diff --git a/DESCRIPTION b/DESCRIPTION
index d3f3bb5..4766acb 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: serosv
 Type: Package
 Title: Model Infectious Disease Parameters from Serosurveys
-Version: 1.2.0
+Version: 1.2.0.9000
 Authors@R: c(
     person("Anh", "Phan Truong Quynh", email = "anhptq@oucru.org", role = c("aut", "cre"), comment = c(ORCID = "0009-0000-2129-435X")),
     person("Nguyen", "Pham Nguyen The", email = "nguyenpnt@oucru.org", role = c("aut"), comment = c(ORCID = "0000-0002-0356-2776")),
@@ -40,6 +40,7 @@ Imports:
     rstan (>= 2.18.1),
     rstantools (>= 2.4.0),
     boot,
+    pROC,
     stats4
 Suggests: 
     covr,
@@ -49,16 +50,13 @@ Suggests:
     testthat (>= 3.0.0)
 Collate: 
     'data.R'
-    'mseir_model.R'
-    'sir_basic_model.R'
-    'sir_static_model.R'
-    'sir_subpops_model.R'
     'fractional_polynomial_models.R'
     'polynomial_models.R'
     'utils.R'
     'compare_models.R'
     'correct_prevalence.R'
     'weibull_model.R'
+    'farrington_model.R'
     'nonparametric.R'
     'semiparametric_models.R'
     'mixture_model.R'
@@ -69,6 +67,8 @@ Collate:
     'plots.R'
     'compute_ci.R'
     'age_time_model.R'
+    'predict.R'
+    'print.R'
 Config/testthat/edition: 3
 URL: https://oucru-modelling.github.io/serosv/, https://github.com/OUCRU-Modelling/serosv
 VignetteBuilder: knitr
diff --git a/NAMESPACE b/NAMESPACE
index 80b1540..ff45bcb 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,5 +1,13 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(compute_ci,age_time_model)
+S3method(compute_ci,default)
+S3method(compute_ci,fp_model)
+S3method(compute_ci,hierarchical_bayesian_model)
+S3method(compute_ci,lp_model)
+S3method(compute_ci,mixture_model)
+S3method(compute_ci,penalized_spline_model)
+S3method(compute_ci,weibull_model)
 S3method(plot,age_time_model)
 S3method(plot,estimate_from_mixture)
 S3method(plot,farrington_model)
@@ -7,34 +15,38 @@ S3method(plot,fp_model)
 S3method(plot,hierarchical_bayesian_model)
 S3method(plot,lp_model)
 S3method(plot,mixture_model)
-S3method(plot,mseir_model)
 S3method(plot,penalized_spline_model)
 S3method(plot,polynomial_model)
-S3method(plot,sir_basic_model)
-S3method(plot,sir_static_model)
-S3method(plot,sir_subpops_model)
 S3method(plot,weibull_model)
+S3method(predict,age_time_model)
+S3method(predict,farrington_model)
+S3method(predict,fp_model)
+S3method(predict,hierarchical_bayesian_model)
+S3method(predict,lp_model)
+S3method(predict,penalized_spline_model)
+S3method(predict,polynomial_model)
+S3method(predict,weibull_model)
+S3method(print,age_time_model)
+S3method(print,estimate_from_mixture)
+S3method(print,farrington_model)
+S3method(print,fp_model)
+S3method(print,hierarchical_bayesian_model)
+S3method(print,lp_model)
+S3method(print,mixture_model)
+S3method(print,penalized_spline_model)
+S3method(print,polynomial_model)
+S3method(print,weibull_model)
 export(add_thresholds)
 export(age_time_model)
 export(compare_models)
-export(compute_ci)
-export(compute_ci.age_time_model)
-export(compute_ci.fp_model)
-export(compute_ci.hierarchical_bayesian_model)
-export(compute_ci.lp_model)
-export(compute_ci.mixture_model)
-export(compute_ci.penalized_spline_model)
-export(compute_ci.weibull_model)
 export(correct_prevalence)
 export(est_foi)
 export(estimate_from_mixture)
 export(farrington_model)
-export(find_best_fp_powers)
 export(fp_model)
 export(hierarchical_bayesian_model)
 export(lp_model)
 export(mixture_model)
-export(mseir_model)
 export(pava)
 export(penalized_spline_model)
 export(plot_corrected_prev)
@@ -43,9 +55,6 @@ export(plot_standard_curve)
 export(plot_titer_qc)
 export(polynomial_model)
 export(set_plot_style)
-export(sir_basic_model)
-export(sir_static_model)
-export(sir_subpops_model)
 export(standardize_data)
 export(to_titer)
 export(transform_data)
@@ -55,15 +64,15 @@ import(assertthat)
 import(dplyr)
 import(ggplot2)
 import(graphics)
+import(locfit)
 import(methods)
+import(pROC)
 import(patchwork)
-import(purrr)
 import(scam)
 import(tidyr)
 importFrom(RcppParallel,RcppParallelLibs)
 importFrom(assertthat,assert_that)
 importFrom(boot,inv.logit)
-importFrom(deSolve,ode)
 importFrom(dplyr,group_by)
 importFrom(dplyr,mutate)
 importFrom(dplyr,n)
@@ -87,7 +96,17 @@ importFrom(mixdist,mix)
 importFrom(mixdist,mixgroup)
 importFrom(mixdist,mixparam)
 importFrom(mvtnorm,rmvnorm)
+importFrom(purrr,as_mapper)
+importFrom(purrr,compact)
 importFrom(purrr,imap_dfr)
+importFrom(purrr,map)
+importFrom(purrr,map2)
+importFrom(purrr,map_dbl)
+importFrom(purrr,map_dfc)
+importFrom(purrr,map_dfr)
+importFrom(purrr,pmap)
+importFrom(purrr,pmap_dfr)
+importFrom(purrr,walk)
 importFrom(rstan,sampling)
 importFrom(rstan,summary)
 importFrom(rstantools,rstan_config)
@@ -103,6 +122,9 @@ importFrom(stats,predict.glm)
 importFrom(stats,prop.test)
 importFrom(stats,qnorm)
 importFrom(stats,qt)
+importFrom(stats4,AIC)
+importFrom(stats4,BIC)
+importFrom(stats4,logLik)
 importFrom(stats4,mle)
 importFrom(stringr,str_detect)
 useDynLib(serosv, .registration=TRUE)
diff --git a/NEWS.md b/NEWS.md
index 76a8a94..f8c5a13 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,10 @@
+# serosv (development version)
+* clean up documentation
+
 # serosv 1.2.0
+* add to_titer() function to convert assay reading to titer
+* update the docs
+* update article for to_titer() on the website
 
 # serosv 1.1.0
 * add correct_prevalence() function to estimate real prevalence from imperfect test
diff --git a/R/age_time_model.R b/R/age_time_model.R
index 9e75d98..a2c9a64 100644
--- a/R/age_time_model.R
+++ b/R/age_time_model.R
@@ -3,15 +3,21 @@
 #'
 #' @description Fit age-stratified seroprevalence across multiple time points. Also try to monotonize age (or birth cohort) - specific seroprevalence.
 #'
-#' @param data - input data, must have`age`, `status`, time, group columns, where group column determines how data is aggregated
-#' @param time_col - name of the column for time (default to `date`)
-#' @param grouping_col - name of the column for time (default to `group`)
-#' @param age_correct - a boolean, if `TRUE`, monotonize age-specific prevalence. Monotonize birth cohort-specific seroprevalence otherwise.
-#' @param le - number of bins to generate age grid, used when monotonizing data
-#' @param ci - confidence interval for smoothing
-#' @param monotonize_method - either "pava" or "scam"
+#' @param data input data, must have age, status, time, group columns, where group column determines how data is aggregated
+#' @param age_col name of the `age` column (default age_col="age").
+#' @param pos_col name of the `pos` column (default pos_col="pos").
+#' @param tot_col name of the `tot` column (default tot_col="tot").
+#' @param status_col name of the `status` column (default status_col="status").
+#' @param time_col name of the column for time (default to "date")
+#' @param grouping_col name of the column for time (default to "group")
+#' @param age_correct a boolean, if `TRUE`, monotonize age-specific prevalence. Monotonize birth cohort-specific seroprevalence otherwise.
+#' @param le number of bins to generate age grid, used when monotonizing data
+#' @param ci confidence interval for smoothing
+#' @param monotonize_method either "pava" or "scam"
+#'
 #' @import scam assertthat
 #' @importFrom mgcv gam predict.gam betar
+#' @importFrom purrr map map_dbl map2
 #'
 #' @return a list of class time_age_model with 4 items
 #'   \item{out}{a data.frame with dimension n_group x 9, where columns `info`, `sp`, `foi` store output for non-monotonized
@@ -20,7 +26,10 @@
 #'   \item{age_correct}{a boolean indicating whether the data is monotonized across age or cohort}
 #'   \item{datatype}{whether the input data is aggregated or line-listing data}
 #' @export
-age_time_model <- function(data, time_col="date", grouping_col="group", age_correct=F, le=512, ci = 0.95, monotonize_method = "pava"){
+age_time_model <- function(data,
+                           age_col="age", status_col="status", pos_col="pos", tot_col="tot",
+                           time_col="date", grouping_col="group",
+                           age_correct=F, le=512, ci = 0.95, monotonize_method = "pava"){
   # work around to resolve no visible binding note NOTE during check()
   x <- label <- family <- fit <- se.fit <- ymin <- ymax <- y <- mean_time <- prevalence <- sim_data <- NULL
   age <- ys <- shift_no <- cohort <- col_time <- monotonized_mod <- df <- info <- sp <- monotonized_info <- monotonized_sp <- NULL
@@ -82,7 +91,8 @@ age_time_model <- function(data, time_col="date", grouping_col="group", age_corr
   model <- list()
 
   # --- preprocess data ------
-  check_input <- check_input(data)
+  check_input <- check_input(data,
+                             stratum_col=age_col,pos_col=pos_col, tot_col=tot_col, status_col=status_col)
   age_range <- range(data$age)
   age_grid <- seq(age_range[1], age_range[2], length.out = le)
 
@@ -230,8 +240,10 @@ age_time_model <- function(data, time_col="date", grouping_col="group", age_corr
     )
 
   model$out <- out
+  model$monotonize_method <- monotonize_method
   model$grouping_col <- grouping_col
   model$age_correct <- age_correct
+  model$ci <- ci
 
 
   class(model) <- "age_time_model"
diff --git a/R/compare_models.R b/R/compare_models.R
index 681b521..fb6100c 100644
--- a/R/compare_models.R
+++ b/R/compare_models.R
@@ -1,37 +1,151 @@
 #' Compare models
 #'
+#' @param data input data to fit into the models
+#' @param method method to compare models. Can be one of the built-in methods or a function to compute the returned metrics (see Details).
 #' @param ... models to be compared. Must be models created by serosv. If models' names are not provided, indices will be used instead for the `model` column in the returned data.frame.
 #'
 #'
 #' @return
 #' a data.frame of 4 columns
-#'   \item{model}{name or index of the model}
+#'   \item{label}{name or index of the model}
 #'   \item{type}{model type of the given model (a serosv model name)}
 #'   \item{AIC}{AIC value for the model (lower value indicates better fit)}
 #'   \item{BIC}{BIC value for the model (lower value indicates better fit)}
 #'
+#' @details
+#' Built-in comparison methods include:
+#' - computing AIC and BIC, which returns AIC, BIC values of the model if available
+#' - cross validation, which returns MSE and logloss (negative )
+#'
 #' @importFrom magrittr %>%
-#' @importFrom purrr imap_dfr
+#' @importFrom purrr imap_dfr as_mapper
 #' @importFrom stringr str_detect
+#' @importFrom assertthat assert_that
 #'
 #' @export
-compare_models <- function(...){
+compare_models <- function(data, method="AIC/BIC",...){
   list(...) %>%
     imap_dfr(~ {
       # return error if input contains non-serosv models
-      if(!all(str_detect(class(.x), "_model"))) {
-        stop("Inputs must be serosv models")
+      # if(!all(str_detect(class(.x), "_model"))) {
+      #   stop("Inputs must be serosv models")
+      # }
+
+      # get function to compute comparison metrics
+      metric_func <- if(is.character(method)){
+        switch(
+          method,
+          "AIC/BIC" = aic_bic,
+          "CV" = cv,
+          method
+        )
+      }else{
+        method
       }
 
-      # TODO: apply cross validation to get matrix instead
+      assert_that(is.function(metric_func),
+                  msg = "Function to compute the metrics must be provided")
+
+      out <- metric_func(data, as_mapper(.x))
 
-      data.frame(
-        model = .y,
-        type = class(.x),
-        AIC = AIC(.x$info),
-        BIC = BIC(.x$info)
+      assert_that("data.frame" %in% class(out),
+                  msg = "Function to compute the metrics must return a data.frame")
+
+      out %>% mutate(
+        label = .y,
+        .before = 1
       )
+
     })
 }
 
+# function returning goodness-of-fit metrics such as AIC/BIC, likelihood (with degree-of-freedom)
+#' @importFrom stats4 logLik AIC BIC
+aic_bic <- function(dat, mod_func){
+  out <- mod_func(dat)
+
+  aic <- tryCatch(stats4::AIC(out$info),
+                  error = \(e){NULL})
+  bic <- tryCatch(stats4::BIC(out$info),
+                  error = \(e){NULL})
+  ll <- tryCatch(stats4::logLik(out$info),
+                     error = \(e){NULL})
+
+  tibble(
+    type = class(out),
+    AIC = aic,
+    BIC = bic,
+    logLik = if (!is.null(ll)) as.numeric(ll) else NA,
+    df = if (!is.null(ll) && !is.null(attr(ll, "df"))) attr(ll, "df") else NA,
+    mod_out = list(out),
+    plots = list(plot(out)+ggtitle(paste("Fitted model using", class(out))))
+  )
+}
+
+
+# function to compute metrics from cross validation
+# assess the generalization/prediction of the model
+#' @importFrom stats4 logLik AIC BIC
+#' @importFrom stats predict.glm
+#' @import tidyr dplyr pROC
+cv <- function(dat, mod_func, k=4){
+  # assign each row of data to each fold
+  idx_fold <- sort(rep(1:k, length.out=nrow(dat)))
+
+  metrics <- lapply(1:k, \(fold){
+    curr_metric <- list()
+
+    # split data
+    fit_dat <- dat[idx_fold != fold, ]
+    test_dat <- dat[idx_fold == fold, ]
+
+    # get model info
+    out <- mod_func(fit_dat)
+    curr_metric$type <- class(out)
+    # generate prediction
+    pred <- predict(out, data.frame(age=test_dat[,1]), type="response")
+
+    if(out$datatype == "aggregated"){
+      # if data is aggregated
+      seroprev_obs <- test_dat$pos/test_dat$tot
+
+      # MSE
+      curr_metric$mse <- sum((pred - seroprev_obs)**2)/nrow(test_dat)
+      # compute logloss (negative binomial loglikelihood)
+      curr_metric$logloss <- -sum(
+        dbinom(test_dat$pos, test_dat$tot, prob=pred, log=TRUE),
+        na.rm = TRUE)
+
+    }else{
+      # if data is linelisting
+      # make sure pred is slightly higher than 0 and lower than 1 to avoid log(0)
+      eps <- .Machine$double.eps  # smallest positive floating point number ~ 2.2e-16
+      pred <- pmax(eps, pmin(1 - eps, pred))
+
+      # compute logloss (negative bernoulli loglikelihood)
+      curr_metric$logloss <- -sum(test_dat$status*log(pred) + (1 - test_dat$status)*log(1-pred),
+                                  na.rm = TRUE)
+      # and estimate auc
+      curr_metric$auc <- as.numeric(pROC::auc(test_dat$status, pred))
+    }
+
+    curr_metric
+  }) %>%
+  bind_rows() %>%
+  summarise(
+    # compute average of the metrics
+    across(where(is.numeric), mean),
+    # for type, simply get the first one
+    type = first(type))
+
+  # also return the model when it is fitted using the whole data
+  out <- mod_func(dat)
+
+  metrics %>%
+    mutate(
+      mod_out = list(out),
+      plots = list(plot(out)+ggtitle(paste("Fitted model using", class(out))))
+    )
+}
+
 
diff --git a/R/compute_ci.R b/R/compute_ci.R
index 54228e5..21bc310 100644
--- a/R/compute_ci.R
+++ b/R/compute_ci.R
@@ -2,12 +2,12 @@ compute_ci <- function(x, ci = 0.95, le = 100, ...){
   UseMethod("compute_ci")
 }
 
-#' Compute confidence interval
+#' Compute confidence interval for a model of serosv
 #'
-#' @param x - serosv models
-#' @param ci - confidence interval
-#' @param le - number of data for computing confidence interval
-#' @param ... - arbitrary argument
+#' @param x serosv models
+#' @param ci confidence interval
+#' @param le number of data for computing confidence interval
+#' @param ... arbitrary argument
 #'
 #' @importFrom stats qt predict.glm
 #' @import dplyr
@@ -15,7 +15,7 @@ compute_ci <- function(x, ci = 0.95, le = 100, ...){
 #' @return confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval
 #'
 #' @export
-compute_ci <- function(x, ci = 0.95, le = 100, ...){
+compute_ci.default <- function(x, ci = 0.95, le = 100, ...){
   # resolve no visible binding issue with CRAN check
   fit <- se.fit <- NULL
 
@@ -23,10 +23,10 @@ compute_ci <- function(x, ci = 0.95, le = 100, ...){
   link_inv <- x$info$family$linkinv
   dataset <- x$info$data
   n <- nrow(dataset) - length(x$info$coefficients)
-  age_range <- range(dataset$Age)
+  age_range <- range(dataset$age)
   ages <- seq(age_range[1], age_range[2], le = le)
 
-  mod1 <- predict.glm(x$info,data.frame(Age = ages), se.fit = TRUE)
+  mod1 <- predict.glm(x$info,data.frame(age = ages), se.fit = TRUE)
   n1 <- mod1 %>% as_tibble() %>%  select(fit, se.fit) %>%
     mutate(age = ages ) %>%
     mutate(lwr = link_inv(fit + qt(    p, n) * se.fit),
@@ -40,10 +40,10 @@ compute_ci <- function(x, ci = 0.95, le = 100, ...){
 
 #' Compute confidence interval for fractional polynomial model
 #'
-#' @param x - serosv models
-#' @param ci - confidence interval
-#' @param le - number of data for computing confidence interval
-#' @param ... - arbitrary argument
+#' @param x serosv models
+#' @param ci confidence interval
+#' @param le number of data for computing confidence interval
+#' @param ... arbitrary argument
 #'
 #' @import dplyr
 #' @return confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval
@@ -74,9 +74,9 @@ compute_ci.fp_model <- function(x, ci = 0.95, le = 100, ...){
 
 #' Compute confidence interval for Weibull model
 #'
-#' @param x - serosv models
-#' @param ci - confidence interval
-#' @param ... - arbitrary argument
+#' @param x serosv models
+#' @param ci confidence interval
+#' @param ... arbitrary argument
 #'
 #' @import dplyr
 #' @return confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval
@@ -109,15 +109,15 @@ compute_ci.weibull_model <- function(x, ci = 0.95, ...){
 
 #' Compute confidence interval for local polynomial model
 #'
-#' @param x - serosv models
-#' @param ci - confidence interval
-#' @param ... - arbitrary arguments
+#' @param x serosv models
+#' @param ci confidence interval
+#' @param ... arbitrary arguments
 #' @return confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval
 #' @export
 compute_ci.lp_model <- function(x,ci = 0.95, ...){
   ages <- x$df$age
-  crit<- crit(x$pi,cov = ci)$crit.val
-  mod1 <- predict(x$pi, data.frame(a = ages),se.fit = TRUE)
+  crit<- crit(x$info,cov = ci)$crit.val
+  mod1 <- predict(x$info, data.frame(a = ages),se.fit = TRUE)
   out.DF <- data.frame(x = ages, y = mod1$fit,ymin= mod1$fit-crit*(mod1$se.fit/100),
                        ymax= mod1$fit+crit*(mod1$se.fit/100))
   out.DF
@@ -126,9 +126,9 @@ compute_ci.lp_model <- function(x,ci = 0.95, ...){
 
 #' Compute confidence interval for penalized_spline_model
 #'
-#' @param x - serosv models
-#' @param ci - confidence interval
-#' @param ... - arbitrary arguments
+#' @param x serosv models
+#' @param ci confidence interval
+#' @param ... arbitrary arguments
 #' @importFrom mgcv predict.gam
 #' @import dplyr
 #'
@@ -181,8 +181,8 @@ compute_ci.penalized_spline_model <- function(x,ci = 0.95, ...){
 
 #' Compute 95\% credible interval for hierarchical Bayesian model
 #'
-#' @param x - serosv models
-#' @param ... - arbitrary arguments
+#' @param x serosv models
+#' @param ... arbitrary arguments
 #' @importFrom mgcv predict.gam
 #' @import dplyr
 #'
@@ -233,9 +233,9 @@ compute_ci.hierarchical_bayesian_model <- function(x, ...){
 
 #' Compute confidence interval for mixture model
 #'
-#' @param x - serosv mixture_model object
-#' @param ci - confidence interval
-#' @param ... - arbitrary arguments
+#' @param x serosv mixture_model object
+#' @param ci confidence interval
+#' @param ... arbitrary arguments
 #' @importFrom stats qnorm
 #'
 #' @return list of confidence interval for susceptible and infected. Each confidence interval is a list with 2 items for lower and upper bound of the interval.
@@ -258,10 +258,10 @@ compute_ci.mixture_model <- function(x,ci = 0.95, ...){
 
 #' Compute confidence interval for time age model
 #'
-#' @param x - serosv models
-#' @param ci - confidence interval
-#' @param le - number of data for computing confidence interval
-#' @param ... - arbitrary argument
+#' @param x serosv models
+#' @param ci confidence interval
+#' @param le number of data for computing confidence interval
+#' @param ... arbitrary argument
 #'
 #' @importFrom mgcv predict.gam
 #' @import dplyr
@@ -340,6 +340,8 @@ compute_ci.age_time_model <- function(x, ci=0.95, le = 100, ...){
       })
     ) %>%
     select(!!sym(x$grouping_col), sp_df, foi_df)
+
+  out
 }
 
 
diff --git a/R/correct_prevalence.R b/R/correct_prevalence.R
index 2bc626d..9659749 100644
--- a/R/correct_prevalence.R
+++ b/R/correct_prevalence.R
@@ -1,6 +1,6 @@
 #' Estimate the true sero prevalence using Frequentist/Bayesian estimation
 #'
-#' @param data the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)
+#' @param data the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR `age`, `status` (for linelisting data)
 #' @param bayesian whether to adjust sero-prevalence using the Bayesian or frequentist approach. If set to `TRUE`, true sero-prevalence is estimated using MCMC.
 #' @param init_se sensitivity of the serological test
 #' @param init_sp specificity of the serological test
@@ -9,6 +9,10 @@
 #' @param chains (applicable when `bayesian=TRUE`) number of Markov chains
 #' @param warmup (applicable when `bayesian=TRUE`) number of warm up runs
 #' @param iter (applicable when `bayesian=TRUE`) number of iterations
+#' @param age_col name of the `age` column (default age_col="age").
+#' @param pos_col name of the `pos` column (default pos_col="pos").
+#' @param tot_col name of the `tot` column (default tot_col="tot").
+#' @param status_col name of the `status` column (default status_col="status").
 #'
 #' @importFrom rstan sampling summary
 #' @importFrom dplyr mutate
@@ -17,7 +21,7 @@
 #'
 #' @return a list of 3 items
 #'   \item{info}{estimated parameters (when `bayesian = TRUE`) or formula to compute corrected prevalence (when `bayesian = FALSE`)}
-#'   \item{df}{data.frame of input data (in aggregated form)}
+#'   \item{df}{data.frame of input data (in aggregated form) with the 95\% confidence interval for apparent (i.e. observed) seroprevalence}
 #'   \item{corrected_sero}{data.frame containing age, the corresponding estimated seroprevalance with 95\% confidence/credible interval, and adjusted tot and pos}
 #' @export
 #'
@@ -25,6 +29,7 @@
 #' data <- rubella_uk_1986_1987
 #' correct_prevalence(data)
 correct_prevalence <- function(data, bayesian=TRUE,
+                               age_col="age",pos_col="pos", tot_col="tot", status_col="status",
                          init_se = 0.95, init_sp = 0.8, study_size_se = 1000, study_size_sp = 1000,
                          chains = 1, warmup = 1000, iter = 2000){
   # resolve no visible binding note
@@ -32,18 +37,26 @@ correct_prevalence <- function(data, bayesian=TRUE,
 
   output <- list()
 
-  data <- check_input(data)
+  data <- check_input(data, stratum_col=age_col,pos_col=pos_col, tot_col=tot_col, status_col=status_col)
   age <- data$age
   pos <- data$pos
   tot <- data$tot
 
   if (data$type == "linelisting"){
-    transform_df <- transform_data(age, pos)
+    transform_df <- transform_data(data, stratum_col="age", status_col="pos")
     age <- transform_df$t
     pos <- transform_df$pos
     tot <- transform_df$tot
   }
 
+  # quantify CI for apparent seroprev
+  apparent_thetas <- data.frame(
+    # use prop.test to get confidence interval
+    lower = mapply(\(pos, tot){prop.test(pos, tot, conf.level = 0.95)$conf.int[1]}, pos, tot),
+    fit = pos/tot,
+    upper = mapply(\(pos, tot){prop.test(pos, tot, conf.level = 0.95)$conf.int[2]}, pos, tot)
+  )
+
   if (bayesian){
 
     # format data
@@ -68,12 +81,7 @@ correct_prevalence <- function(data, bayesian=TRUE,
       upper = summary(fit)$summary[3:(length(age) + 2), "97.5%"]
     )
   }else{
-    thetas <- data.frame(
-      # use prop.test to get confidence interval
-      lower = mapply(\(pos, tot){prop.test(pos, tot, conf.level = 0.95)$conf.int[1]}, pos, tot),
-      fit = pos/tot,
-      upper = mapply(\(pos, tot){prop.test(pos, tot, conf.level = 0.95)$conf.int[2]}, pos, tot)
-    ) %>%
+    thetas <- apparent_thetas %>%
     mutate(
       # estimate true prevalence and lower, upper bound
       lower = pmax(0, (lower - 1 + init_sp)/(init_se + init_sp - 1)),
@@ -88,14 +96,17 @@ correct_prevalence <- function(data, bayesian=TRUE,
   output$df <- data.frame(
     age = age,
     pos = pos,
-    tot = tot
+    tot = tot,
+    sero = apparent_thetas$fit,
+    sero_lwr = apparent_thetas$lower,
+    sero_upr = apparent_thetas$upper
   )
 
   output$corrected_se <- data.frame(
     age = age,
-    sero = thetas$fit,
     pos = thetas$fit*tot, #adjusted pos with estimated sero
     tot = tot,
+    sero = thetas$fit,
     sero_lwr = thetas$lower,
     sero_upr = thetas$upper
   )
diff --git a/R/data.R b/R/data.R
index 50931cb..ea9248d 100644
--- a/R/data.R
+++ b/R/data.R
@@ -7,6 +7,9 @@
 # data are normalized and cleaned for the purpose of this package
 
 
+# ===== Hepatitis A =======
+
+
 #' Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated)
 #'
 #' A study of the prevalence of HAV antibodies conducted in the Flemish
@@ -20,14 +23,12 @@
 #' }
 #'
 #' @examples
-#' # Reproduce Fig 4.1 (upper left panel), p. 63
-#' age <- hav_be_1993_1994$age
-#' pos <- hav_be_1993_1994$pos
-#' tot <- hav_be_1993_1994$tot
-#' plot(
-#'   age, pos / tot,
-#'   pty = "s", cex = 0.06 * tot, pch = 16, xlab = "age",
-#'   ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
+#' with(hav_be_1993_1994,
+#'   plot(
+#'     age, pos / tot,
+#'     pty = "s", cex = 0.34 * sqrt(tot), pch = 16, xlab = "age",
+#'     ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
+#'   )
 #' )
 #'
 #' @source Beutels, M., Van Damme, P., Aelvoet, W. et al. Prevalence of
@@ -48,15 +49,18 @@
 #' }
 #'
 #' @examples
-#' # Reproduce Fig 4.1 (upper right panel), p. 63
 #' library(dplyr)
 #' df <- hav_be_2002 %>%
 #'   group_by(age) %>%
 #'   summarise(pos = sum(seropositive), tot = n())
-#' plot(
-#'   df$age, df$pos / df$tot,
-#'   pty = "s", cex = 0.06 * df$tot, pch = 16, xlab = "age",
-#'   ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
+#'
+#' with(
+#'   df,
+#'   plot(
+#'     age, pos / tot,
+#'     pty = "s", cex = 0.34 * sqrt(tot), pch = 16, xlab = "age",
+#'     ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
+#'   )
 #' )
 #'
 #' @source Thiry, N., Beutels, P., Shkedy, Z. et al. The seroepidemiology
@@ -78,14 +82,13 @@
 #' }
 #'
 #' @examples
-#' # Reproduce Fig 4.1 (lower panel), p. 63
-#' age <- hav_bg_1964$age
-#' pos <- hav_bg_1964$pos
-#' tot <- hav_bg_1964$tot
-#' plot(
+#' with(
+#'   hav_bg_1964,
+#'   plot(
 #'     age, pos / tot,
-#'     pty = "s", cex = 0.08 * tot, pch = 16, xlab = "age",
+#'     pty = "s", cex = 0.6 * sqrt(tot), pch = 16, xlab = "age",
 #'     ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
+#'   )
 #' )
 #'
 #' @source Keiding, Niels. "Age-Specific Incidence and Prevalence: A
@@ -94,6 +97,8 @@
 #' \doi{doi:10.2307/2983150}
 "hav_bg_1964"
 
+# ===== Hepatitis B =======
+
 #' Hepatitis B serological data from Russia in 1999 (aggregated)
 #'
 #' A seroprevalence study conducted in St. Petersburg
@@ -108,7 +113,6 @@
 #' }
 #'
 #' @examples
-#' # Reproduce Fig 4.2, p. 65
 #' library(dplyr)
 #' hbv_ru_1999$age <- trunc(hbv_ru_1999$age / 1) * 1
 #' hbv_ru_1999$age[hbv_ru_1999$age > 40] <- trunc(
@@ -117,9 +121,10 @@
 #' df <- hbv_ru_1999 %>%
 #'   group_by(age) %>%
 #'   summarise(pos = sum(pos), tot = sum(tot))
+#'
 #' plot(
 #'   df$age, df$pos / df$tot,
-#'   cex = 0.05 * df$tot, pch = 16, xlab = "age",
+#'   cex = 0.32 * sqrt(df$tot), pch = 16, xlab = "age",
 #'   ylab = "seroprevalence", xlim = c(0, 72)
 #' )
 #'
@@ -129,6 +134,7 @@
 #' St Petersburg, 2000.
 "hbv_ru_1999"
 
+# ===== Hepatitis C =======
 #' Hepatitis C serological data from Belgium in 2006 (line listing)
 #'
 #' A study of HCV infection among injecting drug users. All injecting drug users
@@ -143,7 +149,6 @@
 #' }
 #'
 #' @examples
-#' # Reproduce Fig 4.3, p. 66
 #' library(dplyr)
 #' # snapping age to aggregated age group
 #' # (credit: https://stackoverflow.com/a/12861810)
@@ -162,11 +167,13 @@
 #'   group_by(dur) %>%
 #'   summarise(tot = n(), pos = sum(seropositive))
 #'
-#' plot(
-#'   hcv_be_2006$dur, hcv_be_2006$pos / hcv_be_2006$tot,
-#'   cex = 0.1 * hcv_be_2006$tot, pch = 16,
-#'   xlab = "duration of injection (years)",
-#'   ylab = "seroprevalence", xlim = c(0, 25), ylim = c(0, 1)
+#' with(hcv_be_2006,
+#'   plot(
+#'     dur, pos / tot,
+#'     cex = 0.42 * sqrt(tot), pch = 16,
+#'     xlab = "duration of injection (years)",
+#'     ylab = "seroprevalence", xlim = c(0, 25), ylim = c(0, 1)
+#'   )
 #' )
 #'
 #' @source Mathei, C., Shkedy, Z., Denis, B., Kabali, C., Aerts,
@@ -177,6 +184,8 @@
 #' \doi{doi:10.1111/j.1365-2893.2006.00725.x}
 "hcv_be_2006"
 
+# ===== Mumps =======
+
 #' Mumps serological data from the UK in 1986 and 1987 (aggregated)
 #'
 #' a large survey of prevalence of antibodies to mumps and rubella viruses in
@@ -185,19 +194,17 @@
 #'
 #' @format A data frame with 3 variables:
 #' \describe{
-#'  \item{age}{Age group}
+#'  \item{age}{Midpoint of the age group (e.g. 1.5 = 1-2 years old, 2.5 = 2-3 years old)}
 #'  \item{pos}{Number of seropositive individuals}
 #'  \item{tot}{Total number of individuals surveyed}
 #' }
 #'
 #' @examples
-#' # Reproduce Fig 4.4 (left panel), p. 67
-#' age <- mumps_uk_1986_1987$age
-#' pos <- mumps_uk_1986_1987$pos
-#' tot <- mumps_uk_1986_1987$tot
-#' plot(age, pos / tot,
-#'   cex = 0.008 * tot, pch = 16, xlab = "age", ylab = "seroprevalence",
-#'   xlim = c(0, 45), ylim = c(0, 1)
+#' with(mumps_uk_1986_1987,
+#'   plot(age, pos / tot,
+#'     cex = 0.1 * sqrt(tot), pch = 16, xlab = "age", ylab = "seroprevalence",
+#'     xlim = c(0, 45), ylim = c(0, 1)
+#'   )
 #' )
 #'
 #' @source  Morgan-Capner P, Wright J, Miller C L, Miller E. Surveillance of
@@ -205,6 +212,8 @@
 #' 1988; 297 :770 \doi{doi:10.1136/bmj.297.6651.770}
 "mumps_uk_1986_1987"
 
+# ===== Parvo 19 =======
+
 #' Parvo B19 serological data from Belgium from 2001-2003 (line listing)
 #'
 #' A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
@@ -222,13 +231,12 @@
 #' }
 #'
 #' @examples
-#' # Reproduce Fig 4.5 (left upper panel), p. 68
 #' library(dplyr)
 #' df <- parvob19_be_2001_2003 %>%
 #'   group_by(age) %>%
 #'   summarise(pos = sum(seropositive), tot = n())
 #' plot(df$age, df$pos / df$tot,
-#'   cex = 0.02 * df$tot, pch = 16, xlab = "age", ylab = "seroprevalence",
+#'   cex = 0.3 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
 #'   xlim = c(0, 82), ylim = c(0, 1)
 #' )
 #'
@@ -256,17 +264,17 @@
 #' }
 #'
 #' @examples
-#' # Reproduce Fig 4.5 (right upper panel), p. 68
-#' # NB: This figure will look different to that of in the book, since we
+#' # Note: This figure will look different to that of in the book, since we
 #' # believe that the original authors has made some errors in specifying
 #' # the sample size of the dots.
+#'
 #' library(dplyr)
 #' df <- parvob19_ew_1996 %>%
 #'   mutate(age = round(age)) %>%
 #'   group_by(age) %>%
 #'   summarise(pos = sum(seropositive), tot = n())
 #' plot(df$age, df$pos / df$tot,
-#'   cex = 0.02 * df$tot, pch = 16, xlab = "age", ylab = "seroprevalence",
+#'   cex = 0.3 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
 #'   xlim = c(0, 82), ylim = c(0, 1)
 #' )
 #'
@@ -294,17 +302,17 @@
 #' }
 #'
 #' @examples
-#' # Reproduce Fig 4.5 (left bottom panel), p. 68
-#' # NB: This figure will look different to that of in the book, since we
+#' # Note: This figure will look different to that of in the book, since we
 #' # believe that the original authors has made some errors in specifying
 #' # the sample size of the dots.
+#'
 #' library(dplyr)
 #' df <- parvob19_fi_1997_1998 %>%
 #'   mutate(age = round(age)) %>%
 #'   group_by(age) %>%
 #'   summarise(pos = sum(seropositive), tot = n())
 #' plot(df$age, df$pos / df$tot,
-#'   cex = 0.07 * df$tot, pch = 16, xlab = "age", ylab = "seroprevalence",
+#'   cex = 0.4 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
 #'   xlim = c(0, 82), ylim = c(0, 1)
 #' )
 #'
@@ -332,16 +340,12 @@
 #' }
 #'
 #' @examples
-#' # Reproduce Fig 4.5 (middle bottom panel), p. 68
-#' # NB: This figure will look different to that of in the book, since we
-#' # believe that the original authors has made some errors in specifying
-#' # the sample size of the dots.
 #' library(dplyr)
 #' df <- parvob19_it_2003_2004 %>%
 #'   group_by(age) %>%
 #'   summarise(pos = sum(seropositive), tot = n())
 #' plot(df$age, df$pos / df$tot,
-#'   cex = 0.07 * df$tot, pch = 16, xlab = "age", ylab = "seroprevalence",
+#'   cex = 0.32 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
 #'   xlim = c(0, 82), ylim = c(0, 1)
 #' )
 #'
@@ -369,17 +373,17 @@
 #' }
 #'
 #' @examples
-#' # Reproduce Fig 4.5 (right bottom panel), p. 68
-#' # NB: This figure will look different to that of in the book, since we
+#' # Note: This figure will look different to that of in the book, since we
 #' # believe that the original authors has made some errors in specifying
 #' # the sample size of the dots.
+#'
 #' library(dplyr)
 #' df <- parvob19_pl_1995_2004 %>%
 #'   mutate(age = round(age)) %>%
 #'   group_by(age) %>%
 #'   summarise(pos = sum(seropositive), tot = n())
 #' plot(df$age, df$pos / df$tot,
-#'   cex = 0.07 * df$tot, pch = 16, xlab = "age", ylab = "seroprevalence",
+#'   cex = 0.32 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
 #'   xlim = c(0, 82), ylim = c(0, 1)
 #' )
 #'
@@ -390,6 +394,9 @@
 #' \doi{doi:10.1017/S0950268807009661}
 "parvob19_pl_1995_2004"
 
+
+# ===== Rubella =======
+
 #' Rubella serological data from the UK in 1986 and 1987 (aggregated)
 #'
 #' Prevalence of rubella in the UK, obtained from a large survey of prevalence
@@ -397,19 +404,17 @@
 #'
 #' @format A data frame with 3 variables:
 #' \describe{
-#'  \item{age}{Age group}
+#'  \item{age}{Midpoint of the age group (e.g. 1.5 = 1-2 years old, 2.5 = 2-3 years old)}
 #'  \item{pos}{Number of seropositive individuals}
 #'  \item{tot}{Total number of individuals surveyed}
 #' }
 #'
 #' @examples
-#' # Reproduce Fig 4.4 (middle panel), p. 67
-#' age <- rubella_uk_1986_1987$age
-#' pos <- rubella_uk_1986_1987$pos
-#' tot <- rubella_uk_1986_1987$tot
-#' plot(age, pos / tot,
-#'   cex = 0.008 * tot, pch = 16, xlab = "age", ylab = "seroprevalence",
-#'   xlim = c(0, 45), ylim = c(0, 1)
+#' with(rubella_uk_1986_1987,
+#'   plot(age, pos / tot,
+#'     cex = 0.2 * sqrt(tot), pch = 16, xlab = "age", ylab = "seroprevalence",
+#'     xlim = c(0, 45), ylim = c(0, 1)
+#'   )
 #' )
 #'
 #' @source  Morgan-Capner P, Wright J, Miller C L, Miller E. Surveillance of
@@ -417,6 +422,8 @@
 #' 1988; 297 :770 \doi{doi:10.1136/bmj.297.6651.770}
 "rubella_uk_1986_1987"
 
+# ===== Tuberculosis =======
+
 #' Tuberculosis serological data from the Netherlands 1966-1973 (aggregated)
 #'
 #' A study of tuberculosis conducted in the Netherlands. Schoolchildren, aged
@@ -432,19 +439,17 @@
 #' }
 #'
 #' @examples
-#' # Reproduce Fig 4.6, p.70
-#' age <- tb_nl_1966_1973$age
-#' birthyr <- tb_nl_1966_1973$birthyr
-#' pos <- tb_nl_1966_1973$pos
-#' tot <- tb_nl_1966_1973$tot
-#' # left panel
-#' plot(age, pos / tot,
-#'   pch = 16, cex = 0.00005 * tot, xlab = "age",
-#'   ylab = "prevalence", xlim = c(6, 18)
+#' with(tb_nl_1966_1973,
+#'   plot(age, pos / tot,
+#'     pch = 16, cex = 0.01 * sqrt(tot), xlab = "age",
+#'     ylab = "prevalence", xlim = c(6, 18)
+#'   )
 #' )
-#' # right panel
-#' plot(birthyr, pos / tot,
-#'   pch = 16, cex = 0.00005 * tot, xlab = "year", ylab = "prevalence"
+#'
+#' with(tb_nl_1966_1973,
+#'   plot(birthyr, pos / tot,
+#'     pch = 16, cex = 0.01 * sqrt(tot), xlab = "year", ylab = "prevalence"
+#'   )
 #' )
 #'
 #' @source Nagelkerke, N., Heisterkamp, S., Borgdorff, M., Broekmans, J. and
@@ -453,6 +458,9 @@
 #' \doi{doi:10.1002/(SICI)1097-0258(19990215)18:3<307::AID-SIM15>3.0.CO;2-Z}
 "tb_nl_1966_1973"
 
+
+# ===== VZV =======
+
 #' VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated)
 #'
 #' Age-specific seroprevalence of VZV antibodies, assessed in Flanders (Belgium)
@@ -467,15 +475,12 @@
 #' }
 #'
 #' @examples
-#' # Reproduce Fig 4.7 (left panel), p.71
-#' age <- vzv_be_1999_2000$age
-#' pos <- vzv_be_1999_2000$pos
-#' tot <- vzv_be_1999_2000$tot
-#' plot(age, pos / tot,
-#'   cex = 0.036 * tot, pch = 19, xlab = "age", ylab = "seroprevalence",
-#'   xlim = c(0, 45), ylim = c(0, 1)
+#' with(vzv_be_1999_2000,
+#'   plot(age, pos / tot,
+#'     cex = 0.1 * sqrt(tot), pch = 19, xlab = "age", ylab = "seroprevalence",
+#'     xlim = c(0, 45), ylim = c(0, 1)
+#'   )
 #' )
-#'
 #' @source Thiry, N., Beutels, P., Shkedy, Z. et al. The seroepidemiology
 #' of primary varicella-zoster virus infection in Flanders (Belgium).
 #' Eur J Pediatr 161, 588-593 (2002).
@@ -496,14 +501,13 @@
 #' }
 #'
 #' @examples
-#' # Reproduce Fig 4.7 (right panel), p.71
 #' library(dplyr)
 #' df <- vzv_be_2001_2003 %>%
 #'   mutate(age = round(age)) %>%
 #'   group_by(age) %>%
 #'   summarise(pos = sum(seropositive), tot = n())
 #' plot(df$age, df$pos / df$tot,
-#'   cex = 0.036 * df$tot, pch = 19, xlab = "age", ylab = "seroprevalence",
+#'   cex = 0.1 * sqrt(df$tot), pch = 19, xlab = "age", ylab = "seroprevalence",
 #'   xlim = c(0, 45), ylim = c(0, 1)
 #' )
 #'
@@ -514,6 +518,9 @@
 #' \doi{doi:10.1017/S0950268807009661}
 "vzv_be_2001_2003"
 
+
+# ===== Rubella - Mumps =======
+
 #' Rubella - Mumps data from the UK (aggregated)
 #'
 #'
@@ -532,6 +539,7 @@
 #' 1988; 297 :770 \doi{doi:10.1136/bmj.297.6651.770}
 "rubella_mumps_uk"
 
+# ===== VZV - Parvo B19 =======
 
 #' VZV and Parvovirus B19 serological data in Belgium (line listing)
 #' @format A data frame with 7 variables:
diff --git a/R/farrington_model.R b/R/farrington_model.R
new file mode 100644
index 0000000..a73d94d
--- /dev/null
+++ b/R/farrington_model.R
@@ -0,0 +1,93 @@
+#' The Farrington (1990) model.
+#'
+#' @description Fit age-stratified seroprevalence data using the Farrington (1990) model, which assumes the
+#' force of infection increases linearly with age and subsequently decreases exponentially.
+#'
+#' @details
+#' The force of infection is defined as followed
+#'
+#' \deqn{
+#' \lambda(a) = (\alpha a - \gamma)e^{-\beta a} + \gamma
+#' }
+#' Where \eqn{\gamma} is called the long term residual for FOI,
+#' as \eqn{a \rightarrow \infty} , \eqn{\lambda (a) \rightarrow \gamma}
+#'
+#' The seroprevalence can thus be estimated using the non-linear model
+#' \deqn{
+#'  \pi(a) = 1 - exp\{ \frac{\alpha}{\beta}ae^{-\beta a} +
+#'  \frac{1}{\beta}(\frac{\alpha}{\beta} -
+#'  \gamma)(e^{-\beta a} - 1) -\gamma a \}
+#' }
+#'
+#' Refer to section 6.1.2. of the the book by Hens et al. (2012) for further details.
+#'
+#' @references
+#' Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+#' and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+#' Serological and Social Contact Data: A Modern Statistical Perspective.
+#' tatistics for Biology and Health. Springer New York.
+#' \doi{https://doi.org/10.1007/978-1-4614-4072-7}.
+#'
+#'
+#' @param data the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR `age`, `status` (for linelisting data)
+#' @param start Named list of vectors or single vector.
+#' Initial values for optimizer.
+#' @param fixed Named list of vectors or single vector.
+#' Parameter values to keep fixed during optimization.
+#' @param age_col name of the `age` column (default age_col="age").
+#' @param pos_col name of the `pos` column (default pos_col="pos").
+#' @param tot_col name of the `tot` column (default tot_col="tot").
+#' @param status_col name of the `status` column (default status_col="status").
+#'
+#' @return a list of class farrington_model with 5 items
+#'   \item{datatype}{type of datatype used for model fitting (aggregated or linelisting)}
+#'   \item{df}{the dataframe used for fitting the model}
+#'   \item{info}{fitted "mle" object}
+#'   \item{sp}{seroprevalence}
+#'   \item{foi}{force of infection}
+#' @seealso [stats4::mle()] for more information on the fitted mle object
+#'
+#' @examples
+#' df <- rubella_uk_1986_1987
+#' model <- farrington_model(
+#'   df,
+#'   start=list(alpha=0.07,beta=0.1,gamma=0.03)
+#'   )
+#' plot(model)
+#'
+#' @importFrom stats4 mle
+#'
+#' @export
+farrington_model <- function(data, start, fixed=list(),
+                             age_col="age",pos_col="pos", tot_col="tot", status_col="status")
+{
+  model <- list()
+
+  # check input whether it is line-listing or aggregated data
+  data <- check_input(data, stratum_col=age_col,pos_col=pos_col, tot_col=tot_col, status_col=status_col)
+  age <- data$age
+  pos <- data$pos
+  tot <- data$tot
+  model$datatype <- data$type
+
+  farrington <- function(alpha,beta,gamma) {
+    p=1-exp((alpha/beta)*age*exp(-beta*age)
+            +(1/beta)*((alpha/beta)-gamma)*(exp(-beta*age)-1)-gamma*age)
+    ll=pos*log(p)+(tot-pos)*log(1-p)
+    return(-sum(ll))
+  }
+
+  model$info <- mle(farrington, fixed=fixed, start=start)
+  alpha <- model$info@coef[1]
+  beta  <- model$info@coef[2]
+  gamma <- model$info@coef[3]
+  model$sp <- 1-exp(
+    (alpha/beta)*age*exp(-beta*age)
+    +(1/beta)*((alpha/beta)-gamma)*(exp(-beta*age)-1)
+    -gamma*age)
+  model$foi <- (alpha*age-gamma)*exp(-beta*age)+gamma
+  model$df <- list(age=age, pos=pos, tot=tot)
+
+  class(model) <- "farrington_model"
+  model
+}
diff --git a/R/fractional_polynomial_models.R b/R/fractional_polynomial_models.R
index 202fe76..759c758 100644
--- a/R/fractional_polynomial_models.R
+++ b/R/fractional_polynomial_models.R
@@ -20,14 +20,15 @@ formulate <- function(p) {
   equation
 }
 
-#' Returns the powers of the GLM fitted model which has the lowest deviance score.
+#' Returns the powers of the fractional polynomial model which has the lowest deviance score.
 #'
-#' Refers to section 6.2.
+#' Return the best powers for a given degree
 #'
-#' @param data the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)
-#' @param p a powers sequence.
+#' @param data the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR
+#' `age`, `status` (for linelisting data)
+#' @param p a powers sequence to be tested.
 #' @param mc indicates if the returned model should be monotonic.
-#' @param degree the degree of the model. Recommended to be <= 2.
+#' @param degree the maximum degree (i.e. number of power terms) to search for the best model. Recommended to be <= 2.
 #' @param link the link function. Defaulted to "logit".
 #'
 #' @return list of 3 elements:
@@ -35,88 +36,126 @@ formulate <- function(p) {
 #'   \item{deviance}{Deviance of the best fitted model.}
 #'   \item{model}{The best model fitted}
 #'
-#' @examples
-#' df <- hav_be_1993_1994
-#' best_p <- find_best_fp_powers(
-#' df,
-#' p=seq(-2,3,0.1), mc=FALSE, degree=2, link="cloglog"
-#' )
-#' best_p
-#'
 #' @importFrom stats glm binomial as.formula
-#'
-#' @export
-find_best_fp_powers <- function(data, p, mc, degree, link="logit"){
-  data <- check_input(data)
+#' @import dplyr tidyr
+#' @importFrom purrr pmap_dfr
+find_best_fp_powers <- function(data,
+                                p, mc, degree, link="logit"){
   age <- data$age
   pos <- data$pos
   tot <- data$tot
 
-  glm_best <- NULL
-  d_best <- NULL
-  p_best <- NULL
-  #----
-  min_p <- 1
-  max_p <- length(p)
-  state <- rep(min_p, degree)
-  i <- degree
-  #----
-
-  get_cur_p <- function(cur_state) {
-    cur_p <- c()
-    for (i in 1:degree) {
-      cur_p <- c(cur_p, p[cur_state[i]])
-    }
-    cur_p
-  }
+  best_mod <- NULL # best model
+  best_p <- NULL # best powers (p vector) for the given degree
 
-  repeat {
-    if (
-      (i < degree && state[i] == max_p)
-      || (i == degree && state[i] == max_p+1)
-    ) {
-      if (i-1 == 0) break
-      if (state[i-1] < max_p) {
-        state[i-1] <- state[i-1]+1
-        for (j in i:degree) state[j] <- state[i-1]
-        i <- degree
-      } else {
-        i <- i-1
-        next
-      }
-    }
-    #------ iteration implementation -------
-    p_cur <- get_cur_p(state)
+  # Starting from the lowest degree
+  # Get the best combinations of powers p
+  # Try increment m and only accept when there is a statistically significant improvement (determined by LRT)
+  for (curr_deg in 1:degree){
+    # generate combinations of powers
+    p_combis <- expand.grid(rep(list(p), curr_deg))
+    # filter the rows that are increasing in order
+    p_combis <- p_combis[apply(p_combis, 1, \(r){all(diff(r)>=0)}), ,drop=FALSE]
+    # arrange by increasing order of power in first->second degree and so on
+    p_combis <- p_combis[do.call(order,p_combis),,drop=FALSE]
 
-    glm_cur <- glm(
-      as.formula(formulate(p_cur)),
-      family=binomial(link=link)
-    )
-    if (glm_cur$converged == TRUE) {
-      # d_cur <- deviance(glm_cur)
-      d_cur <- glm_cur$deviance
-      if (is.null(glm_best) || d_cur < d_best) {
-        if ((mc && is_monotone(glm_cur)) | !mc) {
-          glm_best <- glm_cur
-          d_best <- d_cur
-          p_best <- p_cur
-        }
+    # fit model with all the combinations of p and degree
+    mods <- p_combis %>%
+      pmap_dfr(\(...){
+        curr_p <- as.numeric(c(...))
+
+        curr_mod <- glm(
+          as.formula(formulate(curr_p)),
+          family=binomial(link=link)
+        )
+
+        # only accept the parameters if the model converged
+        if(curr_mod$converged==FALSE) return(NULL)
+
+        # only accept the parameters if the model is monotonic (if enforced)
+        if(mc && !is_monotone(curr_mod)) return(NULL)
+
+        tibble(
+          p = list(curr_p),
+          mod = list(curr_mod),
+          deviance = curr_mod$deviance
+        )
+      })
+
+    # handle scenario where no models converged at current degree
+    if(nrow(mods) == 0) next
+    # get the best model and powers for the current degree
+    best_idx <- which.min(mods$deviance)
+    curr_deg_mod <- mods[best_idx, ][["mod"]][[1]]
+    curr_deg_p <- mods[best_idx, ][["p"]][[1]]
+
+    # check if the best model with current degree is better than the last degree
+    if(!is.null(best_mod)){
+      # perform LRT
+      lrt_out <- anova(best_mod, curr_deg_mod, test="LRT")
+      lrt_out <- as.data.frame(lrt_out)
+
+      # Royston&Altman (1994) suggests significance level of 0.1
+      if(!is.na(lrt_out$`Pr(>Chi)`[2]) && lrt_out$`Pr(>Chi)`[2] < 0.1){
+        best_mod <- curr_deg_mod
+        best_p <- curr_deg_p
       }
+    }else{
+      best_mod <- curr_deg_mod
+      best_p <- curr_deg_p
     }
-    #---------------------------------------
-    if (sum(state != max_p) == 0) break
-    state[i] <- state[i]+1
   }
-  return(list(p=p_best, deviance=d_best, model=glm_best))
+
+  if(is.null(best_mod)) stop("Cannot find a converged model with the given degree and power")
+
+  list(p=best_p, model=best_mod)
 }
 
 #' A fractional polynomial model.
 #'
-#' Refers to section 6.2.
+#' @description Fractional polynomial model is a generalization of polynomial models
+#' where the power of the terms can be fractions, allowing more flexibility and better
+#' fit for data where asymptotic behavior is expected.
 #'
+#' @details
+#' Instead of a polynomial, the linear predictor is now defined as
+#' \deqn{
+#'  \eta_m(a, \beta, p_1, p_2, ...,p_m) = \Sigma^m_{i=0} \beta_i H_i(a)
+#' }
+#' Where \eqn{m} is an integer, \eqn{p_1 \le p_2 \le... \le p_m} is a sequence of powers,
+#' and \eqn{H_i(a)} is a transformation given by
+#'
+#' \deqn{
+#' H_i = \begin{cases}
+#' a^{p_i} & \text{ if } p_i \neq p_{i-1},
+#' \\ H_{i-1}(a) \times log(a)  & \text{ if } p_i = p_{i-1},
+#' \end{cases}
+#' }
+#'
+#' Refers to section 6.2. of the the book by Hens et al. (2012) for further details.
+#'
+#' @references
+#' Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+#' and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+#' Serological and Social Contact Data: A Modern Statistical Perspective.
+#' tatistics for Biology and Health. Springer New York.
+#' \doi{https://doi.org/10.1007/978-1-4614-4072-7}.
 #' @param data the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)
-#' @param p the powers of the predictor.
+#' @param p is either:
+#'   \itemize{
+#'    \item{a numeric vector specifying the powers to apply to the predictors}
+#'    \item{
+#'      a named list with two elements, \code{"p_range"} and \code{"degree"}. \code{"p_range"}
+#'      is a sequence of powers and \code{"degree"} is the maximum degree.
+#'      In which case the package will search for the best degree and power combinations
+#'    }
+#'   }
 #' @param link the link function for model. Defaulted to "logit".
+#' @param age_col name of the `age` column (default age_col="age").
+#' @param pos_col name of the `pos` column (default pos_col="pos").
+#' @param tot_col name of the `tot` column (default tot_col="tot").
+#' @param status_col name of the `status` column (default status_col="status").
+#' @param monotonic whether the returned model should be monotonic (if a search is specified)
 #'
 #' @importFrom stats predict as.formula
 #'
@@ -126,7 +165,10 @@ find_best_fp_powers <- function(data, p, mc, degree, link="logit"){
 #'   \item{info}{a fitted glm model}
 #'   \item{sp}{seroprevalence}
 #'   \item{foi}{force of infection}
-#' @seealso [stats::glm()] for more information on glm object
+#' @seealso
+#' [stats::glm()] for more information on glm object
+#'
+#' [polynomial_models()]
 #'
 #' @examples
 #' df <- hav_be_1993_1994
@@ -136,20 +178,36 @@ find_best_fp_powers <- function(data, p, mc, degree, link="logit"){
 #' plot(model)
 #'
 #' @export
-fp_model <- function(data,p,  link="logit") {
+fp_model <- function(data,p,monotonic=FALSE,link="logit",
+                     age_col="age",pos_col="pos", tot_col="tot", status_col="status") {
   model <- list()
 
-  data <- check_input(data)
+  data <- check_input(data, stratum_col=age_col,pos_col=pos_col, tot_col=tot_col, status_col=status_col)
   age <- data$age
   pos <- data$pos
   tot <- data$tot
-
   model$datatype <- data$type
 
-  model$info <- glm(
-    as.formula(formulate(p)),
-    family=binomial(link=link)
-  )
+  # handle powers input here
+  if(is.numeric(p)){
+    model$info <- glm(
+      as.formula(formulate(p)),
+      family=binomial(link=link)
+    )
+    model$p <- p
+  }else if(is.list(p) && all(c("p_range", "degree") %in% names(p))){
+    out <- find_best_fp_powers(
+      data = data.frame(age=age, pos=pos, tot=tot),
+      p = p$p_range, degree = p$degree, mc = monotonic, link = link
+    )
+    model$p <- out$p
+    model$info <- out$model
+  }else{
+    stop("Invalid value for `p`: either a numeric vector or a named list with
+         2 elements `p_range` and `degree`")
+  }
+
+
   model$sp  <- model$info$fitted.values
   model$foi <- est_foi(
     t=age,
diff --git a/R/hierarchical_bayesian_model.R b/R/hierarchical_bayesian_model.R
index c62d032..de6b02c 100644
--- a/R/hierarchical_bayesian_model.R
+++ b/R/hierarchical_bayesian_model.R
@@ -1,12 +1,69 @@
 #' Hierarchical Bayesian Model
 #'
-#' Refers to section 10.3
 #'
-#' @param data the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)
+#' @description Fit age-stratified seroprevalence to parametric hierarchical Bayesian models.
+#' Supported models including Farrington model (2 and 3 parameters variants)
+#' and Log Logistic model
+#'
+#' @details Consider a model for prevalence that has a parametric form
+#' \eqn{\pi(a_i, \alpha)} where \eqn{\alpha} is a parameter vector
+#'
+#' Under a Bayesian framework, we can constraint the parameter space of the prior distribution \eqn{P(\alpha)}
+#' to achieve monotonicity of the posterior distribution \eqn{P(\pi_1, \pi_2, ..., \pi_m|y,n)}
+#'
+#' Where:
+#'
+#' - \eqn{n = (n_1, n_2, ..., n_m)} and \eqn{n_i} is the sample size at age \eqn{a_i}
+#'
+#' - \eqn{y = (y_1, y_2, ..., y_m)} and \eqn{y_i} is the number of infected individual from the \eqn{n_i} sampled subjects
+#'
+#' For \bold{Farrington} model with 3 parameters, prevalence is formulated as follow
+#'
+#' \deqn{
+#' \pi (a) = 1 - exp\{ \frac{\alpha_1}{\alpha_2}ae^{-\alpha_2 a} +
+#' \frac{1}{\alpha_2}(\frac{\alpha_1}{\alpha_2} - \alpha_3)(e^{-\alpha_2 a} - 1) -\alpha_3 a \}
+#' }
+#'
+#' The likelihood model is defined as \eqn{y_i \sim Bin(n_i, \pi_i), \text{  for } i = 1,2,3,...m}
+#'
+#' The constraint on the parameter space can be incorporated by assuming
+#'  truncated normal distribution for the components of \eqn{\alpha},
+#' \eqn{\alpha = (\alpha_1, \alpha_2, \alpha_3)} in \eqn{\pi_i = \pi(a_i,\alpha)}
+#'
+#' The flat hyperpriors are defined as \eqn{\mu_j \sim \mathcal{N}(0, 10000)} and
+#' \eqn{\tau^{-2}_j \sim \Gamma(100,100)}
+#'
+#' For \bold{Farrington} model with 2 parameters, it is equivalent to the previous model with \eqn{\alpha_3 = 0}
+#'
+#' For \bold{Log logistic model}, seroprevalence is instead defined as
+#'
+#' \deqn{\pi(a) = \frac{\beta a^\alpha}{1 + \beta a^\alpha}, \text{ } \alpha, \beta > 0}
+#'
+#' The likelihood is similarly defined as \eqn{y_i \sim Bin(n_i, \pi_i))}
+#'
+#' The prior model of \eqn{\alpha_1} is specified as \eqn{\alpha_1 \sim \text{truncated  } \mathcal{N}(\mu_1, \tau_1)}
+#' with flat hyperpriors as in Farrington model
+#'
+#' \eqn{\beta} is constrained to be positive by specifying \eqn{\alpha_2 \sim \mathcal{N}(\mu_2, \tau_2)}
+#'
+#' Refer to section Chapter 10.3 of the the book by Hens et al. (2012) for further details.
+#'
+#' @references
+#' Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+#' and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+#' Serological and Social Contact Data: A Modern Statistical Perspective.
+#' tatistics for Biology and Health. Springer New York.
+#' \doi{https://doi.org/10.1007/978-1-4614-4072-7}.
+#'
+#' @param data the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR `age`, `status` (for linelisting data)
 #' @param type type of model ("far2", "far3" or "log_logistic")
 #' @param chains number of Markov chains
 #' @param warmup number of warmup runs
 #' @param iter number of iterations
+#' @param age_col name of the `age` column (default age_col="age").
+#' @param pos_col name of the `pos` column (default pos_col="pos").
+#' @param tot_col name of the `tot` column (default tot_col="tot").
+#' @param status_col name of the `status` column (default status_col="status").
 #'
 #' @importFrom rstan sampling summary
 #' @importFrom boot inv.logit
@@ -30,11 +87,12 @@
 #' plot(model)
 #' }
 hierarchical_bayesian_model <- function(data,
+                            age_col="age",pos_col="pos", tot_col="tot", status_col="status",
                             type="far3",chains = 1,warmup = 1500,iter = 5000){
   model <- list()
 
   # check input whether it is line-listing or aggregated data
-  data <- check_input(data)
+  data <- check_input(data, stratum_col=age_col,pos_col=pos_col, tot_col=tot_col, status_col=status_col)
   model$datatype <- data$type
   age <- data$age
   pos <- data$pos
diff --git a/R/mixture_model.R b/R/mixture_model.R
index ce3f7ee..1604c9d 100644
--- a/R/mixture_model.R
+++ b/R/mixture_model.R
@@ -1,12 +1,47 @@
 #' Fit a mixture model to classify serostatus
 #'
-#' Refers to section 11.1 - 11.4
+#' @description Fit the antibody level data to a 2-component Gaussian mixture model
 #'
-#' @param antibody_level - vector of the corresponding raw antibody level
-#' @param breaks - number of intervals which the antibody_level are grouped into
-#' @param pi - proportion of susceptible, infected
-#' @param mu - a vector of means of component distributions (vector of 2 numbers in ascending order)
-#' @param sigma -  a vector of standard deviations of component distributions (vector of 2 number)
+#' @details
+#' Antibody level (denoted \eqn{Z}) is modeled using a 2-component Gaussian
+#' mixture model. Each component \eqn{Z_j} (\eqn{j \in \{I, S\}}) represents the
+#' antibody level of the latent Infected and Susceptible sub-populations, following density
+#' \eqn{f_j(z_j|\theta_j)}
+#'
+#' Let \eqn{\pi_{\text{TRUE}}(a)} denotes the age-dependent mixing probability
+#' (i.e., the true prevalence), the density of the mixture is formulated as
+#'
+#' \deqn{f(z|z_I, z_S,a) = (1-\pi_{\text{TRUE}}(a))f_S(z_S|\theta_S)+\pi_{\text{TRUE}}(a)f_I(z_I|\theta_I)}
+#'
+#' The mean \eqn{E(Z|a)} thus equals
+#' \deqn{\mu(a) = (1-\pi_{\text{TRUE}}(a))\mu_S+\pi_{\text{TRUE}}(a)\mu_I}
+#'
+#' From which true prevalence can be computed as
+#' \deqn{\pi_{\text{TRUE}}(a) = \frac{\mu(a) - \mu_S}{\mu_I - \mu_S}}
+#'
+#' And FOI can then be inferred as
+#' \deqn{\lambda_{TRUE} = \frac{\mu'(a)}{\mu_I - \mu(a)}}
+#'
+#' Function [serosv::mixture_model()] fits antibody level data to \eqn{f_S(z_S|\theta_S)} and
+#' \eqn{f_I(z_I|\theta_I)}
+#'
+#' Function [serosv::estimate_mixture()] will then estimate age-specific antibody level \eqn{\mu(a)}
+#' and infer the estimation for \eqn{\pi_{\text{TRUE}}(a)} and \eqn{\lambda_{TRUE}}
+#'
+#' Refer to section 11.3. of the the book by Hens et al. (2012) for further details.
+#'
+#' @references
+#' Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+#' and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+#' Serological and Social Contact Data: A Modern Statistical Perspective.
+#' tatistics for Biology and Health. Springer New York.
+#' \doi{https://doi.org/10.1007/978-1-4614-4072-7}.
+#'
+#' @param antibody_level vector of the corresponding raw antibody level
+#' @param breaks number of intervals which the antibody_level are grouped into
+#' @param pi proportion of susceptible, infected
+#' @param mu a vector of means of component distributions (vector of 2 numbers in ascending order)
+#' @param sigma a vector of standard deviations of component distributions (vector of 2 number)
 #'
 #' @importFrom mixdist mix mixgroup mixparam
 #' @importFrom stats fitted
@@ -41,17 +76,53 @@ mixture_model <- function (antibody_level, breaks=40, pi=c(0.2, 0.8), mu=c(2,6),
   model
 }
 
-#' Estimate seroprevalence and foi by combining mixture model and regression
+#' Estimate seroprevalence and FOI from a fixed mixture model
+#'
+#' @description Estimate age-specific seroprevalence and FOI given
+#' a fitted mixture model (generated by [serosv::mixture_model()])
+#'
+#' @details
+#' Antibody level (denoted \eqn{Z}) is modeled using a 2-component Gaussian
+#' mixture model. Each component \eqn{Z_j} (\eqn{j \in \{I, S\}}) represents the
+#' antibody level of the latent Infected and Susceptible sub-populations, following density
+#' \eqn{f_j(z_j|\theta_j)}
+#'
+#' Let \eqn{\pi_{\text{TRUE}}(a)} denotes the age-dependent mixing probability
+#' (i.e., the true prevalence), the density of the mixture is formulated as
+#'
+#' \deqn{f(z|z_I, z_S,a) = (1-\pi_{\text{TRUE}}(a))f_S(z_S|\theta_S)+\pi_{\text{TRUE}}(a)f_I(z_I|\theta_I)}
+#'
+#' The mean \eqn{E(Z|a)} thus equals
+#' \deqn{\mu(a) = (1-\pi_{\text{TRUE}}(a))\mu_S+\pi_{\text{TRUE}}(a)\mu_I}
+#'
+#' From which true prevalence can be computed as
+#' \deqn{\pi_{\text{TRUE}}(a) = \frac{\mu(a) - \mu_S}{\mu_I - \mu_S}}
+#'
+#' And FOI can then be inferred as
+#' \deqn{\lambda_{TRUE} = \frac{\mu'(a)}{\mu_I - \mu(a)}}
+#'
+#' Function [serosv::mixture_model()] fits antibody level data to \eqn{f_S(z_S|\theta_S)} and
+#' \eqn{f_I(z_I|\theta_I)}
+#'
+#' Function [serosv::estimate_mixture()] will then estimate age-specific antibody level \eqn{\mu(a)}
+#' and infer the estimation for \eqn{\pi_{\text{TRUE}}(a)} and \eqn{\lambda_{TRUE}}
+#'
+#' Refer to section 11.3. of the the book by Hens et al. (2012) for further details.
 #'
-#' Refers to section 11.2 - 11.4
+#' @references
+#' Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+#' and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+#' Serological and Social Contact Data: A Modern Statistical Perspective.
+#' tatistics for Biology and Health. Springer New York.
+#' \doi{https://doi.org/10.1007/978-1-4614-4072-7}.
 #'
-#' @param age - vector of age
-#' @param antibody_level - vector of the corresponding raw antibody level
-#' @param mixture_model - mixture_model object generated by serosv::mixture_model()
-#' @param s - smoothing basis used to fit antibody level
-#' @param sp - smoothing parameter
-#' @param threshold_status - sero status using threshold approach in line listing (optional, for visualization and comparison only)
-#' @param monotonize - whether to monotonize seroprevalence (default to TRUE)
+#' @param age vector of age
+#' @param antibody_level vector of the corresponding raw antibody level
+#' @param mixture_model mixture_model object generated by serosv::mixture_model()
+#' @param s smoothing basis used to fit antibody level
+#' @param sp smoothing parameter
+#' @param threshold_status sero status using threshold approach in line listing (optional, for visualization and comparison only)
+#' @param monotonize whether to monotonize seroprevalence (default to TRUE)
 #'
 #' @importFrom mgcv gam
 #' @importFrom stats approx gaussian
@@ -124,6 +195,7 @@ estimate_from_mixture <- function(age, antibody_level, threshold_status = NULL,
 
   # save fitted df
   model$df <- data.frame(age = age, antibody_level = antibody_level)
+  model$monotonize <- monotonize
 
   if (!is.null(threshold_status)){
     model$df[["threshold_status"]] <- threshold_status
diff --git a/R/mseir_model.R b/R/mseir_model.R
deleted file mode 100644
index 0fcfa6c..0000000
--- a/R/mseir_model.R
+++ /dev/null
@@ -1,58 +0,0 @@
-#' MSEIR model
-#'
-#' Refers to section 3.4.
-#'
-#' @param a age sequence
-#' @param gamma time in maternal class.
-#' @param lambda time in susceptible class.
-#' @param sigma time in latent class.
-#' @param nu time in infected class.
-#'
-#' @examples
-#' model <- mseir_model(
-#'   a=seq(from=1,to=20,length=500), # age range from 0 -> 20 yo
-#'   gamma=1/0.5, # 6 months in the maternal antibodies
-#'   lambda=0.2,  # 5 years in the susceptible class
-#'   sigma=26.07, # 14 days in the latent class
-#'   nu=36.5      # 10 days in the infected class
-#' )
-#' model
-#'
-#' @return list of class mseir_model with the following parameters
-#'   \item{parameters}{list of parameters used for fitting the model}
-#'   \item{output}{matrix of proportion for each compartment over time}
-#'
-#' @export
-mseir_model <- function(a, gamma, lambda, sigma, nu)
-{
-
-
-  ma  <- exp(-gamma*a)
-  sa  <- (gamma/(gamma-lambda))*(exp(-lambda*a)-exp(-gamma*a))
-  ea  <- ((lambda*gamma)/(gamma-lambda))*
-    (
-      ((exp(-sigma*a)-exp(-lambda*a))/(lambda-sigma))
-      -((exp(-sigma*a)-exp(-gamma*a))/(gamma-sigma))
-    )
-  ia  <- (sigma*lambda*gamma)*
-    (
-      ((exp(-nu*a)-exp(-sigma*a))/((lambda-sigma)*(gamma-sigma)*(sigma-nu)))
-      +((exp(-nu*a)-exp(-lambda*a))/((lambda-gamma)*(lambda-sigma)*(lambda-nu)))
-      +((exp(-nu*a)-exp(-gamma*a))/((gamma-lambda)*(gamma-sigma)*(gamma-nu)))
-    )
-
-
-  model <- list()
-  model$parameters <- list(gamma = gamma, lambda = lambda, sigma = sigma, nu = nu)
-  model$output <- data.frame(
-    a =  c(0, a),
-    m = c(1, ma),
-    s = c(0, sa),
-    e = c(0, ea),
-    i = c(0, ia),
-    r = c(0, 1 - ma - sa - ea - ia)
-  )
-
-  class(model) <- "mseir_model"
-  model
-}
diff --git a/R/nonparametric.R b/R/nonparametric.R
index 6f1ccf1..24fe1d6 100644
--- a/R/nonparametric.R
+++ b/R/nonparametric.R
@@ -1,16 +1,63 @@
 #' A local polynomial model.
 #'
-#' Refers to section 7.1. and 7.2.
+#' @description Fit the age-specific seroprevalence to a local polynomial model,
+#' where the linear predictor is approximated locally at one particular age.
 #'
-#' @param data the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)
+#' @details
+#' Consider a linear predictor \eqn{\eta(a)} approximated locally at one particular value \eqn{a_0}.
+#'
+#' For a general degree \eqn{p}, the linear predictor for a neighbor of \eqn{a_0}, labeled \eqn{a_i} is equivalent to the Taylor approximation
+#' \deqn{
+#' \eta(a_i) = \eta(a_0) + \eta^{(1)}(a_0)(a_i - a_0) +
+#' \frac{\eta^{(2)}(a_0)}{2}(a_i - a_0)^2 + ... + \frac{\eta^{(p)}(a_0)}{p!}(a_i - a_0)^p
+#' }
+#'
+#' \eqn{\eta(a_i)} can be estimated by maximizing
+#' \deqn{
+#'  \Sigma_{i=1}^{N} \ell_i \{Y_i, g^{-1} (\beta_0 + \beta_1(a_i-a_0)+ \beta_2(a_i-a_0)^2 ... +
+#'  \beta_p(a_i-a_0)^p) \} K_h(a_i - a_0)
+#' }
+#'
+#' The estimator for the \eqn{k}-th derivative of \eqn{\eta(a_0)}, for \eqn{k = 0,1,…,p}
+#' (degree of local polynomial) is thus:
+#' \deqn{
+#'  \hat{\eta}^{(k)}(a_0) = k!\hat{\beta}_k(a_0)
+#' }
+#'
+#' The estimator for the prevalence at age \eqn{a_0} is then given by
+#' \deqn{
+#' \hat{\pi}(a_0) = g^{-1}\{ \hat{\beta}_0(a_0) \}
+#' }
+#' Where \eqn{g} is the link function
+#'
+#' The estimator for the force of infection at age \eqn{a_0} by assuming \eqn{p \ge 1} is as followed
+#' \deqn{
+#' \hat{\lambda}(a_0) = \hat{\beta}_1(a_0) \delta \{ \hat{\beta}_0 (a_0) \}
+#' }
+#' Where \eqn{\delta \{ \hat{\beta}_0(a_0) \} = \frac{dg^{-1} \{ \hat{\beta}_0(a_0) \} } {d\hat{\beta}_0(a_0)}}
+#'
+#' Refer to section 7.1 and 7.2. of the the book by Hens et al. (2012) for further details.
+#'
+#' @references
+#' Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+#' and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+#' Serological and Social Contact Data: A Modern Statistical Perspective.
+#' tatistics for Biology and Health. Springer New York.
+#' \doi{https://doi.org/10.1007/978-1-4614-4072-7}.
+#'
+#' @param data the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR `age`, `status` (for linelisting data)
 #' @param kern Weight function, default = "tcub".
 #' Other choices are "rect", "trwt", "tria", "epan", "bisq" and "gauss".
 #' Choices may be restricted when derivatives are required;
 #' e.g. for confidence bands and some bandwidth selectors.
 #' @param nn Nearest neighbor component of the smoothing parameter.
 #' Default value is 0.7, unless either h is provided, in which case the default is 0.
-#' @param h The constant component of the smoothing parameter. Default: 0.
+#' @param h The constant bandwidth of the smoothing parameter. Default: 0.
 #' @param deg Degree of polynomial to use. Default: 2.
+#' @param age_col name of the `age` column (default age_col="age").
+#' @param pos_col name of the `pos` column (default pos_col="pos").
+#' @param tot_col name of the `tot` column (default tot_col="tot").
+#' @param status_col name of the `status` column (default status_col="status").
 #'
 #' @examples
 #' df <- mumps_uk_1986_1987
@@ -34,28 +81,106 @@
 #' @seealso [locfit::locfit()] for more information on the fitted locfit object
 #'
 #' @export
-lp_model <- function(data, kern="tcub", nn=0, h=0, deg=2) {
-  if (missing(nn) & missing(h)) {
-    nn <- 0.7 # default nn from lp()
+lp_model <- function(data, kern="tcub", nn=0, h=0, deg=2,
+                     age_col="age",pos_col="pos", tot_col="tot", status_col="status") {
+  if (all(nn==0) & all(h==0))  {
+    # default nn from lp()
+    nn <- 0.7
+    h <- 0
   }
-
   model <- list()
 
   # check input whether it is line-listing or aggregated data
-  data <- check_input(data)
+  data <- check_input(data, stratum_col=age_col,pos_col=pos_col, tot_col=tot_col, status_col=status_col)
   age <- data$age
   pos <- data$pos
   tot <- data$tot
   model$datatype <- data$type
 
   y <- pos/tot
-  estimator <- lp(age, deg=deg, nn=nn, h=h)
-  model$pi  <- locfit(y~estimator, family="binomial", kern=kern)
-  model$eta <- locfit(y~estimator, family="binomial", kern=kern, deriv=1)
-  model$sp  <- fitted(model$pi)
-  model$foi <- fitted(model$eta)*fitted(model$pi) # λ(a)=η′(a)π(a)
+
+  if(length(nn) > 1 & length(h) > 1) stop("Tuning both `h` and `nn` may lead to parameter idenfifiability issues, please fix one of the parameters instead")
+
+  if(length(nn) > 1 || length(h)>1){
+    best_param <- best_lp_params(
+      data=data,
+      nn = nn,
+      h = h,
+      family="binomial",
+      kern=kern
+    )
+
+    nn <- best_param$nn
+    h <- best_param$h
+  }
+
+  # print(paste0("nn: ", nn))
+  # print(paste0("h: ", h))
+
+  model$info  <- locfit(y~lp(age, deg=deg, nn=nn, h=h), family="binomial", kern=kern)
+  model$nn <- nn
+  model$h <- h
+  model$deg <- deg
+  model$kern <- kern
+  model$eta <- locfit(y~lp(age, deg=deg, nn=nn, h=h), family="binomial", kern=kern, deriv=1)
+  model$sp  <- fitted(model$info)
+  model$foi <- fitted(model$eta)*fitted(model$info) # λ(a)=η′(a)π(a)
   model$df  <- list(age=age, pos=pos, tot=tot)
 
   class(model) <- "lp_model"
   model
 }
+
+# function to return the best parameter of local polynomial model using GCV
+# nn - range of values for nearest neighbor
+# h - range of values for constant bandwidth
+# if both nn and h are given, select either best nn or h, whichever gives the lowest GCV
+#' @import tidyr dplyr locfit
+best_lp_params <- function(data, nn=0, h=0, kern="tcub",deg=2, family="binomial"){
+  # helper function to get df and GCV
+  summary.gcvplot <- function(object, ...){
+    z <- cbind(object$df, object$values)
+    dimnames(z) <- list(NULL, c("df", object$cri))
+    z
+  }
+
+  # helper function which return parameter value which gives the lowest gcv
+  get_best_gcv <- function(nn_vals=0, h_vals=0){
+    is_nn <- length(nn_vals) > 1 # check if we are tuning for nn or h
+    par_vals <- if (is_nn) nn_vals else h_vals
+    # generate parameters matrix
+    alpha <-
+      if (is_nn)
+        cbind(nn_vals, rep(h_vals, length(nn_vals)))
+      else
+        cbind(rep(nn_vals, length(h_vals)), h_vals)
+
+    # compute gcv
+    gcv_out <- gcvplot(
+      pos / tot ~ age,
+      deg = deg,
+      kern = kern,
+      family = family,
+      alpha = alpha,
+      data=data
+    )
+
+    gcv_out <- cbind(par_vals, gcv_out$values)
+    best_idx <- which.min(gcv_out[,2])
+
+    c(
+      gcv_out[best_idx, 1],
+      gcv_out[best_idx, 2]
+    )
+  }
+
+  res <- list()
+
+  if(length(nn) > 1 & length(h) > 1) stop("Tuning both `h` and `nn` may lead to parameter idenfifiability issues, please fix one of the parameters instead")
+  if(length(nn) > 1) res[c("h", "nn", "nn_gcv")] <- c(h, get_best_gcv(nn_vals=nn, h_vals=h))
+  if(length(h) > 1) res[c("nn", "h", "h_gcv")] <- c(nn, get_best_gcv(nn_vals=nn, h_vals=h))
+
+  res
+}
+
+
diff --git a/R/plots.R b/R/plots.R
index 78b4f89..b287811 100644
--- a/R/plots.R
+++ b/R/plots.R
@@ -28,8 +28,8 @@ set_plot_style <- function(sero = "blueviolet", ci = "royalblue1", foi = "#fc032
 plot_data <- function(x){
   if(x$datatype == "linelisting"){
     # transform data before plotting
-    df_ <- transform_data(x$df$age, x$df$pos)
-    age <- df_$t
+    df_ <- transform_data(x$df, stratum_col="age", status_col="pos")
+    age <- df_$age
     pos <- df_$pos
     tot <- df_$tot
     # use pre-aggregated age for FOI
@@ -45,7 +45,7 @@ plot_data <- function(x){
 }
 
 #=== Helper function for plotting =====
-plot_util <- function(age, pos, tot, sero, foi, cex = 20){
+plot_util <- function(age, pos, tot, sero, foi, scale_foi=1, cex = 20){
   # resolve no visible binding
   x <- y <- ymin <- ymax <- NULL
 
@@ -55,7 +55,7 @@ plot_util <- function(age, pos, tot, sero, foi, cex = 20){
     coord_cartesian(xlim=c(0,max(age)), ylim=c(0, 1)) +
     scale_y_continuous(
       name = "Seroprevalence",
-      sec.axis = sec_axis(~.*1, name = " Force of infection")
+      sec.axis = sec_axis(~.*scale_foi, name = " Force of infection")
     ) + set_plot_style()
 
   # === Add seroprevalence layer
@@ -81,22 +81,51 @@ plot_util <- function(age, pos, tot, sero, foi, cex = 20){
       # --- Handle cases where FOI is a data.frame (with CI)
       # plot <- plot + geom_smooth(aes_auto(foi, col = "foi", linetype="foi", fill="ci"), data=foi,
       #                          stat="identity",lwd=0.5)
-      plot <- plot + geom_smooth(aes(x = x, y=y, ymin =ymin, ymax = ymax, col = "foi", linetype="foi", fill="ci"), data=foi,
-                               stat="identity",lwd=0.5)
+      plot <- plot + geom_smooth(
+        aes(
+          x = x,
+          # scale FOI if specified
+          y = y/scale_foi,
+          ymin = ymin/scale_foi,
+          ymax = ymax/scale_foi,
+          col = "foi",
+          linetype = "foi",
+          fill = "ci"
+        ),
+        data = foi,
+        stat = "identity",
+        lwd = 0.5
+      )
     }else{
       # --- Handle cases where CI for FOI is not computable & length of age for foi differs from provided age vector
-      plot <- plot + geom_line(aes(x = x, y=y, col = "foi", linetype="foi"), data=foi,
-                               stat="identity",lwd=0.5)
+      plot <- plot + geom_line(
+        aes(
+          x = x,
+          # scale FOI if specified
+          y = y/scale_foi,
+          col = "foi",
+          linetype = "foi"
+        ),
+        data = foi,
+        stat = "identity",
+        lwd = 0.5
+      )
     }
   }else if (length(age) != length(foi)){
     # --- handle some cases when length of age differs from length of foi
     age <- age[c(-1,-length(age))]
     foi <- data.frame(x = age, y = foi)
-    plot <- plot + geom_line(aes(x = x, y=y, col = "foi", linetype="foi"), data = foi,
-                             lwd = 0.5)
+    plot <- plot + geom_line(aes(
+      x = x,
+      y = y/scale_foi,
+      col = "foi",
+      linetype = "foi"
+    ),
+    data = foi,
+    lwd = 0.5)
   }else{
     # --- Simply plot foi
-    plot <- plot + geom_line(aes(x = age, y = foi, col = "foi", linetype="foi"),
+    plot <- plot + geom_line(aes(x = age, y = foi/scale_foi, col = "foi", linetype="foi"),
                 lwd = 0.5)
   }
   plot
@@ -107,129 +136,6 @@ plot_util <- function(age, pos, tot, sero, foi, cex = 20){
 #   UseMethod("plot")
 # }
 
-#### SIR model ####
-
-#' plot() overloading for SIR model
-#'
-#' @param x the sir_basic_model object.
-#' @param ... arbitrary params.
-#' @import ggplot2
-#' @importFrom methods is
-#' @importFrom graphics plot
-#'
-#' @return ggplot object
-#' @export
-plot.sir_basic_model <- function(x, ...){
-  comp_lvl <-  c("S", "I", "R")
-  time <- S <- I <- R <- NULL
-
-  ggplot(x$output) +
-    geom_line(aes(x = time, y = S, color = factor("S", levels = comp_lvl))) +
-    geom_line(aes(x = time, y = I, color = factor("I", levels = comp_lvl))) +
-    geom_line(aes(x = time, y = R, color = factor("R", levels = comp_lvl))) +
-    list(
-      scale_colour_manual(
-        values = c("S" = "blueviolet", "I" = "#fc0328", "R" = "royalblue1"),
-        labels = c("S"="susceptible", "I"="infected", "R"="recovered")
-      ),
-      labs( x = "Time",
-            y = "Count",
-            colour = "Compartment")
-    )
-}
-
-#' plot() overloading for SIR static model
-#'
-#' @param x the sir_static_model object.
-#' @param ... arbitrary params.
-#' @import ggplot2
-#' @importFrom methods is
-#' @importFrom graphics plot
-#'
-#' @return ggplot object
-#' @export
-plot.sir_static_model <- function(x, ...){
-  comp_lvl <-  c("s", "i", "r")
-  time <- s <- i <- r <- NULL
-
-  ggplot(x$output) +
-    geom_line(aes(x = time, y = s, color = factor("s", levels = comp_lvl))) +
-    geom_line(aes(x = time, y = i, color = factor("i", levels = comp_lvl))) +
-    geom_line(aes(x = time, y = r, color = factor("r", levels = comp_lvl))) +
-    list(
-      scale_colour_manual(
-        values = c("s" = "blueviolet", "i" = "#fc0328", "r" = "royalblue1"),
-        labels = c("s"="susceptible", "i"="infected", "r"="recovered")
-      ),
-      labs(
-        colour = "Compartment",
-        x = "Age",
-        y = "Fraction")
-    )
-}
-
-
-#' plot() overloading for SIR sub populations model
-#'
-#' @param x the sir_subpops_models object.
-#' @param ... arbitrary params.
-#' @import ggplot2
-#' @importFrom methods is
-#' @importFrom graphics plot
-#'
-#' @return list of ggplot objects, each object is the plot for the corresponding subpopulation
-#' @export
-plot.sir_subpops_model <- function(x, ...){
-  time <- s <- i <- r <- NULL
-  comp_lvl <-  c("s", "i", "r")
-
-  # using for loop here would not work due to ggplot lazy eval
-  subpop_plots <- lapply(1:x$parameters$k, function(subpop) {
-    ggplot(x$output) +
-      geom_line(aes(x = time, y = get(paste0("s", subpop)), color = factor("s", levels = comp_lvl))) +
-      geom_line(aes(x = time, y = get(paste0("i", subpop)), color = factor("i", levels = comp_lvl))) +
-      geom_line(aes(x = time, y = get(paste0("r", subpop)), color = factor("r", levels = comp_lvl))) +
-      scale_colour_manual(
-        values = c("s" = "blueviolet", "i" = "#fc0328", "r" = "royalblue1"),
-        labels = c("s"="susceptible", "i"="infected", "r"="recovered")
-      ) +
-      labs(title= paste0("Plot for subpopulation ", subpop),
-           x = "Time",
-           y = "Fraction",
-           colour = "Compartment")
-  })
-
-  names(subpop_plots) <- paste0("subpop_", 1:x$parameters$k)
-  subpop_plots
-}
-
-#' plot() overloading for MSEIR model
-#'
-#' @param x the mseir_model object.
-#' @param ... arbitrary params.
-#' @import ggplot2
-#' @importFrom methods is
-#' @importFrom graphics plot
-#'
-#' @return ggplot object
-#' @export
-plot.mseir_model <- function(x, ...){
-  a <- m <- s <- e <- i <- r <- NULL
-  # make leveled factor to force legend show color in order
-  comp_lvl <- c("m", "s", "e", "i", "r")
-
-  ggplot(x$output) +
-    geom_line(aes(x = a, y = m, color = factor("m", levels = comp_lvl))) +
-    geom_line(aes(x = a, y = s, color = factor("s", levels = comp_lvl))) +
-    geom_line(aes(x = a, y = e, color = factor("e", levels = comp_lvl))) +
-    geom_line(aes(x = a, y = i, color = factor("i", levels = comp_lvl))) +
-    geom_line(aes(x = a, y = r, color = factor("r", levels = comp_lvl))) +
-    scale_colour_manual(
-      values = c("m"="#3ea379","s" = "blueviolet", "e"="#3e45a3", "i" = "#fc0328", "r" = "royalblue1"),
-      labels = c("m"="maternal immunity", "s"="susceptible", "e"="exposed", "i"="infected", "r"="recovered")
-    ) +
-    labs(x = "Age", y = "Fraction", color = "Compartment")
-}
 
 #### Polynomial model ####
 #' plot() overloading for polynomial model
@@ -511,12 +417,17 @@ plot.estimate_from_mixture <- function(x, ... ){
   returned_plot <- ggplot()
 
   if(!is.null(x$df$threshold_status)){
-    aggregated <- transform_data(round(x$df$age), x$df$threshold_status)
+    aggregated <- transform_data(
+      data.frame(
+        age = round(x$df$age),
+        status = x$df$threshold_status
+      )
+    )
     # resolve no visible binding note
-    t <- pos <- tot <- NULL
+    age <- pos <- tot <- NULL
 
     returned_plot <-  returned_plot +
-      geom_point(aes( x = t, y = pos/tot, size = cex*(pos)/max(tot) ), data = aggregated,
+      geom_point(aes( x = age, y = pos/tot, size = cex*(pos)/max(tot) ), data = aggregated,
                  shape = 1, show.legend = FALSE)
   }
 
@@ -529,6 +440,7 @@ plot.estimate_from_mixture <- function(x, ... ){
   returned_plot + set_plot_style() + labs(x = "Age", y="Seroprevalence")
 }
 
+# ------- Plot age time varying seroprevalence ----------
 #' Plot output for age_time_model
 #'
 #' @param x - a `age_time_model` object
@@ -665,12 +577,13 @@ plot_gcv <- function(age, pos, tot, nn_seq, h_seq, kern="tcub", deg=2) {
   nn_plot + h_plot + plot_layout(ncol=2)
 }
 
+# ----- Plot corrected prevalence -------
 #' Plot output for corrected_prevalence
 #'
 #' @param x - the output of `correct_prevalence()` function
 #' @param y - another output of `correct_prevalence()` function (optional, for comparison only)
 #' @param facet - whether to plot as facets or on the same plot (only when y is provided)
-#' @import ggplot2 tidyr patchwork 
+#' @import ggplot2 tidyr patchwork
 #' @importFrom magrittr %>%
 #' @importFrom assertthat assert_that
 #'
@@ -712,7 +625,7 @@ plot_corrected_prev <- function(x, y=NULL, facet=FALSE){
           ymax = sero_upr,
           color = label
         ),
-        alpha = 0.7, data = data
+        alpha = 0.5, data = data
       )
     }
     # them add label layer
@@ -739,9 +652,15 @@ plot_corrected_prev <- function(x, y=NULL, facet=FALSE){
 
   plot <- ggplot() +
     geom_point(aes(
-      x = age, y = pos / tot,
+      x = age, y = sero,
       color = "apparent prevalence"
-    ), data = dat)
+    ), alpha = 0.8, data = dat)+
+    geom_errorbar(
+      aes(
+        x = age, y = sero, ymin = sero_lwr, ymax = sero_upr,
+        color = "apparent prevalence"
+      ), alpha = 0.5,data = dat
+    )
 
   if(!facet){
     plot <- plot + generate_layers(corrected_dat, title = if(is.null(y)) paste0("Plot for ", x$method, " approach") else NULL)
diff --git a/R/polynomial_models.R b/R/polynomial_models.R
index c6113ef..6095f07 100644
--- a/R/polynomial_models.R
+++ b/R/polynomial_models.R
@@ -10,21 +10,67 @@ X <- function(t, degree) {
 
 #' Polynomial models
 #'
-#' Refers to section 6.1.1
-#' @param data the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)
-#' @param k  degree of the model.
-#' @param type name of method (Muench, Giffith, Grenfell).
-#' @param link link function.
+#' @description Fit age-stratified seroprevalence data to serocatalytic models formulated as polynomials.
+#'
+#' @details
+#' The seroprevalence is assumed to follow the general format
+#' \deqn{
+#' \pi(a)  = 1 - e^{-\Sigma_{i=1}^k \beta_i a^i}
+#' }
+#' Which implies the force of infection to be \eqn{\lambda(a) = \Sigma_{i=1}^k \beta_i i a^{i-1}}
+#'
+#' Where:
+#'
+#' - \eqn{\pi} is the seroprevalence at age \eqn{a}
+#'
+#' - \eqn{a} is the variable age
+#'
+#' - \eqn{k} is the degree of the polynomial
+#'
+#' The seroprevalence \eqn{\pi(a)} is fitted using a GLM with log link with
+#' the linear predictor \eqn{\eta(a) = \Sigma_{i=1}^k \beta_i a^{i}}
+#'
+#' Muench (1934) model is equivalent to a degree 1 (\eqn{k=1}) linear predictor
+#'
+#' Griffith model is equivalent to a degree 2 (\eqn{k=2}) linear predictor
+#'
+#' Grenfell & Anderson (1985) suggested a higher order polynomials (\eqn{k \geq 3})
+#'
+#' Refer to section 6.1.1. of the the book by Hens et al. (2012) for further details.
+#'
+#' @references
+#' Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+#' and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+#' Serological and Social Contact Data: A Modern Statistical Perspective.
+#' tatistics for Biology and Health. Springer New York.
+#' \doi{https://doi.org/10.1007/978-1-4614-4072-7}.
+#'
+#' Grenfell, B. T., and R. M. Anderson. 1985. “The Estimation of
+#' Age-Related Rates of Infection from Case Notifications and Serological Data.”
+#' The Journal of Hygiene 95 (2): 419–36. \doi{https://doi.org/10.1017/s0022172400062859}.
+#'
+#' Muench, Hugo. 1934. “Derivation of Rates from Summation Data by the Catalytic Curve.”
+#' Journal of the American Statistical Association 29 (185):
+#' 25–38. \doi{https://doi.org/10.1080/01621459.1934.10502684}.
+#'
+#' @param data the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR `age`, `status` (for linelisting data)
+#' @param k  degree of the polynomial. (k=1 for Muench model, k=2 for Griffith model, k=3 for Grenfell model).
+#' @param link link function (default link="log").
+#' @param age_col name of the `age` column (default age_col="age").
+#' @param pos_col name of the `pos` column (default pos_col="pos").
+#' @param tot_col name of the `tot` column (default tot_col="tot").
+#' @param status_col name of the `status` column (default status_col="status").
 #'
 #' @examples
 #' data <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age), ]
-#' data$status <- data$seropositive
-#' aggregated <- transform_data(data$age, data$seropositive, stratum_col = "age")
+#' aggregated <- transform_data(data, stratum_col = "age", status_col="seropositive")
 #'
 #' # fit with aggregated data
-#' model <- polynomial_model(aggregated, type = "Muench")
+#' model <- polynomial_model(aggregated, k = 1)
 #' # fit with linelisting data
-#' model <- polynomial_model(data, type = "Muench")
+#' model <- polynomial_model(data,
+#'     status_col = "seropositive",
+#'     k = 1)
 #' plot(model)
 #'
 #' @return a list of class polynomial_model with 5 items
@@ -35,97 +81,102 @@ X <- function(t, degree) {
 #'   \item{foi}{force of infection}
 #'
 #' @export
-polynomial_model <- function(data, k,type, link = "log"){
+polynomial_model <- function(data, k, link = "log",
+                             age_col="age",pos_col="pos", tot_col="tot", status_col="status"){
   model <- list()
-  data <- check_input(data)
+  data <- check_input(data, stratum_col=age_col,pos_col=pos_col, tot_col=tot_col, status_col=status_col)
   model$datatype <- data$type
 
-  Age <- data$age
-  Pos <- data$pos
-  Neg <- data$tot - Pos
+  age <- data$age
+  pos <- data$pos
+  neg <- data$tot - pos
+
+  df <- data.frame(cbind(age, pos,neg))
 
-  df <- data.frame(cbind(Age, Pos,Neg))
-  if(missing(k)){
-    k <- switch(type,
-                "Muench" = 1 ,
-                "Griffith" = 2,
-                "Grenfell" = 3)}
-  age <- function(k){
-    if(k>1){
-      formula<- paste0("I","(",paste("Age", 2:k,sep = "^"),")",collapse = "+")
-      paste0("cbind(Neg,Pos)"," ~","-1+Age+",formula)
-    } else {
-      paste0("cbind(Neg,Pos)"," ~","-1+Age")
+  # helper function to generate the polynomial given a k value
+  # to be used for parameter selection if multiple values for k are given
+  generate_polynomial <- function(k, df, link="log"){
+    Age <- function(k){
+      if(k>1){
+        formula<- paste0("I","(",paste("age", 2:k,sep = "^"),")",collapse = "+")
+        paste0("cbind(neg,pos)"," ~","-1+age+",formula)
+      } else {
+        paste0("cbind(neg,pos)"," ~","-1+age")
+      }
     }
+
+    glm(Age(k), family=binomial(link=link),df)
+  }
+
+  # If a vector of values for k is provided -> select best value
+  if(length(k) > 1){
+    out <- nested_mod_selection(
+      list("k" = k),
+      model_fn = \(k, df){
+        generate_polynomial(k, df, link=link)
+      },
+      dat = df
+    )
+
+    k <- out$best_par$k
+    model$info <- out$mod
+  }else{
+    model$info <- generate_polynomial(k, df, link=link)
   }
-  model$info <- glm(age(k), family=binomial(link=link),df)
-  X <- X(Age, k)
+
+  X <- X(age, k)
   model$sp <- 1 - model$info$fitted.values
   model$foi <- X%*%model$info$coefficients
-  model$df <- list(age=Age, pos=Pos, tot= Pos + Neg)
+  model$df <- list(age=age, pos=pos, tot= pos + neg)
+  model$k <- k
   class(model) <- "polynomial_model"
   model
 }
 
-#' The Farrington (1990) model.
-#'
-#' Refers to section 6.1.2.
-#'
-#' @param data the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)
-#' @param start Named list of vectors or single vector.
-#' Initial values for optimizer.
-#' @param fixed Named list of vectors or single vector.
-#' Parameter values to keep fixed during optimization.
-#'
-#' @return a list of class farrington_model with 5 items
-#'   \item{datatype}{type of datatype used for model fitting (aggregated or linelisting)}
-#'   \item{df}{the dataframe used for fitting the model}
-#'   \item{info}{fitted "mle" object}
-#'   \item{sp}{seroprevalence}
-#'   \item{foi}{force of infection}
-#' @seealso [stats4::mle()] for more information on the fitted mle object
-#'
-#' @examples
-#' df <- rubella_uk_1986_1987
-#' model <- farrington_model(
-#'   df,
-#'   start=list(alpha=0.07,beta=0.1,gamma=0.03)
-#'   )
-#' plot(model)
-#'
-#' @importFrom stats4 mle
-#'
-#' @export
-farrington_model <- function(data, start, fixed=list())
-{
-  model <- list()
+# TODO: check if this can be generalized to other functions as well (e.g. fractional polynomial)
+# function to return the best parameter of nested glm models using LRT
+# par_range - list of parameters and its possible values
+# model_fn - function to fit and return a model, must takes 2 arguments: par, df
+#' @import tidyr
+#' @importFrom purrr pmap
+nested_mod_selection <- function(par_range, model_fn, dat, method="LRT"){
+  # generate all combinations of parameters values
+  par_combs <- tidyr::crossing(!!!par_range)
 
-  # check input whether it is line-listing or aggregated data
-  data <- check_input(data)
-  age <- data$age
-  pos <- data$pos
-  tot <- data$tot
-  model$datatype <- data$type
+  # fit the model using specified parameter values
+  mods_out <- par_combs %>%
+    purrr::pmap(\(...){model_fn(..., df=dat)})
+  # perform LRT
+  lrt_out <- do.call(
+    anova,
+    c(mods_out, list(test="LRT"))
+  )
 
-  farrington <- function(alpha,beta,gamma) {
-    p=1-exp((alpha/beta)*age*exp(-beta*age)
-            +(1/beta)*((alpha/beta)-gamma)*(exp(-beta*age)-1)-gamma*age)
-    ll=pos*log(p)+(tot-pos)*log(1-p)
-    return(-sum(ll))
-  }
+  # get the best model
+  best_idx <- lrt_out %>%
+    as.data.frame() %>%
+    mutate(
+      idx = 1:n()
+    ) %>%
+    filter(
+      `Pr(>Chi)` < 0.05
+    ) %>%
+    arrange(
+      Deviance
+    ) %>%
+    pull(idx)
 
-  model$info <- mle(farrington, fixed=fixed, start=start)
-  alpha <- model$info@coef[1]
-  beta  <- model$info@coef[2]
-  gamma <- model$info@coef[3]
-  model$sp <- 1-exp(
-    (alpha/beta)*age*exp(-beta*age)
-    +(1/beta)*((alpha/beta)-gamma)*(exp(-beta*age)-1)
-    -gamma*age)
-  model$foi <- (alpha*age-gamma)*exp(-beta*age)+gamma
-  model$df <- list(age=age, pos=pos, tot=tot)
+  # handle scenario when the reference model (i.e., the first model) is in fact the best option
+  # i.e., when the other parameter combinations do not result in statistically significant improvement
+  if(length(best_idx)>1){
+    best_idx <- best_idx[1]
+  }else{
+    best_idx <- 1
+  }
 
-  class(model) <- "farrington_model"
-  model
+  list(
+    best_par = par_combs[best_idx, ] %>% as.list(),
+    mod = mods_out[best_idx][[1]]
+  )
 }
 
diff --git a/R/predict.R b/R/predict.R
new file mode 100644
index 0000000..5c65370
--- /dev/null
+++ b/R/predict.R
@@ -0,0 +1,253 @@
+# ====== Predict function for serosv models ======
+#' Prediction for serosv polynomial model
+#'
+#' A wrapper of predict.glm for direct prediction from polynomial_model object
+#'
+#' @param object serosv models
+#' @param newdata data.frame with age column to generate prediction
+#' @param ... arbitrary argument
+#'
+#' @importFrom stats predict.glm
+#' @import dplyr
+#'
+#' @return prediction output
+#' @seealso
+#' [stats::predict.glm()] for more information on the predict function
+#' @export
+predict.polynomial_model <- function(object, newdata=NULL, ...){
+  predict.glm(object$info, newdata, ...)
+}
+
+#' Prediction for serosv fractional polynomial model
+#'
+#' @param object serosv models
+#' @param newdata data.frame with age column to generate prediction
+#' @param ... arbitrary argument
+#'
+#' @importFrom stats predict.glm
+#' @return prediction output
+#' @seealso
+#' [stats::predict.glm()] for more information on the predict function
+#' @export
+predict.fp_model <- function(object, newdata=NULL, ...){
+
+  predict.glm(object$info,newdata=newdata, ...)
+}
+
+#' Prediction for serosv Weibull model
+#'
+#' @param object serosv models
+#' @param newdata data.frame with age column to generate prediction
+#' @param ... arbitrary argument
+#'
+#' @importFrom stats predict.glm
+#' @return prediction output
+#' @seealso
+#' [stats::predict.glm()] for more information on the predict function
+#' @export
+predict.weibull_model <- function(object, newdata=NULL, ...){
+  predict.glm(object$info,data.frame("log(t)" = newdata$`log(t)`), ...)
+}
+
+
+#' Prediction for serosv local polynomial model
+#'
+#' @param object serosv models
+#' @param newdata data.frame with age column to generate prediction
+#' @param ... arbitrary argument
+#'
+#' @return prediction output
+#' @export
+predict.lp_model <- function(object, newdata=NULL,...){
+  predict(object$info, data.frame(age = newdata[[1]]), ...)
+}
+
+
+#' Prediction for serosv penalized spline model
+#'
+#' @param object serosv models
+#' @param newdata data.frame with age column to generate prediction
+#' @param ... arbitrary argument
+#'
+#' @importFrom mgcv predict.gam
+#' @return prediction output
+#' @seealso
+#' [mgcv::predict.gam()] for more information on the predict function
+#' @export
+predict.penalized_spline_model <- function(object, newdata=NULL,...){
+
+  # handle different output for different frameworks
+  if(object$framework == "pl"){
+    gam_obj <- object$info
+  }else{
+    gam_obj <- object$info$gam
+  }
+
+  predict.gam(gam_obj, newdata, ...)
+}
+
+#' Prediction for serosv Farrington model
+#'
+#' @param object serosv models
+#' @param newdata data.frame with age column to generate prediction
+#' @param ... arbitrary argument
+#'
+#' @return prediction output
+#' @export
+predict.farrington_model <- function(object, newdata=NULL,...){
+  alpha <- object$info@coef[1]
+  beta  <- object$info@coef[2]
+  gamma <- object$info@coef[3]
+
+  1-exp(
+    (alpha/beta)*newdata[[1]]*exp(-beta*newdata[[1]])
+    +(1/beta)*((alpha/beta)-gamma)*(exp(-beta*newdata[[1]])-1)
+    -gamma*newdata[[1]])
+}
+
+#' Predict from an hierarchical bayesian model
+#'
+#' @param object serosv models
+#' @param newdata data.frame with age column to generate prediction
+#' @param ... arbitrary arguments
+#' @import dplyr
+#'
+#' @return list of confidence interval for seroprevalence and foi. Each confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval
+#' @export
+predict.hierarchical_bayesian_model <- function(object, newdata=NULL, ...){
+  out_x <- object$df$age
+  out.DF <- NULL
+
+  if (object$type == "far3"){
+    alpha1 <- object$info["alpha1", "50%"]
+    alpha2 <- object$info["alpha2", "50%"]
+    alpha3 <- object$info["alpha3", "50%"]
+
+    out.DF <- data.frame(
+      x = out_x,
+      y = object$sp_func(out_x, alpha1, alpha2, alpha3),
+    )
+  }else if(object$type == "far2"){
+    alpha1 <- object$info["alpha1", "50%"]
+    alpha2 <- object$info["alpha2", "50%"]
+
+    out.DF <- data.frame(
+      x = out_x,
+      y = object$sp_func(out_x, alpha1, alpha2),
+    )
+
+  }else if(object$type == "log_logistic"){
+    alpha1 <- object$info["alpha1", "50%"]
+    alpha2 <- object$info["alpha2", "50%"]
+
+    out.DF <- data.frame(
+      x = out_x,
+      y = object$sp_func(out_x, alpha1, alpha2),
+    )
+  }else{
+    warning('Expect model type to be one of the following: "far3", "far2", "log_logistic"')
+  }
+
+  out.DF
+}
+
+#' Predict from the age_time_mdoel
+#'
+#' @param object serosv models
+#' @param newdata data.frame with age column to generate prediction
+#' @param modtype either "monotonized" (to predict using monotonized model) or "non-monotonized"
+#' @param ... arbitrary argument
+#'
+#' @importFrom mgcv predict.gam
+#' @import dplyr
+#'
+#' @return confidence interval dataframe with n_group x 3 cols, the columns are `group`, `sp_df`, `foi_df`
+#' @export
+predict.age_time_model <- function(object, newdata, modtype="monotonized", ...){
+  # resolve no visible binding note
+  df <- monotonized_info <- monotonized_ci_mod <- age <- info <- fit <- se.fit <- sp_df <- foi_df <- NULL
+
+  # check which type of model user wants to predict
+  modtype <- if (is.null(list(...)[["modtype"]])) "monotonized" else list(...)$modtype
+  assert_that(
+    modtype == "monotonized" | modtype == "non-monotonized",
+    msg = "modtype argument must be eithers 'monotonized' or 'non-monotonized'"
+  )
+
+  p <- (1 - object$ci) / 2
+
+  # check whether newdata match the requirement
+  if(!all(c(object$grouping_col, "age") %in% colnames(newdata)) ){
+    stop(paste0(
+      "Data must have `",
+      object$grouping_col,
+      "`, `age` columns"
+    ))
+  }
+
+
+  # generate the newdata by survey time for prediction
+  out <- newdata %>%
+    group_by(.data[[object$grouping_col]]) %>%
+    nest() %>%
+    rename(age_df = data) %>%
+    left_join(
+      # can only predict for the survey time fitted to the model
+      object$out,
+      join_by(!!sym(object$grouping_col))
+    )
+
+  # --- use the monotonized model for prediction and ci-----
+  if(modtype == "monotonized"){
+    out <- out %>%
+      mutate(
+        sp_df = pmap(list(monotonized_info, monotonized_ci_mod, age_df), \(mod, ci_mod, grid){
+          data.frame(
+            x = grid$age,
+            y = predict(mod, grid, type = "response"),
+            ymin = predict(ci_mod$ymin, grid, type = "response"),
+            ymax = predict(ci_mod$ymax, grid, type = "response")
+          )
+        })
+      )
+  }else{
+    # --- if user specify non-monotonized then simply compute CI from gam model-----
+    out <- out %>%
+      mutate(
+        sp_df = map2(info, age_df, \(mod, grid){
+          link_inv <- mod$family$linkinv
+          dataset <- mod$model[,1:2]
+          n <- nrow(dataset) - length(mod$coefficients)
+
+          predict(mod, grid, se.fit = TRUE)  %>%
+            as_tibble()  %>%
+            select(fit, se.fit) %>%
+            mutate(
+              x = grid,
+              ymin = link_inv(fit + qt(    p, n) * se.fit),
+              ymax = link_inv(fit + qt(1 - p, n) * se.fit),
+              y = link_inv(fit)
+            )  %>%
+            select(- se.fit)
+        })
+      )
+  }
+
+  # --- finally, compute FOI -----
+  out <- out %>%
+    mutate(
+      foi_df = map2(age_df, sp_df, \(grid, sp){
+        foi_x <- sort(unique(grid$age))
+        foi_x <- foi_x[c(-1, -length(foi_x) )]
+
+        data.frame(
+          x = foi_x,
+          y = est_foi(grid$age, sp$y)
+        )
+      })
+    ) %>%
+    select(!!sym(object$grouping_col), sp_df, foi_df)
+
+  out
+}
+
diff --git a/R/print.R b/R/print.R
new file mode 100644
index 0000000..ca26328
--- /dev/null
+++ b/R/print.R
@@ -0,0 +1,143 @@
+#' @export
+print.polynomial_model <- function(x, ...){
+  cat("Polynomial model\n\n")
+  cat("Input type: ", x$datatype, "\n")
+  cat("Degree (k): ",x$k, "\n")
+  print(x$info)
+}
+
+#' @export
+print.fp_model <- function(x, ...){
+  cat("Fractional polynomial model \n\n")
+  cat("Input type: ", x$datatype, "\n")
+  cat("Powers: ", paste(x$p, collapse=", "), "\n")
+  print(x$info)
+}
+
+#' @export
+print.age_time_model <- function(x, ...){
+  cat("Age-time varying seroprevalence model \n\n")
+  cat("Input type: ", x$datatype, "\n")
+  cat("Grouping variable: ", x$grouping_col,"\n")
+  cat("Monotonization method: ", x$monotonize_method, "\n")
+  cat("Monotonize across: ", if(x$age_correct) "birth cohort" else "age group", "\n")
+  print(x$out)
+}
+
+#' @export
+print.farrington_model <- function(x, ...){
+  cat("Farrington model \n\n")
+  cat("Input type: ", x$datatype, "\n")
+  # cat("Fitted parameters: ",
+  #     paste(c("alpha", "beta", "gamma"), sprintf("%.4g", x$info@coef[1:3]), sep="=", collapse=", "),
+  #     "\n\n")
+  print(x$info)
+}
+
+#' @importFrom purrr compact
+#' @export
+print.hierarchical_bayesian_model <- function(x, ...){
+  pars <- intersect(c("alpha1", "alpha2", "alpha3"), rownames(x$info))
+  fitted_pars <- x$info[pars,  "mean"]
+  # get the CrI
+  lower_pars <- x$info[pars,  "2.5%"]
+  upper_pars <- x$info[pars,  "97.5%"]
+  # get the sd
+  sd_pars <- purrr::compact(x$info[pars,  "sd"])
+
+  cat("Hierarchical Bayesian model \n\n")
+  cat("Input type: ", x$datatype, "\n")
+  cat("Model: ",
+      switch(x$type,
+             far2 = "Farrington model with 2 parameters",
+             far3 = "Farrington model with 3 parameters",
+             log_logistic = "Log-logistic model"),
+      "\n\n")
+  cat("Fitted parameters:\n",
+      paste(names(fitted_pars),
+            # print fitted parameter in the format mean (95% CrI=lower-upper, sd=)
+            paste(
+              sprintf("%.4g", fitted_pars),
+              " (95% CrI [",
+              sprintf("%.4g", lower_pars),
+              ", ",
+              sprintf("%.4g", upper_pars),
+              "], sd = ",
+              sprintf("%.4g", sd_pars),
+              ")",
+              sep = ""
+            ),
+            sep=" = ", collapse="\n "),
+      "\n")
+}
+
+#' @export
+print.mixture_model <- function(x, ...){
+  cat("Mixture model \n\n")
+  cat("Estimated proportion:\n",
+      paste(
+        c("Susceptible", "Infected"),
+        sprintf("%.4g", x$info$parameters$pi),
+        sep="=", collapse = ", "
+      ),
+      "\n\n"
+      )
+  cat("Estimated mean Log(Antibody):\n",
+      paste(
+        c("Susceptible", "Infected"),
+        sprintf("%.4g", x$info$parameters$mu),
+        sep="=", collapse = ", "
+      ),
+      "\n")
+}
+
+#' @export
+print.estimate_from_mixture <- function(x, ...){
+  cat("Age-varying seroprevalence estimated from mixture model \n\n")
+  cat("Monotonized seroprevalence: ", x$monotonize)
+  print(x$info)
+}
+
+#' @export
+print.lp_model <- function(x, ...){
+  cat("Local polynomial model \n\n")
+  cat("Input type: ", x$datatype, "\n")
+  cat(
+    "Configs: ",
+    paste(
+      c("nn", "bandwidth(h)", "degree", "kernel"),
+      c(as.character(round(
+        as.numeric(x[c("nn", "h", "deg")]), digits = 4
+      )), x$kern),
+      sep = "=",
+      collapse = ", "
+    ),
+    "\n\n"
+  )
+  print(x$info)
+}
+
+#' @export
+print.penalized_spline_model <- function(x, ...){
+  cat("Penalized spline model \n\n")
+  cat("Input type: ", x$datatype, "\n")
+  cat("Framework: ", if(x$framework=="pl") "Penalized likelihood" else "Mixed model", "\n")
+  print(x$info)
+}
+
+#' @export
+print.weibull_model <- function(x, ...){
+  cat("Weibull model \n\n")
+  cat("Input type: ", x$datatype, "\n")
+  cat(
+    paste(
+      c("b0", "b1"),
+      sprintf("%.4g", coef(x$info)[1:2]),
+      sep="=", collapse=", "
+    ),
+    "\n"
+  )
+
+  print(x$info)
+}
+
diff --git a/R/semiparametric_models.R b/R/semiparametric_models.R
index 4fcd143..d867b8b 100644
--- a/R/semiparametric_models.R
+++ b/R/semiparametric_models.R
@@ -1,10 +1,99 @@
 #' Penalized Spline model
 #'
-#' @param data the input data frame, must either have `age`, `pos`, `tot` column for aggregated data OR `age`, `status` for linelisting data
+#' @description Fit age-specific seroprevalence to a semi-parametric model where
+#' predictor is modeled with penalized splines. The penalized splines can be estimated by either
+#' (1) penalized likelihood framework or (2) mixed model framework
+#'
+#' @details
+#' In the semi-parametric model, the predictor is formulated as a penalized spline
+#' with truncated power basis functions of degree \eqn{p}
+#' and fixed knots \eqn{\kappa_1,..., \kappa_k} as followed
+#'
+#' \deqn{
+#' \eta(a_i) = \beta_0 + \beta_1a_i + ... + \beta_p a_i^p + \Sigma_{k=1}^ku_k(a_i - \kappa_k)^p_+
+#' }
+#'
+#' - Where:
+#' \deqn{
+#' (a_i - \kappa_k)^p_+ = \begin{cases}
+#' 0, & a_i \le \kappa_k \\
+#' (a_i - \kappa_k)^p, & a_i > \kappa_k
+#' \end{cases}
+#' }
+#'
+#' FOI can then be derived by
+#'
+#' \deqn{\hat{\lambda}(a_i) = [\hat{\beta_1} , 2\hat{\beta_2}a_i, ...,
+#' p \hat{\beta} a_i ^{p-1} + \Sigma^k_{k=1} p \hat{u}_k(a_i - \kappa_k)^{p-1}_+] \delta(\hat{\eta}(a_i))
+#' }
+#'
+#' Where \eqn{\delta(.)} is determined by the link function used in the model
+#'
+#' In matrix annotation, the mean structure model for \eqn{\eta(a_i)} becomes
+#' \deqn{\eta = \textbf{X}\beta + \textbf{Zu}}
+#'
+#' Where \eqn{\eta = [\eta(a_i) ... \eta(a_N) ]^T}, \eqn{\beta = [\beta_0 \beta_1 .... \beta_p]^T},
+#' and \eqn{\textbf{u} = [u_1 u_2 ... u_k]^T} are the regression with corresponding design matrices
+#' \deqn{
+#'
+#' \textbf{X} = \begin{bmatrix}
+#' 1 & a_1 & a_1^2 & ... & a_1^p \\
+#' 1 & a_2 & a_2^2 & ... & a_2^p \\
+#' \vdots & \vdots & \vdots & \dots & \vdots \\
+#' 1 & a_N & a_N^2 & ... & a_N^p
+#' \end{bmatrix}, \textbf{Z} = \begin{bmatrix}
+#' (a_1 - \kappa_1 )_+^p & (a_1 - \kappa_2 )_+^p & \dots & (a_1 - \kappa_k)_+^p \\
+#' (a_2 - \kappa_1 )_+^p & (a_2 - \kappa_2 )_+^p & \dots & (a_2 - \kappa_k)_+^p \\
+#' \vdots & \vdots & \dots & \vdots \\
+#' (a_N - \kappa_1 )_+^p & (a_N - \kappa_2 )_+^p & \dots & (a_N - \kappa_k)_+^p
+#' \end{bmatrix}
+#' }
+#'
+#' Under \bold{penalized likelihood framework}, the model is fitted by maximizing
+#' the following likelihood
+#'
+#' \deqn{
+#' \phi^{-1}[y^T(\textbf{X}\beta + \textbf{Zu} ) -  \textbf{1}^Tc(\textbf{X}\beta + \textbf{Zu} )] - \frac{1}{2}\lambda^2
+#' \begin{bmatrix} \beta \\ \textbf{u} \end{bmatrix}^T D\begin{bmatrix} \beta \\ \textbf{u} \end{bmatrix}
+#' }
+#'
+#' Where:
+#'
+#' - \eqn{X\beta + Zu} is the predictor
+#'
+#' - \eqn{D} is a known semi-definite penalty matrix [@Wahba1978], [@Green1993]
+#'
+#' - \eqn{y} is the response vector
+#'
+#' - \eqn{\textbf{1}} the unit vector, \eqn{c(.)} is determined by the link function used
+#'
+#' - \eqn{\lambda} is the smoothing parameter (larger values –> smoother curves)
+#'
+#' - \eqn{\phi} is the overdispersion parameter and equals 1 if there is no overdispersion
+#'
+#' Under the \bold{mixed model} framework,
+#' the model instead treats the coefficients \eqn{\textbf{u}} in the likelihood formulation
+#' as random effects with \eqn{\textbf{u} \sim N(\textbf{0}, \mathbf{\sigma}^2_u \textbf{I})}
+#'
+#' Refer to section 8.1 and 8.2 of the the book by Hens et al. (2012) for further details.
+#'
+#' @references
+#' Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+#' and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+#' Serological and Social Contact Data: A Modern Statistical Perspective.
+#' tatistics for Biology and Health. Springer New York.
+#' \doi{https://doi.org/10.1007/978-1-4614-4072-7}.
+#'
+#' @param data the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR
+#' columns for `age`, `status` (for linelisting data)
 #' @param s smoothing basis to use
 #' @param sp smoothing parameter
 #' @param link link function to use
 #' @param framework which approach to fit the model ("pl" for penalized likelihood framework, "glmm" for generalized linear mixed model framework)
+#' @param age_col name of the `age` column (default age_col="age").
+#' @param pos_col name of the `pos` column (default pos_col="pos").
+#' @param tot_col name of the `tot` column (default tot_col="tot").
+#' @param status_col name of the `status` column (default status_col="status").
 #'
 #' @importFrom mgcv gam gamm
 #' @importFrom stats binomial
@@ -27,10 +116,12 @@
 #' model <- penalized_spline_model(data, framework="glmm")
 #' model$info$gam
 #' plot(model)
-penalized_spline_model <- function(data, s = "bs", link = "logit", framework = "pl", sp = NULL){
+penalized_spline_model <- function(data,
+                                   age_col="age",pos_col="pos", tot_col="tot", status_col="status",
+                                   s = "bs", link = "logit", framework = "pl", sp = NULL){
   model <- list()
 
-  data <- check_input(data)
+  data <- check_input(data, stratum_col=age_col,pos_col=pos_col, tot_col=tot_col, status_col=status_col)
   age <- data$age
   pos <- data$pos
   tot <- data$tot
diff --git a/R/sir_basic_model.R b/R/sir_basic_model.R
deleted file mode 100644
index 50cf76d..0000000
--- a/R/sir_basic_model.R
+++ /dev/null
@@ -1,72 +0,0 @@
-sir_basic <- function(t, state, parameters)
-{
-  with(as.list(c(state, parameters)), {
-     N  <- S + I + R   # total population
-     lambda <- beta*I  # force of infection
-     dS <- N*mu*(1-p) - (lambda + mu)*S
-     dI <- lambda*S - (nu+alpha+mu)*I
-     dR <- N*mu*p + nu*I - mu*R
-     list(c(dS, dI, dR))
-  })
-}
-
-#' Basic SIR model
-#'
-#' Refers to section 3.1.3.
-#'
-#' @details
-#' In \code{state}:
-#'
-#'  - \code{S}: number of susceptible
-#'
-#'  - \code{I}: number of infected
-#'
-#'  - \code{R}: number of recovered
-#'
-#' In \code{parameters}:
-#'
-#'  - \code{alpha}: disease-related death rate
-#'
-#'  - \code{mu}: natural death rate (= 1/life expectancy)
-#'
-#'  - \code{beta}: transmission rate
-#'
-#'  - \code{nu}: recovery rate
-#'
-#'  - \code{p}: percent of population vaccinated at birth
-#'
-#' @param times time sequence.
-#'
-#' @param state the initial state of the model.
-#'
-#' @param parameters the parameters of the model.
-#'
-#' @examples
-#' state <- c(S=4999, I=1, R=0)
-#' parameters <- c(
-#'   mu=1/75, # 1 divided by life expectancy (75 years old)
-#'   alpha=0, # no disease-related death
-#'   beta=0.0005, # transmission rate
-#'   nu=1, # 1 year for infected to recover
-#'   p=0 # no vaccination at birth
-#' )
-#' times <- seq(0, 250, by=0.1)
-#' model <- sir_basic_model(times, state, parameters)
-#' model
-#'
-#' @importFrom deSolve ode
-#' @return list of class sir_basic_model with the following items
-#'   \item{parameters}{list of parameters used for fitting the model}
-#'   \item{output}{matrix of population for each compartment over time}
-#'
-#'
-#' @export
-sir_basic_model <- function(times, state, parameters)
-{
-  model <- list()
-  model$parameters <- parameters
-  model$output <- as.data.frame(ode(y=state,times=times,func=sir_basic,parms=parameters))
-
-  class(model) <- "sir_basic_model"
-  model
-}
diff --git a/R/sir_static_model.R b/R/sir_static_model.R
deleted file mode 100644
index 2708abe..0000000
--- a/R/sir_static_model.R
+++ /dev/null
@@ -1,61 +0,0 @@
-sir_static <- function(t, state, parameters)
-{
-  with(as.list(c(state, parameters)), {
-    ds <- -lambda*s
-    di <- lambda*s - nu*i
-    dr <- nu*i
-    list(c(ds, di, dr))
-  })
-}
-
-#' SIR static model (age-heterogeneous, endemic equilibrium)
-#'
-#' Refers to section 3.2.2.
-#'
-#' @details
-#' In \code{state}:
-#'
-#'   - \code{s}: proportion susceptible
-#'
-#'   - \code{i}: proportion infected
-#'
-#'   - \code{r}: proportion recovered
-#'
-#' In \code{parameters}:
-#'
-#'   - \code{lambda}: natural death rate
-#'
-#'   - \code{nu}: recovery rate
-#'
-#' @param a age sequence.
-#'
-#' @param state the initial state of the system.
-#'
-#' @param parameters the model's parameter.
-#'
-#' @examples
-#' state <- c(s=0.99,i=0.01,r=0)
-#' parameters <- c(
-#'   lambda = 0.05,
-#'   nu=1/(14/365) # 2 weeks to recover
-#' )
-#' ages<-seq(0, 90, by=0.01)
-#' model = sir_static_model(ages, state, parameters)
-#' model
-#'
-#' @return list of class sir_static_model with the following items
-#'   \item{parameters}{list of parameters used for fitting the model}
-#'   \item{output}{matrix of proportion for each compartment over time}
-#'
-#'
-#' @export
-sir_static_model <- function(a, state, parameters)
-{
-
-  model <- list()
-  model$parameters <- parameters
-  model$output <- as.data.frame(ode(y=state,times=a,func=sir_static,parms=parameters))
-
-  class(model) <- "sir_static_model"
-  model
-}
diff --git a/R/sir_subpops_model.R b/R/sir_subpops_model.R
deleted file mode 100644
index 35a2dc8..0000000
--- a/R/sir_subpops_model.R
+++ /dev/null
@@ -1,114 +0,0 @@
-ds <- function(state, parameters, i)
-{
-  with(as.list(c(state, parameters)), {
-    sum_beta_i <- 0
-    for (j in 1:k) {
-      sum_beta_i <- sum_beta_i + beta[i,j]*get(paste0("i", j))
-    }
-    -sum_beta_i*get(paste0("s", i)) + mu - mu*get(paste0("s", i))
-  })
-}
-
-di <- function(state, parameters, i)
-{
-  with(as.list(c(state, parameters)), {
-    sum_beta_i <- 0
-    for (j in 1:k) {
-      sum_beta_i <- sum_beta_i + beta[i,j]*get(paste0("i", j))
-    }
-    sum_beta_i*get(paste0("s", i)) - nu[i]*get(paste0("i", i)) - mu*get(paste0("i", i))
-  })
-}
-
-dr <- function(state, parameters, i)
-{
-  with(as.list(c(state, parameters)), {
-    nu[i]*get(paste0("i", i)) - mu*get(paste0("r", i))
-  })
-}
-
-sir_subpop <- function(t, state, parameters) {
-  with(as.list(c(state, parameters)), {
-    s_states <- c()
-    i_states <- c()
-    r_states <- c()
-
-    for (i in 1:k) {
-      s_states <- c(s_states, ds(state, parameters, i))
-      i_states <- c(i_states, di(state, parameters, i))
-      r_states <- c(r_states, dr(state, parameters, i))
-    }
-
-    list(c(s_states, i_states, r_states))
-  })
-}
-
-#' SIR Model with Interacting Subpopulations
-#'
-#' Refers to section 3.5.1.
-#'
-#' @details
-#' In \code{state}:
-#'
-#' - \code{s}: Percent susceptible
-#'
-#' - \code{i}: Percent infected
-#'
-#' - \code{r}: Percent recovered
-#'
-#' In \code{parameters}:
-#'
-#' - \code{mu}: natural death rate (1/L).
-#'
-#' - \code{beta}: transmission rate w.r.t population (beta tilde)
-#'
-#' - \code{nu}: recovery rate
-#'
-#' - \code{k}: number of subpopulations
-#'
-#' @param times time sequence.
-#'
-#' @param state the initial state of the model.
-#'
-#' @param parameters the parameters of the model.
-#'
-#' @examples
-#' \donttest{
-#' k <- 2
-#' state <- c(
-#'   s = c(0.8, 0.8),
-#'   i = c(0.2, 0.2),
-#'   r = c(  0,   0)
-#' )
-#' beta_matrix <- c(
-#'   c(0.05, 0.00),
-#'   c(0.00, 0.05)
-#' )
-#' parameters <- list(
-#'   beta = matrix(beta_matrix, nrow=k, ncol=k, byrow=TRUE),
-#'   nu = c(1/30, 1/30),
-#'   mu = 0.001,
-#'   k = k
-#' )
-#' times<-seq(0,10000,by=0.5)
-#' model <- sir_subpops_model(times, state, parameters)
-#' model
-#' }
-#'
-#' @return list of class sir_subpops_model with the following items
-#'
-#'   \item{parameters}{list of parameters used for fitting the model}
-#'   \item{output}{matrix of proportion for each compartment over time}
-#'
-#'
-#' @export
-sir_subpops_model <- function(times, state, parameters) {
-  model <- list()
-  model$parameters <- parameters
-  model$output <- as.data.frame(
-    ode(y=state,times=times,func=sir_subpop,parms=parameters)
-  )
-
-  class(model) <- "sir_subpops_model"
-  model
-}
diff --git a/R/to_titer.R b/R/to_titer.R
index 2735f89..5e98220 100644
--- a/R/to_titer.R
+++ b/R/to_titer.R
@@ -1,18 +1,28 @@
-#' Process assay test result to titer
+#' Convert assay readings to titers
+#'
+#' to_titer() converts raw assay readings (e.g., OD, fluorescence intensity) to titer by
+#' fitting a calibrating model
+#'
+#' @param df a standardized data.frame returned by`standardize_data()`
+#' @param model either:
+#'   \enumerate{
+#'     \item {A string naming a built-in model (currently supported: \code{"4PL"}), or}
+#'     \item  {A named list with two functions: \code{$mod} for curve fitting and
+#'       \code{$quantify_ci} for titer estimation with confidence intervals.}
+#'   }
+#' @param positive_threshold if not NULL, processed_data will have the serostatus labeled
+#' @param ci confidence interval for the titer estimates (default is .95 i.e., 95\% CI)
+#' @param negative_control if TRUE, output tibble will include the result for negative controls
 #'
-#' @param df - a standardized data.frame generated by`standardize_data()`
-#' @param model - either the name of a built-in model to fit standard curve or a named list of 2 functions for "mod" and "quantify_ci"
-#' @param positive_threshold - if not NULL, processed_data will have the serostatus labeled
-#' @param ci - confidence interval for the titer estimates
-#' @param negative_control - if TRUE, output tibble will include the result for negative controls
 #' @importFrom magrittr %>%
-#' @import dplyr purrr
+#' @import dplyr
+#' @importFrom purrr map_dfr
 #'
 #' @return a data.frame with 8 columns
 #'   \item{plate_id}{id of the plate}
-#'   \item{data}{list of `data.frame`s containing the results from each plate}
-#'   \item{antitoxin_df}{list of `data.frame`s containing the results for antitoxins from each plate}
-#'   \item{standard_curve_func}{list of functions mapping from OD to titer for each plate}
+#'   \item{data}{list of `data.frame`s containing the raw sample results from each plate}
+#'   \item{antitoxin_df}{list of `data.frame`s containing the raw results for antitoxins from each plate}
+#'   \item{standard_curve_func}{list of functions mapping from assay reading to titer for each plate}
 #'   \item{std_crv_midpoint}{midpoint of the standard curve, for qualitative analysis}
 #'   \item{processed_data}{list of `tibble`s containing samples with titer estimates (lower, median, upper)}
 #'   \item{negative_control}{list of `tibble`s containing negative control check results (if `negative_control=TRUE`)}
@@ -73,13 +83,13 @@ to_titer <- function(df, model="4PL", positive_threshold=NULL, ci = .95,
 
 #' Preprocess data
 #'
-#' @param df - data.frame with columns for plate id, sample id, result, dilution factor, and (optionally) negative controls
-#' @param plate_id_col - name of the column storing plates id
-#' @param id_col - name of the column storing sample id
-#' @param result_col - name of the column storing result
-#' @param dilution_fct_col - name of the column storing dilution factors
-#' @param antitoxin_label - how antitoxin is label in the sample id column
-#' @param negative_col - regex for columns for negative controls, assumed to be a label followed by the dilution factor (e.g. NEGATIVE_50, NEGATIVE_100)
+#' @param df data.frame with columns for plate id, sample id, result, dilution factor, and (optionally) negative controls
+#' @param plate_id_col name of the column storing plates id
+#' @param id_col name of the column storing sample id
+#' @param result_col name of the column storing result
+#' @param dilution_fct_col name of the column storing dilution factors
+#' @param antitoxin_label how antitoxin is label in the sample id column
+#' @param negative_col regex for columns for negative controls, assumed to be a label followed by the dilution factor (e.g. NEGATIVE_50, NEGATIVE_100)
 #'
 #' @importFrom magrittr %>%
 #' @importFrom janitor clean_names
@@ -157,9 +167,10 @@ data2function <- function(df) {
 }
 
 # Function to convert samples' OD to LC
+#' @importFrom purrr map_dfr
 process_samples <- function(plate, std_crv, midpoint=2, positive_threshold = NULL) {
   out <- plate %>%
-    bind_cols(map_dfr(.$result, std_crv))
+    bind_cols(purrr::map_dfr(.$result, std_crv))
 
   if(is.numeric(positive_threshold)){
     out <- out %>% mutate(
@@ -205,6 +216,7 @@ nls4PL <- function(df) {
 # - sample different values for parameters (number of samples = nb)
 # - use model to compute OD for the new set of parameter values
 #' @importFrom mvtnorm rmvnorm
+#' @importFrom purrr map_dfc
 simulate_nls_ci <- function(object, newdata, nb = 9999, alpha = .025) {
   rowsplit <- function(df) split(df, 1:nrow(df))
 
@@ -214,7 +226,7 @@ simulate_nls_ci <- function(object, newdata, nb = 9999, alpha = .025) {
     rowsplit() |>
     map(as.list) |>
     map(~ c(.x, newdata)) |>
-    map_dfc(eval, expr = parse(text = as.character(formula(object))[3])) %>%
+    purrr::map_dfc(eval, expr = parse(text = as.character(formula(object))[3])) %>%
     apply(1, quantile, c(alpha, .5, 1 - alpha))  %>%
     t() %>%  as.data.frame() %>%
     setNames(c("lower", "median", "upper"))
@@ -233,11 +245,11 @@ get_negative_controls <- function(plate, std_crv){
 # ======== Plot functions ==========
 #' Visualize standard curves for each plate
 #'
-#' @param x - output of `to_titer()`
-#' @param facet - whether to faceted by plates or plot all standard curves on a single plot
-#' @param xlab - label of the x axis
-#' @param ylab - label of the y axis
-#' @param datapoint_size - size of the data point (only applicable when `facet=TRUE`)
+#' @param x output of `to_titer()`
+#' @param facet whether to faceted by plates or plot all standard curves on a single plot
+#' @param xlab label of the x axis
+#' @param ylab label of the y axis
+#' @param datapoint_size size of the data point (only applicable when `facet=TRUE`)
 #'
 #' @importFrom magrittr %>%
 #' @import ggplot2 dplyr
@@ -293,7 +305,7 @@ plot_standard_curve <- function(x, facet=TRUE,
 #' @param shift_text adjust how much the text is shifted along the x-axis (relative to the threshold line)
 #'
 #' @importFrom magrittr %>%
-#' @import ggplot2 dplyr purrr
+#' @import ggplot2 dplyr
 #'
 #' @export
 add_thresholds <- function(dilution_factors, positive_threshold = 0.1,
@@ -333,7 +345,8 @@ add_thresholds <- function(dilution_factors, positive_threshold = 0.1,
 #' @param n_dilutions - number of dilutions used for testing
 #'
 #' @importFrom magrittr %>%
-#' @import ggplot2 dplyr purrr
+#' @importFrom purrr walk
+#' @import ggplot2 dplyr
 #'
 #' @export
 plot_titer_qc <- function(x, n_plates=18, n_samples=22, n_dilutions = 3){
@@ -371,7 +384,7 @@ plot_titer_qc <- function(x, n_plates=18, n_samples=22, n_dilutions = 3){
   }
 
   opar <- par(mfrow = c(1, n_plates), mar = c(0,0.25,0,0.25))
-  walk(head(x$processed_data, n=n_plates), plot_heatmap)
+  purrr::walk(head(x$processed_data, n=n_plates), plot_heatmap)
 
   par(opar)
 }
diff --git a/R/utils.R b/R/utils.R
index e288f77..af63e99 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -55,18 +55,19 @@ pava<- function(pos=pos,tot=rep(1,length(pos)))
   return(list(pai1=pai1,pai2=pai2))
 }
 
+# TODO: update aggregate func here to be "pipe"-able
 #' Aggregate data
 #'
 #' Generate a dataframe with `t`, `pos` and `tot` columns from
 #' `t` and `seropositive` vectors.
 #'
-#' @param t the time vector (for stratification).
-#' @param spos the seropositive vector.
-#' @param stratum_col new name for the time vector (default to "t")
+#' @param data a data frame with columns for age and serostatus
+#' @param status_col name of the column for serostatus
+#' @param stratum_col name of the column to stratify by (default to "age")
 #'
 #' @examples
 #' df <- hcv_be_2006
-#' hcv_df <- transform_data(df$dur, df$seropositive)
+#' hcv_df <- transform_data(df, stratum_col="dur", status_col="seropositive")
 #' hcv_df
 #'
 #' @importFrom dplyr group_by
@@ -76,15 +77,29 @@ pava<- function(pos=pos,tot=rep(1,length(pos)))
 #'
 #' @return dataframe in aggregated format
 #' @export
-transform_data <- function(t, spos, stratum_col = "t") {
-  df <- data.frame(t, spos)
+transform_data <- function(data, stratum_col="age", status_col="status") {
+  df <- NULL
+
+  if( all(c(stratum_col, status_col) %in% names(data)) ) {
+    df <- data.frame(
+      age = data[[stratum_col]],
+      status = data[[status_col]]
+    )
+  }else{
+    stop(paste0(
+      "Data must have `",
+      stratum_col,
+      "`, `",status_col ,"` columns"
+    ))
+  }
+
+
   df_agg <- df %>%
-    group_by(t) %>%
+    group_by(age) %>%
     summarize(
-      pos = sum(spos),
+      pos = sum(status),
       tot = n()
     )
-  colnames(df_agg) <- c(stratum_col, "pos", "tot")
 
   df_agg
 }
@@ -94,7 +109,8 @@ transform_data <- function(t, spos, stratum_col = "t") {
 # - type of data (either linelisting or aggregated)
 # - preprocessed pos and tot columns
 #' @importFrom assertthat assert_that
-check_input <- function(data, stratum_col = "age"){
+check_input <- function(data, pos_col="pos",tot_col="tot",status_col="status",
+                        stratum_col = "age"){
   assert_that(
     is.data.frame(data),
     msg = "Input must be a data.frame or tibble"
@@ -106,23 +122,23 @@ check_input <- function(data, stratum_col = "age"){
   type <- NULL
 
 
-  if( all(c(stratum_col, "pos", "tot") %in% colnames(data)) ){
+  if( all(c(stratum_col, pos_col, tot_col) %in% colnames(data)) ){
     age <- as.numeric(data[[stratum_col]])
-    pos <- as.numeric(data$pos)
-    tot <- as.numeric(data$tot)
+    pos <- as.numeric(data[[pos_col]])
+    tot <- as.numeric(data[[tot_col]])
     type <- "aggregated"
-  }else if( all(c(stratum_col, "status") %in% colnames(data)) ){
+  }else if( all(c(stratum_col, status_col) %in% colnames(data)) ){
     age <- as.numeric(data[[stratum_col]])
-    pos <- as.numeric(data$status)
-    tot <- rep(1, length(data$status))
+    pos <- as.numeric(data[[status_col]])
+    tot <- rep(1, length(data[[status_col]]))
     type <- "linelisting"
   }else{
     stop(paste0(
       "Data must have `",
       stratum_col,
-      "`, `pos`, `tot` columns for aggregated data OR `",
+      "`, `", pos_col, "`, `", tot_col,"` columns for aggregated data OR `",
       stratum_col,
-      "`, `status` columns for linelisting data"
+      "`, `",status_col ,"` columns for linelisting data"
     ))
   }
 
diff --git a/R/weibull_model.R b/R/weibull_model.R
index 05631c6..9aac7d0 100644
--- a/R/weibull_model.R
+++ b/R/weibull_model.R
@@ -1,8 +1,35 @@
 #' The Weibull model.
 #'
-#' Refers to section 6.1.2.
+#' @description Model seroprevalence as a function of duration since vaccination using the Weibull
+#' model, where the force of infection is assumed to vary monotonically with duration.
 #'
-#' @param data the input data frame, must either have `t`, `pos`, `tot` column for aggregated data OR `t`, `status` for linelisting data
+#' @details
+#' For a Weibull model, the prevalence is given by
+#' \deqn{
+#'  \pi (d) = 1 - e^{ - \beta_0 d ^ {\beta_1}}
+#' }
+#' Where \eqn{d} is exposure time (difference between age of vaccination and age at test)
+#'
+#' Which implies the force of infection to be the monotonic function
+#' \deqn{
+#'  \lambda(d) = \beta_0 \beta_1 d^{\beta_1 - 1}
+#' }
+#'
+#' Refer to section 6.1.2. of the the book by Hens et al. (2012) for further details.
+#'
+#' @references
+#' Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+#' and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+#' Serological and Social Contact Data: A Modern Statistical Perspective.
+#' tatistics for Biology and Health. Springer New York.
+#' \doi{https://doi.org/10.1007/978-1-4614-4072-7}.
+#'
+#' @param data the input data frame, must either have columns for `t`, `pos`, `tot` (for aggregated data) OR
+#'  `t`, `status` (for linelisting data)
+#' @param t_lab name of the `t` column (default t_lab="t").
+#' @param pos_col name of the `pos` column (default pos_col="pos").
+#' @param tot_col name of the `tot` column (default tot_col="tot").
+#' @param status_col name of the `status` column (default status_col="status").
 #'
 #' @importFrom stats coef
 #'
@@ -10,7 +37,7 @@
 #' df <- hcv_be_2006[order(hcv_be_2006$dur), ]
 #' df$t <- df$dur
 #' df$status <- df$seropositive
-#' model <- weibull_model(df)
+#' model <- weibull_model(df, t_lab="dur", status_col="seropositive")
 #' plot(model)
 #'
 #' @return list of class weibull_model with the following items
@@ -23,12 +50,13 @@
 #' @seealso [stats::glm()] for more information on the fitted "glm" object
 #'
 #' @export
-weibull_model <- function(data)
+weibull_model <- function(data,
+                          t_lab="t",pos_col="pos", tot_col="tot", status_col="status")
 {
   model <- list()
 
   # check input whether it is line-listing or aggregated data
-  data <- check_input(data, stratum_col = "t")
+  data <- check_input(data, stratum_col = t_lab, pos_col=pos_col, tot_col=tot_col, status_col=status_col)
   t <- data$age
   pos <- data$pos
   tot <- data$tot
diff --git a/README.Rmd b/README.Rmd
index dadb13d..4095c80 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -49,14 +49,26 @@ The following methods are available to estimate seroprevalence and force of infe
 
 Parametric approaches:
 
--   Polynomial models:
-    -   Muench's model
-    -   Griffiths' model
-    -   Grenfell and Anderson's model
--   Nonlinear models:
-    -   Farrington's model
-    -   Weibull model
--   Fractional polynomial models
+-   Frequentist methods:
+
+    -   Polynomial models:
+
+        -   Muench's model
+        -   Griffiths' model
+        -   Grenfell and Anderson's model
+
+    -   Nonlinear models:
+
+        -   Farrington's model
+        -   Weibull model
+
+    -   Fractional polynomial models
+
+-   Bayesian methods:
+
+    -   Hierarchical Farrington model
+
+    -   Hierarchical log-logistic model
 
 Nonparametric approaches:
 
@@ -70,12 +82,6 @@ Semiparametric approaches:
 
     -   Generalized Linear Mixed Model framework
 
-Hierarchical Bayesian approaches:
-
--   Hierarchical Farrington model
-
--   Hierarchical log-logistic model
-
 ## Demo
 
 ### Fitting rubella data from the UK
@@ -84,34 +90,33 @@ Load the rubella in UK dataset.
 
 ```{r}
 library(serosv)
+rubella <- rubella_uk_1986_1987
 ```
 
-Find the power for the best second degree fractional polynomial with monotonicity constraint and a logit link function. The power appears to be (-0.9,-0.9).
+Fit the data using a fractional polynomial model via `fp_model()`. In this example, the model searches for the best combination of powers within a specified range.
 
 ```{r}
-rubella <- rubella_uk_1986_1987
-
-best_2d_mn <- find_best_fp_powers(
+rubella_mod <- fp_model(
   rubella,
-  p=seq(-2,3,0.1), mc = T, degree=2, link="logit"
+  p=list(
+    p_range=seq(-2,3,0.1), # range of powers to search over
+    degree=2 # maximum degree for the search
+  ), 
+  monotonic = T, # enforce model to be monotonic
+  link="logit"
 )
-
-best_2d_mn
+rubella_mod
 ```
 
-Finally, fit the second degree fractional polynomial.
+Visualize the model
 
 ```{r}
-fpmd <- fp_model(
-  rubella,
-  p=c(-0.9, -0.9), link="logit")
-
-plot(fpmd)
+plot(rubella_mod)
 ```
 
 ### Fitting Parvo B19 data from Finland
 
-```{r}
+```{r warning=FALSE, message=FALSE}
 library(dplyr)
 parvob19 <- parvob19_fi_1997_1998
 
@@ -120,12 +125,12 @@ transform_data(
   parvob19$age, 
   parvob19$seropositive,
   stratum_col = "age") |>
-  polynomial_model(type = "Muench") |>
+  polynomial_model(k = 1) |>
   plot()
 
 # or fit data as is
 parvob19 |>
   rename(status = seropositive) |>
-  polynomial_model(type = "Muench") |>
+  polynomial_model(k = 1) |>
   plot()
 ```
diff --git a/README.md b/README.md
index eaf53b6..5bb731c 100644
--- a/README.md
+++ b/README.md
@@ -47,14 +47,26 @@ of infection.
 
 Parametric approaches:
 
-- Polynomial models:
-  - Muench’s model
-  - Griffiths’ model
-  - Grenfell and Anderson’s model
-- Nonlinear models:
-  - Farrington’s model
-  - Weibull model
-- Fractional polynomial models
+- Frequentist methods:
+
+  - Polynomial models:
+
+    - Muench’s model
+    - Griffiths’ model
+    - Grenfell and Anderson’s model
+
+  - Nonlinear models:
+
+    - Farrington’s model
+    - Weibull model
+
+  - Fractional polynomial models
+
+- Bayesian methods:
+
+  - Hierarchical Farrington model
+
+  - Hierarchical log-logistic model
 
 Nonparametric approaches:
 
@@ -68,12 +80,6 @@ Semiparametric approaches:
 
   - Generalized Linear Mixed Model framework
 
-Hierarchical Bayesian approaches:
-
-- Hierarchical Farrington model
-
-- Hierarchical log-logistic model
-
 ## Demo
 
 ### Fitting rubella data from the UK
@@ -82,30 +88,30 @@ Load the rubella in UK dataset.
 
 ``` r
 library(serosv)
+rubella <- rubella_uk_1986_1987
 ```
 
-Find the power for the best second degree fractional polynomial with
-monotonicity constraint and a logit link function. The power appears to
-be (-0.9,-0.9).
+Fit the data using a fractional polynomial model via `fp_model()`. In
+this example, the model searches for the best combination of powers
+within a specified range.
 
 ``` r
-rubella <- rubella_uk_1986_1987
-
-best_2d_mn <- find_best_fp_powers(
+rubella_mod <- fp_model(
   rubella,
-  p=seq(-2,3,0.1), mc = T, degree=2, link="logit"
+  p=list(
+    p_range=seq(-2,3,0.1), # range of powers to search over
+    degree=2 # maximum degree for the search
+  ), 
+  monotonic = T, # enforce model to be monotonic
+  link="logit"
 )
-
-best_2d_mn
-#> $p
-#> [1] -0.9 -0.9
-#> 
-#> $deviance
-#> [1] 37.57966
+rubella_mod
+#> Fractional polynomial model 
 #> 
-#> $model
+#> Input type:  aggregated 
+#> Powers:  -0.9, -0.9 
 #> 
-#> Call:  glm(formula = as.formula(formulate(p_cur)), family = binomial(link = link))
+#> Call:  glm(formula = as.formula(formulate(curr_p)), family = binomial(link = link))
 #> 
 #> Coefficients:
 #>               (Intercept)                I(age^-0.9)  
@@ -118,14 +124,10 @@ best_2d_mn
 #> Residual Deviance: 37.58     AIC: 210.1
 ```
 
-Finally, fit the second degree fractional polynomial.
+Visualize the model
 
 ``` r
-fpmd <- fp_model(
-  rubella,
-  p=c(-0.9, -0.9), link="logit")
-
-plot(fpmd)
+plot(rubella_mod)
 ```
 
 <img src="man/figures/README-unnamed-chunk-5-1.png" width="100%" />
@@ -134,15 +136,6 @@ plot(fpmd)
 
 ``` r
 library(dplyr)
-#> Warning: package 'dplyr' was built under R version 4.3.1
-#> 
-#> Attaching package: 'dplyr'
-#> The following objects are masked from 'package:stats':
-#> 
-#>     filter, lag
-#> The following objects are masked from 'package:base':
-#> 
-#>     intersect, setdiff, setequal, union
 parvob19 <- parvob19_fi_1997_1998
 
 # for linelisting data, either transform it to aggregated
@@ -150,7 +143,7 @@ transform_data(
   parvob19$age, 
   parvob19$seropositive,
   stratum_col = "age") |>
-  polynomial_model(type = "Muench") |>
+  polynomial_model(k = 1) |>
   plot()
 ```
 
@@ -161,7 +154,7 @@ transform_data(
 # or fit data as is
 parvob19 |>
   rename(status = seropositive) |>
-  polynomial_model(type = "Muench") |>
+  polynomial_model(k = 1) |>
   plot()
 ```
 
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 3e3609b..d634c66 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -1,6 +1,7 @@
 url: https://oucru-modelling.github.io/serosv/
 template:
   bootstrap: 5
+  math-rendering: mathjax
 
 reference:
 - title: Dataset
@@ -42,10 +43,6 @@ reference:
 
 - title: Models
   contents:
-  - sir_basic_model
-  - sir_static_model
-  - sir_subpops_model
-  - mseir_model
   - polynomial_model
   - farrington_model
   - weibull_model
@@ -68,10 +65,6 @@ reference:
   - plot.weibull_model
   - plot.fp_model
   - plot.lp_model
-  - plot.mseir_model
-  - plot.sir_basic_model
-  - plot.sir_static_model
-  - plot.sir_subpops_model
   - plot.hierarchical_bayesian_model
   - plot.penalized_spline_model
   - plot.mixture_model
@@ -90,7 +83,7 @@ reference:
   - transform_data
   - standardize_data
   - compare_models
-  - compute_ci
+  - compute_ci.default
   - compute_ci.fp_model
   - compute_ci.lp_model
   - compute_ci.weibull_model
@@ -98,6 +91,14 @@ reference:
   - compute_ci.mixture_model
   - compute_ci.age_time_model
   - compute_ci.hierarchical_bayesian_model
+  - predict.age_time_model
+  - predict.farrington_model
+  - predict.fp_model
+  - predict.hierarchical_bayesian_model
+  - predict.lp_model
+  - predict.penalized_spline_model
+  - predict.weibull_model
+  - predict.polynomial_model
   - find_best_fp_powers
 
 articles:
@@ -105,16 +106,15 @@ articles:
   navbar: ~
   contents:
   - linelisting_vs_aggregated
-  - sir_model
   - parametric_model
   - nonparametric_model
   - semiparametric_model
-  - hierarchical_model
   - model_quantitative_data
   - repeated_cross_sectional
 - title: Utilities
   navbar: Utilities
   contents:
+  - reading_to_titer
   - data_transformation
   - imperfect_test
   - visualizing_model
diff --git a/docs/404.html b/docs/404.html
index 39e5eba..4d380e5 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -11,7 +11,13 @@
 <link rel="apple-touch-icon" sizes="180x180" href="https://oucru-modelling.github.io/serosv/apple-touch-icon.png">
 <link rel="icon" sizes="any" href="https://oucru-modelling.github.io/serosv/favicon.ico">
 <link rel="manifest" href="https://oucru-modelling.github.io/serosv/site.webmanifest">
-<script src="https://oucru-modelling.github.io/serosv/deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script src="https://oucru-modelling.github.io/serosv/NA">
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="https://oucru-modelling.github.io/serosv/deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <link href="https://oucru-modelling.github.io/serosv/deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
 <script src="https://oucru-modelling.github.io/serosv/deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="https://oucru-modelling.github.io/serosv/deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
 <link href="https://oucru-modelling.github.io/serosv/deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
@@ -26,7 +32,7 @@
 
     <a class="navbar-brand me-2" href="https://oucru-modelling.github.io/serosv/index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -40,15 +46,14 @@
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
 <li><a class="dropdown-item" href="https://oucru-modelling.github.io/serosv/articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="https://oucru-modelling.github.io/serosv/articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="https://oucru-modelling.github.io/serosv/articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="https://oucru-modelling.github.io/serosv/articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="https://oucru-modelling.github.io/serosv/articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="https://oucru-modelling.github.io/serosv/articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="https://oucru-modelling.github.io/serosv/articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
     <li><a class="dropdown-item" href="https://oucru-modelling.github.io/serosv/articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="https://oucru-modelling.github.io/serosv/articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="https://oucru-modelling.github.io/serosv/articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="https://oucru-modelling.github.io/serosv/articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="https://oucru-modelling.github.io/serosv/articles/visualizing_model.html">Model visualization</a></li>
diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
index d9e2507..23d02de 100644
--- a/docs/LICENSE-text.html
+++ b/docs/LICENSE-text.html
@@ -1,5 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>License • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="License"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>License • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="License"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -19,15 +25,14 @@
 <li class="nav-item dropdown">
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
     <li><a class="dropdown-item" href="articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="articles/visualizing_model.html">Model visualization</a></li>
diff --git a/docs/LICENSE.html b/docs/LICENSE.html
index 422b7f7..bec8bb4 100644
--- a/docs/LICENSE.html
+++ b/docs/LICENSE.html
@@ -1,5 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>MIT License • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="MIT License"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>MIT License • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="MIT License"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -19,15 +25,14 @@
 <li class="nav-item dropdown">
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
     <li><a class="dropdown-item" href="articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="articles/visualizing_model.html">Model visualization</a></li>
diff --git a/docs/articles/data_transformation.html b/docs/articles/data_transformation.html
index aa92df4..2f837a6 100644
--- a/docs/articles/data_transformation.html
+++ b/docs/articles/data_transformation.html
@@ -5,83 +5,71 @@
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
-<meta name="description" content="serosv">
 <title>Data transformation • serosv</title>
-<!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png">
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-<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png">
+<link rel="icon" type="”image/svg+xml”" href="../favicon.svg">
+<link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png">
+<link rel="icon" sizes="any" href="../favicon.ico">
+<link rel="manifest" href="../site.webmanifest">
+<!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
-<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
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-<meta property="og:description" content="serosv">
-<meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Data transformation">
 </head>
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-    
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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-<li class="nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+<li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="active nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a>
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-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
+    <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
+    <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
+    <li><a class="dropdown-item" href="../articles/model_selection.html">Model selection</a></li>
+  </ul>
 </li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
       </ul>
-<form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off">
-</form>
-
-      <ul class="navbar-nav">
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/OUCRU-Modelling/serosv/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/OUCRU-Modelling/serosv/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
 
-    
+
   </div>
 </nav><div class="container template-article">
 
@@ -100,9 +88,7 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
     
     
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://oucru-modelling.github.io/serosv/">serosv</a></span><span class="op">)</span></span>
-<span><span class="co">#&gt; Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when</span></span>
-<span><span class="co">#&gt; loading 'serosv'</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://oucru-modelling.github.io/serosv/">serosv</a></span><span class="op">)</span></span></code></pre></div>
 <div class="section level2">
 <h2 id="aggregate-data">Aggregate data<a class="anchor" aria-label="anchor" href="#aggregate-data"></a>
 </h2>
@@ -120,10 +106,10 @@ <h2 id="aggregate-data">Aggregate data<a class="anchor" aria-label="anchor" href
 </ul>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">linelisting_data</span> <span class="op">&lt;-</span> <span class="va">hcv_be_2006</span></span>
-<span><span class="va">aggregated_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/transform_data.html">transform_data</a></span><span class="op">(</span><span class="va">linelisting_data</span><span class="op">$</span><span class="va">dur</span>, <span class="va">linelisting_data</span><span class="op">$</span><span class="va">seropositive</span><span class="op">)</span></span>
+<span><span class="va">aggregated_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/transform_data.html">transform_data</a></span><span class="op">(</span><span class="va">linelisting_data</span>, stratum_col <span class="op">=</span> <span class="st">"dur"</span>, status_col<span class="op">=</span><span class="st">"seropositive"</span><span class="op">)</span></span>
 <span><span class="va">aggregated_data</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 116 × 3</span></span></span>
-<span><span class="co">#&gt;        t   pos   tot</span></span>
+<span><span class="co">#&gt;      age   pos   tot</span></span>
 <span><span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span>   0.1     0     1</span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span>   0.3     0     1</span></span>
@@ -143,21 +129,19 @@ <h2 id="aggregate-data">Aggregate data<a class="anchor" aria-label="anchor" href
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p>
-<p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
     </footer>
 </div>
 
-  
 
-  
+
+
 
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 </html>
diff --git a/docs/articles/data_transformation.md b/docs/articles/data_transformation.md
index 973eadf..492fd41 100644
--- a/docs/articles/data_transformation.md
+++ b/docs/articles/data_transformation.md
@@ -2,8 +2,6 @@
 
 ``` r
 library(serosv)
-#> Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when
-#> loading 'serosv'
 ```
 
 ## Aggregate data
@@ -24,10 +22,10 @@ Arguments:
 
 ``` r
 linelisting_data <- hcv_be_2006
-aggregated_data <- transform_data(linelisting_data$dur, linelisting_data$seropositive)
+aggregated_data <- transform_data(linelisting_data, stratum_col = "dur", status_col="seropositive")
 aggregated_data
 #> # A tibble: 116 × 3
-#>        t   pos   tot
+#>      age   pos   tot
 #>    <dbl> <int> <int>
 #>  1   0.1     0     1
 #>  2   0.3     0     1
diff --git a/docs/articles/hierarchical_model.md b/docs/articles/hierarchical_model.md
index b2d36ac..8faf30c 100644
--- a/docs/articles/hierarchical_model.md
+++ b/docs/articles/hierarchical_model.md
@@ -10,20 +10,20 @@ Currently, `serosv` only has models under parametric Bayesian framework
 
 **Proposed approach**
 
-Prevalence has a parametric form $\pi\left( a_{i},\alpha \right)$ where
-$\alpha$ is a parameter vector
+Prevalence has a parametric form $`\pi(a_i, \alpha)`$ where $`\alpha`$
+is a parameter vector
 
 One can constraint the parameter space of the prior distribution
-$P(\alpha)$ in order to achieve the desired monotonicity of the
-posterior distribution $P\left( \pi_{1},\pi_{2},...,\pi_{m}|y,n \right)$
+$`P(\alpha)`$ in order to achieve the desired monotonicity of the
+posterior distribution $`P(\pi_1, \pi_2, ..., \pi_m|y,n)`$
 
 Where:
 
-- $n = \left( n_{1},n_{2},...,n_{m} \right)$ and $n_{i}$ is the sample
-  size at age $a_{i}$
+- $`n = (n_1, n_2, ..., n_m)`$ and $`n_i`$ is the sample size at age
+  $`a_i`$
 
-- $y = \left( y_{1},y_{2},...,y_{m} \right)$ and $y_{i}$ is the number
-  of infected individual from the $n_{i}$ sampled subjects
+- $`y = (y_1, y_2, ..., y_m)`$ and $`y_i`$ is the number of infected
+  individual from the $`n_i`$ sampled subjects
 
 ### Farrington
 
@@ -33,33 +33,48 @@ Refer to `Chapter 10.3.1`
 
 The model for prevalence is as followed
 
-$$\pi(a) = 1 - exp\{\frac{\alpha_{1}}{\alpha_{2}}ae^{- \alpha_{2}a} + \frac{1}{\alpha_{2}}\left( \frac{\alpha_{1}}{\alpha_{2}} - \alpha_{3} \right)\left( e^{- \alpha_{2}a} - 1 \right) - \alpha_{3}a\}$$
+``` math
+
+\pi (a) = 1 - exp\{ \frac{\alpha_1}{\alpha_2}ae^{-\alpha_2 a} + \frac{1}{\alpha_2}(\frac{\alpha_1}{\alpha_2} - \alpha_3)(e^{-\alpha_2 a} - 1) -\alpha_3 a \}
+```
 
 For likelihood model, independent binomial distribution are assumed for
-the number of infected individuals at age $a_{i}$
+the number of infected individuals at age $`a_i`$
 
-$$y_{i} \sim Bin\left( n_{i},\pi_{i} \right),{\mspace{6mu}\text{for}\mspace{6mu}}i = 1,2,3,...m$$
+``` math
+
+y_i \sim Bin(n_i, \pi_i), \text{  for } i = 1,2,3,...m
+```
 
 The constraint on the parameter space can be incorporated by assuming
-truncated normal distribution for the components of $\alpha$,
-$\alpha = \left( \alpha_{1},\alpha_{2},\alpha_{3} \right)$ in
-$\pi_{i} = \pi\left( a_{i},\alpha \right)$
+truncated normal distribution for the components of $`\alpha`$,
+$`\alpha = (\alpha_1, \alpha_2, \alpha_3)`$ in
+$`\pi_i = \pi(a_i,\alpha)`$
 
-$$\alpha_{j} \sim {\text{truncated}\mspace{6mu}}\mathcal{N}\left( \mu_{j},\tau_{j} \right),{\mspace{6mu}\mspace{6mu}}j = 1,2,3$$
+``` math
 
-The joint posterior distribution for $\alpha$ can be derived by
+\alpha_j \sim \text{truncated  } \mathcal{N}(\mu_j, \tau_j), \text{ } j = 1,2,3
+```
+
+The joint posterior distribution for $`\alpha`$ can be derived by
 combining the likelihood and prior as followed
 
-$$P\left( \alpha|y \right) \propto \prod\limits_{i = 1}^{m}\text{Bin}\left( y_{i}|n_{i},\pi\left( a_{i},\alpha \right) \right)\prod\limits_{i = 1}^{3} - \frac{1}{\tau_{j}}\text{exp}\left( \frac{1}{2\tau_{j}^{2}}\left( \alpha_{j} - \mu_{j} \right)^{2} \right)$$
+``` math
+
+P(\alpha|y) \propto \prod^m_{i=1} \text{Bin}(y_i|n_i, \pi(a_i, \alpha)) \prod^3_{i=1}-\frac{1}{\tau_j}\text{exp}(\frac{1}{2\tau^2_j} (\alpha_j - \mu_j)^2)
+```
 
 - Where the flat hyperprior distribution is defined as followed:
 
-  - $\mu_{j} \sim \mathcal{N}(0,10000)$
+  - $`\mu_j \sim \mathcal{N}(0, 10000)`$
 
-  - $\tau_{j}^{- 2} \sim \Gamma(100,100)$
+  - $`\tau^{-2}_j \sim \Gamma(100,100)`$
 
-The full conditional distribution of $\alpha_{i}$ is thus
-$$P\left( \alpha_{i}|\alpha_{j},\alpha_{k},k,j \neq i \right) \propto - \frac{1}{\tau_{i}}\text{exp}\left( \frac{1}{2\tau_{i}^{2}}\left( \alpha_{i} - \mu_{i} \right)^{2} \right)\prod\limits_{i = 1}^{m}\text{Bin}\left( y_{i}|n_{i},\pi\left( a_{i},\alpha \right) \right)$$
+The full conditional distribution of $`\alpha_i`$ is thus
+``` math
+
+P(\alpha_i|\alpha_j,\alpha_k, k, j \neq i) \propto  -\frac{1}{\tau_i}\text{exp}(\frac{1}{2\tau^2_i} (\alpha_i - \mu_i)^2) \prod^m_{i=1} \text{Bin}(y_i|n_i, \pi(a_i, \alpha))
+```
 
 **Fitting data**
 
@@ -68,10 +83,10 @@ To fit Farrington model, use
 and define `type = "far2"` or `type = "far3"` where
 
 - `type = "far2"` refers to Farrington model with 2 parameters
-  ($\alpha_{3} = 0$)
+  ($`\alpha_3 = 0`$)
 
 - `type = "far3"` refers to Farrington model with 3 parameters
-  ($\alpha_{3} > 0$)
+  ($`\alpha_3 > 0`$)
 
 ``` r
 df <- mumps_uk_1986_1987
@@ -168,27 +183,37 @@ plot(model)
 
 The model for seroprevalence is as followed
 
-$$\pi(a) = \frac{\beta a^{\alpha}}{1 + \beta a^{\alpha}},{\mspace{6mu}\mspace{6mu}}\alpha,\beta > 0$$
+``` math
+
+\pi(a) = \frac{\beta a^\alpha}{1 + \beta a^\alpha}, \text{ } \alpha, \beta > 0
+```
 
 The likelihood is specified to be the same as Farrington model
-($y_{i} \sim Bin\left( n_{i},\pi_{i} \right)$) with
+($`y_i \sim Bin(n_i, \pi_i)`$) with
 
-$$\text{logit}\left( \pi(a) \right) = \alpha_{2} + \alpha_{1}\log(a)$$
+``` math
 
-- Where $\alpha_{2} = \text{log}(\beta)$
+\text{logit}(\pi(a)) = \alpha_2 + \alpha_1\log(a)
+```
+
+- Where $`\alpha_2 = \text{log}(\beta)`$
 
-The prior model of $\alpha_{1}$ is specified as
-$\alpha_{1} \sim {\text{truncated}\mspace{6mu}}\mathcal{N}\left( \mu_{1},\tau_{1} \right)$
-with flat hyperprior as in Farrington model
+The prior model of $`\alpha_1`$ is specified as
+$`\alpha_1 \sim \text{truncated  } \mathcal{N}(\mu_1, \tau_1)`$ with
+flat hyperprior as in Farrington model
 
-$\beta$ is constrained to be positive by specifying
-$\alpha_{2} \sim \mathcal{N}\left( \mu_{2},\tau_{2} \right)$
+$`\beta`$ is constrained to be positive by specifying
+$`\alpha_2 \sim \mathcal{N}(\mu_2, \tau_2)`$
 
-The full conditional distribution of $\alpha_{1}$ is thus
+The full conditional distribution of $`\alpha_1`$ is thus
 
-$$P\left( \alpha_{1}|\alpha_{2} \right) \propto - \frac{1}{\tau_{1}}\text{exp}\left( \frac{1}{2\tau_{1}^{2}}\left( \alpha_{1} - \mu_{1} \right)^{2} \right)\prod\limits_{i = 1}^{m}\text{Bin}\left( y_{i}|n_{i},\pi\left( a_{i},\alpha_{1},\alpha_{2} \right) \right)$$
+``` math
+
+P(\alpha_1|\alpha_2) \propto -\frac{1}{\tau_1} \text{exp} (\frac{1}{2 \tau_1^2} (\alpha_1 - \mu_1)^2)
+\prod_{i=1}^m \text{Bin}(y_i|n_i,\pi(a_i, \alpha_1, \alpha_2) )
+```
 
-And $\alpha_{2}$ can be derived in the same way
+And $`\alpha_2`$ can be derived in the same way
 
 **Fitting data**
 
diff --git a/docs/articles/imperfect_test.html b/docs/articles/imperfect_test.html
index fe9e954..55f4ac1 100644
--- a/docs/articles/imperfect_test.html
+++ b/docs/articles/imperfect_test.html
@@ -11,7 +11,13 @@
 <link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png">
 <link rel="icon" sizes="any" href="../favicon.ico">
 <link rel="manifest" href="../site.webmanifest">
-<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
 <script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
 <link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
@@ -25,7 +31,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -39,15 +45,14 @@
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
 <li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="../articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="../articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
     <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
@@ -84,8 +89,8 @@
     
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://oucru-modelling.github.io/serosv/">serosv</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span>
-<span><span class="co">#&gt; Warning: package 'ggplot2' was built under R version 4.3.3</span></span></code></pre></div>
+<span><span class="co">#&gt; Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when</span></span>
+<span><span class="co">#&gt; loading 'serosv'</span></span></code></pre></div>
 <div class="section level2">
 <h2 id="imperfect-test">Imperfect test<a class="anchor" aria-label="anchor" href="#imperfect-test"></a>
 </h2>
@@ -96,10 +101,13 @@ <h2 id="imperfect-test">Imperfect test<a class="anchor" aria-label="anchor" href
 <li>
 <p><code>data</code> the input data frame, must either have:</p>
 <ul>
-<li><p><code>age</code>, <code>pos</code>, <code>tot</code> columns (for
-aggregated data)</p></li>
-<li><p><strong>OR</strong> <code>age</code>, <code>status</code> columns
-for (linelisting data)</p></li>
+<li><p>age, pos, tot columns (for aggregated data)</p></li>
+<li><p><strong>OR</strong> age, status columns for (linelisting
+data)</p></li>
+<li><p>Users can specifiy the name for these columns in the input data
+frame using arguments <code>age_col</code>, <code>pos_col</code>,
+<code>tot_col</code>or <code>age_col</code>, <code>status_col</code>
+respectively</p></li>
 </ul>
 </li>
 <li><p><code>bayesian</code> whether to adjust sero-prevalence using the
@@ -145,8 +153,8 @@ <h2 id="imperfect-test">Imperfect test<a class="anchor" aria-label="anchor" href
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; SAMPLING FOR MODEL 'prevalence_correction' NOW (CHAIN 1).</span></span>
 <span><span class="co">#&gt; Chain 1: </span></span>
-<span><span class="co">#&gt; Chain 1: Gradient evaluation took 9.6e-05 seconds</span></span>
-<span><span class="co">#&gt; Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.96 seconds.</span></span>
+<span><span class="co">#&gt; Chain 1: Gradient evaluation took 9.4e-05 seconds</span></span>
+<span><span class="co">#&gt; Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.94 seconds.</span></span>
 <span><span class="co">#&gt; Chain 1: Adjust your expectations accordingly!</span></span>
 <span><span class="co">#&gt; Chain 1: </span></span>
 <span><span class="co">#&gt; Chain 1: </span></span>
@@ -163,9 +171,9 @@ <h2 id="imperfect-test">Imperfect test<a class="anchor" aria-label="anchor" href
 <span><span class="co">#&gt; Chain 1: Iteration: 3800 / 4000 [ 95%]  (Sampling)</span></span>
 <span><span class="co">#&gt; Chain 1: Iteration: 4000 / 4000 [100%]  (Sampling)</span></span>
 <span><span class="co">#&gt; Chain 1: </span></span>
-<span><span class="co">#&gt; Chain 1:  Elapsed Time: 2.026 seconds (Warm-up)</span></span>
-<span><span class="co">#&gt; Chain 1:                4.044 seconds (Sampling)</span></span>
-<span><span class="co">#&gt; Chain 1:                6.07 seconds (Total)</span></span>
+<span><span class="co">#&gt; Chain 1:  Elapsed Time: 2.057 seconds (Warm-up)</span></span>
+<span><span class="co">#&gt; Chain 1:                4.107 seconds (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1:                6.164 seconds (Total)</span></span>
 <span><span class="co">#&gt; Chain 1:</span></span>
 <span></span>
 <span><span class="co"># check fitted value </span></span>
@@ -193,9 +201,9 @@ <h2 id="imperfect-test">Imperfect test<a class="anchor" aria-label="anchor" href
 <code class="sourceCode R"><span></span>
 <span><span class="co"># Plot output of the bayesian approach </span></span>
 <span><span class="fu"><a href="../reference/plot_corrected_prev.html">plot_corrected_prev</a></span><span class="op">(</span><span class="va">output</span><span class="op">)</span></span></code></pre></div>
-<p><img src="imperfect_test_files/figure-html/unnamed-chunk-3-2.png" class="r-plt" width="700">
-To compare both correction methods in a single plot, provide the output
-from the second method as the optional <code>y</code> argument in
+<p><img src="imperfect_test_files/figure-html/unnamed-chunk-3-2.png" class="r-plt" width="700"></p>
+<p>To compare both correction methods in a single plot, provide the
+output from the second method as the optional <code>y</code> argument in
 <code><a href="../reference/plot_corrected_prev.html">plot_corrected_prev()</a></code></p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/plot_corrected_prev.html">plot_corrected_prev</a></span><span class="op">(</span><span class="va">output</span>, <span class="va">freq_output</span><span class="op">)</span></span></code></pre></div>
diff --git a/docs/articles/imperfect_test.md b/docs/articles/imperfect_test.md
index 9cf2a5e..21c5070 100644
--- a/docs/articles/imperfect_test.md
+++ b/docs/articles/imperfect_test.md
@@ -2,8 +2,8 @@
 
 ``` r
 library(serosv)
-library(ggplot2)
-#> Warning: package 'ggplot2' was built under R version 4.3.3
+#> Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when
+#> loading 'serosv'
 ```
 
 ## Imperfect test
@@ -17,9 +17,13 @@ Arguments:
 
 - `data` the input data frame, must either have:
 
-  - `age`, `pos`, `tot` columns (for aggregated data)
+  - age, pos, tot columns (for aggregated data)
 
-  - **OR** `age`, `status` columns for (linelisting data)
+  - **OR** age, status columns for (linelisting data)
+
+  - Users can specifiy the name for these columns in the input data
+    frame using arguments `age_col`, `pos_col`, `tot_col`or `age_col`,
+    `status_col` respectively
 
 - `bayesian` whether to adjust sero-prevalence using the Bayesian or
   frequentist approach. If set to `TRUE`, true sero-prevalence is
@@ -63,8 +67,8 @@ output <- correct_prevalence(data, warmup = 1000, iter = 4000, init_se=0.9, init
 #> 
 #> SAMPLING FOR MODEL 'prevalence_correction' NOW (CHAIN 1).
 #> Chain 1: 
-#> Chain 1: Gradient evaluation took 9.6e-05 seconds
-#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.96 seconds.
+#> Chain 1: Gradient evaluation took 9.4e-05 seconds
+#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.94 seconds.
 #> Chain 1: Adjust your expectations accordingly!
 #> Chain 1: 
 #> Chain 1: 
@@ -81,9 +85,9 @@ output <- correct_prevalence(data, warmup = 1000, iter = 4000, init_se=0.9, init
 #> Chain 1: Iteration: 3800 / 4000 [ 95%]  (Sampling)
 #> Chain 1: Iteration: 4000 / 4000 [100%]  (Sampling)
 #> Chain 1: 
-#> Chain 1:  Elapsed Time: 2.026 seconds (Warm-up)
-#> Chain 1:                4.044 seconds (Sampling)
-#> Chain 1:                6.07 seconds (Total)
+#> Chain 1:  Elapsed Time: 2.057 seconds (Warm-up)
+#> Chain 1:                4.107 seconds (Sampling)
+#> Chain 1:                6.164 seconds (Total)
 #> Chain 1:
 
 # check fitted value 
@@ -120,9 +124,10 @@ plot_corrected_prev(freq_output)
 plot_corrected_prev(output)
 ```
 
-![](imperfect_test_files/figure-html/unnamed-chunk-3-2.png) To compare
-both correction methods in a single plot, provide the output from the
-second method as the optional `y` argument in
+![](imperfect_test_files/figure-html/unnamed-chunk-3-2.png)
+
+To compare both correction methods in a single plot, provide the output
+from the second method as the optional `y` argument in
 [`plot_corrected_prev()`](https://oucru-modelling.github.io/serosv/reference/plot_corrected_prev.md)
 
 ``` r
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diff --git a/docs/articles/index.html b/docs/articles/index.html
index abcc1f2..e1a0ebb 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -1,5 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Articles • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Articles"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Articles • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
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+  };
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     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -19,15 +25,14 @@
 <li class="nav-item dropdown">
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="../articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="../articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
     <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
@@ -52,8 +57,6 @@ <h3>Fitting data</h3>
       <div class="section-desc"></div>
 
       <dl><dt><a href="linelisting_vs_aggregated.html">Input data</a></dt>
-        <dd>
-        </dd><dt><a href="sir_model.html">SIR model</a></dt>
         <dd>
         </dd><dt><a href="parametric_model.html">Parametric models</a></dt>
         <dd>
@@ -61,8 +64,6 @@ <h3>Fitting data</h3>
         <dd>
         </dd><dt><a href="semiparametric_model.html">Semiparametric model</a></dt>
         <dd>
-        </dd><dt><a href="hierarchical_model.html">Hierarchical Bayesian models</a></dt>
-        <dd>
         </dd><dt><a href="model_quantitative_data.html">Modeling directly from antibody levels</a></dt>
         <dd>
         </dd><dt><a href="repeated_cross_sectional.html">Model repeated cross-sectional data</a></dt>
@@ -72,7 +73,9 @@ <h3>Fitting data</h3>
       <h3>Utilities</h3>
       <div class="section-desc"></div>
 
-      <dl><dt><a href="data_transformation.html">Data transformation</a></dt>
+      <dl><dt><a href="reading_to_titer.html">Convert assay readings to titer</a></dt>
+        <dd>
+        </dd><dt><a href="data_transformation.html">Data transformation</a></dt>
         <dd>
         </dd><dt><a href="imperfect_test.html">Imperfect serological test</a></dt>
         <dd>
diff --git a/docs/articles/index.md b/docs/articles/index.md
index 4255453..8b12c9f 100644
--- a/docs/articles/index.md
+++ b/docs/articles/index.md
@@ -4,16 +4,12 @@
 
 - [Input
   data](https://oucru-modelling.github.io/serosv/articles/linelisting_vs_aggregated.md):
-- [SIR
-  model](https://oucru-modelling.github.io/serosv/articles/sir_model.md):
 - [Parametric
   models](https://oucru-modelling.github.io/serosv/articles/parametric_model.md):
 - [Nonparametric
   model](https://oucru-modelling.github.io/serosv/articles/nonparametric_model.md):
 - [Semiparametric
   model](https://oucru-modelling.github.io/serosv/articles/semiparametric_model.md):
-- [Hierarchical Bayesian
-  models](https://oucru-modelling.github.io/serosv/articles/hierarchical_model.md):
 - [Modeling directly from antibody
   levels](https://oucru-modelling.github.io/serosv/articles/model_quantitative_data.md):
 - [Model repeated cross-sectional
@@ -21,6 +17,8 @@
 
 ### Utilities
 
+- [Convert assay readings to
+  titer](https://oucru-modelling.github.io/serosv/articles/reading_to_titer.md):
 - [Data
   transformation](https://oucru-modelling.github.io/serosv/articles/data_transformation.md):
 - [Imperfect serological
diff --git a/docs/articles/linelisting_vs_aggregated.html b/docs/articles/linelisting_vs_aggregated.html
index 2e90857..4c4ec19 100644
--- a/docs/articles/linelisting_vs_aggregated.html
+++ b/docs/articles/linelisting_vs_aggregated.html
@@ -5,83 +5,71 @@
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
-<meta name="description" content="serosv">
 <title>Input data • serosv</title>
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-<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png">
+<link rel="icon" type="”image/svg+xml”" href="../favicon.svg">
+<link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png">
+<link rel="icon" sizes="any" href="../favicon.ico">
+<link rel="manifest" href="../site.webmanifest">
+<!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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-<meta property="og:description" content="serosv">
-<meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
+<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Input data">
 </head>
 <body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
       <ul class="navbar-nav me-auto">
-<li class="nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+<li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="active nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a>
-    <a class="dropdown-item" href="../articles/sir_model.html">SIR model</a>
-    <a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a>
-    <a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a>
-    <a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a>
-    <a class="dropdown-item" href="../articles/hierarchical_model.html">Hierarchical Bayesian models</a>
-    <a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a>
-    <div class="dropdown-divider"></div>
-    <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
-    <a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a>
-    <a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a>
-    <a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a>
-    <a class="dropdown-item" href="../articles/model_selection.html">Model selection</a>
-  </div>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
+    <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
+    <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
+    <li><a class="dropdown-item" href="../articles/model_selection.html">Model selection</a></li>
+  </ul>
 </li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
       </ul>
-<form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off">
-</form>
-
-      <ul class="navbar-nav">
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/OUCRU-Modelling/serosv/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/OUCRU-Modelling/serosv/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
 
-    
+
   </div>
 </nav><div class="container template-article">
 
@@ -101,43 +89,29 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
     
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://oucru-modelling.github.io/serosv/">serosv</a></span><span class="op">)</span></span>
-<span><span class="co">#&gt; Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when</span></span>
-<span><span class="co">#&gt; loading 'serosv'</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span>
-<span><span class="co">#&gt; Warning: package 'dplyr' was built under R version 4.3.1</span></span>
-<span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Attaching package: 'dplyr'</span></span>
-<span><span class="co">#&gt; The following objects are masked from 'package:stats':</span></span>
-<span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt;     filter, lag</span></span>
-<span><span class="co">#&gt; The following objects are masked from 'package:base':</span></span>
-<span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt;     intersect, setdiff, setequal, union</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://magrittr.tidyverse.org" class="external-link">magrittr</a></span><span class="op">)</span></span></code></pre></div>
 <div class="section level2">
 <h2 id="input-data-format">Input data format<a class="anchor" aria-label="anchor" href="#input-data-format"></a>
 </h2>
-<p>Most <code>*_model()</code> functions in <code>serosv</code> require
+<p>All <code>*_model()</code> functions in <code>serosv</code> require
 <code>data</code> argument as the input data to be fitted.</p>
 <p>The package can handle both linelisting and aggregated data, and it
-infers the format from the column names of the input data frame. This
-means that input data is <u>expected to follow a specific
-format.</u></p>
-<p>For linelisting data: data must have <code>age</code>,
-<code>pos</code> and <code>tot</code> columns, where</p>
+infers the format from the structure of the input data frame. This means
+that input data is <u>expected to follow a specific format.</u></p>
+<p>For linelisting data: data must have 3 columns as follows</p>
 <ul>
-<li><p><code>age</code> is the age vector</p></li>
-<li><p><code>pos</code> is the vector of counts of sero positives of
-that age group</p></li>
-<li><p><code>tot</code> is the vector is the total population of that
-age group</p></li>
+<li><p>the age vector (column name specified via <code>age_col</code>
+parameter)</p></li>
+<li><p>the vector of counts of sero positives of that age group (column
+name specified via <code>pos_col</code> parameter)</p></li>
+<li><p>the vector is the total population of that age group (column name
+specified via <code>tot_col</code> parameter)</p></li>
 </ul>
-<p>For aggregated data: data must have <code>age</code>,
-<code>status</code> columns, where</p>
+<p>For aggregated data: data must have 2 columns as follows</p>
 <ul>
-<li><p><code>age</code> is the age vector of individuals</p></li>
-<li><p><code>status</code> is the vector for the sero positivity of that
-individual</p></li>
+<li><p>the age vector of individuals</p></li>
+<li><p>is the vector indicating the serostatus of that individual
+(column name specified via <code>status_col</code> parameter)</p></li>
 </ul>
 <p><strong>Example:</strong> Fitting linelisting and aggregated data
 using <code><a href="../reference/polynomial_model.html">polynomial_model()</a></code></p>
@@ -164,16 +138,14 @@ <h2 id="input-data-format">Input data format<a class="anchor" aria-label="anchor
 <span><span class="co">#&gt; 6   6   4  15</span></span>
 <span></span>
 <span><span class="co"># fit with aggregated data</span></span>
-<span><span class="va">model1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">aggregated</span>, type <span class="op">=</span> <span class="st">"Muench"</span><span class="op">)</span></span>
+<span><span class="va">model1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">aggregated</span>, k<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">model1</span><span class="op">)</span></span></code></pre></div>
-<p><img src="linelisting_vs_aggregated_files/figure-html/unnamed-chunk-2-1.png" width="700"></p>
+<p><img src="linelisting_vs_aggregated_files/figure-html/unnamed-chunk-2-1.png" class="r-plt" width="700"></p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># fit with linelisting data</span></span>
-<span><span class="va">model2</span> <span class="op">&lt;-</span> <span class="va">linelisting</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/rename.html" class="external-link">rename</a></span><span class="op">(</span>status <span class="op">=</span> <span class="va">seropositive</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
-<span>  <span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"Muench"</span><span class="op">)</span></span>
+<span><span class="va">model2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">linelisting</span>, k<span class="op">=</span><span class="fl">1</span>, status_col <span class="op">=</span> <span class="st">"seropositive"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">model2</span><span class="op">)</span></span></code></pre></div>
-<p><img src="linelisting_vs_aggregated_files/figure-html/unnamed-chunk-2-2.png" width="700"></p>
+<p><img src="linelisting_vs_aggregated_files/figure-html/unnamed-chunk-2-2.png" class="r-plt" width="700"></p>
 </div>
 <div class="section level2">
 <h2 id="data-transformation">Data transformation<a class="anchor" aria-label="anchor" href="#data-transformation"></a>
@@ -183,35 +155,33 @@ <h2 id="data-transformation">Data transformation<a class="anchor" aria-label="an
 data. For more information, refer to <a href="data_transformation.html">Data transformation</a></p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/transform_data.html">transform_data</a></span><span class="op">(</span></span>
-<span>  <span class="va">linelisting</span><span class="op">$</span><span class="va">age</span>, </span>
-<span>  <span class="va">linelisting</span><span class="op">$</span><span class="va">seropositive</span>,</span>
-<span>  stratum_col <span class="op">=</span> <span class="st">"age"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
-<span>  <span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"Muench"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
+<span>  <span class="va">linelisting</span>,</span>
+<span>  stratum_col<span class="op">=</span><span class="st">"age"</span>, </span>
+<span>  status_col<span class="op">=</span><span class="st">"seropositive"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
+<span>  <span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span>k<span class="op">=</span><span class="fl">1</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<p><img src="linelisting_vs_aggregated_files/figure-html/unnamed-chunk-3-1.png" width="700"></p>
+<p><img src="linelisting_vs_aggregated_files/figure-html/unnamed-chunk-3-1.png" class="r-plt" width="700"></p>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
 </div>
 
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p>
-<p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
     </footer>
 </div>
 
-  
 
-  
+
+
 
   </body>
 </html>
diff --git a/docs/articles/linelisting_vs_aggregated.md b/docs/articles/linelisting_vs_aggregated.md
index a4673e7..ecf03f9 100644
--- a/docs/articles/linelisting_vs_aggregated.md
+++ b/docs/articles/linelisting_vs_aggregated.md
@@ -2,44 +2,34 @@
 
 ``` r
 library(serosv)
-#> Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when
-#> loading 'serosv'
-library(dplyr)
-#> Warning: package 'dplyr' was built under R version 4.3.1
-#> 
-#> Attaching package: 'dplyr'
-#> The following objects are masked from 'package:stats':
-#> 
-#>     filter, lag
-#> The following objects are masked from 'package:base':
-#> 
-#>     intersect, setdiff, setequal, union
 library(magrittr)
 ```
 
 ## Input data format
 
-Most `*_model()` functions in `serosv` require `data` argument as the
+All `*_model()` functions in `serosv` require `data` argument as the
 input data to be fitted.
 
 The package can handle both linelisting and aggregated data, and it
-infers the format from the column names of the input data frame. This
-means that input data is *expected to follow a specific format.*
+infers the format from the structure of the input data frame. This means
+that input data is *expected to follow a specific format.*
 
-For linelisting data: data must have `age`, `pos` and `tot` columns,
-where
+For linelisting data: data must have 3 columns as follows
 
-- `age` is the age vector
+- the age vector (column name specified via `age_col` parameter)
 
-- `pos` is the vector of counts of sero positives of that age group
+- the vector of counts of sero positives of that age group (column name
+  specified via `pos_col` parameter)
 
-- `tot` is the vector is the total population of that age group
+- the vector is the total population of that age group (column name
+  specified via `tot_col` parameter)
 
-For aggregated data: data must have `age`, `status` columns, where
+For aggregated data: data must have 2 columns as follows
 
-- `age` is the age vector of individuals
+- the age vector of individuals
 
-- `status` is the vector for the sero positivity of that individual
+- is the vector indicating the serostatus of that individual (column
+  name specified via `status_col` parameter)
 
 **Example:** Fitting linelisting and aggregated data using
 [`polynomial_model()`](https://oucru-modelling.github.io/serosv/reference/polynomial_model.md)
@@ -67,7 +57,7 @@ head(aggregated)
 #> 6   6   4  15
 
 # fit with aggregated data
-model1 <- polynomial_model(aggregated, type = "Muench")
+model1 <- polynomial_model(aggregated, k=1)
 plot(model1)
 ```
 
@@ -75,9 +65,7 @@ plot(model1)
 
 ``` r
 # fit with linelisting data
-model2 <- linelisting %>% 
-  rename(status = seropositive) %>% 
-  polynomial_model(type = "Muench")
+model2 <- polynomial_model(linelisting, k=1, status_col = "seropositive")
 plot(model2)
 ```
 
@@ -93,10 +81,10 @@ transformation](https://oucru-modelling.github.io/serosv/articles/data_transform
 
 ``` r
 transform_data(
-  linelisting$age, 
-  linelisting$seropositive,
-  stratum_col = "age") %>% 
-  polynomial_model(type = "Muench") %>% 
+  linelisting,
+  stratum_col="age", 
+  status_col="seropositive") %>% 
+  polynomial_model(k=1) %>% 
   plot()
 ```
 
diff --git a/docs/articles/model_quantitative_data.html b/docs/articles/model_quantitative_data.html
index ec7f607..29c159a 100644
--- a/docs/articles/model_quantitative_data.html
+++ b/docs/articles/model_quantitative_data.html
@@ -5,83 +5,71 @@
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
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+<!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png">
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-  <a class="external-link nav-link" href="https://github.com/OUCRU-Modelling/serosv/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/OUCRU-Modelling/serosv/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
 
-    
+
   </div>
 </nav><div class="container template-article">
 
@@ -100,22 +88,27 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
     
     
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://oucru-modelling.github.io/serosv/">serosv</a></span><span class="op">)</span></span>
-<span><span class="co">#&gt; Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when</span></span>
-<span><span class="co">#&gt; loading 'serosv'</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://oucru-modelling.github.io/serosv/">serosv</a></span><span class="op">)</span></span></code></pre></div>
 <div class="section level2">
 <h2 id="mixture-model">Mixture model<a class="anchor" aria-label="anchor" href="#mixture-model"></a>
 </h2>
 <p><strong>Proposed model</strong></p>
-<p>Two-component mixture model for test result <span class="math inline">\(Z\)</span> with <span class="math inline">\(Z_j (j
-= \{I, S\})\)</span> being the latent mixing component having density
-<span class="math inline">\(f_j(z_j|\theta_j)\)</span> and with <span class="math inline">\(\pi_{\text{TRUE}}(a)\)</span> being the
-age-dependent mixing probability can be represented as</p>
+<p>Consider a two-component Gaussian mixture model for the antibody
+level <span class="math inline">\(Z\)</span>, where each component <span class="math inline">\(Z_j\)</span> represent antibody level arising from
+the 2 latent sub-populations <span class="math inline">\(j \in \{I,
+S\}\)</span> (i.e., Infected and Susceptible groups).</p>
+<p>Let <span class="math inline">\(f_j(z_j|\theta_j)\)</span> denotes
+the density of component <span class="math inline">\(Z_j\)</span>, where
+<span class="math inline">\(\theta_I\)</span> and <span class="math inline">\(\theta_S\)</span> are the parameters for the
+Susceptible and Infected components respectively.</p>
+<p>With <span class="math inline">\(\pi_{\text{TRUE}}(a)\)</span> being
+the age-dependent mixing probability (i.e., the true prevalence), the
+density of the mixture is formulated as</p>
 <p><span class="math display">\[
 f(z|z_I, z_S,a) =
 (1-\pi_{\text{TRUE}}(a))f_S(z_S|\theta_S)+\pi_{\text{TRUE}}(a)f_I(z_I|\theta_I)
 \]</span></p>
-<p>The mean <span class="math inline">\(E(Z|a)\)</span> thus equals</p>
+<p>The age-specific mean antibody level <span class="math inline">\(E(Z|a)\)</span> thus equals</p>
 <p><span class="math display">\[
 \mu(a) =
 (1-\pi_{\text{TRUE}}(a))\mu_S+\pi_{\text{TRUE}}(a)\mu_I\]</span></p>
@@ -123,41 +116,68 @@ <h2 id="mixture-model">Mixture model<a class="anchor" aria-label="anchor" href="
 <p><span class="math display">\[
 \pi_{\text{TRUE}}(a) = \frac{\mu(a) - \mu_S}{\mu_I - \mu_S}
 \]</span></p>
-<p>Force of infection can then be calculated by</p>
+<p>Force of infection can then be inferred by</p>
 <p><span class="math display">\[
 \lambda_{TRUE} = \frac{\mu'(a)}{\mu_I - \mu(a)}
 \]</span></p>
+<p>Refer to Chapter <code>11.3</code> of the book by <span class="citation">Hens et al. (2012)</span> for a more detailed
+explanation of the method.</p>
 <p><strong>Fitting data</strong></p>
-<p>To fit the mixture data, use <code>mixture_model</code> function</p>
+<p>General workflow:</p>
+<ul>
+<li><p>Step 1: Fit the antibody level data to a 2-component mixture
+model</p></li>
+<li><p>Step 2: From the fitted mixture model, estimate the
+seroprevalence and FOI</p></li>
+</ul>
+<p>To fit the antibody data, use <code>mixture_model</code> function</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">vzv_be_2001_2003</span><span class="op">[</span><span class="va">vzv_be_2001_2003</span><span class="op">$</span><span class="va">age</span> <span class="op">&lt;</span> <span class="fl">40.5</span>,<span class="op">]</span></span>
 <span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">df</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/order.html" class="external-link">order</a></span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">age</span><span class="op">)</span>,<span class="op">]</span></span>
 <span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">df</span><span class="op">$</span><span class="va">VZVmIUml</span></span>
 <span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mixture_model.html">mixture_model</a></span><span class="op">(</span>antibody_level <span class="op">=</span> <span class="va">data</span><span class="op">)</span></span>
-<span><span class="va">model</span><span class="op">$</span><span class="va">info</span></span>
-<span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Parameters:</span></span>
-<span><span class="co">#&gt;       pi    mu  sigma</span></span>
-<span><span class="co">#&gt; 1 0.1088 2.349 0.6804</span></span>
-<span><span class="co">#&gt; 2 0.8912 6.439 0.9437</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span>
+<span><span class="co">#&gt; Mixture model </span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Distribution:</span></span>
-<span><span class="co">#&gt; [1] "norm"</span></span>
+<span><span class="co">#&gt; Estimated proportion:</span></span>
+<span><span class="co">#&gt;  Susceptible=0.1088, Infected=0.8912 </span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Constraints:</span></span>
-<span><span class="co">#&gt;    conpi    conmu consigma </span></span>
-<span><span class="co">#&gt;   "NONE"   "NONE"   "NONE"</span></span></code></pre></div>
+<span><span class="co">#&gt; Estimated mean Log(Antibody):</span></span>
+<span><span class="co">#&gt;  Susceptible=2.349, Infected=6.439</span></span></code></pre></div>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></code></pre></div>
-<p><img src="model_quantitative_data_files/figure-html/unnamed-chunk-3-1.png" width="700"></p>
+<p><img src="model_quantitative_data_files/figure-html/unnamed-chunk-3-1.png" class="r-plt" width="700"></p>
 <p>sero-prevalence and FOI can then be esimated using function
 <code>estimate_from_mixture</code></p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">est_mixture</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/estimate_from_mixture.html">estimate_from_mixture</a></span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">age</span>, <span class="va">data</span>, mixture_model <span class="op">=</span> <span class="va">model</span>, threshold_status <span class="op">=</span> <span class="va">df</span><span class="op">$</span><span class="va">seropositive</span>, sp<span class="op">=</span><span class="fl">83</span>, monotonize <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
+<span><span class="va">est_mixture</span></span>
+<span><span class="co">#&gt; Age-varying seroprevalence estimated from mixture model </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Monotonized seroprevalence:  FALSE</span></span>
+<span><span class="co">#&gt; Family: gaussian </span></span>
+<span><span class="co">#&gt; Link function: identity </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Formula:</span></span>
+<span><span class="co">#&gt; log_antibody ~ s(age, bs = "ps", sp = 83)</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Estimated degrees of freedom:</span></span>
+<span><span class="co">#&gt; 5.13  total = 6.13 </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; GCV score: 2.056333</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">est_mixture</span><span class="op">)</span></span>
 <span><span class="co">#&gt; Warning: No shared levels found between `names(values)` of the manual scale and the</span></span>
 <span><span class="co">#&gt; data's <span style="color: #00BB00;">fill</span> values.</span></span></code></pre></div>
-<p><img src="model_quantitative_data_files/figure-html/unnamed-chunk-4-1.png" width="700"></p>
+<p><img src="model_quantitative_data_files/figure-html/unnamed-chunk-4-1.png" class="r-plt" width="700"></p>
+<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0">
+<div id="ref-Hens2012" class="csl-entry">
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. <em>Modeling Infectious Disease Parameters Based
+on Serological and Social Contact Data: A Modern Statistical
+Perspective</em>. <em>Statistics for Biology and Health</em>. Springer
+New York. <a href="https://doi.org/10.1007/978-1-4614-4072-7" class="external-link">https://doi.org/10.1007/978-1-4614-4072-7</a>.
+</div>
+</div>
 </div>
   </main>
 </div>
@@ -165,21 +185,19 @@ <h2 id="mixture-model">Mixture model<a class="anchor" aria-label="anchor" href="
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p>
-<p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
     </footer>
 </div>
 
-  
 
-  
+
+
 
   </body>
 </html>
diff --git a/docs/articles/model_quantitative_data.md b/docs/articles/model_quantitative_data.md
index d710c0a..76295a0 100644
--- a/docs/articles/model_quantitative_data.md
+++ b/docs/articles/model_quantitative_data.md
@@ -2,58 +2,68 @@
 
 ``` r
 library(serosv)
-#> Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when
-#> loading 'serosv'
 ```
 
 ## Mixture model
 
 **Proposed model**
 
-Two-component mixture model for test result \\(Z\\) with \\(Z_j (j =
-\\{I, S\\})\\) being the latent mixing component having density
-\\(f_j(z_j\|\theta_j)\\) and with \\(\pi\_{\text{TRUE}}(a)\\) being the
-age-dependent mixing probability can be represented as
+Consider a two-component Gaussian mixture model for the antibody level
+\\Z\\, where each component \\Z_j\\ represent antibody level arising
+from the 2 latent sub-populations \\j \in \\I, S\\\\ (i.e., Infected and
+Susceptible groups).
 
-\\\[ f(z\|z_I, z_S,a) =
+Let \\f_j(z_j\|\theta_j)\\ denotes the density of component \\Z_j\\,
+where \\\theta_I\\ and \\\theta_S\\ are the parameters for the
+Susceptible and Infected components respectively.
+
+With \\\pi\_{\text{TRUE}}(a)\\ being the age-dependent mixing
+probability (i.e., the true prevalence), the density of the mixture is
+formulated as
+
+\\ f(z\|z_I, z_S,a) =
 (1-\pi\_{\text{TRUE}}(a))f_S(z_S\|\theta_S)+\pi\_{\text{TRUE}}(a)f_I(z_I\|\theta_I)
-\\\]
+\\
 
-The mean \\(E(Z\|a)\\) thus equals
+The age-specific mean antibody level \\E(Z\|a)\\ thus equals
 
-\\\[ \mu(a) =
-(1-\pi\_{\text{TRUE}}(a))\mu_S+\pi\_{\text{TRUE}}(a)\mu_I\\\]
+\\ \mu(a) = (1-\pi\_{\text{TRUE}}(a))\mu_S+\pi\_{\text{TRUE}}(a)\mu_I\\
 
 From which the true prevalence can be calculated by
 
-\\\[ \pi\_{\text{TRUE}}(a) = \frac{\mu(a) - \mu_S}{\mu_I - \mu_S} \\\]
+\\ \pi\_{\text{TRUE}}(a) = \frac{\mu(a) - \mu_S}{\mu_I - \mu_S} \\
+
+Force of infection can then be inferred by
 
-Force of infection can then be calculated by
+\\ \lambda\_{TRUE} = \frac{\mu'(a)}{\mu_I - \mu(a)} \\
 
-\\\[ \lambda\_{TRUE} = \frac{\mu'(a)}{\mu_I - \mu(a)} \\\]
+Refer to Chapter `11.3` of the book by Hens et al. (2012) for a more
+detailed explanation of the method.
 
 **Fitting data**
 
-To fit the mixture data, use `mixture_model` function
+General workflow:
+
+- Step 1: Fit the antibody level data to a 2-component mixture model
+
+- Step 2: From the fitted mixture model, estimate the seroprevalence and
+  FOI
+
+To fit the antibody data, use `mixture_model` function
 
 ``` r
 df <- vzv_be_2001_2003[vzv_be_2001_2003$age < 40.5,]
 df <- df[order(df$age),]
 data <- df$VZVmIUml
 model <- mixture_model(antibody_level = data)
-model$info
+print(model)
+#> Mixture model 
 #> 
-#> Parameters:
-#>       pi    mu  sigma
-#> 1 0.1088 2.349 0.6804
-#> 2 0.8912 6.439 0.9437
+#> Estimated proportion:
+#>  Susceptible=0.1088, Infected=0.8912 
 #> 
-#> Distribution:
-#> [1] "norm"
-#> 
-#> Constraints:
-#>    conpi    conmu consigma 
-#>   "NONE"   "NONE"   "NONE"
+#> Estimated mean Log(Antibody):
+#>  Susceptible=2.349, Infected=6.439
 ```
 
 ``` r
@@ -67,9 +77,29 @@ sero-prevalence and FOI can then be esimated using function
 
 ``` r
 est_mixture <- estimate_from_mixture(df$age, data, mixture_model = model, threshold_status = df$seropositive, sp=83, monotonize = FALSE)
+est_mixture
+#> Age-varying seroprevalence estimated from mixture model 
+#> 
+#> Monotonized seroprevalence:  FALSE
+#> Family: gaussian 
+#> Link function: identity 
+#> 
+#> Formula:
+#> log_antibody ~ s(age, bs = "ps", sp = 83)
+#> 
+#> Estimated degrees of freedom:
+#> 5.13  total = 6.13 
+#> 
+#> GCV score: 2.056333
 plot(est_mixture)
 #> Warning: No shared levels found between `names(values)` of the manual scale and the
 #> data's fill values.
 ```
 
 ![](model_quantitative_data_files/figure-html/unnamed-chunk-4-1.png)
+
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. *Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective*.
+*Statistics for Biology and Health*. Springer New York.
+<https://doi.org/10.1007/978-1-4614-4072-7>.
diff --git a/docs/articles/model_selection.html b/docs/articles/model_selection.html
index ee34dcd..1f1e562 100644
--- a/docs/articles/model_selection.html
+++ b/docs/articles/model_selection.html
@@ -5,83 +5,71 @@
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
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@@ -100,101 +88,214 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
     
     
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://oucru-modelling.github.io/serosv/">serosv</a></span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when</span></span>
-<span><span class="co">## loading 'serosv'</span></span></code></pre>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning: package 'dplyr' was built under R version 4.3.1</span></span></code></pre>
-<pre><code><span><span class="co">## </span></span>
-<span><span class="co">## Attaching package: 'dplyr'</span></span></code></pre>
-<pre><code><span><span class="co">## The following objects are masked from 'package:stats':</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">##     filter, lag</span></span></code></pre>
-<pre><code><span><span class="co">## The following objects are masked from 'package:base':</span></span>
-<span><span class="co">## </span></span>
-<span><span class="co">##     intersect, setdiff, setequal, union</span></span></code></pre>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://magrittr.tidyverse.org" class="external-link">magrittr</a></span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://oucru-modelling.github.io/serosv/">serosv</a></span><span class="op">)</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://magrittr.tidyverse.org" class="external-link">magrittr</a></span><span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">parvob19_fi_1997_1998</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/order.html" class="external-link">order</a></span><span class="op">(</span><span class="va">parvob19_fi_1997_1998</span><span class="op">$</span><span class="va">age</span><span class="op">)</span>, <span class="op">]</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/rename.html" class="external-link">rename</a></span><span class="op">(</span>status <span class="op">=</span> <span class="va">seropositive</span><span class="op">)</span></span>
-<span>  </span>
-<span><span class="va">aggregated</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/transform_data.html">transform_data</a></span><span class="op">(</span><span class="va">data</span><span class="op">$</span><span class="va">age</span>, <span class="va">data</span><span class="op">$</span><span class="va">status</span>, stratum_col <span class="op">=</span> <span class="st">"age"</span><span class="op">)</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/rename.html" class="external-link">rename</a></span><span class="op">(</span>status <span class="op">=</span> <span class="va">seropositive</span><span class="op">)</span> </span>
 <span></span>
-<span><span class="co"># fit with linelisting data</span></span>
-<span><span class="va">model1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">data</span>, type <span class="op">=</span> <span class="st">"Muench"</span><span class="op">)</span></span>
-<span><span class="co"># fit with aggregated data</span></span>
-<span><span class="va">model2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">aggregated</span>, type <span class="op">=</span> <span class="st">"Muench"</span><span class="op">)</span></span>
-<span><span class="co"># fit with aggregated data</span></span>
-<span><span class="va">model3</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">aggregated</span>, type <span class="op">=</span> <span class="st">"Griffith"</span><span class="op">)</span></span>
-<span><span class="co"># fit with gam</span></span>
-<span><span class="va">model4</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/penalized_spline_model.html">penalized_spline_model</a></span><span class="op">(</span><span class="va">aggregated</span><span class="op">)</span></span></code></pre></div>
+<span><span class="va">aggregated</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/transform_data.html">transform_data</a></span><span class="op">(</span><span class="va">data</span>, stratum_col <span class="op">=</span> <span class="st">"age"</span>, status_col<span class="op">=</span><span class="st">"status"</span><span class="op">)</span></span></code></pre></div>
 <div class="section level2">
 <h2 id="generate-models-comparison-data-frame">Generate models comparison <code>data.frame</code><a class="anchor" aria-label="anchor" href="#generate-models-comparison-data-frame"></a>
 </h2>
 <p>Function <code><a href="../reference/compare_models.html">compare_models()</a></code> is used for quickly computing
-AIC and BIC values for given model(s).</p>
-<p>The function can take an arbitrary number of models and all models
-must be created from <code>serosv</code>’s set of <code>*_model()</code>
-functions. It will then return a <code>data.frame</code> of 4
-columns:</p>
+comparison metrics for a set of models on a given dataset.</p>
+<p>The function takes the following arguments:</p>
+<ul>
+<li><p><code>data</code> the dataset that will be fitted to the
+models</p></li>
+<li><p><code>method</code> the method to generate comparison metrics. It
+can be the name of one of the built-in methods, or a user-defined
+function.</p></li>
+<li><p><code>…</code> functions that take a data and return a fitted
+<code>serosv</code> model.</p></li>
+</ul>
+<p>It will then return a <code>data.frame</code> with the following
+columns</p>
 <ul>
-<li><p><code>model</code> model identifier. Either user defined name or
+<li><p><code>label</code> model identifier. Either user defined name or
 index based on the order provided.</p></li>
 <li><p><code>type</code> type of model (a <code>serosv</code> model
 class)</p></li>
-<li><p><code>AIC</code> AIC value for the fitted model (if
-applicable)</p></li>
-<li><p><code>BIC</code> AIC value for the fitted model (if
-applicable)</p></li>
+<li><p>metrics depending on the method selected</p></li>
+</ul>
+<p><strong>Built-in <code>method</code></strong></p>
+<p><code>serosv</code> currently provide 2 built-in metrics generating
+methods</p>
+<ul>
+<li><p><code>"AIC/BIC"</code> which returns fitted model’s AIC, BIC and
+Log-likelihood (where applicable)</p></li>
+<li><p><code>"CV"</code> which split the data into “train” and “test”
+set then return logloss and AUC from model’s prediction on the test
+set</p></li>
 </ul>
 <p><strong>Sample usage</strong></p>
-<p>Compare 4 models defined above</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># provide models with name</span></span>
-<span><span class="fu"><a href="../reference/compare_models.html">compare_models</a></span><span class="op">(</span>muench_linelist <span class="op">=</span> <span class="va">model1</span>, muench_aggregated <span class="op">=</span> <span class="va">model2</span>, griffith <span class="op">=</span> <span class="va">model3</span>, penalized_spline <span class="op">=</span> <span class="va">model4</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">##               model                   type       AIC       BIC</span></span>
-<span><span class="co">## 1   muench_linelist       polynomial_model 1368.9096 1373.9280</span></span>
-<span><span class="co">## 2 muench_aggregated       polynomial_model  505.5269  508.8787</span></span>
-<span><span class="co">## 3          griffith       polynomial_model  489.1185  495.8223</span></span>
-<span><span class="co">## 4  penalized_spline penalized_spline_model  256.7061  271.3522</span></span></code></pre>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># ----- Return AIC, BIC ----- </span></span>
+<span><span class="va">aic_bic_out</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/compare_models.html">compare_models</a></span><span class="op">(</span></span>
+<span>  data <span class="op">=</span> <span class="va">data</span>,</span>
+<span>  method <span class="op">=</span> <span class="st">"AIC/BIC"</span>,</span>
+<span>  griffith <span class="op">=</span> <span class="op">~</span><span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">.x</span>, k<span class="op">=</span><span class="fl">2</span><span class="op">)</span>,</span>
+<span>  penalized_spline <span class="op">=</span> <span class="va">penalized_spline_model</span>,</span>
+<span>  farrington <span class="op">=</span> <span class="op">~</span><span class="fu"><a href="../reference/farrington_model.html">farrington_model</a></span><span class="op">(</span><span class="va">.x</span>, start<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>alpha<span class="op">=</span><span class="fl">0.07</span>,beta<span class="op">=</span><span class="fl">0.1</span>,gamma<span class="op">=</span><span class="fl">0.03</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>  local_polynomial <span class="op">=</span> <span class="va">lp_model</span> <span class="co"># expect to not return any values</span></span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/warning.html" class="external-link">suppressWarnings</a></span><span class="op">(</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># ----- Return Cross-validation metrics ----- </span></span>
+<span><span class="va">cv_out</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/compare_models.html">compare_models</a></span><span class="op">(</span></span>
+<span>    <span class="va">data</span>,</span>
+<span>    method <span class="op">=</span> <span class="st">"CV"</span>,</span>
+<span>    griffith <span class="op">=</span> <span class="op">~</span><span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">.x</span>, k<span class="op">=</span><span class="fl">2</span><span class="op">)</span>,</span>
+<span>    penalized_spline <span class="op">=</span> <span class="va">penalized_spline_model</span>,</span>
+<span>    farrington <span class="op">=</span> <span class="op">~</span><span class="fu"><a href="../reference/farrington_model.html">farrington_model</a></span><span class="op">(</span><span class="va">.x</span>, start<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>alpha<span class="op">=</span><span class="fl">0.07</span>,beta<span class="op">=</span><span class="fl">0.1</span>,gamma<span class="op">=</span><span class="fl">0.03</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>    local_polynomial <span class="op">=</span> <span class="va">lp_model</span> </span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/warning.html" class="external-link">suppressWarnings</a></span><span class="op">(</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">aic_bic_out</span></span></code></pre></div>
+<pre><code><span><span class="co">## <span style="color: #949494;"># A tibble: 4 × 8</span></span></span>
+<span><span class="co">##   label            type                  AIC   BIC logLik    df mod_out    plots</span></span>
+<span><span class="co">##   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>            <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>               <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;list&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;lis&gt;</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">1</span> griffith         polynomial_model    <span style="text-decoration: underline;">1</span>353. <span style="text-decoration: underline;">1</span>363.  -<span style="color: #BB0000;">674.</span>  2    <span style="color: #949494;">&lt;plynml_m&gt;</span> <span style="color: #949494;">&lt;gg&gt;</span> </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">2</span> penalized_spline penalized_spline_m… <span style="text-decoration: underline;">1</span>330. <span style="text-decoration: underline;">1</span>365.  -<span style="color: #BB0000;">658.</span>  6.88 <span style="color: #949494;">&lt;pnlzd_s_&gt;</span> <span style="color: #949494;">&lt;gg&gt;</span> </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">3</span> farrington       farrington_model    <span style="text-decoration: underline;">1</span>337.   <span style="color: #BB0000;">NA</span>   -<span style="color: #BB0000;">665.</span>  3    <span style="color: #949494;">&lt;frrngtn_&gt;</span> <span style="color: #949494;">&lt;gg&gt;</span> </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">4</span> local_polynomial lp_model              <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>     <span style="color: #BB0000;">NA</span>  <span style="color: #BB0000;">NA</span>    <span style="color: #949494;">&lt;lp_model&gt;</span> <span style="color: #949494;">&lt;gg&gt;</span></span></span></code></pre>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">cv_out</span></span></code></pre></div>
+<pre><code><span><span class="co">## <span style="color: #949494;"># A tibble: 4 × 6</span></span></span>
+<span><span class="co">##   label            logloss   auc type                   mod_out    plots </span></span>
+<span><span class="co">##   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>              <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                  <span style="color: #949494; font-style: italic;">&lt;list&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;list&gt;</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">1</span> griffith            277. 0.594 polynomial_model       <span style="color: #949494;">&lt;plynml_m&gt;</span> <span style="color: #949494;">&lt;gg&gt;</span>  </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">2</span> penalized_spline    197. 0.601 penalized_spline_model <span style="color: #949494;">&lt;pnlzd_s_&gt;</span> <span style="color: #949494;">&lt;gg&gt;</span>  </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">3</span> farrington          356. 0.600 farrington_model       <span style="color: #949494;">&lt;frrngtn_&gt;</span> <span style="color: #949494;">&lt;gg&gt;</span>  </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">4</span> local_polynomial    237. 0.588 lp_model               <span style="color: #949494;">&lt;lp_model&gt;</span> <span style="color: #949494;">&lt;gg&gt;</span></span></span></code></pre>
+<p>With aggregated data</p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># ----- Return AIC, BIC ----- </span></span>
+<span><span class="va">aic_bic_out</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/compare_models.html">compare_models</a></span><span class="op">(</span></span>
+<span>  data <span class="op">=</span> <span class="va">aggregated</span>,</span>
+<span>  method <span class="op">=</span> <span class="st">"AIC/BIC"</span>,</span>
+<span>  griffith <span class="op">=</span> <span class="op">~</span><span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">.x</span>, k<span class="op">=</span><span class="fl">2</span><span class="op">)</span>,</span>
+<span>  penalized_spline <span class="op">=</span> <span class="va">penalized_spline_model</span>,</span>
+<span>  farrington <span class="op">=</span> <span class="op">~</span><span class="fu"><a href="../reference/farrington_model.html">farrington_model</a></span><span class="op">(</span><span class="va">.x</span>, start<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>alpha<span class="op">=</span><span class="fl">0.07</span>,beta<span class="op">=</span><span class="fl">0.1</span>,gamma<span class="op">=</span><span class="fl">0.03</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>  local_polynomial <span class="op">=</span> <span class="va">lp_model</span> <span class="co"># expect to not return any values</span></span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/warning.html" class="external-link">suppressWarnings</a></span><span class="op">(</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># ----- Return Cross-validation metrics (default with 4 folds) ----- </span></span>
+<span><span class="va">cv_out</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/compare_models.html">compare_models</a></span><span class="op">(</span></span>
+<span>    data <span class="op">=</span> <span class="va">aggregated</span>,</span>
+<span>    method <span class="op">=</span> <span class="st">"CV"</span>,</span>
+<span>    griffith <span class="op">=</span> <span class="op">~</span><span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">.x</span>, k<span class="op">=</span><span class="fl">2</span><span class="op">)</span>,</span>
+<span>    penalized_spline <span class="op">=</span> <span class="va">penalized_spline_model</span>,</span>
+<span>    farrington <span class="op">=</span> <span class="op">~</span><span class="fu"><a href="../reference/farrington_model.html">farrington_model</a></span><span class="op">(</span><span class="va">.x</span>, start<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>alpha<span class="op">=</span><span class="fl">0.07</span>,beta<span class="op">=</span><span class="fl">0.1</span>,gamma<span class="op">=</span><span class="fl">0.03</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>    local_polynomial <span class="op">=</span> <span class="va">lp_model</span> </span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/warning.html" class="external-link">suppressWarnings</a></span><span class="op">(</span><span class="op">)</span></span>
+<span></span>
+<span></span>
+<span><span class="va">aic_bic_out</span></span></code></pre></div>
+<pre><code><span><span class="co">## <span style="color: #949494;"># A tibble: 4 × 8</span></span></span>
+<span><span class="co">##   label            type                  AIC   BIC logLik    df mod_out    plots</span></span>
+<span><span class="co">##   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>            <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>               <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;list&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;lis&gt;</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">1</span> griffith         polynomial_model     489.  496.  -<span style="color: #BB0000;">243.</span>  2    <span style="color: #949494;">&lt;plynml_m&gt;</span> <span style="color: #949494;">&lt;gg&gt;</span> </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">2</span> penalized_spline penalized_spline_m…  257.  271.  -<span style="color: #BB0000;">124.</span>  4.37 <span style="color: #949494;">&lt;pnlzd_s_&gt;</span> <span style="color: #949494;">&lt;gg&gt;</span> </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">3</span> farrington       farrington_model    <span style="text-decoration: underline;">1</span>337.   <span style="color: #BB0000;">NA</span>   -<span style="color: #BB0000;">665.</span>  3    <span style="color: #949494;">&lt;frrngtn_&gt;</span> <span style="color: #949494;">&lt;gg&gt;</span> </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">4</span> local_polynomial lp_model              <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>     <span style="color: #BB0000;">NA</span>  <span style="color: #BB0000;">NA</span>    <span style="color: #949494;">&lt;lp_model&gt;</span> <span style="color: #949494;">&lt;gg&gt;</span></span></span></code></pre>
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">cv_out</span></span></code></pre></div>
+<pre><code><span><span class="co">## <span style="color: #949494;"># A tibble: 4 × 6</span></span></span>
+<span><span class="co">##   label              mse logloss type                   mod_out    plots </span></span>
+<span><span class="co">##   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>            <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                  <span style="color: #949494; font-style: italic;">&lt;list&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;list&gt;</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">1</span> griffith         0.217   178.  polynomial_model       <span style="color: #949494;">&lt;plynml_m&gt;</span> <span style="color: #949494;">&lt;gg&gt;</span>  </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">2</span> penalized_spline 0.123    79.7 penalized_spline_model <span style="color: #949494;">&lt;pnlzd_s_&gt;</span> <span style="color: #949494;">&lt;gg&gt;</span>  </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">3</span> farrington       0.117    65.5 farrington_model       <span style="color: #949494;">&lt;frrngtn_&gt;</span> <span style="color: #949494;">&lt;gg&gt;</span>  </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">4</span> local_polynomial 0.117    71.3 lp_model               <span style="color: #949494;">&lt;lp_model&gt;</span> <span style="color: #949494;">&lt;gg&gt;</span></span></span></code></pre>
+</div>
+<div class="section level2">
+<h2 id="generate-custom-metrics">Generate custom metrics<a class="anchor" aria-label="anchor" href="#generate-custom-metrics"></a>
+</h2>
+<p>The users can also provide a custom function to generate the
+comparison metrics.</p>
+<p>This function must accepts 2 parameters:</p>
+<ul>
+<li><p><code>dat</code> the input data</p></li>
+<li><p><code>model_func</code> a function that takes an input data and
+returns a <code>serosv</code> model</p></li>
+</ul>
+<p>And it must returns a data frame with 1 row where each column
+represents one metric.</p>
+<p><strong><em>Example:</em></strong></p>
+<p>The following implements holdout validation and returns MAE:</p>
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">generate_mae</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">dat</span>, <span class="va">model_func</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="va">n_train</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">dat</span><span class="op">)</span><span class="op">*</span><span class="fl">0.8</span><span class="op">)</span></span>
+<span>  <span class="va">train</span> <span class="op">&lt;-</span> <span class="va">dat</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="va">n_train</span>,<span class="op">]</span></span>
+<span>  <span class="va">test</span> <span class="op">&lt;-</span> <span class="va">dat</span><span class="op">[</span><span class="va">n_train</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">dat</span><span class="op">)</span>,<span class="op">]</span></span>
+<span>  </span>
+<span>  <span class="va">fit</span> <span class="op">&lt;-</span> <span class="fu">model_func</span><span class="op">(</span><span class="va">dat</span><span class="op">)</span></span>
+<span>  <span class="va">pred</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">fit</span>, <span class="va">test</span><span class="op">[</span>, <span class="fl">1</span>, drop<span class="op">=</span><span class="cn">FALSE</span><span class="op">]</span><span class="op">)</span></span>
+<span>  </span>
+<span>  <span class="co"># handle error differently depending on datatype</span></span>
+<span>  <span class="va">mae</span> <span class="op">&lt;-</span> <span class="kw">if</span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">datatype</span> <span class="op">==</span> <span class="st">"linelisting"</span><span class="op">)</span><span class="op">{</span></span>
+<span>    <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="va">test</span><span class="op">$</span><span class="va">status</span> <span class="op">-</span> <span class="va">pred</span><span class="op">)</span>, na.rm<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">test</span><span class="op">)</span></span>
+<span>  <span class="op">}</span><span class="kw">else</span><span class="op">{</span></span>
+<span>    <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="va">test</span><span class="op">$</span><span class="va">pos</span><span class="op">/</span><span class="va">test</span><span class="op">$</span><span class="va">tot</span> <span class="op">-</span> <span class="va">pred</span><span class="op">)</span>, na.rm<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">test</span><span class="op">)</span></span>
+<span>  <span class="op">}</span></span>
+<span>  </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
+<span>    mae <span class="op">=</span> <span class="va">mae</span></span>
+<span>  <span class="op">)</span></span>
+<span><span class="op">}</span></span></code></pre></div>
+<p>We can then run <code>compare_models</code> with the custom metrics
+function</p>
 <div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># provide models without name</span></span>
-<span><span class="fu"><a href="../reference/compare_models.html">compare_models</a></span><span class="op">(</span><span class="va">model1</span>, <span class="va">model2</span>, <span class="va">model3</span>, <span class="va">model4</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">##   model                   type       AIC       BIC</span></span>
-<span><span class="co">## 1     1       polynomial_model 1368.9096 1373.9280</span></span>
-<span><span class="co">## 2     2       polynomial_model  505.5269  508.8787</span></span>
-<span><span class="co">## 3     3       polynomial_model  489.1185  495.8223</span></span>
-<span><span class="co">## 4     4 penalized_spline_model  256.7061  271.3522</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/compare_models.html">compare_models</a></span><span class="op">(</span></span>
+<span>    data <span class="op">=</span> <span class="va">aggregated</span>,</span>
+<span>    method <span class="op">=</span> <span class="va">generate_mae</span>,</span>
+<span>    griffith <span class="op">=</span> <span class="op">~</span><span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">.x</span>, k<span class="op">=</span><span class="fl">2</span><span class="op">)</span>,</span>
+<span>    penalized_spline <span class="op">=</span> <span class="va">penalized_spline_model</span>,</span>
+<span>    farrington <span class="op">=</span> <span class="op">~</span><span class="fu"><a href="../reference/farrington_model.html">farrington_model</a></span><span class="op">(</span><span class="va">.x</span>, start<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>alpha<span class="op">=</span><span class="fl">0.07</span>,beta<span class="op">=</span><span class="fl">0.1</span>,gamma<span class="op">=</span><span class="fl">0.03</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>    local_polynomial <span class="op">=</span> <span class="va">lp_model</span> </span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/warning.html" class="external-link">suppressWarnings</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">##              label       mae</span></span>
+<span><span class="co">## 1         griffith 1.9127037</span></span>
+<span><span class="co">## 2 penalized_spline 0.4801265</span></span>
+<span><span class="co">## 3       farrington 0.3710685</span></span>
+<span><span class="co">## 4 local_polynomial 0.3285755</span></span></code></pre>
 <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># user can provide arbitrary number of models</span></span>
-<span><span class="fu"><a href="../reference/compare_models.html">compare_models</a></span><span class="op">(</span><span class="va">model3</span>, <span class="va">model4</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">##   model                   type      AIC      BIC</span></span>
-<span><span class="co">## 1     1       polynomial_model 489.1185 495.8223</span></span>
-<span><span class="co">## 2     2 penalized_spline_model 256.7061 271.3522</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/compare_models.html">compare_models</a></span><span class="op">(</span></span>
+<span>    data <span class="op">=</span> <span class="va">data</span>,</span>
+<span>    method <span class="op">=</span> <span class="va">generate_mae</span>,</span>
+<span>    griffith <span class="op">=</span> <span class="op">~</span><span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">.x</span>, k<span class="op">=</span><span class="fl">2</span><span class="op">)</span>,</span>
+<span>    penalized_spline <span class="op">=</span> <span class="va">penalized_spline_model</span>,</span>
+<span>    farrington <span class="op">=</span> <span class="op">~</span><span class="fu"><a href="../reference/farrington_model.html">farrington_model</a></span><span class="op">(</span><span class="va">.x</span>, start<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>alpha<span class="op">=</span><span class="fl">0.07</span>,beta<span class="op">=</span><span class="fl">0.1</span>,gamma<span class="op">=</span><span class="fl">0.03</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>    local_polynomial <span class="op">=</span> <span class="va">lp_model</span> </span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/warning.html" class="external-link">suppressWarnings</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">##              label       mae</span></span>
+<span><span class="co">## 1         griffith 1.6883332</span></span>
+<span><span class="co">## 2 penalized_spline 0.5215807</span></span>
+<span><span class="co">## 3       farrington 0.4636040</span></span>
+<span><span class="co">## 4 local_polynomial 0.4536016</span></span></code></pre>
 </div>
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+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
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-<p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
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diff --git a/docs/articles/model_selection.md b/docs/articles/model_selection.md
index 384c750..3345dc1 100644
--- a/docs/articles/model_selection.md
+++ b/docs/articles/model_selection.md
@@ -2,98 +2,218 @@
 
 ``` r
 library(serosv)
+library(dplyr)
+library(magrittr)
 ```
 
-    ## Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when
-    ## loading 'serosv'
+``` r
+data <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age), ] %>% 
+  rename(status = seropositive) 
+
+aggregated <- transform_data(data, stratum_col = "age", status_col="status")
+```
+
+## Generate models comparison `data.frame`
+
+Function
+[`compare_models()`](https://oucru-modelling.github.io/serosv/reference/compare_models.md)
+is used for quickly computing comparison metrics for a set of models on
+a given dataset.
+
+The function takes the following arguments:
+
+- `data` the dataset that will be fitted to the models
+
+- `method` the method to generate comparison metrics. It can be the name
+  of one of the built-in methods, or a user-defined function.
+
+- `…` functions that take a data and return a fitted `serosv` model.
+
+It will then return a `data.frame` with the following columns
+
+- `label` model identifier. Either user defined name or index based on
+  the order provided.
+
+- `type` type of model (a `serosv` model class)
+
+- metrics depending on the method selected
+
+**Built-in `method`**
+
+`serosv` currently provide 2 built-in metrics generating methods
+
+- `"AIC/BIC"` which returns fitted model’s AIC, BIC and Log-likelihood
+  (where applicable)
+
+- `"CV"` which split the data into “train” and “test” set then return
+  logloss and AUC from model’s prediction on the test set
+
+**Sample usage**
 
 ``` r
-library(dplyr)
+# ----- Return AIC, BIC ----- 
+aic_bic_out <- compare_models(
+  data = data,
+  method = "AIC/BIC",
+  griffith = ~polynomial_model(.x, k=2),
+  penalized_spline = penalized_spline_model,
+  farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
+  local_polynomial = lp_model # expect to not return any values
+  ) %>% suppressWarnings()
+
+# ----- Return Cross-validation metrics ----- 
+cv_out <- compare_models(
+    data,
+    method = "CV",
+    griffith = ~polynomial_model(.x, k=2),
+    penalized_spline = penalized_spline_model,
+    farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
+    local_polynomial = lp_model 
+  ) %>% suppressWarnings()
+
+aic_bic_out
 ```
 
-    ## Warning: package 'dplyr' was built under R version 4.3.1
+    ## # A tibble: 4 × 8
+    ##   label            type                  AIC   BIC logLik    df mod_out    plots
+    ##   <chr>            <chr>               <dbl> <dbl>  <dbl> <dbl> <list>     <lis>
+    ## 1 griffith         polynomial_model    1353. 1363.  -674.  2    <plynml_m> <gg> 
+    ## 2 penalized_spline penalized_spline_m… 1330. 1365.  -658.  6.88 <pnlzd_s_> <gg> 
+    ## 3 farrington       farrington_model    1337.   NA   -665.  3    <frrngtn_> <gg> 
+    ## 4 local_polynomial lp_model              NA    NA     NA  NA    <lp_model> <gg>
 
-    ## 
-    ## Attaching package: 'dplyr'
+``` r
+cv_out
+```
 
-    ## The following objects are masked from 'package:stats':
-    ## 
-    ##     filter, lag
+    ## # A tibble: 4 × 6
+    ##   label            logloss   auc type                   mod_out    plots 
+    ##   <chr>              <dbl> <dbl> <chr>                  <list>     <list>
+    ## 1 griffith            277. 0.594 polynomial_model       <plynml_m> <gg>  
+    ## 2 penalized_spline    197. 0.601 penalized_spline_model <pnlzd_s_> <gg>  
+    ## 3 farrington          356. 0.600 farrington_model       <frrngtn_> <gg>  
+    ## 4 local_polynomial    237. 0.588 lp_model               <lp_model> <gg>
 
-    ## The following objects are masked from 'package:base':
-    ## 
-    ##     intersect, setdiff, setequal, union
+With aggregated data
 
 ``` r
-library(magrittr)
+# ----- Return AIC, BIC ----- 
+aic_bic_out <- compare_models(
+  data = aggregated,
+  method = "AIC/BIC",
+  griffith = ~polynomial_model(.x, k=2),
+  penalized_spline = penalized_spline_model,
+  farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
+  local_polynomial = lp_model # expect to not return any values
+  ) %>% suppressWarnings()
+
+# ----- Return Cross-validation metrics (default with 4 folds) ----- 
+cv_out <- compare_models(
+    data = aggregated,
+    method = "CV",
+    griffith = ~polynomial_model(.x, k=2),
+    penalized_spline = penalized_spline_model,
+    farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
+    local_polynomial = lp_model 
+  ) %>% suppressWarnings()
+
+
+aic_bic_out
 ```
 
+    ## # A tibble: 4 × 8
+    ##   label            type                  AIC   BIC logLik    df mod_out    plots
+    ##   <chr>            <chr>               <dbl> <dbl>  <dbl> <dbl> <list>     <lis>
+    ## 1 griffith         polynomial_model     489.  496.  -243.  2    <plynml_m> <gg> 
+    ## 2 penalized_spline penalized_spline_m…  257.  271.  -124.  4.37 <pnlzd_s_> <gg> 
+    ## 3 farrington       farrington_model    1337.   NA   -665.  3    <frrngtn_> <gg> 
+    ## 4 local_polynomial lp_model              NA    NA     NA  NA    <lp_model> <gg>
+
 ``` r
-data <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age), ] %>% 
-  rename(status = seropositive)
-  
-aggregated <- transform_data(data$age, data$status, stratum_col = "age")
-
-# fit with linelisting data
-model1 <- polynomial_model(data, type = "Muench")
-# fit with aggregated data
-model2 <- polynomial_model(aggregated, type = "Muench")
-# fit with aggregated data
-model3 <- polynomial_model(aggregated, type = "Griffith")
-# fit with gam
-model4 <- penalized_spline_model(aggregated)
+cv_out
 ```
 
-## Generate models comparison `data.frame`
+    ## # A tibble: 4 × 6
+    ##   label              mse logloss type                   mod_out    plots 
+    ##   <chr>            <dbl>   <dbl> <chr>                  <list>     <list>
+    ## 1 griffith         0.217   178.  polynomial_model       <plynml_m> <gg>  
+    ## 2 penalized_spline 0.123    79.7 penalized_spline_model <pnlzd_s_> <gg>  
+    ## 3 farrington       0.117    65.5 farrington_model       <frrngtn_> <gg>  
+    ## 4 local_polynomial 0.117    71.3 lp_model               <lp_model> <gg>
 
-Function
-[`compare_models()`](https://oucru-modelling.github.io/serosv/reference/compare_models.md)
-is used for quickly computing AIC and BIC values for given model(s).
+## Generate custom metrics
 
-The function can take an arbitrary number of models and all models must
-be created from `serosv`’s set of `*_model()` functions. It will then
-return a `data.frame` of 4 columns:
+The users can also provide a custom function to generate the comparison
+metrics.
 
-- `model` model identifier. Either user defined name or index based on
-  the order provided.
+This function must accepts 2 parameters:
 
-- `type` type of model (a `serosv` model class)
+- `dat` the input data
 
-- `AIC` AIC value for the fitted model (if applicable)
+- `model_func` a function that takes an input data and returns a
+  `serosv` model
 
-- `BIC` AIC value for the fitted model (if applicable)
+And it must returns a data frame with 1 row where each column represents
+one metric.
 
-**Sample usage**
+***Example:***
 
-Compare 4 models defined above
+The following implements holdout validation and returns MAE:
 
 ``` r
-# provide models with name
-compare_models(muench_linelist = model1, muench_aggregated = model2, griffith = model3, penalized_spline = model4)
+generate_mae <- function(dat, model_func){
+  n_train <- round(nrow(dat)*0.8)
+  train <- dat[1:n_train,]
+  test <- dat[n_train:nrow(dat),]
+  
+  fit <- model_func(dat)
+  pred <- predict(fit, test[, 1, drop=FALSE])
+  
+  # handle error differently depending on datatype
+  mae <- if(fit$datatype == "linelisting"){
+    sum(abs(test$status - pred), na.rm=TRUE)/nrow(test)
+  }else{
+    sum(abs(test$pos/test$tot - pred), na.rm=TRUE)/nrow(test)
+  }
+  
+  data.frame(
+    mae = mae
+  )
+}
 ```
 
-    ##               model                   type       AIC       BIC
-    ## 1   muench_linelist       polynomial_model 1368.9096 1373.9280
-    ## 2 muench_aggregated       polynomial_model  505.5269  508.8787
-    ## 3          griffith       polynomial_model  489.1185  495.8223
-    ## 4  penalized_spline penalized_spline_model  256.7061  271.3522
+We can then run `compare_models` with the custom metrics function
 
 ``` r
-# provide models without name
-compare_models(model1, model2, model3, model4)
+compare_models(
+    data = aggregated,
+    method = generate_mae,
+    griffith = ~polynomial_model(.x, k=2),
+    penalized_spline = penalized_spline_model,
+    farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
+    local_polynomial = lp_model 
+  ) %>% suppressWarnings()
 ```
 
-    ##   model                   type       AIC       BIC
-    ## 1     1       polynomial_model 1368.9096 1373.9280
-    ## 2     2       polynomial_model  505.5269  508.8787
-    ## 3     3       polynomial_model  489.1185  495.8223
-    ## 4     4 penalized_spline_model  256.7061  271.3522
+    ##              label       mae
+    ## 1         griffith 1.9127037
+    ## 2 penalized_spline 0.4801265
+    ## 3       farrington 0.3710685
+    ## 4 local_polynomial 0.3285755
 
 ``` r
-# user can provide arbitrary number of models
-compare_models(model3, model4)
+compare_models(
+    data = data,
+    method = generate_mae,
+    griffith = ~polynomial_model(.x, k=2),
+    penalized_spline = penalized_spline_model,
+    farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
+    local_polynomial = lp_model 
+  ) %>% suppressWarnings()
 ```
 
-    ##   model                   type      AIC      BIC
-    ## 1     1       polynomial_model 489.1185 495.8223
-    ## 2     2 penalized_spline_model 256.7061 271.3522
+    ##              label       mae
+    ## 1         griffith 1.6883332
+    ## 2 penalized_spline 0.5215807
+    ## 3       farrington 0.4636040
+    ## 4 local_polynomial 0.4536016
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 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://oucru-modelling.github.io/serosv/">serosv</a></span><span class="op">)</span></span>
-<span><span class="co">#&gt; Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when</span></span>
-<span><span class="co">#&gt; loading 'serosv'</span></span></code></pre></div>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://magrittr.tidyverse.org" class="external-link">magrittr</a></span><span class="op">)</span></span></code></pre></div>
 <div class="section level2">
 <h2 id="local-estimation-by-polynomial">Local estimation by polynomial<a class="anchor" aria-label="anchor" href="#local-estimation-by-polynomial"></a>
 </h2>
-<p>Refer to <code>Chapter 7.1</code></p>
 <p><strong>Proposed model</strong></p>
 <p>Within the local polynomial framework, the linear predictor <span class="math inline">\(\eta(a)\)</span> is approximated locally at one
 particular value <span class="math inline">\(a_0\)</span> for age by a
-line (local linear) or a parabola (local quadratic).</p>
+line (local linear, degree <span class="math inline">\(p=1\)</span>) or
+a parabola (local quadratic, degree <span class="math inline">\(p=2\)</span>).</p>
+<p>For a general degree <span class="math inline">\(p\)</span>, the
+linear predictor for a neighbor of <span class="math inline">\(a_0\)</span>, labeled <span class="math inline">\(a_i\)</span> is equivalent to the Taylor
+approximation</p>
+<p><span class="math display">\[
+\eta(a_i) = \eta(a_0) + \eta^{(1)}(a_0)(a_i - a_0) +
+\frac{\eta^{(2)}(a_0)}{2}(a_i - a_0)^2 + ... +
+\frac{\eta^{(p)}(a_0)}{p!}(a_i - a_0)^p
+\]</span></p>
+<p><span class="math inline">\(\eta(a_i)\)</span> can be estimated by
+maximizing</p>
+<p><span class="math display">\[
+\Sigma_{i=1}^{N} \ell_i \{Y_i, g^{-1} (\beta_0 + \beta_1(a_i-a_0)+
+\beta_2(a_i-a_0)^2 ... +
+\beta_p(a_i-a_0)^p) \} K_h(a_i - a_0)
+\]</span></p>
+<p>Where:</p>
+<ul>
+<li><p><span class="math inline">\(\ell_i\)</span> is the binomial
+log-likelihood <span class="math inline">\(\ell_i\{Y_i, \pi\} =
+Y_ilog\{\pi\} + (1-Y_i)log\{1-\pi\}\)</span></p></li>
+<li><p><span class="math inline">\(K_h\)</span> is the kernel function
+with the specified smoothing parameter <span class="math inline">\(h\)</span>, also called “bandwidth” or “window
+width”</p></li>
+</ul>
 <p>The estimator for the <span class="math inline">\(k\)</span>-th
 derivative of <span class="math inline">\(\eta(a_0)\)</span>, for <span class="math inline">\(k = 0,1,…,p\)</span> (degree of local polynomial)
-is as followed:</p>
+is thus</p>
 <p><span class="math display">\[
 \hat{\eta}^{(k)}(a_0) = k!\hat{\beta}_k(a_0)
 \]</span></p>
@@ -135,24 +147,85 @@ <h2 id="local-estimation-by-polynomial">Local estimation by polynomial<a class="
 {d\hat{\beta}_0(a_0)}\)</span>
 </li>
 </ul>
+<p>Refer to <code>Chapter 7.1</code> of the book by <span class="citation">Hens et al. (2012)</span> for a more detailed
+explanation of the method.</p>
 <p><strong>Fitting data</strong></p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">mump</span> <span class="op">&lt;-</span> <span class="va">mumps_uk_1986_1987</span></span></code></pre></div>
-<p>Use <code><a href="../reference/plot_gcv.html">plot_gcv()</a></code> to show GCV curves for the nearest
-neighbor method (left) and constant bandwidth (right).</p>
+<p>To fit a local estimation by polynomials, use <code><a href="../reference/lp_model.html">lp_model()</a></code>
+function with model parameters include:</p>
+<ul>
+<li><p><code>nn</code> the number of nearest neighbors to be included in
+the approximation (i.e., the nearest neighbor component of the smoothing
+parameter)</p></li>
+<li><p><code>h</code> the constant component of the smoothing
+parameter</p></li>
+<li><p><code>deg</code> the degree of the polynomial</p></li>
+<li><p><code>kern</code> the type of kernel</p></li>
+</ul>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">lp</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/lp_model.html">lp_model</a></span><span class="op">(</span><span class="va">mump</span>, kern<span class="op">=</span><span class="st">"tcub"</span>, nn <span class="op">=</span> <span class="fl">0.5</span>, deg<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span>
+<span><span class="va">lp</span></span>
+<span><span class="co">#&gt; Local polynomial model </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Input type:  aggregated </span></span>
+<span><span class="co">#&gt; Configs:  nn=0.5, bandwidth(h)=0, degree=2, kernel=tcub </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Call:</span></span>
+<span><span class="co">#&gt; locfit(formula = y ~ lp(age, deg = deg, nn = nn, h = h), family = "binomial", </span></span>
+<span><span class="co">#&gt;     kern = kern)</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Number of observations:          44 </span></span>
+<span><span class="co">#&gt; Family:  Logistic </span></span>
+<span><span class="co">#&gt; Fitted Degrees of freedom:       6.463 </span></span>
+<span><span class="co">#&gt; Residual scale:                  1</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">lp</span><span class="op">)</span></span></code></pre></div>
+<p><img src="nonparametric_model_files/figure-html/unnamed-chunk-3-1.png" class="r-plt" width="700"></p>
+<p>The users can also provide <code>h</code> or <code>nn</code> as a
+numeric vector, in which case the package will use the parameter value
+that minimize the generalized cross-validation (GCV) criterion.</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># ----- Tune nearest neighbor (nn) parameter</span></span>
+<span><span class="fu"><a href="../reference/lp_model.html">lp_model</a></span><span class="op">(</span><span class="va">mump</span>, kern<span class="op">=</span><span class="st">"tcub"</span>, nn <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0.2</span>, <span class="fl">0.8</span>, <span class="fl">0.1</span><span class="op">)</span>, deg<span class="op">=</span><span class="fl">2</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<p><img src="nonparametric_model_files/figure-html/unnamed-chunk-4-1.png" class="r-plt" width="700"></p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="co"># ----- Tune bandwidth (h) parameter</span></span>
+<span><span class="fu"><a href="../reference/lp_model.html">lp_model</a></span><span class="op">(</span><span class="va">mump</span>, kern<span class="op">=</span><span class="st">"tcub"</span>, h <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">5</span>,<span class="fl">25</span><span class="op">)</span>, deg<span class="op">=</span><span class="fl">2</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<p><img src="nonparametric_model_files/figure-html/unnamed-chunk-4-2.png" class="r-plt" width="700"></p>
+<p>Alternatively, use <code><a href="../reference/plot_gcv.html">plot_gcv()</a></code> to show GCV curves for the
+nearest neighbor method (left) and constant bandwidth (right) for manual
+parameter selection.</p>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/plot_gcv.html">plot_gcv</a></span><span class="op">(</span></span>
 <span>   <span class="va">mump</span><span class="op">$</span><span class="va">age</span>, <span class="va">mump</span><span class="op">$</span><span class="va">pos</span>, <span class="va">mump</span><span class="op">$</span><span class="va">tot</span>,</span>
 <span>   nn_seq <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0.2</span>, <span class="fl">0.8</span>, by<span class="op">=</span><span class="fl">0.1</span><span class="op">)</span>,</span>
 <span>   h_seq <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">25</span>, by<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span>
 <span> <span class="op">)</span></span></code></pre></div>
-<p><img src="nonparametric_model_files/figure-html/unnamed-chunk-3-1.png" width="672"></p>
-<p>Use <code><a href="../reference/lp_model.html">lp_model()</a></code> to fit a local estimation by
-polynomials.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">lp</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/lp_model.html">lp_model</a></span><span class="op">(</span><span class="va">mump</span>, kern<span class="op">=</span><span class="st">"tcub"</span>, nn<span class="op">=</span><span class="fl">0.7</span>, deg<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">lp</span><span class="op">)</span></span></code></pre></div>
-<p><img src="nonparametric_model_files/figure-html/unnamed-chunk-4-1.png" width="700"></p>
+<p><img src="nonparametric_model_files/figure-html/unnamed-chunk-5-1.png" class="r-plt" width="672"></p>
+<p>Based on the plot, we can then fit the model using the parameter
+value that give the lowest GCV (<code>nn = 0.3</code> and
+<code>h = 14</code>)</p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/lp_model.html">lp_model</a></span><span class="op">(</span><span class="va">mump</span>, kern<span class="op">=</span><span class="st">"tcub"</span>, nn <span class="op">=</span> <span class="fl">0.3</span>, deg<span class="op">=</span><span class="fl">2</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<p><img src="nonparametric_model_files/figure-html/unnamed-chunk-6-1.png" class="r-plt" width="700"></p>
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="fu"><a href="../reference/lp_model.html">lp_model</a></span><span class="op">(</span><span class="va">mump</span>, kern<span class="op">=</span><span class="st">"tcub"</span>, h <span class="op">=</span> <span class="fl">14</span>, deg<span class="op">=</span><span class="fl">2</span><span class="op">)</span>  <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<p><img src="nonparametric_model_files/figure-html/unnamed-chunk-6-2.png" class="r-plt" width="700"></p>
+<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0">
+<div id="ref-Hens2012" class="csl-entry">
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. <em>Modeling Infectious Disease Parameters Based
+on Serological and Social Contact Data: A Modern Statistical
+Perspective</em>. <em>Statistics for Biology and Health</em>. Springer
+New York. <a href="https://doi.org/10.1007/978-1-4614-4072-7" class="external-link">https://doi.org/10.1007/978-1-4614-4072-7</a>.
+</div>
+</div>
 </div>
   </main>
 </div>
@@ -160,21 +233,19 @@ <h2 id="local-estimation-by-polynomial">Local estimation by polynomial<a class="
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p>
-<p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
     </footer>
 </div>
 
-  
 
-  
+
+
 
   </body>
 </html>
diff --git a/docs/articles/nonparametric_model.md b/docs/articles/nonparametric_model.md
index d635eae..615bfed 100644
--- a/docs/articles/nonparametric_model.md
+++ b/docs/articles/nonparametric_model.md
@@ -2,39 +2,60 @@
 
 ``` r
 library(serosv)
-#> Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when
-#> loading 'serosv'
+library(magrittr)
 ```
 
 ## Local estimation by polynomial
 
-Refer to `Chapter 7.1`
-
 **Proposed model**
 
-Within the local polynomial framework, the linear predictor
-\\(\eta(a)\\) is approximated locally at one particular value \\(a_0\\)
-for age by a line (local linear) or a parabola (local quadratic).
+Within the local polynomial framework, the linear predictor \\\eta(a)\\
+is approximated locally at one particular value \\a_0\\ for age by a
+line (local linear, degree \\p=1\\) or a parabola (local quadratic,
+degree \\p=2\\).
+
+For a general degree \\p\\, the linear predictor for a neighbor of
+\\a_0\\, labeled \\a_i\\ is equivalent to the Taylor approximation
+
+\\ \eta(a_i) = \eta(a_0) + \eta^{(1)}(a_0)(a_i - a_0) +
+\frac{\eta^{(2)}(a_0)}{2}(a_i - a_0)^2 + ... +
+\frac{\eta^{(p)}(a_0)}{p!}(a_i - a_0)^p \\
+
+\\\eta(a_i)\\ can be estimated by maximizing
+
+\\ \Sigma\_{i=1}^{N} \ell_i \\Y_i, g^{-1} (\beta_0 + \beta_1(a_i-a_0)+
+\beta_2(a_i-a_0)^2 ... + \beta_p(a_i-a_0)^p) \\ K_h(a_i - a_0) \\
+
+Where:
 
-The estimator for the \\(k\\)-th derivative of \\(\eta(a_0)\\), for \\(k
-= 0,1,…,p\\) (degree of local polynomial) is as followed:
+- \\\ell_i\\ is the binomial log-likelihood \\\ell_i\\Y_i, \pi\\ =
+  Y_ilog\\\pi\\ + (1-Y_i)log\\1-\pi\\\\
 
-\\\[ \hat{\eta}^{(k)}(a_0) = k!\hat{\beta}\_k(a_0) \\\]
+- \\K_h\\ is the kernel function with the specified smoothing parameter
+  \\h\\, also called “bandwidth” or “window width”
 
-The estimator for the prevalence at age \\(a_0\\) is then given by
+The estimator for the \\k\\-th derivative of \\\eta(a_0)\\, for \\k =
+0,1,…,p\\ (degree of local polynomial) is thus
 
-\\\[ \hat{\pi}(a_0) = g^{-1}\\{ \hat{\beta}\_0(a_0) \\} \\\]
+\\ \hat{\eta}^{(k)}(a_0) = k!\hat{\beta}\_k(a_0) \\
 
-- Where \\(g\\) is the link function
+The estimator for the prevalence at age \\a_0\\ is then given by
 
-The estimator for the force of infection at age \\(a_0\\) by assuming
-\\(p \ge 1\\) is as followed
+\\ \hat{\pi}(a_0) = g^{-1}\\ \hat{\beta}\_0(a_0) \\ \\
 
-\\\[ \hat{\lambda}(a_0) = \hat{\beta}\_1(a_0) \delta \\{ \hat{\beta}\_0
-(a_0) \\} \\\]
+- Where \\g\\ is the link function
 
-- Where \\(\delta \\{ \hat{\beta}\_0(a_0) \\} = \frac{dg^{-1} \\{
-  \hat{\beta}\_0(a_0) \\} } {d\hat{\beta}\_0(a_0)}\\)
+The estimator for the force of infection at age \\a_0\\ by assuming \\p
+\ge 1\\ is as followed
+
+\\ \hat{\lambda}(a_0) = \hat{\beta}\_1(a_0) \delta \\ \hat{\beta}\_0
+(a_0) \\ \\
+
+- Where \\\delta \\ \hat{\beta}\_0(a_0) \\ = \frac{dg^{-1} \\
+  \hat{\beta}\_0(a_0) \\ } {d\hat{\beta}\_0(a_0)}\\
+
+Refer to `Chapter 7.1` of the book by Hens et al. (2012) for a more
+detailed explanation of the method.
 
 **Fitting data**
 
@@ -42,10 +63,66 @@ The estimator for the force of infection at age \\(a_0\\) by assuming
 mump <- mumps_uk_1986_1987
 ```
 
-Use
+To fit a local estimation by polynomials, use
+[`lp_model()`](https://oucru-modelling.github.io/serosv/reference/lp_model.md)
+function with model parameters include:
+
+- `nn` the number of nearest neighbors to be included in the
+  approximation (i.e., the nearest neighbor component of the smoothing
+  parameter)
+
+- `h` the constant component of the smoothing parameter
+
+- `deg` the degree of the polynomial
+
+- `kern` the type of kernel
+
+``` r
+lp <- lp_model(mump, kern="tcub", nn = 0.5, deg=2)
+lp
+#> Local polynomial model 
+#> 
+#> Input type:  aggregated 
+#> Configs:  nn=0.5, bandwidth(h)=0, degree=2, kernel=tcub 
+#> 
+#> Call:
+#> locfit(formula = y ~ lp(age, deg = deg, nn = nn, h = h), family = "binomial", 
+#>     kern = kern)
+#> 
+#> Number of observations:          44 
+#> Family:  Logistic 
+#> Fitted Degrees of freedom:       6.463 
+#> Residual scale:                  1
+plot(lp)
+```
+
+![](nonparametric_model_files/figure-html/unnamed-chunk-3-1.png)
+
+The users can also provide `h` or `nn` as a numeric vector, in which
+case the package will use the parameter value that minimize the
+generalized cross-validation (GCV) criterion.
+
+``` r
+# ----- Tune nearest neighbor (nn) parameter
+lp_model(mump, kern="tcub", nn = seq(0.2, 0.8, 0.1), deg=2) %>% 
+  plot()
+```
+
+![](nonparametric_model_files/figure-html/unnamed-chunk-4-1.png)
+
+``` r
+
+# ----- Tune bandwidth (h) parameter
+lp_model(mump, kern="tcub", h = seq(5,25), deg=2) %>% 
+  plot()
+```
+
+![](nonparametric_model_files/figure-html/unnamed-chunk-4-2.png)
+
+Alternatively, use
 [`plot_gcv()`](https://oucru-modelling.github.io/serosv/reference/plot_gcv.md)
 to show GCV curves for the nearest neighbor method (left) and constant
-bandwidth (right).
+bandwidth (right) for manual parameter selection.
 
 ``` r
 plot_gcv(
@@ -55,15 +132,28 @@ plot_gcv(
  )
 ```
 
-![](nonparametric_model_files/figure-html/unnamed-chunk-3-1.png)
+![](nonparametric_model_files/figure-html/unnamed-chunk-5-1.png)
 
-Use
-[`lp_model()`](https://oucru-modelling.github.io/serosv/reference/lp_model.md)
-to fit a local estimation by polynomials.
+Based on the plot, we can then fit the model using the parameter value
+that give the lowest GCV (`nn = 0.3` and `h = 14`)
 
 ``` r
-lp <- lp_model(mump, kern="tcub", nn=0.7, deg=2)
-plot(lp)
+lp_model(mump, kern="tcub", nn = 0.3, deg=2) %>% 
+  plot()
 ```
 
-![](nonparametric_model_files/figure-html/unnamed-chunk-4-1.png)
+![](nonparametric_model_files/figure-html/unnamed-chunk-6-1.png)
+
+``` r
+
+lp_model(mump, kern="tcub", h = 14, deg=2)  %>% 
+  plot()
+```
+
+![](nonparametric_model_files/figure-html/unnamed-chunk-6-2.png)
+
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. *Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective*.
+*Statistics for Biology and Health*. Springer New York.
+<https://doi.org/10.1007/978-1-4614-4072-7>.
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@@ -104,136 +92,216 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://magrittr.tidyverse.org" class="external-link">magrittr</a></span><span class="op">)</span></span></code></pre></div>
 <div class="section level2">
-<h2 id="polynomial-models">Polynomial models<a class="anchor" aria-label="anchor" href="#polynomial-models"></a>
+<h2 id="frequentist-methods">Frequentist methods<a class="anchor" aria-label="anchor" href="#frequentist-methods"></a>
 </h2>
-<p>Refer to <code>Chapter 6.1.1</code></p>
-<p>Use <code><a href="../reference/polynomial_model.html">polynomial_model()</a></code> to fit a polynomial model.</p>
-<p>We will use the <code>Hepatitis A</code> data from Belgium 1993–1994
-for this example.</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">hav_bg_1964</span></span></code></pre></div>
 <div class="section level3">
-<h3 id="muench-model">Muench model<a class="anchor" aria-label="anchor" href="#muench-model"></a>
+<h3 id="polynomial-models">Polynomial models<a class="anchor" aria-label="anchor" href="#polynomial-models"></a>
 </h3>
-<p><strong>Proposed model</strong></p>
-<p><span class="citation">(<a href="#ref-muench_derivation_1934" role="doc-biblioref">Muench 1934</a>)</span> suggested to model the
-infection process with so-called “catalytic model”, in which the
-distribution of the time spent in the susceptible class in SIR model is
-exponential with rate <span class="math inline">\(\beta\)</span></p>
+<p>Seroprevalence is modeled using the following serocatalytic model</p>
 <p><span class="math display">\[
-\pi(a) = k(1 - e^{-\beta a} )
+\pi(a)  = 1 - \text{exp}({-\Sigma_{i=1}^k \beta_i a^i})
 \]</span></p>
 <p>Where:</p>
 <ul>
-<li>
-<span class="math inline">\(\pi\)</span> is the seroprevalence at
-age <span class="math inline">\(a\)</span>
-</li>
-<li>
-<span class="math inline">\(1 - k\)</span> is the proportion of
-population that stay uninfected for a lifetime</li>
-<li>
-<span class="math inline">\(a\)</span> is the variable age</li>
+<li><p><span class="math inline">\(a\)</span> is the variable
+age</p></li>
+<li><p><span class="math inline">\(\pi\)</span> is the seroprevalence of
+the population at age <span class="math inline">\(a\)</span></p></li>
+<li><p><span class="math inline">\(k\)</span> is the degree of the
+polynomial</p></li>
+<li><p><span class="math inline">\(\beta_i\)</span> are the model
+parameters</p></li>
+</ul>
+<p>Which implies the force of infection is <span class="math inline">\(\lambda(a) = \Sigma_{i=1}^k \beta_i i
+a^{i-1}\)</span></p>
+<p>This generalization encompasses several classical serocatalytic model
+including</p>
+<ul>
+<li><p><strong>Muench model</strong> (assuming <span class="math inline">\(k=1\)</span>) <span class="citation">(<a href="#ref-muench_derivation_1934">Muench 1934</a>)</span></p></li>
+<li><p><strong>Griffith model</strong> (assuming <span class="math inline">\(k = 2\)</span>)</p></li>
+<li><p><strong>Grenfell and Anderson model</strong> (assuming higher
+degree <span class="math inline">\(k\)</span>) <span class="citation">(<a href="#ref-grenfell_estimation_1985">Grenfell and
+Anderson 1985</a>)</span></p></li>
 </ul>
-<p>Under this catalytic model and assuming that <span class="math inline">\(k = 1\)</span>, force infection would be <span class="math inline">\(\lambda(a) = \beta\)</span></p>
+<p>Refer to <code>Chapter 6.1.1</code> of the book by <span class="citation">Hens et al. (<a href="#ref-Hens2012">2012</a>)</span>
+for a more detailed explanation of the methods.</p>
 <p><strong>Fitting data</strong></p>
-<p><strong>Muench</strong>’s model can be estimated by either defining
-<code>k = 1</code> (a degree one linear predictor, note that it is
-irrelevant to the k in the proposed model) or setting the
-<code>type = "Muench"</code>.</p>
+<p>We will use the <code>Parvo B19</code> data from Finland 1997–1998
+for this example.</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">parvob19_fi_1997_1998</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/order.html" class="external-link">order</a></span><span class="op">(</span><span class="va">parvob19_fi_1997_1998</span><span class="op">$</span><span class="va">age</span><span class="op">)</span>, <span class="op">]</span></span></code></pre></div>
+<p>To fit a polynomial model, use the <code><a href="../reference/polynomial_model.html">polynomial_model()</a></code>
+function.</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">muench1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">data</span>, k <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">muench1</span><span class="op">$</span><span class="va">info</span><span class="op">)</span></span>
+<code class="sourceCode R"><span><span class="co"># Fit a Muench model</span></span>
+<span><span class="va">muench</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">data</span>, k <span class="op">=</span> <span class="fl">1</span>, status_col<span class="op">=</span><span class="st">"seropositive"</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">muench</span><span class="op">$</span><span class="va">info</span><span class="op">)</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Call:</span></span>
-<span><span class="co">#&gt; glm(formula = age(k), family = binomial(link = link), data = df)</span></span>
+<span><span class="co">#&gt; glm(formula = Age(k), family = binomial(link = link), data = df)</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Coefficients:</span></span>
 <span><span class="co">#&gt;      Estimate Std. Error z value Pr(&gt;|z|)    </span></span>
-<span><span class="co">#&gt; Age -0.050500   0.002457  -20.55   &lt;2e-16 ***</span></span>
+<span><span class="co">#&gt; age -0.029088   0.001375  -21.15   &lt;2e-16 ***</span></span>
 <span><span class="co">#&gt; ---</span></span>
 <span><span class="co">#&gt; Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; (Dispersion parameter for binomial family taken to be 1)</span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt;     Null deviance:    Inf  on 83  degrees of freedom</span></span>
-<span><span class="co">#&gt; Residual deviance: 97.275  on 82  degrees of freedom</span></span>
-<span><span class="co">#&gt; AIC: 219.19</span></span>
+<span><span class="co">#&gt;     Null deviance:    Inf  on 1117  degrees of freedom</span></span>
+<span><span class="co">#&gt; Residual deviance: 1366.9  on 1116  degrees of freedom</span></span>
+<span><span class="co">#&gt; AIC: 1368.9</span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Number of Fisher Scoring iterations: 5</span></span>
-<span></span>
-<span><span class="va">muench2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">data</span>, type <span class="op">=</span> <span class="st">"Muench"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">muench2</span><span class="op">$</span><span class="va">info</span><span class="op">)</span></span>
+<span><span class="co">#&gt; Number of Fisher Scoring iterations: 6</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">muench</span><span class="op">)</span> </span></code></pre></div>
+<p><img src="parametric_model_files/figure-html/unnamed-chunk-3-1.png" class="r-plt" width="700"></p>
+<p>The users can also choose to provide a range of values for <span class="math inline">\(k\)</span> in which case the package will try to
+find the best <span class="math inline">\(k\)</span> parameter
+determined by Loglikelihood Ratio test (LRT)</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Provide a range of values for k</span></span>
+<span><span class="va">best_param</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">data</span>, k <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">5</span>, status_col <span class="op">=</span> <span class="st">"seropositive"</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">best_param</span><span class="op">)</span></span></code></pre></div>
+<p><img src="parametric_model_files/figure-html/unnamed-chunk-4-1.png" class="r-plt" width="700"></p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># View the best model here which suggests k = 4 is the best parameter value</span></span>
+<span><span class="va">best_param</span></span>
+<span><span class="co">#&gt; Polynomial model</span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Call:</span></span>
-<span><span class="co">#&gt; glm(formula = age(k), family = binomial(link = link), data = df)</span></span>
+<span><span class="co">#&gt; Input type:  linelisting </span></span>
+<span><span class="co">#&gt; Degree (k):  4 </span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Coefficients:</span></span>
-<span><span class="co">#&gt;      Estimate Std. Error z value Pr(&gt;|z|)    </span></span>
-<span><span class="co">#&gt; Age -0.050500   0.002457  -20.55   &lt;2e-16 ***</span></span>
-<span><span class="co">#&gt; ---</span></span>
-<span><span class="co">#&gt; Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</span></span>
+<span><span class="co">#&gt; Call:  glm(formula = Age(k), family = binomial(link = link), data = df)</span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; (Dispersion parameter for binomial family taken to be 1)</span></span>
-<span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt;     Null deviance:    Inf  on 83  degrees of freedom</span></span>
-<span><span class="co">#&gt; Residual deviance: 97.275  on 82  degrees of freedom</span></span>
-<span><span class="co">#&gt; AIC: 219.19</span></span>
+<span><span class="co">#&gt; Coefficients:</span></span>
+<span><span class="co">#&gt;        age    I(age^2)    I(age^3)    I(age^4)  </span></span>
+<span><span class="co">#&gt; -3.381e-02  -9.950e-04   5.551e-05  -5.737e-07  </span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Number of Fisher Scoring iterations: 5</span></span></code></pre></div>
-<p>We can plot any model with the <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot()</a></code> function.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">muench2</span><span class="op">)</span> </span></code></pre></div>
-<p><img src="parametric_model_files/figure-html/unnamed-chunk-4-1.png" width="700"></p>
+<span><span class="co">#&gt; Degrees of Freedom: 1117 Total (i.e. Null);  1113 Residual</span></span>
+<span><span class="co">#&gt; Null Deviance:       Inf </span></span>
+<span><span class="co">#&gt; Residual Deviance: 1336  AIC: 1344</span></span></code></pre></div>
+<hr>
 </div>
 <div class="section level3">
-<h3 id="griffith-model">Griffith model<a class="anchor" aria-label="anchor" href="#griffith-model"></a>
+<h3 id="fractional-polynomial-model">Fractional polynomial model<a class="anchor" aria-label="anchor" href="#fractional-polynomial-model"></a>
 </h3>
 <p><strong>Proposed model</strong></p>
-<p>Griffith proposed a model for force of infection as followed</p>
+<p>Fractional polynomial model generalize conventional polynomial class
+of functions. In the context of binary responses, a fractional
+polynomial of degree <span class="math inline">\(m\)</span> for the
+linear predictor is defined as followed</p>
 <p><span class="math display">\[
-\lambda(a) = \beta_1 + 2\beta_2a  
+\eta_m(a, \beta, p_1, p_2, ...,p_m) = \Sigma^m_{i=0} \beta_i H_i(a)
 \]</span></p>
-<p>Which can be estimated using a GLM where the for which the linear
-predictor was <span class="math inline">\(\eta(a) = \beta_1 +
-\beta_2a^{2}\)</span></p>
-<p><strong>Fitting data</strong></p>
-<p>Similarly, we can estimate <strong>Griffith</strong>’s model either
-by defining <code>k = 2</code>, or setting the
-<code>type = "Griffith"</code></p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">gf_model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">data</span>, type <span class="op">=</span> <span class="st">"Griffith"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">gf_model</span><span class="op">)</span></span></code></pre></div>
-<p><img src="parametric_model_files/figure-html/unnamed-chunk-5-1.png" width="700"></p>
-</div>
-<div class="section level3">
-<h3 id="grenfell-and-anderson-model">Grenfell and Anderson model<a class="anchor" aria-label="anchor" href="#grenfell-and-anderson-model"></a>
-</h3>
-<p><strong>Proposed model</strong></p>
-<p><span class="citation">(<a href="#ref-grenfell_estimation_1985" role="doc-biblioref">Grenfell and Anderson 1985</a>)</span> extended the
-models of Muench and Griffiths further suggest the use of higher order
-polynomial functions to model the force of infection which assumes
-prevalence model as followed</p>
+<p>Where <span class="math inline">\(m\)</span> is an integer, <span class="math inline">\(p_1 \le p_2 \le... \le p_m\)</span> is a sequence
+of powers, and <span class="math inline">\(H_i(a)\)</span> is a
+transformation given by</p>
 <p><span class="math display">\[
-\pi(a)  = 1 - e^{-\Sigma_i \beta_i a^i}
+H_i = \begin{cases}
+a^{p_i} &amp; \text{ if } p_i \neq p_{i-1},
+\\ H_{i-1}(a) \times log(a)  &amp; \text{ if } p_i = p_{i-1},
+\end{cases}
 \]</span></p>
-<p>Which implies that force of infection equals <span class="math inline">\(\lambda(a) = \Sigma \beta_i i a^{i-1}\)</span></p>
+<p>Refer to <code>Chapter 6.2</code> of the book by <span class="citation">Hens et al. (<a href="#ref-Hens2012">2012</a>)</span>
+for a more detailed explanation of the methods.</p>
 <p><strong>Fitting data</strong></p>
-<p>And Grenfell and Anderson’s model.</p>
+<p>Use <code><a href="../reference/fp_model.html">fp_model()</a></code> to fit a fractional polynomial model.</p>
+<p>The parameter <code>p</code> specifies the powers of each polynomial
+term (length of <code>p</code> is thus the model’s degree)</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">grf_model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">data</span>, type <span class="op">=</span> <span class="st">"Grenfell"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">grf_model</span><span class="op">)</span></span></code></pre></div>
-<p><img src="parametric_model_files/figure-html/unnamed-chunk-6-1.png" width="700"></p>
+<code class="sourceCode R"><span><span class="va">hav</span> <span class="op">&lt;-</span> <span class="va">hav_be_1993_1994</span></span>
+<span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/fp_model.html">fp_model</a></span><span class="op">(</span><span class="va">hav</span>, p<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1.5</span><span class="op">)</span>, link<span class="op">=</span><span class="st">"cloglog"</span><span class="op">)</span></span>
+<span><span class="va">model</span></span>
+<span><span class="co">#&gt; Fractional polynomial model </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Input type:  aggregated </span></span>
+<span><span class="co">#&gt; Powers:  1, 1.5 </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Call:  glm(formula = as.formula(formulate(p)), family = binomial(link = link))</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Coefficients:</span></span>
+<span><span class="co">#&gt; (Intercept)     I(age^1)   I(age^1.5)  </span></span>
+<span><span class="co">#&gt;    -4.56685      0.22340     -0.01775  </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Degrees of Freedom: 85 Total (i.e. Null);  83 Residual</span></span>
+<span><span class="co">#&gt; Null Deviance:       1320 </span></span>
+<span><span class="co">#&gt; Residual Deviance: 85.81     AIC: 365.4</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></code></pre></div>
+<p><img src="parametric_model_files/figure-html/unnamed-chunk-5-1.png" class="r-plt" width="700"></p>
+<p>The users can also tell the package to perform parameter selection by
+providing <code>p</code> as a named list with 2 elements:</p>
+<ul>
+<li>
+<code>degree</code> the maximum number of terms to search over</li>
+<li>
+<code>p_range</code> the possible powers for each term</li>
+</ul>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/fp_model.html">fp_model</a></span><span class="op">(</span><span class="va">hav</span>, </span>
+<span>                  p<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
+<span>                    p_range<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="op">-</span><span class="fl">2</span>,<span class="fl">3</span>,<span class="fl">0.1</span><span class="op">)</span>,</span>
+<span>                    degree<span class="op">=</span><span class="fl">2</span></span>
+<span>                  <span class="op">)</span>, </span>
+<span>                  monotonic<span class="op">=</span><span class="cn">FALSE</span>,</span>
+<span>                  link<span class="op">=</span><span class="st">"cloglog"</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></code></pre></div>
+<p><img src="parametric_model_files/figure-html/unnamed-chunk-6-1.png" class="r-plt" width="700"></p>
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># the best set of powers for this dataset is 1.5 and 1.6</span></span>
+<span><span class="va">model</span></span>
+<span><span class="co">#&gt; Fractional polynomial model </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Input type:  aggregated </span></span>
+<span><span class="co">#&gt; Powers:  1.5, 1.6 </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Call:  glm(formula = as.formula(formulate(curr_p)), family = binomial(link = link))</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Coefficients:</span></span>
+<span><span class="co">#&gt; (Intercept)   I(age^1.5)   I(age^1.6)  </span></span>
+<span><span class="co">#&gt;    -3.61083      0.12443     -0.07656  </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Degrees of Freedom: 85 Total (i.e. Null);  83 Residual</span></span>
+<span><span class="co">#&gt; Null Deviance:       1320 </span></span>
+<span><span class="co">#&gt; Residual Deviance: 81.6  AIC: 361.2</span></span></code></pre></div>
+<p>To restrict the parameters search such that the predictions are
+monotonic (thus ensuring the FOI to be <span class="math inline">\(\lambda \geq 0\)</span>) set
+<code>monotonic=TRUE</code></p>
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># ---- Best model with the monotonic constraint -----</span></span>
+<span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/fp_model.html">fp_model</a></span><span class="op">(</span><span class="va">hav</span>, </span>
+<span>                  p<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
+<span>                    p_range<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="op">-</span><span class="fl">2</span>,<span class="fl">3</span>,<span class="fl">0.1</span><span class="op">)</span>,</span>
+<span>                    degree<span class="op">=</span><span class="fl">2</span></span>
+<span>                  <span class="op">)</span>, </span>
+<span>                  monotonic<span class="op">=</span><span class="cn">TRUE</span>,</span>
+<span>                  link<span class="op">=</span><span class="st">"cloglog"</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></code></pre></div>
+<p><img src="parametric_model_files/figure-html/unnamed-chunk-7-1.png" class="r-plt" width="700"></p>
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># the best set of powers with the monotonic constraint is 0.5 and 1.1</span></span>
+<span><span class="va">model</span></span>
+<span><span class="co">#&gt; Fractional polynomial model </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Input type:  aggregated </span></span>
+<span><span class="co">#&gt; Powers:  0.5, 1.1 </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Call:  glm(formula = as.formula(formulate(curr_p)), family = binomial(link = link))</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Coefficients:</span></span>
+<span><span class="co">#&gt; (Intercept)   I(age^0.5)   I(age^1.1)  </span></span>
+<span><span class="co">#&gt;    -7.64170      1.67492     -0.05304  </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Degrees of Freedom: 85 Total (i.e. Null);  83 Residual</span></span>
+<span><span class="co">#&gt; Null Deviance:       1320 </span></span>
+<span><span class="co">#&gt; Residual Deviance: 106   AIC: 385.5</span></span></code></pre></div>
 <hr>
 </div>
-</div>
-<div class="section level2">
-<h2 id="nonlinear-models">Nonlinear models<a class="anchor" aria-label="anchor" href="#nonlinear-models"></a>
-</h2>
-<p>Refer to <code>Chapter 6.1.2</code></p>
 <div class="section level3">
-<h3 id="farrington-model">Farrington model<a class="anchor" aria-label="anchor" href="#farrington-model"></a>
+<h3 id="nonlinear-models">Nonlinear models<a class="anchor" aria-label="anchor" href="#nonlinear-models"></a>
 </h3>
+<div class="section level4">
+<h4 id="farrington-model">Farrington model<a class="anchor" aria-label="anchor" href="#farrington-model"></a>
+</h4>
 <p><strong>Proposed model</strong></p>
 <p>For Farrington’s model, the force of infection was defined
 non-negative for all a <span class="math inline">\(\lambda(a) \geq
@@ -253,20 +321,33 @@ <h3 id="farrington-model">Farrington model<a class="anchor" aria-label="anchor"
 \frac{\alpha}{\beta}ae^{-\beta a} + \frac{1}{\beta}(\frac{\alpha}{\beta}
 - \gamma)(e^{-\beta a} - 1) -\gamma a \}
 \]</span></p>
+<p>Refer to <code>Chapter 6.1.2</code> of the book by <span class="citation">Hens et al. (<a href="#ref-Hens2012">2012</a>)</span>
+for a more detailed explanation of the methods.</p>
 <p><strong>Fitting data</strong></p>
 <p>Use <code><a href="../reference/farrington_model.html">farrington_model()</a></code> to fit a
 <strong>Farrington</strong>’s model.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">farrington_md</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/farrington_model.html">farrington_model</a></span><span class="op">(</span></span>
 <span>   <span class="va">rubella_uk_1986_1987</span>,</span>
 <span>   start<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>alpha<span class="op">=</span><span class="fl">0.07</span>,beta<span class="op">=</span><span class="fl">0.1</span>,gamma<span class="op">=</span><span class="fl">0.03</span><span class="op">)</span></span>
 <span>   <span class="op">)</span></span>
+<span><span class="va">farrington_md</span></span>
+<span><span class="co">#&gt; Farrington model </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Input type:  aggregated </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Call:</span></span>
+<span><span class="co">#&gt; mle(minuslogl = farrington, start = start, fixed = fixed)</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Coefficients:</span></span>
+<span><span class="co">#&gt;      alpha       beta      gamma </span></span>
+<span><span class="co">#&gt; 0.07034904 0.20243950 0.03665599</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">farrington_md</span><span class="op">)</span></span></code></pre></div>
-<p><img src="parametric_model_files/figure-html/unnamed-chunk-7-1.png" width="700"></p>
+<p><img src="parametric_model_files/figure-html/unnamed-chunk-8-1.png" class="r-plt" width="700"></p>
 </div>
-<div class="section level3">
-<h3 id="weibull-model">Weibull model<a class="anchor" aria-label="anchor" href="#weibull-model"></a>
-</h3>
+<div class="section level4">
+<h4 id="weibull-model">Weibull model<a class="anchor" aria-label="anchor" href="#weibull-model"></a>
+</h4>
 <p><strong>Proposed model</strong></p>
 <p>For a Weibull model, the prevalence is given by</p>
 <p><span class="math display">\[
@@ -283,80 +364,247 @@ <h3 id="weibull-model">Weibull model<a class="anchor" aria-label="anchor" href="
 <p><span class="math display">\[
 \lambda(d) = \beta_0 \beta_1 d^{\beta_1 - 1}
 \]</span></p>
+<p>Refer to <code>Chapter 6.1.2</code> of the book by <span class="citation">Hens et al. (<a href="#ref-Hens2012">2012</a>)</span>
+for a more detailed explanation of the methods.</p>
 <p><strong>Fitting data</strong></p>
 <p>Use <code><a href="../reference/weibull_model.html">weibull_model()</a></code> to fit a Weibull model.</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">hcv</span> <span class="op">&lt;-</span> <span class="va">hcv_be_2006</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/order.html" class="external-link">order</a></span><span class="op">(</span><span class="va">hcv_be_2006</span><span class="op">$</span><span class="va">dur</span><span class="op">)</span>, <span class="op">]</span></span>
 <span></span>
-<span><span class="va">wb_md</span> <span class="op">&lt;-</span> <span class="va">hcv</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/rename.html" class="external-link">rename</a></span><span class="op">(</span></span>
-<span>    t <span class="op">=</span> <span class="va">dur</span>, status <span class="op">=</span> <span class="va">seropositive</span></span>
-<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="../reference/weibull_model.html">weibull_model</a></span><span class="op">(</span><span class="op">)</span></span>
+<span><span class="va">wb_md</span> <span class="op">&lt;-</span> <span class="va">hcv</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="../reference/weibull_model.html">weibull_model</a></span><span class="op">(</span>t_lab <span class="op">=</span> <span class="st">"dur"</span>, status_col<span class="op">=</span><span class="st">"seropositive"</span><span class="op">)</span></span>
+<span><span class="va">wb_md</span></span>
+<span><span class="co">#&gt; Weibull model </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Input type:  linelisting </span></span>
+<span><span class="co">#&gt; b0=-0.276, b1=0.3807 </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Call:  glm(formula = spos ~ log(t), family = binomial(link = "cloglog"))</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Coefficients:</span></span>
+<span><span class="co">#&gt; (Intercept)       log(t)  </span></span>
+<span><span class="co">#&gt;     -0.2760       0.3807  </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Degrees of Freedom: 420 Total (i.e. Null);  419 Residual</span></span>
+<span><span class="co">#&gt; Null Deviance:       452.1 </span></span>
+<span><span class="co">#&gt; Residual Deviance: 419.4     AIC: 423.4</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">wb_md</span><span class="op">)</span> </span></code></pre></div>
-<p><img src="parametric_model_files/figure-html/unnamed-chunk-8-1.png" width="700"></p>
-<hr>
+<p><img src="parametric_model_files/figure-html/unnamed-chunk-9-1.png" class="r-plt" width="700"></p>
+</div>
 </div>
 </div>
 <div class="section level2">
-<h2 id="fractional-polynomial-model">Fractional polynomial model<a class="anchor" aria-label="anchor" href="#fractional-polynomial-model"></a>
+<h2 id="bayesian-methods">Bayesian methods<a class="anchor" aria-label="anchor" href="#bayesian-methods"></a>
 </h2>
-<p>Refer to <code>Chapter 6.2</code></p>
+<p><strong>Proposed approach</strong></p>
+<p>Consider a model for prevalence that has a parametric form <span class="math inline">\(\pi(a_i, \alpha)\)</span> where <span class="math inline">\(\alpha\)</span> is a parameter vector</p>
+<p>One can constraint the parameter space of the prior distribution
+<span class="math inline">\(P(\alpha)\)</span> in order to achieve the
+desired monotonicity of the posterior distribution <span class="math inline">\(P(\pi_1, \pi_2, ..., \pi_m|y,n)\)</span></p>
+<p>Where:</p>
+<ul>
+<li><p><span class="math inline">\(n = (n_1, n_2, ..., n_m)\)</span> and
+<span class="math inline">\(n_i\)</span> is the sample size at age <span class="math inline">\(a_i\)</span></p></li>
+<li><p><span class="math inline">\(y = (y_1, y_2, ..., y_m)\)</span> and
+<span class="math inline">\(y_i\)</span> is the number of infected
+individual from the <span class="math inline">\(n_i\)</span> sampled
+subjects</p></li>
+</ul>
+<div class="section level3">
+<h3 id="farrington">Farrington<a class="anchor" aria-label="anchor" href="#farrington"></a>
+</h3>
 <p><strong>Proposed model</strong></p>
-<p>Fractional polynomial model generalize conventional polynomial class
-of functions. In the context of binary responses, a fractional
-polynomial of degree <span class="math inline">\(m\)</span> for the
-linear predictor is defined as followed</p>
+<p>The model for prevalence is as followed</p>
 <p><span class="math display">\[
-\eta_m(a, \beta, p_1, p_2, ...,p_m) = \Sigma^m_{i=0} \beta_i H_i(a)
+\pi (a) = 1 - exp\{ \frac{\alpha_1}{\alpha_2}ae^{-\alpha_2 a} +
+\frac{1}{\alpha_2}(\frac{\alpha_1}{\alpha_2} - \alpha_3)(e^{-\alpha_2 a}
+- 1) -\alpha_3 a \}
 \]</span></p>
-<p>Where <span class="math inline">\(m\)</span> is an integer, <span class="math inline">\(p_1 \le p_2 \le... \le p_m\)</span> is a sequence
-of powers, and <span class="math inline">\(H_i(a)\)</span> is a
-transformation given by</p>
+<p>For likelihood model, independent binomial distribution are assumed
+for the number of infected individuals at age <span class="math inline">\(a_i\)</span></p>
 <p><span class="math display">\[
-H_i = \begin{cases}
-a^{p_i} &amp; \text{ if } p_i \neq p_{i-1},
-\\ H_{i-1}(a) \times log(a)  &amp; \text{ if } p_i = p_{i-1},
-\end{cases}
+y_i \sim Bin(n_i, \pi_i), \text{  for } i = 1,2,3,...m
 \]</span></p>
-<p><strong>Best power selection</strong></p>
-<p>Use <code><a href="../reference/find_best_fp_powers.html">find_best_fp_powers()</a></code> to find the powers which gives
-the lowest deviance score</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">hav</span> <span class="op">&lt;-</span> <span class="va">hav_be_1993_1994</span></span>
-<span><span class="va">best_p</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/find_best_fp_powers.html">find_best_fp_powers</a></span><span class="op">(</span></span>
-<span>  <span class="va">hav</span>,</span>
-<span>  p<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="op">-</span><span class="fl">2</span>,<span class="fl">3</span>,<span class="fl">0.1</span><span class="op">)</span>, mc<span class="op">=</span><span class="cn">FALSE</span>, degree<span class="op">=</span><span class="fl">2</span>, link<span class="op">=</span><span class="st">"cloglog"</span></span>
-<span><span class="op">)</span></span>
-<span><span class="va">best_p</span></span>
-<span><span class="co">#&gt; $p</span></span>
-<span><span class="co">#&gt; [1] 1.5 1.6</span></span>
+<p>The constraint on the parameter space can be incorporated by assuming
+truncated normal distribution for the components of <span class="math inline">\(\alpha\)</span>, <span class="math inline">\(\alpha = (\alpha_1, \alpha_2, \alpha_3)\)</span>
+in <span class="math inline">\(\pi_i = \pi(a_i,\alpha)\)</span></p>
+<p><span class="math display">\[
+\alpha_j \sim \text{truncated  } \mathcal{N}(\mu_j, \tau_j), \text{ } j
+= 1,2,3
+\]</span></p>
+<p>The joint posterior distribution for <span class="math inline">\(\alpha\)</span> can be derived by combining the
+likelihood and prior as followed</p>
+<p><span class="math display">\[
+P(\alpha|y) \propto \prod^m_{i=1} \text{Bin}(y_i|n_i, \pi(a_i, \alpha))
+\prod^3_{i=1}-\frac{1}{\tau_j}\text{exp}(\frac{1}{2\tau^2_j} (\alpha_j -
+\mu_j)^2)
+\]</span></p>
+<ul>
+<li>
+<p>Where the flat hyperprior distribution is defined as
+followed:</p>
+<ul>
+<li><p><span class="math inline">\(\mu_j \sim \mathcal{N}(0,
+10000)\)</span></p></li>
+<li><p><span class="math inline">\(\tau^{-2}_j \sim
+\Gamma(100,100)\)</span></p></li>
+</ul>
+</li>
+</ul>
+<p>The full conditional distribution of <span class="math inline">\(\alpha_i\)</span> is thus <span class="math display">\[
+P(\alpha_i|\alpha_j,\alpha_k, k, j \neq i)
+\propto  -\frac{1}{\tau_i}\text{exp}(\frac{1}{2\tau^2_i} (\alpha_i -
+\mu_i)^2) \prod^m_{i=1} \text{Bin}(y_i|n_i, \pi(a_i, \alpha))
+\]</span></p>
+<p>Refer to <code>Chapter 10.3.1</code> of the book by <span class="citation">Hens et al. (<a href="#ref-Hens2012">2012</a>)</span>
+for a more detailed explanation of the method.</p>
+<p><strong>Fitting data</strong></p>
+<p>To fit Farrington model, use
+<code><a href="../reference/hierarchical_bayesian_model.html">hierarchical_bayesian_model()</a></code> and define
+<code>type = "far2"</code> or <code>type = "far3"</code> where</p>
+<ul>
+<li><p><code>type = "far2"</code> refers to Farrington model with 2
+parameters (<span class="math inline">\(\alpha_3 = 0\)</span>)</p></li>
+<li><p><code>type = "far3"</code> refers to Farrington model with 3
+parameters (<span class="math inline">\(\alpha_3 &gt;
+0\)</span>)</p></li>
+</ul>
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">mumps_uk_1986_1987</span></span>
+<span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/hierarchical_bayesian_model.html">hierarchical_bayesian_model</a></span><span class="op">(</span><span class="va">df</span>, type<span class="op">=</span><span class="st">"far3"</span><span class="op">)</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; SAMPLING FOR MODEL 'fra_3' NOW (CHAIN 1).</span></span>
+<span><span class="co">#&gt; Chain 1: Rejecting initial value:</span></span>
+<span><span class="co">#&gt; Chain 1:   Log probability evaluates to log(0), i.e. negative infinity.</span></span>
+<span><span class="co">#&gt; Chain 1:   Stan can't start sampling from this initial value.</span></span>
+<span><span class="co">#&gt; Chain 1: </span></span>
+<span><span class="co">#&gt; Chain 1: Gradient evaluation took 0.000165 seconds</span></span>
+<span><span class="co">#&gt; Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.65 seconds.</span></span>
+<span><span class="co">#&gt; Chain 1: Adjust your expectations accordingly!</span></span>
+<span><span class="co">#&gt; Chain 1: </span></span>
+<span><span class="co">#&gt; Chain 1: </span></span>
+<span><span class="co">#&gt; Chain 1: Iteration:    1 / 5000 [  0%]  (Warmup)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration:  500 / 5000 [ 10%]  (Warmup)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 1000 / 5000 [ 20%]  (Warmup)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 1500 / 5000 [ 30%]  (Warmup)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 1501 / 5000 [ 30%]  (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 2000 / 5000 [ 40%]  (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 2500 / 5000 [ 50%]  (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 3000 / 5000 [ 60%]  (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 3500 / 5000 [ 70%]  (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 4000 / 5000 [ 80%]  (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 4500 / 5000 [ 90%]  (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 5000 / 5000 [100%]  (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1: </span></span>
+<span><span class="co">#&gt; Chain 1:  Elapsed Time: 17.494 seconds (Warm-up)</span></span>
+<span><span class="co">#&gt; Chain 1:                100.35 seconds (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1:                117.844 seconds (Total)</span></span>
+<span><span class="co">#&gt; Chain 1:</span></span></code></pre></div>
+<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">model</span></span>
+<span><span class="co">#&gt; Hierarchical Bayesian model </span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; $deviance</span></span>
-<span><span class="co">#&gt; [1] 81.60333</span></span>
+<span><span class="co">#&gt; Input type:  aggregated </span></span>
+<span><span class="co">#&gt; Model:  Farrington model with 3 parameters </span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; $model</span></span>
+<span><span class="co">#&gt; Fitted parameters:</span></span>
+<span><span class="co">#&gt;  alpha1 = 0.1397 (95% CrI [0.129, 0.1521], sd = 0.005927)</span></span>
+<span><span class="co">#&gt;  alpha2 = 0.199 (95% CrI [0.1848, 0.2181], sd = 0.008454)</span></span>
+<span><span class="co">#&gt;  alpha3 = 0.009017 (95% CrI [0.000252, 0.02798], sd = 0.007503)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></code></pre></div>
+<p><img src="parametric_model_files/figure-html/unnamed-chunk-11-1.png" class="r-plt" width="700"></p>
+</div>
+<div class="section level3">
+<h3 id="log-logistic">Log-logistic<a class="anchor" aria-label="anchor" href="#log-logistic"></a>
+</h3>
+<p><strong>Proposed approach</strong></p>
+<p>The model for seroprevalence is as followed</p>
+<p><span class="math display">\[
+\pi(a) = \frac{\beta a^\alpha}{1 + \beta a^\alpha}, \text{ } \alpha,
+\beta &gt; 0
+\]</span></p>
+<p>The likelihood is specified to be the same as Farrington model (<span class="math inline">\(y_i \sim Bin(n_i, \pi_i)\)</span>) with</p>
+<p><span class="math display">\[
+\text{logit}(\pi(a)) = \alpha_2 + \alpha_1\log(a)
+\]</span></p>
+<ul>
+<li>Where <span class="math inline">\(\alpha_2 =
+\text{log}(\beta)\)</span>
+</li>
+</ul>
+<p>The prior model of <span class="math inline">\(\alpha_1\)</span> is
+specified as <span class="math inline">\(\alpha_1 \sim
+\text{truncated  } \mathcal{N}(\mu_1, \tau_1)\)</span> with flat
+hyperprior as in Farrington model</p>
+<p><span class="math inline">\(\beta\)</span> is constrained to be
+positive by specifying <span class="math inline">\(\alpha_2 \sim
+\mathcal{N}(\mu_2, \tau_2)\)</span></p>
+<p>The full conditional distribution of <span class="math inline">\(\alpha_1\)</span> is thus</p>
+<p><span class="math display">\[
+P(\alpha_1|\alpha_2) \propto -\frac{1}{\tau_1} \text{exp} (\frac{1}{2
+\tau_1^2} (\alpha_1 - \mu_1)^2)
+\prod_{i=1}^m \text{Bin}(y_i|n_i,\pi(a_i, \alpha_1, \alpha_2) )
+\]</span></p>
+<p>And <span class="math inline">\(\alpha_2\)</span> can be derived in
+the same way</p>
+<p>Refer to <code>Chapter 10.3.3</code> of the book by <span class="citation">Hens et al. (<a href="#ref-Hens2012">2012</a>)</span>
+for a more detailed explanation of the method.</p>
+<p><strong>Fitting data</strong></p>
+<p>To fit Log-logistic model, use
+<code><a href="../reference/hierarchical_bayesian_model.html">hierarchical_bayesian_model()</a></code> and define
+<code>type = "log_logistic"</code></p>
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">rubella_uk_1986_1987</span></span>
+<span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/hierarchical_bayesian_model.html">hierarchical_bayesian_model</a></span><span class="op">(</span><span class="va">df</span>, type<span class="op">=</span><span class="st">"log_logistic"</span><span class="op">)</span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Call:  glm(formula = as.formula(formulate(p_cur)), family = binomial(link = link))</span></span>
+<span><span class="co">#&gt; SAMPLING FOR MODEL 'log_logistic' NOW (CHAIN 1).</span></span>
+<span><span class="co">#&gt; Chain 1: </span></span>
+<span><span class="co">#&gt; Chain 1: Gradient evaluation took 6.5e-05 seconds</span></span>
+<span><span class="co">#&gt; Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.65 seconds.</span></span>
+<span><span class="co">#&gt; Chain 1: Adjust your expectations accordingly!</span></span>
+<span><span class="co">#&gt; Chain 1: </span></span>
+<span><span class="co">#&gt; Chain 1: </span></span>
+<span><span class="co">#&gt; Chain 1: Iteration:    1 / 5000 [  0%]  (Warmup)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration:  500 / 5000 [ 10%]  (Warmup)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 1000 / 5000 [ 20%]  (Warmup)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 1500 / 5000 [ 30%]  (Warmup)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 1501 / 5000 [ 30%]  (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 2000 / 5000 [ 40%]  (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 2500 / 5000 [ 50%]  (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 3000 / 5000 [ 60%]  (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 3500 / 5000 [ 70%]  (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 4000 / 5000 [ 80%]  (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 4500 / 5000 [ 90%]  (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1: Iteration: 5000 / 5000 [100%]  (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1: </span></span>
+<span><span class="co">#&gt; Chain 1:  Elapsed Time: 4.337 seconds (Warm-up)</span></span>
+<span><span class="co">#&gt; Chain 1:                6.096 seconds (Sampling)</span></span>
+<span><span class="co">#&gt; Chain 1:                10.433 seconds (Total)</span></span>
+<span><span class="co">#&gt; Chain 1:</span></span></code></pre></div>
+<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">model</span></span>
+<span><span class="co">#&gt; Hierarchical Bayesian model </span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Coefficients:</span></span>
-<span><span class="co">#&gt; (Intercept)   I(age^1.5)   I(age^1.6)  </span></span>
-<span><span class="co">#&gt;    -3.61083      0.12443     -0.07656  </span></span>
+<span><span class="co">#&gt; Input type:  aggregated </span></span>
+<span><span class="co">#&gt; Model:  Log-logistic model </span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Degrees of Freedom: 85 Total (i.e. Null);  83 Residual</span></span>
-<span><span class="co">#&gt; Null Deviance:       1320 </span></span>
-<span><span class="co">#&gt; Residual Deviance: 81.6  AIC: 361.2</span></span></code></pre></div>
-<p><strong>Fitting data</strong></p>
-<p>Use <code><a href="../reference/fp_model.html">fp_model()</a></code> to fit a fractional polynomial model</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/fp_model.html">fp_model</a></span><span class="op">(</span><span class="va">hav</span>, p<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.5</span>, <span class="fl">1.6</span><span class="op">)</span>, link<span class="op">=</span><span class="st">"cloglog"</span><span class="op">)</span></span>
+<span><span class="co">#&gt; Fitted parameters:</span></span>
+<span><span class="co">#&gt;  alpha1 = 1.642 (95% CrI [1.539, 1.749], sd = 0.05377)</span></span>
+<span><span class="co">#&gt;  alpha2 = -2.958 (95% CrI [-3.223, -2.722], sd = 0.1275)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></code></pre></div>
-<p><img src="parametric_model_files/figure-html/unnamed-chunk-10-1.png" width="700"></p>
-<div id="refs" class="references csl-bib-body hanging-indent">
+<p><img src="parametric_model_files/figure-html/unnamed-chunk-13-1.png" class="r-plt" width="700"></p>
+<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0">
 <div id="ref-grenfell_estimation_1985" class="csl-entry">
 Grenfell, B. T., and R. M. Anderson. 1985. <span>“The Estimation of
 Age-Related Rates of Infection from Case Notifications and Serological
 Data.”</span> <em>The Journal of Hygiene</em> 95 (2): 419–36. <a href="https://doi.org/10.1017/s0022172400062859" class="external-link">https://doi.org/10.1017/s0022172400062859</a>.
 </div>
+<div id="ref-Hens2012" class="csl-entry">
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. <em>Modeling Infectious Disease Parameters Based
+on Serological and Social Contact Data: A Modern Statistical
+Perspective</em>. <em>Statistics for Biology and Health</em>. Springer
+New York. <a href="https://doi.org/10.1007/978-1-4614-4072-7" class="external-link">https://doi.org/10.1007/978-1-4614-4072-7</a>.
+</div>
 <div id="ref-muench_derivation_1934" class="csl-entry">
 Muench, Hugo. 1934. <span>“Derivation of Rates from Summation Data by
 the Catalytic Curve.”</span> <em>Journal of the American Statistical
@@ -364,28 +612,27 @@ <h2 id="fractional-polynomial-model">Fractional polynomial model<a class="anchor
 </div>
 </div>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
 </div>
 
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p>
-<p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
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-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
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diff --git a/docs/articles/parametric_model.md b/docs/articles/parametric_model.md
index d88c949..cd1a0a5 100644
--- a/docs/articles/parametric_model.md
+++ b/docs/articles/parametric_model.md
@@ -6,173 +6,267 @@ library(dplyr)
 library(magrittr)
 ```
 
-## Polynomial models
+## Frequentist methods
 
-Refer to `Chapter 6.1.1`
+### Polynomial models
 
-Use
-[`polynomial_model()`](https://oucru-modelling.github.io/serosv/reference/polynomial_model.md)
-to fit a polynomial model.
+Seroprevalence is modeled using the following serocatalytic model
 
-We will use the `Hepatitis A` data from Belgium 1993–1994 for this
-example.
+\\ \pi(a) = 1 - \text{exp}({-\Sigma\_{i=1}^k \beta_i a^i}) \\
 
-``` r
-data <- hav_bg_1964
-```
+Where:
 
-### Muench model
+- \\a\\ is the variable age
 
-**Proposed model**
+- \\\pi\\ is the seroprevalence of the population at age \\a\\
 
-([Muench 1934](#ref-muench_derivation_1934)) suggested to model the
-infection process with so-called “catalytic model”, in which the
-distribution of the time spent in the susceptible class in SIR model is
-exponential with rate \\(\beta\\)
+- \\k\\ is the degree of the polynomial
 
-\\\[ \pi(a) = k(1 - e^{-\beta a} ) \\\]
+- \\\beta_i\\ are the model parameters
 
-Where:
+Which implies the force of infection is \\\lambda(a) = \Sigma\_{i=1}^k
+\beta_i i a^{i-1}\\
+
+This generalization encompasses several classical serocatalytic model
+including
+
+- **Muench model** (assuming \\k=1\\) ([Muench
+  1934](#ref-muench_derivation_1934))
 
-- \\(\pi\\) is the seroprevalence at age \\(a\\)
-- \\(1 - k\\) is the proportion of population that stay uninfected for a
-  lifetime
-- \\(a\\) is the variable age
+- **Griffith model** (assuming \\k = 2\\)
 
-Under this catalytic model and assuming that \\(k = 1\\), force
-infection would be \\(\lambda(a) = \beta\\)
+- **Grenfell and Anderson model** (assuming higher degree \\k\\)
+  ([Grenfell and Anderson 1985](#ref-grenfell_estimation_1985))
+
+Refer to `Chapter 6.1.1` of the book by Hens et al.
+([2012](#ref-Hens2012)) for a more detailed explanation of the methods.
 
 **Fitting data**
 
-**Muench**’s model can be estimated by either defining `k = 1` (a degree
-one linear predictor, note that it is irrelevant to the k in the
-proposed model) or setting the `type = "Muench"`.
+We will use the `Parvo B19` data from Finland 1997–1998 for this
+example.
 
 ``` r
-muench1 <- polynomial_model(data, k = 1)
-summary(muench1$info)
-#> 
-#> Call:
-#> glm(formula = age(k), family = binomial(link = link), data = df)
-#> 
-#> Coefficients:
-#>      Estimate Std. Error z value Pr(>|z|)    
-#> Age -0.050500   0.002457  -20.55   <2e-16 ***
-#> ---
-#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#> 
-#> (Dispersion parameter for binomial family taken to be 1)
-#> 
-#>     Null deviance:    Inf  on 83  degrees of freedom
-#> Residual deviance: 97.275  on 82  degrees of freedom
-#> AIC: 219.19
-#> 
-#> Number of Fisher Scoring iterations: 5
+data <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age), ]
+```
+
+To fit a polynomial model, use the
+[`polynomial_model()`](https://oucru-modelling.github.io/serosv/reference/polynomial_model.md)
+function.
 
-muench2 <- polynomial_model(data, type = "Muench")
-summary(muench2$info)
+``` r
+# Fit a Muench model
+muench <- polynomial_model(data, k = 1, status_col="seropositive")
+summary(muench$info)
 #> 
 #> Call:
-#> glm(formula = age(k), family = binomial(link = link), data = df)
+#> glm(formula = Age(k), family = binomial(link = link), data = df)
 #> 
 #> Coefficients:
 #>      Estimate Std. Error z value Pr(>|z|)    
-#> Age -0.050500   0.002457  -20.55   <2e-16 ***
+#> age -0.029088   0.001375  -21.15   <2e-16 ***
 #> ---
 #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 #> 
 #> (Dispersion parameter for binomial family taken to be 1)
 #> 
-#>     Null deviance:    Inf  on 83  degrees of freedom
-#> Residual deviance: 97.275  on 82  degrees of freedom
-#> AIC: 219.19
+#>     Null deviance:    Inf  on 1117  degrees of freedom
+#> Residual deviance: 1366.9  on 1116  degrees of freedom
+#> AIC: 1368.9
 #> 
-#> Number of Fisher Scoring iterations: 5
+#> Number of Fisher Scoring iterations: 6
+plot(muench) 
 ```
 
-We can plot any model with the
-[`plot()`](https://rdrr.io/r/graphics/plot.default.html) function.
+![](parametric_model_files/figure-html/unnamed-chunk-3-1.png)
+
+The users can also choose to provide a range of values for \\k\\ in
+which case the package will try to find the best \\k\\ parameter
+determined by Loglikelihood Ratio test (LRT)
 
 ``` r
-plot(muench2) 
+# Provide a range of values for k
+best_param <- polynomial_model(data, k = 1:5, status_col = "seropositive")
+plot(best_param)
 ```
 
 ![](parametric_model_files/figure-html/unnamed-chunk-4-1.png)
 
-### Griffith model
+``` r
+# View the best model here which suggests k = 4 is the best parameter value
+best_param
+#> Polynomial model
+#> 
+#> Input type:  linelisting 
+#> Degree (k):  4 
+#> 
+#> Call:  glm(formula = Age(k), family = binomial(link = link), data = df)
+#> 
+#> Coefficients:
+#>        age    I(age^2)    I(age^3)    I(age^4)  
+#> -3.381e-02  -9.950e-04   5.551e-05  -5.737e-07  
+#> 
+#> Degrees of Freedom: 1117 Total (i.e. Null);  1113 Residual
+#> Null Deviance:       Inf 
+#> Residual Deviance: 1336  AIC: 1344
+```
+
+------------------------------------------------------------------------
+
+### Fractional polynomial model
 
 **Proposed model**
 
-Griffith proposed a model for force of infection as followed
+Fractional polynomial model generalize conventional polynomial class of
+functions. In the context of binary responses, a fractional polynomial
+of degree \\m\\ for the linear predictor is defined as followed
+
+\\ \eta_m(a, \beta, p_1, p_2, ...,p_m) = \Sigma^m\_{i=0} \beta_i H_i(a)
+\\
+
+Where \\m\\ is an integer, \\p_1 \le p_2 \le... \le p_m\\ is a sequence
+of powers, and \\H_i(a)\\ is a transformation given by
 
-\\\[ \lambda(a) = \beta_1 + 2\beta_2a \\\]
+\\ H_i = \begin{cases} a^{p_i} & \text{ if } p_i \neq p\_{i-1}, \\
+H\_{i-1}(a) \times log(a) & \text{ if } p_i = p\_{i-1}, \end{cases} \\
 
-Which can be estimated using a GLM where the for which the linear
-predictor was \\(\eta(a) = \beta_1 + \beta_2a^{2}\\)
+Refer to `Chapter 6.2` of the book by Hens et al.
+([2012](#ref-Hens2012)) for a more detailed explanation of the methods.
 
 **Fitting data**
 
-Similarly, we can estimate **Griffith**’s model either by defining
-`k = 2`, or setting the `type = "Griffith"`
+Use
+[`fp_model()`](https://oucru-modelling.github.io/serosv/reference/fp_model.md)
+to fit a fractional polynomial model.
+
+The parameter `p` specifies the powers of each polynomial term (length
+of `p` is thus the model’s degree)
 
 ``` r
-gf_model <- polynomial_model(data, type = "Griffith")
-plot(gf_model)
+hav <- hav_be_1993_1994
+model <- fp_model(hav, p=c(1, 1.5), link="cloglog")
+model
+#> Fractional polynomial model 
+#> 
+#> Input type:  aggregated 
+#> Powers:  1, 1.5 
+#> 
+#> Call:  glm(formula = as.formula(formulate(p)), family = binomial(link = link))
+#> 
+#> Coefficients:
+#> (Intercept)     I(age^1)   I(age^1.5)  
+#>    -4.56685      0.22340     -0.01775  
+#> 
+#> Degrees of Freedom: 85 Total (i.e. Null);  83 Residual
+#> Null Deviance:       1320 
+#> Residual Deviance: 85.81     AIC: 365.4
+plot(model)
 ```
 
 ![](parametric_model_files/figure-html/unnamed-chunk-5-1.png)
 
-### Grenfell and Anderson model
-
-**Proposed model**
+The users can also tell the package to perform parameter selection by
+providing `p` as a named list with 2 elements:
 
-([Grenfell and Anderson 1985](#ref-grenfell_estimation_1985)) extended
-the models of Muench and Griffiths further suggest the use of higher
-order polynomial functions to model the force of infection which assumes
-prevalence model as followed
+- `degree` the maximum number of terms to search over
+- `p_range` the possible powers for each term
 
-\\\[ \pi(a) = 1 - e^{-\Sigma_i \beta_i a^i} \\\]
+``` r
+model <- fp_model(hav, 
+                  p=list(
+                    p_range=seq(-2,3,0.1),
+                    degree=2
+                  ), 
+                  monotonic=FALSE,
+                  link="cloglog")
+plot(model)
+```
 
-Which implies that force of infection equals \\(\lambda(a) = \Sigma
-\beta_i i a^{i-1}\\)
+![](parametric_model_files/figure-html/unnamed-chunk-6-1.png)
 
-**Fitting data**
+``` r
+# the best set of powers for this dataset is 1.5 and 1.6
+model
+#> Fractional polynomial model 
+#> 
+#> Input type:  aggregated 
+#> Powers:  1.5, 1.6 
+#> 
+#> Call:  glm(formula = as.formula(formulate(curr_p)), family = binomial(link = link))
+#> 
+#> Coefficients:
+#> (Intercept)   I(age^1.5)   I(age^1.6)  
+#>    -3.61083      0.12443     -0.07656  
+#> 
+#> Degrees of Freedom: 85 Total (i.e. Null);  83 Residual
+#> Null Deviance:       1320 
+#> Residual Deviance: 81.6  AIC: 361.2
+```
 
-And Grenfell and Anderson’s model.
+To restrict the parameters search such that the predictions are
+monotonic (thus ensuring the FOI to be \\\lambda \geq 0\\) set
+`monotonic=TRUE`
 
 ``` r
-grf_model <- polynomial_model(data, type = "Grenfell")
-plot(grf_model)
+# ---- Best model with the monotonic constraint -----
+model <- fp_model(hav, 
+                  p=list(
+                    p_range=seq(-2,3,0.1),
+                    degree=2
+                  ), 
+                  monotonic=TRUE,
+                  link="cloglog")
+plot(model)
 ```
 
-![](parametric_model_files/figure-html/unnamed-chunk-6-1.png)
+![](parametric_model_files/figure-html/unnamed-chunk-7-1.png)
 
-------------------------------------------------------------------------
+``` r
+# the best set of powers with the monotonic constraint is 0.5 and 1.1
+model
+#> Fractional polynomial model 
+#> 
+#> Input type:  aggregated 
+#> Powers:  0.5, 1.1 
+#> 
+#> Call:  glm(formula = as.formula(formulate(curr_p)), family = binomial(link = link))
+#> 
+#> Coefficients:
+#> (Intercept)   I(age^0.5)   I(age^1.1)  
+#>    -7.64170      1.67492     -0.05304  
+#> 
+#> Degrees of Freedom: 85 Total (i.e. Null);  83 Residual
+#> Null Deviance:       1320 
+#> Residual Deviance: 106   AIC: 385.5
+```
 
-## Nonlinear models
+------------------------------------------------------------------------
 
-Refer to `Chapter 6.1.2`
+### Nonlinear models
 
-### Farrington model
+#### Farrington model
 
 **Proposed model**
 
 For Farrington’s model, the force of infection was defined non-negative
-for all a \\(\lambda(a) \geq 0\\) and increases to a peak in a linear
+for all a \\\lambda(a) \geq 0\\ and increases to a peak in a linear
 fashion followed by an exponential decrease
 
-\\\[ \lambda(a) = (\alpha a - \gamma)e^{-\beta a} + \gamma \\\]
+\\ \lambda(a) = (\alpha a - \gamma)e^{-\beta a} + \gamma \\
 
-Where \\(\gamma\\) is called the long term residual for FOI, as \\(a
-\rightarrow \infty\\) , \\(\lambda (a) \rightarrow \gamma\\)
+Where \\\gamma\\ is called the long term residual for FOI, as \\a
+\rightarrow \infty\\ , \\\lambda (a) \rightarrow \gamma\\
 
-Integrating \\(\lambda(a)\\) would results in the following non-linear
+Integrating \\\lambda(a)\\ would results in the following non-linear
 model for prevalence
 
-\\\[ \pi (a) = 1 - e^{-\int_0^a \lambda(s) ds} \\\\ = 1 - exp\\{
+\\ \pi (a) = 1 - e^{-\int_0^a \lambda(s) ds} \\ = 1 - exp\\
 \frac{\alpha}{\beta}ae^{-\beta a} +
 \frac{1}{\beta}(\frac{\alpha}{\beta} - \gamma)(e^{-\beta a} - 1) -\gamma
-a \\} \\\]
+a \\ \\
+
+Refer to `Chapter 6.1.2` of the book by Hens et al.
+([2012](#ref-Hens2012)) for a more detailed explanation of the methods.
 
 **Fitting data**
 
@@ -185,31 +279,45 @@ farrington_md <- farrington_model(
    rubella_uk_1986_1987,
    start=list(alpha=0.07,beta=0.1,gamma=0.03)
    )
+farrington_md
+#> Farrington model 
+#> 
+#> Input type:  aggregated 
+#> 
+#> Call:
+#> mle(minuslogl = farrington, start = start, fixed = fixed)
+#> 
+#> Coefficients:
+#>      alpha       beta      gamma 
+#> 0.07034904 0.20243950 0.03665599
 plot(farrington_md)
 ```
 
-![](parametric_model_files/figure-html/unnamed-chunk-7-1.png)
+![](parametric_model_files/figure-html/unnamed-chunk-8-1.png)
 
-### Weibull model
+#### Weibull model
 
 **Proposed model**
 
 For a Weibull model, the prevalence is given by
 
-\\\[ \pi (d) = 1 - e^{ - \beta_0 d ^ {\beta_1}} \\\]
+\\ \pi (d) = 1 - e^{ - \beta_0 d ^ {\beta_1}} \\
 
-Where \\(d\\) is exposure time (difference between age of injection and
+Where \\d\\ is exposure time (difference between age of injection and
 age at test)
 
 The model was reformulated as a GLM model with log - log link and linear
 predictor using log(d)
 
-\\\[\eta(d) = log(\beta_0) + \beta_1 log(d)\\\]
+\\\eta(d) = log(\beta_0) + \beta_1 log(d)\\
 
 Thus implies that the force of infection is a monotone function of the
 exposure time as followed
 
-\\\[ \lambda(d) = \beta_0 \beta_1 d^{\beta_1 - 1} \\\]
+\\ \lambda(d) = \beta_0 \beta_1 d^{\beta_1 - 1} \\
+
+Refer to `Chapter 6.1.2` of the book by Hens et al.
+([2012](#ref-Hens2012)) for a more detailed explanation of the methods.
 
 **Fitting data**
 
@@ -220,86 +328,246 @@ to fit a Weibull model.
 ``` r
 hcv <- hcv_be_2006[order(hcv_be_2006$dur), ]
 
-wb_md <- hcv %>% 
-  rename(
-    t = dur, status = seropositive
-  ) %>% weibull_model()
+wb_md <- hcv %>% weibull_model(t_lab = "dur", status_col="seropositive")
+wb_md
+#> Weibull model 
+#> 
+#> Input type:  linelisting 
+#> b0=-0.276, b1=0.3807 
+#> 
+#> Call:  glm(formula = spos ~ log(t), family = binomial(link = "cloglog"))
+#> 
+#> Coefficients:
+#> (Intercept)       log(t)  
+#>     -0.2760       0.3807  
+#> 
+#> Degrees of Freedom: 420 Total (i.e. Null);  419 Residual
+#> Null Deviance:       452.1 
+#> Residual Deviance: 419.4     AIC: 423.4
 plot(wb_md) 
 ```
 
-![](parametric_model_files/figure-html/unnamed-chunk-8-1.png)
+![](parametric_model_files/figure-html/unnamed-chunk-9-1.png)
 
-------------------------------------------------------------------------
+## Bayesian methods
+
+**Proposed approach**
+
+Consider a model for prevalence that has a parametric form \\\pi(a_i,
+\alpha)\\ where \\\alpha\\ is a parameter vector
 
-## Fractional polynomial model
+One can constraint the parameter space of the prior distribution
+\\P(\alpha)\\ in order to achieve the desired monotonicity of the
+posterior distribution \\P(\pi_1, \pi_2, ..., \pi_m\|y,n)\\
 
-Refer to `Chapter 6.2`
+Where:
+
+- \\n = (n_1, n_2, ..., n_m)\\ and \\n_i\\ is the sample size at age
+  \\a_i\\
+
+- \\y = (y_1, y_2, ..., y_m)\\ and \\y_i\\ is the number of infected
+  individual from the \\n_i\\ sampled subjects
+
+### Farrington
 
 **Proposed model**
 
-Fractional polynomial model generalize conventional polynomial class of
-functions. In the context of binary responses, a fractional polynomial
-of degree \\(m\\) for the linear predictor is defined as followed
+The model for prevalence is as followed
 
-\\\[ \eta_m(a, \beta, p_1, p_2, ...,p_m) = \Sigma^m\_{i=0} \beta_i
-H_i(a) \\\]
+\\ \pi (a) = 1 - exp\\ \frac{\alpha_1}{\alpha_2}ae^{-\alpha_2 a} +
+\frac{1}{\alpha_2}(\frac{\alpha_1}{\alpha_2} - \alpha_3)(e^{-\alpha_2
+a} - 1) -\alpha_3 a \\ \\
 
-Where \\(m\\) is an integer, \\(p_1 \le p_2 \le... \le p_m\\) is a
-sequence of powers, and \\(H_i(a)\\) is a transformation given by
+For likelihood model, independent binomial distribution are assumed for
+the number of infected individuals at age \\a_i\\
 
-\\\[ H_i = \begin{cases} a^{p_i} & \text{ if } p_i \neq p\_{i-1}, \\\\
-H\_{i-1}(a) \times log(a) & \text{ if } p_i = p\_{i-1}, \end{cases} \\\]
+\\ y_i \sim Bin(n_i, \pi_i), \text{ for } i = 1,2,3,...m \\
 
-**Best power selection**
+The constraint on the parameter space can be incorporated by assuming
+truncated normal distribution for the components of \\\alpha\\, \\\alpha
+= (\alpha_1, \alpha_2, \alpha_3)\\ in \\\pi_i = \pi(a_i,\alpha)\\
 
-Use
-[`find_best_fp_powers()`](https://oucru-modelling.github.io/serosv/reference/find_best_fp_powers.md)
-to find the powers which gives the lowest deviance score
+\\ \alpha_j \sim \text{truncated } \mathcal{N}(\mu_j, \tau_j), \text{ }
+j = 1,2,3 \\
+
+The joint posterior distribution for \\\alpha\\ can be derived by
+combining the likelihood and prior as followed
+
+\\ P(\alpha\|y) \propto \prod^m\_{i=1} \text{Bin}(y_i\|n_i, \pi(a_i,
+\alpha)) \prod^3\_{i=1}-\frac{1}{\tau_j}\text{exp}(\frac{1}{2\tau^2_j}
+(\alpha_j - \mu_j)^2) \\
+
+- Where the flat hyperprior distribution is defined as followed:
+
+  - \\\mu_j \sim \mathcal{N}(0, 10000)\\
+
+  - \\\tau^{-2}\_j \sim \Gamma(100,100)\\
+
+The full conditional distribution of \\\alpha_i\\ is thus \\
+P(\alpha_i\|\alpha_j,\alpha_k, k, j \neq i) \propto
+-\frac{1}{\tau_i}\text{exp}(\frac{1}{2\tau^2_i} (\alpha_i - \mu_i)^2)
+\prod^m\_{i=1} \text{Bin}(y_i\|n_i, \pi(a_i, \alpha)) \\
+
+Refer to `Chapter 10.3.1` of the book by Hens et al.
+([2012](#ref-Hens2012)) for a more detailed explanation of the method.
+
+**Fitting data**
+
+To fit Farrington model, use
+[`hierarchical_bayesian_model()`](https://oucru-modelling.github.io/serosv/reference/hierarchical_bayesian_model.md)
+and define `type = "far2"` or `type = "far3"` where
+
+- `type = "far2"` refers to Farrington model with 2 parameters
+  (\\\alpha_3 = 0\\)
+
+- `type = "far3"` refers to Farrington model with 3 parameters
+  (\\\alpha_3 \> 0\\)
 
 ``` r
-hav <- hav_be_1993_1994
-best_p <- find_best_fp_powers(
-  hav,
-  p=seq(-2,3,0.1), mc=FALSE, degree=2, link="cloglog"
-)
-best_p
-#> $p
-#> [1] 1.5 1.6
+df <- mumps_uk_1986_1987
+model <- hierarchical_bayesian_model(df, type="far3")
 #> 
-#> $deviance
-#> [1] 81.60333
-#> 
-#> $model
-#> 
-#> Call:  glm(formula = as.formula(formulate(p_cur)), family = binomial(link = link))
+#> SAMPLING FOR MODEL 'fra_3' NOW (CHAIN 1).
+#> Chain 1: Rejecting initial value:
+#> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity.
+#> Chain 1:   Stan can't start sampling from this initial value.
+#> Chain 1: 
+#> Chain 1: Gradient evaluation took 0.000165 seconds
+#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.65 seconds.
+#> Chain 1: Adjust your expectations accordingly!
+#> Chain 1: 
+#> Chain 1: 
+#> Chain 1: Iteration:    1 / 5000 [  0%]  (Warmup)
+#> Chain 1: Iteration:  500 / 5000 [ 10%]  (Warmup)
+#> Chain 1: Iteration: 1000 / 5000 [ 20%]  (Warmup)
+#> Chain 1: Iteration: 1500 / 5000 [ 30%]  (Warmup)
+#> Chain 1: Iteration: 1501 / 5000 [ 30%]  (Sampling)
+#> Chain 1: Iteration: 2000 / 5000 [ 40%]  (Sampling)
+#> Chain 1: Iteration: 2500 / 5000 [ 50%]  (Sampling)
+#> Chain 1: Iteration: 3000 / 5000 [ 60%]  (Sampling)
+#> Chain 1: Iteration: 3500 / 5000 [ 70%]  (Sampling)
+#> Chain 1: Iteration: 4000 / 5000 [ 80%]  (Sampling)
+#> Chain 1: Iteration: 4500 / 5000 [ 90%]  (Sampling)
+#> Chain 1: Iteration: 5000 / 5000 [100%]  (Sampling)
+#> Chain 1: 
+#> Chain 1:  Elapsed Time: 17.494 seconds (Warm-up)
+#> Chain 1:                100.35 seconds (Sampling)
+#> Chain 1:                117.844 seconds (Total)
+#> Chain 1:
+```
+
+``` r
+model
+#> Hierarchical Bayesian model 
 #> 
-#> Coefficients:
-#> (Intercept)   I(age^1.5)   I(age^1.6)  
-#>    -3.61083      0.12443     -0.07656  
+#> Input type:  aggregated 
+#> Model:  Farrington model with 3 parameters 
 #> 
-#> Degrees of Freedom: 85 Total (i.e. Null);  83 Residual
-#> Null Deviance:       1320 
-#> Residual Deviance: 81.6  AIC: 361.2
+#> Fitted parameters:
+#>  alpha1 = 0.1397 (95% CrI [0.129, 0.1521], sd = 0.005927)
+#>  alpha2 = 0.199 (95% CrI [0.1848, 0.2181], sd = 0.008454)
+#>  alpha3 = 0.009017 (95% CrI [0.000252, 0.02798], sd = 0.007503)
+plot(model)
 ```
 
+![](parametric_model_files/figure-html/unnamed-chunk-11-1.png)
+
+### Log-logistic
+
+**Proposed approach**
+
+The model for seroprevalence is as followed
+
+\\ \pi(a) = \frac{\beta a^\alpha}{1 + \beta a^\alpha}, \text{ } \alpha,
+\beta \> 0 \\
+
+The likelihood is specified to be the same as Farrington model (\\y_i
+\sim Bin(n_i, \pi_i)\\) with
+
+\\ \text{logit}(\pi(a)) = \alpha_2 + \alpha_1\log(a) \\
+
+- Where \\\alpha_2 = \text{log}(\beta)\\
+
+The prior model of \\\alpha_1\\ is specified as \\\alpha_1 \sim
+\text{truncated } \mathcal{N}(\mu_1, \tau_1)\\ with flat hyperprior as
+in Farrington model
+
+\\\beta\\ is constrained to be positive by specifying \\\alpha_2 \sim
+\mathcal{N}(\mu_2, \tau_2)\\
+
+The full conditional distribution of \\\alpha_1\\ is thus
+
+\\ P(\alpha_1\|\alpha_2) \propto -\frac{1}{\tau_1} \text{exp}
+(\frac{1}{2 \tau_1^2} (\alpha_1 - \mu_1)^2) \prod\_{i=1}^m
+\text{Bin}(y_i\|n_i,\pi(a_i, \alpha_1, \alpha_2) ) \\
+
+And \\\alpha_2\\ can be derived in the same way
+
+Refer to `Chapter 10.3.3` of the book by Hens et al.
+([2012](#ref-Hens2012)) for a more detailed explanation of the method.
+
 **Fitting data**
 
-Use
-[`fp_model()`](https://oucru-modelling.github.io/serosv/reference/fp_model.md)
-to fit a fractional polynomial model
+To fit Log-logistic model, use
+[`hierarchical_bayesian_model()`](https://oucru-modelling.github.io/serosv/reference/hierarchical_bayesian_model.md)
+and define `type = "log_logistic"`
+
+``` r
+df <- rubella_uk_1986_1987
+model <- hierarchical_bayesian_model(df, type="log_logistic")
+#> 
+#> SAMPLING FOR MODEL 'log_logistic' NOW (CHAIN 1).
+#> Chain 1: 
+#> Chain 1: Gradient evaluation took 6.5e-05 seconds
+#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.65 seconds.
+#> Chain 1: Adjust your expectations accordingly!
+#> Chain 1: 
+#> Chain 1: 
+#> Chain 1: Iteration:    1 / 5000 [  0%]  (Warmup)
+#> Chain 1: Iteration:  500 / 5000 [ 10%]  (Warmup)
+#> Chain 1: Iteration: 1000 / 5000 [ 20%]  (Warmup)
+#> Chain 1: Iteration: 1500 / 5000 [ 30%]  (Warmup)
+#> Chain 1: Iteration: 1501 / 5000 [ 30%]  (Sampling)
+#> Chain 1: Iteration: 2000 / 5000 [ 40%]  (Sampling)
+#> Chain 1: Iteration: 2500 / 5000 [ 50%]  (Sampling)
+#> Chain 1: Iteration: 3000 / 5000 [ 60%]  (Sampling)
+#> Chain 1: Iteration: 3500 / 5000 [ 70%]  (Sampling)
+#> Chain 1: Iteration: 4000 / 5000 [ 80%]  (Sampling)
+#> Chain 1: Iteration: 4500 / 5000 [ 90%]  (Sampling)
+#> Chain 1: Iteration: 5000 / 5000 [100%]  (Sampling)
+#> Chain 1: 
+#> Chain 1:  Elapsed Time: 4.337 seconds (Warm-up)
+#> Chain 1:                6.096 seconds (Sampling)
+#> Chain 1:                10.433 seconds (Total)
+#> Chain 1:
+```
 
 ``` r
-model <- fp_model(hav, p=c(1.5, 1.6), link="cloglog")
+model
+#> Hierarchical Bayesian model 
+#> 
+#> Input type:  aggregated 
+#> Model:  Log-logistic model 
+#> 
+#> Fitted parameters:
+#>  alpha1 = 1.642 (95% CrI [1.539, 1.749], sd = 0.05377)
+#>  alpha2 = -2.958 (95% CrI [-3.223, -2.722], sd = 0.1275)
 plot(model)
 ```
 
-![](parametric_model_files/figure-html/unnamed-chunk-10-1.png)
+![](parametric_model_files/figure-html/unnamed-chunk-13-1.png)
 
 Grenfell, B. T., and R. M. Anderson. 1985. “The Estimation of
 Age-Related Rates of Infection from Case Notifications and Serological
 Data.” *The Journal of Hygiene* 95 (2): 419–36.
 <https://doi.org/10.1017/s0022172400062859>.
 
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. *Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective*.
+*Statistics for Biology and Health*. Springer New York.
+<https://doi.org/10.1007/978-1-4614-4072-7>.
+
 Muench, Hugo. 1934. “Derivation of Rates from Summation Data by the
 Catalytic Curve.” *Journal of the American Statistical Association* 29
 (185): 25–38. <https://doi.org/10.1080/01621459.1934.10502684>.
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+      <img src="../logo.svg" class="logo" alt=""><h1>Convert assay readings to titer</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/OUCRU-Modelling/serosv/blob/HEAD/vignettes/reading_to_titer.Rmd" class="external-link"><code>vignettes/reading_to_titer.Rmd</code></a></small>
+      <div class="d-none name"><code>reading_to_titer.Rmd</code></div>
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://oucru-modelling.github.io/serosv/">serosv</a></span><span class="op">)</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://tidyr.tidyverse.org" class="external-link">tidyr</a></span><span class="op">)</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://magrittr.tidyverse.org" class="external-link">magrittr</a></span><span class="op">)</span></span></code></pre></div>
+<div class="section level2">
+<h2 id="overview">Overview<a class="anchor" aria-label="anchor" href="#overview"></a>
+</h2>
+<p>Serological assays often produce raw signals such as optical density
+(OD) or fluorescence intensity. These measurements are not directly
+interpretable and must be converted to titers (e.g., IU/ml) using a
+calibration model.</p>
+<p><code>serosv</code> provides the function <code><a href="../reference/to_titer.html">to_titer()</a></code>
+perform this conversion by fitting a standard curve (per plate) and
+mapping assay readings to estimated titers</p>
+</div>
+<div class="section level2">
+<h2 id="convert-to-titer-workflow">Convert to titer workflow<a class="anchor" aria-label="anchor" href="#convert-to-titer-workflow"></a>
+</h2>
+<p>The input data is expected to have the following information</p>
+<ul>
+<li><p>Sample id containing id for samples <strong>or</strong> label for
+antitoxins</p></li>
+<li><p>Plate id the id of each plate (the standard curve will be fitted
+for each plate)</p></li>
+<li><p>Dilution level (e.g., 50, 100, 200)</p></li>
+<li><p>(Optionally) the columns for result for negative control at
+different dilution level (e.g., negative_50, negative_100, and
+negative_200)</p></li>
+</ul>
+<p>To demonstrate the use of this function, we will use a simulated
+dataset</p>
+<details><summary>Code for simulating data</summary><div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">1</span><span class="op">)</span></span>
+<span><span class="co"># Config</span></span>
+<span><span class="va">n_samples</span> <span class="op">&lt;-</span> <span class="fl">50</span></span>
+<span><span class="va">n_plates</span>  <span class="op">&lt;-</span> <span class="fl">5</span></span>
+<span><span class="va">dilutions</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">50</span>, <span class="fl">100</span>, <span class="fl">200</span><span class="op">)</span></span>
+<span><span class="va">ref_conc_antitoxin</span> <span class="op">&lt;-</span> <span class="fl">10</span></span>
+<span></span>
+<span><span class="co"># 4PL function: OD = D + (A - D) / (1 + 10^((log10(conc) - c) * b))</span></span>
+<span><span class="va">mock_4pl</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">conc</span>, <span class="va">A</span> <span class="op">=</span> <span class="fl">0</span>, <span class="va">B</span> <span class="op">=</span> <span class="fl">1.8</span>, <span class="va">C</span> <span class="op">=</span> <span class="op">-</span><span class="fl">2.5</span>, <span class="va">D</span> <span class="op">=</span> <span class="fl">4</span><span class="op">)</span> <span class="op">{</span></span>
+<span>  <span class="va">D</span> <span class="op">+</span> <span class="op">(</span><span class="va">A</span> <span class="op">-</span> <span class="va">D</span><span class="op">)</span> <span class="op">/</span> <span class="op">(</span><span class="fl">1</span> <span class="op">+</span> <span class="fl">10</span><span class="op">^</span><span class="op">(</span><span class="op">(</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log10</a></span><span class="op">(</span><span class="va">conc</span><span class="op">)</span> <span class="op">-</span> <span class="va">C</span><span class="op">)</span><span class="op">*</span><span class="va">B</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span></span>
+<span><span class="co"># Assign each sample a "true" concentration (UI/ml)</span></span>
+<span><span class="co"># ~60% positive (conc &gt; 0.1), ~40% negative</span></span>
+<span><span class="va">sample_meta</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a></span><span class="op">(</span></span>
+<span>  SAMPLE_ID    <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sprintf.html" class="external-link">sprintf</a></span><span class="op">(</span><span class="st">"S%03d"</span>, <span class="fl">1</span><span class="op">:</span><span class="va">n_samples</span><span class="op">)</span>,</span>
+<span>  PLATE_ID     <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="va">n_plates</span>, length.out <span class="op">=</span> <span class="va">n_samples</span><span class="op">)</span>,</span>
+<span>  true_conc    <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
+<span>    <span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="va">n_samples</span> <span class="op">*</span> <span class="fl">0.8</span><span class="op">)</span>, <span class="fl">0.15</span>, <span class="fl">100</span><span class="op">)</span>,   <span class="co"># positives</span></span>
+<span>    <span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="va">n_samples</span> <span class="op">*</span> <span class="fl">0.2</span><span class="op">)</span>, <span class="fl">0.001</span>, <span class="fl">0.09</span><span class="op">)</span> <span class="co"># negatives</span></span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/sample.html" class="external-link">sample</a></span><span class="op">(</span><span class="op">)</span>                               <span class="co"># shuffle</span></span>
+<span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Negative control concentrations per plate (one fixed OD per plate per dilution)</span></span>
+<span><span class="va">neg_ctrl_conc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a></span><span class="op">(</span></span>
+<span>  PLATE_ID      <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="va">n_plates</span>,</span>
+<span>  true_neg_conc <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="va">n_plates</span>, <span class="fl">0.001</span>, <span class="fl">0.04</span><span class="op">)</span>,</span>
+<span>  neg_conc_50   <span class="op">=</span> <span class="va">true_neg_conc</span><span class="op">/</span><span class="fl">50</span>,</span>
+<span>  neg_conc_100  <span class="op">=</span> <span class="va">true_neg_conc</span><span class="op">/</span><span class="fl">100</span>,</span>
+<span>  neg_conc_200  <span class="op">=</span> <span class="va">true_neg_conc</span><span class="op">/</span><span class="fl">200</span></span>
+<span><span class="op">)</span></span>
+<span><span class="va">neg_ctrl</span> <span class="op">&lt;-</span> <span class="va">neg_ctrl_conc</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span></span>
+<span>    NEGATIVE_50  <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu">mock_4pl</span><span class="op">(</span><span class="va">neg_conc_50</span><span class="op">)</span>  <span class="op">+</span> <span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="va">n_plates</span>, <span class="fl">0</span>, <span class="fl">0.003</span><span class="op">)</span>, <span class="fl">4</span><span class="op">)</span>,</span>
+<span>    NEGATIVE_100 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu">mock_4pl</span><span class="op">(</span><span class="va">neg_conc_100</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="va">n_plates</span>, <span class="fl">0</span>, <span class="fl">0.003</span><span class="op">)</span>, <span class="fl">4</span><span class="op">)</span>,</span>
+<span>    NEGATIVE_200 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu">mock_4pl</span><span class="op">(</span><span class="va">neg_conc_200</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="va">n_plates</span>, <span class="fl">0</span>, <span class="fl">0.003</span><span class="op">)</span>, <span class="fl">4</span><span class="op">)</span></span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">PLATE_ID</span>, <span class="va">NEGATIVE_50</span>, <span class="va">NEGATIVE_100</span>, <span class="va">NEGATIVE_200</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Antitoxin </span></span>
+<span><span class="va">antitoxin_conc</span> <span class="op">&lt;-</span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">50</span>, <span class="fl">100</span>, <span class="fl">200</span>, <span class="fl">400</span>, <span class="fl">800</span>, <span class="fl">1600</span>, <span class="fl">3200</span>, <span class="fl">6400</span>, <span class="fl">12800</span>, <span class="fl">25600</span>, <span class="fl">51200</span>, <span class="fl">102400</span><span class="op">)</span></span>
+<span><span class="va">antitoxin</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a></span><span class="op">(</span></span>
+<span>  SAMPLE_ID    <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"Antitoxin"</span>, <span class="va">n_plates</span><span class="op">)</span>,</span>
+<span>  true_conc    <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="va">ref_conc_antitoxin</span>, <span class="va">n_plates</span><span class="op">)</span>,</span>
+<span>  PLATE_ID <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="va">n_plates</span></span>
+<span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
+<span>  <span class="fu"><a href="https://tidyr.tidyverse.org/reference/expand.html" class="external-link">crossing</a></span><span class="op">(</span></span>
+<span>    DILUTION <span class="op">=</span> <span class="va">antitoxin_conc</span></span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span></span>
+<span>    eff_conc <span class="op">=</span> <span class="va">true_conc</span><span class="op">/</span><span class="va">DILUTION</span></span>
+<span>  <span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Generate one row per sample × dilution, compute OD via 4PL + noise</span></span>
+<span><span class="va">simulated_data</span> <span class="op">&lt;-</span> <span class="va">sample_meta</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://tidyr.tidyverse.org/reference/expand.html" class="external-link">crossing</a></span><span class="op">(</span>DILUTION <span class="op">=</span> <span class="va">dilutions</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span></span>
+<span>    eff_conc  <span class="op">=</span> <span class="va">true_conc</span> <span class="op">/</span> <span class="va">DILUTION</span></span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/bind_rows.html" class="external-link">bind_rows</a></span><span class="op">(</span></span>
+<span>    <span class="va">antitoxin</span></span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span></span>
+<span>    <span class="co"># Effective concentration decreases with dilution</span></span>
+<span>    od_mean   <span class="op">=</span> <span class="fu">mock_4pl</span><span class="op">(</span><span class="va">eff_conc</span>,</span>
+<span>                         B <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/context.html" class="external-link">n</a></span><span class="op">(</span><span class="op">)</span>, <span class="fl">1.7</span>, <span class="fl">1.9</span><span class="op">)</span>,</span>
+<span>                         C <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/context.html" class="external-link">n</a></span><span class="op">(</span><span class="op">)</span>, <span class="op">-</span><span class="fl">2.7</span>, <span class="op">-</span><span class="fl">2.4</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>    noise     <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/context.html" class="external-link">n</a></span><span class="op">(</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">0.008</span><span class="op">)</span>,</span>
+<span>    RESULT    <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">pmax</a></span><span class="op">(</span><span class="va">od_mean</span> <span class="op">+</span> <span class="va">noise</span>, <span class="fl">0.02</span><span class="op">)</span>, <span class="fl">4</span><span class="op">)</span>,</span>
+<span></span>
+<span>    <span class="co"># Blanks: clearly below result (reagent background only)</span></span>
+<span>    BLANK_1   <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/context.html" class="external-link">n</a></span><span class="op">(</span><span class="op">)</span>, <span class="fl">0.010</span>, <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">pmin</a></span><span class="op">(</span><span class="va">RESULT</span> <span class="op">-</span> <span class="fl">0.01</span>, <span class="fl">0.045</span><span class="op">)</span><span class="op">)</span>, <span class="fl">4</span><span class="op">)</span>,</span>
+<span>    BLANK_2   <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/context.html" class="external-link">n</a></span><span class="op">(</span><span class="op">)</span>, <span class="fl">0.010</span>, <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">pmin</a></span><span class="op">(</span><span class="va">RESULT</span> <span class="op">-</span> <span class="fl">0.01</span>, <span class="fl">0.045</span><span class="op">)</span><span class="op">)</span>, <span class="fl">4</span><span class="op">)</span>,</span>
+<span>    BLANK_3   <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/context.html" class="external-link">n</a></span><span class="op">(</span><span class="op">)</span>, <span class="fl">0.010</span>, <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">pmin</a></span><span class="op">(</span><span class="va">RESULT</span> <span class="op">-</span> <span class="fl">0.01</span>, <span class="fl">0.045</span><span class="op">)</span><span class="op">)</span>, <span class="fl">4</span><span class="op">)</span></span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate-joins.html" class="external-link">left_join</a></span><span class="op">(</span><span class="va">neg_ctrl</span>, by <span class="op">=</span> <span class="st">"PLATE_ID"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span></span>
+<span>    <span class="va">SAMPLE_ID</span>, <span class="va">PLATE_ID</span>, </span>
+<span>    <span class="va">DILUTION</span>, <span class="va">RESULT</span>,</span>
+<span>    <span class="co"># BLANK_1, BLANK_2, BLANK_3,</span></span>
+<span>    <span class="va">NEGATIVE_50</span>, <span class="va">NEGATIVE_100</span>, <span class="va">NEGATIVE_200</span></span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/arrange.html" class="external-link">arrange</a></span><span class="op">(</span><span class="va">PLATE_ID</span>, <span class="va">SAMPLE_ID</span>, <span class="va">DILUTION</span><span class="op">)</span></span></code></pre></div>
+</details><div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">simulated_data</span></span></code></pre></div>
+<pre><code><span><span class="co">## <span style="color: #949494;"># A tibble: 210 × 7</span></span></span>
+<span><span class="co">##    SAMPLE_ID PLATE_ID DILUTION RESULT NEGATIVE_50 NEGATIVE_100 NEGATIVE_200</span></span>
+<span><span class="co">##    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>        <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>       <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>        <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>        <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 1</span> Antitoxin        1       50  3.98        0.306       0.093<span style="text-decoration: underline;">9</span>       0.026<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 2</span> Antitoxin        1      100  4.00        0.306       0.093<span style="text-decoration: underline;">9</span>       0.026<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 3</span> Antitoxin        1      200  3.99        0.306       0.093<span style="text-decoration: underline;">9</span>       0.026<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 4</span> Antitoxin        1      400  3.96        0.306       0.093<span style="text-decoration: underline;">9</span>       0.026<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 5</span> Antitoxin        1      800  3.79        0.306       0.093<span style="text-decoration: underline;">9</span>       0.026<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 6</span> Antitoxin        1     <span style="text-decoration: underline;">1</span>600  3.51        0.306       0.093<span style="text-decoration: underline;">9</span>       0.026<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 7</span> Antitoxin        1     <span style="text-decoration: underline;">3</span>200  2.57        0.306       0.093<span style="text-decoration: underline;">9</span>       0.026<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 8</span> Antitoxin        1     <span style="text-decoration: underline;">6</span>400  0.750       0.306       0.093<span style="text-decoration: underline;">9</span>       0.026<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 9</span> Antitoxin        1    <span style="text-decoration: underline;">12</span>800  0.298       0.306       0.093<span style="text-decoration: underline;">9</span>       0.026<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">10</span> Antitoxin        1    <span style="text-decoration: underline;">25</span>600  0.135       0.306       0.093<span style="text-decoration: underline;">9</span>       0.026<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #949494;"># ℹ 200 more rows</span></span></span></code></pre>
+<p><strong>Standardize data</strong></p>
+<p>Before fitting the standard curve, the input data must be
+standardized into the required format. This is handled by the function
+<code><a href="../reference/standardize_data.html">standardize_data()</a></code>.</p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">standardized_dat</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/standardize_data.html">standardize_data</a></span><span class="op">(</span></span>
+<span>  <span class="va">simulated_data</span>,</span>
+<span>  plate_id_col <span class="op">=</span> <span class="st">"PLATE_ID"</span>, <span class="co"># specify the column for plate id</span></span>
+<span>  id_col <span class="op">=</span> <span class="st">"SAMPLE_ID"</span>, <span class="co"># specify the column for sample id</span></span>
+<span>  result_col <span class="op">=</span> <span class="st">"RESULT"</span>, <span class="co"># specify the column for raw assay readings</span></span>
+<span>  dilution_fct_col<span class="op">=</span> <span class="st">"DILUTION"</span>, <span class="co"># specify the column for dilution factor</span></span>
+<span>  antitoxin_label <span class="op">=</span> <span class="st">"Antitoxin"</span>, <span class="co"># specify the label for antitoxin (in the sample id column)</span></span>
+<span>  negative_col <span class="op">=</span> <span class="st">"^NEGATIVE_*"</span> <span class="co"># (optionally) specify the regex (i.e., pattern) for columns for negative controls</span></span>
+<span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">standardized_dat</span></span></code></pre></div>
+<pre><code><span><span class="co">## <span style="color: #949494;"># A tibble: 210 × 7</span></span></span>
+<span><span class="co">##    sample_id plate_id dilution_factors result negative_50 negative_100</span></span>
+<span><span class="co">##    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>        <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>            <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>       <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>        <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 1</span> antitoxin        1               50  3.98        0.306       0.093<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 2</span> antitoxin        1              100  4.00        0.306       0.093<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 3</span> antitoxin        1              200  3.99        0.306       0.093<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 4</span> antitoxin        1              400  3.96        0.306       0.093<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 5</span> antitoxin        1              800  3.79        0.306       0.093<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 6</span> antitoxin        1             <span style="text-decoration: underline;">1</span>600  3.51        0.306       0.093<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 7</span> antitoxin        1             <span style="text-decoration: underline;">3</span>200  2.57        0.306       0.093<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 8</span> antitoxin        1             <span style="text-decoration: underline;">6</span>400  0.750       0.306       0.093<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 9</span> antitoxin        1            <span style="text-decoration: underline;">12</span>800  0.298       0.306       0.093<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">10</span> antitoxin        1            <span style="text-decoration: underline;">25</span>600  0.135       0.306       0.093<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">## <span style="color: #949494;"># ℹ 200 more rows</span></span></span>
+<span><span class="co">## <span style="color: #949494;"># ℹ 1 more variable: negative_200 &lt;dbl&gt;</span></span></span></code></pre>
+<p><strong>Fitting data</strong></p>
+<p>The function <code><a href="../reference/to_titer.html">to_titer()</a></code> fits a standard curve for each
+plate and converts assay readings into titer estimates.</p>
+<p>The users can configure the following:</p>
+<ul>
+<li>
+<code>model</code> either the name of a built-in model
+(<code>"4PL"</code> for 4 parameters log-logistic model) or a named list
+with 2 items <code>"mod"</code> and <code>"quantify_ci"</code>. Section
+<a href="#custom-mod">Custom models</a> will provide more details on
+these functions.</li>
+</ul>
+<!-- --><ul>
+<li><p><code>ci</code> confidence interval of the titer estimate
+(default to <code>.95</code> for 95% CI)</p></li>
+<li><p><code>negative_control</code> whether to include the result for
+negative controls</p></li>
+<li><p>(optionally) <code>positive_threshold</code> the threshold of
+titer for sample to be considered positive. If provided, the output will
+include serostatus</p></li>
+</ul>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/to_titer.html">to_titer</a></span><span class="op">(</span></span>
+<span>  <span class="va">standardized_dat</span>,</span>
+<span>  model <span class="op">=</span> <span class="st">"4PL"</span>,</span>
+<span>  ci <span class="op">=</span> <span class="fl">0.95</span>,</span>
+<span>  positive_threshold <span class="op">=</span> <span class="fl">0.1</span>,</span>
+<span>  negative_control <span class="op">=</span> <span class="cn">TRUE</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+<p><strong>Output format</strong></p>
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">out</span></span></code></pre></div>
+<pre><code><span><span class="co">## <span style="color: #949494;"># A tibble: 5 × 8</span></span></span>
+<span><span class="co">## <span style="color: #949494;"># Groups:   plate_id [5]</span></span></span>
+<span><span class="co">##   plate_id data              antitoxin_df standard_curve_df standard_curve_func</span></span>
+<span><span class="co">##      <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;list&gt;</span>            <span style="color: #949494; font-style: italic;">&lt;list&gt;</span>       <span style="color: #949494; font-style: italic;">&lt;list&gt;</span>            <span style="color: #949494; font-style: italic;">&lt;list&gt;</span>             </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">1</span>        1 <span style="color: #949494;">&lt;tibble [42 × 6]&gt;</span> <span style="color: #949494;">&lt;tibble&gt;</span>     <span style="color: #949494;">&lt;df [512 × 4]&gt;</span>    <span style="color: #949494;">&lt;fn&gt;</span>               </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">2</span>        2 <span style="color: #949494;">&lt;tibble [42 × 6]&gt;</span> <span style="color: #949494;">&lt;tibble&gt;</span>     <span style="color: #949494;">&lt;df [512 × 4]&gt;</span>    <span style="color: #949494;">&lt;fn&gt;</span>               </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">3</span>        3 <span style="color: #949494;">&lt;tibble [42 × 6]&gt;</span> <span style="color: #949494;">&lt;tibble&gt;</span>     <span style="color: #949494;">&lt;df [512 × 4]&gt;</span>    <span style="color: #949494;">&lt;fn&gt;</span>               </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">4</span>        4 <span style="color: #949494;">&lt;tibble [42 × 6]&gt;</span> <span style="color: #949494;">&lt;tibble&gt;</span>     <span style="color: #949494;">&lt;df [512 × 4]&gt;</span>    <span style="color: #949494;">&lt;fn&gt;</span>               </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">5</span>        5 <span style="color: #949494;">&lt;tibble [42 × 6]&gt;</span> <span style="color: #949494;">&lt;tibble&gt;</span>     <span style="color: #949494;">&lt;df [512 × 4]&gt;</span>    <span style="color: #949494;">&lt;fn&gt;</span>               </span></span>
+<span><span class="co">## <span style="color: #949494;"># ℹ 3 more variables: std_crv_midpoint &lt;dbl&gt;, processed_data &lt;list&gt;,</span></span></span>
+<span><span class="co">## <span style="color: #949494;">#   negative_control &lt;list&gt;</span></span></span></code></pre>
+<p>The output is a nested tibble with the following columns</p>
+<ul>
+<li><p><code>plate_id</code> ids of the plate</p></li>
+<li><p><code>data</code> list of <code>data.frame</code>s containing the
+input samples data corresponding to each plate</p></li>
+<li><p><code>antitoxin_df</code> list of <code>data.frame</code>s
+containing the input reference data corresponding to each plate</p></li>
+<li><p><code>standard_curve_func</code> list of functions mapping from
+assay reading to titer for each plate</p></li>
+<li><p><code>std_crv_midpoint</code> midpoint of the standard curve, for
+qualitative analysis</p></li>
+<li><p><code>processed_data</code> list of <code>tibble</code>s
+containing samples with titer estimates (lower, median, upper)</p></li>
+<li><p><code>negative_control</code> list of <code>tibble</code>s
+containing negative control check results (if
+<code>negative_control=TRUE</code>)</p></li>
+</ul>
+<p>To access the estimated titers, simply unnest the
+<code>processed_data</code> column.</p>
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">out</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">plate_id</span>, <span class="va">processed_data</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
+<span>  <span class="fu"><a href="https://tidyr.tidyverse.org/reference/unnest.html" class="external-link">unnest</a></span><span class="op">(</span><span class="va">processed_data</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">plate_id</span>, <span class="va">sample_id</span>, <span class="va">result</span>, <span class="va">lower</span>, <span class="va">median</span>, <span class="va">upper</span>, <span class="va">positive</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## <span style="color: #949494;"># A tibble: 150 × 7</span></span></span>
+<span><span class="co">## <span style="color: #949494;"># Groups:   plate_id [5]</span></span></span>
+<span><span class="co">##    plate_id sample_id result lower median upper positive</span></span>
+<span><span class="co">##       <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span>   </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 1</span>        1 S001        4.00 -<span style="color: #BB0000;">1.71</span>     <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span> TRUE    </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 2</span>        1 S001        4.00 -<span style="color: #BB0000;">1.73</span>     <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span> TRUE    </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 3</span>        1 S001        4.00 -<span style="color: #BB0000;">1.68</span>     <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span> TRUE    </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 4</span>        1 S006        4.02 -<span style="color: #BB0000;">1.54</span>     <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span> TRUE    </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 5</span>        1 S006        4.00 -<span style="color: #BB0000;">1.73</span>     <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span> TRUE    </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 6</span>        1 S006        3.99 -<span style="color: #BB0000;">1.78</span>     <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span> TRUE    </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 7</span>        1 S011        4.00 -<span style="color: #BB0000;">1.74</span>     <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span> TRUE    </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 8</span>        1 S011        3.99 -<span style="color: #BB0000;">1.79</span>     <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span> TRUE    </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;"> 9</span>        1 S011        4.00 -<span style="color: #BB0000;">1.71</span>     <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span> TRUE    </span></span>
+<span><span class="co">## <span style="color: #BCBCBC;">10</span>        1 S016        4.00 -<span style="color: #BB0000;">1.70</span>     <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span> TRUE    </span></span>
+<span><span class="co">## <span style="color: #949494;"># ℹ 140 more rows</span></span></span></code></pre>
+<p>The columns for titer estimates are</p>
+<ul>
+<li><p><code>lower</code> lower bound of the confidence interval for the
+titer estimate.</p></li>
+<li><p><code>median</code> median (predicted) titer value.</p></li>
+<li><p><code>upper</code> upper bound of the confidence interval for
+titer estimates</p></li>
+</ul>
+<p><strong>Visualize standard curves</strong></p>
+<p>Standard curves can be visualized using the function
+<code><a href="../reference/plot_standard_curve.html">plot_standard_curve()</a></code></p>
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># visualize standard curves with datapoints for antitoxin</span></span>
+<span><span class="fu"><a href="../reference/plot_standard_curve.html">plot_standard_curve</a></span><span class="op">(</span><span class="va">out</span>, facet<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<p><img src="reading_to_titer_files/figure-html/unnamed-chunk-6-1.png" class="r-plt" width="700"></p>
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># set facet to FALSE to view all the curves together</span></span>
+<span><span class="fu"><a href="../reference/plot_standard_curve.html">plot_standard_curve</a></span><span class="op">(</span><span class="va">out</span>, facet<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
+<p><img src="reading_to_titer_files/figure-html/unnamed-chunk-6-2.png" class="r-plt" width="700"></p>
+<p>Positive threshold at different dilutions and also be added to the
+plot using <code>add_threshold()</code> function, note that this only
+works when <code>facet=FALSE</code></p>
+<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># set facet to FALSE to view all the curves together</span></span>
+<span><span class="fu"><a href="../reference/plot_standard_curve.html">plot_standard_curve</a></span><span class="op">(</span><span class="va">out</span>, facet<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="../reference/add_thresholds.html">add_thresholds</a></span><span class="op">(</span></span>
+<span>    dilution_factors <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">50</span>, <span class="fl">100</span>, <span class="fl">200</span><span class="op">)</span>,</span>
+<span>    positive_threshold <span class="op">=</span> <span class="fl">0.1</span></span>
+<span>  <span class="op">)</span></span></code></pre></div>
+<p><img src="reading_to_titer_files/figure-html/unnamed-chunk-7-1.png" class="r-plt" width="700"></p>
+<p><strong>Visualizing estimate availability</strong></p>
+<p>The function <code><a href="../reference/plot_titer_qc.html">plot_titer_qc()</a></code> visualizes whether titer
+estimates can be computed for each sample across the tested
+dilutions.</p>
+<p>Each sample is represented as a grid of size
+<code>n_estimates × n_dilutions</code>, where the cell color indicates
+estimate availability:</p>
+<ul>
+<li><p><strong>Green</strong>: estimate available</p></li>
+<li><p><strong>Orange</strong>: assay reading is too low to
+estimate</p></li>
+<li><p><strong>Red</strong>: assay reading is too high to
+estimate</p></li>
+</ul>
+<p>The sample grids are arranged by plate, with each column
+corresponding to a single plate and containing samples from that
+plate.</p>
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/plot_titer_qc.html">plot_titer_qc</a></span><span class="op">(</span></span>
+<span>  <span class="va">out</span>,</span>
+<span>  n_plates <span class="op">=</span> <span class="cn">NULL</span>, <span class="co"># maximum number of plates to visualize, if NULL then plot all</span></span>
+<span>  n_samples <span class="op">=</span> <span class="fl">10</span>, <span class="co"># maximum number of samples per plate to visualize</span></span>
+<span>  n_dilutions <span class="op">=</span> <span class="fl">3</span> <span class="co"># number of dilutions used for testing</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+<p><img src="reading_to_titer_files/figure-html/unnamed-chunk-8-1.png" class="r-plt" width="700"></p>
+</div>
+<div class="section level2">
+<h2 id="custom-mod">Custom models<a class="anchor" aria-label="anchor" href="#custom-mod"></a>
+</h2>
+<p>The users can also define their own model for fitting the standard
+curve and a function for quantifying the confidence intervals.</p>
+<p>To do this, define the <code>model</code> argument of
+<code><a href="../reference/to_titer.html">serosv::to_titer()</a></code> as a named list of 2 items where:</p>
+<ul>
+<li><p><code>mod</code> is a function that takes a <code>df</code> as
+input and return a fitted standard curve model</p></li>
+<li>
+<p><code>quantify_ci</code> is a function to compute the confidence
+intervals, with the following required arguments</p>
+<ul>
+<li><p><code>object</code> the fitted model</p></li>
+<li><p><code>newdata</code> the new predictor values for which
+predictions are made</p></li>
+<li><p><code>nb</code> the number of samples (required for
+bootstrapping, but may be unused otherwise)</p></li>
+<li><p><code>alpha</code> the significance level</p></li>
+</ul>
+<p><code>quantify_ci</code> must return a data.frame/tibble with the
+following columns:</p>
+<ul>
+<li><p><code>lower</code> lower bound of the confidence interval for the
+titer estimate.</p></li>
+<li><p><code>median</code> median (predicted) titer value.</p></li>
+<li><p><code>upper</code> upper bound of the confidence interval for
+titer estimate.</p></li>
+</ul>
+</li>
+</ul>
+<p><strong>Example</strong></p>
+<p>For demonstration purpose, below is an example for the
+<code>mod</code> and <code>quantify_ci</code> functions for a 4PL
+model</p>
+<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="http://mvtnorm.R-forge.R-project.org" class="external-link">mvtnorm</a></span><span class="op">)</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://purrr.tidyverse.org/" class="external-link">purrr</a></span><span class="op">)</span></span>
+<span><span class="co"># custom model function</span></span>
+<span><span class="va">custom_4PL</span> <span class="op">&lt;-</span> <span class="kw">function</span> <span class="op">(</span><span class="va">df</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="va">good_guess4PL</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, <span class="va">eps</span> <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span> <span class="op">{</span></span>
+<span>    <span class="va">nb_rep</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/unique.html" class="external-link">unique</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/table.html" class="external-link">table</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span></span>
+<span>    <span class="va">the_order</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/order.html" class="external-link">order</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span>    <span class="va">x</span> <span class="op">&lt;-</span> <span class="va">x</span><span class="op">[</span><span class="va">the_order</span><span class="op">]</span></span>
+<span>    <span class="va">y</span> <span class="op">&lt;-</span> <span class="va">y</span><span class="op">[</span><span class="va">the_order</span><span class="op">]</span></span>
+<span>    <span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">min</a></span><span class="op">(</span><span class="va">y</span><span class="op">)</span></span>
+<span>    <span class="va">d</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">y</span><span class="op">)</span></span>
+<span>    <span class="va">c</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/approxfun.html" class="external-link">approx</a></span><span class="op">(</span><span class="va">y</span>, <span class="va">x</span>, <span class="op">(</span><span class="va">d</span> <span class="op">-</span> <span class="va">a</span><span class="op">)</span><span class="op">/</span><span class="fl">2</span>, ties <span class="op">=</span> <span class="st">"ordered"</span><span class="op">)</span><span class="op">$</span><span class="va">y</span></span>
+<span>    <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>a <span class="op">=</span> <span class="va">a</span>, c <span class="op">=</span> <span class="va">c</span>, d <span class="op">=</span> <span class="va">d</span>, b <span class="op">=</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/approxfun.html" class="external-link">approx</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, <span class="va">c</span> <span class="op">+</span> <span class="va">eps</span>, ties <span class="op">=</span> <span class="st">"ordered"</span><span class="op">)</span><span class="op">$</span><span class="va">y</span> <span class="op">-</span> </span>
+<span>        <span class="fu"><a href="https://rdrr.io/r/stats/approxfun.html" class="external-link">approx</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, <span class="va">c</span> <span class="op">-</span> <span class="va">eps</span>, ties <span class="op">=</span> <span class="st">"ordered"</span><span class="op">)</span><span class="op">$</span><span class="va">y</span><span class="op">)</span><span class="op">/</span><span class="op">(</span><span class="fl">2</span> <span class="op">*</span> <span class="va">eps</span><span class="op">)</span><span class="op">)</span></span>
+<span>  <span class="op">}</span></span>
+<span>  </span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/nls.html" class="external-link">nls</a></span><span class="op">(</span></span>
+<span>    <span class="va">result</span> <span class="op">~</span> <span class="va">d</span> <span class="op">+</span> <span class="op">(</span><span class="va">a</span> <span class="op">-</span> <span class="va">d</span><span class="op">)</span> <span class="op">/</span> <span class="op">(</span><span class="fl">1</span> <span class="op">+</span> <span class="fl">10</span> <span class="op">^</span> <span class="op">(</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log10</a></span><span class="op">(</span><span class="va">concentration</span><span class="op">)</span> <span class="op">-</span> <span class="va">c</span><span class="op">)</span> <span class="op">*</span> <span class="va">b</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>    data <span class="op">=</span> <span class="va">df</span>,</span>
+<span>    start <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/with.html" class="external-link">with</a></span><span class="op">(</span><span class="va">df</span>, <span class="fu">good_guess4PL</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log10</a></span><span class="op">(</span><span class="va">concentration</span><span class="op">)</span>, <span class="va">result</span><span class="op">)</span><span class="op">)</span></span>
+<span>  <span class="op">)</span></span>
+<span><span class="op">}</span></span>
+<span></span>
+<span><span class="co"># custom quantify CI function for the model</span></span>
+<span><span class="va">custom_quantify_ci</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">object</span>, <span class="va">newdata</span>, <span class="va">nb</span> <span class="op">=</span> <span class="fl">9999</span>, <span class="va">alpha</span> <span class="op">=</span> <span class="fl">0.025</span><span class="op">)</span><span class="op">{</span></span>
+<span>  <span class="va">rowsplit</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">df</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/split.html" class="external-link">split</a></span><span class="op">(</span><span class="va">df</span>, <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">df</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span>  <span class="va">nb</span> <span class="op">|&gt;</span></span>
+<span>    <span class="fu"><a href="https://rdrr.io/pkg/mvtnorm/man/Mvnorm.html" class="external-link">rmvnorm</a></span><span class="op">(</span>mean <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/coef.html" class="external-link">coef</a></span><span class="op">(</span><span class="va">object</span><span class="op">)</span>, sigma <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/vcov.html" class="external-link">vcov</a></span><span class="op">(</span><span class="va">object</span><span class="op">)</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span>    <span class="fu"><a href="https://rdrr.io/r/base/as.data.frame.html" class="external-link">as.data.frame</a></span><span class="op">(</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span>    <span class="fu">rowsplit</span><span class="op">(</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span>    <span class="fu"><a href="https://purrr.tidyverse.org/reference/map.html" class="external-link">map</a></span><span class="op">(</span><span class="va">as.list</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span>    <span class="fu"><a href="https://purrr.tidyverse.org/reference/map.html" class="external-link">map</a></span><span class="op">(</span><span class="op">~</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">.x</span>, <span class="va">newdata</span><span class="op">)</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span>    <span class="fu"><a href="https://purrr.tidyverse.org/reference/map_dfr.html" class="external-link">map_dfc</a></span><span class="op">(</span><span class="va">eval</span>, expr <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/parse.html" class="external-link">parse</a></span><span class="op">(</span>text <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">as.character</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/formula.html" class="external-link">formula</a></span><span class="op">(</span><span class="va">object</span><span class="op">)</span><span class="op">)</span><span class="op">[</span><span class="fl">3</span><span class="op">]</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>    <span class="fu"><a href="https://rdrr.io/r/base/apply.html" class="external-link">apply</a></span><span class="op">(</span><span class="fl">1</span>, <span class="va">quantile</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">alpha</span>, <span class="fl">.5</span>, <span class="fl">1</span> <span class="op">-</span> <span class="va">alpha</span><span class="op">)</span><span class="op">)</span>  <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>    <span class="fu"><a href="https://rdrr.io/r/base/t.html" class="external-link">t</a></span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>  <span class="fu"><a href="https://rdrr.io/r/base/as.data.frame.html" class="external-link">as.data.frame</a></span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>    <span class="fu"><a href="https://rdrr.io/r/stats/setNames.html" class="external-link">setNames</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"lower"</span>, <span class="st">"median"</span>, <span class="st">"upper"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="op">}</span></span></code></pre></div>
+<p>The custom model can be provided as followed</p>
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># use the custom model and quantify ci function</span></span>
+<span><span class="va">custom_mod</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
+<span>  <span class="st">"mod"</span> <span class="op">=</span> <span class="va">custom_4PL</span>,</span>
+<span>  <span class="st">"quantify_ci"</span> <span class="op">=</span> <span class="va">custom_quantify_ci</span></span>
+<span><span class="op">)</span></span>
+<span><span class="va">custom_mod_out</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/to_titer.html">to_titer</a></span><span class="op">(</span></span>
+<span>  <span class="va">standardized_dat</span>,</span>
+<span>  model <span class="op">=</span> <span class="va">custom_mod</span>,</span>
+<span>  positive_threshold <span class="op">=</span> <span class="fl">0.1</span>, ci <span class="op">=</span> <span class="fl">0.95</span>,</span>
+<span>  negative_control <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<p>Visualize standard curves</p>
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/plot_standard_curve.html">plot_standard_curve</a></span><span class="op">(</span><span class="va">custom_mod_out</span>, facet<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<p><img src="reading_to_titer_files/figure-html/unnamed-chunk-11-1.png" class="r-plt" width="700"></p>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
+</div>
+
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
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diff --git a/docs/articles/reading_to_titer.md b/docs/articles/reading_to_titer.md
new file mode 100644
index 0000000..71674ff
--- /dev/null
+++ b/docs/articles/reading_to_titer.md
@@ -0,0 +1,456 @@
+# Convert assay readings to titer
+
+``` r
+library(serosv)
+library(dplyr)
+library(tidyr)
+library(magrittr)
+```
+
+## Overview
+
+Serological assays often produce raw signals such as optical density
+(OD) or fluorescence intensity. These measurements are not directly
+interpretable and must be converted to titers (e.g., IU/ml) using a
+calibration model.
+
+`serosv` provides the function
+[`to_titer()`](https://oucru-modelling.github.io/serosv/reference/to_titer.md)
+perform this conversion by fitting a standard curve (per plate) and
+mapping assay readings to estimated titers
+
+## Convert to titer workflow
+
+The input data is expected to have the following information
+
+- Sample id containing id for samples **or** label for antitoxins
+
+- Plate id the id of each plate (the standard curve will be fitted for
+  each plate)
+
+- Dilution level (e.g., 50, 100, 200)
+
+- (Optionally) the columns for result for negative control at different
+  dilution level (e.g., negative_50, negative_100, and negative_200)
+
+To demonstrate the use of this function, we will use a simulated dataset
+
+Code for simulating data
+
+``` r
+set.seed(1)
+# Config
+n_samples <- 50
+n_plates  <- 5
+dilutions <- c(50, 100, 200)
+ref_conc_antitoxin <- 10
+
+# 4PL function: OD = D + (A - D) / (1 + 10^((log10(conc) - c) * b))
+mock_4pl <- function(conc, A = 0, B = 1.8, C = -2.5, D = 4) {
+  D + (A - D) / (1 + 10^(( log10(conc) - C)*B))
+}
+
+# Assign each sample a "true" concentration (UI/ml)
+# ~60% positive (conc > 0.1), ~40% negative
+sample_meta <- tibble(
+  SAMPLE_ID    = sprintf("S%03d", 1:n_samples),
+  PLATE_ID     = rep(1:n_plates, length.out = n_samples),
+  true_conc    = c(
+    runif(round(n_samples * 0.8), 0.15, 100),   # positives
+    runif(round(n_samples * 0.2), 0.001, 0.09) # negatives
+  ) %>% sample()                               # shuffle
+)
+
+# Negative control concentrations per plate (one fixed OD per plate per dilution)
+neg_ctrl_conc <- tibble(
+  PLATE_ID      = 1:n_plates,
+  true_neg_conc = runif(n_plates, 0.001, 0.04),
+  neg_conc_50   = true_neg_conc/50,
+  neg_conc_100  = true_neg_conc/100,
+  neg_conc_200  = true_neg_conc/200
+)
+neg_ctrl <- neg_ctrl_conc %>%
+  mutate(
+    NEGATIVE_50  = round(mock_4pl(neg_conc_50)  + rnorm(n_plates, 0, 0.003), 4),
+    NEGATIVE_100 = round(mock_4pl(neg_conc_100) + rnorm(n_plates, 0, 0.003), 4),
+    NEGATIVE_200 = round(mock_4pl(neg_conc_200) + rnorm(n_plates, 0, 0.003), 4)
+  ) %>%
+  select(PLATE_ID, NEGATIVE_50, NEGATIVE_100, NEGATIVE_200)
+
+# Antitoxin 
+antitoxin_conc <-  c(50, 100, 200, 400, 800, 1600, 3200, 6400, 12800, 25600, 51200, 102400)
+antitoxin <- tibble(
+  SAMPLE_ID    = rep("Antitoxin", n_plates),
+  true_conc    = rep(ref_conc_antitoxin, n_plates),
+  PLATE_ID = 1:n_plates
+) %>% 
+  crossing(
+    DILUTION = antitoxin_conc
+  ) %>% 
+  mutate(
+    eff_conc = true_conc/DILUTION
+  )
+
+# Generate one row per sample × dilution, compute OD via 4PL + noise
+simulated_data <- sample_meta %>%
+  crossing(DILUTION = dilutions) %>%
+  mutate(
+    eff_conc  = true_conc / DILUTION
+  ) %>% 
+  bind_rows(
+    antitoxin
+  ) %>% 
+  mutate(
+    # Effective concentration decreases with dilution
+    od_mean   = mock_4pl(eff_conc,
+                         B = runif(n(), 1.7, 1.9),
+                         C = runif(n(), -2.7, -2.4)),
+    noise     = rnorm(n(), 0, 0.008),
+    RESULT    = round(pmax(od_mean + noise, 0.02), 4),
+
+    # Blanks: clearly below result (reagent background only)
+    BLANK_1   = round(runif(n(), 0.010, pmin(RESULT - 0.01, 0.045)), 4),
+    BLANK_2   = round(runif(n(), 0.010, pmin(RESULT - 0.01, 0.045)), 4),
+    BLANK_3   = round(runif(n(), 0.010, pmin(RESULT - 0.01, 0.045)), 4)
+  ) %>%
+  left_join(neg_ctrl, by = "PLATE_ID") %>%
+  select(
+    SAMPLE_ID, PLATE_ID, 
+    DILUTION, RESULT,
+    # BLANK_1, BLANK_2, BLANK_3,
+    NEGATIVE_50, NEGATIVE_100, NEGATIVE_200
+  ) %>%
+  arrange(PLATE_ID, SAMPLE_ID, DILUTION)
+```
+
+``` r
+simulated_data
+```
+
+    ## # A tibble: 210 × 7
+    ##    SAMPLE_ID PLATE_ID DILUTION RESULT NEGATIVE_50 NEGATIVE_100 NEGATIVE_200
+    ##    <chr>        <int>    <dbl>  <dbl>       <dbl>        <dbl>        <dbl>
+    ##  1 Antitoxin        1       50  3.98        0.306       0.0939       0.0269
+    ##  2 Antitoxin        1      100  4.00        0.306       0.0939       0.0269
+    ##  3 Antitoxin        1      200  3.99        0.306       0.0939       0.0269
+    ##  4 Antitoxin        1      400  3.96        0.306       0.0939       0.0269
+    ##  5 Antitoxin        1      800  3.79        0.306       0.0939       0.0269
+    ##  6 Antitoxin        1     1600  3.51        0.306       0.0939       0.0269
+    ##  7 Antitoxin        1     3200  2.57        0.306       0.0939       0.0269
+    ##  8 Antitoxin        1     6400  0.750       0.306       0.0939       0.0269
+    ##  9 Antitoxin        1    12800  0.298       0.306       0.0939       0.0269
+    ## 10 Antitoxin        1    25600  0.135       0.306       0.0939       0.0269
+    ## # ℹ 200 more rows
+
+**Standardize data**
+
+Before fitting the standard curve, the input data must be standardized
+into the required format. This is handled by the function
+[`standardize_data()`](https://oucru-modelling.github.io/serosv/reference/standardize_data.md).
+
+``` r
+standardized_dat <- standardize_data(
+  simulated_data,
+  plate_id_col = "PLATE_ID", # specify the column for plate id
+  id_col = "SAMPLE_ID", # specify the column for sample id
+  result_col = "RESULT", # specify the column for raw assay readings
+  dilution_fct_col= "DILUTION", # specify the column for dilution factor
+  antitoxin_label = "Antitoxin", # specify the label for antitoxin (in the sample id column)
+  negative_col = "^NEGATIVE_*" # (optionally) specify the regex (i.e., pattern) for columns for negative controls
+)
+
+standardized_dat
+```
+
+    ## # A tibble: 210 × 7
+    ##    sample_id plate_id dilution_factors result negative_50 negative_100
+    ##    <chr>        <int>            <dbl>  <dbl>       <dbl>        <dbl>
+    ##  1 antitoxin        1               50  3.98        0.306       0.0939
+    ##  2 antitoxin        1              100  4.00        0.306       0.0939
+    ##  3 antitoxin        1              200  3.99        0.306       0.0939
+    ##  4 antitoxin        1              400  3.96        0.306       0.0939
+    ##  5 antitoxin        1              800  3.79        0.306       0.0939
+    ##  6 antitoxin        1             1600  3.51        0.306       0.0939
+    ##  7 antitoxin        1             3200  2.57        0.306       0.0939
+    ##  8 antitoxin        1             6400  0.750       0.306       0.0939
+    ##  9 antitoxin        1            12800  0.298       0.306       0.0939
+    ## 10 antitoxin        1            25600  0.135       0.306       0.0939
+    ## # ℹ 200 more rows
+    ## # ℹ 1 more variable: negative_200 <dbl>
+
+**Fitting data**
+
+The function
+[`to_titer()`](https://oucru-modelling.github.io/serosv/reference/to_titer.md)
+fits a standard curve for each plate and converts assay readings into
+titer estimates.
+
+The users can configure the following:
+
+- `model` either the name of a built-in model (`"4PL"` for 4 parameters
+  log-logistic model) or a named list with 2 items `"mod"` and
+  `"quantify_ci"`. Section [Custom models](#custom-mod) will provide
+  more details on these functions.
+
+&nbsp;
+
+- `ci` confidence interval of the titer estimate (default to `.95` for
+  95% CI)
+
+- `negative_control` whether to include the result for negative controls
+
+- (optionally) `positive_threshold` the threshold of titer for sample to
+  be considered positive. If provided, the output will include
+  serostatus
+
+``` r
+out <- to_titer(
+  standardized_dat,
+  model = "4PL",
+  ci = 0.95,
+  positive_threshold = 0.1,
+  negative_control = TRUE
+)
+```
+
+**Output format**
+
+``` r
+out
+```
+
+    ## # A tibble: 5 × 8
+    ## # Groups:   plate_id [5]
+    ##   plate_id data              antitoxin_df standard_curve_df standard_curve_func
+    ##      <int> <list>            <list>       <list>            <list>             
+    ## 1        1 <tibble [42 × 6]> <tibble>     <df [512 × 4]>    <fn>               
+    ## 2        2 <tibble [42 × 6]> <tibble>     <df [512 × 4]>    <fn>               
+    ## 3        3 <tibble [42 × 6]> <tibble>     <df [512 × 4]>    <fn>               
+    ## 4        4 <tibble [42 × 6]> <tibble>     <df [512 × 4]>    <fn>               
+    ## 5        5 <tibble [42 × 6]> <tibble>     <df [512 × 4]>    <fn>               
+    ## # ℹ 3 more variables: std_crv_midpoint <dbl>, processed_data <list>,
+    ## #   negative_control <list>
+
+The output is a nested tibble with the following columns
+
+- `plate_id` ids of the plate
+
+- `data` list of `data.frame`s containing the input samples data
+  corresponding to each plate
+
+- `antitoxin_df` list of `data.frame`s containing the input reference
+  data corresponding to each plate
+
+- `standard_curve_func` list of functions mapping from assay reading to
+  titer for each plate
+
+- `std_crv_midpoint` midpoint of the standard curve, for qualitative
+  analysis
+
+- `processed_data` list of `tibble`s containing samples with titer
+  estimates (lower, median, upper)
+
+- `negative_control` list of `tibble`s containing negative control check
+  results (if `negative_control=TRUE`)
+
+To access the estimated titers, simply unnest the `processed_data`
+column.
+
+``` r
+out %>% 
+  select(plate_id, processed_data) %>% 
+  unnest(processed_data) %>% 
+  select(plate_id, sample_id, result, lower, median, upper, positive)
+```
+
+    ## # A tibble: 150 × 7
+    ## # Groups:   plate_id [5]
+    ##    plate_id sample_id result lower median upper positive
+    ##       <int> <chr>      <dbl> <dbl>  <dbl> <dbl> <lgl>   
+    ##  1        1 S001        4.00 -1.71     NA    NA TRUE    
+    ##  2        1 S001        4.00 -1.73     NA    NA TRUE    
+    ##  3        1 S001        4.00 -1.68     NA    NA TRUE    
+    ##  4        1 S006        4.02 -1.54     NA    NA TRUE    
+    ##  5        1 S006        4.00 -1.73     NA    NA TRUE    
+    ##  6        1 S006        3.99 -1.78     NA    NA TRUE    
+    ##  7        1 S011        4.00 -1.74     NA    NA TRUE    
+    ##  8        1 S011        3.99 -1.79     NA    NA TRUE    
+    ##  9        1 S011        4.00 -1.71     NA    NA TRUE    
+    ## 10        1 S016        4.00 -1.70     NA    NA TRUE    
+    ## # ℹ 140 more rows
+
+The columns for titer estimates are
+
+- `lower` lower bound of the confidence interval for the titer estimate.
+
+- `median` median (predicted) titer value.
+
+- `upper` upper bound of the confidence interval for titer estimates
+
+**Visualize standard curves**
+
+Standard curves can be visualized using the function
+[`plot_standard_curve()`](https://oucru-modelling.github.io/serosv/reference/plot_standard_curve.md)
+
+``` r
+# visualize standard curves with datapoints for antitoxin
+plot_standard_curve(out, facet=TRUE)
+```
+
+![](reading_to_titer_files/figure-html/unnamed-chunk-6-1.png)
+
+``` r
+# set facet to FALSE to view all the curves together
+plot_standard_curve(out, facet=FALSE)
+```
+
+![](reading_to_titer_files/figure-html/unnamed-chunk-6-2.png)
+
+Positive threshold at different dilutions and also be added to the plot
+using `add_threshold()` function, note that this only works when
+`facet=FALSE`
+
+``` r
+# set facet to FALSE to view all the curves together
+plot_standard_curve(out, facet=FALSE) +
+  add_thresholds(
+    dilution_factors = c(50, 100, 200),
+    positive_threshold = 0.1
+  )
+```
+
+![](reading_to_titer_files/figure-html/unnamed-chunk-7-1.png)
+
+**Visualizing estimate availability**
+
+The function
+[`plot_titer_qc()`](https://oucru-modelling.github.io/serosv/reference/plot_titer_qc.md)
+visualizes whether titer estimates can be computed for each sample
+across the tested dilutions.
+
+Each sample is represented as a grid of size
+`n_estimates × n_dilutions`, where the cell color indicates estimate
+availability:
+
+- **Green**: estimate available
+
+- **Orange**: assay reading is too low to estimate
+
+- **Red**: assay reading is too high to estimate
+
+The sample grids are arranged by plate, with each column corresponding
+to a single plate and containing samples from that plate.
+
+``` r
+plot_titer_qc(
+  out,
+  n_plates = NULL, # maximum number of plates to visualize, if NULL then plot all
+  n_samples = 10, # maximum number of samples per plate to visualize
+  n_dilutions = 3 # number of dilutions used for testing
+)
+```
+
+![](reading_to_titer_files/figure-html/unnamed-chunk-8-1.png)
+
+## Custom models
+
+The users can also define their own model for fitting the standard curve
+and a function for quantifying the confidence intervals.
+
+To do this, define the `model` argument of
+[`serosv::to_titer()`](https://oucru-modelling.github.io/serosv/reference/to_titer.md)
+as a named list of 2 items where:
+
+- `mod` is a function that takes a `df` as input and return a fitted
+  standard curve model
+
+- `quantify_ci` is a function to compute the confidence intervals, with
+  the following required arguments
+
+  - `object` the fitted model
+
+  - `newdata` the new predictor values for which predictions are made
+
+  - `nb` the number of samples (required for bootstrapping, but may be
+    unused otherwise)
+
+  - `alpha` the significance level
+
+  `quantify_ci` must return a data.frame/tibble with the following
+  columns:
+
+  - `lower` lower bound of the confidence interval for the titer
+    estimate.
+
+  - `median` median (predicted) titer value.
+
+  - `upper` upper bound of the confidence interval for titer estimate.
+
+**Example**
+
+For demonstration purpose, below is an example for the `mod` and
+`quantify_ci` functions for a 4PL model
+
+``` r
+library(mvtnorm)
+library(purrr)
+# custom model function
+custom_4PL <- function (df){
+  good_guess4PL <- function(x, y, eps = 0.3) {
+    nb_rep <- unique(table(x))
+    the_order <- order(x)
+    x <- x[the_order]
+    y <- y[the_order]
+    a <- min(y)
+    d <- max(y)
+    c <- approx(y, x, (d - a)/2, ties = "ordered")$y
+    list(a = a, c = c, d = d, b = (approx(x, y, c + eps, ties = "ordered")$y - 
+        approx(x, y, c - eps, ties = "ordered")$y)/(2 * eps))
+  }
+  
+  nls(
+    result ~ d + (a - d) / (1 + 10 ^ ((log10(concentration) - c) * b)),
+    data = df,
+    start = with(df, good_guess4PL(log10(concentration), result))
+  )
+}
+
+# custom quantify CI function for the model
+custom_quantify_ci <- function(object, newdata, nb = 9999, alpha = 0.025){
+  rowsplit <- function(df) split(df, 1:nrow(df))
+
+  nb |>
+    rmvnorm(mean = coef(object), sigma = vcov(object)) |>
+    as.data.frame() |>
+    rowsplit() |>
+    map(as.list) |>
+    map(~ c(.x, newdata)) |>
+    map_dfc(eval, expr = parse(text = as.character(formula(object))[3])) %>%
+    apply(1, quantile, c(alpha, .5, 1 - alpha))  %>%
+    t() %>%  as.data.frame() %>%
+    setNames(c("lower", "median", "upper"))
+}
+```
+
+The custom model can be provided as followed
+
+``` r
+# use the custom model and quantify ci function
+custom_mod <- list(
+  "mod" = custom_4PL,
+  "quantify_ci" = custom_quantify_ci
+)
+custom_mod_out <- to_titer(
+  standardized_dat,
+  model = custom_mod,
+  positive_threshold = 0.1, ci = 0.95,
+  negative_control = TRUE)
+```
+
+Visualize standard curves
+
+``` r
+plot_standard_curve(custom_mod_out, facet=TRUE)
+```
+
+![](reading_to_titer_files/figure-html/unnamed-chunk-11-1.png)
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diff --git a/docs/articles/repeated_cross_sectional.html b/docs/articles/repeated_cross_sectional.html
index 6a96f70..ca404e8 100644
--- a/docs/articles/repeated_cross_sectional.html
+++ b/docs/articles/repeated_cross_sectional.html
@@ -11,7 +11,13 @@
 <link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png">
 <link rel="icon" sizes="any" href="../favicon.ico">
 <link rel="manifest" href="../site.webmanifest">
-<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
 <script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
 <link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
@@ -25,7 +31,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -39,15 +45,14 @@
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
 <li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="../articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="../articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
     <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
@@ -125,7 +130,18 @@ <h2 id="age-time-varying-model">Age-time varying model<a class="anchor" aria-lab
 <span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="op">-</span><span class="va">birthyr</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="va">survey_year</span> <span class="op">&gt;</span> <span class="fl">1966</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">age</span>, <span class="va">survey_year</span>, <span class="va">survey_time</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarize</a></span><span class="op">(</span>pos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">pos</span><span class="op">)</span>, tot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">tot</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span></span></code></pre></div>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarize</a></span><span class="op">(</span>pos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">pos</span><span class="op">)</span>, tot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">tot</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">tb_nl</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 6 × 5</span></span></span>
+<span><span class="co">#&gt;     age survey_year survey_time   pos    tot</span></span>
+<span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>       <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;date&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;int&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span>     6        <span style="text-decoration: underline;">1</span>970 1970-01-01    140  <span style="text-decoration: underline;">40</span>868</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span>     6        <span style="text-decoration: underline;">1</span>971 1971-01-01     55  <span style="text-decoration: underline;">17</span>874</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span>     6        <span style="text-decoration: underline;">1</span>972 1972-01-01      4   <span style="text-decoration: underline;">2</span>163</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span>     7        <span style="text-decoration: underline;">1</span>970 1970-01-01    328 <span style="text-decoration: underline;">105</span>960</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">5</span>     7        <span style="text-decoration: underline;">1</span>971 1971-01-01    308  <span style="text-decoration: underline;">96</span>326</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">6</span>     7        <span style="text-decoration: underline;">1</span>972 1972-01-01     11   <span style="text-decoration: underline;">2</span>807</span></span></code></pre></div>
 <p>The monotonization method can be specified via the
 <code>monotonize_method</code> argument, <code>serosv</code> currently
 supports 2 options:</p>
@@ -182,7 +198,31 @@ <h2 id="age-time-varying-model">Age-time varying model<a class="anchor" aria-lab
 <code>monotonized_sp</code></p></li>
 </ul>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">out_pava</span><span class="op">$</span><span class="va">out</span></span>
+<code class="sourceCode R"><span><span class="va">out_pava</span></span>
+<span><span class="co">#&gt; Age-time varying seroprevalence model </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Input type:  aggregated </span></span>
+<span><span class="co">#&gt; Grouping variable:  survey_year </span></span>
+<span><span class="co">#&gt; Monotonization method:  pava </span></span>
+<span><span class="co">#&gt; Monotonize across:  age group </span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 7 × 9</span></span></span>
+<span><span class="co">#&gt;   survey_year monotonized_info monotonized_ci_mod df       info   sp        </span></span>
+<span><span class="co">#&gt;         <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;list&gt;</span>           <span style="color: #949494; font-style: italic;">&lt;list&gt;</span>             <span style="color: #949494; font-style: italic;">&lt;list&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;list&gt;</span> <span style="color: #949494; font-style: italic;">&lt;list&gt;</span>    </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span>        <span style="text-decoration: underline;">1</span>967 <span style="color: #949494;">&lt;gam&gt;</span>            <span style="color: #949494;">&lt;named list [2]&gt;</span>   <span style="color: #949494;">&lt;tibble&gt;</span> <span style="color: #949494;">&lt;gam&gt;</span>  <span style="color: #949494;">&lt;dbl [5]&gt;</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span>        <span style="text-decoration: underline;">1</span>968 <span style="color: #949494;">&lt;gam&gt;</span>            <span style="color: #949494;">&lt;named list [2]&gt;</span>   <span style="color: #949494;">&lt;tibble&gt;</span> <span style="color: #949494;">&lt;gam&gt;</span>  <span style="color: #949494;">&lt;dbl [5]&gt;</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span>        <span style="text-decoration: underline;">1</span>969 <span style="color: #949494;">&lt;gam&gt;</span>            <span style="color: #949494;">&lt;named list [2]&gt;</span>   <span style="color: #949494;">&lt;tibble&gt;</span> <span style="color: #949494;">&lt;gam&gt;</span>  <span style="color: #949494;">&lt;dbl [6]&gt;</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span>        <span style="text-decoration: underline;">1</span>970 <span style="color: #949494;">&lt;gam&gt;</span>            <span style="color: #949494;">&lt;named list [2]&gt;</span>   <span style="color: #949494;">&lt;tibble&gt;</span> <span style="color: #949494;">&lt;gam&gt;</span>  <span style="color: #949494;">&lt;dbl [13]&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">5</span>        <span style="text-decoration: underline;">1</span>971 <span style="color: #949494;">&lt;gam&gt;</span>            <span style="color: #949494;">&lt;named list [2]&gt;</span>   <span style="color: #949494;">&lt;tibble&gt;</span> <span style="color: #949494;">&lt;gam&gt;</span>  <span style="color: #949494;">&lt;dbl [8]&gt;</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">6</span>        <span style="text-decoration: underline;">1</span>972 <span style="color: #949494;">&lt;gam&gt;</span>            <span style="color: #949494;">&lt;named list [2]&gt;</span>   <span style="color: #949494;">&lt;tibble&gt;</span> <span style="color: #949494;">&lt;gam&gt;</span>  <span style="color: #949494;">&lt;dbl [8]&gt;</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">7</span>        <span style="text-decoration: underline;">1</span>973 <span style="color: #949494;">&lt;gam&gt;</span>            <span style="color: #949494;">&lt;named list [2]&gt;</span>   <span style="color: #949494;">&lt;tibble&gt;</span> <span style="color: #949494;">&lt;gam&gt;</span>  <span style="color: #949494;">&lt;dbl [5]&gt;</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 3 more variables: foi &lt;list&gt;, monotonized_sp &lt;list&gt;, monotonized_foi &lt;list&gt;</span></span></span>
+<span><span class="va">out_scam</span></span>
+<span><span class="co">#&gt; Age-time varying seroprevalence model </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Input type:  aggregated </span></span>
+<span><span class="co">#&gt; Grouping variable:  survey_year </span></span>
+<span><span class="co">#&gt; Monotonization method:  scam </span></span>
+<span><span class="co">#&gt; Monotonize across:  birth cohort </span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 7 × 9</span></span></span>
 <span><span class="co">#&gt;   survey_year monotonized_info monotonized_ci_mod df       info   sp        </span></span>
 <span><span class="co">#&gt;         <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;list&gt;</span>           <span style="color: #949494; font-style: italic;">&lt;list&gt;</span>             <span style="color: #949494; font-style: italic;">&lt;list&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;list&gt;</span> <span style="color: #949494; font-style: italic;">&lt;list&gt;</span>    </span></span>
@@ -207,32 +247,22 @@ <h2 id="age-time-varying-model">Age-time varying model<a class="anchor" aria-lab
 <p><strong><em>Example:</em></strong> output for model with PAVA for
 monotonization</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">out_pava</span>, facet <span class="op">=</span> <span class="cn">TRUE</span>, modtype <span class="op">=</span> <span class="st">"non-monotonized"</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/lims.html" class="external-link">ylim</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0.07</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="co">#&gt; Scale for <span style="color: #00BB00;">y</span> is already present.</span></span>
-<span><span class="co">#&gt; Adding another scale for <span style="color: #00BB00;">y</span>, which will replace the existing scale.</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">out_pava</span>, facet <span class="op">=</span> <span class="cn">TRUE</span>, modtype <span class="op">=</span> <span class="st">"non-monotonized"</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/lims.html" class="external-link">ylim</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0.07</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p><img src="repeated_cross_sectional_files/figure-html/unnamed-chunk-5-1.png" class="r-plt" width="700"></p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">out_pava</span>, facet <span class="op">=</span> <span class="cn">TRUE</span>, modtype <span class="op">=</span> <span class="st">"monotonized"</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/lims.html" class="external-link">ylim</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0.07</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="co">#&gt; Scale for <span style="color: #00BB00;">y</span> is already present.</span></span>
-<span><span class="co">#&gt; Adding another scale for <span style="color: #00BB00;">y</span>, which will replace the existing scale.</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">out_pava</span>, facet <span class="op">=</span> <span class="cn">TRUE</span>, modtype <span class="op">=</span> <span class="st">"monotonized"</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/lims.html" class="external-link">ylim</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0.07</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p><img src="repeated_cross_sectional_files/figure-html/unnamed-chunk-5-2.png" class="r-plt" width="700"></p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">out_pava</span>, facet <span class="op">=</span> <span class="cn">FALSE</span>, modtype <span class="op">=</span> <span class="st">"monotonized"</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/lims.html" class="external-link">ylim</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0.07</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="co">#&gt; Scale for <span style="color: #00BB00;">y</span> is already present.</span></span>
-<span><span class="co">#&gt; Adding another scale for <span style="color: #00BB00;">y</span>, which will replace the existing scale.</span></span></code></pre></div>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">out_pava</span>, facet <span class="op">=</span> <span class="cn">FALSE</span>, modtype <span class="op">=</span> <span class="st">"monotonized"</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/lims.html" class="external-link">ylim</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0.07</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p><img src="repeated_cross_sectional_files/figure-html/unnamed-chunk-5-3.png" class="r-plt" width="700"></p>
 <p><strong><em>Example:</em></strong> output for model with SCAM for
 monotonization</p>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">out_scam</span>, facet <span class="op">=</span> <span class="cn">TRUE</span>, modtype <span class="op">=</span> <span class="st">"non-monotonized"</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/lims.html" class="external-link">ylim</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0.07</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="co">#&gt; Scale for <span style="color: #00BB00;">y</span> is already present.</span></span>
-<span><span class="co">#&gt; Adding another scale for <span style="color: #00BB00;">y</span>, which will replace the existing scale.</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">out_scam</span>, facet <span class="op">=</span> <span class="cn">TRUE</span>, modtype <span class="op">=</span> <span class="st">"non-monotonized"</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/lims.html" class="external-link">ylim</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0.07</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p><img src="repeated_cross_sectional_files/figure-html/unnamed-chunk-6-1.png" class="r-plt" width="700"></p>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">out_scam</span>, facet <span class="op">=</span> <span class="cn">TRUE</span>, modtype <span class="op">=</span> <span class="st">"monotonized"</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/lims.html" class="external-link">ylim</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0.07</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="co">#&gt; Scale for <span style="color: #00BB00;">y</span> is already present.</span></span>
-<span><span class="co">#&gt; Adding another scale for <span style="color: #00BB00;">y</span>, which will replace the existing scale.</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">out_scam</span>, facet <span class="op">=</span> <span class="cn">TRUE</span>, modtype <span class="op">=</span> <span class="st">"monotonized"</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/lims.html" class="external-link">ylim</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0.07</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p><img src="repeated_cross_sectional_files/figure-html/unnamed-chunk-6-2.png" class="r-plt" width="700"></p>
 </div>
   </main>
diff --git a/docs/articles/repeated_cross_sectional.md b/docs/articles/repeated_cross_sectional.md
index 1a2ab85..aa4bb01 100644
--- a/docs/articles/repeated_cross_sectional.md
+++ b/docs/articles/repeated_cross_sectional.md
@@ -47,6 +47,17 @@ tb_nl <- tb_nl_1966_1973 %>%
   filter(survey_year > 1966) %>% 
   group_by(age, survey_year, survey_time) %>% 
   summarize(pos = sum(pos), tot = sum(tot), .groups = "drop")
+
+head(tb_nl)
+#> # A tibble: 6 × 5
+#>     age survey_year survey_time   pos    tot
+#>   <int>       <dbl> <date>      <int>  <int>
+#> 1     6        1970 1970-01-01    140  40868
+#> 2     6        1971 1971-01-01     55  17874
+#> 3     6        1972 1972-01-01      4   2163
+#> 4     7        1970 1970-01-01    328 105960
+#> 5     7        1971 1971-01-01    308  96326
+#> 6     7        1972 1972-01-01     11   2807
 ```
 
 The monotonization method can be specified via the `monotonize_method`
@@ -105,7 +116,31 @@ where each row corresponds to a single survey period. The columns are:
 - `monotonized_foi` - estimated force of infection from `monotonized_sp`
 
 ``` r
-out_pava$out
+out_pava
+#> Age-time varying seroprevalence model 
+#> 
+#> Input type:  aggregated 
+#> Grouping variable:  survey_year 
+#> Monotonization method:  pava 
+#> Monotonize across:  age group 
+#> # A tibble: 7 × 9
+#>   survey_year monotonized_info monotonized_ci_mod df       info   sp        
+#>         <dbl> <list>           <list>             <list>   <list> <list>    
+#> 1        1967 <gam>            <named list [2]>   <tibble> <gam>  <dbl [5]> 
+#> 2        1968 <gam>            <named list [2]>   <tibble> <gam>  <dbl [5]> 
+#> 3        1969 <gam>            <named list [2]>   <tibble> <gam>  <dbl [6]> 
+#> 4        1970 <gam>            <named list [2]>   <tibble> <gam>  <dbl [13]>
+#> 5        1971 <gam>            <named list [2]>   <tibble> <gam>  <dbl [8]> 
+#> 6        1972 <gam>            <named list [2]>   <tibble> <gam>  <dbl [8]> 
+#> 7        1973 <gam>            <named list [2]>   <tibble> <gam>  <dbl [5]> 
+#> # ℹ 3 more variables: foi <list>, monotonized_sp <list>, monotonized_foi <list>
+out_scam
+#> Age-time varying seroprevalence model 
+#> 
+#> Input type:  aggregated 
+#> Grouping variable:  survey_year 
+#> Monotonization method:  scam 
+#> Monotonize across:  birth cohort 
 #> # A tibble: 7 × 9
 #>   survey_year monotonized_info monotonized_ci_mod df       info   sp        
 #>         <dbl> <list>           <list>             <list>   <list> <list>    
@@ -133,16 +168,12 @@ following configurations
 
 ``` r
 plot(out_pava, facet = TRUE, modtype = "non-monotonized") + ylim(c(0, 0.07))
-#> Scale for y is already present.
-#> Adding another scale for y, which will replace the existing scale.
 ```
 
 ![](repeated_cross_sectional_files/figure-html/unnamed-chunk-5-1.png)
 
 ``` r
 plot(out_pava, facet = TRUE, modtype = "monotonized") + ylim(c(0, 0.07))
-#> Scale for y is already present.
-#> Adding another scale for y, which will replace the existing scale.
 ```
 
 ![](repeated_cross_sectional_files/figure-html/unnamed-chunk-5-2.png)
@@ -150,8 +181,6 @@ plot(out_pava, facet = TRUE, modtype = "monotonized") + ylim(c(0, 0.07))
 ``` r
 
 plot(out_pava, facet = FALSE, modtype = "monotonized") + ylim(c(0, 0.07))
-#> Scale for y is already present.
-#> Adding another scale for y, which will replace the existing scale.
 ```
 
 ![](repeated_cross_sectional_files/figure-html/unnamed-chunk-5-3.png)
@@ -160,16 +189,12 @@ plot(out_pava, facet = FALSE, modtype = "monotonized") + ylim(c(0, 0.07))
 
 ``` r
 plot(out_scam, facet = TRUE, modtype = "non-monotonized") + ylim(c(0, 0.07))
-#> Scale for y is already present.
-#> Adding another scale for y, which will replace the existing scale.
 ```
 
 ![](repeated_cross_sectional_files/figure-html/unnamed-chunk-6-1.png)
 
 ``` r
 plot(out_scam, facet = TRUE, modtype = "monotonized") + ylim(c(0, 0.07))
-#> Scale for y is already present.
-#> Adding another scale for y, which will replace the existing scale.
 ```
 
 ![](repeated_cross_sectional_files/figure-html/unnamed-chunk-6-2.png)
diff --git a/docs/articles/repeated_cross_sectional_files/figure-html/unnamed-chunk-5-2.png b/docs/articles/repeated_cross_sectional_files/figure-html/unnamed-chunk-5-2.png
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index 0b31595..096d9e7 100644
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diff --git a/docs/articles/semiparametric_model.html b/docs/articles/semiparametric_model.html
index 3eeb3c8..306fdda 100644
--- a/docs/articles/semiparametric_model.html
+++ b/docs/articles/semiparametric_model.html
@@ -5,83 +5,72 @@
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@@ -99,23 +88,12 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
     
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://oucru-modelling.github.io/serosv/">serosv</a></span><span class="op">)</span></span>
-<span><span class="co">#&gt; Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when</span></span>
-<span><span class="co">#&gt; loading 'serosv'</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span>
-<span><span class="co">#&gt; Warning: package 'dplyr' was built under R version 4.3.1</span></span>
-<span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Attaching package: 'dplyr'</span></span>
-<span><span class="co">#&gt; The following objects are masked from 'package:stats':</span></span>
-<span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt;     filter, lag</span></span>
-<span><span class="co">#&gt; The following objects are masked from 'package:base':</span></span>
-<span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt;     intersect, setdiff, setequal, union</span></span></code></pre></div>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></code></pre></div>
 <div class="section level2">
 <h2 id="sec-penalized-splines">Penalized splines<a class="anchor" aria-label="anchor" href="#sec-penalized-splines"></a>
 </h2>
 <p><strong>Proposed model</strong></p>
-<details><summary><code> Penalized splines </code> </summary><p>A general model relating the prevalence to age can be written as a GLM</p>
+<p>A general model relating the prevalence to age can be written as a GLM</p>
 <p><span class="math display">\[
 g(P(Y_i = 1| a _i)) = g(\pi(a_i)) = \eta(a_i)
 \]</span></p>
@@ -138,16 +116,16 @@ <h2 id="sec-penalized-splines">Penalized splines<a class="anchor" aria-label="an
 </ul>
 <p>In matrix notation, the mean structure model for <span class="math inline">\(\eta(a_i)\)</span> becomes</p>
 <p><span class="math display">\[
-\eta = X\beta + Zu
+\eta = \textbf{X}\beta + \textbf{Zu}
 \]</span></p>
-<p>Where <span class="math inline">\(\eta = [\eta(a_i) ... \eta(a_N) ]^T\)</span>, <span class="math inline">\(\beta = [\beta_0 \beta_1 .... \beta_p]^T\)</span>, and <span class="math inline">\(u = [u_1 u_2 ... u_k]^T\)</span> are the regression with corresponding design matrices</p>
+<p>Where <span class="math inline">\(\eta = [\eta(a_i) ... \eta(a_N) ]^T\)</span>, <span class="math inline">\(\beta = [\beta_0 \beta_1 .... \beta_p]^T\)</span>, and <span class="math inline">\(\textbf{u} = [u_1 u_2 ... u_k]^T\)</span> are the regression with corresponding design matrices</p>
 <p><span class="math display">\[
-X = \begin{bmatrix}
+\textbf{X} = \begin{bmatrix}
 1 &amp; a_1 &amp; a_1^2 &amp; ... &amp; a_1^p \\
 1 &amp; a_2 &amp; a_2^2 &amp; ... &amp; a_2^p \\
 \vdots &amp; \vdots &amp; \vdots &amp; \dots &amp; \vdots \\
 1 &amp; a_N &amp; a_N^2 &amp; ... &amp; a_N^p
-\end{bmatrix}, Z = \begin{bmatrix}
+\end{bmatrix}, \textbf{Z} = \begin{bmatrix}
 (a_1 - \kappa_1 )_+^p &amp; (a_1 - \kappa_2 )_+^p &amp; \dots &amp; (a_1 - \kappa_k)_+^p \\
 (a_2 - \kappa_1 )_+^p &amp; (a_2 - \kappa_2 )_+^p &amp; \dots &amp; (a_2 - \kappa_k)_+^p \\
 \vdots &amp; \vdots &amp; \dots &amp; \vdots \\
@@ -159,44 +137,48 @@ <h2 id="sec-penalized-splines">Penalized splines<a class="anchor" aria-label="an
 \hat{\lambda}(a_i) = [\hat{\beta_1} , 2\hat{\beta_2}a_i, ..., p \hat{\beta} a_i ^{p-1} + \Sigma^k_{k=1} p \hat{u}_k(a_i - \kappa_k)^{p-1}_+] \delta(\hat{\eta}(a_i))
 \]</span></p>
 <ul>
-<li>Where <span class="math inline">\(\delta(.)\)</span> is determined by the link function use in the model</li>
-</ul></details><hr>
+<li>Where <span class="math inline">\(\delta(.)\)</span> is determined by the link function used in the model</li>
+</ul>
+<hr>
 <div class="section level3">
 <h3 id="penalized-likelihood-framework">Penalized likelihood framework<a class="anchor" aria-label="anchor" href="#penalized-likelihood-framework"></a>
 </h3>
-<p>Refer to Chapter <code>8.2.1</code></p>
 <p><strong>Proposed approach</strong></p>
-<p>A first approach to fit the model is by maximizing the following penalized likelihood</p>
-<span class="math display" id="eq:penlikelihood">\[\begin{equation}
-\phi^{-1}[y^T(X\beta + Zu ) -  1^Tc(X\beta + Zu )] - \frac{1}{2}\lambda^2
-\begin{bmatrix} \beta \\u \end{bmatrix}^T D\begin{bmatrix} \beta \\u \end{bmatrix}
+<p>The first approach to fit the model is by maximizing the following penalized likelihood</p>
+<p><span class="math display" id="eq:penlikelihood">\[\begin{equation}
+\phi^{-1}[y^T(\textbf{X}\beta + \textbf{Zu} ) -  \textbf{1}^Tc(\textbf{X}\beta + \textbf{Zu} )] - \frac{1}{2}\lambda^2
+\begin{bmatrix} \beta \\ \textbf{u} \end{bmatrix}^T D\begin{bmatrix} \beta \\ \textbf{u} \end{bmatrix}
 
 \tag{1}
-\end{equation}\]</span>
+\end{equation}\]</span></p>
 <p>Where:</p>
 <ul>
-<li><p><span class="math inline">\(X\beta + Zu\)</span> is the linear predictor</p></li>
-<li><p><span class="math inline">\(D\)</span> is a known semi-definite penalty matrix <span class="citation">(<a href="#ref-Wahba1978" role="doc-biblioref">Wahba 1978</a>)</span>, <span class="citation">(<a href="#ref-Green1993" role="doc-biblioref">Green and Silverman 1993</a>)</span></p></li>
+<li><p><span class="math inline">\(X\beta + Zu\)</span> is the predictor</p></li>
+<li><p><span class="math inline">\(D\)</span> is a known semi-definite penalty matrix <span class="citation">(<a href="#ref-Wahba1978">Wahba 1978</a>)</span>, <span class="citation">(<a href="#ref-Green1993">Green and Silverman 1993</a>)</span></p></li>
 <li><p><span class="math inline">\(y\)</span> is the response vector</p></li>
-<li><p>1 the unit vector, <span class="math inline">\(c(.)\)</span> is determined by the link function used</p></li>
+<li><p><span class="math inline">\(\textbf{1}\)</span> is the unit vector, <span class="math inline">\(c(.)\)</span> is determined by the link function used</p></li>
 <li><p><span class="math inline">\(\lambda\)</span> is the smoothing parameter (larger values –&gt; smoother curves)</p></li>
 <li><p><span class="math inline">\(\phi\)</span> is the overdispersion parameter and equals 1 if there is no overdispersion</p></li>
 </ul>
+<p>Refer to Chapter <code>8.2.1</code> of the book by <span class="citation">Hens et al. (<a href="#ref-Hens2012">2012</a>)</span> for a more detailed explanation of the method.</p>
 <p><strong>Fitting data</strong></p>
 <p>To fit the data using the penalized likelihood framework, specify <code>framework = "pl"</code></p>
 <p>Basis function can be defined via the <code>s</code> parameter, some values for s includes:</p>
 <ul>
 <li><p><code>"tp"</code> thin plate regression splines</p></li>
 <li><p><code>"cr"</code> cubic regression splines</p></li>
-<li><p><code>"ps"</code> P-splines proposed by <span class="citation">(<a href="#ref-Eilers1996" role="doc-biblioref">Eilers and Marx 1996</a>)</span></p></li>
+<li><p><code>"ps"</code> P-splines proposed by <span class="citation">(<a href="#ref-Eilers1996">Eilers and Marx 1996</a>)</span></p></li>
 <li><p><code>"ad"</code> for Adaptive smoothers</p></li>
 </ul>
-<p>For more options, refer to the <a href="https://stat.ethz.ch/R-manual/R-devel/library/mgcv/html/smooth.terms.html" class="external-link">mgcv documentation</a> <span class="citation">(<a href="#ref-Wood2017" role="doc-biblioref">Wood 2017</a>)</span></p>
+<p>For more options, refer to the <a href="https://stat.ethz.ch/R-manual/R-devel/library/mgcv/html/smooth.terms.html" class="external-link">mgcv documentation</a> <span class="citation">(<a href="#ref-Wood2017">Wood 2017</a>)</span></p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pl</span> <span class="op">&lt;-</span> <span class="va">parvob19_be_2001_2003</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/rename.html" class="external-link">rename</a></span><span class="op">(</span>status <span class="op">=</span> <span class="va">seropositive</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
-<span>  <span class="fu"><a href="../reference/penalized_spline_model.html">penalized_spline_model</a></span><span class="op">(</span>s <span class="op">=</span> <span class="st">"tp"</span>, framework <span class="op">=</span> <span class="st">"pl"</span><span class="op">)</span> </span>
-<span><span class="va">pl</span><span class="op">$</span><span class="va">info</span></span>
+<span>  <span class="fu"><a href="../reference/penalized_spline_model.html">penalized_spline_model</a></span><span class="op">(</span>status_col <span class="op">=</span> <span class="st">"seropositive"</span>, s <span class="op">=</span> <span class="st">"tp"</span>, framework <span class="op">=</span> <span class="st">"pl"</span><span class="op">)</span> </span>
+<span><span class="va">pl</span></span>
+<span><span class="co">#&gt; Penalized spline model </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Input type:  linelisting </span></span>
+<span><span class="co">#&gt; Framework:  Penalized likelihood </span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Family: binomial </span></span>
 <span><span class="co">#&gt; Link function: logit </span></span>
@@ -216,11 +198,10 @@ <h3 id="penalized-likelihood-framework">Penalized likelihood framework<a class="
 <div class="section level3">
 <h3 id="generalized-linear-mixed-model-framework">Generalized Linear Mixed Model framework<a class="anchor" aria-label="anchor" href="#generalized-linear-mixed-model-framework"></a>
 </h3>
-<p>Refer to Chapter <code>8.2.2</code></p>
 <p><strong>Proposed approach</strong></p>
-<p>Looking back at <a href="#eq:penlikelihood">(1)</a>, a constraint for <span class="math inline">\(u\)</span> would be <span class="math inline">\(\Sigma_ku_k^2 &lt; C\)</span> for some positive value <span class="math inline">\(C\)</span></p>
-<p>This is equivalent to choosing <span class="math inline">\((\beta, u)\)</span> to maximise <a href="#eq:penlikelihood">(1)</a> with <span class="math inline">\(D = diag(0, 1)\)</span> where <span class="math inline">\(0\)</span> denotes zero vector length <span class="math inline">\(p+1\)</span> and 1 denotes the unit vector of length <span class="math inline">\(K\)</span></p>
-<p>For a fixed value for <span class="math inline">\(\lambda\)</span> this is equivalent to fitting the following generalized linear mixed model <span class="citation">Ngo and Wand (<a href="#ref-Ngo2004" role="doc-biblioref">2004</a>)</span></p>
+<p>Looking back at <a href="#eq:penlikelihood">(1)</a>, a constraint for <span class="math inline">\(\textbf{u}\)</span> would be <span class="math inline">\(\Sigma_ku_k^2 &lt; C\)</span> for some positive value <span class="math inline">\(C\)</span></p>
+<p>This is equivalent to choosing <span class="math inline">\((\beta, \textbf{u})\)</span> to maximise <a href="#eq:penlikelihood">(1)</a> with <span class="math inline">\(D = diag(\textbf{0}, \textbf{1})\)</span> where <span class="math inline">\(\textbf{0}\)</span> denotes zero vector length <span class="math inline">\(p+1\)</span> and <span class="math inline">\(\textbf{1}\)</span> denotes the unit vector of length <span class="math inline">\(K\)</span></p>
+<p>For a fixed value for <span class="math inline">\(\lambda\)</span> this is equivalent to fitting the following generalized linear mixed model <span class="citation">Ngo and Wand (<a href="#ref-Ngo2004">2004</a>)</span></p>
 <p><span class="math display">\[
 f(y|u) = exp\{ \phi^{-1} [y^T(X\beta + Zu) - c(X\beta + Zu)] + 1^Tc(y)\},\\
 u \sim N(0, G)
@@ -229,20 +210,47 @@ <h3 id="generalized-linear-mixed-model-framework">Generalized Linear Mixed Model
 <li>With similar notations as before and <span class="math inline">\(G = \sigma^2_uI_{K \times K}\)</span>
 </li>
 </ul>
-<p>Thus <span class="math inline">\(Z\)</span> is penalized by assuming the corresponding coefficients <span class="math inline">\(u\)</span> are random effect with <span class="math inline">\(u \sim N(0, \sigma^2_uI)\)</span>.</p>
+<p>Thus <span class="math inline">\(Z\)</span> is penalized by assuming the corresponding coefficients <span class="math inline">\(\textbf{u}\)</span> are random effect with <span class="math inline">\(\textbf{u} \sim N(\textbf{0}, \mathbf{\sigma}^2_u \textbf{I})\)</span>.</p>
+<p>Refer to Chapter <code>8.2.2</code> of the book by <span class="citation">Hens et al. (<a href="#ref-Hens2012">2012</a>)</span> for a more detailed explanation of the method.</p>
 <p><strong>Fitting data</strong></p>
 <p>To fit the data using the penalized likelihood framework, specify <code>framework = "glmm"</code></p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">glmm</span> <span class="op">&lt;-</span> <span class="va">parvob19_be_2001_2003</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/rename.html" class="external-link">rename</a></span><span class="op">(</span>status <span class="op">=</span> <span class="va">seropositive</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
-<span>  <span class="fu"><a href="../reference/penalized_spline_model.html">penalized_spline_model</a></span><span class="op">(</span>s <span class="op">=</span> <span class="st">"tp"</span>, framework <span class="op">=</span> <span class="st">"glmm"</span><span class="op">)</span> </span>
+<span>  <span class="fu"><a href="../reference/penalized_spline_model.html">penalized_spline_model</a></span><span class="op">(</span>status_col <span class="op">=</span> <span class="st">"seropositive"</span>, s <span class="op">=</span> <span class="st">"tp"</span>, framework <span class="op">=</span> <span class="st">"glmm"</span><span class="op">)</span> </span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt;  Maximum number of PQL iterations:  20</span></span>
 <span><span class="co">#&gt; iteration 1</span></span>
 <span><span class="co">#&gt; iteration 2</span></span>
 <span><span class="co">#&gt; iteration 3</span></span>
 <span><span class="co">#&gt; iteration 4</span></span>
-<span><span class="va">glmm</span><span class="op">$</span><span class="va">info</span><span class="op">$</span><span class="va">gam</span></span>
+<span><span class="va">glmm</span></span>
+<span><span class="co">#&gt; Penalized spline model </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Input type:  linelisting </span></span>
+<span><span class="co">#&gt; Framework:  Mixed model </span></span>
+<span><span class="co">#&gt; $lme</span></span>
+<span><span class="co">#&gt; Linear mixed-effects model fit by maximum likelihood</span></span>
+<span><span class="co">#&gt;   Data: data </span></span>
+<span><span class="co">#&gt;   Log-likelihood: -6977.429</span></span>
+<span><span class="co">#&gt;   Fixed: fixed </span></span>
+<span><span class="co">#&gt; X(Intercept)   Xs(age)Fx1 </span></span>
+<span><span class="co">#&gt;    0.7122306    3.6123783 </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Random effects:</span></span>
+<span><span class="co">#&gt;  Formula: ~Xr - 1 | g</span></span>
+<span><span class="co">#&gt;  Structure: pdIdnot</span></span>
+<span><span class="co">#&gt;              Xr1      Xr2      Xr3      Xr4      Xr5      Xr6      Xr7      Xr8</span></span>
+<span><span class="co">#&gt; StdDev: 6.020273 6.020273 6.020273 6.020273 6.020273 6.020273 6.020273 6.020273</span></span>
+<span><span class="co">#&gt;         Residual</span></span>
+<span><span class="co">#&gt; StdDev:        1</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Variance function:</span></span>
+<span><span class="co">#&gt;  Structure: fixed weights</span></span>
+<span><span class="co">#&gt;  Formula: ~invwt </span></span>
+<span><span class="co">#&gt; Number of Observations: 3080</span></span>
+<span><span class="co">#&gt; Number of Groups: 1 </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; $gam</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Family: binomial </span></span>
 <span><span class="co">#&gt; Link function: logit </span></span>
@@ -251,17 +259,24 @@ <h3 id="generalized-linear-mixed-model-framework">Generalized Linear Mixed Model
 <span><span class="co">#&gt; spos ~ s(age, bs = s, sp = sp)</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Estimated degrees of freedom:</span></span>
-<span><span class="co">#&gt; 6.45  total = 7.45</span></span></code></pre></div>
+<span><span class="co">#&gt; 6.45  total = 7.45 </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; attr(,"class")</span></span>
+<span><span class="co">#&gt; [1] "gamm" "list"</span></span></code></pre></div>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">glmm</span><span class="op">)</span></span></code></pre></div>
 <p><img src="semiparametric_model_files/figure-html/unnamed-chunk-5-1.png"><!-- --></p>
-<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0">
 <div id="ref-Eilers1996" class="csl-entry">
 Eilers, Paul H. C., and Brian D. Marx. 1996. <span>“Flexible Smoothing with b-Splines and Penalties.”</span> <em>Statistical Science</em> 11 (2). <a href="https://doi.org/10.1214/ss/1038425655" class="external-link">https://doi.org/10.1214/ss/1038425655</a>.
 </div>
 <div id="ref-Green1993" class="csl-entry">
 Green, P. J., and Bernard. W. Silverman. 1993. <em>Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach</em>. Chapman; Hall/CRC. <a href="https://doi.org/10.1201/b15710" class="external-link">https://doi.org/10.1201/b15710</a>.
 </div>
+<div id="ref-Hens2012" class="csl-entry">
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and Philippe Beutels. 2012. <em>Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective</em>. <em>Statistics for Biology and Health</em>. Springer New York. <a href="https://doi.org/10.1007/978-1-4614-4072-7" class="external-link">https://doi.org/10.1007/978-1-4614-4072-7</a>.
+</div>
 <div id="ref-Ngo2004" class="csl-entry">
 Ngo, Long, and Matthew P. Wand. 2004. <span>“Smoothing with Mixed Model Software.”</span> <em>Journal of Statistical Software</em> 9 (1). <a href="https://doi.org/10.18637/jss.v009.i01" class="external-link">https://doi.org/10.18637/jss.v009.i01</a>.
 </div>
@@ -280,28 +295,26 @@ <h3 id="generalized-linear-mixed-model-framework">Generalized Linear Mixed Model
 </div>
 </div>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
 </div>
 
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p>
-<p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
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-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
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diff --git a/docs/articles/semiparametric_model.md b/docs/articles/semiparametric_model.md
index 1f26e2b..0f4b266 100644
--- a/docs/articles/semiparametric_model.md
+++ b/docs/articles/semiparametric_model.md
@@ -2,103 +2,92 @@
 
 ``` r
 library(serosv)
-#> Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when
-#> loading 'serosv'
 library(dplyr)
-#> Warning: package 'dplyr' was built under R version 4.3.1
-#> 
-#> Attaching package: 'dplyr'
-#> The following objects are masked from 'package:stats':
-#> 
-#>     filter, lag
-#> The following objects are masked from 'package:base':
-#> 
-#>     intersect, setdiff, setequal, union
 ```
 
 ## Penalized splines
 
 **Proposed model**
 
-` Penalized splines `
-
 A general model relating the prevalence to age can be written as a GLM
 
-\\\[ g(P(Y_i = 1\| a \_i)) = g(\pi(a_i)) = \eta(a_i) \\\]
+\\ g(P(Y_i = 1\| a \_i)) = g(\pi(a_i)) = \eta(a_i) \\
 
-- Where \\(g\\) is the link function and \\(\eta\\) is the linear
-  predictor
+- Where \\g\\ is the link function and \\\eta\\ is the linear predictor
 
 The linear predictor can be estimated semi-parametrically using
-penalized spline with truncated power basis functions of degree \\(p\\)
-and fixed knots \\(\kappa_1,..., \kappa_k\\) as followed
+penalized spline with truncated power basis functions of degree \\p\\
+and fixed knots \\\kappa_1,..., \kappa_k\\ as followed
 
-\\\[ \eta(a_i) = \beta_0 + \beta_1a_i + ... + \beta_p a_i^p +
-\Sigma\_{k=1}^ku_k(a_i - \kappa_k)^p\_+ \\\]
+\\ \eta(a_i) = \beta_0 + \beta_1a_i + ... + \beta_p a_i^p +
+\Sigma\_{k=1}^ku_k(a_i - \kappa_k)^p\_+ \\
 
 - Where
 
-  \\\[ (a_i - \kappa_k)^p\_+ = \begin{cases} 0, & a_i \le \kappa_k \\\\
-  (a_i - \kappa_k)^p, & a_i \> \kappa_k \end{cases} \\\]
+  \\ (a_i - \kappa_k)^p\_+ = \begin{cases} 0, & a_i \le \kappa_k \\
+  (a_i - \kappa_k)^p, & a_i \> \kappa_k \end{cases} \\
 
-In matrix notation, the mean structure model for \\(\eta(a_i)\\) becomes
+In matrix notation, the mean structure model for \\\eta(a_i)\\ becomes
 
-\\\[ \eta = X\beta + Zu \\\]
+\\ \eta = \textbf{X}\beta + \textbf{Zu} \\
 
-Where \\(\eta = \[\eta(a_i) ... \eta(a_N) \]^T\\), \\(\beta = \[\beta_0
-\beta_1 .... \beta_p\]^T\\), and \\(u = \[u_1 u_2 ... u_k\]^T\\) are the
-regression with corresponding design matrices
+Where \\\eta = \[\eta(a_i) ... \eta(a_N) \]^T\\, \\\beta = \[\beta_0
+\beta_1 .... \beta_p\]^T\\, and \\\textbf{u} = \[u_1 u_2 ... u_k\]^T\\
+are the regression with corresponding design matrices
 
-\\\[ X = \begin{bmatrix} 1 & a_1 & a_1^2 & ... & a_1^p \\\\ 1 & a_2 &
-a_2^2 & ... & a_2^p \\\\ \vdots & \vdots & \vdots & \dots & \vdots \\\\
-1 & a_N & a_N^2 & ... & a_N^p \end{bmatrix}, Z = \begin{bmatrix} (a_1 -
-\kappa_1 )\_+^p & (a_1 - \kappa_2 )\_+^p & \dots & (a_1 - \kappa_k)\_+^p
-\\\\ (a_2 - \kappa_1 )\_+^p & (a_2 - \kappa_2 )\_+^p & \dots & (a_2 -
-\kappa_k)\_+^p \\\\ \vdots & \vdots & \dots & \vdots \\\\ (a_N -
-\kappa_1 )\_+^p & (a_N - \kappa_2 )\_+^p & \dots & (a_N - \kappa_k)\_+^p
-\end{bmatrix} \\\]
+\\ \textbf{X} = \begin{bmatrix} 1 & a_1 & a_1^2 & ... & a_1^p \\ 1 & a_2
+& a_2^2 & ... & a_2^p \\ \vdots & \vdots & \vdots & \dots & \vdots \\ 1
+& a_N & a_N^2 & ... & a_N^p \end{bmatrix}, \textbf{Z} = \begin{bmatrix}
+(a_1 - \kappa_1 )\_+^p & (a_1 - \kappa_2 )\_+^p & \dots & (a_1 -
+\kappa_k)\_+^p \\ (a_2 - \kappa_1 )\_+^p & (a_2 - \kappa_2 )\_+^p &
+\dots & (a_2 - \kappa_k)\_+^p \\ \vdots & \vdots & \dots & \vdots \\
+(a_N - \kappa_1 )\_+^p & (a_N - \kappa_2 )\_+^p & \dots & (a_N -
+\kappa_k)\_+^p \end{bmatrix} \\
 
 FOI can then be derived as
 
-\\\[ \hat{\lambda}(a_i) = \[\hat{\beta_1} , 2\hat{\beta_2}a_i, ..., p
+\\ \hat{\lambda}(a_i) = \[\hat{\beta_1} , 2\hat{\beta_2}a_i, ..., p
 \hat{\beta} a_i ^{p-1} + \Sigma^k\_{k=1} p \hat{u}\_k(a_i -
-\kappa_k)^{p-1}\_+\] \delta(\hat{\eta}(a_i)) \\\]
+\kappa_k)^{p-1}\_+\] \delta(\hat{\eta}(a_i)) \\
 
-- Where \\(\delta(.)\\) is determined by the link function use in the
+- Where \\\delta(.)\\ is determined by the link function used in the
   model
 
 ------------------------------------------------------------------------
 
 ### Penalized likelihood framework
 
-Refer to Chapter `8.2.1`
-
 **Proposed approach**
 
-A first approach to fit the model is by maximizing the following
+The first approach to fit the model is by maximizing the following
 penalized likelihood
 
-\\\[\begin{equation} \phi^{-1}\[y^T(X\beta + Zu ) - 1^Tc(X\beta + Zu
-)\] - \frac{1}{2}\lambda^2 \begin{bmatrix} \beta \\\u \end{bmatrix}^T
-D\begin{bmatrix} \beta \\\u \end{bmatrix} \tag{1} \end{equation}\\\]
+\\\begin{equation} \phi^{-1}\[y^T(\textbf{X}\beta + \textbf{Zu} ) -
+\textbf{1}^Tc(\textbf{X}\beta + \textbf{Zu} )\] - \frac{1}{2}\lambda^2
+\begin{bmatrix} \beta \\ \textbf{u} \end{bmatrix}^T D\begin{bmatrix}
+\beta \\ \textbf{u} \end{bmatrix} \tag{1} \end{equation}\\
 
 Where:
 
-- \\(X\beta + Zu\\) is the linear predictor
+- \\X\beta + Zu\\ is the predictor
 
-- \\(D\\) is a known semi-definite penalty matrix ([Wahba
+- \\D\\ is a known semi-definite penalty matrix ([Wahba
   1978](#ref-Wahba1978)), ([Green and Silverman 1993](#ref-Green1993))
 
-- \\(y\\) is the response vector
+- \\y\\ is the response vector
 
-- 1 the unit vector, \\(c(.)\\) is determined by the link function used
+- \\\textbf{1}\\ is the unit vector, \\c(.)\\ is determined by the link
+  function used
 
-- \\(\lambda\\) is the smoothing parameter (larger values –\> smoother
+- \\\lambda\\ is the smoothing parameter (larger values –\> smoother
   curves)
 
-- \\(\phi\\) is the overdispersion parameter and equals 1 if there is no
+- \\\phi\\ is the overdispersion parameter and equals 1 if there is no
   overdispersion
 
+Refer to Chapter `8.2.1` of the book by Hens et al.
+([2012](#ref-Hens2012)) for a more detailed explanation of the method.
+
 **Fitting data**
 
 To fit the data using the penalized likelihood framework, specify
@@ -121,9 +110,12 @@ documentation](https://stat.ethz.ch/R-manual/R-devel/library/mgcv/html/smooth.te
 
 ``` r
 pl <- parvob19_be_2001_2003 %>% 
-  rename(status = seropositive) %>% 
-  penalized_spline_model(s = "tp", framework = "pl") 
-pl$info
+  penalized_spline_model(status_col = "seropositive", s = "tp", framework = "pl") 
+pl
+#> Penalized spline model 
+#> 
+#> Input type:  linelisting 
+#> Framework:  Penalized likelihood 
 #> 
 #> Family: binomial 
 #> Link function: logit 
@@ -147,30 +139,32 @@ plot(pl)
 
 ### Generalized Linear Mixed Model framework
 
-Refer to Chapter `8.2.2`
-
 **Proposed approach**
 
-Looking back at [(1)](#eq:penlikelihood), a constraint for \\(u\\) would
-be \\(\Sigma_ku_k^2 \< C\\) for some positive value \\(C\\)
+Looking back at [(1)](#eq:penlikelihood), a constraint for
+\\\textbf{u}\\ would be \\\Sigma_ku_k^2 \< C\\ for some positive value
+\\C\\
 
-This is equivalent to choosing \\((\beta, u)\\) to maximise
-[(1)](#eq:penlikelihood) with \\(D = diag(0, 1)\\) where \\(0\\) denotes
-zero vector length \\(p+1\\) and 1 denotes the unit vector of length
-\\(K\\)
+This is equivalent to choosing \\(\beta, \textbf{u})\\ to maximise
+[(1)](#eq:penlikelihood) with \\D = diag(\textbf{0}, \textbf{1})\\ where
+\\\textbf{0}\\ denotes zero vector length \\p+1\\ and \\\textbf{1}\\
+denotes the unit vector of length \\K\\
 
-For a fixed value for \\(\lambda\\) this is equivalent to fitting the
+For a fixed value for \\\lambda\\ this is equivalent to fitting the
 following generalized linear mixed model Ngo and Wand
 ([2004](#ref-Ngo2004))
 
-\\\[ f(y\|u) = exp\\{ \phi^{-1} \[y^T(X\beta + Zu) - c(X\beta + Zu)\] +
-1^Tc(y)\\},\\\\ u \sim N(0, G) \\\]
+\\ f(y\|u) = exp\\ \phi^{-1} \[y^T(X\beta + Zu) - c(X\beta + Zu)\] +
+1^Tc(y)\\,\\ u \sim N(0, G) \\
 
-- With similar notations as before and \\(G = \sigma^2_uI\_{K \times
-  K}\\)
+- With similar notations as before and \\G = \sigma^2_uI\_{K \times K}\\
 
-Thus \\(Z\\) is penalized by assuming the corresponding coefficients
-\\(u\\) are random effect with \\(u \sim N(0, \sigma^2_uI)\\).
+Thus \\Z\\ is penalized by assuming the corresponding coefficients
+\\\textbf{u}\\ are random effect with \\\textbf{u} \sim N(\textbf{0},
+\mathbf{\sigma}^2_u \textbf{I})\\.
+
+Refer to Chapter `8.2.2` of the book by Hens et al.
+([2012](#ref-Hens2012)) for a more detailed explanation of the method.
 
 **Fitting data**
 
@@ -179,15 +173,41 @@ To fit the data using the penalized likelihood framework, specify
 
 ``` r
 glmm <- parvob19_be_2001_2003 %>% 
-  rename(status = seropositive) %>% 
-  penalized_spline_model(s = "tp", framework = "glmm") 
+  penalized_spline_model(status_col = "seropositive", s = "tp", framework = "glmm") 
 #> 
 #>  Maximum number of PQL iterations:  20
 #> iteration 1
 #> iteration 2
 #> iteration 3
 #> iteration 4
-glmm$info$gam
+glmm
+#> Penalized spline model 
+#> 
+#> Input type:  linelisting 
+#> Framework:  Mixed model 
+#> $lme
+#> Linear mixed-effects model fit by maximum likelihood
+#>   Data: data 
+#>   Log-likelihood: -6977.429
+#>   Fixed: fixed 
+#> X(Intercept)   Xs(age)Fx1 
+#>    0.7122306    3.6123783 
+#> 
+#> Random effects:
+#>  Formula: ~Xr - 1 | g
+#>  Structure: pdIdnot
+#>              Xr1      Xr2      Xr3      Xr4      Xr5      Xr6      Xr7      Xr8
+#> StdDev: 6.020273 6.020273 6.020273 6.020273 6.020273 6.020273 6.020273 6.020273
+#>         Residual
+#> StdDev:        1
+#> 
+#> Variance function:
+#>  Structure: fixed weights
+#>  Formula: ~invwt 
+#> Number of Observations: 3080
+#> Number of Groups: 1 
+#> 
+#> $gam
 #> 
 #> Family: binomial 
 #> Link function: logit 
@@ -196,7 +216,11 @@ glmm$info$gam
 #> spos ~ s(age, bs = s, sp = sp)
 #> 
 #> Estimated degrees of freedom:
-#> 6.45  total = 7.45
+#> 6.45  total = 7.45 
+#> 
+#> 
+#> attr(,"class")
+#> [1] "gamm" "list"
 ```
 
 ``` r
@@ -213,6 +237,12 @@ Green, P. J., and Bernard. W. Silverman. 1993. *Nonparametric Regression
 and Generalized Linear Models: A Roughness Penalty Approach*. Chapman;
 Hall/CRC. <https://doi.org/10.1201/b15710>.
 
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. *Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective*.
+*Statistics for Biology and Health*. Springer New York.
+<https://doi.org/10.1007/978-1-4614-4072-7>.
+
 Ngo, Long, and Matthew P. Wand. 2004. “Smoothing with Mixed Model
 Software.” *Journal of Statistical Software* 9 (1).
 <https://doi.org/10.18637/jss.v009.i01>.
diff --git a/docs/articles/sir_model.html b/docs/articles/sir_model.html
index dd84626..f101097 100644
--- a/docs/articles/sir_model.html
+++ b/docs/articles/sir_model.html
@@ -5,83 +5,72 @@
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
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+  window.MathJax = {
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@@ -101,14 +90,12 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
     
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://oucru-modelling.github.io/serosv/">serosv</a></span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when</span></span>
-<span><span class="co">## loading 'serosv'</span></span></code></pre>
 <div class="section level2">
 <h2 id="basic-sir-model">Basic SIR model<a class="anchor" aria-label="anchor" href="#basic-sir-model"></a>
 </h2>
 <p><strong>Proposed model</strong></p>
-<p>A transmission model consists of 3 compartments: susceptible (S),
-infected (I), recovered (R)</p>
+<p>Let’s consider a transmission model consists of 3 compartments:
+susceptible (S), infected (I), recovered (R)</p>
 <p>With the following assumptions:</p>
 <ul>
 <li><p>Individuals are born into susceptible group (exposure time is age
@@ -144,8 +131,8 @@ <h2 id="basic-sir-model">Basic SIR model<a class="anchor" aria-label="anchor" hr
 <span class="math inline">\(p\)</span> is the proportion of newborn
 vaccinated and moved directly to the recovered compartment</li>
 </ul>
-<p><strong>Fitting data</strong></p>
-<p>To fit a basic SIR model, use <code><a href="../reference/sir_basic_model.html">sir_basic_model()</a></code> and
+<p><strong>Simulating data</strong></p>
+<p>To simulate a basic SIR model, use <code><a href="../reference/sir_basic_model.html">sir_basic_model()</a></code> and
 specify the following parameters</p>
 <ul>
 <li><p><code>state</code> - initial population of each
@@ -153,7 +140,7 @@ <h2 id="basic-sir-model">Basic SIR model<a class="anchor" aria-label="anchor" hr
 <li><p><code>times</code> - a time sequence</p></li>
 <li><p><code>parameters</code> - parameters for SIR model</p></li>
 </ul>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">state</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>S<span class="op">=</span><span class="fl">4999</span>, I<span class="op">=</span><span class="fl">1</span>, R<span class="op">=</span><span class="fl">0</span><span class="op">)</span></span>
 <span><span class="va">parameters</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
 <span>  mu<span class="op">=</span><span class="fl">1</span><span class="op">/</span><span class="fl">75</span>, <span class="co"># 1 divided by life expectancy (75 years old)</span></span>
@@ -168,14 +155,14 @@ <h2 id="basic-sir-model">Basic SIR model<a class="anchor" aria-label="anchor" hr
 <span><span class="co">#&gt;         mu      alpha       beta         nu          p </span></span>
 <span><span class="co">#&gt; 0.01333333 0.00000000 0.00050000 1.00000000 0.00000000</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></code></pre></div>
-<p><img src="sir_model_files/figure-html/unnamed-chunk-2-1.png" width="700"></p>
+<p><img src="sir_model_files/figure-html/unnamed-chunk-2-1.png" class="r-plt" width="700"></p>
 </div>
 <div class="section level2">
 <h2 id="sir-model-with-constant-force-of-infection-at-endemic-state">SIR model with constant Force of Infection at Endemic state<a class="anchor" aria-label="anchor" href="#sir-model-with-constant-force-of-infection-at-endemic-state"></a>
 </h2>
 <p><strong>Proposed model</strong></p>
-<p>A transmission model consists of 3 compartments: susceptible (S),
-infected (I), recovered (R)</p>
+<p>Let’s consider a transmission model consists of 3 compartments:
+susceptible (S), infected (I), recovered (R)</p>
 <p>With the following assumptions:</p>
 <ul>
 <li><p>Time homogeneity</p></li>
@@ -200,8 +187,8 @@ <h2 id="sir-model-with-constant-force-of-infection-at-endemic-state">SIR model w
 <li>
 <span class="math inline">\(\nu\)</span> is the recovery rate</li>
 </ul>
-<p><strong>FItting data</strong></p>
-<p>To fit an SIR model with constant FOI, use
+<p><strong>Simulating data</strong></p>
+<p>To simulate an SIR model with constant FOI, use
 <code><a href="../reference/sir_static_model.html">sir_static_model()</a></code> and specify the following parameters</p>
 <ul>
 <li><p><code>state</code> - initial proportion of each
@@ -209,7 +196,7 @@ <h2 id="sir-model-with-constant-force-of-infection-at-endemic-state">SIR model w
 <li><p><code>ages</code> - an age sequence</p></li>
 <li><p><code>parameters</code> - parameters for the model</p></li>
 </ul>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">state</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>s<span class="op">=</span><span class="fl">0.99</span>,i<span class="op">=</span><span class="fl">0.01</span>,r<span class="op">=</span><span class="fl">0</span><span class="op">)</span></span>
 <span><span class="va">parameters</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
 <span>  lambda <span class="op">=</span> <span class="fl">0.05</span>,</span>
@@ -221,7 +208,7 @@ <h2 id="sir-model-with-constant-force-of-infection-at-endemic-state">SIR model w
 <span><span class="co">#&gt;   lambda       nu </span></span>
 <span><span class="co">#&gt;  0.05000 26.07143</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></code></pre></div>
-<p><img src="sir_model_files/figure-html/unnamed-chunk-3-1.png" width="700"></p>
+<p><img src="sir_model_files/figure-html/unnamed-chunk-3-1.png" class="r-plt" width="700"></p>
 </div>
 <div class="section level2">
 <h2 id="sir-model-with-sub-populations">SIR model with sub populations<a class="anchor" aria-label="anchor" href="#sir-model-with-sub-populations"></a>
@@ -249,35 +236,36 @@ <h2 id="sir-model-with-sub-populations">SIR model with sub populations<a class="
 \frac{dR_i(t)}{dt} = \nu_i I_i(t)  - \mu_i R_i(t)
 \end{cases}
 \]</span></p>
-<p><strong>FItting data</strong></p>
-<p>To fit a SIR model with subpopulations, use
+<p><strong>Simulating data</strong></p>
+<p>To simulate a SIR model with subpopulations, use
 <code><a href="../reference/sir_subpops_model.html">sir_subpops_model()</a></code> and specify the following
 parameters</p>
 <ul>
 <li><p><code>state</code> - initial proportion of each compartment for
 each population</p></li>
-<li><p><code>beta_matrix</code> - the WAIFW matrix</p></li>
+<li><p><code>beta_matrix</code> - the WAIFW matrix, with dimensions
+<code>[K, K]</code></p></li>
 <li><p><code>times</code> - a time sequence</p></li>
 <li><p><code>parameters</code> - parameters for the model</p></li>
 </ul>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">k</span> <span class="op">&lt;-</span> <span class="fl">2</span> <span class="co"># number of population</span></span>
-<span><span class="va">state</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
-<span>  <span class="co"># proportion of each compartment for each population</span></span>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">state</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
+<span>  <span class="co"># proportion of each compartment for each population </span></span>
 <span>  s <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.8</span>, <span class="fl">0.6</span><span class="op">)</span>, </span>
 <span>  i <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.2</span>, <span class="fl">0.4</span><span class="op">)</span>,</span>
 <span>  r <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>  <span class="fl">0</span>,   <span class="fl">0</span><span class="op">)</span></span>
 <span><span class="op">)</span></span>
-<span><span class="va">beta_matrix</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.05</span>, <span class="fl">0.00</span><span class="op">)</span>,</span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.00</span>, <span class="fl">0.05</span><span class="op">)</span></span>
+<span><span class="va">beta_matrix</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.05</span>, <span class="fl">0.00</span>,</span>
+<span>    <span class="fl">0.00</span>, <span class="fl">0.05</span><span class="op">)</span>,</span>
+<span>  <span class="fl">2</span></span>
 <span><span class="op">)</span></span>
 <span><span class="va">parameters</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
-<span>  beta <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="va">beta_matrix</span>, nrow<span class="op">=</span><span class="va">k</span>, ncol<span class="op">=</span><span class="va">k</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span>,</span>
+<span>  beta <span class="op">=</span> <span class="va">beta_matrix</span>,</span>
 <span>  nu <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span><span class="op">/</span><span class="fl">30</span>, <span class="fl">1</span><span class="op">/</span><span class="fl">30</span><span class="op">)</span>,</span>
-<span>  mu <span class="op">=</span> <span class="fl">0.001</span>,</span>
-<span>  k <span class="op">=</span> <span class="va">k</span></span>
+<span>  mu <span class="op">=</span> <span class="fl">0.001</span></span>
 <span><span class="op">)</span></span>
+<span></span>
 <span><span class="va">times</span><span class="op">&lt;-</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">10000</span>,by<span class="op">=</span><span class="fl">0.5</span><span class="op">)</span></span>
 <span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/sir_subpops_model.html">sir_subpops_model</a></span><span class="op">(</span><span class="va">times</span>, <span class="va">state</span>, <span class="va">parameters</span><span class="op">)</span></span>
 <span><span class="va">model</span><span class="op">$</span><span class="va">parameters</span></span>
@@ -296,17 +284,17 @@ <h2 id="sir-model-with-sub-populations">SIR model with sub populations<a class="
 <span><span class="co">#&gt; [1] 2</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span> <span class="co"># returns plot for each population</span></span>
 <span><span class="co">#&gt; $subpop_1</span></span></code></pre></div>
-<p><img src="sir_model_files/figure-html/unnamed-chunk-4-1.png" width="700"></p>
+<p><img src="sir_model_files/figure-html/unnamed-chunk-4-1.png" class="r-plt" width="700"></p>
 <pre><code><span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; $subpop_2</span></span></code></pre>
-<p><img src="sir_model_files/figure-html/unnamed-chunk-4-2.png" width="700"></p>
+<p><img src="sir_model_files/figure-html/unnamed-chunk-4-2.png" class="r-plt" width="700"></p>
 </div>
 <div class="section level2">
 <h2 id="mseir-model">MSEIR model<a class="anchor" aria-label="anchor" href="#mseir-model"></a>
 </h2>
 <p><strong>Proposed model</strong></p>
 <p>Extends on SIR model with 2 additional compartments: maternal
-antibody (M) and exposed period (E)</p>
+immunity (M) and exposed period (E)</p>
 <p>And described by the following system of ordinary differential
 equation</p>
 <p><span class="math display">\[
@@ -333,15 +321,15 @@ <h2 id="mseir-model">MSEIR model<a class="anchor" aria-label="anchor" href="#mse
 <li><p><span class="math inline">\(\nu\)</span> is the recovery
 rate</p></li>
 </ul>
-<p><strong>Fitting data</strong></p>
-<p>To fit a MSEIR, use <code><a href="../reference/mseir_model.html">mseir_model()</a></code> and specify the
-following parameters</p>
+<p><strong>Simulating data</strong></p>
+<p>To simulate a MSEIR model, use <code><a href="../reference/mseir_model.html">mseir_model()</a></code> and specify
+the following parameters</p>
 <ul>
 <li><p><code>a</code> - age sequence</p></li>
 <li><p>And model parameters including <code>gamma</code>,
 <code>lambda</code>, <code>sigma</code>, <code>nu</code></p></li>
 </ul>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mseir_model.html">mseir_model</a></span><span class="op">(</span></span>
 <span>  a<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span>from<span class="op">=</span><span class="fl">1</span>,to<span class="op">=</span><span class="fl">20</span>,length<span class="op">=</span><span class="fl">500</span><span class="op">)</span>, <span class="co"># age range from 0 -&gt; 20 yo</span></span>
 <span>  gamma<span class="op">=</span><span class="fl">1</span><span class="op">/</span><span class="fl">0.5</span>, <span class="co"># 6 months in the maternal antibodies</span></span>
@@ -362,8 +350,8 @@ <h2 id="mseir-model">MSEIR model<a class="anchor" aria-label="anchor" href="#mse
 <span><span class="co">#&gt; $nu</span></span>
 <span><span class="co">#&gt; [1] 36.5</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></code></pre></div>
-<p><img src="sir_model_files/figure-html/unnamed-chunk-5-1.png" width="700"></p>
-<div id="refs" class="references csl-bib-body hanging-indent">
+<p><img src="sir_model_files/figure-html/unnamed-chunk-5-1.png" class="r-plt" width="700"></p>
+<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0">
 <div id="ref-Hens2012" class="csl-entry">
 Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
 Philippe Beutels. 2012. <em>Modeling Infectious Disease Parameters Based
@@ -373,28 +361,26 @@ <h2 id="mseir-model">MSEIR model<a class="anchor" aria-label="anchor" href="#mse
 </div>
 </div>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
 </div>
 
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p>
-<p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
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-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
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 </div>
 
-  
 
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+
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 </html>
diff --git a/docs/articles/sir_model.md b/docs/articles/sir_model.md
index 9a84314..9229a1b 100644
--- a/docs/articles/sir_model.md
+++ b/docs/articles/sir_model.md
@@ -4,15 +4,12 @@
 library(serosv)
 ```
 
-    ## Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when
-    ## loading 'serosv'
-
 ## Basic SIR model
 
 **Proposed model**
 
-A transmission model consists of 3 compartments: susceptible (S),
-infected (I), recovered (R)
+Let’s consider a transmission model consists of 3 compartments:
+susceptible (S), infected (I), recovered (R)
 
 With the following assumptions:
 
@@ -25,25 +22,23 @@ With the following assumptions:
 
 And described by a system of 3 differential equations
 
-\\\[ \begin{cases} \frac{dS(t)}{dt} = B(t) (1-p) - \lambda(t)S(t) - \mu
-S(t) \\\\ \frac{dI(t)}{dt} = \lambda(t)S(t) - \nu I(t) - \mu I(t) -
-\alpha I(t) \\\\ \frac{dR(t)}{dt} = B(t) p + \nu I(t) - \mu R(t)
-\end{cases} \\\]
+\\ \begin{cases} \frac{dS(t)}{dt} = B(t) (1-p) - \lambda(t)S(t) - \mu
+S(t) \\ \frac{dI(t)}{dt} = \lambda(t)S(t) - \nu I(t) - \mu I(t) - \alpha
+I(t) \\ \frac{dR(t)}{dt} = B(t) p + \nu I(t) - \mu R(t) \end{cases} \\
 
 Where:
 
-- \\(B(t) = \mu N(t)\\)
-- \\(\lambda(t) = \beta I(t)\\) with \\(\beta\\) is the transmission
-  rate
-- \\(\mu\\) is the natural death rate
-- \\(\nu\\) is the recovery rate
-- \\(\alpha\\) is the disease related death rate
-- \\(p\\) is the proportion of newborn vaccinated and moved directly to
+- \\B(t) = \mu N(t)\\
+- \\\lambda(t) = \beta I(t)\\ with \\\beta\\ is the transmission rate
+- \\\mu\\ is the natural death rate
+- \\\nu\\ is the recovery rate
+- \\\alpha\\ is the disease related death rate
+- \\p\\ is the proportion of newborn vaccinated and moved directly to
   the recovered compartment
 
-**Fitting data**
+**Simulating data**
 
-To fit a basic SIR model, use
+To simulate a basic SIR model, use
 [`sir_basic_model()`](https://oucru-modelling.github.io/serosv/reference/sir_basic_model.md)
 and specify the following parameters
 
@@ -76,8 +71,8 @@ plot(model)
 
 **Proposed model**
 
-A transmission model consists of 3 compartments: susceptible (S),
-infected (I), recovered (R)
+Let’s consider a transmission model consists of 3 compartments:
+susceptible (S), infected (I), recovered (R)
 
 With the following assumptions:
 
@@ -87,20 +82,19 @@ With the following assumptions:
 
 Described by a system of 3 differential equations
 
-\\\[ \begin{cases} \frac{ds(a)}{da} = -\lambda s(a) \\\\
-\frac{di(a)}{da} = \lambda s(a) - \nu i(a) \\\\ \frac{dr(a)}{da} = \nu
-i(a) \end{cases} \\\]
+\\ \begin{cases} \frac{ds(a)}{da} = -\lambda s(a) \\ \frac{di(a)}{da} =
+\lambda s(a) - \nu i(a) \\ \frac{dr(a)}{da} = \nu i(a) \end{cases} \\
 
 Where:
 
-- \\(s(a), i(a), r(a)\\) are proportion of susceptible, infected,
-  recovered population of age group \\(a\\) respectively
-- \\(\lambda\\) is the force of infection
-- \\(\nu\\) is the recovery rate
+- \\s(a), i(a), r(a)\\ are proportion of susceptible, infected,
+  recovered population of age group \\a\\ respectively
+- \\\lambda\\ is the force of infection
+- \\\nu\\ is the recovery rate
 
-**FItting data**
+**Simulating data**
 
-To fit an SIR model with constant FOI, use
+To simulate an SIR model with constant FOI, use
 [`sir_static_model()`](https://oucru-modelling.github.io/serosv/reference/sir_static_model.md)
 and specify the following parameters
 
@@ -135,52 +129,51 @@ Extends on the SIR model by having interacting sub-populations
 
 With K subpopulations, the WAIFW matrix or mixing matrix is given by
 
-\\\[ C = \begin{bmatrix} \beta\_{11} & \beta\_{12} & ... & \beta\_{1K}
-\\\\ \beta\_{21} & \beta\_{22} & ... & \beta\_{2K} \\\\ \vdots & \vdots
-& ... & \vdots \\\\ \beta\_{K1} & \beta\_{K2} & ... & \beta\_{KK} \\\\
-\end{bmatrix} \\\]
+\\ C = \begin{bmatrix} \beta\_{11} & \beta\_{12} & ... & \beta\_{1K} \\
+\beta\_{21} & \beta\_{22} & ... & \beta\_{2K} \\ \vdots & \vdots & ... &
+\vdots \\ \beta\_{K1} & \beta\_{K2} & ... & \beta\_{KK} \\ \end{bmatrix}
+\\
 
-The system of differential equations for the i\\(th\\) subpopulation is
+The system of differential equations for the i\\th\\ subpopulation is
 given by
 
-\\\[ \begin{cases} \frac{dS_i(t)}{dt} =
--(\sum^K\_{j=1}\beta\_{ij}I_j(t)) S_i(t) + N_i\mu_i - \mu_i S_i(t) \\\\
-\frac{dI_i(t)}{dt} = (\sum^K\_{j=1}\beta\_{ij}I_j(t)) S_i(t) - (\nu_i +
-\mu_i) I_i(t) \\\\ \frac{dR_i(t)}{dt} = \nu_i I_i(t) - \mu_i R_i(t)
-\end{cases} \\\]
+\\ \begin{cases} \frac{dS_i(t)}{dt} = -(\sum^K\_{j=1}\beta\_{ij}I_j(t))
+S_i(t) + N_i\mu_i - \mu_i S_i(t) \\ \frac{dI_i(t)}{dt} =
+(\sum^K\_{j=1}\beta\_{ij}I_j(t)) S_i(t) - (\nu_i + \mu_i) I_i(t) \\
+\frac{dR_i(t)}{dt} = \nu_i I_i(t) - \mu_i R_i(t) \end{cases} \\
 
-**FItting data**
+**Simulating data**
 
-To fit a SIR model with subpopulations, use
+To simulate a SIR model with subpopulations, use
 [`sir_subpops_model()`](https://oucru-modelling.github.io/serosv/reference/sir_subpops_model.md)
 and specify the following parameters
 
 - `state` - initial proportion of each compartment for each population
 
-- `beta_matrix` - the WAIFW matrix
+- `beta_matrix` - the WAIFW matrix, with dimensions `[K, K]`
 
 - `times` - a time sequence
 
 - `parameters` - parameters for the model
 
 ``` r
-k <- 2 # number of population
 state <- c(
-  # proportion of each compartment for each population
+  # proportion of each compartment for each population 
   s = c(0.8, 0.6), 
   i = c(0.2, 0.4),
   r = c(  0,   0)
 )
-beta_matrix <- c(
-  c(0.05, 0.00),
-  c(0.00, 0.05)
+beta_matrix <- matrix(
+  c(0.05, 0.00,
+    0.00, 0.05),
+  2
 )
 parameters <- list(
-  beta = matrix(beta_matrix, nrow=k, ncol=k, byrow=TRUE),
+  beta = beta_matrix,
   nu = c(1/30, 1/30),
-  mu = 0.001,
-  k = k
+  mu = 0.001
 )
+
 times<-seq(0,10000,by=0.5)
 model <- sir_subpops_model(times, state, parameters)
 model$parameters
@@ -212,34 +205,34 @@ plot(model) # returns plot for each population
 
 **Proposed model**
 
-Extends on SIR model with 2 additional compartments: maternal antibody
+Extends on SIR model with 2 additional compartments: maternal immunity
 (M) and exposed period (E)
 
 And described by the following system of ordinary differential equation
 
-\\\[ \begin{cases} \frac{dM(a)}{da} = -(\gamma + \mu(a))M(a) \\\\
-\frac{dS(a)}{da} = \gamma M(a) - (\lambda(a) + \mu(a)) S(a) \\\\
-\frac{dE(a)}{da} = \lambda(a) S(a) - (\sigma + \mu(a)) E(a) \\\\
-\frac{dI(a)}{da} = \sigma(a) E(a) - (\nu + \mu(a)) I(a) \\\\
-\frac{dR(a)}{da} = \nu I(a) - \mu(a) R(a) \end{cases} \\\]
+\\ \begin{cases} \frac{dM(a)}{da} = -(\gamma + \mu(a))M(a) \\
+\frac{dS(a)}{da} = \gamma M(a) - (\lambda(a) + \mu(a)) S(a) \\
+\frac{dE(a)}{da} = \lambda(a) S(a) - (\sigma + \mu(a)) E(a) \\
+\frac{dI(a)}{da} = \sigma(a) E(a) - (\nu + \mu(a)) I(a) \\
+\frac{dR(a)}{da} = \nu I(a) - \mu(a) R(a) \end{cases} \\
 
 Where
 
-- \\(M(0)\\) = B, the number of births in the population
+- \\M(0)\\ = B, the number of births in the population
 
-- \\(\gamma\\) is the rate of antibody decaying
+- \\\gamma\\ is the rate of antibody decaying
 
-- \\(\lambda(a)\\) is the force of infection at age \\(a\\)
+- \\\lambda(a)\\ is the force of infection at age \\a\\
 
-- \\(\mu(a)\\) is the natural death rate at age \\(a\\)
+- \\\mu(a)\\ is the natural death rate at age \\a\\
 
-- \\(\sigma\\) is the rate of becoming infected after being exposed
+- \\\sigma\\ is the rate of becoming infected after being exposed
 
-- \\(\nu\\) is the recovery rate
+- \\\nu\\ is the recovery rate
 
-**Fitting data**
+**Simulating data**
 
-To fit a MSEIR, use
+To simulate a MSEIR model, use
 [`mseir_model()`](https://oucru-modelling.github.io/serosv/reference/mseir_model.md)
 and specify the following parameters
 
diff --git a/docs/articles/visualizing_model.html b/docs/articles/visualizing_model.html
index b0d36cd..7c0640a 100644
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+++ b/docs/articles/visualizing_model.html
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-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off">
-</form>
-
-      <ul class="navbar-nav">
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/OUCRU-Modelling/serosv/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/OUCRU-Modelling/serosv/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
 
-    
+
   </div>
 </nav><div class="container template-article">
 
@@ -100,9 +89,7 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
     
     
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://oucru-modelling.github.io/serosv/">serosv</a></span><span class="op">)</span></span>
-<span><span class="co">#&gt; Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when</span></span>
-<span><span class="co">#&gt; loading 'serosv'</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://oucru-modelling.github.io/serosv/">serosv</a></span><span class="op">)</span></span></code></pre></div>
 <div class="section level2">
 <h2 id="visualize-model">Visualize model<a class="anchor" aria-label="anchor" href="#visualize-model"></a>
 </h2>
@@ -114,7 +101,7 @@ <h2 id="visualize-model">Visualize model<a class="anchor" aria-label="anchor" hr
 <span>  <span class="va">hav</span>,</span>
 <span>  p<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.5</span>, <span class="fl">1.6</span><span class="op">)</span>, link<span class="op">=</span><span class="st">"cloglog"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></code></pre></div>
-<p><img src="visualizing_model_files/figure-html/unnamed-chunk-2-1.png" width="700"></p>
+<p><img src="visualizing_model_files/figure-html/unnamed-chunk-2-1.png" class="r-plt" width="700"></p>
 <p>If implementation for confidence interval calculation is not yet
 available, <code>plot</code> function would simply visualize
 seroprevalence line</p>
@@ -126,20 +113,23 @@ <h2 id="visualize-model">Visualize model<a class="anchor" aria-label="anchor" hr
 <span>   start<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>alpha<span class="op">=</span><span class="fl">0.07</span>,beta<span class="op">=</span><span class="fl">0.1</span>,gamma<span class="op">=</span><span class="fl">0.03</span><span class="op">)</span></span>
 <span>   <span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">farrington_md</span><span class="op">)</span></span></code></pre></div>
-<p><img src="visualizing_model_files/figure-html/unnamed-chunk-3-1.png" width="700"></p>
+<p><img src="visualizing_model_files/figure-html/unnamed-chunk-3-1.png" class="r-plt" width="700"></p>
 </div>
 <div class="section level2">
 <h2 id="customize-the-plot">Customize the plot<a class="anchor" aria-label="anchor" href="#customize-the-plot"></a>
 </h2>
-<p>Function <code><a href="../reference/set_plot_style.html">set_plot_style()</a></code> is provided to customize some
-attributes of the plot.</p>
+<div class="section level3">
+<h3 id="built-in-function">Built-in function<a class="anchor" aria-label="anchor" href="#built-in-function"></a>
+</h3>
+<p><code>serosv</code> offers the function <code><a href="../reference/set_plot_style.html">set_plot_style()</a></code>
+to customize some key attributes of the plot.</p>
 <p>Current modifiable attributes include color, linetype for
 seroprevalence, foi and fill color for confidence interval</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">gf_model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">hav_bg_1964</span>, type <span class="op">=</span> <span class="st">"Griffith"</span><span class="op">)</span></span>
+<code class="sourceCode R"><span><span class="va">hav_mod</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span><span class="va">hav_bg_1964</span>, k<span class="op">=</span><span class="fl">3</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># customize plot</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">gf_model</span><span class="op">)</span> <span class="op">+</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">hav_mod</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="../reference/set_plot_style.html">set_plot_style</a></span><span class="op">(</span></span>
 <span>    sero <span class="op">=</span> <span class="st">"#3de071"</span>,</span>
 <span>    foi <span class="op">=</span> <span class="st">"#2f22e0"</span>,</span>
@@ -153,30 +143,70 @@ <h2 id="customize-the-plot">Customize the plot<a class="anchor" aria-label="anch
 <span><span class="co">#&gt; Adding another scale for <span style="color: #00BB00;">linetype</span>, which will replace the existing scale.</span></span>
 <span><span class="co">#&gt; Scale for <span style="color: #00BB00;">fill</span> is already present.</span></span>
 <span><span class="co">#&gt; Adding another scale for <span style="color: #00BB00;">fill</span>, which will replace the existing scale.</span></span></code></pre></div>
-<p><img src="visualizing_model_files/figure-html/unnamed-chunk-4-1.png" width="700"></p>
+<p><img src="visualizing_model_files/figure-html/unnamed-chunk-4-1.png" class="r-plt" width="700"></p>
+</div>
+<div class="section level3">
+<h3 id="ggplot2-functions">
+<code>ggplot2</code> functions<a class="anchor" aria-label="anchor" href="#ggplot2-functions"></a>
+</h3>
+<p>Since <code>serosv</code> uses <code>ggplot2</code> for plotting, the
+returned plot is a <code>gg</code> object meaning any standard
+<code>ggplot2</code> layer can be appended with <code>+</code> for
+further configurations.</p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># return gg object</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/class.html" class="external-link">class</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">hav_mod</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="co">#&gt; [1] "gg"     "ggplot"</span></span></code></pre></div>
+<p><strong>Examples</strong></p>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Set x and y limits</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">hav_mod</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/coord_cartesian.html" class="external-link">coord_cartesian</a></span><span class="op">(</span>xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">50</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1.5</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="co">#&gt; Coordinate system already present. Adding new coordinate system, which will</span></span>
+<span><span class="co">#&gt; replace the existing one.</span></span></code></pre></div>
+<p><img src="visualizing_model_files/figure-html/unnamed-chunk-6-1.png" class="r-plt" width="700"></p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="co"># Configure the plot theme</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">hav_mod</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_bw</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<p><img src="visualizing_model_files/figure-html/unnamed-chunk-6-2.png" class="r-plt" width="700"></p>
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="co"># Set titles and omit legends</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">hav_mod</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_bw</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">ggtitle</a></span><span class="op">(</span><span class="st">"Age-stratified Hepatitis A prevalence in Bulgaria (1964)"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/guides.html" class="external-link">guides</a></span><span class="op">(</span></span>
+<span>    colour <span class="op">=</span> <span class="st">"none"</span>,</span>
+<span>    linetype <span class="op">=</span> <span class="st">"none"</span>,</span>
+<span>    fill <span class="op">=</span> <span class="st">"none"</span></span>
+<span>  <span class="op">)</span></span></code></pre></div>
+<p><img src="visualizing_model_files/figure-html/unnamed-chunk-6-3.png" class="r-plt" width="700"></p>
+</div>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
 </div>
 
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p>
-<p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
     </footer>
 </div>
 
-  
 
-  
+
+
 
   </body>
 </html>
diff --git a/docs/articles/visualizing_model.md b/docs/articles/visualizing_model.md
index 48880b3..f33d3dd 100644
--- a/docs/articles/visualizing_model.md
+++ b/docs/articles/visualizing_model.md
@@ -2,8 +2,6 @@
 
 ``` r
 library(serosv)
-#> Warning: replacing previous import 'magrittr::extract' by 'tidyr::extract' when
-#> loading 'serosv'
 ```
 
 ## Visualize model
@@ -37,18 +35,20 @@ plot(farrington_md)
 
 ## Customize the plot
 
-Function
+### Built-in function
+
+`serosv` offers the function
 [`set_plot_style()`](https://oucru-modelling.github.io/serosv/reference/set_plot_style.md)
-is provided to customize some attributes of the plot.
+to customize some key attributes of the plot.
 
 Current modifiable attributes include color, linetype for
 seroprevalence, foi and fill color for confidence interval
 
 ``` r
-gf_model <- polynomial_model(hav_bg_1964, type = "Griffith")
+hav_mod <- polynomial_model(hav_bg_1964, k=3)
 
 # customize plot
-plot(gf_model) +
+plot(hav_mod) +
   set_plot_style(
     sero = "#3de071",
     foi = "#2f22e0",
@@ -65,3 +65,53 @@ plot(gf_model) +
 ```
 
 ![](visualizing_model_files/figure-html/unnamed-chunk-4-1.png)
+
+### `ggplot2` functions
+
+Since `serosv` uses `ggplot2` for plotting, the returned plot is a `gg`
+object meaning any standard `ggplot2` layer can be appended with `+` for
+further configurations.
+
+``` r
+# return gg object
+class(plot(hav_mod))
+#> [1] "gg"     "ggplot"
+```
+
+**Examples**
+
+``` r
+library(ggplot2)
+
+# Set x and y limits
+plot(hav_mod) +
+  coord_cartesian(xlim = c(0, 50), ylim = c(0, 1.5))
+#> Coordinate system already present. Adding new coordinate system, which will
+#> replace the existing one.
+```
+
+![](visualizing_model_files/figure-html/unnamed-chunk-6-1.png)
+
+``` r
+
+# Configure the plot theme
+plot(hav_mod) +
+  theme_bw()
+```
+
+![](visualizing_model_files/figure-html/unnamed-chunk-6-2.png)
+
+``` r
+
+# Set titles and omit legends
+plot(hav_mod) +
+  theme_bw() +
+  ggtitle("Age-stratified Hepatitis A prevalence in Bulgaria (1964)") +
+  guides(
+    colour = "none",
+    linetype = "none",
+    fill = "none"
+  )
+```
+
+![](visualizing_model_files/figure-html/unnamed-chunk-6-3.png)
diff --git a/docs/articles/visualizing_model_files/figure-html/unnamed-chunk-4-1.png b/docs/articles/visualizing_model_files/figure-html/unnamed-chunk-4-1.png
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diff --git a/docs/authors.html b/docs/authors.html
index 50eef9f..4025aca 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -1,5 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -19,15 +25,14 @@
 <li class="nav-item dropdown">
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
     <li><a class="dropdown-item" href="articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="articles/visualizing_model.html">Model visualization</a></li>
@@ -80,14 +85,14 @@ <h2>Authors</h2>
       <h2 id="citation">Citation</h2>
       <p><small class="dont-index">Source: <a href="https://github.com/OUCRU-Modelling/serosv/blob/HEAD/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
 
-      <p>Phan Truong Quynh A, Pham Nguyen The N, Bui Thanh L, Huynh T, Ong T, Choisy M (2025).
+      <p>Phan Truong Quynh A, Pham Nguyen The N, Bui Thanh L, Huynh T, Ong T, Choisy M (2026).
 <em>serosv: Model Infectious Disease Parameters from Serosurveys</em>.
 https://oucru-modelling.github.io/serosv/, https://github.com/OUCRU-Modelling/serosv.
 </p>
       <pre>@Manual{,
   title = {serosv: Model Infectious Disease Parameters from Serosurveys},
   author = {Anh {Phan Truong Quynh} and Nguyen {Pham Nguyen The} and Long {Bui Thanh} and Tuyen Huynh and Thinh Ong and Marc Choisy},
-  year = {2025},
+  year = {2026},
   note = {https://oucru-modelling.github.io/serosv/, https://github.com/OUCRU-Modelling/serosv},
 }</pre>
     </div>
diff --git a/docs/authors.md b/docs/authors.md
index fd314ae..f8f4d5c 100644
--- a/docs/authors.md
+++ b/docs/authors.md
@@ -22,13 +22,13 @@ Source:
 [`DESCRIPTION`](https://github.com/OUCRU-Modelling/serosv/blob/HEAD/DESCRIPTION)
 
 Phan Truong Quynh A, Pham Nguyen The N, Bui Thanh L, Huynh T, Ong T,
-Choisy M (2025). *serosv: Model Infectious Disease Parameters from
+Choisy M (2026). *serosv: Model Infectious Disease Parameters from
 Serosurveys*. https://oucru-modelling.github.io/serosv/,
 https://github.com/OUCRU-Modelling/serosv.
 
     @Manual{,
       title = {serosv: Model Infectious Disease Parameters from Serosurveys},
       author = {Anh {Phan Truong Quynh} and Nguyen {Pham Nguyen The} and Long {Bui Thanh} and Tuyen Huynh and Thinh Ong and Marc Choisy},
-      year = {2025},
+      year = {2026},
       note = {https://oucru-modelling.github.io/serosv/, https://github.com/OUCRU-Modelling/serosv},
     }
diff --git a/docs/index.html b/docs/index.html
index 671668d..5b031d0 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -11,7 +11,13 @@
 <link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png">
 <link rel="icon" sizes="any" href="favicon.ico">
 <link rel="manifest" href="site.webmanifest">
-<script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
 <script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
 <link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
@@ -28,7 +34,7 @@
 
     <a class="navbar-brand me-2" href="index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -42,15 +48,14 @@
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
 <li><a class="dropdown-item" href="articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
     <li><a class="dropdown-item" href="articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="articles/visualizing_model.html">Model visualization</a></li>
@@ -104,20 +109,34 @@ <h3 id="methods">Methods<a class="anchor" aria-label="anchor" href="#methods"></
 <p>The following methods are available to estimate seroprevalence and force of infection.</p>
 <p>Parametric approaches:</p>
 <ul>
-<li>Polynomial models:
+<li>
+<p>Frequentist methods:</p>
+<ul>
+<li>
+<p>Polynomial models:</p>
 <ul>
 <li>Muench’s model</li>
 <li>Griffiths’ model</li>
 <li>Grenfell and Anderson’s model</li>
 </ul>
 </li>
-<li>Nonlinear models:
+<li>
+<p>Nonlinear models:</p>
 <ul>
 <li>Farrington’s model</li>
 <li>Weibull model</li>
 </ul>
 </li>
-<li>Fractional polynomial models</li>
+<li><p>Fractional polynomial models</p></li>
+</ul>
+</li>
+<li>
+<p>Bayesian methods:</p>
+<ul>
+<li><p>Hierarchical Farrington model</p></li>
+<li><p>Hierarchical log-logistic model</p></li>
+</ul>
+</li>
 </ul>
 <p>Nonparametric approaches:</p>
 <ul>
@@ -133,11 +152,6 @@ <h3 id="methods">Methods<a class="anchor" aria-label="anchor" href="#methods"></
 </ul>
 </li>
 </ul>
-<p>Hierarchical Bayesian approaches:</p>
-<ul>
-<li><p>Hierarchical Farrington model</p></li>
-<li><p>Hierarchical log-logistic model</p></li>
-</ul>
 </div>
 </div>
 <div class="section level2">
@@ -148,26 +162,26 @@ <h3 id="fitting-rubella-data-from-the-uk">Fitting rubella data from the UK<a cla
 </h3>
 <p>Load the rubella in UK dataset.</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://oucru-modelling.github.io/serosv/">serosv</a></span><span class="op">)</span></span></code></pre></div>
-<p>Find the power for the best second degree fractional polynomial with monotonicity constraint and a logit link function. The power appears to be (-0.9,-0.9).</p>
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://oucru-modelling.github.io/serosv/">serosv</a></span><span class="op">)</span></span>
+<span><span class="va">rubella</span> <span class="op">&lt;-</span> <span class="va">rubella_uk_1986_1987</span></span></code></pre></div>
+<p>Fit the data using a fractional polynomial model via <code><a href="reference/fp_model.html">fp_model()</a></code>. In this example, the model searches for the best combination of powers within a specified range.</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">rubella</span> <span class="op">&lt;-</span> <span class="va">rubella_uk_1986_1987</span></span>
-<span></span>
-<span><span class="va">best_2d_mn</span> <span class="op">&lt;-</span> <span class="fu"><a href="reference/find_best_fp_powers.html">find_best_fp_powers</a></span><span class="op">(</span></span>
+<code class="sourceCode R"><span><span class="va">rubella_mod</span> <span class="op">&lt;-</span> <span class="fu"><a href="reference/fp_model.html">fp_model</a></span><span class="op">(</span></span>
 <span>  <span class="va">rubella</span>,</span>
-<span>  p<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="op">-</span><span class="fl">2</span>,<span class="fl">3</span>,<span class="fl">0.1</span><span class="op">)</span>, mc <span class="op">=</span> <span class="cn">T</span>, degree<span class="op">=</span><span class="fl">2</span>, link<span class="op">=</span><span class="st">"logit"</span></span>
+<span>  p<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
+<span>    p_range<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="op">-</span><span class="fl">2</span>,<span class="fl">3</span>,<span class="fl">0.1</span><span class="op">)</span>, <span class="co"># range of powers to search over</span></span>
+<span>    degree<span class="op">=</span><span class="fl">2</span> <span class="co"># maximum degree for the search</span></span>
+<span>  <span class="op">)</span>, </span>
+<span>  monotonic <span class="op">=</span> <span class="cn">T</span>, <span class="co"># enforce model to be monotonic</span></span>
+<span>  link<span class="op">=</span><span class="st">"logit"</span></span>
 <span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">best_2d_mn</span></span>
-<span><span class="co">#&gt; $p</span></span>
-<span><span class="co">#&gt; [1] -0.9 -0.9</span></span>
+<span><span class="va">rubella_mod</span></span>
+<span><span class="co">#&gt; Fractional polynomial model </span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; $deviance</span></span>
-<span><span class="co">#&gt; [1] 37.57966</span></span>
+<span><span class="co">#&gt; Input type:  aggregated </span></span>
+<span><span class="co">#&gt; Powers:  -0.9, -0.9 </span></span>
 <span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; $model</span></span>
-<span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Call:  glm(formula = as.formula(formulate(p_cur)), family = binomial(link = link))</span></span>
+<span><span class="co">#&gt; Call:  glm(formula = as.formula(formulate(curr_p)), family = binomial(link = link))</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Coefficients:</span></span>
 <span><span class="co">#&gt;               (Intercept)                I(age^-0.9)  </span></span>
@@ -178,13 +192,9 @@ <h3 id="fitting-rubella-data-from-the-uk">Fitting rubella data from the UK<a cla
 <span><span class="co">#&gt; Degrees of Freedom: 43 Total (i.e. Null);  41 Residual</span></span>
 <span><span class="co">#&gt; Null Deviance:       1369 </span></span>
 <span><span class="co">#&gt; Residual Deviance: 37.58     AIC: 210.1</span></span></code></pre></div>
-<p>Finally, fit the second degree fractional polynomial.</p>
+<p>Visualize the model</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">fpmd</span> <span class="op">&lt;-</span> <span class="fu"><a href="reference/fp_model.html">fp_model</a></span><span class="op">(</span></span>
-<span>  <span class="va">rubella</span>,</span>
-<span>  p<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">0.9</span>, <span class="op">-</span><span class="fl">0.9</span><span class="op">)</span>, link<span class="op">=</span><span class="st">"logit"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fpmd</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">rubella_mod</span><span class="op">)</span></span></code></pre></div>
 <p><img src="reference/figures/README-unnamed-chunk-5-1.png" width="100%"></p>
 </div>
 <div class="section level3">
@@ -192,15 +202,6 @@ <h3 id="fitting-parvo-b19-data-from-finland">Fitting Parvo B19 data from Finland
 </h3>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span>
-<span><span class="co">#&gt; Warning: package 'dplyr' was built under R version 4.3.1</span></span>
-<span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt; Attaching package: 'dplyr'</span></span>
-<span><span class="co">#&gt; The following objects are masked from 'package:stats':</span></span>
-<span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt;     filter, lag</span></span>
-<span><span class="co">#&gt; The following objects are masked from 'package:base':</span></span>
-<span><span class="co">#&gt; </span></span>
-<span><span class="co">#&gt;     intersect, setdiff, setequal, union</span></span>
 <span><span class="va">parvob19</span> <span class="op">&lt;-</span> <span class="va">parvob19_fi_1997_1998</span></span>
 <span></span>
 <span><span class="co"># for linelisting data, either transform it to aggregated</span></span>
@@ -208,7 +209,7 @@ <h3 id="fitting-parvo-b19-data-from-finland">Fitting Parvo B19 data from Finland
 <span>  <span class="va">parvob19</span><span class="op">$</span><span class="va">age</span>, </span>
 <span>  <span class="va">parvob19</span><span class="op">$</span><span class="va">seropositive</span>,</span>
 <span>  stratum_col <span class="op">=</span> <span class="st">"age"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
-<span>  <span class="fu"><a href="reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"Muench"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span>k <span class="op">=</span> <span class="fl">1</span><span class="op">)</span> <span class="op">|&gt;</span></span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 <p><img src="reference/figures/README-unnamed-chunk-6-1.png" width="100%"></p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
@@ -216,7 +217,7 @@ <h3 id="fitting-parvo-b19-data-from-finland">Fitting Parvo B19 data from Finland
 <span><span class="co"># or fit data as is</span></span>
 <span><span class="va">parvob19</span> <span class="op">|&gt;</span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/rename.html" class="external-link">rename</a></span><span class="op">(</span>status <span class="op">=</span> <span class="va">seropositive</span><span class="op">)</span> <span class="op">|&gt;</span></span>
-<span>  <span class="fu"><a href="reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"Muench"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="reference/polynomial_model.html">polynomial_model</a></span><span class="op">(</span>k <span class="op">=</span> <span class="fl">1</span><span class="op">)</span> <span class="op">|&gt;</span></span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 <p><img src="reference/figures/README-unnamed-chunk-6-2.png" width="100%"></p>
 </div>
diff --git a/docs/index.md b/docs/index.md
index b06d044..7bc8ac7 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -36,14 +36,26 @@ of infection.
 
 Parametric approaches:
 
-- Polynomial models:
-  - Muench’s model
-  - Griffiths’ model
-  - Grenfell and Anderson’s model
-- Nonlinear models:
-  - Farrington’s model
-  - Weibull model
-- Fractional polynomial models
+- Frequentist methods:
+
+  - Polynomial models:
+
+    - Muench’s model
+    - Griffiths’ model
+    - Grenfell and Anderson’s model
+
+  - Nonlinear models:
+
+    - Farrington’s model
+    - Weibull model
+
+  - Fractional polynomial models
+
+- Bayesian methods:
+
+  - Hierarchical Farrington model
+
+  - Hierarchical log-logistic model
 
 Nonparametric approaches:
 
@@ -57,12 +69,6 @@ Semiparametric approaches:
 
   - Generalized Linear Mixed Model framework
 
-Hierarchical Bayesian approaches:
-
-- Hierarchical Farrington model
-
-- Hierarchical log-logistic model
-
 ## Demo
 
 ### Fitting rubella data from the UK
@@ -71,30 +77,31 @@ Load the rubella in UK dataset.
 
 ``` r
 library(serosv)
+rubella <- rubella_uk_1986_1987
 ```
 
-Find the power for the best second degree fractional polynomial with
-monotonicity constraint and a logit link function. The power appears to
-be (-0.9,-0.9).
+Fit the data using a fractional polynomial model via
+[`fp_model()`](https://oucru-modelling.github.io/serosv/reference/fp_model.md).
+In this example, the model searches for the best combination of powers
+within a specified range.
 
 ``` r
-rubella <- rubella_uk_1986_1987
-
-best_2d_mn <- find_best_fp_powers(
+rubella_mod <- fp_model(
   rubella,
-  p=seq(-2,3,0.1), mc = T, degree=2, link="logit"
+  p=list(
+    p_range=seq(-2,3,0.1), # range of powers to search over
+    degree=2 # maximum degree for the search
+  ), 
+  monotonic = T, # enforce model to be monotonic
+  link="logit"
 )
-
-best_2d_mn
-#> $p
-#> [1] -0.9 -0.9
-#> 
-#> $deviance
-#> [1] 37.57966
+rubella_mod
+#> Fractional polynomial model 
 #> 
-#> $model
+#> Input type:  aggregated 
+#> Powers:  -0.9, -0.9 
 #> 
-#> Call:  glm(formula = as.formula(formulate(p_cur)), family = binomial(link = link))
+#> Call:  glm(formula = as.formula(formulate(curr_p)), family = binomial(link = link))
 #> 
 #> Coefficients:
 #>               (Intercept)                I(age^-0.9)  
@@ -107,14 +114,10 @@ best_2d_mn
 #> Residual Deviance: 37.58     AIC: 210.1
 ```
 
-Finally, fit the second degree fractional polynomial.
+Visualize the model
 
 ``` r
-fpmd <- fp_model(
-  rubella,
-  p=c(-0.9, -0.9), link="logit")
-
-plot(fpmd)
+plot(rubella_mod)
 ```
 
 ![](reference/figures/README-unnamed-chunk-5-1.png)
@@ -123,15 +126,6 @@ plot(fpmd)
 
 ``` r
 library(dplyr)
-#> Warning: package 'dplyr' was built under R version 4.3.1
-#> 
-#> Attaching package: 'dplyr'
-#> The following objects are masked from 'package:stats':
-#> 
-#>     filter, lag
-#> The following objects are masked from 'package:base':
-#> 
-#>     intersect, setdiff, setequal, union
 parvob19 <- parvob19_fi_1997_1998
 
 # for linelisting data, either transform it to aggregated
@@ -139,7 +133,7 @@ transform_data(
   parvob19$age, 
   parvob19$seropositive,
   stratum_col = "age") |>
-  polynomial_model(type = "Muench") |>
+  polynomial_model(k = 1) |>
   plot()
 ```
 
@@ -150,7 +144,7 @@ transform_data(
 # or fit data as is
 parvob19 |>
   rename(status = seropositive) |>
-  polynomial_model(type = "Muench") |>
+  polynomial_model(k = 1) |>
   plot()
 ```
 
diff --git a/docs/llms.txt b/docs/llms.txt
index 7ae0307..be37d74 100644
--- a/docs/llms.txt
+++ b/docs/llms.txt
@@ -36,14 +36,26 @@ of infection.
 
 Parametric approaches:
 
-- Polynomial models:
-  - Muench’s model
-  - Griffiths’ model
-  - Grenfell and Anderson’s model
-- Nonlinear models:
-  - Farrington’s model
-  - Weibull model
-- Fractional polynomial models
+- Frequentist methods:
+
+  - Polynomial models:
+
+    - Muench’s model
+    - Griffiths’ model
+    - Grenfell and Anderson’s model
+
+  - Nonlinear models:
+
+    - Farrington’s model
+    - Weibull model
+
+  - Fractional polynomial models
+
+- Bayesian methods:
+
+  - Hierarchical Farrington model
+
+  - Hierarchical log-logistic model
 
 Nonparametric approaches:
 
@@ -57,12 +69,6 @@ Semiparametric approaches:
 
   - Generalized Linear Mixed Model framework
 
-Hierarchical Bayesian approaches:
-
-- Hierarchical Farrington model
-
-- Hierarchical log-logistic model
-
 ## Demo
 
 ### Fitting rubella data from the UK
@@ -71,30 +77,31 @@ Load the rubella in UK dataset.
 
 ``` r
 library(serosv)
+rubella <- rubella_uk_1986_1987
 ```
 
-Find the power for the best second degree fractional polynomial with
-monotonicity constraint and a logit link function. The power appears to
-be (-0.9,-0.9).
+Fit the data using a fractional polynomial model via
+[`fp_model()`](https://oucru-modelling.github.io/serosv/reference/fp_model.md).
+In this example, the model searches for the best combination of powers
+within a specified range.
 
 ``` r
-rubella <- rubella_uk_1986_1987
-
-best_2d_mn <- find_best_fp_powers(
+rubella_mod <- fp_model(
   rubella,
-  p=seq(-2,3,0.1), mc = T, degree=2, link="logit"
+  p=list(
+    p_range=seq(-2,3,0.1), # range of powers to search over
+    degree=2 # maximum degree for the search
+  ), 
+  monotonic = T, # enforce model to be monotonic
+  link="logit"
 )
-
-best_2d_mn
-#> $p
-#> [1] -0.9 -0.9
-#> 
-#> $deviance
-#> [1] 37.57966
+rubella_mod
+#> Fractional polynomial model 
 #> 
-#> $model
+#> Input type:  aggregated 
+#> Powers:  -0.9, -0.9 
 #> 
-#> Call:  glm(formula = as.formula(formulate(p_cur)), family = binomial(link = link))
+#> Call:  glm(formula = as.formula(formulate(curr_p)), family = binomial(link = link))
 #> 
 #> Coefficients:
 #>               (Intercept)                I(age^-0.9)  
@@ -107,14 +114,10 @@ best_2d_mn
 #> Residual Deviance: 37.58     AIC: 210.1
 ```
 
-Finally, fit the second degree fractional polynomial.
+Visualize the model
 
 ``` r
-fpmd <- fp_model(
-  rubella,
-  p=c(-0.9, -0.9), link="logit")
-
-plot(fpmd)
+plot(rubella_mod)
 ```
 
 ![](reference/figures/README-unnamed-chunk-5-1.png)
@@ -123,15 +126,6 @@ plot(fpmd)
 
 ``` r
 library(dplyr)
-#> Warning: package 'dplyr' was built under R version 4.3.1
-#> 
-#> Attaching package: 'dplyr'
-#> The following objects are masked from 'package:stats':
-#> 
-#>     filter, lag
-#> The following objects are masked from 'package:base':
-#> 
-#>     intersect, setdiff, setequal, union
 parvob19 <- parvob19_fi_1997_1998
 
 # for linelisting data, either transform it to aggregated
@@ -139,7 +133,7 @@ transform_data(
   parvob19$age, 
   parvob19$seropositive,
   stratum_col = "age") |>
-  polynomial_model(type = "Muench") |>
+  polynomial_model(k = 1) |>
   plot()
 ```
 
@@ -150,7 +144,7 @@ transform_data(
 # or fit data as is
 parvob19 |>
   rename(status = seropositive) |>
-  polynomial_model(type = "Muench") |>
+  polynomial_model(k = 1) |>
   plot()
 ```
 
@@ -229,14 +223,6 @@ parvob19 |>
 
 ## Models
 
-- [`sir_basic_model()`](https://oucru-modelling.github.io/serosv/reference/sir_basic_model.md)
-  : Basic SIR model
-- [`sir_static_model()`](https://oucru-modelling.github.io/serosv/reference/sir_static_model.md)
-  : SIR static model (age-heterogeneous, endemic equilibrium)
-- [`sir_subpops_model()`](https://oucru-modelling.github.io/serosv/reference/sir_subpops_model.md)
-  : SIR Model with Interacting Subpopulations
-- [`mseir_model()`](https://oucru-modelling.github.io/serosv/reference/mseir_model.md)
-  : MSEIR model
 - [`polynomial_model()`](https://oucru-modelling.github.io/serosv/reference/polynomial_model.md)
   : Polynomial models
 - [`farrington_model()`](https://oucru-modelling.github.io/serosv/reference/farrington_model.md)
@@ -254,8 +240,7 @@ parvob19 |>
 - [`mixture_model()`](https://oucru-modelling.github.io/serosv/reference/mixture_model.md)
   : Fit a mixture model to classify serostatus
 - [`estimate_from_mixture()`](https://oucru-modelling.github.io/serosv/reference/estimate_from_mixture.md)
-  : Estimate seroprevalence and foi by combining mixture model and
-  regression
+  : Estimate seroprevalence and FOI from a fixed mixture model
 - [`age_time_model()`](https://oucru-modelling.github.io/serosv/reference/age_time_model.md)
   : Age-time varying seroprevalence
 
@@ -280,14 +265,6 @@ parvob19 |>
   : plot() overloading for fractional polynomial model
 - [`plot(`*`<lp_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/plot.lp_model.md)
   : plot() overloading for local polynomial model
-- [`plot(`*`<mseir_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/plot.mseir_model.md)
-  : plot() overloading for MSEIR model
-- [`plot(`*`<sir_basic_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/plot.sir_basic_model.md)
-  : plot() overloading for SIR model
-- [`plot(`*`<sir_static_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/plot.sir_static_model.md)
-  : plot() overloading for SIR static model
-- [`plot(`*`<sir_subpops_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/plot.sir_subpops_model.md)
-  : plot() overloading for SIR sub populations model
 - [`plot(`*`<hierarchical_bayesian_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/plot.hierarchical_bayesian_model.md)
   : plot() overloading for hierarchical_bayesian_model
 - [`plot(`*`<penalized_spline_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/plot.penalized_spline_model.md)
@@ -313,7 +290,7 @@ parvob19 |>
 - [`pava()`](https://oucru-modelling.github.io/serosv/reference/pava.md)
   : Monotonize seroprevalence
 - [`to_titer()`](https://oucru-modelling.github.io/serosv/reference/to_titer.md)
-  : Process assay test result to titer
+  : Convert assay readings to titers
 - [`correct_prevalence()`](https://oucru-modelling.github.io/serosv/reference/correct_prevalence.md)
   : Estimate the true sero prevalence using Frequentist/Bayesian
   estimation
@@ -323,25 +300,41 @@ parvob19 |>
   : Preprocess data
 - [`compare_models()`](https://oucru-modelling.github.io/serosv/reference/compare_models.md)
   : Compare models
-- [`compute_ci()`](https://oucru-modelling.github.io/serosv/reference/compute_ci.md)
-  : Compute confidence interval
-- [`compute_ci.fp_model()`](https://oucru-modelling.github.io/serosv/reference/compute_ci.fp_model.md)
+- [`compute_ci(`*`<default>`*`)`](https://oucru-modelling.github.io/serosv/reference/compute_ci.default.md)
+  : Compute confidence interval for a model of serosv
+- [`compute_ci(`*`<fp_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/compute_ci.fp_model.md)
   : Compute confidence interval for fractional polynomial model
-- [`compute_ci.lp_model()`](https://oucru-modelling.github.io/serosv/reference/compute_ci.lp_model.md)
+- [`compute_ci(`*`<lp_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/compute_ci.lp_model.md)
   : Compute confidence interval for local polynomial model
-- [`compute_ci.weibull_model()`](https://oucru-modelling.github.io/serosv/reference/compute_ci.weibull_model.md)
+- [`compute_ci(`*`<weibull_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/compute_ci.weibull_model.md)
   : Compute confidence interval for Weibull model
-- [`compute_ci.penalized_spline_model()`](https://oucru-modelling.github.io/serosv/reference/compute_ci.penalized_spline_model.md)
+- [`compute_ci(`*`<penalized_spline_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/compute_ci.penalized_spline_model.md)
   : Compute confidence interval for penalized_spline_model
-- [`compute_ci.mixture_model()`](https://oucru-modelling.github.io/serosv/reference/compute_ci.mixture_model.md)
+- [`compute_ci(`*`<mixture_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/compute_ci.mixture_model.md)
   : Compute confidence interval for mixture model
-- [`compute_ci.age_time_model()`](https://oucru-modelling.github.io/serosv/reference/compute_ci.age_time_model.md)
+- [`compute_ci(`*`<age_time_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/compute_ci.age_time_model.md)
   : Compute confidence interval for time age model
-- [`compute_ci.hierarchical_bayesian_model()`](https://oucru-modelling.github.io/serosv/reference/compute_ci.hierarchical_bayesian_model.md)
+- [`compute_ci(`*`<hierarchical_bayesian_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/compute_ci.hierarchical_bayesian_model.md)
   : Compute 95% credible interval for hierarchical Bayesian model
+- [`predict(`*`<age_time_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/predict.age_time_model.md)
+  : Predict from the age_time_mdoel
+- [`predict(`*`<farrington_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/predict.farrington_model.md)
+  : Prediction for serosv Farrington model
+- [`predict(`*`<fp_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/predict.fp_model.md)
+  : Prediction for serosv fractional polynomial model
+- [`predict(`*`<hierarchical_bayesian_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/predict.hierarchical_bayesian_model.md)
+  : Predict from an hierarchical bayesian model
+- [`predict(`*`<lp_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/predict.lp_model.md)
+  : Prediction for serosv local polynomial model
+- [`predict(`*`<penalized_spline_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/predict.penalized_spline_model.md)
+  : Prediction for serosv penalized spline model
+- [`predict(`*`<weibull_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/predict.weibull_model.md)
+  : Prediction for serosv Weibull model
+- [`predict(`*`<polynomial_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/predict.polynomial_model.md)
+  : Prediction for serosv polynomial model
 - [`find_best_fp_powers()`](https://oucru-modelling.github.io/serosv/reference/find_best_fp_powers.md)
-  : Returns the powers of the GLM fitted model which has the lowest
-  deviance score.
+  : Returns the powers of the fractional polynomial model which has the
+  lowest deviance score.
 
 # Articles
 
@@ -349,16 +342,12 @@ parvob19 |>
 
 - [Input
   data](https://oucru-modelling.github.io/serosv/articles/linelisting_vs_aggregated.md):
-- [SIR
-  model](https://oucru-modelling.github.io/serosv/articles/sir_model.md):
 - [Parametric
   models](https://oucru-modelling.github.io/serosv/articles/parametric_model.md):
 - [Nonparametric
   model](https://oucru-modelling.github.io/serosv/articles/nonparametric_model.md):
 - [Semiparametric
   model](https://oucru-modelling.github.io/serosv/articles/semiparametric_model.md):
-- [Hierarchical Bayesian
-  models](https://oucru-modelling.github.io/serosv/articles/hierarchical_model.md):
 - [Modeling directly from antibody
   levels](https://oucru-modelling.github.io/serosv/articles/model_quantitative_data.md):
 - [Model repeated cross-sectional
@@ -366,6 +355,8 @@ parvob19 |>
 
 ### Utilities
 
+- [Convert assay readings to
+  titer](https://oucru-modelling.github.io/serosv/articles/reading_to_titer.md):
 - [Data
   transformation](https://oucru-modelling.github.io/serosv/articles/data_transformation.md):
 - [Imperfect serological
diff --git a/docs/news/index.html b/docs/news/index.html
index 87e19d0..45ef970 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -1,5 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -19,15 +25,14 @@
 <li class="nav-item dropdown">
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="../articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="../articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
     <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
@@ -48,9 +53,16 @@
       <small>Source: <a href="https://github.com/OUCRU-Modelling/serosv/blob/HEAD/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
     </div>
 
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="development version" id="serosv-development-version">serosv (development version)<a class="anchor" aria-label="anchor" href="#serosv-development-version"></a></h2>
+<ul><li>clean up documentation</li>
+</ul></div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="1.2.0" id="serosv-120">serosv 1.2.0<a class="anchor" aria-label="anchor" href="#serosv-120"></a></h2>
-</div>
+<ul><li>add to_titer() function to convert assay reading to titer</li>
+<li>update the docs</li>
+<li>update article for to_titer() on the website</li>
+</ul></div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="1.1.0" id="serosv-110">serosv 1.1.0<a class="anchor" aria-label="anchor" href="#serosv-110"></a></h2><p class="text-muted">CRAN release: 2025-04-09</p>
 <ul><li>add correct_prevalence() function to estimate real prevalence from imperfect test</li>
diff --git a/docs/news/index.md b/docs/news/index.md
index a9dc643..66f1f7b 100644
--- a/docs/news/index.md
+++ b/docs/news/index.md
@@ -1,7 +1,15 @@
 # Changelog
 
+## serosv (development version)
+
+- clean up documentation
+
 ## serosv 1.2.0
 
+- add to_titer() function to convert assay reading to titer
+- update the docs
+- update article for to_titer() on the website
+
 ## serosv 1.1.0
 
 CRAN release: 2025-04-09
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 35d0d3f..91b6676 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -1,20 +1,19 @@
-pandoc: 2.19.2
+pandoc: 3.6.3
 pkgdown: 2.2.0
 pkgdown_sha: ~
 articles:
   data_transformation: data_transformation.html
-  hierarchical_model: hierarchical_model.html
   imperfect_test: imperfect_test.html
   linelisting_vs_aggregated: linelisting_vs_aggregated.html
   model_quantitative_data: model_quantitative_data.html
   model_selection: model_selection.html
   nonparametric_model: nonparametric_model.html
   parametric_model: parametric_model.html
+  reading_to_titer: reading_to_titer.html
   repeated_cross_sectional: repeated_cross_sectional.html
   semiparametric_model: semiparametric_model.html
-  sir_model: sir_model.html
   visualizing_model: visualizing_model.html
-last_built: 2025-11-14T10:23Z
+last_built: 2026-03-24T11:10Z
 urls:
   reference: https://oucru-modelling.github.io/serosv/reference
   article: https://oucru-modelling.github.io/serosv/articles
diff --git a/docs/reference/add_thresholds.html b/docs/reference/add_thresholds.html
index 709c1f2..39e64e2 100644
--- a/docs/reference/add_thresholds.html
+++ b/docs/reference/add_thresholds.html
@@ -1,5 +1,11 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Visualize positive threshold at different dilution factors — add_thresholds • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
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@@ -7,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -19,14 +25,14 @@
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-    <li><a class="dropdown-item" href="../articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
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-    <li><a class="dropdown-item" href="../articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
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diff --git a/docs/reference/age_time_model.html b/docs/reference/age_time_model.html
index d179aef..4cf49a5 100644
--- a/docs/reference/age_time_model.html
+++ b/docs/reference/age_time_model.html
@@ -1,5 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Age-time varying seroprevalence — age_time_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Age-time varying seroprevalence — age_time_model"><meta name="description" content="Fit age-stratified seroprevalence across multiple time points. Also try to monotonize age (or birth cohort) - specific seroprevalence."><meta property="og:description" content="Fit age-stratified seroprevalence across multiple time points. Also try to monotonize age (or birth cohort) - specific seroprevalence."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
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     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -19,14 +25,14 @@
 <li class="nav-item dropdown">
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="../articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="../articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
@@ -56,6 +62,10 @@
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">age_time_model</span><span class="op">(</span></span>
 <span>  <span class="va">data</span>,</span>
+<span>  age_col <span class="op">=</span> <span class="st">"age"</span>,</span>
+<span>  status_col <span class="op">=</span> <span class="st">"status"</span>,</span>
+<span>  pos_col <span class="op">=</span> <span class="st">"pos"</span>,</span>
+<span>  tot_col <span class="op">=</span> <span class="st">"tot"</span>,</span>
 <span>  time_col <span class="op">=</span> <span class="st">"date"</span>,</span>
 <span>  grouping_col <span class="op">=</span> <span class="st">"group"</span>,</span>
 <span>  age_correct <span class="op">=</span> <span class="cn">F</span>,</span>
@@ -70,31 +80,47 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
-<dd><p>- input data, must have`age`, `status`, time, group columns, where group column determines how data is aggregated</p></dd>
+<dd><p>input data, must have age, status, time, group columns, where group column determines how data is aggregated</p></dd>
+
+
+<dt id="arg-age-col">age_col<a class="anchor" aria-label="anchor" href="#arg-age-col"></a></dt>
+<dd><p>name of the `age` column (default age_col="age").</p></dd>
+
+
+<dt id="arg-status-col">status_col<a class="anchor" aria-label="anchor" href="#arg-status-col"></a></dt>
+<dd><p>name of the `status` column (default status_col="status").</p></dd>
+
+
+<dt id="arg-pos-col">pos_col<a class="anchor" aria-label="anchor" href="#arg-pos-col"></a></dt>
+<dd><p>name of the `pos` column (default pos_col="pos").</p></dd>
+
+
+<dt id="arg-tot-col">tot_col<a class="anchor" aria-label="anchor" href="#arg-tot-col"></a></dt>
+<dd><p>name of the `tot` column (default tot_col="tot").</p></dd>
 
 
 <dt id="arg-time-col">time_col<a class="anchor" aria-label="anchor" href="#arg-time-col"></a></dt>
-<dd><p>- name of the column for time (default to `date`)</p></dd>
+<dd><p>name of the column for time (default to "date")</p></dd>
 
 
 <dt id="arg-grouping-col">grouping_col<a class="anchor" aria-label="anchor" href="#arg-grouping-col"></a></dt>
-<dd><p>- name of the column for time (default to `group`)</p></dd>
+<dd><p>name of the column for time (default to "group")</p></dd>
 
 
 <dt id="arg-age-correct">age_correct<a class="anchor" aria-label="anchor" href="#arg-age-correct"></a></dt>
-<dd><p>- a boolean, if `TRUE`, monotonize age-specific prevalence. Monotonize birth cohort-specific seroprevalence otherwise.</p></dd>
+<dd><p>a boolean, if `TRUE`, monotonize age-specific prevalence. Monotonize birth cohort-specific seroprevalence otherwise.</p></dd>
 
 
 <dt id="arg-le">le<a class="anchor" aria-label="anchor" href="#arg-le"></a></dt>
-<dd><p>- number of bins to generate age grid, used when monotonizing data</p></dd>
+<dd><p>number of bins to generate age grid, used when monotonizing data</p></dd>
 
 
 <dt id="arg-ci">ci<a class="anchor" aria-label="anchor" href="#arg-ci"></a></dt>
-<dd><p>- confidence interval for smoothing</p></dd>
+<dd><p>confidence interval for smoothing</p></dd>
 
 
 <dt id="arg-monotonize-method">monotonize_method<a class="anchor" aria-label="anchor" href="#arg-monotonize-method"></a></dt>
-<dd><p>- either "pava" or "scam"</p></dd>
+<dd><p>either "pava" or "scam"</p></dd>
 
 </dl></div>
     <div class="section level2">
diff --git a/docs/reference/age_time_model.md b/docs/reference/age_time_model.md
index d78981d..09a33b2 100644
--- a/docs/reference/age_time_model.md
+++ b/docs/reference/age_time_model.md
@@ -8,6 +8,10 @@ to monotonize age (or birth cohort) - specific seroprevalence.
 ``` r
 age_time_model(
   data,
+  age_col = "age",
+  status_col = "status",
+  pos_col = "pos",
+  tot_col = "tot",
   time_col = "date",
   grouping_col = "group",
   age_correct = F,
@@ -21,33 +25,49 @@ age_time_model(
 
 - data:
 
-  \- input data, must have\`age\`, \`status\`, time, group columns,
-  where group column determines how data is aggregated
+  input data, must have age, status, time, group columns, where group
+  column determines how data is aggregated
+
+- age_col:
+
+  name of the \`age\` column (default age_col="age").
+
+- status_col:
+
+  name of the \`status\` column (default status_col="status").
+
+- pos_col:
+
+  name of the \`pos\` column (default pos_col="pos").
+
+- tot_col:
+
+  name of the \`tot\` column (default tot_col="tot").
 
 - time_col:
 
-  \- name of the column for time (default to \`date\`)
+  name of the column for time (default to "date")
 
 - grouping_col:
 
-  \- name of the column for time (default to \`group\`)
+  name of the column for time (default to "group")
 
 - age_correct:
 
-  \- a boolean, if \`TRUE\`, monotonize age-specific prevalence.
-  Monotonize birth cohort-specific seroprevalence otherwise.
+  a boolean, if \`TRUE\`, monotonize age-specific prevalence. Monotonize
+  birth cohort-specific seroprevalence otherwise.
 
 - le:
 
-  \- number of bins to generate age grid, used when monotonizing data
+  number of bins to generate age grid, used when monotonizing data
 
 - ci:
 
-  \- confidence interval for smoothing
+  confidence interval for smoothing
 
 - monotonize_method:
 
-  \- either "pava" or "scam"
+  either "pava" or "scam"
 
 ## Value
 
diff --git a/docs/reference/compare_models.html b/docs/reference/compare_models.html
index 8464b17..ef57ca4 100644
--- a/docs/reference/compare_models.html
+++ b/docs/reference/compare_models.html
@@ -1,59 +1,50 @@
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+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
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@@ -69,21 +60,29 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">compare_models</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">compare_models</span><span class="op">(</span><span class="va">data</span>, method <span class="op">=</span> <span class="st">"AIC/BIC"</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
+<dd><p>input data to fit into the models</p></dd>
+
+
+<dt id="arg-method">method<a class="anchor" aria-label="anchor" href="#arg-method"></a></dt>
+<dd><p>method to compare models. Can be one of the built-in methods or a function to compute the returned metrics (see Details).</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>models to be compared. Must be models created by serosv. If models' names are not provided, indices will be used instead for the `model` column in the returned data.frame.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a data.frame of 4 columns</p>
-<dl><dt>model</dt>
+    <p>a data.frame of 4 columns</p>
+<dl><dt>label</dt>
 <dd><p>name or index of the model</p></dd>
 
   <dt>type</dt>
@@ -96,24 +95,30 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <dd><p>BIC value for the model (lower value indicates better fit)</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Built-in comparison methods include:
+- computing AIC and BIC, which returns AIC, BIC values of the model if available
+- cross validation, which reutns</p>
+    </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
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-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
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diff --git a/docs/reference/compare_models.md b/docs/reference/compare_models.md
index 203a32f..68552e7 100644
--- a/docs/reference/compare_models.md
+++ b/docs/reference/compare_models.md
@@ -5,11 +5,20 @@ Compare models
 ## Usage
 
 ``` r
-compare_models(...)
+compare_models(data, method = "AIC/BIC", ...)
 ```
 
 ## Arguments
 
+- data:
+
+  input data to fit into the models
+
+- method:
+
+  method to compare models. Can be one of the built-in methods or a
+  function to compute the returned metrics (see Details).
+
 - ...:
 
   models to be compared. Must be models created by serosv. If models'
@@ -20,7 +29,7 @@ compare_models(...)
 
 a data.frame of 4 columns
 
-- model:
+- label:
 
   name or index of the model
 
@@ -35,3 +44,9 @@ a data.frame of 4 columns
 - BIC:
 
   BIC value for the model (lower value indicates better fit)
+
+## Details
+
+Built-in comparison methods include: - computing AIC and BIC, which
+returns AIC, BIC values of the model if available - cross validation,
+which reutns
diff --git a/docs/reference/compute_ci.age_time_model.html b/docs/reference/compute_ci.age_time_model.html
index 4b13b43..bc32877 100644
--- a/docs/reference/compute_ci.age_time_model.html
+++ b/docs/reference/compute_ci.age_time_model.html
@@ -1,59 +1,50 @@
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+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Compute confidence interval for time age model — compute_ci.age_time_model"><meta name="description" content="Compute confidence interval for time age model"><meta property="og:description" content="Compute confidence interval for time age model"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
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+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
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@@ -69,51 +60,52 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">compute_ci.age_time_model</span><span class="op">(</span><span class="va">x</span>, ci <span class="op">=</span> <span class="fl">0.95</span>, le <span class="op">=</span> <span class="fl">100</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'age_time_model'</span></span>
+<span><span class="fu">compute_ci</span><span class="op">(</span><span class="va">x</span>, ci <span class="op">=</span> <span class="fl">0.95</span>, le <span class="op">=</span> <span class="fl">100</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
-<dd><p>- serosv models</p></dd>
 
 
-<dt>ci</dt>
-<dd><p>- confidence interval</p></dd>
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
+<dd><p>serosv models</p></dd>
 
 
-<dt>le</dt>
-<dd><p>- number of data for computing confidence interval</p></dd>
+<dt id="arg-ci">ci<a class="anchor" aria-label="anchor" href="#arg-ci"></a></dt>
+<dd><p>confidence interval</p></dd>
 
 
-<dt>...</dt>
-<dd><p>- arbitrary argument</p></dd>
+<dt id="arg-le">le<a class="anchor" aria-label="anchor" href="#arg-le"></a></dt>
+<dd><p>number of data for computing confidence interval</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arbitrary argument</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>confidence interval dataframe with n_group x 3 cols, the columns are `group`, `sp_df`, `foi_df`</p>
+    <p>confidence interval dataframe with n_group x 3 cols, the columns are `group`, `sp_df`, `foi_df`</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
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-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
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diff --git a/docs/reference/compute_ci.age_time_model.md b/docs/reference/compute_ci.age_time_model.md
index 18d0b0f..d5be1e4 100644
--- a/docs/reference/compute_ci.age_time_model.md
+++ b/docs/reference/compute_ci.age_time_model.md
@@ -5,26 +5,27 @@ Compute confidence interval for time age model
 ## Usage
 
 ``` r
-compute_ci.age_time_model(x, ci = 0.95, le = 100, ...)
+# S3 method for class 'age_time_model'
+compute_ci(x, ci = 0.95, le = 100, ...)
 ```
 
 ## Arguments
 
 - x:
 
-  \- serosv models
+  serosv models
 
 - ci:
 
-  \- confidence interval
+  confidence interval
 
 - le:
 
-  \- number of data for computing confidence interval
+  number of data for computing confidence interval
 
 - ...:
 
-  \- arbitrary argument
+  arbitrary argument
 
 ## Value
 
diff --git a/docs/reference/compute_ci.default.html b/docs/reference/compute_ci.default.html
new file mode 100644
index 0000000..57722e5
--- /dev/null
+++ b/docs/reference/compute_ci.default.html
@@ -0,0 +1,111 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Compute confidence interval for a model of serosv — compute_ci.default • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
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+    <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
+    <li><a class="dropdown-item" href="../articles/model_selection.html">Model selection</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Compute confidence interval for a model of serosv</h1>
+      <small class="dont-index">Source: <a href="https://github.com/OUCRU-Modelling/serosv/blob/HEAD/R/compute_ci.R" class="external-link"><code>R/compute_ci.R</code></a></small>
+      <div class="d-none name"><code>compute_ci.default.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Compute confidence interval for a model of serosv</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># Default S3 method</span></span>
+<span><span class="fu">compute_ci</span><span class="op">(</span><span class="va">x</span>, ci <span class="op">=</span> <span class="fl">0.95</span>, le <span class="op">=</span> <span class="fl">100</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
+<dd><p>serosv models</p></dd>
+
+
+<dt id="arg-ci">ci<a class="anchor" aria-label="anchor" href="#arg-ci"></a></dt>
+<dd><p>confidence interval</p></dd>
+
+
+<dt id="arg-le">le<a class="anchor" aria-label="anchor" href="#arg-le"></a></dt>
+<dd><p>number of data for computing confidence interval</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arbitrary argument</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
+</div>
+
+    </footer></div>
+
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+
+
+  </body></html>
+
diff --git a/docs/reference/compute_ci.default.md b/docs/reference/compute_ci.default.md
new file mode 100644
index 0000000..80de181
--- /dev/null
+++ b/docs/reference/compute_ci.default.md
@@ -0,0 +1,33 @@
+# Compute confidence interval for a model of serosv
+
+Compute confidence interval for a model of serosv
+
+## Usage
+
+``` r
+# Default S3 method
+compute_ci(x, ci = 0.95, le = 100, ...)
+```
+
+## Arguments
+
+- x:
+
+  serosv models
+
+- ci:
+
+  confidence interval
+
+- le:
+
+  number of data for computing confidence interval
+
+- ...:
+
+  arbitrary argument
+
+## Value
+
+confidence interval dataframe with 4 variables, x and y for the fitted
+values and ymin and ymax for the confidence interval
diff --git a/docs/reference/compute_ci.fp_model.html b/docs/reference/compute_ci.fp_model.html
index 85618ed..dd8a1b3 100644
--- a/docs/reference/compute_ci.fp_model.html
+++ b/docs/reference/compute_ci.fp_model.html
@@ -1,59 +1,50 @@
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@@ -69,51 +60,52 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">compute_ci.fp_model</span><span class="op">(</span><span class="va">x</span>, ci <span class="op">=</span> <span class="fl">0.95</span>, le <span class="op">=</span> <span class="fl">100</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'fp_model'</span></span>
+<span><span class="fu">compute_ci</span><span class="op">(</span><span class="va">x</span>, ci <span class="op">=</span> <span class="fl">0.95</span>, le <span class="op">=</span> <span class="fl">100</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
-<dd><p>- serosv models</p></dd>
 
 
-<dt>ci</dt>
-<dd><p>- confidence interval</p></dd>
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
+<dd><p>serosv models</p></dd>
 
 
-<dt>le</dt>
-<dd><p>- number of data for computing confidence interval</p></dd>
+<dt id="arg-ci">ci<a class="anchor" aria-label="anchor" href="#arg-ci"></a></dt>
+<dd><p>confidence interval</p></dd>
 
 
-<dt>...</dt>
-<dd><p>- arbitrary argument</p></dd>
+<dt id="arg-le">le<a class="anchor" aria-label="anchor" href="#arg-le"></a></dt>
+<dd><p>number of data for computing confidence interval</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arbitrary argument</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval</p>
+    <p>confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
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diff --git a/docs/reference/compute_ci.fp_model.md b/docs/reference/compute_ci.fp_model.md
index 18bbdc1..2383dbf 100644
--- a/docs/reference/compute_ci.fp_model.md
+++ b/docs/reference/compute_ci.fp_model.md
@@ -5,26 +5,27 @@ Compute confidence interval for fractional polynomial model
 ## Usage
 
 ``` r
-compute_ci.fp_model(x, ci = 0.95, le = 100, ...)
+# S3 method for class 'fp_model'
+compute_ci(x, ci = 0.95, le = 100, ...)
 ```
 
 ## Arguments
 
 - x:
 
-  \- serosv models
+  serosv models
 
 - ci:
 
-  \- confidence interval
+  confidence interval
 
 - le:
 
-  \- number of data for computing confidence interval
+  number of data for computing confidence interval
 
 - ...:
 
-  \- arbitrary argument
+  arbitrary argument
 
 ## Value
 
diff --git a/docs/reference/compute_ci.hierarchical_bayesian_model.html b/docs/reference/compute_ci.hierarchical_bayesian_model.html
index 0a93d38..0ee4907 100644
--- a/docs/reference/compute_ci.hierarchical_bayesian_model.html
+++ b/docs/reference/compute_ci.hierarchical_bayesian_model.html
@@ -1,5 +1,11 @@
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     <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
@@ -55,7 +60,8 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">compute_ci.hierarchical_bayesian_model</span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'hierarchical_bayesian_model'</span></span>
+<span><span class="fu">compute_ci</span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
@@ -63,11 +69,11 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
-<dd><p>- serosv models</p></dd>
+<dd><p>serosv models</p></dd>
 
 
 <dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
-<dd><p>- arbitrary arguments</p></dd>
+<dd><p>arbitrary arguments</p></dd>
 
 </dl></div>
     <div class="section level2">
diff --git a/docs/reference/compute_ci.hierarchical_bayesian_model.md b/docs/reference/compute_ci.hierarchical_bayesian_model.md
index bc1470a..441b7c1 100644
--- a/docs/reference/compute_ci.hierarchical_bayesian_model.md
+++ b/docs/reference/compute_ci.hierarchical_bayesian_model.md
@@ -5,18 +5,19 @@ Compute 95% credible interval for hierarchical Bayesian model
 ## Usage
 
 ``` r
-compute_ci.hierarchical_bayesian_model(x, ...)
+# S3 method for class 'hierarchical_bayesian_model'
+compute_ci(x, ...)
 ```
 
 ## Arguments
 
 - x:
 
-  \- serosv models
+  serosv models
 
 - ...:
 
-  \- arbitrary arguments
+  arbitrary arguments
 
 ## Value
 
diff --git a/docs/reference/compute_ci.html b/docs/reference/compute_ci.html
index 7a9e961..f7d1e6e 100644
--- a/docs/reference/compute_ci.html
+++ b/docs/reference/compute_ci.html
@@ -1,59 +1,44 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Compute confidence interval"><title>Compute confidence interval — compute_ci • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Compute confidence interval — compute_ci"><meta property="og:description" content="Compute confidence interval"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
+    <li><a class="dropdown-item" href="../articles/model_selection.html">Model selection</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
@@ -74,46 +59,46 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>- serosv models</p></dd>
 
 
-<dt>ci</dt>
+<dt id="arg-ci">ci<a class="anchor" aria-label="anchor" href="#arg-ci"></a></dt>
 <dd><p>- confidence interval</p></dd>
 
 
-<dt>le</dt>
+<dt id="arg-le">le<a class="anchor" aria-label="anchor" href="#arg-le"></a></dt>
 <dd><p>- number of data for computing confidence interval</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>- arbitrary argument</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval</p>
+    <p>confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
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diff --git a/docs/reference/compute_ci.lp_model.html b/docs/reference/compute_ci.lp_model.html
index 3befcef..75119b0 100644
--- a/docs/reference/compute_ci.lp_model.html
+++ b/docs/reference/compute_ci.lp_model.html
@@ -1,59 +1,50 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Compute confidence interval for local polynomial model"><title>Compute confidence interval for local polynomial model — compute_ci.lp_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Compute confidence interval for local polynomial model — compute_ci.lp_model"><meta property="og:description" content="Compute confidence interval for local polynomial model"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -69,47 +60,48 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">compute_ci.lp_model</span><span class="op">(</span><span class="va">x</span>, ci <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'lp_model'</span></span>
+<span><span class="fu">compute_ci</span><span class="op">(</span><span class="va">x</span>, ci <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
-<dd><p>- serosv models</p></dd>
 
 
-<dt>ci</dt>
-<dd><p>- confidence interval</p></dd>
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
+<dd><p>serosv models</p></dd>
 
 
-<dt>...</dt>
-<dd><p>- arbitrary arguments</p></dd>
+<dt id="arg-ci">ci<a class="anchor" aria-label="anchor" href="#arg-ci"></a></dt>
+<dd><p>confidence interval</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arbitrary arguments</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval</p>
+    <p>confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
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diff --git a/docs/reference/compute_ci.lp_model.md b/docs/reference/compute_ci.lp_model.md
index f6de7a6..e800fc2 100644
--- a/docs/reference/compute_ci.lp_model.md
+++ b/docs/reference/compute_ci.lp_model.md
@@ -5,22 +5,23 @@ Compute confidence interval for local polynomial model
 ## Usage
 
 ``` r
-compute_ci.lp_model(x, ci = 0.95, ...)
+# S3 method for class 'lp_model'
+compute_ci(x, ci = 0.95, ...)
 ```
 
 ## Arguments
 
 - x:
 
-  \- serosv models
+  serosv models
 
 - ci:
 
-  \- confidence interval
+  confidence interval
 
 - ...:
 
-  \- arbitrary arguments
+  arbitrary arguments
 
 ## Value
 
diff --git a/docs/reference/compute_ci.mixture_model.html b/docs/reference/compute_ci.mixture_model.html
index 180b511..a16574f 100644
--- a/docs/reference/compute_ci.mixture_model.html
+++ b/docs/reference/compute_ci.mixture_model.html
@@ -1,59 +1,50 @@
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@@ -69,47 +60,48 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">compute_ci.mixture_model</span><span class="op">(</span><span class="va">x</span>, ci <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mixture_model'</span></span>
+<span><span class="fu">compute_ci</span><span class="op">(</span><span class="va">x</span>, ci <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
-<dd><p>- serosv mixture_model object</p></dd>
 
 
-<dt>ci</dt>
-<dd><p>- confidence interval</p></dd>
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
+<dd><p>serosv mixture_model object</p></dd>
 
 
-<dt>...</dt>
-<dd><p>- arbitrary arguments</p></dd>
+<dt id="arg-ci">ci<a class="anchor" aria-label="anchor" href="#arg-ci"></a></dt>
+<dd><p>confidence interval</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arbitrary arguments</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>list of confidence interval for susceptible and infected. Each confidence interval is a list with 2 items for lower and upper bound of the interval.</p>
+    <p>list of confidence interval for susceptible and infected. Each confidence interval is a list with 2 items for lower and upper bound of the interval.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
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diff --git a/docs/reference/compute_ci.mixture_model.md b/docs/reference/compute_ci.mixture_model.md
index 61183c0..b49a9ea 100644
--- a/docs/reference/compute_ci.mixture_model.md
+++ b/docs/reference/compute_ci.mixture_model.md
@@ -5,22 +5,23 @@ Compute confidence interval for mixture model
 ## Usage
 
 ``` r
-compute_ci.mixture_model(x, ci = 0.95, ...)
+# S3 method for class 'mixture_model'
+compute_ci(x, ci = 0.95, ...)
 ```
 
 ## Arguments
 
 - x:
 
-  \- serosv mixture_model object
+  serosv mixture_model object
 
 - ci:
 
-  \- confidence interval
+  confidence interval
 
 - ...:
 
-  \- arbitrary arguments
+  arbitrary arguments
 
 ## Value
 
diff --git a/docs/reference/compute_ci.penalized_spline_model.html b/docs/reference/compute_ci.penalized_spline_model.html
index 443e906..7c09b5d 100644
--- a/docs/reference/compute_ci.penalized_spline_model.html
+++ b/docs/reference/compute_ci.penalized_spline_model.html
@@ -1,59 +1,50 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Compute confidence interval for penalized_spline_model"><title>Compute confidence interval for penalized_spline_model — compute_ci.penalized_spline_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Compute confidence interval for penalized_spline_model — compute_ci.penalized_spline_model"><meta property="og:description" content="Compute confidence interval for penalized_spline_model"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -69,47 +60,48 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">compute_ci.penalized_spline_model</span><span class="op">(</span><span class="va">x</span>, ci <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'penalized_spline_model'</span></span>
+<span><span class="fu">compute_ci</span><span class="op">(</span><span class="va">x</span>, ci <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
-<dd><p>- serosv models</p></dd>
 
 
-<dt>ci</dt>
-<dd><p>- confidence interval</p></dd>
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
+<dd><p>serosv models</p></dd>
 
 
-<dt>...</dt>
-<dd><p>- arbitrary arguments</p></dd>
+<dt id="arg-ci">ci<a class="anchor" aria-label="anchor" href="#arg-ci"></a></dt>
+<dd><p>confidence interval</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arbitrary arguments</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>list of confidence interval for seroprevalence and foi Each confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval</p>
+    <p>list of confidence interval for seroprevalence and foi Each confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
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diff --git a/docs/reference/compute_ci.penalized_spline_model.md b/docs/reference/compute_ci.penalized_spline_model.md
index e9c468a..76f1e66 100644
--- a/docs/reference/compute_ci.penalized_spline_model.md
+++ b/docs/reference/compute_ci.penalized_spline_model.md
@@ -5,22 +5,23 @@ Compute confidence interval for penalized_spline_model
 ## Usage
 
 ``` r
-compute_ci.penalized_spline_model(x, ci = 0.95, ...)
+# S3 method for class 'penalized_spline_model'
+compute_ci(x, ci = 0.95, ...)
 ```
 
 ## Arguments
 
 - x:
 
-  \- serosv models
+  serosv models
 
 - ci:
 
-  \- confidence interval
+  confidence interval
 
 - ...:
 
-  \- arbitrary arguments
+  arbitrary arguments
 
 ## Value
 
diff --git a/docs/reference/compute_ci.weibull_model.html b/docs/reference/compute_ci.weibull_model.html
index e26f4cc..2c497b8 100644
--- a/docs/reference/compute_ci.weibull_model.html
+++ b/docs/reference/compute_ci.weibull_model.html
@@ -1,59 +1,50 @@
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@@ -69,47 +60,48 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">compute_ci.weibull_model</span><span class="op">(</span><span class="va">x</span>, ci <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'weibull_model'</span></span>
+<span><span class="fu">compute_ci</span><span class="op">(</span><span class="va">x</span>, ci <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
-<dd><p>- serosv models</p></dd>
 
 
-<dt>ci</dt>
-<dd><p>- confidence interval</p></dd>
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
+<dd><p>serosv models</p></dd>
 
 
-<dt>...</dt>
-<dd><p>- arbitrary argument</p></dd>
+<dt id="arg-ci">ci<a class="anchor" aria-label="anchor" href="#arg-ci"></a></dt>
+<dd><p>confidence interval</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arbitrary argument</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval</p>
+    <p>confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
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diff --git a/docs/reference/compute_ci.weibull_model.md b/docs/reference/compute_ci.weibull_model.md
index d951b39..6f4cfef 100644
--- a/docs/reference/compute_ci.weibull_model.md
+++ b/docs/reference/compute_ci.weibull_model.md
@@ -5,22 +5,23 @@ Compute confidence interval for Weibull model
 ## Usage
 
 ``` r
-compute_ci.weibull_model(x, ci = 0.95, ...)
+# S3 method for class 'weibull_model'
+compute_ci(x, ci = 0.95, ...)
 ```
 
 ## Arguments
 
 - x:
 
-  \- serosv models
+  serosv models
 
 - ci:
 
-  \- confidence interval
+  confidence interval
 
 - ...:
 
-  \- arbitrary argument
+  arbitrary argument
 
 ## Value
 
diff --git a/docs/reference/correct_prevalence.html b/docs/reference/correct_prevalence.html
index b2b8247..f3691e6 100644
--- a/docs/reference/correct_prevalence.html
+++ b/docs/reference/correct_prevalence.html
@@ -1,5 +1,11 @@
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@@ -7,7 +13,7 @@
 
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@@ -19,14 +25,14 @@
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     <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
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+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
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     <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
@@ -57,6 +63,10 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">correct_prevalence</span><span class="op">(</span></span>
 <span>  <span class="va">data</span>,</span>
 <span>  bayesian <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  age_col <span class="op">=</span> <span class="st">"age"</span>,</span>
+<span>  pos_col <span class="op">=</span> <span class="st">"pos"</span>,</span>
+<span>  tot_col <span class="op">=</span> <span class="st">"tot"</span>,</span>
+<span>  status_col <span class="op">=</span> <span class="st">"status"</span>,</span>
 <span>  init_se <span class="op">=</span> <span class="fl">0.95</span>,</span>
 <span>  init_sp <span class="op">=</span> <span class="fl">0.8</span>,</span>
 <span>  study_size_se <span class="op">=</span> <span class="fl">1000</span>,</span>
@@ -72,13 +82,29 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
-<dd><p>the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)</p></dd>
+<dd><p>the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR `age`, `status` (for linelisting data)</p></dd>
 
 
 <dt id="arg-bayesian">bayesian<a class="anchor" aria-label="anchor" href="#arg-bayesian"></a></dt>
 <dd><p>whether to adjust sero-prevalence using the Bayesian or frequentist approach. If set to `TRUE`, true sero-prevalence is estimated using MCMC.</p></dd>
 
 
+<dt id="arg-age-col">age_col<a class="anchor" aria-label="anchor" href="#arg-age-col"></a></dt>
+<dd><p>name of the `age` column (default age_col="age").</p></dd>
+
+
+<dt id="arg-pos-col">pos_col<a class="anchor" aria-label="anchor" href="#arg-pos-col"></a></dt>
+<dd><p>name of the `pos` column (default pos_col="pos").</p></dd>
+
+
+<dt id="arg-tot-col">tot_col<a class="anchor" aria-label="anchor" href="#arg-tot-col"></a></dt>
+<dd><p>name of the `tot` column (default tot_col="tot").</p></dd>
+
+
+<dt id="arg-status-col">status_col<a class="anchor" aria-label="anchor" href="#arg-status-col"></a></dt>
+<dd><p>name of the `status` column (default status_col="status").</p></dd>
+
+
 <dt id="arg-init-se">init_se<a class="anchor" aria-label="anchor" href="#arg-init-se"></a></dt>
 <dd><p>sensitivity of the serological test</p></dd>
 
@@ -114,7 +140,7 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <dd><p>estimated parameters (when `bayesian = TRUE`) or formula to compute corrected prevalence (when `bayesian = FALSE`)</p></dd>
 
   <dt>df</dt>
-<dd><p>data.frame of input data (in aggregated form)</p></dd>
+<dd><p>data.frame of input data (in aggregated form) with the 95% confidence interval for apparent (i.e. observed) seroprevalence</p></dd>
 
   <dt>corrected_sero</dt>
 <dd><p>data.frame containing age, the corresponding estimated seroprevalance with 95% confidence/credible interval, and adjusted tot and pos</p></dd>
@@ -128,8 +154,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> SAMPLING FOR MODEL 'prevalence_correction' NOW (CHAIN 1).</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Gradient evaluation took 0.000244 seconds</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.44 seconds.</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Gradient evaluation took 0.000162 seconds</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.62 seconds.</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Adjust your expectations accordingly!</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: </span>
@@ -146,9 +172,9 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:  Elapsed Time: 1.656 seconds (Warm-up)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:                1.233 seconds (Sampling)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:                2.889 seconds (Total)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:  Elapsed Time: 1.64 seconds (Warm-up)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:                1.235 seconds (Sampling)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:                2.875 seconds (Total)</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $info</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                    mean      se_mean          sd          2.5%           25%</span>
@@ -249,98 +275,98 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> lp__      -2.762797e+03 -2.759032e+03 -2.753133e+03  360.2932 1.0126026</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $df</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     age pos tot</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1   1.5  31 206</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   2.5  30 146</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3   3.5  34 168</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4   4.5  57 189</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5   5.5  95 219</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6   6.5 104 195</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7   7.5  90 164</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8   8.5  96 145</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9   9.5 134 180</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10 10.5 110 160</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11 11.5 111 148</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12 12.5 147 178</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13 13.5 138 177</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14 14.5 141 165</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15 15.5  53  67</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16 16.5  49  58</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17 17.5  73  81</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18 18.5  69  79</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19 19.5  97 111</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20 20.5  65  76</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21 21.5  74  82</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22 22.5  84 101</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23 23.5  82  88</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24 24.5  79  85</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25 25.5  90  94</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26 26.5  84  91</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27 27.5  81  89</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28 28.5  72  76</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29 29.5  71  79</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30 30.5  51  56</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31 31.5  45  52</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32 32.5  45  48</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33 33.5  35  37</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34 34.5  39  41</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35 35.5  36  40</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 36 36.5  37  38</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 37 37.5  37  39</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 38 38.5  37  41</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 39 39.5  28  30</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 40 40.5  26  27</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 41 41.5  25  25</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 42 42.5  21  22</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 43 43.5  18  19</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 44 44.5  18  18</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     age pos tot      sero  sero_lwr  sero_upr</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1   1.5  31 206 0.1504854 0.1060177 0.2083748</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2   2.5  30 146 0.2054795 0.1449496 0.2818842</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3   3.5  34 168 0.2023810 0.1460087 0.2727002</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4   4.5  57 189 0.3015873 0.2382102 0.3731851</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5   5.5  95 219 0.4337900 0.3676479 0.5022934</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6   6.5 104 195 0.5333333 0.4608072 0.6045248</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7   7.5  90 164 0.5487805 0.4693519 0.6258878</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8   8.5  96 145 0.6620690 0.5782402 0.7371628</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9   9.5 134 180 0.7444444 0.6731424 0.8050917</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10 10.5 110 160 0.6875000 0.6087555 0.7570748</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 11 11.5 111 148 0.7500000 0.6709203 0.8158278</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 12 12.5 147 178 0.8258427 0.7603216 0.8769152</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 13 13.5 138 177 0.7796610 0.7099871 0.8369125</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14 14.5 141 165 0.8545455 0.7892898 0.9027796</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 15 15.5  53  67 0.7910448 0.6710850 0.8771184</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 16 16.5  49  58 0.8448276 0.7207490 0.9223300</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 17 17.5  73  81 0.9012346 0.8095518 0.9533386</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 18 18.5  69  79 0.8734177 0.7750073 0.9343581</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 19 19.5  97 111 0.8738739 0.7941533 0.9268253</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 20 20.5  65  76 0.8552632 0.7515326 0.9220985</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 21 21.5  74  82 0.9024390 0.8117280 0.9539169</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 22 22.5  84 101 0.8316832 0.7413312 0.8961330</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 23 23.5  82  88 0.9318182 0.8519298 0.9719801</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 24 24.5  79  85 0.9294118 0.8470005 0.9709798</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 25 25.5  90  94 0.9574468 0.8884991 0.9862810</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 26 26.5  84  91 0.9230769 0.8427965 0.9658735</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 27 27.5  81  89 0.9101124 0.8256740 0.9575953</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 28 28.5  72  76 0.9473684 0.8636358 0.9830057</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 29 29.5  71  79 0.8987342 0.8050453 0.9521374</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 30 30.5  51  56 0.9107143 0.7963017 0.9666611</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 31 31.5  45  52 0.8653846 0.7359542 0.9396995</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 32 32.5  45  48 0.9375000 0.8179078 0.9837187</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 33 33.5  35  37 0.9459459 0.8046945 0.9905819</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 34 34.5  39  41 0.9512195 0.8219181 0.9915057</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 35 35.5  36  40 0.9000000 0.7540414 0.9674737</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 36 36.5  37  38 0.9736842 0.8456597 0.9986248</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 37 37.5  37  39 0.9487179 0.8137029 0.9910676</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 38 38.5  37  41 0.9024390 0.7594053 0.9682791</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 39 39.5  28  30 0.9333333 0.7649271 0.9883682</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 40 40.5  26  27 0.9629630 0.7911082 0.9980636</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 41 41.5  25  25 1.0000000 0.8342270 1.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 42 42.5  21  22 0.9545455 0.7511570 0.9976227</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 43 43.5  18  19 0.9473684 0.7189259 0.9972465</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 44 44.5  18  18 1.0000000 0.7812431 1.0000000</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $corrected_se</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     age       sero        pos tot     sero_lwr   sero_upr</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1   1.5 0.01325944   2.731444 206 0.0003397982 0.05764662</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   2.5 0.03926800   5.733129 146 0.0034973616 0.11797694</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3   3.5 0.03510345   5.897379 168 0.0019009974 0.10590546</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4   4.5 0.14362274  27.144698 189 0.0542534972 0.23632128</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5   5.5 0.31359457  68.677211 219 0.2305849615 0.40512397</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6   6.5 0.44517156  86.808453 195 0.3545909437 0.53456725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7   7.5 0.46503915  76.266421 164 0.3671684261 0.56000455</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8   8.5 0.60719173  88.042801 145 0.5034081474 0.70838708</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9   9.5 0.71874687 129.374436 180 0.6248538646 0.79810121</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10 10.5 0.64131891 102.611026 160 0.5427183649 0.73760906</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11 11.5 0.72758395 107.682425 148 0.6257778650 0.81138031</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12 12.5 0.82320978 146.531341 178 0.7334985417 0.89141914</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13 13.5 0.76309793 135.068334 177 0.6779961210 0.83553609</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14 14.5 0.85962824 141.838659 165 0.7896023026 0.91993691</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15 15.5 0.77685579  52.049338  67 0.6349393644 0.88862239</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16 16.5 0.84407836  48.956545  58 0.7006722751 0.94884869</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17 17.5 0.91729554  74.300939  81 0.8153282665 0.99108683</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18 18.5 0.88320521  69.773211  79 0.7815408701 0.96919022</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19 19.5 0.88350782  98.069368 111 0.7940747981 0.94801151</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20 20.5 0.85513649  64.990373  76 0.7386624226 0.94068610</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21 21.5 0.92020148  75.456521  82 0.8144062288 0.99036682</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22 22.5 0.82648821  83.475309 101 0.7226266670 0.91311217</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23 23.5 0.95350762  83.908671  88 0.8654860615 0.99508220</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24 24.5 0.94977310  80.730713  85 0.8708282569 0.99668585</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25 25.5 0.97622654  91.765295  94 0.9115229908 0.99905070</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26 26.5 0.94392302  85.896995  91 0.8681502083 0.99640880</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27 27.5 0.93055493  82.819389  89 0.8198035939 0.99294163</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28 28.5 0.96530578  73.363239  76 0.8824004970 0.99806516</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29 29.5 0.90942839  71.844843  79 0.8166192321 0.98031092</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30 30.5 0.92396246  51.741898  56 0.8018241060 0.99177343</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31 31.5 0.86450734  44.954381  52 0.7114398199 0.96150283</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32 32.5 0.95145521  45.669850  48 0.8244368835 0.99784321</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 33 33.5 0.94939266  35.127529  37 0.7888680098 0.99823258</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 34 34.5 0.95578440  39.187161  41 0.8334385481 0.99731726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 35 35.5 0.90579729  36.231892  40 0.7444533435 0.98825635</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 36 36.5 0.96630732  36.719678  38 0.8613046445 0.99826479</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 37 37.5 0.95021588  37.058419  39 0.8041305633 0.99763957</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 38 38.5 0.90999106  37.309634  41 0.7442793553 0.99101392</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 39 39.5 0.92804546  27.841364  30 0.7562417314 0.99399646</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 40 40.5 0.95301930  25.731521  27 0.7920169109 0.99752857</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 41 41.5 0.96465707  24.116427  25 0.8275253759 0.99916657</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 42 42.5 0.93287433  20.523235  22 0.7639822308 0.99641675</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 43 43.5 0.92531182  17.580925  19 0.7300474394 0.99597705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 44 44.5 0.95462715  17.183289  18 0.8014756977 0.99781240</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     age        pos tot       sero     sero_lwr   sero_upr</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1   1.5   2.731444 206 0.01325944 0.0003397982 0.05764662</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2   2.5   5.733129 146 0.03926800 0.0034973616 0.11797694</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3   3.5   5.897379 168 0.03510345 0.0019009974 0.10590546</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4   4.5  27.144698 189 0.14362274 0.0542534972 0.23632128</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5   5.5  68.677211 219 0.31359457 0.2305849615 0.40512397</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6   6.5  86.808453 195 0.44517156 0.3545909437 0.53456725</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7   7.5  76.266421 164 0.46503915 0.3671684261 0.56000455</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8   8.5  88.042801 145 0.60719173 0.5034081474 0.70838708</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9   9.5 129.374436 180 0.71874687 0.6248538646 0.79810121</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10 10.5 102.611026 160 0.64131891 0.5427183649 0.73760906</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 11 11.5 107.682425 148 0.72758395 0.6257778650 0.81138031</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 12 12.5 146.531341 178 0.82320978 0.7334985417 0.89141914</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 13 13.5 135.068334 177 0.76309793 0.6779961210 0.83553609</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 14 14.5 141.838659 165 0.85962824 0.7896023026 0.91993691</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 15 15.5  52.049338  67 0.77685579 0.6349393644 0.88862239</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 16 16.5  48.956545  58 0.84407836 0.7006722751 0.94884869</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 17 17.5  74.300939  81 0.91729554 0.8153282665 0.99108683</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 18 18.5  69.773211  79 0.88320521 0.7815408701 0.96919022</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 19 19.5  98.069368 111 0.88350782 0.7940747981 0.94801151</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 20 20.5  64.990373  76 0.85513649 0.7386624226 0.94068610</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 21 21.5  75.456521  82 0.92020148 0.8144062288 0.99036682</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 22 22.5  83.475309 101 0.82648821 0.7226266670 0.91311217</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 23 23.5  83.908671  88 0.95350762 0.8654860615 0.99508220</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 24 24.5  80.730713  85 0.94977310 0.8708282569 0.99668585</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 25 25.5  91.765295  94 0.97622654 0.9115229908 0.99905070</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 26 26.5  85.896995  91 0.94392302 0.8681502083 0.99640880</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 27 27.5  82.819389  89 0.93055493 0.8198035939 0.99294163</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 28 28.5  73.363239  76 0.96530578 0.8824004970 0.99806516</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 29 29.5  71.844843  79 0.90942839 0.8166192321 0.98031092</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 30 30.5  51.741898  56 0.92396246 0.8018241060 0.99177343</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 31 31.5  44.954381  52 0.86450734 0.7114398199 0.96150283</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 32 32.5  45.669850  48 0.95145521 0.8244368835 0.99784321</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 33 33.5  35.127529  37 0.94939266 0.7888680098 0.99823258</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 34 34.5  39.187161  41 0.95578440 0.8334385481 0.99731726</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 35 35.5  36.231892  40 0.90579729 0.7444533435 0.98825635</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 36 36.5  36.719678  38 0.96630732 0.8613046445 0.99826479</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 37 37.5  37.058419  39 0.95021588 0.8041305633 0.99763957</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 38 38.5  37.309634  41 0.90999106 0.7442793553 0.99101392</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 39 39.5  27.841364  30 0.92804546 0.7562417314 0.99399646</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 40 40.5  25.731521  27 0.95301930 0.7920169109 0.99752857</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 41 41.5  24.116427  25 0.96465707 0.8275253759 0.99916657</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 42 42.5  20.523235  22 0.93287433 0.7639822308 0.99641675</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 43 43.5  17.580925  19 0.92531182 0.7300474394 0.99597705</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 44 44.5  17.183289  18 0.95462715 0.8014756977 0.99781240</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $method</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "bayesian"</span>
diff --git a/docs/reference/correct_prevalence.md b/docs/reference/correct_prevalence.md
index 39680fc..8da356c 100644
--- a/docs/reference/correct_prevalence.md
+++ b/docs/reference/correct_prevalence.md
@@ -8,6 +8,10 @@ Estimate the true sero prevalence using Frequentist/Bayesian estimation
 correct_prevalence(
   data,
   bayesian = TRUE,
+  age_col = "age",
+  pos_col = "pos",
+  tot_col = "tot",
+  status_col = "status",
   init_se = 0.95,
   init_sp = 0.8,
   study_size_se = 1000,
@@ -22,8 +26,8 @@ correct_prevalence(
 
 - data:
 
-  the input data frame, must either have \`age\`, \`pos\`, \`tot\`
-  columns (for aggregated data) OR \`age\`, \`status\` for (linelisting
+  the input data frame, must either have columns for \`age\`, \`pos\`,
+  \`tot\` (for aggregated data) OR \`age\`, \`status\` (for linelisting
   data)
 
 - bayesian:
@@ -32,6 +36,22 @@ correct_prevalence(
   approach. If set to \`TRUE\`, true sero-prevalence is estimated using
   MCMC.
 
+- age_col:
+
+  name of the \`age\` column (default age_col="age").
+
+- pos_col:
+
+  name of the \`pos\` column (default pos_col="pos").
+
+- tot_col:
+
+  name of the \`tot\` column (default tot_col="tot").
+
+- status_col:
+
+  name of the \`status\` column (default status_col="status").
+
 - init_se:
 
   sensitivity of the serological test
@@ -75,7 +95,8 @@ a list of 3 items
 
 - df:
 
-  data.frame of input data (in aggregated form)
+  data.frame of input data (in aggregated form) with the 95% confidence
+  interval for apparent (i.e. observed) seroprevalence
 
 - corrected_sero:
 
@@ -90,8 +111,8 @@ correct_prevalence(data)
 #> 
 #> SAMPLING FOR MODEL 'prevalence_correction' NOW (CHAIN 1).
 #> Chain 1: 
-#> Chain 1: Gradient evaluation took 0.000244 seconds
-#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.44 seconds.
+#> Chain 1: Gradient evaluation took 0.000162 seconds
+#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.62 seconds.
 #> Chain 1: Adjust your expectations accordingly!
 #> Chain 1: 
 #> Chain 1: 
@@ -108,9 +129,9 @@ correct_prevalence(data)
 #> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
 #> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
 #> Chain 1: 
-#> Chain 1:  Elapsed Time: 1.656 seconds (Warm-up)
-#> Chain 1:                1.233 seconds (Sampling)
-#> Chain 1:                2.889 seconds (Total)
+#> Chain 1:  Elapsed Time: 1.64 seconds (Warm-up)
+#> Chain 1:                1.235 seconds (Sampling)
+#> Chain 1:                2.875 seconds (Total)
 #> Chain 1: 
 #> $info
 #>                    mean      se_mean          sd          2.5%           25%
@@ -211,98 +232,98 @@ correct_prevalence(data)
 #> lp__      -2.762797e+03 -2.759032e+03 -2.753133e+03  360.2932 1.0126026
 #> 
 #> $df
-#>     age pos tot
-#> 1   1.5  31 206
-#> 2   2.5  30 146
-#> 3   3.5  34 168
-#> 4   4.5  57 189
-#> 5   5.5  95 219
-#> 6   6.5 104 195
-#> 7   7.5  90 164
-#> 8   8.5  96 145
-#> 9   9.5 134 180
-#> 10 10.5 110 160
-#> 11 11.5 111 148
-#> 12 12.5 147 178
-#> 13 13.5 138 177
-#> 14 14.5 141 165
-#> 15 15.5  53  67
-#> 16 16.5  49  58
-#> 17 17.5  73  81
-#> 18 18.5  69  79
-#> 19 19.5  97 111
-#> 20 20.5  65  76
-#> 21 21.5  74  82
-#> 22 22.5  84 101
-#> 23 23.5  82  88
-#> 24 24.5  79  85
-#> 25 25.5  90  94
-#> 26 26.5  84  91
-#> 27 27.5  81  89
-#> 28 28.5  72  76
-#> 29 29.5  71  79
-#> 30 30.5  51  56
-#> 31 31.5  45  52
-#> 32 32.5  45  48
-#> 33 33.5  35  37
-#> 34 34.5  39  41
-#> 35 35.5  36  40
-#> 36 36.5  37  38
-#> 37 37.5  37  39
-#> 38 38.5  37  41
-#> 39 39.5  28  30
-#> 40 40.5  26  27
-#> 41 41.5  25  25
-#> 42 42.5  21  22
-#> 43 43.5  18  19
-#> 44 44.5  18  18
+#>     age pos tot      sero  sero_lwr  sero_upr
+#> 1   1.5  31 206 0.1504854 0.1060177 0.2083748
+#> 2   2.5  30 146 0.2054795 0.1449496 0.2818842
+#> 3   3.5  34 168 0.2023810 0.1460087 0.2727002
+#> 4   4.5  57 189 0.3015873 0.2382102 0.3731851
+#> 5   5.5  95 219 0.4337900 0.3676479 0.5022934
+#> 6   6.5 104 195 0.5333333 0.4608072 0.6045248
+#> 7   7.5  90 164 0.5487805 0.4693519 0.6258878
+#> 8   8.5  96 145 0.6620690 0.5782402 0.7371628
+#> 9   9.5 134 180 0.7444444 0.6731424 0.8050917
+#> 10 10.5 110 160 0.6875000 0.6087555 0.7570748
+#> 11 11.5 111 148 0.7500000 0.6709203 0.8158278
+#> 12 12.5 147 178 0.8258427 0.7603216 0.8769152
+#> 13 13.5 138 177 0.7796610 0.7099871 0.8369125
+#> 14 14.5 141 165 0.8545455 0.7892898 0.9027796
+#> 15 15.5  53  67 0.7910448 0.6710850 0.8771184
+#> 16 16.5  49  58 0.8448276 0.7207490 0.9223300
+#> 17 17.5  73  81 0.9012346 0.8095518 0.9533386
+#> 18 18.5  69  79 0.8734177 0.7750073 0.9343581
+#> 19 19.5  97 111 0.8738739 0.7941533 0.9268253
+#> 20 20.5  65  76 0.8552632 0.7515326 0.9220985
+#> 21 21.5  74  82 0.9024390 0.8117280 0.9539169
+#> 22 22.5  84 101 0.8316832 0.7413312 0.8961330
+#> 23 23.5  82  88 0.9318182 0.8519298 0.9719801
+#> 24 24.5  79  85 0.9294118 0.8470005 0.9709798
+#> 25 25.5  90  94 0.9574468 0.8884991 0.9862810
+#> 26 26.5  84  91 0.9230769 0.8427965 0.9658735
+#> 27 27.5  81  89 0.9101124 0.8256740 0.9575953
+#> 28 28.5  72  76 0.9473684 0.8636358 0.9830057
+#> 29 29.5  71  79 0.8987342 0.8050453 0.9521374
+#> 30 30.5  51  56 0.9107143 0.7963017 0.9666611
+#> 31 31.5  45  52 0.8653846 0.7359542 0.9396995
+#> 32 32.5  45  48 0.9375000 0.8179078 0.9837187
+#> 33 33.5  35  37 0.9459459 0.8046945 0.9905819
+#> 34 34.5  39  41 0.9512195 0.8219181 0.9915057
+#> 35 35.5  36  40 0.9000000 0.7540414 0.9674737
+#> 36 36.5  37  38 0.9736842 0.8456597 0.9986248
+#> 37 37.5  37  39 0.9487179 0.8137029 0.9910676
+#> 38 38.5  37  41 0.9024390 0.7594053 0.9682791
+#> 39 39.5  28  30 0.9333333 0.7649271 0.9883682
+#> 40 40.5  26  27 0.9629630 0.7911082 0.9980636
+#> 41 41.5  25  25 1.0000000 0.8342270 1.0000000
+#> 42 42.5  21  22 0.9545455 0.7511570 0.9976227
+#> 43 43.5  18  19 0.9473684 0.7189259 0.9972465
+#> 44 44.5  18  18 1.0000000 0.7812431 1.0000000
 #> 
 #> $corrected_se
-#>     age       sero        pos tot     sero_lwr   sero_upr
-#> 1   1.5 0.01325944   2.731444 206 0.0003397982 0.05764662
-#> 2   2.5 0.03926800   5.733129 146 0.0034973616 0.11797694
-#> 3   3.5 0.03510345   5.897379 168 0.0019009974 0.10590546
-#> 4   4.5 0.14362274  27.144698 189 0.0542534972 0.23632128
-#> 5   5.5 0.31359457  68.677211 219 0.2305849615 0.40512397
-#> 6   6.5 0.44517156  86.808453 195 0.3545909437 0.53456725
-#> 7   7.5 0.46503915  76.266421 164 0.3671684261 0.56000455
-#> 8   8.5 0.60719173  88.042801 145 0.5034081474 0.70838708
-#> 9   9.5 0.71874687 129.374436 180 0.6248538646 0.79810121
-#> 10 10.5 0.64131891 102.611026 160 0.5427183649 0.73760906
-#> 11 11.5 0.72758395 107.682425 148 0.6257778650 0.81138031
-#> 12 12.5 0.82320978 146.531341 178 0.7334985417 0.89141914
-#> 13 13.5 0.76309793 135.068334 177 0.6779961210 0.83553609
-#> 14 14.5 0.85962824 141.838659 165 0.7896023026 0.91993691
-#> 15 15.5 0.77685579  52.049338  67 0.6349393644 0.88862239
-#> 16 16.5 0.84407836  48.956545  58 0.7006722751 0.94884869
-#> 17 17.5 0.91729554  74.300939  81 0.8153282665 0.99108683
-#> 18 18.5 0.88320521  69.773211  79 0.7815408701 0.96919022
-#> 19 19.5 0.88350782  98.069368 111 0.7940747981 0.94801151
-#> 20 20.5 0.85513649  64.990373  76 0.7386624226 0.94068610
-#> 21 21.5 0.92020148  75.456521  82 0.8144062288 0.99036682
-#> 22 22.5 0.82648821  83.475309 101 0.7226266670 0.91311217
-#> 23 23.5 0.95350762  83.908671  88 0.8654860615 0.99508220
-#> 24 24.5 0.94977310  80.730713  85 0.8708282569 0.99668585
-#> 25 25.5 0.97622654  91.765295  94 0.9115229908 0.99905070
-#> 26 26.5 0.94392302  85.896995  91 0.8681502083 0.99640880
-#> 27 27.5 0.93055493  82.819389  89 0.8198035939 0.99294163
-#> 28 28.5 0.96530578  73.363239  76 0.8824004970 0.99806516
-#> 29 29.5 0.90942839  71.844843  79 0.8166192321 0.98031092
-#> 30 30.5 0.92396246  51.741898  56 0.8018241060 0.99177343
-#> 31 31.5 0.86450734  44.954381  52 0.7114398199 0.96150283
-#> 32 32.5 0.95145521  45.669850  48 0.8244368835 0.99784321
-#> 33 33.5 0.94939266  35.127529  37 0.7888680098 0.99823258
-#> 34 34.5 0.95578440  39.187161  41 0.8334385481 0.99731726
-#> 35 35.5 0.90579729  36.231892  40 0.7444533435 0.98825635
-#> 36 36.5 0.96630732  36.719678  38 0.8613046445 0.99826479
-#> 37 37.5 0.95021588  37.058419  39 0.8041305633 0.99763957
-#> 38 38.5 0.90999106  37.309634  41 0.7442793553 0.99101392
-#> 39 39.5 0.92804546  27.841364  30 0.7562417314 0.99399646
-#> 40 40.5 0.95301930  25.731521  27 0.7920169109 0.99752857
-#> 41 41.5 0.96465707  24.116427  25 0.8275253759 0.99916657
-#> 42 42.5 0.93287433  20.523235  22 0.7639822308 0.99641675
-#> 43 43.5 0.92531182  17.580925  19 0.7300474394 0.99597705
-#> 44 44.5 0.95462715  17.183289  18 0.8014756977 0.99781240
+#>     age        pos tot       sero     sero_lwr   sero_upr
+#> 1   1.5   2.731444 206 0.01325944 0.0003397982 0.05764662
+#> 2   2.5   5.733129 146 0.03926800 0.0034973616 0.11797694
+#> 3   3.5   5.897379 168 0.03510345 0.0019009974 0.10590546
+#> 4   4.5  27.144698 189 0.14362274 0.0542534972 0.23632128
+#> 5   5.5  68.677211 219 0.31359457 0.2305849615 0.40512397
+#> 6   6.5  86.808453 195 0.44517156 0.3545909437 0.53456725
+#> 7   7.5  76.266421 164 0.46503915 0.3671684261 0.56000455
+#> 8   8.5  88.042801 145 0.60719173 0.5034081474 0.70838708
+#> 9   9.5 129.374436 180 0.71874687 0.6248538646 0.79810121
+#> 10 10.5 102.611026 160 0.64131891 0.5427183649 0.73760906
+#> 11 11.5 107.682425 148 0.72758395 0.6257778650 0.81138031
+#> 12 12.5 146.531341 178 0.82320978 0.7334985417 0.89141914
+#> 13 13.5 135.068334 177 0.76309793 0.6779961210 0.83553609
+#> 14 14.5 141.838659 165 0.85962824 0.7896023026 0.91993691
+#> 15 15.5  52.049338  67 0.77685579 0.6349393644 0.88862239
+#> 16 16.5  48.956545  58 0.84407836 0.7006722751 0.94884869
+#> 17 17.5  74.300939  81 0.91729554 0.8153282665 0.99108683
+#> 18 18.5  69.773211  79 0.88320521 0.7815408701 0.96919022
+#> 19 19.5  98.069368 111 0.88350782 0.7940747981 0.94801151
+#> 20 20.5  64.990373  76 0.85513649 0.7386624226 0.94068610
+#> 21 21.5  75.456521  82 0.92020148 0.8144062288 0.99036682
+#> 22 22.5  83.475309 101 0.82648821 0.7226266670 0.91311217
+#> 23 23.5  83.908671  88 0.95350762 0.8654860615 0.99508220
+#> 24 24.5  80.730713  85 0.94977310 0.8708282569 0.99668585
+#> 25 25.5  91.765295  94 0.97622654 0.9115229908 0.99905070
+#> 26 26.5  85.896995  91 0.94392302 0.8681502083 0.99640880
+#> 27 27.5  82.819389  89 0.93055493 0.8198035939 0.99294163
+#> 28 28.5  73.363239  76 0.96530578 0.8824004970 0.99806516
+#> 29 29.5  71.844843  79 0.90942839 0.8166192321 0.98031092
+#> 30 30.5  51.741898  56 0.92396246 0.8018241060 0.99177343
+#> 31 31.5  44.954381  52 0.86450734 0.7114398199 0.96150283
+#> 32 32.5  45.669850  48 0.95145521 0.8244368835 0.99784321
+#> 33 33.5  35.127529  37 0.94939266 0.7888680098 0.99823258
+#> 34 34.5  39.187161  41 0.95578440 0.8334385481 0.99731726
+#> 35 35.5  36.231892  40 0.90579729 0.7444533435 0.98825635
+#> 36 36.5  36.719678  38 0.96630732 0.8613046445 0.99826479
+#> 37 37.5  37.058419  39 0.95021588 0.8041305633 0.99763957
+#> 38 38.5  37.309634  41 0.90999106 0.7442793553 0.99101392
+#> 39 39.5  27.841364  30 0.92804546 0.7562417314 0.99399646
+#> 40 40.5  25.731521  27 0.95301930 0.7920169109 0.99752857
+#> 41 41.5  24.116427  25 0.96465707 0.8275253759 0.99916657
+#> 42 42.5  20.523235  22 0.93287433 0.7639822308 0.99641675
+#> 43 43.5  17.580925  19 0.92531182 0.7300474394 0.99597705
+#> 44 44.5  17.183289  18 0.95462715 0.8014756977 0.99781240
 #> 
 #> $method
 #> [1] "bayesian"
diff --git a/docs/reference/est_foi.html b/docs/reference/est_foi.html
index 92dfcbd..9164500 100644
--- a/docs/reference/est_foi.html
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@@ -1,59 +1,50 @@
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@@ -74,38 +65,38 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>t</dt>
+
+
+<dl><dt id="arg-t">t<a class="anchor" aria-label="anchor" href="#arg-t"></a></dt>
 <dd><p>- time (in this case age) vector</p></dd>
 
 
-<dt>sp</dt>
+<dt id="arg-sp">sp<a class="anchor" aria-label="anchor" href="#arg-sp"></a></dt>
 <dd><p>- seroprevalence vector</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>computed foi vector</p>
+    <p>computed foi vector</p>
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-      <img src="../logo.svg" class="logo" alt=""><h1>Estimate seroprevalence and foi by combining mixture model and regression</h1>
+      <img src="../logo.svg" class="logo" alt=""><h1>Estimate seroprevalence and FOI from a fixed mixture model</h1>
       <small class="dont-index">Source: <a href="https://github.com/OUCRU-Modelling/serosv/blob/HEAD/R/mixture_model.R" class="external-link"><code>R/mixture_model.R</code></a></small>
       <div class="d-none name"><code>estimate_from_mixture.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Refers to section 11.2 - 11.4</p>
+    <p>Estimate age-specific seroprevalence and FOI given
+a fitted mixture model (generated by [serosv::mixture_model()])</p>
     </div>
 
     <div class="section level2">
@@ -82,39 +76,39 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>age</dt>
-<dd><p>- vector of age</p></dd>
 
 
-<dt>antibody_level</dt>
-<dd><p>- vector of the corresponding raw antibody level</p></dd>
+<dl><dt id="arg-age">age<a class="anchor" aria-label="anchor" href="#arg-age"></a></dt>
+<dd><p>vector of age</p></dd>
+
+
+<dt id="arg-antibody-level">antibody_level<a class="anchor" aria-label="anchor" href="#arg-antibody-level"></a></dt>
+<dd><p>vector of the corresponding raw antibody level</p></dd>
 
 
-<dt>threshold_status</dt>
-<dd><p>- sero status using threshold approach in line listing (optional, for visualization and comparison only)</p></dd>
+<dt id="arg-threshold-status">threshold_status<a class="anchor" aria-label="anchor" href="#arg-threshold-status"></a></dt>
+<dd><p>sero status using threshold approach in line listing (optional, for visualization and comparison only)</p></dd>
 
 
-<dt>mixture_model</dt>
-<dd><p>- mixture_model object generated by serosv::mixture_model()</p></dd>
+<dt id="arg-mixture-model">mixture_model<a class="anchor" aria-label="anchor" href="#arg-mixture-model"></a></dt>
+<dd><p>mixture_model object generated by serosv::mixture_model()</p></dd>
 
 
-<dt>s</dt>
-<dd><p>- smoothing basis used to fit antibody level</p></dd>
+<dt id="arg-s">s<a class="anchor" aria-label="anchor" href="#arg-s"></a></dt>
+<dd><p>smoothing basis used to fit antibody level</p></dd>
 
 
-<dt>sp</dt>
-<dd><p>- smoothing parameter</p></dd>
+<dt id="arg-sp">sp<a class="anchor" aria-label="anchor" href="#arg-sp"></a></dt>
+<dd><p>smoothing parameter</p></dd>
 
 
-<dt>monotonize</dt>
-<dd><p>- whether to monotonize seroprevalence (default to TRUE)</p></dd>
+<dt id="arg-monotonize">monotonize<a class="anchor" aria-label="anchor" href="#arg-monotonize"></a></dt>
+<dd><p>whether to monotonize seroprevalence (default to TRUE)</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a list of class estimated_from_mixture with the following items</p>
+    <p>a list of class estimated_from_mixture with the following items</p>
 <dl><dt>df</dt>
 <dd><p>the dataframe used for fitting the model</p></dd>
 
@@ -131,28 +125,58 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <dd><p>serostatus using threshold method only if provided</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Antibody level (denoted \(Z\)) is modeled using a 2-component Gaussian
+mixture model. Each component \(Z_j\) (\(j \in \{I, S\}\)) represents the
+antibody level of the latent Infected and Susceptible sub-populations, following density
+\(f_j(z_j|\theta_j)\)</p>
+<p>Let \(\pi_{\text{TRUE}}(a)\) denotes the age-dependent mixing probability
+(i.e., the true prevalence), the density of the mixture is formulated as</p>
+<p>$$f(z|z_I, z_S,a) = (1-\pi_{\text{TRUE}}(a))f_S(z_S|\theta_S)+\pi_{\text{TRUE}}(a)f_I(z_I|\theta_I)$$</p>
+<p>The mean \(E(Z|a)\) thus equals
+$$\mu(a) = (1-\pi_{\text{TRUE}}(a))\mu_S+\pi_{\text{TRUE}}(a)\mu_I$$</p>
+<p>From which true prevalence can be computed as
+$$\pi_{\text{TRUE}}(a) = \frac{\mu(a) - \mu_S}{\mu_I - \mu_S}$$</p>
+<p>And FOI can then be inferred as
+$$\lambda_{TRUE} = \frac{\mu'(a)}{\mu_I - \mu(a)}$$</p>
+<p>Function [serosv::mixture_model()] fits antibody level data to \(f_S(z_S|\theta_S)\) and
+\(f_I(z_I|\theta_I)\)</p>
+<p>Function [serosv::estimate_mixture()] will then estimate age-specific antibody level \(\mu(a)\)
+and infer the estimation for \(\pi_{\text{TRUE}}(a)\) and \(\lambda_{TRUE}\)</p>
+<p>Refer to section 11.3. of the the book by Hens et al. (2012) for further details.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+<a href="https://doi.org/10.1007/978-1-4614-4072-7" class="external-link">doi:10.1007/978-1-4614-4072-7</a>
+.</p>
+    </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p>[mgcv::gam()] for more information about the fitted gam object</p></div>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
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diff --git a/docs/reference/estimate_from_mixture.md b/docs/reference/estimate_from_mixture.md
index 0249a59..9d9ca9e 100644
--- a/docs/reference/estimate_from_mixture.md
+++ b/docs/reference/estimate_from_mixture.md
@@ -1,6 +1,7 @@
-# Estimate seroprevalence and foi by combining mixture model and regression
+# Estimate seroprevalence and FOI from a fixed mixture model
 
-Refers to section 11.2 - 11.4
+Estimate age-specific seroprevalence and FOI given a fitted mixture
+model (generated by \[serosv::mixture_model()\])
 
 ## Usage
 
@@ -20,32 +21,32 @@ estimate_from_mixture(
 
 - age:
 
-  \- vector of age
+  vector of age
 
 - antibody_level:
 
-  \- vector of the corresponding raw antibody level
+  vector of the corresponding raw antibody level
 
 - threshold_status:
 
-  \- sero status using threshold approach in line listing (optional, for
+  sero status using threshold approach in line listing (optional, for
   visualization and comparison only)
 
 - mixture_model:
 
-  \- mixture_model object generated by serosv::mixture_model()
+  mixture_model object generated by serosv::mixture_model()
 
 - s:
 
-  \- smoothing basis used to fit antibody level
+  smoothing basis used to fit antibody level
 
 - sp:
 
-  \- smoothing parameter
+  smoothing parameter
 
 - monotonize:
 
-  \- whether to monotonize seroprevalence (default to TRUE)
+  whether to monotonize seroprevalence (default to TRUE)
 
 ## Value
 
@@ -71,6 +72,48 @@ a list of class estimated_from_mixture with the following items
 
   serostatus using threshold method only if provided
 
+## Details
+
+Antibody level (denoted \\Z\\) is modeled using a 2-component Gaussian
+mixture model. Each component \\Z_j\\ (\\j \in \\I, S\\\\) represents
+the antibody level of the latent Infected and Susceptible
+sub-populations, following density \\f_j(z_j\|\theta_j)\\
+
+Let \\\pi\_{\text{TRUE}}(a)\\ denotes the age-dependent mixing
+probability (i.e., the true prevalence), the density of the mixture is
+formulated as
+
+\$\$f(z\|z_I, z_S,a) =
+(1-\pi\_{\text{TRUE}}(a))f_S(z_S\|\theta_S)+\pi\_{\text{TRUE}}(a)f_I(z_I\|\theta_I)\$\$
+
+The mean \\E(Z\|a)\\ thus equals \$\$\mu(a) =
+(1-\pi\_{\text{TRUE}}(a))\mu_S+\pi\_{\text{TRUE}}(a)\mu_I\$\$
+
+From which true prevalence can be computed as \$\$\pi\_{\text{TRUE}}(a)
+= \frac{\mu(a) - \mu_S}{\mu_I - \mu_S}\$\$
+
+And FOI can then be inferred as \$\$\lambda\_{TRUE} =
+\frac{\mu'(a)}{\mu_I - \mu(a)}\$\$
+
+Function \[serosv::mixture_model()\] fits antibody level data to
+\\f_S(z_S\|\theta_S)\\ and \\f_I(z_I\|\theta_I)\\
+
+Function \[serosv::estimate_mixture()\] will then estimate age-specific
+antibody level \\\mu(a)\\ and infer the estimation for
+\\\pi\_{\text{TRUE}}(a)\\ and \\\lambda\_{TRUE}\\
+
+Refer to section 11.3. of the the book by Hens et al. (2012) for further
+details.
+
+## References
+
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+[doi:10.1007/978-1-4614-4072-7](https://doi.org/10.1007/978-1-4614-4072-7)
+.
+
 ## See also
 
 \[mgcv::gam()\] for more information about the fitted gam object
diff --git a/docs/reference/farrington_model.html b/docs/reference/farrington_model.html
index c85de21..1aa4ea4 100644
--- a/docs/reference/farrington_model.html
+++ b/docs/reference/farrington_model.html
@@ -1,98 +1,116 @@
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   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.svg" class="logo" alt=""><h1>The Farrington (1990) model.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/OUCRU-Modelling/serosv/blob/HEAD/R/polynomial_models.R" class="external-link"><code>R/polynomial_models.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/OUCRU-Modelling/serosv/blob/HEAD/R/farrington_model.R" class="external-link"><code>R/farrington_model.R</code></a></small>
       <div class="d-none name"><code>farrington_model.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Refers to section 6.1.2.</p>
+    <p>Fit age-stratified seroprevalence data using the Farrington (1990) model, which assumes the
+force of infection increases linearly with age and subsequently decreases exponentially.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">farrington_model</span><span class="op">(</span><span class="va">data</span>, <span class="va">start</span>, fixed <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">farrington_model</span><span class="op">(</span></span>
+<span>  <span class="va">data</span>,</span>
+<span>  <span class="va">start</span>,</span>
+<span>  fixed <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span>  age_col <span class="op">=</span> <span class="st">"age"</span>,</span>
+<span>  pos_col <span class="op">=</span> <span class="st">"pos"</span>,</span>
+<span>  tot_col <span class="op">=</span> <span class="st">"tot"</span>,</span>
+<span>  status_col <span class="op">=</span> <span class="st">"status"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>data</dt>
-<dd><p>the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)</p></dd>
 
 
-<dt>start</dt>
+<dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
+<dd><p>the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR `age`, `status` (for linelisting data)</p></dd>
+
+
+<dt id="arg-start">start<a class="anchor" aria-label="anchor" href="#arg-start"></a></dt>
 <dd><p>Named list of vectors or single vector.
 Initial values for optimizer.</p></dd>
 
 
-<dt>fixed</dt>
+<dt id="arg-fixed">fixed<a class="anchor" aria-label="anchor" href="#arg-fixed"></a></dt>
 <dd><p>Named list of vectors or single vector.
 Parameter values to keep fixed during optimization.</p></dd>
 
+
+<dt id="arg-age-col">age_col<a class="anchor" aria-label="anchor" href="#arg-age-col"></a></dt>
+<dd><p>name of the `age` column (default age_col="age").</p></dd>
+
+
+<dt id="arg-pos-col">pos_col<a class="anchor" aria-label="anchor" href="#arg-pos-col"></a></dt>
+<dd><p>name of the `pos` column (default pos_col="pos").</p></dd>
+
+
+<dt id="arg-tot-col">tot_col<a class="anchor" aria-label="anchor" href="#arg-tot-col"></a></dt>
+<dd><p>name of the `tot` column (default tot_col="tot").</p></dd>
+
+
+<dt id="arg-status-col">status_col<a class="anchor" aria-label="anchor" href="#arg-status-col"></a></dt>
+<dd><p>name of the `status` column (default status_col="status").</p></dd>
+
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a list of class farrington_model with 5 items</p>
+    <p>a list of class farrington_model with 5 items</p>
 <dl><dt>datatype</dt>
 <dd><p>type of datatype used for model fitting (aggregated or linelisting)</p></dd>
 
@@ -109,6 +127,31 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <dd><p>force of infection</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>The force of infection is defined as followed</p>
+<p>$$
+\lambda(a) = (\alpha a - \gamma)e^{-\beta a} + \gamma
+$$
+Where \(\gamma\) is called the long term residual for FOI,
+as \(a \rightarrow \infty\) , \(\lambda (a) \rightarrow \gamma\)</p>
+<p>The seroprevalence can thus be estimated using the non-linear model
+$$
+ \pi(a) = 1 - exp\{ \frac{\alpha}{\beta}ae^{-\beta a} +
+ \frac{1}{\beta}(\frac{\alpha}{\beta} -
+ \gamma)(e^{-\beta a} - 1) -\gamma a \}
+$$</p>
+<p>Refer to section 6.1.2. of the the book by Hens et al. (2012) for further details.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+<a href="https://doi.org/10.1007/978-1-4614-4072-7" class="external-link">doi:10.1007/978-1-4614-4072-7</a>
+.</p>
+    </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p>[stats4::mle()] for more information on the fitted mle object</p></div>
@@ -179,23 +222,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/docs/reference/farrington_model.md b/docs/reference/farrington_model.md
index 6a27b61..8f59261 100644
--- a/docs/reference/farrington_model.md
+++ b/docs/reference/farrington_model.md
@@ -1,19 +1,29 @@
 # The Farrington (1990) model.
 
-Refers to section 6.1.2.
+Fit age-stratified seroprevalence data using the Farrington (1990)
+model, which assumes the force of infection increases linearly with age
+and subsequently decreases exponentially.
 
 ## Usage
 
 ``` r
-farrington_model(data, start, fixed = list())
+farrington_model(
+  data,
+  start,
+  fixed = list(),
+  age_col = "age",
+  pos_col = "pos",
+  tot_col = "tot",
+  status_col = "status"
+)
 ```
 
 ## Arguments
 
 - data:
 
-  the input data frame, must either have \`age\`, \`pos\`, \`tot\`
-  columns (for aggregated data) OR \`age\`, \`status\` for (linelisting
+  the input data frame, must either have columns for \`age\`, \`pos\`,
+  \`tot\` (for aggregated data) OR \`age\`, \`status\` (for linelisting
   data)
 
 - start:
@@ -25,6 +35,22 @@ farrington_model(data, start, fixed = list())
   Named list of vectors or single vector. Parameter values to keep fixed
   during optimization.
 
+- age_col:
+
+  name of the \`age\` column (default age_col="age").
+
+- pos_col:
+
+  name of the \`pos\` column (default pos_col="pos").
+
+- tot_col:
+
+  name of the \`tot\` column (default tot_col="tot").
+
+- status_col:
+
+  name of the \`status\` column (default status_col="status").
+
 ## Value
 
 a list of class farrington_model with 5 items
@@ -49,6 +75,31 @@ a list of class farrington_model with 5 items
 
   force of infection
 
+## Details
+
+The force of infection is defined as followed
+
+\$\$ \lambda(a) = (\alpha a - \gamma)e^{-\beta a} + \gamma \$\$ Where
+\\\gamma\\ is called the long term residual for FOI, as \\a \rightarrow
+\infty\\ , \\\lambda (a) \rightarrow \gamma\\
+
+The seroprevalence can thus be estimated using the non-linear model \$\$
+\pi(a) = 1 - exp\\ \frac{\alpha}{\beta}ae^{-\beta a} +
+\frac{1}{\beta}(\frac{\alpha}{\beta} - \gamma)(e^{-\beta a} - 1) -\gamma
+a \\ \$\$
+
+Refer to section 6.1.2. of the the book by Hens et al. (2012) for
+further details.
+
+## References
+
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+[doi:10.1007/978-1-4614-4072-7](https://doi.org/10.1007/978-1-4614-4072-7)
+.
+
 ## See also
 
 \[stats4::mle()\] for more information on the fitted mle object
diff --git a/docs/reference/find_best_fp_powers.html b/docs/reference/find_best_fp_powers.html
index d799b0d..fc2500c 100644
--- a/docs/reference/find_best_fp_powers.html
+++ b/docs/reference/find_best_fp_powers.html
@@ -1,70 +1,61 @@
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-      <img src="../logo.svg" class="logo" alt=""><h1>Returns the powers of the GLM fitted model which has the lowest deviance score.</h1>
+      <img src="../logo.svg" class="logo" alt=""><h1>Returns the powers of the fractional polynomial model which has the lowest deviance score.</h1>
       <small class="dont-index">Source: <a href="https://github.com/OUCRU-Modelling/serosv/blob/HEAD/R/fractional_polynomial_models.R" class="external-link"><code>R/fractional_polynomial_models.R</code></a></small>
       <div class="d-none name"><code>find_best_fp_powers.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Refers to section 6.2.</p>
+    <p>Return the best powers for a given degree</p>
     </div>
 
     <div class="section level2">
@@ -74,31 +65,32 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>data</dt>
-<dd><p>the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)</p></dd>
 
 
-<dt>p</dt>
-<dd><p>a powers sequence.</p></dd>
+<dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
+<dd><p>the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR
+`age`, `status` (for linelisting data)</p></dd>
+
 
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
+<dd><p>a powers sequence to be tested.</p></dd>
 
-<dt>mc</dt>
+
+<dt id="arg-mc">mc<a class="anchor" aria-label="anchor" href="#arg-mc"></a></dt>
 <dd><p>indicates if the returned model should be monotonic.</p></dd>
 
 
-<dt>degree</dt>
-<dd><p>the degree of the model. Recommended to be &lt;= 2.</p></dd>
+<dt id="arg-degree">degree<a class="anchor" aria-label="anchor" href="#arg-degree"></a></dt>
+<dd><p>the maximum degree (i.e. number of power terms) to search for the best model. Recommended to be &lt;= 2.</p></dd>
 
 
-<dt>link</dt>
+<dt id="arg-link">link<a class="anchor" aria-label="anchor" href="#arg-link"></a></dt>
 <dd><p>the link function. Defaulted to "logit".</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>list of 3 elements:</p>
+    <p>list of 3 elements:</p>
 <dl><dt>p</dt>
 <dd><p>The best power for fp model.</p></dd>
 
@@ -110,429 +102,23 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 
 </dl></div>
 
-    <div class="section level2">
-    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">hav_be_1993_1994</span></span></span>
-<span class="r-in"><span><span class="va">best_p</span> <span class="op">&lt;-</span> <span class="fu">find_best_fp_powers</span><span class="op">(</span></span></span>
-<span class="r-in"><span><span class="va">df</span>,</span></span>
-<span class="r-in"><span>p<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="op">-</span><span class="fl">2</span>,<span class="fl">3</span>,<span class="fl">0.1</span><span class="op">)</span>, mc<span class="op">=</span><span class="cn">FALSE</span>, degree<span class="op">=</span><span class="fl">2</span>, link<span class="op">=</span><span class="st">"cloglog"</span></span></span>
-<span class="r-in"><span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
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-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
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-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
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-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
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-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
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-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
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-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
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-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
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-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: algorithm did not converge</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: algorithm did not converge</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: algorithm did not converge</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: algorithm did not converge</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: algorithm did not converge</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: algorithm did not converge</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: algorithm did not converge</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: algorithm did not converge</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: algorithm did not converge</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: algorithm did not converge</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: algorithm did not converge</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: algorithm did not converge</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: algorithm did not converge</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: algorithm did not converge</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>glm.fit: fitted probabilities numerically 0 or 1 occurred</span>
-<span class="r-in"><span><span class="va">best_p</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $p</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 1.5 1.6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $deviance</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 81.60333</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> $model</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Call:  glm(formula = as.formula(formulate(p_cur)), family = binomial(link = link))</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Coefficients:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> (Intercept)   I(age^1.5)   I(age^1.6)  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    -3.61083      0.12443     -0.07656  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Degrees of Freedom: 85 Total (i.e. Null);  83 Residual</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Null Deviance:	    1320 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Residual Deviance: 81.6 	AIC: 361.2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
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diff --git a/docs/reference/find_best_fp_powers.md b/docs/reference/find_best_fp_powers.md
index 9417724..4b8e2e6 100644
--- a/docs/reference/find_best_fp_powers.md
+++ b/docs/reference/find_best_fp_powers.md
@@ -1,6 +1,6 @@
-# Returns the powers of the GLM fitted model which has the lowest deviance score.
+# Returns the powers of the fractional polynomial model which has the lowest deviance score.
 
-Refers to section 6.2.
+Return the best powers for a given degree
 
 ## Usage
 
@@ -12,13 +12,13 @@ find_best_fp_powers(data, p, mc, degree, link = "logit")
 
 - data:
 
-  the input data frame, must either have \`age\`, \`pos\`, \`tot\`
-  columns (for aggregated data) OR \`age\`, \`status\` for (linelisting
+  the input data frame, must either have columns for \`age\`, \`pos\`,
+  \`tot\` (for aggregated data) OR \`age\`, \`status\` (for linelisting
   data)
 
 - p:
 
-  a powers sequence.
+  a powers sequence to be tested.
 
 - mc:
 
@@ -26,7 +26,8 @@ find_best_fp_powers(data, p, mc, degree, link = "logit")
 
 - degree:
 
-  the degree of the model. Recommended to be \<= 2.
+  the maximum degree (i.e. number of power terms) to search for the best
+  model. Recommended to be \<= 2.
 
 - link:
 
@@ -47,409 +48,3 @@ list of 3 elements:
 - model:
 
   The best model fitted
-
-## Examples
-
-``` r
-df <- hav_be_1993_1994
-best_p <- find_best_fp_powers(
-df,
-p=seq(-2,3,0.1), mc=FALSE, degree=2, link="cloglog"
-)
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: algorithm did not converge
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: algorithm did not converge
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: algorithm did not converge
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: algorithm did not converge
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: algorithm did not converge
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: algorithm did not converge
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: algorithm did not converge
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: algorithm did not converge
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: algorithm did not converge
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: algorithm did not converge
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: algorithm did not converge
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: algorithm did not converge
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: algorithm did not converge
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: algorithm did not converge
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-best_p
-#> $p
-#> [1] 1.5 1.6
-#> 
-#> $deviance
-#> [1] 81.60333
-#> 
-#> $model
-#> 
-#> Call:  glm(formula = as.formula(formulate(p_cur)), family = binomial(link = link))
-#> 
-#> Coefficients:
-#> (Intercept)   I(age^1.5)   I(age^1.6)  
-#>    -3.61083      0.12443     -0.07656  
-#> 
-#> Degrees of Freedom: 85 Total (i.e. Null);  83 Residual
-#> Null Deviance:       1320 
-#> Residual Deviance: 81.6  AIC: 361.2
-#> 
-```
diff --git a/docs/reference/fp_model.html b/docs/reference/fp_model.html
index 953cbc1..0ecfc97 100644
--- a/docs/reference/fp_model.html
+++ b/docs/reference/fp_model.html
@@ -1,59 +1,54 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Refers to section 6.2."><title>A fractional polynomial model. — fp_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="A fractional polynomial model. — fp_model"><meta property="og:description" content="Refers to section 6.2."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+where the power of the terms can be fractions, allowing more flexibility and better
+fit for data where asymptotic behavior is expected."><meta property="og:description" content="Fractional polynomial model is a generalization of polynomial models
+where the power of the terms can be fractions, allowing more flexibility and better
+fit for data where asymptotic behavior is expected."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
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+
 
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+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
+    <li><a class="dropdown-item" href="../articles/model_selection.html">Model selection</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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@@ -64,33 +59,68 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
     </div>
 
     <div class="ref-description section level2">
-    <p>Refers to section 6.2.</p>
+    <p>Fractional polynomial model is a generalization of polynomial models
+where the power of the terms can be fractions, allowing more flexibility and better
+fit for data where asymptotic behavior is expected.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">fp_model</span><span class="op">(</span><span class="va">data</span>, <span class="va">p</span>, link <span class="op">=</span> <span class="st">"logit"</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">fp_model</span><span class="op">(</span></span>
+<span>  <span class="va">data</span>,</span>
+<span>  <span class="va">p</span>,</span>
+<span>  monotonic <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  link <span class="op">=</span> <span class="st">"logit"</span>,</span>
+<span>  age_col <span class="op">=</span> <span class="st">"age"</span>,</span>
+<span>  pos_col <span class="op">=</span> <span class="st">"pos"</span>,</span>
+<span>  tot_col <span class="op">=</span> <span class="st">"tot"</span>,</span>
+<span>  status_col <span class="op">=</span> <span class="st">"status"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>data</dt>
+
+
+<dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
 <dd><p>the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)</p></dd>
 
 
-<dt>p</dt>
-<dd><p>the powers of the predictor.</p></dd>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
+<dd><p>is either:</p><ul><li><p>a numeric vector specifying the powers to apply to the predictors</p></li>
+<li><p>a named list with two elements, <code>"p_range"</code> and <code>"degree"</code>. <code>"p_range"</code>
+   is a sequence of powers and <code>"degree"</code> is the maximum degree.
+   In which case the package will search for the best degree and power combinations</p></li>
+</ul></dd>
+
+
+<dt id="arg-monotonic">monotonic<a class="anchor" aria-label="anchor" href="#arg-monotonic"></a></dt>
+<dd><p>whether the returned model should be monotonic (if a search is specified)</p></dd>
 
 
-<dt>link</dt>
+<dt id="arg-link">link<a class="anchor" aria-label="anchor" href="#arg-link"></a></dt>
 <dd><p>the link function for model. Defaulted to "logit".</p></dd>
 
+
+<dt id="arg-age-col">age_col<a class="anchor" aria-label="anchor" href="#arg-age-col"></a></dt>
+<dd><p>name of the `age` column (default age_col="age").</p></dd>
+
+
+<dt id="arg-pos-col">pos_col<a class="anchor" aria-label="anchor" href="#arg-pos-col"></a></dt>
+<dd><p>name of the `pos` column (default pos_col="pos").</p></dd>
+
+
+<dt id="arg-tot-col">tot_col<a class="anchor" aria-label="anchor" href="#arg-tot-col"></a></dt>
+<dd><p>name of the `tot` column (default tot_col="tot").</p></dd>
+
+
+<dt id="arg-status-col">status_col<a class="anchor" aria-label="anchor" href="#arg-status-col"></a></dt>
+<dd><p>name of the `status` column (default status_col="status").</p></dd>
+
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a list of class fp_model with 5 items</p>
+    <p>a list of class fp_model with 5 items</p>
 <dl><dt>datatype</dt>
 <dd><p>type of data used for fitting model (aggregated or linelisting)</p></dd>
 
@@ -107,9 +137,35 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <dd><p>force of infection</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Instead of a polynomial, the linear predictor is now defined as
+$$
+ \eta_m(a, \beta, p_1, p_2, ...,p_m) = \Sigma^m_{i=0} \beta_i H_i(a)
+$$
+Where \(m\) is an integer, \(p_1 \le p_2 \le... \le p_m\) is a sequence of powers,
+and \(H_i(a)\) is a transformation given by</p>
+<p>$$
+H_i = \begin{cases}
+a^{p_i} &amp; \text{ if } p_i \neq p_{i-1},
+\\ H_{i-1}(a) \times log(a)  &amp; \text{ if } p_i = p_{i-1},
+\end{cases}
+$$</p>
+<p>Refers to section 6.2. of the the book by Hens et al. (2012) for further details.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+<a href="https://doi.org/10.1007/978-1-4614-4072-7" class="external-link">doi:10.1007/978-1-4614-4072-7</a>
+.</p>
+    </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p>[stats::glm()] for more information on glm object</p></div>
+    <div class="dont-index"><p>[stats::glm()] for more information on glm object</p>
+<p>[polynomial_models()]</p></div>
     </div>
 
     <div class="section level2">
@@ -123,23 +179,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/fp_model.md b/docs/reference/fp_model.md
index 766d844..e4f5d11 100644
--- a/docs/reference/fp_model.md
+++ b/docs/reference/fp_model.md
@@ -1,11 +1,22 @@
 # A fractional polynomial model.
 
-Refers to section 6.2.
+Fractional polynomial model is a generalization of polynomial models
+where the power of the terms can be fractions, allowing more flexibility
+and better fit for data where asymptotic behavior is expected.
 
 ## Usage
 
 ``` r
-fp_model(data, p, link = "logit")
+fp_model(
+  data,
+  p,
+  monotonic = FALSE,
+  link = "logit",
+  age_col = "age",
+  pos_col = "pos",
+  tot_col = "tot",
+  status_col = "status"
+)
 ```
 
 ## Arguments
@@ -18,12 +29,40 @@ fp_model(data, p, link = "logit")
 
 - p:
 
-  the powers of the predictor.
+  is either:
+
+  - a numeric vector specifying the powers to apply to the predictors
+
+  - a named list with two elements, `"p_range"` and `"degree"`.
+    `"p_range"` is a sequence of powers and `"degree"` is the maximum
+    degree. In which case the package will search for the best degree
+    and power combinations
+
+- monotonic:
+
+  whether the returned model should be monotonic (if a search is
+  specified)
 
 - link:
 
   the link function for model. Defaulted to "logit".
 
+- age_col:
+
+  name of the \`age\` column (default age_col="age").
+
+- pos_col:
+
+  name of the \`pos\` column (default pos_col="pos").
+
+- tot_col:
+
+  name of the \`tot\` column (default tot_col="tot").
+
+- status_col:
+
+  name of the \`status\` column (default status_col="status").
+
 ## Value
 
 a list of class fp_model with 5 items
@@ -48,10 +87,34 @@ a list of class fp_model with 5 items
 
   force of infection
 
+## Details
+
+Instead of a polynomial, the linear predictor is now defined as \$\$
+\eta_m(a, \beta, p_1, p_2, ...,p_m) = \Sigma^m\_{i=0} \beta_i H_i(a)
+\$\$ Where \\m\\ is an integer, \\p_1 \le p_2 \le... \le p_m\\ is a
+sequence of powers, and \\H_i(a)\\ is a transformation given by
+
+\$\$ H_i = \begin{cases} a^{p_i} & \text{ if } p_i \neq p\_{i-1}, \\
+H\_{i-1}(a) \times log(a) & \text{ if } p_i = p\_{i-1}, \end{cases} \$\$
+
+Refers to section 6.2. of the the book by Hens et al. (2012) for further
+details.
+
+## References
+
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+[doi:10.1007/978-1-4614-4072-7](https://doi.org/10.1007/978-1-4614-4072-7)
+.
+
 ## See also
 
 \[stats::glm()\] for more information on glm object
 
+\[polynomial_models()\]
+
 ## Examples
 
 ``` r
diff --git a/docs/reference/hav_be_1993_1994-1.png b/docs/reference/hav_be_1993_1994-1.png
index 79bfa1f..e9bd5b3 100644
Binary files a/docs/reference/hav_be_1993_1994-1.png and b/docs/reference/hav_be_1993_1994-1.png differ
diff --git a/docs/reference/hav_be_1993_1994.html b/docs/reference/hav_be_1993_1994.html
index 9b43969..dcedbb2 100644
--- a/docs/reference/hav_be_1993_1994.html
+++ b/docs/reference/hav_be_1993_1994.html
@@ -1,61 +1,52 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A study of the prevalence of HAV antibodies conducted in the Flemish
-Community of Belgium in 1993 and early 1994"><title>Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated) — hav_be_1993_1994 • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated) — hav_be_1993_1994"><meta property="og:description" content="A study of the prevalence of HAV antibodies conducted in the Flemish
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@@ -97,36 +88,34 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Reproduce Fig 4.1 (upper left panel), p. 63</span></span></span>
-<span class="r-in"><span><span class="va">age</span> <span class="op">&lt;-</span> <span class="va">hav_be_1993_1994</span><span class="op">$</span><span class="va">age</span></span></span>
-<span class="r-in"><span><span class="va">pos</span> <span class="op">&lt;-</span> <span class="va">hav_be_1993_1994</span><span class="op">$</span><span class="va">pos</span></span></span>
-<span class="r-in"><span><span class="va">tot</span> <span class="op">&lt;-</span> <span class="va">hav_be_1993_1994</span><span class="op">$</span><span class="va">tot</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  <span class="va">age</span>, <span class="va">pos</span> <span class="op">/</span> <span class="va">tot</span>,</span></span>
-<span class="r-in"><span>  pty <span class="op">=</span> <span class="st">"s"</span>, cex <span class="op">=</span> <span class="fl">0.06</span> <span class="op">*</span> <span class="va">tot</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>,</span></span>
-<span class="r-in"><span>  ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>, xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">86</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/with.html" class="external-link">with</a></span><span class="op">(</span><span class="va">hav_be_1993_1994</span>,</span></span>
+<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span></span>
+<span class="r-in"><span>    <span class="va">age</span>, <span class="va">pos</span> <span class="op">/</span> <span class="va">tot</span>,</span></span>
+<span class="r-in"><span>    pty <span class="op">=</span> <span class="st">"s"</span>, cex <span class="op">=</span> <span class="fl">0.34</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">tot</span><span class="op">)</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>,</span></span>
+<span class="r-in"><span>    ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>, xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">86</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span>  <span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="hav_be_1993_1994-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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diff --git a/docs/reference/hav_be_1993_1994.md b/docs/reference/hav_be_1993_1994.md
index 8ff6be8..b9bd364 100644
--- a/docs/reference/hav_be_1993_1994.md
+++ b/docs/reference/hav_be_1993_1994.md
@@ -35,14 +35,12 @@ A, B and C in the Flemish population. Eur J Epidemiol 13, 275-280
 ## Examples
 
 ``` r
-# Reproduce Fig 4.1 (upper left panel), p. 63
-age <- hav_be_1993_1994$age
-pos <- hav_be_1993_1994$pos
-tot <- hav_be_1993_1994$tot
-plot(
-  age, pos / tot,
-  pty = "s", cex = 0.06 * tot, pch = 16, xlab = "age",
-  ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
+with(hav_be_1993_1994,
+  plot(
+    age, pos / tot,
+    pty = "s", cex = 0.34 * sqrt(tot), pch = 16, xlab = "age",
+    ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
+  )
 )
 
 ```
diff --git a/docs/reference/hav_be_2002-1.png b/docs/reference/hav_be_2002-1.png
index 7d114b1..f70f32a 100644
Binary files a/docs/reference/hav_be_2002-1.png and b/docs/reference/hav_be_2002-1.png differ
diff --git a/docs/reference/hav_be_2002.html b/docs/reference/hav_be_2002.html
index 115e43c..ab6283b 100644
--- a/docs/reference/hav_be_2002.html
+++ b/docs/reference/hav_be_2002.html
@@ -1,63 +1,54 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A subset of the serological dataset of Varicella-Zoster Virus (VZV) and
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Hepatitis A serological data from Belgium in 2002 (line listing) — hav_be_2002 • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
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+selected"><meta property="og:description" content="A subset of the serological dataset of Varicella-Zoster Virus (VZV) and
 Parvovirus B19 in Belgium where only individuals living in Flanders were
-selected"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+selected"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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-    
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 </nav><div class="container template-reference-topic">
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@@ -98,8 +89,7 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Reproduce Fig 4.1 (upper right panel), p. 63</span></span></span>
-<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>package ‘dplyr’ was built under R version 4.3.1</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Attaching package: ‘dplyr’</span>
@@ -112,32 +102,36 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">hav_be_2002</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">age</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span>pos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">seropositive</span><span class="op">)</span>, tot <span class="op">=</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/context.html" class="external-link">n</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  <span class="va">df</span><span class="op">$</span><span class="va">age</span>, <span class="va">df</span><span class="op">$</span><span class="va">pos</span> <span class="op">/</span> <span class="va">df</span><span class="op">$</span><span class="va">tot</span>,</span></span>
-<span class="r-in"><span>  pty <span class="op">=</span> <span class="st">"s"</span>, cex <span class="op">=</span> <span class="fl">0.06</span> <span class="op">*</span> <span class="va">df</span><span class="op">$</span><span class="va">tot</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>,</span></span>
-<span class="r-in"><span>  ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>, xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">86</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/with.html" class="external-link">with</a></span><span class="op">(</span></span></span>
+<span class="r-in"><span>  <span class="va">df</span>,</span></span>
+<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span></span>
+<span class="r-in"><span>    <span class="va">age</span>, <span class="va">pos</span> <span class="op">/</span> <span class="va">tot</span>,</span></span>
+<span class="r-in"><span>    pty <span class="op">=</span> <span class="st">"s"</span>, cex <span class="op">=</span> <span class="fl">0.34</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">tot</span><span class="op">)</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>,</span></span>
+<span class="r-in"><span>    ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>, xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">86</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span>  <span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="hav_be_2002-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
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diff --git a/docs/reference/hav_be_2002.md b/docs/reference/hav_be_2002.md
index 1195d99..3b5fd95 100644
--- a/docs/reference/hav_be_2002.md
+++ b/docs/reference/hav_be_2002.md
@@ -32,7 +32,6 @@ Pediatr 161, 588-593 (2002).
 ## Examples
 
 ``` r
-# Reproduce Fig 4.1 (upper right panel), p. 63
 library(dplyr)
 #> Warning: package ‘dplyr’ was built under R version 4.3.1
 #> 
@@ -46,10 +45,14 @@ library(dplyr)
 df <- hav_be_2002 %>%
   group_by(age) %>%
   summarise(pos = sum(seropositive), tot = n())
-plot(
-  df$age, df$pos / df$tot,
-  pty = "s", cex = 0.06 * df$tot, pch = 16, xlab = "age",
-  ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
+
+with(
+  df,
+  plot(
+    age, pos / tot,
+    pty = "s", cex = 0.34 * sqrt(tot), pch = 16, xlab = "age",
+    ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
+  )
 )
 
 ```
diff --git a/docs/reference/hav_bg_1964-1.png b/docs/reference/hav_bg_1964-1.png
index 46ec24f..828ab4e 100644
Binary files a/docs/reference/hav_bg_1964-1.png and b/docs/reference/hav_bg_1964-1.png differ
diff --git a/docs/reference/hav_bg_1964.html b/docs/reference/hav_bg_1964.html
index 78f68d7..9086c53 100644
--- a/docs/reference/hav_bg_1964.html
+++ b/docs/reference/hav_bg_1964.html
@@ -1,61 +1,52 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A cross-sectional survey conducted in 1964 in Bulgaria. Samples were
-collected from schoolchildren and blood donors."><title>Hepatitis A serological data from Bulgaria in 1964 (aggregated) — hav_bg_1964 • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Hepatitis A serological data from Bulgaria in 1964 (aggregated) — hav_bg_1964"><meta property="og:description" content="A cross-sectional survey conducted in 1964 in Bulgaria. Samples were
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+collected from schoolchildren and blood donors."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
@@ -98,36 +89,35 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Reproduce Fig 4.1 (lower panel), p. 63</span></span></span>
-<span class="r-in"><span><span class="va">age</span> <span class="op">&lt;-</span> <span class="va">hav_bg_1964</span><span class="op">$</span><span class="va">age</span></span></span>
-<span class="r-in"><span><span class="va">pos</span> <span class="op">&lt;-</span> <span class="va">hav_bg_1964</span><span class="op">$</span><span class="va">pos</span></span></span>
-<span class="r-in"><span><span class="va">tot</span> <span class="op">&lt;-</span> <span class="va">hav_bg_1964</span><span class="op">$</span><span class="va">tot</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/with.html" class="external-link">with</a></span><span class="op">(</span></span></span>
+<span class="r-in"><span>  <span class="va">hav_bg_1964</span>,</span></span>
+<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span></span>
 <span class="r-in"><span>    <span class="va">age</span>, <span class="va">pos</span> <span class="op">/</span> <span class="va">tot</span>,</span></span>
-<span class="r-in"><span>    pty <span class="op">=</span> <span class="st">"s"</span>, cex <span class="op">=</span> <span class="fl">0.08</span> <span class="op">*</span> <span class="va">tot</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>,</span></span>
+<span class="r-in"><span>    pty <span class="op">=</span> <span class="st">"s"</span>, cex <span class="op">=</span> <span class="fl">0.6</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">tot</span><span class="op">)</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>,</span></span>
 <span class="r-in"><span>    ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>, xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">86</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span>  <span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="hav_bg_1964-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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   </body></html>
 
diff --git a/docs/reference/hav_bg_1964.md b/docs/reference/hav_bg_1964.md
index 5424122..ba6b3e2 100644
--- a/docs/reference/hav_bg_1964.md
+++ b/docs/reference/hav_bg_1964.md
@@ -35,14 +35,13 @@ Perspective." Journal of the Royal Statistical Society. Series A
 ## Examples
 
 ``` r
-# Reproduce Fig 4.1 (lower panel), p. 63
-age <- hav_bg_1964$age
-pos <- hav_bg_1964$pos
-tot <- hav_bg_1964$tot
-plot(
+with(
+  hav_bg_1964,
+  plot(
     age, pos / tot,
-    pty = "s", cex = 0.08 * tot, pch = 16, xlab = "age",
+    pty = "s", cex = 0.6 * sqrt(tot), pch = 16, xlab = "age",
     ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
+  )
 )
 
 ```
diff --git a/docs/reference/hbv_ru_1999-1.png b/docs/reference/hbv_ru_1999-1.png
index cae4b75..a336a3b 100644
Binary files a/docs/reference/hbv_ru_1999-1.png and b/docs/reference/hbv_ru_1999-1.png differ
diff --git a/docs/reference/hbv_ru_1999.html b/docs/reference/hbv_ru_1999.html
index 3196f1b..69c0494 100644
--- a/docs/reference/hbv_ru_1999.html
+++ b/docs/reference/hbv_ru_1999.html
@@ -1,61 +1,52 @@
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@@ -101,8 +92,7 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Reproduce Fig 4.2, p. 65</span></span></span>
-<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">hbv_ru_1999</span><span class="op">$</span><span class="va">age</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">trunc</a></span><span class="op">(</span><span class="va">hbv_ru_1999</span><span class="op">$</span><span class="va">age</span> <span class="op">/</span> <span class="fl">1</span><span class="op">)</span> <span class="op">*</span> <span class="fl">1</span></span></span>
 <span class="r-in"><span><span class="va">hbv_ru_1999</span><span class="op">$</span><span class="va">age</span><span class="op">[</span><span class="va">hbv_ru_1999</span><span class="op">$</span><span class="va">age</span> <span class="op">&gt;</span> <span class="fl">40</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">trunc</a></span><span class="op">(</span></span></span>
 <span class="r-in"><span>  <span class="va">hbv_ru_1999</span><span class="op">$</span><span class="va">age</span><span class="op">[</span><span class="va">hbv_ru_1999</span><span class="op">$</span><span class="va">age</span> <span class="op">&gt;</span> <span class="fl">40</span><span class="op">]</span> <span class="op">/</span> <span class="fl">5</span></span></span>
@@ -110,32 +100,33 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">hbv_ru_1999</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">age</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span>pos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">pos</span><span class="op">)</span>, tot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">tot</span><span class="op">)</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span></span>
 <span class="r-in"><span>  <span class="va">df</span><span class="op">$</span><span class="va">age</span>, <span class="va">df</span><span class="op">$</span><span class="va">pos</span> <span class="op">/</span> <span class="va">df</span><span class="op">$</span><span class="va">tot</span>,</span></span>
-<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.05</span> <span class="op">*</span> <span class="va">df</span><span class="op">$</span><span class="va">tot</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>,</span></span>
+<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.32</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">tot</span><span class="op">)</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>,</span></span>
 <span class="r-in"><span>  ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>, xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">72</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="hbv_ru_1999-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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diff --git a/docs/reference/hbv_ru_1999.md b/docs/reference/hbv_ru_1999.md
index 65fc141..b17722b 100644
--- a/docs/reference/hbv_ru_1999.md
+++ b/docs/reference/hbv_ru_1999.md
@@ -39,7 +39,6 @@ Report 213 (3), 3rd edn. St Petersburg Pasteur Institute, St Petersburg,
 ## Examples
 
 ``` r
-# Reproduce Fig 4.2, p. 65
 library(dplyr)
 hbv_ru_1999$age <- trunc(hbv_ru_1999$age / 1) * 1
 hbv_ru_1999$age[hbv_ru_1999$age > 40] <- trunc(
@@ -48,9 +47,10 @@ hbv_ru_1999$age[hbv_ru_1999$age > 40] <- trunc(
 df <- hbv_ru_1999 %>%
   group_by(age) %>%
   summarise(pos = sum(pos), tot = sum(tot))
+
 plot(
   df$age, df$pos / df$tot,
-  cex = 0.05 * df$tot, pch = 16, xlab = "age",
+  cex = 0.32 * sqrt(df$tot), pch = 16, xlab = "age",
   ylab = "seroprevalence", xlim = c(0, 72)
 )
 
diff --git a/docs/reference/hcv_be_2006-1.png b/docs/reference/hcv_be_2006-1.png
index 0336d2a..63cf695 100644
Binary files a/docs/reference/hcv_be_2006-1.png and b/docs/reference/hcv_be_2006-1.png differ
diff --git a/docs/reference/hcv_be_2006.html b/docs/reference/hcv_be_2006.html
index 109a271..f8860bc 100644
--- a/docs/reference/hcv_be_2006.html
+++ b/docs/reference/hcv_be_2006.html
@@ -1,65 +1,56 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A study of HCV infection among injecting drug users. All injecting drug users
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Hepatitis C serological data from Belgium in 2006 (line listing) — hcv_be_2006 • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
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 <div class="row">
@@ -103,8 +94,7 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Reproduce Fig 4.3, p. 66</span></span></span>
-<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="co"># snapping age to aggregated age group</span></span></span>
 <span class="r-in"><span><span class="co"># (credit: https://stackoverflow.com/a/12861810)</span></span></span>
 <span class="r-in"><span><span class="va">groups</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span><span class="op">:</span><span class="fl">24.5</span><span class="op">)</span></span></span>
@@ -122,33 +112,35 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">dur</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span>tot <span class="op">=</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/context.html" class="external-link">n</a></span><span class="op">(</span><span class="op">)</span>, pos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">seropositive</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  <span class="va">hcv_be_2006</span><span class="op">$</span><span class="va">dur</span>, <span class="va">hcv_be_2006</span><span class="op">$</span><span class="va">pos</span> <span class="op">/</span> <span class="va">hcv_be_2006</span><span class="op">$</span><span class="va">tot</span>,</span></span>
-<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.1</span> <span class="op">*</span> <span class="va">hcv_be_2006</span><span class="op">$</span><span class="va">tot</span>, pch <span class="op">=</span> <span class="fl">16</span>,</span></span>
-<span class="r-in"><span>  xlab <span class="op">=</span> <span class="st">"duration of injection (years)"</span>,</span></span>
-<span class="r-in"><span>  ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>, xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">25</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/with.html" class="external-link">with</a></span><span class="op">(</span><span class="va">hcv_be_2006</span>,</span></span>
+<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span></span>
+<span class="r-in"><span>    <span class="va">dur</span>, <span class="va">pos</span> <span class="op">/</span> <span class="va">tot</span>,</span></span>
+<span class="r-in"><span>    cex <span class="op">=</span> <span class="fl">0.42</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">tot</span><span class="op">)</span>, pch <span class="op">=</span> <span class="fl">16</span>,</span></span>
+<span class="r-in"><span>    xlab <span class="op">=</span> <span class="st">"duration of injection (years)"</span>,</span></span>
+<span class="r-in"><span>    ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>, xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">25</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span>  <span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="hcv_be_2006-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
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+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
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diff --git a/docs/reference/hcv_be_2006.md b/docs/reference/hcv_be_2006.md
index 4d8bb19..6e1e6e7 100644
--- a/docs/reference/hcv_be_2006.md
+++ b/docs/reference/hcv_be_2006.md
@@ -35,7 +35,6 @@ Viral Hepatitis, 13: 560-570.
 ## Examples
 
 ``` r
-# Reproduce Fig 4.3, p. 66
 library(dplyr)
 # snapping age to aggregated age group
 # (credit: https://stackoverflow.com/a/12861810)
@@ -54,11 +53,13 @@ hcv_be_2006 <- hcv_be_2006 %>%
   group_by(dur) %>%
   summarise(tot = n(), pos = sum(seropositive))
 
-plot(
-  hcv_be_2006$dur, hcv_be_2006$pos / hcv_be_2006$tot,
-  cex = 0.1 * hcv_be_2006$tot, pch = 16,
-  xlab = "duration of injection (years)",
-  ylab = "seroprevalence", xlim = c(0, 25), ylim = c(0, 1)
+with(hcv_be_2006,
+  plot(
+    dur, pos / tot,
+    cex = 0.42 * sqrt(tot), pch = 16,
+    xlab = "duration of injection (years)",
+    ylab = "seroprevalence", xlim = c(0, 25), ylim = c(0, 1)
+  )
 )
 
 ```
diff --git a/docs/reference/hierarchical_bayesian_model-1.png b/docs/reference/hierarchical_bayesian_model-1.png
index 344940c..3b9bc1b 100644
Binary files a/docs/reference/hierarchical_bayesian_model-1.png and b/docs/reference/hierarchical_bayesian_model-1.png differ
diff --git a/docs/reference/hierarchical_bayesian_model.html b/docs/reference/hierarchical_bayesian_model.html
index 53ece89..7f55aca 100644
--- a/docs/reference/hierarchical_bayesian_model.html
+++ b/docs/reference/hierarchical_bayesian_model.html
@@ -1,5 +1,15 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Hierarchical Bayesian Model — hierarchical_bayesian_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Hierarchical Bayesian Model — hierarchical_bayesian_model"><meta name="description" content="Refers to section 10.3"><meta property="og:description" content="Refers to section 10.3"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
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+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Hierarchical Bayesian Model — hierarchical_bayesian_model"><meta name="description" content="Fit age-stratified seroprevalence to parametric hierarchical Bayesian models.
+Supported models including Farrington model (2 and 3 parameters variants)
+and Log Logistic model"><meta property="og:description" content="Fit age-stratified seroprevalence to parametric hierarchical Bayesian models.
+Supported models including Farrington model (2 and 3 parameters variants)
+and Log Logistic model"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +17,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -19,14 +29,14 @@
 <li class="nav-item dropdown">
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="../articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="../articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
@@ -49,13 +59,19 @@
     </div>
 
     <div class="ref-description section level2">
-    <p>Refers to section 10.3</p>
+    <p>Fit age-stratified seroprevalence to parametric hierarchical Bayesian models.
+Supported models including Farrington model (2 and 3 parameters variants)
+and Log Logistic model</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">hierarchical_bayesian_model</span><span class="op">(</span></span>
 <span>  <span class="va">data</span>,</span>
+<span>  age_col <span class="op">=</span> <span class="st">"age"</span>,</span>
+<span>  pos_col <span class="op">=</span> <span class="st">"pos"</span>,</span>
+<span>  tot_col <span class="op">=</span> <span class="st">"tot"</span>,</span>
+<span>  status_col <span class="op">=</span> <span class="st">"status"</span>,</span>
 <span>  type <span class="op">=</span> <span class="st">"far3"</span>,</span>
 <span>  chains <span class="op">=</span> <span class="fl">1</span>,</span>
 <span>  warmup <span class="op">=</span> <span class="fl">1500</span>,</span>
@@ -68,7 +84,23 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
-<dd><p>the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)</p></dd>
+<dd><p>the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR `age`, `status` (for linelisting data)</p></dd>
+
+
+<dt id="arg-age-col">age_col<a class="anchor" aria-label="anchor" href="#arg-age-col"></a></dt>
+<dd><p>name of the `age` column (default age_col="age").</p></dd>
+
+
+<dt id="arg-pos-col">pos_col<a class="anchor" aria-label="anchor" href="#arg-pos-col"></a></dt>
+<dd><p>name of the `pos` column (default pos_col="pos").</p></dd>
+
+
+<dt id="arg-tot-col">tot_col<a class="anchor" aria-label="anchor" href="#arg-tot-col"></a></dt>
+<dd><p>name of the `tot` column (default tot_col="tot").</p></dd>
+
+
+<dt id="arg-status-col">status_col<a class="anchor" aria-label="anchor" href="#arg-status-col"></a></dt>
+<dd><p>name of the `status` column (default status_col="status").</p></dd>
 
 
 <dt id="arg-type">type<a class="anchor" aria-label="anchor" href="#arg-type"></a></dt>
@@ -115,6 +147,44 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <dd><p>function to compute force of infection given age and model parameters</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Consider a model for prevalence that has a parametric form
+\(\pi(a_i, \alpha)\) where \(\alpha\) is a parameter vector</p>
+<p>Under a Bayesian framework, we can constraint the parameter space of the prior distribution \(P(\alpha)\)
+to achieve monotonicity of the posterior distribution \(P(\pi_1, \pi_2, ..., \pi_m|y,n)\)</p>
+<p>Where:</p>
+<p>- \(n = (n_1, n_2, ..., n_m)\) and \(n_i\) is the sample size at age \(a_i\)</p>
+<p>- \(y = (y_1, y_2, ..., y_m)\) and \(y_i\) is the number of infected individual from the \(n_i\) sampled subjects</p>
+<p>For <b>Farrington</b> model with 3 parameters, prevalence is formulated as follow</p>
+<p>$$
+\pi (a) = 1 - exp\{ \frac{\alpha_1}{\alpha_2}ae^{-\alpha_2 a} +
+\frac{1}{\alpha_2}(\frac{\alpha_1}{\alpha_2} - \alpha_3)(e^{-\alpha_2 a} - 1) -\alpha_3 a \}
+$$</p>
+<p>The likelihood model is defined as \(y_i \sim Bin(n_i, \pi_i), \text{  for } i = 1,2,3,...m\)</p>
+<p>The constraint on the parameter space can be incorporated by assuming
+ truncated normal distribution for the components of \(\alpha\),
+\(\alpha = (\alpha_1, \alpha_2, \alpha_3)\) in \(\pi_i = \pi(a_i,\alpha)\)</p>
+<p>The flat hyperpriors are defined as \(\mu_j \sim \mathcal{N}(0, 10000)\) and
+\(\tau^{-2}_j \sim \Gamma(100,100)\)</p>
+<p>For <b>Farrington</b> model with 2 parameters, it is equivalent to the previous model with \(\alpha_3 = 0\)</p>
+<p>For <b>Log logistic model</b>, seroprevalence is instead defined as</p>
+<p>$$\pi(a) = \frac{\beta a^\alpha}{1 + \beta a^\alpha}, \text{ } \alpha, \beta &gt; 0$$</p>
+<p>The likelihood is similarly defined as \(y_i \sim Bin(n_i, \pi_i))\)</p>
+<p>The prior model of \(\alpha_1\) is specified as \(\alpha_1 \sim \text{truncated  } \mathcal{N}(\mu_1, \tau_1)\)
+with flat hyperpriors as in Farrington model</p>
+<p>\(\beta\) is constrained to be positive by specifying \(\alpha_2 \sim \mathcal{N}(\mu_2, \tau_2)\)</p>
+<p>Refer to section Chapter 10.3 of the the book by Hens et al. (2012) for further details.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+<a href="https://doi.org/10.1007/978-1-4614-4072-7" class="external-link">doi:10.1007/978-1-4614-4072-7</a>
+.</p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
@@ -135,15 +205,9 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Rejecting initial value:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity.</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:   Stan can't start sampling from this initial value.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Rejecting initial value:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:   Stan can't start sampling from this initial value.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Rejecting initial value:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:   Stan can't start sampling from this initial value.</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Gradient evaluation took 0.000123 seconds</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.23 seconds.</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Gradient evaluation took 0.00013 seconds</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.3 seconds.</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Adjust your expectations accordingly!</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: </span>
@@ -160,17 +224,16 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 4500 / 5000 [ 90%]  (Sampling)</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 5000 / 5000 [100%]  (Sampling)</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:  Elapsed Time: 14.459 seconds (Warm-up)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:                3.506 seconds (Sampling)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:                17.965 seconds (Total)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:  Elapsed Time: 15.235 seconds (Warm-up)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:                139.842 seconds (Sampling)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:                155.077 seconds (Total)</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: </span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>There were 1634 divergent transitions after warmup. See</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>There were 324 divergent transitions after warmup. See</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> to find out why this is a problem and how to eliminate them.</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>There were 143 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Examine the pairs() plot to diagnose sampling problems</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>The largest R-hat is 1.3, indicating chains have not mixed.</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> Running the chains for more iterations may help. See</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> https://mc-stan.org/misc/warnings.html#r-hat</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> Running the chains for more iterations may help. See</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> https://mc-stan.org/misc/warnings.html#bulk-ess</span>
@@ -179,47 +242,47 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> https://mc-stan.org/misc/warnings.html#tail-ess</span>
 <span class="r-in"><span><span class="va">model</span><span class="op">$</span><span class="va">info</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                       mean      se_mean           sd          2.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> alpha1        1.374349e-01 2.469659e-03 7.304112e-03  1.256108e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> alpha2        1.971191e-01 2.365928e-03 8.668356e-03  1.840890e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> alpha3        8.361884e-03 9.115459e-04 6.405086e-03  6.530832e-04</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha1    3.232071e-02 8.114025e-03 7.619259e-02  1.649675e-05</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha2    6.679641e-01 2.734528e-01 1.028098e+00  6.526141e-06</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha3    5.874325e-02 1.502771e-02 1.094519e-01  6.639239e-06</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha1    -5.831428e+00 1.717162e+00 3.215799e+01 -7.580347e+01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha2     1.899729e+00 2.488519e+00 3.501723e+01 -7.316053e+01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha3     7.615985e+00 1.031699e+01 6.091801e+01 -9.637830e+01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha1  8.046101e+01 3.723283e+01 1.601118e+03  1.845717e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha2  5.050277e+01 1.023182e+01 2.116087e+02  5.704865e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha3  6.396098e+01 1.470747e+01 3.041621e+02  1.532948e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> lp__         -2.535346e+03 8.603892e-01 3.512983e+00 -2.543226e+03</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                        25%           50%           75%         97.5%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> alpha1        1.309418e-01  1.381552e-01  1.433022e-01  1.496570e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> alpha2        1.903549e-01  1.978268e-01  2.027656e-01  2.136976e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> alpha3        2.918675e-03  7.742625e-03  1.200969e-02  2.372548e-02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha1    8.305591e-04  3.669129e-03  1.367782e-02  2.935416e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha2    1.304788e-03  6.628968e-02  1.133317e+00  3.072622e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha3    5.987982e-04  7.336155e-03  5.747848e-02  4.255445e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha1    -1.143099e+01 -2.676034e+00  2.767768e+00  4.919579e+01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha2    -1.520815e+00  4.404344e-01  2.365690e+00  8.958812e+01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha3    -3.895228e+00 -3.255963e-03  4.586351e+00  2.369248e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha1  8.550509e+00  1.650901e+01  3.469882e+01  2.462098e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha2  9.393433e-01  3.883981e+00  2.768415e+01  3.914472e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha3  4.171069e+00  1.167549e+01  4.086578e+01  3.880977e+02</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> lp__         -2.537755e+03 -2.535076e+03 -2.532816e+03 -2.529997e+03</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                    n_eff      Rhat</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> alpha1          8.747031 1.1212692</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> alpha2         13.423642 1.0897105</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> alpha3         49.373384 1.0283578</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha1     88.176492 1.0039526</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha2     14.135280 1.3042242</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha3     53.047004 0.9997529</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha1     350.715887 1.0015192</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha2     198.007524 1.0074621</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha3      34.864649 1.0304100</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha1 1849.244902 0.9999612</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha2  427.721511 1.0054612</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha3  427.695365 1.0184599</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> lp__           16.671016 1.1939938</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> alpha1        1.394994e-01 2.208144e-04 5.686602e-03  1.290184e-01</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> alpha2        1.984613e-01 3.072933e-04 7.829686e-03  1.844645e-01</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> alpha3        8.289398e-03 2.217734e-04 6.839120e-03  3.133171e-04</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha1    6.381566e+00 1.529942e+00 1.636028e+01  6.597716e-06</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha2    1.371545e+01 8.454139e+00 4.030987e+01  5.871040e-06</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha3    5.780803e+00 1.085321e+00 1.356397e+01  7.111779e-06</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha1     8.127640e-01 1.294292e+00 2.957688e+01 -7.133897e+01</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha2     3.575058e+00 1.799463e+00 3.726214e+01 -7.253911e+01</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha3     3.883757e+00 1.307763e+00 3.405219e+01 -6.454653e+01</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha1  6.352800e+01 1.682171e+01 7.004084e+02  1.213045e-01</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha2  7.855927e+01 1.684436e+01 6.470535e+02  7.736001e-02</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha3  1.873854e+02 1.323037e+02 3.877173e+03  1.431469e-01</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> lp__         -2.532504e+03 3.710684e-01 4.655392e+00 -2.542452e+03</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>                        25%           50%           75%         97.5%      n_eff</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> alpha1        1.357106e-01  1.390419e-01  1.430492e-01  1.511064e-01  663.20993</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> alpha2        1.931963e-01  1.983732e-01  2.027147e-01  2.165249e-01  649.20593</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> alpha3        2.841082e-03  6.688870e-03  1.163816e-02  2.583495e-02  951.00250</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha1    1.733407e-03  9.495067e-02  2.687740e+00  6.795883e+01  114.34889</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha2    1.342653e-03  6.464717e-02  2.511530e+00  1.670964e+02   22.73443</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha3    1.111049e-03  5.337217e-02  2.822276e+00  4.880180e+01  156.19150</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha1    -1.276472e+00  1.729980e-01  2.532245e+00  5.703259e+01  522.20403</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha2    -1.528020e+00  2.032933e-01  2.920524e+00  1.045006e+02  428.79515</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha3    -1.524451e+00  3.939297e-02  3.640311e+00  9.311625e+01  678.00364</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha1  6.099697e-01  3.245271e+00  2.401891e+01  3.893194e+02 1733.65511</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha2  6.310022e-01  3.933036e+00  2.729092e+01  4.127492e+02 1475.60823</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha3  5.952566e-01  4.328557e+00  3.000085e+01  3.749841e+02  858.78884</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> lp__         -2.535634e+03 -2.532144e+03 -2.528948e+03 -2.524553e+03  157.39993</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>                   Rhat</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> alpha1       1.0002098</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> alpha2       0.9998023</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> alpha3       0.9997596</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha1   1.0003182</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha2   1.0444726</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> tau_alpha3   1.0101265</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha1    1.0073358</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha2    1.0048162</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mu_alpha3    1.0031755</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha1 0.9997210</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha2 1.0000959</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sigma_alpha3 1.0009114</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> lp__         0.9998177</span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="hierarchical_bayesian_model-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span><span class="co"># }</span></span></span>
diff --git a/docs/reference/hierarchical_bayesian_model.md b/docs/reference/hierarchical_bayesian_model.md
index cf9bff3..d522ff4 100644
--- a/docs/reference/hierarchical_bayesian_model.md
+++ b/docs/reference/hierarchical_bayesian_model.md
@@ -1,12 +1,18 @@
 # Hierarchical Bayesian Model
 
-Refers to section 10.3
+Fit age-stratified seroprevalence to parametric hierarchical Bayesian
+models. Supported models including Farrington model (2 and 3 parameters
+variants) and Log Logistic model
 
 ## Usage
 
 ``` r
 hierarchical_bayesian_model(
   data,
+  age_col = "age",
+  pos_col = "pos",
+  tot_col = "tot",
+  status_col = "status",
   type = "far3",
   chains = 1,
   warmup = 1500,
@@ -18,10 +24,26 @@ hierarchical_bayesian_model(
 
 - data:
 
-  the input data frame, must either have \`age\`, \`pos\`, \`tot\`
-  columns (for aggregated data) OR \`age\`, \`status\` for (linelisting
+  the input data frame, must either have columns for \`age\`, \`pos\`,
+  \`tot\` (for aggregated data) OR \`age\`, \`status\` (for linelisting
   data)
 
+- age_col:
+
+  name of the \`age\` column (default age_col="age").
+
+- pos_col:
+
+  name of the \`pos\` column (default pos_col="pos").
+
+- tot_col:
+
+  name of the \`tot\` column (default tot_col="tot").
+
+- status_col:
+
+  name of the \`status\` column (default status_col="status").
+
 - type:
 
   type of model ("far2", "far3" or "log_logistic")
@@ -74,6 +96,69 @@ a list of class hierarchical_bayesian_model with 6 items
 
   function to compute force of infection given age and model parameters
 
+## Details
+
+Consider a model for prevalence that has a parametric form \\\pi(a_i,
+\alpha)\\ where \\\alpha\\ is a parameter vector
+
+Under a Bayesian framework, we can constraint the parameter space of the
+prior distribution \\P(\alpha)\\ to achieve monotonicity of the
+posterior distribution \\P(\pi_1, \pi_2, ..., \pi_m\|y,n)\\
+
+Where:
+
+\- \\n = (n_1, n_2, ..., n_m)\\ and \\n_i\\ is the sample size at age
+\\a_i\\
+
+\- \\y = (y_1, y_2, ..., y_m)\\ and \\y_i\\ is the number of infected
+individual from the \\n_i\\ sampled subjects
+
+For **Farrington** model with 3 parameters, prevalence is formulated as
+follow
+
+\$\$ \pi (a) = 1 - exp\\ \frac{\alpha_1}{\alpha_2}ae^{-\alpha_2 a} +
+\frac{1}{\alpha_2}(\frac{\alpha_1}{\alpha_2} - \alpha_3)(e^{-\alpha_2
+a} - 1) -\alpha_3 a \\ \$\$
+
+The likelihood model is defined as \\y_i \sim Bin(n_i, \pi_i), \text{
+for } i = 1,2,3,...m\\
+
+The constraint on the parameter space can be incorporated by assuming
+truncated normal distribution for the components of \\\alpha\\, \\\alpha
+= (\alpha_1, \alpha_2, \alpha_3)\\ in \\\pi_i = \pi(a_i,\alpha)\\
+
+The flat hyperpriors are defined as \\\mu_j \sim \mathcal{N}(0, 10000)\\
+and \\\tau^{-2}\_j \sim \Gamma(100,100)\\
+
+For **Farrington** model with 2 parameters, it is equivalent to the
+previous model with \\\alpha_3 = 0\\
+
+For **Log logistic model**, seroprevalence is instead defined as
+
+\$\$\pi(a) = \frac{\beta a^\alpha}{1 + \beta a^\alpha}, \text{ } \alpha,
+\beta \> 0\$\$
+
+The likelihood is similarly defined as \\y_i \sim Bin(n_i, \pi_i))\\
+
+The prior model of \\\alpha_1\\ is specified as \\\alpha_1 \sim
+\text{truncated } \mathcal{N}(\mu_1, \tau_1)\\ with flat hyperpriors as
+in Farrington model
+
+\\\beta\\ is constrained to be positive by specifying \\\alpha_2 \sim
+\mathcal{N}(\mu_2, \tau_2)\\
+
+Refer to section Chapter 10.3 of the the book by Hens et al. (2012) for
+further details.
+
+## References
+
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+[doi:10.1007/978-1-4614-4072-7](https://doi.org/10.1007/978-1-4614-4072-7)
+.
+
 ## Examples
 
 ``` r
@@ -94,15 +179,9 @@ model <- hierarchical_bayesian_model(df, type="far3")
 #> Chain 1: Rejecting initial value:
 #> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity.
 #> Chain 1:   Stan can't start sampling from this initial value.
-#> Chain 1: Rejecting initial value:
-#> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity.
-#> Chain 1:   Stan can't start sampling from this initial value.
-#> Chain 1: Rejecting initial value:
-#> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity.
-#> Chain 1:   Stan can't start sampling from this initial value.
 #> Chain 1: 
-#> Chain 1: Gradient evaluation took 0.000123 seconds
-#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.23 seconds.
+#> Chain 1: Gradient evaluation took 0.00013 seconds
+#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.3 seconds.
 #> Chain 1: Adjust your expectations accordingly!
 #> Chain 1: 
 #> Chain 1: 
@@ -119,17 +198,16 @@ model <- hierarchical_bayesian_model(df, type="far3")
 #> Chain 1: Iteration: 4500 / 5000 [ 90%]  (Sampling)
 #> Chain 1: Iteration: 5000 / 5000 [100%]  (Sampling)
 #> Chain 1: 
-#> Chain 1:  Elapsed Time: 14.459 seconds (Warm-up)
-#> Chain 1:                3.506 seconds (Sampling)
-#> Chain 1:                17.965 seconds (Total)
+#> Chain 1:  Elapsed Time: 15.235 seconds (Warm-up)
+#> Chain 1:                139.842 seconds (Sampling)
+#> Chain 1:                155.077 seconds (Total)
 #> Chain 1: 
-#> Warning: There were 1634 divergent transitions after warmup. See
+#> Warning: There were 324 divergent transitions after warmup. See
 #> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 #> to find out why this is a problem and how to eliminate them.
+#> Warning: There were 143 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See
+#> https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded
 #> Warning: Examine the pairs() plot to diagnose sampling problems
-#> Warning: The largest R-hat is 1.3, indicating chains have not mixed.
-#> Running the chains for more iterations may help. See
-#> https://mc-stan.org/misc/warnings.html#r-hat
 #> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
 #> Running the chains for more iterations may help. See
 #> https://mc-stan.org/misc/warnings.html#bulk-ess
@@ -138,47 +216,47 @@ model <- hierarchical_bayesian_model(df, type="far3")
 #> https://mc-stan.org/misc/warnings.html#tail-ess
 model$info
 #>                       mean      se_mean           sd          2.5%
-#> alpha1        1.374349e-01 2.469659e-03 7.304112e-03  1.256108e-01
-#> alpha2        1.971191e-01 2.365928e-03 8.668356e-03  1.840890e-01
-#> alpha3        8.361884e-03 9.115459e-04 6.405086e-03  6.530832e-04
-#> tau_alpha1    3.232071e-02 8.114025e-03 7.619259e-02  1.649675e-05
-#> tau_alpha2    6.679641e-01 2.734528e-01 1.028098e+00  6.526141e-06
-#> tau_alpha3    5.874325e-02 1.502771e-02 1.094519e-01  6.639239e-06
-#> mu_alpha1    -5.831428e+00 1.717162e+00 3.215799e+01 -7.580347e+01
-#> mu_alpha2     1.899729e+00 2.488519e+00 3.501723e+01 -7.316053e+01
-#> mu_alpha3     7.615985e+00 1.031699e+01 6.091801e+01 -9.637830e+01
-#> sigma_alpha1  8.046101e+01 3.723283e+01 1.601118e+03  1.845717e+00
-#> sigma_alpha2  5.050277e+01 1.023182e+01 2.116087e+02  5.704865e-01
-#> sigma_alpha3  6.396098e+01 1.470747e+01 3.041621e+02  1.532948e+00
-#> lp__         -2.535346e+03 8.603892e-01 3.512983e+00 -2.543226e+03
-#>                        25%           50%           75%         97.5%
-#> alpha1        1.309418e-01  1.381552e-01  1.433022e-01  1.496570e-01
-#> alpha2        1.903549e-01  1.978268e-01  2.027656e-01  2.136976e-01
-#> alpha3        2.918675e-03  7.742625e-03  1.200969e-02  2.372548e-02
-#> tau_alpha1    8.305591e-04  3.669129e-03  1.367782e-02  2.935416e-01
-#> tau_alpha2    1.304788e-03  6.628968e-02  1.133317e+00  3.072622e+00
-#> tau_alpha3    5.987982e-04  7.336155e-03  5.747848e-02  4.255445e-01
-#> mu_alpha1    -1.143099e+01 -2.676034e+00  2.767768e+00  4.919579e+01
-#> mu_alpha2    -1.520815e+00  4.404344e-01  2.365690e+00  8.958812e+01
-#> mu_alpha3    -3.895228e+00 -3.255963e-03  4.586351e+00  2.369248e+02
-#> sigma_alpha1  8.550509e+00  1.650901e+01  3.469882e+01  2.462098e+02
-#> sigma_alpha2  9.393433e-01  3.883981e+00  2.768415e+01  3.914472e+02
-#> sigma_alpha3  4.171069e+00  1.167549e+01  4.086578e+01  3.880977e+02
-#> lp__         -2.537755e+03 -2.535076e+03 -2.532816e+03 -2.529997e+03
-#>                    n_eff      Rhat
-#> alpha1          8.747031 1.1212692
-#> alpha2         13.423642 1.0897105
-#> alpha3         49.373384 1.0283578
-#> tau_alpha1     88.176492 1.0039526
-#> tau_alpha2     14.135280 1.3042242
-#> tau_alpha3     53.047004 0.9997529
-#> mu_alpha1     350.715887 1.0015192
-#> mu_alpha2     198.007524 1.0074621
-#> mu_alpha3      34.864649 1.0304100
-#> sigma_alpha1 1849.244902 0.9999612
-#> sigma_alpha2  427.721511 1.0054612
-#> sigma_alpha3  427.695365 1.0184599
-#> lp__           16.671016 1.1939938
+#> alpha1        1.394994e-01 2.208144e-04 5.686602e-03  1.290184e-01
+#> alpha2        1.984613e-01 3.072933e-04 7.829686e-03  1.844645e-01
+#> alpha3        8.289398e-03 2.217734e-04 6.839120e-03  3.133171e-04
+#> tau_alpha1    6.381566e+00 1.529942e+00 1.636028e+01  6.597716e-06
+#> tau_alpha2    1.371545e+01 8.454139e+00 4.030987e+01  5.871040e-06
+#> tau_alpha3    5.780803e+00 1.085321e+00 1.356397e+01  7.111779e-06
+#> mu_alpha1     8.127640e-01 1.294292e+00 2.957688e+01 -7.133897e+01
+#> mu_alpha2     3.575058e+00 1.799463e+00 3.726214e+01 -7.253911e+01
+#> mu_alpha3     3.883757e+00 1.307763e+00 3.405219e+01 -6.454653e+01
+#> sigma_alpha1  6.352800e+01 1.682171e+01 7.004084e+02  1.213045e-01
+#> sigma_alpha2  7.855927e+01 1.684436e+01 6.470535e+02  7.736001e-02
+#> sigma_alpha3  1.873854e+02 1.323037e+02 3.877173e+03  1.431469e-01
+#> lp__         -2.532504e+03 3.710684e-01 4.655392e+00 -2.542452e+03
+#>                        25%           50%           75%         97.5%      n_eff
+#> alpha1        1.357106e-01  1.390419e-01  1.430492e-01  1.511064e-01  663.20993
+#> alpha2        1.931963e-01  1.983732e-01  2.027147e-01  2.165249e-01  649.20593
+#> alpha3        2.841082e-03  6.688870e-03  1.163816e-02  2.583495e-02  951.00250
+#> tau_alpha1    1.733407e-03  9.495067e-02  2.687740e+00  6.795883e+01  114.34889
+#> tau_alpha2    1.342653e-03  6.464717e-02  2.511530e+00  1.670964e+02   22.73443
+#> tau_alpha3    1.111049e-03  5.337217e-02  2.822276e+00  4.880180e+01  156.19150
+#> mu_alpha1    -1.276472e+00  1.729980e-01  2.532245e+00  5.703259e+01  522.20403
+#> mu_alpha2    -1.528020e+00  2.032933e-01  2.920524e+00  1.045006e+02  428.79515
+#> mu_alpha3    -1.524451e+00  3.939297e-02  3.640311e+00  9.311625e+01  678.00364
+#> sigma_alpha1  6.099697e-01  3.245271e+00  2.401891e+01  3.893194e+02 1733.65511
+#> sigma_alpha2  6.310022e-01  3.933036e+00  2.729092e+01  4.127492e+02 1475.60823
+#> sigma_alpha3  5.952566e-01  4.328557e+00  3.000085e+01  3.749841e+02  858.78884
+#> lp__         -2.535634e+03 -2.532144e+03 -2.528948e+03 -2.524553e+03  157.39993
+#>                   Rhat
+#> alpha1       1.0002098
+#> alpha2       0.9998023
+#> alpha3       0.9997596
+#> tau_alpha1   1.0003182
+#> tau_alpha2   1.0444726
+#> tau_alpha3   1.0101265
+#> mu_alpha1    1.0073358
+#> mu_alpha2    1.0048162
+#> mu_alpha3    1.0031755
+#> sigma_alpha1 0.9997210
+#> sigma_alpha2 1.0000959
+#> sigma_alpha3 1.0009114
+#> lp__         0.9998177
 plot(model)
 
 # }
diff --git a/docs/reference/index.html b/docs/reference/index.html
index d9dd4a1..81f92ce 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -1,5 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Package index • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Package index"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Package index • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Package index"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -19,15 +25,14 @@
 <li class="nav-item dropdown">
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="../articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="../articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
     <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
@@ -264,34 +269,6 @@ <h2 id="models">Models<a class="anchor" aria-label="anchor" href="#models"></a><
 
       <dl><dt>
 
-          <code><a href="sir_basic_model.html">sir_basic_model()</a></code>
-
-        </dt>
-        <dd>Basic SIR model</dd>
-
-        <dt>
-
-          <code><a href="sir_static_model.html">sir_static_model()</a></code>
-
-        </dt>
-        <dd>SIR static model (age-heterogeneous, endemic equilibrium)</dd>
-
-        <dt>
-
-          <code><a href="sir_subpops_model.html">sir_subpops_model()</a></code>
-
-        </dt>
-        <dd>SIR Model with Interacting Subpopulations</dd>
-
-        <dt>
-
-          <code><a href="mseir_model.html">mseir_model()</a></code>
-
-        </dt>
-        <dd>MSEIR model</dd>
-
-        <dt>
-
           <code><a href="polynomial_model.html">polynomial_model()</a></code>
 
         </dt>
@@ -351,7 +328,7 @@ <h2 id="models">Models<a class="anchor" aria-label="anchor" href="#models"></a><
           <code><a href="estimate_from_mixture.html">estimate_from_mixture()</a></code>
 
         </dt>
-        <dd>Estimate seroprevalence and foi by combining mixture model and regression</dd>
+        <dd>Estimate seroprevalence and FOI from a fixed mixture model</dd>
 
         <dt>
 
@@ -432,34 +409,6 @@ <h2 id="plotting-functions">Plotting functions<a class="anchor" aria-label="anch
         </dt>
         <dd>plot() overloading for local polynomial model</dd>
 
-        <dt>
-
-          <code><a href="plot.mseir_model.html">plot(<i>&lt;mseir_model&gt;</i>)</a></code>
-
-        </dt>
-        <dd>plot() overloading for MSEIR model</dd>
-
-        <dt>
-
-          <code><a href="plot.sir_basic_model.html">plot(<i>&lt;sir_basic_model&gt;</i>)</a></code>
-
-        </dt>
-        <dd>plot() overloading for SIR model</dd>
-
-        <dt>
-
-          <code><a href="plot.sir_static_model.html">plot(<i>&lt;sir_static_model&gt;</i>)</a></code>
-
-        </dt>
-        <dd>plot() overloading for SIR static model</dd>
-
-        <dt>
-
-          <code><a href="plot.sir_subpops_model.html">plot(<i>&lt;sir_subpops_model&gt;</i>)</a></code>
-
-        </dt>
-        <dd>plot() overloading for SIR sub populations model</dd>
-
         <dt>
 
           <code><a href="plot.hierarchical_bayesian_model.html">plot(<i>&lt;hierarchical_bayesian_model&gt;</i>)</a></code>
@@ -544,7 +493,7 @@ <h2 id="other-utilities">Other utilities<a class="anchor" aria-label="anchor" hr
           <code><a href="to_titer.html">to_titer()</a></code>
 
         </dt>
-        <dd>Process assay test result to titer</dd>
+        <dd>Convert assay readings to titers</dd>
 
         <dt>
 
@@ -576,66 +525,122 @@ <h2 id="other-utilities">Other utilities<a class="anchor" aria-label="anchor" hr
 
         <dt>
 
-          <code><a href="compute_ci.html">compute_ci()</a></code>
+          <code><a href="compute_ci.default.html">compute_ci(<i>&lt;default&gt;</i>)</a></code>
 
         </dt>
-        <dd>Compute confidence interval</dd>
+        <dd>Compute confidence interval for a model of serosv</dd>
 
         <dt>
 
-          <code><a href="compute_ci.fp_model.html">compute_ci.fp_model()</a></code>
+          <code><a href="compute_ci.fp_model.html">compute_ci(<i>&lt;fp_model&gt;</i>)</a></code>
 
         </dt>
         <dd>Compute confidence interval for fractional polynomial model</dd>
 
         <dt>
 
-          <code><a href="compute_ci.lp_model.html">compute_ci.lp_model()</a></code>
+          <code><a href="compute_ci.lp_model.html">compute_ci(<i>&lt;lp_model&gt;</i>)</a></code>
 
         </dt>
         <dd>Compute confidence interval for local polynomial model</dd>
 
         <dt>
 
-          <code><a href="compute_ci.weibull_model.html">compute_ci.weibull_model()</a></code>
+          <code><a href="compute_ci.weibull_model.html">compute_ci(<i>&lt;weibull_model&gt;</i>)</a></code>
 
         </dt>
         <dd>Compute confidence interval for Weibull model</dd>
 
         <dt>
 
-          <code><a href="compute_ci.penalized_spline_model.html">compute_ci.penalized_spline_model()</a></code>
+          <code><a href="compute_ci.penalized_spline_model.html">compute_ci(<i>&lt;penalized_spline_model&gt;</i>)</a></code>
 
         </dt>
         <dd>Compute confidence interval for penalized_spline_model</dd>
 
         <dt>
 
-          <code><a href="compute_ci.mixture_model.html">compute_ci.mixture_model()</a></code>
+          <code><a href="compute_ci.mixture_model.html">compute_ci(<i>&lt;mixture_model&gt;</i>)</a></code>
 
         </dt>
         <dd>Compute confidence interval for mixture model</dd>
 
         <dt>
 
-          <code><a href="compute_ci.age_time_model.html">compute_ci.age_time_model()</a></code>
+          <code><a href="compute_ci.age_time_model.html">compute_ci(<i>&lt;age_time_model&gt;</i>)</a></code>
 
         </dt>
         <dd>Compute confidence interval for time age model</dd>
 
         <dt>
 
-          <code><a href="compute_ci.hierarchical_bayesian_model.html">compute_ci.hierarchical_bayesian_model()</a></code>
+          <code><a href="compute_ci.hierarchical_bayesian_model.html">compute_ci(<i>&lt;hierarchical_bayesian_model&gt;</i>)</a></code>
 
         </dt>
         <dd>Compute 95% credible interval for hierarchical Bayesian model</dd>
 
+        <dt>
+
+          <code><a href="predict.age_time_model.html">predict(<i>&lt;age_time_model&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Predict from the age_time_mdoel</dd>
+
+        <dt>
+
+          <code><a href="predict.farrington_model.html">predict(<i>&lt;farrington_model&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Prediction for serosv Farrington model</dd>
+
+        <dt>
+
+          <code><a href="predict.fp_model.html">predict(<i>&lt;fp_model&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Prediction for serosv fractional polynomial model</dd>
+
+        <dt>
+
+          <code><a href="predict.hierarchical_bayesian_model.html">predict(<i>&lt;hierarchical_bayesian_model&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Predict from an hierarchical bayesian model</dd>
+
+        <dt>
+
+          <code><a href="predict.lp_model.html">predict(<i>&lt;lp_model&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Prediction for serosv local polynomial model</dd>
+
+        <dt>
+
+          <code><a href="predict.penalized_spline_model.html">predict(<i>&lt;penalized_spline_model&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Prediction for serosv penalized spline model</dd>
+
+        <dt>
+
+          <code><a href="predict.weibull_model.html">predict(<i>&lt;weibull_model&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Prediction for serosv Weibull model</dd>
+
+        <dt>
+
+          <code><a href="predict.polynomial_model.html">predict(<i>&lt;polynomial_model&gt;</i>)</a></code>
+
+        </dt>
+        <dd>Prediction for serosv polynomial model</dd>
+
         <dt>
 
           <code><a href="find_best_fp_powers.html">find_best_fp_powers()</a></code>
 
         </dt>
-        <dd>Returns the powers of the GLM fitted model which has the lowest deviance score.</dd>
+        <dd>Returns the powers of the fractional polynomial model which has the lowest deviance score.</dd>
       </dl></div>
   </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
diff --git a/docs/reference/index.md b/docs/reference/index.md
index 1b56d22..2498558 100644
--- a/docs/reference/index.md
+++ b/docs/reference/index.md
@@ -71,14 +71,6 @@
 
 ## Models
 
-- [`sir_basic_model()`](https://oucru-modelling.github.io/serosv/reference/sir_basic_model.md)
-  : Basic SIR model
-- [`sir_static_model()`](https://oucru-modelling.github.io/serosv/reference/sir_static_model.md)
-  : SIR static model (age-heterogeneous, endemic equilibrium)
-- [`sir_subpops_model()`](https://oucru-modelling.github.io/serosv/reference/sir_subpops_model.md)
-  : SIR Model with Interacting Subpopulations
-- [`mseir_model()`](https://oucru-modelling.github.io/serosv/reference/mseir_model.md)
-  : MSEIR model
 - [`polynomial_model()`](https://oucru-modelling.github.io/serosv/reference/polynomial_model.md)
   : Polynomial models
 - [`farrington_model()`](https://oucru-modelling.github.io/serosv/reference/farrington_model.md)
@@ -96,8 +88,7 @@
 - [`mixture_model()`](https://oucru-modelling.github.io/serosv/reference/mixture_model.md)
   : Fit a mixture model to classify serostatus
 - [`estimate_from_mixture()`](https://oucru-modelling.github.io/serosv/reference/estimate_from_mixture.md)
-  : Estimate seroprevalence and foi by combining mixture model and
-  regression
+  : Estimate seroprevalence and FOI from a fixed mixture model
 - [`age_time_model()`](https://oucru-modelling.github.io/serosv/reference/age_time_model.md)
   : Age-time varying seroprevalence
 
@@ -122,14 +113,6 @@
   : plot() overloading for fractional polynomial model
 - [`plot(`*`<lp_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/plot.lp_model.md)
   : plot() overloading for local polynomial model
-- [`plot(`*`<mseir_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/plot.mseir_model.md)
-  : plot() overloading for MSEIR model
-- [`plot(`*`<sir_basic_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/plot.sir_basic_model.md)
-  : plot() overloading for SIR model
-- [`plot(`*`<sir_static_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/plot.sir_static_model.md)
-  : plot() overloading for SIR static model
-- [`plot(`*`<sir_subpops_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/plot.sir_subpops_model.md)
-  : plot() overloading for SIR sub populations model
 - [`plot(`*`<hierarchical_bayesian_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/plot.hierarchical_bayesian_model.md)
   : plot() overloading for hierarchical_bayesian_model
 - [`plot(`*`<penalized_spline_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/plot.penalized_spline_model.md)
@@ -155,7 +138,7 @@
 - [`pava()`](https://oucru-modelling.github.io/serosv/reference/pava.md)
   : Monotonize seroprevalence
 - [`to_titer()`](https://oucru-modelling.github.io/serosv/reference/to_titer.md)
-  : Process assay test result to titer
+  : Convert assay readings to titers
 - [`correct_prevalence()`](https://oucru-modelling.github.io/serosv/reference/correct_prevalence.md)
   : Estimate the true sero prevalence using Frequentist/Bayesian
   estimation
@@ -165,22 +148,38 @@
   : Preprocess data
 - [`compare_models()`](https://oucru-modelling.github.io/serosv/reference/compare_models.md)
   : Compare models
-- [`compute_ci()`](https://oucru-modelling.github.io/serosv/reference/compute_ci.md)
-  : Compute confidence interval
-- [`compute_ci.fp_model()`](https://oucru-modelling.github.io/serosv/reference/compute_ci.fp_model.md)
+- [`compute_ci(`*`<default>`*`)`](https://oucru-modelling.github.io/serosv/reference/compute_ci.default.md)
+  : Compute confidence interval for a model of serosv
+- [`compute_ci(`*`<fp_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/compute_ci.fp_model.md)
   : Compute confidence interval for fractional polynomial model
-- [`compute_ci.lp_model()`](https://oucru-modelling.github.io/serosv/reference/compute_ci.lp_model.md)
+- [`compute_ci(`*`<lp_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/compute_ci.lp_model.md)
   : Compute confidence interval for local polynomial model
-- [`compute_ci.weibull_model()`](https://oucru-modelling.github.io/serosv/reference/compute_ci.weibull_model.md)
+- [`compute_ci(`*`<weibull_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/compute_ci.weibull_model.md)
   : Compute confidence interval for Weibull model
-- [`compute_ci.penalized_spline_model()`](https://oucru-modelling.github.io/serosv/reference/compute_ci.penalized_spline_model.md)
+- [`compute_ci(`*`<penalized_spline_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/compute_ci.penalized_spline_model.md)
   : Compute confidence interval for penalized_spline_model
-- [`compute_ci.mixture_model()`](https://oucru-modelling.github.io/serosv/reference/compute_ci.mixture_model.md)
+- [`compute_ci(`*`<mixture_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/compute_ci.mixture_model.md)
   : Compute confidence interval for mixture model
-- [`compute_ci.age_time_model()`](https://oucru-modelling.github.io/serosv/reference/compute_ci.age_time_model.md)
+- [`compute_ci(`*`<age_time_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/compute_ci.age_time_model.md)
   : Compute confidence interval for time age model
-- [`compute_ci.hierarchical_bayesian_model()`](https://oucru-modelling.github.io/serosv/reference/compute_ci.hierarchical_bayesian_model.md)
+- [`compute_ci(`*`<hierarchical_bayesian_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/compute_ci.hierarchical_bayesian_model.md)
   : Compute 95% credible interval for hierarchical Bayesian model
+- [`predict(`*`<age_time_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/predict.age_time_model.md)
+  : Predict from the age_time_mdoel
+- [`predict(`*`<farrington_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/predict.farrington_model.md)
+  : Prediction for serosv Farrington model
+- [`predict(`*`<fp_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/predict.fp_model.md)
+  : Prediction for serosv fractional polynomial model
+- [`predict(`*`<hierarchical_bayesian_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/predict.hierarchical_bayesian_model.md)
+  : Predict from an hierarchical bayesian model
+- [`predict(`*`<lp_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/predict.lp_model.md)
+  : Prediction for serosv local polynomial model
+- [`predict(`*`<penalized_spline_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/predict.penalized_spline_model.md)
+  : Prediction for serosv penalized spline model
+- [`predict(`*`<weibull_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/predict.weibull_model.md)
+  : Prediction for serosv Weibull model
+- [`predict(`*`<polynomial_model>`*`)`](https://oucru-modelling.github.io/serosv/reference/predict.polynomial_model.md)
+  : Prediction for serosv polynomial model
 - [`find_best_fp_powers()`](https://oucru-modelling.github.io/serosv/reference/find_best_fp_powers.md)
-  : Returns the powers of the GLM fitted model which has the lowest
-  deviance score.
+  : Returns the powers of the fractional polynomial model which has the
+  lowest deviance score.
diff --git a/docs/reference/lp_model.html b/docs/reference/lp_model.html
index 1b65802..18e2b3e 100644
--- a/docs/reference/lp_model.html
+++ b/docs/reference/lp_model.html
@@ -1,59 +1,52 @@
 <!DOCTYPE html>
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+where the linear predictor is approximated locally at one particular age."><meta property="og:description" content="Fit the age-specific seroprevalence to a local polynomial model,
+where the linear predictor is approximated locally at one particular age."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
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+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
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@@ -64,45 +57,72 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
     </div>
 
     <div class="ref-description section level2">
-    <p>Refers to section 7.1. and 7.2.</p>
+    <p>Fit the age-specific seroprevalence to a local polynomial model,
+where the linear predictor is approximated locally at one particular age.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">lp_model</span><span class="op">(</span><span class="va">data</span>, kern <span class="op">=</span> <span class="st">"tcub"</span>, nn <span class="op">=</span> <span class="fl">0</span>, h <span class="op">=</span> <span class="fl">0</span>, deg <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">lp_model</span><span class="op">(</span></span>
+<span>  <span class="va">data</span>,</span>
+<span>  kern <span class="op">=</span> <span class="st">"tcub"</span>,</span>
+<span>  nn <span class="op">=</span> <span class="fl">0</span>,</span>
+<span>  h <span class="op">=</span> <span class="fl">0</span>,</span>
+<span>  deg <span class="op">=</span> <span class="fl">2</span>,</span>
+<span>  age_col <span class="op">=</span> <span class="st">"age"</span>,</span>
+<span>  pos_col <span class="op">=</span> <span class="st">"pos"</span>,</span>
+<span>  tot_col <span class="op">=</span> <span class="st">"tot"</span>,</span>
+<span>  status_col <span class="op">=</span> <span class="st">"status"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>data</dt>
-<dd><p>the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)</p></dd>
 
 
-<dt>kern</dt>
+<dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
+<dd><p>the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR `age`, `status` (for linelisting data)</p></dd>
+
+
+<dt id="arg-kern">kern<a class="anchor" aria-label="anchor" href="#arg-kern"></a></dt>
 <dd><p>Weight function, default = "tcub".
 Other choices are "rect", "trwt", "tria", "epan", "bisq" and "gauss".
 Choices may be restricted when derivatives are required;
 e.g. for confidence bands and some bandwidth selectors.</p></dd>
 
 
-<dt>nn</dt>
+<dt id="arg-nn">nn<a class="anchor" aria-label="anchor" href="#arg-nn"></a></dt>
 <dd><p>Nearest neighbor component of the smoothing parameter.
 Default value is 0.7, unless either h is provided, in which case the default is 0.</p></dd>
 
 
-<dt>h</dt>
-<dd><p>The constant component of the smoothing parameter. Default: 0.</p></dd>
+<dt id="arg-h">h<a class="anchor" aria-label="anchor" href="#arg-h"></a></dt>
+<dd><p>The constant bandwidth of the smoothing parameter. Default: 0.</p></dd>
 
 
-<dt>deg</dt>
+<dt id="arg-deg">deg<a class="anchor" aria-label="anchor" href="#arg-deg"></a></dt>
 <dd><p>Degree of polynomial to use. Default: 2.</p></dd>
 
+
+<dt id="arg-age-col">age_col<a class="anchor" aria-label="anchor" href="#arg-age-col"></a></dt>
+<dd><p>name of the `age` column (default age_col="age").</p></dd>
+
+
+<dt id="arg-pos-col">pos_col<a class="anchor" aria-label="anchor" href="#arg-pos-col"></a></dt>
+<dd><p>name of the `pos` column (default pos_col="pos").</p></dd>
+
+
+<dt id="arg-tot-col">tot_col<a class="anchor" aria-label="anchor" href="#arg-tot-col"></a></dt>
+<dd><p>name of the `tot` column (default tot_col="tot").</p></dd>
+
+
+<dt id="arg-status-col">status_col<a class="anchor" aria-label="anchor" href="#arg-status-col"></a></dt>
+<dd><p>name of the `status` column (default status_col="status").</p></dd>
+
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a list of class lp_model with 6 items</p>
+    <p>a list of class lp_model with 6 items</p>
 <dl><dt>datatype</dt>
 <dd><p>type of datatype used for model fitting (aggregated or linelisting)</p></dd>
 
@@ -122,6 +142,45 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <dd><p>force of infection</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Consider a linear predictor \(\eta(a)\) approximated locally at one particular value \(a_0\).</p>
+<p>For a general degree \(p\), the linear predictor for a neighbor of \(a_0\), labeled \(a_i\) is equivalent to the Taylor approximation
+$$
+\eta(a_i) = \eta(a_0) + \eta^{(1)}(a_0)(a_i - a_0) +
+\frac{\eta^{(2)}(a_0)}{2}(a_i - a_0)^2 + ... + \frac{\eta^{(p)}(a_0)}{p!}(a_i - a_0)^p
+$$</p>
+<p>\(\eta(a_i)\) can be estimated by maximizing
+$$
+ \Sigma_{i=1}^{N} \ell_i \{Y_i, g^{-1} (\beta_0 + \beta_1(a_i-a_0)+ \beta_2(a_i-a_0)^2 ... +
+ \beta_p(a_i-a_0)^p) \} K_h(a_i - a_0)
+$$</p>
+<p>The estimator for the \(k\)-th derivative of \(\eta(a_0)\), for \(k = 0,1,…,p\)
+(degree of local polynomial) is thus:
+$$
+ \hat{\eta}^{(k)}(a_0) = k!\hat{\beta}_k(a_0)
+$$</p>
+<p>The estimator for the prevalence at age \(a_0\) is then given by
+$$
+\hat{\pi}(a_0) = g^{-1}\{ \hat{\beta}_0(a_0) \}
+$$
+Where \(g\) is the link function</p>
+<p>The estimator for the force of infection at age \(a_0\) by assuming \(p \ge 1\) is as followed
+$$
+\hat{\lambda}(a_0) = \hat{\beta}_1(a_0) \delta \{ \hat{\beta}_0 (a_0) \}
+$$
+Where \(\delta \{ \hat{\beta}_0(a_0) \} = \frac{dg^{-1} \{ \hat{\beta}_0(a_0) \} } {d\hat{\beta}_0(a_0)}\)</p>
+<p>Refer to section 7.1 and 7.2. of the the book by Hens et al. (2012) for further details.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+<a href="https://doi.org/10.1007/978-1-4614-4072-7" class="external-link">doi:10.1007/978-1-4614-4072-7</a>
+.</p>
+    </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p>[locfit::locfit()] for more information on the fitted locfit object</p></div>
@@ -139,23 +198,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/lp_model.md b/docs/reference/lp_model.md
index 5265f2d..0f93357 100644
--- a/docs/reference/lp_model.md
+++ b/docs/reference/lp_model.md
@@ -1,19 +1,30 @@
 # A local polynomial model.
 
-Refers to section 7.1. and 7.2.
+Fit the age-specific seroprevalence to a local polynomial model, where
+the linear predictor is approximated locally at one particular age.
 
 ## Usage
 
 ``` r
-lp_model(data, kern = "tcub", nn = 0, h = 0, deg = 2)
+lp_model(
+  data,
+  kern = "tcub",
+  nn = 0,
+  h = 0,
+  deg = 2,
+  age_col = "age",
+  pos_col = "pos",
+  tot_col = "tot",
+  status_col = "status"
+)
 ```
 
 ## Arguments
 
 - data:
 
-  the input data frame, must either have \`age\`, \`pos\`, \`tot\`
-  columns (for aggregated data) OR \`age\`, \`status\` for (linelisting
+  the input data frame, must either have columns for \`age\`, \`pos\`,
+  \`tot\` (for aggregated data) OR \`age\`, \`status\` (for linelisting
   data)
 
 - kern:
@@ -30,12 +41,28 @@ lp_model(data, kern = "tcub", nn = 0, h = 0, deg = 2)
 
 - h:
 
-  The constant component of the smoothing parameter. Default: 0.
+  The constant bandwidth of the smoothing parameter. Default: 0.
 
 - deg:
 
   Degree of polynomial to use. Default: 2.
 
+- age_col:
+
+  name of the \`age\` column (default age_col="age").
+
+- pos_col:
+
+  name of the \`pos\` column (default pos_col="pos").
+
+- tot_col:
+
+  name of the \`tot\` column (default tot_col="tot").
+
+- status_col:
+
+  name of the \`status\` column (default status_col="status").
+
 ## Value
 
 a list of class lp_model with 6 items
@@ -64,6 +91,47 @@ a list of class lp_model with 6 items
 
   force of infection
 
+## Details
+
+Consider a linear predictor \\\eta(a)\\ approximated locally at one
+particular value \\a_0\\.
+
+For a general degree \\p\\, the linear predictor for a neighbor of
+\\a_0\\, labeled \\a_i\\ is equivalent to the Taylor approximation \$\$
+\eta(a_i) = \eta(a_0) + \eta^{(1)}(a_0)(a_i - a_0) +
+\frac{\eta^{(2)}(a_0)}{2}(a_i - a_0)^2 + ... +
+\frac{\eta^{(p)}(a_0)}{p!}(a_i - a_0)^p \$\$
+
+\\\eta(a_i)\\ can be estimated by maximizing \$\$ \Sigma\_{i=1}^{N}
+\ell_i \\Y_i, g^{-1} (\beta_0 + \beta_1(a_i-a_0)+ \beta_2(a_i-a_0)^2
+... + \beta_p(a_i-a_0)^p) \\ K_h(a_i - a_0) \$\$
+
+The estimator for the \\k\\-th derivative of \\\eta(a_0)\\, for \\k =
+0,1,…,p\\ (degree of local polynomial) is thus: \$\$
+\hat{\eta}^{(k)}(a_0) = k!\hat{\beta}\_k(a_0) \$\$
+
+The estimator for the prevalence at age \\a_0\\ is then given by \$\$
+\hat{\pi}(a_0) = g^{-1}\\ \hat{\beta}\_0(a_0) \\ \$\$ Where \\g\\ is the
+link function
+
+The estimator for the force of infection at age \\a_0\\ by assuming \\p
+\ge 1\\ is as followed \$\$ \hat{\lambda}(a_0) = \hat{\beta}\_1(a_0)
+\delta \\ \hat{\beta}\_0 (a_0) \\ \$\$ Where \\\delta \\
+\hat{\beta}\_0(a_0) \\ = \frac{dg^{-1} \\ \hat{\beta}\_0(a_0) \\ }
+{d\hat{\beta}\_0(a_0)}\\
+
+Refer to section 7.1 and 7.2. of the the book by Hens et al. (2012) for
+further details.
+
+## References
+
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+[doi:10.1007/978-1-4614-4072-7](https://doi.org/10.1007/978-1-4614-4072-7)
+.
+
 ## See also
 
 \[locfit::locfit()\] for more information on the fitted locfit object
diff --git a/docs/reference/mixture_model.html b/docs/reference/mixture_model.html
index 2be1cd0..75a6b31 100644
--- a/docs/reference/mixture_model.html
+++ b/docs/reference/mixture_model.html
@@ -1,59 +1,50 @@
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@@ -64,7 +55,7 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
     </div>
 
     <div class="ref-description section level2">
-    <p>Refers to section 11.1 - 11.4</p>
+    <p>Fit the antibody level data to a 2-component Gaussian mixture model</p>
     </div>
 
     <div class="section level2">
@@ -80,31 +71,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>antibody_level</dt>
-<dd><p>- vector of the corresponding raw antibody level</p></dd>
 
 
-<dt>breaks</dt>
-<dd><p>- number of intervals which the antibody_level are grouped into</p></dd>
+<dl><dt id="arg-antibody-level">antibody_level<a class="anchor" aria-label="anchor" href="#arg-antibody-level"></a></dt>
+<dd><p>vector of the corresponding raw antibody level</p></dd>
+
+
+<dt id="arg-breaks">breaks<a class="anchor" aria-label="anchor" href="#arg-breaks"></a></dt>
+<dd><p>number of intervals which the antibody_level are grouped into</p></dd>
 
 
-<dt>pi</dt>
-<dd><p>- proportion of susceptible, infected</p></dd>
+<dt id="arg-pi">pi<a class="anchor" aria-label="anchor" href="#arg-pi"></a></dt>
+<dd><p>proportion of susceptible, infected</p></dd>
 
 
-<dt>mu</dt>
-<dd><p>- a vector of means of component distributions (vector of 2 numbers in ascending order)</p></dd>
+<dt id="arg-mu">mu<a class="anchor" aria-label="anchor" href="#arg-mu"></a></dt>
+<dd><p>a vector of means of component distributions (vector of 2 numbers in ascending order)</p></dd>
 
 
-<dt>sigma</dt>
-<dd><p>-  a vector of standard deviations of component distributions (vector of 2 number)</p></dd>
+<dt id="arg-sigma">sigma<a class="anchor" aria-label="anchor" href="#arg-sigma"></a></dt>
+<dd><p>a vector of standard deviations of component distributions (vector of 2 number)</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a list of class mixture_model with the following items</p>
+    <p>a list of class mixture_model with the following items</p>
 <dl><dt>df</dt>
 <dd><p>the dataframe used for fitting the model</p></dd>
 
@@ -118,6 +109,36 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <dd><p>fitted distribution for infected</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Antibody level (denoted \(Z\)) is modeled using a 2-component Gaussian
+mixture model. Each component \(Z_j\) (\(j \in \{I, S\}\)) represents the
+antibody level of the latent Infected and Susceptible sub-populations, following density
+\(f_j(z_j|\theta_j)\)</p>
+<p>Let \(\pi_{\text{TRUE}}(a)\) denotes the age-dependent mixing probability
+(i.e., the true prevalence), the density of the mixture is formulated as</p>
+<p>$$f(z|z_I, z_S,a) = (1-\pi_{\text{TRUE}}(a))f_S(z_S|\theta_S)+\pi_{\text{TRUE}}(a)f_I(z_I|\theta_I)$$</p>
+<p>The mean \(E(Z|a)\) thus equals
+$$\mu(a) = (1-\pi_{\text{TRUE}}(a))\mu_S+\pi_{\text{TRUE}}(a)\mu_I$$</p>
+<p>From which true prevalence can be computed as
+$$\pi_{\text{TRUE}}(a) = \frac{\mu(a) - \mu_S}{\mu_I - \mu_S}$$</p>
+<p>And FOI can then be inferred as
+$$\lambda_{TRUE} = \frac{\mu'(a)}{\mu_I - \mu(a)}$$</p>
+<p>Function [serosv::mixture_model()] fits antibody level data to \(f_S(z_S|\theta_S)\) and
+\(f_I(z_I|\theta_I)\)</p>
+<p>Function [serosv::estimate_mixture()] will then estimate age-specific antibody level \(\mu(a)\)
+and infer the estimation for \(\pi_{\text{TRUE}}(a)\) and \(\lambda_{TRUE}\)</p>
+<p>Refer to section 11.3. of the the book by Hens et al. (2012) for further details.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+<a href="https://doi.org/10.1007/978-1-4614-4072-7" class="external-link">doi:10.1007/978-1-4614-4072-7</a>
+.</p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
@@ -142,23 +163,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="mixture_model-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/docs/reference/mixture_model.md b/docs/reference/mixture_model.md
index 3d0e813..bf7e2e4 100644
--- a/docs/reference/mixture_model.md
+++ b/docs/reference/mixture_model.md
@@ -1,6 +1,6 @@
 # Fit a mixture model to classify serostatus
 
-Refers to section 11.1 - 11.4
+Fit the antibody level data to a 2-component Gaussian mixture model
 
 ## Usage
 
@@ -18,25 +18,25 @@ mixture_model(
 
 - antibody_level:
 
-  \- vector of the corresponding raw antibody level
+  vector of the corresponding raw antibody level
 
 - breaks:
 
-  \- number of intervals which the antibody_level are grouped into
+  number of intervals which the antibody_level are grouped into
 
 - pi:
 
-  \- proportion of susceptible, infected
+  proportion of susceptible, infected
 
 - mu:
 
-  \- a vector of means of component distributions (vector of 2 numbers
-  in ascending order)
+  a vector of means of component distributions (vector of 2 numbers in
+  ascending order)
 
 - sigma:
 
-  \- a vector of standard deviations of component distributions (vector
-  of 2 number)
+  a vector of standard deviations of component distributions (vector of
+  2 number)
 
 ## Value
 
@@ -59,6 +59,48 @@ a list of class mixture_model with the following items
 
   fitted distribution for infected
 
+## Details
+
+Antibody level (denoted \\Z\\) is modeled using a 2-component Gaussian
+mixture model. Each component \\Z_j\\ (\\j \in \\I, S\\\\) represents
+the antibody level of the latent Infected and Susceptible
+sub-populations, following density \\f_j(z_j\|\theta_j)\\
+
+Let \\\pi\_{\text{TRUE}}(a)\\ denotes the age-dependent mixing
+probability (i.e., the true prevalence), the density of the mixture is
+formulated as
+
+\$\$f(z\|z_I, z_S,a) =
+(1-\pi\_{\text{TRUE}}(a))f_S(z_S\|\theta_S)+\pi\_{\text{TRUE}}(a)f_I(z_I\|\theta_I)\$\$
+
+The mean \\E(Z\|a)\\ thus equals \$\$\mu(a) =
+(1-\pi\_{\text{TRUE}}(a))\mu_S+\pi\_{\text{TRUE}}(a)\mu_I\$\$
+
+From which true prevalence can be computed as \$\$\pi\_{\text{TRUE}}(a)
+= \frac{\mu(a) - \mu_S}{\mu_I - \mu_S}\$\$
+
+And FOI can then be inferred as \$\$\lambda\_{TRUE} =
+\frac{\mu'(a)}{\mu_I - \mu(a)}\$\$
+
+Function \[serosv::mixture_model()\] fits antibody level data to
+\\f_S(z_S\|\theta_S)\\ and \\f_I(z_I\|\theta_I)\\
+
+Function \[serosv::estimate_mixture()\] will then estimate age-specific
+antibody level \\\mu(a)\\ and infer the estimation for
+\\\pi\_{\text{TRUE}}(a)\\ and \\\lambda\_{TRUE}\\
+
+Refer to section 11.3. of the the book by Hens et al. (2012) for further
+details.
+
+## References
+
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+[doi:10.1007/978-1-4614-4072-7](https://doi.org/10.1007/978-1-4614-4072-7)
+.
+
 ## Examples
 
 ``` r
diff --git a/docs/reference/mseir_model.html b/docs/reference/mseir_model.html
index c449fb0..c707b43 100644
--- a/docs/reference/mseir_model.html
+++ b/docs/reference/mseir_model.html
@@ -1,59 +1,51 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Refers to section 3.4."><title>MSEIR model — mseir_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="MSEIR model — mseir_model"><meta property="og:description" content="Refers to section 3.4."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
@@ -64,7 +56,7 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
     </div>
 
     <div class="ref-description section level2">
-    <p>Refers to section 3.4.</p>
+    <p>Simulate a transmission model with 5 compartments: maternal immunity (M), susceptible (S), exposed (E), infected (I), recovered (R)</p>
     </div>
 
     <div class="section level2">
@@ -74,31 +66,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>a</dt>
+
+
+<dl><dt id="arg-a">a<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>age sequence</p></dd>
 
 
-<dt>gamma</dt>
+<dt id="arg-gamma">gamma<a class="anchor" aria-label="anchor" href="#arg-gamma"></a></dt>
 <dd><p>time in maternal class.</p></dd>
 
 
-<dt>lambda</dt>
+<dt id="arg-lambda">lambda<a class="anchor" aria-label="anchor" href="#arg-lambda"></a></dt>
 <dd><p>time in susceptible class.</p></dd>
 
 
-<dt>sigma</dt>
+<dt id="arg-sigma">sigma<a class="anchor" aria-label="anchor" href="#arg-sigma"></a></dt>
 <dd><p>time in latent class.</p></dd>
 
 
-<dt>nu</dt>
+<dt id="arg-nu">nu<a class="anchor" aria-label="anchor" href="#arg-nu"></a></dt>
 <dd><p>time in infected class.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>list of class mseir_model with the following parameters</p>
+    <p>list of class mseir_model with the following parameters</p>
 <dl><dt>parameters</dt>
 <dd><p>list of parameters used for fitting the model</p></dd>
 
@@ -106,6 +98,36 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <dd><p>matrix of proportion for each compartment over time</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Follow the MSEIR described in the book by Hens et al. (section 3.5.1.)</p>
+<p>The model is described by the following system of equations
+$$
+ \begin{cases}
+   \frac{dM(a)}{da} = -(\gamma + \mu(a))M(a) \\
+   \frac{dS(a)}{da} = \gamma M(a) - (\lambda(a) + \mu(a)) S(a) \\
+   \frac{dE(a)}{da} = \lambda(a) S(a)  - (\sigma + \mu(a)) E(a) \\
+   \frac{dI(a)}{da} = \sigma(a) E(a)  - (\nu + \mu(a)) I(a)  \\
+   \frac{dR(a)}{da} = \nu I(a)  - \mu(a) R(a)
+   \end{cases}
+$$</p>
+<p>Where:</p>
+<p>-   \(M(0)\) = B, the number of births in the population</p>
+<p>-   \(\gamma\) is the rate of antibody decaying</p>
+<p>-   \(\lambda(a)\) is the force of infection at age \(a\)</p>
+<p>-   \(\mu(a)\) is the natural death rate at age \(a\)</p>
+<p>-   \(\sigma\) is the rate of becoming infected after being exposed</p>
+<p>-   \(\nu\) is the recovery rate</p>
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+<a href="https://doi.org/10.1007/978-1-4614-4072-7" class="external-link">doi:10.1007/978-1-4614-4072-7</a>
+.</p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
@@ -640,23 +662,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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   </body></html>
 
diff --git a/docs/reference/mseir_model.md b/docs/reference/mseir_model.md
index 5eff024..c088c2e 100644
--- a/docs/reference/mseir_model.md
+++ b/docs/reference/mseir_model.md
@@ -1,6 +1,7 @@
 # MSEIR model
 
-Refers to section 3.4.
+Simulate a transmission model with 5 compartments: maternal immunity
+(M), susceptible (S), exposed (E), infected (I), recovered (R)
 
 ## Usage
 
@@ -42,6 +43,40 @@ list of class mseir_model with the following parameters
 
   matrix of proportion for each compartment over time
 
+## Details
+
+Follow the MSEIR described in the book by Hens et al. (section 3.5.1.)
+
+The model is described by the following system of equations \$\$
+\begin{cases} \frac{dM(a)}{da} = -(\gamma + \mu(a))M(a) \\
+\frac{dS(a)}{da} = \gamma M(a) - (\lambda(a) + \mu(a)) S(a) \\
+\frac{dE(a)}{da} = \lambda(a) S(a) - (\sigma + \mu(a)) E(a) \\
+\frac{dI(a)}{da} = \sigma(a) E(a) - (\nu + \mu(a)) I(a) \\
+\frac{dR(a)}{da} = \nu I(a) - \mu(a) R(a) \end{cases} \$\$
+
+Where:
+
+\- \\M(0)\\ = B, the number of births in the population
+
+\- \\\gamma\\ is the rate of antibody decaying
+
+\- \\\lambda(a)\\ is the force of infection at age \\a\\
+
+\- \\\mu(a)\\ is the natural death rate at age \\a\\
+
+\- \\\sigma\\ is the rate of becoming infected after being exposed
+
+\- \\\nu\\ is the recovery rate
+
+## References
+
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+[doi:10.1007/978-1-4614-4072-7](https://doi.org/10.1007/978-1-4614-4072-7)
+.
+
 ## Examples
 
 ``` r
diff --git a/docs/reference/mumps_uk_1986_1987-1.png b/docs/reference/mumps_uk_1986_1987-1.png
index dbb331b..21d2a7a 100644
Binary files a/docs/reference/mumps_uk_1986_1987-1.png and b/docs/reference/mumps_uk_1986_1987-1.png differ
diff --git a/docs/reference/mumps_uk_1986_1987.html b/docs/reference/mumps_uk_1986_1987.html
index 1268ba6..5aa1ceb 100644
--- a/docs/reference/mumps_uk_1986_1987.html
+++ b/docs/reference/mumps_uk_1986_1987.html
@@ -1,63 +1,54 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="a large survey of prevalence of antibodies to mumps and rubella viruses in
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Mumps serological data from the UK in 1986 and 1987 (aggregated) — mumps_uk_1986_1987 • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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@@ -81,7 +72,7 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <div class="section level2">
     <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>A data frame with 3 variables:</p><dl><dt>age</dt>
-<dd><p>Age group</p></dd>
+<dd><p>Midpoint of the age group (e.g. 1.5 = 1-2 years old, 2.5 = 2-3 years old)</p></dd>
 
  <dt>pos</dt>
 <dd><p>Number of seropositive individuals</p></dd>
@@ -100,35 +91,33 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Reproduce Fig 4.4 (left panel), p. 67</span></span></span>
-<span class="r-in"><span><span class="va">age</span> <span class="op">&lt;-</span> <span class="va">mumps_uk_1986_1987</span><span class="op">$</span><span class="va">age</span></span></span>
-<span class="r-in"><span><span class="va">pos</span> <span class="op">&lt;-</span> <span class="va">mumps_uk_1986_1987</span><span class="op">$</span><span class="va">pos</span></span></span>
-<span class="r-in"><span><span class="va">tot</span> <span class="op">&lt;-</span> <span class="va">mumps_uk_1986_1987</span><span class="op">$</span><span class="va">tot</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">age</span>, <span class="va">pos</span> <span class="op">/</span> <span class="va">tot</span>,</span></span>
-<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.008</span> <span class="op">*</span> <span class="va">tot</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
-<span class="r-in"><span>  xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">45</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/with.html" class="external-link">with</a></span><span class="op">(</span><span class="va">mumps_uk_1986_1987</span>,</span></span>
+<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">age</span>, <span class="va">pos</span> <span class="op">/</span> <span class="va">tot</span>,</span></span>
+<span class="r-in"><span>    cex <span class="op">=</span> <span class="fl">0.1</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">tot</span><span class="op">)</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
+<span class="r-in"><span>    xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">45</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span>  <span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="mumps_uk_1986_1987-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
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diff --git a/docs/reference/mumps_uk_1986_1987.md b/docs/reference/mumps_uk_1986_1987.md
index 5101b1f..ec49d74 100644
--- a/docs/reference/mumps_uk_1986_1987.md
+++ b/docs/reference/mumps_uk_1986_1987.md
@@ -16,7 +16,8 @@ A data frame with 3 variables:
 
 - age:
 
-  Age group
+  Midpoint of the age group (e.g. 1.5 = 1-2 years old, 2.5 = 2-3 years
+  old)
 
 - pos:
 
@@ -36,13 +37,11 @@ antibody to measles, mumps, and rubella by age. British Medical Journal
 ## Examples
 
 ``` r
-# Reproduce Fig 4.4 (left panel), p. 67
-age <- mumps_uk_1986_1987$age
-pos <- mumps_uk_1986_1987$pos
-tot <- mumps_uk_1986_1987$tot
-plot(age, pos / tot,
-  cex = 0.008 * tot, pch = 16, xlab = "age", ylab = "seroprevalence",
-  xlim = c(0, 45), ylim = c(0, 1)
+with(mumps_uk_1986_1987,
+  plot(age, pos / tot,
+    cex = 0.1 * sqrt(tot), pch = 16, xlab = "age", ylab = "seroprevalence",
+    xlim = c(0, 45), ylim = c(0, 1)
+  )
 )
 
 ```
diff --git a/docs/reference/parvob19_be_2001_2003-1.png b/docs/reference/parvob19_be_2001_2003-1.png
index 3adb0aa..948a9d5 100644
Binary files a/docs/reference/parvob19_be_2001_2003-1.png and b/docs/reference/parvob19_be_2001_2003-1.png differ
diff --git a/docs/reference/parvob19_be_2001_2003.html b/docs/reference/parvob19_be_2001_2003.html
index e7cc31c..e79987a 100644
--- a/docs/reference/parvob19_be_2001_2003.html
+++ b/docs/reference/parvob19_be_2001_2003.html
@@ -1,65 +1,56 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Parvo B19 serological data from Belgium from 2001-2003 (line listing) — parvob19_be_2001_2003 • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
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 large representative national serum banks in Belgium, England and Wales,
 Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and
-were obtained from residual sera submitted for routine laboratory testing."><title>Parvo B19 serological data from Belgium from 2001-2003 (line listing) — parvob19_be_2001_2003 • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Parvo B19 serological data from Belgium from 2001-2003 (line listing) — parvob19_be_2001_2003"><meta property="og:description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
+were obtained from residual sera submitted for routine laboratory testing."><meta property="og:description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
 large representative national serum banks in Belgium, England and Wales,
 Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and
-were obtained from residual sera submitted for routine laboratory testing."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -111,36 +102,35 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Reproduce Fig 4.5 (left upper panel), p. 68</span></span></span>
-<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">parvob19_be_2001_2003</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">age</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span>pos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">seropositive</span><span class="op">)</span>, tot <span class="op">=</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/context.html" class="external-link">n</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">age</span>, <span class="va">df</span><span class="op">$</span><span class="va">pos</span> <span class="op">/</span> <span class="va">df</span><span class="op">$</span><span class="va">tot</span>,</span></span>
-<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.02</span> <span class="op">*</span> <span class="va">df</span><span class="op">$</span><span class="va">tot</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
+<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.3</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">tot</span><span class="op">)</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
 <span class="r-in"><span>  xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">82</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="parvob19_be_2001_2003-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
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diff --git a/docs/reference/parvob19_be_2001_2003.md b/docs/reference/parvob19_be_2001_2003.md
index 7b1462f..afc86d5 100644
--- a/docs/reference/parvob19_be_2001_2003.md
+++ b/docs/reference/parvob19_be_2001_2003.md
@@ -48,13 +48,12 @@ Risk of Infection." Epidemiology and Infection 136, no. 8 (2008):
 ## Examples
 
 ``` r
-# Reproduce Fig 4.5 (left upper panel), p. 68
 library(dplyr)
 df <- parvob19_be_2001_2003 %>%
   group_by(age) %>%
   summarise(pos = sum(seropositive), tot = n())
 plot(df$age, df$pos / df$tot,
-  cex = 0.02 * df$tot, pch = 16, xlab = "age", ylab = "seroprevalence",
+  cex = 0.3 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
   xlim = c(0, 82), ylim = c(0, 1)
 )
 
diff --git a/docs/reference/parvob19_ew_1996-1.png b/docs/reference/parvob19_ew_1996-1.png
index acd5664..e8b89b7 100644
Binary files a/docs/reference/parvob19_ew_1996-1.png and b/docs/reference/parvob19_ew_1996-1.png differ
diff --git a/docs/reference/parvob19_ew_1996.html b/docs/reference/parvob19_ew_1996.html
index 14b16a8..442adad 100644
--- a/docs/reference/parvob19_ew_1996.html
+++ b/docs/reference/parvob19_ew_1996.html
@@ -1,65 +1,56 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Parvo B19 serological data from England and Wales in 1996 (line listing) — parvob19_ew_1996 • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
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+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Parvo B19 serological data from England and Wales in 1996 (line listing) — parvob19_ew_1996"><meta name="description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
 large representative national serum banks in Belgium, England and Wales,
 Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and
-were obtained from residual sera submitted for routine laboratory testing."><title>Parvo B19 serological data from England and Wales in 1996 (line listing) — parvob19_ew_1996 • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Parvo B19 serological data from England and Wales in 1996 (line listing) — parvob19_ew_1996"><meta property="og:description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
+were obtained from residual sera submitted for routine laboratory testing."><meta property="og:description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
 large representative national serum banks in Belgium, England and Wales,
 Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and
-were obtained from residual sera submitted for routine laboratory testing."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <div class="row">
@@ -111,40 +102,40 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Reproduce Fig 4.5 (right upper panel), p. 68</span></span></span>
-<span class="r-in"><span><span class="co"># NB: This figure will look different to that of in the book, since we</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Note: This figure will look different to that of in the book, since we</span></span></span>
 <span class="r-in"><span><span class="co"># believe that the original authors has made some errors in specifying</span></span></span>
 <span class="r-in"><span><span class="co"># the sample size of the dots.</span></span></span>
+<span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">parvob19_ew_1996</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>age <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="va">age</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">age</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span>pos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">seropositive</span><span class="op">)</span>, tot <span class="op">=</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/context.html" class="external-link">n</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">age</span>, <span class="va">df</span><span class="op">$</span><span class="va">pos</span> <span class="op">/</span> <span class="va">df</span><span class="op">$</span><span class="va">tot</span>,</span></span>
-<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.02</span> <span class="op">*</span> <span class="va">df</span><span class="op">$</span><span class="va">tot</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
+<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.3</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">tot</span><span class="op">)</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
 <span class="r-in"><span>  xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">82</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="parvob19_ew_1996-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
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diff --git a/docs/reference/parvob19_ew_1996.md b/docs/reference/parvob19_ew_1996.md
index 7e0a520..1cbf8c6 100644
--- a/docs/reference/parvob19_ew_1996.md
+++ b/docs/reference/parvob19_ew_1996.md
@@ -48,17 +48,17 @@ Risk of Infection." Epidemiology and Infection 136, no. 8 (2008):
 ## Examples
 
 ``` r
-# Reproduce Fig 4.5 (right upper panel), p. 68
-# NB: This figure will look different to that of in the book, since we
+# Note: This figure will look different to that of in the book, since we
 # believe that the original authors has made some errors in specifying
 # the sample size of the dots.
+
 library(dplyr)
 df <- parvob19_ew_1996 %>%
   mutate(age = round(age)) %>%
   group_by(age) %>%
   summarise(pos = sum(seropositive), tot = n())
 plot(df$age, df$pos / df$tot,
-  cex = 0.02 * df$tot, pch = 16, xlab = "age", ylab = "seroprevalence",
+  cex = 0.3 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
   xlim = c(0, 82), ylim = c(0, 1)
 )
 
diff --git a/docs/reference/parvob19_fi_1997_1998-1.png b/docs/reference/parvob19_fi_1997_1998-1.png
index 2ed6636..165fa1d 100644
Binary files a/docs/reference/parvob19_fi_1997_1998-1.png and b/docs/reference/parvob19_fi_1997_1998-1.png differ
diff --git a/docs/reference/parvob19_fi_1997_1998.html b/docs/reference/parvob19_fi_1997_1998.html
index 242c1f9..494b519 100644
--- a/docs/reference/parvob19_fi_1997_1998.html
+++ b/docs/reference/parvob19_fi_1997_1998.html
@@ -1,65 +1,56 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Parvo B19 serological data from Finland from 1997-1998 (line listing) — parvob19_fi_1997_1998 • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Parvo B19 serological data from Finland from 1997-1998 (line listing) — parvob19_fi_1997_1998"><meta name="description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
 large representative national serum banks in Belgium, England and Wales,
 Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and
-were obtained from residual sera submitted for routine laboratory testing."><title>Parvo B19 serological data from Finland from 1997-1998 (line listing) — parvob19_fi_1997_1998 • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Parvo B19 serological data from Finland from 1997-1998 (line listing) — parvob19_fi_1997_1998"><meta property="og:description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
+were obtained from residual sera submitted for routine laboratory testing."><meta property="og:description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
 large representative national serum banks in Belgium, England and Wales,
 Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and
-were obtained from residual sera submitted for routine laboratory testing."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <div class="row">
@@ -111,40 +102,40 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Reproduce Fig 4.5 (left bottom panel), p. 68</span></span></span>
-<span class="r-in"><span><span class="co"># NB: This figure will look different to that of in the book, since we</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Note: This figure will look different to that of in the book, since we</span></span></span>
 <span class="r-in"><span><span class="co"># believe that the original authors has made some errors in specifying</span></span></span>
 <span class="r-in"><span><span class="co"># the sample size of the dots.</span></span></span>
+<span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">parvob19_fi_1997_1998</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>age <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="va">age</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">age</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span>pos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">seropositive</span><span class="op">)</span>, tot <span class="op">=</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/context.html" class="external-link">n</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">age</span>, <span class="va">df</span><span class="op">$</span><span class="va">pos</span> <span class="op">/</span> <span class="va">df</span><span class="op">$</span><span class="va">tot</span>,</span></span>
-<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.07</span> <span class="op">*</span> <span class="va">df</span><span class="op">$</span><span class="va">tot</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
+<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.4</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">tot</span><span class="op">)</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
 <span class="r-in"><span>  xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">82</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="parvob19_fi_1997_1998-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
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diff --git a/docs/reference/parvob19_fi_1997_1998.md b/docs/reference/parvob19_fi_1997_1998.md
index da6e98a..f9d0152 100644
--- a/docs/reference/parvob19_fi_1997_1998.md
+++ b/docs/reference/parvob19_fi_1997_1998.md
@@ -48,17 +48,17 @@ Risk of Infection." Epidemiology and Infection 136, no. 8 (2008):
 ## Examples
 
 ``` r
-# Reproduce Fig 4.5 (left bottom panel), p. 68
-# NB: This figure will look different to that of in the book, since we
+# Note: This figure will look different to that of in the book, since we
 # believe that the original authors has made some errors in specifying
 # the sample size of the dots.
+
 library(dplyr)
 df <- parvob19_fi_1997_1998 %>%
   mutate(age = round(age)) %>%
   group_by(age) %>%
   summarise(pos = sum(seropositive), tot = n())
 plot(df$age, df$pos / df$tot,
-  cex = 0.07 * df$tot, pch = 16, xlab = "age", ylab = "seroprevalence",
+  cex = 0.4 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
   xlim = c(0, 82), ylim = c(0, 1)
 )
 
diff --git a/docs/reference/parvob19_it_2003_2004-1.png b/docs/reference/parvob19_it_2003_2004-1.png
index 9715650..f987b08 100644
Binary files a/docs/reference/parvob19_it_2003_2004-1.png and b/docs/reference/parvob19_it_2003_2004-1.png differ
diff --git a/docs/reference/parvob19_it_2003_2004.html b/docs/reference/parvob19_it_2003_2004.html
index 054a183..89486cf 100644
--- a/docs/reference/parvob19_it_2003_2004.html
+++ b/docs/reference/parvob19_it_2003_2004.html
@@ -1,65 +1,56 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Parvo B19 serological data from Italy from 2003-2004 (line listing) — parvob19_it_2003_2004 • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Parvo B19 serological data from Italy from 2003-2004 (line listing) — parvob19_it_2003_2004"><meta name="description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
 large representative national serum banks in Belgium, England and Wales,
 Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and
-were obtained from residual sera submitted for routine laboratory testing."><title>Parvo B19 serological data from Italy from 2003-2004 (line listing) — parvob19_it_2003_2004 • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Parvo B19 serological data from Italy from 2003-2004 (line listing) — parvob19_it_2003_2004"><meta property="og:description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
+were obtained from residual sera submitted for routine laboratory testing."><meta property="og:description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
 large representative national serum banks in Belgium, England and Wales,
 Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and
-were obtained from residual sera submitted for routine laboratory testing."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -111,39 +102,35 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Reproduce Fig 4.5 (middle bottom panel), p. 68</span></span></span>
-<span class="r-in"><span><span class="co"># NB: This figure will look different to that of in the book, since we</span></span></span>
-<span class="r-in"><span><span class="co"># believe that the original authors has made some errors in specifying</span></span></span>
-<span class="r-in"><span><span class="co"># the sample size of the dots.</span></span></span>
-<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">parvob19_it_2003_2004</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">age</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span>pos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">seropositive</span><span class="op">)</span>, tot <span class="op">=</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/context.html" class="external-link">n</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">age</span>, <span class="va">df</span><span class="op">$</span><span class="va">pos</span> <span class="op">/</span> <span class="va">df</span><span class="op">$</span><span class="va">tot</span>,</span></span>
-<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.07</span> <span class="op">*</span> <span class="va">df</span><span class="op">$</span><span class="va">tot</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
+<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.32</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">tot</span><span class="op">)</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
 <span class="r-in"><span>  xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">82</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="parvob19_it_2003_2004-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
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+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
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diff --git a/docs/reference/parvob19_it_2003_2004.md b/docs/reference/parvob19_it_2003_2004.md
index ac4499c..0d5cf36 100644
--- a/docs/reference/parvob19_it_2003_2004.md
+++ b/docs/reference/parvob19_it_2003_2004.md
@@ -48,16 +48,12 @@ Risk of Infection." Epidemiology and Infection 136, no. 8 (2008):
 ## Examples
 
 ``` r
-# Reproduce Fig 4.5 (middle bottom panel), p. 68
-# NB: This figure will look different to that of in the book, since we
-# believe that the original authors has made some errors in specifying
-# the sample size of the dots.
 library(dplyr)
 df <- parvob19_it_2003_2004 %>%
   group_by(age) %>%
   summarise(pos = sum(seropositive), tot = n())
 plot(df$age, df$pos / df$tot,
-  cex = 0.07 * df$tot, pch = 16, xlab = "age", ylab = "seroprevalence",
+  cex = 0.32 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
   xlim = c(0, 82), ylim = c(0, 1)
 )
 
diff --git a/docs/reference/parvob19_pl_1995_2004-1.png b/docs/reference/parvob19_pl_1995_2004-1.png
index 70b6ed8..68b8de9 100644
Binary files a/docs/reference/parvob19_pl_1995_2004-1.png and b/docs/reference/parvob19_pl_1995_2004-1.png differ
diff --git a/docs/reference/parvob19_pl_1995_2004.html b/docs/reference/parvob19_pl_1995_2004.html
index 88ab808..cfeea0c 100644
--- a/docs/reference/parvob19_pl_1995_2004.html
+++ b/docs/reference/parvob19_pl_1995_2004.html
@@ -1,65 +1,56 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Parvo B19 serological data from Poland from 1995-2004 (line listing) — parvob19_pl_1995_2004 • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Parvo B19 serological data from Poland from 1995-2004 (line listing) — parvob19_pl_1995_2004"><meta name="description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
 large representative national serum banks in Belgium, England and Wales,
 Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and
-were obtained from residual sera submitted for routine laboratory testing."><title>Parvo B19 serological data from Poland from 1995-2004 (line listing) — parvob19_pl_1995_2004 • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Parvo B19 serological data from Poland from 1995-2004 (line listing) — parvob19_pl_1995_2004"><meta property="og:description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
+were obtained from residual sera submitted for routine laboratory testing."><meta property="og:description" content="A seroprevalence survey testing for parvovirus B19 IgG antibody, performed on
 large representative national serum banks in Belgium, England and Wales,
 Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and
-were obtained from residual sera submitted for routine laboratory testing."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -111,40 +102,40 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Reproduce Fig 4.5 (right bottom panel), p. 68</span></span></span>
-<span class="r-in"><span><span class="co"># NB: This figure will look different to that of in the book, since we</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Note: This figure will look different to that of in the book, since we</span></span></span>
 <span class="r-in"><span><span class="co"># believe that the original authors has made some errors in specifying</span></span></span>
 <span class="r-in"><span><span class="co"># the sample size of the dots.</span></span></span>
+<span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">parvob19_pl_1995_2004</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>age <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="va">age</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">age</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span>pos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">seropositive</span><span class="op">)</span>, tot <span class="op">=</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/context.html" class="external-link">n</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">age</span>, <span class="va">df</span><span class="op">$</span><span class="va">pos</span> <span class="op">/</span> <span class="va">df</span><span class="op">$</span><span class="va">tot</span>,</span></span>
-<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.07</span> <span class="op">*</span> <span class="va">df</span><span class="op">$</span><span class="va">tot</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
+<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.32</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">tot</span><span class="op">)</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
 <span class="r-in"><span>  xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">82</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="parvob19_pl_1995_2004-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
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diff --git a/docs/reference/parvob19_pl_1995_2004.md b/docs/reference/parvob19_pl_1995_2004.md
index 25dd71b..86d25c4 100644
--- a/docs/reference/parvob19_pl_1995_2004.md
+++ b/docs/reference/parvob19_pl_1995_2004.md
@@ -48,17 +48,17 @@ Risk of Infection." Epidemiology and Infection 136, no. 8 (2008):
 ## Examples
 
 ``` r
-# Reproduce Fig 4.5 (right bottom panel), p. 68
-# NB: This figure will look different to that of in the book, since we
+# Note: This figure will look different to that of in the book, since we
 # believe that the original authors has made some errors in specifying
 # the sample size of the dots.
+
 library(dplyr)
 df <- parvob19_pl_1995_2004 %>%
   mutate(age = round(age)) %>%
   group_by(age) %>%
   summarise(pos = sum(seropositive), tot = n())
 plot(df$age, df$pos / df$tot,
-  cex = 0.07 * df$tot, pch = 16, xlab = "age", ylab = "seroprevalence",
+  cex = 0.32 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
   xlim = c(0, 82), ylim = c(0, 1)
 )
 
diff --git a/docs/reference/pava.html b/docs/reference/pava.html
index 05e7961..facc542 100644
--- a/docs/reference/pava.html
+++ b/docs/reference/pava.html
@@ -1,59 +1,50 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Monotonize seroprevalence"><title>Monotonize seroprevalence — pava • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Monotonize seroprevalence — pava"><meta property="og:description" content="Monotonize seroprevalence"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Monotonize seroprevalence — pava • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
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     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
+
 
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+    <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
+    <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
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+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
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 </nav><div class="container template-reference-topic">
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@@ -74,38 +65,38 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>pos</dt>
+
+
+<dl><dt id="arg-pos">pos<a class="anchor" aria-label="anchor" href="#arg-pos"></a></dt>
 <dd><p>the positive count vector.</p></dd>
 
 
-<dt>tot</dt>
+<dt id="arg-tot">tot<a class="anchor" aria-label="anchor" href="#arg-tot"></a></dt>
 <dd><p>the total count vector.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>computed list of 2 items pai1 for original values and pai2 for monotonized value</p>
+    <p>computed list of 2 items pai1 for original values and pai2 for monotonized value</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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     <footer><div class="pkgdown-footer-left">
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+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
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diff --git a/docs/reference/penalized_spline_model.html b/docs/reference/penalized_spline_model.html
index dcc18d6..b99ae34 100644
--- a/docs/reference/penalized_spline_model.html
+++ b/docs/reference/penalized_spline_model.html
@@ -1,59 +1,54 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Penalized Spline model"><title>Penalized Spline model — penalized_spline_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Penalized Spline model — penalized_spline_model"><meta property="og:description" content="Penalized Spline model"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Penalized Spline model — penalized_spline_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
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+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Penalized Spline model — penalized_spline_model"><meta name="description" content="Fit age-specific seroprevalence to a semi-parametric model where
+predictor is modeled with penalized splines. The penalized splines can be estimated by either
+(1) penalized likelihood framework or (2) mixed model framework"><meta property="og:description" content="Fit age-specific seroprevalence to a semi-parametric model where
+predictor is modeled with penalized splines. The penalized splines can be estimated by either
+(1) penalized likelihood framework or (2) mixed model framework"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
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-    
 
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+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
+    <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
+    <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
+    <li><a class="dropdown-item" href="../articles/model_selection.html">Model selection</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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@@ -64,13 +59,19 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
     </div>
 
     <div class="ref-description section level2">
-    <p>Penalized Spline model</p>
+    <p>Fit age-specific seroprevalence to a semi-parametric model where
+predictor is modeled with penalized splines. The penalized splines can be estimated by either
+(1) penalized likelihood framework or (2) mixed model framework</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">penalized_spline_model</span><span class="op">(</span></span>
 <span>  <span class="va">data</span>,</span>
+<span>  age_col <span class="op">=</span> <span class="st">"age"</span>,</span>
+<span>  pos_col <span class="op">=</span> <span class="st">"pos"</span>,</span>
+<span>  tot_col <span class="op">=</span> <span class="st">"tot"</span>,</span>
+<span>  status_col <span class="op">=</span> <span class="st">"status"</span>,</span>
 <span>  s <span class="op">=</span> <span class="st">"bs"</span>,</span>
 <span>  link <span class="op">=</span> <span class="st">"logit"</span>,</span>
 <span>  framework <span class="op">=</span> <span class="st">"pl"</span>,</span>
@@ -80,31 +81,48 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>data</dt>
-<dd><p>the input data frame, must either have `age`, `pos`, `tot` column for aggregated data OR `age`, `status` for linelisting data</p></dd>
 
 
-<dt>s</dt>
+<dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
+<dd><p>the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR
+columns for `age`, `status` (for linelisting data)</p></dd>
+
+
+<dt id="arg-age-col">age_col<a class="anchor" aria-label="anchor" href="#arg-age-col"></a></dt>
+<dd><p>name of the `age` column (default age_col="age").</p></dd>
+
+
+<dt id="arg-pos-col">pos_col<a class="anchor" aria-label="anchor" href="#arg-pos-col"></a></dt>
+<dd><p>name of the `pos` column (default pos_col="pos").</p></dd>
+
+
+<dt id="arg-tot-col">tot_col<a class="anchor" aria-label="anchor" href="#arg-tot-col"></a></dt>
+<dd><p>name of the `tot` column (default tot_col="tot").</p></dd>
+
+
+<dt id="arg-status-col">status_col<a class="anchor" aria-label="anchor" href="#arg-status-col"></a></dt>
+<dd><p>name of the `status` column (default status_col="status").</p></dd>
+
+
+<dt id="arg-s">s<a class="anchor" aria-label="anchor" href="#arg-s"></a></dt>
 <dd><p>smoothing basis to use</p></dd>
 
 
-<dt>link</dt>
+<dt id="arg-link">link<a class="anchor" aria-label="anchor" href="#arg-link"></a></dt>
 <dd><p>link function to use</p></dd>
 
 
-<dt>framework</dt>
+<dt id="arg-framework">framework<a class="anchor" aria-label="anchor" href="#arg-framework"></a></dt>
 <dd><p>which approach to fit the model ("pl" for penalized likelihood framework, "glmm" for generalized linear mixed model framework)</p></dd>
 
 
-<dt>sp</dt>
+<dt id="arg-sp">sp<a class="anchor" aria-label="anchor" href="#arg-sp"></a></dt>
 <dd><p>smoothing parameter</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a list of class penalized_spline_model with 6 attributes</p>
+    <p>a list of class penalized_spline_model with 6 attributes</p>
 <dl><dt>datatype</dt>
 <dd><p>type of datatype used for model fitting (aggregated or linelisting)</p></dd>
 
@@ -124,6 +142,69 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <dd><p>force of infection</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>In the semi-parametric model, the predictor is formulated as a penalized spline
+with truncated power basis functions of degree \(p\)
+and fixed knots \(\kappa_1,..., \kappa_k\) as followed</p>
+<p>$$
+\eta(a_i) = \beta_0 + \beta_1a_i + ... + \beta_p a_i^p + \Sigma_{k=1}^ku_k(a_i - \kappa_k)^p_+
+$$</p>
+<p>- Where:
+$$
+(a_i - \kappa_k)^p_+ = \begin{cases}
+0, &amp; a_i \le \kappa_k \\
+(a_i - \kappa_k)^p, &amp; a_i &gt; \kappa_k
+\end{cases}
+$$</p>
+<p>FOI can then be derived by</p>
+<p>$$\hat{\lambda}(a_i) = [\hat{\beta_1} , 2\hat{\beta_2}a_i, ...,
+p \hat{\beta} a_i ^{p-1} + \Sigma^k_{k=1} p \hat{u}_k(a_i - \kappa_k)^{p-1}_+] \delta(\hat{\eta}(a_i))
+$$</p>
+<p>Where \(\delta(.)\) is determined by the link function used in the model</p>
+<p>In matrix annotation, the mean structure model for \(\eta(a_i)\) becomes
+$$\eta = \textbf{X}\beta + \textbf{Zu}$$</p>
+<p>Where \(\eta = [\eta(a_i) ... \eta(a_N) ]^T\), \(\beta = [\beta_0 \beta_1 .... \beta_p]^T\),
+and \(\textbf{u} = [u_1 u_2 ... u_k]^T\) are the regression with corresponding design matrices
+$$\textbf{X} = \begin{bmatrix}
+1 &amp; a_1 &amp; a_1^2 &amp; ... &amp; a_1^p \\
+1 &amp; a_2 &amp; a_2^2 &amp; ... &amp; a_2^p \\
+\vdots &amp; \vdots &amp; \vdots &amp; \dots &amp; \vdots \\
+1 &amp; a_N &amp; a_N^2 &amp; ... &amp; a_N^p
+\end{bmatrix}, \textbf{Z} = \begin{bmatrix}
+(a_1 - \kappa_1 )_+^p &amp; (a_1 - \kappa_2 )_+^p &amp; \dots &amp; (a_1 - \kappa_k)_+^p \\
+(a_2 - \kappa_1 )_+^p &amp; (a_2 - \kappa_2 )_+^p &amp; \dots &amp; (a_2 - \kappa_k)_+^p \\
+\vdots &amp; \vdots &amp; \dots &amp; \vdots \\
+(a_N - \kappa_1 )_+^p &amp; (a_N - \kappa_2 )_+^p &amp; \dots &amp; (a_N - \kappa_k)_+^p
+\end{bmatrix}
+$$</p>
+<p>Under <b>penalized likelihood framework</b>, the model is fitted by maximizing
+the following likelihood</p>
+<p>$$
+\phi^{-1}[y^T(\textbf{X}\beta + \textbf{Zu} ) -  \textbf{1}^Tc(\textbf{X}\beta + \textbf{Zu} )] - \frac{1}{2}\lambda^2
+\begin{bmatrix} \beta \\ \textbf{u} \end{bmatrix}^T D\begin{bmatrix} \beta \\ \textbf{u} \end{bmatrix}
+$$</p>
+<p>Where:</p>
+<p>- \(X\beta + Zu\) is the predictor</p>
+<p>- \(D\) is a known semi-definite penalty matrix [@Wahba1978], [@Green1993]</p>
+<p>- \(y\) is the response vector</p>
+<p>- \(\textbf{1}\) the unit vector, \(c(.)\) is determined by the link function used</p>
+<p>- \(\lambda\) is the smoothing parameter (larger values –&gt; smoother curves)</p>
+<p>- \(\phi\) is the overdispersion parameter and equals 1 if there is no overdispersion</p>
+<p>Under the <b>mixed model</b> framework,
+the model instead treats the coefficients \(\textbf{u}\) in the likelihood formulation
+as random effects with \(\textbf{u} \sim N(\textbf{0}, \mathbf{\sigma}^2_u \textbf{I})\)</p>
+<p>Refer to section 8.1 and 8.2 of the the book by Hens et al. (2012) for further details.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+<a href="https://doi.org/10.1007/978-1-4614-4072-7" class="external-link">doi:10.1007/978-1-4614-4072-7</a>
+.</p>
+    </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p>[mgcv::gam()], [mgcv::gamm()] for more information the fitted gam and gamm model</p></div>
@@ -155,23 +236,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="penalized_spline_model-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/penalized_spline_model.md b/docs/reference/penalized_spline_model.md
index 8fc907c..47aa4c6 100644
--- a/docs/reference/penalized_spline_model.md
+++ b/docs/reference/penalized_spline_model.md
@@ -1,12 +1,19 @@
 # Penalized Spline model
 
-Penalized Spline model
+Fit age-specific seroprevalence to a semi-parametric model where
+predictor is modeled with penalized splines. The penalized splines can
+be estimated by either (1) penalized likelihood framework or (2) mixed
+model framework
 
 ## Usage
 
 ``` r
 penalized_spline_model(
   data,
+  age_col = "age",
+  pos_col = "pos",
+  tot_col = "tot",
+  status_col = "status",
   s = "bs",
   link = "logit",
   framework = "pl",
@@ -18,8 +25,25 @@ penalized_spline_model(
 
 - data:
 
-  the input data frame, must either have \`age\`, \`pos\`, \`tot\`
-  column for aggregated data OR \`age\`, \`status\` for linelisting data
+  the input data frame, must either have columns for \`age\`, \`pos\`,
+  \`tot\` (for aggregated data) OR columns for \`age\`, \`status\` (for
+  linelisting data)
+
+- age_col:
+
+  name of the \`age\` column (default age_col="age").
+
+- pos_col:
+
+  name of the \`pos\` column (default pos_col="pos").
+
+- tot_col:
+
+  name of the \`tot\` column (default tot_col="tot").
+
+- status_col:
+
+  name of the \`status\` column (default status_col="status").
 
 - s:
 
@@ -67,6 +91,84 @@ a list of class penalized_spline_model with 6 attributes
 
   force of infection
 
+## Details
+
+In the semi-parametric model, the predictor is formulated as a penalized
+spline with truncated power basis functions of degree \\p\\ and fixed
+knots \\\kappa_1,..., \kappa_k\\ as followed
+
+\$\$ \eta(a_i) = \beta_0 + \beta_1a_i + ... + \beta_p a_i^p +
+\Sigma\_{k=1}^ku_k(a_i - \kappa_k)^p\_+ \$\$
+
+\- Where: \$\$ (a_i - \kappa_k)^p\_+ = \begin{cases} 0, & a_i \le
+\kappa_k \\ (a_i - \kappa_k)^p, & a_i \> \kappa_k \end{cases} \$\$
+
+FOI can then be derived by
+
+\$\$\hat{\lambda}(a_i) = \[\hat{\beta_1} , 2\hat{\beta_2}a_i, ..., p
+\hat{\beta} a_i ^{p-1} + \Sigma^k\_{k=1} p \hat{u}\_k(a_i -
+\kappa_k)^{p-1}\_+\] \delta(\hat{\eta}(a_i)) \$\$
+
+Where \\\delta(.)\\ is determined by the link function used in the model
+
+In matrix annotation, the mean structure model for \\\eta(a_i)\\ becomes
+\$\$\eta = \textbf{X}\beta + \textbf{Zu}\$\$
+
+Where \\\eta = \[\eta(a_i) ... \eta(a_N) \]^T\\, \\\beta = \[\beta_0
+\beta_1 .... \beta_p\]^T\\, and \\\textbf{u} = \[u_1 u_2 ... u_k\]^T\\
+are the regression with corresponding design matrices \$\$\textbf{X} =
+\begin{bmatrix} 1 & a_1 & a_1^2 & ... & a_1^p \\ 1 & a_2 & a_2^2 & ... &
+a_2^p \\ \vdots & \vdots & \vdots & \dots & \vdots \\ 1 & a_N & a_N^2 &
+... & a_N^p \end{bmatrix}, \textbf{Z} = \begin{bmatrix} (a_1 - \kappa_1
+)\_+^p & (a_1 - \kappa_2 )\_+^p & \dots & (a_1 - \kappa_k)\_+^p \\
+(a_2 - \kappa_1 )\_+^p & (a_2 - \kappa_2 )\_+^p & \dots & (a_2 -
+\kappa_k)\_+^p \\ \vdots & \vdots & \dots & \vdots \\ (a_N - \kappa_1
+)\_+^p & (a_N - \kappa_2 )\_+^p & \dots & (a_N - \kappa_k)\_+^p
+\end{bmatrix} \$\$
+
+Under **penalized likelihood framework**, the model is fitted by
+maximizing the following likelihood
+
+\$\$ \phi^{-1}\[y^T(\textbf{X}\beta + \textbf{Zu} ) -
+\textbf{1}^Tc(\textbf{X}\beta + \textbf{Zu} )\] - \frac{1}{2}\lambda^2
+\begin{bmatrix} \beta \\ \textbf{u} \end{bmatrix}^T D\begin{bmatrix}
+\beta \\ \textbf{u} \end{bmatrix} \$\$
+
+Where:
+
+\- \\X\beta + Zu\\ is the predictor
+
+\- \\D\\ is a known semi-definite penalty matrix \[@Wahba1978\],
+\[@Green1993\]
+
+\- \\y\\ is the response vector
+
+\- \\\textbf{1}\\ the unit vector, \\c(.)\\ is determined by the link
+function used
+
+\- \\\lambda\\ is the smoothing parameter (larger values –\> smoother
+curves)
+
+\- \\\phi\\ is the overdispersion parameter and equals 1 if there is no
+overdispersion
+
+Under the **mixed model** framework, the model instead treats the
+coefficients \\\textbf{u}\\ in the likelihood formulation as random
+effects with \\\textbf{u} \sim N(\textbf{0}, \mathbf{\sigma}^2_u
+\textbf{I})\\
+
+Refer to section 8.1 and 8.2 of the the book by Hens et al. (2012) for
+further details.
+
+## References
+
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+[doi:10.1007/978-1-4614-4072-7](https://doi.org/10.1007/978-1-4614-4072-7)
+.
+
 ## See also
 
 \[mgcv::gam()\], \[mgcv::gamm()\] for more information the fitted gam
diff --git a/docs/reference/plot.age_time_model.html b/docs/reference/plot.age_time_model.html
index 3e8b3df..a811a42 100644
--- a/docs/reference/plot.age_time_model.html
+++ b/docs/reference/plot.age_time_model.html
@@ -1,5 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Plot output for age_time_model — plot.age_time_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plot output for age_time_model — plot.age_time_model"><meta name="description" content="Plot output for age_time_model"><meta property="og:description" content="Plot output for age_time_model"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Plot output for age_time_model — plot.age_time_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
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+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plot output for age_time_model — plot.age_time_model"><meta name="description" content="Plot output for age_time_model"><meta property="og:description" content="Plot output for age_time_model"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +13,7 @@
 
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -19,14 +25,14 @@
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-    <li><a class="dropdown-item" href="../articles/sir_model.html">SIR model</a></li>
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-    <li><a class="dropdown-item" href="../articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
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     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
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diff --git a/docs/reference/plot.estimate_from_mixture.html b/docs/reference/plot.estimate_from_mixture.html
index 95c390a..4fcca74 100644
--- a/docs/reference/plot.estimate_from_mixture.html
+++ b/docs/reference/plot.estimate_from_mixture.html
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@@ -69,44 +60,44 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 
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     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for estimate_from_mixture</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'estimate_from_mixture'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
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     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>the mixture_model</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>arbitrary params.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>ggplot object</p>
+    <p>ggplot object</p>
     </div>
 
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diff --git a/docs/reference/plot.estimate_from_mixture.md b/docs/reference/plot.estimate_from_mixture.md
index 00b374b..cef9c32 100644
--- a/docs/reference/plot.estimate_from_mixture.md
+++ b/docs/reference/plot.estimate_from_mixture.md
@@ -5,7 +5,7 @@ plot() overloading for result of estimate_from_mixture
 ## Usage
 
 ``` r
-# S3 method for estimate_from_mixture
+# S3 method for class 'estimate_from_mixture'
 plot(x, ...)
 ```
 
diff --git a/docs/reference/plot.farrington_model.html b/docs/reference/plot.farrington_model.html
index 4a4d7cc..28a6461 100644
--- a/docs/reference/plot.farrington_model.html
+++ b/docs/reference/plot.farrington_model.html
@@ -1,59 +1,50 @@
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+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
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@@ -69,44 +60,44 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for farrington_model</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'farrington_model'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>the Farrington model object.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>arbitrary params.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>ggplot object</p>
+    <p>ggplot object</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
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diff --git a/docs/reference/plot.farrington_model.md b/docs/reference/plot.farrington_model.md
index a79f919..088eed5 100644
--- a/docs/reference/plot.farrington_model.md
+++ b/docs/reference/plot.farrington_model.md
@@ -5,7 +5,7 @@ plot() overloading for Farrington model
 ## Usage
 
 ``` r
-# S3 method for farrington_model
+# S3 method for class 'farrington_model'
 plot(x, ...)
 ```
 
diff --git a/docs/reference/plot.fp_model.html b/docs/reference/plot.fp_model.html
index 0a75f3b..ef814d8 100644
--- a/docs/reference/plot.fp_model.html
+++ b/docs/reference/plot.fp_model.html
@@ -1,59 +1,50 @@
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+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for fp_model</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'fp_model'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>the fractional polynomial model object.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>arbitrary params.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>ggplot object</p>
+    <p>ggplot object</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
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diff --git a/docs/reference/plot.fp_model.md b/docs/reference/plot.fp_model.md
index 43a7a20..cd2760a 100644
--- a/docs/reference/plot.fp_model.md
+++ b/docs/reference/plot.fp_model.md
@@ -5,7 +5,7 @@ plot() overloading for fractional polynomial model
 ## Usage
 
 ``` r
-# S3 method for fp_model
+# S3 method for class 'fp_model'
 plot(x, ...)
 ```
 
diff --git a/docs/reference/plot.hierarchical_bayesian_model.html b/docs/reference/plot.hierarchical_bayesian_model.html
index 1d89f05..97f8c9f 100644
--- a/docs/reference/plot.hierarchical_bayesian_model.html
+++ b/docs/reference/plot.hierarchical_bayesian_model.html
@@ -1,59 +1,50 @@
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+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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@@ -69,44 +60,44 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for hierarchical_bayesian_model</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'hierarchical_bayesian_model'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>hierarchical_bayesian_model object created by serosv.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>arbitrary params.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>ggplot object</p>
+    <p>ggplot object</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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diff --git a/docs/reference/plot.hierarchical_bayesian_model.md b/docs/reference/plot.hierarchical_bayesian_model.md
index 28dbdb4..9a57e83 100644
--- a/docs/reference/plot.hierarchical_bayesian_model.md
+++ b/docs/reference/plot.hierarchical_bayesian_model.md
@@ -5,7 +5,7 @@ plot() overloading for hierarchical_bayesian_model
 ## Usage
 
 ``` r
-# S3 method for hierarchical_bayesian_model
+# S3 method for class 'hierarchical_bayesian_model'
 plot(x, ...)
 ```
 
diff --git a/docs/reference/plot.lp_model.html b/docs/reference/plot.lp_model.html
index 6e8b309..3032cf9 100644
--- a/docs/reference/plot.lp_model.html
+++ b/docs/reference/plot.lp_model.html
@@ -1,59 +1,50 @@
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@@ -69,44 +60,44 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for lp_model</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'lp_model'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>the local polynomial model object.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>arbitrary params.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>ggplot object</p>
+    <p>ggplot object</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
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diff --git a/docs/reference/plot.lp_model.md b/docs/reference/plot.lp_model.md
index bfa8c4b..26a548e 100644
--- a/docs/reference/plot.lp_model.md
+++ b/docs/reference/plot.lp_model.md
@@ -5,7 +5,7 @@ plot() overloading for local polynomial model
 ## Usage
 
 ``` r
-# S3 method for lp_model
+# S3 method for class 'lp_model'
 plot(x, ...)
 ```
 
diff --git a/docs/reference/plot.mixture_model.html b/docs/reference/plot.mixture_model.html
index e9b7b89..f82e986 100644
--- a/docs/reference/plot.mixture_model.html
+++ b/docs/reference/plot.mixture_model.html
@@ -1,59 +1,50 @@
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+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
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     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mixture_model</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mixture_model'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>the mixture_model</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>arbitrary params.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>ggplot object</p>
+    <p>ggplot object</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
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diff --git a/docs/reference/plot.mixture_model.md b/docs/reference/plot.mixture_model.md
index d0f52a6..69d7781 100644
--- a/docs/reference/plot.mixture_model.md
+++ b/docs/reference/plot.mixture_model.md
@@ -5,7 +5,7 @@ plot() overloading for mixture model
 ## Usage
 
 ``` r
-# S3 method for mixture_model
+# S3 method for class 'mixture_model'
 plot(x, ...)
 ```
 
diff --git a/docs/reference/plot.mseir_model.html b/docs/reference/plot.mseir_model.html
index 615fd8b..92786ac 100644
--- a/docs/reference/plot.mseir_model.html
+++ b/docs/reference/plot.mseir_model.html
@@ -1,59 +1,51 @@
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+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
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 <div class="row">
@@ -69,44 +61,44 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mseir_model</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'mseir_model'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>the mseir_model object.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>arbitrary params.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>ggplot object</p>
+    <p>ggplot object</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
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diff --git a/docs/reference/plot.mseir_model.md b/docs/reference/plot.mseir_model.md
index 4c01a66..6b6cd08 100644
--- a/docs/reference/plot.mseir_model.md
+++ b/docs/reference/plot.mseir_model.md
@@ -5,7 +5,7 @@ plot() overloading for MSEIR model
 ## Usage
 
 ``` r
-# S3 method for mseir_model
+# S3 method for class 'mseir_model'
 plot(x, ...)
 ```
 
diff --git a/docs/reference/plot.penalized_spline_model.html b/docs/reference/plot.penalized_spline_model.html
index 329a82b..890afb6 100644
--- a/docs/reference/plot.penalized_spline_model.html
+++ b/docs/reference/plot.penalized_spline_model.html
@@ -1,59 +1,50 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="plot() overloading for penalized spline"><title>plot() overloading for penalized spline — plot.penalized_spline_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="plot() overloading for penalized spline — plot.penalized_spline_model"><meta property="og:description" content="plot() overloading for penalized spline"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -69,44 +60,44 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for penalized_spline_model</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'penalized_spline_model'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
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     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>the penalized_spline_model object</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>arbitrary params.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>ggplot object</p>
+    <p>ggplot object</p>
     </div>
 
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diff --git a/docs/reference/plot.penalized_spline_model.md b/docs/reference/plot.penalized_spline_model.md
index d60896b..dd8b6a5 100644
--- a/docs/reference/plot.penalized_spline_model.md
+++ b/docs/reference/plot.penalized_spline_model.md
@@ -5,7 +5,7 @@ plot() overloading for penalized spline
 ## Usage
 
 ``` r
-# S3 method for penalized_spline_model
+# S3 method for class 'penalized_spline_model'
 plot(x, ...)
 ```
 
diff --git a/docs/reference/plot.polynomial_model.html b/docs/reference/plot.polynomial_model.html
index 518f3c8..0f5054c 100644
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+++ b/docs/reference/plot.polynomial_model.html
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+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
@@ -69,44 +60,44 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for polynomial_model</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'polynomial_model'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>the polynomial model object</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>arbitrary params.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>ggplot object</p>
+    <p>ggplot object</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
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diff --git a/docs/reference/plot.polynomial_model.md b/docs/reference/plot.polynomial_model.md
index dfaee20..05c1db4 100644
--- a/docs/reference/plot.polynomial_model.md
+++ b/docs/reference/plot.polynomial_model.md
@@ -5,7 +5,7 @@ plot() overloading for polynomial model
 ## Usage
 
 ``` r
-# S3 method for polynomial_model
+# S3 method for class 'polynomial_model'
 plot(x, ...)
 ```
 
diff --git a/docs/reference/plot.sir_basic_model.html b/docs/reference/plot.sir_basic_model.html
index 0530e1f..c6c2a41 100644
--- a/docs/reference/plot.sir_basic_model.html
+++ b/docs/reference/plot.sir_basic_model.html
@@ -1,59 +1,51 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="plot() overloading for SIR model"><title>plot() overloading for SIR model — plot.sir_basic_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="plot() overloading for SIR model — plot.sir_basic_model"><meta property="og:description" content="plot() overloading for SIR model"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
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 <div class="row">
@@ -69,44 +61,44 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for sir_basic_model</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'sir_basic_model'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>the sir_basic_model object.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>arbitrary params.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>ggplot object</p>
+    <p>ggplot object</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
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diff --git a/docs/reference/plot.sir_basic_model.md b/docs/reference/plot.sir_basic_model.md
index 6f365ab..dd211ee 100644
--- a/docs/reference/plot.sir_basic_model.md
+++ b/docs/reference/plot.sir_basic_model.md
@@ -5,7 +5,7 @@ plot() overloading for SIR model
 ## Usage
 
 ``` r
-# S3 method for sir_basic_model
+# S3 method for class 'sir_basic_model'
 plot(x, ...)
 ```
 
diff --git a/docs/reference/plot.sir_static_model.html b/docs/reference/plot.sir_static_model.html
index 4f41718..08c371d 100644
--- a/docs/reference/plot.sir_static_model.html
+++ b/docs/reference/plot.sir_static_model.html
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@@ -69,44 +61,44 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for sir_static_model</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'sir_static_model'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>the sir_static_model object.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>arbitrary params.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>ggplot object</p>
+    <p>ggplot object</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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diff --git a/docs/reference/plot.sir_static_model.md b/docs/reference/plot.sir_static_model.md
index 4cd3fe4..b122aba 100644
--- a/docs/reference/plot.sir_static_model.md
+++ b/docs/reference/plot.sir_static_model.md
@@ -5,7 +5,7 @@ plot() overloading for SIR static model
 ## Usage
 
 ``` r
-# S3 method for sir_static_model
+# S3 method for class 'sir_static_model'
 plot(x, ...)
 ```
 
diff --git a/docs/reference/plot.sir_subpops_model.html b/docs/reference/plot.sir_subpops_model.html
index ca05a4d..dfb90dc 100644
--- a/docs/reference/plot.sir_subpops_model.html
+++ b/docs/reference/plot.sir_subpops_model.html
@@ -1,59 +1,51 @@
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     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
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+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
+    <li><a class="dropdown-item" href="../articles/model_selection.html">Model selection</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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 </nav><div class="container template-reference-topic">
 <div class="row">
@@ -69,44 +61,44 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for sir_subpops_model</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'sir_subpops_model'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>the sir_subpops_models object.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>arbitrary params.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>list of ggplot objects, each object is the plot for the corresponding subpopulation</p>
+    <p>list of ggplot objects, each object is the plot for the corresponding subpopulation</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
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diff --git a/docs/reference/plot.sir_subpops_model.md b/docs/reference/plot.sir_subpops_model.md
index 8f76783..53fcac2 100644
--- a/docs/reference/plot.sir_subpops_model.md
+++ b/docs/reference/plot.sir_subpops_model.md
@@ -5,7 +5,7 @@ plot() overloading for SIR sub populations model
 ## Usage
 
 ``` r
-# S3 method for sir_subpops_model
+# S3 method for class 'sir_subpops_model'
 plot(x, ...)
 ```
 
diff --git a/docs/reference/plot.weibull_model.html b/docs/reference/plot.weibull_model.html
index d38b082..5e75539 100644
--- a/docs/reference/plot.weibull_model.html
+++ b/docs/reference/plot.weibull_model.html
@@ -1,59 +1,50 @@
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+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
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     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for weibull_model</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'weibull_model'</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>the Weibull model object.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>arbitrary params.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>ggplot object</p>
+    <p>ggplot object</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
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-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
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diff --git a/docs/reference/plot.weibull_model.md b/docs/reference/plot.weibull_model.md
index 995ff8d..4129acc 100644
--- a/docs/reference/plot.weibull_model.md
+++ b/docs/reference/plot.weibull_model.md
@@ -5,7 +5,7 @@ plot() overloading for Weibull model
 ## Usage
 
 ``` r
-# S3 method for weibull_model
+# S3 method for class 'weibull_model'
 plot(x, ...)
 ```
 
diff --git a/docs/reference/plot_corrected_prev.html b/docs/reference/plot_corrected_prev.html
index 7bbf57b..ff16117 100644
--- a/docs/reference/plot_corrected_prev.html
+++ b/docs/reference/plot_corrected_prev.html
@@ -1,5 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Plot output for corrected_prevalence — plot_corrected_prev • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plot output for corrected_prevalence — plot_corrected_prev"><meta name="description" content="Plot output for corrected_prevalence"><meta property="og:description" content="Plot output for corrected_prevalence"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Plot output for corrected_prevalence — plot_corrected_prev • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plot output for corrected_prevalence — plot_corrected_prev"><meta name="description" content="Plot output for corrected_prevalence"><meta property="og:description" content="Plot output for corrected_prevalence"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -19,14 +25,14 @@
 <li class="nav-item dropdown">
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="../articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="../articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
diff --git a/docs/reference/plot_gcv.html b/docs/reference/plot_gcv.html
index c536da5..ce364d0 100644
--- a/docs/reference/plot_gcv.html
+++ b/docs/reference/plot_gcv.html
@@ -1,59 +1,50 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Refers to section 7.2."><title>Plotting GCV values with respect to different nn-s and h-s parameters. — plot_gcv • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plotting GCV values with respect to different nn-s and h-s parameters. — plot_gcv"><meta property="og:description" content="Refers to section 7.2."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Plotting GCV values with respect to different nn-s and h-s parameters. — plot_gcv • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plotting GCV values with respect to different nn-s and h-s parameters. — plot_gcv"><meta name="description" content="Refers to section 7.2."><meta property="og:description" content="Refers to section 7.2."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
+    <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
+    <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
+    <li><a class="dropdown-item" href="../articles/model_selection.html">Model selection</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/OUCRU-Modelling/serosv/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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-    
+
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 </nav><div class="container template-reference-topic">
 <div class="row">
@@ -74,42 +65,42 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>age</dt>
+
+
+<dl><dt id="arg-age">age<a class="anchor" aria-label="anchor" href="#arg-age"></a></dt>
 <dd><p>the age vector.</p></dd>
 
 
-<dt>pos</dt>
+<dt id="arg-pos">pos<a class="anchor" aria-label="anchor" href="#arg-pos"></a></dt>
 <dd><p>the pos vector.</p></dd>
 
 
-<dt>tot</dt>
+<dt id="arg-tot">tot<a class="anchor" aria-label="anchor" href="#arg-tot"></a></dt>
 <dd><p>the tot vector.#'</p></dd>
 
 
-<dt>nn_seq</dt>
+<dt id="arg-nn-seq">nn_seq<a class="anchor" aria-label="anchor" href="#arg-nn-seq"></a></dt>
 <dd><p>Nearest neighbor sequence.</p></dd>
 
 
-<dt>h_seq</dt>
+<dt id="arg-h-seq">h_seq<a class="anchor" aria-label="anchor" href="#arg-h-seq"></a></dt>
 <dd><p>Smoothing parameter sequence.</p></dd>
 
 
-<dt>kern</dt>
+<dt id="arg-kern">kern<a class="anchor" aria-label="anchor" href="#arg-kern"></a></dt>
 <dd><p>Weight function, default = "tcub".
 Other choices are "rect", "trwt", "tria", "epan", "bisq" and "gauss".
 Choices may be restricted when derivatives are required;
 e.g. for confidence bands and some bandwidth selectors.</p></dd>
 
 
-<dt>deg</dt>
+<dt id="arg-deg">deg<a class="anchor" aria-label="anchor" href="#arg-deg"></a></dt>
 <dd><p>Degree of polynomial to use. Default: 2.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>plot of gcv value</p>
+    <p>plot of gcv value</p>
     </div>
 
     <div class="section level2">
@@ -124,23 +115,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
     </footer></div>
 
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diff --git a/docs/reference/plot_standard_curve.html b/docs/reference/plot_standard_curve.html
index a29b28f..d5070fe 100644
--- a/docs/reference/plot_standard_curve.html
+++ b/docs/reference/plot_standard_curve.html
@@ -1,5 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Visualize standard curves for each plate — plot_standard_curve • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Visualize standard curves for each plate — plot_standard_curve"><meta name="description" content="Visualize standard curves for each plate"><meta property="og:description" content="Visualize standard curves for each plate"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Visualize standard curves for each plate — plot_standard_curve • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Visualize standard curves for each plate — plot_standard_curve"><meta name="description" content="Visualize standard curves for each plate"><meta property="og:description" content="Visualize standard curves for each plate"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -19,14 +25,14 @@
 <li class="nav-item dropdown">
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="../articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="../articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
@@ -68,23 +74,23 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
-<dd><p>- output of `to_titer()`</p></dd>
+<dd><p>output of `to_titer()`</p></dd>
 
 
 <dt id="arg-facet">facet<a class="anchor" aria-label="anchor" href="#arg-facet"></a></dt>
-<dd><p>- whether to faceted by plates or plot all standard curves on a single plot</p></dd>
+<dd><p>whether to faceted by plates or plot all standard curves on a single plot</p></dd>
 
 
 <dt id="arg-xlab">xlab<a class="anchor" aria-label="anchor" href="#arg-xlab"></a></dt>
-<dd><p>- label of the x axis</p></dd>
+<dd><p>label of the x axis</p></dd>
 
 
 <dt id="arg-ylab">ylab<a class="anchor" aria-label="anchor" href="#arg-ylab"></a></dt>
-<dd><p>- label of the y axis</p></dd>
+<dd><p>label of the y axis</p></dd>
 
 
 <dt id="arg-datapoint-size">datapoint_size<a class="anchor" aria-label="anchor" href="#arg-datapoint-size"></a></dt>
-<dd><p>- size of the data point (only applicable when `facet=TRUE`)</p></dd>
+<dd><p>size of the data point (only applicable when `facet=TRUE`)</p></dd>
 
 </dl></div>
 
diff --git a/docs/reference/plot_standard_curve.md b/docs/reference/plot_standard_curve.md
index 0f5b127..a07310f 100644
--- a/docs/reference/plot_standard_curve.md
+++ b/docs/reference/plot_standard_curve.md
@@ -18,21 +18,21 @@ plot_standard_curve(
 
 - x:
 
-  \- output of \`to_titer()\`
+  output of \`to_titer()\`
 
 - facet:
 
-  \- whether to faceted by plates or plot all standard curves on a
-  single plot
+  whether to faceted by plates or plot all standard curves on a single
+  plot
 
 - xlab:
 
-  \- label of the x axis
+  label of the x axis
 
 - ylab:
 
-  \- label of the y axis
+  label of the y axis
 
 - datapoint_size:
 
-  \- size of the data point (only applicable when \`facet=TRUE\`)
+  size of the data point (only applicable when \`facet=TRUE\`)
diff --git a/docs/reference/plot_titer_qc.html b/docs/reference/plot_titer_qc.html
index e1cef88..5642ade 100644
--- a/docs/reference/plot_titer_qc.html
+++ b/docs/reference/plot_titer_qc.html
@@ -1,5 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Quality control plot — plot_titer_qc • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Quality control plot — plot_titer_qc"><meta name="description" content="Visualize for each sample, whether titer estimates can be computed at the tested dilutions."><meta property="og:description" content="Visualize for each sample, whether titer estimates can be computed at the tested dilutions."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Quality control plot — plot_titer_qc • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Quality control plot — plot_titer_qc"><meta name="description" content="Visualize for each sample, whether titer estimates can be computed at the tested dilutions."><meta property="og:description" content="Visualize for each sample, whether titer estimates can be computed at the tested dilutions."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -19,14 +25,14 @@
 <li class="nav-item dropdown">
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="../articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="../articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
diff --git a/docs/reference/polynomial_model.html b/docs/reference/polynomial_model.html
index fdee498..4b50329 100644
--- a/docs/reference/polynomial_model.html
+++ b/docs/reference/polynomial_model.html
@@ -1,59 +1,50 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Refers to section 6.1.1"><title>Polynomial models — polynomial_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Polynomial models — polynomial_model"><meta property="og:description" content="Refers to section 6.1.1"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Polynomial models — polynomial_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
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 </nav><div class="container template-reference-topic">
 <div class="row">
@@ -64,37 +55,57 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
     </div>
 
     <div class="ref-description section level2">
-    <p>Refers to section 6.1.1</p>
+    <p>Fit age-stratified seroprevalence data to serocatalytic models formulated as polynomials.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">polynomial_model</span><span class="op">(</span><span class="va">data</span>, <span class="va">k</span>, <span class="va">type</span>, link <span class="op">=</span> <span class="st">"log"</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">polynomial_model</span><span class="op">(</span></span>
+<span>  <span class="va">data</span>,</span>
+<span>  <span class="va">k</span>,</span>
+<span>  link <span class="op">=</span> <span class="st">"log"</span>,</span>
+<span>  age_col <span class="op">=</span> <span class="st">"age"</span>,</span>
+<span>  pos_col <span class="op">=</span> <span class="st">"pos"</span>,</span>
+<span>  tot_col <span class="op">=</span> <span class="st">"tot"</span>,</span>
+<span>  status_col <span class="op">=</span> <span class="st">"status"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>data</dt>
-<dd><p>the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)</p></dd>
 
 
-<dt>k</dt>
-<dd><p>degree of the model.</p></dd>
+<dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
+<dd><p>the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR `age`, `status` (for linelisting data)</p></dd>
+
+
+<dt id="arg-k">k<a class="anchor" aria-label="anchor" href="#arg-k"></a></dt>
+<dd><p>degree of the polynomial. (k=1 for Muench model, k=2 for Griffith model, k=3 for Grenfell model).</p></dd>
+
+
+<dt id="arg-link">link<a class="anchor" aria-label="anchor" href="#arg-link"></a></dt>
+<dd><p>link function (default link="log").</p></dd>
+
 
+<dt id="arg-age-col">age_col<a class="anchor" aria-label="anchor" href="#arg-age-col"></a></dt>
+<dd><p>name of the `age` column (default age_col="age").</p></dd>
 
-<dt>type</dt>
-<dd><p>name of method (Muench, Giffith, Grenfell).</p></dd>
 
+<dt id="arg-pos-col">pos_col<a class="anchor" aria-label="anchor" href="#arg-pos-col"></a></dt>
+<dd><p>name of the `pos` column (default pos_col="pos").</p></dd>
 
-<dt>link</dt>
-<dd><p>link function.</p></dd>
+
+<dt id="arg-tot-col">tot_col<a class="anchor" aria-label="anchor" href="#arg-tot-col"></a></dt>
+<dd><p>name of the `tot` column (default tot_col="tot").</p></dd>
+
+
+<dt id="arg-status-col">status_col<a class="anchor" aria-label="anchor" href="#arg-status-col"></a></dt>
+<dd><p>name of the `status` column (default status_col="status").</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a list of class polynomial_model with 5 items</p>
+    <p>a list of class polynomial_model with 5 items</p>
 <dl><dt>datatype</dt>
 <dd><p>type of datatype used for model fitting (aggregated or linelisting)</p></dd>
 
@@ -111,39 +122,75 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <dd><p>force of infection</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>The seroprevalence is assumed to follow the general format
+$$
+\pi(a)  = 1 - e^{-\Sigma_{i=1}^k \beta_i a^i}
+$$
+Which implies the force of infection to be \(\lambda(a) = \Sigma_{i=1}^k \beta_i i a^{i-1}\)</p>
+<p>Where:</p>
+<p>- \(\pi\) is the seroprevalence at age \(a\)</p>
+<p>- \(a\) is the variable age</p>
+<p>- \(k\) is the degree of the polynomial</p>
+<p>The seroprevalence \(\pi(a)\) is fitted using a GLM with log link with
+the linear predictor \(\eta(a) = \Sigma_{i=1}^k \beta_i a^{i}\)</p>
+<p>Muench (1934) model is equivalent to a degree 1 (\(k=1\)) linear predictor</p>
+<p>Griffith model is equivalent to a degree 2 (\(k=2\)) linear predictor</p>
+<p>Grenfell &amp; Anderson (1985) suggested a higher order polynomials (\(k \geq 3\))</p>
+<p>Refer to section 6.1.1. of the the book by Hens et al. (2012) for further details.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+<a href="https://doi.org/10.1007/978-1-4614-4072-7" class="external-link">doi:10.1007/978-1-4614-4072-7</a>
+.</p>
+<p>Grenfell, B. T., and R. M. Anderson. 1985. “The Estimation of
+Age-Related Rates of Infection from Case Notifications and Serological Data.”
+The Journal of Hygiene 95 (2): 419–36. <a href="https://doi.org/10.1017/s0022172400062859" class="external-link">doi:10.1017/s0022172400062859</a>
+.</p>
+<p>Muench, Hugo. 1934. “Derivation of Rates from Summation Data by the Catalytic Curve.”
+Journal of the American Statistical Association 29 (185):
+25–38. <a href="https://doi.org/10.1080/01621459.1934.10502684" class="external-link">doi:10.1080/01621459.1934.10502684</a>
+.</p>
+    </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">parvob19_fi_1997_1998</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/order.html" class="external-link">order</a></span><span class="op">(</span><span class="va">parvob19_fi_1997_1998</span><span class="op">$</span><span class="va">age</span><span class="op">)</span>, <span class="op">]</span></span></span>
-<span class="r-in"><span><span class="va">data</span><span class="op">$</span><span class="va">status</span> <span class="op">&lt;-</span> <span class="va">data</span><span class="op">$</span><span class="va">seropositive</span></span></span>
-<span class="r-in"><span><span class="va">aggregated</span> <span class="op">&lt;-</span> <span class="fu"><a href="transform_data.html">transform_data</a></span><span class="op">(</span><span class="va">data</span><span class="op">$</span><span class="va">age</span>, <span class="va">data</span><span class="op">$</span><span class="va">seropositive</span>, stratum_col <span class="op">=</span> <span class="st">"age"</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">aggregated</span> <span class="op">&lt;-</span> <span class="fu"><a href="transform_data.html">transform_data</a></span><span class="op">(</span><span class="va">data</span>, stratum_col <span class="op">=</span> <span class="st">"age"</span>, status_col<span class="op">=</span><span class="st">"seropositive"</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># fit with aggregated data</span></span></span>
-<span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">polynomial_model</span><span class="op">(</span><span class="va">aggregated</span>, type <span class="op">=</span> <span class="st">"Muench"</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">polynomial_model</span><span class="op">(</span><span class="va">aggregated</span>, k <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="co"># fit with linelisting data</span></span></span>
-<span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">polynomial_model</span><span class="op">(</span><span class="va">data</span>, type <span class="op">=</span> <span class="st">"Muench"</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">polynomial_model</span><span class="op">(</span><span class="va">data</span>,</span></span>
+<span class="r-in"><span>    status_col <span class="op">=</span> <span class="st">"seropositive"</span>,</span></span>
+<span class="r-in"><span>    k <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="polynomial_model-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
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diff --git a/docs/reference/polynomial_model.md b/docs/reference/polynomial_model.md
index 1ad3d45..53ab7aa 100644
--- a/docs/reference/polynomial_model.md
+++ b/docs/reference/polynomial_model.md
@@ -1,32 +1,54 @@
 # Polynomial models
 
-Refers to section 6.1.1
+Fit age-stratified seroprevalence data to serocatalytic models
+formulated as polynomials.
 
 ## Usage
 
 ``` r
-polynomial_model(data, k, type, link = "log")
+polynomial_model(
+  data,
+  k,
+  link = "log",
+  age_col = "age",
+  pos_col = "pos",
+  tot_col = "tot",
+  status_col = "status"
+)
 ```
 
 ## Arguments
 
 - data:
 
-  the input data frame, must either have \`age\`, \`pos\`, \`tot\`
-  columns (for aggregated data) OR \`age\`, \`status\` for (linelisting
+  the input data frame, must either have columns for \`age\`, \`pos\`,
+  \`tot\` (for aggregated data) OR \`age\`, \`status\` (for linelisting
   data)
 
 - k:
 
-  degree of the model.
+  degree of the polynomial. (k=1 for Muench model, k=2 for Griffith
+  model, k=3 for Grenfell model).
 
-- type:
+- link:
 
-  name of method (Muench, Giffith, Grenfell).
+  link function (default link="log").
 
-- link:
+- age_col:
+
+  name of the \`age\` column (default age_col="age").
+
+- pos_col:
+
+  name of the \`pos\` column (default pos_col="pos").
+
+- tot_col:
 
-  link function.
+  name of the \`tot\` column (default tot_col="tot").
+
+- status_col:
+
+  name of the \`status\` column (default status_col="status").
 
 ## Value
 
@@ -52,17 +74,67 @@ a list of class polynomial_model with 5 items
 
   force of infection
 
+## Details
+
+The seroprevalence is assumed to follow the general format \$\$ \pi(a) =
+1 - e^{-\Sigma\_{i=1}^k \beta_i a^i} \$\$ Which implies the force of
+infection to be \\\lambda(a) = \Sigma\_{i=1}^k \beta_i i a^{i-1}\\
+
+Where:
+
+\- \\\pi\\ is the seroprevalence at age \\a\\
+
+\- \\a\\ is the variable age
+
+\- \\k\\ is the degree of the polynomial
+
+The seroprevalence \\\pi(a)\\ is fitted using a GLM with log link with
+the linear predictor \\\eta(a) = \Sigma\_{i=1}^k \beta_i a^{i}\\
+
+Muench (1934) model is equivalent to a degree 1 (\\k=1\\) linear
+predictor
+
+Griffith model is equivalent to a degree 2 (\\k=2\\) linear predictor
+
+Grenfell & Anderson (1985) suggested a higher order polynomials (\\k
+\geq 3\\)
+
+Refer to section 6.1.1. of the the book by Hens et al. (2012) for
+further details.
+
+## References
+
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+[doi:10.1007/978-1-4614-4072-7](https://doi.org/10.1007/978-1-4614-4072-7)
+.
+
+Grenfell, B. T., and R. M. Anderson. 1985. “The Estimation of
+Age-Related Rates of Infection from Case Notifications and Serological
+Data.” The Journal of Hygiene 95 (2): 419–36.
+[doi:10.1017/s0022172400062859](https://doi.org/10.1017/s0022172400062859)
+.
+
+Muench, Hugo. 1934. “Derivation of Rates from Summation Data by the
+Catalytic Curve.” Journal of the American Statistical Association 29
+(185): 25–38.
+[doi:10.1080/01621459.1934.10502684](https://doi.org/10.1080/01621459.1934.10502684)
+.
+
 ## Examples
 
 ``` r
 data <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age), ]
-data$status <- data$seropositive
-aggregated <- transform_data(data$age, data$seropositive, stratum_col = "age")
+aggregated <- transform_data(data, stratum_col = "age", status_col="seropositive")
 
 # fit with aggregated data
-model <- polynomial_model(aggregated, type = "Muench")
+model <- polynomial_model(aggregated, k = 1)
 # fit with linelisting data
-model <- polynomial_model(data, type = "Muench")
+model <- polynomial_model(data,
+    status_col = "seropositive",
+    k = 1)
 plot(model)
 
 ```
diff --git a/docs/reference/predict.age_time_model.html b/docs/reference/predict.age_time_model.html
new file mode 100644
index 0000000..a171244
--- /dev/null
+++ b/docs/reference/predict.age_time_model.html
@@ -0,0 +1,111 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Predict from the age_time_mdoel — predict.age_time_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
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+    <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
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+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Predict from the age_time_mdoel</h1>
+      <small class="dont-index">Source: <a href="https://github.com/OUCRU-Modelling/serosv/blob/HEAD/R/predict.R" class="external-link"><code>R/predict.R</code></a></small>
+      <div class="d-none name"><code>predict.age_time_model.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Predict from the age_time_mdoel</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'age_time_model'</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">newdata</span>, modtype <span class="op">=</span> <span class="st">"monotonized"</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>serosv models</p></dd>
+
+
+<dt id="arg-newdata">newdata<a class="anchor" aria-label="anchor" href="#arg-newdata"></a></dt>
+<dd><p>data.frame with age column to generate prediction</p></dd>
+
+
+<dt id="arg-modtype">modtype<a class="anchor" aria-label="anchor" href="#arg-modtype"></a></dt>
+<dd><p>either "monotonized" (to predict using monotonized model) or "non-monotonized"</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arbitrary argument</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>confidence interval dataframe with n_group x 3 cols, the columns are `group`, `sp_df`, `foi_df`</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
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+
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+
diff --git a/docs/reference/predict.age_time_model.md b/docs/reference/predict.age_time_model.md
new file mode 100644
index 0000000..7412b82
--- /dev/null
+++ b/docs/reference/predict.age_time_model.md
@@ -0,0 +1,34 @@
+# Predict from the age_time_mdoel
+
+Predict from the age_time_mdoel
+
+## Usage
+
+``` r
+# S3 method for class 'age_time_model'
+predict(object, newdata, modtype = "monotonized", ...)
+```
+
+## Arguments
+
+- object:
+
+  serosv models
+
+- newdata:
+
+  data.frame with age column to generate prediction
+
+- modtype:
+
+  either "monotonized" (to predict using monotonized model) or
+  "non-monotonized"
+
+- ...:
+
+  arbitrary argument
+
+## Value
+
+confidence interval dataframe with n_group x 3 cols, the columns are
+\`group\`, \`sp_df\`, \`foi_df\`
diff --git a/docs/reference/predict.farrington_model.html b/docs/reference/predict.farrington_model.html
new file mode 100644
index 0000000..f0edfad
--- /dev/null
+++ b/docs/reference/predict.farrington_model.html
@@ -0,0 +1,107 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction for serosv Farrington model — predict.farrington_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
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+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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+
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+    <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
+    <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
+    <li><a class="dropdown-item" href="../articles/model_selection.html">Model selection</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Prediction for serosv Farrington model</h1>
+      <small class="dont-index">Source: <a href="https://github.com/OUCRU-Modelling/serosv/blob/HEAD/R/predict.R" class="external-link"><code>R/predict.R</code></a></small>
+      <div class="d-none name"><code>predict.farrington_model.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Prediction for serosv Farrington model</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'farrington_model'</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">object</span>, newdata <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>serosv models</p></dd>
+
+
+<dt id="arg-newdata">newdata<a class="anchor" aria-label="anchor" href="#arg-newdata"></a></dt>
+<dd><p>data.frame with age column to generate prediction</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arbitrary argument</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>prediction output</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/predict.farrington_model.md b/docs/reference/predict.farrington_model.md
new file mode 100644
index 0000000..3fa0077
--- /dev/null
+++ b/docs/reference/predict.farrington_model.md
@@ -0,0 +1,28 @@
+# Prediction for serosv Farrington model
+
+Prediction for serosv Farrington model
+
+## Usage
+
+``` r
+# S3 method for class 'farrington_model'
+predict(object, newdata = NULL, ...)
+```
+
+## Arguments
+
+- object:
+
+  serosv models
+
+- newdata:
+
+  data.frame with age column to generate prediction
+
+- ...:
+
+  arbitrary argument
+
+## Value
+
+prediction output
diff --git a/docs/reference/predict.fp_model.html b/docs/reference/predict.fp_model.html
new file mode 100644
index 0000000..c46946e
--- /dev/null
+++ b/docs/reference/predict.fp_model.html
@@ -0,0 +1,111 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction for serosv fractional polynomial model — predict.fp_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
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+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction for serosv fractional polynomial model — predict.fp_model"><meta name="description" content="Prediction for serosv fractional polynomial model"><meta property="og:description" content="Prediction for serosv fractional polynomial model"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">serosv</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
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+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
+    <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
+    <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
+    <li><a class="dropdown-item" href="../articles/model_selection.html">Model selection</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/OUCRU-Modelling/serosv/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Prediction for serosv fractional polynomial model</h1>
+      <small class="dont-index">Source: <a href="https://github.com/OUCRU-Modelling/serosv/blob/HEAD/R/predict.R" class="external-link"><code>R/predict.R</code></a></small>
+      <div class="d-none name"><code>predict.fp_model.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Prediction for serosv fractional polynomial model</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'fp_model'</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">object</span>, newdata <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>serosv models</p></dd>
+
+
+<dt id="arg-newdata">newdata<a class="anchor" aria-label="anchor" href="#arg-newdata"></a></dt>
+<dd><p>data.frame with age column to generate prediction</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arbitrary argument</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>prediction output</p>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>[stats::predict.glm()] for more information on the predict function</p></div>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/predict.fp_model.md b/docs/reference/predict.fp_model.md
new file mode 100644
index 0000000..4678552
--- /dev/null
+++ b/docs/reference/predict.fp_model.md
@@ -0,0 +1,32 @@
+# Prediction for serosv fractional polynomial model
+
+Prediction for serosv fractional polynomial model
+
+## Usage
+
+``` r
+# S3 method for class 'fp_model'
+predict(object, newdata = NULL, ...)
+```
+
+## Arguments
+
+- object:
+
+  serosv models
+
+- newdata:
+
+  data.frame with age column to generate prediction
+
+- ...:
+
+  arbitrary argument
+
+## Value
+
+prediction output
+
+## See also
+
+\[stats::predict.glm()\] for more information on the predict function
diff --git a/docs/reference/predict.hierarchical_bayesian_model.html b/docs/reference/predict.hierarchical_bayesian_model.html
new file mode 100644
index 0000000..62f8c02
--- /dev/null
+++ b/docs/reference/predict.hierarchical_bayesian_model.html
@@ -0,0 +1,107 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Predict from an hierarchical bayesian model — predict.hierarchical_bayesian_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
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+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
+    <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
+    <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
+    <li><a class="dropdown-item" href="../articles/model_selection.html">Model selection</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Predict from an hierarchical bayesian model</h1>
+      <small class="dont-index">Source: <a href="https://github.com/OUCRU-Modelling/serosv/blob/HEAD/R/predict.R" class="external-link"><code>R/predict.R</code></a></small>
+      <div class="d-none name"><code>predict.hierarchical_bayesian_model.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Predict from an hierarchical bayesian model</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'hierarchical_bayesian_model'</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">object</span>, newdata <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>serosv models</p></dd>
+
+
+<dt id="arg-newdata">newdata<a class="anchor" aria-label="anchor" href="#arg-newdata"></a></dt>
+<dd><p>data.frame with age column to generate prediction</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arbitrary arguments</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>list of confidence interval for seroprevalence and foi. Each confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/predict.hierarchical_bayesian_model.md b/docs/reference/predict.hierarchical_bayesian_model.md
new file mode 100644
index 0000000..8b6cf0a
--- /dev/null
+++ b/docs/reference/predict.hierarchical_bayesian_model.md
@@ -0,0 +1,30 @@
+# Predict from an hierarchical bayesian model
+
+Predict from an hierarchical bayesian model
+
+## Usage
+
+``` r
+# S3 method for class 'hierarchical_bayesian_model'
+predict(object, newdata = NULL, ...)
+```
+
+## Arguments
+
+- object:
+
+  serosv models
+
+- newdata:
+
+  data.frame with age column to generate prediction
+
+- ...:
+
+  arbitrary arguments
+
+## Value
+
+list of confidence interval for seroprevalence and foi. Each confidence
+interval dataframe with 4 variables, x and y for the fitted values and
+ymin and ymax for the confidence interval
diff --git a/docs/reference/predict.lp_model.html b/docs/reference/predict.lp_model.html
new file mode 100644
index 0000000..246bd58
--- /dev/null
+++ b/docs/reference/predict.lp_model.html
@@ -0,0 +1,107 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction for serosv local polynomial model — predict.lp_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
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+    chtml: {
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+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction for serosv local polynomial model — predict.lp_model"><meta name="description" content="Prediction for serosv local polynomial model"><meta property="og:description" content="Prediction for serosv local polynomial model"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">serosv</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
+    <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
+    <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
+    <li><a class="dropdown-item" href="../articles/model_selection.html">Model selection</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/OUCRU-Modelling/serosv/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Prediction for serosv local polynomial model</h1>
+      <small class="dont-index">Source: <a href="https://github.com/OUCRU-Modelling/serosv/blob/HEAD/R/predict.R" class="external-link"><code>R/predict.R</code></a></small>
+      <div class="d-none name"><code>predict.lp_model.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Prediction for serosv local polynomial model</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'lp_model'</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">object</span>, newdata <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>serosv models</p></dd>
+
+
+<dt id="arg-newdata">newdata<a class="anchor" aria-label="anchor" href="#arg-newdata"></a></dt>
+<dd><p>data.frame with age column to generate prediction</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arbitrary argument</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>prediction output</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/predict.lp_model.md b/docs/reference/predict.lp_model.md
new file mode 100644
index 0000000..197cc4c
--- /dev/null
+++ b/docs/reference/predict.lp_model.md
@@ -0,0 +1,28 @@
+# Prediction for serosv local polynomial model
+
+Prediction for serosv local polynomial model
+
+## Usage
+
+``` r
+# S3 method for class 'lp_model'
+predict(object, newdata = NULL, ...)
+```
+
+## Arguments
+
+- object:
+
+  serosv models
+
+- newdata:
+
+  data.frame with age column to generate prediction
+
+- ...:
+
+  arbitrary argument
+
+## Value
+
+prediction output
diff --git a/docs/reference/predict.penalized_spline_model.html b/docs/reference/predict.penalized_spline_model.html
new file mode 100644
index 0000000..58fc424
--- /dev/null
+++ b/docs/reference/predict.penalized_spline_model.html
@@ -0,0 +1,111 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction for serosv penalized spline model — predict.penalized_spline_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
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+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
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+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction for serosv penalized spline model — predict.penalized_spline_model"><meta name="description" content="Prediction for serosv penalized spline model"><meta property="og:description" content="Prediction for serosv penalized spline model"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
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+    <a class="navbar-brand me-2" href="../index.html">serosv</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
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+
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+<li class="nav-item dropdown">
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
+    <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
+    <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
+    <li><a class="dropdown-item" href="../articles/model_selection.html">Model selection</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Prediction for serosv penalized spline model</h1>
+      <small class="dont-index">Source: <a href="https://github.com/OUCRU-Modelling/serosv/blob/HEAD/R/predict.R" class="external-link"><code>R/predict.R</code></a></small>
+      <div class="d-none name"><code>predict.penalized_spline_model.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Prediction for serosv penalized spline model</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'penalized_spline_model'</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">object</span>, newdata <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>serosv models</p></dd>
+
+
+<dt id="arg-newdata">newdata<a class="anchor" aria-label="anchor" href="#arg-newdata"></a></dt>
+<dd><p>data.frame with age column to generate prediction</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arbitrary argument</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>prediction output</p>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>[mgcv::predict.gam()] for more information on the predict function</p></div>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
+</div>
+
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+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/predict.penalized_spline_model.md b/docs/reference/predict.penalized_spline_model.md
new file mode 100644
index 0000000..c3f7591
--- /dev/null
+++ b/docs/reference/predict.penalized_spline_model.md
@@ -0,0 +1,32 @@
+# Prediction for serosv penalized spline model
+
+Prediction for serosv penalized spline model
+
+## Usage
+
+``` r
+# S3 method for class 'penalized_spline_model'
+predict(object, newdata = NULL, ...)
+```
+
+## Arguments
+
+- object:
+
+  serosv models
+
+- newdata:
+
+  data.frame with age column to generate prediction
+
+- ...:
+
+  arbitrary argument
+
+## Value
+
+prediction output
+
+## See also
+
+\[mgcv::predict.gam()\] for more information on the predict function
diff --git a/docs/reference/predict.polynomial_model.html b/docs/reference/predict.polynomial_model.html
new file mode 100644
index 0000000..b5c0b56
--- /dev/null
+++ b/docs/reference/predict.polynomial_model.html
@@ -0,0 +1,111 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction for serosv polynomial model — predict.polynomial_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
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+    chtml: {
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+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Prediction for serosv polynomial model — predict.polynomial_model"><meta name="description" content="A wrapper of predict.glm for direct prediction from polynomial_model object"><meta property="og:description" content="A wrapper of predict.glm for direct prediction from polynomial_model object"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">serosv</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
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+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+    <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
+    <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
+    <li><a class="dropdown-item" href="../articles/model_selection.html">Model selection</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Prediction for serosv polynomial model</h1>
+      <small class="dont-index">Source: <a href="https://github.com/OUCRU-Modelling/serosv/blob/HEAD/R/predict.R" class="external-link"><code>R/predict.R</code></a></small>
+      <div class="d-none name"><code>predict.polynomial_model.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>A wrapper of predict.glm for direct prediction from polynomial_model object</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'polynomial_model'</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">object</span>, newdata <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>serosv models</p></dd>
+
+
+<dt id="arg-newdata">newdata<a class="anchor" aria-label="anchor" href="#arg-newdata"></a></dt>
+<dd><p>data.frame with age column to generate prediction</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arbitrary argument</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>prediction output</p>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>[stats::predict.glm()] for more information on the predict function</p></div>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/predict.polynomial_model.md b/docs/reference/predict.polynomial_model.md
new file mode 100644
index 0000000..4ed9642
--- /dev/null
+++ b/docs/reference/predict.polynomial_model.md
@@ -0,0 +1,33 @@
+# Prediction for serosv polynomial model
+
+A wrapper of predict.glm for direct prediction from polynomial_model
+object
+
+## Usage
+
+``` r
+# S3 method for class 'polynomial_model'
+predict(object, newdata = NULL, ...)
+```
+
+## Arguments
+
+- object:
+
+  serosv models
+
+- newdata:
+
+  data.frame with age column to generate prediction
+
+- ...:
+
+  arbitrary argument
+
+## Value
+
+prediction output
+
+## See also
+
+\[stats::predict.glm()\] for more information on the predict function
diff --git a/docs/reference/predict.weibull_model.html b/docs/reference/predict.weibull_model.html
new file mode 100644
index 0000000..69c13a2
--- /dev/null
+++ b/docs/reference/predict.weibull_model.html
@@ -0,0 +1,111 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prediction for serosv Weibull model — predict.weibull_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
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+    chtml: {
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+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
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+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
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+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
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+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
+    <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
+    <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
+    <li><a class="dropdown-item" href="../articles/model_selection.html">Model selection</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/OUCRU-Modelling/serosv/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Prediction for serosv Weibull model</h1>
+      <small class="dont-index">Source: <a href="https://github.com/OUCRU-Modelling/serosv/blob/HEAD/R/predict.R" class="external-link"><code>R/predict.R</code></a></small>
+      <div class="d-none name"><code>predict.weibull_model.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Prediction for serosv Weibull model</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'weibull_model'</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">object</span>, newdata <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>serosv models</p></dd>
+
+
+<dt id="arg-newdata">newdata<a class="anchor" aria-label="anchor" href="#arg-newdata"></a></dt>
+<dd><p>data.frame with age column to generate prediction</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>arbitrary argument</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>prediction output</p>
+    </div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>[stats::predict.glm()] for more information on the predict function</p></div>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
+</div>
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+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/predict.weibull_model.md b/docs/reference/predict.weibull_model.md
new file mode 100644
index 0000000..12ce41e
--- /dev/null
+++ b/docs/reference/predict.weibull_model.md
@@ -0,0 +1,32 @@
+# Prediction for serosv Weibull model
+
+Prediction for serosv Weibull model
+
+## Usage
+
+``` r
+# S3 method for class 'weibull_model'
+predict(object, newdata = NULL, ...)
+```
+
+## Arguments
+
+- object:
+
+  serosv models
+
+- newdata:
+
+  data.frame with age column to generate prediction
+
+- ...:
+
+  arbitrary argument
+
+## Value
+
+prediction output
+
+## See also
+
+\[stats::predict.glm()\] for more information on the predict function
diff --git a/docs/reference/rubella_mumps_uk.html b/docs/reference/rubella_mumps_uk.html
index b2e9fec..341f379 100644
--- a/docs/reference/rubella_mumps_uk.html
+++ b/docs/reference/rubella_mumps_uk.html
@@ -1,59 +1,50 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Rubella - Mumps data from the UK (aggregated)"><title>Rubella - Mumps data from the UK (aggregated) — rubella_mumps_uk • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Rubella - Mumps data from the UK (aggregated) — rubella_mumps_uk"><meta property="og:description" content="Rubella - Mumps data from the UK (aggregated)"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+
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@@ -98,23 +89,23 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 1988; 297 :770 <a href="https://doi.org/10.1136/bmj.297.6651.770" class="external-link">doi:10.1136/bmj.297.6651.770</a></p>
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 </div>
 
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+of antibodies to both mumps and rubella viruses."><meta property="og:description" content="Prevalence of rubella in the UK, obtained from a large survey of prevalence
+of antibodies to both mumps and rubella viruses."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
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 <div class="row">
@@ -78,7 +69,7 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <div class="section level2">
     <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>A data frame with 3 variables:</p><dl><dt>age</dt>
-<dd><p>Age group</p></dd>
+<dd><p>Midpoint of the age group (e.g. 1.5 = 1-2 years old, 2.5 = 2-3 years old)</p></dd>
 
  <dt>pos</dt>
 <dd><p>Number of seropositive individuals</p></dd>
@@ -97,35 +88,33 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Reproduce Fig 4.4 (middle panel), p. 67</span></span></span>
-<span class="r-in"><span><span class="va">age</span> <span class="op">&lt;-</span> <span class="va">rubella_uk_1986_1987</span><span class="op">$</span><span class="va">age</span></span></span>
-<span class="r-in"><span><span class="va">pos</span> <span class="op">&lt;-</span> <span class="va">rubella_uk_1986_1987</span><span class="op">$</span><span class="va">pos</span></span></span>
-<span class="r-in"><span><span class="va">tot</span> <span class="op">&lt;-</span> <span class="va">rubella_uk_1986_1987</span><span class="op">$</span><span class="va">tot</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">age</span>, <span class="va">pos</span> <span class="op">/</span> <span class="va">tot</span>,</span></span>
-<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.008</span> <span class="op">*</span> <span class="va">tot</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
-<span class="r-in"><span>  xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">45</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/with.html" class="external-link">with</a></span><span class="op">(</span><span class="va">rubella_uk_1986_1987</span>,</span></span>
+<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">age</span>, <span class="va">pos</span> <span class="op">/</span> <span class="va">tot</span>,</span></span>
+<span class="r-in"><span>    cex <span class="op">=</span> <span class="fl">0.2</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">tot</span><span class="op">)</span>, pch <span class="op">=</span> <span class="fl">16</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
+<span class="r-in"><span>    xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">45</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span>  <span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="rubella_uk_1986_1987-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
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diff --git a/docs/reference/rubella_uk_1986_1987.md b/docs/reference/rubella_uk_1986_1987.md
index d4bc6cf..c9fa5de 100644
--- a/docs/reference/rubella_uk_1986_1987.md
+++ b/docs/reference/rubella_uk_1986_1987.md
@@ -15,7 +15,8 @@ A data frame with 3 variables:
 
 - age:
 
-  Age group
+  Midpoint of the age group (e.g. 1.5 = 1-2 years old, 2.5 = 2-3 years
+  old)
 
 - pos:
 
@@ -35,13 +36,11 @@ antibody to measles, mumps, and rubella by age. British Medical Journal
 ## Examples
 
 ``` r
-# Reproduce Fig 4.4 (middle panel), p. 67
-age <- rubella_uk_1986_1987$age
-pos <- rubella_uk_1986_1987$pos
-tot <- rubella_uk_1986_1987$tot
-plot(age, pos / tot,
-  cex = 0.008 * tot, pch = 16, xlab = "age", ylab = "seroprevalence",
-  xlim = c(0, 45), ylim = c(0, 1)
+with(rubella_uk_1986_1987,
+  plot(age, pos / tot,
+    cex = 0.2 * sqrt(tot), pch = 16, xlab = "age", ylab = "seroprevalence",
+    xlim = c(0, 45), ylim = c(0, 1)
+  )
 )
 
 ```
diff --git a/docs/reference/serosv-package.html b/docs/reference/serosv-package.html
index b551d21..3f8399c 100644
--- a/docs/reference/serosv-package.html
+++ b/docs/reference/serosv-package.html
@@ -1,61 +1,52 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='An easy-to-use and efficient tool to estimate infectious diseases parameters using serological data. Implemented models include SIR models (basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()), parametric models (polynomial_model(), fp_model()), nonparametric models (lp_model()), semiparametric models (penalized_splines_model()), hierarchical models (hierarchical_bayesian_model()). The package is based on the book "Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective" (Hens, Niel &amp;amp; Shkedy, Ziv &amp;amp; Aerts, Marc &amp;amp; Faes, Christel &amp;amp; Damme, Pierre &amp;amp; Beutels, Philippe., 2013) doi:10.1007/978-1-4614-4072-7
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diff --git a/docs/reference/set_plot_style.html b/docs/reference/set_plot_style.html
index 3221b67..1dfe241 100644
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+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
@@ -81,54 +72,54 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>sero</dt>
+
+
+<dl><dt id="arg-sero">sero<a class="anchor" aria-label="anchor" href="#arg-sero"></a></dt>
 <dd><p>- color for seroprevalence line</p></dd>
 
 
-<dt>ci</dt>
+<dt id="arg-ci">ci<a class="anchor" aria-label="anchor" href="#arg-ci"></a></dt>
 <dd><p>- color for confidence interval</p></dd>
 
 
-<dt>foi</dt>
+<dt id="arg-foi">foi<a class="anchor" aria-label="anchor" href="#arg-foi"></a></dt>
 <dd><p>- color for force of infection line</p></dd>
 
 
-<dt>sero_line</dt>
+<dt id="arg-sero-line">sero_line<a class="anchor" aria-label="anchor" href="#arg-sero-line"></a></dt>
 <dd><p>- linetype for seroprevalence line</p></dd>
 
 
-<dt>foi_line</dt>
+<dt id="arg-foi-line">foi_line<a class="anchor" aria-label="anchor" href="#arg-foi-line"></a></dt>
 <dd><p>- linetype for force of infection line</p></dd>
 
 
-<dt>xlabel</dt>
+<dt id="arg-xlabel">xlabel<a class="anchor" aria-label="anchor" href="#arg-xlabel"></a></dt>
 <dd><p>- x label</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>list of updated aesthetic values</p>
+    <p>list of updated aesthetic values</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
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diff --git a/docs/reference/sir_basic_model.html b/docs/reference/sir_basic_model.html
index 4e7a4ae..23fd399 100644
--- a/docs/reference/sir_basic_model.html
+++ b/docs/reference/sir_basic_model.html
@@ -1,59 +1,51 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Refers to section 3.1.3."><title>Basic SIR model — sir_basic_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Basic SIR model — sir_basic_model"><meta property="og:description" content="Refers to section 3.1.3."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Basic SIR model — sir_basic_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
+    <li><a class="dropdown-item" href="../articles/sir_model.html">SIR model</a></li>
+    <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
+    <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
+    <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
+    <li><a class="dropdown-item" href="../articles/model_selection.html">Model selection</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/OUCRU-Modelling/serosv/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
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@@ -64,7 +56,7 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
     </div>
 
     <div class="ref-description section level2">
-    <p>Refers to section 3.1.3.</p>
+    <p>Simulate a transmission model with 3 compartments: susceptible (S), infected (I), recovered (R)</p>
     </div>
 
     <div class="section level2">
@@ -74,23 +66,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>times</dt>
+
+
+<dl><dt id="arg-times">times<a class="anchor" aria-label="anchor" href="#arg-times"></a></dt>
 <dd><p>time sequence.</p></dd>
 
 
-<dt>state</dt>
-<dd><p>the initial state of the model.</p></dd>
+<dt id="arg-state">state<a class="anchor" aria-label="anchor" href="#arg-state"></a></dt>
+<dd><p>the initial state of the model. A named vector with the following</p><ul><li><p><code>S</code>: initial susceptible population</p></li>
+<li><p><code>I</code>: initial infected population</p></li>
+<li><p><code>R</code>: initial recovered population</p></li>
+</ul></dd>
 
 
-<dt>parameters</dt>
-<dd><p>the parameters of the model.</p></dd>
+<dt id="arg-parameters">parameters<a class="anchor" aria-label="anchor" href="#arg-parameters"></a></dt>
+<dd><p>the parameters of the model. A named vector with the following</p><ul><li><p><code>alpha</code>: disease-related death rate</p></li>
+<li><p><code>mu</code>: natural death rate (= 1/life expectancy)</p></li>
+<li><p><code>beta</code>: transmission rate</p></li>
+<li><p><code>nu</code>: recovery rate</p></li>
+<li><p><code>p</code>: proportion vaccinated at birth</p></li>
+</ul></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>list of class sir_basic_model with the following items</p>
+    <p>list of class sir_basic_model with the following items</p>
 <dl><dt>parameters</dt>
 <dd><p>list of parameters used for fitting the model</p></dd>
 
@@ -100,16 +100,35 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </dl></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>In <code>state</code>:</p>
-<p>- <code>S</code>: number of susceptible</p>
-<p>- <code>I</code>: number of infected</p>
-<p>- <code>R</code>: number of recovered</p>
-<p>In <code>parameters</code>:</p>
-<p>- <code>alpha</code>: disease-related death rate</p>
-<p>- <code>mu</code>: natural death rate (= 1/life expectancy)</p>
-<p>- <code>beta</code>: transmission rate</p>
-<p>- <code>nu</code>: recovery rate</p>
-<p>- <code>p</code>: percent of population vaccinated at birth</p>
+    <p>Follow the SIR model described in the book by Hens et al. (section 3.1.3.)</p>
+<p>Assumptions:</p>
+<p>- Individuals are born into susceptible group (exposure time is age of the individual) then transfer to infected class and recovered class</p>
+<p>- Recovered individuals gained lifelong immunity</p>
+<p>- Age homogeneity</p>
+<p>The model is described by a system of 3 differential equations</p>
+<p>$$
+\begin{cases}
+\frac{dS(t)}{dt} = B(t) (1-p) - \lambda(t)S(t) - \mu S(t) \\
+\frac{dI(t)}{dt} = \lambda(t)S(t) - \nu I(t) - \mu I(t) - \alpha I(t)  \\
+\frac{dR(t)}{dt} =  B(t) p + \nu I(t) - \mu R(t)
+\end{cases}
+$$</p>
+<p>Where:</p>
+<p>-   \(B(t) = \mu N(t)\)</p>
+<p>-   \(\lambda(t) = \beta I(t)\) with \(\beta\) is the transmission rate</p>
+<p>-   \(\mu\) is the natural death rate</p>
+<p>-   \(\nu\) is the recovery rate</p>
+<p>-   \(\alpha\) is the disease related death rate</p>
+<p>-   \(p\) is the proportion of newborn vaccinated and moved directly to the recovered compartment</p>
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+<a href="https://doi.org/10.1007/978-1-4614-4072-7" class="external-link">doi:10.1007/978-1-4614-4072-7</a>
+.</p>
     </div>
 
     <div class="section level2">
@@ -2638,23 +2657,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/sir_basic_model.md b/docs/reference/sir_basic_model.md
index 9feb9d0..fcce84e 100644
--- a/docs/reference/sir_basic_model.md
+++ b/docs/reference/sir_basic_model.md
@@ -1,6 +1,7 @@
 # Basic SIR model
 
-Refers to section 3.1.3.
+Simulate a transmission model with 3 compartments: susceptible (S),
+infected (I), recovered (R)
 
 ## Usage
 
@@ -16,11 +17,27 @@ sir_basic_model(times, state, parameters)
 
 - state:
 
-  the initial state of the model.
+  the initial state of the model. A named vector with the following
+
+  - `S`: initial susceptible population
+
+  - `I`: initial infected population
+
+  - `R`: initial recovered population
 
 - parameters:
 
-  the parameters of the model.
+  the parameters of the model. A named vector with the following
+
+  - `alpha`: disease-related death rate
+
+  - `mu`: natural death rate (= 1/life expectancy)
+
+  - `beta`: transmission rate
+
+  - `nu`: recovery rate
+
+  - `p`: proportion vaccinated at birth
 
 ## Value
 
@@ -36,25 +53,47 @@ list of class sir_basic_model with the following items
 
 ## Details
 
-In `state`:
+Follow the SIR model described in the book by Hens et al. (section
+3.1.3.)
+
+Assumptions:
+
+\- Individuals are born into susceptible group (exposure time is age of
+the individual) then transfer to infected class and recovered class
+
+\- Recovered individuals gained lifelong immunity
+
+\- Age homogeneity
+
+The model is described by a system of 3 differential equations
+
+\$\$ \begin{cases} \frac{dS(t)}{dt} = B(t) (1-p) - \lambda(t)S(t) - \mu
+S(t) \\ \frac{dI(t)}{dt} = \lambda(t)S(t) - \nu I(t) - \mu I(t) - \alpha
+I(t) \\ \frac{dR(t)}{dt} = B(t) p + \nu I(t) - \mu R(t) \end{cases} \$\$
 
-\- `S`: number of susceptible
+Where:
 
-\- `I`: number of infected
+\- \\B(t) = \mu N(t)\\
 
-\- `R`: number of recovered
+\- \\\lambda(t) = \beta I(t)\\ with \\\beta\\ is the transmission rate
 
-In `parameters`:
+\- \\\mu\\ is the natural death rate
 
-\- `alpha`: disease-related death rate
+\- \\\nu\\ is the recovery rate
 
-\- `mu`: natural death rate (= 1/life expectancy)
+\- \\\alpha\\ is the disease related death rate
 
-\- `beta`: transmission rate
+\- \\p\\ is the proportion of newborn vaccinated and moved directly to
+the recovered compartment
 
-\- `nu`: recovery rate
+## References
 
-\- `p`: percent of population vaccinated at birth
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+[doi:10.1007/978-1-4614-4072-7](https://doi.org/10.1007/978-1-4614-4072-7)
+.
 
 ## Examples
 
diff --git a/docs/reference/sir_static_model.html b/docs/reference/sir_static_model.html
index bc03fc3..659cc14 100644
--- a/docs/reference/sir_static_model.html
+++ b/docs/reference/sir_static_model.html
@@ -1,59 +1,51 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Refers to section 3.2.2."><title>SIR static model (age-heterogeneous, endemic equilibrium) — sir_static_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="SIR static model (age-heterogeneous, endemic equilibrium) — sir_static_model"><meta property="og:description" content="Refers to section 3.2.2."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -64,7 +56,7 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
     </div>
 
     <div class="ref-description section level2">
-    <p>Refers to section 3.2.2.</p>
+    <p>Simulate transmission model with constant force of infection and consists of 3 compartments: susceptible (S), infected (I), recovered (R)</p>
     </div>
 
     <div class="section level2">
@@ -74,23 +66,28 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>a</dt>
+
+
+<dl><dt id="arg-a">a<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>age sequence.</p></dd>
 
 
-<dt>state</dt>
-<dd><p>the initial state of the system.</p></dd>
+<dt id="arg-state">state<a class="anchor" aria-label="anchor" href="#arg-state"></a></dt>
+<dd><p>the initial state of the system. A named vector with the following</p><ul><li><p><code>s</code>: initial susceptible proportion</p></li>
+<li><p><code>i</code>: initial infected proportion</p></li>
+<li><p><code>r</code>: initial recovered proportion</p></li>
+</ul></dd>
 
 
-<dt>parameters</dt>
-<dd><p>the model's parameter.</p></dd>
+<dt id="arg-parameters">parameters<a class="anchor" aria-label="anchor" href="#arg-parameters"></a></dt>
+<dd><p>the model's parameter. A named vector with the following</p><ul><li><p><code>lambda</code>: natural death rate</p></li>
+<li><p><code>nu</code>: recovery rate</p></li>
+</ul></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>list of class sir_static_model with the following items</p>
+    <p>list of class sir_static_model with the following items</p>
 <dl><dt>parameters</dt>
 <dd><p>list of parameters used for fitting the model</p></dd>
 
@@ -100,13 +97,32 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </dl></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>In <code>state</code>:</p>
-<p>- <code>s</code>: proportion susceptible</p>
-<p>- <code>i</code>: proportion infected</p>
-<p>- <code>r</code>: proportion recovered</p>
-<p>In <code>parameters</code>:</p>
-<p>- <code>lambda</code>: natural death rate</p>
-<p>- <code>nu</code>: recovery rate</p>
+    <p>Follow the SIR model at endemic state described in the book by Hens et al. (section 3.2.2.)</p>
+<p>Assumptions:</p>
+<p>- Time homogeneity</p>
+<p>- Age heterogeneity</p>
+<p>- Constant force of infection</p>
+<p>The model is described by a system of 3 differential equations</p>
+<p>$$
+\begin{cases}
+\frac{ds(a)}{da} = -\lambda s(a) \\
+\frac{di(a)}{da} = \lambda s(a) - \nu i(a)  \\
+\frac{dr(a)}{da} =  \nu i(a)
+\end{cases}
+$$</p>
+<p>Where:</p>
+<p>-   \(s(a), i(a), r(a)\) are proportion of susceptible, infected, recovered population of age group \(a\) respectively</p>
+<p>-   \(\lambda\) is the force of infection</p>
+<p>-   \(\nu\) is the recovery rate</p>
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+<a href="https://doi.org/10.1007/978-1-4614-4072-7" class="external-link">doi:10.1007/978-1-4614-4072-7</a>
+.</p>
     </div>
 
     <div class="section level2">
@@ -9132,23 +9148,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
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+
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diff --git a/docs/reference/sir_static_model.md b/docs/reference/sir_static_model.md
index ad3f61c..322362b 100644
--- a/docs/reference/sir_static_model.md
+++ b/docs/reference/sir_static_model.md
@@ -1,6 +1,7 @@
 # SIR static model (age-heterogeneous, endemic equilibrium)
 
-Refers to section 3.2.2.
+Simulate transmission model with constant force of infection and
+consists of 3 compartments: susceptible (S), infected (I), recovered (R)
 
 ## Usage
 
@@ -16,11 +17,21 @@ sir_static_model(a, state, parameters)
 
 - state:
 
-  the initial state of the system.
+  the initial state of the system. A named vector with the following
+
+  - `s`: initial susceptible proportion
+
+  - `i`: initial infected proportion
+
+  - `r`: initial recovered proportion
 
 - parameters:
 
-  the model's parameter.
+  the model's parameter. A named vector with the following
+
+  - `lambda`: natural death rate
+
+  - `nu`: recovery rate
 
 ## Value
 
@@ -36,19 +47,40 @@ list of class sir_static_model with the following items
 
 ## Details
 
-In `state`:
+Follow the SIR model at endemic state described in the book by Hens et
+al. (section 3.2.2.)
+
+Assumptions:
+
+\- Time homogeneity
+
+\- Age heterogeneity
+
+\- Constant force of infection
+
+The model is described by a system of 3 differential equations
+
+\$\$ \begin{cases} \frac{ds(a)}{da} = -\lambda s(a) \\ \frac{di(a)}{da}
+= \lambda s(a) - \nu i(a) \\ \frac{dr(a)}{da} = \nu i(a) \end{cases}
+\$\$
 
-\- `s`: proportion susceptible
+Where:
 
-\- `i`: proportion infected
+\- \\s(a), i(a), r(a)\\ are proportion of susceptible, infected,
+recovered population of age group \\a\\ respectively
 
-\- `r`: proportion recovered
+\- \\\lambda\\ is the force of infection
 
-In `parameters`:
+\- \\\nu\\ is the recovery rate
 
-\- `lambda`: natural death rate
+## References
 
-\- `nu`: recovery rate
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+[doi:10.1007/978-1-4614-4072-7](https://doi.org/10.1007/978-1-4614-4072-7)
+.
 
 ## Examples
 
diff --git a/docs/reference/sir_subpops_model.html b/docs/reference/sir_subpops_model.html
index c455431..e29adc6 100644
--- a/docs/reference/sir_subpops_model.html
+++ b/docs/reference/sir_subpops_model.html
@@ -1,59 +1,51 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Refers to section 3.5.1."><title>SIR Model with Interacting Subpopulations — sir_subpops_model • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="SIR Model with Interacting Subpopulations — sir_subpops_model"><meta property="og:description" content="Refers to section 3.5.1."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <div class="row">
@@ -64,7 +56,7 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
     </div>
 
     <div class="ref-description section level2">
-    <p>Refers to section 3.5.1.</p>
+    <p>An extension of the basic SIR model that incorporates interaction between sub-populations</p>
     </div>
 
     <div class="section level2">
@@ -74,23 +66,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>times</dt>
+
+
+<dl><dt id="arg-times">times<a class="anchor" aria-label="anchor" href="#arg-times"></a></dt>
 <dd><p>time sequence.</p></dd>
 
 
-<dt>state</dt>
-<dd><p>the initial state of the model.</p></dd>
+<dt id="arg-state">state<a class="anchor" aria-label="anchor" href="#arg-state"></a></dt>
+<dd><p>the initial state of the model. A named vector with the following</p><ul><li><p><code>s</code>: initial susceptible proportion</p></li>
+<li><p><code>i</code>: initial infected proportion</p></li>
+<li><p><code>r</code>: initial recovered proportion</p></li>
+</ul></dd>
 
 
-<dt>parameters</dt>
-<dd><p>the parameters of the model.</p></dd>
+<dt id="arg-parameters">parameters<a class="anchor" aria-label="anchor" href="#arg-parameters"></a></dt>
+<dd><p>the parameters of the model. A named vector with the following</p><ul><li><p><code>mu</code>: natural death rate (1/L).</p></li>
+<li><p><code>beta</code>: the WAIFW matrix, with dimensions <code>[K, K]</code>.</p></li>
+<li><p><code>nu</code>: recovery rate</p></li>
+</ul></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>list of class sir_subpops_model with the following items</p>
+    <p>list of class sir_subpops_model with the following items</p>
+<p></p>
 <dl><dt>parameters</dt>
 <dd><p>list of parameters used for fitting the model</p></dd>
 
@@ -100,35 +99,52 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </dl></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>In <code>state</code>:</p>
-<p>- <code>s</code>: Percent susceptible</p>
-<p>- <code>i</code>: Percent infected</p>
-<p>- <code>r</code>: Percent recovered</p>
-<p>In <code>parameters</code>:</p>
-<p>- <code>mu</code>: natural death rate (1/L).</p>
-<p>- <code>beta</code>: transmission rate w.r.t population (beta tilde)</p>
-<p>- <code>nu</code>: recovery rate</p>
-<p>- <code>k</code>: number of subpopulations</p>
+    <p>Follow the SIR model with sub populations described in the book by Hens et al. (section 3.5.1.)</p>
+<p>With K subpopulations, the WAIFW matrix or mixing matrix is given by</p>
+<p>$$
+C = \begin{bmatrix}
+\beta_{11} &amp; \beta_{12}  &amp; ... &amp; \beta_{1K} \\
+\beta_{21} &amp; \beta_{22}  &amp; ... &amp; \beta_{2K} \\
+\vdots &amp; \vdots  &amp; ... &amp; \vdots \\
+\beta_{K1} &amp; \beta_{K2}  &amp; ... &amp; \beta_{KK} \\
+\end{bmatrix}
+$$</p>
+<p>And the \(i^{th}\) sub population is described by the following system of differential equations
+$$
+\begin{cases}
+\frac{dS_i(t)}{dt} = -(\sum^K_{j=1}\beta_{ij}I_j(t)) S_i(t) + N_i\mu_i - \mu_i S_i(t) \\
+\frac{dI_i(t)}{dt} = (\sum^K_{j=1}\beta_{ij}I_j(t)) S_i(t)  - (\nu_i + \mu_i) I_i(t)  \\
+\frac{dR_i(t)}{dt} = \nu_i I_i(t)  - \mu_i R_i(t)
+\end{cases}
+$$</p>
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+<a href="https://doi.org/10.1007/978-1-4614-4072-7" class="external-link">doi:10.1007/978-1-4614-4072-7</a>
+.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \donttest{</span></span></span>
-<span class="r-in"><span><span class="va">k</span> <span class="op">&lt;-</span> <span class="fl">2</span></span></span>
 <span class="r-in"><span><span class="va">state</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span></span>
 <span class="r-in"><span>  s <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.8</span>, <span class="fl">0.8</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>  i <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.2</span>, <span class="fl">0.2</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>  r <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>  <span class="fl">0</span>,   <span class="fl">0</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">beta_matrix</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.05</span>, <span class="fl">0.00</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.00</span>, <span class="fl">0.05</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">beta_matrix</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span></span></span>
+<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.05</span>, <span class="fl">0.00</span>,</span></span>
+<span class="r-in"><span>  <span class="fl">0.00</span>, <span class="fl">0.05</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  <span class="fl">2</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">parameters</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>  beta <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="va">beta_matrix</span>, nrow<span class="op">=</span><span class="va">k</span>, ncol<span class="op">=</span><span class="va">k</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  beta <span class="op">=</span> <span class="va">beta_matrix</span>,</span></span>
 <span class="r-in"><span>  nu <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span><span class="op">/</span><span class="fl">30</span>, <span class="fl">1</span><span class="op">/</span><span class="fl">30</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>  mu <span class="op">=</span> <span class="fl">0.001</span>,</span></span>
-<span class="r-in"><span>  k <span class="op">=</span> <span class="va">k</span></span></span>
+<span class="r-in"><span>  mu <span class="op">=</span> <span class="fl">0.001</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">times</span><span class="op">&lt;-</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">10000</span>,by<span class="op">=</span><span class="fl">0.5</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">sir_subpops_model</span><span class="op">(</span><span class="va">times</span>, <span class="va">state</span>, <span class="va">parameters</span><span class="op">)</span></span></span>
@@ -28730,23 +28746,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/sir_subpops_model.md b/docs/reference/sir_subpops_model.md
index 78d5928..dff97d8 100644
--- a/docs/reference/sir_subpops_model.md
+++ b/docs/reference/sir_subpops_model.md
@@ -1,6 +1,7 @@
 # SIR Model with Interacting Subpopulations
 
-Refers to section 3.5.1.
+An extension of the basic SIR model that incorporates interaction
+between sub-populations
 
 ## Usage
 
@@ -16,11 +17,23 @@ sir_subpops_model(times, state, parameters)
 
 - state:
 
-  the initial state of the model.
+  the initial state of the model. A named vector with the following
+
+  - `s`: initial susceptible proportion
+
+  - `i`: initial infected proportion
+
+  - `r`: initial recovered proportion
 
 - parameters:
 
-  the parameters of the model.
+  the parameters of the model. A named vector with the following
+
+  - `mu`: natural death rate (1/L).
+
+  - `beta`: the WAIFW matrix, with dimensions `[K, K]`.
+
+  - `nu`: recovery rate
 
 ## Value
 
@@ -36,43 +49,50 @@ list of class sir_subpops_model with the following items
 
 ## Details
 
-In `state`:
-
-\- `s`: Percent susceptible
-
-\- `i`: Percent infected
-
-\- `r`: Percent recovered
+Follow the SIR model with sub populations described in the book by Hens
+et al. (section 3.5.1.)
 
-In `parameters`:
+With K subpopulations, the WAIFW matrix or mixing matrix is given by
 
-\- `mu`: natural death rate (1/L).
+\$\$ C = \begin{bmatrix} \beta\_{11} & \beta\_{12} & ... & \beta\_{1K}
+\\ \beta\_{21} & \beta\_{22} & ... & \beta\_{2K} \\ \vdots & \vdots &
+... & \vdots \\ \beta\_{K1} & \beta\_{K2} & ... & \beta\_{KK} \\
+\end{bmatrix} \$\$
 
-\- `beta`: transmission rate w.r.t population (beta tilde)
+And the \\i^{th}\\ sub population is described by the following system
+of differential equations \$\$ \begin{cases} \frac{dS_i(t)}{dt} =
+-(\sum^K\_{j=1}\beta\_{ij}I_j(t)) S_i(t) + N_i\mu_i - \mu_i S_i(t) \\
+\frac{dI_i(t)}{dt} = (\sum^K\_{j=1}\beta\_{ij}I_j(t)) S_i(t) - (\nu_i +
+\mu_i) I_i(t) \\ \frac{dR_i(t)}{dt} = \nu_i I_i(t) - \mu_i R_i(t)
+\end{cases} \$\$
 
-\- `nu`: recovery rate
+## References
 
-\- `k`: number of subpopulations
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+[doi:10.1007/978-1-4614-4072-7](https://doi.org/10.1007/978-1-4614-4072-7)
+.
 
 ## Examples
 
 ``` r
 # \donttest{
-k <- 2
 state <- c(
   s = c(0.8, 0.8),
   i = c(0.2, 0.2),
   r = c(  0,   0)
 )
-beta_matrix <- c(
-  c(0.05, 0.00),
-  c(0.00, 0.05)
+beta_matrix <- matrix(
+  c(0.05, 0.00,
+  0.00, 0.05),
+  2
 )
 parameters <- list(
-  beta = matrix(beta_matrix, nrow=k, ncol=k, byrow=TRUE),
+  beta = beta_matrix,
   nu = c(1/30, 1/30),
-  mu = 0.001,
-  k = k
+  mu = 0.001
 )
 times<-seq(0,10000,by=0.5)
 model <- sir_subpops_model(times, state, parameters)
diff --git a/docs/reference/standardize_data.html b/docs/reference/standardize_data.html
index b5e1e6b..0d6b8eb 100644
--- a/docs/reference/standardize_data.html
+++ b/docs/reference/standardize_data.html
@@ -1,5 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Preprocess data — standardize_data • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Preprocess data — standardize_data"><meta name="description" content="Preprocess data"><meta property="og:description" content="Preprocess data"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Preprocess data — standardize_data • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Preprocess data — standardize_data"><meta name="description" content="Preprocess data"><meta property="og:description" content="Preprocess data"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -19,14 +25,14 @@
 <li class="nav-item dropdown">
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="../articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="../articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
@@ -70,31 +76,31 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dl><dt id="arg-df">df<a class="anchor" aria-label="anchor" href="#arg-df"></a></dt>
-<dd><p>- data.frame with columns for plate id, sample id, result, dilution factor, and (optionally) negative controls</p></dd>
+<dd><p>data.frame with columns for plate id, sample id, result, dilution factor, and (optionally) negative controls</p></dd>
 
 
 <dt id="arg-plate-id-col">plate_id_col<a class="anchor" aria-label="anchor" href="#arg-plate-id-col"></a></dt>
-<dd><p>- name of the column storing plates id</p></dd>
+<dd><p>name of the column storing plates id</p></dd>
 
 
 <dt id="arg-id-col">id_col<a class="anchor" aria-label="anchor" href="#arg-id-col"></a></dt>
-<dd><p>- name of the column storing sample id</p></dd>
+<dd><p>name of the column storing sample id</p></dd>
 
 
 <dt id="arg-result-col">result_col<a class="anchor" aria-label="anchor" href="#arg-result-col"></a></dt>
-<dd><p>- name of the column storing result</p></dd>
+<dd><p>name of the column storing result</p></dd>
 
 
 <dt id="arg-dilution-fct-col">dilution_fct_col<a class="anchor" aria-label="anchor" href="#arg-dilution-fct-col"></a></dt>
-<dd><p>- name of the column storing dilution factors</p></dd>
+<dd><p>name of the column storing dilution factors</p></dd>
 
 
 <dt id="arg-antitoxin-label">antitoxin_label<a class="anchor" aria-label="anchor" href="#arg-antitoxin-label"></a></dt>
-<dd><p>- how antitoxin is label in the sample id column</p></dd>
+<dd><p>how antitoxin is label in the sample id column</p></dd>
 
 
 <dt id="arg-negative-col">negative_col<a class="anchor" aria-label="anchor" href="#arg-negative-col"></a></dt>
-<dd><p>- regex for columns for negative controls, assumed to be a label followed by the dilution factor (e.g. NEGATIVE_50, NEGATIVE_100)</p></dd>
+<dd><p>regex for columns for negative controls, assumed to be a label followed by the dilution factor (e.g. NEGATIVE_50, NEGATIVE_100)</p></dd>
 
 </dl></div>
     <div class="section level2">
diff --git a/docs/reference/standardize_data.md b/docs/reference/standardize_data.md
index b83bd2e..c22f54a 100644
--- a/docs/reference/standardize_data.md
+++ b/docs/reference/standardize_data.md
@@ -20,32 +20,32 @@ standardize_data(
 
 - df:
 
-  \- data.frame with columns for plate id, sample id, result, dilution
+  data.frame with columns for plate id, sample id, result, dilution
   factor, and (optionally) negative controls
 
 - plate_id_col:
 
-  \- name of the column storing plates id
+  name of the column storing plates id
 
 - id_col:
 
-  \- name of the column storing sample id
+  name of the column storing sample id
 
 - result_col:
 
-  \- name of the column storing result
+  name of the column storing result
 
 - dilution_fct_col:
 
-  \- name of the column storing dilution factors
+  name of the column storing dilution factors
 
 - antitoxin_label:
 
-  \- how antitoxin is label in the sample id column
+  how antitoxin is label in the sample id column
 
 - negative_col:
 
-  \- regex for columns for negative controls, assumed to be a label
+  regex for columns for negative controls, assumed to be a label
   followed by the dilution factor (e.g. NEGATIVE_50, NEGATIVE_100)
 
 ## Value
diff --git a/docs/reference/tb_nl_1966_1973-1.png b/docs/reference/tb_nl_1966_1973-1.png
index 89d60f4..a00f30f 100644
Binary files a/docs/reference/tb_nl_1966_1973-1.png and b/docs/reference/tb_nl_1966_1973-1.png differ
diff --git a/docs/reference/tb_nl_1966_1973-2.png b/docs/reference/tb_nl_1966_1973-2.png
index 403e1f2..8e3c2c4 100644
Binary files a/docs/reference/tb_nl_1966_1973-2.png and b/docs/reference/tb_nl_1966_1973-2.png differ
diff --git a/docs/reference/tb_nl_1966_1973.html b/docs/reference/tb_nl_1966_1973.html
index 8e2651b..4a137e2 100644
--- a/docs/reference/tb_nl_1966_1973.html
+++ b/docs/reference/tb_nl_1966_1973.html
@@ -1,61 +1,52 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A study of tuberculosis conducted in the Netherlands. Schoolchildren, aged
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 <div class="row">
@@ -104,42 +95,40 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Reproduce Fig 4.6, p.70</span></span></span>
-<span class="r-in"><span><span class="va">age</span> <span class="op">&lt;-</span> <span class="va">tb_nl_1966_1973</span><span class="op">$</span><span class="va">age</span></span></span>
-<span class="r-in"><span><span class="va">birthyr</span> <span class="op">&lt;-</span> <span class="va">tb_nl_1966_1973</span><span class="op">$</span><span class="va">birthyr</span></span></span>
-<span class="r-in"><span><span class="va">pos</span> <span class="op">&lt;-</span> <span class="va">tb_nl_1966_1973</span><span class="op">$</span><span class="va">pos</span></span></span>
-<span class="r-in"><span><span class="va">tot</span> <span class="op">&lt;-</span> <span class="va">tb_nl_1966_1973</span><span class="op">$</span><span class="va">tot</span></span></span>
-<span class="r-in"><span><span class="co"># left panel</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">age</span>, <span class="va">pos</span> <span class="op">/</span> <span class="va">tot</span>,</span></span>
-<span class="r-in"><span>  pch <span class="op">=</span> <span class="fl">16</span>, cex <span class="op">=</span> <span class="fl">0.00005</span> <span class="op">*</span> <span class="va">tot</span>, xlab <span class="op">=</span> <span class="st">"age"</span>,</span></span>
-<span class="r-in"><span>  ylab <span class="op">=</span> <span class="st">"prevalence"</span>, xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6</span>, <span class="fl">18</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/with.html" class="external-link">with</a></span><span class="op">(</span><span class="va">tb_nl_1966_1973</span>,</span></span>
+<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">age</span>, <span class="va">pos</span> <span class="op">/</span> <span class="va">tot</span>,</span></span>
+<span class="r-in"><span>    pch <span class="op">=</span> <span class="fl">16</span>, cex <span class="op">=</span> <span class="fl">0.01</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">tot</span><span class="op">)</span>, xlab <span class="op">=</span> <span class="st">"age"</span>,</span></span>
+<span class="r-in"><span>    ylab <span class="op">=</span> <span class="st">"prevalence"</span>, xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6</span>, <span class="fl">18</span><span class="op">)</span></span></span>
+<span class="r-in"><span>  <span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="tb_nl_1966_1973-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span><span class="co"># right panel</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">birthyr</span>, <span class="va">pos</span> <span class="op">/</span> <span class="va">tot</span>,</span></span>
-<span class="r-in"><span>  pch <span class="op">=</span> <span class="fl">16</span>, cex <span class="op">=</span> <span class="fl">0.00005</span> <span class="op">*</span> <span class="va">tot</span>, xlab <span class="op">=</span> <span class="st">"year"</span>, ylab <span class="op">=</span> <span class="st">"prevalence"</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/with.html" class="external-link">with</a></span><span class="op">(</span><span class="va">tb_nl_1966_1973</span>,</span></span>
+<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">birthyr</span>, <span class="va">pos</span> <span class="op">/</span> <span class="va">tot</span>,</span></span>
+<span class="r-in"><span>    pch <span class="op">=</span> <span class="fl">16</span>, cex <span class="op">=</span> <span class="fl">0.01</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">tot</span><span class="op">)</span>, xlab <span class="op">=</span> <span class="st">"year"</span>, ylab <span class="op">=</span> <span class="st">"prevalence"</span></span></span>
+<span class="r-in"><span>  <span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="tb_nl_1966_1973-2.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
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diff --git a/docs/reference/tb_nl_1966_1973.md b/docs/reference/tb_nl_1966_1973.md
index 568a08e..2587360 100644
--- a/docs/reference/tb_nl_1966_1973.md
+++ b/docs/reference/tb_nl_1966_1973.md
@@ -44,20 +44,18 @@ infection incidence from serial prevalence data. Statist. Med., 18:
 ## Examples
 
 ``` r
-# Reproduce Fig 4.6, p.70
-age <- tb_nl_1966_1973$age
-birthyr <- tb_nl_1966_1973$birthyr
-pos <- tb_nl_1966_1973$pos
-tot <- tb_nl_1966_1973$tot
-# left panel
-plot(age, pos / tot,
-  pch = 16, cex = 0.00005 * tot, xlab = "age",
-  ylab = "prevalence", xlim = c(6, 18)
+with(tb_nl_1966_1973,
+  plot(age, pos / tot,
+    pch = 16, cex = 0.01 * sqrt(tot), xlab = "age",
+    ylab = "prevalence", xlim = c(6, 18)
+  )
 )
 
-# right panel
-plot(birthyr, pos / tot,
-  pch = 16, cex = 0.00005 * tot, xlab = "year", ylab = "prevalence"
+
+with(tb_nl_1966_1973,
+  plot(birthyr, pos / tot,
+    pch = 16, cex = 0.01 * sqrt(tot), xlab = "year", ylab = "prevalence"
+  )
 )
 
 ```
diff --git a/docs/reference/to_titer.html b/docs/reference/to_titer.html
index 27c7a79..f1d030a 100644
--- a/docs/reference/to_titer.html
+++ b/docs/reference/to_titer.html
@@ -1,5 +1,13 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Process assay test result to titer — to_titer • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Process assay test result to titer — to_titer"><meta name="description" content="Process assay test result to titer"><meta property="og:description" content="Process assay test result to titer"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Convert assay readings to titers — to_titer • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Convert assay readings to titers — to_titer"><meta name="description" content="to_titer() converts raw assay readings (e.g., OD, fluorescence intensity) to titer by
+fitting a calibrating model"><meta property="og:description" content="to_titer() converts raw assay readings (e.g., OD, fluorescence intensity) to titer by
+fitting a calibrating model"><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +15,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -19,14 +27,14 @@
 <li class="nav-item dropdown">
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="../articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="../articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
@@ -43,13 +51,14 @@
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="../logo.svg" class="logo" alt=""><h1>Process assay test result to titer</h1>
+      <img src="../logo.svg" class="logo" alt=""><h1>Convert assay readings to titers</h1>
       <small class="dont-index">Source: <a href="https://github.com/OUCRU-Modelling/serosv/blob/HEAD/R/to_titer.R" class="external-link"><code>R/to_titer.R</code></a></small>
       <div class="d-none name"><code>to_titer.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Process assay test result to titer</p>
+    <p>to_titer() converts raw assay readings (e.g., OD, fluorescence intensity) to titer by
+fitting a calibrating model</p>
     </div>
 
     <div class="section level2">
@@ -68,23 +77,26 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dl><dt id="arg-df">df<a class="anchor" aria-label="anchor" href="#arg-df"></a></dt>
-<dd><p>- a standardized data.frame generated by`standardize_data()`</p></dd>
+<dd><p>a standardized data.frame returned by`standardize_data()`</p></dd>
 
 
 <dt id="arg-model">model<a class="anchor" aria-label="anchor" href="#arg-model"></a></dt>
-<dd><p>- either the name of a built-in model to fit standard curve or a named list of 2 functions for "mod" and "quantify_ci"</p></dd>
+<dd><p>either:</p><ol><li><p>A string naming a built-in model (currently supported: <code>"4PL"</code>), or</p></li>
+<li><p>A named list with two functions: <code>$mod</code> for curve fitting and
+    <code>$quantify_ci</code> for titer estimation with confidence intervals.</p></li>
+</ol></dd>
 
 
 <dt id="arg-positive-threshold">positive_threshold<a class="anchor" aria-label="anchor" href="#arg-positive-threshold"></a></dt>
-<dd><p>- if not NULL, processed_data will have the serostatus labeled</p></dd>
+<dd><p>if not NULL, processed_data will have the serostatus labeled</p></dd>
 
 
 <dt id="arg-ci">ci<a class="anchor" aria-label="anchor" href="#arg-ci"></a></dt>
-<dd><p>- confidence interval for the titer estimates</p></dd>
+<dd><p>confidence interval for the titer estimates (default is .95 i.e., 95% CI)</p></dd>
 
 
 <dt id="arg-negative-control">negative_control<a class="anchor" aria-label="anchor" href="#arg-negative-control"></a></dt>
-<dd><p>- if TRUE, output tibble will include the result for negative controls</p></dd>
+<dd><p>if TRUE, output tibble will include the result for negative controls</p></dd>
 
 </dl></div>
     <div class="section level2">
@@ -94,13 +106,13 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <dd><p>id of the plate</p></dd>
 
   <dt>data</dt>
-<dd><p>list of `data.frame`s containing the results from each plate</p></dd>
+<dd><p>list of `data.frame`s containing the raw sample results from each plate</p></dd>
 
   <dt>antitoxin_df</dt>
-<dd><p>list of `data.frame`s containing the results for antitoxins from each plate</p></dd>
+<dd><p>list of `data.frame`s containing the raw results for antitoxins from each plate</p></dd>
 
   <dt>standard_curve_func</dt>
-<dd><p>list of functions mapping from OD to titer for each plate</p></dd>
+<dd><p>list of functions mapping from assay reading to titer for each plate</p></dd>
 
   <dt>std_crv_midpoint</dt>
 <dd><p>midpoint of the standard curve, for qualitative analysis</p></dd>
diff --git a/docs/reference/to_titer.md b/docs/reference/to_titer.md
index f0f16a6..2ac3627 100644
--- a/docs/reference/to_titer.md
+++ b/docs/reference/to_titer.md
@@ -1,6 +1,7 @@
-# Process assay test result to titer
+# Convert assay readings to titers
 
-Process assay test result to titer
+to_titer() converts raw assay readings (e.g., OD, fluorescence
+intensity) to titer by fitting a calibrating model
 
 ## Usage
 
@@ -18,25 +19,30 @@ to_titer(
 
 - df:
 
-  \- a standardized data.frame generated by\`standardize_data()\`
+  a standardized data.frame returned by\`standardize_data()\`
 
 - model:
 
-  \- either the name of a built-in model to fit standard curve or a
-  named list of 2 functions for "mod" and "quantify_ci"
+  either:
+
+  1.  A string naming a built-in model (currently supported: `"4PL"`),
+      or
+
+  2.  A named list with two functions: `$mod` for curve fitting and
+      `$quantify_ci` for titer estimation with confidence intervals.
 
 - positive_threshold:
 
-  \- if not NULL, processed_data will have the serostatus labeled
+  if not NULL, processed_data will have the serostatus labeled
 
 - ci:
 
-  \- confidence interval for the titer estimates
+  confidence interval for the titer estimates (default is .95 i.e., 95%
+  CI)
 
 - negative_control:
 
-  \- if TRUE, output tibble will include the result for negative
-  controls
+  if TRUE, output tibble will include the result for negative controls
 
 ## Value
 
@@ -48,16 +54,17 @@ a data.frame with 8 columns
 
 - data:
 
-  list of \`data.frame\`s containing the results from each plate
+  list of \`data.frame\`s containing the raw sample results from each
+  plate
 
 - antitoxin_df:
 
-  list of \`data.frame\`s containing the results for antitoxins from
+  list of \`data.frame\`s containing the raw results for antitoxins from
   each plate
 
 - standard_curve_func:
 
-  list of functions mapping from OD to titer for each plate
+  list of functions mapping from assay reading to titer for each plate
 
 - std_crv_midpoint:
 
diff --git a/docs/reference/transform_data.html b/docs/reference/transform_data.html
index d95bce1..94d4eee 100644
--- a/docs/reference/transform_data.html
+++ b/docs/reference/transform_data.html
@@ -1,5 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Aggregate data — transform_data • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Aggregate data — transform_data"><meta name="description" content="Generate a dataframe with `t`, `pos` and `tot` columns from
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Aggregate data — transform_data • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
+  window.MathJax = {
+    chtml: {
+      fontURL: "https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/output/chtml/fonts/woff-v2"
+    }
+  };
+</script><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Aggregate data — transform_data"><meta name="description" content="Generate a dataframe with `t`, `pos` and `tot` columns from
 `t` and `seropositive` vectors."><meta property="og:description" content="Generate a dataframe with `t`, `pos` and `tot` columns from
 `t` and `seropositive` vectors."><meta property="og:image" content="https://oucru-modelling.github.io/serosv/logo.svg"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
@@ -9,7 +15,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">serosv</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.0.9000</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -21,15 +27,14 @@
 <li class="nav-item dropdown">
   <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
   <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
-    <li><a class="dropdown-item" href="../articles/sir_model.html">SIR model</a></li>
     <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
     <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
     <li><a class="dropdown-item" href="../articles/semiparametric_model.html">Semiparametric model</a></li>
-    <li><a class="dropdown-item" href="../articles/hierarchical_model.html">Hierarchical Bayesian models</a></li>
     <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
     <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
     <li><hr class="dropdown-divider"></li>
     <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
     <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
     <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
     <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
@@ -58,23 +63,23 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">transform_data</span><span class="op">(</span><span class="va">t</span>, <span class="va">spos</span>, stratum_col <span class="op">=</span> <span class="st">"t"</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">transform_data</span><span class="op">(</span><span class="va">data</span>, stratum_col <span class="op">=</span> <span class="st">"age"</span>, status_col <span class="op">=</span> <span class="st">"status"</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
 
 
-<dl><dt id="arg-t">t<a class="anchor" aria-label="anchor" href="#arg-t"></a></dt>
-<dd><p>the time vector (for stratification).</p></dd>
+<dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
+<dd><p>a data frame with columns for age and serostatus</p></dd>
 
 
-<dt id="arg-spos">spos<a class="anchor" aria-label="anchor" href="#arg-spos"></a></dt>
-<dd><p>the seropositive vector.</p></dd>
+<dt id="arg-stratum-col">stratum_col<a class="anchor" aria-label="anchor" href="#arg-stratum-col"></a></dt>
+<dd><p>name of the column to stratify by (default to "age")</p></dd>
 
 
-<dt id="arg-stratum-col">stratum_col<a class="anchor" aria-label="anchor" href="#arg-stratum-col"></a></dt>
-<dd><p>new name for the time vector (default to "t")</p></dd>
+<dt id="arg-status-col">status_col<a class="anchor" aria-label="anchor" href="#arg-status-col"></a></dt>
+<dd><p>name of the column for serostatus</p></dd>
 
 </dl></div>
     <div class="section level2">
@@ -85,10 +90,10 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">hcv_be_2006</span></span></span>
-<span class="r-in"><span><span class="va">hcv_df</span> <span class="op">&lt;-</span> <span class="fu">transform_data</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">dur</span>, <span class="va">df</span><span class="op">$</span><span class="va">seropositive</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">hcv_df</span> <span class="op">&lt;-</span> <span class="fu">transform_data</span><span class="op">(</span><span class="va">df</span>, stratum_col<span class="op">=</span><span class="st">"dur"</span>, status_col<span class="op">=</span><span class="st">"seropositive"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">hcv_df</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 116 × 3</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        t   pos   tot</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>      age   pos   tot</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span>   0.1     0     1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span>   0.3     0     1</span>
diff --git a/docs/reference/transform_data.md b/docs/reference/transform_data.md
index 2325dc6..32eb282 100644
--- a/docs/reference/transform_data.md
+++ b/docs/reference/transform_data.md
@@ -6,22 +6,22 @@ and \`seropositive\` vectors.
 ## Usage
 
 ``` r
-transform_data(t, spos, stratum_col = "t")
+transform_data(data, stratum_col = "age", status_col = "status")
 ```
 
 ## Arguments
 
-- t:
+- data:
 
-  the time vector (for stratification).
+  a data frame with columns for age and serostatus
 
-- spos:
+- stratum_col:
 
-  the seropositive vector.
+  name of the column to stratify by (default to "age")
 
-- stratum_col:
+- status_col:
 
-  new name for the time vector (default to "t")
+  name of the column for serostatus
 
 ## Value
 
@@ -31,10 +31,10 @@ dataframe in aggregated format
 
 ``` r
 df <- hcv_be_2006
-hcv_df <- transform_data(df$dur, df$seropositive)
+hcv_df <- transform_data(df, stratum_col="dur", status_col="seropositive")
 hcv_df
 #> # A tibble: 116 × 3
-#>        t   pos   tot
+#>      age   pos   tot
 #>    <dbl> <int> <int>
 #>  1   0.1     0     1
 #>  2   0.3     0     1
diff --git a/docs/reference/vzv_be_1999_2000-1.png b/docs/reference/vzv_be_1999_2000-1.png
index ed4f0d7..d22e4dc 100644
Binary files a/docs/reference/vzv_be_1999_2000-1.png and b/docs/reference/vzv_be_1999_2000-1.png differ
diff --git a/docs/reference/vzv_be_1999_2000.html b/docs/reference/vzv_be_1999_2000.html
index 6f5711a..941a234 100644
--- a/docs/reference/vzv_be_1999_2000.html
+++ b/docs/reference/vzv_be_1999_2000.html
@@ -1,63 +1,54 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Age-specific seroprevalence of VZV antibodies, assessed in Flanders (Belgium)
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated) — vzv_be_1999_2000 • serosv</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><!-- mathjax math --><script src="https://cdn.jsdelivr.net/npm/mathjax@3.2.2/es5/tex-mml-chtml.js" integrity="sha256-MASABpB4tYktI2Oitl4t+78w/lyA+D7b/s9GEP0JOGI=" crossorigin="anonymous"></script><script>
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 between October 1999 and April 2000. This population was stratified by age
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+in order to obtain approximately 100 observations per age group."><meta property="og:description" content="Age-specific seroprevalence of VZV antibodies, assessed in Flanders (Belgium)
 between October 1999 and April 2000. This population was stratified by age
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 </nav><div class="container template-reference-topic">
 <div class="row">
@@ -101,35 +92,32 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Reproduce Fig 4.7 (left panel), p.71</span></span></span>
-<span class="r-in"><span><span class="va">age</span> <span class="op">&lt;-</span> <span class="va">vzv_be_1999_2000</span><span class="op">$</span><span class="va">age</span></span></span>
-<span class="r-in"><span><span class="va">pos</span> <span class="op">&lt;-</span> <span class="va">vzv_be_1999_2000</span><span class="op">$</span><span class="va">pos</span></span></span>
-<span class="r-in"><span><span class="va">tot</span> <span class="op">&lt;-</span> <span class="va">vzv_be_1999_2000</span><span class="op">$</span><span class="va">tot</span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">age</span>, <span class="va">pos</span> <span class="op">/</span> <span class="va">tot</span>,</span></span>
-<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.036</span> <span class="op">*</span> <span class="va">tot</span>, pch <span class="op">=</span> <span class="fl">19</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
-<span class="r-in"><span>  xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">45</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/with.html" class="external-link">with</a></span><span class="op">(</span><span class="va">vzv_be_1999_2000</span>,</span></span>
+<span class="r-in"><span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">age</span>, <span class="va">pos</span> <span class="op">/</span> <span class="va">tot</span>,</span></span>
+<span class="r-in"><span>    cex <span class="op">=</span> <span class="fl">0.1</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">tot</span><span class="op">)</span>, pch <span class="op">=</span> <span class="fl">19</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
+<span class="r-in"><span>    xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">45</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
+<span class="r-in"><span>  <span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="vzv_be_1999_2000-1.png" alt="" width="700" height="433"></span>
-<span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
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+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
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diff --git a/docs/reference/vzv_be_1999_2000.md b/docs/reference/vzv_be_1999_2000.md
index f5d58e2..35d5685 100644
--- a/docs/reference/vzv_be_1999_2000.md
+++ b/docs/reference/vzv_be_1999_2000.md
@@ -37,13 +37,10 @@ Pediatr 161, 588-593 (2002).
 ## Examples
 
 ``` r
-# Reproduce Fig 4.7 (left panel), p.71
-age <- vzv_be_1999_2000$age
-pos <- vzv_be_1999_2000$pos
-tot <- vzv_be_1999_2000$tot
-plot(age, pos / tot,
-  cex = 0.036 * tot, pch = 19, xlab = "age", ylab = "seroprevalence",
-  xlim = c(0, 45), ylim = c(0, 1)
+with(vzv_be_1999_2000,
+  plot(age, pos / tot,
+    cex = 0.1 * sqrt(tot), pch = 19, xlab = "age", ylab = "seroprevalence",
+    xlim = c(0, 45), ylim = c(0, 1)
+  )
 )
-
 ```
diff --git a/docs/reference/vzv_be_2001_2003-1.png b/docs/reference/vzv_be_2001_2003-1.png
index 923dd58..cbf6d78 100644
Binary files a/docs/reference/vzv_be_2001_2003-1.png and b/docs/reference/vzv_be_2001_2003-1.png differ
diff --git a/docs/reference/vzv_be_2001_2003.html b/docs/reference/vzv_be_2001_2003.html
index 40195e7..ef584c3 100644
--- a/docs/reference/vzv_be_2001_2003.html
+++ b/docs/reference/vzv_be_2001_2003.html
@@ -1,61 +1,52 @@
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@@ -102,37 +93,36 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Reproduce Fig 4.7 (right panel), p.71</span></span></span>
-<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">vzv_be_2001_2003</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>age <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="va">age</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">age</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span>pos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">seropositive</span><span class="op">)</span>, tot <span class="op">=</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/context.html" class="external-link">n</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">age</span>, <span class="va">df</span><span class="op">$</span><span class="va">pos</span> <span class="op">/</span> <span class="va">df</span><span class="op">$</span><span class="va">tot</span>,</span></span>
-<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.036</span> <span class="op">*</span> <span class="va">df</span><span class="op">$</span><span class="va">tot</span>, pch <span class="op">=</span> <span class="fl">19</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
+<span class="r-in"><span>  cex <span class="op">=</span> <span class="fl">0.1</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">tot</span><span class="op">)</span>, pch <span class="op">=</span> <span class="fl">19</span>, xlab <span class="op">=</span> <span class="st">"age"</span>, ylab <span class="op">=</span> <span class="st">"seroprevalence"</span>,</span></span>
 <span class="r-in"><span>  xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">45</span><span class="op">)</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="vzv_be_2001_2003-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
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diff --git a/docs/reference/vzv_be_2001_2003.md b/docs/reference/vzv_be_2001_2003.md
index 8b3ebe8..1dba4c6 100644
--- a/docs/reference/vzv_be_2001_2003.md
+++ b/docs/reference/vzv_be_2001_2003.md
@@ -41,14 +41,13 @@ Risk of Infection." Epidemiology and Infection 136, no. 8 (2008):
 ## Examples
 
 ``` r
-# Reproduce Fig 4.7 (right panel), p.71
 library(dplyr)
 df <- vzv_be_2001_2003 %>%
   mutate(age = round(age)) %>%
   group_by(age) %>%
   summarise(pos = sum(seropositive), tot = n())
 plot(df$age, df$pos / df$tot,
-  cex = 0.036 * df$tot, pch = 19, xlab = "age", ylab = "seroprevalence",
+  cex = 0.1 * sqrt(df$tot), pch = 19, xlab = "age", ylab = "seroprevalence",
   xlim = c(0, 45), ylim = c(0, 1)
 )
 
diff --git a/docs/reference/vzv_parvo_be.html b/docs/reference/vzv_parvo_be.html
index 35f4b3c..8c73a6e 100644
--- a/docs/reference/vzv_parvo_be.html
+++ b/docs/reference/vzv_parvo_be.html
@@ -1,59 +1,50 @@
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diff --git a/docs/reference/weibull_model.html b/docs/reference/weibull_model.html
index 569cc2f..69cdac9 100644
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@@ -1,59 +1,52 @@
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-    <a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a>
-    <div class="dropdown-divider"></div>
-    <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
-    <a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a>
-    <a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a>
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-  <a class="nav-link" href="../news/index.html">Changelog</a>
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-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
-      <ul class="navbar-nav"><li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/OUCRU-Modelling/serosv/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/linelisting_vs_aggregated.html">Input data</a></li>
+    <li><a class="dropdown-item" href="../articles/parametric_model.html">Parametric models</a></li>
+    <li><a class="dropdown-item" href="../articles/nonparametric_model.html">Nonparametric model</a></li>
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+    <li><a class="dropdown-item" href="../articles/model_quantitative_data.html">Modeling directly from antibody levels</a></li>
+    <li><a class="dropdown-item" href="../articles/repeated_cross_sectional.html">Model repeated cross-sectional data</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Utilities</h6></li>
+    <li><a class="dropdown-item" href="../articles/reading_to_titer.html">Convert assay readings to titer</a></li>
+    <li><a class="dropdown-item" href="../articles/data_transformation.html">Data transformation</a></li>
+    <li><a class="dropdown-item" href="../articles/imperfect_test.html">Imperfect serological test</a></li>
+    <li><a class="dropdown-item" href="../articles/visualizing_model.html">Model visualization</a></li>
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+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/OUCRU-Modelling/serosv/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
@@ -64,25 +57,49 @@ <h6 class="dropdown-header" data-toc-skip>Utilities</h6>
     </div>
 
     <div class="ref-description section level2">
-    <p>Refers to section 6.1.2.</p>
+    <p>Model seroprevalence as a function of duration since vaccination using the Weibull
+model, where the force of infection is assumed to vary monotonically with duration.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">weibull_model</span><span class="op">(</span><span class="va">data</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">weibull_model</span><span class="op">(</span></span>
+<span>  <span class="va">data</span>,</span>
+<span>  t_lab <span class="op">=</span> <span class="st">"t"</span>,</span>
+<span>  pos_col <span class="op">=</span> <span class="st">"pos"</span>,</span>
+<span>  tot_col <span class="op">=</span> <span class="st">"tot"</span>,</span>
+<span>  status_col <span class="op">=</span> <span class="st">"status"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>data</dt>
-<dd><p>the input data frame, must either have `t`, `pos`, `tot` column for aggregated data OR `t`, `status` for linelisting data</p></dd>
+
+
+<dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
+<dd><p>the input data frame, must either have columns for `t`, `pos`, `tot` (for aggregated data) OR
+`t`, `status` (for linelisting data)</p></dd>
+
+
+<dt id="arg-t-lab">t_lab<a class="anchor" aria-label="anchor" href="#arg-t-lab"></a></dt>
+<dd><p>name of the `t` column (default t_lab="t").</p></dd>
+
+
+<dt id="arg-pos-col">pos_col<a class="anchor" aria-label="anchor" href="#arg-pos-col"></a></dt>
+<dd><p>name of the `pos` column (default pos_col="pos").</p></dd>
+
+
+<dt id="arg-tot-col">tot_col<a class="anchor" aria-label="anchor" href="#arg-tot-col"></a></dt>
+<dd><p>name of the `tot` column (default tot_col="tot").</p></dd>
+
+
+<dt id="arg-status-col">status_col<a class="anchor" aria-label="anchor" href="#arg-status-col"></a></dt>
+<dd><p>name of the `status` column (default status_col="status").</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>list of class weibull_model with the following items</p>
+    <p>list of class weibull_model with the following items</p>
 <dl><dt>datatype</dt>
 <dd><p>type of datatype used for model fitting (aggregated or linelisting)</p></dd>
 
@@ -99,6 +116,28 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 <dd><p>force of infection</p></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>For a Weibull model, the prevalence is given by
+$$
+ \pi (d) = 1 - e^{ - \beta_0 d ^ {\beta_1}}
+$$
+Where \(d\) is exposure time (difference between age of vaccination and age at test)</p>
+<p>Which implies the force of infection to be the monotonic function
+$$
+ \lambda(d) = \beta_0 \beta_1 d^{\beta_1 - 1}
+$$</p>
+<p>Refer to section 6.1.2. of the the book by Hens et al. (2012) for further details.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
+    <p>Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+<a href="https://doi.org/10.1007/978-1-4614-4072-7" class="external-link">doi:10.1007/978-1-4614-4072-7</a>
+.</p>
+    </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p>[stats::glm()] for more information on the fitted "glm" object</p></div>
@@ -109,29 +148,29 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">hcv_be_2006</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/order.html" class="external-link">order</a></span><span class="op">(</span><span class="va">hcv_be_2006</span><span class="op">$</span><span class="va">dur</span><span class="op">)</span>, <span class="op">]</span></span></span>
 <span class="r-in"><span><span class="va">df</span><span class="op">$</span><span class="va">t</span> <span class="op">&lt;-</span> <span class="va">df</span><span class="op">$</span><span class="va">dur</span></span></span>
 <span class="r-in"><span><span class="va">df</span><span class="op">$</span><span class="va">status</span> <span class="op">&lt;-</span> <span class="va">df</span><span class="op">$</span><span class="va">seropositive</span></span></span>
-<span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">weibull_model</span><span class="op">(</span><span class="va">df</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">weibull_model</span><span class="op">(</span><span class="va">df</span>, t_lab<span class="op">=</span><span class="st">"dur"</span>, status_col<span class="op">=</span><span class="st">"seropositive"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="weibull_model-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
+  <p>Developed by Anh Phan Truong Quynh, Nguyen Pham Nguyen The, Long Bui Thanh, Tuyen Huynh, Thinh Ong, Marc Choisy.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.2.0.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/docs/reference/weibull_model.md b/docs/reference/weibull_model.md
index cb750a8..2b37a84 100644
--- a/docs/reference/weibull_model.md
+++ b/docs/reference/weibull_model.md
@@ -1,19 +1,44 @@
 # The Weibull model.
 
-Refers to section 6.1.2.
+Model seroprevalence as a function of duration since vaccination using
+the Weibull model, where the force of infection is assumed to vary
+monotonically with duration.
 
 ## Usage
 
 ``` r
-weibull_model(data)
+weibull_model(
+  data,
+  t_lab = "t",
+  pos_col = "pos",
+  tot_col = "tot",
+  status_col = "status"
+)
 ```
 
 ## Arguments
 
 - data:
 
-  the input data frame, must either have \`t\`, \`pos\`, \`tot\` column
-  for aggregated data OR \`t\`, \`status\` for linelisting data
+  the input data frame, must either have columns for \`t\`, \`pos\`,
+  \`tot\` (for aggregated data) OR \`t\`, \`status\` (for linelisting
+  data)
+
+- t_lab:
+
+  name of the \`t\` column (default t_lab="t").
+
+- pos_col:
+
+  name of the \`pos\` column (default pos_col="pos").
+
+- tot_col:
+
+  name of the \`tot\` column (default tot_col="tot").
+
+- status_col:
+
+  name of the \`status\` column (default status_col="status").
 
 ## Value
 
@@ -39,6 +64,27 @@ list of class weibull_model with the following items
 
   force of infection
 
+## Details
+
+For a Weibull model, the prevalence is given by \$\$ \pi (d) = 1 - e^{ -
+\beta_0 d ^ {\beta_1}} \$\$ Where \\d\\ is exposure time (difference
+between age of vaccination and age at test)
+
+Which implies the force of infection to be the monotonic function \$\$
+\lambda(d) = \beta_0 \beta_1 d^{\beta_1 - 1} \$\$
+
+Refer to section 6.1.2. of the the book by Hens et al. (2012) for
+further details.
+
+## References
+
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and
+Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+[doi:10.1007/978-1-4614-4072-7](https://doi.org/10.1007/978-1-4614-4072-7)
+.
+
 ## See also
 
 \[stats::glm()\] for more information on the fitted "glm" object
@@ -49,7 +95,7 @@ list of class weibull_model with the following items
 df <- hcv_be_2006[order(hcv_be_2006$dur), ]
 df$t <- df$dur
 df$status <- df$seropositive
-model <- weibull_model(df)
+model <- weibull_model(df, t_lab="dur", status_col="seropositive")
 plot(model)
 
 ```
diff --git a/docs/search.json b/docs/search.json
index 901925d..d8b8137 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":"https://oucru-modelling.github.io/serosv/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2024 serosv authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://oucru-modelling.github.io/serosv/articles/data_transformation.html","id":"aggregate-data","dir":"Articles","previous_headings":"","what":"Aggregate data","title":"Data transformation","text":"Use function transform_data() convert line listing data (data t, seropositive columns) aggregated data (data t, pos, tot columns) Arguments: t time vector spos seropositive vector (TRUE/FALSE 1/0) stratum_col new name time vector, default stratum_col = \"t\"","code":"linelisting_data <- hcv_be_2006 aggregated_data <- transform_data(linelisting_data$dur, linelisting_data$seropositive) aggregated_data #> # A tibble: 116 × 3 #>        t   pos   tot #>    <dbl> <int> <int> #>  1   0.1     0     1 #>  2   0.3     0     1 #>  3   0.4     0     3 #>  4   0.5     0     1 #>  5   0.7     2     5 #>  6   0.8     3     4 #>  7   0.9     3     4 #>  8   1       5     7 #>  9   1.1     1     2 #> 10   1.2     3     4 #> # ℹ 106 more rows"},{"path":"https://oucru-modelling.github.io/serosv/articles/hierarchical_model.html","id":"parametric-bayesian-framework","dir":"Articles","previous_headings":"","what":"Parametric Bayesian framework","title":"Hierarchical Bayesian models","text":"Currently, serosv models parametric Bayesian framework Proposed approach Prevalence parametric form π(ai,α)\\pi(a_i, \\alpha) α\\alpha parameter vector One can constraint parameter space prior distribution P(α)P(\\alpha) order achieve desired monotonicity posterior distribution P(π1,π2,...,πm|y,n)P(\\pi_1, \\pi_2, ..., \\pi_m|y,n) : n=(n1,n2,...,nm)n = (n_1, n_2, ..., n_m) nin_i sample size age aia_i y=(y1,y2,...,ym)y = (y_1, y_2, ..., y_m) yiy_i number infected individual nin_i sampled subjects","code":""},{"path":"https://oucru-modelling.github.io/serosv/articles/hierarchical_model.html","id":"farrington","dir":"Articles","previous_headings":"Parametric Bayesian framework","what":"Farrington","title":"Hierarchical Bayesian models","text":"Refer Chapter 10.3.1 Proposed model model prevalence followed π()=1−exp{α1α2ae−α2a+1α2(α1α2−α3)(e−α2a−1)−α3a} \\pi () = 1 - exp\\{ \\frac{\\alpha_1}{\\alpha_2}ae^{-\\alpha_2 } + \\frac{1}{\\alpha_2}(\\frac{\\alpha_1}{\\alpha_2} - \\alpha_3)(e^{-\\alpha_2 } - 1) -\\alpha_3 \\} likelihood model, independent binomial distribution assumed number infected individuals age aia_i yi∼Bin(ni,πi), =1,2,3,...m y_i \\sim Bin(n_i, \\pi_i), \\text{  } = 1,2,3,...m constraint parameter space can incorporated assuming truncated normal distribution components α\\alpha, α=(α1,α2,α3)\\alpha = (\\alpha_1, \\alpha_2, \\alpha_3) πi=π(ai,α)\\pi_i = \\pi(a_i,\\alpha) αj∼truncated 𝒩(μj,τj),j=1,2,3 \\alpha_j \\sim \\text{truncated  } \\mathcal{N}(\\mu_j, \\tau_j), \\text{ } j = 1,2,3 joint posterior distribution α\\alpha can derived combining likelihood prior followed P(α|y)∝∏=1mBin(yi|ni,π(ai,α))∏=13−1τjexp(12τj2(αj−μj)2) P(\\alpha|y) \\propto \\prod^m_{=1} \\text{Bin}(y_i|n_i, \\pi(a_i, \\alpha)) \\prod^3_{=1}-\\frac{1}{\\tau_j}\\text{exp}(\\frac{1}{2\\tau^2_j} (\\alpha_j - \\mu_j)^2) flat hyperprior distribution defined followed: μj∼𝒩(0,10000)\\mu_j \\sim \\mathcal{N}(0, 10000) τj−2∼Γ(100,100)\\tau^{-2}_j \\sim \\Gamma(100,100) full conditional distribution αi\\alpha_i thus P(αi|αj,αk,k,j≠)∝−1τiexp(12τi2(αi−μi)2)∏=1mBin(yi|ni,π(ai,α)) P(\\alpha_i|\\alpha_j,\\alpha_k, k, j \\neq ) \\propto  -\\frac{1}{\\tau_i}\\text{exp}(\\frac{1}{2\\tau^2_i} (\\alpha_i - \\mu_i)^2) \\prod^m_{=1} \\text{Bin}(y_i|n_i, \\pi(a_i, \\alpha)) Fitting data fit Farrington model, use hierarchical_bayesian_model() define type = \"far2\" type = \"far3\" type = \"far2\" refers Farrington model 2 parameters (α3=0\\alpha_3 = 0) type = \"far3\" refers Farrington model 3 parameters (α3>0\\alpha_3 > 0)","code":"df <- mumps_uk_1986_1987 model <- hierarchical_bayesian_model(df, type=\"far3\") #>  #> SAMPLING FOR MODEL 'fra_3' NOW (CHAIN 1). #> Chain 1: Rejecting initial value: #> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity. #> Chain 1:   Stan can't start sampling from this initial value. #> Chain 1:  #> Chain 1: Gradient evaluation took 0.000154 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.54 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1:  #> Chain 1:  #> Chain 1: Iteration:    1 / 5000 [  0%]  (Warmup) #> Chain 1: Iteration:  500 / 5000 [ 10%]  (Warmup) #> Chain 1: Iteration: 1000 / 5000 [ 20%]  (Warmup) #> Chain 1: Iteration: 1500 / 5000 [ 30%]  (Warmup) #> Chain 1: Iteration: 1501 / 5000 [ 30%]  (Sampling) #> Chain 1: Iteration: 2000 / 5000 [ 40%]  (Sampling) #> Chain 1: Iteration: 2500 / 5000 [ 50%]  (Sampling) #> Chain 1: Iteration: 3000 / 5000 [ 60%]  (Sampling) #> Chain 1: Iteration: 3500 / 5000 [ 70%]  (Sampling) #> Chain 1: Iteration: 4000 / 5000 [ 80%]  (Sampling) #> Chain 1: Iteration: 4500 / 5000 [ 90%]  (Sampling) #> Chain 1: Iteration: 5000 / 5000 [100%]  (Sampling) #> Chain 1:  #> Chain 1:  Elapsed Time: 16.591 seconds (Warm-up) #> Chain 1:                96.031 seconds (Sampling) #> Chain 1:                112.622 seconds (Total) #> Chain 1: #> Warning: There were 288 divergent transitions after warmup. See #> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup #> to find out why this is a problem and how to eliminate them. #> Warning: There were 11 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See #> https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded #> Warning: Examine the pairs() plot to diagnose sampling problems #> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable. #> Running the chains for more iterations may help. See #> https://mc-stan.org/misc/warnings.html#tail-ess  model$info #>                       mean      se_mean           sd          2.5% #> alpha1        1.396732e-01 2.328172e-04 5.926643e-03  1.290067e-01 #> alpha2        1.989632e-01 3.342249e-04 8.454176e-03  1.847781e-01 #> alpha3        9.017286e-03 2.957517e-04 7.503320e-03  2.519932e-04 #> tau_alpha1    2.051039e+00 2.925237e-01 5.992958e+00  1.814318e-06 #> tau_alpha2    4.280878e+00 1.545375e+00 1.261621e+01  5.514011e-06 #> tau_alpha3    1.594944e+00 2.713658e-01 4.513571e+00  1.717967e-06 #> mu_alpha1    -2.434489e+00 4.143249e+00 4.467754e+01 -1.656101e+02 #> mu_alpha2    -9.043922e-01 1.406475e+00 3.373188e+01 -9.031034e+01 #> mu_alpha3     2.147579e+00 1.666808e+00 4.272829e+01 -9.818932e+01 #> sigma_alpha1  9.673678e+03 9.523462e+03 2.012224e+05  2.027127e-01 #> sigma_alpha2  8.037575e+01 1.664371e+01 5.816331e+02  1.342710e-01 #> sigma_alpha3  1.665419e+02 4.235920e+01 1.470059e+03  2.354869e-01 #> lp__         -2.534311e+03 2.684310e-01 4.176499e+00 -2.542650e+03 #>                        25%           50%           75%         97.5%      n_eff #> alpha1        1.353546e-01  1.393536e-01  1.435425e-01  1.520502e-01  648.01829 #> alpha2        1.931939e-01  1.981250e-01  2.037919e-01  2.181404e-01  639.83066 #> alpha3        3.301399e-03  6.948630e-03  1.310897e-02  2.798314e-02  643.65402 #> tau_alpha1    4.699710e-04  1.339575e-02  4.377299e-01  2.433537e+01  419.72073 #> tau_alpha2    1.099782e-03  3.547442e-02  9.730938e-01  5.546799e+01   66.64842 #> tau_alpha3    3.847899e-04  1.424805e-02  4.511886e-01  1.803311e+01  276.64990 #> mu_alpha1    -4.777740e+00  1.761566e-01  4.990839e+00  8.616947e+01  116.27771 #> mu_alpha2    -3.043304e+00  1.912986e-01  2.809284e+00  6.930927e+01  575.19790 #> mu_alpha3    -4.356601e+00  8.834931e-02  7.109717e+00  1.097394e+02  657.14323 #> sigma_alpha1  1.511461e+00  8.640059e+00  4.612799e+01  7.424105e+02  446.43988 #> sigma_alpha2  1.013731e+00  5.309408e+00  3.015428e+01  4.258861e+02 1221.23065 #> sigma_alpha3  1.488748e+00  8.377652e+00  5.097869e+01  7.629637e+02 1204.40906 #> lp__         -2.537035e+03 -2.534291e+03 -2.531361e+03 -2.526569e+03  242.08025 #>                   Rhat #> alpha1       1.0006390 #> alpha2       0.9997486 #> alpha3       0.9999655 #> tau_alpha1   1.0023789 #> tau_alpha2   1.0060590 #> tau_alpha3   1.0005442 #> mu_alpha1    1.0039620 #> mu_alpha2    0.9997144 #> mu_alpha3    1.0015442 #> sigma_alpha1 1.0019918 #> sigma_alpha2 1.0000931 #> sigma_alpha3 0.9997146 #> lp__         1.0215691 plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/hierarchical_model.html","id":"log-logistic","dir":"Articles","previous_headings":"Parametric Bayesian framework","what":"Log-logistic","title":"Hierarchical Bayesian models","text":"Proposed approach model seroprevalence followed π()=βaα1+βaα,α,β>0 \\pi() = \\frac{\\beta ^\\alpha}{1 + \\beta ^\\alpha}, \\text{ } \\alpha, \\beta > 0 likelihood specified Farrington model (yi∼Bin(ni,πi)y_i \\sim Bin(n_i, \\pi_i)) logit(π())=α2+α1log() \\text{logit}(\\pi()) = \\alpha_2 + \\alpha_1\\log() α2=log(β)\\alpha_2 = \\text{log}(\\beta) prior model α1\\alpha_1 specified α1∼truncated 𝒩(μ1,τ1)\\alpha_1 \\sim \\text{truncated  } \\mathcal{N}(\\mu_1, \\tau_1) flat hyperprior Farrington model β\\beta constrained positive specifying α2∼𝒩(μ2,τ2)\\alpha_2 \\sim \\mathcal{N}(\\mu_2, \\tau_2) full conditional distribution α1\\alpha_1 thus P(α1|α2)∝−1τ1exp(12τ12(α1−μ1)2)∏=1mBin(yi|ni,π(ai,α1,α2)) P(\\alpha_1|\\alpha_2) \\propto -\\frac{1}{\\tau_1} \\text{exp} (\\frac{1}{2 \\tau_1^2} (\\alpha_1 - \\mu_1)^2) \\prod_{=1}^m \\text{Bin}(y_i|n_i,\\pi(a_i, \\alpha_1, \\alpha_2) ) α2\\alpha_2 can derived way Fitting data fit Log-logistic model, use hierarchical_bayesian_model() define type = \"log_logistic\"","code":"df <- rubella_uk_1986_1987 model <- hierarchical_bayesian_model(df, type=\"log_logistic\") #>  #> SAMPLING FOR MODEL 'log_logistic' NOW (CHAIN 1). #> Chain 1:  #> Chain 1: Gradient evaluation took 6.4e-05 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.64 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1:  #> Chain 1:  #> Chain 1: Iteration:    1 / 5000 [  0%]  (Warmup) #> Chain 1: Iteration:  500 / 5000 [ 10%]  (Warmup) #> Chain 1: Iteration: 1000 / 5000 [ 20%]  (Warmup) #> Chain 1: Iteration: 1500 / 5000 [ 30%]  (Warmup) #> Chain 1: Iteration: 1501 / 5000 [ 30%]  (Sampling) #> Chain 1: Iteration: 2000 / 5000 [ 40%]  (Sampling) #> Chain 1: Iteration: 2500 / 5000 [ 50%]  (Sampling) #> Chain 1: Iteration: 3000 / 5000 [ 60%]  (Sampling) #> Chain 1: Iteration: 3500 / 5000 [ 70%]  (Sampling) #> Chain 1: Iteration: 4000 / 5000 [ 80%]  (Sampling) #> Chain 1: Iteration: 4500 / 5000 [ 90%]  (Sampling) #> Chain 1: Iteration: 5000 / 5000 [100%]  (Sampling) #> Chain 1:  #> Chain 1:  Elapsed Time: 4.15 seconds (Warm-up) #> Chain 1:                5.77 seconds (Sampling) #> Chain 1:                9.92 seconds (Total) #> Chain 1: #> Warning: There were 583 divergent transitions after warmup. See #> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup #> to find out why this is a problem and how to eliminate them. #> Warning: Examine the pairs() plot to diagnose sampling problems #> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable. #> Running the chains for more iterations may help. See #> https://mc-stan.org/misc/warnings.html#bulk-ess #> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable. #> Running the chains for more iterations may help. See #> https://mc-stan.org/misc/warnings.html#tail-ess  model$type #> [1] \"log_logistic\" plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/imperfect_test.html","id":"imperfect-test","dir":"Articles","previous_headings":"","what":"Imperfect test","title":"Imperfect serological test","text":"Function correct_prevalence() used estimating true prevalence serological test used imperfect Arguments: data input data frame, must either : age, pos, tot columns (aggregated data) age, status columns (linelisting data) bayesian whether adjust sero-prevalence using Bayesian frequentist approach. set TRUE, true sero-prevalence estimated using MCMC. init_se sensitivity serological test (default value 0.95) init_sp specificity serological test (default value 0.8) study_size_se (applicable bayesian=TRUE) sample size sensitivity validation study (default value 1000) study_size_sp (applicable bayesian=TRUE) sample size specificity validation study (default value 1000) chains (applicable bayesian=TRUE) number Markov chains (default 1) warmup (applicable bayesian=TRUE) number warm runs (default value 1000) iter (applicable bayesian=TRUE) number iterations (default value 2000) function return list 2 items: info bayesian = TRUE contains estimated values se, sp corrected seroprevalence else return formula computing corrected seroprevalence corrected_sero return data.frame age, sero (corrected sero) pos, tot (adjusted based corrected prevalence) User can visualize output using plot_corrected_prev() function  compare correction methods single plot, provide output second method optional y argument plot_corrected_prev()","code":"# ---- estimate real prevalence using Bayesian approach ---- data <- rubella_uk_1986_1987 output <- correct_prevalence(data, warmup = 1000, iter = 4000, init_se=0.9, init_sp = 0.8, study_size_se=1000, study_size_sp=3000) #>  #> SAMPLING FOR MODEL 'prevalence_correction' NOW (CHAIN 1). #> Chain 1:  #> Chain 1: Gradient evaluation took 9.6e-05 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.96 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1:  #> Chain 1:  #> Chain 1: Iteration:    1 / 4000 [  0%]  (Warmup) #> Chain 1: Iteration:  400 / 4000 [ 10%]  (Warmup) #> Chain 1: Iteration:  800 / 4000 [ 20%]  (Warmup) #> Chain 1: Iteration: 1001 / 4000 [ 25%]  (Sampling) #> Chain 1: Iteration: 1400 / 4000 [ 35%]  (Sampling) #> Chain 1: Iteration: 1800 / 4000 [ 45%]  (Sampling) #> Chain 1: Iteration: 2200 / 4000 [ 55%]  (Sampling) #> Chain 1: Iteration: 2600 / 4000 [ 65%]  (Sampling) #> Chain 1: Iteration: 3000 / 4000 [ 75%]  (Sampling) #> Chain 1: Iteration: 3400 / 4000 [ 85%]  (Sampling) #> Chain 1: Iteration: 3800 / 4000 [ 95%]  (Sampling) #> Chain 1: Iteration: 4000 / 4000 [100%]  (Sampling) #> Chain 1:  #> Chain 1:  Elapsed Time: 2.026 seconds (Warm-up) #> Chain 1:                4.044 seconds (Sampling) #> Chain 1:                6.07 seconds (Total) #> Chain 1:  # check fitted value  output$info[1:2, ] #>             mean      se_mean          sd      2.5%       25%       50% #> est_se 0.9278179 0.0001098350 0.006095913 0.9151759 0.9239329 0.9279511 #> est_sp 0.8028005 0.0001011728 0.006973970 0.7888135 0.7981642 0.8028934 #>              75%     97.5%    n_eff      Rhat #> est_se 0.9318726 0.9390499 3080.323 0.9998417 #> est_sp 0.8075049 0.8167427 4751.518 0.9996668  # ---- estimate real prevalence using frequentist approach ---- freq_output <- correct_prevalence(data, bayesian = FALSE, init_se=0.9, init_sp = 0.8)  # check info freq_output$info #> [1] \"Formula: real_sero = (observed_sero + sp - 1) / (se + sp -1)\" # Plot output of the frequentist approach plot_corrected_prev(freq_output) # Plot output of the bayesian approach  plot_corrected_prev(output) plot_corrected_prev(output, freq_output) # set facet = TRUE to display the confidence or credible intervals for each method plot_corrected_prev(output, freq_output, facet = TRUE)"},{"path":"https://oucru-modelling.github.io/serosv/articles/imperfect_test.html","id":"fitting-corrected-data","dir":"Articles","previous_headings":"Imperfect test","what":"Fitting corrected data","title":"Imperfect serological test","text":"Data seroprevalence correction Bayesian approach  Frequentist approach  Original data","code":"suppressWarnings(   corrected_data <- farrington_model(   output$corrected_se,   start=list(alpha=0.07,beta=0.1,gamma=0.03)) )  plot(corrected_data) #> Warning: No shared levels found between `names(values)` of the manual scale and the #> data's fill values. suppressWarnings(   corrected_data <- farrington_model(   freq_output$corrected_se,   start=list(alpha=0.07,beta=0.1,gamma=0.03)) )  plot(corrected_data) #> Warning: No shared levels found between `names(values)` of the manual scale and the #> data's fill values. suppressWarnings(   original_data <- farrington_model(   data,   start=list(alpha=0.07,beta=0.1,gamma=0.03)) ) plot(original_data) #> Warning: No shared levels found between `names(values)` of the manual scale and the #> data's fill values."},{"path":"https://oucru-modelling.github.io/serosv/articles/linelisting_vs_aggregated.html","id":"input-data-format","dir":"Articles","previous_headings":"","what":"Input data format","title":"Input data","text":"*_model() functions serosv require data argument input data fitted. package can handle linelisting aggregated data, infers format column names input data frame. means input data expected follow specific format. linelisting data: data must age, pos tot columns, age age vector pos vector counts sero positives age group tot vector total population age group aggregated data: data must age, status columns, age age vector individuals status vector sero positivity individual Example: Fitting linelisting aggregated data using polynomial_model()","code":"linelisting <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age), ] aggregated <- hav_bg_1964  # View the 2 different data format head(linelisting) #>     age seropositive year gender parvouml #> 1     1            0 2001      m        8 #> 2     1            0 2001      m        7 #> 3     1            0 2001      m        6 #> 406   1            0 2001      f       11 #> 407   1            0 2001      f        7 #> 408   1            0 2001      f        5 head(aggregated) #>   age pos tot #> 1   1   3  16 #> 2   2   3  15 #> 3   3   3  16 #> 4   4   4  13 #> 5   5   7  12 #> 6   6   4  15  # fit with aggregated data model1 <- polynomial_model(aggregated, type = \"Muench\") plot(model1) # fit with linelisting data model2 <- linelisting %>%    rename(status = seropositive) %>%    polynomial_model(type = \"Muench\") plot(model2)"},{"path":"https://oucru-modelling.github.io/serosv/articles/linelisting_vs_aggregated.html","id":"data-transformation","dir":"Articles","previous_headings":"","what":"Data transformation","title":"Input data","text":"serosv also offers function transform_data() convert linelisting aggregated data. information, refer Data transformation","code":"transform_data(   linelisting$age,    linelisting$seropositive,   stratum_col = \"age\") %>%    polynomial_model(type = \"Muench\") %>%    plot()"},{"path":"https://oucru-modelling.github.io/serosv/articles/model_multivariate_data.html","id":"bivariate-dale-model","dir":"Articles","previous_headings":"","what":"Bivariate Dale model","title":"Model multivariate serological data","text":"Fitting bivariate Dale model using bivariate_dale_model() use rubella_mumps_uk data example plot function bivariate_dale_model object takes 3 additional parameters: y1 y2 labels 2 diseases modeled plot_type type plot returned \"ci\" returns fitted line marginal prevalence 95% confidence interval \"sp\" returns conditional joint seroprevalence FOI inputs returns individual plots  line listing data vzv_parvo_be , use generate_quad_matrix() function create (NN, NP, PN, PP) fitting model","code":"data <- rubella_mumps_uk model <- bivariate_dale_model(age = data$age, y = data[, c(\"NN\", \"NP\", \"PN\", \"PP\")], monotonized=TRUE) plot(model, y1 = \"Rubella\", y2 = \"Mumps\", plot_type = \"sp\") generate_quad_matrix(vzv_parvo_be, vzv_res, parvo_res, age, discrete_age = F) #> # A tibble: 1,751 × 6 #>       age    NN    NP    PN    PP total #>     <dbl> <int> <int> <int> <int> <int> #>  1 0.0329     0     0     1     0     1 #>  2 0.0521     1     0     0     0     1 #>  3 0.266      0     0     1     0     1 #>  4 0.403      1     0     0     0     1 #>  5 0.493      0     0     0     1     1 #>  6 0.663      1     0     0     0     1 #>  7 0.811      1     0     0     0     1 #>  8 1.05       0     0     0     1     1 #>  9 1.07       1     0     1     0     2 #> 10 1.10       0     0     1     0     1 #> # ℹ 1,741 more rows"},{"path":"https://oucru-modelling.github.io/serosv/articles/model_quantitative_data.html","id":"mixture-model","dir":"Articles","previous_headings":"","what":"Mixture model","title":"Modeling directly from antibody levels","text":"Proposed model Two-component mixture model test result \\(Z\\) \\(Z_j (j = \\{, S\\})\\) latent mixing component density \\(f_j(z_j|\\theta_j)\\) \\(\\pi_{\\text{TRUE}}()\\) age-dependent mixing probability can represented \\[ f(z|z_I, z_S,) = (1-\\pi_{\\text{TRUE}}())f_S(z_S|\\theta_S)+\\pi_{\\text{TRUE}}()f_I(z_I|\\theta_I) \\] mean \\(E(Z|)\\) thus equals \\[ \\mu() = (1-\\pi_{\\text{TRUE}}())\\mu_S+\\pi_{\\text{TRUE}}()\\mu_I\\] true prevalence can calculated \\[ \\pi_{\\text{TRUE}}() = \\frac{\\mu() - \\mu_S}{\\mu_I - \\mu_S} \\] Force infection can calculated \\[ \\lambda_{TRUE} = \\frac{\\mu'()}{\\mu_I - \\mu()} \\] Fitting data fit mixture data, use mixture_model function  sero-prevalence FOI can esimated using function estimate_from_mixture","code":"df <- vzv_be_2001_2003[vzv_be_2001_2003$age < 40.5,] df <- df[order(df$age),] data <- df$VZVmIUml model <- mixture_model(antibody_level = data) model$info #>  #> Parameters: #>       pi    mu  sigma #> 1 0.1088 2.349 0.6804 #> 2 0.8912 6.439 0.9437 #>  #> Distribution: #> [1] \"norm\" #>  #> Constraints: #>    conpi    conmu consigma  #>   \"NONE\"   \"NONE\"   \"NONE\" plot(model) est_mixture <- estimate_from_mixture(df$age, data, mixture_model = model, threshold_status = df$seropositive, sp=83, monotonize = FALSE) plot(est_mixture) #> Warning: No shared levels found between `names(values)` of the manual scale and the #> data's fill values."},{"path":"https://oucru-modelling.github.io/serosv/articles/model_selection.html","id":"generate-models-comparison-data-frame","dir":"Articles","previous_headings":"","what":"Generate models comparison data.frame","title":"Model selection","text":"Function compare_models() used quickly computing AIC BIC values given model(s). function can take arbitrary number models models must created serosv’s set *_model() functions. return data.frame 4 columns: model model identifier. Either user defined name index based order provided. type type model (serosv model class) AIC AIC value fitted model (applicable) BIC AIC value fitted model (applicable) Sample usage Compare 4 models defined ","code":"# provide models with name compare_models(muench_linelist = model1, muench_aggregated = model2, griffith = model3, penalized_spline = model4) ##               model                   type       AIC       BIC ## 1   muench_linelist       polynomial_model 1368.9096 1373.9280 ## 2 muench_aggregated       polynomial_model  505.5269  508.8787 ## 3          griffith       polynomial_model  489.1185  495.8223 ## 4  penalized_spline penalized_spline_model  256.7061  271.3522 # provide models without name compare_models(model1, model2, model3, model4) ##   model                   type       AIC       BIC ## 1     1       polynomial_model 1368.9096 1373.9280 ## 2     2       polynomial_model  505.5269  508.8787 ## 3     3       polynomial_model  489.1185  495.8223 ## 4     4 penalized_spline_model  256.7061  271.3522 # user can provide arbitrary number of models compare_models(model3, model4) ##   model                   type      AIC      BIC ## 1     1       polynomial_model 489.1185 495.8223 ## 2     2 penalized_spline_model 256.7061 271.3522"},{"path":"https://oucru-modelling.github.io/serosv/articles/nonparametric_model.html","id":"local-estimation-by-polynomial","dir":"Articles","previous_headings":"","what":"Local estimation by polynomial","title":"Nonparametric model","text":"Refer Chapter 7.1 Proposed model Within local polynomial framework, linear predictor \\(\\eta()\\) approximated locally one particular value \\(a_0\\) age line (local linear) parabola (local quadratic). estimator \\(k\\)-th derivative \\(\\eta(a_0)\\), \\(k = 0,1,…,p\\) (degree local polynomial) followed: \\[ \\hat{\\eta}^{(k)}(a_0) = k!\\hat{\\beta}_k(a_0) \\] estimator prevalence age \\(a_0\\) given \\[ \\hat{\\pi}(a_0) = g^{-1}\\{ \\hat{\\beta}_0(a_0) \\} \\] \\(g\\) link function estimator force infection age \\(a_0\\) assuming \\(p \\ge 1\\) followed \\[ \\hat{\\lambda}(a_0) = \\hat{\\beta}_1(a_0) \\delta \\{ \\hat{\\beta}_0 (a_0) \\} \\] \\(\\delta \\{ \\hat{\\beta}_0(a_0) \\} = \\frac{dg^{-1} \\{ \\hat{\\beta}_0(a_0) \\} } {d\\hat{\\beta}_0(a_0)}\\) Fitting data Use plot_gcv() show GCV curves nearest neighbor method (left) constant bandwidth (right).  Use lp_model() fit local estimation polynomials.","code":"mump <- mumps_uk_1986_1987 plot_gcv(    mump$age, mump$pos, mump$tot,    nn_seq = seq(0.2, 0.8, by=0.1),    h_seq = seq(5, 25, by=1)  ) lp <- lp_model(mump, kern=\"tcub\", nn=0.7, deg=2) plot(lp)"},{"path":"https://oucru-modelling.github.io/serosv/articles/parametric_model.html","id":"polynomial-models","dir":"Articles","previous_headings":"","what":"Polynomial models","title":"Parametric models","text":"Refer Chapter 6.1.1 Use polynomial_model() fit polynomial model. use Hepatitis data Belgium 1993–1994 example.","code":"data <- hav_bg_1964"},{"path":"https://oucru-modelling.github.io/serosv/articles/parametric_model.html","id":"muench-model","dir":"Articles","previous_headings":"Polynomial models","what":"Muench model","title":"Parametric models","text":"Proposed model (Muench 1934) suggested model infection process -called “catalytic model”, distribution time spent susceptible class SIR model exponential rate \\(\\beta\\) \\[ \\pi() = k(1 - e^{-\\beta } ) \\] : \\(\\pi\\) seroprevalence age \\(\\) \\(1 - k\\) proportion population stay uninfected lifetime \\(\\) variable age catalytic model assuming \\(k = 1\\), force infection \\(\\lambda() = \\beta\\) Fitting data Muench’s model can estimated either defining k = 1 (degree one linear predictor, note irrelevant k proposed model) setting type = \"Muench\". can plot model plot() function.","code":"muench1 <- polynomial_model(data, k = 1) summary(muench1$info) #>  #> Call: #> glm(formula = age(k), family = binomial(link = link), data = df) #>  #> Coefficients: #>      Estimate Std. Error z value Pr(>|z|)     #> Age -0.050500   0.002457  -20.55   <2e-16 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance:    Inf  on 83  degrees of freedom #> Residual deviance: 97.275  on 82  degrees of freedom #> AIC: 219.19 #>  #> Number of Fisher Scoring iterations: 5  muench2 <- polynomial_model(data, type = \"Muench\") summary(muench2$info) #>  #> Call: #> glm(formula = age(k), family = binomial(link = link), data = df) #>  #> Coefficients: #>      Estimate Std. Error z value Pr(>|z|)     #> Age -0.050500   0.002457  -20.55   <2e-16 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance:    Inf  on 83  degrees of freedom #> Residual deviance: 97.275  on 82  degrees of freedom #> AIC: 219.19 #>  #> Number of Fisher Scoring iterations: 5 plot(muench2)"},{"path":"https://oucru-modelling.github.io/serosv/articles/parametric_model.html","id":"griffith-model","dir":"Articles","previous_headings":"Polynomial models","what":"Griffith model","title":"Parametric models","text":"Proposed model Griffith proposed model force infection followed \\[ \\lambda() = \\beta_1 + 2\\beta_2a   \\] can estimated using GLM linear predictor \\(\\eta() = \\beta_1 + \\beta_2a^{2}\\) Fitting data Similarly, can estimate Griffith’s model either defining k = 2, setting type = \"Griffith\"","code":"gf_model <- polynomial_model(data, type = \"Griffith\") plot(gf_model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/parametric_model.html","id":"grenfell-and-anderson-model","dir":"Articles","previous_headings":"Polynomial models","what":"Grenfell and Anderson model","title":"Parametric models","text":"Proposed model (Grenfell Anderson 1985) extended models Muench Griffiths suggest use higher order polynomial functions model force infection assumes prevalence model followed \\[ \\pi()  = 1 - e^{-\\Sigma_i \\beta_i ^} \\] implies force infection equals \\(\\lambda() = \\Sigma \\beta_i ^{-1}\\) Fitting data Grenfell Anderson’s model.","code":"grf_model <- polynomial_model(data, type = \"Grenfell\") plot(grf_model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/parametric_model.html","id":"nonlinear-models","dir":"Articles","previous_headings":"","what":"Nonlinear models","title":"Parametric models","text":"Refer Chapter 6.1.2","code":""},{"path":"https://oucru-modelling.github.io/serosv/articles/parametric_model.html","id":"farrington-model","dir":"Articles","previous_headings":"Nonlinear models","what":"Farrington model","title":"Parametric models","text":"Proposed model Farrington’s model, force infection defined non-negative \\(\\lambda() \\geq 0\\) increases peak linear fashion followed exponential decrease \\[ \\lambda() = (\\alpha - \\gamma)e^{-\\beta } + \\gamma \\] \\(\\gamma\\) called long term residual FOI, \\(\\rightarrow \\infty\\) , \\(\\lambda () \\rightarrow \\gamma\\) Integrating \\(\\lambda()\\) results following non-linear model prevalence \\[ \\pi () = 1 - e^{-\\int_0^\\lambda(s) ds} \\\\ = 1 - exp\\{ \\frac{\\alpha}{\\beta}ae^{-\\beta } + \\frac{1}{\\beta}(\\frac{\\alpha}{\\beta} - \\gamma)(e^{-\\beta } - 1) -\\gamma \\} \\] Fitting data Use farrington_model() fit Farrington’s model.","code":"farrington_md <- farrington_model(    rubella_uk_1986_1987,    start=list(alpha=0.07,beta=0.1,gamma=0.03)    ) plot(farrington_md)"},{"path":"https://oucru-modelling.github.io/serosv/articles/parametric_model.html","id":"weibull-model","dir":"Articles","previous_headings":"Nonlinear models","what":"Weibull model","title":"Parametric models","text":"Proposed model Weibull model, prevalence given \\[ \\pi (d) = 1 - e^{ - \\beta_0 d ^ {\\beta_1}} \\] \\(d\\) exposure time (difference age injection age test) model reformulated GLM model log - log link linear predictor using log(d) \\[\\eta(d) = log(\\beta_0) + \\beta_1 log(d)\\] Thus implies force infection monotone function exposure time followed \\[ \\lambda(d) = \\beta_0 \\beta_1 d^{\\beta_1 - 1} \\] Fitting data Use weibull_model() fit Weibull model.","code":"hcv <- hcv_be_2006[order(hcv_be_2006$dur), ]  wb_md <- hcv %>%    rename(     t = dur, status = seropositive   ) %>% weibull_model() plot(wb_md)"},{"path":"https://oucru-modelling.github.io/serosv/articles/parametric_model.html","id":"fractional-polynomial-model","dir":"Articles","previous_headings":"","what":"Fractional polynomial model","title":"Parametric models","text":"Refer Chapter 6.2 Proposed model Fractional polynomial model generalize conventional polynomial class functions. context binary responses, fractional polynomial degree \\(m\\) linear predictor defined followed \\[ \\eta_m(, \\beta, p_1, p_2, ...,p_m) = \\Sigma^m_{=0} \\beta_i H_i() \\] \\(m\\) integer, \\(p_1 \\le p_2 \\le... \\le p_m\\) sequence powers, \\(H_i()\\) transformation given \\[ H_i = \\begin{cases} ^{p_i} & \\text{ } p_i \\neq p_{-1}, \\\\ H_{-1}() \\times log()  & \\text{ } p_i = p_{-1}, \\end{cases} \\] Best power selection Use find_best_fp_powers() find powers gives lowest deviance score Fitting data Use fp_model() fit fractional polynomial model","code":"hav <- hav_be_1993_1994 best_p <- find_best_fp_powers(   hav,   p=seq(-2,3,0.1), mc=FALSE, degree=2, link=\"cloglog\" ) best_p #> $p #> [1] 1.5 1.6 #>  #> $deviance #> [1] 81.60333 #>  #> $model #>  #> Call:  glm(formula = as.formula(formulate(p_cur)), family = binomial(link = link)) #>  #> Coefficients: #> (Intercept)   I(age^1.5)   I(age^1.6)   #>    -3.61083      0.12443     -0.07656   #>  #> Degrees of Freedom: 85 Total (i.e. Null);  83 Residual #> Null Deviance:       1320  #> Residual Deviance: 81.6  AIC: 361.2 model <- fp_model(hav, p=c(1.5, 1.6), link=\"cloglog\") plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/repeated_cross_sectional.html","id":"age-time-varying-model","dir":"Articles","previous_headings":"","what":"Age-time varying model","title":"Model repeated cross-sectional data","text":"monitor changes population’s seroprevalence time, modelers often conduct multiple cross-sectional surveys different time points, using new representative sample. resulting data known repeated cross-sectional data. Proposed approach model repeated cross-sectional serological data, serosv offers age_time_model() function implements following workflow: Fit age-specific seroprevalence survey period Monotonize age-specific birth-cohort-specific prevalence time Fit monotonized age-specific seroprevalence survey period Fitting data function expects input data following columns: Either age status (linelisting) age pos + tot (aggregated) column date survey (specified via time_col argument) column id survey period (specified via grouping_col argument) monotonization method can specified via monotonize_method argument, serosv currently supports 2 options: Pooled adjacent violators average (monotonize_method = \"pava\") Shape constrained additive model (monotonize_method = \"scam\") users can also configure monotonize either: Age-specific seroprevalence time (age_correct = FALSE) birth cohort specific seroprevalence time (age_correct=TRUE) output data.frame dimension [number survey, 9], row corresponds single survey period. columns : column id survey period df - input data.frame corresponding survey period info - model seroprevalence monotonized_info - model monotonized seroprevalence monotonized_ci_mod - model monotonized confidence interval sp - predicted seroprevalence given input data foi - estimated force infection sp monotonized_sp - predicted monotonized seroprevalence given input data monotonized_foi - estimated force infection monotonized_sp visualization, plot function age_time_model offers following configurations facet whether visualize result survey period separately (facet=TRUE) single plot (facet=FALSE) modtype choose model visualize, model fitted input data (modtype=\"non-monotonized\") monotonized data (modtype=\"monotonized\") Example: output model PAVA monotonization    Example: output model SCAM monotonization","code":"# Prepare data  tb_nl <- tb_nl_1966_1973 %>%    mutate(     survey_year = age + birthyr,     survey_time = as.Date(paste0(survey_year, \"-01-01\"))   ) %>% select(-birthyr) %>%    filter(survey_year > 1966) %>%    group_by(age, survey_year, survey_time) %>%    summarize(pos = sum(pos), tot = sum(tot), .groups = \"drop\") out_pava <- tb_nl %>%    age_time_model(     time_col = \"survey_time\",      grouping_col = \"survey_year\",     age_correct = F,     monotonize_method = \"pava\"   ) %>%    suppressWarnings()  out_scam <- tb_nl %>%    age_time_model(     time_col = \"survey_time\",      grouping_col = \"survey_year\",     age_correct = T,     monotonize_method = \"scam\"   ) %>%    suppressWarnings() out_pava$out #> # A tibble: 7 × 9 #>   survey_year monotonized_info monotonized_ci_mod df       info   sp         #>         <dbl> <list>           <list>             <list>   <list> <list>     #> 1        1967 <gam>            <named list [2]>   <tibble> <gam>  <dbl [5]>  #> 2        1968 <gam>            <named list [2]>   <tibble> <gam>  <dbl [5]>  #> 3        1969 <gam>            <named list [2]>   <tibble> <gam>  <dbl [6]>  #> 4        1970 <gam>            <named list [2]>   <tibble> <gam>  <dbl [13]> #> 5        1971 <gam>            <named list [2]>   <tibble> <gam>  <dbl [8]>  #> 6        1972 <gam>            <named list [2]>   <tibble> <gam>  <dbl [8]>  #> 7        1973 <gam>            <named list [2]>   <tibble> <gam>  <dbl [5]>  #> # ℹ 3 more variables: foi <list>, monotonized_sp <list>, monotonized_foi <list> plot(out_pava, facet = TRUE, modtype = \"non-monotonized\") + ylim(c(0, 0.07)) #> Scale for y is already present. #> Adding another scale for y, which will replace the existing scale. plot(out_pava, facet = TRUE, modtype = \"monotonized\") + ylim(c(0, 0.07)) #> Scale for y is already present. #> Adding another scale for y, which will replace the existing scale. plot(out_pava, facet = FALSE, modtype = \"monotonized\") + ylim(c(0, 0.07)) #> Scale for y is already present. #> Adding another scale for y, which will replace the existing scale. plot(out_scam, facet = TRUE, modtype = \"non-monotonized\") + ylim(c(0, 0.07)) #> Scale for y is already present. #> Adding another scale for y, which will replace the existing scale. plot(out_scam, facet = TRUE, modtype = \"monotonized\") + ylim(c(0, 0.07)) #> Scale for y is already present. #> Adding another scale for y, which will replace the existing scale."},{"path":"https://oucru-modelling.github.io/serosv/articles/semiparametric_model.html","id":"sec-penalized-splines","dir":"Articles","previous_headings":"","what":"Penalized splines","title":"Semiparametric model","text":"Proposed model general model relating prevalence age can written GLM \\[ g(P(Y_i = 1| _i)) = g(\\pi(a_i)) = \\eta(a_i) \\] \\(g\\) link function \\(\\eta\\) linear predictor linear predictor can estimated semi-parametrically using penalized spline truncated power basis functions degree \\(p\\) fixed knots \\(\\kappa_1,..., \\kappa_k\\) followed \\[ \\eta(a_i) = \\beta_0 + \\beta_1a_i + ... + \\beta_p a_i^p + \\Sigma_{k=1}^ku_k(a_i - \\kappa_k)^p_+ \\] \\[ (a_i - \\kappa_k)^p_+ = \\begin{cases} 0, & a_i \\le \\kappa_k \\\\ (a_i - \\kappa_k)^p, & a_i > \\kappa_k \\end{cases} \\] matrix notation, mean structure model \\(\\eta(a_i)\\) becomes \\[ \\eta = X\\beta + Zu \\] \\(\\eta = [\\eta(a_i) ... \\eta(a_N) ]^T\\), \\(\\beta = [\\beta_0 \\beta_1 .... \\beta_p]^T\\), \\(u = [u_1 u_2 ... u_k]^T\\) regression corresponding design matrices \\[ X = \\begin{bmatrix} 1 & a_1 & a_1^2 & ... & a_1^p \\\\ 1 & a_2 & a_2^2 & ... & a_2^p \\\\ \\vdots & \\vdots & \\vdots & \\dots & \\vdots \\\\ 1 & a_N & a_N^2 & ... & a_N^p \\end{bmatrix}, Z = \\begin{bmatrix} (a_1 - \\kappa_1 )_+^p & (a_1 - \\kappa_2 )_+^p & \\dots & (a_1 - \\kappa_k)_+^p \\\\ (a_2 - \\kappa_1 )_+^p & (a_2 - \\kappa_2 )_+^p & \\dots & (a_2 - \\kappa_k)_+^p \\\\ \\vdots & \\vdots & \\dots & \\vdots \\\\ (a_N - \\kappa_1 )_+^p & (a_N - \\kappa_2 )_+^p & \\dots & (a_N - \\kappa_k)_+^p \\end{bmatrix} \\] FOI can derived \\[ \\hat{\\lambda}(a_i) = [\\hat{\\beta_1} , 2\\hat{\\beta_2}a_i, ..., p \\hat{\\beta} a_i ^{p-1} + \\Sigma^k_{k=1} p \\hat{u}_k(a_i - \\kappa_k)^{p-1}_+] \\delta(\\hat{\\eta}(a_i)) \\] \\(\\delta(.)\\) determined link function use model","code":""},{"path":"https://oucru-modelling.github.io/serosv/articles/semiparametric_model.html","id":"penalized-likelihood-framework","dir":"Articles","previous_headings":"Penalized splines","what":"Penalized likelihood framework","title":"Semiparametric model","text":"Refer Chapter 8.2.1 Proposed approach first approach fit model maximizing following penalized likelihood : \\(X\\beta + Zu\\) linear predictor \\(D\\) known semi-definite penalty matrix (Wahba 1978), (Green Silverman 1993) \\(y\\) response vector 1 unit vector, \\(c(.)\\) determined link function used \\(\\lambda\\) smoothing parameter (larger values –> smoother curves) \\(\\phi\\) overdispersion parameter equals 1 overdispersion Fitting data fit data using penalized likelihood framework, specify framework = \"pl\" Basis function can defined via s parameter, values s includes: \"tp\" thin plate regression splines \"cr\" cubic regression splines \"ps\" P-splines proposed (Eilers Marx 1996) \"ad\" Adaptive smoothers options, refer mgcv documentation (Wood 2017)","code":"pl <- parvob19_be_2001_2003 %>%    rename(status = seropositive) %>%    penalized_spline_model(s = \"tp\", framework = \"pl\")  pl$info #>  #> Family: binomial  #> Link function: logit  #>  #> Formula: #> spos ~ s(age, bs = s, sp = sp) #>  #> Estimated degrees of freedom: #> 6.16  total = 7.16  #>  #> UBRE score: 0.1206458 plot(pl)"},{"path":"https://oucru-modelling.github.io/serosv/articles/semiparametric_model.html","id":"generalized-linear-mixed-model-framework","dir":"Articles","previous_headings":"Penalized splines","what":"Generalized Linear Mixed Model framework","title":"Semiparametric model","text":"Refer Chapter 8.2.2 Proposed approach Looking back (1), constraint \\(u\\) \\(\\Sigma_ku_k^2 < C\\) positive value \\(C\\) equivalent choosing \\((\\beta, u)\\) maximise (1) \\(D = diag(0, 1)\\) \\(0\\) denotes zero vector length \\(p+1\\) 1 denotes unit vector length \\(K\\) fixed value \\(\\lambda\\) equivalent fitting following generalized linear mixed model Ngo Wand (2004) \\[ f(y|u) = exp\\{ \\phi^{-1} [y^T(X\\beta + Zu) - c(X\\beta + Zu)] + 1^Tc(y)\\},\\\\ u \\sim N(0, G) \\] similar notations \\(G = \\sigma^2_uI_{K \\times K}\\) Thus \\(Z\\) penalized assuming corresponding coefficients \\(u\\) random effect \\(u \\sim N(0, \\sigma^2_uI)\\). Fitting data fit data using penalized likelihood framework, specify framework = \"glmm\"","code":"glmm <- parvob19_be_2001_2003 %>%    rename(status = seropositive) %>%    penalized_spline_model(s = \"tp\", framework = \"glmm\")  #>  #>  Maximum number of PQL iterations:  20 #> iteration 1 #> iteration 2 #> iteration 3 #> iteration 4 glmm$info$gam #>  #> Family: binomial  #> Link function: logit  #>  #> Formula: #> spos ~ s(age, bs = s, sp = sp) #>  #> Estimated degrees of freedom: #> 6.45  total = 7.45 plot(glmm)"},{"path":"https://oucru-modelling.github.io/serosv/articles/sir_model.html","id":"basic-sir-model","dir":"Articles","previous_headings":"","what":"Basic SIR model","title":"SIR model","text":"Proposed model transmission model consists 3 compartments: susceptible (S), infected (), recovered (R) following assumptions: Individuals born susceptible group (exposure time age individual) transfer infected class recovered class Recovered individuals gained lifelong immunity Age homogeneity described system 3 differential equations \\[ \\begin{cases} \\frac{dS(t)}{dt} = B(t) (1-p) - \\lambda(t)S(t) - \\mu S(t) \\\\ \\frac{dI(t)}{dt} = \\lambda(t)S(t) - \\nu (t) - \\mu (t) - \\alpha (t)  \\\\ \\frac{dR(t)}{dt} =  B(t) p + \\nu (t) - \\mu R(t) \\end{cases} \\] : \\(B(t) = \\mu N(t)\\) \\(\\lambda(t) = \\beta (t)\\) \\(\\beta\\) transmission rate \\(\\mu\\) natural death rate \\(\\nu\\) recovery rate \\(\\alpha\\) disease related death rate \\(p\\) proportion newborn vaccinated moved directly recovered compartment Fitting data fit basic SIR model, use sir_basic_model() specify following parameters state - initial population compartment times - time sequence parameters - parameters SIR model","code":"state <- c(S=4999, I=1, R=0) parameters <- c(   mu=1/75, # 1 divided by life expectancy (75 years old)   alpha=0, # no disease-related death   beta=0.0005, # transmission rate   nu=1, # 1 year for infected to recover   p=0 # no vaccination at birth ) times <- seq(0, 250, by=0.1) model <- sir_basic_model(times, state, parameters) model$parameters #>         mu      alpha       beta         nu          p  #> 0.01333333 0.00000000 0.00050000 1.00000000 0.00000000 plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/sir_model.html","id":"sir-model-with-constant-force-of-infection-at-endemic-state","dir":"Articles","previous_headings":"","what":"SIR model with constant Force of Infection at Endemic state","title":"SIR model","text":"Proposed model transmission model consists 3 compartments: susceptible (S), infected (), recovered (R) following assumptions: Time homogeneity Age heterogeneity Described system 3 differential equations \\[ \\begin{cases} \\frac{ds()}{da} = -\\lambda s() \\\\ \\frac{di()}{da} = \\lambda s() - \\nu ()  \\\\ \\frac{dr()}{da} =  \\nu () \\end{cases} \\] : \\(s(), (), r()\\) proportion susceptible, infected, recovered population age group \\(\\) respectively \\(\\lambda\\) force infection \\(\\nu\\) recovery rate FItting data fit SIR model constant FOI, use sir_static_model() specify following parameters state - initial proportion compartment ages - age sequence parameters - parameters model","code":"state <- c(s=0.99,i=0.01,r=0) parameters <- c(   lambda = 0.05,   nu=1/(14/365) # 2 weeks to recover ) ages<-seq(0, 90, by=0.01) model <- sir_static_model(ages, state, parameters) model$parameters #>   lambda       nu  #>  0.05000 26.07143 plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/sir_model.html","id":"sir-model-with-sub-populations","dir":"Articles","previous_headings":"","what":"SIR model with sub populations","title":"SIR model","text":"Proposed model Extends SIR model interacting sub-populations (different age groups) K subpopulations, WAIFW matrix mixing matrix given \\[ C = \\begin{bmatrix} \\beta_{11} & \\beta_{12}  & ... & \\beta_{1K} \\\\ \\beta_{21} & \\beta_{22}  & ... & \\beta_{2K} \\\\ \\vdots & \\vdots  & ... & \\vdots \\\\ \\beta_{K1} & \\beta_{K2}  & ... & \\beta_{KK} \\\\ \\end{bmatrix} \\] system differential equations \\(th\\) subpopulation given \\[ \\begin{cases} \\frac{dS_i(t)}{dt} = -(\\sum^K_{j=1}\\beta_{ij}I_j(t)) S_i(t) + N_i\\mu_i - \\mu_i S_i(t) \\\\ \\frac{dI_i(t)}{dt} = (\\sum^K_{j=1}\\beta_{ij}I_j(t)) S_i(t)  - (\\nu_i + \\mu_i) I_i(t)  \\\\ \\frac{dR_i(t)}{dt} = \\nu_i I_i(t)  - \\mu_i R_i(t) \\end{cases} \\] FItting data fit SIR model subpopulations, use sir_subpops_model() specify following parameters state - initial proportion compartment population beta_matrix - WAIFW matrix times - time sequence parameters - parameters model","code":"k <- 2 # number of population state <- c(   # proportion of each compartment for each population   s = c(0.8, 0.6),    i = c(0.2, 0.4),   r = c(  0,   0) ) beta_matrix <- c(   c(0.05, 0.00),   c(0.00, 0.05) ) parameters <- list(   beta = matrix(beta_matrix, nrow=k, ncol=k, byrow=TRUE),   nu = c(1/30, 1/30),   mu = 0.001,   k = k ) times<-seq(0,10000,by=0.5) model <- sir_subpops_model(times, state, parameters) model$parameters #> $beta #>      [,1] [,2] #> [1,] 0.05 0.00 #> [2,] 0.00 0.05 #>  #> $nu #> [1] 0.03333333 0.03333333 #>  #> $mu #> [1] 0.001 #>  #> $k #> [1] 2 plot(model) # returns plot for each population #> $subpop_1 #>  #> $subpop_2"},{"path":"https://oucru-modelling.github.io/serosv/articles/sir_model.html","id":"mseir-model","dir":"Articles","previous_headings":"","what":"MSEIR model","title":"SIR model","text":"Proposed model Extends SIR model 2 additional compartments: maternal antibody (M) exposed period (E) described following system ordinary differential equation \\[ \\begin{cases} \\frac{dM()}{da} = -(\\gamma + \\mu())M() \\\\ \\frac{dS()}{da} = \\gamma M() - (\\lambda() + \\mu()) S() \\\\ \\frac{dE()}{da} = \\lambda() S()  - (\\sigma + \\mu()) E() \\\\ \\frac{dI()}{da} = \\sigma() E()  - (\\nu + \\mu()) ()  \\\\ \\frac{dR()}{da} = \\nu ()  - \\mu() R() \\end{cases} \\] \\(M(0)\\) = B, number births population \\(\\gamma\\) rate antibody decaying \\(\\lambda()\\) force infection age \\(\\) \\(\\mu()\\) natural death rate age \\(\\) \\(\\sigma\\) rate becoming infected exposed \\(\\nu\\) recovery rate Fitting data fit MSEIR, use mseir_model() specify following parameters - age sequence model parameters including gamma, lambda, sigma, nu","code":"model <- mseir_model(   a=seq(from=1,to=20,length=500), # age range from 0 -> 20 yo   gamma=1/0.5, # 6 months in the maternal antibodies   lambda=0.2,  # 5 years in the susceptible class   sigma=26.07, # 14 days in the latent class   nu=36.5      # 10 days in the infected class ) model$parameters #> $gamma #> [1] 2 #>  #> $lambda #> [1] 0.2 #>  #> $sigma #> [1] 26.07 #>  #> $nu #> [1] 36.5 plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/visualizing_model.html","id":"visualize-model","dir":"Articles","previous_headings":"","what":"Visualize model","title":"Model visualization","text":"visualize model, user can simply use function plot  implementation confidence interval calculation yet available, plot function simply visualize seroprevalence line","code":"hav <- hav_be_1993_1994 model <- fp_model(   hav,   p=c(1.5, 1.6), link=\"cloglog\") plot(model) rubella <- rubella_uk_1986_1987  farrington_md <- farrington_model(    rubella,    start=list(alpha=0.07,beta=0.1,gamma=0.03)    ) plot(farrington_md)"},{"path":"https://oucru-modelling.github.io/serosv/articles/visualizing_model.html","id":"customize-the-plot","dir":"Articles","previous_headings":"","what":"Customize the plot","title":"Model visualization","text":"Function set_plot_style() provided customize attributes plot. Current modifiable attributes include color, linetype seroprevalence, foi fill color confidence interval","code":"gf_model <- polynomial_model(hav_bg_1964, type = \"Griffith\")  # customize plot plot(gf_model) +   set_plot_style(     sero = \"#3de071\",     foi = \"#2f22e0\",     ci = \"#aaf2b2\",     foi_line = \"dotted\",      sero_line = \"dotdash\"   ) #> Scale for colour is already present. #> Adding another scale for colour, which will replace the existing scale. #> Scale for linetype is already present. #> Adding another scale for linetype, which will replace the existing scale. #> Scale for fill is already present. #> Adding another scale for fill, which will replace the existing scale."},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"polynomial-models","dir":"Articles > Web-only","previous_headings":"","what":"Polynomial models","title":"serosv: model infectious disease parameters from serosurveys","text":"Refer Chapter 6.1.1 Use polynomial_model() fit polynomial model. use Hepatitis data Belgium 1993–1994 example.","code":"a <- hav_bg_1964 neg <- a$tot -a$pos pos <- a$pos age <- a$age tot <- a$tot"},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"muench-model","dir":"Articles > Web-only","previous_headings":"Polynomial models","what":"Muench model","title":"serosv: model infectious disease parameters from serosurveys","text":"Muench’s model can estimated either defining k = 1 (degree one linear predictor) setting type = \"Muench\". can plot model plot() function.","code":"muench1 <- polynomial_model(age, pos, neg, k = 1) summary(muench1$info) #>  #> Call: #> glm(formula = age(k), family = binomial(link = \"log\"), data = df) #>  #> Coefficients: #>      Estimate Std. Error z value Pr(>|z|)     #> Age -0.050500   0.002457  -20.55   <2e-16 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance:    Inf  on 83  degrees of freedom #> Residual deviance: 97.275  on 82  degrees of freedom #> AIC: 219.19 #>  #> Number of Fisher Scoring iterations: 5  muench2 <- polynomial_model(age, pos, neg, type = \"Muench\") summary(muench2$info) #>  #> Call: #> glm(formula = age(k), family = binomial(link = \"log\"), data = df) #>  #> Coefficients: #>      Estimate Std. Error z value Pr(>|z|)     #> Age -0.050500   0.002457  -20.55   <2e-16 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance:    Inf  on 83  degrees of freedom #> Residual deviance: 97.275  on 82  degrees of freedom #> AIC: 219.19 #>  #> Number of Fisher Scoring iterations: 5 plot(muench2)"},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"griffith-model","dir":"Articles > Web-only","previous_headings":"Polynomial models","what":"Griffith model","title":"serosv: model infectious disease parameters from serosurveys","text":"Similarly, can estimate Griffith’s model.","code":"gf_model <- polynomial_model(age, pos, neg, type = \"Griffith\") plot(gf_model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"grenfell-and-anderson-model","dir":"Articles > Web-only","previous_headings":"Polynomial models","what":"Grenfell and Anderson model","title":"serosv: model infectious disease parameters from serosurveys","text":"Grenfell Anderson’s model.","code":"grf_model <- polynomial_model(age,pos,neg,type = \"Grenfell\") plot(grf_model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"nonlinear-models","dir":"Articles > Web-only","previous_headings":"","what":"Nonlinear models","title":"serosv: model infectious disease parameters from serosurveys","text":"Refer Chapter 6.1.2","code":""},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"farrington-model","dir":"Articles > Web-only","previous_headings":"Nonlinear models","what":"Farrington model","title":"serosv: model infectious disease parameters from serosurveys","text":"Use farrington_model() fit Farrington’s model.","code":"rubella <- rubella_uk_1986_1987 rubella$neg <- rubella$tot - rubella$pos  farrington_md <- farrington_model(    rubella$age, rubella$pos, rubella$tot,    start=list(alpha=0.07,beta=0.1,gamma=0.03)    ) plot(farrington_md)"},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"weibull-model","dir":"Articles > Web-only","previous_headings":"Nonlinear models","what":"Weibull model","title":"serosv: model infectious disease parameters from serosurveys","text":"Use weibull_model() fit Weibull model.","code":"hcv <- hcv_be_2006[order(hcv_be_2006$dur), ] dur <- hcv$dur infected <- hcv$seropositive  wb_md <- weibull_model(    t = dur,    spos = infected    ) plot(wb_md)"},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"fractional-polynomial-model","dir":"Articles > Web-only","previous_headings":"","what":"Fractional polynomial model","title":"serosv: model infectious disease parameters from serosurveys","text":"Refer Chapter 6.2 Use find_best_fp_powers() find powers gives lowest deviance score Use fp_model() fit fractional polynomial model","code":"hav <- hav_be_1993_1994 best_p <- find_best_fp_powers(   hav$age, hav$pos, hav$tot,   p=seq(-2,3,0.1), mc=FALSE, degree=2, link=\"cloglog\" ) best_p #> $p #> [1] 1.5 1.6 #>  #> $deviance #> [1] 81.60333 #>  #> $model #>  #> Call:  glm(formula = as.formula(formulate(p_cur)), family = binomial(link = link)) #>  #> Coefficients: #> (Intercept)   I(age^1.5)   I(age^1.6)   #>    -3.61083      0.12443     -0.07656   #>  #> Degrees of Freedom: 85 Total (i.e. Null);  83 Residual #> Null Deviance:       1320  #> Residual Deviance: 81.6  AIC: 361.2 model <- fp_model(   hav$age, hav$pos, hav$tot,   p=c(1.5, 1.6), link=\"cloglog\") plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"nonparametric-model","dir":"Articles > Web-only","previous_headings":"","what":"Nonparametric model","title":"serosv: model infectious disease parameters from serosurveys","text":"Refer Chapter 7.1","code":""},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"local-estimation-by-polynomial","dir":"Articles > Web-only","previous_headings":"Nonparametric model","what":"Local estimation by polynomial","title":"serosv: model infectious disease parameters from serosurveys","text":"Use plot_gcv() show GCV curves nearest neighbor method (left) constant bandwidth (right).  Use lp_model() fit local estimation polynomials.","code":"mump <- mumps_uk_1986_1987 age <- mump$age pos <- mump$pos tot <- mump$tot y <- pos/tot plot_gcv(    age, pos, tot,    nn_seq = seq(0.2, 0.8, by=0.1),    h_seq = seq(5, 25, by=1)  ) lp <- lp_model(age, pos, tot, kern=\"tcub\", nn=0.7, deg=2) plot(lp)"},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"modeling-multivariate-serological-data","dir":"Articles > Web-only","previous_headings":"","what":"Modeling Multivariate serological data","title":"serosv: model infectious disease parameters from serosurveys","text":"Fitting bivariate Dale model using bivariate_dale_model() use rubella_mumps_uk data example plot function bivariate_dale_model object takes 3 additional parameters: y1 y2 labels 2 diseases modeled plot_type type plot returned \"ci\" returns fitted line marginal prevalence 95% confidence interval \"sp\" returns conditional joint seroprevalence FOI inputs returns individual plots  line listing data vzv_parvo_be , use generate_quad_matrix() function create (NN, NP, PN, PP) fitting model","code":"data <- rubella_mumps_uk model <- bivariate_dale_model(age = data$age, y = data[, c(\"NN\", \"NP\", \"PN\", \"PP\")], monotonized=TRUE) plot(model, y1 = \"Rubella\", y2 = \"Mumps\", plot_type = \"ci\") generate_quad_matrix(vzv_parvo_be, vzv_res, parvo_res, age, discrete_age = F)"},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Anh Phan Truong Quynh. Author, maintainer. Nguyen Pham Nguyen . Author. Long Bui Thanh. Author. Tuyen Huynh. Author. Thinh Ong. Author. Marc Choisy. Author.","code":""},{"path":"https://oucru-modelling.github.io/serosv/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Phan Truong Quynh , Pham Nguyen N, Bui Thanh L, Huynh T, Ong T, Choisy M (2025). serosv: Model Infectious Disease Parameters Serosurveys. https://oucru-modelling.github.io/serosv/, https://github.com/OUCRU-Modelling/serosv.","code":"@Manual{,   title = {serosv: Model Infectious Disease Parameters from Serosurveys},   author = {Anh {Phan Truong Quynh} and Nguyen {Pham Nguyen The} and Long {Bui Thanh} and Tuyen Huynh and Thinh Ong and Marc Choisy},   year = {2025},   note = {https://oucru-modelling.github.io/serosv/, https://github.com/OUCRU-Modelling/serosv}, }"},{"path":"https://oucru-modelling.github.io/serosv/index.html","id":"serosv-","dir":"","previous_headings":"","what":"Model Infectious Disease Parameters from Serosurveys","title":"Model Infectious Disease Parameters from Serosurveys","text":"serosv easy--use efficient tool estimate infectious diseases parameters (seroprevalence force infection) using serological data. current version mostly based book “Modeling Infectious Disease Parameters Based Serological Social Contact Data – Modern Statistical Perspective” Hens et al., 2012 Springer.","code":""},{"path":"https://oucru-modelling.github.io/serosv/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Model Infectious Disease Parameters from Serosurveys","text":"can install development version serosv :","code":"# install.packages(\"devtools\") devtools::install_github(\"OUCRU-Modelling/serosv\")"},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/index.html","id":"datasets","dir":"","previous_headings":"Feature overview","what":"Datasets","title":"Model Infectious Disease Parameters from Serosurveys","text":"serosv contains 15 built-serological datasets provided Hens et al., 2012 Springer. Simply call name load dataset, example:","code":"rubella <- rubella_uk_1986_1987"},{"path":"https://oucru-modelling.github.io/serosv/index.html","id":"methods","dir":"","previous_headings":"Feature overview","what":"Methods","title":"Model Infectious Disease Parameters from Serosurveys","text":"following methods available estimate seroprevalence force infection. Parametric approaches: Muench’s model Griffiths’ model Grenfell Anderson’s model Farrington’s model Weibull model Fractional polynomial models Nonparametric approaches: Local estimation polynomials Semiparametric approaches: Penalized splines: Penalized likelihood framework Generalized Linear Mixed Model framework Hierarchical Bayesian approaches: Hierarchical Farrington model Hierarchical log-logistic model","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/index.html","id":"fitting-rubella-data-from-the-uk","dir":"","previous_headings":"Demo","what":"Fitting rubella data from the UK","title":"Model Infectious Disease Parameters from Serosurveys","text":"Load rubella UK dataset. Find power best second degree fractional polynomial monotonicity constraint logit link function. power appears (-0.9,-0.9). Finally, fit second degree fractional polynomial.","code":"library(serosv) rubella <- rubella_uk_1986_1987  best_2d_mn <- find_best_fp_powers(   rubella,   p=seq(-2,3,0.1), mc = T, degree=2, link=\"logit\" )  best_2d_mn #> $p #> [1] -0.9 -0.9 #>  #> $deviance #> [1] 37.57966 #>  #> $model #>  #> Call:  glm(formula = as.formula(formulate(p_cur)), family = binomial(link = link)) #>  #> Coefficients: #>               (Intercept)                I(age^-0.9)   #>                     4.342                     -4.696   #> I(I(age^-0.9) * log(age))   #>                    -9.845   #>  #> Degrees of Freedom: 43 Total (i.e. Null);  41 Residual #> Null Deviance:       1369  #> Residual Deviance: 37.58     AIC: 210.1 fpmd <- fp_model(   rubella,   p=c(-0.9, -0.9), link=\"logit\")  plot(fpmd)"},{"path":"https://oucru-modelling.github.io/serosv/index.html","id":"fitting-parvo-b19-data-from-finland","dir":"","previous_headings":"Demo","what":"Fitting Parvo B19 data from Finland","title":"Model Infectious Disease Parameters from Serosurveys","text":"","code":"library(dplyr) #> Warning: package 'dplyr' was built under R version 4.3.1 #>  #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #>  #>     filter, lag #> The following objects are masked from 'package:base': #>  #>     intersect, setdiff, setequal, union parvob19 <- parvob19_fi_1997_1998  # for linelisting data, either transform it to aggregated transform_data(   parvob19$age,    parvob19$seropositive,   stratum_col = \"age\") |>   polynomial_model(type = \"Muench\") |>   plot() # or fit data as is parvob19 |>   rename(status = seropositive) |>   polynomial_model(type = \"Muench\") |>   plot()"},{"path":"https://oucru-modelling.github.io/serosv/reference/add_thresholds.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize positive threshold at different dilution factors — add_thresholds","title":"Visualize positive threshold at different dilution factors — add_thresholds","text":"Visualize positive threshold different dilution factors","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/add_thresholds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize positive threshold at different dilution factors — add_thresholds","text":"","code":"add_thresholds(dilution_factors, positive_threshold = 0.1, shift_text = 0.15)"},{"path":"https://oucru-modelling.github.io/serosv/reference/add_thresholds.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize positive threshold at different dilution factors — add_thresholds","text":"dilution_factors dilution factors visualized positive_threshold titer threshold sample considered positive shift_text adjust much text shifted along x-axis (relative threshold line)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/age_time_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Age-time varying seroprevalence — age_time_model","title":"Age-time varying seroprevalence — age_time_model","text":"Fit age-stratified seroprevalence across multiple time points. Also try monotonize age (birth cohort) - specific seroprevalence.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/age_time_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Age-time varying seroprevalence — age_time_model","text":"","code":"age_time_model(   data,   time_col = \"date\",   grouping_col = \"group\",   age_correct = F,   le = 512,   ci = 0.95,   monotonize_method = \"pava\" )"},{"path":"https://oucru-modelling.github.io/serosv/reference/age_time_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Age-time varying seroprevalence — age_time_model","text":"data - input data, must `age`, `status`, time, group columns, group column determines data aggregated time_col - name column time (default `date`) grouping_col - name column time (default `group`) age_correct - boolean, `TRUE`, monotonize age-specific prevalence. Monotonize birth cohort-specific seroprevalence otherwise. le - number bins generate age grid, used monotonizing data ci - confidence interval smoothing monotonize_method - either \"pava\" \"scam\"","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/age_time_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Age-time varying seroprevalence — age_time_model","text":"list class time_age_model 4 items data.frame dimension n_group x 9, columns `info`, `sp`, `foi` store output non-monotonized data `monotonized_info`, `monotonized_sp`,  `monotonized_foi`,  `monotonized_ci_mod` store output monotonized data grouping_col name column grouping age_correct boolean indicating whether data monotonized across age cohort datatype whether input data aggregated line-listing data","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/bivariate_dale_model.html","id":null,"dir":"Reference","previous_headings":"","what":"A bivariate Dale model — bivariate_dale_model","title":"A bivariate Dale model — bivariate_dale_model","text":"Refers section 12.2","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/bivariate_dale_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A bivariate Dale model — bivariate_dale_model","text":"","code":"bivariate_dale_model(age, y, constant_or = F, monotonized = T, sparopt = 0)"},{"path":"https://oucru-modelling.github.io/serosv/reference/bivariate_dale_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A bivariate Dale model — bivariate_dale_model","text":"age age vector y dataframe predictors (NN, NP, PN, PP), order constant_or whether set constant restriction monotonized whether monotonize seroprevalence sparopt smoothing parameter VGAM model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/bivariate_dale_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A bivariate Dale model — bivariate_dale_model","text":"","code":"data <- rubella_mumps_uk model <- bivariate_dale_model(age = data$age,         y = data[, c(\"NN\", \"NP\", \"PN\", \"PP\")], monotonized=TRUE) plot(model, y1 = \"Rubella\", y2 = \"Mumps\", plot_type = \"sp\")"},{"path":"https://oucru-modelling.github.io/serosv/reference/compare_models.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare models — compare_models","title":"Compare models — compare_models","text":"Compare models","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compare_models.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare models — compare_models","text":"","code":"compare_models(...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compare_models.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare models — compare_models","text":"... models compared. Must models created serosv. models' names provided, indices used instead `model` column returned data.frame.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compare_models.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare models — compare_models","text":"data.frame 4 columns model name index model type model type given model (serosv model name) AIC AIC value model (lower value indicates better fit) BIC BIC value model (lower value indicates better fit)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.age_time_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute confidence interval for time age model — compute_ci.age_time_model","title":"Compute confidence interval for time age model — compute_ci.age_time_model","text":"Compute confidence interval time age model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.age_time_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute confidence interval for time age model — compute_ci.age_time_model","text":"","code":"compute_ci.age_time_model(x, ci = 0.95, le = 100, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.age_time_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute confidence interval for time age model — compute_ci.age_time_model","text":"x - serosv models ci - confidence interval le - number data computing confidence interval ... - arbitrary argument","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.age_time_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute confidence interval for time age model — compute_ci.age_time_model","text":"confidence interval dataframe n_group x 3 cols, columns `group`, `sp_df`, `foi_df`","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.fp_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute confidence interval for fractional polynomial model — compute_ci.fp_model","title":"Compute confidence interval for fractional polynomial model — compute_ci.fp_model","text":"Compute confidence interval fractional polynomial model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.fp_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute confidence interval for fractional polynomial model — compute_ci.fp_model","text":"","code":"compute_ci.fp_model(x, ci = 0.95, le = 100, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.fp_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute confidence interval for fractional polynomial model — compute_ci.fp_model","text":"x - serosv models ci - confidence interval le - number data computing confidence interval ... - arbitrary argument","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.fp_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute confidence interval for fractional polynomial model — compute_ci.fp_model","text":"confidence interval dataframe 4 variables, x y fitted values ymin ymax confidence interval","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.glmm_ps_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute confidence interval for GLMM — compute_ci.glmm_ps_model","title":"Compute confidence interval for GLMM — compute_ci.glmm_ps_model","text":"Compute confidence interval GLMM","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.glmm_ps_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute confidence interval for GLMM — compute_ci.glmm_ps_model","text":"","code":"compute_ci.glmm_ps_model(x, ci = 0.95, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.glmm_ps_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute confidence interval for GLMM — compute_ci.glmm_ps_model","text":"x - serosv models ci - confidence interval ... - arbitrary arguments","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.hierarchical_bayesian_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute 95% credible interval for hierarchical Bayesian model — compute_ci.hierarchical_bayesian_model","title":"Compute 95% credible interval for hierarchical Bayesian model — compute_ci.hierarchical_bayesian_model","text":"Compute 95% credible interval hierarchical Bayesian model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.hierarchical_bayesian_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute 95% credible interval for hierarchical Bayesian model — compute_ci.hierarchical_bayesian_model","text":"","code":"compute_ci.hierarchical_bayesian_model(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.hierarchical_bayesian_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute 95% credible interval for hierarchical Bayesian model — compute_ci.hierarchical_bayesian_model","text":"x - serosv models ... - arbitrary arguments","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.hierarchical_bayesian_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute 95% credible interval for hierarchical Bayesian model — compute_ci.hierarchical_bayesian_model","text":"list confidence interval seroprevalence foi. confidence interval dataframe 4 variables, x y fitted values ymin ymax confidence interval","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute confidence interval — compute_ci","title":"Compute confidence interval — compute_ci","text":"Compute confidence interval","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute confidence interval — compute_ci","text":"","code":"compute_ci(x, ci = 0.95, le = 100, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute confidence interval — compute_ci","text":"x - serosv models ci - confidence interval le - number data computing confidence interval ... - arbitrary argument","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute confidence interval — compute_ci","text":"confidence interval dataframe 4 variables, x y fitted values ymin ymax confidence interval","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.lp_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute confidence interval for local polynomial model — compute_ci.lp_model","title":"Compute confidence interval for local polynomial model — compute_ci.lp_model","text":"Compute confidence interval local polynomial model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.lp_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute confidence interval for local polynomial model — compute_ci.lp_model","text":"","code":"compute_ci.lp_model(x, ci = 0.95, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.lp_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute confidence interval for local polynomial model — compute_ci.lp_model","text":"x - serosv models ci - confidence interval ... - arbitrary arguments","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.lp_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute confidence interval for local polynomial model — compute_ci.lp_model","text":"confidence interval dataframe 4 variables, x y fitted values ymin ymax confidence interval","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.mixture_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute confidence interval for mixture model — compute_ci.mixture_model","title":"Compute confidence interval for mixture model — compute_ci.mixture_model","text":"Compute confidence interval mixture model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.mixture_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute confidence interval for mixture model — compute_ci.mixture_model","text":"","code":"compute_ci.mixture_model(x, ci = 0.95, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.mixture_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute confidence interval for mixture model — compute_ci.mixture_model","text":"x - serosv mixture_model object ci - confidence interval ... - arbitrary arguments","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.mixture_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute confidence interval for mixture model — compute_ci.mixture_model","text":"list confidence interval susceptible infected. confidence interval list 2 items lower upper bound interval.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.penalized_spline_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute confidence interval for penalized_spline_model — compute_ci.penalized_spline_model","title":"Compute confidence interval for penalized_spline_model — compute_ci.penalized_spline_model","text":"Compute confidence interval penalized_spline_model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.penalized_spline_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute confidence interval for penalized_spline_model — compute_ci.penalized_spline_model","text":"","code":"compute_ci.penalized_spline_model(x, ci = 0.95, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.penalized_spline_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute confidence interval for penalized_spline_model — compute_ci.penalized_spline_model","text":"x - serosv models ci - confidence interval ... - arbitrary arguments","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.penalized_spline_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute confidence interval for penalized_spline_model — compute_ci.penalized_spline_model","text":"list confidence interval seroprevalence foi confidence interval dataframe 4 variables, x y fitted values ymin ymax confidence interval","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.weibull_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute confidence interval for Weibull model — compute_ci.weibull_model","title":"Compute confidence interval for Weibull model — compute_ci.weibull_model","text":"Compute confidence interval Weibull model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.weibull_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute confidence interval for Weibull model — compute_ci.weibull_model","text":"","code":"compute_ci.weibull_model(x, ci = 0.95, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.weibull_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute confidence interval for Weibull model — compute_ci.weibull_model","text":"x - serosv models ci - confidence interval ... - arbitrary argument","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.weibull_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute confidence interval for Weibull model — compute_ci.weibull_model","text":"confidence interval dataframe 4 variables, x y fitted values ymin ymax confidence interval","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/correct_prevalence.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the true sero prevalence using Frequentist/Bayesian estimation — correct_prevalence","title":"Estimate the true sero prevalence using Frequentist/Bayesian estimation — correct_prevalence","text":"Estimate true sero prevalence using Frequentist/Bayesian estimation","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/correct_prevalence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the true sero prevalence using Frequentist/Bayesian estimation — correct_prevalence","text":"","code":"correct_prevalence(   data,   bayesian = TRUE,   init_se = 0.95,   init_sp = 0.8,   study_size_se = 1000,   study_size_sp = 1000,   chains = 1,   warmup = 1000,   iter = 2000 )"},{"path":"https://oucru-modelling.github.io/serosv/reference/correct_prevalence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the true sero prevalence using Frequentist/Bayesian estimation — correct_prevalence","text":"data input data frame, must either `age`, `pos`, `tot` columns (aggregated data) `age`, `status` (linelisting data) bayesian whether adjust sero-prevalence using Bayesian frequentist approach. set `TRUE`, true sero-prevalence estimated using MCMC. init_se sensitivity serological test init_sp specificity serological test study_size_se (applicable `bayesian=TRUE`) study size sensitivity validation study (.e., number confirmed infected patients study) study_size_sp (applicable `bayesian=TRUE`) study size specificity validation study (.e., number confirmed non-infected patients study) chains (applicable `bayesian=TRUE`) number Markov chains warmup (applicable `bayesian=TRUE`) number warm runs iter (applicable `bayesian=TRUE`) number iterations","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/correct_prevalence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the true sero prevalence using Frequentist/Bayesian estimation — correct_prevalence","text":"list 3 items info estimated parameters (`bayesian = TRUE`) formula compute corrected prevalence (`bayesian = FALSE`) df data.frame input data (aggregated form) corrected_sero data.frame containing age, corresponding estimated seroprevalance 95% confidence/credible interval, adjusted tot pos","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/correct_prevalence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the true sero prevalence using Frequentist/Bayesian estimation — correct_prevalence","text":"","code":"data <- rubella_uk_1986_1987 correct_prevalence(data) #>  #> SAMPLING FOR MODEL 'prevalence_correction' NOW (CHAIN 1). #> Chain 1:  #> Chain 1: Gradient evaluation took 0.000244 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.44 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1:  #> Chain 1:  #> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup) #> Chain 1: Iteration:  200 / 2000 [ 10%]  (Warmup) #> Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup) #> Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup) #> Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup) #> Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup) #> Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling) #> Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling) #> Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling) #> Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling) #> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling) #> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling) #> Chain 1:  #> Chain 1:  Elapsed Time: 1.656 seconds (Warm-up) #> Chain 1:                1.233 seconds (Sampling) #> Chain 1:                2.889 seconds (Total) #> Chain 1:  #> $info #>                    mean      se_mean          sd          2.5%           25% #> est_se     9.579691e-01 0.0001720772 0.005234571  9.468221e-01  9.545560e-01 #> est_sp     8.070363e-01 0.0003593900 0.010998038  7.851243e-01  8.001180e-01 #> theta[1]   1.747732e-02 0.0004012933 0.015518658  3.397982e-04  5.556424e-03 #> theta[2]   4.521156e-02 0.0009719303 0.032103002  3.497362e-03  1.967008e-02 #> theta[3]   3.934172e-02 0.0008073821 0.027757712  1.900997e-03  1.793613e-02 #> theta[4]   1.439986e-01 0.0014055351 0.046463549  5.425350e-02  1.123683e-01 #> theta[5]   3.158280e-01 0.0011278041 0.045792461  2.305850e-01  2.838526e-01 #> theta[6]   4.452249e-01 0.0012812842 0.047140675  3.545909e-01  4.120292e-01 #> theta[7]   4.636578e-01 0.0013137642 0.049670685  3.671684e-01  4.296514e-01 #> theta[8]   6.077679e-01 0.0012322838 0.051758945  5.034081e-01  5.746207e-01 #> theta[9]   7.173174e-01 0.0011569343 0.042494430  6.248539e-01  6.900703e-01 #> theta[10]  6.414107e-01 0.0013163957 0.050844687  5.427184e-01  6.076674e-01 #> theta[11]  7.251551e-01 0.0012041984 0.046902001  6.257779e-01  6.937983e-01 #> theta[12]  8.200245e-01 0.0010676660 0.039122057  7.334985e-01  7.953583e-01 #> theta[13]  7.607357e-01 0.0010292102 0.041323713  6.779961e-01  7.336668e-01 #> theta[14]  8.589639e-01 0.0008862776 0.033589290  7.896023e-01  8.380959e-01 #> theta[15]  7.716052e-01 0.0017929505 0.065516404  6.349394e-01  7.285860e-01 #> theta[16]  8.381310e-01 0.0017053329 0.066149986  7.006723e-01  7.966934e-01 #> theta[17]  9.133985e-01 0.0014411494 0.044158724  8.153283e-01  8.879187e-01 #> theta[18]  8.803590e-01 0.0013704992 0.048219400  7.815409e-01  8.477237e-01 #> theta[19]  8.801362e-01 0.0009873217 0.041440481  7.940748e-01  8.510319e-01 #> theta[20]  8.516456e-01 0.0013062293 0.051569875  7.386624e-01  8.196764e-01 #> theta[21]  9.149110e-01 0.0014600247 0.046220606  8.144062e-01  8.874402e-01 #> theta[22]  8.248331e-01 0.0012972922 0.049342001  7.226267e-01  7.924436e-01 #> theta[23]  9.461869e-01 0.0012688504 0.035172720  8.654861e-01  9.237220e-01 #> theta[24]  9.458786e-01 0.0010783110 0.033650049  8.708283e-01  9.239385e-01 #> theta[25]  9.706552e-01 0.0006278715 0.023829751  9.115230e-01  9.589298e-01 #> theta[26]  9.400029e-01 0.0010821960 0.033457664  8.681502e-01  9.181518e-01 #> theta[27]  9.251679e-01 0.0012477057 0.043365359  8.198036e-01  9.011331e-01 #> theta[28]  9.578219e-01 0.0008736670 0.032194552  8.824005e-01  9.384708e-01 #> theta[29]  9.064031e-01 0.0012458483 0.043045202  8.166192e-01  8.788169e-01 #> theta[30]  9.164445e-01 0.0013366119 0.049352158  8.018241e-01  8.855027e-01 #> theta[31]  8.607109e-01 0.0017207072 0.062663082  7.114398e-01  8.265632e-01 #> theta[32]  9.407438e-01 0.0012121067 0.045559916  8.244369e-01  9.173058e-01 #> theta[33]  9.357270e-01 0.0013794554 0.054278925  7.888680e-01  9.085377e-01 #> theta[34]  9.458862e-01 0.0012548097 0.043638644  8.334385e-01  9.249963e-01 #> theta[35]  8.961572e-01 0.0018858060 0.062735450  7.444533e-01  8.625101e-01 #> theta[36]  9.553792e-01 0.0009240727 0.037492222  8.613046e-01  9.363582e-01 #> theta[37]  9.389160e-01 0.0014208188 0.049938528  8.041306e-01  9.158440e-01 #> theta[38]  8.990585e-01 0.0018333159 0.066536570  7.442794e-01  8.581524e-01 #> theta[39]  9.168747e-01 0.0018935154 0.062687616  7.562417e-01  8.885957e-01 #> theta[40]  9.371065e-01 0.0015140064 0.056832194  7.920169e-01  9.115977e-01 #> theta[41]  9.528511e-01 0.0010380880 0.045128543  8.275254e-01  9.341298e-01 #> theta[42]  9.173219e-01 0.0017494957 0.065112154  7.639822e-01  8.834364e-01 #> theta[43]  9.080154e-01 0.0019124502 0.072102410  7.300474e-01  8.720032e-01 #> theta[44]  9.386586e-01 0.0014204252 0.055390859  8.014757e-01  9.138788e-01 #> lp__      -2.763145e+03 0.3011237007 5.715746786 -2.775866e+03 -2.766777e+03 #>                     50%           75%         97.5%     n_eff      Rhat #> est_se     9.582485e-01  9.613921e-01  9.678490e-01  925.3698 0.9990461 #> est_sp     8.070742e-01  8.144335e-01  8.281304e-01  936.4798 1.0003010 #> theta[1]   1.325944e-02  2.580038e-02  5.764662e-02 1495.4931 0.9990388 #> theta[2]   3.926800e-02  6.313427e-02  1.179769e-01 1090.9906 0.9991753 #> theta[3]   3.510345e-02  5.608013e-02  1.059055e-01 1181.9772 0.9992290 #> theta[4]   1.436227e-01  1.740261e-01  2.363213e-01 1092.8017 1.0041265 #> theta[5]   3.135946e-01  3.461306e-01  4.051240e-01 1648.6201 1.0032065 #> theta[6]   4.451716e-01  4.781418e-01  5.345672e-01 1353.6321 1.0045891 #> theta[7]   4.650392e-01  4.964724e-01  5.600045e-01 1429.4384 0.9991162 #> theta[8]   6.071917e-01  6.426559e-01  7.083871e-01 1764.2062 0.9996147 #> theta[9]   7.187469e-01  7.459264e-01  7.981012e-01 1349.1078 0.9994776 #> theta[10]  6.413189e-01  6.777565e-01  7.376091e-01 1491.8264 0.9999393 #> theta[11]  7.275840e-01  7.571155e-01  8.113803e-01 1517.0036 0.9990510 #> theta[12]  8.232098e-01  8.466952e-01  8.914191e-01 1342.6802 0.9990800 #> theta[13]  7.630979e-01  7.894624e-01  8.355361e-01 1612.0945 0.9994269 #> theta[14]  8.596282e-01  8.830362e-01  9.199369e-01 1436.3561 1.0028516 #> theta[15]  7.768558e-01  8.169429e-01  8.886224e-01 1335.2529 0.9990301 #> theta[16]  8.440784e-01  8.872369e-01  9.488487e-01 1504.6697 0.9990161 #> theta[17]  9.172955e-01  9.451696e-01  9.910868e-01  938.8907 0.9990891 #> theta[18]  8.832052e-01  9.150528e-01  9.691902e-01 1237.9014 1.0004986 #> theta[19]  8.835078e-01  9.116064e-01  9.480115e-01 1761.7010 0.9998162 #> theta[20]  8.551365e-01  8.881845e-01  9.406861e-01 1558.6669 1.0006793 #> theta[21]  9.202015e-01  9.487129e-01  9.903668e-01 1002.1918 1.0008532 #> theta[22]  8.264882e-01  8.589395e-01  9.131122e-01 1446.6315 0.9993236 #> theta[23]  9.535076e-01  9.739242e-01  9.950822e-01  768.4065 0.9994326 #> theta[24]  9.497731e-01  9.711970e-01  9.966858e-01  973.8305 0.9995677 #> theta[25]  9.762265e-01  9.886092e-01  9.990507e-01 1440.4476 1.0010816 #> theta[26]  9.439230e-01  9.641349e-01  9.964088e-01  955.8272 1.0003354 #> theta[27]  9.305549e-01  9.561449e-01  9.929416e-01 1207.9850 0.9998152 #> theta[28]  9.653058e-01  9.828475e-01  9.980652e-01 1357.9160 0.9997075 #> theta[29]  9.094284e-01  9.377292e-01  9.803109e-01 1193.7659 0.9990008 #> theta[30]  9.239625e-01  9.526448e-01  9.917734e-01 1363.3321 0.9990114 #> theta[31]  8.645073e-01  9.066013e-01  9.615028e-01 1326.2016 0.9995796 #> theta[32]  9.514552e-01  9.741705e-01  9.978432e-01 1412.8111 0.9994082 #> theta[33]  9.493927e-01  9.758949e-01  9.982326e-01 1548.2716 1.0000931 #> theta[34]  9.557844e-01  9.797785e-01  9.973173e-01 1209.4468 0.9998171 #> theta[35]  9.057973e-01  9.406812e-01  9.882563e-01 1106.7053 0.9990242 #> theta[36]  9.663073e-01  9.840566e-01  9.982648e-01 1646.1524 0.9992741 #> theta[37]  9.502159e-01  9.773138e-01  9.976396e-01 1235.3616 0.9992080 #> theta[38]  9.099911e-01  9.501315e-01  9.910139e-01 1317.1832 0.9990001 #> theta[39]  9.280455e-01  9.629734e-01  9.939965e-01 1096.0386 0.9992802 #> theta[40]  9.530193e-01  9.777640e-01  9.975286e-01 1409.0728 0.9993224 #> theta[41]  9.646571e-01  9.854371e-01  9.991666e-01 1889.8803 0.9991952 #> theta[42]  9.328743e-01  9.681655e-01  9.964167e-01 1385.1550 0.9990917 #> theta[43]  9.253118e-01  9.602544e-01  9.959770e-01 1421.4097 0.9991980 #> theta[44]  9.546272e-01  9.817446e-01  9.978124e-01 1520.6858 0.9991256 #> lp__      -2.762797e+03 -2.759032e+03 -2.753133e+03  360.2932 1.0126026 #>  #> $df #>     age pos tot #> 1   1.5  31 206 #> 2   2.5  30 146 #> 3   3.5  34 168 #> 4   4.5  57 189 #> 5   5.5  95 219 #> 6   6.5 104 195 #> 7   7.5  90 164 #> 8   8.5  96 145 #> 9   9.5 134 180 #> 10 10.5 110 160 #> 11 11.5 111 148 #> 12 12.5 147 178 #> 13 13.5 138 177 #> 14 14.5 141 165 #> 15 15.5  53  67 #> 16 16.5  49  58 #> 17 17.5  73  81 #> 18 18.5  69  79 #> 19 19.5  97 111 #> 20 20.5  65  76 #> 21 21.5  74  82 #> 22 22.5  84 101 #> 23 23.5  82  88 #> 24 24.5  79  85 #> 25 25.5  90  94 #> 26 26.5  84  91 #> 27 27.5  81  89 #> 28 28.5  72  76 #> 29 29.5  71  79 #> 30 30.5  51  56 #> 31 31.5  45  52 #> 32 32.5  45  48 #> 33 33.5  35  37 #> 34 34.5  39  41 #> 35 35.5  36  40 #> 36 36.5  37  38 #> 37 37.5  37  39 #> 38 38.5  37  41 #> 39 39.5  28  30 #> 40 40.5  26  27 #> 41 41.5  25  25 #> 42 42.5  21  22 #> 43 43.5  18  19 #> 44 44.5  18  18 #>  #> $corrected_se #>     age       sero        pos tot     sero_lwr   sero_upr #> 1   1.5 0.01325944   2.731444 206 0.0003397982 0.05764662 #> 2   2.5 0.03926800   5.733129 146 0.0034973616 0.11797694 #> 3   3.5 0.03510345   5.897379 168 0.0019009974 0.10590546 #> 4   4.5 0.14362274  27.144698 189 0.0542534972 0.23632128 #> 5   5.5 0.31359457  68.677211 219 0.2305849615 0.40512397 #> 6   6.5 0.44517156  86.808453 195 0.3545909437 0.53456725 #> 7   7.5 0.46503915  76.266421 164 0.3671684261 0.56000455 #> 8   8.5 0.60719173  88.042801 145 0.5034081474 0.70838708 #> 9   9.5 0.71874687 129.374436 180 0.6248538646 0.79810121 #> 10 10.5 0.64131891 102.611026 160 0.5427183649 0.73760906 #> 11 11.5 0.72758395 107.682425 148 0.6257778650 0.81138031 #> 12 12.5 0.82320978 146.531341 178 0.7334985417 0.89141914 #> 13 13.5 0.76309793 135.068334 177 0.6779961210 0.83553609 #> 14 14.5 0.85962824 141.838659 165 0.7896023026 0.91993691 #> 15 15.5 0.77685579  52.049338  67 0.6349393644 0.88862239 #> 16 16.5 0.84407836  48.956545  58 0.7006722751 0.94884869 #> 17 17.5 0.91729554  74.300939  81 0.8153282665 0.99108683 #> 18 18.5 0.88320521  69.773211  79 0.7815408701 0.96919022 #> 19 19.5 0.88350782  98.069368 111 0.7940747981 0.94801151 #> 20 20.5 0.85513649  64.990373  76 0.7386624226 0.94068610 #> 21 21.5 0.92020148  75.456521  82 0.8144062288 0.99036682 #> 22 22.5 0.82648821  83.475309 101 0.7226266670 0.91311217 #> 23 23.5 0.95350762  83.908671  88 0.8654860615 0.99508220 #> 24 24.5 0.94977310  80.730713  85 0.8708282569 0.99668585 #> 25 25.5 0.97622654  91.765295  94 0.9115229908 0.99905070 #> 26 26.5 0.94392302  85.896995  91 0.8681502083 0.99640880 #> 27 27.5 0.93055493  82.819389  89 0.8198035939 0.99294163 #> 28 28.5 0.96530578  73.363239  76 0.8824004970 0.99806516 #> 29 29.5 0.90942839  71.844843  79 0.8166192321 0.98031092 #> 30 30.5 0.92396246  51.741898  56 0.8018241060 0.99177343 #> 31 31.5 0.86450734  44.954381  52 0.7114398199 0.96150283 #> 32 32.5 0.95145521  45.669850  48 0.8244368835 0.99784321 #> 33 33.5 0.94939266  35.127529  37 0.7888680098 0.99823258 #> 34 34.5 0.95578440  39.187161  41 0.8334385481 0.99731726 #> 35 35.5 0.90579729  36.231892  40 0.7444533435 0.98825635 #> 36 36.5 0.96630732  36.719678  38 0.8613046445 0.99826479 #> 37 37.5 0.95021588  37.058419  39 0.8041305633 0.99763957 #> 38 38.5 0.90999106  37.309634  41 0.7442793553 0.99101392 #> 39 39.5 0.92804546  27.841364  30 0.7562417314 0.99399646 #> 40 40.5 0.95301930  25.731521  27 0.7920169109 0.99752857 #> 41 41.5 0.96465707  24.116427  25 0.8275253759 0.99916657 #> 42 42.5 0.93287433  20.523235  22 0.7639822308 0.99641675 #> 43 43.5 0.92531182  17.580925  19 0.7300474394 0.99597705 #> 44 44.5 0.95462715  17.183289  18 0.8014756977 0.99781240 #>  #> $method #> [1] \"bayesian\" #>"},{"path":"https://oucru-modelling.github.io/serosv/reference/est_foi.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate force of infection — est_foi","title":"Estimate force of infection — est_foi","text":"Estimate force infection","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/est_foi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate force of infection — est_foi","text":"","code":"est_foi(t, sp)"},{"path":"https://oucru-modelling.github.io/serosv/reference/est_foi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate force of infection — est_foi","text":"t - time (case age) vector sp - seroprevalence vector","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/est_foi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate force of infection — est_foi","text":"computed foi vector","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/estimate_from_mixture.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate seroprevalence and foi by combining mixture model and regression — estimate_from_mixture","title":"Estimate seroprevalence and foi by combining mixture model and regression — estimate_from_mixture","text":"Refers section 11.2 - 11.4","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/estimate_from_mixture.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate seroprevalence and foi by combining mixture model and regression — estimate_from_mixture","text":"","code":"estimate_from_mixture(   age,   antibody_level,   threshold_status = NULL,   mixture_model,   s = \"ps\",   sp = 83,   monotonize = TRUE )"},{"path":"https://oucru-modelling.github.io/serosv/reference/estimate_from_mixture.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate seroprevalence and foi by combining mixture model and regression — estimate_from_mixture","text":"age - vector age antibody_level - vector corresponding raw antibody level threshold_status - sero status using threshold approach line listing (optional, visualization comparison ) mixture_model - mixture_model object generated serosv::mixture_model() s - smoothing basis used fit antibody level sp - smoothing parameter monotonize - whether monotonize seroprevalence (default TRUE)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/estimate_from_mixture.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate seroprevalence and foi by combining mixture model and regression — estimate_from_mixture","text":"list class estimated_from_mixture following items df dataframe used fitting model info fitted \"gam\" model mu() sp seroprevalence foi force infection threshold_status serostatus using threshold method provided","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/farrington_model.html","id":null,"dir":"Reference","previous_headings":"","what":"The Farrington (1990) model. — farrington_model","title":"The Farrington (1990) model. — farrington_model","text":"Refers section 6.1.2.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/farrington_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Farrington (1990) model. — farrington_model","text":"","code":"farrington_model(data, start, fixed = list())"},{"path":"https://oucru-modelling.github.io/serosv/reference/farrington_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Farrington (1990) model. — farrington_model","text":"data input data frame, must either `age`, `pos`, `tot` columns (aggregated data) `age`, `status` (linelisting data) start Named list vectors single vector. Initial values optimizer. fixed Named list vectors single vector. Parameter values keep fixed optimization.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/farrington_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Farrington (1990) model. — farrington_model","text":"list class farrington_model 5 items datatype type datatype used model fitting (aggregated linelisting) df dataframe used fitting model info fitted \"mle\" object sp seroprevalence foi force infection","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/farrington_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Farrington (1990) model. — farrington_model","text":"","code":"df <- rubella_uk_1986_1987 model <- farrington_model(   df,   start=list(alpha=0.07,beta=0.1,gamma=0.03)   ) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced plot(model) #> Warning: No shared levels found between `names(values)` of the manual scale and the #> data's fill values."},{"path":"https://oucru-modelling.github.io/serosv/reference/find_best_bdm_param.html","id":null,"dir":"Reference","previous_headings":"","what":"Find best smoothing parameter for Bivariate Dale model — find_best_bdm_param","title":"Find best smoothing parameter for Bivariate Dale model — find_best_bdm_param","text":"Find best smoothing parameter Bivariate Dale model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/find_best_bdm_param.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find best smoothing parameter for Bivariate Dale model — find_best_bdm_param","text":"","code":"find_best_bdm_param(age, y, spar_seq = seq(0, 0.1, 0.001))"},{"path":"https://oucru-modelling.github.io/serosv/reference/find_best_bdm_param.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find best smoothing parameter for Bivariate Dale model — find_best_bdm_param","text":"age age vector y dataframe predictors (NN, NP, PN, PP), order spar_seq sequence smoothing parameters evaluated","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/find_best_bdm_param.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find best smoothing parameter for Bivariate Dale model — find_best_bdm_param","text":"data frame optimal parameter different criteria","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/find_best_fp_powers.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the powers of the GLM fitted model which has the lowest deviance score. — find_best_fp_powers","title":"Returns the powers of the GLM fitted model which has the lowest deviance score. — find_best_fp_powers","text":"Refers section 6.2.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/find_best_fp_powers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the powers of the GLM fitted model which has the lowest deviance score. — find_best_fp_powers","text":"","code":"find_best_fp_powers(data, p, mc, degree, link = \"logit\")"},{"path":"https://oucru-modelling.github.io/serosv/reference/find_best_fp_powers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the powers of the GLM fitted model which has the lowest deviance score. — find_best_fp_powers","text":"data input data frame, must either `age`, `pos`, `tot` columns (aggregated data) `age`, `status` (linelisting data) p powers sequence. mc indicates returned model monotonic. degree degree model. Recommended <= 2. link link function. Defaulted \"logit\".","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/find_best_fp_powers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the powers of the GLM fitted model which has the lowest deviance score. — find_best_fp_powers","text":"list 3 elements: p best power fp model. deviance Deviance best fitted model. model best model fitted","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/find_best_fp_powers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns the powers of the GLM fitted model which has the lowest deviance score. — find_best_fp_powers","text":"","code":"df <- hav_be_1993_1994 best_p <- find_best_fp_powers( df, p=seq(-2,3,0.1), mc=FALSE, degree=2, link=\"cloglog\" ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred #> Warning: 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$p #> [1] 1.5 1.6 #>  #> $deviance #> [1] 81.60333 #>  #> $model #>  #> Call:  glm(formula = as.formula(formulate(p_cur)), family = binomial(link = link)) #>  #> Coefficients: #> (Intercept)   I(age^1.5)   I(age^1.6)   #>    -3.61083      0.12443     -0.07656   #>  #> Degrees of Freedom: 85 Total (i.e. Null);  83 Residual #> Null Deviance:\t    1320  #> Residual Deviance: 81.6 \tAIC: 361.2 #>"},{"path":"https://oucru-modelling.github.io/serosv/reference/fp_model.html","id":null,"dir":"Reference","previous_headings":"","what":"A fractional polynomial model. — fp_model","title":"A fractional polynomial model. — fp_model","text":"Refers section 6.2.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/fp_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A fractional polynomial model. — fp_model","text":"","code":"fp_model(data, p, link = \"logit\")"},{"path":"https://oucru-modelling.github.io/serosv/reference/fp_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A fractional polynomial model. — fp_model","text":"data input data frame, must either `age`, `pos`, `tot` columns (aggregated data) `age`, `status` (linelisting data) p powers predictor. link link function model. Defaulted \"logit\".","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/fp_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A fractional polynomial model. — fp_model","text":"list class fp_model 5 items datatype type data used fitting model (aggregated linelisting) df dataframe used fitting model info fitted glm model sp seroprevalence foi force infection","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/fp_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A fractional polynomial model. — fp_model","text":"","code":"df <- hav_be_1993_1994 model <- fp_model(   df,   p=c(1.5, 1.6), link=\"cloglog\") plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/reference/generate_quad_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate the (NN, NP, PN, PP) matrix from line-listing data — generate_quad_matrix","title":"Generate the (NN, NP, PN, PP) matrix from line-listing data — generate_quad_matrix","text":"Generate (NN, NP, PN, PP) matrix line-listing data","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/generate_quad_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate the (NN, NP, PN, PP) matrix from line-listing data — generate_quad_matrix","text":"","code":"generate_quad_matrix(data, y1, y2, age, discrete_age = F)"},{"path":"https://oucru-modelling.github.io/serosv/reference/generate_quad_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate the (NN, NP, PN, PP) matrix from line-listing data — generate_quad_matrix","text":"data - dataframe transformed y1 - serostatus y1 y2 - serostatus y2 age - individual's age discrete_age - round age discrete","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/generate_quad_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate the (NN, NP, PN, PP) matrix from line-listing data — generate_quad_matrix","text":"transformed matrix","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/generate_quad_matrix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate the (NN, NP, PN, PP) matrix from line-listing data — generate_quad_matrix","text":"","code":"data <- vzv_parvo_be data <- generate_quad_matrix(data, parvo_res, vzv_res, age)"},{"path":"https://oucru-modelling.github.io/serosv/reference/glmm_ps_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Fitting penalized spline model with Generalized Linear Mixture Model framework — glmm_ps_model","title":"Fitting penalized spline model with Generalized Linear Mixture Model framework — glmm_ps_model","text":"Fitting penalized spline model Generalized Linear Mixture Model framework","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/glmm_ps_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fitting penalized spline model with Generalized Linear Mixture Model framework — glmm_ps_model","text":"","code":"glmm_ps_model(age, res, s = \"bs\", link = \"logit\")"},{"path":"https://oucru-modelling.github.io/serosv/reference/glmm_ps_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fitting penalized spline model with Generalized Linear Mixture Model framework — glmm_ps_model","text":"age age vector. res serostatus vector. s smoothing basis use. link link function use.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/glmm_ps_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fitting penalized spline model with Generalized Linear Mixture Model framework — glmm_ps_model","text":"","code":"data <- parvob19_be_2001_2003 mod <- glmm_ps_model(data$age, data$seropositive) #>  #>  Maximum number of PQL iterations:  20  #> iteration 1 #> iteration 2 #> iteration 3 #> iteration 4 mod$gam$info #> NULL"},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_1993_1994.html","id":null,"dir":"Reference","previous_headings":"","what":"Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated) — hav_be_1993_1994","title":"Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated) — hav_be_1993_1994","text":"study prevalence HAV antibodies conducted Flemish Community Belgium 1993 early 1994","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_1993_1994.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated) — hav_be_1993_1994","text":"","code":"hav_be_1993_1994"},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_1993_1994.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated) — hav_be_1993_1994","text":"data frame 3 variables: age Age group pos Number seropositive individuals tot Total number individuals surveyed","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_1993_1994.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated) — hav_be_1993_1994","text":"Beutels, M., Van Damme, P., Aelvoet, W. et al. Prevalence hepatitis , B C Flemish population. Eur J Epidemiol 13, 275-280 (1997). doi:10.1023/:1007393405966","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_1993_1994.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated) — hav_be_1993_1994","text":"","code":"# Reproduce Fig 4.1 (upper left panel), p. 63 age <- hav_be_1993_1994$age pos <- hav_be_1993_1994$pos tot <- hav_be_1993_1994$tot plot(   age, pos / tot,   pty = \"s\", cex = 0.06 * tot, pch = 16, xlab = \"age\",   ylab = \"seroprevalence\", xlim = c(0, 86), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_2002.html","id":null,"dir":"Reference","previous_headings":"","what":"Hepatitis A serological data from Belgium in 2002 (line listing) — hav_be_2002","title":"Hepatitis A serological data from Belgium in 2002 (line listing) — hav_be_2002","text":"subset serological dataset Varicella-Zoster Virus (VZV) Parvovirus B19 Belgium individuals living Flanders selected","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_2002.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hepatitis A serological data from Belgium in 2002 (line listing) — hav_be_2002","text":"","code":"hav_be_2002"},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_2002.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hepatitis A serological data from Belgium in 2002 (line listing) — hav_be_2002","text":"data frame 2 variables: age Age individual seropositive individual seropositive ","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_2002.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Hepatitis A serological data from Belgium in 2002 (line listing) — hav_be_2002","text":"Thiry, N., Beutels, P., Shkedy, Z. et al. seroepidemiology primary varicella-zoster virus infection Flanders (Belgium). Eur J Pediatr 161, 588-593 (2002). doi:10.1007/s00431-002-1053-2","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_2002.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hepatitis A serological data from Belgium in 2002 (line listing) — hav_be_2002","text":"","code":"# Reproduce Fig 4.1 (upper right panel), p. 63 library(dplyr) #> Warning: package ‘dplyr’ was built under R version 4.3.1 #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union df <- hav_be_2002 %>%   group_by(age) %>%   summarise(pos = sum(seropositive), tot = n()) plot(   df$age, df$pos / df$tot,   pty = \"s\", cex = 0.06 * df$tot, pch = 16, xlab = \"age\",   ylab = \"seroprevalence\", xlim = c(0, 86), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_bg_1964.html","id":null,"dir":"Reference","previous_headings":"","what":"Hepatitis A serological data from Bulgaria in 1964 (aggregated) — hav_bg_1964","title":"Hepatitis A serological data from Bulgaria in 1964 (aggregated) — hav_bg_1964","text":"cross-sectional survey conducted 1964 Bulgaria. Samples collected schoolchildren blood donors.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_bg_1964.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hepatitis A serological data from Bulgaria in 1964 (aggregated) — hav_bg_1964","text":"","code":"hav_bg_1964"},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_bg_1964.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hepatitis A serological data from Bulgaria in 1964 (aggregated) — hav_bg_1964","text":"data frame 3 variables: age Age group pos Number seropositive individuals tot Total number individuals surveyed","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_bg_1964.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Hepatitis A serological data from Bulgaria in 1964 (aggregated) — hav_bg_1964","text":"Keiding, Niels. \"Age-Specific Incidence Prevalence: Statistical Perspective.\" Journal Royal Statistical Society. Series (Statistics Society) 154, . 3 (1991): 371-412. doi:10.2307/2983150","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_bg_1964.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hepatitis A serological data from Bulgaria in 1964 (aggregated) — hav_bg_1964","text":"","code":"# Reproduce Fig 4.1 (lower panel), p. 63 age <- hav_bg_1964$age pos <- hav_bg_1964$pos tot <- hav_bg_1964$tot plot(     age, pos / tot,     pty = \"s\", cex = 0.08 * tot, pch = 16, xlab = \"age\",     ylab = \"seroprevalence\", xlim = c(0, 86), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/hbv_ru_1999.html","id":null,"dir":"Reference","previous_headings":"","what":"Hepatitis B serological data from Russia in 1999 (aggregated) — hbv_ru_1999","title":"Hepatitis B serological data from Russia in 1999 (aggregated) — hbv_ru_1999","text":"seroprevalence study conducted St. Petersburg (information book)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hbv_ru_1999.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hepatitis B serological data from Russia in 1999 (aggregated) — hbv_ru_1999","text":"","code":"hbv_ru_1999"},{"path":"https://oucru-modelling.github.io/serosv/reference/hbv_ru_1999.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hepatitis B serological data from Russia in 1999 (aggregated) — hbv_ru_1999","text":"data frame 4 variables: age Age group pos Number seropositive individuals tot Total number individuals surveyed gender Gender cohort (unsure 1 2 means)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hbv_ru_1999.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Hepatitis B serological data from Russia in 1999 (aggregated) — hbv_ru_1999","text":"Mukomolov, S., L. Shliakhtenko, . Levakova, E. Shargorodskaya. Viral hepatitis Russian federation. analytical overview. Technical Report 213 (3), 3rd edn. St Petersburg Pasteur Institute, St Petersburg, 2000.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hbv_ru_1999.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hepatitis B serological data from Russia in 1999 (aggregated) — hbv_ru_1999","text":"","code":"# Reproduce Fig 4.2, p. 65 library(dplyr) hbv_ru_1999$age <- trunc(hbv_ru_1999$age / 1) * 1 hbv_ru_1999$age[hbv_ru_1999$age > 40] <- trunc(   hbv_ru_1999$age[hbv_ru_1999$age > 40] / 5 ) * 5 df <- hbv_ru_1999 %>%   group_by(age) %>%   summarise(pos = sum(pos), tot = sum(tot)) plot(   df$age, df$pos / df$tot,   cex = 0.05 * df$tot, pch = 16, xlab = \"age\",   ylab = \"seroprevalence\", xlim = c(0, 72) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/hcv_be_2006.html","id":null,"dir":"Reference","previous_headings":"","what":"Hepatitis C serological data from Belgium in 2006 (line listing) — hcv_be_2006","title":"Hepatitis C serological data from Belgium in 2006 (line listing) — hcv_be_2006","text":"study HCV infection among injecting drug users. injecting drug users interviewed means standardized face--face interview information socio-demographic status, drug use history, drug use, related risk behavior recorded","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hcv_be_2006.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hepatitis C serological data from Belgium in 2006 (line listing) — hcv_be_2006","text":"","code":"hcv_be_2006"},{"path":"https://oucru-modelling.github.io/serosv/reference/hcv_be_2006.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hepatitis C serological data from Belgium in 2006 (line listing) — hcv_be_2006","text":"data frame 3 variables: dur Duration injection/Exposure time (years) seropositive individual seropositive ","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hcv_be_2006.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Hepatitis C serological data from Belgium in 2006 (line listing) — hcv_be_2006","text":"Mathei, C., Shkedy, Z., Denis, B., Kabali, C., Aerts, M., Molenberghs, G., Van Damme, P. Buntinx, F. (2006), Evidence substantial role sharing injecting paraphernalia syringes/needles spread hepatitis C among injecting drug users. Journal Viral Hepatitis, 13: 560-570. doi:10.1111/j.1365-2893.2006.00725.x","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hcv_be_2006.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hepatitis C serological data from Belgium in 2006 (line listing) — hcv_be_2006","text":"","code":"# Reproduce Fig 4.3, p. 66 library(dplyr) # snapping age to aggregated age group # (credit: https://stackoverflow.com/a/12861810) groups <- c(0.5:24.5) range <- 0.5 low <- findInterval(hcv_be_2006$dur, groups) high <- low + 1 low_diff <- hcv_be_2006$dur - groups[ifelse(low == 0, NA, low)] high_diff <- groups[ifelse(high == 0, NA, high)] - hcv_be_2006$dur mins <- pmin(low_diff, high_diff, na.rm = TRUE) pick <- ifelse(!is.na(low_diff) & mins == low_diff, low, high) hcv_be_2006$dur <- ifelse(   mins <= range + .Machine$double.eps, groups[pick], hcv_be_2006$dur ) hcv_be_2006 <- hcv_be_2006 %>%   group_by(dur) %>%   summarise(tot = n(), pos = sum(seropositive))  plot(   hcv_be_2006$dur, hcv_be_2006$pos / hcv_be_2006$tot,   cex = 0.1 * hcv_be_2006$tot, pch = 16,   xlab = \"duration of injection (years)\",   ylab = \"seroprevalence\", xlim = c(0, 25), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/hierarchical_bayesian_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Hierarchical Bayesian Model — hierarchical_bayesian_model","title":"Hierarchical Bayesian Model — hierarchical_bayesian_model","text":"Refers section 10.3","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hierarchical_bayesian_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hierarchical Bayesian Model — hierarchical_bayesian_model","text":"","code":"hierarchical_bayesian_model(   data,   type = \"far3\",   chains = 1,   warmup = 1500,   iter = 5000 )"},{"path":"https://oucru-modelling.github.io/serosv/reference/hierarchical_bayesian_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hierarchical Bayesian Model — hierarchical_bayesian_model","text":"data input data frame, must either `age`, `pos`, `tot` columns (aggregated data) `age`, `status` (linelisting data) type type model (\"far2\", \"far3\" \"log_logistic\") chains number Markov chains warmup number warmup runs iter number iterations","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hierarchical_bayesian_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hierarchical Bayesian Model — hierarchical_bayesian_model","text":"list class hierarchical_bayesian_model 6 items datatype type datatype used model fitting (aggregated linelisting) df dataframe used fitting model type type bayesian model far2, far3 log_logistic info parameters fitted model sp seroprevalence foi force infection sp_func function compute seroprevalence given age model parameters foi function compute force infection given age model parameters","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hierarchical_bayesian_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hierarchical Bayesian Model — hierarchical_bayesian_model","text":"","code":"# \\donttest{ df <- mumps_uk_1986_1987 model <- hierarchical_bayesian_model(df, type=\"far3\") #>  #> SAMPLING FOR MODEL 'fra_3' NOW (CHAIN 1). #> Chain 1: Rejecting initial value: #> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity. #> Chain 1:   Stan can't start sampling from this initial value. #> Chain 1: Rejecting initial value: #> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity. #> Chain 1:   Stan can't start sampling from this initial value. #> Chain 1: Rejecting initial value: #> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity. #> Chain 1:   Stan can't start sampling from this initial value. #> Chain 1: Rejecting initial value: #> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity. #> Chain 1:   Stan can't start sampling from this initial value. #> Chain 1: Rejecting initial value: #> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity. #> Chain 1:   Stan can't start sampling from this initial value. #> Chain 1: Rejecting initial value: #> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity. #> Chain 1:   Stan can't start sampling from this initial value. #> Chain 1:  #> Chain 1: Gradient evaluation took 0.000123 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.23 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1:  #> Chain 1:  #> Chain 1: Iteration:    1 / 5000 [  0%]  (Warmup) #> Chain 1: Iteration:  500 / 5000 [ 10%]  (Warmup) #> Chain 1: Iteration: 1000 / 5000 [ 20%]  (Warmup) #> Chain 1: Iteration: 1500 / 5000 [ 30%]  (Warmup) #> Chain 1: Iteration: 1501 / 5000 [ 30%]  (Sampling) #> Chain 1: Iteration: 2000 / 5000 [ 40%]  (Sampling) #> Chain 1: Iteration: 2500 / 5000 [ 50%]  (Sampling) #> Chain 1: Iteration: 3000 / 5000 [ 60%]  (Sampling) #> Chain 1: Iteration: 3500 / 5000 [ 70%]  (Sampling) #> Chain 1: Iteration: 4000 / 5000 [ 80%]  (Sampling) #> Chain 1: Iteration: 4500 / 5000 [ 90%]  (Sampling) #> Chain 1: Iteration: 5000 / 5000 [100%]  (Sampling) #> Chain 1:  #> Chain 1:  Elapsed Time: 14.459 seconds (Warm-up) #> Chain 1:                3.506 seconds (Sampling) #> Chain 1:                17.965 seconds (Total) #> Chain 1:  #> Warning: There were 1634 divergent transitions after warmup. See #> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup #> to find out why this is a problem and how to eliminate them. #> Warning: Examine the pairs() plot to diagnose sampling problems #> Warning: The largest R-hat is 1.3, indicating chains have not mixed. #> Running the chains for more iterations may help. See #> https://mc-stan.org/misc/warnings.html#r-hat #> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable. #> Running the chains for more iterations may help. See #> https://mc-stan.org/misc/warnings.html#bulk-ess #> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable. #> Running the chains for more iterations may help. See #> https://mc-stan.org/misc/warnings.html#tail-ess model$info #>                       mean      se_mean           sd          2.5% #> alpha1        1.374349e-01 2.469659e-03 7.304112e-03  1.256108e-01 #> alpha2        1.971191e-01 2.365928e-03 8.668356e-03  1.840890e-01 #> alpha3        8.361884e-03 9.115459e-04 6.405086e-03  6.530832e-04 #> tau_alpha1    3.232071e-02 8.114025e-03 7.619259e-02  1.649675e-05 #> tau_alpha2    6.679641e-01 2.734528e-01 1.028098e+00  6.526141e-06 #> tau_alpha3    5.874325e-02 1.502771e-02 1.094519e-01  6.639239e-06 #> mu_alpha1    -5.831428e+00 1.717162e+00 3.215799e+01 -7.580347e+01 #> mu_alpha2     1.899729e+00 2.488519e+00 3.501723e+01 -7.316053e+01 #> mu_alpha3     7.615985e+00 1.031699e+01 6.091801e+01 -9.637830e+01 #> sigma_alpha1  8.046101e+01 3.723283e+01 1.601118e+03  1.845717e+00 #> sigma_alpha2  5.050277e+01 1.023182e+01 2.116087e+02  5.704865e-01 #> sigma_alpha3  6.396098e+01 1.470747e+01 3.041621e+02  1.532948e+00 #> lp__         -2.535346e+03 8.603892e-01 3.512983e+00 -2.543226e+03 #>                        25%           50%           75%         97.5% #> alpha1        1.309418e-01  1.381552e-01  1.433022e-01  1.496570e-01 #> alpha2        1.903549e-01  1.978268e-01  2.027656e-01  2.136976e-01 #> alpha3        2.918675e-03  7.742625e-03  1.200969e-02  2.372548e-02 #> tau_alpha1    8.305591e-04  3.669129e-03  1.367782e-02  2.935416e-01 #> tau_alpha2    1.304788e-03  6.628968e-02  1.133317e+00  3.072622e+00 #> tau_alpha3    5.987982e-04  7.336155e-03  5.747848e-02  4.255445e-01 #> mu_alpha1    -1.143099e+01 -2.676034e+00  2.767768e+00  4.919579e+01 #> mu_alpha2    -1.520815e+00  4.404344e-01  2.365690e+00  8.958812e+01 #> mu_alpha3    -3.895228e+00 -3.255963e-03  4.586351e+00  2.369248e+02 #> sigma_alpha1  8.550509e+00  1.650901e+01  3.469882e+01  2.462098e+02 #> sigma_alpha2  9.393433e-01  3.883981e+00  2.768415e+01  3.914472e+02 #> sigma_alpha3  4.171069e+00  1.167549e+01  4.086578e+01  3.880977e+02 #> lp__         -2.537755e+03 -2.535076e+03 -2.532816e+03 -2.529997e+03 #>                    n_eff      Rhat #> alpha1          8.747031 1.1212692 #> alpha2         13.423642 1.0897105 #> alpha3         49.373384 1.0283578 #> tau_alpha1     88.176492 1.0039526 #> tau_alpha2     14.135280 1.3042242 #> tau_alpha3     53.047004 0.9997529 #> mu_alpha1     350.715887 1.0015192 #> mu_alpha2     198.007524 1.0074621 #> mu_alpha3      34.864649 1.0304100 #> sigma_alpha1 1849.244902 0.9999612 #> sigma_alpha2  427.721511 1.0054612 #> sigma_alpha3  427.695365 1.0184599 #> lp__           16.671016 1.1939938 plot(model)  # }"},{"path":"https://oucru-modelling.github.io/serosv/reference/lp_model.html","id":null,"dir":"Reference","previous_headings":"","what":"A local polynomial model. — lp_model","title":"A local polynomial model. — lp_model","text":"Refers section 7.1. 7.2.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/lp_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A local polynomial model. — lp_model","text":"","code":"lp_model(data, kern = \"tcub\", nn = 0, h = 0, deg = 2)"},{"path":"https://oucru-modelling.github.io/serosv/reference/lp_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A local polynomial model. — lp_model","text":"data input data frame, must either `age`, `pos`, `tot` columns (aggregated data) `age`, `status` (linelisting data) kern Weight function, default = \"tcub\". choices \"rect\", \"trwt\", \"tria\", \"epan\", \"bisq\" \"gauss\". Choices may restricted derivatives required; e.g. confidence bands bandwidth selectors. nn Nearest neighbor component smoothing parameter. Default value 0.7, unless either h provided, case default 0. h constant component smoothing parameter. Default: 0. deg Degree polynomial use. Default: 2.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/lp_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A local polynomial model. — lp_model","text":"list class lp_model 6 items datatype type datatype used model fitting (aggregated linelisting) df dataframe used fitting model pi fitted locfit object pi eta fitted locfit object eta sp seroprevalence foi force infection","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/lp_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A local polynomial model. — lp_model","text":"","code":"df <- mumps_uk_1986_1987 model <- lp_model(   df,   nn=0.7, kern=\"tcub\"   ) plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/reference/mixture_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a mixture model to classify serostatus — mixture_model","title":"Fit a mixture model to classify serostatus — mixture_model","text":"Refers section 11.1 - 11.4","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mixture_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a mixture model to classify serostatus — mixture_model","text":"","code":"mixture_model(   antibody_level,   breaks = 40,   pi = c(0.2, 0.8),   mu = c(2, 6),   sigma = c(0.5, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/mixture_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a mixture model to classify serostatus — mixture_model","text":"antibody_level - vector corresponding raw antibody level breaks - number intervals antibody_level grouped pi - proportion susceptible, infected mu - vector means component distributions (vector 2 numbers ascending order) sigma -  vector standard deviations component distributions (vector 2 number)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mixture_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit a mixture model to classify serostatus — mixture_model","text":"list class mixture_model following items df dataframe used fitting model info list 3 items parameters, distribution constraints fitted model susceptible fitted distribution susceptible infected fitted distribution infected","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mixture_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit a mixture model to classify serostatus — mixture_model","text":"","code":"df <- vzv_be_2001_2003[vzv_be_2001_2003$age < 40.5,] data <- df$VZVmIUml[order(df$age)] model <- mixture_model(antibody_level = data) model$info #>  #> Parameters: #>       pi    mu  sigma #> 1 0.1088 2.349 0.6804 #> 2 0.8912 6.439 0.9437 #>  #> Distribution: #> [1] \"norm\" #>  #> Constraints: #>    conpi    conmu consigma  #>   \"NONE\"   \"NONE\"   \"NONE\"  #>  plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/reference/mseir_model.html","id":null,"dir":"Reference","previous_headings":"","what":"MSEIR model — mseir_model","title":"MSEIR model — mseir_model","text":"Refers section 3.4.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mseir_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MSEIR model — mseir_model","text":"","code":"mseir_model(a, gamma, lambda, sigma, nu)"},{"path":"https://oucru-modelling.github.io/serosv/reference/mseir_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MSEIR model — mseir_model","text":"age sequence gamma time maternal class. lambda time susceptible class. sigma time latent class. nu time infected class.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mseir_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MSEIR model — mseir_model","text":"list class mseir_model following parameters parameters list parameters used fitting model output matrix proportion compartment time","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mseir_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MSEIR model — mseir_model","text":"","code":"model <- mseir_model(   a=seq(from=1,to=20,length=500), # age range from 0 -> 20 yo   gamma=1/0.5, # 6 months in the maternal antibodies   lambda=0.2,  # 5 years in the susceptible class   sigma=26.07, # 14 days in the latent class   nu=36.5      # 10 days in the infected class ) model #> $parameters #> $parameters$gamma #> [1] 2 #>  #> $parameters$lambda #> [1] 0.2 #>  #> $parameters$sigma #> [1] 26.07 #>  #> $parameters$nu #> [1] 36.5 #>  #>  #> $output #>             a            m          s            e            i         r #> 1    0.000000 1.000000e+00 0.00000000 0.0000000000 0.0000000000 0.0000000 #> 2    1.000000 1.353353e-01 0.75932830 0.0057834028 0.0041067171 0.0954463 #> 3    1.038076 1.254118e-01 0.76345309 0.0058216660 0.0041376298 0.1011758 #> 4    1.076152 1.162160e-01 0.76682174 0.0058536161 0.0041637570 0.1069449 #> 5    1.114228 1.076945e-01 0.76949316 0.0058797427 0.0041854686 0.1127471 #> 6    1.152305 9.979779e-02 0.77152186 0.0059004992 0.0042031073 0.1185767 #> 7    1.190381 9.248012e-02 0.77295838 0.0059163055 0.0042169906 0.1244282 #> 8    1.228457 8.569903e-02 0.77384950 0.0059275507 0.0042274125 0.1302965 #> 9    1.266533 7.941515e-02 0.77423855 0.0059345950 0.0042346455 0.1361771 #> 10   1.304609 7.359204e-02 0.77416565 0.0059377721 0.0042389416 0.1420656 #> 11   1.342685 6.819591e-02 0.77366797 0.0059373908 0.0042405345 0.1479582 #> 12   1.380762 6.319545e-02 0.77277991 0.0059337375 0.0042396406 0.1538513 #> 13   1.418838 5.856164e-02 0.77153335 0.0059270770 0.0042364602 0.1597415 #> 14   1.456914 5.426761e-02 0.76995777 0.0059176546 0.0042311787 0.1656258 #> 15   1.494990 5.028844e-02 0.76808050 0.0059056976 0.0042239679 0.1715014 #> 16   1.533066 4.660105e-02 0.76592682 0.0058914163 0.0042149869 0.1773657 #> 17   1.571142 4.318403e-02 0.76352014 0.0058750053 0.0042043830 0.1832164 #> 18   1.609218 4.001756e-02 0.76088213 0.0058566450 0.0041922923 0.1890514 #> 19   1.647295 3.708328e-02 0.75803284 0.0058365022 0.0041788412 0.1948685 #> 20   1.685371 3.436415e-02 0.75499084 0.0058147313 0.0041641464 0.2006661 #> 21   1.723447 3.184440e-02 0.75177331 0.0057914753 0.0041483160 0.2064425 #> 22   1.761523 2.950941e-02 0.74839613 0.0057668664 0.0041314501 0.2121961 #> 23   1.799599 2.734563e-02 0.74487402 0.0057410270 0.0041136413 0.2179257 #> 24   1.837675 2.534052e-02 0.74122058 0.0057140705 0.0040949753 0.2236299 #> 25   1.875752 2.348243e-02 0.73744840 0.0056861015 0.0040755312 0.2293075 #> 26   1.913828 2.176058e-02 0.73356912 0.0056572169 0.0040553825 0.2349577 #> 27   1.951904 2.016498e-02 0.72959349 0.0056275065 0.0040345970 0.2405794 #> 28   1.989980 1.868639e-02 0.72553148 0.0055970530 0.0040132374 0.2461718 #> 29   2.028056 1.731621e-02 0.72139228 0.0055659332 0.0039913617 0.2517342 #> 30   2.066132 1.604650e-02 0.71718441 0.0055342179 0.0039690236 0.2572659 #> 31   2.104208 1.486989e-02 0.71291571 0.0055019726 0.0039462726 0.2627662 #> 32   2.142285 1.377956e-02 0.70859345 0.0054692578 0.0039231546 0.2682346 #> 33   2.180361 1.276917e-02 0.70422434 0.0054361294 0.0038997119 0.2736706 #> 34   2.218437 1.183287e-02 0.69981457 0.0054026391 0.0038759837 0.2790739 #> 35   2.256513 1.096523e-02 0.69536987 0.0053688346 0.0038520061 0.2844441 #> 36   2.294589 1.016120e-02 0.69089552 0.0053347600 0.0038278125 0.2897807 #> 37   2.332665 9.416134e-03 0.68639637 0.0053004560 0.0038034337 0.2950836 #> 38   2.370741 8.725697e-03 0.68187694 0.0052659599 0.0037788981 0.3003525 #> 39   2.408818 8.085885e-03 0.67734136 0.0052313065 0.0037542320 0.3055872 #> 40   2.446894 7.492988e-03 0.67279346 0.0051965277 0.0037294596 0.3107876 #> 41   2.484970 6.943565e-03 0.66823675 0.0051616529 0.0037046032 0.3159534 #> 42   2.523046 6.434429e-03 0.66367448 0.0051267092 0.0036796834 0.3210847 #> 43   2.561122 5.962625e-03 0.65910963 0.0050917216 0.0036547192 0.3261813 #> 44   2.599198 5.525416e-03 0.65454495 0.0050567131 0.0036297279 0.3312432 #> 45   2.637275 5.120265e-03 0.64998296 0.0050217048 0.0036047258 0.3362703 #> 46   2.675351 4.744822e-03 0.64542599 0.0049867162 0.0035797275 0.3412627 #> 47   2.713427 4.396908e-03 0.64087617 0.0049517651 0.0035547466 0.3462204 #> 48   2.751503 4.074505e-03 0.63633544 0.0049168681 0.0035297957 0.3511434 #> 49   2.789579 3.775742e-03 0.63180562 0.0048820401 0.0035048863 0.3560317 #> 50   2.827655 3.498886e-03 0.62728834 0.0048472950 0.0034800288 0.3608854 #> 51   2.865731 3.242330e-03 0.62278511 0.0048126455 0.0034552329 0.3657047 #> 52   2.903808 3.004587e-03 0.61829730 0.0047781031 0.0034305074 0.3704895 #> 53   2.941884 2.784276e-03 0.61382617 0.0047436785 0.0034058605 0.3752400 #> 54   2.979960 2.580119e-03 0.60937286 0.0047093814 0.0033812995 0.3799563 #> 55   3.018036 2.390932e-03 0.60493843 0.0046752205 0.0033568312 0.3846386 #> 56   3.056112 2.215617e-03 0.60052382 0.0046412039 0.0033324618 0.3892869 #> 57   3.094188 2.053157e-03 0.59612990 0.0046073390 0.0033081968 0.3939014 #> 58   3.132265 1.902609e-03 0.59175744 0.0045736324 0.0032840415 0.3984823 #> 59   3.170341 1.763101e-03 0.58740716 0.0045400902 0.0032600003 0.4030296 #> 60   3.208417 1.633821e-03 0.58307969 0.0045067177 0.0032360775 0.4075437 #> 61   3.246493 1.514021e-03 0.57877561 0.0044735200 0.0032122769 0.4120246 #> 62   3.284569 1.403006e-03 0.57449543 0.0044405014 0.0031886020 0.4164725 #> 63   3.322645 1.300131e-03 0.57023961 0.0044076659 0.0031650557 0.4208875 #> 64   3.360721 1.204799e-03 0.56600856 0.0043750170 0.0031416409 0.4252700 #> 65   3.398798 1.116457e-03 0.56180265 0.0043425580 0.0031183600 0.4296200 #> 66   3.436874 1.034593e-03 0.55762219 0.0043102916 0.0030952152 0.4339377 #> 67   3.474950 9.587312e-04 0.55346746 0.0042782203 0.0030722084 0.4382234 #> 68   3.513026 8.884323e-04 0.54933872 0.0042463463 0.0030493414 0.4424772 #> 69   3.551102 8.232881e-04 0.54523616 0.0042146714 0.0030266156 0.4466993 #> 70   3.589178 7.629205e-04 0.54115996 0.0041831973 0.0030040323 0.4508899 #> 71   3.627255 7.069794e-04 0.53711028 0.0041519253 0.0029815927 0.4550492 #> 72   3.665331 6.551402e-04 0.53308724 0.0041208566 0.0029592977 0.4591775 #> 73   3.703407 6.071021e-04 0.52909093 0.0040899922 0.0029371480 0.4632748 #> 74   3.741483 5.625863e-04 0.52512143 0.0040593327 0.0029151443 0.4673415 #> 75   3.779559 5.213347e-04 0.52117879 0.0040288788 0.0028932871 0.4713777 #> 76   3.817635 4.831079e-04 0.51726304 0.0039986309 0.0028715769 0.4753836 #> 77   3.855711 4.476840e-04 0.51337422 0.0039685892 0.0028500138 0.4793595 #> 78   3.893788 4.148576e-04 0.50951230 0.0039387540 0.0028285980 0.4833055 #> 79   3.931864 3.844382e-04 0.50567729 0.0039091252 0.0028073296 0.4872218 #> 80   3.969940 3.562493e-04 0.50186914 0.0038797027 0.0027862087 0.4911087 #> 81   4.008016 3.301274e-04 0.49808783 0.0038504863 0.0027652350 0.4949663 #> 82   4.046092 3.059208e-04 0.49433330 0.0038214757 0.0027444085 0.4987949 #> 83   4.084168 2.834892e-04 0.49060548 0.0037926704 0.0027237289 0.5025946 #> 84   4.122244 2.627023e-04 0.48690430 0.0037640701 0.0027031958 0.5063657 #> 85   4.160321 2.434397e-04 0.48322968 0.0037356742 0.0026828091 0.5101084 #> 86   4.198397 2.255895e-04 0.47958153 0.0037074819 0.0026625681 0.5138228 #> 87   4.236473 2.090482e-04 0.47595976 0.0036794928 0.0026424725 0.5175092 #> 88   4.274549 1.937197e-04 0.47236425 0.0036517059 0.0026225217 0.5211678 #> 89   4.312625 1.795152e-04 0.46879490 0.0036241205 0.0026027153 0.5247987 #> 90   4.350701 1.663523e-04 0.46525159 0.0035967357 0.0025830527 0.5284023 #> 91   4.388778 1.541545e-04 0.46173420 0.0035695507 0.0025635331 0.5319786 #> 92   4.426854 1.428511e-04 0.45824260 0.0035425646 0.0025441561 0.5355278 #> 93   4.464930 1.323766e-04 0.45477666 0.0035157763 0.0025249208 0.5390503 #> 94   4.503006 1.226701e-04 0.45133626 0.0034891849 0.0025058267 0.5425461 #> 95   4.541082 1.136753e-04 0.44792124 0.0034627893 0.0024868730 0.5460154 #> 96   4.579158 1.053401e-04 0.44453148 0.0034365885 0.0024680589 0.5494585 #> 97   4.617234 9.761602e-05 0.44116682 0.0034105814 0.0024493838 0.5528756 #> 98   4.655311 9.045833e-05 0.43782712 0.0033847670 0.0024308468 0.5562668 #> 99   4.693387 8.382548e-05 0.43451224 0.0033591440 0.0024124471 0.5596323 #> 100  4.731463 7.767898e-05 0.43122202 0.0033337113 0.0023941840 0.5629724 #> 101  4.769539 7.198317e-05 0.42795631 0.0033084679 0.0023760566 0.5662872 #> 102  4.807615 6.670501e-05 0.42471496 0.0032834126 0.0023580641 0.5695769 #> 103  4.845691 6.181387e-05 0.42149781 0.0032585441 0.0023402057 0.5728416 #> 104  4.883768 5.728137e-05 0.41830471 0.0032338613 0.0023224805 0.5760817 #> 105  4.921844 5.308122e-05 0.41513551 0.0032093630 0.0023048878 0.5792972 #> 106  4.959920 4.918904e-05 0.41199004 0.0031850480 0.0022874265 0.5824883 #> 107  4.997996 4.558226e-05 0.40886815 0.0031609151 0.0022700960 0.5856553 #> 108  5.036072 4.223994e-05 0.40576968 0.0031369630 0.0022528952 0.5887982 #> 109  5.074148 3.914270e-05 0.40269447 0.0031131907 0.0022358234 0.5919174 #> 110  5.112224 3.627256e-05 0.39964237 0.0030895968 0.0022188797 0.5950129 #> 111  5.150301 3.361288e-05 0.39661321 0.0030661802 0.0022020633 0.5980849 #> 112  5.188377 3.114822e-05 0.39360683 0.0030429396 0.0021853731 0.6011337 #> 113  5.226453 2.886428e-05 0.39062308 0.0030198739 0.0021688085 0.6041594 #> 114  5.264529 2.674781e-05 0.38766180 0.0029969817 0.0021523685 0.6071621 #> 115  5.302605 2.478652e-05 0.38472284 0.0029742619 0.0021360522 0.6101421 #> 116  5.340681 2.296905e-05 0.38180602 0.0029517133 0.0021198589 0.6130994 #> 117  5.378758 2.128485e-05 0.37891120 0.0029293346 0.0021037875 0.6160344 #> 118  5.416834 1.972414e-05 0.37603822 0.0029071247 0.0020878373 0.6189471 #> 119  5.454910 1.827787e-05 0.37318692 0.0028850823 0.0020720073 0.6218377 #> 120  5.492986 1.693764e-05 0.37035714 0.0028632063 0.0020562968 0.6247064 #> 121  5.531062 1.569569e-05 0.36754874 0.0028414954 0.0020407049 0.6275534 #> 122  5.569138 1.454481e-05 0.36476154 0.0028199485 0.0020252307 0.6303787 #> 123  5.607214 1.347831e-05 0.36199541 0.0027985643 0.0020098733 0.6331827 #> 124  5.645291 1.249001e-05 0.35925019 0.0027773417 0.0019946320 0.6359653 #> 125  5.683367 1.157418e-05 0.35652572 0.0027562795 0.0019795058 0.6387269 #> 126  5.721443 1.072551e-05 0.35382185 0.0027353766 0.0019644940 0.6414676 #> 127  5.759519 9.939060e-06 0.35113843 0.0027146317 0.0019495957 0.6441874 #> 128  5.797595 9.210279e-06 0.34847531 0.0026940437 0.0019348100 0.6468866 #> 129  5.835671 8.534936e-06 0.34583234 0.0026736114 0.0019201361 0.6495654 #> 130  5.873747 7.909112e-06 0.34320938 0.0026533337 0.0019055733 0.6522238 #> 131  5.911824 7.329177e-06 0.34060626 0.0026332095 0.0018911207 0.6548621 #> 132  5.949900 6.791766e-06 0.33802285 0.0026132377 0.0018767774 0.6574803 #> 133  5.987976 6.293760e-06 0.33545901 0.0025934169 0.0018625427 0.6600787 #> 134  6.026052 5.832270e-06 0.33291457 0.0025737463 0.0018484158 0.6626574 #> 135  6.064128 5.404619e-06 0.33038940 0.0025542246 0.0018343958 0.6652166 #> 136  6.102204 5.008326e-06 0.32788336 0.0025348508 0.0018204820 0.6677563 #> 137  6.140281 4.641091e-06 0.32539630 0.0025156236 0.0018066736 0.6702768 #> 138  6.178357 4.300783e-06 0.32292809 0.0024965422 0.0017929697 0.6727781 #> 139  6.216433 3.985428e-06 0.32047857 0.0024776052 0.0017793697 0.6752605 #> 140  6.254509 3.693197e-06 0.31804761 0.0024588118 0.0017658726 0.6777240 #> 141  6.292585 3.422394e-06 0.31563507 0.0024401607 0.0017524779 0.6801689 #> 142  6.330661 3.171447e-06 0.31324082 0.0024216510 0.0017391846 0.6825952 #> 143  6.368737 2.938901e-06 0.31086471 0.0024032815 0.0017259921 0.6850031 #> 144  6.406814 2.723406e-06 0.30850661 0.0023850513 0.0017128995 0.6873927 #> 145  6.444890 2.523712e-06 0.30616638 0.0023669592 0.0016999062 0.6897642 #> 146  6.482966 2.338661e-06 0.30384389 0.0023490042 0.0016870113 0.6921178 #> 147  6.521042 2.167179e-06 0.30153901 0.0023311854 0.0016742142 0.6944534 #> 148  6.559118 2.008271e-06 0.29925160 0.0023135016 0.0016615141 0.6967714 #> 149  6.597194 1.861015e-06 0.29698153 0.0022959519 0.0016489103 0.6990717 #> 150  6.635271 1.724556e-06 0.29472867 0.0022785352 0.0016364020 0.7013547 #> 151  6.673347 1.598103e-06 0.29249290 0.0022612506 0.0016239885 0.7036203 #> 152  6.711423 1.480922e-06 0.29027407 0.0022440970 0.0016116692 0.7058687 #> 153  6.749499 1.372333e-06 0.28807207 0.0022270735 0.0015994432 0.7081000 #> 154  6.787575 1.271707e-06 0.28588677 0.0022101791 0.0015873100 0.7103145 #> 155  6.825651 1.178459e-06 0.28371804 0.0021934128 0.0015752688 0.7125121 #> 156  6.863727 1.092049e-06 0.28156575 0.0021767736 0.0015633188 0.7146931 #> 157  6.901804 1.011974e-06 0.27942979 0.0021602606 0.0015514595 0.7168575 #> 158  6.939880 9.377715e-07 0.27731002 0.0021438728 0.0015396901 0.7190055 #> 159  6.977956 8.690095e-07 0.27520633 0.0021276093 0.0015280100 0.7211372 #> 160  7.016032 8.052894e-07 0.27311859 0.0021114691 0.0015164184 0.7232527 #> 161  7.054108 7.462416e-07 0.27104669 0.0020954513 0.0015049148 0.7253522 #> 162  7.092184 6.915235e-07 0.26899050 0.0020795550 0.0014934984 0.7274358 #> 163  7.130261 6.408176e-07 0.26694991 0.0020637793 0.0014821686 0.7295035 #> 164  7.168337 5.938296e-07 0.26492479 0.0020481232 0.0014709247 0.7315556 #> 165  7.206413 5.502871e-07 0.26291503 0.0020325859 0.0014597660 0.7335921 #> 166  7.244489 5.099374e-07 0.26092052 0.0020171664 0.0014486921 0.7356131 #> 167  7.282565 4.725462e-07 0.25894113 0.0020018639 0.0014377021 0.7376188 #> 168  7.320641 4.378968e-07 0.25697676 0.0019866774 0.0014267954 0.7396093 #> 169  7.358717 4.057880e-07 0.25502729 0.0019716061 0.0014159715 0.7415847 #> 170  7.396794 3.760337e-07 0.25309260 0.0019566492 0.0014052297 0.7435451 #> 171  7.434870 3.484610e-07 0.25117259 0.0019418056 0.0013945694 0.7454907 #> 172  7.472946 3.229101e-07 0.24926714 0.0019270747 0.0013839899 0.7474215 #> 173  7.511022 2.992328e-07 0.24737615 0.0019124555 0.0013734907 0.7493376 #> 174  7.549098 2.772915e-07 0.24549950 0.0018979472 0.0013630711 0.7512392 #> 175  7.587174 2.569592e-07 0.24363708 0.0018835490 0.0013527305 0.7531264 #> 176  7.625251 2.381176e-07 0.24178880 0.0018692600 0.0013424684 0.7549992 #> 177  7.663327 2.206577e-07 0.23995453 0.0018550793 0.0013322842 0.7568579 #> 178  7.701403 2.044780e-07 0.23813418 0.0018410063 0.0013221771 0.7587024 #> 179  7.739479 1.894846e-07 0.23632763 0.0018270399 0.0013121468 0.7605330 #> 180  7.777555 1.755907e-07 0.23453479 0.0018131796 0.0013021925 0.7623497 #> 181  7.815631 1.627155e-07 0.23275555 0.0017994243 0.0012923138 0.7641525 #> 182  7.853707 1.507844e-07 0.23098981 0.0017857734 0.0012825100 0.7659418 #> 183  7.891784 1.397281e-07 0.22923746 0.0017722261 0.0012727805 0.7677174 #> 184  7.929860 1.294826e-07 0.22749840 0.0017587815 0.0012631249 0.7694796 #> 185  7.967936 1.199883e-07 0.22577254 0.0017454390 0.0012535425 0.7712284 #> 186  8.006012 1.111901e-07 0.22405977 0.0017321976 0.0012440328 0.7729639 #> 187  8.044088 1.030371e-07 0.22235999 0.0017190567 0.0012345952 0.7746863 #> 188  8.082164 9.548195e-08 0.22067311 0.0017060154 0.0012252292 0.7763956 #> 189  8.120240 8.848074e-08 0.21899902 0.0016930731 0.0012159343 0.7780919 #> 190  8.158317 8.199289e-08 0.21733763 0.0016802290 0.0012067099 0.7797753 #> 191  8.196393 7.598077e-08 0.21568885 0.0016674823 0.0011975555 0.7814460 #> 192  8.234469 7.040948e-08 0.21405257 0.0016548324 0.0011884705 0.7831041 #> 193  8.272545 6.524671e-08 0.21242871 0.0016422783 0.0011794544 0.7847495 #> 194  8.310621 6.046250e-08 0.21081716 0.0016298195 0.0011705067 0.7863825 #> 195  8.348697 5.602909e-08 0.20921784 0.0016174553 0.0011616269 0.7880030 #> 196  8.386774 5.192076e-08 0.20763066 0.0016051848 0.0011528145 0.7896113 #> 197  8.424850 4.811367e-08 0.20605551 0.0015930074 0.0011440689 0.7912074 #> 198  8.462926 4.458574e-08 0.20449231 0.0015809224 0.0011353897 0.7927913 #> 199  8.501002 4.131650e-08 0.20294097 0.0015689290 0.0011267763 0.7943633 #> 200  8.539078 3.828697e-08 0.20140140 0.0015570267 0.0011182282 0.7959233 #> 201  8.577154 3.547958e-08 0.19987351 0.0015452146 0.0011097450 0.7974715 #> 202  8.615230 3.287804e-08 0.19835721 0.0015334922 0.0011013262 0.7990079 #> 203  8.653307 3.046726e-08 0.19685242 0.0015218586 0.0010929712 0.8005327 #> 204  8.691383 2.823325e-08 0.19535904 0.0015103134 0.0010846796 0.8020459 #> 205  8.729459 2.616305e-08 0.19387699 0.0014988557 0.0010764509 0.8035477 #> 206  8.767535 2.424464e-08 0.19240618 0.0014874849 0.0010682846 0.8050380 #> 207  8.805611 2.246691e-08 0.19094653 0.0014762004 0.0010601803 0.8065171 #> 208  8.843687 2.081952e-08 0.18949795 0.0014650015 0.0010521374 0.8079849 #> 209  8.881764 1.929293e-08 0.18806036 0.0014538876 0.0010441556 0.8094416 #> 210  8.919840 1.787828e-08 0.18663368 0.0014428580 0.0010362343 0.8108872 #> 211  8.957916 1.656735e-08 0.18521782 0.0014319120 0.0010283732 0.8123219 #> 212  8.995992 1.535255e-08 0.18381270 0.0014210491 0.0010205716 0.8137457 #> 213  9.034068 1.422683e-08 0.18241824 0.0014102686 0.0010128292 0.8151586 #> 214  9.072144 1.318365e-08 0.18103436 0.0013995699 0.0010051456 0.8165609 #> 215  9.110220 1.221696e-08 0.17966098 0.0013889523 0.0009975203 0.8179525 #> 216  9.148297 1.132115e-08 0.17829802 0.0013784153 0.0009899528 0.8193336 #> 217  9.186373 1.049103e-08 0.17694539 0.0013679582 0.0009824427 0.8207042 #> 218  9.224449 9.721773e-09 0.17560303 0.0013575805 0.0009749896 0.8220644 #> 219  9.262525 9.008925e-09 0.17427086 0.0013472814 0.0009675930 0.8234143 #> 220  9.300601 8.348346e-09 0.17294878 0.0013370606 0.0009602526 0.8247539 #> 221  9.338677 7.736204e-09 0.17163674 0.0013269172 0.0009529678 0.8260834 #> 222  9.376754 7.168947e-09 0.17033465 0.0013168508 0.0009457383 0.8274028 #> 223  9.414830 6.643285e-09 0.16904244 0.0013068608 0.0009385636 0.8287121 #> 224  9.452906 6.156166e-09 0.16776003 0.0012969465 0.0009314434 0.8300116 #> 225  9.490982 5.704766e-09 0.16648736 0.0012871075 0.0009243772 0.8313012 #> 226  9.529058 5.286464e-09 0.16522433 0.0012773431 0.0009173646 0.8325810 #> 227  9.567134 4.898834e-09 0.16397089 0.0012676528 0.0009104052 0.8338510 #> 228  9.605210 4.539628e-09 0.16272696 0.0012580360 0.0009034986 0.8351115 #> 229  9.643287 4.206760e-09 0.16149246 0.0012484921 0.0008966444 0.8363624 #> 230  9.681363 3.898299e-09 0.16026733 0.0012390207 0.0008898421 0.8376038 #> 231  9.719439 3.612457e-09 0.15905149 0.0012296211 0.0008830915 0.8388358 #> 232  9.757515 3.347574e-09 0.15784488 0.0012202928 0.0008763921 0.8400584 #> 233  9.795591 3.102113e-09 0.15664742 0.0012110353 0.0008697435 0.8412718 #> 234  9.833667 2.874651e-09 0.15545904 0.0012018480 0.0008631454 0.8424760 #> 235  9.871743 2.663867e-09 0.15427968 0.0011927304 0.0008565973 0.8436710 #> 236  9.909820 2.468539e-09 0.15310927 0.0011836820 0.0008500989 0.8448569 #> 237  9.947896 2.287534e-09 0.15194774 0.0011747022 0.0008436498 0.8460339 #> 238  9.985972 2.119800e-09 0.15079501 0.0011657906 0.0008372496 0.8472019 #> 239 10.024048 1.964366e-09 0.14965104 0.0011569466 0.0008308980 0.8483611 #> 240 10.062124 1.820329e-09 0.14851574 0.0011481696 0.0008245945 0.8495115 #> 241 10.100200 1.686854e-09 0.14738905 0.0011394592 0.0008183389 0.8506531 #> 242 10.138277 1.563165e-09 0.14627091 0.0011308149 0.0008121307 0.8517861 #> 243 10.176353 1.448546e-09 0.14516126 0.0011222362 0.0008059697 0.8529105 #> 244 10.214429 1.342332e-09 0.14406002 0.0011137226 0.0007998553 0.8540264 #> 245 10.252505 1.243905e-09 0.14296714 0.0011052736 0.0007937874 0.8551338 #> 246 10.290581 1.152696e-09 0.14188254 0.0010968886 0.0007877655 0.8562328 #> 247 10.328657 1.068175e-09 0.14080618 0.0010885673 0.0007817892 0.8573235 #> 248 10.366733 9.898508e-10 0.13973798 0.0010803091 0.0007758583 0.8584059 #> 249 10.404810 9.172700e-10 0.13867788 0.0010721135 0.0007699724 0.8594800 #> 250 10.442886 8.500112e-10 0.13762583 0.0010639801 0.0007641312 0.8605461 #> 251 10.480962 7.876842e-10 0.13658176 0.0010559085 0.0007583343 0.8616040 #> 252 10.519038 7.299273e-10 0.13554561 0.0010478980 0.0007525813 0.8626539 #> 253 10.557114 6.764054e-10 0.13451732 0.0010399483 0.0007468720 0.8636959 #> 254 10.595190 6.268081e-10 0.13349683 0.0010320590 0.0007412060 0.8647299 #> 255 10.633267 5.808474e-10 0.13248408 0.0010242295 0.0007355830 0.8657561 #> 256 10.671343 5.382568e-10 0.13147901 0.0010164593 0.0007300026 0.8667745 #> 257 10.709419 4.987891e-10 0.13048157 0.0010087482 0.0007244646 0.8677852 #> 258 10.747495 4.622155e-10 0.12949170 0.0010010955 0.0007189686 0.8687882 #> 259 10.785571 4.283235e-10 0.12850934 0.0009935009 0.0007135143 0.8697836 #> 260 10.823647 3.969167e-10 0.12753443 0.0009859639 0.0007081013 0.8707715 #> 261 10.861723 3.678128e-10 0.12656691 0.0009784840 0.0007027294 0.8717519 #> 262 10.899800 3.408430e-10 0.12560673 0.0009710610 0.0006973983 0.8727248 #> 263 10.937876 3.158507e-10 0.12465384 0.0009636942 0.0006921076 0.8736904 #> 264 10.975952 2.926910e-10 0.12370818 0.0009563833 0.0006868571 0.8746486 #> 265 11.014028 2.712294e-10 0.12276969 0.0009491279 0.0006816464 0.8755995 #> 266 11.052104 2.513415e-10 0.12183832 0.0009419275 0.0006764752 0.8765433 #> 267 11.090180 2.329119e-10 0.12091402 0.0009347818 0.0006713433 0.8774799 #> 268 11.128257 2.158337e-10 0.11999673 0.0009276902 0.0006662502 0.8784093 #> 269 11.166333 2.000077e-10 0.11908640 0.0009206525 0.0006611959 0.8793318 #> 270 11.204409 1.853421e-10 0.11818297 0.0009136681 0.0006561798 0.8802472 #> 271 11.242485 1.717519e-10 0.11728640 0.0009067368 0.0006512019 0.8811557 #> 272 11.280561 1.591582e-10 0.11639663 0.0008998580 0.0006462616 0.8820573 #> 273 11.318637 1.474880e-10 0.11551361 0.0008930314 0.0006413589 0.8829520 #> 274 11.356713 1.366734e-10 0.11463729 0.0008862566 0.0006364933 0.8838400 #> 275 11.394790 1.266518e-10 0.11376761 0.0008795331 0.0006316647 0.8847212 #> 276 11.432866 1.173651e-10 0.11290454 0.0008728607 0.0006268727 0.8855957 #> 277 11.470942 1.087593e-10 0.11204801 0.0008662389 0.0006221171 0.8864636 #> 278 11.509018 1.007845e-10 0.11119798 0.0008596674 0.0006173975 0.8873250 #> 279 11.547094 9.339453e-11 0.11035439 0.0008531457 0.0006127137 0.8881797 #> 280 11.585170 8.654638e-11 0.10951721 0.0008466735 0.0006080655 0.8890280 #> 281 11.623246 8.020037e-11 0.10868638 0.0008402503 0.0006034525 0.8898699 #> 282 11.661323 7.431968e-11 0.10786185 0.0008338759 0.0005988745 0.8907054 #> 283 11.699399 6.887020e-11 0.10704358 0.0008275499 0.0005943313 0.8915345 #> 284 11.737475 6.382029e-11 0.10623151 0.0008212718 0.0005898225 0.8923574 #> 285 11.775551 5.914067e-11 0.10542561 0.0008150414 0.0005853479 0.8931740 #> 286 11.813627 5.480419e-11 0.10462582 0.0008088583 0.0005809073 0.8939844 #> 287 11.851703 5.078567e-11 0.10383209 0.0008027220 0.0005765004 0.8947887 #> 288 11.889780 4.706182e-11 0.10304439 0.0007966323 0.0005721269 0.8955868 #> 289 11.927856 4.361101e-11 0.10226267 0.0007905888 0.0005677865 0.8963790 #> 290 11.965932 4.041324e-11 0.10148687 0.0007845912 0.0005634791 0.8971651 #> 291 12.004008 3.744994e-11 0.10071696 0.0007786390 0.0005592044 0.8979452 #> 292 12.042084 3.470392e-11 0.09995289 0.0007727320 0.0005549621 0.8987194 #> 293 12.080160 3.215926e-11 0.09919462 0.0007668699 0.0005507520 0.8994878 #> 294 12.118236 2.980119e-11 0.09844209 0.0007610521 0.0005465738 0.9002503 #> 295 12.156313 2.761602e-11 0.09769528 0.0007552786 0.0005424273 0.9010070 #> 296 12.194389 2.559107e-11 0.09695414 0.0007495488 0.0005383123 0.9017580 #> 297 12.232465 2.371461e-11 0.09621861 0.0007438625 0.0005342285 0.9025033 #> 298 12.270541 2.197574e-11 0.09548867 0.0007382193 0.0005301757 0.9032429 #> 299 12.308617 2.036437e-11 0.09476426 0.0007326190 0.0005261536 0.9039770 #> 300 12.346693 1.887115e-11 0.09404535 0.0007270611 0.0005221620 0.9047054 #> 301 12.384770 1.748742e-11 0.09333189 0.0007215454 0.0005182008 0.9054284 #> 302 12.422846 1.620516e-11 0.09262385 0.0007160715 0.0005142695 0.9061458 #> 303 12.460922 1.501692e-11 0.09192118 0.0007106392 0.0005103681 0.9068578 #> 304 12.498998 1.391580e-11 0.09122383 0.0007052480 0.0005064963 0.9075644 #> 305 12.537074 1.289543e-11 0.09053178 0.0006998978 0.0005026539 0.9082657 #> 306 12.575150 1.194987e-11 0.08984498 0.0006945882 0.0004988406 0.9089616 #> 307 12.613226 1.107365e-11 0.08916339 0.0006893188 0.0004950562 0.9096522 #> 308 12.651303 1.026167e-11 0.08848697 0.0006840894 0.0004913006 0.9103376 #> 309 12.689379 9.509237e-12 0.08781568 0.0006788997 0.0004875734 0.9110178 #> 310 12.727455 8.811972e-12 0.08714948 0.0006737494 0.0004838745 0.9116929 #> 311 12.765531 8.165835e-12 0.08648834 0.0006686381 0.0004802037 0.9123628 #> 312 12.803607 7.567076e-12 0.08583221 0.0006635656 0.0004765608 0.9130277 #> 313 12.841683 7.012220e-12 0.08518106 0.0006585316 0.0004729454 0.9136875 #> 314 12.879760 6.498050e-12 0.08453485 0.0006535358 0.0004693575 0.9143423 #> 315 12.917836 6.021581e-12 0.08389354 0.0006485778 0.0004657968 0.9149921 #> 316 12.955912 5.580049e-12 0.08325710 0.0006436575 0.0004622631 0.9156370 #> 317 12.993988 5.170892e-12 0.08262549 0.0006387745 0.0004587563 0.9162770 #> 318 13.032064 4.791736e-12 0.08199867 0.0006339286 0.0004552760 0.9169121 #> 319 13.070140 4.440383e-12 0.08137660 0.0006291194 0.0004518221 0.9175425 #> 320 13.108216 4.114792e-12 0.08075925 0.0006243467 0.0004483945 0.9181680 #> 321 13.146293 3.813075e-12 0.08014659 0.0006196102 0.0004449928 0.9187888 #> 322 13.184369 3.533481e-12 0.07953857 0.0006149097 0.0004416170 0.9194049 #> 323 13.222445 3.274389e-12 0.07893516 0.0006102448 0.0004382667 0.9200163 #> 324 13.260521 3.034295e-12 0.07833634 0.0006056153 0.0004349419 0.9206231 #> 325 13.298597 2.811805e-12 0.07774205 0.0006010209 0.0004316423 0.9212253 #> 326 13.336673 2.605630e-12 0.07715228 0.0005964614 0.0004283677 0.9218229 #> 327 13.374749 2.414572e-12 0.07656698 0.0005919364 0.0004251180 0.9224160 #> 328 13.412826 2.237524e-12 0.07598612 0.0005874458 0.0004218929 0.9230045 #> 329 13.450902 2.073458e-12 0.07540966 0.0005829893 0.0004186923 0.9235887 #> 330 13.488978 1.921421e-12 0.07483758 0.0005785665 0.0004155160 0.9241683 #> 331 13.527054 1.780533e-12 0.07426984 0.0005741774 0.0004123637 0.9247436 #> 332 13.565130 1.649976e-12 0.07370641 0.0005698215 0.0004092354 0.9253145 #> 333 13.603206 1.528991e-12 0.07314725 0.0005654986 0.0004061308 0.9258811 #> 334 13.641283 1.416878e-12 0.07259233 0.0005612086 0.0004030498 0.9264434 #> 335 13.679359 1.312986e-12 0.07204162 0.0005569511 0.0003999921 0.9270014 #> 336 13.717435 1.216711e-12 0.07149509 0.0005527259 0.0003969577 0.9275552 #> 337 13.755511 1.127496e-12 0.07095271 0.0005485327 0.0003939462 0.9281048 #> 338 13.793587 1.044822e-12 0.07041444 0.0005443714 0.0003909576 0.9286502 #> 339 13.831663 9.682107e-13 0.06988025 0.0005402416 0.0003879917 0.9291915 #> 340 13.869739 8.972167e-13 0.06935012 0.0005361432 0.0003850483 0.9297287 #> 341 13.907816 8.314284e-13 0.06882401 0.0005320758 0.0003821272 0.9302618 #> 342 13.945892 7.704639e-13 0.06830189 0.0005280394 0.0003792283 0.9307908 #> 343 13.983968 7.139697e-13 0.06778373 0.0005240335 0.0003763513 0.9313159 #> 344 14.022044 6.616179e-13 0.06726950 0.0005200580 0.0003734962 0.9318369 #> 345 14.060120 6.131048e-13 0.06675918 0.0005161127 0.0003706627 0.9323540 #> 346 14.098196 5.681489e-13 0.06625272 0.0005121973 0.0003678508 0.9328672 #> 347 14.136273 5.264895e-13 0.06575011 0.0005083116 0.0003650602 0.9333765 #> 348 14.174349 4.878846e-13 0.06525131 0.0005044554 0.0003622907 0.9338819 #> 349 14.212425 4.521105e-13 0.06475629 0.0005006284 0.0003595422 0.9343835 #> 350 14.250501 4.189596e-13 0.06426503 0.0004968305 0.0003568146 0.9348813 #> 351 14.288577 3.882394e-13 0.06377749 0.0004930614 0.0003541077 0.9353753 #> 352 14.326653 3.597717e-13 0.06329366 0.0004893209 0.0003514214 0.9358656 #> 353 14.364729 3.333915e-13 0.06281349 0.0004856088 0.0003487554 0.9363521 #> 354 14.402806 3.089456e-13 0.06233697 0.0004819248 0.0003461096 0.9368350 #> 355 14.440882 2.862922e-13 0.06186406 0.0004782687 0.0003434839 0.9373142 #> 356 14.478958 2.652998e-13 0.06139474 0.0004746405 0.0003408781 0.9377897 #> 357 14.517034 2.458467e-13 0.06092898 0.0004710397 0.0003382921 0.9382617 #> 358 14.555110 2.278200e-13 0.06046676 0.0004674662 0.0003357258 0.9387301 #> 359 14.593186 2.111151e-13 0.06000804 0.0004639199 0.0003331788 0.9391949 #> 360 14.631263 1.956351e-13 0.05955280 0.0004604005 0.0003306512 0.9396561 #> 361 14.669339 1.812902e-13 0.05910101 0.0004569077 0.0003281428 0.9401139 #> 362 14.707415 1.679971e-13 0.05865265 0.0004534415 0.0003256534 0.9405683 #> 363 14.745491 1.556787e-13 0.05820770 0.0004500015 0.0003231829 0.9410191 #> 364 14.783567 1.442636e-13 0.05776612 0.0004465877 0.0003207311 0.9414666 #> 365 14.821643 1.336855e-13 0.05732788 0.0004431997 0.0003182980 0.9419106 #> 366 14.859719 1.238830e-13 0.05689298 0.0004398375 0.0003158833 0.9423513 #> 367 14.897796 1.147993e-13 0.05646137 0.0004365007 0.0003134869 0.9427886 #> 368 14.935872 1.063816e-13 0.05603304 0.0004331893 0.0003111087 0.9432227 #> 369 14.973948 9.858120e-14 0.05560795 0.0004299030 0.0003087485 0.9436534 #> 370 15.012024 9.135274e-14 0.05518609 0.0004266416 0.0003064063 0.9440809 #> 371 15.050100 8.465431e-14 0.05476743 0.0004234050 0.0003040818 0.9445051 #> 372 15.088176 7.844704e-14 0.05435195 0.0004201929 0.0003017749 0.9449261 #> 373 15.126253 7.269491e-14 0.05393962 0.0004170052 0.0002994855 0.9453439 #> 374 15.164329 6.736456e-14 0.05353042 0.0004138417 0.0002972136 0.9457585 #> 375 15.202405 6.242506e-14 0.05312432 0.0004107021 0.0002949588 0.9461700 #> 376 15.240481 5.784774e-14 0.05272130 0.0004075864 0.0002927212 0.9465784 #> 377 15.278557 5.360606e-14 0.05232134 0.0004044943 0.0002905005 0.9469837 #> 378 15.316633 4.967540e-14 0.05192442 0.0004014257 0.0002882967 0.9473859 #> 379 15.354709 4.603296e-14 0.05153050 0.0003983804 0.0002861095 0.9477850 #> 380 15.392786 4.265759e-14 0.05113958 0.0003953581 0.0002839390 0.9481811 #> 381 15.430862 3.952973e-14 0.05075162 0.0003923588 0.0002817850 0.9485742 #> 382 15.468938 3.663121e-14 0.05036660 0.0003893823 0.0002796473 0.9489644 #> 383 15.507014 3.394523e-14 0.04998450 0.0003864283 0.0002775258 0.9493515 #> 384 15.545090 3.145620e-14 0.04960530 0.0003834967 0.0002754204 0.9497358 #> 385 15.583166 2.914967e-14 0.04922898 0.0003805874 0.0002733310 0.9501171 #> 386 15.621242 2.701228e-14 0.04885552 0.0003777002 0.0002712574 0.9504955 #> 387 15.659319 2.503160e-14 0.04848488 0.0003748348 0.0002691995 0.9508711 #> 388 15.697395 2.319616e-14 0.04811706 0.0003719912 0.0002671573 0.9512438 #> 389 15.735471 2.149530e-14 0.04775203 0.0003691692 0.0002651306 0.9516137 #> 390 15.773547 1.991916e-14 0.04738977 0.0003663685 0.0002631192 0.9519807 #> 391 15.811623 1.845859e-14 0.04703026 0.0003635891 0.0002611231 0.9523450 #> 392 15.849699 1.710512e-14 0.04667347 0.0003608308 0.0002591422 0.9527066 #> 393 15.887776 1.585088e-14 0.04631939 0.0003580935 0.0002571762 0.9530653 #> 394 15.925852 1.468862e-14 0.04596800 0.0003553769 0.0002552252 0.9534214 #> 395 15.963928 1.361158e-14 0.04561927 0.0003526809 0.0002532890 0.9537748 #> 396 16.002004 1.261351e-14 0.04527319 0.0003500053 0.0002513675 0.9541254 #> 397 16.040080 1.168862e-14 0.04492973 0.0003473501 0.0002494605 0.9544735 #> 398 16.078156 1.083156e-14 0.04458888 0.0003447150 0.0002475680 0.9548188 #> 399 16.116232 1.003733e-14 0.04425062 0.0003420999 0.0002456899 0.9551616 #> 400 16.154309 9.301346e-15 0.04391492 0.0003395046 0.0002438260 0.9555018 #> 401 16.192385 8.619326e-15 0.04358177 0.0003369290 0.0002419763 0.9558393 #> 402 16.230461 7.987314e-15 0.04325114 0.0003343729 0.0002401406 0.9561743 #> 403 16.268537 7.401645e-15 0.04292302 0.0003318363 0.0002383188 0.9565068 #> 404 16.306613 6.858920e-15 0.04259740 0.0003293189 0.0002365108 0.9568368 #> 405 16.344689 6.355990e-15 0.04227424 0.0003268206 0.0002347166 0.9571642 #> 406 16.382766 5.889937e-15 0.04195353 0.0003243412 0.0002329360 0.9574892 #> 407 16.420842 5.458058e-15 0.04163526 0.0003218807 0.0002311688 0.9578117 #> 408 16.458918 5.057846e-15 0.04131940 0.0003194388 0.0002294151 0.9581317 #> 409 16.496994 4.686980e-15 0.04100594 0.0003170154 0.0002276747 0.9584494 #> 410 16.535070 4.343307e-15 0.04069486 0.0003146104 0.0002259475 0.9587646 #> 411 16.573146 4.024835e-15 0.04038614 0.0003122237 0.0002242334 0.9590774 #> 412 16.611222 3.729714e-15 0.04007975 0.0003098551 0.0002225323 0.9593879 #> 413 16.649299 3.456233e-15 0.03977570 0.0003075044 0.0002208441 0.9596960 #> 414 16.687375 3.202805e-15 0.03947395 0.0003051716 0.0002191687 0.9600017 #> 415 16.725451 2.967959e-15 0.03917448 0.0003028565 0.0002175060 0.9603052 #> 416 16.763527 2.750334e-15 0.03887729 0.0003005589 0.0002158559 0.9606063 #> 417 16.801603 2.548666e-15 0.03858236 0.0002982788 0.0002142184 0.9609051 #> 418 16.839679 2.361785e-15 0.03828966 0.0002960159 0.0002125933 0.9612017 #> 419 16.877756 2.188607e-15 0.03799918 0.0002937703 0.0002109805 0.9614961 #> 420 16.915832 2.028128e-15 0.03771091 0.0002915416 0.0002093799 0.9617882 #> 421 16.953908 1.879415e-15 0.03742482 0.0002893299 0.0002077915 0.9620781 #> 422 16.991984 1.741607e-15 0.03714091 0.0002871350 0.0002062151 0.9623657 #> 423 17.030060 1.613904e-15 0.03685915 0.0002849567 0.0002046507 0.9626512 #> 424 17.068136 1.495565e-15 0.03657952 0.0002827949 0.0002030982 0.9629346 #> 425 17.106212 1.385902e-15 0.03630202 0.0002806495 0.0002015574 0.9632158 #> 426 17.144289 1.284281e-15 0.03602662 0.0002785204 0.0002000283 0.9634948 #> 427 17.182365 1.190111e-15 0.03575331 0.0002764075 0.0001985108 0.9637718 #> 428 17.220441 1.102847e-15 0.03548207 0.0002743106 0.0001970049 0.9640466 #> 429 17.258517 1.021980e-15 0.03521290 0.0002722296 0.0001955103 0.9643194 #> 430 17.296593 9.470438e-16 0.03494576 0.0002701644 0.0001940271 0.9645900 #> 431 17.334669 8.776018e-16 0.03468065 0.0002681148 0.0001925552 0.9648587 #> 432 17.372745 8.132517e-16 0.03441755 0.0002660808 0.0001910944 0.9651253 #> 433 17.410822 7.536201e-16 0.03415645 0.0002640622 0.0001896447 0.9653898 #> 434 17.448898 6.983609e-16 0.03389733 0.0002620590 0.0001882060 0.9656524 #> 435 17.486974 6.471537e-16 0.03364017 0.0002600709 0.0001867782 0.9659130 #> 436 17.525050 5.997012e-16 0.03338497 0.0002580979 0.0001853613 0.9661716 #> 437 17.563126 5.557281e-16 0.03313170 0.0002561399 0.0001839550 0.9664282 #> 438 17.601202 5.149794e-16 0.03288035 0.0002541968 0.0001825595 0.9666829 #> 439 17.639279 4.772185e-16 0.03263091 0.0002522684 0.0001811746 0.9669356 #> 440 17.677355 4.422265e-16 0.03238336 0.0002503546 0.0001798001 0.9671865 #> 441 17.715431 4.098003e-16 0.03213769 0.0002484553 0.0001784361 0.9674354 #> 442 17.753507 3.797517e-16 0.03189389 0.0002465705 0.0001770824 0.9676825 #> 443 17.791583 3.519064e-16 0.03165193 0.0002446999 0.0001757390 0.9679276 #> 444 17.829659 3.261029e-16 0.03141181 0.0002428435 0.0001744058 0.9681709 #> 445 17.867735 3.021914e-16 0.03117351 0.0002410012 0.0001730827 0.9684124 #> 446 17.905812 2.800333e-16 0.03093702 0.0002391729 0.0001717696 0.9686520 #> 447 17.943888 2.594998e-16 0.03070232 0.0002373585 0.0001704666 0.9688899 #> 448 17.981964 2.404720e-16 0.03046940 0.0002355578 0.0001691733 0.9691259 #> 449 18.020040 2.228394e-16 0.03023825 0.0002337708 0.0001678899 0.9693601 #> 450 18.058116 2.064998e-16 0.03000886 0.0002319973 0.0001666163 0.9695925 #> 451 18.096192 1.913582e-16 0.02978120 0.0002302373 0.0001653523 0.9698232 #> 452 18.134269 1.773268e-16 0.02955527 0.0002284907 0.0001640979 0.9700521 #> 453 18.172345 1.643244e-16 0.02933106 0.0002267573 0.0001628530 0.9702793 #> 454 18.210421 1.522753e-16 0.02910854 0.0002250370 0.0001616175 0.9705048 #> 455 18.248497 1.411097e-16 0.02888771 0.0002233298 0.0001603914 0.9707286 #> 456 18.286573 1.307629e-16 0.02866856 0.0002216356 0.0001591746 0.9709506 #> 457 18.324649 1.211747e-16 0.02845107 0.0002199542 0.0001579671 0.9711710 #> 458 18.362725 1.122895e-16 0.02823524 0.0002182856 0.0001567687 0.9713897 #> 459 18.400802 1.040559e-16 0.02802103 0.0002166296 0.0001555794 0.9716068 #> 460 18.438878 9.642603e-17 0.02780846 0.0002149862 0.0001543991 0.9718222 #> 461 18.476954 8.935559e-17 0.02759750 0.0002133552 0.0001532278 0.9720359 #> 462 18.515030 8.280360e-17 0.02738813 0.0002117366 0.0001520654 0.9722481 #> 463 18.553106 7.673203e-17 0.02718036 0.0002101303 0.0001509118 0.9724586 #> 464 18.591182 7.110566e-17 0.02697416 0.0002085362 0.0001497669 0.9726675 #> 465 18.629259 6.589184e-17 0.02676952 0.0002069542 0.0001486307 0.9728749 #> 466 18.667335 6.106033e-17 0.02656644 0.0002053842 0.0001475032 0.9730807 #> 467 18.705411 5.658308e-17 0.02636490 0.0002038261 0.0001463842 0.9732849 #> 468 18.743487 5.243413e-17 0.02616489 0.0002022798 0.0001452737 0.9734876 #> 469 18.781563 4.858940e-17 0.02596639 0.0002007452 0.0001441716 0.9736887 #> 470 18.819639 4.502659e-17 0.02576941 0.0001992223 0.0001430778 0.9738883 #> 471 18.857715 4.172501e-17 0.02557391 0.0001977109 0.0001419924 0.9740864 #> 472 18.895792 3.866553e-17 0.02537990 0.0001962110 0.0001409152 0.9742830 #> 473 18.933868 3.583038e-17 0.02518736 0.0001947225 0.0001398462 0.9744781 #> 474 18.971944 3.320312e-17 0.02499628 0.0001932453 0.0001387853 0.9746717 #> 475 19.010020 3.076850e-17 0.02480665 0.0001917793 0.0001377324 0.9748638 #> 476 19.048096 2.851241e-17 0.02461846 0.0001903244 0.0001366875 0.9750545 #> 477 19.086172 2.642174e-17 0.02443170 0.0001888805 0.0001356506 0.9752438 #> 478 19.124248 2.448436e-17 0.02424635 0.0001874476 0.0001346215 0.9754316 #> 479 19.162325 2.268905e-17 0.02406241 0.0001860256 0.0001336002 0.9756180 #> 480 19.200401 2.102538e-17 0.02387986 0.0001846143 0.0001325867 0.9758029 #> 481 19.238477 1.948369e-17 0.02369870 0.0001832138 0.0001315808 0.9759865 #> 482 19.276553 1.805505e-17 0.02351892 0.0001818239 0.0001305826 0.9761687 #> 483 19.314629 1.673117e-17 0.02334050 0.0001804445 0.0001295920 0.9763495 #> 484 19.352705 1.550435e-17 0.02316343 0.0001790756 0.0001286088 0.9765289 #> 485 19.390782 1.436750e-17 0.02298770 0.0001777171 0.0001276332 0.9767069 #> 486 19.428858 1.331400e-17 0.02281331 0.0001763689 0.0001266649 0.9768837 #> 487 19.466934 1.233775e-17 0.02264024 0.0001750309 0.0001257040 0.9770590 #> 488 19.505010 1.143309e-17 0.02246849 0.0001737030 0.0001247504 0.9772331 #> 489 19.543086 1.059476e-17 0.02229804 0.0001723853 0.0001238040 0.9774058 #> 490 19.581162 9.817898e-18 0.02212888 0.0001710775 0.0001228648 0.9775772 #> 491 19.619238 9.098001e-18 0.02196100 0.0001697797 0.0001219327 0.9777473 #> 492 19.657315 8.430891e-18 0.02179440 0.0001684917 0.0001210076 0.9779161 #> 493 19.695391 7.812696e-18 0.02162906 0.0001672134 0.0001200896 0.9780836 #> 494 19.733467 7.239831e-18 0.02146497 0.0001659449 0.0001191786 0.9782499 #> 495 19.771543 6.708970e-18 0.02130213 0.0001646860 0.0001182745 0.9784149 #> 496 19.809619 6.217036e-18 0.02114053 0.0001634366 0.0001173772 0.9785787 #> 497 19.847695 5.761172e-18 0.02098015 0.0001621967 0.0001164868 0.9787412 #> 498 19.885772 5.338734e-18 0.02082099 0.0001609663 0.0001156031 0.9789024 #> 499 19.923848 4.947272e-18 0.02066303 0.0001597451 0.0001147261 0.9790625 #> 500 19.961924 4.584513e-18 0.02050628 0.0001585333 0.0001138557 0.9792213 #> 501 20.000000 4.248354e-18 0.02035071 0.0001573306 0.0001129920 0.9793790 #>  #> attr(,\"class\") #> [1] \"mseir_model\""},{"path":"https://oucru-modelling.github.io/serosv/reference/mumps_uk_1986_1987.html","id":null,"dir":"Reference","previous_headings":"","what":"Mumps serological data from the UK in 1986 and 1987 (aggregated) — mumps_uk_1986_1987","title":"Mumps serological data from the UK in 1986 and 1987 (aggregated) — mumps_uk_1986_1987","text":"large survey prevalence antibodies mumps rubella viruses UK. survey, covering subjects 1 65 years age, provides information prevalence antibody age","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mumps_uk_1986_1987.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mumps serological data from the UK in 1986 and 1987 (aggregated) — mumps_uk_1986_1987","text":"","code":"mumps_uk_1986_1987"},{"path":"https://oucru-modelling.github.io/serosv/reference/mumps_uk_1986_1987.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Mumps serological data from the UK in 1986 and 1987 (aggregated) — mumps_uk_1986_1987","text":"data frame 3 variables: age Age group pos Number seropositive individuals tot Total number individuals surveyed","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mumps_uk_1986_1987.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Mumps serological data from the UK in 1986 and 1987 (aggregated) — mumps_uk_1986_1987","text":"Morgan-Capner P, Wright J, Miller C L, Miller E. Surveillance antibody measles, mumps, rubella age. British Medical Journal 1988; 297 :770 doi:10.1136/bmj.297.6651.770","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mumps_uk_1986_1987.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mumps serological data from the UK in 1986 and 1987 (aggregated) — mumps_uk_1986_1987","text":"","code":"# Reproduce Fig 4.4 (left panel), p. 67 age <- mumps_uk_1986_1987$age pos <- mumps_uk_1986_1987$pos tot <- mumps_uk_1986_1987$tot plot(age, pos / tot,   cex = 0.008 * tot, pch = 16, xlab = \"age\", ylab = \"seroprevalence\",   xlim = c(0, 45), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_be_2001_2003.html","id":null,"dir":"Reference","previous_headings":"","what":"Parvo B19 serological data from Belgium from 2001-2003 (line listing) — parvob19_be_2001_2003","title":"Parvo B19 serological data from Belgium from 2001-2003 (line listing) — parvob19_be_2001_2003","text":"seroprevalence survey testing parvovirus B19 IgG antibody, performed large representative national serum banks Belgium, England Wales, Finland, Italy, Poland. sera collected 1995 2004 obtained residual sera submitted routine laboratory testing.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_be_2001_2003.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parvo B19 serological data from Belgium from 2001-2003 (line listing) — parvob19_be_2001_2003","text":"","code":"parvob19_be_2001_2003"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_be_2001_2003.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Parvo B19 serological data from Belgium from 2001-2003 (line listing) — parvob19_be_2001_2003","text":"data frame 5 variables: age Age individual seropositive individual seropositive year Year surveyed gender Gender individual parvouml Parvo B19 antibody units per ml","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_be_2001_2003.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Parvo B19 serological data from Belgium from 2001-2003 (line listing) — parvob19_be_2001_2003","text":"MOSSONG, J., N. HENS, V. FRIEDERICHS, . DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. \"Parvovirus B19 Infection Five European Countries: Seroepidemiology, Force Infection Maternal Risk Infection.\" Epidemiology Infection 136, . 8 (2008): 1059-68. doi:10.1017/S0950268807009661","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_be_2001_2003.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parvo B19 serological data from Belgium from 2001-2003 (line listing) — parvob19_be_2001_2003","text":"","code":"# Reproduce Fig 4.5 (left upper panel), p. 68 library(dplyr) df <- parvob19_be_2001_2003 %>%   group_by(age) %>%   summarise(pos = sum(seropositive), tot = n()) plot(df$age, df$pos / df$tot,   cex = 0.02 * df$tot, pch = 16, xlab = \"age\", ylab = \"seroprevalence\",   xlim = c(0, 82), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_ew_1996.html","id":null,"dir":"Reference","previous_headings":"","what":"Parvo B19 serological data from England and Wales in 1996 (line listing) — parvob19_ew_1996","title":"Parvo B19 serological data from England and Wales in 1996 (line listing) — parvob19_ew_1996","text":"seroprevalence survey testing parvovirus B19 IgG antibody, performed large representative national serum banks Belgium, England Wales, Finland, Italy, Poland. sera collected 1995 2004 obtained residual sera submitted routine laboratory testing.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_ew_1996.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parvo B19 serological data from England and Wales in 1996 (line listing) — parvob19_ew_1996","text":"","code":"parvob19_ew_1996"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_ew_1996.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Parvo B19 serological data from England and Wales in 1996 (line listing) — parvob19_ew_1996","text":"data frame 5 variables: age Age individual seropositive individual seropositive year Year surveyed gender Gender individual parvouml Parvo B19 antibody units per ml","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_ew_1996.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Parvo B19 serological data from England and Wales in 1996 (line listing) — parvob19_ew_1996","text":"MOSSONG, J., N. HENS, V. FRIEDERICHS, . DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. \"Parvovirus B19 Infection Five European Countries: Seroepidemiology, Force Infection Maternal Risk Infection.\" Epidemiology Infection 136, . 8 (2008): 1059-68. doi:10.1017/S0950268807009661","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_ew_1996.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parvo B19 serological data from England and Wales in 1996 (line listing) — parvob19_ew_1996","text":"","code":"# Reproduce Fig 4.5 (right upper panel), p. 68 # NB: This figure will look different to that of in the book, since we # believe that the original authors has made some errors in specifying # the sample size of the dots. library(dplyr) df <- parvob19_ew_1996 %>%   mutate(age = round(age)) %>%   group_by(age) %>%   summarise(pos = sum(seropositive), tot = n()) plot(df$age, df$pos / df$tot,   cex = 0.02 * df$tot, pch = 16, xlab = \"age\", ylab = \"seroprevalence\",   xlim = c(0, 82), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_fi_1997_1998.html","id":null,"dir":"Reference","previous_headings":"","what":"Parvo B19 serological data from Finland from 1997-1998 (line listing) — parvob19_fi_1997_1998","title":"Parvo B19 serological data from Finland from 1997-1998 (line listing) — parvob19_fi_1997_1998","text":"seroprevalence survey testing parvovirus B19 IgG antibody, performed large representative national serum banks Belgium, England Wales, Finland, Italy, Poland. sera collected 1995 2004 obtained residual sera submitted routine laboratory testing.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_fi_1997_1998.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parvo B19 serological data from Finland from 1997-1998 (line listing) — parvob19_fi_1997_1998","text":"","code":"parvob19_fi_1997_1998"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_fi_1997_1998.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Parvo B19 serological data from Finland from 1997-1998 (line listing) — parvob19_fi_1997_1998","text":"data frame 5 variables: age Age individual seropositive individual seropositive year Year surveyed gender Gender individual parvouml Parvo B19 antibody units per ml","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_fi_1997_1998.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Parvo B19 serological data from Finland from 1997-1998 (line listing) — parvob19_fi_1997_1998","text":"MOSSONG, J., N. HENS, V. FRIEDERICHS, . DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. \"Parvovirus B19 Infection Five European Countries: Seroepidemiology, Force Infection Maternal Risk Infection.\" Epidemiology Infection 136, . 8 (2008): 1059-68. doi:10.1017/S0950268807009661","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_fi_1997_1998.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parvo B19 serological data from Finland from 1997-1998 (line listing) — parvob19_fi_1997_1998","text":"","code":"# Reproduce Fig 4.5 (left bottom panel), p. 68 # NB: This figure will look different to that of in the book, since we # believe that the original authors has made some errors in specifying # the sample size of the dots. library(dplyr) df <- parvob19_fi_1997_1998 %>%   mutate(age = round(age)) %>%   group_by(age) %>%   summarise(pos = sum(seropositive), tot = n()) plot(df$age, df$pos / df$tot,   cex = 0.07 * df$tot, pch = 16, xlab = \"age\", ylab = \"seroprevalence\",   xlim = c(0, 82), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_it_2003_2004.html","id":null,"dir":"Reference","previous_headings":"","what":"Parvo B19 serological data from Italy from 2003-2004 (line listing) — parvob19_it_2003_2004","title":"Parvo B19 serological data from Italy from 2003-2004 (line listing) — parvob19_it_2003_2004","text":"seroprevalence survey testing parvovirus B19 IgG antibody, performed large representative national serum banks Belgium, England Wales, Finland, Italy, Poland. sera collected 1995 2004 obtained residual sera submitted routine laboratory testing.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_it_2003_2004.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parvo B19 serological data from Italy from 2003-2004 (line listing) — parvob19_it_2003_2004","text":"","code":"parvob19_it_2003_2004"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_it_2003_2004.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Parvo B19 serological data from Italy from 2003-2004 (line listing) — parvob19_it_2003_2004","text":"data frame 5 variables: age Age individual seropositive individual seropositive year Year surveyed gender Gender individual parvouml Parvo B19 antibody units per ml","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_it_2003_2004.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Parvo B19 serological data from Italy from 2003-2004 (line listing) — parvob19_it_2003_2004","text":"MOSSONG, J., N. HENS, V. FRIEDERICHS, . DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. \"Parvovirus B19 Infection Five European Countries: Seroepidemiology, Force Infection Maternal Risk Infection.\" Epidemiology Infection 136, . 8 (2008): 1059-68. doi:10.1017/S0950268807009661","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_it_2003_2004.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parvo B19 serological data from Italy from 2003-2004 (line listing) — parvob19_it_2003_2004","text":"","code":"# Reproduce Fig 4.5 (middle bottom panel), p. 68 # NB: This figure will look different to that of in the book, since we # believe that the original authors has made some errors in specifying # the sample size of the dots. library(dplyr) df <- parvob19_it_2003_2004 %>%   group_by(age) %>%   summarise(pos = sum(seropositive), tot = n()) plot(df$age, df$pos / df$tot,   cex = 0.07 * df$tot, pch = 16, xlab = \"age\", ylab = \"seroprevalence\",   xlim = c(0, 82), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_pl_1995_2004.html","id":null,"dir":"Reference","previous_headings":"","what":"Parvo B19 serological data from Poland from 1995-2004 (line listing) — parvob19_pl_1995_2004","title":"Parvo B19 serological data from Poland from 1995-2004 (line listing) — parvob19_pl_1995_2004","text":"seroprevalence survey testing parvovirus B19 IgG antibody, performed large representative national serum banks Belgium, England Wales, Finland, Italy, Poland. sera collected 1995 2004 obtained residual sera submitted routine laboratory testing.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_pl_1995_2004.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parvo B19 serological data from Poland from 1995-2004 (line listing) — parvob19_pl_1995_2004","text":"","code":"parvob19_pl_1995_2004"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_pl_1995_2004.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Parvo B19 serological data from Poland from 1995-2004 (line listing) — parvob19_pl_1995_2004","text":"data frame 5 variables: age Age individual seropositive individual seropositive year Year surveyed gender Gender individual parvouml Parvo B19 antibody units per ml","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_pl_1995_2004.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Parvo B19 serological data from Poland from 1995-2004 (line listing) — parvob19_pl_1995_2004","text":"MOSSONG, J., N. HENS, V. FRIEDERICHS, . DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. \"Parvovirus B19 Infection Five European Countries: Seroepidemiology, Force Infection Maternal Risk Infection.\" Epidemiology Infection 136, . 8 (2008): 1059-68. doi:10.1017/S0950268807009661","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_pl_1995_2004.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parvo B19 serological data from Poland from 1995-2004 (line listing) — parvob19_pl_1995_2004","text":"","code":"# Reproduce Fig 4.5 (right bottom panel), p. 68 # NB: This figure will look different to that of in the book, since we # believe that the original authors has made some errors in specifying # the sample size of the dots. library(dplyr) df <- parvob19_pl_1995_2004 %>%   mutate(age = round(age)) %>%   group_by(age) %>%   summarise(pos = sum(seropositive), tot = n()) plot(df$age, df$pos / df$tot,   cex = 0.07 * df$tot, pch = 16, xlab = \"age\", ylab = \"seroprevalence\",   xlim = c(0, 82), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/pava.html","id":null,"dir":"Reference","previous_headings":"","what":"Monotonize seroprevalence — pava","title":"Monotonize seroprevalence — pava","text":"Monotonize seroprevalence","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/pava.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Monotonize seroprevalence — pava","text":"","code":"pava(pos = pos, tot = rep(1, length(pos)))"},{"path":"https://oucru-modelling.github.io/serosv/reference/pava.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Monotonize seroprevalence — pava","text":"pos positive count vector. tot total count vector.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/pava.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Monotonize seroprevalence — pava","text":"computed list 2 items pai1 original values pai2 monotonized value","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/penalized_spline_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Penalized Spline model — penalized_spline_model","title":"Penalized Spline model — penalized_spline_model","text":"Penalized Spline model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/penalized_spline_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Penalized Spline model — penalized_spline_model","text":"","code":"penalized_spline_model(   data,   s = \"bs\",   link = \"logit\",   framework = \"pl\",   sp = NULL )"},{"path":"https://oucru-modelling.github.io/serosv/reference/penalized_spline_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Penalized Spline model — penalized_spline_model","text":"data input data frame, must either `age`, `pos`, `tot` column aggregated data `age`, `status` linelisting data s smoothing basis use link link function use framework approach fit model (\"pl\" penalized likelihood framework, \"glmm\" generalized linear mixed model framework) sp smoothing parameter","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/penalized_spline_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Penalized Spline model — penalized_spline_model","text":"list class penalized_spline_model 6 attributes datatype type datatype used model fitting (aggregated linelisting) df dataframe used fitting model framework either pl glmm info fitted \"gam\" model framework pl \"gamm\" model framework glmm sp seroprevalence foi force infection","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/penalized_spline_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Penalized Spline model — penalized_spline_model","text":"","code":"data <- parvob19_be_2001_2003 data$status <- data$seropositive model <- penalized_spline_model(data, framework=\"glmm\") #>  #>  Maximum number of PQL iterations:  20  #> iteration 1 #> iteration 2 #> iteration 3 #> iteration 4 model$info$gam #>  #> Family: binomial  #> Link function: logit  #>  #> Formula: #> spos ~ s(age, bs = s, sp = sp) #>  #> Estimated degrees of freedom: #> 5.8  total = 6.8  #>  plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.age_time_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot output for age_time_model — plot.age_time_model","title":"Plot output for age_time_model — plot.age_time_model","text":"Plot output age_time_model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.age_time_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot output for age_time_model — plot.age_time_model","text":"","code":"# S3 method for class 'age_time_model' plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.age_time_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot output for age_time_model — plot.age_time_model","text":"x - `age_time_model` object ... arbitrary params. Supported options include: facet: Whether facet plot group. modtype: model plot, either \"monotonized\" \"non-monotonized\". le: Number bins used generate x-axis; higher values produce smoother curves. cex: Adjusts size data points (facet = TRUE).","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.age_time_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot output for age_time_model — plot.age_time_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.bivariate_dale_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for bivariate_dale_model — plot.bivariate_dale_model","title":"plot() overloading for bivariate_dale_model — plot.bivariate_dale_model","text":"plot() overloading bivariate_dale_model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.bivariate_dale_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for bivariate_dale_model — plot.bivariate_dale_model","text":"","code":"# S3 method for bivariate_dale_model plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.bivariate_dale_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for bivariate_dale_model — plot.bivariate_dale_model","text":"x bivariate_dale_model object ... arbitrary params including y1, y2 (label predictors), plot_type (\"ci\" \"sp\")","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.bivariate_dale_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for bivariate_dale_model — plot.bivariate_dale_model","text":"patchwork object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.estimate_from_mixture.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for result of estimate_from_mixture — plot.estimate_from_mixture","title":"plot() overloading for result of estimate_from_mixture — plot.estimate_from_mixture","text":"plot() overloading result estimate_from_mixture","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.estimate_from_mixture.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for result of estimate_from_mixture — plot.estimate_from_mixture","text":"","code":"# S3 method for estimate_from_mixture plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.estimate_from_mixture.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for result of estimate_from_mixture — plot.estimate_from_mixture","text":"x mixture_model ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.estimate_from_mixture.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for result of estimate_from_mixture — plot.estimate_from_mixture","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.farrington_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for Farrington model — plot.farrington_model","title":"plot() overloading for Farrington model — plot.farrington_model","text":"plot() overloading Farrington model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.farrington_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for Farrington model — plot.farrington_model","text":"","code":"# S3 method for farrington_model plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.farrington_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for Farrington model — plot.farrington_model","text":"x Farrington model object. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.farrington_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for Farrington model — plot.farrington_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.fp_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for fractional polynomial model — plot.fp_model","title":"plot() overloading for fractional polynomial model — plot.fp_model","text":"plot() overloading fractional polynomial model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.fp_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for fractional polynomial model — plot.fp_model","text":"","code":"# S3 method for fp_model plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.fp_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for fractional polynomial model — plot.fp_model","text":"x fractional polynomial model object. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.fp_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for fractional polynomial model — plot.fp_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.glmm_ps_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for penalized spline fitted with GLMM — plot.glmm_ps_model","title":"plot() overloading for penalized spline fitted with GLMM — plot.glmm_ps_model","text":"plot() overloading penalized spline fitted GLMM","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.glmm_ps_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for penalized spline fitted with GLMM — plot.glmm_ps_model","text":"","code":"# S3 method for glmm_ps_model plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.glmm_ps_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for penalized spline fitted with GLMM — plot.glmm_ps_model","text":"x glmm_ps_model object ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.hierarchical_bayesian_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for hierarchical_bayesian_model — plot.hierarchical_bayesian_model","title":"plot() overloading for hierarchical_bayesian_model — plot.hierarchical_bayesian_model","text":"plot() overloading hierarchical_bayesian_model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.hierarchical_bayesian_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for hierarchical_bayesian_model — plot.hierarchical_bayesian_model","text":"","code":"# S3 method for hierarchical_bayesian_model plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.hierarchical_bayesian_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for hierarchical_bayesian_model — plot.hierarchical_bayesian_model","text":"x hierarchical_bayesian_model object created serosv. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.hierarchical_bayesian_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for hierarchical_bayesian_model — plot.hierarchical_bayesian_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.lp_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for local polynomial model — plot.lp_model","title":"plot() overloading for local polynomial model — plot.lp_model","text":"plot() overloading local polynomial model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.lp_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for local polynomial model — plot.lp_model","text":"","code":"# S3 method for lp_model plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.lp_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for local polynomial model — plot.lp_model","text":"x local polynomial model object. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.lp_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for local polynomial model — plot.lp_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.mixture_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for mixture model — plot.mixture_model","title":"plot() overloading for mixture model — plot.mixture_model","text":"plot() overloading mixture model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.mixture_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for mixture model — plot.mixture_model","text":"","code":"# S3 method for mixture_model plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.mixture_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for mixture model — plot.mixture_model","text":"x mixture_model ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.mixture_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for mixture model — plot.mixture_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.mseir_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for MSEIR model — plot.mseir_model","title":"plot() overloading for MSEIR model — plot.mseir_model","text":"plot() overloading MSEIR model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.mseir_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for MSEIR model — plot.mseir_model","text":"","code":"# S3 method for mseir_model plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.mseir_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for MSEIR model — plot.mseir_model","text":"x mseir_model object. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.mseir_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for MSEIR model — plot.mseir_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.penalized_spline_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for penalized spline — plot.penalized_spline_model","title":"plot() overloading for penalized spline — plot.penalized_spline_model","text":"plot() overloading penalized spline","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.penalized_spline_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for penalized spline — plot.penalized_spline_model","text":"","code":"# S3 method for penalized_spline_model plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.penalized_spline_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for penalized spline — plot.penalized_spline_model","text":"x penalized_spline_model object ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.penalized_spline_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for penalized spline — plot.penalized_spline_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.polynomial_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for polynomial model — plot.polynomial_model","title":"plot() overloading for polynomial model — plot.polynomial_model","text":"plot() overloading polynomial model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.polynomial_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for polynomial model — plot.polynomial_model","text":"","code":"# S3 method for polynomial_model plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.polynomial_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for polynomial model — plot.polynomial_model","text":"x polynomial model object ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.polynomial_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for polynomial model — plot.polynomial_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_basic_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for SIR model — plot.sir_basic_model","title":"plot() overloading for SIR model — plot.sir_basic_model","text":"plot() overloading SIR model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_basic_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for SIR model — plot.sir_basic_model","text":"","code":"# S3 method for sir_basic_model plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_basic_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for SIR model — plot.sir_basic_model","text":"x sir_basic_model object. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_basic_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for SIR model — plot.sir_basic_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_static_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for SIR static model — plot.sir_static_model","title":"plot() overloading for SIR static model — plot.sir_static_model","text":"plot() overloading SIR static model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_static_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for SIR static model — plot.sir_static_model","text":"","code":"# S3 method for sir_static_model plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_static_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for SIR static model — plot.sir_static_model","text":"x sir_static_model object. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_static_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for SIR static model — plot.sir_static_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_subpops_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for SIR sub populations model — plot.sir_subpops_model","title":"plot() overloading for SIR sub populations model — plot.sir_subpops_model","text":"plot() overloading SIR sub populations model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_subpops_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for SIR sub populations model — plot.sir_subpops_model","text":"","code":"# S3 method for sir_subpops_model plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_subpops_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for SIR sub populations model — plot.sir_subpops_model","text":"x sir_subpops_models object. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_subpops_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for SIR sub populations model — plot.sir_subpops_model","text":"list ggplot objects, object plot corresponding subpopulation","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.weibull_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for Weibull model — plot.weibull_model","title":"plot() overloading for Weibull model — plot.weibull_model","text":"plot() overloading Weibull model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.weibull_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for Weibull model — plot.weibull_model","text":"","code":"# S3 method for weibull_model plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.weibull_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for Weibull model — plot.weibull_model","text":"x Weibull model object. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.weibull_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for Weibull model — plot.weibull_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_corrected_prev.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot output for corrected_prevalence — plot_corrected_prev","title":"Plot output for corrected_prevalence — plot_corrected_prev","text":"Plot output corrected_prevalence","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_corrected_prev.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot output for corrected_prevalence — plot_corrected_prev","text":"","code":"plot_corrected_prev(x, y = NULL, facet = FALSE)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_corrected_prev.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot output for corrected_prevalence — plot_corrected_prev","text":"x - output `correct_prevalence()` function y - another output `correct_prevalence()` function (optional, comparison ) facet - whether plot facets plot (y provided)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_corrected_prev.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot output for corrected_prevalence — plot_corrected_prev","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_gcv.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting GCV values with respect to different nn-s and h-s parameters. — plot_gcv","title":"Plotting GCV values with respect to different nn-s and h-s parameters. — plot_gcv","text":"Refers section 7.2.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_gcv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting GCV values with respect to different nn-s and h-s parameters. — plot_gcv","text":"","code":"plot_gcv(age, pos, tot, nn_seq, h_seq, kern = \"tcub\", deg = 2)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_gcv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting GCV values with respect to different nn-s and h-s parameters. — plot_gcv","text":"age age vector. pos pos vector. tot tot vector.#' nn_seq Nearest neighbor sequence. h_seq Smoothing parameter sequence. kern Weight function, default = \"tcub\". choices \"rect\", \"trwt\", \"tria\", \"epan\", \"bisq\" \"gauss\". Choices may restricted derivatives required; e.g. confidence bands bandwidth selectors. deg Degree polynomial use. Default: 2.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_gcv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting GCV values with respect to different nn-s and h-s parameters. — plot_gcv","text":"plot gcv value","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_gcv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting GCV values with respect to different nn-s and h-s parameters. — plot_gcv","text":"","code":"df <- mumps_uk_1986_1987 plot_gcv(   df$age, df$pos, df$tot,   nn_seq = seq(0.2, 0.8, by=0.1),   h_seq = seq(5, 25, by=1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_standard_curve.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize standard curves for each plate — plot_standard_curve","title":"Visualize standard curves for each plate — plot_standard_curve","text":"Visualize standard curves plate","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_standard_curve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize standard curves for each plate — plot_standard_curve","text":"","code":"plot_standard_curve(   x,   facet = TRUE,   xlab = \"log10(concentration)\",   ylab = \"Optical density\",   datapoint_size = 2 )"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_standard_curve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize standard curves for each plate — plot_standard_curve","text":"x - output `to_titer()` facet - whether faceted plates plot standard curves single plot xlab - label x axis ylab - label y axis datapoint_size - size data point (applicable `facet=TRUE`)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_titer_qc.html","id":null,"dir":"Reference","previous_headings":"","what":"Quality control plot — plot_titer_qc","title":"Quality control plot — plot_titer_qc","text":"Visualize sample, whether titer estimates can computed tested dilutions.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_titer_qc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quality control plot — plot_titer_qc","text":"","code":"plot_titer_qc(x, n_plates = 18, n_samples = 22, n_dilutions = 3)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_titer_qc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quality control plot — plot_titer_qc","text":"x - output `to_titer()` n_plates - maximum number plates plot n_samples - maximum number samples per plate plot n_dilutions - number dilutions used testing","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_titer_qc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Quality control plot — plot_titer_qc","text":"sample represented `n_estimates x n_dilutions` grid cell color indicate estimate availability (green = estimate available, orange = result low, red = result high) sample grids arranged columns column represent samples plate figure demonstrates interpretation plot.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/polynomial_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Polynomial models — polynomial_model","title":"Polynomial models — polynomial_model","text":"Refers section 6.1.1","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/polynomial_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polynomial models — polynomial_model","text":"","code":"polynomial_model(data, k, type, link = \"log\")"},{"path":"https://oucru-modelling.github.io/serosv/reference/polynomial_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Polynomial models — polynomial_model","text":"data input data frame, must either `age`, `pos`, `tot` columns (aggregated data) `age`, `status` (linelisting data) k degree model. type name method (Muench, Giffith, Grenfell). link link function.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/polynomial_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Polynomial models — polynomial_model","text":"list class polynomial_model 5 items datatype type datatype used model fitting (aggregated linelisting) df dataframe used fitting model info fitted \"glm\" object sp seroprevalence foi force infection","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/polynomial_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Polynomial models — polynomial_model","text":"","code":"data <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age), ] data$status <- data$seropositive aggregated <- transform_data(data$age, data$seropositive, stratum_col = \"age\")  # fit with aggregated data model <- polynomial_model(aggregated, type = \"Muench\") # fit with linelisting data model <- polynomial_model(data, type = \"Muench\") plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_mumps_uk.html","id":null,"dir":"Reference","previous_headings":"","what":"Rubella - Mumps data from the UK (aggregated) — rubella_mumps_uk","title":"Rubella - Mumps data from the UK (aggregated) — rubella_mumps_uk","text":"Rubella - Mumps data UK (aggregated)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_mumps_uk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rubella - Mumps data from the UK (aggregated) — rubella_mumps_uk","text":"","code":"rubella_mumps_uk"},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_mumps_uk.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Rubella - Mumps data from the UK (aggregated) — rubella_mumps_uk","text":"data frame 5 variables: age Age group NN Number individuals negative rubella mumps NP Number individuals negative rubella positive mumps PN Number individuals positive rubella negative mumps PP Number individuals positive rubella mumps","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_mumps_uk.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Rubella - Mumps data from the UK (aggregated) — rubella_mumps_uk","text":"Morgan-Capner P, Wright J, Miller C L, Miller E. Surveillance antibody measles, mumps, rubella age. British Medical Journal 1988; 297 :770 doi:10.1136/bmj.297.6651.770","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_uk_1986_1987.html","id":null,"dir":"Reference","previous_headings":"","what":"Rubella serological data from the UK in 1986 and 1987 (aggregated) — rubella_uk_1986_1987","title":"Rubella serological data from the UK in 1986 and 1987 (aggregated) — rubella_uk_1986_1987","text":"Prevalence rubella UK, obtained large survey prevalence antibodies mumps rubella viruses.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_uk_1986_1987.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rubella serological data from the UK in 1986 and 1987 (aggregated) — rubella_uk_1986_1987","text":"","code":"rubella_uk_1986_1987"},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_uk_1986_1987.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Rubella serological data from the UK in 1986 and 1987 (aggregated) — rubella_uk_1986_1987","text":"data frame 3 variables: age Age group pos Number seropositive individuals tot Total number individuals surveyed","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_uk_1986_1987.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Rubella serological data from the UK in 1986 and 1987 (aggregated) — rubella_uk_1986_1987","text":"Morgan-Capner P, Wright J, Miller C L, Miller E. Surveillance antibody measles, mumps, rubella age. British Medical Journal 1988; 297 :770 doi:10.1136/bmj.297.6651.770","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_uk_1986_1987.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rubella serological data from the UK in 1986 and 1987 (aggregated) — rubella_uk_1986_1987","text":"","code":"# Reproduce Fig 4.4 (middle panel), p. 67 age <- rubella_uk_1986_1987$age pos <- rubella_uk_1986_1987$pos tot <- rubella_uk_1986_1987$tot plot(age, pos / tot,   cex = 0.008 * tot, pch = 16, xlab = \"age\", ylab = \"seroprevalence\",   xlim = c(0, 45), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/serosv-package.html","id":null,"dir":"Reference","previous_headings":"","what":"serosv: model infectious disease parameters — serosv-package","title":"serosv: model infectious disease parameters — serosv-package","text":"easy--use efficient tool estimate infectious diseases parameters using serological data. Implemented models include SIR models (basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()), parametric models (polynomial_model(), fp_model()), nonparametric models (lp_model()), semiparametric models (penalized_splines_model()), hierarchical models (hierarchical_bayesian_model()). package based book \"Modeling Infectious Disease Parameters Based Serological Social Contact Data: Modern Statistical Perspective\" (Hens, Niel & Shkedy, Ziv & Aerts, Marc & Faes, Christel & Damme, Pierre & Beutels, Philippe., 2013) doi:10.1007/978-1-4614-4072-7 .","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/serosv-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"serosv: model infectious disease parameters — serosv-package","text":"Maintainer: Anh Phan Truong Quynh anhptq@oucru.org (ORCID) Authors: Nguyen Pham Nguyen nguyenpnt@oucru.org (ORCID) Long Bui Thanh Tuyen Huynh tuyenhn@oucru.org Thinh Ong thinhop@oucru.org (ORCID) Marc Choisy mchoisy@oucru.org (ORCID)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/set_plot_style.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper to adjust styling of a plot — set_plot_style","title":"Helper to adjust styling of a plot — set_plot_style","text":"Helper adjust styling plot","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/set_plot_style.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper to adjust styling of a plot — set_plot_style","text":"","code":"set_plot_style(   sero = \"blueviolet\",   ci = \"royalblue1\",   foi = \"#fc0328\",   sero_line = \"solid\",   foi_line = \"dashed\",   xlabel = \"Age\" )"},{"path":"https://oucru-modelling.github.io/serosv/reference/set_plot_style.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper to adjust styling of a plot — set_plot_style","text":"sero - color seroprevalence line ci - color confidence interval foi - color force infection line sero_line - linetype seroprevalence line foi_line - linetype force infection line xlabel - x label","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/set_plot_style.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper to adjust styling of a plot — set_plot_style","text":"list updated aesthetic values","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_basic_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Basic SIR model — sir_basic_model","title":"Basic SIR model — sir_basic_model","text":"Refers section 3.1.3.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_basic_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basic SIR model — sir_basic_model","text":"","code":"sir_basic_model(times, state, parameters)"},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_basic_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Basic SIR model — sir_basic_model","text":"times time sequence. state initial state model. parameters parameters model.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_basic_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Basic SIR model — sir_basic_model","text":"list class sir_basic_model following items parameters list parameters used fitting model output matrix population compartment time","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_basic_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Basic SIR model — sir_basic_model","text":"state: - S: number susceptible - : number infected - R: number recovered parameters: - alpha: disease-related death rate - mu: natural death rate (= 1/life expectancy) - beta: transmission rate - nu: recovery rate - p: percent population vaccinated birth","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_basic_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Basic SIR model — sir_basic_model","text":"","code":"state <- c(S=4999, I=1, R=0) parameters <- c(   mu=1/75, # 1 divided by life expectancy (75 years old)   alpha=0, # no disease-related death   beta=0.0005, # transmission rate   nu=1, # 1 year for infected to recover   p=0 # no vaccination at birth ) times <- seq(0, 250, by=0.1) model <- sir_basic_model(times, state, parameters) model #> $parameters #>         mu      alpha       beta         nu          p  #> 0.01333333 0.00000000 0.00050000 1.00000000 0.00000000  #>  #> $output #>       time         S            I            R #> 1      0.0 4999.0000 1.000000e+00    0.0000000 #> 2      0.1 4998.7320 1.160221e+00    0.1077427 #> 3      0.2 4998.4213 1.346094e+00    0.2326044 #> 4      0.3 4998.0610 1.561716e+00    0.3773237 #> 5      0.4 4997.6431 1.811844e+00    0.5450805 #> 6      0.5 4997.1585 2.101986e+00    0.7395605 #> 7      0.6 4996.5964 2.438526e+00    0.9650385 #> 8      0.7 4995.9447 2.828861e+00    1.2264683 #> 9      0.8 4995.1888 3.281562e+00    1.5295984 #> 10     0.9 4994.3124 3.806555e+00    1.8810889 #> 11     1.0 4993.2960 4.415328e+00    2.2886597 #> 12     1.1 4992.1176 5.121181e+00    2.7612570 #> 13     1.2 4990.7513 5.939496e+00    3.3092454 #> 14     1.3 4989.1673 6.888062e+00    3.9446311 #> 15     1.4 4987.3312 7.987438e+00    4.6813171 #> 16     1.5 4985.2032 9.261364e+00    5.5353974 #> 17     1.6 4982.7373 1.073724e+01    6.5254956 #> 18     1.7 4979.8802 1.244666e+01    7.6731536 #> 19     1.8 4976.5707 1.442600e+01    9.0032756 #> 20     1.9 4972.7382 1.671712e+01   10.5446365 #> 21     2.0 4968.3014 1.936813e+01   12.3304612 #> 22     2.1 4963.1667 2.243418e+01   14.3990819 #> 23     2.2 4957.2269 2.597840e+01   16.7946833 #> 24     2.3 4950.3589 3.007295e+01   19.5681420 #> 25     2.4 4942.4220 3.479999e+01   22.7779683 #> 26     2.5 4933.2558 4.025286e+01   26.4913554 #> 27     2.6 4922.6774 4.653722e+01   30.7853403 #> 28     2.7 4910.4798 5.377212e+01   35.7480765 #> 29     2.8 4896.4287 6.209111e+01   41.4802135 #> 30     2.9 4880.2606 7.164304e+01   48.0963714 #> 31     3.0 4861.6806 8.259275e+01   55.7266884 #> 32     3.1 4840.3604 9.512119e+01   64.5184070 #> 33     3.2 4815.9375 1.094250e+02   74.6374493 #> 34     3.3 4788.0147 1.257154e+02   86.2699071 #> 35     3.4 4756.1611 1.442155e+02   99.6233541 #> 36     3.5 4719.9151 1.651570e+02  114.9278525 #> 37     3.6 4678.7892 1.887743e+02  132.4364981 #> 38     3.7 4632.2776 2.152971e+02  152.4253126 #> 39     3.8 4579.8673 2.449404e+02  175.1922628 #> 40     3.9 4521.0529 2.778920e+02  201.0551572 #> 41     4.0 4455.3547 3.142972e+02  230.3481645 #> 42     4.1 4382.3416 3.542416e+02  263.4167064 #> 43     4.2 4301.6574 3.977321e+02  300.6105180 #> 44     4.3 4213.0491 4.446762e+02  342.2747557 #> 45     4.4 4116.3969 4.948639e+02  388.7391584 #> 46     4.5 4011.7435 5.479511e+02  440.3054474 #> 47     4.6 3899.3180 6.034486e+02  497.2333690 #> 48     4.7 3779.5549 6.607191e+02  559.7260108 #> 49     4.8 3653.1014 7.189833e+02  627.9153170 #> 50     4.9 3520.8135 7.773377e+02  701.8487820 #> 51     5.0 3383.7376 8.347838e+02  781.4785087 #> 52     5.1 3243.0786 8.902676e+02  866.6537354 #> 53     5.2 3100.1558 9.427265e+02  957.1177216 #> 54     5.3 2956.3499 9.911405e+02 1052.5095881 #> 55     5.4 2813.0466 1.034582e+03 1152.3712323 #> 56     5.5 2671.5798 1.072261e+03 1256.1589966 #> 57     5.6 2533.1819 1.103559e+03 1363.2593013 #> 58     5.7 2398.9427 1.128050e+03 1473.0071156 #> 59     5.8 2269.7810 1.145513e+03 1584.7059460 #> 60     5.9 2146.4291 1.155923e+03 1697.6479867 #> 61     6.0 2029.4295 1.159437e+03 1811.1332030 #> 62     6.1 1919.1419 1.156372e+03 1924.4863558 #> 63     6.2 1815.7593 1.147169e+03 2037.0712745 #> 64     6.3 1719.3283 1.132370e+03 2148.3020058 #> 65     6.4 1629.7729 1.112576e+03 2257.6507525 #> 66     6.5 1546.9188 1.088428e+03 2364.6527513 #> 67     6.6 1470.5167 1.060575e+03 2468.9084084 #> 68     6.7 1400.2634 1.029653e+03 2570.0831096 #> 69     6.8 1335.8203 9.962746e+02 2667.9051667 #> 70     6.9 1276.8286 9.610090e+02 2762.1623582 #> 71     7.0 1222.9220 9.243805e+02 2852.6974870 #> 72     7.1 1173.7359 8.868608e+02 2939.4033246 #> 73     7.2 1128.9149 8.488678e+02 3022.2172493 #> 74     7.3 1088.1178 8.107663e+02 3101.1158224 #> 75     7.4 1051.0210 7.728695e+02 3176.1094867 #> 76     7.5 1017.3203 7.354422e+02 3247.2375209 #> 77     7.6  986.7324 6.987042e+02 3314.5633374 #> 78     7.7  958.9949 6.628349e+02 3378.1701781 #> 79     7.8  933.8656 6.279772e+02 3438.1572322 #> 80     7.9  911.1224 5.942414e+02 3494.6361813 #> 81     8.0  890.5618 5.617100e+02 3547.7281617 #> 82     8.1  871.9980 5.304408e+02 3597.5611217 #> 83     8.2  855.2616 5.004708e+02 3644.2675485 #> 84     8.3  840.1983 4.718192e+02 3687.9825323 #> 85     8.4  826.6677 4.444902e+02 3728.8421358 #> 86     8.5  814.5421 4.184759e+02 3766.9820366 #> 87     8.6  803.7054 3.937582e+02 3802.5364101 #> 88     8.7  794.0521 3.703109e+02 3835.6370249 #> 89     8.8  785.4861 3.481014e+02 3866.4125218 #> 90     8.9  777.9201 3.270920e+02 3894.9878513 #> 91     9.0  771.2746 3.072416e+02 3921.4838479 #> 92     9.1  765.4769 2.885062e+02 3946.0169197 #> 93     9.2  760.4611 2.708401e+02 3968.6988359 #> 94     9.3  756.1668 2.541966e+02 3989.6365965 #> 95     9.4  752.5390 2.385287e+02 4008.9323696 #> 96     9.5  749.5271 2.237894e+02 4026.6834856 #> 97     9.6  747.0852 2.099323e+02 4042.9824762 #> 98     9.7  745.1710 1.969118e+02 4057.9171510 #> 99     9.8  743.7458 1.846835e+02 4071.5707024 #> 100    9.9  742.7739 1.732042e+02 4084.0218338 #> 101   10.0  742.2228 1.624323e+02 4095.3449045 #> 102   10.1  742.0623 1.523276e+02 4105.6100871 #> 103   10.2  742.2648 1.428517e+02 4114.8835338 #> 104   10.3  742.8047 1.339677e+02 4123.2275477 #> 105   10.4  743.6586 1.256407e+02 4130.7007573 #> 106   10.5  744.8046 1.178371e+02 4137.3582912 #> 107   10.6  746.2228 1.105253e+02 4143.2519513 #> 108   10.7  747.8944 1.036752e+02 4148.4303843 #> 109   10.8  749.8024 9.725838e+01 4152.9392487 #> 110   10.9  751.9307 9.124792e+01 4156.8213773 #> 111   11.0  754.2646 8.561846e+01 4160.1169347 #> 112   11.1  756.7904 8.034607e+01 4162.8635691 #> 113   11.2  759.4952 7.540822e+01 4165.0965576 #> 114   11.3  762.3673 7.078371e+01 4166.8489457 #> 115   11.4  765.3957 6.645260e+01 4168.1516805 #> 116   11.5  768.5701 6.239619e+01 4169.0337371 #> 117   11.6  771.8809 5.859689e+01 4169.5222389 #> 118   11.7  775.3192 5.503821e+01 4169.6425718 #> 119   11.8  778.8768 5.170471e+01 4169.4184920 #> 120   11.9  782.5459 4.858188e+01 4168.8722284 #> 121   12.0  786.3193 4.565616e+01 4168.0245782 #> 122   12.1  790.1902 4.291483e+01 4166.8949983 #> 123   12.2  794.1523 4.034600e+01 4165.5016907 #> 124   12.3  798.1998 3.793854e+01 4163.8616822 #> 125   12.4  802.3271 3.568202e+01 4161.9909009 #> 126   12.5  806.5290 3.356671e+01 4159.9042463 #> 127   12.6  810.8009 3.158349e+01 4157.6156567 #> 128   12.7  815.1380 2.972384e+01 4155.1381709 #> 129   12.8  819.5362 2.797979e+01 4152.4839869 #> 130   12.9  823.9916 2.634391e+01 4149.6645170 #> 131   13.0  828.5003 2.480923e+01 4146.6904387 #> 132   13.1  833.0590 2.336926e+01 4143.5717431 #> 133   13.2  837.6643 2.201790e+01 4140.3177793 #> 134   13.3  842.3132 2.074949e+01 4136.9372970 #> 135   13.4  847.0028 1.955872e+01 4133.4384854 #> 136   13.5  851.7304 1.844062e+01 4129.8290100 #> 137   13.6  856.4934 1.739057e+01 4126.1160468 #> 138   13.7  861.2895 1.640423e+01 4122.3063147 #> 139   13.8  866.1163 1.547756e+01 4118.4061050 #> 140   13.9  870.9719 1.460676e+01 4114.4213098 #> 141   14.0  875.8542 1.378832e+01 4110.3574477 #> 142   14.1  880.7614 1.301892e+01 4106.2196884 #> 143   14.2  885.6916 1.229548e+01 4102.0128756 #> 144   14.3  890.6433 1.161511e+01 4097.7415479 #> 145   14.4  895.6149 1.097511e+01 4093.4099590 #> 146   14.5  900.6049 1.037295e+01 4089.0220961 #> 147   14.6  905.6120 9.806289e+00 4084.5816972 #> 148   14.7  910.6348 9.272905e+00 4080.0922671 #> 149   14.8  915.6722 8.770739e+00 4075.5570929 #> 150   14.9  920.7229 8.297859e+00 4070.9792576 #> 151   15.0  925.7859 7.852460e+00 4066.3616536 #> 152   15.1  930.8602 7.432852e+00 4061.7069946 #> 153   15.2  935.9447 7.037453e+00 4057.0178275 #> 154   15.3  941.0387 6.664784e+00 4052.2965428 #> 155   15.4  946.1412 6.313459e+00 4047.5453848 #> 156   15.5  951.2514 5.982181e+00 4042.7664607 #> 157   15.6  956.3685 5.669734e+00 4037.9617495 #> 158   15.7  961.4919 5.374983e+00 4033.1331100 #> 159   15.8  966.6209 5.096861e+00 4028.2822886 #> 160   15.9  971.7547 4.834371e+00 4023.4109263 #> 161   16.0  976.8929 4.586576e+00 4018.5205652 #> 162   16.1  982.0347 4.352601e+00 4013.6126550 #> 163   16.2  987.1798 4.131624e+00 4008.6885587 #> 164   16.3  992.3276 3.922876e+00 4003.7495579 #> 165   16.4  997.4775 3.725633e+00 3998.7968580 #> 166   16.5 1002.6292 3.539219e+00 3993.8315927 #> 167   16.6 1007.7822 3.362999e+00 3988.8548290 #> 168   16.7 1012.9361 3.196376e+00 3983.8675708 #> 169   16.8 1018.0904 3.038792e+00 3978.8707629 #> 170   16.9 1023.2450 2.889721e+00 3973.8652947 #> 171   17.0 1028.3993 2.748672e+00 3968.8520038 #> 172   17.1 1033.5531 2.615181e+00 3963.8316788 #> 173   17.2 1038.7061 2.488814e+00 3958.8050624 #> 174   17.3 1043.8580 2.369164e+00 3953.7728545 #> 175   17.4 1049.0084 2.255847e+00 3948.7357143 #> 176   17.5 1054.1572 2.148503e+00 3943.6942630 #> 177   17.6 1059.3041 2.046794e+00 3938.6490863 #> 178   17.7 1064.4489 1.950401e+00 3933.6007362 #> 179   17.8 1069.5912 1.859026e+00 3928.5497330 #> 180   17.9 1074.7310 1.772387e+00 3923.4965676 #> 181   18.0 1079.8681 1.690221e+00 3918.4417028 #> 182   18.1 1085.0021 1.612277e+00 3913.3855752 #> 183   18.2 1090.1331 1.538323e+00 3908.3285967 #> 184   18.3 1095.2607 1.468137e+00 3903.2711559 #> 185   18.4 1100.3849 1.401512e+00 3898.2136197 #> 186   18.5 1105.5054 1.338254e+00 3893.1563342 #> 187   18.6 1110.6222 1.278178e+00 3888.0996261 #> 188   18.7 1115.7351 1.221111e+00 3883.0438041 #> 189   18.8 1120.8440 1.166891e+00 3877.9891593 #> 190   18.9 1125.9487 1.115362e+00 3872.9359668 #> 191   19.0 1131.0491 1.066381e+00 3867.8844866 #> 192   19.1 1136.1452 1.019811e+00 3862.8349639 #> 193   19.2 1141.2368 9.755230e-01 3857.7876306 #> 194   19.3 1146.3239 9.333959e-01 3852.7427058 #> 195   19.4 1151.4063 8.933151e-01 3847.7003963 #> 196   19.5 1156.4839 8.551726e-01 3842.6608978 #> 197   19.6 1161.5567 8.188665e-01 3837.6243950 #> 198   19.7 1166.6246 7.843005e-01 3832.5910626 #> 199   19.8 1171.6876 7.513840e-01 3827.5610658 #> 200   19.9 1176.7454 7.200310e-01 3822.5345605 #> 201   20.0 1181.7981 6.901608e-01 3817.5116943 #> 202   20.1 1186.8457 6.616967e-01 3812.4926066 #> 203   20.2 1191.8880 6.345667e-01 3807.4774294 #> 204   20.3 1196.9250 6.087024e-01 3802.4662872 #> 205   20.4 1201.9567 5.840392e-01 3797.4592979 #> 206   20.5 1206.9829 5.605163e-01 3792.4565730 #> 207   20.6 1212.0037 5.380759e-01 3787.4582178 #> 208   20.7 1217.0190 5.166636e-01 3782.4643319 #> 209   20.8 1222.0288 4.962277e-01 3777.4750095 #> 210   20.9 1227.0329 4.767194e-01 3772.4903396 #> 211   21.0 1232.0315 4.580926e-01 3767.5104064 #> 212   21.1 1237.0244 4.403036e-01 3762.5352895 #> 213   21.2 1242.0116 4.233110e-01 3757.5650641 #> 214   21.3 1246.9931 4.070756e-01 3752.5998012 #> 215   21.4 1251.9689 3.915603e-01 3747.6395681 #> 216   21.5 1256.9388 3.767301e-01 3742.6844283 #> 217   21.6 1261.9030 3.625516e-01 3737.7344418 #> 218   21.7 1266.8613 3.489932e-01 3732.7896651 #> 219   21.8 1271.8138 3.360252e-01 3727.8501520 #> 220   21.9 1276.7604 3.236191e-01 3722.9159528 #> 221   22.0 1281.7011 3.117481e-01 3717.9871154 #> 222   22.1 1286.6359 3.003866e-01 3713.0636847 #> 223   22.2 1291.5648 2.895107e-01 3708.1457033 #> 224   22.3 1296.4877 2.790972e-01 3703.2332113 #> 225   22.4 1301.4046 2.691245e-01 3698.3262464 #> 226   22.5 1306.3156 2.595719e-01 3693.4248444 #> 227   22.6 1311.2205 2.504199e-01 3688.5290387 #> 228   22.7 1316.1195 2.416497e-01 3683.6388611 #> 229   22.8 1321.0124 2.332438e-01 3678.7543413 #> 230   22.9 1325.8993 2.251853e-01 3673.8755074 #> 231   23.0 1330.7802 2.174583e-01 3669.0023856 #> 232   23.1 1335.6550 2.100477e-01 3664.1350007 #> 233   23.2 1340.5237 2.029391e-01 3659.2733759 #> 234   23.3 1345.3863 1.961188e-01 3654.4175332 #> 235   23.4 1350.2429 1.895737e-01 3649.5674929 #> 236   23.5 1355.0934 1.832915e-01 3644.7232741 #> 237   23.6 1359.9378 1.772605e-01 3639.8848949 #> 238   23.7 1364.7762 1.714694e-01 3635.0523719 #> 239   23.8 1369.6084 1.659077e-01 3630.2257208 #> 240   23.9 1374.4345 1.605650e-01 3625.4049562 #> 241   24.0 1379.2545 1.554319e-01 3620.5900916 #> 242   24.1 1384.0684 1.504992e-01 3615.7811397 #> 243   24.2 1388.8761 1.457581e-01 3610.9781122 #> 244   24.3 1393.6778 1.412002e-01 3606.1810198 #> 245   24.4 1398.4733 1.368177e-01 3601.3898727 #> 246   24.5 1403.2627 1.326029e-01 3596.6046800 #> 247   24.6 1408.0460 1.285488e-01 3591.8254502 #> 248   24.7 1412.8232 1.246484e-01 3587.0521910 #> 249   24.8 1417.5942 1.208952e-01 3582.2849096 #> 250   24.9 1422.3591 1.172830e-01 3577.5236123 #> 251   25.0 1427.1179 1.138058e-01 3572.7683051 #> 252   25.1 1431.8705 1.104579e-01 3568.0189930 #> 253   25.2 1436.6171 1.072340e-01 3563.2756808 #> 254   25.3 1441.3575 1.041289e-01 3558.5383726 #> 255   25.4 1446.0918 1.011377e-01 3553.8070721 #> 256   25.5 1450.8200 9.825558e-02 3549.0817823 #> 257   25.6 1455.5420 9.547818e-02 3544.3625059 #> 258   25.7 1460.2580 9.280119e-02 3539.6492452 #> 259   25.8 1464.9678 9.022051e-02 3534.9420020 #> 260   25.9 1469.6715 8.773224e-02 3530.2407777 #> 261   26.0 1474.3691 8.533265e-02 3525.5455733 #> 262   26.1 1479.0606 8.301818e-02 3520.8563895 #> 263   26.2 1483.7460 8.078542e-02 3516.1732267 #> 264   26.3 1488.4253 7.863111e-02 3511.4960846 #> 265   26.4 1493.0985 7.655215e-02 3506.8249631 #> 266   26.5 1497.7656 7.454556e-02 3502.1598616 #> 267   26.6 1502.4266 7.260850e-02 3497.5007789 #> 268   26.7 1507.0815 7.073825e-02 3492.8477141 #> 269   26.8 1511.7304 6.893220e-02 3488.2006655 #> 270   26.9 1516.3732 6.718787e-02 3483.5596315 #> 271   27.0 1521.0099 6.550287e-02 3478.9246100 #> 272   27.1 1525.6405 6.387493e-02 3474.2955990 #> 273   27.2 1530.2651 6.230186e-02 3469.6725959 #> 274   27.3 1534.8836 6.078157e-02 3465.0555982 #> 275   27.4 1539.4961 5.931207e-02 3460.4446030 #> 276   27.5 1544.1025 5.789144e-02 3455.8396073 #> 277   27.6 1548.7029 5.651784e-02 3451.2406079 #> 278   27.7 1553.2972 5.518952e-02 3446.6476014 #> 279   27.8 1557.8855 5.390479e-02 3442.0605843 #> 280   27.9 1562.4678 5.266203e-02 3437.4795529 #> 281   28.0 1567.0440 5.145972e-02 3432.9045033 #> 282   28.1 1571.6143 5.029635e-02 3428.3354314 #> 283   28.2 1576.1785 4.917050e-02 3423.7723333 #> 284   28.3 1580.7367 4.808083e-02 3419.2152045 #> 285   28.4 1585.2889 4.702601e-02 3414.6640408 #> 286   28.5 1589.8352 4.600480e-02 3410.1188374 #> 287   28.6 1594.3754 4.501598e-02 3405.5795899 #> 288   28.7 1598.9096 4.405842e-02 3401.0462935 #> 289   28.8 1603.4379 4.313099e-02 3396.5189432 #> 290   28.9 1607.9602 4.223264e-02 3391.9975341 #> 291   29.0 1612.4766 4.136235e-02 3387.4820613 #> 292   29.1 1616.9870 4.051913e-02 3382.9725194 #> 293   29.2 1621.4914 3.970206e-02 3378.4689033 #> 294   29.3 1625.9899 3.891021e-02 3373.9712076 #> 295   29.4 1630.4824 3.814273e-02 3369.4794269 #> 296   29.5 1634.9690 3.739878e-02 3364.9935558 #> 297   29.6 1639.4497 3.667757e-02 3360.5135887 #> 298   29.7 1643.9245 3.597831e-02 3356.0395199 #> 299   29.8 1648.3934 3.530028e-02 3351.5713437 #> 300   29.9 1652.8563 3.464277e-02 3347.1090544 #> 301   30.0 1657.3133 3.400508e-02 3342.6526462 #> 302   30.1 1661.7645 3.338656e-02 3338.2021131 #> 303   30.2 1666.2098 3.278659e-02 3333.7574492 #> 304   30.3 1670.6491 3.220455e-02 3329.3186485 #> 305   30.4 1675.0827 3.163986e-02 3324.8857050 #> 306   30.5 1679.5103 3.109196e-02 3320.4586126 #> 307   30.6 1683.9321 3.056031e-02 3316.0373650 #> 308   30.7 1688.3480 3.004438e-02 3311.6219563 #> 309   30.8 1692.7581 2.954369e-02 3307.2123800 #> 310   30.9 1697.1623 2.905774e-02 3302.8086299 #> 311   31.0 1701.5607 2.858607e-02 3298.4106997 #> 312   31.1 1705.9533 2.812824e-02 3294.0185831 #> 313   31.2 1710.3400 2.768382e-02 3289.6322737 #> 314   31.3 1714.7210 2.725239e-02 3285.2517650 #> 315   31.4 1719.0961 2.683356e-02 3280.8770506 #> 316   31.5 1723.4654 2.642694e-02 3276.5081241 #> 317   31.6 1727.8290 2.603217e-02 3272.1449788 #> 318   31.7 1732.1867 2.564889e-02 3267.7876084 #> 319   31.8 1736.5387 2.527675e-02 3263.4360061 #> 320   31.9 1740.8849 2.491543e-02 3259.0901655 #> 321   32.0 1745.2254 2.456461e-02 3254.7500799 #> 322   32.1 1749.5600 2.422398e-02 3250.4157427 #> 323   32.2 1753.8890 2.389325e-02 3246.0871472 #> 324   32.3 1758.2121 2.357213e-02 3241.7642867 #> 325   32.4 1762.5296 2.326036e-02 3237.4471546 #> 326   32.5 1766.8413 2.295766e-02 3233.1357441 #> 327   32.6 1771.1473 2.266378e-02 3228.8300486 #> 328   32.7 1775.4476 2.237847e-02 3224.5300612 #> 329   32.8 1779.7421 2.210151e-02 3220.2357752 #> 330   32.9 1784.0310 2.183266e-02 3215.9471838 #> 331   33.0 1788.3141 2.157170e-02 3211.6642803 #> 332   33.1 1792.5916 2.131842e-02 3207.3870578 #> 333   33.2 1796.8634 2.107262e-02 3203.1155095 #> 334   33.3 1801.1295 2.083410e-02 3198.8496287 #> 335   33.4 1805.3900 2.060267e-02 3194.5894084 #> 336   33.5 1809.6448 2.037815e-02 3190.3348418 #> 337   33.6 1813.8939 2.016036e-02 3186.0859222 #> 338   33.7 1818.1374 1.994914e-02 3181.8426425 #> 339   33.8 1822.3753 1.974431e-02 3177.6049960 #> 340   33.9 1826.6075 1.954573e-02 3173.3729758 #> 341   34.0 1830.8341 1.935323e-02 3169.1465749 #> 342   34.1 1835.0550 1.916668e-02 3164.9257866 #> 343   34.2 1839.2704 1.898593e-02 3160.7106038 #> 344   34.3 1843.4802 1.881085e-02 3156.5010198 #> 345   34.4 1847.6843 1.864130e-02 3152.2970277 #> 346   34.5 1851.8829 1.847716e-02 3148.0986204 #> 347   34.6 1856.0759 1.831831e-02 3143.9057911 #> 348   34.7 1860.2633 1.816463e-02 3139.7185330 #> 349   34.8 1864.4451 1.801601e-02 3135.5368390 #> 350   34.9 1868.6214 1.787233e-02 3131.3607023 #> 351   35.0 1872.7922 1.773351e-02 3127.1901160 #> 352   35.1 1876.9573 1.759943e-02 3123.0250731 #> 353   35.2 1881.1170 1.747000e-02 3118.8655667 #> 354   35.3 1885.2711 1.734512e-02 3114.7115899 #> 355   35.4 1889.4196 1.722472e-02 3110.5631358 #> 356   35.5 1893.5627 1.710869e-02 3106.4201975 #> 357   35.6 1897.7002 1.699697e-02 3102.2827681 #> 358   35.7 1901.8323 1.688946e-02 3098.1508406 #> 359   35.8 1905.9588 1.678610e-02 3094.0244081 #> 360   35.9 1910.0798 1.668682e-02 3089.9034637 #> 361   36.0 1914.1954 1.659153e-02 3085.7880006 #> 362   36.1 1918.3055 1.650019e-02 3081.6780117 #> 363   36.2 1922.4101 1.641271e-02 3077.5734903 #> 364   36.3 1926.5092 1.632905e-02 3073.4744293 #> 365   36.4 1930.6029 1.624915e-02 3069.3808220 #> 366   36.5 1934.6912 1.617294e-02 3065.2926613 #> 367   36.6 1938.7740 1.610038e-02 3061.2099405 #> 368   36.7 1942.8513 1.603141e-02 3057.1326526 #> 369   36.8 1946.9232 1.596600e-02 3053.0607908 #> 370   36.9 1950.9897 1.590408e-02 3048.9943481 #> 371   37.0 1955.0508 1.584562e-02 3044.9333177 #> 372   37.1 1959.1065 1.579059e-02 3040.8776927 #> 373   37.2 1963.1568 1.573893e-02 3036.8274663 #> 374   37.3 1967.2017 1.569062e-02 3032.7826316 #> 375   37.4 1971.2412 1.564562e-02 3028.7431818 #> 376   37.5 1975.2753 1.560389e-02 3024.7091100 #> 377   37.6 1979.3040 1.556542e-02 3020.6804094 #> 378   37.7 1983.3274 1.553016e-02 3016.6570731 #> 379   37.8 1987.3454 1.549810e-02 3012.6390943 #> 380   37.9 1991.3581 1.546921e-02 3008.6264662 #> 381   38.0 1995.3654 1.544347e-02 3004.6191819 #> 382   38.1 1999.3673 1.542087e-02 3000.6172348 #> 383   38.2 2003.3640 1.540137e-02 2996.6206179 #> 384   38.3 2007.3553 1.538497e-02 2992.6293245 #> 385   38.4 2011.3413 1.537166e-02 2988.6433477 #> 386   38.5 2015.3220 1.536141e-02 2984.6626809 #> 387   38.6 2019.2973 1.535423e-02 2980.6873173 #> 388   38.7 2023.2674 1.535009e-02 2976.7172500 #> 389   38.8 2027.2322 1.534901e-02 2972.7524724 #> 390   38.9 2031.1917 1.535096e-02 2968.7929776 #> 391   39.0 2035.1459 1.535595e-02 2964.8387591 #> 392   39.1 2039.0948 1.536398e-02 2960.8898099 #> 393   39.2 2043.0385 1.537505e-02 2956.9461235 #> 394   39.3 2046.9769 1.538915e-02 2953.0076930 #> 395   39.4 2050.9101 1.540630e-02 2949.0745119 #> 396   39.5 2054.8380 1.542651e-02 2945.1465733 #> 397   39.6 2058.7607 1.544977e-02 2941.2238708 #> 398   39.7 2062.6781 1.547609e-02 2937.3063974 #> 399   39.8 2066.5903 1.550550e-02 2933.3941467 #> 400   39.9 2070.4974 1.553800e-02 2929.4871119 #> 401   40.0 2074.3991 1.557361e-02 2925.5852865 #> 402   40.1 2078.2957 1.561234e-02 2921.6886637 #> 403   40.2 2082.1871 1.565422e-02 2917.7972369 #> 404   40.3 2086.0733 1.569926e-02 2913.9109997 #> 405   40.4 2089.9543 1.574749e-02 2910.0299453 #> 406   40.5 2093.8301 1.579894e-02 2906.1540671 #> 407   40.6 2097.7008 1.585361e-02 2902.2833587 #> 408   40.7 2101.5663 1.591156e-02 2898.4178134 #> 409   40.8 2105.4266 1.597280e-02 2894.5574247 #> 410   40.9 2109.2818 1.603737e-02 2890.7021860 #> 411   41.0 2113.1318 1.610531e-02 2886.8520908 #> 412   41.1 2116.9767 1.617664e-02 2883.0071326 #> 413   41.2 2120.8164 1.625141e-02 2879.1673049 #> 414   41.3 2124.6511 1.632966e-02 2875.3326013 #> 415   41.4 2128.4806 1.641143e-02 2871.5030151 #> 416   41.5 2132.3050 1.649677e-02 2867.6785400 #> 417   41.6 2136.1242 1.658572e-02 2863.8591695 #> 418   41.7 2139.9384 1.667833e-02 2860.0448972 #> 419   41.8 2143.7475 1.677465e-02 2856.2357167 #> 420   41.9 2147.5515 1.687474e-02 2852.4316214 #> 421   42.0 2151.3504 1.697866e-02 2848.6326052 #> 422   42.1 2155.1443 1.708646e-02 2844.8386614 #> 423   42.2 2158.9330 1.719820e-02 2841.0497839 #> 424   42.3 2162.7167 1.731396e-02 2837.2659662 #> 425   42.4 2166.4954 1.743378e-02 2833.4872020 #> 426   42.5 2170.2690 1.755775e-02 2829.7134850 #> 427   42.6 2174.0375 1.768594e-02 2825.9448088 #> 428   42.7 2177.8010 1.781842e-02 2822.1811673 #> 429   42.8 2181.5595 1.795527e-02 2818.4225540 #> 430   42.9 2185.3129 1.809656e-02 2814.6689627 #> 431   43.0 2189.0614 1.824239e-02 2810.9203873 #> 432   43.1 2192.8048 1.839284e-02 2807.1768215 #> 433   43.2 2196.5432 1.854799e-02 2803.4382590 #> 434   43.3 2200.2766 1.870796e-02 2799.7046936 #> 435   43.4 2204.0050 1.887282e-02 2795.9761193 #> 436   43.5 2207.7284 1.904268e-02 2792.2525298 #> 437   43.6 2211.4469 1.921764e-02 2788.5339191 #> 438   43.7 2215.1603 1.939782e-02 2784.8202809 #> 439   43.8 2218.8688 1.958332e-02 2781.1116092 #> 440   43.9 2222.5723 1.977425e-02 2777.4078979 #> 441   44.0 2226.2709 1.997075e-02 2773.7091409 #> 442   44.1 2229.9645 2.017292e-02 2770.0153323 #> 443   44.2 2233.6532 2.038090e-02 2766.3264660 #> 444   44.3 2237.3369 2.059482e-02 2762.6425359 #> 445   44.4 2241.0156 2.081482e-02 2758.9635362 #> 446   44.5 2244.6895 2.104103e-02 2755.2894608 #> 447   44.6 2248.3584 2.127361e-02 2751.6203039 #> 448   44.7 2252.0224 2.151270e-02 2747.9560594 #> 449   44.8 2255.6815 2.175846e-02 2744.2967216 #> 450   44.9 2259.3357 2.201106e-02 2740.6422846 #> 451   45.0 2262.9850 2.227065e-02 2736.9927425 #> 452   45.1 2266.6294 2.253741e-02 2733.3480895 #> 453   45.2 2270.2689 2.281152e-02 2729.7083198 #> 454   45.3 2273.9035 2.309317e-02 2726.0734277 #> 455   45.4 2277.5332 2.338254e-02 2722.4434074 #> 456   45.5 2281.1581 2.367982e-02 2718.8182532 #> 457   45.6 2284.7781 2.398524e-02 2715.1979594 #> 458   45.7 2288.3932 2.429898e-02 2711.5825205 #> 459   45.8 2292.0034 2.462128e-02 2707.9719306 #> 460   45.9 2295.6089 2.495235e-02 2704.3661844 #> 461   46.0 2299.2094 2.529243e-02 2700.7652761 #> 462   46.1 2302.8052 2.564176e-02 2697.1692003 #> 463   46.2 2306.3960 2.600058e-02 2693.5779514 #> 464   46.3 2309.9821 2.636916e-02 2689.9915240 #> 465   46.4 2313.5633 2.674775e-02 2686.4099127 #> 466   46.5 2317.1398 2.713664e-02 2682.8331120 #> 467   46.6 2320.7113 2.753609e-02 2679.2611166 #> 468   46.7 2324.2781 2.794642e-02 2675.6939212 #> 469   46.8 2327.8401 2.836791e-02 2672.1315204 #> 470   46.9 2331.3973 2.880089e-02 2668.5739090 #> 471   47.0 2334.9497 2.924567e-02 2665.0210817 #> 472   47.1 2338.4973 2.970260e-02 2661.4730335 #> 473   47.2 2342.0401 3.017201e-02 2657.9297591 #> 474   47.3 2345.5781 3.065426e-02 2654.3912535 #> 475   47.4 2349.1113 3.114973e-02 2650.8575116 #> 476   47.5 2352.6398 3.165880e-02 2647.3285284 #> 477   47.6 2356.1635 3.218186e-02 2643.8042990 #> 478   47.7 2359.6825 3.271932e-02 2640.2848184 #> 479   47.8 2363.1966 3.327161e-02 2636.7700817 #> 480   47.9 2366.7061 3.383916e-02 2633.2600842 #> 481   48.0 2370.2108 3.442243e-02 2629.7548211 #> 482   48.1 2373.7107 3.502188e-02 2626.2542876 #> 483   48.2 2377.2059 3.563801e-02 2622.7584791 #> 484   48.3 2380.6963 3.627130e-02 2619.2673911 #> 485   48.4 2384.1821 3.692229e-02 2615.7810189 #> 486   48.5 2387.6631 3.759151e-02 2612.2993580 #> 487   48.6 2391.1393 3.827952e-02 2608.8224041 #> 488   48.7 2394.6109 3.898689e-02 2605.3501527 #> 489   48.8 2398.0777 3.971422e-02 2601.8825996 #> 490   48.9 2401.5398 4.046213e-02 2598.4197406 #> 491   49.0 2404.9972 4.123126e-02 2594.9615714 #> 492   49.1 2408.4499 4.202226e-02 2591.5080879 #> 493   49.2 2411.8979 4.283583e-02 2588.0592862 #> 494   49.3 2415.3412 4.367267e-02 2584.6151624 #> 495   49.4 2418.7798 4.453353e-02 2581.1757124 #> 496   49.5 2422.2136 4.541915e-02 2577.7409326 #> 497   49.6 2425.6429 4.633033e-02 2574.3108192 #> 498   49.7 2429.0674 4.726790e-02 2570.8853686 #> 499   49.8 2432.4872 4.823268e-02 2567.4645773 #> 500   49.9 2435.9023 4.922558e-02 2564.0484418 #> 501   50.0 2439.3128 5.024748e-02 2560.6369588 #> 502   50.1 2442.7186 5.129934e-02 2557.2301250 #> 503   50.2 2446.1197 5.238213e-02 2553.8279373 #> 504   50.3 2449.5161 5.349687e-02 2550.4303926 #> 505   50.4 2452.9079 5.464460e-02 2547.0374880 #> 506   50.5 2456.2950 5.582641e-02 2543.6492206 #> 507   50.6 2459.6774 5.704344e-02 2540.2655878 #> 508   50.7 2463.0551 5.829685e-02 2536.8865869 #> 509   50.8 2466.4282 5.958786e-02 2533.5122155 #> 510   50.9 2469.7966 6.091773e-02 2530.1424711 #> 511   51.0 2473.1604 6.228775e-02 2526.7773517 #> 512   51.1 2476.5194 6.369929e-02 2523.4168551 #> 513   51.2 2479.8739 6.515376e-02 2520.0609793 #> 514   51.3 2483.2236 6.665260e-02 2516.7097227 #> 515   51.4 2486.5687 6.819734e-02 2513.3630834 #> 516   51.5 2489.9092 6.978955e-02 2510.0210601 #> 517   51.6 2493.2449 7.143085e-02 2506.6836514 #> 518   51.7 2496.5760 7.312293e-02 2503.3508562 #> 519   51.8 2499.9025 7.486756e-02 2500.0226734 #> 520   51.9 2503.2242 7.666655e-02 2496.6991023 #> 521   52.0 2506.5413 7.852180e-02 2493.3801423 #> 522   52.1 2509.8538 8.043528e-02 2490.0657929 #> 523   52.2 2513.1615 8.240902e-02 2486.7560538 #> 524   52.3 2516.4646 8.444516e-02 2483.4509252 #> 525   52.4 2519.7630 8.654588e-02 2480.1504072 #> 526   52.5 2523.0568 8.871348e-02 2476.8545001 #> 527   52.6 2526.3458 9.095034e-02 2473.5632048 #> 528   52.7 2529.6302 9.325892e-02 2470.2765220 #> 529   52.8 2532.9099 9.564179e-02 2466.9944529 #> 530   52.9 2536.1849 9.810163e-02 2463.7169990 #> 531   53.0 2539.4552 1.006412e-01 2460.4441619 #> 532   53.1 2542.7208 1.032634e-01 2457.1759435 #> 533   53.2 2545.9817 1.059712e-01 2453.9123462 #> 534   53.3 2549.2379 1.087677e-01 2450.6533724 #> 535   53.4 2552.4893 1.116561e-01 2447.3990251 #> 536   53.5 2555.7361 1.146399e-01 2444.1493073 #> 537   53.6 2558.9781 1.177226e-01 2440.9042228 #> 538   53.7 2562.2153 1.209077e-01 2437.6637753 #> 539   53.8 2565.4478 1.241991e-01 2434.4279691 #> 540   53.9 2568.6756 1.276006e-01 2431.1968088 #> 541   54.0 2571.8986 1.311165e-01 2427.9702995 #> 542   54.1 2575.1168 1.347510e-01 2424.7484465 #> 543   54.2 2578.3302 1.385085e-01 2421.5312558 #> 544   54.3 2581.5389 1.423936e-01 2418.3187337 #> 545   54.4 2584.7427 1.464111e-01 2415.1108869 #> 546   54.5 2587.9417 1.505661e-01 2411.9077227 #> 547   54.6 2591.1359 1.548638e-01 2408.7092489 #> 548   54.7 2594.3252 1.593096e-01 2405.5154737 #> 549   54.8 2597.5097 1.639091e-01 2402.3264059 #> 550   54.9 2600.6893 1.686682e-01 2399.1420550 #> 551   55.0 2603.8640 1.735931e-01 2395.9624308 #> 552   55.1 2607.0338 1.786901e-01 2392.7875441 #> 553   55.2 2610.1986 1.839659e-01 2389.6174059 #> 554   55.3 2613.3585 1.894275e-01 2386.4520282 #> 555   55.4 2616.5135 1.950819e-01 2383.2914236 #> 556   55.5 2619.6635 2.009369e-01 2380.1356054 #> 557   55.6 2622.8084 2.070001e-01 2376.9845875 #> 558   55.7 2625.9483 2.132798e-01 2373.8383849 #> 559   55.8 2629.0832 2.197845e-01 2370.6970131 #> 560   55.9 2632.2130 2.265230e-01 2367.5604887 #> 561   56.0 2635.3377 2.335046e-01 2364.4288290 #> 562   56.1 2638.4572 2.407390e-01 2361.3020524 #> 563   56.2 2641.5716 2.482362e-01 2358.1801780 #> 564   56.3 2644.6808 2.560068e-01 2355.0632261 #> 565   56.4 2647.7847 2.640615e-01 2351.9512180 #> 566   56.5 2650.8834 2.724120e-01 2348.8441761 #> 567   56.6 2653.9768 2.810700e-01 2345.7421239 #> 568   56.7 2657.0649 2.900480e-01 2342.6450862 #> 569   56.8 2660.1476 2.993590e-01 2339.5530888 #> 570   56.9 2663.2248 3.090164e-01 2336.4661591 #> 571   57.0 2666.2966 3.190345e-01 2333.3843255 #> 572   57.1 2669.3630 3.294279e-01 2330.3076180 #> 573   57.2 2672.4237 3.402119e-01 2327.2360681 #> 574   57.3 2675.4789 3.514027e-01 2324.1697088 #> 575   57.4 2678.5284 3.630171e-01 2321.1085746 #> 576   57.5 2681.5722 3.750724e-01 2318.0527017 #> 577   57.6 2684.6103 3.875870e-01 2315.0021281 #> 578   57.7 2687.6425 4.005799e-01 2311.9568937 #> 579   57.8 2690.6689 4.140712e-01 2308.9170400 #> 580   57.9 2693.6893 4.280814e-01 2305.8826107 #> 581   58.0 2696.7037 4.426326e-01 2302.8536515 #> 582   58.1 2699.7120 4.577472e-01 2299.8302104 #> 583   58.2 2702.7142 4.734491e-01 2296.8123375 #> 584   58.3 2705.7102 4.897631e-01 2293.8000853 #> 585   58.4 2708.6998 5.067150e-01 2290.7935086 #> 586   58.5 2711.6830 5.243319e-01 2287.7926652 #> 587   58.6 2714.6597 5.426422e-01 2284.7976151 #> 588   58.7 2717.6299 5.616754e-01 2281.8084215 #> 589   58.8 2720.5934 5.814625e-01 2278.8251502 #> 590   58.9 2723.5501 6.020357e-01 2275.8478704 #> 591   59.0 2726.4999 6.234288e-01 2272.8766544 #> 592   59.1 2729.4427 6.456773e-01 2269.9115776 #> 593   59.2 2732.3785 6.688181e-01 2266.9527193 #> 594   59.3 2735.3069 6.928898e-01 2264.0001622 #> 595   59.4 2738.2281 7.179328e-01 2261.0539930 #> 596   59.5 2741.1417 7.439895e-01 2258.1143023 #> 597   59.6 2744.0477 7.711041e-01 2255.1811851 #> 598   59.7 2746.9459 7.993229e-01 2252.2547405 #> 599   59.8 2749.8362 8.286942e-01 2249.3350724 #> 600   59.9 2752.7184 8.592688e-01 2246.4222895 #> 601   60.0 2755.5924 8.910996e-01 2243.5165055 #> 602   60.1 2758.4579 9.242422e-01 2240.6178393 #> 603   60.2 2761.3148 9.587547e-01 2237.7264155 #> 604   60.3 2764.1629 9.946977e-01 2234.8423642 #> 605   60.4 2767.0020 1.032135e+00 2231.9658219 #> 606   60.5 2769.8319 1.071133e+00 2229.0969310 #> 607   60.6 2772.6524 1.111762e+00 2226.2358409 #> 608   60.7 2775.4632 1.154094e+00 2223.3827076 #> 609   60.8 2778.2641 1.198206e+00 2220.5376948 #> 610   60.9 2781.0548 1.244178e+00 2217.7009733 #> 611   61.0 2783.8352 1.292094e+00 2214.8727221 #> 612   61.1 2786.6048 1.342041e+00 2212.0531286 #> 613   61.2 2789.3635 1.394112e+00 2209.2423888 #> 614   61.3 2792.1109 1.448402e+00 2206.4407079 #> 615   61.4 2794.8467 1.505013e+00 2203.6483006 #> 616   61.5 2797.5706 1.564050e+00 2200.8653917 #> 617   61.6 2800.2822 1.625624e+00 2198.0922166 #> 618   61.7 2802.9811 1.689851e+00 2195.3290213 #> 619   61.8 2805.6671 1.756851e+00 2192.5760639 #> 620   61.9 2808.3396 1.826753e+00 2189.8336141 #> 621   62.0 2810.9984 1.899689e+00 2187.1019546 #> 622   62.1 2813.6428 1.975799e+00 2184.3813810 #> 623   62.2 2816.2726 2.055230e+00 2181.6722030 #> 624   62.3 2818.8871 2.138134e+00 2178.9747449 #> 625   62.4 2821.4860 2.224672e+00 2176.2893462 #> 626   62.5 2824.0686 2.315012e+00 2173.6163623 #> 627   62.6 2826.6345 2.409332e+00 2170.9561653 #> 628   62.7 2829.1830 2.507814e+00 2168.3091450 #> 629   62.8 2831.7136 2.610654e+00 2165.6757096 #> 630   62.9 2834.2257 2.718053e+00 2163.0562863 #> 631   63.0 2836.7185 2.830225e+00 2160.4513228 #> 632   63.1 2839.1913 2.947392e+00 2157.8612877 #> 633   63.2 2841.6435 3.069788e+00 2155.2866718 #> 634   63.3 2844.0744 3.197656e+00 2152.7279894 #> 635   63.4 2846.4830 3.331254e+00 2150.1857787 #> 636   63.5 2848.8685 3.470849e+00 2147.6606038 #> 637   63.6 2851.2302 3.616724e+00 2145.1530551 #> 638   63.7 2853.5671 3.769172e+00 2142.6637513 #> 639   63.8 2855.8782 3.928502e+00 2140.1933404 #> 640   63.9 2858.1625 4.095038e+00 2137.7425010 #> 641   64.0 2860.4189 4.269118e+00 2135.3119438 #> 642   64.1 2862.6465 4.451098e+00 2132.9024134 #> 643   64.2 2864.8440 4.641348e+00 2130.5146895 #> 644   64.3 2867.0102 4.840257e+00 2128.1495890 #> 645   64.4 2869.1438 5.048234e+00 2125.8079672 #> 646   64.5 2871.2436 5.265705e+00 2123.4907202 #> 647   64.6 2873.3081 5.493116e+00 2121.1987864 #> 648   64.7 2875.3359 5.730934e+00 2118.9331485 #> 649   64.8 2877.3255 5.979650e+00 2116.6948359 #> 650   64.9 2879.2753 6.239773e+00 2114.4849265 #> 651   65.0 2881.1836 6.511840e+00 2112.3045491 #> 652   65.1 2883.0487 6.796412e+00 2110.1548856 #> 653   65.2 2884.8688 7.094073e+00 2108.0371738 #> 654   65.3 2886.6419 7.405435e+00 2105.9527096 #> 655   65.4 2888.3660 7.731140e+00 2103.9028496 #> 656   65.5 2890.0391 8.071855e+00 2101.8890142 #> 657   65.6 2891.6590 8.428279e+00 2099.9126901 #> 658   65.7 2893.2234 8.801143e+00 2097.9754335 #> 659   65.8 2894.7299 9.191208e+00 2096.0788729 #> 660   65.9 2896.1760 9.599269e+00 2094.2247124 #> 661   66.0 2897.5591 1.002616e+01 2092.4147353 #> 662   66.1 2898.8765 1.047273e+01 2090.6508068 #> 663   66.2 2900.1252 1.093990e+01 2088.9348782 #> 664   66.3 2901.3024 1.142861e+01 2087.2689902 #> 665   66.4 2902.4049 1.193982e+01 2085.6552765 #> 666   66.5 2903.4295 1.247457e+01 2084.0959678 #> 667   66.6 2904.3727 1.303391e+01 2082.5933959 #> 668   66.7 2905.2311 1.361894e+01 2081.1499973 #> 669   66.8 2906.0009 1.423081e+01 2079.7683177 #> 670   66.9 2906.6783 1.487071e+01 2078.4510160 #> 671   67.0 2907.2593 1.553987e+01 2077.2008687 #> 672   67.1 2907.7397 1.623957e+01 2076.0207742 #> 673   67.2 2908.1151 1.697114e+01 2074.9137573 #> 674   67.3 2908.3811 1.773596e+01 2073.8829734 #> 675   67.4 2908.5329 1.853543e+01 2072.9317136 #> 676   67.5 2908.5656 1.937103e+01 2072.0634085 #> 677   67.6 2908.4741 2.024427e+01 2071.2816331 #> 678   67.7 2908.2532 2.115671e+01 2070.5901112 #> 679   67.8 2907.8973 2.210996e+01 2069.9927200 #> 680   67.9 2907.4008 2.310567e+01 2069.4934942 #> 681   68.0 2906.7578 2.414553e+01 2069.0966304 #> 682   68.1 2905.9622 2.523128e+01 2068.8064910 #> 683   68.2 2905.0077 2.636470e+01 2068.6276086 #> 684   68.3 2903.8877 2.754761e+01 2068.5646891 #> 685   68.4 2902.5955 2.878186e+01 2068.6226158 #> 686   68.5 2901.1242 3.006933e+01 2068.8064520 #> 687   68.6 2899.4666 3.141193e+01 2069.1214441 #> 688   68.7 2897.6154 3.281161e+01 2069.5730235 #> 689   68.8 2895.5629 3.427031e+01 2070.1668091 #> 690   68.9 2893.3014 3.579000e+01 2070.9086081 #> 691   69.0 2890.8229 3.737265e+01 2071.8044168 #> 692   69.1 2888.1193 3.902024e+01 2072.8604204 #> 693   69.2 2885.1823 4.073471e+01 2074.0829924 #> 694   69.3 2882.0033 4.251802e+01 2075.4786926 #> 695   69.4 2878.5737 4.437207e+01 2077.0542646 #> 696   69.5 2874.8846 4.629872e+01 2078.8166319 #> 697   69.6 2870.9273 4.829980e+01 2080.7728928 #> 698   69.7 2866.6926 5.037705e+01 2082.9303143 #> 699   69.8 2862.1715 5.253215e+01 2085.2963248 #> 700   69.9 2857.3548 5.476665e+01 2087.8785043 #> 701   70.0 2852.2334 5.708201e+01 2090.6845742 #> 702   70.1 2846.7981 5.947956e+01 2093.7223845 #> 703   70.2 2841.0396 6.196048e+01 2096.9998998 #> 704   70.3 2834.9491 6.452576e+01 2100.5251831 #> 705   70.4 2828.5174 6.717622e+01 2104.3063775 #> 706   70.5 2821.7359 6.991245e+01 2108.3516856 #> 707   70.6 2814.5958 7.273482e+01 2112.6693475 #> 708   70.7 2807.0890 7.564343e+01 2117.2676155 #> 709   70.8 2799.2072 7.863810e+01 2122.1547272 #> 710   70.9 2790.9428 8.171833e+01 2127.3388758 #> 711   71.0 2782.2885 8.488330e+01 2132.8281782 #> 712   71.1 2773.2375 8.813184e+01 2138.6306406 #> 713   71.2 2763.7835 9.146238e+01 2144.7541219 #> 714   71.3 2753.9207 9.487296e+01 2151.2062944 #> 715   71.4 2743.6442 9.836118e+01 2157.9946028 #> 716   71.5 2732.9496 1.019242e+02 2165.1262201 #> 717   71.6 2721.8333 1.055587e+02 2172.6080031 #> 718   71.7 2710.2926 1.092610e+02 2180.4464441 #> 719   71.8 2698.3258 1.130266e+02 2188.6476229 #> 720   71.9 2685.9320 1.168508e+02 2197.2171562 #> 721   72.0 2673.1116 1.207283e+02 2206.1601469 #> 722   72.1 2659.8657 1.246532e+02 2215.4811321 #> 723   72.2 2646.1969 1.286191e+02 2225.1840310 #> 724   72.3 2632.1088 1.326191e+02 2235.2720936 #> 725   72.4 2617.6063 1.366459e+02 2245.7478493 #> 726   72.5 2602.6956 1.406914e+02 2256.6130570 #> 727   72.6 2587.3841 1.447472e+02 2267.8686569 #> 728   72.7 2571.6807 1.488046e+02 2279.5147247 #> 729   72.8 2555.5955 1.528541e+02 2291.5504283 #> 730   72.9 2539.1399 1.568862e+02 2303.9739879 #> 731   73.0 2522.3267 1.608907e+02 2316.7826403 #> 732   73.1 2505.1700 1.648574e+02 2329.9726081 #> 733   73.2 2487.6853 1.687756e+02 2343.5390725 #> 734   73.3 2469.8892 1.726346e+02 2357.4761536 #> 735   73.4 2451.7996 1.764235e+02 2371.7768953 #> 736   73.5 2433.4354 1.801313e+02 2386.4332570 #> 737   73.6 2414.8168 1.837470e+02 2401.4361126 #> 738   73.7 2395.9649 1.872598e+02 2416.7752561 #> 739   73.8 2376.9016 1.906590e+02 2432.4394145 #> 740   73.9 2357.6498 1.939339e+02 2448.4162682 #> 741   74.0 2338.2330 1.970745e+02 2464.6924790 #> 742   74.1 2318.6754 2.000709e+02 2481.2537254 #> 743   74.2 2299.0015 2.029137e+02 2498.0847446 #> 744   74.3 2279.2365 2.055941e+02 2515.1693824 #> 745   74.4 2259.4056 2.081037e+02 2532.4906488 #> 746   74.5 2239.5342 2.104350e+02 2550.0307807 #> 747   74.6 2219.6477 2.125810e+02 2567.7713090 #> 748   74.7 2199.7714 2.145355e+02 2585.6931323 #> 749   74.8 2179.9303 2.162931e+02 2603.7765936 #> 750   74.9 2160.1493 2.178491e+02 2622.0015614 #> 751   75.0 2140.4526 2.191999e+02 2640.3475140 #> 752   75.1 2120.8639 2.203425e+02 2658.7936251 #> 753   75.2 2101.4062 2.212749e+02 2677.3188515 #> 754   75.3 2082.1019 2.219960e+02 2695.9020208 #> 755   75.4 2062.9725 2.225056e+02 2714.5219191 #> 756   75.5 2044.0384 2.228042e+02 2733.1573780 #> 757   75.6 2025.3194 2.228933e+02 2751.7873587 #> 758   75.7 2006.8339 2.227751e+02 2770.3910353 #> 759   75.8 1988.5995 2.224526e+02 2788.9478731 #> 760   75.9 1970.6327 2.219296e+02 2807.4377046 #> 761   76.0 1952.9486 2.212106e+02 2825.8408005 #> 762   76.1 1935.5614 2.203007e+02 2844.1379366 #> 763   76.2 1918.4840 2.192056e+02 2862.3104546 #> 764   76.3 1901.7282 2.179314e+02 2880.3403186 #> 765   76.4 1885.3048 2.164851e+02 2898.2101655 #> 766   76.5 1869.2230 2.148736e+02 2915.9033494 #> 767   76.6 1853.4914 2.131046e+02 2933.4039807 #> 768   76.7 1838.1172 2.111858e+02 2950.6969590 #> 769   76.8 1823.1066 2.091254e+02 2967.7679997 #> 770   76.9 1808.4647 2.069317e+02 2984.6036563 #> 771   77.0 1794.1957 2.046130e+02 3001.1913356 #> 772   77.1 1780.3028 2.021779e+02 3017.5193082 #> 773   77.2 1766.7884 1.996349e+02 3033.5767142 #> 774   77.3 1753.6538 1.969926e+02 3049.3535636 #> 775   77.4 1740.8997 1.942596e+02 3064.8407323 #> 776   77.5 1728.5259 1.914441e+02 3080.0299540 #> 777   77.6 1716.5316 1.885546e+02 3094.9138082 #> 778   77.7 1704.9152 1.855991e+02 3109.4857047 #> 779   77.8 1693.6746 1.825855e+02 3123.7398646 #> 780   77.9 1682.8071 1.795216e+02 3137.6712994 #> 781   78.0 1672.3093 1.764149e+02 3151.2757866 #> 782   78.1 1662.1775 1.732726e+02 3164.5498437 #> 783   78.2 1652.4077 1.701016e+02 3177.4907007 #> 784   78.3 1642.9952 1.669086e+02 3190.0962706 #> 785   78.4 1633.9350 1.636999e+02 3202.3651186 #> 786   78.5 1625.2220 1.604815e+02 3214.2964317 #> 787   78.6 1616.8507 1.572593e+02 3225.8899858 #> 788   78.7 1608.8154 1.540385e+02 3237.1461137 #> 789   78.8 1601.1099 1.508244e+02 3248.0656732 #> 790   78.9 1593.7283 1.476216e+02 3258.6500137 #> 791   79.0 1586.6643 1.444347e+02 3268.9009447 #> 792   79.1 1579.9115 1.412678e+02 3278.8207042 #> 793   79.2 1573.4633 1.381247e+02 3288.4119269 #> 794   79.3 1567.3133 1.350091e+02 3297.6776141 #> 795   79.4 1561.4548 1.319241e+02 3306.6211039 #> 796   79.5 1555.8812 1.288728e+02 3315.2460423 #> 797   79.6 1550.5858 1.258578e+02 3323.5563553 #> 798   79.7 1545.5621 1.228817e+02 3331.5562223 #> 799   79.8 1540.8033 1.199466e+02 3339.2500499 #> 800   79.9 1536.3030 1.170546e+02 3346.6424477 #> 801   80.0 1532.0546 1.142072e+02 3353.7382044 #> 802   80.1 1528.0515 1.114062e+02 3360.5422655 #> 803   80.2 1524.2875 1.086528e+02 3367.0597123 #> 804   80.3 1520.7562 1.059481e+02 3373.2957416 #> 805   80.4 1517.4513 1.032930e+02 3379.2556469 #> 806   80.5 1514.3668 1.006884e+02 3384.9448005 #> 807   80.6 1511.4964 9.813494e+01 3390.3686372 #> 808   80.7 1508.8344 9.563296e+01 3395.5326382 #> 809   80.8 1506.3748 9.318284e+01 3400.4423170 #> 810   80.9 1504.1120 9.078478e+01 3405.1032058 #> 811   81.0 1502.0403 8.843885e+01 3409.5208431 #> 812   81.1 1500.1542 8.614501e+01 3413.7007617 #> 813   81.2 1498.4484 8.390314e+01 3417.6484790 #> 814   81.3 1496.9175 8.171300e+01 3421.3694861 #> 815   81.4 1495.5565 7.957428e+01 3424.8692402 #> 816   81.5 1494.3603 7.748659e+01 3428.1531552 #> 817   81.6 1493.3239 7.544946e+01 3431.2265954 #> 818   81.7 1492.4428 7.346236e+01 3434.0948683 #> 819   81.8 1491.7121 7.152472e+01 3436.7632189 #> 820   81.9 1491.1273 6.963590e+01 3439.2368242 #> 821   82.0 1490.6840 6.779521e+01 3441.5207887 #> 822   82.1 1490.3779 6.600194e+01 3443.6201401 #> 823   82.2 1490.2048 6.425534e+01 3445.5398258 #> 824   82.3 1490.1607 6.255462e+01 3447.2847096 #> 825   82.4 1490.2415 6.089898e+01 3448.8595693 #> 826   82.5 1490.4433 5.928757e+01 3450.2690942 #> 827   82.6 1490.7626 5.771955e+01 3451.5178833 #> 828   82.7 1491.1955 5.619406e+01 3452.6104441 #> 829   82.8 1491.7386 5.471023e+01 3453.5511910 #> 830   82.9 1492.3884 5.326717e+01 3454.3444447 #> 831   83.0 1493.1416 5.186398e+01 3454.9944319 #> 832   83.1 1493.9949 5.049979e+01 3455.5052846 #> 833   83.2 1494.9453 4.917371e+01 3455.8810402 #> 834   83.3 1495.9895 4.788483e+01 3456.1256416 #> 835   83.4 1497.1248 4.663227e+01 3456.2429379 #> 836   83.5 1498.3482 4.541516e+01 3456.2366846 #> 837   83.6 1499.6568 4.423262e+01 3456.1105443 #> 838   83.7 1501.0481 4.308377e+01 3455.8680878 #> 839   83.8 1502.5194 4.196777e+01 3455.5127950 #> 840   83.9 1504.0682 4.088376e+01 3455.0480558 #> 841   84.0 1505.6919 3.983091e+01 3454.4771714 #> 842   84.1 1507.3882 3.880840e+01 3453.8033559 #> 843   84.2 1509.1549 3.781541e+01 3453.0297372 #> 844   84.3 1510.9895 3.685114e+01 3452.1593587 #> 845   84.4 1512.8900 3.591482e+01 3451.1951805 #> 846   84.5 1514.8542 3.500567e+01 3450.1400815 #> 847   84.6 1516.8802 3.412294e+01 3448.9968605 #> 848   84.7 1518.9659 3.326588e+01 3447.7682379 #> 849   84.8 1521.1094 3.243379e+01 3446.4568573 #> 850   84.9 1523.3088 3.162594e+01 3445.0652875 #> 851   85.0 1525.5623 3.084164e+01 3443.5960236 #> 852   85.1 1527.8683 3.008023e+01 3442.0514892 #> 853   85.2 1530.2249 2.934103e+01 3440.4340378 #> 854   85.3 1532.6306 2.862340e+01 3438.7459544 #> 855   85.4 1535.0838 2.792672e+01 3436.9894576 #> 856   85.5 1537.5829 2.725037e+01 3435.1667006 #> 857   85.6 1540.1265 2.659375e+01 3433.2797735 #> 858   85.7 1542.7130 2.595629e+01 3431.3307045 #> 859   85.8 1545.3411 2.533740e+01 3429.3214618 #> 860   85.9 1548.0095 2.473655e+01 3427.2539548 #> 861   86.0 1550.7168 2.415319e+01 3425.1300363 #> 862   86.1 1553.4617 2.358681e+01 3422.9515034 #> 863   86.2 1556.2430 2.303689e+01 3420.7200996 #> 864   86.3 1559.0595 2.250294e+01 3418.4375157 #> 865   86.4 1561.9101 2.198448e+01 3416.1053918 #> 866   86.5 1564.7936 2.148104e+01 3413.7253186 #> 867   86.6 1567.7090 2.099218e+01 3411.2988387 #> 868   86.7 1570.6551 2.051744e+01 3408.8274480 #> 869   86.8 1573.6310 2.005642e+01 3406.3125971 #> 870   86.9 1576.6356 1.960868e+01 3403.7556928 #> 871   87.0 1579.6681 1.917383e+01 3401.1580991 #> 872   87.1 1582.7274 1.875148e+01 3398.5211386 #> 873   87.2 1585.8126 1.834126e+01 3395.8460940 #> 874   87.3 1588.9230 1.794278e+01 3393.1342086 #> 875   87.4 1592.0576 1.755571e+01 3390.3866884 #> 876   87.5 1595.2156 1.717968e+01 3387.6047025 #> 877   87.6 1598.3962 1.681438e+01 3384.7893845 #> 878   87.7 1601.5987 1.645947e+01 3381.9418337 #> 879   87.8 1604.8222 1.611464e+01 3379.0631160 #> 880   87.9 1608.0662 1.577958e+01 3376.1542650 #> 881   88.0 1611.3297 1.545400e+01 3373.2162828 #> 882   88.1 1614.6122 1.513762e+01 3370.2501414 #> 883   88.2 1617.9131 1.483016e+01 3367.2567832 #> 884   88.3 1621.2315 1.453135e+01 3364.2371222 #> 885   88.4 1624.5670 1.424092e+01 3361.1920447 #> 886   88.5 1627.9190 1.395864e+01 3358.1224103 #> 887   88.6 1631.2867 1.368425e+01 3355.0290529 #> 888   88.7 1634.6697 1.341751e+01 3351.9127809 #> 889   88.8 1638.0674 1.315821e+01 3348.7743789 #> 890   88.9 1641.4793 1.290611e+01 3345.6146075 #> 891   89.0 1644.9048 1.266101e+01 3342.4342049 #> 892   89.1 1648.3434 1.242270e+01 3339.2338870 #> 893   89.2 1651.7947 1.219097e+01 3336.0143484 #> 894   89.3 1655.2581 1.196563e+01 3332.7762632 #> 895   89.4 1658.7332 1.174649e+01 3329.5202852 #> 896   89.5 1662.2196 1.153337e+01 3326.2470489 #> 897   89.6 1665.7167 1.132610e+01 3322.9571702 #> 898   89.7 1669.2243 1.112450e+01 3319.6512465 #> 899   89.8 1672.7417 1.092841e+01 3316.3298579 #> 900   89.9 1676.2688 1.073767e+01 3312.9935673 #> 901   90.0 1679.8050 1.055211e+01 3309.6429212 #> 902   90.1 1683.3499 1.037161e+01 3306.2784500 #> 903   90.2 1686.9033 1.019599e+01 3302.9006688 #> 904   90.3 1690.4648 1.002514e+01 3299.5100777 #> 905   90.4 1694.0339 9.858901e+00 3296.1071621 #> 906   90.5 1697.6105 9.697154e+00 3292.6923937 #> 907   90.6 1701.1940 9.539768e+00 3289.2662303 #> 908   90.7 1704.7843 9.386620e+00 3285.8291168 #> 909   90.8 1708.3809 9.237590e+00 3282.3814853 #> 910   90.9 1711.9837 9.092563e+00 3278.9237555 #> 911   91.0 1715.5922 8.951426e+00 3275.4563352 #> 912   91.1 1719.2063 8.814071e+00 3271.9796207 #> 913   91.2 1722.8256 8.680394e+00 3268.4939970 #> 914   91.3 1726.4499 8.550292e+00 3264.9998385 #> 915   91.4 1730.0788 8.423668e+00 3261.4975087 #> 916   91.5 1733.7122 8.300425e+00 3257.9873615 #> 917   91.6 1737.3498 8.180473e+00 3254.4697405 #> 918   91.7 1740.9913 8.063721e+00 3250.9449799 #> 919   91.8 1744.6365 7.950084e+00 3247.4134049 #> 920   91.9 1748.2852 7.839477e+00 3243.8753315 #> 921   92.0 1751.9371 7.731821e+00 3240.3310672 #> 922   92.1 1755.5921 7.627036e+00 3236.7809111 #> 923   92.2 1759.2498 7.525046e+00 3233.2251542 #> 924   92.3 1762.9101 7.425779e+00 3229.6640797 #> 925   92.4 1766.5729 7.329163e+00 3226.0979631 #> 926   92.5 1770.2378 7.235130e+00 3222.5270726 #> 927   92.6 1773.9047 7.143612e+00 3218.9516694 #> 928   92.7 1777.5734 7.054545e+00 3215.3720075 #> 929   92.8 1781.2438 6.967867e+00 3211.7883344 #> 930   92.9 1784.9156 6.883518e+00 3208.2008913 #> 931   93.0 1788.5886 6.801438e+00 3204.6099129 #> 932   93.1 1792.2628 6.721572e+00 3201.0156278 #> 933   93.2 1795.9379 6.643864e+00 3197.4182589 #> 934   93.3 1799.6137 6.568262e+00 3193.8180233 #> 935   93.4 1803.2902 6.494713e+00 3190.2151328 #> 936   93.5 1806.9670 6.423169e+00 3186.6097935 #> 937   93.6 1810.6442 6.353580e+00 3183.0022066 #> 938   93.7 1814.3215 6.285902e+00 3179.3925682 #> 939   93.8 1817.9988 6.220088e+00 3175.7810697 #> 940   93.9 1821.6760 6.156094e+00 3172.1678976 #> 941   94.0 1825.3529 6.093879e+00 3168.5532341 #> 942   94.1 1829.0293 6.033403e+00 3164.9372568 #> 943   94.2 1832.7052 5.974624e+00 3161.3201391 #> 944   94.3 1836.3804 5.917505e+00 3157.7020503 #> 945   94.4 1840.0548 5.862010e+00 3154.0831558 #> 946   94.5 1843.7283 5.808102e+00 3150.4636170 #> 947   94.6 1847.4007 5.755746e+00 3146.8435918 #> 948   94.7 1851.0719 5.704910e+00 3143.2232341 #> 949   94.8 1854.7417 5.655560e+00 3139.6026947 #> 950   94.9 1858.4102 5.607666e+00 3135.9821209 #> 951   95.0 1862.0771 5.561198e+00 3132.3616565 #> 952   95.1 1865.7424 5.516125e+00 3128.7414424 #> 953   95.2 1869.4060 5.472421e+00 3125.1216164 #> 954   95.3 1873.0676 5.430057e+00 3121.5023133 #> 955   95.4 1876.7273 5.389007e+00 3117.8836651 #> 956   95.5 1880.3850 5.349246e+00 3114.2658009 #> 957   95.6 1884.0404 5.310749e+00 3110.6488472 #> 958   95.7 1887.6936 5.273493e+00 3107.0329280 #> 959   95.8 1891.3444 5.237454e+00 3103.4181647 #> 960   95.9 1894.9927 5.202611e+00 3099.8046764 #> 961   96.0 1898.6385 5.168942e+00 3096.1925797 #> 962   96.1 1902.2816 5.136427e+00 3092.5819891 #> 963   96.2 1905.9219 5.105047e+00 3088.9730169 #> 964   96.3 1909.5594 5.074781e+00 3085.3657734 #> 965   96.4 1913.1940 5.045611e+00 3081.7603667 #> 966   96.5 1916.8256 5.017521e+00 3078.1569031 #> 967   96.6 1920.4540 4.990493e+00 3074.5554870 #> 968   96.7 1924.0793 4.964510e+00 3070.9562209 #> 969   96.8 1927.7012 4.939558e+00 3067.3592058 #> 970   96.9 1931.3198 4.915621e+00 3063.7645407 #> 971   97.0 1934.9350 4.892684e+00 3060.1723234 #> 972   97.1 1938.5466 4.870735e+00 3056.5826498 #> 973   97.2 1942.1546 4.849759e+00 3052.9956146 #> 974   97.3 1945.7589 4.829744e+00 3049.4113108 #> 975   97.4 1949.3595 4.810679e+00 3045.8298304 #> 976   97.5 1952.9562 4.792551e+00 3042.2512637 #> 977   97.6 1956.5490 4.775349e+00 3038.6757000 #> 978   97.7 1960.1377 4.759064e+00 3035.1032274 #> 979   97.8 1963.7224 4.743684e+00 3031.5339329 #> 980   97.9 1967.3029 4.729202e+00 3027.9679023 #> 981   98.0 1970.8792 4.715607e+00 3024.4052203 #> 982   98.1 1974.4511 4.702892e+00 3020.8459709 #> 983   98.2 1978.0187 4.691048e+00 3017.2902369 #> 984   98.3 1981.5818 4.680068e+00 3013.7381004 #> 985   98.4 1985.1404 4.669946e+00 3010.1896426 #> 986   98.5 1988.6944 4.660674e+00 3006.6449439 #> 987   98.6 1992.2437 4.652246e+00 3003.1040840 #> 988   98.7 1995.7882 4.644658e+00 2999.5671418 #> 989   98.8 1999.3279 4.637903e+00 2996.0341958 #> 990   98.9 2002.8627 4.631977e+00 2992.5053237 #> 991   99.0 2006.3925 4.626875e+00 2988.9806026 #> 992   99.1 2009.9173 4.622595e+00 2985.4601093 #> 993   99.2 2013.4369 4.619132e+00 2981.9439199 #> 994   99.3 2016.9514 4.616483e+00 2978.4321104 #> 995   99.4 2020.4606 4.614646e+00 2974.9247560 #> 996   99.5 2023.9644 4.613618e+00 2971.4219318 #> 997   99.6 2027.4629 4.613399e+00 2967.9237127 #> 998   99.7 2030.9558 4.613985e+00 2964.4301731 #> 999   99.8 2034.4432 4.615377e+00 2960.9413874 #> 1000  99.9 2037.9250 4.617574e+00 2957.4574296 #> 1001 100.0 2041.4011 4.620576e+00 2953.9783737 #> 1002 100.1 2044.8713 4.624383e+00 2950.5042938 #> 1003 100.2 2048.3357 4.628995e+00 2947.0352634 #> 1004 100.3 2051.7942 4.634414e+00 2943.5713566 #> 1005 100.4 2055.2467 4.640641e+00 2940.1126472 #> 1006 100.5 2058.6931 4.647677e+00 2936.6592090 #> 1007 100.6 2062.1334 4.655526e+00 2933.2111161 #> 1008 100.7 2065.5674 4.664190e+00 2929.7684427 #> 1009 100.8 2068.9951 4.673671e+00 2926.3312631 #> 1010 100.9 2072.4164 4.683974e+00 2922.8996518 #> 1011 101.0 2075.8312 4.695102e+00 2919.4736838 #> 1012 101.1 2079.2395 4.707059e+00 2916.0534341 #> 1013 101.2 2082.6412 4.719850e+00 2912.6389783 #> 1014 101.3 2086.0361 4.733480e+00 2909.2303921 #> 1015 101.4 2089.4243 4.747954e+00 2905.8277520 #> 1016 101.5 2092.8056 4.763279e+00 2902.4311346 #> 1017 101.6 2096.1799 4.779460e+00 2899.0406172 #> 1018 101.7 2099.5472 4.796505e+00 2895.6562776 #> 1019 101.8 2102.9074 4.814420e+00 2892.2781943 #> 1020 101.9 2106.2603 4.833213e+00 2888.9064462 #> 1021 102.0 2109.6060 4.852892e+00 2885.5411130 #> 1022 102.1 2112.9443 4.873465e+00 2882.1822751 #> 1023 102.2 2116.2750 4.894942e+00 2878.8300138 #> 1024 102.3 2119.5983 4.917331e+00 2875.4844109 #> 1025 102.4 2122.9138 4.940643e+00 2872.1455493 #> 1026 102.5 2126.2216 4.964887e+00 2868.8135127 #> 1027 102.6 2129.5215 4.990074e+00 2865.4883856 #> 1028 102.7 2132.8135 5.016216e+00 2862.1702538 #> 1029 102.8 2136.0975 5.043324e+00 2858.8592039 #> 1030 102.9 2139.3733 5.071410e+00 2855.5553234 #> 1031 103.0 2142.6408 5.100486e+00 2852.2587014 #> 1032 103.1 2145.9000 5.130566e+00 2848.9694277 #> 1033 103.2 2149.1507 5.161664e+00 2845.6875935 #> 1034 103.3 2152.3929 5.193793e+00 2842.4132913 #> 1035 103.4 2155.6264 5.226969e+00 2839.1466149 #> 1036 103.5 2158.8511 5.261205e+00 2835.8876593 #> 1037 103.6 2162.0670 5.296519e+00 2832.6365213 #> 1038 103.7 2165.2738 5.332926e+00 2829.3932987 #> 1039 103.8 2168.4715 5.370443e+00 2826.1580911 #> 1040 103.9 2171.6599 5.409087e+00 2822.9309996 #> 1041 104.0 2174.8390 5.448877e+00 2819.7121271 #> 1042 104.1 2178.0086 5.489831e+00 2816.5015778 #> 1043 104.2 2181.1686 5.531968e+00 2813.2994581 #> 1044 104.3 2184.3188 5.575307e+00 2810.1058758 #> 1045 104.4 2187.4592 5.619871e+00 2806.9209408 #> 1046 104.5 2190.5896 5.665678e+00 2803.7447650 #> 1047 104.6 2193.7098 5.712751e+00 2800.5774620 #> 1048 104.7 2196.8197 5.761113e+00 2797.4191476 #> 1049 104.8 2199.9193 5.810786e+00 2794.2699398 #> 1050 104.9 2203.0082 5.861794e+00 2791.1299587 #> 1051 105.0 2206.0865 5.914162e+00 2787.9993267 #> 1052 105.1 2209.1539 5.967915e+00 2784.8781683 #> 1053 105.2 2212.2103 6.023078e+00 2781.7666106 #> 1054 105.3 2215.2555 6.079678e+00 2778.6647831 #> 1055 105.4 2218.2894 6.137743e+00 2775.5728179 #> 1056 105.5 2221.3118 6.197301e+00 2772.4908496 #> 1057 105.6 2224.3226 6.258380e+00 2769.4190154 #> 1058 105.7 2227.3215 6.321012e+00 2766.3574554 #> 1059 105.8 2230.3085 6.385225e+00 2763.3063126 #> 1060 105.9 2233.2832 6.451052e+00 2760.2657327 #> 1061 106.0 2236.2456 6.518525e+00 2757.2358645 #> 1062 106.1 2239.1955 6.587678e+00 2754.2168599 #> 1063 106.2 2242.1326 6.658544e+00 2751.2088740 #> 1064 106.3 2245.0568 6.731159e+00 2748.2120650 #> 1065 106.4 2247.9678 6.805558e+00 2745.2265946 #> 1066 106.5 2250.8656 6.881779e+00 2742.2526279 #> 1067 106.6 2253.7498 6.959860e+00 2739.2903335 #> 1068 106.7 2256.6203 7.039839e+00 2736.3398836 #> 1069 106.8 2259.4768 7.121757e+00 2733.4014542 #> 1070 106.9 2262.3191 7.205654e+00 2730.4752250 #> 1071 107.0 2265.1470 7.291574e+00 2727.5613797 #> 1072 107.1 2267.9603 7.379559e+00 2724.6601060 #> 1073 107.2 2270.7588 7.469653e+00 2721.7715958 #> 1074 107.3 2273.5421 7.561902e+00 2718.8960451 #> 1075 107.4 2276.3100 7.656353e+00 2716.0336544 #> 1076 107.5 2279.0623 7.753054e+00 2713.1846284 #> 1077 107.6 2281.7988 7.852054e+00 2710.3491768 #> 1078 107.7 2284.5191 7.953402e+00 2707.5275136 #> 1079 107.8 2287.2230 8.057152e+00 2704.7198577 #> 1080 107.9 2289.9102 8.163354e+00 2701.9264330 #> 1081 108.0 2292.5805 8.272065e+00 2699.1474686 #> 1082 108.1 2295.2335 8.383339e+00 2696.3831984 #> 1083 108.2 2297.8689 8.497233e+00 2693.6338618 #> 1084 108.3 2300.4865 8.613805e+00 2690.8997038 #> 1085 108.4 2303.0859 8.733116e+00 2688.1809747 #> 1086 108.5 2305.6668 8.855226e+00 2685.4779307 #> 1087 108.6 2308.2290 8.980198e+00 2682.7908338 #> 1088 108.7 2310.7720 9.108096e+00 2680.1199518 #> 1089 108.8 2313.2955 9.238986e+00 2677.4655589 #> 1090 108.9 2315.7991 9.372935e+00 2674.8279353 #> 1091 109.0 2318.2826 9.510011e+00 2672.2073680 #> 1092 109.1 2320.7456 9.650286e+00 2669.6041501 #> 1093 109.2 2323.1876 9.793830e+00 2667.0185819 #> 1094 109.3 2325.6083 9.940718e+00 2664.4509701 #> 1095 109.4 2328.0073 1.009102e+01 2661.9016287 #> 1096 109.5 2330.3843 1.024483e+01 2659.3708789 #> 1097 109.6 2332.7387 1.040220e+01 2656.8590493 #> 1098 109.7 2335.0703 1.056324e+01 2654.3664757 #> 1099 109.8 2337.3785 1.072801e+01 2651.8935019 #> 1100 109.9 2339.6629 1.089660e+01 2649.4404795 #> 1101 110.0 2341.9231 1.106909e+01 2647.0077680 #> 1102 110.1 2344.1587 1.124559e+01 2644.5957350 #> 1103 110.2 2346.3691 1.142616e+01 2642.2047569 #> 1104 110.3 2348.5539 1.161092e+01 2639.8352180 #> 1105 110.4 2350.7126 1.179994e+01 2637.4875119 #> 1106 110.5 2352.8446 1.199332e+01 2635.1620406 #> 1107 110.6 2354.9496 1.219116e+01 2632.8592155 #> 1108 110.7 2357.0270 1.239357e+01 2630.5794571 #> 1109 110.8 2359.0762 1.260063e+01 2628.3231954 #> 1110 110.9 2361.0967 1.281246e+01 2626.0908698 #> 1111 111.0 2363.0879 1.302915e+01 2623.8829296 #> 1112 111.1 2365.0493 1.325082e+01 2621.6998342 #> 1113 111.2 2366.9804 1.347758e+01 2619.5420529 #> 1114 111.3 2368.8804 1.370952e+01 2617.4100655 #> 1115 111.4 2370.7489 1.394677e+01 2615.3043621 #> 1116 111.5 2372.5851 1.418944e+01 2613.2254436 #> 1117 111.6 2374.3885 1.443765e+01 2611.1738219 #> 1118 111.7 2376.1585 1.469151e+01 2609.1500195 #> 1119 111.8 2377.8943 1.495115e+01 2607.1545706 #> 1120 111.9 2379.5953 1.521668e+01 2605.1880203 #> 1121 112.0 2381.2608 1.548823e+01 2603.2509256 #> 1122 112.1 2382.8902 1.576592e+01 2601.3438549 #> 1123 112.2 2384.4827 1.604989e+01 2599.4673887 #> 1124 112.3 2386.0376 1.634026e+01 2597.6221194 #> 1125 112.4 2387.5542 1.663716e+01 2595.8086514 #> 1126 112.5 2389.0317 1.694072e+01 2594.0276017 #> 1127 112.6 2390.4693 1.725107e+01 2592.2795994 #> 1128 112.7 2391.8664 1.756836e+01 2590.5652866 #> 1129 112.8 2393.2220 1.789271e+01 2588.8853175 #> 1130 112.9 2394.5354 1.822427e+01 2587.2403597 #> 1131 113.0 2395.8057 1.856317e+01 2585.6310932 #> 1132 113.1 2397.0322 1.890956e+01 2584.0582115 #> 1133 113.2 2398.2140 1.926357e+01 2582.5224206 #> 1134 113.3 2399.3502 1.962534e+01 2581.0244402 #> 1135 113.4 2400.4400 1.999502e+01 2579.5650028 #> 1136 113.5 2401.4824 2.037275e+01 2578.1448545 #> 1137 113.6 2402.4766 2.075867e+01 2576.7647544 #> 1138 113.7 2403.4216 2.115293e+01 2575.4254751 #> 1139 113.8 2404.3165 2.155566e+01 2574.1278024 #> 1140 113.9 2405.1604 2.196702e+01 2572.8725354 #> 1141 114.0 2405.9524 2.238715e+01 2571.6604864 #> 1142 114.1 2406.6913 2.281618e+01 2570.4924811 #> 1143 114.2 2407.3764 2.325427e+01 2569.3693580 #> 1144 114.3 2408.0065 2.370154e+01 2568.2919688 #> 1145 114.4 2408.5807 2.415815e+01 2567.2611780 #> 1146 114.5 2409.0979 2.462422e+01 2566.2778629 #> 1147 114.6 2409.5572 2.509990e+01 2565.3429132 #> 1148 114.7 2409.9575 2.558532e+01 2564.4572310 #> 1149 114.8 2410.2977 2.608060e+01 2563.6217306 #> 1150 114.9 2410.5768 2.658589e+01 2562.8373379 #> 1151 115.0 2410.7937 2.710130e+01 2562.1049904 #> 1152 115.1 2410.9474 2.762697e+01 2561.4256369 #> 1153 115.2 2411.0368 2.816299e+01 2560.8002369 #> 1154 115.3 2411.0607 2.870951e+01 2560.2297603 #> 1155 115.4 2411.0182 2.926661e+01 2559.7151868 #> 1156 115.5 2410.9081 2.983441e+01 2559.2575058 #> 1157 115.6 2410.7293 3.041300e+01 2558.8577155 #> 1158 115.7 2410.4807 3.100249e+01 2558.5168223 #> 1159 115.8 2410.1612 3.160295e+01 2558.2358404 #> 1160 115.9 2409.7697 3.221447e+01 2558.0157911 #> 1161 116.0 2409.3052 3.283712e+01 2557.8577018 #> 1162 116.1 2408.7664 3.347096e+01 2557.7626058 #> 1163 116.2 2408.1524 3.411606e+01 2557.7315406 #> 1164 116.3 2407.4620 3.477246e+01 2557.7655478 #> 1165 116.4 2406.6941 3.544019e+01 2557.8656720 #> 1166 116.5 2405.8478 3.611929e+01 2558.0329595 #> 1167 116.6 2404.9218 3.680977e+01 2558.2684573 #> 1168 116.7 2403.9152 3.751164e+01 2558.5732126 #> 1169 116.8 2402.8268 3.822488e+01 2558.9482707 #> 1170 116.9 2401.6558 3.894949e+01 2559.3946744 #> 1171 117.0 2400.4011 3.968543e+01 2559.9134626 #> 1172 117.1 2399.0617 4.043266e+01 2560.5056690 #> 1173 117.2 2397.6366 4.119110e+01 2561.1723202 #> 1174 117.3 2396.1249 4.196070e+01 2561.9144350 #> 1175 117.4 2394.5256 4.274134e+01 2562.7330222 #> 1176 117.5 2392.8380 4.353294e+01 2563.6290793 #> 1177 117.6 2391.0611 4.433535e+01 2564.6035910 #> 1178 117.7 2389.1940 4.514844e+01 2565.6575270 #> 1179 117.8 2387.2361 4.597205e+01 2566.7918405 #> 1180 117.9 2385.1865 4.680599e+01 2568.0074666 #> 1181 118.0 2383.0446 4.765006e+01 2569.3053197 #> 1182 118.1 2380.8097 4.850405e+01 2570.6862922 #> 1183 118.2 2378.4810 4.936772e+01 2572.1512523 #> 1184 118.3 2376.0582 5.024079e+01 2573.7010418 #> 1185 118.4 2373.5405 5.112299e+01 2575.3364739 #> 1186 118.5 2370.9277 5.201401e+01 2577.0583315 #> 1187 118.6 2368.2191 5.291352e+01 2578.8673643 #> 1188 118.7 2365.4146 5.382116e+01 2580.7642872 #> 1189 118.8 2362.5137 5.473657e+01 2582.7497777 #> 1190 118.9 2359.5162 5.565933e+01 2584.8244736 #> 1191 119.0 2356.4220 5.658904e+01 2586.9889707 #> 1192 119.1 2353.2309 5.752523e+01 2589.2438205 #> 1193 119.2 2349.9430 5.846745e+01 2591.5895277 #> 1194 119.3 2346.5583 5.941518e+01 2594.0265480 #> 1195 119.4 2343.0768 6.036791e+01 2596.5552854 #> 1196 119.5 2339.4988 6.132510e+01 2599.1760903 #> 1197 119.6 2335.8246 6.228616e+01 2601.8892567 #> 1198 119.7 2332.0545 6.325052e+01 2604.6950200 #> 1199 119.8 2328.1889 6.421755e+01 2607.5935549 #> 1200 119.9 2324.2284 6.518660e+01 2610.5849728 #> 1201 120.0 2320.1737 6.615703e+01 2613.6693198 #> 1202 120.1 2316.0253 6.712813e+01 2616.8465743 #> 1203 120.2 2311.7842 6.809920e+01 2620.1166451 #> 1204 120.3 2307.4511 6.906951e+01 2623.4793693 #> 1205 120.4 2303.0272 7.003831e+01 2626.9345100 #> 1206 120.5 2298.5134 7.100483e+01 2630.4817551 #> 1207 120.6 2293.9110 7.196829e+01 2634.1207149 #> 1208 120.7 2289.2212 7.292787e+01 2637.8509207 #> 1209 120.8 2284.4454 7.388276e+01 2641.6718231 #> 1210 120.9 2279.5851 7.483213e+01 2645.5827909 #> 1211 121.0 2274.6418 7.577512e+01 2649.5831095 #> 1212 121.1 2269.6171 7.671088e+01 2653.6719796 #> 1213 121.2 2264.5130 7.763852e+01 2657.8485168 #> 1214 121.3 2259.3311 7.855718e+01 2662.1117502 #> 1215 121.4 2254.0734 7.946597e+01 2666.4606219 #> 1216 121.5 2248.7420 8.036400e+01 2670.8939867 #> 1217 121.6 2243.3390 8.125036e+01 2675.4106116 #> 1218 121.7 2237.8667 8.212417e+01 2680.0091756 #> 1219 121.8 2232.3272 8.298452e+01 2684.6882700 #> 1220 121.9 2226.7231 8.383054e+01 2689.4463985 #> 1221 122.0 2221.0567 8.466131e+01 2694.2819778 #> 1222 122.1 2215.3307 8.547597e+01 2699.1933382 #> 1223 122.2 2209.5476 8.627365e+01 2704.1787246 #> 1224 122.3 2203.7102 8.705347e+01 2709.2362974 #> 1225 122.4 2197.8213 8.781459e+01 2714.3641339 #> 1226 122.5 2191.8836 8.855618e+01 2719.5602301 #> 1227 122.6 2185.9001 8.927742e+01 2724.8225019 #> 1228 122.7 2179.8737 8.997751e+01 2730.1487877 #> 1229 122.8 2173.8075 9.065569e+01 2735.5368499 #> 1230 122.9 2167.7044 9.131118e+01 2740.9843777 #> 1231 123.0 2161.5677 9.194328e+01 2746.4889895 #> 1232 123.1 2155.4005 9.255128e+01 2752.0482356 #> 1233 123.2 2149.2059 9.313451e+01 2757.6596013 #> 1234 123.3 2142.9872 9.369234e+01 2763.3205099 #> 1235 123.4 2136.7475 9.422415e+01 2769.0283258 #> 1236 123.5 2130.4903 9.472938e+01 2774.7803582 #> 1237 123.6 2124.2186 9.520749e+01 2780.5738648 #> 1238 123.7 2117.9360 9.565799e+01 2786.4060549 #> 1239 123.8 2111.6455 9.608041e+01 2792.2740941 #> 1240 123.9 2105.3506 9.647433e+01 2798.1751077 #> 1241 124.0 2099.0544 9.683939e+01 2804.1061847 #> 1242 124.1 2092.7604 9.717523e+01 2810.0643825 #> 1243 124.2 2086.4717 9.748156e+01 2816.0467307 #> 1244 124.3 2080.1916 9.775814e+01 2822.0502356 #> 1245 124.4 2073.9234 9.800475e+01 2828.0718842 #> 1246 124.5 2067.6701 9.822123e+01 2834.1086493 #> 1247 124.6 2061.4350 9.840746e+01 2840.1574933 #> 1248 124.7 2055.2213 9.856336e+01 2846.2153726 #> 1249 124.8 2049.0319 9.868890e+01 2852.2792425 #> 1250 124.9 2042.8698 9.878409e+01 2858.3460612 #> 1251 125.0 2036.7382 9.884899e+01 2864.4127943 #> 1252 125.1 2030.6399 9.888368e+01 2870.4764189 #> 1253 125.2 2024.5777 9.888833e+01 2876.5339283 #> 1254 125.3 2018.5546 9.886310e+01 2882.5823355 #> 1255 125.4 2012.5731 9.880821e+01 2888.6186779 #> 1256 125.5 2006.6360 9.872393e+01 2894.6400212 #> 1257 125.6 2000.7460 9.861057e+01 2900.6434627 #> 1258 125.7 1994.9054 9.846845e+01 2906.6261359 #> 1259 125.8 1989.1168 9.829795e+01 2912.5852135 #> 1260 125.9 1983.3826 9.809948e+01 2918.5179111 #> 1261 126.0 1977.7050 9.787349e+01 2924.4214906 #> 1262 126.1 1972.0863 9.762044e+01 2930.2932635 #> 1263 126.2 1966.5286 9.734084e+01 2936.1305936 #> 1264 126.3 1961.0339 9.703523e+01 2941.9309000 #> 1265 126.4 1955.6042 9.670417e+01 2947.6916600 #> 1266 126.5 1950.2414 9.634823e+01 2953.4104116 #> 1267 126.6 1944.9472 9.596803e+01 2959.0847555 #> 1268 126.7 1939.7234 9.556420e+01 2964.7123575 #> 1269 126.8 1934.5717 9.513738e+01 2970.2909507 #> 1270 126.9 1929.4934 9.468825e+01 2975.8183369 #> 1271 127.0 1924.4901 9.421750e+01 2981.2923886 #> 1272 127.1 1919.5631 9.372581e+01 2986.7110500 #> 1273 127.2 1914.7138 9.321390e+01 2992.0723387 #> 1274 127.3 1909.9432 9.268249e+01 2997.3743465 #> 1275 127.4 1905.2524 9.213232e+01 3002.6152405 #> 1276 127.5 1900.6426 9.156412e+01 3007.7932638 #> 1277 127.6 1896.1146 9.097864e+01 3012.9067356 #> 1278 127.7 1891.6693 9.037663e+01 3017.9540525 #> 1279 127.8 1887.3075 8.975883e+01 3022.9336878 #> 1280 127.9 1883.0298 8.912601e+01 3027.8441922 #> 1281 128.0 1878.8369 8.847890e+01 3032.6841934 #> 1282 128.1 1874.7293 8.781828e+01 3037.4523962 #> 1283 128.2 1870.7076 8.714487e+01 3042.1475818 #> 1284 128.3 1866.7720 8.645942e+01 3046.7686075 #> 1285 128.4 1862.9229 8.576267e+01 3051.3144059 #> 1286 128.5 1859.1607 8.505535e+01 3055.7839845 #> 1287 128.6 1855.4854 8.433818e+01 3060.1764246 #> 1288 128.7 1851.8972 8.361187e+01 3064.4908803 #> 1289 128.8 1848.3963 8.287713e+01 3068.7265776 #> 1290 128.9 1844.9825 8.213465e+01 3072.8828133 #> 1291 129.0 1841.6559 8.138510e+01 3076.9589534 #> 1292 129.1 1838.4164 8.062916e+01 3080.9544323 #> 1293 129.2 1835.2638 7.986747e+01 3084.8687511 #> 1294 129.3 1832.1978 7.910068e+01 3088.7014761 #> 1295 129.4 1829.2183 7.832942e+01 3092.4522374 #> 1296 129.5 1826.3250 7.755428e+01 3096.1207275 #> 1297 129.6 1823.5174 7.677588e+01 3099.7066994 #> 1298 129.7 1820.7953 7.599478e+01 3103.2099651 #> 1299 129.8 1818.1581 7.521155e+01 3106.6303938 #> 1300 129.9 1815.6054 7.442673e+01 3109.9679105 #> 1301 130.0 1813.1366 7.364086e+01 3113.2224937 #> 1302 130.1 1810.7514 7.285444e+01 3116.3941743 #> 1303 130.2 1808.4490 7.206798e+01 3119.4830333 #> 1304 130.3 1806.2288 7.128195e+01 3122.4892004 #> 1305 130.4 1804.0903 7.049681e+01 3125.4128518 #> 1306 130.5 1802.0328 6.971300e+01 3128.2542090 #> 1307 130.6 1800.0555 6.893096e+01 3131.0135364 #> 1308 130.7 1798.1578 6.815108e+01 3133.6911399 #> 1309 130.8 1796.3389 6.737377e+01 3136.2873651 #> 1310 130.9 1794.5980 6.659940e+01 3138.8025955 #> 1311 131.0 1792.9344 6.582833e+01 3141.2372508 #> 1312 131.1 1791.3473 6.506089e+01 3143.5917852 #> 1313 131.2 1789.8359 6.429742e+01 3145.8666855 #> 1314 131.3 1788.3993 6.353823e+01 3148.0624699 #> 1315 131.4 1787.0367 6.278361e+01 3150.1796859 #> 1316 131.5 1785.7473 6.203384e+01 3152.2189092 #> 1317 131.6 1784.5301 6.128918e+01 3154.1807415 #> 1318 131.7 1783.3843 6.054988e+01 3156.0658096 #> 1319 131.8 1782.3091 5.981618e+01 3157.8747633 #> 1320 131.9 1781.3034 5.908830e+01 3159.6082745 #> 1321 132.0 1780.3665 5.836644e+01 3161.2670355 #> 1322 132.1 1779.4974 5.765080e+01 3162.8517575 #> 1323 132.2 1778.6953 5.694155e+01 3164.3631694 #> 1324 132.3 1777.9591 5.623886e+01 3165.8020165 #> 1325 132.4 1777.2880 5.554290e+01 3167.1690592 #> 1326 132.5 1776.6811 5.485379e+01 3168.4650717 #> 1327 132.6 1776.1375 5.417167e+01 3169.6908408 #> 1328 132.7 1775.6562 5.349667e+01 3170.8471651 #> 1329 132.8 1775.2363 5.282888e+01 3171.9348531 #> 1330 132.9 1774.8769 5.216842e+01 3172.9547231 #> 1331 133.0 1774.5770 5.151536e+01 3173.9076013 #> 1332 133.1 1774.3359 5.086980e+01 3174.7943213 #> 1333 133.2 1774.1525 5.023178e+01 3175.6157232 #> 1334 133.3 1774.0260 4.960139e+01 3176.3726522 #> 1335 133.4 1773.9554 4.897867e+01 3177.0659583 #> 1336 133.5 1773.9398 4.836366e+01 3177.6964949 #> 1337 133.6 1773.9785 4.775640e+01 3178.2651185 #> 1338 133.7 1774.0704 4.715692e+01 3178.7726875 #> 1339 133.8 1774.2147 4.656524e+01 3179.2200618 #> 1340 133.9 1774.4105 4.598137e+01 3179.6081019 #> 1341 134.0 1774.6570 4.540533e+01 3179.9376684 #> 1342 134.1 1774.9533 4.483712e+01 3180.2096210 #> 1343 134.2 1775.2985 4.427672e+01 3180.4248183 #> 1344 134.3 1775.6917 4.372414e+01 3180.5841172 #> 1345 134.4 1776.1323 4.317935e+01 3180.6883721 #> 1346 134.5 1776.6192 4.264234e+01 3180.7384344 #> 1347 134.6 1777.1518 4.211308e+01 3180.7351524 #> 1348 134.7 1777.7291 4.159154e+01 3180.6793703 #> 1349 134.8 1778.3504 4.107770e+01 3180.5719284 #> 1350 134.9 1779.0148 4.057150e+01 3180.4136621 #> 1351 135.0 1779.7217 4.007292e+01 3180.2054019 #> 1352 135.1 1780.4701 3.958191e+01 3179.9479727 #> 1353 135.2 1781.2594 3.909841e+01 3179.6421941 #> 1354 135.3 1782.0887 3.862239e+01 3179.2888794 #> 1355 135.4 1782.9574 3.815378e+01 3178.8888356 #> 1356 135.5 1783.8646 3.769252e+01 3178.4428633 #> 1357 135.6 1784.8097 3.723857e+01 3177.9517562 #> 1358 135.7 1785.7918 3.679186e+01 3177.4163010 #> 1359 135.8 1786.8104 3.635233e+01 3176.8372771 #> 1360 135.9 1787.8646 3.591991e+01 3176.2154567 #> 1361 136.0 1788.9539 3.549453e+01 3175.5516042 #> 1362 136.1 1790.0774 3.507613e+01 3174.8464764 #> 1363 136.2 1791.2345 3.466464e+01 3174.1008220 #> 1364 136.3 1792.4246 3.425999e+01 3173.3153820 #> 1365 136.4 1793.6470 3.386211e+01 3172.4908891 #> 1366 136.5 1794.9010 3.347091e+01 3171.6280679 #> 1367 136.6 1796.1860 3.308634e+01 3170.7276346 #> 1368 136.7 1797.5014 3.270831e+01 3169.7902972 #> 1369 136.8 1798.8465 3.233676e+01 3168.8167550 #> 1370 136.9 1800.2207 3.197159e+01 3167.8076992 #> 1371 137.0 1801.6234 3.161275e+01 3166.7638123 #> 1372 137.1 1803.0541 3.126014e+01 3165.6857683 #> 1373 137.2 1804.5121 3.091370e+01 3164.5742326 #> 1374 137.3 1805.9968 3.057336e+01 3163.4298621 #> 1375 137.4 1807.5077 3.023902e+01 3162.2533053 #> 1376 137.5 1809.0442 2.991061e+01 3161.0452020 #> 1377 137.6 1810.6057 2.958807e+01 3159.8061833 #> 1378 137.7 1812.1918 2.927131e+01 3158.5368722 #> 1379 137.8 1813.8019 2.896025e+01 3157.2378830 #> 1380 137.9 1815.4354 2.865482e+01 3155.9098214 #> 1381 138.0 1817.0918 2.835494e+01 3154.5532851 #> 1382 138.1 1818.7706 2.806055e+01 3153.1688630 #> 1383 138.2 1820.4713 2.777155e+01 3151.7571361 #> 1384 138.3 1822.1934 2.748788e+01 3150.3186770 #> 1385 138.4 1823.9365 2.720947e+01 3148.8540500 #> 1386 138.5 1825.6999 2.693624e+01 3147.3638115 #> 1387 138.6 1827.4834 2.666812e+01 3145.8485097 #> 1388 138.7 1829.2863 2.640503e+01 3144.3086849 #> 1389 138.8 1831.1082 2.614691e+01 3142.7448694 #> 1390 138.9 1832.9487 2.589369e+01 3141.1575879 #> 1391 139.0 1834.8074 2.564528e+01 3139.5473572 #> 1392 139.1 1836.6837 2.540163e+01 3137.9146865 #> 1393 139.2 1838.5773 2.516267e+01 3136.2600774 #> 1394 139.3 1840.4876 2.492833e+01 3134.5840241 #> 1395 139.4 1842.4145 2.469854e+01 3132.8870134 #> 1396 139.5 1844.3572 2.447323e+01 3131.1695246 #> 1397 139.6 1846.3156 2.425234e+01 3129.4320302 #> 1398 139.7 1848.2892 2.403582e+01 3127.6749954 #> 1399 139.8 1850.2775 2.382358e+01 3125.8988782 #> 1400 139.9 1852.2803 2.361557e+01 3124.1041301 #> 1401 140.0 1854.2971 2.341174e+01 3122.2911954 #> 1402 140.1 1856.3275 2.321201e+01 3120.4605121 #> 1403 140.2 1858.3712 2.301632e+01 3118.6125111 #> 1404 140.3 1860.4278 2.282463e+01 3116.7476173 #> 1405 140.4 1862.4969 2.263687e+01 3114.8662488 #> 1406 140.5 1864.5782 2.245298e+01 3112.9688177 #> 1407 140.6 1866.6714 2.227291e+01 3111.0557296 #> 1408 140.7 1868.7760 2.209661e+01 3109.1273843 #> 1409 140.8 1870.8918 2.192401e+01 3107.1841753 #> 1410 140.9 1873.0184 2.175507e+01 3105.2264905 #> 1411 141.0 1875.1556 2.158973e+01 3103.2547118 #> 1412 141.1 1877.3028 2.142795e+01 3101.2692155 #> 1413 141.2 1879.4600 2.126966e+01 3099.2703723 #> 1414 141.3 1881.6266 2.111483e+01 3097.2585472 #> 1415 141.4 1883.8025 2.096339e+01 3095.2341002 #> 1416 141.5 1885.9873 2.081532e+01 3093.1973857 #> 1417 141.6 1888.1807 2.067055e+01 3091.1487530 #> 1418 141.7 1890.3824 2.052904e+01 3089.0885462 #> 1419 141.8 1892.5921 2.039076e+01 3087.0171046 #> 1420 141.9 1894.8096 2.025564e+01 3084.9347625 #> 1421 142.0 1897.0345 2.012366e+01 3082.8418493 #> 1422 142.1 1899.2665 1.999476e+01 3080.7386898 #> 1423 142.2 1901.5055 1.986891e+01 3078.6256042 #> 1424 142.3 1903.7510 1.974607e+01 3076.5029080 #> 1425 142.4 1906.0029 1.962619e+01 3074.3709126 #> 1426 142.5 1908.2608 1.950924e+01 3072.2299246 #> 1427 142.6 1910.5246 1.939517e+01 3070.0802467 #> 1428 142.7 1912.7939 1.928396e+01 3067.9221775 #> 1429 142.8 1915.0684 1.917557e+01 3065.7560112 #> 1430 142.9 1917.3480 1.906996e+01 3063.5820382 #> 1431 143.0 1919.6324 1.896709e+01 3061.4005452 #> 1432 143.1 1921.9213 1.886693e+01 3059.2118147 #> 1433 143.2 1924.2144 1.876945e+01 3057.0161258 #> 1434 143.3 1926.5116 1.867462e+01 3054.8137539 #> 1435 143.4 1928.8126 1.858241e+01 3052.6049707 #> 1436 143.5 1931.1172 1.849278e+01 3050.3900446 #> 1437 143.6 1933.4251 1.840570e+01 3048.1692405 #> 1438 143.7 1935.7360 1.832115e+01 3045.9428202 #> 1439 143.8 1938.0499 1.823910e+01 3043.7110420 #> 1440 143.9 1940.3663 1.815951e+01 3041.4741613 #> 1441 144.0 1942.6852 1.808237e+01 3039.2324303 #> 1442 144.1 1945.0063 1.800765e+01 3036.9860982 #> 1443 144.2 1947.3293 1.793531e+01 3034.7354114 #> 1444 144.3 1949.6540 1.786534e+01 3032.4806134 #> 1445 144.4 1951.9803 1.779772e+01 3030.2219449 #> 1446 144.5 1954.3079 1.773241e+01 3027.9596440 #> 1447 144.6 1956.6367 1.766940e+01 3025.6939461 #> 1448 144.7 1958.9663 1.760866e+01 3023.4250841 #> 1449 144.8 1961.2965 1.755018e+01 3021.1532885 #> 1450 144.9 1963.6273 1.749393e+01 3018.8787874 #> 1451 145.0 1965.9583 1.743990e+01 3016.6018064 #> 1452 145.1 1968.2894 1.738805e+01 3014.3225691 #> 1453 145.2 1970.6203 1.733838e+01 3012.0412966 #> 1454 145.3 1972.9509 1.729087e+01 3009.7582081 #> 1455 145.4 1975.2810 1.724550e+01 3007.4735207 #> 1456 145.5 1977.6103 1.720225e+01 3005.1874495 #> 1457 145.6 1979.9387 1.716110e+01 3002.9002077 #> 1458 145.7 1982.2659 1.712205e+01 3000.6120066 #> 1459 145.8 1984.5919 1.708508e+01 2998.3230556 #> 1460 145.9 1986.9163 1.705016e+01 2996.0335626 #> 1461 146.0 1989.2390 1.701730e+01 2993.7437337 #> 1462 146.1 1991.5598 1.698647e+01 2991.4537732 #> 1463 146.2 1993.8785 1.695766e+01 2989.1638842 #> 1464 146.3 1996.1949 1.693086e+01 2986.8742681 #> 1465 146.4 1998.5088 1.690606e+01 2984.5851248 #> 1466 146.5 2000.8201 1.688325e+01 2982.2966530 #> 1467 146.6 2003.1285 1.686242e+01 2980.0090500 #> 1468 146.7 2005.4339 1.684356e+01 2977.7225119 #> 1469 146.8 2007.7361 1.682666e+01 2975.4372333 #> 1470 146.9 2010.0349 1.681170e+01 2973.1534081 #> 1471 147.0 2012.3301 1.679869e+01 2970.8712289 #> 1472 147.1 2014.6215 1.678762e+01 2968.5908870 #> 1473 147.2 2016.9090 1.677847e+01 2966.3125732 #> 1474 147.3 2019.1923 1.677124e+01 2964.0364771 #> 1475 147.4 2021.4713 1.676593e+01 2961.7627873 #> 1476 147.5 2023.7458 1.676253e+01 2959.4916919 #> 1477 147.6 2026.0156 1.676103e+01 2957.2233779 #> 1478 147.7 2028.2805 1.676144e+01 2954.9580317 #> 1479 147.8 2030.5404 1.676374e+01 2952.6958392 #> 1480 147.9 2032.7951 1.676793e+01 2950.4369853 #> 1481 148.0 2035.0443 1.677401e+01 2948.1816548 #> 1482 148.1 2037.2880 1.678198e+01 2945.9300315 #> 1483 148.2 2039.5259 1.679184e+01 2943.6822990 #> 1484 148.3 2041.7578 1.680358e+01 2941.4386404 #> 1485 148.4 2043.9836 1.681720e+01 2939.1992385 #> 1486 148.5 2046.2030 1.683270e+01 2936.9642756 #> 1487 148.6 2048.4160 1.685008e+01 2934.7339338 #> 1488 148.7 2050.6223 1.686935e+01 2932.5083950 #> 1489 148.8 2052.8217 1.689049e+01 2930.2878410 #> 1490 148.9 2055.0140 1.691352e+01 2928.0724530 #> 1491 149.0 2057.1991 1.693844e+01 2925.8624127 #> 1492 149.1 2059.3769 1.696524e+01 2923.6579013 #> 1493 149.2 2061.5470 1.699393e+01 2921.4591002 #> 1494 149.3 2063.7093 1.702452e+01 2919.2661907 #> 1495 149.4 2065.8636 1.705700e+01 2917.0793543 #> 1496 149.5 2068.0099 1.709138e+01 2914.8987724 #> 1497 149.6 2070.1477 1.712766e+01 2912.7246268 #> 1498 149.7 2072.2770 1.716585e+01 2910.5570993 #> 1499 149.8 2074.3977 1.720596e+01 2908.3963721 #> 1500 149.9 2076.5094 1.724798e+01 2906.2426276 #> 1501 150.0 2078.6120 1.729193e+01 2904.0960485 #> 1502 150.1 2080.7054 1.733780e+01 2901.9568178 #> 1503 150.2 2082.7893 1.738562e+01 2899.8251191 #> 1504 150.3 2084.8635 1.743538e+01 2897.7011361 #> 1505 150.4 2086.9279 1.748709e+01 2895.5850534 #> 1506 150.5 2088.9822 1.754076e+01 2893.4770558 #> 1507 150.6 2091.0263 1.759640e+01 2891.3773288 #> 1508 150.7 2093.0599 1.765401e+01 2889.2860583 #> 1509 150.8 2095.0830 1.771361e+01 2887.2034311 #> 1510 150.9 2097.0952 1.777521e+01 2885.1296344 #> 1511 151.0 2099.0963 1.783880e+01 2883.0648562 #> 1512 151.1 2101.0863 1.790442e+01 2881.0092854 #> 1513 151.2 2103.0648 1.797205e+01 2878.9631114 #> 1514 151.3 2105.0318 1.804172e+01 2876.9265245 #> 1515 151.4 2106.9868 1.811344e+01 2874.8997158 #> 1516 151.5 2108.9299 1.818721e+01 2872.8828774 #> 1517 151.6 2110.8607 1.826306e+01 2870.8762021 #> 1518 151.7 2112.7791 1.834098e+01 2868.8798839 #> 1519 151.8 2114.6849 1.842100e+01 2866.8941174 #> 1520 151.9 2116.5778 1.850313e+01 2864.9190985 #> 1521 152.0 2118.4576 1.858737e+01 2862.9550240 #> 1522 152.1 2120.3242 1.867374e+01 2861.0020917 #> 1523 152.2 2122.1772 1.876227e+01 2859.0605005 #> 1524 152.3 2124.0166 1.885295e+01 2857.1304506 #> 1525 152.4 2125.8421 1.894581e+01 2855.2121431 #> 1526 152.5 2127.6534 1.904085e+01 2853.3057802 #> 1527 152.6 2129.4503 1.913810e+01 2851.4115656 #> 1528 152.7 2131.2327 1.923756e+01 2849.5297040 #> 1529 152.8 2133.0003 1.933926e+01 2847.6604012 #> 1530 152.9 2134.7529 1.944321e+01 2845.8038646 #> 1531 153.0 2136.4903 1.954942e+01 2843.9603026 #> 1532 153.1 2138.2122 1.965791e+01 2842.1299250 #> 1533 153.2 2139.9184 1.976870e+01 2840.3129428 #> 1534 153.3 2141.6086 1.988180e+01 2838.5095686 #> 1535 153.4 2143.2828 1.999723e+01 2836.7200161 #> 1536 153.5 2144.9405 2.011501e+01 2834.9445005 #> 1537 153.6 2146.5816 2.023515e+01 2833.1832383 #> 1538 153.7 2148.2059 2.035766e+01 2831.4364474 #> 1539 153.8 2149.8131 2.048258e+01 2829.7043472 #> 1540 153.9 2151.4029 2.060991e+01 2827.9871585 #> 1541 154.0 2152.9752 2.073966e+01 2826.2851035 #> 1542 154.1 2154.5297 2.087187e+01 2824.5984058 #> 1543 154.2 2156.0662 2.100654e+01 2822.9272906 #> 1544 154.3 2157.5843 2.114370e+01 2821.2719843 #> 1545 154.4 2159.0839 2.128336e+01 2819.6327150 #> 1546 154.5 2160.5648 2.142553e+01 2818.0097121 #> 1547 154.6 2162.0265 2.157025e+01 2816.4032066 #> 1548 154.7 2163.4691 2.171752e+01 2814.8134308 #> 1549 154.8 2164.8920 2.186735e+01 2813.2406186 #> 1550 154.9 2166.2952 2.201978e+01 2811.6850054 #> 1551 155.0 2167.6784 2.217482e+01 2810.1468278 #> 1552 155.1 2169.0412 2.233248e+01 2808.6263240 #> 1553 155.2 2170.3835 2.249278e+01 2807.1237337 #> 1554 155.3 2171.7050 2.265574e+01 2805.6392979 #> 1555 155.4 2173.0054 2.282138e+01 2804.1732590 #> 1556 155.5 2174.2844 2.298972e+01 2802.7258608 #> 1557 155.6 2175.5419 2.316076e+01 2801.2973484 #> 1558 155.7 2176.7775 2.333453e+01 2799.8879685 #> 1559 155.8 2177.9910 2.351104e+01 2798.4979688 #> 1560 155.9 2179.1821 2.369031e+01 2797.1275985 #> 1561 156.0 2180.3505 2.387236e+01 2795.7771080 #> 1562 156.1 2181.4961 2.405720e+01 2794.4467488 #> 1563 156.2 2182.6184 2.424484e+01 2793.1367738 #> 1564 156.3 2183.7173 2.443530e+01 2791.8474370 #> 1565 156.4 2184.7924 2.462860e+01 2790.5789934 #> 1566 156.5 2185.8436 2.482475e+01 2789.3316991 #> 1567 156.6 2186.8704 2.502376e+01 2788.1058115 #> 1568 156.7 2187.8728 2.522564e+01 2786.9015885 #> 1569 156.8 2188.8503 2.543042e+01 2785.7192893 #> 1570 156.9 2189.8027 2.563809e+01 2784.5591738 #> 1571 157.0 2190.7298 2.584867e+01 2783.4215028 #> 1572 157.1 2191.6313 2.606217e+01 2782.3065378 #> 1573 157.2 2192.5069 2.627861e+01 2781.2145408 #> 1574 157.3 2193.3562 2.649798e+01 2780.1457747 #> 1575 157.4 2194.1792 2.672030e+01 2779.1005027 #> 1576 157.5 2194.9754 2.694558e+01 2778.0789887 #> 1577 157.6 2195.7447 2.717382e+01 2777.0814965 #> 1578 157.7 2196.4867 2.740503e+01 2776.1082908 #> 1579 157.8 2197.2011 2.763922e+01 2775.1596359 #> 1580 157.9 2197.8878 2.787638e+01 2774.2357967 #> 1581 158.0 2198.5464 2.811652e+01 2773.3370377 #> 1582 158.1 2199.1767 2.835964e+01 2772.4636235 #> 1583 158.2 2199.7784 2.860575e+01 2771.6158184 #> 1584 158.3 2200.3513 2.885484e+01 2770.7938864 #> 1585 158.4 2200.8950 2.910692e+01 2769.9980912 #> 1586 158.5 2201.4093 2.936197e+01 2769.2286957 #> 1587 158.6 2201.8940 2.961999e+01 2768.4859622 #> 1588 158.7 2202.3489 2.988099e+01 2767.7701522 #> 1589 158.8 2202.7735 3.014495e+01 2767.0815262 #> 1590 158.9 2203.1678 3.041186e+01 2766.4203438 #> 1591 159.0 2203.5314 3.068172e+01 2765.7868630 #> 1592 159.1 2203.8642 3.095451e+01 2765.1813407 #> 1593 159.2 2204.1658 3.123022e+01 2764.6040322 #> 1594 159.3 2204.4360 3.150883e+01 2764.0551910 #> 1595 159.4 2204.6746 3.179034e+01 2763.5350689 #> 1596 159.5 2204.8814 3.207472e+01 2763.0439155 #> 1597 159.6 2205.0561 3.236195e+01 2762.5819783 #> 1598 159.7 2205.1985 3.265202e+01 2762.1495024 #> 1599 159.8 2205.3084 3.294489e+01 2761.7467302 #> 1600 159.9 2205.3856 3.324054e+01 2761.3739016 #> 1601 160.0 2205.4298 3.353895e+01 2761.0312533 #> 1602 160.1 2205.4409 3.384008e+01 2760.7190190 #> 1603 160.2 2205.4187 3.414391e+01 2760.4374290 #> 1604 160.3 2205.3629 3.445040e+01 2760.1867101 #> 1605 160.4 2205.2734 3.475952e+01 2759.9670852 #> 1606 160.5 2205.1500 3.507122e+01 2759.7787734 #> 1607 160.6 2204.9925 3.538547e+01 2759.6219895 #> 1608 160.7 2204.8008 3.570222e+01 2759.4969439 #> 1609 160.8 2204.5747 3.602143e+01 2759.4038423 #> 1610 160.9 2204.3141 3.634305e+01 2759.3428855 #> 1611 161.0 2204.0187 3.666704e+01 2759.3142692 #> 1612 161.1 2203.6885 3.699333e+01 2759.3181839 #> 1613 161.2 2203.3233 3.732188e+01 2759.3548142 #> 1614 161.3 2202.9230 3.765263e+01 2759.4243390 #> 1615 161.4 2202.4876 3.798551e+01 2759.5269312 #> 1616 161.5 2202.0168 3.832047e+01 2759.6627572 #> 1617 161.6 2201.5106 3.865744e+01 2759.8319768 #> 1618 161.7 2200.9689 3.899635e+01 2760.0347429 #> 1619 161.8 2200.3917 3.933713e+01 2760.2712014 #> 1620 161.9 2199.7788 3.967971e+01 2760.5414906 #> 1621 162.0 2199.1303 4.002401e+01 2760.8457414 #> 1622 162.1 2198.4460 4.036995e+01 2761.1840765 #> 1623 162.2 2197.7259 4.071745e+01 2761.5566107 #> 1624 162.3 2196.9701 4.106643e+01 2761.9634500 #> 1625 162.4 2196.1785 4.141680e+01 2762.4046921 #> 1626 162.5 2195.3511 4.176846e+01 2762.8804252 #> 1627 162.6 2194.4879 4.212134e+01 2763.3907287 #> 1628 162.7 2193.5890 4.247532e+01 2763.9356721 #> 1629 162.8 2192.6544 4.283031e+01 2764.5153154 #> 1630 162.9 2191.6841 4.318621e+01 2765.1297083 #> 1631 163.0 2190.6782 4.354292e+01 2765.7788903 #> 1632 163.1 2189.6368 4.390033e+01 2766.4628904 #> 1633 163.2 2188.5599 4.425833e+01 2767.1817266 #> 1634 163.3 2187.4478 4.461681e+01 2767.9354057 #> 1635 163.4 2186.3004 4.497565e+01 2768.7239235 #> 1636 163.5 2185.1180 4.533473e+01 2769.5472638 #> 1637 163.6 2183.9007 4.569394e+01 2770.4053989 #> 1638 163.7 2182.6486 4.605315e+01 2771.2982888 #> 1639 163.8 2181.3619 4.641224e+01 2772.2258813 #> 1640 163.9 2180.0408 4.677109e+01 2773.1881116 #> 1641 164.0 2178.6855 4.712955e+01 2774.1849021 #> 1642 164.1 2177.2963 4.748750e+01 2775.2161624 #> 1643 164.2 2175.8734 4.784481e+01 2776.2817888 #> 1644 164.3 2174.4170 4.820133e+01 2777.3816641 #> 1645 164.4 2172.9274 4.855694e+01 2778.5156579 #> 1646 164.5 2171.4049 4.891148e+01 2779.6836257 #> 1647 164.6 2169.8498 4.926482e+01 2780.8854093 #> 1648 164.7 2168.2623 4.961682e+01 2782.1208363 #> 1649 164.8 2166.6430 4.996733e+01 2783.3897201 #> 1650 164.9 2164.9919 5.031620e+01 2784.6918599 #> 1651 165.0 2163.3097 5.066328e+01 2786.0270402 #> 1652 165.1 2161.5965 5.100842e+01 2787.3950310 #> 1653 165.2 2159.8529 5.135148e+01 2788.7955877 #> 1654 165.3 2158.0792 5.169231e+01 2790.2284506 #> 1655 165.4 2156.2759 5.203073e+01 2791.6933456 #> 1656 165.5 2154.4434 5.236662e+01 2793.1899831 #> 1657 165.6 2152.5821 5.269981e+01 2794.7180591 #> 1658 165.7 2150.6926 5.303014e+01 2796.2772541 #> 1659 165.8 2148.7753 5.335747e+01 2797.8672340 #> 1660 165.9 2146.8307 5.368163e+01 2799.4876492 #> 1661 166.0 2144.8594 5.400248e+01 2801.1381356 #> 1662 166.1 2142.8618 5.431985e+01 2802.8183137 #> 1663 166.2 2140.8386 5.463359e+01 2804.5277892 #> 1664 166.3 2138.7903 5.494356e+01 2806.2661529 #> 1665 166.4 2136.7174 5.524959e+01 2808.0329809 #> 1666 166.5 2134.6206 5.555153e+01 2809.8278344 #> 1667 166.6 2132.5005 5.584924e+01 2811.6502601 #> 1668 166.7 2130.3577 5.614256e+01 2813.4997902 #> 1669 166.8 2128.1927 5.643134e+01 2815.3759427 #> 1670 166.9 2126.0063 5.671544e+01 2817.2782211 #> 1671 167.0 2123.7992 5.699470e+01 2819.2061154 #> 1672 167.1 2121.5719 5.726899e+01 2821.1591013 #> 1673 167.2 2119.3252 5.753817e+01 2823.1366412 #> 1674 167.3 2117.0597 5.780209e+01 2825.1381843 #> 1675 167.4 2114.7762 5.806061e+01 2827.1631663 #> 1676 167.5 2112.4754 5.831361e+01 2829.2110104 #> 1677 167.6 2110.1579 5.856095e+01 2831.2811272 #> 1678 167.7 2107.8246 5.880250e+01 2833.3729148 #> 1679 167.8 2105.4761 5.903814e+01 2835.4857597 #> 1680 167.9 2103.1132 5.926774e+01 2837.6190365 #> 1681 168.0 2100.7367 5.949118e+01 2839.7721089 #> 1682 168.1 2098.3473 5.970835e+01 2841.9443292 #> 1683 168.2 2095.9458 5.991914e+01 2844.1350396 #> 1684 168.3 2093.5330 6.012343e+01 2846.3435719 #> 1685 168.4 2091.1096 6.032113e+01 2848.5692484 #> 1686 168.5 2088.6765 6.051212e+01 2850.8113817 #> 1687 168.6 2086.2344 6.069633e+01 2853.0692759 #> 1688 168.7 2083.7841 6.087365e+01 2855.3422265 #> 1689 168.8 2081.3265 6.104399e+01 2857.6295208 #> 1690 168.9 2078.8623 6.120729e+01 2859.9304389 #> 1691 169.0 2076.3923 6.136344e+01 2862.2442535 #> 1692 169.1 2073.9174 6.151240e+01 2864.5702309 #> 1693 169.2 2071.4383 6.165407e+01 2866.9076312 #> 1694 169.3 2068.9559 6.178841e+01 2869.2557091 #> 1695 169.4 2066.4709 6.191535e+01 2871.6137140 #> 1696 169.5 2063.9843 6.203484e+01 2873.9808908 #> 1697 169.6 2061.4967 6.214684e+01 2876.3564801 #> 1698 169.7 2059.0090 6.225129e+01 2878.7397194 #> 1699 169.8 2056.5220 6.234816e+01 2881.1298428 #> 1700 169.9 2054.0365 6.243742e+01 2883.5260821 #> 1701 170.0 2051.5533 6.251904e+01 2885.9276671 #> 1702 170.1 2049.0732 6.259300e+01 2888.3338261 #> 1703 170.2 2046.5969 6.265929e+01 2890.7437866 #> 1704 170.3 2044.1253 6.271788e+01 2893.1567757 #> 1705 170.4 2041.6592 6.276878e+01 2895.5720208 #> 1706 170.5 2039.1993 6.281199e+01 2897.9887498 #> 1707 170.6 2036.7463 6.284751e+01 2900.4061919 #> 1708 170.7 2034.3011 6.287534e+01 2902.8235783 #> 1709 170.8 2031.8643 6.289551e+01 2905.2401422 #> 1710 170.9 2029.4368 6.290804e+01 2907.6551197 #> 1711 171.0 2027.0193 6.291295e+01 2910.0677502 #> 1712 171.1 2024.6125 6.291027e+01 2912.4772772 #> 1713 171.2 2022.2170 6.290004e+01 2914.8829480 #> 1714 171.3 2019.8337 6.288230e+01 2917.2840153 #> 1715 171.4 2017.4632 6.285709e+01 2919.6797366 #> 1716 171.5 2015.1062 6.282446e+01 2922.0693756 #> 1717 171.6 2012.7633 6.278448e+01 2924.4522018 #> 1718 171.7 2010.4353 6.273719e+01 2926.8274919 #> 1719 171.8 2008.1228 6.268267e+01 2929.1945293 #> 1720 171.9 2005.8264 6.262098e+01 2931.5526053 #> 1721 172.0 2003.5468 6.255219e+01 2933.9010190 #> 1722 172.1 2001.2845 6.247638e+01 2936.2390779 #> 1723 172.2 1999.0403 6.239364e+01 2938.5660985 #> 1724 172.3 1996.8146 6.230404e+01 2940.8814063 #> 1725 172.4 1994.6080 6.220768e+01 2943.1843364 #> 1726 172.5 1992.4211 6.210464e+01 2945.4742339 #> 1727 172.6 1990.2545 6.199503e+01 2947.7504539 #> 1728 172.7 1988.1087 6.187894e+01 2950.0123625 #> 1729 172.8 1985.9842 6.175647e+01 2952.2593363 #> 1730 172.9 1983.8815 6.162773e+01 2954.4907633 #> 1731 173.0 1981.8011 6.149283e+01 2956.7060429 #> 1732 173.1 1979.7435 6.135188e+01 2958.9045863 #> 1733 173.2 1977.7092 6.120499e+01 2961.0858166 #> 1734 173.3 1975.6986 6.105228e+01 2963.2491693 #> 1735 173.4 1973.7121 6.089386e+01 2965.3940920 #> 1736 173.5 1971.7501 6.072986e+01 2967.5200455 #> 1737 173.6 1969.8131 6.056039e+01 2969.6265029 #> 1738 173.7 1967.9015 6.038559e+01 2971.7129507 #> 1739 173.8 1966.0155 6.020558e+01 2973.7788883 #> 1740 173.9 1964.1557 6.002048e+01 2975.8238287 #> 1741 174.0 1962.3223 5.983043e+01 2977.8472982 #> 1742 174.1 1960.5156 5.963555e+01 2979.8488366 #> 1743 174.2 1958.7360 5.943597e+01 2981.8279975 #> 1744 174.3 1956.9838 5.923184e+01 2983.7843481 #> 1745 174.4 1955.2593 5.902327e+01 2985.7174696 #> 1746 174.5 1953.5626 5.881041e+01 2987.6269569 #> 1747 174.6 1951.8942 5.859339e+01 2989.5124190 #> 1748 174.7 1950.2542 5.837234e+01 2991.3734786 #> 1749 174.8 1948.6428 5.814740e+01 2993.2097725 #> 1750 174.9 1947.0604 5.791870e+01 2995.0209514 #> 1751 175.0 1945.5069 5.768637e+01 2996.8066800 #> 1752 175.1 1943.9828 5.745056e+01 2998.5666368 #> 1753 175.2 1942.4881 5.721140e+01 3000.3005144 #> 1754 175.3 1941.0230 5.696901e+01 3002.0080189 #> 1755 175.4 1939.5876 5.672354e+01 3003.6888704 #> 1756 175.5 1938.1821 5.647511e+01 3005.3428026 #> 1757 175.6 1936.8066 5.622386e+01 3006.9695628 #> 1758 175.7 1935.4612 5.596993e+01 3008.5689120 #> 1759 175.8 1934.1459 5.571343e+01 3010.1406244 #> 1760 175.9 1932.8610 5.545450e+01 3011.6844876 #> 1761 176.0 1931.6064 5.519328e+01 3013.2003023 #> 1762 176.1 1930.3822 5.492988e+01 3014.6878823 #> 1763 176.2 1929.1885 5.466443e+01 3016.1470544 #> 1764 176.3 1928.0253 5.439706e+01 3017.5776580 #> 1765 176.4 1926.8926 5.412789e+01 3018.9795453 #> 1766 176.5 1925.7904 5.385704e+01 3020.3525809 #> 1767 176.6 1924.7187 5.358464e+01 3021.6966416 #> 1768 176.7 1923.6776 5.331080e+01 3023.0116163 #> 1769 176.8 1922.6670 5.303564e+01 3024.2974060 #> 1770 176.9 1921.6868 5.275927e+01 3025.5539233 #> 1771 177.0 1920.7371 5.248181e+01 3026.7810924 #> 1772 177.1 1919.8178 5.220337e+01 3027.9788490 #> 1773 177.2 1918.9288 5.192406e+01 3029.1471397 #> 1774 177.3 1918.0701 5.164398e+01 3030.2859224 #> 1775 177.4 1917.2416 5.136326e+01 3031.3951654 #> 1776 177.5 1916.4432 5.108198e+01 3032.4748479 #> 1777 177.6 1915.6748 5.080025e+01 3033.5249592 #> 1778 177.7 1914.9363 5.051817e+01 3034.5454988 #> 1779 177.8 1914.2277 5.023584e+01 3035.5364761 #> 1780 177.9 1913.5487 4.995335e+01 3036.4979103 #> 1781 178.0 1912.8994 4.967081e+01 3037.4298300 #> 1782 178.1 1912.2794 4.938829e+01 3038.3322729 #> 1783 178.2 1911.6888 4.910590e+01 3039.2052860 #> 1784 178.3 1911.1274 4.882371e+01 3040.0489249 #> 1785 178.4 1910.5949 4.854182e+01 3040.8632540 #> 1786 178.5 1910.0913 4.826031e+01 3041.6483457 #> 1787 178.6 1909.6165 4.797925e+01 3042.4042811 #> 1788 178.7 1909.1701 4.769873e+01 3043.1311486 #> 1789 178.8 1908.7521 4.741883e+01 3043.8290448 #> 1790 178.9 1908.3623 4.713962e+01 3044.4980734 #> 1791 179.0 1908.0005 4.686117e+01 3045.1383457 #> 1792 179.1 1907.6665 4.658356e+01 3045.7499798 #> 1793 179.2 1907.3600 4.630685e+01 3046.3331007 #> 1794 179.3 1907.0810 4.603111e+01 3046.8878400 #> 1795 179.4 1906.8293 4.575641e+01 3047.4143358 #> 1796 179.5 1906.6045 4.548280e+01 3047.9127322 #> 1797 179.6 1906.4065 4.521035e+01 3048.3831793 #> 1798 179.7 1906.2350 4.493912e+01 3048.8258332 #> 1799 179.8 1906.0900 4.466916e+01 3049.2408554 #> 1800 179.9 1905.9711 4.440053e+01 3049.6284127 #> 1801 180.0 1905.8780 4.413328e+01 3049.9886771 #> 1802 180.1 1905.8107 4.386747e+01 3050.3218257 #> 1803 180.2 1905.7688 4.360313e+01 3050.6280403 #> 1804 180.3 1905.7522 4.334033e+01 3050.9075072 #> 1805 180.4 1905.7605 4.307910e+01 3051.1604172 #> 1806 180.5 1905.7935 4.281949e+01 3051.3869653 #> 1807 180.6 1905.8511 4.256154e+01 3051.5873505 #> 1808 180.7 1905.9329 4.230529e+01 3051.7617757 #> 1809 180.8 1906.0388 4.205079e+01 3051.9104474 #> 1810 180.9 1906.1684 4.179806e+01 3052.0335758 #> 1811 181.0 1906.3215 4.154714e+01 3052.1313743 #> 1812 181.1 1906.4979 4.129807e+01 3052.2040594 #> 1813 181.2 1906.6973 4.105088e+01 3052.2518508 #> 1814 181.3 1906.9194 4.080559e+01 3052.2749709 #> 1815 181.4 1907.1641 4.056225e+01 3052.2736450 #> 1816 181.5 1907.4310 4.032087e+01 3052.2481008 #> 1817 181.6 1907.7199 4.008149e+01 3052.1985684 #> 1818 181.7 1908.0306 3.984412e+01 3052.1252804 #> 1819 181.8 1908.3627 3.960880e+01 3052.0284712 #> 1820 181.9 1908.7161 3.937554e+01 3051.9083774 #> 1821 182.0 1909.0904 3.914437e+01 3051.7652375 #> 1822 182.1 1909.4854 3.891531e+01 3051.5992916 #> 1823 182.2 1909.9009 3.868836e+01 3051.4107815 #> 1824 182.3 1910.3365 3.846356e+01 3051.1999503 #> 1825 182.4 1910.7920 3.824092e+01 3050.9670428 #> 1826 182.5 1911.2672 3.802045e+01 3050.7123047 #> 1827 182.6 1911.7618 3.780217e+01 3050.4359831 #> 1828 182.7 1912.2756 3.758609e+01 3050.1383261 #> 1829 182.8 1912.8082 3.737223e+01 3049.8195826 #> 1830 182.9 1913.3594 3.716058e+01 3049.4800023 #> 1831 183.0 1913.9290 3.695117e+01 3049.1198358 #> 1832 183.1 1914.5167 3.674401e+01 3048.7393342 #> 1833 183.2 1915.1222 3.653909e+01 3048.3387493 #> 1834 183.3 1915.7452 3.633643e+01 3047.9183331 #> 1835 183.4 1916.3856 3.613604e+01 3047.4783382 #> 1836 183.5 1917.0431 3.593792e+01 3047.0190172 #> 1837 183.6 1917.7173 3.574208e+01 3046.5406233 #> 1838 183.7 1918.4081 3.554852e+01 3046.0434094 #> 1839 183.8 1919.1151 3.535724e+01 3045.5276287 #> 1840 183.9 1919.8382 3.516825e+01 3044.9935344 #> 1841 184.0 1920.5771 3.498154e+01 3044.4413795 #> 1842 184.1 1921.3315 3.479713e+01 3043.8714168 #> 1843 184.2 1922.1011 3.461501e+01 3043.2838989 #> 1844 184.3 1922.8857 3.443517e+01 3042.6790783 #> 1845 184.4 1923.6852 3.425763e+01 3042.0572069 #> 1846 184.5 1924.4991 3.408238e+01 3041.4185364 #> 1847 184.6 1925.3273 3.390942e+01 3040.7633179 #> 1848 184.7 1926.1695 3.373875e+01 3040.0918022 #> 1849 184.8 1927.0254 3.357036e+01 3039.4042392 #> 1850 184.9 1927.8949 3.340425e+01 3038.7008785 #> 1851 185.0 1928.7776 3.324042e+01 3037.9819691 #> 1852 185.1 1929.6734 3.307886e+01 3037.2477590 #> 1853 185.2 1930.5819 3.291957e+01 3036.4984958 #> 1854 185.3 1931.5030 3.276255e+01 3035.7344260 #> 1855 185.4 1932.4364 3.260779e+01 3034.9557958 #> 1856 185.5 1933.3819 3.245528e+01 3034.1628500 #> 1857 185.6 1934.3391 3.230503e+01 3033.3558329 #> 1858 185.7 1935.3080 3.215702e+01 3032.5349878 #> 1859 185.8 1936.2882 3.201124e+01 3031.7005571 #> 1860 185.9 1937.2795 3.186770e+01 3030.8527823 #> 1861 186.0 1938.2817 3.172639e+01 3029.9919036 #> 1862 186.1 1939.2946 3.158729e+01 3029.1181607 #> 1863 186.2 1940.3178 3.145040e+01 3028.2317919 #> 1864 186.3 1941.3513 3.131571e+01 3027.3330346 #> 1865 186.4 1942.3946 3.118322e+01 3026.4221252 #> 1866 186.5 1943.4478 3.105292e+01 3025.4992988 #> 1867 186.6 1944.5104 3.092480e+01 3024.5647897 #> 1868 186.7 1945.5823 3.079885e+01 3023.6188309 #> 1869 186.8 1946.6633 3.067507e+01 3022.6616542 #> 1870 186.9 1947.7531 3.055344e+01 3021.6934905 #> 1871 187.0 1948.8515 3.043396e+01 3020.7145694 #> 1872 187.1 1949.9583 3.031662e+01 3019.7251194 #> 1873 187.2 1951.0732 3.020140e+01 3018.7253678 #> 1874 187.3 1952.1961 3.008831e+01 3017.7155407 #> 1875 187.4 1953.3268 2.997733e+01 3016.6958631 #> 1876 187.5 1954.4650 2.986845e+01 3015.6665588 #> 1877 187.6 1955.6105 2.976167e+01 3014.6278503 #> 1878 187.7 1956.7631 2.965698e+01 3013.5799592 #> 1879 187.8 1957.9225 2.955436e+01 3012.5231054 #> 1880 187.9 1959.0887 2.945380e+01 3011.4575082 #> 1881 188.0 1960.2613 2.935531e+01 3010.3833852 #> 1882 188.1 1961.4402 2.925887e+01 3009.3009531 #> 1883 188.2 1962.6251 2.916446e+01 3008.2104274 #> 1884 188.3 1963.8159 2.907209e+01 3007.1120222 #> 1885 188.4 1965.0123 2.898174e+01 3006.0059506 #> 1886 188.5 1966.2142 2.889340e+01 3004.8924244 #> 1887 188.6 1967.4213 2.880707e+01 3003.7716543 #> 1888 188.7 1968.6334 2.872273e+01 3002.6438498 #> 1889 188.8 1969.8504 2.864038e+01 3001.5092191 #> 1890 188.9 1971.0720 2.856000e+01 3000.3679694 #> 1891 189.0 1972.2981 2.848160e+01 2999.2203068 #> 1892 189.1 1973.5284 2.840515e+01 2998.0664359 #> 1893 189.2 1974.7628 2.833065e+01 2996.9065604 #> 1894 189.3 1976.0010 2.825810e+01 2995.7408830 #> 1895 189.4 1977.2429 2.818748e+01 2994.5696048 #> 1896 189.5 1978.4883 2.811878e+01 2993.3929263 #> 1897 189.6 1979.7369 2.805200e+01 2992.2110465 #> 1898 189.7 1980.9887 2.798713e+01 2991.0241635 #> 1899 189.8 1982.2434 2.792416e+01 2989.8324742 #> 1900 189.9 1983.5007 2.786308e+01 2988.6361744 #> 1901 190.0 1984.7607 2.780389e+01 2987.4354588 #> 1902 190.1 1986.0229 2.774656e+01 2986.2305213 #> 1903 190.2 1987.2873 2.769111e+01 2985.0215544 #> 1904 190.3 1988.5537 2.763752e+01 2983.8087496 #> 1905 190.4 1989.8219 2.758577e+01 2982.5922976 #> 1906 190.5 1991.0917 2.753587e+01 2981.3723878 #> 1907 190.6 1992.3630 2.748781e+01 2980.1492087 #> 1908 190.7 1993.6355 2.744158e+01 2978.9229480 #> 1909 190.8 1994.9090 2.739717e+01 2977.6937919 #> 1910 190.9 1996.1835 2.735457e+01 2976.4619262 #> 1911 191.0 1997.4587 2.731378e+01 2975.2275353 #> 1912 191.1 1998.7344 2.727479e+01 2973.9908030 #> 1913 191.2 2000.0105 2.723759e+01 2972.7519117 #> 1914 191.3 2001.2868 2.720218e+01 2971.5110434 #> 1915 191.4 2002.5631 2.716855e+01 2970.2683787 #> 1916 191.5 2003.8392 2.713669e+01 2969.0240977 #> 1917 191.6 2005.1150 2.710660e+01 2967.7783794 #> 1918 191.7 2006.3903 2.707827e+01 2966.5314018 #> 1919 191.8 2007.6650 2.705170e+01 2965.2833424 #> 1920 191.9 2008.9388 2.702687e+01 2964.0343774 #> 1921 192.0 2010.2115 2.700378e+01 2962.7846826 #> 1922 192.1 2011.4831 2.698243e+01 2961.5344326 #> 1923 192.2 2012.7534 2.696281e+01 2960.2838013 #> 1924 192.3 2014.0221 2.694491e+01 2959.0329620 #> 1925 192.4 2015.2892 2.692874e+01 2957.7820869 #> 1926 192.5 2016.5544 2.691427e+01 2956.5313477 #> 1927 192.6 2017.8176 2.690152e+01 2955.2809150 #> 1928 192.7 2019.0786 2.689047e+01 2954.0309590 #> 1929 192.8 2020.3372 2.688111e+01 2952.7816489 #> 1930 192.9 2021.5934 2.687345e+01 2951.5331533 #> 1931 193.0 2022.8469 2.686748e+01 2950.2856399 #> 1932 193.1 2024.0975 2.686319e+01 2949.0392760 #> 1933 193.2 2025.3452 2.686057e+01 2947.7942280 #> 1934 193.3 2026.5897 2.685964e+01 2946.5506615 #> 1935 193.4 2027.8309 2.686037e+01 2945.3087416 #> 1936 193.5 2029.0686 2.686276e+01 2944.0686328 #> 1937 193.6 2030.3027 2.686682e+01 2942.8304987 #> 1938 193.7 2031.5330 2.687253e+01 2941.5945025 #> 1939 193.8 2032.7593 2.687989e+01 2940.3608066 #> 1940 193.9 2033.9815 2.688890e+01 2939.1295728 #> 1941 194.0 2035.1995 2.689956e+01 2937.9009623 #> 1942 194.1 2036.4130 2.691186e+01 2936.6751357 #> 1943 194.2 2037.6220 2.692579e+01 2935.4522531 #> 1944 194.3 2038.8262 2.694135e+01 2934.2324738 #> 1945 194.4 2040.0255 2.695855e+01 2933.0159566 #> 1946 194.5 2041.2198 2.697737e+01 2931.8028598 #> 1947 194.6 2042.4089 2.699781e+01 2930.5933412 #> 1948 194.7 2043.5926 2.701986e+01 2929.3875577 #> 1949 194.8 2044.7708 2.704354e+01 2928.1856660 #> 1950 194.9 2045.9434 2.706882e+01 2926.9878222 #> 1951 195.0 2047.1101 2.709572e+01 2925.7941816 #> 1952 195.1 2048.2709 2.712422e+01 2924.6048993 #> 1953 195.2 2049.4256 2.715432e+01 2923.4201297 #> 1954 195.3 2050.5740 2.718601e+01 2922.2400266 #> 1955 195.4 2051.7159 2.721931e+01 2921.0647435 #> 1956 195.5 2052.8514 2.725420e+01 2919.8944333 #> 1957 195.6 2053.9801 2.729067e+01 2918.7292483 #> 1958 195.7 2055.1019 2.732874e+01 2917.5693403 #> 1959 195.8 2056.2168 2.736838e+01 2916.4148607 #> 1960 195.9 2057.3244 2.740961e+01 2915.2659604 #> 1961 196.0 2058.4248 2.745241e+01 2914.1227898 #> 1962 196.1 2059.5177 2.749679e+01 2912.9854986 #> 1963 196.2 2060.6030 2.754274e+01 2911.8542363 #> 1964 196.3 2061.6806 2.759026e+01 2910.7291517 #> 1965 196.4 2062.7503 2.763934e+01 2909.6103932 #> 1966 196.5 2063.8119 2.768999e+01 2908.4981087 #> 1967 196.6 2064.8654 2.774220e+01 2907.3924455 #> 1968 196.7 2065.9105 2.779596e+01 2906.2935507 #> 1969 196.8 2066.9472 2.785128e+01 2905.2015705 #> 1970 196.9 2067.9752 2.790815e+01 2904.1166509 #> 1971 197.0 2068.9945 2.796656e+01 2903.0389372 #> 1972 197.1 2070.0049 2.802652e+01 2901.9685745 #> 1973 197.2 2071.0063 2.808802e+01 2900.9057069 #> 1974 197.3 2071.9985 2.815106e+01 2899.8504785 #> 1975 197.4 2072.9813 2.821564e+01 2898.8030325 #> 1976 197.5 2073.9547 2.828174e+01 2897.7635119 #> 1977 197.6 2074.9186 2.834937e+01 2896.7320588 #> 1978 197.7 2075.8727 2.841853e+01 2895.7088150 #> 1979 197.8 2076.8169 2.848921e+01 2894.6939218 #> 1980 197.9 2077.7511 2.856140e+01 2893.6875198 #> 1981 198.0 2078.6751 2.863511e+01 2892.6897491 #> 1982 198.1 2079.5889 2.871033e+01 2891.7007493 #> 1983 198.2 2080.4923 2.878705e+01 2890.7206592 #> 1984 198.3 2081.3851 2.886528e+01 2889.7496173 #> 1985 198.4 2082.2672 2.894500e+01 2888.7877613 #> 1986 198.5 2083.1386 2.902621e+01 2887.8352283 #> 1987 198.6 2083.9989 2.910891e+01 2886.8921549 #> 1988 198.7 2084.8482 2.919310e+01 2885.9586768 #> 1989 198.8 2085.6863 2.927876e+01 2885.0349294 #> 1990 198.9 2086.5131 2.936590e+01 2884.1210471 #> 1991 199.0 2087.3283 2.945450e+01 2883.2171639 #> 1992 199.1 2088.1320 2.954457e+01 2882.3234129 #> 1993 199.2 2088.9240 2.963610e+01 2881.4399265 #> 1994 199.3 2089.7041 2.972908e+01 2880.5668364 #> 1995 199.4 2090.4722 2.982350e+01 2879.7042736 #> 1996 199.5 2091.2283 2.991936e+01 2878.8523683 #> 1997 199.6 2091.9721 3.001666e+01 2878.0112499 #> 1998 199.7 2092.7036 3.011539e+01 2877.1810468 #> 1999 199.8 2093.4226 3.021553e+01 2876.3618870 #> 2000 199.9 2094.1290 3.031709e+01 2875.5538972 #> 2001 200.0 2094.8227 3.042006e+01 2874.7572034 #> 2002 200.1 2095.5036 3.052442e+01 2873.9719308 #> 2003 200.2 2096.1716 3.063017e+01 2873.1982035 #> 2004 200.3 2096.8265 3.073731e+01 2872.4361448 #> 2005 200.4 2097.4683 3.084582e+01 2871.6858768 #> 2006 200.5 2098.0968 3.095570e+01 2870.9475209 #> 2007 200.6 2098.7119 3.106694e+01 2870.2211972 #> 2008 200.7 2099.3135 3.117952e+01 2869.5070249 #> 2009 200.8 2099.9014 3.129344e+01 2868.8051221 #> 2010 200.9 2100.4757 3.140870e+01 2868.1156056 #> 2011 201.0 2101.0361 3.152527e+01 2867.4385913 #> 2012 201.1 2101.5827 3.164314e+01 2866.7741938 #> 2013 201.2 2102.1152 3.176232e+01 2866.1225265 #> 2014 201.3 2102.6335 3.188278e+01 2865.4837015 #> 2015 201.4 2103.1377 3.200452e+01 2864.8578298 #> 2016 201.5 2103.6275 3.212752e+01 2864.2450208 #> 2017 201.6 2104.1028 3.225177e+01 2863.6453828 #> 2018 201.7 2104.5637 3.237726e+01 2863.0590227 #> 2019 201.8 2105.0100 3.250398e+01 2862.4860458 #> 2020 201.9 2105.4415 3.263190e+01 2861.9265562 #> 2021 202.0 2105.8583 3.276103e+01 2861.3806562 #> 2022 202.1 2106.2602 3.289134e+01 2860.8484469 #> 2023 202.2 2106.6472 3.302282e+01 2860.3300276 #> 2024 202.3 2107.0191 3.315545e+01 2859.8254960 #> 2025 202.4 2107.3758 3.328922e+01 2859.3349483 #> 2026 202.5 2107.7174 3.342412e+01 2858.8584788 #> 2027 202.6 2108.0437 3.356012e+01 2858.3961804 #> 2028 202.7 2108.3546 3.369722e+01 2857.9481438 #> 2029 202.8 2108.6502 3.383538e+01 2857.5144582 #> 2030 202.9 2108.9302 3.397461e+01 2857.0952108 #> 2031 203.0 2109.1946 3.411486e+01 2856.6904869 #> 2032 203.1 2109.4435 3.425614e+01 2856.3003699 #> 2033 203.2 2109.6766 3.439842e+01 2855.9249411 #> 2034 203.3 2109.8940 3.454168e+01 2855.5642801 #> 2035 203.4 2110.0956 3.468589e+01 2855.2184639 #> 2036 203.5 2110.2814 3.483105e+01 2854.8875677 #> 2037 203.6 2110.4512 3.497713e+01 2854.5716645 #> 2038 203.7 2110.6051 3.512410e+01 2854.2708250 #> 2039 203.8 2110.7429 3.527194e+01 2853.9851176 #> 2040 203.9 2110.8647 3.542064e+01 2853.7146086 #> 2041 204.0 2110.9705 3.557017e+01 2853.4593616 #> 2042 204.1 2111.0601 3.572051e+01 2853.2194380 #> 2043 204.2 2111.1335 3.587162e+01 2852.9948968 #> 2044 204.3 2111.1907 3.602349e+01 2852.7857943 #> 2045 204.4 2111.2317 3.617610e+01 2852.5921843 #> 2046 204.5 2111.2565 3.632941e+01 2852.4141180 #> 2047 204.6 2111.2650 3.648340e+01 2852.2516440 #> 2048 204.7 2111.2571 3.663804e+01 2852.1048082 #> 2049 204.8 2111.2330 3.679331e+01 2851.9736537 #> 2050 204.9 2111.1926 3.694918e+01 2851.8582206 #> 2051 205.0 2111.1358 3.710562e+01 2851.7585464 #> 2052 205.1 2111.0627 3.726260e+01 2851.6746658 #> 2053 205.2 2110.9733 3.742009e+01 2851.6066103 #> 2054 205.3 2110.8675 3.757807e+01 2851.5544087 #> 2055 205.4 2110.7454 3.773649e+01 2851.5180864 #> 2056 205.5 2110.6070 3.789534e+01 2851.4976658 #> 2057 205.6 2110.4523 3.805458e+01 2851.4931663 #> 2058 205.7 2110.2812 3.821417e+01 2851.5046041 #> 2059 205.8 2110.0939 3.837409e+01 2851.5319920 #> 2060 205.9 2109.8904 3.853430e+01 2851.5753397 #> 2061 206.0 2109.6706 3.869478e+01 2851.6346535 #> 2062 206.1 2109.4346 3.885547e+01 2851.7099363 #> 2063 206.2 2109.1824 3.901636e+01 2851.8011878 #> 2064 206.3 2108.9142 3.917741e+01 2851.9084040 #> 2065 206.4 2108.6298 3.933858e+01 2852.0315776 #> 2066 206.5 2108.3295 3.949983e+01 2852.1706976 #> 2067 206.6 2108.0131 3.966113e+01 2852.3257494 #> 2068 206.7 2107.6808 3.982245e+01 2852.4967151 #> 2069 206.8 2107.3327 3.998374e+01 2852.6835729 #> 2070 206.9 2106.9687 4.014497e+01 2852.8862972 #> 2071 207.0 2106.5890 4.030610e+01 2853.1048590 #> 2072 207.1 2106.1937 4.046709e+01 2853.3392253 #> 2073 207.2 2105.7827 4.062791e+01 2853.5893594 #> 2074 207.3 2105.3563 4.078851e+01 2853.8552208 #> 2075 207.4 2104.9144 4.094886e+01 2854.1367651 #> 2076 207.5 2104.4571 4.110891e+01 2854.4339440 #> 2077 207.6 2103.9847 4.126863e+01 2854.7467053 #> 2078 207.7 2103.4970 4.142798e+01 2855.0749930 #> 2079 207.8 2102.9943 4.158691e+01 2855.4187469 #> 2080 207.9 2102.4767 4.174539e+01 2855.7779031 #> 2081 208.0 2101.9442 4.190337e+01 2856.1523934 #> 2082 208.1 2101.3970 4.206081e+01 2856.5421457 #> 2083 208.2 2100.8352 4.221768e+01 2856.9470840 #> 2084 208.3 2100.2590 4.237392e+01 2857.3671281 #> 2085 208.4 2099.6683 4.252950e+01 2857.8021937 #> 2086 208.5 2099.0634 4.268438e+01 2858.2521926 #> 2087 208.6 2098.4445 4.283851e+01 2858.7170322 #> 2088 208.7 2097.8115 4.299185e+01 2859.1966161 #> 2089 208.8 2097.1648 4.314436e+01 2859.6908437 #> 2090 208.9 2096.5044 4.329600e+01 2860.1996103 #> 2091 209.0 2095.8305 4.344672e+01 2860.7228070 #> 2092 209.1 2095.1432 4.359648e+01 2861.2603208 #> 2093 209.2 2094.4427 4.374523e+01 2861.8120349 #> 2094 209.3 2093.7292 4.389295e+01 2862.3778279 #> 2095 209.4 2093.0028 4.403958e+01 2862.9575748 #> 2096 209.5 2092.2638 4.418508e+01 2863.5511462 #> 2097 209.6 2091.5122 4.432940e+01 2864.1584087 #> 2098 209.7 2090.7483 4.447252e+01 2864.7792249 #> 2099 209.8 2089.9722 4.461438e+01 2865.4134534 #> 2100 209.9 2089.1841 4.475494e+01 2866.0609488 #> 2101 210.0 2088.3843 4.489416e+01 2866.7215616 #> 2102 210.1 2087.5729 4.503200e+01 2867.3951383 #> 2103 210.2 2086.7501 4.516842e+01 2868.0815216 #> 2104 210.3 2085.9161 4.530337e+01 2868.7805504 #> 2105 210.4 2085.0711 4.543683e+01 2869.4920594 #> 2106 210.5 2084.2154 4.556873e+01 2870.2158798 #> 2107 210.6 2083.3491 4.569905e+01 2870.9518387 #> 2108 210.7 2082.4725 4.582775e+01 2871.6997597 #> 2109 210.8 2081.5858 4.595479e+01 2872.4594626 #> 2110 210.9 2080.6891 4.608012e+01 2873.2307634 #> 2111 211.0 2079.7828 4.620371e+01 2874.0134746 #> 2112 211.1 2078.8671 4.632552e+01 2874.8074053 #> 2113 211.2 2077.9421 4.644552e+01 2875.6123607 #> 2114 211.3 2077.0082 4.656366e+01 2876.4281429 #> 2115 211.4 2076.0655 4.667992e+01 2877.2545505 #> 2116 211.5 2075.1144 4.679424e+01 2878.0913785 #> 2117 211.6 2074.1550 4.690661e+01 2878.9384190 #> 2118 211.7 2073.1876 4.701698e+01 2879.7954608 #> 2119 211.8 2072.2124 4.712533e+01 2880.6622893 #> 2120 211.9 2071.2297 4.723161e+01 2881.5386872 #> 2121 212.0 2070.2398 4.733580e+01 2882.4244340 #> 2122 212.1 2069.2428 4.743786e+01 2883.3193062 #> 2123 212.2 2068.2392 4.753776e+01 2884.2230776 #> 2124 212.3 2067.2290 4.763547e+01 2885.1355193 #> 2125 212.4 2066.2126 4.773097e+01 2886.0563996 #> 2126 212.5 2065.1903 4.782422e+01 2886.9854841 #> 2127 212.6 2064.1623 4.791520e+01 2887.9225360 #> 2128 212.7 2063.1288 4.800387e+01 2888.8673162 #> 2129 212.8 2062.0902 4.809022e+01 2889.8195831 #> 2130 212.9 2061.0467 4.817422e+01 2890.7790929 #> 2131 213.0 2059.9986 4.825583e+01 2891.7455996 #> 2132 213.1 2058.9461 4.833505e+01 2892.7188551 #> 2133 213.2 2057.8895 4.841185e+01 2893.6986096 #> 2134 213.3 2056.8292 4.848620e+01 2894.6846112 #> 2135 213.4 2055.7653 4.855809e+01 2895.6766063 #> 2136 213.5 2054.6982 4.862749e+01 2896.6743395 #> 2137 213.6 2053.6281 4.869439e+01 2897.6775541 #> 2138 213.7 2052.5552 4.875877e+01 2898.6859918 #> 2139 213.8 2051.4800 4.882062e+01 2899.6993929 #> 2140 213.9 2050.4026 4.887991e+01 2900.7174965 #> 2141 214.0 2049.3233 4.893663e+01 2901.7400406 #> 2142 214.1 2048.2425 4.899078e+01 2902.7667621 #> 2143 214.2 2047.1603 4.904233e+01 2903.7973970 #> 2144 214.3 2046.0770 4.909128e+01 2904.8316804 #> 2145 214.4 2044.9930 4.913762e+01 2905.8693468 #> 2146 214.5 2043.9085 4.918133e+01 2906.9101300 #> 2147 214.6 2042.8238 4.922241e+01 2907.9537634 #> 2148 214.7 2041.7392 4.926086e+01 2908.9999800 #> 2149 214.8 2040.6548 4.929666e+01 2910.0485124 #> 2150 214.9 2039.5711 4.932981e+01 2911.0990932 #> 2151 215.0 2038.4882 4.936032e+01 2912.1514549 #> 2152 215.1 2037.4065 4.938816e+01 2913.2053299 #> 2153 215.2 2036.3262 4.941336e+01 2914.2604510 #> 2154 215.3 2035.2476 4.943589e+01 2915.3165511 #> 2155 215.4 2034.1709 4.945578e+01 2916.3733636 #> 2156 215.5 2033.0964 4.947301e+01 2917.4306224 #> 2157 215.6 2032.0243 4.948759e+01 2918.4880618 #> 2158 215.7 2030.9551 4.949952e+01 2919.5454171 #> 2159 215.8 2029.8888 4.950882e+01 2920.6024242 #> 2160 215.9 2028.8257 4.951548e+01 2921.6588200 #> 2161 216.0 2027.7661 4.951951e+01 2922.7143424 #> 2162 216.1 2026.7103 4.952093e+01 2923.7687305 #> 2163 216.2 2025.6585 4.951973e+01 2924.8217246 #> 2164 216.3 2024.6110 4.951594e+01 2925.8730662 #> 2165 216.4 2023.5679 4.950956e+01 2926.9224984 #> 2166 216.5 2022.5296 4.950060e+01 2927.9697658 #> 2167 216.6 2021.4963 4.948908e+01 2929.0146146 #> 2168 216.7 2020.4682 4.947502e+01 2930.0567928 #> 2169 216.8 2019.4455 4.945842e+01 2931.0960501 #> 2170 216.9 2018.4286 4.943931e+01 2932.1321382 #> 2171 217.0 2017.4175 4.941770e+01 2933.1648109 #> 2172 217.1 2016.4126 4.939361e+01 2934.1938238 #> 2173 217.2 2015.4140 4.936706e+01 2935.2189351 #> 2174 217.3 2014.4220 4.933806e+01 2936.2399049 #> 2175 217.4 2013.4369 4.930665e+01 2937.2564958 #> 2176 217.5 2012.4587 4.927284e+01 2938.2684729 #> 2177 217.6 2011.4877 4.923665e+01 2939.2756038 #> 2178 217.7 2010.5242 4.919811e+01 2940.2776585 #> 2179 217.8 2009.5683 4.915724e+01 2941.2744101 #> 2180 217.9 2008.6203 4.911407e+01 2942.2656340 #> 2181 218.0 2007.6803 4.906862e+01 2943.2511086 #> 2182 218.1 2006.7485 4.902091e+01 2944.2306152 #> 2183 218.2 2005.8251 4.897098e+01 2945.2039382 #> 2184 218.3 2004.9103 4.891886e+01 2946.1708646 #> 2185 218.4 2004.0043 4.886456e+01 2947.1311850 #> 2186 218.5 2003.1072 4.880813e+01 2948.0846928 #> 2187 218.6 2002.2192 4.874959e+01 2949.0311846 #> 2188 218.7 2001.3406 4.868896e+01 2949.9704605 #> 2189 218.8 2000.4714 4.862629e+01 2950.9023237 #> 2190 218.9 1999.6118 4.856159e+01 2951.8265808 #> 2191 219.0 1998.7620 4.849491e+01 2952.7430419 #> 2192 219.1 1997.9222 4.842628e+01 2953.6515205 #> 2193 219.2 1997.0924 4.835573e+01 2954.5518335 #> 2194 219.3 1996.2729 4.828328e+01 2955.4438015 #> 2195 219.4 1995.4638 4.820899e+01 2956.3272486 #> 2196 219.5 1994.6651 4.813287e+01 2957.2020025 #> 2197 219.6 1993.8771 4.805496e+01 2958.0678947 #> 2198 219.7 1993.0999 4.797531e+01 2958.9247601 #> 2199 219.8 1992.3336 4.789394e+01 2959.7724376 #> 2200 219.9 1991.5783 4.781089e+01 2960.6107696 #> 2201 220.0 1990.8342 4.772619e+01 2961.4396024 #> 2202 220.1 1990.1013 4.763988e+01 2962.2587861 #> 2203 220.2 1989.3798 4.755201e+01 2963.0681745 #> 2204 220.3 1988.6698 4.746259e+01 2963.8676254 #> 2205 220.4 1987.9713 4.737168e+01 2964.6570001 #> 2206 220.5 1987.2845 4.727930e+01 2965.4361642 #> 2207 220.6 1986.6095 4.718549e+01 2966.2049867 #> 2208 220.7 1985.9464 4.709030e+01 2966.9633408 #> 2209 220.8 1985.2951 4.699375e+01 2967.7111036 #> 2210 220.9 1984.6560 4.689589e+01 2968.4481557 #> 2211 221.0 1984.0289 4.679675e+01 2969.1743819 #> 2212 221.1 1983.4140 4.669637e+01 2969.8896709 #> 2213 221.2 1982.8113 4.659479e+01 2970.5939151 #> 2214 221.3 1982.2209 4.649204e+01 2971.2870109 #> 2215 221.4 1981.6430 4.638816e+01 2971.9688585 #> 2216 221.5 1981.0774 4.628320e+01 2972.6393620 #> 2217 221.6 1980.5244 4.617717e+01 2973.2984293 #> 2218 221.7 1979.9839 4.607014e+01 2973.9459723 #> 2219 221.8 1979.4560 4.596212e+01 2974.5819065 #> 2220 221.9 1978.9407 4.585316e+01 2975.2061513 #> 2221 222.0 1978.4381 4.574329e+01 2975.8186301 #> 2222 222.1 1977.9482 4.563256e+01 2976.4192697 #> 2223 222.2 1977.4710 4.552099e+01 2977.0080010 #> 2224 222.3 1977.0066 4.540863e+01 2977.5847584 #> 2225 222.4 1976.5550 4.529550e+01 2978.1494801 #> 2226 222.5 1976.1162 4.518165e+01 2978.7021081 #> 2227 222.6 1975.6903 4.506712e+01 2979.2425878 #> 2228 222.7 1975.2772 4.495193e+01 2979.7708686 #> 2229 222.8 1974.8770 4.483612e+01 2980.2869031 #> 2230 222.9 1974.4896 4.471974e+01 2980.7906478 #> 2231 223.0 1974.1151 4.460280e+01 2981.2820627 #> 2232 223.1 1973.7535 4.448535e+01 2981.7611111 #> 2233 223.2 1973.4048 4.436743e+01 2982.2277601 #> 2234 223.3 1973.0690 4.424906e+01 2982.6819800 #> 2235 223.4 1972.7460 4.413027e+01 2983.1237445 #> 2236 223.5 1972.4359 4.401111e+01 2983.5530310 #> 2237 223.6 1972.1386 4.389161e+01 2983.9698199 #> 2238 223.7 1971.8541 4.377179e+01 2984.3740951 #> 2239 223.8 1971.5825 4.365170e+01 2984.7658436 #> 2240 223.9 1971.3236 4.353135e+01 2985.1450559 #> 2241 224.0 1971.0775 4.341079e+01 2985.5117253 #> 2242 224.1 1970.8441 4.329004e+01 2985.8658486 #> 2243 224.2 1970.6234 4.316914e+01 2986.2074256 #> 2244 224.3 1970.4154 4.304812e+01 2986.5364591 #> 2245 224.4 1970.2200 4.292700e+01 2986.8529549 #> 2246 224.5 1970.0373 4.280582e+01 2987.1569219 #> 2247 224.6 1969.8670 4.268461e+01 2987.4483718 #> 2248 224.7 1969.7093 4.256338e+01 2987.7273194 #> 2249 224.8 1969.5640 4.244219e+01 2987.9937821 #> 2250 224.9 1969.4312 4.232104e+01 2988.2477802 #> 2251 225.0 1969.3107 4.219997e+01 2988.4893370 #> 2252 225.1 1969.2025 4.207900e+01 2988.7184780 #> 2253 225.2 1969.1066 4.195817e+01 2988.9352318 #> 2254 225.3 1969.0229 4.183750e+01 2989.1396295 #> 2255 225.4 1968.9513 4.171701e+01 2989.3317047 #> 2256 225.5 1968.8918 4.159674e+01 2989.5114937 #> 2257 225.6 1968.8443 4.147670e+01 2989.6790351 #> 2258 225.7 1968.8087 4.135692e+01 2989.8343701 #> 2259 225.8 1968.7850 4.123742e+01 2989.9775421 #> 2260 225.9 1968.7732 4.111824e+01 2990.1085970 #> 2261 226.0 1968.7730 4.099938e+01 2990.2275831 #> 2262 226.1 1968.7846 4.088088e+01 2990.3345507 #> 2263 226.2 1968.8077 4.076276e+01 2990.4295525 #> 2264 226.3 1968.8423 4.064504e+01 2990.5126433 #> 2265 226.4 1968.8884 4.052774e+01 2990.5838800 #> 2266 226.5 1968.9458 4.041089e+01 2990.6433217 #> 2267 226.6 1969.0145 4.029450e+01 2990.6910294 #> 2268 226.7 1969.0943 4.017859e+01 2990.7270661 #> 2269 226.8 1969.1853 4.006318e+01 2990.7514967 #> 2270 226.9 1969.2873 3.994831e+01 2990.7643883 #> 2271 227.0 1969.4002 3.983397e+01 2990.7658094 #> 2272 227.1 1969.5240 3.972020e+01 2990.7558306 #> 2273 227.2 1969.6585 3.960700e+01 2990.7345243 #> 2274 227.3 1969.8036 3.949441e+01 2990.7019644 #> 2275 227.4 1969.9593 3.938243e+01 2990.6582266 #> 2276 227.5 1970.1255 3.927109e+01 2990.6033884 #> 2277 227.6 1970.3021 3.916040e+01 2990.5375286 #> 2278 227.7 1970.4889 3.905037e+01 2990.4607278 #> 2279 227.8 1970.6859 3.894102e+01 2990.3730679 #> 2280 227.9 1970.8930 3.883238e+01 2990.2746325 #> 2281 228.0 1971.1100 3.872445e+01 2990.1655065 #> 2282 228.1 1971.3370 3.861724e+01 2990.0457762 #> 2283 228.2 1971.5737 3.851078e+01 2989.9155292 #> 2284 228.3 1971.8201 3.840508e+01 2989.7748547 #> 2285 228.4 1972.0760 3.830015e+01 2989.6238428 #> 2286 228.5 1972.3414 3.819600e+01 2989.4625850 #> 2287 228.6 1972.6162 3.809265e+01 2989.2911741 #> 2288 228.7 1972.9002 3.799011e+01 2989.1097039 #> 2289 228.8 1973.1933 3.788840e+01 2988.9182695 #> 2290 228.9 1973.4955 3.778752e+01 2988.7169669 #> 2291 229.0 1973.8066 3.768748e+01 2988.5058933 #> 2292 229.1 1974.1265 3.758830e+01 2988.2851469 #> 2293 229.2 1974.4552 3.748999e+01 2988.0548268 #> 2294 229.3 1974.7924 3.739257e+01 2987.8150332 #> 2295 229.4 1975.1381 3.729603e+01 2987.5658671 #> 2296 229.5 1975.4922 3.720039e+01 2987.3074305 #> 2297 229.6 1975.8545 3.710566e+01 2987.0398260 #> 2298 229.7 1976.2250 3.701185e+01 2986.7631574 #> 2299 229.8 1976.6035 3.691897e+01 2986.4775291 #> 2300 229.9 1976.9899 3.682702e+01 2986.1830462 #> 2301 230.0 1977.3842 3.673603e+01 2985.8798146 #> 2302 230.1 1977.7861 3.664598e+01 2985.5679410 #> 2303 230.2 1978.1956 3.655690e+01 2985.2475326 #> 2304 230.3 1978.6125 3.646879e+01 2984.9186973 #> 2305 230.4 1979.0368 3.638166e+01 2984.5815437 #> 2306 230.5 1979.4683 3.629551e+01 2984.2361809 #> 2307 230.6 1979.9069 3.621035e+01 2983.8827186 #> 2308 230.7 1980.3525 3.612619e+01 2983.5212670 #> 2309 230.8 1980.8050 3.604304e+01 2983.1519370 #> 2310 230.9 1981.2643 3.596089e+01 2982.7748397 #> 2311 231.0 1981.7301 3.587977e+01 2982.3900867 #> 2312 231.1 1982.2025 3.579966e+01 2981.9977903 #> 2313 231.2 1982.6813 3.572059e+01 2981.5980630 #> 2314 231.3 1983.1664 3.564255e+01 2981.1910177 #> 2315 231.4 1983.6577 3.556554e+01 2980.7767677 #> 2316 231.5 1984.1550 3.548959e+01 2980.3554265 #> 2317 231.6 1984.6582 3.541467e+01 2979.9271083 #> 2318 231.7 1985.1673 3.534082e+01 2979.4919272 #> 2319 231.8 1985.6820 3.526801e+01 2979.0499978 #> 2320 231.9 1986.2023 3.519627e+01 2978.6014348 #> 2321 232.0 1986.7281 3.512559e+01 2978.1463534 #> 2322 232.1 1987.2591 3.505599e+01 2977.6848687 #> 2323 232.2 1987.7955 3.498745e+01 2977.2170961 #> 2324 232.3 1988.3369 3.491999e+01 2976.7431515 #> 2325 232.4 1988.8832 3.485360e+01 2976.2631504 #> 2326 232.5 1989.4345 3.478830e+01 2975.7772089 #> 2327 232.6 1989.9905 3.472408e+01 2975.2854430 #> 2328 232.7 1990.5511 3.466095e+01 2974.7879689 #> 2329 232.8 1991.1162 3.459890e+01 2974.2849029 #> 2330 232.9 1991.6857 3.453795e+01 2973.7763612 #> 2331 233.0 1992.2595 3.447808e+01 2973.2624604 #> 2332 233.1 1992.8374 3.441932e+01 2972.7433168 #> 2333 233.2 1993.4193 3.436165e+01 2972.2190470 #> 2334 233.3 1994.0052 3.430507e+01 2971.6897673 #> 2335 233.4 1994.5948 3.424960e+01 2971.1555944 #> 2336 233.5 1995.1881 3.419523e+01 2970.6166447 #> 2337 233.6 1995.7850 3.414196e+01 2970.0730346 #> 2338 233.7 1996.3853 3.408979e+01 2969.5248806 #> 2339 233.8 1996.9890 3.403873e+01 2968.9722990 #> 2340 233.9 1997.5958 3.398877e+01 2968.4154062 #> 2341 234.0 1998.2058 3.393992e+01 2967.8543182 #> 2342 234.1 1998.8187 3.389218e+01 2967.2891513 #> 2343 234.2 1999.4344 3.384554e+01 2966.7200215 #> 2344 234.3 2000.0529 3.380001e+01 2966.1470446 #> 2345 234.4 2000.6741 3.375559e+01 2965.5703365 #> 2346 234.5 2001.2977 3.371227e+01 2964.9900128 #> 2347 234.6 2001.9237 3.367006e+01 2964.4061890 #> 2348 234.7 2002.5521 3.362896e+01 2963.8189804 #> 2349 234.8 2003.1825 3.358897e+01 2963.2285021 #> 2350 234.9 2003.8151 3.355008e+01 2962.6348693 #> 2351 235.0 2004.4495 3.351230e+01 2962.0381966 #> 2352 235.1 2005.0858 3.347562e+01 2961.4385988 #> 2353 235.2 2005.7238 3.344006e+01 2960.8361901 #> 2354 235.3 2006.3633 3.340559e+01 2960.2310847 #> 2355 235.4 2007.0044 3.337223e+01 2959.6233967 #> 2356 235.5 2007.6468 3.333997e+01 2959.0132397 #> 2357 235.6 2008.2905 3.330882e+01 2958.4007272 #> 2358 235.7 2008.9353 3.327876e+01 2957.7859724 #> 2359 235.8 2009.5811 3.324981e+01 2957.1690883 #> 2360 235.9 2010.2279 3.322195e+01 2956.5501876 #> 2361 236.0 2010.8754 3.319520e+01 2955.9293828 #> 2362 236.1 2011.5237 3.316953e+01 2955.3067859 #> 2363 236.2 2012.1725 3.314497e+01 2954.6825088 #> 2364 236.3 2012.8218 3.312149e+01 2954.0566632 #> 2365 236.4 2013.4715 3.309911e+01 2953.4293601 #> 2366 236.5 2014.1215 3.307782e+01 2952.8007106 #> 2367 236.6 2014.7716 3.305761e+01 2952.1708253 #> 2368 236.7 2015.4217 3.303849e+01 2951.5398144 #> 2369 236.8 2016.0718 3.302046e+01 2950.9077880 #> 2370 236.9 2016.7216 3.300351e+01 2950.2748557 #> 2371 237.0 2017.3712 3.298764e+01 2949.6411267 #> 2372 237.1 2018.0204 3.297285e+01 2949.0067099 #> 2373 237.2 2018.6692 3.295913e+01 2948.3717140 #> 2374 237.3 2019.3173 3.294649e+01 2947.7362470 #> 2375 237.4 2019.9647 3.293491e+01 2947.1004169 #> 2376 237.5 2020.6113 3.292441e+01 2946.4643311 #> 2377 237.6 2021.2569 3.291498e+01 2945.8280967 #> 2378 237.7 2021.9016 3.290660e+01 2945.1918202 #> 2379 237.8 2022.5451 3.289929e+01 2944.5556081 #> 2380 237.9 2023.1874 3.289304e+01 2943.9195661 #> 2381 238.0 2023.8284 3.288785e+01 2943.2837998 #> 2382 238.1 2024.4679 3.288371e+01 2942.6484142 #> 2383 238.2 2025.1059 3.288062e+01 2942.0135139 #> 2384 238.3 2025.7422 3.287858e+01 2941.3792031 #> 2385 238.4 2026.3768 3.287758e+01 2940.7455857 #> 2386 238.5 2027.0096 3.287762e+01 2940.1127648 #> 2387 238.6 2027.6405 3.287870e+01 2939.4808435 #> 2388 238.7 2028.2693 3.288082e+01 2938.8499242 #> 2389 238.8 2028.8959 3.288397e+01 2938.2201087 #> 2390 238.9 2029.5203 3.288815e+01 2937.5914988 #> 2391 239.0 2030.1424 3.289336e+01 2936.9641954 #> 2392 239.1 2030.7621 3.289958e+01 2936.3382991 #> 2393 239.2 2031.3793 3.290683e+01 2935.7139099 #> 2394 239.3 2031.9938 3.291509e+01 2935.0911276 #> 2395 239.4 2032.6056 3.292436e+01 2934.4700512 #> 2396 239.5 2033.2146 3.293464e+01 2933.8507794 #> 2397 239.6 2033.8207 3.294593e+01 2933.2334103 #> 2398 239.7 2034.4237 3.295821e+01 2932.6180414 #> 2399 239.8 2035.0237 3.297149e+01 2932.0047699 #> 2400 239.9 2035.6205 3.298576e+01 2931.3936923 #> 2401 240.0 2036.2141 3.300102e+01 2930.7849047 #> 2402 240.1 2036.8042 3.301727e+01 2930.1785026 #> 2403 240.2 2037.3909 3.303449e+01 2929.5745809 #> 2404 240.3 2037.9741 3.305270e+01 2928.9732341 #> 2405 240.4 2038.5536 3.307187e+01 2928.3745559 #> 2406 240.5 2039.1294 3.309201e+01 2927.7786397 #> 2407 240.6 2039.7013 3.311311e+01 2927.1855782 #> 2408 240.7 2040.2694 3.313517e+01 2926.5954636 #> 2409 240.8 2040.8334 3.315818e+01 2926.0083874 #> 2410 240.9 2041.3934 3.318214e+01 2925.4244405 #> 2411 241.0 2041.9492 3.320704e+01 2924.8437134 #> 2412 241.1 2042.5008 3.323289e+01 2924.2662958 #> 2413 241.2 2043.0481 3.325966e+01 2923.6922768 #> 2414 241.3 2043.5909 3.328737e+01 2923.1217451 #> 2415 241.4 2044.1292 3.331600e+01 2922.5547885 #> 2416 241.5 2044.6630 3.334555e+01 2921.9914942 #> 2417 241.6 2045.1920 3.337601e+01 2921.4319490 #> 2418 241.7 2045.7164 3.340737e+01 2920.8762387 #> 2419 241.8 2046.2359 3.343964e+01 2920.3244487 #> 2420 241.9 2046.7505 3.347281e+01 2919.7766637 #> 2421 242.0 2047.2602 3.350687e+01 2919.2329677 #> 2422 242.1 2047.7647 3.354181e+01 2918.6934438 #> 2423 242.2 2048.2642 3.357763e+01 2918.1581748 #> 2424 242.3 2048.7584 3.361433e+01 2917.6272425 #> 2425 242.4 2049.2474 3.365189e+01 2917.1007281 #> 2426 242.5 2049.7310 3.369031e+01 2916.5787121 #> 2427 242.6 2050.2091 3.372959e+01 2916.0612743 #> 2428 242.7 2050.6818 3.376972e+01 2915.5484936 #> 2429 242.8 2051.1489 3.381069e+01 2915.0404484 #> 2430 242.9 2051.6103 3.385249e+01 2914.5372161 #> 2431 243.0 2052.0660 3.389513e+01 2914.0388734 #> 2432 243.1 2052.5159 3.393858e+01 2913.5454965 #> 2433 243.2 2052.9600 3.398285e+01 2913.0571604 #> 2434 243.3 2053.3981 3.402793e+01 2912.5739395 #> 2435 243.4 2053.8303 3.407381e+01 2912.0959076 #> 2436 243.5 2054.2564 3.412049e+01 2911.6231372 #> 2437 243.6 2054.6764 3.416795e+01 2911.1557006 #> 2438 243.7 2055.0901 3.421619e+01 2910.6936686 #> 2439 243.8 2055.4977 3.426520e+01 2910.2371117 #> 2440 243.9 2055.8989 3.431498e+01 2909.7860994 #> 2441 244.0 2056.2938 3.436551e+01 2909.3407000 #> 2442 244.1 2056.6822 3.441679e+01 2908.9009815 #> 2443 244.2 2057.0642 3.446881e+01 2908.4670106 #> 2444 244.3 2057.4396 3.452156e+01 2908.0388531 #> 2445 244.4 2057.8084 3.457504e+01 2907.6165743 #> 2446 244.5 2058.1705 3.462923e+01 2907.2002380 #> 2447 244.6 2058.5260 3.468413e+01 2906.7899076 #> 2448 244.7 2058.8746 3.473973e+01 2906.3856452 #> 2449 244.8 2059.2165 3.479602e+01 2905.9875122 #> 2450 244.9 2059.5514 3.485299e+01 2905.5955689 #> 2451 245.0 2059.8795 3.491063e+01 2905.2098745 #> 2452 245.1 2060.2006 3.496893e+01 2904.8304876 #> 2453 245.2 2060.5146 3.502789e+01 2904.4574654 #> 2454 245.3 2060.8216 3.508749e+01 2904.0908644 #> 2455 245.4 2061.1215 3.514773e+01 2903.7307398 #> 2456 245.5 2061.4143 3.520859e+01 2903.3771460 #> 2457 245.6 2061.6998 3.527007e+01 2903.0301363 #> 2458 245.7 2061.9781 3.533215e+01 2902.6897628 #> 2459 245.8 2062.2491 3.539483e+01 2902.3560768 #> 2460 245.9 2062.5128 3.545809e+01 2902.0291283 #> 2461 246.0 2062.7691 3.552193e+01 2901.7089663 #> 2462 246.1 2063.0180 3.558633e+01 2901.3956387 #> 2463 246.2 2063.2595 3.565128e+01 2901.0891922 #> 2464 246.3 2063.4935 3.571678e+01 2900.7896726 #> 2465 246.4 2063.7201 3.578281e+01 2900.4971242 #> 2466 246.5 2063.9390 3.584937e+01 2900.2115906 #> 2467 246.6 2064.1505 3.591643e+01 2899.9331138 #> 2468 246.7 2064.3543 3.598399e+01 2899.6617349 #> 2469 246.8 2064.5505 3.605204e+01 2899.3974937 #> 2470 246.9 2064.7390 3.612057e+01 2899.1404290 #> 2471 247.0 2064.9199 3.618956e+01 2898.8905780 #> 2472 247.1 2065.0930 3.625900e+01 2898.6479771 #> 2473 247.2 2065.2585 3.632888e+01 2898.4126612 #> 2474 247.3 2065.4161 3.639919e+01 2898.1846641 #> 2475 247.4 2065.5661 3.646992e+01 2897.9640182 #> 2476 247.5 2065.7082 3.654106e+01 2897.7507548 #> 2477 247.6 2065.8425 3.661258e+01 2897.5449038 #> 2478 247.7 2065.9690 3.668448e+01 2897.3464939 #> 2479 247.8 2066.0877 3.675675e+01 2897.1555523 #> 2480 247.9 2066.1985 3.682938e+01 2896.9721052 #> 2481 248.0 2066.3015 3.690234e+01 2896.7961772 #> 2482 248.1 2066.3966 3.697564e+01 2896.6277917 #> 2483 248.2 2066.4838 3.704924e+01 2896.4669708 #> 2484 248.3 2066.5631 3.712315e+01 2896.3137351 #> 2485 248.4 2066.6345 3.719735e+01 2896.1681040 #> 2486 248.5 2066.6981 3.727182e+01 2896.0300953 #> 2487 248.6 2066.7537 3.734655e+01 2895.8997257 #> 2488 248.7 2066.8015 3.742152e+01 2895.7770103 #> 2489 248.8 2066.8413 3.749673e+01 2895.6619627 #> 2490 248.9 2066.8732 3.757216e+01 2895.5545954 #> 2491 249.0 2066.8973 3.764779e+01 2895.4549193 #> 2492 249.1 2066.9134 3.772362e+01 2895.3629438 #> 2493 249.2 2066.9217 3.779961e+01 2895.2786769 #> 2494 249.3 2066.9221 3.787577e+01 2895.2021253 #> 2495 249.4 2066.9146 3.795208e+01 2895.1332939 #> 2496 249.5 2066.8993 3.802852e+01 2895.0721866 #> 2497 249.6 2066.8761 3.810507e+01 2895.0188054 #> 2498 249.7 2066.8451 3.818173e+01 2894.9731510 #> 2499 249.8 2066.8063 3.825848e+01 2894.9352226 #> 2500 249.9 2066.7597 3.833529e+01 2894.9050179 #> 2501 250.0 2066.7053 3.841217e+01 2894.8825331 #>  #> attr(,\"class\") #> [1] \"sir_basic_model\""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_static_model.html","id":null,"dir":"Reference","previous_headings":"","what":"SIR static model (age-heterogeneous, endemic equilibrium) — sir_static_model","title":"SIR static model (age-heterogeneous, endemic equilibrium) — sir_static_model","text":"Refers section 3.2.2.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_static_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SIR static model (age-heterogeneous, endemic equilibrium) — sir_static_model","text":"","code":"sir_static_model(a, state, parameters)"},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_static_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SIR static model (age-heterogeneous, endemic equilibrium) — sir_static_model","text":"age sequence. state initial state system. parameters model's parameter.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_static_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SIR static model (age-heterogeneous, endemic equilibrium) — sir_static_model","text":"list class sir_static_model following items parameters list parameters used fitting model output matrix proportion compartment time","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_static_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SIR static model (age-heterogeneous, endemic equilibrium) — sir_static_model","text":"state: - s: proportion susceptible - : proportion infected - r: proportion recovered parameters: - lambda: natural death rate - nu: recovery rate","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_static_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SIR static model (age-heterogeneous, endemic equilibrium) — sir_static_model","text":"","code":"state <- c(s=0.99,i=0.01,r=0) parameters <- c(   lambda = 0.05,   nu=1/(14/365) # 2 weeks to recover ) ages<-seq(0, 90, by=0.01) model = sir_static_model(ages, state, parameters) model #> $parameters #>   lambda       nu  #>  0.05000 26.07143  #>  #> $output #>       time          s            i           r #> 1     0.00 0.99000000 1.000000e-02 0.000000000 #> 2     0.01 0.98950512 8.139822e-03 0.002355054 #> 3     0.02 0.98901049 6.708036e-03 0.004281469 #> 4     0.03 0.98851611 5.603950e-03 0.005879937 #> 5     0.04 0.98802198 4.752794e-03 0.007225227 #> 6     0.05 0.98752809 4.096558e-03 0.008375351 #> 7     0.06 0.98703445 3.590872e-03 0.009374678 #> 8     0.07 0.98654106 3.201102e-03 0.010257841 #> 9     0.08 0.98604791 2.900525e-03 0.011051565 #> 10    0.09 0.98555501 2.668724e-03 0.011776268 #> 11    0.10 0.98506235 2.489909e-03 0.012447737 #> 12    0.11 0.98456995 2.351916e-03 0.013078137 #> 13    0.12 0.98407778 2.245378e-03 0.013676837 #> 14    0.13 0.98358587 2.163076e-03 0.014251056 #> 15    0.14 0.98309420 2.099446e-03 0.014806355 #> 16    0.15 0.98260277 2.050204e-03 0.015347022 #> 17    0.16 0.98211160 2.012047e-03 0.015876357 #> 18    0.17 0.98162066 1.982432e-03 0.016396906 #> 19    0.18 0.98112997 1.959397e-03 0.016910628 #> 20    0.19 0.98063953 1.941438e-03 0.017419030 #> 21    0.20 0.98014934 1.927389e-03 0.017923276 #> 22    0.21 0.97965938 1.916345e-03 0.018424271 #> 23    0.22 0.97916968 1.907612e-03 0.018922712 #> 24    0.23 0.97868021 1.900660e-03 0.019419127 #> 25    0.24 0.97819100 1.895079e-03 0.019913926 #> 26    0.25 0.97770202 1.890571e-03 0.020407406 #> 27    0.26 0.97721329 1.886886e-03 0.020899820 #> 28    0.27 0.97672481 1.883834e-03 0.021391357 #> 29    0.28 0.97623657 1.881269e-03 0.021882162 #> 30    0.29 0.97574857 1.879079e-03 0.022372348 #> 31    0.30 0.97526082 1.877178e-03 0.022862001 #> 32    0.31 0.97477331 1.875500e-03 0.023351189 #> 33    0.32 0.97428605 1.873993e-03 0.023839961 #> 34    0.33 0.97379903 1.872617e-03 0.024328357 #> 35    0.34 0.97331225 1.871344e-03 0.024816409 #> 36    0.35 0.97282571 1.870148e-03 0.025304138 #> 37    0.36 0.97233942 1.869014e-03 0.025791565 #> 38    0.37 0.97185337 1.867925e-03 0.026278701 #> 39    0.38 0.97136757 1.866873e-03 0.026765558 #> 40    0.39 0.97088201 1.865849e-03 0.027252145 #> 41    0.40 0.97039669 1.864846e-03 0.027738468 #> 42    0.41 0.96991161 1.863860e-03 0.028224531 #> 43    0.42 0.96942677 1.862886e-03 0.028710339 #> 44    0.43 0.96894218 1.861923e-03 0.029195894 #> 45    0.44 0.96845783 1.860968e-03 0.029681200 #> 46    0.45 0.96797372 1.860019e-03 0.030166257 #> 47    0.46 0.96748986 1.859074e-03 0.030651067 #> 48    0.47 0.96700623 1.858134e-03 0.031135632 #> 49    0.48 0.96652285 1.857196e-03 0.031619952 #> 50    0.49 0.96603971 1.856261e-03 0.032104028 #> 51    0.50 0.96555681 1.855328e-03 0.032587860 #> 52    0.51 0.96507415 1.854396e-03 0.033071449 #> 53    0.52 0.96459174 1.853466e-03 0.033554795 #> 54    0.53 0.96410956 1.852538e-03 0.034037900 #> 55    0.54 0.96362763 1.851610e-03 0.034520762 #> 56    0.55 0.96314594 1.850683e-03 0.035003382 #> 57    0.56 0.96266448 1.849757e-03 0.035485761 #> 58    0.57 0.96218327 1.848831e-03 0.035967898 #> 59    0.58 0.96170230 1.847906e-03 0.036449794 #> 60    0.59 0.96122157 1.846982e-03 0.036931450 #> 61    0.60 0.96074108 1.846059e-03 0.037412864 #> 62    0.61 0.96026083 1.845136e-03 0.037894037 #> 63    0.62 0.95978082 1.844213e-03 0.038374970 #> 64    0.63 0.95930105 1.843291e-03 0.038855663 #> 65    0.64 0.95882152 1.842369e-03 0.039336115 #> 66    0.65 0.95834222 1.841448e-03 0.039816327 #> 67    0.66 0.95786317 1.840528e-03 0.040296299 #> 68    0.67 0.95738436 1.839608e-03 0.040776031 #> 69    0.68 0.95690579 1.838688e-03 0.041255523 #> 70    0.69 0.95642746 1.837769e-03 0.041734776 #> 71    0.70 0.95594936 1.836850e-03 0.042213789 #> 72    0.71 0.95547151 1.835932e-03 0.042692562 #> 73    0.72 0.95499389 1.835014e-03 0.043171096 #> 74    0.73 0.95451651 1.834097e-03 0.043649391 #> 75    0.74 0.95403937 1.833180e-03 0.044127447 #> 76    0.75 0.95356247 1.832264e-03 0.044605264 #> 77    0.76 0.95308581 1.831348e-03 0.045082842 #> 78    0.77 0.95260939 1.830433e-03 0.045560181 #> 79    0.78 0.95213320 1.829518e-03 0.046037282 #> 80    0.79 0.95165725 1.828603e-03 0.046514144 #> 81    0.80 0.95118154 1.827689e-03 0.046990768 #> 82    0.81 0.95070607 1.826775e-03 0.047467153 #> 83    0.82 0.95023084 1.825862e-03 0.047943300 #> 84    0.83 0.94975584 1.824949e-03 0.048419210 #> 85    0.84 0.94928108 1.824037e-03 0.048894881 #> 86    0.85 0.94880656 1.823125e-03 0.049370315 #> 87    0.86 0.94833227 1.822214e-03 0.049845511 #> 88    0.87 0.94785823 1.821303e-03 0.050320470 #> 89    0.88 0.94738442 1.820393e-03 0.050795191 #> 90    0.89 0.94691084 1.819483e-03 0.051269675 #> 91    0.90 0.94643751 1.818573e-03 0.051743921 #> 92    0.91 0.94596441 1.817664e-03 0.052217931 #> 93    0.92 0.94549154 1.816756e-03 0.052691703 #> 94    0.93 0.94501891 1.815847e-03 0.053165239 #> 95    0.94 0.94454652 1.814940e-03 0.053638538 #> 96    0.95 0.94407437 1.814033e-03 0.054111601 #> 97    0.96 0.94360245 1.813126e-03 0.054584427 #> 98    0.97 0.94313076 1.812219e-03 0.055057016 #> 99    0.98 0.94265932 1.811314e-03 0.055529370 #> 100   0.99 0.94218810 1.810408e-03 0.056001487 #> 101   1.00 0.94171713 1.809503e-03 0.056473368 #> 102   1.01 0.94124639 1.808599e-03 0.056945014 #> 103   1.02 0.94077588 1.807695e-03 0.057416423 #> 104   1.03 0.94030561 1.806791e-03 0.057887597 #> 105   1.04 0.93983558 1.805888e-03 0.058358536 #> 106   1.05 0.93936578 1.804985e-03 0.058829239 #> 107   1.06 0.93889621 1.804083e-03 0.059299707 #> 108   1.07 0.93842688 1.803181e-03 0.059769939 #> 109   1.08 0.93795778 1.802280e-03 0.060239937 #> 110   1.09 0.93748892 1.801379e-03 0.060709699 #> 111   1.10 0.93702029 1.800478e-03 0.061179227 #> 112   1.11 0.93655190 1.799578e-03 0.061648520 #> 113   1.12 0.93608374 1.798679e-03 0.062117579 #> 114   1.13 0.93561582 1.797780e-03 0.062586403 #> 115   1.14 0.93514813 1.796881e-03 0.063054992 #> 116   1.15 0.93468067 1.795983e-03 0.063523348 #> 117   1.16 0.93421345 1.795085e-03 0.063991469 #> 118   1.17 0.93374646 1.794188e-03 0.064459356 #> 119   1.18 0.93327970 1.793291e-03 0.064927010 #> 120   1.19 0.93281318 1.792394e-03 0.065394430 #> 121   1.20 0.93234689 1.791498e-03 0.065861615 #> 122   1.21 0.93188083 1.790603e-03 0.066328568 #> 123   1.22 0.93141501 1.789708e-03 0.066795287 #> 124   1.23 0.93094941 1.788813e-03 0.067261773 #> 125   1.24 0.93048406 1.787919e-03 0.067728025 #> 126   1.25 0.93001893 1.787025e-03 0.068194045 #> 127   1.26 0.92955404 1.786132e-03 0.068659831 #> 128   1.27 0.92908938 1.785239e-03 0.069125385 #> 129   1.28 0.92862495 1.784347e-03 0.069590706 #> 130   1.29 0.92816075 1.783455e-03 0.070055794 #> 131   1.30 0.92769679 1.782563e-03 0.070520650 #> 132   1.31 0.92723305 1.781672e-03 0.070985274 #> 133   1.32 0.92676955 1.780781e-03 0.071449665 #> 134   1.33 0.92630628 1.779891e-03 0.071913824 #> 135   1.34 0.92584325 1.779002e-03 0.072377751 #> 136   1.35 0.92538044 1.778112e-03 0.072841447 #> 137   1.36 0.92491787 1.777223e-03 0.073304910 #> 138   1.37 0.92445552 1.776335e-03 0.073768142 #> 139   1.38 0.92399341 1.775447e-03 0.074231142 #> 140   1.39 0.92353153 1.774560e-03 0.074693911 #> 141   1.40 0.92306988 1.773673e-03 0.075156448 #> 142   1.41 0.92260846 1.772786e-03 0.075618754 #> 143   1.42 0.92214727 1.771900e-03 0.076080829 #> 144   1.43 0.92168631 1.771014e-03 0.076542674 #> 145   1.44 0.92122558 1.770129e-03 0.077004287 #> 146   1.45 0.92076509 1.769244e-03 0.077465669 #> 147   1.46 0.92030482 1.768360e-03 0.077926821 #> 148   1.47 0.91984478 1.767476e-03 0.078387743 #> 149   1.48 0.91938497 1.766592e-03 0.078848434 #> 150   1.49 0.91892540 1.765709e-03 0.079308894 #> 151   1.50 0.91846605 1.764826e-03 0.079769125 #> 152   1.51 0.91800693 1.763944e-03 0.080229125 #> 153   1.52 0.91754804 1.763062e-03 0.080688896 #> 154   1.53 0.91708938 1.762181e-03 0.081148436 #> 155   1.54 0.91663095 1.761300e-03 0.081607747 #> 156   1.55 0.91617275 1.760420e-03 0.082066829 #> 157   1.56 0.91571478 1.759540e-03 0.082525681 #> 158   1.57 0.91525704 1.758660e-03 0.082984303 #> 159   1.58 0.91479952 1.757781e-03 0.083442696 #> 160   1.59 0.91434224 1.756902e-03 0.083900860 #> 161   1.60 0.91388518 1.756024e-03 0.084358796 #> 162   1.61 0.91342835 1.755146e-03 0.084816502 #> 163   1.62 0.91297175 1.754269e-03 0.085273979 #> 164   1.63 0.91251538 1.753392e-03 0.085731228 #> 165   1.64 0.91205924 1.752516e-03 0.086188248 #> 166   1.65 0.91160332 1.751640e-03 0.086645040 #> 167   1.66 0.91114763 1.750764e-03 0.087101603 #> 168   1.67 0.91069217 1.749889e-03 0.087557938 #> 169   1.68 0.91023694 1.749014e-03 0.088014045 #> 170   1.69 0.90978194 1.748140e-03 0.088469924 #> 171   1.70 0.90932716 1.747266e-03 0.088925575 #> 172   1.71 0.90887261 1.746393e-03 0.089380999 #> 173   1.72 0.90841829 1.745520e-03 0.089836194 #> 174   1.73 0.90796419 1.744647e-03 0.090291163 #> 175   1.74 0.90751032 1.743775e-03 0.090745903 #> 176   1.75 0.90705668 1.742903e-03 0.091200417 #> 177   1.76 0.90660326 1.742032e-03 0.091654703 #> 178   1.77 0.90615008 1.741161e-03 0.092108762 #> 179   1.78 0.90569711 1.740291e-03 0.092562594 #> 180   1.79 0.90524438 1.739421e-03 0.093016200 #> 181   1.80 0.90479187 1.738551e-03 0.093469578 #> 182   1.81 0.90433959 1.737682e-03 0.093922730 #> 183   1.82 0.90388753 1.736814e-03 0.094375655 #> 184   1.83 0.90343570 1.735946e-03 0.094828354 #> 185   1.84 0.90298409 1.735078e-03 0.095280827 #> 186   1.85 0.90253272 1.734211e-03 0.095733074 #> 187   1.86 0.90208156 1.733344e-03 0.096185094 #> 188   1.87 0.90163063 1.732477e-03 0.096636889 #> 189   1.88 0.90117993 1.731611e-03 0.097088457 #> 190   1.89 0.90072945 1.730746e-03 0.097539800 #> 191   1.90 0.90027920 1.729880e-03 0.097990918 #> 192   1.91 0.89982917 1.729016e-03 0.098441809 #> 193   1.92 0.89937937 1.728151e-03 0.098892476 #> 194   1.93 0.89892980 1.727288e-03 0.099342917 #> 195   1.94 0.89848044 1.726424e-03 0.099793133 #> 196   1.95 0.89803131 1.725561e-03 0.100243124 #> 197   1.96 0.89758241 1.724699e-03 0.100692890 #> 198   1.97 0.89713373 1.723836e-03 0.101142431 #> 199   1.98 0.89668528 1.722975e-03 0.101591748 #> 200   1.99 0.89623705 1.722113e-03 0.102040840 #> 201   2.00 0.89578904 1.721253e-03 0.102489707 #> 202   2.01 0.89534126 1.720392e-03 0.102938350 #> 203   2.02 0.89489370 1.719532e-03 0.103386769 #> 204   2.03 0.89444636 1.718673e-03 0.103834963 #> 205   2.04 0.89399925 1.717814e-03 0.104282934 #> 206   2.05 0.89355236 1.716955e-03 0.104730680 #> 207   2.06 0.89310570 1.716097e-03 0.105178203 #> 208   2.07 0.89265926 1.715239e-03 0.105625502 #> 209   2.08 0.89221304 1.714381e-03 0.106072578 #> 210   2.09 0.89176705 1.713524e-03 0.106519430 #> 211   2.10 0.89132127 1.712668e-03 0.106966058 #> 212   2.11 0.89087572 1.711812e-03 0.107412464 #> 213   2.12 0.89043040 1.710956e-03 0.107858646 #> 214   2.13 0.88998529 1.710101e-03 0.108304605 #> 215   2.14 0.88954041 1.709246e-03 0.108750341 #> 216   2.15 0.88909575 1.708392e-03 0.109195855 #> 217   2.16 0.88865132 1.707538e-03 0.109641146 #> 218   2.17 0.88820710 1.706684e-03 0.110086214 #> 219   2.18 0.88776311 1.705831e-03 0.110531060 #> 220   2.19 0.88731934 1.704978e-03 0.110975683 #> 221   2.20 0.88687579 1.704126e-03 0.111420084 #> 222   2.21 0.88643246 1.703274e-03 0.111864263 #> 223   2.22 0.88598936 1.702423e-03 0.112308220 #> 224   2.23 0.88554647 1.701572e-03 0.112751955 #> 225   2.24 0.88510381 1.700721e-03 0.113195468 #> 226   2.25 0.88466137 1.699871e-03 0.113638759 #> 227   2.26 0.88421915 1.699021e-03 0.114081829 #> 228   2.27 0.88377715 1.698172e-03 0.114524678 #> 229   2.28 0.88333537 1.697323e-03 0.114967305 #> 230   2.29 0.88289382 1.696475e-03 0.115409710 #> 231   2.30 0.88245248 1.695627e-03 0.115851895 #> 232   2.31 0.88201136 1.694779e-03 0.116293858 #> 233   2.32 0.88157047 1.693932e-03 0.116735601 #> 234   2.33 0.88112979 1.693085e-03 0.117177123 #> 235   2.34 0.88068934 1.692239e-03 0.117618424 #> 236   2.35 0.88024910 1.691393e-03 0.118059505 #> 237   2.36 0.87980909 1.690547e-03 0.118500365 #> 238   2.37 0.87936929 1.689702e-03 0.118941004 #> 239   2.38 0.87892972 1.688858e-03 0.119381424 #> 240   2.39 0.87849036 1.688013e-03 0.119821623 #> 241   2.40 0.87805123 1.687170e-03 0.120261602 #> 242   2.41 0.87761231 1.686326e-03 0.120701361 #> 243   2.42 0.87717362 1.685483e-03 0.121140901 #> 244   2.43 0.87673514 1.684641e-03 0.121580221 #> 245   2.44 0.87629688 1.683799e-03 0.122019321 #> 246   2.45 0.87585884 1.682957e-03 0.122458201 #> 247   2.46 0.87542102 1.682116e-03 0.122896863 #> 248   2.47 0.87498342 1.681275e-03 0.123335305 #> 249   2.48 0.87454604 1.680434e-03 0.123773527 #> 250   2.49 0.87410887 1.679594e-03 0.124211531 #> 251   2.50 0.87367193 1.678755e-03 0.124649316 #> 252   2.51 0.87323520 1.677916e-03 0.125086882 #> 253   2.52 0.87279869 1.677077e-03 0.125524229 #> 254   2.53 0.87236240 1.676238e-03 0.125961358 #> 255   2.54 0.87192633 1.675401e-03 0.126398268 #> 256   2.55 0.87149048 1.674563e-03 0.126834960 #> 257   2.56 0.87105484 1.673726e-03 0.127271433 #> 258   2.57 0.87061942 1.672889e-03 0.127707688 #> 259   2.58 0.87018422 1.672053e-03 0.128143725 #> 260   2.59 0.86974924 1.671217e-03 0.128579545 #> 261   2.60 0.86931447 1.670382e-03 0.129015146 #> 262   2.61 0.86887992 1.669547e-03 0.129450529 #> 263   2.62 0.86844559 1.668712e-03 0.129885695 #> 264   2.63 0.86801148 1.667878e-03 0.130320644 #> 265   2.64 0.86757758 1.667044e-03 0.130755375 #> 266   2.65 0.86714390 1.666211e-03 0.131189889 #> 267   2.66 0.86671044 1.665378e-03 0.131624185 #> 268   2.67 0.86627719 1.664546e-03 0.132058264 #> 269   2.68 0.86584416 1.663714e-03 0.132492127 #> 270   2.69 0.86541135 1.662882e-03 0.132925772 #> 271   2.70 0.86497875 1.662051e-03 0.133359201 #> 272   2.71 0.86454637 1.661220e-03 0.133792413 #> 273   2.72 0.86411420 1.660390e-03 0.134225409 #> 274   2.73 0.86368225 1.659560e-03 0.134658188 #> 275   2.74 0.86325052 1.658730e-03 0.135090751 #> 276   2.75 0.86281900 1.657901e-03 0.135523097 #> 277   2.76 0.86238770 1.657072e-03 0.135955228 #> 278   2.77 0.86195661 1.656244e-03 0.136387142 #> 279   2.78 0.86152574 1.655416e-03 0.136818841 #> 280   2.79 0.86109509 1.654588e-03 0.137250323 #> 281   2.80 0.86066465 1.653761e-03 0.137681590 #> 282   2.81 0.86023442 1.652935e-03 0.138112642 #> 283   2.82 0.85980441 1.652108e-03 0.138543478 #> 284   2.83 0.85937462 1.651283e-03 0.138974098 #> 285   2.84 0.85894504 1.650457e-03 0.139404504 #> 286   2.85 0.85851567 1.649632e-03 0.139834694 #> 287   2.86 0.85808652 1.648807e-03 0.140264669 #> 288   2.87 0.85765759 1.647983e-03 0.140694429 #> 289   2.88 0.85722887 1.647160e-03 0.141123975 #> 290   2.89 0.85680036 1.646336e-03 0.141553305 #> 291   2.90 0.85637207 1.645513e-03 0.141982422 #> 292   2.91 0.85594399 1.644691e-03 0.142411323 #> 293   2.92 0.85551612 1.643868e-03 0.142840010 #> 294   2.93 0.85508847 1.643047e-03 0.143268483 #> 295   2.94 0.85466103 1.642225e-03 0.143696742 #> 296   2.95 0.85423381 1.641405e-03 0.144124786 #> 297   2.96 0.85380680 1.640584e-03 0.144552617 #> 298   2.97 0.85338000 1.639764e-03 0.144980234 #> 299   2.98 0.85295342 1.638944e-03 0.145407637 #> 300   2.99 0.85252705 1.638125e-03 0.145834826 #> 301   3.00 0.85210089 1.637306e-03 0.146261802 #> 302   3.01 0.85167495 1.636488e-03 0.146688565 #> 303   3.02 0.85124922 1.635670e-03 0.147115114 #> 304   3.03 0.85082370 1.634852e-03 0.147541450 #> 305   3.04 0.85039839 1.634035e-03 0.147967572 #> 306   3.05 0.84997330 1.633218e-03 0.148393482 #> 307   3.06 0.84954842 1.632402e-03 0.148819179 #> 308   3.07 0.84912375 1.631586e-03 0.149244663 #> 309   3.08 0.84869930 1.630770e-03 0.149669934 #> 310   3.09 0.84827505 1.629955e-03 0.150094993 #> 311   3.10 0.84785102 1.629140e-03 0.150519839 #> 312   3.11 0.84742720 1.628326e-03 0.150944473 #> 313   3.12 0.84700359 1.627512e-03 0.151368895 #> 314   3.13 0.84658020 1.626698e-03 0.151793104 #> 315   3.14 0.84615701 1.625885e-03 0.152217102 #> 316   3.15 0.84573404 1.625072e-03 0.152640887 #> 317   3.16 0.84531128 1.624260e-03 0.153064461 #> 318   3.17 0.84488873 1.623448e-03 0.153487823 #> 319   3.18 0.84446639 1.622636e-03 0.153910973 #> 320   3.19 0.84404426 1.621825e-03 0.154333912 #> 321   3.20 0.84362235 1.621015e-03 0.154756640 #> 322   3.21 0.84320064 1.620204e-03 0.155179156 #> 323   3.22 0.84277915 1.619394e-03 0.155601460 #> 324   3.23 0.84235786 1.618585e-03 0.156023554 #> 325   3.24 0.84193679 1.617776e-03 0.156445437 #> 326   3.25 0.84151592 1.616967e-03 0.156867109 #> 327   3.26 0.84109527 1.616159e-03 0.157288570 #> 328   3.27 0.84067483 1.615351e-03 0.157709820 #> 329   3.28 0.84025460 1.614544e-03 0.158130860 #> 330   3.29 0.83983457 1.613736e-03 0.158551690 #> 331   3.30 0.83941476 1.612930e-03 0.158972309 #> 332   3.31 0.83899516 1.612124e-03 0.159392717 #> 333   3.32 0.83857577 1.611318e-03 0.159812916 #> 334   3.33 0.83815658 1.610512e-03 0.160232904 #> 335   3.34 0.83773761 1.609707e-03 0.160652683 #> 336   3.35 0.83731885 1.608903e-03 0.161072252 #> 337   3.36 0.83690029 1.608098e-03 0.161491611 #> 338   3.37 0.83648195 1.607294e-03 0.161910760 #> 339   3.38 0.83606381 1.606491e-03 0.162329700 #> 340   3.39 0.83564588 1.605688e-03 0.162748431 #> 341   3.40 0.83522816 1.604885e-03 0.163166952 #> 342   3.41 0.83481065 1.604083e-03 0.163585264 #> 343   3.42 0.83439335 1.603281e-03 0.164003367 #> 344   3.43 0.83397626 1.602480e-03 0.164421261 #> 345   3.44 0.83355938 1.601679e-03 0.164838945 #> 346   3.45 0.83314270 1.600878e-03 0.165256422 #> 347   3.46 0.83272623 1.600078e-03 0.165673689 #> 348   3.47 0.83230997 1.599278e-03 0.166090748 #> 349   3.48 0.83189392 1.598479e-03 0.166507598 #> 350   3.49 0.83147808 1.597680e-03 0.166924241 #> 351   3.50 0.83106244 1.596881e-03 0.167340674 #> 352   3.51 0.83064702 1.596083e-03 0.167756900 #> 353   3.52 0.83023180 1.595285e-03 0.168172917 #> 354   3.53 0.82981679 1.594487e-03 0.168588727 #> 355   3.54 0.82940198 1.593690e-03 0.169004329 #> 356   3.55 0.82898738 1.592894e-03 0.169419723 #> 357   3.56 0.82857299 1.592097e-03 0.169834909 #> 358   3.57 0.82815881 1.591302e-03 0.170249888 #> 359   3.58 0.82774483 1.590506e-03 0.170664659 #> 360   3.59 0.82733107 1.589711e-03 0.171079223 #> 361   3.60 0.82691750 1.588916e-03 0.171493580 #> 362   3.61 0.82650415 1.588122e-03 0.171907730 #> 363   3.62 0.82609100 1.587328e-03 0.172321672 #> 364   3.63 0.82567806 1.586535e-03 0.172735408 #> 365   3.64 0.82526532 1.585742e-03 0.173148937 #> 366   3.65 0.82485279 1.584949e-03 0.173562259 #> 367   3.66 0.82444047 1.584157e-03 0.173975375 #> 368   3.67 0.82402835 1.583365e-03 0.174388284 #> 369   3.68 0.82361644 1.582573e-03 0.174800987 #> 370   3.69 0.82320473 1.581782e-03 0.175213483 #> 371   3.70 0.82279323 1.580992e-03 0.175625773 #> 372   3.71 0.82238194 1.580201e-03 0.176037857 #> 373   3.72 0.82197085 1.579411e-03 0.176449736 #> 374   3.73 0.82155997 1.578622e-03 0.176861408 #> 375   3.74 0.82114929 1.577833e-03 0.177272874 #> 376   3.75 0.82073882 1.577044e-03 0.177684135 #> 377   3.76 0.82032855 1.576256e-03 0.178095190 #> 378   3.77 0.81991849 1.575468e-03 0.178506040 #> 379   3.78 0.81950864 1.574680e-03 0.178916684 #> 380   3.79 0.81909898 1.573893e-03 0.179327123 #> 381   3.80 0.81868954 1.573106e-03 0.179737357 #> 382   3.81 0.81828029 1.572320e-03 0.180147386 #> 383   3.82 0.81787126 1.571534e-03 0.180557210 #> 384   3.83 0.81746242 1.570749e-03 0.180966829 #> 385   3.84 0.81705379 1.569963e-03 0.181376243 #> 386   3.85 0.81664537 1.569179e-03 0.181785453 #> 387   3.86 0.81623715 1.568394e-03 0.182194458 #> 388   3.87 0.81582913 1.567610e-03 0.182603258 #> 389   3.88 0.81542132 1.566827e-03 0.183011855 #> 390   3.89 0.81501371 1.566043e-03 0.183420246 #> 391   3.90 0.81460631 1.565261e-03 0.183828434 #> 392   3.91 0.81419910 1.564478e-03 0.184236418 #> 393   3.92 0.81379211 1.563696e-03 0.184644198 #> 394   3.93 0.81338531 1.562914e-03 0.185051774 #> 395   3.94 0.81297872 1.562133e-03 0.185459146 #> 396   3.95 0.81257233 1.561352e-03 0.185866315 #> 397   3.96 0.81216615 1.560572e-03 0.186273280 #> 398   3.97 0.81176017 1.559792e-03 0.186680042 #> 399   3.98 0.81135439 1.559012e-03 0.187086600 #> 400   3.99 0.81094881 1.558233e-03 0.187492955 #> 401   4.00 0.81054344 1.557454e-03 0.187899107 #> 402   4.01 0.81013827 1.556675e-03 0.188305056 #> 403   4.02 0.80973330 1.555897e-03 0.188710802 #> 404   4.03 0.80932854 1.555119e-03 0.189116345 #> 405   4.04 0.80892397 1.554342e-03 0.189521686 #> 406   4.05 0.80851961 1.553565e-03 0.189926824 #> 407   4.06 0.80811545 1.552788e-03 0.190331759 #> 408   4.07 0.80771150 1.552012e-03 0.190736492 #> 409   4.08 0.80730774 1.551236e-03 0.191141023 #> 410   4.09 0.80690419 1.550461e-03 0.191545351 #> 411   4.10 0.80650084 1.549686e-03 0.191949477 #> 412   4.11 0.80609769 1.548911e-03 0.192353402 #> 413   4.12 0.80569474 1.548137e-03 0.192757124 #> 414   4.13 0.80529199 1.547363e-03 0.193160645 #> 415   4.14 0.80488945 1.546590e-03 0.193563963 #> 416   4.15 0.80448710 1.545817e-03 0.193967081 #> 417   4.16 0.80408496 1.545044e-03 0.194369996 #> 418   4.17 0.80368302 1.544272e-03 0.194772711 #> 419   4.18 0.80328128 1.543500e-03 0.195175224 #> 420   4.19 0.80287974 1.542728e-03 0.195577536 #> 421   4.20 0.80247840 1.541957e-03 0.195979646 #> 422   4.21 0.80207726 1.541186e-03 0.196381556 #> 423   4.22 0.80167632 1.540416e-03 0.196783265 #> 424   4.23 0.80127558 1.539646e-03 0.197184773 #> 425   4.24 0.80087504 1.538876e-03 0.197586080 #> 426   4.25 0.80047471 1.538107e-03 0.197987187 #> 427   4.26 0.80007457 1.537338e-03 0.198388093 #> 428   4.27 0.79967463 1.536569e-03 0.198788799 #> 429   4.28 0.79927489 1.535801e-03 0.199189304 #> 430   4.29 0.79887536 1.535034e-03 0.199589610 #> 431   4.30 0.79847602 1.534266e-03 0.199989715 #> 432   4.31 0.79807688 1.533499e-03 0.200389620 #> 433   4.32 0.79767794 1.532733e-03 0.200789325 #> 434   4.33 0.79727920 1.531967e-03 0.201188831 #> 435   4.34 0.79688066 1.531201e-03 0.201588136 #> 436   4.35 0.79648232 1.530435e-03 0.201987243 #> 437   4.36 0.79608418 1.529670e-03 0.202386149 #> 438   4.37 0.79568624 1.528906e-03 0.202784857 #> 439   4.38 0.79528849 1.528141e-03 0.203183365 #> 440   4.39 0.79489095 1.527378e-03 0.203581673 #> 441   4.40 0.79449360 1.526614e-03 0.203979783 #> 442   4.41 0.79409646 1.525851e-03 0.204377693 #> 443   4.42 0.79369951 1.525088e-03 0.204775405 #> 444   4.43 0.79330276 1.524326e-03 0.205172918 #> 445   4.44 0.79290620 1.523564e-03 0.205570232 #> 446   4.45 0.79250985 1.522802e-03 0.205967348 #> 447   4.46 0.79211369 1.522041e-03 0.206364265 #> 448   4.47 0.79171774 1.521280e-03 0.206760984 #> 449   4.48 0.79132198 1.520520e-03 0.207157504 #> 450   4.49 0.79092641 1.519760e-03 0.207553826 #> 451   4.50 0.79053105 1.519000e-03 0.207949950 #> 452   4.51 0.79013588 1.518241e-03 0.208345876 #> 453   4.52 0.78974091 1.517482e-03 0.208741605 #> 454   4.53 0.78934614 1.516723e-03 0.209137135 #> 455   4.54 0.78895157 1.515965e-03 0.209532468 #> 456   4.55 0.78855719 1.515207e-03 0.209927603 #> 457   4.56 0.78816301 1.514450e-03 0.210322540 #> 458   4.57 0.78776903 1.513693e-03 0.210717280 #> 459   4.58 0.78737524 1.512936e-03 0.211111823 #> 460   4.59 0.78698165 1.512180e-03 0.211506168 #> 461   4.60 0.78658826 1.511424e-03 0.211900317 #> 462   4.61 0.78619506 1.510669e-03 0.212294268 #> 463   4.62 0.78580206 1.509913e-03 0.212688022 #> 464   4.63 0.78540926 1.509159e-03 0.213081580 #> 465   4.64 0.78501665 1.508404e-03 0.213474941 #> 466   4.65 0.78462424 1.507650e-03 0.213868105 #> 467   4.66 0.78423203 1.506897e-03 0.214261073 #> 468   4.67 0.78384001 1.506143e-03 0.214653844 #> 469   4.68 0.78344819 1.505390e-03 0.215046419 #> 470   4.69 0.78305656 1.504638e-03 0.215438798 #> 471   4.70 0.78266513 1.503886e-03 0.215830980 #> 472   4.71 0.78227390 1.503134e-03 0.216222967 #> 473   4.72 0.78188286 1.502383e-03 0.216614757 #> 474   4.73 0.78149202 1.501632e-03 0.217006352 #> 475   4.74 0.78110137 1.500881e-03 0.217397751 #> 476   4.75 0.78071091 1.500131e-03 0.217788955 #> 477   4.76 0.78032066 1.499381e-03 0.218179962 #> 478   4.77 0.77993059 1.498631e-03 0.218570775 #> 479   4.78 0.77954073 1.497882e-03 0.218961392 #> 480   4.79 0.77915105 1.497134e-03 0.219351813 #> 481   4.80 0.77876157 1.496385e-03 0.219742040 #> 482   4.81 0.77837229 1.495637e-03 0.220132071 #> 483   4.82 0.77798320 1.494889e-03 0.220521908 #> 484   4.83 0.77759431 1.494142e-03 0.220911550 #> 485   4.84 0.77720561 1.493395e-03 0.221300996 #> 486   4.85 0.77681710 1.492649e-03 0.221690249 #> 487   4.86 0.77642879 1.491903e-03 0.222079306 #> 488   4.87 0.77604067 1.491157e-03 0.222468169 #> 489   4.88 0.77565275 1.490412e-03 0.222856838 #> 490   4.89 0.77526502 1.489667e-03 0.223245313 #> 491   4.90 0.77487749 1.488922e-03 0.223633593 #> 492   4.91 0.77449014 1.488178e-03 0.224021679 #> 493   4.92 0.77410300 1.487434e-03 0.224409571 #> 494   4.93 0.77371604 1.486690e-03 0.224797270 #> 495   4.94 0.77332928 1.485947e-03 0.225184774 #> 496   4.95 0.77294271 1.485204e-03 0.225572085 #> 497   4.96 0.77255634 1.484462e-03 0.225959202 #> 498   4.97 0.77217015 1.483720e-03 0.226346126 #> 499   4.98 0.77178417 1.482978e-03 0.226732856 #> 500   4.99 0.77139837 1.482237e-03 0.227119393 #> 501   5.00 0.77101277 1.481496e-03 0.227505737 #> 502   5.01 0.77062736 1.480755e-03 0.227891887 #> 503   5.02 0.77024214 1.480015e-03 0.228277845 #> 504   5.03 0.76985712 1.479275e-03 0.228663609 #> 505   5.04 0.76947228 1.478536e-03 0.229049181 #> 506   5.05 0.76908764 1.477797e-03 0.229434560 #> 507   5.06 0.76870320 1.477058e-03 0.229819747 #> 508   5.07 0.76831894 1.476320e-03 0.230204741 #> 509   5.08 0.76793488 1.475582e-03 0.230589542 #> 510   5.09 0.76755100 1.474844e-03 0.230974151 #> 511   5.10 0.76716733 1.474107e-03 0.231358568 #> 512   5.11 0.76678384 1.473370e-03 0.231742793 #> 513   5.12 0.76640054 1.472633e-03 0.232126825 #> 514   5.13 0.76601744 1.471897e-03 0.232510666 #> 515   5.14 0.76563452 1.471162e-03 0.232894315 #> 516   5.15 0.76525180 1.470426e-03 0.233277772 #> 517   5.16 0.76486927 1.469691e-03 0.233661037 #> 518   5.17 0.76448693 1.468956e-03 0.234044111 #> 519   5.18 0.76410478 1.468222e-03 0.234426993 #> 520   5.19 0.76372283 1.467488e-03 0.234809684 #> 521   5.20 0.76334106 1.466755e-03 0.235192183 #> 522   5.21 0.76295949 1.466022e-03 0.235574492 #> 523   5.22 0.76257810 1.465289e-03 0.235956609 #> 524   5.23 0.76219691 1.464556e-03 0.236338535 #> 525   5.24 0.76181591 1.463824e-03 0.236720270 #> 526   5.25 0.76143509 1.463092e-03 0.237101815 #> 527   5.26 0.76105447 1.462361e-03 0.237483169 #> 528   5.27 0.76067404 1.461630e-03 0.237864332 #> 529   5.28 0.76029380 1.460899e-03 0.238245304 #> 530   5.29 0.75991374 1.460169e-03 0.238626087 #> 531   5.30 0.75953388 1.459439e-03 0.239006678 #> 532   5.31 0.75915421 1.458710e-03 0.239387080 #> 533   5.32 0.75877473 1.457981e-03 0.239767291 #> 534   5.33 0.75839544 1.457252e-03 0.240147313 #> 535   5.34 0.75801633 1.456523e-03 0.240527144 #> 536   5.35 0.75763742 1.455795e-03 0.240906786 #> 537   5.36 0.75725870 1.455067e-03 0.241286237 #> 538   5.37 0.75688016 1.454340e-03 0.241665499 #> 539   5.38 0.75650181 1.453613e-03 0.242044572 #> 540   5.39 0.75612366 1.452887e-03 0.242423455 #> 541   5.40 0.75574569 1.452160e-03 0.242802149 #> 542   5.41 0.75536791 1.451434e-03 0.243180653 #> 543   5.42 0.75499032 1.450709e-03 0.243558968 #> 544   5.43 0.75461292 1.449984e-03 0.243937094 #> 545   5.44 0.75423571 1.449259e-03 0.244315031 #> 546   5.45 0.75385869 1.448534e-03 0.244692779 #> 547   5.46 0.75348185 1.447810e-03 0.245070338 #> 548   5.47 0.75310520 1.447087e-03 0.245447709 #> 549   5.48 0.75272875 1.446363e-03 0.245824891 #> 550   5.49 0.75235248 1.445640e-03 0.246201884 #> 551   5.50 0.75197639 1.444918e-03 0.246578689 #> 552   5.51 0.75160050 1.444195e-03 0.246955305 #> 553   5.52 0.75122479 1.443473e-03 0.247331733 #> 554   5.53 0.75084927 1.442752e-03 0.247707974 #> 555   5.54 0.75047394 1.442031e-03 0.248084026 #> 556   5.55 0.75009880 1.441310e-03 0.248459890 #> 557   5.56 0.74972384 1.440589e-03 0.248835566 #> 558   5.57 0.74934908 1.439869e-03 0.249211054 #> 559   5.58 0.74897450 1.439149e-03 0.249586355 #> 560   5.59 0.74860010 1.438430e-03 0.249961468 #> 561   5.60 0.74822590 1.437711e-03 0.250336393 #> 562   5.61 0.74785188 1.436992e-03 0.250711131 #> 563   5.62 0.74747804 1.436274e-03 0.251085682 #> 564   5.63 0.74710440 1.435556e-03 0.251460046 #> 565   5.64 0.74673094 1.434838e-03 0.251834222 #> 566   5.65 0.74635767 1.434121e-03 0.252208212 #> 567   5.66 0.74598458 1.433404e-03 0.252582014 #> 568   5.67 0.74561168 1.432688e-03 0.252955630 #> 569   5.68 0.74523897 1.431972e-03 0.253329059 #> 570   5.69 0.74486644 1.431256e-03 0.253702301 #> 571   5.70 0.74449410 1.430540e-03 0.254075356 #> 572   5.71 0.74412195 1.429825e-03 0.254448225 #> 573   5.72 0.74374998 1.429111e-03 0.254820908 #> 574   5.73 0.74337820 1.428396e-03 0.255193405 #> 575   5.74 0.74300660 1.427682e-03 0.255565715 #> 576   5.75 0.74263519 1.426969e-03 0.255937839 #> 577   5.76 0.74226397 1.426255e-03 0.256309777 #> 578   5.77 0.74189293 1.425542e-03 0.256681529 #> 579   5.78 0.74152207 1.424830e-03 0.257053096 #> 580   5.79 0.74115141 1.424117e-03 0.257424476 #> 581   5.80 0.74078092 1.423406e-03 0.257795671 #> 582   5.81 0.74041063 1.422694e-03 0.258166681 #> 583   5.82 0.74004051 1.421983e-03 0.258537505 #> 584   5.83 0.73967058 1.421272e-03 0.258908143 #> 585   5.84 0.73930084 1.420562e-03 0.259278596 #> 586   5.85 0.73893128 1.419851e-03 0.259648865 #> 587   5.86 0.73856191 1.419142e-03 0.260018948 #> 588   5.87 0.73819272 1.418432e-03 0.260388846 #> 589   5.88 0.73782372 1.417723e-03 0.260758559 #> 590   5.89 0.73745490 1.417015e-03 0.261128087 #> 591   5.90 0.73708626 1.416306e-03 0.261497431 #> 592   5.91 0.73671781 1.415598e-03 0.261866590 #> 593   5.92 0.73634954 1.414891e-03 0.262235564 #> 594   5.93 0.73598146 1.414183e-03 0.262604354 #> 595   5.94 0.73561356 1.413477e-03 0.262972960 #> 596   5.95 0.73524585 1.412770e-03 0.263341381 #> 597   5.96 0.73487832 1.412064e-03 0.263709619 #> 598   5.97 0.73451097 1.411358e-03 0.264077672 #> 599   5.98 0.73414381 1.410652e-03 0.264445541 #> 600   5.99 0.73377683 1.409947e-03 0.264813226 #> 601   6.00 0.73341003 1.409242e-03 0.265180728 #> 602   6.01 0.73304342 1.408538e-03 0.265548046 #> 603   6.02 0.73267699 1.407834e-03 0.265915180 #> 604   6.03 0.73231074 1.407130e-03 0.266282131 #> 605   6.04 0.73194468 1.406427e-03 0.266648898 #> 606   6.05 0.73157879 1.405724e-03 0.267015482 #> 607   6.06 0.73121310 1.405021e-03 0.267381882 #> 608   6.07 0.73084758 1.404319e-03 0.267748100 #> 609   6.08 0.73048225 1.403617e-03 0.268114134 #> 610   6.09 0.73011710 1.402915e-03 0.268479986 #> 611   6.10 0.72975213 1.402214e-03 0.268845655 #> 612   6.11 0.72938735 1.401513e-03 0.269211140 #> 613   6.12 0.72902274 1.400812e-03 0.269576443 #> 614   6.13 0.72865832 1.400112e-03 0.269941564 #> 615   6.14 0.72829409 1.399412e-03 0.270306502 #> 616   6.15 0.72793003 1.398713e-03 0.270671258 #> 617   6.16 0.72756616 1.398013e-03 0.271035831 #> 618   6.17 0.72720246 1.397315e-03 0.271400222 #> 619   6.18 0.72683895 1.396616e-03 0.271764431 #> 620   6.19 0.72647562 1.395918e-03 0.272128457 #> 621   6.20 0.72611248 1.395220e-03 0.272492302 #> 622   6.21 0.72574951 1.394523e-03 0.272855965 #> 623   6.22 0.72538673 1.393826e-03 0.273219446 #> 624   6.23 0.72502413 1.393129e-03 0.273582746 #> 625   6.24 0.72466170 1.392433e-03 0.273945864 #> 626   6.25 0.72429946 1.391737e-03 0.274308800 #> 627   6.26 0.72393740 1.391041e-03 0.274671555 #> 628   6.27 0.72357553 1.390346e-03 0.275034128 #> 629   6.28 0.72321383 1.389651e-03 0.275396521 #> 630   6.29 0.72285231 1.388956e-03 0.275758732 #> 631   6.30 0.72249098 1.388262e-03 0.276120762 #> 632   6.31 0.72212982 1.387568e-03 0.276482611 #> 633   6.32 0.72176885 1.386874e-03 0.276844280 #> 634   6.33 0.72140805 1.386181e-03 0.277205767 #> 635   6.34 0.72104744 1.385488e-03 0.277567074 #> 636   6.35 0.72068700 1.384795e-03 0.277928200 #> 637   6.36 0.72032675 1.384103e-03 0.278289146 #> 638   6.37 0.71996668 1.383411e-03 0.278649911 #> 639   6.38 0.71960678 1.382720e-03 0.279010496 #> 640   6.39 0.71924707 1.382028e-03 0.279370901 #> 641   6.40 0.71888754 1.381338e-03 0.279731125 #> 642   6.41 0.71852818 1.380647e-03 0.280091169 #> 643   6.42 0.71816901 1.379957e-03 0.280451034 #> 644   6.43 0.71781001 1.379267e-03 0.280810718 #> 645   6.44 0.71745120 1.378578e-03 0.281170223 #> 646   6.45 0.71709256 1.377889e-03 0.281529548 #> 647   6.46 0.71673411 1.377200e-03 0.281888694 #> 648   6.47 0.71637583 1.376511e-03 0.282247660 #> 649   6.48 0.71601773 1.375823e-03 0.282606446 #> 650   6.49 0.71565981 1.375136e-03 0.282965053 #> 651   6.50 0.71530207 1.374448e-03 0.283323481 #> 652   6.51 0.71494451 1.373761e-03 0.283681730 #> 653   6.52 0.71458713 1.373074e-03 0.284039799 #> 654   6.53 0.71422992 1.372388e-03 0.284397690 #> 655   6.54 0.71387290 1.371702e-03 0.284755402 #> 656   6.55 0.71351605 1.371016e-03 0.285112935 #> 657   6.56 0.71315938 1.370331e-03 0.285470289 #> 658   6.57 0.71280289 1.369646e-03 0.285827465 #> 659   6.58 0.71244658 1.368961e-03 0.286184462 #> 660   6.59 0.71209044 1.368277e-03 0.286541280 #> 661   6.60 0.71173449 1.367593e-03 0.286897920 #> 662   6.61 0.71137871 1.366909e-03 0.287254382 #> 663   6.62 0.71102311 1.366226e-03 0.287610666 #> 664   6.63 0.71066769 1.365543e-03 0.287966772 #> 665   6.64 0.71031244 1.364861e-03 0.288322699 #> 666   6.65 0.70995737 1.364178e-03 0.288678449 #> 667   6.66 0.70960248 1.363496e-03 0.289034021 #> 668   6.67 0.70924777 1.362815e-03 0.289389415 #> 669   6.68 0.70889323 1.362134e-03 0.289744632 #> 670   6.69 0.70853888 1.361453e-03 0.290099671 #> 671   6.70 0.70818470 1.360772e-03 0.290454532 #> 672   6.71 0.70783069 1.360092e-03 0.290809216 #> 673   6.72 0.70747687 1.359412e-03 0.291163723 #> 674   6.73 0.70712322 1.358733e-03 0.291518052 #> 675   6.74 0.70676974 1.358053e-03 0.291872205 #> 676   6.75 0.70641645 1.357374e-03 0.292226180 #> 677   6.76 0.70606333 1.356696e-03 0.292579979 #> 678   6.77 0.70571038 1.356018e-03 0.292933600 #> 679   6.78 0.70535761 1.355340e-03 0.293287045 #> 680   6.79 0.70500502 1.354662e-03 0.293640313 #> 681   6.80 0.70465261 1.353985e-03 0.293993405 #> 682   6.81 0.70430037 1.353308e-03 0.294346320 #> 683   6.82 0.70394831 1.352632e-03 0.294699059 #> 684   6.83 0.70359642 1.351956e-03 0.295051621 #> 685   6.84 0.70324471 1.351280e-03 0.295404007 #> 686   6.85 0.70289318 1.350605e-03 0.295756217 #> 687   6.86 0.70254182 1.349929e-03 0.296108251 #> 688   6.87 0.70219064 1.349255e-03 0.296460109 #> 689   6.88 0.70183963 1.348580e-03 0.296811791 #> 690   6.89 0.70148880 1.347906e-03 0.297163297 #> 691   6.90 0.70113814 1.347232e-03 0.297514628 #> 692   6.91 0.70078766 1.346559e-03 0.297865783 #> 693   6.92 0.70043735 1.345886e-03 0.298216762 #> 694   6.93 0.70008722 1.345213e-03 0.298567566 #> 695   6.94 0.69973727 1.344540e-03 0.298918194 #> 696   6.95 0.69938748 1.343868e-03 0.299268648 #> 697   6.96 0.69903788 1.343197e-03 0.299618926 #> 698   6.97 0.69868845 1.342525e-03 0.299969029 #> 699   6.98 0.69833919 1.341854e-03 0.300318957 #> 700   6.99 0.69799011 1.341183e-03 0.300668710 #> 701   7.00 0.69764120 1.340513e-03 0.301018288 #> 702   7.01 0.69729247 1.339843e-03 0.301367692 #> 703   7.02 0.69694391 1.339173e-03 0.301716921 #> 704   7.03 0.69659552 1.338504e-03 0.302065975 #> 705   7.04 0.69624731 1.337835e-03 0.302414855 #> 706   7.05 0.69589927 1.337166e-03 0.302763560 #> 707   7.06 0.69555141 1.336497e-03 0.303112091 #> 708   7.07 0.69520372 1.335829e-03 0.303460448 #> 709   7.08 0.69485621 1.335162e-03 0.303808631 #> 710   7.09 0.69450887 1.334494e-03 0.304156639 #> 711   7.10 0.69416170 1.333827e-03 0.304504474 #> 712   7.11 0.69381470 1.333160e-03 0.304852135 #> 713   7.12 0.69346788 1.332494e-03 0.305199622 #> 714   7.13 0.69312124 1.331828e-03 0.305546935 #> 715   7.14 0.69277476 1.331162e-03 0.305894075 #> 716   7.15 0.69242846 1.330497e-03 0.306241041 #> 717   7.16 0.69208233 1.329832e-03 0.306587834 #> 718   7.17 0.69173638 1.329167e-03 0.306934454 #> 719   7.18 0.69139060 1.328502e-03 0.307280900 #> 720   7.19 0.69104499 1.327838e-03 0.307627173 #> 721   7.20 0.69069955 1.327175e-03 0.307973273 #> 722   7.21 0.69035429 1.326511e-03 0.308319200 #> 723   7.22 0.69000920 1.325848e-03 0.308664954 #> 724   7.23 0.68966428 1.325185e-03 0.309010535 #> 725   7.24 0.68931953 1.324523e-03 0.309355943 #> 726   7.25 0.68897496 1.323861e-03 0.309701179 #> 727   7.26 0.68863056 1.323199e-03 0.310046242 #> 728   7.27 0.68828633 1.322538e-03 0.310391133 #> 729   7.28 0.68794227 1.321876e-03 0.310735851 #> 730   7.29 0.68759839 1.321216e-03 0.311080397 #> 731   7.30 0.68725467 1.320555e-03 0.311424771 #> 732   7.31 0.68691113 1.319895e-03 0.311768972 #> 733   7.32 0.68656776 1.319235e-03 0.312113002 #> 734   7.33 0.68622456 1.318576e-03 0.312456859 #> 735   7.34 0.68588154 1.317917e-03 0.312800545 #> 736   7.35 0.68553868 1.317258e-03 0.313144059 #> 737   7.36 0.68519600 1.316600e-03 0.313487401 #> 738   7.37 0.68485349 1.315941e-03 0.313830571 #> 739   7.38 0.68451115 1.315284e-03 0.314173570 #> 740   7.39 0.68416898 1.314626e-03 0.314516398 #> 741   7.40 0.68382698 1.313969e-03 0.314859054 #> 742   7.41 0.68348515 1.313312e-03 0.315201539 #> 743   7.42 0.68314349 1.312656e-03 0.315543852 #> 744   7.43 0.68280201 1.311999e-03 0.315885995 #> 745   7.44 0.68246069 1.311344e-03 0.316227967 #> 746   7.45 0.68211954 1.310688e-03 0.316569767 #> 747   7.46 0.68177857 1.310033e-03 0.316911397 #> 748   7.47 0.68143777 1.309378e-03 0.317252856 #> 749   7.48 0.68109713 1.308724e-03 0.317594144 #> 750   7.49 0.68075667 1.308069e-03 0.317935262 #> 751   7.50 0.68041638 1.307415e-03 0.318276209 #> 752   7.51 0.68007625 1.306762e-03 0.318616986 #> 753   7.52 0.67973630 1.306109e-03 0.318957592 #> 754   7.53 0.67939652 1.305456e-03 0.319298028 #> 755   7.54 0.67905690 1.304803e-03 0.319638294 #> 756   7.55 0.67871746 1.304151e-03 0.319978390 #> 757   7.56 0.67837819 1.303499e-03 0.320318316 #> 758   7.57 0.67803908 1.302848e-03 0.320658072 #> 759   7.58 0.67770015 1.302196e-03 0.320997658 #> 760   7.59 0.67736138 1.301545e-03 0.321337074 #> 761   7.60 0.67702278 1.300895e-03 0.321676321 #> 762   7.61 0.67668436 1.300244e-03 0.322015398 #> 763   7.62 0.67634610 1.299594e-03 0.322354305 #> 764   7.63 0.67600801 1.298945e-03 0.322693043 #> 765   7.64 0.67567009 1.298296e-03 0.323031612 #> 766   7.65 0.67533234 1.297647e-03 0.323370012 #> 767   7.66 0.67499476 1.296998e-03 0.323708242 #> 768   7.67 0.67465735 1.296350e-03 0.324046304 #> 769   7.68 0.67432010 1.295702e-03 0.324384196 #> 770   7.69 0.67398303 1.295054e-03 0.324721920 #> 771   7.70 0.67364612 1.294406e-03 0.325059474 #> 772   7.71 0.67330938 1.293759e-03 0.325396860 #> 773   7.72 0.67297281 1.293113e-03 0.325734077 #> 774   7.73 0.67263641 1.292466e-03 0.326071126 #> 775   7.74 0.67230017 1.291820e-03 0.326408006 #> 776   7.75 0.67196411 1.291175e-03 0.326744718 #> 777   7.76 0.67162821 1.290529e-03 0.327081262 #> 778   7.77 0.67129248 1.289884e-03 0.327417637 #> 779   7.78 0.67095692 1.289239e-03 0.327753844 #> 780   7.79 0.67062152 1.288595e-03 0.328089883 #> 781   7.80 0.67028630 1.287951e-03 0.328425754 #> 782   7.81 0.66995124 1.287307e-03 0.328761457 #> 783   7.82 0.66961634 1.286663e-03 0.329096993 #> 784   7.83 0.66928162 1.286020e-03 0.329432360 #> 785   7.84 0.66894706 1.285377e-03 0.329767561 #> 786   7.85 0.66861267 1.284735e-03 0.330102593 #> 787   7.86 0.66827845 1.284093e-03 0.330437458 #> 788   7.87 0.66794439 1.283451e-03 0.330772156 #> 789   7.88 0.66761050 1.282809e-03 0.331106686 #> 790   7.89 0.66727678 1.282168e-03 0.331441049 #> 791   7.90 0.66694323 1.281527e-03 0.331775245 #> 792   7.91 0.66660984 1.280886e-03 0.332109274 #> 793   7.92 0.66627662 1.280246e-03 0.332443136 #> 794   7.93 0.66594356 1.279606e-03 0.332776831 #> 795   7.94 0.66561067 1.278966e-03 0.333110359 #> 796   7.95 0.66527795 1.278327e-03 0.333443720 #> 797   7.96 0.66494540 1.277688e-03 0.333776915 #> 798   7.97 0.66461301 1.277049e-03 0.334109944 #> 799   7.98 0.66428078 1.276411e-03 0.334442805 #> 800   7.99 0.66394873 1.275773e-03 0.334775501 #> 801   8.00 0.66361683 1.275135e-03 0.335108030 #> 802   8.01 0.66328511 1.274498e-03 0.335440393 #> 803   8.02 0.66295355 1.273861e-03 0.335772590 #> 804   8.03 0.66262216 1.273224e-03 0.336104620 #> 805   8.04 0.66229093 1.272588e-03 0.336436485 #> 806   8.05 0.66195986 1.271951e-03 0.336768184 #> 807   8.06 0.66162897 1.271316e-03 0.337099717 #> 808   8.07 0.66129824 1.270680e-03 0.337431084 #> 809   8.08 0.66096767 1.270045e-03 0.337762286 #> 810   8.09 0.66063727 1.269410e-03 0.338093322 #> 811   8.10 0.66030703 1.268776e-03 0.338424193 #> 812   8.11 0.65997696 1.268141e-03 0.338754898 #> 813   8.12 0.65964705 1.267507e-03 0.339085438 #> 814   8.13 0.65931731 1.266874e-03 0.339415812 #> 815   8.14 0.65898774 1.266241e-03 0.339746022 #> 816   8.15 0.65865833 1.265608e-03 0.340076066 #> 817   8.16 0.65832908 1.264975e-03 0.340405946 #> 818   8.17 0.65800000 1.264343e-03 0.340735661 #> 819   8.18 0.65767108 1.263711e-03 0.341065210 #> 820   8.19 0.65734233 1.263079e-03 0.341394595 #> 821   8.20 0.65701374 1.262447e-03 0.341723816 #> 822   8.21 0.65668531 1.261816e-03 0.342052872 #> 823   8.22 0.65635705 1.261186e-03 0.342381763 #> 824   8.23 0.65602895 1.260555e-03 0.342710490 #> 825   8.24 0.65570102 1.259925e-03 0.343039053 #> 826   8.25 0.65537325 1.259295e-03 0.343367451 #> 827   8.26 0.65504565 1.258666e-03 0.343695685 #> 828   8.27 0.65471821 1.258037e-03 0.344023755 #> 829   8.28 0.65439093 1.257408e-03 0.344351662 #> 830   8.29 0.65406382 1.256779e-03 0.344679404 #> 831   8.30 0.65373687 1.256151e-03 0.345006982 #> 832   8.31 0.65341008 1.255523e-03 0.345334397 #> 833   8.32 0.65308346 1.254895e-03 0.345661648 #> 834   8.33 0.65275700 1.254268e-03 0.345988735 #> 835   8.34 0.65243070 1.253641e-03 0.346315659 #> 836   8.35 0.65210457 1.253015e-03 0.346642420 #> 837   8.36 0.65177860 1.252388e-03 0.346969017 #> 838   8.37 0.65145279 1.251762e-03 0.347295451 #> 839   8.38 0.65112714 1.251136e-03 0.347621721 #> 840   8.39 0.65080166 1.250511e-03 0.347947829 #> 841   8.40 0.65047634 1.249886e-03 0.348273774 #> 842   8.41 0.65015118 1.249261e-03 0.348599555 #> 843   8.42 0.64982619 1.248637e-03 0.348925174 #> 844   8.43 0.64950136 1.248012e-03 0.349250630 #> 845   8.44 0.64917669 1.247389e-03 0.349575923 #> 846   8.45 0.64885218 1.246765e-03 0.349901054 #> 847   8.46 0.64852784 1.246142e-03 0.350226023 #> 848   8.47 0.64820365 1.245519e-03 0.350550828 #> 849   8.48 0.64787963 1.244896e-03 0.350875472 #> 850   8.49 0.64755577 1.244274e-03 0.351199953 #> 851   8.50 0.64723208 1.243652e-03 0.351524272 #> 852   8.51 0.64690854 1.243030e-03 0.351848429 #> 853   8.52 0.64658517 1.242409e-03 0.352172423 #> 854   8.53 0.64626196 1.241788e-03 0.352496256 #> 855   8.54 0.64593891 1.241167e-03 0.352819927 #> 856   8.55 0.64561602 1.240547e-03 0.353143436 #> 857   8.56 0.64529329 1.239927e-03 0.353466784 #> 858   8.57 0.64497072 1.239307e-03 0.353789970 #> 859   8.58 0.64464832 1.238687e-03 0.354112994 #> 860   8.59 0.64432608 1.238068e-03 0.354435857 #> 861   8.60 0.64400399 1.237449e-03 0.354758558 #> 862   8.61 0.64368207 1.236831e-03 0.355081098 #> 863   8.62 0.64336031 1.236213e-03 0.355403477 #> 864   8.63 0.64303871 1.235595e-03 0.355725695 #> 865   8.64 0.64271727 1.234977e-03 0.356047751 #> 866   8.65 0.64239599 1.234360e-03 0.356369647 #> 867   8.66 0.64207488 1.233743e-03 0.356691382 #> 868   8.67 0.64175392 1.233126e-03 0.357012956 #> 869   8.68 0.64143312 1.232509e-03 0.357334369 #> 870   8.69 0.64111249 1.231893e-03 0.357655621 #> 871   8.70 0.64079201 1.231278e-03 0.357976713 #> 872   8.71 0.64047169 1.230662e-03 0.358297645 #> 873   8.72 0.64015154 1.230047e-03 0.358618416 #> 874   8.73 0.63983154 1.229432e-03 0.358939026 #> 875   8.74 0.63951171 1.228817e-03 0.359259477 #> 876   8.75 0.63919203 1.228203e-03 0.359579767 #> 877   8.76 0.63887251 1.227589e-03 0.359899897 #> 878   8.77 0.63855316 1.226976e-03 0.360219867 #> 879   8.78 0.63823396 1.226362e-03 0.360539677 #> 880   8.79 0.63791492 1.225749e-03 0.360859328 #> 881   8.80 0.63759605 1.225137e-03 0.361178818 #> 882   8.81 0.63727733 1.224524e-03 0.361498149 #> 883   8.82 0.63695877 1.223912e-03 0.361817320 #> 884   8.83 0.63664037 1.223300e-03 0.362136331 #> 885   8.84 0.63632213 1.222689e-03 0.362455184 #> 886   8.85 0.63600405 1.222077e-03 0.362773876 #> 887   8.86 0.63568612 1.221467e-03 0.363092410 #> 888   8.87 0.63536836 1.220856e-03 0.363410784 #> 889   8.88 0.63505076 1.220246e-03 0.363728999 #> 890   8.89 0.63473331 1.219636e-03 0.364047055 #> 891   8.90 0.63441602 1.219026e-03 0.364364952 #> 892   8.91 0.63409889 1.218417e-03 0.364682690 #> 893   8.92 0.63378192 1.217808e-03 0.365000269 #> 894   8.93 0.63346511 1.217199e-03 0.365317690 #> 895   8.94 0.63314846 1.216591e-03 0.365634952 #> 896   8.95 0.63283196 1.215982e-03 0.365952055 #> 897   8.96 0.63251563 1.215375e-03 0.366269000 #> 898   8.97 0.63219945 1.214767e-03 0.366585786 #> 899   8.98 0.63188343 1.214160e-03 0.366902414 #> 900   8.99 0.63156756 1.213553e-03 0.367218884 #> 901   9.00 0.63125186 1.212946e-03 0.367535195 #> 902   9.01 0.63093631 1.212340e-03 0.367851349 #> 903   9.02 0.63062092 1.211734e-03 0.368167344 #> 904   9.03 0.63030569 1.211128e-03 0.368483181 #> 905   9.04 0.62999062 1.210523e-03 0.368798861 #> 906   9.05 0.62967570 1.209918e-03 0.369114383 #> 907   9.06 0.62936094 1.209313e-03 0.369429747 #> 908   9.07 0.62904634 1.208708e-03 0.369744953 #> 909   9.08 0.62873189 1.208104e-03 0.370060002 #> 910   9.09 0.62841761 1.207500e-03 0.370374893 #> 911   9.10 0.62810348 1.206897e-03 0.370689627 #> 912   9.11 0.62778950 1.206293e-03 0.371004203 #> 913   9.12 0.62747569 1.205690e-03 0.371318623 #> 914   9.13 0.62716203 1.205088e-03 0.371632885 #> 915   9.14 0.62684853 1.204485e-03 0.371946990 #> 916   9.15 0.62653518 1.203883e-03 0.372260938 #> 917   9.16 0.62622199 1.203281e-03 0.372574729 #> 918   9.17 0.62590896 1.202680e-03 0.372888363 #> 919   9.18 0.62559608 1.202079e-03 0.373201841 #> 920   9.19 0.62528336 1.201478e-03 0.373515161 #> 921   9.20 0.62497080 1.200877e-03 0.373828325 #> 922   9.21 0.62465839 1.200277e-03 0.374141333 #> 923   9.22 0.62434614 1.199677e-03 0.374454184 #> 924   9.23 0.62403404 1.199077e-03 0.374766879 #> 925   9.24 0.62372210 1.198478e-03 0.375079417 #> 926   9.25 0.62341032 1.197879e-03 0.375391800 #> 927   9.26 0.62309869 1.197280e-03 0.375704026 #> 928   9.27 0.62278722 1.196681e-03 0.376016096 #> 929   9.28 0.62247591 1.196083e-03 0.376328010 #> 930   9.29 0.62216475 1.195485e-03 0.376639768 #> 931   9.30 0.62185374 1.194888e-03 0.376951370 #> 932   9.31 0.62154289 1.194290e-03 0.377262816 #> 933   9.32 0.62123220 1.193693e-03 0.377574107 #> 934   9.33 0.62092166 1.193097e-03 0.377885242 #> 935   9.34 0.62061128 1.192500e-03 0.378196222 #> 936   9.35 0.62030105 1.191904e-03 0.378507046 #> 937   9.36 0.61999098 1.191308e-03 0.378817715 #> 938   9.37 0.61968106 1.190713e-03 0.379128228 #> 939   9.38 0.61937130 1.190118e-03 0.379438587 #> 940   9.39 0.61906169 1.189523e-03 0.379748790 #> 941   9.40 0.61875223 1.188928e-03 0.380058838 #> 942   9.41 0.61844293 1.188334e-03 0.380368731 #> 943   9.42 0.61813379 1.187740e-03 0.380678469 #> 944   9.43 0.61782480 1.187146e-03 0.380988053 #> 945   9.44 0.61751597 1.186553e-03 0.381297481 #> 946   9.45 0.61720729 1.185960e-03 0.381606755 #> 947   9.46 0.61689876 1.185367e-03 0.381915875 #> 948   9.47 0.61659039 1.184774e-03 0.382224839 #> 949   9.48 0.61628217 1.184182e-03 0.382533650 #> 950   9.49 0.61597410 1.183590e-03 0.382842306 #> 951   9.50 0.61566619 1.182998e-03 0.383150808 #> 952   9.51 0.61535844 1.182407e-03 0.383459155 #> 953   9.52 0.61505084 1.181816e-03 0.383767348 #> 954   9.53 0.61474339 1.181225e-03 0.384075388 #> 955   9.54 0.61443609 1.180635e-03 0.384383273 #> 956   9.55 0.61412895 1.180045e-03 0.384691005 #> 957   9.56 0.61382196 1.179455e-03 0.384998582 #> 958   9.57 0.61351513 1.178865e-03 0.385306006 #> 959   9.58 0.61320845 1.178276e-03 0.385613276 #> 960   9.59 0.61290192 1.177687e-03 0.385920393 #> 961   9.60 0.61259555 1.177098e-03 0.386227356 #> 962   9.61 0.61228932 1.176510e-03 0.386534165 #> 963   9.62 0.61198326 1.175922e-03 0.386840822 #> 964   9.63 0.61167734 1.175334e-03 0.387147325 #> 965   9.64 0.61137158 1.174746e-03 0.387453674 #> 966   9.65 0.61106597 1.174159e-03 0.387759871 #> 967   9.66 0.61076051 1.173572e-03 0.388065915 #> 968   9.67 0.61045521 1.172986e-03 0.388371805 #> 969   9.68 0.61015006 1.172399e-03 0.388677543 #> 970   9.69 0.60984506 1.171813e-03 0.388983128 #> 971   9.70 0.60954021 1.171227e-03 0.389288560 #> 972   9.71 0.60923552 1.170642e-03 0.389593839 #> 973   9.72 0.60893098 1.170057e-03 0.389898966 #> 974   9.73 0.60862659 1.169472e-03 0.390203940 #> 975   9.74 0.60832235 1.168887e-03 0.390508762 #> 976   9.75 0.60801827 1.168303e-03 0.390813432 #> 977   9.76 0.60771433 1.167719e-03 0.391117949 #> 978   9.77 0.60741055 1.167135e-03 0.391422314 #> 979   9.78 0.60710692 1.166552e-03 0.391726527 #> 980   9.79 0.60680344 1.165969e-03 0.392030587 #> 981   9.80 0.60650012 1.165386e-03 0.392334496 #> 982   9.81 0.60619694 1.164803e-03 0.392638253 #> 983   9.82 0.60589392 1.164221e-03 0.392941858 #> 984   9.83 0.60559105 1.163639e-03 0.393245311 #> 985   9.84 0.60528833 1.163057e-03 0.393548612 #> 986   9.85 0.60498576 1.162476e-03 0.393851762 #> 987   9.86 0.60468334 1.161895e-03 0.394154761 #> 988   9.87 0.60438108 1.161314e-03 0.394457608 #> 989   9.88 0.60407896 1.160734e-03 0.394760303 #> 990   9.89 0.60377700 1.160153e-03 0.395062847 #> 991   9.90 0.60347519 1.159574e-03 0.395365240 #> 992   9.91 0.60317352 1.158994e-03 0.395667482 #> 993   9.92 0.60287201 1.158415e-03 0.395969573 #> 994   9.93 0.60257065 1.157835e-03 0.396271513 #> 995   9.94 0.60226944 1.157257e-03 0.396573302 #> 996   9.95 0.60196838 1.156678e-03 0.396874939 #> 997   9.96 0.60166747 1.156100e-03 0.397176427 #> 998   9.97 0.60136671 1.155522e-03 0.397477763 #> 999   9.98 0.60106611 1.154944e-03 0.397778949 #> 1000  9.99 0.60076565 1.154367e-03 0.398079984 #> 1001 10.00 0.60046534 1.153790e-03 0.398380869 #> 1002 10.01 0.60016518 1.153213e-03 0.398681603 #> 1003 10.02 0.59986518 1.152637e-03 0.398982187 #> 1004 10.03 0.59956532 1.152061e-03 0.399282621 #> 1005 10.04 0.59926561 1.151485e-03 0.399582905 #> 1006 10.05 0.59896605 1.150909e-03 0.399883038 #> 1007 10.06 0.59866664 1.150334e-03 0.400183022 #> 1008 10.07 0.59836739 1.149759e-03 0.400482855 #> 1009 10.08 0.59806828 1.149184e-03 0.400782539 #> 1010 10.09 0.59776932 1.148610e-03 0.401082073 #> 1011 10.10 0.59747051 1.148036e-03 0.401381457 #> 1012 10.11 0.59717185 1.147462e-03 0.401680692 #> 1013 10.12 0.59687334 1.146888e-03 0.401979776 #> 1014 10.13 0.59657497 1.146315e-03 0.402278712 #> 1015 10.14 0.59627676 1.145742e-03 0.402577498 #> 1016 10.15 0.59597870 1.145169e-03 0.402876134 #> 1017 10.16 0.59568078 1.144597e-03 0.403174622 #> 1018 10.17 0.59538302 1.144024e-03 0.403472960 #> 1019 10.18 0.59508540 1.143453e-03 0.403771149 #> 1020 10.19 0.59478793 1.142881e-03 0.404069189 #> 1021 10.20 0.59449061 1.142310e-03 0.404367080 #> 1022 10.21 0.59419344 1.141739e-03 0.404664822 #> 1023 10.22 0.59389642 1.141168e-03 0.404962415 #> 1024 10.23 0.59359954 1.140598e-03 0.405259859 #> 1025 10.24 0.59330282 1.140027e-03 0.405557155 #> 1026 10.25 0.59300624 1.139458e-03 0.405854302 #> 1027 10.26 0.59270981 1.138888e-03 0.406151301 #> 1028 10.27 0.59241353 1.138319e-03 0.406448151 #> 1029 10.28 0.59211740 1.137750e-03 0.406744853 #> 1030 10.29 0.59182141 1.137181e-03 0.407041406 #> 1031 10.30 0.59152558 1.136612e-03 0.407337811 #> 1032 10.31 0.59122989 1.136044e-03 0.407634068 #> 1033 10.32 0.59093435 1.135476e-03 0.407930177 #> 1034 10.33 0.59063895 1.134909e-03 0.408226138 #> 1035 10.34 0.59034371 1.134341e-03 0.408521951 #> 1036 10.35 0.59004861 1.133774e-03 0.408817616 #> 1037 10.36 0.58975366 1.133208e-03 0.409113134 #> 1038 10.37 0.58945886 1.132641e-03 0.409408503 #> 1039 10.38 0.58916420 1.132075e-03 0.409703725 #> 1040 10.39 0.58886969 1.131509e-03 0.409998800 #> 1041 10.40 0.58857533 1.130944e-03 0.410293726 #> 1042 10.41 0.58828112 1.130378e-03 0.410588506 #> 1043 10.42 0.58798705 1.129813e-03 0.410883138 #> 1044 10.43 0.58769313 1.129248e-03 0.411177623 #> 1045 10.44 0.58739936 1.128684e-03 0.411471960 #> 1046 10.45 0.58710573 1.128120e-03 0.411766151 #> 1047 10.46 0.58681225 1.127556e-03 0.412060194 #> 1048 10.47 0.58651892 1.126992e-03 0.412354091 #> 1049 10.48 0.58622573 1.126429e-03 0.412647840 #> 1050 10.49 0.58593269 1.125866e-03 0.412941443 #> 1051 10.50 0.58563980 1.125303e-03 0.413234899 #> 1052 10.51 0.58534705 1.124740e-03 0.413528208 #> 1053 10.52 0.58505445 1.124178e-03 0.413821371 #> 1054 10.53 0.58476200 1.123616e-03 0.414114387 #> 1055 10.54 0.58446969 1.123055e-03 0.414407256 #> 1056 10.55 0.58417753 1.122493e-03 0.414699980 #> 1057 10.56 0.58388551 1.121932e-03 0.414992556 #> 1058 10.57 0.58359364 1.121371e-03 0.415284987 #> 1059 10.58 0.58330192 1.120811e-03 0.415577272 #> 1060 10.59 0.58301034 1.120250e-03 0.415869410 #> 1061 10.60 0.58271891 1.119690e-03 0.416161402 #> 1062 10.61 0.58242762 1.119131e-03 0.416453248 #> 1063 10.62 0.58213648 1.118571e-03 0.416744949 #> 1064 10.63 0.58184548 1.118012e-03 0.417036504 #> 1065 10.64 0.58155463 1.117453e-03 0.417327912 #> 1066 10.65 0.58126393 1.116895e-03 0.417619176 #> 1067 10.66 0.58097337 1.116336e-03 0.417910293 #> 1068 10.67 0.58068296 1.115778e-03 0.418201266 #> 1069 10.68 0.58039269 1.115221e-03 0.418492092 #> 1070 10.69 0.58010256 1.114663e-03 0.418782773 #> 1071 10.70 0.57981258 1.114106e-03 0.419073309 #> 1072 10.71 0.57952275 1.113549e-03 0.419363700 #> 1073 10.72 0.57923306 1.112992e-03 0.419653946 #> 1074 10.73 0.57894352 1.112436e-03 0.419944046 #> 1075 10.74 0.57865412 1.111880e-03 0.420234002 #> 1076 10.75 0.57836486 1.111324e-03 0.420523812 #> 1077 10.76 0.57807575 1.110769e-03 0.420813478 #> 1078 10.77 0.57778679 1.110213e-03 0.421102999 #> 1079 10.78 0.57749797 1.109658e-03 0.421392375 #> 1080 10.79 0.57720929 1.109104e-03 0.421681607 #> 1081 10.80 0.57692076 1.108549e-03 0.421970693 #> 1082 10.81 0.57663237 1.107995e-03 0.422259636 #> 1083 10.82 0.57634412 1.107441e-03 0.422548434 #> 1084 10.83 0.57605602 1.106888e-03 0.422837088 #> 1085 10.84 0.57576807 1.106334e-03 0.423125597 #> 1086 10.85 0.57548026 1.105781e-03 0.423413962 #> 1087 10.86 0.57519259 1.105229e-03 0.423702183 #> 1088 10.87 0.57490506 1.104676e-03 0.423990260 #> 1089 10.88 0.57461768 1.104124e-03 0.424278193 #> 1090 10.89 0.57433045 1.103572e-03 0.424565982 #> 1091 10.90 0.57404335 1.103020e-03 0.424853627 #> 1092 10.91 0.57375640 1.102469e-03 0.425141128 #> 1093 10.92 0.57346960 1.101918e-03 0.425428486 #> 1094 10.93 0.57318293 1.101367e-03 0.425715700 #> 1095 10.94 0.57289641 1.100817e-03 0.426002770 #> 1096 10.95 0.57261004 1.100266e-03 0.426289697 #> 1097 10.96 0.57232380 1.099716e-03 0.426576480 #> 1098 10.97 0.57203771 1.099167e-03 0.426863121 #> 1099 10.98 0.57175177 1.098617e-03 0.427149617 #> 1100 10.99 0.57146596 1.098068e-03 0.427435971 #> 1101 11.00 0.57118030 1.097519e-03 0.427722181 #> 1102 11.01 0.57089478 1.096970e-03 0.428008249 #> 1103 11.02 0.57060940 1.096422e-03 0.428294173 #> 1104 11.03 0.57032417 1.095874e-03 0.428579955 #> 1105 11.04 0.57003908 1.095326e-03 0.428865593 #> 1106 11.05 0.56975413 1.094779e-03 0.429151089 #> 1107 11.06 0.56946933 1.094231e-03 0.429436442 #> 1108 11.07 0.56918466 1.093685e-03 0.429721653 #> 1109 11.08 0.56890014 1.093138e-03 0.430006721 #> 1110 11.09 0.56861576 1.092591e-03 0.430291646 #> 1111 11.10 0.56833153 1.092045e-03 0.430576429 #> 1112 11.11 0.56804743 1.091499e-03 0.430861070 #> 1113 11.12 0.56776348 1.090954e-03 0.431145568 #> 1114 11.13 0.56747967 1.090408e-03 0.431429924 #> 1115 11.14 0.56719600 1.089863e-03 0.431714138 #> 1116 11.15 0.56691247 1.089319e-03 0.431998210 #> 1117 11.16 0.56662909 1.088774e-03 0.432282140 #> 1118 11.17 0.56634584 1.088230e-03 0.432565928 #> 1119 11.18 0.56606274 1.087686e-03 0.432849574 #> 1120 11.19 0.56577978 1.087142e-03 0.433133078 #> 1121 11.20 0.56549696 1.086599e-03 0.433416441 #> 1122 11.21 0.56521428 1.086055e-03 0.433699662 #> 1123 11.22 0.56493175 1.085513e-03 0.433982741 #> 1124 11.23 0.56464935 1.084970e-03 0.434265679 #> 1125 11.24 0.56436710 1.084428e-03 0.434548476 #> 1126 11.25 0.56408498 1.083886e-03 0.434831131 #> 1127 11.26 0.56380301 1.083344e-03 0.435113645 #> 1128 11.27 0.56352118 1.082802e-03 0.435396017 #> 1129 11.28 0.56323949 1.082261e-03 0.435678249 #> 1130 11.29 0.56295794 1.081720e-03 0.435960339 #> 1131 11.30 0.56267653 1.081179e-03 0.436242289 #> 1132 11.31 0.56239526 1.080639e-03 0.436524097 #> 1133 11.32 0.56211414 1.080099e-03 0.436805764 #> 1134 11.33 0.56183315 1.079559e-03 0.437087291 #> 1135 11.34 0.56155230 1.079019e-03 0.437368677 #> 1136 11.35 0.56127160 1.078480e-03 0.437649923 #> 1137 11.36 0.56099103 1.077940e-03 0.437931027 #> 1138 11.37 0.56071061 1.077402e-03 0.438211992 #> 1139 11.38 0.56043032 1.076863e-03 0.438492815 #> 1140 11.39 0.56015018 1.076325e-03 0.438773499 #> 1141 11.40 0.55987017 1.075787e-03 0.439054042 #> 1142 11.41 0.55959031 1.075249e-03 0.439334445 #> 1143 11.42 0.55931058 1.074712e-03 0.439614708 #> 1144 11.43 0.55903100 1.074174e-03 0.439894830 #> 1145 11.44 0.55875155 1.073637e-03 0.440174813 #> 1146 11.45 0.55847224 1.073101e-03 0.440454655 #> 1147 11.46 0.55819308 1.072564e-03 0.440734358 #> 1148 11.47 0.55791405 1.072028e-03 0.441013921 #> 1149 11.48 0.55763516 1.071492e-03 0.441293344 #> 1150 11.49 0.55735642 1.070957e-03 0.441572628 #> 1151 11.50 0.55707781 1.070421e-03 0.441851772 #> 1152 11.51 0.55679934 1.069886e-03 0.442130776 #> 1153 11.52 0.55652101 1.069351e-03 0.442409641 #> 1154 11.53 0.55624282 1.068817e-03 0.442688366 #> 1155 11.54 0.55596476 1.068283e-03 0.442966953 #> 1156 11.55 0.55568685 1.067749e-03 0.443245400 #> 1157 11.56 0.55540908 1.067215e-03 0.443523707 #> 1158 11.57 0.55513144 1.066681e-03 0.443801876 #> 1159 11.58 0.55485395 1.066148e-03 0.444079905 #> 1160 11.59 0.55457659 1.065615e-03 0.444357796 #> 1161 11.60 0.55429937 1.065083e-03 0.444635548 #> 1162 11.61 0.55402229 1.064550e-03 0.444913161 #> 1163 11.62 0.55374535 1.064018e-03 0.445190635 #> 1164 11.63 0.55346854 1.063486e-03 0.445467970 #> 1165 11.64 0.55319188 1.062954e-03 0.445745167 #> 1166 11.65 0.55291535 1.062423e-03 0.446022225 #> 1167 11.66 0.55263896 1.061892e-03 0.446299145 #> 1168 11.67 0.55236271 1.061361e-03 0.446575926 #> 1169 11.68 0.55208660 1.060831e-03 0.446852569 #> 1170 11.69 0.55181063 1.060300e-03 0.447129073 #> 1171 11.70 0.55153479 1.059770e-03 0.447405440 #> 1172 11.71 0.55125909 1.059241e-03 0.447681668 #> 1173 11.72 0.55098353 1.058711e-03 0.447957758 #> 1174 11.73 0.55070811 1.058182e-03 0.448233710 #> 1175 11.74 0.55043282 1.057653e-03 0.448509524 #> 1176 11.75 0.55015768 1.057124e-03 0.448785201 #> 1177 11.76 0.54988266 1.056596e-03 0.449060739 #> 1178 11.77 0.54960779 1.056068e-03 0.449336140 #> 1179 11.78 0.54933306 1.055540e-03 0.449611403 #> 1180 11.79 0.54905846 1.055012e-03 0.449886529 #> 1181 11.80 0.54878400 1.054485e-03 0.450161517 #> 1182 11.81 0.54850968 1.053958e-03 0.450436367 #> 1183 11.82 0.54823549 1.053431e-03 0.450711080 #> 1184 11.83 0.54796144 1.052904e-03 0.450985656 #> 1185 11.84 0.54768753 1.052378e-03 0.451260095 #> 1186 11.85 0.54741375 1.051852e-03 0.451534396 #> 1187 11.86 0.54714011 1.051326e-03 0.451808560 #> 1188 11.87 0.54686661 1.050801e-03 0.452082588 #> 1189 11.88 0.54659325 1.050275e-03 0.452356478 #> 1190 11.89 0.54632002 1.049750e-03 0.452630231 #> 1191 11.90 0.54604693 1.049225e-03 0.452903848 #> 1192 11.91 0.54577397 1.048701e-03 0.453177327 #> 1193 11.92 0.54550115 1.048177e-03 0.453450670 #> 1194 11.93 0.54522847 1.047653e-03 0.453723877 #> 1195 11.94 0.54495592 1.047129e-03 0.453996946 #> 1196 11.95 0.54468351 1.046606e-03 0.454269880 #> 1197 11.96 0.54441124 1.046083e-03 0.454542677 #> 1198 11.97 0.54413910 1.045560e-03 0.454815337 #> 1199 11.98 0.54386710 1.045037e-03 0.455087861 #> 1200 11.99 0.54359524 1.044515e-03 0.455360249 #> 1201 12.00 0.54332351 1.043992e-03 0.455632501 #> 1202 12.01 0.54305191 1.043471e-03 0.455904617 #> 1203 12.02 0.54278045 1.042949e-03 0.456176597 #> 1204 12.03 0.54250913 1.042428e-03 0.456448440 #> 1205 12.04 0.54223795 1.041907e-03 0.456720148 #> 1206 12.05 0.54196689 1.041386e-03 0.456991720 #> 1207 12.06 0.54169598 1.040865e-03 0.457263156 #> 1208 12.07 0.54142520 1.040345e-03 0.457534457 #> 1209 12.08 0.54115455 1.039825e-03 0.457805622 #> 1210 12.09 0.54088404 1.039305e-03 0.458076652 #> 1211 12.10 0.54061367 1.038786e-03 0.458347545 #> 1212 12.11 0.54034343 1.038266e-03 0.458618304 #> 1213 12.12 0.54007333 1.037747e-03 0.458888927 #> 1214 12.13 0.53980336 1.037229e-03 0.459159415 #> 1215 12.14 0.53953352 1.036710e-03 0.459429768 #> 1216 12.15 0.53926382 1.036192e-03 0.459699985 #> 1217 12.16 0.53899426 1.035674e-03 0.459970068 #> 1218 12.17 0.53872483 1.035156e-03 0.460240015 #> 1219 12.18 0.53845553 1.034639e-03 0.460509828 #> 1220 12.19 0.53818637 1.034121e-03 0.460779506 #> 1221 12.20 0.53791735 1.033605e-03 0.461049048 #> 1222 12.21 0.53764846 1.033088e-03 0.461318457 #> 1223 12.22 0.53737970 1.032571e-03 0.461587730 #> 1224 12.23 0.53711108 1.032055e-03 0.461856869 #> 1225 12.24 0.53684259 1.031539e-03 0.462125873 #> 1226 12.25 0.53657423 1.031024e-03 0.462394743 #> 1227 12.26 0.53630601 1.030508e-03 0.462663479 #> 1228 12.27 0.53603793 1.029993e-03 0.462932080 #> 1229 12.28 0.53576998 1.029478e-03 0.463200547 #> 1230 12.29 0.53550216 1.028964e-03 0.463468879 #> 1231 12.30 0.53523447 1.028449e-03 0.463737078 #> 1232 12.31 0.53496692 1.027935e-03 0.464005142 #> 1233 12.32 0.53469951 1.027422e-03 0.464273073 #> 1234 12.33 0.53443222 1.026908e-03 0.464540869 #> 1235 12.34 0.53416507 1.026395e-03 0.464808532 #> 1236 12.35 0.53389806 1.025882e-03 0.465076061 #> 1237 12.36 0.53363118 1.025369e-03 0.465343456 #> 1238 12.37 0.53336443 1.024856e-03 0.465610717 #> 1239 12.38 0.53309781 1.024344e-03 0.465877845 #> 1240 12.39 0.53283133 1.023832e-03 0.466144839 #> 1241 12.40 0.53256498 1.023320e-03 0.466411700 #> 1242 12.41 0.53229876 1.022808e-03 0.466678428 #> 1243 12.42 0.53203268 1.022297e-03 0.466945022 #> 1244 12.43 0.53176673 1.021786e-03 0.467211483 #> 1245 12.44 0.53150091 1.021275e-03 0.467477810 #> 1246 12.45 0.53123523 1.020765e-03 0.467744005 #> 1247 12.46 0.53096968 1.020255e-03 0.468010066 #> 1248 12.47 0.53070426 1.019745e-03 0.468275995 #> 1249 12.48 0.53043897 1.019235e-03 0.468541791 #> 1250 12.49 0.53017382 1.018725e-03 0.468807453 #> 1251 12.50 0.52990880 1.018216e-03 0.469072983 #> 1252 12.51 0.52964391 1.017707e-03 0.469338380 #> 1253 12.52 0.52937916 1.017198e-03 0.469603645 #> 1254 12.53 0.52911453 1.016690e-03 0.469868777 #> 1255 12.54 0.52885004 1.016182e-03 0.470133776 #> 1256 12.55 0.52858568 1.015674e-03 0.470398643 #> 1257 12.56 0.52832146 1.015166e-03 0.470663377 #> 1258 12.57 0.52805736 1.014659e-03 0.470927980 #> 1259 12.58 0.52779340 1.014151e-03 0.471192450 #> 1260 12.59 0.52752957 1.013645e-03 0.471456787 #> 1261 12.60 0.52726587 1.013138e-03 0.471720993 #> 1262 12.61 0.52700230 1.012631e-03 0.471985066 #> 1263 12.62 0.52673887 1.012125e-03 0.472249008 #> 1264 12.63 0.52647556 1.011619e-03 0.472512817 #> 1265 12.64 0.52621239 1.011114e-03 0.472776495 #> 1266 12.65 0.52594935 1.010608e-03 0.473040041 #> 1267 12.66 0.52568644 1.010103e-03 0.473303455 #> 1268 12.67 0.52542366 1.009598e-03 0.473566737 #> 1269 12.68 0.52516102 1.009093e-03 0.473829888 #> 1270 12.69 0.52489850 1.008589e-03 0.474092908 #> 1271 12.70 0.52463612 1.008085e-03 0.474355795 #> 1272 12.71 0.52437387 1.007581e-03 0.474618552 #> 1273 12.72 0.52411175 1.007077e-03 0.474881177 #> 1274 12.73 0.52384976 1.006574e-03 0.475143671 #> 1275 12.74 0.52358790 1.006071e-03 0.475406033 #> 1276 12.75 0.52332617 1.005568e-03 0.475668265 #> 1277 12.76 0.52306457 1.005065e-03 0.475930365 #> 1278 12.77 0.52280310 1.004563e-03 0.476192334 #> 1279 12.78 0.52254177 1.004060e-03 0.476454173 #> 1280 12.79 0.52228056 1.003559e-03 0.476715880 #> 1281 12.80 0.52201949 1.003057e-03 0.476977457 #> 1282 12.81 0.52175854 1.002556e-03 0.477238903 #> 1283 12.82 0.52149773 1.002054e-03 0.477500218 #> 1284 12.83 0.52123704 1.001553e-03 0.477761403 #> 1285 12.84 0.52097649 1.001053e-03 0.478022457 #> 1286 12.85 0.52071607 1.000552e-03 0.478283380 #> 1287 12.86 0.52045577 1.000052e-03 0.478544173 #> 1288 12.87 0.52019561 9.995524e-04 0.478804836 #> 1289 12.88 0.51993558 9.990527e-04 0.479065369 #> 1290 12.89 0.51967568 9.985533e-04 0.479325771 #> 1291 12.90 0.51941590 9.980542e-04 0.479586043 #> 1292 12.91 0.51915626 9.975553e-04 0.479846185 #> 1293 12.92 0.51889675 9.970566e-04 0.480106197 #> 1294 12.93 0.51863736 9.965582e-04 0.480366079 #> 1295 12.94 0.51837811 9.960601e-04 0.480625831 #> 1296 12.95 0.51811898 9.955621e-04 0.480885453 #> 1297 12.96 0.51785999 9.950645e-04 0.481144945 #> 1298 12.97 0.51760112 9.945671e-04 0.481404308 #> 1299 12.98 0.51734239 9.940699e-04 0.481663541 #> 1300 12.99 0.51708378 9.935730e-04 0.481922644 #> 1301 13.00 0.51682531 9.930763e-04 0.482181618 #> 1302 13.01 0.51656696 9.925799e-04 0.482440463 #> 1303 13.02 0.51630874 9.920838e-04 0.482699178 #> 1304 13.03 0.51605065 9.915879e-04 0.482957764 #> 1305 13.04 0.51579269 9.910922e-04 0.483216220 #> 1306 13.05 0.51553486 9.905968e-04 0.483474548 #> 1307 13.06 0.51527715 9.901016e-04 0.483732746 #> 1308 13.07 0.51501958 9.896067e-04 0.483990815 #> 1309 13.08 0.51476213 9.891120e-04 0.484248755 #> 1310 13.09 0.51450482 9.886175e-04 0.484506566 #> 1311 13.10 0.51424763 9.881234e-04 0.484764248 #> 1312 13.11 0.51399057 9.876294e-04 0.485021802 #> 1313 13.12 0.51373364 9.871357e-04 0.485279227 #> 1314 13.13 0.51347683 9.866423e-04 0.485536523 #> 1315 13.14 0.51322016 9.861491e-04 0.485793690 #> 1316 13.15 0.51296361 9.856561e-04 0.486050729 #> 1317 13.16 0.51270720 9.851634e-04 0.486307640 #> 1318 13.17 0.51245091 9.846710e-04 0.486564422 #> 1319 13.18 0.51219475 9.841788e-04 0.486821075 #> 1320 13.19 0.51193871 9.836868e-04 0.487077600 #> 1321 13.20 0.51168281 9.831951e-04 0.487333998 #> 1322 13.21 0.51142703 9.827036e-04 0.487590267 #> 1323 13.22 0.51117138 9.822124e-04 0.487846407 #> 1324 13.23 0.51091586 9.817214e-04 0.488102420 #> 1325 13.24 0.51066046 9.812306e-04 0.488358305 #> 1326 13.25 0.51040520 9.807402e-04 0.488614062 #> 1327 13.26 0.51015006 9.802499e-04 0.488869691 #> 1328 13.27 0.50989505 9.797599e-04 0.489125192 #> 1329 13.28 0.50964016 9.792701e-04 0.489380566 #> 1330 13.29 0.50938541 9.787806e-04 0.489635812 #> 1331 13.30 0.50913078 9.782914e-04 0.489890930 #> 1332 13.31 0.50887628 9.778023e-04 0.490145921 #> 1333 13.32 0.50862190 9.773136e-04 0.490400784 #> 1334 13.33 0.50836765 9.768250e-04 0.490655520 #> 1335 13.34 0.50811353 9.763367e-04 0.490910129 #> 1336 13.35 0.50785954 9.758487e-04 0.491164610 #> 1337 13.36 0.50760567 9.753609e-04 0.491418964 #> 1338 13.37 0.50735194 9.748733e-04 0.491673191 #> 1339 13.38 0.50709832 9.743860e-04 0.491927291 #> 1340 13.39 0.50684484 9.738989e-04 0.492181264 #> 1341 13.40 0.50659148 9.734121e-04 0.492435110 #> 1342 13.41 0.50633825 9.729255e-04 0.492688829 #> 1343 13.42 0.50608514 9.724392e-04 0.492942421 #> 1344 13.43 0.50583216 9.719531e-04 0.493195886 #> 1345 13.44 0.50557931 9.714672e-04 0.493449225 #> 1346 13.45 0.50532658 9.709816e-04 0.493702437 #> 1347 13.46 0.50507398 9.704963e-04 0.493955523 #> 1348 13.47 0.50482151 9.700111e-04 0.494208482 #> 1349 13.48 0.50456916 9.695262e-04 0.494461314 #> 1350 13.49 0.50431694 9.690416e-04 0.494714020 #> 1351 13.50 0.50406484 9.685572e-04 0.494966600 #> 1352 13.51 0.50381287 9.680730e-04 0.495219054 #> 1353 13.52 0.50356103 9.675891e-04 0.495471381 #> 1354 13.53 0.50330931 9.671055e-04 0.495723583 #> 1355 13.54 0.50305772 9.666220e-04 0.495975658 #> 1356 13.55 0.50280625 9.661388e-04 0.496227607 #> 1357 13.56 0.50255491 9.656559e-04 0.496479430 #> 1358 13.57 0.50230370 9.651732e-04 0.496731128 #> 1359 13.58 0.50205261 9.646907e-04 0.496982699 #> 1360 13.59 0.50180165 9.642085e-04 0.497234145 #> 1361 13.60 0.50155081 9.637265e-04 0.497485465 #> 1362 13.61 0.50130010 9.632448e-04 0.497736659 #> 1363 13.62 0.50104951 9.627633e-04 0.497987728 #> 1364 13.63 0.50079905 9.622820e-04 0.498238672 #> 1365 13.64 0.50054871 9.618010e-04 0.498489490 #> 1366 13.65 0.50029850 9.613202e-04 0.498740182 #> 1367 13.66 0.50004841 9.608397e-04 0.498990750 #> 1368 13.67 0.49979845 9.603594e-04 0.499241192 #> 1369 13.68 0.49954861 9.598793e-04 0.499491508 #> 1370 13.69 0.49929890 9.593995e-04 0.499741700 #> 1371 13.70 0.49904931 9.589199e-04 0.499991767 #> 1372 13.71 0.49879985 9.584406e-04 0.500241708 #> 1373 13.72 0.49855051 9.579615e-04 0.500491525 #> 1374 13.73 0.49830130 9.574826e-04 0.500741217 #> 1375 13.74 0.49805221 9.570040e-04 0.500990784 #> 1376 13.75 0.49780325 9.565256e-04 0.501240226 #> 1377 13.76 0.49755441 9.560475e-04 0.501489544 #> 1378 13.77 0.49730569 9.555695e-04 0.501738737 #> 1379 13.78 0.49705710 9.550919e-04 0.501987805 #> 1380 13.79 0.49680864 9.546145e-04 0.502236749 #> 1381 13.80 0.49656029 9.541373e-04 0.502485568 #> 1382 13.81 0.49631208 9.536603e-04 0.502734263 #> 1383 13.82 0.49606398 9.531836e-04 0.502982834 #> 1384 13.83 0.49581601 9.527071e-04 0.503231281 #> 1385 13.84 0.49556817 9.522309e-04 0.503479603 #> 1386 13.85 0.49532044 9.517549e-04 0.503727801 #> 1387 13.86 0.49507285 9.512791e-04 0.503975875 #> 1388 13.87 0.49482537 9.508036e-04 0.504223825 #> 1389 13.88 0.49457802 9.503283e-04 0.504471651 #> 1390 13.89 0.49433079 9.498533e-04 0.504719354 #> 1391 13.90 0.49408369 9.493785e-04 0.504966932 #> 1392 13.91 0.49383671 9.489039e-04 0.505214387 #> 1393 13.92 0.49358985 9.484296e-04 0.505461718 #> 1394 13.93 0.49334312 9.479555e-04 0.505708925 #> 1395 13.94 0.49309651 9.474816e-04 0.505956009 #> 1396 13.95 0.49285002 9.470080e-04 0.506202969 #> 1397 13.96 0.49260366 9.465346e-04 0.506449806 #> 1398 13.97 0.49235742 9.460615e-04 0.506696519 #> 1399 13.98 0.49211130 9.455886e-04 0.506943109 #> 1400 13.99 0.49186531 9.451159e-04 0.507189576 #> 1401 14.00 0.49161944 9.446434e-04 0.507435920 #> 1402 14.01 0.49137369 9.441712e-04 0.507682140 #> 1403 14.02 0.49112806 9.436993e-04 0.507928238 #> 1404 14.03 0.49088256 9.432275e-04 0.508174212 #> 1405 14.04 0.49063718 9.427560e-04 0.508420064 #> 1406 14.05 0.49039192 9.422848e-04 0.508665792 #> 1407 14.06 0.49014679 9.418138e-04 0.508911398 #> 1408 14.07 0.48990178 9.413430e-04 0.509156881 #> 1409 14.08 0.48965689 9.408724e-04 0.509402241 #> 1410 14.09 0.48941212 9.404021e-04 0.509647479 #> 1411 14.10 0.48916747 9.399320e-04 0.509892594 #> 1412 14.11 0.48892295 9.394622e-04 0.510137586 #> 1413 14.12 0.48867855 9.389926e-04 0.510382456 #> 1414 14.13 0.48843427 9.385232e-04 0.510627204 #> 1415 14.14 0.48819012 9.380540e-04 0.510871829 #> 1416 14.15 0.48794608 9.375851e-04 0.511116332 #> 1417 14.16 0.48770217 9.371164e-04 0.511360713 #> 1418 14.17 0.48745838 9.366480e-04 0.511604971 #> 1419 14.18 0.48721471 9.361798e-04 0.511849108 #> 1420 14.19 0.48697117 9.357118e-04 0.512093122 #> 1421 14.20 0.48672774 9.352441e-04 0.512337015 #> 1422 14.21 0.48648444 9.347766e-04 0.512580785 #> 1423 14.22 0.48624126 9.343093e-04 0.512824434 #> 1424 14.23 0.48599820 9.338423e-04 0.513067961 #> 1425 14.24 0.48575526 9.333755e-04 0.513311366 #> 1426 14.25 0.48551244 9.329089e-04 0.513554649 #> 1427 14.26 0.48526975 9.324426e-04 0.513797811 #> 1428 14.27 0.48502717 9.319764e-04 0.514040852 #> 1429 14.28 0.48478472 9.315106e-04 0.514283771 #> 1430 14.29 0.48454239 9.310449e-04 0.514526568 #> 1431 14.30 0.48430018 9.305795e-04 0.514769244 #> 1432 14.31 0.48405809 9.301144e-04 0.515011799 #> 1433 14.32 0.48381612 9.296494e-04 0.515254232 #> 1434 14.33 0.48357427 9.291847e-04 0.515496545 #> 1435 14.34 0.48333254 9.287202e-04 0.515738736 #> 1436 14.35 0.48309094 9.282560e-04 0.515980806 #> 1437 14.36 0.48284945 9.277920e-04 0.516222755 #> 1438 14.37 0.48260809 9.273282e-04 0.516464583 #> 1439 14.38 0.48236684 9.268646e-04 0.516706290 #> 1440 14.39 0.48212572 9.264013e-04 0.516947877 #> 1441 14.40 0.48188472 9.259382e-04 0.517189343 #> 1442 14.41 0.48164384 9.254754e-04 0.517430688 #> 1443 14.42 0.48140308 9.250128e-04 0.517671912 #> 1444 14.43 0.48116243 9.245504e-04 0.517913016 #> 1445 14.44 0.48092191 9.240882e-04 0.518153999 #> 1446 14.45 0.48068151 9.236263e-04 0.518394862 #> 1447 14.46 0.48044123 9.231646e-04 0.518635604 #> 1448 14.47 0.48020107 9.227031e-04 0.518876226 #> 1449 14.48 0.47996103 9.222419e-04 0.519116728 #> 1450 14.49 0.47972111 9.217809e-04 0.519357109 #> 1451 14.50 0.47948131 9.213201e-04 0.519597371 #> 1452 14.51 0.47924163 9.208596e-04 0.519837512 #> 1453 14.52 0.47900207 9.203993e-04 0.520077533 #> 1454 14.53 0.47876263 9.199392e-04 0.520317435 #> 1455 14.54 0.47852330 9.194793e-04 0.520557216 #> 1456 14.55 0.47828410 9.190197e-04 0.520796877 #> 1457 14.56 0.47804502 9.185603e-04 0.521036419 #> 1458 14.57 0.47780606 9.181011e-04 0.521275841 #> 1459 14.58 0.47756721 9.176422e-04 0.521515143 #> 1460 14.59 0.47732849 9.171835e-04 0.521754326 #> 1461 14.60 0.47708989 9.167250e-04 0.521993389 #> 1462 14.61 0.47685140 9.162668e-04 0.522232333 #> 1463 14.62 0.47661303 9.158087e-04 0.522471157 #> 1464 14.63 0.47637479 9.153510e-04 0.522709862 #> 1465 14.64 0.47613666 9.148934e-04 0.522948447 #> 1466 14.65 0.47589865 9.144361e-04 0.523186913 #> 1467 14.66 0.47566076 9.139790e-04 0.523425260 #> 1468 14.67 0.47542299 9.135221e-04 0.523663488 #> 1469 14.68 0.47518534 9.130654e-04 0.523901597 #> 1470 14.69 0.47494780 9.126090e-04 0.524139586 #> 1471 14.70 0.47471039 9.121528e-04 0.524377457 #> 1472 14.71 0.47447309 9.116969e-04 0.524615209 #> 1473 14.72 0.47423592 9.112411e-04 0.524852842 #> 1474 14.73 0.47399886 9.107856e-04 0.525090356 #> 1475 14.74 0.47376192 9.103303e-04 0.525327752 #> 1476 14.75 0.47352510 9.098753e-04 0.525565028 #> 1477 14.76 0.47328839 9.094205e-04 0.525802187 #> 1478 14.77 0.47305181 9.089659e-04 0.526039226 #> 1479 14.78 0.47281534 9.085115e-04 0.526276147 #> 1480 14.79 0.47257899 9.080574e-04 0.526512950 #> 1481 14.80 0.47234276 9.076034e-04 0.526749635 #> 1482 14.81 0.47210665 9.071498e-04 0.526986201 #> 1483 14.82 0.47187066 9.066963e-04 0.527222648 #> 1484 14.83 0.47163478 9.062431e-04 0.527458978 #> 1485 14.84 0.47139902 9.057900e-04 0.527695189 #> 1486 14.85 0.47116338 9.053373e-04 0.527931283 #> 1487 14.86 0.47092786 9.048847e-04 0.528167258 #> 1488 14.87 0.47069245 9.044324e-04 0.528403116 #> 1489 14.88 0.47045716 9.039803e-04 0.528638855 #> 1490 14.89 0.47022199 9.035284e-04 0.528874477 #> 1491 14.90 0.46998694 9.030767e-04 0.529109981 #> 1492 14.91 0.46975201 9.026253e-04 0.529345367 #> 1493 14.92 0.46951719 9.021741e-04 0.529580635 #> 1494 14.93 0.46928249 9.017231e-04 0.529815786 #> 1495 14.94 0.46904791 9.012724e-04 0.530050820 #> 1496 14.95 0.46881344 9.008219e-04 0.530285735 #> 1497 14.96 0.46857909 9.003716e-04 0.530520534 #> 1498 14.97 0.46834486 8.999215e-04 0.530755215 #> 1499 14.98 0.46811075 8.994717e-04 0.530989779 #> 1500 14.99 0.46787675 8.990220e-04 0.531224225 #> 1501 15.00 0.46764287 8.985726e-04 0.531458555 #> 1502 15.01 0.46740911 8.981235e-04 0.531692767 #> 1503 15.02 0.46717546 8.976745e-04 0.531926862 #> 1504 15.03 0.46694193 8.972258e-04 0.532160840 #> 1505 15.04 0.46670852 8.967773e-04 0.532394701 #> 1506 15.05 0.46647523 8.963290e-04 0.532628445 #> 1507 15.06 0.46624205 8.958810e-04 0.532862073 #> 1508 15.07 0.46600898 8.954331e-04 0.533095583 #> 1509 15.08 0.46577604 8.949855e-04 0.533328977 #> 1510 15.09 0.46554321 8.945381e-04 0.533562254 #> 1511 15.10 0.46531049 8.940910e-04 0.533795415 #> 1512 15.11 0.46507790 8.936441e-04 0.534028459 #> 1513 15.12 0.46484542 8.931973e-04 0.534261386 #> 1514 15.13 0.46461305 8.927509e-04 0.534494198 #> 1515 15.14 0.46438080 8.923046e-04 0.534726892 #> 1516 15.15 0.46414867 8.918585e-04 0.534959471 #> 1517 15.16 0.46391665 8.914127e-04 0.535191933 #> 1518 15.17 0.46368475 8.909671e-04 0.535424279 #> 1519 15.18 0.46345297 8.905218e-04 0.535656509 #> 1520 15.19 0.46322130 8.900766e-04 0.535888622 #> 1521 15.20 0.46298975 8.896317e-04 0.536120620 #> 1522 15.21 0.46275831 8.891870e-04 0.536352502 #> 1523 15.22 0.46252699 8.887425e-04 0.536584268 #> 1524 15.23 0.46229578 8.882982e-04 0.536815917 #> 1525 15.24 0.46206469 8.878542e-04 0.537047452 #> 1526 15.25 0.46183372 8.874104e-04 0.537278870 #> 1527 15.26 0.46160286 8.869668e-04 0.537510173 #> 1528 15.27 0.46137212 8.865234e-04 0.537741360 #> 1529 15.28 0.46114149 8.860803e-04 0.537972432 #> 1530 15.29 0.46091098 8.856373e-04 0.538203388 #> 1531 15.30 0.46068058 8.851946e-04 0.538434228 #> 1532 15.31 0.46045029 8.847521e-04 0.538664953 #> 1533 15.32 0.46022013 8.843099e-04 0.538895563 #> 1534 15.33 0.45999007 8.838678e-04 0.539126058 #> 1535 15.34 0.45976014 8.834260e-04 0.539356437 #> 1536 15.35 0.45953031 8.829844e-04 0.539586701 #> 1537 15.36 0.45930061 8.825430e-04 0.539816851 #> 1538 15.37 0.45907101 8.821019e-04 0.540046885 #> 1539 15.38 0.45884154 8.816609e-04 0.540276804 #> 1540 15.39 0.45861217 8.812202e-04 0.540506608 #> 1541 15.40 0.45838292 8.807797e-04 0.540736297 #> 1542 15.41 0.45815379 8.803394e-04 0.540965872 #> 1543 15.42 0.45792477 8.798994e-04 0.541195331 #> 1544 15.43 0.45769586 8.794595e-04 0.541424676 #> 1545 15.44 0.45746707 8.790199e-04 0.541653907 #> 1546 15.45 0.45723840 8.785805e-04 0.541883022 #> 1547 15.46 0.45700983 8.781413e-04 0.542112024 #> 1548 15.47 0.45678139 8.777024e-04 0.542340910 #> 1549 15.48 0.45655305 8.772636e-04 0.542569683 #> 1550 15.49 0.45632483 8.768251e-04 0.542798341 #> 1551 15.50 0.45609673 8.763868e-04 0.543026884 #> 1552 15.51 0.45586874 8.759487e-04 0.543255314 #> 1553 15.52 0.45564086 8.755108e-04 0.543483629 #> 1554 15.53 0.45541310 8.750732e-04 0.543711830 #> 1555 15.54 0.45518545 8.746358e-04 0.543939917 #> 1556 15.55 0.45495791 8.741986e-04 0.544167890 #> 1557 15.56 0.45473049 8.737616e-04 0.544395750 #> 1558 15.57 0.45450318 8.733248e-04 0.544623495 #> 1559 15.58 0.45427599 8.728883e-04 0.544851126 #> 1560 15.59 0.45404890 8.724519e-04 0.545078644 #> 1561 15.60 0.45382194 8.720158e-04 0.545306048 #> 1562 15.61 0.45359508 8.715799e-04 0.545533338 #> 1563 15.62 0.45336834 8.711442e-04 0.545760514 #> 1564 15.63 0.45314171 8.707088e-04 0.545987577 #> 1565 15.64 0.45291520 8.702735e-04 0.546214527 #> 1566 15.65 0.45268880 8.698385e-04 0.546441363 #> 1567 15.66 0.45246251 8.694037e-04 0.546668085 #> 1568 15.67 0.45223634 8.689691e-04 0.546894695 #> 1569 15.68 0.45201027 8.685347e-04 0.547121191 #> 1570 15.69 0.45178433 8.681005e-04 0.547347574 #> 1571 15.70 0.45155849 8.676666e-04 0.547573843 #> 1572 15.71 0.45133277 8.672329e-04 0.547800000 #> 1573 15.72 0.45110716 8.667994e-04 0.548026043 #> 1574 15.73 0.45088166 8.663661e-04 0.548251974 #> 1575 15.74 0.45065628 8.659330e-04 0.548477791 #> 1576 15.75 0.45043100 8.655001e-04 0.548703496 #> 1577 15.76 0.45020584 8.650675e-04 0.548929088 #> 1578 15.77 0.44998080 8.646351e-04 0.549154567 #> 1579 15.78 0.44975586 8.642029e-04 0.549379933 #> 1580 15.79 0.44953104 8.637709e-04 0.549605187 #> 1581 15.80 0.44930633 8.633391e-04 0.549830328 #> 1582 15.81 0.44908174 8.629075e-04 0.550055357 #> 1583 15.82 0.44885725 8.624762e-04 0.550280273 #> 1584 15.83 0.44863288 8.620451e-04 0.550505076 #> 1585 15.84 0.44840862 8.616141e-04 0.550729768 #> 1586 15.85 0.44818447 8.611834e-04 0.550954347 #> 1587 15.86 0.44796043 8.607530e-04 0.551178813 #> 1588 15.87 0.44773651 8.603227e-04 0.551403168 #> 1589 15.88 0.44751270 8.598926e-04 0.551627410 #> 1590 15.89 0.44728900 8.594628e-04 0.551851541 #> 1591 15.90 0.44706541 8.590332e-04 0.552075559 #> 1592 15.91 0.44684193 8.586038e-04 0.552299465 #> 1593 15.92 0.44661857 8.581746e-04 0.552523259 #> 1594 15.93 0.44639531 8.577456e-04 0.552746942 #> 1595 15.94 0.44617217 8.573168e-04 0.552970512 #> 1596 15.95 0.44594914 8.568883e-04 0.553193971 #> 1597 15.96 0.44572622 8.564599e-04 0.553417319 #> 1598 15.97 0.44550341 8.560318e-04 0.553640554 #> 1599 15.98 0.44528072 8.556039e-04 0.553863678 #> 1600 15.99 0.44505813 8.551762e-04 0.554086690 #> 1601 16.00 0.44483566 8.547487e-04 0.554309591 #> 1602 16.01 0.44461330 8.543215e-04 0.554532381 #> 1603 16.02 0.44439105 8.538944e-04 0.554755059 #> 1604 16.03 0.44416891 8.534676e-04 0.554977626 #> 1605 16.04 0.44394688 8.530409e-04 0.555200081 #> 1606 16.05 0.44372496 8.526145e-04 0.555422426 #> 1607 16.06 0.44350315 8.521883e-04 0.555644659 #> 1608 16.07 0.44328146 8.517623e-04 0.555866781 #> 1609 16.08 0.44305987 8.513366e-04 0.556088792 #> 1610 16.09 0.44283840 8.509110e-04 0.556310692 #> 1611 16.10 0.44261703 8.504857e-04 0.556532482 #> 1612 16.11 0.44239578 8.500605e-04 0.556754160 #> 1613 16.12 0.44217464 8.496356e-04 0.556975728 #> 1614 16.13 0.44195360 8.492109e-04 0.557197184 #> 1615 16.14 0.44173268 8.487864e-04 0.557418530 #> 1616 16.15 0.44151187 8.483621e-04 0.557639766 #> 1617 16.16 0.44129117 8.479380e-04 0.557860891 #> 1618 16.17 0.44107058 8.475142e-04 0.558081905 #> 1619 16.18 0.44085010 8.470905e-04 0.558302809 #> 1620 16.19 0.44062973 8.466671e-04 0.558523602 #> 1621 16.20 0.44040947 8.462438e-04 0.558744285 #> 1622 16.21 0.44018932 8.458208e-04 0.558964858 #> 1623 16.22 0.43996928 8.453980e-04 0.559185320 #> 1624 16.23 0.43974935 8.449754e-04 0.559405673 #> 1625 16.24 0.43952953 8.445530e-04 0.559625915 #> 1626 16.25 0.43930982 8.441309e-04 0.559846047 #> 1627 16.26 0.43909022 8.437089e-04 0.560066069 #> 1628 16.27 0.43887073 8.432872e-04 0.560285981 #> 1629 16.28 0.43865135 8.428656e-04 0.560505783 #> 1630 16.29 0.43843208 8.424443e-04 0.560725475 #> 1631 16.30 0.43821292 8.420232e-04 0.560945057 #> 1632 16.31 0.43799387 8.416023e-04 0.561164530 #> 1633 16.32 0.43777493 8.411816e-04 0.561383893 #> 1634 16.33 0.43755609 8.407611e-04 0.561603146 #> 1635 16.34 0.43733737 8.403408e-04 0.561822290 #> 1636 16.35 0.43711876 8.399207e-04 0.562041324 #> 1637 16.36 0.43690025 8.395009e-04 0.562260249 #> 1638 16.37 0.43668186 8.390812e-04 0.562479064 #> 1639 16.38 0.43646357 8.386618e-04 0.562697769 #> 1640 16.39 0.43624539 8.382426e-04 0.562916366 #> 1641 16.40 0.43602732 8.378236e-04 0.563134853 #> 1642 16.41 0.43580936 8.374048e-04 0.563353231 #> 1643 16.42 0.43559151 8.369862e-04 0.563571500 #> 1644 16.43 0.43537377 8.365678e-04 0.563789660 #> 1645 16.44 0.43515614 8.361496e-04 0.564007710 #> 1646 16.45 0.43493862 8.357316e-04 0.564225652 #> 1647 16.46 0.43472120 8.353139e-04 0.564443485 #> 1648 16.47 0.43450390 8.348963e-04 0.564661209 #> 1649 16.48 0.43428670 8.344790e-04 0.564878824 #> 1650 16.49 0.43406961 8.340618e-04 0.565096330 #> 1651 16.50 0.43385263 8.336449e-04 0.565313727 #> 1652 16.51 0.43363576 8.332282e-04 0.565531016 #> 1653 16.52 0.43341899 8.328117e-04 0.565748196 #> 1654 16.53 0.43320234 8.323954e-04 0.565965268 #> 1655 16.54 0.43298579 8.319793e-04 0.566182231 #> 1656 16.55 0.43276935 8.315634e-04 0.566399086 #> 1657 16.56 0.43255302 8.311477e-04 0.566615832 #> 1658 16.57 0.43233680 8.307322e-04 0.566832470 #> 1659 16.58 0.43212068 8.303170e-04 0.567049000 #> 1660 16.59 0.43190468 8.299019e-04 0.567265421 #> 1661 16.60 0.43168878 8.294871e-04 0.567481734 #> 1662 16.61 0.43147299 8.290724e-04 0.567697939 #> 1663 16.62 0.43125731 8.286580e-04 0.567914036 #> 1664 16.63 0.43104173 8.282438e-04 0.568130025 #> 1665 16.64 0.43082626 8.278298e-04 0.568345906 #> 1666 16.65 0.43061090 8.274160e-04 0.568561679 #> 1667 16.66 0.43039565 8.270024e-04 0.568777345 #> 1668 16.67 0.43018051 8.265890e-04 0.568992902 #> 1669 16.68 0.42996547 8.261758e-04 0.569208352 #> 1670 16.69 0.42975054 8.257628e-04 0.569423694 #> 1671 16.70 0.42953572 8.253500e-04 0.569638928 #> 1672 16.71 0.42932101 8.249374e-04 0.569854055 #> 1673 16.72 0.42910640 8.245251e-04 0.570069074 #> 1674 16.73 0.42889190 8.241129e-04 0.570283986 #> 1675 16.74 0.42867751 8.237010e-04 0.570498790 #> 1676 16.75 0.42846322 8.232892e-04 0.570713487 #> 1677 16.76 0.42824905 8.228777e-04 0.570928077 #> 1678 16.77 0.42803497 8.224663e-04 0.571142559 #> 1679 16.78 0.42782101 8.220552e-04 0.571356934 #> 1680 16.79 0.42760715 8.216443e-04 0.571571202 #> 1681 16.80 0.42739340 8.212336e-04 0.571785363 #> 1682 16.81 0.42717976 8.208230e-04 0.571999417 #> 1683 16.82 0.42696622 8.204127e-04 0.572213364 #> 1684 16.83 0.42675279 8.200026e-04 0.572427203 #> 1685 16.84 0.42653947 8.195927e-04 0.572640936 #> 1686 16.85 0.42632625 8.191830e-04 0.572854563 #> 1687 16.86 0.42611314 8.187735e-04 0.573068082 #> 1688 16.87 0.42590014 8.183643e-04 0.573281495 #> 1689 16.88 0.42568724 8.179552e-04 0.573494800 #> 1690 16.89 0.42547445 8.175463e-04 0.573708000 #> 1691 16.90 0.42526177 8.171376e-04 0.573921092 #> 1692 16.91 0.42504919 8.167292e-04 0.574134079 #> 1693 16.92 0.42483672 8.163209e-04 0.574346958 #> 1694 16.93 0.42462436 8.159128e-04 0.574559732 #> 1695 16.94 0.42441210 8.155050e-04 0.574772399 #> 1696 16.95 0.42419994 8.150973e-04 0.574984959 #> 1697 16.96 0.42398790 8.146899e-04 0.575197414 #> 1698 16.97 0.42377596 8.142826e-04 0.575409762 #> 1699 16.98 0.42356412 8.138756e-04 0.575622004 #> 1700 16.99 0.42335239 8.134688e-04 0.575834140 #> 1701 17.00 0.42314077 8.130621e-04 0.576046170 #> 1702 17.01 0.42292925 8.126557e-04 0.576258094 #> 1703 17.02 0.42271784 8.122495e-04 0.576469912 #> 1704 17.03 0.42250653 8.118435e-04 0.576681624 #> 1705 17.04 0.42229533 8.114376e-04 0.576893230 #> 1706 17.05 0.42208424 8.110320e-04 0.577104731 #> 1707 17.06 0.42187325 8.106266e-04 0.577316126 #> 1708 17.07 0.42166236 8.102214e-04 0.577527415 #> 1709 17.08 0.42145159 8.098164e-04 0.577738598 #> 1710 17.09 0.42124091 8.094116e-04 0.577949676 #> 1711 17.10 0.42103034 8.090070e-04 0.578160649 #> 1712 17.11 0.42081988 8.086026e-04 0.578371516 #> 1713 17.12 0.42060952 8.081984e-04 0.578582277 #> 1714 17.13 0.42039927 8.077944e-04 0.578792933 #> 1715 17.14 0.42018913 8.073906e-04 0.579003484 #> 1716 17.15 0.41997908 8.069870e-04 0.579213930 #> 1717 17.16 0.41976915 8.065836e-04 0.579424270 #> 1718 17.17 0.41955931 8.061804e-04 0.579634506 #> 1719 17.18 0.41934959 8.057774e-04 0.579844636 #> 1720 17.19 0.41913996 8.053746e-04 0.580054661 #> 1721 17.20 0.41893045 8.049720e-04 0.580264581 #> 1722 17.21 0.41872103 8.045697e-04 0.580474396 #> 1723 17.22 0.41851173 8.041675e-04 0.580684107 #> 1724 17.23 0.41830252 8.037655e-04 0.580893712 #> 1725 17.24 0.41809342 8.033637e-04 0.581103213 #> 1726 17.25 0.41788443 8.029621e-04 0.581312609 #> 1727 17.26 0.41767554 8.025607e-04 0.581521901 #> 1728 17.27 0.41746675 8.021596e-04 0.581731087 #> 1729 17.28 0.41725807 8.017586e-04 0.581940170 #> 1730 17.29 0.41704949 8.013578e-04 0.582149147 #> 1731 17.30 0.41684102 8.009572e-04 0.582358020 #> 1732 17.31 0.41663265 8.005568e-04 0.582566789 #> 1733 17.32 0.41642439 8.001567e-04 0.582775454 #> 1734 17.33 0.41621623 7.997567e-04 0.582984014 #> 1735 17.34 0.41600817 7.993569e-04 0.583192470 #> 1736 17.35 0.41580022 7.989573e-04 0.583400821 #> 1737 17.36 0.41559237 7.985579e-04 0.583609069 #> 1738 17.37 0.41538463 7.981588e-04 0.583817212 #> 1739 17.38 0.41517699 7.977598e-04 0.584025252 #> 1740 17.39 0.41496945 7.973610e-04 0.584233187 #> 1741 17.40 0.41476202 7.969624e-04 0.584441019 #> 1742 17.41 0.41455469 7.965640e-04 0.584648746 #> 1743 17.42 0.41434746 7.961659e-04 0.584856370 #> 1744 17.43 0.41414034 7.957679e-04 0.585063890 #> 1745 17.44 0.41393332 7.953701e-04 0.585271306 #> 1746 17.45 0.41372641 7.949725e-04 0.585478619 #> 1747 17.46 0.41351960 7.945751e-04 0.585685827 #> 1748 17.47 0.41331289 7.941779e-04 0.585892933 #> 1749 17.48 0.41310628 7.937809e-04 0.586099935 #> 1750 17.49 0.41289978 7.933842e-04 0.586306833 #> 1751 17.50 0.41269338 7.929876e-04 0.586513628 #> 1752 17.51 0.41248709 7.925912e-04 0.586720319 #> 1753 17.52 0.41228090 7.921950e-04 0.586926907 #> 1754 17.53 0.41207481 7.917990e-04 0.587133392 #> 1755 17.54 0.41186882 7.914032e-04 0.587339774 #> 1756 17.55 0.41166294 7.910076e-04 0.587546053 #> 1757 17.56 0.41145716 7.906122e-04 0.587752228 #> 1758 17.57 0.41125148 7.902170e-04 0.587958301 #> 1759 17.58 0.41104591 7.898219e-04 0.588164270 #> 1760 17.59 0.41084044 7.894271e-04 0.588370136 #> 1761 17.60 0.41063507 7.890325e-04 0.588575900 #> 1762 17.61 0.41042980 7.886381e-04 0.588781560 #> 1763 17.62 0.41022464 7.882439e-04 0.588987118 #> 1764 17.63 0.41001958 7.878499e-04 0.589192573 #> 1765 17.64 0.40981462 7.874560e-04 0.589397926 #> 1766 17.65 0.40960976 7.870624e-04 0.589603175 #> 1767 17.66 0.40940501 7.866690e-04 0.589808323 #> 1768 17.67 0.40920036 7.862757e-04 0.590013367 #> 1769 17.68 0.40899581 7.858827e-04 0.590218309 #> 1770 17.69 0.40879136 7.854899e-04 0.590423149 #> 1771 17.70 0.40858702 7.850972e-04 0.590627886 #> 1772 17.71 0.40838277 7.847048e-04 0.590832521 #> 1773 17.72 0.40817863 7.843125e-04 0.591037054 #> 1774 17.73 0.40797460 7.839204e-04 0.591241484 #> 1775 17.74 0.40777066 7.835286e-04 0.591445812 #> 1776 17.75 0.40756682 7.831369e-04 0.591650038 #> 1777 17.76 0.40736309 7.827454e-04 0.591854162 #> 1778 17.77 0.40715946 7.823542e-04 0.592058184 #> 1779 17.78 0.40695593 7.819631e-04 0.592262104 #> 1780 17.79 0.40675251 7.815722e-04 0.592465922 #> 1781 17.80 0.40654918 7.811815e-04 0.592669638 #> 1782 17.81 0.40634596 7.807910e-04 0.592873252 #> 1783 17.82 0.40614283 7.804007e-04 0.593076765 #> 1784 17.83 0.40593981 7.800106e-04 0.593280176 #> 1785 17.84 0.40573689 7.796207e-04 0.593483485 #> 1786 17.85 0.40553408 7.792310e-04 0.593686692 #> 1787 17.86 0.40533136 7.788415e-04 0.593889798 #> 1788 17.87 0.40512875 7.784522e-04 0.594092802 #> 1789 17.88 0.40492623 7.780630e-04 0.594295705 #> 1790 17.89 0.40472382 7.776741e-04 0.594498507 #> 1791 17.90 0.40452151 7.772854e-04 0.594701207 #> 1792 17.91 0.40431930 7.768968e-04 0.594903806 #> 1793 17.92 0.40411719 7.765085e-04 0.595106303 #> 1794 17.93 0.40391518 7.761203e-04 0.595308699 #> 1795 17.94 0.40371327 7.757323e-04 0.595510994 #> 1796 17.95 0.40351147 7.753446e-04 0.595713188 #> 1797 17.96 0.40330976 7.749570e-04 0.595915281 #> 1798 17.97 0.40310816 7.745696e-04 0.596117273 #> 1799 17.98 0.40290665 7.741824e-04 0.596319164 #> 1800 17.99 0.40270525 7.737954e-04 0.596520954 #> 1801 18.00 0.40250395 7.734086e-04 0.596722643 #> 1802 18.01 0.40230275 7.730220e-04 0.596924231 #> 1803 18.02 0.40210165 7.726356e-04 0.597125719 #> 1804 18.03 0.40190064 7.722494e-04 0.597327106 #> 1805 18.04 0.40169974 7.718634e-04 0.597528392 #> 1806 18.05 0.40149895 7.714775e-04 0.597729577 #> 1807 18.06 0.40129825 7.710919e-04 0.597930662 #> 1808 18.07 0.40109765 7.707064e-04 0.598131647 #> 1809 18.08 0.40089715 7.703212e-04 0.598332531 #> 1810 18.09 0.40069675 7.699361e-04 0.598533314 #> 1811 18.10 0.40049645 7.695512e-04 0.598733997 #> 1812 18.11 0.40029625 7.691666e-04 0.598934580 #> 1813 18.12 0.40009616 7.687821e-04 0.599135063 #> 1814 18.13 0.39989616 7.683978e-04 0.599335445 #> 1815 18.14 0.39969626 7.680137e-04 0.599535727 #> 1816 18.15 0.39949646 7.676298e-04 0.599735910 #> 1817 18.16 0.39929676 7.672460e-04 0.599935992 #> 1818 18.17 0.39909716 7.668625e-04 0.600135974 #> 1819 18.18 0.39889767 7.664792e-04 0.600335856 #> 1820 18.19 0.39869827 7.660960e-04 0.600535638 #> 1821 18.20 0.39849897 7.657131e-04 0.600735320 #> 1822 18.21 0.39829977 7.653303e-04 0.600934902 #> 1823 18.22 0.39810067 7.649478e-04 0.601134385 #> 1824 18.23 0.39790167 7.645654e-04 0.601333768 #> 1825 18.24 0.39770277 7.641832e-04 0.601533051 #> 1826 18.25 0.39750396 7.638012e-04 0.601732235 #> 1827 18.26 0.39730526 7.634194e-04 0.601931319 #> 1828 18.27 0.39710666 7.630378e-04 0.602130304 #> 1829 18.28 0.39690815 7.626563e-04 0.602329189 #> 1830 18.29 0.39670975 7.622751e-04 0.602527975 #> 1831 18.30 0.39651144 7.618941e-04 0.602726661 #> 1832 18.31 0.39631324 7.615132e-04 0.602925248 #> 1833 18.32 0.39611513 7.611326e-04 0.603123736 #> 1834 18.33 0.39591712 7.607521e-04 0.603322124 #> 1835 18.34 0.39571921 7.603718e-04 0.603520414 #> 1836 18.35 0.39552140 7.599917e-04 0.603718604 #> 1837 18.36 0.39532369 7.596118e-04 0.603916695 #> 1838 18.37 0.39512608 7.592321e-04 0.604114687 #> 1839 18.38 0.39492857 7.588526e-04 0.604312580 #> 1840 18.39 0.39473115 7.584733e-04 0.604510375 #> 1841 18.40 0.39453384 7.580941e-04 0.604708070 #> 1842 18.41 0.39433662 7.577152e-04 0.604905667 #> 1843 18.42 0.39413950 7.573364e-04 0.605103164 #> 1844 18.43 0.39394248 7.569578e-04 0.605300564 #> 1845 18.44 0.39374556 7.565794e-04 0.605497864 #> 1846 18.45 0.39354873 7.562012e-04 0.605695066 #> 1847 18.46 0.39335201 7.558232e-04 0.605892169 #> 1848 18.47 0.39315538 7.554454e-04 0.606089174 #> 1849 18.48 0.39295885 7.550678e-04 0.606286080 #> 1850 18.49 0.39276242 7.546904e-04 0.606482888 #> 1851 18.50 0.39256609 7.543131e-04 0.606679597 #> 1852 18.51 0.39236986 7.539360e-04 0.606876208 #> 1853 18.52 0.39217372 7.535592e-04 0.607072721 #> 1854 18.53 0.39197768 7.531825e-04 0.607269135 #> 1855 18.54 0.39178174 7.528060e-04 0.607465452 #> 1856 18.55 0.39158590 7.524297e-04 0.607661670 #> 1857 18.56 0.39139016 7.520536e-04 0.607857790 #> 1858 18.57 0.39119451 7.516776e-04 0.608053812 #> 1859 18.58 0.39099896 7.513019e-04 0.608249736 #> 1860 18.59 0.39080351 7.509263e-04 0.608445562 #> 1861 18.60 0.39060816 7.505509e-04 0.608641291 #> 1862 18.61 0.39041290 7.501758e-04 0.608836921 #> 1863 18.62 0.39021775 7.498008e-04 0.609032454 #> 1864 18.63 0.39002269 7.494260e-04 0.609227889 #> 1865 18.64 0.38982772 7.490513e-04 0.609423226 #> 1866 18.65 0.38963286 7.486769e-04 0.609618466 #> 1867 18.66 0.38943809 7.483027e-04 0.609813608 #> 1868 18.67 0.38924342 7.479286e-04 0.610008652 #> 1869 18.68 0.38904885 7.475547e-04 0.610203599 #> 1870 18.69 0.38885437 7.471811e-04 0.610398448 #> 1871 18.70 0.38865999 7.468076e-04 0.610593200 #> 1872 18.71 0.38846571 7.464343e-04 0.610787855 #> 1873 18.72 0.38827153 7.460611e-04 0.610982413 #> 1874 18.73 0.38807744 7.456882e-04 0.611176873 #> 1875 18.74 0.38788345 7.453154e-04 0.611371236 #> 1876 18.75 0.38768956 7.449429e-04 0.611565502 #> 1877 18.76 0.38749576 7.445705e-04 0.611759670 #> 1878 18.77 0.38730206 7.441983e-04 0.611953742 #> 1879 18.78 0.38710846 7.438263e-04 0.612147717 #> 1880 18.79 0.38691495 7.434545e-04 0.612341594 #> 1881 18.80 0.38672154 7.430828e-04 0.612535375 #> 1882 18.81 0.38652823 7.427114e-04 0.612729059 #> 1883 18.82 0.38633501 7.423401e-04 0.612922646 #> 1884 18.83 0.38614189 7.419691e-04 0.613116136 #> 1885 18.84 0.38594887 7.415982e-04 0.613309530 #> 1886 18.85 0.38575595 7.412275e-04 0.613502827 #> 1887 18.86 0.38556312 7.408569e-04 0.613696027 #> 1888 18.87 0.38537038 7.404866e-04 0.613889131 #> 1889 18.88 0.38517775 7.401164e-04 0.614082138 #> 1890 18.89 0.38498520 7.397465e-04 0.614275049 #> 1891 18.90 0.38479276 7.393767e-04 0.614467863 #> 1892 18.91 0.38460041 7.390071e-04 0.614660581 #> 1893 18.92 0.38440816 7.386377e-04 0.614853202 #> 1894 18.93 0.38421600 7.382685e-04 0.615045728 #> 1895 18.94 0.38402394 7.378994e-04 0.615238157 #> 1896 18.95 0.38383198 7.375306e-04 0.615430490 #> 1897 18.96 0.38364011 7.371619e-04 0.615622726 #> 1898 18.97 0.38344834 7.367934e-04 0.615814867 #> 1899 18.98 0.38325666 7.364251e-04 0.616006912 #> 1900 18.99 0.38306508 7.360570e-04 0.616198860 #> 1901 19.00 0.38287360 7.356890e-04 0.616390713 #> 1902 19.01 0.38268221 7.353213e-04 0.616582469 #> 1903 19.02 0.38249092 7.349537e-04 0.616774130 #> 1904 19.03 0.38229972 7.345863e-04 0.616965695 #> 1905 19.04 0.38210862 7.342191e-04 0.617157165 #> 1906 19.05 0.38191761 7.338521e-04 0.617348538 #> 1907 19.06 0.38172670 7.334853e-04 0.617539816 #> 1908 19.07 0.38153588 7.331186e-04 0.617730998 #> 1909 19.08 0.38134516 7.327522e-04 0.617922085 #> 1910 19.09 0.38115454 7.323859e-04 0.618113076 #> 1911 19.10 0.38096401 7.320198e-04 0.618303972 #> 1912 19.11 0.38077357 7.316539e-04 0.618494772 #> 1913 19.12 0.38058323 7.312881e-04 0.618685477 #> 1914 19.13 0.38039299 7.309226e-04 0.618876087 #> 1915 19.14 0.38020284 7.305572e-04 0.619066601 #> 1916 19.15 0.38001279 7.301920e-04 0.619257020 #> 1917 19.16 0.37982283 7.298270e-04 0.619447344 #> 1918 19.17 0.37963296 7.294622e-04 0.619637573 #> 1919 19.18 0.37944320 7.290975e-04 0.619827707 #> 1920 19.19 0.37925352 7.287331e-04 0.620017745 #> 1921 19.20 0.37906394 7.283688e-04 0.620207689 #> 1922 19.21 0.37887446 7.280047e-04 0.620397538 #> 1923 19.22 0.37868507 7.276408e-04 0.620587291 #> 1924 19.23 0.37849577 7.272771e-04 0.620776950 #> 1925 19.24 0.37830657 7.269135e-04 0.620966515 #> 1926 19.25 0.37811747 7.265502e-04 0.621155984 #> 1927 19.26 0.37792845 7.261870e-04 0.621345359 #> 1928 19.27 0.37773954 7.258240e-04 0.621534639 #> 1929 19.28 0.37755071 7.254612e-04 0.621723824 #> 1930 19.29 0.37736199 7.250985e-04 0.621912915 #> 1931 19.30 0.37717335 7.247361e-04 0.622101911 #> 1932 19.31 0.37698481 7.243738e-04 0.622290813 #> 1933 19.32 0.37679637 7.240117e-04 0.622479620 #> 1934 19.33 0.37660802 7.236498e-04 0.622668333 #> 1935 19.34 0.37641976 7.232880e-04 0.622856952 #> 1936 19.35 0.37623160 7.229265e-04 0.623045476 #> 1937 19.36 0.37604353 7.225651e-04 0.623233907 #> 1938 19.37 0.37585555 7.222039e-04 0.623422243 #> 1939 19.38 0.37566767 7.218429e-04 0.623610484 #> 1940 19.39 0.37547989 7.214821e-04 0.623798632 #> 1941 19.40 0.37529219 7.211214e-04 0.623986686 #> 1942 19.41 0.37510459 7.207610e-04 0.624174645 #> 1943 19.42 0.37491709 7.204007e-04 0.624362511 #> 1944 19.43 0.37472968 7.200406e-04 0.624550283 #> 1945 19.44 0.37454236 7.196806e-04 0.624737961 #> 1946 19.45 0.37435513 7.193209e-04 0.624925545 #> 1947 19.46 0.37416800 7.189613e-04 0.625113035 #> 1948 19.47 0.37398097 7.186019e-04 0.625300432 #> 1949 19.48 0.37379402 7.182427e-04 0.625487735 #> 1950 19.49 0.37360717 7.178837e-04 0.625674944 #> 1951 19.50 0.37342042 7.175248e-04 0.625862060 #> 1952 19.51 0.37323375 7.171661e-04 0.626049082 #> 1953 19.52 0.37304718 7.168077e-04 0.626236011 #> 1954 19.53 0.37286070 7.164493e-04 0.626422846 #> 1955 19.54 0.37267432 7.160912e-04 0.626609588 #> 1956 19.55 0.37248803 7.157332e-04 0.626796237 #> 1957 19.56 0.37230183 7.153755e-04 0.626982792 #> 1958 19.57 0.37211573 7.150179e-04 0.627169254 #> 1959 19.58 0.37192972 7.146604e-04 0.627355623 #> 1960 19.59 0.37174380 7.143032e-04 0.627541898 #> 1961 19.60 0.37155797 7.139461e-04 0.627728081 #> 1962 19.61 0.37137224 7.135893e-04 0.627914170 #> 1963 19.62 0.37118660 7.132326e-04 0.628100167 #> 1964 19.63 0.37100105 7.128760e-04 0.628286070 #> 1965 19.64 0.37081560 7.125197e-04 0.628471881 #> 1966 19.65 0.37063024 7.121635e-04 0.628657598 #> 1967 19.66 0.37044497 7.118075e-04 0.628843223 #> 1968 19.67 0.37025979 7.114517e-04 0.629028755 #> 1969 19.68 0.37007471 7.110961e-04 0.629214194 #> 1970 19.69 0.36988972 7.107406e-04 0.629399541 #> 1971 19.70 0.36970482 7.103853e-04 0.629584795 #> 1972 19.71 0.36952001 7.100302e-04 0.629769956 #> 1973 19.72 0.36933530 7.096753e-04 0.629955025 #> 1974 19.73 0.36915068 7.093205e-04 0.630140001 #> 1975 19.74 0.36896615 7.089660e-04 0.630324885 #> 1976 19.75 0.36878171 7.086116e-04 0.630509676 #> 1977 19.76 0.36859737 7.082574e-04 0.630694375 #> 1978 19.77 0.36841311 7.079033e-04 0.630878982 #> 1979 19.78 0.36822895 7.075495e-04 0.631063496 #> 1980 19.79 0.36804489 7.071958e-04 0.631247919 #> 1981 19.80 0.36786091 7.068423e-04 0.631432249 #> 1982 19.81 0.36767702 7.064889e-04 0.631616486 #> 1983 19.82 0.36749323 7.061358e-04 0.631800632 #> 1984 19.83 0.36730953 7.057828e-04 0.631984686 #> 1985 19.84 0.36712592 7.054300e-04 0.632168647 #> 1986 19.85 0.36694241 7.050774e-04 0.632352517 #> 1987 19.86 0.36675898 7.047249e-04 0.632536295 #> 1988 19.87 0.36657565 7.043726e-04 0.632719981 #> 1989 19.88 0.36639240 7.040205e-04 0.632903575 #> 1990 19.89 0.36620925 7.036686e-04 0.633087077 #> 1991 19.90 0.36602620 7.033169e-04 0.633270488 #> 1992 19.91 0.36584323 7.029653e-04 0.633453807 #> 1993 19.92 0.36566035 7.026139e-04 0.633637034 #> 1994 19.93 0.36547757 7.022627e-04 0.633820170 #> 1995 19.94 0.36529487 7.019116e-04 0.634003214 #> 1996 19.95 0.36511227 7.015608e-04 0.634186167 #> 1997 19.96 0.36492976 7.012101e-04 0.634369028 #> 1998 19.97 0.36474734 7.008596e-04 0.634551798 #> 1999 19.98 0.36456501 7.005092e-04 0.634734476 #> 2000 19.99 0.36438278 7.001591e-04 0.634917063 #> 2001 20.00 0.36420063 6.998091e-04 0.635099559 #> 2002 20.01 0.36401858 6.994592e-04 0.635281964 #> 2003 20.02 0.36383661 6.991096e-04 0.635464277 #> 2004 20.03 0.36365474 6.987601e-04 0.635646500 #> 2005 20.04 0.36347296 6.984108e-04 0.635828631 #> 2006 20.05 0.36329127 6.980617e-04 0.636010671 #> 2007 20.06 0.36310967 6.977128e-04 0.636192620 #> 2008 20.07 0.36292816 6.973640e-04 0.636374478 #> 2009 20.08 0.36274674 6.970154e-04 0.636556246 #> 2010 20.09 0.36256541 6.966670e-04 0.636737922 #> 2011 20.10 0.36238417 6.963188e-04 0.636919508 #> 2012 20.11 0.36220303 6.959707e-04 0.637101003 #> 2013 20.12 0.36202197 6.956228e-04 0.637282407 #> 2014 20.13 0.36184100 6.952751e-04 0.637463720 #> 2015 20.14 0.36166013 6.949275e-04 0.637644943 #> 2016 20.15 0.36147934 6.945801e-04 0.637826075 #> 2017 20.16 0.36129865 6.942329e-04 0.638007117 #> 2018 20.17 0.36111805 6.938859e-04 0.638188068 #> 2019 20.18 0.36093753 6.935390e-04 0.638368929 #> 2020 20.19 0.36075711 6.931924e-04 0.638549699 #> 2021 20.20 0.36057677 6.928458e-04 0.638730379 #> 2022 20.21 0.36039653 6.924995e-04 0.638910969 #> 2023 20.22 0.36021638 6.921533e-04 0.639091468 #> 2024 20.23 0.36003632 6.918074e-04 0.639271878 #> 2025 20.24 0.35985634 6.914615e-04 0.639452197 #> 2026 20.25 0.35967646 6.911159e-04 0.639632425 #> 2027 20.26 0.35949667 6.907704e-04 0.639812564 #> 2028 20.27 0.35931696 6.904251e-04 0.639992613 #> 2029 20.28 0.35913735 6.900800e-04 0.640172572 #> 2030 20.29 0.35895782 6.897350e-04 0.640352440 #> 2031 20.30 0.35877839 6.893903e-04 0.640532219 #> 2032 20.31 0.35859905 6.890457e-04 0.640711908 #> 2033 20.32 0.35841979 6.887012e-04 0.640891507 #> 2034 20.33 0.35824063 6.883570e-04 0.641071017 #> 2035 20.34 0.35806155 6.880129e-04 0.641250436 #> 2036 20.35 0.35788256 6.876689e-04 0.641429766 #> 2037 20.36 0.35770367 6.873252e-04 0.641609007 #> 2038 20.37 0.35752486 6.869816e-04 0.641788157 #> 2039 20.38 0.35734614 6.866382e-04 0.641967218 #> 2040 20.39 0.35716751 6.862950e-04 0.642146190 #> 2041 20.40 0.35698898 6.859519e-04 0.642325072 #> 2042 20.41 0.35681053 6.856090e-04 0.642503865 #> 2043 20.42 0.35663217 6.852663e-04 0.642682568 #> 2044 20.43 0.35645389 6.849238e-04 0.642861182 #> 2045 20.44 0.35627571 6.845814e-04 0.643039707 #> 2046 20.45 0.35609762 6.842392e-04 0.643218143 #> 2047 20.46 0.35591961 6.838971e-04 0.643396489 #> 2048 20.47 0.35574170 6.835553e-04 0.643574746 #> 2049 20.48 0.35556387 6.832136e-04 0.643752914 #> 2050 20.49 0.35538613 6.828721e-04 0.643930993 #> 2051 20.50 0.35520849 6.825307e-04 0.644108983 #> 2052 20.51 0.35503093 6.821895e-04 0.644286885 #> 2053 20.52 0.35485345 6.818485e-04 0.644464697 #> 2054 20.53 0.35467607 6.815077e-04 0.644642420 #> 2055 20.54 0.35449878 6.811670e-04 0.644820054 #> 2056 20.55 0.35432157 6.808265e-04 0.644997600 #> 2057 20.56 0.35414446 6.804862e-04 0.645175057 #> 2058 20.57 0.35396743 6.801460e-04 0.645352425 #> 2059 20.58 0.35379049 6.798060e-04 0.645529704 #> 2060 20.59 0.35361364 6.794662e-04 0.645706895 #> 2061 20.60 0.35343688 6.791266e-04 0.645883997 #> 2062 20.61 0.35326020 6.787871e-04 0.646061011 #> 2063 20.62 0.35308362 6.784478e-04 0.646237936 #> 2064 20.63 0.35290712 6.781087e-04 0.646414773 #> 2065 20.64 0.35273071 6.777697e-04 0.646591522 #> 2066 20.65 0.35255439 6.774309e-04 0.646768182 #> 2067 20.66 0.35237815 6.770923e-04 0.646944754 #> 2068 20.67 0.35220201 6.767538e-04 0.647121237 #> 2069 20.68 0.35202595 6.764155e-04 0.647297632 #> 2070 20.69 0.35184998 6.760774e-04 0.647473939 #> 2071 20.70 0.35167410 6.757394e-04 0.647650158 #> 2072 20.71 0.35149831 6.754016e-04 0.647826289 #> 2073 20.72 0.35132260 6.750640e-04 0.648002332 #> 2074 20.73 0.35114699 6.747266e-04 0.648178287 #> 2075 20.74 0.35097146 6.743893e-04 0.648354154 #> 2076 20.75 0.35079601 6.740522e-04 0.648529933 #> 2077 20.76 0.35062066 6.737152e-04 0.648705624 #> 2078 20.77 0.35044539 6.733785e-04 0.648881227 #> 2079 20.78 0.35027022 6.730419e-04 0.649056743 #> 2080 20.79 0.35009512 6.727054e-04 0.649232171 #> 2081 20.80 0.34992012 6.723692e-04 0.649407511 #> 2082 20.81 0.34974520 6.720331e-04 0.649582763 #> 2083 20.82 0.34957037 6.716971e-04 0.649757928 #> 2084 20.83 0.34939563 6.713614e-04 0.649933005 #> 2085 20.84 0.34922098 6.710258e-04 0.650107995 #> 2086 20.85 0.34904641 6.706903e-04 0.650282897 #> 2087 20.86 0.34887193 6.703551e-04 0.650457712 #> 2088 20.87 0.34869754 6.700200e-04 0.650632440 #> 2089 20.88 0.34852324 6.696851e-04 0.650807080 #> 2090 20.89 0.34834902 6.693503e-04 0.650981633 #> 2091 20.90 0.34817489 6.690157e-04 0.651156098 #> 2092 20.91 0.34800084 6.686813e-04 0.651330476 #> 2093 20.92 0.34782689 6.683470e-04 0.651504768 #> 2094 20.93 0.34765302 6.680129e-04 0.651678972 #> 2095 20.94 0.34747923 6.676790e-04 0.651853089 #> 2096 20.95 0.34730554 6.673453e-04 0.652027119 #> 2097 20.96 0.34713193 6.670117e-04 0.652201062 #> 2098 20.97 0.34695840 6.666782e-04 0.652374918 #> 2099 20.98 0.34678497 6.663450e-04 0.652548687 #> 2100 20.99 0.34661162 6.660119e-04 0.652722369 #> 2101 21.00 0.34643836 6.656790e-04 0.652895964 #> 2102 21.01 0.34626518 6.653462e-04 0.653069473 #> 2103 21.02 0.34609209 6.650136e-04 0.653242895 #> 2104 21.03 0.34591909 6.646812e-04 0.653416230 #> 2105 21.04 0.34574617 6.643489e-04 0.653589479 #> 2106 21.05 0.34557334 6.640169e-04 0.653762641 #> 2107 21.06 0.34540060 6.636849e-04 0.653935716 #> 2108 21.07 0.34522794 6.633532e-04 0.654108705 #> 2109 21.08 0.34505537 6.630216e-04 0.654281607 #> 2110 21.09 0.34488289 6.626901e-04 0.654454423 #> 2111 21.10 0.34471049 6.623589e-04 0.654627153 #> 2112 21.11 0.34453818 6.620278e-04 0.654799796 #> 2113 21.12 0.34436595 6.616969e-04 0.654972353 #> 2114 21.13 0.34419381 6.613661e-04 0.655144824 #> 2115 21.14 0.34402176 6.610355e-04 0.655317208 #> 2116 21.15 0.34384979 6.607051e-04 0.655489507 #> 2117 21.16 0.34367791 6.603748e-04 0.655661719 #> 2118 21.17 0.34350611 6.600447e-04 0.655833845 #> 2119 21.18 0.34333440 6.597147e-04 0.656005885 #> 2120 21.19 0.34316278 6.593850e-04 0.656177839 #> 2121 21.20 0.34299124 6.590554e-04 0.656349707 #> 2122 21.21 0.34281978 6.587259e-04 0.656521490 #> 2123 21.22 0.34264842 6.583966e-04 0.656693186 #> 2124 21.23 0.34247714 6.580675e-04 0.656864796 #> 2125 21.24 0.34230594 6.577386e-04 0.657036321 #> 2126 21.25 0.34213483 6.574098e-04 0.657207760 #> 2127 21.26 0.34196381 6.570812e-04 0.657379113 #> 2128 21.27 0.34179287 6.567527e-04 0.657550381 #> 2129 21.28 0.34162201 6.564244e-04 0.657721563 #> 2130 21.29 0.34145124 6.560963e-04 0.657892660 #> 2131 21.30 0.34128056 6.557683e-04 0.658063670 #> 2132 21.31 0.34110996 6.554405e-04 0.658234596 #> 2133 21.32 0.34093945 6.551129e-04 0.658405436 #> 2134 21.33 0.34076902 6.547854e-04 0.658576190 #> 2135 21.34 0.34059868 6.544581e-04 0.658746860 #> 2136 21.35 0.34042843 6.541309e-04 0.658917444 #> 2137 21.36 0.34025825 6.538039e-04 0.659087942 #> 2138 21.37 0.34008817 6.534771e-04 0.659258356 #> 2139 21.38 0.33991817 6.531505e-04 0.659428684 #> 2140 21.39 0.33974825 6.528240e-04 0.659598927 #> 2141 21.40 0.33957842 6.524976e-04 0.659769085 #> 2142 21.41 0.33940867 6.521715e-04 0.659939158 #> 2143 21.42 0.33923901 6.518455e-04 0.660109146 #> 2144 21.43 0.33906943 6.515196e-04 0.660279049 #> 2145 21.44 0.33889994 6.511940e-04 0.660448867 #> 2146 21.45 0.33873053 6.508684e-04 0.660618600 #> 2147 21.46 0.33856121 6.505431e-04 0.660788248 #> 2148 21.47 0.33839197 6.502179e-04 0.660957812 #> 2149 21.48 0.33822282 6.498929e-04 0.661127291 #> 2150 21.49 0.33805375 6.495680e-04 0.661296685 #> 2151 21.50 0.33788476 6.492433e-04 0.661465994 #> 2152 21.51 0.33771586 6.489188e-04 0.661635219 #> 2153 21.52 0.33754705 6.485944e-04 0.661804359 #> 2154 21.53 0.33737832 6.482702e-04 0.661973414 #> 2155 21.54 0.33720967 6.479461e-04 0.662142385 #> 2156 21.55 0.33704111 6.476222e-04 0.662311272 #> 2157 21.56 0.33687263 6.472985e-04 0.662480074 #> 2158 21.57 0.33670423 6.469749e-04 0.662648792 #> 2159 21.58 0.33653592 6.466515e-04 0.662817425 #> 2160 21.59 0.33636770 6.463283e-04 0.662985974 #> 2161 21.60 0.33619956 6.460052e-04 0.663154439 #> 2162 21.61 0.33603150 6.456823e-04 0.663322820 #> 2163 21.62 0.33586352 6.453595e-04 0.663491117 #> 2164 21.63 0.33569563 6.450369e-04 0.663659329 #> 2165 21.64 0.33552783 6.447145e-04 0.663827457 #> 2166 21.65 0.33536011 6.443922e-04 0.663995502 #> 2167 21.66 0.33519247 6.440701e-04 0.664163462 #> 2168 21.67 0.33502491 6.437481e-04 0.664331338 #> 2169 21.68 0.33485744 6.434263e-04 0.664499130 #> 2170 21.69 0.33469006 6.431047e-04 0.664666839 #> 2171 21.70 0.33452275 6.427832e-04 0.664834464 #> 2172 21.71 0.33435553 6.424619e-04 0.665002005 #> 2173 21.72 0.33418840 6.421408e-04 0.665169462 #> 2174 21.73 0.33402135 6.418198e-04 0.665336835 #> 2175 21.74 0.33385438 6.414989e-04 0.665504125 #> 2176 21.75 0.33368749 6.411783e-04 0.665671331 #> 2177 21.76 0.33352069 6.408578e-04 0.665838454 #> 2178 21.77 0.33335397 6.405374e-04 0.666005493 #> 2179 21.78 0.33318733 6.402172e-04 0.666172448 #> 2180 21.79 0.33302078 6.398972e-04 0.666339320 #> 2181 21.80 0.33285431 6.395773e-04 0.666506109 #> 2182 21.81 0.33268793 6.392576e-04 0.666672814 #> 2183 21.82 0.33252163 6.389381e-04 0.666839436 #> 2184 21.83 0.33235541 6.386187e-04 0.667005975 #> 2185 21.84 0.33218927 6.382994e-04 0.667172430 #> 2186 21.85 0.33202322 6.379804e-04 0.667338802 #> 2187 21.86 0.33185725 6.376615e-04 0.667505091 #> 2188 21.87 0.33169136 6.373427e-04 0.667671297 #> 2189 21.88 0.33152556 6.370241e-04 0.667837420 #> 2190 21.89 0.33135983 6.367057e-04 0.668003460 #> 2191 21.90 0.33119420 6.363874e-04 0.668169417 #> 2192 21.91 0.33102864 6.360693e-04 0.668335290 #> 2193 21.92 0.33086317 6.357514e-04 0.668501081 #> 2194 21.93 0.33069778 6.354336e-04 0.668666789 #> 2195 21.94 0.33053247 6.351159e-04 0.668832415 #> 2196 21.95 0.33036724 6.347984e-04 0.668997957 #> 2197 21.96 0.33020210 6.344811e-04 0.669163417 #> 2198 21.97 0.33003704 6.341640e-04 0.669328794 #> 2199 21.98 0.32987207 6.338470e-04 0.669494088 #> 2200 21.99 0.32970717 6.335301e-04 0.669659300 #> 2201 22.00 0.32954236 6.332134e-04 0.669824429 #> 2202 22.01 0.32937763 6.328969e-04 0.669989475 #> 2203 22.02 0.32921298 6.325805e-04 0.670154439 #> 2204 22.03 0.32904841 6.322643e-04 0.670319321 #> 2205 22.04 0.32888393 6.319483e-04 0.670484120 #> 2206 22.05 0.32871953 6.316324e-04 0.670648837 #> 2207 22.06 0.32855521 6.313166e-04 0.670813471 #> 2208 22.07 0.32839098 6.310011e-04 0.670978023 #> 2209 22.08 0.32822682 6.306856e-04 0.671142493 #> 2210 22.09 0.32806275 6.303704e-04 0.671306881 #> 2211 22.10 0.32789876 6.300553e-04 0.671471186 #> 2212 22.11 0.32773485 6.297403e-04 0.671635410 #> 2213 22.12 0.32757102 6.294255e-04 0.671799551 #> 2214 22.13 0.32740728 6.291109e-04 0.671963610 #> 2215 22.14 0.32724362 6.287964e-04 0.672127587 #> 2216 22.15 0.32708004 6.284821e-04 0.672291482 #> 2217 22.16 0.32691654 6.281679e-04 0.672455296 #> 2218 22.17 0.32675312 6.278539e-04 0.672619027 #> 2219 22.18 0.32658978 6.275401e-04 0.672782677 #> 2220 22.19 0.32642653 6.272264e-04 0.672946245 #> 2221 22.20 0.32626336 6.269128e-04 0.673109731 #> 2222 22.21 0.32610027 6.265995e-04 0.673273135 #> 2223 22.22 0.32593726 6.262862e-04 0.673436457 #> 2224 22.23 0.32577433 6.259732e-04 0.673599698 #> 2225 22.24 0.32561148 6.256603e-04 0.673762858 #> 2226 22.25 0.32544872 6.253475e-04 0.673925936 #> 2227 22.26 0.32528603 6.250349e-04 0.674088932 #> 2228 22.27 0.32512343 6.247225e-04 0.674251847 #> 2229 22.28 0.32496091 6.244102e-04 0.674414680 #> 2230 22.29 0.32479847 6.240981e-04 0.674577432 #> 2231 22.30 0.32463611 6.237861e-04 0.674740103 #> 2232 22.31 0.32447383 6.234743e-04 0.674902692 #> 2233 22.32 0.32431164 6.231626e-04 0.675065200 #> 2234 22.33 0.32414952 6.228511e-04 0.675227627 #> 2235 22.34 0.32398749 6.225398e-04 0.675389972 #> 2236 22.35 0.32382553 6.222286e-04 0.675552237 #> 2237 22.36 0.32366366 6.219176e-04 0.675714420 #> 2238 22.37 0.32350187 6.216067e-04 0.675876522 #> 2239 22.38 0.32334016 6.212959e-04 0.676038544 #> 2240 22.39 0.32317853 6.209854e-04 0.676200484 #> 2241 22.40 0.32301698 6.206750e-04 0.676362343 #> 2242 22.41 0.32285551 6.203647e-04 0.676524122 #> 2243 22.42 0.32269413 6.200546e-04 0.676685819 #> 2244 22.43 0.32253282 6.197446e-04 0.676847436 #> 2245 22.44 0.32237159 6.194348e-04 0.677008972 #> 2246 22.45 0.32221045 6.191252e-04 0.677170427 #> 2247 22.46 0.32204938 6.188157e-04 0.677331801 #> 2248 22.47 0.32188840 6.185064e-04 0.677493095 #> 2249 22.48 0.32172749 6.181972e-04 0.677654308 #> 2250 22.49 0.32156667 6.178882e-04 0.677815441 #> 2251 22.50 0.32140593 6.175793e-04 0.677976493 #> 2252 22.51 0.32124527 6.172706e-04 0.678137464 #> 2253 22.52 0.32108468 6.169621e-04 0.678298355 #> 2254 22.53 0.32092418 6.166537e-04 0.678459166 #> 2255 22.54 0.32076376 6.163454e-04 0.678619896 #> 2256 22.55 0.32060342 6.160373e-04 0.678780546 #> 2257 22.56 0.32044315 6.157294e-04 0.678941116 #> 2258 22.57 0.32028297 6.154216e-04 0.679101605 #> 2259 22.58 0.32012287 6.151139e-04 0.679262014 #> 2260 22.59 0.31996285 6.148065e-04 0.679422343 #> 2261 22.60 0.31980291 6.144991e-04 0.679582592 #> 2262 22.61 0.31964305 6.141920e-04 0.679742761 #> 2263 22.62 0.31948327 6.138849e-04 0.679902849 #> 2264 22.63 0.31932356 6.135781e-04 0.680062858 #> 2265 22.64 0.31916394 6.132714e-04 0.680222786 #> 2266 22.65 0.31900440 6.129648e-04 0.680382635 #> 2267 22.66 0.31884494 6.126584e-04 0.680542404 #> 2268 22.67 0.31868556 6.123522e-04 0.680702093 #> 2269 22.68 0.31852625 6.120461e-04 0.680861702 #> 2270 22.69 0.31836703 6.117401e-04 0.681021231 #> 2271 22.70 0.31820789 6.114343e-04 0.681180680 #> 2272 22.71 0.31804882 6.111287e-04 0.681340050 #> 2273 22.72 0.31788984 6.108232e-04 0.681499340 #> 2274 22.73 0.31773093 6.105178e-04 0.681658551 #> 2275 22.74 0.31757211 6.102127e-04 0.681817682 #> 2276 22.75 0.31741336 6.099076e-04 0.681976733 #> 2277 22.76 0.31725469 6.096028e-04 0.682135705 #> 2278 22.77 0.31709610 6.092980e-04 0.682294598 #> 2279 22.78 0.31693760 6.089935e-04 0.682453411 #> 2280 22.79 0.31677917 6.086890e-04 0.682612144 #> 2281 22.80 0.31662082 6.083848e-04 0.682770798 #> 2282 22.81 0.31646255 6.080807e-04 0.682929373 #> 2283 22.82 0.31630435 6.077767e-04 0.683087869 #> 2284 22.83 0.31614624 6.074729e-04 0.683246286 #> 2285 22.84 0.31598821 6.071692e-04 0.683404623 #> 2286 22.85 0.31583025 6.068657e-04 0.683562881 #> 2287 22.86 0.31567238 6.065624e-04 0.683721060 #> 2288 22.87 0.31551458 6.062591e-04 0.683879160 #> 2289 22.88 0.31535686 6.059561e-04 0.684037181 #> 2290 22.89 0.31519922 6.056532e-04 0.684195123 #> 2291 22.90 0.31504166 6.053504e-04 0.684352986 #> 2292 22.91 0.31488418 6.050478e-04 0.684510770 #> 2293 22.92 0.31472678 6.047454e-04 0.684668475 #> 2294 22.93 0.31456946 6.044431e-04 0.684826101 #> 2295 22.94 0.31441221 6.041409e-04 0.684983649 #> 2296 22.95 0.31425504 6.038390e-04 0.685141118 #> 2297 22.96 0.31409796 6.035371e-04 0.685298508 #> 2298 22.97 0.31394095 6.032354e-04 0.685455819 #> 2299 22.98 0.31378401 6.029339e-04 0.685613052 #> 2300 22.99 0.31362716 6.026325e-04 0.685770206 #> 2301 23.00 0.31347039 6.023312e-04 0.685927282 #> 2302 23.01 0.31331369 6.020301e-04 0.686084279 #> 2303 23.02 0.31315707 6.017292e-04 0.686241198 #> 2304 23.03 0.31300053 6.014284e-04 0.686398038 #> 2305 23.04 0.31284407 6.011278e-04 0.686554800 #> 2306 23.05 0.31268769 6.008273e-04 0.686711483 #> 2307 23.06 0.31253138 6.005270e-04 0.686868088 #> 2308 23.07 0.31237516 6.002268e-04 0.687024615 #> 2309 23.08 0.31221901 5.999267e-04 0.687181064 #> 2310 23.09 0.31206294 5.996268e-04 0.687337434 #> 2311 23.10 0.31190695 5.993271e-04 0.687493726 #> 2312 23.11 0.31175103 5.990275e-04 0.687649940 #> 2313 23.12 0.31159520 5.987281e-04 0.687806076 #> 2314 23.13 0.31143944 5.984288e-04 0.687962134 #> 2315 23.14 0.31128376 5.981296e-04 0.688118114 #> 2316 23.15 0.31112815 5.978307e-04 0.688274016 #> 2317 23.16 0.31097263 5.975318e-04 0.688429840 #> 2318 23.17 0.31081718 5.972331e-04 0.688585586 #> 2319 23.18 0.31066181 5.969346e-04 0.688741255 #> 2320 23.19 0.31050652 5.966362e-04 0.688896845 #> 2321 23.20 0.31035130 5.963379e-04 0.689052358 #> 2322 23.21 0.31019617 5.960398e-04 0.689207793 #> 2323 23.22 0.31004111 5.957419e-04 0.689363150 #> 2324 23.23 0.30988613 5.954441e-04 0.689518430 #> 2325 23.24 0.30973122 5.951465e-04 0.689673632 #> 2326 23.25 0.30957640 5.948490e-04 0.689828756 #> 2327 23.26 0.30942165 5.945516e-04 0.689983803 #> 2328 23.27 0.30926697 5.942544e-04 0.690138772 #> 2329 23.28 0.30911238 5.939574e-04 0.690293664 #> 2330 23.29 0.30895786 5.936605e-04 0.690448479 #> 2331 23.30 0.30880342 5.933637e-04 0.690603216 #> 2332 23.31 0.30864906 5.930671e-04 0.690757875 #> 2333 23.32 0.30849477 5.927706e-04 0.690912458 #> 2334 23.33 0.30834056 5.924743e-04 0.691066963 #> 2335 23.34 0.30818643 5.921782e-04 0.691221391 #> 2336 23.35 0.30803238 5.918821e-04 0.691375742 #> 2337 23.36 0.30787840 5.915863e-04 0.691530015 #> 2338 23.37 0.30772450 5.912906e-04 0.691684212 #> 2339 23.38 0.30757067 5.909950e-04 0.691838331 #> 2340 23.39 0.30741693 5.906996e-04 0.691992373 #> 2341 23.40 0.30726326 5.904043e-04 0.692146339 #> 2342 23.41 0.30710966 5.901092e-04 0.692300227 #> 2343 23.42 0.30695615 5.898142e-04 0.692454038 #> 2344 23.43 0.30680271 5.895193e-04 0.692607773 #> 2345 23.44 0.30664934 5.892247e-04 0.692761431 #> 2346 23.45 0.30649606 5.889301e-04 0.692915012 #> 2347 23.46 0.30634285 5.886357e-04 0.693068516 #> 2348 23.47 0.30618972 5.883415e-04 0.693221943 #> 2349 23.48 0.30603666 5.880474e-04 0.693375294 #> 2350 23.49 0.30588368 5.877534e-04 0.693528568 #> 2351 23.50 0.30573078 5.874596e-04 0.693681765 #> 2352 23.51 0.30557795 5.871660e-04 0.693834886 #> 2353 23.52 0.30542520 5.868725e-04 0.693987930 #> 2354 23.53 0.30527252 5.865791e-04 0.694140898 #> 2355 23.54 0.30511992 5.862859e-04 0.694293790 #> 2356 23.55 0.30496740 5.859928e-04 0.694446604 #> 2357 23.56 0.30481496 5.856999e-04 0.694599343 #> 2358 23.57 0.30466259 5.854071e-04 0.694752005 #> 2359 23.58 0.30451029 5.851145e-04 0.694904591 #> 2360 23.59 0.30435808 5.848220e-04 0.695057101 #> 2361 23.60 0.30420594 5.845297e-04 0.695209534 #> 2362 23.61 0.30405387 5.842375e-04 0.695361891 #> 2363 23.62 0.30390188 5.839454e-04 0.695514172 #> 2364 23.63 0.30374997 5.836535e-04 0.695666377 #> 2365 23.64 0.30359813 5.833618e-04 0.695818506 #> 2366 23.65 0.30344637 5.830702e-04 0.695970558 #> 2367 23.66 0.30329469 5.827787e-04 0.696122535 #> 2368 23.67 0.30314308 5.824874e-04 0.696274436 #> 2369 23.68 0.30299154 5.821962e-04 0.696426261 #> 2370 23.69 0.30284009 5.819052e-04 0.696578010 #> 2371 23.70 0.30268870 5.816143e-04 0.696729683 #> 2372 23.71 0.30253740 5.813236e-04 0.696881280 #> 2373 23.72 0.30238617 5.810330e-04 0.697032801 #> 2374 23.73 0.30223501 5.807425e-04 0.697184247 #> 2375 23.74 0.30208393 5.804522e-04 0.697335617 #> 2376 23.75 0.30193293 5.801621e-04 0.697486912 #> 2377 23.76 0.30178200 5.798721e-04 0.697638130 #> 2378 23.77 0.30163114 5.795822e-04 0.697789274 #> 2379 23.78 0.30148037 5.792925e-04 0.697940341 #> 2380 23.79 0.30132966 5.790029e-04 0.698091333 #> 2381 23.80 0.30117904 5.787135e-04 0.698242250 #> 2382 23.81 0.30102848 5.784242e-04 0.698393091 #> 2383 23.82 0.30087801 5.781351e-04 0.698543857 #> 2384 23.83 0.30072761 5.778461e-04 0.698694547 #> 2385 23.84 0.30057728 5.775572e-04 0.698845162 #> 2386 23.85 0.30042703 5.772685e-04 0.698995702 #> 2387 23.86 0.30027685 5.769800e-04 0.699146167 #> 2388 23.87 0.30012675 5.766915e-04 0.699296556 #> 2389 23.88 0.29997673 5.764033e-04 0.699446870 #> 2390 23.89 0.29982678 5.761151e-04 0.699597109 #> 2391 23.90 0.29967690 5.758271e-04 0.699747273 #> 2392 23.91 0.29952710 5.755393e-04 0.699897362 #> 2393 23.92 0.29937737 5.752516e-04 0.700047376 #> 2394 23.93 0.29922772 5.749641e-04 0.700197314 #> 2395 23.94 0.29907815 5.746766e-04 0.700347178 #> 2396 23.95 0.29892864 5.743894e-04 0.700496967 #> 2397 23.96 0.29877922 5.741023e-04 0.700646681 #> 2398 23.97 0.29862986 5.738153e-04 0.700796321 #> 2399 23.98 0.29848059 5.735284e-04 0.700945885 #> 2400 23.99 0.29833138 5.732417e-04 0.701095375 #> 2401 24.00 0.29818226 5.729552e-04 0.701244790 #> 2402 24.01 0.29803320 5.726688e-04 0.701394130 #> 2403 24.02 0.29788422 5.723825e-04 0.701543396 #> 2404 24.03 0.29773532 5.720964e-04 0.701692587 #> 2405 24.04 0.29758649 5.718104e-04 0.701841703 #> 2406 24.05 0.29743773 5.715246e-04 0.701990745 #> 2407 24.06 0.29728905 5.712389e-04 0.702139712 #> 2408 24.07 0.29714044 5.709534e-04 0.702288605 #> 2409 24.08 0.29699191 5.706680e-04 0.702437424 #> 2410 24.09 0.29684345 5.703827e-04 0.702586168 #> 2411 24.10 0.29669506 5.700976e-04 0.702734838 #> 2412 24.11 0.29654675 5.698126e-04 0.702883433 #> 2413 24.12 0.29639852 5.695278e-04 0.703031954 #> 2414 24.13 0.29625036 5.692431e-04 0.703180401 #> 2415 24.14 0.29610227 5.689585e-04 0.703328774 #> 2416 24.15 0.29595425 5.686741e-04 0.703477072 #> 2417 24.16 0.29580631 5.683898e-04 0.703625297 #> 2418 24.17 0.29565845 5.681057e-04 0.703773447 #> 2419 24.18 0.29551065 5.678217e-04 0.703921523 #> 2420 24.19 0.29536294 5.675379e-04 0.704069526 #> 2421 24.20 0.29521529 5.672542e-04 0.704217454 #> 2422 24.21 0.29506772 5.669706e-04 0.704365308 #> 2423 24.22 0.29492022 5.666872e-04 0.704513089 #> 2424 24.23 0.29477280 5.664040e-04 0.704660795 #> 2425 24.24 0.29462545 5.661208e-04 0.704808428 #> 2426 24.25 0.29447818 5.658378e-04 0.704955987 #> 2427 24.26 0.29433097 5.655550e-04 0.705103472 #> 2428 24.27 0.29418384 5.652723e-04 0.705250883 #> 2429 24.28 0.29403679 5.649897e-04 0.705398221 #> 2430 24.29 0.29388981 5.647073e-04 0.705545485 #> 2431 24.30 0.29374290 5.644250e-04 0.705692676 #> 2432 24.31 0.29359606 5.641429e-04 0.705839792 #> 2433 24.32 0.29344930 5.638609e-04 0.705986836 #> 2434 24.33 0.29330262 5.635790e-04 0.706133806 #> 2435 24.34 0.29315600 5.632973e-04 0.706280702 #> 2436 24.35 0.29300946 5.630157e-04 0.706427525 #> 2437 24.36 0.29286299 5.627343e-04 0.706574274 #> 2438 24.37 0.29271660 5.624530e-04 0.706720951 #> 2439 24.38 0.29257027 5.621718e-04 0.706867554 #> 2440 24.39 0.29242403 5.618908e-04 0.707014083 #> 2441 24.40 0.29227785 5.616099e-04 0.707160539 #> 2442 24.41 0.29213175 5.613292e-04 0.707306923 #> 2443 24.42 0.29198572 5.610486e-04 0.707453233 #> 2444 24.43 0.29183976 5.607681e-04 0.707599469 #> 2445 24.44 0.29169388 5.604878e-04 0.707745633 #> 2446 24.45 0.29154807 5.602077e-04 0.707891724 #> 2447 24.46 0.29140233 5.599276e-04 0.708037741 #> 2448 24.47 0.29125667 5.596477e-04 0.708183686 #> 2449 24.48 0.29111107 5.593680e-04 0.708329558 #> 2450 24.49 0.29096556 5.590884e-04 0.708475357 #> 2451 24.50 0.29082011 5.588089e-04 0.708621082 #> 2452 24.51 0.29067473 5.585295e-04 0.708766735 #> 2453 24.52 0.29052943 5.582504e-04 0.708912316 #> 2454 24.53 0.29038421 5.579713e-04 0.709057823 #> 2455 24.54 0.29023905 5.576924e-04 0.709203258 #> 2456 24.55 0.29009397 5.574136e-04 0.709348620 #> 2457 24.56 0.28994896 5.571350e-04 0.709493909 #> 2458 24.57 0.28980402 5.568565e-04 0.709639126 #> 2459 24.58 0.28965915 5.565781e-04 0.709784270 #> 2460 24.59 0.28951436 5.562999e-04 0.709929342 #> 2461 24.60 0.28936964 5.560218e-04 0.710074341 #> 2462 24.61 0.28922499 5.557439e-04 0.710219268 #> 2463 24.62 0.28908041 5.554661e-04 0.710364122 #> 2464 24.63 0.28893591 5.551884e-04 0.710508903 #> 2465 24.64 0.28879148 5.549109e-04 0.710653613 #> 2466 24.65 0.28864712 5.546335e-04 0.710798250 #> 2467 24.66 0.28850283 5.543562e-04 0.710942815 #> 2468 24.67 0.28835861 5.540791e-04 0.711087307 #> 2469 24.68 0.28821447 5.538022e-04 0.711231727 #> 2470 24.69 0.28807040 5.535253e-04 0.711376075 #> 2471 24.70 0.28792640 5.532486e-04 0.711520351 #> 2472 24.71 0.28778247 5.529721e-04 0.711664555 #> 2473 24.72 0.28763862 5.526957e-04 0.711808687 #> 2474 24.73 0.28749483 5.524194e-04 0.711952746 #> 2475 24.74 0.28735112 5.521432e-04 0.712096734 #> 2476 24.75 0.28720748 5.518672e-04 0.712240650 #> 2477 24.76 0.28706392 5.515914e-04 0.712384493 #> 2478 24.77 0.28692042 5.513157e-04 0.712528265 #> 2479 24.78 0.28677699 5.510401e-04 0.712671965 #> 2480 24.79 0.28663364 5.507646e-04 0.712815593 #> 2481 24.80 0.28649036 5.504893e-04 0.712959150 #> 2482 24.81 0.28634715 5.502141e-04 0.713102634 #> 2483 24.82 0.28620401 5.499391e-04 0.713246047 #> 2484 24.83 0.28606095 5.496642e-04 0.713389388 #> 2485 24.84 0.28591795 5.493894e-04 0.713532658 #> 2486 24.85 0.28577503 5.491148e-04 0.713675855 #> 2487 24.86 0.28563218 5.488403e-04 0.713818982 #> 2488 24.87 0.28548940 5.485660e-04 0.713962037 #> 2489 24.88 0.28534669 5.482917e-04 0.714105020 #> 2490 24.89 0.28520405 5.480177e-04 0.714247932 #> 2491 24.90 0.28506148 5.477437e-04 0.714390772 #> 2492 24.91 0.28491899 5.474699e-04 0.714533541 #> 2493 24.92 0.28477657 5.471963e-04 0.714676238 #> 2494 24.93 0.28463421 5.469227e-04 0.714818865 #> 2495 24.94 0.28449193 5.466493e-04 0.714961419 #> 2496 24.95 0.28434972 5.463761e-04 0.715103903 #> 2497 24.96 0.28420758 5.461030e-04 0.715246316 #> 2498 24.97 0.28406551 5.458300e-04 0.715388657 #> 2499 24.98 0.28392352 5.455571e-04 0.715530927 #> 2500 24.99 0.28378159 5.452844e-04 0.715673126 #> 2501 25.00 0.28363973 5.450118e-04 0.715815254 #> 2502 25.01 0.28349795 5.447394e-04 0.715957311 #> 2503 25.02 0.28335624 5.444671e-04 0.716099297 #> 2504 25.03 0.28321459 5.441949e-04 0.716241211 #> 2505 25.04 0.28307302 5.439229e-04 0.716383055 #> 2506 25.05 0.28293152 5.436510e-04 0.716524828 #> 2507 25.06 0.28279009 5.433793e-04 0.716666531 #> 2508 25.07 0.28264873 5.431076e-04 0.716808162 #> 2509 25.08 0.28250744 5.428361e-04 0.716949722 #> 2510 25.09 0.28236622 5.425648e-04 0.717091212 #> 2511 25.10 0.28222508 5.422936e-04 0.717232631 #> 2512 25.11 0.28208400 5.420225e-04 0.717373980 #> 2513 25.12 0.28194299 5.417516e-04 0.717515257 #> 2514 25.13 0.28180205 5.414808e-04 0.717656464 #> 2515 25.14 0.28166119 5.412101e-04 0.717797601 #> 2516 25.15 0.28152039 5.409395e-04 0.717938667 #> 2517 25.16 0.28137967 5.406691e-04 0.718079662 #> 2518 25.17 0.28123901 5.403989e-04 0.718220587 #> 2519 25.18 0.28109843 5.401287e-04 0.718361442 #> 2520 25.19 0.28095792 5.398587e-04 0.718502226 #> 2521 25.20 0.28081747 5.395889e-04 0.718642939 #> 2522 25.21 0.28067710 5.393192e-04 0.718783583 #> 2523 25.22 0.28053679 5.390496e-04 0.718924156 #> 2524 25.23 0.28039656 5.387801e-04 0.719064659 #> 2525 25.24 0.28025640 5.385108e-04 0.719205091 #> 2526 25.25 0.28011630 5.382416e-04 0.719345454 #> 2527 25.26 0.27997628 5.379725e-04 0.719485746 #> 2528 25.27 0.27983633 5.377036e-04 0.719625968 #> 2529 25.28 0.27969645 5.374348e-04 0.719766120 #> 2530 25.29 0.27955663 5.371662e-04 0.719906202 #> 2531 25.30 0.27941689 5.368977e-04 0.720046214 #> 2532 25.31 0.27927722 5.366293e-04 0.720186156 #> 2533 25.32 0.27913761 5.363610e-04 0.720326028 #> 2534 25.33 0.27899808 5.360929e-04 0.720465830 #> 2535 25.34 0.27885861 5.358250e-04 0.720605562 #> 2536 25.35 0.27871922 5.355571e-04 0.720745224 #> 2537 25.36 0.27857989 5.352894e-04 0.720884817 #> 2538 25.37 0.27844064 5.350218e-04 0.721024339 #> 2539 25.38 0.27830145 5.347544e-04 0.721163792 #> 2540 25.39 0.27816234 5.344871e-04 0.721303176 #> 2541 25.40 0.27802329 5.342199e-04 0.721442489 #> 2542 25.41 0.27788431 5.339528e-04 0.721581733 #> 2543 25.42 0.27774541 5.336859e-04 0.721720907 #> 2544 25.43 0.27760657 5.334192e-04 0.721860012 #> 2545 25.44 0.27746780 5.331525e-04 0.721999047 #> 2546 25.45 0.27732910 5.328860e-04 0.722138013 #> 2547 25.46 0.27719047 5.326196e-04 0.722276909 #> 2548 25.47 0.27705191 5.323534e-04 0.722415736 #> 2549 25.48 0.27691342 5.320873e-04 0.722554494 #> 2550 25.49 0.27677500 5.318213e-04 0.722693182 #> 2551 25.50 0.27663664 5.315555e-04 0.722831801 #> 2552 25.51 0.27649836 5.312897e-04 0.722970350 #> 2553 25.52 0.27636015 5.310242e-04 0.723108830 #> 2554 25.53 0.27622200 5.307587e-04 0.723247241 #> 2555 25.54 0.27608392 5.304934e-04 0.723385583 #> 2556 25.55 0.27594592 5.302282e-04 0.723523856 #> 2557 25.56 0.27580798 5.299632e-04 0.723662059 #> 2558 25.57 0.27567011 5.296983e-04 0.723800194 #> 2559 25.58 0.27553231 5.294335e-04 0.723938259 #> 2560 25.59 0.27539458 5.291688e-04 0.724076255 #> 2561 25.60 0.27525691 5.289043e-04 0.724214183 #> 2562 25.61 0.27511932 5.286399e-04 0.724352041 #> 2563 25.62 0.27498179 5.283757e-04 0.724489831 #> 2564 25.63 0.27484434 5.281115e-04 0.724627551 #> 2565 25.64 0.27470695 5.278476e-04 0.724765203 #> 2566 25.65 0.27456963 5.275837e-04 0.724902786 #> 2567 25.66 0.27443238 5.273200e-04 0.725040300 #> 2568 25.67 0.27429520 5.270564e-04 0.725177746 #> 2569 25.68 0.27415808 5.267929e-04 0.725315123 #> 2570 25.69 0.27402104 5.265296e-04 0.725452431 #> 2571 25.70 0.27388406 5.262664e-04 0.725589670 #> 2572 25.71 0.27374716 5.260033e-04 0.725726841 #> 2573 25.72 0.27361032 5.257404e-04 0.725863943 #> 2574 25.73 0.27347355 5.254776e-04 0.726000977 #> 2575 25.74 0.27333684 5.252149e-04 0.726137943 #> 2576 25.75 0.27320021 5.249524e-04 0.726274839 #> 2577 25.76 0.27306364 5.246900e-04 0.726411668 #> 2578 25.77 0.27292714 5.244277e-04 0.726548428 #> 2579 25.78 0.27279072 5.241655e-04 0.726685119 #> 2580 25.79 0.27265435 5.239035e-04 0.726821743 #> 2581 25.80 0.27251806 5.236416e-04 0.726958298 #> 2582 25.81 0.27238184 5.233799e-04 0.727094784 #> 2583 25.82 0.27224568 5.231182e-04 0.727231203 #> 2584 25.83 0.27210959 5.228567e-04 0.727367553 #> 2585 25.84 0.27197357 5.225954e-04 0.727503835 #> 2586 25.85 0.27183762 5.223342e-04 0.727640049 #> 2587 25.86 0.27170173 5.220731e-04 0.727776195 #> 2588 25.87 0.27156591 5.218121e-04 0.727912273 #> 2589 25.88 0.27143017 5.215512e-04 0.728048283 #> 2590 25.89 0.27129448 5.212905e-04 0.728184225 #> 2591 25.90 0.27115887 5.210299e-04 0.728320099 #> 2592 25.91 0.27102333 5.207695e-04 0.728455905 #> 2593 25.92 0.27088785 5.205092e-04 0.728591643 #> 2594 25.93 0.27075244 5.202490e-04 0.728727313 #> 2595 25.94 0.27061710 5.199889e-04 0.728862916 #> 2596 25.95 0.27048182 5.197290e-04 0.728998450 #> 2597 25.96 0.27034661 5.194692e-04 0.729133917 #> 2598 25.97 0.27021147 5.192095e-04 0.729269316 #> 2599 25.98 0.27007640 5.189500e-04 0.729404648 #> 2600 25.99 0.26994140 5.186906e-04 0.729539912 #> 2601 26.00 0.26980646 5.184313e-04 0.729675108 #> 2602 26.01 0.26967159 5.181721e-04 0.729810237 #> 2603 26.02 0.26953679 5.179131e-04 0.729945298 #> 2604 26.03 0.26940205 5.176542e-04 0.730080291 #> 2605 26.04 0.26926739 5.173955e-04 0.730215218 #> 2606 26.05 0.26913279 5.171368e-04 0.730350076 #> 2607 26.06 0.26899825 5.168783e-04 0.730484868 #> 2608 26.07 0.26886379 5.166200e-04 0.730619591 #> 2609 26.08 0.26872939 5.163617e-04 0.730754248 #> 2610 26.09 0.26859506 5.161036e-04 0.730888837 #> 2611 26.10 0.26846080 5.158456e-04 0.731023359 #> 2612 26.11 0.26832660 5.155878e-04 0.731157814 #> 2613 26.12 0.26819247 5.153300e-04 0.731292201 #> 2614 26.13 0.26805841 5.150724e-04 0.731426522 #> 2615 26.14 0.26792441 5.148150e-04 0.731560775 #> 2616 26.15 0.26779048 5.145576e-04 0.731694961 #> 2617 26.16 0.26765662 5.143004e-04 0.731829080 #> 2618 26.17 0.26752282 5.140433e-04 0.731963132 #> 2619 26.18 0.26738910 5.137864e-04 0.732097117 #> 2620 26.19 0.26725544 5.135295e-04 0.732231035 #> 2621 26.20 0.26712184 5.132728e-04 0.732364886 #> 2622 26.21 0.26698831 5.130162e-04 0.732498670 #> 2623 26.22 0.26685485 5.127598e-04 0.732632387 #> 2624 26.23 0.26672146 5.125035e-04 0.732766038 #> 2625 26.24 0.26658813 5.122473e-04 0.732899621 #> 2626 26.25 0.26645487 5.119912e-04 0.733033138 #> 2627 26.26 0.26632168 5.117353e-04 0.733166588 #> 2628 26.27 0.26618855 5.114795e-04 0.733299971 #> 2629 26.28 0.26605549 5.112238e-04 0.733433288 #> 2630 26.29 0.26592249 5.109683e-04 0.733566538 #> 2631 26.30 0.26578957 5.107129e-04 0.733699722 #> 2632 26.31 0.26565670 5.104576e-04 0.733832838 #> 2633 26.32 0.26552391 5.102024e-04 0.733965889 #> 2634 26.33 0.26539118 5.099474e-04 0.734098873 #> 2635 26.34 0.26525852 5.096925e-04 0.734231790 #> 2636 26.35 0.26512592 5.094377e-04 0.734364641 #> 2637 26.36 0.26499339 5.091830e-04 0.734497425 #> 2638 26.37 0.26486093 5.089285e-04 0.734630143 #> 2639 26.38 0.26472853 5.086741e-04 0.734762795 #> 2640 26.39 0.26459620 5.084198e-04 0.734895381 #> 2641 26.40 0.26446393 5.081657e-04 0.735027900 #> 2642 26.41 0.26433174 5.079117e-04 0.735160353 #> 2643 26.42 0.26419960 5.076578e-04 0.735292739 #> 2644 26.43 0.26406754 5.074040e-04 0.735425060 #> 2645 26.44 0.26393554 5.071504e-04 0.735557314 #> 2646 26.45 0.26380360 5.068968e-04 0.735689503 #> 2647 26.46 0.26367173 5.066435e-04 0.735821625 #> 2648 26.47 0.26353993 5.063902e-04 0.735953681 #> 2649 26.48 0.26340819 5.061371e-04 0.736085671 #> 2650 26.49 0.26327652 5.058841e-04 0.736217595 #> 2651 26.50 0.26314492 5.056312e-04 0.736349454 #> 2652 26.51 0.26301338 5.053784e-04 0.736481246 #> 2653 26.52 0.26288190 5.051258e-04 0.736612972 #> 2654 26.53 0.26275049 5.048733e-04 0.736744633 #> 2655 26.54 0.26261915 5.046209e-04 0.736876228 #> 2656 26.55 0.26248787 5.043687e-04 0.737007757 #> 2657 26.56 0.26235666 5.041166e-04 0.737139220 #> 2658 26.57 0.26222552 5.038646e-04 0.737270618 #> 2659 26.58 0.26209444 5.036127e-04 0.737401950 #> 2660 26.59 0.26196342 5.033610e-04 0.737533216 #> 2661 26.60 0.26183247 5.031093e-04 0.737664416 #> 2662 26.61 0.26170159 5.028578e-04 0.737795551 #> 2663 26.62 0.26157077 5.026065e-04 0.737926621 #> 2664 26.63 0.26144002 5.023552e-04 0.738057625 #> 2665 26.64 0.26130933 5.021041e-04 0.738188563 #> 2666 26.65 0.26117871 5.018531e-04 0.738319436 #> 2667 26.66 0.26104815 5.016023e-04 0.738450244 #> 2668 26.67 0.26091766 5.013515e-04 0.738580986 #> 2669 26.68 0.26078724 5.011009e-04 0.738711663 #> 2670 26.69 0.26065688 5.008504e-04 0.738842274 #> 2671 26.70 0.26052658 5.006001e-04 0.738972821 #> 2672 26.71 0.26039635 5.003498e-04 0.739103302 #> 2673 26.72 0.26026618 5.000997e-04 0.739233717 #> 2674 26.73 0.26013608 4.998497e-04 0.739364068 #> 2675 26.74 0.26000605 4.995999e-04 0.739494353 #> 2676 26.75 0.25987608 4.993501e-04 0.739624573 #> 2677 26.76 0.25974617 4.991005e-04 0.739754729 #> 2678 26.77 0.25961633 4.988510e-04 0.739884819 #> 2679 26.78 0.25948655 4.986017e-04 0.740014844 #> 2680 26.79 0.25935684 4.983524e-04 0.740144804 #> 2681 26.80 0.25922720 4.981033e-04 0.740274699 #> 2682 26.81 0.25909762 4.978543e-04 0.740404529 #> 2683 26.82 0.25896810 4.976055e-04 0.740534295 #> 2684 26.83 0.25883865 4.973567e-04 0.740663995 #> 2685 26.84 0.25870926 4.971081e-04 0.740793631 #> 2686 26.85 0.25857994 4.968596e-04 0.740923201 #> 2687 26.86 0.25845068 4.966112e-04 0.741052707 #> 2688 26.87 0.25832149 4.963630e-04 0.741182149 #> 2689 26.88 0.25819236 4.961149e-04 0.741311525 #> 2690 26.89 0.25806330 4.958669e-04 0.741440837 #> 2691 26.90 0.25793430 4.956190e-04 0.741570084 #> 2692 26.91 0.25780536 4.953713e-04 0.741699267 #> 2693 26.92 0.25767649 4.951236e-04 0.741828385 #> 2694 26.93 0.25754769 4.948761e-04 0.741957439 #> 2695 26.94 0.25741894 4.946288e-04 0.742086428 #> 2696 26.95 0.25729027 4.943815e-04 0.742215352 #> 2697 26.96 0.25716165 4.941344e-04 0.742344212 #> 2698 26.97 0.25703310 4.938874e-04 0.742473008 #> 2699 26.98 0.25690462 4.936405e-04 0.742601740 #> 2700 26.99 0.25677620 4.933937e-04 0.742730406 #> 2701 27.00 0.25664784 4.931471e-04 0.742859009 #> 2702 27.01 0.25651955 4.929006e-04 0.742987547 #> 2703 27.02 0.25639132 4.926542e-04 0.743116022 #> 2704 27.03 0.25626316 4.924079e-04 0.743244431 #> 2705 27.04 0.25613506 4.921618e-04 0.743372777 #> 2706 27.05 0.25600703 4.919158e-04 0.743501059 #> 2707 27.06 0.25587905 4.916699e-04 0.743629276 #> 2708 27.07 0.25575115 4.914241e-04 0.743757429 #> 2709 27.08 0.25562330 4.911785e-04 0.743885519 #> 2710 27.09 0.25549552 4.909329e-04 0.744013544 #> 2711 27.10 0.25536781 4.906875e-04 0.744141505 #> 2712 27.11 0.25524016 4.904422e-04 0.744269402 #> 2713 27.12 0.25511257 4.901971e-04 0.744397236 #> 2714 27.13 0.25498504 4.899520e-04 0.744525005 #> 2715 27.14 0.25485758 4.897071e-04 0.744652711 #> 2716 27.15 0.25473019 4.894623e-04 0.744780353 #> 2717 27.16 0.25460285 4.892177e-04 0.744907931 #> 2718 27.17 0.25447558 4.889731e-04 0.745035445 #> 2719 27.18 0.25434838 4.887287e-04 0.745162895 #> 2720 27.19 0.25422123 4.884844e-04 0.745290282 #> 2721 27.20 0.25409415 4.882402e-04 0.745417605 #> 2722 27.21 0.25396714 4.879962e-04 0.745544864 #> 2723 27.22 0.25384019 4.877522e-04 0.745672060 #> 2724 27.23 0.25371330 4.875084e-04 0.745799192 #> 2725 27.24 0.25358647 4.872647e-04 0.745926261 #> 2726 27.25 0.25345971 4.870211e-04 0.746053266 #> 2727 27.26 0.25333301 4.867777e-04 0.746180208 #> 2728 27.27 0.25320638 4.865344e-04 0.746307086 #> 2729 27.28 0.25307981 4.862911e-04 0.746433901 #> 2730 27.29 0.25295330 4.860481e-04 0.746560652 #> 2731 27.30 0.25282686 4.858051e-04 0.746687340 #> 2732 27.31 0.25270047 4.855623e-04 0.746813965 #> 2733 27.32 0.25257415 4.853195e-04 0.746940526 #> 2734 27.33 0.25244790 4.850769e-04 0.747067024 #> 2735 27.34 0.25232171 4.848345e-04 0.747193459 #> 2736 27.35 0.25219558 4.845921e-04 0.747319831 #> 2737 27.36 0.25206951 4.843499e-04 0.747446139 #> 2738 27.37 0.25194351 4.841078e-04 0.747572385 #> 2739 27.38 0.25181757 4.838658e-04 0.747698567 #> 2740 27.39 0.25169169 4.836239e-04 0.747824686 #> 2741 27.40 0.25156588 4.833821e-04 0.747950742 #> 2742 27.41 0.25144012 4.831405e-04 0.748076735 #> 2743 27.42 0.25131444 4.828990e-04 0.748202665 #> 2744 27.43 0.25118881 4.826576e-04 0.748328533 #> 2745 27.44 0.25106325 4.824163e-04 0.748454337 #> 2746 27.45 0.25093775 4.821752e-04 0.748580078 #> 2747 27.46 0.25081231 4.819342e-04 0.748705757 #> 2748 27.47 0.25068693 4.816933e-04 0.748831373 #> 2749 27.48 0.25056162 4.814525e-04 0.748956925 #> 2750 27.49 0.25043637 4.812118e-04 0.749082416 #> 2751 27.50 0.25031119 4.809713e-04 0.749207843 #> 2752 27.51 0.25018606 4.807308e-04 0.749333208 #> 2753 27.52 0.25006100 4.804905e-04 0.749458510 #> 2754 27.53 0.24993600 4.802503e-04 0.749583749 #> 2755 27.54 0.24981106 4.800103e-04 0.749708926 #> 2756 27.55 0.24968619 4.797703e-04 0.749834040 #> 2757 27.56 0.24956138 4.795305e-04 0.749959092 #> 2758 27.57 0.24943663 4.792908e-04 0.750084081 #> 2759 27.58 0.24931194 4.790512e-04 0.750209008 #> 2760 27.59 0.24918732 4.788118e-04 0.750333872 #> 2761 27.60 0.24906275 4.785724e-04 0.750458674 #> 2762 27.61 0.24893825 4.783332e-04 0.750583414 #> 2763 27.62 0.24881382 4.780941e-04 0.750708091 #> 2764 27.63 0.24868944 4.778551e-04 0.750832706 #> 2765 27.64 0.24856513 4.776162e-04 0.750957258 #> 2766 27.65 0.24844087 4.773775e-04 0.751081748 #> 2767 27.66 0.24831668 4.771388e-04 0.751206176 #> 2768 27.67 0.24819256 4.769003e-04 0.751330542 #> 2769 27.68 0.24806849 4.766619e-04 0.751454846 #> 2770 27.69 0.24794449 4.764237e-04 0.751579087 #> 2771 27.70 0.24782055 4.761855e-04 0.751703267 #> 2772 27.71 0.24769667 4.759475e-04 0.751827384 #> 2773 27.72 0.24757285 4.757096e-04 0.751951439 #> 2774 27.73 0.24744910 4.754718e-04 0.752075433 #> 2775 27.74 0.24732540 4.752341e-04 0.752199364 #> 2776 27.75 0.24720177 4.749965e-04 0.752323233 #> 2777 27.76 0.24707820 4.747591e-04 0.752447041 #> 2778 27.77 0.24695469 4.745218e-04 0.752570786 #> 2779 27.78 0.24683125 4.742846e-04 0.752694470 #> 2780 27.79 0.24670786 4.740475e-04 0.752818092 #> 2781 27.80 0.24658454 4.738105e-04 0.752941652 #> 2782 27.81 0.24646128 4.735737e-04 0.753065150 #> 2783 27.82 0.24633808 4.733370e-04 0.753188587 #> 2784 27.83 0.24621494 4.731003e-04 0.753311962 #> 2785 27.84 0.24609186 4.728639e-04 0.753435275 #> 2786 27.85 0.24596885 4.726275e-04 0.753558526 #> 2787 27.86 0.24584589 4.723912e-04 0.753681716 #> 2788 27.87 0.24572300 4.721551e-04 0.753804845 #> 2789 27.88 0.24560017 4.719191e-04 0.753927912 #> 2790 27.89 0.24547740 4.716832e-04 0.754050917 #> 2791 27.90 0.24535469 4.714474e-04 0.754173861 #> 2792 27.91 0.24523205 4.712117e-04 0.754296743 #> 2793 27.92 0.24510946 4.709762e-04 0.754419564 #> 2794 27.93 0.24498694 4.707407e-04 0.754542324 #> 2795 27.94 0.24486447 4.705054e-04 0.754665022 #> 2796 27.95 0.24474207 4.702702e-04 0.754787659 #> 2797 27.96 0.24461973 4.700352e-04 0.754910234 #> 2798 27.97 0.24449745 4.698002e-04 0.755032748 #> 2799 27.98 0.24437523 4.695654e-04 0.755155201 #> 2800 27.99 0.24425308 4.693306e-04 0.755277593 #> 2801 28.00 0.24413098 4.690960e-04 0.755399924 #> 2802 28.01 0.24400895 4.688615e-04 0.755522193 #> 2803 28.02 0.24388697 4.686272e-04 0.755644402 #> 2804 28.03 0.24376506 4.683929e-04 0.755766549 #> 2805 28.04 0.24364321 4.681588e-04 0.755888635 #> 2806 28.05 0.24352142 4.679248e-04 0.756010660 #> 2807 28.06 0.24339968 4.676909e-04 0.756132624 #> 2808 28.07 0.24327802 4.674571e-04 0.756254528 #> 2809 28.08 0.24315641 4.672234e-04 0.756376370 #> 2810 28.09 0.24303486 4.669898e-04 0.756498151 #> 2811 28.10 0.24291337 4.667564e-04 0.756619872 #> 2812 28.11 0.24279195 4.665231e-04 0.756741531 #> 2813 28.12 0.24267058 4.662899e-04 0.756863130 #> 2814 28.13 0.24254927 4.660568e-04 0.756984668 #> 2815 28.14 0.24242803 4.658238e-04 0.757106146 #> 2816 28.15 0.24230685 4.655910e-04 0.757227562 #> 2817 28.16 0.24218572 4.653582e-04 0.757348918 #> 2818 28.17 0.24206466 4.651256e-04 0.757470213 #> 2819 28.18 0.24194366 4.648931e-04 0.757591448 #> 2820 28.19 0.24182272 4.646607e-04 0.757712622 #> 2821 28.20 0.24170184 4.644285e-04 0.757833735 #> 2822 28.21 0.24158102 4.641963e-04 0.757954788 #> 2823 28.22 0.24146026 4.639643e-04 0.758075780 #> 2824 28.23 0.24133956 4.637323e-04 0.758196712 #> 2825 28.24 0.24121892 4.635005e-04 0.758317584 #> 2826 28.25 0.24109834 4.632688e-04 0.758438395 #> 2827 28.26 0.24097782 4.630373e-04 0.758559145 #> 2828 28.27 0.24085736 4.628058e-04 0.758679836 #> 2829 28.28 0.24073696 4.625744e-04 0.758800466 #> 2830 28.29 0.24061662 4.623432e-04 0.758921035 #> 2831 28.30 0.24049634 4.621121e-04 0.759041545 #> 2832 28.31 0.24037613 4.618811e-04 0.759161994 #> 2833 28.32 0.24025597 4.616502e-04 0.759282383 #> 2834 28.33 0.24013587 4.614195e-04 0.759402711 #> 2835 28.34 0.24001583 4.611888e-04 0.759522980 #> 2836 28.35 0.23989585 4.609583e-04 0.759643188 #> 2837 28.36 0.23977594 4.607278e-04 0.759763337 #> 2838 28.37 0.23965608 4.604975e-04 0.759883425 #> 2839 28.38 0.23953628 4.602673e-04 0.760003453 #> 2840 28.39 0.23941654 4.600373e-04 0.760123422 #> 2841 28.40 0.23929686 4.598073e-04 0.760243330 #> 2842 28.41 0.23917724 4.595775e-04 0.760363178 #> 2843 28.42 0.23905769 4.593477e-04 0.760482967 #> 2844 28.43 0.23893819 4.591181e-04 0.760602695 #> 2845 28.44 0.23881875 4.588886e-04 0.760722364 #> 2846 28.45 0.23869937 4.586592e-04 0.760841973 #> 2847 28.46 0.23858005 4.584300e-04 0.760961522 #> 2848 28.47 0.23846079 4.582008e-04 0.761081012 #> 2849 28.48 0.23834159 4.579718e-04 0.761200441 #> 2850 28.49 0.23822245 4.577428e-04 0.761319811 #> 2851 28.50 0.23810336 4.575140e-04 0.761439121 #> 2852 28.51 0.23798434 4.572853e-04 0.761558372 #> 2853 28.52 0.23786538 4.570567e-04 0.761677563 #> 2854 28.53 0.23774648 4.568283e-04 0.761796694 #> 2855 28.54 0.23762763 4.565999e-04 0.761915766 #> 2856 28.55 0.23750885 4.563717e-04 0.762034779 #> 2857 28.56 0.23739012 4.561435e-04 0.762153732 #> 2858 28.57 0.23727146 4.559155e-04 0.762272625 #> 2859 28.58 0.23715285 4.556876e-04 0.762391459 #> 2860 28.59 0.23703431 4.554598e-04 0.762510234 #> 2861 28.60 0.23691582 4.552322e-04 0.762628949 #> 2862 28.61 0.23679739 4.550046e-04 0.762747605 #> 2863 28.62 0.23667902 4.547771e-04 0.762866201 #> 2864 28.63 0.23656071 4.545498e-04 0.762984738 #> 2865 28.64 0.23644246 4.543226e-04 0.763103216 #> 2866 28.65 0.23632427 4.540955e-04 0.763221635 #> 2867 28.66 0.23620614 4.538685e-04 0.763339995 #> 2868 28.67 0.23608806 4.536416e-04 0.763458295 #> 2869 28.68 0.23597005 4.534149e-04 0.763576537 #> 2870 28.69 0.23585209 4.531882e-04 0.763694719 #> 2871 28.70 0.23573420 4.529617e-04 0.763812842 #> 2872 28.71 0.23561636 4.527352e-04 0.763930906 #> 2873 28.72 0.23549858 4.525089e-04 0.764048911 #> 2874 28.73 0.23538086 4.522827e-04 0.764166857 #> 2875 28.74 0.23526320 4.520567e-04 0.764284744 #> 2876 28.75 0.23514560 4.518307e-04 0.764402572 #> 2877 28.76 0.23502805 4.516048e-04 0.764520342 #> 2878 28.77 0.23491057 4.513791e-04 0.764638052 #> 2879 28.78 0.23479314 4.511534e-04 0.764755704 #> 2880 28.79 0.23467578 4.509279e-04 0.764873296 #> 2881 28.80 0.23455847 4.507025e-04 0.764990830 #> 2882 28.81 0.23444122 4.504772e-04 0.765108306 #> 2883 28.82 0.23432403 4.502520e-04 0.765225722 #> 2884 28.83 0.23420689 4.500270e-04 0.765343080 #> 2885 28.84 0.23408982 4.498020e-04 0.765460379 #> 2886 28.85 0.23397280 4.495772e-04 0.765577619 #> 2887 28.86 0.23385585 4.493524e-04 0.765694801 #> 2888 28.87 0.23373895 4.491278e-04 0.765811925 #> 2889 28.88 0.23362211 4.489033e-04 0.765928989 #> 2890 28.89 0.23350533 4.486789e-04 0.766045996 #> 2891 28.90 0.23338860 4.484546e-04 0.766162943 #> 2892 28.91 0.23327194 4.482305e-04 0.766279833 #> 2893 28.92 0.23315533 4.480064e-04 0.766396664 #> 2894 28.93 0.23303878 4.477825e-04 0.766513436 #> 2895 28.94 0.23292229 4.475586e-04 0.766630150 #> 2896 28.95 0.23280586 4.473349e-04 0.766746806 #> 2897 28.96 0.23268949 4.471113e-04 0.766863403 #> 2898 28.97 0.23257317 4.468878e-04 0.766979943 #> 2899 28.98 0.23245691 4.466644e-04 0.767096424 #> 2900 28.99 0.23234071 4.464411e-04 0.767212846 #> 2901 29.00 0.23222457 4.462180e-04 0.767329211 #> 2902 29.01 0.23210849 4.459949e-04 0.767445517 #> 2903 29.02 0.23199246 4.457720e-04 0.767561765 #> 2904 29.03 0.23187650 4.455491e-04 0.767677955 #> 2905 29.04 0.23176059 4.453264e-04 0.767794087 #> 2906 29.05 0.23164473 4.451038e-04 0.767910161 #> 2907 29.06 0.23152894 4.448813e-04 0.768026177 #> 2908 29.07 0.23141321 4.446589e-04 0.768142135 #> 2909 29.08 0.23129753 4.444366e-04 0.768258035 #> 2910 29.09 0.23118191 4.442145e-04 0.768373877 #> 2911 29.10 0.23106635 4.439924e-04 0.768489661 #> 2912 29.11 0.23095084 4.437705e-04 0.768605387 #> 2913 29.12 0.23083540 4.435487e-04 0.768721056 #> 2914 29.13 0.23072001 4.433269e-04 0.768836666 #> 2915 29.14 0.23060468 4.431053e-04 0.768952219 #> 2916 29.15 0.23048940 4.428838e-04 0.769067714 #> 2917 29.16 0.23037419 4.426624e-04 0.769183151 #> 2918 29.17 0.23025903 4.424412e-04 0.769298531 #> 2919 29.18 0.23014393 4.422200e-04 0.769413853 #> 2920 29.19 0.23002888 4.419990e-04 0.769529117 #> 2921 29.20 0.22991390 4.417780e-04 0.769644324 #> 2922 29.21 0.22979897 4.415572e-04 0.769759473 #> 2923 29.22 0.22968410 4.413365e-04 0.769874564 #> 2924 29.23 0.22956929 4.411158e-04 0.769989598 #> 2925 29.24 0.22945453 4.408953e-04 0.770104575 #> 2926 29.25 0.22933983 4.406749e-04 0.770219494 #> 2927 29.26 0.22922519 4.404547e-04 0.770334355 #> 2928 29.27 0.22911061 4.402345e-04 0.770449159 #> 2929 29.28 0.22899608 4.400144e-04 0.770563906 #> 2930 29.29 0.22888161 4.397945e-04 0.770678596 #> 2931 29.30 0.22876720 4.395746e-04 0.770793228 #> 2932 29.31 0.22865284 4.393549e-04 0.770907802 #> 2933 29.32 0.22853854 4.391353e-04 0.771022320 #> 2934 29.33 0.22842430 4.389158e-04 0.771136780 #> 2935 29.34 0.22831012 4.386964e-04 0.771251183 #> 2936 29.35 0.22819599 4.384771e-04 0.771365529 #> 2937 29.36 0.22808192 4.382579e-04 0.771479818 #> 2938 29.37 0.22796791 4.380388e-04 0.771594049 #> 2939 29.38 0.22785396 4.378198e-04 0.771708224 #> 2940 29.39 0.22774006 4.376010e-04 0.771822341 #> 2941 29.40 0.22762622 4.373822e-04 0.771936401 #> 2942 29.41 0.22751243 4.371636e-04 0.772050404 #> 2943 29.42 0.22739870 4.369451e-04 0.772164351 #> 2944 29.43 0.22728503 4.367267e-04 0.772278240 #> 2945 29.44 0.22717142 4.365084e-04 0.772392073 #> 2946 29.45 0.22705786 4.362902e-04 0.772505848 #> 2947 29.46 0.22694436 4.360721e-04 0.772619567 #> 2948 29.47 0.22683092 4.358541e-04 0.772733229 #> 2949 29.48 0.22671753 4.356362e-04 0.772846834 #> 2950 29.49 0.22660420 4.354184e-04 0.772960382 #> 2951 29.50 0.22649093 4.352008e-04 0.773073873 #> 2952 29.51 0.22637771 4.349832e-04 0.773187308 #> 2953 29.52 0.22626455 4.347658e-04 0.773300686 #> 2954 29.53 0.22615144 4.345485e-04 0.773414007 #> 2955 29.54 0.22603840 4.343313e-04 0.773527272 #> 2956 29.55 0.22592541 4.341141e-04 0.773640480 #> 2957 29.56 0.22581247 4.338971e-04 0.773753631 #> 2958 29.57 0.22569959 4.336803e-04 0.773866726 #> 2959 29.58 0.22558677 4.334635e-04 0.773979765 #> 2960 29.59 0.22547401 4.332468e-04 0.774092747 #> 2961 29.60 0.22536130 4.330302e-04 0.774205672 #> 2962 29.61 0.22524865 4.328138e-04 0.774318541 #> 2963 29.62 0.22513605 4.325974e-04 0.774431353 #> 2964 29.63 0.22502351 4.323812e-04 0.774544110 #> 2965 29.64 0.22491103 4.321650e-04 0.774656809 #> 2966 29.65 0.22479860 4.319490e-04 0.774769453 #> 2967 29.66 0.22468623 4.317331e-04 0.774882040 #> 2968 29.67 0.22457391 4.315173e-04 0.774994571 #> 2969 29.68 0.22446165 4.313016e-04 0.775107045 #> 2970 29.69 0.22434945 4.310860e-04 0.775219464 #> 2971 29.70 0.22423730 4.308705e-04 0.775331826 #> 2972 29.71 0.22412521 4.306551e-04 0.775444132 #> 2973 29.72 0.22401318 4.304398e-04 0.775556382 #> 2974 29.73 0.22390120 4.302246e-04 0.775668576 #> 2975 29.74 0.22378928 4.300096e-04 0.775780713 #> 2976 29.75 0.22367741 4.297946e-04 0.775892795 #> 2977 29.76 0.22356560 4.295798e-04 0.776004821 #> 2978 29.77 0.22345384 4.293651e-04 0.776116790 #> 2979 29.78 0.22334215 4.291504e-04 0.776228704 #> 2980 29.79 0.22323050 4.289359e-04 0.776340561 #> 2981 29.80 0.22311892 4.287215e-04 0.776452363 #> 2982 29.81 0.22300738 4.285072e-04 0.776564109 #> 2983 29.82 0.22289591 4.282930e-04 0.776675799 #> 2984 29.83 0.22278449 4.280789e-04 0.776787433 #> 2985 29.84 0.22267312 4.278649e-04 0.776899012 #> 2986 29.85 0.22256181 4.276510e-04 0.777010534 #> 2987 29.86 0.22245056 4.274373e-04 0.777122001 #> 2988 29.87 0.22233936 4.272236e-04 0.777233412 #> 2989 29.88 0.22222822 4.270100e-04 0.777344768 #> 2990 29.89 0.22211714 4.267966e-04 0.777456068 #> 2991 29.90 0.22200611 4.265832e-04 0.777567312 #> 2992 29.91 0.22189513 4.263700e-04 0.777678500 #> 2993 29.92 0.22178421 4.261569e-04 0.777789633 #> 2994 29.93 0.22167335 4.259438e-04 0.777900711 #> 2995 29.94 0.22156254 4.257309e-04 0.778011733 #> 2996 29.95 0.22145178 4.255181e-04 0.778122699 #> 2997 29.96 0.22134108 4.253054e-04 0.778233610 #> 2998 29.97 0.22123044 4.250928e-04 0.778344465 #> 2999 29.98 0.22111985 4.248803e-04 0.778455265 #> 3000 29.99 0.22100932 4.246679e-04 0.778566010 #> 3001 30.00 0.22089884 4.244556e-04 0.778676699 #> 3002 30.01 0.22078842 4.242435e-04 0.778787333 #> 3003 30.02 0.22067806 4.240314e-04 0.778897912 #> 3004 30.03 0.22056775 4.238194e-04 0.779008436 #> 3005 30.04 0.22045749 4.236076e-04 0.779118904 #> 3006 30.05 0.22034729 4.233958e-04 0.779229317 #> 3007 30.06 0.22023714 4.231842e-04 0.779339674 #> 3008 30.07 0.22012705 4.229726e-04 0.779449977 #> 3009 30.08 0.22001701 4.227612e-04 0.779560224 #> 3010 30.09 0.21990703 4.225499e-04 0.779670417 #> 3011 30.10 0.21979711 4.223387e-04 0.779780554 #> 3012 30.11 0.21968724 4.221275e-04 0.779890636 #> 3013 30.12 0.21957742 4.219165e-04 0.780000663 #> 3014 30.13 0.21946766 4.217056e-04 0.780110636 #> 3015 30.14 0.21935795 4.214948e-04 0.780220553 #> 3016 30.15 0.21924830 4.212841e-04 0.780330415 #> 3017 30.16 0.21913870 4.210735e-04 0.780440222 #> 3018 30.17 0.21902916 4.208631e-04 0.780549975 #> 3019 30.18 0.21891967 4.206527e-04 0.780659672 #> 3020 30.19 0.21881024 4.204424e-04 0.780769315 #> 3021 30.20 0.21870086 4.202322e-04 0.780878903 #> 3022 30.21 0.21859154 4.200222e-04 0.780988436 #> 3023 30.22 0.21848227 4.198122e-04 0.781097915 #> 3024 30.23 0.21837306 4.196024e-04 0.781207338 #> 3025 30.24 0.21826390 4.193926e-04 0.781316707 #> 3026 30.25 0.21815480 4.191830e-04 0.781426022 #> 3027 30.26 0.21804575 4.189734e-04 0.781535281 #> 3028 30.27 0.21793675 4.187640e-04 0.781644487 #> 3029 30.28 0.21782781 4.185547e-04 0.781753637 #> 3030 30.29 0.21771892 4.183454e-04 0.781862733 #> 3031 30.30 0.21761009 4.181363e-04 0.781971774 #> 3032 30.31 0.21750131 4.179273e-04 0.782080761 #> 3033 30.32 0.21739259 4.177184e-04 0.782189694 #> 3034 30.33 0.21728392 4.175096e-04 0.782298571 #> 3035 30.34 0.21717530 4.173009e-04 0.782407395 #> 3036 30.35 0.21706674 4.170923e-04 0.782516164 #> 3037 30.36 0.21695824 4.168838e-04 0.782624879 #> 3038 30.37 0.21684979 4.166754e-04 0.782733539 #> 3039 30.38 0.21674139 4.164671e-04 0.782842145 #> 3040 30.39 0.21663304 4.162589e-04 0.782950697 #> 3041 30.40 0.21652475 4.160509e-04 0.783059195 #> 3042 30.41 0.21641652 4.158429e-04 0.783167638 #> 3043 30.42 0.21630834 4.156350e-04 0.783276027 #> 3044 30.43 0.21620021 4.154273e-04 0.783384362 #> 3045 30.44 0.21609214 4.152196e-04 0.783492643 #> 3046 30.45 0.21598412 4.150120e-04 0.783600869 #> 3047 30.46 0.21587615 4.148046e-04 0.783709042 #> 3048 30.47 0.21576824 4.145972e-04 0.783817160 #> 3049 30.48 0.21566039 4.143900e-04 0.783925225 #> 3050 30.49 0.21555258 4.141828e-04 0.784033235 #> 3051 30.50 0.21544483 4.139758e-04 0.784141191 #> 3052 30.51 0.21533714 4.137689e-04 0.784249094 #> 3053 30.52 0.21522950 4.135620e-04 0.784356942 #> 3054 30.53 0.21512191 4.133553e-04 0.784464737 #> 3055 30.54 0.21501437 4.131487e-04 0.784572478 #> 3056 30.55 0.21490689 4.129422e-04 0.784680164 #> 3057 30.56 0.21479947 4.127357e-04 0.784787798 #> 3058 30.57 0.21469209 4.125294e-04 0.784895377 #> 3059 30.58 0.21458477 4.123232e-04 0.785002902 #> 3060 30.59 0.21447751 4.121171e-04 0.785110374 #> 3061 30.60 0.21437030 4.119111e-04 0.785217792 #> 3062 30.61 0.21426314 4.117052e-04 0.785325156 #> 3063 30.62 0.21415603 4.114994e-04 0.785432467 #> 3064 30.63 0.21404898 4.112937e-04 0.785539724 #> 3065 30.64 0.21394198 4.110881e-04 0.785646927 #> 3066 30.65 0.21383504 4.108826e-04 0.785754077 #> 3067 30.66 0.21372815 4.106772e-04 0.785861173 #> 3068 30.67 0.21362131 4.104719e-04 0.785968216 #> 3069 30.68 0.21351453 4.102667e-04 0.786075205 #> 3070 30.69 0.21340780 4.100616e-04 0.786182140 #> 3071 30.70 0.21330112 4.098567e-04 0.786289023 #> 3072 30.71 0.21319450 4.096518e-04 0.786395851 #> 3073 30.72 0.21308793 4.094470e-04 0.786502627 #> 3074 30.73 0.21298141 4.092423e-04 0.786609349 #> 3075 30.74 0.21287494 4.090378e-04 0.786716017 #> 3076 30.75 0.21276853 4.088333e-04 0.786822633 #> 3077 30.76 0.21266218 4.086289e-04 0.786929195 #> 3078 30.77 0.21255587 4.084247e-04 0.787035704 #> 3079 30.78 0.21244962 4.082205e-04 0.787142159 #> 3080 30.79 0.21234342 4.080165e-04 0.787248561 #> 3081 30.80 0.21223728 4.078125e-04 0.787354911 #> 3082 30.81 0.21213118 4.076086e-04 0.787461207 #> 3083 30.82 0.21202515 4.074049e-04 0.787567449 #> 3084 30.83 0.21191916 4.072012e-04 0.787673639 #> 3085 30.84 0.21181323 4.069977e-04 0.787779776 #> 3086 30.85 0.21170735 4.067942e-04 0.787885859 #> 3087 30.86 0.21160152 4.065909e-04 0.787991890 #> 3088 30.87 0.21149574 4.063877e-04 0.788097867 #> 3089 30.88 0.21139002 4.061845e-04 0.788203792 #> 3090 30.89 0.21128435 4.059815e-04 0.788309664 #> 3091 30.90 0.21117874 4.057785e-04 0.788415482 #> 3092 30.91 0.21107318 4.055757e-04 0.788521248 #> 3093 30.92 0.21096767 4.053730e-04 0.788626961 #> 3094 30.93 0.21086221 4.051703e-04 0.788732621 #> 3095 30.94 0.21075680 4.049678e-04 0.788838229 #> 3096 30.95 0.21065145 4.047653e-04 0.788943783 #> 3097 30.96 0.21054615 4.045630e-04 0.789049285 #> 3098 30.97 0.21044091 4.043608e-04 0.789154734 #> 3099 30.98 0.21033571 4.041587e-04 0.789260130 #> 3100 30.99 0.21023057 4.039566e-04 0.789365474 #> 3101 31.00 0.21012548 4.037547e-04 0.789470765 #> 3102 31.01 0.21002044 4.035529e-04 0.789576003 #> 3103 31.02 0.20991546 4.033511e-04 0.789681189 #> 3104 31.03 0.20981053 4.031495e-04 0.789786322 #> 3105 31.04 0.20970565 4.029480e-04 0.789891402 #> 3106 31.05 0.20960082 4.027466e-04 0.789996430 #> 3107 31.06 0.20949605 4.025452e-04 0.790101406 #> 3108 31.07 0.20939133 4.023440e-04 0.790206329 #> 3109 31.08 0.20928666 4.021429e-04 0.790311200 #> 3110 31.09 0.20918204 4.019419e-04 0.790416018 #> 3111 31.10 0.20907748 4.017410e-04 0.790520784 #> 3112 31.11 0.20897296 4.015401e-04 0.790625497 #> 3113 31.12 0.20886850 4.013394e-04 0.790730158 #> 3114 31.13 0.20876409 4.011388e-04 0.790834767 #> 3115 31.14 0.20865974 4.009383e-04 0.790939323 #> 3116 31.15 0.20855543 4.007379e-04 0.791043828 #> 3117 31.16 0.20845118 4.005375e-04 0.791148280 #> 3118 31.17 0.20834698 4.003373e-04 0.791252679 #> 3119 31.18 0.20824284 4.001372e-04 0.791357027 #> 3120 31.19 0.20813874 3.999372e-04 0.791461322 #> 3121 31.20 0.20803470 3.997373e-04 0.791565566 #> 3122 31.21 0.20793071 3.995375e-04 0.791669757 #> 3123 31.22 0.20782677 3.993377e-04 0.791773896 #> 3124 31.23 0.20772288 3.991381e-04 0.791877983 #> 3125 31.24 0.20761904 3.989386e-04 0.791982018 #> 3126 31.25 0.20751526 3.987392e-04 0.792086001 #> 3127 31.26 0.20741153 3.985399e-04 0.792189932 #> 3128 31.27 0.20730785 3.983406e-04 0.792293811 #> 3129 31.28 0.20720422 3.981415e-04 0.792397638 #> 3130 31.29 0.20710064 3.979425e-04 0.792501413 #> 3131 31.30 0.20699712 3.977436e-04 0.792605137 #> 3132 31.31 0.20689365 3.975448e-04 0.792708808 #> 3133 31.32 0.20679023 3.973460e-04 0.792812428 #> 3134 31.33 0.20668686 3.971474e-04 0.792915996 #> 3135 31.34 0.20658354 3.969489e-04 0.793019512 #> 3136 31.35 0.20648027 3.967505e-04 0.793122976 #> 3137 31.36 0.20637706 3.965521e-04 0.793226389 #> 3138 31.37 0.20627390 3.963539e-04 0.793329750 #> 3139 31.38 0.20617078 3.961558e-04 0.793433059 #> 3140 31.39 0.20606773 3.959577e-04 0.793536317 #> 3141 31.40 0.20596472 3.957598e-04 0.793639523 #> 3142 31.41 0.20586176 3.955620e-04 0.793742677 #> 3143 31.42 0.20575886 3.953643e-04 0.793845780 #> 3144 31.43 0.20565600 3.951666e-04 0.793948832 #> 3145 31.44 0.20555320 3.949691e-04 0.794051831 #> 3146 31.45 0.20545045 3.947717e-04 0.794154780 #> 3147 31.46 0.20534775 3.945743e-04 0.794257677 #> 3148 31.47 0.20524510 3.943771e-04 0.794360522 #> 3149 31.48 0.20514250 3.941799e-04 0.794463316 #> 3150 31.49 0.20503996 3.939829e-04 0.794566059 #> 3151 31.50 0.20493746 3.937860e-04 0.794668750 #> 3152 31.51 0.20483502 3.935891e-04 0.794771390 #> 3153 31.52 0.20473263 3.933924e-04 0.794873979 #> 3154 31.53 0.20463029 3.931957e-04 0.794976516 #> 3155 31.54 0.20452800 3.929992e-04 0.795079002 #> 3156 31.55 0.20442576 3.928027e-04 0.795181437 #> 3157 31.56 0.20432357 3.926064e-04 0.795283821 #> 3158 31.57 0.20422144 3.924101e-04 0.795386153 #> 3159 31.58 0.20411935 3.922140e-04 0.795488435 #> 3160 31.59 0.20401732 3.920179e-04 0.795590665 #> 3161 31.60 0.20391533 3.918219e-04 0.795692844 #> 3162 31.61 0.20381340 3.916261e-04 0.795794972 #> 3163 31.62 0.20371152 3.914303e-04 0.795897049 #> 3164 31.63 0.20360969 3.912347e-04 0.795999075 #> 3165 31.64 0.20350791 3.910391e-04 0.796101050 #> 3166 31.65 0.20340618 3.908436e-04 0.796202974 #> 3167 31.66 0.20330450 3.906482e-04 0.796304847 #> 3168 31.67 0.20320288 3.904530e-04 0.796406669 #> 3169 31.68 0.20310130 3.902578e-04 0.796508440 #> 3170 31.69 0.20299978 3.900627e-04 0.796610161 #> 3171 31.70 0.20289830 3.898677e-04 0.796711830 #> 3172 31.71 0.20279688 3.896728e-04 0.796813449 #> 3173 31.72 0.20269551 3.894780e-04 0.796915017 #> 3174 31.73 0.20259418 3.892834e-04 0.797016534 #> 3175 31.74 0.20249291 3.890888e-04 0.797118000 #> 3176 31.75 0.20239169 3.888943e-04 0.797219416 #> 3177 31.76 0.20229052 3.886999e-04 0.797320781 #> 3178 31.77 0.20218940 3.885056e-04 0.797422095 #> 3179 31.78 0.20208833 3.883114e-04 0.797523359 #> 3180 31.79 0.20198731 3.881173e-04 0.797624572 #> 3181 31.80 0.20188634 3.879232e-04 0.797725734 #> 3182 31.81 0.20178542 3.877293e-04 0.797826846 #> 3183 31.82 0.20168456 3.875355e-04 0.797927908 #> 3184 31.83 0.20158374 3.873418e-04 0.798028918 #> 3185 31.84 0.20148297 3.871482e-04 0.798129879 #> 3186 31.85 0.20138226 3.869547e-04 0.798230788 #> 3187 31.86 0.20128159 3.867612e-04 0.798331648 #> 3188 31.87 0.20118098 3.865679e-04 0.798432457 #> 3189 31.88 0.20108041 3.863747e-04 0.798533215 #> 3190 31.89 0.20097989 3.861815e-04 0.798633924 #> 3191 31.90 0.20087943 3.859885e-04 0.798734582 #> 3192 31.91 0.20077902 3.857955e-04 0.798835189 #> 3193 31.92 0.20067865 3.856027e-04 0.798935746 #> 3194 31.93 0.20057834 3.854099e-04 0.799036253 #> 3195 31.94 0.20047807 3.852173e-04 0.799136710 #> 3196 31.95 0.20037786 3.850247e-04 0.799237117 #> 3197 31.96 0.20027769 3.848322e-04 0.799337473 #> 3198 31.97 0.20017758 3.846399e-04 0.799437779 #> 3199 31.98 0.20007752 3.844476e-04 0.799538035 #> 3200 31.99 0.19997750 3.842554e-04 0.799638241 #> 3201 32.00 0.19987754 3.840633e-04 0.799738397 #> 3202 32.01 0.19977763 3.838714e-04 0.799838503 #> 3203 32.02 0.19967776 3.836795e-04 0.799938559 #> 3204 32.03 0.19957795 3.834877e-04 0.800038564 #> 3205 32.04 0.19947818 3.832960e-04 0.800138520 #> 3206 32.05 0.19937847 3.831044e-04 0.800238426 #> 3207 32.06 0.19927881 3.829129e-04 0.800338282 #> 3208 32.07 0.19917919 3.827215e-04 0.800438087 #> 3209 32.08 0.19907963 3.825302e-04 0.800537844 #> 3210 32.09 0.19898011 3.823389e-04 0.800637550 #> 3211 32.10 0.19888065 3.821478e-04 0.800737206 #> 3212 32.11 0.19878123 3.819568e-04 0.800836812 #> 3213 32.12 0.19868186 3.817659e-04 0.800936369 #> 3214 32.13 0.19858255 3.815750e-04 0.801035876 #> 3215 32.14 0.19848328 3.813843e-04 0.801135333 #> 3216 32.15 0.19838407 3.811936e-04 0.801234741 #> 3217 32.16 0.19828490 3.810031e-04 0.801334099 #> 3218 32.17 0.19818578 3.808126e-04 0.801433407 #> 3219 32.18 0.19808671 3.806223e-04 0.801532665 #> 3220 32.19 0.19798769 3.804320e-04 0.801631874 #> 3221 32.20 0.19788872 3.802419e-04 0.801731033 #> 3222 32.21 0.19778981 3.800518e-04 0.801830143 #> 3223 32.22 0.19769093 3.798618e-04 0.801929203 #> 3224 32.23 0.19759211 3.796719e-04 0.802028214 #> 3225 32.24 0.19749334 3.794821e-04 0.802127175 #> 3226 32.25 0.19739462 3.792924e-04 0.802226087 #> 3227 32.26 0.19729595 3.791028e-04 0.802324949 #> 3228 32.27 0.19719732 3.789133e-04 0.802423762 #> 3229 32.28 0.19709875 3.787239e-04 0.802522525 #> 3230 32.29 0.19700023 3.785346e-04 0.802621239 #> 3231 32.30 0.19690175 3.783454e-04 0.802719904 #> 3232 32.31 0.19680332 3.781563e-04 0.802818519 #> 3233 32.32 0.19670495 3.779672e-04 0.802917085 #> 3234 32.33 0.19660662 3.777783e-04 0.803015602 #> 3235 32.34 0.19650834 3.775895e-04 0.803114070 #> 3236 32.35 0.19641011 3.774007e-04 0.803212488 #> 3237 32.36 0.19631193 3.772121e-04 0.803310857 #> 3238 32.37 0.19621380 3.770235e-04 0.803409177 #> 3239 32.38 0.19611572 3.768350e-04 0.803507448 #> 3240 32.39 0.19601768 3.766467e-04 0.803605670 #> 3241 32.40 0.19591970 3.764584e-04 0.803703843 #> 3242 32.41 0.19582176 3.762702e-04 0.803801966 #> 3243 32.42 0.19572388 3.760821e-04 0.803900041 #> 3244 32.43 0.19562604 3.758941e-04 0.803998066 #> 3245 32.44 0.19552825 3.757062e-04 0.804096043 #> 3246 32.45 0.19543051 3.755184e-04 0.804193970 #> 3247 32.46 0.19533282 3.753307e-04 0.804291849 #> 3248 32.47 0.19523518 3.751431e-04 0.804389678 #> 3249 32.48 0.19513759 3.749556e-04 0.804487459 #> 3250 32.49 0.19504004 3.747681e-04 0.804585191 #> 3251 32.50 0.19494255 3.745808e-04 0.804682874 #> 3252 32.51 0.19484510 3.743935e-04 0.804780508 #> 3253 32.52 0.19474770 3.742064e-04 0.804878093 #> 3254 32.53 0.19465035 3.740193e-04 0.804975630 #> 3255 32.54 0.19455305 3.738324e-04 0.805073118 #> 3256 32.55 0.19445580 3.736455e-04 0.805170557 #> 3257 32.56 0.19435859 3.734587e-04 0.805267947 #> 3258 32.57 0.19426144 3.732720e-04 0.805365289 #> 3259 32.58 0.19416433 3.730855e-04 0.805462582 #> 3260 32.59 0.19406727 3.728990e-04 0.805559826 #> 3261 32.60 0.19397027 3.727126e-04 0.805657022 #> 3262 32.61 0.19387330 3.725263e-04 0.805754169 #> 3263 32.62 0.19377639 3.723400e-04 0.805851268 #> 3264 32.63 0.19367953 3.721539e-04 0.805948318 #> 3265 32.64 0.19358271 3.719679e-04 0.806045320 #> 3266 32.65 0.19348595 3.717819e-04 0.806142273 #> 3267 32.66 0.19338923 3.715961e-04 0.806239177 #> 3268 32.67 0.19329256 3.714103e-04 0.806336034 #> 3269 32.68 0.19319593 3.712247e-04 0.806432841 #> 3270 32.69 0.19309936 3.710391e-04 0.806529601 #> 3271 32.70 0.19300283 3.708536e-04 0.806626312 #> 3272 32.71 0.19290636 3.706683e-04 0.806722974 #> 3273 32.72 0.19280993 3.704830e-04 0.806819589 #> 3274 32.73 0.19271355 3.702978e-04 0.806916155 #> 3275 32.74 0.19261721 3.701127e-04 0.807012673 #> 3276 32.75 0.19252093 3.699277e-04 0.807109142 #> 3277 32.76 0.19242469 3.697428e-04 0.807205564 #> 3278 32.77 0.19232851 3.695579e-04 0.807301937 #> 3279 32.78 0.19223237 3.693732e-04 0.807398262 #> 3280 32.79 0.19213627 3.691886e-04 0.807494538 #> 3281 32.80 0.19204023 3.690040e-04 0.807590767 #> 3282 32.81 0.19194423 3.688196e-04 0.807686948 #> 3283 32.82 0.19184828 3.686352e-04 0.807783080 #> 3284 32.83 0.19175238 3.684509e-04 0.807879165 #> 3285 32.84 0.19165653 3.682667e-04 0.807975201 #> 3286 32.85 0.19156073 3.680827e-04 0.808071189 #> 3287 32.86 0.19146497 3.678987e-04 0.808167130 #> 3288 32.87 0.19136926 3.677148e-04 0.808263022 #> 3289 32.88 0.19127360 3.675309e-04 0.808358867 #> 3290 32.89 0.19117799 3.673472e-04 0.808454663 #> 3291 32.90 0.19108242 3.671636e-04 0.808550412 #> 3292 32.91 0.19098691 3.669801e-04 0.808646113 #> 3293 32.92 0.19089144 3.667966e-04 0.808741766 #> 3294 32.93 0.19079602 3.666133e-04 0.808837371 #> 3295 32.94 0.19070064 3.664300e-04 0.808932929 #> 3296 32.95 0.19060531 3.662468e-04 0.809028438 #> 3297 32.96 0.19051004 3.660638e-04 0.809123900 #> 3298 32.97 0.19041480 3.658808e-04 0.809219315 #> 3299 32.98 0.19031962 3.656979e-04 0.809314681 #> 3300 32.99 0.19022449 3.655151e-04 0.809410000 #> 3301 33.00 0.19012940 3.653324e-04 0.809505271 #> 3302 33.01 0.19003436 3.651497e-04 0.809600495 #> 3303 33.02 0.18993936 3.649672e-04 0.809695671 #> 3304 33.03 0.18984442 3.647848e-04 0.809790799 #> 3305 33.04 0.18974952 3.646024e-04 0.809885880 #> 3306 33.05 0.18965467 3.644202e-04 0.809980913 #> 3307 33.06 0.18955986 3.642380e-04 0.810075899 #> 3308 33.07 0.18946511 3.640559e-04 0.810170837 #> 3309 33.08 0.18937040 3.638739e-04 0.810265728 #> 3310 33.09 0.18927574 3.636921e-04 0.810360571 #> 3311 33.10 0.18918112 3.635103e-04 0.810455367 #> 3312 33.11 0.18908656 3.633285e-04 0.810550116 #> 3313 33.12 0.18899204 3.631469e-04 0.810644817 #> 3314 33.13 0.18889756 3.629654e-04 0.810739471 #> 3315 33.14 0.18880314 3.627840e-04 0.810834078 #> 3316 33.15 0.18870876 3.626026e-04 0.810928637 #> 3317 33.16 0.18861443 3.624214e-04 0.811023149 #> 3318 33.17 0.18852015 3.622402e-04 0.811117614 #> 3319 33.18 0.18842591 3.620591e-04 0.811212032 #> 3320 33.19 0.18833172 3.618781e-04 0.811306402 #> 3321 33.20 0.18823758 3.616972e-04 0.811400725 #> 3322 33.21 0.18814348 3.615164e-04 0.811495001 #> 3323 33.22 0.18804943 3.613357e-04 0.811589230 #> 3324 33.23 0.18795543 3.611551e-04 0.811683412 #> 3325 33.24 0.18786148 3.609746e-04 0.811777547 #> 3326 33.25 0.18776757 3.607941e-04 0.811871635 #> 3327 33.26 0.18767371 3.606138e-04 0.811965675 #> 3328 33.27 0.18757990 3.604335e-04 0.812059669 #> 3329 33.28 0.18748613 3.602533e-04 0.812153616 #> 3330 33.29 0.18739241 3.600733e-04 0.812247515 #> 3331 33.30 0.18729874 3.598933e-04 0.812341368 #> 3332 33.31 0.18720511 3.597134e-04 0.812435174 #> 3333 33.32 0.18711153 3.595336e-04 0.812528933 #> 3334 33.33 0.18701800 3.593538e-04 0.812622645 #> 3335 33.34 0.18692452 3.591742e-04 0.812716310 #> 3336 33.35 0.18683108 3.589947e-04 0.812809929 #> 3337 33.36 0.18673768 3.588152e-04 0.812903500 #> 3338 33.37 0.18664434 3.586358e-04 0.812997025 #> 3339 33.38 0.18655104 3.584566e-04 0.813090503 #> 3340 33.39 0.18645779 3.582774e-04 0.813183935 #> 3341 33.40 0.18636458 3.580983e-04 0.813277320 #> 3342 33.41 0.18627142 3.579193e-04 0.813370658 #> 3343 33.42 0.18617831 3.577404e-04 0.813463949 #> 3344 33.43 0.18608524 3.575615e-04 0.813557194 #> 3345 33.44 0.18599223 3.573828e-04 0.813650392 #> 3346 33.45 0.18589925 3.572042e-04 0.813743543 #> 3347 33.46 0.18580633 3.570256e-04 0.813836648 #> 3348 33.47 0.18571345 3.568471e-04 0.813929707 #> 3349 33.48 0.18562061 3.566688e-04 0.814022719 #> 3350 33.49 0.18552783 3.564905e-04 0.814115684 #> 3351 33.50 0.18543508 3.563123e-04 0.814208603 #> 3352 33.51 0.18534239 3.561342e-04 0.814301475 #> 3353 33.52 0.18524974 3.559561e-04 0.814394301 #> 3354 33.53 0.18515714 3.557782e-04 0.814487081 #> 3355 33.54 0.18506459 3.556004e-04 0.814579814 #> 3356 33.55 0.18497208 3.554226e-04 0.814672501 #> 3357 33.56 0.18487961 3.552449e-04 0.814765142 #> 3358 33.57 0.18478720 3.550674e-04 0.814857736 #> 3359 33.58 0.18469483 3.548899e-04 0.814950284 #> 3360 33.59 0.18460250 3.547125e-04 0.815042786 #> 3361 33.60 0.18451022 3.545352e-04 0.815135241 #> 3362 33.61 0.18441799 3.543579e-04 0.815227651 #> 3363 33.62 0.18432581 3.541808e-04 0.815320014 #> 3364 33.63 0.18423367 3.540037e-04 0.815412331 #> 3365 33.64 0.18414157 3.538268e-04 0.815504601 #> 3366 33.65 0.18404952 3.536499e-04 0.815596826 #> 3367 33.66 0.18395752 3.534731e-04 0.815689005 #> 3368 33.67 0.18386557 3.532964e-04 0.815781137 #> 3369 33.68 0.18377366 3.531198e-04 0.815873223 #> 3370 33.69 0.18368179 3.529433e-04 0.815965264 #> 3371 33.70 0.18358997 3.527669e-04 0.816057258 #> 3372 33.71 0.18349820 3.525906e-04 0.816149207 #> 3373 33.72 0.18340648 3.524143e-04 0.816241109 #> 3374 33.73 0.18331480 3.522381e-04 0.816332965 #> 3375 33.74 0.18322316 3.520621e-04 0.816424776 #> 3376 33.75 0.18313157 3.518861e-04 0.816516541 #> 3377 33.76 0.18304003 3.517102e-04 0.816608260 #> 3378 33.77 0.18294853 3.515344e-04 0.816699933 #> 3379 33.78 0.18285708 3.513587e-04 0.816791560 #> 3380 33.79 0.18276568 3.511830e-04 0.816883141 #> 3381 33.80 0.18267432 3.510075e-04 0.816974677 #> 3382 33.81 0.18258300 3.508320e-04 0.817066166 #> 3383 33.82 0.18249173 3.506566e-04 0.817157610 #> 3384 33.83 0.18240051 3.504814e-04 0.817249009 #> 3385 33.84 0.18230933 3.503062e-04 0.817340361 #> 3386 33.85 0.18221820 3.501310e-04 0.817431668 #> 3387 33.86 0.18212711 3.499560e-04 0.817522930 #> 3388 33.87 0.18203607 3.497811e-04 0.817614145 #> 3389 33.88 0.18194508 3.496062e-04 0.817705316 #> 3390 33.89 0.18185413 3.494315e-04 0.817796440 #> 3391 33.90 0.18176322 3.492568e-04 0.817887519 #> 3392 33.91 0.18167237 3.490822e-04 0.817978553 #> 3393 33.92 0.18158155 3.489077e-04 0.818069541 #> 3394 33.93 0.18149078 3.487333e-04 0.818160483 #> 3395 33.94 0.18140006 3.485590e-04 0.818251380 #> 3396 33.95 0.18130938 3.483848e-04 0.818342232 #> 3397 33.96 0.18121875 3.482106e-04 0.818433038 #> 3398 33.97 0.18112816 3.480366e-04 0.818523799 #> 3399 33.98 0.18103762 3.478626e-04 0.818614514 #> 3400 33.99 0.18094713 3.476887e-04 0.818705184 #> 3401 34.00 0.18085668 3.475149e-04 0.818795809 #> 3402 34.01 0.18076627 3.473412e-04 0.818886388 #> 3403 34.02 0.18067591 3.471675e-04 0.818976923 #> 3404 34.03 0.18058559 3.469940e-04 0.819067412 #> 3405 34.04 0.18049532 3.468206e-04 0.819157855 #> 3406 34.05 0.18040510 3.466472e-04 0.819248254 #> 3407 34.06 0.18031492 3.464739e-04 0.819338607 #> 3408 34.07 0.18022478 3.463007e-04 0.819428915 #> 3409 34.08 0.18013469 3.461276e-04 0.819519178 #> 3410 34.09 0.18004465 3.459546e-04 0.819609396 #> 3411 34.10 0.17995465 3.457816e-04 0.819699569 #> 3412 34.11 0.17986469 3.456088e-04 0.819789696 #> 3413 34.12 0.17977478 3.454360e-04 0.819879779 #> 3414 34.13 0.17968492 3.452634e-04 0.819969817 #> 3415 34.14 0.17959510 3.450908e-04 0.820059809 #> 3416 34.15 0.17950532 3.449183e-04 0.820149757 #> 3417 34.16 0.17941559 3.447459e-04 0.820239659 #> 3418 34.17 0.17932591 3.445735e-04 0.820329517 #> 3419 34.18 0.17923627 3.444013e-04 0.820419330 #> 3420 34.19 0.17914667 3.442291e-04 0.820509098 #> 3421 34.20 0.17905712 3.440571e-04 0.820598821 #> 3422 34.21 0.17896762 3.438851e-04 0.820688499 #> 3423 34.22 0.17887815 3.437132e-04 0.820778132 #> 3424 34.23 0.17878874 3.435414e-04 0.820867721 #> 3425 34.24 0.17869937 3.433696e-04 0.820957265 #> 3426 34.25 0.17861004 3.431980e-04 0.821046764 #> 3427 34.26 0.17852076 3.430264e-04 0.821136218 #> 3428 34.27 0.17843152 3.428550e-04 0.821225627 #> 3429 34.28 0.17834232 3.426836e-04 0.821314992 #> 3430 34.29 0.17825318 3.425123e-04 0.821404313 #> 3431 34.30 0.17816407 3.423411e-04 0.821493588 #> 3432 34.31 0.17807501 3.421699e-04 0.821582819 #> 3433 34.32 0.17798600 3.419989e-04 0.821672005 #> 3434 34.33 0.17789703 3.418279e-04 0.821761147 #> 3435 34.34 0.17780810 3.416571e-04 0.821850244 #> 3436 34.35 0.17771922 3.414863e-04 0.821939297 #> 3437 34.36 0.17763038 3.413156e-04 0.822028305 #> 3438 34.37 0.17754159 3.411450e-04 0.822117268 #> 3439 34.38 0.17745284 3.409744e-04 0.822206188 #> 3440 34.39 0.17736413 3.408040e-04 0.822295062 #> 3441 34.40 0.17727547 3.406336e-04 0.822383893 #> 3442 34.41 0.17718686 3.404634e-04 0.822472678 #> 3443 34.42 0.17709829 3.402932e-04 0.822561420 #> 3444 34.43 0.17700976 3.401231e-04 0.822650117 #> 3445 34.44 0.17692128 3.399530e-04 0.822738770 #> 3446 34.45 0.17683284 3.397831e-04 0.822827378 #> 3447 34.46 0.17674444 3.396133e-04 0.822915942 #> 3448 34.47 0.17665609 3.394435e-04 0.823004462 #> 3449 34.48 0.17656779 3.392738e-04 0.823092938 #> 3450 34.49 0.17647953 3.391042e-04 0.823181369 #> 3451 34.50 0.17639131 3.389347e-04 0.823269757 #> 3452 34.51 0.17630314 3.387653e-04 0.823358100 #> 3453 34.52 0.17621501 3.385959e-04 0.823446398 #> 3454 34.53 0.17612692 3.384267e-04 0.823534653 #> 3455 34.54 0.17603888 3.382575e-04 0.823622864 #> 3456 34.55 0.17595088 3.380884e-04 0.823711030 #> 3457 34.56 0.17586293 3.379194e-04 0.823799153 #> 3458 34.57 0.17577502 3.377505e-04 0.823887231 #> 3459 34.58 0.17568715 3.375817e-04 0.823975266 #> 3460 34.59 0.17559933 3.374129e-04 0.824063256 #> 3461 34.60 0.17551155 3.372443e-04 0.824151202 #> 3462 34.61 0.17542382 3.370757e-04 0.824239105 #> 3463 34.62 0.17533613 3.369072e-04 0.824326963 #> 3464 34.63 0.17524848 3.367388e-04 0.824414778 #> 3465 34.64 0.17516088 3.365705e-04 0.824502549 #> 3466 34.65 0.17507332 3.364022e-04 0.824590275 #> 3467 34.66 0.17498581 3.362341e-04 0.824677958 #> 3468 34.67 0.17489834 3.360660e-04 0.824765597 #> 3469 34.68 0.17481091 3.358980e-04 0.824853193 #> 3470 34.69 0.17472353 3.357301e-04 0.824940744 #> 3471 34.70 0.17463619 3.355623e-04 0.825028252 #> 3472 34.71 0.17454889 3.353945e-04 0.825115716 #> 3473 34.72 0.17446164 3.352269e-04 0.825203136 #> 3474 34.73 0.17437443 3.350593e-04 0.825290513 #> 3475 34.74 0.17428726 3.348918e-04 0.825377846 #> 3476 34.75 0.17420014 3.347244e-04 0.825465135 #> 3477 34.76 0.17411306 3.345571e-04 0.825552381 #> 3478 34.77 0.17402603 3.343898e-04 0.825639583 #> 3479 34.78 0.17393904 3.342227e-04 0.825726741 #> 3480 34.79 0.17385209 3.340556e-04 0.825813856 #> 3481 34.80 0.17376518 3.338886e-04 0.825900927 #> 3482 34.81 0.17367832 3.337217e-04 0.825987955 #> 3483 34.82 0.17359151 3.335549e-04 0.826074939 #> 3484 34.83 0.17350473 3.333882e-04 0.826161880 #> 3485 34.84 0.17341800 3.332215e-04 0.826248777 #> 3486 34.85 0.17333131 3.330550e-04 0.826335631 #> 3487 34.86 0.17324467 3.328885e-04 0.826422442 #> 3488 34.87 0.17315807 3.327221e-04 0.826509209 #> 3489 34.88 0.17307151 3.325557e-04 0.826595933 #> 3490 34.89 0.17298500 3.323895e-04 0.826682613 #> 3491 34.90 0.17289853 3.322234e-04 0.826769250 #> 3492 34.91 0.17281210 3.320573e-04 0.826855844 #> 3493 34.92 0.17272571 3.318913e-04 0.826942394 #> 3494 34.93 0.17263937 3.317254e-04 0.827028901 #> 3495 34.94 0.17255308 3.315596e-04 0.827115365 #> 3496 34.95 0.17246682 3.313938e-04 0.827201786 #> 3497 34.96 0.17238061 3.312282e-04 0.827288164 #> 3498 34.97 0.17229444 3.310626e-04 0.827374498 #> 3499 34.98 0.17220831 3.308971e-04 0.827460789 #> 3500 34.99 0.17212223 3.307317e-04 0.827547037 #> 3501 35.00 0.17203619 3.305664e-04 0.827633242 #> 3502 35.01 0.17195020 3.304011e-04 0.827719404 #> 3503 35.02 0.17186424 3.302360e-04 0.827805523 #> 3504 35.03 0.17177833 3.300709e-04 0.827891598 #> 3505 35.04 0.17169246 3.299059e-04 0.827977631 #> 3506 35.05 0.17160664 3.297410e-04 0.828063621 #> 3507 35.06 0.17152086 3.295762e-04 0.828149567 #> 3508 35.07 0.17143512 3.294114e-04 0.828235471 #> 3509 35.08 0.17134942 3.292468e-04 0.828321332 #> 3510 35.09 0.17126377 3.290822e-04 0.828407150 #> 3511 35.10 0.17117816 3.289177e-04 0.828492925 #> 3512 35.11 0.17109259 3.287533e-04 0.828578657 #> 3513 35.12 0.17100706 3.285889e-04 0.828664346 #> 3514 35.13 0.17092158 3.284247e-04 0.828749993 #> 3515 35.14 0.17083614 3.282605e-04 0.828835596 #> 3516 35.15 0.17075075 3.280964e-04 0.828921157 #> 3517 35.16 0.17066539 3.279324e-04 0.829006675 #> 3518 35.17 0.17058008 3.277685e-04 0.829092150 #> 3519 35.18 0.17049481 3.276046e-04 0.829177583 #> 3520 35.19 0.17040959 3.274409e-04 0.829262973 #> 3521 35.20 0.17032440 3.272772e-04 0.829348320 #> 3522 35.21 0.17023926 3.271136e-04 0.829433624 #> 3523 35.22 0.17015416 3.269501e-04 0.829518886 #> 3524 35.23 0.17006911 3.267866e-04 0.829604106 #> 3525 35.24 0.16998409 3.266233e-04 0.829689282 #> 3526 35.25 0.16989912 3.264600e-04 0.829774416 #> 3527 35.26 0.16981420 3.262968e-04 0.829859508 #> 3528 35.27 0.16972931 3.261337e-04 0.829944557 #> 3529 35.28 0.16964447 3.259707e-04 0.830029563 #> 3530 35.29 0.16955966 3.258078e-04 0.830114527 #> 3531 35.30 0.16947491 3.256449e-04 0.830199449 #> 3532 35.31 0.16939019 3.254821e-04 0.830284328 #> 3533 35.32 0.16930552 3.253194e-04 0.830369164 #> 3534 35.33 0.16922088 3.251568e-04 0.830453959 #> 3535 35.34 0.16913630 3.249943e-04 0.830538711 #> 3536 35.35 0.16905175 3.248318e-04 0.830623420 #> 3537 35.36 0.16896724 3.246694e-04 0.830708087 #> 3538 35.37 0.16888278 3.245071e-04 0.830792712 #> 3539 35.38 0.16879836 3.243449e-04 0.830877294 #> 3540 35.39 0.16871398 3.241828e-04 0.830961835 #> 3541 35.40 0.16862965 3.240207e-04 0.831046333 #> 3542 35.41 0.16854535 3.238588e-04 0.831130788 #> 3543 35.42 0.16846110 3.236969e-04 0.831215202 #> 3544 35.43 0.16837689 3.235351e-04 0.831299573 #> 3545 35.44 0.16829272 3.233733e-04 0.831383902 #> 3546 35.45 0.16820860 3.232117e-04 0.831468189 #> 3547 35.46 0.16812452 3.230501e-04 0.831552434 #> 3548 35.47 0.16804047 3.228886e-04 0.831636637 #> 3549 35.48 0.16795648 3.227272e-04 0.831720798 #> 3550 35.49 0.16787252 3.225659e-04 0.831804916 #> 3551 35.50 0.16778860 3.224047e-04 0.831888993 #> 3552 35.51 0.16770473 3.222435e-04 0.831973027 #> 3553 35.52 0.16762090 3.220824e-04 0.832057020 #> 3554 35.53 0.16753711 3.219214e-04 0.832140970 #> 3555 35.54 0.16745336 3.217605e-04 0.832224879 #> 3556 35.55 0.16736966 3.215997e-04 0.832308745 #> 3557 35.56 0.16728599 3.214389e-04 0.832392570 #> 3558 35.57 0.16720237 3.212782e-04 0.832476353 #> 3559 35.58 0.16711879 3.211176e-04 0.832560094 #> 3560 35.59 0.16703525 3.209571e-04 0.832643793 #> 3561 35.60 0.16695175 3.207967e-04 0.832727450 #> 3562 35.61 0.16686830 3.206363e-04 0.832811065 #> 3563 35.62 0.16678489 3.204760e-04 0.832894639 #> 3564 35.63 0.16670151 3.203158e-04 0.832978171 #> 3565 35.64 0.16661818 3.201557e-04 0.833061661 #> 3566 35.65 0.16653490 3.199957e-04 0.833145109 #> 3567 35.66 0.16645165 3.198357e-04 0.833228515 #> 3568 35.67 0.16636844 3.196758e-04 0.833311880 #> 3569 35.68 0.16628528 3.195160e-04 0.833395204 #> 3570 35.69 0.16620216 3.193563e-04 0.833478485 #> 3571 35.70 0.16611908 3.191967e-04 0.833561725 #> 3572 35.71 0.16603604 3.190371e-04 0.833644923 #> 3573 35.72 0.16595304 3.188777e-04 0.833728080 #> 3574 35.73 0.16587009 3.187183e-04 0.833811195 #> 3575 35.74 0.16578717 3.185589e-04 0.833894269 #> 3576 35.75 0.16570430 3.183997e-04 0.833977301 #> 3577 35.76 0.16562147 3.182405e-04 0.834060292 #> 3578 35.77 0.16553868 3.180815e-04 0.834143241 #> 3579 35.78 0.16545593 3.179225e-04 0.834226149 #> 3580 35.79 0.16537322 3.177635e-04 0.834309015 #> 3581 35.80 0.16529056 3.176047e-04 0.834391840 #> 3582 35.81 0.16520793 3.174459e-04 0.834474623 #> 3583 35.82 0.16512535 3.172872e-04 0.834557365 #> 3584 35.83 0.16504281 3.171286e-04 0.834640066 #> 3585 35.84 0.16496031 3.169701e-04 0.834722725 #> 3586 35.85 0.16487785 3.168117e-04 0.834805343 #> 3587 35.86 0.16479543 3.166533e-04 0.834887920 #> 3588 35.87 0.16471305 3.164950e-04 0.834970455 #> 3589 35.88 0.16463071 3.163368e-04 0.835052949 #> 3590 35.89 0.16454842 3.161787e-04 0.835135402 #> 3591 35.90 0.16446617 3.160206e-04 0.835217814 #> 3592 35.91 0.16438395 3.158627e-04 0.835300184 #> 3593 35.92 0.16430178 3.157048e-04 0.835382514 #> 3594 35.93 0.16421965 3.155470e-04 0.835464802 #> 3595 35.94 0.16413756 3.153892e-04 0.835547049 #> 3596 35.95 0.16405551 3.152316e-04 0.835629255 #> 3597 35.96 0.16397351 3.150740e-04 0.835711420 #> 3598 35.97 0.16389154 3.149165e-04 0.835793543 #> 3599 35.98 0.16380961 3.147591e-04 0.835875626 #> 3600 35.99 0.16372773 3.146017e-04 0.835957668 #> 3601 36.00 0.16364589 3.144445e-04 0.836039668 #> 3602 36.01 0.16356408 3.142873e-04 0.836121628 #> 3603 36.02 0.16348232 3.141302e-04 0.836203547 #> 3604 36.03 0.16340060 3.139732e-04 0.836285424 #> 3605 36.04 0.16331892 3.138162e-04 0.836367261 #> 3606 36.05 0.16323728 3.136593e-04 0.836449057 #> 3607 36.06 0.16315569 3.135026e-04 0.836530812 #> 3608 36.07 0.16307413 3.133458e-04 0.836612526 #> 3609 36.08 0.16299261 3.131892e-04 0.836694200 #> 3610 36.09 0.16291114 3.130327e-04 0.836775832 #> 3611 36.10 0.16282970 3.128762e-04 0.836857424 #> 3612 36.11 0.16274831 3.127198e-04 0.836938975 #> 3613 36.12 0.16266695 3.125635e-04 0.837020485 #> 3614 36.13 0.16258564 3.124072e-04 0.837101954 #> 3615 36.14 0.16250437 3.122510e-04 0.837183383 #> 3616 36.15 0.16242313 3.120950e-04 0.837264771 #> 3617 36.16 0.16234194 3.119390e-04 0.837346118 #> 3618 36.17 0.16226079 3.117830e-04 0.837427425 #> 3619 36.18 0.16217968 3.116272e-04 0.837508691 #> 3620 36.19 0.16209861 3.114714e-04 0.837589916 #> 3621 36.20 0.16201758 3.113157e-04 0.837671101 #> 3622 36.21 0.16193659 3.111601e-04 0.837752245 #> 3623 36.22 0.16185565 3.110045e-04 0.837833349 #> 3624 36.23 0.16177474 3.108491e-04 0.837914412 #> 3625 36.24 0.16169387 3.106937e-04 0.837995434 #> 3626 36.25 0.16161305 3.105384e-04 0.838076416 #> 3627 36.26 0.16153226 3.103831e-04 0.838157358 #> 3628 36.27 0.16145151 3.102280e-04 0.838238259 #> 3629 36.28 0.16137081 3.100729e-04 0.838319120 #> 3630 36.29 0.16129014 3.099179e-04 0.838399940 #> 3631 36.30 0.16120952 3.097630e-04 0.838480720 #> 3632 36.31 0.16112893 3.096082e-04 0.838561459 #> 3633 36.32 0.16104839 3.094534e-04 0.838642158 #> 3634 36.33 0.16096788 3.092987e-04 0.838722817 #> 3635 36.34 0.16088742 3.091441e-04 0.838803436 #> 3636 36.35 0.16080700 3.089896e-04 0.838884014 #> 3637 36.36 0.16072661 3.088351e-04 0.838964552 #> 3638 36.37 0.16064627 3.086807e-04 0.839045049 #> 3639 36.38 0.16056597 3.085264e-04 0.839125506 #> 3640 36.39 0.16048570 3.083722e-04 0.839205924 #> 3641 36.40 0.16040548 3.082181e-04 0.839286301 #> 3642 36.41 0.16032530 3.080640e-04 0.839366637 #> 3643 36.42 0.16024516 3.079100e-04 0.839446934 #> 3644 36.43 0.16016505 3.077561e-04 0.839527190 #> 3645 36.44 0.16008499 3.076022e-04 0.839607407 #> 3646 36.45 0.16000497 3.074485e-04 0.839687583 #> 3647 36.46 0.15992499 3.072948e-04 0.839767719 #> 3648 36.47 0.15984504 3.071412e-04 0.839847815 #> 3649 36.48 0.15976514 3.069876e-04 0.839927871 #> 3650 36.49 0.15968528 3.068342e-04 0.840007887 #> 3651 36.50 0.15960546 3.066808e-04 0.840087864 #> 3652 36.51 0.15952567 3.065275e-04 0.840167800 #> 3653 36.52 0.15944593 3.063743e-04 0.840247696 #> 3654 36.53 0.15936623 3.062211e-04 0.840327552 #> 3655 36.54 0.15928656 3.060681e-04 0.840407368 #> 3656 36.55 0.15920694 3.059151e-04 0.840487145 #> 3657 36.56 0.15912736 3.057621e-04 0.840566881 #> 3658 36.57 0.15904781 3.056093e-04 0.840646578 #> 3659 36.58 0.15896831 3.054565e-04 0.840726234 #> 3660 36.59 0.15888884 3.053038e-04 0.840805851 #> 3661 36.60 0.15880942 3.051512e-04 0.840885429 #> 3662 36.61 0.15873004 3.049987e-04 0.840964966 #> 3663 36.62 0.15865069 3.048462e-04 0.841044464 #> 3664 36.63 0.15857138 3.046938e-04 0.841123922 #> 3665 36.64 0.15849212 3.045415e-04 0.841203340 #> 3666 36.65 0.15841289 3.043893e-04 0.841282718 #> 3667 36.66 0.15833371 3.042372e-04 0.841362057 #> 3668 36.67 0.15825456 3.040851e-04 0.841441356 #> 3669 36.68 0.15817545 3.039331e-04 0.841520616 #> 3670 36.69 0.15809638 3.037811e-04 0.841599836 #> 3671 36.70 0.15801735 3.036293e-04 0.841679016 #> 3672 36.71 0.15793837 3.034775e-04 0.841758157 #> 3673 36.72 0.15785942 3.033258e-04 0.841837258 #> 3674 36.73 0.15778051 3.031742e-04 0.841916319 #> 3675 36.74 0.15770164 3.030226e-04 0.841995341 #> 3676 36.75 0.15762280 3.028712e-04 0.842074324 #> 3677 36.76 0.15754401 3.027198e-04 0.842153267 #> 3678 36.77 0.15746526 3.025684e-04 0.842232171 #> 3679 36.78 0.15738655 3.024172e-04 0.842311035 #> 3680 36.79 0.15730787 3.022660e-04 0.842389860 #> 3681 36.80 0.15722924 3.021149e-04 0.842468645 #> 3682 36.81 0.15715064 3.019639e-04 0.842547391 #> 3683 36.82 0.15707209 3.018130e-04 0.842626098 #> 3684 36.83 0.15699357 3.016621e-04 0.842704765 #> 3685 36.84 0.15691510 3.015113e-04 0.842783393 #> 3686 36.85 0.15683666 3.013606e-04 0.842861982 #> 3687 36.86 0.15675826 3.012099e-04 0.842940531 #> 3688 36.87 0.15667990 3.010594e-04 0.843019041 #> 3689 36.88 0.15660158 3.009089e-04 0.843097512 #> 3690 36.89 0.15652330 3.007585e-04 0.843175944 #> 3691 36.90 0.15644506 3.006081e-04 0.843254336 #> 3692 36.91 0.15636685 3.004579e-04 0.843332689 #> 3693 36.92 0.15628869 3.003077e-04 0.843411003 #> 3694 36.93 0.15621056 3.001575e-04 0.843489278 #> 3695 36.94 0.15613248 3.000075e-04 0.843567514 #> 3696 36.95 0.15605443 2.998575e-04 0.843645711 #> 3697 36.96 0.15597642 2.997076e-04 0.843723868 #> 3698 36.97 0.15589846 2.995578e-04 0.843801987 #> 3699 36.98 0.15582053 2.994081e-04 0.843880066 #> 3700 36.99 0.15574263 2.992584e-04 0.843958107 #> 3701 37.00 0.15566478 2.991088e-04 0.844036108 #> 3702 37.01 0.15558697 2.989593e-04 0.844114071 #> 3703 37.02 0.15550920 2.988099e-04 0.844191994 #> 3704 37.03 0.15543146 2.986605e-04 0.844269879 #> 3705 37.04 0.15535376 2.985112e-04 0.844347724 #> 3706 37.05 0.15527611 2.983620e-04 0.844425531 #> 3707 37.06 0.15519849 2.982129e-04 0.844503299 #> 3708 37.07 0.15512091 2.980638e-04 0.844581028 #> 3709 37.08 0.15504337 2.979148e-04 0.844658718 #> 3710 37.09 0.15496586 2.977659e-04 0.844736369 #> 3711 37.10 0.15488840 2.976170e-04 0.844813982 #> 3712 37.11 0.15481098 2.974683e-04 0.844891555 #> 3713 37.12 0.15473359 2.973196e-04 0.844969090 #> 3714 37.13 0.15465624 2.971709e-04 0.845046586 #> 3715 37.14 0.15457893 2.970224e-04 0.845124043 #> 3716 37.15 0.15450166 2.968739e-04 0.845201462 #> 3717 37.16 0.15442443 2.967255e-04 0.845278842 #> 3718 37.17 0.15434724 2.965772e-04 0.845356183 #> 3719 37.18 0.15427009 2.964289e-04 0.845433486 #> 3720 37.19 0.15419297 2.962808e-04 0.845510750 #> 3721 37.20 0.15411589 2.961327e-04 0.845587975 #> 3722 37.21 0.15403885 2.959846e-04 0.845665162 #> 3723 37.22 0.15396185 2.958367e-04 0.845742310 #> 3724 37.23 0.15388489 2.956888e-04 0.845819420 #> 3725 37.24 0.15380797 2.955410e-04 0.845896491 #> 3726 37.25 0.15373108 2.953932e-04 0.845973523 #> 3727 37.26 0.15365424 2.952456e-04 0.846050517 #> 3728 37.27 0.15357743 2.950980e-04 0.846127473 #> 3729 37.28 0.15350066 2.949505e-04 0.846204390 #> 3730 37.29 0.15342393 2.948030e-04 0.846281268 #> 3731 37.30 0.15334724 2.946557e-04 0.846358108 #> 3732 37.31 0.15327058 2.945084e-04 0.846434910 #> 3733 37.32 0.15319397 2.943612e-04 0.846511673 #> 3734 37.33 0.15311739 2.942140e-04 0.846588398 #> 3735 37.34 0.15304085 2.940670e-04 0.846665085 #> 3736 37.35 0.15296435 2.939200e-04 0.846741733 #> 3737 37.36 0.15288788 2.937730e-04 0.846818343 #> 3738 37.37 0.15281146 2.936262e-04 0.846894915 #> 3739 37.38 0.15273507 2.934794e-04 0.846971448 #> 3740 37.39 0.15265872 2.933327e-04 0.847047944 #> 3741 37.40 0.15258241 2.931861e-04 0.847124400 #> 3742 37.41 0.15250614 2.930395e-04 0.847200819 #> 3743 37.42 0.15242991 2.928930e-04 0.847277200 #> 3744 37.43 0.15235371 2.927466e-04 0.847353542 #> 3745 37.44 0.15227755 2.926003e-04 0.847429846 #> 3746 37.45 0.15220143 2.924540e-04 0.847506112 #> 3747 37.46 0.15212535 2.923078e-04 0.847582340 #> 3748 37.47 0.15204931 2.921617e-04 0.847658530 #> 3749 37.48 0.15197330 2.920157e-04 0.847734682 #> 3750 37.49 0.15189734 2.918697e-04 0.847810795 #> 3751 37.50 0.15182141 2.917238e-04 0.847886871 #> 3752 37.51 0.15174551 2.915780e-04 0.847962908 #> 3753 37.52 0.15166966 2.914322e-04 0.848038908 #> 3754 37.53 0.15159384 2.912866e-04 0.848114869 #> 3755 37.54 0.15151807 2.911409e-04 0.848190793 #> 3756 37.55 0.15144233 2.909954e-04 0.848266679 #> 3757 37.56 0.15136662 2.908499e-04 0.848342526 #> 3758 37.57 0.15129096 2.907046e-04 0.848418336 #> 3759 37.58 0.15121533 2.905592e-04 0.848494108 #> 3760 37.59 0.15113974 2.904140e-04 0.848569842 #> 3761 37.60 0.15106419 2.902688e-04 0.848645538 #> 3762 37.61 0.15098868 2.901237e-04 0.848721196 #> 3763 37.62 0.15091320 2.899787e-04 0.848796817 #> 3764 37.63 0.15083777 2.898338e-04 0.848872400 #> 3765 37.64 0.15076237 2.896889e-04 0.848947945 #> 3766 37.65 0.15068700 2.895441e-04 0.849023452 #> 3767 37.66 0.15061168 2.893993e-04 0.849098921 #> 3768 37.67 0.15053639 2.892547e-04 0.849174353 #> 3769 37.68 0.15046114 2.891101e-04 0.849249747 #> 3770 37.69 0.15038593 2.889656e-04 0.849325103 #> 3771 37.70 0.15031076 2.888211e-04 0.849400422 #> 3772 37.71 0.15023562 2.886767e-04 0.849475703 #> 3773 37.72 0.15016052 2.885324e-04 0.849550946 #> 3774 37.73 0.15008546 2.883882e-04 0.849626152 #> 3775 37.74 0.15001044 2.882440e-04 0.849701320 #> 3776 37.75 0.14993545 2.881000e-04 0.849776450 #> 3777 37.76 0.14986050 2.879559e-04 0.849851543 #> 3778 37.77 0.14978559 2.878120e-04 0.849926599 #> 3779 37.78 0.14971071 2.876681e-04 0.850001617 #> 3780 37.79 0.14963588 2.875243e-04 0.850076597 #> 3781 37.80 0.14956108 2.873806e-04 0.850151540 #> 3782 37.81 0.14948632 2.872370e-04 0.850226446 #> 3783 37.82 0.14941159 2.870934e-04 0.850301314 #> 3784 37.83 0.14933691 2.869499e-04 0.850376145 #> 3785 37.84 0.14926226 2.868064e-04 0.850450938 #> 3786 37.85 0.14918764 2.866631e-04 0.850525694 #> 3787 37.86 0.14911307 2.865198e-04 0.850600412 #> 3788 37.87 0.14903853 2.863765e-04 0.850675093 #> 3789 37.88 0.14896403 2.862334e-04 0.850749737 #> 3790 37.89 0.14888957 2.860903e-04 0.850824343 #> 3791 37.90 0.14881514 2.859473e-04 0.850898913 #> 3792 37.91 0.14874075 2.858044e-04 0.850973445 #> 3793 37.92 0.14866640 2.856615e-04 0.851047939 #> 3794 37.93 0.14859208 2.855187e-04 0.851122397 #> 3795 37.94 0.14851781 2.853760e-04 0.851196817 #> 3796 37.95 0.14844357 2.852333e-04 0.851271200 #> 3797 37.96 0.14836936 2.850907e-04 0.851345546 #> 3798 37.97 0.14829520 2.849482e-04 0.851419854 #> 3799 37.98 0.14822107 2.848058e-04 0.851494126 #> 3800 37.99 0.14814698 2.846634e-04 0.851568360 #> 3801 38.00 0.14807292 2.845211e-04 0.851642557 #> 3802 38.01 0.14799890 2.843789e-04 0.851716718 #> 3803 38.02 0.14792492 2.842367e-04 0.851790841 #> 3804 38.03 0.14785098 2.840947e-04 0.851864927 #> 3805 38.04 0.14777707 2.839526e-04 0.851938976 #> 3806 38.05 0.14770320 2.838107e-04 0.852012988 #> 3807 38.06 0.14762937 2.836688e-04 0.852086963 #> 3808 38.07 0.14755557 2.835270e-04 0.852160901 #> 3809 38.08 0.14748181 2.833853e-04 0.852234802 #> 3810 38.09 0.14740809 2.832437e-04 0.852308666 #> 3811 38.10 0.14733440 2.831021e-04 0.852382493 #> 3812 38.11 0.14726076 2.829606e-04 0.852456284 #> 3813 38.12 0.14718714 2.828191e-04 0.852530037 #> 3814 38.13 0.14711357 2.826777e-04 0.852603754 #> 3815 38.14 0.14704003 2.825364e-04 0.852677433 #> 3816 38.15 0.14696653 2.823952e-04 0.852751076 #> 3817 38.16 0.14689306 2.822540e-04 0.852824682 #> 3818 38.17 0.14681964 2.821129e-04 0.852898252 #> 3819 38.18 0.14674624 2.819719e-04 0.852971784 #> 3820 38.19 0.14667289 2.818310e-04 0.853045280 #> 3821 38.20 0.14659957 2.816901e-04 0.853118739 #> 3822 38.21 0.14652629 2.815493e-04 0.853192161 #> 3823 38.22 0.14645304 2.814085e-04 0.853265547 #> 3824 38.23 0.14637984 2.812679e-04 0.853338895 #> 3825 38.24 0.14630667 2.811273e-04 0.853412208 #> 3826 38.25 0.14623353 2.809867e-04 0.853485483 #> 3827 38.26 0.14616043 2.808463e-04 0.853558722 #> 3828 38.27 0.14608737 2.807059e-04 0.853631925 #> 3829 38.28 0.14601434 2.805656e-04 0.853705090 #> 3830 38.29 0.14594136 2.804253e-04 0.853778219 #> 3831 38.30 0.14586840 2.802852e-04 0.853851312 #> 3832 38.31 0.14579549 2.801450e-04 0.853924368 #> 3833 38.32 0.14572261 2.800050e-04 0.853997388 #> 3834 38.33 0.14564976 2.798650e-04 0.854070371 #> 3835 38.34 0.14557696 2.797251e-04 0.854143317 #> 3836 38.35 0.14550419 2.795853e-04 0.854216228 #> 3837 38.36 0.14543145 2.794456e-04 0.854289101 #> 3838 38.37 0.14535876 2.793059e-04 0.854361938 #> 3839 38.38 0.14528609 2.791663e-04 0.854434739 #> 3840 38.39 0.14521347 2.790267e-04 0.854507504 #> 3841 38.40 0.14514088 2.788872e-04 0.854580232 #> 3842 38.41 0.14506833 2.787478e-04 0.854652923 #> 3843 38.42 0.14499581 2.786085e-04 0.854725579 #> 3844 38.43 0.14492333 2.784692e-04 0.854798198 #> 3845 38.44 0.14485089 2.783300e-04 0.854870781 #> 3846 38.45 0.14477848 2.781909e-04 0.854943327 #> 3847 38.46 0.14470611 2.780518e-04 0.855015837 #> 3848 38.47 0.14463378 2.779128e-04 0.855088311 #> 3849 38.48 0.14456148 2.777739e-04 0.855160749 #> 3850 38.49 0.14448921 2.776351e-04 0.855233151 #> 3851 38.50 0.14441699 2.774963e-04 0.855305516 #> 3852 38.51 0.14434480 2.773576e-04 0.855377845 #> 3853 38.52 0.14427264 2.772189e-04 0.855450138 #> 3854 38.53 0.14420052 2.770803e-04 0.855522395 #> 3855 38.54 0.14412844 2.769418e-04 0.855594616 #> 3856 38.55 0.14405640 2.768034e-04 0.855666800 #> 3857 38.56 0.14398439 2.766650e-04 0.855738949 #> 3858 38.57 0.14391241 2.765267e-04 0.855811061 #> 3859 38.58 0.14384047 2.763885e-04 0.855883138 #> 3860 38.59 0.14376857 2.762503e-04 0.855955178 #> 3861 38.60 0.14369671 2.761123e-04 0.856027183 #> 3862 38.61 0.14362487 2.759742e-04 0.856099151 #> 3863 38.62 0.14355308 2.758363e-04 0.856171084 #> 3864 38.63 0.14348132 2.756984e-04 0.856242980 #> 3865 38.64 0.14340960 2.755606e-04 0.856314841 #> 3866 38.65 0.14333791 2.754228e-04 0.856386665 #> 3867 38.66 0.14326626 2.752852e-04 0.856458454 #> 3868 38.67 0.14319465 2.751475e-04 0.856530207 #> 3869 38.68 0.14312307 2.750100e-04 0.856601924 #> 3870 38.69 0.14305152 2.748725e-04 0.856673605 #> 3871 38.70 0.14298001 2.747351e-04 0.856745250 #> 3872 38.71 0.14290854 2.745978e-04 0.856816860 #> 3873 38.72 0.14283711 2.744605e-04 0.856888433 #> 3874 38.73 0.14276571 2.743233e-04 0.856959971 #> 3875 38.74 0.14269434 2.741862e-04 0.857031473 #> 3876 38.75 0.14262301 2.740492e-04 0.857102940 #> 3877 38.76 0.14255172 2.739122e-04 0.857174370 #> 3878 38.77 0.14248046 2.737752e-04 0.857245765 #> 3879 38.78 0.14240924 2.736384e-04 0.857317125 #> 3880 38.79 0.14233805 2.735016e-04 0.857388448 #> 3881 38.80 0.14226690 2.733649e-04 0.857459736 #> 3882 38.81 0.14219578 2.732282e-04 0.857530989 #> 3883 38.82 0.14212470 2.730917e-04 0.857602205 #> 3884 38.83 0.14205366 2.729551e-04 0.857673386 #> 3885 38.84 0.14198265 2.728187e-04 0.857744532 #> 3886 38.85 0.14191168 2.726823e-04 0.857815642 #> 3887 38.86 0.14184074 2.725460e-04 0.857886716 #> 3888 38.87 0.14176984 2.724098e-04 0.857957755 #> 3889 38.88 0.14169897 2.722736e-04 0.858028758 #> 3890 38.89 0.14162814 2.721375e-04 0.858099726 #> 3891 38.90 0.14155734 2.720015e-04 0.858170659 #> 3892 38.91 0.14148658 2.718655e-04 0.858241556 #> 3893 38.92 0.14141585 2.717296e-04 0.858312417 #> 3894 38.93 0.14134516 2.715938e-04 0.858383243 #> 3895 38.94 0.14127451 2.714580e-04 0.858454034 #> 3896 38.95 0.14120389 2.713223e-04 0.858524789 #> 3897 38.96 0.14113330 2.711867e-04 0.858595509 #> 3898 38.97 0.14106276 2.710511e-04 0.858666194 #> 3899 38.98 0.14099224 2.709156e-04 0.858736843 #> 3900 38.99 0.14092176 2.707802e-04 0.858807457 #> 3901 39.00 0.14085132 2.706449e-04 0.858878036 #> 3902 39.01 0.14078091 2.705096e-04 0.858948579 #> 3903 39.02 0.14071054 2.703744e-04 0.859019087 #> 3904 39.03 0.14064020 2.702392e-04 0.859089560 #> 3905 39.04 0.14056990 2.701041e-04 0.859159997 #> 3906 39.05 0.14049963 2.699691e-04 0.859230400 #> 3907 39.06 0.14042940 2.698341e-04 0.859300767 #> 3908 39.07 0.14035920 2.696993e-04 0.859371099 #> 3909 39.08 0.14028904 2.695644e-04 0.859441396 #> 3910 39.09 0.14021891 2.694297e-04 0.859511658 #> 3911 39.10 0.14014882 2.692950e-04 0.859581884 #> 3912 39.11 0.14007876 2.691604e-04 0.859652076 #> 3913 39.12 0.14000874 2.690259e-04 0.859722232 #> 3914 39.13 0.13993875 2.688914e-04 0.859792354 #> 3915 39.14 0.13986880 2.687570e-04 0.859862440 #> 3916 39.15 0.13979889 2.686226e-04 0.859932491 #> 3917 39.16 0.13972900 2.684883e-04 0.860002507 #> 3918 39.17 0.13965916 2.683541e-04 0.860072489 #> 3919 39.18 0.13958935 2.682200e-04 0.860142435 #> 3920 39.19 0.13951957 2.680859e-04 0.860212346 #> 3921 39.20 0.13944983 2.679519e-04 0.860282223 #> 3922 39.21 0.13938012 2.678180e-04 0.860352064 #> 3923 39.22 0.13931045 2.676841e-04 0.860421871 #> 3924 39.23 0.13924081 2.675503e-04 0.860491642 #> 3925 39.24 0.13917120 2.674165e-04 0.860561379 #> 3926 39.25 0.13910164 2.672829e-04 0.860631081 #> 3927 39.26 0.13903210 2.671493e-04 0.860700748 #> 3928 39.27 0.13896260 2.670157e-04 0.860770380 #> 3929 39.28 0.13889314 2.668822e-04 0.860839978 #> 3930 39.29 0.13882371 2.667488e-04 0.860909540 #> 3931 39.30 0.13875432 2.666155e-04 0.860979068 #> 3932 39.31 0.13868496 2.664822e-04 0.861048561 #> 3933 39.32 0.13861563 2.663490e-04 0.861118019 #> 3934 39.33 0.13854634 2.662159e-04 0.861187443 #> 3935 39.34 0.13847709 2.660828e-04 0.861256832 #> 3936 39.35 0.13840786 2.659498e-04 0.861326186 #> 3937 39.36 0.13833868 2.658168e-04 0.861395506 #> 3938 39.37 0.13826953 2.656840e-04 0.861464791 #> 3939 39.38 0.13820041 2.655512e-04 0.861534041 #> 3940 39.39 0.13813132 2.654184e-04 0.861603257 #> 3941 39.40 0.13806228 2.652857e-04 0.861672438 #> 3942 39.41 0.13799326 2.651531e-04 0.861741584 #> 3943 39.42 0.13792428 2.650206e-04 0.861810696 #> 3944 39.43 0.13785534 2.648881e-04 0.861879774 #> 3945 39.44 0.13778643 2.647557e-04 0.861948816 #> 3946 39.45 0.13771755 2.646233e-04 0.862017825 #> 3947 39.46 0.13764871 2.644911e-04 0.862086799 #> 3948 39.47 0.13757990 2.643589e-04 0.862155738 #> 3949 39.48 0.13751113 2.642267e-04 0.862224643 #> 3950 39.49 0.13744239 2.640946e-04 0.862293513 #> 3951 39.50 0.13737369 2.639626e-04 0.862362349 #> 3952 39.51 0.13730502 2.638307e-04 0.862431151 #> 3953 39.52 0.13723638 2.636988e-04 0.862499918 #> 3954 39.53 0.13716778 2.635670e-04 0.862568651 #> 3955 39.54 0.13709922 2.634352e-04 0.862637350 #> 3956 39.55 0.13703068 2.633035e-04 0.862706014 #> 3957 39.56 0.13696218 2.631719e-04 0.862774644 #> 3958 39.57 0.13689372 2.630404e-04 0.862843239 #> 3959 39.58 0.13682529 2.629089e-04 0.862911800 #> 3960 39.59 0.13675690 2.627775e-04 0.862980327 #> 3961 39.60 0.13668853 2.626461e-04 0.863048820 #> 3962 39.61 0.13662021 2.625148e-04 0.863117279 #> 3963 39.62 0.13655191 2.623836e-04 0.863185703 #> 3964 39.63 0.13648365 2.622524e-04 0.863254093 #> 3965 39.64 0.13641543 2.621213e-04 0.863322449 #> 3966 39.65 0.13634724 2.619903e-04 0.863390770 #> 3967 39.66 0.13627908 2.618593e-04 0.863459058 #> 3968 39.67 0.13621096 2.617284e-04 0.863527311 #> 3969 39.68 0.13614287 2.615976e-04 0.863595531 #> 3970 39.69 0.13607482 2.614668e-04 0.863663716 #> 3971 39.70 0.13600680 2.613361e-04 0.863731867 #> 3972 39.71 0.13593881 2.612055e-04 0.863799984 #> 3973 39.72 0.13587086 2.610749e-04 0.863868067 #> 3974 39.73 0.13580294 2.609444e-04 0.863936116 #> 3975 39.74 0.13573506 2.608140e-04 0.864004131 #> 3976 39.75 0.13566720 2.606836e-04 0.864072112 #> 3977 39.76 0.13559939 2.605533e-04 0.864140059 #> 3978 39.77 0.13553161 2.604231e-04 0.864207972 #> 3979 39.78 0.13546386 2.602929e-04 0.864275851 #> 3980 39.79 0.13539614 2.601628e-04 0.864343696 #> 3981 39.80 0.13532846 2.600327e-04 0.864411507 #> 3982 39.81 0.13526081 2.599027e-04 0.864479284 #> 3983 39.82 0.13519320 2.597728e-04 0.864547028 #> 3984 39.83 0.13512562 2.596430e-04 0.864614737 #> 3985 39.84 0.13505807 2.595132e-04 0.864682413 #> 3986 39.85 0.13499056 2.593835e-04 0.864750055 #> 3987 39.86 0.13492308 2.592538e-04 0.864817663 #> 3988 39.87 0.13485564 2.591242e-04 0.864885237 #> 3989 39.88 0.13478823 2.589947e-04 0.864952778 #> 3990 39.89 0.13472085 2.588652e-04 0.865020285 #> 3991 39.90 0.13465351 2.587358e-04 0.865087758 #> 3992 39.91 0.13458620 2.586065e-04 0.865155197 #> 3993 39.92 0.13451892 2.584772e-04 0.865222602 #> 3994 39.93 0.13445168 2.583480e-04 0.865289974 #> 3995 39.94 0.13438447 2.582189e-04 0.865357312 #> 3996 39.95 0.13431729 2.580898e-04 0.865424617 #> 3997 39.96 0.13425015 2.579608e-04 0.865491888 #> 3998 39.97 0.13418304 2.578318e-04 0.865559125 #> 3999 39.98 0.13411597 2.577029e-04 0.865626329 #> 4000 39.99 0.13404893 2.575741e-04 0.865693499 #> 4001 40.00 0.13398192 2.574454e-04 0.865760635 #> 4002 40.01 0.13391495 2.573167e-04 0.865827738 #> 4003 40.02 0.13384800 2.571880e-04 0.865894808 #> 4004 40.03 0.13378110 2.570595e-04 0.865961843 #> 4005 40.04 0.13371422 2.569310e-04 0.866028846 #> 4006 40.05 0.13364738 2.568025e-04 0.866095815 #> 4007 40.06 0.13358058 2.566742e-04 0.866162750 #> 4008 40.07 0.13351380 2.565459e-04 0.866229652 #> 4009 40.08 0.13344706 2.564176e-04 0.866296520 #> 4010 40.09 0.13338036 2.562895e-04 0.866363355 #> 4011 40.10 0.13331368 2.561613e-04 0.866430157 #> 4012 40.11 0.13324704 2.560333e-04 0.866496925 #> 4013 40.12 0.13318043 2.559053e-04 0.866563660 #> 4014 40.13 0.13311386 2.557774e-04 0.866630362 #> 4015 40.14 0.13304732 2.556495e-04 0.866697030 #> 4016 40.15 0.13298081 2.555217e-04 0.866763665 #> 4017 40.16 0.13291434 2.553940e-04 0.866830266 #> 4018 40.17 0.13284790 2.552663e-04 0.866896834 #> 4019 40.18 0.13278149 2.551387e-04 0.866963369 #> 4020 40.19 0.13271512 2.550112e-04 0.867029871 #> 4021 40.20 0.13264878 2.548837e-04 0.867096339 #> 4022 40.21 0.13258247 2.547563e-04 0.867162775 #> 4023 40.22 0.13251619 2.546290e-04 0.867229177 #> 4024 40.23 0.13244995 2.545017e-04 0.867295545 #> 4025 40.24 0.13238374 2.543745e-04 0.867361881 #> 4026 40.25 0.13231757 2.542473e-04 0.867428184 #> 4027 40.26 0.13225143 2.541202e-04 0.867494453 #> 4028 40.27 0.13218532 2.539932e-04 0.867560689 #> 4029 40.28 0.13211924 2.538662e-04 0.867626892 #> 4030 40.29 0.13205320 2.537393e-04 0.867693062 #> 4031 40.30 0.13198719 2.536125e-04 0.867759199 #> 4032 40.31 0.13192121 2.534857e-04 0.867825303 #> 4033 40.32 0.13185527 2.533590e-04 0.867891374 #> 4034 40.33 0.13178936 2.532324e-04 0.867957412 #> 4035 40.34 0.13172348 2.531058e-04 0.868023416 #> 4036 40.35 0.13165763 2.529793e-04 0.868089388 #> 4037 40.36 0.13159182 2.528528e-04 0.868155327 #> 4038 40.37 0.13152604 2.527264e-04 0.868221233 #> 4039 40.38 0.13146029 2.526001e-04 0.868287106 #> 4040 40.39 0.13139458 2.524738e-04 0.868352946 #> 4041 40.40 0.13132890 2.523476e-04 0.868418753 #> 4042 40.41 0.13126325 2.522215e-04 0.868484527 #> 4043 40.42 0.13119764 2.520954e-04 0.868550268 #> 4044 40.43 0.13113205 2.519694e-04 0.868615977 #> 4045 40.44 0.13106650 2.518434e-04 0.868681652 #> 4046 40.45 0.13100099 2.517175e-04 0.868747295 #> 4047 40.46 0.13093550 2.515917e-04 0.868812905 #> 4048 40.47 0.13087005 2.514659e-04 0.868878482 #> 4049 40.48 0.13080463 2.513402e-04 0.868944027 #> 4050 40.49 0.13073925 2.512146e-04 0.869009538 #> 4051 40.50 0.13067389 2.510890e-04 0.869075017 #> 4052 40.51 0.13060857 2.509635e-04 0.869140463 #> 4053 40.52 0.13054329 2.508380e-04 0.869205877 #> 4054 40.53 0.13047803 2.507127e-04 0.869271257 #> 4055 40.54 0.13041281 2.505873e-04 0.869336605 #> 4056 40.55 0.13034762 2.504621e-04 0.869401921 #> 4057 40.56 0.13028246 2.503369e-04 0.869467204 #> 4058 40.57 0.13021733 2.502117e-04 0.869532454 #> 4059 40.58 0.13015224 2.500867e-04 0.869597671 #> 4060 40.59 0.13008718 2.499616e-04 0.869662856 #> 4061 40.60 0.13002216 2.498367e-04 0.869728008 #> 4062 40.61 0.12995716 2.497118e-04 0.869793128 #> 4063 40.62 0.12989220 2.495870e-04 0.869858215 #> 4064 40.63 0.12982727 2.494622e-04 0.869923270 #> 4065 40.64 0.12976237 2.493375e-04 0.869988292 #> 4066 40.65 0.12969751 2.492129e-04 0.870053281 #> 4067 40.66 0.12963267 2.490883e-04 0.870118239 #> 4068 40.67 0.12956787 2.489638e-04 0.870183163 #> 4069 40.68 0.12950311 2.488393e-04 0.870248055 #> 4070 40.69 0.12943837 2.487150e-04 0.870312915 #> 4071 40.70 0.12937367 2.485906e-04 0.870377743 #> 4072 40.71 0.12930900 2.484664e-04 0.870442537 #> 4073 40.72 0.12924436 2.483422e-04 0.870507300 #> 4074 40.73 0.12917975 2.482180e-04 0.870572030 #> 4075 40.74 0.12911518 2.480939e-04 0.870636728 #> 4076 40.75 0.12905064 2.479699e-04 0.870701393 #> 4077 40.76 0.12898613 2.478460e-04 0.870766027 #> 4078 40.77 0.12892165 2.477221e-04 0.870830627 #> 4079 40.78 0.12885721 2.475983e-04 0.870895196 #> 4080 40.79 0.12879279 2.474745e-04 0.870959732 #> 4081 40.80 0.12872841 2.473508e-04 0.871024236 #> 4082 40.81 0.12866406 2.472271e-04 0.871088708 #> 4083 40.82 0.12859975 2.471036e-04 0.871153148 #> 4084 40.83 0.12853547 2.469800e-04 0.871217555 #> 4085 40.84 0.12847121 2.468566e-04 0.871281930 #> 4086 40.85 0.12840699 2.467332e-04 0.871346273 #> 4087 40.86 0.12834281 2.466098e-04 0.871410584 #> 4088 40.87 0.12827865 2.464866e-04 0.871474862 #> 4089 40.88 0.12821453 2.463634e-04 0.871539109 #> 4090 40.89 0.12815044 2.462402e-04 0.871603323 #> 4091 40.90 0.12808638 2.461171e-04 0.871667506 #> 4092 40.91 0.12802235 2.459941e-04 0.871731656 #> 4093 40.92 0.12795835 2.458711e-04 0.871795774 #> 4094 40.93 0.12789439 2.457482e-04 0.871859860 #> 4095 40.94 0.12783046 2.456254e-04 0.871923914 #> 4096 40.95 0.12776656 2.455026e-04 0.871987936 #> 4097 40.96 0.12770269 2.453799e-04 0.872051926 #> 4098 40.97 0.12763886 2.452572e-04 0.872115884 #> 4099 40.98 0.12757506 2.451346e-04 0.872179810 #> 4100 40.99 0.12751128 2.450121e-04 0.872243704 #> 4101 41.00 0.12744754 2.448896e-04 0.872307567 #> 4102 41.01 0.12738384 2.447672e-04 0.872371397 #> 4103 41.02 0.12732016 2.446448e-04 0.872435195 #> 4104 41.03 0.12725652 2.445225e-04 0.872498962 #> 4105 41.04 0.12719290 2.444003e-04 0.872562696 #> 4106 41.05 0.12712932 2.442781e-04 0.872626399 #> 4107 41.06 0.12706577 2.441560e-04 0.872690070 #> 4108 41.07 0.12700226 2.440340e-04 0.872753709 #> 4109 41.08 0.12693877 2.439120e-04 0.872817316 #> 4110 41.09 0.12687532 2.437901e-04 0.872880892 #> 4111 41.10 0.12681190 2.436682e-04 0.872944435 #> 4112 41.11 0.12674851 2.435464e-04 0.873007947 #> 4113 41.12 0.12668515 2.434247e-04 0.873071427 #> 4114 41.13 0.12662182 2.433030e-04 0.873134876 #> 4115 41.14 0.12655853 2.431814e-04 0.873198293 #> 4116 41.15 0.12649526 2.430598e-04 0.873261678 #> 4117 41.16 0.12643203 2.429383e-04 0.873325031 #> 4118 41.17 0.12636883 2.428169e-04 0.873388353 #> 4119 41.18 0.12630566 2.426955e-04 0.873451643 #> 4120 41.19 0.12624252 2.425742e-04 0.873514901 #> 4121 41.20 0.12617942 2.424529e-04 0.873578128 #> 4122 41.21 0.12611635 2.423317e-04 0.873641323 #> 4123 41.22 0.12605330 2.422106e-04 0.873704486 #> 4124 41.23 0.12599029 2.420895e-04 0.873767618 #> 4125 41.24 0.12592731 2.419685e-04 0.873830719 #> 4126 41.25 0.12586436 2.418475e-04 0.873893788 #> 4127 41.26 0.12580145 2.417266e-04 0.873956825 #> 4128 41.27 0.12573856 2.416058e-04 0.874019831 #> 4129 41.28 0.12567571 2.414850e-04 0.874082805 #> 4130 41.29 0.12561289 2.413643e-04 0.874145748 #> 4131 41.30 0.12555010 2.412437e-04 0.874208659 #> 4132 41.31 0.12548734 2.411231e-04 0.874271539 #> 4133 41.32 0.12542461 2.410025e-04 0.874334388 #> 4134 41.33 0.12536191 2.408821e-04 0.874397205 #> 4135 41.34 0.12529925 2.407617e-04 0.874459991 #> 4136 41.35 0.12523661 2.406413e-04 0.874522745 #> 4137 41.36 0.12517401 2.405210e-04 0.874585468 #> 4138 41.37 0.12511144 2.404008e-04 0.874648160 #> 4139 41.38 0.12504890 2.402806e-04 0.874710820 #> 4140 41.39 0.12498639 2.401605e-04 0.874773449 #> 4141 41.40 0.12492391 2.400405e-04 0.874836046 #> 4142 41.41 0.12486147 2.399205e-04 0.874898613 #> 4143 41.42 0.12479905 2.398005e-04 0.874961148 #> 4144 41.43 0.12473667 2.396807e-04 0.875023652 #> 4145 41.44 0.12467432 2.395609e-04 0.875086124 #> 4146 41.45 0.12461199 2.394411e-04 0.875148565 #> 4147 41.46 0.12454970 2.393214e-04 0.875210976 #> 4148 41.47 0.12448744 2.392018e-04 0.875273354 #> 4149 41.48 0.12442522 2.390822e-04 0.875335702 #> 4150 41.49 0.12436302 2.389627e-04 0.875398019 #> 4151 41.50 0.12430085 2.388433e-04 0.875460304 #> 4152 41.51 0.12423872 2.387239e-04 0.875522558 #> 4153 41.52 0.12417661 2.386045e-04 0.875584782 #> 4154 41.53 0.12411454 2.384853e-04 0.875646974 #> 4155 41.54 0.12405250 2.383660e-04 0.875709135 #> 4156 41.55 0.12399049 2.382469e-04 0.875771265 #> 4157 41.56 0.12392851 2.381278e-04 0.875833363 #> 4158 41.57 0.12386656 2.380088e-04 0.875895431 #> 4159 41.58 0.12380464 2.378898e-04 0.875957468 #> 4160 41.59 0.12374276 2.377709e-04 0.876019474 #> 4161 41.60 0.12368090 2.376520e-04 0.876081449 #> 4162 41.61 0.12361907 2.375332e-04 0.876143392 #> 4163 41.62 0.12355728 2.374145e-04 0.876205305 #> 4164 41.63 0.12349552 2.372958e-04 0.876267187 #> 4165 41.64 0.12343378 2.371772e-04 0.876329038 #> 4166 41.65 0.12337208 2.370586e-04 0.876390858 #> 4167 41.66 0.12331041 2.369401e-04 0.876452647 #> 4168 41.67 0.12324877 2.368217e-04 0.876514405 #> 4169 41.68 0.12318716 2.367033e-04 0.876576133 #> 4170 41.69 0.12312559 2.365850e-04 0.876637829 #> 4171 41.70 0.12306404 2.364667e-04 0.876699495 #> 4172 41.71 0.12300252 2.363485e-04 0.876761130 #> 4173 41.72 0.12294104 2.362304e-04 0.876822734 #> 4174 41.73 0.12287958 2.361123e-04 0.876884307 #> 4175 41.74 0.12281816 2.359943e-04 0.876945849 #> 4176 41.75 0.12275676 2.358763e-04 0.877007361 #> 4177 41.76 0.12269540 2.357584e-04 0.877068842 #> 4178 41.77 0.12263407 2.356405e-04 0.877130292 #> 4179 41.78 0.12257277 2.355227e-04 0.877191712 #> 4180 41.79 0.12251149 2.354050e-04 0.877253101 #> 4181 41.80 0.12245025 2.352873e-04 0.877314459 #> 4182 41.81 0.12238904 2.351697e-04 0.877375786 #> 4183 41.82 0.12232786 2.350522e-04 0.877437083 #> 4184 41.83 0.12226672 2.349347e-04 0.877498349 #> 4185 41.84 0.12220560 2.348172e-04 0.877559585 #> 4186 41.85 0.12214451 2.346999e-04 0.877620790 #> 4187 41.86 0.12208345 2.345825e-04 0.877681964 #> 4188 41.87 0.12202243 2.344653e-04 0.877743108 #> 4189 41.88 0.12196143 2.343481e-04 0.877804221 #> 4190 41.89 0.12190047 2.342309e-04 0.877865303 #> 4191 41.90 0.12183953 2.341138e-04 0.877926355 #> 4192 41.91 0.12177863 2.339968e-04 0.877987377 #> 4193 41.92 0.12171775 2.338798e-04 0.878048368 #> 4194 41.93 0.12165691 2.337629e-04 0.878109329 #> 4195 41.94 0.12159610 2.336461e-04 0.878170259 #> 4196 41.95 0.12153531 2.335293e-04 0.878231158 #> 4197 41.96 0.12147456 2.334125e-04 0.878292028 #> 4198 41.97 0.12141384 2.332959e-04 0.878352866 #> 4199 41.98 0.12135315 2.331793e-04 0.878413675 #> 4200 41.99 0.12129248 2.330627e-04 0.878474453 #> 4201 42.00 0.12123185 2.329462e-04 0.878535200 #> 4202 42.01 0.12117125 2.328297e-04 0.878595918 #> 4203 42.02 0.12111068 2.327134e-04 0.878656604 #> 4204 42.03 0.12105014 2.325970e-04 0.878717261 #> 4205 42.04 0.12098963 2.324808e-04 0.878777887 #> 4206 42.05 0.12092915 2.323646e-04 0.878838483 #> 4207 42.06 0.12086870 2.322484e-04 0.878899049 #> 4208 42.07 0.12080828 2.321323e-04 0.878959584 #> 4209 42.08 0.12074789 2.320163e-04 0.879020089 #> 4210 42.09 0.12068754 2.319003e-04 0.879080564 #> 4211 42.10 0.12062721 2.317844e-04 0.879141009 #> 4212 42.11 0.12056691 2.316685e-04 0.879201423 #> 4213 42.12 0.12050664 2.315527e-04 0.879261807 #> 4214 42.13 0.12044640 2.314370e-04 0.879322161 #> 4215 42.14 0.12038619 2.313213e-04 0.879382485 #> 4216 42.15 0.12032602 2.312056e-04 0.879442779 #> 4217 42.16 0.12026587 2.310901e-04 0.879503042 #> 4218 42.17 0.12020575 2.309745e-04 0.879563276 #> 4219 42.18 0.12014566 2.308591e-04 0.879623479 #> 4220 42.19 0.12008560 2.307437e-04 0.879683652 #> 4221 42.20 0.12002558 2.306283e-04 0.879743795 #> 4222 42.21 0.11996558 2.305131e-04 0.879803908 #> 4223 42.22 0.11990561 2.303978e-04 0.879863991 #> 4224 42.23 0.11984567 2.302827e-04 0.879924044 #> 4225 42.24 0.11978577 2.301675e-04 0.879984067 #> 4226 42.25 0.11972589 2.300525e-04 0.880044060 #> 4227 42.26 0.11966604 2.299375e-04 0.880104023 #> 4228 42.27 0.11960622 2.298225e-04 0.880163956 #> 4229 42.28 0.11954643 2.297077e-04 0.880223859 #> 4230 42.29 0.11948667 2.295928e-04 0.880283733 #> 4231 42.30 0.11942695 2.294781e-04 0.880343576 #> 4232 42.31 0.11936725 2.293634e-04 0.880403389 #> 4233 42.32 0.11930758 2.292487e-04 0.880463172 #> 4234 42.33 0.11924794 2.291341e-04 0.880522926 #> 4235 42.34 0.11918833 2.290196e-04 0.880582649 #> 4236 42.35 0.11912875 2.289051e-04 0.880642343 #> 4237 42.36 0.11906920 2.287907e-04 0.880702007 #> 4238 42.37 0.11900968 2.286763e-04 0.880761641 #> 4239 42.38 0.11895019 2.285620e-04 0.880821245 #> 4240 42.39 0.11889073 2.284477e-04 0.880880820 #> 4241 42.40 0.11883130 2.283335e-04 0.880940365 #> 4242 42.41 0.11877190 2.282194e-04 0.880999880 #> 4243 42.42 0.11871253 2.281053e-04 0.881059365 #> 4244 42.43 0.11865319 2.279913e-04 0.881118820 #> 4245 42.44 0.11859388 2.278773e-04 0.881178246 #> 4246 42.45 0.11853459 2.277634e-04 0.881237642 #> 4247 42.46 0.11847534 2.276496e-04 0.881297008 #> 4248 42.47 0.11841612 2.275358e-04 0.881356345 #> 4249 42.48 0.11835693 2.274220e-04 0.881415652 #> 4250 42.49 0.11829776 2.273084e-04 0.881474929 #> 4251 42.50 0.11823863 2.271947e-04 0.881534177 #> 4252 42.51 0.11817952 2.270812e-04 0.881593395 #> 4253 42.52 0.11812045 2.269676e-04 0.881652584 #> 4254 42.53 0.11806140 2.268542e-04 0.881711743 #> 4255 42.54 0.11800239 2.267408e-04 0.881770872 #> 4256 42.55 0.11794340 2.266275e-04 0.881829972 #> 4257 42.56 0.11788444 2.265142e-04 0.881889042 #> 4258 42.57 0.11782552 2.264009e-04 0.881948083 #> 4259 42.58 0.11776662 2.262878e-04 0.882007094 #> 4260 42.59 0.11770775 2.261746e-04 0.882066076 #> 4261 42.60 0.11764891 2.260616e-04 0.882125028 #> 4262 42.61 0.11759010 2.259486e-04 0.882183951 #> 4263 42.62 0.11753132 2.258356e-04 0.882242844 #> 4264 42.63 0.11747257 2.257228e-04 0.882301708 #> 4265 42.64 0.11741385 2.256099e-04 0.882360542 #> 4266 42.65 0.11735516 2.254971e-04 0.882419347 #> 4267 42.66 0.11729649 2.253844e-04 0.882478123 #> 4268 42.67 0.11723786 2.252718e-04 0.882536869 #> 4269 42.68 0.11717925 2.251592e-04 0.882595586 #> 4270 42.69 0.11712068 2.250466e-04 0.882654274 #> 4271 42.70 0.11706213 2.249341e-04 0.882712932 #> 4272 42.71 0.11700362 2.248217e-04 0.882771561 #> 4273 42.72 0.11694513 2.247093e-04 0.882830160 #> 4274 42.73 0.11688667 2.245970e-04 0.882888730 #> 4275 42.74 0.11682824 2.244847e-04 0.882947271 #> 4276 42.75 0.11676984 2.243725e-04 0.883005783 #> 4277 42.76 0.11671147 2.242603e-04 0.883064266 #> 4278 42.77 0.11665313 2.241482e-04 0.883122719 #> 4279 42.78 0.11659482 2.240362e-04 0.883181143 #> 4280 42.79 0.11653654 2.239242e-04 0.883239538 #> 4281 42.80 0.11647828 2.238122e-04 0.883297903 #> 4282 42.81 0.11642006 2.237004e-04 0.883356240 #> 4283 42.82 0.11636186 2.235885e-04 0.883414547 #> 4284 42.83 0.11630370 2.234768e-04 0.883472825 #> 4285 42.84 0.11624556 2.233651e-04 0.883531074 #> 4286 42.85 0.11618745 2.232534e-04 0.883589294 #> 4287 42.86 0.11612937 2.231418e-04 0.883647485 #> 4288 42.87 0.11607132 2.230303e-04 0.883705647 #> 4289 42.88 0.11601330 2.229188e-04 0.883763779 #> 4290 42.89 0.11595531 2.228073e-04 0.883821883 #> 4291 42.90 0.11589735 2.226960e-04 0.883879958 #> 4292 42.91 0.11583941 2.225847e-04 0.883938003 #> 4293 42.92 0.11578151 2.224734e-04 0.883996020 #> 4294 42.93 0.11572363 2.223622e-04 0.884054007 #> 4295 42.94 0.11566578 2.222510e-04 0.884111966 #> 4296 42.95 0.11560796 2.221399e-04 0.884169895 #> 4297 42.96 0.11555018 2.220289e-04 0.884227796 #> 4298 42.97 0.11549241 2.219179e-04 0.884285667 #> 4299 42.98 0.11543468 2.218070e-04 0.884343510 #> 4300 42.99 0.11537698 2.216961e-04 0.884401324 #> 4301 43.00 0.11531931 2.215853e-04 0.884459109 #> 4302 43.01 0.11526166 2.214745e-04 0.884516865 #> 4303 43.02 0.11520404 2.213638e-04 0.884574592 #> 4304 43.03 0.11514646 2.212531e-04 0.884632290 #> 4305 43.04 0.11508890 2.211425e-04 0.884689960 #> 4306 43.05 0.11503137 2.210320e-04 0.884747600 #> 4307 43.06 0.11497387 2.209215e-04 0.884805212 #> 4308 43.07 0.11491639 2.208111e-04 0.884862795 #> 4309 43.08 0.11485895 2.207007e-04 0.884920349 #> 4310 43.09 0.11480153 2.205904e-04 0.884977875 #> 4311 43.10 0.11474415 2.204801e-04 0.885035371 #> 4312 43.11 0.11468679 2.203699e-04 0.885092839 #> 4313 43.12 0.11462946 2.202597e-04 0.885150279 #> 4314 43.13 0.11457216 2.201496e-04 0.885207689 #> 4315 43.14 0.11451489 2.200396e-04 0.885265071 #> 4316 43.15 0.11445765 2.199296e-04 0.885322424 #> 4317 43.16 0.11440043 2.198197e-04 0.885379748 #> 4318 43.17 0.11434325 2.197098e-04 0.885437044 #> 4319 43.18 0.11428609 2.196000e-04 0.885494311 #> 4320 43.19 0.11422896 2.194902e-04 0.885551550 #> 4321 43.20 0.11417186 2.193805e-04 0.885608760 #> 4322 43.21 0.11411479 2.192708e-04 0.885665941 #> 4323 43.22 0.11405774 2.191612e-04 0.885723094 #> 4324 43.23 0.11400073 2.190516e-04 0.885780218 #> 4325 43.24 0.11394374 2.189421e-04 0.885837314 #> 4326 43.25 0.11388679 2.188327e-04 0.885894381 #> 4327 43.26 0.11382986 2.187233e-04 0.885951419 #> 4328 43.27 0.11377296 2.186140e-04 0.886008429 #> 4329 43.28 0.11371608 2.185047e-04 0.886065411 #> 4330 43.29 0.11365924 2.183955e-04 0.886122364 #> 4331 43.30 0.11360243 2.182863e-04 0.886179289 #> 4332 43.31 0.11354564 2.181772e-04 0.886236185 #> 4333 43.32 0.11348888 2.180681e-04 0.886293052 #> 4334 43.33 0.11343215 2.179591e-04 0.886349892 #> 4335 43.34 0.11337545 2.178502e-04 0.886406703 #> 4336 43.35 0.11331877 2.177413e-04 0.886463485 #> 4337 43.36 0.11326213 2.176324e-04 0.886520239 #> 4338 43.37 0.11320551 2.175236e-04 0.886576965 #> 4339 43.38 0.11314892 2.174149e-04 0.886633662 #> 4340 43.39 0.11309236 2.173062e-04 0.886690331 #> 4341 43.40 0.11303583 2.171976e-04 0.886746972 #> 4342 43.41 0.11297933 2.170890e-04 0.886803584 #> 4343 43.42 0.11292285 2.169805e-04 0.886860168 #> 4344 43.43 0.11286640 2.168720e-04 0.886916724 #> 4345 43.44 0.11280998 2.167636e-04 0.886973252 #> 4346 43.45 0.11275359 2.166553e-04 0.887029751 #> 4347 43.46 0.11269723 2.165470e-04 0.887086222 #> 4348 43.47 0.11264090 2.164387e-04 0.887142665 #> 4349 43.48 0.11258459 2.163305e-04 0.887199079 #> 4350 43.49 0.11252831 2.162224e-04 0.887255465 #> 4351 43.50 0.11247206 2.161143e-04 0.887311824 #> 4352 43.51 0.11241584 2.160063e-04 0.887368154 #> 4353 43.52 0.11235965 2.158983e-04 0.887424456 #> 4354 43.53 0.11230348 2.157904e-04 0.887480729 #> 4355 43.54 0.11224734 2.156825e-04 0.887536975 #> 4356 43.55 0.11219123 2.155747e-04 0.887593192 #> 4357 43.56 0.11213515 2.154669e-04 0.887649382 #> 4358 43.57 0.11207910 2.153592e-04 0.887705543 #> 4359 43.58 0.11202307 2.152516e-04 0.887761676 #> 4360 43.59 0.11196707 2.151440e-04 0.887817781 #> 4361 43.60 0.11191111 2.150364e-04 0.887873858 #> 4362 43.61 0.11185516 2.149289e-04 0.887929907 #> 4363 43.62 0.11179925 2.148215e-04 0.887985928 #> 4364 43.63 0.11174336 2.147141e-04 0.888041921 #> 4365 43.64 0.11168751 2.146068e-04 0.888097886 #> 4366 43.65 0.11163168 2.144995e-04 0.888153824 #> 4367 43.66 0.11157588 2.143923e-04 0.888209733 #> 4368 43.67 0.11152010 2.142851e-04 0.888265614 #> 4369 43.68 0.11146435 2.141780e-04 0.888321467 #> 4370 43.69 0.11140864 2.140709e-04 0.888377292 #> 4371 43.70 0.11135295 2.139639e-04 0.888433090 #> 4372 43.71 0.11129728 2.138570e-04 0.888488859 #> 4373 43.72 0.11124165 2.137501e-04 0.888544601 #> 4374 43.73 0.11118604 2.136432e-04 0.888600315 #> 4375 43.74 0.11113046 2.135364e-04 0.888656001 #> 4376 43.75 0.11107491 2.134297e-04 0.888711659 #> 4377 43.76 0.11101939 2.133230e-04 0.888767289 #> 4378 43.77 0.11096389 2.132164e-04 0.888822891 #> 4379 43.78 0.11090842 2.131098e-04 0.888878466 #> 4380 43.79 0.11085298 2.130033e-04 0.888934013 #> 4381 43.80 0.11079757 2.128968e-04 0.888989532 #> 4382 43.81 0.11074219 2.127904e-04 0.889045023 #> 4383 43.82 0.11068683 2.126840e-04 0.889100487 #> 4384 43.83 0.11063150 2.125777e-04 0.889155923 #> 4385 43.84 0.11057620 2.124714e-04 0.889211331 #> 4386 43.85 0.11052092 2.123652e-04 0.889266712 #> 4387 43.86 0.11046568 2.122591e-04 0.889322064 #> 4388 43.87 0.11041046 2.121530e-04 0.889377390 #> 4389 43.88 0.11035527 2.120469e-04 0.889432687 #> 4390 43.89 0.11030010 2.119409e-04 0.889487957 #> 4391 43.90 0.11024497 2.118350e-04 0.889543199 #> 4392 43.91 0.11018986 2.117291e-04 0.889598414 #> 4393 43.92 0.11013478 2.116232e-04 0.889653601 #> 4394 43.93 0.11007972 2.115174e-04 0.889708760 #> 4395 43.94 0.11002470 2.114117e-04 0.889763892 #> 4396 43.95 0.10996970 2.113060e-04 0.889818996 #> 4397 43.96 0.10991473 2.112004e-04 0.889874073 #> 4398 43.97 0.10985978 2.110948e-04 0.889929122 #> 4399 43.98 0.10980487 2.109893e-04 0.889984144 #> 4400 43.99 0.10974998 2.108838e-04 0.890039138 #> 4401 44.00 0.10969512 2.107784e-04 0.890094105 #> 4402 44.01 0.10964028 2.106731e-04 0.890149044 #> 4403 44.02 0.10958548 2.105678e-04 0.890203956 #> 4404 44.03 0.10953070 2.104625e-04 0.890258840 #> 4405 44.04 0.10947595 2.103573e-04 0.890313697 #> 4406 44.05 0.10942122 2.102521e-04 0.890368526 #> 4407 44.06 0.10936652 2.101470e-04 0.890423328 #> 4408 44.07 0.10931185 2.100420e-04 0.890478103 #> 4409 44.08 0.10925721 2.099370e-04 0.890532850 #> 4410 44.09 0.10920260 2.098321e-04 0.890587570 #> 4411 44.10 0.10914801 2.097272e-04 0.890642263 #> 4412 44.11 0.10909345 2.096223e-04 0.890696928 #> 4413 44.12 0.10903892 2.095175e-04 0.890751566 #> 4414 44.13 0.10898441 2.094128e-04 0.890806176 #> 4415 44.14 0.10892993 2.093081e-04 0.890860760 #> 4416 44.15 0.10887548 2.092035e-04 0.890915316 #> 4417 44.16 0.10882106 2.090989e-04 0.890969844 #> 4418 44.17 0.10876666 2.089944e-04 0.891024346 #> 4419 44.18 0.10871229 2.088899e-04 0.891078820 #> 4420 44.19 0.10865795 2.087855e-04 0.891133267 #> 4421 44.20 0.10860363 2.086811e-04 0.891187687 #> 4422 44.21 0.10854934 2.085768e-04 0.891242079 #> 4423 44.22 0.10849508 2.084726e-04 0.891296445 #> 4424 44.23 0.10844085 2.083684e-04 0.891350783 #> 4425 44.24 0.10838664 2.082642e-04 0.891405094 #> 4426 44.25 0.10833246 2.081601e-04 0.891459378 #> 4427 44.26 0.10827831 2.080560e-04 0.891513635 #> 4428 44.27 0.10822418 2.079520e-04 0.891567864 #> 4429 44.28 0.10817009 2.078481e-04 0.891622067 #> 4430 44.29 0.10811601 2.077442e-04 0.891676242 #> 4431 44.30 0.10806197 2.076403e-04 0.891730390 #> 4432 44.31 0.10800795 2.075366e-04 0.891784512 #> 4433 44.32 0.10795396 2.074328e-04 0.891838606 #> 4434 44.33 0.10790000 2.073291e-04 0.891892673 #> 4435 44.34 0.10784606 2.072255e-04 0.891946713 #> 4436 44.35 0.10779215 2.071219e-04 0.892000726 #> 4437 44.36 0.10773827 2.070184e-04 0.892054713 #> 4438 44.37 0.10768441 2.069149e-04 0.892108672 #> 4439 44.38 0.10763058 2.068114e-04 0.892162604 #> 4440 44.39 0.10757678 2.067081e-04 0.892216509 #> 4441 44.40 0.10752301 2.066047e-04 0.892270387 #> 4442 44.41 0.10746926 2.065015e-04 0.892324239 #> 4443 44.42 0.10741554 2.063982e-04 0.892378063 #> 4444 44.43 0.10736184 2.062951e-04 0.892431861 #> 4445 44.44 0.10730818 2.061919e-04 0.892485631 #> 4446 44.45 0.10725454 2.060889e-04 0.892539375 #> 4447 44.46 0.10720092 2.059859e-04 0.892593092 #> 4448 44.47 0.10714734 2.058829e-04 0.892646782 #> 4449 44.48 0.10709377 2.057800e-04 0.892700445 #> 4450 44.49 0.10704024 2.056771e-04 0.892754082 #> 4451 44.50 0.10698673 2.055743e-04 0.892807691 #> 4452 44.51 0.10693325 2.054715e-04 0.892861274 #> 4453 44.52 0.10687980 2.053688e-04 0.892914830 #> 4454 44.53 0.10682637 2.052662e-04 0.892968359 #> 4455 44.54 0.10677297 2.051636e-04 0.893021861 #> 4456 44.55 0.10671960 2.050610e-04 0.893075337 #> 4457 44.56 0.10666626 2.049585e-04 0.893128786 #> 4458 44.57 0.10661294 2.048560e-04 0.893182208 #> 4459 44.58 0.10655964 2.047536e-04 0.893235604 #> 4460 44.59 0.10650638 2.046513e-04 0.893288973 #> 4461 44.60 0.10645314 2.045490e-04 0.893342315 #> 4462 44.61 0.10639992 2.044467e-04 0.893395630 #> 4463 44.62 0.10634674 2.043445e-04 0.893448919 #> 4464 44.63 0.10629358 2.042424e-04 0.893502182 #> 4465 44.64 0.10624044 2.041403e-04 0.893555417 #> 4466 44.65 0.10618734 2.040383e-04 0.893608626 #> 4467 44.66 0.10613426 2.039363e-04 0.893661809 #> 4468 44.67 0.10608120 2.038343e-04 0.893714964 #> 4469 44.68 0.10602817 2.037324e-04 0.893768094 #> 4470 44.69 0.10597517 2.036306e-04 0.893821196 #> 4471 44.70 0.10592220 2.035288e-04 0.893874272 #> 4472 44.71 0.10586925 2.034271e-04 0.893927322 #> 4473 44.72 0.10581633 2.033254e-04 0.893980345 #> 4474 44.73 0.10576343 2.032237e-04 0.894033342 #> 4475 44.74 0.10571057 2.031221e-04 0.894086312 #> 4476 44.75 0.10565772 2.030206e-04 0.894139255 #> 4477 44.76 0.10560491 2.029191e-04 0.894192173 #> 4478 44.77 0.10555212 2.028177e-04 0.894245063 #> 4479 44.78 0.10549936 2.027163e-04 0.894297927 #> 4480 44.79 0.10544662 2.026150e-04 0.894350765 #> 4481 44.80 0.10539391 2.025137e-04 0.894403577 #> 4482 44.81 0.10534123 2.024125e-04 0.894456362 #> 4483 44.82 0.10528857 2.023113e-04 0.894509120 #> 4484 44.83 0.10523594 2.022101e-04 0.894561853 #> 4485 44.84 0.10518333 2.021091e-04 0.894614559 #> 4486 44.85 0.10513075 2.020080e-04 0.894667238 #> 4487 44.86 0.10507820 2.019071e-04 0.894719891 #> 4488 44.87 0.10502568 2.018061e-04 0.894772518 #> 4489 44.88 0.10497318 2.017053e-04 0.894825119 #> 4490 44.89 0.10492070 2.016044e-04 0.894877693 #> 4491 44.90 0.10486826 2.015036e-04 0.894930241 #> 4492 44.91 0.10481583 2.014029e-04 0.894982763 #> 4493 44.92 0.10476344 2.013022e-04 0.895035258 #> 4494 44.93 0.10471107 2.012016e-04 0.895087728 #> 4495 44.94 0.10465873 2.011010e-04 0.895140171 #> 4496 44.95 0.10460641 2.010005e-04 0.895192587 #> 4497 44.96 0.10455412 2.009000e-04 0.895244978 #> 4498 44.97 0.10450186 2.007996e-04 0.895297342 #> 4499 44.98 0.10444962 2.006992e-04 0.895349681 #> 4500 44.99 0.10439741 2.005989e-04 0.895401993 #> 4501 45.00 0.10434522 2.004986e-04 0.895454279 #> 4502 45.01 0.10429306 2.003984e-04 0.895506539 #> 4503 45.02 0.10424093 2.002982e-04 0.895558772 #> 4504 45.03 0.10418882 2.001981e-04 0.895610980 #> 4505 45.04 0.10413674 2.000980e-04 0.895663161 #> 4506 45.05 0.10408469 1.999980e-04 0.895715317 #> 4507 45.06 0.10403266 1.998980e-04 0.895767446 #> 4508 45.07 0.10398065 1.997981e-04 0.895819549 #> 4509 45.08 0.10392868 1.996983e-04 0.895871626 #> 4510 45.09 0.10387672 1.995984e-04 0.895923678 #> 4511 45.10 0.10382480 1.994987e-04 0.895975703 #> 4512 45.11 0.10377290 1.993989e-04 0.896027702 #> 4513 45.12 0.10372103 1.992993e-04 0.896079675 #> 4514 45.13 0.10366918 1.991996e-04 0.896131622 #> 4515 45.14 0.10361736 1.991001e-04 0.896183543 #> 4516 45.15 0.10356556 1.990005e-04 0.896235439 #> 4517 45.16 0.10351379 1.989011e-04 0.896287308 #> 4518 45.17 0.10346205 1.988016e-04 0.896339151 #> 4519 45.18 0.10341033 1.987023e-04 0.896390969 #> 4520 45.19 0.10335864 1.986029e-04 0.896442760 #> 4521 45.20 0.10330697 1.985036e-04 0.896494526 #> 4522 45.21 0.10325533 1.984044e-04 0.896546266 #> 4523 45.22 0.10320371 1.983052e-04 0.896597980 #> 4524 45.23 0.10315213 1.982061e-04 0.896649668 #> 4525 45.24 0.10310056 1.981070e-04 0.896701330 #> 4526 45.25 0.10304903 1.980080e-04 0.896752967 #> 4527 45.26 0.10299751 1.979090e-04 0.896804577 #> 4528 45.27 0.10294603 1.978101e-04 0.896856162 #> 4529 45.28 0.10289457 1.977112e-04 0.896907721 #> 4530 45.29 0.10284313 1.976124e-04 0.896959254 #> 4531 45.30 0.10279172 1.975136e-04 0.897010762 #> 4532 45.31 0.10274034 1.974149e-04 0.897062244 #> 4533 45.32 0.10268898 1.973162e-04 0.897113700 #> 4534 45.33 0.10263765 1.972176e-04 0.897165130 #> 4535 45.34 0.10258635 1.971190e-04 0.897216534 #> 4536 45.35 0.10253507 1.970204e-04 0.897267913 #> 4537 45.36 0.10248381 1.969220e-04 0.897319267 #> 4538 45.37 0.10243258 1.968235e-04 0.897370594 #> 4539 45.38 0.10238138 1.967251e-04 0.897421896 #> 4540 45.39 0.10233020 1.966268e-04 0.897473172 #> 4541 45.40 0.10227905 1.965285e-04 0.897524423 #> 4542 45.41 0.10222792 1.964303e-04 0.897575648 #> 4543 45.42 0.10217682 1.963321e-04 0.897626847 #> 4544 45.43 0.10212575 1.962339e-04 0.897678021 #> 4545 45.44 0.10207470 1.961358e-04 0.897729169 #> 4546 45.45 0.10202367 1.960378e-04 0.897780292 #> 4547 45.46 0.10197267 1.959398e-04 0.897831389 #> 4548 45.47 0.10192170 1.958419e-04 0.897882460 #> 4549 45.48 0.10187075 1.957440e-04 0.897933506 #> 4550 45.49 0.10181983 1.956461e-04 0.897984527 #> 4551 45.50 0.10176893 1.955483e-04 0.898035522 #> 4552 45.51 0.10171806 1.954506e-04 0.898086491 #> 4553 45.52 0.10166721 1.953529e-04 0.898137435 #> 4554 45.53 0.10161639 1.952552e-04 0.898188354 #> 4555 45.54 0.10156560 1.951576e-04 0.898239247 #> 4556 45.55 0.10151483 1.950601e-04 0.898290115 #> 4557 45.56 0.10146408 1.949625e-04 0.898340957 #> 4558 45.57 0.10141336 1.948651e-04 0.898391774 #> 4559 45.58 0.10136267 1.947677e-04 0.898442565 #> 4560 45.59 0.10131200 1.946703e-04 0.898493331 #> 4561 45.60 0.10126136 1.945730e-04 0.898544072 #> 4562 45.61 0.10121074 1.944758e-04 0.898594787 #> 4563 45.62 0.10116014 1.943785e-04 0.898645477 #> 4564 45.63 0.10110958 1.942814e-04 0.898696142 #> 4565 45.64 0.10105903 1.941843e-04 0.898746781 #> 4566 45.65 0.10100852 1.940872e-04 0.898797395 #> 4567 45.66 0.10095803 1.939902e-04 0.898847984 #> 4568 45.67 0.10090756 1.938932e-04 0.898898547 #> 4569 45.68 0.10085712 1.937963e-04 0.898949085 #> 4570 45.69 0.10080670 1.936994e-04 0.898999598 #> 4571 45.70 0.10075631 1.936026e-04 0.899050085 #> 4572 45.71 0.10070595 1.935058e-04 0.899100548 #> 4573 45.72 0.10065561 1.934091e-04 0.899150985 #> 4574 45.73 0.10060529 1.933124e-04 0.899201397 #> 4575 45.74 0.10055500 1.932158e-04 0.899251784 #> 4576 45.75 0.10050474 1.931192e-04 0.899302145 #> 4577 45.76 0.10045450 1.930226e-04 0.899352481 #> 4578 45.77 0.10040428 1.929262e-04 0.899402793 #> 4579 45.78 0.10035409 1.928297e-04 0.899453079 #> 4580 45.79 0.10030393 1.927333e-04 0.899503339 #> 4581 45.80 0.10025379 1.926370e-04 0.899553575 #> 4582 45.81 0.10020367 1.925407e-04 0.899603786 #> 4583 45.82 0.10015358 1.924444e-04 0.899653971 #> 4584 45.83 0.10010352 1.923482e-04 0.899704132 #> 4585 45.84 0.10005348 1.922521e-04 0.899754267 #> 4586 45.85 0.10000347 1.921560e-04 0.899804378 #> 4587 45.86 0.09995348 1.920599e-04 0.899854463 #> 4588 45.87 0.09990351 1.919639e-04 0.899904523 #> 4589 45.88 0.09985357 1.918680e-04 0.899954558 #> 4590 45.89 0.09980366 1.917721e-04 0.900004569 #> 4591 45.90 0.09975377 1.916762e-04 0.900054554 #> 4592 45.91 0.09970391 1.915804e-04 0.900104514 #> 4593 45.92 0.09965407 1.914846e-04 0.900154449 #> 4594 45.93 0.09960425 1.913889e-04 0.900204360 #> 4595 45.94 0.09955446 1.912932e-04 0.900254245 #> 4596 45.95 0.09950470 1.911976e-04 0.900304105 #> 4597 45.96 0.09945496 1.911020e-04 0.900353941 #> 4598 45.97 0.09940524 1.910065e-04 0.900403752 #> 4599 45.98 0.09935555 1.909110e-04 0.900453537 #> 4600 45.99 0.09930589 1.908156e-04 0.900503298 #> 4601 46.00 0.09925625 1.907202e-04 0.900553034 #> 4602 46.01 0.09920663 1.906249e-04 0.900602745 #> 4603 46.02 0.09915704 1.905296e-04 0.900652431 #> 4604 46.03 0.09910747 1.904343e-04 0.900702093 #> 4605 46.04 0.09905793 1.903391e-04 0.900751729 #> 4606 46.05 0.09900842 1.902440e-04 0.900801341 #> 4607 46.06 0.09895892 1.901489e-04 0.900850928 #> 4608 46.07 0.09890946 1.900539e-04 0.900900490 #> 4609 46.08 0.09886001 1.899589e-04 0.900950027 #> 4610 46.09 0.09881060 1.898639e-04 0.900999540 #> 4611 46.10 0.09876120 1.897690e-04 0.901049028 #> 4612 46.11 0.09871183 1.896741e-04 0.901098491 #> 4613 46.12 0.09866249 1.895793e-04 0.901147929 #> 4614 46.13 0.09861317 1.894845e-04 0.901197343 #> 4615 46.14 0.09856388 1.893898e-04 0.901246732 #> 4616 46.15 0.09851461 1.892952e-04 0.901296096 #> 4617 46.16 0.09846536 1.892005e-04 0.901345436 #> 4618 46.17 0.09841614 1.891060e-04 0.901394751 #> 4619 46.18 0.09836695 1.890114e-04 0.901444041 #> 4620 46.19 0.09831778 1.889169e-04 0.901493307 #> 4621 46.20 0.09826863 1.888225e-04 0.901542548 #> 4622 46.21 0.09821951 1.887281e-04 0.901591764 #> 4623 46.22 0.09817041 1.886338e-04 0.901640956 #> 4624 46.23 0.09812134 1.885395e-04 0.901690123 #> 4625 46.24 0.09807229 1.884452e-04 0.901739266 #> 4626 46.25 0.09802326 1.883510e-04 0.901788384 #> 4627 46.26 0.09797427 1.882569e-04 0.901837478 #> 4628 46.27 0.09792529 1.881628e-04 0.901886547 #> 4629 46.28 0.09787634 1.880687e-04 0.901935591 #> 4630 46.29 0.09782741 1.879747e-04 0.901984611 #> 4631 46.30 0.09777851 1.878808e-04 0.902033606 #> 4632 46.31 0.09772964 1.877868e-04 0.902082577 #> 4633 46.32 0.09768078 1.876930e-04 0.902131524 #> 4634 46.33 0.09763195 1.875991e-04 0.902180446 #> 4635 46.34 0.09758315 1.875054e-04 0.902229343 #> 4636 46.35 0.09753437 1.874116e-04 0.902278217 #> 4637 46.36 0.09748562 1.873180e-04 0.902327065 #> 4638 46.37 0.09743689 1.872243e-04 0.902375889 #> 4639 46.38 0.09738818 1.871307e-04 0.902424689 #> 4640 46.39 0.09733950 1.870372e-04 0.902473465 #> 4641 46.40 0.09729084 1.869437e-04 0.902522216 #> 4642 46.41 0.09724221 1.868502e-04 0.902570943 #> 4643 46.42 0.09719360 1.867568e-04 0.902619645 #> 4644 46.43 0.09714501 1.866635e-04 0.902668323 #> 4645 46.44 0.09709645 1.865702e-04 0.902716977 #> 4646 46.45 0.09704792 1.864769e-04 0.902765606 #> 4647 46.46 0.09699941 1.863837e-04 0.902814211 #> 4648 46.47 0.09695092 1.862905e-04 0.902862792 #> 4649 46.48 0.09690245 1.861974e-04 0.902911348 #> 4650 46.49 0.09685402 1.861043e-04 0.902959880 #> 4651 46.50 0.09680560 1.860113e-04 0.903008388 #> 4652 46.51 0.09675721 1.859183e-04 0.903056872 #> 4653 46.52 0.09670884 1.858254e-04 0.903105332 #> 4654 46.53 0.09666050 1.857325e-04 0.903153767 #> 4655 46.54 0.09661218 1.856397e-04 0.903202178 #> 4656 46.55 0.09656389 1.855469e-04 0.903250565 #> 4657 46.56 0.09651562 1.854541e-04 0.903298927 #> 4658 46.57 0.09646737 1.853614e-04 0.903347266 #> 4659 46.58 0.09641915 1.852688e-04 0.903395580 #> 4660 46.59 0.09637095 1.851761e-04 0.903443870 #> 4661 46.60 0.09632278 1.850836e-04 0.903492136 #> 4662 46.61 0.09627463 1.849911e-04 0.903540378 #> 4663 46.62 0.09622651 1.848986e-04 0.903588596 #> 4664 46.63 0.09617840 1.848062e-04 0.903636789 #> 4665 46.64 0.09613033 1.847138e-04 0.903684959 #> 4666 46.65 0.09608227 1.846214e-04 0.903733104 #> 4667 46.66 0.09603425 1.845292e-04 0.903781226 #> 4668 46.67 0.09598624 1.844369e-04 0.903829323 #> 4669 46.68 0.09593826 1.843447e-04 0.903877397 #> 4670 46.69 0.09589030 1.842526e-04 0.903925446 #> 4671 46.70 0.09584237 1.841605e-04 0.903973471 #> 4672 46.71 0.09579446 1.840684e-04 0.904021472 #> 4673 46.72 0.09574657 1.839764e-04 0.904069450 #> 4674 46.73 0.09569871 1.838844e-04 0.904117403 #> 4675 46.74 0.09565088 1.837925e-04 0.904165332 #> 4676 46.75 0.09560306 1.837006e-04 0.904213238 #> 4677 46.76 0.09555527 1.836088e-04 0.904261119 #> 4678 46.77 0.09550751 1.835170e-04 0.904308976 #> 4679 46.78 0.09545976 1.834253e-04 0.904356810 #> 4680 46.79 0.09541205 1.833336e-04 0.904404620 #> 4681 46.80 0.09536435 1.832420e-04 0.904452405 #> 4682 46.81 0.09531668 1.831504e-04 0.904500167 #> 4683 46.82 0.09526904 1.830588e-04 0.904547905 #> 4684 46.83 0.09522141 1.829673e-04 0.904595619 #> 4685 46.84 0.09517381 1.828758e-04 0.904643310 #> 4686 46.85 0.09512624 1.827844e-04 0.904690976 #> 4687 46.86 0.09507869 1.826931e-04 0.904738619 #> 4688 46.87 0.09503116 1.826017e-04 0.904786237 #> 4689 46.88 0.09498366 1.825105e-04 0.904833832 #> 4690 46.89 0.09493618 1.824192e-04 0.904881404 #> 4691 46.90 0.09488872 1.823280e-04 0.904928951 #> 4692 46.91 0.09484129 1.822369e-04 0.904976475 #> 4693 46.92 0.09479388 1.821458e-04 0.905023975 #> 4694 46.93 0.09474649 1.820548e-04 0.905071451 #> 4695 46.94 0.09469913 1.819637e-04 0.905118903 #> 4696 46.95 0.09465180 1.818728e-04 0.905166332 #> 4697 46.96 0.09460448 1.817819e-04 0.905213737 #> 4698 46.97 0.09455719 1.816910e-04 0.905261118 #> 4699 46.98 0.09450992 1.816002e-04 0.905308476 #> 4700 46.99 0.09446268 1.815094e-04 0.905355810 #> 4701 47.00 0.09441546 1.814187e-04 0.905403120 #> 4702 47.01 0.09436827 1.813280e-04 0.905450407 #> 4703 47.02 0.09432109 1.812373e-04 0.905497669 #> 4704 47.03 0.09427394 1.811468e-04 0.905544909 #> 4705 47.04 0.09422682 1.810562e-04 0.905592125 #> 4706 47.05 0.09417972 1.809657e-04 0.905639317 #> 4707 47.06 0.09413264 1.808752e-04 0.905686485 #> 4708 47.07 0.09408558 1.807848e-04 0.905733630 #> 4709 47.08 0.09403855 1.806944e-04 0.905780752 #> 4710 47.09 0.09399155 1.806041e-04 0.905827850 #> 4711 47.10 0.09394456 1.805138e-04 0.905874924 #> 4712 47.11 0.09389760 1.804236e-04 0.905921975 #> 4713 47.12 0.09385066 1.803334e-04 0.905969002 #> 4714 47.13 0.09380375 1.802433e-04 0.906016006 #> 4715 47.14 0.09375686 1.801532e-04 0.906062986 #> 4716 47.15 0.09370999 1.800631e-04 0.906109943 #> 4717 47.16 0.09366315 1.799731e-04 0.906156876 #> 4718 47.17 0.09361633 1.798832e-04 0.906203786 #> 4719 47.18 0.09356953 1.797932e-04 0.906250672 #> 4720 47.19 0.09352276 1.797034e-04 0.906297535 #> 4721 47.20 0.09347601 1.796135e-04 0.906344375 #> 4722 47.21 0.09342929 1.795237e-04 0.906391191 #> 4723 47.22 0.09338258 1.794340e-04 0.906437983 #> 4724 47.23 0.09333590 1.793443e-04 0.906484753 #> 4725 47.24 0.09328925 1.792547e-04 0.906531499 #> 4726 47.25 0.09324261 1.791651e-04 0.906578221 #> 4727 47.26 0.09319600 1.790755e-04 0.906624920 #> 4728 47.27 0.09314942 1.789860e-04 0.906671596 #> 4729 47.28 0.09310285 1.788965e-04 0.906718249 #> 4730 47.29 0.09305631 1.788071e-04 0.906764878 #> 4731 47.30 0.09300980 1.787177e-04 0.906811484 #> 4732 47.31 0.09296331 1.786284e-04 0.906858067 #> 4733 47.32 0.09291683 1.785391e-04 0.906904626 #> 4734 47.33 0.09287039 1.784498e-04 0.906951162 #> 4735 47.34 0.09282396 1.783606e-04 0.906997675 #> 4736 47.35 0.09277756 1.782715e-04 0.907044164 #> 4737 47.36 0.09273119 1.781824e-04 0.907090631 #> 4738 47.37 0.09268483 1.780933e-04 0.907137074 #> 4739 47.38 0.09263850 1.780043e-04 0.907183494 #> 4740 47.39 0.09259219 1.779153e-04 0.907229890 #> 4741 47.40 0.09254591 1.778263e-04 0.907276264 #> 4742 47.41 0.09249965 1.777375e-04 0.907322614 #> 4743 47.42 0.09245341 1.776486e-04 0.907368941 #> 4744 47.43 0.09240720 1.775598e-04 0.907415245 #> 4745 47.44 0.09236100 1.774710e-04 0.907461526 #> 4746 47.45 0.09231483 1.773823e-04 0.907507784 #> 4747 47.46 0.09226869 1.772937e-04 0.907554018 #> 4748 47.47 0.09222257 1.772050e-04 0.907600230 #> 4749 47.48 0.09217647 1.771165e-04 0.907646418 #> 4750 47.49 0.09213039 1.770279e-04 0.907692583 #> 4751 47.50 0.09208434 1.769394e-04 0.907738725 #> 4752 47.51 0.09203830 1.768510e-04 0.907784844 #> 4753 47.52 0.09199230 1.767626e-04 0.907830940 #> 4754 47.53 0.09194631 1.766742e-04 0.907877013 #> 4755 47.54 0.09190035 1.765859e-04 0.907923063 #> 4756 47.55 0.09185441 1.764976e-04 0.907969090 #> 4757 47.56 0.09180850 1.764094e-04 0.908015094 #> 4758 47.57 0.09176260 1.763212e-04 0.908061075 #> 4759 47.58 0.09171673 1.762331e-04 0.908107033 #> 4760 47.59 0.09167089 1.761450e-04 0.908152968 #> 4761 47.60 0.09162506 1.760569e-04 0.908198880 #> 4762 47.61 0.09157926 1.759689e-04 0.908244769 #> 4763 47.62 0.09153348 1.758810e-04 0.908290636 #> 4764 47.63 0.09148773 1.757931e-04 0.908336479 #> 4765 47.64 0.09144200 1.757052e-04 0.908382299 #> 4766 47.65 0.09139629 1.756173e-04 0.908428096 #> 4767 47.66 0.09135060 1.755296e-04 0.908473871 #> 4768 47.67 0.09130494 1.754418e-04 0.908519623 #> 4769 47.68 0.09125929 1.753541e-04 0.908565351 #> 4770 47.69 0.09121368 1.752665e-04 0.908611057 #> 4771 47.70 0.09116808 1.751789e-04 0.908656740 #> 4772 47.71 0.09112251 1.750913e-04 0.908702401 #> 4773 47.72 0.09107696 1.750038e-04 0.908748038 #> 4774 47.73 0.09103143 1.749163e-04 0.908793653 #> 4775 47.74 0.09098593 1.748288e-04 0.908839244 #> 4776 47.75 0.09094045 1.747415e-04 0.908884813 #> 4777 47.76 0.09089499 1.746541e-04 0.908930359 #> 4778 47.77 0.09084955 1.745668e-04 0.908975883 #> 4779 47.78 0.09080414 1.744795e-04 0.909021384 #> 4780 47.79 0.09075875 1.743923e-04 0.909066862 #> 4781 47.80 0.09071338 1.743051e-04 0.909112317 #> 4782 47.81 0.09066803 1.742180e-04 0.909157749 #> 4783 47.82 0.09062271 1.741309e-04 0.909203159 #> 4784 47.83 0.09057741 1.740439e-04 0.909248546 #> 4785 47.84 0.09053213 1.739569e-04 0.909293910 #> 4786 47.85 0.09048688 1.738699e-04 0.909339252 #> 4787 47.86 0.09044165 1.737830e-04 0.909384571 #> 4788 47.87 0.09039644 1.736961e-04 0.909429868 #> 4789 47.88 0.09035125 1.736093e-04 0.909475141 #> 4790 47.89 0.09030608 1.735225e-04 0.909520392 #> 4791 47.90 0.09026094 1.734358e-04 0.909565621 #> 4792 47.91 0.09021582 1.733491e-04 0.909610827 #> 4793 47.92 0.09017073 1.732624e-04 0.909656010 #> 4794 47.93 0.09012565 1.731758e-04 0.909701171 #> 4795 47.94 0.09008060 1.730893e-04 0.909746309 #> 4796 47.95 0.09003557 1.730027e-04 0.909791425 #> 4797 47.96 0.08999057 1.729163e-04 0.909836518 #> 4798 47.97 0.08994558 1.728298e-04 0.909881588 #> 4799 47.98 0.08990062 1.727434e-04 0.909926636 #> 4800 47.99 0.08985568 1.726571e-04 0.909971661 #> 4801 48.00 0.08981076 1.725708e-04 0.910016664 #> 4802 48.01 0.08976587 1.724845e-04 0.910061645 #> 4803 48.02 0.08972100 1.723983e-04 0.910106603 #> 4804 48.03 0.08967615 1.723121e-04 0.910151538 #> 4805 48.04 0.08963132 1.722260e-04 0.910196451 #> 4806 48.05 0.08958652 1.721399e-04 0.910241342 #> 4807 48.06 0.08954174 1.720538e-04 0.910286210 #> 4808 48.07 0.08949698 1.719678e-04 0.910331056 #> 4809 48.08 0.08945224 1.718819e-04 0.910375879 #> 4810 48.09 0.08940752 1.717960e-04 0.910420680 #> 4811 48.10 0.08936283 1.717101e-04 0.910465458 #> 4812 48.11 0.08931816 1.716242e-04 0.910510214 #> 4813 48.12 0.08927351 1.715385e-04 0.910554948 #> 4814 48.13 0.08922889 1.714527e-04 0.910599659 #> 4815 48.14 0.08918428 1.713670e-04 0.910644348 #> 4816 48.15 0.08913970 1.712813e-04 0.910689015 #> 4817 48.16 0.08909515 1.711957e-04 0.910733659 #> 4818 48.17 0.08905061 1.711101e-04 0.910778281 #> 4819 48.18 0.08900609 1.710246e-04 0.910822881 #> 4820 48.19 0.08896160 1.709391e-04 0.910867458 #> 4821 48.20 0.08891713 1.708537e-04 0.910912014 #> 4822 48.21 0.08887269 1.707683e-04 0.910956546 #> 4823 48.22 0.08882826 1.706829e-04 0.911001057 #> 4824 48.23 0.08878386 1.705976e-04 0.911045545 #> 4825 48.24 0.08873948 1.705123e-04 0.911090011 #> 4826 48.25 0.08869512 1.704271e-04 0.911134455 #> 4827 48.26 0.08865078 1.703419e-04 0.911178877 #> 4828 48.27 0.08860647 1.702567e-04 0.911223276 #> 4829 48.28 0.08856217 1.701716e-04 0.911267654 #> 4830 48.29 0.08851790 1.700866e-04 0.911312009 #> 4831 48.30 0.08847366 1.700015e-04 0.911356342 #> 4832 48.31 0.08842943 1.699166e-04 0.911400653 #> 4833 48.32 0.08838523 1.698316e-04 0.911444941 #> 4834 48.33 0.08834105 1.697467e-04 0.911489208 #> 4835 48.34 0.08829689 1.696619e-04 0.911533452 #> 4836 48.35 0.08825275 1.695771e-04 0.911577674 #> 4837 48.36 0.08820863 1.694923e-04 0.911621874 #> 4838 48.37 0.08816454 1.694076e-04 0.911666052 #> 4839 48.38 0.08812047 1.693229e-04 0.911710208 #> 4840 48.39 0.08807642 1.692382e-04 0.911754342 #> 4841 48.40 0.08803239 1.691537e-04 0.911798454 #> 4842 48.41 0.08798839 1.690691e-04 0.911842544 #> 4843 48.42 0.08794440 1.689846e-04 0.911886611 #> 4844 48.43 0.08790044 1.689001e-04 0.911930657 #> 4845 48.44 0.08785650 1.688157e-04 0.911974681 #> 4846 48.45 0.08781259 1.687313e-04 0.912018682 #> 4847 48.46 0.08776869 1.686469e-04 0.912062662 #> 4848 48.47 0.08772482 1.685626e-04 0.912106620 #> 4849 48.48 0.08768097 1.684784e-04 0.912150555 #> 4850 48.49 0.08763714 1.683942e-04 0.912194469 #> 4851 48.50 0.08759333 1.683100e-04 0.912238361 #> 4852 48.51 0.08754954 1.682259e-04 0.912282231 #> 4853 48.52 0.08750578 1.681418e-04 0.912326079 #> 4854 48.53 0.08746204 1.680577e-04 0.912369905 #> 4855 48.54 0.08741832 1.679737e-04 0.912413709 #> 4856 48.55 0.08737462 1.678897e-04 0.912457491 #> 4857 48.56 0.08733094 1.678058e-04 0.912501251 #> 4858 48.57 0.08728729 1.677219e-04 0.912544990 #> 4859 48.58 0.08724366 1.676381e-04 0.912588706 #> 4860 48.59 0.08720004 1.675543e-04 0.912632401 #> 4861 48.60 0.08715646 1.674705e-04 0.912676074 #> 4862 48.61 0.08711289 1.673868e-04 0.912719725 #> 4863 48.62 0.08706934 1.673032e-04 0.912763354 #> 4864 48.63 0.08702582 1.672195e-04 0.912806962 #> 4865 48.64 0.08698232 1.671359e-04 0.912850547 #> 4866 48.65 0.08693884 1.670524e-04 0.912894111 #> 4867 48.66 0.08689538 1.669689e-04 0.912937653 #> 4868 48.67 0.08685194 1.668854e-04 0.912981173 #> 4869 48.68 0.08680853 1.668020e-04 0.913024672 #> 4870 48.69 0.08676513 1.667186e-04 0.913068149 #> 4871 48.70 0.08672176 1.666353e-04 0.913111604 #> 4872 48.71 0.08667841 1.665520e-04 0.913155037 #> 4873 48.72 0.08663508 1.664687e-04 0.913198449 #> 4874 48.73 0.08659178 1.663855e-04 0.913241839 #> 4875 48.74 0.08654849 1.663023e-04 0.913285207 #> 4876 48.75 0.08650523 1.662192e-04 0.913328554 #> 4877 48.76 0.08646199 1.661361e-04 0.913371878 #> 4878 48.77 0.08641877 1.660531e-04 0.913415182 #> 4879 48.78 0.08637557 1.659701e-04 0.913458463 #> 4880 48.79 0.08633239 1.658871e-04 0.913501723 #> 4881 48.80 0.08628923 1.658042e-04 0.913544962 #> 4882 48.81 0.08624610 1.657213e-04 0.913588178 #> 4883 48.82 0.08620299 1.656385e-04 0.913631373 #> 4884 48.83 0.08615990 1.655557e-04 0.913674547 #> 4885 48.84 0.08611683 1.654729e-04 0.913717699 #> 4886 48.85 0.08607378 1.653902e-04 0.913760829 #> 4887 48.86 0.08603075 1.653075e-04 0.913803938 #> 4888 48.87 0.08598775 1.652249e-04 0.913847025 #> 4889 48.88 0.08594477 1.651423e-04 0.913890091 #> 4890 48.89 0.08590181 1.650597e-04 0.913933135 #> 4891 48.90 0.08585886 1.649772e-04 0.913976158 #> 4892 48.91 0.08581595 1.648948e-04 0.914019159 #> 4893 48.92 0.08577305 1.648123e-04 0.914062139 #> 4894 48.93 0.08573017 1.647300e-04 0.914105097 #> 4895 48.94 0.08568732 1.646476e-04 0.914148034 #> 4896 48.95 0.08564449 1.645653e-04 0.914190949 #> 4897 48.96 0.08560167 1.644830e-04 0.914233843 #> 4898 48.97 0.08555888 1.644008e-04 0.914276715 #> 4899 48.98 0.08551612 1.643186e-04 0.914319566 #> 4900 48.99 0.08547337 1.642365e-04 0.914362396 #> 4901 49.00 0.08543064 1.641544e-04 0.914405204 #> 4902 49.01 0.08538794 1.640723e-04 0.914447990 #> 4903 49.02 0.08534525 1.639903e-04 0.914490756 #> 4904 49.03 0.08530259 1.639084e-04 0.914533500 #> 4905 49.04 0.08525995 1.638264e-04 0.914576222 #> 4906 49.05 0.08521733 1.637445e-04 0.914618923 #> 4907 49.06 0.08517473 1.636627e-04 0.914661603 #> 4908 49.07 0.08513216 1.635809e-04 0.914704262 #> 4909 49.08 0.08508960 1.634991e-04 0.914746899 #> 4910 49.09 0.08504707 1.634174e-04 0.914789515 #> 4911 49.10 0.08500455 1.633357e-04 0.914832109 #> 4912 49.11 0.08496206 1.632540e-04 0.914874683 #> 4913 49.12 0.08491959 1.631724e-04 0.914917235 #> 4914 49.13 0.08487714 1.630909e-04 0.914959766 #> 4915 49.14 0.08483472 1.630093e-04 0.915002275 #> 4916 49.15 0.08479231 1.629279e-04 0.915044763 #> 4917 49.16 0.08474992 1.628464e-04 0.915087230 #> 4918 49.17 0.08470756 1.627650e-04 0.915129676 #> 4919 49.18 0.08466522 1.626836e-04 0.915172101 #> 4920 49.19 0.08462289 1.626023e-04 0.915214504 #> 4921 49.20 0.08458059 1.625210e-04 0.915256886 #> 4922 49.21 0.08453831 1.624398e-04 0.915299247 #> 4923 49.22 0.08449605 1.623586e-04 0.915341587 #> 4924 49.23 0.08445382 1.622774e-04 0.915383905 #> 4925 49.24 0.08441160 1.621963e-04 0.915426203 #> 4926 49.25 0.08436941 1.621152e-04 0.915468479 #> 4927 49.26 0.08432723 1.620342e-04 0.915510734 #> 4928 49.27 0.08428508 1.619532e-04 0.915552969 #> 4929 49.28 0.08424295 1.618723e-04 0.915595182 #> 4930 49.29 0.08420084 1.617913e-04 0.915637373 #> 4931 49.30 0.08415875 1.617105e-04 0.915679544 #> 4932 49.31 0.08411668 1.616296e-04 0.915721694 #> 4933 49.32 0.08407463 1.615488e-04 0.915763822 #> 4934 49.33 0.08403260 1.614681e-04 0.915805930 #> 4935 49.34 0.08399060 1.613874e-04 0.915848017 #> 4936 49.35 0.08394861 1.613067e-04 0.915890082 #> 4937 49.36 0.08390665 1.612261e-04 0.915932126 #> 4938 49.37 0.08386470 1.611455e-04 0.915974150 #> 4939 49.38 0.08382278 1.610649e-04 0.916016152 #> 4940 49.39 0.08378088 1.609844e-04 0.916058134 #> 4941 49.40 0.08373900 1.609039e-04 0.916100094 #> 4942 49.41 0.08369714 1.608235e-04 0.916142034 #> 4943 49.42 0.08365530 1.607431e-04 0.916183952 #> 4944 49.43 0.08361349 1.606628e-04 0.916225850 #> 4945 49.44 0.08357169 1.605824e-04 0.916267726 #> 4946 49.45 0.08352992 1.605022e-04 0.916309582 #> 4947 49.46 0.08348816 1.604219e-04 0.916351417 #> 4948 49.47 0.08344643 1.603417e-04 0.916393231 #> 4949 49.48 0.08340471 1.602616e-04 0.916435024 #> 4950 49.49 0.08336302 1.601815e-04 0.916476796 #> 4951 49.50 0.08332135 1.601014e-04 0.916518547 #> 4952 49.51 0.08327970 1.600214e-04 0.916560277 #> 4953 49.52 0.08323807 1.599414e-04 0.916601986 #> 4954 49.53 0.08319646 1.598614e-04 0.916643675 #> 4955 49.54 0.08315488 1.597815e-04 0.916685343 #> 4956 49.55 0.08311331 1.597017e-04 0.916726990 #> 4957 49.56 0.08307176 1.596218e-04 0.916768616 #> 4958 49.57 0.08303024 1.595420e-04 0.916810221 #> 4959 49.58 0.08298873 1.594623e-04 0.916851806 #> 4960 49.59 0.08294725 1.593826e-04 0.916893369 #> 4961 49.60 0.08290578 1.593029e-04 0.916934912 #> 4962 49.61 0.08286434 1.592233e-04 0.916976434 #> 4963 49.62 0.08282292 1.591437e-04 0.917017936 #> 4964 49.63 0.08278152 1.590641e-04 0.917059416 #> 4965 49.64 0.08274014 1.589846e-04 0.917100876 #> 4966 49.65 0.08269878 1.589051e-04 0.917142316 #> 4967 49.66 0.08265744 1.588257e-04 0.917183734 #> 4968 49.67 0.08261612 1.587463e-04 0.917225132 #> 4969 49.68 0.08257482 1.586670e-04 0.917266509 #> 4970 49.69 0.08253355 1.585877e-04 0.917307865 #> 4971 49.70 0.08249229 1.585084e-04 0.917349201 #> 4972 49.71 0.08245105 1.584291e-04 0.917390516 #> 4973 49.72 0.08240984 1.583500e-04 0.917431811 #> 4974 49.73 0.08236864 1.582708e-04 0.917473084 #> 4975 49.74 0.08232747 1.581917e-04 0.917514338 #> 4976 49.75 0.08228632 1.581126e-04 0.917555570 #> 4977 49.76 0.08224518 1.580336e-04 0.917596782 #> 4978 49.77 0.08220407 1.579546e-04 0.917637973 #> 4979 49.78 0.08216298 1.578756e-04 0.917679144 #> 4980 49.79 0.08212191 1.577967e-04 0.917720294 #> 4981 49.80 0.08208086 1.577178e-04 0.917761424 #> 4982 49.81 0.08203983 1.576390e-04 0.917802533 #> 4983 49.82 0.08199882 1.575602e-04 0.917843621 #> 4984 49.83 0.08195783 1.574814e-04 0.917884689 #> 4985 49.84 0.08191686 1.574027e-04 0.917925737 #> 4986 49.85 0.08187591 1.573240e-04 0.917966763 #> 4987 49.86 0.08183498 1.572454e-04 0.918007770 #> 4988 49.87 0.08179408 1.571668e-04 0.918048756 #> 4989 49.88 0.08175319 1.570882e-04 0.918089721 #> 4990 49.89 0.08171232 1.570097e-04 0.918130666 #> 4991 49.90 0.08167148 1.569312e-04 0.918171590 #> 4992 49.91 0.08163065 1.568528e-04 0.918212494 #> 4993 49.92 0.08158985 1.567743e-04 0.918253378 #> 4994 49.93 0.08154906 1.566960e-04 0.918294241 #> 4995 49.94 0.08150830 1.566176e-04 0.918335084 #> 4996 49.95 0.08146755 1.565394e-04 0.918375906 #> 4997 49.96 0.08142683 1.564611e-04 0.918416708 #> 4998 49.97 0.08138613 1.563829e-04 0.918457489 #> 4999 49.98 0.08134545 1.563047e-04 0.918498250 #> 5000 49.99 0.08130478 1.562266e-04 0.918538991 #> 5001 50.00 0.08126414 1.561485e-04 0.918579711 #> 5002 50.01 0.08122352 1.560704e-04 0.918620411 #> 5003 50.02 0.08118292 1.559924e-04 0.918661091 #> 5004 50.03 0.08114234 1.559145e-04 0.918701750 #> 5005 50.04 0.08110177 1.558365e-04 0.918742389 #> 5006 50.05 0.08106123 1.557586e-04 0.918783008 #> 5007 50.06 0.08102071 1.556808e-04 0.918823606 #> 5008 50.07 0.08098021 1.556029e-04 0.918864184 #> 5009 50.08 0.08093973 1.555252e-04 0.918904742 #> 5010 50.09 0.08089927 1.554474e-04 0.918945279 #> 5011 50.10 0.08085883 1.553697e-04 0.918985797 #> 5012 50.11 0.08081841 1.552920e-04 0.919026294 #> 5013 50.12 0.08077802 1.552144e-04 0.919066770 #> 5014 50.13 0.08073764 1.551368e-04 0.919107227 #> 5015 50.14 0.08069728 1.550593e-04 0.919147663 #> 5016 50.15 0.08065694 1.549818e-04 0.919188079 #> 5017 50.16 0.08061662 1.549043e-04 0.919228475 #> 5018 50.17 0.08057632 1.548269e-04 0.919268851 #> 5019 50.18 0.08053604 1.547495e-04 0.919309206 #> 5020 50.19 0.08049579 1.546721e-04 0.919349542 #> 5021 50.20 0.08045555 1.545948e-04 0.919389857 #> 5022 50.21 0.08041533 1.545175e-04 0.919430152 #> 5023 50.22 0.08037513 1.544403e-04 0.919470427 #> 5024 50.23 0.08033496 1.543631e-04 0.919510681 #> 5025 50.24 0.08029480 1.542859e-04 0.919550916 #> 5026 50.25 0.08025466 1.542088e-04 0.919591130 #> 5027 50.26 0.08021454 1.541317e-04 0.919631325 #> 5028 50.27 0.08017445 1.540547e-04 0.919671499 #> 5029 50.28 0.08013437 1.539777e-04 0.919711653 #> 5030 50.29 0.08009431 1.539007e-04 0.919751787 #> 5031 50.30 0.08005427 1.538238e-04 0.919791901 #> 5032 50.31 0.08001426 1.537469e-04 0.919831996 #> 5033 50.32 0.07997426 1.536700e-04 0.919872069 #> 5034 50.33 0.07993428 1.535932e-04 0.919912123 #> 5035 50.34 0.07989433 1.535164e-04 0.919952157 #> 5036 50.35 0.07985439 1.534397e-04 0.919992171 #> 5037 50.36 0.07981447 1.533630e-04 0.920032165 #> 5038 50.37 0.07977457 1.532863e-04 0.920072139 #> 5039 50.38 0.07973470 1.532097e-04 0.920112093 #> 5040 50.39 0.07969484 1.531331e-04 0.920152027 #> 5041 50.40 0.07965500 1.530566e-04 0.920191941 #> 5042 50.41 0.07961518 1.529800e-04 0.920231835 #> 5043 50.42 0.07957539 1.529036e-04 0.920271709 #> 5044 50.43 0.07953561 1.528271e-04 0.920311563 #> 5045 50.44 0.07949585 1.527507e-04 0.920351398 #> 5046 50.45 0.07945611 1.526744e-04 0.920391212 #> 5047 50.46 0.07941640 1.525981e-04 0.920431006 #> 5048 50.47 0.07937670 1.525218e-04 0.920470781 #> 5049 50.48 0.07933702 1.524455e-04 0.920510536 #> 5050 50.49 0.07929736 1.523693e-04 0.920550270 #> 5051 50.50 0.07925772 1.522932e-04 0.920589985 #> 5052 50.51 0.07921810 1.522171e-04 0.920629681 #> 5053 50.52 0.07917850 1.521410e-04 0.920669356 #> 5054 50.53 0.07913892 1.520649e-04 0.920709011 #> 5055 50.54 0.07909936 1.519889e-04 0.920748647 #> 5056 50.55 0.07905982 1.519129e-04 0.920788263 #> 5057 50.56 0.07902030 1.518370e-04 0.920827858 #> 5058 50.57 0.07898080 1.517611e-04 0.920867435 #> 5059 50.58 0.07894132 1.516852e-04 0.920906991 #> 5060 50.59 0.07890186 1.516094e-04 0.920946528 #> 5061 50.60 0.07886242 1.515336e-04 0.920986045 #> 5062 50.61 0.07882300 1.514579e-04 0.921025542 #> 5063 50.62 0.07878360 1.513822e-04 0.921065019 #> 5064 50.63 0.07874422 1.513065e-04 0.921104477 #> 5065 50.64 0.07870485 1.512308e-04 0.921143915 #> 5066 50.65 0.07866551 1.511553e-04 0.921183333 #> 5067 50.66 0.07862619 1.510797e-04 0.921222731 #> 5068 50.67 0.07858689 1.510042e-04 0.921262110 #> 5069 50.68 0.07854760 1.509287e-04 0.921301469 #> 5070 50.69 0.07850834 1.508532e-04 0.921340809 #> 5071 50.70 0.07846909 1.507778e-04 0.921380128 #> 5072 50.71 0.07842987 1.507025e-04 0.921419428 #> 5073 50.72 0.07839066 1.506271e-04 0.921458709 #> 5074 50.73 0.07835148 1.505518e-04 0.921497970 #> 5075 50.74 0.07831231 1.504766e-04 0.921537211 #> 5076 50.75 0.07827317 1.504014e-04 0.921576433 #> 5077 50.76 0.07823404 1.503262e-04 0.921615634 #> 5078 50.77 0.07819493 1.502510e-04 0.921654817 #> 5079 50.78 0.07815584 1.501759e-04 0.921693980 #> 5080 50.79 0.07811678 1.501009e-04 0.921733123 #> 5081 50.80 0.07807773 1.500258e-04 0.921772247 #> 5082 50.81 0.07803870 1.499508e-04 0.921811351 #> 5083 50.82 0.07799969 1.498759e-04 0.921850435 #> 5084 50.83 0.07796070 1.498010e-04 0.921889500 #> 5085 50.84 0.07792173 1.497261e-04 0.921928546 #> 5086 50.85 0.07788278 1.496512e-04 0.921967572 #> 5087 50.86 0.07784385 1.495764e-04 0.922006578 #> 5088 50.87 0.07780493 1.495017e-04 0.922045565 #> 5089 50.88 0.07776604 1.494269e-04 0.922084533 #> 5090 50.89 0.07772717 1.493522e-04 0.922123481 #> 5091 50.90 0.07768831 1.492776e-04 0.922162409 #> 5092 50.91 0.07764948 1.492030e-04 0.922201318 #> 5093 50.92 0.07761066 1.491284e-04 0.922240208 #> 5094 50.93 0.07757187 1.490538e-04 0.922279078 #> 5095 50.94 0.07753309 1.489793e-04 0.922317929 #> 5096 50.95 0.07749434 1.489048e-04 0.922356760 #> 5097 50.96 0.07745560 1.488304e-04 0.922395572 #> 5098 50.97 0.07741688 1.487560e-04 0.922434365 #> 5099 50.98 0.07737818 1.486817e-04 0.922473138 #> 5100 50.99 0.07733950 1.486073e-04 0.922511891 #> 5101 51.00 0.07730084 1.485330e-04 0.922550626 #> 5102 51.01 0.07726220 1.484588e-04 0.922589341 #> 5103 51.02 0.07722358 1.483846e-04 0.922628036 #> 5104 51.03 0.07718498 1.483104e-04 0.922666713 #> 5105 51.04 0.07714639 1.482363e-04 0.922705370 #> 5106 51.05 0.07710783 1.481622e-04 0.922744007 #> 5107 51.06 0.07706929 1.480881e-04 0.922782626 #> 5108 51.07 0.07703076 1.480141e-04 0.922821225 #> 5109 51.08 0.07699226 1.479401e-04 0.922859804 #> 5110 51.09 0.07695377 1.478661e-04 0.922898365 #> 5111 51.10 0.07691530 1.477922e-04 0.922936906 #> 5112 51.11 0.07687685 1.477184e-04 0.922975428 #> 5113 51.12 0.07683842 1.476445e-04 0.923013931 #> 5114 51.13 0.07680002 1.475707e-04 0.923052414 #> 5115 51.14 0.07676162 1.474969e-04 0.923090878 #> 5116 51.15 0.07672325 1.474232e-04 0.923129323 #> 5117 51.16 0.07668490 1.473495e-04 0.923167749 #> 5118 51.17 0.07664657 1.472759e-04 0.923206156 #> 5119 51.18 0.07660825 1.472022e-04 0.923244543 #> 5120 51.19 0.07656996 1.471287e-04 0.923282911 #> 5121 51.20 0.07653168 1.470551e-04 0.923321260 #> 5122 51.21 0.07649343 1.469816e-04 0.923359590 #> 5123 51.22 0.07645519 1.469081e-04 0.923397900 #> 5124 51.23 0.07641697 1.468347e-04 0.923436192 #> 5125 51.24 0.07637877 1.467613e-04 0.923474464 #> 5126 51.25 0.07634059 1.466879e-04 0.923512717 #> 5127 51.26 0.07630243 1.466146e-04 0.923550951 #> 5128 51.27 0.07626429 1.465413e-04 0.923589166 #> 5129 51.28 0.07622617 1.464681e-04 0.923627362 #> 5130 51.29 0.07618807 1.463949e-04 0.923665539 #> 5131 51.30 0.07614998 1.463217e-04 0.923703697 #> 5132 51.31 0.07611192 1.462485e-04 0.923741835 #> 5133 51.32 0.07607387 1.461754e-04 0.923779955 #> 5134 51.33 0.07603584 1.461024e-04 0.923818055 #> 5135 51.34 0.07599783 1.460293e-04 0.923856137 #> 5136 51.35 0.07595984 1.459563e-04 0.923894199 #> 5137 51.36 0.07592187 1.458834e-04 0.923932243 #> 5138 51.37 0.07588392 1.458104e-04 0.923970267 #> 5139 51.38 0.07584599 1.457376e-04 0.924008272 #> 5140 51.39 0.07580808 1.456647e-04 0.924046259 #> 5141 51.40 0.07577018 1.455919e-04 0.924084226 #> 5142 51.41 0.07573231 1.455191e-04 0.924122175 #> 5143 51.42 0.07569445 1.454464e-04 0.924160104 #> 5144 51.43 0.07565661 1.453737e-04 0.924198014 #> 5145 51.44 0.07561879 1.453010e-04 0.924235906 #> 5146 51.45 0.07558099 1.452284e-04 0.924273779 #> 5147 51.46 0.07554321 1.451558e-04 0.924311632 #> 5148 51.47 0.07550545 1.450832e-04 0.924349467 #> 5149 51.48 0.07546771 1.450107e-04 0.924387283 #> 5150 51.49 0.07542998 1.449382e-04 0.924425080 #> 5151 51.50 0.07539228 1.448658e-04 0.924462858 #> 5152 51.51 0.07535459 1.447933e-04 0.924500617 #> 5153 51.52 0.07531692 1.447210e-04 0.924538357 #> 5154 51.53 0.07527927 1.446486e-04 0.924576078 #> 5155 51.54 0.07524164 1.445763e-04 0.924613781 #> 5156 51.55 0.07520403 1.445040e-04 0.924651465 #> 5157 51.56 0.07516644 1.444318e-04 0.924689130 #> 5158 51.57 0.07512886 1.443596e-04 0.924726776 #> 5159 51.58 0.07509131 1.442874e-04 0.924764403 #> 5160 51.59 0.07505377 1.442153e-04 0.924802011 #> 5161 51.60 0.07501626 1.441432e-04 0.924839601 #> 5162 51.61 0.07497876 1.440712e-04 0.924877172 #> 5163 51.62 0.07494128 1.439992e-04 0.924914724 #> 5164 51.63 0.07490382 1.439272e-04 0.924952257 #> 5165 51.64 0.07486637 1.438552e-04 0.924989771 #> 5166 51.65 0.07482895 1.437833e-04 0.925027267 #> 5167 51.66 0.07479154 1.437114e-04 0.925064744 #> 5168 51.67 0.07475416 1.436396e-04 0.925102202 #> 5169 51.68 0.07471679 1.435678e-04 0.925139642 #> 5170 51.69 0.07467944 1.434960e-04 0.925177063 #> 5171 51.70 0.07464211 1.434243e-04 0.925214465 #> 5172 51.71 0.07460480 1.433526e-04 0.925251848 #> 5173 51.72 0.07456751 1.432810e-04 0.925289213 #> 5174 51.73 0.07453023 1.432093e-04 0.925326559 #> 5175 51.74 0.07449298 1.431378e-04 0.925363886 #> 5176 51.75 0.07445574 1.430662e-04 0.925401195 #> 5177 51.76 0.07441852 1.429947e-04 0.925438485 #> 5178 51.77 0.07438132 1.429232e-04 0.925475757 #> 5179 51.78 0.07434414 1.428518e-04 0.925513010 #> 5180 51.79 0.07430698 1.427804e-04 0.925550244 #> 5181 51.80 0.07426983 1.427090e-04 0.925587459 #> 5182 51.81 0.07423271 1.426376e-04 0.925624656 #> 5183 51.82 0.07419560 1.425663e-04 0.925661835 #> 5184 51.83 0.07415851 1.424951e-04 0.925698994 #> 5185 51.84 0.07412144 1.424238e-04 0.925736136 #> 5186 51.85 0.07408439 1.423527e-04 0.925773258 #> 5187 51.86 0.07404736 1.422815e-04 0.925810362 #> 5188 51.87 0.07401034 1.422104e-04 0.925847448 #> 5189 51.88 0.07397335 1.421393e-04 0.925884515 #> 5190 51.89 0.07393637 1.420682e-04 0.925921563 #> 5191 51.90 0.07389941 1.419972e-04 0.925958593 #> 5192 51.91 0.07386247 1.419262e-04 0.925995605 #> 5193 51.92 0.07382555 1.418553e-04 0.926032598 #> 5194 51.93 0.07378864 1.417844e-04 0.926069572 #> 5195 51.94 0.07375176 1.417135e-04 0.926106528 #> 5196 51.95 0.07371489 1.416427e-04 0.926143466 #> 5197 51.96 0.07367804 1.415719e-04 0.926180385 #> 5198 51.97 0.07364121 1.415011e-04 0.926217285 #> 5199 51.98 0.07360440 1.414304e-04 0.926254167 #> 5200 51.99 0.07356761 1.413597e-04 0.926291031 #> 5201 52.00 0.07353083 1.412890e-04 0.926327876 #> 5202 52.01 0.07349408 1.412184e-04 0.926364703 #> 5203 52.02 0.07345734 1.411478e-04 0.926401512 #> 5204 52.03 0.07342062 1.410772e-04 0.926438302 #> 5205 52.04 0.07338392 1.410067e-04 0.926475073 #> 5206 52.05 0.07334724 1.409362e-04 0.926511827 #> 5207 52.06 0.07331057 1.408658e-04 0.926548562 #> 5208 52.07 0.07327393 1.407954e-04 0.926585278 #> 5209 52.08 0.07323730 1.407250e-04 0.926621976 #> 5210 52.09 0.07320069 1.406546e-04 0.926658656 #> 5211 52.10 0.07316410 1.405843e-04 0.926695318 #> 5212 52.11 0.07312753 1.405140e-04 0.926731961 #> 5213 52.12 0.07309097 1.404438e-04 0.926768586 #> 5214 52.13 0.07305443 1.403736e-04 0.926805192 #> 5215 52.14 0.07301792 1.403034e-04 0.926841781 #> 5216 52.15 0.07298142 1.402333e-04 0.926878351 #> 5217 52.16 0.07294493 1.401632e-04 0.926914902 #> 5218 52.17 0.07290847 1.400931e-04 0.926951436 #> 5219 52.18 0.07287203 1.400231e-04 0.926987951 #> 5220 52.19 0.07283560 1.399531e-04 0.927024448 #> 5221 52.20 0.07279919 1.398832e-04 0.927060926 #> 5222 52.21 0.07276280 1.398132e-04 0.927097387 #> 5223 52.22 0.07272643 1.397433e-04 0.927133829 #> 5224 52.23 0.07269007 1.396735e-04 0.927170253 #> 5225 52.24 0.07265374 1.396037e-04 0.927206659 #> 5226 52.25 0.07261742 1.395339e-04 0.927243046 #> 5227 52.26 0.07258112 1.394641e-04 0.927279416 #> 5228 52.27 0.07254484 1.393944e-04 0.927315767 #> 5229 52.28 0.07250858 1.393247e-04 0.927352100 #> 5230 52.29 0.07247233 1.392551e-04 0.927388415 #> 5231 52.30 0.07243610 1.391855e-04 0.927424712 #> 5232 52.31 0.07239989 1.391159e-04 0.927460990 #> 5233 52.32 0.07236370 1.390464e-04 0.927497251 #> 5234 52.33 0.07232753 1.389769e-04 0.927533493 #> 5235 52.34 0.07229138 1.389074e-04 0.927569717 #> 5236 52.35 0.07225524 1.388380e-04 0.927605923 #> 5237 52.36 0.07221912 1.387686e-04 0.927642111 #> 5238 52.37 0.07218302 1.386992e-04 0.927678281 #> 5239 52.38 0.07214694 1.386299e-04 0.927714433 #> 5240 52.39 0.07211087 1.385606e-04 0.927750567 #> 5241 52.40 0.07207483 1.384913e-04 0.927786682 #> 5242 52.41 0.07203880 1.384221e-04 0.927822780 #> 5243 52.42 0.07200279 1.383529e-04 0.927858860 #> 5244 52.43 0.07196680 1.382837e-04 0.927894921 #> 5245 52.44 0.07193082 1.382146e-04 0.927930965 #> 5246 52.45 0.07189486 1.381455e-04 0.927966990 #> 5247 52.46 0.07185893 1.380764e-04 0.928002998 #> 5248 52.47 0.07182301 1.380074e-04 0.928038987 #> 5249 52.48 0.07178710 1.379384e-04 0.928074959 #> 5250 52.49 0.07175122 1.378695e-04 0.928110912 #> 5251 52.50 0.07171535 1.378006e-04 0.928146848 #> 5252 52.51 0.07167950 1.377317e-04 0.928182765 #> 5253 52.52 0.07164367 1.376628e-04 0.928218665 #> 5254 52.53 0.07160786 1.375940e-04 0.928254547 #> 5255 52.54 0.07157206 1.375252e-04 0.928290410 #> 5256 52.55 0.07153629 1.374565e-04 0.928326256 #> 5257 52.56 0.07150053 1.373878e-04 0.928362084 #> 5258 52.57 0.07146479 1.373191e-04 0.928397894 #> 5259 52.58 0.07142906 1.372505e-04 0.928433686 #> 5260 52.59 0.07139336 1.371819e-04 0.928469461 #> 5261 52.60 0.07135767 1.371133e-04 0.928505217 #> 5262 52.61 0.07132200 1.370447e-04 0.928540955 #> 5263 52.62 0.07128635 1.369762e-04 0.928576676 #> 5264 52.63 0.07125071 1.369078e-04 0.928612379 #> 5265 52.64 0.07121510 1.368393e-04 0.928648064 #> 5266 52.65 0.07117950 1.367709e-04 0.928683731 #> 5267 52.66 0.07114392 1.367026e-04 0.928719380 #> 5268 52.67 0.07110835 1.366342e-04 0.928755011 #> 5269 52.68 0.07107281 1.365659e-04 0.928790625 #> 5270 52.69 0.07103728 1.364977e-04 0.928826221 #> 5271 52.70 0.07100177 1.364294e-04 0.928861799 #> 5272 52.71 0.07096628 1.363612e-04 0.928897359 #> 5273 52.72 0.07093081 1.362931e-04 0.928932901 #> 5274 52.73 0.07089535 1.362249e-04 0.928968426 #> 5275 52.74 0.07085991 1.361568e-04 0.929003933 #> 5276 52.75 0.07082449 1.360888e-04 0.929039422 #> 5277 52.76 0.07078909 1.360208e-04 0.929074893 #> 5278 52.77 0.07075370 1.359528e-04 0.929110347 #> 5279 52.78 0.07071833 1.358848e-04 0.929145783 #> 5280 52.79 0.07068298 1.358169e-04 0.929181201 #> 5281 52.80 0.07064765 1.357490e-04 0.929216602 #> 5282 52.81 0.07061233 1.356811e-04 0.929251985 #> 5283 52.82 0.07057704 1.356133e-04 0.929287350 #> 5284 52.83 0.07054176 1.355455e-04 0.929322697 #> 5285 52.84 0.07050650 1.354778e-04 0.929358027 #> 5286 52.85 0.07047125 1.354100e-04 0.929393339 #> 5287 52.86 0.07043602 1.353423e-04 0.929428634 #> 5288 52.87 0.07040081 1.352747e-04 0.929463911 #> 5289 52.88 0.07036562 1.352071e-04 0.929499170 #> 5290 52.89 0.07033045 1.351395e-04 0.929534412 #> 5291 52.90 0.07029529 1.350719e-04 0.929569636 #> 5292 52.91 0.07026015 1.350044e-04 0.929604842 #> 5293 52.92 0.07022503 1.349369e-04 0.929640031 #> 5294 52.93 0.07018993 1.348695e-04 0.929675202 #> 5295 52.94 0.07015484 1.348021e-04 0.929710356 #> 5296 52.95 0.07011977 1.347347e-04 0.929745492 #> 5297 52.96 0.07008472 1.346673e-04 0.929780610 #> 5298 52.97 0.07004969 1.346000e-04 0.929815711 #> 5299 52.98 0.07001467 1.345327e-04 0.929850794 #> 5300 52.99 0.06997967 1.344655e-04 0.929885860 #> 5301 53.00 0.06994469 1.343983e-04 0.929920908 #> 5302 53.01 0.06990973 1.343311e-04 0.929955939 #> 5303 53.02 0.06987478 1.342639e-04 0.929990953 #> 5304 53.03 0.06983985 1.341968e-04 0.930025948 #> 5305 53.04 0.06980494 1.341297e-04 0.930060927 #> 5306 53.05 0.06977005 1.340627e-04 0.930095887 #> 5307 53.06 0.06973517 1.339957e-04 0.930130831 #> 5308 53.07 0.06970031 1.339287e-04 0.930165757 #> 5309 53.08 0.06966547 1.338617e-04 0.930200665 #> 5310 53.09 0.06963065 1.337948e-04 0.930235556 #> 5311 53.10 0.06959584 1.337279e-04 0.930270429 #> 5312 53.11 0.06956105 1.336611e-04 0.930305285 #> 5313 53.12 0.06952628 1.335943e-04 0.930340124 #> 5314 53.13 0.06949153 1.335275e-04 0.930374945 #> 5315 53.14 0.06945679 1.334608e-04 0.930409749 #> 5316 53.15 0.06942207 1.333940e-04 0.930444536 #> 5317 53.16 0.06938737 1.333274e-04 0.930479305 #> 5318 53.17 0.06935268 1.332607e-04 0.930514056 #> 5319 53.18 0.06931802 1.331941e-04 0.930548791 #> 5320 53.19 0.06928336 1.331275e-04 0.930583508 #> 5321 53.20 0.06924873 1.330610e-04 0.930618207 #> 5322 53.21 0.06921412 1.329945e-04 0.930652889 #> 5323 53.22 0.06917952 1.329280e-04 0.930687554 #> 5324 53.23 0.06914494 1.328615e-04 0.930722202 #> 5325 53.24 0.06911037 1.327951e-04 0.930756832 #> 5326 53.25 0.06907583 1.327287e-04 0.930791445 #> 5327 53.26 0.06904130 1.326624e-04 0.930826041 #> 5328 53.27 0.06900679 1.325961e-04 0.930860619 #> 5329 53.28 0.06897229 1.325298e-04 0.930895180 #> 5330 53.29 0.06893781 1.324635e-04 0.930929724 #> 5331 53.30 0.06890335 1.323973e-04 0.930964250 #> 5332 53.31 0.06886891 1.323311e-04 0.930998760 #> 5333 53.32 0.06883448 1.322650e-04 0.931033251 #> 5334 53.33 0.06880007 1.321989e-04 0.931067726 #> 5335 53.34 0.06876568 1.321328e-04 0.931102184 #> 5336 53.35 0.06873131 1.320667e-04 0.931136624 #> 5337 53.36 0.06869695 1.320007e-04 0.931171047 #> 5338 53.37 0.06866261 1.319347e-04 0.931205453 #> 5339 53.38 0.06862829 1.318688e-04 0.931239842 #> 5340 53.39 0.06859398 1.318029e-04 0.931274213 #> 5341 53.40 0.06855970 1.317370e-04 0.931308568 #> 5342 53.41 0.06852542 1.316711e-04 0.931342905 #> 5343 53.42 0.06849117 1.316053e-04 0.931377225 #> 5344 53.43 0.06845693 1.315395e-04 0.931411527 #> 5345 53.44 0.06842271 1.314738e-04 0.931445813 #> 5346 53.45 0.06838851 1.314081e-04 0.931480082 #> 5347 53.46 0.06835432 1.313424e-04 0.931514333 #> 5348 53.47 0.06832016 1.312767e-04 0.931548567 #> 5349 53.48 0.06828600 1.312111e-04 0.931582784 #> 5350 53.49 0.06825187 1.311455e-04 0.931616985 #> 5351 53.50 0.06821775 1.310800e-04 0.931651167 #> 5352 53.51 0.06818365 1.310144e-04 0.931685333 #> 5353 53.52 0.06814957 1.309489e-04 0.931719482 #> 5354 53.53 0.06811550 1.308835e-04 0.931753614 #> 5355 53.54 0.06808145 1.308181e-04 0.931787729 #> 5356 53.55 0.06804742 1.307527e-04 0.931821826 #> 5357 53.56 0.06801341 1.306873e-04 0.931855907 #> 5358 53.57 0.06797941 1.306220e-04 0.931889970 #> 5359 53.58 0.06794543 1.305567e-04 0.931924017 #> 5360 53.59 0.06791146 1.304914e-04 0.931958046 #> 5361 53.60 0.06787752 1.304262e-04 0.931992059 #> 5362 53.61 0.06784358 1.303610e-04 0.932026054 #> 5363 53.62 0.06780967 1.302958e-04 0.932060033 #> 5364 53.63 0.06777578 1.302307e-04 0.932093994 #> 5365 53.64 0.06774190 1.301656e-04 0.932127939 #> 5366 53.65 0.06770803 1.301005e-04 0.932161866 #> 5367 53.66 0.06767419 1.300355e-04 0.932195777 #> 5368 53.67 0.06764036 1.299705e-04 0.932229670 #> 5369 53.68 0.06760655 1.299055e-04 0.932263547 #> 5370 53.69 0.06757275 1.298406e-04 0.932297407 #> 5371 53.70 0.06753897 1.297757e-04 0.932331250 #> 5372 53.71 0.06750521 1.297108e-04 0.932365076 #> 5373 53.72 0.06747147 1.296460e-04 0.932398885 #> 5374 53.73 0.06743774 1.295812e-04 0.932432677 #> 5375 53.74 0.06740403 1.295164e-04 0.932466452 #> 5376 53.75 0.06737034 1.294517e-04 0.932500210 #> 5377 53.76 0.06733666 1.293869e-04 0.932533952 #> 5378 53.77 0.06730300 1.293223e-04 0.932567676 #> 5379 53.78 0.06726936 1.292576e-04 0.932601384 #> 5380 53.79 0.06723573 1.291930e-04 0.932635075 #> 5381 53.80 0.06720212 1.291284e-04 0.932668749 #> 5382 53.81 0.06716853 1.290639e-04 0.932702406 #> 5383 53.82 0.06713495 1.289994e-04 0.932736047 #> 5384 53.83 0.06710139 1.289349e-04 0.932769670 #> 5385 53.84 0.06706785 1.288704e-04 0.932803277 #> 5386 53.85 0.06703433 1.288060e-04 0.932836867 #> 5387 53.86 0.06700082 1.287416e-04 0.932870440 #> 5388 53.87 0.06696733 1.286773e-04 0.932903997 #> 5389 53.88 0.06693385 1.286129e-04 0.932937536 #> 5390 53.89 0.06690039 1.285487e-04 0.932971059 #> 5391 53.90 0.06686695 1.284844e-04 0.933004565 #> 5392 53.91 0.06683353 1.284202e-04 0.933038054 #> 5393 53.92 0.06680012 1.283560e-04 0.933071527 #> 5394 53.93 0.06676673 1.282918e-04 0.933104983 #> 5395 53.94 0.06673335 1.282277e-04 0.933138422 #> 5396 53.95 0.06669999 1.281636e-04 0.933171844 #> 5397 53.96 0.06666665 1.280995e-04 0.933205250 #> 5398 53.97 0.06663333 1.280355e-04 0.933238639 #> 5399 53.98 0.06660002 1.279715e-04 0.933272012 #> 5400 53.99 0.06656673 1.279075e-04 0.933305367 #> 5401 54.00 0.06653345 1.278436e-04 0.933338706 #> 5402 54.01 0.06650019 1.277797e-04 0.933372029 #> 5403 54.02 0.06646695 1.277158e-04 0.933405334 #> 5404 54.03 0.06643372 1.276520e-04 0.933438623 #> 5405 54.04 0.06640052 1.275881e-04 0.933471896 #> 5406 54.05 0.06636732 1.275244e-04 0.933505151 #> 5407 54.06 0.06633415 1.274606e-04 0.933538390 #> 5408 54.07 0.06630099 1.273969e-04 0.933571613 #> 5409 54.08 0.06626785 1.273332e-04 0.933604819 #> 5410 54.09 0.06623472 1.272696e-04 0.933638008 #> 5411 54.10 0.06620161 1.272060e-04 0.933671181 #> 5412 54.11 0.06616852 1.271424e-04 0.933704337 #> 5413 54.12 0.06613544 1.270788e-04 0.933737476 #> 5414 54.13 0.06610239 1.270153e-04 0.933770599 #> 5415 54.14 0.06606934 1.269518e-04 0.933803706 #> 5416 54.15 0.06603632 1.268883e-04 0.933836796 #> 5417 54.16 0.06600331 1.268249e-04 0.933869869 #> 5418 54.17 0.06597031 1.267615e-04 0.933902926 #> 5419 54.18 0.06593734 1.266981e-04 0.933935966 #> 5420 54.19 0.06590438 1.266348e-04 0.933968990 #> 5421 54.20 0.06587143 1.265715e-04 0.934001997 #> 5422 54.21 0.06583850 1.265082e-04 0.934034988 #> 5423 54.22 0.06580559 1.264450e-04 0.934067962 #> 5424 54.23 0.06577270 1.263818e-04 0.934100920 #> 5425 54.24 0.06573982 1.263186e-04 0.934133861 #> 5426 54.25 0.06570696 1.262555e-04 0.934166786 #> 5427 54.26 0.06567411 1.261924e-04 0.934199695 #> 5428 54.27 0.06564128 1.261293e-04 0.934232586 #> 5429 54.28 0.06560847 1.260662e-04 0.934265462 #> 5430 54.29 0.06557568 1.260032e-04 0.934298321 #> 5431 54.30 0.06554290 1.259402e-04 0.934331164 #> 5432 54.31 0.06551013 1.258773e-04 0.934363990 #> 5433 54.32 0.06547739 1.258144e-04 0.934396800 #> 5434 54.33 0.06544466 1.257515e-04 0.934429593 #> 5435 54.34 0.06541194 1.256886e-04 0.934462370 #> 5436 54.35 0.06537924 1.256258e-04 0.934495131 #> 5437 54.36 0.06534656 1.255630e-04 0.934527875 #> 5438 54.37 0.06531390 1.255002e-04 0.934560603 #> 5439 54.38 0.06528125 1.254375e-04 0.934593314 #> 5440 54.39 0.06524862 1.253748e-04 0.934626010 #> 5441 54.40 0.06521600 1.253121e-04 0.934658688 #> 5442 54.41 0.06518340 1.252495e-04 0.934691351 #> 5443 54.42 0.06515082 1.251869e-04 0.934723997 #> 5444 54.43 0.06511825 1.251243e-04 0.934756627 #> 5445 54.44 0.06508570 1.250617e-04 0.934789240 #> 5446 54.45 0.06505316 1.249992e-04 0.934821838 #> 5447 54.46 0.06502064 1.249367e-04 0.934854419 #> 5448 54.47 0.06498814 1.248743e-04 0.934886983 #> 5449 54.48 0.06495566 1.248119e-04 0.934919532 #> 5450 54.49 0.06492319 1.247495e-04 0.934952064 #> 5451 54.50 0.06489073 1.246871e-04 0.934984580 #> 5452 54.51 0.06485830 1.246248e-04 0.935017079 #> 5453 54.52 0.06482588 1.245625e-04 0.935049562 #> 5454 54.53 0.06479347 1.245002e-04 0.935082030 #> 5455 54.54 0.06476108 1.244380e-04 0.935114480 #> 5456 54.55 0.06472871 1.243758e-04 0.935146915 #> 5457 54.56 0.06469635 1.243136e-04 0.935179333 #> 5458 54.57 0.06466401 1.242515e-04 0.935211736 #> 5459 54.58 0.06463169 1.241894e-04 0.935244122 #> 5460 54.59 0.06459938 1.241273e-04 0.935276492 #> 5461 54.60 0.06456709 1.240652e-04 0.935308845 #> 5462 54.61 0.06453481 1.240032e-04 0.935341183 #> 5463 54.62 0.06450255 1.239412e-04 0.935373504 #> 5464 54.63 0.06447031 1.238793e-04 0.935405809 #> 5465 54.64 0.06443808 1.238173e-04 0.935438098 #> 5466 54.65 0.06440587 1.237555e-04 0.935470371 #> 5467 54.66 0.06437368 1.236936e-04 0.935502628 #> 5468 54.67 0.06434150 1.236318e-04 0.935534869 #> 5469 54.68 0.06430934 1.235700e-04 0.935567093 #> 5470 54.69 0.06427719 1.235082e-04 0.935599301 #> 5471 54.70 0.06424506 1.234465e-04 0.935631494 #> 5472 54.71 0.06421295 1.233847e-04 0.935663670 #> 5473 54.72 0.06418085 1.233231e-04 0.935695830 #> 5474 54.73 0.06414876 1.232614e-04 0.935727974 #> 5475 54.74 0.06411670 1.231998e-04 0.935760102 #> 5476 54.75 0.06408465 1.231382e-04 0.935792214 #> 5477 54.76 0.06405261 1.230767e-04 0.935824310 #> 5478 54.77 0.06402060 1.230151e-04 0.935856390 #> 5479 54.78 0.06398859 1.229537e-04 0.935888454 #> 5480 54.79 0.06395661 1.228922e-04 0.935920501 #> 5481 54.80 0.06392464 1.228308e-04 0.935952533 #> 5482 54.81 0.06389268 1.227694e-04 0.935984549 #> 5483 54.82 0.06386074 1.227080e-04 0.936016549 #> 5484 54.83 0.06382882 1.226467e-04 0.936048532 #> 5485 54.84 0.06379691 1.225853e-04 0.936080500 #> 5486 54.85 0.06376502 1.225241e-04 0.936112452 #> 5487 54.86 0.06373315 1.224628e-04 0.936144388 #> 5488 54.87 0.06370129 1.224016e-04 0.936176307 #> 5489 54.88 0.06366945 1.223404e-04 0.936208211 #> 5490 54.89 0.06363762 1.222793e-04 0.936240099 #> 5491 54.90 0.06360581 1.222181e-04 0.936271971 #> 5492 54.91 0.06357402 1.221570e-04 0.936303827 #> 5493 54.92 0.06354224 1.220960e-04 0.936335667 #> 5494 54.93 0.06351047 1.220349e-04 0.936367492 #> 5495 54.94 0.06347873 1.219739e-04 0.936399300 #> 5496 54.95 0.06344699 1.219130e-04 0.936431092 #> 5497 54.96 0.06341528 1.218520e-04 0.936462869 #> 5498 54.97 0.06338358 1.217911e-04 0.936494629 #> 5499 54.98 0.06335190 1.217302e-04 0.936526374 #> 5500 54.99 0.06332023 1.216694e-04 0.936558103 #> 5501 55.00 0.06328858 1.216086e-04 0.936589816 #> 5502 55.01 0.06325694 1.215478e-04 0.936621513 #> 5503 55.02 0.06322532 1.214870e-04 0.936653195 #> 5504 55.03 0.06319371 1.214263e-04 0.936684860 #> 5505 55.04 0.06316212 1.213656e-04 0.936716510 #> 5506 55.05 0.06313055 1.213049e-04 0.936748143 #> 5507 55.06 0.06309899 1.212443e-04 0.936779762 #> 5508 55.07 0.06306745 1.211837e-04 0.936811364 #> 5509 55.08 0.06303593 1.211231e-04 0.936842950 #> 5510 55.09 0.06300442 1.210626e-04 0.936874521 #> 5511 55.10 0.06297292 1.210020e-04 0.936906076 #> 5512 55.11 0.06294144 1.209416e-04 0.936937615 #> 5513 55.12 0.06290998 1.208811e-04 0.936969138 #> 5514 55.13 0.06287853 1.208207e-04 0.937000646 #> 5515 55.14 0.06284710 1.207603e-04 0.937032137 #> 5516 55.15 0.06281569 1.206999e-04 0.937063613 #> 5517 55.16 0.06278429 1.206396e-04 0.937095074 #> 5518 55.17 0.06275290 1.205793e-04 0.937126518 #> 5519 55.18 0.06272153 1.205190e-04 0.937157947 #> 5520 55.19 0.06269018 1.204588e-04 0.937189360 #> 5521 55.20 0.06265884 1.203985e-04 0.937220758 #> 5522 55.21 0.06262752 1.203384e-04 0.937252140 #> 5523 55.22 0.06259622 1.202782e-04 0.937283506 #> 5524 55.23 0.06256493 1.202181e-04 0.937314856 #> 5525 55.24 0.06253365 1.201580e-04 0.937346191 #> 5526 55.25 0.06250239 1.200979e-04 0.937377510 #> 5527 55.26 0.06247115 1.200379e-04 0.937408813 #> 5528 55.27 0.06243992 1.199779e-04 0.937440101 #> 5529 55.28 0.06240871 1.199179e-04 0.937471373 #> 5530 55.29 0.06237751 1.198580e-04 0.937502630 #> 5531 55.30 0.06234633 1.197981e-04 0.937533871 #> 5532 55.31 0.06231517 1.197382e-04 0.937565096 #> 5533 55.32 0.06228402 1.196783e-04 0.937596306 #> 5534 55.33 0.06225288 1.196185e-04 0.937627500 #> 5535 55.34 0.06222176 1.195587e-04 0.937658678 #> 5536 55.35 0.06219066 1.194989e-04 0.937689841 #> 5537 55.36 0.06215957 1.194392e-04 0.937720988 #> 5538 55.37 0.06212850 1.193795e-04 0.937752120 #> 5539 55.38 0.06209744 1.193198e-04 0.937783236 #> 5540 55.39 0.06206640 1.192602e-04 0.937814337 #> 5541 55.40 0.06203538 1.192006e-04 0.937845422 #> 5542 55.41 0.06200437 1.191410e-04 0.937876491 #> 5543 55.42 0.06197337 1.190814e-04 0.937907545 #> 5544 55.43 0.06194239 1.190219e-04 0.937938584 #> 5545 55.44 0.06191143 1.189624e-04 0.937969607 #> 5546 55.45 0.06188048 1.189029e-04 0.938000614 #> 5547 55.46 0.06184955 1.188435e-04 0.938031606 #> 5548 55.47 0.06181863 1.187841e-04 0.938062583 #> 5549 55.48 0.06178773 1.187247e-04 0.938093544 #> 5550 55.49 0.06175685 1.186654e-04 0.938124489 #> 5551 55.50 0.06172597 1.186060e-04 0.938155419 #> 5552 55.51 0.06169512 1.185468e-04 0.938186334 #> 5553 55.52 0.06166428 1.184875e-04 0.938217233 #> 5554 55.53 0.06163346 1.184283e-04 0.938248116 #> 5555 55.54 0.06160265 1.183691e-04 0.938278985 #> 5556 55.55 0.06157185 1.183099e-04 0.938309837 #> 5557 55.56 0.06154107 1.182508e-04 0.938340675 #> 5558 55.57 0.06151031 1.181916e-04 0.938371497 #> 5559 55.58 0.06147956 1.181326e-04 0.938402303 #> 5560 55.59 0.06144883 1.180735e-04 0.938433094 #> 5561 55.60 0.06141812 1.180145e-04 0.938463870 #> 5562 55.61 0.06138741 1.179555e-04 0.938494631 #> 5563 55.62 0.06135673 1.178965e-04 0.938525376 #> 5564 55.63 0.06132606 1.178376e-04 0.938556105 #> 5565 55.64 0.06129540 1.177787e-04 0.938586820 #> 5566 55.65 0.06126476 1.177198e-04 0.938617518 #> 5567 55.66 0.06123414 1.176610e-04 0.938648202 #> 5568 55.67 0.06120353 1.176022e-04 0.938678870 #> 5569 55.68 0.06117293 1.175434e-04 0.938709523 #> 5570 55.69 0.06114235 1.174846e-04 0.938740161 #> 5571 55.70 0.06111179 1.174259e-04 0.938770783 #> 5572 55.71 0.06108124 1.173672e-04 0.938801390 #> 5573 55.72 0.06105071 1.173085e-04 0.938831982 #> 5574 55.73 0.06102019 1.172499e-04 0.938862558 #> 5575 55.74 0.06098969 1.171913e-04 0.938893119 #> 5576 55.75 0.06095920 1.171327e-04 0.938923665 #> 5577 55.76 0.06092873 1.170741e-04 0.938954195 #> 5578 55.77 0.06089827 1.170156e-04 0.938984711 #> 5579 55.78 0.06086783 1.169571e-04 0.939015211 #> 5580 55.79 0.06083741 1.168987e-04 0.939045695 #> 5581 55.80 0.06080699 1.168402e-04 0.939076165 #> 5582 55.81 0.06077660 1.167818e-04 0.939106619 #> 5583 55.82 0.06074622 1.167234e-04 0.939137058 #> 5584 55.83 0.06071585 1.166651e-04 0.939167482 #> 5585 55.84 0.06068550 1.166068e-04 0.939197891 #> 5586 55.85 0.06065517 1.165485e-04 0.939228284 #> 5587 55.86 0.06062485 1.164902e-04 0.939258663 #> 5588 55.87 0.06059454 1.164320e-04 0.939289026 #> 5589 55.88 0.06056425 1.163738e-04 0.939319374 #> 5590 55.89 0.06053398 1.163156e-04 0.939349706 #> 5591 55.90 0.06050372 1.162575e-04 0.939380024 #> 5592 55.91 0.06047347 1.161994e-04 0.939410326 #> 5593 55.92 0.06044325 1.161413e-04 0.939440614 #> 5594 55.93 0.06041303 1.160832e-04 0.939470886 #> 5595 55.94 0.06038283 1.160252e-04 0.939501143 #> 5596 55.95 0.06035265 1.159672e-04 0.939531385 #> 5597 55.96 0.06032248 1.159092e-04 0.939561611 #> 5598 55.97 0.06029233 1.158513e-04 0.939591823 #> 5599 55.98 0.06026219 1.157934e-04 0.939622020 #> 5600 55.99 0.06023206 1.157355e-04 0.939652201 #> 5601 56.00 0.06020196 1.156777e-04 0.939682367 #> 5602 56.01 0.06017186 1.156198e-04 0.939712519 #> 5603 56.02 0.06014178 1.155620e-04 0.939742655 #> 5604 56.03 0.06011172 1.155043e-04 0.939772776 #> 5605 56.04 0.06008167 1.154465e-04 0.939802882 #> 5606 56.05 0.06005164 1.153888e-04 0.939832973 #> 5607 56.06 0.06002162 1.153311e-04 0.939863049 #> 5608 56.07 0.05999162 1.152735e-04 0.939893110 #> 5609 56.08 0.05996163 1.152159e-04 0.939923156 #> 5610 56.09 0.05993165 1.151583e-04 0.939953187 #> 5611 56.10 0.05990170 1.151007e-04 0.939983203 #> 5612 56.11 0.05987175 1.150432e-04 0.940013204 #> 5613 56.12 0.05984182 1.149857e-04 0.940043190 #> 5614 56.13 0.05981191 1.149282e-04 0.940073160 #> 5615 56.14 0.05978201 1.148707e-04 0.940103116 #> 5616 56.15 0.05975213 1.148133e-04 0.940133057 #> 5617 56.16 0.05972226 1.147559e-04 0.940162983 #> 5618 56.17 0.05969241 1.146986e-04 0.940192894 #> 5619 56.18 0.05966257 1.146412e-04 0.940222790 #> 5620 56.19 0.05963274 1.145839e-04 0.940252672 #> 5621 56.20 0.05960294 1.145266e-04 0.940282538 #> 5622 56.21 0.05957314 1.144694e-04 0.940312389 #> 5623 56.22 0.05954336 1.144122e-04 0.940342225 #> 5624 56.23 0.05951360 1.143550e-04 0.940372047 #> 5625 56.24 0.05948385 1.142978e-04 0.940401853 #> 5626 56.25 0.05945411 1.142407e-04 0.940431645 #> 5627 56.26 0.05942439 1.141836e-04 0.940461422 #> 5628 56.27 0.05939469 1.141265e-04 0.940491184 #> 5629 56.28 0.05936500 1.140694e-04 0.940520931 #> 5630 56.29 0.05933532 1.140124e-04 0.940550663 #> 5631 56.30 0.05930566 1.139554e-04 0.940580380 #> 5632 56.31 0.05927602 1.138985e-04 0.940610082 #> 5633 56.32 0.05924639 1.138415e-04 0.940639770 #> 5634 56.33 0.05921677 1.137846e-04 0.940669443 #> 5635 56.34 0.05918717 1.137278e-04 0.940699100 #> 5636 56.35 0.05915759 1.136709e-04 0.940728743 #> 5637 56.36 0.05912801 1.136141e-04 0.940758372 #> 5638 56.37 0.05909846 1.135573e-04 0.940787985 #> 5639 56.38 0.05906892 1.135005e-04 0.940817584 #> 5640 56.39 0.05903939 1.134438e-04 0.940847167 #> 5641 56.40 0.05900988 1.133871e-04 0.940876737 #> 5642 56.41 0.05898038 1.133304e-04 0.940906291 #> 5643 56.42 0.05895090 1.132738e-04 0.940935830 #> 5644 56.43 0.05892143 1.132171e-04 0.940965355 #> 5645 56.44 0.05889197 1.131605e-04 0.940994865 #> 5646 56.45 0.05886254 1.131040e-04 0.941024360 #> 5647 56.46 0.05883311 1.130474e-04 0.941053841 #> 5648 56.47 0.05880370 1.129909e-04 0.941083306 #> 5649 56.48 0.05877431 1.129344e-04 0.941112757 #> 5650 56.49 0.05874493 1.128780e-04 0.941142193 #> 5651 56.50 0.05871556 1.128216e-04 0.941171615 #> 5652 56.51 0.05868621 1.127652e-04 0.941201022 #> 5653 56.52 0.05865688 1.127088e-04 0.941230414 #> 5654 56.53 0.05862756 1.126525e-04 0.941259791 #> 5655 56.54 0.05859825 1.125961e-04 0.941289154 #> 5656 56.55 0.05856896 1.125399e-04 0.941318502 #> 5657 56.56 0.05853968 1.124836e-04 0.941347836 #> 5658 56.57 0.05851042 1.124274e-04 0.941377154 #> 5659 56.58 0.05848117 1.123712e-04 0.941406459 #> 5660 56.59 0.05845194 1.123150e-04 0.941435748 #> 5661 56.60 0.05842272 1.122589e-04 0.941465023 #> 5662 56.61 0.05839351 1.122027e-04 0.941494283 #> 5663 56.62 0.05836432 1.121467e-04 0.941523529 #> 5664 56.63 0.05833515 1.120906e-04 0.941552759 #> 5665 56.64 0.05830599 1.120346e-04 0.941581976 #> 5666 56.65 0.05827684 1.119786e-04 0.941611178 #> 5667 56.66 0.05824771 1.119226e-04 0.941640365 #> 5668 56.67 0.05821860 1.118666e-04 0.941669537 #> 5669 56.68 0.05818949 1.118107e-04 0.941698695 #> 5670 56.69 0.05816041 1.117548e-04 0.941727838 #> 5671 56.70 0.05813133 1.116990e-04 0.941756967 #> 5672 56.71 0.05810228 1.116431e-04 0.941786082 #> 5673 56.72 0.05807323 1.115873e-04 0.941815181 #> 5674 56.73 0.05804420 1.115315e-04 0.941844266 #> 5675 56.74 0.05801519 1.114758e-04 0.941873337 #> 5676 56.75 0.05798619 1.114201e-04 0.941902393 #> 5677 56.76 0.05795720 1.113644e-04 0.941931435 #> 5678 56.77 0.05792823 1.113087e-04 0.941960462 #> 5679 56.78 0.05789927 1.112531e-04 0.941989474 #> 5680 56.79 0.05787033 1.111975e-04 0.942018472 #> 5681 56.80 0.05784140 1.111419e-04 0.942047456 #> 5682 56.81 0.05781249 1.110863e-04 0.942076425 #> 5683 56.82 0.05778359 1.110308e-04 0.942105379 #> 5684 56.83 0.05775471 1.109753e-04 0.942134319 #> 5685 56.84 0.05772584 1.109198e-04 0.942163245 #> 5686 56.85 0.05769698 1.108644e-04 0.942192156 #> 5687 56.86 0.05766814 1.108089e-04 0.942221053 #> 5688 56.87 0.05763931 1.107535e-04 0.942249935 #> 5689 56.88 0.05761050 1.106982e-04 0.942278803 #> 5690 56.89 0.05758170 1.106429e-04 0.942307656 #> 5691 56.90 0.05755292 1.105875e-04 0.942336495 #> 5692 56.91 0.05752415 1.105323e-04 0.942365320 #> 5693 56.92 0.05749539 1.104770e-04 0.942394130 #> 5694 56.93 0.05746665 1.104218e-04 0.942422926 #> 5695 56.94 0.05743793 1.103666e-04 0.942451707 #> 5696 56.95 0.05740921 1.103114e-04 0.942480474 #> 5697 56.96 0.05738052 1.102563e-04 0.942509226 #> 5698 56.97 0.05735183 1.102012e-04 0.942537965 #> 5699 56.98 0.05732317 1.101461e-04 0.942566688 #> 5700 56.99 0.05729451 1.100910e-04 0.942595398 #> 5701 57.00 0.05726587 1.100360e-04 0.942624093 #> 5702 57.01 0.05723725 1.099810e-04 0.942652774 #> 5703 57.02 0.05720863 1.099260e-04 0.942681440 #> 5704 57.03 0.05718004 1.098711e-04 0.942710092 #> 5705 57.04 0.05715145 1.098161e-04 0.942738730 #> 5706 57.05 0.05712289 1.097612e-04 0.942767354 #> 5707 57.06 0.05709433 1.097064e-04 0.942795963 #> 5708 57.07 0.05706579 1.096515e-04 0.942824558 #> 5709 57.08 0.05703726 1.095967e-04 0.942853138 #> 5710 57.09 0.05700875 1.095419e-04 0.942881705 #> 5711 57.10 0.05698026 1.094872e-04 0.942910257 #> 5712 57.11 0.05695177 1.094324e-04 0.942938794 #> 5713 57.12 0.05692330 1.093777e-04 0.942967318 #> 5714 57.13 0.05689485 1.093231e-04 0.942995827 #> 5715 57.14 0.05686641 1.092684e-04 0.943024322 #> 5716 57.15 0.05683798 1.092138e-04 0.943052803 #> 5717 57.16 0.05680957 1.091592e-04 0.943081269 #> 5718 57.17 0.05678117 1.091046e-04 0.943109721 #> 5719 57.18 0.05675279 1.090501e-04 0.943138160 #> 5720 57.19 0.05672442 1.089956e-04 0.943166583 #> 5721 57.20 0.05669607 1.089411e-04 0.943194993 #> 5722 57.21 0.05666773 1.088867e-04 0.943223388 #> 5723 57.22 0.05663940 1.088322e-04 0.943251770 #> 5724 57.23 0.05661109 1.087778e-04 0.943280137 #> 5725 57.24 0.05658279 1.087234e-04 0.943308489 #> 5726 57.25 0.05655450 1.086691e-04 0.943336828 #> 5727 57.26 0.05652623 1.086148e-04 0.943365153 #> 5728 57.27 0.05649798 1.085605e-04 0.943393463 #> 5729 57.28 0.05646973 1.085062e-04 0.943421759 #> 5730 57.29 0.05644151 1.084520e-04 0.943450041 #> 5731 57.30 0.05641329 1.083978e-04 0.943478309 #> 5732 57.31 0.05638509 1.083436e-04 0.943506563 #> 5733 57.32 0.05635691 1.082894e-04 0.943534803 #> 5734 57.33 0.05632874 1.082353e-04 0.943563028 #> 5735 57.34 0.05630058 1.081812e-04 0.943591240 #> 5736 57.35 0.05627244 1.081271e-04 0.943619437 #> 5737 57.36 0.05624431 1.080731e-04 0.943647620 #> 5738 57.37 0.05621619 1.080190e-04 0.943675789 #> 5739 57.38 0.05618809 1.079650e-04 0.943703944 #> 5740 57.39 0.05616000 1.079111e-04 0.943732085 #> 5741 57.40 0.05613193 1.078571e-04 0.943760212 #> 5742 57.41 0.05610387 1.078032e-04 0.943788325 #> 5743 57.42 0.05607583 1.077493e-04 0.943816424 #> 5744 57.43 0.05604780 1.076955e-04 0.943844509 #> 5745 57.44 0.05601978 1.076416e-04 0.943872579 #> 5746 57.45 0.05599178 1.075878e-04 0.943900636 #> 5747 57.46 0.05596379 1.075340e-04 0.943928679 #> 5748 57.47 0.05593581 1.074803e-04 0.943956707 #> 5749 57.48 0.05590785 1.074266e-04 0.943984722 #> 5750 57.49 0.05587990 1.073729e-04 0.944012723 #> 5751 57.50 0.05585197 1.073192e-04 0.944040709 #> 5752 57.51 0.05582405 1.072655e-04 0.944068682 #> 5753 57.52 0.05579615 1.072119e-04 0.944096641 #> 5754 57.53 0.05576826 1.071583e-04 0.944124585 #> 5755 57.54 0.05574038 1.071048e-04 0.944152516 #> 5756 57.55 0.05571252 1.070512e-04 0.944180433 #> 5757 57.56 0.05568467 1.069977e-04 0.944208336 #> 5758 57.57 0.05565683 1.069442e-04 0.944236225 #> 5759 57.58 0.05562901 1.068908e-04 0.944264099 #> 5760 57.59 0.05560120 1.068373e-04 0.944291960 #> 5761 57.60 0.05557341 1.067839e-04 0.944319807 #> 5762 57.61 0.05554563 1.067306e-04 0.944347641 #> 5763 57.62 0.05551786 1.066772e-04 0.944375460 #> 5764 57.63 0.05549011 1.066239e-04 0.944403265 #> 5765 57.64 0.05546237 1.065706e-04 0.944431057 #> 5766 57.65 0.05543465 1.065173e-04 0.944458834 #> 5767 57.66 0.05540694 1.064641e-04 0.944486598 #> 5768 57.67 0.05537924 1.064108e-04 0.944514348 #> 5769 57.68 0.05535156 1.063576e-04 0.944542083 #> 5770 57.69 0.05532389 1.063045e-04 0.944569805 #> 5771 57.70 0.05529624 1.062513e-04 0.944597514 #> 5772 57.71 0.05526859 1.061982e-04 0.944625208 #> 5773 57.72 0.05524097 1.061451e-04 0.944652888 #> 5774 57.73 0.05521335 1.060921e-04 0.944680555 #> 5775 57.74 0.05518575 1.060391e-04 0.944708208 #> 5776 57.75 0.05515817 1.059860e-04 0.944735847 #> 5777 57.76 0.05513059 1.059331e-04 0.944763472 #> 5778 57.77 0.05510304 1.058801e-04 0.944791083 #> 5779 57.78 0.05507549 1.058272e-04 0.944818681 #> 5780 57.79 0.05504796 1.057743e-04 0.944846265 #> 5781 57.80 0.05502044 1.057214e-04 0.944873835 #> 5782 57.81 0.05499294 1.056686e-04 0.944901391 #> 5783 57.82 0.05496545 1.056157e-04 0.944928933 #> 5784 57.83 0.05493798 1.055629e-04 0.944956462 #> 5785 57.84 0.05491051 1.055102e-04 0.944983977 #> 5786 57.85 0.05488306 1.054574e-04 0.945011478 #> 5787 57.86 0.05485563 1.054047e-04 0.945038965 #> 5788 57.87 0.05482821 1.053520e-04 0.945066439 #> 5789 57.88 0.05480080 1.052994e-04 0.945093899 #> 5790 57.89 0.05477341 1.052467e-04 0.945121345 #> 5791 57.90 0.05474603 1.051941e-04 0.945148778 #> 5792 57.91 0.05471866 1.051415e-04 0.945176196 #> 5793 57.92 0.05469131 1.050890e-04 0.945203601 #> 5794 57.93 0.05466397 1.050365e-04 0.945230993 #> 5795 57.94 0.05463665 1.049839e-04 0.945258370 #> 5796 57.95 0.05460933 1.049315e-04 0.945285734 #> 5797 57.96 0.05458204 1.048790e-04 0.945313085 #> 5798 57.97 0.05455475 1.048266e-04 0.945340421 #> 5799 57.98 0.05452748 1.047742e-04 0.945367744 #> 5800 57.99 0.05450022 1.047218e-04 0.945395054 #> 5801 58.00 0.05447298 1.046695e-04 0.945422349 #> 5802 58.01 0.05444575 1.046171e-04 0.945449631 #> 5803 58.02 0.05441854 1.045649e-04 0.945476900 #> 5804 58.03 0.05439133 1.045126e-04 0.945504154 #> 5805 58.04 0.05436414 1.044603e-04 0.945531395 #> 5806 58.05 0.05433697 1.044081e-04 0.945558623 #> 5807 58.06 0.05430981 1.043559e-04 0.945585837 #> 5808 58.07 0.05428266 1.043038e-04 0.945613037 #> 5809 58.08 0.05425552 1.042516e-04 0.945640224 #> 5810 58.09 0.05422840 1.041995e-04 0.945667397 #> 5811 58.10 0.05420130 1.041474e-04 0.945694556 #> 5812 58.11 0.05417420 1.040954e-04 0.945721702 #> 5813 58.12 0.05414712 1.040433e-04 0.945748835 #> 5814 58.13 0.05412006 1.039913e-04 0.945775953 #> 5815 58.14 0.05409300 1.039393e-04 0.945803059 #> 5816 58.15 0.05406596 1.038874e-04 0.945830150 #> 5817 58.16 0.05403894 1.038355e-04 0.945857229 #> 5818 58.17 0.05401192 1.037835e-04 0.945884293 #> 5819 58.18 0.05398492 1.037317e-04 0.945911344 #> 5820 58.19 0.05395794 1.036798e-04 0.945938382 #> 5821 58.20 0.05393097 1.036280e-04 0.945965406 #> 5822 58.21 0.05390401 1.035762e-04 0.945992416 #> 5823 58.22 0.05387706 1.035244e-04 0.946019414 #> 5824 58.23 0.05385013 1.034727e-04 0.946046397 #> 5825 58.24 0.05382321 1.034209e-04 0.946073367 #> 5826 58.25 0.05379631 1.033692e-04 0.946100324 #> 5827 58.26 0.05376942 1.033176e-04 0.946127267 #> 5828 58.27 0.05374254 1.032659e-04 0.946154196 #> 5829 58.28 0.05371567 1.032143e-04 0.946181113 #> 5830 58.29 0.05368882 1.031627e-04 0.946208015 #> 5831 58.30 0.05366198 1.031111e-04 0.946234905 #> 5832 58.31 0.05363516 1.030596e-04 0.946261780 #> 5833 58.32 0.05360835 1.030081e-04 0.946288643 #> 5834 58.33 0.05358155 1.029566e-04 0.946315492 #> 5835 58.34 0.05355477 1.029051e-04 0.946342327 #> 5836 58.35 0.05352800 1.028537e-04 0.946369149 #> 5837 58.36 0.05350124 1.028023e-04 0.946395958 #> 5838 58.37 0.05347450 1.027509e-04 0.946422754 #> 5839 58.38 0.05344777 1.026995e-04 0.946449535 #> 5840 58.39 0.05342105 1.026482e-04 0.946476304 #> 5841 58.40 0.05339434 1.025969e-04 0.946503059 #> 5842 58.41 0.05336765 1.025456e-04 0.946529801 #> 5843 58.42 0.05334098 1.024943e-04 0.946556529 #> 5844 58.43 0.05331431 1.024431e-04 0.946583244 #> 5845 58.44 0.05328766 1.023919e-04 0.946609946 #> 5846 58.45 0.05326102 1.023407e-04 0.946636634 #> 5847 58.46 0.05323440 1.022895e-04 0.946663309 #> 5848 58.47 0.05320779 1.022384e-04 0.946689971 #> 5849 58.48 0.05318119 1.021873e-04 0.946716620 #> 5850 58.49 0.05315461 1.021362e-04 0.946743255 #> 5851 58.50 0.05312804 1.020852e-04 0.946769876 #> 5852 58.51 0.05310148 1.020341e-04 0.946796485 #> 5853 58.52 0.05307494 1.019831e-04 0.946823080 #> 5854 58.53 0.05304841 1.019322e-04 0.946849662 #> 5855 58.54 0.05302189 1.018812e-04 0.946876230 #> 5856 58.55 0.05299538 1.018303e-04 0.946902785 #> 5857 58.56 0.05296889 1.017794e-04 0.946929327 #> 5858 58.57 0.05294242 1.017285e-04 0.946955856 #> 5859 58.58 0.05291595 1.016776e-04 0.946982371 #> 5860 58.59 0.05288950 1.016268e-04 0.947008874 #> 5861 58.60 0.05286306 1.015760e-04 0.947035363 #> 5862 58.61 0.05283664 1.015252e-04 0.947061838 #> 5863 58.62 0.05281022 1.014745e-04 0.947088301 #> 5864 58.63 0.05278383 1.014238e-04 0.947114750 #> 5865 58.64 0.05275744 1.013731e-04 0.947141186 #> 5866 58.65 0.05273107 1.013224e-04 0.947167609 #> 5867 58.66 0.05270471 1.012717e-04 0.947194018 #> 5868 58.67 0.05267836 1.012211e-04 0.947220415 #> 5869 58.68 0.05265203 1.011705e-04 0.947246798 #> 5870 58.69 0.05262571 1.011200e-04 0.947273168 #> 5871 58.70 0.05259941 1.010694e-04 0.947299525 #> 5872 58.71 0.05257311 1.010189e-04 0.947325869 #> 5873 58.72 0.05254683 1.009684e-04 0.947352199 #> 5874 58.73 0.05252057 1.009179e-04 0.947378516 #> 5875 58.74 0.05249431 1.008675e-04 0.947404820 #> 5876 58.75 0.05246807 1.008170e-04 0.947431112 #> 5877 58.76 0.05244184 1.007667e-04 0.947457389 #> 5878 58.77 0.05241563 1.007163e-04 0.947483654 #> 5879 58.78 0.05238943 1.006659e-04 0.947509906 #> 5880 58.79 0.05236324 1.006156e-04 0.947536144 #> 5881 58.80 0.05233707 1.005653e-04 0.947562370 #> 5882 58.81 0.05231090 1.005150e-04 0.947588582 #> 5883 58.82 0.05228475 1.004648e-04 0.947614781 #> 5884 58.83 0.05225862 1.004146e-04 0.947640967 #> 5885 58.84 0.05223250 1.003644e-04 0.947667140 #> 5886 58.85 0.05220639 1.003142e-04 0.947693300 #> 5887 58.86 0.05218029 1.002641e-04 0.947719447 #> 5888 58.87 0.05215421 1.002140e-04 0.947745580 #> 5889 58.88 0.05212813 1.001639e-04 0.947771701 #> 5890 58.89 0.05210208 1.001138e-04 0.947797809 #> 5891 58.90 0.05207603 1.000637e-04 0.947823903 #> 5892 58.91 0.05205000 1.000137e-04 0.947849985 #> 5893 58.92 0.05202398 9.996373e-05 0.947876053 #> 5894 58.93 0.05199798 9.991376e-05 0.947902109 #> 5895 58.94 0.05197198 9.986382e-05 0.947928151 #> 5896 58.95 0.05194601 9.981390e-05 0.947954181 #> 5897 58.96 0.05192004 9.976401e-05 0.947980197 #> 5898 58.97 0.05189409 9.971414e-05 0.948006201 #> 5899 58.98 0.05186814 9.966429e-05 0.948032191 #> 5900 58.99 0.05184222 9.961447e-05 0.948058168 #> 5901 59.00 0.05181630 9.956468e-05 0.948084133 #> 5902 59.01 0.05179040 9.951491e-05 0.948110084 #> 5903 59.02 0.05176451 9.946516e-05 0.948136023 #> 5904 59.03 0.05173864 9.941544e-05 0.948161948 #> 5905 59.04 0.05171277 9.936575e-05 0.948187861 #> 5906 59.05 0.05168692 9.931608e-05 0.948213760 #> 5907 59.06 0.05166109 9.926643e-05 0.948239647 #> 5908 59.07 0.05163526 9.921681e-05 0.948265521 #> 5909 59.08 0.05160945 9.916721e-05 0.948291381 #> 5910 59.09 0.05158365 9.911764e-05 0.948317229 #> 5911 59.10 0.05155787 9.906810e-05 0.948343064 #> 5912 59.11 0.05153210 9.901857e-05 0.948368886 #> 5913 59.12 0.05150634 9.896908e-05 0.948394695 #> 5914 59.13 0.05148059 9.891961e-05 0.948420492 #> 5915 59.14 0.05145485 9.887016e-05 0.948446275 #> 5916 59.15 0.05142913 9.882074e-05 0.948472045 #> 5917 59.16 0.05140343 9.877134e-05 0.948497803 #> 5918 59.17 0.05137773 9.872196e-05 0.948523547 #> 5919 59.18 0.05135205 9.867262e-05 0.948549279 #> 5920 59.19 0.05132638 9.862329e-05 0.948574998 #> 5921 59.20 0.05130072 9.857399e-05 0.948600704 #> 5922 59.21 0.05127508 9.852472e-05 0.948626398 #> 5923 59.22 0.05124945 9.847547e-05 0.948652078 #> 5924 59.23 0.05122383 9.842624e-05 0.948677745 #> 5925 59.24 0.05119822 9.837704e-05 0.948703400 #> 5926 59.25 0.05117263 9.832786e-05 0.948729042 #> 5927 59.26 0.05114705 9.827871e-05 0.948754671 #> 5928 59.27 0.05112148 9.822959e-05 0.948780287 #> 5929 59.28 0.05109593 9.818048e-05 0.948805891 #> 5930 59.29 0.05107039 9.813141e-05 0.948831481 #> 5931 59.30 0.05104486 9.808235e-05 0.948857059 #> 5932 59.31 0.05101934 9.803332e-05 0.948882624 #> 5933 59.32 0.05099384 9.798432e-05 0.948908177 #> 5934 59.33 0.05096835 9.793534e-05 0.948933716 #> 5935 59.34 0.05094287 9.788638e-05 0.948959243 #> 5936 59.35 0.05091741 9.783745e-05 0.948984757 #> 5937 59.36 0.05089195 9.778855e-05 0.949010258 #> 5938 59.37 0.05086651 9.773966e-05 0.949035747 #> 5939 59.38 0.05084109 9.769081e-05 0.949061223 #> 5940 59.39 0.05081567 9.764197e-05 0.949086686 #> 5941 59.40 0.05079027 9.759316e-05 0.949112136 #> 5942 59.41 0.05076488 9.754438e-05 0.949137573 #> 5943 59.42 0.05073951 9.749562e-05 0.949162998 #> 5944 59.43 0.05071414 9.744688e-05 0.949188410 #> 5945 59.44 0.05068879 9.739817e-05 0.949213810 #> 5946 59.45 0.05066345 9.734949e-05 0.949239197 #> 5947 59.46 0.05063813 9.730082e-05 0.949264571 #> 5948 59.47 0.05061282 9.725219e-05 0.949289932 #> 5949 59.48 0.05058752 9.720357e-05 0.949315281 #> 5950 59.49 0.05056223 9.715498e-05 0.949340617 #> 5951 59.50 0.05053695 9.710642e-05 0.949365940 #> 5952 59.51 0.05051169 9.705788e-05 0.949391251 #> 5953 59.52 0.05048644 9.700936e-05 0.949416549 #> 5954 59.53 0.05046120 9.696087e-05 0.949441834 #> 5955 59.54 0.05043598 9.691240e-05 0.949467107 #> 5956 59.55 0.05041077 9.686395e-05 0.949492367 #> 5957 59.56 0.05038557 9.681553e-05 0.949517615 #> 5958 59.57 0.05036038 9.676714e-05 0.949542850 #> 5959 59.58 0.05033521 9.671877e-05 0.949568072 #> 5960 59.59 0.05031005 9.667042e-05 0.949593282 #> 5961 59.60 0.05028490 9.662210e-05 0.949618479 #> 5962 59.61 0.05025976 9.657380e-05 0.949643663 #> 5963 59.62 0.05023464 9.652552e-05 0.949668835 #> 5964 59.63 0.05020953 9.647727e-05 0.949693994 #> 5965 59.64 0.05018443 9.642904e-05 0.949719141 #> 5966 59.65 0.05015934 9.638084e-05 0.949744275 #> 5967 59.66 0.05013427 9.633266e-05 0.949769397 #> 5968 59.67 0.05010921 9.628451e-05 0.949794506 #> 5969 59.68 0.05008416 9.623638e-05 0.949819602 #> 5970 59.69 0.05005913 9.618827e-05 0.949844686 #> 5971 59.70 0.05003410 9.614019e-05 0.949869757 #> 5972 59.71 0.05000909 9.609213e-05 0.949894816 #> 5973 59.72 0.04998409 9.604410e-05 0.949919863 #> 5974 59.73 0.04995911 9.599609e-05 0.949944896 #> 5975 59.74 0.04993413 9.594810e-05 0.949969918 #> 5976 59.75 0.04990917 9.590014e-05 0.949994927 #> 5977 59.76 0.04988422 9.585220e-05 0.950019923 #> 5978 59.77 0.04985929 9.580429e-05 0.950044907 #> 5979 59.78 0.04983437 9.575640e-05 0.950069878 #> 5980 59.79 0.04980945 9.570853e-05 0.950094837 #> 5981 59.80 0.04978456 9.566069e-05 0.950119783 #> 5982 59.81 0.04975967 9.561287e-05 0.950144717 #> 5983 59.82 0.04973480 9.556508e-05 0.950169638 #> 5984 59.83 0.04970994 9.551731e-05 0.950194547 #> 5985 59.84 0.04968509 9.546956e-05 0.950219444 #> 5986 59.85 0.04966025 9.542184e-05 0.950244328 #> 5987 59.86 0.04963543 9.537414e-05 0.950269200 #> 5988 59.87 0.04961061 9.532646e-05 0.950294059 #> 5989 59.88 0.04958582 9.527881e-05 0.950318905 #> 5990 59.89 0.04956103 9.523118e-05 0.950343740 #> 5991 59.90 0.04953625 9.518358e-05 0.950368562 #> 5992 59.91 0.04951149 9.513600e-05 0.950393371 #> 5993 59.92 0.04948674 9.508844e-05 0.950418168 #> 5994 59.93 0.04946201 9.504091e-05 0.950442953 #> 5995 59.94 0.04943728 9.499340e-05 0.950467725 #> 5996 59.95 0.04941257 9.494592e-05 0.950492485 #> 5997 59.96 0.04938787 9.489846e-05 0.950517233 #> 5998 59.97 0.04936318 9.485102e-05 0.950541968 #> 5999 59.98 0.04933851 9.480361e-05 0.950566691 #> 6000 59.99 0.04931384 9.475622e-05 0.950591401 #> 6001 60.00 0.04928919 9.470885e-05 0.950616100 #> 6002 60.01 0.04926455 9.466151e-05 0.950640785 #> 6003 60.02 0.04923993 9.461419e-05 0.950665459 #> 6004 60.03 0.04921531 9.456689e-05 0.950690120 #> 6005 60.04 0.04919071 9.451962e-05 0.950714769 #> 6006 60.05 0.04916612 9.447237e-05 0.950739405 #> 6007 60.06 0.04914155 9.442515e-05 0.950764029 #> 6008 60.07 0.04911698 9.437795e-05 0.950788641 #> 6009 60.08 0.04909243 9.433077e-05 0.950813241 #> 6010 60.09 0.04906789 9.428362e-05 0.950837828 #> 6011 60.10 0.04904336 9.423649e-05 0.950862403 #> 6012 60.11 0.04901885 9.418938e-05 0.950886965 #> 6013 60.12 0.04899434 9.414230e-05 0.950911516 #> 6014 60.13 0.04896985 9.409524e-05 0.950936054 #> 6015 60.14 0.04894537 9.404820e-05 0.950960580 #> 6016 60.15 0.04892091 9.400119e-05 0.950985093 #> 6017 60.16 0.04889645 9.395420e-05 0.951009595 #> 6018 60.17 0.04887201 9.390724e-05 0.951034084 #> 6019 60.18 0.04884758 9.386029e-05 0.951058561 #> 6020 60.19 0.04882316 9.381338e-05 0.951083025 #> 6021 60.20 0.04879876 9.376648e-05 0.951107478 #> 6022 60.21 0.04877436 9.371961e-05 0.951131918 #> 6023 60.22 0.04874998 9.367276e-05 0.951156346 #> 6024 60.23 0.04872561 9.362594e-05 0.951180761 #> 6025 60.24 0.04870126 9.357914e-05 0.951205165 #> 6026 60.25 0.04867691 9.353236e-05 0.951229556 #> 6027 60.26 0.04865258 9.348560e-05 0.951253935 #> 6028 60.27 0.04862826 9.343887e-05 0.951278302 #> 6029 60.28 0.04860395 9.339216e-05 0.951302657 #> 6030 60.29 0.04857965 9.334548e-05 0.951327000 #> 6031 60.30 0.04855537 9.329882e-05 0.951351330 #> 6032 60.31 0.04853110 9.325218e-05 0.951375648 #> 6033 60.32 0.04850684 9.320557e-05 0.951399954 #> 6034 60.33 0.04848259 9.315898e-05 0.951424248 #> 6035 60.34 0.04845836 9.311241e-05 0.951448530 #> 6036 60.35 0.04843413 9.306586e-05 0.951472800 #> 6037 60.36 0.04840992 9.301934e-05 0.951497057 #> 6038 60.37 0.04838572 9.297284e-05 0.951521303 #> 6039 60.38 0.04836154 9.292637e-05 0.951545536 #> 6040 60.39 0.04833736 9.287992e-05 0.951569757 #> 6041 60.40 0.04831320 9.283349e-05 0.951593966 #> 6042 60.41 0.04828905 9.278708e-05 0.951618163 #> 6043 60.42 0.04826491 9.274070e-05 0.951642348 #> 6044 60.43 0.04824078 9.269434e-05 0.951666521 #> 6045 60.44 0.04821667 9.264801e-05 0.951690682 #> 6046 60.45 0.04819257 9.260170e-05 0.951714830 #> 6047 60.46 0.04816848 9.255541e-05 0.951738967 #> 6048 60.47 0.04814440 9.250914e-05 0.951763091 #> 6049 60.48 0.04812033 9.246290e-05 0.951787204 #> 6050 60.49 0.04809628 9.241668e-05 0.951811304 #> 6051 60.50 0.04807224 9.237048e-05 0.951835392 #> 6052 60.51 0.04804821 9.232431e-05 0.951859469 #> 6053 60.52 0.04802419 9.227816e-05 0.951883533 #> 6054 60.53 0.04800018 9.223203e-05 0.951907585 #> 6055 60.54 0.04797619 9.218592e-05 0.951931625 #> 6056 60.55 0.04795221 9.213984e-05 0.951955654 #> 6057 60.56 0.04792824 9.209378e-05 0.951979670 #> 6058 60.57 0.04790428 9.204775e-05 0.952003674 #> 6059 60.58 0.04788033 9.200174e-05 0.952027666 #> 6060 60.59 0.04785640 9.195575e-05 0.952051646 #> 6061 60.60 0.04783248 9.190978e-05 0.952075614 #> 6062 60.61 0.04780857 9.186384e-05 0.952099571 #> 6063 60.62 0.04778467 9.181792e-05 0.952123515 #> 6064 60.63 0.04776078 9.177202e-05 0.952147447 #> 6065 60.64 0.04773691 9.172614e-05 0.952171367 #> 6066 60.65 0.04771304 9.168029e-05 0.952195276 #> 6067 60.66 0.04768919 9.163446e-05 0.952219172 #> 6068 60.67 0.04766535 9.158866e-05 0.952243057 #> 6069 60.68 0.04764153 9.154288e-05 0.952266929 #> 6070 60.69 0.04761771 9.149712e-05 0.952290790 #> 6071 60.70 0.04759391 9.145138e-05 0.952314638 #> 6072 60.71 0.04757012 9.140566e-05 0.952338475 #> 6073 60.72 0.04754634 9.135997e-05 0.952362300 #> 6074 60.73 0.04752257 9.131430e-05 0.952386113 #> 6075 60.74 0.04749882 9.126866e-05 0.952409914 #> 6076 60.75 0.04747507 9.122304e-05 0.952433703 #> 6077 60.76 0.04745134 9.117744e-05 0.952457480 #> 6078 60.77 0.04742762 9.113186e-05 0.952481245 #> 6079 60.78 0.04740391 9.108630e-05 0.952504999 #> 6080 60.79 0.04738022 9.104077e-05 0.952528740 #> 6081 60.80 0.04735653 9.099526e-05 0.952552470 #> 6082 60.81 0.04733286 9.094978e-05 0.952576188 #> 6083 60.82 0.04730920 9.090431e-05 0.952599894 #> 6084 60.83 0.04728555 9.085887e-05 0.952623588 #> 6085 60.84 0.04726192 9.081345e-05 0.952647270 #> 6086 60.85 0.04723829 9.076806e-05 0.952670941 #> 6087 60.86 0.04721468 9.072269e-05 0.952694599 #> 6088 60.87 0.04719108 9.067734e-05 0.952718246 #> 6089 60.88 0.04716749 9.063201e-05 0.952741881 #> 6090 60.89 0.04714391 9.058670e-05 0.952765504 #> 6091 60.90 0.04712034 9.054142e-05 0.952789116 #> 6092 60.91 0.04709679 9.049616e-05 0.952812715 #> 6093 60.92 0.04707325 9.045093e-05 0.952836303 #> 6094 60.93 0.04704972 9.040571e-05 0.952859879 #> 6095 60.94 0.04702620 9.036052e-05 0.952883443 #> 6096 60.95 0.04700269 9.031535e-05 0.952906995 #> 6097 60.96 0.04697919 9.027020e-05 0.952930536 #> 6098 60.97 0.04695571 9.022508e-05 0.952954065 #> 6099 60.98 0.04693224 9.017998e-05 0.952977582 #> 6100 60.99 0.04690878 9.013490e-05 0.953001087 #> 6101 61.00 0.04688533 9.008984e-05 0.953024581 #> 6102 61.01 0.04686189 9.004481e-05 0.953048063 #> 6103 61.02 0.04683847 8.999980e-05 0.953071533 #> 6104 61.03 0.04681505 8.995481e-05 0.953094991 #> 6105 61.04 0.04679165 8.990985e-05 0.953118438 #> 6106 61.05 0.04676826 8.986490e-05 0.953141873 #> 6107 61.06 0.04674488 8.981998e-05 0.953165296 #> 6108 61.07 0.04672152 8.977508e-05 0.953188708 #> 6109 61.08 0.04669816 8.973021e-05 0.953212107 #> 6110 61.09 0.04667482 8.968535e-05 0.953235495 #> 6111 61.10 0.04665149 8.964052e-05 0.953258872 #> 6112 61.11 0.04662817 8.959571e-05 0.953282237 #> 6113 61.12 0.04660486 8.955092e-05 0.953305590 #> 6114 61.13 0.04658156 8.950616e-05 0.953328931 #> 6115 61.14 0.04655828 8.946142e-05 0.953352261 #> 6116 61.15 0.04653500 8.941670e-05 0.953375579 #> 6117 61.16 0.04651174 8.937200e-05 0.953398885 #> 6118 61.17 0.04648849 8.932733e-05 0.953422180 #> 6119 61.18 0.04646525 8.928267e-05 0.953445463 #> 6120 61.19 0.04644203 8.923804e-05 0.953468734 #> 6121 61.20 0.04641881 8.919344e-05 0.953491994 #> 6122 61.21 0.04639561 8.914885e-05 0.953515242 #> 6123 61.22 0.04637242 8.910429e-05 0.953538479 #> 6124 61.23 0.04634924 8.905975e-05 0.953561704 #> 6125 61.24 0.04632607 8.901523e-05 0.953584917 #> 6126 61.25 0.04630291 8.897073e-05 0.953608119 #> 6127 61.26 0.04627976 8.892626e-05 0.953631309 #> 6128 61.27 0.04625663 8.888180e-05 0.953654488 #> 6129 61.28 0.04623351 8.883737e-05 0.953677655 #> 6130 61.29 0.04621040 8.879297e-05 0.953700810 #> 6131 61.30 0.04618730 8.874858e-05 0.953723954 #> 6132 61.31 0.04616421 8.870422e-05 0.953747086 #> 6133 61.32 0.04614113 8.865988e-05 0.953770207 #> 6134 61.33 0.04611807 8.861556e-05 0.953793316 #> 6135 61.34 0.04609502 8.857126e-05 0.953816413 #> 6136 61.35 0.04607197 8.852699e-05 0.953839499 #> 6137 61.36 0.04604894 8.848273e-05 0.953862574 #> 6138 61.37 0.04602592 8.843850e-05 0.953885637 #> 6139 61.38 0.04600292 8.839430e-05 0.953908688 #> 6140 61.39 0.04597992 8.835011e-05 0.953931728 #> 6141 61.40 0.04595694 8.830595e-05 0.953954757 #> 6142 61.41 0.04593396 8.826180e-05 0.953977773 #> 6143 61.42 0.04591100 8.821768e-05 0.954000779 #> 6144 61.43 0.04588805 8.817359e-05 0.954023773 #> 6145 61.44 0.04586512 8.812951e-05 0.954046755 #> 6146 61.45 0.04584219 8.808546e-05 0.954069726 #> 6147 61.46 0.04581927 8.804143e-05 0.954092685 #> 6148 61.47 0.04579637 8.799742e-05 0.954115633 #> 6149 61.48 0.04577348 8.795343e-05 0.954138570 #> 6150 61.49 0.04575060 8.790946e-05 0.954161495 #> 6151 61.50 0.04572773 8.786552e-05 0.954184408 #> 6152 61.51 0.04570487 8.782160e-05 0.954207310 #> 6153 61.52 0.04568202 8.777770e-05 0.954230201 #> 6154 61.53 0.04565919 8.773382e-05 0.954253080 #> 6155 61.54 0.04563636 8.768996e-05 0.954275948 #> 6156 61.55 0.04561355 8.764613e-05 0.954298804 #> 6157 61.56 0.04559075 8.760232e-05 0.954321649 #> 6158 61.57 0.04556796 8.755853e-05 0.954344482 #> 6159 61.58 0.04554518 8.751476e-05 0.954367305 #> 6160 61.59 0.04552241 8.747101e-05 0.954390115 #> 6161 61.60 0.04549966 8.742729e-05 0.954412914 #> 6162 61.61 0.04547691 8.738358e-05 0.954435702 #> 6163 61.62 0.04545418 8.733990e-05 0.954458479 #> 6164 61.63 0.04543146 8.729624e-05 0.954481244 #> 6165 61.64 0.04540875 8.725261e-05 0.954503997 #> 6166 61.65 0.04538605 8.720899e-05 0.954526740 #> 6167 61.66 0.04536336 8.716540e-05 0.954549471 #> 6168 61.67 0.04534069 8.712183e-05 0.954572190 #> 6169 61.68 0.04531802 8.707828e-05 0.954594899 #> 6170 61.69 0.04529537 8.703475e-05 0.954617595 #> 6171 61.70 0.04527273 8.699124e-05 0.954640281 #> 6172 61.71 0.04525010 8.694776e-05 0.954662955 #> 6173 61.72 0.04522748 8.690429e-05 0.954685618 #> 6174 61.73 0.04520487 8.686085e-05 0.954708270 #> 6175 61.74 0.04518227 8.681743e-05 0.954730910 #> 6176 61.75 0.04515969 8.677404e-05 0.954753539 #> 6177 61.76 0.04513711 8.673066e-05 0.954776156 #> 6178 61.77 0.04511455 8.668730e-05 0.954798762 #> 6179 61.78 0.04509200 8.664397e-05 0.954821357 #> 6180 61.79 0.04506946 8.660066e-05 0.954843941 #> 6181 61.80 0.04504693 8.655737e-05 0.954866514 #> 6182 61.81 0.04502441 8.651410e-05 0.954889075 #> 6183 61.82 0.04500190 8.647086e-05 0.954911624 #> 6184 61.83 0.04497941 8.642763e-05 0.954934163 #> 6185 61.84 0.04495693 8.638443e-05 0.954956690 #> 6186 61.85 0.04493445 8.634125e-05 0.954979206 #> 6187 61.86 0.04491199 8.629809e-05 0.955001711 #> 6188 61.87 0.04488954 8.625495e-05 0.955024205 #> 6189 61.88 0.04486710 8.621183e-05 0.955046687 #> 6190 61.89 0.04484467 8.616874e-05 0.955069158 #> 6191 61.90 0.04482226 8.612566e-05 0.955091618 #> 6192 61.91 0.04479985 8.608261e-05 0.955114066 #> 6193 61.92 0.04477746 8.603958e-05 0.955136504 #> 6194 61.93 0.04475507 8.599657e-05 0.955158930 #> 6195 61.94 0.04473270 8.595359e-05 0.955181345 #> 6196 61.95 0.04471034 8.591062e-05 0.955203748 #> 6197 61.96 0.04468799 8.586767e-05 0.955226141 #> 6198 61.97 0.04466565 8.582475e-05 0.955248522 #> 6199 61.98 0.04464333 8.578185e-05 0.955270892 #> 6200 61.99 0.04462101 8.573897e-05 0.955293251 #> 6201 62.00 0.04459870 8.569611e-05 0.955315599 #> 6202 62.01 0.04457641 8.565327e-05 0.955337936 #> 6203 62.02 0.04455413 8.561046e-05 0.955360261 #> 6204 62.03 0.04453186 8.556766e-05 0.955382576 #> 6205 62.04 0.04450960 8.552489e-05 0.955404879 #> 6206 62.05 0.04448735 8.548214e-05 0.955427171 #> 6207 62.06 0.04446511 8.543941e-05 0.955449452 #> 6208 62.07 0.04444288 8.539670e-05 0.955471721 #> 6209 62.08 0.04442067 8.535401e-05 0.955493980 #> 6210 62.09 0.04439846 8.531134e-05 0.955516227 #> 6211 62.10 0.04437627 8.526870e-05 0.955538464 #> 6212 62.11 0.04435409 8.522608e-05 0.955560689 #> 6213 62.12 0.04433191 8.518347e-05 0.955582903 #> 6214 62.13 0.04430975 8.514089e-05 0.955605106 #> 6215 62.14 0.04428760 8.509833e-05 0.955627298 #> 6216 62.15 0.04426547 8.505579e-05 0.955649479 #> 6217 62.16 0.04424334 8.501328e-05 0.955671648 #> 6218 62.17 0.04422122 8.497078e-05 0.955693807 #> 6219 62.18 0.04419912 8.492831e-05 0.955715955 #> 6220 62.19 0.04417702 8.488585e-05 0.955738091 #> 6221 62.20 0.04415494 8.484342e-05 0.955760216 #> 6222 62.21 0.04413287 8.480101e-05 0.955782331 #> 6223 62.22 0.04411081 8.475862e-05 0.955804434 #> 6224 62.23 0.04408876 8.471625e-05 0.955826526 #> 6225 62.24 0.04406672 8.467390e-05 0.955848608 #> 6226 62.25 0.04404469 8.463158e-05 0.955870678 #> 6227 62.26 0.04402267 8.458927e-05 0.955892737 #> 6228 62.27 0.04400067 8.454699e-05 0.955914785 #> 6229 62.28 0.04397867 8.450472e-05 0.955936822 #> 6230 62.29 0.04395669 8.446248e-05 0.955958848 #> 6231 62.30 0.04393472 8.442026e-05 0.955980863 #> 6232 62.31 0.04391275 8.437806e-05 0.956002867 #> 6233 62.32 0.04389080 8.433588e-05 0.956024860 #> 6234 62.33 0.04386886 8.429373e-05 0.956046843 #> 6235 62.34 0.04384693 8.425159e-05 0.956068814 #> 6236 62.35 0.04382502 8.420948e-05 0.956090774 #> 6237 62.36 0.04380311 8.416738e-05 0.956112723 #> 6238 62.37 0.04378121 8.412531e-05 0.956134661 #> 6239 62.38 0.04375933 8.408326e-05 0.956156588 #> 6240 62.39 0.04373745 8.404122e-05 0.956178504 #> 6241 62.40 0.04371559 8.399921e-05 0.956200410 #> 6242 62.41 0.04369374 8.395723e-05 0.956222304 #> 6243 62.42 0.04367190 8.391526e-05 0.956244187 #> 6244 62.43 0.04365007 8.387331e-05 0.956266060 #> 6245 62.44 0.04362825 8.383138e-05 0.956287921 #> 6246 62.45 0.04360644 8.378948e-05 0.956309772 #> 6247 62.46 0.04358464 8.374759e-05 0.956331612 #> 6248 62.47 0.04356285 8.370573e-05 0.956353440 #> 6249 62.48 0.04354108 8.366389e-05 0.956375258 #> 6250 62.49 0.04351931 8.362207e-05 0.956397065 #> 6251 62.50 0.04349756 8.358027e-05 0.956418861 #> 6252 62.51 0.04347582 8.353849e-05 0.956440646 #> 6253 62.52 0.04345408 8.349673e-05 0.956462420 #> 6254 62.53 0.04343236 8.345499e-05 0.956484184 #> 6255 62.54 0.04341065 8.341327e-05 0.956505936 #> 6256 62.55 0.04338895 8.337158e-05 0.956527678 #> 6257 62.56 0.04336726 8.332990e-05 0.956549409 #> 6258 62.57 0.04334558 8.328825e-05 0.956571128 #> 6259 62.58 0.04332392 8.324661e-05 0.956592837 #> 6260 62.59 0.04330226 8.320500e-05 0.956614536 #> 6261 62.60 0.04328061 8.316341e-05 0.956636223 #> 6262 62.61 0.04325898 8.312184e-05 0.956657899 #> 6263 62.62 0.04323735 8.308029e-05 0.956679565 #> 6264 62.63 0.04321574 8.303876e-05 0.956701220 #> 6265 62.64 0.04319414 8.299725e-05 0.956722864 #> 6266 62.65 0.04317255 8.295576e-05 0.956744497 #> 6267 62.66 0.04315097 8.291429e-05 0.956766119 #> 6268 62.67 0.04312940 8.287284e-05 0.956787731 #> 6269 62.68 0.04310784 8.283142e-05 0.956809332 #> 6270 62.69 0.04308629 8.279001e-05 0.956830922 #> 6271 62.70 0.04306475 8.274863e-05 0.956852501 #> 6272 62.71 0.04304322 8.270726e-05 0.956874069 #> 6273 62.72 0.04302171 8.266592e-05 0.956895627 #> 6274 62.73 0.04300020 8.262460e-05 0.956917173 #> 6275 62.74 0.04297871 8.258330e-05 0.956938709 #> 6276 62.75 0.04295722 8.254202e-05 0.956960235 #> 6277 62.76 0.04293575 8.250075e-05 0.956981749 #> 6278 62.77 0.04291429 8.245951e-05 0.957003253 #> 6279 62.78 0.04289284 8.241830e-05 0.957024746 #> 6280 62.79 0.04287139 8.237710e-05 0.957046228 #> 6281 62.80 0.04284996 8.233592e-05 0.957067700 #> 6282 62.81 0.04282854 8.229476e-05 0.957089161 #> 6283 62.82 0.04280714 8.225362e-05 0.957110611 #> 6284 62.83 0.04278574 8.221251e-05 0.957132050 #> 6285 62.84 0.04276435 8.217141e-05 0.957153479 #> 6286 62.85 0.04274297 8.213034e-05 0.957174896 #> 6287 62.86 0.04272161 8.208928e-05 0.957196304 #> 6288 62.87 0.04270025 8.204825e-05 0.957217700 #> 6289 62.88 0.04267891 8.200723e-05 0.957239086 #> 6290 62.89 0.04265757 8.196624e-05 0.957260461 #> 6291 62.90 0.04263625 8.192527e-05 0.957281825 #> 6292 62.91 0.04261494 8.188431e-05 0.957303179 #> 6293 62.92 0.04259363 8.184338e-05 0.957324522 #> 6294 62.93 0.04257234 8.180247e-05 0.957345855 #> 6295 62.94 0.04255106 8.176158e-05 0.957367176 #> 6296 62.95 0.04252979 8.172071e-05 0.957388488 #> 6297 62.96 0.04250853 8.167986e-05 0.957409788 #> 6298 62.97 0.04248728 8.163903e-05 0.957431078 #> 6299 62.98 0.04246604 8.159822e-05 0.957452357 #> 6300 62.99 0.04244482 8.155743e-05 0.957473625 #> 6301 63.00 0.04242360 8.151666e-05 0.957494883 #> 6302 63.01 0.04240239 8.147591e-05 0.957516131 #> 6303 63.02 0.04238120 8.143519e-05 0.957537367 #> 6304 63.03 0.04236001 8.139448e-05 0.957558593 #> 6305 63.04 0.04233884 8.135379e-05 0.957579809 #> 6306 63.05 0.04231767 8.131313e-05 0.957601013 #> 6307 63.06 0.04229652 8.127248e-05 0.957622208 #> 6308 63.07 0.04227538 8.123185e-05 0.957643391 #> 6309 63.08 0.04225424 8.119125e-05 0.957664564 #> 6310 63.09 0.04223312 8.115066e-05 0.957685727 #> 6311 63.10 0.04221201 8.111010e-05 0.957706878 #> 6312 63.11 0.04219091 8.106955e-05 0.957728020 #> 6313 63.12 0.04216982 8.102903e-05 0.957749150 #> 6314 63.13 0.04214874 8.098852e-05 0.957770271 #> 6315 63.14 0.04212767 8.094804e-05 0.957791380 #> 6316 63.15 0.04210661 8.090757e-05 0.957812479 #> 6317 63.16 0.04208557 8.086713e-05 0.957833568 #> 6318 63.17 0.04206453 8.082671e-05 0.957854646 #> 6319 63.18 0.04204350 8.078630e-05 0.957875713 #> 6320 63.19 0.04202248 8.074592e-05 0.957896770 #> 6321 63.20 0.04200148 8.070556e-05 0.957917816 #> 6322 63.21 0.04198048 8.066522e-05 0.957938852 #> 6323 63.22 0.04195950 8.062489e-05 0.957959877 #> 6324 63.23 0.04193852 8.058459e-05 0.957980892 #> 6325 63.24 0.04191756 8.054431e-05 0.958001897 #> 6326 63.25 0.04189661 8.050405e-05 0.958022890 #> 6327 63.26 0.04187566 8.046380e-05 0.958043874 #> 6328 63.27 0.04185473 8.042358e-05 0.958064846 #> 6329 63.28 0.04183381 8.038338e-05 0.958085809 #> 6330 63.29 0.04181290 8.034320e-05 0.958106761 #> 6331 63.30 0.04179199 8.030304e-05 0.958127702 #> 6332 63.31 0.04177110 8.026290e-05 0.958148633 #> 6333 63.32 0.04175022 8.022277e-05 0.958169553 #> 6334 63.33 0.04172935 8.018267e-05 0.958190463 #> 6335 63.34 0.04170849 8.014259e-05 0.958211363 #> 6336 63.35 0.04168765 8.010253e-05 0.958232252 #> 6337 63.36 0.04166681 8.006249e-05 0.958253131 #> 6338 63.37 0.04164598 8.002247e-05 0.958273999 #> 6339 63.38 0.04162516 7.998247e-05 0.958294857 #> 6340 63.39 0.04160435 7.994249e-05 0.958315704 #> 6341 63.40 0.04158356 7.990252e-05 0.958336541 #> 6342 63.41 0.04156277 7.986258e-05 0.958357368 #> 6343 63.42 0.04154199 7.982266e-05 0.958378184 #> 6344 63.43 0.04152123 7.978276e-05 0.958398989 #> 6345 63.44 0.04150047 7.974288e-05 0.958419785 #> 6346 63.45 0.04147973 7.970302e-05 0.958440570 #> 6347 63.46 0.04145899 7.966318e-05 0.958461344 #> 6348 63.47 0.04143827 7.962335e-05 0.958482108 #> 6349 63.48 0.04141755 7.958355e-05 0.958502862 #> 6350 63.49 0.04139685 7.954377e-05 0.958523605 #> 6351 63.50 0.04137616 7.950401e-05 0.958544338 #> 6352 63.51 0.04135547 7.946427e-05 0.958565061 #> 6353 63.52 0.04133480 7.942454e-05 0.958585773 #> 6354 63.53 0.04131414 7.938484e-05 0.958606475 #> 6355 63.54 0.04129349 7.934516e-05 0.958627167 #> 6356 63.55 0.04127285 7.930550e-05 0.958647848 #> 6357 63.56 0.04125222 7.926585e-05 0.958668519 #> 6358 63.57 0.04123159 7.922623e-05 0.958689180 #> 6359 63.58 0.04121098 7.918663e-05 0.958709830 #> 6360 63.59 0.04119038 7.914704e-05 0.958730470 #> 6361 63.60 0.04116979 7.910748e-05 0.958751099 #> 6362 63.61 0.04114921 7.906794e-05 0.958771719 #> 6363 63.62 0.04112864 7.902841e-05 0.958792328 #> 6364 63.63 0.04110808 7.898891e-05 0.958812926 #> 6365 63.64 0.04108754 7.894942e-05 0.958833515 #> 6366 63.65 0.04106700 7.890996e-05 0.958854093 #> 6367 63.66 0.04104647 7.887051e-05 0.958874661 #> 6368 63.67 0.04102595 7.883109e-05 0.958895218 #> 6369 63.68 0.04100544 7.879168e-05 0.958915765 #> 6370 63.69 0.04098495 7.875230e-05 0.958936302 #> 6371 63.70 0.04096446 7.871293e-05 0.958956829 #> 6372 63.71 0.04094398 7.867358e-05 0.958977346 #> 6373 63.72 0.04092351 7.863426e-05 0.958997852 #> 6374 63.73 0.04090306 7.859495e-05 0.959018348 #> 6375 63.74 0.04088261 7.855566e-05 0.959038833 #> 6376 63.75 0.04086217 7.851639e-05 0.959059309 #> 6377 63.76 0.04084175 7.847715e-05 0.959079774 #> 6378 63.77 0.04082133 7.843792e-05 0.959100229 #> 6379 63.78 0.04080093 7.839871e-05 0.959120674 #> 6380 63.79 0.04078053 7.835952e-05 0.959141108 #> 6381 63.80 0.04076015 7.832035e-05 0.959161533 #> 6382 63.81 0.04073977 7.828120e-05 0.959181947 #> 6383 63.82 0.04071941 7.824207e-05 0.959202351 #> 6384 63.83 0.04069905 7.820296e-05 0.959222745 #> 6385 63.84 0.04067871 7.816386e-05 0.959243128 #> 6386 63.85 0.04065837 7.812479e-05 0.959263501 #> 6387 63.86 0.04063805 7.808574e-05 0.959283865 #> 6388 63.87 0.04061774 7.804671e-05 0.959304218 #> 6389 63.88 0.04059743 7.800769e-05 0.959324560 #> 6390 63.89 0.04057714 7.796870e-05 0.959344893 #> 6391 63.90 0.04055685 7.792972e-05 0.959365216 #> 6392 63.91 0.04053658 7.789077e-05 0.959385528 #> 6393 63.92 0.04051632 7.785183e-05 0.959405830 #> 6394 63.93 0.04049607 7.781292e-05 0.959426122 #> 6395 63.94 0.04047582 7.777402e-05 0.959446404 #> 6396 63.95 0.04045559 7.773514e-05 0.959466676 #> 6397 63.96 0.04043537 7.769628e-05 0.959486937 #> 6398 63.97 0.04041515 7.765745e-05 0.959507189 #> 6399 63.98 0.04039495 7.761863e-05 0.959527430 #> 6400 63.99 0.04037476 7.757983e-05 0.959547661 #> 6401 64.00 0.04035458 7.754105e-05 0.959567882 #> 6402 64.01 0.04033440 7.750229e-05 0.959588093 #> 6403 64.02 0.04031424 7.746355e-05 0.959608294 #> 6404 64.03 0.04029409 7.742482e-05 0.959628485 #> 6405 64.04 0.04027395 7.738612e-05 0.959648666 #> 6406 64.05 0.04025382 7.734744e-05 0.959668836 #> 6407 64.06 0.04023369 7.730877e-05 0.959688997 #> 6408 64.07 0.04021358 7.727013e-05 0.959709147 #> 6409 64.08 0.04019348 7.723150e-05 0.959729288 #> 6410 64.09 0.04017339 7.719290e-05 0.959749418 #> 6411 64.10 0.04015331 7.715431e-05 0.959769538 #> 6412 64.11 0.04013324 7.711574e-05 0.959789649 #> 6413 64.12 0.04011317 7.707719e-05 0.959809749 #> 6414 64.13 0.04009312 7.703867e-05 0.959829839 #> 6415 64.14 0.04007308 7.700016e-05 0.959849919 #> 6416 64.15 0.04005305 7.696167e-05 0.959869989 #> 6417 64.16 0.04003303 7.692319e-05 0.959890049 #> 6418 64.17 0.04001302 7.688474e-05 0.959910099 #> 6419 64.18 0.03999301 7.684631e-05 0.959930139 #> 6420 64.19 0.03997302 7.680790e-05 0.959950169 #> 6421 64.20 0.03995304 7.676950e-05 0.959970189 #> 6422 64.21 0.03993307 7.673113e-05 0.959990199 #> 6423 64.22 0.03991311 7.669277e-05 0.960010198 #> 6424 64.23 0.03989316 7.665443e-05 0.960030188 #> 6425 64.24 0.03987322 7.661612e-05 0.960050168 #> 6426 64.25 0.03985328 7.657782e-05 0.960070138 #> 6427 64.26 0.03983336 7.653954e-05 0.960090098 #> 6428 64.27 0.03981345 7.650128e-05 0.960110048 #> 6429 64.28 0.03979355 7.646304e-05 0.960129988 #> 6430 64.29 0.03977366 7.642481e-05 0.960149918 #> 6431 64.30 0.03975378 7.638661e-05 0.960169838 #> 6432 64.31 0.03973390 7.634843e-05 0.960189748 #> 6433 64.32 0.03971404 7.631026e-05 0.960209648 #> 6434 64.33 0.03969419 7.627212e-05 0.960229539 #> 6435 64.34 0.03967435 7.623399e-05 0.960249419 #> 6436 64.35 0.03965451 7.619588e-05 0.960269289 #> 6437 64.36 0.03963469 7.615780e-05 0.960289150 #> 6438 64.37 0.03961488 7.611973e-05 0.960309000 #> 6439 64.38 0.03959508 7.608168e-05 0.960328841 #> 6440 64.39 0.03957529 7.604364e-05 0.960348671 #> 6441 64.40 0.03955550 7.600563e-05 0.960368492 #> 6442 64.41 0.03953573 7.596764e-05 0.960388303 #> 6443 64.42 0.03951597 7.592966e-05 0.960408104 #> 6444 64.43 0.03949621 7.589171e-05 0.960427895 #> 6445 64.44 0.03947647 7.585377e-05 0.960447676 #> 6446 64.45 0.03945674 7.581586e-05 0.960467447 #> 6447 64.46 0.03943701 7.577796e-05 0.960487208 #> 6448 64.47 0.03941730 7.574008e-05 0.960506960 #> 6449 64.48 0.03939760 7.570222e-05 0.960526701 #> 6450 64.49 0.03937790 7.566437e-05 0.960546433 #> 6451 64.50 0.03935822 7.562655e-05 0.960566155 #> 6452 64.51 0.03933854 7.558875e-05 0.960585867 #> 6453 64.52 0.03931888 7.555096e-05 0.960605569 #> 6454 64.53 0.03929923 7.551320e-05 0.960625261 #> 6455 64.54 0.03927958 7.547545e-05 0.960644944 #> 6456 64.55 0.03925995 7.543772e-05 0.960664616 #> 6457 64.56 0.03924032 7.540001e-05 0.960684279 #> 6458 64.57 0.03922071 7.536232e-05 0.960703932 #> 6459 64.58 0.03920110 7.532465e-05 0.960723575 #> 6460 64.59 0.03918150 7.528700e-05 0.960743209 #> 6461 64.60 0.03916192 7.524936e-05 0.960762832 #> 6462 64.61 0.03914234 7.521175e-05 0.960782446 #> 6463 64.62 0.03912278 7.517415e-05 0.960802050 #> 6464 64.63 0.03910322 7.513657e-05 0.960821644 #> 6465 64.64 0.03908367 7.509901e-05 0.960841228 #> 6466 64.65 0.03906414 7.506147e-05 0.960860802 #> 6467 64.66 0.03904461 7.502395e-05 0.960880367 #> 6468 64.67 0.03902509 7.498645e-05 0.960899922 #> 6469 64.68 0.03900558 7.494897e-05 0.960919467 #> 6470 64.69 0.03898609 7.491150e-05 0.960939003 #> 6471 64.70 0.03896660 7.487406e-05 0.960958528 #> 6472 64.71 0.03894712 7.483663e-05 0.960978044 #> 6473 64.72 0.03892765 7.479922e-05 0.960997550 #> 6474 64.73 0.03890819 7.476183e-05 0.961017047 #> 6475 64.74 0.03888874 7.472446e-05 0.961036533 #> 6476 64.75 0.03886930 7.468710e-05 0.961056010 #> 6477 64.76 0.03884987 7.464977e-05 0.961075477 #> 6478 64.77 0.03883045 7.461245e-05 0.961094935 #> 6479 64.78 0.03881104 7.457516e-05 0.961114382 #> 6480 64.79 0.03879164 7.453788e-05 0.961133820 #> 6481 64.80 0.03877225 7.450062e-05 0.961153248 #> 6482 64.81 0.03875287 7.446338e-05 0.961172667 #> 6483 64.82 0.03873350 7.442616e-05 0.961192076 #> 6484 64.83 0.03871414 7.438895e-05 0.961211475 #> 6485 64.84 0.03869478 7.435177e-05 0.961230864 #> 6486 64.85 0.03867544 7.431460e-05 0.961250244 #> 6487 64.86 0.03865611 7.427745e-05 0.961269614 #> 6488 64.87 0.03863679 7.424032e-05 0.961288974 #> 6489 64.88 0.03861747 7.420321e-05 0.961308325 #> 6490 64.89 0.03859817 7.416612e-05 0.961327666 #> 6491 64.90 0.03857887 7.412905e-05 0.961346997 #> 6492 64.91 0.03855959 7.409199e-05 0.961366319 #> 6493 64.92 0.03854031 7.405495e-05 0.961385631 #> 6494 64.93 0.03852105 7.401794e-05 0.961404933 #> 6495 64.94 0.03850179 7.398094e-05 0.961424226 #> 6496 64.95 0.03848255 7.394395e-05 0.961443509 #> 6497 64.96 0.03846331 7.390699e-05 0.961462783 #> 6498 64.97 0.03844408 7.387005e-05 0.961482046 #> 6499 64.98 0.03842487 7.383312e-05 0.961501301 #> 6500 64.99 0.03840566 7.379621e-05 0.961520545 #> 6501 65.00 0.03838646 7.375933e-05 0.961539780 #> 6502 65.01 0.03836727 7.372246e-05 0.961559005 #> 6503 65.02 0.03834809 7.368560e-05 0.961578221 #> 6504 65.03 0.03832892 7.364877e-05 0.961597427 #> 6505 65.04 0.03830976 7.361195e-05 0.961616624 #> 6506 65.05 0.03829061 7.357516e-05 0.961635811 #> 6507 65.06 0.03827147 7.353838e-05 0.961654988 #> 6508 65.07 0.03825234 7.350162e-05 0.961674156 #> 6509 65.08 0.03823322 7.346488e-05 0.961693314 #> 6510 65.09 0.03821411 7.342815e-05 0.961712462 #> 6511 65.10 0.03819501 7.339145e-05 0.961731601 #> 6512 65.11 0.03817591 7.335476e-05 0.961750731 #> 6513 65.12 0.03815683 7.331809e-05 0.961769851 #> 6514 65.13 0.03813776 7.328145e-05 0.961788961 #> 6515 65.14 0.03811869 7.324481e-05 0.961808062 #> 6516 65.15 0.03809964 7.320820e-05 0.961827153 #> 6517 65.16 0.03808059 7.317161e-05 0.961846234 #> 6518 65.17 0.03806156 7.313503e-05 0.961865307 #> 6519 65.18 0.03804253 7.309847e-05 0.961884369 #> 6520 65.19 0.03802352 7.306193e-05 0.961903422 #> 6521 65.20 0.03800451 7.302541e-05 0.961922466 #> 6522 65.21 0.03798551 7.298890e-05 0.961941500 #> 6523 65.22 0.03796652 7.295242e-05 0.961960524 #> 6524 65.23 0.03794754 7.291595e-05 0.961979539 #> 6525 65.24 0.03792858 7.287950e-05 0.961998545 #> 6526 65.25 0.03790962 7.284307e-05 0.962017541 #> 6527 65.26 0.03789067 7.280666e-05 0.962036527 #> 6528 65.27 0.03787173 7.277027e-05 0.962055504 #> 6529 65.28 0.03785279 7.273389e-05 0.962074472 #> 6530 65.29 0.03783387 7.269753e-05 0.962093430 #> 6531 65.30 0.03781496 7.266119e-05 0.962112378 #> 6532 65.31 0.03779606 7.262487e-05 0.962131317 #> 6533 65.32 0.03777716 7.258857e-05 0.962150247 #> 6534 65.33 0.03775828 7.255228e-05 0.962169167 #> 6535 65.34 0.03773941 7.251602e-05 0.962188078 #> 6536 65.35 0.03772054 7.247977e-05 0.962206979 #> 6537 65.36 0.03770169 7.244354e-05 0.962225871 #> 6538 65.37 0.03768284 7.240732e-05 0.962244753 #> 6539 65.38 0.03766400 7.237113e-05 0.962263626 #> 6540 65.39 0.03764518 7.233495e-05 0.962282489 #> 6541 65.40 0.03762636 7.229879e-05 0.962301343 #> 6542 65.41 0.03760755 7.226265e-05 0.962320188 #> 6543 65.42 0.03758875 7.222653e-05 0.962339023 #> 6544 65.43 0.03756996 7.219043e-05 0.962357849 #> 6545 65.44 0.03755118 7.215434e-05 0.962376665 #> 6546 65.45 0.03753241 7.211827e-05 0.962395472 #> 6547 65.46 0.03751365 7.208222e-05 0.962414270 #> 6548 65.47 0.03749490 7.204619e-05 0.962433058 #> 6549 65.48 0.03747615 7.201018e-05 0.962451837 #> 6550 65.49 0.03745742 7.197418e-05 0.962470606 #> 6551 65.50 0.03743870 7.193820e-05 0.962489366 #> 6552 65.51 0.03741998 7.190224e-05 0.962508117 #> 6553 65.52 0.03740128 7.186630e-05 0.962526858 #> 6554 65.53 0.03738258 7.183038e-05 0.962545590 #> 6555 65.54 0.03736389 7.179447e-05 0.962564313 #> 6556 65.55 0.03734522 7.175858e-05 0.962583026 #> 6557 65.56 0.03732655 7.172271e-05 0.962601730 #> 6558 65.57 0.03730789 7.168686e-05 0.962620424 #> 6559 65.58 0.03728924 7.165102e-05 0.962639109 #> 6560 65.59 0.03727060 7.161521e-05 0.962657785 #> 6561 65.60 0.03725197 7.157941e-05 0.962676451 #> 6562 65.61 0.03723335 7.154363e-05 0.962695109 #> 6563 65.62 0.03721474 7.150786e-05 0.962713756 #> 6564 65.63 0.03719613 7.147212e-05 0.962732395 #> 6565 65.64 0.03717754 7.143639e-05 0.962751024 #> 6566 65.65 0.03715896 7.140068e-05 0.962769644 #> 6567 65.66 0.03714038 7.136499e-05 0.962788254 #> 6568 65.67 0.03712182 7.132932e-05 0.962806855 #> 6569 65.68 0.03710326 7.129366e-05 0.962825447 #> 6570 65.69 0.03708471 7.125802e-05 0.962844030 #> 6571 65.70 0.03706617 7.122240e-05 0.962862603 #> 6572 65.71 0.03704765 7.118680e-05 0.962881167 #> 6573 65.72 0.03702913 7.115122e-05 0.962899722 #> 6574 65.73 0.03701062 7.111565e-05 0.962918268 #> 6575 65.74 0.03699212 7.108010e-05 0.962936804 #> 6576 65.75 0.03697362 7.104457e-05 0.962955331 #> 6577 65.76 0.03695514 7.100906e-05 0.962973849 #> 6578 65.77 0.03693667 7.097356e-05 0.962992357 #> 6579 65.78 0.03691821 7.093808e-05 0.963010856 #> 6580 65.79 0.03689975 7.090262e-05 0.963029346 #> 6581 65.80 0.03688131 7.086718e-05 0.963047827 #> 6582 65.81 0.03686287 7.083176e-05 0.963066298 #> 6583 65.82 0.03684444 7.079635e-05 0.963084761 #> 6584 65.83 0.03682603 7.076096e-05 0.963103214 #> 6585 65.84 0.03680762 7.072559e-05 0.963121657 #> 6586 65.85 0.03678922 7.069023e-05 0.963140092 #> 6587 65.86 0.03677083 7.065490e-05 0.963158517 #> 6588 65.87 0.03675245 7.061958e-05 0.963176933 #> 6589 65.88 0.03673408 7.058428e-05 0.963195340 #> 6590 65.89 0.03671571 7.054900e-05 0.963213738 #> 6591 65.90 0.03669736 7.051373e-05 0.963232127 #> 6592 65.91 0.03667902 7.047848e-05 0.963250506 #> 6593 65.92 0.03666068 7.044325e-05 0.963268876 #> 6594 65.93 0.03664235 7.040804e-05 0.963287237 #> 6595 65.94 0.03662404 7.037284e-05 0.963305589 #> 6596 65.95 0.03660573 7.033767e-05 0.963323932 #> 6597 65.96 0.03658743 7.030251e-05 0.963342265 #> 6598 65.97 0.03656914 7.026736e-05 0.963360589 #> 6599 65.98 0.03655086 7.023224e-05 0.963378904 #> 6600 65.99 0.03653259 7.019713e-05 0.963397210 #> 6601 66.00 0.03651433 7.016204e-05 0.963415507 #> 6602 66.01 0.03649608 7.012697e-05 0.963433795 #> 6603 66.02 0.03647783 7.009191e-05 0.963452073 #> 6604 66.03 0.03645960 7.005688e-05 0.963470343 #> 6605 66.04 0.03644138 7.002186e-05 0.963488603 #> 6606 66.05 0.03642316 6.998685e-05 0.963506854 #> 6607 66.06 0.03640495 6.995187e-05 0.963525096 #> 6608 66.07 0.03638675 6.991690e-05 0.963543329 #> 6609 66.08 0.03636857 6.988195e-05 0.963561553 #> 6610 66.09 0.03635039 6.984702e-05 0.963579768 #> 6611 66.10 0.03633221 6.981211e-05 0.963597973 #> 6612 66.11 0.03631405 6.977721e-05 0.963616170 #> 6613 66.12 0.03629590 6.974233e-05 0.963634357 #> 6614 66.13 0.03627776 6.970747e-05 0.963652535 #> 6615 66.14 0.03625962 6.967262e-05 0.963670704 #> 6616 66.15 0.03624150 6.963779e-05 0.963688865 #> 6617 66.16 0.03622338 6.960298e-05 0.963707016 #> 6618 66.17 0.03620527 6.956819e-05 0.963725158 #> 6619 66.18 0.03618718 6.953342e-05 0.963743290 #> 6620 66.19 0.03616909 6.949866e-05 0.963761414 #> 6621 66.20 0.03615101 6.946392e-05 0.963779529 #> 6622 66.21 0.03613294 6.942919e-05 0.963797635 #> 6623 66.22 0.03611487 6.939449e-05 0.963815731 #> 6624 66.23 0.03609682 6.935980e-05 0.963833819 #> 6625 66.24 0.03607878 6.932513e-05 0.963851898 #> 6626 66.25 0.03606074 6.929047e-05 0.963869967 #> 6627 66.26 0.03604272 6.925584e-05 0.963888028 #> 6628 66.27 0.03602470 6.922122e-05 0.963906079 #> 6629 66.28 0.03600669 6.918662e-05 0.963924122 #> 6630 66.29 0.03598869 6.915203e-05 0.963942155 #> 6631 66.30 0.03597070 6.911746e-05 0.963960179 #> 6632 66.31 0.03595272 6.908291e-05 0.963978195 #> 6633 66.32 0.03593475 6.904838e-05 0.963996201 #> 6634 66.33 0.03591679 6.901387e-05 0.964014199 #> 6635 66.34 0.03589883 6.897937e-05 0.964032187 #> 6636 66.35 0.03588089 6.894489e-05 0.964050166 #> 6637 66.36 0.03586295 6.891042e-05 0.964068137 #> 6638 66.37 0.03584503 6.887598e-05 0.964086098 #> 6639 66.38 0.03582711 6.884155e-05 0.964104051 #> 6640 66.39 0.03580920 6.880713e-05 0.964121994 #> 6641 66.40 0.03579130 6.877274e-05 0.964139929 #> 6642 66.41 0.03577341 6.873836e-05 0.964157854 #> 6643 66.42 0.03575553 6.870400e-05 0.964175771 #> 6644 66.43 0.03573765 6.866966e-05 0.964193679 #> 6645 66.44 0.03571979 6.863533e-05 0.964211577 #> 6646 66.45 0.03570193 6.860102e-05 0.964229467 #> 6647 66.46 0.03568409 6.856673e-05 0.964247348 #> 6648 66.47 0.03566625 6.853246e-05 0.964265220 #> 6649 66.48 0.03564842 6.849820e-05 0.964283083 #> 6650 66.49 0.03563060 6.846396e-05 0.964300937 #> 6651 66.50 0.03561279 6.842973e-05 0.964318782 #> 6652 66.51 0.03559499 6.839553e-05 0.964336618 #> 6653 66.52 0.03557719 6.836134e-05 0.964354445 #> 6654 66.53 0.03555941 6.832717e-05 0.964372263 #> 6655 66.54 0.03554163 6.829301e-05 0.964390073 #> 6656 66.55 0.03552387 6.825887e-05 0.964407873 #> 6657 66.56 0.03550611 6.822475e-05 0.964425665 #> 6658 66.57 0.03548836 6.819065e-05 0.964443448 #> 6659 66.58 0.03547062 6.815656e-05 0.964461221 #> 6660 66.59 0.03545289 6.812249e-05 0.964478986 #> 6661 66.60 0.03543517 6.808844e-05 0.964496742 #> 6662 66.61 0.03541746 6.805440e-05 0.964514490 #> 6663 66.62 0.03539975 6.802038e-05 0.964532228 #> 6664 66.63 0.03538206 6.798638e-05 0.964549957 #> 6665 66.64 0.03536437 6.795240e-05 0.964567678 #> 6666 66.65 0.03534669 6.791843e-05 0.964585390 #> 6667 66.66 0.03532902 6.788448e-05 0.964603093 #> 6668 66.67 0.03531136 6.785055e-05 0.964620787 #> 6669 66.68 0.03529371 6.781663e-05 0.964638472 #> 6670 66.69 0.03527607 6.778273e-05 0.964656148 #> 6671 66.70 0.03525844 6.774885e-05 0.964673816 #> 6672 66.71 0.03524081 6.771498e-05 0.964691474 #> 6673 66.72 0.03522319 6.768113e-05 0.964709124 #> 6674 66.73 0.03520559 6.764730e-05 0.964726765 #> 6675 66.74 0.03518799 6.761348e-05 0.964744397 #> 6676 66.75 0.03517040 6.757969e-05 0.964762021 #> 6677 66.76 0.03515282 6.754590e-05 0.964779635 #> 6678 66.77 0.03513525 6.751214e-05 0.964797241 #> 6679 66.78 0.03511768 6.747839e-05 0.964814838 #> 6680 66.79 0.03510013 6.744466e-05 0.964832426 #> 6681 66.80 0.03508258 6.741095e-05 0.964850006 #> 6682 66.81 0.03506505 6.737725e-05 0.964867576 #> 6683 66.82 0.03504752 6.734357e-05 0.964885138 #> 6684 66.83 0.03503000 6.730991e-05 0.964902691 #> 6685 66.84 0.03501249 6.727626e-05 0.964920235 #> 6686 66.85 0.03499499 6.724263e-05 0.964937771 #> 6687 66.86 0.03497749 6.720902e-05 0.964955298 #> 6688 66.87 0.03496001 6.717542e-05 0.964972816 #> 6689 66.88 0.03494253 6.714184e-05 0.964990325 #> 6690 66.89 0.03492507 6.710828e-05 0.965007825 #> 6691 66.90 0.03490761 6.707473e-05 0.965025317 #> 6692 66.91 0.03489016 6.704121e-05 0.965042800 #> 6693 66.92 0.03487272 6.700769e-05 0.965060274 #> 6694 66.93 0.03485529 6.697420e-05 0.965077740 #> 6695 66.94 0.03483786 6.694072e-05 0.965095197 #> 6696 66.95 0.03482045 6.690726e-05 0.965112645 #> 6697 66.96 0.03480304 6.687381e-05 0.965130084 #> 6698 66.97 0.03478565 6.684038e-05 0.965147515 #> 6699 66.98 0.03476826 6.680697e-05 0.965164936 #> 6700 66.99 0.03475088 6.677358e-05 0.965182350 #> 6701 67.00 0.03473351 6.674020e-05 0.965199754 #> 6702 67.01 0.03471614 6.670684e-05 0.965217150 #> 6703 67.02 0.03469879 6.667349e-05 0.965234537 #> 6704 67.03 0.03468144 6.664016e-05 0.965251915 #> 6705 67.04 0.03466411 6.660685e-05 0.965269285 #> 6706 67.05 0.03464678 6.657356e-05 0.965286646 #> 6707 67.06 0.03462946 6.654028e-05 0.965303998 #> 6708 67.07 0.03461215 6.650702e-05 0.965321342 #> 6709 67.08 0.03459485 6.647377e-05 0.965338677 #> 6710 67.09 0.03457756 6.644054e-05 0.965356003 #> 6711 67.10 0.03456027 6.640733e-05 0.965373321 #> 6712 67.11 0.03454300 6.637413e-05 0.965390630 #> 6713 67.12 0.03452573 6.634096e-05 0.965407930 #> 6714 67.13 0.03450847 6.630779e-05 0.965425222 #> 6715 67.14 0.03449122 6.627465e-05 0.965442505 #> 6716 67.15 0.03447398 6.624152e-05 0.965459780 #> 6717 67.16 0.03445675 6.620841e-05 0.965477045 #> 6718 67.17 0.03443952 6.617531e-05 0.965494303 #> 6719 67.18 0.03442231 6.614223e-05 0.965511551 #> 6720 67.19 0.03440510 6.610917e-05 0.965528791 #> 6721 67.20 0.03438790 6.607612e-05 0.965546022 #> 6722 67.21 0.03437071 6.604309e-05 0.965563245 #> 6723 67.22 0.03435353 6.601008e-05 0.965580459 #> 6724 67.23 0.03433636 6.597708e-05 0.965597665 #> 6725 67.24 0.03431919 6.594410e-05 0.965614861 #> 6726 67.25 0.03430204 6.591114e-05 0.965632050 #> 6727 67.26 0.03428489 6.587819e-05 0.965649229 #> 6728 67.27 0.03426775 6.584526e-05 0.965666400 #> 6729 67.28 0.03425062 6.581235e-05 0.965683563 #> 6730 67.29 0.03423350 6.577945e-05 0.965700717 #> 6731 67.30 0.03421639 6.574657e-05 0.965717862 #> 6732 67.31 0.03419929 6.571370e-05 0.965734999 #> 6733 67.32 0.03418219 6.568085e-05 0.965752127 #> 6734 67.33 0.03416511 6.564802e-05 0.965769247 #> 6735 67.34 0.03414803 6.561520e-05 0.965786358 #> 6736 67.35 0.03413096 6.558240e-05 0.965803461 #> 6737 67.36 0.03411390 6.554962e-05 0.965820555 #> 6738 67.37 0.03409684 6.551685e-05 0.965837640 #> 6739 67.38 0.03407980 6.548410e-05 0.965854717 #> 6740 67.39 0.03406276 6.545137e-05 0.965871785 #> 6741 67.40 0.03404574 6.541865e-05 0.965888845 #> 6742 67.41 0.03402872 6.538595e-05 0.965905896 #> 6743 67.42 0.03401171 6.535327e-05 0.965922939 #> 6744 67.43 0.03399471 6.532060e-05 0.965939973 #> 6745 67.44 0.03397771 6.528795e-05 0.965956999 #> 6746 67.45 0.03396073 6.525531e-05 0.965974016 #> 6747 67.46 0.03394375 6.522269e-05 0.965991025 #> 6748 67.47 0.03392678 6.519009e-05 0.966008025 #> 6749 67.48 0.03390983 6.515750e-05 0.966025017 #> 6750 67.49 0.03389287 6.512493e-05 0.966042000 #> 6751 67.50 0.03387593 6.509238e-05 0.966058975 #> 6752 67.51 0.03385900 6.505984e-05 0.966075941 #> 6753 67.52 0.03384207 6.502732e-05 0.966092899 #> 6754 67.53 0.03382516 6.499481e-05 0.966109849 #> 6755 67.54 0.03380825 6.496232e-05 0.966126789 #> 6756 67.55 0.03379135 6.492985e-05 0.966143722 #> 6757 67.56 0.03377446 6.489739e-05 0.966160646 #> 6758 67.57 0.03375757 6.486495e-05 0.966177561 #> 6759 67.58 0.03374070 6.483253e-05 0.966194468 #> 6760 67.59 0.03372383 6.480012e-05 0.966211367 #> 6761 67.60 0.03370698 6.476773e-05 0.966228257 #> 6762 67.61 0.03369013 6.473535e-05 0.966245138 #> 6763 67.62 0.03367329 6.470299e-05 0.966262012 #> 6764 67.63 0.03365645 6.467065e-05 0.966278876 #> 6765 67.64 0.03363963 6.463832e-05 0.966295733 #> 6766 67.65 0.03362281 6.460601e-05 0.966312581 #> 6767 67.66 0.03360601 6.457371e-05 0.966329420 #> 6768 67.67 0.03358921 6.454144e-05 0.966346251 #> 6769 67.68 0.03357242 6.450917e-05 0.966363074 #> 6770 67.69 0.03355563 6.447693e-05 0.966379888 #> 6771 67.70 0.03353886 6.444470e-05 0.966396694 #> 6772 67.71 0.03352210 6.441248e-05 0.966413492 #> 6773 67.72 0.03350534 6.438028e-05 0.966430281 #> 6774 67.73 0.03348859 6.434810e-05 0.966447061 #> 6775 67.74 0.03347185 6.431594e-05 0.966463834 #> 6776 67.75 0.03345512 6.428379e-05 0.966480597 #> 6777 67.76 0.03343840 6.425165e-05 0.966497353 #> 6778 67.77 0.03342168 6.421953e-05 0.966514100 #> 6779 67.78 0.03340497 6.418743e-05 0.966530839 #> 6780 67.79 0.03338828 6.415535e-05 0.966547569 #> 6781 67.80 0.03337159 6.412328e-05 0.966564291 #> 6782 67.81 0.03335490 6.409122e-05 0.966581005 #> 6783 67.82 0.03333823 6.405919e-05 0.966597710 #> 6784 67.83 0.03332157 6.402716e-05 0.966614407 #> 6785 67.84 0.03330491 6.399516e-05 0.966631096 #> 6786 67.85 0.03328826 6.396317e-05 0.966647776 #> 6787 67.86 0.03327162 6.393120e-05 0.966664448 #> 6788 67.87 0.03325499 6.389924e-05 0.966681112 #> 6789 67.88 0.03323837 6.386730e-05 0.966697767 #> 6790 67.89 0.03322175 6.383537e-05 0.966714414 #> 6791 67.90 0.03320514 6.380346e-05 0.966731053 #> 6792 67.91 0.03318855 6.377157e-05 0.966747683 #> 6793 67.92 0.03317196 6.373969e-05 0.966764305 #> 6794 67.93 0.03315537 6.370783e-05 0.966780919 #> 6795 67.94 0.03313880 6.367598e-05 0.966797524 #> 6796 67.95 0.03312223 6.364415e-05 0.966814121 #> 6797 67.96 0.03310568 6.361234e-05 0.966830710 #> 6798 67.97 0.03308913 6.358054e-05 0.966847290 #> 6799 67.98 0.03307259 6.354876e-05 0.966863863 #> 6800 67.99 0.03305606 6.351699e-05 0.966880426 #> 6801 68.00 0.03303953 6.348524e-05 0.966896982 #> 6802 68.01 0.03302302 6.345351e-05 0.966913530 #> 6803 68.02 0.03300651 6.342179e-05 0.966930069 #> 6804 68.03 0.03299001 6.339008e-05 0.966946599 #> 6805 68.04 0.03297352 6.335840e-05 0.966963122 #> 6806 68.05 0.03295704 6.332672e-05 0.966979636 #> 6807 68.06 0.03294056 6.329507e-05 0.966996142 #> 6808 68.07 0.03292410 6.326343e-05 0.967012640 #> 6809 68.08 0.03290764 6.323181e-05 0.967029130 #> 6810 68.09 0.03289119 6.320020e-05 0.967045611 #> 6811 68.10 0.03287475 6.316861e-05 0.967062084 #> 6812 68.11 0.03285831 6.313703e-05 0.967078549 #> 6813 68.12 0.03284189 6.310547e-05 0.967095006 #> 6814 68.13 0.03282547 6.307392e-05 0.967111454 #> 6815 68.14 0.03280906 6.304239e-05 0.967127894 #> 6816 68.15 0.03279266 6.301088e-05 0.967144326 #> 6817 68.16 0.03277627 6.297938e-05 0.967160750 #> 6818 68.17 0.03275989 6.294790e-05 0.967177165 #> 6819 68.18 0.03274351 6.291644e-05 0.967193573 #> 6820 68.19 0.03272714 6.288499e-05 0.967209972 #> 6821 68.20 0.03271078 6.285355e-05 0.967226363 #> 6822 68.21 0.03269443 6.282213e-05 0.967242745 #> 6823 68.22 0.03267809 6.279073e-05 0.967259120 #> 6824 68.23 0.03266175 6.275934e-05 0.967275486 #> 6825 68.24 0.03264543 6.272797e-05 0.967291844 #> 6826 68.25 0.03262911 6.269661e-05 0.967308194 #> 6827 68.26 0.03261280 6.266527e-05 0.967324536 #> 6828 68.27 0.03259650 6.263395e-05 0.967340870 #> 6829 68.28 0.03258020 6.260264e-05 0.967357195 #> 6830 68.29 0.03256392 6.257135e-05 0.967373513 #> 6831 68.30 0.03254764 6.254007e-05 0.967389822 #> 6832 68.31 0.03253137 6.250881e-05 0.967406123 #> 6833 68.32 0.03251511 6.247756e-05 0.967422416 #> 6834 68.33 0.03249885 6.244633e-05 0.967438700 #> 6835 68.34 0.03248261 6.241511e-05 0.967454977 #> 6836 68.35 0.03246637 6.238391e-05 0.967471246 #> 6837 68.36 0.03245014 6.235273e-05 0.967487506 #> 6838 68.37 0.03243392 6.232156e-05 0.967503758 #> 6839 68.38 0.03241771 6.229041e-05 0.967520002 #> 6840 68.39 0.03240150 6.225927e-05 0.967536238 #> 6841 68.40 0.03238531 6.222815e-05 0.967552466 #> 6842 68.41 0.03236912 6.219704e-05 0.967568686 #> 6843 68.42 0.03235294 6.216595e-05 0.967584897 #> 6844 68.43 0.03233676 6.213488e-05 0.967601101 #> 6845 68.44 0.03232060 6.210382e-05 0.967617296 #> 6846 68.45 0.03230444 6.207277e-05 0.967633483 #> 6847 68.46 0.03228830 6.204174e-05 0.967649663 #> 6848 68.47 0.03227216 6.201073e-05 0.967665834 #> 6849 68.48 0.03225602 6.197973e-05 0.967681997 #> 6850 68.49 0.03223990 6.194875e-05 0.967698152 #> 6851 68.50 0.03222378 6.191778e-05 0.967714299 #> 6852 68.51 0.03220768 6.188683e-05 0.967730437 #> 6853 68.52 0.03219158 6.185590e-05 0.967746568 #> 6854 68.53 0.03217548 6.182498e-05 0.967762691 #> 6855 68.54 0.03215940 6.179407e-05 0.967778805 #> 6856 68.55 0.03214332 6.176318e-05 0.967794912 #> 6857 68.56 0.03212726 6.173231e-05 0.967811011 #> 6858 68.57 0.03211120 6.170145e-05 0.967827101 #> 6859 68.58 0.03209515 6.167061e-05 0.967843183 #> 6860 68.59 0.03207910 6.163978e-05 0.967859258 #> 6861 68.60 0.03206307 6.160897e-05 0.967875324 #> 6862 68.61 0.03204704 6.157817e-05 0.967891383 #> 6863 68.62 0.03203102 6.154739e-05 0.967907433 #> 6864 68.63 0.03201501 6.151662e-05 0.967923475 #> 6865 68.64 0.03199900 6.148587e-05 0.967939509 #> 6866 68.65 0.03198301 6.145514e-05 0.967955536 #> 6867 68.66 0.03196702 6.142442e-05 0.967971554 #> 6868 68.67 0.03195104 6.139371e-05 0.967987564 #> 6869 68.68 0.03193507 6.136302e-05 0.968003566 #> 6870 68.69 0.03191911 6.133235e-05 0.968019560 #> 6871 68.70 0.03190315 6.130169e-05 0.968035547 #> 6872 68.71 0.03188720 6.127105e-05 0.968051525 #> 6873 68.72 0.03187126 6.124042e-05 0.968067495 #> 6874 68.73 0.03185533 6.120981e-05 0.968083457 #> 6875 68.74 0.03183941 6.117921e-05 0.968099412 #> 6876 68.75 0.03182349 6.114863e-05 0.968115358 #> 6877 68.76 0.03180759 6.111806e-05 0.968131296 #> 6878 68.77 0.03179169 6.108751e-05 0.968147227 #> 6879 68.78 0.03177579 6.105697e-05 0.968163149 #> 6880 68.79 0.03175991 6.102645e-05 0.968179064 #> 6881 68.80 0.03174403 6.099595e-05 0.968194970 #> 6882 68.81 0.03172817 6.096546e-05 0.968210869 #> 6883 68.82 0.03171231 6.093498e-05 0.968226759 #> 6884 68.83 0.03169645 6.090452e-05 0.968242642 #> 6885 68.84 0.03168061 6.087408e-05 0.968258517 #> 6886 68.85 0.03166477 6.084365e-05 0.968274383 #> 6887 68.86 0.03164894 6.081323e-05 0.968290242 #> 6888 68.87 0.03163312 6.078284e-05 0.968306093 #> 6889 68.88 0.03161731 6.075245e-05 0.968321936 #> 6890 68.89 0.03160151 6.072208e-05 0.968337771 #> 6891 68.90 0.03158571 6.069173e-05 0.968353598 #> 6892 68.91 0.03156992 6.066139e-05 0.968369418 #> 6893 68.92 0.03155414 6.063107e-05 0.968385229 #> 6894 68.93 0.03153837 6.060076e-05 0.968401032 #> 6895 68.94 0.03152260 6.057047e-05 0.968416828 #> 6896 68.95 0.03150684 6.054019e-05 0.968432616 #> 6897 68.96 0.03149109 6.050993e-05 0.968448395 #> 6898 68.97 0.03147535 6.047968e-05 0.968464167 #> 6899 68.98 0.03145962 6.044945e-05 0.968479931 #> 6900 68.99 0.03144389 6.041923e-05 0.968495687 #> 6901 69.00 0.03142818 6.038903e-05 0.968511435 #> 6902 69.01 0.03141247 6.035884e-05 0.968527176 #> 6903 69.02 0.03139676 6.032867e-05 0.968542908 #> 6904 69.03 0.03138107 6.029851e-05 0.968558633 #> 6905 69.04 0.03136538 6.026837e-05 0.968574350 #> 6906 69.05 0.03134970 6.023824e-05 0.968590059 #> 6907 69.06 0.03133403 6.020813e-05 0.968605760 #> 6908 69.07 0.03131837 6.017804e-05 0.968621453 #> 6909 69.08 0.03130271 6.014795e-05 0.968637138 #> 6910 69.09 0.03128707 6.011789e-05 0.968652816 #> 6911 69.10 0.03127143 6.008784e-05 0.968668485 #> 6912 69.11 0.03125580 6.005780e-05 0.968684147 #> 6913 69.12 0.03124017 6.002778e-05 0.968699801 #> 6914 69.13 0.03122455 5.999777e-05 0.968715447 #> 6915 69.14 0.03120895 5.996778e-05 0.968731086 #> 6916 69.15 0.03119335 5.993780e-05 0.968746716 #> 6917 69.16 0.03117775 5.990784e-05 0.968762339 #> 6918 69.17 0.03116217 5.987790e-05 0.968777954 #> 6919 69.18 0.03114659 5.984797e-05 0.968793561 #> 6920 69.19 0.03113102 5.981805e-05 0.968809160 #> 6921 69.20 0.03111546 5.978815e-05 0.968824752 #> 6922 69.21 0.03109991 5.975826e-05 0.968840336 #> 6923 69.22 0.03108436 5.972839e-05 0.968855912 #> 6924 69.23 0.03106882 5.969853e-05 0.968871480 #> 6925 69.24 0.03105329 5.966869e-05 0.968887040 #> 6926 69.25 0.03103777 5.963886e-05 0.968902593 #> 6927 69.26 0.03102225 5.960905e-05 0.968918138 #> 6928 69.27 0.03100675 5.957925e-05 0.968933675 #> 6929 69.28 0.03099125 5.954947e-05 0.968949204 #> 6930 69.29 0.03097575 5.951970e-05 0.968964725 #> 6931 69.30 0.03096027 5.948995e-05 0.968980239 #> 6932 69.31 0.03094479 5.946021e-05 0.968995745 #> 6933 69.32 0.03092933 5.943049e-05 0.969011243 #> 6934 69.33 0.03091387 5.940078e-05 0.969026734 #> 6935 69.34 0.03089841 5.937109e-05 0.969042217 #> 6936 69.35 0.03088297 5.934141e-05 0.969057692 #> 6937 69.36 0.03086753 5.931175e-05 0.969073159 #> 6938 69.37 0.03085210 5.928210e-05 0.969088619 #> 6939 69.38 0.03083668 5.925247e-05 0.969104070 #> 6940 69.39 0.03082126 5.922285e-05 0.969119514 #> 6941 69.40 0.03080586 5.919325e-05 0.969134951 #> 6942 69.41 0.03079046 5.916366e-05 0.969150379 #> 6943 69.42 0.03077507 5.913408e-05 0.969165800 #> 6944 69.43 0.03075968 5.910452e-05 0.969181214 #> 6945 69.44 0.03074431 5.907498e-05 0.969196619 #> 6946 69.45 0.03072894 5.904545e-05 0.969212017 #> 6947 69.46 0.03071358 5.901593e-05 0.969227407 #> 6948 69.47 0.03069822 5.898643e-05 0.969242790 #> 6949 69.48 0.03068288 5.895694e-05 0.969258164 #> 6950 69.49 0.03066754 5.892747e-05 0.969273532 #> 6951 69.50 0.03065221 5.889802e-05 0.969288891 #> 6952 69.51 0.03063689 5.886858e-05 0.969304243 #> 6953 69.52 0.03062157 5.883915e-05 0.969319587 #> 6954 69.53 0.03060627 5.880974e-05 0.969334923 #> 6955 69.54 0.03059097 5.878034e-05 0.969350252 #> 6956 69.55 0.03057568 5.875096e-05 0.969365573 #> 6957 69.56 0.03056039 5.872159e-05 0.969380886 #> 6958 69.57 0.03054512 5.869223e-05 0.969396192 #> 6959 69.58 0.03052985 5.866290e-05 0.969411490 #> 6960 69.59 0.03051459 5.863357e-05 0.969426780 #> 6961 69.60 0.03049933 5.860426e-05 0.969442063 #> 6962 69.61 0.03048409 5.857497e-05 0.969457338 #> 6963 69.62 0.03046885 5.854569e-05 0.969472606 #> 6964 69.63 0.03045362 5.851642e-05 0.969487866 #> 6965 69.64 0.03043839 5.848717e-05 0.969503118 #> 6966 69.65 0.03042318 5.845793e-05 0.969518363 #> 6967 69.66 0.03040797 5.842871e-05 0.969533600 #> 6968 69.67 0.03039277 5.839951e-05 0.969548829 #> 6969 69.68 0.03037758 5.837031e-05 0.969564051 #> 6970 69.69 0.03036239 5.834114e-05 0.969579265 #> 6971 69.70 0.03034722 5.831197e-05 0.969594472 #> 6972 69.71 0.03033205 5.828282e-05 0.969609671 #> 6973 69.72 0.03031688 5.825369e-05 0.969624862 #> 6974 69.73 0.03030173 5.822457e-05 0.969640046 #> 6975 69.74 0.03028658 5.819546e-05 0.969655222 #> 6976 69.75 0.03027144 5.816637e-05 0.969670390 #> 6977 69.76 0.03025631 5.813730e-05 0.969685551 #> 6978 69.77 0.03024119 5.810824e-05 0.969700705 #> 6979 69.78 0.03022607 5.807919e-05 0.969715851 #> 6980 69.79 0.03021096 5.805016e-05 0.969730989 #> 6981 69.80 0.03019586 5.802114e-05 0.969746120 #> 6982 69.81 0.03018076 5.799214e-05 0.969761243 #> 6983 69.82 0.03016568 5.796315e-05 0.969776359 #> 6984 69.83 0.03015060 5.793417e-05 0.969791467 #> 6985 69.84 0.03013553 5.790521e-05 0.969806567 #> 6986 69.85 0.03012046 5.787627e-05 0.969821660 #> 6987 69.86 0.03010541 5.784734e-05 0.969836745 #> 6988 69.87 0.03009036 5.781842e-05 0.969851823 #> 6989 69.88 0.03007532 5.778952e-05 0.969866894 #> 6990 69.89 0.03006028 5.776063e-05 0.969881956 #> 6991 69.90 0.03004526 5.773176e-05 0.969897012 #> 6992 69.91 0.03003024 5.770290e-05 0.969912059 #> 6993 69.92 0.03001523 5.767406e-05 0.969927100 #> 6994 69.93 0.03000022 5.764523e-05 0.969942132 #> 6995 69.94 0.02998523 5.761641e-05 0.969957157 #> 6996 69.95 0.02997024 5.758761e-05 0.969972175 #> 6997 69.96 0.02995526 5.755882e-05 0.969987185 #> 6998 69.97 0.02994028 5.753005e-05 0.970002188 #> 6999 69.98 0.02992532 5.750129e-05 0.970017183 #> 7000 69.99 0.02991036 5.747255e-05 0.970032171 #> 7001 70.00 0.02989541 5.744382e-05 0.970047151 #> 7002 70.01 0.02988046 5.741511e-05 0.970062124 #> 7003 70.02 0.02986552 5.738641e-05 0.970077089 #> 7004 70.03 0.02985060 5.735772e-05 0.970092047 #> 7005 70.04 0.02983567 5.732905e-05 0.970106997 #> 7006 70.05 0.02982076 5.730039e-05 0.970121940 #> 7007 70.06 0.02980585 5.727175e-05 0.970136875 #> 7008 70.07 0.02979095 5.724312e-05 0.970151803 #> 7009 70.08 0.02977606 5.721450e-05 0.970166723 #> 7010 70.09 0.02976118 5.718590e-05 0.970181636 #> 7011 70.10 0.02974630 5.715732e-05 0.970196541 #> 7012 70.11 0.02973143 5.712875e-05 0.970211439 #> 7013 70.12 0.02971657 5.710019e-05 0.970226330 #> 7014 70.13 0.02970172 5.707165e-05 0.970241213 #> 7015 70.14 0.02968687 5.704312e-05 0.970256089 #> 7016 70.15 0.02967203 5.701460e-05 0.970270957 #> 7017 70.16 0.02965720 5.698610e-05 0.970285818 #> 7018 70.17 0.02964237 5.695762e-05 0.970300671 #> 7019 70.18 0.02962755 5.692915e-05 0.970315517 #> 7020 70.19 0.02961274 5.690069e-05 0.970330356 #> 7021 70.20 0.02959794 5.687224e-05 0.970345187 #> 7022 70.21 0.02958315 5.684382e-05 0.970360010 #> 7023 70.22 0.02956836 5.681540e-05 0.970374827 #> 7024 70.23 0.02955358 5.678700e-05 0.970389636 #> 7025 70.24 0.02953880 5.675861e-05 0.970404437 #> 7026 70.25 0.02952404 5.673024e-05 0.970419231 #> 7027 70.26 0.02950928 5.670188e-05 0.970434018 #> 7028 70.27 0.02949453 5.667354e-05 0.970448797 #> 7029 70.28 0.02947979 5.664521e-05 0.970463569 #> 7030 70.29 0.02946505 5.661689e-05 0.970478334 #> 7031 70.30 0.02945032 5.658859e-05 0.970493091 #> 7032 70.31 0.02943560 5.656031e-05 0.970507841 #> 7033 70.32 0.02942089 5.653203e-05 0.970522583 #> 7034 70.33 0.02940618 5.650377e-05 0.970537318 #> 7035 70.34 0.02939148 5.647553e-05 0.970552046 #> 7036 70.35 0.02937679 5.644730e-05 0.970566766 #> 7037 70.36 0.02936210 5.641908e-05 0.970581479 #> 7038 70.37 0.02934742 5.639088e-05 0.970596184 #> 7039 70.38 0.02933275 5.636269e-05 0.970610883 #> 7040 70.39 0.02931809 5.633452e-05 0.970625574 #> 7041 70.40 0.02930344 5.630636e-05 0.970640257 #> 7042 70.41 0.02928879 5.627821e-05 0.970654933 #> 7043 70.42 0.02927415 5.625008e-05 0.970669602 #> 7044 70.43 0.02925951 5.622196e-05 0.970684264 #> 7045 70.44 0.02924489 5.619386e-05 0.970698918 #> 7046 70.45 0.02923027 5.616577e-05 0.970713565 #> 7047 70.46 0.02921566 5.613769e-05 0.970728204 #> 7048 70.47 0.02920105 5.610963e-05 0.970742837 #> 7049 70.48 0.02918646 5.608158e-05 0.970757462 #> 7050 70.49 0.02917187 5.605355e-05 0.970772079 #> 7051 70.50 0.02915729 5.602553e-05 0.970786689 #> 7052 70.51 0.02914271 5.599752e-05 0.970801292 #> 7053 70.52 0.02912814 5.596953e-05 0.970815888 #> 7054 70.53 0.02911358 5.594155e-05 0.970830477 #> 7055 70.54 0.02909903 5.591359e-05 0.970845058 #> 7056 70.55 0.02908448 5.588564e-05 0.970859631 #> 7057 70.56 0.02906994 5.585770e-05 0.970874198 #> 7058 70.57 0.02905541 5.582978e-05 0.970888757 #> 7059 70.58 0.02904089 5.580187e-05 0.970903309 #> 7060 70.59 0.02902637 5.577398e-05 0.970917854 #> 7061 70.60 0.02901186 5.574610e-05 0.970932391 #> 7062 70.61 0.02899736 5.571823e-05 0.970946922 #> 7063 70.62 0.02898287 5.569038e-05 0.970961445 #> 7064 70.63 0.02896838 5.566254e-05 0.970975960 #> 7065 70.64 0.02895390 5.563472e-05 0.970990469 #> 7066 70.65 0.02893942 5.560691e-05 0.971004970 #> 7067 70.66 0.02892496 5.557911e-05 0.971019464 #> 7068 70.67 0.02891050 5.555133e-05 0.971033950 #> 7069 70.68 0.02889605 5.552356e-05 0.971048430 #> 7070 70.69 0.02888160 5.549580e-05 0.971062902 #> 7071 70.70 0.02886717 5.546806e-05 0.971077367 #> 7072 70.71 0.02885274 5.544034e-05 0.971091824 #> 7073 70.72 0.02883831 5.541262e-05 0.971106275 #> 7074 70.73 0.02882390 5.538492e-05 0.971120718 #> 7075 70.74 0.02880949 5.535724e-05 0.971135154 #> 7076 70.75 0.02879509 5.532957e-05 0.971149583 #> 7077 70.76 0.02878069 5.530191e-05 0.971164005 #> 7078 70.77 0.02876631 5.527426e-05 0.971178419 #> 7079 70.78 0.02875193 5.524663e-05 0.971192826 #> 7080 70.79 0.02873755 5.521902e-05 0.971207226 #> 7081 70.80 0.02872319 5.519142e-05 0.971221619 #> 7082 70.81 0.02870883 5.516383e-05 0.971236005 #> 7083 70.82 0.02869448 5.513625e-05 0.971250383 #> 7084 70.83 0.02868014 5.510869e-05 0.971264754 #> 7085 70.84 0.02866580 5.508114e-05 0.971279118 #> 7086 70.85 0.02865147 5.505361e-05 0.971293475 #> 7087 70.86 0.02863715 5.502609e-05 0.971307825 #> 7088 70.87 0.02862283 5.499858e-05 0.971322167 #> 7089 70.88 0.02860853 5.497109e-05 0.971336503 #> 7090 70.89 0.02859423 5.494361e-05 0.971350831 #> 7091 70.90 0.02857993 5.491615e-05 0.971365152 #> 7092 70.91 0.02856565 5.488870e-05 0.971379466 #> 7093 70.92 0.02855137 5.486126e-05 0.971393772 #> 7094 70.93 0.02853709 5.483383e-05 0.971408072 #> 7095 70.94 0.02852283 5.480642e-05 0.971422364 #> 7096 70.95 0.02850857 5.477903e-05 0.971436649 #> 7097 70.96 0.02849432 5.475165e-05 0.971450928 #> 7098 70.97 0.02848008 5.472428e-05 0.971465199 #> 7099 70.98 0.02846584 5.469692e-05 0.971479462 #> 7100 70.99 0.02845161 5.466958e-05 0.971493719 #> 7101 71.00 0.02843739 5.464225e-05 0.971507969 #> 7102 71.01 0.02842317 5.461494e-05 0.971522211 #> 7103 71.02 0.02840897 5.458764e-05 0.971536446 #> 7104 71.03 0.02839476 5.456035e-05 0.971550675 #> 7105 71.04 0.02838057 5.453308e-05 0.971564896 #> 7106 71.05 0.02836638 5.450582e-05 0.971579110 #> 7107 71.06 0.02835220 5.447857e-05 0.971593317 #> 7108 71.07 0.02833803 5.445134e-05 0.971607516 #> 7109 71.08 0.02832387 5.442412e-05 0.971621709 #> 7110 71.09 0.02830971 5.439691e-05 0.971635895 #> 7111 71.10 0.02829556 5.436972e-05 0.971650073 #> 7112 71.11 0.02828141 5.434254e-05 0.971664245 #> 7113 71.12 0.02826728 5.431538e-05 0.971678409 #> 7114 71.13 0.02825315 5.428823e-05 0.971692566 #> 7115 71.14 0.02823902 5.426109e-05 0.971706716 #> 7116 71.15 0.02822491 5.423397e-05 0.971720860 #> 7117 71.16 0.02821080 5.420686e-05 0.971734996 #> 7118 71.17 0.02819670 5.417976e-05 0.971749125 #> 7119 71.18 0.02818260 5.415268e-05 0.971763246 #> 7120 71.19 0.02816851 5.412561e-05 0.971777361 #> 7121 71.20 0.02815443 5.409855e-05 0.971791469 #> 7122 71.21 0.02814036 5.407151e-05 0.971805570 #> 7123 71.22 0.02812629 5.404448e-05 0.971819664 #> 7124 71.23 0.02811223 5.401747e-05 0.971833750 #> 7125 71.24 0.02809818 5.399046e-05 0.971847830 #> 7126 71.25 0.02808413 5.396347e-05 0.971861902 #> 7127 71.26 0.02807010 5.393650e-05 0.971875968 #> 7128 71.27 0.02805606 5.390954e-05 0.971890026 #> 7129 71.28 0.02804204 5.388259e-05 0.971904078 #> 7130 71.29 0.02802802 5.385566e-05 0.971918122 #> 7131 71.30 0.02801401 5.382873e-05 0.971932160 #> 7132 71.31 0.02800001 5.380183e-05 0.971946190 #> 7133 71.32 0.02798601 5.377493e-05 0.971960214 #> 7134 71.33 0.02797202 5.374805e-05 0.971974230 #> 7135 71.34 0.02795804 5.372118e-05 0.971988239 #> 7136 71.35 0.02794406 5.369433e-05 0.972002242 #> 7137 71.36 0.02793010 5.366749e-05 0.972016237 #> 7138 71.37 0.02791613 5.364066e-05 0.972030225 #> 7139 71.38 0.02790218 5.361385e-05 0.972044207 #> 7140 71.39 0.02788823 5.358705e-05 0.972058181 #> 7141 71.40 0.02787429 5.356026e-05 0.972072149 #> 7142 71.41 0.02786036 5.353349e-05 0.972086109 #> 7143 71.42 0.02784643 5.350673e-05 0.972100063 #> 7144 71.43 0.02783251 5.347998e-05 0.972114009 #> 7145 71.44 0.02781860 5.345325e-05 0.972127949 #> 7146 71.45 0.02780469 5.342653e-05 0.972141881 #> 7147 71.46 0.02779079 5.339982e-05 0.972155807 #> 7148 71.47 0.02777690 5.337313e-05 0.972169725 #> 7149 71.48 0.02776302 5.334645e-05 0.972183637 #> 7150 71.49 0.02774914 5.331978e-05 0.972197542 #> 7151 71.50 0.02773527 5.329313e-05 0.972211439 #> 7152 71.51 0.02772140 5.326649e-05 0.972225330 #> 7153 71.52 0.02770755 5.323986e-05 0.972239214 #> 7154 71.53 0.02769370 5.321325e-05 0.972253091 #> 7155 71.54 0.02767985 5.318665e-05 0.972266961 #> 7156 71.55 0.02766602 5.316006e-05 0.972280824 #> 7157 71.56 0.02765219 5.313349e-05 0.972294680 #> 7158 71.57 0.02763836 5.310693e-05 0.972308529 #> 7159 71.58 0.02762455 5.308038e-05 0.972322372 #> 7160 71.59 0.02761074 5.305385e-05 0.972336207 #> 7161 71.60 0.02759694 5.302733e-05 0.972350035 #> 7162 71.61 0.02758314 5.300082e-05 0.972363857 #> 7163 71.62 0.02756935 5.297433e-05 0.972377672 #> 7164 71.63 0.02755557 5.294785e-05 0.972391479 #> 7165 71.64 0.02754180 5.292138e-05 0.972405280 #> 7166 71.65 0.02752803 5.289493e-05 0.972419074 #> 7167 71.66 0.02751427 5.286849e-05 0.972432861 #> 7168 71.67 0.02750052 5.284206e-05 0.972446641 #> 7169 71.68 0.02748677 5.281564e-05 0.972460414 #> 7170 71.69 0.02747303 5.278924e-05 0.972474181 #> 7171 71.70 0.02745930 5.276285e-05 0.972487940 #> 7172 71.71 0.02744557 5.273648e-05 0.972501693 #> 7173 71.72 0.02743185 5.271012e-05 0.972515439 #> 7174 71.73 0.02741814 5.268377e-05 0.972529177 #> 7175 71.74 0.02740443 5.265743e-05 0.972542909 #> 7176 71.75 0.02739073 5.263111e-05 0.972556635 #> 7177 71.76 0.02737704 5.260480e-05 0.972570353 #> 7178 71.77 0.02736336 5.257851e-05 0.972584064 #> 7179 71.78 0.02734968 5.255222e-05 0.972597769 #> 7180 71.79 0.02733601 5.252595e-05 0.972611466 #> 7181 71.80 0.02732234 5.249970e-05 0.972625157 #> 7182 71.81 0.02730869 5.247346e-05 0.972638841 #> 7183 71.82 0.02729503 5.244722e-05 0.972652518 #> 7184 71.83 0.02728139 5.242101e-05 0.972666189 #> 7185 71.84 0.02726775 5.239480e-05 0.972679852 #> 7186 71.85 0.02725412 5.236861e-05 0.972693509 #> 7187 71.86 0.02724050 5.234244e-05 0.972707159 #> 7188 71.87 0.02722688 5.231627e-05 0.972720802 #> 7189 71.88 0.02721327 5.229012e-05 0.972734438 #> 7190 71.89 0.02719967 5.226398e-05 0.972748067 #> 7191 71.90 0.02718607 5.223786e-05 0.972761690 #> 7192 71.91 0.02717248 5.221174e-05 0.972775306 #> 7193 71.92 0.02715890 5.218564e-05 0.972788915 #> 7194 71.93 0.02714532 5.215956e-05 0.972802517 #> 7195 71.94 0.02713175 5.213348e-05 0.972816112 #> 7196 71.95 0.02711819 5.210742e-05 0.972829701 #> 7197 71.96 0.02710464 5.208138e-05 0.972843282 #> 7198 71.97 0.02709109 5.205534e-05 0.972856857 #> 7199 71.98 0.02707755 5.202932e-05 0.972870425 #> 7200 71.99 0.02706401 5.200331e-05 0.972883987 #> 7201 72.00 0.02705048 5.197732e-05 0.972897541 #> 7202 72.01 0.02703696 5.195134e-05 0.972911089 #> 7203 72.02 0.02702344 5.192537e-05 0.972924630 #> 7204 72.03 0.02700994 5.189941e-05 0.972938165 #> 7205 72.04 0.02699643 5.187347e-05 0.972951692 #> 7206 72.05 0.02698294 5.184754e-05 0.972965213 #> 7207 72.06 0.02696945 5.182162e-05 0.972978727 #> 7208 72.07 0.02695597 5.179572e-05 0.972992234 #> 7209 72.08 0.02694250 5.176982e-05 0.973005735 #> 7210 72.09 0.02692903 5.174395e-05 0.973019229 #> 7211 72.10 0.02691557 5.171808e-05 0.973032716 #> 7212 72.11 0.02690211 5.169223e-05 0.973046196 #> 7213 72.12 0.02688866 5.166639e-05 0.973059669 #> 7214 72.13 0.02687522 5.164056e-05 0.973073136 #> 7215 72.14 0.02686179 5.161475e-05 0.973086596 #> 7216 72.15 0.02684836 5.158895e-05 0.973100050 #> 7217 72.16 0.02683494 5.156316e-05 0.973113496 #> 7218 72.17 0.02682153 5.153738e-05 0.973126936 #> 7219 72.18 0.02680812 5.151162e-05 0.973140369 #> 7220 72.19 0.02679472 5.148587e-05 0.973153796 #> 7221 72.20 0.02678132 5.146013e-05 0.973167215 #> 7222 72.21 0.02676794 5.143441e-05 0.973180629 #> 7223 72.22 0.02675456 5.140870e-05 0.973194035 #> 7224 72.23 0.02674118 5.138300e-05 0.973207434 #> 7225 72.24 0.02672782 5.135732e-05 0.973220827 #> 7226 72.25 0.02671445 5.133165e-05 0.973234214 #> 7227 72.26 0.02670110 5.130599e-05 0.973247593 #> 7228 72.27 0.02668775 5.128034e-05 0.973260966 #> 7229 72.28 0.02667441 5.125471e-05 0.973274332 #> 7230 72.29 0.02666108 5.122908e-05 0.973287692 #> 7231 72.30 0.02664775 5.120348e-05 0.973301045 #> 7232 72.31 0.02663443 5.117788e-05 0.973314391 #> 7233 72.32 0.02662112 5.115230e-05 0.973327730 #> 7234 72.33 0.02660781 5.112673e-05 0.973341063 #> 7235 72.34 0.02659451 5.110117e-05 0.973354389 #> 7236 72.35 0.02658122 5.107563e-05 0.973367709 #> 7237 72.36 0.02656793 5.105010e-05 0.973381021 #> 7238 72.37 0.02655465 5.102458e-05 0.973394328 #> 7239 72.38 0.02654137 5.099907e-05 0.973407627 #> 7240 72.39 0.02652811 5.097358e-05 0.973420920 #> 7241 72.40 0.02651485 5.094810e-05 0.973434206 #> 7242 72.41 0.02650159 5.092263e-05 0.973447486 #> 7243 72.42 0.02648834 5.089718e-05 0.973460759 #> 7244 72.43 0.02647510 5.087173e-05 0.973474025 #> 7245 72.44 0.02646187 5.084630e-05 0.973487285 #> 7246 72.45 0.02644864 5.082089e-05 0.973500538 #> 7247 72.46 0.02643542 5.079548e-05 0.973513784 #> 7248 72.47 0.02642221 5.077009e-05 0.973527024 #> 7249 72.48 0.02640900 5.074471e-05 0.973540257 #> 7250 72.49 0.02639580 5.071935e-05 0.973553484 #> 7251 72.50 0.02638260 5.069399e-05 0.973566704 #> 7252 72.51 0.02636941 5.066865e-05 0.973579917 #> 7253 72.52 0.02635623 5.064332e-05 0.973593124 #> 7254 72.53 0.02634306 5.061801e-05 0.973606324 #> 7255 72.54 0.02632989 5.059271e-05 0.973619517 #> 7256 72.55 0.02631673 5.056742e-05 0.973632704 #> 7257 72.56 0.02630357 5.054214e-05 0.973645885 #> 7258 72.57 0.02629042 5.051687e-05 0.973659058 #> 7259 72.58 0.02627728 5.049162e-05 0.973672226 #> 7260 72.59 0.02626415 5.046638e-05 0.973685386 #> 7261 72.60 0.02625102 5.044116e-05 0.973698540 #> 7262 72.61 0.02623790 5.041594e-05 0.973711688 #> 7263 72.62 0.02622478 5.039074e-05 0.973724829 #> 7264 72.63 0.02621167 5.036555e-05 0.973737963 #> 7265 72.64 0.02619857 5.034037e-05 0.973751091 #> 7266 72.65 0.02618547 5.031521e-05 0.973764212 #> 7267 72.66 0.02617238 5.029006e-05 0.973777326 #> 7268 72.67 0.02615930 5.026492e-05 0.973790434 #> 7269 72.68 0.02614622 5.023979e-05 0.973803536 #> 7270 72.69 0.02613315 5.021468e-05 0.973816631 #> 7271 72.70 0.02612009 5.018958e-05 0.973829719 #> 7272 72.71 0.02610703 5.016449e-05 0.973842801 #> 7273 72.72 0.02609398 5.013941e-05 0.973855877 #> 7274 72.73 0.02608094 5.011435e-05 0.973868945 #> 7275 72.74 0.02606790 5.008930e-05 0.973882008 #> 7276 72.75 0.02605487 5.006426e-05 0.973895063 #> 7277 72.76 0.02604185 5.003924e-05 0.973908112 #> 7278 72.77 0.02602883 5.001422e-05 0.973921155 #> 7279 72.78 0.02601582 4.998922e-05 0.973934191 #> 7280 72.79 0.02600281 4.996423e-05 0.973947221 #> 7281 72.80 0.02598982 4.993926e-05 0.973960244 #> 7282 72.81 0.02597682 4.991429e-05 0.973973261 #> 7283 72.82 0.02596384 4.988934e-05 0.973986271 #> 7284 72.83 0.02595086 4.986441e-05 0.973999274 #> 7285 72.84 0.02593789 4.983948e-05 0.974012272 #> 7286 72.85 0.02592492 4.981457e-05 0.974025262 #> 7287 72.86 0.02591196 4.978966e-05 0.974038246 #> 7288 72.87 0.02589901 4.976478e-05 0.974051224 #> 7289 72.88 0.02588606 4.973990e-05 0.974064195 #> 7290 72.89 0.02587313 4.971504e-05 0.974077160 #> 7291 72.90 0.02586019 4.969018e-05 0.974090118 #> 7292 72.91 0.02584726 4.966535e-05 0.974103070 #> 7293 72.92 0.02583434 4.964052e-05 0.974116015 #> 7294 72.93 0.02582143 4.961571e-05 0.974128954 #> 7295 72.94 0.02580852 4.959090e-05 0.974141886 #> 7296 72.95 0.02579562 4.956611e-05 0.974154812 #> 7297 72.96 0.02578273 4.954134e-05 0.974167731 #> 7298 72.97 0.02576984 4.951657e-05 0.974180644 #> 7299 72.98 0.02575696 4.949182e-05 0.974193550 #> 7300 72.99 0.02574408 4.946708e-05 0.974206450 #> 7301 73.00 0.02573121 4.944235e-05 0.974219344 #> 7302 73.01 0.02571835 4.941764e-05 0.974232231 #> 7303 73.02 0.02570550 4.939294e-05 0.974245112 #> 7304 73.03 0.02569265 4.936825e-05 0.974257986 #> 7305 73.04 0.02567980 4.934357e-05 0.974270854 #> 7306 73.05 0.02566697 4.931890e-05 0.974283715 #> 7307 73.06 0.02565414 4.929425e-05 0.974296570 #> 7308 73.07 0.02564131 4.926961e-05 0.974309419 #> 7309 73.08 0.02562849 4.924498e-05 0.974322261 #> 7310 73.09 0.02561568 4.922036e-05 0.974335096 #> 7311 73.10 0.02560288 4.919576e-05 0.974347926 #> 7312 73.11 0.02559008 4.917117e-05 0.974360748 #> 7313 73.12 0.02557729 4.914659e-05 0.974373565 #> 7314 73.13 0.02556450 4.912202e-05 0.974386375 #> 7315 73.14 0.02555172 4.909747e-05 0.974399178 #> 7316 73.15 0.02553895 4.907292e-05 0.974411976 #> 7317 73.16 0.02552619 4.904839e-05 0.974424767 #> 7318 73.17 0.02551343 4.902387e-05 0.974437551 #> 7319 73.18 0.02550067 4.899937e-05 0.974450329 #> 7320 73.19 0.02548792 4.897488e-05 0.974463101 #> 7321 73.20 0.02547518 4.895039e-05 0.974475866 #> 7322 73.21 0.02546245 4.892593e-05 0.974488625 #> 7323 73.22 0.02544972 4.890147e-05 0.974501377 #> 7324 73.23 0.02543700 4.887702e-05 0.974514123 #> 7325 73.24 0.02542428 4.885259e-05 0.974526863 #> 7326 73.25 0.02541158 4.882817e-05 0.974539597 #> 7327 73.26 0.02539887 4.880376e-05 0.974552324 #> 7328 73.27 0.02538618 4.877937e-05 0.974565044 #> 7329 73.28 0.02537349 4.875498e-05 0.974577758 #> 7330 73.29 0.02536080 4.873061e-05 0.974590466 #> 7331 73.30 0.02534813 4.870625e-05 0.974603168 #> 7332 73.31 0.02533545 4.868191e-05 0.974615863 #> 7333 73.32 0.02532279 4.865757e-05 0.974628552 #> 7334 73.33 0.02531013 4.863325e-05 0.974641235 #> 7335 73.34 0.02529748 4.860894e-05 0.974653911 #> 7336 73.35 0.02528483 4.858464e-05 0.974666581 #> 7337 73.36 0.02527220 4.856035e-05 0.974679244 #> 7338 73.37 0.02525956 4.853608e-05 0.974691902 #> 7339 73.38 0.02524694 4.851182e-05 0.974704552 #> 7340 73.39 0.02523432 4.848757e-05 0.974717197 #> 7341 73.40 0.02522170 4.846333e-05 0.974729835 #> 7342 73.41 0.02520909 4.843910e-05 0.974742467 #> 7343 73.42 0.02519649 4.841489e-05 0.974755093 #> 7344 73.43 0.02518390 4.839069e-05 0.974767712 #> 7345 73.44 0.02517131 4.836650e-05 0.974780325 #> 7346 73.45 0.02515873 4.834232e-05 0.974792932 #> 7347 73.46 0.02514615 4.831816e-05 0.974805532 #> 7348 73.47 0.02513358 4.829400e-05 0.974818126 #> 7349 73.48 0.02512102 4.826986e-05 0.974830714 #> 7350 73.49 0.02510846 4.824573e-05 0.974843296 #> 7351 73.50 0.02509591 4.822162e-05 0.974855871 #> 7352 73.51 0.02508336 4.819751e-05 0.974868440 #> 7353 73.52 0.02507082 4.817342e-05 0.974881002 #> 7354 73.53 0.02505829 4.814934e-05 0.974893559 #> 7355 73.54 0.02504577 4.812527e-05 0.974906109 #> 7356 73.55 0.02503325 4.810121e-05 0.974918653 #> 7357 73.56 0.02502073 4.807717e-05 0.974931190 #> 7358 73.57 0.02500823 4.805314e-05 0.974943721 #> 7359 73.58 0.02499572 4.802912e-05 0.974956246 #> 7360 73.59 0.02498323 4.800511e-05 0.974968765 #> 7361 73.60 0.02497074 4.798111e-05 0.974981278 #> 7362 73.61 0.02495826 4.795713e-05 0.974993784 #> 7363 73.62 0.02494578 4.793315e-05 0.975006284 #> 7364 73.63 0.02493331 4.790919e-05 0.975018778 #> 7365 73.64 0.02492085 4.788525e-05 0.975031265 #> 7366 73.65 0.02490839 4.786131e-05 0.975043746 #> 7367 73.66 0.02489594 4.783738e-05 0.975056221 #> 7368 73.67 0.02488350 4.781347e-05 0.975068690 #> 7369 73.68 0.02487106 4.778957e-05 0.975081153 #> 7370 73.69 0.02485863 4.776568e-05 0.975093609 #> 7371 73.70 0.02484620 4.774180e-05 0.975106059 #> 7372 73.71 0.02483378 4.771794e-05 0.975118503 #> 7373 73.72 0.02482137 4.769409e-05 0.975130941 #> 7374 73.73 0.02480896 4.767025e-05 0.975143372 #> 7375 73.74 0.02479656 4.764642e-05 0.975155797 #> 7376 73.75 0.02478416 4.762260e-05 0.975168216 #> 7377 73.76 0.02477177 4.759879e-05 0.975180629 #> 7378 73.77 0.02475939 4.757500e-05 0.975193036 #> 7379 73.78 0.02474701 4.755122e-05 0.975205436 #> 7380 73.79 0.02473464 4.752745e-05 0.975217830 #> 7381 73.80 0.02472228 4.750369e-05 0.975230218 #> 7382 73.81 0.02470992 4.747995e-05 0.975242600 #> 7383 73.82 0.02469757 4.745621e-05 0.975254975 #> 7384 73.83 0.02468522 4.743249e-05 0.975267345 #> 7385 73.84 0.02467288 4.740878e-05 0.975279708 #> 7386 73.85 0.02466055 4.738508e-05 0.975292065 #> 7387 73.86 0.02464822 4.736139e-05 0.975304416 #> 7388 73.87 0.02463590 4.733772e-05 0.975316761 #> 7389 73.88 0.02462359 4.731406e-05 0.975329099 #> 7390 73.89 0.02461128 4.729041e-05 0.975341432 #> 7391 73.90 0.02459898 4.726677e-05 0.975353758 #> 7392 73.91 0.02458668 4.724314e-05 0.975366078 #> 7393 73.92 0.02457439 4.721952e-05 0.975378392 #> 7394 73.93 0.02456210 4.719592e-05 0.975390700 #> 7395 73.94 0.02454983 4.717233e-05 0.975403001 #> 7396 73.95 0.02453755 4.714875e-05 0.975415297 #> 7397 73.96 0.02452529 4.712518e-05 0.975427586 #> 7398 73.97 0.02451303 4.710162e-05 0.975439869 #> 7399 73.98 0.02450078 4.707808e-05 0.975452146 #> 7400 73.99 0.02448853 4.705454e-05 0.975464417 #> 7401 74.00 0.02447629 4.703102e-05 0.975476682 #> 7402 74.01 0.02446405 4.700751e-05 0.975488940 #> 7403 74.02 0.02445182 4.698401e-05 0.975501193 #> 7404 74.03 0.02443960 4.696053e-05 0.975513439 #> 7405 74.04 0.02442738 4.693705e-05 0.975525679 #> 7406 74.05 0.02441517 4.691359e-05 0.975537913 #> 7407 74.06 0.02440297 4.689014e-05 0.975550141 #> 7408 74.07 0.02439077 4.686670e-05 0.975562363 #> 7409 74.08 0.02437858 4.684327e-05 0.975574579 #> 7410 74.09 0.02436639 4.681986e-05 0.975586789 #> 7411 74.10 0.02435421 4.679645e-05 0.975598992 #> 7412 74.11 0.02434204 4.677306e-05 0.975611190 #> 7413 74.12 0.02432987 4.674968e-05 0.975623381 #> 7414 74.13 0.02431771 4.672631e-05 0.975635566 #> 7415 74.14 0.02430555 4.670295e-05 0.975647745 #> 7416 74.15 0.02429340 4.667961e-05 0.975659918 #> 7417 74.16 0.02428126 4.665627e-05 0.975672085 #> 7418 74.17 0.02426912 4.663295e-05 0.975684246 #> 7419 74.18 0.02425699 4.660964e-05 0.975696401 #> 7420 74.19 0.02424486 4.658634e-05 0.975708550 #> 7421 74.20 0.02423274 4.656306e-05 0.975720693 #> 7422 74.21 0.02422063 4.653978e-05 0.975732829 #> 7423 74.22 0.02420852 4.651652e-05 0.975744960 #> 7424 74.23 0.02419642 4.649326e-05 0.975757084 #> 7425 74.24 0.02418433 4.647002e-05 0.975769203 #> 7426 74.25 0.02417224 4.644679e-05 0.975781315 #> 7427 74.26 0.02416016 4.642358e-05 0.975793421 #> 7428 74.27 0.02414808 4.640037e-05 0.975805522 #> 7429 74.28 0.02413601 4.637718e-05 0.975817616 #> 7430 74.29 0.02412394 4.635399e-05 0.975829704 #> 7431 74.30 0.02411188 4.633082e-05 0.975841786 #> 7432 74.31 0.02409983 4.630766e-05 0.975853862 #> 7433 74.32 0.02408778 4.628451e-05 0.975865932 #> 7434 74.33 0.02407574 4.626138e-05 0.975877996 #> 7435 74.34 0.02406371 4.623825e-05 0.975890054 #> 7436 74.35 0.02405168 4.621514e-05 0.975902106 #> 7437 74.36 0.02403966 4.619204e-05 0.975914152 #> 7438 74.37 0.02402764 4.616895e-05 0.975926192 #> 7439 74.38 0.02401563 4.614587e-05 0.975938226 #> 7440 74.39 0.02400362 4.612280e-05 0.975950254 #> 7441 74.40 0.02399162 4.609975e-05 0.975962276 #> 7442 74.41 0.02397963 4.607670e-05 0.975974292 #> 7443 74.42 0.02396764 4.605367e-05 0.975986301 #> 7444 74.43 0.02395566 4.603065e-05 0.975998305 #> 7445 74.44 0.02394369 4.600764e-05 0.976010303 #> 7446 74.45 0.02393172 4.598464e-05 0.976022295 #> 7447 74.46 0.02391976 4.596165e-05 0.976034281 #> 7448 74.47 0.02390780 4.593868e-05 0.976046261 #> 7449 74.48 0.02389585 4.591571e-05 0.976058235 #> 7450 74.49 0.02388390 4.589276e-05 0.976070203 #> 7451 74.50 0.02387197 4.586982e-05 0.976082164 #> 7452 74.51 0.02386003 4.584689e-05 0.976094120 #> 7453 74.52 0.02384811 4.582397e-05 0.976106070 #> 7454 74.53 0.02383618 4.580107e-05 0.976118014 #> 7455 74.54 0.02382427 4.577817e-05 0.976129952 #> 7456 74.55 0.02381236 4.575529e-05 0.976141884 #> 7457 74.56 0.02380046 4.573242e-05 0.976153810 #> 7458 74.57 0.02378856 4.570956e-05 0.976165731 #> 7459 74.58 0.02377667 4.568671e-05 0.976177645 #> 7460 74.59 0.02376478 4.566387e-05 0.976189553 #> 7461 74.60 0.02375290 4.564104e-05 0.976201455 #> 7462 74.61 0.02374103 4.561823e-05 0.976213351 #> 7463 74.62 0.02372916 4.559543e-05 0.976225242 #> 7464 74.63 0.02371730 4.557263e-05 0.976237126 #> 7465 74.64 0.02370545 4.554985e-05 0.976249005 #> 7466 74.65 0.02369360 4.552708e-05 0.976260877 #> 7467 74.66 0.02368175 4.550433e-05 0.976272744 #> 7468 74.67 0.02366991 4.548158e-05 0.976284604 #> 7469 74.68 0.02365808 4.545885e-05 0.976296459 #> 7470 74.69 0.02364626 4.543612e-05 0.976308308 #> 7471 74.70 0.02363444 4.541341e-05 0.976320151 #> 7472 74.71 0.02362262 4.539071e-05 0.976331988 #> 7473 74.72 0.02361081 4.536802e-05 0.976343819 #> 7474 74.73 0.02359901 4.534534e-05 0.976355644 #> 7475 74.74 0.02358721 4.532267e-05 0.976367463 #> 7476 74.75 0.02357542 4.530002e-05 0.976379277 #> 7477 74.76 0.02356364 4.527737e-05 0.976391084 #> 7478 74.77 0.02355186 4.525474e-05 0.976402885 #> 7479 74.78 0.02354009 4.523212e-05 0.976414681 #> 7480 74.79 0.02352832 4.520951e-05 0.976426471 #> 7481 74.80 0.02351656 4.518691e-05 0.976438255 #> 7482 74.81 0.02350480 4.516432e-05 0.976450033 #> 7483 74.82 0.02349305 4.514174e-05 0.976461805 #> 7484 74.83 0.02348131 4.511918e-05 0.976473571 #> 7485 74.84 0.02346957 4.509663e-05 0.976485331 #> 7486 74.85 0.02345784 4.507408e-05 0.976497085 #> 7487 74.86 0.02344611 4.505155e-05 0.976508834 #> 7488 74.87 0.02343439 4.502903e-05 0.976520577 #> 7489 74.88 0.02342268 4.500652e-05 0.976532313 #> 7490 74.89 0.02341097 4.498402e-05 0.976544044 #> 7491 74.90 0.02339927 4.496154e-05 0.976555769 #> 7492 74.91 0.02338757 4.493906e-05 0.976567489 #> 7493 74.92 0.02337588 4.491660e-05 0.976579202 #> 7494 74.93 0.02336420 4.489415e-05 0.976590909 #> 7495 74.94 0.02335252 4.487171e-05 0.976602611 #> 7496 74.95 0.02334084 4.484927e-05 0.976614307 #> 7497 74.96 0.02332918 4.482686e-05 0.976625997 #> 7498 74.97 0.02331751 4.480445e-05 0.976637681 #> 7499 74.98 0.02330586 4.478205e-05 0.976649359 #> 7500 74.99 0.02329421 4.475967e-05 0.976661031 #> 7501 75.00 0.02328256 4.473729e-05 0.976672698 #> 7502 75.01 0.02327093 4.471493e-05 0.976684359 #> 7503 75.02 0.02325929 4.469258e-05 0.976696014 #> 7504 75.03 0.02324767 4.467024e-05 0.976707663 #> 7505 75.04 0.02323605 4.464791e-05 0.976719306 #> 7506 75.05 0.02322443 4.462559e-05 0.976730943 #> 7507 75.06 0.02321282 4.460328e-05 0.976742575 #> 7508 75.07 0.02320122 4.458098e-05 0.976754201 #> 7509 75.08 0.02318962 4.455870e-05 0.976765821 #> 7510 75.09 0.02317803 4.453643e-05 0.976777435 #> 7511 75.10 0.02316644 4.451416e-05 0.976789043 #> 7512 75.11 0.02315486 4.449191e-05 0.976800646 #> 7513 75.12 0.02314329 4.446967e-05 0.976812243 #> 7514 75.13 0.02313172 4.444744e-05 0.976823834 #> 7515 75.14 0.02312016 4.442522e-05 0.976835419 #> 7516 75.15 0.02310860 4.440302e-05 0.976846998 #> 7517 75.16 0.02309705 4.438082e-05 0.976858572 #> 7518 75.17 0.02308550 4.435864e-05 0.976870140 #> 7519 75.18 0.02307396 4.433646e-05 0.976881702 #> 7520 75.19 0.02306243 4.431430e-05 0.976893258 #> 7521 75.20 0.02305090 4.429215e-05 0.976904808 #> 7522 75.21 0.02303938 4.427001e-05 0.976916353 #> 7523 75.22 0.02302786 4.424788e-05 0.976927892 #> 7524 75.23 0.02301635 4.422576e-05 0.976939425 #> 7525 75.24 0.02300484 4.420365e-05 0.976950953 #> 7526 75.25 0.02299334 4.418156e-05 0.976962474 #> 7527 75.26 0.02298185 4.415947e-05 0.976973990 #> 7528 75.27 0.02297036 4.413740e-05 0.976985500 #> 7529 75.28 0.02295888 4.411533e-05 0.976997005 #> 7530 75.29 0.02294740 4.409328e-05 0.977008503 #> 7531 75.30 0.02293593 4.407124e-05 0.977019996 #> 7532 75.31 0.02292447 4.404921e-05 0.977031483 #> 7533 75.32 0.02291301 4.402719e-05 0.977042965 #> 7534 75.33 0.02290155 4.400518e-05 0.977054440 #> 7535 75.34 0.02289011 4.398319e-05 0.977065910 #> 7536 75.35 0.02287866 4.396120e-05 0.977077374 #> 7537 75.36 0.02286723 4.393922e-05 0.977088833 #> 7538 75.37 0.02285580 4.391726e-05 0.977100286 #> 7539 75.38 0.02284437 4.389531e-05 0.977111733 #> 7540 75.39 0.02283295 4.387337e-05 0.977123174 #> 7541 75.40 0.02282154 4.385143e-05 0.977134609 #> 7542 75.41 0.02281013 4.382951e-05 0.977146039 #> 7543 75.42 0.02279873 4.380760e-05 0.977157463 #> 7544 75.43 0.02278733 4.378571e-05 0.977168882 #> 7545 75.44 0.02277594 4.376382e-05 0.977180295 #> 7546 75.45 0.02276456 4.374194e-05 0.977191702 #> 7547 75.46 0.02275318 4.372008e-05 0.977203103 #> 7548 75.47 0.02274180 4.369822e-05 0.977214498 #> 7549 75.48 0.02273044 4.367638e-05 0.977225888 #> 7550 75.49 0.02271907 4.365455e-05 0.977237273 #> 7551 75.50 0.02270772 4.363272e-05 0.977248651 #> 7552 75.51 0.02269637 4.361091e-05 0.977260024 #> 7553 75.52 0.02268502 4.358911e-05 0.977271391 #> 7554 75.53 0.02267368 4.356732e-05 0.977282753 #> 7555 75.54 0.02266235 4.354555e-05 0.977294108 #> 7556 75.55 0.02265102 4.352378e-05 0.977305458 #> 7557 75.56 0.02263970 4.350202e-05 0.977316803 #> 7558 75.57 0.02262838 4.348028e-05 0.977328142 #> 7559 75.58 0.02261707 4.345854e-05 0.977339475 #> 7560 75.59 0.02260576 4.343682e-05 0.977350802 #> 7561 75.60 0.02259446 4.341510e-05 0.977362124 #> 7562 75.61 0.02258317 4.339340e-05 0.977373440 #> 7563 75.62 0.02257188 4.337171e-05 0.977384750 #> 7564 75.63 0.02256059 4.335003e-05 0.977396055 #> 7565 75.64 0.02254932 4.332836e-05 0.977407354 #> 7566 75.65 0.02253805 4.330670e-05 0.977418648 #> 7567 75.66 0.02252678 4.328505e-05 0.977429936 #> 7568 75.67 0.02251552 4.326342e-05 0.977441218 #> 7569 75.68 0.02250426 4.324179e-05 0.977452495 #> 7570 75.69 0.02249301 4.322018e-05 0.977463765 #> 7571 75.70 0.02248177 4.319857e-05 0.977475031 #> 7572 75.71 0.02247053 4.317698e-05 0.977486290 #> 7573 75.72 0.02245930 4.315539e-05 0.977497545 #> 7574 75.73 0.02244807 4.313382e-05 0.977508793 #> 7575 75.74 0.02243685 4.311226e-05 0.977520036 #> 7576 75.75 0.02242564 4.309071e-05 0.977531273 #> 7577 75.76 0.02241443 4.306917e-05 0.977542504 #> 7578 75.77 0.02240322 4.304764e-05 0.977553730 #> 7579 75.78 0.02239202 4.302612e-05 0.977564951 #> 7580 75.79 0.02238083 4.300461e-05 0.977576165 #> 7581 75.80 0.02236964 4.298312e-05 0.977587375 #> 7582 75.81 0.02235846 4.296163e-05 0.977598578 #> 7583 75.82 0.02234728 4.294016e-05 0.977609776 #> 7584 75.83 0.02233611 4.291869e-05 0.977620968 #> 7585 75.84 0.02232495 4.289724e-05 0.977632155 #> 7586 75.85 0.02231379 4.287579e-05 0.977643336 #> 7587 75.86 0.02230263 4.285436e-05 0.977654512 #> 7588 75.87 0.02229149 4.283294e-05 0.977665682 #> 7589 75.88 0.02228034 4.281153e-05 0.977676846 #> 7590 75.89 0.02226921 4.279013e-05 0.977688005 #> 7591 75.90 0.02225807 4.276874e-05 0.977699158 #> 7592 75.91 0.02224695 4.274736e-05 0.977710306 #> 7593 75.92 0.02223583 4.272599e-05 0.977721448 #> 7594 75.93 0.02222471 4.270463e-05 0.977732584 #> 7595 75.94 0.02221360 4.268329e-05 0.977743715 #> 7596 75.95 0.02220250 4.266195e-05 0.977754840 #> 7597 75.96 0.02219140 4.264062e-05 0.977765960 #> 7598 75.97 0.02218031 4.261931e-05 0.977777075 #> 7599 75.98 0.02216922 4.259800e-05 0.977788183 #> 7600 75.99 0.02215814 4.257671e-05 0.977799286 #> 7601 76.00 0.02214706 4.255543e-05 0.977810384 #> 7602 76.01 0.02213599 4.253416e-05 0.977821476 #> 7603 76.02 0.02212492 4.251289e-05 0.977832562 #> 7604 76.03 0.02211386 4.249164e-05 0.977843643 #> 7605 76.04 0.02210281 4.247040e-05 0.977854719 #> 7606 76.05 0.02209176 4.244917e-05 0.977865789 #> 7607 76.06 0.02208072 4.242795e-05 0.977876853 #> 7608 76.07 0.02206968 4.240674e-05 0.977887912 #> 7609 76.08 0.02205865 4.238555e-05 0.977898965 #> 7610 76.09 0.02204762 4.236436e-05 0.977910013 #> 7611 76.10 0.02203660 4.234318e-05 0.977921055 #> 7612 76.11 0.02202559 4.232202e-05 0.977932092 #> 7613 76.12 0.02201458 4.230086e-05 0.977943123 #> 7614 76.13 0.02200357 4.227971e-05 0.977954149 #> 7615 76.14 0.02199257 4.225858e-05 0.977965169 #> 7616 76.15 0.02198158 4.223746e-05 0.977976184 #> 7617 76.16 0.02197059 4.221634e-05 0.977987193 #> 7618 76.17 0.02195961 4.219524e-05 0.977998196 #> 7619 76.18 0.02194863 4.217415e-05 0.978009194 #> 7620 76.19 0.02193766 4.215307e-05 0.978020187 #> 7621 76.20 0.02192669 4.213199e-05 0.978031174 #> 7622 76.21 0.02191573 4.211093e-05 0.978042156 #> 7623 76.22 0.02190478 4.208988e-05 0.978053132 #> 7624 76.23 0.02189383 4.206884e-05 0.978064103 #> 7625 76.24 0.02188288 4.204781e-05 0.978075068 #> 7626 76.25 0.02187195 4.202680e-05 0.978086028 #> 7627 76.26 0.02186101 4.200579e-05 0.978096982 #> 7628 76.27 0.02185008 4.198479e-05 0.978107931 #> 7629 76.28 0.02183916 4.196380e-05 0.978118874 #> 7630 76.29 0.02182825 4.194283e-05 0.978129812 #> 7631 76.30 0.02181733 4.192186e-05 0.978140744 #> 7632 76.31 0.02180643 4.190090e-05 0.978151671 #> 7633 76.32 0.02179553 4.187996e-05 0.978162593 #> 7634 76.33 0.02178463 4.185902e-05 0.978173509 #> 7635 76.34 0.02177374 4.183810e-05 0.978184419 #> 7636 76.35 0.02176286 4.181719e-05 0.978195324 #> 7637 76.36 0.02175198 4.179628e-05 0.978206224 #> 7638 76.37 0.02174111 4.177539e-05 0.978217118 #> 7639 76.38 0.02173024 4.175451e-05 0.978228007 #> 7640 76.39 0.02171938 4.173364e-05 0.978238890 #> 7641 76.40 0.02170852 4.171277e-05 0.978249768 #> 7642 76.41 0.02169767 4.169192e-05 0.978260640 #> 7643 76.42 0.02168682 4.167108e-05 0.978271507 #> 7644 76.43 0.02167598 4.165025e-05 0.978282369 #> 7645 76.44 0.02166515 4.162943e-05 0.978293225 #> 7646 76.45 0.02165432 4.160862e-05 0.978304075 #> 7647 76.46 0.02164349 4.158782e-05 0.978314921 #> 7648 76.47 0.02163267 4.156703e-05 0.978325761 #> 7649 76.48 0.02162186 4.154626e-05 0.978336595 #> 7650 76.49 0.02161105 4.152549e-05 0.978347424 #> 7651 76.50 0.02160025 4.150473e-05 0.978358248 #> 7652 76.51 0.02158945 4.148398e-05 0.978369066 #> 7653 76.52 0.02157866 4.146325e-05 0.978379878 #> 7654 76.53 0.02156787 4.144252e-05 0.978390686 #> 7655 76.54 0.02155709 4.142180e-05 0.978401488 #> 7656 76.55 0.02154631 4.140110e-05 0.978412284 #> 7657 76.56 0.02153554 4.138040e-05 0.978423075 #> 7658 76.57 0.02152478 4.135972e-05 0.978433861 #> 7659 76.58 0.02151402 4.133904e-05 0.978444642 #> 7660 76.59 0.02150327 4.131838e-05 0.978455417 #> 7661 76.60 0.02149252 4.129773e-05 0.978466186 #> 7662 76.61 0.02148177 4.127708e-05 0.978476950 #> 7663 76.62 0.02147103 4.125645e-05 0.978487709 #> 7664 76.63 0.02146030 4.123582e-05 0.978498463 #> 7665 76.64 0.02144957 4.121521e-05 0.978509211 #> 7666 76.65 0.02143885 4.119461e-05 0.978519954 #> 7667 76.66 0.02142814 4.117402e-05 0.978530691 #> 7668 76.67 0.02141742 4.115344e-05 0.978541423 #> 7669 76.68 0.02140672 4.113286e-05 0.978552149 #> 7670 76.69 0.02139602 4.111230e-05 0.978562871 #> 7671 76.70 0.02138532 4.109175e-05 0.978573587 #> 7672 76.71 0.02137463 4.107121e-05 0.978584297 #> 7673 76.72 0.02136395 4.105068e-05 0.978595002 #> 7674 76.73 0.02135327 4.103016e-05 0.978605702 #> 7675 76.74 0.02134259 4.100965e-05 0.978616397 #> 7676 76.75 0.02133193 4.098915e-05 0.978627086 #> 7677 76.76 0.02132126 4.096866e-05 0.978637769 #> 7678 76.77 0.02131060 4.094818e-05 0.978648448 #> 7679 76.78 0.02129995 4.092771e-05 0.978659121 #> 7680 76.79 0.02128930 4.090725e-05 0.978669789 #> 7681 76.80 0.02127866 4.088681e-05 0.978680451 #> 7682 76.81 0.02126803 4.086637e-05 0.978691108 #> 7683 76.82 0.02125739 4.084594e-05 0.978701760 #> 7684 76.83 0.02124677 4.082552e-05 0.978712407 #> 7685 76.84 0.02123615 4.080511e-05 0.978723048 #> 7686 76.85 0.02122553 4.078472e-05 0.978733684 #> 7687 76.86 0.02121492 4.076433e-05 0.978744314 #> 7688 76.87 0.02120432 4.074395e-05 0.978754939 #> 7689 76.88 0.02119372 4.072359e-05 0.978765559 #> 7690 76.89 0.02118312 4.070323e-05 0.978776174 #> 7691 76.90 0.02117253 4.068288e-05 0.978786783 #> 7692 76.91 0.02116195 4.066255e-05 0.978797387 #> 7693 76.92 0.02115137 4.064222e-05 0.978807986 #> 7694 76.93 0.02114080 4.062190e-05 0.978818579 #> 7695 76.94 0.02113023 4.060160e-05 0.978829167 #> 7696 76.95 0.02111967 4.058130e-05 0.978839750 #> 7697 76.96 0.02110911 4.056102e-05 0.978850327 #> 7698 76.97 0.02109856 4.054074e-05 0.978860899 #> 7699 76.98 0.02108801 4.052048e-05 0.978871466 #> 7700 76.99 0.02107747 4.050022e-05 0.978882028 #> 7701 77.00 0.02106694 4.047998e-05 0.978892584 #> 7702 77.01 0.02105640 4.045974e-05 0.978903135 #> 7703 77.02 0.02104588 4.043952e-05 0.978913681 #> 7704 77.03 0.02103536 4.041930e-05 0.978924222 #> 7705 77.04 0.02102484 4.039910e-05 0.978934757 #> 7706 77.05 0.02101433 4.037890e-05 0.978945287 #> 7707 77.06 0.02100383 4.035872e-05 0.978955812 #> 7708 77.07 0.02099333 4.033854e-05 0.978966331 #> 7709 77.08 0.02098284 4.031838e-05 0.978976845 #> 7710 77.09 0.02097235 4.029822e-05 0.978987354 #> 7711 77.10 0.02096186 4.027808e-05 0.978997858 #> 7712 77.11 0.02095139 4.025795e-05 0.979008356 #> 7713 77.12 0.02094091 4.023782e-05 0.979018850 #> 7714 77.13 0.02093044 4.021771e-05 0.979029338 #> 7715 77.14 0.02091998 4.019760e-05 0.979039820 #> 7716 77.15 0.02090952 4.017751e-05 0.979050298 #> 7717 77.16 0.02089907 4.015743e-05 0.979060770 #> 7718 77.17 0.02088863 4.013735e-05 0.979071237 #> 7719 77.18 0.02087818 4.011729e-05 0.979081699 #> 7720 77.19 0.02086775 4.009724e-05 0.979092155 #> 7721 77.20 0.02085732 4.007719e-05 0.979102607 #> 7722 77.21 0.02084689 4.005716e-05 0.979113053 #> 7723 77.22 0.02083647 4.003714e-05 0.979123494 #> 7724 77.23 0.02082605 4.001712e-05 0.979133929 #> 7725 77.24 0.02081564 3.999712e-05 0.979144360 #> 7726 77.25 0.02080524 3.997712e-05 0.979154785 #> 7727 77.26 0.02079484 3.995714e-05 0.979165205 #> 7728 77.27 0.02078444 3.993717e-05 0.979175620 #> 7729 77.28 0.02077405 3.991720e-05 0.979186029 #> 7730 77.29 0.02076367 3.989725e-05 0.979196434 #> 7731 77.30 0.02075329 3.987731e-05 0.979206833 #> 7732 77.31 0.02074292 3.985737e-05 0.979217227 #> 7733 77.32 0.02073255 3.983745e-05 0.979227616 #> 7734 77.33 0.02072218 3.981754e-05 0.979237999 #> 7735 77.34 0.02071182 3.979763e-05 0.979248378 #> 7736 77.35 0.02070147 3.977774e-05 0.979258751 #> 7737 77.36 0.02069112 3.975785e-05 0.979269119 #> 7738 77.37 0.02068078 3.973798e-05 0.979279482 #> 7739 77.38 0.02067044 3.971812e-05 0.979289839 #> 7740 77.39 0.02066011 3.969826e-05 0.979300192 #> 7741 77.40 0.02064978 3.967842e-05 0.979310539 #> 7742 77.41 0.02063946 3.965858e-05 0.979320881 #> 7743 77.42 0.02062914 3.963876e-05 0.979331218 #> 7744 77.43 0.02061883 3.961895e-05 0.979341550 #> 7745 77.44 0.02060852 3.959914e-05 0.979351877 #> 7746 77.45 0.02059822 3.957935e-05 0.979362198 #> 7747 77.46 0.02058793 3.955956e-05 0.979372515 #> 7748 77.47 0.02057763 3.953979e-05 0.979382826 #> 7749 77.48 0.02056735 3.952002e-05 0.979393132 #> 7750 77.49 0.02055707 3.950027e-05 0.979403433 #> 7751 77.50 0.02054679 3.948052e-05 0.979413728 #> 7752 77.51 0.02053652 3.946079e-05 0.979424019 #> 7753 77.52 0.02052625 3.944106e-05 0.979434304 #> 7754 77.53 0.02051599 3.942134e-05 0.979444585 #> 7755 77.54 0.02050574 3.940164e-05 0.979454860 #> 7756 77.55 0.02049549 3.938194e-05 0.979465130 #> 7757 77.56 0.02048524 3.936226e-05 0.979475395 #> 7758 77.57 0.02047500 3.934258e-05 0.979485654 #> 7759 77.58 0.02046477 3.932291e-05 0.979495909 #> 7760 77.59 0.02045454 3.930326e-05 0.979506158 #> 7761 77.60 0.02044431 3.928361e-05 0.979516403 #> 7762 77.61 0.02043409 3.926397e-05 0.979526642 #> 7763 77.62 0.02042388 3.924435e-05 0.979536876 #> 7764 77.63 0.02041367 3.922473e-05 0.979547105 #> 7765 77.64 0.02040347 3.920512e-05 0.979557329 #> 7766 77.65 0.02039327 3.918552e-05 0.979567548 #> 7767 77.66 0.02038307 3.916594e-05 0.979577761 #> 7768 77.67 0.02037288 3.914636e-05 0.979587970 #> 7769 77.68 0.02036270 3.912679e-05 0.979598173 #> 7770 77.69 0.02035252 3.910723e-05 0.979608372 #> 7771 77.70 0.02034235 3.908768e-05 0.979618565 #> 7772 77.71 0.02033218 3.906814e-05 0.979628753 #> 7773 77.72 0.02032202 3.904862e-05 0.979638936 #> 7774 77.73 0.02031186 3.902910e-05 0.979649114 #> 7775 77.74 0.02030170 3.900959e-05 0.979659287 #> 7776 77.75 0.02029155 3.899009e-05 0.979669455 #> 7777 77.76 0.02028141 3.897060e-05 0.979679618 #> 7778 77.77 0.02027127 3.895112e-05 0.979689775 #> 7779 77.78 0.02026114 3.893164e-05 0.979699928 #> 7780 77.79 0.02025101 3.891218e-05 0.979710076 #> 7781 77.80 0.02024089 3.889273e-05 0.979720218 #> 7782 77.81 0.02023077 3.887329e-05 0.979730355 #> 7783 77.82 0.02022066 3.885386e-05 0.979740488 #> 7784 77.83 0.02021055 3.883444e-05 0.979750615 #> 7785 77.84 0.02020045 3.881502e-05 0.979760737 #> 7786 77.85 0.02019035 3.879562e-05 0.979770854 #> 7787 77.86 0.02018026 3.877623e-05 0.979780966 #> 7788 77.87 0.02017017 3.875685e-05 0.979791073 #> 7789 77.88 0.02016009 3.873747e-05 0.979801175 #> 7790 77.89 0.02015001 3.871811e-05 0.979811272 #> 7791 77.90 0.02013994 3.869875e-05 0.979821364 #> 7792 77.91 0.02012987 3.867941e-05 0.979831451 #> 7793 77.92 0.02011981 3.866007e-05 0.979841532 #> 7794 77.93 0.02010975 3.864075e-05 0.979851609 #> 7795 77.94 0.02009970 3.862143e-05 0.979861681 #> 7796 77.95 0.02008965 3.860213e-05 0.979871747 #> 7797 77.96 0.02007961 3.858283e-05 0.979881809 #> 7798 77.97 0.02006957 3.856355e-05 0.979891866 #> 7799 77.98 0.02005954 3.854427e-05 0.979901917 #> 7800 77.99 0.02004951 3.852500e-05 0.979911964 #> 7801 78.00 0.02003949 3.850574e-05 0.979922005 #> 7802 78.01 0.02002947 3.848650e-05 0.979932042 #> 7803 78.02 0.02001946 3.846726e-05 0.979942073 #> 7804 78.03 0.02000945 3.844803e-05 0.979952100 #> 7805 78.04 0.01999945 3.842881e-05 0.979962121 #> 7806 78.05 0.01998945 3.840960e-05 0.979972137 #> 7807 78.06 0.01997946 3.839040e-05 0.979982149 #> 7808 78.07 0.01996947 3.837121e-05 0.979992155 #> 7809 78.08 0.01995949 3.835203e-05 0.980002157 #> 7810 78.09 0.01994951 3.833286e-05 0.980012153 #> 7811 78.10 0.01993954 3.831370e-05 0.980022145 #> 7812 78.11 0.01992957 3.829454e-05 0.980032131 #> 7813 78.12 0.01991961 3.827540e-05 0.980042112 #> 7814 78.13 0.01990965 3.825627e-05 0.980052089 #> 7815 78.14 0.01989970 3.823714e-05 0.980062060 #> 7816 78.15 0.01988976 3.821803e-05 0.980072027 #> 7817 78.16 0.01987981 3.819893e-05 0.980081988 #> 7818 78.17 0.01986988 3.817983e-05 0.980091945 #> 7819 78.18 0.01985994 3.816075e-05 0.980101896 #> 7820 78.19 0.01985002 3.814167e-05 0.980111843 #> 7821 78.20 0.01984009 3.812260e-05 0.980121785 #> 7822 78.21 0.01983018 3.810355e-05 0.980131721 #> 7823 78.22 0.01982026 3.808450e-05 0.980141653 #> 7824 78.23 0.01981036 3.806546e-05 0.980151580 #> 7825 78.24 0.01980045 3.804644e-05 0.980161501 #> 7826 78.25 0.01979055 3.802742e-05 0.980171418 #> 7827 78.26 0.01978066 3.800841e-05 0.980181330 #> 7828 78.27 0.01977077 3.798941e-05 0.980191237 #> 7829 78.28 0.01976089 3.797042e-05 0.980201139 #> 7830 78.29 0.01975101 3.795144e-05 0.980211036 #> 7831 78.30 0.01974114 3.793247e-05 0.980220928 #> 7832 78.31 0.01973127 3.791351e-05 0.980230815 #> 7833 78.32 0.01972141 3.789455e-05 0.980240697 #> 7834 78.33 0.01971155 3.787561e-05 0.980250574 #> 7835 78.34 0.01970170 3.785668e-05 0.980260446 #> 7836 78.35 0.01969185 3.783775e-05 0.980270313 #> 7837 78.36 0.01968201 3.781884e-05 0.980280176 #> 7838 78.37 0.01967217 3.779994e-05 0.980290033 #> 7839 78.38 0.01966233 3.778104e-05 0.980299886 #> 7840 78.39 0.01965250 3.776216e-05 0.980309733 #> 7841 78.40 0.01964268 3.774328e-05 0.980319576 #> 7842 78.41 0.01963286 3.772441e-05 0.980329414 #> 7843 78.42 0.01962305 3.770555e-05 0.980339247 #> 7844 78.43 0.01961324 3.768671e-05 0.980349075 #> 7845 78.44 0.01960343 3.766787e-05 0.980358898 #> 7846 78.45 0.01959364 3.764904e-05 0.980368716 #> 7847 78.46 0.01958384 3.763022e-05 0.980378529 #> 7848 78.47 0.01957405 3.761141e-05 0.980388337 #> 7849 78.48 0.01956427 3.759261e-05 0.980398141 #> 7850 78.49 0.01955449 3.757382e-05 0.980407939 #> 7851 78.50 0.01954471 3.755503e-05 0.980417733 #> 7852 78.51 0.01953494 3.753626e-05 0.980427521 #> 7853 78.52 0.01952518 3.751750e-05 0.980437305 #> 7854 78.53 0.01951542 3.749874e-05 0.980447084 #> 7855 78.54 0.01950566 3.748000e-05 0.980456858 #> 7856 78.55 0.01949591 3.746126e-05 0.980466627 #> 7857 78.56 0.01948617 3.744254e-05 0.980476391 #> 7858 78.57 0.01947643 3.742382e-05 0.980486151 #> 7859 78.58 0.01946669 3.740511e-05 0.980495905 #> 7860 78.59 0.01945696 3.738642e-05 0.980505655 #> 7861 78.60 0.01944723 3.736773e-05 0.980515400 #> 7862 78.61 0.01943751 3.734905e-05 0.980525139 #> 7863 78.62 0.01942780 3.733038e-05 0.980534874 #> 7864 78.63 0.01941808 3.731172e-05 0.980544605 #> 7865 78.64 0.01940838 3.729307e-05 0.980554330 #> 7866 78.65 0.01939868 3.727442e-05 0.980564050 #> 7867 78.66 0.01938898 3.725579e-05 0.980573766 #> 7868 78.67 0.01937929 3.723717e-05 0.980583477 #> 7869 78.68 0.01936960 3.721855e-05 0.980593182 #> 7870 78.69 0.01935992 3.719995e-05 0.980602883 #> 7871 78.70 0.01935024 3.718135e-05 0.980612579 #> 7872 78.71 0.01934057 3.716277e-05 0.980622271 #> 7873 78.72 0.01933090 3.714419e-05 0.980631957 #> 7874 78.73 0.01932124 3.712562e-05 0.980641639 #> 7875 78.74 0.01931158 3.710707e-05 0.980651316 #> 7876 78.75 0.01930192 3.708852e-05 0.980660987 #> 7877 78.76 0.01929228 3.706998e-05 0.980670655 #> 7878 78.77 0.01928263 3.705145e-05 0.980680317 #> 7879 78.78 0.01927299 3.703293e-05 0.980689974 #> 7880 78.79 0.01926336 3.701441e-05 0.980699627 #> 7881 78.80 0.01925373 3.699591e-05 0.980709275 #> 7882 78.81 0.01924411 3.697742e-05 0.980718918 #> 7883 78.82 0.01923449 3.695893e-05 0.980728556 #> 7884 78.83 0.01922487 3.694046e-05 0.980738189 #> 7885 78.84 0.01921526 3.692199e-05 0.980747818 #> 7886 78.85 0.01920566 3.690354e-05 0.980757441 #> 7887 78.86 0.01919605 3.688509e-05 0.980767060 #> 7888 78.87 0.01918646 3.686665e-05 0.980776674 #> 7889 78.88 0.01917687 3.684822e-05 0.980786283 #> 7890 78.89 0.01916728 3.682980e-05 0.980795888 #> 7891 78.90 0.01915770 3.681139e-05 0.980805488 #> 7892 78.91 0.01914812 3.679299e-05 0.980815082 #> 7893 78.92 0.01913855 3.677460e-05 0.980824672 #> 7894 78.93 0.01912899 3.675622e-05 0.980834258 #> 7895 78.94 0.01911942 3.673784e-05 0.980843838 #> 7896 78.95 0.01910987 3.671948e-05 0.980853414 #> 7897 78.96 0.01910031 3.670113e-05 0.980862985 #> 7898 78.97 0.01909077 3.668278e-05 0.980872551 #> 7899 78.98 0.01908122 3.666444e-05 0.980882112 #> 7900 78.99 0.01907169 3.664611e-05 0.980891669 #> 7901 79.00 0.01906215 3.662780e-05 0.980901221 #> 7902 79.01 0.01905262 3.660949e-05 0.980910768 #> 7903 79.02 0.01904310 3.659119e-05 0.980920310 #> 7904 79.03 0.01903358 3.657290e-05 0.980929847 #> 7905 79.04 0.01902407 3.655461e-05 0.980939380 #> 7906 79.05 0.01901456 3.653634e-05 0.980948908 #> 7907 79.06 0.01900505 3.651808e-05 0.980958431 #> 7908 79.07 0.01899555 3.649982e-05 0.980967949 #> 7909 79.08 0.01898606 3.648158e-05 0.980977463 #> 7910 79.09 0.01897656 3.646334e-05 0.980986972 #> 7911 79.10 0.01896708 3.644511e-05 0.980996476 #> 7912 79.11 0.01895760 3.642690e-05 0.981005976 #> 7913 79.12 0.01894812 3.640869e-05 0.981015470 #> 7914 79.13 0.01893865 3.639049e-05 0.981024960 #> 7915 79.14 0.01892918 3.637230e-05 0.981034445 #> 7916 79.15 0.01891972 3.635412e-05 0.981043926 #> 7917 79.16 0.01891026 3.633594e-05 0.981053401 #> 7918 79.17 0.01890081 3.631778e-05 0.981062872 #> 7919 79.18 0.01889136 3.629962e-05 0.981072338 #> 7920 79.19 0.01888192 3.628148e-05 0.981081800 #> 7921 79.20 0.01887248 3.626334e-05 0.981091257 #> 7922 79.21 0.01886305 3.624522e-05 0.981100709 #> 7923 79.22 0.01885362 3.622710e-05 0.981110156 #> 7924 79.23 0.01884419 3.620899e-05 0.981119598 #> 7925 79.24 0.01883477 3.619089e-05 0.981129036 #> 7926 79.25 0.01882536 3.617280e-05 0.981138469 #> 7927 79.26 0.01881595 3.615472e-05 0.981147898 #> 7928 79.27 0.01880654 3.613664e-05 0.981157322 #> 7929 79.28 0.01879714 3.611858e-05 0.981166741 #> 7930 79.29 0.01878774 3.610053e-05 0.981176155 #> 7931 79.30 0.01877835 3.608248e-05 0.981185564 #> 7932 79.31 0.01876897 3.606444e-05 0.981194969 #> 7933 79.32 0.01875958 3.604641e-05 0.981204369 #> 7934 79.33 0.01875021 3.602840e-05 0.981213765 #> 7935 79.34 0.01874083 3.601039e-05 0.981223156 #> 7936 79.35 0.01873147 3.599239e-05 0.981232542 #> 7937 79.36 0.01872210 3.597439e-05 0.981241923 #> 7938 79.37 0.01871274 3.595641e-05 0.981251300 #> 7939 79.38 0.01870339 3.593844e-05 0.981260672 #> 7940 79.39 0.01869404 3.592047e-05 0.981270039 #> 7941 79.40 0.01868470 3.590252e-05 0.981279402 #> 7942 79.41 0.01867536 3.588457e-05 0.981288760 #> 7943 79.42 0.01866602 3.586663e-05 0.981298113 #> 7944 79.43 0.01865669 3.584870e-05 0.981307462 #> 7945 79.44 0.01864736 3.583078e-05 0.981316806 #> 7946 79.45 0.01863804 3.581287e-05 0.981326145 #> 7947 79.46 0.01862873 3.579497e-05 0.981335479 #> 7948 79.47 0.01861941 3.577708e-05 0.981344809 #> 7949 79.48 0.01861011 3.575919e-05 0.981354135 #> 7950 79.49 0.01860080 3.574132e-05 0.981363455 #> 7951 79.50 0.01859151 3.572345e-05 0.981372771 #> 7952 79.51 0.01858221 3.570560e-05 0.981382082 #> 7953 79.52 0.01857292 3.568775e-05 0.981391389 #> 7954 79.53 0.01856364 3.566991e-05 0.981400691 #> 7955 79.54 0.01855436 3.565208e-05 0.981409988 #> 7956 79.55 0.01854508 3.563426e-05 0.981419281 #> 7957 79.56 0.01853581 3.561644e-05 0.981428569 #> 7958 79.57 0.01852655 3.559864e-05 0.981437852 #> 7959 79.58 0.01851729 3.558084e-05 0.981447131 #> 7960 79.59 0.01850803 3.556306e-05 0.981456405 #> 7961 79.60 0.01849878 3.554528e-05 0.981465675 #> 7962 79.61 0.01848953 3.552751e-05 0.981474940 #> 7963 79.62 0.01848029 3.550975e-05 0.981484200 #> 7964 79.63 0.01847105 3.549200e-05 0.981493455 #> 7965 79.64 0.01846182 3.547426e-05 0.981502706 #> 7966 79.65 0.01845259 3.545653e-05 0.981511953 #> 7967 79.66 0.01844337 3.543881e-05 0.981521194 #> 7968 79.67 0.01843415 3.542109e-05 0.981530432 #> 7969 79.68 0.01842493 3.540338e-05 0.981539664 #> 7970 79.69 0.01841572 3.538569e-05 0.981548892 #> 7971 79.70 0.01840652 3.536800e-05 0.981558115 #> 7972 79.71 0.01839732 3.535032e-05 0.981567334 #> 7973 79.72 0.01838812 3.533265e-05 0.981576548 #> 7974 79.73 0.01837893 3.531499e-05 0.981585757 #> 7975 79.74 0.01836974 3.529733e-05 0.981594962 #> 7976 79.75 0.01836056 3.527969e-05 0.981604162 #> 7977 79.76 0.01835138 3.526205e-05 0.981613358 #> 7978 79.77 0.01834221 3.524443e-05 0.981622549 #> 7979 79.78 0.01833304 3.522681e-05 0.981631735 #> 7980 79.79 0.01832387 3.520920e-05 0.981640917 #> 7981 79.80 0.01831471 3.519160e-05 0.981650094 #> 7982 79.81 0.01830556 3.517401e-05 0.981659267 #> 7983 79.82 0.01829641 3.515643e-05 0.981668435 #> 7984 79.83 0.01828726 3.513885e-05 0.981677599 #> 7985 79.84 0.01827812 3.512129e-05 0.981686758 #> 7986 79.85 0.01826898 3.510373e-05 0.981695912 #> 7987 79.86 0.01825985 3.508618e-05 0.981705062 #> 7988 79.87 0.01825072 3.506864e-05 0.981714207 #> 7989 79.88 0.01824160 3.505111e-05 0.981723347 #> 7990 79.89 0.01823248 3.503359e-05 0.981732484 #> 7991 79.90 0.01822337 3.501608e-05 0.981741615 #> 7992 79.91 0.01821426 3.499858e-05 0.981750742 #> 7993 79.92 0.01820515 3.498108e-05 0.981759864 #> 7994 79.93 0.01819605 3.496360e-05 0.981768982 #> 7995 79.94 0.01818696 3.494612e-05 0.981778095 #> 7996 79.95 0.01817787 3.492865e-05 0.981787204 #> 7997 79.96 0.01816878 3.491119e-05 0.981796308 #> 7998 79.97 0.01815970 3.489374e-05 0.981805408 #> 7999 79.98 0.01815062 3.487630e-05 0.981814503 #> 8000 79.99 0.01814155 3.485886e-05 0.981823593 #> 8001 80.00 0.01813248 3.484144e-05 0.981832679 #> 8002 80.01 0.01812342 3.482402e-05 0.981841760 #> 8003 80.02 0.01811436 3.480661e-05 0.981850837 #> 8004 80.03 0.01810530 3.478921e-05 0.981859910 #> 8005 80.04 0.01809625 3.477182e-05 0.981868977 #> 8006 80.05 0.01808720 3.475444e-05 0.981878041 #> 8007 80.06 0.01807816 3.473707e-05 0.981887099 #> 8008 80.07 0.01806913 3.471971e-05 0.981896154 #> 8009 80.08 0.01806009 3.470235e-05 0.981905203 #> 8010 80.09 0.01805107 3.468500e-05 0.981914248 #> 8011 80.10 0.01804204 3.466767e-05 0.981923289 #> 8012 80.11 0.01803302 3.465034e-05 0.981932325 #> 8013 80.12 0.01802401 3.463301e-05 0.981941357 #> 8014 80.13 0.01801500 3.461570e-05 0.981950384 #> 8015 80.14 0.01800600 3.459840e-05 0.981959406 #> 8016 80.15 0.01799699 3.458110e-05 0.981968424 #> 8017 80.16 0.01798800 3.456382e-05 0.981977438 #> 8018 80.17 0.01797901 3.454654e-05 0.981986447 #> 8019 80.18 0.01797002 3.452927e-05 0.981995451 #> 8020 80.19 0.01796104 3.451201e-05 0.982004451 #> 8021 80.20 0.01795206 3.449476e-05 0.982013447 #> 8022 80.21 0.01794308 3.447752e-05 0.982022438 #> 8023 80.22 0.01793412 3.446028e-05 0.982031424 #> 8024 80.23 0.01792515 3.444306e-05 0.982040407 #> 8025 80.24 0.01791619 3.442584e-05 0.982049384 #> 8026 80.25 0.01790723 3.440863e-05 0.982058357 #> 8027 80.26 0.01789828 3.439143e-05 0.982067326 #> 8028 80.27 0.01788934 3.437424e-05 0.982076290 #> 8029 80.28 0.01788039 3.435706e-05 0.982085249 #> 8030 80.29 0.01787146 3.433988e-05 0.982094205 #> 8031 80.30 0.01786252 3.432272e-05 0.982103155 #> 8032 80.31 0.01785359 3.430556e-05 0.982112101 #> 8033 80.32 0.01784467 3.428841e-05 0.982121043 #> 8034 80.33 0.01783575 3.427127e-05 0.982129980 #> 8035 80.34 0.01782683 3.425414e-05 0.982138913 #> 8036 80.35 0.01781792 3.423702e-05 0.982147841 #> 8037 80.36 0.01780901 3.421990e-05 0.982156765 #> 8038 80.37 0.01780011 3.420280e-05 0.982165685 #> 8039 80.38 0.01779121 3.418570e-05 0.982174600 #> 8040 80.39 0.01778232 3.416861e-05 0.982183510 #> 8041 80.40 0.01777343 3.415153e-05 0.982192416 #> 8042 80.41 0.01776455 3.413446e-05 0.982201318 #> 8043 80.42 0.01775567 3.411740e-05 0.982210215 #> 8044 80.43 0.01774679 3.410034e-05 0.982219107 #> 8045 80.44 0.01773792 3.408330e-05 0.982227996 #> 8046 80.45 0.01772905 3.406626e-05 0.982236879 #> 8047 80.46 0.01772019 3.404923e-05 0.982245759 #> 8048 80.47 0.01771133 3.403221e-05 0.982254634 #> 8049 80.48 0.01770248 3.401520e-05 0.982263504 #> 8050 80.49 0.01769363 3.399819e-05 0.982272370 #> 8051 80.50 0.01768479 3.398120e-05 0.982281232 #> 8052 80.51 0.01767595 3.396421e-05 0.982290089 #> 8053 80.52 0.01766711 3.394724e-05 0.982298942 #> 8054 80.53 0.01765828 3.393027e-05 0.982307790 #> 8055 80.54 0.01764945 3.391330e-05 0.982316634 #> 8056 80.55 0.01764063 3.389635e-05 0.982325473 #> 8057 80.56 0.01763181 3.387941e-05 0.982334308 #> 8058 80.57 0.01762300 3.386247e-05 0.982343139 #> 8059 80.58 0.01761419 3.384555e-05 0.982351965 #> 8060 80.59 0.01760538 3.382863e-05 0.982360787 #> 8061 80.60 0.01759658 3.381172e-05 0.982369605 #> 8062 80.61 0.01758779 3.379482e-05 0.982378418 #> 8063 80.62 0.01757900 3.377792e-05 0.982387226 #> 8064 80.63 0.01757021 3.376104e-05 0.982396030 #> 8065 80.64 0.01756143 3.374416e-05 0.982404830 #> 8066 80.65 0.01755265 3.372729e-05 0.982413626 #> 8067 80.66 0.01754387 3.371043e-05 0.982422416 #> 8068 80.67 0.01753510 3.369358e-05 0.982431203 #> 8069 80.68 0.01752634 3.367674e-05 0.982439985 #> 8070 80.69 0.01751758 3.365991e-05 0.982448763 #> 8071 80.70 0.01750882 3.364308e-05 0.982457537 #> 8072 80.71 0.01750007 3.362626e-05 0.982466306 #> 8073 80.72 0.01749132 3.360945e-05 0.982475070 #> 8074 80.73 0.01748258 3.359265e-05 0.982483830 #> 8075 80.74 0.01747384 3.357586e-05 0.982492586 #> 8076 80.75 0.01746510 3.355908e-05 0.982501338 #> 8077 80.76 0.01745637 3.354230e-05 0.982510085 #> 8078 80.77 0.01744765 3.352554e-05 0.982518828 #> 8079 80.78 0.01743892 3.350878e-05 0.982527566 #> 8080 80.79 0.01743021 3.349203e-05 0.982536300 #> 8081 80.80 0.01742149 3.347529e-05 0.982545030 #> 8082 80.81 0.01741279 3.345855e-05 0.982553755 #> 8083 80.82 0.01740408 3.344183e-05 0.982562476 #> 8084 80.83 0.01739538 3.342511e-05 0.982571193 #> 8085 80.84 0.01738669 3.340840e-05 0.982579905 #> 8086 80.85 0.01737800 3.339170e-05 0.982588613 #> 8087 80.86 0.01736931 3.337501e-05 0.982597316 #> 8088 80.87 0.01736063 3.335833e-05 0.982606016 #> 8089 80.88 0.01735195 3.334165e-05 0.982614710 #> 8090 80.89 0.01734327 3.332498e-05 0.982623401 #> 8091 80.90 0.01733460 3.330833e-05 0.982632087 #> 8092 80.91 0.01732594 3.329168e-05 0.982640769 #> 8093 80.92 0.01731728 3.327503e-05 0.982649446 #> 8094 80.93 0.01730862 3.325840e-05 0.982658119 #> 8095 80.94 0.01729997 3.324178e-05 0.982666788 #> 8096 80.95 0.01729132 3.322516e-05 0.982675453 #> 8097 80.96 0.01728268 3.320855e-05 0.982684113 #> 8098 80.97 0.01727404 3.319195e-05 0.982692768 #> 8099 80.98 0.01726540 3.317536e-05 0.982701420 #> 8100 80.99 0.01725677 3.315878e-05 0.982710067 #> 8101 81.00 0.01724815 3.314220e-05 0.982718710 #> 8102 81.01 0.01723953 3.312563e-05 0.982727348 #> 8103 81.02 0.01723091 3.310907e-05 0.982735982 #> 8104 81.03 0.01722230 3.309252e-05 0.982744612 #> 8105 81.04 0.01721369 3.307598e-05 0.982753238 #> 8106 81.05 0.01720508 3.305945e-05 0.982761859 #> 8107 81.06 0.01719648 3.304292e-05 0.982770476 #> 8108 81.07 0.01718789 3.302641e-05 0.982779089 #> 8109 81.08 0.01717929 3.300990e-05 0.982787697 #> 8110 81.09 0.01717071 3.299340e-05 0.982796301 #> 8111 81.10 0.01716212 3.297690e-05 0.982804901 #> 8112 81.11 0.01715354 3.296042e-05 0.982813496 #> 8113 81.12 0.01714497 3.294394e-05 0.982822087 #> 8114 81.13 0.01713640 3.292747e-05 0.982830674 #> 8115 81.14 0.01712783 3.291102e-05 0.982839256 #> 8116 81.15 0.01711927 3.289456e-05 0.982847835 #> 8117 81.16 0.01711071 3.287812e-05 0.982856409 #> 8118 81.17 0.01710216 3.286169e-05 0.982864978 #> 8119 81.18 0.01709361 3.284526e-05 0.982873544 #> 8120 81.19 0.01708507 3.282884e-05 0.982882105 #> 8121 81.20 0.01707653 3.281243e-05 0.982890662 #> 8122 81.21 0.01706799 3.279603e-05 0.982899214 #> 8123 81.22 0.01705946 3.277963e-05 0.982907762 #> 8124 81.23 0.01705093 3.276325e-05 0.982916306 #> 8125 81.24 0.01704241 3.274687e-05 0.982924846 #> 8126 81.25 0.01703389 3.273050e-05 0.982933381 #> 8127 81.26 0.01702537 3.271414e-05 0.982941913 #> 8128 81.27 0.01701686 3.269779e-05 0.982950440 #> 8129 81.28 0.01700836 3.268144e-05 0.982958962 #> 8130 81.29 0.01699985 3.266511e-05 0.982967481 #> 8131 81.30 0.01699136 3.264878e-05 0.982975995 #> 8132 81.31 0.01698286 3.263246e-05 0.982984505 #> 8133 81.32 0.01697437 3.261614e-05 0.982993010 #> 8134 81.33 0.01696589 3.259984e-05 0.983001512 #> 8135 81.34 0.01695741 3.258354e-05 0.983010009 #> 8136 81.35 0.01694893 3.256726e-05 0.983018502 #> 8137 81.36 0.01694046 3.255098e-05 0.983026990 #> 8138 81.37 0.01693199 3.253471e-05 0.983035475 #> 8139 81.38 0.01692353 3.251844e-05 0.983043955 #> 8140 81.39 0.01691507 3.250219e-05 0.983052431 #> 8141 81.40 0.01690661 3.248594e-05 0.983060902 #> 8142 81.41 0.01689816 3.246970e-05 0.983069370 #> 8143 81.42 0.01688971 3.245347e-05 0.983077833 #> 8144 81.43 0.01688127 3.243725e-05 0.983086292 #> 8145 81.44 0.01687283 3.242103e-05 0.983094747 #> 8146 81.45 0.01686440 3.240483e-05 0.983103197 #> 8147 81.46 0.01685597 3.238863e-05 0.983111643 #> 8148 81.47 0.01684754 3.237244e-05 0.983120085 #> 8149 81.48 0.01683912 3.235626e-05 0.983128523 #> 8150 81.49 0.01683070 3.234008e-05 0.983136957 #> 8151 81.50 0.01682229 3.232392e-05 0.983145386 #> 8152 81.51 0.01681388 3.230776e-05 0.983153812 #> 8153 81.52 0.01680548 3.229161e-05 0.983162233 #> 8154 81.53 0.01679708 3.227547e-05 0.983170649 #> 8155 81.54 0.01678868 3.225933e-05 0.983179062 #> 8156 81.55 0.01678029 3.224321e-05 0.983187470 #> 8157 81.56 0.01677190 3.222709e-05 0.983195874 #> 8158 81.57 0.01676351 3.221098e-05 0.983204274 #> 8159 81.58 0.01675513 3.219488e-05 0.983212670 #> 8160 81.59 0.01674676 3.217879e-05 0.983221062 #> 8161 81.60 0.01673839 3.216270e-05 0.983229449 #> 8162 81.61 0.01673002 3.214662e-05 0.983237832 #> 8163 81.62 0.01672166 3.213055e-05 0.983246211 #> 8164 81.63 0.01671330 3.211449e-05 0.983254586 #> 8165 81.64 0.01670494 3.209844e-05 0.983262957 #> 8166 81.65 0.01669659 3.208239e-05 0.983271323 #> 8167 81.66 0.01668825 3.206636e-05 0.983279685 #> 8168 81.67 0.01667991 3.205033e-05 0.983288043 #> 8169 81.68 0.01667157 3.203431e-05 0.983296397 #> 8170 81.69 0.01666323 3.201829e-05 0.983304747 #> 8171 81.70 0.01665491 3.200229e-05 0.983313093 #> 8172 81.71 0.01664658 3.198629e-05 0.983321434 #> 8173 81.72 0.01663826 3.197030e-05 0.983329771 #> 8174 81.73 0.01662994 3.195432e-05 0.983338104 #> 8175 81.74 0.01662163 3.193835e-05 0.983346433 #> 8176 81.75 0.01661332 3.192238e-05 0.983354758 #> 8177 81.76 0.01660502 3.190643e-05 0.983363078 #> 8178 81.77 0.01659671 3.189048e-05 0.983371395 #> 8179 81.78 0.01658842 3.187453e-05 0.983379707 #> 8180 81.79 0.01658013 3.185860e-05 0.983388015 #> 8181 81.80 0.01657184 3.184268e-05 0.983396319 #> 8182 81.81 0.01656355 3.182676e-05 0.983404619 #> 8183 81.82 0.01655527 3.181085e-05 0.983412914 #> 8184 81.83 0.01654700 3.179495e-05 0.983421206 #> 8185 81.84 0.01653873 3.177905e-05 0.983429493 #> 8186 81.85 0.01653046 3.176317e-05 0.983437776 #> 8187 81.86 0.01652220 3.174729e-05 0.983446055 #> 8188 81.87 0.01651394 3.173142e-05 0.983454330 #> 8189 81.88 0.01650568 3.171556e-05 0.983462601 #> 8190 81.89 0.01649743 3.169971e-05 0.983470868 #> 8191 81.90 0.01648919 3.168386e-05 0.983479130 #> 8192 81.91 0.01648094 3.166802e-05 0.983487388 #> 8193 81.92 0.01647271 3.165219e-05 0.983495643 #> 8194 81.93 0.01646447 3.163637e-05 0.983503893 #> 8195 81.94 0.01645624 3.162056e-05 0.983512139 #> 8196 81.95 0.01644801 3.160475e-05 0.983520381 #> 8197 81.96 0.01643979 3.158895e-05 0.983528618 #> 8198 81.97 0.01643157 3.157316e-05 0.983536852 #> 8199 81.98 0.01642336 3.155738e-05 0.983545082 #> 8200 81.99 0.01641515 3.154160e-05 0.983553307 #> 8201 82.00 0.01640695 3.152584e-05 0.983561528 #> 8202 82.01 0.01639874 3.151008e-05 0.983569745 #> 8203 82.02 0.01639055 3.149433e-05 0.983577959 #> 8204 82.03 0.01638235 3.147858e-05 0.983586168 #> 8205 82.04 0.01637416 3.146285e-05 0.983594372 #> 8206 82.05 0.01636598 3.144712e-05 0.983602573 #> 8207 82.06 0.01635780 3.143140e-05 0.983610770 #> 8208 82.07 0.01634962 3.141569e-05 0.983618962 #> 8209 82.08 0.01634145 3.139998e-05 0.983627151 #> 8210 82.09 0.01633328 3.138429e-05 0.983635335 #> 8211 82.10 0.01632512 3.136860e-05 0.983643516 #> 8212 82.11 0.01631696 3.135292e-05 0.983651692 #> 8213 82.12 0.01630880 3.133725e-05 0.983659864 #> 8214 82.13 0.01630065 3.132158e-05 0.983668032 #> 8215 82.14 0.01629250 3.130593e-05 0.983676196 #> 8216 82.15 0.01628435 3.129028e-05 0.983684356 #> 8217 82.16 0.01627621 3.127464e-05 0.983692511 #> 8218 82.17 0.01626808 3.125900e-05 0.983700663 #> 8219 82.18 0.01625995 3.124338e-05 0.983708811 #> 8220 82.19 0.01625182 3.122776e-05 0.983716954 #> 8221 82.20 0.01624369 3.121215e-05 0.983725094 #> 8222 82.21 0.01623557 3.119655e-05 0.983733229 #> 8223 82.22 0.01622746 3.118095e-05 0.983741361 #> 8224 82.23 0.01621935 3.116537e-05 0.983749488 #> 8225 82.24 0.01621124 3.114979e-05 0.983757611 #> 8226 82.25 0.01620314 3.113422e-05 0.983765730 #> 8227 82.26 0.01619504 3.111865e-05 0.983773845 #> 8228 82.27 0.01618694 3.110310e-05 0.983781956 #> 8229 82.28 0.01617885 3.108755e-05 0.983790063 #> 8230 82.29 0.01617076 3.107201e-05 0.983798166 #> 8231 82.30 0.01616268 3.105648e-05 0.983806265 #> 8232 82.31 0.01615460 3.104095e-05 0.983814360 #> 8233 82.32 0.01614652 3.102544e-05 0.983822451 #> 8234 82.33 0.01613845 3.100993e-05 0.983830538 #> 8235 82.34 0.01613039 3.099443e-05 0.983838620 #> 8236 82.35 0.01612232 3.097893e-05 0.983846699 #> 8237 82.36 0.01611426 3.096345e-05 0.983854774 #> 8238 82.37 0.01610621 3.094797e-05 0.983862844 #> 8239 82.38 0.01609816 3.093250e-05 0.983870911 #> 8240 82.39 0.01609011 3.091704e-05 0.983878973 #> 8241 82.40 0.01608207 3.090158e-05 0.983887032 #> 8242 82.41 0.01607403 3.088614e-05 0.983895086 #> 8243 82.42 0.01606599 3.087070e-05 0.983903137 #> 8244 82.43 0.01605796 3.085527e-05 0.983911183 #> 8245 82.44 0.01604993 3.083984e-05 0.983919226 #> 8246 82.45 0.01604191 3.082443e-05 0.983927264 #> 8247 82.46 0.01603389 3.080902e-05 0.983935298 #> 8248 82.47 0.01602588 3.079362e-05 0.983943329 #> 8249 82.48 0.01601787 3.077822e-05 0.983951355 #> 8250 82.49 0.01600986 3.076284e-05 0.983959377 #> 8251 82.50 0.01600186 3.074746e-05 0.983967396 #> 8252 82.51 0.01599386 3.073209e-05 0.983975410 #> 8253 82.52 0.01598586 3.071673e-05 0.983983420 #> 8254 82.53 0.01597787 3.070137e-05 0.983991427 #> 8255 82.54 0.01596989 3.068603e-05 0.983999429 #> 8256 82.55 0.01596190 3.067069e-05 0.984007427 #> 8257 82.56 0.01595392 3.065536e-05 0.984015421 #> 8258 82.57 0.01594595 3.064003e-05 0.984023412 #> 8259 82.58 0.01593798 3.062472e-05 0.984031398 #> 8260 82.59 0.01593001 3.060941e-05 0.984039380 #> 8261 82.60 0.01592205 3.059411e-05 0.984047359 #> 8262 82.61 0.01591409 3.057881e-05 0.984055333 #> 8263 82.62 0.01590613 3.056353e-05 0.984063303 #> 8264 82.63 0.01589818 3.054825e-05 0.984071270 #> 8265 82.64 0.01589024 3.053298e-05 0.984079232 #> 8266 82.65 0.01588229 3.051772e-05 0.984087190 #> 8267 82.66 0.01587435 3.050246e-05 0.984095145 #> 8268 82.67 0.01586642 3.048721e-05 0.984103095 #> 8269 82.68 0.01585849 3.047197e-05 0.984111042 #> 8270 82.69 0.01585056 3.045674e-05 0.984118984 #> 8271 82.70 0.01584264 3.044152e-05 0.984126923 #> 8272 82.71 0.01583472 3.042630e-05 0.984134857 #> 8273 82.72 0.01582680 3.041109e-05 0.984142788 #> 8274 82.73 0.01581889 3.039589e-05 0.984150714 #> 8275 82.74 0.01581098 3.038070e-05 0.984158637 #> 8276 82.75 0.01580308 3.036551e-05 0.984166556 #> 8277 82.76 0.01579518 3.035033e-05 0.984174471 #> 8278 82.77 0.01578728 3.033516e-05 0.984182381 #> 8279 82.78 0.01577939 3.032000e-05 0.984190288 #> 8280 82.79 0.01577150 3.030484e-05 0.984198191 #> 8281 82.80 0.01576362 3.028969e-05 0.984206090 #> 8282 82.81 0.01575574 3.027455e-05 0.984213985 #> 8283 82.82 0.01574786 3.025942e-05 0.984221876 #> 8284 82.83 0.01573999 3.024429e-05 0.984229763 #> 8285 82.84 0.01573212 3.022917e-05 0.984237646 #> 8286 82.85 0.01572426 3.021406e-05 0.984245525 #> 8287 82.86 0.01571640 3.019896e-05 0.984253401 #> 8288 82.87 0.01570854 3.018386e-05 0.984261272 #> 8289 82.88 0.01570069 3.016877e-05 0.984269139 #> 8290 82.89 0.01569284 3.015369e-05 0.984277003 #> 8291 82.90 0.01568500 3.013862e-05 0.984284862 #> 8292 82.91 0.01567716 3.012355e-05 0.984292718 #> 8293 82.92 0.01566932 3.010850e-05 0.984300570 #> 8294 82.93 0.01566149 3.009345e-05 0.984308418 #> 8295 82.94 0.01565366 3.007840e-05 0.984316261 #> 8296 82.95 0.01564584 3.006337e-05 0.984324101 #> 8297 82.96 0.01563801 3.004834e-05 0.984331937 #> 8298 82.97 0.01563020 3.003332e-05 0.984339769 #> 8299 82.98 0.01562238 3.001831e-05 0.984347597 #> 8300 82.99 0.01561457 3.000330e-05 0.984355422 #> 8301 83.00 0.01560677 2.998830e-05 0.984363242 #> 8302 83.01 0.01559897 2.997331e-05 0.984371058 #> 8303 83.02 0.01559117 2.995833e-05 0.984378871 #> 8304 83.03 0.01558338 2.994335e-05 0.984386680 #> 8305 83.04 0.01557559 2.992839e-05 0.984394484 #> 8306 83.05 0.01556780 2.991343e-05 0.984402285 #> 8307 83.06 0.01556002 2.989847e-05 0.984410082 #> 8308 83.07 0.01555224 2.988353e-05 0.984417875 #> 8309 83.08 0.01554447 2.986859e-05 0.984425664 #> 8310 83.09 0.01553670 2.985366e-05 0.984433449 #> 8311 83.10 0.01552893 2.983874e-05 0.984441231 #> 8312 83.11 0.01552117 2.982382e-05 0.984449008 #> 8313 83.12 0.01551341 2.980891e-05 0.984456782 #> 8314 83.13 0.01550565 2.979401e-05 0.984464551 #> 8315 83.14 0.01549790 2.977912e-05 0.984472317 #> 8316 83.15 0.01549016 2.976423e-05 0.984480079 #> 8317 83.16 0.01548241 2.974935e-05 0.984487837 #> 8318 83.17 0.01547467 2.973448e-05 0.984495591 #> 8319 83.18 0.01546694 2.971962e-05 0.984503341 #> 8320 83.19 0.01545921 2.970476e-05 0.984511088 #> 8321 83.20 0.01545148 2.968991e-05 0.984518830 #> 8322 83.21 0.01544376 2.967507e-05 0.984526569 #> 8323 83.22 0.01543604 2.966024e-05 0.984534304 #> 8324 83.23 0.01542832 2.964541e-05 0.984542035 #> 8325 83.24 0.01542061 2.963059e-05 0.984549762 #> 8326 83.25 0.01541290 2.961578e-05 0.984557485 #> 8327 83.26 0.01540519 2.960098e-05 0.984565204 #> 8328 83.27 0.01539749 2.958618e-05 0.984572920 #> 8329 83.28 0.01538980 2.957139e-05 0.984580631 #> 8330 83.29 0.01538210 2.955661e-05 0.984588339 #> 8331 83.30 0.01537442 2.954184e-05 0.984596043 #> 8332 83.31 0.01536673 2.952707e-05 0.984603743 #> 8333 83.32 0.01535905 2.951231e-05 0.984611439 #> 8334 83.33 0.01535137 2.949756e-05 0.984619132 #> 8335 83.34 0.01534370 2.948281e-05 0.984626820 #> 8336 83.35 0.01533603 2.946807e-05 0.984634505 #> 8337 83.36 0.01532836 2.945334e-05 0.984642186 #> 8338 83.37 0.01532070 2.943862e-05 0.984649863 #> 8339 83.38 0.01531304 2.942390e-05 0.984657536 #> 8340 83.39 0.01530539 2.940920e-05 0.984665205 #> 8341 83.40 0.01529773 2.939449e-05 0.984672871 #> 8342 83.41 0.01529009 2.937980e-05 0.984680532 #> 8343 83.42 0.01528244 2.936511e-05 0.984688190 #> 8344 83.43 0.01527481 2.935044e-05 0.984695844 #> 8345 83.44 0.01526717 2.933576e-05 0.984703494 #> 8346 83.45 0.01525954 2.932110e-05 0.984711141 #> 8347 83.46 0.01525191 2.930644e-05 0.984718783 #> 8348 83.47 0.01524429 2.929179e-05 0.984726422 #> 8349 83.48 0.01523667 2.927715e-05 0.984734057 #> 8350 83.49 0.01522905 2.926252e-05 0.984741688 #> 8351 83.50 0.01522144 2.924789e-05 0.984749315 #> 8352 83.51 0.01521383 2.923327e-05 0.984756938 #> 8353 83.52 0.01520622 2.921866e-05 0.984764558 #> 8354 83.53 0.01519862 2.920405e-05 0.984772174 #> 8355 83.54 0.01519102 2.918945e-05 0.984779786 #> 8356 83.55 0.01518343 2.917486e-05 0.984787394 #> 8357 83.56 0.01517584 2.916028e-05 0.984794998 #> 8358 83.57 0.01516826 2.914570e-05 0.984802599 #> 8359 83.58 0.01516067 2.913113e-05 0.984810196 #> 8360 83.59 0.01515309 2.911657e-05 0.984817789 #> 8361 83.60 0.01514552 2.910201e-05 0.984825378 #> 8362 83.61 0.01513795 2.908747e-05 0.984832964 #> 8363 83.62 0.01513038 2.907293e-05 0.984840545 #> 8364 83.63 0.01512282 2.905839e-05 0.984848123 #> 8365 83.64 0.01511526 2.904387e-05 0.984855697 #> 8366 83.65 0.01510770 2.902935e-05 0.984863267 #> 8367 83.66 0.01510015 2.901484e-05 0.984870834 #> 8368 83.67 0.01509260 2.900034e-05 0.984878396 #> 8369 83.68 0.01508506 2.898584e-05 0.984885955 #> 8370 83.69 0.01507752 2.897135e-05 0.984893511 #> 8371 83.70 0.01506998 2.895687e-05 0.984901062 #> 8372 83.71 0.01506245 2.894239e-05 0.984908609 #> 8373 83.72 0.01505492 2.892793e-05 0.984916153 #> 8374 83.73 0.01504739 2.891346e-05 0.984923693 #> 8375 83.74 0.01503987 2.889901e-05 0.984931230 #> 8376 83.75 0.01503235 2.888457e-05 0.984938762 #> 8377 83.76 0.01502484 2.887013e-05 0.984946291 #> 8378 83.77 0.01501733 2.885570e-05 0.984953816 #> 8379 83.78 0.01500982 2.884127e-05 0.984961337 #> 8380 83.79 0.01500232 2.882685e-05 0.984968854 #> 8381 83.80 0.01499482 2.881244e-05 0.984976368 #> 8382 83.81 0.01498732 2.879804e-05 0.984983878 #> 8383 83.82 0.01497983 2.878365e-05 0.984991384 #> 8384 83.83 0.01497234 2.876926e-05 0.984998887 #> 8385 83.84 0.01496486 2.875488e-05 0.985006385 #> 8386 83.85 0.01495738 2.874050e-05 0.985013880 #> 8387 83.86 0.01494990 2.872614e-05 0.985021371 #> 8388 83.87 0.01494243 2.871178e-05 0.985028859 #> 8389 83.88 0.01493496 2.869743e-05 0.985036343 #> 8390 83.89 0.01492749 2.868308e-05 0.985043823 #> 8391 83.90 0.01492003 2.866874e-05 0.985051299 #> 8392 83.91 0.01491257 2.865441e-05 0.985058771 #> 8393 83.92 0.01490512 2.864009e-05 0.985066240 #> 8394 83.93 0.01489767 2.862577e-05 0.985073705 #> 8395 83.94 0.01489022 2.861146e-05 0.985081166 #> 8396 83.95 0.01488278 2.859716e-05 0.985088624 #> 8397 83.96 0.01487534 2.858286e-05 0.985096078 #> 8398 83.97 0.01486790 2.856858e-05 0.985103528 #> 8399 83.98 0.01486047 2.855430e-05 0.985110974 #> 8400 83.99 0.01485304 2.854002e-05 0.985118417 #> 8401 84.00 0.01484562 2.852576e-05 0.985125856 #> 8402 84.01 0.01483820 2.851150e-05 0.985133291 #> 8403 84.02 0.01483078 2.849724e-05 0.985140722 #> 8404 84.03 0.01482337 2.848300e-05 0.985148150 #> 8405 84.04 0.01481596 2.846876e-05 0.985155574 #> 8406 84.05 0.01480855 2.845453e-05 0.985162995 #> 8407 84.06 0.01480115 2.844031e-05 0.985170411 #> 8408 84.07 0.01479375 2.842609e-05 0.985177824 #> 8409 84.08 0.01478635 2.841188e-05 0.985185234 #> 8410 84.09 0.01477896 2.839768e-05 0.985192639 #> 8411 84.10 0.01477158 2.838348e-05 0.985200041 #> 8412 84.11 0.01476419 2.836930e-05 0.985207439 #> 8413 84.12 0.01475681 2.835511e-05 0.985214833 #> 8414 84.13 0.01474943 2.834094e-05 0.985222224 #> 8415 84.14 0.01474206 2.832677e-05 0.985229611 #> 8416 84.15 0.01473469 2.831261e-05 0.985236995 #> 8417 84.16 0.01472733 2.829846e-05 0.985244374 #> 8418 84.17 0.01471997 2.828431e-05 0.985251750 #> 8419 84.18 0.01471261 2.827018e-05 0.985259122 #> 8420 84.19 0.01470525 2.825604e-05 0.985266491 #> 8421 84.20 0.01469790 2.824192e-05 0.985273856 #> 8422 84.21 0.01469055 2.822780e-05 0.985281217 #> 8423 84.22 0.01468321 2.821369e-05 0.985288575 #> 8424 84.23 0.01467587 2.819959e-05 0.985295929 #> 8425 84.24 0.01466854 2.818549e-05 0.985303279 #> 8426 84.25 0.01466120 2.817140e-05 0.985310625 #> 8427 84.26 0.01465387 2.815732e-05 0.985317968 #> 8428 84.27 0.01464655 2.814325e-05 0.985325307 #> 8429 84.28 0.01463923 2.812918e-05 0.985332643 #> 8430 84.29 0.01463191 2.811512e-05 0.985339975 #> 8431 84.30 0.01462460 2.810106e-05 0.985347303 #> 8432 84.31 0.01461729 2.808702e-05 0.985354627 #> 8433 84.32 0.01460998 2.807298e-05 0.985361948 #> 8434 84.33 0.01460268 2.805894e-05 0.985369266 #> 8435 84.34 0.01459538 2.804492e-05 0.985376579 #> 8436 84.35 0.01458808 2.803090e-05 0.985383889 #> 8437 84.36 0.01458079 2.801689e-05 0.985391195 #> 8438 84.37 0.01457350 2.800288e-05 0.985398498 #> 8439 84.38 0.01456621 2.798888e-05 0.985405797 #> 8440 84.39 0.01455893 2.797489e-05 0.985413092 #> 8441 84.40 0.01455166 2.796091e-05 0.985420384 #> 8442 84.41 0.01454438 2.794693e-05 0.985427672 #> 8443 84.42 0.01453711 2.793296e-05 0.985434956 #> 8444 84.43 0.01452984 2.791900e-05 0.985442237 #> 8445 84.44 0.01452258 2.790504e-05 0.985449514 #> 8446 84.45 0.01451532 2.789109e-05 0.985456787 #> 8447 84.46 0.01450807 2.787715e-05 0.985464057 #> 8448 84.47 0.01450081 2.786322e-05 0.985471323 #> 8449 84.48 0.01449357 2.784929e-05 0.985478586 #> 8450 84.49 0.01448632 2.783537e-05 0.985485844 #> 8451 84.50 0.01447908 2.782145e-05 0.985493100 #> 8452 84.51 0.01447184 2.780755e-05 0.985500351 #> 8453 84.52 0.01446461 2.779365e-05 0.985507599 #> 8454 84.53 0.01445738 2.777975e-05 0.985514844 #> 8455 84.54 0.01445015 2.776587e-05 0.985522085 #> 8456 84.55 0.01444293 2.775199e-05 0.985529322 #> 8457 84.56 0.01443571 2.773811e-05 0.985536555 #> 8458 84.57 0.01442849 2.772425e-05 0.985543785 #> 8459 84.58 0.01442128 2.771039e-05 0.985551011 #> 8460 84.59 0.01441407 2.769654e-05 0.985558234 #> 8461 84.60 0.01440686 2.768269e-05 0.985565453 #> 8462 84.61 0.01439966 2.766885e-05 0.985572669 #> 8463 84.62 0.01439246 2.765502e-05 0.985579881 #> 8464 84.63 0.01438527 2.764120e-05 0.985587089 #> 8465 84.64 0.01437808 2.762738e-05 0.985594293 #> 8466 84.65 0.01437089 2.761357e-05 0.985601494 #> 8467 84.66 0.01436371 2.759977e-05 0.985608692 #> 8468 84.67 0.01435653 2.758597e-05 0.985615886 #> 8469 84.68 0.01434935 2.757218e-05 0.985623076 #> 8470 84.69 0.01434218 2.755840e-05 0.985630263 #> 8471 84.70 0.01433501 2.754462e-05 0.985637446 #> 8472 84.71 0.01432784 2.753086e-05 0.985644625 #> 8473 84.72 0.01432068 2.751709e-05 0.985651801 #> 8474 84.73 0.01431352 2.750334e-05 0.985658973 #> 8475 84.74 0.01430637 2.748959e-05 0.985666142 #> 8476 84.75 0.01429922 2.747585e-05 0.985673307 #> 8477 84.76 0.01429207 2.746211e-05 0.985680469 #> 8478 84.77 0.01428492 2.744839e-05 0.985687627 #> 8479 84.78 0.01427778 2.743467e-05 0.985694781 #> 8480 84.79 0.01427065 2.742095e-05 0.985701932 #> 8481 84.80 0.01426351 2.740725e-05 0.985709079 #> 8482 84.81 0.01425638 2.739354e-05 0.985716223 #> 8483 84.82 0.01424926 2.737985e-05 0.985723363 #> 8484 84.83 0.01424213 2.736617e-05 0.985730500 #> 8485 84.84 0.01423501 2.735249e-05 0.985737633 #> 8486 84.85 0.01422790 2.733881e-05 0.985744762 #> 8487 84.86 0.01422079 2.732515e-05 0.985751888 #> 8488 84.87 0.01421368 2.731149e-05 0.985759010 #> 8489 84.88 0.01420657 2.729784e-05 0.985766129 #> 8490 84.89 0.01419947 2.728419e-05 0.985773244 #> 8491 84.90 0.01419237 2.727055e-05 0.985780356 #> 8492 84.91 0.01418528 2.725692e-05 0.985787464 #> 8493 84.92 0.01417819 2.724329e-05 0.985794568 #> 8494 84.93 0.01417110 2.722968e-05 0.985801669 #> 8495 84.94 0.01416402 2.721606e-05 0.985808766 #> 8496 84.95 0.01415694 2.720246e-05 0.985815860 #> 8497 84.96 0.01414986 2.718886e-05 0.985822951 #> 8498 84.97 0.01414279 2.717527e-05 0.985830037 #> 8499 84.98 0.01413572 2.716169e-05 0.985837121 #> 8500 84.99 0.01412865 2.714811e-05 0.985844200 #> 8501 85.00 0.01412159 2.713454e-05 0.985851276 #> 8502 85.01 0.01411453 2.712097e-05 0.985858349 #> 8503 85.02 0.01410747 2.710742e-05 0.985865418 #> 8504 85.03 0.01410042 2.709387e-05 0.985872484 #> 8505 85.04 0.01409337 2.708032e-05 0.985879546 #> 8506 85.05 0.01408633 2.706679e-05 0.985886604 #> 8507 85.06 0.01407929 2.705326e-05 0.985893659 #> 8508 85.07 0.01407225 2.703973e-05 0.985900710 #> 8509 85.08 0.01406522 2.702622e-05 0.985907758 #> 8510 85.09 0.01405818 2.701271e-05 0.985914803 #> 8511 85.10 0.01405116 2.699920e-05 0.985921843 #> 8512 85.11 0.01404413 2.698571e-05 0.985928881 #> 8513 85.12 0.01403711 2.697222e-05 0.985935915 #> 8514 85.13 0.01403010 2.695874e-05 0.985942945 #> 8515 85.14 0.01402308 2.694526e-05 0.985949972 #> 8516 85.15 0.01401607 2.693179e-05 0.985956995 #> 8517 85.16 0.01400907 2.691833e-05 0.985964015 #> 8518 85.17 0.01400206 2.690487e-05 0.985971031 #> 8519 85.18 0.01399506 2.689142e-05 0.985978044 #> 8520 85.19 0.01398807 2.687798e-05 0.985985053 #> 8521 85.20 0.01398108 2.686455e-05 0.985992059 #> 8522 85.21 0.01397409 2.685112e-05 0.985999061 #> 8523 85.22 0.01396710 2.683769e-05 0.986006059 #> 8524 85.23 0.01396012 2.682428e-05 0.986013055 #> 8525 85.24 0.01395314 2.681087e-05 0.986020046 #> 8526 85.25 0.01394617 2.679747e-05 0.986027035 #> 8527 85.26 0.01393920 2.678407e-05 0.986034019 #> 8528 85.27 0.01393223 2.677068e-05 0.986041001 #> 8529 85.28 0.01392526 2.675730e-05 0.986047978 #> 8530 85.29 0.01391830 2.674393e-05 0.986054953 #> 8531 85.30 0.01391135 2.673056e-05 0.986061923 #> 8532 85.31 0.01390439 2.671720e-05 0.986068891 #> 8533 85.32 0.01389744 2.670384e-05 0.986075855 #> 8534 85.33 0.01389049 2.669049e-05 0.986082815 #> 8535 85.34 0.01388355 2.667715e-05 0.986089772 #> 8536 85.35 0.01387661 2.666381e-05 0.986096725 #> 8537 85.36 0.01386967 2.665049e-05 0.986103675 #> 8538 85.37 0.01386274 2.663716e-05 0.986110621 #> 8539 85.38 0.01385581 2.662385e-05 0.986117564 #> 8540 85.39 0.01384889 2.661054e-05 0.986124504 #> 8541 85.40 0.01384196 2.659724e-05 0.986131440 #> 8542 85.41 0.01383504 2.658394e-05 0.986138372 #> 8543 85.42 0.01382813 2.657065e-05 0.986145302 #> 8544 85.43 0.01382122 2.655737e-05 0.986152227 #> 8545 85.44 0.01381431 2.654410e-05 0.986159149 #> 8546 85.45 0.01380740 2.653083e-05 0.986166068 #> 8547 85.46 0.01380050 2.651757e-05 0.986172983 #> 8548 85.47 0.01379360 2.650431e-05 0.986179895 #> 8549 85.48 0.01378671 2.649106e-05 0.986186803 #> 8550 85.49 0.01377981 2.647782e-05 0.986193708 #> 8551 85.50 0.01377293 2.646458e-05 0.986200610 #> 8552 85.51 0.01376604 2.645136e-05 0.986207508 #> 8553 85.52 0.01375916 2.643813e-05 0.986214402 #> 8554 85.53 0.01375228 2.642492e-05 0.986221293 #> 8555 85.54 0.01374541 2.641171e-05 0.986228181 #> 8556 85.55 0.01373854 2.639851e-05 0.986235065 #> 8557 85.56 0.01373167 2.638531e-05 0.986241946 #> 8558 85.57 0.01372480 2.637212e-05 0.986248823 #> 8559 85.58 0.01371794 2.635894e-05 0.986255697 #> 8560 85.59 0.01371109 2.634576e-05 0.986262567 #> 8561 85.60 0.01370423 2.633259e-05 0.986269434 #> 8562 85.61 0.01369738 2.631943e-05 0.986276298 #> 8563 85.62 0.01369054 2.630627e-05 0.986283158 #> 8564 85.63 0.01368369 2.629312e-05 0.986290015 #> 8565 85.64 0.01367685 2.627998e-05 0.986296868 #> 8566 85.65 0.01367002 2.626684e-05 0.986303718 #> 8567 85.66 0.01366318 2.625371e-05 0.986310564 #> 8568 85.67 0.01365635 2.624059e-05 0.986317407 #> 8569 85.68 0.01364953 2.622747e-05 0.986324247 #> 8570 85.69 0.01364270 2.621436e-05 0.986331083 #> 8571 85.70 0.01363588 2.620126e-05 0.986337916 #> 8572 85.71 0.01362907 2.618816e-05 0.986344745 #> 8573 85.72 0.01362225 2.617507e-05 0.986351571 #> 8574 85.73 0.01361544 2.616198e-05 0.986358394 #> 8575 85.74 0.01360864 2.614891e-05 0.986365213 #> 8576 85.75 0.01360184 2.613584e-05 0.986372028 #> 8577 85.76 0.01359504 2.612277e-05 0.986378841 #> 8578 85.77 0.01358824 2.610971e-05 0.986385650 #> 8579 85.78 0.01358145 2.609666e-05 0.986392455 #> 8580 85.79 0.01357466 2.608362e-05 0.986399257 #> 8581 85.80 0.01356787 2.607058e-05 0.986406056 #> 8582 85.81 0.01356109 2.605755e-05 0.986412851 #> 8583 85.82 0.01355431 2.604452e-05 0.986419643 #> 8584 85.83 0.01354754 2.603150e-05 0.986426431 #> 8585 85.84 0.01354077 2.601849e-05 0.986433217 #> 8586 85.85 0.01353400 2.600548e-05 0.986439998 #> 8587 85.86 0.01352723 2.599248e-05 0.986446777 #> 8588 85.87 0.01352047 2.597949e-05 0.986453551 #> 8589 85.88 0.01351371 2.596650e-05 0.986460323 #> 8590 85.89 0.01350696 2.595352e-05 0.986467091 #> 8591 85.90 0.01350020 2.594055e-05 0.986473856 #> 8592 85.91 0.01349346 2.592758e-05 0.986480617 #> 8593 85.92 0.01348671 2.591462e-05 0.986487375 #> 8594 85.93 0.01347997 2.590167e-05 0.986494130 #> 8595 85.94 0.01347323 2.588872e-05 0.986500881 #> 8596 85.95 0.01346650 2.587578e-05 0.986507629 #> 8597 85.96 0.01345976 2.586285e-05 0.986514373 #> 8598 85.97 0.01345304 2.584992e-05 0.986521115 #> 8599 85.98 0.01344631 2.583700e-05 0.986527852 #> 8600 85.99 0.01343959 2.582408e-05 0.986534587 #> 8601 86.00 0.01343287 2.581117e-05 0.986541318 #> 8602 86.01 0.01342616 2.579827e-05 0.986548045 #> 8603 86.02 0.01341944 2.578537e-05 0.986554770 #> 8604 86.03 0.01341274 2.577248e-05 0.986561491 #> 8605 86.04 0.01340603 2.575960e-05 0.986568208 #> 8606 86.05 0.01339933 2.574672e-05 0.986574922 #> 8607 86.06 0.01339263 2.573385e-05 0.986581633 #> 8608 86.07 0.01338594 2.572099e-05 0.986588341 #> 8609 86.08 0.01337925 2.570813e-05 0.986595045 #> 8610 86.09 0.01337256 2.569528e-05 0.986601746 #> 8611 86.10 0.01336587 2.568244e-05 0.986608443 #> 8612 86.11 0.01335919 2.566960e-05 0.986615137 #> 8613 86.12 0.01335252 2.565677e-05 0.986621828 #> 8614 86.13 0.01334584 2.564394e-05 0.986628516 #> 8615 86.14 0.01333917 2.563112e-05 0.986635200 #> 8616 86.15 0.01333250 2.561831e-05 0.986641880 #> 8617 86.16 0.01332584 2.560551e-05 0.986648558 #> 8618 86.17 0.01331918 2.559271e-05 0.986655232 #> 8619 86.18 0.01331252 2.557991e-05 0.986661902 #> 8620 86.19 0.01330586 2.556713e-05 0.986668570 #> 8621 86.20 0.01329921 2.555435e-05 0.986675234 #> 8622 86.21 0.01329256 2.554157e-05 0.986681895 #> 8623 86.22 0.01328592 2.552880e-05 0.986688552 #> 8624 86.23 0.01327928 2.551604e-05 0.986695206 #> 8625 86.24 0.01327264 2.550329e-05 0.986701857 #> 8626 86.25 0.01326601 2.549054e-05 0.986708504 #> 8627 86.26 0.01325937 2.547780e-05 0.986715148 #> 8628 86.27 0.01325275 2.546506e-05 0.986721789 #> 8629 86.28 0.01324612 2.545233e-05 0.986728427 #> 8630 86.29 0.01323950 2.543961e-05 0.986735061 #> 8631 86.30 0.01323288 2.542689e-05 0.986741691 #> 8632 86.31 0.01322627 2.541418e-05 0.986748319 #> 8633 86.32 0.01321966 2.540148e-05 0.986754943 #> 8634 86.33 0.01321305 2.538878e-05 0.986761564 #> 8635 86.34 0.01320644 2.537609e-05 0.986768182 #> 8636 86.35 0.01319984 2.536341e-05 0.986774796 #> 8637 86.36 0.01319324 2.535073e-05 0.986781407 #> 8638 86.37 0.01318665 2.533805e-05 0.986788014 #> 8639 86.38 0.01318006 2.532539e-05 0.986794619 #> 8640 86.39 0.01317347 2.531273e-05 0.986801220 #> 8641 86.40 0.01316688 2.530008e-05 0.986807818 #> 8642 86.41 0.01316030 2.528743e-05 0.986814412 #> 8643 86.42 0.01315372 2.527479e-05 0.986821003 #> 8644 86.43 0.01314715 2.526215e-05 0.986827591 #> 8645 86.44 0.01314057 2.524953e-05 0.986834176 #> 8646 86.45 0.01313401 2.523690e-05 0.986840757 #> 8647 86.46 0.01312744 2.522429e-05 0.986847335 #> 8648 86.47 0.01312088 2.521168e-05 0.986853910 #> 8649 86.48 0.01311432 2.519908e-05 0.986860481 #> 8650 86.49 0.01310776 2.518648e-05 0.986867049 #> 8651 86.50 0.01310121 2.517389e-05 0.986873614 #> 8652 86.51 0.01309466 2.516131e-05 0.986880175 #> 8653 86.52 0.01308812 2.514873e-05 0.986886734 #> 8654 86.53 0.01308158 2.513616e-05 0.986893289 #> 8655 86.54 0.01307504 2.512359e-05 0.986899840 #> 8656 86.55 0.01306850 2.511104e-05 0.986906389 #> 8657 86.56 0.01306197 2.509848e-05 0.986912934 #> 8658 86.57 0.01305544 2.508594e-05 0.986919476 #> 8659 86.58 0.01304891 2.507340e-05 0.986926015 #> 8660 86.59 0.01304239 2.506086e-05 0.986932550 #> 8661 86.60 0.01303587 2.504834e-05 0.986939082 #> 8662 86.61 0.01302935 2.503581e-05 0.986945611 #> 8663 86.62 0.01302284 2.502330e-05 0.986952136 #> 8664 86.63 0.01301633 2.501079e-05 0.986958659 #> 8665 86.64 0.01300982 2.499829e-05 0.986965178 #> 8666 86.65 0.01300332 2.498579e-05 0.986971693 #> 8667 86.66 0.01299682 2.497330e-05 0.986978206 #> 8668 86.67 0.01299032 2.496082e-05 0.986984715 #> 8669 86.68 0.01298383 2.494834e-05 0.986991221 #> 8670 86.69 0.01297734 2.493587e-05 0.986997724 #> 8671 86.70 0.01297085 2.492341e-05 0.987004224 #> 8672 86.71 0.01296437 2.491095e-05 0.987010720 #> 8673 86.72 0.01295789 2.489850e-05 0.987017213 #> 8674 86.73 0.01295141 2.488605e-05 0.987023703 #> 8675 86.74 0.01294494 2.487361e-05 0.987030189 #> 8676 86.75 0.01293847 2.486118e-05 0.987036672 #> 8677 86.76 0.01293200 2.484875e-05 0.987043152 #> 8678 86.77 0.01292553 2.483633e-05 0.987049629 #> 8679 86.78 0.01291907 2.482391e-05 0.987056103 #> 8680 86.79 0.01291262 2.481150e-05 0.987062573 #> 8681 86.80 0.01290616 2.479910e-05 0.987069040 #> 8682 86.81 0.01289971 2.478670e-05 0.987075504 #> 8683 86.82 0.01289326 2.477431e-05 0.987081965 #> 8684 86.83 0.01288682 2.476193e-05 0.987088422 #> 8685 86.84 0.01288037 2.474955e-05 0.987094876 #> 8686 86.85 0.01287394 2.473718e-05 0.987101327 #> 8687 86.86 0.01286750 2.472481e-05 0.987107775 #> 8688 86.87 0.01286107 2.471246e-05 0.987114220 #> 8689 86.88 0.01285464 2.470010e-05 0.987120661 #> 8690 86.89 0.01284821 2.468776e-05 0.987127099 #> 8691 86.90 0.01284179 2.467541e-05 0.987133534 #> 8692 86.91 0.01283537 2.466308e-05 0.987139965 #> 8693 86.92 0.01282896 2.465075e-05 0.987146394 #> 8694 86.93 0.01282254 2.463843e-05 0.987152819 #> 8695 86.94 0.01281613 2.462611e-05 0.987159241 #> 8696 86.95 0.01280973 2.461380e-05 0.987165660 #> 8697 86.96 0.01280332 2.460150e-05 0.987172075 #> 8698 86.97 0.01279692 2.458920e-05 0.987178488 #> 8699 86.98 0.01279053 2.457691e-05 0.987184897 #> 8700 86.99 0.01278413 2.456462e-05 0.987191303 #> 8701 87.00 0.01277774 2.455235e-05 0.987197705 #> 8702 87.01 0.01277135 2.454007e-05 0.987204105 #> 8703 87.02 0.01276497 2.452781e-05 0.987210501 #> 8704 87.03 0.01275859 2.451554e-05 0.987216895 #> 8705 87.04 0.01275221 2.450329e-05 0.987223284 #> 8706 87.05 0.01274584 2.449104e-05 0.987229671 #> 8707 87.06 0.01273947 2.447880e-05 0.987236055 #> 8708 87.07 0.01273310 2.446656e-05 0.987242435 #> 8709 87.08 0.01272673 2.445433e-05 0.987248812 #> 8710 87.09 0.01272037 2.444211e-05 0.987255186 #> 8711 87.10 0.01271401 2.442989e-05 0.987261557 #> 8712 87.11 0.01270766 2.441768e-05 0.987267925 #> 8713 87.12 0.01270131 2.440547e-05 0.987274289 #> 8714 87.13 0.01269496 2.439327e-05 0.987280651 #> 8715 87.14 0.01268861 2.438108e-05 0.987287009 #> 8716 87.15 0.01268227 2.436889e-05 0.987293364 #> 8717 87.16 0.01267593 2.435671e-05 0.987299715 #> 8718 87.17 0.01266959 2.434454e-05 0.987306064 #> 8719 87.18 0.01266326 2.433237e-05 0.987312409 #> 8720 87.19 0.01265693 2.432020e-05 0.987318751 #> 8721 87.20 0.01265060 2.430805e-05 0.987325090 #> 8722 87.21 0.01264428 2.429589e-05 0.987331426 #> 8723 87.22 0.01263796 2.428375e-05 0.987337759 #> 8724 87.23 0.01263164 2.427161e-05 0.987344089 #> 8725 87.24 0.01262533 2.425948e-05 0.987350415 #> 8726 87.25 0.01261901 2.424735e-05 0.987356738 #> 8727 87.26 0.01261271 2.423523e-05 0.987363058 #> 8728 87.27 0.01260640 2.422312e-05 0.987369375 #> 8729 87.28 0.01260010 2.421101e-05 0.987375689 #> 8730 87.29 0.01259380 2.419891e-05 0.987381999 #> 8731 87.30 0.01258751 2.418681e-05 0.987388307 #> 8732 87.31 0.01258121 2.417472e-05 0.987394611 #> 8733 87.32 0.01257493 2.416263e-05 0.987400912 #> 8734 87.33 0.01256864 2.415056e-05 0.987407210 #> 8735 87.34 0.01256236 2.413848e-05 0.987413505 #> 8736 87.35 0.01255608 2.412642e-05 0.987419797 #> 8737 87.36 0.01254980 2.411436e-05 0.987426085 #> 8738 87.37 0.01254353 2.410230e-05 0.987432371 #> 8739 87.38 0.01253726 2.409025e-05 0.987438653 #> 8740 87.39 0.01253099 2.407821e-05 0.987444932 #> 8741 87.40 0.01252473 2.406618e-05 0.987451208 #> 8742 87.41 0.01251847 2.405415e-05 0.987457481 #> 8743 87.42 0.01251221 2.404212e-05 0.987463750 #> 8744 87.43 0.01250595 2.403010e-05 0.987470017 #> 8745 87.44 0.01249970 2.401809e-05 0.987476280 #> 8746 87.45 0.01249345 2.400609e-05 0.987482541 #> 8747 87.46 0.01248721 2.399409e-05 0.987488798 #> 8748 87.47 0.01248097 2.398209e-05 0.987495052 #> 8749 87.48 0.01247473 2.397010e-05 0.987501303 #> 8750 87.49 0.01246849 2.395812e-05 0.987507551 #> 8751 87.50 0.01246226 2.394615e-05 0.987513795 #> 8752 87.51 0.01245603 2.393418e-05 0.987520037 #> 8753 87.52 0.01244980 2.392221e-05 0.987526275 #> 8754 87.53 0.01244358 2.391025e-05 0.987532511 #> 8755 87.54 0.01243736 2.389830e-05 0.987538743 #> 8756 87.55 0.01243114 2.388636e-05 0.987544972 #> 8757 87.56 0.01242493 2.387442e-05 0.987551198 #> 8758 87.57 0.01241872 2.386248e-05 0.987557421 #> 8759 87.58 0.01241251 2.385055e-05 0.987563640 #> 8760 87.59 0.01240630 2.383863e-05 0.987569857 #> 8761 87.60 0.01240010 2.382671e-05 0.987576070 #> 8762 87.61 0.01239390 2.381480e-05 0.987582281 #> 8763 87.62 0.01238771 2.380290e-05 0.987588488 #> 8764 87.63 0.01238152 2.379100e-05 0.987594692 #> 8765 87.64 0.01237533 2.377911e-05 0.987600894 #> 8766 87.65 0.01236914 2.376722e-05 0.987607092 #> 8767 87.66 0.01236296 2.375534e-05 0.987613286 #> 8768 87.67 0.01235678 2.374347e-05 0.987619478 #> 8769 87.68 0.01235060 2.373160e-05 0.987625667 #> 8770 87.69 0.01234443 2.371973e-05 0.987631853 #> 8771 87.70 0.01233826 2.370788e-05 0.987638035 #> 8772 87.71 0.01233209 2.369603e-05 0.987644215 #> 8773 87.72 0.01232592 2.368418e-05 0.987650391 #> 8774 87.73 0.01231976 2.367234e-05 0.987656564 #> 8775 87.74 0.01231361 2.366051e-05 0.987662734 #> 8776 87.75 0.01230745 2.364868e-05 0.987668901 #> 8777 87.76 0.01230130 2.363686e-05 0.987675065 #> 8778 87.77 0.01229515 2.362505e-05 0.987681226 #> 8779 87.78 0.01228900 2.361324e-05 0.987687384 #> 8780 87.79 0.01228286 2.360143e-05 0.987693539 #> 8781 87.80 0.01227672 2.358963e-05 0.987699691 #> 8782 87.81 0.01227058 2.357784e-05 0.987705839 #> 8783 87.82 0.01226445 2.356606e-05 0.987711985 #> 8784 87.83 0.01225832 2.355428e-05 0.987718127 #> 8785 87.84 0.01225219 2.354250e-05 0.987724267 #> 8786 87.85 0.01224607 2.353073e-05 0.987730403 #> 8787 87.86 0.01223994 2.351897e-05 0.987736536 #> 8788 87.87 0.01223383 2.350721e-05 0.987742666 #> 8789 87.88 0.01222771 2.349546e-05 0.987748794 #> 8790 87.89 0.01222160 2.348372e-05 0.987754918 #> 8791 87.90 0.01221549 2.347198e-05 0.987761039 #> 8792 87.91 0.01220938 2.346025e-05 0.987767157 #> 8793 87.92 0.01220328 2.344852e-05 0.987773272 #> 8794 87.93 0.01219718 2.343680e-05 0.987779383 #> 8795 87.94 0.01219108 2.342508e-05 0.987785492 #> 8796 87.95 0.01218499 2.341337e-05 0.987791598 #> 8797 87.96 0.01217890 2.340167e-05 0.987797701 #> 8798 87.97 0.01217281 2.338997e-05 0.987803800 #> 8799 87.98 0.01216672 2.337828e-05 0.987809897 #> 8800 87.99 0.01216064 2.336659e-05 0.987815990 #> 8801 88.00 0.01215456 2.335491e-05 0.987822081 #> 8802 88.01 0.01214849 2.334324e-05 0.987828168 #> 8803 88.02 0.01214242 2.333157e-05 0.987834253 #> 8804 88.03 0.01213635 2.331991e-05 0.987840334 #> 8805 88.04 0.01213028 2.330825e-05 0.987846412 #> 8806 88.05 0.01212422 2.329660e-05 0.987852488 #> 8807 88.06 0.01211816 2.328495e-05 0.987858560 #> 8808 88.07 0.01211210 2.327331e-05 0.987864629 #> 8809 88.08 0.01210604 2.326168e-05 0.987870695 #> 8810 88.09 0.01209999 2.325005e-05 0.987876758 #> 8811 88.10 0.01209394 2.323843e-05 0.987882818 #> 8812 88.11 0.01208790 2.322681e-05 0.987888875 #> 8813 88.12 0.01208186 2.321520e-05 0.987894930 #> 8814 88.13 0.01207582 2.320360e-05 0.987900981 #> 8815 88.14 0.01206978 2.319200e-05 0.987907029 #> 8816 88.15 0.01206375 2.318041e-05 0.987913074 #> 8817 88.16 0.01205772 2.316882e-05 0.987919115 #> 8818 88.17 0.01205169 2.315724e-05 0.987925154 #> 8819 88.18 0.01204566 2.314566e-05 0.987931190 #> 8820 88.19 0.01203964 2.313409e-05 0.987937223 #> 8821 88.20 0.01203362 2.312253e-05 0.987943253 #> 8822 88.21 0.01202761 2.311097e-05 0.987949280 #> 8823 88.22 0.01202160 2.309942e-05 0.987955304 #> 8824 88.23 0.01201559 2.308787e-05 0.987961325 #> 8825 88.24 0.01200958 2.307633e-05 0.987967343 #> 8826 88.25 0.01200358 2.306479e-05 0.987973357 #> 8827 88.26 0.01199758 2.305326e-05 0.987979369 #> 8828 88.27 0.01199158 2.304174e-05 0.987985378 #> 8829 88.28 0.01198559 2.303022e-05 0.987991384 #> 8830 88.29 0.01197959 2.301871e-05 0.987997387 #> 8831 88.30 0.01197361 2.300720e-05 0.988003386 #> 8832 88.31 0.01196762 2.299570e-05 0.988009383 #> 8833 88.32 0.01196164 2.298421e-05 0.988015377 #> 8834 88.33 0.01195566 2.297272e-05 0.988021368 #> 8835 88.34 0.01194968 2.296124e-05 0.988027356 #> 8836 88.35 0.01194371 2.294976e-05 0.988033340 #> 8837 88.36 0.01193774 2.293829e-05 0.988039322 #> 8838 88.37 0.01193177 2.292682e-05 0.988045301 #> 8839 88.38 0.01192581 2.291536e-05 0.988051277 #> 8840 88.39 0.01191985 2.290390e-05 0.988057250 #> 8841 88.40 0.01191389 2.289246e-05 0.988063220 #> 8842 88.41 0.01190793 2.288101e-05 0.988069187 #> 8843 88.42 0.01190198 2.286957e-05 0.988075151 #> 8844 88.43 0.01189603 2.285814e-05 0.988081112 #> 8845 88.44 0.01189008 2.284672e-05 0.988087069 #> 8846 88.45 0.01188414 2.283530e-05 0.988093024 #> 8847 88.46 0.01187820 2.282388e-05 0.988098976 #> 8848 88.47 0.01187226 2.281247e-05 0.988104925 #> 8849 88.48 0.01186633 2.280107e-05 0.988110872 #> 8850 88.49 0.01186040 2.278967e-05 0.988116815 #> 8851 88.50 0.01185447 2.277828e-05 0.988122755 #> 8852 88.51 0.01184854 2.276689e-05 0.988128692 #> 8853 88.52 0.01184262 2.275551e-05 0.988134626 #> 8854 88.53 0.01183670 2.274414e-05 0.988140557 #> 8855 88.54 0.01183078 2.273277e-05 0.988146485 #> 8856 88.55 0.01182487 2.272140e-05 0.988152411 #> 8857 88.56 0.01181896 2.271005e-05 0.988158333 #> 8858 88.57 0.01181305 2.269869e-05 0.988164252 #> 8859 88.58 0.01180714 2.268735e-05 0.988170169 #> 8860 88.59 0.01180124 2.267601e-05 0.988176082 #> 8861 88.60 0.01179534 2.266467e-05 0.988181993 #> 8862 88.61 0.01178945 2.265334e-05 0.988187900 #> 8863 88.62 0.01178355 2.264202e-05 0.988193805 #> 8864 88.63 0.01177766 2.263070e-05 0.988199706 #> 8865 88.64 0.01177178 2.261939e-05 0.988205605 #> 8866 88.65 0.01176589 2.260808e-05 0.988211501 #> 8867 88.66 0.01176001 2.259678e-05 0.988217394 #> 8868 88.67 0.01175413 2.258548e-05 0.988223283 #> 8869 88.68 0.01174826 2.257419e-05 0.988229170 #> 8870 88.69 0.01174238 2.256291e-05 0.988235054 #> 8871 88.70 0.01173651 2.255163e-05 0.988240935 #> 8872 88.71 0.01173065 2.254036e-05 0.988246813 #> 8873 88.72 0.01172478 2.252909e-05 0.988252688 #> 8874 88.73 0.01171892 2.251783e-05 0.988258561 #> 8875 88.74 0.01171306 2.250657e-05 0.988264430 #> 8876 88.75 0.01170721 2.249532e-05 0.988270296 #> 8877 88.76 0.01170136 2.248408e-05 0.988276160 #> 8878 88.77 0.01169551 2.247284e-05 0.988282020 #> 8879 88.78 0.01168966 2.246160e-05 0.988287878 #> 8880 88.79 0.01168382 2.245038e-05 0.988293732 #> 8881 88.80 0.01167798 2.243915e-05 0.988299584 #> 8882 88.81 0.01167214 2.242794e-05 0.988305433 #> 8883 88.82 0.01166630 2.241673e-05 0.988311278 #> 8884 88.83 0.01166047 2.240552e-05 0.988317121 #> 8885 88.84 0.01165464 2.239432e-05 0.988322961 #> 8886 88.85 0.01164882 2.238313e-05 0.988328798 #> 8887 88.86 0.01164300 2.237194e-05 0.988334633 #> 8888 88.87 0.01163718 2.236075e-05 0.988340464 #> 8889 88.88 0.01163136 2.234958e-05 0.988346292 #> 8890 88.89 0.01162554 2.233840e-05 0.988352117 #> 8891 88.90 0.01161973 2.232724e-05 0.988357940 #> 8892 88.91 0.01161392 2.231608e-05 0.988363760 #> 8893 88.92 0.01160812 2.230492e-05 0.988369576 #> 8894 88.93 0.01160232 2.229377e-05 0.988375390 #> 8895 88.94 0.01159652 2.228263e-05 0.988381201 #> 8896 88.95 0.01159072 2.227149e-05 0.988387009 #> 8897 88.96 0.01158493 2.226036e-05 0.988392814 #> 8898 88.97 0.01157913 2.224923e-05 0.988398616 #> 8899 88.98 0.01157335 2.223811e-05 0.988404415 #> 8900 88.99 0.01156756 2.222699e-05 0.988410211 #> 8901 89.00 0.01156178 2.221588e-05 0.988416005 #> 8902 89.01 0.01155600 2.220478e-05 0.988421795 #> 8903 89.02 0.01155022 2.219368e-05 0.988427583 #> 8904 89.03 0.01154445 2.218258e-05 0.988433368 #> 8905 89.04 0.01153868 2.217149e-05 0.988439150 #> 8906 89.05 0.01153291 2.216041e-05 0.988444929 #> 8907 89.06 0.01152715 2.214933e-05 0.988450705 #> 8908 89.07 0.01152138 2.213826e-05 0.988456478 #> 8909 89.08 0.01151562 2.212719e-05 0.988462248 #> 8910 89.09 0.01150987 2.211613e-05 0.988468016 #> 8911 89.10 0.01150411 2.210508e-05 0.988473780 #> 8912 89.11 0.01149836 2.209403e-05 0.988479542 #> 8913 89.12 0.01149262 2.208298e-05 0.988485301 #> 8914 89.13 0.01148687 2.207195e-05 0.988491057 #> 8915 89.14 0.01148113 2.206091e-05 0.988496810 #> 8916 89.15 0.01147539 2.204989e-05 0.988502560 #> 8917 89.16 0.01146965 2.203886e-05 0.988508307 #> 8918 89.17 0.01146392 2.202785e-05 0.988514052 #> 8919 89.18 0.01145819 2.201684e-05 0.988519793 #> 8920 89.19 0.01145246 2.200583e-05 0.988525532 #> 8921 89.20 0.01144674 2.199483e-05 0.988531268 #> 8922 89.21 0.01144102 2.198383e-05 0.988537001 #> 8923 89.22 0.01143530 2.197285e-05 0.988542731 #> 8924 89.23 0.01142958 2.196186e-05 0.988548458 #> 8925 89.24 0.01142387 2.195088e-05 0.988554182 #> 8926 89.25 0.01141816 2.193991e-05 0.988559904 #> 8927 89.26 0.01141245 2.192894e-05 0.988565622 #> 8928 89.27 0.01140674 2.191798e-05 0.988571338 #> 8929 89.28 0.01140104 2.190703e-05 0.988577051 #> 8930 89.29 0.01139534 2.189607e-05 0.988582761 #> 8931 89.30 0.01138965 2.188513e-05 0.988588468 #> 8932 89.31 0.01138395 2.187419e-05 0.988594173 #> 8933 89.32 0.01137826 2.186326e-05 0.988599874 #> 8934 89.33 0.01137258 2.185233e-05 0.988605573 #> 8935 89.34 0.01136689 2.184140e-05 0.988611268 #> 8936 89.35 0.01136121 2.183049e-05 0.988616961 #> 8937 89.36 0.01135553 2.181957e-05 0.988622651 #> 8938 89.37 0.01134985 2.180867e-05 0.988628339 #> 8939 89.38 0.01134418 2.179776e-05 0.988634023 #> 8940 89.39 0.01133851 2.178687e-05 0.988639705 #> 8941 89.40 0.01133284 2.177598e-05 0.988645383 #> 8942 89.41 0.01132718 2.176509e-05 0.988651059 #> 8943 89.42 0.01132151 2.175421e-05 0.988656732 #> 8944 89.43 0.01131585 2.174334e-05 0.988662403 #> 8945 89.44 0.01131020 2.173247e-05 0.988668070 #> 8946 89.45 0.01130454 2.172161e-05 0.988673735 #> 8947 89.46 0.01129889 2.171075e-05 0.988679396 #> 8948 89.47 0.01129324 2.169989e-05 0.988685055 #> 8949 89.48 0.01128760 2.168905e-05 0.988690711 #> 8950 89.49 0.01128196 2.167821e-05 0.988696364 #> 8951 89.50 0.01127632 2.166737e-05 0.988702015 #> 8952 89.51 0.01127068 2.165654e-05 0.988707662 #> 8953 89.52 0.01126505 2.164571e-05 0.988713307 #> 8954 89.53 0.01125942 2.163489e-05 0.988718949 #> 8955 89.54 0.01125379 2.162408e-05 0.988724588 #> 8956 89.55 0.01124816 2.161327e-05 0.988730225 #> 8957 89.56 0.01124254 2.160246e-05 0.988735858 #> 8958 89.57 0.01123692 2.159167e-05 0.988741489 #> 8959 89.58 0.01123130 2.158087e-05 0.988747116 #> 8960 89.59 0.01122569 2.157008e-05 0.988752742 #> 8961 89.60 0.01122008 2.155930e-05 0.988758364 #> 8962 89.61 0.01121447 2.154853e-05 0.988763983 #> 8963 89.62 0.01120886 2.153775e-05 0.988769600 #> 8964 89.63 0.01120326 2.152699e-05 0.988775214 #> 8965 89.64 0.01119766 2.151623e-05 0.988780825 #> 8966 89.65 0.01119206 2.150547e-05 0.988786433 #> 8967 89.66 0.01118647 2.149472e-05 0.988792038 #> 8968 89.67 0.01118088 2.148398e-05 0.988797641 #> 8969 89.68 0.01117529 2.147324e-05 0.988803240 #> 8970 89.69 0.01116970 2.146250e-05 0.988808837 #> 8971 89.70 0.01116412 2.145177e-05 0.988814432 #> 8972 89.71 0.01115854 2.144105e-05 0.988820023 #> 8973 89.72 0.01115296 2.143033e-05 0.988825612 #> 8974 89.73 0.01114738 2.141962e-05 0.988831197 #> 8975 89.74 0.01114181 2.140891e-05 0.988836780 #> 8976 89.75 0.01113624 2.139821e-05 0.988842361 #> 8977 89.76 0.01113067 2.138752e-05 0.988847938 #> 8978 89.77 0.01112511 2.137682e-05 0.988853513 #> 8979 89.78 0.01111955 2.136614e-05 0.988859085 #> 8980 89.79 0.01111399 2.135546e-05 0.988864654 #> 8981 89.80 0.01110844 2.134478e-05 0.988870220 #> 8982 89.81 0.01110288 2.133411e-05 0.988875783 #> 8983 89.82 0.01109733 2.132345e-05 0.988881344 #> 8984 89.83 0.01109179 2.131279e-05 0.988886902 #> 8985 89.84 0.01108624 2.130214e-05 0.988892457 #> 8986 89.85 0.01108070 2.129149e-05 0.988898010 #> 8987 89.86 0.01107516 2.128085e-05 0.988903559 #> 8988 89.87 0.01106962 2.127021e-05 0.988909106 #> 8989 89.88 0.01106409 2.125958e-05 0.988914650 #> 8990 89.89 0.01105856 2.124895e-05 0.988920191 #> 8991 89.90 0.01105303 2.123833e-05 0.988925730 #> 8992 89.91 0.01104751 2.122771e-05 0.988931266 #> 8993 89.92 0.01104198 2.121710e-05 0.988936799 #> 8994 89.93 0.01103646 2.120649e-05 0.988942329 #> 8995 89.94 0.01103095 2.119589e-05 0.988947856 #> 8996 89.95 0.01102543 2.118530e-05 0.988953381 #> 8997 89.96 0.01101992 2.117471e-05 0.988958903 #> 8998 89.97 0.01101441 2.116412e-05 0.988964422 #> 8999 89.98 0.01100891 2.115354e-05 0.988969939 #> 9000 89.99 0.01100340 2.114297e-05 0.988975452 #> 9001 90.00 0.01099790 2.113240e-05 0.988980963 #>  #> attr(,\"class\") #> [1] \"sir_static_model\""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_subpops_model.html","id":null,"dir":"Reference","previous_headings":"","what":"SIR Model with Interacting Subpopulations — sir_subpops_model","title":"SIR Model with Interacting Subpopulations — sir_subpops_model","text":"Refers section 3.5.1.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_subpops_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SIR Model with Interacting Subpopulations — sir_subpops_model","text":"","code":"sir_subpops_model(times, state, parameters)"},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_subpops_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SIR Model with Interacting Subpopulations — sir_subpops_model","text":"times time sequence. state initial state model. parameters parameters model.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_subpops_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SIR Model with Interacting Subpopulations — sir_subpops_model","text":"list class sir_subpops_model following items parameters list parameters used fitting model output matrix proportion compartment time","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_subpops_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SIR Model with Interacting Subpopulations — sir_subpops_model","text":"state: - s: Percent susceptible - : Percent infected - r: Percent recovered parameters: - mu: natural death rate (1/L). - beta: transmission rate w.r.t population (beta tilde) - nu: recovery rate - k: number subpopulations","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_subpops_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SIR Model with Interacting Subpopulations — sir_subpops_model","text":"","code":"# \\donttest{ k <- 2 state <- c(   s = c(0.8, 0.8),   i = c(0.2, 0.2),   r = c(  0,   0) ) beta_matrix <- c(   c(0.05, 0.00),   c(0.00, 0.05) ) parameters <- list(   beta = matrix(beta_matrix, nrow=k, ncol=k, byrow=TRUE),   nu = c(1/30, 1/30),   mu = 0.001,   k = k ) times<-seq(0,10000,by=0.5) model <- sir_subpops_model(times, state, parameters) model #> $parameters #> $parameters$beta #>      [,1] [,2] #> [1,] 0.05 0.00 #> [2,] 0.00 0.05 #>  #> $parameters$nu #> [1] 0.03333333 0.03333333 #>  #> $parameters$mu #> [1] 0.001 #>  #> $parameters$k #> [1] 2 #>  #>  #> $output #>         time        s1        s2           i1           i2          r1 #> 1        0.0 0.8000000 0.8000000 2.000000e-01 2.000000e-01 0.000000000 #> 2        0.5 0.7961052 0.7961052 2.005577e-01 2.005577e-01 0.003337146 #> 3        1.0 0.7922208 0.7922208 2.010974e-01 2.010974e-01 0.006681767 #> 4        1.5 0.7883475 0.7883475 2.016190e-01 2.016190e-01 0.010033558 #> 5        2.0 0.7844854 0.7844854 2.021224e-01 2.021224e-01 0.013392251 #> 6        2.5 0.7806350 0.7806350 2.026075e-01 2.026075e-01 0.016757489 #> 7        3.0 0.7767967 0.7767967 2.030743e-01 2.030743e-01 0.020128975 #> 8        3.5 0.7729709 0.7729709 2.035227e-01 2.035227e-01 0.023506400 #> 9        4.0 0.7691580 0.7691580 2.039525e-01 2.039525e-01 0.026889453 #> 10       4.5 0.7653583 0.7653583 2.043639e-01 2.043639e-01 0.030277824 #> 11       5.0 0.7615722 0.7615722 2.047566e-01 2.047566e-01 0.033671200 #> 12       5.5 0.7578000 0.7578000 2.051307e-01 2.051307e-01 0.037069269 #> 13       6.0 0.7540421 0.7540421 2.054862e-01 2.054862e-01 0.040471717 #> 14       6.5 0.7502988 0.7502988 2.058229e-01 2.058229e-01 0.043878231 #> 15       7.0 0.7465705 0.7465705 2.061410e-01 2.061410e-01 0.047288499 #> 16       7.5 0.7428574 0.7428574 2.064404e-01 2.064404e-01 0.050702206 #> 17       8.0 0.7391599 0.7391599 2.067210e-01 2.067210e-01 0.054119038 #> 18       8.5 0.7354784 0.7354784 2.069829e-01 2.069829e-01 0.057538683 #> 19       9.0 0.7318130 0.7318130 2.072262e-01 2.072262e-01 0.060960827 #> 20       9.5 0.7281641 0.7281641 2.074507e-01 2.074507e-01 0.064385157 #> 21      10.0 0.7245320 0.7245320 2.076566e-01 2.076566e-01 0.067811362 #> 22      10.5 0.7209170 0.7209170 2.078439e-01 2.078439e-01 0.071239129 #> 23      11.0 0.7173193 0.7173193 2.080126e-01 2.080126e-01 0.074668149 #> 24      11.5 0.7137392 0.7137392 2.081627e-01 2.081627e-01 0.078098110 #> 25      12.0 0.7101769 0.7101769 2.082944e-01 2.082944e-01 0.081528705 #> 26      12.5 0.7066328 0.7066328 2.084076e-01 2.084076e-01 0.084959625 #> 27      13.0 0.7031070 0.7031070 2.085024e-01 2.085024e-01 0.088390563 #> 28      13.5 0.6995998 0.6995998 2.085790e-01 2.085790e-01 0.091821214 #> 29      14.0 0.6961114 0.6961114 2.086373e-01 2.086373e-01 0.095251274 #> 30      14.5 0.6926420 0.6926420 2.086776e-01 2.086776e-01 0.098680441 #> 31      15.0 0.6891919 0.6891919 2.086997e-01 2.086997e-01 0.102108412 #> 32      15.5 0.6857612 0.6857612 2.087039e-01 2.087039e-01 0.105534890 #> 33      16.0 0.6823501 0.6823501 2.086903e-01 2.086903e-01 0.108959577 #> 34      16.5 0.6789589 0.6789589 2.086589e-01 2.086589e-01 0.112382176 #> 35      17.0 0.6755877 0.6755877 2.086099e-01 2.086099e-01 0.115802395 #> 36      17.5 0.6722366 0.6722366 2.085434e-01 2.085434e-01 0.119219941 #> 37      18.0 0.6689060 0.6689060 2.084595e-01 2.084595e-01 0.122634526 #> 38      18.5 0.6655958 0.6655958 2.083583e-01 2.083583e-01 0.126045862 #> 39      19.0 0.6623063 0.6623063 2.082400e-01 2.082400e-01 0.129453664 #> 40      19.5 0.6590376 0.6590376 2.081047e-01 2.081047e-01 0.132857649 #> 41      20.0 0.6557899 0.6557899 2.079526e-01 2.079526e-01 0.136257538 #> 42      20.5 0.6525632 0.6525632 2.077838e-01 2.077838e-01 0.139653053 #> 43      21.0 0.6493577 0.6493577 2.075983e-01 2.075983e-01 0.143043919 #> 44      21.5 0.6461736 0.6461736 2.073965e-01 2.073965e-01 0.146429864 #> 45      22.0 0.6430109 0.6430109 2.071785e-01 2.071785e-01 0.149810618 #> 46      22.5 0.6398697 0.6398697 2.069444e-01 2.069444e-01 0.153185914 #> 47      23.0 0.6367502 0.6367502 2.066943e-01 2.066943e-01 0.156555490 #> 48      23.5 0.6336524 0.6336524 2.064286e-01 2.064286e-01 0.159919084 #> 49      24.0 0.6305764 0.6305764 2.061472e-01 2.061472e-01 0.163276437 #> 50      24.5 0.6275222 0.6275222 2.058505e-01 2.058505e-01 0.166627296 #> 51      25.0 0.6244901 0.6244901 2.055385e-01 2.055385e-01 0.169971409 #> 52      25.5 0.6214799 0.6214799 2.052115e-01 2.052115e-01 0.173308527 #> 53      26.0 0.6184919 0.6184919 2.048697e-01 2.048697e-01 0.176638404 #> 54      26.5 0.6155259 0.6155259 2.045133e-01 2.045133e-01 0.179960799 #> 55      27.0 0.6125822 0.6125822 2.041423e-01 2.041423e-01 0.183275473 #> 56      27.5 0.6096607 0.6096607 2.037571e-01 2.037571e-01 0.186582190 #> 57      28.0 0.6067614 0.6067614 2.033579e-01 2.033579e-01 0.189880719 #> 58      28.5 0.6038844 0.6038844 2.029447e-01 2.029447e-01 0.193170830 #> 59      29.0 0.6010298 0.6010298 2.025179e-01 2.025179e-01 0.196452298 #> 60      29.5 0.5981974 0.5981974 2.020777e-01 2.020777e-01 0.199724902 #> 61      30.0 0.5953875 0.5953875 2.016241e-01 2.016241e-01 0.202988423 #> 62      30.5 0.5925998 0.5925998 2.011575e-01 2.011575e-01 0.206242646 #> 63      31.0 0.5898346 0.5898346 2.006781e-01 2.006781e-01 0.209487361 #> 64      31.5 0.5870917 0.5870917 2.001860e-01 2.001860e-01 0.212722359 #> 65      32.0 0.5843711 0.5843711 1.996815e-01 1.996815e-01 0.215947437 #> 66      32.5 0.5816729 0.5816729 1.991647e-01 1.991647e-01 0.219162394 #> 67      33.0 0.5789970 0.5789970 1.986360e-01 1.986360e-01 0.222367034 #> 68      33.5 0.5763434 0.5763434 1.980954e-01 1.980954e-01 0.225561163 #> 69      34.0 0.5737121 0.5737121 1.975433e-01 1.975433e-01 0.228744592 #> 70      34.5 0.5711030 0.5711030 1.969798e-01 1.969798e-01 0.231917135 #> 71      35.0 0.5685162 0.5685162 1.964052e-01 1.964052e-01 0.235078610 #> 72      35.5 0.5659515 0.5659515 1.958197e-01 1.958197e-01 0.238228838 #> 73      36.0 0.5634090 0.5634090 1.952234e-01 1.952234e-01 0.241367645 #> 74      36.5 0.5608886 0.5608886 1.946166e-01 1.946166e-01 0.244494860 #> 75      37.0 0.5583902 0.5583902 1.939995e-01 1.939995e-01 0.247610314 #> 76      37.5 0.5559138 0.5559138 1.933724e-01 1.933724e-01 0.250713846 #> 77      38.0 0.5534593 0.5534593 1.927354e-01 1.927354e-01 0.253805294 #> 78      38.5 0.5510267 0.5510267 1.920888e-01 1.920888e-01 0.256884502 #> 79      39.0 0.5486160 0.5486160 1.914327e-01 1.914327e-01 0.259951318 #> 80      39.5 0.5462269 0.5462269 1.907675e-01 1.907675e-01 0.263005592 #> 81      40.0 0.5438596 0.5438596 1.900932e-01 1.900932e-01 0.266047180 #> 82      40.5 0.5415139 0.5415139 1.894102e-01 1.894102e-01 0.269075939 #> 83      41.0 0.5391896 0.5391896 1.887186e-01 1.887186e-01 0.272091732 #> 84      41.5 0.5368869 0.5368869 1.880187e-01 1.880187e-01 0.275094424 #> 85      42.0 0.5346055 0.5346055 1.873106e-01 1.873106e-01 0.278083884 #> 86      42.5 0.5323454 0.5323454 1.865946e-01 1.865946e-01 0.281059985 #> 87      43.0 0.5301065 0.5301065 1.858709e-01 1.858709e-01 0.284022604 #> 88      43.5 0.5278887 0.5278887 1.851397e-01 1.851397e-01 0.286971620 #> 89      44.0 0.5256919 0.5256919 1.844012e-01 1.844012e-01 0.289906917 #> 90      44.5 0.5235161 0.5235161 1.836556e-01 1.836556e-01 0.292828382 #> 91      45.0 0.5213610 0.5213610 1.829031e-01 1.829031e-01 0.295735905 #> 92      45.5 0.5192267 0.5192267 1.821439e-01 1.821439e-01 0.298629380 #> 93      46.0 0.5171131 0.5171131 1.813782e-01 1.813782e-01 0.301508705 #> 94      46.5 0.5150199 0.5150199 1.806063e-01 1.806063e-01 0.304373780 #> 95      47.0 0.5129472 0.5129472 1.798283e-01 1.798283e-01 0.307224510 #> 96      47.5 0.5108948 0.5108948 1.790444e-01 1.790444e-01 0.310060801 #> 97      48.0 0.5088626 0.5088626 1.782548e-01 1.782548e-01 0.312882565 #> 98      48.5 0.5068506 0.5068506 1.774597e-01 1.774597e-01 0.315689717 #> 99      49.0 0.5048585 0.5048585 1.766593e-01 1.766593e-01 0.318482173 #> 100     49.5 0.5028863 0.5028863 1.758539e-01 1.758539e-01 0.321259854 #> 101     50.0 0.5009338 0.5009338 1.750435e-01 1.750435e-01 0.324022684 #> 102     50.5 0.4990010 0.4990010 1.742284e-01 1.742284e-01 0.326770590 #> 103     51.0 0.4970878 0.4970878 1.734087e-01 1.734087e-01 0.329503503 #> 104     51.5 0.4951939 0.4951939 1.725847e-01 1.725847e-01 0.332221355 #> 105     52.0 0.4933194 0.4933194 1.717565e-01 1.717565e-01 0.334924084 #> 106     52.5 0.4914640 0.4914640 1.709243e-01 1.709243e-01 0.337611628 #> 107     53.0 0.4896277 0.4896277 1.700883e-01 1.700883e-01 0.340283931 #> 108     53.5 0.4878104 0.4878104 1.692487e-01 1.692487e-01 0.342940938 #> 109     54.0 0.4860118 0.4860118 1.684056e-01 1.684056e-01 0.345582596 #> 110     54.5 0.4842320 0.4842320 1.675592e-01 1.675592e-01 0.348208859 #> 111     55.0 0.4824707 0.4824707 1.667096e-01 1.667096e-01 0.350819678 #> 112     55.5 0.4807278 0.4807278 1.658572e-01 1.658572e-01 0.353415013 #> 113     56.0 0.4790033 0.4790033 1.650019e-01 1.650019e-01 0.355994822 #> 114     56.5 0.4772970 0.4772970 1.641440e-01 1.641440e-01 0.358559069 #> 115     57.0 0.4756087 0.4756087 1.632836e-01 1.632836e-01 0.361107718 #> 116     57.5 0.4739384 0.4739384 1.624209e-01 1.624209e-01 0.363640737 #> 117     58.0 0.4722858 0.4722858 1.615561e-01 1.615561e-01 0.366158098 #> 118     58.5 0.4706510 0.4706510 1.606892e-01 1.606892e-01 0.368659773 #> 119     59.0 0.4690337 0.4690337 1.598205e-01 1.598205e-01 0.371145738 #> 120     59.5 0.4674339 0.4674339 1.589502e-01 1.589502e-01 0.373615972 #> 121     60.0 0.4658513 0.4658513 1.580782e-01 1.580782e-01 0.376070455 #> 122     60.5 0.4642859 0.4642859 1.572049e-01 1.572049e-01 0.378509171 #> 123     61.0 0.4627376 0.4627376 1.563303e-01 1.563303e-01 0.380932105 #> 124     61.5 0.4612061 0.4612061 1.554546e-01 1.554546e-01 0.383339246 #> 125     62.0 0.4596915 0.4596915 1.545779e-01 1.545779e-01 0.385730583 #> 126     62.5 0.4581935 0.4581935 1.537004e-01 1.537004e-01 0.388106110 #> 127     63.0 0.4567120 0.4567120 1.528221e-01 1.528221e-01 0.390465822 #> 128     63.5 0.4552470 0.4552470 1.519433e-01 1.519433e-01 0.392809716 #> 129     64.0 0.4537982 0.4537982 1.510641e-01 1.510641e-01 0.395137790 #> 130     64.5 0.4523655 0.4523655 1.501845e-01 1.501845e-01 0.397450047 #> 131     65.0 0.4509488 0.4509488 1.493047e-01 1.493047e-01 0.399746490 #> 132     65.5 0.4495481 0.4495481 1.484248e-01 1.484248e-01 0.402027125 #> 133     66.0 0.4481630 0.4481630 1.475450e-01 1.475450e-01 0.404291960 #> 134     66.5 0.4467936 0.4467936 1.466654e-01 1.466654e-01 0.406541004 #> 135     67.0 0.4454397 0.4454397 1.457860e-01 1.457860e-01 0.408774268 #> 136     67.5 0.4441012 0.4441012 1.449070e-01 1.449070e-01 0.410991767 #> 137     68.0 0.4427779 0.4427779 1.440286e-01 1.440286e-01 0.413193516 #> 138     68.5 0.4414698 0.4414698 1.431507e-01 1.431507e-01 0.415379531 #> 139     69.0 0.4401766 0.4401766 1.422736e-01 1.422736e-01 0.417549833 #> 140     69.5 0.4388983 0.4388983 1.413973e-01 1.413973e-01 0.419704442 #> 141     70.0 0.4376347 0.4376347 1.405219e-01 1.405219e-01 0.421843379 #> 142     70.5 0.4363857 0.4363857 1.396476e-01 1.396476e-01 0.423966671 #> 143     71.0 0.4351513 0.4351513 1.387744e-01 1.387744e-01 0.426074342 #> 144     71.5 0.4339311 0.4339311 1.379024e-01 1.379024e-01 0.428166419 #> 145     72.0 0.4327253 0.4327253 1.370318e-01 1.370318e-01 0.430242933 #> 146     72.5 0.4315335 0.4315335 1.361626e-01 1.361626e-01 0.432303913 #> 147     73.0 0.4303558 0.4303558 1.352948e-01 1.352948e-01 0.434349392 #> 148     73.5 0.4291919 0.4291919 1.344287e-01 1.344287e-01 0.436379403 #> 149     74.0 0.4280417 0.4280417 1.335643e-01 1.335643e-01 0.438393982 #> 150     74.5 0.4269052 0.4269052 1.327016e-01 1.327016e-01 0.440393164 #> 151     75.0 0.4257823 0.4257823 1.318408e-01 1.318408e-01 0.442376988 #> 152     75.5 0.4246726 0.4246726 1.309819e-01 1.309819e-01 0.444345492 #> 153     76.0 0.4235763 0.4235763 1.301250e-01 1.301250e-01 0.446298718 #> 154     76.5 0.4224931 0.4224931 1.292702e-01 1.292702e-01 0.448236707 #> 155     77.0 0.4214230 0.4214230 1.284175e-01 1.284175e-01 0.450159501 #> 156     77.5 0.4203658 0.4203658 1.275671e-01 1.275671e-01 0.452067145 #> 157     78.0 0.4193214 0.4193214 1.267190e-01 1.267190e-01 0.453959685 #> 158     78.5 0.4182896 0.4182896 1.258732e-01 1.258732e-01 0.455837167 #> 159     79.0 0.4172705 0.4172705 1.250299e-01 1.250299e-01 0.457699638 #> 160     79.5 0.4162637 0.4162637 1.241891e-01 1.241891e-01 0.459547147 #> 161     80.0 0.4152694 0.4152694 1.233509e-01 1.233509e-01 0.461379744 #> 162     80.5 0.4142872 0.4142872 1.225153e-01 1.225153e-01 0.463197480 #> 163     81.0 0.4133172 0.4133172 1.216824e-01 1.216824e-01 0.465000407 #> 164     81.5 0.4123592 0.4123592 1.208522e-01 1.208522e-01 0.466788577 #> 165     82.0 0.4114131 0.4114131 1.200248e-01 1.200248e-01 0.468562043 #> 166     82.5 0.4104788 0.4104788 1.192004e-01 1.192004e-01 0.470320861 #> 167     83.0 0.4095561 0.4095561 1.183788e-01 1.183788e-01 0.472065086 #> 168     83.5 0.4086451 0.4086451 1.175602e-01 1.175602e-01 0.473794774 #> 169     84.0 0.4077454 0.4077454 1.167446e-01 1.167446e-01 0.475509983 #> 170     84.5 0.4068572 0.4068572 1.159321e-01 1.159321e-01 0.477210770 #> 171     85.0 0.4059801 0.4059801 1.151227e-01 1.151227e-01 0.478897194 #> 172     85.5 0.4051143 0.4051143 1.143164e-01 1.143164e-01 0.480569315 #> 173     86.0 0.4042594 0.4042594 1.135134e-01 1.135134e-01 0.482227192 #> 174     86.5 0.4034155 0.4034155 1.127136e-01 1.127136e-01 0.483870887 #> 175     87.0 0.4025825 0.4025825 1.119171e-01 1.119171e-01 0.485500461 #> 176     87.5 0.4017601 0.4017601 1.111239e-01 1.111239e-01 0.487115976 #> 177     88.0 0.4009484 0.4009484 1.103341e-01 1.103341e-01 0.488717496 #> 178     88.5 0.4001472 0.4001472 1.095477e-01 1.095477e-01 0.490305083 #> 179     89.0 0.3993565 0.3993565 1.087647e-01 1.087647e-01 0.491878801 #> 180     89.5 0.3985761 0.3985761 1.079852e-01 1.079852e-01 0.493438716 #> 181     90.0 0.3978059 0.3978059 1.072093e-01 1.072093e-01 0.494984892 #> 182     90.5 0.3970458 0.3970458 1.064368e-01 1.064368e-01 0.496517395 #> 183     91.0 0.3962958 0.3962958 1.056679e-01 1.056679e-01 0.498036291 #> 184     91.5 0.3955557 0.3955557 1.049027e-01 1.049027e-01 0.499541646 #> 185     92.0 0.3948254 0.3948254 1.041410e-01 1.041410e-01 0.501033528 #> 186     92.5 0.3941049 0.3941049 1.033831e-01 1.033831e-01 0.502512003 #> 187     93.0 0.3933941 0.3933941 1.026288e-01 1.026288e-01 0.503977140 #> 188     93.5 0.3926928 0.3926928 1.018782e-01 1.018782e-01 0.505429007 #> 189     94.0 0.3920010 0.3920010 1.011313e-01 1.011313e-01 0.506867673 #> 190     94.5 0.3913186 0.3913186 1.003882e-01 1.003882e-01 0.508293205 #> 191     95.0 0.3906455 0.3906455 9.964882e-02 9.964882e-02 0.509705675 #> 192     95.5 0.3899816 0.3899816 9.891327e-02 9.891327e-02 0.511105150 #> 193     96.0 0.3893268 0.3893268 9.818152e-02 9.818152e-02 0.512491702 #> 194     96.5 0.3886810 0.3886810 9.745361e-02 9.745361e-02 0.513865399 #> 195     97.0 0.3880441 0.3880441 9.672954e-02 9.672954e-02 0.515226314 #> 196     97.5 0.3874162 0.3874162 9.600933e-02 9.600933e-02 0.516574515 #> 197     98.0 0.3867969 0.3867969 9.529300e-02 9.529300e-02 0.517910074 #> 198     98.5 0.3861864 0.3861864 9.458056e-02 9.458056e-02 0.519233062 #> 199     99.0 0.3855844 0.3855844 9.387203e-02 9.387203e-02 0.520543550 #> 200     99.5 0.3849910 0.3849910 9.316741e-02 9.316741e-02 0.521841609 #> 201    100.0 0.3844060 0.3844060 9.246671e-02 9.246671e-02 0.523127312 #> 202    100.5 0.3838293 0.3838293 9.176996e-02 9.176996e-02 0.524400730 #> 203    101.0 0.3832609 0.3832609 9.107715e-02 9.107715e-02 0.525661934 #> 204    101.5 0.3827007 0.3827007 9.038829e-02 9.038829e-02 0.526910997 #> 205    102.0 0.3821486 0.3821486 8.970340e-02 8.970340e-02 0.528147990 #> 206    102.5 0.3816045 0.3816045 8.902248e-02 8.902248e-02 0.529372986 #> 207    103.0 0.3810684 0.3810684 8.834553e-02 8.834553e-02 0.530586057 #> 208    103.5 0.3805402 0.3805402 8.767257e-02 8.767257e-02 0.531787275 #> 209    104.0 0.3800197 0.3800197 8.700359e-02 8.700359e-02 0.532976713 #> 210    104.5 0.3795070 0.3795070 8.633860e-02 8.633860e-02 0.534154442 #> 211    105.0 0.3790019 0.3790019 8.567760e-02 8.567760e-02 0.535320536 #> 212    105.5 0.3785043 0.3785043 8.502060e-02 8.502060e-02 0.536475066 #> 213    106.0 0.3780143 0.3780143 8.436760e-02 8.436760e-02 0.537618105 #> 214    106.5 0.3775317 0.3775317 8.371859e-02 8.371859e-02 0.538749725 #> 215    107.0 0.3770564 0.3770564 8.307358e-02 8.307358e-02 0.539869999 #> 216    107.5 0.3765884 0.3765884 8.243257e-02 8.243257e-02 0.540978999 #> 217    108.0 0.3761277 0.3761277 8.179555e-02 8.179555e-02 0.542076796 #> 218    108.5 0.3756740 0.3756740 8.116253e-02 8.116253e-02 0.543163464 #> 219    109.0 0.3752274 0.3752274 8.053350e-02 8.053350e-02 0.544239075 #> 220    109.5 0.3747878 0.3747878 7.990847e-02 7.990847e-02 0.545303699 #> 221    110.0 0.3743552 0.3743552 7.928741e-02 7.928741e-02 0.546357411 #> 222    110.5 0.3739294 0.3739294 7.867035e-02 7.867035e-02 0.547400280 #> 223    111.0 0.3735104 0.3735104 7.805726e-02 7.805726e-02 0.548432379 #> 224    111.5 0.3730981 0.3730981 7.744814e-02 7.744814e-02 0.549453780 #> 225    112.0 0.3726925 0.3726925 7.684299e-02 7.684299e-02 0.550464554 #> 226    112.5 0.3722934 0.3722934 7.624180e-02 7.624180e-02 0.551464772 #> 227    113.0 0.3719009 0.3719009 7.564457e-02 7.564457e-02 0.552454506 #> 228    113.5 0.3715149 0.3715149 7.505128e-02 7.505128e-02 0.553433827 #> 229    114.0 0.3711353 0.3711353 7.446194e-02 7.446194e-02 0.554402805 #> 230    114.5 0.3707620 0.3707620 7.387652e-02 7.387652e-02 0.555361512 #> 231    115.0 0.3703949 0.3703949 7.329504e-02 7.329504e-02 0.556310018 #> 232    115.5 0.3700341 0.3700341 7.271746e-02 7.271746e-02 0.557248393 #> 233    116.0 0.3696795 0.3696795 7.214380e-02 7.214380e-02 0.558176708 #> 234    116.5 0.3693309 0.3693309 7.157403e-02 7.157403e-02 0.559095033 #> 235    117.0 0.3689884 0.3689884 7.100814e-02 7.100814e-02 0.560003438 #> 236    117.5 0.3686519 0.3686519 7.044614e-02 7.044614e-02 0.560901991 #> 237    118.0 0.3683212 0.3683212 6.988800e-02 6.988800e-02 0.561790763 #> 238    118.5 0.3679965 0.3679965 6.933371e-02 6.933371e-02 0.562669823 #> 239    119.0 0.3676775 0.3676775 6.878326e-02 6.878326e-02 0.563539240 #> 240    119.5 0.3673643 0.3673643 6.823665e-02 6.823665e-02 0.564399082 #> 241    120.0 0.3670567 0.3670567 6.769385e-02 6.769385e-02 0.565249419 #> 242    120.5 0.3667548 0.3667548 6.715486e-02 6.715486e-02 0.566090317 #> 243    121.0 0.3664585 0.3664585 6.661967e-02 6.661967e-02 0.566921846 #> 244    121.5 0.3661677 0.3661677 6.608825e-02 6.608825e-02 0.567744073 #> 245    122.0 0.3658823 0.3658823 6.556060e-02 6.556060e-02 0.568557066 #> 246    122.5 0.3656024 0.3656024 6.503670e-02 6.503670e-02 0.569360892 #> 247    123.0 0.3653278 0.3653278 6.451655e-02 6.451655e-02 0.570155618 #> 248    123.5 0.3650586 0.3650586 6.400012e-02 6.400012e-02 0.570941310 #> 249    124.0 0.3647946 0.3647946 6.348740e-02 6.348740e-02 0.571718036 #> 250    124.5 0.3645358 0.3645358 6.297837e-02 6.297837e-02 0.572485861 #> 251    125.0 0.3642821 0.3642821 6.247303e-02 6.247303e-02 0.573244851 #> 252    125.5 0.3640336 0.3640336 6.197135e-02 6.197135e-02 0.573995072 #> 253    126.0 0.3637901 0.3637901 6.147332e-02 6.147332e-02 0.574736589 #> 254    126.5 0.3635516 0.3635516 6.097893e-02 6.097893e-02 0.575469468 #> 255    127.0 0.3633181 0.3633181 6.048815e-02 6.048815e-02 0.576193772 #> 256    127.5 0.3630894 0.3630894 6.000098e-02 6.000098e-02 0.576909567 #> 257    128.0 0.3628657 0.3628657 5.951740e-02 5.951740e-02 0.577616917 #> 258    128.5 0.3626467 0.3626467 5.903739e-02 5.903739e-02 0.578315885 #> 259    129.0 0.3624325 0.3624325 5.856093e-02 5.856093e-02 0.579006535 #> 260    129.5 0.3622231 0.3622231 5.808801e-02 5.808801e-02 0.579688930 #> 261    130.0 0.3620183 0.3620183 5.761861e-02 5.761861e-02 0.580363134 #> 262    130.5 0.3618181 0.3618181 5.715272e-02 5.715272e-02 0.581029209 #> 263    131.0 0.3616225 0.3616225 5.669031e-02 5.669031e-02 0.581687216 #> 264    131.5 0.3614314 0.3614314 5.623137e-02 5.623137e-02 0.582337219 #> 265    132.0 0.3612448 0.3612448 5.577588e-02 5.577588e-02 0.582979278 #> 266    132.5 0.3610627 0.3610627 5.532383e-02 5.532383e-02 0.583613456 #> 267    133.0 0.3608850 0.3608850 5.487519e-02 5.487519e-02 0.584239813 #> 268    133.5 0.3607116 0.3607116 5.442995e-02 5.442995e-02 0.584858409 #> 269    134.0 0.3605426 0.3605426 5.398810e-02 5.398810e-02 0.585469306 #> 270    134.5 0.3603778 0.3603778 5.354961e-02 5.354961e-02 0.586072563 #> 271    135.0 0.3602173 0.3602173 5.311446e-02 5.311446e-02 0.586668240 #> 272    135.5 0.3600610 0.3600610 5.268264e-02 5.268264e-02 0.587256397 #> 273    136.0 0.3599088 0.3599088 5.225413e-02 5.225413e-02 0.587837092 #> 274    136.5 0.3597607 0.3597607 5.182891e-02 5.182891e-02 0.588410384 #> 275    137.0 0.3596167 0.3596167 5.140697e-02 5.140697e-02 0.588976331 #> 276    137.5 0.3594767 0.3594767 5.098828e-02 5.098828e-02 0.589534992 #> 277    138.0 0.3593408 0.3593408 5.057282e-02 5.057282e-02 0.590086425 #> 278    138.5 0.3592087 0.3592087 5.016058e-02 5.016058e-02 0.590630686 #> 279    139.0 0.3590806 0.3590806 4.975154e-02 4.975154e-02 0.591167832 #> 280    139.5 0.3589564 0.3589564 4.934568e-02 4.934568e-02 0.591697921 #> 281    140.0 0.3588360 0.3588360 4.894298e-02 4.894298e-02 0.592221009 #> 282    140.5 0.3587194 0.3587194 4.854342e-02 4.854342e-02 0.592737151 #> 283    141.0 0.3586066 0.3586066 4.814699e-02 4.814699e-02 0.593246404 #> 284    141.5 0.3584975 0.3584975 4.775366e-02 4.775366e-02 0.593748823 #> 285    142.0 0.3583921 0.3583921 4.736342e-02 4.736342e-02 0.594244463 #> 286    142.5 0.3582904 0.3582904 4.697624e-02 4.697624e-02 0.594733377 #> 287    143.0 0.3581923 0.3581923 4.659212e-02 4.659212e-02 0.595215622 #> 288    143.5 0.3580977 0.3580977 4.621102e-02 4.621102e-02 0.595691250 #> 289    144.0 0.3580067 0.3580067 4.583294e-02 4.583294e-02 0.596160316 #> 290    144.5 0.3579193 0.3579193 4.545784e-02 4.545784e-02 0.596622872 #> 291    145.0 0.3578353 0.3578353 4.508572e-02 4.508572e-02 0.597078972 #> 292    145.5 0.3577548 0.3577548 4.471656e-02 4.471656e-02 0.597528668 #> 293    146.0 0.3576777 0.3576777 4.435033e-02 4.435033e-02 0.597972013 #> 294    146.5 0.3576039 0.3576039 4.398701e-02 4.398701e-02 0.598409058 #> 295    147.0 0.3575336 0.3575336 4.362660e-02 4.362660e-02 0.598839854 #> 296    147.5 0.3574665 0.3574665 4.326906e-02 4.326906e-02 0.599264455 #> 297    148.0 0.3574027 0.3574027 4.291438e-02 4.291438e-02 0.599682909 #> 298    148.5 0.3573422 0.3573422 4.256254e-02 4.256254e-02 0.600095268 #> 299    149.0 0.3572849 0.3572849 4.221353e-02 4.221353e-02 0.600501582 #> 300    149.5 0.3572308 0.3572308 4.186732e-02 4.186732e-02 0.600901900 #> 301    150.0 0.3571798 0.3571798 4.152389e-02 4.152389e-02 0.601296274 #> 302    150.5 0.3571320 0.3571320 4.118323e-02 4.118323e-02 0.601684750 #> 303    151.0 0.3570873 0.3570873 4.084532e-02 4.084532e-02 0.602067379 #> 304    151.5 0.3570457 0.3570457 4.051014e-02 4.051014e-02 0.602444210 #> 305    152.0 0.3570070 0.3570070 4.017767e-02 4.017767e-02 0.602815289 #> 306    152.5 0.3569714 0.3569714 3.984788e-02 3.984788e-02 0.603180666 #> 307    153.0 0.3569388 0.3569388 3.952078e-02 3.952078e-02 0.603540387 #> 308    153.5 0.3569092 0.3569092 3.919632e-02 3.919632e-02 0.603894500 #> 309    154.0 0.3568824 0.3568824 3.887451e-02 3.887451e-02 0.604243052 #> 310    154.5 0.3568586 0.3568586 3.855531e-02 3.855531e-02 0.604586090 #> 311    155.0 0.3568376 0.3568376 3.823871e-02 3.823871e-02 0.604923659 #> 312    155.5 0.3568195 0.3568195 3.792469e-02 3.792469e-02 0.605255805 #> 313    156.0 0.3568042 0.3568042 3.761323e-02 3.761323e-02 0.605582574 #> 314    156.5 0.3567917 0.3567917 3.730432e-02 3.730432e-02 0.605904012 #> 315    157.0 0.3567819 0.3567819 3.699794e-02 3.699794e-02 0.606220163 #> 316    157.5 0.3567749 0.3567749 3.669406e-02 3.669406e-02 0.606531071 #> 317    158.0 0.3567705 0.3567705 3.639268e-02 3.639268e-02 0.606836782 #> 318    158.5 0.3567689 0.3567689 3.609377e-02 3.609377e-02 0.607137338 #> 319    159.0 0.3567699 0.3567699 3.579731e-02 3.579731e-02 0.607432784 #> 320    159.5 0.3567735 0.3567735 3.550329e-02 3.550329e-02 0.607723164 #> 321    160.0 0.3567798 0.3567798 3.521169e-02 3.521169e-02 0.608008519 #> 322    160.5 0.3567886 0.3567886 3.492249e-02 3.492249e-02 0.608288892 #> 323    161.0 0.3568000 0.3568000 3.463568e-02 3.463568e-02 0.608564327 #> 324    161.5 0.3568139 0.3568139 3.435123e-02 3.435123e-02 0.608834864 #> 325    162.0 0.3568303 0.3568303 3.406913e-02 3.406913e-02 0.609100547 #> 326    162.5 0.3568492 0.3568492 3.378936e-02 3.378936e-02 0.609361415 #> 327    163.0 0.3568706 0.3568706 3.351191e-02 3.351191e-02 0.609617511 #> 328    163.5 0.3568944 0.3568944 3.323675e-02 3.323675e-02 0.609868875 #> 329    164.0 0.3569206 0.3569206 3.296387e-02 3.296387e-02 0.610115547 #> 330    164.5 0.3569492 0.3569492 3.269326e-02 3.269326e-02 0.610357569 #> 331    165.0 0.3569801 0.3569801 3.242489e-02 3.242489e-02 0.610594978 #> 332    165.5 0.3570134 0.3570134 3.215875e-02 3.215875e-02 0.610827816 #> 333    166.0 0.3570491 0.3570491 3.189482e-02 3.189482e-02 0.611056122 #> 334    166.5 0.3570870 0.3570870 3.163309e-02 3.163309e-02 0.611279934 #> 335    167.0 0.3571272 0.3571272 3.137354e-02 3.137354e-02 0.611499291 #> 336    167.5 0.3571696 0.3571696 3.111614e-02 3.111614e-02 0.611714232 #> 337    168.0 0.3572143 0.3572143 3.086090e-02 3.086090e-02 0.611924794 #> 338    168.5 0.3572612 0.3572612 3.060778e-02 3.060778e-02 0.612131016 #> 339    169.0 0.3573103 0.3573103 3.035677e-02 3.035677e-02 0.612332935 #> 340    169.5 0.3573615 0.3573615 3.010786e-02 3.010786e-02 0.612530588 #> 341    170.0 0.3574150 0.3574150 2.986103e-02 2.986103e-02 0.612724012 #> 342    170.5 0.3574705 0.3574705 2.961627e-02 2.961627e-02 0.612913244 #> 343    171.0 0.3575281 0.3575281 2.937355e-02 2.937355e-02 0.613098320 #> 344    171.5 0.3575879 0.3575879 2.913287e-02 2.913287e-02 0.613279276 #> 345    172.0 0.3576497 0.3576497 2.889420e-02 2.889420e-02 0.613456148 #> 346    172.5 0.3577135 0.3577135 2.865753e-02 2.865753e-02 0.613628972 #> 347    173.0 0.3577794 0.3577794 2.842284e-02 2.842284e-02 0.613797782 #> 348    173.5 0.3578473 0.3578473 2.819013e-02 2.819013e-02 0.613962614 #> 349    174.0 0.3579171 0.3579171 2.795936e-02 2.795936e-02 0.614123502 #> 350    174.5 0.3579890 0.3579890 2.773054e-02 2.773054e-02 0.614280480 #> 351    175.0 0.3580628 0.3580628 2.750364e-02 2.750364e-02 0.614433584 #> 352    175.5 0.3581385 0.3581385 2.727864e-02 2.727864e-02 0.614582846 #> 353    176.0 0.3582162 0.3582162 2.705554e-02 2.705554e-02 0.614728300 #> 354    176.5 0.3582957 0.3582957 2.683432e-02 2.683432e-02 0.614869980 #> 355    177.0 0.3583771 0.3583771 2.661496e-02 2.661496e-02 0.615007918 #> 356    177.5 0.3584604 0.3584604 2.639744e-02 2.639744e-02 0.615142148 #> 357    178.0 0.3585455 0.3585455 2.618176e-02 2.618176e-02 0.615272702 #> 358    178.5 0.3586325 0.3586325 2.596790e-02 2.596790e-02 0.615399611 #> 359    179.0 0.3587213 0.3587213 2.575584e-02 2.575584e-02 0.615522909 #> 360    179.5 0.3588118 0.3588118 2.554557e-02 2.554557e-02 0.615642627 #> 361    180.0 0.3589041 0.3589041 2.533707e-02 2.533707e-02 0.615758796 #> 362    180.5 0.3589982 0.3589982 2.513034e-02 2.513034e-02 0.615871448 #> 363    181.0 0.3590940 0.3590940 2.492535e-02 2.492535e-02 0.615980613 #> 364    181.5 0.3591916 0.3591916 2.472209e-02 2.472209e-02 0.616086322 #> 365    182.0 0.3592908 0.3592908 2.452055e-02 2.452055e-02 0.616188606 #> 366    182.5 0.3593918 0.3593918 2.432071e-02 2.432071e-02 0.616287495 #> 367    183.0 0.3594944 0.3594944 2.412257e-02 2.412257e-02 0.616383019 #> 368    183.5 0.3595987 0.3595987 2.392610e-02 2.392610e-02 0.616475208 #> 369    184.0 0.3597046 0.3597046 2.373129e-02 2.373129e-02 0.616564090 #> 370    184.5 0.3598122 0.3598122 2.353813e-02 2.353813e-02 0.616649696 #> 371    185.0 0.3599213 0.3599213 2.334661e-02 2.334661e-02 0.616732054 #> 372    185.5 0.3600321 0.3600321 2.315671e-02 2.315671e-02 0.616811194 #> 373    186.0 0.3601444 0.3601444 2.296841e-02 2.296841e-02 0.616887143 #> 374    186.5 0.3602584 0.3602584 2.278172e-02 2.278172e-02 0.616959930 #> 375    187.0 0.3603738 0.3603738 2.259660e-02 2.259660e-02 0.617029583 #> 376    187.5 0.3604908 0.3604908 2.241306e-02 2.241306e-02 0.617096130 #> 377    188.0 0.3606093 0.3606093 2.223107e-02 2.223107e-02 0.617159598 #> 378    188.5 0.3607294 0.3607294 2.205062e-02 2.205062e-02 0.617220015 #> 379    189.0 0.3608509 0.3608509 2.187171e-02 2.187171e-02 0.617277407 #> 380    189.5 0.3609739 0.3609739 2.169431e-02 2.169431e-02 0.617331803 #> 381    190.0 0.3610984 0.3610984 2.151842e-02 2.151842e-02 0.617383228 #> 382    190.5 0.3612243 0.3612243 2.134402e-02 2.134402e-02 0.617431709 #> 383    191.0 0.3613516 0.3613516 2.117110e-02 2.117110e-02 0.617477272 #> 384    191.5 0.3614804 0.3614804 2.099965e-02 2.099965e-02 0.617519944 #> 385    192.0 0.3616106 0.3616106 2.082966e-02 2.082966e-02 0.617559750 #> 386    192.5 0.3617422 0.3617422 2.066111e-02 2.066111e-02 0.617596715 #> 387    193.0 0.3618751 0.3618751 2.049399e-02 2.049399e-02 0.617630865 #> 388    193.5 0.3620095 0.3620095 2.032829e-02 2.032829e-02 0.617662225 #> 389    194.0 0.3621452 0.3621452 2.016400e-02 2.016400e-02 0.617690821 #> 390    194.5 0.3622822 0.3622822 2.000110e-02 2.000110e-02 0.617716676 #> 391    195.0 0.3624206 0.3624206 1.983959e-02 1.983959e-02 0.617739815 #> 392    195.5 0.3625603 0.3625603 1.967945e-02 1.967945e-02 0.617760264 #> 393    196.0 0.3627013 0.3627013 1.952068e-02 1.952068e-02 0.617778045 #> 394    196.5 0.3628436 0.3628436 1.936325e-02 1.936325e-02 0.617793183 #> 395    197.0 0.3629871 0.3629871 1.920716e-02 1.920716e-02 0.617805701 #> 396    197.5 0.3631320 0.3631320 1.905240e-02 1.905240e-02 0.617815624 #> 397    198.0 0.3632781 0.3632781 1.889895e-02 1.889895e-02 0.617822973 #> 398    198.5 0.3634254 0.3634254 1.874681e-02 1.874681e-02 0.617827773 #> 399    199.0 0.3635740 0.3635740 1.859596e-02 1.859596e-02 0.617830047 #> 400    199.5 0.3637238 0.3637238 1.844639e-02 1.844639e-02 0.617829816 #> 401    200.0 0.3638748 0.3638748 1.829810e-02 1.829810e-02 0.617827104 #> 402    200.5 0.3640270 0.3640270 1.815107e-02 1.815107e-02 0.617821933 #> 403    201.0 0.3641804 0.3641804 1.800528e-02 1.800528e-02 0.617814325 #> 404    201.5 0.3643350 0.3643350 1.786074e-02 1.786074e-02 0.617804302 #> 405    202.0 0.3644907 0.3644907 1.771743e-02 1.771743e-02 0.617791886 #> 406    202.5 0.3646476 0.3646476 1.757533e-02 1.757533e-02 0.617777099 #> 407    203.0 0.3648056 0.3648056 1.743444e-02 1.743444e-02 0.617759961 #> 408    203.5 0.3649648 0.3649648 1.729475e-02 1.729475e-02 0.617740494 #> 409    204.0 0.3651250 0.3651250 1.715625e-02 1.715625e-02 0.617718719 #> 410    204.5 0.3652864 0.3652864 1.701893e-02 1.701893e-02 0.617694657 #> 411    205.0 0.3654489 0.3654489 1.688277e-02 1.688277e-02 0.617668329 #> 412    205.5 0.3656125 0.3656125 1.674777e-02 1.674777e-02 0.617639754 #> 413    206.0 0.3657771 0.3657771 1.661392e-02 1.661392e-02 0.617608955 #> 414    206.5 0.3659428 0.3659428 1.648121e-02 1.648121e-02 0.617575950 #> 415    207.0 0.3661096 0.3661096 1.634962e-02 1.634962e-02 0.617540759 #> 416    207.5 0.3662774 0.3662774 1.621915e-02 1.621915e-02 0.617503403 #> 417    208.0 0.3664463 0.3664463 1.608980e-02 1.608980e-02 0.617463901 #> 418    208.5 0.3666162 0.3666162 1.596154e-02 1.596154e-02 0.617422272 #> 419    209.0 0.3667871 0.3667871 1.583437e-02 1.583437e-02 0.617378536 #> 420    209.5 0.3669590 0.3669590 1.570828e-02 1.570828e-02 0.617332712 #> 421    210.0 0.3671319 0.3671319 1.558326e-02 1.558326e-02 0.617284819 #> 422    210.5 0.3673058 0.3673058 1.545931e-02 1.545931e-02 0.617234875 #> 423    211.0 0.3674807 0.3674807 1.533640e-02 1.533640e-02 0.617182900 #> 424    211.5 0.3676565 0.3676565 1.521455e-02 1.521455e-02 0.617128912 #> 425    212.0 0.3678333 0.3678333 1.509372e-02 1.509372e-02 0.617072929 #> 426    212.5 0.3680111 0.3680111 1.497392e-02 1.497392e-02 0.617014969 #> 427    213.0 0.3681898 0.3681898 1.485514e-02 1.485514e-02 0.616955051 #> 428    213.5 0.3683694 0.3683694 1.473737e-02 1.473737e-02 0.616893192 #> 429    214.0 0.3685500 0.3685500 1.462060e-02 1.462060e-02 0.616829409 #> 430    214.5 0.3687315 0.3687315 1.450481e-02 1.450481e-02 0.616763721 #> 431    215.0 0.3689138 0.3689138 1.439001e-02 1.439001e-02 0.616696145 #> 432    215.5 0.3690971 0.3690971 1.427619e-02 1.427619e-02 0.616626698 #> 433    216.0 0.3692813 0.3692813 1.416333e-02 1.416333e-02 0.616555397 #> 434    216.5 0.3694663 0.3694663 1.405142e-02 1.405142e-02 0.616482259 #> 435    217.0 0.3696522 0.3696522 1.394047e-02 1.394047e-02 0.616407301 #> 436    217.5 0.3698390 0.3698390 1.383045e-02 1.383045e-02 0.616330540 #> 437    218.0 0.3700266 0.3700266 1.372137e-02 1.372137e-02 0.616251991 #> 438    218.5 0.3702151 0.3702151 1.361321e-02 1.361321e-02 0.616171672 #> 439    219.0 0.3704044 0.3704044 1.350597e-02 1.350597e-02 0.616089599 #> 440    219.5 0.3705946 0.3705946 1.339964e-02 1.339964e-02 0.616005787 #> 441    220.0 0.3707855 0.3707855 1.329421e-02 1.329421e-02 0.615920253 #> 442    220.5 0.3709773 0.3709773 1.318967e-02 1.318967e-02 0.615833012 #> 443    221.0 0.3711699 0.3711699 1.308601e-02 1.308601e-02 0.615744081 #> 444    221.5 0.3713633 0.3713633 1.298324e-02 1.298324e-02 0.615653474 #> 445    222.0 0.3715575 0.3715575 1.288133e-02 1.288133e-02 0.615561207 #> 446    222.5 0.3717524 0.3717524 1.278028e-02 1.278028e-02 0.615467295 #> 447    223.0 0.3719482 0.3719482 1.268009e-02 1.268009e-02 0.615371754 #> 448    223.5 0.3721447 0.3721447 1.258075e-02 1.258075e-02 0.615274598 #> 449    224.0 0.3723419 0.3723419 1.248224e-02 1.248224e-02 0.615175843 #> 450    224.5 0.3725399 0.3725399 1.238457e-02 1.238457e-02 0.615075502 #> 451    225.0 0.3727387 0.3727387 1.228772e-02 1.228772e-02 0.614973591 #> 452    225.5 0.3729382 0.3729382 1.219170e-02 1.219170e-02 0.614870124 #> 453    226.0 0.3731384 0.3731384 1.209648e-02 1.209648e-02 0.614765115 #> 454    226.5 0.3733394 0.3733394 1.200206e-02 1.200206e-02 0.614658579 #> 455    227.0 0.3735410 0.3735410 1.190845e-02 1.190845e-02 0.614550530 #> 456    227.5 0.3737434 0.3737434 1.181562e-02 1.181562e-02 0.614440982 #> 457    228.0 0.3739465 0.3739465 1.172358e-02 1.172358e-02 0.614329948 #> 458    228.5 0.3741503 0.3741503 1.163231e-02 1.163231e-02 0.614217442 #> 459    229.0 0.3743547 0.3743547 1.154181e-02 1.154181e-02 0.614103479 #> 460    229.5 0.3745599 0.3745599 1.145207e-02 1.145207e-02 0.613988070 #> 461    230.0 0.3747657 0.3747657 1.136309e-02 1.136309e-02 0.613871231 #> 462    230.5 0.3749722 0.3749722 1.127486e-02 1.127486e-02 0.613752973 #> 463    231.0 0.3751793 0.3751793 1.118738e-02 1.118738e-02 0.613633311 #> 464    231.5 0.3753871 0.3753871 1.110062e-02 1.110062e-02 0.613512257 #> 465    232.0 0.3755956 0.3755956 1.101460e-02 1.101460e-02 0.613389824 #> 466    232.5 0.3758047 0.3758047 1.092931e-02 1.092931e-02 0.613266025 #> 467    233.0 0.3760144 0.3760144 1.084473e-02 1.084473e-02 0.613140872 #> 468    233.5 0.3762248 0.3762248 1.076086e-02 1.076086e-02 0.613014379 #> 469    234.0 0.3764358 0.3764358 1.067769e-02 1.067769e-02 0.612886557 #> 470    234.5 0.3766474 0.3766474 1.059523e-02 1.059523e-02 0.612757419 #> 471    235.0 0.3768596 0.3768596 1.051345e-02 1.051345e-02 0.612626978 #> 472    235.5 0.3770724 0.3770724 1.043237e-02 1.043237e-02 0.612495245 #> 473    236.0 0.3772858 0.3772858 1.035196e-02 1.035196e-02 0.612362232 #> 474    236.5 0.3774998 0.3774998 1.027223e-02 1.027223e-02 0.612227952 #> 475    237.0 0.3777144 0.3777144 1.019317e-02 1.019317e-02 0.612092416 #> 476    237.5 0.3779296 0.3779296 1.011477e-02 1.011477e-02 0.611955636 #> 477    238.0 0.3781454 0.3781454 1.003703e-02 1.003703e-02 0.611817623 #> 478    238.5 0.3783617 0.3783617 9.959935e-03 9.959935e-03 0.611678389 #> 479    239.0 0.3785786 0.3785786 9.883490e-03 9.883490e-03 0.611537946 #> 480    239.5 0.3787960 0.3787960 9.807684e-03 9.807684e-03 0.611396305 #> 481    240.0 0.3790140 0.3790140 9.732513e-03 9.732513e-03 0.611253477 #> 482    240.5 0.3792326 0.3792326 9.657971e-03 9.657971e-03 0.611109472 #> 483    241.0 0.3794516 0.3794516 9.584052e-03 9.584052e-03 0.610964303 #> 484    241.5 0.3796713 0.3796713 9.510751e-03 9.510751e-03 0.610817980 #> 485    242.0 0.3798914 0.3798914 9.438062e-03 9.438062e-03 0.610670514 #> 486    242.5 0.3801121 0.3801121 9.365981e-03 9.365981e-03 0.610521915 #> 487    243.0 0.3803333 0.3803333 9.294501e-03 9.294501e-03 0.610372195 #> 488    243.5 0.3805550 0.3805550 9.223618e-03 9.223618e-03 0.610221363 #> 489    244.0 0.3807772 0.3807772 9.153326e-03 9.153326e-03 0.610069431 #> 490    244.5 0.3810000 0.3810000 9.083621e-03 9.083621e-03 0.609916408 #> 491    245.0 0.3812232 0.3812232 9.014497e-03 9.014497e-03 0.609762305 #> 492    245.5 0.3814469 0.3814469 8.945948e-03 8.945948e-03 0.609607132 #> 493    246.0 0.3816711 0.3816711 8.877971e-03 8.877971e-03 0.609450900 #> 494    246.5 0.3818958 0.3818958 8.810560e-03 8.810560e-03 0.609293617 #> 495    247.0 0.3821210 0.3821210 8.743709e-03 8.743709e-03 0.609135295 #> 496    247.5 0.3823466 0.3823466 8.677415e-03 8.677415e-03 0.608975942 #> 497    248.0 0.3825728 0.3825728 8.611672e-03 8.611672e-03 0.608815569 #> 498    248.5 0.3827993 0.3827993 8.546476e-03 8.546476e-03 0.608654185 #> 499    249.0 0.3830264 0.3830264 8.481821e-03 8.481821e-03 0.608491801 #> 500    249.5 0.3832539 0.3832539 8.417703e-03 8.417703e-03 0.608328424 #> 501    250.0 0.3834818 0.3834818 8.354117e-03 8.354117e-03 0.608164065 #> 502    250.5 0.3837102 0.3837102 8.291059e-03 8.291059e-03 0.607998734 #> 503    251.0 0.3839390 0.3839390 8.228524e-03 8.228524e-03 0.607832438 #> 504    251.5 0.3841683 0.3841683 8.166508e-03 8.166508e-03 0.607665188 #> 505    252.0 0.3843980 0.3843980 8.105005e-03 8.105005e-03 0.607496993 #> 506    252.5 0.3846281 0.3846281 8.044012e-03 8.044012e-03 0.607327861 #> 507    253.0 0.3848587 0.3848587 7.983524e-03 7.983524e-03 0.607157802 #> 508    253.5 0.3850896 0.3850896 7.923536e-03 7.923536e-03 0.606986824 #> 509    254.0 0.3853210 0.3853210 7.864044e-03 7.864044e-03 0.606814936 #> 510    254.5 0.3855528 0.3855528 7.805045e-03 7.805045e-03 0.606642147 #> 511    255.0 0.3857850 0.3857850 7.746532e-03 7.746532e-03 0.606468465 #> 512    255.5 0.3860176 0.3860176 7.688504e-03 7.688504e-03 0.606293899 #> 513    256.0 0.3862506 0.3862506 7.630954e-03 7.630954e-03 0.606118457 #> 514    256.5 0.3864840 0.3864840 7.573879e-03 7.573879e-03 0.605942148 #> 515    257.0 0.3867177 0.3867177 7.517275e-03 7.517275e-03 0.605764980 #> 516    257.5 0.3869519 0.3869519 7.461138e-03 7.461138e-03 0.605586962 #> 517    258.0 0.3871864 0.3871864 7.405463e-03 7.405463e-03 0.605408101 #> 518    258.5 0.3874213 0.3874213 7.350247e-03 7.350247e-03 0.605228405 #> 519    259.0 0.3876566 0.3876566 7.295486e-03 7.295486e-03 0.605047883 #> 520    259.5 0.3878923 0.3878923 7.241175e-03 7.241175e-03 0.604866543 #> 521    260.0 0.3881283 0.3881283 7.187310e-03 7.187310e-03 0.604684392 #> 522    260.5 0.3883647 0.3883647 7.133889e-03 7.133889e-03 0.604501438 #> 523    261.0 0.3886014 0.3886014 7.080906e-03 7.080906e-03 0.604317689 #> 524    261.5 0.3888385 0.3888385 7.028359e-03 7.028359e-03 0.604133153 #> 525    262.0 0.3890759 0.3890759 6.976243e-03 6.976243e-03 0.603947837 #> 526    262.5 0.3893137 0.3893137 6.924555e-03 6.924555e-03 0.603761749 #> 527    263.0 0.3895518 0.3895518 6.873290e-03 6.873290e-03 0.603574896 #> 528    263.5 0.3897903 0.3897903 6.822445e-03 6.822445e-03 0.603387286 #> 529    264.0 0.3900291 0.3900291 6.772018e-03 6.772018e-03 0.603198926 #> 530    264.5 0.3902682 0.3902682 6.722003e-03 6.722003e-03 0.603009824 #> 531    265.0 0.3905076 0.3905076 6.672397e-03 6.672397e-03 0.602819986 #> 532    265.5 0.3907474 0.3907474 6.623197e-03 6.623197e-03 0.602629419 #> 533    266.0 0.3909875 0.3909875 6.574399e-03 6.574399e-03 0.602438132 #> 534    266.5 0.3912279 0.3912279 6.526000e-03 6.526000e-03 0.602246130 #> 535    267.0 0.3914686 0.3914686 6.477996e-03 6.477996e-03 0.602053421 #> 536    267.5 0.3917096 0.3917096 6.430384e-03 6.430384e-03 0.601860012 #> 537    268.0 0.3919509 0.3919509 6.383161e-03 6.383161e-03 0.601665910 #> 538    268.5 0.3921926 0.3921926 6.336322e-03 6.336322e-03 0.601471121 #> 539    269.0 0.3924345 0.3924345 6.289865e-03 6.289865e-03 0.601275652 #> 540    269.5 0.3926767 0.3926767 6.243787e-03 6.243787e-03 0.601079509 #> 541    270.0 0.3929192 0.3929192 6.198084e-03 6.198084e-03 0.600882701 #> 542    270.5 0.3931620 0.3931620 6.152752e-03 6.152752e-03 0.600685232 #> 543    271.0 0.3934051 0.3934051 6.107790e-03 6.107790e-03 0.600487109 #> 544    271.5 0.3936485 0.3936485 6.063192e-03 6.063192e-03 0.600288340 #> 545    272.0 0.3938921 0.3938921 6.018957e-03 6.018957e-03 0.600088929 #> 546    272.5 0.3941360 0.3941360 5.975081e-03 5.975081e-03 0.599888885 #> 547    273.0 0.3943802 0.3943802 5.931562e-03 5.931562e-03 0.599688212 #> 548    273.5 0.3946247 0.3946247 5.888395e-03 5.888395e-03 0.599486917 #> 549    274.0 0.3948694 0.3948694 5.845578e-03 5.845578e-03 0.599285007 #> 550    274.5 0.3951144 0.3951144 5.803108e-03 5.803108e-03 0.599082487 #> 551    275.0 0.3953597 0.3953597 5.760981e-03 5.760981e-03 0.598879363 #> 552    275.5 0.3956052 0.3956052 5.719196e-03 5.719196e-03 0.598675642 #> 553    276.0 0.3958509 0.3958509 5.677748e-03 5.677748e-03 0.598471330 #> 554    276.5 0.3960969 0.3960969 5.636636e-03 5.636636e-03 0.598266431 #> 555    277.0 0.3963432 0.3963432 5.595856e-03 5.595856e-03 0.598060953 #> 556    277.5 0.3965897 0.3965897 5.555405e-03 5.555405e-03 0.597854901 #> 557    278.0 0.3968364 0.3968364 5.515280e-03 5.515280e-03 0.597648280 #> 558    278.5 0.3970834 0.3970834 5.475479e-03 5.475479e-03 0.597441097 #> 559    279.0 0.3973306 0.3973306 5.435999e-03 5.435999e-03 0.597233357 #> 560    279.5 0.3975781 0.3975781 5.396836e-03 5.396836e-03 0.597025065 #> 561    280.0 0.3978258 0.3978258 5.357990e-03 5.357990e-03 0.596816228 #> 562    280.5 0.3980737 0.3980737 5.319455e-03 5.319455e-03 0.596606851 #> 563    281.0 0.3983218 0.3983218 5.281231e-03 5.281231e-03 0.596396938 #> 564    281.5 0.3985702 0.3985702 5.243314e-03 5.243314e-03 0.596186497 #> 565    282.0 0.3988188 0.3988188 5.205701e-03 5.205701e-03 0.595975531 #> 566    282.5 0.3990676 0.3990676 5.168390e-03 5.168390e-03 0.595764046 #> 567    283.0 0.3993166 0.3993166 5.131379e-03 5.131379e-03 0.595552048 #> 568    283.5 0.3995658 0.3995658 5.094664e-03 5.094664e-03 0.595339542 #> 569    284.0 0.3998152 0.3998152 5.058244e-03 5.058244e-03 0.595126532 #> 570    284.5 0.4000649 0.4000649 5.022115e-03 5.022115e-03 0.594913025 #> 571    285.0 0.4003147 0.4003147 4.986276e-03 4.986276e-03 0.594699025 #> 572    285.5 0.4005647 0.4005647 4.950723e-03 4.950723e-03 0.594484537 #> 573    286.0 0.4008150 0.4008150 4.915454e-03 4.915454e-03 0.594269566 #> 574    286.5 0.4010654 0.4010654 4.880467e-03 4.880467e-03 0.594054118 #> 575    287.0 0.4013160 0.4013160 4.845760e-03 4.845760e-03 0.593838196 #> 576    287.5 0.4015669 0.4015669 4.811330e-03 4.811330e-03 0.593621806 #> 577    288.0 0.4018179 0.4018179 4.777174e-03 4.777174e-03 0.593404953 #> 578    288.5 0.4020691 0.4020691 4.743290e-03 4.743290e-03 0.593187642 #> 579    289.0 0.4023204 0.4023204 4.709677e-03 4.709677e-03 0.592969877 #> 580    289.5 0.4025720 0.4025720 4.676331e-03 4.676331e-03 0.592751663 #> 581    290.0 0.4028237 0.4028237 4.643250e-03 4.643250e-03 0.592533005 #> 582    290.5 0.4030757 0.4030757 4.610432e-03 4.610432e-03 0.592313906 #> 583    291.0 0.4033278 0.4033278 4.577875e-03 4.577875e-03 0.592094373 #> 584    291.5 0.4035800 0.4035800 4.545577e-03 4.545577e-03 0.591874409 #> 585    292.0 0.4038324 0.4038324 4.513535e-03 4.513535e-03 0.591654020 #> 586    292.5 0.4040850 0.4040850 4.481747e-03 4.481747e-03 0.591433208 #> 587    293.0 0.4043378 0.4043378 4.450211e-03 4.450211e-03 0.591211979 #> 588    293.5 0.4045907 0.4045907 4.418925e-03 4.418925e-03 0.590990338 #> 589    294.0 0.4048438 0.4048438 4.387887e-03 4.387887e-03 0.590768288 #> 590    294.5 0.4050971 0.4050971 4.357094e-03 4.357094e-03 0.590545834 #> 591    295.0 0.4053505 0.4053505 4.326545e-03 4.326545e-03 0.590322980 #> 592    295.5 0.4056040 0.4056040 4.296237e-03 4.296237e-03 0.590099731 #> 593    296.0 0.4058577 0.4058577 4.266169e-03 4.266169e-03 0.589876090 #> 594    296.5 0.4061116 0.4061116 4.236338e-03 4.236338e-03 0.589652061 #> 595    297.0 0.4063656 0.4063656 4.206742e-03 4.206742e-03 0.589427650 #> 596    297.5 0.4066198 0.4066198 4.177379e-03 4.177379e-03 0.589202860 #> 597    298.0 0.4068741 0.4068741 4.148248e-03 4.148248e-03 0.588977695 #> 598    298.5 0.4071285 0.4071285 4.119346e-03 4.119346e-03 0.588752158 #> 599    299.0 0.4073831 0.4073831 4.090672e-03 4.090672e-03 0.588526255 #> 600    299.5 0.4076378 0.4076378 4.062223e-03 4.062223e-03 0.588299989 #> 601    300.0 0.4078926 0.4078926 4.033997e-03 4.033997e-03 0.588073364 #> 602    300.5 0.4081476 0.4081476 4.005993e-03 4.005993e-03 0.587846384 #> 603    301.0 0.4084027 0.4084027 3.978209e-03 3.978209e-03 0.587619052 #> 604    301.5 0.4086580 0.4086580 3.950643e-03 3.950643e-03 0.587391373 #> 605    302.0 0.4089134 0.4089134 3.923293e-03 3.923293e-03 0.587163350 #> 606    302.5 0.4091689 0.4091689 3.896157e-03 3.896157e-03 0.586934987 #> 607    303.0 0.4094245 0.4094245 3.869234e-03 3.869234e-03 0.586706288 #> 608    303.5 0.4096802 0.4096802 3.842521e-03 3.842521e-03 0.586477257 #> 609    304.0 0.4099361 0.4099361 3.816017e-03 3.816017e-03 0.586247896 #> 610    304.5 0.4101921 0.4101921 3.789720e-03 3.789720e-03 0.586018211 #> 611    305.0 0.4104482 0.4104482 3.763628e-03 3.763628e-03 0.585788203 #> 612    305.5 0.4107044 0.4107044 3.737740e-03 3.737740e-03 0.585557878 #> 613    306.0 0.4109607 0.4109607 3.712054e-03 3.712054e-03 0.585327238 #> 614    306.5 0.4112171 0.4112171 3.686568e-03 3.686568e-03 0.585096287 #> 615    307.0 0.4114737 0.4114737 3.661280e-03 3.661280e-03 0.584865028 #> 616    307.5 0.4117303 0.4117303 3.636189e-03 3.636189e-03 0.584633466 #> 617    308.0 0.4119871 0.4119871 3.611293e-03 3.611293e-03 0.584401602 #> 618    308.5 0.4122440 0.4122440 3.586591e-03 3.586591e-03 0.584169442 #> 619    309.0 0.4125009 0.4125009 3.562081e-03 3.562081e-03 0.583936987 #> 620    309.5 0.4127580 0.4127580 3.537761e-03 3.537761e-03 0.583704242 #> 621    310.0 0.4130152 0.4130152 3.513629e-03 3.513629e-03 0.583471209 #> 622    310.5 0.4132724 0.4132724 3.489685e-03 3.489685e-03 0.583237893 #> 623    311.0 0.4135298 0.4135298 3.465926e-03 3.465926e-03 0.583004295 #> 624    311.5 0.4137872 0.4137872 3.442350e-03 3.442350e-03 0.582770420 #> 625    312.0 0.4140448 0.4140448 3.418958e-03 3.418958e-03 0.582536271 #> 626    312.5 0.4143024 0.4143024 3.395746e-03 3.395746e-03 0.582301850 #> 627    313.0 0.4145601 0.4145601 3.372713e-03 3.372713e-03 0.582067162 #> 628    313.5 0.4148179 0.4148179 3.349858e-03 3.349858e-03 0.581832208 #> 629    314.0 0.4150758 0.4150758 3.327180e-03 3.327180e-03 0.581596992 #> 630    314.5 0.4153338 0.4153338 3.304676e-03 3.304676e-03 0.581361518 #> 631    315.0 0.4155919 0.4155919 3.282346e-03 3.282346e-03 0.581125788 #> 632    315.5 0.4158500 0.4158500 3.260188e-03 3.260188e-03 0.580889805 #> 633    316.0 0.4161082 0.4161082 3.238200e-03 3.238200e-03 0.580653572 #> 634    316.5 0.4163665 0.4163665 3.216381e-03 3.216381e-03 0.580417092 #> 635    317.0 0.4166249 0.4166249 3.194730e-03 3.194730e-03 0.580180368 #> 636    317.5 0.4168834 0.4168834 3.173245e-03 3.173245e-03 0.579943404 #> 637    318.0 0.4171419 0.4171419 3.151925e-03 3.151925e-03 0.579706201 #> 638    318.5 0.4174005 0.4174005 3.130769e-03 3.130769e-03 0.579468763 #> 639    319.0 0.4176591 0.4176591 3.109774e-03 3.109774e-03 0.579231092 #> 640    319.5 0.4179179 0.4179179 3.088941e-03 3.088941e-03 0.578993192 #> 641    320.0 0.4181767 0.4181767 3.068266e-03 3.068266e-03 0.578755064 #> 642    320.5 0.4184355 0.4184355 3.047750e-03 3.047750e-03 0.578516713 #> 643    321.0 0.4186945 0.4186945 3.027391e-03 3.027391e-03 0.578278140 #> 644    321.5 0.4189535 0.4189535 3.007187e-03 3.007187e-03 0.578039349 #> 645    322.0 0.4192125 0.4192125 2.987137e-03 2.987137e-03 0.577800341 #> 646    322.5 0.4194716 0.4194716 2.967240e-03 2.967240e-03 0.577561120 #> 647    323.0 0.4197308 0.4197308 2.947495e-03 2.947495e-03 0.577321689 #> 648    323.5 0.4199901 0.4199901 2.927900e-03 2.927900e-03 0.577082049 #> 649    324.0 0.4202493 0.4202493 2.908455e-03 2.908455e-03 0.576842205 #> 650    324.5 0.4205087 0.4205087 2.889157e-03 2.889157e-03 0.576602157 #> 651    325.0 0.4207681 0.4207681 2.870006e-03 2.870006e-03 0.576361908 #> 652    325.5 0.4210275 0.4210275 2.851000e-03 2.851000e-03 0.576121462 #> 653    326.0 0.4212870 0.4212870 2.832138e-03 2.832138e-03 0.575880821 #> 654    326.5 0.4215466 0.4215466 2.813420e-03 2.813420e-03 0.575639987 #> 655    327.0 0.4218062 0.4218062 2.794843e-03 2.794843e-03 0.575398963 #> 656    327.5 0.4220658 0.4220658 2.776407e-03 2.776407e-03 0.575157750 #> 657    328.0 0.4223255 0.4223255 2.758111e-03 2.758111e-03 0.574916353 #> 658    328.5 0.4225853 0.4225853 2.739953e-03 2.739953e-03 0.574674772 #> 659    329.0 0.4228451 0.4228451 2.721932e-03 2.721932e-03 0.574433010 #> 660    329.5 0.4231049 0.4231049 2.704047e-03 2.704047e-03 0.574191071 #> 661    330.0 0.4233647 0.4233647 2.686297e-03 2.686297e-03 0.573948955 #> 662    330.5 0.4236247 0.4236247 2.668682e-03 2.668682e-03 0.573706666 #> 663    331.0 0.4238846 0.4238846 2.651198e-03 2.651198e-03 0.573464205 #> 664    331.5 0.4241446 0.4241446 2.633847e-03 2.633847e-03 0.573221576 #> 665    332.0 0.4244046 0.4244046 2.616626e-03 2.616626e-03 0.572978779 #> 666    332.5 0.4246646 0.4246646 2.599534e-03 2.599534e-03 0.572735818 #> 667    333.0 0.4249247 0.4249247 2.582571e-03 2.582571e-03 0.572492695 #> 668    333.5 0.4251849 0.4251849 2.565736e-03 2.565736e-03 0.572249412 #> 669    334.0 0.4254450 0.4254450 2.549026e-03 2.549026e-03 0.572005971 #> 670    334.5 0.4257052 0.4257052 2.532442e-03 2.532442e-03 0.571762374 #> 671    335.0 0.4259654 0.4259654 2.515982e-03 2.515982e-03 0.571518624 #> 672    335.5 0.4262256 0.4262256 2.499646e-03 2.499646e-03 0.571274723 #> 673    336.0 0.4264859 0.4264859 2.483432e-03 2.483432e-03 0.571030672 #> 674    336.5 0.4267462 0.4267462 2.467339e-03 2.467339e-03 0.570786474 #> 675    337.0 0.4270065 0.4270065 2.451366e-03 2.451366e-03 0.570542131 #> 676    337.5 0.4272668 0.4272668 2.435512e-03 2.435512e-03 0.570297644 #> 677    338.0 0.4275272 0.4275272 2.419777e-03 2.419777e-03 0.570053017 #> 678    338.5 0.4277876 0.4277876 2.404159e-03 2.404159e-03 0.569808251 #> 679    339.0 0.4280480 0.4280480 2.388657e-03 2.388657e-03 0.569563348 #> 680    339.5 0.4283084 0.4283084 2.373271e-03 2.373271e-03 0.569318311 #> 681    340.0 0.4285689 0.4285689 2.357999e-03 2.357999e-03 0.569073140 #> 682    340.5 0.4288293 0.4288293 2.342841e-03 2.342841e-03 0.568827838 #> 683    341.0 0.4290898 0.4290898 2.327795e-03 2.327795e-03 0.568582407 #> 684    341.5 0.4293503 0.4293503 2.312861e-03 2.312861e-03 0.568336849 #> 685    342.0 0.4296108 0.4296108 2.298038e-03 2.298038e-03 0.568091166 #> 686    342.5 0.4298713 0.4298713 2.283325e-03 2.283325e-03 0.567845360 #> 687    343.0 0.4301318 0.4301318 2.268720e-03 2.268720e-03 0.567599432 #> 688    343.5 0.4303924 0.4303924 2.254224e-03 2.254224e-03 0.567353385 #> 689    344.0 0.4306529 0.4306529 2.239835e-03 2.239835e-03 0.567107221 #> 690    344.5 0.4309135 0.4309135 2.225552e-03 2.225552e-03 0.566860940 #> 691    345.0 0.4311741 0.4311741 2.211375e-03 2.211375e-03 0.566614545 #> 692    345.5 0.4314347 0.4314347 2.197303e-03 2.197303e-03 0.566368038 #> 693    346.0 0.4316952 0.4316952 2.183334e-03 2.183334e-03 0.566121421 #> 694    346.5 0.4319558 0.4319558 2.169468e-03 2.169468e-03 0.565874695 #> 695    347.0 0.4322164 0.4322164 2.155704e-03 2.155704e-03 0.565627863 #> 696    347.5 0.4324770 0.4324770 2.142042e-03 2.142042e-03 0.565380925 #> 697    348.0 0.4327376 0.4327376 2.128480e-03 2.128480e-03 0.565133884 #> 698    348.5 0.4329982 0.4329982 2.115017e-03 2.115017e-03 0.564886741 #> 699    349.0 0.4332588 0.4332588 2.101654e-03 2.101654e-03 0.564639498 #> 700    349.5 0.4335195 0.4335195 2.088388e-03 2.088388e-03 0.564392157 #> 701    350.0 0.4337801 0.4337801 2.075220e-03 2.075220e-03 0.564144720 #> 702    350.5 0.4340407 0.4340407 2.062148e-03 2.062148e-03 0.563897187 #> 703    351.0 0.4343013 0.4343013 2.049172e-03 2.049172e-03 0.563649561 #> 704    351.5 0.4345619 0.4345619 2.036291e-03 2.036291e-03 0.563401844 #> 705    352.0 0.4348225 0.4348225 2.023504e-03 2.023504e-03 0.563154036 #> 706    352.5 0.4350830 0.4350830 2.010810e-03 2.010810e-03 0.562906140 #> 707    353.0 0.4353436 0.4353436 1.998209e-03 1.998209e-03 0.562658158 #> 708    353.5 0.4356042 0.4356042 1.985700e-03 1.985700e-03 0.562410090 #> 709    354.0 0.4358648 0.4358648 1.973282e-03 1.973282e-03 0.562161938 #> 710    354.5 0.4361253 0.4361253 1.960955e-03 1.960955e-03 0.561913704 #> 711    355.0 0.4363859 0.4363859 1.948717e-03 1.948717e-03 0.561665390 #> 712    355.5 0.4366464 0.4366464 1.936568e-03 1.936568e-03 0.561416997 #> 713    356.0 0.4369070 0.4369070 1.924508e-03 1.924508e-03 0.561168526 #> 714    356.5 0.4371675 0.4371675 1.912535e-03 1.912535e-03 0.560919979 #> 715    357.0 0.4374280 0.4374280 1.900649e-03 1.900649e-03 0.560671358 #> 716    357.5 0.4376885 0.4376885 1.888849e-03 1.888849e-03 0.560422663 #> 717    358.0 0.4379490 0.4379490 1.877134e-03 1.877134e-03 0.560173897 #> 718    358.5 0.4382094 0.4382094 1.865505e-03 1.865505e-03 0.559925061 #> 719    359.0 0.4384699 0.4384699 1.853959e-03 1.853959e-03 0.559676156 #> 720    359.5 0.4387303 0.4387303 1.842497e-03 1.842497e-03 0.559427184 #> 721    360.0 0.4389907 0.4389907 1.831118e-03 1.831118e-03 0.559178146 #> 722    360.5 0.4392511 0.4392511 1.819821e-03 1.819821e-03 0.558929043 #> 723    361.0 0.4395115 0.4395115 1.808605e-03 1.808605e-03 0.558679878 #> 724    361.5 0.4397719 0.4397719 1.797470e-03 1.797470e-03 0.558430651 #> 725    362.0 0.4400322 0.4400322 1.786416e-03 1.786416e-03 0.558181363 #> 726    362.5 0.4402925 0.4402925 1.775441e-03 1.775441e-03 0.557932017 #> 727    363.0 0.4405528 0.4405528 1.764544e-03 1.764544e-03 0.557682613 #> 728    363.5 0.4408131 0.4408131 1.753726e-03 1.753726e-03 0.557433153 #> 729    364.0 0.4410734 0.4410734 1.742986e-03 1.742986e-03 0.557183638 #> 730    364.5 0.4413336 0.4413336 1.732323e-03 1.732323e-03 0.556934069 #> 731    365.0 0.4415938 0.4415938 1.721736e-03 1.721736e-03 0.556684448 #> 732    365.5 0.4418540 0.4418540 1.711225e-03 1.711225e-03 0.556434777 #> 733    366.0 0.4421142 0.4421142 1.700789e-03 1.700789e-03 0.556185055 #> 734    366.5 0.4423743 0.4423743 1.690428e-03 1.690428e-03 0.555935285 #> 735    367.0 0.4426344 0.4426344 1.680141e-03 1.680141e-03 0.555685468 #> 736    367.5 0.4428945 0.4428945 1.669928e-03 1.669928e-03 0.555435605 #> 737    368.0 0.4431545 0.4431545 1.659787e-03 1.659787e-03 0.555185697 #> 738    368.5 0.4434145 0.4434145 1.649719e-03 1.649719e-03 0.554935745 #> 739    369.0 0.4436745 0.4436745 1.639722e-03 1.639722e-03 0.554685752 #> 740    369.5 0.4439345 0.4439345 1.629796e-03 1.629796e-03 0.554435718 #> 741    370.0 0.4441944 0.4441944 1.619942e-03 1.619942e-03 0.554185643 #> 742    370.5 0.4444543 0.4444543 1.610157e-03 1.610157e-03 0.553935530 #> 743    371.0 0.4447142 0.4447142 1.600441e-03 1.600441e-03 0.553685380 #> 744    371.5 0.4449740 0.4449740 1.590795e-03 1.590795e-03 0.553435193 #> 745    372.0 0.4452338 0.4452338 1.581217e-03 1.581217e-03 0.553184972 #> 746    372.5 0.4454936 0.4454936 1.571707e-03 1.571707e-03 0.552934716 #> 747    373.0 0.4457533 0.4457533 1.562264e-03 1.562264e-03 0.552684428 #> 748    373.5 0.4460130 0.4460130 1.552888e-03 1.552888e-03 0.552434107 #> 749    374.0 0.4462727 0.4462727 1.543579e-03 1.543579e-03 0.552183757 #> 750    374.5 0.4465323 0.4465323 1.534335e-03 1.534335e-03 0.551933377 #> 751    375.0 0.4467919 0.4467919 1.525156e-03 1.525156e-03 0.551682968 #> 752    375.5 0.4470514 0.4470514 1.516042e-03 1.516042e-03 0.551432533 #> 753    376.0 0.4473109 0.4473109 1.506993e-03 1.506993e-03 0.551182071 #> 754    376.5 0.4475704 0.4475704 1.498007e-03 1.498007e-03 0.550931584 #> 755    377.0 0.4478298 0.4478298 1.489084e-03 1.489084e-03 0.550681073 #> 756    377.5 0.4480892 0.4480892 1.480224e-03 1.480224e-03 0.550430539 #> 757    378.0 0.4483486 0.4483486 1.471427e-03 1.471427e-03 0.550179984 #> 758    378.5 0.4486079 0.4486079 1.462691e-03 1.462691e-03 0.549929407 #> 759    379.0 0.4488672 0.4488672 1.454016e-03 1.454016e-03 0.549678811 #> 760    379.5 0.4491264 0.4491264 1.445402e-03 1.445402e-03 0.549428196 #> 761    380.0 0.4493856 0.4493856 1.436849e-03 1.436849e-03 0.549177563 #> 762    380.5 0.4496447 0.4496447 1.428355e-03 1.428355e-03 0.548926914 #> 763    381.0 0.4499038 0.4499038 1.419921e-03 1.419921e-03 0.548676249 #> 764    381.5 0.4501629 0.4501629 1.411546e-03 1.411546e-03 0.548425569 #> 765    382.0 0.4504219 0.4504219 1.403229e-03 1.403229e-03 0.548174875 #> 766    382.5 0.4506809 0.4506809 1.394970e-03 1.394970e-03 0.547924168 #> 767    383.0 0.4509398 0.4509398 1.386769e-03 1.386769e-03 0.547673450 #> 768    383.5 0.4511987 0.4511987 1.378625e-03 1.378625e-03 0.547422721 #> 769    384.0 0.4514575 0.4514575 1.370538e-03 1.370538e-03 0.547171982 #> 770    384.5 0.4517163 0.4517163 1.362507e-03 1.362507e-03 0.546921234 #> 771    385.0 0.4519750 0.4519750 1.354532e-03 1.354532e-03 0.546670478 #> 772    385.5 0.4522337 0.4522337 1.346612e-03 1.346612e-03 0.546419715 #> 773    386.0 0.4524923 0.4524923 1.338747e-03 1.338747e-03 0.546168945 #> 774    386.5 0.4527509 0.4527509 1.330937e-03 1.330937e-03 0.545918171 #> 775    387.0 0.4530094 0.4530094 1.323181e-03 1.323181e-03 0.545667392 #> 776    387.5 0.4532679 0.4532679 1.315478e-03 1.315478e-03 0.545416610 #> 777    388.0 0.4535263 0.4535263 1.307829e-03 1.307829e-03 0.545165825 #> 778    388.5 0.4537847 0.4537847 1.300233e-03 1.300233e-03 0.544915039 #> 779    389.0 0.4540431 0.4540431 1.292689e-03 1.292689e-03 0.544664251 #> 780    389.5 0.4543013 0.4543013 1.285197e-03 1.285197e-03 0.544413464 #> 781    390.0 0.4545596 0.4545596 1.277757e-03 1.277757e-03 0.544162678 #> 782    390.5 0.4548177 0.4548177 1.270368e-03 1.270368e-03 0.543911894 #> 783    391.0 0.4550759 0.4550759 1.263030e-03 1.263030e-03 0.543661112 #> 784    391.5 0.4553339 0.4553339 1.255743e-03 1.255743e-03 0.543410334 #> 785    392.0 0.4555919 0.4555919 1.248505e-03 1.248505e-03 0.543159560 #> 786    392.5 0.4558499 0.4558499 1.241318e-03 1.241318e-03 0.542908792 #> 787    393.0 0.4561078 0.4561078 1.234179e-03 1.234179e-03 0.542658029 #> 788    393.5 0.4563656 0.4563656 1.227090e-03 1.227090e-03 0.542407273 #> 789    394.0 0.4566234 0.4566234 1.220049e-03 1.220049e-03 0.542156525 #> 790    394.5 0.4568812 0.4568812 1.213057e-03 1.213057e-03 0.541905785 #> 791    395.0 0.4571388 0.4571388 1.206112e-03 1.206112e-03 0.541655055 #> 792    395.5 0.4573965 0.4573965 1.199215e-03 1.199215e-03 0.541404334 #> 793    396.0 0.4576540 0.4576540 1.192365e-03 1.192365e-03 0.541153624 #> 794    396.5 0.4579115 0.4579115 1.185561e-03 1.185561e-03 0.540902926 #> 795    397.0 0.4581690 0.4581690 1.178804e-03 1.178804e-03 0.540652240 #> 796    397.5 0.4584263 0.4584263 1.172093e-03 1.172093e-03 0.540401568 #> 797    398.0 0.4586837 0.4586837 1.165428e-03 1.165428e-03 0.540150909 #> 798    398.5 0.4589409 0.4589409 1.158808e-03 1.158808e-03 0.539900265 #> 799    399.0 0.4591981 0.4591981 1.152234e-03 1.152234e-03 0.539649636 #> 800    399.5 0.4594553 0.4594553 1.145703e-03 1.145703e-03 0.539399023 #> 801    400.0 0.4597124 0.4597124 1.139218e-03 1.139218e-03 0.539148427 #> 802    400.5 0.4599694 0.4599694 1.132776e-03 1.132776e-03 0.538897849 #> 803    401.0 0.4602263 0.4602263 1.126378e-03 1.126378e-03 0.538647289 #> 804    401.5 0.4604832 0.4604832 1.120023e-03 1.120023e-03 0.538396748 #> 805    402.0 0.4607401 0.4607401 1.113711e-03 1.113711e-03 0.538146226 #> 806    402.5 0.4609968 0.4609968 1.107442e-03 1.107442e-03 0.537895725 #> 807    403.0 0.4612535 0.4612535 1.101215e-03 1.101215e-03 0.537645246 #> 808    403.5 0.4615102 0.4615102 1.095030e-03 1.095030e-03 0.537394788 #> 809    404.0 0.4617668 0.4617668 1.088887e-03 1.088887e-03 0.537144352 #> 810    404.5 0.4620233 0.4620233 1.082786e-03 1.082786e-03 0.536893940 #> 811    405.0 0.4622797 0.4622797 1.076725e-03 1.076725e-03 0.536643551 #> 812    405.5 0.4625361 0.4625361 1.070705e-03 1.070705e-03 0.536393187 #> 813    406.0 0.4627924 0.4627924 1.064726e-03 1.064726e-03 0.536142848 #> 814    406.5 0.4630487 0.4630487 1.058787e-03 1.058787e-03 0.535892535 #> 815    407.0 0.4633049 0.4633049 1.052888e-03 1.052888e-03 0.535642249 #> 816    407.5 0.4635610 0.4635610 1.047028e-03 1.047028e-03 0.535391990 #> 817    408.0 0.4638170 0.4638170 1.041208e-03 1.041208e-03 0.535141758 #> 818    408.5 0.4640730 0.4640730 1.035427e-03 1.035427e-03 0.534891555 #> 819    409.0 0.4643289 0.4643289 1.029684e-03 1.029684e-03 0.534641381 #> 820    409.5 0.4645848 0.4645848 1.023980e-03 1.023980e-03 0.534391237 #> 821    410.0 0.4648406 0.4648406 1.018313e-03 1.018313e-03 0.534141123 #> 822    410.5 0.4650963 0.4650963 1.012685e-03 1.012685e-03 0.533891039 #> 823    411.0 0.4653519 0.4653519 1.007094e-03 1.007094e-03 0.533640988 #> 824    411.5 0.4656075 0.4656075 1.001541e-03 1.001541e-03 0.533390969 #> 825    412.0 0.4658630 0.4658630 9.960242e-04 9.960242e-04 0.533140982 #> 826    412.5 0.4661184 0.4661184 9.905443e-04 9.905443e-04 0.532891029 #> 827    413.0 0.4663738 0.4663738 9.851010e-04 9.851010e-04 0.532641109 #> 828    413.5 0.4666291 0.4666291 9.796937e-04 9.796937e-04 0.532391224 #> 829    414.0 0.4668843 0.4668843 9.743224e-04 9.743224e-04 0.532141375 #> 830    414.5 0.4671395 0.4671395 9.689867e-04 9.689867e-04 0.531891561 #> 831    415.0 0.4673945 0.4673945 9.636864e-04 9.636864e-04 0.531641783 #> 832    415.5 0.4676495 0.4676495 9.584211e-04 9.584211e-04 0.531392042 #> 833    416.0 0.4679045 0.4679045 9.531907e-04 9.531907e-04 0.531142338 #> 834    416.5 0.4681593 0.4681593 9.479949e-04 9.479949e-04 0.530892673 #> 835    417.0 0.4684141 0.4684141 9.428335e-04 9.428335e-04 0.530643046 #> 836    417.5 0.4686688 0.4686688 9.377061e-04 9.377061e-04 0.530393458 #> 837    418.0 0.4689235 0.4689235 9.326125e-04 9.326125e-04 0.530143909 #> 838    418.5 0.4691780 0.4691780 9.275525e-04 9.275525e-04 0.529894401 #> 839    419.0 0.4694325 0.4694325 9.225258e-04 9.225258e-04 0.529644933 #> 840    419.5 0.4696870 0.4696870 9.175322e-04 9.175322e-04 0.529395507 #> 841    420.0 0.4699413 0.4699413 9.125714e-04 9.125714e-04 0.529146122 #> 842    420.5 0.4701956 0.4701956 9.076432e-04 9.076432e-04 0.528896780 #> 843    421.0 0.4704498 0.4704498 9.027474e-04 9.027474e-04 0.528647481 #> 844    421.5 0.4707039 0.4707039 8.978837e-04 8.978837e-04 0.528398224 #> 845    422.0 0.4709579 0.4709579 8.930519e-04 8.930519e-04 0.528149012 #> 846    422.5 0.4712119 0.4712119 8.882517e-04 8.882517e-04 0.527899844 #> 847    423.0 0.4714658 0.4714658 8.834829e-04 8.834829e-04 0.527650721 #> 848    423.5 0.4717196 0.4717196 8.787453e-04 8.787453e-04 0.527401643 #> 849    424.0 0.4719734 0.4719734 8.740386e-04 8.740386e-04 0.527152611 #> 850    424.5 0.4722270 0.4722270 8.693627e-04 8.693627e-04 0.526903625 #> 851    425.0 0.4724806 0.4724806 8.647173e-04 8.647173e-04 0.526654686 #> 852    425.5 0.4727341 0.4727341 8.601021e-04 8.601021e-04 0.526405794 #> 853    426.0 0.4729875 0.4729875 8.555170e-04 8.555170e-04 0.526156951 #> 854    426.5 0.4732409 0.4732409 8.509617e-04 8.509617e-04 0.525908155 #> 855    427.0 0.4734942 0.4734942 8.464361e-04 8.464361e-04 0.525659408 #> 856    427.5 0.4737474 0.4737474 8.419398e-04 8.419398e-04 0.525410710 #> 857    428.0 0.4740005 0.4740005 8.374727e-04 8.374727e-04 0.525162062 #> 858    428.5 0.4742535 0.4742535 8.330346e-04 8.330346e-04 0.524913464 #> 859    429.0 0.4745065 0.4745065 8.286253e-04 8.286253e-04 0.524664917 #> 860    429.5 0.4747593 0.4747593 8.242445e-04 8.242445e-04 0.524416420 #> 861    430.0 0.4750121 0.4750121 8.198920e-04 8.198920e-04 0.524167975 #> 862    430.5 0.4752649 0.4752649 8.155677e-04 8.155677e-04 0.523919582 #> 863    431.0 0.4755175 0.4755175 8.112713e-04 8.112713e-04 0.523671241 #> 864    431.5 0.4757700 0.4757700 8.070027e-04 8.070027e-04 0.523422953 #> 865    432.0 0.4760225 0.4760225 8.027616e-04 8.027616e-04 0.523174719 #> 866    432.5 0.4762749 0.4762749 7.985478e-04 7.985478e-04 0.522926537 #> 867    433.0 0.4765272 0.4765272 7.943611e-04 7.943611e-04 0.522678410 #> 868    433.5 0.4767795 0.4767795 7.902014e-04 7.902014e-04 0.522430338 #> 869    434.0 0.4770316 0.4770316 7.860684e-04 7.860684e-04 0.522182320 #> 870    434.5 0.4772837 0.4772837 7.819619e-04 7.819619e-04 0.521934358 #> 871    435.0 0.4775357 0.4775357 7.778818e-04 7.778818e-04 0.521686451 #> 872    435.5 0.4777876 0.4777876 7.738279e-04 7.738279e-04 0.521438601 #> 873    436.0 0.4780394 0.4780394 7.698000e-04 7.698000e-04 0.521190807 #> 874    436.5 0.4782911 0.4782911 7.657978e-04 7.657978e-04 0.520943070 #> 875    437.0 0.4785428 0.4785428 7.618212e-04 7.618212e-04 0.520695391 #> 876    437.5 0.4787944 0.4787944 7.578701e-04 7.578701e-04 0.520447769 #> 877    438.0 0.4790459 0.4790459 7.539442e-04 7.539442e-04 0.520200206 #> 878    438.5 0.4792973 0.4792973 7.500433e-04 7.500433e-04 0.519952701 #> 879    439.0 0.4795486 0.4795486 7.461673e-04 7.461673e-04 0.519705254 #> 880    439.5 0.4797998 0.4797998 7.423160e-04 7.423160e-04 0.519457868 #> 881    440.0 0.4800510 0.4800510 7.384892e-04 7.384892e-04 0.519210541 #> 882    440.5 0.4803020 0.4803020 7.346868e-04 7.346868e-04 0.518963274 #> 883    441.0 0.4805530 0.4805530 7.309085e-04 7.309085e-04 0.518716067 #> 884    441.5 0.4808039 0.4808039 7.271542e-04 7.271542e-04 0.518468921 #> 885    442.0 0.4810547 0.4810547 7.234237e-04 7.234237e-04 0.518221837 #> 886    442.5 0.4813055 0.4813055 7.197169e-04 7.197169e-04 0.517974814 #> 887    443.0 0.4815561 0.4815561 7.160336e-04 7.160336e-04 0.517727852 #> 888    443.5 0.4818067 0.4818067 7.123735e-04 7.123735e-04 0.517480954 #> 889    444.0 0.4820571 0.4820571 7.087367e-04 7.087367e-04 0.517234117 #> 890    444.5 0.4823075 0.4823075 7.051228e-04 7.051228e-04 0.516987344 #> 891    445.0 0.4825578 0.4825578 7.015317e-04 7.015317e-04 0.516740634 #> 892    445.5 0.4828080 0.4828080 6.979633e-04 6.979633e-04 0.516493988 #> 893    446.0 0.4830582 0.4830582 6.944174e-04 6.944174e-04 0.516247406 #> 894    446.5 0.4833082 0.4833082 6.908938e-04 6.908938e-04 0.516000888 #> 895    447.0 0.4835582 0.4835582 6.873924e-04 6.873924e-04 0.515754435 #> 896    447.5 0.4838080 0.4838080 6.839130e-04 6.839130e-04 0.515508047 #> 897    448.0 0.4840578 0.4840578 6.804554e-04 6.804554e-04 0.515261724 #> 898    448.5 0.4843075 0.4843075 6.770196e-04 6.770196e-04 0.515015467 #> 899    449.0 0.4845571 0.4845571 6.736053e-04 6.736053e-04 0.514769276 #> 900    449.5 0.4848066 0.4848066 6.702124e-04 6.702124e-04 0.514523151 #> 901    450.0 0.4850561 0.4850561 6.668408e-04 6.668408e-04 0.514277093 #> 902    450.5 0.4853054 0.4853054 6.634903e-04 6.634903e-04 0.514031102 #> 903    451.0 0.4855547 0.4855547 6.601607e-04 6.601607e-04 0.513785179 #> 904    451.5 0.4858038 0.4858038 6.568519e-04 6.568519e-04 0.513539323 #> 905    452.0 0.4860529 0.4860529 6.535638e-04 6.535638e-04 0.513293535 #> 906    452.5 0.4863019 0.4863019 6.502962e-04 6.502962e-04 0.513047815 #> 907    453.0 0.4865508 0.4865508 6.470489e-04 6.470489e-04 0.512802163 #> 908    453.5 0.4867996 0.4867996 6.438219e-04 6.438219e-04 0.512556581 #> 909    454.0 0.4870483 0.4870483 6.406149e-04 6.406149e-04 0.512311068 #> 910    454.5 0.4872969 0.4872969 6.374279e-04 6.374279e-04 0.512065624 #> 911    455.0 0.4875455 0.4875455 6.342607e-04 6.342607e-04 0.511820250 #> 912    455.5 0.4877939 0.4877939 6.311131e-04 6.311131e-04 0.511574946 #> 913    456.0 0.4880423 0.4880423 6.279851e-04 6.279851e-04 0.511329712 #> 914    456.5 0.4882906 0.4882906 6.248764e-04 6.248764e-04 0.511084549 #> 915    457.0 0.4885388 0.4885388 6.217870e-04 6.217870e-04 0.510839457 #> 916    457.5 0.4887868 0.4887868 6.187167e-04 6.187167e-04 0.510594436 #> 917    458.0 0.4890348 0.4890348 6.156654e-04 6.156654e-04 0.510349486 #> 918    458.5 0.4892828 0.4892828 6.126329e-04 6.126329e-04 0.510104609 #> 919    459.0 0.4895306 0.4895306 6.096192e-04 6.096192e-04 0.509859803 #> 920    459.5 0.4897783 0.4897783 6.066240e-04 6.066240e-04 0.509615069 #> 921    460.0 0.4900259 0.4900259 6.036473e-04 6.036473e-04 0.509370409 #> 922    460.5 0.4902735 0.4902735 6.006889e-04 6.006889e-04 0.509125821 #> 923    461.0 0.4905209 0.4905209 5.977487e-04 5.977487e-04 0.508881306 #> 924    461.5 0.4907683 0.4907683 5.948265e-04 5.948265e-04 0.508636864 #> 925    462.0 0.4910156 0.4910156 5.919223e-04 5.919223e-04 0.508392497 #> 926    462.5 0.4912628 0.4912628 5.890360e-04 5.890360e-04 0.508148203 #> 927    463.0 0.4915098 0.4915098 5.861673e-04 5.861673e-04 0.507903983 #> 928    463.5 0.4917568 0.4917568 5.833162e-04 5.833162e-04 0.507659838 #> 929    464.0 0.4920038 0.4920038 5.804826e-04 5.804826e-04 0.507415767 #> 930    464.5 0.4922506 0.4922506 5.776662e-04 5.776662e-04 0.507171772 #> 931    465.0 0.4924973 0.4924973 5.748671e-04 5.748671e-04 0.506927851 #> 932    465.5 0.4927439 0.4927439 5.720851e-04 5.720851e-04 0.506684006 #> 933    466.0 0.4929904 0.4929904 5.693201e-04 5.693201e-04 0.506440237 #> 934    466.5 0.4932369 0.4932369 5.665719e-04 5.665719e-04 0.506196543 #> 935    467.0 0.4934832 0.4934832 5.638404e-04 5.638404e-04 0.505952926 #> 936    467.5 0.4937295 0.4937295 5.611256e-04 5.611256e-04 0.505709385 #> 937    468.0 0.4939757 0.4939757 5.584273e-04 5.584273e-04 0.505465921 #> 938    468.5 0.4942217 0.4942217 5.557454e-04 5.557454e-04 0.505222533 #> 939    469.0 0.4944677 0.4944677 5.530797e-04 5.530797e-04 0.504979223 #> 940    469.5 0.4947136 0.4947136 5.504303e-04 5.504303e-04 0.504735990 #> 941    470.0 0.4949594 0.4949594 5.477969e-04 5.477969e-04 0.504492835 #> 942    470.5 0.4952051 0.4952051 5.451794e-04 5.451794e-04 0.504249757 #> 943    471.0 0.4954507 0.4954507 5.425778e-04 5.425778e-04 0.504006758 #> 944    471.5 0.4956962 0.4956962 5.399919e-04 5.399919e-04 0.503763837 #> 945    472.0 0.4959416 0.4959416 5.374216e-04 5.374216e-04 0.503520994 #> 946    472.5 0.4961869 0.4961869 5.348669e-04 5.348669e-04 0.503278230 #> 947    473.0 0.4964321 0.4964321 5.323275e-04 5.323275e-04 0.503035545 #> 948    473.5 0.4966773 0.4966773 5.298035e-04 5.298035e-04 0.502792939 #> 949    474.0 0.4969223 0.4969223 5.272946e-04 5.272946e-04 0.502550412 #> 950    474.5 0.4971672 0.4971672 5.248009e-04 5.248009e-04 0.502307965 #> 951    475.0 0.4974121 0.4974121 5.223221e-04 5.223221e-04 0.502065597 #> 952    475.5 0.4976568 0.4976568 5.198583e-04 5.198583e-04 0.501823310 #> 953    476.0 0.4979015 0.4979015 5.174092e-04 5.174092e-04 0.501581103 #> 954    476.5 0.4981460 0.4981460 5.149748e-04 5.149748e-04 0.501338976 #> 955    477.0 0.4983905 0.4983905 5.125550e-04 5.125550e-04 0.501096930 #> 956    477.5 0.4986349 0.4986349 5.101497e-04 5.101497e-04 0.500854964 #> 957    478.0 0.4988792 0.4988792 5.077587e-04 5.077587e-04 0.500613080 #> 958    478.5 0.4991233 0.4991233 5.053821e-04 5.053821e-04 0.500371276 #> 959    479.0 0.4993674 0.4993674 5.030196e-04 5.030196e-04 0.500129555 #> 960    479.5 0.4996114 0.4996114 5.006713e-04 5.006713e-04 0.499887914 #> 961    480.0 0.4998553 0.4998553 4.983370e-04 4.983370e-04 0.499646356 #> 962    480.5 0.5000991 0.5000991 4.960165e-04 4.960165e-04 0.499404879 #> 963    481.0 0.5003428 0.5003428 4.937099e-04 4.937099e-04 0.499163485 #> 964    481.5 0.5005864 0.5005864 4.914170e-04 4.914170e-04 0.498922173 #> 965    482.0 0.5008299 0.5008299 4.891377e-04 4.891377e-04 0.498680943 #> 966    482.5 0.5010733 0.5010733 4.868720e-04 4.868720e-04 0.498439797 #> 967    483.0 0.5013166 0.5013166 4.846197e-04 4.846197e-04 0.498198733 #> 968    483.5 0.5015599 0.5015599 4.823807e-04 4.823807e-04 0.497957752 #> 969    484.0 0.5018030 0.5018030 4.801550e-04 4.801550e-04 0.497716854 #> 970    484.5 0.5020460 0.5020460 4.779425e-04 4.779425e-04 0.497476040 #> 971    485.0 0.5022889 0.5022889 4.757431e-04 4.757431e-04 0.497235310 #> 972    485.5 0.5025318 0.5025318 4.735567e-04 4.735567e-04 0.496994663 #> 973    486.0 0.5027745 0.5027745 4.713832e-04 4.713832e-04 0.496754100 #> 974    486.5 0.5030172 0.5030172 4.692225e-04 4.692225e-04 0.496513622 #> 975    487.0 0.5032597 0.5032597 4.670745e-04 4.670745e-04 0.496273228 #> 976    487.5 0.5035021 0.5035021 4.649392e-04 4.649392e-04 0.496032918 #> 977    488.0 0.5037445 0.5037445 4.628165e-04 4.628165e-04 0.495792693 #> 978    488.5 0.5039867 0.5039867 4.607062e-04 4.607062e-04 0.495552552 #> 979    489.0 0.5042289 0.5042289 4.586084e-04 4.586084e-04 0.495312497 #> 980    489.5 0.5044710 0.5044710 4.565228e-04 4.565228e-04 0.495072527 #> 981    490.0 0.5047129 0.5047129 4.544495e-04 4.544495e-04 0.494832642 #> 982    490.5 0.5049548 0.5049548 4.523883e-04 4.523883e-04 0.494592843 #> 983    491.0 0.5051965 0.5051965 4.503393e-04 4.503393e-04 0.494353129 #> 984    491.5 0.5054382 0.5054382 4.483022e-04 4.483022e-04 0.494113501 #> 985    492.0 0.5056798 0.5056798 4.462770e-04 4.462770e-04 0.493873959 #> 986    492.5 0.5059212 0.5059212 4.442636e-04 4.442636e-04 0.493634503 #> 987    493.0 0.5061626 0.5061626 4.422620e-04 4.422620e-04 0.493395133 #> 988    493.5 0.5064039 0.5064039 4.402721e-04 4.402721e-04 0.493155850 #> 989    494.0 0.5066451 0.5066451 4.382938e-04 4.382938e-04 0.492916653 #> 990    494.5 0.5068861 0.5068861 4.363269e-04 4.363269e-04 0.492677543 #> 991    495.0 0.5071271 0.5071271 4.343716e-04 4.343716e-04 0.492438520 #> 992    495.5 0.5073680 0.5073680 4.324276e-04 4.324276e-04 0.492199584 #> 993    496.0 0.5076088 0.5076088 4.304949e-04 4.304949e-04 0.491960734 #> 994    496.5 0.5078495 0.5078495 4.285734e-04 4.285734e-04 0.491721973 #> 995    497.0 0.5080900 0.5080900 4.266630e-04 4.266630e-04 0.491483298 #> 996    497.5 0.5083305 0.5083305 4.247638e-04 4.247638e-04 0.491244712 #> 997    498.0 0.5085709 0.5085709 4.228755e-04 4.228755e-04 0.491006212 #> 998    498.5 0.5088112 0.5088112 4.209981e-04 4.209981e-04 0.490767801 #> 999    499.0 0.5090514 0.5090514 4.191316e-04 4.191316e-04 0.490529478 #> 1000   499.5 0.5092915 0.5092915 4.172759e-04 4.172759e-04 0.490291243 #> 1001   500.0 0.5095315 0.5095315 4.154309e-04 4.154309e-04 0.490053096 #> 1002   500.5 0.5097714 0.5097714 4.135965e-04 4.135965e-04 0.489815037 #> 1003   501.0 0.5100112 0.5100112 4.117727e-04 4.117727e-04 0.489577068 #> 1004   501.5 0.5102509 0.5102509 4.099594e-04 4.099594e-04 0.489339186 #> 1005   502.0 0.5104904 0.5104904 4.081565e-04 4.081565e-04 0.489101394 #> 1006   502.5 0.5107299 0.5107299 4.063640e-04 4.063640e-04 0.488863690 #> 1007   503.0 0.5109693 0.5109693 4.045817e-04 4.045817e-04 0.488626076 #> 1008   503.5 0.5112086 0.5112086 4.028098e-04 4.028098e-04 0.488388550 #> 1009   504.0 0.5114478 0.5114478 4.010479e-04 4.010479e-04 0.488151114 #> 1010   504.5 0.5116869 0.5116869 3.992962e-04 3.992962e-04 0.487913767 #> 1011   505.0 0.5119259 0.5119259 3.975545e-04 3.975545e-04 0.487676510 #> 1012   505.5 0.5121648 0.5121648 3.958227e-04 3.958227e-04 0.487439343 #> 1013   506.0 0.5124036 0.5124036 3.941008e-04 3.941008e-04 0.487202265 #> 1014   506.5 0.5126423 0.5126423 3.923888e-04 3.923888e-04 0.486965277 #> 1015   507.0 0.5128809 0.5128809 3.906866e-04 3.906866e-04 0.486728379 #> 1016   507.5 0.5131194 0.5131194 3.889940e-04 3.889940e-04 0.486491572 #> 1017   508.0 0.5133578 0.5133578 3.873111e-04 3.873111e-04 0.486254854 #> 1018   508.5 0.5135961 0.5135961 3.856378e-04 3.856378e-04 0.486018227 #> 1019   509.0 0.5138343 0.5138343 3.839740e-04 3.839740e-04 0.485781691 #> 1020   509.5 0.5140724 0.5140724 3.823196e-04 3.823196e-04 0.485545245 #> 1021   510.0 0.5143104 0.5143104 3.806746e-04 3.806746e-04 0.485308889 #> 1022   510.5 0.5145483 0.5145483 3.790390e-04 3.790390e-04 0.485072625 #> 1023   511.0 0.5147861 0.5147861 3.774126e-04 3.774126e-04 0.484836451 #> 1024   511.5 0.5150238 0.5150238 3.757955e-04 3.757955e-04 0.484600369 #> 1025   512.0 0.5152614 0.5152614 3.741875e-04 3.741875e-04 0.484364378 #> 1026   512.5 0.5154989 0.5154989 3.725886e-04 3.725886e-04 0.484128478 #> 1027   513.0 0.5157363 0.5157363 3.709987e-04 3.709987e-04 0.483892669 #> 1028   513.5 0.5159736 0.5159736 3.694178e-04 3.694178e-04 0.483656952 #> 1029   514.0 0.5162108 0.5162108 3.678458e-04 3.678458e-04 0.483421326 #> 1030   514.5 0.5164479 0.5164479 3.662827e-04 3.662827e-04 0.483185792 #> 1031   515.0 0.5166849 0.5166849 3.647284e-04 3.647284e-04 0.482950350 #> 1032   515.5 0.5169218 0.5169218 3.631828e-04 3.631828e-04 0.482714999 #> 1033   516.0 0.5171586 0.5171586 3.616460e-04 3.616460e-04 0.482479741 #> 1034   516.5 0.5173953 0.5173953 3.601177e-04 3.601177e-04 0.482244574 #> 1035   517.0 0.5176319 0.5176319 3.585981e-04 3.585981e-04 0.482009500 #> 1036   517.5 0.5178684 0.5178684 3.570869e-04 3.570869e-04 0.481774518 #> 1037   518.0 0.5181048 0.5181048 3.555843e-04 3.555843e-04 0.481539628 #> 1038   518.5 0.5183411 0.5183411 3.540900e-04 3.540900e-04 0.481304831 #> 1039   519.0 0.5185773 0.5185773 3.526041e-04 3.526041e-04 0.481070127 #> 1040   519.5 0.5188134 0.5188134 3.511265e-04 3.511265e-04 0.480835515 #> 1041   520.0 0.5190493 0.5190493 3.496572e-04 3.496572e-04 0.480600995 #> 1042   520.5 0.5192852 0.5192852 3.481961e-04 3.481961e-04 0.480366569 #> 1043   521.0 0.5195210 0.5195210 3.467431e-04 3.467431e-04 0.480132235 #> 1044   521.5 0.5197567 0.5197567 3.452982e-04 3.452982e-04 0.479897995 #> 1045   522.0 0.5199923 0.5199923 3.438614e-04 3.438614e-04 0.479663847 #> 1046   522.5 0.5202278 0.5202278 3.424326e-04 3.424326e-04 0.479429793 #> 1047   523.0 0.5204632 0.5204632 3.410117e-04 3.410117e-04 0.479195832 #> 1048   523.5 0.5206984 0.5206984 3.395987e-04 3.395987e-04 0.478961964 #> 1049   524.0 0.5209336 0.5209336 3.381936e-04 3.381936e-04 0.478728190 #> 1050   524.5 0.5211687 0.5211687 3.367962e-04 3.367962e-04 0.478494509 #> 1051   525.0 0.5214037 0.5214037 3.354066e-04 3.354066e-04 0.478260922 #> 1052   525.5 0.5216385 0.5216385 3.340247e-04 3.340247e-04 0.478027429 #> 1053   526.0 0.5218733 0.5218733 3.326504e-04 3.326504e-04 0.477794029 #> 1054   526.5 0.5221080 0.5221080 3.312837e-04 3.312837e-04 0.477560723 #> 1055   527.0 0.5223426 0.5223426 3.299246e-04 3.299246e-04 0.477327511 #> 1056   527.5 0.5225770 0.5225770 3.285730e-04 3.285730e-04 0.477094393 #> 1057   528.0 0.5228114 0.5228114 3.272288e-04 3.272288e-04 0.476861369 #> 1058   528.5 0.5230457 0.5230457 3.258921e-04 3.258921e-04 0.476628439 #> 1059   529.0 0.5232798 0.5232798 3.245627e-04 3.245627e-04 0.476395604 #> 1060   529.5 0.5235139 0.5235139 3.232406e-04 3.232406e-04 0.476162862 #> 1061   530.0 0.5237479 0.5237479 3.219258e-04 3.219258e-04 0.475930215 #> 1062   530.5 0.5239817 0.5239817 3.206182e-04 3.206182e-04 0.475697663 #> 1063   531.0 0.5242155 0.5242155 3.193178e-04 3.193178e-04 0.475465205 #> 1064   531.5 0.5244491 0.5244491 3.180245e-04 3.180245e-04 0.475232842 #> 1065   532.0 0.5246827 0.5246827 3.167383e-04 3.167383e-04 0.475000573 #> 1066   532.5 0.5249161 0.5249161 3.154591e-04 3.154591e-04 0.474768399 #> 1067   533.0 0.5251495 0.5251495 3.141870e-04 3.141870e-04 0.474536320 #> 1068   533.5 0.5253827 0.5253827 3.129218e-04 3.129218e-04 0.474304336 #> 1069   534.0 0.5256159 0.5256159 3.116635e-04 3.116635e-04 0.474072446 #> 1070   534.5 0.5258489 0.5258489 3.104121e-04 3.104121e-04 0.473840652 #> 1071   535.0 0.5260819 0.5260819 3.091675e-04 3.091675e-04 0.473608953 #> 1072   535.5 0.5263147 0.5263147 3.079297e-04 3.079297e-04 0.473377349 #> 1073   536.0 0.5265475 0.5265475 3.066986e-04 3.066986e-04 0.473145840 #> 1074   536.5 0.5267801 0.5267801 3.054743e-04 3.054743e-04 0.472914426 #> 1075   537.0 0.5270126 0.5270126 3.042566e-04 3.042566e-04 0.472683108 #> 1076   537.5 0.5272451 0.5272451 3.030455e-04 3.030455e-04 0.472451885 #> 1077   538.0 0.5274774 0.5274774 3.018409e-04 3.018409e-04 0.472220758 #> 1078   538.5 0.5277096 0.5277096 3.006430e-04 3.006430e-04 0.471989726 #> 1079   539.0 0.5279418 0.5279418 2.994515e-04 2.994515e-04 0.471758789 #> 1080   539.5 0.5281738 0.5281738 2.982664e-04 2.982664e-04 0.471527949 #> 1081   540.0 0.5284057 0.5284057 2.970878e-04 2.970878e-04 0.471297204 #> 1082   540.5 0.5286375 0.5286375 2.959155e-04 2.959155e-04 0.471066554 #> 1083   541.0 0.5288692 0.5288692 2.947496e-04 2.947496e-04 0.470836001 #> 1084   541.5 0.5291009 0.5291009 2.935900e-04 2.935900e-04 0.470605543 #> 1085   542.0 0.5293324 0.5293324 2.924366e-04 2.924366e-04 0.470375182 #> 1086   542.5 0.5295638 0.5295638 2.912895e-04 2.912895e-04 0.470144916 #> 1087   543.0 0.5297951 0.5297951 2.901485e-04 2.901485e-04 0.469914747 #> 1088   543.5 0.5300263 0.5300263 2.890137e-04 2.890137e-04 0.469684673 #> 1089   544.0 0.5302574 0.5302574 2.878849e-04 2.878849e-04 0.469454696 #> 1090   544.5 0.5304884 0.5304884 2.867622e-04 2.867622e-04 0.469224814 #> 1091   545.0 0.5307193 0.5307193 2.856456e-04 2.856456e-04 0.468995030 #> 1092   545.5 0.5309501 0.5309501 2.845349e-04 2.845349e-04 0.468765341 #> 1093   546.0 0.5311808 0.5311808 2.834303e-04 2.834303e-04 0.468535749 #> 1094   546.5 0.5314114 0.5314114 2.823315e-04 2.823315e-04 0.468306253 #> 1095   547.0 0.5316419 0.5316419 2.812386e-04 2.812386e-04 0.468076854 #> 1096   547.5 0.5318723 0.5318723 2.801515e-04 2.801515e-04 0.467847551 #> 1097   548.0 0.5321026 0.5321026 2.790703e-04 2.790703e-04 0.467618344 #> 1098   548.5 0.5323328 0.5323328 2.779948e-04 2.779948e-04 0.467389235 #> 1099   549.0 0.5325629 0.5325629 2.769250e-04 2.769250e-04 0.467160222 #> 1100   549.5 0.5327928 0.5327928 2.758610e-04 2.758610e-04 0.466931305 #> 1101   550.0 0.5330227 0.5330227 2.748026e-04 2.748026e-04 0.466702486 #> 1102   550.5 0.5332525 0.5332525 2.737499e-04 2.737499e-04 0.466473763 #> 1103   551.0 0.5334822 0.5334822 2.727028e-04 2.727028e-04 0.466245137 #> 1104   551.5 0.5337117 0.5337117 2.716612e-04 2.716612e-04 0.466016608 #> 1105   552.0 0.5339412 0.5339412 2.706252e-04 2.706252e-04 0.465788176 #> 1106   552.5 0.5341706 0.5341706 2.695946e-04 2.695946e-04 0.465559841 #> 1107   553.0 0.5343998 0.5343998 2.685696e-04 2.685696e-04 0.465331602 #> 1108   553.5 0.5346290 0.5346290 2.675499e-04 2.675499e-04 0.465103461 #> 1109   554.0 0.5348580 0.5348580 2.665357e-04 2.665357e-04 0.464875417 #> 1110   554.5 0.5350870 0.5350870 2.655268e-04 2.655268e-04 0.464647470 #> 1111   555.0 0.5353159 0.5353159 2.645232e-04 2.645232e-04 0.464419621 #> 1112   555.5 0.5355446 0.5355446 2.635250e-04 2.635250e-04 0.464191868 #> 1113   556.0 0.5357733 0.5357733 2.625320e-04 2.625320e-04 0.463964213 #> 1114   556.5 0.5360018 0.5360018 2.615442e-04 2.615442e-04 0.463736655 #> 1115   557.0 0.5362302 0.5362302 2.605617e-04 2.605617e-04 0.463509195 #> 1116   557.5 0.5364586 0.5364586 2.595843e-04 2.595843e-04 0.463281831 #> 1117   558.0 0.5366868 0.5366868 2.586121e-04 2.586121e-04 0.463054566 #> 1118   558.5 0.5369150 0.5369150 2.576450e-04 2.576450e-04 0.462827397 #> 1119   559.0 0.5371430 0.5371430 2.566830e-04 2.566830e-04 0.462600326 #> 1120   559.5 0.5373709 0.5373709 2.557260e-04 2.557260e-04 0.462373353 #> 1121   560.0 0.5375987 0.5375987 2.547740e-04 2.547740e-04 0.462146477 #> 1122   560.5 0.5378265 0.5378265 2.538270e-04 2.538270e-04 0.461919699 #> 1123   561.0 0.5380541 0.5380541 2.528850e-04 2.528850e-04 0.461693018 #> 1124   561.5 0.5382816 0.5382816 2.519479e-04 2.519479e-04 0.461466436 #> 1125   562.0 0.5385090 0.5385090 2.510157e-04 2.510157e-04 0.461239950 #> 1126   562.5 0.5387363 0.5387363 2.500884e-04 2.500884e-04 0.461013563 #> 1127   563.0 0.5389636 0.5389636 2.491660e-04 2.491660e-04 0.460787273 #> 1128   563.5 0.5391907 0.5391907 2.482483e-04 2.482483e-04 0.460561081 #> 1129   564.0 0.5394177 0.5394177 2.473354e-04 2.473354e-04 0.460334987 #> 1130   564.5 0.5396446 0.5396446 2.464273e-04 2.464273e-04 0.460108991 #> 1131   565.0 0.5398714 0.5398714 2.455239e-04 2.455239e-04 0.459883092 #> 1132   565.5 0.5400981 0.5400981 2.446252e-04 2.446252e-04 0.459657292 #> 1133   566.0 0.5403247 0.5403247 2.437312e-04 2.437312e-04 0.459431589 #> 1134   566.5 0.5405512 0.5405512 2.428418e-04 2.428418e-04 0.459205984 #> 1135   567.0 0.5407776 0.5407776 2.419570e-04 2.419570e-04 0.458980478 #> 1136   567.5 0.5410039 0.5410039 2.410768e-04 2.410768e-04 0.458755069 #> 1137   568.0 0.5412300 0.5412300 2.402012e-04 2.402012e-04 0.458529759 #> 1138   568.5 0.5414561 0.5414561 2.393301e-04 2.393301e-04 0.458304546 #> 1139   569.0 0.5416821 0.5416821 2.384635e-04 2.384635e-04 0.458079432 #> 1140   569.5 0.5419080 0.5419080 2.376014e-04 2.376014e-04 0.457854415 #> 1141   570.0 0.5421338 0.5421338 2.367438e-04 2.367438e-04 0.457629497 #> 1142   570.5 0.5423594 0.5423594 2.358906e-04 2.358906e-04 0.457404677 #> 1143   571.0 0.5425850 0.5425850 2.350417e-04 2.350417e-04 0.457179956 #> 1144   571.5 0.5428105 0.5428105 2.341973e-04 2.341973e-04 0.456955332 #> 1145   572.0 0.5430358 0.5430358 2.333572e-04 2.333572e-04 0.456730807 #> 1146   572.5 0.5432611 0.5432611 2.325214e-04 2.325214e-04 0.456506380 #> 1147   573.0 0.5434863 0.5434863 2.316899e-04 2.316899e-04 0.456282051 #> 1148   573.5 0.5437113 0.5437113 2.308627e-04 2.308627e-04 0.456057821 #> 1149   574.0 0.5439363 0.5439363 2.300398e-04 2.300398e-04 0.455833689 #> 1150   574.5 0.5441611 0.5441611 2.292210e-04 2.292210e-04 0.455609655 #> 1151   575.0 0.5443859 0.5443859 2.284065e-04 2.284065e-04 0.455385720 #> 1152   575.5 0.5446105 0.5446105 2.275961e-04 2.275961e-04 0.455161883 #> 1153   576.0 0.5448351 0.5448351 2.267899e-04 2.267899e-04 0.454938145 #> 1154   576.5 0.5450595 0.5450595 2.259878e-04 2.259878e-04 0.454714505 #> 1155   577.0 0.5452838 0.5452838 2.251898e-04 2.251898e-04 0.454490963 #> 1156   577.5 0.5455081 0.5455081 2.243959e-04 2.243959e-04 0.454267520 #> 1157   578.0 0.5457322 0.5457322 2.236061e-04 2.236061e-04 0.454044175 #> 1158   578.5 0.5459563 0.5459563 2.228202e-04 2.228202e-04 0.453820929 #> 1159   579.0 0.5461802 0.5461802 2.220384e-04 2.220384e-04 0.453597782 #> 1160   579.5 0.5464040 0.5464040 2.212605e-04 2.212605e-04 0.453374733 #> 1161   580.0 0.5466277 0.5466277 2.204867e-04 2.204867e-04 0.453151782 #> 1162   580.5 0.5468514 0.5468514 2.197167e-04 2.197167e-04 0.452928931 #> 1163   581.0 0.5470749 0.5470749 2.189507e-04 2.189507e-04 0.452706177 #> 1164   581.5 0.5472983 0.5472983 2.181885e-04 2.181885e-04 0.452483523 #> 1165   582.0 0.5475216 0.5475216 2.174302e-04 2.174302e-04 0.452260967 #> 1166   582.5 0.5477448 0.5477448 2.166758e-04 2.166758e-04 0.452038510 #> 1167   583.0 0.5479679 0.5479679 2.159252e-04 2.159252e-04 0.451816151 #> 1168   583.5 0.5481909 0.5481909 2.151784e-04 2.151784e-04 0.451593891 #> 1169   584.0 0.5484138 0.5484138 2.144353e-04 2.144353e-04 0.451371730 #> 1170   584.5 0.5486366 0.5486366 2.136960e-04 2.136960e-04 0.451149667 #> 1171   585.0 0.5488593 0.5488593 2.129605e-04 2.129605e-04 0.450927703 #> 1172   585.5 0.5490819 0.5490819 2.122286e-04 2.122286e-04 0.450705838 #> 1173   586.0 0.5493044 0.5493044 2.115005e-04 2.115005e-04 0.450484072 #> 1174   586.5 0.5495268 0.5495268 2.107760e-04 2.107760e-04 0.450262404 #> 1175   587.0 0.5497491 0.5497491 2.100552e-04 2.100552e-04 0.450040835 #> 1176   587.5 0.5499713 0.5499713 2.093380e-04 2.093380e-04 0.449819365 #> 1177   588.0 0.5501934 0.5501934 2.086244e-04 2.086244e-04 0.449597994 #> 1178   588.5 0.5504154 0.5504154 2.079144e-04 2.079144e-04 0.449376721 #> 1179   589.0 0.5506372 0.5506372 2.072080e-04 2.072080e-04 0.449155547 #> 1180   589.5 0.5508590 0.5508590 2.065051e-04 2.065051e-04 0.448934473 #> 1181   590.0 0.5510807 0.5510807 2.058057e-04 2.058057e-04 0.448713496 #> 1182   590.5 0.5513023 0.5513023 2.051098e-04 2.051098e-04 0.448492619 #> 1183   591.0 0.5515237 0.5515237 2.044175e-04 2.044175e-04 0.448271841 #> 1184   591.5 0.5517451 0.5517451 2.037286e-04 2.037286e-04 0.448051161 #> 1185   592.0 0.5519664 0.5519664 2.030431e-04 2.030431e-04 0.447830581 #> 1186   592.5 0.5521875 0.5521875 2.023611e-04 2.023611e-04 0.447610099 #> 1187   593.0 0.5524086 0.5524086 2.016824e-04 2.016824e-04 0.447389716 #> 1188   593.5 0.5526296 0.5526296 2.010072e-04 2.010072e-04 0.447169432 #> 1189   594.0 0.5528504 0.5528504 2.003353e-04 2.003353e-04 0.446949247 #> 1190   594.5 0.5530712 0.5530712 1.996668e-04 1.996668e-04 0.446729160 #> 1191   595.0 0.5532918 0.5532918 1.990015e-04 1.990015e-04 0.446509173 #> 1192   595.5 0.5535124 0.5535124 1.983397e-04 1.983397e-04 0.446289285 #> 1193   596.0 0.5537328 0.5537328 1.976811e-04 1.976811e-04 0.446069495 #> 1194   596.5 0.5539532 0.5539532 1.970257e-04 1.970257e-04 0.445849805 #> 1195   597.0 0.5541734 0.5541734 1.963736e-04 1.963736e-04 0.445630213 #> 1196   597.5 0.5543936 0.5543936 1.957248e-04 1.957248e-04 0.445410720 #> 1197   598.0 0.5546136 0.5546136 1.950792e-04 1.950792e-04 0.445191326 #> 1198   598.5 0.5548335 0.5548335 1.944368e-04 1.944368e-04 0.444972031 #> 1199   599.0 0.5550534 0.5550534 1.937975e-04 1.937975e-04 0.444752836 #> 1200   599.5 0.5552731 0.5552731 1.931614e-04 1.931614e-04 0.444533739 #> 1201   600.0 0.5554927 0.5554927 1.925285e-04 1.925285e-04 0.444314740 #> 1202   600.5 0.5557123 0.5557123 1.918987e-04 1.918987e-04 0.444095841 #> 1203   601.0 0.5559317 0.5559317 1.912720e-04 1.912720e-04 0.443877041 #> 1204   601.5 0.5561510 0.5561510 1.906484e-04 1.906484e-04 0.443658340 #> 1205   602.0 0.5563702 0.5563702 1.900278e-04 1.900278e-04 0.443439738 #> 1206   602.5 0.5565894 0.5565894 1.894104e-04 1.894104e-04 0.443221235 #> 1207   603.0 0.5568084 0.5568084 1.887959e-04 1.887959e-04 0.443002830 #> 1208   603.5 0.5570273 0.5570273 1.881845e-04 1.881845e-04 0.442784525 #> 1209   604.0 0.5572461 0.5572461 1.875761e-04 1.875761e-04 0.442566319 #> 1210   604.5 0.5574648 0.5574648 1.869707e-04 1.869707e-04 0.442348211 #> 1211   605.0 0.5576834 0.5576834 1.863683e-04 1.863683e-04 0.442130203 #> 1212   605.5 0.5579019 0.5579019 1.857688e-04 1.857688e-04 0.441912293 #> 1213   606.0 0.5581203 0.5581203 1.851722e-04 1.851722e-04 0.441694483 #> 1214   606.5 0.5583387 0.5583387 1.845786e-04 1.845786e-04 0.441476771 #> 1215   607.0 0.5585569 0.5585569 1.839879e-04 1.839879e-04 0.441259159 #> 1216   607.5 0.5587750 0.5587750 1.834001e-04 1.834001e-04 0.441041645 #> 1217   608.0 0.5589930 0.5589930 1.828151e-04 1.828151e-04 0.440824230 #> 1218   608.5 0.5592109 0.5592109 1.822330e-04 1.822330e-04 0.440606915 #> 1219   609.0 0.5594286 0.5594286 1.816538e-04 1.816538e-04 0.440389698 #> 1220   609.5 0.5596463 0.5596463 1.810774e-04 1.810774e-04 0.440172580 #> 1221   610.0 0.5598639 0.5598639 1.805037e-04 1.805037e-04 0.439955561 #> 1222   610.5 0.5600814 0.5600814 1.799329e-04 1.799329e-04 0.439738641 #> 1223   611.0 0.5602988 0.5602988 1.793649e-04 1.793649e-04 0.439521820 #> 1224   611.5 0.5605161 0.5605161 1.787996e-04 1.787996e-04 0.439305098 #> 1225   612.0 0.5607333 0.5607333 1.782371e-04 1.782371e-04 0.439088475 #> 1226   612.5 0.5609504 0.5609504 1.776773e-04 1.776773e-04 0.438871951 #> 1227   613.0 0.5611674 0.5611674 1.771202e-04 1.771202e-04 0.438655526 #> 1228   613.5 0.5613842 0.5613842 1.765659e-04 1.765659e-04 0.438439199 #> 1229   614.0 0.5616010 0.5616010 1.760142e-04 1.760142e-04 0.438222972 #> 1230   614.5 0.5618177 0.5618177 1.754652e-04 1.754652e-04 0.438006844 #> 1231   615.0 0.5620343 0.5620343 1.749189e-04 1.749189e-04 0.437790814 #> 1232   615.5 0.5622507 0.5622507 1.743752e-04 1.743752e-04 0.437574883 #> 1233   616.0 0.5624671 0.5624671 1.738341e-04 1.738341e-04 0.437359052 #> 1234   616.5 0.5626834 0.5626834 1.732957e-04 1.732957e-04 0.437143319 #> 1235   617.0 0.5628996 0.5628996 1.727598e-04 1.727598e-04 0.436927685 #> 1236   617.5 0.5631156 0.5631156 1.722266e-04 1.722266e-04 0.436712150 #> 1237   618.0 0.5633316 0.5633316 1.716959e-04 1.716959e-04 0.436496714 #> 1238   618.5 0.5635475 0.5635475 1.711677e-04 1.711677e-04 0.436281376 #> 1239   619.0 0.5637632 0.5637632 1.706422e-04 1.706422e-04 0.436066138 #> 1240   619.5 0.5639789 0.5639789 1.701191e-04 1.701191e-04 0.435850998 #> 1241   620.0 0.5641944 0.5641944 1.695986e-04 1.695986e-04 0.435635958 #> 1242   620.5 0.5644099 0.5644099 1.690805e-04 1.690805e-04 0.435421016 #> 1243   621.0 0.5646253 0.5646253 1.685650e-04 1.685650e-04 0.435206173 #> 1244   621.5 0.5648405 0.5648405 1.680519e-04 1.680519e-04 0.434991428 #> 1245   622.0 0.5650557 0.5650557 1.675413e-04 1.675413e-04 0.434776783 #> 1246   622.5 0.5652707 0.5652707 1.670332e-04 1.670332e-04 0.434562236 #> 1247   623.0 0.5654857 0.5654857 1.665275e-04 1.665275e-04 0.434347788 #> 1248   623.5 0.5657005 0.5657005 1.660242e-04 1.660242e-04 0.434133439 #> 1249   624.0 0.5659153 0.5659153 1.655233e-04 1.655233e-04 0.433919189 #> 1250   624.5 0.5661299 0.5661299 1.650248e-04 1.650248e-04 0.433705038 #> 1251   625.0 0.5663445 0.5663445 1.645287e-04 1.645287e-04 0.433490985 #> 1252   625.5 0.5665589 0.5665589 1.640350e-04 1.640350e-04 0.433277031 #> 1253   626.0 0.5667733 0.5667733 1.635436e-04 1.635436e-04 0.433063176 #> 1254   626.5 0.5669875 0.5669875 1.630546e-04 1.630546e-04 0.432849419 #> 1255   627.0 0.5672017 0.5672017 1.625679e-04 1.625679e-04 0.432635761 #> 1256   627.5 0.5674157 0.5674157 1.620835e-04 1.620835e-04 0.432422202 #> 1257   628.0 0.5676297 0.5676297 1.616015e-04 1.616015e-04 0.432208742 #> 1258   628.5 0.5678435 0.5678435 1.611217e-04 1.611217e-04 0.431995380 #> 1259   629.0 0.5680572 0.5680572 1.606442e-04 1.606442e-04 0.431782117 #> 1260   629.5 0.5682709 0.5682709 1.601690e-04 1.601690e-04 0.431568953 #> 1261   630.0 0.5684844 0.5684844 1.596960e-04 1.596960e-04 0.431355887 #> 1262   630.5 0.5686979 0.5686979 1.592253e-04 1.592253e-04 0.431142920 #> 1263   631.0 0.5689112 0.5689112 1.587569e-04 1.587569e-04 0.430930052 #> 1264   631.5 0.5691244 0.5691244 1.582906e-04 1.582906e-04 0.430717282 #> 1265   632.0 0.5693376 0.5693376 1.578266e-04 1.578266e-04 0.430504611 #> 1266   632.5 0.5695506 0.5695506 1.573647e-04 1.573647e-04 0.430292038 #> 1267   633.0 0.5697635 0.5697635 1.569051e-04 1.569051e-04 0.430079564 #> 1268   633.5 0.5699764 0.5699764 1.564476e-04 1.564476e-04 0.429867189 #> 1269   634.0 0.5701891 0.5701891 1.559923e-04 1.559923e-04 0.429654912 #> 1270   634.5 0.5704017 0.5704017 1.555391e-04 1.555391e-04 0.429442734 #> 1271   635.0 0.5706143 0.5706143 1.550881e-04 1.550881e-04 0.429230654 #> 1272   635.5 0.5708267 0.5708267 1.546392e-04 1.546392e-04 0.429018673 #> 1273   636.0 0.5710390 0.5710390 1.541924e-04 1.541924e-04 0.428806790 #> 1274   636.5 0.5712512 0.5712512 1.537477e-04 1.537477e-04 0.428595006 #> 1275   637.0 0.5714634 0.5714634 1.533051e-04 1.533051e-04 0.428383320 #> 1276   637.5 0.5716754 0.5716754 1.528646e-04 1.528646e-04 0.428171733 #> 1277   638.0 0.5718873 0.5718873 1.524262e-04 1.524262e-04 0.427960244 #> 1278   638.5 0.5720992 0.5720992 1.519899e-04 1.519899e-04 0.427748853 #> 1279   639.0 0.5723109 0.5723109 1.515556e-04 1.515556e-04 0.427537561 #> 1280   639.5 0.5725225 0.5725225 1.511233e-04 1.511233e-04 0.427326367 #> 1281   640.0 0.5727340 0.5727340 1.506931e-04 1.506931e-04 0.427115272 #> 1282   640.5 0.5729455 0.5729455 1.502649e-04 1.502649e-04 0.426904275 #> 1283   641.0 0.5731568 0.5731568 1.498386e-04 1.498386e-04 0.426693377 #> 1284   641.5 0.5733680 0.5733680 1.494144e-04 1.494144e-04 0.426482576 #> 1285   642.0 0.5735791 0.5735791 1.489922e-04 1.489922e-04 0.426271875 #> 1286   642.5 0.5737902 0.5737902 1.485720e-04 1.485720e-04 0.426061271 #> 1287   643.0 0.5740011 0.5740011 1.481537e-04 1.481537e-04 0.425850766 #> 1288   643.5 0.5742119 0.5742119 1.477374e-04 1.477374e-04 0.425640359 #> 1289   644.0 0.5744226 0.5744226 1.473230e-04 1.473230e-04 0.425430050 #> 1290   644.5 0.5746333 0.5746333 1.469106e-04 1.469106e-04 0.425219839 #> 1291   645.0 0.5748438 0.5748438 1.465001e-04 1.465001e-04 0.425009727 #> 1292   645.5 0.5750542 0.5750542 1.460915e-04 1.460915e-04 0.424799713 #> 1293   646.0 0.5752645 0.5752645 1.456848e-04 1.456848e-04 0.424589797 #> 1294   646.5 0.5754747 0.5754747 1.452800e-04 1.452800e-04 0.424379979 #> 1295   647.0 0.5756849 0.5756849 1.448771e-04 1.448771e-04 0.424170260 #> 1296   647.5 0.5758949 0.5758949 1.444760e-04 1.444760e-04 0.423960638 #> 1297   648.0 0.5761048 0.5761048 1.440769e-04 1.440769e-04 0.423751115 #> 1298   648.5 0.5763146 0.5763146 1.436796e-04 1.436796e-04 0.423541690 #> 1299   649.0 0.5765244 0.5765244 1.432841e-04 1.432841e-04 0.423332363 #> 1300   649.5 0.5767340 0.5767340 1.428905e-04 1.428905e-04 0.423123134 #> 1301   650.0 0.5769435 0.5769435 1.424987e-04 1.424987e-04 0.422914002 #> 1302   650.5 0.5771529 0.5771529 1.421087e-04 1.421087e-04 0.422704969 #> 1303   651.0 0.5773622 0.5773622 1.417205e-04 1.417205e-04 0.422496034 #> 1304   651.5 0.5775715 0.5775715 1.413342e-04 1.413342e-04 0.422287197 #> 1305   652.0 0.5777806 0.5777806 1.409496e-04 1.409496e-04 0.422078458 #> 1306   652.5 0.5779896 0.5779896 1.405668e-04 1.405668e-04 0.421869817 #> 1307   653.0 0.5781985 0.5781985 1.401858e-04 1.401858e-04 0.421661274 #> 1308   653.5 0.5784074 0.5784074 1.398065e-04 1.398065e-04 0.421452829 #> 1309   654.0 0.5786161 0.5786161 1.394290e-04 1.394290e-04 0.421244482 #> 1310   654.5 0.5788247 0.5788247 1.390532e-04 1.390532e-04 0.421036232 #> 1311   655.0 0.5790332 0.5790332 1.386792e-04 1.386792e-04 0.420828080 #> 1312   655.5 0.5792417 0.5792417 1.383069e-04 1.383069e-04 0.420620027 #> 1313   656.0 0.5794500 0.5794500 1.379363e-04 1.379363e-04 0.420412071 #> 1314   656.5 0.5796582 0.5796582 1.375675e-04 1.375675e-04 0.420204212 #> 1315   657.0 0.5798663 0.5798663 1.372003e-04 1.372003e-04 0.419996452 #> 1316   657.5 0.5800744 0.5800744 1.368348e-04 1.368348e-04 0.419788789 #> 1317   658.0 0.5802823 0.5802823 1.364710e-04 1.364710e-04 0.419581224 #> 1318   658.5 0.5804901 0.5804901 1.361089e-04 1.361089e-04 0.419373757 #> 1319   659.0 0.5806979 0.5806979 1.357484e-04 1.357484e-04 0.419166388 #> 1320   659.5 0.5809055 0.5809055 1.353896e-04 1.353896e-04 0.418959116 #> 1321   660.0 0.5811130 0.5811130 1.350325e-04 1.350325e-04 0.418751941 #> 1322   660.5 0.5813205 0.5813205 1.346770e-04 1.346770e-04 0.418544865 #> 1323   661.0 0.5815278 0.5815278 1.343231e-04 1.343231e-04 0.418337886 #> 1324   661.5 0.5817350 0.5817350 1.339708e-04 1.339708e-04 0.418131004 #> 1325   662.0 0.5819422 0.5819422 1.336202e-04 1.336202e-04 0.417924220 #> 1326   662.5 0.5821492 0.5821492 1.332712e-04 1.332712e-04 0.417717534 #> 1327   663.0 0.5823561 0.5823561 1.329237e-04 1.329237e-04 0.417510945 #> 1328   663.5 0.5825630 0.5825630 1.325779e-04 1.325779e-04 0.417304454 #> 1329   664.0 0.5827697 0.5827697 1.322336e-04 1.322336e-04 0.417098060 #> 1330   664.5 0.5829763 0.5829763 1.318910e-04 1.318910e-04 0.416891764 #> 1331   665.0 0.5831829 0.5831829 1.315498e-04 1.315498e-04 0.416685565 #> 1332   665.5 0.5833893 0.5833893 1.312103e-04 1.312103e-04 0.416479463 #> 1333   666.0 0.5835957 0.5835957 1.308723e-04 1.308723e-04 0.416273459 #> 1334   666.5 0.5838019 0.5838019 1.305358e-04 1.305358e-04 0.416067552 #> 1335   667.0 0.5840081 0.5840081 1.302009e-04 1.302009e-04 0.415861742 #> 1336   667.5 0.5842141 0.5842141 1.298675e-04 1.298675e-04 0.415656030 #> 1337   668.0 0.5844200 0.5844200 1.295356e-04 1.295356e-04 0.415450415 #> 1338   668.5 0.5846259 0.5846259 1.292053e-04 1.292053e-04 0.415244898 #> 1339   669.0 0.5848316 0.5848316 1.288764e-04 1.288764e-04 0.415039477 #> 1340   669.5 0.5850373 0.5850373 1.285491e-04 1.285491e-04 0.414834154 #> 1341   670.0 0.5852428 0.5852428 1.282232e-04 1.282232e-04 0.414628928 #> 1342   670.5 0.5854483 0.5854483 1.278988e-04 1.278988e-04 0.414423799 #> 1343   671.0 0.5856537 0.5856537 1.275759e-04 1.275759e-04 0.414218768 #> 1344   671.5 0.5858589 0.5858589 1.272545e-04 1.272545e-04 0.414013833 #> 1345   672.0 0.5860641 0.5860641 1.269345e-04 1.269345e-04 0.413808996 #> 1346   672.5 0.5862691 0.5862691 1.266160e-04 1.266160e-04 0.413604255 #> 1347   673.0 0.5864741 0.5864741 1.262989e-04 1.262989e-04 0.413399612 #> 1348   673.5 0.5866790 0.5866790 1.259833e-04 1.259833e-04 0.413195066 #> 1349   674.0 0.5868837 0.5868837 1.256691e-04 1.256691e-04 0.412990616 #> 1350   674.5 0.5870884 0.5870884 1.253563e-04 1.253563e-04 0.412786264 #> 1351   675.0 0.5872929 0.5872929 1.250449e-04 1.250449e-04 0.412582008 #> 1352   675.5 0.5874974 0.5874974 1.247350e-04 1.247350e-04 0.412377850 #> 1353   676.0 0.5877018 0.5877018 1.244264e-04 1.244264e-04 0.412173788 #> 1354   676.5 0.5879061 0.5879061 1.241193e-04 1.241193e-04 0.411969824 #> 1355   677.0 0.5881102 0.5881102 1.238135e-04 1.238135e-04 0.411765956 #> 1356   677.5 0.5883143 0.5883143 1.235091e-04 1.235091e-04 0.411562185 #> 1357   678.0 0.5885183 0.5885183 1.232061e-04 1.232061e-04 0.411358511 #> 1358   678.5 0.5887222 0.5887222 1.229045e-04 1.229045e-04 0.411154933 #> 1359   679.0 0.5889259 0.5889259 1.226043e-04 1.226043e-04 0.410951453 #> 1360   679.5 0.5891296 0.5891296 1.223053e-04 1.223053e-04 0.410748069 #> 1361   680.0 0.5893332 0.5893332 1.220078e-04 1.220078e-04 0.410544781 #> 1362   680.5 0.5895367 0.5895367 1.217116e-04 1.217116e-04 0.410341591 #> 1363   681.0 0.5897401 0.5897401 1.214167e-04 1.214167e-04 0.410138497 #> 1364   681.5 0.5899434 0.5899434 1.211231e-04 1.211231e-04 0.409935499 #> 1365   682.0 0.5901466 0.5901466 1.208309e-04 1.208309e-04 0.409732599 #> 1366   682.5 0.5903497 0.5903497 1.205400e-04 1.205400e-04 0.409529795 #> 1367   683.0 0.5905527 0.5905527 1.202504e-04 1.202504e-04 0.409327087 #> 1368   683.5 0.5907556 0.5907556 1.199621e-04 1.199621e-04 0.409124476 #> 1369   684.0 0.5909584 0.5909584 1.196751e-04 1.196751e-04 0.408921961 #> 1370   684.5 0.5911611 0.5911611 1.193894e-04 1.193894e-04 0.408719543 #> 1371   685.0 0.5913637 0.5913637 1.191050e-04 1.191050e-04 0.408517221 #> 1372   685.5 0.5915662 0.5915662 1.188219e-04 1.188219e-04 0.408314996 #> 1373   686.0 0.5917686 0.5917686 1.185400e-04 1.185400e-04 0.408112867 #> 1374   686.5 0.5919709 0.5919709 1.182594e-04 1.182594e-04 0.407910834 #> 1375   687.0 0.5921731 0.5921731 1.179801e-04 1.179801e-04 0.407708898 #> 1376   687.5 0.5923752 0.5923752 1.177020e-04 1.177020e-04 0.407507058 #> 1377   688.0 0.5925773 0.5925773 1.174252e-04 1.174252e-04 0.407305314 #> 1378   688.5 0.5927792 0.5927792 1.171496e-04 1.171496e-04 0.407103667 #> 1379   689.0 0.5929810 0.5929810 1.168752e-04 1.168752e-04 0.406902116 #> 1380   689.5 0.5931827 0.5931827 1.166021e-04 1.166021e-04 0.406700661 #> 1381   690.0 0.5933844 0.5933844 1.163302e-04 1.163302e-04 0.406499302 #> 1382   690.5 0.5935859 0.5935859 1.160595e-04 1.160595e-04 0.406298039 #> 1383   691.0 0.5937873 0.5937873 1.157900e-04 1.157900e-04 0.406096872 #> 1384   691.5 0.5939887 0.5939887 1.155218e-04 1.155218e-04 0.405895802 #> 1385   692.0 0.5941899 0.5941899 1.152547e-04 1.152547e-04 0.405694827 #> 1386   692.5 0.5943911 0.5943911 1.149889e-04 1.149889e-04 0.405493949 #> 1387   693.0 0.5945921 0.5945921 1.147242e-04 1.147242e-04 0.405293166 #> 1388   693.5 0.5947931 0.5947931 1.144607e-04 1.144607e-04 0.405092480 #> 1389   694.0 0.5949939 0.5949939 1.141984e-04 1.141984e-04 0.404891889 #> 1390   694.5 0.5951947 0.5951947 1.139373e-04 1.139373e-04 0.404691395 #> 1391   695.0 0.5953953 0.5953953 1.136773e-04 1.136773e-04 0.404490996 #> 1392   695.5 0.5955959 0.5955959 1.134185e-04 1.134185e-04 0.404290693 #> 1393   696.0 0.5957964 0.5957964 1.131608e-04 1.131608e-04 0.404090486 #> 1394   696.5 0.5959967 0.5959967 1.129043e-04 1.129043e-04 0.403890374 #> 1395   697.0 0.5961970 0.5961970 1.126490e-04 1.126490e-04 0.403690359 #> 1396   697.5 0.5963972 0.5963972 1.123948e-04 1.123948e-04 0.403490439 #> 1397   698.0 0.5965972 0.5965972 1.121417e-04 1.121417e-04 0.403290615 #> 1398   698.5 0.5967972 0.5967972 1.118897e-04 1.118897e-04 0.403090886 #> 1399   699.0 0.5969971 0.5969971 1.116389e-04 1.116389e-04 0.402891254 #> 1400   699.5 0.5971969 0.5971969 1.113892e-04 1.113892e-04 0.402691716 #> 1401   700.0 0.5973966 0.5973966 1.111406e-04 1.111406e-04 0.402492275 #> 1402   700.5 0.5975962 0.5975962 1.108931e-04 1.108931e-04 0.402292929 #> 1403   701.0 0.5977957 0.5977957 1.106467e-04 1.106467e-04 0.402093678 #> 1404   701.5 0.5979951 0.5979951 1.104015e-04 1.104015e-04 0.401894523 #> 1405   702.0 0.5981944 0.5981944 1.101573e-04 1.101573e-04 0.401695464 #> 1406   702.5 0.5983936 0.5983936 1.099142e-04 1.099142e-04 0.401496500 #> 1407   703.0 0.5985927 0.5985927 1.096721e-04 1.096721e-04 0.401297631 #> 1408   703.5 0.5987917 0.5987917 1.094312e-04 1.094312e-04 0.401098858 #> 1409   704.0 0.5989906 0.5989906 1.091913e-04 1.091913e-04 0.400900180 #> 1410   704.5 0.5991895 0.5991895 1.089525e-04 1.089525e-04 0.400701597 #> 1411   705.0 0.5993882 0.5993882 1.087148e-04 1.087148e-04 0.400503110 #> 1412   705.5 0.5995868 0.5995868 1.084781e-04 1.084781e-04 0.400304718 #> 1413   706.0 0.5997853 0.5997853 1.082425e-04 1.082425e-04 0.400106421 #> 1414   706.5 0.5999838 0.5999838 1.080079e-04 1.080079e-04 0.399908220 #> 1415   707.0 0.6001821 0.6001821 1.077743e-04 1.077743e-04 0.399710113 #> 1416   707.5 0.6003804 0.6003804 1.075418e-04 1.075418e-04 0.399512102 #> 1417   708.0 0.6005785 0.6005785 1.073104e-04 1.073104e-04 0.399314186 #> 1418   708.5 0.6007766 0.6007766 1.070799e-04 1.070799e-04 0.399116365 #> 1419   709.0 0.6009745 0.6009745 1.068505e-04 1.068505e-04 0.398918639 #> 1420   709.5 0.6011724 0.6011724 1.066221e-04 1.066221e-04 0.398721008 #> 1421   710.0 0.6013701 0.6013701 1.063947e-04 1.063947e-04 0.398523472 #> 1422   710.5 0.6015678 0.6015678 1.061684e-04 1.061684e-04 0.398326031 #> 1423   711.0 0.6017654 0.6017654 1.059430e-04 1.059430e-04 0.398128685 #> 1424   711.5 0.6019628 0.6019628 1.057186e-04 1.057186e-04 0.397931434 #> 1425   712.0 0.6021602 0.6021602 1.054952e-04 1.054952e-04 0.397734277 #> 1426   712.5 0.6023575 0.6023575 1.052729e-04 1.052729e-04 0.397537216 #> 1427   713.0 0.6025547 0.6025547 1.050515e-04 1.050515e-04 0.397340249 #> 1428   713.5 0.6027518 0.6027518 1.048311e-04 1.048311e-04 0.397143377 #> 1429   714.0 0.6029488 0.6029488 1.046116e-04 1.046116e-04 0.396946600 #> 1430   714.5 0.6031457 0.6031457 1.043932e-04 1.043932e-04 0.396749918 #> 1431   715.0 0.6033425 0.6033425 1.041757e-04 1.041757e-04 0.396553330 #> 1432   715.5 0.6035392 0.6035392 1.039591e-04 1.039591e-04 0.396356837 #> 1433   716.0 0.6037358 0.6037358 1.037436e-04 1.037436e-04 0.396160439 #> 1434   716.5 0.6039323 0.6039323 1.035290e-04 1.035290e-04 0.395964135 #> 1435   717.0 0.6041288 0.6041288 1.033153e-04 1.033153e-04 0.395767925 #> 1436   717.5 0.6043251 0.6043251 1.031026e-04 1.031026e-04 0.395571811 #> 1437   718.0 0.6045213 0.6045213 1.028908e-04 1.028908e-04 0.395375790 #> 1438   718.5 0.6047175 0.6047175 1.026800e-04 1.026800e-04 0.395179864 #> 1439   719.0 0.6049135 0.6049135 1.024701e-04 1.024701e-04 0.394984033 #> 1440   719.5 0.6051094 0.6051094 1.022611e-04 1.022611e-04 0.394788296 #> 1441   720.0 0.6053053 0.6053053 1.020531e-04 1.020531e-04 0.394592653 #> 1442   720.5 0.6055010 0.6055010 1.018460e-04 1.018460e-04 0.394397105 #> 1443   721.0 0.6056967 0.6056967 1.016398e-04 1.016398e-04 0.394201651 #> 1444   721.5 0.6058923 0.6058923 1.014345e-04 1.014345e-04 0.394006292 #> 1445   722.0 0.6060877 0.6060877 1.012301e-04 1.012301e-04 0.393811026 #> 1446   722.5 0.6062831 0.6062831 1.010266e-04 1.010266e-04 0.393615855 #> 1447   723.0 0.6064784 0.6064784 1.008241e-04 1.008241e-04 0.393420778 #> 1448   723.5 0.6066736 0.6066736 1.006224e-04 1.006224e-04 0.393225795 #> 1449   724.0 0.6068687 0.6068687 1.004216e-04 1.004216e-04 0.393030906 #> 1450   724.5 0.6070637 0.6070637 1.002217e-04 1.002217e-04 0.392836111 #> 1451   725.0 0.6072586 0.6072586 1.000227e-04 1.000227e-04 0.392641411 #> 1452   725.5 0.6074534 0.6074534 9.982458e-05 9.982458e-05 0.392446804 #> 1453   726.0 0.6076481 0.6076481 9.962733e-05 9.962733e-05 0.392252291 #> 1454   726.5 0.6078427 0.6078427 9.943095e-05 9.943095e-05 0.392057873 #> 1455   727.0 0.6080372 0.6080372 9.923545e-05 9.923545e-05 0.391863548 #> 1456   727.5 0.6082316 0.6082316 9.904081e-05 9.904081e-05 0.391669317 #> 1457   728.0 0.6084260 0.6084260 9.884704e-05 9.884704e-05 0.391475180 #> 1458   728.5 0.6086202 0.6086202 9.865412e-05 9.865412e-05 0.391281137 #> 1459   729.0 0.6088144 0.6088144 9.846205e-05 9.846205e-05 0.391087187 #> 1460   729.5 0.6090084 0.6090084 9.827084e-05 9.827084e-05 0.390893331 #> 1461   730.0 0.6092024 0.6092024 9.808048e-05 9.808048e-05 0.390699569 #> 1462   730.5 0.6093962 0.6093962 9.789095e-05 9.789095e-05 0.390505901 #> 1463   731.0 0.6095900 0.6095900 9.770227e-05 9.770227e-05 0.390312327 #> 1464   731.5 0.6097836 0.6097836 9.751442e-05 9.751442e-05 0.390118846 #> 1465   732.0 0.6099772 0.6099772 9.732741e-05 9.732741e-05 0.389925458 #> 1466   732.5 0.6101707 0.6101707 9.714122e-05 9.714122e-05 0.389732164 #> 1467   733.0 0.6103641 0.6103641 9.695586e-05 9.695586e-05 0.389538964 #> 1468   733.5 0.6105574 0.6105574 9.677132e-05 9.677132e-05 0.389345857 #> 1469   734.0 0.6107506 0.6107506 9.658760e-05 9.658760e-05 0.389152844 #> 1470   734.5 0.6109437 0.6109437 9.640469e-05 9.640469e-05 0.388959924 #> 1471   735.0 0.6111367 0.6111367 9.622260e-05 9.622260e-05 0.388767098 #> 1472   735.5 0.6113296 0.6113296 9.604131e-05 9.604131e-05 0.388574364 #> 1473   736.0 0.6115224 0.6115224 9.586082e-05 9.586082e-05 0.388381725 #> 1474   736.5 0.6117151 0.6117151 9.568114e-05 9.568114e-05 0.388189178 #> 1475   737.0 0.6119078 0.6119078 9.550225e-05 9.550225e-05 0.387996725 #> 1476   737.5 0.6121003 0.6121003 9.532415e-05 9.532415e-05 0.387804365 #> 1477   738.0 0.6122928 0.6122928 9.514685e-05 9.514685e-05 0.387612098 #> 1478   738.5 0.6124851 0.6124851 9.497033e-05 9.497033e-05 0.387419924 #> 1479   739.0 0.6126774 0.6126774 9.479460e-05 9.479460e-05 0.387227844 #> 1480   739.5 0.6128695 0.6128695 9.461964e-05 9.461964e-05 0.387035856 #> 1481   740.0 0.6130616 0.6130616 9.444546e-05 9.444546e-05 0.386843962 #> 1482   740.5 0.6132536 0.6132536 9.427206e-05 9.427206e-05 0.386652160 #> 1483   741.0 0.6134454 0.6134454 9.409942e-05 9.409942e-05 0.386460452 #> 1484   741.5 0.6136372 0.6136372 9.392755e-05 9.392755e-05 0.386268836 #> 1485   742.0 0.6138289 0.6138289 9.375645e-05 9.375645e-05 0.386077314 #> 1486   742.5 0.6140205 0.6140205 9.358610e-05 9.358610e-05 0.385885884 #> 1487   743.0 0.6142120 0.6142120 9.341651e-05 9.341651e-05 0.385694548 #> 1488   743.5 0.6144034 0.6144034 9.324768e-05 9.324768e-05 0.385503304 #> 1489   744.0 0.6145948 0.6145948 9.307959e-05 9.307959e-05 0.385312153 #> 1490   744.5 0.6147860 0.6147860 9.291225e-05 9.291225e-05 0.385121094 #> 1491   745.0 0.6149771 0.6149771 9.274566e-05 9.274566e-05 0.384930128 #> 1492   745.5 0.6151682 0.6151682 9.257981e-05 9.257981e-05 0.384739256 #> 1493   746.0 0.6153591 0.6153591 9.241469e-05 9.241469e-05 0.384548475 #> 1494   746.5 0.6155500 0.6155500 9.225031e-05 9.225031e-05 0.384357788 #> 1495   747.0 0.6157407 0.6157407 9.208667e-05 9.208667e-05 0.384167192 #> 1496   747.5 0.6159314 0.6159314 9.192375e-05 9.192375e-05 0.383976690 #> 1497   748.0 0.6161220 0.6161220 9.176155e-05 9.176155e-05 0.383786280 #> 1498   748.5 0.6163124 0.6163124 9.160008e-05 9.160008e-05 0.383595962 #> 1499   749.0 0.6165028 0.6165028 9.143933e-05 9.143933e-05 0.383405737 #> 1500   749.5 0.6166931 0.6166931 9.127929e-05 9.127929e-05 0.383215605 #> 1501   750.0 0.6168833 0.6168833 9.111997e-05 9.111997e-05 0.383025564 #> 1502   750.5 0.6170734 0.6170734 9.096136e-05 9.096136e-05 0.382835616 #> 1503   751.0 0.6172634 0.6172634 9.080345e-05 9.080345e-05 0.382645761 #> 1504   751.5 0.6174534 0.6174534 9.064626e-05 9.064626e-05 0.382455997 #> 1505   752.0 0.6176432 0.6176432 9.048976e-05 9.048976e-05 0.382266326 #> 1506   752.5 0.6178329 0.6178329 9.033396e-05 9.033396e-05 0.382076747 #> 1507   753.0 0.6180226 0.6180226 9.017886e-05 9.017886e-05 0.381887261 #> 1508   753.5 0.6182121 0.6182121 9.002445e-05 9.002445e-05 0.381697866 #> 1509   754.0 0.6184016 0.6184016 8.987073e-05 8.987073e-05 0.381508564 #> 1510   754.5 0.6185909 0.6185909 8.971769e-05 8.971769e-05 0.381319353 #> 1511   755.0 0.6187802 0.6187802 8.956534e-05 8.956534e-05 0.381130235 #> 1512   755.5 0.6189694 0.6189694 8.941368e-05 8.941368e-05 0.380941208 #> 1513   756.0 0.6191585 0.6191585 8.926269e-05 8.926269e-05 0.380752274 #> 1514   756.5 0.6193475 0.6193475 8.911238e-05 8.911238e-05 0.380563432 #> 1515   757.0 0.6195364 0.6195364 8.896274e-05 8.896274e-05 0.380374681 #> 1516   757.5 0.6197252 0.6197252 8.881377e-05 8.881377e-05 0.380186022 #> 1517   758.0 0.6199139 0.6199139 8.866547e-05 8.866547e-05 0.379997455 #> 1518   758.5 0.6201025 0.6201025 8.851784e-05 8.851784e-05 0.379808980 #> 1519   759.0 0.6202910 0.6202910 8.837087e-05 8.837087e-05 0.379620597 #> 1520   759.5 0.6204795 0.6204795 8.822455e-05 8.822455e-05 0.379432305 #> 1521   760.0 0.6206678 0.6206678 8.807890e-05 8.807890e-05 0.379244105 #> 1522   760.5 0.6208561 0.6208561 8.793390e-05 8.793390e-05 0.379055997 #> 1523   761.0 0.6210442 0.6210442 8.778955e-05 8.778955e-05 0.378867981 #> 1524   761.5 0.6212323 0.6212323 8.764585e-05 8.764585e-05 0.378680056 #> 1525   762.0 0.6214203 0.6214203 8.750280e-05 8.750280e-05 0.378492222 #> 1526   762.5 0.6216082 0.6216082 8.736039e-05 8.736039e-05 0.378304480 #> 1527   763.0 0.6217960 0.6217960 8.721862e-05 8.721862e-05 0.378116830 #> 1528   763.5 0.6219837 0.6219837 8.707749e-05 8.707749e-05 0.377929271 #> 1529   764.0 0.6221713 0.6221713 8.693700e-05 8.693700e-05 0.377741803 #> 1530   764.5 0.6223588 0.6223588 8.679714e-05 8.679714e-05 0.377554427 #> 1531   765.0 0.6225462 0.6225462 8.665791e-05 8.665791e-05 0.377367142 #> 1532   765.5 0.6227335 0.6227335 8.651931e-05 8.651931e-05 0.377179948 #> 1533   766.0 0.6229208 0.6229208 8.638133e-05 8.638133e-05 0.376992846 #> 1534   766.5 0.6231079 0.6231079 8.624398e-05 8.624398e-05 0.376805835 #> 1535   767.0 0.6232950 0.6232950 8.610726e-05 8.610726e-05 0.376618915 #> 1536   767.5 0.6234819 0.6234819 8.597115e-05 8.597115e-05 0.376432086 #> 1537   768.0 0.6236688 0.6236688 8.583565e-05 8.583565e-05 0.376245348 #> 1538   768.5 0.6238556 0.6238556 8.570077e-05 8.570077e-05 0.376058702 #> 1539   769.0 0.6240423 0.6240423 8.556650e-05 8.556650e-05 0.375872146 #> 1540   769.5 0.6242289 0.6242289 8.543284e-05 8.543284e-05 0.375685682 #> 1541   770.0 0.6244154 0.6244154 8.529979e-05 8.529979e-05 0.375499308 #> 1542   770.5 0.6246018 0.6246018 8.516734e-05 8.516734e-05 0.375313026 #> 1543   771.0 0.6247881 0.6247881 8.503549e-05 8.503549e-05 0.375126834 #> 1544   771.5 0.6249744 0.6249744 8.490425e-05 8.490425e-05 0.374940733 #> 1545   772.0 0.6251605 0.6251605 8.477360e-05 8.477360e-05 0.374754724 #> 1546   772.5 0.6253466 0.6253466 8.464354e-05 8.464354e-05 0.374568804 #> 1547   773.0 0.6255325 0.6255325 8.451408e-05 8.451408e-05 0.374382976 #> 1548   773.5 0.6257184 0.6257184 8.438521e-05 8.438521e-05 0.374197239 #> 1549   774.0 0.6259042 0.6259042 8.425692e-05 8.425692e-05 0.374011592 #> 1550   774.5 0.6260898 0.6260898 8.412922e-05 8.412922e-05 0.373826036 #> 1551   775.0 0.6262754 0.6262754 8.400211e-05 8.400211e-05 0.373640570 #> 1552   775.5 0.6264609 0.6264609 8.387557e-05 8.387557e-05 0.373455195 #> 1553   776.0 0.6266463 0.6266463 8.374962e-05 8.374962e-05 0.373269911 #> 1554   776.5 0.6268317 0.6268317 8.362424e-05 8.362424e-05 0.373084717 #> 1555   777.0 0.6270169 0.6270169 8.349943e-05 8.349943e-05 0.372899613 #> 1556   777.5 0.6272020 0.6272020 8.337520e-05 8.337520e-05 0.372714601 #> 1557   778.0 0.6273871 0.6273871 8.325154e-05 8.325154e-05 0.372529678 #> 1558   778.5 0.6275720 0.6275720 8.312844e-05 8.312844e-05 0.372344846 #> 1559   779.0 0.6277569 0.6277569 8.300591e-05 8.300591e-05 0.372160104 #> 1560   779.5 0.6279417 0.6279417 8.288395e-05 8.288395e-05 0.371975453 #> 1561   780.0 0.6281263 0.6281263 8.276255e-05 8.276255e-05 0.371790891 #> 1562   780.5 0.6283109 0.6283109 8.264170e-05 8.264170e-05 0.371606420 #> 1563   781.0 0.6284954 0.6284954 8.252141e-05 8.252141e-05 0.371422040 #> 1564   781.5 0.6286798 0.6286798 8.240168e-05 8.240168e-05 0.371237749 #> 1565   782.0 0.6288642 0.6288642 8.228250e-05 8.228250e-05 0.371053549 #> 1566   782.5 0.6290484 0.6290484 8.216387e-05 8.216387e-05 0.370869438 #> 1567   783.0 0.6292325 0.6292325 8.204580e-05 8.204580e-05 0.370685418 #> 1568   783.5 0.6294166 0.6294166 8.192826e-05 8.192826e-05 0.370501488 #> 1569   784.0 0.6296005 0.6296005 8.181127e-05 8.181127e-05 0.370317647 #> 1570   784.5 0.6297844 0.6297844 8.169483e-05 8.169483e-05 0.370133897 #> 1571   785.0 0.6299682 0.6299682 8.157892e-05 8.157892e-05 0.369950237 #> 1572   785.5 0.6301519 0.6301519 8.146356e-05 8.146356e-05 0.369766666 #> 1573   786.0 0.6303355 0.6303355 8.134873e-05 8.134873e-05 0.369583185 #> 1574   786.5 0.6305190 0.6305190 8.123443e-05 8.123443e-05 0.369399795 #> 1575   787.0 0.6307024 0.6307024 8.112067e-05 8.112067e-05 0.369216494 #> 1576   787.5 0.6308857 0.6308857 8.100744e-05 8.100744e-05 0.369033282 #> 1577   788.0 0.6310689 0.6310689 8.089474e-05 8.089474e-05 0.368850160 #> 1578   788.5 0.6312521 0.6312521 8.078256e-05 8.078256e-05 0.368667128 #> 1579   789.0 0.6314351 0.6314351 8.067091e-05 8.067091e-05 0.368484186 #> 1580   789.5 0.6316181 0.6316181 8.055978e-05 8.055978e-05 0.368301333 #> 1581   790.0 0.6318010 0.6318010 8.044917e-05 8.044917e-05 0.368118570 #> 1582   790.5 0.6319838 0.6319838 8.033909e-05 8.033909e-05 0.367935896 #> 1583   791.0 0.6321665 0.6321665 8.022952e-05 8.022952e-05 0.367753312 #> 1584   791.5 0.6323491 0.6323491 8.012046e-05 8.012046e-05 0.367570817 #> 1585   792.0 0.6325316 0.6325316 8.001192e-05 8.001192e-05 0.367388412 #> 1586   792.5 0.6327140 0.6327140 7.990389e-05 7.990389e-05 0.367206096 #> 1587   793.0 0.6328963 0.6328963 7.979637e-05 7.979637e-05 0.367023869 #> 1588   793.5 0.6330786 0.6330786 7.968935e-05 7.968935e-05 0.366841732 #> 1589   794.0 0.6332607 0.6332607 7.958284e-05 7.958284e-05 0.366659684 #> 1590   794.5 0.6334428 0.6334428 7.947684e-05 7.947684e-05 0.366477725 #> 1591   795.0 0.6336248 0.6336248 7.937134e-05 7.937134e-05 0.366295855 #> 1592   795.5 0.6338067 0.6338067 7.926634e-05 7.926634e-05 0.366114075 #> 1593   796.0 0.6339885 0.6339885 7.916184e-05 7.916184e-05 0.365932384 #> 1594   796.5 0.6341702 0.6341702 7.905784e-05 7.905784e-05 0.365750781 #> 1595   797.0 0.6343518 0.6343518 7.895433e-05 7.895433e-05 0.365569268 #> 1596   797.5 0.6345333 0.6345333 7.885131e-05 7.885131e-05 0.365387844 #> 1597   798.0 0.6347147 0.6347147 7.874879e-05 7.874879e-05 0.365206508 #> 1598   798.5 0.6348961 0.6348961 7.864675e-05 7.864675e-05 0.365025262 #> 1599   799.0 0.6350774 0.6350774 7.854521e-05 7.854521e-05 0.364844105 #> 1600   799.5 0.6352585 0.6352585 7.844415e-05 7.844415e-05 0.364663036 #> 1601   800.0 0.6354396 0.6354396 7.834357e-05 7.834357e-05 0.364482057 #> 1602   800.5 0.6356206 0.6356206 7.824348e-05 7.824348e-05 0.364301166 #> 1603   801.0 0.6358015 0.6358015 7.814387e-05 7.814387e-05 0.364120363 #> 1604   801.5 0.6359823 0.6359823 7.804474e-05 7.804474e-05 0.363939650 #> 1605   802.0 0.6361630 0.6361630 7.794609e-05 7.794609e-05 0.363759025 #> 1606   802.5 0.6363437 0.6363437 7.784791e-05 7.784791e-05 0.363578489 #> 1607   803.0 0.6365242 0.6365242 7.775021e-05 7.775021e-05 0.363398042 #> 1608   803.5 0.6367047 0.6367047 7.765298e-05 7.765298e-05 0.363217683 #> 1609   804.0 0.6368850 0.6368850 7.755622e-05 7.755622e-05 0.363037412 #> 1610   804.5 0.6370653 0.6370653 7.745994e-05 7.745994e-05 0.362857231 #> 1611   805.0 0.6372455 0.6372455 7.736412e-05 7.736412e-05 0.362677137 #> 1612   805.5 0.6374256 0.6374256 7.726877e-05 7.726877e-05 0.362497132 #> 1613   806.0 0.6376056 0.6376056 7.717388e-05 7.717388e-05 0.362317216 #> 1614   806.5 0.6377855 0.6377855 7.707945e-05 7.707945e-05 0.362137387 #> 1615   807.0 0.6379654 0.6379654 7.698549e-05 7.698549e-05 0.361957648 #> 1616   807.5 0.6381451 0.6381451 7.689199e-05 7.689199e-05 0.361777996 #> 1617   808.0 0.6383248 0.6383248 7.679894e-05 7.679894e-05 0.361598433 #> 1618   808.5 0.6385043 0.6385043 7.670636e-05 7.670636e-05 0.361418958 #> 1619   809.0 0.6386838 0.6386838 7.661423e-05 7.661423e-05 0.361239571 #> 1620   809.5 0.6388632 0.6388632 7.652255e-05 7.652255e-05 0.361060272 #> 1621   810.0 0.6390425 0.6390425 7.643132e-05 7.643132e-05 0.360881061 #> 1622   810.5 0.6392217 0.6392217 7.634055e-05 7.634055e-05 0.360701938 #> 1623   811.0 0.6394008 0.6394008 7.625022e-05 7.625022e-05 0.360522904 #> 1624   811.5 0.6395799 0.6395799 7.616035e-05 7.616035e-05 0.360343957 #> 1625   812.0 0.6397588 0.6397588 7.607092e-05 7.607092e-05 0.360165098 #> 1626   812.5 0.6399377 0.6399377 7.598193e-05 7.598193e-05 0.359986328 #> 1627   813.0 0.6401165 0.6401165 7.589339e-05 7.589339e-05 0.359807645 #> 1628   813.5 0.6402951 0.6402951 7.580529e-05 7.580529e-05 0.359629050 #> 1629   814.0 0.6404737 0.6404737 7.571763e-05 7.571763e-05 0.359450543 #> 1630   814.5 0.6406522 0.6406522 7.563041e-05 7.563041e-05 0.359272123 #> 1631   815.0 0.6408307 0.6408307 7.554362e-05 7.554362e-05 0.359093792 #> 1632   815.5 0.6410090 0.6410090 7.545727e-05 7.545727e-05 0.358915548 #> 1633   816.0 0.6411872 0.6411872 7.537136e-05 7.537136e-05 0.358737391 #> 1634   816.5 0.6413654 0.6413654 7.528588e-05 7.528588e-05 0.358559323 #> 1635   817.0 0.6415435 0.6415435 7.520083e-05 7.520083e-05 0.358381341 #> 1636   817.5 0.6417214 0.6417214 7.511622e-05 7.511622e-05 0.358203448 #> 1637   818.0 0.6418993 0.6418993 7.503203e-05 7.503203e-05 0.358025642 #> 1638   818.5 0.6420771 0.6420771 7.494827e-05 7.494827e-05 0.357847923 #> 1639   819.0 0.6422548 0.6422548 7.486493e-05 7.486493e-05 0.357670292 #> 1640   819.5 0.6424325 0.6424325 7.478202e-05 7.478202e-05 0.357492748 #> 1641   820.0 0.6426100 0.6426100 7.469953e-05 7.469953e-05 0.357315292 #> 1642   820.5 0.6427875 0.6427875 7.461747e-05 7.461747e-05 0.357137923 #> 1643   821.0 0.6429648 0.6429648 7.453583e-05 7.453583e-05 0.356960641 #> 1644   821.5 0.6431421 0.6431421 7.445460e-05 7.445460e-05 0.356783447 #> 1645   822.0 0.6433193 0.6433193 7.437380e-05 7.437380e-05 0.356606340 #> 1646   822.5 0.6434964 0.6434964 7.429341e-05 7.429341e-05 0.356429320 #> 1647   823.0 0.6436734 0.6436734 7.421343e-05 7.421343e-05 0.356252387 #> 1648   823.5 0.6438503 0.6438503 7.413387e-05 7.413387e-05 0.356075541 #> 1649   824.0 0.6440272 0.6440272 7.405472e-05 7.405472e-05 0.355898783 #> 1650   824.5 0.6442039 0.6442039 7.397599e-05 7.397599e-05 0.355722111 #> 1651   825.0 0.6443806 0.6443806 7.389766e-05 7.389766e-05 0.355545526 #> 1652   825.5 0.6445572 0.6445572 7.381975e-05 7.381975e-05 0.355369029 #> 1653   826.0 0.6447336 0.6447336 7.374224e-05 7.374224e-05 0.355192618 #> 1654   826.5 0.6449100 0.6449100 7.366513e-05 7.366513e-05 0.355016294 #> 1655   827.0 0.6450864 0.6450864 7.358844e-05 7.358844e-05 0.354840057 #> 1656   827.5 0.6452626 0.6452626 7.351214e-05 7.351214e-05 0.354663907 #> 1657   828.0 0.6454387 0.6454387 7.343625e-05 7.343625e-05 0.354487844 #> 1658   828.5 0.6456148 0.6456148 7.336076e-05 7.336076e-05 0.354311867 #> 1659   829.0 0.6457907 0.6457907 7.328567e-05 7.328567e-05 0.354135977 #> 1660   829.5 0.6459666 0.6459666 7.321098e-05 7.321098e-05 0.353960174 #> 1661   830.0 0.6461424 0.6461424 7.313669e-05 7.313669e-05 0.353784457 #> 1662   830.5 0.6463181 0.6463181 7.306279e-05 7.306279e-05 0.353608827 #> 1663   831.0 0.6464937 0.6464937 7.298929e-05 7.298929e-05 0.353433284 #> 1664   831.5 0.6466693 0.6466693 7.291618e-05 7.291618e-05 0.353257827 #> 1665   832.0 0.6468447 0.6468447 7.284346e-05 7.284346e-05 0.353082457 #> 1666   832.5 0.6470201 0.6470201 7.277114e-05 7.277114e-05 0.352907173 #> 1667   833.0 0.6471953 0.6471953 7.269921e-05 7.269921e-05 0.352731975 #> 1668   833.5 0.6473705 0.6473705 7.262766e-05 7.262766e-05 0.352556864 #> 1669   834.0 0.6475456 0.6475456 7.255651e-05 7.255651e-05 0.352381839 #> 1670   834.5 0.6477206 0.6477206 7.248574e-05 7.248574e-05 0.352206901 #> 1671   835.0 0.6478955 0.6478955 7.241535e-05 7.241535e-05 0.352032048 #> 1672   835.5 0.6480704 0.6480704 7.234536e-05 7.234536e-05 0.351857282 #> 1673   836.0 0.6482451 0.6482451 7.227574e-05 7.227574e-05 0.351682603 #> 1674   836.5 0.6484198 0.6484198 7.220651e-05 7.220651e-05 0.351508009 #> 1675   837.0 0.6485944 0.6485944 7.213766e-05 7.213766e-05 0.351333501 #> 1676   837.5 0.6487689 0.6487689 7.206919e-05 7.206919e-05 0.351159080 #> 1677   838.0 0.6489433 0.6489433 7.200109e-05 7.200109e-05 0.350984745 #> 1678   838.5 0.6491176 0.6491176 7.193338e-05 7.193338e-05 0.350810495 #> 1679   839.0 0.6492918 0.6492918 7.186604e-05 7.186604e-05 0.350636332 #> 1680   839.5 0.6494659 0.6494659 7.179908e-05 7.179908e-05 0.350462254 #> 1681   840.0 0.6496400 0.6496400 7.173249e-05 7.173249e-05 0.350288263 #> 1682   840.5 0.6498140 0.6498140 7.166628e-05 7.166628e-05 0.350114357 #> 1683   841.0 0.6499879 0.6499879 7.160044e-05 7.160044e-05 0.349940537 #> 1684   841.5 0.6501617 0.6501617 7.153497e-05 7.153497e-05 0.349766803 #> 1685   842.0 0.6503354 0.6503354 7.146987e-05 7.146987e-05 0.349593155 #> 1686   842.5 0.6505090 0.6505090 7.140514e-05 7.140514e-05 0.349419593 #> 1687   843.0 0.6506825 0.6506825 7.134077e-05 7.134077e-05 0.349246116 #> 1688   843.5 0.6508560 0.6508560 7.127678e-05 7.127678e-05 0.349072724 #> 1689   844.0 0.6510294 0.6510294 7.121315e-05 7.121315e-05 0.348899419 #> 1690   844.5 0.6512027 0.6512027 7.114989e-05 7.114989e-05 0.348726199 #> 1691   845.0 0.6513758 0.6513758 7.108699e-05 7.108699e-05 0.348553064 #> 1692   845.5 0.6515490 0.6515490 7.102445e-05 7.102445e-05 0.348380015 #> 1693   846.0 0.6517220 0.6517220 7.096228e-05 7.096228e-05 0.348207052 #> 1694   846.5 0.6518949 0.6518949 7.090046e-05 7.090046e-05 0.348034174 #> 1695   847.0 0.6520678 0.6520678 7.083901e-05 7.083901e-05 0.347861381 #> 1696   847.5 0.6522405 0.6522405 7.077791e-05 7.077791e-05 0.347688674 #> 1697   848.0 0.6524132 0.6524132 7.071718e-05 7.071718e-05 0.347516052 #> 1698   848.5 0.6525858 0.6525858 7.065680e-05 7.065680e-05 0.347343515 #> 1699   849.0 0.6527583 0.6527583 7.059678e-05 7.059678e-05 0.347171063 #> 1700   849.5 0.6529308 0.6529308 7.053711e-05 7.053711e-05 0.346998697 #> 1701   850.0 0.6531031 0.6531031 7.047779e-05 7.047779e-05 0.346826416 #> 1702   850.5 0.6532754 0.6532754 7.041883e-05 7.041883e-05 0.346654220 #> 1703   851.0 0.6534475 0.6534475 7.036022e-05 7.036022e-05 0.346482109 #> 1704   851.5 0.6536196 0.6536196 7.030197e-05 7.030197e-05 0.346310083 #> 1705   852.0 0.6537916 0.6537916 7.024406e-05 7.024406e-05 0.346138142 #> 1706   852.5 0.6539635 0.6539635 7.018650e-05 7.018650e-05 0.345966286 #> 1707   853.0 0.6541354 0.6541354 7.012929e-05 7.012929e-05 0.345794515 #> 1708   853.5 0.6543071 0.6543071 7.007243e-05 7.007243e-05 0.345622829 #> 1709   854.0 0.6544788 0.6544788 7.001592e-05 7.001592e-05 0.345451228 #> 1710   854.5 0.6546503 0.6546503 6.995975e-05 6.995975e-05 0.345279712 #> 1711   855.0 0.6548218 0.6548218 6.990393e-05 6.990393e-05 0.345108281 #> 1712   855.5 0.6549932 0.6549932 6.984845e-05 6.984845e-05 0.344936934 #> 1713   856.0 0.6551645 0.6551645 6.979331e-05 6.979331e-05 0.344765672 #> 1714   856.5 0.6553358 0.6553358 6.973851e-05 6.973851e-05 0.344594495 #> 1715   857.0 0.6555069 0.6555069 6.968406e-05 6.968406e-05 0.344423402 #> 1716   857.5 0.6556780 0.6556780 6.962995e-05 6.962995e-05 0.344252394 #> 1717   858.0 0.6558490 0.6558490 6.957617e-05 6.957617e-05 0.344081471 #> 1718   858.5 0.6560198 0.6560198 6.952274e-05 6.952274e-05 0.343910632 #> 1719   859.0 0.6561907 0.6561907 6.946964e-05 6.946964e-05 0.343739877 #> 1720   859.5 0.6563614 0.6563614 6.941688e-05 6.941688e-05 0.343569207 #> 1721   860.0 0.6565320 0.6565320 6.936445e-05 6.936445e-05 0.343398622 #> 1722   860.5 0.6567026 0.6567026 6.931236e-05 6.931236e-05 0.343228121 #> 1723   861.0 0.6568730 0.6568730 6.926061e-05 6.926061e-05 0.343057704 #> 1724   861.5 0.6570434 0.6570434 6.920919e-05 6.920919e-05 0.342887372 #> 1725   862.0 0.6572137 0.6572137 6.915810e-05 6.915810e-05 0.342717124 #> 1726   862.5 0.6573839 0.6573839 6.910734e-05 6.910734e-05 0.342546960 #> 1727   863.0 0.6575541 0.6575541 6.905692e-05 6.905692e-05 0.342376880 #> 1728   863.5 0.6577241 0.6577241 6.900682e-05 6.900682e-05 0.342206885 #> 1729   864.0 0.6578941 0.6578941 6.895705e-05 6.895705e-05 0.342036974 #> 1730   864.5 0.6580639 0.6580639 6.890762e-05 6.890762e-05 0.341867147 #> 1731   865.0 0.6582337 0.6582337 6.885851e-05 6.885851e-05 0.341697404 #> 1732   865.5 0.6584034 0.6584034 6.880972e-05 6.880972e-05 0.341527745 #> 1733   866.0 0.6585731 0.6585731 6.876127e-05 6.876127e-05 0.341358169 #> 1734   866.5 0.6587426 0.6587426 6.871314e-05 6.871314e-05 0.341188678 #> 1735   867.0 0.6589121 0.6589121 6.866533e-05 6.866533e-05 0.341019271 #> 1736   867.5 0.6590814 0.6590814 6.861785e-05 6.861785e-05 0.340849948 #> 1737   868.0 0.6592507 0.6592507 6.857069e-05 6.857069e-05 0.340680709 #> 1738   868.5 0.6594199 0.6594199 6.852385e-05 6.852385e-05 0.340511553 #> 1739   869.0 0.6595890 0.6595890 6.847733e-05 6.847733e-05 0.340342481 #> 1740   869.5 0.6597581 0.6597581 6.843114e-05 6.843114e-05 0.340173493 #> 1741   870.0 0.6599270 0.6599270 6.838527e-05 6.838527e-05 0.340004589 #> 1742   870.5 0.6600959 0.6600959 6.833971e-05 6.833971e-05 0.339835768 #> 1743   871.0 0.6602647 0.6602647 6.829447e-05 6.829447e-05 0.339667031 #> 1744   871.5 0.6604334 0.6604334 6.824955e-05 6.824955e-05 0.339498377 #> 1745   872.0 0.6606020 0.6606020 6.820495e-05 6.820495e-05 0.339329807 #> 1746   872.5 0.6607705 0.6607705 6.816067e-05 6.816067e-05 0.339161321 #> 1747   873.0 0.6609390 0.6609390 6.811670e-05 6.811670e-05 0.338992918 #> 1748   873.5 0.6611073 0.6611073 6.807304e-05 6.807304e-05 0.338824599 #> 1749   874.0 0.6612756 0.6612756 6.802970e-05 6.802970e-05 0.338656363 #> 1750   874.5 0.6614438 0.6614438 6.798667e-05 6.798667e-05 0.338488210 #> 1751   875.0 0.6616119 0.6616119 6.794396e-05 6.794396e-05 0.338320141 #> 1752   875.5 0.6617799 0.6617799 6.790156e-05 6.790156e-05 0.338152155 #> 1753   876.0 0.6619479 0.6619479 6.785947e-05 6.785947e-05 0.337984252 #> 1754   876.5 0.6621157 0.6621157 6.781769e-05 6.781769e-05 0.337816432 #> 1755   877.0 0.6622835 0.6622835 6.777622e-05 6.777622e-05 0.337648696 #> 1756   877.5 0.6624512 0.6624512 6.773506e-05 6.773506e-05 0.337481043 #> 1757   878.0 0.6626188 0.6626188 6.769421e-05 6.769421e-05 0.337313473 #> 1758   878.5 0.6627864 0.6627864 6.765366e-05 6.765366e-05 0.337145986 #> 1759   879.0 0.6629538 0.6629538 6.761343e-05 6.761343e-05 0.336978582 #> 1760   879.5 0.6631212 0.6631212 6.757350e-05 6.757350e-05 0.336811261 #> 1761   880.0 0.6632884 0.6632884 6.753387e-05 6.753387e-05 0.336644023 #> 1762   880.5 0.6634556 0.6634556 6.749456e-05 6.749456e-05 0.336476868 #> 1763   881.0 0.6636227 0.6636227 6.745554e-05 6.745554e-05 0.336309796 #> 1764   881.5 0.6637898 0.6637898 6.741683e-05 6.741683e-05 0.336142807 #> 1765   882.0 0.6639567 0.6639567 6.737843e-05 6.737843e-05 0.335975900 #> 1766   882.5 0.6641236 0.6641236 6.734033e-05 6.734033e-05 0.335809077 #> 1767   883.0 0.6642904 0.6642904 6.730252e-05 6.730252e-05 0.335642336 #> 1768   883.5 0.6644571 0.6644571 6.726503e-05 6.726503e-05 0.335475678 #> 1769   884.0 0.6646237 0.6646237 6.722783e-05 6.722783e-05 0.335309102 #> 1770   884.5 0.6647902 0.6647902 6.719093e-05 6.719093e-05 0.335142610 #> 1771   885.0 0.6649566 0.6649566 6.715433e-05 6.715433e-05 0.334976200 #> 1772   885.5 0.6651230 0.6651230 6.711803e-05 6.711803e-05 0.334809872 #> 1773   886.0 0.6652893 0.6652893 6.708203e-05 6.708203e-05 0.334643627 #> 1774   886.5 0.6654555 0.6654555 6.704633e-05 6.704633e-05 0.334477464 #> 1775   887.0 0.6656216 0.6656216 6.701093e-05 6.701093e-05 0.334311384 #> 1776   887.5 0.6657876 0.6657876 6.697582e-05 6.697582e-05 0.334145387 #> 1777   888.0 0.6659536 0.6659536 6.694101e-05 6.694101e-05 0.333979471 #> 1778   888.5 0.6661195 0.6661195 6.690649e-05 6.690649e-05 0.333813639 #> 1779   889.0 0.6662852 0.6662852 6.687227e-05 6.687227e-05 0.333647888 #> 1780   889.5 0.6664509 0.6664509 6.683834e-05 6.683834e-05 0.333482220 #> 1781   890.0 0.6666166 0.6666166 6.680471e-05 6.680471e-05 0.333316634 #> 1782   890.5 0.6667821 0.6667821 6.677137e-05 6.677137e-05 0.333151130 #> 1783   891.0 0.6669476 0.6669476 6.673832e-05 6.673832e-05 0.332985708 #> 1784   891.5 0.6671129 0.6671129 6.670557e-05 6.670557e-05 0.332820369 #> 1785   892.0 0.6672782 0.6672782 6.667311e-05 6.667311e-05 0.332655111 #> 1786   892.5 0.6674434 0.6674434 6.664094e-05 6.664094e-05 0.332489936 #> 1787   893.0 0.6676085 0.6676085 6.660905e-05 6.660905e-05 0.332324843 #> 1788   893.5 0.6677736 0.6677736 6.657746e-05 6.657746e-05 0.332159832 #> 1789   894.0 0.6679386 0.6679386 6.654616e-05 6.654616e-05 0.331994902 #> 1790   894.5 0.6681034 0.6681034 6.651515e-05 6.651515e-05 0.331830055 #> 1791   895.0 0.6682682 0.6682682 6.648442e-05 6.648442e-05 0.331665289 #> 1792   895.5 0.6684329 0.6684329 6.645399e-05 6.645399e-05 0.331500606 #> 1793   896.0 0.6685976 0.6685976 6.642384e-05 6.642384e-05 0.331336004 #> 1794   896.5 0.6687621 0.6687621 6.639398e-05 6.639398e-05 0.331171484 #> 1795   897.0 0.6689266 0.6689266 6.636440e-05 6.636440e-05 0.331007046 #> 1796   897.5 0.6690910 0.6690910 6.633511e-05 6.633511e-05 0.330842689 #> 1797   898.0 0.6692553 0.6692553 6.630611e-05 6.630611e-05 0.330678414 #> 1798   898.5 0.6694195 0.6694195 6.627739e-05 6.627739e-05 0.330514221 #> 1799   899.0 0.6695836 0.6695836 6.624895e-05 6.624895e-05 0.330350109 #> 1800   899.5 0.6697477 0.6697477 6.622080e-05 6.622080e-05 0.330186079 #> 1801   900.0 0.6699117 0.6699117 6.619293e-05 6.619293e-05 0.330022130 #> 1802   900.5 0.6700756 0.6700756 6.616535e-05 6.616535e-05 0.329858263 #> 1803   901.0 0.6702394 0.6702394 6.613804e-05 6.613804e-05 0.329694478 #> 1804   901.5 0.6704031 0.6704031 6.611102e-05 6.611102e-05 0.329530773 #> 1805   902.0 0.6705668 0.6705668 6.608428e-05 6.608428e-05 0.329367150 #> 1806   902.5 0.6707303 0.6707303 6.605783e-05 6.605783e-05 0.329203609 #> 1807   903.0 0.6708938 0.6708938 6.603165e-05 6.603165e-05 0.329040149 #> 1808   903.5 0.6710572 0.6710572 6.600575e-05 6.600575e-05 0.328876770 #> 1809   904.0 0.6712205 0.6712205 6.598013e-05 6.598013e-05 0.328713472 #> 1810   904.5 0.6713838 0.6713838 6.595479e-05 6.595479e-05 0.328550256 #> 1811   905.0 0.6715469 0.6715469 6.592973e-05 6.592973e-05 0.328387120 #> 1812   905.5 0.6717100 0.6717100 6.590495e-05 6.590495e-05 0.328224066 #> 1813   906.0 0.6718730 0.6718730 6.588045e-05 6.588045e-05 0.328061093 #> 1814   906.5 0.6720359 0.6720359 6.585622e-05 6.585622e-05 0.327898201 #> 1815   907.0 0.6721988 0.6721988 6.583227e-05 6.583227e-05 0.327735390 #> 1816   907.5 0.6723615 0.6723615 6.580860e-05 6.580860e-05 0.327572660 #> 1817   908.0 0.6725242 0.6725242 6.578520e-05 6.578520e-05 0.327410011 #> 1818   908.5 0.6726868 0.6726868 6.576208e-05 6.576208e-05 0.327247443 #> 1819   909.0 0.6728493 0.6728493 6.573923e-05 6.573923e-05 0.327084956 #> 1820   909.5 0.6730117 0.6730117 6.571666e-05 6.571666e-05 0.326922549 #> 1821   910.0 0.6731741 0.6731741 6.569437e-05 6.569437e-05 0.326760224 #> 1822   910.5 0.6733363 0.6733363 6.567234e-05 6.567234e-05 0.326597979 #> 1823   911.0 0.6734985 0.6734985 6.565059e-05 6.565059e-05 0.326435815 #> 1824   911.5 0.6736606 0.6736606 6.562912e-05 6.562912e-05 0.326273731 #> 1825   912.0 0.6738227 0.6738227 6.560792e-05 6.560792e-05 0.326111729 #> 1826   912.5 0.6739846 0.6739846 6.558698e-05 6.558698e-05 0.325949807 #> 1827   913.0 0.6741465 0.6741465 6.556633e-05 6.556633e-05 0.325787965 #> 1828   913.5 0.6743082 0.6743082 6.554594e-05 6.554594e-05 0.325626204 #> 1829   914.0 0.6744700 0.6744700 6.552582e-05 6.552582e-05 0.325464524 #> 1830   914.5 0.6746316 0.6746316 6.550598e-05 6.550598e-05 0.325302924 #> 1831   915.0 0.6747931 0.6747931 6.548640e-05 6.548640e-05 0.325141404 #> 1832   915.5 0.6749546 0.6749546 6.546710e-05 6.546710e-05 0.324979965 #> 1833   916.0 0.6751159 0.6751159 6.544807e-05 6.544807e-05 0.324818607 #> 1834   916.5 0.6752772 0.6752772 6.542930e-05 6.542930e-05 0.324657328 #> 1835   917.0 0.6754385 0.6754385 6.541081e-05 6.541081e-05 0.324496130 #> 1836   917.5 0.6755996 0.6755996 6.539258e-05 6.539258e-05 0.324335012 #> 1837   918.0 0.6757607 0.6757607 6.537462e-05 6.537462e-05 0.324173975 #> 1838   918.5 0.6759216 0.6759216 6.535693e-05 6.535693e-05 0.324013018 #> 1839   919.0 0.6760825 0.6760825 6.533951e-05 6.533951e-05 0.323852140 #> 1840   919.5 0.6762433 0.6762433 6.532235e-05 6.532235e-05 0.323691343 #> 1841   920.0 0.6764041 0.6764041 6.530546e-05 6.530546e-05 0.323530627 #> 1842   920.5 0.6765647 0.6765647 6.528884e-05 6.528884e-05 0.323369990 #> 1843   921.0 0.6767253 0.6767253 6.527249e-05 6.527249e-05 0.323209433 #> 1844   921.5 0.6768858 0.6768858 6.525640e-05 6.525640e-05 0.323048956 #> 1845   922.0 0.6770462 0.6770462 6.524057e-05 6.524057e-05 0.322888559 #> 1846   922.5 0.6772065 0.6772065 6.522502e-05 6.522502e-05 0.322728242 #> 1847   923.0 0.6773668 0.6773668 6.520972e-05 6.520972e-05 0.322568005 #> 1848   923.5 0.6775270 0.6775270 6.519469e-05 6.519469e-05 0.322407848 #> 1849   924.0 0.6776870 0.6776870 6.517993e-05 6.517993e-05 0.322247770 #> 1850   924.5 0.6778471 0.6778471 6.516543e-05 6.516543e-05 0.322087773 #> 1851   925.0 0.6780070 0.6780070 6.515119e-05 6.515119e-05 0.321927855 #> 1852   925.5 0.6781668 0.6781668 6.513722e-05 6.513722e-05 0.321768016 #> 1853   926.0 0.6783266 0.6783266 6.512351e-05 6.512351e-05 0.321608258 #> 1854   926.5 0.6784863 0.6784863 6.511006e-05 6.511006e-05 0.321448579 #> 1855   927.0 0.6786459 0.6786459 6.509688e-05 6.509688e-05 0.321288980 #> 1856   927.5 0.6788055 0.6788055 6.508396e-05 6.508396e-05 0.321129460 #> 1857   928.0 0.6789649 0.6789649 6.507130e-05 6.507130e-05 0.320970020 #> 1858   928.5 0.6791243 0.6791243 6.505890e-05 6.505890e-05 0.320810659 #> 1859   929.0 0.6792836 0.6792836 6.504676e-05 6.504676e-05 0.320651378 #> 1860   929.5 0.6794428 0.6794428 6.503488e-05 6.503488e-05 0.320492176 #> 1861   930.0 0.6796019 0.6796019 6.502327e-05 6.502327e-05 0.320333053 #> 1862   930.5 0.6797610 0.6797610 6.501192e-05 6.501192e-05 0.320174010 #> 1863   931.0 0.6799200 0.6799200 6.500082e-05 6.500082e-05 0.320015046 #> 1864   931.5 0.6800788 0.6800788 6.498999e-05 6.498999e-05 0.319856162 #> 1865   932.0 0.6802377 0.6802377 6.497942e-05 6.497942e-05 0.319697356 #> 1866   932.5 0.6803964 0.6803964 6.496910e-05 6.496910e-05 0.319538630 #> 1867   933.0 0.6805551 0.6805551 6.495905e-05 6.495905e-05 0.319379983 #> 1868   933.5 0.6807136 0.6807136 6.494925e-05 6.494925e-05 0.319221416 #> 1869   934.0 0.6808721 0.6808721 6.493971e-05 6.493971e-05 0.319062927 #> 1870   934.5 0.6810306 0.6810306 6.493044e-05 6.493044e-05 0.318904517 #> 1871   935.0 0.6811889 0.6811889 6.492142e-05 6.492142e-05 0.318746187 #> 1872   935.5 0.6813472 0.6813472 6.491265e-05 6.491265e-05 0.318587935 #> 1873   936.0 0.6815053 0.6815053 6.490415e-05 6.490415e-05 0.318429763 #> 1874   936.5 0.6816634 0.6816634 6.489591e-05 6.489591e-05 0.318271669 #> 1875   937.0 0.6818215 0.6818215 6.488792e-05 6.488792e-05 0.318113654 #> 1876   937.5 0.6819794 0.6819794 6.488019e-05 6.488019e-05 0.317955718 #> 1877   938.0 0.6821373 0.6821373 6.487271e-05 6.487271e-05 0.317797861 #> 1878   938.5 0.6822951 0.6822951 6.486549e-05 6.486549e-05 0.317640083 #> 1879   939.0 0.6824528 0.6824528 6.485853e-05 6.485853e-05 0.317482383 #> 1880   939.5 0.6826104 0.6826104 6.485183e-05 6.485183e-05 0.317324762 #> 1881   940.0 0.6827679 0.6827679 6.484538e-05 6.484538e-05 0.317167220 #> 1882   940.5 0.6829254 0.6829254 6.483919e-05 6.483919e-05 0.317009757 #> 1883   941.0 0.6830828 0.6830828 6.483325e-05 6.483325e-05 0.316852372 #> 1884   941.5 0.6832401 0.6832401 6.482757e-05 6.482757e-05 0.316695065 #> 1885   942.0 0.6833973 0.6833973 6.482214e-05 6.482214e-05 0.316537837 #> 1886   942.5 0.6835545 0.6835545 6.481697e-05 6.481697e-05 0.316380688 #> 1887   943.0 0.6837116 0.6837116 6.481206e-05 6.481206e-05 0.316223617 #> 1888   943.5 0.6838686 0.6838686 6.480740e-05 6.480740e-05 0.316066625 #> 1889   944.0 0.6840255 0.6840255 6.480299e-05 6.480299e-05 0.315909711 #> 1890   944.5 0.6841823 0.6841823 6.479884e-05 6.479884e-05 0.315752875 #> 1891   945.0 0.6843391 0.6843391 6.479494e-05 6.479494e-05 0.315596118 #> 1892   945.5 0.6844958 0.6844958 6.479130e-05 6.479130e-05 0.315439439 #> 1893   946.0 0.6846524 0.6846524 6.478791e-05 6.478791e-05 0.315282838 #> 1894   946.5 0.6848089 0.6848089 6.478477e-05 6.478477e-05 0.315126316 #> 1895   947.0 0.6849653 0.6849653 6.478189e-05 6.478189e-05 0.314969871 #> 1896   947.5 0.6851217 0.6851217 6.477926e-05 6.477926e-05 0.314813505 #> 1897   948.0 0.6852780 0.6852780 6.477689e-05 6.477689e-05 0.314657217 #> 1898   948.5 0.6854342 0.6854342 6.477476e-05 6.477476e-05 0.314501007 #> 1899   949.0 0.6855904 0.6855904 6.477290e-05 6.477290e-05 0.314344875 #> 1900   949.5 0.6857464 0.6857464 6.477128e-05 6.477128e-05 0.314188822 #> 1901   950.0 0.6859024 0.6859024 6.476991e-05 6.476991e-05 0.314032846 #> 1902   950.5 0.6860583 0.6860583 6.476880e-05 6.476880e-05 0.313876948 #> 1903   951.0 0.6862141 0.6862141 6.476794e-05 6.476794e-05 0.313721128 #> 1904   951.5 0.6863698 0.6863698 6.476734e-05 6.476734e-05 0.313565385 #> 1905   952.0 0.6865255 0.6865255 6.476698e-05 6.476698e-05 0.313409721 #> 1906   952.5 0.6866811 0.6866811 6.476688e-05 6.476688e-05 0.313254135 #> 1907   953.0 0.6868366 0.6868366 6.476703e-05 6.476703e-05 0.313098626 #> 1908   953.5 0.6869920 0.6869920 6.476743e-05 6.476743e-05 0.312943195 #> 1909   954.0 0.6871474 0.6871474 6.476808e-05 6.476808e-05 0.312787842 #> 1910   954.5 0.6873027 0.6873027 6.476899e-05 6.476899e-05 0.312632566 #> 1911   955.0 0.6874579 0.6874579 6.477014e-05 6.477014e-05 0.312477368 #> 1912   955.5 0.6876130 0.6876130 6.477155e-05 6.477155e-05 0.312322248 #> 1913   956.0 0.6877680 0.6877680 6.477321e-05 6.477321e-05 0.312167205 #> 1914   956.5 0.6879230 0.6879230 6.477512e-05 6.477512e-05 0.312012239 #> 1915   957.0 0.6880779 0.6880779 6.477728e-05 6.477728e-05 0.311857352 #> 1916   957.5 0.6882327 0.6882327 6.477969e-05 6.477969e-05 0.311702541 #> 1917   958.0 0.6883874 0.6883874 6.478235e-05 6.478235e-05 0.311547808 #> 1918   958.5 0.6885421 0.6885421 6.478526e-05 6.478526e-05 0.311393153 #> 1919   959.0 0.6886966 0.6886966 6.478842e-05 6.478842e-05 0.311238575 #> 1920   959.5 0.6888511 0.6888511 6.479184e-05 6.479184e-05 0.311084074 #> 1921   960.0 0.6890056 0.6890056 6.479550e-05 6.479550e-05 0.310929650 #> 1922   960.5 0.6891599 0.6891599 6.479941e-05 6.479941e-05 0.310775304 #> 1923   961.0 0.6893142 0.6893142 6.480358e-05 6.480358e-05 0.310621035 #> 1924   961.5 0.6894683 0.6894683 6.480799e-05 6.480799e-05 0.310466843 #> 1925   962.0 0.6896225 0.6896225 6.481266e-05 6.481266e-05 0.310312729 #> 1926   962.5 0.6897765 0.6897765 6.481757e-05 6.481757e-05 0.310158691 #> 1927   963.0 0.6899304 0.6899304 6.482274e-05 6.482274e-05 0.310004730 #> 1928   963.5 0.6900843 0.6900843 6.482815e-05 6.482815e-05 0.309850847 #> 1929   964.0 0.6902381 0.6902381 6.483381e-05 6.483381e-05 0.309697040 #> 1930   964.5 0.6903918 0.6903918 6.483973e-05 6.483973e-05 0.309543311 #> 1931   965.0 0.6905455 0.6905455 6.484589e-05 6.484589e-05 0.309389658 #> 1932   965.5 0.6906991 0.6906991 6.485230e-05 6.485230e-05 0.309236083 #> 1933   966.0 0.6908526 0.6908526 6.485897e-05 6.485897e-05 0.309082584 #> 1934   966.5 0.6910060 0.6910060 6.486588e-05 6.486588e-05 0.308929162 #> 1935   967.0 0.6911593 0.6911593 6.487304e-05 6.487304e-05 0.308775817 #> 1936   967.5 0.6913126 0.6913126 6.488045e-05 6.488045e-05 0.308622549 #> 1937   968.0 0.6914658 0.6914658 6.488811e-05 6.488811e-05 0.308469357 #> 1938   968.5 0.6916189 0.6916189 6.489602e-05 6.489602e-05 0.308316242 #> 1939   969.0 0.6917719 0.6917719 6.490418e-05 6.490418e-05 0.308163204 #> 1940   969.5 0.6919248 0.6919248 6.491259e-05 6.491259e-05 0.308010243 #> 1941   970.0 0.6920777 0.6920777 6.492125e-05 6.492125e-05 0.307857358 #> 1942   970.5 0.6922305 0.6922305 6.493015e-05 6.493015e-05 0.307704549 #> 1943   971.0 0.6923832 0.6923832 6.493931e-05 6.493931e-05 0.307551817 #> 1944   971.5 0.6925359 0.6925359 6.494871e-05 6.494871e-05 0.307399162 #> 1945   972.0 0.6926885 0.6926885 6.495837e-05 6.495837e-05 0.307246583 #> 1946   972.5 0.6928410 0.6928410 6.496827e-05 6.496827e-05 0.307094081 #> 1947   973.0 0.6929934 0.6929934 6.497843e-05 6.497843e-05 0.306941655 #> 1948   973.5 0.6931457 0.6931457 6.498883e-05 6.498883e-05 0.306789305 #> 1949   974.0 0.6932980 0.6932980 6.499948e-05 6.499948e-05 0.306637032 #> 1950   974.5 0.6934502 0.6934502 6.501038e-05 6.501038e-05 0.306484835 #> 1951   975.0 0.6936023 0.6936023 6.502153e-05 6.502153e-05 0.306332714 #> 1952   975.5 0.6937543 0.6937543 6.503293e-05 6.503293e-05 0.306180669 #> 1953   976.0 0.6939063 0.6939063 6.504458e-05 6.504458e-05 0.306028701 #> 1954   976.5 0.6940581 0.6940581 6.505647e-05 6.505647e-05 0.305876809 #> 1955   977.0 0.6942099 0.6942099 6.506862e-05 6.506862e-05 0.305724993 #> 1956   977.5 0.6943617 0.6943617 6.508101e-05 6.508101e-05 0.305573253 #> 1957   978.0 0.6945133 0.6945133 6.509366e-05 6.509366e-05 0.305421589 #> 1958   978.5 0.6946649 0.6946649 6.510655e-05 6.510655e-05 0.305270001 #> 1959   979.0 0.6948164 0.6948164 6.511970e-05 6.511970e-05 0.305118489 #> 1960   979.5 0.6949678 0.6949678 6.513309e-05 6.513309e-05 0.304967053 #> 1961   980.0 0.6951192 0.6951192 6.514673e-05 6.514673e-05 0.304815693 #> 1962   980.5 0.6952704 0.6952704 6.516062e-05 6.516062e-05 0.304664409 #> 1963   981.0 0.6954216 0.6954216 6.517476e-05 6.517476e-05 0.304513201 #> 1964   981.5 0.6955727 0.6955727 6.518915e-05 6.518915e-05 0.304362068 #> 1965   982.0 0.6957238 0.6957238 6.520379e-05 6.520379e-05 0.304211012 #> 1966   982.5 0.6958748 0.6958748 6.521868e-05 6.521868e-05 0.304060031 #> 1967   983.0 0.6960256 0.6960256 6.523382e-05 6.523382e-05 0.303909126 #> 1968   983.5 0.6961765 0.6961765 6.524920e-05 6.524920e-05 0.303758296 #> 1969   984.0 0.6963272 0.6963272 6.526484e-05 6.526484e-05 0.303607542 #> 1970   984.5 0.6964779 0.6964779 6.528073e-05 6.528073e-05 0.303456864 #> 1971   985.0 0.6966284 0.6966284 6.529687e-05 6.529687e-05 0.303306261 #> 1972   985.5 0.6967790 0.6967790 6.531325e-05 6.531325e-05 0.303155734 #> 1973   986.0 0.6969294 0.6969294 6.532989e-05 6.532989e-05 0.303005283 #> 1974   986.5 0.6970797 0.6970797 6.534678e-05 6.534678e-05 0.302854907 #> 1975   987.0 0.6972300 0.6972300 6.536391e-05 6.536391e-05 0.302704606 #> 1976   987.5 0.6973802 0.6973802 6.538130e-05 6.538130e-05 0.302554381 #> 1977   988.0 0.6975304 0.6975304 6.539894e-05 6.539894e-05 0.302404231 #> 1978   988.5 0.6976804 0.6976804 6.541682e-05 6.541682e-05 0.302254157 #> 1979   989.0 0.6978304 0.6978304 6.543496e-05 6.543496e-05 0.302104157 #> 1980   989.5 0.6979803 0.6979803 6.545335e-05 6.545335e-05 0.301954234 #> 1981   990.0 0.6981301 0.6981301 6.547199e-05 6.547199e-05 0.301804385 #> 1982   990.5 0.6982799 0.6982799 6.549087e-05 6.549087e-05 0.301654612 #> 1983   991.0 0.6984296 0.6984296 6.551001e-05 6.551001e-05 0.301504913 #> 1984   991.5 0.6985792 0.6985792 6.552940e-05 6.552940e-05 0.301355290 #> 1985   992.0 0.6987287 0.6987287 6.554905e-05 6.554905e-05 0.301205742 #> 1986   992.5 0.6988782 0.6988782 6.556894e-05 6.556894e-05 0.301056269 #> 1987   993.0 0.6990275 0.6990275 6.558908e-05 6.558908e-05 0.300906872 #> 1988   993.5 0.6991768 0.6991768 6.560947e-05 6.560947e-05 0.300757549 #> 1989   994.0 0.6993261 0.6993261 6.563012e-05 6.563012e-05 0.300608301 #> 1990   994.5 0.6994752 0.6994752 6.565102e-05 6.565102e-05 0.300459128 #> 1991   995.0 0.6996243 0.6996243 6.567216e-05 6.567216e-05 0.300310030 #> 1992   995.5 0.6997733 0.6997733 6.569356e-05 6.569356e-05 0.300161007 #> 1993   996.0 0.6999222 0.6999222 6.571521e-05 6.571521e-05 0.300012059 #> 1994   996.5 0.7000711 0.7000711 6.573712e-05 6.573712e-05 0.299863186 #> 1995   997.0 0.7002199 0.7002199 6.575927e-05 6.575927e-05 0.299714387 #> 1996   997.5 0.7003686 0.7003686 6.578168e-05 6.578168e-05 0.299565663 #> 1997   998.0 0.7005172 0.7005172 6.580434e-05 6.580434e-05 0.299417014 #> 1998   998.5 0.7006657 0.7006657 6.582725e-05 6.582725e-05 0.299268440 #> 1999   999.0 0.7008142 0.7008142 6.585042e-05 6.585042e-05 0.299119940 #> 2000   999.5 0.7009626 0.7009626 6.587383e-05 6.587383e-05 0.298971515 #> 2001  1000.0 0.7011109 0.7011109 6.589750e-05 6.589750e-05 0.298823164 #> 2002  1000.5 0.7012592 0.7012592 6.592143e-05 6.592143e-05 0.298674888 #> 2003  1001.0 0.7014074 0.7014074 6.594560e-05 6.594560e-05 0.298526687 #> 2004  1001.5 0.7015555 0.7015555 6.597003e-05 6.597003e-05 0.298378560 #> 2005  1002.0 0.7017035 0.7017035 6.599471e-05 6.599471e-05 0.298230507 #> 2006  1002.5 0.7018515 0.7018515 6.601965e-05 6.601965e-05 0.298082529 #> 2007  1003.0 0.7019993 0.7019993 6.604484e-05 6.604484e-05 0.297934625 #> 2008  1003.5 0.7021471 0.7021471 6.607028e-05 6.607028e-05 0.297786796 #> 2009  1004.0 0.7022949 0.7022949 6.609598e-05 6.609598e-05 0.297639041 #> 2010  1004.5 0.7024425 0.7024425 6.612193e-05 6.612193e-05 0.297491360 #> 2011  1005.0 0.7025901 0.7025901 6.614813e-05 6.614813e-05 0.297343753 #> 2012  1005.5 0.7027376 0.7027376 6.617459e-05 6.617459e-05 0.297196221 #> 2013  1006.0 0.7028850 0.7028850 6.620131e-05 6.620131e-05 0.297048763 #> 2014  1006.5 0.7030324 0.7030324 6.622828e-05 6.622828e-05 0.296901379 #> 2015  1007.0 0.7031797 0.7031797 6.625550e-05 6.625550e-05 0.296754069 #> 2016  1007.5 0.7033269 0.7033269 6.628298e-05 6.628298e-05 0.296606834 #> 2017  1008.0 0.7034740 0.7034740 6.631072e-05 6.631072e-05 0.296459672 #> 2018  1008.5 0.7036211 0.7036211 6.633871e-05 6.633871e-05 0.296312584 #> 2019  1009.0 0.7037681 0.7037681 6.636695e-05 6.636695e-05 0.296165571 #> 2020  1009.5 0.7039150 0.7039150 6.639545e-05 6.639545e-05 0.296018631 #> 2021  1010.0 0.7040618 0.7040618 6.642421e-05 6.642421e-05 0.295871765 #> 2022  1010.5 0.7042086 0.7042086 6.645323e-05 6.645323e-05 0.295724973 #> 2023  1011.0 0.7043553 0.7043553 6.648250e-05 6.648250e-05 0.295578255 #> 2024  1011.5 0.7045019 0.7045019 6.651203e-05 6.651203e-05 0.295431611 #> 2025  1012.0 0.7046484 0.7046484 6.654181e-05 6.654181e-05 0.295285041 #> 2026  1012.5 0.7047949 0.7047949 6.657185e-05 6.657185e-05 0.295138544 #> 2027  1013.0 0.7049413 0.7049413 6.660215e-05 6.660215e-05 0.294992121 #> 2028  1013.5 0.7050876 0.7050876 6.663271e-05 6.663271e-05 0.294845772 #> 2029  1014.0 0.7052338 0.7052338 6.666352e-05 6.666352e-05 0.294699497 #> 2030  1014.5 0.7053800 0.7053800 6.669460e-05 6.669460e-05 0.294553295 #> 2031  1015.0 0.7055261 0.7055261 6.672593e-05 6.672593e-05 0.294407166 #> 2032  1015.5 0.7056721 0.7056721 6.675752e-05 6.675752e-05 0.294261112 #> 2033  1016.0 0.7058181 0.7058181 6.678937e-05 6.678937e-05 0.294115130 #> 2034  1016.5 0.7059640 0.7059640 6.682147e-05 6.682147e-05 0.293969223 #> 2035  1017.0 0.7061098 0.7061098 6.685384e-05 6.685384e-05 0.293823389 #> 2036  1017.5 0.7062555 0.7062555 6.688647e-05 6.688647e-05 0.293677628 #> 2037  1018.0 0.7064011 0.7064011 6.691935e-05 6.691935e-05 0.293531941 #> 2038  1018.5 0.7065467 0.7065467 6.695250e-05 6.695250e-05 0.293386327 #> 2039  1019.0 0.7066922 0.7066922 6.698590e-05 6.698590e-05 0.293240786 #> 2040  1019.5 0.7068377 0.7068377 6.701957e-05 6.701957e-05 0.293095319 #> 2041  1020.0 0.7069830 0.7069830 6.705350e-05 6.705350e-05 0.292949925 #> 2042  1020.5 0.7071283 0.7071283 6.708768e-05 6.708768e-05 0.292804604 #> 2043  1021.0 0.7072735 0.7072735 6.712213e-05 6.712213e-05 0.292659356 #> 2044  1021.5 0.7074187 0.7074187 6.715684e-05 6.715684e-05 0.292514182 #> 2045  1022.0 0.7075637 0.7075637 6.719181e-05 6.719181e-05 0.292369081 #> 2046  1022.5 0.7077087 0.7077087 6.722705e-05 6.722705e-05 0.292224052 #> 2047  1023.0 0.7078536 0.7078536 6.726254e-05 6.726254e-05 0.292079097 #> 2048  1023.5 0.7079985 0.7079985 6.729830e-05 6.729830e-05 0.291934215 #> 2049  1024.0 0.7081433 0.7081433 6.733432e-05 6.733432e-05 0.291789407 #> 2050  1024.5 0.7082880 0.7082880 6.737061e-05 6.737061e-05 0.291644671 #> 2051  1025.0 0.7084326 0.7084326 6.740716e-05 6.740716e-05 0.291500008 #> 2052  1025.5 0.7085771 0.7085771 6.744397e-05 6.744397e-05 0.291355417 #> 2053  1026.0 0.7087216 0.7087216 6.748104e-05 6.748104e-05 0.291210900 #> 2054  1026.5 0.7088660 0.7088660 6.751838e-05 6.751838e-05 0.291066456 #> 2055  1027.0 0.7090104 0.7090104 6.755599e-05 6.755599e-05 0.290922084 #> 2056  1027.5 0.7091546 0.7091546 6.759385e-05 6.759385e-05 0.290777786 #> 2057  1028.0 0.7092988 0.7092988 6.763199e-05 6.763199e-05 0.290633560 #> 2058  1028.5 0.7094429 0.7094429 6.767039e-05 6.767039e-05 0.290489406 #> 2059  1029.0 0.7095870 0.7095870 6.770905e-05 6.770905e-05 0.290345326 #> 2060  1029.5 0.7097309 0.7097309 6.774798e-05 6.774798e-05 0.290201318 #> 2061  1030.0 0.7098748 0.7098748 6.778718e-05 6.778718e-05 0.290057383 #> 2062  1030.5 0.7100187 0.7100187 6.782664e-05 6.782664e-05 0.289913520 #> 2063  1031.0 0.7101624 0.7101624 6.786637e-05 6.786637e-05 0.289769730 #> 2064  1031.5 0.7103061 0.7103061 6.790637e-05 6.790637e-05 0.289626013 #> 2065  1032.0 0.7104497 0.7104497 6.794664e-05 6.794664e-05 0.289482368 #> 2066  1032.5 0.7105932 0.7105932 6.798717e-05 6.798717e-05 0.289338795 #> 2067  1033.0 0.7107367 0.7107367 6.802797e-05 6.802797e-05 0.289195295 #> 2068  1033.5 0.7108801 0.7108801 6.806904e-05 6.806904e-05 0.289051868 #> 2069  1034.0 0.7110234 0.7110234 6.811038e-05 6.811038e-05 0.288908512 #> 2070  1034.5 0.7111666 0.7111666 6.815199e-05 6.815199e-05 0.288765229 #> 2071  1035.0 0.7113098 0.7113098 6.819387e-05 6.819387e-05 0.288622019 #> 2072  1035.5 0.7114529 0.7114529 6.823602e-05 6.823602e-05 0.288478880 #> 2073  1036.0 0.7115959 0.7115959 6.827843e-05 6.827843e-05 0.288335814 #> 2074  1036.5 0.7117389 0.7117389 6.832112e-05 6.832112e-05 0.288192821 #> 2075  1037.0 0.7118817 0.7118817 6.836408e-05 6.836408e-05 0.288049899 #> 2076  1037.5 0.7120245 0.7120245 6.840731e-05 6.840731e-05 0.287907050 #> 2077  1038.0 0.7121673 0.7121673 6.845082e-05 6.845082e-05 0.287764272 #> 2078  1038.5 0.7123099 0.7123099 6.849459e-05 6.849459e-05 0.287621567 #> 2079  1039.0 0.7124525 0.7124525 6.853864e-05 6.853864e-05 0.287478934 #> 2080  1039.5 0.7125950 0.7125950 6.858296e-05 6.858296e-05 0.287336373 #> 2081  1040.0 0.7127375 0.7127375 6.862755e-05 6.862755e-05 0.287193883 #> 2082  1040.5 0.7128799 0.7128799 6.867242e-05 6.867242e-05 0.287051466 #> 2083  1041.0 0.7130222 0.7130222 6.871756e-05 6.871756e-05 0.286909121 #> 2084  1041.5 0.7131644 0.7131644 6.876297e-05 6.876297e-05 0.286766848 #> 2085  1042.0 0.7133065 0.7133065 6.880866e-05 6.880866e-05 0.286624646 #> 2086  1042.5 0.7134486 0.7134486 6.885462e-05 6.885462e-05 0.286482517 #> 2087  1043.0 0.7135906 0.7135906 6.890086e-05 6.890086e-05 0.286340459 #> 2088  1043.5 0.7137326 0.7137326 6.894738e-05 6.894738e-05 0.286198473 #> 2089  1044.0 0.7138744 0.7138744 6.899417e-05 6.899417e-05 0.286056559 #> 2090  1044.5 0.7140162 0.7140162 6.904124e-05 6.904124e-05 0.285914716 #> 2091  1045.0 0.7141580 0.7141580 6.908858e-05 6.908858e-05 0.285772945 #> 2092  1045.5 0.7142996 0.7142996 6.913620e-05 6.913620e-05 0.285631246 #> 2093  1046.0 0.7144412 0.7144412 6.918410e-05 6.918410e-05 0.285489619 #> 2094  1046.5 0.7145827 0.7145827 6.923228e-05 6.923228e-05 0.285348063 #> 2095  1047.0 0.7147241 0.7147241 6.928074e-05 6.928074e-05 0.285206578 #> 2096  1047.5 0.7148655 0.7148655 6.932947e-05 6.932947e-05 0.285065165 #> 2097  1048.0 0.7150068 0.7150068 6.937849e-05 6.937849e-05 0.284923824 #> 2098  1048.5 0.7151480 0.7151480 6.942778e-05 6.942778e-05 0.284782554 #> 2099  1049.0 0.7152892 0.7152892 6.947736e-05 6.947736e-05 0.284641356 #> 2100  1049.5 0.7154302 0.7154302 6.952721e-05 6.952721e-05 0.284500229 #> 2101  1050.0 0.7155712 0.7155712 6.957735e-05 6.957735e-05 0.284359173 #> 2102  1050.5 0.7157122 0.7157122 6.962777e-05 6.962777e-05 0.284218189 #> 2103  1051.0 0.7158530 0.7158530 6.967847e-05 6.967847e-05 0.284077276 #> 2104  1051.5 0.7159938 0.7159938 6.972945e-05 6.972945e-05 0.283936434 #> 2105  1052.0 0.7161346 0.7161346 6.978072e-05 6.978072e-05 0.283795664 #> 2106  1052.5 0.7162752 0.7162752 6.983227e-05 6.983227e-05 0.283654965 #> 2107  1053.0 0.7164158 0.7164158 6.988410e-05 6.988410e-05 0.283514336 #> 2108  1053.5 0.7165563 0.7165563 6.993622e-05 6.993622e-05 0.283373780 #> 2109  1054.0 0.7166967 0.7166967 6.998862e-05 6.998862e-05 0.283233294 #> 2110  1054.5 0.7168371 0.7168371 7.004131e-05 7.004131e-05 0.283092879 #> 2111  1055.0 0.7169774 0.7169774 7.009428e-05 7.009428e-05 0.282952536 #> 2112  1055.5 0.7171176 0.7171176 7.014754e-05 7.014754e-05 0.282812263 #> 2113  1056.0 0.7172577 0.7172577 7.020108e-05 7.020108e-05 0.282672062 #> 2114  1056.5 0.7173978 0.7173978 7.025491e-05 7.025491e-05 0.282531931 #> 2115  1057.0 0.7175378 0.7175378 7.030903e-05 7.030903e-05 0.282391872 #> 2116  1057.5 0.7176778 0.7176778 7.036344e-05 7.036344e-05 0.282251883 #> 2117  1058.0 0.7178176 0.7178176 7.041813e-05 7.041813e-05 0.282111965 #> 2118  1058.5 0.7179574 0.7179574 7.047312e-05 7.047312e-05 0.281972118 #> 2119  1059.0 0.7180971 0.7180971 7.052839e-05 7.052839e-05 0.281832342 #> 2120  1059.5 0.7182368 0.7182368 7.058396e-05 7.058396e-05 0.281692637 #> 2121  1060.0 0.7183764 0.7183764 7.063981e-05 7.063981e-05 0.281553002 #> 2122  1060.5 0.7185159 0.7185159 7.069595e-05 7.069595e-05 0.281413438 #> 2123  1061.0 0.7186553 0.7186553 7.075239e-05 7.075239e-05 0.281273945 #> 2124  1061.5 0.7187947 0.7187947 7.080912e-05 7.080912e-05 0.281134523 #> 2125  1062.0 0.7189340 0.7189340 7.086614e-05 7.086614e-05 0.280995171 #> 2126  1062.5 0.7190732 0.7190732 7.092345e-05 7.092345e-05 0.280855890 #> 2127  1063.0 0.7192123 0.7192123 7.098106e-05 7.098106e-05 0.280716679 #> 2128  1063.5 0.7193514 0.7193514 7.103896e-05 7.103896e-05 0.280577539 #> 2129  1064.0 0.7194904 0.7194904 7.109715e-05 7.109715e-05 0.280438470 #> 2130  1064.5 0.7196294 0.7196294 7.115564e-05 7.115564e-05 0.280299471 #> 2131  1065.0 0.7197682 0.7197682 7.121443e-05 7.121443e-05 0.280160542 #> 2132  1065.5 0.7199070 0.7199070 7.127351e-05 7.127351e-05 0.280021684 #> 2133  1066.0 0.7200458 0.7200458 7.133288e-05 7.133288e-05 0.279882896 #> 2134  1066.5 0.7201844 0.7201844 7.139256e-05 7.139256e-05 0.279744179 #> 2135  1067.0 0.7203230 0.7203230 7.145253e-05 7.145253e-05 0.279605532 #> 2136  1067.5 0.7204615 0.7204615 7.151280e-05 7.151280e-05 0.279466955 #> 2137  1068.0 0.7206000 0.7206000 7.157337e-05 7.157337e-05 0.279328449 #> 2138  1068.5 0.7207384 0.7207384 7.163424e-05 7.163424e-05 0.279190012 #> 2139  1069.0 0.7208767 0.7208767 7.169540e-05 7.169540e-05 0.279051646 #> 2140  1069.5 0.7210149 0.7210149 7.175687e-05 7.175687e-05 0.278913351 #> 2141  1070.0 0.7211531 0.7211531 7.181864e-05 7.181864e-05 0.278775125 #> 2142  1070.5 0.7212911 0.7212911 7.188071e-05 7.188071e-05 0.278636969 #> 2143  1071.0 0.7214292 0.7214292 7.194308e-05 7.194308e-05 0.278498884 #> 2144  1071.5 0.7215671 0.7215671 7.200576e-05 7.200576e-05 0.278360869 #> 2145  1072.0 0.7217050 0.7217050 7.206873e-05 7.206873e-05 0.278222923 #> 2146  1072.5 0.7218428 0.7218428 7.213202e-05 7.213202e-05 0.278085048 #> 2147  1073.0 0.7219806 0.7219806 7.219560e-05 7.219560e-05 0.277947243 #> 2148  1073.5 0.7221182 0.7221182 7.225949e-05 7.225949e-05 0.277809507 #> 2149  1074.0 0.7222558 0.7222558 7.232369e-05 7.232369e-05 0.277671842 #> 2150  1074.5 0.7223934 0.7223934 7.238819e-05 7.238819e-05 0.277534246 #> 2151  1075.0 0.7225308 0.7225308 7.245300e-05 7.245300e-05 0.277396720 #> 2152  1075.5 0.7226682 0.7226682 7.251811e-05 7.251811e-05 0.277259265 #> 2153  1076.0 0.7228055 0.7228055 7.258353e-05 7.258353e-05 0.277121878 #> 2154  1076.5 0.7229428 0.7229428 7.264927e-05 7.264927e-05 0.276984562 #> 2155  1077.0 0.7230800 0.7230800 7.271531e-05 7.271531e-05 0.276847315 #> 2156  1077.5 0.7232171 0.7232171 7.278166e-05 7.278166e-05 0.276710139 #> 2157  1078.0 0.7233541 0.7233541 7.284832e-05 7.284832e-05 0.276573031 #> 2158  1078.5 0.7234911 0.7234911 7.291529e-05 7.291529e-05 0.276435994 #> 2159  1079.0 0.7236280 0.7236280 7.298257e-05 7.298257e-05 0.276299026 #> 2160  1079.5 0.7237648 0.7237648 7.305016e-05 7.305016e-05 0.276162128 #> 2161  1080.0 0.7239016 0.7239016 7.311807e-05 7.311807e-05 0.276025299 #> 2162  1080.5 0.7240383 0.7240383 7.318629e-05 7.318629e-05 0.275888540 #> 2163  1081.0 0.7241749 0.7241749 7.325483e-05 7.325483e-05 0.275751850 #> 2164  1081.5 0.7243114 0.7243114 7.332368e-05 7.332368e-05 0.275615229 #> 2165  1082.0 0.7244479 0.7244479 7.339284e-05 7.339284e-05 0.275478679 #> 2166  1082.5 0.7245843 0.7245843 7.346232e-05 7.346232e-05 0.275342197 #> 2167  1083.0 0.7247207 0.7247207 7.353212e-05 7.353212e-05 0.275205785 #> 2168  1083.5 0.7248570 0.7248570 7.360223e-05 7.360223e-05 0.275069442 #> 2169  1084.0 0.7249932 0.7249932 7.367266e-05 7.367266e-05 0.274933169 #> 2170  1084.5 0.7251293 0.7251293 7.374341e-05 7.374341e-05 0.274796965 #> 2171  1085.0 0.7252654 0.7252654 7.381448e-05 7.381448e-05 0.274660830 #> 2172  1085.5 0.7254013 0.7254013 7.388587e-05 7.388587e-05 0.274524765 #> 2173  1086.0 0.7255373 0.7255373 7.395758e-05 7.395758e-05 0.274388768 #> 2174  1086.5 0.7256731 0.7256731 7.402961e-05 7.402961e-05 0.274252841 #> 2175  1087.0 0.7258089 0.7258089 7.410196e-05 7.410196e-05 0.274116983 #> 2176  1087.5 0.7259446 0.7259446 7.417463e-05 7.417463e-05 0.273981194 #> 2177  1088.0 0.7260803 0.7260803 7.424763e-05 7.424763e-05 0.273845474 #> 2178  1088.5 0.7262159 0.7262159 7.432095e-05 7.432095e-05 0.273709824 #> 2179  1089.0 0.7263514 0.7263514 7.439460e-05 7.439460e-05 0.273574242 #> 2180  1089.5 0.7264868 0.7264868 7.446857e-05 7.446857e-05 0.273438729 #> 2181  1090.0 0.7266222 0.7266222 7.454286e-05 7.454286e-05 0.273303286 #> 2182  1090.5 0.7267575 0.7267575 7.461749e-05 7.461749e-05 0.273167911 #> 2183  1091.0 0.7268927 0.7268927 7.469244e-05 7.469244e-05 0.273032605 #> 2184  1091.5 0.7270279 0.7270279 7.476772e-05 7.476772e-05 0.272897368 #> 2185  1092.0 0.7271630 0.7271630 7.484332e-05 7.484332e-05 0.272762200 #> 2186  1092.5 0.7272980 0.7272980 7.491926e-05 7.491926e-05 0.272627100 #> 2187  1093.0 0.7274329 0.7274329 7.499553e-05 7.499553e-05 0.272492070 #> 2188  1093.5 0.7275678 0.7275678 7.507213e-05 7.507213e-05 0.272357108 #> 2189  1094.0 0.7277026 0.7277026 7.514905e-05 7.514905e-05 0.272222215 #> 2190  1094.5 0.7278374 0.7278374 7.522632e-05 7.522632e-05 0.272087391 #> 2191  1095.0 0.7279721 0.7279721 7.530391e-05 7.530391e-05 0.271952635 #> 2192  1095.5 0.7281067 0.7281067 7.538184e-05 7.538184e-05 0.271817948 #> 2193  1096.0 0.7282412 0.7282412 7.546010e-05 7.546010e-05 0.271683330 #> 2194  1096.5 0.7283757 0.7283757 7.553870e-05 7.553870e-05 0.271548780 #> 2195  1097.0 0.7285101 0.7285101 7.561764e-05 7.561764e-05 0.271414299 #> 2196  1097.5 0.7286444 0.7286444 7.569691e-05 7.569691e-05 0.271279887 #> 2197  1098.0 0.7287787 0.7287787 7.577651e-05 7.577651e-05 0.271145543 #> 2198  1098.5 0.7289129 0.7289129 7.585646e-05 7.585646e-05 0.271011267 #> 2199  1099.0 0.7290470 0.7290470 7.593675e-05 7.593675e-05 0.270877060 #> 2200  1099.5 0.7291811 0.7291811 7.601737e-05 7.601737e-05 0.270742921 #> 2201  1100.0 0.7293151 0.7293151 7.609834e-05 7.609834e-05 0.270608851 #> 2202  1100.5 0.7294490 0.7294490 7.617965e-05 7.617965e-05 0.270474849 #> 2203  1101.0 0.7295828 0.7295828 7.626130e-05 7.626130e-05 0.270340915 #> 2204  1101.5 0.7297166 0.7297166 7.634329e-05 7.634329e-05 0.270207050 #> 2205  1102.0 0.7298503 0.7298503 7.642563e-05 7.642563e-05 0.270073253 #> 2206  1102.5 0.7299840 0.7299840 7.650831e-05 7.650831e-05 0.269939524 #> 2207  1103.0 0.7301175 0.7301175 7.659133e-05 7.659133e-05 0.269805864 #> 2208  1103.5 0.7302511 0.7302511 7.667470e-05 7.667470e-05 0.269672271 #> 2209  1104.0 0.7303845 0.7303845 7.675842e-05 7.675842e-05 0.269538747 #> 2210  1104.5 0.7305179 0.7305179 7.684249e-05 7.684249e-05 0.269405291 #> 2211  1105.0 0.7306512 0.7306512 7.692691e-05 7.692691e-05 0.269271903 #> 2212  1105.5 0.7307844 0.7307844 7.701167e-05 7.701167e-05 0.269138584 #> 2213  1106.0 0.7309176 0.7309176 7.709678e-05 7.709678e-05 0.269005332 #> 2214  1106.5 0.7310507 0.7310507 7.718225e-05 7.718225e-05 0.268872148 #> 2215  1107.0 0.7311837 0.7311837 7.726807e-05 7.726807e-05 0.268739033 #> 2216  1107.5 0.7313167 0.7313167 7.735424e-05 7.735424e-05 0.268605985 #> 2217  1108.0 0.7314496 0.7314496 7.744076e-05 7.744076e-05 0.268473005 #> 2218  1108.5 0.7315824 0.7315824 7.752764e-05 7.752764e-05 0.268340093 #> 2219  1109.0 0.7317151 0.7317151 7.761487e-05 7.761487e-05 0.268207249 #> 2220  1109.5 0.7318478 0.7318478 7.770246e-05 7.770246e-05 0.268074473 #> 2221  1110.0 0.7319804 0.7319804 7.779041e-05 7.779041e-05 0.267941765 #> 2222  1110.5 0.7321130 0.7321130 7.787871e-05 7.787871e-05 0.267809124 #> 2223  1111.0 0.7322455 0.7322455 7.796737e-05 7.796737e-05 0.267676552 #> 2224  1111.5 0.7323779 0.7323779 7.805639e-05 7.805639e-05 0.267544047 #> 2225  1112.0 0.7325102 0.7325102 7.814578e-05 7.814578e-05 0.267411609 #> 2226  1112.5 0.7326425 0.7326425 7.823552e-05 7.823552e-05 0.267279240 #> 2227  1113.0 0.7327747 0.7327747 7.832562e-05 7.832562e-05 0.267146938 #> 2228  1113.5 0.7329069 0.7329069 7.841609e-05 7.841609e-05 0.267014704 #> 2229  1114.0 0.7330390 0.7330390 7.850692e-05 7.850692e-05 0.266882537 #> 2230  1114.5 0.7331710 0.7331710 7.859812e-05 7.859812e-05 0.266750438 #> 2231  1115.0 0.7333029 0.7333029 7.868968e-05 7.868968e-05 0.266618407 #> 2232  1115.5 0.7334348 0.7334348 7.878161e-05 7.878161e-05 0.266486443 #> 2233  1116.0 0.7335666 0.7335666 7.887390e-05 7.887390e-05 0.266354546 #> 2234  1116.5 0.7336983 0.7336983 7.896657e-05 7.896657e-05 0.266222717 #> 2235  1117.0 0.7338300 0.7338300 7.905960e-05 7.905960e-05 0.266090956 #> 2236  1117.5 0.7339616 0.7339616 7.915300e-05 7.915300e-05 0.265959262 #> 2237  1118.0 0.7340931 0.7340931 7.924678e-05 7.924678e-05 0.265827635 #> 2238  1118.5 0.7342246 0.7342246 7.934092e-05 7.934092e-05 0.265696076 #> 2239  1119.0 0.7343560 0.7343560 7.943544e-05 7.943544e-05 0.265564584 #> 2240  1119.5 0.7344873 0.7344873 7.953033e-05 7.953033e-05 0.265433159 #> 2241  1120.0 0.7346186 0.7346186 7.962560e-05 7.962560e-05 0.265301801 #> 2242  1120.5 0.7347498 0.7347498 7.972124e-05 7.972124e-05 0.265170511 #> 2243  1121.0 0.7348809 0.7348809 7.981726e-05 7.981726e-05 0.265039288 #> 2244  1121.5 0.7350120 0.7350120 7.991366e-05 7.991366e-05 0.264908132 #> 2245  1122.0 0.7351429 0.7351429 8.001044e-05 8.001044e-05 0.264777044 #> 2246  1122.5 0.7352739 0.7352739 8.010759e-05 8.010759e-05 0.264646022 #> 2247  1123.0 0.7354047 0.7354047 8.020513e-05 8.020513e-05 0.264515068 #> 2248  1123.5 0.7355355 0.7355355 8.030304e-05 8.030304e-05 0.264384181 #> 2249  1124.0 0.7356662 0.7356662 8.040134e-05 8.040134e-05 0.264253361 #> 2250  1124.5 0.7357969 0.7357969 8.050003e-05 8.050003e-05 0.264122608 #> 2251  1125.0 0.7359275 0.7359275 8.059909e-05 8.059909e-05 0.263991921 #> 2252  1125.5 0.7360580 0.7360580 8.069855e-05 8.069855e-05 0.263861302 #> 2253  1126.0 0.7361885 0.7361885 8.079838e-05 8.079838e-05 0.263730750 #> 2254  1126.5 0.7363188 0.7363188 8.089861e-05 8.089861e-05 0.263600265 #> 2255  1127.0 0.7364492 0.7364492 8.099922e-05 8.099922e-05 0.263469846 #> 2256  1127.5 0.7365794 0.7365794 8.110023e-05 8.110023e-05 0.263339495 #> 2257  1128.0 0.7367096 0.7367096 8.120162e-05 8.120162e-05 0.263209210 #> 2258  1128.5 0.7368397 0.7368397 8.130341e-05 8.130341e-05 0.263078992 #> 2259  1129.0 0.7369698 0.7369698 8.140558e-05 8.140558e-05 0.262948841 #> 2260  1129.5 0.7370997 0.7370997 8.150815e-05 8.150815e-05 0.262818757 #> 2261  1130.0 0.7372296 0.7372296 8.161112e-05 8.161112e-05 0.262688739 #> 2262  1130.5 0.7373595 0.7373595 8.171448e-05 8.171448e-05 0.262558789 #> 2263  1131.0 0.7374893 0.7374893 8.181824e-05 8.181824e-05 0.262428905 #> 2264  1131.5 0.7376190 0.7376190 8.192239e-05 8.192239e-05 0.262299087 #> 2265  1132.0 0.7377486 0.7377486 8.202694e-05 8.202694e-05 0.262169336 #> 2266  1132.5 0.7378782 0.7378782 8.213190e-05 8.213190e-05 0.262039652 #> 2267  1133.0 0.7380077 0.7380077 8.223725e-05 8.223725e-05 0.261910034 #> 2268  1133.5 0.7381372 0.7381372 8.234300e-05 8.234300e-05 0.261780483 #> 2269  1134.0 0.7382666 0.7382666 8.244916e-05 8.244916e-05 0.261650998 #> 2270  1134.5 0.7383959 0.7383959 8.255572e-05 8.255572e-05 0.261521580 #> 2271  1135.0 0.7385251 0.7385251 8.266269e-05 8.266269e-05 0.261392229 #> 2272  1135.5 0.7386543 0.7386543 8.277006e-05 8.277006e-05 0.261262944 #> 2273  1136.0 0.7387834 0.7387834 8.287784e-05 8.287784e-05 0.261133725 #> 2274  1136.5 0.7389124 0.7389124 8.298603e-05 8.298603e-05 0.261004572 #> 2275  1137.0 0.7390414 0.7390414 8.309462e-05 8.309462e-05 0.260875486 #> 2276  1137.5 0.7391703 0.7391703 8.320363e-05 8.320363e-05 0.260746467 #> 2277  1138.0 0.7392992 0.7392992 8.331305e-05 8.331305e-05 0.260617513 #> 2278  1138.5 0.7394280 0.7394280 8.342288e-05 8.342288e-05 0.260488626 #> 2279  1139.0 0.7395567 0.7395567 8.353312e-05 8.353312e-05 0.260359806 #> 2280  1139.5 0.7396853 0.7396853 8.364378e-05 8.364378e-05 0.260231051 #> 2281  1140.0 0.7398139 0.7398139 8.375486e-05 8.375486e-05 0.260102363 #> 2282  1140.5 0.7399424 0.7399424 8.386635e-05 8.386635e-05 0.259973740 #> 2283  1141.0 0.7400708 0.7400708 8.397826e-05 8.397826e-05 0.259845184 #> 2284  1141.5 0.7401992 0.7401992 8.409059e-05 8.409059e-05 0.259716695 #> 2285  1142.0 0.7403275 0.7403275 8.420334e-05 8.420334e-05 0.259588271 #> 2286  1142.5 0.7404558 0.7404558 8.431651e-05 8.431651e-05 0.259459913 #> 2287  1143.0 0.7405839 0.7405839 8.443011e-05 8.443011e-05 0.259331621 #> 2288  1143.5 0.7407121 0.7407121 8.454412e-05 8.454412e-05 0.259203396 #> 2289  1144.0 0.7408401 0.7408401 8.465857e-05 8.465857e-05 0.259075236 #> 2290  1144.5 0.7409681 0.7409681 8.477344e-05 8.477344e-05 0.258947142 #> 2291  1145.0 0.7410960 0.7410960 8.488873e-05 8.488873e-05 0.258819115 #> 2292  1145.5 0.7412238 0.7412238 8.500446e-05 8.500446e-05 0.258691153 #> 2293  1146.0 0.7413516 0.7413516 8.512061e-05 8.512061e-05 0.258563257 #> 2294  1146.5 0.7414793 0.7414793 8.523720e-05 8.523720e-05 0.258435427 #> 2295  1147.0 0.7416070 0.7416070 8.535422e-05 8.535422e-05 0.258307663 #> 2296  1147.5 0.7417346 0.7417346 8.547167e-05 8.547167e-05 0.258179964 #> 2297  1148.0 0.7418621 0.7418621 8.558956e-05 8.558956e-05 0.258052332 #> 2298  1148.5 0.7419895 0.7419895 8.570788e-05 8.570788e-05 0.257924765 #> 2299  1149.0 0.7421169 0.7421169 8.582664e-05 8.582664e-05 0.257797264 #> 2300  1149.5 0.7422442 0.7422442 8.594583e-05 8.594583e-05 0.257669829 #> 2301  1150.0 0.7423715 0.7423715 8.606547e-05 8.606547e-05 0.257542459 #> 2302  1150.5 0.7424987 0.7424987 8.618555e-05 8.618555e-05 0.257415155 #> 2303  1151.0 0.7426258 0.7426258 8.630607e-05 8.630607e-05 0.257287917 #> 2304  1151.5 0.7427528 0.7427528 8.642703e-05 8.642703e-05 0.257160744 #> 2305  1152.0 0.7428798 0.7428798 8.654844e-05 8.654844e-05 0.257033637 #> 2306  1152.5 0.7430067 0.7430067 8.667029e-05 8.667029e-05 0.256906595 #> 2307  1153.0 0.7431336 0.7431336 8.679259e-05 8.679259e-05 0.256779619 #> 2308  1153.5 0.7432604 0.7432604 8.691533e-05 8.691533e-05 0.256652709 #> 2309  1154.0 0.7433871 0.7433871 8.703853e-05 8.703853e-05 0.256525864 #> 2310  1154.5 0.7435138 0.7435138 8.716218e-05 8.716218e-05 0.256399084 #> 2311  1155.0 0.7436403 0.7436403 8.728628e-05 8.728628e-05 0.256272370 #> 2312  1155.5 0.7437669 0.7437669 8.741083e-05 8.741083e-05 0.256145721 #> 2313  1156.0 0.7438933 0.7438933 8.753583e-05 8.753583e-05 0.256019138 #> 2314  1156.5 0.7440197 0.7440197 8.766130e-05 8.766130e-05 0.255892620 #> 2315  1157.0 0.7441460 0.7441460 8.778722e-05 8.778722e-05 0.255766167 #> 2316  1157.5 0.7442723 0.7442723 8.791360e-05 8.791360e-05 0.255639780 #> 2317  1158.0 0.7443985 0.7443985 8.804043e-05 8.804043e-05 0.255513458 #> 2318  1158.5 0.7445246 0.7445246 8.816773e-05 8.816773e-05 0.255387201 #> 2319  1159.0 0.7446507 0.7446507 8.829549e-05 8.829549e-05 0.255261010 #> 2320  1159.5 0.7447767 0.7447767 8.842372e-05 8.842372e-05 0.255134884 #> 2321  1160.0 0.7449026 0.7449026 8.855241e-05 8.855241e-05 0.255008822 #> 2322  1160.5 0.7450285 0.7450285 8.868157e-05 8.868157e-05 0.254882826 #> 2323  1161.0 0.7451543 0.7451543 8.881119e-05 8.881119e-05 0.254756896 #> 2324  1161.5 0.7452800 0.7452800 8.894129e-05 8.894129e-05 0.254631030 #> 2325  1162.0 0.7454057 0.7454057 8.907185e-05 8.907185e-05 0.254505229 #> 2326  1162.5 0.7455313 0.7455313 8.920289e-05 8.920289e-05 0.254379494 #> 2327  1163.0 0.7456568 0.7456568 8.933439e-05 8.933439e-05 0.254253823 #> 2328  1163.5 0.7457823 0.7457823 8.946638e-05 8.946638e-05 0.254128218 #> 2329  1164.0 0.7459077 0.7459077 8.959884e-05 8.959884e-05 0.254002677 #> 2330  1164.5 0.7460331 0.7460331 8.973178e-05 8.973178e-05 0.253877202 #> 2331  1165.0 0.7461583 0.7461583 8.986519e-05 8.986519e-05 0.253751791 #> 2332  1165.5 0.7462836 0.7462836 8.999909e-05 8.999909e-05 0.253626445 #> 2333  1166.0 0.7464087 0.7464087 9.013347e-05 9.013347e-05 0.253501165 #> 2334  1166.5 0.7465338 0.7465338 9.026833e-05 9.026833e-05 0.253375949 #> 2335  1167.0 0.7466588 0.7466588 9.040367e-05 9.040367e-05 0.253250798 #> 2336  1167.5 0.7467837 0.7467837 9.053950e-05 9.053950e-05 0.253125711 #> 2337  1168.0 0.7469086 0.7469086 9.067582e-05 9.067582e-05 0.253000690 #> 2338  1168.5 0.7470335 0.7470335 9.081263e-05 9.081263e-05 0.252875733 #> 2339  1169.0 0.7471582 0.7471582 9.094992e-05 9.094992e-05 0.252750841 #> 2340  1169.5 0.7472829 0.7472829 9.108771e-05 9.108771e-05 0.252626014 #> 2341  1170.0 0.7474075 0.7474075 9.122599e-05 9.122599e-05 0.252501251 #> 2342  1170.5 0.7475321 0.7475321 9.136477e-05 9.136477e-05 0.252376554 #> 2343  1171.0 0.7476566 0.7476566 9.150404e-05 9.150404e-05 0.252251920 #> 2344  1171.5 0.7477810 0.7477810 9.164381e-05 9.164381e-05 0.252127352 #> 2345  1172.0 0.7479054 0.7479054 9.178408e-05 9.178408e-05 0.252002848 #> 2346  1172.5 0.7480297 0.7480297 9.192485e-05 9.192485e-05 0.251878408 #> 2347  1173.0 0.7481539 0.7481539 9.206612e-05 9.206612e-05 0.251754034 #> 2348  1173.5 0.7482781 0.7482781 9.220789e-05 9.220789e-05 0.251629723 #> 2349  1174.0 0.7484022 0.7484022 9.235017e-05 9.235017e-05 0.251505477 #> 2350  1174.5 0.7485262 0.7485262 9.249296e-05 9.249296e-05 0.251381296 #> 2351  1175.0 0.7486502 0.7486502 9.263625e-05 9.263625e-05 0.251257179 #> 2352  1175.5 0.7487741 0.7487741 9.278006e-05 9.278006e-05 0.251133127 #> 2353  1176.0 0.7488979 0.7488979 9.292437e-05 9.292437e-05 0.251009139 #> 2354  1176.5 0.7490217 0.7490217 9.306920e-05 9.306920e-05 0.250885215 #> 2355  1177.0 0.7491454 0.7491454 9.321454e-05 9.321454e-05 0.250761356 #> 2356  1177.5 0.7492691 0.7492691 9.336039e-05 9.336039e-05 0.250637561 #> 2357  1178.0 0.7493927 0.7493927 9.350677e-05 9.350677e-05 0.250513830 #> 2358  1178.5 0.7495162 0.7495162 9.365366e-05 9.365366e-05 0.250390164 #> 2359  1179.0 0.7496396 0.7496396 9.380107e-05 9.380107e-05 0.250266562 #> 2360  1179.5 0.7497630 0.7497630 9.394901e-05 9.394901e-05 0.250143024 #> 2361  1180.0 0.7498864 0.7498864 9.409746e-05 9.409746e-05 0.250019550 #> 2362  1180.5 0.7500096 0.7500096 9.424645e-05 9.424645e-05 0.249896141 #> 2363  1181.0 0.7501328 0.7501328 9.439596e-05 9.439596e-05 0.249772796 #> 2364  1181.5 0.7502559 0.7502559 9.454599e-05 9.454599e-05 0.249649515 #> 2365  1182.0 0.7503790 0.7503790 9.469656e-05 9.469656e-05 0.249526298 #> 2366  1182.5 0.7505020 0.7505020 9.484766e-05 9.484766e-05 0.249403145 #> 2367  1183.0 0.7506249 0.7506249 9.499930e-05 9.499930e-05 0.249280056 #> 2368  1183.5 0.7507478 0.7507478 9.515146e-05 9.515146e-05 0.249157032 #> 2369  1184.0 0.7508706 0.7508706 9.530417e-05 9.530417e-05 0.249034071 #> 2370  1184.5 0.7509934 0.7509934 9.545741e-05 9.545741e-05 0.248911174 #> 2371  1185.0 0.7511160 0.7511160 9.561119e-05 9.561119e-05 0.248788342 #> 2372  1185.5 0.7512387 0.7512387 9.576551e-05 9.576551e-05 0.248665573 #> 2373  1186.0 0.7513612 0.7513612 9.592038e-05 9.592038e-05 0.248542868 #> 2374  1186.5 0.7514837 0.7514837 9.607579e-05 9.607579e-05 0.248420228 #> 2375  1187.0 0.7516061 0.7516061 9.623175e-05 9.623175e-05 0.248297651 #> 2376  1187.5 0.7517285 0.7517285 9.638825e-05 9.638825e-05 0.248175138 #> 2377  1188.0 0.7518508 0.7518508 9.654531e-05 9.654531e-05 0.248052688 #> 2378  1188.5 0.7519730 0.7519730 9.670291e-05 9.670291e-05 0.247930303 #> 2379  1189.0 0.7520952 0.7520952 9.686107e-05 9.686107e-05 0.247807982 #> 2380  1189.5 0.7522173 0.7522173 9.701979e-05 9.701979e-05 0.247685724 #> 2381  1190.0 0.7523393 0.7523393 9.717906e-05 9.717906e-05 0.247563530 #> 2382  1190.5 0.7524613 0.7524613 9.733889e-05 9.733889e-05 0.247441400 #> 2383  1191.0 0.7525832 0.7525832 9.749928e-05 9.749928e-05 0.247319333 #> 2384  1191.5 0.7527050 0.7527050 9.766023e-05 9.766023e-05 0.247197330 #> 2385  1192.0 0.7528268 0.7528268 9.782175e-05 9.782175e-05 0.247075391 #> 2386  1192.5 0.7529485 0.7529485 9.798383e-05 9.798383e-05 0.246953516 #> 2387  1193.0 0.7530701 0.7530701 9.814647e-05 9.814647e-05 0.246831704 #> 2388  1193.5 0.7531917 0.7531917 9.830969e-05 9.830969e-05 0.246709955 #> 2389  1194.0 0.7533133 0.7533133 9.847348e-05 9.847348e-05 0.246588271 #> 2390  1194.5 0.7534347 0.7534347 9.863784e-05 9.863784e-05 0.246466650 #> 2391  1195.0 0.7535561 0.7535561 9.880277e-05 9.880277e-05 0.246345092 #> 2392  1195.5 0.7536774 0.7536774 9.896828e-05 9.896828e-05 0.246223598 #> 2393  1196.0 0.7537987 0.7537987 9.913437e-05 9.913437e-05 0.246102167 #> 2394  1196.5 0.7539199 0.7539199 9.930103e-05 9.930103e-05 0.245980800 #> 2395  1197.0 0.7540410 0.7540410 9.946828e-05 9.946828e-05 0.245859497 #> 2396  1197.5 0.7541621 0.7541621 9.963612e-05 9.963612e-05 0.245738256 #> 2397  1198.0 0.7542831 0.7542831 9.980453e-05 9.980453e-05 0.245617079 #> 2398  1198.5 0.7544041 0.7544041 9.997354e-05 9.997354e-05 0.245495966 #> 2399  1199.0 0.7545249 0.7545249 1.001431e-04 1.001431e-04 0.245374916 #> 2400  1199.5 0.7546458 0.7546458 1.003133e-04 1.003133e-04 0.245253929 #> 2401  1200.0 0.7547665 0.7547665 1.004841e-04 1.004841e-04 0.245133006 #> 2402  1200.5 0.7548872 0.7548872 1.006555e-04 1.006555e-04 0.245012146 #> 2403  1201.0 0.7550078 0.7550078 1.008274e-04 1.008274e-04 0.244891349 #> 2404  1201.5 0.7551284 0.7551284 1.010000e-04 1.010000e-04 0.244770615 #> 2405  1202.0 0.7552489 0.7552489 1.011732e-04 1.011732e-04 0.244649945 #> 2406  1202.5 0.7553693 0.7553693 1.013469e-04 1.013469e-04 0.244529338 #> 2407  1203.0 0.7554897 0.7554897 1.015213e-04 1.015213e-04 0.244408794 #> 2408  1203.5 0.7556100 0.7556100 1.016963e-04 1.016963e-04 0.244288313 #> 2409  1204.0 0.7557302 0.7557302 1.018719e-04 1.018719e-04 0.244167895 #> 2410  1204.5 0.7558504 0.7558504 1.020481e-04 1.020481e-04 0.244047541 #> 2411  1205.0 0.7559705 0.7559705 1.022249e-04 1.022249e-04 0.243927249 #> 2412  1205.5 0.7560906 0.7560906 1.024023e-04 1.024023e-04 0.243807021 #> 2413  1206.0 0.7562106 0.7562106 1.025803e-04 1.025803e-04 0.243686856 #> 2414  1206.5 0.7563305 0.7563305 1.027590e-04 1.027590e-04 0.243566753 #> 2415  1207.0 0.7564503 0.7564503 1.029383e-04 1.029383e-04 0.243446714 #> 2416  1207.5 0.7565701 0.7565701 1.031182e-04 1.031182e-04 0.243326738 #> 2417  1208.0 0.7566899 0.7566899 1.032987e-04 1.032987e-04 0.243206825 #> 2418  1208.5 0.7568095 0.7568095 1.034798e-04 1.034798e-04 0.243086974 #> 2419  1209.0 0.7569292 0.7569292 1.036616e-04 1.036616e-04 0.242967187 #> 2420  1209.5 0.7570487 0.7570487 1.038440e-04 1.038440e-04 0.242847463 #> 2421  1210.0 0.7571682 0.7571682 1.040270e-04 1.040270e-04 0.242727801 #> 2422  1210.5 0.7572876 0.7572876 1.042107e-04 1.042107e-04 0.242608202 #> 2423  1211.0 0.7574069 0.7574069 1.043950e-04 1.043950e-04 0.242488667 #> 2424  1211.5 0.7575262 0.7575262 1.045800e-04 1.045800e-04 0.242369194 #> 2425  1212.0 0.7576455 0.7576455 1.047655e-04 1.047655e-04 0.242249783 #> 2426  1212.5 0.7577646 0.7577646 1.049518e-04 1.049518e-04 0.242130436 #> 2427  1213.0 0.7578837 0.7578837 1.051386e-04 1.051386e-04 0.242011151 #> 2428  1213.5 0.7580027 0.7580027 1.053261e-04 1.053261e-04 0.241891929 #> 2429  1214.0 0.7581217 0.7581217 1.055143e-04 1.055143e-04 0.241772770 #> 2430  1214.5 0.7582406 0.7582406 1.057031e-04 1.057031e-04 0.241653674 #> 2431  1215.0 0.7583595 0.7583595 1.058926e-04 1.058926e-04 0.241534640 #> 2432  1215.5 0.7584782 0.7584782 1.060827e-04 1.060827e-04 0.241415669 #> 2433  1216.0 0.7585970 0.7585970 1.062735e-04 1.062735e-04 0.241296760 #> 2434  1216.5 0.7587156 0.7587156 1.064649e-04 1.064649e-04 0.241177915 #> 2435  1217.0 0.7588342 0.7588342 1.066570e-04 1.066570e-04 0.241059131 #> 2436  1217.5 0.7589527 0.7589527 1.068498e-04 1.068498e-04 0.240940411 #> 2437  1218.0 0.7590712 0.7590712 1.070432e-04 1.070432e-04 0.240821753 #> 2438  1218.5 0.7591896 0.7591896 1.072373e-04 1.072373e-04 0.240703157 #> 2439  1219.0 0.7593079 0.7593079 1.074321e-04 1.074321e-04 0.240584624 #> 2440  1219.5 0.7594262 0.7594262 1.076275e-04 1.076275e-04 0.240466153 #> 2441  1220.0 0.7595444 0.7595444 1.078236e-04 1.078236e-04 0.240347745 #> 2442  1220.5 0.7596626 0.7596626 1.080204e-04 1.080204e-04 0.240229400 #> 2443  1221.0 0.7597807 0.7597807 1.082179e-04 1.082179e-04 0.240111117 #> 2444  1221.5 0.7598987 0.7598987 1.084160e-04 1.084160e-04 0.239992896 #> 2445  1222.0 0.7600166 0.7600166 1.086148e-04 1.086148e-04 0.239874738 #> 2446  1222.5 0.7601345 0.7601345 1.088144e-04 1.088144e-04 0.239756642 #> 2447  1223.0 0.7602524 0.7602524 1.090146e-04 1.090146e-04 0.239638608 #> 2448  1223.5 0.7603701 0.7603701 1.092155e-04 1.092155e-04 0.239520637 #> 2449  1224.0 0.7604879 0.7604879 1.094170e-04 1.094170e-04 0.239402728 #> 2450  1224.5 0.7606055 0.7606055 1.096193e-04 1.096193e-04 0.239284881 #> 2451  1225.0 0.7607231 0.7607231 1.098223e-04 1.098223e-04 0.239167097 #> 2452  1225.5 0.7608406 0.7608406 1.100260e-04 1.100260e-04 0.239049375 #> 2453  1226.0 0.7609581 0.7609581 1.102304e-04 1.102304e-04 0.238931715 #> 2454  1226.5 0.7610754 0.7610754 1.104354e-04 1.104354e-04 0.238814118 #> 2455  1227.0 0.7611928 0.7611928 1.106412e-04 1.106412e-04 0.238696582 #> 2456  1227.5 0.7613100 0.7613100 1.108477e-04 1.108477e-04 0.238579109 #> 2457  1228.0 0.7614272 0.7614272 1.110549e-04 1.110549e-04 0.238461698 #> 2458  1228.5 0.7615444 0.7615444 1.112628e-04 1.112628e-04 0.238344349 #> 2459  1229.0 0.7616615 0.7616615 1.114715e-04 1.114715e-04 0.238227063 #> 2460  1229.5 0.7617785 0.7617785 1.116808e-04 1.116808e-04 0.238109838 #> 2461  1230.0 0.7618954 0.7618954 1.118909e-04 1.118909e-04 0.237992675 #> 2462  1230.5 0.7620123 0.7620123 1.121017e-04 1.121017e-04 0.237875575 #> 2463  1231.0 0.7621292 0.7621292 1.123132e-04 1.123132e-04 0.237758537 #> 2464  1231.5 0.7622459 0.7622459 1.125255e-04 1.125255e-04 0.237641560 #> 2465  1232.0 0.7623626 0.7623626 1.127385e-04 1.127385e-04 0.237524646 #> 2466  1232.5 0.7624793 0.7624793 1.129522e-04 1.129522e-04 0.237407793 #> 2467  1233.0 0.7625958 0.7625958 1.131666e-04 1.131666e-04 0.237291003 #> 2468  1233.5 0.7627123 0.7627123 1.133818e-04 1.133818e-04 0.237174275 #> 2469  1234.0 0.7628288 0.7628288 1.135977e-04 1.135977e-04 0.237057608 #> 2470  1234.5 0.7629452 0.7629452 1.138144e-04 1.138144e-04 0.236941004 #> 2471  1235.0 0.7630615 0.7630615 1.140318e-04 1.140318e-04 0.236824461 #> 2472  1235.5 0.7631778 0.7631778 1.142500e-04 1.142500e-04 0.236707980 #> 2473  1236.0 0.7632940 0.7632940 1.144689e-04 1.144689e-04 0.236591561 #> 2474  1236.5 0.7634101 0.7634101 1.146885e-04 1.146885e-04 0.236475204 #> 2475  1237.0 0.7635262 0.7635262 1.149090e-04 1.149090e-04 0.236358909 #> 2476  1237.5 0.7636422 0.7636422 1.151301e-04 1.151301e-04 0.236242676 #> 2477  1238.0 0.7637581 0.7637581 1.153521e-04 1.153521e-04 0.236126504 #> 2478  1238.5 0.7638740 0.7638740 1.155748e-04 1.155748e-04 0.236010394 #> 2479  1239.0 0.7639899 0.7639899 1.157982e-04 1.157982e-04 0.235894346 #> 2480  1239.5 0.7641056 0.7641056 1.160225e-04 1.160225e-04 0.235778360 #> 2481  1240.0 0.7642213 0.7642213 1.162475e-04 1.162475e-04 0.235662435 #> 2482  1240.5 0.7643370 0.7643370 1.164732e-04 1.164732e-04 0.235546572 #> 2483  1241.0 0.7644525 0.7644525 1.166998e-04 1.166998e-04 0.235430771 #> 2484  1241.5 0.7645680 0.7645680 1.169271e-04 1.169271e-04 0.235315032 #> 2485  1242.0 0.7646835 0.7646835 1.171552e-04 1.171552e-04 0.235199354 #> 2486  1242.5 0.7647989 0.7647989 1.173841e-04 1.173841e-04 0.235083737 #> 2487  1243.0 0.7649142 0.7649142 1.176138e-04 1.176138e-04 0.234968183 #> 2488  1243.5 0.7650295 0.7650295 1.178443e-04 1.178443e-04 0.234852690 #> 2489  1244.0 0.7651447 0.7651447 1.180755e-04 1.180755e-04 0.234737258 #> 2490  1244.5 0.7652598 0.7652598 1.183076e-04 1.183076e-04 0.234621888 #> 2491  1245.0 0.7653749 0.7653749 1.185405e-04 1.185405e-04 0.234506580 #> 2492  1245.5 0.7654899 0.7654899 1.187741e-04 1.187741e-04 0.234391333 #> 2493  1246.0 0.7656048 0.7656048 1.190086e-04 1.190086e-04 0.234276148 #> 2494  1246.5 0.7657197 0.7657197 1.192438e-04 1.192438e-04 0.234161024 #> 2495  1247.0 0.7658346 0.7658346 1.194799e-04 1.194799e-04 0.234045961 #> 2496  1247.5 0.7659493 0.7659493 1.197168e-04 1.197168e-04 0.233930960 #> 2497  1248.0 0.7660640 0.7660640 1.199545e-04 1.199545e-04 0.233816021 #> 2498  1248.5 0.7661787 0.7661787 1.201930e-04 1.201930e-04 0.233701143 #> 2499  1249.0 0.7662932 0.7662932 1.204323e-04 1.204323e-04 0.233586326 #> 2500  1249.5 0.7664078 0.7664078 1.206725e-04 1.206725e-04 0.233471571 #> 2501  1250.0 0.7665222 0.7665222 1.209134e-04 1.209134e-04 0.233356877 #> 2502  1250.5 0.7666366 0.7666366 1.211552e-04 1.211552e-04 0.233242245 #> 2503  1251.0 0.7667509 0.7667509 1.213979e-04 1.213979e-04 0.233127673 #> 2504  1251.5 0.7668652 0.7668652 1.216413e-04 1.216413e-04 0.233013164 #> 2505  1252.0 0.7669794 0.7669794 1.218856e-04 1.218856e-04 0.232898715 #> 2506  1252.5 0.7670935 0.7670935 1.221308e-04 1.221308e-04 0.232784328 #> 2507  1253.0 0.7672076 0.7672076 1.223768e-04 1.223768e-04 0.232670002 #> 2508  1253.5 0.7673216 0.7673216 1.226236e-04 1.226236e-04 0.232555737 #> 2509  1254.0 0.7674356 0.7674356 1.228713e-04 1.228713e-04 0.232441533 #> 2510  1254.5 0.7675495 0.7675495 1.231198e-04 1.231198e-04 0.232327391 #> 2511  1255.0 0.7676633 0.7676633 1.233692e-04 1.233692e-04 0.232213310 #> 2512  1255.5 0.7677771 0.7677771 1.236194e-04 1.236194e-04 0.232099290 #> 2513  1256.0 0.7678908 0.7678908 1.238705e-04 1.238705e-04 0.231985331 #> 2514  1256.5 0.7680044 0.7680044 1.241225e-04 1.241225e-04 0.231871434 #> 2515  1257.0 0.7681180 0.7681180 1.243753e-04 1.243753e-04 0.231757597 #> 2516  1257.5 0.7682315 0.7682315 1.246290e-04 1.246290e-04 0.231643822 #> 2517  1258.0 0.7683450 0.7683450 1.248836e-04 1.248836e-04 0.231530108 #> 2518  1258.5 0.7684584 0.7684584 1.251391e-04 1.251391e-04 0.231416455 #> 2519  1259.0 0.7685717 0.7685717 1.253954e-04 1.253954e-04 0.231302863 #> 2520  1259.5 0.7686850 0.7686850 1.256526e-04 1.256526e-04 0.231189332 #> 2521  1260.0 0.7687982 0.7687982 1.259107e-04 1.259107e-04 0.231075862 #> 2522  1260.5 0.7689114 0.7689114 1.261696e-04 1.261696e-04 0.230962453 #> 2523  1261.0 0.7690245 0.7690245 1.264295e-04 1.264295e-04 0.230849105 #> 2524  1261.5 0.7691375 0.7691375 1.266903e-04 1.266903e-04 0.230735818 #> 2525  1262.0 0.7692505 0.7692505 1.269519e-04 1.269519e-04 0.230622592 #> 2526  1262.5 0.7693634 0.7693634 1.272145e-04 1.272145e-04 0.230509427 #> 2527  1263.0 0.7694762 0.7694762 1.274779e-04 1.274779e-04 0.230396323 #> 2528  1263.5 0.7695890 0.7695890 1.277423e-04 1.277423e-04 0.230283280 #> 2529  1264.0 0.7697017 0.7697017 1.280076e-04 1.280076e-04 0.230170298 #> 2530  1264.5 0.7698143 0.7698143 1.282738e-04 1.282738e-04 0.230057377 #> 2531  1265.0 0.7699269 0.7699269 1.285409e-04 1.285409e-04 0.229944516 #> 2532  1265.5 0.7700395 0.7700395 1.288089e-04 1.288089e-04 0.229831717 #> 2533  1266.0 0.7701519 0.7701519 1.290778e-04 1.290778e-04 0.229718978 #> 2534  1266.5 0.7702644 0.7702644 1.293477e-04 1.293477e-04 0.229606300 #> 2535  1267.0 0.7703767 0.7703767 1.296185e-04 1.296185e-04 0.229493683 #> 2536  1267.5 0.7704890 0.7704890 1.298902e-04 1.298902e-04 0.229381127 #> 2537  1268.0 0.7706012 0.7706012 1.301629e-04 1.301629e-04 0.229268632 #> 2538  1268.5 0.7707134 0.7707134 1.304365e-04 1.304365e-04 0.229156197 #> 2539  1269.0 0.7708255 0.7708255 1.307110e-04 1.307110e-04 0.229043824 #> 2540  1269.5 0.7709375 0.7709375 1.309865e-04 1.309865e-04 0.228931511 #> 2541  1270.0 0.7710495 0.7710495 1.312629e-04 1.312629e-04 0.228819258 #> 2542  1270.5 0.7711614 0.7711614 1.315403e-04 1.315403e-04 0.228707067 #> 2543  1271.0 0.7712732 0.7712732 1.318186e-04 1.318186e-04 0.228594936 #> 2544  1271.5 0.7713850 0.7713850 1.320979e-04 1.320979e-04 0.228482866 #> 2545  1272.0 0.7714968 0.7714968 1.323782e-04 1.323782e-04 0.228370856 #> 2546  1272.5 0.7716084 0.7716084 1.326594e-04 1.326594e-04 0.228258908 #> 2547  1273.0 0.7717200 0.7717200 1.329416e-04 1.329416e-04 0.228147019 #> 2548  1273.5 0.7718316 0.7718316 1.332248e-04 1.332248e-04 0.228035192 #> 2549  1274.0 0.7719431 0.7719431 1.335089e-04 1.335089e-04 0.227923425 #> 2550  1274.5 0.7720545 0.7720545 1.337940e-04 1.337940e-04 0.227811719 #> 2551  1275.0 0.7721658 0.7721658 1.340801e-04 1.340801e-04 0.227700073 #> 2552  1275.5 0.7722771 0.7722771 1.343672e-04 1.343672e-04 0.227588488 #> 2553  1276.0 0.7723884 0.7723884 1.346553e-04 1.346553e-04 0.227476964 #> 2554  1276.5 0.7724996 0.7724996 1.349444e-04 1.349444e-04 0.227365500 #> 2555  1277.0 0.7726107 0.7726107 1.352344e-04 1.352344e-04 0.227254096 #> 2556  1277.5 0.7727217 0.7727217 1.355255e-04 1.355255e-04 0.227142753 #> 2557  1278.0 0.7728327 0.7728327 1.358176e-04 1.358176e-04 0.227031471 #> 2558  1278.5 0.7729436 0.7729436 1.361106e-04 1.361106e-04 0.226920249 #> 2559  1279.0 0.7730545 0.7730545 1.364047e-04 1.364047e-04 0.226809088 #> 2560  1279.5 0.7731653 0.7731653 1.366998e-04 1.366998e-04 0.226697987 #> 2561  1280.0 0.7732761 0.7732761 1.369960e-04 1.369960e-04 0.226586946 #> 2562  1280.5 0.7733867 0.7733867 1.372931e-04 1.372931e-04 0.226475966 #> 2563  1281.0 0.7734974 0.7734974 1.375913e-04 1.375913e-04 0.226365047 #> 2564  1281.5 0.7736079 0.7736079 1.378905e-04 1.378905e-04 0.226254188 #> 2565  1282.0 0.7737184 0.7737184 1.381907e-04 1.381907e-04 0.226143389 #> 2566  1282.5 0.7738289 0.7738289 1.384919e-04 1.384919e-04 0.226032651 #> 2567  1283.0 0.7739392 0.7739392 1.387942e-04 1.387942e-04 0.225921973 #> 2568  1283.5 0.7740495 0.7740495 1.390976e-04 1.390976e-04 0.225811355 #> 2569  1284.0 0.7741598 0.7741598 1.394020e-04 1.394020e-04 0.225700798 #> 2570  1284.5 0.7742700 0.7742700 1.397074e-04 1.397074e-04 0.225590301 #> 2571  1285.0 0.7743801 0.7743801 1.400139e-04 1.400139e-04 0.225479865 #> 2572  1285.5 0.7744902 0.7744902 1.403215e-04 1.403215e-04 0.225369488 #> 2573  1286.0 0.7746002 0.7746002 1.406301e-04 1.406301e-04 0.225259173 #> 2574  1286.5 0.7747101 0.7747101 1.409398e-04 1.409398e-04 0.225148917 #> 2575  1287.0 0.7748200 0.7748200 1.412505e-04 1.412505e-04 0.225038722 #> 2576  1287.5 0.7749299 0.7749299 1.415624e-04 1.415624e-04 0.224928587 #> 2577  1288.0 0.7750396 0.7750396 1.418753e-04 1.418753e-04 0.224818512 #> 2578  1288.5 0.7751493 0.7751493 1.421893e-04 1.421893e-04 0.224708497 #> 2579  1289.0 0.7752590 0.7752590 1.425043e-04 1.425043e-04 0.224598543 #> 2580  1289.5 0.7753685 0.7753685 1.428205e-04 1.428205e-04 0.224488649 #> 2581  1290.0 0.7754780 0.7754780 1.431378e-04 1.431378e-04 0.224378815 #> 2582  1290.5 0.7755875 0.7755875 1.434561e-04 1.434561e-04 0.224269041 #> 2583  1291.0 0.7756969 0.7756969 1.437756e-04 1.437756e-04 0.224159328 #> 2584  1291.5 0.7758062 0.7758062 1.440961e-04 1.440961e-04 0.224049674 #> 2585  1292.0 0.7759155 0.7759155 1.444178e-04 1.444178e-04 0.223940081 #> 2586  1292.5 0.7760247 0.7760247 1.447406e-04 1.447406e-04 0.223830548 #> 2587  1293.0 0.7761339 0.7761339 1.450645e-04 1.450645e-04 0.223721075 #> 2588  1293.5 0.7762429 0.7762429 1.453895e-04 1.453895e-04 0.223611663 #> 2589  1294.0 0.7763520 0.7763520 1.457157e-04 1.457157e-04 0.223502310 #> 2590  1294.5 0.7764609 0.7764609 1.460430e-04 1.460430e-04 0.223393018 #> 2591  1295.0 0.7765698 0.7765698 1.463714e-04 1.463714e-04 0.223283785 #> 2592  1295.5 0.7766787 0.7766787 1.467009e-04 1.467009e-04 0.223174613 #> 2593  1296.0 0.7767875 0.7767875 1.470316e-04 1.470316e-04 0.223065501 #> 2594  1296.5 0.7768962 0.7768962 1.473635e-04 1.473635e-04 0.222956448 #> 2595  1297.0 0.7770048 0.7770048 1.476964e-04 1.476964e-04 0.222847456 #> 2596  1297.5 0.7771134 0.7771134 1.480306e-04 1.480306e-04 0.222738524 #> 2597  1298.0 0.7772220 0.7772220 1.483659e-04 1.483659e-04 0.222629652 #> 2598  1298.5 0.7773305 0.7773305 1.487024e-04 1.487024e-04 0.222520840 #> 2599  1299.0 0.7774389 0.7774389 1.490400e-04 1.490400e-04 0.222412088 #> 2600  1299.5 0.7775472 0.7775472 1.493788e-04 1.493788e-04 0.222303396 #> 2601  1300.0 0.7776555 0.7776555 1.497188e-04 1.497188e-04 0.222194764 #> 2602  1300.5 0.7777637 0.7777637 1.500599e-04 1.500599e-04 0.222086192 #> 2603  1301.0 0.7778719 0.7778719 1.504023e-04 1.504023e-04 0.221977680 #> 2604  1301.5 0.7779800 0.7779800 1.507458e-04 1.507458e-04 0.221869227 #> 2605  1302.0 0.7780881 0.7780881 1.510905e-04 1.510905e-04 0.221760835 #> 2606  1302.5 0.7781961 0.7781961 1.514365e-04 1.514365e-04 0.221652503 #> 2607  1303.0 0.7783040 0.7783040 1.517836e-04 1.517836e-04 0.221544231 #> 2608  1303.5 0.7784118 0.7784118 1.521319e-04 1.521319e-04 0.221436018 #> 2609  1304.0 0.7785197 0.7785197 1.524814e-04 1.524814e-04 0.221327866 #> 2610  1304.5 0.7786274 0.7786274 1.528322e-04 1.528322e-04 0.221219773 #> 2611  1305.0 0.7787351 0.7787351 1.531842e-04 1.531842e-04 0.221111740 #> 2612  1305.5 0.7788427 0.7788427 1.535374e-04 1.535374e-04 0.221003767 #> 2613  1306.0 0.7789503 0.7789503 1.538918e-04 1.538918e-04 0.220895854 #> 2614  1306.5 0.7790578 0.7790578 1.542475e-04 1.542475e-04 0.220788001 #> 2615  1307.0 0.7791652 0.7791652 1.546044e-04 1.546044e-04 0.220680208 #> 2616  1307.5 0.7792726 0.7792726 1.549625e-04 1.549625e-04 0.220572475 #> 2617  1308.0 0.7793799 0.7793799 1.553219e-04 1.553219e-04 0.220464801 #> 2618  1308.5 0.7794871 0.7794871 1.556825e-04 1.556825e-04 0.220357187 #> 2619  1309.0 0.7795943 0.7795943 1.560444e-04 1.560444e-04 0.220249633 #> 2620  1309.5 0.7797015 0.7797015 1.564076e-04 1.564076e-04 0.220142139 #> 2621  1310.0 0.7798085 0.7798085 1.567720e-04 1.567720e-04 0.220034705 #> 2622  1310.5 0.7799155 0.7799155 1.571377e-04 1.571377e-04 0.219927330 #> 2623  1311.0 0.7800225 0.7800225 1.575046e-04 1.575046e-04 0.219820015 #> 2624  1311.5 0.7801294 0.7801294 1.578729e-04 1.578729e-04 0.219712760 #> 2625  1312.0 0.7802362 0.7802362 1.582424e-04 1.582424e-04 0.219605565 #> 2626  1312.5 0.7803430 0.7803430 1.586132e-04 1.586132e-04 0.219498429 #> 2627  1313.0 0.7804497 0.7804497 1.589853e-04 1.589853e-04 0.219391354 #> 2628  1313.5 0.7805563 0.7805563 1.593587e-04 1.593587e-04 0.219284338 #> 2629  1314.0 0.7806629 0.7806629 1.597334e-04 1.597334e-04 0.219177381 #> 2630  1314.5 0.7807694 0.7807694 1.601094e-04 1.601094e-04 0.219070485 #> 2631  1315.0 0.7808759 0.7808759 1.604868e-04 1.604868e-04 0.218963648 #> 2632  1315.5 0.7809823 0.7809823 1.608654e-04 1.608654e-04 0.218856870 #> 2633  1316.0 0.7810886 0.7810886 1.612454e-04 1.612454e-04 0.218750153 #> 2634  1316.5 0.7811949 0.7811949 1.616266e-04 1.616266e-04 0.218643495 #> 2635  1317.0 0.7813011 0.7813011 1.620093e-04 1.620093e-04 0.218536897 #> 2636  1317.5 0.7814072 0.7814072 1.623932e-04 1.623932e-04 0.218430359 #> 2637  1318.0 0.7815133 0.7815133 1.627785e-04 1.627785e-04 0.218323880 #> 2638  1318.5 0.7816194 0.7816194 1.631652e-04 1.631652e-04 0.218217461 #> 2639  1319.0 0.7817253 0.7817253 1.635532e-04 1.635532e-04 0.218111101 #> 2640  1319.5 0.7818313 0.7818313 1.639425e-04 1.639425e-04 0.218004801 #> 2641  1320.0 0.7819371 0.7819371 1.643333e-04 1.643333e-04 0.217898561 #> 2642  1320.5 0.7820429 0.7820429 1.647254e-04 1.647254e-04 0.217792381 #> 2643  1321.0 0.7821486 0.7821486 1.651188e-04 1.651188e-04 0.217686260 #> 2644  1321.5 0.7822543 0.7822543 1.655136e-04 1.655136e-04 0.217580198 #> 2645  1322.0 0.7823599 0.7823599 1.659099e-04 1.659099e-04 0.217474197 #> 2646  1322.5 0.7824654 0.7824654 1.663075e-04 1.663075e-04 0.217368254 #> 2647  1323.0 0.7825709 0.7825709 1.667065e-04 1.667065e-04 0.217262372 #> 2648  1323.5 0.7826763 0.7826763 1.671069e-04 1.671069e-04 0.217156549 #> 2649  1324.0 0.7827817 0.7827817 1.675087e-04 1.675087e-04 0.217050786 #> 2650  1324.5 0.7828870 0.7828870 1.679119e-04 1.679119e-04 0.216945082 #> 2651  1325.0 0.7829922 0.7829922 1.683165e-04 1.683165e-04 0.216839437 #> 2652  1325.5 0.7830974 0.7830974 1.687225e-04 1.687225e-04 0.216733853 #> 2653  1326.0 0.7832025 0.7832025 1.691300e-04 1.691300e-04 0.216628328 #> 2654  1326.5 0.7833076 0.7833076 1.695389e-04 1.695389e-04 0.216522862 #> 2655  1327.0 0.7834126 0.7834126 1.699492e-04 1.699492e-04 0.216417456 #> 2656  1327.5 0.7835175 0.7835175 1.703610e-04 1.703610e-04 0.216312110 #> 2657  1328.0 0.7836224 0.7836224 1.707742e-04 1.707742e-04 0.216206823 #> 2658  1328.5 0.7837272 0.7837272 1.711889e-04 1.711889e-04 0.216101595 #> 2659  1329.0 0.7838320 0.7838320 1.716050e-04 1.716050e-04 0.215996427 #> 2660  1329.5 0.7839367 0.7839367 1.720226e-04 1.720226e-04 0.215891319 #> 2661  1330.0 0.7840413 0.7840413 1.724416e-04 1.724416e-04 0.215786270 #> 2662  1330.5 0.7841459 0.7841459 1.728621e-04 1.728621e-04 0.215681281 #> 2663  1331.0 0.7842504 0.7842504 1.732841e-04 1.732841e-04 0.215576351 #> 2664  1331.5 0.7843548 0.7843548 1.737076e-04 1.737076e-04 0.215471481 #> 2665  1332.0 0.7844592 0.7844592 1.741326e-04 1.741326e-04 0.215366670 #> 2666  1332.5 0.7845635 0.7845635 1.745591e-04 1.745591e-04 0.215261918 #> 2667  1333.0 0.7846678 0.7846678 1.749870e-04 1.749870e-04 0.215157226 #> 2668  1333.5 0.7847720 0.7847720 1.754165e-04 1.754165e-04 0.215052594 #> 2669  1334.0 0.7848761 0.7848761 1.758475e-04 1.758475e-04 0.214948021 #> 2670  1334.5 0.7849802 0.7849802 1.762800e-04 1.762800e-04 0.214843508 #> 2671  1335.0 0.7850842 0.7850842 1.767141e-04 1.767141e-04 0.214739054 #> 2672  1335.5 0.7851882 0.7851882 1.771496e-04 1.771496e-04 0.214634659 #> 2673  1336.0 0.7852921 0.7852921 1.775867e-04 1.775867e-04 0.214530324 #> 2674  1336.5 0.7853959 0.7853959 1.780253e-04 1.780253e-04 0.214426048 #> 2675  1337.0 0.7854997 0.7854997 1.784655e-04 1.784655e-04 0.214321832 #> 2676  1337.5 0.7856034 0.7856034 1.789073e-04 1.789073e-04 0.214217675 #> 2677  1338.0 0.7857071 0.7857071 1.793506e-04 1.793506e-04 0.214113578 #> 2678  1338.5 0.7858107 0.7858107 1.797954e-04 1.797954e-04 0.214009540 #> 2679  1339.0 0.7859142 0.7859142 1.802418e-04 1.802418e-04 0.213905562 #> 2680  1339.5 0.7860177 0.7860177 1.806898e-04 1.806898e-04 0.213801643 #> 2681  1340.0 0.7861211 0.7861211 1.811394e-04 1.811394e-04 0.213697783 #> 2682  1340.5 0.7862244 0.7862244 1.815906e-04 1.815906e-04 0.213593983 #> 2683  1341.0 0.7863277 0.7863277 1.820434e-04 1.820434e-04 0.213490242 #> 2684  1341.5 0.7864309 0.7864309 1.824977e-04 1.824977e-04 0.213386561 #> 2685  1342.0 0.7865341 0.7865341 1.829537e-04 1.829537e-04 0.213282939 #> 2686  1342.5 0.7866372 0.7866372 1.834113e-04 1.834113e-04 0.213179376 #> 2687  1343.0 0.7867403 0.7867403 1.838705e-04 1.838705e-04 0.213075873 #> 2688  1343.5 0.7868432 0.7868432 1.843313e-04 1.843313e-04 0.212972429 #> 2689  1344.0 0.7869462 0.7869462 1.847938e-04 1.847938e-04 0.212869045 #> 2690  1344.5 0.7870490 0.7870490 1.852579e-04 1.852579e-04 0.212765720 #> 2691  1345.0 0.7871518 0.7871518 1.857236e-04 1.857236e-04 0.212662454 #> 2692  1345.5 0.7872546 0.7872546 1.861910e-04 1.861910e-04 0.212559248 #> 2693  1346.0 0.7873572 0.7873572 1.866600e-04 1.866600e-04 0.212456102 #> 2694  1346.5 0.7874599 0.7874599 1.871307e-04 1.871307e-04 0.212353014 #> 2695  1347.0 0.7875624 0.7875624 1.876031e-04 1.876031e-04 0.212249986 #> 2696  1347.5 0.7876649 0.7876649 1.880772e-04 1.880772e-04 0.212147018 #> 2697  1348.0 0.7877673 0.7877673 1.885529e-04 1.885529e-04 0.212044108 #> 2698  1348.5 0.7878697 0.7878697 1.890303e-04 1.890303e-04 0.211941259 #> 2699  1349.0 0.7879720 0.7879720 1.895094e-04 1.895094e-04 0.211838468 #> 2700  1349.5 0.7880743 0.7880743 1.899902e-04 1.899902e-04 0.211735737 #> 2701  1350.0 0.7881765 0.7881765 1.904727e-04 1.904727e-04 0.211633066 #> 2702  1350.5 0.7882786 0.7882786 1.909570e-04 1.909570e-04 0.211530453 #> 2703  1351.0 0.7883807 0.7883807 1.914429e-04 1.914429e-04 0.211427900 #> 2704  1351.5 0.7884827 0.7884827 1.919306e-04 1.919306e-04 0.211325407 #> 2705  1352.0 0.7885846 0.7885846 1.924200e-04 1.924200e-04 0.211222973 #> 2706  1352.5 0.7886865 0.7886865 1.929111e-04 1.929111e-04 0.211120598 #> 2707  1353.0 0.7887883 0.7887883 1.934040e-04 1.934040e-04 0.211018282 #> 2708  1353.5 0.7888901 0.7888901 1.938987e-04 1.938987e-04 0.210916026 #> 2709  1354.0 0.7889918 0.7889918 1.943951e-04 1.943951e-04 0.210813830 #> 2710  1354.5 0.7890934 0.7890934 1.948933e-04 1.948933e-04 0.210711692 #> 2711  1355.0 0.7891950 0.7891950 1.953932e-04 1.953932e-04 0.210609614 #> 2712  1355.5 0.7892965 0.7892965 1.958949e-04 1.958949e-04 0.210507596 #> 2713  1356.0 0.7893980 0.7893980 1.963984e-04 1.963984e-04 0.210405637 #> 2714  1356.5 0.7894994 0.7894994 1.969037e-04 1.969037e-04 0.210303737 #> 2715  1357.0 0.7896007 0.7896007 1.974109e-04 1.974109e-04 0.210201896 #> 2716  1357.5 0.7897020 0.7897020 1.979198e-04 1.979198e-04 0.210100115 #> 2717  1358.0 0.7898032 0.7898032 1.984305e-04 1.984305e-04 0.209998394 #> 2718  1358.5 0.7899043 0.7899043 1.989430e-04 1.989430e-04 0.209896731 #> 2719  1359.0 0.7900054 0.7900054 1.994574e-04 1.994574e-04 0.209795128 #> 2720  1359.5 0.7901064 0.7901064 1.999736e-04 1.999736e-04 0.209693585 #> 2721  1360.0 0.7902074 0.7902074 2.004917e-04 2.004917e-04 0.209592100 #> 2722  1360.5 0.7903083 0.7903083 2.010116e-04 2.010116e-04 0.209490676 #> 2723  1361.0 0.7904092 0.7904092 2.015333e-04 2.015333e-04 0.209389310 #> 2724  1361.5 0.7905099 0.7905099 2.020570e-04 2.020570e-04 0.209288004 #> 2725  1362.0 0.7906107 0.7906107 2.025825e-04 2.025825e-04 0.209186757 #> 2726  1362.5 0.7907113 0.7907113 2.031098e-04 2.031098e-04 0.209085570 #> 2727  1363.0 0.7908119 0.7908119 2.036391e-04 2.036391e-04 0.208984442 #> 2728  1363.5 0.7909125 0.7909125 2.041702e-04 2.041702e-04 0.208883374 #> 2729  1364.0 0.7910129 0.7910129 2.047033e-04 2.047033e-04 0.208782364 #> 2730  1364.5 0.7911133 0.7911133 2.052382e-04 2.052382e-04 0.208681415 #> 2731  1365.0 0.7912137 0.7912137 2.057751e-04 2.057751e-04 0.208580524 #> 2732  1365.5 0.7913140 0.7913140 2.063139e-04 2.063139e-04 0.208479693 #> 2733  1366.0 0.7914142 0.7914142 2.068546e-04 2.068546e-04 0.208378922 #> 2734  1366.5 0.7915144 0.7915144 2.073973e-04 2.073973e-04 0.208278210 #> 2735  1367.0 0.7916145 0.7916145 2.079419e-04 2.079419e-04 0.208177557 #> 2736  1367.5 0.7917145 0.7917145 2.084884e-04 2.084884e-04 0.208076963 #> 2737  1368.0 0.7918145 0.7918145 2.090369e-04 2.090369e-04 0.207976429 #> 2738  1368.5 0.7919145 0.7919145 2.095874e-04 2.095874e-04 0.207875955 #> 2739  1369.0 0.7920143 0.7920143 2.101399e-04 2.101399e-04 0.207775540 #> 2740  1369.5 0.7921141 0.7921141 2.106943e-04 2.106943e-04 0.207675184 #> 2741  1370.0 0.7922139 0.7922139 2.112507e-04 2.112507e-04 0.207574888 #> 2742  1370.5 0.7923135 0.7923135 2.118092e-04 2.118092e-04 0.207474651 #> 2743  1371.0 0.7924132 0.7924132 2.123696e-04 2.123696e-04 0.207374473 #> 2744  1371.5 0.7925127 0.7925127 2.129320e-04 2.129320e-04 0.207274355 #> 2745  1372.0 0.7926122 0.7926122 2.134965e-04 2.134965e-04 0.207174297 #> 2746  1372.5 0.7927116 0.7927116 2.140630e-04 2.140630e-04 0.207074298 #> 2747  1373.0 0.7928110 0.7928110 2.146315e-04 2.146315e-04 0.206974358 #> 2748  1373.5 0.7929103 0.7929103 2.152021e-04 2.152021e-04 0.206874478 #> 2749  1374.0 0.7930096 0.7930096 2.157747e-04 2.157747e-04 0.206774657 #> 2750  1374.5 0.7931088 0.7931088 2.163494e-04 2.163494e-04 0.206674895 #> 2751  1375.0 0.7932079 0.7932079 2.169261e-04 2.169261e-04 0.206575194 #> 2752  1375.5 0.7933069 0.7933069 2.175050e-04 2.175050e-04 0.206475551 #> 2753  1376.0 0.7934059 0.7934059 2.180859e-04 2.180859e-04 0.206375968 #> 2754  1376.5 0.7935049 0.7935049 2.186689e-04 2.186689e-04 0.206276445 #> 2755  1377.0 0.7936038 0.7936038 2.192540e-04 2.192540e-04 0.206176981 #> 2756  1377.5 0.7937026 0.7937026 2.198412e-04 2.198412e-04 0.206077576 #> 2757  1378.0 0.7938013 0.7938013 2.204305e-04 2.204305e-04 0.205978231 #> 2758  1378.5 0.7939000 0.7939000 2.210220e-04 2.210220e-04 0.205878946 #> 2759  1379.0 0.7939987 0.7939987 2.216156e-04 2.216156e-04 0.205779720 #> 2760  1379.5 0.7940972 0.7940972 2.222113e-04 2.222113e-04 0.205680553 #> 2761  1380.0 0.7941957 0.7941957 2.228092e-04 2.228092e-04 0.205581446 #> 2762  1380.5 0.7942942 0.7942942 2.234093e-04 2.234093e-04 0.205482399 #> 2763  1381.0 0.7943926 0.7943926 2.240115e-04 2.240115e-04 0.205383411 #> 2764  1381.5 0.7944909 0.7944909 2.246158e-04 2.246158e-04 0.205284482 #> 2765  1382.0 0.7945892 0.7945892 2.252224e-04 2.252224e-04 0.205185613 #> 2766  1382.5 0.7946874 0.7946874 2.258312e-04 2.258312e-04 0.205086804 #> 2767  1383.0 0.7947855 0.7947855 2.264421e-04 2.264421e-04 0.204988054 #> 2768  1383.5 0.7948836 0.7948836 2.270553e-04 2.270553e-04 0.204889364 #> 2769  1384.0 0.7949816 0.7949816 2.276707e-04 2.276707e-04 0.204790733 #> 2770  1384.5 0.7950795 0.7950795 2.282883e-04 2.282883e-04 0.204692162 #> 2771  1385.0 0.7951774 0.7951774 2.289082e-04 2.289082e-04 0.204593651 #> 2772  1385.5 0.7952753 0.7952753 2.295303e-04 2.295303e-04 0.204495199 #> 2773  1386.0 0.7953730 0.7953730 2.301546e-04 2.301546e-04 0.204396807 #> 2774  1386.5 0.7954707 0.7954707 2.307812e-04 2.307812e-04 0.204298474 #> 2775  1387.0 0.7955684 0.7955684 2.314101e-04 2.314101e-04 0.204200201 #> 2776  1387.5 0.7956660 0.7956660 2.320413e-04 2.320413e-04 0.204101987 #> 2777  1388.0 0.7957635 0.7957635 2.326748e-04 2.326748e-04 0.204003834 #> 2778  1388.5 0.7958610 0.7958610 2.333105e-04 2.333105e-04 0.203905739 #> 2779  1389.0 0.7959583 0.7959583 2.339486e-04 2.339486e-04 0.203807705 #> 2780  1389.5 0.7960557 0.7960557 2.345889e-04 2.345889e-04 0.203709730 #> 2781  1390.0 0.7961530 0.7961530 2.352316e-04 2.352316e-04 0.203611815 #> 2782  1390.5 0.7962502 0.7962502 2.358767e-04 2.358767e-04 0.203513959 #> 2783  1391.0 0.7963473 0.7963473 2.365240e-04 2.365240e-04 0.203416163 #> 2784  1391.5 0.7964444 0.7964444 2.371738e-04 2.371738e-04 0.203318427 #> 2785  1392.0 0.7965414 0.7965414 2.378259e-04 2.378259e-04 0.203220751 #> 2786  1392.5 0.7966384 0.7966384 2.384803e-04 2.384803e-04 0.203123134 #> 2787  1393.0 0.7967353 0.7967353 2.391372e-04 2.391372e-04 0.203025577 #> 2788  1393.5 0.7968321 0.7968321 2.397964e-04 2.397964e-04 0.202928080 #> 2789  1394.0 0.7969289 0.7969289 2.404580e-04 2.404580e-04 0.202830642 #> 2790  1394.5 0.7970256 0.7970256 2.411221e-04 2.411221e-04 0.202733264 #> 2791  1395.0 0.7971223 0.7971223 2.417886e-04 2.417886e-04 0.202635946 #> 2792  1395.5 0.7972189 0.7972189 2.424574e-04 2.424574e-04 0.202538688 #> 2793  1396.0 0.7973154 0.7973154 2.431288e-04 2.431288e-04 0.202441490 #> 2794  1396.5 0.7974118 0.7974118 2.438025e-04 2.438025e-04 0.202344351 #> 2795  1397.0 0.7975082 0.7975082 2.444788e-04 2.444788e-04 0.202247272 #> 2796  1397.5 0.7976046 0.7976046 2.451575e-04 2.451575e-04 0.202150253 #> 2797  1398.0 0.7977009 0.7977009 2.458386e-04 2.458386e-04 0.202053294 #> 2798  1398.5 0.7977971 0.7977971 2.465223e-04 2.465223e-04 0.201956394 #> 2799  1399.0 0.7978932 0.7978932 2.472084e-04 2.472084e-04 0.201859555 #> 2800  1399.5 0.7979893 0.7979893 2.478971e-04 2.478971e-04 0.201762775 #> 2801  1400.0 0.7980854 0.7980854 2.485883e-04 2.485883e-04 0.201666055 #> 2802  1400.5 0.7981813 0.7981813 2.492820e-04 2.492820e-04 0.201569395 #> 2803  1401.0 0.7982772 0.7982772 2.499782e-04 2.499782e-04 0.201472795 #> 2804  1401.5 0.7983731 0.7983731 2.506770e-04 2.506770e-04 0.201376255 #> 2805  1402.0 0.7984688 0.7984688 2.513783e-04 2.513783e-04 0.201279775 #> 2806  1402.5 0.7985646 0.7985646 2.520822e-04 2.520822e-04 0.201183354 #> 2807  1403.0 0.7986602 0.7986602 2.527887e-04 2.527887e-04 0.201086994 #> 2808  1403.5 0.7987558 0.7987558 2.534978e-04 2.534978e-04 0.200990694 #> 2809  1404.0 0.7988513 0.7988513 2.542094e-04 2.542094e-04 0.200894453 #> 2810  1404.5 0.7989468 0.7989468 2.549237e-04 2.549237e-04 0.200798273 #> 2811  1405.0 0.7990422 0.7990422 2.556406e-04 2.556406e-04 0.200702153 #> 2812  1405.5 0.7991375 0.7991375 2.563601e-04 2.563601e-04 0.200606092 #> 2813  1406.0 0.7992328 0.7992328 2.570822e-04 2.570822e-04 0.200510092 #> 2814  1406.5 0.7993280 0.7993280 2.578070e-04 2.578070e-04 0.200414151 #> 2815  1407.0 0.7994232 0.7994232 2.585345e-04 2.585345e-04 0.200318271 #> 2816  1407.5 0.7995183 0.7995183 2.592646e-04 2.592646e-04 0.200222451 #> 2817  1408.0 0.7996133 0.7996133 2.599974e-04 2.599974e-04 0.200126691 #> 2818  1408.5 0.7997083 0.7997083 2.607329e-04 2.607329e-04 0.200030991 #> 2819  1409.0 0.7998032 0.7998032 2.614711e-04 2.614711e-04 0.199935351 #> 2820  1409.5 0.7998980 0.7998980 2.622120e-04 2.622120e-04 0.199839771 #> 2821  1410.0 0.7999928 0.7999928 2.629556e-04 2.629556e-04 0.199744252 #> 2822  1410.5 0.8000875 0.8000875 2.637020e-04 2.637020e-04 0.199648792 #> 2823  1411.0 0.8001822 0.8001822 2.644511e-04 2.644511e-04 0.199553393 #> 2824  1411.5 0.8002767 0.8002767 2.652029e-04 2.652029e-04 0.199458054 #> 2825  1412.0 0.8003713 0.8003713 2.659576e-04 2.659576e-04 0.199362775 #> 2826  1412.5 0.8004657 0.8004657 2.667150e-04 2.667150e-04 0.199267556 #> 2827  1413.0 0.8005601 0.8005601 2.674752e-04 2.674752e-04 0.199172398 #> 2828  1413.5 0.8006545 0.8006545 2.682382e-04 2.682382e-04 0.199077300 #> 2829  1414.0 0.8007487 0.8007487 2.690040e-04 2.690040e-04 0.198982262 #> 2830  1414.5 0.8008429 0.8008429 2.697726e-04 2.697726e-04 0.198887284 #> 2831  1415.0 0.8009371 0.8009371 2.705441e-04 2.705441e-04 0.198792367 #> 2832  1415.5 0.8010312 0.8010312 2.713184e-04 2.713184e-04 0.198697510 #> 2833  1416.0 0.8011252 0.8011252 2.720955e-04 2.720955e-04 0.198602714 #> 2834  1416.5 0.8012191 0.8012191 2.728755e-04 2.728755e-04 0.198507977 #> 2835  1417.0 0.8013130 0.8013130 2.736585e-04 2.736585e-04 0.198413301 #> 2836  1417.5 0.8014069 0.8014069 2.744442e-04 2.744442e-04 0.198318686 #> 2837  1418.0 0.8015006 0.8015006 2.752329e-04 2.752329e-04 0.198224131 #> 2838  1418.5 0.8015943 0.8015943 2.760246e-04 2.760246e-04 0.198129636 #> 2839  1419.0 0.8016880 0.8016880 2.768191e-04 2.768191e-04 0.198035202 #> 2840  1419.5 0.8017816 0.8017816 2.776166e-04 2.776166e-04 0.197940828 #> 2841  1420.0 0.8018751 0.8018751 2.784170e-04 2.784170e-04 0.197846515 #> 2842  1420.5 0.8019685 0.8019685 2.792204e-04 2.792204e-04 0.197752262 #> 2843  1421.0 0.8020619 0.8020619 2.800267e-04 2.800267e-04 0.197658070 #> 2844  1421.5 0.8021552 0.8021552 2.808361e-04 2.808361e-04 0.197563939 #> 2845  1422.0 0.8022485 0.8022485 2.816484e-04 2.816484e-04 0.197469867 #> 2846  1422.5 0.8023417 0.8023417 2.824637e-04 2.824637e-04 0.197375857 #> 2847  1423.0 0.8024348 0.8024348 2.832821e-04 2.832821e-04 0.197281907 #> 2848  1423.5 0.8025279 0.8025279 2.841035e-04 2.841035e-04 0.197188018 #> 2849  1424.0 0.8026209 0.8026209 2.849279e-04 2.849279e-04 0.197094189 #> 2850  1424.5 0.8027138 0.8027138 2.857554e-04 2.857554e-04 0.197000421 #> 2851  1425.0 0.8028067 0.8028067 2.865860e-04 2.865860e-04 0.196906714 #> 2852  1425.5 0.8028995 0.8028995 2.874196e-04 2.874196e-04 0.196813067 #> 2853  1426.0 0.8029923 0.8029923 2.882564e-04 2.882564e-04 0.196719482 #> 2854  1426.5 0.8030849 0.8030849 2.890962e-04 2.890962e-04 0.196625956 #> 2855  1427.0 0.8031776 0.8031776 2.899392e-04 2.899392e-04 0.196532492 #> 2856  1427.5 0.8032701 0.8032701 2.907853e-04 2.907853e-04 0.196439089 #> 2857  1428.0 0.8033626 0.8033626 2.916345e-04 2.916345e-04 0.196345746 #> 2858  1428.5 0.8034550 0.8034550 2.924869e-04 2.924869e-04 0.196252464 #> 2859  1429.0 0.8035474 0.8035474 2.933425e-04 2.933425e-04 0.196159243 #> 2860  1429.5 0.8036397 0.8036397 2.942012e-04 2.942012e-04 0.196066083 #> 2861  1430.0 0.8037320 0.8037320 2.950632e-04 2.950632e-04 0.195972984 #> 2862  1430.5 0.8038241 0.8038241 2.959283e-04 2.959283e-04 0.195879945 #> 2863  1431.0 0.8039162 0.8039162 2.967967e-04 2.967967e-04 0.195786968 #> 2864  1431.5 0.8040083 0.8040083 2.976683e-04 2.976683e-04 0.195694052 #> 2865  1432.0 0.8041003 0.8041003 2.985431e-04 2.985431e-04 0.195601196 #> 2866  1432.5 0.8041922 0.8041922 2.994212e-04 2.994212e-04 0.195508402 #> 2867  1433.0 0.8042840 0.8042840 3.003026e-04 3.003026e-04 0.195415669 #> 2868  1433.5 0.8043758 0.8043758 3.011873e-04 3.011873e-04 0.195322996 #> 2869  1434.0 0.8044675 0.8044675 3.020752e-04 3.020752e-04 0.195230385 #> 2870  1434.5 0.8045592 0.8045592 3.029665e-04 3.029665e-04 0.195137835 #> 2871  1435.0 0.8046508 0.8046508 3.038611e-04 3.038611e-04 0.195045346 #> 2872  1435.5 0.8047423 0.8047423 3.047591e-04 3.047591e-04 0.194952918 #> 2873  1436.0 0.8048338 0.8048338 3.056603e-04 3.056603e-04 0.194860552 #> 2874  1436.5 0.8049252 0.8049252 3.065650e-04 3.065650e-04 0.194768247 #> 2875  1437.0 0.8050165 0.8050165 3.074730e-04 3.074730e-04 0.194676002 #> 2876  1437.5 0.8051078 0.8051078 3.083845e-04 3.083845e-04 0.194583820 #> 2877  1438.0 0.8051990 0.8051990 3.092993e-04 3.092993e-04 0.194491698 #> 2878  1438.5 0.8052901 0.8052901 3.102176e-04 3.102176e-04 0.194399638 #> 2879  1439.0 0.8053812 0.8053812 3.111393e-04 3.111393e-04 0.194307639 #> 2880  1439.5 0.8054722 0.8054722 3.120644e-04 3.120644e-04 0.194215702 #> 2881  1440.0 0.8055632 0.8055632 3.129930e-04 3.129930e-04 0.194123826 #> 2882  1440.5 0.8056541 0.8056541 3.139251e-04 3.139251e-04 0.194032011 #> 2883  1441.0 0.8057449 0.8057449 3.148607e-04 3.148607e-04 0.193940258 #> 2884  1441.5 0.8058356 0.8058356 3.157998e-04 3.157998e-04 0.193848566 #> 2885  1442.0 0.8059263 0.8059263 3.167424e-04 3.167424e-04 0.193756936 #> 2886  1442.5 0.8060169 0.8060169 3.176885e-04 3.176885e-04 0.193665367 #> 2887  1443.0 0.8061075 0.8061075 3.186382e-04 3.186382e-04 0.193573860 #> 2888  1443.5 0.8061980 0.8061980 3.195914e-04 3.195914e-04 0.193482415 #> 2889  1444.0 0.8062884 0.8062884 3.205483e-04 3.205483e-04 0.193391031 #> 2890  1444.5 0.8063788 0.8063788 3.215087e-04 3.215087e-04 0.193299709 #> 2891  1445.0 0.8064691 0.8064691 3.224727e-04 3.224727e-04 0.193208448 #> 2892  1445.5 0.8065593 0.8065593 3.234403e-04 3.234403e-04 0.193117249 #> 2893  1446.0 0.8066495 0.8066495 3.244116e-04 3.244116e-04 0.193026112 #> 2894  1446.5 0.8067396 0.8067396 3.253865e-04 3.253865e-04 0.192935037 #> 2895  1447.0 0.8068296 0.8068296 3.263651e-04 3.263651e-04 0.192844023 #> 2896  1447.5 0.8069196 0.8069196 3.273474e-04 3.273474e-04 0.192753072 #> 2897  1448.0 0.8070095 0.8070095 3.283333e-04 3.283333e-04 0.192662182 #> 2898  1448.5 0.8070993 0.8070993 3.293230e-04 3.293230e-04 0.192571354 #> 2899  1449.0 0.8071891 0.8071891 3.303164e-04 3.303164e-04 0.192480588 #> 2900  1449.5 0.8072788 0.8072788 3.313135e-04 3.313135e-04 0.192389884 #> 2901  1450.0 0.8073684 0.8073684 3.323144e-04 3.323144e-04 0.192299242 #> 2902  1450.5 0.8074580 0.8074580 3.333191e-04 3.333191e-04 0.192208662 #> 2903  1451.0 0.8075475 0.8075475 3.343275e-04 3.343275e-04 0.192118144 #> 2904  1451.5 0.8076370 0.8076370 3.353398e-04 3.353398e-04 0.192027688 #> 2905  1452.0 0.8077264 0.8077264 3.363559e-04 3.363559e-04 0.191937294 #> 2906  1452.5 0.8078157 0.8078157 3.373758e-04 3.373758e-04 0.191846962 #> 2907  1453.0 0.8079049 0.8079049 3.383995e-04 3.383995e-04 0.191756693 #> 2908  1453.5 0.8079941 0.8079941 3.394271e-04 3.394271e-04 0.191666486 #> 2909  1454.0 0.8080832 0.8080832 3.404586e-04 3.404586e-04 0.191576341 #> 2910  1454.5 0.8081722 0.8081722 3.414940e-04 3.414940e-04 0.191486258 #> 2911  1455.0 0.8082612 0.8082612 3.425333e-04 3.425333e-04 0.191396238 #> 2912  1455.5 0.8083501 0.8083501 3.435765e-04 3.435765e-04 0.191306280 #> 2913  1456.0 0.8084390 0.8084390 3.446237e-04 3.446237e-04 0.191216384 #> 2914  1456.5 0.8085278 0.8085278 3.456748e-04 3.456748e-04 0.191126551 #> 2915  1457.0 0.8086165 0.8086165 3.467299e-04 3.467299e-04 0.191036780 #> 2916  1457.5 0.8087051 0.8087051 3.477890e-04 3.477890e-04 0.190947072 #> 2917  1458.0 0.8087937 0.8087937 3.488521e-04 3.488521e-04 0.190857426 #> 2918  1458.5 0.8088822 0.8088822 3.499192e-04 3.499192e-04 0.190767843 #> 2919  1459.0 0.8089707 0.8089707 3.509903e-04 3.509903e-04 0.190678322 #> 2920  1459.5 0.8090591 0.8090591 3.520656e-04 3.520656e-04 0.190588864 #> 2921  1460.0 0.8091474 0.8091474 3.531449e-04 3.531449e-04 0.190499469 #> 2922  1460.5 0.8092356 0.8092356 3.542282e-04 3.542282e-04 0.190410136 #> 2923  1461.0 0.8093238 0.8093238 3.553157e-04 3.553157e-04 0.190320866 #> 2924  1461.5 0.8094119 0.8094119 3.564073e-04 3.564073e-04 0.190231659 #> 2925  1462.0 0.8095000 0.8095000 3.575031e-04 3.575031e-04 0.190142515 #> 2926  1462.5 0.8095880 0.8095880 3.586030e-04 3.586030e-04 0.190053433 #> 2927  1463.0 0.8096759 0.8096759 3.597071e-04 3.597071e-04 0.189964415 #> 2928  1463.5 0.8097637 0.8097637 3.608154e-04 3.608154e-04 0.189875459 #> 2929  1464.0 0.8098515 0.8098515 3.619279e-04 3.619279e-04 0.189786566 #> 2930  1464.5 0.8099392 0.8099392 3.630446e-04 3.630446e-04 0.189697737 #> 2931  1465.0 0.8100269 0.8100269 3.641656e-04 3.641656e-04 0.189608970 #> 2932  1465.5 0.8101144 0.8101144 3.652908e-04 3.652908e-04 0.189520267 #> 2933  1466.0 0.8102020 0.8102020 3.664203e-04 3.664203e-04 0.189431626 #> 2934  1466.5 0.8102894 0.8102894 3.675541e-04 3.675541e-04 0.189343049 #> 2935  1467.0 0.8103768 0.8103768 3.686922e-04 3.686922e-04 0.189254535 #> 2936  1467.5 0.8104641 0.8104641 3.698346e-04 3.698346e-04 0.189166084 #> 2937  1468.0 0.8105513 0.8105513 3.709814e-04 3.709814e-04 0.189077697 #> 2938  1468.5 0.8106385 0.8106385 3.721326e-04 3.721326e-04 0.188989373 #> 2939  1469.0 0.8107256 0.8107256 3.732881e-04 3.732881e-04 0.188901112 #> 2940  1469.5 0.8108126 0.8108126 3.744481e-04 3.744481e-04 0.188812915 #> 2941  1470.0 0.8108996 0.8108996 3.756124e-04 3.756124e-04 0.188724781 #> 2942  1470.5 0.8109865 0.8109865 3.767813e-04 3.767813e-04 0.188636710 #> 2943  1471.0 0.8110733 0.8110733 3.779545e-04 3.779545e-04 0.188548703 #> 2944  1471.5 0.8111601 0.8111601 3.791323e-04 3.791323e-04 0.188460760 #> 2945  1472.0 0.8112468 0.8112468 3.803145e-04 3.803145e-04 0.188372880 #> 2946  1472.5 0.8113334 0.8113334 3.815012e-04 3.815012e-04 0.188285064 #> 2947  1473.0 0.8114200 0.8114200 3.826925e-04 3.826925e-04 0.188197312 #> 2948  1473.5 0.8115065 0.8115065 3.838884e-04 3.838884e-04 0.188109623 #> 2949  1474.0 0.8115929 0.8115929 3.850888e-04 3.850888e-04 0.188021999 #> 2950  1474.5 0.8116793 0.8116793 3.862938e-04 3.862938e-04 0.187934438 #> 2951  1475.0 0.8117656 0.8117656 3.875033e-04 3.875033e-04 0.187846941 #> 2952  1475.5 0.8118518 0.8118518 3.887176e-04 3.887176e-04 0.187759508 #> 2953  1476.0 0.8119379 0.8119379 3.899364e-04 3.899364e-04 0.187672138 #> 2954  1476.5 0.8120240 0.8120240 3.911600e-04 3.911600e-04 0.187584833 #> 2955  1477.0 0.8121100 0.8121100 3.923882e-04 3.923882e-04 0.187497592 #> 2956  1477.5 0.8121960 0.8121960 3.936211e-04 3.936211e-04 0.187410415 #> 2957  1478.0 0.8122818 0.8122818 3.948587e-04 3.948587e-04 0.187323303 #> 2958  1478.5 0.8123676 0.8123676 3.961011e-04 3.961011e-04 0.187236254 #> 2959  1479.0 0.8124534 0.8124534 3.973483e-04 3.973483e-04 0.187149270 #> 2960  1479.5 0.8125391 0.8125391 3.986002e-04 3.986002e-04 0.187062350 #> 2961  1480.0 0.8126246 0.8126246 3.998569e-04 3.998569e-04 0.186975494 #> 2962  1480.5 0.8127102 0.8127102 4.011184e-04 4.011184e-04 0.186888703 #> 2963  1481.0 0.8127956 0.8127956 4.023848e-04 4.023848e-04 0.186801976 #> 2964  1481.5 0.8128810 0.8128810 4.036561e-04 4.036561e-04 0.186715314 #> 2965  1482.0 0.8129664 0.8129664 4.049322e-04 4.049322e-04 0.186628716 #> 2966  1482.5 0.8130516 0.8130516 4.062132e-04 4.062132e-04 0.186542183 #> 2967  1483.0 0.8131368 0.8131368 4.074992e-04 4.074992e-04 0.186455714 #> 2968  1483.5 0.8132219 0.8132219 4.087901e-04 4.087901e-04 0.186369310 #> 2969  1484.0 0.8133069 0.8133069 4.100859e-04 4.100859e-04 0.186282971 #> 2970  1484.5 0.8133919 0.8133919 4.113867e-04 4.113867e-04 0.186196697 #> 2971  1485.0 0.8134768 0.8134768 4.126926e-04 4.126926e-04 0.186110487 #> 2972  1485.5 0.8135617 0.8135617 4.140034e-04 4.140034e-04 0.186024343 #> 2973  1486.0 0.8136464 0.8136464 4.153193e-04 4.153193e-04 0.185938263 #> 2974  1486.5 0.8137311 0.8137311 4.166403e-04 4.166403e-04 0.185852249 #> 2975  1487.0 0.8138157 0.8138157 4.179663e-04 4.179663e-04 0.185766299 #> 2976  1487.5 0.8139003 0.8139003 4.192975e-04 4.192975e-04 0.185680414 #> 2977  1488.0 0.8139848 0.8139848 4.206338e-04 4.206338e-04 0.185594595 #> 2978  1488.5 0.8140692 0.8140692 4.219752e-04 4.219752e-04 0.185508841 #> 2979  1489.0 0.8141535 0.8141535 4.233218e-04 4.233218e-04 0.185423152 #> 2980  1489.5 0.8142378 0.8142378 4.246736e-04 4.246736e-04 0.185337529 #> 2981  1490.0 0.8143220 0.8143220 4.260306e-04 4.260306e-04 0.185251970 #> 2982  1490.5 0.8144061 0.8144061 4.273929e-04 4.273929e-04 0.185166478 #> 2983  1491.0 0.8144902 0.8144902 4.287604e-04 4.287604e-04 0.185081050 #> 2984  1491.5 0.8145742 0.8145742 4.301332e-04 4.301332e-04 0.184995689 #> 2985  1492.0 0.8146581 0.8146581 4.315113e-04 4.315113e-04 0.184910392 #> 2986  1492.5 0.8147419 0.8147419 4.328947e-04 4.328947e-04 0.184825162 #> 2987  1493.0 0.8148257 0.8148257 4.342834e-04 4.342834e-04 0.184739997 #> 2988  1493.5 0.8149094 0.8149094 4.356775e-04 4.356775e-04 0.184654898 #> 2989  1494.0 0.8149931 0.8149931 4.370770e-04 4.370770e-04 0.184569865 #> 2990  1494.5 0.8150766 0.8150766 4.384820e-04 4.384820e-04 0.184484898 #> 2991  1495.0 0.8151601 0.8151601 4.398923e-04 4.398923e-04 0.184399996 #> 2992  1495.5 0.8152435 0.8152435 4.413081e-04 4.413081e-04 0.184315161 #> 2993  1496.0 0.8153269 0.8153269 4.427294e-04 4.427294e-04 0.184230391 #> 2994  1496.5 0.8154102 0.8154102 4.441562e-04 4.441562e-04 0.184145688 #> 2995  1497.0 0.8154934 0.8154934 4.455886e-04 4.455886e-04 0.184061051 #> 2996  1497.5 0.8155765 0.8155765 4.470264e-04 4.470264e-04 0.183976480 #> 2997  1498.0 0.8156596 0.8156596 4.484699e-04 4.484699e-04 0.183891975 #> 2998  1498.5 0.8157425 0.8157425 4.499189e-04 4.499189e-04 0.183807537 #> 2999  1499.0 0.8158255 0.8158255 4.513736e-04 4.513736e-04 0.183723165 #> 3000  1499.5 0.8159083 0.8159083 4.528339e-04 4.528339e-04 0.183638859 #> 3001  1500.0 0.8159911 0.8159911 4.542998e-04 4.542998e-04 0.183554621 #> 3002  1500.5 0.8160738 0.8160738 4.557715e-04 4.557715e-04 0.183470448 #> 3003  1501.0 0.8161564 0.8161564 4.572488e-04 4.572488e-04 0.183386343 #> 3004  1501.5 0.8162390 0.8162390 4.587319e-04 4.587319e-04 0.183302304 #> 3005  1502.0 0.8163214 0.8163214 4.602208e-04 4.602208e-04 0.183218331 #> 3006  1502.5 0.8164039 0.8164039 4.617154e-04 4.617154e-04 0.183134426 #> 3007  1503.0 0.8164862 0.8164862 4.632159e-04 4.632159e-04 0.183050587 #> 3008  1503.5 0.8165685 0.8165685 4.647222e-04 4.647222e-04 0.182966816 #> 3009  1504.0 0.8166507 0.8166507 4.662343e-04 4.662343e-04 0.182883111 #> 3010  1504.5 0.8167328 0.8167328 4.677523e-04 4.677523e-04 0.182799474 #> 3011  1505.0 0.8168148 0.8168148 4.692762e-04 4.692762e-04 0.182715904 #> 3012  1505.5 0.8168968 0.8168968 4.708061e-04 4.708061e-04 0.182632401 #> 3013  1506.0 0.8169787 0.8169787 4.723419e-04 4.723419e-04 0.182548965 #> 3014  1506.5 0.8170605 0.8170605 4.738837e-04 4.738837e-04 0.182465596 #> 3015  1507.0 0.8171423 0.8171423 4.754315e-04 4.754315e-04 0.182382295 #> 3016  1507.5 0.8172240 0.8172240 4.769853e-04 4.769853e-04 0.182299062 #> 3017  1508.0 0.8173056 0.8173056 4.785452e-04 4.785452e-04 0.182215896 #> 3018  1508.5 0.8173871 0.8173871 4.801112e-04 4.801112e-04 0.182132797 #> 3019  1509.0 0.8174686 0.8174686 4.816832e-04 4.816832e-04 0.182049767 #> 3020  1509.5 0.8175499 0.8175499 4.832614e-04 4.832614e-04 0.181966804 #> 3021  1510.0 0.8176312 0.8176312 4.848458e-04 4.848458e-04 0.181883909 #> 3022  1510.5 0.8177125 0.8177125 4.864363e-04 4.864363e-04 0.181801081 #> 3023  1511.0 0.8177936 0.8177936 4.880330e-04 4.880330e-04 0.181718322 #> 3024  1511.5 0.8178747 0.8178747 4.896360e-04 4.896360e-04 0.181635631 #> 3025  1512.0 0.8179557 0.8179557 4.912453e-04 4.912453e-04 0.181553008 #> 3026  1512.5 0.8180367 0.8180367 4.928608e-04 4.928608e-04 0.181470453 #> 3027  1513.0 0.8181176 0.8181176 4.944826e-04 4.944826e-04 0.181387966 #> 3028  1513.5 0.8181983 0.8181983 4.961108e-04 4.961108e-04 0.181305548 #> 3029  1514.0 0.8182791 0.8182791 4.977454e-04 4.977454e-04 0.181223198 #> 3030  1514.5 0.8183597 0.8183597 4.993863e-04 4.993863e-04 0.181140916 #> 3031  1515.0 0.8184403 0.8184403 5.010336e-04 5.010336e-04 0.181058703 #> 3032  1515.5 0.8185208 0.8185208 5.026875e-04 5.026875e-04 0.180976558 #> 3033  1516.0 0.8186012 0.8186012 5.043477e-04 5.043477e-04 0.180894483 #> 3034  1516.5 0.8186815 0.8186815 5.060145e-04 5.060145e-04 0.180812475 #> 3035  1517.0 0.8187618 0.8187618 5.076878e-04 5.076878e-04 0.180730537 #> 3036  1517.5 0.8188420 0.8188420 5.093677e-04 5.093677e-04 0.180648668 #> 3037  1518.0 0.8189221 0.8189221 5.110541e-04 5.110541e-04 0.180566868 #> 3038  1518.5 0.8190021 0.8190021 5.127472e-04 5.127472e-04 0.180485136 #> 3039  1519.0 0.8190821 0.8190821 5.144468e-04 5.144468e-04 0.180403474 #> 3040  1519.5 0.8191620 0.8191620 5.161532e-04 5.161532e-04 0.180321881 #> 3041  1520.0 0.8192418 0.8192418 5.178663e-04 5.178663e-04 0.180240357 #> 3042  1520.5 0.8193215 0.8193215 5.195860e-04 5.195860e-04 0.180158903 #> 3043  1521.0 0.8194012 0.8194012 5.213125e-04 5.213125e-04 0.180077518 #> 3044  1521.5 0.8194808 0.8194808 5.230458e-04 5.230458e-04 0.179996202 #> 3045  1522.0 0.8195603 0.8195603 5.247859e-04 5.247859e-04 0.179914957 #> 3046  1522.5 0.8196397 0.8196397 5.265329e-04 5.265329e-04 0.179833780 #> 3047  1523.0 0.8197190 0.8197190 5.282867e-04 5.282867e-04 0.179752674 #> 3048  1523.5 0.8197983 0.8197983 5.300474e-04 5.300474e-04 0.179671637 #> 3049  1524.0 0.8198775 0.8198775 5.318150e-04 5.318150e-04 0.179590671 #> 3050  1524.5 0.8199566 0.8199566 5.335896e-04 5.335896e-04 0.179509774 #> 3051  1525.0 0.8200357 0.8200357 5.353711e-04 5.353711e-04 0.179428947 #> 3052  1525.5 0.8201146 0.8201146 5.371597e-04 5.371597e-04 0.179348191 #> 3053  1526.0 0.8201935 0.8201935 5.389553e-04 5.389553e-04 0.179267504 #> 3054  1526.5 0.8202724 0.8202724 5.407579e-04 5.407579e-04 0.179186888 #> 3055  1527.0 0.8203511 0.8203511 5.425677e-04 5.425677e-04 0.179106343 #> 3056  1527.5 0.8204297 0.8204297 5.443846e-04 5.443846e-04 0.179025868 #> 3057  1528.0 0.8205083 0.8205083 5.462086e-04 5.462086e-04 0.178945463 #> 3058  1528.5 0.8205868 0.8205868 5.480399e-04 5.480399e-04 0.178865129 #> 3059  1529.0 0.8206653 0.8206653 5.498783e-04 5.498783e-04 0.178784866 #> 3060  1529.5 0.8207436 0.8207436 5.517240e-04 5.517240e-04 0.178704674 #> 3061  1530.0 0.8208219 0.8208219 5.535770e-04 5.535770e-04 0.178624552 #> 3062  1530.5 0.8209001 0.8209001 5.554373e-04 5.554373e-04 0.178544502 #> 3063  1531.0 0.8209782 0.8209782 5.573049e-04 5.573049e-04 0.178464522 #> 3064  1531.5 0.8210562 0.8210562 5.591799e-04 5.591799e-04 0.178384614 #> 3065  1532.0 0.8211342 0.8211342 5.610623e-04 5.610623e-04 0.178304777 #> 3066  1532.5 0.8212120 0.8212120 5.629522e-04 5.629522e-04 0.178225011 #> 3067  1533.0 0.8212898 0.8212898 5.648495e-04 5.648495e-04 0.178145317 #> 3068  1533.5 0.8213676 0.8213676 5.667543e-04 5.667543e-04 0.178065694 #> 3069  1534.0 0.8214452 0.8214452 5.686666e-04 5.686666e-04 0.177986143 #> 3070  1534.5 0.8215227 0.8215227 5.705865e-04 5.705865e-04 0.177906664 #> 3071  1535.0 0.8216002 0.8216002 5.725140e-04 5.725140e-04 0.177827256 #> 3072  1535.5 0.8216776 0.8216776 5.744491e-04 5.744491e-04 0.177747920 #> 3073  1536.0 0.8217550 0.8217550 5.763918e-04 5.763918e-04 0.177668656 #> 3074  1536.5 0.8218322 0.8218322 5.783423e-04 5.783423e-04 0.177589465 #> 3075  1537.0 0.8219094 0.8219094 5.803004e-04 5.803004e-04 0.177510345 #> 3076  1537.5 0.8219864 0.8219864 5.822663e-04 5.822663e-04 0.177431298 #> 3077  1538.0 0.8220634 0.8220634 5.842400e-04 5.842400e-04 0.177352323 #> 3078  1538.5 0.8221404 0.8221404 5.862216e-04 5.862216e-04 0.177273420 #> 3079  1539.0 0.8222172 0.8222172 5.882109e-04 5.882109e-04 0.177194590 #> 3080  1539.5 0.8222940 0.8222940 5.902082e-04 5.902082e-04 0.177115833 #> 3081  1540.0 0.8223706 0.8223706 5.922134e-04 5.922134e-04 0.177037148 #> 3082  1540.5 0.8224472 0.8224472 5.942265e-04 5.942265e-04 0.176958536 #> 3083  1541.0 0.8225238 0.8225238 5.962476e-04 5.962476e-04 0.176879997 #> 3084  1541.5 0.8226002 0.8226002 5.982767e-04 5.982767e-04 0.176801531 #> 3085  1542.0 0.8226765 0.8226765 6.003139e-04 6.003139e-04 0.176723138 #> 3086  1542.5 0.8227528 0.8227528 6.023592e-04 6.023592e-04 0.176644818 #> 3087  1543.0 0.8228290 0.8228290 6.044126e-04 6.044126e-04 0.176566572 #> 3088  1543.5 0.8229051 0.8229051 6.064741e-04 6.064741e-04 0.176488399 #> 3089  1544.0 0.8229812 0.8229812 6.085438e-04 6.085438e-04 0.176410299 #> 3090  1544.5 0.8230571 0.8230571 6.106218e-04 6.106218e-04 0.176332273 #> 3091  1545.0 0.8231330 0.8231330 6.127080e-04 6.127080e-04 0.176254321 #> 3092  1545.5 0.8232088 0.8232088 6.148025e-04 6.148025e-04 0.176176443 #> 3093  1546.0 0.8232845 0.8232845 6.169053e-04 6.169053e-04 0.176098638 #> 3094  1546.5 0.8233601 0.8233601 6.190165e-04 6.190165e-04 0.176020908 #> 3095  1547.0 0.8234356 0.8234356 6.211361e-04 6.211361e-04 0.175943251 #> 3096  1547.5 0.8235111 0.8235111 6.232641e-04 6.232641e-04 0.175865669 #> 3097  1548.0 0.8235864 0.8235864 6.254006e-04 6.254006e-04 0.175788161 #> 3098  1548.5 0.8236617 0.8236617 6.275456e-04 6.275456e-04 0.175710727 #> 3099  1549.0 0.8237369 0.8237369 6.296992e-04 6.296992e-04 0.175633368 #> 3100  1549.5 0.8238121 0.8238121 6.318613e-04 6.318613e-04 0.175556084 #> 3101  1550.0 0.8238871 0.8238871 6.340320e-04 6.340320e-04 0.175478874 #> 3102  1550.5 0.8239620 0.8239620 6.362114e-04 6.362114e-04 0.175401740 #> 3103  1551.0 0.8240369 0.8240369 6.383995e-04 6.383995e-04 0.175324680 #> 3104  1551.5 0.8241117 0.8241117 6.405963e-04 6.405963e-04 0.175247695 #> 3105  1552.0 0.8241864 0.8241864 6.428018e-04 6.428018e-04 0.175170785 #> 3106  1552.5 0.8242610 0.8242610 6.450162e-04 6.450162e-04 0.175093951 #> 3107  1553.0 0.8243356 0.8243356 6.472394e-04 6.472394e-04 0.175017192 #> 3108  1553.5 0.8244100 0.8244100 6.494714e-04 6.494714e-04 0.174940508 #> 3109  1554.0 0.8244844 0.8244844 6.517124e-04 6.517124e-04 0.174863901 #> 3110  1554.5 0.8245587 0.8245587 6.539623e-04 6.539623e-04 0.174787368 #> 3111  1555.0 0.8246329 0.8246329 6.562212e-04 6.562212e-04 0.174710912 #> 3112  1555.5 0.8247070 0.8247070 6.584891e-04 6.584891e-04 0.174634531 #> 3113  1556.0 0.8247810 0.8247810 6.607661e-04 6.607661e-04 0.174558227 #> 3114  1556.5 0.8248549 0.8248549 6.630522e-04 6.630522e-04 0.174481999 #> 3115  1557.0 0.8249288 0.8249288 6.653475e-04 6.653475e-04 0.174405847 #> 3116  1557.5 0.8250026 0.8250026 6.676519e-04 6.676519e-04 0.174329771 #> 3117  1558.0 0.8250763 0.8250763 6.699655e-04 6.699655e-04 0.174253772 #> 3118  1558.5 0.8251499 0.8251499 6.722884e-04 6.722884e-04 0.174177850 #> 3119  1559.0 0.8252234 0.8252234 6.746205e-04 6.746205e-04 0.174102004 #> 3120  1559.5 0.8252968 0.8252968 6.769620e-04 6.769620e-04 0.174026235 #> 3121  1560.0 0.8253701 0.8253701 6.793129e-04 6.793129e-04 0.173950543 #> 3122  1560.5 0.8254434 0.8254434 6.816732e-04 6.816732e-04 0.173874928 #> 3123  1561.0 0.8255166 0.8255166 6.840429e-04 6.840429e-04 0.173799391 #> 3124  1561.5 0.8255896 0.8255896 6.864222e-04 6.864222e-04 0.173723930 #> 3125  1562.0 0.8256626 0.8256626 6.888109e-04 6.888109e-04 0.173648548 #> 3126  1562.5 0.8257355 0.8257355 6.912093e-04 6.912093e-04 0.173573242 #> 3127  1563.0 0.8258084 0.8258084 6.936172e-04 6.936172e-04 0.173498015 #> 3128  1563.5 0.8258811 0.8258811 6.960348e-04 6.960348e-04 0.173422865 #> 3129  1564.0 0.8259537 0.8259537 6.984622e-04 6.984622e-04 0.173347793 #> 3130  1564.5 0.8260263 0.8260263 7.008992e-04 7.008992e-04 0.173272799 #> 3131  1565.0 0.8260988 0.8260988 7.033460e-04 7.033460e-04 0.173197884 #> 3132  1565.5 0.8261712 0.8261712 7.058027e-04 7.058027e-04 0.173123046 #> 3133  1566.0 0.8262434 0.8262434 7.082692e-04 7.082692e-04 0.173048287 #> 3134  1566.5 0.8263156 0.8263156 7.107456e-04 7.107456e-04 0.172973607 #> 3135  1567.0 0.8263878 0.8263878 7.132319e-04 7.132319e-04 0.172899005 #> 3136  1567.5 0.8264598 0.8264598 7.157283e-04 7.157283e-04 0.172824482 #> 3137  1568.0 0.8265317 0.8265317 7.182346e-04 7.182346e-04 0.172750038 #> 3138  1568.5 0.8266036 0.8266036 7.207511e-04 7.207511e-04 0.172675674 #> 3139  1569.0 0.8266753 0.8266753 7.232776e-04 7.232776e-04 0.172601388 #> 3140  1569.5 0.8267470 0.8267470 7.258143e-04 7.258143e-04 0.172527181 #> 3141  1570.0 0.8268186 0.8268186 7.283613e-04 7.283613e-04 0.172453055 #> 3142  1570.5 0.8268901 0.8268901 7.309184e-04 7.309184e-04 0.172379007 #> 3143  1571.0 0.8269615 0.8269615 7.334859e-04 7.334859e-04 0.172305040 #> 3144  1571.5 0.8270328 0.8270328 7.360637e-04 7.360637e-04 0.172231152 #> 3145  1572.0 0.8271040 0.8271040 7.386518e-04 7.386518e-04 0.172157344 #> 3146  1572.5 0.8271751 0.8271751 7.412504e-04 7.412504e-04 0.172083616 #> 3147  1573.0 0.8272462 0.8272462 7.438594e-04 7.438594e-04 0.172009969 #> 3148  1573.5 0.8273171 0.8273171 7.464790e-04 7.464790e-04 0.171936402 #> 3149  1574.0 0.8273880 0.8273880 7.491091e-04 7.491091e-04 0.171862915 #> 3150  1574.5 0.8274587 0.8274587 7.517498e-04 7.517498e-04 0.171789509 #> 3151  1575.0 0.8275294 0.8275294 7.544011e-04 7.544011e-04 0.171716184 #> 3152  1575.5 0.8276000 0.8276000 7.570631e-04 7.570631e-04 0.171642940 #> 3153  1576.0 0.8276705 0.8276705 7.597359e-04 7.597359e-04 0.171569776 #> 3154  1576.5 0.8277409 0.8277409 7.624194e-04 7.624194e-04 0.171496694 #> 3155  1577.0 0.8278112 0.8278112 7.651138e-04 7.651138e-04 0.171423694 #> 3156  1577.5 0.8278814 0.8278814 7.678190e-04 7.678190e-04 0.171350774 #> 3157  1578.0 0.8279515 0.8279515 7.705352e-04 7.705352e-04 0.171277937 #> 3158  1578.5 0.8280216 0.8280216 7.732623e-04 7.732623e-04 0.171205181 #> 3159  1579.0 0.8280915 0.8280915 7.760004e-04 7.760004e-04 0.171132507 #> 3160  1579.5 0.8281613 0.8281613 7.787496e-04 7.787496e-04 0.171059915 #> 3161  1580.0 0.8282311 0.8282311 7.815099e-04 7.815099e-04 0.170987406 #> 3162  1580.5 0.8283007 0.8283007 7.842813e-04 7.842813e-04 0.170914978 #> 3163  1581.0 0.8283703 0.8283703 7.870639e-04 7.870639e-04 0.170842633 #> 3164  1581.5 0.8284398 0.8284398 7.898578e-04 7.898578e-04 0.170770371 #> 3165  1582.0 0.8285091 0.8285091 7.926630e-04 7.926630e-04 0.170698192 #> 3166  1582.5 0.8285784 0.8285784 7.954795e-04 7.954795e-04 0.170626095 #> 3167  1583.0 0.8286476 0.8286476 7.983074e-04 7.983074e-04 0.170554082 #> 3168  1583.5 0.8287167 0.8287167 8.011467e-04 8.011467e-04 0.170482151 #> 3169  1584.0 0.8287857 0.8287857 8.039976e-04 8.039976e-04 0.170410304 #> 3170  1584.5 0.8288546 0.8288546 8.068599e-04 8.068599e-04 0.170338541 #> 3171  1585.0 0.8289234 0.8289234 8.097339e-04 8.097339e-04 0.170266861 #> 3172  1585.5 0.8289921 0.8289921 8.126194e-04 8.126194e-04 0.170195265 #> 3173  1586.0 0.8290607 0.8290607 8.155167e-04 8.155167e-04 0.170123753 #> 3174  1586.5 0.8291292 0.8291292 8.184257e-04 8.184257e-04 0.170052326 #> 3175  1587.0 0.8291977 0.8291977 8.213465e-04 8.213465e-04 0.169980982 #> 3176  1587.5 0.8292660 0.8292660 8.242791e-04 8.242791e-04 0.169909723 #> 3177  1588.0 0.8293342 0.8293342 8.272236e-04 8.272236e-04 0.169838548 #> 3178  1588.5 0.8294024 0.8294024 8.301800e-04 8.301800e-04 0.169767458 #> 3179  1589.0 0.8294704 0.8294704 8.331484e-04 8.331484e-04 0.169696453 #> 3180  1589.5 0.8295383 0.8295383 8.361288e-04 8.361288e-04 0.169625534 #> 3181  1590.0 0.8296062 0.8296062 8.391214e-04 8.391214e-04 0.169554699 #> 3182  1590.5 0.8296739 0.8296739 8.421260e-04 8.421260e-04 0.169483950 #> 3183  1591.0 0.8297416 0.8297416 8.451429e-04 8.451429e-04 0.169413286 #> 3184  1591.5 0.8298091 0.8298091 8.481720e-04 8.481720e-04 0.169342708 #> 3185  1592.0 0.8298766 0.8298766 8.512134e-04 8.512134e-04 0.169272216 #> 3186  1592.5 0.8299439 0.8299439 8.542671e-04 8.542671e-04 0.169201810 #> 3187  1593.0 0.8300112 0.8300112 8.573332e-04 8.573332e-04 0.169131490 #> 3188  1593.5 0.8300783 0.8300783 8.604118e-04 8.604118e-04 0.169061256 #> 3189  1594.0 0.8301454 0.8301454 8.635029e-04 8.635029e-04 0.168991109 #> 3190  1594.5 0.8302123 0.8302123 8.666066e-04 8.666066e-04 0.168921048 #> 3191  1595.0 0.8302792 0.8302792 8.697228e-04 8.697228e-04 0.168851075 #> 3192  1595.5 0.8303460 0.8303460 8.728517e-04 8.728517e-04 0.168781188 #> 3193  1596.0 0.8304126 0.8304126 8.759934e-04 8.759934e-04 0.168711389 #> 3194  1596.5 0.8304792 0.8304792 8.791478e-04 8.791478e-04 0.168641676 #> 3195  1597.0 0.8305456 0.8305456 8.823150e-04 8.823150e-04 0.168572052 #> 3196  1597.5 0.8306120 0.8306120 8.854951e-04 8.854951e-04 0.168502515 #> 3197  1598.0 0.8306782 0.8306782 8.886882e-04 8.886882e-04 0.168433066 #> 3198  1598.5 0.8307444 0.8307444 8.918942e-04 8.918942e-04 0.168363705 #> 3199  1599.0 0.8308105 0.8308105 8.951133e-04 8.951133e-04 0.168294432 #> 3200  1599.5 0.8308764 0.8308764 8.983455e-04 8.983455e-04 0.168225248 #> 3201  1600.0 0.8309423 0.8309423 9.015908e-04 9.015908e-04 0.168156152 #> 3202  1600.5 0.8310080 0.8310080 9.048494e-04 9.048494e-04 0.168087145 #> 3203  1601.0 0.8310737 0.8310737 9.081212e-04 9.081212e-04 0.168018226 #> 3204  1601.5 0.8311392 0.8311392 9.114064e-04 9.114064e-04 0.167949397 #> 3205  1602.0 0.8312046 0.8312046 9.147049e-04 9.147049e-04 0.167880657 #> 3206  1602.5 0.8312700 0.8312700 9.180169e-04 9.180169e-04 0.167812007 #> 3207  1603.0 0.8313352 0.8313352 9.213423e-04 9.213423e-04 0.167743446 #> 3208  1603.5 0.8314003 0.8314003 9.246813e-04 9.246813e-04 0.167674975 #> 3209  1604.0 0.8314654 0.8314654 9.280340e-04 9.280340e-04 0.167606594 #> 3210  1604.5 0.8315303 0.8315303 9.314003e-04 9.314003e-04 0.167538303 #> 3211  1605.0 0.8315951 0.8315951 9.347803e-04 9.347803e-04 0.167470102 #> 3212  1605.5 0.8316598 0.8316598 9.381741e-04 9.381741e-04 0.167401992 #> 3213  1606.0 0.8317244 0.8317244 9.415817e-04 9.415817e-04 0.167333973 #> 3214  1606.5 0.8317890 0.8317890 9.450033e-04 9.450033e-04 0.167266044 #> 3215  1607.0 0.8318534 0.8318534 9.484388e-04 9.484388e-04 0.167198207 #> 3216  1607.5 0.8319177 0.8319177 9.518884e-04 9.518884e-04 0.167130461 #> 3217  1608.0 0.8319818 0.8319818 9.553520e-04 9.553520e-04 0.167062806 #> 3218  1608.5 0.8320459 0.8320459 9.588297e-04 9.588297e-04 0.166995243 #> 3219  1609.0 0.8321099 0.8321099 9.623217e-04 9.623217e-04 0.166927772 #> 3220  1609.5 0.8321738 0.8321738 9.658279e-04 9.658279e-04 0.166860393 #> 3221  1610.0 0.8322375 0.8322375 9.693485e-04 9.693485e-04 0.166793106 #> 3222  1610.5 0.8323012 0.8323012 9.728834e-04 9.728834e-04 0.166725911 #> 3223  1611.0 0.8323648 0.8323648 9.764327e-04 9.764327e-04 0.166658809 #> 3224  1611.5 0.8324282 0.8324282 9.799966e-04 9.799966e-04 0.166591800 #> 3225  1612.0 0.8324915 0.8324915 9.835751e-04 9.835751e-04 0.166524884 #> 3226  1612.5 0.8325548 0.8325548 9.871681e-04 9.871681e-04 0.166458061 #> 3227  1613.0 0.8326179 0.8326179 9.907759e-04 9.907759e-04 0.166391332 #> 3228  1613.5 0.8326809 0.8326809 9.943984e-04 9.943984e-04 0.166324696 #> 3229  1614.0 0.8327438 0.8327438 9.980357e-04 9.980357e-04 0.166258154 #> 3230  1614.5 0.8328066 0.8328066 1.001688e-03 1.001688e-03 0.166191705 #> 3231  1615.0 0.8328693 0.8328693 1.005355e-03 1.005355e-03 0.166125352 #> 3232  1615.5 0.8329319 0.8329319 1.009037e-03 1.009037e-03 0.166059092 #> 3233  1616.0 0.8329943 0.8329943 1.012734e-03 1.012734e-03 0.165992927 #> 3234  1616.5 0.8330567 0.8330567 1.016447e-03 1.016447e-03 0.165926857 #> 3235  1617.0 0.8331189 0.8331189 1.020174e-03 1.020174e-03 0.165860882 #> 3236  1617.5 0.8331811 0.8331811 1.023917e-03 1.023917e-03 0.165795002 #> 3237  1618.0 0.8332431 0.8332431 1.027675e-03 1.027675e-03 0.165729218 #> 3238  1618.5 0.8333050 0.8333050 1.031449e-03 1.031449e-03 0.165663529 #> 3239  1619.0 0.8333668 0.8333668 1.035238e-03 1.035238e-03 0.165597936 #> 3240  1619.5 0.8334285 0.8334285 1.039042e-03 1.039042e-03 0.165532439 #> 3241  1620.0 0.8334901 0.8334901 1.042862e-03 1.042862e-03 0.165467038 #> 3242  1620.5 0.8335516 0.8335516 1.046698e-03 1.046698e-03 0.165401734 #> 3243  1621.0 0.8336129 0.8336129 1.050550e-03 1.050550e-03 0.165336526 #> 3244  1621.5 0.8336742 0.8336742 1.054417e-03 1.054417e-03 0.165271416 #> 3245  1622.0 0.8337353 0.8337353 1.058300e-03 1.058300e-03 0.165206402 #> 3246  1622.5 0.8337963 0.8337963 1.062199e-03 1.062199e-03 0.165141486 #> 3247  1623.0 0.8338572 0.8338572 1.066114e-03 1.066114e-03 0.165076667 #> 3248  1623.5 0.8339180 0.8339180 1.070045e-03 1.070045e-03 0.165011947 #> 3249  1624.0 0.8339787 0.8339787 1.073992e-03 1.073992e-03 0.164947324 #> 3250  1624.5 0.8340392 0.8340392 1.077956e-03 1.077956e-03 0.164882799 #> 3251  1625.0 0.8340997 0.8340997 1.081935e-03 1.081935e-03 0.164818373 #> 3252  1625.5 0.8341600 0.8341600 1.085931e-03 1.085931e-03 0.164754045 #> 3253  1626.0 0.8342202 0.8342202 1.089944e-03 1.089944e-03 0.164689817 #> 3254  1626.5 0.8342803 0.8342803 1.093973e-03 1.093973e-03 0.164625687 #> 3255  1627.0 0.8343403 0.8343403 1.098018e-03 1.098018e-03 0.164561657 #> 3256  1627.5 0.8344002 0.8344002 1.102080e-03 1.102080e-03 0.164497726 #> 3257  1628.0 0.8344599 0.8344599 1.106159e-03 1.106159e-03 0.164433895 #> 3258  1628.5 0.8345196 0.8345196 1.110254e-03 1.110254e-03 0.164370164 #> 3259  1629.0 0.8345791 0.8345791 1.114367e-03 1.114367e-03 0.164306534 #> 3260  1629.5 0.8346385 0.8346385 1.118496e-03 1.118496e-03 0.164243003 #> 3261  1630.0 0.8346978 0.8346978 1.122642e-03 1.122642e-03 0.164179574 #> 3262  1630.5 0.8347569 0.8347569 1.126805e-03 1.126805e-03 0.164116245 #> 3263  1631.0 0.8348160 0.8348160 1.130985e-03 1.130985e-03 0.164053018 #> 3264  1631.5 0.8348749 0.8348749 1.135183e-03 1.135183e-03 0.163989892 #> 3265  1632.0 0.8349337 0.8349337 1.139398e-03 1.139398e-03 0.163926867 #> 3266  1632.5 0.8349924 0.8349924 1.143630e-03 1.143630e-03 0.163863945 #> 3267  1633.0 0.8350510 0.8350510 1.147879e-03 1.147879e-03 0.163801125 #> 3268  1633.5 0.8351094 0.8351094 1.152146e-03 1.152146e-03 0.163738407 #> 3269  1634.0 0.8351678 0.8351678 1.156431e-03 1.156431e-03 0.163675791 #> 3270  1634.5 0.8352260 0.8352260 1.160733e-03 1.160733e-03 0.163613279 #> 3271  1635.0 0.8352841 0.8352841 1.165052e-03 1.165052e-03 0.163550869 #> 3272  1635.5 0.8353420 0.8353420 1.169390e-03 1.169390e-03 0.163488563 #> 3273  1636.0 0.8353999 0.8353999 1.173745e-03 1.173745e-03 0.163426360 #> 3274  1636.5 0.8354576 0.8354576 1.178119e-03 1.178119e-03 0.163364261 #> 3275  1637.0 0.8355152 0.8355152 1.182510e-03 1.182510e-03 0.163302267 #> 3276  1637.5 0.8355727 0.8355727 1.186920e-03 1.186920e-03 0.163240376 #> 3277  1638.0 0.8356301 0.8356301 1.191347e-03 1.191347e-03 0.163178590 #> 3278  1638.5 0.8356873 0.8356873 1.195793e-03 1.195793e-03 0.163116909 #> 3279  1639.0 0.8357444 0.8357444 1.200257e-03 1.200257e-03 0.163055333 #> 3280  1639.5 0.8358014 0.8358014 1.204740e-03 1.204740e-03 0.162993863 #> 3281  1640.0 0.8358583 0.8358583 1.209241e-03 1.209241e-03 0.162932497 #> 3282  1640.5 0.8359150 0.8359150 1.213760e-03 1.213760e-03 0.162871238 #> 3283  1641.0 0.8359716 0.8359716 1.218298e-03 1.218298e-03 0.162810085 #> 3284  1641.5 0.8360281 0.8360281 1.222855e-03 1.222855e-03 0.162749038 #> 3285  1642.0 0.8360845 0.8360845 1.227431e-03 1.227431e-03 0.162688098 #> 3286  1642.5 0.8361407 0.8361407 1.232025e-03 1.232025e-03 0.162627264 #> 3287  1643.0 0.8361968 0.8361968 1.236638e-03 1.236638e-03 0.162566538 #> 3288  1643.5 0.8362528 0.8362528 1.241271e-03 1.241271e-03 0.162505919 #> 3289  1644.0 0.8363087 0.8363087 1.245922e-03 1.245922e-03 0.162445408 #> 3290  1644.5 0.8363644 0.8363644 1.250593e-03 1.250593e-03 0.162385005 #> 3291  1645.0 0.8364200 0.8364200 1.255283e-03 1.255283e-03 0.162324710 #> 3292  1645.5 0.8364755 0.8364755 1.259992e-03 1.259992e-03 0.162264523 #> 3293  1646.0 0.8365308 0.8365308 1.264721e-03 1.264721e-03 0.162204445 #> 3294  1646.5 0.8365861 0.8365861 1.269469e-03 1.269469e-03 0.162144476 #> 3295  1647.0 0.8366411 0.8366411 1.274237e-03 1.274237e-03 0.162084616 #> 3296  1647.5 0.8366961 0.8366961 1.279024e-03 1.279024e-03 0.162024866 #> 3297  1648.0 0.8367509 0.8367509 1.283831e-03 1.283831e-03 0.161965225 #> 3298  1648.5 0.8368056 0.8368056 1.288658e-03 1.288658e-03 0.161905695 #> 3299  1649.0 0.8368602 0.8368602 1.293505e-03 1.293505e-03 0.161846275 #> 3300  1649.5 0.8369147 0.8369147 1.298372e-03 1.298372e-03 0.161786966 #> 3301  1650.0 0.8369690 0.8369690 1.303259e-03 1.303259e-03 0.161727767 #> 3302  1650.5 0.8370232 0.8370232 1.308166e-03 1.308166e-03 0.161668680 #> 3303  1651.0 0.8370772 0.8370772 1.313094e-03 1.313094e-03 0.161609704 #> 3304  1651.5 0.8371311 0.8371311 1.318041e-03 1.318041e-03 0.161550840 #> 3305  1652.0 0.8371849 0.8371849 1.323010e-03 1.323010e-03 0.161492088 #> 3306  1652.5 0.8372386 0.8372386 1.327998e-03 1.327998e-03 0.161433449 #> 3307  1653.0 0.8372921 0.8372921 1.333008e-03 1.333008e-03 0.161374922 #> 3308  1653.5 0.8373455 0.8373455 1.338038e-03 1.338038e-03 0.161316507 #> 3309  1654.0 0.8373987 0.8373987 1.343088e-03 1.343088e-03 0.161258206 #> 3310  1654.5 0.8374518 0.8374518 1.348160e-03 1.348160e-03 0.161200019 #> 3311  1655.0 0.8375048 0.8375048 1.353253e-03 1.353253e-03 0.161141945 #> 3312  1655.5 0.8375576 0.8375576 1.358366e-03 1.358366e-03 0.161083985 #> 3313  1656.0 0.8376104 0.8376104 1.363501e-03 1.363501e-03 0.161026140 #> 3314  1656.5 0.8376629 0.8376629 1.368657e-03 1.368657e-03 0.160968409 #> 3315  1657.0 0.8377154 0.8377154 1.373834e-03 1.373834e-03 0.160910794 #> 3316  1657.5 0.8377677 0.8377677 1.379033e-03 1.379033e-03 0.160853293 #> 3317  1658.0 0.8378198 0.8378198 1.384253e-03 1.384253e-03 0.160795908 #> 3318  1658.5 0.8378719 0.8378719 1.389494e-03 1.389494e-03 0.160738639 #> 3319  1659.0 0.8379238 0.8379238 1.394758e-03 1.394758e-03 0.160681486 #> 3320  1659.5 0.8379755 0.8379755 1.400043e-03 1.400043e-03 0.160624450 #> 3321  1660.0 0.8380271 0.8380271 1.405350e-03 1.405350e-03 0.160567530 #> 3322  1660.5 0.8380786 0.8380786 1.410678e-03 1.410678e-03 0.160510727 #> 3323  1661.0 0.8381299 0.8381299 1.416029e-03 1.416029e-03 0.160454042 #> 3324  1661.5 0.8381811 0.8381811 1.421402e-03 1.421402e-03 0.160397474 #> 3325  1662.0 0.8382322 0.8382322 1.426797e-03 1.426797e-03 0.160341025 #> 3326  1662.5 0.8382831 0.8382831 1.432215e-03 1.432215e-03 0.160284693 #> 3327  1663.0 0.8383339 0.8383339 1.437655e-03 1.437655e-03 0.160228481 #> 3328  1663.5 0.8383845 0.8383845 1.443117e-03 1.443117e-03 0.160172387 #> 3329  1664.0 0.8384350 0.8384350 1.448602e-03 1.448602e-03 0.160116412 #> 3330  1664.5 0.8384853 0.8384853 1.454110e-03 1.454110e-03 0.160060557 #> 3331  1665.0 0.8385355 0.8385355 1.459640e-03 1.459640e-03 0.160004822 #> 3332  1665.5 0.8385856 0.8385856 1.465194e-03 1.465194e-03 0.159949207 #> 3333  1666.0 0.8386355 0.8386355 1.470770e-03 1.470770e-03 0.159893713 #> 3334  1666.5 0.8386853 0.8386853 1.476369e-03 1.476369e-03 0.159838339 #> 3335  1667.0 0.8387349 0.8387349 1.481992e-03 1.481992e-03 0.159783087 #> 3336  1667.5 0.8387844 0.8387844 1.487637e-03 1.487637e-03 0.159727956 #> 3337  1668.0 0.8388337 0.8388337 1.493306e-03 1.493306e-03 0.159672947 #> 3338  1668.5 0.8388829 0.8388829 1.498999e-03 1.498999e-03 0.159618060 #> 3339  1669.0 0.8389320 0.8389320 1.504715e-03 1.504715e-03 0.159563296 #> 3340  1669.5 0.8389809 0.8389809 1.510455e-03 1.510455e-03 0.159508654 #> 3341  1670.0 0.8390296 0.8390296 1.516218e-03 1.516218e-03 0.159454135 #> 3342  1670.5 0.8390783 0.8390783 1.522006e-03 1.522006e-03 0.159399741 #> 3343  1671.0 0.8391267 0.8391267 1.527817e-03 1.527817e-03 0.159345469 #> 3344  1671.5 0.8391750 0.8391750 1.533652e-03 1.533652e-03 0.159291322 #> 3345  1672.0 0.8392232 0.8392232 1.539512e-03 1.539512e-03 0.159237300 #> 3346  1672.5 0.8392712 0.8392712 1.545395e-03 1.545395e-03 0.159183402 #> 3347  1673.0 0.8393191 0.8393191 1.551303e-03 1.551303e-03 0.159129630 #> 3348  1673.5 0.8393668 0.8393668 1.557236e-03 1.557236e-03 0.159075983 #> 3349  1674.0 0.8394143 0.8394143 1.563193e-03 1.563193e-03 0.159022462 #> 3350  1674.5 0.8394618 0.8394618 1.569175e-03 1.569175e-03 0.158969067 #> 3351  1675.0 0.8395090 0.8395090 1.575181e-03 1.575181e-03 0.158915799 #> 3352  1675.5 0.8395561 0.8395561 1.581212e-03 1.581212e-03 0.158862657 #> 3353  1676.0 0.8396031 0.8396031 1.587269e-03 1.587269e-03 0.158809643 #> 3354  1676.5 0.8396499 0.8396499 1.593350e-03 1.593350e-03 0.158756757 #> 3355  1677.0 0.8396965 0.8396965 1.599457e-03 1.599457e-03 0.158703998 #> 3356  1677.5 0.8397430 0.8397430 1.605588e-03 1.605588e-03 0.158651368 #> 3357  1678.0 0.8397894 0.8397894 1.611745e-03 1.611745e-03 0.158598867 #> 3358  1678.5 0.8398356 0.8398356 1.617928e-03 1.617928e-03 0.158546494 #> 3359  1679.0 0.8398816 0.8398816 1.624136e-03 1.624136e-03 0.158494251 #> 3360  1679.5 0.8399275 0.8399275 1.630370e-03 1.630370e-03 0.158442138 #> 3361  1680.0 0.8399732 0.8399732 1.636630e-03 1.636630e-03 0.158390155 #> 3362  1680.5 0.8400188 0.8400188 1.642916e-03 1.642916e-03 0.158338302 #> 3363  1681.0 0.8400642 0.8400642 1.649227e-03 1.649227e-03 0.158286581 #> 3364  1681.5 0.8401094 0.8401094 1.655565e-03 1.655565e-03 0.158234990 #> 3365  1682.0 0.8401545 0.8401545 1.661929e-03 1.661929e-03 0.158183531 #> 3366  1682.5 0.8401995 0.8401995 1.668319e-03 1.668319e-03 0.158132204 #> 3367  1683.0 0.8402443 0.8402443 1.674736e-03 1.674736e-03 0.158081010 #> 3368  1683.5 0.8402889 0.8402889 1.681179e-03 1.681179e-03 0.158029948 #> 3369  1684.0 0.8403333 0.8403333 1.687649e-03 1.687649e-03 0.157979019 #> 3370  1684.5 0.8403776 0.8403776 1.694146e-03 1.694146e-03 0.157928224 #> 3371  1685.0 0.8404218 0.8404218 1.700670e-03 1.700670e-03 0.157877563 #> 3372  1685.5 0.8404657 0.8404657 1.707221e-03 1.707221e-03 0.157827036 #> 3373  1686.0 0.8405096 0.8405096 1.713798e-03 1.713798e-03 0.157776643 #> 3374  1686.5 0.8405532 0.8405532 1.720403e-03 1.720403e-03 0.157726386 #> 3375  1687.0 0.8405967 0.8405967 1.727036e-03 1.727036e-03 0.157676264 #> 3376  1687.5 0.8406400 0.8406400 1.733696e-03 1.733696e-03 0.157626277 #> 3377  1688.0 0.8406832 0.8406832 1.740383e-03 1.740383e-03 0.157576427 #> 3378  1688.5 0.8407262 0.8407262 1.747098e-03 1.747098e-03 0.157526714 #> 3379  1689.0 0.8407690 0.8407690 1.753841e-03 1.753841e-03 0.157477137 #> 3380  1689.5 0.8408117 0.8408117 1.760612e-03 1.760612e-03 0.157427698 #> 3381  1690.0 0.8408542 0.8408542 1.767410e-03 1.767410e-03 0.157378397 #> 3382  1690.5 0.8408965 0.8408965 1.774237e-03 1.774237e-03 0.157329234 #> 3383  1691.0 0.8409387 0.8409387 1.781092e-03 1.781092e-03 0.157280209 #> 3384  1691.5 0.8409807 0.8409807 1.787976e-03 1.787976e-03 0.157231323 #> 3385  1692.0 0.8410225 0.8410225 1.794888e-03 1.794888e-03 0.157182577 #> 3386  1692.5 0.8410642 0.8410642 1.801829e-03 1.801829e-03 0.157133970 #> 3387  1693.0 0.8411057 0.8411057 1.808798e-03 1.808798e-03 0.157085504 #> 3388  1693.5 0.8411470 0.8411470 1.815796e-03 1.815796e-03 0.157037178 #> 3389  1694.0 0.8411882 0.8411882 1.822823e-03 1.822823e-03 0.156988994 #> 3390  1694.5 0.8412292 0.8412292 1.829879e-03 1.829879e-03 0.156940950 #> 3391  1695.0 0.8412700 0.8412700 1.836965e-03 1.836965e-03 0.156893049 #> 3392  1695.5 0.8413106 0.8413106 1.844079e-03 1.844079e-03 0.156845290 #> 3393  1696.0 0.8413511 0.8413511 1.851224e-03 1.851224e-03 0.156797673 #> 3394  1696.5 0.8413914 0.8413914 1.858397e-03 1.858397e-03 0.156750199 #> 3395  1697.0 0.8414315 0.8414315 1.865601e-03 1.865601e-03 0.156702869 #> 3396  1697.5 0.8414715 0.8414715 1.872834e-03 1.872834e-03 0.156655683 #> 3397  1698.0 0.8415113 0.8415113 1.880097e-03 1.880097e-03 0.156608642 #> 3398  1698.5 0.8415509 0.8415509 1.887390e-03 1.887390e-03 0.156561745 #> 3399  1699.0 0.8415903 0.8415903 1.894713e-03 1.894713e-03 0.156514993 #> 3400  1699.5 0.8416295 0.8416295 1.902067e-03 1.902067e-03 0.156468387 #> 3401  1700.0 0.8416686 0.8416686 1.909451e-03 1.909451e-03 0.156421927 #> 3402  1700.5 0.8417075 0.8417075 1.916865e-03 1.916865e-03 0.156375614 #> 3403  1701.0 0.8417462 0.8417462 1.924310e-03 1.924310e-03 0.156329447 #> 3404  1701.5 0.8417848 0.8417848 1.931786e-03 1.931786e-03 0.156283428 #> 3405  1702.0 0.8418232 0.8418232 1.939293e-03 1.939293e-03 0.156237557 #> 3406  1702.5 0.8418613 0.8418613 1.946831e-03 1.946831e-03 0.156191834 #> 3407  1703.0 0.8418993 0.8418993 1.954400e-03 1.954400e-03 0.156146259 #> 3408  1703.5 0.8419372 0.8419372 1.962001e-03 1.962001e-03 0.156100834 #> 3409  1704.0 0.8419748 0.8419748 1.969632e-03 1.969632e-03 0.156055559 #> 3410  1704.5 0.8420123 0.8420123 1.977296e-03 1.977296e-03 0.156010433 #> 3411  1705.0 0.8420496 0.8420496 1.984991e-03 1.984991e-03 0.155965458 #> 3412  1705.5 0.8420866 0.8420866 1.992717e-03 1.992717e-03 0.155920634 #> 3413  1706.0 0.8421236 0.8421236 2.000476e-03 2.000476e-03 0.155875962 #> 3414  1706.5 0.8421603 0.8421603 2.008267e-03 2.008267e-03 0.155831441 #> 3415  1707.0 0.8421968 0.8421968 2.016090e-03 2.016090e-03 0.155787073 #> 3416  1707.5 0.8422332 0.8422332 2.023945e-03 2.023945e-03 0.155742857 #> 3417  1708.0 0.8422694 0.8422694 2.031833e-03 2.031833e-03 0.155698795 #> 3418  1708.5 0.8423054 0.8423054 2.039753e-03 2.039753e-03 0.155654886 #> 3419  1709.0 0.8423412 0.8423412 2.047706e-03 2.047706e-03 0.155611132 #> 3420  1709.5 0.8423768 0.8423768 2.055692e-03 2.055692e-03 0.155567532 #> 3421  1710.0 0.8424122 0.8424122 2.063711e-03 2.063711e-03 0.155524088 #> 3422  1710.5 0.8424474 0.8424474 2.071762e-03 2.071762e-03 0.155480799 #> 3423  1711.0 0.8424825 0.8424825 2.079848e-03 2.079848e-03 0.155437666 #> 3424  1711.5 0.8425173 0.8425173 2.087966e-03 2.087966e-03 0.155394689 #> 3425  1712.0 0.8425520 0.8425520 2.096118e-03 2.096118e-03 0.155351870 #> 3426  1712.5 0.8425865 0.8425865 2.104304e-03 2.104304e-03 0.155309208 #> 3427  1713.0 0.8426208 0.8426208 2.112523e-03 2.112523e-03 0.155266704 #> 3428  1713.5 0.8426549 0.8426549 2.120777e-03 2.120777e-03 0.155224359 #> 3429  1714.0 0.8426888 0.8426888 2.129064e-03 2.129064e-03 0.155182173 #> 3430  1714.5 0.8427225 0.8427225 2.137386e-03 2.137386e-03 0.155140146 #> 3431  1715.0 0.8427560 0.8427560 2.145742e-03 2.145742e-03 0.155098279 #> 3432  1715.5 0.8427893 0.8427893 2.154132e-03 2.154132e-03 0.155056573 #> 3433  1716.0 0.8428224 0.8428224 2.162557e-03 2.162557e-03 0.155015027 #> 3434  1716.5 0.8428553 0.8428553 2.171017e-03 2.171017e-03 0.154973643 #> 3435  1717.0 0.8428881 0.8428881 2.179512e-03 2.179512e-03 0.154932421 #> 3436  1717.5 0.8429206 0.8429206 2.188041e-03 2.188041e-03 0.154891361 #> 3437  1718.0 0.8429529 0.8429529 2.196606e-03 2.196606e-03 0.154850464 #> 3438  1718.5 0.8429851 0.8429851 2.205206e-03 2.205206e-03 0.154809731 #> 3439  1719.0 0.8430170 0.8430170 2.213842e-03 2.213842e-03 0.154769162 #> 3440  1719.5 0.8430487 0.8430487 2.222513e-03 2.222513e-03 0.154728757 #> 3441  1720.0 0.8430803 0.8430803 2.231220e-03 2.231220e-03 0.154688517 #> 3442  1720.5 0.8431116 0.8431116 2.239963e-03 2.239963e-03 0.154648442 #> 3443  1721.0 0.8431427 0.8431427 2.248742e-03 2.248742e-03 0.154608534 #> 3444  1721.5 0.8431737 0.8431737 2.257557e-03 2.257557e-03 0.154568792 #> 3445  1722.0 0.8432044 0.8432044 2.266408e-03 2.266408e-03 0.154529217 #> 3446  1722.5 0.8432349 0.8432349 2.275296e-03 2.275296e-03 0.154489810 #> 3447  1723.0 0.8432652 0.8432652 2.284220e-03 2.284220e-03 0.154450571 #> 3448  1723.5 0.8432953 0.8432953 2.293181e-03 2.293181e-03 0.154411500 #> 3449  1724.0 0.8433252 0.8433252 2.302179e-03 2.302179e-03 0.154372599 #> 3450  1724.5 0.8433549 0.8433549 2.311214e-03 2.311214e-03 0.154333867 #> 3451  1725.0 0.8433844 0.8433844 2.320286e-03 2.320286e-03 0.154295306 #> 3452  1725.5 0.8434137 0.8434137 2.329395e-03 2.329395e-03 0.154256915 #> 3453  1726.0 0.8434428 0.8434428 2.338542e-03 2.338542e-03 0.154218695 #> 3454  1726.5 0.8434716 0.8434716 2.347727e-03 2.347727e-03 0.154180648 #> 3455  1727.0 0.8435003 0.8435003 2.356949e-03 2.356949e-03 0.154142772 #> 3456  1727.5 0.8435287 0.8435287 2.366209e-03 2.366209e-03 0.154105070 #> 3457  1728.0 0.8435570 0.8435570 2.375508e-03 2.375508e-03 0.154067541 #> 3458  1728.5 0.8435850 0.8435850 2.384844e-03 2.384844e-03 0.154030186 #> 3459  1729.0 0.8436128 0.8436128 2.394219e-03 2.394219e-03 0.153993006 #> 3460  1729.5 0.8436404 0.8436404 2.403632e-03 2.403632e-03 0.153956001 #> 3461  1730.0 0.8436677 0.8436677 2.413084e-03 2.413084e-03 0.153919171 #> 3462  1730.5 0.8436949 0.8436949 2.422575e-03 2.422575e-03 0.153882518 #> 3463  1731.0 0.8437219 0.8437219 2.432105e-03 2.432105e-03 0.153846041 #> 3464  1731.5 0.8437486 0.8437486 2.441674e-03 2.441674e-03 0.153809742 #> 3465  1732.0 0.8437751 0.8437751 2.451282e-03 2.451282e-03 0.153773621 #> 3466  1732.5 0.8438014 0.8438014 2.460930e-03 2.460930e-03 0.153737678 #> 3467  1733.0 0.8438275 0.8438275 2.470617e-03 2.470617e-03 0.153701915 #> 3468  1733.5 0.8438533 0.8438533 2.480344e-03 2.480344e-03 0.153666330 #> 3469  1734.0 0.8438790 0.8438790 2.490111e-03 2.490111e-03 0.153630927 #> 3470  1734.5 0.8439044 0.8439044 2.499918e-03 2.499918e-03 0.153595703 #> 3471  1735.0 0.8439296 0.8439296 2.509765e-03 2.509765e-03 0.153560662 #> 3472  1735.5 0.8439545 0.8439545 2.519653e-03 2.519653e-03 0.153525802 #> 3473  1736.0 0.8439793 0.8439793 2.529581e-03 2.529581e-03 0.153491124 #> 3474  1736.5 0.8440038 0.8440038 2.539550e-03 2.539550e-03 0.153456630 #> 3475  1737.0 0.8440281 0.8440281 2.549559e-03 2.549559e-03 0.153422320 #> 3476  1737.5 0.8440522 0.8440522 2.559610e-03 2.559610e-03 0.153388193 #> 3477  1738.0 0.8440760 0.8440760 2.569701e-03 2.569701e-03 0.153354252 #> 3478  1738.5 0.8440997 0.8440997 2.579835e-03 2.579835e-03 0.153320496 #> 3479  1739.0 0.8441231 0.8441231 2.590009e-03 2.590009e-03 0.153286926 #> 3480  1739.5 0.8441462 0.8441462 2.600225e-03 2.600225e-03 0.153253543 #> 3481  1740.0 0.8441692 0.8441692 2.610483e-03 2.610483e-03 0.153220347 #> 3482  1740.5 0.8441919 0.8441919 2.620783e-03 2.620783e-03 0.153187339 #> 3483  1741.0 0.8442144 0.8442144 2.631125e-03 2.631125e-03 0.153154519 #> 3484  1741.5 0.8442366 0.8442366 2.641510e-03 2.641510e-03 0.153121889 #> 3485  1742.0 0.8442586 0.8442586 2.651936e-03 2.651936e-03 0.153089448 #> 3486  1742.5 0.8442804 0.8442804 2.662406e-03 2.662406e-03 0.153057197 #> 3487  1743.0 0.8443019 0.8443019 2.672918e-03 2.672918e-03 0.153025138 #> 3488  1743.5 0.8443233 0.8443233 2.683473e-03 2.683473e-03 0.152993270 #> 3489  1744.0 0.8443443 0.8443443 2.694071e-03 2.694071e-03 0.152961594 #> 3490  1744.5 0.8443652 0.8443652 2.704713e-03 2.704713e-03 0.152930111 #> 3491  1745.0 0.8443858 0.8443858 2.715398e-03 2.715398e-03 0.152898821 #> 3492  1745.5 0.8444061 0.8444061 2.726127e-03 2.726127e-03 0.152867725 #> 3493  1746.0 0.8444263 0.8444263 2.736899e-03 2.736899e-03 0.152836824 #> 3494  1746.5 0.8444462 0.8444462 2.747715e-03 2.747715e-03 0.152806119 #> 3495  1747.0 0.8444658 0.8444658 2.758576e-03 2.758576e-03 0.152775609 #> 3496  1747.5 0.8444852 0.8444852 2.769480e-03 2.769480e-03 0.152745296 #> 3497  1748.0 0.8445044 0.8445044 2.780429e-03 2.780429e-03 0.152715180 #> 3498  1748.5 0.8445233 0.8445233 2.791423e-03 2.791423e-03 0.152685262 #> 3499  1749.0 0.8445420 0.8445420 2.802462e-03 2.802462e-03 0.152655542 #> 3500  1749.5 0.8445604 0.8445604 2.813545e-03 2.813545e-03 0.152626022 #> 3501  1750.0 0.8445786 0.8445786 2.824674e-03 2.824674e-03 0.152596701 #> 3502  1750.5 0.8445966 0.8445966 2.835848e-03 2.835848e-03 0.152567581 #> 3503  1751.0 0.8446143 0.8446143 2.847067e-03 2.847067e-03 0.152538662 #> 3504  1751.5 0.8446317 0.8446317 2.858332e-03 2.858332e-03 0.152509945 #> 3505  1752.0 0.8446489 0.8446489 2.869643e-03 2.869643e-03 0.152481430 #> 3506  1752.5 0.8446659 0.8446659 2.881000e-03 2.881000e-03 0.152453119 #> 3507  1753.0 0.8446826 0.8446826 2.892403e-03 2.892403e-03 0.152425011 #> 3508  1753.5 0.8446990 0.8446990 2.903852e-03 2.903852e-03 0.152397107 #> 3509  1754.0 0.8447152 0.8447152 2.915348e-03 2.915348e-03 0.152369409 #> 3510  1754.5 0.8447312 0.8447312 2.926891e-03 2.926891e-03 0.152341916 #> 3511  1755.0 0.8447469 0.8447469 2.938480e-03 2.938480e-03 0.152314630 #> 3512  1755.5 0.8447623 0.8447623 2.950117e-03 2.950117e-03 0.152287551 #> 3513  1756.0 0.8447775 0.8447775 2.961800e-03 2.961800e-03 0.152260680 #> 3514  1756.5 0.8447925 0.8447925 2.973531e-03 2.973531e-03 0.152234018 #> 3515  1757.0 0.8448071 0.8448071 2.985310e-03 2.985310e-03 0.152207564 #> 3516  1757.5 0.8448215 0.8448215 2.997136e-03 2.997136e-03 0.152181321 #> 3517  1758.0 0.8448357 0.8448357 3.009010e-03 3.009010e-03 0.152155288 #> 3518  1758.5 0.8448496 0.8448496 3.020933e-03 3.020933e-03 0.152129466 #> 3519  1759.0 0.8448632 0.8448632 3.032904e-03 3.032904e-03 0.152103856 #> 3520  1759.5 0.8448766 0.8448766 3.044923e-03 3.044923e-03 0.152078459 #> 3521  1760.0 0.8448897 0.8448897 3.056990e-03 3.056990e-03 0.152053275 #> 3522  1760.5 0.8449026 0.8449026 3.069107e-03 3.069107e-03 0.152028306 #> 3523  1761.0 0.8449152 0.8449152 3.081273e-03 3.081273e-03 0.152003551 #> 3524  1761.5 0.8449275 0.8449275 3.093487e-03 3.093487e-03 0.151979011 #> 3525  1762.0 0.8449396 0.8449396 3.105752e-03 3.105752e-03 0.151954688 #> 3526  1762.5 0.8449514 0.8449514 3.118065e-03 3.118065e-03 0.151930582 #> 3527  1763.0 0.8449629 0.8449629 3.130429e-03 3.130429e-03 0.151906693 #> 3528  1763.5 0.8449741 0.8449741 3.142842e-03 3.142842e-03 0.151883023 #> 3529  1764.0 0.8449851 0.8449851 3.155306e-03 3.155306e-03 0.151859572 #> 3530  1764.5 0.8449958 0.8449958 3.167819e-03 3.167819e-03 0.151836341 #> 3531  1765.0 0.8450063 0.8450063 3.180384e-03 3.180384e-03 0.151813330 #> 3532  1765.5 0.8450165 0.8450165 3.192998e-03 3.192998e-03 0.151790540 #> 3533  1766.0 0.8450264 0.8450264 3.205664e-03 3.205664e-03 0.151767973 #> 3534  1766.5 0.8450360 0.8450360 3.218381e-03 3.218381e-03 0.151745628 #> 3535  1767.0 0.8450453 0.8450453 3.231149e-03 3.231149e-03 0.151723507 #> 3536  1767.5 0.8450544 0.8450544 3.243968e-03 3.243968e-03 0.151701610 #> 3537  1768.0 0.8450632 0.8450632 3.256839e-03 3.256839e-03 0.151679938 #> 3538  1768.5 0.8450717 0.8450717 3.269762e-03 3.269762e-03 0.151658491 #> 3539  1769.0 0.8450800 0.8450800 3.282737e-03 3.282737e-03 0.151637272 #> 3540  1769.5 0.8450880 0.8450880 3.295763e-03 3.295763e-03 0.151616279 #> 3541  1770.0 0.8450956 0.8450956 3.308843e-03 3.308843e-03 0.151595514 #> 3542  1770.5 0.8451030 0.8451030 3.321974e-03 3.321974e-03 0.151574979 #> 3543  1771.0 0.8451102 0.8451102 3.335159e-03 3.335159e-03 0.151554672 #> 3544  1771.5 0.8451170 0.8451170 3.348396e-03 3.348396e-03 0.151534596 #> 3545  1772.0 0.8451236 0.8451236 3.361687e-03 3.361687e-03 0.151514751 #> 3546  1772.5 0.8451298 0.8451298 3.375030e-03 3.375030e-03 0.151495138 #> 3547  1773.0 0.8451358 0.8451358 3.388427e-03 3.388427e-03 0.151475757 #> 3548  1773.5 0.8451415 0.8451415 3.401878e-03 3.401878e-03 0.151456610 #> 3549  1774.0 0.8451469 0.8451469 3.415383e-03 3.415383e-03 0.151437697 #> 3550  1774.5 0.8451520 0.8451520 3.428942e-03 3.428942e-03 0.151419019 #> 3551  1775.0 0.8451569 0.8451569 3.442555e-03 3.442555e-03 0.151400576 #> 3552  1775.5 0.8451614 0.8451614 3.456223e-03 3.456223e-03 0.151382370 #> 3553  1776.0 0.8451657 0.8451657 3.469945e-03 3.469945e-03 0.151364401 #> 3554  1776.5 0.8451696 0.8451696 3.483722e-03 3.483722e-03 0.151346671 #> 3555  1777.0 0.8451733 0.8451733 3.497554e-03 3.497554e-03 0.151329179 #> 3556  1777.5 0.8451766 0.8451766 3.511441e-03 3.511441e-03 0.151311927 #> 3557  1778.0 0.8451797 0.8451797 3.525384e-03 3.525384e-03 0.151294915 #> 3558  1778.5 0.8451825 0.8451825 3.539382e-03 3.539382e-03 0.151278145 #> 3559  1779.0 0.8451849 0.8451849 3.553436e-03 3.553436e-03 0.151261617 #> 3560  1779.5 0.8451871 0.8451871 3.567546e-03 3.567546e-03 0.151245332 #> 3561  1780.0 0.8451890 0.8451890 3.581713e-03 3.581713e-03 0.151229290 #> 3562  1780.5 0.8451906 0.8451906 3.595936e-03 3.595936e-03 0.151213493 #> 3563  1781.0 0.8451918 0.8451918 3.610215e-03 3.610215e-03 0.151197941 #> 3564  1781.5 0.8451928 0.8451928 3.624551e-03 3.624551e-03 0.151182636 #> 3565  1782.0 0.8451935 0.8451935 3.638944e-03 3.638944e-03 0.151167577 #> 3566  1782.5 0.8451938 0.8451938 3.653395e-03 3.653395e-03 0.151152767 #> 3567  1783.0 0.8451939 0.8451939 3.667903e-03 3.667903e-03 0.151138205 #> 3568  1783.5 0.8451936 0.8451936 3.682468e-03 3.682468e-03 0.151123892 #> 3569  1784.0 0.8451931 0.8451931 3.697091e-03 3.697091e-03 0.151109830 #> 3570  1784.5 0.8451922 0.8451922 3.711772e-03 3.711772e-03 0.151096019 #> 3571  1785.0 0.8451910 0.8451910 3.726512e-03 3.726512e-03 0.151082460 #> 3572  1785.5 0.8451895 0.8451895 3.741310e-03 3.741310e-03 0.151069154 #> 3573  1786.0 0.8451877 0.8451877 3.756166e-03 3.756166e-03 0.151056101 #> 3574  1786.5 0.8451856 0.8451856 3.771081e-03 3.771081e-03 0.151043304 #> 3575  1787.0 0.8451832 0.8451832 3.786055e-03 3.786055e-03 0.151030761 #> 3576  1787.5 0.8451804 0.8451804 3.801089e-03 3.801089e-03 0.151018475 #> 3577  1788.0 0.8451774 0.8451774 3.816182e-03 3.816182e-03 0.151006446 #> 3578  1788.5 0.8451740 0.8451740 3.831334e-03 3.831334e-03 0.150994675 #> 3579  1789.0 0.8451703 0.8451703 3.846547e-03 3.846547e-03 0.150983163 #> 3580  1789.5 0.8451663 0.8451663 3.861819e-03 3.861819e-03 0.150971910 #> 3581  1790.0 0.8451619 0.8451619 3.877151e-03 3.877151e-03 0.150960918 #> 3582  1790.5 0.8451573 0.8451573 3.892544e-03 3.892544e-03 0.150950188 #> 3583  1791.0 0.8451523 0.8451523 3.907998e-03 3.907998e-03 0.150939720 #> 3584  1791.5 0.8451470 0.8451470 3.923513e-03 3.923513e-03 0.150929515 #> 3585  1792.0 0.8451413 0.8451413 3.939088e-03 3.939088e-03 0.150919575 #> 3586  1792.5 0.8451354 0.8451354 3.954725e-03 3.954725e-03 0.150909899 #> 3587  1793.0 0.8451291 0.8451291 3.970423e-03 3.970423e-03 0.150900489 #> 3588  1793.5 0.8451225 0.8451225 3.986183e-03 3.986183e-03 0.150891346 #> 3589  1794.0 0.8451155 0.8451155 4.002005e-03 4.002005e-03 0.150882471 #> 3590  1794.5 0.8451082 0.8451082 4.017889e-03 4.017889e-03 0.150873864 #> 3591  1795.0 0.8451006 0.8451006 4.033835e-03 4.033835e-03 0.150865527 #> 3592  1795.5 0.8450927 0.8450927 4.049844e-03 4.049844e-03 0.150857460 #> 3593  1796.0 0.8450844 0.8450844 4.065915e-03 4.065915e-03 0.150849665 #> 3594  1796.5 0.8450758 0.8450758 4.082050e-03 4.082050e-03 0.150842141 #> 3595  1797.0 0.8450669 0.8450669 4.098247e-03 4.098247e-03 0.150834891 #> 3596  1797.5 0.8450576 0.8450576 4.114508e-03 4.114508e-03 0.150827915 #> 3597  1798.0 0.8450480 0.8450480 4.130832e-03 4.130832e-03 0.150821214 #> 3598  1798.5 0.8450380 0.8450380 4.147220e-03 4.147220e-03 0.150814788 #> 3599  1799.0 0.8450277 0.8450277 4.163672e-03 4.163672e-03 0.150808640 #> 3600  1799.5 0.8450170 0.8450170 4.180188e-03 4.180188e-03 0.150802769 #> 3601  1800.0 0.8450061 0.8450061 4.196769e-03 4.196769e-03 0.150797177 #> 3602  1800.5 0.8449947 0.8449947 4.213414e-03 4.213414e-03 0.150791865 #> 3603  1801.0 0.8449830 0.8449830 4.230124e-03 4.230124e-03 0.150786833 #> 3604  1801.5 0.8449710 0.8449710 4.246899e-03 4.246899e-03 0.150782082 #> 3605  1802.0 0.8449586 0.8449586 4.263739e-03 4.263739e-03 0.150777614 #> 3606  1802.5 0.8449459 0.8449459 4.280645e-03 4.280645e-03 0.150773429 #> 3607  1803.0 0.8449329 0.8449329 4.297616e-03 4.297616e-03 0.150769529 #> 3608  1803.5 0.8449194 0.8449194 4.314653e-03 4.314653e-03 0.150765914 #> 3609  1804.0 0.8449057 0.8449057 4.331757e-03 4.331757e-03 0.150762585 #> 3610  1804.5 0.8448915 0.8448915 4.348926e-03 4.348926e-03 0.150759544 #> 3611  1805.0 0.8448770 0.8448770 4.366162e-03 4.366162e-03 0.150756790 #> 3612  1805.5 0.8448622 0.8448622 4.383465e-03 4.383465e-03 0.150754326 #> 3613  1806.0 0.8448470 0.8448470 4.400834e-03 4.400834e-03 0.150752152 #> 3614  1806.5 0.8448315 0.8448315 4.418271e-03 4.418271e-03 0.150750269 #> 3615  1807.0 0.8448155 0.8448155 4.435775e-03 4.435775e-03 0.150748678 #> 3616  1807.5 0.8447993 0.8447993 4.453347e-03 4.453347e-03 0.150747380 #> 3617  1808.0 0.8447826 0.8447826 4.470986e-03 4.470986e-03 0.150746375 #> 3618  1808.5 0.8447656 0.8447656 4.488694e-03 4.488694e-03 0.150745666 #> 3619  1809.0 0.8447483 0.8447483 4.506469e-03 4.506469e-03 0.150745253 #> 3620  1809.5 0.8447305 0.8447305 4.524313e-03 4.524313e-03 0.150745137 #> 3621  1810.0 0.8447125 0.8447125 4.542226e-03 4.542226e-03 0.150745319 #> 3622  1810.5 0.8446940 0.8446940 4.560207e-03 4.560207e-03 0.150745800 #> 3623  1811.0 0.8446752 0.8446752 4.578258e-03 4.578258e-03 0.150746580 #> 3624  1811.5 0.8446560 0.8446560 4.596378e-03 4.596378e-03 0.150747662 #> 3625  1812.0 0.8446364 0.8446364 4.614567e-03 4.614567e-03 0.150749046 #> 3626  1812.5 0.8446164 0.8446164 4.632826e-03 4.632826e-03 0.150750732 #> 3627  1813.0 0.8445961 0.8445961 4.651155e-03 4.651155e-03 0.150752723 #> 3628  1813.5 0.8445754 0.8445754 4.669553e-03 4.669553e-03 0.150755018 #> 3629  1814.0 0.8445544 0.8445544 4.688023e-03 4.688023e-03 0.150757620 #> 3630  1814.5 0.8445329 0.8445329 4.706563e-03 4.706563e-03 0.150760528 #> 3631  1815.0 0.8445111 0.8445111 4.725173e-03 4.725173e-03 0.150763745 #> 3632  1815.5 0.8444889 0.8444889 4.743855e-03 4.743855e-03 0.150767271 #> 3633  1816.0 0.8444663 0.8444663 4.762608e-03 4.762608e-03 0.150771107 #> 3634  1816.5 0.8444433 0.8444433 4.781432e-03 4.781432e-03 0.150775254 #> 3635  1817.0 0.8444200 0.8444200 4.800328e-03 4.800328e-03 0.150779713 #> 3636  1817.5 0.8443962 0.8443962 4.819295e-03 4.819295e-03 0.150784485 #> 3637  1818.0 0.8443721 0.8443721 4.838335e-03 4.838335e-03 0.150789572 #> 3638  1818.5 0.8443476 0.8443476 4.857447e-03 4.857447e-03 0.150794974 #> 3639  1819.0 0.8443227 0.8443227 4.876631e-03 4.876631e-03 0.150800692 #> 3640  1819.5 0.8442974 0.8442974 4.895888e-03 4.895888e-03 0.150806728 #> 3641  1820.0 0.8442717 0.8442717 4.915218e-03 4.915218e-03 0.150813082 #> 3642  1820.5 0.8442456 0.8442456 4.934622e-03 4.934622e-03 0.150819756 #> 3643  1821.0 0.8442192 0.8442192 4.954098e-03 4.954098e-03 0.150826750 #> 3644  1821.5 0.8441923 0.8441923 4.973648e-03 4.973648e-03 0.150834066 #> 3645  1822.0 0.8441650 0.8441650 4.993272e-03 4.993272e-03 0.150841704 #> 3646  1822.5 0.8441374 0.8441374 5.012970e-03 5.012970e-03 0.150849667 #> 3647  1823.0 0.8441093 0.8441093 5.032742e-03 5.032742e-03 0.150857954 #> 3648  1823.5 0.8440808 0.8440808 5.052588e-03 5.052588e-03 0.150866567 #> 3649  1824.0 0.8440520 0.8440520 5.072509e-03 5.072509e-03 0.150875508 #> 3650  1824.5 0.8440227 0.8440227 5.092505e-03 5.092505e-03 0.150884776 #> 3651  1825.0 0.8439931 0.8439931 5.112576e-03 5.112576e-03 0.150894373 #> 3652  1825.5 0.8439630 0.8439630 5.132723e-03 5.132723e-03 0.150904301 #> 3653  1826.0 0.8439325 0.8439325 5.152944e-03 5.152944e-03 0.150914560 #> 3654  1826.5 0.8439016 0.8439016 5.173242e-03 5.173242e-03 0.150925152 #> 3655  1827.0 0.8438703 0.8438703 5.193615e-03 5.193615e-03 0.150936077 #> 3656  1827.5 0.8438386 0.8438386 5.214065e-03 5.214065e-03 0.150947336 #> 3657  1828.0 0.8438065 0.8438065 5.234590e-03 5.234590e-03 0.150958932 #> 3658  1828.5 0.8437739 0.8437739 5.255193e-03 5.255193e-03 0.150970864 #> 3659  1829.0 0.8437410 0.8437410 5.275872e-03 5.275872e-03 0.150983135 #> 3660  1829.5 0.8437076 0.8437076 5.296628e-03 5.296628e-03 0.150995744 #> 3661  1830.0 0.8436738 0.8436738 5.317461e-03 5.317461e-03 0.151008693 #> 3662  1830.5 0.8436396 0.8436396 5.338372e-03 5.338372e-03 0.151021984 #> 3663  1831.0 0.8436050 0.8436050 5.359361e-03 5.359361e-03 0.151035618 #> 3664  1831.5 0.8435700 0.8435700 5.380427e-03 5.380427e-03 0.151049594 #> 3665  1832.0 0.8435345 0.8435345 5.401571e-03 5.401571e-03 0.151063916 #> 3666  1832.5 0.8434986 0.8434986 5.422793e-03 5.422793e-03 0.151078583 #> 3667  1833.0 0.8434623 0.8434623 5.444094e-03 5.444094e-03 0.151093598 #> 3668  1833.5 0.8434256 0.8434256 5.465474e-03 5.465474e-03 0.151108960 #> 3669  1834.0 0.8433884 0.8433884 5.486933e-03 5.486933e-03 0.151124671 #> 3670  1834.5 0.8433508 0.8433508 5.508470e-03 5.508470e-03 0.151140733 #> 3671  1835.0 0.8433128 0.8433128 5.530087e-03 5.530087e-03 0.151157147 #> 3672  1835.5 0.8432743 0.8432743 5.551784e-03 5.551784e-03 0.151173913 #> 3673  1836.0 0.8432354 0.8432354 5.573560e-03 5.573560e-03 0.151191033 #> 3674  1836.5 0.8431961 0.8431961 5.595416e-03 5.595416e-03 0.151208507 #> 3675  1837.0 0.8431563 0.8431563 5.617352e-03 5.617352e-03 0.151226338 #> 3676  1837.5 0.8431161 0.8431161 5.639369e-03 5.639369e-03 0.151244527 #> 3677  1838.0 0.8430755 0.8430755 5.661466e-03 5.661466e-03 0.151263073 #> 3678  1838.5 0.8430344 0.8430344 5.683644e-03 5.683644e-03 0.151281979 #> 3679  1839.0 0.8429929 0.8429929 5.705903e-03 5.705903e-03 0.151301246 #> 3680  1839.5 0.8429509 0.8429509 5.728243e-03 5.728243e-03 0.151320875 #> 3681  1840.0 0.8429085 0.8429085 5.750665e-03 5.750665e-03 0.151340867 #> 3682  1840.5 0.8428656 0.8428656 5.773168e-03 5.773168e-03 0.151361224 #> 3683  1841.0 0.8428223 0.8428223 5.795753e-03 5.795753e-03 0.151381945 #> 3684  1841.5 0.8427785 0.8427785 5.818420e-03 5.818420e-03 0.151403034 #> 3685  1842.0 0.8427343 0.8427343 5.841169e-03 5.841169e-03 0.151424490 #> 3686  1842.5 0.8426897 0.8426897 5.864001e-03 5.864001e-03 0.151446315 #> 3687  1843.0 0.8426446 0.8426446 5.886915e-03 5.886915e-03 0.151468511 #> 3688  1843.5 0.8425990 0.8425990 5.909912e-03 5.909912e-03 0.151491078 #> 3689  1844.0 0.8425530 0.8425530 5.932993e-03 5.932993e-03 0.151514017 #> 3690  1844.5 0.8425065 0.8425065 5.956156e-03 5.956156e-03 0.151537331 #> 3691  1845.0 0.8424596 0.8424596 5.979403e-03 5.979403e-03 0.151561019 #> 3692  1845.5 0.8424122 0.8424122 6.002734e-03 6.002734e-03 0.151585083 #> 3693  1846.0 0.8423643 0.8423643 6.026148e-03 6.026148e-03 0.151609525 #> 3694  1846.5 0.8423160 0.8423160 6.049647e-03 6.049647e-03 0.151634346 #> 3695  1847.0 0.8422672 0.8422672 6.073229e-03 6.073229e-03 0.151659546 #> 3696  1847.5 0.8422180 0.8422180 6.096897e-03 6.096897e-03 0.151685128 #> 3697  1848.0 0.8421683 0.8421683 6.120648e-03 6.120648e-03 0.151711091 #> 3698  1848.5 0.8421181 0.8421181 6.144485e-03 6.144485e-03 0.151737439 #> 3699  1849.0 0.8420674 0.8420674 6.168407e-03 6.168407e-03 0.151764171 #> 3700  1849.5 0.8420163 0.8420163 6.192414e-03 6.192414e-03 0.151791289 #> 3701  1850.0 0.8419647 0.8419647 6.216506e-03 6.216506e-03 0.151818794 #> 3702  1850.5 0.8419126 0.8419126 6.240685e-03 6.240685e-03 0.151846687 #> 3703  1851.0 0.8418601 0.8418601 6.264949e-03 6.264949e-03 0.151874970 #> 3704  1851.5 0.8418071 0.8418071 6.289299e-03 6.289299e-03 0.151903644 #> 3705  1852.0 0.8417536 0.8417536 6.313735e-03 6.313735e-03 0.151932710 #> 3706  1852.5 0.8416996 0.8416996 6.338258e-03 6.338258e-03 0.151962170 #> 3707  1853.0 0.8416451 0.8416451 6.362867e-03 6.362867e-03 0.151992024 #> 3708  1853.5 0.8415902 0.8415902 6.387563e-03 6.387563e-03 0.152022274 #> 3709  1854.0 0.8415347 0.8415347 6.412346e-03 6.412346e-03 0.152052921 #> 3710  1854.5 0.8414788 0.8414788 6.437216e-03 6.437216e-03 0.152083966 #> 3711  1855.0 0.8414224 0.8414224 6.462174e-03 6.462174e-03 0.152115411 #> 3712  1855.5 0.8413655 0.8413655 6.487219e-03 6.487219e-03 0.152147257 #> 3713  1856.0 0.8413081 0.8413081 6.512352e-03 6.512352e-03 0.152179505 #> 3714  1856.5 0.8412503 0.8412503 6.537573e-03 6.537573e-03 0.152212156 #> 3715  1857.0 0.8411919 0.8411919 6.562883e-03 6.562883e-03 0.152245212 #> 3716  1857.5 0.8411330 0.8411330 6.588280e-03 6.588280e-03 0.152278674 #> 3717  1858.0 0.8410737 0.8410737 6.613766e-03 6.613766e-03 0.152312543 #> 3718  1858.5 0.8410138 0.8410138 6.639341e-03 6.639341e-03 0.152346821 #> 3719  1859.0 0.8409535 0.8409535 6.665004e-03 6.665004e-03 0.152381508 #> 3720  1859.5 0.8408926 0.8408926 6.690757e-03 6.690757e-03 0.152416607 #> 3721  1860.0 0.8408313 0.8408313 6.716599e-03 6.716599e-03 0.152452117 #> 3722  1860.5 0.8407694 0.8407694 6.742530e-03 6.742530e-03 0.152488041 #> 3723  1861.0 0.8407071 0.8407071 6.768551e-03 6.768551e-03 0.152524381 #> 3724  1861.5 0.8406442 0.8406442 6.794662e-03 6.794662e-03 0.152561136 #> 3725  1862.0 0.8405808 0.8405808 6.820862e-03 6.820862e-03 0.152598309 #> 3726  1862.5 0.8405169 0.8405169 6.847153e-03 6.847153e-03 0.152635900 #> 3727  1863.0 0.8404526 0.8404526 6.873534e-03 6.873534e-03 0.152673912 #> 3728  1863.5 0.8403876 0.8403876 6.900006e-03 6.900006e-03 0.152712344 #> 3729  1864.0 0.8403222 0.8403222 6.926568e-03 6.926568e-03 0.152751200 #> 3730  1864.5 0.8402563 0.8402563 6.953221e-03 6.953221e-03 0.152790479 #> 3731  1865.0 0.8401899 0.8401899 6.979965e-03 6.979965e-03 0.152830184 #> 3732  1865.5 0.8401229 0.8401229 7.006800e-03 7.006800e-03 0.152870315 #> 3733  1866.0 0.8400554 0.8400554 7.033727e-03 7.033727e-03 0.152910874 #> 3734  1866.5 0.8399874 0.8399874 7.060745e-03 7.060745e-03 0.152951862 #> 3735  1867.0 0.8399189 0.8399189 7.087855e-03 7.087855e-03 0.152993281 #> 3736  1867.5 0.8398498 0.8398498 7.115057e-03 7.115057e-03 0.153035131 #> 3737  1868.0 0.8397802 0.8397802 7.142350e-03 7.142350e-03 0.153077415 #> 3738  1868.5 0.8397101 0.8397101 7.169736e-03 7.169736e-03 0.153120132 #> 3739  1869.0 0.8396395 0.8396395 7.197215e-03 7.197215e-03 0.153163286 #> 3740  1869.5 0.8395683 0.8395683 7.224785e-03 7.224785e-03 0.153206877 #> 3741  1870.0 0.8394966 0.8394966 7.252449e-03 7.252449e-03 0.153250906 #> 3742  1870.5 0.8394244 0.8394244 7.280205e-03 7.280205e-03 0.153295375 #> 3743  1871.0 0.8393517 0.8393517 7.308054e-03 7.308054e-03 0.153340284 #> 3744  1871.5 0.8392784 0.8392784 7.335997e-03 7.335997e-03 0.153385637 #> 3745  1872.0 0.8392045 0.8392045 7.364032e-03 7.364032e-03 0.153431432 #> 3746  1872.5 0.8391302 0.8391302 7.392161e-03 7.392161e-03 0.153477673 #> 3747  1873.0 0.8390553 0.8390553 7.420384e-03 7.420384e-03 0.153524361 #> 3748  1873.5 0.8389798 0.8389798 7.448700e-03 7.448700e-03 0.153571496 #> 3749  1874.0 0.8389038 0.8389038 7.477111e-03 7.477111e-03 0.153619080 #> 3750  1874.5 0.8388273 0.8388273 7.505615e-03 7.505615e-03 0.153667114 #> 3751  1875.0 0.8387502 0.8387502 7.534214e-03 7.534214e-03 0.153715600 #> 3752  1875.5 0.8386726 0.8386726 7.562907e-03 7.562907e-03 0.153764539 #> 3753  1876.0 0.8385944 0.8385944 7.591694e-03 7.591694e-03 0.153813933 #> 3754  1876.5 0.8385156 0.8385156 7.620576e-03 7.620576e-03 0.153863782 #> 3755  1877.0 0.8384364 0.8384364 7.649553e-03 7.649553e-03 0.153914089 #> 3756  1877.5 0.8383565 0.8383565 7.678625e-03 7.678625e-03 0.153964854 #> 3757  1878.0 0.8382761 0.8382761 7.707792e-03 7.707792e-03 0.154016078 #> 3758  1878.5 0.8381952 0.8381952 7.737053e-03 7.737053e-03 0.154067764 #> 3759  1879.0 0.8381137 0.8381137 7.766411e-03 7.766411e-03 0.154119913 #> 3760  1879.5 0.8380316 0.8380316 7.795863e-03 7.795863e-03 0.154172525 #> 3761  1880.0 0.8379490 0.8379490 7.825412e-03 7.825412e-03 0.154225603 #> 3762  1880.5 0.8378658 0.8378658 7.855056e-03 7.855056e-03 0.154279147 #> 3763  1881.0 0.8377820 0.8377820 7.884796e-03 7.884796e-03 0.154333159 #> 3764  1881.5 0.8376977 0.8376977 7.914632e-03 7.914632e-03 0.154387641 #> 3765  1882.0 0.8376128 0.8376128 7.944564e-03 7.944564e-03 0.154442593 #> 3766  1882.5 0.8375274 0.8375274 7.974592e-03 7.974592e-03 0.154498018 #> 3767  1883.0 0.8374414 0.8374414 8.004716e-03 8.004716e-03 0.154553916 #> 3768  1883.5 0.8373548 0.8373548 8.034937e-03 8.034937e-03 0.154610288 #> 3769  1884.0 0.8372676 0.8372676 8.065255e-03 8.065255e-03 0.154667137 #> 3770  1884.5 0.8371799 0.8371799 8.095669e-03 8.095669e-03 0.154724463 #> 3771  1885.0 0.8370916 0.8370916 8.126180e-03 8.126180e-03 0.154782269 #> 3772  1885.5 0.8370027 0.8370027 8.156788e-03 8.156788e-03 0.154840554 #> 3773  1886.0 0.8369132 0.8369132 8.187493e-03 8.187493e-03 0.154899321 #> 3774  1886.5 0.8368231 0.8368231 8.218295e-03 8.218295e-03 0.154958572 #> 3775  1887.0 0.8367325 0.8367325 8.249195e-03 8.249195e-03 0.155018306 #> 3776  1887.5 0.8366413 0.8366413 8.280191e-03 8.280191e-03 0.155078527 #> 3777  1888.0 0.8365495 0.8365495 8.311286e-03 8.311286e-03 0.155139235 #> 3778  1888.5 0.8364571 0.8364571 8.342477e-03 8.342477e-03 0.155200431 #> 3779  1889.0 0.8363641 0.8363641 8.373767e-03 8.373767e-03 0.155262117 #> 3780  1889.5 0.8362706 0.8362706 8.405154e-03 8.405154e-03 0.155324295 #> 3781  1890.0 0.8361764 0.8361764 8.436639e-03 8.436639e-03 0.155386965 #> 3782  1890.5 0.8360816 0.8360816 8.468222e-03 8.468222e-03 0.155450130 #> 3783  1891.0 0.8359863 0.8359863 8.499903e-03 8.499903e-03 0.155513790 #> 3784  1891.5 0.8358904 0.8358904 8.531682e-03 8.531682e-03 0.155577947 #> 3785  1892.0 0.8357938 0.8357938 8.563559e-03 8.563559e-03 0.155642602 #> 3786  1892.5 0.8356967 0.8356967 8.595535e-03 8.595535e-03 0.155707756 #> 3787  1893.0 0.8355990 0.8355990 8.627609e-03 8.627609e-03 0.155773412 #> 3788  1893.5 0.8355006 0.8355006 8.659782e-03 8.659782e-03 0.155839570 #> 3789  1894.0 0.8354017 0.8354017 8.692053e-03 8.692053e-03 0.155906232 #> 3790  1894.5 0.8353022 0.8353022 8.724422e-03 8.724422e-03 0.155973400 #> 3791  1895.0 0.8352020 0.8352020 8.756891e-03 8.756891e-03 0.156041074 #> 3792  1895.5 0.8351013 0.8351013 8.789458e-03 8.789458e-03 0.156109255 #> 3793  1896.0 0.8349999 0.8349999 8.822124e-03 8.822124e-03 0.156177947 #> 3794  1896.5 0.8348980 0.8348980 8.854888e-03 8.854888e-03 0.156247149 #> 3795  1897.0 0.8347954 0.8347954 8.887752e-03 8.887752e-03 0.156316863 #> 3796  1897.5 0.8346922 0.8346922 8.920715e-03 8.920715e-03 0.156387091 #> 3797  1898.0 0.8345884 0.8345884 8.953777e-03 8.953777e-03 0.156457834 #> 3798  1898.5 0.8344840 0.8344840 8.986938e-03 8.986938e-03 0.156529093 #> 3799  1899.0 0.8343789 0.8343789 9.020198e-03 9.020198e-03 0.156600870 #> 3800  1899.5 0.8342733 0.8342733 9.053558e-03 9.053558e-03 0.156673166 #> 3801  1900.0 0.8341670 0.8341670 9.087016e-03 9.087016e-03 0.156745982 #> 3802  1900.5 0.8340601 0.8340601 9.120575e-03 9.120575e-03 0.156819321 #> 3803  1901.0 0.8339526 0.8339526 9.154232e-03 9.154232e-03 0.156893182 #> 3804  1901.5 0.8338444 0.8338444 9.187989e-03 9.187989e-03 0.156967569 #> 3805  1902.0 0.8337357 0.8337357 9.221845e-03 9.221845e-03 0.157042482 #> 3806  1902.5 0.8336263 0.8336263 9.255801e-03 9.255801e-03 0.157117922 #> 3807  1903.0 0.8335163 0.8335163 9.289857e-03 9.289857e-03 0.157193891 #> 3808  1903.5 0.8334056 0.8334056 9.324012e-03 9.324012e-03 0.157270390 #> 3809  1904.0 0.8332943 0.8332943 9.358267e-03 9.358267e-03 0.157347421 #> 3810  1904.5 0.8331824 0.8331824 9.392621e-03 9.392621e-03 0.157424985 #> 3811  1905.0 0.8330698 0.8330698 9.427075e-03 9.427075e-03 0.157503084 #> 3812  1905.5 0.8329567 0.8329567 9.461629e-03 9.461629e-03 0.157581719 #> 3813  1906.0 0.8328428 0.8328428 9.496282e-03 9.496282e-03 0.157660891 #> 3814  1906.5 0.8327284 0.8327284 9.531035e-03 9.531035e-03 0.157740601 #> 3815  1907.0 0.8326133 0.8326133 9.565888e-03 9.565888e-03 0.157820852 #> 3816  1907.5 0.8324975 0.8324975 9.600841e-03 9.600841e-03 0.157901644 #> 3817  1908.0 0.8323811 0.8323811 9.635893e-03 9.635893e-03 0.157982978 #> 3818  1908.5 0.8322641 0.8322641 9.671045e-03 9.671045e-03 0.158064857 #> 3819  1909.0 0.8321464 0.8321464 9.706297e-03 9.706297e-03 0.158147282 #> 3820  1909.5 0.8320281 0.8320281 9.741649e-03 9.741649e-03 0.158230254 #> 3821  1910.0 0.8319091 0.8319091 9.777100e-03 9.777100e-03 0.158313774 #> 3822  1910.5 0.8317895 0.8317895 9.812651e-03 9.812651e-03 0.158397844 #> 3823  1911.0 0.8316692 0.8316692 9.848302e-03 9.848302e-03 0.158482465 #> 3824  1911.5 0.8315483 0.8315483 9.884053e-03 9.884053e-03 0.158567639 #> 3825  1912.0 0.8314267 0.8314267 9.919903e-03 9.919903e-03 0.158653366 #> 3826  1912.5 0.8313045 0.8313045 9.955853e-03 9.955853e-03 0.158739649 #> 3827  1913.0 0.8311816 0.8311816 9.991902e-03 9.991902e-03 0.158826489 #> 3828  1913.5 0.8310581 0.8310581 1.002805e-02 1.002805e-02 0.158913887 #> 3829  1914.0 0.8309339 0.8309339 1.006430e-02 1.006430e-02 0.159001844 #> 3830  1914.5 0.8308090 0.8308090 1.010065e-02 1.010065e-02 0.159090362 #> 3831  1915.0 0.8306835 0.8306835 1.013710e-02 1.013710e-02 0.159179442 #> 3832  1915.5 0.8305573 0.8305573 1.017364e-02 1.017364e-02 0.159269086 #> 3833  1916.0 0.8304304 0.8304304 1.021029e-02 1.021029e-02 0.159359295 #> 3834  1916.5 0.8303029 0.8303029 1.024704e-02 1.024704e-02 0.159450070 #> 3835  1917.0 0.8301747 0.8301747 1.028388e-02 1.028388e-02 0.159541413 #> 3836  1917.5 0.8300458 0.8300458 1.032083e-02 1.032083e-02 0.159633326 #> 3837  1918.0 0.8299163 0.8299163 1.035787e-02 1.035787e-02 0.159725808 #> 3838  1918.5 0.8297861 0.8297861 1.039502e-02 1.039502e-02 0.159818863 #> 3839  1919.0 0.8296552 0.8296552 1.043226e-02 1.043226e-02 0.159912490 #> 3840  1919.5 0.8295237 0.8295237 1.046960e-02 1.046960e-02 0.160006693 #> 3841  1920.0 0.8293915 0.8293915 1.050704e-02 1.050704e-02 0.160101471 #> 3842  1920.5 0.8292586 0.8292586 1.054458e-02 1.054458e-02 0.160196826 #> 3843  1921.0 0.8291250 0.8291250 1.058222e-02 1.058222e-02 0.160292761 #> 3844  1921.5 0.8289908 0.8289908 1.061996e-02 1.061996e-02 0.160389275 #> 3845  1922.0 0.8288558 0.8288558 1.065780e-02 1.065780e-02 0.160486370 #> 3846  1922.5 0.8287202 0.8287202 1.069573e-02 1.069573e-02 0.160584049 #> 3847  1923.0 0.8285839 0.8285839 1.073376e-02 1.073376e-02 0.160682311 #> 3848  1923.5 0.8284469 0.8284469 1.077190e-02 1.077190e-02 0.160781159 #> 3849  1924.0 0.8283093 0.8283093 1.081013e-02 1.081013e-02 0.160880594 #> 3850  1924.5 0.8281709 0.8281709 1.084845e-02 1.084845e-02 0.160980616 #> 3851  1925.0 0.8280319 0.8280319 1.088688e-02 1.088688e-02 0.161081229 #> 3852  1925.5 0.8278922 0.8278922 1.092541e-02 1.092541e-02 0.161182432 #> 3853  1926.0 0.8277517 0.8277517 1.096403e-02 1.096403e-02 0.161284227 #> 3854  1926.5 0.8276106 0.8276106 1.100275e-02 1.100275e-02 0.161386615 #> 3855  1927.0 0.8274688 0.8274688 1.104157e-02 1.104157e-02 0.161489599 #> 3856  1927.5 0.8273263 0.8273263 1.108048e-02 1.108048e-02 0.161593178 #> 3857  1928.0 0.8271831 0.8271831 1.111950e-02 1.111950e-02 0.161697355 #> 3858  1928.5 0.8270393 0.8270393 1.115861e-02 1.115861e-02 0.161802131 #> 3859  1929.0 0.8268947 0.8268947 1.119781e-02 1.119781e-02 0.161907507 #> 3860  1929.5 0.8267494 0.8267494 1.123712e-02 1.123712e-02 0.162013485 #> 3861  1930.0 0.8266034 0.8266034 1.127652e-02 1.127652e-02 0.162120065 #> 3862  1930.5 0.8264567 0.8264567 1.131602e-02 1.131602e-02 0.162227249 #> 3863  1931.0 0.8263093 0.8263093 1.135561e-02 1.135561e-02 0.162335039 #> 3864  1931.5 0.8261613 0.8261613 1.139530e-02 1.139530e-02 0.162443435 #> 3865  1932.0 0.8260125 0.8260125 1.143509e-02 1.143509e-02 0.162552439 #> 3866  1932.5 0.8258630 0.8258630 1.147498e-02 1.147498e-02 0.162662053 #> 3867  1933.0 0.8257128 0.8257128 1.151496e-02 1.151496e-02 0.162772277 #> 3868  1933.5 0.8255619 0.8255619 1.155503e-02 1.155503e-02 0.162883113 #> 3869  1934.0 0.8254102 0.8254102 1.159520e-02 1.159520e-02 0.162994562 #> 3870  1934.5 0.8252579 0.8252579 1.163547e-02 1.163547e-02 0.163106625 #> 3871  1935.0 0.8251049 0.8251049 1.167583e-02 1.167583e-02 0.163219305 #> 3872  1935.5 0.8249511 0.8249511 1.171629e-02 1.171629e-02 0.163332601 #> 3873  1936.0 0.8247966 0.8247966 1.175684e-02 1.175684e-02 0.163446515 #> 3874  1936.5 0.8246415 0.8246415 1.179749e-02 1.179749e-02 0.163561049 #> 3875  1937.0 0.8244856 0.8244856 1.183823e-02 1.183823e-02 0.163676204 #> 3876  1937.5 0.8243290 0.8243290 1.187906e-02 1.187906e-02 0.163791981 #> 3877  1938.0 0.8241716 0.8241716 1.191999e-02 1.191999e-02 0.163908381 #> 3878  1938.5 0.8240136 0.8240136 1.196102e-02 1.196102e-02 0.164025406 #> 3879  1939.0 0.8238548 0.8238548 1.200213e-02 1.200213e-02 0.164143057 #> 3880  1939.5 0.8236953 0.8236953 1.204334e-02 1.204334e-02 0.164261335 #> 3881  1940.0 0.8235351 0.8235351 1.208465e-02 1.208465e-02 0.164380241 #> 3882  1940.5 0.8233742 0.8233742 1.212604e-02 1.212604e-02 0.164499776 #> 3883  1941.0 0.8232125 0.8232125 1.216753e-02 1.216753e-02 0.164619943 #> 3884  1941.5 0.8230501 0.8230501 1.220912e-02 1.220912e-02 0.164740741 #> 3885  1942.0 0.8228870 0.8228870 1.225079e-02 1.225079e-02 0.164862173 #> 3886  1942.5 0.8227232 0.8227232 1.229256e-02 1.229256e-02 0.164984239 #> 3887  1943.0 0.8225586 0.8225586 1.233441e-02 1.233441e-02 0.165106941 #> 3888  1943.5 0.8223934 0.8223934 1.237636e-02 1.237636e-02 0.165230280 #> 3889  1944.0 0.8222273 0.8222273 1.241840e-02 1.241840e-02 0.165354257 #> 3890  1944.5 0.8220606 0.8220606 1.246053e-02 1.246053e-02 0.165478873 #> 3891  1945.0 0.8218931 0.8218931 1.250276e-02 1.250276e-02 0.165604129 #> 3892  1945.5 0.8217249 0.8217249 1.254507e-02 1.254507e-02 0.165730028 #> 3893  1946.0 0.8215560 0.8215560 1.258747e-02 1.258747e-02 0.165856569 #> 3894  1946.5 0.8213863 0.8213863 1.262997e-02 1.262997e-02 0.165983754 #> 3895  1947.0 0.8212159 0.8212159 1.267255e-02 1.267255e-02 0.166111584 #> 3896  1947.5 0.8210447 0.8210447 1.271522e-02 1.271522e-02 0.166240061 #> 3897  1948.0 0.8208728 0.8208728 1.275798e-02 1.275798e-02 0.166369185 #> 3898  1948.5 0.8207002 0.8207002 1.280083e-02 1.280083e-02 0.166498958 #> 3899  1949.0 0.8205269 0.8205269 1.284377e-02 1.284377e-02 0.166629381 #> 3900  1949.5 0.8203528 0.8203528 1.288679e-02 1.288679e-02 0.166760455 #> 3901  1950.0 0.8201779 0.8201779 1.292991e-02 1.292991e-02 0.166892181 #> 3902  1950.5 0.8200023 0.8200023 1.297311e-02 1.297311e-02 0.167024560 #> 3903  1951.0 0.8198260 0.8198260 1.301640e-02 1.301640e-02 0.167157593 #> 3904  1951.5 0.8196489 0.8196489 1.305977e-02 1.305977e-02 0.167291282 #> 3905  1952.0 0.8194711 0.8194711 1.310323e-02 1.310323e-02 0.167425628 #> 3906  1952.5 0.8192926 0.8192926 1.314678e-02 1.314678e-02 0.167560632 #> 3907  1953.0 0.8191133 0.8191133 1.319041e-02 1.319041e-02 0.167696294 #> 3908  1953.5 0.8189333 0.8189333 1.323413e-02 1.323413e-02 0.167832616 #> 3909  1954.0 0.8187525 0.8187525 1.327793e-02 1.327793e-02 0.167969599 #> 3910  1954.5 0.8185709 0.8185709 1.332182e-02 1.332182e-02 0.168107244 #> 3911  1955.0 0.8183887 0.8183887 1.336579e-02 1.336579e-02 0.168245553 #> 3912  1955.5 0.8182056 0.8182056 1.340985e-02 1.340985e-02 0.168384526 #> 3913  1956.0 0.8180218 0.8180218 1.345399e-02 1.345399e-02 0.168524164 #> 3914  1956.5 0.8178373 0.8178373 1.349821e-02 1.349821e-02 0.168664468 #> 3915  1957.0 0.8176520 0.8176520 1.354252e-02 1.354252e-02 0.168805440 #> 3916  1957.5 0.8174660 0.8174660 1.358690e-02 1.358690e-02 0.168947080 #> 3917  1958.0 0.8172792 0.8172792 1.363137e-02 1.363137e-02 0.169089390 #> 3918  1958.5 0.8170917 0.8170917 1.367593e-02 1.367593e-02 0.169232370 #> 3919  1959.0 0.8169034 0.8169034 1.372056e-02 1.372056e-02 0.169376022 #> 3920  1959.5 0.8167144 0.8167144 1.376527e-02 1.376527e-02 0.169520347 #> 3921  1960.0 0.8165246 0.8165246 1.381007e-02 1.381007e-02 0.169665345 #> 3922  1960.5 0.8163340 0.8163340 1.385494e-02 1.385494e-02 0.169811017 #> 3923  1961.0 0.8161427 0.8161427 1.389989e-02 1.389989e-02 0.169957365 #> 3924  1961.5 0.8159507 0.8159507 1.394492e-02 1.394492e-02 0.170104390 #> 3925  1962.0 0.8157579 0.8157579 1.399003e-02 1.399003e-02 0.170252092 #> 3926  1962.5 0.8155643 0.8155643 1.403522e-02 1.403522e-02 0.170400473 #> 3927  1963.0 0.8153700 0.8153700 1.408049e-02 1.408049e-02 0.170549533 #> 3928  1963.5 0.8151749 0.8151749 1.412583e-02 1.412583e-02 0.170699273 #> 3929  1964.0 0.8149791 0.8149791 1.417125e-02 1.417125e-02 0.170849695 #> 3930  1964.5 0.8147825 0.8147825 1.421675e-02 1.421675e-02 0.171000799 #> 3931  1965.0 0.8145851 0.8145851 1.426232e-02 1.426232e-02 0.171152586 #> 3932  1965.5 0.8143870 0.8143870 1.430797e-02 1.430797e-02 0.171305057 #> 3933  1966.0 0.8141881 0.8141881 1.435369e-02 1.435369e-02 0.171458214 #> 3934  1966.5 0.8139885 0.8139885 1.439949e-02 1.439949e-02 0.171612056 #> 3935  1967.0 0.8137881 0.8137881 1.444536e-02 1.444536e-02 0.171766585 #> 3936  1967.5 0.8135869 0.8135869 1.449131e-02 1.449131e-02 0.171921802 #> 3937  1968.0 0.8133850 0.8133850 1.453732e-02 1.453732e-02 0.172077707 #> 3938  1968.5 0.8131823 0.8131823 1.458341e-02 1.458341e-02 0.172234302 #> 3939  1969.0 0.8129788 0.8129788 1.462958e-02 1.462958e-02 0.172391587 #> 3940  1969.5 0.8127746 0.8127746 1.467581e-02 1.467581e-02 0.172549563 #> 3941  1970.0 0.8125697 0.8125697 1.472211e-02 1.472211e-02 0.172708231 #> 3942  1970.5 0.8123639 0.8123639 1.476849e-02 1.476849e-02 0.172867592 #> 3943  1971.0 0.8121574 0.8121574 1.481493e-02 1.481493e-02 0.173027647 #> 3944  1971.5 0.8119502 0.8119502 1.486144e-02 1.486144e-02 0.173188396 #> 3945  1972.0 0.8117421 0.8117421 1.490802e-02 1.490802e-02 0.173349840 #> 3946  1972.5 0.8115333 0.8115333 1.495467e-02 1.495467e-02 0.173511980 #> 3947  1973.0 0.8113238 0.8113238 1.500139e-02 1.500139e-02 0.173674817 #> 3948  1973.5 0.8111135 0.8111135 1.504818e-02 1.504818e-02 0.173838352 #> 3949  1974.0 0.8109024 0.8109024 1.509503e-02 1.509503e-02 0.174002585 #> 3950  1974.5 0.8106905 0.8106905 1.514194e-02 1.514194e-02 0.174167517 #> 3951  1975.0 0.8104779 0.8104779 1.518892e-02 1.518892e-02 0.174333149 #> 3952  1975.5 0.8102645 0.8102645 1.523597e-02 1.523597e-02 0.174499482 #> 3953  1976.0 0.8100504 0.8100504 1.528308e-02 1.528308e-02 0.174666516 #> 3954  1976.5 0.8098355 0.8098355 1.533025e-02 1.533025e-02 0.174834251 #> 3955  1977.0 0.8096198 0.8096198 1.537749e-02 1.537749e-02 0.175002690 #> 3956  1977.5 0.8094034 0.8094034 1.542479e-02 1.542479e-02 0.175171832 #> 3957  1978.0 0.8091862 0.8091862 1.547215e-02 1.547215e-02 0.175341678 #> 3958  1978.5 0.8089682 0.8089682 1.551957e-02 1.551957e-02 0.175512229 #> 3959  1979.0 0.8087495 0.8087495 1.556705e-02 1.556705e-02 0.175683485 #> 3960  1979.5 0.8085300 0.8085300 1.561459e-02 1.561459e-02 0.175855447 #> 3961  1980.0 0.8083097 0.8083097 1.566219e-02 1.566219e-02 0.176028116 #> 3962  1980.5 0.8080887 0.8080887 1.570985e-02 1.570985e-02 0.176201493 #> 3963  1981.0 0.8078669 0.8078669 1.575757e-02 1.575757e-02 0.176375577 #> 3964  1981.5 0.8076443 0.8076443 1.580534e-02 1.580534e-02 0.176550370 #> 3965  1982.0 0.8074210 0.8074210 1.585318e-02 1.585318e-02 0.176725871 #> 3966  1982.5 0.8071969 0.8071969 1.590106e-02 1.590106e-02 0.176902083 #> 3967  1983.0 0.8069720 0.8069720 1.594900e-02 1.594900e-02 0.177079005 #> 3968  1983.5 0.8067464 0.8067464 1.599700e-02 1.599700e-02 0.177256638 #> 3969  1984.0 0.8065200 0.8065200 1.604505e-02 1.604505e-02 0.177434982 #> 3970  1984.5 0.8062928 0.8062928 1.609315e-02 1.609315e-02 0.177614038 #> 3971  1985.0 0.8060649 0.8060649 1.614131e-02 1.614131e-02 0.177793806 #> 3972  1985.5 0.8058362 0.8058362 1.618951e-02 1.618951e-02 0.177974288 #> 3973  1986.0 0.8056067 0.8056067 1.623777e-02 1.623777e-02 0.178155483 #> 3974  1986.5 0.8053765 0.8053765 1.628608e-02 1.628608e-02 0.178337391 #> 3975  1987.0 0.8051455 0.8051455 1.633444e-02 1.633444e-02 0.178520015 #> 3976  1987.5 0.8049138 0.8049138 1.638284e-02 1.638284e-02 0.178703353 #> 3977  1988.0 0.8046813 0.8046813 1.643130e-02 1.643130e-02 0.178887406 #> 3978  1988.5 0.8044480 0.8044480 1.647980e-02 1.647980e-02 0.179072175 #> 3979  1989.0 0.8042140 0.8042140 1.652835e-02 1.652835e-02 0.179257661 #> 3980  1989.5 0.8039792 0.8039792 1.657694e-02 1.657694e-02 0.179443863 #> 3981  1990.0 0.8037436 0.8037436 1.662558e-02 1.662558e-02 0.179630782 #> 3982  1990.5 0.8035073 0.8035073 1.667427e-02 1.667427e-02 0.179818418 #> 3983  1991.0 0.8032702 0.8032702 1.672299e-02 1.672299e-02 0.180006772 #> 3984  1991.5 0.8030324 0.8030324 1.677176e-02 1.677176e-02 0.180195845 #> 3985  1992.0 0.8027938 0.8027938 1.682058e-02 1.682058e-02 0.180385635 #> 3986  1992.5 0.8025544 0.8025544 1.686943e-02 1.686943e-02 0.180576145 #> 3987  1993.0 0.8023143 0.8023143 1.691832e-02 1.691832e-02 0.180767374 #> 3988  1993.5 0.8020734 0.8020734 1.696726e-02 1.696726e-02 0.180959322 #> 3989  1994.0 0.8018318 0.8018318 1.701623e-02 1.701623e-02 0.181151990 #> 3990  1994.5 0.8015894 0.8015894 1.706524e-02 1.706524e-02 0.181345378 #> 3991  1995.0 0.8013462 0.8013462 1.711429e-02 1.711429e-02 0.181539486 #> 3992  1995.5 0.8011023 0.8011023 1.716337e-02 1.716337e-02 0.181734315 #> 3993  1996.0 0.8008576 0.8008576 1.721249e-02 1.721249e-02 0.181929864 #> 3994  1996.5 0.8006122 0.8006122 1.726165e-02 1.726165e-02 0.182126135 #> 3995  1997.0 0.8003660 0.8003660 1.731084e-02 1.731084e-02 0.182323126 #> 3996  1997.5 0.8001191 0.8001191 1.736006e-02 1.736006e-02 0.182520839 #> 3997  1998.0 0.7998714 0.7998714 1.740931e-02 1.740931e-02 0.182719274 #> 3998  1998.5 0.7996230 0.7996230 1.745860e-02 1.745860e-02 0.182918431 #> 3999  1999.0 0.7993738 0.7993738 1.750792e-02 1.750792e-02 0.183118309 #> 4000  1999.5 0.7991238 0.7991238 1.755726e-02 1.755726e-02 0.183318910 #> 4001  2000.0 0.7988731 0.7988731 1.760664e-02 1.760664e-02 0.183520232 #> 4002  2000.5 0.7986217 0.7986217 1.765604e-02 1.765604e-02 0.183722277 #> 4003  2001.0 0.7983695 0.7983695 1.770547e-02 1.770547e-02 0.183925045 #> 4004  2001.5 0.7981165 0.7981165 1.775493e-02 1.775493e-02 0.184128535 #> 4005  2002.0 0.7978628 0.7978628 1.780441e-02 1.780441e-02 0.184332747 #> 4006  2002.5 0.7976084 0.7976084 1.785392e-02 1.785392e-02 0.184537683 #> 4007  2003.0 0.7973532 0.7973532 1.790345e-02 1.790345e-02 0.184743340 #> 4008  2003.5 0.7970973 0.7970973 1.795300e-02 1.795300e-02 0.184949721 #> 4009  2004.0 0.7968406 0.7968406 1.800258e-02 1.800258e-02 0.185156824 #> 4010  2004.5 0.7965832 0.7965832 1.805217e-02 1.805217e-02 0.185364650 #> 4011  2005.0 0.7963250 0.7963250 1.810179e-02 1.810179e-02 0.185573198 #> 4012  2005.5 0.7960661 0.7960661 1.815142e-02 1.815142e-02 0.185782470 #> 4013  2006.0 0.7958065 0.7958065 1.820108e-02 1.820108e-02 0.185992463 #> 4014  2006.5 0.7955461 0.7955461 1.825075e-02 1.825075e-02 0.186203180 #> 4015  2007.0 0.7952850 0.7952850 1.830043e-02 1.830043e-02 0.186414618 #> 4016  2007.5 0.7950231 0.7950231 1.835013e-02 1.835013e-02 0.186626779 #> 4017  2008.0 0.7947605 0.7947605 1.839985e-02 1.839985e-02 0.186839663 #> 4018  2008.5 0.7944972 0.7944972 1.844958e-02 1.844958e-02 0.187053268 #> 4019  2009.0 0.7942331 0.7942331 1.849932e-02 1.849932e-02 0.187267595 #> 4020  2009.5 0.7939683 0.7939683 1.854907e-02 1.854907e-02 0.187482644 #> 4021  2010.0 0.7937027 0.7937027 1.859884e-02 1.859884e-02 0.187698415 #> 4022  2010.5 0.7934365 0.7934365 1.864861e-02 1.864861e-02 0.187914907 #> 4023  2011.0 0.7931695 0.7931695 1.869839e-02 1.869839e-02 0.188132120 #> 4024  2011.5 0.7929018 0.7929018 1.874818e-02 1.874818e-02 0.188350055 #> 4025  2012.0 0.7926333 0.7926333 1.879798e-02 1.879798e-02 0.188568710 #> 4026  2012.5 0.7923641 0.7923641 1.884778e-02 1.884778e-02 0.188788085 #> 4027  2013.0 0.7920942 0.7920942 1.889759e-02 1.889759e-02 0.189008181 #> 4028  2013.5 0.7918236 0.7918236 1.894740e-02 1.894740e-02 0.189228996 #> 4029  2014.0 0.7915523 0.7915523 1.899721e-02 1.899721e-02 0.189450531 #> 4030  2014.5 0.7912802 0.7912802 1.904702e-02 1.904702e-02 0.189672786 #> 4031  2015.0 0.7910074 0.7910074 1.909684e-02 1.909684e-02 0.189895759 #> 4032  2015.5 0.7907339 0.7907339 1.914665e-02 1.914665e-02 0.190119451 #> 4033  2016.0 0.7904597 0.7904597 1.919646e-02 1.919646e-02 0.190343861 #> 4034  2016.5 0.7901847 0.7901847 1.924627e-02 1.924627e-02 0.190568989 #> 4035  2017.0 0.7899091 0.7899091 1.929608e-02 1.929608e-02 0.190794835 #> 4036  2017.5 0.7896327 0.7896327 1.934588e-02 1.934588e-02 0.191021397 #> 4037  2018.0 0.7893556 0.7893556 1.939568e-02 1.939568e-02 0.191248676 #> 4038  2018.5 0.7890779 0.7890779 1.944547e-02 1.944547e-02 0.191476671 #> 4039  2019.0 0.7887994 0.7887994 1.949525e-02 1.949525e-02 0.191705381 #> 4040  2019.5 0.7885202 0.7885202 1.954502e-02 1.954502e-02 0.191934807 #> 4041  2020.0 0.7882403 0.7882403 1.959478e-02 1.959478e-02 0.192164947 #> 4042  2020.5 0.7879597 0.7879597 1.964454e-02 1.964454e-02 0.192395801 #> 4043  2021.0 0.7876784 0.7876784 1.969428e-02 1.969428e-02 0.192627369 #> 4044  2021.5 0.7873963 0.7873963 1.974400e-02 1.974400e-02 0.192859649 #> 4045  2022.0 0.7871136 0.7871136 1.979372e-02 1.979372e-02 0.193092642 #> 4046  2022.5 0.7868302 0.7868302 1.984341e-02 1.984341e-02 0.193326347 #> 4047  2023.0 0.7865461 0.7865461 1.989310e-02 1.989310e-02 0.193560763 #> 4048  2023.5 0.7862614 0.7862614 1.994276e-02 1.994276e-02 0.193795889 #> 4049  2024.0 0.7859759 0.7859759 1.999241e-02 1.999241e-02 0.194031726 #> 4050  2024.5 0.7856897 0.7856897 2.004203e-02 2.004203e-02 0.194268271 #> 4051  2025.0 0.7854028 0.7854028 2.009164e-02 2.009164e-02 0.194505525 #> 4052  2025.5 0.7851153 0.7851153 2.014122e-02 2.014122e-02 0.194743486 #> 4053  2026.0 0.7848271 0.7848271 2.019078e-02 2.019078e-02 0.194982155 #> 4054  2026.5 0.7845382 0.7845382 2.024032e-02 2.024032e-02 0.195221530 #> 4055  2027.0 0.7842486 0.7842486 2.028983e-02 2.028983e-02 0.195461610 #> 4056  2027.5 0.7839583 0.7839583 2.033931e-02 2.033931e-02 0.195702396 #> 4057  2028.0 0.7836673 0.7836673 2.038877e-02 2.038877e-02 0.195943885 #> 4058  2028.5 0.7833757 0.7833757 2.043820e-02 2.043820e-02 0.196186077 #> 4059  2029.0 0.7830834 0.7830834 2.048760e-02 2.048760e-02 0.196428972 #> 4060  2029.5 0.7827905 0.7827905 2.053696e-02 2.053696e-02 0.196672568 #> 4061  2030.0 0.7824968 0.7824968 2.058630e-02 2.058630e-02 0.196916864 #> 4062  2030.5 0.7822025 0.7822025 2.063560e-02 2.063560e-02 0.197161860 #> 4063  2031.0 0.7819076 0.7819076 2.068487e-02 2.068487e-02 0.197407555 #> 4064  2031.5 0.7816119 0.7816119 2.073410e-02 2.073410e-02 0.197653948 #> 4065  2032.0 0.7813157 0.7813157 2.078330e-02 2.078330e-02 0.197901038 #> 4066  2032.5 0.7810187 0.7810187 2.083246e-02 2.083246e-02 0.198148824 #> 4067  2033.0 0.7807211 0.7807211 2.088158e-02 2.088158e-02 0.198397304 #> 4068  2033.5 0.7804229 0.7804229 2.093066e-02 2.093066e-02 0.198646479 #> 4069  2034.0 0.7801240 0.7801240 2.097970e-02 2.097970e-02 0.198896346 #> 4070  2034.5 0.7798244 0.7798244 2.102870e-02 2.102870e-02 0.199146905 #> 4071  2035.0 0.7795242 0.7795242 2.107765e-02 2.107765e-02 0.199398155 #> 4072  2035.5 0.7792233 0.7792233 2.112656e-02 2.112656e-02 0.199650095 #> 4073  2036.0 0.7789219 0.7789219 2.117542e-02 2.117542e-02 0.199902723 #> 4074  2036.5 0.7786197 0.7786197 2.122423e-02 2.122423e-02 0.200156039 #> 4075  2037.0 0.7783170 0.7783170 2.127300e-02 2.127300e-02 0.200410041 #> 4076  2037.5 0.7780136 0.7780136 2.132172e-02 2.132172e-02 0.200664729 #> 4077  2038.0 0.7777095 0.7777095 2.137038e-02 2.137038e-02 0.200920100 #> 4078  2038.5 0.7774048 0.7774048 2.141900e-02 2.141900e-02 0.201176154 #> 4079  2039.0 0.7770996 0.7770996 2.146756e-02 2.146756e-02 0.201432890 #> 4080  2039.5 0.7767936 0.7767936 2.151606e-02 2.151606e-02 0.201690306 #> 4081  2040.0 0.7764871 0.7764871 2.156452e-02 2.156452e-02 0.201948402 #> 4082  2040.5 0.7761799 0.7761799 2.161291e-02 2.161291e-02 0.202207175 #> 4083  2041.0 0.7758721 0.7758721 2.166125e-02 2.166125e-02 0.202466624 #> 4084  2041.5 0.7755637 0.7755637 2.170953e-02 2.170953e-02 0.202726749 #> 4085  2042.0 0.7752547 0.7752547 2.175774e-02 2.175774e-02 0.202987548 #> 4086  2042.5 0.7749451 0.7749451 2.180590e-02 2.180590e-02 0.203249019 #> 4087  2043.0 0.7746348 0.7746348 2.185399e-02 2.185399e-02 0.203511162 #> 4088  2043.5 0.7743240 0.7743240 2.190202e-02 2.190202e-02 0.203773974 #> 4089  2044.0 0.7740126 0.7740126 2.194999e-02 2.194999e-02 0.204037455 #> 4090  2044.5 0.7737005 0.7737005 2.199788e-02 2.199788e-02 0.204301602 #> 4091  2045.0 0.7733879 0.7733879 2.204571e-02 2.204571e-02 0.204566415 #> 4092  2045.5 0.7730746 0.7730746 2.209347e-02 2.209347e-02 0.204831893 #> 4093  2046.0 0.7727608 0.7727608 2.214117e-02 2.214117e-02 0.205098032 #> 4094  2046.5 0.7724464 0.7724464 2.218879e-02 2.218879e-02 0.205364833 #> 4095  2047.0 0.7721314 0.7721314 2.223633e-02 2.223633e-02 0.205632293 #> 4096  2047.5 0.7718158 0.7718158 2.228381e-02 2.228381e-02 0.205900411 #> 4097  2048.0 0.7714996 0.7714996 2.233120e-02 2.233120e-02 0.206169186 #> 4098  2048.5 0.7711829 0.7711829 2.237853e-02 2.237853e-02 0.206438615 #> 4099  2049.0 0.7708655 0.7708655 2.242577e-02 2.242577e-02 0.206708697 #> 4100  2049.5 0.7705476 0.7705476 2.247294e-02 2.247294e-02 0.206979431 #> 4101  2050.0 0.7702292 0.7702292 2.252002e-02 2.252002e-02 0.207250815 #> 4102  2050.5 0.7699101 0.7699101 2.256703e-02 2.256703e-02 0.207522847 #> 4103  2051.0 0.7695905 0.7695905 2.261395e-02 2.261395e-02 0.207795526 #> 4104  2051.5 0.7692704 0.7692704 2.266079e-02 2.266079e-02 0.208068850 #> 4105  2052.0 0.7689496 0.7689496 2.270754e-02 2.270754e-02 0.208342816 #> 4106  2052.5 0.7686284 0.7686284 2.275421e-02 2.275421e-02 0.208617424 #> 4107  2053.0 0.7683065 0.7683065 2.280079e-02 2.280079e-02 0.208892672 #> 4108  2053.5 0.7679842 0.7679842 2.284728e-02 2.284728e-02 0.209168557 #> 4109  2054.0 0.7676612 0.7676612 2.289369e-02 2.289369e-02 0.209445079 #> 4110  2054.5 0.7673378 0.7673378 2.294000e-02 2.294000e-02 0.209722235 #> 4111  2055.0 0.7670138 0.7670138 2.298622e-02 2.298622e-02 0.210000023 #> 4112  2055.5 0.7666892 0.7666892 2.303234e-02 2.303234e-02 0.210278441 #> 4113  2056.0 0.7663641 0.7663641 2.307837e-02 2.307837e-02 0.210557488 #> 4114  2056.5 0.7660385 0.7660385 2.312430e-02 2.312430e-02 0.210837162 #> 4115  2057.0 0.7657124 0.7657124 2.317014e-02 2.317014e-02 0.211117461 #> 4116  2057.5 0.7653857 0.7653857 2.321588e-02 2.321588e-02 0.211398382 #> 4117  2058.0 0.7650586 0.7650586 2.326152e-02 2.326152e-02 0.211679924 #> 4118  2058.5 0.7647309 0.7647309 2.330705e-02 2.330705e-02 0.211962085 #> 4119  2059.0 0.7644026 0.7644026 2.335249e-02 2.335249e-02 0.212244863 #> 4120  2059.5 0.7640739 0.7640739 2.339782e-02 2.339782e-02 0.212528256 #> 4121  2060.0 0.7637447 0.7637447 2.344305e-02 2.344305e-02 0.212812262 #> 4122  2060.5 0.7634150 0.7634150 2.348817e-02 2.348817e-02 0.213096878 #> 4123  2061.0 0.7630847 0.7630847 2.353318e-02 2.353318e-02 0.213382103 #> 4124  2061.5 0.7627540 0.7627540 2.357808e-02 2.357808e-02 0.213667934 #> 4125  2062.0 0.7624228 0.7624228 2.362288e-02 2.362288e-02 0.213954370 #> 4126  2062.5 0.7620910 0.7620910 2.366756e-02 2.366756e-02 0.214241409 #> 4127  2063.0 0.7617588 0.7617588 2.371213e-02 2.371213e-02 0.214529047 #> 4128  2063.5 0.7614261 0.7614261 2.375659e-02 2.375659e-02 0.214817283 #> 4129  2064.0 0.7610929 0.7610929 2.380094e-02 2.380094e-02 0.215106115 #> 4130  2064.5 0.7607593 0.7607593 2.384516e-02 2.384516e-02 0.215395541 #> 4131  2065.0 0.7604252 0.7604252 2.388927e-02 2.388927e-02 0.215685558 #> 4132  2065.5 0.7600906 0.7600906 2.393326e-02 2.393326e-02 0.215976164 #> 4133  2066.0 0.7597555 0.7597555 2.397714e-02 2.397714e-02 0.216267356 #> 4134  2066.5 0.7594200 0.7594200 2.402089e-02 2.402089e-02 0.216559133 #> 4135  2067.0 0.7590840 0.7590840 2.406452e-02 2.406452e-02 0.216851493 #> 4136  2067.5 0.7587475 0.7587475 2.410802e-02 2.410802e-02 0.217144432 #> 4137  2068.0 0.7584107 0.7584107 2.415140e-02 2.415140e-02 0.217437948 #> 4138  2068.5 0.7580733 0.7580733 2.419466e-02 2.419466e-02 0.217732040 #> 4139  2069.0 0.7577355 0.7577355 2.423778e-02 2.423778e-02 0.218026704 #> 4140  2069.5 0.7573973 0.7573973 2.428078e-02 2.428078e-02 0.218321938 #> 4141  2070.0 0.7570586 0.7570586 2.432365e-02 2.432365e-02 0.218617740 #> 4142  2070.5 0.7567195 0.7567195 2.436639e-02 2.436639e-02 0.218914108 #> 4143  2071.0 0.7563800 0.7563800 2.440900e-02 2.440900e-02 0.219211039 #> 4144  2071.5 0.7560400 0.7560400 2.445147e-02 2.445147e-02 0.219508530 #> 4145  2072.0 0.7556996 0.7556996 2.449381e-02 2.449381e-02 0.219806578 #> 4146  2072.5 0.7553588 0.7553588 2.453602e-02 2.453602e-02 0.220105183 #> 4147  2073.0 0.7550176 0.7550176 2.457808e-02 2.457808e-02 0.220404340 #> 4148  2073.5 0.7546759 0.7546759 2.462001e-02 2.462001e-02 0.220704047 #> 4149  2074.0 0.7543339 0.7543339 2.466180e-02 2.466180e-02 0.221004302 #> 4150  2074.5 0.7539914 0.7539914 2.470345e-02 2.470345e-02 0.221305102 #> 4151  2075.0 0.7536486 0.7536486 2.474496e-02 2.474496e-02 0.221606444 #> 4152  2075.5 0.7533053 0.7533053 2.478633e-02 2.478633e-02 0.221908326 #> 4153  2076.0 0.7529617 0.7529617 2.482755e-02 2.482755e-02 0.222210746 #> 4154  2076.5 0.7526177 0.7526177 2.486862e-02 2.486862e-02 0.222513700 #> 4155  2077.0 0.7522733 0.7522733 2.490955e-02 2.490955e-02 0.222817185 #> 4156  2077.5 0.7519285 0.7519285 2.495033e-02 2.495033e-02 0.223121200 #> 4157  2078.0 0.7515833 0.7515833 2.499097e-02 2.499097e-02 0.223425741 #> 4158  2078.5 0.7512377 0.7512377 2.503145e-02 2.503145e-02 0.223730805 #> 4159  2079.0 0.7508918 0.7508918 2.507178e-02 2.507178e-02 0.224036391 #> 4160  2079.5 0.7505455 0.7505455 2.511196e-02 2.511196e-02 0.224342494 #> 4161  2080.0 0.7501989 0.7501989 2.515199e-02 2.515199e-02 0.224649113 #> 4162  2080.5 0.7498519 0.7498519 2.519186e-02 2.519186e-02 0.224956244 #> 4163  2081.0 0.7495045 0.7495045 2.523157e-02 2.523157e-02 0.225263884 #> 4164  2081.5 0.7491568 0.7491568 2.527113e-02 2.527113e-02 0.225572031 #> 4165  2082.0 0.7488088 0.7488088 2.531053e-02 2.531053e-02 0.225880682 #> 4166  2082.5 0.7484604 0.7484604 2.534978e-02 2.534978e-02 0.226189834 #> 4167  2083.0 0.7481117 0.7481117 2.538886e-02 2.538886e-02 0.226499484 #> 4168  2083.5 0.7477626 0.7477626 2.542778e-02 2.542778e-02 0.226809629 #> 4169  2084.0 0.7474132 0.7474132 2.546653e-02 2.546653e-02 0.227120266 #> 4170  2084.5 0.7470635 0.7470635 2.550513e-02 2.550513e-02 0.227431392 #> 4171  2085.0 0.7467134 0.7467134 2.554355e-02 2.554355e-02 0.227743004 #> 4172  2085.5 0.7463631 0.7463631 2.558182e-02 2.558182e-02 0.228055100 #> 4173  2086.0 0.7460124 0.7460124 2.561991e-02 2.561991e-02 0.228367675 #> 4174  2086.5 0.7456614 0.7456614 2.565784e-02 2.565784e-02 0.228680728 #> 4175  2087.0 0.7453102 0.7453102 2.569559e-02 2.569559e-02 0.228994255 #> 4176  2087.5 0.7449586 0.7449586 2.573318e-02 2.573318e-02 0.229308253 #> 4177  2088.0 0.7446067 0.7446067 2.577060e-02 2.577060e-02 0.229622718 #> 4178  2088.5 0.7442545 0.7442545 2.580784e-02 2.580784e-02 0.229937649 #> 4179  2089.0 0.7439021 0.7439021 2.584491e-02 2.584491e-02 0.230253041 #> 4180  2089.5 0.7435493 0.7435493 2.588180e-02 2.588180e-02 0.230568892 #> 4181  2090.0 0.7431963 0.7431963 2.591852e-02 2.591852e-02 0.230885198 #> 4182  2090.5 0.7428430 0.7428430 2.595506e-02 2.595506e-02 0.231201956 #> 4183  2091.0 0.7424894 0.7424894 2.599142e-02 2.599142e-02 0.231519163 #> 4184  2091.5 0.7421356 0.7421356 2.602760e-02 2.602760e-02 0.231836816 #> 4185  2092.0 0.7417815 0.7417815 2.606361e-02 2.606361e-02 0.232154912 #> 4186  2092.5 0.7414271 0.7414271 2.609943e-02 2.609943e-02 0.232473447 #> 4187  2093.0 0.7410725 0.7410725 2.613507e-02 2.613507e-02 0.232792418 #> 4188  2093.5 0.7407177 0.7407177 2.617052e-02 2.617052e-02 0.233111822 #> 4189  2094.0 0.7403626 0.7403626 2.620579e-02 2.620579e-02 0.233431656 #> 4190  2094.5 0.7400072 0.7400072 2.624088e-02 2.624088e-02 0.233751916 #> 4191  2095.0 0.7396516 0.7396516 2.627578e-02 2.627578e-02 0.234072599 #> 4192  2095.5 0.7392958 0.7392958 2.631049e-02 2.631049e-02 0.234393702 #> 4193  2096.0 0.7389398 0.7389398 2.634501e-02 2.634501e-02 0.234715221 #> 4194  2096.5 0.7385835 0.7385835 2.637935e-02 2.637935e-02 0.235037152 #> 4195  2097.0 0.7382270 0.7382270 2.641349e-02 2.641349e-02 0.235359494 #> 4196  2097.5 0.7378703 0.7378703 2.644744e-02 2.644744e-02 0.235682241 #> 4197  2098.0 0.7375134 0.7375134 2.648120e-02 2.648120e-02 0.236005392 #> 4198  2098.5 0.7371563 0.7371563 2.651476e-02 2.651476e-02 0.236328941 #> 4199  2099.0 0.7367990 0.7367990 2.654813e-02 2.654813e-02 0.236652887 #> 4200  2099.5 0.7364415 0.7364415 2.658131e-02 2.658131e-02 0.236977225 #> 4201  2100.0 0.7360838 0.7360838 2.661429e-02 2.661429e-02 0.237301952 #> 4202  2100.5 0.7357259 0.7357259 2.664707e-02 2.664707e-02 0.237627065 #> 4203  2101.0 0.7353678 0.7353678 2.667965e-02 2.667965e-02 0.237952560 #> 4204  2101.5 0.7350095 0.7350095 2.671203e-02 2.671203e-02 0.238278433 #> 4205  2102.0 0.7346511 0.7346511 2.674422e-02 2.674422e-02 0.238604681 #> 4206  2102.5 0.7342925 0.7342925 2.677620e-02 2.677620e-02 0.238931301 #> 4207  2103.0 0.7339337 0.7339337 2.680798e-02 2.680798e-02 0.239258289 #> 4208  2103.5 0.7335748 0.7335748 2.683956e-02 2.683956e-02 0.239585641 #> 4209  2104.0 0.7332157 0.7332157 2.687093e-02 2.687093e-02 0.239913354 #> 4210  2104.5 0.7328565 0.7328565 2.690210e-02 2.690210e-02 0.240241424 #> 4211  2105.0 0.7324971 0.7324971 2.693306e-02 2.693306e-02 0.240569848 #> 4212  2105.5 0.7321376 0.7321376 2.696381e-02 2.696381e-02 0.240898622 #> 4213  2106.0 0.7317779 0.7317779 2.699436e-02 2.699436e-02 0.241227742 #> 4214  2106.5 0.7314181 0.7314181 2.702470e-02 2.702470e-02 0.241557205 #> 4215  2107.0 0.7310582 0.7310582 2.705483e-02 2.705483e-02 0.241887007 #> 4216  2107.5 0.7306981 0.7306981 2.708475e-02 2.708475e-02 0.242217144 #> 4217  2108.0 0.7303379 0.7303379 2.711446e-02 2.711446e-02 0.242547613 #> 4218  2108.5 0.7299776 0.7299776 2.714396e-02 2.714396e-02 0.242878411 #> 4219  2109.0 0.7296172 0.7296172 2.717324e-02 2.717324e-02 0.243209532 #> 4220  2109.5 0.7292567 0.7292567 2.720231e-02 2.720231e-02 0.243540975 #> 4221  2110.0 0.7288961 0.7288961 2.723117e-02 2.723117e-02 0.243872734 #> 4222  2110.5 0.7285354 0.7285354 2.725981e-02 2.725981e-02 0.244204806 #> 4223  2111.0 0.7281746 0.7281746 2.728823e-02 2.728823e-02 0.244537188 #> 4224  2111.5 0.7278137 0.7278137 2.731644e-02 2.731644e-02 0.244869876 #> 4225  2112.0 0.7274527 0.7274527 2.734443e-02 2.734443e-02 0.245202865 #> 4226  2112.5 0.7270916 0.7270916 2.737221e-02 2.737221e-02 0.245536153 #> 4227  2113.0 0.7267305 0.7267305 2.739976e-02 2.739976e-02 0.245869734 #> 4228  2113.5 0.7263693 0.7263693 2.742709e-02 2.742709e-02 0.246203607 #> 4229  2114.0 0.7260080 0.7260080 2.745421e-02 2.745421e-02 0.246537766 #> 4230  2114.5 0.7256467 0.7256467 2.748110e-02 2.748110e-02 0.246872208 #> 4231  2115.0 0.7252853 0.7252853 2.750777e-02 2.750777e-02 0.247206929 #> 4232  2115.5 0.7249239 0.7249239 2.753421e-02 2.753421e-02 0.247541925 #> 4233  2116.0 0.7245624 0.7245624 2.756044e-02 2.756044e-02 0.247877193 #> 4234  2116.5 0.7242008 0.7242008 2.758643e-02 2.758643e-02 0.248212728 #> 4235  2117.0 0.7238393 0.7238393 2.761221e-02 2.761221e-02 0.248548527 #> 4236  2117.5 0.7234777 0.7234777 2.763775e-02 2.763775e-02 0.248884585 #> 4237  2118.0 0.7231160 0.7231160 2.766308e-02 2.766308e-02 0.249220899 #> 4238  2118.5 0.7227544 0.7227544 2.768817e-02 2.768817e-02 0.249557465 #> 4239  2119.0 0.7223927 0.7223927 2.771303e-02 2.771303e-02 0.249894280 #> 4240  2119.5 0.7220310 0.7220310 2.773767e-02 2.773767e-02 0.250231338 #> 4241  2120.0 0.7216693 0.7216693 2.776208e-02 2.776208e-02 0.250568636 #> 4242  2120.5 0.7213076 0.7213076 2.778626e-02 2.778626e-02 0.250906170 #> 4243  2121.0 0.7209459 0.7209459 2.781021e-02 2.781021e-02 0.251243937 #> 4244  2121.5 0.7205841 0.7205841 2.783392e-02 2.783392e-02 0.251581932 #> 4245  2122.0 0.7202224 0.7202224 2.785741e-02 2.785741e-02 0.251920151 #> 4246  2122.5 0.7198607 0.7198607 2.788066e-02 2.788066e-02 0.252258591 #> 4247  2123.0 0.7194991 0.7194991 2.790368e-02 2.790368e-02 0.252597247 #> 4248  2123.5 0.7191374 0.7191374 2.792647e-02 2.792647e-02 0.252936115 #> 4249  2124.0 0.7187758 0.7187758 2.794902e-02 2.794902e-02 0.253275192 #> 4250  2124.5 0.7184142 0.7184142 2.797134e-02 2.797134e-02 0.253614473 #> 4251  2125.0 0.7180526 0.7180526 2.799342e-02 2.799342e-02 0.253953954 #> 4252  2125.5 0.7176911 0.7176911 2.801527e-02 2.801527e-02 0.254293632 #> 4253  2126.0 0.7173296 0.7173296 2.803688e-02 2.803688e-02 0.254633501 #> 4254  2126.5 0.7169682 0.7169682 2.805826e-02 2.805826e-02 0.254973559 #> 4255  2127.0 0.7166068 0.7166068 2.807939e-02 2.807939e-02 0.255313802 #> 4256  2127.5 0.7162455 0.7162455 2.810029e-02 2.810029e-02 0.255654224 #> 4257  2128.0 0.7158842 0.7158842 2.812095e-02 2.812095e-02 0.255994822 #> 4258  2128.5 0.7155230 0.7155230 2.814137e-02 2.814137e-02 0.256335593 #> 4259  2129.0 0.7151619 0.7151619 2.816155e-02 2.816155e-02 0.256676531 #> 4260  2129.5 0.7148009 0.7148009 2.818150e-02 2.818150e-02 0.257017633 #> 4261  2130.0 0.7144399 0.7144399 2.820120e-02 2.820120e-02 0.257358895 #> 4262  2130.5 0.7140790 0.7140790 2.822066e-02 2.822066e-02 0.257700313 #> 4263  2131.0 0.7137182 0.7137182 2.823988e-02 2.823988e-02 0.258041882 #> 4264  2131.5 0.7133575 0.7133575 2.825885e-02 2.825885e-02 0.258383599 #> 4265  2132.0 0.7129970 0.7129970 2.827759e-02 2.827759e-02 0.258725459 #> 4266  2132.5 0.7126365 0.7126365 2.829608e-02 2.829608e-02 0.259067458 #> 4267  2133.0 0.7122761 0.7122761 2.831433e-02 2.831433e-02 0.259409593 #> 4268  2133.5 0.7119158 0.7119158 2.833234e-02 2.833234e-02 0.259751859 #> 4269  2134.0 0.7115557 0.7115557 2.835010e-02 2.835010e-02 0.260094251 #> 4270  2134.5 0.7111956 0.7111956 2.836762e-02 2.836762e-02 0.260436766 #> 4271  2135.0 0.7108357 0.7108357 2.838489e-02 2.838489e-02 0.260779400 #> 4272  2135.5 0.7104759 0.7104759 2.840192e-02 2.840192e-02 0.261122148 #> 4273  2136.0 0.7101163 0.7101163 2.841870e-02 2.841870e-02 0.261465007 #> 4274  2136.5 0.7097568 0.7097568 2.843524e-02 2.843524e-02 0.261807972 #> 4275  2137.0 0.7093974 0.7093974 2.845153e-02 2.845153e-02 0.262151039 #> 4276  2137.5 0.7090382 0.7090382 2.846757e-02 2.846757e-02 0.262494204 #> 4277  2138.0 0.7086792 0.7086792 2.848337e-02 2.848337e-02 0.262837463 #> 4278  2138.5 0.7083203 0.7083203 2.849892e-02 2.849892e-02 0.263180811 #> 4279  2139.0 0.7079615 0.7079615 2.851423e-02 2.851423e-02 0.263524245 #> 4280  2139.5 0.7076030 0.7076030 2.852928e-02 2.852928e-02 0.263867760 #> 4281  2140.0 0.7072446 0.7072446 2.854409e-02 2.854409e-02 0.264211352 #> 4282  2140.5 0.7068863 0.7068863 2.855865e-02 2.855865e-02 0.264555017 #> 4283  2141.0 0.7065283 0.7065283 2.857297e-02 2.857297e-02 0.264898750 #> 4284  2141.5 0.7061704 0.7061704 2.858703e-02 2.858703e-02 0.265242549 #> 4285  2142.0 0.7058127 0.7058127 2.860084e-02 2.860084e-02 0.265586407 #> 4286  2142.5 0.7054553 0.7054553 2.861441e-02 2.861441e-02 0.265930322 #> 4287  2143.0 0.7050980 0.7050980 2.862772e-02 2.862772e-02 0.266274289 #> 4288  2143.5 0.7047409 0.7047409 2.864079e-02 2.864079e-02 0.266618304 #> 4289  2144.0 0.7043840 0.7043840 2.865361e-02 2.865361e-02 0.266962363 #> 4290  2144.5 0.7040274 0.7040274 2.866617e-02 2.866617e-02 0.267306460 #> 4291  2145.0 0.7036709 0.7036709 2.867849e-02 2.867849e-02 0.267650594 #> 4292  2145.5 0.7033147 0.7033147 2.869056e-02 2.869056e-02 0.267994758 #> 4293  2146.0 0.7029587 0.7029587 2.870237e-02 2.870237e-02 0.268338949 #> 4294  2146.5 0.7026029 0.7026029 2.871394e-02 2.871394e-02 0.268683164 #> 4295  2147.0 0.7022474 0.7022474 2.872525e-02 2.872525e-02 0.269027396 #> 4296  2147.5 0.7018920 0.7018920 2.873632e-02 2.873632e-02 0.269371643 #> 4297  2148.0 0.7015370 0.7015370 2.874713e-02 2.874713e-02 0.269715900 #> 4298  2148.5 0.7011821 0.7011821 2.875769e-02 2.875769e-02 0.270060163 #> 4299  2149.0 0.7008276 0.7008276 2.876800e-02 2.876800e-02 0.270404428 #> 4300  2149.5 0.7004733 0.7004733 2.877806e-02 2.877806e-02 0.270748691 #> 4301  2150.0 0.7001192 0.7001192 2.878787e-02 2.878787e-02 0.271092947 #> 4302  2150.5 0.6997654 0.6997654 2.879742e-02 2.879742e-02 0.271437192 #> 4303  2151.0 0.6994119 0.6994119 2.880673e-02 2.880673e-02 0.271781422 #> 4304  2151.5 0.6990586 0.6990586 2.881578e-02 2.881578e-02 0.272125633 #> 4305  2152.0 0.6987056 0.6987056 2.882458e-02 2.882458e-02 0.272469821 #> 4306  2152.5 0.6983529 0.6983529 2.883313e-02 2.883313e-02 0.272813982 #> 4307  2153.0 0.6980005 0.6980005 2.884143e-02 2.884143e-02 0.273158110 #> 4308  2153.5 0.6976483 0.6976483 2.884947e-02 2.884947e-02 0.273502203 #> 4309  2154.0 0.6972965 0.6972965 2.885727e-02 2.885727e-02 0.273846256 #> 4310  2154.5 0.6969449 0.6969449 2.886481e-02 2.886481e-02 0.274190264 #> 4311  2155.0 0.6965937 0.6965937 2.887210e-02 2.887210e-02 0.274534224 #> 4312  2155.5 0.6962427 0.6962427 2.887914e-02 2.887914e-02 0.274878132 #> 4313  2156.0 0.6958921 0.6958921 2.888593e-02 2.888593e-02 0.275221983 #> 4314  2156.5 0.6955418 0.6955418 2.889247e-02 2.889247e-02 0.275565773 #> 4315  2157.0 0.6951918 0.6951918 2.889875e-02 2.889875e-02 0.275909498 #> 4316  2157.5 0.6948421 0.6948421 2.890478e-02 2.890478e-02 0.276253154 #> 4317  2158.0 0.6944927 0.6944927 2.891057e-02 2.891057e-02 0.276596736 #> 4318  2158.5 0.6941437 0.6941437 2.891610e-02 2.891610e-02 0.276940241 #> 4319  2159.0 0.6937950 0.6937950 2.892138e-02 2.892138e-02 0.277283665 #> 4320  2159.5 0.6934466 0.6934466 2.892641e-02 2.892641e-02 0.277627002 #> 4321  2160.0 0.6930986 0.6930986 2.893118e-02 2.893118e-02 0.277970250 #> 4322  2160.5 0.6927509 0.6927509 2.893571e-02 2.893571e-02 0.278313403 #> 4323  2161.0 0.6924036 0.6924036 2.893999e-02 2.893999e-02 0.278656459 #> 4324  2161.5 0.6920566 0.6920566 2.894401e-02 2.894401e-02 0.278999412 #> 4325  2162.0 0.6917100 0.6917100 2.894779e-02 2.894779e-02 0.279342258 #> 4326  2162.5 0.6913637 0.6913637 2.895131e-02 2.895131e-02 0.279684994 #> 4327  2163.0 0.6910178 0.6910178 2.895458e-02 2.895458e-02 0.280027616 #> 4328  2163.5 0.6906723 0.6906723 2.895761e-02 2.895761e-02 0.280370118 #> 4329  2164.0 0.6903271 0.6903271 2.896038e-02 2.896038e-02 0.280712498 #> 4330  2164.5 0.6899823 0.6899823 2.896291e-02 2.896291e-02 0.281054750 #> 4331  2165.0 0.6896379 0.6896379 2.896519e-02 2.896519e-02 0.281396872 #> 4332  2165.5 0.6892939 0.6892939 2.896721e-02 2.896721e-02 0.281738858 #> 4333  2166.0 0.6889503 0.6889503 2.896899e-02 2.896899e-02 0.282080705 #> 4334  2166.5 0.6886071 0.6886071 2.897052e-02 2.897052e-02 0.282422409 #> 4335  2167.0 0.6882642 0.6882642 2.897180e-02 2.897180e-02 0.282763966 #> 4336  2167.5 0.6879218 0.6879218 2.897284e-02 2.897284e-02 0.283105371 #> 4337  2168.0 0.6875798 0.6875798 2.897362e-02 2.897362e-02 0.283446620 #> 4338  2168.5 0.6872381 0.6872381 2.897416e-02 2.897416e-02 0.283787710 #> 4339  2169.0 0.6868969 0.6868969 2.897445e-02 2.897445e-02 0.284128636 #> 4340  2169.5 0.6865561 0.6865561 2.897449e-02 2.897449e-02 0.284469395 #> 4341  2170.0 0.6862157 0.6862157 2.897429e-02 2.897429e-02 0.284809982 #> 4342  2170.5 0.6858758 0.6858758 2.897384e-02 2.897384e-02 0.285150393 #> 4343  2171.0 0.6855362 0.6855362 2.897314e-02 2.897314e-02 0.285490625 #> 4344  2171.5 0.6851971 0.6851971 2.897220e-02 2.897220e-02 0.285830673 #> 4345  2172.0 0.6848585 0.6848585 2.897101e-02 2.897101e-02 0.286170533 #> 4346  2172.5 0.6845202 0.6845202 2.896958e-02 2.896958e-02 0.286510201 #> 4347  2173.0 0.6841824 0.6841824 2.896791e-02 2.896791e-02 0.286849674 #> 4348  2173.5 0.6838451 0.6838451 2.896598e-02 2.896598e-02 0.287188947 #> 4349  2174.0 0.6835082 0.6835082 2.896382e-02 2.896382e-02 0.287528017 #> 4350  2174.5 0.6831717 0.6831717 2.896141e-02 2.896141e-02 0.287866879 #> 4351  2175.0 0.6828357 0.6828357 2.895876e-02 2.895876e-02 0.288205529 #> 4352  2175.5 0.6825002 0.6825002 2.895586e-02 2.895586e-02 0.288543964 #> 4353  2176.0 0.6821651 0.6821651 2.895273e-02 2.895273e-02 0.288882179 #> 4354  2176.5 0.6818305 0.6818305 2.894935e-02 2.894935e-02 0.289220171 #> 4355  2177.0 0.6814963 0.6814963 2.894573e-02 2.894573e-02 0.289557936 #> 4356  2177.5 0.6811627 0.6811627 2.894186e-02 2.894186e-02 0.289895469 #> 4357  2178.0 0.6808295 0.6808295 2.893776e-02 2.893776e-02 0.290232768 #> 4358  2178.5 0.6804968 0.6804968 2.893342e-02 2.893342e-02 0.290569827 #> 4359  2179.0 0.6801645 0.6801645 2.892884e-02 2.892884e-02 0.290906644 #> 4360  2179.5 0.6798328 0.6798328 2.892401e-02 2.892401e-02 0.291243214 #> 4361  2180.0 0.6795015 0.6795015 2.891895e-02 2.891895e-02 0.291579533 #> 4362  2180.5 0.6791707 0.6791707 2.891365e-02 2.891365e-02 0.291915598 #> 4363  2181.0 0.6788405 0.6788405 2.890812e-02 2.890812e-02 0.292251405 #> 4364  2181.5 0.6785107 0.6785107 2.890234e-02 2.890234e-02 0.292586949 #> 4365  2182.0 0.6781814 0.6781814 2.889633e-02 2.889633e-02 0.292922228 #> 4366  2182.5 0.6778527 0.6778527 2.889008e-02 2.889008e-02 0.293257237 #> 4367  2183.0 0.6775244 0.6775244 2.888360e-02 2.888360e-02 0.293591972 #> 4368  2183.5 0.6771967 0.6771967 2.887688e-02 2.887688e-02 0.293926430 #> 4369  2184.0 0.6768695 0.6768695 2.886993e-02 2.886993e-02 0.294260607 #> 4370  2184.5 0.6765428 0.6765428 2.886274e-02 2.886274e-02 0.294594499 #> 4371  2185.0 0.6762166 0.6762166 2.885532e-02 2.885532e-02 0.294928103 #> 4372  2185.5 0.6758909 0.6758909 2.884766e-02 2.884766e-02 0.295261414 #> 4373  2186.0 0.6755658 0.6755658 2.883977e-02 2.883977e-02 0.295594429 #> 4374  2186.5 0.6752412 0.6752412 2.883165e-02 2.883165e-02 0.295927144 #> 4375  2187.0 0.6749171 0.6749171 2.882330e-02 2.882330e-02 0.296259555 #> 4376  2187.5 0.6745936 0.6745936 2.881472e-02 2.881472e-02 0.296591660 #> 4377  2188.0 0.6742706 0.6742706 2.880591e-02 2.880591e-02 0.296923453 #> 4378  2188.5 0.6739482 0.6739482 2.879687e-02 2.879687e-02 0.297254932 #> 4379  2189.0 0.6736263 0.6736263 2.878760e-02 2.878760e-02 0.297586093 #> 4380  2189.5 0.6733050 0.6733050 2.877810e-02 2.877810e-02 0.297916931 #> 4381  2190.0 0.6729842 0.6729842 2.876837e-02 2.876837e-02 0.298247444 #> 4382  2190.5 0.6726640 0.6726640 2.875842e-02 2.875842e-02 0.298577628 #> 4383  2191.0 0.6723443 0.6723443 2.874824e-02 2.874824e-02 0.298907479 #> 4384  2191.5 0.6720252 0.6720252 2.873783e-02 2.873783e-02 0.299236994 #> 4385  2192.0 0.6717066 0.6717066 2.872720e-02 2.872720e-02 0.299566169 #> 4386  2192.5 0.6713887 0.6713887 2.871634e-02 2.871634e-02 0.299895000 #> 4387  2193.0 0.6710713 0.6710713 2.870526e-02 2.870526e-02 0.300223484 #> 4388  2193.5 0.6707544 0.6707544 2.869396e-02 2.869396e-02 0.300551617 #> 4389  2194.0 0.6704382 0.6704382 2.868243e-02 2.868243e-02 0.300879397 #> 4390  2194.5 0.6701225 0.6701225 2.867068e-02 2.867068e-02 0.301206818 #> 4391  2195.0 0.6698074 0.6698074 2.865872e-02 2.865872e-02 0.301533878 #> 4392  2195.5 0.6694929 0.6694929 2.864653e-02 2.864653e-02 0.301860573 #> 4393  2196.0 0.6691790 0.6691790 2.863412e-02 2.863412e-02 0.302186901 #> 4394  2196.5 0.6688657 0.6688657 2.862149e-02 2.862149e-02 0.302512856 #> 4395  2197.0 0.6685529 0.6685529 2.860864e-02 2.860864e-02 0.302838436 #> 4396  2197.5 0.6682408 0.6682408 2.859558e-02 2.859558e-02 0.303163638 #> 4397  2198.0 0.6679292 0.6679292 2.858230e-02 2.858230e-02 0.303488457 #> 4398  2198.5 0.6676183 0.6676183 2.856880e-02 2.856880e-02 0.303812891 #> 4399  2199.0 0.6673080 0.6673080 2.855509e-02 2.855509e-02 0.304136937 #> 4400  2199.5 0.6669982 0.6669982 2.854116e-02 2.854116e-02 0.304460590 #> 4401  2200.0 0.6666891 0.6666891 2.852702e-02 2.852702e-02 0.304783847 #> 4402  2200.5 0.6663806 0.6663806 2.851267e-02 2.851267e-02 0.305106705 #> 4403  2201.0 0.6660727 0.6660727 2.849810e-02 2.849810e-02 0.305429162 #> 4404  2201.5 0.6657655 0.6657655 2.848332e-02 2.848332e-02 0.305751212 #> 4405  2202.0 0.6654588 0.6654588 2.846833e-02 2.846833e-02 0.306072853 #> 4406  2202.5 0.6651528 0.6651528 2.845314e-02 2.845314e-02 0.306394082 #> 4407  2203.0 0.6648474 0.6648474 2.843773e-02 2.843773e-02 0.306714896 #> 4408  2203.5 0.6645426 0.6645426 2.842211e-02 2.842211e-02 0.307035291 #> 4409  2204.0 0.6642384 0.6642384 2.840629e-02 2.840629e-02 0.307355263 #> 4410  2204.5 0.6639349 0.6639349 2.839026e-02 2.839026e-02 0.307674811 #> 4411  2205.0 0.6636321 0.6636321 2.837402e-02 2.837402e-02 0.307993929 #> 4412  2205.5 0.6633298 0.6633298 2.835758e-02 2.835758e-02 0.308312616 #> 4413  2206.0 0.6630282 0.6630282 2.834093e-02 2.834093e-02 0.308630868 #> 4414  2206.5 0.6627272 0.6627272 2.832408e-02 2.832408e-02 0.308948682 #> 4415  2207.0 0.6624269 0.6624269 2.830703e-02 2.830703e-02 0.309266055 #> 4416  2207.5 0.6621272 0.6621272 2.828977e-02 2.828977e-02 0.309582983 #> 4417  2208.0 0.6618282 0.6618282 2.827232e-02 2.827232e-02 0.309899463 #> 4418  2208.5 0.6615298 0.6615298 2.825466e-02 2.825466e-02 0.310215493 #> 4419  2209.0 0.6612321 0.6612321 2.823681e-02 2.823681e-02 0.310531069 #> 4420  2209.5 0.6609351 0.6609351 2.821875e-02 2.821875e-02 0.310846188 #> 4421  2210.0 0.6606387 0.6606387 2.820050e-02 2.820050e-02 0.311160847 #> 4422  2210.5 0.6603429 0.6603429 2.818205e-02 2.818205e-02 0.311475042 #> 4423  2211.0 0.6600478 0.6600478 2.816341e-02 2.816341e-02 0.311788772 #> 4424  2211.5 0.6597534 0.6597534 2.814457e-02 2.814457e-02 0.312102033 #> 4425  2212.0 0.6594596 0.6594596 2.812554e-02 2.812554e-02 0.312414822 #> 4426  2212.5 0.6591666 0.6591666 2.810631e-02 2.810631e-02 0.312727135 #> 4427  2213.0 0.6588741 0.6588741 2.808689e-02 2.808689e-02 0.313038970 #> 4428  2213.5 0.6585824 0.6585824 2.806728e-02 2.806728e-02 0.313350325 #> 4429  2214.0 0.6582913 0.6582913 2.804748e-02 2.804748e-02 0.313661195 #> 4430  2214.5 0.6580009 0.6580009 2.802749e-02 2.802749e-02 0.313971578 #> 4431  2215.0 0.6577112 0.6577112 2.800731e-02 2.800731e-02 0.314281472 #> 4432  2215.5 0.6574222 0.6574222 2.798694e-02 2.798694e-02 0.314590873 #> 4433  2216.0 0.6571338 0.6571338 2.796638e-02 2.796638e-02 0.314899778 #> 4434  2216.5 0.6568462 0.6568462 2.794564e-02 2.794564e-02 0.315208185 #> 4435  2217.0 0.6565592 0.6565592 2.792472e-02 2.792472e-02 0.315516091 #> 4436  2217.5 0.6562729 0.6562729 2.790361e-02 2.790361e-02 0.315823492 #> 4437  2218.0 0.6559873 0.6559873 2.788231e-02 2.788231e-02 0.316130386 #> 4438  2218.5 0.6557024 0.6557024 2.786084e-02 2.786084e-02 0.316436771 #> 4439  2219.0 0.6554182 0.6554182 2.783918e-02 2.783918e-02 0.316742643 #> 4440  2219.5 0.6551347 0.6551347 2.781734e-02 2.781734e-02 0.317048000 #> 4441  2220.0 0.6548518 0.6548518 2.779532e-02 2.779532e-02 0.317352839 #> 4442  2220.5 0.6545697 0.6545697 2.777312e-02 2.777312e-02 0.317657157 #> 4443  2221.0 0.6542883 0.6542883 2.775075e-02 2.775075e-02 0.317960952 #> 4444  2221.5 0.6540076 0.6540076 2.772820e-02 2.772820e-02 0.318264220 #> 4445  2222.0 0.6537276 0.6537276 2.770547e-02 2.770547e-02 0.318566960 #> 4446  2222.5 0.6534483 0.6534483 2.768257e-02 2.768257e-02 0.318869168 #> 4447  2223.0 0.6531697 0.6531697 2.765949e-02 2.765949e-02 0.319170842 #> 4448  2223.5 0.6528918 0.6528918 2.763624e-02 2.763624e-02 0.319471979 #> 4449  2224.0 0.6526146 0.6526146 2.761282e-02 2.761282e-02 0.319772577 #> 4450  2224.5 0.6523381 0.6523381 2.758923e-02 2.758923e-02 0.320072633 #> 4451  2225.0 0.6520624 0.6520624 2.756547e-02 2.756547e-02 0.320372145 #> 4452  2225.5 0.6517874 0.6517874 2.754154e-02 2.754154e-02 0.320671109 #> 4453  2226.0 0.6515130 0.6515130 2.751744e-02 2.751744e-02 0.320969524 #> 4454  2226.5 0.6512394 0.6512394 2.749317e-02 2.749317e-02 0.321267387 #> 4455  2227.0 0.6509666 0.6509666 2.746874e-02 2.746874e-02 0.321564695 #> 4456  2227.5 0.6506944 0.6506944 2.744414e-02 2.744414e-02 0.321861446 #> 4457  2228.0 0.6504230 0.6504230 2.741938e-02 2.741938e-02 0.322157637 #> 4458  2228.5 0.6501523 0.6501523 2.739445e-02 2.739445e-02 0.322453267 #> 4459  2229.0 0.6498823 0.6498823 2.736936e-02 2.736936e-02 0.322748332 #> 4460  2229.5 0.6496131 0.6496131 2.734411e-02 2.734411e-02 0.323042830 #> 4461  2230.0 0.6493445 0.6493445 2.731870e-02 2.731870e-02 0.323336758 #> 4462  2230.5 0.6490768 0.6490768 2.729313e-02 2.729313e-02 0.323630115 #> 4463  2231.0 0.6488097 0.6488097 2.726741e-02 2.726741e-02 0.323922899 #> 4464  2231.5 0.6485434 0.6485434 2.724152e-02 2.724152e-02 0.324215105 #> 4465  2232.0 0.6482778 0.6482778 2.721548e-02 2.721548e-02 0.324506733 #> 4466  2232.5 0.6480129 0.6480129 2.718928e-02 2.718928e-02 0.324797780 #> 4467  2233.0 0.6477488 0.6477488 2.716293e-02 2.716293e-02 0.325088244 #> 4468  2233.5 0.6474855 0.6474855 2.713643e-02 2.713643e-02 0.325378122 #> 4469  2234.0 0.6472228 0.6472228 2.710977e-02 2.710977e-02 0.325667413 #> 4470  2234.5 0.6469609 0.6469609 2.708296e-02 2.708296e-02 0.325956113 #> 4471  2235.0 0.6466998 0.6466998 2.705600e-02 2.705600e-02 0.326244221 #> 4472  2235.5 0.6464394 0.6464394 2.702889e-02 2.702889e-02 0.326531735 #> 4473  2236.0 0.6461797 0.6461797 2.700164e-02 2.700164e-02 0.326818652 #> 4474  2236.5 0.6459208 0.6459208 2.697423e-02 2.697423e-02 0.327104970 #> 4475  2237.0 0.6456626 0.6456626 2.694668e-02 2.694668e-02 0.327390688 #> 4476  2237.5 0.6454052 0.6454052 2.691899e-02 2.691899e-02 0.327675802 #> 4477  2238.0 0.6451485 0.6451485 2.689115e-02 2.689115e-02 0.327960311 #> 4478  2238.5 0.6448926 0.6448926 2.686316e-02 2.686316e-02 0.328244212 #> 4479  2239.0 0.6446375 0.6446375 2.683504e-02 2.683504e-02 0.328527505 #> 4480  2239.5 0.6443830 0.6443830 2.680677e-02 2.680677e-02 0.328810185 #> 4481  2240.0 0.6441294 0.6441294 2.677837e-02 2.677837e-02 0.329092253 #> 4482  2240.5 0.6438765 0.6438765 2.674982e-02 2.674982e-02 0.329373705 #> 4483  2241.0 0.6436243 0.6436243 2.672113e-02 2.672113e-02 0.329654539 #> 4484  2241.5 0.6433729 0.6433729 2.669231e-02 2.669231e-02 0.329934754 #> 4485  2242.0 0.6431223 0.6431223 2.666335e-02 2.666335e-02 0.330214347 #> 4486  2242.5 0.6428724 0.6428724 2.663426e-02 2.663426e-02 0.330493317 #> 4487  2243.0 0.6426233 0.6426233 2.660503e-02 2.660503e-02 0.330771662 #> 4488  2243.5 0.6423749 0.6423749 2.657567e-02 2.657567e-02 0.331049380 #> 4489  2244.0 0.6421274 0.6421274 2.654618e-02 2.654618e-02 0.331326468 #> 4490  2244.5 0.6418805 0.6418805 2.651655e-02 2.651655e-02 0.331602925 #> 4491  2245.0 0.6416344 0.6416344 2.648680e-02 2.648680e-02 0.331878749 #> 4492  2245.5 0.6413891 0.6413891 2.645692e-02 2.645692e-02 0.332153939 #> 4493  2246.0 0.6411446 0.6411446 2.642691e-02 2.642691e-02 0.332428492 #> 4494  2246.5 0.6409008 0.6409008 2.639677e-02 2.639677e-02 0.332702407 #> 4495  2247.0 0.6406578 0.6406578 2.636650e-02 2.636650e-02 0.332975681 #> 4496  2247.5 0.6404156 0.6404156 2.633611e-02 2.633611e-02 0.333248314 #> 4497  2248.0 0.6401741 0.6401741 2.630560e-02 2.630560e-02 0.333520303 #> 4498  2248.5 0.6399334 0.6399334 2.627496e-02 2.627496e-02 0.333791646 #> 4499  2249.0 0.6396935 0.6396935 2.624420e-02 2.624420e-02 0.334062343 #> 4500  2249.5 0.6394543 0.6394543 2.621332e-02 2.621332e-02 0.334332390 #> 4501  2250.0 0.6392159 0.6392159 2.618232e-02 2.618232e-02 0.334601787 #> 4502  2250.5 0.6389783 0.6389783 2.615121e-02 2.615121e-02 0.334870532 #> 4503  2251.0 0.6387414 0.6387414 2.611997e-02 2.611997e-02 0.335138623 #> 4504  2251.5 0.6385053 0.6385053 2.608862e-02 2.608862e-02 0.335406059 #> 4505  2252.0 0.6382700 0.6382700 2.605715e-02 2.605715e-02 0.335672837 #> 4506  2252.5 0.6380355 0.6380355 2.602556e-02 2.602556e-02 0.335938957 #> 4507  2253.0 0.6378017 0.6378017 2.599386e-02 2.599386e-02 0.336204416 #> 4508  2253.5 0.6375687 0.6375687 2.596205e-02 2.596205e-02 0.336469214 #> 4509  2254.0 0.6373365 0.6373365 2.593013e-02 2.593013e-02 0.336733348 #> 4510  2254.5 0.6371051 0.6371051 2.589810e-02 2.589810e-02 0.336996818 #> 4511  2255.0 0.6368744 0.6368744 2.586595e-02 2.586595e-02 0.337259621 #> 4512  2255.5 0.6366445 0.6366445 2.583370e-02 2.583370e-02 0.337521756 #> 4513  2256.0 0.6364154 0.6364154 2.580134e-02 2.580134e-02 0.337783222 #> 4514  2256.5 0.6361871 0.6361871 2.576887e-02 2.576887e-02 0.338044017 #> 4515  2257.0 0.6359596 0.6359596 2.573630e-02 2.573630e-02 0.338304140 #> 4516  2257.5 0.6357328 0.6357328 2.570362e-02 2.570362e-02 0.338563589 #> 4517  2258.0 0.6355068 0.6355068 2.567084e-02 2.567084e-02 0.338822363 #> 4518  2258.5 0.6352816 0.6352816 2.563796e-02 2.563796e-02 0.339080461 #> 4519  2259.0 0.6350571 0.6350571 2.560497e-02 2.560497e-02 0.339337881 #> 4520  2259.5 0.6348335 0.6348335 2.557189e-02 2.557189e-02 0.339594622 #> 4521  2260.0 0.6346106 0.6346106 2.553870e-02 2.553870e-02 0.339850682 #> 4522  2260.5 0.6343885 0.6343885 2.550541e-02 2.550541e-02 0.340106061 #> 4523  2261.0 0.6341672 0.6341672 2.547203e-02 2.547203e-02 0.340360756 #> 4524  2261.5 0.6339467 0.6339467 2.543855e-02 2.543855e-02 0.340614767 #> 4525  2262.0 0.6337269 0.6337269 2.540497e-02 2.540497e-02 0.340868093 #> 4526  2262.5 0.6335080 0.6335080 2.537130e-02 2.537130e-02 0.341120731 #> 4527  2263.0 0.6332898 0.6332898 2.533754e-02 2.533754e-02 0.341372682 #> 4528  2263.5 0.6330724 0.6330724 2.530368e-02 2.530368e-02 0.341623943 #> 4529  2264.0 0.6328558 0.6328558 2.526973e-02 2.526973e-02 0.341874513 #> 4530  2264.5 0.6326399 0.6326399 2.523569e-02 2.523569e-02 0.342124392 #> 4531  2265.0 0.6324249 0.6324249 2.520156e-02 2.520156e-02 0.342373578 #> 4532  2265.5 0.6322106 0.6322106 2.516734e-02 2.516734e-02 0.342622070 #> 4533  2266.0 0.6319971 0.6319971 2.513304e-02 2.513304e-02 0.342869867 #> 4534  2266.5 0.6317844 0.6317844 2.509864e-02 2.509864e-02 0.343116968 #> 4535  2267.0 0.6315725 0.6315725 2.506416e-02 2.506416e-02 0.343363372 #> 4536  2267.5 0.6313613 0.6313613 2.502960e-02 2.502960e-02 0.343609077 #> 4537  2268.0 0.6311510 0.6311510 2.499495e-02 2.499495e-02 0.343854082 #> 4538  2268.5 0.6309414 0.6309414 2.496022e-02 2.496022e-02 0.344098387 #> 4539  2269.0 0.6307326 0.6307326 2.492541e-02 2.492541e-02 0.344341991 #> 4540  2269.5 0.6305246 0.6305246 2.489051e-02 2.489051e-02 0.344584892 #> 4541  2270.0 0.6303174 0.6303174 2.485554e-02 2.485554e-02 0.344827089 #> 4542  2270.5 0.6301109 0.6301109 2.482048e-02 2.482048e-02 0.345068582 #> 4543  2271.0 0.6299053 0.6299053 2.478535e-02 2.478535e-02 0.345309370 #> 4544  2271.5 0.6297004 0.6297004 2.475014e-02 2.475014e-02 0.345549451 #> 4545  2272.0 0.6294963 0.6294963 2.471485e-02 2.471485e-02 0.345788825 #> 4546  2272.5 0.6292930 0.6292930 2.467949e-02 2.467949e-02 0.346027490 #> 4547  2273.0 0.6290905 0.6290905 2.464406e-02 2.464406e-02 0.346265447 #> 4548  2273.5 0.6288888 0.6288888 2.460855e-02 2.460855e-02 0.346502693 #> 4549  2274.0 0.6286878 0.6286878 2.457297e-02 2.457297e-02 0.346739229 #> 4550  2274.5 0.6284876 0.6284876 2.453731e-02 2.453731e-02 0.346975052 #> 4551  2275.0 0.6282882 0.6282882 2.450159e-02 2.450159e-02 0.347210164 #> 4552  2275.5 0.6280896 0.6280896 2.446579e-02 2.446579e-02 0.347444562 #> 4553  2276.0 0.6278918 0.6278918 2.442993e-02 2.442993e-02 0.347678245 #> 4554  2276.5 0.6276948 0.6276948 2.439400e-02 2.439400e-02 0.347911214 #> 4555  2277.0 0.6274985 0.6274985 2.435800e-02 2.435800e-02 0.348143467 #> 4556  2277.5 0.6273031 0.6273031 2.432194e-02 2.432194e-02 0.348375004 #> 4557  2278.0 0.6271084 0.6271084 2.428581e-02 2.428581e-02 0.348605823 #> 4558  2278.5 0.6269145 0.6269145 2.424962e-02 2.424962e-02 0.348835925 #> 4559  2279.0 0.6267213 0.6267213 2.421336e-02 2.421336e-02 0.349065308 #> 4560  2279.5 0.6265290 0.6265290 2.417705e-02 2.417705e-02 0.349293971 #> 4561  2280.0 0.6263374 0.6263374 2.414067e-02 2.414067e-02 0.349521915 #> 4562  2280.5 0.6261466 0.6261466 2.410423e-02 2.410423e-02 0.349749138 #> 4563  2281.0 0.6259566 0.6259566 2.406773e-02 2.406773e-02 0.349975640 #> 4564  2281.5 0.6257674 0.6257674 2.403117e-02 2.403117e-02 0.350201420 #> 4565  2282.0 0.6255790 0.6255790 2.399455e-02 2.399455e-02 0.350426477 #> 4566  2282.5 0.6253913 0.6253913 2.395788e-02 2.395788e-02 0.350650812 #> 4567  2283.0 0.6252044 0.6252044 2.392115e-02 2.392115e-02 0.350874422 #> 4568  2283.5 0.6250183 0.6250183 2.388437e-02 2.388437e-02 0.351097309 #> 4569  2284.0 0.6248330 0.6248330 2.384753e-02 2.384753e-02 0.351319471 #> 4570  2284.5 0.6246485 0.6246485 2.381064e-02 2.381064e-02 0.351540907 #> 4571  2285.0 0.6244647 0.6244647 2.377370e-02 2.377370e-02 0.351761618 #> 4572  2285.5 0.6242817 0.6242817 2.373670e-02 2.373670e-02 0.351981602 #> 4573  2286.0 0.6240995 0.6240995 2.369966e-02 2.369966e-02 0.352200859 #> 4574  2286.5 0.6239180 0.6239180 2.366256e-02 2.366256e-02 0.352419389 #> 4575  2287.0 0.6237374 0.6237374 2.362542e-02 2.362542e-02 0.352637192 #> 4576  2287.5 0.6235575 0.6235575 2.358823e-02 2.358823e-02 0.352854266 #> 4577  2288.0 0.6233784 0.6233784 2.355099e-02 2.355099e-02 0.353070611 #> 4578  2288.5 0.6232001 0.6232001 2.351370e-02 2.351370e-02 0.353286228 #> 4579  2289.0 0.6230225 0.6230225 2.347637e-02 2.347637e-02 0.353501115 #> 4580  2289.5 0.6228457 0.6228457 2.343899e-02 2.343899e-02 0.353715272 #> 4581  2290.0 0.6226697 0.6226697 2.340157e-02 2.340157e-02 0.353928700 #> 4582  2290.5 0.6224945 0.6224945 2.336411e-02 2.336411e-02 0.354141396 #> 4583  2291.0 0.6223200 0.6223200 2.332661e-02 2.332661e-02 0.354353362 #> 4584  2291.5 0.6221463 0.6221463 2.328906e-02 2.328906e-02 0.354564596 #> 4585  2292.0 0.6219734 0.6219734 2.325148e-02 2.325148e-02 0.354775099 #> 4586  2292.5 0.6218013 0.6218013 2.321385e-02 2.321385e-02 0.354984870 #> 4587  2293.0 0.6216299 0.6216299 2.317619e-02 2.317619e-02 0.355193909 #> 4588  2293.5 0.6214593 0.6214593 2.313849e-02 2.313849e-02 0.355402216 #> 4589  2294.0 0.6212895 0.6212895 2.310075e-02 2.310075e-02 0.355609790 #> 4590  2294.5 0.6211204 0.6211204 2.306298e-02 2.306298e-02 0.355816631 #> 4591  2295.0 0.6209521 0.6209521 2.302517e-02 2.302517e-02 0.356022739 #> 4592  2295.5 0.6207846 0.6207846 2.298732e-02 2.298732e-02 0.356228114 #> 4593  2296.0 0.6206178 0.6206178 2.294944e-02 2.294944e-02 0.356432755 #> 4594  2296.5 0.6204518 0.6204518 2.291153e-02 2.291153e-02 0.356636662 #> 4595  2297.0 0.6202866 0.6202866 2.287359e-02 2.287359e-02 0.356839836 #> 4596  2297.5 0.6201221 0.6201221 2.283562e-02 2.283562e-02 0.357042276 #> 4597  2298.0 0.6199584 0.6199584 2.279761e-02 2.279761e-02 0.357243981 #> 4598  2298.5 0.6197955 0.6197955 2.275958e-02 2.275958e-02 0.357444952 #> 4599  2299.0 0.6196333 0.6196333 2.272151e-02 2.272151e-02 0.357645188 #> 4600  2299.5 0.6194719 0.6194719 2.268342e-02 2.268342e-02 0.357844690 #> 4601  2300.0 0.6193112 0.6193112 2.264530e-02 2.264530e-02 0.358043458 #> 4602  2300.5 0.6191514 0.6191514 2.260716e-02 2.260716e-02 0.358241490 #> 4603  2301.0 0.6189922 0.6189922 2.256899e-02 2.256899e-02 0.358438788 #> 4604  2301.5 0.6188339 0.6188339 2.253079e-02 2.253079e-02 0.358635351 #> 4605  2302.0 0.6186763 0.6186763 2.249257e-02 2.249257e-02 0.358831179 #> 4606  2302.5 0.6185194 0.6185194 2.245433e-02 2.245433e-02 0.359026272 #> 4607  2303.0 0.6183633 0.6183633 2.241606e-02 2.241606e-02 0.359220630 #> 4608  2303.5 0.6182080 0.6182080 2.237777e-02 2.237777e-02 0.359414254 #> 4609  2304.0 0.6180534 0.6180534 2.233946e-02 2.233946e-02 0.359607142 #> 4610  2304.5 0.6178996 0.6178996 2.230113e-02 2.230113e-02 0.359799295 #> 4611  2305.0 0.6177465 0.6177465 2.226278e-02 2.226278e-02 0.359990714 #> 4612  2305.5 0.6175942 0.6175942 2.222442e-02 2.222442e-02 0.360181397 #> 4613  2306.0 0.6174426 0.6174426 2.218603e-02 2.218603e-02 0.360371346 #> 4614  2306.5 0.6172918 0.6172918 2.214763e-02 2.214763e-02 0.360560560 #> 4615  2307.0 0.6171418 0.6171418 2.210921e-02 2.210921e-02 0.360749040 #> 4616  2307.5 0.6169924 0.6169924 2.207077e-02 2.207077e-02 0.360936785 #> 4617  2308.0 0.6168439 0.6168439 2.203232e-02 2.203232e-02 0.361123795 #> 4618  2308.5 0.6166961 0.6166961 2.199385e-02 2.199385e-02 0.361310072 #> 4619  2309.0 0.6165490 0.6165490 2.195537e-02 2.195537e-02 0.361495614 #> 4620  2309.5 0.6164027 0.6164027 2.191688e-02 2.191688e-02 0.361680422 #> 4621  2310.0 0.6162571 0.6162571 2.187837e-02 2.187837e-02 0.361864496 #> 4622  2310.5 0.6161123 0.6161123 2.183986e-02 2.183986e-02 0.362047837 #> 4623  2311.0 0.6159682 0.6159682 2.180133e-02 2.180133e-02 0.362230443 #> 4624  2311.5 0.6158249 0.6158249 2.176279e-02 2.176279e-02 0.362412317 #> 4625  2312.0 0.6156823 0.6156823 2.172424e-02 2.172424e-02 0.362593458 #> 4626  2312.5 0.6155404 0.6155404 2.168569e-02 2.168569e-02 0.362773865 #> 4627  2313.0 0.6153993 0.6153993 2.164712e-02 2.164712e-02 0.362953540 #> 4628  2313.5 0.6152590 0.6152590 2.160855e-02 2.160855e-02 0.363132482 #> 4629  2314.0 0.6151193 0.6151193 2.156997e-02 2.156997e-02 0.363310693 #> 4630  2314.5 0.6149804 0.6149804 2.153139e-02 2.153139e-02 0.363488171 #> 4631  2315.0 0.6148423 0.6148423 2.149280e-02 2.149280e-02 0.363664918 #> 4632  2315.5 0.6147049 0.6147049 2.145420e-02 2.145420e-02 0.363840933 #> 4633  2316.0 0.6145682 0.6145682 2.141560e-02 2.141560e-02 0.364016217 #> 4634  2316.5 0.6144322 0.6144322 2.137700e-02 2.137700e-02 0.364190770 #> 4635  2317.0 0.6142970 0.6142970 2.133840e-02 2.133840e-02 0.364364593 #> 4636  2317.5 0.6141625 0.6141625 2.129979e-02 2.129979e-02 0.364537685 #> 4637  2318.0 0.6140288 0.6140288 2.126118e-02 2.126118e-02 0.364710048 #> 4638  2318.5 0.6138957 0.6138957 2.122257e-02 2.122257e-02 0.364881681 #> 4639  2319.0 0.6137635 0.6137635 2.118396e-02 2.118396e-02 0.365052586 #> 4640  2319.5 0.6136319 0.6136319 2.114535e-02 2.114535e-02 0.365222761 #> 4641  2320.0 0.6135010 0.6135010 2.110674e-02 2.110674e-02 0.365392208 #> 4642  2320.5 0.6133709 0.6133709 2.106814e-02 2.106814e-02 0.365560927 #> 4643  2321.0 0.6132415 0.6132415 2.102953e-02 2.102953e-02 0.365728918 #> 4644  2321.5 0.6131129 0.6131129 2.099093e-02 2.099093e-02 0.365896183 #> 4645  2322.0 0.6129849 0.6129849 2.095234e-02 2.095234e-02 0.366062720 #> 4646  2322.5 0.6128577 0.6128577 2.091374e-02 2.091374e-02 0.366228531 #> 4647  2323.0 0.6127312 0.6127312 2.087515e-02 2.087515e-02 0.366393617 #> 4648  2323.5 0.6126055 0.6126055 2.083657e-02 2.083657e-02 0.366557976 #> 4649  2324.0 0.6124804 0.6124804 2.079800e-02 2.079800e-02 0.366721611 #> 4650  2324.5 0.6123561 0.6123561 2.075943e-02 2.075943e-02 0.366884521 #> 4651  2325.0 0.6122324 0.6122324 2.072086e-02 2.072086e-02 0.367046708 #> 4652  2325.5 0.6121095 0.6121095 2.068231e-02 2.068231e-02 0.367208170 #> 4653  2326.0 0.6119873 0.6119873 2.064376e-02 2.064376e-02 0.367368910 #> 4654  2326.5 0.6118658 0.6118658 2.060523e-02 2.060523e-02 0.367528927 #> 4655  2327.0 0.6117451 0.6117451 2.056670e-02 2.056670e-02 0.367688222 #> 4656  2327.5 0.6116250 0.6116250 2.052818e-02 2.052818e-02 0.367846796 #> 4657  2328.0 0.6115057 0.6115057 2.048968e-02 2.048968e-02 0.368004648 #> 4658  2328.5 0.6113870 0.6113870 2.045118e-02 2.045118e-02 0.368161780 #> 4659  2329.0 0.6112691 0.6112691 2.041270e-02 2.041270e-02 0.368318193 #> 4660  2329.5 0.6111519 0.6111519 2.037423e-02 2.037423e-02 0.368473886 #> 4661  2330.0 0.6110354 0.6110354 2.033577e-02 2.033577e-02 0.368628860 #> 4662  2330.5 0.6109196 0.6109196 2.029733e-02 2.029733e-02 0.368783116 #> 4663  2331.0 0.6108044 0.6108044 2.025890e-02 2.025890e-02 0.368936654 #> 4664  2331.5 0.6106900 0.6106900 2.022048e-02 2.022048e-02 0.369089476 #> 4665  2332.0 0.6105763 0.6105763 2.018208e-02 2.018208e-02 0.369241581 #> 4666  2332.5 0.6104633 0.6104633 2.014370e-02 2.014370e-02 0.369392971 #> 4667  2333.0 0.6103510 0.6103510 2.010533e-02 2.010533e-02 0.369543645 #> 4668  2333.5 0.6102394 0.6102394 2.006698e-02 2.006698e-02 0.369693605 #> 4669  2334.0 0.6101285 0.6101285 2.002865e-02 2.002865e-02 0.369842851 #> 4670  2334.5 0.6100183 0.6100183 1.999033e-02 1.999033e-02 0.369991384 #> 4671  2335.0 0.6099088 0.6099088 1.995204e-02 1.995204e-02 0.370139205 #> 4672  2335.5 0.6097999 0.6097999 1.991376e-02 1.991376e-02 0.370286313 #> 4673  2336.0 0.6096918 0.6096918 1.987550e-02 1.987550e-02 0.370432710 #> 4674  2336.5 0.6095843 0.6095843 1.983727e-02 1.983727e-02 0.370578397 #> 4675  2337.0 0.6094776 0.6094776 1.979905e-02 1.979905e-02 0.370723374 #> 4676  2337.5 0.6093715 0.6093715 1.976085e-02 1.976085e-02 0.370867643 #> 4677  2338.0 0.6092661 0.6092661 1.972268e-02 1.972268e-02 0.371011202 #> 4678  2338.5 0.6091614 0.6091614 1.968453e-02 1.968453e-02 0.371154054 #> 4679  2339.0 0.6090574 0.6090574 1.964640e-02 1.964640e-02 0.371296199 #> 4680  2339.5 0.6089541 0.6089541 1.960829e-02 1.960829e-02 0.371437638 #> 4681  2340.0 0.6088514 0.6088514 1.957020e-02 1.957020e-02 0.371578371 #> 4682  2340.5 0.6087495 0.6087495 1.953215e-02 1.953215e-02 0.371718400 #> 4683  2341.0 0.6086482 0.6086482 1.949411e-02 1.949411e-02 0.371857725 #> 4684  2341.5 0.6085476 0.6085476 1.945610e-02 1.945610e-02 0.371996346 #> 4685  2342.0 0.6084476 0.6084476 1.941812e-02 1.941812e-02 0.372134265 #> 4686  2342.5 0.6083484 0.6083484 1.938016e-02 1.938016e-02 0.372271483 #> 4687  2343.0 0.6082498 0.6082498 1.934222e-02 1.934222e-02 0.372407999 #> 4688  2343.5 0.6081519 0.6081519 1.930432e-02 1.930432e-02 0.372543816 #> 4689  2344.0 0.6080546 0.6080546 1.926644e-02 1.926644e-02 0.372678933 #> 4690  2344.5 0.6079581 0.6079581 1.922859e-02 1.922859e-02 0.372813352 #> 4691  2345.0 0.6078622 0.6078622 1.919077e-02 1.919077e-02 0.372947073 #> 4692  2345.5 0.6077669 0.6077669 1.915298e-02 1.915298e-02 0.373080097 #> 4693  2346.0 0.6076724 0.6076724 1.911521e-02 1.911521e-02 0.373212426 #> 4694  2346.5 0.6075785 0.6075785 1.907748e-02 1.907748e-02 0.373344059 #> 4695  2347.0 0.6074852 0.6074852 1.903977e-02 1.903977e-02 0.373474998 #> 4696  2347.5 0.6073927 0.6073927 1.900210e-02 1.900210e-02 0.373605243 #> 4697  2348.0 0.6073008 0.6073008 1.896445e-02 1.896445e-02 0.373734796 #> 4698  2348.5 0.6072095 0.6072095 1.892684e-02 1.892684e-02 0.373863657 #> 4699  2349.0 0.6071189 0.6071189 1.888926e-02 1.888926e-02 0.373991827 #> 4700  2349.5 0.6070290 0.6070290 1.885171e-02 1.885171e-02 0.374119308 #> 4701  2350.0 0.6069397 0.6069397 1.881419e-02 1.881419e-02 0.374246099 #> 4702  2350.5 0.6068511 0.6068511 1.877671e-02 1.877671e-02 0.374372202 #> 4703  2351.0 0.6067631 0.6067631 1.873926e-02 1.873926e-02 0.374497617 #> 4704  2351.5 0.6066758 0.6066758 1.870184e-02 1.870184e-02 0.374622346 #> 4705  2352.0 0.6065891 0.6065891 1.866446e-02 1.866446e-02 0.374746390 #> 4706  2352.5 0.6065031 0.6065031 1.862711e-02 1.862711e-02 0.374869749 #> 4707  2353.0 0.6064178 0.6064178 1.858980e-02 1.858980e-02 0.374992424 #> 4708  2353.5 0.6063331 0.6063331 1.855252e-02 1.855252e-02 0.375114417 #> 4709  2354.0 0.6062490 0.6062490 1.851528e-02 1.851528e-02 0.375235728 #> 4710  2354.5 0.6061656 0.6061656 1.847808e-02 1.847808e-02 0.375356357 #> 4711  2355.0 0.6060828 0.6060828 1.844091e-02 1.844091e-02 0.375476307 #> 4712  2355.5 0.6060006 0.6060006 1.840377e-02 1.840377e-02 0.375595578 #> 4713  2356.0 0.6059192 0.6059192 1.836668e-02 1.836668e-02 0.375714171 #> 4714  2356.5 0.6058383 0.6058383 1.832962e-02 1.832962e-02 0.375832087 #> 4715  2357.0 0.6057581 0.6057581 1.829260e-02 1.829260e-02 0.375949327 #> 4716  2357.5 0.6056785 0.6056785 1.825562e-02 1.825562e-02 0.376065891 #> 4717  2358.0 0.6055995 0.6055995 1.821868e-02 1.821868e-02 0.376181782 #> 4718  2358.5 0.6055212 0.6055212 1.818177e-02 1.818177e-02 0.376296999 #> 4719  2359.0 0.6054435 0.6054435 1.814491e-02 1.814491e-02 0.376411544 #> 4720  2359.5 0.6053665 0.6053665 1.810808e-02 1.810808e-02 0.376525418 #> 4721  2360.0 0.6052901 0.6052901 1.807130e-02 1.807130e-02 0.376638622 #> 4722  2360.5 0.6052143 0.6052143 1.803455e-02 1.803455e-02 0.376751157 #> 4723  2361.0 0.6051391 0.6051391 1.799785e-02 1.799785e-02 0.376863023 #> 4724  2361.5 0.6050646 0.6050646 1.796119e-02 1.796119e-02 0.376974222 #> 4725  2362.0 0.6049907 0.6049907 1.792457e-02 1.792457e-02 0.377084755 #> 4726  2362.5 0.6049174 0.6049174 1.788799e-02 1.788799e-02 0.377194623 #> 4727  2363.0 0.6048447 0.6048447 1.785145e-02 1.785145e-02 0.377303827 #> 4728  2363.5 0.6047727 0.6047727 1.781496e-02 1.781496e-02 0.377412368 #> 4729  2364.0 0.6047012 0.6047012 1.777850e-02 1.777850e-02 0.377520247 #> 4730  2364.5 0.6046304 0.6046304 1.774210e-02 1.774210e-02 0.377627465 #> 4731  2365.0 0.6045602 0.6045602 1.770573e-02 1.770573e-02 0.377734023 #> 4732  2365.5 0.6044907 0.6044907 1.766941e-02 1.766941e-02 0.377839923 #> 4733  2366.0 0.6044217 0.6044217 1.763313e-02 1.763313e-02 0.377945164 #> 4734  2366.5 0.6043534 0.6043534 1.759690e-02 1.759690e-02 0.378049749 #> 4735  2367.0 0.6042856 0.6042856 1.756071e-02 1.756071e-02 0.378153678 #> 4736  2367.5 0.6042185 0.6042185 1.752456e-02 1.752456e-02 0.378256952 #> 4737  2368.0 0.6041520 0.6041520 1.748846e-02 1.748846e-02 0.378359573 #> 4738  2368.5 0.6040860 0.6040860 1.745241e-02 1.745241e-02 0.378461542 #> 4739  2369.0 0.6040207 0.6040207 1.741640e-02 1.741640e-02 0.378562859 #> 4740  2369.5 0.6039560 0.6039560 1.738044e-02 1.738044e-02 0.378663526 #> 4741  2370.0 0.6038919 0.6038919 1.734452e-02 1.734452e-02 0.378763544 #> 4742  2370.5 0.6038284 0.6038284 1.730866e-02 1.730866e-02 0.378862914 #> 4743  2371.0 0.6037655 0.6037655 1.727283e-02 1.727283e-02 0.378961637 #> 4744  2371.5 0.6037032 0.6037032 1.723706e-02 1.723706e-02 0.379059714 #> 4745  2372.0 0.6036415 0.6036415 1.720133e-02 1.720133e-02 0.379157146 #> 4746  2372.5 0.6035804 0.6035804 1.716565e-02 1.716565e-02 0.379253935 #> 4747  2373.0 0.6035199 0.6035199 1.713002e-02 1.713002e-02 0.379350081 #> 4748  2373.5 0.6034600 0.6034600 1.709444e-02 1.709444e-02 0.379445586 #> 4749  2374.0 0.6034006 0.6034006 1.705890e-02 1.705890e-02 0.379540450 #> 4750  2374.5 0.6033419 0.6033419 1.702342e-02 1.702342e-02 0.379634676 #> 4751  2375.0 0.6032838 0.6032838 1.698798e-02 1.698798e-02 0.379728263 #> 4752  2375.5 0.6032262 0.6032262 1.695259e-02 1.695259e-02 0.379821214 #> 4753  2376.0 0.6031692 0.6031692 1.691725e-02 1.691725e-02 0.379913528 #> 4754  2376.5 0.6031128 0.6031128 1.688196e-02 1.688196e-02 0.380005209 #> 4755  2377.0 0.6030570 0.6030570 1.684672e-02 1.684672e-02 0.380096256 #> 4756  2377.5 0.6030018 0.6030018 1.681154e-02 1.681154e-02 0.380186670 #> 4757  2378.0 0.6029471 0.6029471 1.677640e-02 1.677640e-02 0.380276454 #> 4758  2378.5 0.6028931 0.6028931 1.674131e-02 1.674131e-02 0.380365608 #> 4759  2379.0 0.6028396 0.6028396 1.670627e-02 1.670627e-02 0.380454132 #> 4760  2379.5 0.6027867 0.6027867 1.667129e-02 1.667129e-02 0.380542030 #> 4761  2380.0 0.6027343 0.6027343 1.663635e-02 1.663635e-02 0.380629300 #> 4762  2380.5 0.6026826 0.6026826 1.660147e-02 1.660147e-02 0.380715946 #> 4763  2381.0 0.6026314 0.6026314 1.656664e-02 1.656664e-02 0.380801967 #> 4764  2381.5 0.6025808 0.6025808 1.653186e-02 1.653186e-02 0.380887366 #> 4765  2382.0 0.6025307 0.6025307 1.649714e-02 1.649714e-02 0.380972142 #> 4766  2382.5 0.6024812 0.6024812 1.646246e-02 1.646246e-02 0.381056299 #> 4767  2383.0 0.6024323 0.6024323 1.642784e-02 1.642784e-02 0.381139835 #> 4768  2383.5 0.6023840 0.6023840 1.639327e-02 1.639327e-02 0.381222754 #> 4769  2384.0 0.6023362 0.6023362 1.635876e-02 1.635876e-02 0.381305055 #> 4770  2384.5 0.6022890 0.6022890 1.632430e-02 1.632430e-02 0.381386741 #> 4771  2385.0 0.6022423 0.6022423 1.628989e-02 1.628989e-02 0.381467812 #> 4772  2385.5 0.6021962 0.6021962 1.625553e-02 1.625553e-02 0.381548270 #> 4773  2386.0 0.6021507 0.6021507 1.622123e-02 1.622123e-02 0.381628115 #> 4774  2386.5 0.6021057 0.6021057 1.618699e-02 1.618699e-02 0.381707350 #> 4775  2387.0 0.6020612 0.6020612 1.615279e-02 1.615279e-02 0.381785975 #> 4776  2387.5 0.6020174 0.6020174 1.611866e-02 1.611866e-02 0.381863991 #> 4777  2388.0 0.6019740 0.6019740 1.608457e-02 1.608457e-02 0.381941400 #> 4778  2388.5 0.6019313 0.6019313 1.605054e-02 1.605054e-02 0.382018202 #> 4779  2389.0 0.6018890 0.6018890 1.601657e-02 1.601657e-02 0.382094400 #> 4780  2389.5 0.6018474 0.6018474 1.598265e-02 1.598265e-02 0.382169994 #> 4781  2390.0 0.6018062 0.6018062 1.594879e-02 1.594879e-02 0.382244985 #> 4782  2390.5 0.6017656 0.6017656 1.591498e-02 1.591498e-02 0.382319376 #> 4783  2391.0 0.6017256 0.6017256 1.588123e-02 1.588123e-02 0.382393166 #> 4784  2391.5 0.6016861 0.6016861 1.584753e-02 1.584753e-02 0.382466357 #> 4785  2392.0 0.6016472 0.6016472 1.581389e-02 1.581389e-02 0.382538951 #> 4786  2392.5 0.6016087 0.6016087 1.578031e-02 1.578031e-02 0.382610948 #> 4787  2393.0 0.6015709 0.6015709 1.574678e-02 1.574678e-02 0.382682351 #> 4788  2393.5 0.6015335 0.6015335 1.571331e-02 1.571331e-02 0.382753159 #> 4789  2394.0 0.6014967 0.6014967 1.567989e-02 1.567989e-02 0.382823375 #> 4790  2394.5 0.6014605 0.6014605 1.564654e-02 1.564654e-02 0.382892999 #> 4791  2395.0 0.6014247 0.6014247 1.561323e-02 1.561323e-02 0.382962033 #> 4792  2395.5 0.6013895 0.6013895 1.557999e-02 1.557999e-02 0.383030479 #> 4793  2396.0 0.6013549 0.6013549 1.554680e-02 1.554680e-02 0.383098336 #> 4794  2396.5 0.6013207 0.6013207 1.551367e-02 1.551367e-02 0.383165607 #> 4795  2397.0 0.6012871 0.6012871 1.548060e-02 1.548060e-02 0.383232294 #> 4796  2397.5 0.6012540 0.6012540 1.544759e-02 1.544759e-02 0.383298396 #> 4797  2398.0 0.6012215 0.6012215 1.541463e-02 1.541463e-02 0.383363915 #> 4798  2398.5 0.6011894 0.6011894 1.538173e-02 1.538173e-02 0.383428853 #> 4799  2399.0 0.6011579 0.6011579 1.534889e-02 1.534889e-02 0.383493211 #> 4800  2399.5 0.6011269 0.6011269 1.531611e-02 1.531611e-02 0.383556990 #> 4801  2400.0 0.6010964 0.6010964 1.528338e-02 1.528338e-02 0.383620191 #> 4802  2400.5 0.6010665 0.6010665 1.525072e-02 1.525072e-02 0.383682816 #> 4803  2401.0 0.6010370 0.6010370 1.521811e-02 1.521811e-02 0.383744866 #> 4804  2401.5 0.6010081 0.6010081 1.518556e-02 1.518556e-02 0.383806342 #> 4805  2402.0 0.6009797 0.6009797 1.515307e-02 1.515307e-02 0.383867246 #> 4806  2402.5 0.6009518 0.6009518 1.512064e-02 1.512064e-02 0.383927578 #> 4807  2403.0 0.6009244 0.6009244 1.508827e-02 1.508827e-02 0.383987340 #> 4808  2403.5 0.6008975 0.6008975 1.505596e-02 1.505596e-02 0.384046533 #> 4809  2404.0 0.6008711 0.6008711 1.502370e-02 1.502370e-02 0.384105159 #> 4810  2404.5 0.6008453 0.6008453 1.499151e-02 1.499151e-02 0.384163219 #> 4811  2405.0 0.6008199 0.6008199 1.495937e-02 1.495937e-02 0.384220713 #> 4812  2405.5 0.6007951 0.6007951 1.492730e-02 1.492730e-02 0.384277644 #> 4813  2406.0 0.6007707 0.6007707 1.489528e-02 1.489528e-02 0.384334013 #> 4814  2406.5 0.6007469 0.6007469 1.486333e-02 1.486333e-02 0.384389820 #> 4815  2407.0 0.6007235 0.6007235 1.483143e-02 1.483143e-02 0.384445068 #> 4816  2407.5 0.6007007 0.6007007 1.479959e-02 1.479959e-02 0.384499756 #> 4817  2408.0 0.6006783 0.6006783 1.476782e-02 1.476782e-02 0.384553888 #> 4818  2408.5 0.6006564 0.6006564 1.473610e-02 1.473610e-02 0.384607464 #> 4819  2409.0 0.6006351 0.6006351 1.470444e-02 1.470444e-02 0.384660484 #> 4820  2409.5 0.6006142 0.6006142 1.467285e-02 1.467285e-02 0.384712952 #> 4821  2410.0 0.6005938 0.6005938 1.464131e-02 1.464131e-02 0.384764867 #> 4822  2410.5 0.6005739 0.6005739 1.460984e-02 1.460984e-02 0.384816231 #> 4823  2411.0 0.6005545 0.6005545 1.457842e-02 1.457842e-02 0.384867045 #> 4824  2411.5 0.6005356 0.6005356 1.454707e-02 1.454707e-02 0.384917312 #> 4825  2412.0 0.6005172 0.6005172 1.451577e-02 1.451577e-02 0.384967031 #> 4826  2412.5 0.6004993 0.6004993 1.448454e-02 1.448454e-02 0.385016204 #> 4827  2413.0 0.6004818 0.6004818 1.445337e-02 1.445337e-02 0.385064833 #> 4828  2413.5 0.6004648 0.6004648 1.442226e-02 1.442226e-02 0.385112919 #> 4829  2414.0 0.6004483 0.6004483 1.439121e-02 1.439121e-02 0.385160462 #> 4830  2414.5 0.6004323 0.6004323 1.436022e-02 1.436022e-02 0.385207466 #> 4831  2415.0 0.6004168 0.6004168 1.432929e-02 1.432929e-02 0.385253929 #> 4832  2415.5 0.6004017 0.6004017 1.429842e-02 1.429842e-02 0.385299855 #> 4833  2416.0 0.6003871 0.6003871 1.426762e-02 1.426762e-02 0.385345244 #> 4834  2416.5 0.6003730 0.6003730 1.423687e-02 1.423687e-02 0.385390098 #> 4835  2417.0 0.6003594 0.6003594 1.420619e-02 1.420619e-02 0.385434417 #> 4836  2417.5 0.6003462 0.6003462 1.417557e-02 1.417557e-02 0.385478203 #> 4837  2418.0 0.6003335 0.6003335 1.414501e-02 1.414501e-02 0.385521458 #> 4838  2418.5 0.6003213 0.6003213 1.411451e-02 1.411451e-02 0.385564183 #> 4839  2419.0 0.6003095 0.6003095 1.408407e-02 1.408407e-02 0.385606378 #> 4840  2419.5 0.6002983 0.6002983 1.405370e-02 1.405370e-02 0.385648046 #> 4841  2420.0 0.6002874 0.6002874 1.402338e-02 1.402338e-02 0.385689187 #> 4842  2420.5 0.6002771 0.6002771 1.399313e-02 1.399313e-02 0.385729803 #> 4843  2421.0 0.6002672 0.6002672 1.396294e-02 1.396294e-02 0.385769895 #> 4844  2421.5 0.6002577 0.6002577 1.393281e-02 1.393281e-02 0.385809465 #> 4845  2422.0 0.6002487 0.6002487 1.390274e-02 1.390274e-02 0.385848513 #> 4846  2422.5 0.6002402 0.6002402 1.387274e-02 1.387274e-02 0.385887042 #> 4847  2423.0 0.6002322 0.6002322 1.384280e-02 1.384280e-02 0.385925051 #> 4848  2423.5 0.6002245 0.6002245 1.381291e-02 1.381291e-02 0.385962543 #> 4849  2424.0 0.6002174 0.6002174 1.378309e-02 1.378309e-02 0.385999520 #> 4850  2424.5 0.6002107 0.6002107 1.375334e-02 1.375334e-02 0.386035981 #> 4851  2425.0 0.6002044 0.6002044 1.372364e-02 1.372364e-02 0.386071929 #> 4852  2425.5 0.6001986 0.6001986 1.369401e-02 1.369401e-02 0.386107364 #> 4853  2426.0 0.6001933 0.6001933 1.366444e-02 1.366444e-02 0.386142289 #> 4854  2426.5 0.6001884 0.6001884 1.363493e-02 1.363493e-02 0.386176703 #> 4855  2427.0 0.6001839 0.6001839 1.360548e-02 1.360548e-02 0.386210610 #> 4856  2427.5 0.6001799 0.6001799 1.357610e-02 1.357610e-02 0.386244009 #> 4857  2428.0 0.6001763 0.6001763 1.354677e-02 1.354677e-02 0.386276903 #> 4858  2428.5 0.6001732 0.6001732 1.351751e-02 1.351751e-02 0.386309292 #> 4859  2429.0 0.6001705 0.6001705 1.348831e-02 1.348831e-02 0.386341178 #> 4860  2429.5 0.6001683 0.6001683 1.345918e-02 1.345918e-02 0.386372562 #> 4861  2430.0 0.6001665 0.6001665 1.343010e-02 1.343010e-02 0.386403445 #> 4862  2430.5 0.6001651 0.6001651 1.340109e-02 1.340109e-02 0.386433829 #> 4863  2431.0 0.6001641 0.6001641 1.337214e-02 1.337214e-02 0.386463715 #> 4864  2431.5 0.6001636 0.6001636 1.334326e-02 1.334326e-02 0.386493104 #> 4865  2432.0 0.6001636 0.6001636 1.331443e-02 1.331443e-02 0.386521997 #> 4866  2432.5 0.6001639 0.6001639 1.328567e-02 1.328567e-02 0.386550396 #> 4867  2433.0 0.6001647 0.6001647 1.325697e-02 1.325697e-02 0.386578303 #> 4868  2433.5 0.6001660 0.6001660 1.322833e-02 1.322833e-02 0.386605717 #> 4869  2434.0 0.6001676 0.6001676 1.319976e-02 1.319976e-02 0.386632642 #> 4870  2434.5 0.6001697 0.6001697 1.317124e-02 1.317124e-02 0.386659077 #> 4871  2435.0 0.6001722 0.6001722 1.314279e-02 1.314279e-02 0.386685025 #> 4872  2435.5 0.6001751 0.6001751 1.311440e-02 1.311440e-02 0.386710486 #> 4873  2436.0 0.6001785 0.6001785 1.308608e-02 1.308608e-02 0.386735461 #> 4874  2436.5 0.6001822 0.6001822 1.305781e-02 1.305781e-02 0.386759953 #> 4875  2437.0 0.6001864 0.6001864 1.302961e-02 1.302961e-02 0.386783962 #> 4876  2437.5 0.6001910 0.6001910 1.300147e-02 1.300147e-02 0.386807490 #> 4877  2438.0 0.6001961 0.6001961 1.297340e-02 1.297340e-02 0.386830538 #> 4878  2438.5 0.6002015 0.6002015 1.294538e-02 1.294538e-02 0.386853107 #> 4879  2439.0 0.6002074 0.6002074 1.291743e-02 1.291743e-02 0.386875198 #> 4880  2439.5 0.6002136 0.6002136 1.288954e-02 1.288954e-02 0.386896813 #> 4881  2440.0 0.6002203 0.6002203 1.286171e-02 1.286171e-02 0.386917953 #> 4882  2440.5 0.6002274 0.6002274 1.283395e-02 1.283395e-02 0.386938619 #> 4883  2441.0 0.6002349 0.6002349 1.280625e-02 1.280625e-02 0.386958813 #> 4884  2441.5 0.6002429 0.6002429 1.277861e-02 1.277861e-02 0.386978536 #> 4885  2442.0 0.6002512 0.6002512 1.275103e-02 1.275103e-02 0.386997788 #> 4886  2442.5 0.6002599 0.6002599 1.272351e-02 1.272351e-02 0.387016573 #> 4887  2443.0 0.6002691 0.6002691 1.269606e-02 1.269606e-02 0.387034889 #> 4888  2443.5 0.6002786 0.6002786 1.266867e-02 1.266867e-02 0.387052740 #> 4889  2444.0 0.6002885 0.6002885 1.264134e-02 1.264134e-02 0.387070126 #> 4890  2444.5 0.6002989 0.6002989 1.261407e-02 1.261407e-02 0.387087048 #> 4891  2445.0 0.6003096 0.6003096 1.258686e-02 1.258686e-02 0.387103508 #> 4892  2445.5 0.6003208 0.6003208 1.255972e-02 1.255972e-02 0.387119507 #> 4893  2446.0 0.6003323 0.6003323 1.253264e-02 1.253264e-02 0.387135047 #> 4894  2446.5 0.6003443 0.6003443 1.250562e-02 1.250562e-02 0.387150127 #> 4895  2447.0 0.6003566 0.6003566 1.247866e-02 1.247866e-02 0.387164751 #> 4896  2447.5 0.6003693 0.6003693 1.245177e-02 1.245177e-02 0.387178919 #> 4897  2448.0 0.6003824 0.6003824 1.242494e-02 1.242494e-02 0.387192632 #> 4898  2448.5 0.6003959 0.6003959 1.239817e-02 1.239817e-02 0.387205891 #> 4899  2449.0 0.6004098 0.6004098 1.237146e-02 1.237146e-02 0.387218698 #> 4900  2449.5 0.6004241 0.6004241 1.234481e-02 1.234481e-02 0.387231055 #> 4901  2450.0 0.6004388 0.6004388 1.231822e-02 1.231822e-02 0.387242961 #> 4902  2450.5 0.6004539 0.6004539 1.229170e-02 1.229170e-02 0.387254420 #> 4903  2451.0 0.6004693 0.6004693 1.226524e-02 1.226524e-02 0.387265431 #> 4904  2451.5 0.6004852 0.6004852 1.223884e-02 1.223884e-02 0.387275996 #> 4905  2452.0 0.6005014 0.6005014 1.221250e-02 1.221250e-02 0.387286116 #> 4906  2452.5 0.6005180 0.6005180 1.218623e-02 1.218623e-02 0.387295794 #> 4907  2453.0 0.6005350 0.6005350 1.216001e-02 1.216001e-02 0.387305029 #> 4908  2453.5 0.6005523 0.6005523 1.213386e-02 1.213386e-02 0.387313823 #> 4909  2454.0 0.6005701 0.6005701 1.210777e-02 1.210777e-02 0.387322177 #> 4910  2454.5 0.6005882 0.6005882 1.208174e-02 1.208174e-02 0.387330093 #> 4911  2455.0 0.6006067 0.6006067 1.205577e-02 1.205577e-02 0.387337572 #> 4912  2455.5 0.6006255 0.6006255 1.202986e-02 1.202986e-02 0.387344615 #> 4913  2456.0 0.6006448 0.6006448 1.200401e-02 1.200401e-02 0.387351223 #> 4914  2456.5 0.6006644 0.6006644 1.197823e-02 1.197823e-02 0.387357398 #> 4915  2457.0 0.6006844 0.6006844 1.195251e-02 1.195251e-02 0.387363140 #> 4916  2457.5 0.6007047 0.6007047 1.192684e-02 1.192684e-02 0.387368452 #> 4917  2458.0 0.6007254 0.6007254 1.190124e-02 1.190124e-02 0.387373334 #> 4918  2458.5 0.6007465 0.6007465 1.187570e-02 1.187570e-02 0.387377787 #> 4919  2459.0 0.6007680 0.6007680 1.185022e-02 1.185022e-02 0.387381813 #> 4920  2459.5 0.6007898 0.6007898 1.182481e-02 1.182481e-02 0.387385413 #> 4921  2460.0 0.6008120 0.6008120 1.179945e-02 1.179945e-02 0.387388588 #> 4922  2460.5 0.6008345 0.6008345 1.177416e-02 1.177416e-02 0.387391340 #> 4923  2461.0 0.6008574 0.6008574 1.174892e-02 1.174892e-02 0.387393669 #> 4924  2461.5 0.6008807 0.6008807 1.172375e-02 1.172375e-02 0.387395577 #> 4925  2462.0 0.6009043 0.6009043 1.169863e-02 1.169863e-02 0.387397066 #> 4926  2462.5 0.6009283 0.6009283 1.167358e-02 1.167358e-02 0.387398135 #> 4927  2463.0 0.6009526 0.6009526 1.164859e-02 1.164859e-02 0.387398787 #> 4928  2463.5 0.6009773 0.6009773 1.162366e-02 1.162366e-02 0.387399023 #> 4929  2464.0 0.6010024 0.6010024 1.159879e-02 1.159879e-02 0.387398844 #> 4930  2464.5 0.6010278 0.6010278 1.157398e-02 1.157398e-02 0.387398251 #> 4931  2465.0 0.6010535 0.6010535 1.154923e-02 1.154923e-02 0.387397245 #> 4932  2465.5 0.6010796 0.6010796 1.152454e-02 1.152454e-02 0.387395828 #> 4933  2466.0 0.6011061 0.6011061 1.149991e-02 1.149991e-02 0.387394001 #> 4934  2466.5 0.6011329 0.6011329 1.147534e-02 1.147534e-02 0.387391765 #> 4935  2467.0 0.6011600 0.6011600 1.145084e-02 1.145084e-02 0.387389121 #> 4936  2467.5 0.6011875 0.6011875 1.142639e-02 1.142639e-02 0.387386071 #> 4937  2468.0 0.6012154 0.6012154 1.140200e-02 1.140200e-02 0.387382615 #> 4938  2468.5 0.6012436 0.6012436 1.137767e-02 1.137767e-02 0.387378756 #> 4939  2469.0 0.6012721 0.6012721 1.135340e-02 1.135340e-02 0.387374493 #> 4940  2469.5 0.6013010 0.6013010 1.132920e-02 1.132920e-02 0.387369828 #> 4941  2470.0 0.6013302 0.6013302 1.130505e-02 1.130505e-02 0.387364763 #> 4942  2470.5 0.6013597 0.6013597 1.128096e-02 1.128096e-02 0.387359299 #> 4943  2471.0 0.6013896 0.6013896 1.125693e-02 1.125693e-02 0.387353436 #> 4944  2471.5 0.6014199 0.6014199 1.123296e-02 1.123296e-02 0.387347177 #> 4945  2472.0 0.6014504 0.6014504 1.120905e-02 1.120905e-02 0.387340522 #> 4946  2472.5 0.6014813 0.6014813 1.118520e-02 1.118520e-02 0.387333472 #> 4947  2473.0 0.6015126 0.6015126 1.116141e-02 1.116141e-02 0.387326029 #> 4948  2473.5 0.6015441 0.6015441 1.113768e-02 1.113768e-02 0.387318193 #> 4949  2474.0 0.6015760 0.6015760 1.111401e-02 1.111401e-02 0.387309967 #> 4950  2474.5 0.6016083 0.6016083 1.109039e-02 1.109039e-02 0.387301350 #> 4951  2475.0 0.6016408 0.6016408 1.106684e-02 1.106684e-02 0.387292346 #> 4952  2475.5 0.6016737 0.6016737 1.104335e-02 1.104335e-02 0.387282953 #> 4953  2476.0 0.6017069 0.6017069 1.101991e-02 1.101991e-02 0.387273175 #> 4954  2476.5 0.6017405 0.6017405 1.099653e-02 1.099653e-02 0.387263011 #> 4955  2477.0 0.6017743 0.6017743 1.097322e-02 1.097322e-02 0.387252463 #> 4956  2477.5 0.6018085 0.6018085 1.094996e-02 1.094996e-02 0.387241533 #> 4957  2478.0 0.6018430 0.6018430 1.092676e-02 1.092676e-02 0.387230220 #> 4958  2478.5 0.6018779 0.6018779 1.090361e-02 1.090361e-02 0.387218528 #> 4959  2479.0 0.6019130 0.6019130 1.088053e-02 1.088053e-02 0.387206456 #> 4960  2479.5 0.6019485 0.6019485 1.085751e-02 1.085751e-02 0.387194006 #> 4961  2480.0 0.6019843 0.6019843 1.083454e-02 1.083454e-02 0.387181179 #> 4962  2480.5 0.6020204 0.6020204 1.081163e-02 1.081163e-02 0.387167977 #> 4963  2481.0 0.6020568 0.6020568 1.078878e-02 1.078878e-02 0.387154400 #> 4964  2481.5 0.6020936 0.6020936 1.076599e-02 1.076599e-02 0.387140449 #> 4965  2482.0 0.6021306 0.6021306 1.074326e-02 1.074326e-02 0.387126126 #> 4966  2482.5 0.6021680 0.6021680 1.072058e-02 1.072058e-02 0.387111432 #> 4967  2483.0 0.6022057 0.6022057 1.069796e-02 1.069796e-02 0.387096367 #> 4968  2483.5 0.6022437 0.6022437 1.067540e-02 1.067540e-02 0.387080934 #> 4969  2484.0 0.6022820 0.6022820 1.065290e-02 1.065290e-02 0.387065134 #> 4970  2484.5 0.6023206 0.6023206 1.063045e-02 1.063045e-02 0.387048966 #> 4971  2485.0 0.6023595 0.6023595 1.060807e-02 1.060807e-02 0.387032433 #> 4972  2485.5 0.6023987 0.6023987 1.058574e-02 1.058574e-02 0.387015536 #> 4973  2486.0 0.6024383 0.6024383 1.056347e-02 1.056347e-02 0.386998276 #> 4974  2486.5 0.6024781 0.6024781 1.054125e-02 1.054125e-02 0.386980654 #> 4975  2487.0 0.6025182 0.6025182 1.051909e-02 1.051909e-02 0.386962671 #> 4976  2487.5 0.6025587 0.6025587 1.049699e-02 1.049699e-02 0.386944328 #> 4977  2488.0 0.6025994 0.6025994 1.047495e-02 1.047495e-02 0.386925627 #> 4978  2488.5 0.6026405 0.6026405 1.045296e-02 1.045296e-02 0.386906568 #> 4979  2489.0 0.6026818 0.6026818 1.043103e-02 1.043103e-02 0.386887153 #> 4980  2489.5 0.6027235 0.6027235 1.040916e-02 1.040916e-02 0.386867382 #> 4981  2490.0 0.6027654 0.6027654 1.038734e-02 1.038734e-02 0.386847258 #> 4982  2490.5 0.6028076 0.6028076 1.036558e-02 1.036558e-02 0.386826780 #> 4983  2491.0 0.6028502 0.6028502 1.034388e-02 1.034388e-02 0.386805951 #> 4984  2491.5 0.6028930 0.6028930 1.032223e-02 1.032223e-02 0.386784771 #> 4985  2492.0 0.6029361 0.6029361 1.030064e-02 1.030064e-02 0.386763241 #> 4986  2492.5 0.6029795 0.6029795 1.027911e-02 1.027911e-02 0.386741363 #> 4987  2493.0 0.6030232 0.6030232 1.025763e-02 1.025763e-02 0.386719137 #> 4988  2493.5 0.6030672 0.6030672 1.023621e-02 1.023621e-02 0.386696565 #> 4989  2494.0 0.6031115 0.6031115 1.021485e-02 1.021485e-02 0.386673648 #> 4990  2494.5 0.6031561 0.6031561 1.019354e-02 1.019354e-02 0.386650386 #> 4991  2495.0 0.6032009 0.6032009 1.017228e-02 1.017228e-02 0.386626782 #> 4992  2495.5 0.6032461 0.6032461 1.015108e-02 1.015108e-02 0.386602836 #> 4993  2496.0 0.6032915 0.6032915 1.012994e-02 1.012994e-02 0.386578549 #> 4994  2496.5 0.6033372 0.6033372 1.010886e-02 1.010886e-02 0.386553923 #> 4995  2497.0 0.6033832 0.6033832 1.008782e-02 1.008782e-02 0.386528958 #> 4996  2497.5 0.6034295 0.6034295 1.006685e-02 1.006685e-02 0.386503655 #> 4997  2498.0 0.6034761 0.6034761 1.004593e-02 1.004593e-02 0.386478016 #> 4998  2498.5 0.6035229 0.6035229 1.002506e-02 1.002506e-02 0.386452041 #> 4999  2499.0 0.6035700 0.6035700 1.000425e-02 1.000425e-02 0.386425733 #> 5000  2499.5 0.6036174 0.6036174 9.983495e-03 9.983495e-03 0.386399091 #> 5001  2500.0 0.6036651 0.6036651 9.962795e-03 9.962795e-03 0.386372117 #> 5002  2500.5 0.6037130 0.6037130 9.942149e-03 9.942149e-03 0.386344813 #> 5003  2501.0 0.6037613 0.6037613 9.921558e-03 9.921558e-03 0.386317178 #> 5004  2501.5 0.6038098 0.6038098 9.901021e-03 9.901021e-03 0.386289214 #> 5005  2502.0 0.6038585 0.6038585 9.880539e-03 9.880539e-03 0.386260923 #> 5006  2502.5 0.6039076 0.6039076 9.860112e-03 9.860112e-03 0.386232305 #> 5007  2503.0 0.6039569 0.6039569 9.839739e-03 9.839739e-03 0.386203361 #> 5008  2503.5 0.6040065 0.6040065 9.819420e-03 9.819420e-03 0.386174093 #> 5009  2504.0 0.6040563 0.6040563 9.799155e-03 9.799155e-03 0.386144502 #> 5010  2504.5 0.6041065 0.6041065 9.778944e-03 9.778944e-03 0.386114588 #> 5011  2505.0 0.6041569 0.6041569 9.758787e-03 9.758787e-03 0.386084352 #> 5012  2505.5 0.6042075 0.6042075 9.738684e-03 9.738684e-03 0.386053797 #> 5013  2506.0 0.6042584 0.6042584 9.718635e-03 9.718635e-03 0.386022922 #> 5014  2506.5 0.6043096 0.6043096 9.698640e-03 9.698640e-03 0.385991729 #> 5015  2507.0 0.6043611 0.6043611 9.678698e-03 9.678698e-03 0.385960218 #> 5016  2507.5 0.6044128 0.6044128 9.658809e-03 9.658809e-03 0.385928392 #> 5017  2508.0 0.6044648 0.6044648 9.638974e-03 9.638974e-03 0.385896251 #> 5018  2508.5 0.6045170 0.6045170 9.619192e-03 9.619192e-03 0.385863795 #> 5019  2509.0 0.6045695 0.6045695 9.599463e-03 9.599463e-03 0.385831027 #> 5020  2509.5 0.6046223 0.6046223 9.579787e-03 9.579787e-03 0.385797947 #> 5021  2510.0 0.6046753 0.6046753 9.560165e-03 9.560165e-03 0.385764555 #> 5022  2510.5 0.6047286 0.6047286 9.540595e-03 9.540595e-03 0.385730855 #> 5023  2511.0 0.6047821 0.6047821 9.521078e-03 9.521078e-03 0.385696845 #> 5024  2511.5 0.6048359 0.6048359 9.501613e-03 9.501613e-03 0.385662527 #> 5025  2512.0 0.6048899 0.6048899 9.482201e-03 9.482201e-03 0.385627903 #> 5026  2512.5 0.6049442 0.6049442 9.462842e-03 9.462842e-03 0.385592973 #> 5027  2513.0 0.6049987 0.6049987 9.443535e-03 9.443535e-03 0.385557738 #> 5028  2513.5 0.6050535 0.6050535 9.424280e-03 9.424280e-03 0.385522200 #> 5029  2514.0 0.6051086 0.6051086 9.405078e-03 9.405078e-03 0.385486359 #> 5030  2514.5 0.6051639 0.6051639 9.385927e-03 9.385927e-03 0.385450217 #> 5031  2515.0 0.6052194 0.6052194 9.366829e-03 9.366829e-03 0.385413774 #> 5032  2515.5 0.6052752 0.6052752 9.347782e-03 9.347782e-03 0.385377031 #> 5033  2516.0 0.6053312 0.6053312 9.328787e-03 9.328787e-03 0.385339990 #> 5034  2516.5 0.6053875 0.6053875 9.309844e-03 9.309844e-03 0.385302651 #> 5035  2517.0 0.6054440 0.6054440 9.290952e-03 9.290952e-03 0.385265016 #> 5036  2517.5 0.6055008 0.6055008 9.272112e-03 9.272112e-03 0.385227085 #> 5037  2518.0 0.6055578 0.6055578 9.253323e-03 9.253323e-03 0.385188859 #> 5038  2518.5 0.6056151 0.6056151 9.234586e-03 9.234586e-03 0.385150340 #> 5039  2519.0 0.6056726 0.6056726 9.215899e-03 9.215899e-03 0.385111529 #> 5040  2519.5 0.6057303 0.6057303 9.197264e-03 9.197264e-03 0.385072426 #> 5041  2520.0 0.6057883 0.6057883 9.178680e-03 9.178680e-03 0.385033032 #> 5042  2520.5 0.6058465 0.6058465 9.160146e-03 9.160146e-03 0.384993349 #> 5043  2521.0 0.6059050 0.6059050 9.141664e-03 9.141664e-03 0.384953377 #> 5044  2521.5 0.6059637 0.6059637 9.123231e-03 9.123231e-03 0.384913118 #> 5045  2522.0 0.6060226 0.6060226 9.104850e-03 9.104850e-03 0.384872572 #> 5046  2522.5 0.6060817 0.6060817 9.086519e-03 9.086519e-03 0.384831741 #> 5047  2523.0 0.6061411 0.6061411 9.068238e-03 9.068238e-03 0.384790625 #> 5048  2523.5 0.6062008 0.6062008 9.050008e-03 9.050008e-03 0.384749225 #> 5049  2524.0 0.6062606 0.6062606 9.031827e-03 9.031827e-03 0.384707543 #> 5050  2524.5 0.6063207 0.6063207 9.013697e-03 9.013697e-03 0.384665579 #> 5051  2525.0 0.6063810 0.6063810 8.995616e-03 8.995616e-03 0.384623334 #> 5052  2525.5 0.6064416 0.6064416 8.977586e-03 8.977586e-03 0.384580810 #> 5053  2526.0 0.6065024 0.6065024 8.959605e-03 8.959605e-03 0.384538006 #> 5054  2526.5 0.6065634 0.6065634 8.941674e-03 8.941674e-03 0.384494925 #> 5055  2527.0 0.6066246 0.6066246 8.923792e-03 8.923792e-03 0.384451568 #> 5056  2527.5 0.6066861 0.6066861 8.905960e-03 8.905960e-03 0.384407934 #> 5057  2528.0 0.6067478 0.6067478 8.888177e-03 8.888177e-03 0.384364025 #> 5058  2528.5 0.6068097 0.6068097 8.870443e-03 8.870443e-03 0.384319843 #> 5059  2529.0 0.6068719 0.6068719 8.852759e-03 8.852759e-03 0.384275387 #> 5060  2529.5 0.6069342 0.6069342 8.835123e-03 8.835123e-03 0.384230660 #> 5061  2530.0 0.6069968 0.6069968 8.817536e-03 8.817536e-03 0.384185661 #> 5062  2530.5 0.6070596 0.6070596 8.799998e-03 8.799998e-03 0.384140392 #> 5063  2531.0 0.6071226 0.6071226 8.782509e-03 8.782509e-03 0.384094854 #> 5064  2531.5 0.6071859 0.6071859 8.765068e-03 8.765068e-03 0.384049048 #> 5065  2532.0 0.6072493 0.6072493 8.747676e-03 8.747676e-03 0.384002974 #> 5066  2532.5 0.6073130 0.6073130 8.730333e-03 8.730333e-03 0.383956635 #> 5067  2533.0 0.6073769 0.6073769 8.713037e-03 8.713037e-03 0.383910029 #> 5068  2533.5 0.6074411 0.6074411 8.695790e-03 8.695790e-03 0.383863159 #> 5069  2534.0 0.6075054 0.6075054 8.678591e-03 8.678591e-03 0.383816026 #> 5070  2534.5 0.6075699 0.6075699 8.661439e-03 8.661439e-03 0.383768630 #> 5071  2535.0 0.6076347 0.6076347 8.644336e-03 8.644336e-03 0.383720972 #> 5072  2535.5 0.6076997 0.6076997 8.627280e-03 8.627280e-03 0.383673054 #> 5073  2536.0 0.6077649 0.6077649 8.610272e-03 8.610272e-03 0.383624876 #> 5074  2536.5 0.6078302 0.6078302 8.593312e-03 8.593312e-03 0.383576438 #> 5075  2537.0 0.6078959 0.6078959 8.576399e-03 8.576399e-03 0.383527743 #> 5076  2537.5 0.6079617 0.6079617 8.559533e-03 8.559533e-03 0.383478791 #> 5077  2538.0 0.6080277 0.6080277 8.542715e-03 8.542715e-03 0.383429582 #> 5078  2538.5 0.6080939 0.6080939 8.525944e-03 8.525944e-03 0.383380119 #> 5079  2539.0 0.6081604 0.6081604 8.509220e-03 8.509220e-03 0.383330401 #> 5080  2539.5 0.6082270 0.6082270 8.492543e-03 8.492543e-03 0.383280429 #> 5081  2540.0 0.6082939 0.6082939 8.475912e-03 8.475912e-03 0.383230205 #> 5082  2540.5 0.6083609 0.6083609 8.459328e-03 8.459328e-03 0.383179730 #> 5083  2541.0 0.6084282 0.6084282 8.442791e-03 8.442791e-03 0.383129004 #> 5084  2541.5 0.6084957 0.6084957 8.426301e-03 8.426301e-03 0.383078028 #> 5085  2542.0 0.6085633 0.6085633 8.409857e-03 8.409857e-03 0.383026803 #> 5086  2542.5 0.6086312 0.6086312 8.393459e-03 8.393459e-03 0.382975330 #> 5087  2543.0 0.6086993 0.6086993 8.377107e-03 8.377107e-03 0.382923610 #> 5088  2543.5 0.6087676 0.6087676 8.360802e-03 8.360802e-03 0.382871643 #> 5089  2544.0 0.6088360 0.6088360 8.344542e-03 8.344542e-03 0.382819432 #> 5090  2544.5 0.6089047 0.6089047 8.328329e-03 8.328329e-03 0.382766975 #> 5091  2545.0 0.6089736 0.6089736 8.312161e-03 8.312161e-03 0.382714276 #> 5092  2545.5 0.6090426 0.6090426 8.296039e-03 8.296039e-03 0.382661334 #> 5093  2546.0 0.6091119 0.6091119 8.279962e-03 8.279962e-03 0.382608149 #> 5094  2546.5 0.6091813 0.6091813 8.263931e-03 8.263931e-03 0.382554724 #> 5095  2547.0 0.6092510 0.6092510 8.247946e-03 8.247946e-03 0.382501059 #> 5096  2547.5 0.6093208 0.6093208 8.232005e-03 8.232005e-03 0.382447155 #> 5097  2548.0 0.6093909 0.6093909 8.216110e-03 8.216110e-03 0.382393013 #> 5098  2548.5 0.6094611 0.6094611 8.200260e-03 8.200260e-03 0.382338633 #> 5099  2549.0 0.6095315 0.6095315 8.184455e-03 8.184455e-03 0.382284016 #> 5100  2549.5 0.6096021 0.6096021 8.168694e-03 8.168694e-03 0.382229164 #> 5101  2550.0 0.6096729 0.6096729 8.152979e-03 8.152979e-03 0.382174077 #> 5102  2550.5 0.6097439 0.6097439 8.137308e-03 8.137308e-03 0.382118756 #> 5103  2551.0 0.6098151 0.6098151 8.121682e-03 8.121682e-03 0.382063202 #> 5104  2551.5 0.6098865 0.6098865 8.106100e-03 8.106100e-03 0.382007416 #> 5105  2552.0 0.6099580 0.6099580 8.090562e-03 8.090562e-03 0.381951399 #> 5106  2552.5 0.6100298 0.6100298 8.075069e-03 8.075069e-03 0.381895150 #> 5107  2553.0 0.6101017 0.6101017 8.059620e-03 8.059620e-03 0.381838673 #> 5108  2553.5 0.6101738 0.6101738 8.044215e-03 8.044215e-03 0.381781966 #> 5109  2554.0 0.6102461 0.6102461 8.028853e-03 8.028853e-03 0.381725031 #> 5110  2554.5 0.6103186 0.6103186 8.013536e-03 8.013536e-03 0.381667870 #> 5111  2555.0 0.6103913 0.6103913 7.998263e-03 7.998263e-03 0.381610482 #> 5112  2555.5 0.6104641 0.6104641 7.983033e-03 7.983033e-03 0.381552868 #> 5113  2556.0 0.6105371 0.6105371 7.967846e-03 7.967846e-03 0.381495030 #> 5114  2556.5 0.6106103 0.6106103 7.952703e-03 7.952703e-03 0.381436968 #> 5115  2557.0 0.6106837 0.6106837 7.937603e-03 7.937603e-03 0.381378684 #> 5116  2557.5 0.6107573 0.6107573 7.922547e-03 7.922547e-03 0.381320177 #> 5117  2558.0 0.6108310 0.6108310 7.907534e-03 7.907534e-03 0.381261449 #> 5118  2558.5 0.6109049 0.6109049 7.892563e-03 7.892563e-03 0.381202500 #> 5119  2559.0 0.6109790 0.6109790 7.877636e-03 7.877636e-03 0.381143332 #> 5120  2559.5 0.6110533 0.6110533 7.862751e-03 7.862751e-03 0.381083945 #> 5121  2560.0 0.6111278 0.6111278 7.847909e-03 7.847909e-03 0.381024340 #> 5122  2560.5 0.6112024 0.6112024 7.833110e-03 7.833110e-03 0.380964518 #> 5123  2561.0 0.6112772 0.6112772 7.818353e-03 7.818353e-03 0.380904479 #> 5124  2561.5 0.6113521 0.6113521 7.803639e-03 7.803639e-03 0.380844225 #> 5125  2562.0 0.6114273 0.6114273 7.788967e-03 7.788967e-03 0.380783757 #> 5126  2562.5 0.6115026 0.6115026 7.774337e-03 7.774337e-03 0.380723074 #> 5127  2563.0 0.6115781 0.6115781 7.759749e-03 7.759749e-03 0.380662178 #> 5128  2563.5 0.6116537 0.6116537 7.745204e-03 7.745204e-03 0.380601071 #> 5129  2564.0 0.6117295 0.6117295 7.730700e-03 7.730700e-03 0.380539751 #> 5130  2564.5 0.6118055 0.6118055 7.716238e-03 7.716238e-03 0.380478221 #> 5131  2565.0 0.6118817 0.6118817 7.701817e-03 7.701817e-03 0.380416481 #> 5132  2565.5 0.6119580 0.6119580 7.687439e-03 7.687439e-03 0.380354532 #> 5133  2566.0 0.6120345 0.6120345 7.673102e-03 7.673102e-03 0.380292375 #> 5134  2566.5 0.6121112 0.6121112 7.658806e-03 7.658806e-03 0.380230010 #> 5135  2567.0 0.6121880 0.6121880 7.644551e-03 7.644551e-03 0.380167439 #> 5136  2567.5 0.6122650 0.6122650 7.630338e-03 7.630338e-03 0.380104661 #> 5137  2568.0 0.6123422 0.6123422 7.616166e-03 7.616166e-03 0.380041679 #> 5138  2568.5 0.6124195 0.6124195 7.602034e-03 7.602034e-03 0.379978492 #> 5139  2569.0 0.6124970 0.6124970 7.587944e-03 7.587944e-03 0.379915102 #> 5140  2569.5 0.6125746 0.6125746 7.573895e-03 7.573895e-03 0.379851509 #> 5141  2570.0 0.6126524 0.6126524 7.559886e-03 7.559886e-03 0.379787714 #> 5142  2570.5 0.6127304 0.6127304 7.545918e-03 7.545918e-03 0.379723717 #> 5143  2571.0 0.6128085 0.6128085 7.531990e-03 7.531990e-03 0.379659521 #> 5144  2571.5 0.6128868 0.6128868 7.518103e-03 7.518103e-03 0.379595124 #> 5145  2572.0 0.6129652 0.6129652 7.504256e-03 7.504256e-03 0.379530529 #> 5146  2572.5 0.6130438 0.6130438 7.490449e-03 7.490449e-03 0.379465736 #> 5147  2573.0 0.6131226 0.6131226 7.476682e-03 7.476682e-03 0.379400745 #> 5148  2573.5 0.6132015 0.6132015 7.462956e-03 7.462956e-03 0.379335558 #> 5149  2574.0 0.6132806 0.6132806 7.449269e-03 7.449269e-03 0.379270175 #> 5150  2574.5 0.6133598 0.6133598 7.435622e-03 7.435622e-03 0.379204597 #> 5151  2575.0 0.6134392 0.6134392 7.422015e-03 7.422015e-03 0.379138825 #> 5152  2575.5 0.6135187 0.6135187 7.408447e-03 7.408447e-03 0.379072859 #> 5153  2576.0 0.6135984 0.6135984 7.394919e-03 7.394919e-03 0.379006701 #> 5154  2576.5 0.6136782 0.6136782 7.381430e-03 7.381430e-03 0.378940350 #> 5155  2577.0 0.6137582 0.6137582 7.367981e-03 7.367981e-03 0.378873808 #> 5156  2577.5 0.6138384 0.6138384 7.354571e-03 7.354571e-03 0.378807076 #> 5157  2578.0 0.6139186 0.6139186 7.341200e-03 7.341200e-03 0.378740154 #> 5158  2578.5 0.6139991 0.6139991 7.327868e-03 7.327868e-03 0.378673043 #> 5159  2579.0 0.6140797 0.6140797 7.314575e-03 7.314575e-03 0.378605743 #> 5160  2579.5 0.6141604 0.6141604 7.301321e-03 7.301321e-03 0.378538256 #> 5161  2580.0 0.6142413 0.6142413 7.288105e-03 7.288105e-03 0.378470583 #> 5162  2580.5 0.6143223 0.6143223 7.274929e-03 7.274929e-03 0.378402723 #> 5163  2581.0 0.6144035 0.6144035 7.261790e-03 7.261790e-03 0.378334678 #> 5164  2581.5 0.6144849 0.6144849 7.248691e-03 7.248691e-03 0.378266448 #> 5165  2582.0 0.6145663 0.6145663 7.235629e-03 7.235629e-03 0.378198035 #> 5166  2582.5 0.6146480 0.6146480 7.222606e-03 7.222606e-03 0.378129438 #> 5167  2583.0 0.6147297 0.6147297 7.209621e-03 7.209621e-03 0.378060659 #> 5168  2583.5 0.6148116 0.6148116 7.196674e-03 7.196674e-03 0.377991698 #> 5169  2584.0 0.6148937 0.6148937 7.183765e-03 7.183765e-03 0.377922557 #> 5170  2584.5 0.6149759 0.6149759 7.170894e-03 7.170894e-03 0.377853235 #> 5171  2585.0 0.6150582 0.6150582 7.158061e-03 7.158061e-03 0.377783733 #> 5172  2585.5 0.6151407 0.6151407 7.145265e-03 7.145265e-03 0.377714053 #> 5173  2586.0 0.6152233 0.6152233 7.132507e-03 7.132507e-03 0.377644195 #> 5174  2586.5 0.6153061 0.6153061 7.119787e-03 7.119787e-03 0.377574160 #> 5175  2587.0 0.6153889 0.6153889 7.107103e-03 7.107103e-03 0.377503947 #> 5176  2587.5 0.6154720 0.6154720 7.094458e-03 7.094458e-03 0.377433559 #> 5177  2588.0 0.6155552 0.6155552 7.081849e-03 7.081849e-03 0.377362996 #> 5178  2588.5 0.6156385 0.6156385 7.069278e-03 7.069278e-03 0.377292258 #> 5179  2589.0 0.6157219 0.6157219 7.056743e-03 7.056743e-03 0.377221346 #> 5180  2589.5 0.6158055 0.6158055 7.044246e-03 7.044246e-03 0.377150262 #> 5181  2590.0 0.6158892 0.6158892 7.031785e-03 7.031785e-03 0.377079005 #> 5182  2590.5 0.6159731 0.6159731 7.019361e-03 7.019361e-03 0.377007576 #> 5183  2591.0 0.6160571 0.6160571 7.006974e-03 7.006974e-03 0.376935976 #> 5184  2591.5 0.6161412 0.6161412 6.994623e-03 6.994623e-03 0.376864206 #> 5185  2592.0 0.6162254 0.6162254 6.982309e-03 6.982309e-03 0.376792266 #> 5186  2592.5 0.6163098 0.6163098 6.970031e-03 6.970031e-03 0.376720157 #> 5187  2593.0 0.6163943 0.6163943 6.957789e-03 6.957789e-03 0.376647881 #> 5188  2593.5 0.6164790 0.6164790 6.945584e-03 6.945584e-03 0.376575436 #> 5189  2594.0 0.6165638 0.6165638 6.933414e-03 6.933414e-03 0.376502825 #> 5190  2594.5 0.6166487 0.6166487 6.921281e-03 6.921281e-03 0.376430047 #> 5191  2595.0 0.6167337 0.6167337 6.909184e-03 6.909184e-03 0.376357104 #> 5192  2595.5 0.6168189 0.6168189 6.897122e-03 6.897122e-03 0.376283997 #> 5193  2596.0 0.6169042 0.6169042 6.885096e-03 6.885096e-03 0.376210725 #> 5194  2596.5 0.6169896 0.6169896 6.873106e-03 6.873106e-03 0.376137289 #> 5195  2597.0 0.6170752 0.6170752 6.861151e-03 6.861151e-03 0.376063691 #> 5196  2597.5 0.6171608 0.6171608 6.849232e-03 6.849232e-03 0.375989931 #> 5197  2598.0 0.6172466 0.6172466 6.837348e-03 6.837348e-03 0.375916009 #> 5198  2598.5 0.6173326 0.6173326 6.825500e-03 6.825500e-03 0.375841927 #> 5199  2599.0 0.6174186 0.6174186 6.813686e-03 6.813686e-03 0.375767684 #> 5200  2599.5 0.6175048 0.6175048 6.801908e-03 6.801908e-03 0.375693282 #> 5201  2600.0 0.6175911 0.6175911 6.790164e-03 6.790164e-03 0.375618721 #> 5202  2600.5 0.6176775 0.6176775 6.778456e-03 6.778456e-03 0.375544002 #> 5203  2601.0 0.6177641 0.6177641 6.766782e-03 6.766782e-03 0.375469126 #> 5204  2601.5 0.6178508 0.6178508 6.755144e-03 6.755144e-03 0.375394093 #> 5205  2602.0 0.6179376 0.6179376 6.743539e-03 6.743539e-03 0.375318904 #> 5206  2602.5 0.6180245 0.6180245 6.731970e-03 6.731970e-03 0.375243559 #> 5207  2603.0 0.6181115 0.6181115 6.720434e-03 6.720434e-03 0.375168059 #> 5208  2603.5 0.6181987 0.6181987 6.708934e-03 6.708934e-03 0.375092405 #> 5209  2604.0 0.6182859 0.6182859 6.697467e-03 6.697467e-03 0.375016598 #> 5210  2604.5 0.6183733 0.6183733 6.686035e-03 6.686035e-03 0.374940638 #> 5211  2605.0 0.6184608 0.6184608 6.674636e-03 6.674636e-03 0.374864526 #> 5212  2605.5 0.6185485 0.6185485 6.663272e-03 6.663272e-03 0.374788262 #> 5213  2606.0 0.6186362 0.6186362 6.651942e-03 6.651942e-03 0.374711847 #> 5214  2606.5 0.6187241 0.6187241 6.640645e-03 6.640645e-03 0.374635281 #> 5215  2607.0 0.6188121 0.6188121 6.629383e-03 6.629383e-03 0.374558566 #> 5216  2607.5 0.6189001 0.6189001 6.618154e-03 6.618154e-03 0.374481702 #> 5217  2608.0 0.6189884 0.6189884 6.606958e-03 6.606958e-03 0.374404690 #> 5218  2608.5 0.6190767 0.6190767 6.595796e-03 6.595796e-03 0.374327530 #> 5219  2609.0 0.6191651 0.6191651 6.584668e-03 6.584668e-03 0.374250223 #> 5220  2609.5 0.6192537 0.6192537 6.573572e-03 6.573572e-03 0.374172769 #> 5221  2610.0 0.6193423 0.6193423 6.562510e-03 6.562510e-03 0.374095169 #> 5222  2610.5 0.6194311 0.6194311 6.551482e-03 6.551482e-03 0.374017424 #> 5223  2611.0 0.6195200 0.6195200 6.540486e-03 6.540486e-03 0.373939535 #> 5224  2611.5 0.6196090 0.6196090 6.529523e-03 6.529523e-03 0.373861501 #> 5225  2612.0 0.6196981 0.6196981 6.518593e-03 6.518593e-03 0.373783324 #> 5226  2612.5 0.6197873 0.6197873 6.507696e-03 6.507696e-03 0.373705004 #> 5227  2613.0 0.6198766 0.6198766 6.496832e-03 6.496832e-03 0.373626542 #> 5228  2613.5 0.6199661 0.6199661 6.486000e-03 6.486000e-03 0.373547939 #> 5229  2614.0 0.6200556 0.6200556 6.475200e-03 6.475200e-03 0.373469195 #> 5230  2614.5 0.6201453 0.6201453 6.464434e-03 6.464434e-03 0.373390310 #> 5231  2615.0 0.6202350 0.6202350 6.453699e-03 6.453699e-03 0.373311286 #> 5232  2615.5 0.6203249 0.6203249 6.442997e-03 6.442997e-03 0.373232122 #> 5233  2616.0 0.6204149 0.6204149 6.432327e-03 6.432327e-03 0.373152820 #> 5234  2616.5 0.6205049 0.6205049 6.421689e-03 6.421689e-03 0.373073381 #> 5235  2617.0 0.6205951 0.6205951 6.411084e-03 6.411084e-03 0.372993804 #> 5236  2617.5 0.6206854 0.6206854 6.400510e-03 6.400510e-03 0.372914090 #> 5237  2618.0 0.6207758 0.6207758 6.389968e-03 6.389968e-03 0.372834240 #> 5238  2618.5 0.6208663 0.6208663 6.379458e-03 6.379458e-03 0.372754255 #> 5239  2619.0 0.6209569 0.6209569 6.368979e-03 6.368979e-03 0.372674135 #> 5240  2619.5 0.6210476 0.6210476 6.358533e-03 6.358533e-03 0.372593880 #> 5241  2620.0 0.6211384 0.6211384 6.348117e-03 6.348117e-03 0.372513492 #> 5242  2620.5 0.6212293 0.6212293 6.337734e-03 6.337734e-03 0.372432971 #> 5243  2621.0 0.6213203 0.6213203 6.327381e-03 6.327381e-03 0.372352317 #> 5244  2621.5 0.6214114 0.6214114 6.317060e-03 6.317060e-03 0.372271531 #> 5245  2622.0 0.6215026 0.6215026 6.306770e-03 6.306770e-03 0.372190614 #> 5246  2622.5 0.6215939 0.6215939 6.296511e-03 6.296511e-03 0.372109567 #> 5247  2623.0 0.6216853 0.6216853 6.286283e-03 6.286283e-03 0.372028389 #> 5248  2623.5 0.6217768 0.6217768 6.276087e-03 6.276087e-03 0.371947081 #> 5249  2624.0 0.6218684 0.6218684 6.265921e-03 6.265921e-03 0.371865645 #> 5250  2624.5 0.6219601 0.6219601 6.255786e-03 6.255786e-03 0.371784080 #> 5251  2625.0 0.6220519 0.6220519 6.245681e-03 6.245681e-03 0.371702387 #> 5252  2625.5 0.6221438 0.6221438 6.235607e-03 6.235607e-03 0.371620567 #> 5253  2626.0 0.6222358 0.6222358 6.225564e-03 6.225564e-03 0.371538620 #> 5254  2626.5 0.6223279 0.6223279 6.215551e-03 6.215551e-03 0.371456547 #> 5255  2627.0 0.6224201 0.6224201 6.205569e-03 6.205569e-03 0.371374349 #> 5256  2627.5 0.6225124 0.6225124 6.195617e-03 6.195617e-03 0.371292025 #> 5257  2628.0 0.6226047 0.6226047 6.185695e-03 6.185695e-03 0.371209578 #> 5258  2628.5 0.6226972 0.6226972 6.175804e-03 6.175804e-03 0.371127006 #> 5259  2629.0 0.6227897 0.6227897 6.165942e-03 6.165942e-03 0.371044311 #> 5260  2629.5 0.6228824 0.6228824 6.156111e-03 6.156111e-03 0.370961493 #> 5261  2630.0 0.6229751 0.6229751 6.146309e-03 6.146309e-03 0.370878553 #> 5262  2630.5 0.6230680 0.6230680 6.136537e-03 6.136537e-03 0.370795492 #> 5263  2631.0 0.6231609 0.6231609 6.126795e-03 6.126795e-03 0.370712309 #> 5264  2631.5 0.6232539 0.6232539 6.117083e-03 6.117083e-03 0.370629006 #> 5265  2632.0 0.6233470 0.6233470 6.107400e-03 6.107400e-03 0.370545583 #> 5266  2632.5 0.6234402 0.6234402 6.097747e-03 6.097747e-03 0.370462041 #> 5267  2633.0 0.6235335 0.6235335 6.088123e-03 6.088123e-03 0.370378379 #> 5268  2633.5 0.6236269 0.6236269 6.078529e-03 6.078529e-03 0.370294600 #> 5269  2634.0 0.6237203 0.6237203 6.068964e-03 6.068964e-03 0.370210703 #> 5270  2634.5 0.6238139 0.6238139 6.059428e-03 6.059428e-03 0.370126688 #> 5271  2635.0 0.6239075 0.6239075 6.049921e-03 6.049921e-03 0.370042557 #> 5272  2635.5 0.6240012 0.6240012 6.040443e-03 6.040443e-03 0.369958310 #> 5273  2636.0 0.6240951 0.6240951 6.030995e-03 6.030995e-03 0.369873947 #> 5274  2636.5 0.6241890 0.6241890 6.021575e-03 6.021575e-03 0.369789469 #> 5275  2637.0 0.6242829 0.6242829 6.012184e-03 6.012184e-03 0.369704877 #> 5276  2637.5 0.6243770 0.6243770 6.002822e-03 6.002822e-03 0.369620171 #> 5277  2638.0 0.6244712 0.6244712 5.993488e-03 5.993488e-03 0.369535351 #> 5278  2638.5 0.6245654 0.6245654 5.984183e-03 5.984183e-03 0.369450418 #> 5279  2639.0 0.6246597 0.6246597 5.974907e-03 5.974907e-03 0.369365374 #> 5280  2639.5 0.6247541 0.6247541 5.965659e-03 5.965659e-03 0.369280217 #> 5281  2640.0 0.6248486 0.6248486 5.956439e-03 5.956439e-03 0.369194949 #> 5282  2640.5 0.6249432 0.6249432 5.947248e-03 5.947248e-03 0.369109570 #> 5283  2641.0 0.6250378 0.6250378 5.938085e-03 5.938085e-03 0.369024081 #> 5284  2641.5 0.6251326 0.6251326 5.928950e-03 5.928950e-03 0.368938482 #> 5285  2642.0 0.6252274 0.6252274 5.919843e-03 5.919843e-03 0.368852774 #> 5286  2642.5 0.6253223 0.6253223 5.910765e-03 5.910765e-03 0.368766958 #> 5287  2643.0 0.6254173 0.6254173 5.901714e-03 5.901714e-03 0.368681033 #> 5288  2643.5 0.6255123 0.6255123 5.892691e-03 5.892691e-03 0.368595001 #> 5289  2644.0 0.6256074 0.6256074 5.883696e-03 5.883696e-03 0.368508861 #> 5290  2644.5 0.6257027 0.6257027 5.874728e-03 5.874728e-03 0.368422615 #> 5291  2645.0 0.6257979 0.6257979 5.865788e-03 5.865788e-03 0.368336263 #> 5292  2645.5 0.6258933 0.6258933 5.856876e-03 5.856876e-03 0.368249805 #> 5293  2646.0 0.6259888 0.6259888 5.847991e-03 5.847991e-03 0.368163243 #> 5294  2646.5 0.6260843 0.6260843 5.839134e-03 5.839134e-03 0.368076575 #> 5295  2647.0 0.6261799 0.6261799 5.830304e-03 5.830304e-03 0.367989804 #> 5296  2647.5 0.6262756 0.6262756 5.821501e-03 5.821501e-03 0.367902929 #> 5297  2648.0 0.6263713 0.6263713 5.812725e-03 5.812725e-03 0.367815951 #> 5298  2648.5 0.6264672 0.6264672 5.803977e-03 5.803977e-03 0.367728871 #> 5299  2649.0 0.6265631 0.6265631 5.795255e-03 5.795255e-03 0.367641688 #> 5300  2649.5 0.6266590 0.6266590 5.786561e-03 5.786561e-03 0.367554405 #> 5301  2650.0 0.6267551 0.6267551 5.777894e-03 5.777894e-03 0.367467020 #> 5302  2650.5 0.6268512 0.6268512 5.769253e-03 5.769253e-03 0.367379534 #> 5303  2651.0 0.6269474 0.6269474 5.760639e-03 5.760639e-03 0.367291949 #> 5304  2651.5 0.6270437 0.6270437 5.752052e-03 5.752052e-03 0.367204264 #> 5305  2652.0 0.6271400 0.6271400 5.743491e-03 5.743491e-03 0.367116480 #> 5306  2652.5 0.6272364 0.6272364 5.734957e-03 5.734957e-03 0.367028597 #> 5307  2653.0 0.6273329 0.6273329 5.726450e-03 5.726450e-03 0.366940616 #> 5308  2653.5 0.6274295 0.6274295 5.717969e-03 5.717969e-03 0.366852538 #> 5309  2654.0 0.6275261 0.6275261 5.709514e-03 5.709514e-03 0.366764363 #> 5310  2654.5 0.6276228 0.6276228 5.701085e-03 5.701085e-03 0.366676091 #> 5311  2655.0 0.6277196 0.6277196 5.692683e-03 5.692683e-03 0.366587723 #> 5312  2655.5 0.6278164 0.6278164 5.684307e-03 5.684307e-03 0.366499260 #> 5313  2656.0 0.6279133 0.6279133 5.675957e-03 5.675957e-03 0.366410701 #> 5314  2656.5 0.6280103 0.6280103 5.667633e-03 5.667633e-03 0.366322048 #> 5315  2657.0 0.6281074 0.6281074 5.659335e-03 5.659335e-03 0.366233300 #> 5316  2657.5 0.6282045 0.6282045 5.651063e-03 5.651063e-03 0.366144459 #> 5317  2658.0 0.6283017 0.6283017 5.642816e-03 5.642816e-03 0.366055525 #> 5318  2658.5 0.6283989 0.6283989 5.634595e-03 5.634595e-03 0.365966498 #> 5319  2659.0 0.6284962 0.6284962 5.626400e-03 5.626400e-03 0.365877378 #> 5320  2659.5 0.6285936 0.6285936 5.618231e-03 5.618231e-03 0.365788167 #> 5321  2660.0 0.6286910 0.6286910 5.610087e-03 5.610087e-03 0.365698865 #> 5322  2660.5 0.6287886 0.6287886 5.601968e-03 5.601968e-03 0.365609471 #> 5323  2661.0 0.6288861 0.6288861 5.593875e-03 5.593875e-03 0.365519988 #> 5324  2661.5 0.6289838 0.6289838 5.585808e-03 5.585808e-03 0.365430414 #> 5325  2662.0 0.6290815 0.6290815 5.577765e-03 5.577765e-03 0.365340751 #> 5326  2662.5 0.6291793 0.6291793 5.569748e-03 5.569748e-03 0.365250999 #> 5327  2663.0 0.6292771 0.6292771 5.561756e-03 5.561756e-03 0.365161158 #> 5328  2663.5 0.6293750 0.6293750 5.553788e-03 5.553788e-03 0.365071230 #> 5329  2664.0 0.6294729 0.6294729 5.545846e-03 5.545846e-03 0.364981214 #> 5330  2664.5 0.6295710 0.6295710 5.537929e-03 5.537929e-03 0.364891110 #> 5331  2665.0 0.6296690 0.6296690 5.530037e-03 5.530037e-03 0.364800920 #> 5332  2665.5 0.6297672 0.6297672 5.522169e-03 5.522169e-03 0.364710644 #> 5333  2666.0 0.6298654 0.6298654 5.514326e-03 5.514326e-03 0.364620282 #> 5334  2666.5 0.6299637 0.6299637 5.506508e-03 5.506508e-03 0.364529835 #> 5335  2667.0 0.6300620 0.6300620 5.498714e-03 5.498714e-03 0.364439303 #> 5336  2667.5 0.6301604 0.6301604 5.490945e-03 5.490945e-03 0.364348686 #> 5337  2668.0 0.6302588 0.6302588 5.483201e-03 5.483201e-03 0.364257986 #> 5338  2668.5 0.6303573 0.6303573 5.475480e-03 5.475480e-03 0.364167202 #> 5339  2669.0 0.6304559 0.6304559 5.467785e-03 5.467785e-03 0.364076334 #> 5340  2669.5 0.6305545 0.6305545 5.460113e-03 5.460113e-03 0.363985385 #> 5341  2670.0 0.6306532 0.6306532 5.452466e-03 5.452466e-03 0.363894353 #> 5342  2670.5 0.6307519 0.6307519 5.444842e-03 5.444842e-03 0.363803239 #> 5343  2671.0 0.6308507 0.6308507 5.437243e-03 5.437243e-03 0.363712045 #> 5344  2671.5 0.6309496 0.6309496 5.429668e-03 5.429668e-03 0.363620769 #> 5345  2672.0 0.6310485 0.6310485 5.422117e-03 5.422117e-03 0.363529413 #> 5346  2672.5 0.6311474 0.6311474 5.414590e-03 5.414590e-03 0.363437977 #> 5347  2673.0 0.6312465 0.6312465 5.407086e-03 5.407086e-03 0.363346461 #> 5348  2673.5 0.6313455 0.6313455 5.399607e-03 5.399607e-03 0.363254867 #> 5349  2674.0 0.6314447 0.6314447 5.392151e-03 5.392151e-03 0.363163194 #> 5350  2674.5 0.6315438 0.6315438 5.384718e-03 5.384718e-03 0.363071442 #> 5351  2675.0 0.6316431 0.6316431 5.377310e-03 5.377310e-03 0.362979613 #> 5352  2675.5 0.6317424 0.6317424 5.369924e-03 5.369924e-03 0.362887706 #> 5353  2676.0 0.6318417 0.6318417 5.362563e-03 5.362563e-03 0.362795723 #> 5354  2676.5 0.6319411 0.6319411 5.355224e-03 5.355224e-03 0.362703663 #> 5355  2677.0 0.6320406 0.6320406 5.347909e-03 5.347909e-03 0.362611527 #> 5356  2677.5 0.6321401 0.6321401 5.340617e-03 5.340617e-03 0.362519315 #> 5357  2678.0 0.6322396 0.6322396 5.333349e-03 5.333349e-03 0.362427028 #> 5358  2678.5 0.6323392 0.6323392 5.326103e-03 5.326103e-03 0.362334667 #> 5359  2679.0 0.6324389 0.6324389 5.318881e-03 5.318881e-03 0.362242230 #> 5360  2679.5 0.6325386 0.6325386 5.311682e-03 5.311682e-03 0.362149720 #> 5361  2680.0 0.6326384 0.6326384 5.304506e-03 5.304506e-03 0.362057137 #> 5362  2680.5 0.6327382 0.6327382 5.297352e-03 5.297352e-03 0.361964480 #> 5363  2681.0 0.6328380 0.6328380 5.290222e-03 5.290222e-03 0.361871751 #> 5364  2681.5 0.6329379 0.6329379 5.283114e-03 5.283114e-03 0.361778949 #> 5365  2682.0 0.6330379 0.6330379 5.276029e-03 5.276029e-03 0.361686076 #> 5366  2682.5 0.6331379 0.6331379 5.268967e-03 5.268967e-03 0.361593131 #> 5367  2683.0 0.6332380 0.6332380 5.261927e-03 5.261927e-03 0.361500115 #> 5368  2683.5 0.6333381 0.6333381 5.254910e-03 5.254910e-03 0.361407029 #> 5369  2684.0 0.6334382 0.6334382 5.247915e-03 5.247915e-03 0.361313872 #> 5370  2684.5 0.6335384 0.6335384 5.240943e-03 5.240943e-03 0.361220645 #> 5371  2685.0 0.6336387 0.6336387 5.233993e-03 5.233993e-03 0.361127350 #> 5372  2685.5 0.6337390 0.6337390 5.227065e-03 5.227065e-03 0.361033985 #> 5373  2686.0 0.6338393 0.6338393 5.220160e-03 5.220160e-03 0.360940551 #> 5374  2686.5 0.6339397 0.6339397 5.213277e-03 5.213277e-03 0.360847050 #> 5375  2687.0 0.6340401 0.6340401 5.206416e-03 5.206416e-03 0.360753480 #> 5376  2687.5 0.6341406 0.6341406 5.199577e-03 5.199577e-03 0.360659843 #> 5377  2688.0 0.6342411 0.6342411 5.192760e-03 5.192760e-03 0.360566140 #> 5378  2688.5 0.6343417 0.6343417 5.185965e-03 5.185965e-03 0.360472369 #> 5379  2689.0 0.6344423 0.6344423 5.179192e-03 5.179192e-03 0.360378533 #> 5380  2689.5 0.6345429 0.6345429 5.172441e-03 5.172441e-03 0.360284631 #> 5381  2690.0 0.6346436 0.6346436 5.165712e-03 5.165712e-03 0.360190663 #> 5382  2690.5 0.6347444 0.6347444 5.159005e-03 5.159005e-03 0.360096631 #> 5383  2691.0 0.6348451 0.6348451 5.152319e-03 5.152319e-03 0.360002534 #> 5384  2691.5 0.6349460 0.6349460 5.145654e-03 5.145654e-03 0.359908372 #> 5385  2692.0 0.6350468 0.6350468 5.139012e-03 5.139012e-03 0.359814147 #> 5386  2692.5 0.6351478 0.6351478 5.132391e-03 5.132391e-03 0.359719859 #> 5387  2693.0 0.6352487 0.6352487 5.125791e-03 5.125791e-03 0.359625507 #> 5388  2693.5 0.6353497 0.6353497 5.119213e-03 5.119213e-03 0.359531093 #> 5389  2694.0 0.6354507 0.6354507 5.112656e-03 5.112656e-03 0.359436617 #> 5390  2694.5 0.6355518 0.6355518 5.106120e-03 5.106120e-03 0.359342078 #> 5391  2695.0 0.6356529 0.6356529 5.099606e-03 5.099606e-03 0.359247479 #> 5392  2695.5 0.6357541 0.6357541 5.093113e-03 5.093113e-03 0.359152818 #> 5393  2696.0 0.6358553 0.6358553 5.086641e-03 5.086641e-03 0.359058096 #> 5394  2696.5 0.6359565 0.6359565 5.080190e-03 5.080190e-03 0.358963315 #> 5395  2697.0 0.6360578 0.6360578 5.073760e-03 5.073760e-03 0.358868473 #> 5396  2697.5 0.6361591 0.6361591 5.067351e-03 5.067351e-03 0.358773572 #> 5397  2698.0 0.6362604 0.6362604 5.060963e-03 5.060963e-03 0.358678611 #> 5398  2698.5 0.6363618 0.6363618 5.054596e-03 5.054596e-03 0.358583592 #> 5399  2699.0 0.6364632 0.6364632 5.048249e-03 5.048249e-03 0.358488514 #> 5400  2699.5 0.6365647 0.6365647 5.041924e-03 5.041924e-03 0.358393378 #> 5401  2700.0 0.6366662 0.6366662 5.035619e-03 5.035619e-03 0.358298185 #> 5402  2700.5 0.6367677 0.6367677 5.029334e-03 5.029334e-03 0.358202934 #> 5403  2701.0 0.6368693 0.6368693 5.023071e-03 5.023071e-03 0.358107627 #> 5404  2701.5 0.6369709 0.6369709 5.016828e-03 5.016828e-03 0.358012262 #> 5405  2702.0 0.6370726 0.6370726 5.010605e-03 5.010605e-03 0.357916842 #> 5406  2702.5 0.6371742 0.6371742 5.004403e-03 5.004403e-03 0.357821366 #> 5407  2703.0 0.6372759 0.6372759 4.998221e-03 4.998221e-03 0.357725834 #> 5408  2703.5 0.6373777 0.6373777 4.992060e-03 4.992060e-03 0.357630248 #> 5409  2704.0 0.6374795 0.6374795 4.985918e-03 4.985918e-03 0.357534606 #> 5410  2704.5 0.6375813 0.6375813 4.979797e-03 4.979797e-03 0.357438910 #> 5411  2705.0 0.6376831 0.6376831 4.973697e-03 4.973697e-03 0.357343161 #> 5412  2705.5 0.6377850 0.6377850 4.967616e-03 4.967616e-03 0.357247357 #> 5413  2706.0 0.6378869 0.6378869 4.961555e-03 4.961555e-03 0.357151501 #> 5414  2706.5 0.6379889 0.6379889 4.955515e-03 4.955515e-03 0.357055591 #> 5415  2707.0 0.6380909 0.6380909 4.949494e-03 4.949494e-03 0.356959629 #> 5416  2707.5 0.6381929 0.6381929 4.943493e-03 4.943493e-03 0.356863615 #> 5417  2708.0 0.6382949 0.6382949 4.937513e-03 4.937513e-03 0.356767549 #> 5418  2708.5 0.6383970 0.6383970 4.931552e-03 4.931552e-03 0.356671431 #> 5419  2709.0 0.6384991 0.6384991 4.925610e-03 4.925610e-03 0.356575262 #> 5420  2709.5 0.6386013 0.6386013 4.919689e-03 4.919689e-03 0.356479043 #> 5421  2710.0 0.6387034 0.6387034 4.913787e-03 4.913787e-03 0.356382773 #> 5422  2710.5 0.6388056 0.6388056 4.907905e-03 4.907905e-03 0.356286453 #> 5423  2711.0 0.6389079 0.6389079 4.902042e-03 4.902042e-03 0.356190083 #> 5424  2711.5 0.6390101 0.6390101 4.896199e-03 4.896199e-03 0.356093665 #> 5425  2712.0 0.6391124 0.6391124 4.890375e-03 4.890375e-03 0.355997197 #> 5426  2712.5 0.6392147 0.6392147 4.884571e-03 4.884571e-03 0.355900680 #> 5427  2713.0 0.6393171 0.6393171 4.878786e-03 4.878786e-03 0.355804115 #> 5428  2713.5 0.6394195 0.6394195 4.873021e-03 4.873021e-03 0.355707502 #> 5429  2714.0 0.6395219 0.6395219 4.867274e-03 4.867274e-03 0.355610842 #> 5430  2714.5 0.6396243 0.6396243 4.861547e-03 4.861547e-03 0.355514134 #> 5431  2715.0 0.6397268 0.6397268 4.855839e-03 4.855839e-03 0.355417379 #> 5432  2715.5 0.6398293 0.6398293 4.850151e-03 4.850151e-03 0.355320578 #> 5433  2716.0 0.6399318 0.6399318 4.844481e-03 4.844481e-03 0.355223730 #> 5434  2716.5 0.6400343 0.6400343 4.838830e-03 4.838830e-03 0.355126837 #> 5435  2717.0 0.6401369 0.6401369 4.833199e-03 4.833199e-03 0.355029898 #> 5436  2717.5 0.6402395 0.6402395 4.827586e-03 4.827586e-03 0.354932914 #> 5437  2718.0 0.6403421 0.6403421 4.821992e-03 4.821992e-03 0.354835885 #> 5438  2718.5 0.6404448 0.6404448 4.816417e-03 4.816417e-03 0.354738811 #> 5439  2719.0 0.6405474 0.6405474 4.810861e-03 4.810861e-03 0.354641693 #> 5440  2719.5 0.6406501 0.6406501 4.805323e-03 4.805323e-03 0.354544532 #> 5441  2720.0 0.6407529 0.6407529 4.799805e-03 4.799805e-03 0.354447326 #> 5442  2720.5 0.6408556 0.6408556 4.794305e-03 4.794305e-03 0.354350078 #> 5443  2721.0 0.6409584 0.6409584 4.788823e-03 4.788823e-03 0.354252787 #> 5444  2721.5 0.6410612 0.6410612 4.783360e-03 4.783360e-03 0.354155453 #> 5445  2722.0 0.6411640 0.6411640 4.777916e-03 4.777916e-03 0.354058077 #> 5446  2722.5 0.6412669 0.6412669 4.772490e-03 4.772490e-03 0.353960659 #> 5447  2723.0 0.6413697 0.6413697 4.767082e-03 4.767082e-03 0.353863199 #> 5448  2723.5 0.6414726 0.6414726 4.761693e-03 4.761693e-03 0.353765698 #> 5449  2724.0 0.6415755 0.6415755 4.756322e-03 4.756322e-03 0.353668156 #> 5450  2724.5 0.6416785 0.6416785 4.750970e-03 4.750970e-03 0.353570574 #> 5451  2725.0 0.6417814 0.6417814 4.745635e-03 4.745635e-03 0.353472952 #> 5452  2725.5 0.6418844 0.6418844 4.740319e-03 4.740319e-03 0.353375289 #> 5453  2726.0 0.6419874 0.6419874 4.735021e-03 4.735021e-03 0.353277587 #> 5454  2726.5 0.6420904 0.6420904 4.729741e-03 4.729741e-03 0.353179846 #> 5455  2727.0 0.6421935 0.6421935 4.724479e-03 4.724479e-03 0.353082065 #> 5456  2727.5 0.6422965 0.6422965 4.719236e-03 4.719236e-03 0.352984246 #> 5457  2728.0 0.6423996 0.6423996 4.714010e-03 4.714010e-03 0.352886389 #> 5458  2728.5 0.6425027 0.6425027 4.708802e-03 4.708802e-03 0.352788494 #> 5459  2729.0 0.6426058 0.6426058 4.703612e-03 4.703612e-03 0.352690561 #> 5460  2729.5 0.6427090 0.6427090 4.698439e-03 4.698439e-03 0.352592590 #> 5461  2730.0 0.6428121 0.6428121 4.693285e-03 4.693285e-03 0.352494583 #> 5462  2730.5 0.6429153 0.6429153 4.688148e-03 4.688148e-03 0.352396539 #> 5463  2731.0 0.6430185 0.6430185 4.683029e-03 4.683029e-03 0.352298458 #> 5464  2731.5 0.6431217 0.6431217 4.677928e-03 4.677928e-03 0.352200341 #> 5465  2732.0 0.6432250 0.6432250 4.672844e-03 4.672844e-03 0.352102189 #> 5466  2732.5 0.6433282 0.6433282 4.667778e-03 4.667778e-03 0.352004001 #> 5467  2733.0 0.6434315 0.6434315 4.662730e-03 4.662730e-03 0.351905778 #> 5468  2733.5 0.6435348 0.6435348 4.657699e-03 4.657699e-03 0.351807519 #> 5469  2734.0 0.6436381 0.6436381 4.652685e-03 4.652685e-03 0.351709227 #> 5470  2734.5 0.6437414 0.6437414 4.647689e-03 4.647689e-03 0.351610900 #> 5471  2735.0 0.6438448 0.6438448 4.642710e-03 4.642710e-03 0.351512539 #> 5472  2735.5 0.6439481 0.6439481 4.637748e-03 4.637748e-03 0.351414144 #> 5473  2736.0 0.6440515 0.6440515 4.632804e-03 4.632804e-03 0.351315716 #> 5474  2736.5 0.6441549 0.6441549 4.627877e-03 4.627877e-03 0.351217256 #> 5475  2737.0 0.6442583 0.6442583 4.622967e-03 4.622967e-03 0.351118762 #> 5476  2737.5 0.6443617 0.6443617 4.618074e-03 4.618074e-03 0.351020236 #> 5477  2738.0 0.6444651 0.6444651 4.613198e-03 4.613198e-03 0.350921678 #> 5478  2738.5 0.6445686 0.6445686 4.608340e-03 4.608340e-03 0.350823087 #> 5479  2739.0 0.6446720 0.6446720 4.603498e-03 4.603498e-03 0.350724466 #> 5480  2739.5 0.6447755 0.6447755 4.598674e-03 4.598674e-03 0.350625813 #> 5481  2740.0 0.6448790 0.6448790 4.593866e-03 4.593866e-03 0.350527129 #> 5482  2740.5 0.6449825 0.6449825 4.589075e-03 4.589075e-03 0.350428415 #> 5483  2741.0 0.6450860 0.6450860 4.584301e-03 4.584301e-03 0.350329670 #> 5484  2741.5 0.6451896 0.6451896 4.579544e-03 4.579544e-03 0.350230895 #> 5485  2742.0 0.6452931 0.6452931 4.574804e-03 4.574804e-03 0.350132091 #> 5486  2742.5 0.6453967 0.6453967 4.570081e-03 4.570081e-03 0.350033257 #> 5487  2743.0 0.6455002 0.6455002 4.565374e-03 4.565374e-03 0.349934394 #> 5488  2743.5 0.6456038 0.6456038 4.560684e-03 4.560684e-03 0.349835502 #> 5489  2744.0 0.6457074 0.6457074 4.556010e-03 4.556010e-03 0.349736581 #> 5490  2744.5 0.6458110 0.6458110 4.551353e-03 4.551353e-03 0.349637632 #> 5491  2745.0 0.6459146 0.6459146 4.546713e-03 4.546713e-03 0.349538655 #> 5492  2745.5 0.6460183 0.6460183 4.542089e-03 4.542089e-03 0.349439651 #> 5493  2746.0 0.6461219 0.6461219 4.537481e-03 4.537481e-03 0.349340619 #> 5494  2746.5 0.6462256 0.6462256 4.532890e-03 4.532890e-03 0.349241560 #> 5495  2747.0 0.6463292 0.6463292 4.528315e-03 4.528315e-03 0.349142474 #> 5496  2747.5 0.6464329 0.6464329 4.523757e-03 4.523757e-03 0.349043361 #> 5497  2748.0 0.6465366 0.6465366 4.519215e-03 4.519215e-03 0.348944222 #> 5498  2748.5 0.6466403 0.6466403 4.514689e-03 4.514689e-03 0.348845057 #> 5499  2749.0 0.6467440 0.6467440 4.510180e-03 4.510180e-03 0.348745867 #> 5500  2749.5 0.6468477 0.6468477 4.505686e-03 4.505686e-03 0.348646651 #> 5501  2750.0 0.6469514 0.6469514 4.501209e-03 4.501209e-03 0.348547410 #> 5502  2750.5 0.6470551 0.6470551 4.496748e-03 4.496748e-03 0.348448144 #> 5503  2751.0 0.6471588 0.6471588 4.492303e-03 4.492303e-03 0.348348853 #> 5504  2751.5 0.6472626 0.6472626 4.487874e-03 4.487874e-03 0.348249539 #> 5505  2752.0 0.6473663 0.6473663 4.483461e-03 4.483461e-03 0.348150200 #> 5506  2752.5 0.6474701 0.6474701 4.479064e-03 4.479064e-03 0.348050837 #> 5507  2753.0 0.6475739 0.6475739 4.474683e-03 4.474683e-03 0.347951451 #> 5508  2753.5 0.6476776 0.6476776 4.470317e-03 4.470317e-03 0.347852042 #> 5509  2754.0 0.6477814 0.6477814 4.465968e-03 4.465968e-03 0.347752610 #> 5510  2754.5 0.6478852 0.6478852 4.461634e-03 4.461634e-03 0.347653155 #> 5511  2755.0 0.6479890 0.6479890 4.457316e-03 4.457316e-03 0.347553678 #> 5512  2755.5 0.6480928 0.6480928 4.453014e-03 4.453014e-03 0.347454179 #> 5513  2756.0 0.6481966 0.6481966 4.448728e-03 4.448728e-03 0.347354658 #> 5514  2756.5 0.6483004 0.6483004 4.444457e-03 4.444457e-03 0.347255115 #> 5515  2757.0 0.6484042 0.6484042 4.440202e-03 4.440202e-03 0.347155551 #> 5516  2757.5 0.6485081 0.6485081 4.435963e-03 4.435963e-03 0.347055966 #> 5517  2758.0 0.6486119 0.6486119 4.431739e-03 4.431739e-03 0.346956361 #> 5518  2758.5 0.6487157 0.6487157 4.427530e-03 4.427530e-03 0.346856735 #> 5519  2759.0 0.6488196 0.6488196 4.423337e-03 4.423337e-03 0.346757088 #> 5520  2759.5 0.6489234 0.6489234 4.419160e-03 4.419160e-03 0.346657422 #> 5521  2760.0 0.6490273 0.6490273 4.414997e-03 4.414997e-03 0.346557736 #> 5522  2760.5 0.6491311 0.6491311 4.410851e-03 4.410851e-03 0.346458031 #> 5523  2761.0 0.6492350 0.6492350 4.406719e-03 4.406719e-03 0.346358307 #> 5524  2761.5 0.6493388 0.6493388 4.402603e-03 4.402603e-03 0.346258564 #> 5525  2762.0 0.6494427 0.6494427 4.398502e-03 4.398502e-03 0.346158802 #> 5526  2762.5 0.6495466 0.6495466 4.394417e-03 4.394417e-03 0.346059022 #> 5527  2763.0 0.6496504 0.6496504 4.390346e-03 4.390346e-03 0.345959223 #> 5528  2763.5 0.6497543 0.6497543 4.386291e-03 4.386291e-03 0.345859407 #> 5529  2764.0 0.6498582 0.6498582 4.382251e-03 4.382251e-03 0.345759574 #> 5530  2764.5 0.6499621 0.6499621 4.378226e-03 4.378226e-03 0.345659723 #> 5531  2765.0 0.6500659 0.6500659 4.374216e-03 4.374216e-03 0.345559855 #> 5532  2765.5 0.6501698 0.6501698 4.370221e-03 4.370221e-03 0.345459970 #> 5533  2766.0 0.6502737 0.6502737 4.366241e-03 4.366241e-03 0.345360069 #> 5534  2766.5 0.6503776 0.6503776 4.362276e-03 4.362276e-03 0.345260152 #> 5535  2767.0 0.6504815 0.6504815 4.358326e-03 4.358326e-03 0.345160218 #> 5536  2767.5 0.6505853 0.6505853 4.354390e-03 4.354390e-03 0.345060269 #> 5537  2768.0 0.6506892 0.6506892 4.350470e-03 4.350470e-03 0.344960304 #> 5538  2768.5 0.6507931 0.6507931 4.346564e-03 4.346564e-03 0.344860324 #> 5539  2769.0 0.6508970 0.6508970 4.342674e-03 4.342674e-03 0.344760330 #> 5540  2769.5 0.6510009 0.6510009 4.338798e-03 4.338798e-03 0.344660320 #> 5541  2770.0 0.6511048 0.6511048 4.334936e-03 4.334936e-03 0.344560296 #> 5542  2770.5 0.6512087 0.6512087 4.331090e-03 4.331090e-03 0.344460258 #> 5543  2771.0 0.6513125 0.6513125 4.327258e-03 4.327258e-03 0.344360205 #> 5544  2771.5 0.6514164 0.6514164 4.323440e-03 4.323440e-03 0.344260139 #> 5545  2772.0 0.6515203 0.6515203 4.319637e-03 4.319637e-03 0.344160060 #> 5546  2772.5 0.6516242 0.6516242 4.315849e-03 4.315849e-03 0.344059967 #> 5547  2773.0 0.6517281 0.6517281 4.312076e-03 4.312076e-03 0.343959862 #> 5548  2773.5 0.6518319 0.6518319 4.308316e-03 4.308316e-03 0.343859743 #> 5549  2774.0 0.6519358 0.6519358 4.304572e-03 4.304572e-03 0.343759613 #> 5550  2774.5 0.6520397 0.6520397 4.300841e-03 4.300841e-03 0.343659470 #> 5551  2775.0 0.6521436 0.6521436 4.297125e-03 4.297125e-03 0.343559315 #> 5552  2775.5 0.6522474 0.6522474 4.293424e-03 4.293424e-03 0.343459148 #> 5553  2776.0 0.6523513 0.6523513 4.289736e-03 4.289736e-03 0.343358970 #> 5554  2776.5 0.6524552 0.6524552 4.286064e-03 4.286064e-03 0.343258780 #> 5555  2777.0 0.6525590 0.6525590 4.282405e-03 4.282405e-03 0.343158580 #> 5556  2777.5 0.6526629 0.6526629 4.278760e-03 4.278760e-03 0.343058369 #> 5557  2778.0 0.6527667 0.6527667 4.275130e-03 4.275130e-03 0.342958147 #> 5558  2778.5 0.6528706 0.6528706 4.271514e-03 4.271514e-03 0.342857915 #> 5559  2779.0 0.6529744 0.6529744 4.267912e-03 4.267912e-03 0.342757673 #> 5560  2779.5 0.6530783 0.6530783 4.264324e-03 4.264324e-03 0.342657421 #> 5561  2780.0 0.6531821 0.6531821 4.260750e-03 4.260750e-03 0.342557160 #> 5562  2780.5 0.6532859 0.6532859 4.257191e-03 4.257191e-03 0.342456889 #> 5563  2781.0 0.6533897 0.6533897 4.253645e-03 4.253645e-03 0.342356609 #> 5564  2781.5 0.6534936 0.6534936 4.250113e-03 4.250113e-03 0.342256321 #> 5565  2782.0 0.6535974 0.6535974 4.246596e-03 4.246596e-03 0.342156023 #> 5566  2782.5 0.6537012 0.6537012 4.243092e-03 4.243092e-03 0.342055718 #> 5567  2783.0 0.6538050 0.6538050 4.239602e-03 4.239602e-03 0.341955404 #> 5568  2783.5 0.6539088 0.6539088 4.236126e-03 4.236126e-03 0.341855083 #> 5569  2784.0 0.6540126 0.6540126 4.232663e-03 4.232663e-03 0.341754753 #> 5570  2784.5 0.6541164 0.6541164 4.229215e-03 4.229215e-03 0.341654417 #> 5571  2785.0 0.6542201 0.6542201 4.225780e-03 4.225780e-03 0.341554073 #> 5572  2785.5 0.6543239 0.6543239 4.222359e-03 4.222359e-03 0.341453722 #> 5573  2786.0 0.6544277 0.6544277 4.218952e-03 4.218952e-03 0.341353364 #> 5574  2786.5 0.6545314 0.6545314 4.215559e-03 4.215559e-03 0.341253000 #> 5575  2787.0 0.6546352 0.6546352 4.212179e-03 4.212179e-03 0.341152630 #> 5576  2787.5 0.6547389 0.6547389 4.208813e-03 4.208813e-03 0.341052254 #> 5577  2788.0 0.6548427 0.6548427 4.205460e-03 4.205460e-03 0.340951872 #> 5578  2788.5 0.6549464 0.6549464 4.202121e-03 4.202121e-03 0.340851484 #> 5579  2789.0 0.6550501 0.6550501 4.198795e-03 4.198795e-03 0.340751091 #> 5580  2789.5 0.6551538 0.6551538 4.195483e-03 4.195483e-03 0.340650693 #> 5581  2790.0 0.6552575 0.6552575 4.192185e-03 4.192185e-03 0.340550290 #> 5582  2790.5 0.6553612 0.6553612 4.188900e-03 4.188900e-03 0.340449882 #> 5583  2791.0 0.6554649 0.6554649 4.185628e-03 4.185628e-03 0.340349470 #> 5584  2791.5 0.6555686 0.6555686 4.182370e-03 4.182370e-03 0.340249054 #> 5585  2792.0 0.6556722 0.6556722 4.179125e-03 4.179125e-03 0.340148633 #> 5586  2792.5 0.6557759 0.6557759 4.175893e-03 4.175893e-03 0.340048209 #> 5587  2793.0 0.6558795 0.6558795 4.172675e-03 4.172675e-03 0.339947782 #> 5588  2793.5 0.6559832 0.6559832 4.169470e-03 4.169470e-03 0.339847351 #> 5589  2794.0 0.6560868 0.6560868 4.166278e-03 4.166278e-03 0.339746917 #> 5590  2794.5 0.6561904 0.6561904 4.163100e-03 4.163100e-03 0.339646480 #> 5591  2795.0 0.6562940 0.6562940 4.159934e-03 4.159934e-03 0.339546040 #> 5592  2795.5 0.6563976 0.6563976 4.156782e-03 4.156782e-03 0.339445598 #> 5593  2796.0 0.6565012 0.6565012 4.153643e-03 4.153643e-03 0.339345154 #> 5594  2796.5 0.6566048 0.6566048 4.150517e-03 4.150517e-03 0.339244708 #> 5595  2797.0 0.6567083 0.6567083 4.147405e-03 4.147405e-03 0.339144260 #> 5596  2797.5 0.6568119 0.6568119 4.144305e-03 4.144305e-03 0.339043811 #> 5597  2798.0 0.6569154 0.6569154 4.141218e-03 4.141218e-03 0.338943360 #> 5598  2798.5 0.6570189 0.6570189 4.138145e-03 4.138145e-03 0.338842908 #> 5599  2799.0 0.6571225 0.6571225 4.135084e-03 4.135084e-03 0.338742455 #> 5600  2799.5 0.6572260 0.6572260 4.132036e-03 4.132036e-03 0.338642002 #> 5601  2800.0 0.6573295 0.6573295 4.129001e-03 4.129001e-03 0.338541548 #> 5602  2800.5 0.6574329 0.6574329 4.125980e-03 4.125980e-03 0.338441094 #> 5603  2801.0 0.6575364 0.6575364 4.122971e-03 4.122971e-03 0.338340639 #> 5604  2801.5 0.6576398 0.6576398 4.119974e-03 4.119974e-03 0.338240185 #> 5605  2802.0 0.6577433 0.6577433 4.116991e-03 4.116991e-03 0.338139732 #> 5606  2802.5 0.6578467 0.6578467 4.114020e-03 4.114020e-03 0.338039279 #> 5607  2803.0 0.6579501 0.6579501 4.111063e-03 4.111063e-03 0.337938827 #> 5608  2803.5 0.6580535 0.6580535 4.108118e-03 4.108118e-03 0.337838375 #> 5609  2804.0 0.6581569 0.6581569 4.105185e-03 4.105185e-03 0.337737926 #> 5610  2804.5 0.6582603 0.6582603 4.102266e-03 4.102266e-03 0.337637477 #> 5611  2805.0 0.6583636 0.6583636 4.099359e-03 4.099359e-03 0.337537030 #> 5612  2805.5 0.6584669 0.6584669 4.096465e-03 4.096465e-03 0.337436585 #> 5613  2806.0 0.6585703 0.6585703 4.093583e-03 4.093583e-03 0.337336143 #> 5614  2806.5 0.6586736 0.6586736 4.090714e-03 4.090714e-03 0.337235702 #> 5615  2807.0 0.6587769 0.6587769 4.087857e-03 4.087857e-03 0.337135264 #> 5616  2807.5 0.6588802 0.6588802 4.085013e-03 4.085013e-03 0.337034829 #> 5617  2808.0 0.6589834 0.6589834 4.082182e-03 4.082182e-03 0.336934397 #> 5618  2808.5 0.6590867 0.6590867 4.079363e-03 4.079363e-03 0.336833967 #> 5619  2809.0 0.6591899 0.6591899 4.076556e-03 4.076556e-03 0.336733541 #> 5620  2809.5 0.6592931 0.6592931 4.073762e-03 4.073762e-03 0.336633119 #> 5621  2810.0 0.6593963 0.6593963 4.070981e-03 4.070981e-03 0.336532700 #> 5622  2810.5 0.6594995 0.6594995 4.068212e-03 4.068212e-03 0.336432286 #> 5623  2811.0 0.6596027 0.6596027 4.065455e-03 4.065455e-03 0.336331875 #> 5624  2811.5 0.6597058 0.6597058 4.062710e-03 4.062710e-03 0.336231469 #> 5625  2812.0 0.6598090 0.6598090 4.059978e-03 4.059978e-03 0.336131068 #> 5626  2812.5 0.6599121 0.6599121 4.057258e-03 4.057258e-03 0.336030671 #> 5627  2813.0 0.6600152 0.6600152 4.054550e-03 4.054550e-03 0.335930279 #> 5628  2813.5 0.6601183 0.6601183 4.051855e-03 4.051855e-03 0.335829892 #> 5629  2814.0 0.6602213 0.6602213 4.049172e-03 4.049172e-03 0.335729511 #> 5630  2814.5 0.6603244 0.6603244 4.046501e-03 4.046501e-03 0.335629135 #> 5631  2815.0 0.6604274 0.6604274 4.043842e-03 4.043842e-03 0.335528765 #> 5632  2815.5 0.6605304 0.6605304 4.041196e-03 4.041196e-03 0.335428401 #> 5633  2816.0 0.6606334 0.6606334 4.038561e-03 4.038561e-03 0.335328043 #> 5634  2816.5 0.6607364 0.6607364 4.035939e-03 4.035939e-03 0.335227692 #> 5635  2817.0 0.6608393 0.6608393 4.033328e-03 4.033328e-03 0.335127347 #> 5636  2817.5 0.6609423 0.6609423 4.030730e-03 4.030730e-03 0.335027009 #> 5637  2818.0 0.6610452 0.6610452 4.028144e-03 4.028144e-03 0.334926678 #> 5638  2818.5 0.6611481 0.6611481 4.025570e-03 4.025570e-03 0.334826354 #> 5639  2819.0 0.6612510 0.6612510 4.023008e-03 4.023008e-03 0.334726037 #> 5640  2819.5 0.6613538 0.6613538 4.020457e-03 4.020457e-03 0.334625728 #> 5641  2820.0 0.6614567 0.6614567 4.017919e-03 4.017919e-03 0.334525427 #> 5642  2820.5 0.6615595 0.6615595 4.015393e-03 4.015393e-03 0.334425133 #> 5643  2821.0 0.6616623 0.6616623 4.012878e-03 4.012878e-03 0.334324848 #> 5644  2821.5 0.6617651 0.6617651 4.010376e-03 4.010376e-03 0.334224571 #> 5645  2822.0 0.6618678 0.6618678 4.007885e-03 4.007885e-03 0.334124303 #> 5646  2822.5 0.6619706 0.6619706 4.005406e-03 4.005406e-03 0.334024043 #> 5647  2823.0 0.6620733 0.6620733 4.002939e-03 4.002939e-03 0.333923792 #> 5648  2823.5 0.6621760 0.6621760 4.000484e-03 4.000484e-03 0.333823551 #> 5649  2824.0 0.6622786 0.6622786 3.998041e-03 3.998041e-03 0.333723318 #> 5650  2824.5 0.6623813 0.6623813 3.995609e-03 3.995609e-03 0.333623095 #> 5651  2825.0 0.6624839 0.6624839 3.993189e-03 3.993189e-03 0.333522882 #> 5652  2825.5 0.6625865 0.6625865 3.990780e-03 3.990780e-03 0.333422679 #> 5653  2826.0 0.6626891 0.6626891 3.988384e-03 3.988384e-03 0.333322486 #> 5654  2826.5 0.6627917 0.6627917 3.985999e-03 3.985999e-03 0.333222303 #> 5655  2827.0 0.6628942 0.6628942 3.983626e-03 3.983626e-03 0.333122130 #> 5656  2827.5 0.6629968 0.6629968 3.981264e-03 3.981264e-03 0.333021968 #> 5657  2828.0 0.6630993 0.6630993 3.978914e-03 3.978914e-03 0.332921817 #> 5658  2828.5 0.6632017 0.6632017 3.976575e-03 3.976575e-03 0.332821677 #> 5659  2829.0 0.6633042 0.6633042 3.974248e-03 3.974248e-03 0.332721548 #> 5660  2829.5 0.6634066 0.6634066 3.971933e-03 3.971933e-03 0.332621430 #> 5661  2830.0 0.6635090 0.6635090 3.969629e-03 3.969629e-03 0.332521324 #> 5662  2830.5 0.6636114 0.6636114 3.967336e-03 3.967336e-03 0.332421230 #> 5663  2831.0 0.6637138 0.6637138 3.965055e-03 3.965055e-03 0.332321148 #> 5664  2831.5 0.6638161 0.6638161 3.962786e-03 3.962786e-03 0.332221077 #> 5665  2832.0 0.6639185 0.6639185 3.960528e-03 3.960528e-03 0.332121019 #> 5666  2832.5 0.6640207 0.6640207 3.958281e-03 3.958281e-03 0.332020974 #> 5667  2833.0 0.6641230 0.6641230 3.956046e-03 3.956046e-03 0.331920941 #> 5668  2833.5 0.6642253 0.6642253 3.953822e-03 3.953822e-03 0.331820921 #> 5669  2834.0 0.6643275 0.6643275 3.951609e-03 3.951609e-03 0.331720914 #> 5670  2834.5 0.6644297 0.6644297 3.949408e-03 3.949408e-03 0.331620921 #> 5671  2835.0 0.6645318 0.6645318 3.947218e-03 3.947218e-03 0.331520940 #> 5672  2835.5 0.6646340 0.6646340 3.945040e-03 3.945040e-03 0.331420974 #> 5673  2836.0 0.6647361 0.6647361 3.942872e-03 3.942872e-03 0.331321021 #> 5674  2836.5 0.6648382 0.6648382 3.940716e-03 3.940716e-03 0.331221082 #> 5675  2837.0 0.6649403 0.6649403 3.938571e-03 3.938571e-03 0.331121157 #> 5676  2837.5 0.6650423 0.6650423 3.936438e-03 3.936438e-03 0.331021247 #> 5677  2838.0 0.6651443 0.6651443 3.934315e-03 3.934315e-03 0.330921350 #> 5678  2838.5 0.6652463 0.6652463 3.932204e-03 3.932204e-03 0.330821469 #> 5679  2839.0 0.6653483 0.6653483 3.930104e-03 3.930104e-03 0.330721603 #> 5680  2839.5 0.6654502 0.6654502 3.928015e-03 3.928015e-03 0.330621751 #> 5681  2840.0 0.6655521 0.6655521 3.925937e-03 3.925937e-03 0.330521915 #> 5682  2840.5 0.6656540 0.6656540 3.923870e-03 3.923870e-03 0.330422094 #> 5683  2841.0 0.6657559 0.6657559 3.921814e-03 3.921814e-03 0.330322288 #> 5684  2841.5 0.6658577 0.6658577 3.919770e-03 3.919770e-03 0.330222499 #> 5685  2842.0 0.6659595 0.6659595 3.917736e-03 3.917736e-03 0.330122725 #> 5686  2842.5 0.6660613 0.6660613 3.915713e-03 3.915713e-03 0.330022967 #> 5687  2843.0 0.6661631 0.6661631 3.913702e-03 3.913702e-03 0.329923226 #> 5688  2843.5 0.6662648 0.6662648 3.911701e-03 3.911701e-03 0.329823501 #> 5689  2844.0 0.6663665 0.6663665 3.909711e-03 3.909711e-03 0.329723792 #> 5690  2844.5 0.6664682 0.6664682 3.907733e-03 3.907733e-03 0.329624101 #> 5691  2845.0 0.6665698 0.6665698 3.905765e-03 3.905765e-03 0.329524426 #> 5692  2845.5 0.6666714 0.6666714 3.903808e-03 3.903808e-03 0.329424768 #> 5693  2846.0 0.6667730 0.6667730 3.901862e-03 3.901862e-03 0.329325128 #> 5694  2846.5 0.6668746 0.6668746 3.899927e-03 3.899927e-03 0.329225505 #> 5695  2847.0 0.6669761 0.6669761 3.898003e-03 3.898003e-03 0.329125900 #> 5696  2847.5 0.6670776 0.6670776 3.896089e-03 3.896089e-03 0.329026313 #> 5697  2848.0 0.6671791 0.6671791 3.894186e-03 3.894186e-03 0.328926744 #> 5698  2848.5 0.6672805 0.6672805 3.892295e-03 3.892295e-03 0.328827193 #> 5699  2849.0 0.6673819 0.6673819 3.890414e-03 3.890414e-03 0.328727660 #> 5700  2849.5 0.6674833 0.6674833 3.888543e-03 3.888543e-03 0.328628145 #> 5701  2850.0 0.6675847 0.6675847 3.886684e-03 3.886684e-03 0.328528650 #> 5702  2850.5 0.6676860 0.6676860 3.884835e-03 3.884835e-03 0.328429173 #> 5703  2851.0 0.6677873 0.6677873 3.882997e-03 3.882997e-03 0.328329715 #> 5704  2851.5 0.6678886 0.6678886 3.881170e-03 3.881170e-03 0.328230277 #> 5705  2852.0 0.6679898 0.6679898 3.879353e-03 3.879353e-03 0.328130857 #> 5706  2852.5 0.6680910 0.6680910 3.877547e-03 3.877547e-03 0.328031457 #> 5707  2853.0 0.6681922 0.6681922 3.875752e-03 3.875752e-03 0.327932077 #> 5708  2853.5 0.6682933 0.6682933 3.873967e-03 3.873967e-03 0.327832717 #> 5709  2854.0 0.6683944 0.6683944 3.872193e-03 3.872193e-03 0.327733377 #> 5710  2854.5 0.6684955 0.6684955 3.870429e-03 3.870429e-03 0.327634057 #> 5711  2855.0 0.6685966 0.6685966 3.868676e-03 3.868676e-03 0.327534757 #> 5712  2855.5 0.6686976 0.6686976 3.866934e-03 3.866934e-03 0.327435478 #> 5713  2856.0 0.6687986 0.6687986 3.865202e-03 3.865202e-03 0.327336220 #> 5714  2856.5 0.6688995 0.6688995 3.863481e-03 3.863481e-03 0.327236982 #> 5715  2857.0 0.6690005 0.6690005 3.861770e-03 3.861770e-03 0.327137765 #> 5716  2857.5 0.6691014 0.6691014 3.860069e-03 3.860069e-03 0.327038570 #> 5717  2858.0 0.6692022 0.6692022 3.858380e-03 3.858380e-03 0.326939396 #> 5718  2858.5 0.6693031 0.6693031 3.856700e-03 3.856700e-03 0.326840243 #> 5719  2859.0 0.6694039 0.6694039 3.855031e-03 3.855031e-03 0.326741112 #> 5720  2859.5 0.6695046 0.6695046 3.853373e-03 3.853373e-03 0.326642003 #> 5721  2860.0 0.6696054 0.6696054 3.851725e-03 3.851725e-03 0.326542916 #> 5722  2860.5 0.6697061 0.6697061 3.850087e-03 3.850087e-03 0.326443851 #> 5723  2861.0 0.6698067 0.6698067 3.848460e-03 3.848460e-03 0.326344809 #> 5724  2861.5 0.6699074 0.6699074 3.846843e-03 3.846843e-03 0.326245788 #> 5725  2862.0 0.6700080 0.6700080 3.845236e-03 3.845236e-03 0.326146791 #> 5726  2862.5 0.6701085 0.6701085 3.843640e-03 3.843640e-03 0.326047816 #> 5727  2863.0 0.6702091 0.6702091 3.842054e-03 3.842054e-03 0.325948865 #> 5728  2863.5 0.6703096 0.6703096 3.840478e-03 3.840478e-03 0.325849936 #> 5729  2864.0 0.6704101 0.6704101 3.838913e-03 3.838913e-03 0.325751031 #> 5730  2864.5 0.6705105 0.6705105 3.837358e-03 3.837358e-03 0.325652149 #> 5731  2865.0 0.6706109 0.6706109 3.835813e-03 3.835813e-03 0.325553290 #> 5732  2865.5 0.6707113 0.6707113 3.834279e-03 3.834279e-03 0.325454456 #> 5733  2866.0 0.6708116 0.6708116 3.832754e-03 3.832754e-03 0.325355645 #> 5734  2866.5 0.6709119 0.6709119 3.831240e-03 3.831240e-03 0.325256859 #> 5735  2867.0 0.6710122 0.6710122 3.829736e-03 3.829736e-03 0.325158096 #> 5736  2867.5 0.6711124 0.6711124 3.828243e-03 3.828243e-03 0.325059359 #> 5737  2868.0 0.6712126 0.6712126 3.826759e-03 3.826759e-03 0.324960645 #> 5738  2868.5 0.6713128 0.6713128 3.825286e-03 3.825286e-03 0.324861957 #> 5739  2869.0 0.6714129 0.6714129 3.823822e-03 3.823822e-03 0.324763293 #> 5740  2869.5 0.6715130 0.6715130 3.822369e-03 3.822369e-03 0.324664654 #> 5741  2870.0 0.6716130 0.6716130 3.820926e-03 3.820926e-03 0.324566041 #> 5742  2870.5 0.6717131 0.6717131 3.819493e-03 3.819493e-03 0.324467452 #> 5743  2871.0 0.6718130 0.6718130 3.818070e-03 3.818070e-03 0.324368890 #> 5744  2871.5 0.6719130 0.6719130 3.816658e-03 3.816658e-03 0.324270353 #> 5745  2872.0 0.6720129 0.6720129 3.815255e-03 3.815255e-03 0.324171841 #> 5746  2872.5 0.6721128 0.6721128 3.813862e-03 3.813862e-03 0.324073356 #> 5747  2873.0 0.6722126 0.6722126 3.812480e-03 3.812480e-03 0.323974897 #> 5748  2873.5 0.6723124 0.6723124 3.811107e-03 3.811107e-03 0.323876464 #> 5749  2874.0 0.6724122 0.6724122 3.809744e-03 3.809744e-03 0.323778057 #> 5750  2874.5 0.6725119 0.6725119 3.808392e-03 3.808392e-03 0.323679677 #> 5751  2875.0 0.6726116 0.6726116 3.807049e-03 3.807049e-03 0.323581324 #> 5752  2875.5 0.6727113 0.6727113 3.805716e-03 3.805716e-03 0.323482998 #> 5753  2876.0 0.6728109 0.6728109 3.804393e-03 3.804393e-03 0.323384698 #> 5754  2876.5 0.6729105 0.6729105 3.803081e-03 3.803081e-03 0.323286426 #> 5755  2877.0 0.6730100 0.6730100 3.801778e-03 3.801778e-03 0.323188181 #> 5756  2877.5 0.6731096 0.6731096 3.800485e-03 3.800485e-03 0.323089964 #> 5757  2878.0 0.6732090 0.6732090 3.799201e-03 3.799201e-03 0.322991774 #> 5758  2878.5 0.6733085 0.6733085 3.797928e-03 3.797928e-03 0.322893612 #> 5759  2879.0 0.6734079 0.6734079 3.796665e-03 3.796665e-03 0.322795478 #> 5760  2879.5 0.6735072 0.6735072 3.795411e-03 3.795411e-03 0.322697372 #> 5761  2880.0 0.6736065 0.6736065 3.794167e-03 3.794167e-03 0.322599295 #> 5762  2880.5 0.6737058 0.6737058 3.792934e-03 3.792934e-03 0.322501245 #> 5763  2881.0 0.6738051 0.6738051 3.791710e-03 3.791710e-03 0.322403224 #> 5764  2881.5 0.6739043 0.6739043 3.790495e-03 3.790495e-03 0.322305232 #> 5765  2882.0 0.6740034 0.6740034 3.789291e-03 3.789291e-03 0.322207269 #> 5766  2882.5 0.6741026 0.6741026 3.788096e-03 3.788096e-03 0.322109335 #> 5767  2883.0 0.6742017 0.6742017 3.786911e-03 3.786911e-03 0.322011430 #> 5768  2883.5 0.6743007 0.6743007 3.785736e-03 3.785736e-03 0.321913554 #> 5769  2884.0 0.6743997 0.6743997 3.784570e-03 3.784570e-03 0.321815707 #> 5770  2884.5 0.6744987 0.6744987 3.783415e-03 3.783415e-03 0.321717890 #> 5771  2885.0 0.6745976 0.6745976 3.782268e-03 3.782268e-03 0.321620103 #> 5772  2885.5 0.6746965 0.6746965 3.781132e-03 3.781132e-03 0.321522346 #> 5773  2886.0 0.6747954 0.6747954 3.780005e-03 3.780005e-03 0.321424619 #> 5774  2886.5 0.6748942 0.6748942 3.778888e-03 3.778888e-03 0.321326922 #> 5775  2887.0 0.6749930 0.6749930 3.777781e-03 3.777781e-03 0.321229255 #> 5776  2887.5 0.6750917 0.6750917 3.776683e-03 3.776683e-03 0.321131618 #> 5777  2888.0 0.6751904 0.6751904 3.775595e-03 3.775595e-03 0.321034013 #> 5778  2888.5 0.6752890 0.6752890 3.774517e-03 3.774517e-03 0.320936438 #> 5779  2889.0 0.6753877 0.6753877 3.773448e-03 3.773448e-03 0.320838894 #> 5780  2889.5 0.6754862 0.6754862 3.772389e-03 3.772389e-03 0.320741380 #> 5781  2890.0 0.6755848 0.6755848 3.771339e-03 3.771339e-03 0.320643899 #> 5782  2890.5 0.6756833 0.6756833 3.770299e-03 3.770299e-03 0.320546448 #> 5783  2891.0 0.6757817 0.6757817 3.769269e-03 3.769269e-03 0.320449029 #> 5784  2891.5 0.6758801 0.6758801 3.768248e-03 3.768248e-03 0.320351641 #> 5785  2892.0 0.6759785 0.6759785 3.767236e-03 3.767236e-03 0.320254285 #> 5786  2892.5 0.6760768 0.6760768 3.766234e-03 3.766234e-03 0.320156962 #> 5787  2893.0 0.6761751 0.6761751 3.765242e-03 3.765242e-03 0.320059670 #> 5788  2893.5 0.6762733 0.6762733 3.764259e-03 3.764259e-03 0.319962410 #> 5789  2894.0 0.6763715 0.6763715 3.763286e-03 3.763286e-03 0.319865183 #> 5790  2894.5 0.6764697 0.6764697 3.762322e-03 3.762322e-03 0.319767988 #> 5791  2895.0 0.6765678 0.6765678 3.761368e-03 3.761368e-03 0.319670825 #> 5792  2895.5 0.6766659 0.6766659 3.760423e-03 3.760423e-03 0.319573696 #> 5793  2896.0 0.6767639 0.6767639 3.759487e-03 3.759487e-03 0.319476599 #> 5794  2896.5 0.6768619 0.6768619 3.758561e-03 3.758561e-03 0.319379536 #> 5795  2897.0 0.6769599 0.6769599 3.757645e-03 3.757645e-03 0.319282505 #> 5796  2897.5 0.6770578 0.6770578 3.756738e-03 3.756738e-03 0.319185508 #> 5797  2898.0 0.6771556 0.6771556 3.755840e-03 3.755840e-03 0.319088544 #> 5798  2898.5 0.6772534 0.6772534 3.754952e-03 3.754952e-03 0.318991614 #> 5799  2899.0 0.6773512 0.6773512 3.754073e-03 3.754073e-03 0.318894718 #> 5800  2899.5 0.6774489 0.6774489 3.753203e-03 3.753203e-03 0.318797855 #> 5801  2900.0 0.6775466 0.6775466 3.752343e-03 3.752343e-03 0.318701027 #> 5802  2900.5 0.6776443 0.6776443 3.751492e-03 3.751492e-03 0.318604232 #> 5803  2901.0 0.6777419 0.6777419 3.750650e-03 3.750650e-03 0.318507472 #> 5804  2901.5 0.6778394 0.6778394 3.749818e-03 3.749818e-03 0.318410747 #> 5805  2902.0 0.6779369 0.6779369 3.748995e-03 3.748995e-03 0.318314055 #> 5806  2902.5 0.6780344 0.6780344 3.748182e-03 3.748182e-03 0.318217399 #> 5807  2903.0 0.6781318 0.6781318 3.747378e-03 3.747378e-03 0.318120777 #> 5808  2903.5 0.6782292 0.6782292 3.746583e-03 3.746583e-03 0.318024191 #> 5809  2904.0 0.6783266 0.6783266 3.745797e-03 3.745797e-03 0.317927639 #> 5810  2904.5 0.6784239 0.6784239 3.745021e-03 3.745021e-03 0.317831123 #> 5811  2905.0 0.6785211 0.6785211 3.744254e-03 3.744254e-03 0.317734642 #> 5812  2905.5 0.6786183 0.6786183 3.743496e-03 3.743496e-03 0.317638197 #> 5813  2906.0 0.6787155 0.6787155 3.742747e-03 3.742747e-03 0.317541787 #> 5814  2906.5 0.6788126 0.6788126 3.742008e-03 3.742008e-03 0.317445414 #> 5815  2907.0 0.6789096 0.6789096 3.741278e-03 3.741278e-03 0.317349076 #> 5816  2907.5 0.6790067 0.6790067 3.740557e-03 3.740557e-03 0.317252774 #> 5817  2908.0 0.6791036 0.6791036 3.739845e-03 3.739845e-03 0.317156508 #> 5818  2908.5 0.6792006 0.6792006 3.739142e-03 3.739142e-03 0.317060279 #> 5819  2909.0 0.6792975 0.6792975 3.738449e-03 3.738449e-03 0.316964086 #> 5820  2909.5 0.6793943 0.6793943 3.737765e-03 3.737765e-03 0.316867930 #> 5821  2910.0 0.6794911 0.6794911 3.737090e-03 3.737090e-03 0.316771810 #> 5822  2910.5 0.6795878 0.6795878 3.736424e-03 3.736424e-03 0.316675728 #> 5823  2911.0 0.6796846 0.6796846 3.735768e-03 3.735768e-03 0.316579682 #> 5824  2911.5 0.6797812 0.6797812 3.735120e-03 3.735120e-03 0.316483674 #> 5825  2912.0 0.6798778 0.6798778 3.734482e-03 3.734482e-03 0.316387702 #> 5826  2912.5 0.6799744 0.6799744 3.733852e-03 3.733852e-03 0.316291769 #> 5827  2913.0 0.6800709 0.6800709 3.733232e-03 3.733232e-03 0.316195872 #> 5828  2913.5 0.6801674 0.6801674 3.732621e-03 3.732621e-03 0.316100014 #> 5829  2914.0 0.6802638 0.6802638 3.732019e-03 3.732019e-03 0.316004193 #> 5830  2914.5 0.6803602 0.6803602 3.731426e-03 3.731426e-03 0.315908410 #> 5831  2915.0 0.6804565 0.6804565 3.730843e-03 3.730843e-03 0.315812666 #> 5832  2915.5 0.6805528 0.6805528 3.730268e-03 3.730268e-03 0.315716959 #> 5833  2916.0 0.6806490 0.6806490 3.729702e-03 3.729702e-03 0.315621291 #> 5834  2916.5 0.6807452 0.6807452 3.729146e-03 3.729146e-03 0.315525661 #> 5835  2917.0 0.6808413 0.6808413 3.728598e-03 3.728598e-03 0.315430070 #> 5836  2917.5 0.6809374 0.6809374 3.728060e-03 3.728060e-03 0.315334518 #> 5837  2918.0 0.6810335 0.6810335 3.727530e-03 3.727530e-03 0.315239004 #> 5838  2918.5 0.6811295 0.6811295 3.727010e-03 3.727010e-03 0.315143530 #> 5839  2919.0 0.6812254 0.6812254 3.726498e-03 3.726498e-03 0.315048095 #> 5840  2919.5 0.6813213 0.6813213 3.725996e-03 3.725996e-03 0.314952699 #> 5841  2920.0 0.6814172 0.6814172 3.725503e-03 3.725503e-03 0.314857342 #> 5842  2920.5 0.6815130 0.6815130 3.725018e-03 3.725018e-03 0.314762025 #> 5843  2921.0 0.6816087 0.6816087 3.724543e-03 3.724543e-03 0.314666747 #> 5844  2921.5 0.6817044 0.6817044 3.724076e-03 3.724076e-03 0.314571509 #> 5845  2922.0 0.6818001 0.6818001 3.723619e-03 3.723619e-03 0.314476312 #> 5846  2922.5 0.6818957 0.6818957 3.723170e-03 3.723170e-03 0.314381154 #> 5847  2923.0 0.6819912 0.6819912 3.722730e-03 3.722730e-03 0.314286036 #> 5848  2923.5 0.6820867 0.6820867 3.722300e-03 3.722300e-03 0.314190959 #> 5849  2924.0 0.6821822 0.6821822 3.721878e-03 3.721878e-03 0.314095922 #> 5850  2924.5 0.6822776 0.6822776 3.721465e-03 3.721465e-03 0.314000925 #> 5851  2925.0 0.6823730 0.6823730 3.721061e-03 3.721061e-03 0.313905970 #> 5852  2925.5 0.6824683 0.6824683 3.720666e-03 3.720666e-03 0.313811055 #> 5853  2926.0 0.6825635 0.6825635 3.720280e-03 3.720280e-03 0.313716181 #> 5854  2926.5 0.6826587 0.6826587 3.719903e-03 3.719903e-03 0.313621348 #> 5855  2927.0 0.6827539 0.6827539 3.719535e-03 3.719535e-03 0.313526556 #> 5856  2927.5 0.6828490 0.6828490 3.719176e-03 3.719176e-03 0.313431806 #> 5857  2928.0 0.6829441 0.6829441 3.718825e-03 3.718825e-03 0.313337097 #> 5858  2928.5 0.6830391 0.6830391 3.718483e-03 3.718483e-03 0.313242430 #> 5859  2929.0 0.6831340 0.6831340 3.718151e-03 3.718151e-03 0.313147804 #> 5860  2929.5 0.6832290 0.6832290 3.717827e-03 3.717827e-03 0.313053220 #> 5861  2930.0 0.6833238 0.6833238 3.717511e-03 3.717511e-03 0.312958679 #> 5862  2930.5 0.6834186 0.6834186 3.717205e-03 3.717205e-03 0.312864179 #> 5863  2931.0 0.6835134 0.6835134 3.716908e-03 3.716908e-03 0.312769721 #> 5864  2931.5 0.6836081 0.6836081 3.716619e-03 3.716619e-03 0.312675306 #> 5865  2932.0 0.6837027 0.6837027 3.716339e-03 3.716339e-03 0.312580933 #> 5866  2932.5 0.6837973 0.6837973 3.716068e-03 3.716068e-03 0.312486603 #> 5867  2933.0 0.6838919 0.6838919 3.715806e-03 3.715806e-03 0.312392316 #> 5868  2933.5 0.6839864 0.6839864 3.715553e-03 3.715553e-03 0.312298071 #> 5869  2934.0 0.6840808 0.6840808 3.715308e-03 3.715308e-03 0.312203870 #> 5870  2934.5 0.6841752 0.6841752 3.715073e-03 3.715073e-03 0.312109711 #> 5871  2935.0 0.6842696 0.6842696 3.714846e-03 3.714846e-03 0.312015596 #> 5872  2935.5 0.6843638 0.6843638 3.714627e-03 3.714627e-03 0.311921524 #> 5873  2936.0 0.6844581 0.6844581 3.714418e-03 3.714418e-03 0.311827495 #> 5874  2936.5 0.6845523 0.6845523 3.714217e-03 3.714217e-03 0.311733510 #> 5875  2937.0 0.6846464 0.6846464 3.714025e-03 3.714025e-03 0.311639569 #> 5876  2937.5 0.6847405 0.6847405 3.713842e-03 3.713842e-03 0.311545671 #> 5877  2938.0 0.6848345 0.6848345 3.713667e-03 3.713667e-03 0.311451818 #> 5878  2938.5 0.6849285 0.6849285 3.713502e-03 3.713502e-03 0.311358009 #> 5879  2939.0 0.6850224 0.6850224 3.713345e-03 3.713345e-03 0.311264243 #> 5880  2939.5 0.6851163 0.6851163 3.713196e-03 3.713196e-03 0.311170522 #> 5881  2940.0 0.6852101 0.6852101 3.713057e-03 3.713057e-03 0.311076846 #> 5882  2940.5 0.6853039 0.6853039 3.712926e-03 3.712926e-03 0.310983214 #> 5883  2941.0 0.6853976 0.6853976 3.712804e-03 3.712804e-03 0.310889627 #> 5884  2941.5 0.6854912 0.6854912 3.712690e-03 3.712690e-03 0.310796085 #> 5885  2942.0 0.6855848 0.6855848 3.712586e-03 3.712586e-03 0.310702587 #> 5886  2942.5 0.6856784 0.6856784 3.712490e-03 3.712490e-03 0.310609135 #> 5887  2943.0 0.6857719 0.6857719 3.712402e-03 3.712402e-03 0.310515728 #> 5888  2943.5 0.6858653 0.6858653 3.712324e-03 3.712324e-03 0.310422366 #> 5889  2944.0 0.6859587 0.6859587 3.712253e-03 3.712253e-03 0.310329050 #> 5890  2944.5 0.6860520 0.6860520 3.712192e-03 3.712192e-03 0.310235779 #> 5891  2945.0 0.6861453 0.6861453 3.712139e-03 3.712139e-03 0.310142554 #> 5892  2945.5 0.6862385 0.6862385 3.712095e-03 3.712095e-03 0.310049374 #> 5893  2946.0 0.6863317 0.6863317 3.712060e-03 3.712060e-03 0.309956241 #> 5894  2946.5 0.6864248 0.6864248 3.712033e-03 3.712033e-03 0.309863153 #> 5895  2947.0 0.6865179 0.6865179 3.712015e-03 3.712015e-03 0.309770112 #> 5896  2947.5 0.6866109 0.6866109 3.712006e-03 3.712006e-03 0.309677117 #> 5897  2948.0 0.6867038 0.6867038 3.712005e-03 3.712005e-03 0.309584169 #> 5898  2948.5 0.6867967 0.6867967 3.712012e-03 3.712012e-03 0.309491267 #> 5899  2949.0 0.6868896 0.6868896 3.712029e-03 3.712029e-03 0.309398411 #> 5900  2949.5 0.6869823 0.6869823 3.712054e-03 3.712054e-03 0.309305603 #> 5901  2950.0 0.6870751 0.6870751 3.712087e-03 3.712087e-03 0.309212841 #> 5902  2950.5 0.6871677 0.6871677 3.712130e-03 3.712130e-03 0.309120126 #> 5903  2951.0 0.6872604 0.6872604 3.712180e-03 3.712180e-03 0.309027458 #> 5904  2951.5 0.6873529 0.6873529 3.712240e-03 3.712240e-03 0.308934838 #> 5905  2952.0 0.6874454 0.6874454 3.712308e-03 3.712308e-03 0.308842265 #> 5906  2952.5 0.6875379 0.6875379 3.712384e-03 3.712384e-03 0.308749739 #> 5907  2953.0 0.6876303 0.6876303 3.712470e-03 3.712470e-03 0.308657261 #> 5908  2953.5 0.6877226 0.6877226 3.712563e-03 3.712563e-03 0.308564831 #> 5909  2954.0 0.6878149 0.6878149 3.712666e-03 3.712666e-03 0.308472449 #> 5910  2954.5 0.6879071 0.6879071 3.712776e-03 3.712776e-03 0.308380114 #> 5911  2955.0 0.6879993 0.6879993 3.712896e-03 3.712896e-03 0.308287828 #> 5912  2955.5 0.6880914 0.6880914 3.713024e-03 3.713024e-03 0.308195590 #> 5913  2956.0 0.6881834 0.6881834 3.713160e-03 3.713160e-03 0.308103400 #> 5914  2956.5 0.6882754 0.6882754 3.713305e-03 3.713305e-03 0.308011258 #> 5915  2957.0 0.6883674 0.6883674 3.713459e-03 3.713459e-03 0.307919165 #> 5916  2957.5 0.6884593 0.6884593 3.713621e-03 3.713621e-03 0.307827121 #> 5917  2958.0 0.6885511 0.6885511 3.713792e-03 3.713792e-03 0.307735126 #> 5918  2958.5 0.6886428 0.6886428 3.713971e-03 3.713971e-03 0.307643179 #> 5919  2959.0 0.6887346 0.6887346 3.714159e-03 3.714159e-03 0.307551281 #> 5920  2959.5 0.6888262 0.6888262 3.714355e-03 3.714355e-03 0.307459433 #> 5921  2960.0 0.6889178 0.6889178 3.714560e-03 3.714560e-03 0.307367634 #> 5922  2960.5 0.6890093 0.6890093 3.714773e-03 3.714773e-03 0.307275884 #> 5923  2961.0 0.6891008 0.6891008 3.714995e-03 3.714995e-03 0.307184184 #> 5924  2961.5 0.6891922 0.6891922 3.715225e-03 3.715225e-03 0.307092533 #> 5925  2962.0 0.6892836 0.6892836 3.715464e-03 3.715464e-03 0.307000932 #> 5926  2962.5 0.6893749 0.6893749 3.715711e-03 3.715711e-03 0.306909381 #> 5927  2963.0 0.6894662 0.6894662 3.715967e-03 3.715967e-03 0.306817880 #> 5928  2963.5 0.6895573 0.6895573 3.716232e-03 3.716232e-03 0.306726429 #> 5929  2964.0 0.6896485 0.6896485 3.716504e-03 3.716504e-03 0.306635028 #> 5930  2964.5 0.6897395 0.6897395 3.716786e-03 3.716786e-03 0.306543677 #> 5931  2965.0 0.6898305 0.6898305 3.717075e-03 3.717075e-03 0.306452377 #> 5932  2965.5 0.6899215 0.6899215 3.717374e-03 3.717374e-03 0.306361127 #> 5933  2966.0 0.6900124 0.6900124 3.717680e-03 3.717680e-03 0.306269928 #> 5934  2966.5 0.6901032 0.6901032 3.717996e-03 3.717996e-03 0.306178780 #> 5935  2967.0 0.6901940 0.6901940 3.718319e-03 3.718319e-03 0.306087683 #> 5936  2967.5 0.6902847 0.6902847 3.718651e-03 3.718651e-03 0.305996637 #> 5937  2968.0 0.6903754 0.6903754 3.718992e-03 3.718992e-03 0.305905641 #> 5938  2968.5 0.6904660 0.6904660 3.719341e-03 3.719341e-03 0.305814697 #> 5939  2969.0 0.6905565 0.6905565 3.719699e-03 3.719699e-03 0.305723805 #> 5940  2969.5 0.6906470 0.6906470 3.720065e-03 3.720065e-03 0.305632964 #> 5941  2970.0 0.6907374 0.6907374 3.720439e-03 3.720439e-03 0.305542174 #> 5942  2970.5 0.6908277 0.6908277 3.720822e-03 3.720822e-03 0.305451436 #> 5943  2971.0 0.6909180 0.6909180 3.721213e-03 3.721213e-03 0.305360750 #> 5944  2971.5 0.6910083 0.6910083 3.721613e-03 3.721613e-03 0.305270116 #> 5945  2972.0 0.6910984 0.6910984 3.722021e-03 3.722021e-03 0.305179534 #> 5946  2972.5 0.6911886 0.6911886 3.722438e-03 3.722438e-03 0.305089004 #> 5947  2973.0 0.6912786 0.6912786 3.722863e-03 3.722863e-03 0.304998526 #> 5948  2973.5 0.6913686 0.6913686 3.723296e-03 3.723296e-03 0.304908101 #> 5949  2974.0 0.6914585 0.6914585 3.723738e-03 3.723738e-03 0.304817728 #> 5950  2974.5 0.6915484 0.6915484 3.724188e-03 3.724188e-03 0.304727408 #> 5951  2975.0 0.6916382 0.6916382 3.724647e-03 3.724647e-03 0.304637140 #> 5952  2975.5 0.6917280 0.6917280 3.725114e-03 3.725114e-03 0.304546925 #> 5953  2976.0 0.6918176 0.6918176 3.725590e-03 3.725590e-03 0.304456764 #> 5954  2976.5 0.6919073 0.6919073 3.726074e-03 3.726074e-03 0.304366655 #> 5955  2977.0 0.6919968 0.6919968 3.726566e-03 3.726566e-03 0.304276599 #> 5956  2977.5 0.6920863 0.6920863 3.727067e-03 3.727067e-03 0.304186597 #> 5957  2978.0 0.6921758 0.6921758 3.727576e-03 3.727576e-03 0.304096648 #> 5958  2978.5 0.6922652 0.6922652 3.728094e-03 3.728094e-03 0.304006753 #> 5959  2979.0 0.6923545 0.6923545 3.728619e-03 3.728619e-03 0.303916911 #> 5960  2979.5 0.6924437 0.6924437 3.729154e-03 3.729154e-03 0.303827124 #> 5961  2980.0 0.6925329 0.6925329 3.729697e-03 3.729697e-03 0.303737390 #> 5962  2980.5 0.6926220 0.6926220 3.730248e-03 3.730248e-03 0.303647709 #> 5963  2981.0 0.6927111 0.6927111 3.730807e-03 3.730807e-03 0.303558083 #> 5964  2981.5 0.6928001 0.6928001 3.731375e-03 3.731375e-03 0.303468512 #> 5965  2982.0 0.6928891 0.6928891 3.731952e-03 3.731952e-03 0.303378994 #> 5966  2982.5 0.6929779 0.6929779 3.732536e-03 3.732536e-03 0.303289531 #> 5967  2983.0 0.6930667 0.6930667 3.733130e-03 3.733130e-03 0.303200123 #> 5968  2983.5 0.6931555 0.6931555 3.733731e-03 3.733731e-03 0.303110769 #> 5969  2984.0 0.6932442 0.6932442 3.734341e-03 3.734341e-03 0.303021470 #> 5970  2984.5 0.6933328 0.6933328 3.734959e-03 3.734959e-03 0.302932225 #> 5971  2985.0 0.6934214 0.6934214 3.735586e-03 3.735586e-03 0.302843036 #> 5972  2985.5 0.6935099 0.6935099 3.736221e-03 3.736221e-03 0.302753902 #> 5973  2986.0 0.6935983 0.6935983 3.736864e-03 3.736864e-03 0.302664823 #> 5974  2986.5 0.6936867 0.6936867 3.737516e-03 3.737516e-03 0.302575799 #> 5975  2987.0 0.6937750 0.6937750 3.738176e-03 3.738176e-03 0.302486831 #> 5976  2987.5 0.6938632 0.6938632 3.738845e-03 3.738845e-03 0.302397918 #> 5977  2988.0 0.6939514 0.6939514 3.739521e-03 3.739521e-03 0.302309061 #> 5978  2988.5 0.6940395 0.6940395 3.740207e-03 3.740207e-03 0.302220260 #> 5979  2989.0 0.6941276 0.6941276 3.740900e-03 3.740900e-03 0.302131515 #> 5980  2989.5 0.6942156 0.6942156 3.741602e-03 3.741602e-03 0.302042825 #> 5981  2990.0 0.6943035 0.6943035 3.742313e-03 3.742313e-03 0.301954192 #> 5982  2990.5 0.6943914 0.6943914 3.743031e-03 3.743031e-03 0.301865615 #> 5983  2991.0 0.6944791 0.6944791 3.743758e-03 3.743758e-03 0.301777094 #> 5984  2991.5 0.6945669 0.6945669 3.744494e-03 3.744494e-03 0.301688630 #> 5985  2992.0 0.6946545 0.6946545 3.745237e-03 3.745237e-03 0.301600222 #> 5986  2992.5 0.6947421 0.6947421 3.745989e-03 3.745989e-03 0.301511871 #> 5987  2993.0 0.6948297 0.6948297 3.746750e-03 3.746750e-03 0.301423576 #> 5988  2993.5 0.6949171 0.6949171 3.747519e-03 3.747519e-03 0.301335339 #> 5989  2994.0 0.6950045 0.6950045 3.748296e-03 3.748296e-03 0.301247158 #> 5990  2994.5 0.6950919 0.6950919 3.749081e-03 3.749081e-03 0.301159035 #> 5991  2995.0 0.6951792 0.6951792 3.749875e-03 3.749875e-03 0.301070969 #> 5992  2995.5 0.6952664 0.6952664 3.750677e-03 3.750677e-03 0.300982960 #> 5993  2996.0 0.6953535 0.6953535 3.751488e-03 3.751488e-03 0.300895008 #> 5994  2996.5 0.6954406 0.6954406 3.752307e-03 3.752307e-03 0.300807114 #> 5995  2997.0 0.6955276 0.6955276 3.753134e-03 3.753134e-03 0.300719278 #> 5996  2997.5 0.6956145 0.6956145 3.753970e-03 3.753970e-03 0.300631500 #> 5997  2998.0 0.6957014 0.6957014 3.754813e-03 3.754813e-03 0.300543779 #> 5998  2998.5 0.6957882 0.6957882 3.755666e-03 3.755666e-03 0.300456116 #> 5999  2999.0 0.6958750 0.6958750 3.756526e-03 3.756526e-03 0.300368512 #> 6000  2999.5 0.6959616 0.6959616 3.757395e-03 3.757395e-03 0.300280965 #> 6001  3000.0 0.6960483 0.6960483 3.758273e-03 3.758273e-03 0.300193477 #> 6002  3000.5 0.6961348 0.6961348 3.759158e-03 3.759158e-03 0.300106048 #> 6003  3001.0 0.6962213 0.6962213 3.760052e-03 3.760052e-03 0.300018677 #> 6004  3001.5 0.6963077 0.6963077 3.760955e-03 3.760955e-03 0.299931364 #> 6005  3002.0 0.6963940 0.6963940 3.761865e-03 3.761865e-03 0.299844110 #> 6006  3002.5 0.6964803 0.6964803 3.762784e-03 3.762784e-03 0.299756916 #> 6007  3003.0 0.6965665 0.6965665 3.763711e-03 3.763711e-03 0.299669780 #> 6008  3003.5 0.6966526 0.6966526 3.764647e-03 3.764647e-03 0.299582703 #> 6009  3004.0 0.6967387 0.6967387 3.765591e-03 3.765591e-03 0.299495685 #> 6010  3004.5 0.6968247 0.6968247 3.766544e-03 3.766544e-03 0.299408727 #> 6011  3005.0 0.6969107 0.6969107 3.767504e-03 3.767504e-03 0.299321828 #> 6012  3005.5 0.6969965 0.6969965 3.768473e-03 3.768473e-03 0.299234989 #> 6013  3006.0 0.6970823 0.6970823 3.769451e-03 3.769451e-03 0.299148209 #> 6014  3006.5 0.6971681 0.6971681 3.770436e-03 3.770436e-03 0.299061489 #> 6015  3007.0 0.6972537 0.6972537 3.771430e-03 3.771430e-03 0.298974829 #> 6016  3007.5 0.6973393 0.6973393 3.772433e-03 3.772433e-03 0.298888228 #> 6017  3008.0 0.6974249 0.6974249 3.773443e-03 3.773443e-03 0.298801688 #> 6018  3008.5 0.6975103 0.6975103 3.774463e-03 3.774463e-03 0.298715208 #> 6019  3009.0 0.6975957 0.6975957 3.775490e-03 3.775490e-03 0.298628788 #> 6020  3009.5 0.6976810 0.6976810 3.776526e-03 3.776526e-03 0.298542429 #> 6021  3010.0 0.6977663 0.6977663 3.777570e-03 3.777570e-03 0.298456130 #> 6022  3010.5 0.6978515 0.6978515 3.778622e-03 3.778622e-03 0.298369892 #> 6023  3011.0 0.6979366 0.6979366 3.779683e-03 3.779683e-03 0.298283714 #> 6024  3011.5 0.6980217 0.6980217 3.780752e-03 3.780752e-03 0.298197598 #> 6025  3012.0 0.6981066 0.6981066 3.781829e-03 3.781829e-03 0.298111542 #> 6026  3012.5 0.6981915 0.6981915 3.782915e-03 3.782915e-03 0.298025547 #> 6027  3013.0 0.6982764 0.6982764 3.784009e-03 3.784009e-03 0.297939614 #> 6028  3013.5 0.6983611 0.6983611 3.785112e-03 3.785112e-03 0.297853741 #> 6029  3014.0 0.6984458 0.6984458 3.786223e-03 3.786223e-03 0.297767930 #> 6030  3014.5 0.6985305 0.6985305 3.787342e-03 3.787342e-03 0.297682181 #> 6031  3015.0 0.6986150 0.6986150 3.788469e-03 3.788469e-03 0.297596493 #> 6032  3015.5 0.6986995 0.6986995 3.789605e-03 3.789605e-03 0.297510867 #> 6033  3016.0 0.6987839 0.6987839 3.790749e-03 3.790749e-03 0.297425302 #> 6034  3016.5 0.6988683 0.6988683 3.791902e-03 3.791902e-03 0.297339800 #> 6035  3017.0 0.6989526 0.6989526 3.793062e-03 3.793062e-03 0.297254359 #> 6036  3017.5 0.6990368 0.6990368 3.794232e-03 3.794232e-03 0.297168981 #> 6037  3018.0 0.6991209 0.6991209 3.795409e-03 3.795409e-03 0.297083665 #> 6038  3018.5 0.6992050 0.6992050 3.796595e-03 3.796595e-03 0.296998411 #> 6039  3019.0 0.6992890 0.6992890 3.797789e-03 3.797789e-03 0.296913219 #> 6040  3019.5 0.6993729 0.6993729 3.798992e-03 3.798992e-03 0.296828091 #> 6041  3020.0 0.6994568 0.6994568 3.800203e-03 3.800203e-03 0.296743024 #> 6042  3020.5 0.6995406 0.6995406 3.801422e-03 3.801422e-03 0.296658021 #> 6043  3021.0 0.6996243 0.6996243 3.802650e-03 3.802650e-03 0.296573080 #> 6044  3021.5 0.6997079 0.6997079 3.803886e-03 3.803886e-03 0.296488203 #> 6045  3022.0 0.6997915 0.6997915 3.805130e-03 3.805130e-03 0.296403388 #> 6046  3022.5 0.6998750 0.6998750 3.806383e-03 3.806383e-03 0.296318637 #> 6047  3023.0 0.6999584 0.6999584 3.807644e-03 3.807644e-03 0.296233949 #> 6048  3023.5 0.7000418 0.7000418 3.808913e-03 3.808913e-03 0.296149325 #> 6049  3024.0 0.7001250 0.7001250 3.810191e-03 3.810191e-03 0.296064764 #> 6050  3024.5 0.7002083 0.7002083 3.811477e-03 3.811477e-03 0.295980266 #> 6051  3025.0 0.7002914 0.7002914 3.812772e-03 3.812772e-03 0.295895833 #> 6052  3025.5 0.7003745 0.7003745 3.814075e-03 3.814075e-03 0.295811463 #> 6053  3026.0 0.7004575 0.7004575 3.815386e-03 3.815386e-03 0.295727157 #> 6054  3026.5 0.7005404 0.7005404 3.816706e-03 3.816706e-03 0.295642915 #> 6055  3027.0 0.7006232 0.7006232 3.818034e-03 3.818034e-03 0.295558738 #> 6056  3027.5 0.7007060 0.7007060 3.819370e-03 3.819370e-03 0.295474625 #> 6057  3028.0 0.7007887 0.7007887 3.820715e-03 3.820715e-03 0.295390576 #> 6058  3028.5 0.7008713 0.7008713 3.822068e-03 3.822068e-03 0.295306591 #> 6059  3029.0 0.7009539 0.7009539 3.823429e-03 3.823429e-03 0.295222671 #> 6060  3029.5 0.7010364 0.7010364 3.824799e-03 3.824799e-03 0.295138816 #> 6061  3030.0 0.7011188 0.7011188 3.826177e-03 3.826177e-03 0.295055026 #> 6062  3030.5 0.7012011 0.7012011 3.827564e-03 3.827564e-03 0.294971300 #> 6063  3031.0 0.7012834 0.7012834 3.828959e-03 3.828959e-03 0.294887640 #> 6064  3031.5 0.7013656 0.7013656 3.830362e-03 3.830362e-03 0.294804045 #> 6065  3032.0 0.7014477 0.7014477 3.831774e-03 3.831774e-03 0.294720515 #> 6066  3032.5 0.7015298 0.7015298 3.833194e-03 3.833194e-03 0.294637050 #> 6067  3033.0 0.7016117 0.7016117 3.834623e-03 3.834623e-03 0.294553651 #> 6068  3033.5 0.7016936 0.7016936 3.836060e-03 3.836060e-03 0.294470317 #> 6069  3034.0 0.7017754 0.7017754 3.837505e-03 3.837505e-03 0.294387049 #> 6070  3034.5 0.7018572 0.7018572 3.838959e-03 3.838959e-03 0.294303847 #> 6071  3035.0 0.7019389 0.7019389 3.840421e-03 3.840421e-03 0.294220711 #> 6072  3035.5 0.7020205 0.7020205 3.841891e-03 3.841891e-03 0.294137640 #> 6073  3036.0 0.7021020 0.7021020 3.843370e-03 3.843370e-03 0.294054636 #> 6074  3036.5 0.7021834 0.7021834 3.844857e-03 3.844857e-03 0.293971698 #> 6075  3037.0 0.7022648 0.7022648 3.846353e-03 3.846353e-03 0.293888826 #> 6076  3037.5 0.7023461 0.7023461 3.847857e-03 3.847857e-03 0.293806021 #> 6077  3038.0 0.7024273 0.7024273 3.849370e-03 3.849370e-03 0.293723282 #> 6078  3038.5 0.7025085 0.7025085 3.850891e-03 3.850891e-03 0.293640610 #> 6079  3039.0 0.7025896 0.7025896 3.852420e-03 3.852420e-03 0.293558005 #> 6080  3039.5 0.7026706 0.7026706 3.853958e-03 3.853958e-03 0.293475466 #> 6081  3040.0 0.7027515 0.7027515 3.855504e-03 3.855504e-03 0.293392995 #> 6082  3040.5 0.7028324 0.7028324 3.857059e-03 3.857059e-03 0.293310590 #> 6083  3041.0 0.7029131 0.7029131 3.858622e-03 3.858622e-03 0.293228253 #> 6084  3041.5 0.7029938 0.7029938 3.860193e-03 3.860193e-03 0.293145983 #> 6085  3042.0 0.7030744 0.7030744 3.861773e-03 3.861773e-03 0.293063780 #> 6086  3042.5 0.7031550 0.7031550 3.863361e-03 3.863361e-03 0.292981645 #> 6087  3043.0 0.7032355 0.7032355 3.864958e-03 3.864958e-03 0.292899577 #> 6088  3043.5 0.7033159 0.7033159 3.866563e-03 3.866563e-03 0.292817577 #> 6089  3044.0 0.7033962 0.7033962 3.868177e-03 3.868177e-03 0.292735645 #> 6090  3044.5 0.7034764 0.7034764 3.869799e-03 3.869799e-03 0.292653781 #> 6091  3045.0 0.7035566 0.7035566 3.871429e-03 3.871429e-03 0.292571984 #> 6092  3045.5 0.7036367 0.7036367 3.873068e-03 3.873068e-03 0.292490256 #> 6093  3046.0 0.7037167 0.7037167 3.874715e-03 3.874715e-03 0.292408596 #> 6094  3046.5 0.7037966 0.7037966 3.876371e-03 3.876371e-03 0.292327005 #> 6095  3047.0 0.7038765 0.7038765 3.878036e-03 3.878036e-03 0.292245482 #> 6096  3047.5 0.7039563 0.7039563 3.879708e-03 3.879708e-03 0.292164027 #> 6097  3048.0 0.7040360 0.7040360 3.881389e-03 3.881389e-03 0.292082641 #> 6098  3048.5 0.7041156 0.7041156 3.883079e-03 3.883079e-03 0.292001324 #> 6099  3049.0 0.7041951 0.7041951 3.884777e-03 3.884777e-03 0.291920076 #> 6100  3049.5 0.7042746 0.7042746 3.886484e-03 3.886484e-03 0.291838897 #> 6101  3050.0 0.7043540 0.7043540 3.888199e-03 3.888199e-03 0.291757787 #> 6102  3050.5 0.7044333 0.7044333 3.889922e-03 3.889922e-03 0.291676746 #> 6103  3051.0 0.7045126 0.7045126 3.891654e-03 3.891654e-03 0.291595774 #> 6104  3051.5 0.7045917 0.7045917 3.893395e-03 3.893395e-03 0.291514872 #> 6105  3052.0 0.7046708 0.7046708 3.895144e-03 3.895144e-03 0.291434039 #> 6106  3052.5 0.7047498 0.7047498 3.896901e-03 3.896901e-03 0.291353276 #> 6107  3053.0 0.7048288 0.7048288 3.898667e-03 3.898667e-03 0.291272583 #> 6108  3053.5 0.7049076 0.7049076 3.900442e-03 3.900442e-03 0.291191959 #> 6109  3054.0 0.7049864 0.7049864 3.902225e-03 3.902225e-03 0.291111405 #> 6110  3054.5 0.7050651 0.7050651 3.904016e-03 3.904016e-03 0.291030922 #> 6111  3055.0 0.7051437 0.7051437 3.905816e-03 3.905816e-03 0.290950508 #> 6112  3055.5 0.7052222 0.7052222 3.907625e-03 3.907625e-03 0.290870165 #> 6113  3056.0 0.7053007 0.7053007 3.909441e-03 3.909441e-03 0.290789892 #> 6114  3056.5 0.7053790 0.7053790 3.911267e-03 3.911267e-03 0.290709690 #> 6115  3057.0 0.7054573 0.7054573 3.913101e-03 3.913101e-03 0.290629558 #> 6116  3057.5 0.7055356 0.7055356 3.914943e-03 3.914943e-03 0.290549497 #> 6117  3058.0 0.7056137 0.7056137 3.916794e-03 3.916794e-03 0.290469507 #> 6118  3058.5 0.7056918 0.7056918 3.918654e-03 3.918654e-03 0.290389588 #> 6119  3059.0 0.7057697 0.7057697 3.920522e-03 3.920522e-03 0.290309739 #> 6120  3059.5 0.7058476 0.7058476 3.922399e-03 3.922399e-03 0.290229962 #> 6121  3060.0 0.7059255 0.7059255 3.924284e-03 3.924284e-03 0.290150256 #> 6122  3060.5 0.7060032 0.7060032 3.926178e-03 3.926178e-03 0.290070621 #> 6123  3061.0 0.7060809 0.7060809 3.928080e-03 3.928080e-03 0.289991058 #> 6124  3061.5 0.7061584 0.7061584 3.929991e-03 3.929991e-03 0.289911566 #> 6125  3062.0 0.7062359 0.7062359 3.931910e-03 3.931910e-03 0.289832146 #> 6126  3062.5 0.7063134 0.7063134 3.933838e-03 3.933838e-03 0.289752798 #> 6127  3063.0 0.7063907 0.7063907 3.935774e-03 3.935774e-03 0.289673521 #> 6128  3063.5 0.7064680 0.7064680 3.937719e-03 3.937719e-03 0.289594317 #> 6129  3064.0 0.7065451 0.7065451 3.939673e-03 3.939673e-03 0.289515184 #> 6130  3064.5 0.7066222 0.7066222 3.941635e-03 3.941635e-03 0.289436124 #> 6131  3065.0 0.7066993 0.7066993 3.943606e-03 3.943606e-03 0.289357136 #> 6132  3065.5 0.7067762 0.7067762 3.945585e-03 3.945585e-03 0.289278220 #> 6133  3066.0 0.7068530 0.7068530 3.947573e-03 3.947573e-03 0.289199377 #> 6134  3066.5 0.7069298 0.7069298 3.949569e-03 3.949569e-03 0.289120607 #> 6135  3067.0 0.7070065 0.7070065 3.951575e-03 3.951575e-03 0.289041909 #> 6136  3067.5 0.7070831 0.7070831 3.953588e-03 3.953588e-03 0.288963284 #> 6137  3068.0 0.7071597 0.7071597 3.955610e-03 3.955610e-03 0.288884732 #> 6138  3068.5 0.7072361 0.7072361 3.957641e-03 3.957641e-03 0.288806253 #> 6139  3069.0 0.7073125 0.7073125 3.959681e-03 3.959681e-03 0.288727847 #> 6140  3069.5 0.7073888 0.7073888 3.961729e-03 3.961729e-03 0.288649515 #> 6141  3070.0 0.7074650 0.7074650 3.963786e-03 3.963786e-03 0.288571255 #> 6142  3070.5 0.7075411 0.7075411 3.965851e-03 3.965851e-03 0.288493070 #> 6143  3071.0 0.7076171 0.7076171 3.967925e-03 3.967925e-03 0.288414957 #> 6144  3071.5 0.7076931 0.7076931 3.970007e-03 3.970007e-03 0.288336919 #> 6145  3072.0 0.7077689 0.7077689 3.972099e-03 3.972099e-03 0.288258954 #> 6146  3072.5 0.7078447 0.7078447 3.974198e-03 3.974198e-03 0.288181063 #> 6147  3073.0 0.7079204 0.7079204 3.976307e-03 3.976307e-03 0.288103246 #> 6148  3073.5 0.7079961 0.7079961 3.978424e-03 3.978424e-03 0.288025504 #> 6149  3074.0 0.7080716 0.7080716 3.980550e-03 3.980550e-03 0.287947835 #> 6150  3074.5 0.7081471 0.7081471 3.982684e-03 3.982684e-03 0.287870241 #> 6151  3075.0 0.7082225 0.7082225 3.984827e-03 3.984827e-03 0.287792721 #> 6152  3075.5 0.7082977 0.7082977 3.986979e-03 3.986979e-03 0.287715276 #> 6153  3076.0 0.7083730 0.7083730 3.989139e-03 3.989139e-03 0.287637905 #> 6154  3076.5 0.7084481 0.7084481 3.991308e-03 3.991308e-03 0.287560609 #> 6155  3077.0 0.7085231 0.7085231 3.993486e-03 3.993486e-03 0.287483388 #> 6156  3077.5 0.7085981 0.7085981 3.995673e-03 3.995673e-03 0.287406242 #> 6157  3078.0 0.7086730 0.7086730 3.997868e-03 3.997868e-03 0.287329171 #> 6158  3078.5 0.7087478 0.7087478 4.000071e-03 4.000071e-03 0.287252175 #> 6159  3079.0 0.7088225 0.7088225 4.002284e-03 4.002284e-03 0.287175254 #> 6160  3079.5 0.7088971 0.7088971 4.004505e-03 4.004505e-03 0.287098409 #> 6161  3080.0 0.7089716 0.7089716 4.006735e-03 4.006735e-03 0.287021640 #> 6162  3080.5 0.7090461 0.7090461 4.008974e-03 4.008974e-03 0.286944946 #> 6163  3081.0 0.7091205 0.7091205 4.011221e-03 4.011221e-03 0.286868327 #> 6164  3081.5 0.7091947 0.7091947 4.013477e-03 4.013477e-03 0.286791785 #> 6165  3082.0 0.7092689 0.7092689 4.015742e-03 4.015742e-03 0.286715318 #> 6166  3082.5 0.7093431 0.7093431 4.018015e-03 4.018015e-03 0.286638927 #> 6167  3083.0 0.7094171 0.7094171 4.020297e-03 4.020297e-03 0.286562613 #> 6168  3083.5 0.7094910 0.7094910 4.022588e-03 4.022588e-03 0.286486375 #> 6169  3084.0 0.7095649 0.7095649 4.024888e-03 4.024888e-03 0.286410213 #> 6170  3084.5 0.7096387 0.7096387 4.027197e-03 4.027197e-03 0.286334127 #> 6171  3085.0 0.7097124 0.7097124 4.029514e-03 4.029514e-03 0.286258119 #> 6172  3085.5 0.7097860 0.7097860 4.031840e-03 4.031840e-03 0.286182186 #> 6173  3086.0 0.7098595 0.7098595 4.034174e-03 4.034174e-03 0.286106331 #> 6174  3086.5 0.7099329 0.7099329 4.036518e-03 4.036518e-03 0.286030553 #> 6175  3087.0 0.7100063 0.7100063 4.038870e-03 4.038870e-03 0.285954851 #> 6176  3087.5 0.7100795 0.7100795 4.041231e-03 4.041231e-03 0.285879227 #> 6177  3088.0 0.7101527 0.7101527 4.043601e-03 4.043601e-03 0.285803680 #> 6178  3088.5 0.7102258 0.7102258 4.045980e-03 4.045980e-03 0.285728210 #> 6179  3089.0 0.7102988 0.7102988 4.048367e-03 4.048367e-03 0.285652818 #> 6180  3089.5 0.7103717 0.7103717 4.050763e-03 4.050763e-03 0.285577503 #> 6181  3090.0 0.7104446 0.7104446 4.053168e-03 4.053168e-03 0.285502266 #> 6182  3090.5 0.7105173 0.7105173 4.055582e-03 4.055582e-03 0.285427106 #> 6183  3091.0 0.7105900 0.7105900 4.058005e-03 4.058005e-03 0.285352025 #> 6184  3091.5 0.7106625 0.7106625 4.060436e-03 4.060436e-03 0.285277021 #> 6185  3092.0 0.7107350 0.7107350 4.062876e-03 4.062876e-03 0.285202095 #> 6186  3092.5 0.7108074 0.7108074 4.065325e-03 4.065325e-03 0.285127248 #> 6187  3093.0 0.7108797 0.7108797 4.067783e-03 4.067783e-03 0.285052479 #> 6188  3093.5 0.7109520 0.7109520 4.070250e-03 4.070250e-03 0.284977788 #> 6189  3094.0 0.7110241 0.7110241 4.072726e-03 4.072726e-03 0.284903176 #> 6190  3094.5 0.7110961 0.7110961 4.075210e-03 4.075210e-03 0.284828643 #> 6191  3095.0 0.7111681 0.7111681 4.077703e-03 4.077703e-03 0.284754188 #> 6192  3095.5 0.7112400 0.7112400 4.080206e-03 4.080206e-03 0.284679812 #> 6193  3096.0 0.7113118 0.7113118 4.082717e-03 4.082717e-03 0.284605515 #> 6194  3096.5 0.7113835 0.7113835 4.085237e-03 4.085237e-03 0.284531297 #> 6195  3097.0 0.7114551 0.7114551 4.087765e-03 4.087765e-03 0.284457158 #> 6196  3097.5 0.7115266 0.7115266 4.090303e-03 4.090303e-03 0.284383099 #> 6197  3098.0 0.7115980 0.7115980 4.092850e-03 4.092850e-03 0.284309119 #> 6198  3098.5 0.7116694 0.7116694 4.095405e-03 4.095405e-03 0.284235218 #> 6199  3099.0 0.7117406 0.7117406 4.097969e-03 4.097969e-03 0.284161397 #> 6200  3099.5 0.7118118 0.7118118 4.100543e-03 4.100543e-03 0.284087656 #> 6201  3100.0 0.7118829 0.7118829 4.103125e-03 4.103125e-03 0.284013994 #> 6202  3100.5 0.7119539 0.7119539 4.105716e-03 4.105716e-03 0.283940413 #> 6203  3101.0 0.7120248 0.7120248 4.108316e-03 4.108316e-03 0.283866911 #> 6204  3101.5 0.7120956 0.7120956 4.110925e-03 4.110925e-03 0.283793490 #> 6205  3102.0 0.7121663 0.7121663 4.113543e-03 4.113543e-03 0.283720148 #> 6206  3102.5 0.7122369 0.7122369 4.116170e-03 4.116170e-03 0.283646888 #> 6207  3103.0 0.7123075 0.7123075 4.118805e-03 4.118805e-03 0.283573707 #> 6208  3103.5 0.7123779 0.7123779 4.121450e-03 4.121450e-03 0.283500607 #> 6209  3104.0 0.7124483 0.7124483 4.124104e-03 4.124104e-03 0.283427588 #> 6210  3104.5 0.7125186 0.7125186 4.126766e-03 4.126766e-03 0.283354650 #> 6211  3105.0 0.7125888 0.7125888 4.129438e-03 4.129438e-03 0.283281793 #> 6212  3105.5 0.7126589 0.7126589 4.132119e-03 4.132119e-03 0.283209016 #> 6213  3106.0 0.7127289 0.7127289 4.134808e-03 4.134808e-03 0.283136321 #> 6214  3106.5 0.7127988 0.7127988 4.137507e-03 4.137507e-03 0.283063707 #> 6215  3107.0 0.7128686 0.7128686 4.140214e-03 4.140214e-03 0.282991174 #> 6216  3107.5 0.7129383 0.7129383 4.142931e-03 4.142931e-03 0.282918723 #> 6217  3108.0 0.7130080 0.7130080 4.145656e-03 4.145656e-03 0.282846353 #> 6218  3108.5 0.7130775 0.7130775 4.148391e-03 4.148391e-03 0.282774065 #> 6219  3109.0 0.7131470 0.7131470 4.151134e-03 4.151134e-03 0.282701859 #> 6220  3109.5 0.7132164 0.7132164 4.153887e-03 4.153887e-03 0.282629734 #> 6221  3110.0 0.7132857 0.7132857 4.156649e-03 4.156649e-03 0.282557692 #> 6222  3110.5 0.7133548 0.7133548 4.159419e-03 4.159419e-03 0.282485732 #> 6223  3111.0 0.7134239 0.7134239 4.162199e-03 4.162199e-03 0.282413854 #> 6224  3111.5 0.7134930 0.7134930 4.164988e-03 4.164988e-03 0.282342058 #> 6225  3112.0 0.7135619 0.7135619 4.167786e-03 4.167786e-03 0.282270344 #> 6226  3112.5 0.7136307 0.7136307 4.170592e-03 4.170592e-03 0.282198714 #> 6227  3113.0 0.7136994 0.7136994 4.173408e-03 4.173408e-03 0.282127166 #> 6228  3113.5 0.7137681 0.7137681 4.176233e-03 4.176233e-03 0.282055700 #> 6229  3114.0 0.7138366 0.7138366 4.179067e-03 4.179067e-03 0.281984318 #> 6230  3114.5 0.7139051 0.7139051 4.181911e-03 4.181911e-03 0.281913018 #> 6231  3115.0 0.7139734 0.7139734 4.184763e-03 4.184763e-03 0.281841802 #> 6232  3115.5 0.7140417 0.7140417 4.187624e-03 4.187624e-03 0.281770668 #> 6233  3116.0 0.7141099 0.7141099 4.190495e-03 4.190495e-03 0.281699619 #> 6234  3116.5 0.7141780 0.7141780 4.193374e-03 4.193374e-03 0.281628652 #> 6235  3117.0 0.7142460 0.7142460 4.196263e-03 4.196263e-03 0.281557769 #> 6236  3117.5 0.7143139 0.7143139 4.199161e-03 4.199161e-03 0.281486970 #> 6237  3118.0 0.7143817 0.7143817 4.202068e-03 4.202068e-03 0.281416254 #> 6238  3118.5 0.7144494 0.7144494 4.204984e-03 4.204984e-03 0.281345622 #> 6239  3119.0 0.7145170 0.7145170 4.207909e-03 4.207909e-03 0.281275075 #> 6240  3119.5 0.7145845 0.7145845 4.210843e-03 4.210843e-03 0.281204611 #> 6241  3120.0 0.7146520 0.7146520 4.213787e-03 4.213787e-03 0.281134232 #> 6242  3120.5 0.7147193 0.7147193 4.216740e-03 4.216740e-03 0.281063936 #> 6243  3121.0 0.7147866 0.7147866 4.219701e-03 4.219701e-03 0.280993726 #> 6244  3121.5 0.7148537 0.7148537 4.222672e-03 4.222672e-03 0.280923599 #> 6245  3122.0 0.7149208 0.7149208 4.225653e-03 4.225653e-03 0.280853558 #> 6246  3122.5 0.7149878 0.7149878 4.228642e-03 4.228642e-03 0.280783601 #> 6247  3123.0 0.7150546 0.7150546 4.231641e-03 4.231641e-03 0.280713729 #> 6248  3123.5 0.7151214 0.7151214 4.234648e-03 4.234648e-03 0.280643942 #> 6249  3124.0 0.7151881 0.7151881 4.237665e-03 4.237665e-03 0.280574240 #> 6250  3124.5 0.7152547 0.7152547 4.240692e-03 4.240692e-03 0.280504623 #> 6251  3125.0 0.7153212 0.7153212 4.243727e-03 4.243727e-03 0.280435092 #> 6252  3125.5 0.7153876 0.7153876 4.246772e-03 4.246772e-03 0.280365646 #> 6253  3126.0 0.7154539 0.7154539 4.249826e-03 4.249826e-03 0.280296285 #> 6254  3126.5 0.7155201 0.7155201 4.252889e-03 4.252889e-03 0.280227010 #> 6255  3127.0 0.7155862 0.7155862 4.255961e-03 4.255961e-03 0.280157821 #> 6256  3127.5 0.7156522 0.7156522 4.259043e-03 4.259043e-03 0.280088718 #> 6257  3128.0 0.7157182 0.7157182 4.262134e-03 4.262134e-03 0.280019701 #> 6258  3128.5 0.7157840 0.7157840 4.265234e-03 4.265234e-03 0.279950770 #> 6259  3129.0 0.7158497 0.7158497 4.268343e-03 4.268343e-03 0.279881924 #> 6260  3129.5 0.7159154 0.7159154 4.271462e-03 4.271462e-03 0.279813166 #> 6261  3130.0 0.7159809 0.7159809 4.274590e-03 4.274590e-03 0.279744493 #> 6262  3130.5 0.7160464 0.7160464 4.277727e-03 4.277727e-03 0.279675908 #> 6263  3131.0 0.7161117 0.7161117 4.280874e-03 4.280874e-03 0.279607408 #> 6264  3131.5 0.7161770 0.7161770 4.284030e-03 4.284030e-03 0.279538996 #> 6265  3132.0 0.7162421 0.7162421 4.287195e-03 4.287195e-03 0.279470670 #> 6266  3132.5 0.7163072 0.7163072 4.290370e-03 4.290370e-03 0.279402432 #> 6267  3133.0 0.7163722 0.7163722 4.293554e-03 4.293554e-03 0.279334280 #> 6268  3133.5 0.7164370 0.7164370 4.296747e-03 4.296747e-03 0.279266216 #> 6269  3134.0 0.7165018 0.7165018 4.299949e-03 4.299949e-03 0.279198239 #> 6270  3134.5 0.7165665 0.7165665 4.303161e-03 4.303161e-03 0.279130350 #> 6271  3135.0 0.7166311 0.7166311 4.306383e-03 4.306383e-03 0.279062548 #> 6272  3135.5 0.7166956 0.7166956 4.309613e-03 4.309613e-03 0.278994833 #> 6273  3136.0 0.7167599 0.7167599 4.312853e-03 4.312853e-03 0.278927207 #> 6274  3136.5 0.7168242 0.7168242 4.316103e-03 4.316103e-03 0.278859668 #> 6275  3137.0 0.7168884 0.7168884 4.319361e-03 4.319361e-03 0.278792217 #> 6276  3137.5 0.7169525 0.7169525 4.322630e-03 4.322630e-03 0.278724854 #> 6277  3138.0 0.7170165 0.7170165 4.325907e-03 4.325907e-03 0.278657580 #> 6278  3138.5 0.7170804 0.7170804 4.329194e-03 4.329194e-03 0.278590394 #> 6279  3139.0 0.7171442 0.7171442 4.332491e-03 4.332491e-03 0.278523296 #> 6280  3139.5 0.7172079 0.7172079 4.335796e-03 4.335796e-03 0.278456287 #> 6281  3140.0 0.7172715 0.7172715 4.339112e-03 4.339112e-03 0.278389366 #> 6282  3140.5 0.7173350 0.7173350 4.342436e-03 4.342436e-03 0.278322534 #> 6283  3141.0 0.7173984 0.7173984 4.345770e-03 4.345770e-03 0.278255792 #> 6284  3141.5 0.7174617 0.7174617 4.349114e-03 4.349114e-03 0.278189138 #> 6285  3142.0 0.7175250 0.7175250 4.352467e-03 4.352467e-03 0.278122573 #> 6286  3142.5 0.7175881 0.7175881 4.355829e-03 4.355829e-03 0.278056097 #> 6287  3143.0 0.7176511 0.7176511 4.359201e-03 4.359201e-03 0.277989711 #> 6288  3143.5 0.7177140 0.7177140 4.362583e-03 4.362583e-03 0.277923414 #> 6289  3144.0 0.7177768 0.7177768 4.365974e-03 4.365974e-03 0.277857207 #> 6290  3144.5 0.7178395 0.7178395 4.369374e-03 4.369374e-03 0.277791090 #> 6291  3145.0 0.7179022 0.7179022 4.372784e-03 4.372784e-03 0.277725062 #> 6292  3145.5 0.7179647 0.7179647 4.376203e-03 4.376203e-03 0.277659124 #> 6293  3146.0 0.7180271 0.7180271 4.379632e-03 4.379632e-03 0.277593276 #> 6294  3146.5 0.7180894 0.7180894 4.383071e-03 4.383071e-03 0.277527519 #> 6295  3147.0 0.7181516 0.7181516 4.386519e-03 4.386519e-03 0.277461851 #> 6296  3147.5 0.7182137 0.7182137 4.389976e-03 4.389976e-03 0.277396274 #> 6297  3148.0 0.7182758 0.7182758 4.393443e-03 4.393443e-03 0.277330788 #> 6298  3148.5 0.7183377 0.7183377 4.396920e-03 4.396920e-03 0.277265392 #> 6299  3149.0 0.7183995 0.7183995 4.400406e-03 4.400406e-03 0.277200086 #> 6300  3149.5 0.7184612 0.7184612 4.403902e-03 4.403902e-03 0.277134872 #> 6301  3150.0 0.7185228 0.7185228 4.407407e-03 4.407407e-03 0.277069748 #> 6302  3150.5 0.7185844 0.7185844 4.410922e-03 4.410922e-03 0.277004716 #> 6303  3151.0 0.7186458 0.7186458 4.414446e-03 4.414446e-03 0.276939774 #> 6304  3151.5 0.7187071 0.7187071 4.417980e-03 4.417980e-03 0.276874924 #> 6305  3152.0 0.7187683 0.7187683 4.421524e-03 4.421524e-03 0.276810165 #> 6306  3152.5 0.7188294 0.7188294 4.425077e-03 4.425077e-03 0.276745498 #> 6307  3153.0 0.7188904 0.7188904 4.428640e-03 4.428640e-03 0.276680922 #> 6308  3153.5 0.7189513 0.7189513 4.432213e-03 4.432213e-03 0.276616438 #> 6309  3154.0 0.7190122 0.7190122 4.435795e-03 4.435795e-03 0.276552046 #> 6310  3154.5 0.7190729 0.7190729 4.439387e-03 4.439387e-03 0.276487746 #> 6311  3155.0 0.7191335 0.7191335 4.442988e-03 4.442988e-03 0.276423538 #> 6312  3155.5 0.7191940 0.7191940 4.446599e-03 4.446599e-03 0.276359422 #> 6313  3156.0 0.7192544 0.7192544 4.450220e-03 4.450220e-03 0.276295399 #> 6314  3156.5 0.7193147 0.7193147 4.453850e-03 4.453850e-03 0.276231468 #> 6315  3157.0 0.7193749 0.7193749 4.457490e-03 4.457490e-03 0.276167629 #> 6316  3157.5 0.7194350 0.7194350 4.461140e-03 4.461140e-03 0.276103883 #> 6317  3158.0 0.7194950 0.7194950 4.464799e-03 4.464799e-03 0.276040230 #> 6318  3158.5 0.7195549 0.7195549 4.468469e-03 4.468469e-03 0.275976670 #> 6319  3159.0 0.7196147 0.7196147 4.472147e-03 4.472147e-03 0.275913202 #> 6320  3159.5 0.7196743 0.7196743 4.475836e-03 4.475836e-03 0.275849828 #> 6321  3160.0 0.7197339 0.7197339 4.479534e-03 4.479534e-03 0.275786547 #> 6322  3160.5 0.7197934 0.7197934 4.483242e-03 4.483242e-03 0.275723359 #> 6323  3161.0 0.7198528 0.7198528 4.486960e-03 4.486960e-03 0.275660265 #> 6324  3161.5 0.7199120 0.7199120 4.490688e-03 4.490688e-03 0.275597264 #> 6325  3162.0 0.7199712 0.7199712 4.494425e-03 4.494425e-03 0.275534357 #> 6326  3162.5 0.7200303 0.7200303 4.498172e-03 4.498172e-03 0.275471544 #> 6327  3163.0 0.7200892 0.7200892 4.501929e-03 4.501929e-03 0.275408825 #> 6328  3163.5 0.7201481 0.7201481 4.505695e-03 4.505695e-03 0.275346200 #> 6329  3164.0 0.7202069 0.7202069 4.509471e-03 4.509471e-03 0.275283669 #> 6330  3164.5 0.7202655 0.7202655 4.513257e-03 4.513257e-03 0.275221232 #> 6331  3165.0 0.7203241 0.7203241 4.517053e-03 4.517053e-03 0.275158889 #> 6332  3165.5 0.7203825 0.7203825 4.520859e-03 4.520859e-03 0.275096641 #> 6333  3166.0 0.7204408 0.7204408 4.524675e-03 4.524675e-03 0.275034488 #> 6334  3166.5 0.7204991 0.7204991 4.528500e-03 4.528500e-03 0.274972429 #> 6335  3167.0 0.7205572 0.7205572 4.532335e-03 4.532335e-03 0.274910466 #> 6336  3167.5 0.7206152 0.7206152 4.536180e-03 4.536180e-03 0.274848597 #> 6337  3168.0 0.7206731 0.7206731 4.540035e-03 4.540035e-03 0.274786823 #> 6338  3168.5 0.7207310 0.7207310 4.543900e-03 4.543900e-03 0.274725145 #> 6339  3169.0 0.7207887 0.7207887 4.547774e-03 4.547774e-03 0.274663561 #> 6340  3169.5 0.7208463 0.7208463 4.551659e-03 4.551659e-03 0.274602074 #> 6341  3170.0 0.7209038 0.7209038 4.555553e-03 4.555553e-03 0.274540681 #> 6342  3170.5 0.7209612 0.7209612 4.559457e-03 4.559457e-03 0.274479385 #> 6343  3171.0 0.7210184 0.7210184 4.563371e-03 4.563371e-03 0.274418184 #> 6344  3171.5 0.7210756 0.7210756 4.567295e-03 4.567295e-03 0.274357079 #> 6345  3172.0 0.7211327 0.7211327 4.571229e-03 4.571229e-03 0.274296070 #> 6346  3172.5 0.7211897 0.7211897 4.575173e-03 4.575173e-03 0.274235157 #> 6347  3173.0 0.7212465 0.7212465 4.579126e-03 4.579126e-03 0.274174341 #> 6348  3173.5 0.7213033 0.7213033 4.583090e-03 4.583090e-03 0.274113620 #> 6349  3174.0 0.7213599 0.7213599 4.587063e-03 4.587063e-03 0.274052997 #> 6350  3174.5 0.7214165 0.7214165 4.591047e-03 4.591047e-03 0.273992470 #> 6351  3175.0 0.7214729 0.7214729 4.595040e-03 4.595040e-03 0.273932039 #> 6352  3175.5 0.7215293 0.7215293 4.599044e-03 4.599044e-03 0.273871706 #> 6353  3176.0 0.7215855 0.7215855 4.603057e-03 4.603057e-03 0.273811469 #> 6354  3176.5 0.7216416 0.7216416 4.607080e-03 4.607080e-03 0.273751329 #> 6355  3177.0 0.7216976 0.7216976 4.611114e-03 4.611114e-03 0.273691287 #> 6356  3177.5 0.7217535 0.7217535 4.615157e-03 4.615157e-03 0.273631342 #> 6357  3178.0 0.7218093 0.7218093 4.619210e-03 4.619210e-03 0.273571494 #> 6358  3178.5 0.7218650 0.7218650 4.623274e-03 4.623274e-03 0.273511744 #> 6359  3179.0 0.7219206 0.7219206 4.627347e-03 4.627347e-03 0.273452092 #> 6360  3179.5 0.7219760 0.7219760 4.631430e-03 4.631430e-03 0.273392537 #> 6361  3180.0 0.7220314 0.7220314 4.635524e-03 4.635524e-03 0.273333080 #> 6362  3180.5 0.7220867 0.7220867 4.639627e-03 4.639627e-03 0.273273721 #> 6363  3181.0 0.7221418 0.7221418 4.643740e-03 4.643740e-03 0.273214461 #> 6364  3181.5 0.7221968 0.7221968 4.647864e-03 4.647864e-03 0.273155298 #> 6365  3182.0 0.7222518 0.7222518 4.651997e-03 4.651997e-03 0.273096234 #> 6366  3182.5 0.7223066 0.7223066 4.656141e-03 4.656141e-03 0.273037269 #> 6367  3183.0 0.7223613 0.7223613 4.660295e-03 4.660295e-03 0.272978402 #> 6368  3183.5 0.7224159 0.7224159 4.664458e-03 4.664458e-03 0.272919633 #> 6369  3184.0 0.7224704 0.7224704 4.668632e-03 4.668632e-03 0.272860964 #> 6370  3184.5 0.7225248 0.7225248 4.672816e-03 4.672816e-03 0.272802394 #> 6371  3185.0 0.7225791 0.7225791 4.677010e-03 4.677010e-03 0.272743922 #> 6372  3185.5 0.7226332 0.7226332 4.681214e-03 4.681214e-03 0.272685550 #> 6373  3186.0 0.7226873 0.7226873 4.685428e-03 4.685428e-03 0.272627277 #> 6374  3186.5 0.7227412 0.7227412 4.689653e-03 4.689653e-03 0.272569104 #> 6375  3187.0 0.7227951 0.7227951 4.693887e-03 4.693887e-03 0.272511030 #> 6376  3187.5 0.7228488 0.7228488 4.698132e-03 4.698132e-03 0.272453056 #> 6377  3188.0 0.7229024 0.7229024 4.702387e-03 4.702387e-03 0.272395181 #> 6378  3188.5 0.7229559 0.7229559 4.706652e-03 4.706652e-03 0.272337407 #> 6379  3189.0 0.7230093 0.7230093 4.710927e-03 4.710927e-03 0.272279732 #> 6380  3189.5 0.7230626 0.7230626 4.715212e-03 4.715212e-03 0.272222158 #> 6381  3190.0 0.7231158 0.7231158 4.719507e-03 4.719507e-03 0.272164684 #> 6382  3190.5 0.7231689 0.7231689 4.723813e-03 4.723813e-03 0.272107310 #> 6383  3191.0 0.7232218 0.7232218 4.728129e-03 4.728129e-03 0.272050037 #> 6384  3191.5 0.7232747 0.7232747 4.732455e-03 4.732455e-03 0.271992865 #> 6385  3192.0 0.7233274 0.7233274 4.736791e-03 4.736791e-03 0.271935793 #> 6386  3192.5 0.7233800 0.7233800 4.741138e-03 4.741138e-03 0.271878822 #> 6387  3193.0 0.7234326 0.7234326 4.745494e-03 4.745494e-03 0.271821952 #> 6388  3193.5 0.7234850 0.7234850 4.749861e-03 4.749861e-03 0.271765183 #> 6389  3194.0 0.7235372 0.7235372 4.754238e-03 4.754238e-03 0.271708515 #> 6390  3194.5 0.7235894 0.7235894 4.758626e-03 4.758626e-03 0.271651949 #> 6391  3195.0 0.7236415 0.7236415 4.763024e-03 4.763024e-03 0.271595484 #> 6392  3195.5 0.7236934 0.7236934 4.767431e-03 4.767431e-03 0.271539121 #> 6393  3196.0 0.7237453 0.7237453 4.771850e-03 4.771850e-03 0.271482860 #> 6394  3196.5 0.7237970 0.7237970 4.776278e-03 4.776278e-03 0.271426700 #> 6395  3197.0 0.7238486 0.7238486 4.780717e-03 4.780717e-03 0.271370642 #> 6396  3197.5 0.7239001 0.7239001 4.785166e-03 4.785166e-03 0.271314687 #> 6397  3198.0 0.7239515 0.7239515 4.789625e-03 4.789625e-03 0.271258833 #> 6398  3198.5 0.7240028 0.7240028 4.794095e-03 4.794095e-03 0.271203082 #> 6399  3199.0 0.7240540 0.7240540 4.798575e-03 4.798575e-03 0.271147433 #> 6400  3199.5 0.7241050 0.7241050 4.803065e-03 4.803065e-03 0.271091887 #> 6401  3200.0 0.7241560 0.7241560 4.807566e-03 4.807566e-03 0.271036444 #> 6402  3200.5 0.7242068 0.7242068 4.812077e-03 4.812077e-03 0.270981103 #> 6403  3201.0 0.7242575 0.7242575 4.816598e-03 4.816598e-03 0.270925865 #> 6404  3201.5 0.7243081 0.7243081 4.821130e-03 4.821130e-03 0.270870730 #> 6405  3202.0 0.7243586 0.7243586 4.825672e-03 4.825672e-03 0.270815699 #> 6406  3202.5 0.7244090 0.7244090 4.830224e-03 4.830224e-03 0.270760770 #> 6407  3203.0 0.7244593 0.7244593 4.834787e-03 4.834787e-03 0.270705946 #> 6408  3203.5 0.7245094 0.7245094 4.839360e-03 4.839360e-03 0.270651224 #> 6409  3204.0 0.7245594 0.7245594 4.843944e-03 4.843944e-03 0.270596606 #> 6410  3204.5 0.7246094 0.7246094 4.848538e-03 4.848538e-03 0.270542092 #> 6411  3205.0 0.7246592 0.7246592 4.853142e-03 4.853142e-03 0.270487682 #> 6412  3205.5 0.7247089 0.7247089 4.857757e-03 4.857757e-03 0.270433376 #> 6413  3206.0 0.7247584 0.7247584 4.862382e-03 4.862382e-03 0.270379174 #> 6414  3206.5 0.7248079 0.7248079 4.867018e-03 4.867018e-03 0.270325076 #> 6415  3207.0 0.7248573 0.7248573 4.871664e-03 4.871664e-03 0.270271083 #> 6416  3207.5 0.7249065 0.7249065 4.876320e-03 4.876320e-03 0.270217194 #> 6417  3208.0 0.7249556 0.7249556 4.880987e-03 4.880987e-03 0.270163410 #> 6418  3208.5 0.7250046 0.7250046 4.885665e-03 4.885665e-03 0.270109730 #> 6419  3209.0 0.7250535 0.7250535 4.890353e-03 4.890353e-03 0.270056156 #> 6420  3209.5 0.7251023 0.7251023 4.895051e-03 4.895051e-03 0.270002686 #> 6421  3210.0 0.7251509 0.7251509 4.899760e-03 4.899760e-03 0.269949321 #> 6422  3210.5 0.7251995 0.7251995 4.904479e-03 4.904479e-03 0.269896062 #> 6423  3211.0 0.7252479 0.7252479 4.909209e-03 4.909209e-03 0.269842908 #> 6424  3211.5 0.7252962 0.7252962 4.913949e-03 4.913949e-03 0.269789860 #> 6425  3212.0 0.7253444 0.7253444 4.918700e-03 4.918700e-03 0.269736917 #> 6426  3212.5 0.7253925 0.7253925 4.923462e-03 4.923462e-03 0.269684079 #> 6427  3213.0 0.7254404 0.7254404 4.928234e-03 4.928234e-03 0.269631348 #> 6428  3213.5 0.7254883 0.7254883 4.933016e-03 4.933016e-03 0.269578722 #> 6429  3214.0 0.7255360 0.7255360 4.937809e-03 4.937809e-03 0.269526203 #> 6430  3214.5 0.7255836 0.7255836 4.942612e-03 4.942612e-03 0.269473790 #> 6431  3215.0 0.7256311 0.7256311 4.947427e-03 4.947427e-03 0.269421483 #> 6432  3215.5 0.7256785 0.7256785 4.952251e-03 4.952251e-03 0.269369283 #> 6433  3216.0 0.7257257 0.7257257 4.957086e-03 4.957086e-03 0.269317189 #> 6434  3216.5 0.7257729 0.7257729 4.961932e-03 4.961932e-03 0.269265202 #> 6435  3217.0 0.7258199 0.7258199 4.966788e-03 4.966788e-03 0.269213321 #> 6436  3217.5 0.7258668 0.7258668 4.971655e-03 4.971655e-03 0.269161548 #> 6437  3218.0 0.7259136 0.7259136 4.976533e-03 4.976533e-03 0.269109882 #> 6438  3218.5 0.7259603 0.7259603 4.981421e-03 4.981421e-03 0.269058323 #> 6439  3219.0 0.7260068 0.7260068 4.986320e-03 4.986320e-03 0.269006871 #> 6440  3219.5 0.7260532 0.7260532 4.991229e-03 4.991229e-03 0.268955527 #> 6441  3220.0 0.7260996 0.7260996 4.996149e-03 4.996149e-03 0.268904290 #> 6442  3220.5 0.7261458 0.7261458 5.001080e-03 5.001080e-03 0.268853161 #> 6443  3221.0 0.7261918 0.7261918 5.006021e-03 5.006021e-03 0.268802139 #> 6444  3221.5 0.7262378 0.7262378 5.010973e-03 5.010973e-03 0.268751226 #> 6445  3222.0 0.7262836 0.7262836 5.015936e-03 5.015936e-03 0.268700420 #> 6446  3222.5 0.7263294 0.7263294 5.020909e-03 5.020909e-03 0.268649723 #> 6447  3223.0 0.7263750 0.7263750 5.025893e-03 5.025893e-03 0.268599134 #> 6448  3223.5 0.7264205 0.7264205 5.030887e-03 5.030887e-03 0.268548654 #> 6449  3224.0 0.7264658 0.7264658 5.035893e-03 5.035893e-03 0.268498282 #> 6450  3224.5 0.7265111 0.7265111 5.040909e-03 5.040909e-03 0.268448019 #> 6451  3225.0 0.7265562 0.7265562 5.045935e-03 5.045935e-03 0.268397864 #> 6452  3225.5 0.7266012 0.7266012 5.050973e-03 5.050973e-03 0.268347819 #> 6453  3226.0 0.7266461 0.7266461 5.056021e-03 5.056021e-03 0.268297882 #> 6454  3226.5 0.7266909 0.7266909 5.061079e-03 5.061079e-03 0.268248055 #> 6455  3227.0 0.7267355 0.7267355 5.066149e-03 5.066149e-03 0.268198337 #> 6456  3227.5 0.7267800 0.7267800 5.071229e-03 5.071229e-03 0.268148728 #> 6457  3228.0 0.7268245 0.7268245 5.076320e-03 5.076320e-03 0.268099229 #> 6458  3228.5 0.7268687 0.7268687 5.081422e-03 5.081422e-03 0.268049840 #> 6459  3229.0 0.7269129 0.7269129 5.086534e-03 5.086534e-03 0.268000560 #> 6460  3229.5 0.7269570 0.7269570 5.091658e-03 5.091658e-03 0.267951390 #> 6461  3230.0 0.7270009 0.7270009 5.096791e-03 5.096791e-03 0.267902331 #> 6462  3230.5 0.7270447 0.7270447 5.101936e-03 5.101936e-03 0.267853381 #> 6463  3231.0 0.7270884 0.7270884 5.107092e-03 5.107092e-03 0.267804542 #> 6464  3231.5 0.7271319 0.7271319 5.112258e-03 5.112258e-03 0.267755813 #> 6465  3232.0 0.7271754 0.7271754 5.117435e-03 5.117435e-03 0.267707195 #> 6466  3232.5 0.7272187 0.7272187 5.122623e-03 5.122623e-03 0.267658687 #> 6467  3233.0 0.7272619 0.7272619 5.127822e-03 5.127822e-03 0.267610290 #> 6468  3233.5 0.7273050 0.7273050 5.133031e-03 5.133031e-03 0.267562004 #> 6469  3234.0 0.7273479 0.7273479 5.138252e-03 5.138252e-03 0.267513829 #> 6470  3234.5 0.7273908 0.7273908 5.143483e-03 5.143483e-03 0.267465766 #> 6471  3235.0 0.7274335 0.7274335 5.148725e-03 5.148725e-03 0.267417813 #> 6472  3235.5 0.7274761 0.7274761 5.153978e-03 5.153978e-03 0.267369972 #> 6473  3236.0 0.7275185 0.7275185 5.159241e-03 5.159241e-03 0.267322242 #> 6474  3236.5 0.7275609 0.7275609 5.164516e-03 5.164516e-03 0.267274624 #> 6475  3237.0 0.7276031 0.7276031 5.169801e-03 5.169801e-03 0.267227118 #> 6476  3237.5 0.7276452 0.7276452 5.175098e-03 5.175098e-03 0.267179724 #> 6477  3238.0 0.7276872 0.7276872 5.180405e-03 5.180405e-03 0.267132442 #> 6478  3238.5 0.7277290 0.7277290 5.185723e-03 5.185723e-03 0.267085272 #> 6479  3239.0 0.7277707 0.7277707 5.191052e-03 5.191052e-03 0.267038214 #> 6480  3239.5 0.7278123 0.7278123 5.196391e-03 5.196391e-03 0.266991269 #> 6481  3240.0 0.7278538 0.7278538 5.201742e-03 5.201742e-03 0.266944436 #> 6482  3240.5 0.7278952 0.7278952 5.207104e-03 5.207104e-03 0.266897716 #> 6483  3241.0 0.7279364 0.7279364 5.212476e-03 5.212476e-03 0.266851108 #> 6484  3241.5 0.7279775 0.7279775 5.217860e-03 5.217860e-03 0.266804614 #> 6485  3242.0 0.7280185 0.7280185 5.223254e-03 5.223254e-03 0.266758232 #> 6486  3242.5 0.7280594 0.7280594 5.228659e-03 5.228659e-03 0.266711964 #> 6487  3243.0 0.7281001 0.7281001 5.234075e-03 5.234075e-03 0.266665809 #> 6488  3243.5 0.7281407 0.7281407 5.239502e-03 5.239502e-03 0.266619768 #> 6489  3244.0 0.7281812 0.7281812 5.244941e-03 5.244941e-03 0.266573840 #> 6490  3244.5 0.7282216 0.7282216 5.250390e-03 5.250390e-03 0.266528025 #> 6491  3245.0 0.7282618 0.7282618 5.255850e-03 5.255850e-03 0.266482325 #> 6492  3245.5 0.7283019 0.7283019 5.261320e-03 5.261320e-03 0.266436738 #> 6493  3246.0 0.7283419 0.7283419 5.266802e-03 5.266802e-03 0.266391265 #> 6494  3246.5 0.7283818 0.7283818 5.272295e-03 5.272295e-03 0.266345907 #> 6495  3247.0 0.7284215 0.7284215 5.277799e-03 5.277799e-03 0.266300662 #> 6496  3247.5 0.7284612 0.7284612 5.283314e-03 5.283314e-03 0.266255533 #> 6497  3248.0 0.7285006 0.7285006 5.288840e-03 5.288840e-03 0.266210517 #> 6498  3248.5 0.7285400 0.7285400 5.294377e-03 5.294377e-03 0.266165617 #> 6499  3249.0 0.7285792 0.7285792 5.299924e-03 5.299924e-03 0.266120831 #> 6500  3249.5 0.7286184 0.7286184 5.305483e-03 5.305483e-03 0.266076160 #> 6501  3250.0 0.7286573 0.7286573 5.311053e-03 5.311053e-03 0.266031604 #> 6502  3250.5 0.7286962 0.7286962 5.316634e-03 5.316634e-03 0.265987164 #> 6503  3251.0 0.7287349 0.7287349 5.322226e-03 5.322226e-03 0.265942838 #> 6504  3251.5 0.7287735 0.7287735 5.327829e-03 5.327829e-03 0.265898629 #> 6505  3252.0 0.7288120 0.7288120 5.333443e-03 5.333443e-03 0.265854534 #> 6506  3252.5 0.7288504 0.7288504 5.339068e-03 5.339068e-03 0.265810555 #> 6507  3253.0 0.7288886 0.7288886 5.344704e-03 5.344704e-03 0.265766693 #> 6508  3253.5 0.7289267 0.7289267 5.350351e-03 5.350351e-03 0.265722946 #> 6509  3254.0 0.7289647 0.7289647 5.356009e-03 5.356009e-03 0.265679315 #> 6510  3254.5 0.7290025 0.7290025 5.361678e-03 5.361678e-03 0.265635800 #> 6511  3255.0 0.7290402 0.7290402 5.367359e-03 5.367359e-03 0.265592402 #> 6512  3255.5 0.7290778 0.7290778 5.373050e-03 5.373050e-03 0.265549120 #> 6513  3256.0 0.7291153 0.7291153 5.378753e-03 5.378753e-03 0.265505954 #> 6514  3256.5 0.7291526 0.7291526 5.384466e-03 5.384466e-03 0.265462905 #> 6515  3257.0 0.7291898 0.7291898 5.390191e-03 5.390191e-03 0.265419973 #> 6516  3257.5 0.7292269 0.7292269 5.395927e-03 5.395927e-03 0.265377159 #> 6517  3258.0 0.7292639 0.7292639 5.401673e-03 5.401673e-03 0.265334461 #> 6518  3258.5 0.7293007 0.7293007 5.407431e-03 5.407431e-03 0.265291880 #> 6519  3259.0 0.7293374 0.7293374 5.413200e-03 5.413200e-03 0.265249416 #> 6520  3259.5 0.7293739 0.7293739 5.418981e-03 5.418981e-03 0.265207071 #> 6521  3260.0 0.7294104 0.7294104 5.424772e-03 5.424772e-03 0.265164842 #> 6522  3260.5 0.7294467 0.7294467 5.430574e-03 5.430574e-03 0.265122731 #> 6523  3261.0 0.7294829 0.7294829 5.436388e-03 5.436388e-03 0.265080739 #> 6524  3261.5 0.7295189 0.7295189 5.442213e-03 5.442213e-03 0.265038864 #> 6525  3262.0 0.7295548 0.7295548 5.448049e-03 5.448049e-03 0.264997107 #> 6526  3262.5 0.7295906 0.7295906 5.453896e-03 5.453896e-03 0.264955468 #> 6527  3263.0 0.7296263 0.7296263 5.459754e-03 5.459754e-03 0.264913948 #> 6528  3263.5 0.7296618 0.7296618 5.465623e-03 5.465623e-03 0.264872546 #> 6529  3264.0 0.7296972 0.7296972 5.471503e-03 5.471503e-03 0.264831263 #> 6530  3264.5 0.7297325 0.7297325 5.477395e-03 5.477395e-03 0.264790098 #> 6531  3265.0 0.7297676 0.7297676 5.483298e-03 5.483298e-03 0.264749053 #> 6532  3265.5 0.7298027 0.7298027 5.489212e-03 5.489212e-03 0.264708126 #> 6533  3266.0 0.7298375 0.7298375 5.495137e-03 5.495137e-03 0.264667318 #> 6534  3266.5 0.7298723 0.7298723 5.501073e-03 5.501073e-03 0.264626630 #> 6535  3267.0 0.7299069 0.7299069 5.507021e-03 5.507021e-03 0.264586061 #> 6536  3267.5 0.7299414 0.7299414 5.512980e-03 5.512980e-03 0.264545611 #> 6537  3268.0 0.7299758 0.7299758 5.518950e-03 5.518950e-03 0.264505281 #> 6538  3268.5 0.7300100 0.7300100 5.524931e-03 5.524931e-03 0.264465071 #> 6539  3269.0 0.7300441 0.7300441 5.530923e-03 5.530923e-03 0.264424981 #> 6540  3269.5 0.7300781 0.7300781 5.536927e-03 5.536927e-03 0.264385010 #> 6541  3270.0 0.7301119 0.7301119 5.542941e-03 5.542941e-03 0.264345160 #> 6542  3270.5 0.7301456 0.7301456 5.548967e-03 5.548967e-03 0.264305430 #> 6543  3271.0 0.7301792 0.7301792 5.555004e-03 5.555004e-03 0.264265820 #> 6544  3271.5 0.7302126 0.7302126 5.561053e-03 5.561053e-03 0.264226331 #> 6545  3272.0 0.7302459 0.7302459 5.567112e-03 5.567112e-03 0.264186962 #> 6546  3272.5 0.7302791 0.7302791 5.573183e-03 5.573183e-03 0.264147714 #> 6547  3273.0 0.7303121 0.7303121 5.579265e-03 5.579265e-03 0.264108587 #> 6548  3273.5 0.7303451 0.7303451 5.585359e-03 5.585359e-03 0.264069581 #> 6549  3274.0 0.7303778 0.7303778 5.591463e-03 5.591463e-03 0.264030696 #> 6550  3274.5 0.7304105 0.7304105 5.597579e-03 5.597579e-03 0.263991933 #> 6551  3275.0 0.7304430 0.7304430 5.603706e-03 5.603706e-03 0.263953290 #> 6552  3275.5 0.7304754 0.7304754 5.609845e-03 5.609845e-03 0.263914770 #> 6553  3276.0 0.7305076 0.7305076 5.615994e-03 5.615994e-03 0.263876371 #> 6554  3276.5 0.7305398 0.7305398 5.622155e-03 5.622155e-03 0.263838093 #> 6555  3277.0 0.7305717 0.7305717 5.628328e-03 5.628328e-03 0.263799938 #> 6556  3277.5 0.7306036 0.7306036 5.634511e-03 5.634511e-03 0.263761904 #> 6557  3278.0 0.7306353 0.7306353 5.640706e-03 5.640706e-03 0.263723993 #> 6558  3278.5 0.7306669 0.7306669 5.646912e-03 5.646912e-03 0.263686204 #> 6559  3279.0 0.7306983 0.7306983 5.653129e-03 5.653129e-03 0.263648537 #> 6560  3279.5 0.7307296 0.7307296 5.659357e-03 5.659357e-03 0.263610993 #> 6561  3280.0 0.7307608 0.7307608 5.665597e-03 5.665597e-03 0.263573571 #> 6562  3280.5 0.7307919 0.7307919 5.671848e-03 5.671848e-03 0.263536273 #> 6563  3281.0 0.7308228 0.7308228 5.678111e-03 5.678111e-03 0.263499097 #> 6564  3281.5 0.7308536 0.7308536 5.684385e-03 5.684385e-03 0.263462044 #> 6565  3282.0 0.7308842 0.7308842 5.690670e-03 5.690670e-03 0.263425114 #> 6566  3282.5 0.7309147 0.7309147 5.696966e-03 5.696966e-03 0.263388308 #> 6567  3283.0 0.7309451 0.7309451 5.703274e-03 5.703274e-03 0.263351625 #> 6568  3283.5 0.7309753 0.7309753 5.709593e-03 5.709593e-03 0.263315065 #> 6569  3284.0 0.7310054 0.7310054 5.715923e-03 5.715923e-03 0.263278630 #> 6570  3284.5 0.7310354 0.7310354 5.722264e-03 5.722264e-03 0.263242318 #> 6571  3285.0 0.7310653 0.7310653 5.728617e-03 5.728617e-03 0.263206129 #> 6572  3285.5 0.7310950 0.7310950 5.734982e-03 5.734982e-03 0.263170065 #> 6573  3286.0 0.7311245 0.7311245 5.741357e-03 5.741357e-03 0.263134125 #> 6574  3286.5 0.7311539 0.7311539 5.747744e-03 5.747744e-03 0.263098310 #> 6575  3287.0 0.7311832 0.7311832 5.754142e-03 5.754142e-03 0.263062619 #> 6576  3287.5 0.7312124 0.7312124 5.760552e-03 5.760552e-03 0.263027052 #> 6577  3288.0 0.7312414 0.7312414 5.766973e-03 5.766973e-03 0.262991610 #> 6578  3288.5 0.7312703 0.7312703 5.773405e-03 5.773405e-03 0.262956293 #> 6579  3289.0 0.7312991 0.7312991 5.779848e-03 5.779848e-03 0.262921101 #> 6580  3289.5 0.7313277 0.7313277 5.786303e-03 5.786303e-03 0.262886033 #> 6581  3290.0 0.7313561 0.7313561 5.792769e-03 5.792769e-03 0.262851091 #> 6582  3290.5 0.7313845 0.7313845 5.799247e-03 5.799247e-03 0.262816275 #> 6583  3291.0 0.7314127 0.7314127 5.805736e-03 5.805736e-03 0.262781583 #> 6584  3291.5 0.7314407 0.7314407 5.812236e-03 5.812236e-03 0.262747018 #> 6585  3292.0 0.7314687 0.7314687 5.818748e-03 5.818748e-03 0.262712578 #> 6586  3292.5 0.7314965 0.7314965 5.825271e-03 5.825271e-03 0.262678263 #> 6587  3293.0 0.7315241 0.7315241 5.831805e-03 5.831805e-03 0.262644075 #> 6588  3293.5 0.7315516 0.7315516 5.838351e-03 5.838351e-03 0.262610013 #> 6589  3294.0 0.7315790 0.7315790 5.844908e-03 5.844908e-03 0.262576077 #> 6590  3294.5 0.7316063 0.7316063 5.851476e-03 5.851476e-03 0.262542267 #> 6591  3295.0 0.7316334 0.7316334 5.858056e-03 5.858056e-03 0.262508584 #> 6592  3295.5 0.7316603 0.7316603 5.864647e-03 5.864647e-03 0.262475027 #> 6593  3296.0 0.7316872 0.7316872 5.871250e-03 5.871250e-03 0.262441597 #> 6594  3296.5 0.7317138 0.7317138 5.877863e-03 5.877863e-03 0.262408294 #> 6595  3297.0 0.7317404 0.7317404 5.884489e-03 5.884489e-03 0.262375118 #> 6596  3297.5 0.7317668 0.7317668 5.891125e-03 5.891125e-03 0.262342068 #> 6597  3298.0 0.7317931 0.7317931 5.897773e-03 5.897773e-03 0.262309146 #> 6598  3298.5 0.7318192 0.7318192 5.904432e-03 5.904432e-03 0.262276352 #> 6599  3299.0 0.7318452 0.7318452 5.911103e-03 5.911103e-03 0.262243685 #> 6600  3299.5 0.7318711 0.7318711 5.917785e-03 5.917785e-03 0.262211145 #> 6601  3300.0 0.7318968 0.7318968 5.924479e-03 5.924479e-03 0.262178733 #> 6602  3300.5 0.7319224 0.7319224 5.931183e-03 5.931183e-03 0.262146449 #> 6603  3301.0 0.7319478 0.7319478 5.937900e-03 5.937900e-03 0.262114293 #> 6604  3301.5 0.7319731 0.7319731 5.944627e-03 5.944627e-03 0.262082265 #> 6605  3302.0 0.7319983 0.7319983 5.951366e-03 5.951366e-03 0.262050365 #> 6606  3302.5 0.7320233 0.7320233 5.958116e-03 5.958116e-03 0.262018593 #> 6607  3303.0 0.7320482 0.7320482 5.964878e-03 5.964878e-03 0.261986950 #> 6608  3303.5 0.7320729 0.7320729 5.971651e-03 5.971651e-03 0.261955435 #> 6609  3304.0 0.7320975 0.7320975 5.978436e-03 5.978436e-03 0.261924050 #> 6610  3304.5 0.7321220 0.7321220 5.985232e-03 5.985232e-03 0.261892793 #> 6611  3305.0 0.7321463 0.7321463 5.992039e-03 5.992039e-03 0.261861665 #> 6612  3305.5 0.7321705 0.7321705 5.998857e-03 5.998857e-03 0.261830666 #> 6613  3306.0 0.7321945 0.7321945 6.005687e-03 6.005687e-03 0.261799796 #> 6614  3306.5 0.7322184 0.7322184 6.012529e-03 6.012529e-03 0.261769055 #> 6615  3307.0 0.7322422 0.7322422 6.019381e-03 6.019381e-03 0.261738444 #> 6616  3307.5 0.7322658 0.7322658 6.026245e-03 6.026245e-03 0.261707963 #> 6617  3308.0 0.7322893 0.7322893 6.033121e-03 6.033121e-03 0.261677611 #> 6618  3308.5 0.7323126 0.7323126 6.040008e-03 6.040008e-03 0.261647390 #> 6619  3309.0 0.7323358 0.7323358 6.046906e-03 6.046906e-03 0.261617298 #> 6620  3309.5 0.7323588 0.7323588 6.053816e-03 6.053816e-03 0.261587336 #> 6621  3310.0 0.7323818 0.7323818 6.060737e-03 6.060737e-03 0.261557504 #> 6622  3310.5 0.7324045 0.7324045 6.067669e-03 6.067669e-03 0.261527803 #> 6623  3311.0 0.7324272 0.7324272 6.074613e-03 6.074613e-03 0.261498232 #> 6624  3311.5 0.7324496 0.7324496 6.081568e-03 6.081568e-03 0.261468792 #> 6625  3312.0 0.7324720 0.7324720 6.088534e-03 6.088534e-03 0.261439482 #> 6626  3312.5 0.7324942 0.7324942 6.095512e-03 6.095512e-03 0.261410304 #> 6627  3313.0 0.7325162 0.7325162 6.102501e-03 6.102501e-03 0.261381256 #> 6628  3313.5 0.7325382 0.7325382 6.109502e-03 6.109502e-03 0.261352339 #> 6629  3314.0 0.7325599 0.7325599 6.116514e-03 6.116514e-03 0.261323554 #> 6630  3314.5 0.7325816 0.7325816 6.123537e-03 6.123537e-03 0.261294899 #> 6631  3315.0 0.7326031 0.7326031 6.130572e-03 6.130572e-03 0.261266377 #> 6632  3315.5 0.7326244 0.7326244 6.137618e-03 6.137618e-03 0.261237985 #> 6633  3316.0 0.7326456 0.7326456 6.144676e-03 6.144676e-03 0.261209726 #> 6634  3316.5 0.7326667 0.7326667 6.151744e-03 6.151744e-03 0.261181598 #> 6635  3317.0 0.7326876 0.7326876 6.158825e-03 6.158825e-03 0.261153602 #> 6636  3317.5 0.7327083 0.7327083 6.165916e-03 6.165916e-03 0.261125739 #> 6637  3318.0 0.7327290 0.7327290 6.173019e-03 6.173019e-03 0.261098007 #> 6638  3318.5 0.7327495 0.7327495 6.180133e-03 6.180133e-03 0.261070408 #> 6639  3319.0 0.7327698 0.7327698 6.187259e-03 6.187259e-03 0.261042941 #> 6640  3319.5 0.7327900 0.7327900 6.194396e-03 6.194396e-03 0.261015607 #> 6641  3320.0 0.7328100 0.7328100 6.201544e-03 6.201544e-03 0.260988406 #> 6642  3320.5 0.7328300 0.7328300 6.208704e-03 6.208704e-03 0.260961337 #> 6643  3321.0 0.7328497 0.7328497 6.215875e-03 6.215875e-03 0.260934401 #> 6644  3321.5 0.7328693 0.7328693 6.223057e-03 6.223057e-03 0.260907598 #> 6645  3322.0 0.7328888 0.7328888 6.230251e-03 6.230251e-03 0.260880929 #> 6646  3322.5 0.7329082 0.7329082 6.237456e-03 6.237456e-03 0.260854393 #> 6647  3323.0 0.7329273 0.7329273 6.244673e-03 6.244673e-03 0.260827990 #> 6648  3323.5 0.7329464 0.7329464 6.251900e-03 6.251900e-03 0.260801720 #> 6649  3324.0 0.7329653 0.7329653 6.259139e-03 6.259139e-03 0.260775585 #> 6650  3324.5 0.7329840 0.7329840 6.266390e-03 6.266390e-03 0.260749583 #> 6651  3325.0 0.7330026 0.7330026 6.273652e-03 6.273652e-03 0.260723715 #> 6652  3325.5 0.7330211 0.7330211 6.280925e-03 6.280925e-03 0.260697981 #> 6653  3326.0 0.7330394 0.7330394 6.288209e-03 6.288209e-03 0.260672381 #> 6654  3326.5 0.7330576 0.7330576 6.295505e-03 6.295505e-03 0.260646916 #> 6655  3327.0 0.7330756 0.7330756 6.302812e-03 6.302812e-03 0.260621584 #> 6656  3327.5 0.7330935 0.7330935 6.310130e-03 6.310130e-03 0.260596388 #> 6657  3328.0 0.7331112 0.7331112 6.317460e-03 6.317460e-03 0.260571326 #> 6658  3328.5 0.7331288 0.7331288 6.324801e-03 6.324801e-03 0.260546398 #> 6659  3329.0 0.7331462 0.7331462 6.332153e-03 6.332153e-03 0.260521606 #> 6660  3329.5 0.7331635 0.7331635 6.339516e-03 6.339516e-03 0.260496949 #> 6661  3330.0 0.7331807 0.7331807 6.346891e-03 6.346891e-03 0.260472426 #> 6662  3330.5 0.7331977 0.7331977 6.354277e-03 6.354277e-03 0.260448039 #> 6663  3331.0 0.7332145 0.7332145 6.361675e-03 6.361675e-03 0.260423788 #> 6664  3331.5 0.7332312 0.7332312 6.369083e-03 6.369083e-03 0.260399671 #> 6665  3332.0 0.7332478 0.7332478 6.376503e-03 6.376503e-03 0.260375691 #> 6666  3332.5 0.7332642 0.7332642 6.383935e-03 6.383935e-03 0.260351846 #> 6667  3333.0 0.7332805 0.7332805 6.391377e-03 6.391377e-03 0.260328137 #> 6668  3333.5 0.7332966 0.7332966 6.398831e-03 6.398831e-03 0.260304564 #> 6669  3334.0 0.7333126 0.7333126 6.406296e-03 6.406296e-03 0.260281127 #> 6670  3334.5 0.7333284 0.7333284 6.413772e-03 6.413772e-03 0.260257826 #> 6671  3335.0 0.7333441 0.7333441 6.421260e-03 6.421260e-03 0.260234661 #> 6672  3335.5 0.7333596 0.7333596 6.428759e-03 6.428759e-03 0.260211633 #> 6673  3336.0 0.7333750 0.7333750 6.436269e-03 6.436269e-03 0.260188742 #> 6674  3336.5 0.7333902 0.7333902 6.443790e-03 6.443790e-03 0.260165987 #> 6675  3337.0 0.7334053 0.7334053 6.451323e-03 6.451323e-03 0.260143369 #> 6676  3337.5 0.7334202 0.7334202 6.458866e-03 6.458866e-03 0.260120887 #> 6677  3338.0 0.7334350 0.7334350 6.466421e-03 6.466421e-03 0.260098543 #> 6678  3338.5 0.7334497 0.7334497 6.473988e-03 6.473988e-03 0.260076336 #> 6679  3339.0 0.7334642 0.7334642 6.481565e-03 6.481565e-03 0.260054267 #> 6680  3339.5 0.7334785 0.7334785 6.489154e-03 6.489154e-03 0.260032334 #> 6681  3340.0 0.7334927 0.7334927 6.496753e-03 6.496753e-03 0.260010539 #> 6682  3340.5 0.7335068 0.7335068 6.504365e-03 6.504365e-03 0.259988882 #> 6683  3341.0 0.7335207 0.7335207 6.511987e-03 6.511987e-03 0.259967363 #> 6684  3341.5 0.7335344 0.7335344 6.519620e-03 6.519620e-03 0.259945981 #> 6685  3342.0 0.7335480 0.7335480 6.527265e-03 6.527265e-03 0.259924737 #> 6686  3342.5 0.7335614 0.7335614 6.534920e-03 6.534920e-03 0.259903632 #> 6687  3343.0 0.7335747 0.7335747 6.542587e-03 6.542587e-03 0.259882665 #> 6688  3343.5 0.7335879 0.7335879 6.550265e-03 6.550265e-03 0.259861836 #> 6689  3344.0 0.7336009 0.7336009 6.557955e-03 6.557955e-03 0.259841145 #> 6690  3344.5 0.7336138 0.7336138 6.565655e-03 6.565655e-03 0.259820593 #> 6691  3345.0 0.7336265 0.7336265 6.573367e-03 6.573367e-03 0.259800180 #> 6692  3345.5 0.7336390 0.7336390 6.581089e-03 6.581089e-03 0.259779905 #> 6693  3346.0 0.7336514 0.7336514 6.588823e-03 6.588823e-03 0.259759769 #> 6694  3346.5 0.7336637 0.7336637 6.596568e-03 6.596568e-03 0.259739773 #> 6695  3347.0 0.7336758 0.7336758 6.604324e-03 6.604324e-03 0.259719915 #> 6696  3347.5 0.7336877 0.7336877 6.612091e-03 6.612091e-03 0.259700197 #> 6697  3348.0 0.7336995 0.7336995 6.619869e-03 6.619869e-03 0.259680618 #> 6698  3348.5 0.7337112 0.7337112 6.627659e-03 6.627659e-03 0.259661179 #> 6699  3349.0 0.7337227 0.7337227 6.635459e-03 6.635459e-03 0.259641879 #> 6700  3349.5 0.7337340 0.7337340 6.643271e-03 6.643271e-03 0.259622719 #> 6701  3350.0 0.7337452 0.7337452 6.651093e-03 6.651093e-03 0.259603699 #> 6702  3350.5 0.7337563 0.7337563 6.658927e-03 6.658927e-03 0.259584818 #> 6703  3351.0 0.7337672 0.7337672 6.666772e-03 6.666772e-03 0.259566078 #> 6704  3351.5 0.7337779 0.7337779 6.674627e-03 6.674627e-03 0.259547478 #> 6705  3352.0 0.7337885 0.7337885 6.682494e-03 6.682494e-03 0.259529018 #> 6706  3352.5 0.7337989 0.7337989 6.690372e-03 6.690372e-03 0.259510699 #> 6707  3353.0 0.7338092 0.7338092 6.698261e-03 6.698261e-03 0.259492520 #> 6708  3353.5 0.7338194 0.7338194 6.706160e-03 6.706160e-03 0.259474482 #> 6709  3354.0 0.7338293 0.7338293 6.714071e-03 6.714071e-03 0.259456584 #> 6710  3354.5 0.7338392 0.7338392 6.721993e-03 6.721993e-03 0.259438827 #> 6711  3355.0 0.7338489 0.7338489 6.729926e-03 6.729926e-03 0.259421212 #> 6712  3355.5 0.7338584 0.7338584 6.737870e-03 6.737870e-03 0.259403737 #> 6713  3356.0 0.7338678 0.7338678 6.745825e-03 6.745825e-03 0.259386404 #> 6714  3356.5 0.7338770 0.7338770 6.753790e-03 6.753790e-03 0.259369212 #> 6715  3357.0 0.7338861 0.7338861 6.761767e-03 6.761767e-03 0.259352161 #> 6716  3357.5 0.7338950 0.7338950 6.769755e-03 6.769755e-03 0.259335252 #> 6717  3358.0 0.7339038 0.7339038 6.777753e-03 6.777753e-03 0.259318484 #> 6718  3358.5 0.7339124 0.7339124 6.785763e-03 6.785763e-03 0.259301858 #> 6719  3359.0 0.7339208 0.7339208 6.793783e-03 6.793783e-03 0.259285374 #> 6720  3359.5 0.7339292 0.7339292 6.801814e-03 6.801814e-03 0.259269032 #> 6721  3360.0 0.7339373 0.7339373 6.809857e-03 6.809857e-03 0.259252833 #> 6722  3360.5 0.7339453 0.7339453 6.817910e-03 6.817910e-03 0.259236775 #> 6723  3361.0 0.7339532 0.7339532 6.825974e-03 6.825974e-03 0.259220860 #> 6724  3361.5 0.7339609 0.7339609 6.834049e-03 6.834049e-03 0.259205087 #> 6725  3362.0 0.7339684 0.7339684 6.842134e-03 6.842134e-03 0.259189456 #> 6726  3362.5 0.7339758 0.7339758 6.850231e-03 6.850231e-03 0.259173968 #> 6727  3363.0 0.7339830 0.7339830 6.858338e-03 6.858338e-03 0.259158623 #> 6728  3363.5 0.7339901 0.7339901 6.866456e-03 6.866456e-03 0.259143421 #> 6729  3364.0 0.7339971 0.7339971 6.874585e-03 6.874585e-03 0.259128362 #> 6730  3364.5 0.7340038 0.7340038 6.882725e-03 6.882725e-03 0.259113445 #> 6731  3365.0 0.7340105 0.7340105 6.890876e-03 6.890876e-03 0.259098672 #> 6732  3365.5 0.7340169 0.7340169 6.899037e-03 6.899037e-03 0.259084043 #> 6733  3366.0 0.7340232 0.7340232 6.907209e-03 6.907209e-03 0.259069556 #> 6734  3366.5 0.7340294 0.7340294 6.915392e-03 6.915392e-03 0.259055213 #> 6735  3367.0 0.7340354 0.7340354 6.923586e-03 6.923586e-03 0.259041014 #> 6736  3367.5 0.7340413 0.7340413 6.931790e-03 6.931790e-03 0.259026959 #> 6737  3368.0 0.7340469 0.7340469 6.940005e-03 6.940005e-03 0.259013047 #> 6738  3368.5 0.7340525 0.7340525 6.948231e-03 6.948231e-03 0.258999279 #> 6739  3369.0 0.7340579 0.7340579 6.956468e-03 6.956468e-03 0.258985655 #> 6740  3369.5 0.7340631 0.7340631 6.964715e-03 6.964715e-03 0.258972176 #> 6741  3370.0 0.7340682 0.7340682 6.972973e-03 6.972973e-03 0.258958840 #> 6742  3370.5 0.7340731 0.7340731 6.981242e-03 6.981242e-03 0.258945649 #> 6743  3371.0 0.7340779 0.7340779 6.989521e-03 6.989521e-03 0.258932603 #> 6744  3371.5 0.7340825 0.7340825 6.997811e-03 6.997811e-03 0.258919701 #> 6745  3372.0 0.7340869 0.7340869 7.006111e-03 7.006111e-03 0.258906944 #> 6746  3372.5 0.7340912 0.7340912 7.014422e-03 7.014422e-03 0.258894331 #> 6747  3373.0 0.7340954 0.7340954 7.022744e-03 7.022744e-03 0.258881863 #> 6748  3373.5 0.7340994 0.7340994 7.031076e-03 7.031076e-03 0.258869541 #> 6749  3374.0 0.7341032 0.7341032 7.039419e-03 7.039419e-03 0.258857363 #> 6750  3374.5 0.7341069 0.7341069 7.047772e-03 7.047772e-03 0.258845331 #> 6751  3375.0 0.7341104 0.7341104 7.056136e-03 7.056136e-03 0.258833444 #> 6752  3375.5 0.7341138 0.7341138 7.064511e-03 7.064511e-03 0.258821702 #> 6753  3376.0 0.7341170 0.7341170 7.072896e-03 7.072896e-03 0.258810106 #> 6754  3376.5 0.7341201 0.7341201 7.081292e-03 7.081292e-03 0.258798655 #> 6755  3377.0 0.7341230 0.7341230 7.089698e-03 7.089698e-03 0.258787350 #> 6756  3377.5 0.7341257 0.7341257 7.098114e-03 7.098114e-03 0.258776191 #> 6757  3378.0 0.7341283 0.7341283 7.106541e-03 7.106541e-03 0.258765178 #> 6758  3378.5 0.7341307 0.7341307 7.114978e-03 7.114978e-03 0.258754310 #> 6759  3379.0 0.7341330 0.7341330 7.123426e-03 7.123426e-03 0.258743589 #> 6760  3379.5 0.7341351 0.7341351 7.131885e-03 7.131885e-03 0.258733014 #> 6761  3380.0 0.7341371 0.7341371 7.140353e-03 7.140353e-03 0.258722586 #> 6762  3380.5 0.7341389 0.7341389 7.148832e-03 7.148832e-03 0.258712304 #> 6763  3381.0 0.7341405 0.7341405 7.157322e-03 7.157322e-03 0.258702168 #> 6764  3381.5 0.7341420 0.7341420 7.165822e-03 7.165822e-03 0.258692179 #> 6765  3382.0 0.7341433 0.7341433 7.174332e-03 7.174332e-03 0.258682337 #> 6766  3382.5 0.7341445 0.7341445 7.182852e-03 7.182852e-03 0.258672641 #> 6767  3383.0 0.7341455 0.7341455 7.191383e-03 7.191383e-03 0.258663092 #> 6768  3383.5 0.7341464 0.7341464 7.199924e-03 7.199924e-03 0.258653691 #> 6769  3384.0 0.7341471 0.7341471 7.208476e-03 7.208476e-03 0.258644436 #> 6770  3384.5 0.7341476 0.7341476 7.217037e-03 7.217037e-03 0.258635329 #> 6771  3385.0 0.7341480 0.7341480 7.225609e-03 7.225609e-03 0.258626369 #> 6772  3385.5 0.7341483 0.7341483 7.234191e-03 7.234191e-03 0.258617556 #> 6773  3386.0 0.7341483 0.7341483 7.242784e-03 7.242784e-03 0.258608891 #> 6774  3386.5 0.7341482 0.7341482 7.251386e-03 7.251386e-03 0.258600373 #> 6775  3387.0 0.7341480 0.7341480 7.259999e-03 7.259999e-03 0.258592003 #> 6776  3387.5 0.7341476 0.7341476 7.268622e-03 7.268622e-03 0.258583781 #> 6777  3388.0 0.7341470 0.7341470 7.277255e-03 7.277255e-03 0.258575707 #> 6778  3388.5 0.7341463 0.7341463 7.285898e-03 7.285898e-03 0.258567781 #> 6779  3389.0 0.7341454 0.7341454 7.294552e-03 7.294552e-03 0.258560003 #> 6780  3389.5 0.7341444 0.7341444 7.303215e-03 7.303215e-03 0.258552373 #> 6781  3390.0 0.7341432 0.7341432 7.311889e-03 7.311889e-03 0.258544891 #> 6782  3390.5 0.7341419 0.7341419 7.320572e-03 7.320572e-03 0.258537557 #> 6783  3391.0 0.7341404 0.7341404 7.329266e-03 7.329266e-03 0.258530372 #> 6784  3391.5 0.7341387 0.7341387 7.337970e-03 7.337970e-03 0.258523336 #> 6785  3392.0 0.7341369 0.7341369 7.346683e-03 7.346683e-03 0.258516448 #> 6786  3392.5 0.7341349 0.7341349 7.355407e-03 7.355407e-03 0.258509709 #> 6787  3393.0 0.7341327 0.7341327 7.364141e-03 7.364141e-03 0.258503119 #> 6788  3393.5 0.7341304 0.7341304 7.372884e-03 7.372884e-03 0.258496677 #> 6789  3394.0 0.7341280 0.7341280 7.381638e-03 7.381638e-03 0.258490385 #> 6790  3394.5 0.7341254 0.7341254 7.390402e-03 7.390402e-03 0.258484241 #> 6791  3395.0 0.7341226 0.7341226 7.399175e-03 7.399175e-03 0.258478247 #> 6792  3395.5 0.7341196 0.7341196 7.407958e-03 7.407958e-03 0.258472402 #> 6793  3396.0 0.7341165 0.7341165 7.416751e-03 7.416751e-03 0.258466707 #> 6794  3396.5 0.7341133 0.7341133 7.425554e-03 7.425554e-03 0.258461161 #> 6795  3397.0 0.7341099 0.7341099 7.434367e-03 7.434367e-03 0.258455764 #> 6796  3397.5 0.7341063 0.7341063 7.443190e-03 7.443190e-03 0.258450517 #> 6797  3398.0 0.7341026 0.7341026 7.452022e-03 7.452022e-03 0.258445420 #> 6798  3398.5 0.7340987 0.7340987 7.460865e-03 7.460865e-03 0.258440473 #> 6799  3399.0 0.7340946 0.7340946 7.469717e-03 7.469717e-03 0.258435675 #> 6800  3399.5 0.7340904 0.7340904 7.478578e-03 7.478578e-03 0.258431028 #> 6801  3400.0 0.7340860 0.7340860 7.487450e-03 7.487450e-03 0.258426530 #> 6802  3400.5 0.7340815 0.7340815 7.496331e-03 7.496331e-03 0.258422183 #> 6803  3401.0 0.7340768 0.7340768 7.505222e-03 7.505222e-03 0.258417986 #> 6804  3401.5 0.7340719 0.7340719 7.514122e-03 7.514122e-03 0.258413939 #> 6805  3402.0 0.7340669 0.7340669 7.523032e-03 7.523032e-03 0.258410042 #> 6806  3402.5 0.7340618 0.7340618 7.531952e-03 7.531952e-03 0.258406297 #> 6807  3403.0 0.7340564 0.7340564 7.540881e-03 7.540881e-03 0.258402701 #> 6808  3403.5 0.7340509 0.7340509 7.549820e-03 7.549820e-03 0.258399257 #> 6809  3404.0 0.7340453 0.7340453 7.558768e-03 7.558768e-03 0.258395963 #> 6810  3404.5 0.7340395 0.7340395 7.567726e-03 7.567726e-03 0.258392820 #> 6811  3405.0 0.7340335 0.7340335 7.576693e-03 7.576693e-03 0.258389827 #> 6812  3405.5 0.7340273 0.7340273 7.585670e-03 7.585670e-03 0.258386986 #> 6813  3406.0 0.7340210 0.7340210 7.594656e-03 7.594656e-03 0.258384296 #> 6814  3406.5 0.7340146 0.7340146 7.603652e-03 7.603652e-03 0.258381757 #> 6815  3407.0 0.7340080 0.7340080 7.612657e-03 7.612657e-03 0.258379369 #> 6816  3407.5 0.7340012 0.7340012 7.621672e-03 7.621672e-03 0.258377133 #> 6817  3408.0 0.7339943 0.7339943 7.630696e-03 7.630696e-03 0.258375048 #> 6818  3408.5 0.7339872 0.7339872 7.639729e-03 7.639729e-03 0.258373115 #> 6819  3409.0 0.7339799 0.7339799 7.648771e-03 7.648771e-03 0.258371333 #> 6820  3409.5 0.7339725 0.7339725 7.657823e-03 7.657823e-03 0.258369702 #> 6821  3410.0 0.7339649 0.7339649 7.666884e-03 7.666884e-03 0.258368224 #> 6822  3410.5 0.7339571 0.7339571 7.675955e-03 7.675955e-03 0.258366897 #> 6823  3411.0 0.7339492 0.7339492 7.685034e-03 7.685034e-03 0.258365722 #> 6824  3411.5 0.7339412 0.7339412 7.694123e-03 7.694123e-03 0.258364699 #> 6825  3412.0 0.7339330 0.7339330 7.703221e-03 7.703221e-03 0.258363828 #> 6826  3412.5 0.7339246 0.7339246 7.712328e-03 7.712328e-03 0.258363109 #> 6827  3413.0 0.7339160 0.7339160 7.721444e-03 7.721444e-03 0.258362543 #> 6828  3413.5 0.7339073 0.7339073 7.730570e-03 7.730570e-03 0.258362128 #> 6829  3414.0 0.7338984 0.7338984 7.739704e-03 7.739704e-03 0.258361866 #> 6830  3414.5 0.7338894 0.7338894 7.748848e-03 7.748848e-03 0.258361756 #> 6831  3415.0 0.7338802 0.7338802 7.758000e-03 7.758000e-03 0.258361799 #> 6832  3415.5 0.7338708 0.7338708 7.767162e-03 7.767162e-03 0.258361995 #> 6833  3416.0 0.7338613 0.7338613 7.776333e-03 7.776333e-03 0.258362343 #> 6834  3416.5 0.7338516 0.7338516 7.785512e-03 7.785512e-03 0.258362843 #> 6835  3417.0 0.7338418 0.7338418 7.794700e-03 7.794700e-03 0.258363497 #> 6836  3417.5 0.7338318 0.7338318 7.803898e-03 7.803898e-03 0.258364303 #> 6837  3418.0 0.7338216 0.7338216 7.813104e-03 7.813104e-03 0.258365263 #> 6838  3418.5 0.7338113 0.7338113 7.822319e-03 7.822319e-03 0.258366375 #> 6839  3419.0 0.7338008 0.7338008 7.831543e-03 7.831543e-03 0.258367640 #> 6840  3419.5 0.7337902 0.7337902 7.840776e-03 7.840776e-03 0.258369059 #> 6841  3420.0 0.7337794 0.7337794 7.850017e-03 7.850017e-03 0.258370630 #> 6842  3420.5 0.7337684 0.7337684 7.859268e-03 7.859268e-03 0.258372355 #> 6843  3421.0 0.7337572 0.7337572 7.868527e-03 7.868527e-03 0.258374234 #> 6844  3421.5 0.7337459 0.7337459 7.877794e-03 7.877794e-03 0.258376265 #> 6845  3422.0 0.7337345 0.7337345 7.887071e-03 7.887071e-03 0.258378451 #> 6846  3422.5 0.7337229 0.7337229 7.896356e-03 7.896356e-03 0.258380789 #> 6847  3423.0 0.7337111 0.7337111 7.905649e-03 7.905649e-03 0.258383282 #> 6848  3423.5 0.7336991 0.7336991 7.914951e-03 7.914951e-03 0.258385928 #> 6849  3424.0 0.7336870 0.7336870 7.924262e-03 7.924262e-03 0.258388727 #> 6850  3424.5 0.7336747 0.7336747 7.933581e-03 7.933581e-03 0.258391681 #> 6851  3425.0 0.7336623 0.7336623 7.942909e-03 7.942909e-03 0.258394789 #> 6852  3425.5 0.7336497 0.7336497 7.952246e-03 7.952246e-03 0.258398050 #> 6853  3426.0 0.7336369 0.7336369 7.961590e-03 7.961590e-03 0.258401465 #> 6854  3426.5 0.7336240 0.7336240 7.970943e-03 7.970943e-03 0.258405035 #> 6855  3427.0 0.7336109 0.7336109 7.980305e-03 7.980305e-03 0.258408758 #> 6856  3427.5 0.7335977 0.7335977 7.989675e-03 7.989675e-03 0.258412636 #> 6857  3428.0 0.7335843 0.7335843 7.999053e-03 7.999053e-03 0.258416668 #> 6858  3428.5 0.7335707 0.7335707 8.008440e-03 8.008440e-03 0.258420855 #> 6859  3429.0 0.7335570 0.7335570 8.017835e-03 8.017835e-03 0.258425195 #> 6860  3429.5 0.7335431 0.7335431 8.027238e-03 8.027238e-03 0.258429691 #> 6861  3430.0 0.7335290 0.7335290 8.036649e-03 8.036649e-03 0.258434340 #> 6862  3430.5 0.7335148 0.7335148 8.046068e-03 8.046068e-03 0.258439145 #> 6863  3431.0 0.7335004 0.7335004 8.055496e-03 8.055496e-03 0.258444104 #> 6864  3431.5 0.7334859 0.7334859 8.064932e-03 8.064932e-03 0.258449217 #> 6865  3432.0 0.7334711 0.7334711 8.074376e-03 8.074376e-03 0.258454485 #> 6866  3432.5 0.7334563 0.7334563 8.083828e-03 8.083828e-03 0.258459909 #> 6867  3433.0 0.7334412 0.7334412 8.093288e-03 8.093288e-03 0.258465486 #> 6868  3433.5 0.7334260 0.7334260 8.102756e-03 8.102756e-03 0.258471219 #> 6869  3434.0 0.7334107 0.7334107 8.112232e-03 8.112232e-03 0.258477107 #> 6870  3434.5 0.7333951 0.7333951 8.121716e-03 8.121716e-03 0.258483150 #> 6871  3435.0 0.7333794 0.7333794 8.131207e-03 8.131207e-03 0.258489348 #> 6872  3435.5 0.7333636 0.7333636 8.140707e-03 8.140707e-03 0.258495701 #> 6873  3436.0 0.7333476 0.7333476 8.150215e-03 8.150215e-03 0.258502209 #> 6874  3436.5 0.7333314 0.7333314 8.159730e-03 8.159730e-03 0.258508872 #> 6875  3437.0 0.7333151 0.7333151 8.169253e-03 8.169253e-03 0.258515691 #> 6876  3437.5 0.7332986 0.7332986 8.178784e-03 8.178784e-03 0.258522665 #> 6877  3438.0 0.7332819 0.7332819 8.188323e-03 8.188323e-03 0.258529795 #> 6878  3438.5 0.7332651 0.7332651 8.197869e-03 8.197869e-03 0.258537080 #> 6879  3439.0 0.7332481 0.7332481 8.207423e-03 8.207423e-03 0.258544520 #> 6880  3439.5 0.7332309 0.7332309 8.216985e-03 8.216985e-03 0.258552116 #> 6881  3440.0 0.7332136 0.7332136 8.226554e-03 8.226554e-03 0.258559867 #> 6882  3440.5 0.7331961 0.7331961 8.236131e-03 8.236131e-03 0.258567774 #> 6883  3441.0 0.7331784 0.7331784 8.245715e-03 8.245715e-03 0.258575837 #> 6884  3441.5 0.7331606 0.7331606 8.255307e-03 8.255307e-03 0.258584056 #> 6885  3442.0 0.7331427 0.7331427 8.264906e-03 8.264906e-03 0.258592430 #> 6886  3442.5 0.7331245 0.7331245 8.274513e-03 8.274513e-03 0.258600960 #> 6887  3443.0 0.7331062 0.7331062 8.284127e-03 8.284127e-03 0.258609646 #> 6888  3443.5 0.7330878 0.7330878 8.293749e-03 8.293749e-03 0.258618488 #> 6889  3444.0 0.7330691 0.7330691 8.303377e-03 8.303377e-03 0.258627486 #> 6890  3444.5 0.7330503 0.7330503 8.313013e-03 8.313013e-03 0.258636640 #> 6891  3445.0 0.7330314 0.7330314 8.322657e-03 8.322657e-03 0.258645950 #> 6892  3445.5 0.7330123 0.7330123 8.332307e-03 8.332307e-03 0.258655416 #> 6893  3446.0 0.7329930 0.7329930 8.341965e-03 8.341965e-03 0.258665038 #> 6894  3446.5 0.7329736 0.7329736 8.351630e-03 8.351630e-03 0.258674816 #> 6895  3447.0 0.7329539 0.7329539 8.361302e-03 8.361302e-03 0.258684751 #> 6896  3447.5 0.7329342 0.7329342 8.370981e-03 8.370981e-03 0.258694842 #> 6897  3448.0 0.7329142 0.7329142 8.380667e-03 8.380667e-03 0.258705089 #> 6898  3448.5 0.7328941 0.7328941 8.390360e-03 8.390360e-03 0.258715492 #> 6899  3449.0 0.7328739 0.7328739 8.400060e-03 8.400060e-03 0.258726052 #> 6900  3449.5 0.7328535 0.7328535 8.409767e-03 8.409767e-03 0.258736768 #> 6901  3450.0 0.7328329 0.7328329 8.419481e-03 8.419481e-03 0.258747641 #> 6902  3450.5 0.7328121 0.7328121 8.429202e-03 8.429202e-03 0.258758670 #> 6903  3451.0 0.7327912 0.7327912 8.438930e-03 8.438930e-03 0.258769856 #> 6904  3451.5 0.7327701 0.7327701 8.448664e-03 8.448664e-03 0.258781198 #> 6905  3452.0 0.7327489 0.7327489 8.458405e-03 8.458405e-03 0.258792697 #> 6906  3452.5 0.7327275 0.7327275 8.468153e-03 8.468153e-03 0.258804352 #> 6907  3453.0 0.7327059 0.7327059 8.477908e-03 8.477908e-03 0.258816164 #> 6908  3453.5 0.7326842 0.7326842 8.487669e-03 8.487669e-03 0.258828133 #> 6909  3454.0 0.7326623 0.7326623 8.497437e-03 8.497437e-03 0.258840258 #> 6910  3454.5 0.7326402 0.7326402 8.507211e-03 8.507211e-03 0.258852540 #> 6911  3455.0 0.7326180 0.7326180 8.516992e-03 8.516992e-03 0.258864979 #> 6912  3455.5 0.7325956 0.7325956 8.526780e-03 8.526780e-03 0.258877575 #> 6913  3456.0 0.7325731 0.7325731 8.536574e-03 8.536574e-03 0.258890328 #> 6914  3456.5 0.7325504 0.7325504 8.546374e-03 8.546374e-03 0.258903237 #> 6915  3457.0 0.7325275 0.7325275 8.556181e-03 8.556181e-03 0.258916304 #> 6916  3457.5 0.7325045 0.7325045 8.565994e-03 8.565994e-03 0.258929527 #> 6917  3458.0 0.7324813 0.7324813 8.575813e-03 8.575813e-03 0.258942907 #> 6918  3458.5 0.7324579 0.7324579 8.585638e-03 8.585638e-03 0.258956445 #> 6919  3459.0 0.7324344 0.7324344 8.595470e-03 8.595470e-03 0.258970139 #> 6920  3459.5 0.7324107 0.7324107 8.605308e-03 8.605308e-03 0.258983990 #> 6921  3460.0 0.7323868 0.7323868 8.615152e-03 8.615152e-03 0.258997998 #> 6922  3460.5 0.7323628 0.7323628 8.625003e-03 8.625003e-03 0.259012164 #> 6923  3461.0 0.7323387 0.7323387 8.634859e-03 8.634859e-03 0.259026486 #> 6924  3461.5 0.7323143 0.7323143 8.644721e-03 8.644721e-03 0.259040966 #> 6925  3462.0 0.7322898 0.7322898 8.654589e-03 8.654589e-03 0.259055603 #> 6926  3462.5 0.7322651 0.7322651 8.664464e-03 8.664464e-03 0.259070397 #> 6927  3463.0 0.7322403 0.7322403 8.674344e-03 8.674344e-03 0.259085348 #> 6928  3463.5 0.7322153 0.7322153 8.684230e-03 8.684230e-03 0.259100456 #> 6929  3464.0 0.7321902 0.7321902 8.694121e-03 8.694121e-03 0.259115722 #> 6930  3464.5 0.7321648 0.7321648 8.704019e-03 8.704019e-03 0.259131144 #> 6931  3465.0 0.7321394 0.7321394 8.713922e-03 8.713922e-03 0.259146725 #> 6932  3465.5 0.7321137 0.7321137 8.723831e-03 8.723831e-03 0.259162462 #> 6933  3466.0 0.7320879 0.7320879 8.733746e-03 8.733746e-03 0.259178356 #> 6934  3466.5 0.7320619 0.7320619 8.743666e-03 8.743666e-03 0.259194408 #> 6935  3467.0 0.7320358 0.7320358 8.753592e-03 8.753592e-03 0.259210618 #> 6936  3467.5 0.7320095 0.7320095 8.763523e-03 8.763523e-03 0.259226984 #> 6937  3468.0 0.7319830 0.7319830 8.773460e-03 8.773460e-03 0.259243508 #> 6938  3468.5 0.7319564 0.7319564 8.783402e-03 8.783402e-03 0.259260189 #> 6939  3469.0 0.7319296 0.7319296 8.793350e-03 8.793350e-03 0.259277028 #> 6940  3469.5 0.7319027 0.7319027 8.803303e-03 8.803303e-03 0.259294024 #> 6941  3470.0 0.7318756 0.7318756 8.813261e-03 8.813261e-03 0.259311177 #> 6942  3470.5 0.7318483 0.7318483 8.823225e-03 8.823225e-03 0.259328488 #> 6943  3471.0 0.7318209 0.7318209 8.833194e-03 8.833194e-03 0.259345956 #> 6944  3471.5 0.7317933 0.7317933 8.843167e-03 8.843167e-03 0.259363582 #> 6945  3472.0 0.7317655 0.7317655 8.853147e-03 8.853147e-03 0.259381365 #> 6946  3472.5 0.7317376 0.7317376 8.863131e-03 8.863131e-03 0.259399305 #> 6947  3473.0 0.7317095 0.7317095 8.873120e-03 8.873120e-03 0.259417403 #> 6948  3473.5 0.7316812 0.7316812 8.883114e-03 8.883114e-03 0.259435659 #> 6949  3474.0 0.7316528 0.7316528 8.893114e-03 8.893114e-03 0.259454072 #> 6950  3474.5 0.7316242 0.7316242 8.903118e-03 8.903118e-03 0.259472642 #> 6951  3475.0 0.7315955 0.7315955 8.913127e-03 8.913127e-03 0.259491369 #> 6952  3475.5 0.7315666 0.7315666 8.923140e-03 8.923140e-03 0.259510255 #> 6953  3476.0 0.7315375 0.7315375 8.933159e-03 8.933159e-03 0.259529297 #> 6954  3476.5 0.7315083 0.7315083 8.943182e-03 8.943182e-03 0.259548497 #> 6955  3477.0 0.7314789 0.7314789 8.953210e-03 8.953210e-03 0.259567855 #> 6956  3477.5 0.7314494 0.7314494 8.963243e-03 8.963243e-03 0.259587370 #> 6957  3478.0 0.7314197 0.7314197 8.973280e-03 8.973280e-03 0.259607042 #> 6958  3478.5 0.7313898 0.7313898 8.983322e-03 8.983322e-03 0.259626872 #> 6959  3479.0 0.7313598 0.7313598 8.993368e-03 8.993368e-03 0.259646859 #> 6960  3479.5 0.7313296 0.7313296 9.003419e-03 9.003419e-03 0.259667004 #> 6961  3480.0 0.7312992 0.7312992 9.013474e-03 9.013474e-03 0.259687306 #> 6962  3480.5 0.7312687 0.7312687 9.023534e-03 9.023534e-03 0.259707766 #> 6963  3481.0 0.7312380 0.7312380 9.033598e-03 9.033598e-03 0.259728383 #> 6964  3481.5 0.7312072 0.7312072 9.043666e-03 9.043666e-03 0.259749158 #> 6965  3482.0 0.7311762 0.7311762 9.053738e-03 9.053738e-03 0.259770089 #> 6966  3482.5 0.7311450 0.7311450 9.063815e-03 9.063815e-03 0.259791179 #> 6967  3483.0 0.7311137 0.7311137 9.073895e-03 9.073895e-03 0.259812425 #> 6968  3483.5 0.7310822 0.7310822 9.083980e-03 9.083980e-03 0.259833829 #> 6969  3484.0 0.7310505 0.7310505 9.094069e-03 9.094069e-03 0.259855391 #> 6970  3484.5 0.7310187 0.7310187 9.104162e-03 9.104162e-03 0.259877110 #> 6971  3485.0 0.7309868 0.7309868 9.114258e-03 9.114258e-03 0.259898986 #> 6972  3485.5 0.7309546 0.7309546 9.124359e-03 9.124359e-03 0.259921019 #> 6973  3486.0 0.7309223 0.7309223 9.134463e-03 9.134463e-03 0.259943210 #> 6974  3486.5 0.7308899 0.7308899 9.144571e-03 9.144571e-03 0.259965558 #> 6975  3487.0 0.7308573 0.7308573 9.154683e-03 9.154683e-03 0.259988064 #> 6976  3487.5 0.7308245 0.7308245 9.164799e-03 9.164799e-03 0.260010726 #> 6977  3488.0 0.7307915 0.7307915 9.174918e-03 9.174918e-03 0.260033546 #> 6978  3488.5 0.7307584 0.7307584 9.185041e-03 9.185041e-03 0.260056523 #> 6979  3489.0 0.7307252 0.7307252 9.195167e-03 9.195167e-03 0.260079658 #> 6980  3489.5 0.7306918 0.7306918 9.205297e-03 9.205297e-03 0.260102949 #> 6981  3490.0 0.7306582 0.7306582 9.215430e-03 9.215430e-03 0.260126398 #> 6982  3490.5 0.7306244 0.7306244 9.225567e-03 9.225567e-03 0.260150004 #> 6983  3491.0 0.7305905 0.7305905 9.235707e-03 9.235707e-03 0.260173767 #> 6984  3491.5 0.7305565 0.7305565 9.245851e-03 9.245851e-03 0.260197687 #> 6985  3492.0 0.7305222 0.7305222 9.255997e-03 9.255997e-03 0.260221764 #> 6986  3492.5 0.7304879 0.7304879 9.266147e-03 9.266147e-03 0.260245998 #> 6987  3493.0 0.7304533 0.7304533 9.276300e-03 9.276300e-03 0.260270390 #> 6988  3493.5 0.7304186 0.7304186 9.286456e-03 9.286456e-03 0.260294938 #> 6989  3494.0 0.7303837 0.7303837 9.296615e-03 9.296615e-03 0.260319643 #> 6990  3494.5 0.7303487 0.7303487 9.306777e-03 9.306777e-03 0.260344506 #> 6991  3495.0 0.7303135 0.7303135 9.316942e-03 9.316942e-03 0.260369525 #> 6992  3495.5 0.7302782 0.7302782 9.327110e-03 9.327110e-03 0.260394701 #> 6993  3496.0 0.7302427 0.7302427 9.337280e-03 9.337280e-03 0.260420034 #> 6994  3496.5 0.7302070 0.7302070 9.347454e-03 9.347454e-03 0.260445523 #> 6995  3497.0 0.7301712 0.7301712 9.357630e-03 9.357630e-03 0.260471170 #> 6996  3497.5 0.7301352 0.7301352 9.367809e-03 9.367809e-03 0.260496973 #> 6997  3498.0 0.7300991 0.7300991 9.377990e-03 9.377990e-03 0.260522933 #> 6998  3498.5 0.7300628 0.7300628 9.388174e-03 9.388174e-03 0.260549050 #> 6999  3499.0 0.7300263 0.7300263 9.398360e-03 9.398360e-03 0.260575323 #> 7000  3499.5 0.7299897 0.7299897 9.408549e-03 9.408549e-03 0.260601753 #> 7001  3500.0 0.7299529 0.7299529 9.418741e-03 9.418741e-03 0.260628340 #> 7002  3500.5 0.7299160 0.7299160 9.428935e-03 9.428935e-03 0.260655083 #> 7003  3501.0 0.7298789 0.7298789 9.439131e-03 9.439131e-03 0.260681983 #> 7004  3501.5 0.7298416 0.7298416 9.449329e-03 9.449329e-03 0.260709039 #> 7005  3502.0 0.7298042 0.7298042 9.459529e-03 9.459529e-03 0.260736251 #> 7006  3502.5 0.7297666 0.7297666 9.469732e-03 9.469732e-03 0.260763620 #> 7007  3503.0 0.7297289 0.7297289 9.479936e-03 9.479936e-03 0.260791145 #> 7008  3503.5 0.7296910 0.7296910 9.490143e-03 9.490143e-03 0.260818827 #> 7009  3504.0 0.7296530 0.7296530 9.500352e-03 9.500352e-03 0.260846664 #> 7010  3504.5 0.7296148 0.7296148 9.510562e-03 9.510562e-03 0.260874658 #> 7011  3505.0 0.7295764 0.7295764 9.520775e-03 9.520775e-03 0.260902809 #> 7012  3505.5 0.7295379 0.7295379 9.530989e-03 9.530989e-03 0.260931115 #> 7013  3506.0 0.7294992 0.7294992 9.541205e-03 9.541205e-03 0.260959577 #> 7014  3506.5 0.7294604 0.7294604 9.551423e-03 9.551423e-03 0.260988195 #> 7015  3507.0 0.7294214 0.7294214 9.561642e-03 9.561642e-03 0.261016970 #> 7016  3507.5 0.7293822 0.7293822 9.571863e-03 9.571863e-03 0.261045900 #> 7017  3508.0 0.7293429 0.7293429 9.582085e-03 9.582085e-03 0.261074986 #> 7018  3508.5 0.7293035 0.7293035 9.592309e-03 9.592309e-03 0.261104228 #> 7019  3509.0 0.7292638 0.7292638 9.602535e-03 9.602535e-03 0.261133625 #> 7020  3509.5 0.7292241 0.7292241 9.612761e-03 9.612761e-03 0.261163178 #> 7021  3510.0 0.7291841 0.7291841 9.622989e-03 9.622989e-03 0.261192887 #> 7022  3510.5 0.7291440 0.7291440 9.633219e-03 9.633219e-03 0.261222752 #> 7023  3511.0 0.7291038 0.7291038 9.643449e-03 9.643449e-03 0.261252772 #> 7024  3511.5 0.7290634 0.7290634 9.653681e-03 9.653681e-03 0.261282947 #> 7025  3512.0 0.7290228 0.7290228 9.663913e-03 9.663913e-03 0.261313278 #> 7026  3512.5 0.7289821 0.7289821 9.674147e-03 9.674147e-03 0.261343764 #> 7027  3513.0 0.7289412 0.7289412 9.684382e-03 9.684382e-03 0.261374406 #> 7028  3513.5 0.7289002 0.7289002 9.694617e-03 9.694617e-03 0.261405203 #> 7029  3514.0 0.7288590 0.7288590 9.704853e-03 9.704853e-03 0.261436155 #> 7030  3514.5 0.7288176 0.7288176 9.715091e-03 9.715091e-03 0.261467262 #> 7031  3515.0 0.7287761 0.7287761 9.725328e-03 9.725328e-03 0.261498524 #> 7032  3515.5 0.7287345 0.7287345 9.735567e-03 9.735567e-03 0.261529941 #> 7033  3516.0 0.7286927 0.7286927 9.745806e-03 9.745806e-03 0.261561513 #> 7034  3516.5 0.7286507 0.7286507 9.756046e-03 9.756046e-03 0.261593239 #> 7035  3517.0 0.7286086 0.7286086 9.766286e-03 9.766286e-03 0.261625121 #> 7036  3517.5 0.7285663 0.7285663 9.776527e-03 9.776527e-03 0.261657157 #> 7037  3518.0 0.7285239 0.7285239 9.786768e-03 9.786768e-03 0.261689348 #> 7038  3518.5 0.7284813 0.7284813 9.797009e-03 9.797009e-03 0.261721693 #> 7039  3519.0 0.7284386 0.7284386 9.807251e-03 9.807251e-03 0.261754193 #> 7040  3519.5 0.7283957 0.7283957 9.817492e-03 9.817492e-03 0.261786848 #> 7041  3520.0 0.7283526 0.7283526 9.827734e-03 9.827734e-03 0.261819656 #> 7042  3520.5 0.7283094 0.7283094 9.837976e-03 9.837976e-03 0.261852619 #> 7043  3521.0 0.7282660 0.7282660 9.848218e-03 9.848218e-03 0.261885736 #> 7044  3521.5 0.7282225 0.7282225 9.858460e-03 9.858460e-03 0.261919007 #> 7045  3522.0 0.7281789 0.7281789 9.868702e-03 9.868702e-03 0.261952432 #> 7046  3522.5 0.7281350 0.7281350 9.878944e-03 9.878944e-03 0.261986011 #> 7047  3523.0 0.7280911 0.7280911 9.889185e-03 9.889185e-03 0.262019744 #> 7048  3523.5 0.7280469 0.7280469 9.899426e-03 9.899426e-03 0.262053631 #> 7049  3524.0 0.7280027 0.7280027 9.909667e-03 9.909667e-03 0.262087672 #> 7050  3524.5 0.7279582 0.7279582 9.919908e-03 9.919908e-03 0.262121866 #> 7051  3525.0 0.7279136 0.7279136 9.930148e-03 9.930148e-03 0.262156213 #> 7052  3525.5 0.7278689 0.7278689 9.940387e-03 9.940387e-03 0.262190714 #> 7053  3526.0 0.7278240 0.7278240 9.950626e-03 9.950626e-03 0.262225369 #> 7054  3526.5 0.7277790 0.7277790 9.960864e-03 9.960864e-03 0.262260177 #> 7055  3527.0 0.7277338 0.7277338 9.971102e-03 9.971102e-03 0.262295137 #> 7056  3527.5 0.7276884 0.7276884 9.981338e-03 9.981338e-03 0.262330251 #> 7057  3528.0 0.7276429 0.7276429 9.991574e-03 9.991574e-03 0.262365518 #> 7058  3528.5 0.7275973 0.7275973 1.000181e-02 1.000181e-02 0.262400938 #> 7059  3529.0 0.7275514 0.7275514 1.001204e-02 1.001204e-02 0.262436511 #> 7060  3529.5 0.7275055 0.7275055 1.002228e-02 1.002228e-02 0.262472237 #> 7061  3530.0 0.7274594 0.7274594 1.003251e-02 1.003251e-02 0.262508115 #> 7062  3530.5 0.7274131 0.7274131 1.004274e-02 1.004274e-02 0.262544145 #> 7063  3531.0 0.7273667 0.7273667 1.005297e-02 1.005297e-02 0.262580328 #> 7064  3531.5 0.7273201 0.7273201 1.006320e-02 1.006320e-02 0.262616664 #> 7065  3532.0 0.7272734 0.7272734 1.007342e-02 1.007342e-02 0.262653152 #> 7066  3532.5 0.7272266 0.7272266 1.008365e-02 1.008365e-02 0.262689792 #> 7067  3533.0 0.7271795 0.7271795 1.009387e-02 1.009387e-02 0.262726583 #> 7068  3533.5 0.7271324 0.7271324 1.010410e-02 1.010410e-02 0.262763527 #> 7069  3534.0 0.7270851 0.7270851 1.011432e-02 1.011432e-02 0.262800623 #> 7070  3534.5 0.7270376 0.7270376 1.012454e-02 1.012454e-02 0.262837871 #> 7071  3535.0 0.7269900 0.7269900 1.013475e-02 1.013475e-02 0.262875270 #> 7072  3535.5 0.7269422 0.7269422 1.014497e-02 1.014497e-02 0.262912820 #> 7073  3536.0 0.7268943 0.7268943 1.015518e-02 1.015518e-02 0.262950522 #> 7074  3536.5 0.7268462 0.7268462 1.016539e-02 1.016539e-02 0.262988376 #> 7075  3537.0 0.7267980 0.7267980 1.017561e-02 1.017561e-02 0.263026381 #> 7076  3537.5 0.7267497 0.7267497 1.018581e-02 1.018581e-02 0.263064536 #> 7077  3538.0 0.7267011 0.7267011 1.019602e-02 1.019602e-02 0.263102843 #> 7078  3538.5 0.7266525 0.7266525 1.020622e-02 1.020622e-02 0.263141301 #> 7079  3539.0 0.7266037 0.7266037 1.021642e-02 1.021642e-02 0.263179909 #> 7080  3539.5 0.7265547 0.7265547 1.022662e-02 1.022662e-02 0.263218668 #> 7081  3540.0 0.7265056 0.7265056 1.023682e-02 1.023682e-02 0.263257578 #> 7082  3540.5 0.7264563 0.7264563 1.024701e-02 1.024701e-02 0.263296638 #> 7083  3541.0 0.7264069 0.7264069 1.025721e-02 1.025721e-02 0.263335848 #> 7084  3541.5 0.7263574 0.7263574 1.026740e-02 1.026740e-02 0.263375209 #> 7085  3542.0 0.7263077 0.7263077 1.027758e-02 1.027758e-02 0.263414720 #> 7086  3542.5 0.7262579 0.7262579 1.028777e-02 1.028777e-02 0.263454380 #> 7087  3543.0 0.7262079 0.7262079 1.029795e-02 1.029795e-02 0.263494191 #> 7088  3543.5 0.7261577 0.7261577 1.030813e-02 1.030813e-02 0.263534151 #> 7089  3544.0 0.7261074 0.7261074 1.031830e-02 1.031830e-02 0.263574261 #> 7090  3544.5 0.7260570 0.7260570 1.032847e-02 1.032847e-02 0.263614520 #> 7091  3545.0 0.7260064 0.7260064 1.033864e-02 1.033864e-02 0.263654929 #> 7092  3545.5 0.7259557 0.7259557 1.034881e-02 1.034881e-02 0.263695487 #> 7093  3546.0 0.7259048 0.7259048 1.035897e-02 1.035897e-02 0.263736193 #> 7094  3546.5 0.7258538 0.7258538 1.036913e-02 1.036913e-02 0.263777049 #> 7095  3547.0 0.7258027 0.7258027 1.037929e-02 1.037929e-02 0.263818054 #> 7096  3547.5 0.7257513 0.7257513 1.038944e-02 1.038944e-02 0.263859208 #> 7097  3548.0 0.7256999 0.7256999 1.039959e-02 1.039959e-02 0.263900510 #> 7098  3548.5 0.7256483 0.7256483 1.040974e-02 1.040974e-02 0.263941960 #> 7099  3549.0 0.7255966 0.7255966 1.041988e-02 1.041988e-02 0.263983559 #> 7100  3549.5 0.7255447 0.7255447 1.043002e-02 1.043002e-02 0.264025306 #> 7101  3550.0 0.7254926 0.7254926 1.044016e-02 1.044016e-02 0.264067201 #> 7102  3550.5 0.7254405 0.7254405 1.045029e-02 1.045029e-02 0.264109244 #> 7103  3551.0 0.7253881 0.7253881 1.046042e-02 1.046042e-02 0.264151435 #> 7104  3551.5 0.7253357 0.7253357 1.047054e-02 1.047054e-02 0.264193773 #> 7105  3552.0 0.7252831 0.7252831 1.048066e-02 1.048066e-02 0.264236259 #> 7106  3552.5 0.7252303 0.7252303 1.049078e-02 1.049078e-02 0.264278892 #> 7107  3553.0 0.7251774 0.7251774 1.050089e-02 1.050089e-02 0.264321673 #> 7108  3553.5 0.7251244 0.7251244 1.051100e-02 1.051100e-02 0.264364600 #> 7109  3554.0 0.7250712 0.7250712 1.052110e-02 1.052110e-02 0.264407675 #> 7110  3554.5 0.7250179 0.7250179 1.053120e-02 1.053120e-02 0.264450896 #> 7111  3555.0 0.7249644 0.7249644 1.054130e-02 1.054130e-02 0.264494264 #> 7112  3555.5 0.7249108 0.7249108 1.055139e-02 1.055139e-02 0.264537778 #> 7113  3556.0 0.7248571 0.7248571 1.056147e-02 1.056147e-02 0.264581439 #> 7114  3556.5 0.7248032 0.7248032 1.057155e-02 1.057155e-02 0.264625246 #> 7115  3557.0 0.7247492 0.7247492 1.058163e-02 1.058163e-02 0.264669199 #> 7116  3557.5 0.7246950 0.7246950 1.059170e-02 1.059170e-02 0.264713297 #> 7117  3558.0 0.7246407 0.7246407 1.060177e-02 1.060177e-02 0.264757542 #> 7118  3558.5 0.7245862 0.7245862 1.061183e-02 1.061183e-02 0.264801932 #> 7119  3559.0 0.7245316 0.7245316 1.062189e-02 1.062189e-02 0.264846468 #> 7120  3559.5 0.7244769 0.7244769 1.063194e-02 1.063194e-02 0.264891149 #> 7121  3560.0 0.7244220 0.7244220 1.064199e-02 1.064199e-02 0.264935975 #> 7122  3560.5 0.7243670 0.7243670 1.065203e-02 1.065203e-02 0.264980945 #> 7123  3561.0 0.7243119 0.7243119 1.066207e-02 1.066207e-02 0.265026061 #> 7124  3561.5 0.7242566 0.7242566 1.067210e-02 1.067210e-02 0.265071322 #> 7125  3562.0 0.7242011 0.7242011 1.068213e-02 1.068213e-02 0.265116727 #> 7126  3562.5 0.7241456 0.7241456 1.069215e-02 1.069215e-02 0.265162276 #> 7127  3563.0 0.7240899 0.7240899 1.070216e-02 1.070216e-02 0.265207969 #> 7128  3563.5 0.7240340 0.7240340 1.071217e-02 1.071217e-02 0.265253806 #> 7129  3564.0 0.7239780 0.7239780 1.072218e-02 1.072218e-02 0.265299788 #> 7130  3564.5 0.7239219 0.7239219 1.073218e-02 1.073218e-02 0.265345913 #> 7131  3565.0 0.7238657 0.7238657 1.074217e-02 1.074217e-02 0.265392181 #> 7132  3565.5 0.7238093 0.7238093 1.075216e-02 1.075216e-02 0.265438593 #> 7133  3566.0 0.7237527 0.7237527 1.076214e-02 1.076214e-02 0.265485147 #> 7134  3566.5 0.7236960 0.7236960 1.077211e-02 1.077211e-02 0.265531845 #> 7135  3567.0 0.7236392 0.7236392 1.078208e-02 1.078208e-02 0.265578686 #> 7136  3567.5 0.7235823 0.7235823 1.079204e-02 1.079204e-02 0.265625669 #> 7137  3568.0 0.7235252 0.7235252 1.080200e-02 1.080200e-02 0.265672795 #> 7138  3568.5 0.7234680 0.7234680 1.081195e-02 1.081195e-02 0.265720063 #> 7139  3569.0 0.7234106 0.7234106 1.082190e-02 1.082190e-02 0.265767473 #> 7140  3569.5 0.7233531 0.7233531 1.083183e-02 1.083183e-02 0.265815025 #> 7141  3570.0 0.7232955 0.7232955 1.084176e-02 1.084176e-02 0.265862719 #> 7142  3570.5 0.7232378 0.7232378 1.085169e-02 1.085169e-02 0.265910555 #> 7143  3571.0 0.7231799 0.7231799 1.086161e-02 1.086161e-02 0.265958532 #> 7144  3571.5 0.7231218 0.7231218 1.087152e-02 1.087152e-02 0.266006650 #> 7145  3572.0 0.7230637 0.7230637 1.088142e-02 1.088142e-02 0.266054909 #> 7146  3572.5 0.7230054 0.7230054 1.089132e-02 1.089132e-02 0.266103309 #> 7147  3573.0 0.7229469 0.7229469 1.090121e-02 1.090121e-02 0.266151849 #> 7148  3573.5 0.7228884 0.7228884 1.091110e-02 1.091110e-02 0.266200531 #> 7149  3574.0 0.7228297 0.7228297 1.092097e-02 1.092097e-02 0.266249352 #> 7150  3574.5 0.7227708 0.7227708 1.093084e-02 1.093084e-02 0.266298314 #> 7151  3575.0 0.7227119 0.7227119 1.094071e-02 1.094071e-02 0.266347415 #> 7152  3575.5 0.7226528 0.7226528 1.095056e-02 1.095056e-02 0.266396656 #> 7153  3576.0 0.7225936 0.7225936 1.096041e-02 1.096041e-02 0.266446037 #> 7154  3576.5 0.7225342 0.7225342 1.097025e-02 1.097025e-02 0.266495557 #> 7155  3577.0 0.7224747 0.7224747 1.098008e-02 1.098008e-02 0.266545216 #> 7156  3577.5 0.7224151 0.7224151 1.098991e-02 1.098991e-02 0.266595015 #> 7157  3578.0 0.7223553 0.7223553 1.099973e-02 1.099973e-02 0.266644952 #> 7158  3578.5 0.7222954 0.7222954 1.100954e-02 1.100954e-02 0.266695027 #> 7159  3579.0 0.7222354 0.7222354 1.101934e-02 1.101934e-02 0.266745241 #> 7160  3579.5 0.7221753 0.7221753 1.102913e-02 1.102913e-02 0.266795593 #> 7161  3580.0 0.7221150 0.7221150 1.103892e-02 1.103892e-02 0.266846083 #> 7162  3580.5 0.7220546 0.7220546 1.104870e-02 1.104870e-02 0.266896711 #> 7163  3581.0 0.7219941 0.7219941 1.105847e-02 1.105847e-02 0.266947476 #> 7164  3581.5 0.7219334 0.7219334 1.106823e-02 1.106823e-02 0.266998379 #> 7165  3582.0 0.7218726 0.7218726 1.107799e-02 1.107799e-02 0.267049419 #> 7166  3582.5 0.7218117 0.7218117 1.108773e-02 1.108773e-02 0.267100596 #> 7167  3583.0 0.7217506 0.7217506 1.109747e-02 1.109747e-02 0.267151910 #> 7168  3583.5 0.7216894 0.7216894 1.110720e-02 1.110720e-02 0.267203360 #> 7169  3584.0 0.7216281 0.7216281 1.111692e-02 1.111692e-02 0.267254946 #> 7170  3584.5 0.7215667 0.7215667 1.112663e-02 1.112663e-02 0.267306669 #> 7171  3585.0 0.7215051 0.7215051 1.113634e-02 1.113634e-02 0.267358527 #> 7172  3585.5 0.7214434 0.7214434 1.114603e-02 1.114603e-02 0.267410521 #> 7173  3586.0 0.7213816 0.7213816 1.115572e-02 1.115572e-02 0.267462651 #> 7174  3586.5 0.7213197 0.7213197 1.116540e-02 1.116540e-02 0.267514916 #> 7175  3587.0 0.7212576 0.7212576 1.117506e-02 1.117506e-02 0.267567316 #> 7176  3587.5 0.7211954 0.7211954 1.118472e-02 1.118472e-02 0.267619851 #> 7177  3588.0 0.7211331 0.7211331 1.119437e-02 1.119437e-02 0.267672520 #> 7178  3588.5 0.7210707 0.7210707 1.120401e-02 1.120401e-02 0.267725324 #> 7179  3589.0 0.7210081 0.7210081 1.121365e-02 1.121365e-02 0.267778262 #> 7180  3589.5 0.7209454 0.7209454 1.122327e-02 1.122327e-02 0.267831334 #> 7181  3590.0 0.7208826 0.7208826 1.123288e-02 1.123288e-02 0.267884539 #> 7182  3590.5 0.7208196 0.7208196 1.124249e-02 1.124249e-02 0.267937878 #> 7183  3591.0 0.7207566 0.7207566 1.125208e-02 1.125208e-02 0.267991351 #> 7184  3591.5 0.7206934 0.7206934 1.126167e-02 1.126167e-02 0.268044956 #> 7185  3592.0 0.7206301 0.7206301 1.127124e-02 1.127124e-02 0.268098695 #> 7186  3592.5 0.7205666 0.7205666 1.128081e-02 1.128081e-02 0.268152566 #> 7187  3593.0 0.7205031 0.7205031 1.129036e-02 1.129036e-02 0.268206569 #> 7188  3593.5 0.7204394 0.7204394 1.129991e-02 1.129991e-02 0.268260704 #> 7189  3594.0 0.7203756 0.7203756 1.130944e-02 1.130944e-02 0.268314972 #> 7190  3594.5 0.7203117 0.7203117 1.131897e-02 1.131897e-02 0.268369371 #> 7191  3595.0 0.7202476 0.7202476 1.132848e-02 1.132848e-02 0.268423901 #> 7192  3595.5 0.7201834 0.7201834 1.133799e-02 1.133799e-02 0.268478563 #> 7193  3596.0 0.7201192 0.7201192 1.134748e-02 1.134748e-02 0.268533356 #> 7194  3596.5 0.7200548 0.7200548 1.135697e-02 1.135697e-02 0.268588279 #> 7195  3597.0 0.7199902 0.7199902 1.136644e-02 1.136644e-02 0.268643333 #> 7196  3597.5 0.7199256 0.7199256 1.137591e-02 1.137591e-02 0.268698517 #> 7197  3598.0 0.7198608 0.7198608 1.138536e-02 1.138536e-02 0.268753831 #> 7198  3598.5 0.7197959 0.7197959 1.139480e-02 1.139480e-02 0.268809275 #> 7199  3599.0 0.7197309 0.7197309 1.140424e-02 1.140424e-02 0.268864849 #> 7200  3599.5 0.7196658 0.7196658 1.141366e-02 1.141366e-02 0.268920551 #> 7201  3600.0 0.7196005 0.7196005 1.142307e-02 1.142307e-02 0.268976383 #> 7202  3600.5 0.7195352 0.7195352 1.143247e-02 1.143247e-02 0.269032344 #> 7203  3601.0 0.7194697 0.7194697 1.144186e-02 1.144186e-02 0.269088433 #> 7204  3601.5 0.7194041 0.7194041 1.145123e-02 1.145123e-02 0.269144651 #> 7205  3602.0 0.7193384 0.7193384 1.146060e-02 1.146060e-02 0.269200996 #> 7206  3602.5 0.7192726 0.7192726 1.146996e-02 1.146996e-02 0.269257470 #> 7207  3603.0 0.7192066 0.7192066 1.147930e-02 1.147930e-02 0.269314071 #> 7208  3603.5 0.7191406 0.7191406 1.148863e-02 1.148863e-02 0.269370799 #> 7209  3604.0 0.7190744 0.7190744 1.149795e-02 1.149795e-02 0.269427654 #> 7210  3604.5 0.7190081 0.7190081 1.150726e-02 1.150726e-02 0.269484636 #> 7211  3605.0 0.7189417 0.7189417 1.151656e-02 1.151656e-02 0.269541745 #> 7212  3605.5 0.7188752 0.7188752 1.152585e-02 1.152585e-02 0.269598980 #> 7213  3606.0 0.7188085 0.7188085 1.153512e-02 1.153512e-02 0.269656341 #> 7214  3606.5 0.7187418 0.7187418 1.154439e-02 1.154439e-02 0.269713828 #> 7215  3607.0 0.7186749 0.7186749 1.155364e-02 1.155364e-02 0.269771440 #> 7216  3607.5 0.7186079 0.7186079 1.156288e-02 1.156288e-02 0.269829177 #> 7217  3608.0 0.7185409 0.7185409 1.157211e-02 1.157211e-02 0.269887040 #> 7218  3608.5 0.7184737 0.7184737 1.158132e-02 1.158132e-02 0.269945027 #> 7219  3609.0 0.7184063 0.7184063 1.159052e-02 1.159052e-02 0.270003139 #> 7220  3609.5 0.7183389 0.7183389 1.159971e-02 1.159971e-02 0.270061375 #> 7221  3610.0 0.7182714 0.7182714 1.160889e-02 1.160889e-02 0.270119734 #> 7222  3610.5 0.7182037 0.7182037 1.161806e-02 1.161806e-02 0.270178218 #> 7223  3611.0 0.7181360 0.7181360 1.162721e-02 1.162721e-02 0.270236825 #> 7224  3611.5 0.7180681 0.7180681 1.163635e-02 1.163635e-02 0.270295555 #> 7225  3612.0 0.7180001 0.7180001 1.164548e-02 1.164548e-02 0.270354408 #> 7226  3612.5 0.7179320 0.7179320 1.165460e-02 1.165460e-02 0.270413383 #> 7227  3613.0 0.7178638 0.7178638 1.166370e-02 1.166370e-02 0.270472481 #> 7228  3613.5 0.7177955 0.7177955 1.167279e-02 1.167279e-02 0.270531701 #> 7229  3614.0 0.7177271 0.7177271 1.168187e-02 1.168187e-02 0.270591042 #> 7230  3614.5 0.7176586 0.7176586 1.169093e-02 1.169093e-02 0.270650505 #> 7231  3615.0 0.7175899 0.7175899 1.169999e-02 1.169999e-02 0.270710089 #> 7232  3615.5 0.7175212 0.7175212 1.170902e-02 1.170902e-02 0.270769794 #> 7233  3616.0 0.7174523 0.7174523 1.171805e-02 1.171805e-02 0.270829620 #> 7234  3616.5 0.7173834 0.7173834 1.172706e-02 1.172706e-02 0.270889566 #> 7235  3617.0 0.7173143 0.7173143 1.173606e-02 1.173606e-02 0.270949633 #> 7236  3617.5 0.7172451 0.7172451 1.174505e-02 1.174505e-02 0.271009819 #> 7237  3618.0 0.7171759 0.7171759 1.175402e-02 1.175402e-02 0.271070124 #> 7238  3618.5 0.7171065 0.7171065 1.176298e-02 1.176298e-02 0.271130549 #> 7239  3619.0 0.7170370 0.7170370 1.177192e-02 1.177192e-02 0.271191093 #> 7240  3619.5 0.7169674 0.7169674 1.178085e-02 1.178085e-02 0.271251755 #> 7241  3620.0 0.7168977 0.7168977 1.178977e-02 1.178977e-02 0.271312536 #> 7242  3620.5 0.7168279 0.7168279 1.179867e-02 1.179867e-02 0.271373435 #> 7243  3621.0 0.7167580 0.7167580 1.180756e-02 1.180756e-02 0.271434452 #> 7244  3621.5 0.7166880 0.7166880 1.181644e-02 1.181644e-02 0.271495586 #> 7245  3622.0 0.7166179 0.7166179 1.182530e-02 1.182530e-02 0.271556837 #> 7246  3622.5 0.7165476 0.7165476 1.183415e-02 1.183415e-02 0.271618206 #> 7247  3623.0 0.7164773 0.7164773 1.184298e-02 1.184298e-02 0.271679690 #> 7248  3623.5 0.7164069 0.7164069 1.185180e-02 1.185180e-02 0.271741292 #> 7249  3624.0 0.7163364 0.7163364 1.186060e-02 1.186060e-02 0.271803009 #> 7250  3624.5 0.7162658 0.7162658 1.186939e-02 1.186939e-02 0.271864842 #> 7251  3625.0 0.7161950 0.7161950 1.187817e-02 1.187817e-02 0.271926791 #> 7252  3625.5 0.7161242 0.7161242 1.188693e-02 1.188693e-02 0.271988854 #> 7253  3626.0 0.7160533 0.7160533 1.189568e-02 1.189568e-02 0.272051033 #> 7254  3626.5 0.7159823 0.7159823 1.190441e-02 1.190441e-02 0.272113326 #> 7255  3627.0 0.7159111 0.7159111 1.191313e-02 1.191313e-02 0.272175733 #> 7256  3627.5 0.7158399 0.7158399 1.192183e-02 1.192183e-02 0.272238254 #> 7257  3628.0 0.7157686 0.7157686 1.193052e-02 1.193052e-02 0.272300889 #> 7258  3628.5 0.7156972 0.7156972 1.193919e-02 1.193919e-02 0.272363637 #> 7259  3629.0 0.7156257 0.7156257 1.194785e-02 1.194785e-02 0.272426498 #> 7260  3629.5 0.7155540 0.7155540 1.195649e-02 1.195649e-02 0.272489472 #> 7261  3630.0 0.7154823 0.7154823 1.196512e-02 1.196512e-02 0.272552558 #> 7262  3630.5 0.7154105 0.7154105 1.197373e-02 1.197373e-02 0.272615757 #> 7263  3631.0 0.7153386 0.7153386 1.198233e-02 1.198233e-02 0.272679067 #> 7264  3631.5 0.7152666 0.7152666 1.199091e-02 1.199091e-02 0.272742488 #> 7265  3632.0 0.7151945 0.7151945 1.199947e-02 1.199947e-02 0.272806021 #> 7266  3632.5 0.7151223 0.7151223 1.200802e-02 1.200802e-02 0.272869665 #> 7267  3633.0 0.7150500 0.7150500 1.201656e-02 1.201656e-02 0.272933419 #> 7268  3633.5 0.7149776 0.7149776 1.202508e-02 1.202508e-02 0.272997283 #> 7269  3634.0 0.7149052 0.7149052 1.203358e-02 1.203358e-02 0.273061257 #> 7270  3634.5 0.7148326 0.7148326 1.204207e-02 1.204207e-02 0.273125341 #> 7271  3635.0 0.7147599 0.7147599 1.205054e-02 1.205054e-02 0.273189534 #> 7272  3635.5 0.7146872 0.7146872 1.205899e-02 1.205899e-02 0.273253836 #> 7273  3636.0 0.7146143 0.7146143 1.206743e-02 1.206743e-02 0.273318247 #> 7274  3636.5 0.7145414 0.7145414 1.207586e-02 1.207586e-02 0.273382765 #> 7275  3637.0 0.7144683 0.7144683 1.208426e-02 1.208426e-02 0.273447392 #> 7276  3637.5 0.7143952 0.7143952 1.209265e-02 1.209265e-02 0.273512127 #> 7277  3638.0 0.7143220 0.7143220 1.210103e-02 1.210103e-02 0.273576968 #> 7278  3638.5 0.7142487 0.7142487 1.210939e-02 1.210939e-02 0.273641917 #> 7279  3639.0 0.7141753 0.7141753 1.211773e-02 1.211773e-02 0.273706973 #> 7280  3639.5 0.7141018 0.7141018 1.212605e-02 1.212605e-02 0.273772134 #> 7281  3640.0 0.7140282 0.7140282 1.213436e-02 1.213436e-02 0.273837402 #> 7282  3640.5 0.7139546 0.7139546 1.214266e-02 1.214266e-02 0.273902776 #> 7283  3641.0 0.7138808 0.7138808 1.215093e-02 1.215093e-02 0.273968255 #> 7284  3641.5 0.7138070 0.7138070 1.215919e-02 1.215919e-02 0.274033838 #> 7285  3642.0 0.7137330 0.7137330 1.216743e-02 1.216743e-02 0.274099527 #> 7286  3642.5 0.7136590 0.7136590 1.217566e-02 1.217566e-02 0.274165320 #> 7287  3643.0 0.7135849 0.7135849 1.218386e-02 1.218386e-02 0.274231217 #> 7288  3643.5 0.7135107 0.7135107 1.219205e-02 1.219205e-02 0.274297217 #> 7289  3644.0 0.7134365 0.7134365 1.220023e-02 1.220023e-02 0.274363321 #> 7290  3644.5 0.7133621 0.7133621 1.220838e-02 1.220838e-02 0.274429528 #> 7291  3645.0 0.7132876 0.7132876 1.221652e-02 1.221652e-02 0.274495838 #> 7292  3645.5 0.7132131 0.7132131 1.222464e-02 1.222464e-02 0.274562250 #> 7293  3646.0 0.7131385 0.7131385 1.223275e-02 1.223275e-02 0.274628763 #> 7294  3646.5 0.7130638 0.7130638 1.224084e-02 1.224084e-02 0.274695379 #> 7295  3647.0 0.7129890 0.7129890 1.224891e-02 1.224891e-02 0.274762096 #> 7296  3647.5 0.7129141 0.7129141 1.225696e-02 1.225696e-02 0.274828914 #> 7297  3648.0 0.7128392 0.7128392 1.226499e-02 1.226499e-02 0.274895832 #> 7298  3648.5 0.7127641 0.7127641 1.227301e-02 1.227301e-02 0.274962851 #> 7299  3649.0 0.7126890 0.7126890 1.228101e-02 1.228101e-02 0.275029969 #> 7300  3649.5 0.7126138 0.7126138 1.228899e-02 1.228899e-02 0.275097187 #> 7301  3650.0 0.7125385 0.7125385 1.229695e-02 1.229695e-02 0.275164505 #> 7302  3650.5 0.7124632 0.7124632 1.230489e-02 1.230489e-02 0.275231921 #> 7303  3651.0 0.7123877 0.7123877 1.231282e-02 1.231282e-02 0.275299436 #> 7304  3651.5 0.7123122 0.7123122 1.232073e-02 1.232073e-02 0.275367049 #> 7305  3652.0 0.7122366 0.7122366 1.232862e-02 1.232862e-02 0.275434760 #> 7306  3652.5 0.7121609 0.7121609 1.233649e-02 1.233649e-02 0.275502568 #> 7307  3653.0 0.7120852 0.7120852 1.234434e-02 1.234434e-02 0.275570473 #> 7308  3653.5 0.7120093 0.7120093 1.235218e-02 1.235218e-02 0.275638475 #> 7309  3654.0 0.7119334 0.7119334 1.236000e-02 1.236000e-02 0.275706574 #> 7310  3654.5 0.7118574 0.7118574 1.236779e-02 1.236779e-02 0.275774769 #> 7311  3655.0 0.7117814 0.7117814 1.237557e-02 1.237557e-02 0.275843059 #> 7312  3655.5 0.7117052 0.7117052 1.238333e-02 1.238333e-02 0.275911445 #> 7313  3656.0 0.7116290 0.7116290 1.239108e-02 1.239108e-02 0.275979925 #> 7314  3656.5 0.7115527 0.7115527 1.239880e-02 1.239880e-02 0.276048500 #> 7315  3657.0 0.7114763 0.7114763 1.240650e-02 1.240650e-02 0.276117170 #> 7316  3657.5 0.7113999 0.7113999 1.241419e-02 1.241419e-02 0.276185933 #> 7317  3658.0 0.7113234 0.7113234 1.242186e-02 1.242186e-02 0.276254790 #> 7318  3658.5 0.7112468 0.7112468 1.242950e-02 1.242950e-02 0.276323740 #> 7319  3659.0 0.7111701 0.7111701 1.243713e-02 1.243713e-02 0.276392783 #> 7320  3659.5 0.7110933 0.7110933 1.244474e-02 1.244474e-02 0.276461918 #> 7321  3660.0 0.7110165 0.7110165 1.245233e-02 1.245233e-02 0.276531146 #> 7322  3660.5 0.7109396 0.7109396 1.245990e-02 1.245990e-02 0.276600465 #> 7323  3661.0 0.7108627 0.7108627 1.246745e-02 1.246745e-02 0.276669875 #> 7324  3661.5 0.7107856 0.7107856 1.247499e-02 1.247499e-02 0.276739377 #> 7325  3662.0 0.7107085 0.7107085 1.248250e-02 1.248250e-02 0.276808969 #> 7326  3662.5 0.7106314 0.7106314 1.248999e-02 1.248999e-02 0.276878651 #> 7327  3663.0 0.7105541 0.7105541 1.249746e-02 1.249746e-02 0.276948423 #> 7328  3663.5 0.7104768 0.7104768 1.250492e-02 1.250492e-02 0.277018284 #> 7329  3664.0 0.7103994 0.7103994 1.251235e-02 1.251235e-02 0.277088235 #> 7330  3664.5 0.7103220 0.7103220 1.251976e-02 1.251976e-02 0.277158274 #> 7331  3665.0 0.7102444 0.7102444 1.252716e-02 1.252716e-02 0.277228402 #> 7332  3665.5 0.7101668 0.7101668 1.253453e-02 1.253453e-02 0.277298618 #> 7333  3666.0 0.7100892 0.7100892 1.254189e-02 1.254189e-02 0.277368921 #> 7334  3666.5 0.7100115 0.7100115 1.254922e-02 1.254922e-02 0.277439312 #> 7335  3667.0 0.7099337 0.7099337 1.255653e-02 1.255653e-02 0.277509789 #> 7336  3667.5 0.7098558 0.7098558 1.256383e-02 1.256383e-02 0.277580353 #> 7337  3668.0 0.7097779 0.7097779 1.257110e-02 1.257110e-02 0.277651003 #> 7338  3668.5 0.7096999 0.7096999 1.257835e-02 1.257835e-02 0.277721738 #> 7339  3669.0 0.7096219 0.7096219 1.258559e-02 1.258559e-02 0.277792559 #> 7340  3669.5 0.7095437 0.7095437 1.259280e-02 1.259280e-02 0.277863465 #> 7341  3670.0 0.7094656 0.7094656 1.259999e-02 1.259999e-02 0.277934456 #> 7342  3670.5 0.7093873 0.7093873 1.260716e-02 1.260716e-02 0.278005530 #> 7343  3671.0 0.7093090 0.7093090 1.261431e-02 1.261431e-02 0.278076689 #> 7344  3671.5 0.7092306 0.7092306 1.262144e-02 1.262144e-02 0.278147931 #> 7345  3672.0 0.7091522 0.7091522 1.262855e-02 1.262855e-02 0.278219256 #> 7346  3672.5 0.7090737 0.7090737 1.263564e-02 1.263564e-02 0.278290663 #> 7347  3673.0 0.7089951 0.7089951 1.264271e-02 1.264271e-02 0.278362153 #> 7348  3673.5 0.7089165 0.7089165 1.264976e-02 1.264976e-02 0.278433725 #> 7349  3674.0 0.7088378 0.7088378 1.265678e-02 1.265678e-02 0.278505378 #> 7350  3674.5 0.7087591 0.7087591 1.266379e-02 1.266379e-02 0.278577112 #> 7351  3675.0 0.7086803 0.7086803 1.267077e-02 1.267077e-02 0.278648927 #> 7352  3675.5 0.7086014 0.7086014 1.267773e-02 1.267773e-02 0.278720822 #> 7353  3676.0 0.7085225 0.7085225 1.268468e-02 1.268468e-02 0.278792797 #> 7354  3676.5 0.7084436 0.7084436 1.269160e-02 1.269160e-02 0.278864851 #> 7355  3677.0 0.7083645 0.7083645 1.269849e-02 1.269849e-02 0.278936985 #> 7356  3677.5 0.7082854 0.7082854 1.270537e-02 1.270537e-02 0.279009197 #> 7357  3678.0 0.7082063 0.7082063 1.271223e-02 1.271223e-02 0.279081488 #> 7358  3678.5 0.7081271 0.7081271 1.271906e-02 1.271906e-02 0.279153857 #> 7359  3679.0 0.7080478 0.7080478 1.272588e-02 1.272588e-02 0.279226303 #> 7360  3679.5 0.7079685 0.7079685 1.273267e-02 1.273267e-02 0.279298826 #> 7361  3680.0 0.7078891 0.7078891 1.273944e-02 1.273944e-02 0.279371426 #> 7362  3680.5 0.7078097 0.7078097 1.274619e-02 1.274619e-02 0.279444103 #> 7363  3681.0 0.7077302 0.7077302 1.275291e-02 1.275291e-02 0.279516855 #> 7364  3681.5 0.7076507 0.7076507 1.275962e-02 1.275962e-02 0.279589683 #> 7365  3682.0 0.7075711 0.7075711 1.276630e-02 1.276630e-02 0.279662586 #> 7366  3682.5 0.7074915 0.7074915 1.277296e-02 1.277296e-02 0.279735563 #> 7367  3683.0 0.7074118 0.7074118 1.277960e-02 1.277960e-02 0.279808615 #> 7368  3683.5 0.7073320 0.7073320 1.278622e-02 1.278622e-02 0.279881741 #> 7369  3684.0 0.7072522 0.7072522 1.279281e-02 1.279281e-02 0.279954941 #> 7370  3684.5 0.7071724 0.7071724 1.279938e-02 1.279938e-02 0.280028213 #> 7371  3685.0 0.7070925 0.7070925 1.280593e-02 1.280593e-02 0.280101558 #> 7372  3685.5 0.7070126 0.7070126 1.281246e-02 1.281246e-02 0.280174976 #> 7373  3686.0 0.7069326 0.7069326 1.281897e-02 1.281897e-02 0.280248465 #> 7374  3686.5 0.7068525 0.7068525 1.282545e-02 1.282545e-02 0.280322026 #> 7375  3687.0 0.7067724 0.7067724 1.283191e-02 1.283191e-02 0.280395658 #> 7376  3687.5 0.7066923 0.7066923 1.283835e-02 1.283835e-02 0.280469361 #> 7377  3688.0 0.7066121 0.7066121 1.284477e-02 1.284477e-02 0.280543134 #> 7378  3688.5 0.7065319 0.7065319 1.285116e-02 1.285116e-02 0.280616977 #> 7379  3689.0 0.7064516 0.7064516 1.285753e-02 1.285753e-02 0.280690889 #> 7380  3689.5 0.7063712 0.7063712 1.286388e-02 1.286388e-02 0.280764870 #> 7381  3690.0 0.7062909 0.7062909 1.287021e-02 1.287021e-02 0.280838920 #> 7382  3690.5 0.7062105 0.7062105 1.287651e-02 1.287651e-02 0.280913038 #> 7383  3691.0 0.7061300 0.7061300 1.288279e-02 1.288279e-02 0.280987223 #> 7384  3691.5 0.7060495 0.7060495 1.288905e-02 1.288905e-02 0.281061477 #> 7385  3692.0 0.7059689 0.7059689 1.289528e-02 1.289528e-02 0.281135797 #> 7386  3692.5 0.7058883 0.7058883 1.290149e-02 1.290149e-02 0.281210183 #> 7387  3693.0 0.7058077 0.7058077 1.290768e-02 1.290768e-02 0.281284636 #> 7388  3693.5 0.7057270 0.7057270 1.291384e-02 1.291384e-02 0.281359155 #> 7389  3694.0 0.7056463 0.7056463 1.291999e-02 1.291999e-02 0.281433738 #> 7390  3694.5 0.7055655 0.7055655 1.292611e-02 1.292611e-02 0.281508387 #> 7391  3695.0 0.7054847 0.7054847 1.293220e-02 1.293220e-02 0.281583100 #> 7392  3695.5 0.7054038 0.7054038 1.293827e-02 1.293827e-02 0.281657877 #> 7393  3696.0 0.7053230 0.7053230 1.294432e-02 1.294432e-02 0.281732718 #> 7394  3696.5 0.7052420 0.7052420 1.295035e-02 1.295035e-02 0.281807622 #> 7395  3697.0 0.7051611 0.7051611 1.295635e-02 1.295635e-02 0.281882588 #> 7396  3697.5 0.7050801 0.7050801 1.296233e-02 1.296233e-02 0.281957617 #> 7397  3698.0 0.7049990 0.7049990 1.296828e-02 1.296828e-02 0.282032708 #> 7398  3698.5 0.7049179 0.7049179 1.297422e-02 1.297422e-02 0.282107860 #> 7399  3699.0 0.7048368 0.7048368 1.298012e-02 1.298012e-02 0.282183074 #> 7400  3699.5 0.7047556 0.7047556 1.298601e-02 1.298601e-02 0.282258348 #> 7401  3700.0 0.7046744 0.7046744 1.299187e-02 1.299187e-02 0.282333682 #> 7402  3700.5 0.7045932 0.7045932 1.299771e-02 1.299771e-02 0.282409076 #> 7403  3701.0 0.7045120 0.7045120 1.300352e-02 1.300352e-02 0.282484530 #> 7404  3701.5 0.7044306 0.7044306 1.300931e-02 1.300931e-02 0.282560042 #> 7405  3702.0 0.7043493 0.7043493 1.301507e-02 1.301507e-02 0.282635613 #> 7406  3702.5 0.7042679 0.7042679 1.302082e-02 1.302082e-02 0.282711242 #> 7407  3703.0 0.7041865 0.7041865 1.302653e-02 1.302653e-02 0.282786929 #> 7408  3703.5 0.7041051 0.7041051 1.303223e-02 1.303223e-02 0.282862673 #> 7409  3704.0 0.7040236 0.7040236 1.303790e-02 1.303790e-02 0.282938474 #> 7410  3704.5 0.7039421 0.7039421 1.304354e-02 1.304354e-02 0.283014331 #> 7411  3705.0 0.7038606 0.7038606 1.304916e-02 1.304916e-02 0.283090244 #> 7412  3705.5 0.7037790 0.7037790 1.305476e-02 1.305476e-02 0.283166213 #> 7413  3706.0 0.7036974 0.7036974 1.306033e-02 1.306033e-02 0.283242236 #> 7414  3706.5 0.7036158 0.7036158 1.306588e-02 1.306588e-02 0.283318315 #> 7415  3707.0 0.7035341 0.7035341 1.307141e-02 1.307141e-02 0.283394447 #> 7416  3707.5 0.7034525 0.7034525 1.307691e-02 1.307691e-02 0.283470634 #> 7417  3708.0 0.7033707 0.7033707 1.308238e-02 1.308238e-02 0.283546873 #> 7418  3708.5 0.7032890 0.7032890 1.308783e-02 1.308783e-02 0.283623166 #> 7419  3709.0 0.7032072 0.7032072 1.309326e-02 1.309326e-02 0.283699511 #> 7420  3709.5 0.7031254 0.7031254 1.309866e-02 1.309866e-02 0.283775908 #> 7421  3710.0 0.7030436 0.7030436 1.310404e-02 1.310404e-02 0.283852357 #> 7422  3710.5 0.7029618 0.7029618 1.310939e-02 1.310939e-02 0.283928857 #> 7423  3711.0 0.7028799 0.7028799 1.311472e-02 1.311472e-02 0.284005408 #> 7424  3711.5 0.7027980 0.7027980 1.312002e-02 1.312002e-02 0.284082009 #> 7425  3712.0 0.7027160 0.7027160 1.312530e-02 1.312530e-02 0.284158660 #> 7426  3712.5 0.7026341 0.7026341 1.313055e-02 1.313055e-02 0.284235360 #> 7427  3713.0 0.7025521 0.7025521 1.313578e-02 1.313578e-02 0.284312109 #> 7428  3713.5 0.7024701 0.7024701 1.314099e-02 1.314099e-02 0.284388907 #> 7429  3714.0 0.7023881 0.7023881 1.314617e-02 1.314617e-02 0.284465753 #> 7430  3714.5 0.7023060 0.7023060 1.315132e-02 1.315132e-02 0.284542647 #> 7431  3715.0 0.7022240 0.7022240 1.315645e-02 1.315645e-02 0.284619588 #> 7432  3715.5 0.7021419 0.7021419 1.316156e-02 1.316156e-02 0.284696576 #> 7433  3716.0 0.7020598 0.7020598 1.316664e-02 1.316664e-02 0.284773610 #> 7434  3716.5 0.7019776 0.7019776 1.317169e-02 1.317169e-02 0.284850690 #> 7435  3717.0 0.7018955 0.7018955 1.317672e-02 1.317672e-02 0.284927815 #> 7436  3717.5 0.7018133 0.7018133 1.318172e-02 1.318172e-02 0.285004986 #> 7437  3718.0 0.7017311 0.7017311 1.318670e-02 1.318670e-02 0.285082201 #> 7438  3718.5 0.7016489 0.7016489 1.319166e-02 1.319166e-02 0.285159460 #> 7439  3719.0 0.7015667 0.7015667 1.319658e-02 1.319658e-02 0.285236763 #> 7440  3719.5 0.7014844 0.7014844 1.320149e-02 1.320149e-02 0.285314109 #> 7441  3720.0 0.7014021 0.7014021 1.320636e-02 1.320636e-02 0.285391498 #> 7442  3720.5 0.7013199 0.7013199 1.321122e-02 1.321122e-02 0.285468930 #> 7443  3721.0 0.7012376 0.7012376 1.321604e-02 1.321604e-02 0.285546403 #> 7444  3721.5 0.7011552 0.7011552 1.322085e-02 1.322085e-02 0.285623918 #> 7445  3722.0 0.7010729 0.7010729 1.322562e-02 1.322562e-02 0.285701474 #> 7446  3722.5 0.7009906 0.7009906 1.323037e-02 1.323037e-02 0.285779070 #> 7447  3723.0 0.7009082 0.7009082 1.323510e-02 1.323510e-02 0.285856707 #> 7448  3723.5 0.7008258 0.7008258 1.323980e-02 1.323980e-02 0.285934383 #> 7449  3724.0 0.7007434 0.7007434 1.324447e-02 1.324447e-02 0.286012099 #> 7450  3724.5 0.7006610 0.7006610 1.324912e-02 1.324912e-02 0.286089853 #> 7451  3725.0 0.7005786 0.7005786 1.325374e-02 1.325374e-02 0.286167646 #> 7452  3725.5 0.7004962 0.7004962 1.325834e-02 1.325834e-02 0.286245477 #> 7453  3726.0 0.7004137 0.7004137 1.326291e-02 1.326291e-02 0.286323345 #> 7454  3726.5 0.7003313 0.7003313 1.326745e-02 1.326745e-02 0.286401251 #> 7455  3727.0 0.7002488 0.7002488 1.327197e-02 1.327197e-02 0.286479192 #> 7456  3727.5 0.7001664 0.7001664 1.327647e-02 1.327647e-02 0.286557170 #> 7457  3728.0 0.7000839 0.7000839 1.328093e-02 1.328093e-02 0.286635184 #> 7458  3728.5 0.7000014 0.7000014 1.328538e-02 1.328538e-02 0.286713233 #> 7459  3729.0 0.6999189 0.6999189 1.328979e-02 1.328979e-02 0.286791316 #> 7460  3729.5 0.6998364 0.6998364 1.329418e-02 1.329418e-02 0.286869434 #> 7461  3730.0 0.6997539 0.6997539 1.329855e-02 1.329855e-02 0.286947586 #> 7462  3730.5 0.6996713 0.6996713 1.330288e-02 1.330288e-02 0.287025771 #> 7463  3731.0 0.6995888 0.6995888 1.330720e-02 1.330720e-02 0.287103990 #> 7464  3731.5 0.6995063 0.6995063 1.331148e-02 1.331148e-02 0.287182241 #> 7465  3732.0 0.6994237 0.6994237 1.331574e-02 1.331574e-02 0.287260523 #> 7466  3732.5 0.6993412 0.6993412 1.331998e-02 1.331998e-02 0.287338838 #> 7467  3733.0 0.6992586 0.6992586 1.332418e-02 1.332418e-02 0.287417184 #> 7468  3733.5 0.6991761 0.6991761 1.332836e-02 1.332836e-02 0.287495560 #> 7469  3734.0 0.6990935 0.6990935 1.333252e-02 1.333252e-02 0.287573967 #> 7470  3734.5 0.6990109 0.6990109 1.333665e-02 1.333665e-02 0.287652403 #> 7471  3735.0 0.6989284 0.6989284 1.334075e-02 1.334075e-02 0.287730869 #> 7472  3735.5 0.6988458 0.6988458 1.334483e-02 1.334483e-02 0.287809364 #> 7473  3736.0 0.6987632 0.6987632 1.334888e-02 1.334888e-02 0.287887887 #> 7474  3736.5 0.6986807 0.6986807 1.335290e-02 1.335290e-02 0.287966439 #> 7475  3737.0 0.6985981 0.6985981 1.335690e-02 1.335690e-02 0.288045017 #> 7476  3737.5 0.6985155 0.6985155 1.336087e-02 1.336087e-02 0.288123623 #> 7477  3738.0 0.6984329 0.6984329 1.336481e-02 1.336481e-02 0.288202256 #> 7478  3738.5 0.6983504 0.6983504 1.336873e-02 1.336873e-02 0.288280915 #> 7479  3739.0 0.6982678 0.6982678 1.337262e-02 1.337262e-02 0.288359599 #> 7480  3739.5 0.6981852 0.6981852 1.337649e-02 1.337649e-02 0.288438309 #> 7481  3740.0 0.6981026 0.6981026 1.338033e-02 1.338033e-02 0.288517044 #> 7482  3740.5 0.6980201 0.6980201 1.338414e-02 1.338414e-02 0.288595803 #> 7483  3741.0 0.6979375 0.6979375 1.338793e-02 1.338793e-02 0.288674586 #> 7484  3741.5 0.6978549 0.6978549 1.339168e-02 1.339168e-02 0.288753392 #> 7485  3742.0 0.6977724 0.6977724 1.339542e-02 1.339542e-02 0.288832222 #> 7486  3742.5 0.6976898 0.6976898 1.339912e-02 1.339912e-02 0.288911074 #> 7487  3743.0 0.6976072 0.6976072 1.340280e-02 1.340280e-02 0.288989948 #> 7488  3743.5 0.6975247 0.6975247 1.340645e-02 1.340645e-02 0.289068844 #> 7489  3744.0 0.6974422 0.6974422 1.341008e-02 1.341008e-02 0.289147761 #> 7490  3744.5 0.6973596 0.6973596 1.341368e-02 1.341368e-02 0.289226699 #> 7491  3745.0 0.6972771 0.6972771 1.341725e-02 1.341725e-02 0.289305657 #> 7492  3745.5 0.6971946 0.6971946 1.342080e-02 1.342080e-02 0.289384634 #> 7493  3746.0 0.6971121 0.6971121 1.342432e-02 1.342432e-02 0.289463632 #> 7494  3746.5 0.6970295 0.6970295 1.342781e-02 1.342781e-02 0.289542648 #> 7495  3747.0 0.6969470 0.6969470 1.343127e-02 1.343127e-02 0.289621683 #> 7496  3747.5 0.6968646 0.6968646 1.343471e-02 1.343471e-02 0.289700735 #> 7497  3748.0 0.6967821 0.6967821 1.343812e-02 1.343812e-02 0.289779805 #> 7498  3748.5 0.6966996 0.6966996 1.344151e-02 1.344151e-02 0.289858893 #> 7499  3749.0 0.6966171 0.6966171 1.344487e-02 1.344487e-02 0.289937997 #> 7500  3749.5 0.6965347 0.6965347 1.344820e-02 1.344820e-02 0.290017117 #> 7501  3750.0 0.6964522 0.6964522 1.345150e-02 1.345150e-02 0.290096253 #> 7502  3750.5 0.6963698 0.6963698 1.345478e-02 1.345478e-02 0.290175404 #> 7503  3751.0 0.6962874 0.6962874 1.345803e-02 1.345803e-02 0.290254570 #> 7504  3751.5 0.6962050 0.6962050 1.346125e-02 1.346125e-02 0.290333750 #> 7505  3752.0 0.6961226 0.6961226 1.346445e-02 1.346445e-02 0.290412944 #> 7506  3752.5 0.6960402 0.6960402 1.346762e-02 1.346762e-02 0.290492152 #> 7507  3753.0 0.6959579 0.6959579 1.347076e-02 1.347076e-02 0.290571373 #> 7508  3753.5 0.6958755 0.6958755 1.347388e-02 1.347388e-02 0.290650606 #> 7509  3754.0 0.6957932 0.6957932 1.347697e-02 1.347697e-02 0.290729851 #> 7510  3754.5 0.6957109 0.6957109 1.348003e-02 1.348003e-02 0.290809108 #> 7511  3755.0 0.6956286 0.6956286 1.348306e-02 1.348306e-02 0.290888376 #> 7512  3755.5 0.6955463 0.6955463 1.348607e-02 1.348607e-02 0.290967655 #> 7513  3756.0 0.6954640 0.6954640 1.348905e-02 1.348905e-02 0.291046944 #> 7514  3756.5 0.6953818 0.6953818 1.349200e-02 1.349200e-02 0.291126243 #> 7515  3757.0 0.6952995 0.6952995 1.349493e-02 1.349493e-02 0.291205551 #> 7516  3757.5 0.6952173 0.6952173 1.349783e-02 1.349783e-02 0.291284868 #> 7517  3758.0 0.6951351 0.6951351 1.350070e-02 1.350070e-02 0.291364194 #> 7518  3758.5 0.6950529 0.6950529 1.350354e-02 1.350354e-02 0.291443527 #> 7519  3759.0 0.6949708 0.6949708 1.350636e-02 1.350636e-02 0.291522868 #> 7520  3759.5 0.6948886 0.6948886 1.350915e-02 1.350915e-02 0.291602216 #> 7521  3760.0 0.6948065 0.6948065 1.351192e-02 1.351192e-02 0.291681571 #> 7522  3760.5 0.6947244 0.6947244 1.351465e-02 1.351465e-02 0.291760932 #> 7523  3761.0 0.6946423 0.6946423 1.351736e-02 1.351736e-02 0.291840298 #> 7524  3761.5 0.6945603 0.6945603 1.352004e-02 1.352004e-02 0.291919670 #> 7525  3762.0 0.6944783 0.6944783 1.352270e-02 1.352270e-02 0.291999046 #> 7526  3762.5 0.6943962 0.6943962 1.352532e-02 1.352532e-02 0.292078427 #> 7527  3763.0 0.6943143 0.6943143 1.352792e-02 1.352792e-02 0.292157812 #> 7528  3763.5 0.6942323 0.6942323 1.353050e-02 1.353050e-02 0.292237200 #> 7529  3764.0 0.6941504 0.6941504 1.353304e-02 1.353304e-02 0.292316591 #> 7530  3764.5 0.6940685 0.6940685 1.353556e-02 1.353556e-02 0.292395985 #> 7531  3765.0 0.6939866 0.6939866 1.353805e-02 1.353805e-02 0.292475380 #> 7532  3765.5 0.6939047 0.6939047 1.354052e-02 1.354052e-02 0.292554778 #> 7533  3766.0 0.6938229 0.6938229 1.354295e-02 1.354295e-02 0.292634176 #> 7534  3766.5 0.6937411 0.6937411 1.354536e-02 1.354536e-02 0.292713575 #> 7535  3767.0 0.6936593 0.6936593 1.354774e-02 1.354774e-02 0.292792974 #> 7536  3767.5 0.6935775 0.6935775 1.355010e-02 1.355010e-02 0.292872373 #> 7537  3768.0 0.6934958 0.6934958 1.355243e-02 1.355243e-02 0.292951772 #> 7538  3768.5 0.6934141 0.6934141 1.355473e-02 1.355473e-02 0.293031169 #> 7539  3769.0 0.6933324 0.6933324 1.355700e-02 1.355700e-02 0.293110564 #> 7540  3769.5 0.6932508 0.6932508 1.355924e-02 1.355924e-02 0.293189958 #> 7541  3770.0 0.6931692 0.6931692 1.356146e-02 1.356146e-02 0.293269349 #> 7542  3770.5 0.6930876 0.6930876 1.356365e-02 1.356365e-02 0.293348737 #> 7543  3771.0 0.6930061 0.6930061 1.356582e-02 1.356582e-02 0.293428122 #> 7544  3771.5 0.6929245 0.6929245 1.356795e-02 1.356795e-02 0.293507503 #> 7545  3772.0 0.6928431 0.6928431 1.357006e-02 1.357006e-02 0.293586879 #> 7546  3772.5 0.6927616 0.6927616 1.357214e-02 1.357214e-02 0.293666251 #> 7547  3773.0 0.6926802 0.6926802 1.357420e-02 1.357420e-02 0.293745618 #> 7548  3773.5 0.6925988 0.6925988 1.357622e-02 1.357622e-02 0.293824979 #> 7549  3774.0 0.6925174 0.6925174 1.357822e-02 1.357822e-02 0.293904334 #> 7550  3774.5 0.6924361 0.6924361 1.358020e-02 1.358020e-02 0.293983682 #> 7551  3775.0 0.6923548 0.6923548 1.358214e-02 1.358214e-02 0.294063023 #> 7552  3775.5 0.6922736 0.6922736 1.358406e-02 1.358406e-02 0.294142357 #> 7553  3776.0 0.6921924 0.6921924 1.358595e-02 1.358595e-02 0.294221682 #> 7554  3776.5 0.6921112 0.6921112 1.358781e-02 1.358781e-02 0.294301000 #> 7555  3777.0 0.6920300 0.6920300 1.358965e-02 1.358965e-02 0.294380308 #> 7556  3777.5 0.6919489 0.6919489 1.359146e-02 1.359146e-02 0.294459608 #> 7557  3778.0 0.6918679 0.6918679 1.359324e-02 1.359324e-02 0.294538897 #> 7558  3778.5 0.6917868 0.6917868 1.359499e-02 1.359499e-02 0.294618177 #> 7559  3779.0 0.6917058 0.6917058 1.359672e-02 1.359672e-02 0.294697445 #> 7560  3779.5 0.6916249 0.6916249 1.359842e-02 1.359842e-02 0.294776703 #> 7561  3780.0 0.6915440 0.6915440 1.360009e-02 1.360009e-02 0.294855949 #> 7562  3780.5 0.6914631 0.6914631 1.360174e-02 1.360174e-02 0.294935183 #> 7563  3781.0 0.6913822 0.6913822 1.360335e-02 1.360335e-02 0.295014405 #> 7564  3781.5 0.6913014 0.6913014 1.360494e-02 1.360494e-02 0.295093614 #> 7565  3782.0 0.6912207 0.6912207 1.360651e-02 1.360651e-02 0.295172809 #> 7566  3782.5 0.6911400 0.6911400 1.360804e-02 1.360804e-02 0.295251991 #> 7567  3783.0 0.6910593 0.6910593 1.360955e-02 1.360955e-02 0.295331159 #> 7568  3783.5 0.6909787 0.6909787 1.361103e-02 1.361103e-02 0.295410311 #> 7569  3784.0 0.6908981 0.6908981 1.361248e-02 1.361248e-02 0.295489449 #> 7570  3784.5 0.6908175 0.6908175 1.361391e-02 1.361391e-02 0.295568571 #> 7571  3785.0 0.6907370 0.6907370 1.361531e-02 1.361531e-02 0.295647677 #> 7572  3785.5 0.6906566 0.6906566 1.361668e-02 1.361668e-02 0.295726767 #> 7573  3786.0 0.6905761 0.6905761 1.361803e-02 1.361803e-02 0.295805840 #> 7574  3786.5 0.6904958 0.6904958 1.361934e-02 1.361934e-02 0.295884895 #> 7575  3787.0 0.6904154 0.6904154 1.362063e-02 1.362063e-02 0.295963933 #> 7576  3787.5 0.6903352 0.6903352 1.362190e-02 1.362190e-02 0.296042952 #> 7577  3788.0 0.6902549 0.6902549 1.362313e-02 1.362313e-02 0.296121953 #> 7578  3788.5 0.6901747 0.6901747 1.362434e-02 1.362434e-02 0.296200935 #> 7579  3789.0 0.6900946 0.6900946 1.362552e-02 1.362552e-02 0.296279897 #> 7580  3789.5 0.6900145 0.6900145 1.362668e-02 1.362668e-02 0.296358839 #> 7581  3790.0 0.6899344 0.6899344 1.362780e-02 1.362780e-02 0.296437760 #> 7582  3790.5 0.6898544 0.6898544 1.362890e-02 1.362890e-02 0.296516661 #> 7583  3791.0 0.6897745 0.6897745 1.362997e-02 1.362997e-02 0.296595540 #> 7584  3791.5 0.6896946 0.6896946 1.363102e-02 1.363102e-02 0.296674397 #> 7585  3792.0 0.6896147 0.6896147 1.363204e-02 1.363204e-02 0.296753233 #> 7586  3792.5 0.6895349 0.6895349 1.363303e-02 1.363303e-02 0.296832045 #> 7587  3793.0 0.6894552 0.6894552 1.363399e-02 1.363399e-02 0.296910835 #> 7588  3793.5 0.6893755 0.6893755 1.363493e-02 1.363493e-02 0.296989601 #> 7589  3794.0 0.6892958 0.6892958 1.363584e-02 1.363584e-02 0.297068343 #> 7590  3794.5 0.6892162 0.6892162 1.363672e-02 1.363672e-02 0.297147060 #> 7591  3795.0 0.6891367 0.6891367 1.363758e-02 1.363758e-02 0.297225753 #> 7592  3795.5 0.6890572 0.6890572 1.363841e-02 1.363841e-02 0.297304420 #> 7593  3796.0 0.6889777 0.6889777 1.363921e-02 1.363921e-02 0.297383062 #> 7594  3796.5 0.6888983 0.6888983 1.363998e-02 1.363998e-02 0.297461677 #> 7595  3797.0 0.6888190 0.6888190 1.364073e-02 1.364073e-02 0.297540266 #> 7596  3797.5 0.6887397 0.6887397 1.364145e-02 1.364145e-02 0.297618828 #> 7597  3798.0 0.6886605 0.6886605 1.364214e-02 1.364214e-02 0.297697362 #> 7598  3798.5 0.6885813 0.6885813 1.364281e-02 1.364281e-02 0.297775869 #> 7599  3799.0 0.6885022 0.6885022 1.364345e-02 1.364345e-02 0.297854347 #> 7600  3799.5 0.6884231 0.6884231 1.364406e-02 1.364406e-02 0.297932796 #> 7601  3800.0 0.6883441 0.6883441 1.364465e-02 1.364465e-02 0.298011216 #> 7602  3800.5 0.6882652 0.6882652 1.364521e-02 1.364521e-02 0.298089606 #> 7603  3801.0 0.6881863 0.6881863 1.364574e-02 1.364574e-02 0.298167966 #> 7604  3801.5 0.6881075 0.6881075 1.364624e-02 1.364624e-02 0.298246296 #> 7605  3802.0 0.6880287 0.6880287 1.364672e-02 1.364672e-02 0.298324595 #> 7606  3802.5 0.6879500 0.6879500 1.364717e-02 1.364717e-02 0.298402862 #> 7607  3803.0 0.6878713 0.6878713 1.364760e-02 1.364760e-02 0.298481098 #> 7608  3803.5 0.6877927 0.6877927 1.364800e-02 1.364800e-02 0.298559301 #> 7609  3804.0 0.6877142 0.6877142 1.364837e-02 1.364837e-02 0.298637471 #> 7610  3804.5 0.6876357 0.6876357 1.364871e-02 1.364871e-02 0.298715609 #> 7611  3805.0 0.6875573 0.6875573 1.364903e-02 1.364903e-02 0.298793713 #> 7612  3805.5 0.6874789 0.6874789 1.364932e-02 1.364932e-02 0.298871783 #> 7613  3806.0 0.6874006 0.6874006 1.364958e-02 1.364958e-02 0.298949818 #> 7614  3806.5 0.6873224 0.6873224 1.364982e-02 1.364982e-02 0.299027819 #> 7615  3807.0 0.6872442 0.6872442 1.365003e-02 1.365003e-02 0.299105784 #> 7616  3807.5 0.6871661 0.6871661 1.365021e-02 1.365021e-02 0.299183714 #> 7617  3808.0 0.6870880 0.6870880 1.365037e-02 1.365037e-02 0.299261607 #> 7618  3808.5 0.6870100 0.6870100 1.365050e-02 1.365050e-02 0.299339464 #> 7619  3809.0 0.6869321 0.6869321 1.365061e-02 1.365061e-02 0.299417284 #> 7620  3809.5 0.6868543 0.6868543 1.365068e-02 1.365068e-02 0.299495067 #> 7621  3810.0 0.6867765 0.6867765 1.365073e-02 1.365073e-02 0.299572812 #> 7622  3810.5 0.6866987 0.6866987 1.365076e-02 1.365076e-02 0.299650519 #> 7623  3811.0 0.6866211 0.6866211 1.365076e-02 1.365076e-02 0.299728187 #> 7624  3811.5 0.6865435 0.6865435 1.365073e-02 1.365073e-02 0.299805815 #> 7625  3812.0 0.6864659 0.6864659 1.365067e-02 1.365067e-02 0.299883405 #> 7626  3812.5 0.6863885 0.6863885 1.365059e-02 1.365059e-02 0.299960954 #> 7627  3813.0 0.6863111 0.6863111 1.365048e-02 1.365048e-02 0.300038463 #> 7628  3813.5 0.6862337 0.6862337 1.365035e-02 1.365035e-02 0.300115932 #> 7629  3814.0 0.6861565 0.6861565 1.365019e-02 1.365019e-02 0.300193359 #> 7630  3814.5 0.6860793 0.6860793 1.365000e-02 1.365000e-02 0.300270744 #> 7631  3815.0 0.6860021 0.6860021 1.364978e-02 1.364978e-02 0.300348088 #> 7632  3815.5 0.6859251 0.6859251 1.364954e-02 1.364954e-02 0.300425389 #> 7633  3816.0 0.6858481 0.6858481 1.364928e-02 1.364928e-02 0.300502647 #> 7634  3816.5 0.6857712 0.6857712 1.364899e-02 1.364899e-02 0.300579862 #> 7635  3817.0 0.6856943 0.6856943 1.364867e-02 1.364867e-02 0.300657033 #> 7636  3817.5 0.6856175 0.6856175 1.364832e-02 1.364832e-02 0.300734161 #> 7637  3818.0 0.6855408 0.6855408 1.364795e-02 1.364795e-02 0.300811243 #> 7638  3818.5 0.6854642 0.6854642 1.364755e-02 1.364755e-02 0.300888281 #> 7639  3819.0 0.6853876 0.6853876 1.364713e-02 1.364713e-02 0.300965273 #> 7640  3819.5 0.6853111 0.6853111 1.364668e-02 1.364668e-02 0.301042220 #> 7641  3820.0 0.6852347 0.6852347 1.364621e-02 1.364621e-02 0.301119120 #> 7642  3820.5 0.6851583 0.6851583 1.364570e-02 1.364570e-02 0.301195974 #> 7643  3821.0 0.6850820 0.6850820 1.364518e-02 1.364518e-02 0.301272781 #> 7644  3821.5 0.6850058 0.6850058 1.364462e-02 1.364462e-02 0.301349540 #> 7645  3822.0 0.6849297 0.6849297 1.364404e-02 1.364404e-02 0.301426252 #> 7646  3822.5 0.6848536 0.6848536 1.364344e-02 1.364344e-02 0.301502915 #> 7647  3823.0 0.6847777 0.6847777 1.364281e-02 1.364281e-02 0.301579530 #> 7648  3823.5 0.6847018 0.6847018 1.364215e-02 1.364215e-02 0.301656096 #> 7649  3824.0 0.6846259 0.6846259 1.364147e-02 1.364147e-02 0.301732612 #> 7650  3824.5 0.6845502 0.6845502 1.364076e-02 1.364076e-02 0.301809079 #> 7651  3825.0 0.6844745 0.6844745 1.364002e-02 1.364002e-02 0.301885495 #> 7652  3825.5 0.6843989 0.6843989 1.363926e-02 1.363926e-02 0.301961861 #> 7653  3826.0 0.6843233 0.6843233 1.363848e-02 1.363848e-02 0.302038175 #> 7654  3826.5 0.6842479 0.6842479 1.363766e-02 1.363766e-02 0.302114438 #> 7655  3827.0 0.6841725 0.6841725 1.363683e-02 1.363683e-02 0.302190649 #> 7656  3827.5 0.6840972 0.6840972 1.363596e-02 1.363596e-02 0.302266808 #> 7657  3828.0 0.6840220 0.6840220 1.363508e-02 1.363508e-02 0.302342915 #> 7658  3828.5 0.6839469 0.6839469 1.363416e-02 1.363416e-02 0.302418968 #> 7659  3829.0 0.6838718 0.6838718 1.363322e-02 1.363322e-02 0.302494968 #> 7660  3829.5 0.6837968 0.6837968 1.363226e-02 1.363226e-02 0.302570913 #> 7661  3830.0 0.6837219 0.6837219 1.363127e-02 1.363127e-02 0.302646805 #> 7662  3830.5 0.6836471 0.6836471 1.363025e-02 1.363025e-02 0.302722642 #> 7663  3831.0 0.6835724 0.6835724 1.362921e-02 1.362921e-02 0.302798424 #> 7664  3831.5 0.6834977 0.6834977 1.362814e-02 1.362814e-02 0.302874150 #> 7665  3832.0 0.6834231 0.6834231 1.362705e-02 1.362705e-02 0.302949821 #> 7666  3832.5 0.6833486 0.6833486 1.362593e-02 1.362593e-02 0.303025435 #> 7667  3833.0 0.6832742 0.6832742 1.362479e-02 1.362479e-02 0.303100993 #> 7668  3833.5 0.6831999 0.6831999 1.362362e-02 1.362362e-02 0.303176494 #> 7669  3834.0 0.6831256 0.6831256 1.362243e-02 1.362243e-02 0.303251937 #> 7670  3834.5 0.6830515 0.6830515 1.362121e-02 1.362121e-02 0.303327323 #> 7671  3835.0 0.6829774 0.6829774 1.361996e-02 1.361996e-02 0.303402650 #> 7672  3835.5 0.6829034 0.6829034 1.361870e-02 1.361870e-02 0.303477919 #> 7673  3836.0 0.6828295 0.6828295 1.361740e-02 1.361740e-02 0.303553128 #> 7674  3836.5 0.6827556 0.6827556 1.361608e-02 1.361608e-02 0.303628279 #> 7675  3837.0 0.6826819 0.6826819 1.361474e-02 1.361474e-02 0.303703369 #> 7676  3837.5 0.6826082 0.6826082 1.361337e-02 1.361337e-02 0.303778400 #> 7677  3838.0 0.6825347 0.6825347 1.361198e-02 1.361198e-02 0.303853370 #> 7678  3838.5 0.6824612 0.6824612 1.361056e-02 1.361056e-02 0.303928279 #> 7679  3839.0 0.6823878 0.6823878 1.360911e-02 1.360911e-02 0.304003127 #> 7680  3839.5 0.6823144 0.6823144 1.360765e-02 1.360765e-02 0.304077913 #> 7681  3840.0 0.6822412 0.6822412 1.360615e-02 1.360615e-02 0.304152637 #> 7682  3840.5 0.6821681 0.6821681 1.360464e-02 1.360464e-02 0.304227299 #> 7683  3841.0 0.6820950 0.6820950 1.360309e-02 1.360309e-02 0.304301897 #> 7684  3841.5 0.6820220 0.6820220 1.360153e-02 1.360153e-02 0.304376433 #> 7685  3842.0 0.6819492 0.6819492 1.359993e-02 1.359993e-02 0.304450905 #> 7686  3842.5 0.6818764 0.6818764 1.359832e-02 1.359832e-02 0.304525313 #> 7687  3843.0 0.6818037 0.6818037 1.359668e-02 1.359668e-02 0.304599657 #> 7688  3843.5 0.6817311 0.6817311 1.359501e-02 1.359501e-02 0.304673936 #> 7689  3844.0 0.6816585 0.6816585 1.359332e-02 1.359332e-02 0.304748150 #> 7690  3844.5 0.6815861 0.6815861 1.359161e-02 1.359161e-02 0.304822298 #> 7691  3845.0 0.6815137 0.6815137 1.358987e-02 1.358987e-02 0.304896381 #> 7692  3845.5 0.6814415 0.6814415 1.358811e-02 1.358811e-02 0.304970398 #> 7693  3846.0 0.6813693 0.6813693 1.358632e-02 1.358632e-02 0.305044347 #> 7694  3846.5 0.6812973 0.6812973 1.358451e-02 1.358451e-02 0.305118230 #> 7695  3847.0 0.6812253 0.6812253 1.358267e-02 1.358267e-02 0.305192046 #> 7696  3847.5 0.6811534 0.6811534 1.358081e-02 1.358081e-02 0.305265794 #> 7697  3848.0 0.6810816 0.6810816 1.357893e-02 1.357893e-02 0.305339474 #> 7698  3848.5 0.6810099 0.6810099 1.357702e-02 1.357702e-02 0.305413085 #> 7699  3849.0 0.6809383 0.6809383 1.357509e-02 1.357509e-02 0.305486628 #> 7700  3849.5 0.6808668 0.6808668 1.357313e-02 1.357313e-02 0.305560102 #> 7701  3850.0 0.6807953 0.6807953 1.357115e-02 1.357115e-02 0.305633506 #> 7702  3850.5 0.6807240 0.6807240 1.356915e-02 1.356915e-02 0.305706840 #> 7703  3851.0 0.6806528 0.6806528 1.356712e-02 1.356712e-02 0.305780104 #> 7704  3851.5 0.6805816 0.6805816 1.356507e-02 1.356507e-02 0.305853297 #> 7705  3852.0 0.6805106 0.6805106 1.356299e-02 1.356299e-02 0.305926419 #> 7706  3852.5 0.6804396 0.6804396 1.356089e-02 1.356089e-02 0.305999470 #> 7707  3853.0 0.6803688 0.6803688 1.355877e-02 1.355877e-02 0.306072449 #> 7708  3853.5 0.6802980 0.6802980 1.355663e-02 1.355663e-02 0.306145357 #> 7709  3854.0 0.6802274 0.6802274 1.355445e-02 1.355445e-02 0.306218191 #> 7710  3854.5 0.6801568 0.6801568 1.355226e-02 1.355226e-02 0.306290953 #> 7711  3855.0 0.6800863 0.6800863 1.355004e-02 1.355004e-02 0.306363642 #> 7712  3855.5 0.6800159 0.6800159 1.354780e-02 1.354780e-02 0.306436258 #> 7713  3856.0 0.6799457 0.6799457 1.354554e-02 1.354554e-02 0.306508799 #> 7714  3856.5 0.6798755 0.6798755 1.354325e-02 1.354325e-02 0.306581267 #> 7715  3857.0 0.6798054 0.6798054 1.354094e-02 1.354094e-02 0.306653660 #> 7716  3857.5 0.6797354 0.6797354 1.353861e-02 1.353861e-02 0.306725978 #> 7717  3858.0 0.6796655 0.6796655 1.353625e-02 1.353625e-02 0.306798221 #> 7718  3858.5 0.6795957 0.6795957 1.353387e-02 1.353387e-02 0.306870388 #> 7719  3859.0 0.6795261 0.6795261 1.353146e-02 1.353146e-02 0.306942479 #> 7720  3859.5 0.6794565 0.6794565 1.352904e-02 1.352904e-02 0.307014494 #> 7721  3860.0 0.6793870 0.6793870 1.352659e-02 1.352659e-02 0.307086432 #> 7722  3860.5 0.6793176 0.6793176 1.352411e-02 1.352411e-02 0.307158294 #> 7723  3861.0 0.6792483 0.6792483 1.352162e-02 1.352162e-02 0.307230078 #> 7724  3861.5 0.6791791 0.6791791 1.351910e-02 1.351910e-02 0.307301784 #> 7725  3862.0 0.6791100 0.6791100 1.351656e-02 1.351656e-02 0.307373412 #> 7726  3862.5 0.6790410 0.6790410 1.351399e-02 1.351399e-02 0.307444962 #> 7727  3863.0 0.6789722 0.6789722 1.351140e-02 1.351140e-02 0.307516433 #> 7728  3863.5 0.6789034 0.6789034 1.350879e-02 1.350879e-02 0.307587826 #> 7729  3864.0 0.6788347 0.6788347 1.350616e-02 1.350616e-02 0.307659139 #> 7730  3864.5 0.6787661 0.6787661 1.350350e-02 1.350350e-02 0.307730372 #> 7731  3865.0 0.6786977 0.6786977 1.350082e-02 1.350082e-02 0.307801525 #> 7732  3865.5 0.6786293 0.6786293 1.349812e-02 1.349812e-02 0.307872598 #> 7733  3866.0 0.6785610 0.6785610 1.349540e-02 1.349540e-02 0.307943590 #> 7734  3866.5 0.6784928 0.6784928 1.349265e-02 1.349265e-02 0.308014501 #> 7735  3867.0 0.6784248 0.6784248 1.348989e-02 1.348989e-02 0.308085330 #> 7736  3867.5 0.6783568 0.6783568 1.348710e-02 1.348710e-02 0.308156078 #> 7737  3868.0 0.6782890 0.6782890 1.348428e-02 1.348428e-02 0.308226744 #> 7738  3868.5 0.6782212 0.6782212 1.348145e-02 1.348145e-02 0.308297327 #> 7739  3869.0 0.6781536 0.6781536 1.347859e-02 1.347859e-02 0.308367828 #> 7740  3869.5 0.6780860 0.6780860 1.347571e-02 1.347571e-02 0.308438246 #> 7741  3870.0 0.6780186 0.6780186 1.347281e-02 1.347281e-02 0.308508580 #> 7742  3870.5 0.6779513 0.6779513 1.346988e-02 1.346988e-02 0.308578830 #> 7743  3871.0 0.6778841 0.6778841 1.346694e-02 1.346694e-02 0.308648997 #> 7744  3871.5 0.6778170 0.6778170 1.346397e-02 1.346397e-02 0.308719079 #> 7745  3872.0 0.6777499 0.6777499 1.346098e-02 1.346098e-02 0.308789077 #> 7746  3872.5 0.6776830 0.6776830 1.345797e-02 1.345797e-02 0.308858989 #> 7747  3873.0 0.6776162 0.6776162 1.345494e-02 1.345494e-02 0.308928817 #> 7748  3873.5 0.6775496 0.6775496 1.345188e-02 1.345188e-02 0.308998558 #> 7749  3874.0 0.6774830 0.6774830 1.344880e-02 1.344880e-02 0.309068214 #> 7750  3874.5 0.6774165 0.6774165 1.344570e-02 1.344570e-02 0.309137784 #> 7751  3875.0 0.6773501 0.6773501 1.344258e-02 1.344258e-02 0.309207266 #> 7752  3875.5 0.6772839 0.6772839 1.343944e-02 1.343944e-02 0.309276662 #> 7753  3876.0 0.6772177 0.6772177 1.343628e-02 1.343628e-02 0.309345971 #> 7754  3876.5 0.6771517 0.6771517 1.343309e-02 1.343309e-02 0.309415192 #> 7755  3877.0 0.6770858 0.6770858 1.342989e-02 1.342989e-02 0.309484326 #> 7756  3877.5 0.6770200 0.6770200 1.342666e-02 1.342666e-02 0.309553371 #> 7757  3878.0 0.6769543 0.6769543 1.342341e-02 1.342341e-02 0.309622327 #> 7758  3878.5 0.6768887 0.6768887 1.342014e-02 1.342014e-02 0.309691195 #> 7759  3879.0 0.6768232 0.6768232 1.341685e-02 1.341685e-02 0.309759974 #> 7760  3879.5 0.6767578 0.6767578 1.341354e-02 1.341354e-02 0.309828664 #> 7761  3880.0 0.6766925 0.6766925 1.341021e-02 1.341021e-02 0.309897263 #> 7762  3880.5 0.6766274 0.6766274 1.340685e-02 1.340685e-02 0.309965773 #> 7763  3881.0 0.6765623 0.6765623 1.340348e-02 1.340348e-02 0.310034193 #> 7764  3881.5 0.6764974 0.6764974 1.340008e-02 1.340008e-02 0.310102521 #> 7765  3882.0 0.6764326 0.6764326 1.339666e-02 1.339666e-02 0.310170759 #> 7766  3882.5 0.6763679 0.6763679 1.339322e-02 1.339322e-02 0.310238906 #> 7767  3883.0 0.6763033 0.6763033 1.338977e-02 1.338977e-02 0.310306961 #> 7768  3883.5 0.6762388 0.6762388 1.338629e-02 1.338629e-02 0.310374925 #> 7769  3884.0 0.6761744 0.6761744 1.338279e-02 1.338279e-02 0.310442796 #> 7770  3884.5 0.6761102 0.6761102 1.337927e-02 1.337927e-02 0.310510575 #> 7771  3885.0 0.6760460 0.6760460 1.337572e-02 1.337572e-02 0.310578261 #> 7772  3885.5 0.6759820 0.6759820 1.337216e-02 1.337216e-02 0.310645854 #> 7773  3886.0 0.6759181 0.6759181 1.336858e-02 1.336858e-02 0.310713353 #> 7774  3886.5 0.6758543 0.6758543 1.336498e-02 1.336498e-02 0.310780760 #> 7775  3887.0 0.6757906 0.6757906 1.336136e-02 1.336136e-02 0.310848072 #> 7776  3887.5 0.6757270 0.6757270 1.335771e-02 1.335771e-02 0.310915290 #> 7777  3888.0 0.6756635 0.6756635 1.335405e-02 1.335405e-02 0.310982414 #> 7778  3888.5 0.6756002 0.6756002 1.335037e-02 1.335037e-02 0.311049442 #> 7779  3889.0 0.6755370 0.6755370 1.334666e-02 1.334666e-02 0.311116376 #> 7780  3889.5 0.6754738 0.6754738 1.334294e-02 1.334294e-02 0.311183215 #> 7781  3890.0 0.6754108 0.6754108 1.333919e-02 1.333919e-02 0.311249957 #> 7782  3890.5 0.6753480 0.6753480 1.333543e-02 1.333543e-02 0.311316604 #> 7783  3891.0 0.6752852 0.6752852 1.333165e-02 1.333165e-02 0.311383155 #> 7784  3891.5 0.6752225 0.6752225 1.332784e-02 1.332784e-02 0.311449609 #> 7785  3892.0 0.6751600 0.6751600 1.332402e-02 1.332402e-02 0.311515967 #> 7786  3892.5 0.6750976 0.6750976 1.332018e-02 1.332018e-02 0.311582227 #> 7787  3893.0 0.6750353 0.6750353 1.331632e-02 1.331632e-02 0.311648391 #> 7788  3893.5 0.6749731 0.6749731 1.331243e-02 1.331243e-02 0.311714456 #> 7789  3894.0 0.6749110 0.6749110 1.330853e-02 1.330853e-02 0.311780424 #> 7790  3894.5 0.6748491 0.6748491 1.330461e-02 1.330461e-02 0.311846293 #> 7791  3895.0 0.6747873 0.6747873 1.330067e-02 1.330067e-02 0.311912065 #> 7792  3895.5 0.6747256 0.6747256 1.329671e-02 1.329671e-02 0.311977737 #> 7793  3896.0 0.6746640 0.6746640 1.329273e-02 1.329273e-02 0.312043310 #> 7794  3896.5 0.6746025 0.6746025 1.328873e-02 1.328873e-02 0.312108785 #> 7795  3897.0 0.6745411 0.6745411 1.328472e-02 1.328472e-02 0.312174159 #> 7796  3897.5 0.6744799 0.6744799 1.328068e-02 1.328068e-02 0.312239434 #> 7797  3898.0 0.6744188 0.6744188 1.327662e-02 1.327662e-02 0.312304609 #> 7798  3898.5 0.6743578 0.6743578 1.327255e-02 1.327255e-02 0.312369684 #> 7799  3899.0 0.6742969 0.6742969 1.326846e-02 1.326846e-02 0.312434658 #> 7800  3899.5 0.6742361 0.6742361 1.326434e-02 1.326434e-02 0.312499531 #> 7801  3900.0 0.6741755 0.6741755 1.326021e-02 1.326021e-02 0.312564303 #> 7802  3900.5 0.6741150 0.6741150 1.325606e-02 1.325606e-02 0.312628974 #> 7803  3901.0 0.6740546 0.6740546 1.325189e-02 1.325189e-02 0.312693543 #> 7804  3901.5 0.6739943 0.6739943 1.324770e-02 1.324770e-02 0.312758010 #> 7805  3902.0 0.6739341 0.6739341 1.324350e-02 1.324350e-02 0.312822375 #> 7806  3902.5 0.6738741 0.6738741 1.323927e-02 1.323927e-02 0.312886637 #> 7807  3903.0 0.6738142 0.6738142 1.323503e-02 1.323503e-02 0.312950797 #> 7808  3903.5 0.6737544 0.6737544 1.323077e-02 1.323077e-02 0.313014854 #> 7809  3904.0 0.6736947 0.6736947 1.322649e-02 1.322649e-02 0.313078808 #> 7810  3904.5 0.6736352 0.6736352 1.322219e-02 1.322219e-02 0.313142658 #> 7811  3905.0 0.6735757 0.6735757 1.321787e-02 1.321787e-02 0.313206405 #> 7812  3905.5 0.6735164 0.6735164 1.321354e-02 1.321354e-02 0.313270047 #> 7813  3906.0 0.6734572 0.6734572 1.320919e-02 1.320919e-02 0.313333586 #> 7814  3906.5 0.6733982 0.6733982 1.320481e-02 1.320481e-02 0.313397020 #> 7815  3907.0 0.6733392 0.6733392 1.320042e-02 1.320042e-02 0.313460349 #> 7816  3907.5 0.6732804 0.6732804 1.319602e-02 1.319602e-02 0.313523574 #> 7817  3908.0 0.6732217 0.6732217 1.319159e-02 1.319159e-02 0.313586693 #> 7818  3908.5 0.6731631 0.6731631 1.318715e-02 1.318715e-02 0.313649706 #> 7819  3909.0 0.6731047 0.6731047 1.318269e-02 1.318269e-02 0.313712615 #> 7820  3909.5 0.6730464 0.6730464 1.317821e-02 1.317821e-02 0.313775417 #> 7821  3910.0 0.6729882 0.6729882 1.317371e-02 1.317371e-02 0.313838113 #> 7822  3910.5 0.6729301 0.6729301 1.316920e-02 1.316920e-02 0.313900702 #> 7823  3911.0 0.6728721 0.6728721 1.316467e-02 1.316467e-02 0.313963185 #> 7824  3911.5 0.6728143 0.6728143 1.316012e-02 1.316012e-02 0.314025561 #> 7825  3912.0 0.6727566 0.6727566 1.315555e-02 1.315555e-02 0.314087830 #> 7826  3912.5 0.6726990 0.6726990 1.315097e-02 1.315097e-02 0.314149992 #> 7827  3913.0 0.6726416 0.6726416 1.314637e-02 1.314637e-02 0.314212046 #> 7828  3913.5 0.6725843 0.6725843 1.314175e-02 1.314175e-02 0.314273992 #> 7829  3914.0 0.6725271 0.6725271 1.313712e-02 1.313712e-02 0.314335830 #> 7830  3914.5 0.6724700 0.6724700 1.313246e-02 1.313246e-02 0.314397560 #> 7831  3915.0 0.6724130 0.6724130 1.312779e-02 1.312779e-02 0.314459182 #> 7832  3915.5 0.6723562 0.6723562 1.312311e-02 1.312311e-02 0.314520694 #> 7833  3916.0 0.6722995 0.6722995 1.311841e-02 1.311841e-02 0.314582098 #> 7834  3916.5 0.6722429 0.6722429 1.311368e-02 1.311368e-02 0.314643392 #> 7835  3917.0 0.6721865 0.6721865 1.310895e-02 1.310895e-02 0.314704577 #> 7836  3917.5 0.6721302 0.6721302 1.310419e-02 1.310419e-02 0.314765652 #> 7837  3918.0 0.6720740 0.6720740 1.309942e-02 1.309942e-02 0.314826618 #> 7838  3918.5 0.6720179 0.6720179 1.309464e-02 1.309464e-02 0.314887473 #> 7839  3919.0 0.6719619 0.6719619 1.308983e-02 1.308983e-02 0.314948218 #> 7840  3919.5 0.6719061 0.6719061 1.308501e-02 1.308501e-02 0.315008853 #> 7841  3920.0 0.6718504 0.6718504 1.308018e-02 1.308018e-02 0.315069376 #> 7842  3920.5 0.6717949 0.6717949 1.307532e-02 1.307532e-02 0.315129789 #> 7843  3921.0 0.6717395 0.6717395 1.307045e-02 1.307045e-02 0.315190090 #> 7844  3921.5 0.6716842 0.6716842 1.306557e-02 1.306557e-02 0.315250281 #> 7845  3922.0 0.6716290 0.6716290 1.306066e-02 1.306066e-02 0.315310359 #> 7846  3922.5 0.6715739 0.6715739 1.305575e-02 1.305575e-02 0.315370326 #> 7847  3923.0 0.6715190 0.6715190 1.305081e-02 1.305081e-02 0.315430180 #> 7848  3923.5 0.6714642 0.6714642 1.304586e-02 1.304586e-02 0.315489922 #> 7849  3924.0 0.6714096 0.6714096 1.304090e-02 1.304090e-02 0.315549552 #> 7850  3924.5 0.6713550 0.6713550 1.303591e-02 1.303591e-02 0.315609069 #> 7851  3925.0 0.6713006 0.6713006 1.303092e-02 1.303092e-02 0.315668474 #> 7852  3925.5 0.6712463 0.6712463 1.302590e-02 1.302590e-02 0.315727765 #> 7853  3926.0 0.6711922 0.6711922 1.302087e-02 1.302087e-02 0.315786942 #> 7854  3926.5 0.6711382 0.6711382 1.301583e-02 1.301583e-02 0.315846007 #> 7855  3927.0 0.6710843 0.6710843 1.301077e-02 1.301077e-02 0.315904957 #> 7856  3927.5 0.6710305 0.6710305 1.300569e-02 1.300569e-02 0.315963794 #> 7857  3928.0 0.6709769 0.6709769 1.300060e-02 1.300060e-02 0.316022516 #> 7858  3928.5 0.6709234 0.6709234 1.299549e-02 1.299549e-02 0.316081125 #> 7859  3929.0 0.6708700 0.6708700 1.299037e-02 1.299037e-02 0.316139618 #> 7860  3929.5 0.6708168 0.6708168 1.298523e-02 1.298523e-02 0.316197997 #> 7861  3930.0 0.6707637 0.6707637 1.298008e-02 1.298008e-02 0.316256261 #> 7862  3930.5 0.6707107 0.6707107 1.297491e-02 1.297491e-02 0.316314410 #> 7863  3931.0 0.6706578 0.6706578 1.296973e-02 1.296973e-02 0.316372444 #> 7864  3931.5 0.6706051 0.6706051 1.296453e-02 1.296453e-02 0.316430362 #> 7865  3932.0 0.6705525 0.6705525 1.295932e-02 1.295932e-02 0.316488164 #> 7866  3932.5 0.6705001 0.6705001 1.295409e-02 1.295409e-02 0.316545851 #> 7867  3933.0 0.6704477 0.6704477 1.294884e-02 1.294884e-02 0.316603421 #> 7868  3933.5 0.6703955 0.6703955 1.294359e-02 1.294359e-02 0.316660876 #> 7869  3934.0 0.6703435 0.6703435 1.293831e-02 1.293831e-02 0.316718213 #> 7870  3934.5 0.6702915 0.6702915 1.293303e-02 1.293303e-02 0.316775435 #> 7871  3935.0 0.6702397 0.6702397 1.292773e-02 1.292773e-02 0.316832539 #> 7872  3935.5 0.6701881 0.6701881 1.292241e-02 1.292241e-02 0.316889526 #> 7873  3936.0 0.6701365 0.6701365 1.291708e-02 1.291708e-02 0.316946396 #> 7874  3936.5 0.6700851 0.6700851 1.291173e-02 1.291173e-02 0.317003149 #> 7875  3937.0 0.6700338 0.6700338 1.290637e-02 1.290637e-02 0.317059784 #> 7876  3937.5 0.6699827 0.6699827 1.290100e-02 1.290100e-02 0.317116302 #> 7877  3938.0 0.6699317 0.6699317 1.289561e-02 1.289561e-02 0.317172701 #> 7878  3938.5 0.6698808 0.6698808 1.289021e-02 1.289021e-02 0.317228982 #> 7879  3939.0 0.6698301 0.6698301 1.288479e-02 1.288479e-02 0.317285146 #> 7880  3939.5 0.6697794 0.6697794 1.287936e-02 1.287936e-02 0.317341190 #> 7881  3940.0 0.6697290 0.6697290 1.287392e-02 1.287392e-02 0.317397116 #> 7882  3940.5 0.6696786 0.6696786 1.286846e-02 1.286846e-02 0.317452924 #> 7883  3941.0 0.6696284 0.6696284 1.286299e-02 1.286299e-02 0.317508612 #> 7884  3941.5 0.6695783 0.6695783 1.285750e-02 1.285750e-02 0.317564181 #> 7885  3942.0 0.6695284 0.6695284 1.285200e-02 1.285200e-02 0.317619631 #> 7886  3942.5 0.6694785 0.6694785 1.284649e-02 1.284649e-02 0.317674962 #> 7887  3943.0 0.6694289 0.6694289 1.284096e-02 1.284096e-02 0.317730173 #> 7888  3943.5 0.6693793 0.6693793 1.283542e-02 1.283542e-02 0.317785264 #> 7889  3944.0 0.6693299 0.6693299 1.282987e-02 1.282987e-02 0.317840235 #> 7890  3944.5 0.6692806 0.6692806 1.282430e-02 1.282430e-02 0.317895086 #> 7891  3945.0 0.6692315 0.6692315 1.281872e-02 1.281872e-02 0.317949816 #> 7892  3945.5 0.6691824 0.6691824 1.281312e-02 1.281312e-02 0.318004426 #> 7893  3946.0 0.6691336 0.6691336 1.280752e-02 1.280752e-02 0.318058916 #> 7894  3946.5 0.6690848 0.6690848 1.280190e-02 1.280190e-02 0.318113285 #> 7895  3947.0 0.6690362 0.6690362 1.279626e-02 1.279626e-02 0.318167532 #> 7896  3947.5 0.6689877 0.6689877 1.279062e-02 1.279062e-02 0.318221659 #> 7897  3948.0 0.6689394 0.6689394 1.278496e-02 1.278496e-02 0.318275665 #> 7898  3948.5 0.6688912 0.6688912 1.277928e-02 1.277928e-02 0.318329549 #> 7899  3949.0 0.6688431 0.6688431 1.277360e-02 1.277360e-02 0.318383311 #> 7900  3949.5 0.6687951 0.6687951 1.276790e-02 1.276790e-02 0.318436952 #> 7901  3950.0 0.6687473 0.6687473 1.276219e-02 1.276219e-02 0.318490471 #> 7902  3950.5 0.6686997 0.6686997 1.275647e-02 1.275647e-02 0.318543868 #> 7903  3951.0 0.6686521 0.6686521 1.275073e-02 1.275073e-02 0.318597143 #> 7904  3951.5 0.6686047 0.6686047 1.274498e-02 1.274498e-02 0.318650295 #> 7905  3952.0 0.6685575 0.6685575 1.273922e-02 1.273922e-02 0.318703325 #> 7906  3952.5 0.6685103 0.6685103 1.273345e-02 1.273345e-02 0.318756232 #> 7907  3953.0 0.6684633 0.6684633 1.272766e-02 1.272766e-02 0.318809017 #> 7908  3953.5 0.6684165 0.6684165 1.272186e-02 1.272186e-02 0.318861679 #> 7909  3954.0 0.6683697 0.6683697 1.271605e-02 1.271605e-02 0.318914217 #> 7910  3954.5 0.6683231 0.6683231 1.271023e-02 1.271023e-02 0.318966633 #> 7911  3955.0 0.6682767 0.6682767 1.270439e-02 1.270439e-02 0.319018925 #> 7912  3955.5 0.6682304 0.6682304 1.269855e-02 1.269855e-02 0.319071094 #> 7913  3956.0 0.6681842 0.6681842 1.269269e-02 1.269269e-02 0.319123139 #> 7914  3956.5 0.6681381 0.6681381 1.268682e-02 1.268682e-02 0.319175060 #> 7915  3957.0 0.6680922 0.6680922 1.268094e-02 1.268094e-02 0.319226857 #> 7916  3957.5 0.6680464 0.6680464 1.267504e-02 1.267504e-02 0.319278531 #> 7917  3958.0 0.6680008 0.6680008 1.266914e-02 1.266914e-02 0.319330080 #> 7918  3958.5 0.6679553 0.6679553 1.266322e-02 1.266322e-02 0.319381505 #> 7919  3959.0 0.6679099 0.6679099 1.265729e-02 1.265729e-02 0.319432806 #> 7920  3959.5 0.6678647 0.6678647 1.265135e-02 1.265135e-02 0.319483982 #> 7921  3960.0 0.6678196 0.6678196 1.264539e-02 1.264539e-02 0.319535034 #> 7922  3960.5 0.6677746 0.6677746 1.263943e-02 1.263943e-02 0.319585960 #> 7923  3961.0 0.6677298 0.6677298 1.263346e-02 1.263346e-02 0.319636762 #> 7924  3961.5 0.6676851 0.6676851 1.262747e-02 1.262747e-02 0.319687439 #> 7925  3962.0 0.6676405 0.6676405 1.262147e-02 1.262147e-02 0.319737990 #> 7926  3962.5 0.6675961 0.6675961 1.261546e-02 1.261546e-02 0.319788416 #> 7927  3963.0 0.6675518 0.6675518 1.260944e-02 1.260944e-02 0.319838717 #> 7928  3963.5 0.6675077 0.6675077 1.260341e-02 1.260341e-02 0.319888892 #> 7929  3964.0 0.6674637 0.6674637 1.259737e-02 1.259737e-02 0.319938942 #> 7930  3964.5 0.6674198 0.6674198 1.259131e-02 1.259131e-02 0.319988865 #> 7931  3965.0 0.6673761 0.6673761 1.258525e-02 1.258525e-02 0.320038663 #> 7932  3965.5 0.6673325 0.6673325 1.257918e-02 1.257918e-02 0.320088335 #> 7933  3966.0 0.6672890 0.6672890 1.257309e-02 1.257309e-02 0.320137881 #> 7934  3966.5 0.6672457 0.6672457 1.256699e-02 1.256699e-02 0.320187300 #> 7935  3967.0 0.6672025 0.6672025 1.256089e-02 1.256089e-02 0.320236593 #> 7936  3967.5 0.6671595 0.6671595 1.255477e-02 1.255477e-02 0.320285760 #> 7937  3968.0 0.6671166 0.6671166 1.254864e-02 1.254864e-02 0.320334800 #> 7938  3968.5 0.6670738 0.6670738 1.254250e-02 1.254250e-02 0.320383713 #> 7939  3969.0 0.6670311 0.6670311 1.253635e-02 1.253635e-02 0.320432499 #> 7940  3969.5 0.6669886 0.6669886 1.253020e-02 1.253020e-02 0.320481159 #> 7941  3970.0 0.6669463 0.6669463 1.252403e-02 1.252403e-02 0.320529691 #> 7942  3970.5 0.6669041 0.6669041 1.251785e-02 1.251785e-02 0.320578097 #> 7943  3971.0 0.6668620 0.6668620 1.251166e-02 1.251166e-02 0.320626375 #> 7944  3971.5 0.6668200 0.6668200 1.250546e-02 1.250546e-02 0.320674526 #> 7945  3972.0 0.6667782 0.6667782 1.249925e-02 1.249925e-02 0.320722549 #> 7946  3972.5 0.6667365 0.6667365 1.249303e-02 1.249303e-02 0.320770445 #> 7947  3973.0 0.6666950 0.6666950 1.248680e-02 1.248680e-02 0.320818213 #> 7948  3973.5 0.6666536 0.6666536 1.248056e-02 1.248056e-02 0.320865853 #> 7949  3974.0 0.6666123 0.6666123 1.247431e-02 1.247431e-02 0.320913365 #> 7950  3974.5 0.6665712 0.6665712 1.246805e-02 1.246805e-02 0.320960750 #> 7951  3975.0 0.6665302 0.6665302 1.246178e-02 1.246178e-02 0.321008006 #> 7952  3975.5 0.6664894 0.6664894 1.245550e-02 1.245550e-02 0.321055135 #> 7953  3976.0 0.6664486 0.6664486 1.244922e-02 1.244922e-02 0.321102135 #> 7954  3976.5 0.6664081 0.6664081 1.244292e-02 1.244292e-02 0.321149006 #> 7955  3977.0 0.6663676 0.6663676 1.243661e-02 1.243661e-02 0.321195750 #> 7956  3977.5 0.6663273 0.6663273 1.243030e-02 1.243030e-02 0.321242364 #> 7957  3978.0 0.6662872 0.6662872 1.242397e-02 1.242397e-02 0.321288850 #> 7958  3978.5 0.6662472 0.6662472 1.241764e-02 1.241764e-02 0.321335208 #> 7959  3979.0 0.6662073 0.6662073 1.241129e-02 1.241129e-02 0.321381436 #> 7960  3979.5 0.6661675 0.6661675 1.240494e-02 1.240494e-02 0.321427536 #> 7961  3980.0 0.6661279 0.6661279 1.239858e-02 1.239858e-02 0.321473507 #> 7962  3980.5 0.6660884 0.6660884 1.239221e-02 1.239221e-02 0.321519348 #> 7963  3981.0 0.6660491 0.6660491 1.238583e-02 1.238583e-02 0.321565061 #> 7964  3981.5 0.6660099 0.6660099 1.237944e-02 1.237944e-02 0.321610644 #> 7965  3982.0 0.6659709 0.6659709 1.237304e-02 1.237304e-02 0.321656098 #> 7966  3982.5 0.6659319 0.6659319 1.236664e-02 1.236664e-02 0.321701423 #> 7967  3983.0 0.6658932 0.6658932 1.236022e-02 1.236022e-02 0.321746618 #> 7968  3983.5 0.6658545 0.6658545 1.235380e-02 1.235380e-02 0.321791684 #> 7969  3984.0 0.6658160 0.6658160 1.234737e-02 1.234737e-02 0.321836620 #> 7970  3984.5 0.6657776 0.6657776 1.234093e-02 1.234093e-02 0.321881426 #> 7971  3985.0 0.6657394 0.6657394 1.233448e-02 1.233448e-02 0.321926102 #> 7972  3985.5 0.6657013 0.6657013 1.232802e-02 1.232802e-02 0.321970649 #> 7973  3986.0 0.6656634 0.6656634 1.232155e-02 1.232155e-02 0.322015066 #> 7974  3986.5 0.6656256 0.6656256 1.231508e-02 1.231508e-02 0.322059352 #> 7975  3987.0 0.6655879 0.6655879 1.230860e-02 1.230860e-02 0.322103509 #> 7976  3987.5 0.6655504 0.6655504 1.230211e-02 1.230211e-02 0.322147535 #> 7977  3988.0 0.6655130 0.6655130 1.229561e-02 1.229561e-02 0.322191432 #> 7978  3988.5 0.6654757 0.6654757 1.228910e-02 1.228910e-02 0.322235198 #> 7979  3989.0 0.6654386 0.6654386 1.228259e-02 1.228259e-02 0.322278833 #> 7980  3989.5 0.6654016 0.6654016 1.227607e-02 1.227607e-02 0.322322338 #> 7981  3990.0 0.6653648 0.6653648 1.226953e-02 1.226953e-02 0.322365713 #> 7982  3990.5 0.6653280 0.6653280 1.226300e-02 1.226300e-02 0.322408957 #> 7983  3991.0 0.6652915 0.6652915 1.225645e-02 1.225645e-02 0.322452070 #> 7984  3991.5 0.6652550 0.6652550 1.224990e-02 1.224990e-02 0.322495053 #> 7985  3992.0 0.6652188 0.6652188 1.224334e-02 1.224334e-02 0.322537905 #> 7986  3992.5 0.6651826 0.6651826 1.223677e-02 1.223677e-02 0.322580627 #> 7987  3993.0 0.6651466 0.6651466 1.223019e-02 1.223019e-02 0.322623217 #> 7988  3993.5 0.6651107 0.6651107 1.222361e-02 1.222361e-02 0.322665676 #> 7989  3994.0 0.6650750 0.6650750 1.221702e-02 1.221702e-02 0.322708005 #> 7990  3994.5 0.6650394 0.6650394 1.221042e-02 1.221042e-02 0.322750202 #> 7991  3995.0 0.6650039 0.6650039 1.220381e-02 1.220381e-02 0.322792269 #> 7992  3995.5 0.6649686 0.6649686 1.219720e-02 1.219720e-02 0.322834204 #> 7993  3996.0 0.6649334 0.6649334 1.219058e-02 1.219058e-02 0.322876008 #> 7994  3996.5 0.6648984 0.6648984 1.218395e-02 1.218395e-02 0.322917680 #> 7995  3997.0 0.6648635 0.6648635 1.217732e-02 1.217732e-02 0.322959222 #> 7996  3997.5 0.6648287 0.6648287 1.217068e-02 1.217068e-02 0.323000632 #> 7997  3998.0 0.6647941 0.6647941 1.216403e-02 1.216403e-02 0.323041911 #> 7998  3998.5 0.6647596 0.6647596 1.215738e-02 1.215738e-02 0.323083058 #> 7999  3999.0 0.6647252 0.6647252 1.215071e-02 1.215071e-02 0.323124073 #> 8000  3999.5 0.6646910 0.6646910 1.214405e-02 1.214405e-02 0.323164957 #> 8001  4000.0 0.6646569 0.6646569 1.213737e-02 1.213737e-02 0.323205710 #> 8002  4000.5 0.6646230 0.6646230 1.213069e-02 1.213069e-02 0.323246331 #> 8003  4001.0 0.6645892 0.6645892 1.212400e-02 1.212400e-02 0.323286820 #> 8004  4001.5 0.6645555 0.6645555 1.211731e-02 1.211731e-02 0.323327177 #> 8005  4002.0 0.6645220 0.6645220 1.211060e-02 1.211060e-02 0.323367403 #> 8006  4002.5 0.6644886 0.6644886 1.210390e-02 1.210390e-02 0.323407497 #> 8007  4003.0 0.6644554 0.6644554 1.209718e-02 1.209718e-02 0.323447458 #> 8008  4003.5 0.6644223 0.6644223 1.209046e-02 1.209046e-02 0.323487288 #> 8009  4004.0 0.6643893 0.6643893 1.208373e-02 1.208373e-02 0.323526986 #> 8010  4004.5 0.6643564 0.6643564 1.207700e-02 1.207700e-02 0.323566553 #> 8011  4005.0 0.6643238 0.6643238 1.207026e-02 1.207026e-02 0.323605987 #> 8012  4005.5 0.6642912 0.6642912 1.206352e-02 1.206352e-02 0.323645289 #> 8013  4006.0 0.6642588 0.6642588 1.205677e-02 1.205677e-02 0.323684459 #> 8014  4006.5 0.6642265 0.6642265 1.205001e-02 1.205001e-02 0.323723496 #> 8015  4007.0 0.6641944 0.6641944 1.204325e-02 1.204325e-02 0.323762402 #> 8016  4007.5 0.6641623 0.6641623 1.203648e-02 1.203648e-02 0.323801175 #> 8017  4008.0 0.6641305 0.6641305 1.202970e-02 1.202970e-02 0.323839817 #> 8018  4008.5 0.6640988 0.6640988 1.202292e-02 1.202292e-02 0.323878326 #> 8019  4009.0 0.6640672 0.6640672 1.201614e-02 1.201614e-02 0.323916702 #> 8020  4009.5 0.6640357 0.6640357 1.200934e-02 1.200934e-02 0.323954947 #> 8021  4010.0 0.6640044 0.6640044 1.200255e-02 1.200255e-02 0.323993059 #> 8022  4010.5 0.6639732 0.6639732 1.199574e-02 1.199574e-02 0.324031039 #> 8023  4011.0 0.6639422 0.6639422 1.198894e-02 1.198894e-02 0.324068886 #> 8024  4011.5 0.6639113 0.6639113 1.198212e-02 1.198212e-02 0.324106601 #> 8025  4012.0 0.6638805 0.6638805 1.197530e-02 1.197530e-02 0.324144183 #> 8026  4012.5 0.6638499 0.6638499 1.196848e-02 1.196848e-02 0.324181634 #> 8027  4013.0 0.6638194 0.6638194 1.196165e-02 1.196165e-02 0.324218951 #> 8028  4013.5 0.6637891 0.6637891 1.195481e-02 1.195481e-02 0.324256136 #> 8029  4014.0 0.6637588 0.6637588 1.194797e-02 1.194797e-02 0.324293189 #> 8030  4014.5 0.6637288 0.6637288 1.194113e-02 1.194113e-02 0.324330109 #> 8031  4015.0 0.6636988 0.6636988 1.193428e-02 1.193428e-02 0.324366896 #> 8032  4015.5 0.6636690 0.6636690 1.192742e-02 1.192742e-02 0.324403551 #> 8033  4016.0 0.6636394 0.6636394 1.192056e-02 1.192056e-02 0.324440073 #> 8034  4016.5 0.6636098 0.6636098 1.191370e-02 1.191370e-02 0.324476463 #> 8035  4017.0 0.6635805 0.6635805 1.190683e-02 1.190683e-02 0.324512720 #> 8036  4017.5 0.6635512 0.6635512 1.189995e-02 1.189995e-02 0.324548845 #> 8037  4018.0 0.6635221 0.6635221 1.189307e-02 1.189307e-02 0.324584837 #> 8038  4018.5 0.6634931 0.6634931 1.188619e-02 1.188619e-02 0.324620696 #> 8039  4019.0 0.6634643 0.6634643 1.187930e-02 1.187930e-02 0.324656422 #> 8040  4019.5 0.6634356 0.6634356 1.187241e-02 1.187241e-02 0.324692016 #> 8041  4020.0 0.6634070 0.6634070 1.186551e-02 1.186551e-02 0.324727477 #> 8042  4020.5 0.6633786 0.6633786 1.185861e-02 1.185861e-02 0.324762806 #> 8043  4021.0 0.6633503 0.6633503 1.185170e-02 1.185170e-02 0.324798001 #> 8044  4021.5 0.6633221 0.6633221 1.184479e-02 1.184479e-02 0.324833064 #> 8045  4022.0 0.6632941 0.6632941 1.183788e-02 1.183788e-02 0.324867995 #> 8046  4022.5 0.6632662 0.6632662 1.183096e-02 1.183096e-02 0.324902792 #> 8047  4023.0 0.6632385 0.6632385 1.182404e-02 1.182404e-02 0.324937457 #> 8048  4023.5 0.6632109 0.6632109 1.181711e-02 1.181711e-02 0.324971989 #> 8049  4024.0 0.6631834 0.6631834 1.181018e-02 1.181018e-02 0.325006389 #> 8050  4024.5 0.6631561 0.6631561 1.180324e-02 1.180324e-02 0.325040656 #> 8051  4025.0 0.6631289 0.6631289 1.179630e-02 1.179630e-02 0.325074790 #> 8052  4025.5 0.6631019 0.6631019 1.178936e-02 1.178936e-02 0.325108791 #> 8053  4026.0 0.6630749 0.6630749 1.178241e-02 1.178241e-02 0.325142659 #> 8054  4026.5 0.6630481 0.6630481 1.177546e-02 1.177546e-02 0.325176395 #> 8055  4027.0 0.6630215 0.6630215 1.176851e-02 1.176851e-02 0.325209998 #> 8056  4027.5 0.6629950 0.6629950 1.176155e-02 1.176155e-02 0.325243469 #> 8057  4028.0 0.6629686 0.6629686 1.175458e-02 1.175458e-02 0.325276807 #> 8058  4028.5 0.6629424 0.6629424 1.174762e-02 1.174762e-02 0.325310011 #> 8059  4029.0 0.6629163 0.6629163 1.174065e-02 1.174065e-02 0.325343084 #> 8060  4029.5 0.6628903 0.6628903 1.173368e-02 1.173368e-02 0.325376023 #> 8061  4030.0 0.6628645 0.6628645 1.172670e-02 1.172670e-02 0.325408830 #> 8062  4030.5 0.6628388 0.6628388 1.171972e-02 1.171972e-02 0.325441505 #> 8063  4031.0 0.6628132 0.6628132 1.171274e-02 1.171274e-02 0.325474046 #> 8064  4031.5 0.6627878 0.6627878 1.170575e-02 1.170575e-02 0.325506455 #> 8065  4032.0 0.6627625 0.6627625 1.169876e-02 1.169876e-02 0.325538731 #> 8066  4032.5 0.6627374 0.6627374 1.169177e-02 1.169177e-02 0.325570875 #> 8067  4033.0 0.6627123 0.6627123 1.168477e-02 1.168477e-02 0.325602886 #> 8068  4033.5 0.6626875 0.6626875 1.167777e-02 1.167777e-02 0.325634765 #> 8069  4034.0 0.6626627 0.6626627 1.167077e-02 1.167077e-02 0.325666511 #> 8070  4034.5 0.6626381 0.6626381 1.166377e-02 1.166377e-02 0.325698124 #> 8071  4035.0 0.6626136 0.6626136 1.165676e-02 1.165676e-02 0.325729605 #> 8072  4035.5 0.6625893 0.6625893 1.164975e-02 1.164975e-02 0.325760953 #> 8073  4036.0 0.6625651 0.6625651 1.164273e-02 1.164273e-02 0.325792169 #> 8074  4036.5 0.6625410 0.6625410 1.163572e-02 1.163572e-02 0.325823252 #> 8075  4037.0 0.6625171 0.6625171 1.162870e-02 1.162870e-02 0.325854203 #> 8076  4037.5 0.6624933 0.6624933 1.162168e-02 1.162168e-02 0.325885021 #> 8077  4038.0 0.6624696 0.6624696 1.161465e-02 1.161465e-02 0.325915707 #> 8078  4038.5 0.6624461 0.6624461 1.160762e-02 1.160762e-02 0.325946260 #> 8079  4039.0 0.6624227 0.6624227 1.160059e-02 1.160059e-02 0.325976681 #> 8080  4039.5 0.6623995 0.6623995 1.159356e-02 1.159356e-02 0.326006970 #> 8081  4040.0 0.6623763 0.6623763 1.158653e-02 1.158653e-02 0.326037126 #> 8082  4040.5 0.6623534 0.6623534 1.157949e-02 1.157949e-02 0.326067150 #> 8083  4041.0 0.6623305 0.6623305 1.157245e-02 1.157245e-02 0.326097042 #> 8084  4041.5 0.6623078 0.6623078 1.156541e-02 1.156541e-02 0.326126802 #> 8085  4042.0 0.6622852 0.6622852 1.155836e-02 1.155836e-02 0.326156429 #> 8086  4042.5 0.6622628 0.6622628 1.155132e-02 1.155132e-02 0.326185924 #> 8087  4043.0 0.6622404 0.6622404 1.154427e-02 1.154427e-02 0.326215287 #> 8088  4043.5 0.6622183 0.6622183 1.153722e-02 1.153722e-02 0.326244518 #> 8089  4044.0 0.6621962 0.6621962 1.153017e-02 1.153017e-02 0.326273616 #> 8090  4044.5 0.6621743 0.6621743 1.152311e-02 1.152311e-02 0.326302583 #> 8091  4045.0 0.6621525 0.6621525 1.151605e-02 1.151605e-02 0.326331418 #> 8092  4045.5 0.6621309 0.6621309 1.150900e-02 1.150900e-02 0.326360120 #> 8093  4046.0 0.6621094 0.6621094 1.150194e-02 1.150194e-02 0.326388691 #> 8094  4046.5 0.6620880 0.6620880 1.149487e-02 1.149487e-02 0.326417129 #> 8095  4047.0 0.6620668 0.6620668 1.148781e-02 1.148781e-02 0.326445436 #> 8096  4047.5 0.6620456 0.6620456 1.148074e-02 1.148074e-02 0.326473611 #> 8097  4048.0 0.6620247 0.6620247 1.147368e-02 1.147368e-02 0.326501654 #> 8098  4048.5 0.6620038 0.6620038 1.146661e-02 1.146661e-02 0.326529565 #> 8099  4049.0 0.6619831 0.6619831 1.145954e-02 1.145954e-02 0.326557345 #> 8100  4049.5 0.6619625 0.6619625 1.145246e-02 1.145246e-02 0.326584992 #> 8101  4050.0 0.6619421 0.6619421 1.144539e-02 1.144539e-02 0.326612508 #> 8102  4050.5 0.6619218 0.6619218 1.143831e-02 1.143831e-02 0.326639893 #> 8103  4051.0 0.6619016 0.6619016 1.143124e-02 1.143124e-02 0.326667146 #> 8104  4051.5 0.6618816 0.6618816 1.142416e-02 1.142416e-02 0.326694267 #> 8105  4052.0 0.6618617 0.6618617 1.141708e-02 1.141708e-02 0.326721257 #> 8106  4052.5 0.6618419 0.6618419 1.141000e-02 1.141000e-02 0.326748115 #> 8107  4053.0 0.6618222 0.6618222 1.140292e-02 1.140292e-02 0.326774842 #> 8108  4053.5 0.6618027 0.6618027 1.139583e-02 1.139583e-02 0.326801437 #> 8109  4054.0 0.6617833 0.6617833 1.138875e-02 1.138875e-02 0.326827902 #> 8110  4054.5 0.6617641 0.6617641 1.138166e-02 1.138166e-02 0.326854234 #> 8111  4055.0 0.6617450 0.6617450 1.137458e-02 1.137458e-02 0.326880436 #> 8112  4055.5 0.6617260 0.6617260 1.136749e-02 1.136749e-02 0.326906507 #> 8113  4056.0 0.6617072 0.6617072 1.136040e-02 1.136040e-02 0.326932446 #> 8114  4056.5 0.6616884 0.6616884 1.135331e-02 1.135331e-02 0.326958254 #> 8115  4057.0 0.6616698 0.6616698 1.134622e-02 1.134622e-02 0.326983932 #> 8116  4057.5 0.6616514 0.6616514 1.133913e-02 1.133913e-02 0.327009478 #> 8117  4058.0 0.6616331 0.6616331 1.133204e-02 1.133204e-02 0.327034893 #> 8118  4058.5 0.6616149 0.6616149 1.132495e-02 1.132495e-02 0.327060178 #> 8119  4059.0 0.6615968 0.6615968 1.131786e-02 1.131786e-02 0.327085331 #> 8120  4059.5 0.6615789 0.6615789 1.131076e-02 1.131076e-02 0.327110354 #> 8121  4060.0 0.6615611 0.6615611 1.130367e-02 1.130367e-02 0.327135246 #> 8122  4060.5 0.6615434 0.6615434 1.129657e-02 1.129657e-02 0.327160008 #> 8123  4061.0 0.6615259 0.6615259 1.128948e-02 1.128948e-02 0.327184639 #> 8124  4061.5 0.6615085 0.6615085 1.128238e-02 1.128238e-02 0.327209139 #> 8125  4062.0 0.6614912 0.6614912 1.127529e-02 1.127529e-02 0.327233509 #> 8126  4062.5 0.6614741 0.6614741 1.126819e-02 1.126819e-02 0.327257749 #> 8127  4063.0 0.6614571 0.6614571 1.126109e-02 1.126109e-02 0.327281858 #> 8128  4063.5 0.6614402 0.6614402 1.125399e-02 1.125399e-02 0.327305836 #> 8129  4064.0 0.6614234 0.6614234 1.124690e-02 1.124690e-02 0.327329685 #> 8130  4064.5 0.6614068 0.6614068 1.123980e-02 1.123980e-02 0.327353403 #> 8131  4065.0 0.6613903 0.6613903 1.123270e-02 1.123270e-02 0.327376992 #> 8132  4065.5 0.6613739 0.6613739 1.122560e-02 1.122560e-02 0.327400450 #> 8133  4066.0 0.6613577 0.6613577 1.121850e-02 1.121850e-02 0.327423778 #> 8134  4066.5 0.6613416 0.6613416 1.121141e-02 1.121141e-02 0.327446976 #> 8135  4067.0 0.6613256 0.6613256 1.120431e-02 1.120431e-02 0.327470044 #> 8136  4067.5 0.6613098 0.6613098 1.119721e-02 1.119721e-02 0.327492983 #> 8137  4068.0 0.6612941 0.6612941 1.119011e-02 1.119011e-02 0.327515792 #> 8138  4068.5 0.6612785 0.6612785 1.118301e-02 1.118301e-02 0.327538471 #> 8139  4069.0 0.6612631 0.6612631 1.117592e-02 1.117592e-02 0.327561021 #> 8140  4069.5 0.6612477 0.6612477 1.116882e-02 1.116882e-02 0.327583441 #> 8141  4070.0 0.6612325 0.6612325 1.116172e-02 1.116172e-02 0.327605731 #> 8142  4070.5 0.6612175 0.6612175 1.115462e-02 1.115462e-02 0.327627892 #> 8143  4071.0 0.6612025 0.6612025 1.114753e-02 1.114753e-02 0.327649924 #> 8144  4071.5 0.6611877 0.6611877 1.114043e-02 1.114043e-02 0.327671827 #> 8145  4072.0 0.6611731 0.6611731 1.113333e-02 1.113333e-02 0.327693600 #> 8146  4072.5 0.6611585 0.6611585 1.112624e-02 1.112624e-02 0.327715244 #> 8147  4073.0 0.6611441 0.6611441 1.111914e-02 1.111914e-02 0.327736759 #> 8148  4073.5 0.6611298 0.6611298 1.111205e-02 1.111205e-02 0.327758146 #> 8149  4074.0 0.6611156 0.6611156 1.110496e-02 1.110496e-02 0.327779403 #> 8150  4074.5 0.6611016 0.6611016 1.109786e-02 1.109786e-02 0.327800531 #> 8151  4075.0 0.6610877 0.6610877 1.109077e-02 1.109077e-02 0.327821531 #> 8152  4075.5 0.6610739 0.6610739 1.108368e-02 1.108368e-02 0.327842402 #> 8153  4076.0 0.6610603 0.6610603 1.107659e-02 1.107659e-02 0.327863145 #> 8154  4076.5 0.6610467 0.6610467 1.106950e-02 1.106950e-02 0.327883759 #> 8155  4077.0 0.6610333 0.6610333 1.106241e-02 1.106241e-02 0.327904244 #> 8156  4077.5 0.6610201 0.6610201 1.105532e-02 1.105532e-02 0.327924601 #> 8157  4078.0 0.6610069 0.6610069 1.104823e-02 1.104823e-02 0.327944830 #> 8158  4078.5 0.6609939 0.6609939 1.104114e-02 1.104114e-02 0.327964931 #> 8159  4079.0 0.6609810 0.6609810 1.103406e-02 1.103406e-02 0.327984903 #> 8160  4079.5 0.6609683 0.6609683 1.102697e-02 1.102697e-02 0.328004748 #> 8161  4080.0 0.6609556 0.6609556 1.101989e-02 1.101989e-02 0.328024464 #> 8162  4080.5 0.6609431 0.6609431 1.101281e-02 1.101281e-02 0.328044053 #> 8163  4081.0 0.6609308 0.6609308 1.100572e-02 1.100572e-02 0.328063514 #> 8164  4081.5 0.6609185 0.6609185 1.099864e-02 1.099864e-02 0.328082847 #> 8165  4082.0 0.6609064 0.6609064 1.099156e-02 1.099156e-02 0.328102053 #> 8166  4082.5 0.6608944 0.6608944 1.098449e-02 1.098449e-02 0.328121131 #> 8167  4083.0 0.6608825 0.6608825 1.097741e-02 1.097741e-02 0.328140081 #> 8168  4083.5 0.6608708 0.6608708 1.097033e-02 1.097033e-02 0.328158904 #> 8169  4084.0 0.6608591 0.6608591 1.096326e-02 1.096326e-02 0.328177600 #> 8170  4084.5 0.6608476 0.6608476 1.095619e-02 1.095619e-02 0.328196169 #> 8171  4085.0 0.6608363 0.6608363 1.094912e-02 1.094912e-02 0.328214610 #> 8172  4085.5 0.6608250 0.6608250 1.094205e-02 1.094205e-02 0.328232925 #> 8173  4086.0 0.6608139 0.6608139 1.093498e-02 1.093498e-02 0.328251112 #> 8174  4086.5 0.6608029 0.6608029 1.092791e-02 1.092791e-02 0.328269173 #> 8175  4087.0 0.6607920 0.6607920 1.092085e-02 1.092085e-02 0.328287107 #> 8176  4087.5 0.6607813 0.6607813 1.091378e-02 1.091378e-02 0.328304914 #> 8177  4088.0 0.6607707 0.6607707 1.090672e-02 1.090672e-02 0.328322595 #> 8178  4088.5 0.6607602 0.6607602 1.089966e-02 1.089966e-02 0.328340149 #> 8179  4089.0 0.6607498 0.6607498 1.089260e-02 1.089260e-02 0.328357577 #> 8180  4089.5 0.6607396 0.6607396 1.088555e-02 1.088555e-02 0.328374878 #> 8181  4090.0 0.6607295 0.6607295 1.087849e-02 1.087849e-02 0.328392053 #> 8182  4090.5 0.6607195 0.6607195 1.087144e-02 1.087144e-02 0.328409102 #> 8183  4091.0 0.6607096 0.6607096 1.086439e-02 1.086439e-02 0.328426025 #> 8184  4091.5 0.6606998 0.6606998 1.085734e-02 1.085734e-02 0.328442823 #> 8185  4092.0 0.6606902 0.6606902 1.085029e-02 1.085029e-02 0.328459494 #> 8186  4092.5 0.6606807 0.6606807 1.084325e-02 1.084325e-02 0.328476039 #> 8187  4093.0 0.6606713 0.6606713 1.083620e-02 1.083620e-02 0.328492459 #> 8188  4093.5 0.6606621 0.6606621 1.082916e-02 1.082916e-02 0.328508754 #> 8189  4094.0 0.6606530 0.6606530 1.082212e-02 1.082212e-02 0.328524923 #> 8190  4094.5 0.6606439 0.6606439 1.081508e-02 1.081508e-02 0.328540966 #> 8191  4095.0 0.6606351 0.6606351 1.080805e-02 1.080805e-02 0.328556885 #> 8192  4095.5 0.6606263 0.6606263 1.080102e-02 1.080102e-02 0.328572678 #> 8193  4096.0 0.6606177 0.6606177 1.079399e-02 1.079399e-02 0.328588346 #> 8194  4096.5 0.6606092 0.6606092 1.078696e-02 1.078696e-02 0.328603889 #> 8195  4097.0 0.6606008 0.6606008 1.077993e-02 1.077993e-02 0.328619307 #> 8196  4097.5 0.6605925 0.6605925 1.077291e-02 1.077291e-02 0.328634601 #> 8197  4098.0 0.6605843 0.6605843 1.076589e-02 1.076589e-02 0.328649770 #> 8198  4098.5 0.6605763 0.6605763 1.075887e-02 1.075887e-02 0.328664814 #> 8199  4099.0 0.6605684 0.6605684 1.075185e-02 1.075185e-02 0.328679734 #> 8200  4099.5 0.6605606 0.6605606 1.074484e-02 1.074484e-02 0.328694529 #> 8201  4100.0 0.6605530 0.6605530 1.073783e-02 1.073783e-02 0.328709201 #> 8202  4100.5 0.6605454 0.6605454 1.073082e-02 1.073082e-02 0.328723748 #> 8203  4101.0 0.6605380 0.6605380 1.072381e-02 1.072381e-02 0.328738171 #> 8204  4101.5 0.6605307 0.6605307 1.071681e-02 1.071681e-02 0.328752470 #> 8205  4102.0 0.6605235 0.6605235 1.070981e-02 1.070981e-02 0.328766645 #> 8206  4102.5 0.6605165 0.6605165 1.070281e-02 1.070281e-02 0.328780697 #> 8207  4103.0 0.6605096 0.6605096 1.069581e-02 1.069581e-02 0.328794625 #> 8208  4103.5 0.6605027 0.6605027 1.068882e-02 1.068882e-02 0.328808430 #> 8209  4104.0 0.6604961 0.6604961 1.068183e-02 1.068183e-02 0.328822111 #> 8210  4104.5 0.6604895 0.6604895 1.067484e-02 1.067484e-02 0.328835669 #> 8211  4105.0 0.6604830 0.6604830 1.066786e-02 1.066786e-02 0.328849103 #> 8212  4105.5 0.6604767 0.6604767 1.066088e-02 1.066088e-02 0.328862415 #> 8213  4106.0 0.6604705 0.6604705 1.065390e-02 1.065390e-02 0.328875603 #> 8214  4106.5 0.6604644 0.6604644 1.064692e-02 1.064692e-02 0.328888669 #> 8215  4107.0 0.6604584 0.6604584 1.063995e-02 1.063995e-02 0.328901612 #> 8216  4107.5 0.6604526 0.6604526 1.063298e-02 1.063298e-02 0.328914433 #> 8217  4108.0 0.6604469 0.6604469 1.062601e-02 1.062601e-02 0.328927131 #> 8218  4108.5 0.6604412 0.6604412 1.061905e-02 1.061905e-02 0.328939706 #> 8219  4109.0 0.6604358 0.6604358 1.061209e-02 1.061209e-02 0.328952159 #> 8220  4109.5 0.6604304 0.6604304 1.060513e-02 1.060513e-02 0.328964490 #> 8221  4110.0 0.6604251 0.6604251 1.059818e-02 1.059818e-02 0.328976699 #> 8222  4110.5 0.6604200 0.6604200 1.059123e-02 1.059123e-02 0.328988786 #> 8223  4111.0 0.6604150 0.6604150 1.058428e-02 1.058428e-02 0.329000751 #> 8224  4111.5 0.6604101 0.6604101 1.057733e-02 1.057733e-02 0.329012595 #> 8225  4112.0 0.6604053 0.6604053 1.057039e-02 1.057039e-02 0.329024316 #> 8226  4112.5 0.6604006 0.6604006 1.056345e-02 1.056345e-02 0.329035917 #> 8227  4113.0 0.6603961 0.6603961 1.055652e-02 1.055652e-02 0.329047396 #> 8228  4113.5 0.6603917 0.6603917 1.054959e-02 1.054959e-02 0.329058753 #> 8229  4114.0 0.6603874 0.6603874 1.054266e-02 1.054266e-02 0.329069990 #> 8230  4114.5 0.6603832 0.6603832 1.053573e-02 1.053573e-02 0.329081105 #> 8231  4115.0 0.6603791 0.6603791 1.052881e-02 1.052881e-02 0.329092100 #> 8232  4115.5 0.6603751 0.6603751 1.052190e-02 1.052190e-02 0.329102974 #> 8233  4116.0 0.6603713 0.6603713 1.051498e-02 1.051498e-02 0.329113727 #> 8234  4116.5 0.6603676 0.6603676 1.050807e-02 1.050807e-02 0.329124359 #> 8235  4117.0 0.6603640 0.6603640 1.050116e-02 1.050116e-02 0.329134872 #> 8236  4117.5 0.6603605 0.6603605 1.049426e-02 1.049426e-02 0.329145263 #> 8237  4118.0 0.6603571 0.6603571 1.048736e-02 1.048736e-02 0.329155535 #> 8238  4118.5 0.6603538 0.6603538 1.048046e-02 1.048046e-02 0.329165687 #> 8239  4119.0 0.6603507 0.6603507 1.047357e-02 1.047357e-02 0.329175718 #> 8240  4119.5 0.6603477 0.6603477 1.046668e-02 1.046668e-02 0.329185630 #> 8241  4120.0 0.6603448 0.6603448 1.045980e-02 1.045980e-02 0.329195422 #> 8242  4120.5 0.6603420 0.6603420 1.045292e-02 1.045292e-02 0.329205094 #> 8243  4121.0 0.6603393 0.6603393 1.044604e-02 1.044604e-02 0.329214647 #> 8244  4121.5 0.6603368 0.6603368 1.043917e-02 1.043917e-02 0.329224081 #> 8245  4122.0 0.6603343 0.6603343 1.043230e-02 1.043230e-02 0.329233396 #> 8246  4122.5 0.6603320 0.6603320 1.042543e-02 1.042543e-02 0.329242591 #> 8247  4123.0 0.6603298 0.6603298 1.041857e-02 1.041857e-02 0.329251667 #> 8248  4123.5 0.6603277 0.6603277 1.041171e-02 1.041171e-02 0.329260625 #> 8249  4124.0 0.6603257 0.6603257 1.040486e-02 1.040486e-02 0.329269464 #> 8250  4124.5 0.6603238 0.6603238 1.039801e-02 1.039801e-02 0.329278184 #> 8251  4125.0 0.6603221 0.6603221 1.039116e-02 1.039116e-02 0.329286786 #> 8252  4125.5 0.6603204 0.6603204 1.038432e-02 1.038432e-02 0.329295270 #> 8253  4126.0 0.6603189 0.6603189 1.037748e-02 1.037748e-02 0.329303635 #> 8254  4126.5 0.6603175 0.6603175 1.037065e-02 1.037065e-02 0.329311882 #> 8255  4127.0 0.6603162 0.6603162 1.036382e-02 1.036382e-02 0.329320011 #> 8256  4127.5 0.6603150 0.6603150 1.035699e-02 1.035699e-02 0.329328023 #> 8257  4128.0 0.6603139 0.6603139 1.035017e-02 1.035017e-02 0.329335916 #> 8258  4128.5 0.6603130 0.6603130 1.034336e-02 1.034336e-02 0.329343693 #> 8259  4129.0 0.6603121 0.6603121 1.033654e-02 1.033654e-02 0.329351351 #> 8260  4129.5 0.6603114 0.6603114 1.032974e-02 1.032974e-02 0.329358893 #> 8261  4130.0 0.6603108 0.6603108 1.032293e-02 1.032293e-02 0.329366317 #> 8262  4130.5 0.6603102 0.6603102 1.031613e-02 1.031613e-02 0.329373624 #> 8263  4131.0 0.6603098 0.6603098 1.030934e-02 1.030934e-02 0.329380815 #> 8264  4131.5 0.6603096 0.6603096 1.030255e-02 1.030255e-02 0.329387888 #> 8265  4132.0 0.6603094 0.6603094 1.029576e-02 1.029576e-02 0.329394845 #> 8266  4132.5 0.6603093 0.6603093 1.028898e-02 1.028898e-02 0.329401685 #> 8267  4133.0 0.6603094 0.6603094 1.028220e-02 1.028220e-02 0.329408409 #> 8268  4133.5 0.6603096 0.6603096 1.027543e-02 1.027543e-02 0.329415017 #> 8269  4134.0 0.6603098 0.6603098 1.026866e-02 1.026866e-02 0.329421508 #> 8270  4134.5 0.6603102 0.6603102 1.026189e-02 1.026189e-02 0.329427884 #> 8271  4135.0 0.6603107 0.6603107 1.025514e-02 1.025514e-02 0.329434144 #> 8272  4135.5 0.6603113 0.6603113 1.024838e-02 1.024838e-02 0.329440288 #> 8273  4136.0 0.6603121 0.6603121 1.024163e-02 1.024163e-02 0.329446316 #> 8274  4136.5 0.6603129 0.6603129 1.023488e-02 1.023488e-02 0.329452229 #> 8275  4137.0 0.6603138 0.6603138 1.022814e-02 1.022814e-02 0.329458027 #> 8276  4137.5 0.6603149 0.6603149 1.022141e-02 1.022141e-02 0.329463709 #> 8277  4138.0 0.6603160 0.6603160 1.021468e-02 1.021468e-02 0.329469277 #> 8278  4138.5 0.6603173 0.6603173 1.020795e-02 1.020795e-02 0.329474729 #> 8279  4139.0 0.6603187 0.6603187 1.020123e-02 1.020123e-02 0.329480067 #> 8280  4139.5 0.6603202 0.6603202 1.019451e-02 1.019451e-02 0.329485290 #> 8281  4140.0 0.6603218 0.6603218 1.018780e-02 1.018780e-02 0.329490399 #> 8282  4140.5 0.6603235 0.6603235 1.018109e-02 1.018109e-02 0.329495393 #> 8283  4141.0 0.6603253 0.6603253 1.017439e-02 1.017439e-02 0.329500273 #> 8284  4141.5 0.6603273 0.6603273 1.016769e-02 1.016769e-02 0.329505039 #> 8285  4142.0 0.6603293 0.6603293 1.016100e-02 1.016100e-02 0.329509691 #> 8286  4142.5 0.6603315 0.6603315 1.015431e-02 1.015431e-02 0.329514230 #> 8287  4143.0 0.6603337 0.6603337 1.014763e-02 1.014763e-02 0.329518654 #> 8288  4143.5 0.6603361 0.6603361 1.014095e-02 1.014095e-02 0.329522965 #> 8289  4144.0 0.6603386 0.6603386 1.013428e-02 1.013428e-02 0.329527163 #> 8290  4144.5 0.6603411 0.6603411 1.012761e-02 1.012761e-02 0.329531247 #> 8291  4145.0 0.6603438 0.6603438 1.012095e-02 1.012095e-02 0.329535219 #> 8292  4145.5 0.6603466 0.6603466 1.011429e-02 1.011429e-02 0.329539077 #> 8293  4146.0 0.6603495 0.6603495 1.010764e-02 1.010764e-02 0.329542822 #> 8294  4146.5 0.6603526 0.6603526 1.010099e-02 1.010099e-02 0.329546455 #> 8295  4147.0 0.6603557 0.6603557 1.009435e-02 1.009435e-02 0.329549976 #> 8296  4147.5 0.6603589 0.6603589 1.008771e-02 1.008771e-02 0.329553384 #> 8297  4148.0 0.6603622 0.6603622 1.008108e-02 1.008108e-02 0.329556679 #> 8298  4148.5 0.6603657 0.6603657 1.007445e-02 1.007445e-02 0.329559863 #> 8299  4149.0 0.6603692 0.6603692 1.006783e-02 1.006783e-02 0.329562934 #> 8300  4149.5 0.6603729 0.6603729 1.006121e-02 1.006121e-02 0.329565894 #> 8301  4150.0 0.6603767 0.6603767 1.005460e-02 1.005460e-02 0.329568742 #> 8302  4150.5 0.6603805 0.6603805 1.004799e-02 1.004799e-02 0.329571479 #> 8303  4151.0 0.6603845 0.6603845 1.004139e-02 1.004139e-02 0.329574104 #> 8304  4151.5 0.6603886 0.6603886 1.003480e-02 1.003480e-02 0.329576618 #> 8305  4152.0 0.6603928 0.6603928 1.002821e-02 1.002821e-02 0.329579021 #> 8306  4152.5 0.6603971 0.6603971 1.002162e-02 1.002162e-02 0.329581313 #> 8307  4153.0 0.6604015 0.6604015 1.001505e-02 1.001505e-02 0.329583494 #> 8308  4153.5 0.6604060 0.6604060 1.000847e-02 1.000847e-02 0.329585564 #> 8309  4154.0 0.6604106 0.6604106 1.000190e-02 1.000190e-02 0.329587524 #> 8310  4154.5 0.6604153 0.6604153 9.995342e-03 9.995342e-03 0.329589373 #> 8311  4155.0 0.6604201 0.6604201 9.988785e-03 9.988785e-03 0.329591113 #> 8312  4155.5 0.6604250 0.6604250 9.982233e-03 9.982233e-03 0.329592742 #> 8313  4156.0 0.6604301 0.6604301 9.975688e-03 9.975688e-03 0.329594261 #> 8314  4156.5 0.6604352 0.6604352 9.969147e-03 9.969147e-03 0.329595670 #> 8315  4157.0 0.6604404 0.6604404 9.962612e-03 9.962612e-03 0.329596970 #> 8316  4157.5 0.6604458 0.6604458 9.956083e-03 9.956083e-03 0.329598161 #> 8317  4158.0 0.6604512 0.6604512 9.949559e-03 9.949559e-03 0.329599242 #> 8318  4158.5 0.6604567 0.6604567 9.943041e-03 9.943041e-03 0.329600213 #> 8319  4159.0 0.6604624 0.6604624 9.936529e-03 9.936529e-03 0.329601076 #> 8320  4159.5 0.6604681 0.6604681 9.930022e-03 9.930022e-03 0.329601830 #> 8321  4160.0 0.6604740 0.6604740 9.923521e-03 9.923521e-03 0.329602475 #> 8322  4160.5 0.6604800 0.6604800 9.917026e-03 9.917026e-03 0.329603012 #> 8323  4161.0 0.6604860 0.6604860 9.910537e-03 9.910537e-03 0.329603440 #> 8324  4161.5 0.6604922 0.6604922 9.904053e-03 9.904053e-03 0.329603759 #> 8325  4162.0 0.6604985 0.6604985 9.897575e-03 9.897575e-03 0.329603971 #> 8326  4162.5 0.6605048 0.6605048 9.891103e-03 9.891103e-03 0.329604075 #> 8327  4163.0 0.6605113 0.6605113 9.884637e-03 9.884637e-03 0.329604070 #> 8328  4163.5 0.6605179 0.6605179 9.878176e-03 9.878176e-03 0.329603959 #> 8329  4164.0 0.6605245 0.6605245 9.871722e-03 9.871722e-03 0.329603739 #> 8330  4164.5 0.6605313 0.6605313 9.865273e-03 9.865273e-03 0.329603412 #> 8331  4165.0 0.6605382 0.6605382 9.858830e-03 9.858830e-03 0.329602978 #> 8332  4165.5 0.6605452 0.6605452 9.852393e-03 9.852393e-03 0.329602437 #> 8333  4166.0 0.6605522 0.6605522 9.845962e-03 9.845962e-03 0.329601789 #> 8334  4166.5 0.6605594 0.6605594 9.839537e-03 9.839537e-03 0.329601034 #> 8335  4167.0 0.6605667 0.6605667 9.833118e-03 9.833118e-03 0.329600172 #> 8336  4167.5 0.6605741 0.6605741 9.826705e-03 9.826705e-03 0.329599204 #> 8337  4168.0 0.6605816 0.6605816 9.820298e-03 9.820298e-03 0.329598130 #> 8338  4168.5 0.6605892 0.6605892 9.813897e-03 9.813897e-03 0.329596950 #> 8339  4169.0 0.6605968 0.6605968 9.807502e-03 9.807502e-03 0.329595663 #> 8340  4169.5 0.6606046 0.6606046 9.801113e-03 9.801113e-03 0.329594271 #> 8341  4170.0 0.6606125 0.6606125 9.794730e-03 9.794730e-03 0.329592773 #> 8342  4170.5 0.6606205 0.6606205 9.788353e-03 9.788353e-03 0.329591169 #> 8343  4171.0 0.6606286 0.6606286 9.781982e-03 9.781982e-03 0.329589460 #> 8344  4171.5 0.6606367 0.6606367 9.775618e-03 9.775618e-03 0.329587646 #> 8345  4172.0 0.6606450 0.6606450 9.769259e-03 9.769259e-03 0.329585726 #> 8346  4172.5 0.6606534 0.6606534 9.762907e-03 9.762907e-03 0.329583702 #> 8347  4173.0 0.6606619 0.6606619 9.756561e-03 9.756561e-03 0.329581573 #> 8348  4173.5 0.6606704 0.6606704 9.750221e-03 9.750221e-03 0.329579339 #> 8349  4174.0 0.6606791 0.6606791 9.743888e-03 9.743888e-03 0.329577001 #> 8350  4174.5 0.6606879 0.6606879 9.737560e-03 9.737560e-03 0.329574558 #> 8351  4175.0 0.6606967 0.6606967 9.731239e-03 9.731239e-03 0.329572012 #> 8352  4175.5 0.6607057 0.6607057 9.724924e-03 9.724924e-03 0.329569361 #> 8353  4176.0 0.6607148 0.6607148 9.718616e-03 9.718616e-03 0.329566607 #> 8354  4176.5 0.6607239 0.6607239 9.712313e-03 9.712313e-03 0.329563748 #> 8355  4177.0 0.6607332 0.6607332 9.706017e-03 9.706017e-03 0.329560787 #> 8356  4177.5 0.6607426 0.6607426 9.699728e-03 9.699728e-03 0.329557722 #> 8357  4178.0 0.6607520 0.6607520 9.693445e-03 9.693445e-03 0.329554553 #> 8358  4178.5 0.6607616 0.6607616 9.687168e-03 9.687168e-03 0.329551282 #> 8359  4179.0 0.6607712 0.6607712 9.680897e-03 9.680897e-03 0.329547908 #> 8360  4179.5 0.6607809 0.6607809 9.674633e-03 9.674633e-03 0.329544431 #> 8361  4180.0 0.6607908 0.6607908 9.668376e-03 9.668376e-03 0.329540851 #> 8362  4180.5 0.6608007 0.6608007 9.662124e-03 9.662124e-03 0.329537169 #> 8363  4181.0 0.6608107 0.6608107 9.655880e-03 9.655880e-03 0.329533385 #> 8364  4181.5 0.6608209 0.6608209 9.649641e-03 9.649641e-03 0.329529498 #> 8365  4182.0 0.6608311 0.6608311 9.643409e-03 9.643409e-03 0.329525510 #> 8366  4182.5 0.6608414 0.6608414 9.637184e-03 9.637184e-03 0.329521420 #> 8367  4183.0 0.6608518 0.6608518 9.630965e-03 9.630965e-03 0.329517228 #> 8368  4183.5 0.6608623 0.6608623 9.624753e-03 9.624753e-03 0.329512935 #> 8369  4184.0 0.6608729 0.6608729 9.618547e-03 9.618547e-03 0.329508540 #> 8370  4184.5 0.6608836 0.6608836 9.612348e-03 9.612348e-03 0.329504045 #> 8371  4185.0 0.6608944 0.6608944 9.606155e-03 9.606155e-03 0.329499448 #> 8372  4185.5 0.6609053 0.6609053 9.599969e-03 9.599969e-03 0.329494750 #> 8373  4186.0 0.6609163 0.6609163 9.593790e-03 9.593790e-03 0.329489952 #> 8374  4186.5 0.6609273 0.6609273 9.587617e-03 9.587617e-03 0.329485054 #> 8375  4187.0 0.6609385 0.6609385 9.581451e-03 9.581451e-03 0.329480054 #> 8376  4187.5 0.6609498 0.6609498 9.575292e-03 9.575292e-03 0.329474955 #> 8377  4188.0 0.6609611 0.6609611 9.569139e-03 9.569139e-03 0.329469756 #> 8378  4188.5 0.6609726 0.6609726 9.562993e-03 9.562993e-03 0.329464457 #> 8379  4189.0 0.6609841 0.6609841 9.556853e-03 9.556853e-03 0.329459058 #> 8380  4189.5 0.6609957 0.6609957 9.550720e-03 9.550720e-03 0.329453560 #> 8381  4190.0 0.6610074 0.6610074 9.544594e-03 9.544594e-03 0.329447962 #> 8382  4190.5 0.6610193 0.6610193 9.538475e-03 9.538475e-03 0.329442265 #> 8383  4191.0 0.6610312 0.6610312 9.532362e-03 9.532362e-03 0.329436469 #> 8384  4191.5 0.6610432 0.6610432 9.526257e-03 9.526257e-03 0.329430574 #> 8385  4192.0 0.6610553 0.6610553 9.520158e-03 9.520158e-03 0.329424580 #> 8386  4192.5 0.6610674 0.6610674 9.514065e-03 9.514065e-03 0.329418488 #> 8387  4193.0 0.6610797 0.6610797 9.507980e-03 9.507980e-03 0.329412297 #> 8388  4193.5 0.6610921 0.6610921 9.501901e-03 9.501901e-03 0.329406008 #> 8389  4194.0 0.6611045 0.6611045 9.495830e-03 9.495830e-03 0.329399621 #> 8390  4194.5 0.6611171 0.6611171 9.489765e-03 9.489765e-03 0.329393136 #> 8391  4195.0 0.6611297 0.6611297 9.483707e-03 9.483707e-03 0.329386554 #> 8392  4195.5 0.6611425 0.6611425 9.477656e-03 9.477656e-03 0.329379873 #> 8393  4196.0 0.6611553 0.6611553 9.471611e-03 9.471611e-03 0.329373096 #> 8394  4196.5 0.6611682 0.6611682 9.465574e-03 9.465574e-03 0.329366221 #> 8395  4197.0 0.6611812 0.6611812 9.459543e-03 9.459543e-03 0.329359249 #> 8396  4197.5 0.6611943 0.6611943 9.453520e-03 9.453520e-03 0.329352180 #> 8397  4198.0 0.6612075 0.6612075 9.447503e-03 9.447503e-03 0.329345014 #> 8398  4198.5 0.6612208 0.6612208 9.441494e-03 9.441494e-03 0.329337752 #> 8399  4199.0 0.6612341 0.6612341 9.435491e-03 9.435491e-03 0.329330393 #> 8400  4199.5 0.6612476 0.6612476 9.429495e-03 9.429495e-03 0.329322938 #> 8401  4200.0 0.6612611 0.6612611 9.423506e-03 9.423506e-03 0.329315386 #> 8402  4200.5 0.6612747 0.6612747 9.417525e-03 9.417525e-03 0.329307739 #> 8403  4201.0 0.6612885 0.6612885 9.411550e-03 9.411550e-03 0.329299996 #> 8404  4201.5 0.6613023 0.6613023 9.405582e-03 9.405582e-03 0.329292158 #> 8405  4202.0 0.6613162 0.6613162 9.399622e-03 9.399622e-03 0.329284223 #> 8406  4202.5 0.6613301 0.6613301 9.393668e-03 9.393668e-03 0.329276194 #> 8407  4203.0 0.6613442 0.6613442 9.387721e-03 9.387721e-03 0.329268070 #> 8408  4203.5 0.6613584 0.6613584 9.381782e-03 9.381782e-03 0.329259850 #> 8409  4204.0 0.6613726 0.6613726 9.375850e-03 9.375850e-03 0.329251536 #> 8410  4204.5 0.6613869 0.6613869 9.369924e-03 9.369924e-03 0.329243127 #> 8411  4205.0 0.6614014 0.6614014 9.364006e-03 9.364006e-03 0.329234624 #> 8412  4205.5 0.6614159 0.6614159 9.358095e-03 9.358095e-03 0.329226026 #> 8413  4206.0 0.6614305 0.6614305 9.352191e-03 9.352191e-03 0.329217334 #> 8414  4206.5 0.6614452 0.6614452 9.346294e-03 9.346294e-03 0.329208548 #> 8415  4207.0 0.6614599 0.6614599 9.340405e-03 9.340405e-03 0.329199669 #> 8416  4207.5 0.6614748 0.6614748 9.334522e-03 9.334522e-03 0.329190696 #> 8417  4208.0 0.6614897 0.6614897 9.328647e-03 9.328647e-03 0.329181629 #> 8418  4208.5 0.6615048 0.6615048 9.322779e-03 9.322779e-03 0.329172469 #> 8419  4209.0 0.6615199 0.6615199 9.316918e-03 9.316918e-03 0.329163216 #> 8420  4209.5 0.6615351 0.6615351 9.311065e-03 9.311065e-03 0.329153870 #> 8421  4210.0 0.6615504 0.6615504 9.305218e-03 9.305218e-03 0.329144431 #> 8422  4210.5 0.6615657 0.6615657 9.299379e-03 9.299379e-03 0.329134899 #> 8423  4211.0 0.6615812 0.6615812 9.293547e-03 9.293547e-03 0.329125275 #> 8424  4211.5 0.6615967 0.6615967 9.287722e-03 9.287722e-03 0.329115559 #> 8425  4212.0 0.6616123 0.6616123 9.281905e-03 9.281905e-03 0.329105750 #> 8426  4212.5 0.6616281 0.6616281 9.276095e-03 9.276095e-03 0.329095850 #> 8427  4213.0 0.6616439 0.6616439 9.270292e-03 9.270292e-03 0.329085858 #> 8428  4213.5 0.6616597 0.6616597 9.264496e-03 9.264496e-03 0.329075774 #> 8429  4214.0 0.6616757 0.6616757 9.258708e-03 9.258708e-03 0.329065599 #> 8430  4214.5 0.6616917 0.6616917 9.252927e-03 9.252927e-03 0.329055332 #> 8431  4215.0 0.6617079 0.6617079 9.247153e-03 9.247153e-03 0.329044974 #> 8432  4215.5 0.6617241 0.6617241 9.241387e-03 9.241387e-03 0.329034526 #> 8433  4216.0 0.6617404 0.6617404 9.235628e-03 9.235628e-03 0.329023986 #> 8434  4216.5 0.6617568 0.6617568 9.229877e-03 9.229877e-03 0.329013356 #> 8435  4217.0 0.6617732 0.6617732 9.224132e-03 9.224132e-03 0.329002635 #> 8436  4217.5 0.6617898 0.6617898 9.218396e-03 9.218396e-03 0.328991824 #> 8437  4218.0 0.6618064 0.6618064 9.212666e-03 9.212666e-03 0.328980923 #> 8438  4218.5 0.6618231 0.6618231 9.206944e-03 9.206944e-03 0.328969932 #> 8439  4219.0 0.6618399 0.6618399 9.201230e-03 9.201230e-03 0.328958852 #> 8440  4219.5 0.6618568 0.6618568 9.195522e-03 9.195522e-03 0.328947681 #> 8441  4220.0 0.6618738 0.6618738 9.189823e-03 9.189823e-03 0.328936421 #> 8442  4220.5 0.6618908 0.6618908 9.184130e-03 9.184130e-03 0.328925072 #> 8443  4221.0 0.6619079 0.6619079 9.178445e-03 9.178445e-03 0.328913634 #> 8444  4221.5 0.6619251 0.6619251 9.172768e-03 9.172768e-03 0.328902107 #> 8445  4222.0 0.6619424 0.6619424 9.167098e-03 9.167098e-03 0.328890491 #> 8446  4222.5 0.6619598 0.6619598 9.161435e-03 9.161435e-03 0.328878786 #> 8447  4223.0 0.6619772 0.6619772 9.155780e-03 9.155780e-03 0.328866993 #> 8448  4223.5 0.6619948 0.6619948 9.150133e-03 9.150133e-03 0.328855112 #> 8449  4224.0 0.6620124 0.6620124 9.144493e-03 9.144493e-03 0.328843143 #> 8450  4224.5 0.6620301 0.6620301 9.138860e-03 9.138860e-03 0.328831085 #> 8451  4225.0 0.6620478 0.6620478 9.133235e-03 9.133235e-03 0.328818940 #> 8452  4225.5 0.6620657 0.6620657 9.127618e-03 9.127618e-03 0.328806708 #> 8453  4226.0 0.6620836 0.6620836 9.122008e-03 9.122008e-03 0.328794388 #> 8454  4226.5 0.6621016 0.6621016 9.116405e-03 9.116405e-03 0.328781980 #> 8455  4227.0 0.6621197 0.6621197 9.110811e-03 9.110811e-03 0.328769486 #> 8456  4227.5 0.6621379 0.6621379 9.105223e-03 9.105223e-03 0.328756905 #> 8457  4228.0 0.6621561 0.6621561 9.099644e-03 9.099644e-03 0.328744237 #> 8458  4228.5 0.6621744 0.6621744 9.094071e-03 9.094071e-03 0.328731482 #> 8459  4229.0 0.6621929 0.6621929 9.088507e-03 9.088507e-03 0.328718641 #> 8460  4229.5 0.6622113 0.6622113 9.082950e-03 9.082950e-03 0.328705714 #> 8461  4230.0 0.6622299 0.6622299 9.077401e-03 9.077401e-03 0.328692700 #> 8462  4230.5 0.6622485 0.6622485 9.071859e-03 9.071859e-03 0.328679601 #> 8463  4231.0 0.6622673 0.6622673 9.066325e-03 9.066325e-03 0.328666416 #> 8464  4231.5 0.6622861 0.6622861 9.060798e-03 9.060798e-03 0.328653145 #> 8465  4232.0 0.6623049 0.6623049 9.055279e-03 9.055279e-03 0.328639789 #> 8466  4232.5 0.6623239 0.6623239 9.049768e-03 9.049768e-03 0.328626348 #> 8467  4233.0 0.6623429 0.6623429 9.044264e-03 9.044264e-03 0.328612822 #> 8468  4233.5 0.6623620 0.6623620 9.038769e-03 9.038769e-03 0.328599211 #> 8469  4234.0 0.6623812 0.6623812 9.033280e-03 9.033280e-03 0.328585515 #> 8470  4234.5 0.6624005 0.6624005 9.027800e-03 9.027800e-03 0.328571735 #> 8471  4235.0 0.6624198 0.6624198 9.022327e-03 9.022327e-03 0.328557870 #> 8472  4235.5 0.6624392 0.6624392 9.016862e-03 9.016862e-03 0.328543921 #> 8473  4236.0 0.6624587 0.6624587 9.011404e-03 9.011404e-03 0.328529888 #> 8474  4236.5 0.6624783 0.6624783 9.005954e-03 9.005954e-03 0.328515772 #> 8475  4237.0 0.6624979 0.6624979 9.000512e-03 9.000512e-03 0.328501571 #> 8476  4237.5 0.6625176 0.6625176 8.995078e-03 8.995078e-03 0.328487287 #> 8477  4238.0 0.6625374 0.6625374 8.989651e-03 8.989651e-03 0.328472920 #> 8478  4238.5 0.6625573 0.6625573 8.984232e-03 8.984232e-03 0.328458469 #> 8479  4239.0 0.6625772 0.6625772 8.978821e-03 8.978821e-03 0.328443936 #> 8480  4239.5 0.6625973 0.6625973 8.973417e-03 8.973417e-03 0.328429319 #> 8481  4240.0 0.6626174 0.6626174 8.968021e-03 8.968021e-03 0.328414620 #> 8482  4240.5 0.6626375 0.6626375 8.962633e-03 8.962633e-03 0.328399839 #> 8483  4241.0 0.6626578 0.6626578 8.957253e-03 8.957253e-03 0.328384975 #> 8484  4241.5 0.6626781 0.6626781 8.951881e-03 8.951881e-03 0.328370029 #> 8485  4242.0 0.6626985 0.6626985 8.946516e-03 8.946516e-03 0.328355001 #> 8486  4242.5 0.6627189 0.6627189 8.941159e-03 8.941159e-03 0.328339891 #> 8487  4243.0 0.6627395 0.6627395 8.935810e-03 8.935810e-03 0.328324700 #> 8488  4243.5 0.6627601 0.6627601 8.930469e-03 8.930469e-03 0.328309427 #> 8489  4244.0 0.6627808 0.6627808 8.925135e-03 8.925135e-03 0.328294073 #> 8490  4244.5 0.6628016 0.6628016 8.919810e-03 8.919810e-03 0.328278638 #> 8491  4245.0 0.6628224 0.6628224 8.914492e-03 8.914492e-03 0.328263121 #> 8492  4245.5 0.6628433 0.6628433 8.909182e-03 8.909182e-03 0.328247524 #> 8493  4246.0 0.6628643 0.6628643 8.903879e-03 8.903879e-03 0.328231847 #> 8494  4246.5 0.6628853 0.6628853 8.898585e-03 8.898585e-03 0.328216088 #> 8495  4247.0 0.6629065 0.6629065 8.893298e-03 8.893298e-03 0.328200250 #> 8496  4247.5 0.6629276 0.6629276 8.888020e-03 8.888020e-03 0.328184332 #> 8497  4248.0 0.6629489 0.6629489 8.882749e-03 8.882749e-03 0.328168333 #> 8498  4248.5 0.6629703 0.6629703 8.877486e-03 8.877486e-03 0.328152255 #> 8499  4249.0 0.6629917 0.6629917 8.872230e-03 8.872230e-03 0.328136097 #> 8500  4249.5 0.6630132 0.6630132 8.866983e-03 8.866983e-03 0.328119860 #> 8501  4250.0 0.6630347 0.6630347 8.861744e-03 8.861744e-03 0.328103543 #> 8502  4250.5 0.6630563 0.6630563 8.856512e-03 8.856512e-03 0.328087148 #> 8503  4251.0 0.6630780 0.6630780 8.851288e-03 8.851288e-03 0.328070673 #> 8504  4251.5 0.6630998 0.6630998 8.846073e-03 8.846073e-03 0.328054120 #> 8505  4252.0 0.6631216 0.6631216 8.840865e-03 8.840865e-03 0.328037488 #> 8506  4252.5 0.6631436 0.6631436 8.835665e-03 8.835665e-03 0.328020778 #> 8507  4253.0 0.6631655 0.6631655 8.830473e-03 8.830473e-03 0.328003990 #> 8508  4253.5 0.6631876 0.6631876 8.825288e-03 8.825288e-03 0.327987123 #> 8509  4254.0 0.6632097 0.6632097 8.820112e-03 8.820112e-03 0.327970179 #> 8510  4254.5 0.6632319 0.6632319 8.814944e-03 8.814944e-03 0.327953157 #> 8511  4255.0 0.6632542 0.6632542 8.809783e-03 8.809783e-03 0.327936057 #> 8512  4255.5 0.6632765 0.6632765 8.804631e-03 8.804631e-03 0.327918880 #> 8513  4256.0 0.6632989 0.6632989 8.799486e-03 8.799486e-03 0.327901626 #> 8514  4256.5 0.6633214 0.6633214 8.794350e-03 8.794350e-03 0.327884295 #> 8515  4257.0 0.6633439 0.6633439 8.789221e-03 8.789221e-03 0.327866887 #> 8516  4257.5 0.6633665 0.6633665 8.784100e-03 8.784100e-03 0.327849402 #> 8517  4258.0 0.6633892 0.6633892 8.778987e-03 8.778987e-03 0.327831841 #> 8518  4258.5 0.6634119 0.6634119 8.773882e-03 8.773882e-03 0.327814203 #> 8519  4259.0 0.6634347 0.6634347 8.768786e-03 8.768786e-03 0.327796490 #> 8520  4259.5 0.6634576 0.6634576 8.763697e-03 8.763697e-03 0.327778700 #> 8521  4260.0 0.6634806 0.6634806 8.758616e-03 8.758616e-03 0.327760834 #> 8522  4260.5 0.6635036 0.6635036 8.753543e-03 8.753543e-03 0.327742893 #> 8523  4261.0 0.6635266 0.6635266 8.748478e-03 8.748478e-03 0.327724876 #> 8524  4261.5 0.6635498 0.6635498 8.743421e-03 8.743421e-03 0.327706784 #> 8525  4262.0 0.6635730 0.6635730 8.738372e-03 8.738372e-03 0.327688617 #> 8526  4262.5 0.6635963 0.6635963 8.733331e-03 8.733331e-03 0.327670375 #> 8527  4263.0 0.6636196 0.6636196 8.728298e-03 8.728298e-03 0.327652057 #> 8528  4263.5 0.6636431 0.6636431 8.723273e-03 8.723273e-03 0.327633666 #> 8529  4264.0 0.6636665 0.6636665 8.718256e-03 8.718256e-03 0.327615200 #> 8530  4264.5 0.6636901 0.6636901 8.713247e-03 8.713247e-03 0.327596659 #> 8531  4265.0 0.6637137 0.6637137 8.708246e-03 8.708246e-03 0.327578045 #> 8532  4265.5 0.6637374 0.6637374 8.703253e-03 8.703253e-03 0.327559356 #> 8533  4266.0 0.6637611 0.6637611 8.698268e-03 8.698268e-03 0.327540594 #> 8534  4266.5 0.6637850 0.6637850 8.693291e-03 8.693291e-03 0.327521758 #> 8535  4267.0 0.6638088 0.6638088 8.688322e-03 8.688322e-03 0.327502848 #> 8536  4267.5 0.6638328 0.6638328 8.683361e-03 8.683361e-03 0.327483866 #> 8537  4268.0 0.6638568 0.6638568 8.678408e-03 8.678408e-03 0.327464810 #> 8538  4268.5 0.6638809 0.6638809 8.673463e-03 8.673463e-03 0.327445681 #> 8539  4269.0 0.6639050 0.6639050 8.668526e-03 8.668526e-03 0.327426480 #> 8540  4269.5 0.6639292 0.6639292 8.663598e-03 8.663598e-03 0.327407206 #> 8541  4270.0 0.6639535 0.6639535 8.658677e-03 8.658677e-03 0.327387859 #> 8542  4270.5 0.6639778 0.6639778 8.653764e-03 8.653764e-03 0.327368440 #> 8543  4271.0 0.6640022 0.6640022 8.648860e-03 8.648860e-03 0.327348950 #> 8544  4271.5 0.6640266 0.6640266 8.643963e-03 8.643963e-03 0.327329387 #> 8545  4272.0 0.6640512 0.6640512 8.639075e-03 8.639075e-03 0.327309752 #> 8546  4272.5 0.6640758 0.6640758 8.634194e-03 8.634194e-03 0.327290046 #> 8547  4273.0 0.6641004 0.6641004 8.629322e-03 8.629322e-03 0.327270269 #> 8548  4273.5 0.6641251 0.6641251 8.624458e-03 8.624458e-03 0.327250420 #> 8549  4274.0 0.6641499 0.6641499 8.619602e-03 8.619602e-03 0.327230500 #> 8550  4274.5 0.6641747 0.6641747 8.614754e-03 8.614754e-03 0.327210510 #> 8551  4275.0 0.6641996 0.6641996 8.609914e-03 8.609914e-03 0.327190448 #> 8552  4275.5 0.6642246 0.6642246 8.605082e-03 8.605082e-03 0.327170317 #> 8553  4276.0 0.6642496 0.6642496 8.600258e-03 8.600258e-03 0.327150114 #> 8554  4276.5 0.6642747 0.6642747 8.595442e-03 8.595442e-03 0.327129842 #> 8555  4277.0 0.6642999 0.6642999 8.590634e-03 8.590634e-03 0.327109499 #> 8556  4277.5 0.6643251 0.6643251 8.585835e-03 8.585835e-03 0.327089087 #> 8557  4278.0 0.6643504 0.6643504 8.581043e-03 8.581043e-03 0.327068605 #> 8558  4278.5 0.6643757 0.6643757 8.576260e-03 8.576260e-03 0.327048053 #> 8559  4279.0 0.6644011 0.6644011 8.571485e-03 8.571485e-03 0.327027432 #> 8560  4279.5 0.6644265 0.6644265 8.566717e-03 8.566717e-03 0.327006742 #> 8561  4280.0 0.6644521 0.6644521 8.561958e-03 8.561958e-03 0.326985983 #> 8562  4280.5 0.6644776 0.6644776 8.557207e-03 8.557207e-03 0.326965154 #> 8563  4281.0 0.6645033 0.6645033 8.552465e-03 8.552465e-03 0.326944258 #> 8564  4281.5 0.6645290 0.6645290 8.547730e-03 8.547730e-03 0.326923292 #> 8565  4282.0 0.6645547 0.6645547 8.543003e-03 8.543003e-03 0.326902259 #> 8566  4282.5 0.6645806 0.6645806 8.538285e-03 8.538285e-03 0.326881157 #> 8567  4283.0 0.6646064 0.6646064 8.533574e-03 8.533574e-03 0.326859987 #> 8568  4283.5 0.6646324 0.6646324 8.528872e-03 8.528872e-03 0.326838750 #> 8569  4284.0 0.6646584 0.6646584 8.524178e-03 8.524178e-03 0.326817444 #> 8570  4284.5 0.6646844 0.6646844 8.519492e-03 8.519492e-03 0.326796071 #> 8571  4285.0 0.6647106 0.6647106 8.514814e-03 8.514814e-03 0.326774631 #> 8572  4285.5 0.6647367 0.6647367 8.510144e-03 8.510144e-03 0.326753124 #> 8573  4286.0 0.6647630 0.6647630 8.505483e-03 8.505483e-03 0.326731550 #> 8574  4286.5 0.6647893 0.6647893 8.500829e-03 8.500829e-03 0.326709909 #> 8575  4287.0 0.6648156 0.6648156 8.496184e-03 8.496184e-03 0.326688201 #> 8576  4287.5 0.6648420 0.6648420 8.491547e-03 8.491547e-03 0.326666427 #> 8577  4288.0 0.6648685 0.6648685 8.486918e-03 8.486918e-03 0.326644586 #> 8578  4288.5 0.6648950 0.6648950 8.482297e-03 8.482297e-03 0.326622679 #> 8579  4289.0 0.6649216 0.6649216 8.477684e-03 8.477684e-03 0.326600707 #> 8580  4289.5 0.6649483 0.6649483 8.473079e-03 8.473079e-03 0.326578668 #> 8581  4290.0 0.6649750 0.6649750 8.468483e-03 8.468483e-03 0.326556564 #> 8582  4290.5 0.6650017 0.6650017 8.463895e-03 8.463895e-03 0.326534394 #> 8583  4291.0 0.6650285 0.6650285 8.459314e-03 8.459314e-03 0.326512159 #> 8584  4291.5 0.6650554 0.6650554 8.454742e-03 8.454742e-03 0.326489859 #> 8585  4292.0 0.6650823 0.6650823 8.450179e-03 8.450179e-03 0.326467494 #> 8586  4292.5 0.6651093 0.6651093 8.445623e-03 8.445623e-03 0.326445065 #> 8587  4293.0 0.6651364 0.6651364 8.441075e-03 8.441075e-03 0.326422570 #> 8588  4293.5 0.6651635 0.6651635 8.436536e-03 8.436536e-03 0.326400011 #> 8589  4294.0 0.6651906 0.6651906 8.432005e-03 8.432005e-03 0.326377388 #> 8590  4294.5 0.6652178 0.6652178 8.427482e-03 8.427482e-03 0.326354701 #> 8591  4295.0 0.6652451 0.6652451 8.422967e-03 8.422967e-03 0.326331949 #> 8592  4295.5 0.6652724 0.6652724 8.418460e-03 8.418460e-03 0.326309134 #> 8593  4296.0 0.6652998 0.6652998 8.413961e-03 8.413961e-03 0.326286256 #> 8594  4296.5 0.6653272 0.6653272 8.409471e-03 8.409471e-03 0.326263314 #> 8595  4297.0 0.6653547 0.6653547 8.404989e-03 8.404989e-03 0.326240308 #> 8596  4297.5 0.6653822 0.6653822 8.400515e-03 8.400515e-03 0.326217240 #> 8597  4298.0 0.6654098 0.6654098 8.396049e-03 8.396049e-03 0.326194108 #> 8598  4298.5 0.6654375 0.6654375 8.391591e-03 8.391591e-03 0.326170914 #> 8599  4299.0 0.6654652 0.6654652 8.387141e-03 8.387141e-03 0.326147657 #> 8600  4299.5 0.6654930 0.6654930 8.382700e-03 8.382700e-03 0.326124338 #> 8601  4300.0 0.6655208 0.6655208 8.378267e-03 8.378267e-03 0.326100956 #> 8602  4300.5 0.6655486 0.6655486 8.373842e-03 8.373842e-03 0.326077512 #> 8603  4301.0 0.6655766 0.6655766 8.369425e-03 8.369425e-03 0.326054007 #> 8604  4301.5 0.6656045 0.6656045 8.365016e-03 8.365016e-03 0.326030439 #> 8605  4302.0 0.6656326 0.6656326 8.360616e-03 8.360616e-03 0.326006810 #> 8606  4302.5 0.6656607 0.6656607 8.356223e-03 8.356223e-03 0.325983120 #> 8607  4303.0 0.6656888 0.6656888 8.351839e-03 8.351839e-03 0.325959368 #> 8608  4303.5 0.6657170 0.6657170 8.347463e-03 8.347463e-03 0.325935555 #> 8609  4304.0 0.6657452 0.6657452 8.343095e-03 8.343095e-03 0.325911681 #> 8610  4304.5 0.6657735 0.6657735 8.338735e-03 8.338735e-03 0.325887746 #> 8611  4305.0 0.6658019 0.6658019 8.334384e-03 8.334384e-03 0.325863751 #> 8612  4305.5 0.6658303 0.6658303 8.330041e-03 8.330041e-03 0.325839696 #> 8613  4306.0 0.6658587 0.6658587 8.325706e-03 8.325706e-03 0.325815580 #> 8614  4306.5 0.6658872 0.6658872 8.321379e-03 8.321379e-03 0.325791404 #> 8615  4307.0 0.6659158 0.6659158 8.317060e-03 8.317060e-03 0.325767168 #> 8616  4307.5 0.6659444 0.6659444 8.312749e-03 8.312749e-03 0.325742872 #> 8617  4308.0 0.6659730 0.6659730 8.308447e-03 8.308447e-03 0.325718516 #> 8618  4308.5 0.6660017 0.6660017 8.304153e-03 8.304153e-03 0.325694102 #> 8619  4309.0 0.6660305 0.6660305 8.299867e-03 8.299867e-03 0.325669627 #> 8620  4309.5 0.6660593 0.6660593 8.295589e-03 8.295589e-03 0.325645094 #> 8621  4310.0 0.6660882 0.6660882 8.291319e-03 8.291319e-03 0.325620502 #> 8622  4310.5 0.6661171 0.6661171 8.287058e-03 8.287058e-03 0.325595851 #> 8623  4311.0 0.6661461 0.6661461 8.282804e-03 8.282804e-03 0.325571141 #> 8624  4311.5 0.6661751 0.6661751 8.278559e-03 8.278559e-03 0.325546373 #> 8625  4312.0 0.6662041 0.6662041 8.274322e-03 8.274322e-03 0.325521547 #> 8626  4312.5 0.6662332 0.6662332 8.270094e-03 8.270094e-03 0.325496663 #> 8627  4313.0 0.6662624 0.6662624 8.265873e-03 8.265873e-03 0.325471720 #> 8628  4313.5 0.6662916 0.6662916 8.261661e-03 8.261661e-03 0.325446720 #> 8629  4314.0 0.6663209 0.6663209 8.257457e-03 8.257457e-03 0.325421662 #> 8630  4314.5 0.6663502 0.6663502 8.253260e-03 8.253260e-03 0.325396547 #> 8631  4315.0 0.6663796 0.6663796 8.249073e-03 8.249073e-03 0.325371375 #> 8632  4315.5 0.6664090 0.6664090 8.244893e-03 8.244893e-03 0.325346145 #> 8633  4316.0 0.6664384 0.6664384 8.240722e-03 8.240722e-03 0.325320858 #> 8634  4316.5 0.6664679 0.6664679 8.236558e-03 8.236558e-03 0.325295515 #> 8635  4317.0 0.6664975 0.6664975 8.232403e-03 8.232403e-03 0.325270115 #> 8636  4317.5 0.6665271 0.6665271 8.228256e-03 8.228256e-03 0.325244658 #> 8637  4318.0 0.6665567 0.6665567 8.224117e-03 8.224117e-03 0.325219145 #> 8638  4318.5 0.6665864 0.6665864 8.219987e-03 8.219987e-03 0.325193576 #> 8639  4319.0 0.6666162 0.6666162 8.215864e-03 8.215864e-03 0.325167951 #> 8640  4319.5 0.6666460 0.6666460 8.211750e-03 8.211750e-03 0.325142271 #> 8641  4320.0 0.6666758 0.6666758 8.207644e-03 8.207644e-03 0.325116534 #> 8642  4320.5 0.6667057 0.6667057 8.203546e-03 8.203546e-03 0.325090742 #> 8643  4321.0 0.6667356 0.6667356 8.199457e-03 8.199457e-03 0.325064895 #> 8644  4321.5 0.6667656 0.6667656 8.195375e-03 8.195375e-03 0.325038993 #> 8645  4322.0 0.6667957 0.6667957 8.191302e-03 8.191302e-03 0.325013035 #> 8646  4322.5 0.6668257 0.6668257 8.187237e-03 8.187237e-03 0.324987023 #> 8647  4323.0 0.6668559 0.6668559 8.183180e-03 8.183180e-03 0.324960956 #> 8648  4323.5 0.6668860 0.6668860 8.179131e-03 8.179131e-03 0.324934835 #> 8649  4324.0 0.6669163 0.6669163 8.175090e-03 8.175090e-03 0.324908659 #> 8650  4324.5 0.6669465 0.6669465 8.171058e-03 8.171058e-03 0.324882429 #> 8651  4325.0 0.6669768 0.6669768 8.167034e-03 8.167034e-03 0.324856145 #> 8652  4325.5 0.6670072 0.6670072 8.163018e-03 8.163018e-03 0.324829808 #> 8653  4326.0 0.6670376 0.6670376 8.159010e-03 8.159010e-03 0.324803416 #> 8654  4326.5 0.6670680 0.6670680 8.155010e-03 8.155010e-03 0.324776971 #> 8655  4327.0 0.6670985 0.6670985 8.151018e-03 8.151018e-03 0.324750473 #> 8656  4327.5 0.6671290 0.6671290 8.147035e-03 8.147035e-03 0.324723921 #> 8657  4328.0 0.6671596 0.6671596 8.143060e-03 8.143060e-03 0.324697317 #> 8658  4328.5 0.6671902 0.6671902 8.139092e-03 8.139092e-03 0.324670659 #> 8659  4329.0 0.6672209 0.6672209 8.135134e-03 8.135134e-03 0.324643949 #> 8660  4329.5 0.6672516 0.6672516 8.131183e-03 8.131183e-03 0.324617187 #> 8661  4330.0 0.6672824 0.6672824 8.127240e-03 8.127240e-03 0.324590372 #> 8662  4330.5 0.6673132 0.6673132 8.123306e-03 8.123306e-03 0.324563504 #> 8663  4331.0 0.6673440 0.6673440 8.119379e-03 8.119379e-03 0.324536585 #> 8664  4331.5 0.6673749 0.6673749 8.115461e-03 8.115461e-03 0.324509614 #> 8665  4332.0 0.6674059 0.6674059 8.111551e-03 8.111551e-03 0.324482591 #> 8666  4332.5 0.6674368 0.6674368 8.107650e-03 8.107650e-03 0.324455516 #> 8667  4333.0 0.6674679 0.6674679 8.103756e-03 8.103756e-03 0.324428390 #> 8668  4333.5 0.6674989 0.6674989 8.099870e-03 8.099870e-03 0.324401213 #> 8669  4334.0 0.6675300 0.6675300 8.095993e-03 8.095993e-03 0.324373985 #> 8670  4334.5 0.6675612 0.6675612 8.092124e-03 8.092124e-03 0.324346706 #> 8671  4335.0 0.6675924 0.6675924 8.088263e-03 8.088263e-03 0.324319376 #> 8672  4335.5 0.6676236 0.6676236 8.084410e-03 8.084410e-03 0.324291995 #> 8673  4336.0 0.6676549 0.6676549 8.080565e-03 8.080565e-03 0.324264564 #> 8674  4336.5 0.6676862 0.6676862 8.076729e-03 8.076729e-03 0.324237083 #> 8675  4337.0 0.6677175 0.6677175 8.072900e-03 8.072900e-03 0.324209551 #> 8676  4337.5 0.6677490 0.6677490 8.069080e-03 8.069080e-03 0.324181970 #> 8677  4338.0 0.6677804 0.6677804 8.065268e-03 8.065268e-03 0.324154339 #> 8678  4338.5 0.6678119 0.6678119 8.061464e-03 8.061464e-03 0.324126658 #> 8679  4339.0 0.6678434 0.6678434 8.057668e-03 8.057668e-03 0.324098928 #> 8680  4339.5 0.6678750 0.6678750 8.053880e-03 8.053880e-03 0.324071148 #> 8681  4340.0 0.6679066 0.6679066 8.050101e-03 8.050101e-03 0.324043319 #> 8682  4340.5 0.6679382 0.6679382 8.046329e-03 8.046329e-03 0.324015441 #> 8683  4341.0 0.6679699 0.6679699 8.042566e-03 8.042566e-03 0.323987515 #> 8684  4341.5 0.6680016 0.6680016 8.038811e-03 8.038811e-03 0.323959539 #> 8685  4342.0 0.6680334 0.6680334 8.035064e-03 8.035064e-03 0.323931516 #> 8686  4342.5 0.6680652 0.6680652 8.031325e-03 8.031325e-03 0.323903443 #> 8687  4343.0 0.6680971 0.6680971 8.027594e-03 8.027594e-03 0.323875323 #> 8688  4343.5 0.6681290 0.6681290 8.023871e-03 8.023871e-03 0.323847155 #> 8689  4344.0 0.6681609 0.6681609 8.020157e-03 8.020157e-03 0.323818938 #> 8690  4344.5 0.6681929 0.6681929 8.016450e-03 8.016450e-03 0.323790674 #> 8691  4345.0 0.6682249 0.6682249 8.012752e-03 8.012752e-03 0.323762363 #> 8692  4345.5 0.6682569 0.6682569 8.009062e-03 8.009062e-03 0.323734004 #> 8693  4346.0 0.6682890 0.6682890 8.005380e-03 8.005380e-03 0.323705598 #> 8694  4346.5 0.6683211 0.6683211 8.001706e-03 8.001706e-03 0.323677144 #> 8695  4347.0 0.6683533 0.6683533 7.998040e-03 7.998040e-03 0.323648644 #> 8696  4347.5 0.6683855 0.6683855 7.994382e-03 7.994382e-03 0.323620097 #> 8697  4348.0 0.6684178 0.6684178 7.990733e-03 7.990733e-03 0.323591503 #> 8698  4348.5 0.6684500 0.6684500 7.987091e-03 7.987091e-03 0.323562863 #> 8699  4349.0 0.6684824 0.6684824 7.983458e-03 7.983458e-03 0.323534177 #> 8700  4349.5 0.6685147 0.6685147 7.979833e-03 7.979833e-03 0.323505444 #> 8701  4350.0 0.6685471 0.6685471 7.976215e-03 7.976215e-03 0.323476666 #> 8702  4350.5 0.6685796 0.6685796 7.972606e-03 7.972606e-03 0.323447842 #> 8703  4351.0 0.6686120 0.6686120 7.969005e-03 7.969005e-03 0.323418972 #> 8704  4351.5 0.6686445 0.6686445 7.965412e-03 7.965412e-03 0.323390056 #> 8705  4352.0 0.6686771 0.6686771 7.961828e-03 7.961828e-03 0.323361095 #> 8706  4352.5 0.6687097 0.6687097 7.958251e-03 7.958251e-03 0.323332089 #> 8707  4353.0 0.6687423 0.6687423 7.954682e-03 7.954682e-03 0.323303038 #> 8708  4353.5 0.6687749 0.6687749 7.951122e-03 7.951122e-03 0.323273943 #> 8709  4354.0 0.6688076 0.6688076 7.947569e-03 7.947569e-03 0.323244802 #> 8710  4354.5 0.6688404 0.6688404 7.944025e-03 7.944025e-03 0.323215617 #> 8711  4355.0 0.6688731 0.6688731 7.940489e-03 7.940489e-03 0.323186387 #> 8712  4355.5 0.6689059 0.6689059 7.936961e-03 7.936961e-03 0.323157113 #> 8713  4356.0 0.6689388 0.6689388 7.933440e-03 7.933440e-03 0.323127795 #> 8714  4356.5 0.6689716 0.6689716 7.929928e-03 7.929928e-03 0.323098434 #> 8715  4357.0 0.6690045 0.6690045 7.926424e-03 7.926424e-03 0.323069028 #> 8716  4357.5 0.6690375 0.6690375 7.922928e-03 7.922928e-03 0.323039579 #> 8717  4358.0 0.6690705 0.6690705 7.919441e-03 7.919441e-03 0.323010086 #> 8718  4358.5 0.6691035 0.6691035 7.915961e-03 7.915961e-03 0.322980550 #> 8719  4359.0 0.6691365 0.6691365 7.912489e-03 7.912489e-03 0.322950971 #> 8720  4359.5 0.6691696 0.6691696 7.909026e-03 7.909026e-03 0.322921349 #> 8721  4360.0 0.6692027 0.6692027 7.905570e-03 7.905570e-03 0.322891684 #> 8722  4360.5 0.6692359 0.6692359 7.902122e-03 7.902122e-03 0.322861976 #> 8723  4361.0 0.6692691 0.6692691 7.898683e-03 7.898683e-03 0.322832226 #> 8724  4361.5 0.6693023 0.6693023 7.895252e-03 7.895252e-03 0.322802433 #> 8725  4362.0 0.6693356 0.6693356 7.891828e-03 7.891828e-03 0.322772599 #> 8726  4362.5 0.6693689 0.6693689 7.888413e-03 7.888413e-03 0.322742722 #> 8727  4363.0 0.6694022 0.6694022 7.885006e-03 7.885006e-03 0.322712803 #> 8728  4363.5 0.6694356 0.6694356 7.881606e-03 7.881606e-03 0.322682843 #> 8729  4364.0 0.6694689 0.6694689 7.878215e-03 7.878215e-03 0.322652841 #> 8730  4364.5 0.6695024 0.6695024 7.874832e-03 7.874832e-03 0.322622797 #> 8731  4365.0 0.6695358 0.6695358 7.871457e-03 7.871457e-03 0.322592712 #> 8732  4365.5 0.6695693 0.6695693 7.868090e-03 7.868090e-03 0.322562586 #> 8733  4366.0 0.6696029 0.6696029 7.864731e-03 7.864731e-03 0.322532419 #> 8734  4366.5 0.6696364 0.6696364 7.861380e-03 7.861380e-03 0.322502212 #> 8735  4367.0 0.6696700 0.6696700 7.858037e-03 7.858037e-03 0.322471963 #> 8736  4367.5 0.6697036 0.6697036 7.854702e-03 7.854702e-03 0.322441674 #> 8737  4368.0 0.6697373 0.6697373 7.851375e-03 7.851375e-03 0.322411345 #> 8738  4368.5 0.6697710 0.6697710 7.848056e-03 7.848056e-03 0.322380975 #> 8739  4369.0 0.6698047 0.6698047 7.844745e-03 7.844745e-03 0.322350566 #> 8740  4369.5 0.6698384 0.6698384 7.841442e-03 7.841442e-03 0.322320116 #> 8741  4370.0 0.6698722 0.6698722 7.838147e-03 7.838147e-03 0.322289627 #> 8742  4370.5 0.6699060 0.6699060 7.834860e-03 7.834860e-03 0.322259098 #> 8743  4371.0 0.6699399 0.6699399 7.831581e-03 7.831581e-03 0.322228530 #> 8744  4371.5 0.6699738 0.6699738 7.828310e-03 7.828310e-03 0.322197923 #> 8745  4372.0 0.6700077 0.6700077 7.825047e-03 7.825047e-03 0.322167276 #> 8746  4372.5 0.6700416 0.6700416 7.821792e-03 7.821792e-03 0.322136590 #> 8747  4373.0 0.6700756 0.6700756 7.818545e-03 7.818545e-03 0.322105866 #> 8748  4373.5 0.6701096 0.6701096 7.815306e-03 7.815306e-03 0.322075103 #> 8749  4374.0 0.6701436 0.6701436 7.812075e-03 7.812075e-03 0.322044301 #> 8750  4374.5 0.6701777 0.6701777 7.808852e-03 7.808852e-03 0.322013461 #> 8751  4375.0 0.6702118 0.6702118 7.805637e-03 7.805637e-03 0.321982583 #> 8752  4375.5 0.6702459 0.6702459 7.802430e-03 7.802430e-03 0.321951666 #> 8753  4376.0 0.6702801 0.6702801 7.799231e-03 7.799231e-03 0.321920712 #> 8754  4376.5 0.6703142 0.6703142 7.796040e-03 7.796040e-03 0.321889720 #> 8755  4377.0 0.6703485 0.6703485 7.792857e-03 7.792857e-03 0.321858690 #> 8756  4377.5 0.6703827 0.6703827 7.789682e-03 7.789682e-03 0.321827623 #> 8757  4378.0 0.6704170 0.6704170 7.786515e-03 7.786515e-03 0.321796519 #> 8758  4378.5 0.6704513 0.6704513 7.783355e-03 7.783355e-03 0.321765377 #> 8759  4379.0 0.6704856 0.6704856 7.780204e-03 7.780204e-03 0.321734199 #> 8760  4379.5 0.6705200 0.6705200 7.777061e-03 7.777061e-03 0.321702983 #> 8761  4380.0 0.6705543 0.6705543 7.773925e-03 7.773925e-03 0.321671731 #> 8762  4380.5 0.6705888 0.6705888 7.770798e-03 7.770798e-03 0.321640442 #> 8763  4381.0 0.6706232 0.6706232 7.767678e-03 7.767678e-03 0.321609117 #> 8764  4381.5 0.6706577 0.6706577 7.764567e-03 7.764567e-03 0.321577756 #> 8765  4382.0 0.6706922 0.6706922 7.761463e-03 7.761463e-03 0.321546359 #> 8766  4382.5 0.6707267 0.6707267 7.758368e-03 7.758368e-03 0.321514925 #> 8767  4383.0 0.6707613 0.6707613 7.755280e-03 7.755280e-03 0.321483456 #> 8768  4383.5 0.6707958 0.6707958 7.752200e-03 7.752200e-03 0.321451951 #> 8769  4384.0 0.6708305 0.6708305 7.749128e-03 7.749128e-03 0.321420411 #> 8770  4384.5 0.6708651 0.6708651 7.746064e-03 7.746064e-03 0.321388835 #> 8771  4385.0 0.6708998 0.6708998 7.743008e-03 7.743008e-03 0.321357224 #> 8772  4385.5 0.6709345 0.6709345 7.739960e-03 7.739960e-03 0.321325578 #> 8773  4386.0 0.6709692 0.6709692 7.736920e-03 7.736920e-03 0.321293897 #> 8774  4386.5 0.6710039 0.6710039 7.733888e-03 7.733888e-03 0.321262182 #> 8775  4387.0 0.6710387 0.6710387 7.730863e-03 7.730863e-03 0.321230431 #> 8776  4387.5 0.6710735 0.6710735 7.727847e-03 7.727847e-03 0.321198647 #> 8777  4388.0 0.6711083 0.6711083 7.724838e-03 7.724838e-03 0.321166828 #> 8778  4388.5 0.6711432 0.6711432 7.721837e-03 7.721837e-03 0.321134974 #> 8779  4389.0 0.6711781 0.6711781 7.718845e-03 7.718845e-03 0.321103087 #> 8780  4389.5 0.6712130 0.6712130 7.715860e-03 7.715860e-03 0.321071166 #> 8781  4390.0 0.6712479 0.6712479 7.712883e-03 7.712883e-03 0.321039211 #> 8782  4390.5 0.6712829 0.6712829 7.709914e-03 7.709914e-03 0.321007223 #> 8783  4391.0 0.6713178 0.6713178 7.706952e-03 7.706952e-03 0.320975201 #> 8784  4391.5 0.6713529 0.6713529 7.703999e-03 7.703999e-03 0.320943146 #> 8785  4392.0 0.6713879 0.6713879 7.701053e-03 7.701053e-03 0.320911058 #> 8786  4392.5 0.6714229 0.6714229 7.698116e-03 7.698116e-03 0.320878937 #> 8787  4393.0 0.6714580 0.6714580 7.695186e-03 7.695186e-03 0.320846783 #> 8788  4393.5 0.6714931 0.6714931 7.692264e-03 7.692264e-03 0.320814597 #> 8789  4394.0 0.6715283 0.6715283 7.689350e-03 7.689350e-03 0.320782377 #> 8790  4394.5 0.6715634 0.6715634 7.686444e-03 7.686444e-03 0.320750126 #> 8791  4395.0 0.6715986 0.6715986 7.683546e-03 7.683546e-03 0.320717842 #> 8792  4395.5 0.6716338 0.6716338 7.680655e-03 7.680655e-03 0.320685526 #> 8793  4396.0 0.6716690 0.6716690 7.677772e-03 7.677772e-03 0.320653179 #> 8794  4396.5 0.6717043 0.6717043 7.674898e-03 7.674898e-03 0.320620799 #> 8795  4397.0 0.6717396 0.6717396 7.672031e-03 7.672031e-03 0.320588388 #> 8796  4397.5 0.6717749 0.6717749 7.669172e-03 7.669172e-03 0.320555945 #> 8797  4398.0 0.6718102 0.6718102 7.666320e-03 7.666320e-03 0.320523471 #> 8798  4398.5 0.6718456 0.6718456 7.663477e-03 7.663477e-03 0.320490966 #> 8799  4399.0 0.6718809 0.6718809 7.660641e-03 7.660641e-03 0.320458429 #> 8800  4399.5 0.6719163 0.6719163 7.657814e-03 7.657814e-03 0.320425862 #> 8801  4400.0 0.6719517 0.6719517 7.654994e-03 7.654994e-03 0.320393264 #> 8802  4400.5 0.6719872 0.6719872 7.652181e-03 7.652181e-03 0.320360635 #> 8803  4401.0 0.6720226 0.6720226 7.649377e-03 7.649377e-03 0.320327976 #> 8804  4401.5 0.6720581 0.6720581 7.646581e-03 7.646581e-03 0.320295286 #> 8805  4402.0 0.6720936 0.6720936 7.643792e-03 7.643792e-03 0.320262567 #> 8806  4402.5 0.6721292 0.6721292 7.641011e-03 7.641011e-03 0.320229817 #> 8807  4403.0 0.6721647 0.6721647 7.638238e-03 7.638238e-03 0.320197037 #> 8808  4403.5 0.6722003 0.6722003 7.635472e-03 7.635472e-03 0.320164228 #> 8809  4404.0 0.6722359 0.6722359 7.632715e-03 7.632715e-03 0.320131389 #> 8810  4404.5 0.6722715 0.6722715 7.629965e-03 7.629965e-03 0.320098520 #> 8811  4405.0 0.6723072 0.6723072 7.627223e-03 7.627223e-03 0.320065623 #> 8812  4405.5 0.6723428 0.6723428 7.624489e-03 7.624489e-03 0.320032696 #> 8813  4406.0 0.6723785 0.6723785 7.621763e-03 7.621763e-03 0.319999740 #> 8814  4406.5 0.6724142 0.6724142 7.619044e-03 7.619044e-03 0.319966755 #> 8815  4407.0 0.6724499 0.6724499 7.616333e-03 7.616333e-03 0.319933741 #> 8816  4407.5 0.6724857 0.6724857 7.613630e-03 7.613630e-03 0.319900699 #> 8817  4408.0 0.6725214 0.6725214 7.610935e-03 7.610935e-03 0.319867628 #> 8818  4408.5 0.6725572 0.6725572 7.608247e-03 7.608247e-03 0.319834529 #> 8819  4409.0 0.6725930 0.6725930 7.605567e-03 7.605567e-03 0.319801402 #> 8820  4409.5 0.6726289 0.6726289 7.602895e-03 7.602895e-03 0.319768247 #> 8821  4410.0 0.6726647 0.6726647 7.600231e-03 7.600231e-03 0.319735063 #> 8822  4410.5 0.6727006 0.6727006 7.597574e-03 7.597574e-03 0.319701853 #> 8823  4411.0 0.6727365 0.6727365 7.594925e-03 7.594925e-03 0.319668614 #> 8824  4411.5 0.6727724 0.6727724 7.592284e-03 7.592284e-03 0.319635348 #> 8825  4412.0 0.6728083 0.6728083 7.589651e-03 7.589651e-03 0.319602055 #> 8826  4412.5 0.6728442 0.6728442 7.587025e-03 7.587025e-03 0.319568735 #> 8827  4413.0 0.6728802 0.6728802 7.584407e-03 7.584407e-03 0.319535387 #> 8828  4413.5 0.6729162 0.6729162 7.581797e-03 7.581797e-03 0.319502013 #> 8829  4414.0 0.6729522 0.6729522 7.579195e-03 7.579195e-03 0.319468612 #> 8830  4414.5 0.6729882 0.6729882 7.576600e-03 7.576600e-03 0.319435184 #> 8831  4415.0 0.6730243 0.6730243 7.574013e-03 7.574013e-03 0.319401730 #> 8832  4415.5 0.6730603 0.6730603 7.571433e-03 7.571433e-03 0.319368249 #> 8833  4416.0 0.6730964 0.6730964 7.568862e-03 7.568862e-03 0.319334743 #> 8834  4416.5 0.6731325 0.6731325 7.566298e-03 7.566298e-03 0.319301210 #> 8835  4417.0 0.6731686 0.6731686 7.563742e-03 7.563742e-03 0.319267652 #> 8836  4417.5 0.6732047 0.6732047 7.561193e-03 7.561193e-03 0.319234067 #> 8837  4418.0 0.6732409 0.6732409 7.558652e-03 7.558652e-03 0.319200457 #> 8838  4418.5 0.6732771 0.6732771 7.556119e-03 7.556119e-03 0.319166822 #> 8839  4419.0 0.6733132 0.6733132 7.553594e-03 7.553594e-03 0.319133161 #> 8840  4419.5 0.6733494 0.6733494 7.551076e-03 7.551076e-03 0.319099475 #> 8841  4420.0 0.6733857 0.6733857 7.548566e-03 7.548566e-03 0.319065764 #> 8842  4420.5 0.6734219 0.6734219 7.546063e-03 7.546063e-03 0.319032028 #> 8843  4421.0 0.6734582 0.6734582 7.543568e-03 7.543568e-03 0.318998268 #> 8844  4421.5 0.6734944 0.6734944 7.541081e-03 7.541081e-03 0.318964482 #> 8845  4422.0 0.6735307 0.6735307 7.538602e-03 7.538602e-03 0.318930673 #> 8846  4422.5 0.6735670 0.6735670 7.536130e-03 7.536130e-03 0.318896839 #> 8847  4423.0 0.6736034 0.6736034 7.533666e-03 7.533666e-03 0.318862980 #> 8848  4423.5 0.6736397 0.6736397 7.531209e-03 7.531209e-03 0.318829098 #> 8849  4424.0 0.6736760 0.6736760 7.528760e-03 7.528760e-03 0.318795191 #> 8850  4424.5 0.6737124 0.6737124 7.526319e-03 7.526319e-03 0.318761261 #> 8851  4425.0 0.6737488 0.6737488 7.523885e-03 7.523885e-03 0.318727308 #> 8852  4425.5 0.6737852 0.6737852 7.521459e-03 7.521459e-03 0.318693330 #> 8853  4426.0 0.6738216 0.6738216 7.519041e-03 7.519041e-03 0.318659330 #> 8854  4426.5 0.6738581 0.6738581 7.516630e-03 7.516630e-03 0.318625306 #> 8855  4427.0 0.6738945 0.6738945 7.514227e-03 7.514227e-03 0.318591259 #> 8856  4427.5 0.6739310 0.6739310 7.511832e-03 7.511832e-03 0.318557189 #> 8857  4428.0 0.6739675 0.6739675 7.509444e-03 7.509444e-03 0.318523096 #> 8858  4428.5 0.6740040 0.6740040 7.507063e-03 7.507063e-03 0.318488980 #> 8859  4429.0 0.6740405 0.6740405 7.504691e-03 7.504691e-03 0.318454842 #> 8860  4429.5 0.6740770 0.6740770 7.502326e-03 7.502326e-03 0.318420682 #> 8861  4430.0 0.6741135 0.6741135 7.499968e-03 7.499968e-03 0.318386499 #> 8862  4430.5 0.6741501 0.6741501 7.497618e-03 7.497618e-03 0.318352294 #> 8863  4431.0 0.6741867 0.6741867 7.495276e-03 7.495276e-03 0.318318068 #> 8864  4431.5 0.6742232 0.6742232 7.492941e-03 7.492941e-03 0.318283819 #> 8865  4432.0 0.6742598 0.6742598 7.490614e-03 7.490614e-03 0.318249549 #> 8866  4432.5 0.6742964 0.6742964 7.488294e-03 7.488294e-03 0.318215257 #> 8867  4433.0 0.6743331 0.6743331 7.485982e-03 7.485982e-03 0.318180943 #> 8868  4433.5 0.6743697 0.6743697 7.483678e-03 7.483678e-03 0.318146608 #> 8869  4434.0 0.6744064 0.6744064 7.481381e-03 7.481381e-03 0.318112253 #> 8870  4434.5 0.6744430 0.6744430 7.479092e-03 7.479092e-03 0.318077876 #> 8871  4435.0 0.6744797 0.6744797 7.476810e-03 7.476810e-03 0.318043478 #> 8872  4435.5 0.6745164 0.6745164 7.474536e-03 7.474536e-03 0.318009059 #> 8873  4436.0 0.6745531 0.6745531 7.472269e-03 7.472269e-03 0.317974620 #> 8874  4436.5 0.6745898 0.6745898 7.470010e-03 7.470010e-03 0.317940160 #> 8875  4437.0 0.6746266 0.6746266 7.467758e-03 7.467758e-03 0.317905680 #> 8876  4437.5 0.6746633 0.6746633 7.465514e-03 7.465514e-03 0.317871180 #> 8877  4438.0 0.6747001 0.6747001 7.463278e-03 7.463278e-03 0.317836659 #> 8878  4438.5 0.6747368 0.6747368 7.461049e-03 7.461049e-03 0.317802119 #> 8879  4439.0 0.6747736 0.6747736 7.458827e-03 7.458827e-03 0.317767559 #> 8880  4439.5 0.6748104 0.6748104 7.456613e-03 7.456613e-03 0.317732979 #> 8881  4440.0 0.6748472 0.6748472 7.454407e-03 7.454407e-03 0.317698380 #> 8882  4440.5 0.6748840 0.6748840 7.452208e-03 7.452208e-03 0.317663761 #> 8883  4441.0 0.6749209 0.6749209 7.450017e-03 7.450017e-03 0.317629123 #> 8884  4441.5 0.6749577 0.6749577 7.447833e-03 7.447833e-03 0.317594466 #> 8885  4442.0 0.6749946 0.6749946 7.445656e-03 7.445656e-03 0.317559790 #> 8886  4442.5 0.6750314 0.6750314 7.443487e-03 7.443487e-03 0.317525095 #> 8887  4443.0 0.6750683 0.6750683 7.441326e-03 7.441326e-03 0.317490381 #> 8888  4443.5 0.6751052 0.6751052 7.439172e-03 7.439172e-03 0.317455649 #> 8889  4444.0 0.6751421 0.6751421 7.437026e-03 7.437026e-03 0.317420898 #> 8890  4444.5 0.6751790 0.6751790 7.434887e-03 7.434887e-03 0.317386129 #> 8891  4445.0 0.6752159 0.6752159 7.432755e-03 7.432755e-03 0.317351341 #> 8892  4445.5 0.6752528 0.6752528 7.430631e-03 7.430631e-03 0.317316536 #> 8893  4446.0 0.6752898 0.6752898 7.428514e-03 7.428514e-03 0.317281712 #> 8894  4446.5 0.6753267 0.6753267 7.426405e-03 7.426405e-03 0.317246871 #> 8895  4447.0 0.6753637 0.6753637 7.424304e-03 7.424304e-03 0.317212012 #> 8896  4447.5 0.6754007 0.6754007 7.422210e-03 7.422210e-03 0.317177136 #> 8897  4448.0 0.6754376 0.6754376 7.420123e-03 7.420123e-03 0.317142242 #> 8898  4448.5 0.6754746 0.6754746 7.418043e-03 7.418043e-03 0.317107331 #> 8899  4449.0 0.6755116 0.6755116 7.415972e-03 7.415972e-03 0.317072403 #> 8900  4449.5 0.6755486 0.6755486 7.413907e-03 7.413907e-03 0.317037458 #> 8901  4450.0 0.6755857 0.6755857 7.411850e-03 7.411850e-03 0.317002496 #> 8902  4450.5 0.6756227 0.6756227 7.409801e-03 7.409801e-03 0.316967517 #> 8903  4451.0 0.6756597 0.6756597 7.407759e-03 7.407759e-03 0.316932522 #> 8904  4451.5 0.6756968 0.6756968 7.405724e-03 7.405724e-03 0.316897510 #> 8905  4452.0 0.6757338 0.6757338 7.403697e-03 7.403697e-03 0.316862482 #> 8906  4452.5 0.6757709 0.6757709 7.401677e-03 7.401677e-03 0.316827437 #> 8907  4453.0 0.6758080 0.6758080 7.399664e-03 7.399664e-03 0.316792377 #> 8908  4453.5 0.6758450 0.6758450 7.397659e-03 7.397659e-03 0.316757301 #> 8909  4454.0 0.6758821 0.6758821 7.395661e-03 7.395661e-03 0.316722208 #> 8910  4454.5 0.6759192 0.6759192 7.393671e-03 7.393671e-03 0.316687101 #> 8911  4455.0 0.6759563 0.6759563 7.391688e-03 7.391688e-03 0.316651977 #> 8912  4455.5 0.6759934 0.6759934 7.389713e-03 7.389713e-03 0.316616838 #> 8913  4456.0 0.6760306 0.6760306 7.387745e-03 7.387745e-03 0.316581684 #> 8914  4456.5 0.6760677 0.6760677 7.385784e-03 7.385784e-03 0.316546515 #> 8915  4457.0 0.6761048 0.6761048 7.383831e-03 7.383831e-03 0.316511330 #> 8916  4457.5 0.6761420 0.6761420 7.381885e-03 7.381885e-03 0.316476131 #> 8917  4458.0 0.6761791 0.6761791 7.379946e-03 7.379946e-03 0.316440917 #> 8918  4458.5 0.6762163 0.6762163 7.378015e-03 7.378015e-03 0.316405688 #> 8919  4459.0 0.6762535 0.6762535 7.376091e-03 7.376091e-03 0.316370445 #> 8920  4459.5 0.6762906 0.6762906 7.374174e-03 7.374174e-03 0.316335188 #> 8921  4460.0 0.6763278 0.6763278 7.372265e-03 7.372265e-03 0.316299916 #> 8922  4460.5 0.6763650 0.6763650 7.370363e-03 7.370363e-03 0.316264630 #> 8923  4461.0 0.6764022 0.6764022 7.368469e-03 7.368469e-03 0.316229330 #> 8924  4461.5 0.6764394 0.6764394 7.366582e-03 7.366582e-03 0.316194016 #> 8925  4462.0 0.6764766 0.6764766 7.364702e-03 7.364702e-03 0.316158689 #> 8926  4462.5 0.6765138 0.6765138 7.362829e-03 7.362829e-03 0.316123348 #> 8927  4463.0 0.6765510 0.6765510 7.360964e-03 7.360964e-03 0.316087993 #> 8928  4463.5 0.6765883 0.6765883 7.359106e-03 7.359106e-03 0.316052625 #> 8929  4464.0 0.6766255 0.6766255 7.357256e-03 7.357256e-03 0.316017244 #> 8930  4464.5 0.6766627 0.6766627 7.355412e-03 7.355412e-03 0.315981850 #> 8931  4465.0 0.6767000 0.6767000 7.353577e-03 7.353577e-03 0.315946443 #> 8932  4465.5 0.6767372 0.6767372 7.351748e-03 7.351748e-03 0.315911023 #> 8933  4466.0 0.6767745 0.6767745 7.349927e-03 7.349927e-03 0.315875590 #> 8934  4466.5 0.6768117 0.6768117 7.348113e-03 7.348113e-03 0.315840145 #> 8935  4467.0 0.6768490 0.6768490 7.346306e-03 7.346306e-03 0.315804687 #> 8936  4467.5 0.6768863 0.6768863 7.344506e-03 7.344506e-03 0.315769217 #> 8937  4468.0 0.6769236 0.6769236 7.342714e-03 7.342714e-03 0.315733735 #> 8938  4468.5 0.6769608 0.6769608 7.340929e-03 7.340929e-03 0.315698240 #> 8939  4469.0 0.6769981 0.6769981 7.339152e-03 7.339152e-03 0.315662734 #> 8940  4469.5 0.6770354 0.6770354 7.337381e-03 7.337381e-03 0.315627216 #> 8941  4470.0 0.6770727 0.6770727 7.335618e-03 7.335618e-03 0.315591686 #> 8942  4470.5 0.6771100 0.6771100 7.333863e-03 7.333863e-03 0.315556145 #> 8943  4471.0 0.6771473 0.6771473 7.332114e-03 7.332114e-03 0.315520592 #> 8944  4471.5 0.6771846 0.6771846 7.330373e-03 7.330373e-03 0.315485028 #> 8945  4472.0 0.6772219 0.6772219 7.328639e-03 7.328639e-03 0.315449453 #> 8946  4472.5 0.6772592 0.6772592 7.326912e-03 7.326912e-03 0.315413867 #> 8947  4473.0 0.6772965 0.6772965 7.325192e-03 7.325192e-03 0.315378270 #> 8948  4473.5 0.6773339 0.6773339 7.323480e-03 7.323480e-03 0.315342662 #> 8949  4474.0 0.6773712 0.6773712 7.321775e-03 7.321775e-03 0.315307043 #> 8950  4474.5 0.6774085 0.6774085 7.320077e-03 7.320077e-03 0.315271414 #> 8951  4475.0 0.6774458 0.6774458 7.318386e-03 7.318386e-03 0.315235774 #> 8952  4475.5 0.6774832 0.6774832 7.316703e-03 7.316703e-03 0.315200124 #> 8953  4476.0 0.6775205 0.6775205 7.315027e-03 7.315027e-03 0.315164464 #> 8954  4476.5 0.6775578 0.6775578 7.313358e-03 7.313358e-03 0.315128794 #> 8955  4477.0 0.6775952 0.6775952 7.311696e-03 7.311696e-03 0.315093114 #> 8956  4477.5 0.6776325 0.6776325 7.310041e-03 7.310041e-03 0.315057424 #> 8957  4478.0 0.6776699 0.6776699 7.308394e-03 7.308394e-03 0.315021725 #> 8958  4478.5 0.6777072 0.6777072 7.306754e-03 7.306754e-03 0.314986016 #> 8959  4479.0 0.6777446 0.6777446 7.305121e-03 7.305121e-03 0.314950297 #> 8960  4479.5 0.6777819 0.6777819 7.303495e-03 7.303495e-03 0.314914570 #> 8961  4480.0 0.6778193 0.6778193 7.301876e-03 7.301876e-03 0.314878833 #> 8962  4480.5 0.6778566 0.6778566 7.300265e-03 7.300265e-03 0.314843087 #> 8963  4481.0 0.6778940 0.6778940 7.298660e-03 7.298660e-03 0.314807332 #> 8964  4481.5 0.6779314 0.6779314 7.297063e-03 7.297063e-03 0.314771568 #> 8965  4482.0 0.6779687 0.6779687 7.295473e-03 7.295473e-03 0.314735796 #> 8966  4482.5 0.6780061 0.6780061 7.293890e-03 7.293890e-03 0.314700015 #> 8967  4483.0 0.6780435 0.6780435 7.292315e-03 7.292315e-03 0.314664225 #> 8968  4483.5 0.6780808 0.6780808 7.290746e-03 7.290746e-03 0.314628428 #> 8969  4484.0 0.6781182 0.6781182 7.289185e-03 7.289185e-03 0.314592622 #> 8970  4484.5 0.6781556 0.6781556 7.287631e-03 7.287631e-03 0.314556808 #> 8971  4485.0 0.6781929 0.6781929 7.286084e-03 7.286084e-03 0.314520986 #> 8972  4485.5 0.6782303 0.6782303 7.284544e-03 7.284544e-03 0.314485156 #> 8973  4486.0 0.6782677 0.6782677 7.283011e-03 7.283011e-03 0.314449319 #> 8974  4486.5 0.6783050 0.6783050 7.281485e-03 7.281485e-03 0.314413474 #> 8975  4487.0 0.6783424 0.6783424 7.279967e-03 7.279967e-03 0.314377622 #> 8976  4487.5 0.6783798 0.6783798 7.278455e-03 7.278455e-03 0.314341762 #> 8977  4488.0 0.6784172 0.6784172 7.276951e-03 7.276951e-03 0.314305895 #> 8978  4488.5 0.6784545 0.6784545 7.275454e-03 7.275454e-03 0.314270021 #> 8979  4489.0 0.6784919 0.6784919 7.273963e-03 7.273963e-03 0.314234140 #> 8980  4489.5 0.6785293 0.6785293 7.272480e-03 7.272480e-03 0.314198253 #> 8981  4490.0 0.6785666 0.6785666 7.271005e-03 7.271005e-03 0.314162358 #> 8982  4490.5 0.6786040 0.6786040 7.269536e-03 7.269536e-03 0.314126457 #> 8983  4491.0 0.6786414 0.6786414 7.268074e-03 7.268074e-03 0.314090550 #> 8984  4491.5 0.6786787 0.6786787 7.266619e-03 7.266619e-03 0.314054636 #> 8985  4492.0 0.6787161 0.6787161 7.265172e-03 7.265172e-03 0.314018716 #> 8986  4492.5 0.6787535 0.6787535 7.263731e-03 7.263731e-03 0.313982789 #> 8987  4493.0 0.6787908 0.6787908 7.262298e-03 7.262298e-03 0.313946857 #> 8988  4493.5 0.6788282 0.6788282 7.260871e-03 7.260871e-03 0.313910919 #> 8989  4494.0 0.6788656 0.6788656 7.259452e-03 7.259452e-03 0.313874975 #> 8990  4494.5 0.6789029 0.6789029 7.258040e-03 7.258040e-03 0.313839026 #> 8991  4495.0 0.6789403 0.6789403 7.256635e-03 7.256635e-03 0.313803071 #> 8992  4495.5 0.6789777 0.6789777 7.255236e-03 7.255236e-03 0.313767111 #> 8993  4496.0 0.6790150 0.6790150 7.253845e-03 7.253845e-03 0.313731145 #> 8994  4496.5 0.6790524 0.6790524 7.252461e-03 7.252461e-03 0.313695175 #> 8995  4497.0 0.6790897 0.6790897 7.251084e-03 7.251084e-03 0.313659199 #> 8996  4497.5 0.6791271 0.6791271 7.249714e-03 7.249714e-03 0.313623218 #> 8997  4498.0 0.6791644 0.6791644 7.248351e-03 7.248351e-03 0.313587233 #> 8998  4498.5 0.6792018 0.6792018 7.246995e-03 7.246995e-03 0.313551243 #> 8999  4499.0 0.6792391 0.6792391 7.245646e-03 7.245646e-03 0.313515248 #> 9000  4499.5 0.6792764 0.6792764 7.244304e-03 7.244304e-03 0.313479249 #> 9001  4500.0 0.6793138 0.6793138 7.242969e-03 7.242969e-03 0.313443246 #> 9002  4500.5 0.6793511 0.6793511 7.241641e-03 7.241641e-03 0.313407238 #> 9003  4501.0 0.6793885 0.6793885 7.240321e-03 7.240321e-03 0.313371227 #> 9004  4501.5 0.6794258 0.6794258 7.239007e-03 7.239007e-03 0.313335211 #> 9005  4502.0 0.6794631 0.6794631 7.237700e-03 7.237700e-03 0.313299192 #> 9006  4502.5 0.6795004 0.6795004 7.236400e-03 7.236400e-03 0.313263168 #> 9007  4503.0 0.6795378 0.6795378 7.235107e-03 7.235107e-03 0.313227142 #> 9008  4503.5 0.6795751 0.6795751 7.233821e-03 7.233821e-03 0.313191112 #> 9009  4504.0 0.6796124 0.6796124 7.232542e-03 7.232542e-03 0.313155078 #> 9010  4504.5 0.6796497 0.6796497 7.231270e-03 7.231270e-03 0.313119041 #> 9011  4505.0 0.6796870 0.6796870 7.230005e-03 7.230005e-03 0.313083001 #> 9012  4505.5 0.6797243 0.6797243 7.228747e-03 7.228747e-03 0.313046959 #> 9013  4506.0 0.6797616 0.6797616 7.227496e-03 7.227496e-03 0.313010913 #> 9014  4506.5 0.6797989 0.6797989 7.226251e-03 7.226251e-03 0.312974864 #> 9015  4507.0 0.6798362 0.6798362 7.225014e-03 7.225014e-03 0.312938813 #> 9016  4507.5 0.6798735 0.6798735 7.223784e-03 7.223784e-03 0.312902759 #> 9017  4508.0 0.6799107 0.6799107 7.222560e-03 7.222560e-03 0.312866703 #> 9018  4508.5 0.6799480 0.6799480 7.221344e-03 7.221344e-03 0.312830645 #> 9019  4509.0 0.6799853 0.6799853 7.220135e-03 7.220135e-03 0.312794584 #> 9020  4509.5 0.6800225 0.6800225 7.218932e-03 7.218932e-03 0.312758521 #> 9021  4510.0 0.6800598 0.6800598 7.217736e-03 7.217736e-03 0.312722456 #> 9022  4510.5 0.6800971 0.6800971 7.216548e-03 7.216548e-03 0.312686390 #> 9023  4511.0 0.6801343 0.6801343 7.215366e-03 7.215366e-03 0.312650322 #> 9024  4511.5 0.6801716 0.6801716 7.214191e-03 7.214191e-03 0.312614252 #> 9025  4512.0 0.6802088 0.6802088 7.213023e-03 7.213023e-03 0.312578181 #> 9026  4512.5 0.6802460 0.6802460 7.211862e-03 7.211862e-03 0.312542108 #> 9027  4513.0 0.6802833 0.6802833 7.210708e-03 7.210708e-03 0.312506034 #> 9028  4513.5 0.6803205 0.6803205 7.209561e-03 7.209561e-03 0.312469959 #> 9029  4514.0 0.6803577 0.6803577 7.208420e-03 7.208420e-03 0.312433883 #> 9030  4514.5 0.6803949 0.6803949 7.207287e-03 7.207287e-03 0.312397806 #> 9031  4515.0 0.6804321 0.6804321 7.206160e-03 7.206160e-03 0.312361728 #> 9032  4515.5 0.6804693 0.6804693 7.205040e-03 7.205040e-03 0.312325649 #> 9033  4516.0 0.6805065 0.6805065 7.203927e-03 7.203927e-03 0.312289570 #> 9034  4516.5 0.6805437 0.6805437 7.202821e-03 7.202821e-03 0.312253491 #> 9035  4517.0 0.6805809 0.6805809 7.201722e-03 7.201722e-03 0.312217411 #> 9036  4517.5 0.6806180 0.6806180 7.200630e-03 7.200630e-03 0.312181331 #> 9037  4518.0 0.6806552 0.6806552 7.199545e-03 7.199545e-03 0.312145250 #> 9038  4518.5 0.6806924 0.6806924 7.198466e-03 7.198466e-03 0.312109170 #> 9039  4519.0 0.6807295 0.6807295 7.197394e-03 7.197394e-03 0.312073090 #> 9040  4519.5 0.6807667 0.6807667 7.196329e-03 7.196329e-03 0.312037010 #> 9041  4520.0 0.6808038 0.6808038 7.195271e-03 7.195271e-03 0.312000931 #> 9042  4520.5 0.6808409 0.6808409 7.194220e-03 7.194220e-03 0.311964852 #> 9043  4521.0 0.6808781 0.6808781 7.193176e-03 7.193176e-03 0.311928773 #> 9044  4521.5 0.6809152 0.6809152 7.192138e-03 7.192138e-03 0.311892696 #> 9045  4522.0 0.6809523 0.6809523 7.191108e-03 7.191108e-03 0.311856619 #> 9046  4522.5 0.6809894 0.6809894 7.190084e-03 7.190084e-03 0.311820543 #> 9047  4523.0 0.6810265 0.6810265 7.189066e-03 7.189066e-03 0.311784468 #> 9048  4523.5 0.6810636 0.6810636 7.188056e-03 7.188056e-03 0.311748394 #> 9049  4524.0 0.6811006 0.6811006 7.187053e-03 7.187053e-03 0.311712321 #> 9050  4524.5 0.6811377 0.6811377 7.186056e-03 7.186056e-03 0.311676250 #> 9051  4525.0 0.6811748 0.6811748 7.185066e-03 7.185066e-03 0.311640180 #> 9052  4525.5 0.6812118 0.6812118 7.184083e-03 7.184083e-03 0.311604112 #> 9053  4526.0 0.6812488 0.6812488 7.183107e-03 7.183107e-03 0.311568045 #> 9054  4526.5 0.6812859 0.6812859 7.182137e-03 7.182137e-03 0.311531981 #> 9055  4527.0 0.6813229 0.6813229 7.181174e-03 7.181174e-03 0.311495918 #> 9056  4527.5 0.6813599 0.6813599 7.180218e-03 7.180218e-03 0.311459857 #> 9057  4528.0 0.6813969 0.6813969 7.179269e-03 7.179269e-03 0.311423799 #> 9058  4528.5 0.6814339 0.6814339 7.178327e-03 7.178327e-03 0.311387743 #> 9059  4529.0 0.6814709 0.6814709 7.177391e-03 7.177391e-03 0.311351689 #> 9060  4529.5 0.6815079 0.6815079 7.176462e-03 7.176462e-03 0.311315637 #> 9061  4530.0 0.6815449 0.6815449 7.175540e-03 7.175540e-03 0.311279589 #> 9062  4530.5 0.6815818 0.6815818 7.174625e-03 7.174625e-03 0.311243542 #> 9063  4531.0 0.6816188 0.6816188 7.173716e-03 7.173716e-03 0.311207499 #> 9064  4531.5 0.6816557 0.6816557 7.172814e-03 7.172814e-03 0.311171459 #> 9065  4532.0 0.6816927 0.6816927 7.171919e-03 7.171919e-03 0.311135422 #> 9066  4532.5 0.6817296 0.6817296 7.171030e-03 7.171030e-03 0.311099387 #> 9067  4533.0 0.6817665 0.6817665 7.170149e-03 7.170149e-03 0.311063357 #> 9068  4533.5 0.6818034 0.6818034 7.169274e-03 7.169274e-03 0.311027329 #> 9069  4534.0 0.6818403 0.6818403 7.168405e-03 7.168405e-03 0.310991305 #> 9070  4534.5 0.6818772 0.6818772 7.167544e-03 7.167544e-03 0.310955285 #> 9071  4535.0 0.6819140 0.6819140 7.166689e-03 7.166689e-03 0.310919268 #> 9072  4535.5 0.6819509 0.6819509 7.165841e-03 7.165841e-03 0.310883255 #> 9073  4536.0 0.6819878 0.6819878 7.164999e-03 7.164999e-03 0.310847246 #> 9074  4536.5 0.6820246 0.6820246 7.164165e-03 7.164165e-03 0.310811241 #> 9075  4537.0 0.6820614 0.6820614 7.163336e-03 7.163336e-03 0.310775240 #> 9076  4537.5 0.6820982 0.6820982 7.162515e-03 7.162515e-03 0.310739244 #> 9077  4538.0 0.6821350 0.6821350 7.161700e-03 7.161700e-03 0.310703252 #> 9078  4538.5 0.6821718 0.6821718 7.160892e-03 7.160892e-03 0.310667264 #> 9079  4539.0 0.6822086 0.6822086 7.160091e-03 7.160091e-03 0.310631281 #> 9080  4539.5 0.6822454 0.6822454 7.159296e-03 7.159296e-03 0.310595302 #> 9081  4540.0 0.6822822 0.6822822 7.158508e-03 7.158508e-03 0.310559329 #> 9082  4540.5 0.6823189 0.6823189 7.157727e-03 7.157727e-03 0.310523360 #> 9083  4541.0 0.6823557 0.6823557 7.156952e-03 7.156952e-03 0.310487396 #> 9084  4541.5 0.6823924 0.6823924 7.156184e-03 7.156184e-03 0.310451438 #> 9085  4542.0 0.6824291 0.6824291 7.155423e-03 7.155423e-03 0.310415484 #> 9086  4542.5 0.6824658 0.6824658 7.154668e-03 7.154668e-03 0.310379536 #> 9087  4543.0 0.6825025 0.6825025 7.153920e-03 7.153920e-03 0.310343594 #> 9088  4543.5 0.6825392 0.6825392 7.153179e-03 7.153179e-03 0.310307657 #> 9089  4544.0 0.6825758 0.6825758 7.152444e-03 7.152444e-03 0.310271726 #> 9090  4544.5 0.6826125 0.6826125 7.151716e-03 7.151716e-03 0.310235800 #> 9091  4545.0 0.6826491 0.6826491 7.150994e-03 7.150994e-03 0.310199880 #> 9092  4545.5 0.6826858 0.6826858 7.150279e-03 7.150279e-03 0.310163967 #> 9093  4546.0 0.6827224 0.6827224 7.149571e-03 7.149571e-03 0.310128059 #> 9094  4546.5 0.6827590 0.6827590 7.148869e-03 7.148869e-03 0.310092158 #> 9095  4547.0 0.6827956 0.6827956 7.148174e-03 7.148174e-03 0.310056263 #> 9096  4547.5 0.6828321 0.6828321 7.147486e-03 7.147486e-03 0.310020374 #> 9097  4548.0 0.6828687 0.6828687 7.146804e-03 7.146804e-03 0.309984492 #> 9098  4548.5 0.6829053 0.6829053 7.146129e-03 7.146129e-03 0.309948616 #> 9099  4549.0 0.6829418 0.6829418 7.145460e-03 7.145460e-03 0.309912747 #> 9100  4549.5 0.6829783 0.6829783 7.144798e-03 7.144798e-03 0.309876886 #> 9101  4550.0 0.6830148 0.6830148 7.144142e-03 7.144142e-03 0.309841031 #> 9102  4550.5 0.6830513 0.6830513 7.143493e-03 7.143493e-03 0.309805183 #> 9103  4551.0 0.6830878 0.6830878 7.142851e-03 7.142851e-03 0.309769342 #> 9104  4551.5 0.6831243 0.6831243 7.142215e-03 7.142215e-03 0.309733508 #> 9105  4552.0 0.6831607 0.6831607 7.141586e-03 7.141586e-03 0.309697682 #> 9106  4552.5 0.6831972 0.6831972 7.140963e-03 7.140963e-03 0.309661864 #> 9107  4553.0 0.6832336 0.6832336 7.140347e-03 7.140347e-03 0.309626052 #> 9108  4553.5 0.6832700 0.6832700 7.139738e-03 7.139738e-03 0.309590249 #> 9109  4554.0 0.6833064 0.6833064 7.139135e-03 7.139135e-03 0.309554453 #> 9110  4554.5 0.6833428 0.6833428 7.138538e-03 7.138538e-03 0.309518666 #> 9111  4555.0 0.6833792 0.6833792 7.137948e-03 7.137948e-03 0.309482886 #> 9112  4555.5 0.6834155 0.6834155 7.137365e-03 7.137365e-03 0.309447115 #> 9113  4556.0 0.6834519 0.6834519 7.136788e-03 7.136788e-03 0.309411351 #> 9114  4556.5 0.6834882 0.6834882 7.136218e-03 7.136218e-03 0.309375596 #> 9115  4557.0 0.6835245 0.6835245 7.135654e-03 7.135654e-03 0.309339850 #> 9116  4557.5 0.6835608 0.6835608 7.135097e-03 7.135097e-03 0.309304112 #> 9117  4558.0 0.6835971 0.6835971 7.134546e-03 7.134546e-03 0.309268382 #> 9118  4558.5 0.6836333 0.6836333 7.134002e-03 7.134002e-03 0.309232661 #> 9119  4559.0 0.6836696 0.6836696 7.133464e-03 7.133464e-03 0.309196949 #> 9120  4559.5 0.6837058 0.6837058 7.132933e-03 7.132933e-03 0.309161247 #> 9121  4560.0 0.6837420 0.6837420 7.132408e-03 7.132408e-03 0.309125553 #> 9122  4560.5 0.6837782 0.6837782 7.131890e-03 7.131890e-03 0.309089868 #> 9123  4561.0 0.6838144 0.6838144 7.131378e-03 7.131378e-03 0.309054193 #> 9124  4561.5 0.6838506 0.6838506 7.130873e-03 7.130873e-03 0.309018526 #> 9125  4562.0 0.6838868 0.6838868 7.130374e-03 7.130374e-03 0.308982870 #> 9126  4562.5 0.6839229 0.6839229 7.129881e-03 7.129881e-03 0.308947223 #> 9127  4563.0 0.6839590 0.6839590 7.129396e-03 7.129396e-03 0.308911585 #> 9128  4563.5 0.6839951 0.6839951 7.128916e-03 7.128916e-03 0.308875958 #> 9129  4564.0 0.6840312 0.6840312 7.128443e-03 7.128443e-03 0.308840340 #> 9130  4564.5 0.6840673 0.6840673 7.127977e-03 7.127977e-03 0.308804732 #> 9131  4565.0 0.6841033 0.6841033 7.127517e-03 7.127517e-03 0.308769134 #> 9132  4565.5 0.6841394 0.6841394 7.127063e-03 7.127063e-03 0.308733547 #> 9133  4566.0 0.6841754 0.6841754 7.126616e-03 7.126616e-03 0.308697970 #> 9134  4566.5 0.6842114 0.6842114 7.126176e-03 7.126176e-03 0.308662403 #> 9135  4567.0 0.6842474 0.6842474 7.125742e-03 7.125742e-03 0.308626847 #> 9136  4567.5 0.6842834 0.6842834 7.125314e-03 7.125314e-03 0.308591301 #> 9137  4568.0 0.6843193 0.6843193 7.124892e-03 7.124892e-03 0.308555766 #> 9138  4568.5 0.6843553 0.6843553 7.124478e-03 7.124478e-03 0.308520241 #> 9139  4569.0 0.6843912 0.6843912 7.124069e-03 7.124069e-03 0.308484728 #> 9140  4569.5 0.6844271 0.6844271 7.123667e-03 7.123667e-03 0.308449226 #> 9141  4570.0 0.6844630 0.6844630 7.123271e-03 7.123271e-03 0.308413734 #> 9142  4570.5 0.6844989 0.6844989 7.122882e-03 7.122882e-03 0.308378254 #> 9143  4571.0 0.6845347 0.6845347 7.122499e-03 7.122499e-03 0.308342786 #> 9144  4571.5 0.6845705 0.6845705 7.122123e-03 7.122123e-03 0.308307328 #> 9145  4572.0 0.6846064 0.6846064 7.121753e-03 7.121753e-03 0.308271882 #> 9146  4572.5 0.6846422 0.6846422 7.121389e-03 7.121389e-03 0.308236448 #> 9147  4573.0 0.6846779 0.6846779 7.121032e-03 7.121032e-03 0.308201026 #> 9148  4573.5 0.6847137 0.6847137 7.120681e-03 7.120681e-03 0.308165615 #> 9149  4574.0 0.6847494 0.6847494 7.120337e-03 7.120337e-03 0.308130216 #> 9150  4574.5 0.6847852 0.6847852 7.119999e-03 7.119999e-03 0.308094829 #> 9151  4575.0 0.6848209 0.6848209 7.119667e-03 7.119667e-03 0.308059455 #> 9152  4575.5 0.6848566 0.6848566 7.119342e-03 7.119342e-03 0.308024092 #> 9153  4576.0 0.6848922 0.6848922 7.119023e-03 7.119023e-03 0.307988742 #> 9154  4576.5 0.6849279 0.6849279 7.118710e-03 7.118710e-03 0.307953404 #> 9155  4577.0 0.6849635 0.6849635 7.118404e-03 7.118404e-03 0.307918079 #> 9156  4577.5 0.6849991 0.6849991 7.118104e-03 7.118104e-03 0.307882766 #> 9157  4578.0 0.6850347 0.6850347 7.117810e-03 7.117810e-03 0.307847466 #> 9158  4578.5 0.6850703 0.6850703 7.117523e-03 7.117523e-03 0.307812179 #> 9159  4579.0 0.6851059 0.6851059 7.117242e-03 7.117242e-03 0.307776905 #> 9160  4579.5 0.6851414 0.6851414 7.116968e-03 7.116968e-03 0.307741644 #> 9161  4580.0 0.6851769 0.6851769 7.116699e-03 7.116699e-03 0.307706396 #> 9162  4580.5 0.6852124 0.6852124 7.116437e-03 7.116437e-03 0.307671161 #> 9163  4581.0 0.6852479 0.6852479 7.116182e-03 7.116182e-03 0.307635939 #> 9164  4581.5 0.6852833 0.6852833 7.115933e-03 7.115933e-03 0.307600731 #> 9165  4582.0 0.6853188 0.6853188 7.115690e-03 7.115690e-03 0.307565536 #> 9166  4582.5 0.6853542 0.6853542 7.115453e-03 7.115453e-03 0.307530355 #> 9167  4583.0 0.6853896 0.6853896 7.115223e-03 7.115223e-03 0.307495188 #> 9168  4583.5 0.6854250 0.6854250 7.114999e-03 7.114999e-03 0.307460034 #> 9169  4584.0 0.6854603 0.6854603 7.114781e-03 7.114781e-03 0.307424894 #> 9170  4584.5 0.6854957 0.6854957 7.114570e-03 7.114570e-03 0.307389768 #> 9171  4585.0 0.6855310 0.6855310 7.114364e-03 7.114364e-03 0.307354657 #> 9172  4585.5 0.6855663 0.6855663 7.114166e-03 7.114166e-03 0.307319559 #> 9173  4586.0 0.6856016 0.6856016 7.113973e-03 7.113973e-03 0.307284476 #> 9174  4586.5 0.6856368 0.6856368 7.113787e-03 7.113787e-03 0.307249407 #> 9175  4587.0 0.6856720 0.6856720 7.113607e-03 7.113607e-03 0.307214353 #> 9176  4587.5 0.6857073 0.6857073 7.113433e-03 7.113433e-03 0.307179313 #> 9177  4588.0 0.6857424 0.6857424 7.113265e-03 7.113265e-03 0.307144288 #> 9178  4588.5 0.6857776 0.6857776 7.113104e-03 7.113104e-03 0.307109278 #> 9179  4589.0 0.6858128 0.6858128 7.112949e-03 7.112949e-03 0.307074282 #> 9180  4589.5 0.6858479 0.6858479 7.112801e-03 7.112801e-03 0.307039302 #> 9181  4590.0 0.6858830 0.6858830 7.112658e-03 7.112658e-03 0.307004336 #> 9182  4590.5 0.6859181 0.6859181 7.112522e-03 7.112522e-03 0.306969386 #> 9183  4591.0 0.6859532 0.6859532 7.112392e-03 7.112392e-03 0.306934451 #> 9184  4591.5 0.6859882 0.6859882 7.112268e-03 7.112268e-03 0.306899531 #> 9185  4592.0 0.6860232 0.6860232 7.112151e-03 7.112151e-03 0.306864627 #> 9186  4592.5 0.6860582 0.6860582 7.112039e-03 7.112039e-03 0.306829739 #> 9187  4593.0 0.6860932 0.6860932 7.111934e-03 7.111934e-03 0.306794865 #> 9188  4593.5 0.6861282 0.6861282 7.111835e-03 7.111835e-03 0.306760008 #> 9189  4594.0 0.6861631 0.6861631 7.111743e-03 7.111743e-03 0.306725167 #> 9190  4594.5 0.6861980 0.6861980 7.111656e-03 7.111656e-03 0.306690341 #> 9191  4595.0 0.6862329 0.6862329 7.111576e-03 7.111576e-03 0.306655532 #> 9192  4595.5 0.6862678 0.6862678 7.111502e-03 7.111502e-03 0.306620738 #> 9193  4596.0 0.6863026 0.6863026 7.111434e-03 7.111434e-03 0.306585961 #> 9194  4596.5 0.6863374 0.6863374 7.111373e-03 7.111373e-03 0.306551200 #> 9195  4597.0 0.6863722 0.6863722 7.111317e-03 7.111317e-03 0.306516456 #> 9196  4597.5 0.6864070 0.6864070 7.111268e-03 7.111268e-03 0.306481727 #> 9197  4598.0 0.6864418 0.6864418 7.111225e-03 7.111225e-03 0.306447016 #> 9198  4598.5 0.6864765 0.6864765 7.111188e-03 7.111188e-03 0.306412321 #> 9199  4599.0 0.6865112 0.6865112 7.111157e-03 7.111157e-03 0.306377643 #> 9200  4599.5 0.6865459 0.6865459 7.111133e-03 7.111133e-03 0.306342982 #> 9201  4600.0 0.6865805 0.6865805 7.111114e-03 7.111114e-03 0.306308338 #> 9202  4600.5 0.6866152 0.6866152 7.111102e-03 7.111102e-03 0.306273711 #> 9203  4601.0 0.6866498 0.6866498 7.111096e-03 7.111096e-03 0.306239101 #> 9204  4601.5 0.6866844 0.6866844 7.111096e-03 7.111096e-03 0.306204509 #> 9205  4602.0 0.6867190 0.6867190 7.111102e-03 7.111102e-03 0.306169933 #> 9206  4602.5 0.6867535 0.6867535 7.111115e-03 7.111115e-03 0.306135375 #> 9207  4603.0 0.6867880 0.6867880 7.111133e-03 7.111133e-03 0.306100835 #> 9208  4603.5 0.6868225 0.6868225 7.111158e-03 7.111158e-03 0.306066312 #> 9209  4604.0 0.6868570 0.6868570 7.111189e-03 7.111189e-03 0.306031807 #> 9210  4604.5 0.6868915 0.6868915 7.111225e-03 7.111225e-03 0.305997320 #> 9211  4605.0 0.6869259 0.6869259 7.111268e-03 7.111268e-03 0.305962851 #> 9212  4605.5 0.6869603 0.6869603 7.111318e-03 7.111318e-03 0.305928400 #> 9213  4606.0 0.6869947 0.6869947 7.111373e-03 7.111373e-03 0.305893967 #> 9214  4606.5 0.6870290 0.6870290 7.111434e-03 7.111434e-03 0.305859552 #> 9215  4607.0 0.6870633 0.6870633 7.111502e-03 7.111502e-03 0.305825155 #> 9216  4607.5 0.6870976 0.6870976 7.111575e-03 7.111575e-03 0.305790776 #> 9217  4608.0 0.6871319 0.6871319 7.111655e-03 7.111655e-03 0.305756417 #> 9218  4608.5 0.6871662 0.6871662 7.111741e-03 7.111741e-03 0.305722075 #> 9219  4609.0 0.6872004 0.6872004 7.111833e-03 7.111833e-03 0.305687753 #> 9220  4609.5 0.6872346 0.6872346 7.111931e-03 7.111931e-03 0.305653449 #> 9221  4610.0 0.6872688 0.6872688 7.112035e-03 7.112035e-03 0.305619163 #> 9222  4610.5 0.6873030 0.6873030 7.112145e-03 7.112145e-03 0.305584897 #> 9223  4611.0 0.6873371 0.6873371 7.112261e-03 7.112261e-03 0.305550650 #> 9224  4611.5 0.6873712 0.6873712 7.112383e-03 7.112383e-03 0.305516422 #> 9225  4612.0 0.6874053 0.6874053 7.112511e-03 7.112511e-03 0.305482213 #> 9226  4612.5 0.6874393 0.6874393 7.112646e-03 7.112646e-03 0.305448024 #> 9227  4613.0 0.6874734 0.6874734 7.112786e-03 7.112786e-03 0.305413853 #> 9228  4613.5 0.6875074 0.6875074 7.112933e-03 7.112933e-03 0.305379703 #> 9229  4614.0 0.6875413 0.6875413 7.113085e-03 7.113085e-03 0.305345571 #> 9230  4614.5 0.6875753 0.6875753 7.113244e-03 7.113244e-03 0.305311460 #> 9231  4615.0 0.6876092 0.6876092 7.113408e-03 7.113408e-03 0.305277368 #> 9232  4615.5 0.6876431 0.6876431 7.113579e-03 7.113579e-03 0.305243296 #> 9233  4616.0 0.6876770 0.6876770 7.113756e-03 7.113756e-03 0.305209244 #> 9234  4616.5 0.6877108 0.6877108 7.113938e-03 7.113938e-03 0.305175212 #> 9235  4617.0 0.6877447 0.6877447 7.114127e-03 7.114127e-03 0.305141200 #> 9236  4617.5 0.6877785 0.6877785 7.114322e-03 7.114322e-03 0.305107209 #> 9237  4618.0 0.6878122 0.6878122 7.114522e-03 7.114522e-03 0.305073237 #> 9238  4618.5 0.6878460 0.6878460 7.114729e-03 7.114729e-03 0.305039286 #> 9239  4619.0 0.6878797 0.6878797 7.114942e-03 7.114942e-03 0.305005356 #> 9240  4619.5 0.6879134 0.6879134 7.115161e-03 7.115161e-03 0.304971445 #> 9241  4620.0 0.6879471 0.6879471 7.115386e-03 7.115386e-03 0.304937556 #> 9242  4620.5 0.6879807 0.6879807 7.115616e-03 7.115616e-03 0.304903687 #> 9243  4621.0 0.6880143 0.6880143 7.115853e-03 7.115853e-03 0.304869840 #> 9244  4621.5 0.6880479 0.6880479 7.116096e-03 7.116096e-03 0.304836013 #> 9245  4622.0 0.6880814 0.6880814 7.116344e-03 7.116344e-03 0.304802207 #> 9246  4622.5 0.6881150 0.6881150 7.116599e-03 7.116599e-03 0.304768422 #> 9247  4623.0 0.6881485 0.6881485 7.116860e-03 7.116860e-03 0.304734658 #> 9248  4623.5 0.6881820 0.6881820 7.117126e-03 7.117126e-03 0.304700916 #> 9249  4624.0 0.6882154 0.6882154 7.117399e-03 7.117399e-03 0.304667195 #> 9250  4624.5 0.6882488 0.6882488 7.117678e-03 7.117678e-03 0.304633496 #> 9251  4625.0 0.6882822 0.6882822 7.117962e-03 7.117962e-03 0.304599818 #> 9252  4625.5 0.6883156 0.6883156 7.118253e-03 7.118253e-03 0.304566161 #> 9253  4626.0 0.6883489 0.6883489 7.118549e-03 7.118549e-03 0.304532527 #> 9254  4626.5 0.6883822 0.6883822 7.118851e-03 7.118851e-03 0.304498914 #> 9255  4627.0 0.6884155 0.6884155 7.119160e-03 7.119160e-03 0.304465323 #> 9256  4627.5 0.6884488 0.6884488 7.119474e-03 7.119474e-03 0.304431754 #> 9257  4628.0 0.6884820 0.6884820 7.119794e-03 7.119794e-03 0.304398207 #> 9258  4628.5 0.6885152 0.6885152 7.120120e-03 7.120120e-03 0.304364682 #> 9259  4629.0 0.6885484 0.6885484 7.120452e-03 7.120452e-03 0.304331179 #> 9260  4629.5 0.6885815 0.6885815 7.120790e-03 7.120790e-03 0.304297699 #> 9261  4630.0 0.6886146 0.6886146 7.121134e-03 7.121134e-03 0.304264241 #> 9262  4630.5 0.6886477 0.6886477 7.121484e-03 7.121484e-03 0.304230806 #> 9263  4631.0 0.6886808 0.6886808 7.121839e-03 7.121839e-03 0.304197393 #> 9264  4631.5 0.6887138 0.6887138 7.122201e-03 7.122201e-03 0.304164003 #> 9265  4632.0 0.6887468 0.6887468 7.122568e-03 7.122568e-03 0.304130636 #> 9266  4632.5 0.6887798 0.6887798 7.122942e-03 7.122942e-03 0.304097292 #> 9267  4633.0 0.6888127 0.6888127 7.123321e-03 7.123321e-03 0.304063970 #> 9268  4633.5 0.6888456 0.6888456 7.123706e-03 7.123706e-03 0.304030672 #> 9269  4634.0 0.6888785 0.6888785 7.124097e-03 7.124097e-03 0.303997397 #> 9270  4634.5 0.6889114 0.6889114 7.124494e-03 7.124494e-03 0.303964144 #> 9271  4635.0 0.6889442 0.6889442 7.124897e-03 7.124897e-03 0.303930916 #> 9272  4635.5 0.6889770 0.6889770 7.125305e-03 7.125305e-03 0.303897710 #> 9273  4636.0 0.6890098 0.6890098 7.125720e-03 7.125720e-03 0.303864528 #> 9274  4636.5 0.6890425 0.6890425 7.126140e-03 7.126140e-03 0.303831370 #> 9275  4637.0 0.6890752 0.6890752 7.126566e-03 7.126566e-03 0.303798235 #> 9276  4637.5 0.6891079 0.6891079 7.126998e-03 7.126998e-03 0.303765124 #> 9277  4638.0 0.6891405 0.6891405 7.127436e-03 7.127436e-03 0.303732036 #> 9278  4638.5 0.6891731 0.6891731 7.127880e-03 7.127880e-03 0.303698973 #> 9279  4639.0 0.6892057 0.6892057 7.128329e-03 7.128329e-03 0.303665933 #> 9280  4639.5 0.6892383 0.6892383 7.128785e-03 7.128785e-03 0.303632918 #> 9281  4640.0 0.6892708 0.6892708 7.129246e-03 7.129246e-03 0.303599927 #> 9282  4640.5 0.6893033 0.6893033 7.129713e-03 7.129713e-03 0.303566959 #> 9283  4641.0 0.6893358 0.6893358 7.130186e-03 7.130186e-03 0.303534017 #> 9284  4641.5 0.6893682 0.6893682 7.130665e-03 7.130665e-03 0.303501098 #> 9285  4642.0 0.6894006 0.6894006 7.131149e-03 7.131149e-03 0.303468204 #> 9286  4642.5 0.6894330 0.6894330 7.131639e-03 7.131639e-03 0.303435335 #> 9287  4643.0 0.6894654 0.6894654 7.132136e-03 7.132136e-03 0.303402490 #> 9288  4643.5 0.6894977 0.6894977 7.132637e-03 7.132637e-03 0.303369671 #> 9289  4644.0 0.6895300 0.6895300 7.133145e-03 7.133145e-03 0.303336875 #> 9290  4644.5 0.6895622 0.6895622 7.133659e-03 7.133659e-03 0.303304105 #> 9291  4645.0 0.6895945 0.6895945 7.134178e-03 7.134178e-03 0.303271360 #> 9292  4645.5 0.6896267 0.6896267 7.134703e-03 7.134703e-03 0.303238640 #> 9293  4646.0 0.6896588 0.6896588 7.135234e-03 7.135234e-03 0.303205945 #> 9294  4646.5 0.6896910 0.6896910 7.135771e-03 7.135771e-03 0.303173275 #> 9295  4647.0 0.6897231 0.6897231 7.136313e-03 7.136313e-03 0.303140631 #> 9296  4647.5 0.6897551 0.6897551 7.136861e-03 7.136861e-03 0.303108011 #> 9297  4648.0 0.6897872 0.6897872 7.137415e-03 7.137415e-03 0.303075418 #> 9298  4648.5 0.6898192 0.6898192 7.137975e-03 7.137975e-03 0.303042850 #> 9299  4649.0 0.6898512 0.6898512 7.138540e-03 7.138540e-03 0.303010308 #> 9300  4649.5 0.6898831 0.6898831 7.139111e-03 7.139111e-03 0.302977791 #> 9301  4650.0 0.6899150 0.6899150 7.139688e-03 7.139688e-03 0.302945300 #> 9302  4650.5 0.6899469 0.6899469 7.140271e-03 7.140271e-03 0.302912835 #> 9303  4651.0 0.6899787 0.6899787 7.140859e-03 7.140859e-03 0.302880396 #> 9304  4651.5 0.6900106 0.6900106 7.141454e-03 7.141454e-03 0.302847984 #> 9305  4652.0 0.6900423 0.6900423 7.142053e-03 7.142053e-03 0.302815597 #> 9306  4652.5 0.6900741 0.6900741 7.142659e-03 7.142659e-03 0.302783236 #> 9307  4653.0 0.6901058 0.6901058 7.143270e-03 7.143270e-03 0.302750902 #> 9308  4653.5 0.6901375 0.6901375 7.143887e-03 7.143887e-03 0.302718595 #> 9309  4654.0 0.6901692 0.6901692 7.144510e-03 7.144510e-03 0.302686313 #> 9310  4654.5 0.6902008 0.6902008 7.145139e-03 7.145139e-03 0.302654059 #> 9311  4655.0 0.6902324 0.6902324 7.145773e-03 7.145773e-03 0.302621831 #> 9312  4655.5 0.6902640 0.6902640 7.146413e-03 7.146413e-03 0.302589629 #> 9313  4656.0 0.6902955 0.6902955 7.147058e-03 7.147058e-03 0.302557455 #> 9314  4656.5 0.6903270 0.6903270 7.147709e-03 7.147709e-03 0.302525307 #> 9315  4657.0 0.6903584 0.6903584 7.148366e-03 7.148366e-03 0.302493186 #> 9316  4657.5 0.6903899 0.6903899 7.149029e-03 7.149029e-03 0.302461093 #> 9317  4658.0 0.6904213 0.6904213 7.149697e-03 7.149697e-03 0.302429026 #> 9318  4658.5 0.6904526 0.6904526 7.150371e-03 7.150371e-03 0.302396987 #> 9319  4659.0 0.6904840 0.6904840 7.151051e-03 7.151051e-03 0.302364975 #> 9320  4659.5 0.6905153 0.6905153 7.151736e-03 7.151736e-03 0.302332990 #> 9321  4660.0 0.6905465 0.6905465 7.152427e-03 7.152427e-03 0.302301033 #> 9322  4660.5 0.6905778 0.6905778 7.153123e-03 7.153123e-03 0.302269104 #> 9323  4661.0 0.6906090 0.6906090 7.153826e-03 7.153826e-03 0.302237202 #> 9324  4661.5 0.6906401 0.6906401 7.154534e-03 7.154534e-03 0.302205327 #> 9325  4662.0 0.6906713 0.6906713 7.155247e-03 7.155247e-03 0.302173481 #> 9326  4662.5 0.6907024 0.6907024 7.155966e-03 7.155966e-03 0.302141662 #> 9327  4663.0 0.6907334 0.6907334 7.156691e-03 7.156691e-03 0.302109871 #> 9328  4663.5 0.6907645 0.6907645 7.157421e-03 7.157421e-03 0.302078109 #> 9329  4664.0 0.6907955 0.6907955 7.158158e-03 7.158158e-03 0.302046374 #> 9330  4664.5 0.6908264 0.6908264 7.158899e-03 7.158899e-03 0.302014668 #> 9331  4665.0 0.6908574 0.6908574 7.159646e-03 7.159646e-03 0.301982989 #> 9332  4665.5 0.6908883 0.6908883 7.160399e-03 7.160399e-03 0.301951340 #> 9333  4666.0 0.6909191 0.6909191 7.161158e-03 7.161158e-03 0.301919718 #> 9334  4666.5 0.6909500 0.6909500 7.161922e-03 7.161922e-03 0.301888125 #> 9335  4667.0 0.6909807 0.6909807 7.162692e-03 7.162692e-03 0.301856561 #> 9336  4667.5 0.6910115 0.6910115 7.163467e-03 7.163467e-03 0.301825025 #> 9337  4668.0 0.6910422 0.6910422 7.164248e-03 7.164248e-03 0.301793518 #> 9338  4668.5 0.6910729 0.6910729 7.165034e-03 7.165034e-03 0.301762040 #> 9339  4669.0 0.6911036 0.6911036 7.165826e-03 7.165826e-03 0.301730590 #> 9340  4669.5 0.6911342 0.6911342 7.166624e-03 7.166624e-03 0.301699170 #> 9341  4670.0 0.6911648 0.6911648 7.167427e-03 7.167427e-03 0.301667779 #> 9342  4670.5 0.6911953 0.6911953 7.168236e-03 7.168236e-03 0.301636417 #> 9343  4671.0 0.6912259 0.6912259 7.169050e-03 7.169050e-03 0.301605084 #> 9344  4671.5 0.6912563 0.6912563 7.169870e-03 7.169870e-03 0.301573780 #> 9345  4672.0 0.6912868 0.6912868 7.170696e-03 7.170696e-03 0.301542505 #> 9346  4672.5 0.6913172 0.6913172 7.171527e-03 7.171527e-03 0.301511261 #> 9347  4673.0 0.6913476 0.6913476 7.172363e-03 7.172363e-03 0.301480045 #> 9348  4673.5 0.6913779 0.6913779 7.173206e-03 7.173206e-03 0.301448859 #> 9349  4674.0 0.6914082 0.6914082 7.174053e-03 7.174053e-03 0.301417703 #> 9350  4674.5 0.6914385 0.6914385 7.174906e-03 7.174906e-03 0.301386577 #> 9351  4675.0 0.6914688 0.6914688 7.175765e-03 7.175765e-03 0.301355480 #> 9352  4675.5 0.6914990 0.6914990 7.176629e-03 7.176629e-03 0.301324413 #> 9353  4676.0 0.6915291 0.6915291 7.177499e-03 7.177499e-03 0.301293377 #> 9354  4676.5 0.6915593 0.6915593 7.178374e-03 7.178374e-03 0.301262370 #> 9355  4677.0 0.6915894 0.6915894 7.179255e-03 7.179255e-03 0.301231394 #> 9356  4677.5 0.6916194 0.6916194 7.180141e-03 7.180141e-03 0.301200447 #> 9357  4678.0 0.6916494 0.6916494 7.181033e-03 7.181033e-03 0.301169531 #> 9358  4678.5 0.6916794 0.6916794 7.181930e-03 7.181930e-03 0.301138645 #> 9359  4679.0 0.6917094 0.6917094 7.182833e-03 7.182833e-03 0.301107790 #> 9360  4679.5 0.6917393 0.6917393 7.183741e-03 7.183741e-03 0.301076965 #> 9361  4680.0 0.6917692 0.6917692 7.184655e-03 7.184655e-03 0.301046171 #> 9362  4680.5 0.6917990 0.6917990 7.185574e-03 7.185574e-03 0.301015408 #> 9363  4681.0 0.6918288 0.6918288 7.186499e-03 7.186499e-03 0.300984675 #> 9364  4681.5 0.6918586 0.6918586 7.187429e-03 7.187429e-03 0.300953973 #> 9365  4682.0 0.6918883 0.6918883 7.188365e-03 7.188365e-03 0.300923302 #> 9366  4682.5 0.6919180 0.6919180 7.189306e-03 7.189306e-03 0.300892662 #> 9367  4683.0 0.6919477 0.6919477 7.190253e-03 7.190253e-03 0.300862053 #> 9368  4683.5 0.6919773 0.6919773 7.191205e-03 7.191205e-03 0.300831475 #> 9369  4684.0 0.6920069 0.6920069 7.192162e-03 7.192162e-03 0.300800928 #> 9370  4684.5 0.6920365 0.6920365 7.193125e-03 7.193125e-03 0.300770413 #> 9371  4685.0 0.6920660 0.6920660 7.194093e-03 7.194093e-03 0.300739929 #> 9372  4685.5 0.6920955 0.6920955 7.195067e-03 7.195067e-03 0.300709476 #> 9373  4686.0 0.6921249 0.6921249 7.196046e-03 7.196046e-03 0.300679055 #> 9374  4686.5 0.6921543 0.6921543 7.197031e-03 7.197031e-03 0.300648665 #> 9375  4687.0 0.6921837 0.6921837 7.198021e-03 7.198021e-03 0.300618307 #> 9376  4687.5 0.6922130 0.6922130 7.199017e-03 7.199017e-03 0.300587981 #> 9377  4688.0 0.6922423 0.6922423 7.200018e-03 7.200018e-03 0.300557687 #> 9378  4688.5 0.6922716 0.6922716 7.201024e-03 7.201024e-03 0.300527424 #> 9379  4689.0 0.6923008 0.6923008 7.202036e-03 7.202036e-03 0.300497193 #> 9380  4689.5 0.6923300 0.6923300 7.203053e-03 7.203053e-03 0.300466995 #> 9381  4690.0 0.6923591 0.6923591 7.204075e-03 7.204075e-03 0.300436828 #> 9382  4690.5 0.6923882 0.6923882 7.205103e-03 7.205103e-03 0.300406694 #> 9383  4691.0 0.6924173 0.6924173 7.206137e-03 7.206137e-03 0.300376592 #> 9384  4691.5 0.6924463 0.6924463 7.207175e-03 7.207175e-03 0.300346522 #> 9385  4692.0 0.6924753 0.6924753 7.208219e-03 7.208219e-03 0.300316484 #> 9386  4692.5 0.6925043 0.6925043 7.209269e-03 7.209269e-03 0.300286479 #> 9387  4693.0 0.6925332 0.6925332 7.210323e-03 7.210323e-03 0.300256507 #> 9388  4693.5 0.6925620 0.6925620 7.211384e-03 7.211384e-03 0.300226567 #> 9389  4694.0 0.6925909 0.6925909 7.212449e-03 7.212449e-03 0.300196660 #> 9390  4694.5 0.6926197 0.6926197 7.213520e-03 7.213520e-03 0.300166785 #> 9391  4695.0 0.6926485 0.6926485 7.214596e-03 7.214596e-03 0.300136944 #> 9392  4695.5 0.6926772 0.6926772 7.215678e-03 7.215678e-03 0.300107135 #> 9393  4696.0 0.6927059 0.6927059 7.216765e-03 7.216765e-03 0.300077359 #> 9394  4696.5 0.6927345 0.6927345 7.217857e-03 7.217857e-03 0.300047616 #> 9395  4697.0 0.6927631 0.6927631 7.218955e-03 7.218955e-03 0.300017907 #> 9396  4697.5 0.6927917 0.6927917 7.220058e-03 7.220058e-03 0.299988230 #> 9397  4698.0 0.6928202 0.6928202 7.221166e-03 7.221166e-03 0.299958587 #> 9398  4698.5 0.6928487 0.6928487 7.222279e-03 7.222279e-03 0.299928977 #> 9399  4699.0 0.6928772 0.6928772 7.223398e-03 7.223398e-03 0.299899401 #> 9400  4699.5 0.6929056 0.6929056 7.224522e-03 7.224522e-03 0.299869858 #> 9401  4700.0 0.6929340 0.6929340 7.225652e-03 7.225652e-03 0.299840348 #> 9402  4700.5 0.6929623 0.6929623 7.226787e-03 7.226787e-03 0.299810873 #> 9403  4701.0 0.6929906 0.6929906 7.227927e-03 7.227927e-03 0.299781430 #> 9404  4701.5 0.6930189 0.6930189 7.229072e-03 7.229072e-03 0.299752022 #> 9405  4702.0 0.6930471 0.6930471 7.230223e-03 7.230223e-03 0.299722647 #> 9406  4702.5 0.6930753 0.6930753 7.231379e-03 7.231379e-03 0.299693307 #> 9407  4703.0 0.6931035 0.6931035 7.232540e-03 7.232540e-03 0.299664000 #> 9408  4703.5 0.6931316 0.6931316 7.233706e-03 7.233706e-03 0.299634728 #> 9409  4704.0 0.6931596 0.6931596 7.234878e-03 7.234878e-03 0.299605489 #> 9410  4704.5 0.6931877 0.6931877 7.236055e-03 7.236055e-03 0.299576285 #> 9411  4705.0 0.6932156 0.6932156 7.237238e-03 7.237238e-03 0.299547115 #> 9412  4705.5 0.6932436 0.6932436 7.238425e-03 7.238425e-03 0.299517979 #> 9413  4706.0 0.6932715 0.6932715 7.239618e-03 7.239618e-03 0.299488877 #> 9414  4706.5 0.6932994 0.6932994 7.240816e-03 7.240816e-03 0.299459811 #> 9415  4707.0 0.6933272 0.6933272 7.242019e-03 7.242019e-03 0.299430778 #> 9416  4707.5 0.6933550 0.6933550 7.243228e-03 7.243228e-03 0.299401780 #> 9417  4708.0 0.6933827 0.6933827 7.244441e-03 7.244441e-03 0.299372817 #> 9418  4708.5 0.6934105 0.6934105 7.245660e-03 7.245660e-03 0.299343889 #> 9419  4709.0 0.6934381 0.6934381 7.246885e-03 7.246885e-03 0.299314996 #> 9420  4709.5 0.6934657 0.6934657 7.248114e-03 7.248114e-03 0.299286137 #> 9421  4710.0 0.6934933 0.6934933 7.249349e-03 7.249349e-03 0.299257313 #> 9422  4710.5 0.6935209 0.6935209 7.250588e-03 7.250588e-03 0.299228525 #> 9423  4711.0 0.6935484 0.6935484 7.251833e-03 7.251833e-03 0.299199771 #> 9424  4711.5 0.6935759 0.6935759 7.253084e-03 7.253084e-03 0.299171053 #> 9425  4712.0 0.6936033 0.6936033 7.254339e-03 7.254339e-03 0.299142369 #> 9426  4712.5 0.6936307 0.6936307 7.255600e-03 7.255600e-03 0.299113722 #> 9427  4713.0 0.6936580 0.6936580 7.256866e-03 7.256866e-03 0.299085109 #> 9428  4713.5 0.6936853 0.6936853 7.258136e-03 7.258136e-03 0.299056532 #> 9429  4714.0 0.6937126 0.6937126 7.259413e-03 7.259413e-03 0.299027990 #> 9430  4714.5 0.6937398 0.6937398 7.260694e-03 7.260694e-03 0.298999484 #> 9431  4715.0 0.6937670 0.6937670 7.261980e-03 7.261980e-03 0.298971014 #> 9432  4715.5 0.6937941 0.6937941 7.263272e-03 7.263272e-03 0.298942579 #> 9433  4716.0 0.6938213 0.6938213 7.264569e-03 7.264569e-03 0.298914181 #> 9434  4716.5 0.6938483 0.6938483 7.265871e-03 7.265871e-03 0.298885818 #> 9435  4717.0 0.6938753 0.6938753 7.267178e-03 7.267178e-03 0.298857490 #> 9436  4717.5 0.6939023 0.6939023 7.268490e-03 7.268490e-03 0.298829199 #> 9437  4718.0 0.6939292 0.6939292 7.269808e-03 7.269808e-03 0.298800944 #> 9438  4718.5 0.6939561 0.6939561 7.271130e-03 7.271130e-03 0.298772725 #> 9439  4719.0 0.6939830 0.6939830 7.272458e-03 7.272458e-03 0.298744543 #> 9440  4719.5 0.6940098 0.6940098 7.273790e-03 7.273790e-03 0.298716396 #> 9441  4720.0 0.6940366 0.6940366 7.275128e-03 7.275128e-03 0.298688286 #> 9442  4720.5 0.6940633 0.6940633 7.276471e-03 7.276471e-03 0.298660212 #> 9443  4721.0 0.6940900 0.6940900 7.277819e-03 7.277819e-03 0.298632175 #> 9444  4721.5 0.6941167 0.6941167 7.279173e-03 7.279173e-03 0.298604174 #> 9445  4722.0 0.6941433 0.6941433 7.280531e-03 7.280531e-03 0.298576210 #> 9446  4722.5 0.6941698 0.6941698 7.281894e-03 7.281894e-03 0.298548282 #> 9447  4723.0 0.6941963 0.6941963 7.283263e-03 7.283263e-03 0.298520391 #> 9448  4723.5 0.6942228 0.6942228 7.284636e-03 7.284636e-03 0.298492537 #> 9449  4724.0 0.6942493 0.6942493 7.286015e-03 7.286015e-03 0.298464720 #> 9450  4724.5 0.6942757 0.6942757 7.287399e-03 7.287399e-03 0.298436940 #> 9451  4725.0 0.6943020 0.6943020 7.288787e-03 7.288787e-03 0.298409196 #> 9452  4725.5 0.6943283 0.6943283 7.290181e-03 7.290181e-03 0.298381490 #> 9453  4726.0 0.6943546 0.6943546 7.291580e-03 7.291580e-03 0.298353821 #> 9454  4726.5 0.6943808 0.6943808 7.292984e-03 7.292984e-03 0.298326189 #> 9455  4727.0 0.6944070 0.6944070 7.294393e-03 7.294393e-03 0.298298594 #> 9456  4727.5 0.6944332 0.6944332 7.295807e-03 7.295807e-03 0.298271037 #> 9457  4728.0 0.6944593 0.6944593 7.297226e-03 7.297226e-03 0.298243517 #> 9458  4728.5 0.6944853 0.6944853 7.298650e-03 7.298650e-03 0.298216034 #> 9459  4729.0 0.6945113 0.6945113 7.300080e-03 7.300080e-03 0.298188589 #> 9460  4729.5 0.6945373 0.6945373 7.301514e-03 7.301514e-03 0.298161182 #> 9461  4730.0 0.6945632 0.6945632 7.302953e-03 7.302953e-03 0.298133812 #> 9462  4730.5 0.6945891 0.6945891 7.304397e-03 7.304397e-03 0.298106480 #> 9463  4731.0 0.6946150 0.6946150 7.305846e-03 7.305846e-03 0.298079185 #> 9464  4731.5 0.6946408 0.6946408 7.307301e-03 7.307301e-03 0.298051929 #> 9465  4732.0 0.6946665 0.6946665 7.308760e-03 7.308760e-03 0.298024710 #> 9466  4732.5 0.6946922 0.6946922 7.310224e-03 7.310224e-03 0.297997529 #> 9467  4733.0 0.6947179 0.6947179 7.311693e-03 7.311693e-03 0.297970387 #> 9468  4733.5 0.6947436 0.6947436 7.313167e-03 7.313167e-03 0.297943282 #> 9469  4734.0 0.6947691 0.6947691 7.314647e-03 7.314647e-03 0.297916216 #> 9470  4734.5 0.6947947 0.6947947 7.316131e-03 7.316131e-03 0.297889187 #> 9471  4735.0 0.6948202 0.6948202 7.317620e-03 7.317620e-03 0.297862197 #> 9472  4735.5 0.6948456 0.6948456 7.319114e-03 7.319114e-03 0.297835246 #> 9473  4736.0 0.6948711 0.6948711 7.320613e-03 7.320613e-03 0.297808333 #> 9474  4736.5 0.6948964 0.6948964 7.322117e-03 7.322117e-03 0.297781458 #> 9475  4737.0 0.6949218 0.6949218 7.323626e-03 7.323626e-03 0.297754622 #> 9476  4737.5 0.6949470 0.6949470 7.325140e-03 7.325140e-03 0.297727824 #> 9477  4738.0 0.6949723 0.6949723 7.326659e-03 7.326659e-03 0.297701065 #> 9478  4738.5 0.6949975 0.6949975 7.328182e-03 7.328182e-03 0.297674345 #> 9479  4739.0 0.6950226 0.6950226 7.329711e-03 7.329711e-03 0.297647664 #> 9480  4739.5 0.6950477 0.6950477 7.331245e-03 7.331245e-03 0.297621021 #> 9481  4740.0 0.6950728 0.6950728 7.332783e-03 7.332783e-03 0.297594418 #> 9482  4740.5 0.6950978 0.6950978 7.334327e-03 7.334327e-03 0.297567853 #> 9483  4741.0 0.6951228 0.6951228 7.335875e-03 7.335875e-03 0.297541327 #> 9484  4741.5 0.6951477 0.6951477 7.337429e-03 7.337429e-03 0.297514841 #> 9485  4742.0 0.6951726 0.6951726 7.338987e-03 7.338987e-03 0.297488393 #> 9486  4742.5 0.6951975 0.6951975 7.340550e-03 7.340550e-03 0.297461985 #> 9487  4743.0 0.6952223 0.6952223 7.342118e-03 7.342118e-03 0.297435616 #> 9488  4743.5 0.6952470 0.6952470 7.343691e-03 7.343691e-03 0.297409287 #> 9489  4744.0 0.6952717 0.6952717 7.345268e-03 7.345268e-03 0.297382997 #> 9490  4744.5 0.6952964 0.6952964 7.346851e-03 7.346851e-03 0.297356746 #> 9491  4745.0 0.6953210 0.6953210 7.348438e-03 7.348438e-03 0.297330535 #> 9492  4745.5 0.6953456 0.6953456 7.350031e-03 7.350031e-03 0.297304364 #> 9493  4746.0 0.6953701 0.6953701 7.351628e-03 7.351628e-03 0.297278232 #> 9494  4746.5 0.6953946 0.6953946 7.353230e-03 7.353230e-03 0.297252140 #> 9495  4747.0 0.6954191 0.6954191 7.354837e-03 7.354837e-03 0.297226087 #> 9496  4747.5 0.6954435 0.6954435 7.356449e-03 7.356449e-03 0.297200075 #> 9497  4748.0 0.6954678 0.6954678 7.358065e-03 7.358065e-03 0.297174102 #> 9498  4748.5 0.6954921 0.6954921 7.359687e-03 7.359687e-03 0.297148170 #> 9499  4749.0 0.6955164 0.6955164 7.361313e-03 7.361313e-03 0.297122277 #> 9500  4749.5 0.6955406 0.6955406 7.362944e-03 7.362944e-03 0.297096424 #> 9501  4750.0 0.6955648 0.6955648 7.364580e-03 7.364580e-03 0.297070612 #> 9502  4750.5 0.6955889 0.6955889 7.366220e-03 7.366220e-03 0.297044840 #> 9503  4751.0 0.6956130 0.6956130 7.367866e-03 7.367866e-03 0.297019108 #> 9504  4751.5 0.6956371 0.6956371 7.369516e-03 7.369516e-03 0.296993416 #> 9505  4752.0 0.6956611 0.6956611 7.371171e-03 7.371171e-03 0.296967765 #> 9506  4752.5 0.6956850 0.6956850 7.372831e-03 7.372831e-03 0.296942154 #> 9507  4753.0 0.6957089 0.6957089 7.374496e-03 7.374496e-03 0.296916584 #> 9508  4753.5 0.6957328 0.6957328 7.376165e-03 7.376165e-03 0.296891054 #> 9509  4754.0 0.6957566 0.6957566 7.377839e-03 7.377839e-03 0.296865565 #> 9510  4754.5 0.6957804 0.6957804 7.379518e-03 7.379518e-03 0.296840117 #> 9511  4755.0 0.6958041 0.6958041 7.381202e-03 7.381202e-03 0.296814709 #> 9512  4755.5 0.6958278 0.6958278 7.382891e-03 7.382891e-03 0.296789342 #> 9513  4756.0 0.6958514 0.6958514 7.384584e-03 7.384584e-03 0.296764016 #> 9514  4756.5 0.6958750 0.6958750 7.386282e-03 7.386282e-03 0.296738731 #> 9515  4757.0 0.6958985 0.6958985 7.387985e-03 7.387985e-03 0.296713487 #> 9516  4757.5 0.6959220 0.6959220 7.389692e-03 7.389692e-03 0.296688284 #> 9517  4758.0 0.6959455 0.6959455 7.391404e-03 7.391404e-03 0.296663121 #> 9518  4758.5 0.6959689 0.6959689 7.393121e-03 7.393121e-03 0.296638001 #> 9519  4759.0 0.6959922 0.6959922 7.394843e-03 7.394843e-03 0.296612921 #> 9520  4759.5 0.6960155 0.6960155 7.396569e-03 7.396569e-03 0.296587882 #> 9521  4760.0 0.6960388 0.6960388 7.398300e-03 7.398300e-03 0.296562885 #> 9522  4760.5 0.6960620 0.6960620 7.400036e-03 7.400036e-03 0.296537930 #> 9523  4761.0 0.6960852 0.6960852 7.401777e-03 7.401777e-03 0.296513015 #> 9524  4761.5 0.6961083 0.6961083 7.403522e-03 7.403522e-03 0.296488142 #> 9525  4762.0 0.6961314 0.6961314 7.405272e-03 7.405272e-03 0.296463311 #> 9526  4762.5 0.6961545 0.6961545 7.407026e-03 7.407026e-03 0.296438522 #> 9527  4763.0 0.6961774 0.6961774 7.408786e-03 7.408786e-03 0.296413774 #> 9528  4763.5 0.6962004 0.6962004 7.410550e-03 7.410550e-03 0.296389067 #> 9529  4764.0 0.6962233 0.6962233 7.412318e-03 7.412318e-03 0.296364403 #> 9530  4764.5 0.6962461 0.6962461 7.414091e-03 7.414091e-03 0.296339780 #> 9531  4765.0 0.6962689 0.6962689 7.415869e-03 7.415869e-03 0.296315200 #> 9532  4765.5 0.6962917 0.6962917 7.417652e-03 7.417652e-03 0.296290661 #> 9533  4766.0 0.6963144 0.6963144 7.419439e-03 7.419439e-03 0.296266164 #> 9534  4766.5 0.6963371 0.6963371 7.421231e-03 7.421231e-03 0.296241709 #> 9535  4767.0 0.6963597 0.6963597 7.423027e-03 7.423027e-03 0.296217297 #> 9536  4767.5 0.6963822 0.6963822 7.424828e-03 7.424828e-03 0.296192926 #> 9537  4768.0 0.6964048 0.6964048 7.426634e-03 7.426634e-03 0.296168598 #> 9538  4768.5 0.6964272 0.6964272 7.428445e-03 7.428445e-03 0.296144312 #> 9539  4769.0 0.6964497 0.6964497 7.430260e-03 7.430260e-03 0.296120069 #> 9540  4769.5 0.6964721 0.6964721 7.432079e-03 7.432079e-03 0.296095867 #> 9541  4770.0 0.6964944 0.6964944 7.433903e-03 7.433903e-03 0.296071709 #> 9542  4770.5 0.6965167 0.6965167 7.435732e-03 7.435732e-03 0.296047592 #> 9543  4771.0 0.6965389 0.6965389 7.437565e-03 7.437565e-03 0.296023519 #> 9544  4771.5 0.6965611 0.6965611 7.439403e-03 7.439403e-03 0.295999488 #> 9545  4772.0 0.6965833 0.6965833 7.441246e-03 7.441246e-03 0.295975499 #> 9546  4772.5 0.6966054 0.6966054 7.443093e-03 7.443093e-03 0.295951554 #> 9547  4773.0 0.6966274 0.6966274 7.444944e-03 7.444944e-03 0.295927651 #> 9548  4773.5 0.6966494 0.6966494 7.446801e-03 7.446801e-03 0.295903791 #> 9549  4774.0 0.6966714 0.6966714 7.448661e-03 7.448661e-03 0.295879974 #> 9550  4774.5 0.6966933 0.6966933 7.450527e-03 7.450527e-03 0.295856200 #> 9551  4775.0 0.6967151 0.6967151 7.452397e-03 7.452397e-03 0.295832469 #> 9552  4775.5 0.6967369 0.6967369 7.454271e-03 7.454271e-03 0.295808781 #> 9553  4776.0 0.6967587 0.6967587 7.456150e-03 7.456150e-03 0.295785136 #> 9554  4776.5 0.6967804 0.6967804 7.458033e-03 7.458033e-03 0.295761534 #> 9555  4777.0 0.6968021 0.6968021 7.459921e-03 7.459921e-03 0.295737975 #> 9556  4777.5 0.6968237 0.6968237 7.461814e-03 7.461814e-03 0.295714460 #> 9557  4778.0 0.6968453 0.6968453 7.463711e-03 7.463711e-03 0.295690988 #> 9558  4778.5 0.6968668 0.6968668 7.465612e-03 7.465612e-03 0.295667559 #> 9559  4779.0 0.6968883 0.6968883 7.467518e-03 7.467518e-03 0.295644174 #> 9560  4779.5 0.6969097 0.6969097 7.469429e-03 7.469429e-03 0.295620832 #> 9561  4780.0 0.6969311 0.6969311 7.471344e-03 7.471344e-03 0.295597534 #> 9562  4780.5 0.6969525 0.6969525 7.473263e-03 7.473263e-03 0.295574280 #> 9563  4781.0 0.6969737 0.6969737 7.475187e-03 7.475187e-03 0.295551069 #> 9564  4781.5 0.6969950 0.6969950 7.477116e-03 7.477116e-03 0.295527901 #> 9565  4782.0 0.6970162 0.6970162 7.479049e-03 7.479049e-03 0.295504778 #> 9566  4782.5 0.6970373 0.6970373 7.480986e-03 7.480986e-03 0.295481698 #> 9567  4783.0 0.6970584 0.6970584 7.482928e-03 7.482928e-03 0.295458663 #> 9568  4783.5 0.6970795 0.6970795 7.484874e-03 7.484874e-03 0.295435671 #> 9569  4784.0 0.6971005 0.6971005 7.486825e-03 7.486825e-03 0.295412723 #> 9570  4784.5 0.6971214 0.6971214 7.488780e-03 7.488780e-03 0.295389819 #> 9571  4785.0 0.6971423 0.6971423 7.490739e-03 7.490739e-03 0.295366959 #> 9572  4785.5 0.6971632 0.6971632 7.492703e-03 7.492703e-03 0.295344143 #> 9573  4786.0 0.6971840 0.6971840 7.494672e-03 7.494672e-03 0.295321372 #> 9574  4786.5 0.6972047 0.6972047 7.496644e-03 7.496644e-03 0.295298644 #> 9575  4787.0 0.6972254 0.6972254 7.498622e-03 7.498622e-03 0.295275961 #> 9576  4787.5 0.6972461 0.6972461 7.500603e-03 7.500603e-03 0.295253322 #> 9577  4788.0 0.6972667 0.6972667 7.502589e-03 7.502589e-03 0.295230728 #> 9578  4788.5 0.6972872 0.6972872 7.504580e-03 7.504580e-03 0.295208178 #> 9579  4789.0 0.6973078 0.6973078 7.506574e-03 7.506574e-03 0.295185672 #> 9580  4789.5 0.6973282 0.6973282 7.508574e-03 7.508574e-03 0.295163211 #> 9581  4790.0 0.6973486 0.6973486 7.510577e-03 7.510577e-03 0.295140795 #> 9582  4790.5 0.6973690 0.6973690 7.512585e-03 7.512585e-03 0.295118423 #> 9583  4791.0 0.6973893 0.6973893 7.514597e-03 7.514597e-03 0.295096096 #> 9584  4791.5 0.6974096 0.6974096 7.516614e-03 7.516614e-03 0.295073814 #> 9585  4792.0 0.6974298 0.6974298 7.518635e-03 7.518635e-03 0.295051576 #> 9586  4792.5 0.6974500 0.6974500 7.520660e-03 7.520660e-03 0.295029383 #> 9587  4793.0 0.6974701 0.6974701 7.522690e-03 7.522690e-03 0.295007235 #> 9588  4793.5 0.6974901 0.6974901 7.524723e-03 7.524723e-03 0.294985132 #> 9589  4794.0 0.6975102 0.6975102 7.526762e-03 7.526762e-03 0.294963074 #> 9590  4794.5 0.6975301 0.6975301 7.528804e-03 7.528804e-03 0.294941061 #> 9591  4795.0 0.6975501 0.6975501 7.530851e-03 7.530851e-03 0.294919093 #> 9592  4795.5 0.6975699 0.6975699 7.532902e-03 7.532902e-03 0.294897171 #> 9593  4796.0 0.6975897 0.6975897 7.534958e-03 7.534958e-03 0.294875293 #> 9594  4796.5 0.6976095 0.6976095 7.537018e-03 7.537018e-03 0.294853461 #> 9595  4797.0 0.6976292 0.6976292 7.539082e-03 7.539082e-03 0.294831673 #> 9596  4797.5 0.6976489 0.6976489 7.541150e-03 7.541150e-03 0.294809932 #> 9597  4798.0 0.6976685 0.6976685 7.543223e-03 7.543223e-03 0.294788235 #> 9598  4798.5 0.6976881 0.6976881 7.545299e-03 7.545299e-03 0.294766584 #> 9599  4799.0 0.6977076 0.6977076 7.547381e-03 7.547381e-03 0.294744979 #> 9600  4799.5 0.6977271 0.6977271 7.549466e-03 7.549466e-03 0.294723419 #> 9601  4800.0 0.6977465 0.6977465 7.551556e-03 7.551556e-03 0.294701904 #> 9602  4800.5 0.6977659 0.6977659 7.553650e-03 7.553650e-03 0.294680435 #> 9603  4801.0 0.6977852 0.6977852 7.555748e-03 7.555748e-03 0.294659012 #> 9604  4801.5 0.6978045 0.6978045 7.557850e-03 7.557850e-03 0.294637634 #> 9605  4802.0 0.6978237 0.6978237 7.559957e-03 7.559957e-03 0.294616303 #> 9606  4802.5 0.6978429 0.6978429 7.562067e-03 7.562067e-03 0.294595017 #> 9607  4803.0 0.6978620 0.6978620 7.564182e-03 7.564182e-03 0.294573777 #> 9608  4803.5 0.6978811 0.6978811 7.566302e-03 7.566302e-03 0.294552582 #> 9609  4804.0 0.6979001 0.6979001 7.568425e-03 7.568425e-03 0.294531434 #> 9610  4804.5 0.6979191 0.6979191 7.570552e-03 7.570552e-03 0.294510332 #> 9611  4805.0 0.6979380 0.6979380 7.572684e-03 7.572684e-03 0.294489276 #> 9612  4805.5 0.6979569 0.6979569 7.574820e-03 7.574820e-03 0.294468265 #> 9613  4806.0 0.6979757 0.6979757 7.576960e-03 7.576960e-03 0.294447301 #> 9614  4806.5 0.6979945 0.6979945 7.579105e-03 7.579105e-03 0.294426383 #> 9615  4807.0 0.6980132 0.6980132 7.581253e-03 7.581253e-03 0.294405512 #> 9616  4807.5 0.6980319 0.6980319 7.583406e-03 7.583406e-03 0.294384686 #> 9617  4808.0 0.6980505 0.6980505 7.585562e-03 7.585562e-03 0.294363907 #> 9618  4808.5 0.6980691 0.6980691 7.587723e-03 7.587723e-03 0.294343175 #> 9619  4809.0 0.6980876 0.6980876 7.589888e-03 7.589888e-03 0.294322488 #> 9620  4809.5 0.6981061 0.6981061 7.592058e-03 7.592058e-03 0.294301848 #> 9621  4810.0 0.6981245 0.6981245 7.594231e-03 7.594231e-03 0.294281255 #> 9622  4810.5 0.6981429 0.6981429 7.596408e-03 7.596408e-03 0.294260708 #> 9623  4811.0 0.6981612 0.6981612 7.598590e-03 7.598590e-03 0.294240208 #> 9624  4811.5 0.6981795 0.6981795 7.600775e-03 7.600775e-03 0.294219754 #> 9625  4812.0 0.6981977 0.6981977 7.602965e-03 7.602965e-03 0.294199347 #> 9626  4812.5 0.6982159 0.6982159 7.605159e-03 7.605159e-03 0.294178987 #> 9627  4813.0 0.6982340 0.6982340 7.607357e-03 7.607357e-03 0.294158674 #> 9628  4813.5 0.6982520 0.6982520 7.609559e-03 7.609559e-03 0.294138407 #> 9629  4814.0 0.6982700 0.6982700 7.611765e-03 7.611765e-03 0.294118187 #> 9630  4814.5 0.6982880 0.6982880 7.613975e-03 7.613975e-03 0.294098014 #> 9631  4815.0 0.6983059 0.6983059 7.616189e-03 7.616189e-03 0.294077888 #> 9632  4815.5 0.6983238 0.6983238 7.618407e-03 7.618407e-03 0.294057809 #> 9633  4816.0 0.6983416 0.6983416 7.620629e-03 7.620629e-03 0.294037777 #> 9634  4816.5 0.6983594 0.6983594 7.622856e-03 7.622856e-03 0.294017793 #> 9635  4817.0 0.6983771 0.6983771 7.625086e-03 7.625086e-03 0.293997855 #> 9636  4817.5 0.6983947 0.6983947 7.627320e-03 7.627320e-03 0.293977964 #> 9637  4818.0 0.6984123 0.6984123 7.629559e-03 7.629559e-03 0.293958121 #> 9638  4818.5 0.6984299 0.6984299 7.631801e-03 7.631801e-03 0.293938325 #> 9639  4819.0 0.6984474 0.6984474 7.634047e-03 7.634047e-03 0.293918576 #> 9640  4819.5 0.6984648 0.6984648 7.636298e-03 7.636298e-03 0.293898875 #> 9641  4820.0 0.6984822 0.6984822 7.638552e-03 7.638552e-03 0.293879220 #> 9642  4820.5 0.6984996 0.6984996 7.640810e-03 7.640810e-03 0.293859614 #> 9643  4821.0 0.6985169 0.6985169 7.643073e-03 7.643073e-03 0.293840054 #> 9644  4821.5 0.6985341 0.6985341 7.645339e-03 7.645339e-03 0.293820543 #> 9645  4822.0 0.6985513 0.6985513 7.647609e-03 7.647609e-03 0.293801079 #> 9646  4822.5 0.6985685 0.6985685 7.649883e-03 7.649883e-03 0.293781662 #> 9647  4823.0 0.6985855 0.6985855 7.652162e-03 7.652162e-03 0.293762293 #> 9648  4823.5 0.6986026 0.6986026 7.654444e-03 7.654444e-03 0.293742972 #> 9649  4824.0 0.6986196 0.6986196 7.656730e-03 7.656730e-03 0.293723698 #> 9650  4824.5 0.6986365 0.6986365 7.659020e-03 7.659020e-03 0.293704472 #> 9651  4825.0 0.6986534 0.6986534 7.661314e-03 7.661314e-03 0.293685294 #> 9652  4825.5 0.6986702 0.6986702 7.663611e-03 7.663611e-03 0.293666164 #> 9653  4826.0 0.6986870 0.6986870 7.665913e-03 7.665913e-03 0.293647082 #> 9654  4826.5 0.6987037 0.6987037 7.668219e-03 7.668219e-03 0.293628048 #> 9655  4827.0 0.6987204 0.6987204 7.670528e-03 7.670528e-03 0.293609061 #> 9656  4827.5 0.6987370 0.6987370 7.672842e-03 7.672842e-03 0.293590123 #> 9657  4828.0 0.6987536 0.6987536 7.675159e-03 7.675159e-03 0.293571233 #> 9658  4828.5 0.6987701 0.6987701 7.677480e-03 7.677480e-03 0.293552390 #> 9659  4829.0 0.6987866 0.6987866 7.679805e-03 7.679805e-03 0.293533596 #> 9660  4829.5 0.6988030 0.6988030 7.682134e-03 7.682134e-03 0.293514850 #> 9661  4830.0 0.6988194 0.6988194 7.684467e-03 7.684467e-03 0.293496152 #> 9662  4830.5 0.6988357 0.6988357 7.686803e-03 7.686803e-03 0.293477503 #> 9663  4831.0 0.6988520 0.6988520 7.689144e-03 7.689144e-03 0.293458902 #> 9664  4831.5 0.6988682 0.6988682 7.691488e-03 7.691488e-03 0.293440349 #> 9665  4832.0 0.6988843 0.6988843 7.693836e-03 7.693836e-03 0.293421844 #> 9666  4832.5 0.6989004 0.6989004 7.696188e-03 7.696188e-03 0.293403388 #> 9667  4833.0 0.6989165 0.6989165 7.698544e-03 7.698544e-03 0.293384981 #> 9668  4833.5 0.6989325 0.6989325 7.700903e-03 7.700903e-03 0.293366621 #> 9669  4834.0 0.6989484 0.6989484 7.703267e-03 7.703267e-03 0.293348311 #> 9670  4834.5 0.6989643 0.6989643 7.705634e-03 7.705634e-03 0.293330049 #> 9671  4835.0 0.6989802 0.6989802 7.708005e-03 7.708005e-03 0.293311835 #> 9672  4835.5 0.6989960 0.6989960 7.710379e-03 7.710379e-03 0.293293670 #> 9673  4836.0 0.6990117 0.6990117 7.712758e-03 7.712758e-03 0.293275554 #> 9674  4836.5 0.6990274 0.6990274 7.715140e-03 7.715140e-03 0.293257487 #> 9675  4837.0 0.6990430 0.6990430 7.717526e-03 7.717526e-03 0.293239468 #> 9676  4837.5 0.6990586 0.6990586 7.719916e-03 7.719916e-03 0.293221498 #> 9677  4838.0 0.6990741 0.6990741 7.722309e-03 7.722309e-03 0.293203577 #> 9678  4838.5 0.6990896 0.6990896 7.724707e-03 7.724707e-03 0.293185705 #> 9679  4839.0 0.6991050 0.6991050 7.727108e-03 7.727108e-03 0.293167882 #> 9680  4839.5 0.6991204 0.6991204 7.729512e-03 7.729512e-03 0.293150107 #> 9681  4840.0 0.6991357 0.6991357 7.731921e-03 7.731921e-03 0.293132382 #> 9682  4840.5 0.6991510 0.6991510 7.734333e-03 7.734333e-03 0.293114706 #> 9683  4841.0 0.6991662 0.6991662 7.736749e-03 7.736749e-03 0.293097078 #> 9684  4841.5 0.6991813 0.6991813 7.739168e-03 7.739168e-03 0.293079500 #> 9685  4842.0 0.6991964 0.6991964 7.741591e-03 7.741591e-03 0.293061971 #> 9686  4842.5 0.6992115 0.6992115 7.744018e-03 7.744018e-03 0.293044491 #> 9687  4843.0 0.6992265 0.6992265 7.746449e-03 7.746449e-03 0.293027061 #> 9688  4843.5 0.6992414 0.6992414 7.748883e-03 7.748883e-03 0.293009679 #> 9689  4844.0 0.6992563 0.6992563 7.751321e-03 7.751321e-03 0.292992347 #> 9690  4844.5 0.6992712 0.6992712 7.753762e-03 7.753762e-03 0.292975064 #> 9691  4845.0 0.6992860 0.6992860 7.756207e-03 7.756207e-03 0.292957831 #> 9692  4845.5 0.6993007 0.6993007 7.758656e-03 7.758656e-03 0.292940647 #> 9693  4846.0 0.6993154 0.6993154 7.761108e-03 7.761108e-03 0.292923512 #> 9694  4846.5 0.6993300 0.6993300 7.763564e-03 7.763564e-03 0.292906427 #> 9695  4847.0 0.6993446 0.6993446 7.766024e-03 7.766024e-03 0.292889391 #> 9696  4847.5 0.6993591 0.6993591 7.768487e-03 7.768487e-03 0.292872405 #> 9697  4848.0 0.6993736 0.6993736 7.770954e-03 7.770954e-03 0.292855468 #> 9698  4848.5 0.6993880 0.6993880 7.773424e-03 7.773424e-03 0.292838581 #> 9699  4849.0 0.6994024 0.6994024 7.775898e-03 7.775898e-03 0.292821744 #> 9700  4849.5 0.6994167 0.6994167 7.778376e-03 7.778376e-03 0.292804956 #> 9701  4850.0 0.6994309 0.6994309 7.780857e-03 7.780857e-03 0.292788218 #> 9702  4850.5 0.6994451 0.6994451 7.783342e-03 7.783342e-03 0.292771530 #> 9703  4851.0 0.6994593 0.6994593 7.785830e-03 7.785830e-03 0.292754891 #> 9704  4851.5 0.6994734 0.6994734 7.788322e-03 7.788322e-03 0.292738302 #> 9705  4852.0 0.6994874 0.6994874 7.790817e-03 7.790817e-03 0.292721764 #> 9706  4852.5 0.6995014 0.6995014 7.793316e-03 7.793316e-03 0.292705275 #> 9707  4853.0 0.6995153 0.6995153 7.795818e-03 7.795818e-03 0.292688836 #> 9708  4853.5 0.6995292 0.6995292 7.798324e-03 7.798324e-03 0.292672446 #> 9709  4854.0 0.6995431 0.6995431 7.800834e-03 7.800834e-03 0.292656107 #> 9710  4854.5 0.6995568 0.6995568 7.803347e-03 7.803347e-03 0.292639818 #> 9711  4855.0 0.6995706 0.6995706 7.805863e-03 7.805863e-03 0.292623579 #> 9712  4855.5 0.6995842 0.6995842 7.808383e-03 7.808383e-03 0.292607390 #> 9713  4856.0 0.6995978 0.6995978 7.810906e-03 7.810906e-03 0.292591251 #> 9714  4856.5 0.6996114 0.6996114 7.813433e-03 7.813433e-03 0.292575162 #> 9715  4857.0 0.6996249 0.6996249 7.815964e-03 7.815964e-03 0.292559124 #> 9716  4857.5 0.6996384 0.6996384 7.818497e-03 7.818497e-03 0.292543135 #> 9717  4858.0 0.6996518 0.6996518 7.821034e-03 7.821034e-03 0.292527197 #> 9718  4858.5 0.6996651 0.6996651 7.823575e-03 7.823575e-03 0.292511309 #> 9719  4859.0 0.6996784 0.6996784 7.826119e-03 7.826119e-03 0.292495472 #> 9720  4859.5 0.6996916 0.6996916 7.828667e-03 7.828667e-03 0.292479684 #> 9721  4860.0 0.6997048 0.6997048 7.831218e-03 7.831218e-03 0.292463948 #> 9722  4860.5 0.6997180 0.6997180 7.833772e-03 7.833772e-03 0.292448261 #> 9723  4861.0 0.6997310 0.6997310 7.836330e-03 7.836330e-03 0.292432625 #> 9724  4861.5 0.6997441 0.6997441 7.838891e-03 7.838891e-03 0.292417039 #> 9725  4862.0 0.6997570 0.6997570 7.841455e-03 7.841455e-03 0.292401504 #> 9726  4862.5 0.6997700 0.6997700 7.844023e-03 7.844023e-03 0.292386020 #> 9727  4863.0 0.6997828 0.6997828 7.846594e-03 7.846594e-03 0.292370586 #> 9728  4863.5 0.6997956 0.6997956 7.849169e-03 7.849169e-03 0.292355202 #> 9729  4864.0 0.6998084 0.6998084 7.851747e-03 7.851747e-03 0.292339870 #> 9730  4864.5 0.6998211 0.6998211 7.854328e-03 7.854328e-03 0.292324587 #> 9731  4865.0 0.6998337 0.6998337 7.856913e-03 7.856913e-03 0.292309356 #> 9732  4865.5 0.6998463 0.6998463 7.859501e-03 7.859501e-03 0.292294175 #> 9733  4866.0 0.6998589 0.6998589 7.862092e-03 7.862092e-03 0.292279045 #> 9734  4866.5 0.6998713 0.6998713 7.864687e-03 7.864687e-03 0.292263966 #> 9735  4867.0 0.6998838 0.6998838 7.867285e-03 7.867285e-03 0.292248937 #> 9736  4867.5 0.6998962 0.6998962 7.869886e-03 7.869886e-03 0.292233960 #> 9737  4868.0 0.6999085 0.6999085 7.872490e-03 7.872490e-03 0.292219033 #> 9738  4868.5 0.6999207 0.6999207 7.875098e-03 7.875098e-03 0.292204157 #> 9739  4869.0 0.6999330 0.6999330 7.877709e-03 7.877709e-03 0.292189332 #> 9740  4869.5 0.6999451 0.6999451 7.880324e-03 7.880324e-03 0.292174558 #> 9741  4870.0 0.6999572 0.6999572 7.882941e-03 7.882941e-03 0.292159835 #> 9742  4870.5 0.6999693 0.6999693 7.885562e-03 7.885562e-03 0.292145163 #> 9743  4871.0 0.6999813 0.6999813 7.888186e-03 7.888186e-03 0.292130542 #> 9744  4871.5 0.6999932 0.6999932 7.890813e-03 7.890813e-03 0.292115972 #> 9745  4872.0 0.7000051 0.7000051 7.893444e-03 7.893444e-03 0.292101453 #> 9746  4872.5 0.7000169 0.7000169 7.896078e-03 7.896078e-03 0.292086985 #> 9747  4873.0 0.7000287 0.7000287 7.898715e-03 7.898715e-03 0.292072569 #> 9748  4873.5 0.7000404 0.7000404 7.901355e-03 7.901355e-03 0.292058203 #> 9749  4874.0 0.7000521 0.7000521 7.903998e-03 7.903998e-03 0.292043889 #> 9750  4874.5 0.7000637 0.7000637 7.906645e-03 7.906645e-03 0.292029626 #> 9751  4875.0 0.7000753 0.7000753 7.909295e-03 7.909295e-03 0.292015414 #> 9752  4875.5 0.7000868 0.7000868 7.911947e-03 7.911947e-03 0.292001254 #> 9753  4876.0 0.7000983 0.7000983 7.914604e-03 7.914604e-03 0.291987145 #> 9754  4876.5 0.7001097 0.7001097 7.917263e-03 7.917263e-03 0.291973087 #> 9755  4877.0 0.7001210 0.7001210 7.919925e-03 7.919925e-03 0.291959080 #> 9756  4877.5 0.7001323 0.7001323 7.922591e-03 7.922591e-03 0.291945125 #> 9757  4878.0 0.7001435 0.7001435 7.925259e-03 7.925259e-03 0.291931221 #> 9758  4878.5 0.7001547 0.7001547 7.927931e-03 7.927931e-03 0.291917369 #> 9759  4879.0 0.7001658 0.7001658 7.930606e-03 7.930606e-03 0.291903568 #> 9760  4879.5 0.7001769 0.7001769 7.933284e-03 7.933284e-03 0.291889819 #> 9761  4880.0 0.7001879 0.7001879 7.935965e-03 7.935965e-03 0.291876121 #> 9762  4880.5 0.7001989 0.7001989 7.938649e-03 7.938649e-03 0.291862475 #> 9763  4881.0 0.7002098 0.7002098 7.941336e-03 7.941336e-03 0.291848881 #> 9764  4881.5 0.7002206 0.7002206 7.944027e-03 7.944027e-03 0.291835338 #> 9765  4882.0 0.7002314 0.7002314 7.946720e-03 7.946720e-03 0.291821846 #> 9766  4882.5 0.7002422 0.7002422 7.949416e-03 7.949416e-03 0.291808406 #> 9767  4883.0 0.7002529 0.7002529 7.952116e-03 7.952116e-03 0.291795018 #> 9768  4883.5 0.7002635 0.7002635 7.954818e-03 7.954818e-03 0.291781682 #> 9769  4884.0 0.7002741 0.7002741 7.957524e-03 7.957524e-03 0.291768397 #> 9770  4884.5 0.7002846 0.7002846 7.960232e-03 7.960232e-03 0.291755164 #> 9771  4885.0 0.7002951 0.7002951 7.962944e-03 7.962944e-03 0.291741983 #> 9772  4885.5 0.7003055 0.7003055 7.965658e-03 7.965658e-03 0.291728854 #> 9773  4886.0 0.7003158 0.7003158 7.968376e-03 7.968376e-03 0.291715776 #> 9774  4886.5 0.7003262 0.7003262 7.971097e-03 7.971097e-03 0.291702751 #> 9775  4887.0 0.7003364 0.7003364 7.973820e-03 7.973820e-03 0.291689777 #> 9776  4887.5 0.7003466 0.7003466 7.976547e-03 7.976547e-03 0.291676855 #> 9777  4888.0 0.7003567 0.7003567 7.979276e-03 7.979276e-03 0.291663985 #> 9778  4888.5 0.7003668 0.7003668 7.982008e-03 7.982008e-03 0.291651167 #> 9779  4889.0 0.7003769 0.7003769 7.984744e-03 7.984744e-03 0.291638401 #> 9780  4889.5 0.7003868 0.7003868 7.987482e-03 7.987482e-03 0.291625686 #> 9781  4890.0 0.7003968 0.7003968 7.990223e-03 7.990223e-03 0.291613024 #> 9782  4890.5 0.7004066 0.7004066 7.992967e-03 7.992967e-03 0.291600414 #> 9783  4891.0 0.7004164 0.7004164 7.995714e-03 7.995714e-03 0.291587856 #> 9784  4891.5 0.7004262 0.7004262 7.998464e-03 7.998464e-03 0.291575350 #> 9785  4892.0 0.7004359 0.7004359 8.001217e-03 8.001217e-03 0.291562896 #> 9786  4892.5 0.7004455 0.7004455 8.003973e-03 8.003973e-03 0.291550494 #> 9787  4893.0 0.7004551 0.7004551 8.006731e-03 8.006731e-03 0.291538145 #> 9788  4893.5 0.7004647 0.7004647 8.009493e-03 8.009493e-03 0.291525847 #> 9789  4894.0 0.7004741 0.7004741 8.012257e-03 8.012257e-03 0.291513602 #> 9790  4894.5 0.7004836 0.7004836 8.015024e-03 8.015024e-03 0.291501409 #> 9791  4895.0 0.7004929 0.7004929 8.017794e-03 8.017794e-03 0.291489268 #> 9792  4895.5 0.7005023 0.7005023 8.020567e-03 8.020567e-03 0.291477180 #> 9793  4896.0 0.7005115 0.7005115 8.023343e-03 8.023343e-03 0.291465143 #> 9794  4896.5 0.7005207 0.7005207 8.026121e-03 8.026121e-03 0.291453159 #> 9795  4897.0 0.7005299 0.7005299 8.028902e-03 8.028902e-03 0.291441227 #> 9796  4897.5 0.7005390 0.7005390 8.031686e-03 8.031686e-03 0.291429348 #> 9797  4898.0 0.7005480 0.7005480 8.034473e-03 8.034473e-03 0.291417521 #> 9798  4898.5 0.7005570 0.7005570 8.037263e-03 8.037263e-03 0.291405746 #> 9799  4899.0 0.7005659 0.7005659 8.040055e-03 8.040055e-03 0.291394024 #> 9800  4899.5 0.7005748 0.7005748 8.042850e-03 8.042850e-03 0.291382354 #> 9801  4900.0 0.7005836 0.7005836 8.045648e-03 8.045648e-03 0.291370737 #> 9802  4900.5 0.7005924 0.7005924 8.048449e-03 8.048449e-03 0.291359172 #> 9803  4901.0 0.7006011 0.7006011 8.051252e-03 8.051252e-03 0.291347659 #> 9804  4901.5 0.7006097 0.7006097 8.054058e-03 8.054058e-03 0.291336199 #> 9805  4902.0 0.7006183 0.7006183 8.056867e-03 8.056867e-03 0.291324792 #> 9806  4902.5 0.7006269 0.7006269 8.059679e-03 8.059679e-03 0.291313437 #> 9807  4903.0 0.7006354 0.7006354 8.062493e-03 8.062493e-03 0.291302134 #> 9808  4903.5 0.7006438 0.7006438 8.065310e-03 8.065310e-03 0.291290884 #> 9809  4904.0 0.7006522 0.7006522 8.068130e-03 8.068130e-03 0.291279687 #> 9810  4904.5 0.7006605 0.7006605 8.070952e-03 8.070952e-03 0.291268542 #> 9811  4905.0 0.7006688 0.7006688 8.073777e-03 8.073777e-03 0.291257450 #> 9812  4905.5 0.7006770 0.7006770 8.076604e-03 8.076604e-03 0.291246411 #> 9813  4906.0 0.7006851 0.7006851 8.079434e-03 8.079434e-03 0.291235424 #> 9814  4906.5 0.7006932 0.7006932 8.082267e-03 8.082267e-03 0.291224490 #> 9815  4907.0 0.7007013 0.7007013 8.085103e-03 8.085103e-03 0.291213608 #> 9816  4907.5 0.7007093 0.7007093 8.087941e-03 8.087941e-03 0.291202780 #> 9817  4908.0 0.7007172 0.7007172 8.090782e-03 8.090782e-03 0.291192004 #> 9818  4908.5 0.7007251 0.7007251 8.093625e-03 8.093625e-03 0.291181280 #> 9819  4909.0 0.7007329 0.7007329 8.096471e-03 8.096471e-03 0.291170610 #> 9820  4909.5 0.7007407 0.7007407 8.099319e-03 8.099319e-03 0.291159992 #> 9821  4910.0 0.7007484 0.7007484 8.102170e-03 8.102170e-03 0.291149427 #> 9822  4910.5 0.7007561 0.7007561 8.105024e-03 8.105024e-03 0.291138915 #> 9823  4911.0 0.7007637 0.7007637 8.107880e-03 8.107880e-03 0.291128456 #> 9824  4911.5 0.7007712 0.7007712 8.110739e-03 8.110739e-03 0.291118049 #> 9825  4912.0 0.7007787 0.7007787 8.113600e-03 8.113600e-03 0.291107696 #> 9826  4912.5 0.7007861 0.7007861 8.116464e-03 8.116464e-03 0.291097395 #> 9827  4913.0 0.7007935 0.7007935 8.119330e-03 8.119330e-03 0.291087147 #> 9828  4913.5 0.7008008 0.7008008 8.122199e-03 8.122199e-03 0.291076952 #> 9829  4914.0 0.7008081 0.7008081 8.125070e-03 8.125070e-03 0.291066810 #> 9830  4914.5 0.7008153 0.7008153 8.127944e-03 8.127944e-03 0.291056721 #> 9831  4915.0 0.7008225 0.7008225 8.130820e-03 8.130820e-03 0.291046685 #> 9832  4915.5 0.7008296 0.7008296 8.133699e-03 8.133699e-03 0.291036701 #> 9833  4916.0 0.7008366 0.7008366 8.136580e-03 8.136580e-03 0.291026771 #> 9834  4916.5 0.7008436 0.7008436 8.139463e-03 8.139463e-03 0.291016894 #> 9835  4917.0 0.7008506 0.7008506 8.142349e-03 8.142349e-03 0.291007070 #> 9836  4917.5 0.7008575 0.7008575 8.145238e-03 8.145238e-03 0.290997299 #> 9837  4918.0 0.7008643 0.7008643 8.148129e-03 8.148129e-03 0.290987580 #> 9838  4918.5 0.7008711 0.7008711 8.151022e-03 8.151022e-03 0.290977915 #> 9839  4919.0 0.7008778 0.7008778 8.153918e-03 8.153918e-03 0.290968303 #> 9840  4919.5 0.7008844 0.7008844 8.156816e-03 8.156816e-03 0.290958744 #> 9841  4920.0 0.7008910 0.7008910 8.159716e-03 8.159716e-03 0.290949238 #> 9842  4920.5 0.7008976 0.7008976 8.162619e-03 8.162619e-03 0.290939786 #> 9843  4921.0 0.7009041 0.7009041 8.165524e-03 8.165524e-03 0.290930386 #> 9844  4921.5 0.7009105 0.7009105 8.168432e-03 8.168432e-03 0.290921039 #> 9845  4922.0 0.7009169 0.7009169 8.171342e-03 8.171342e-03 0.290911746 #> 9846  4922.5 0.7009232 0.7009232 8.174254e-03 8.174254e-03 0.290902506 #> 9847  4923.0 0.7009295 0.7009295 8.177169e-03 8.177169e-03 0.290893319 #> 9848  4923.5 0.7009357 0.7009357 8.180086e-03 8.180086e-03 0.290884185 #> 9849  4924.0 0.7009419 0.7009419 8.183005e-03 8.183005e-03 0.290875104 #> 9850  4924.5 0.7009480 0.7009480 8.185926e-03 8.185926e-03 0.290866076 #> 9851  4925.0 0.7009540 0.7009540 8.188850e-03 8.188850e-03 0.290857102 #> 9852  4925.5 0.7009600 0.7009600 8.191776e-03 8.191776e-03 0.290848181 #> 9853  4926.0 0.7009660 0.7009660 8.194705e-03 8.194705e-03 0.290839313 #> 9854  4926.5 0.7009719 0.7009719 8.197635e-03 8.197635e-03 0.290830498 #> 9855  4927.0 0.7009777 0.7009777 8.200568e-03 8.200568e-03 0.290821737 #> 9856  4927.5 0.7009835 0.7009835 8.203503e-03 8.203503e-03 0.290813029 #> 9857  4928.0 0.7009892 0.7009892 8.206440e-03 8.206440e-03 0.290804374 #> 9858  4928.5 0.7009948 0.7009948 8.209380e-03 8.209380e-03 0.290795773 #> 9859  4929.0 0.7010005 0.7010005 8.212322e-03 8.212322e-03 0.290787224 #> 9860  4929.5 0.7010060 0.7010060 8.215266e-03 8.215266e-03 0.290778730 #> 9861  4930.0 0.7010115 0.7010115 8.218212e-03 8.218212e-03 0.290770288 #> 9862  4930.5 0.7010169 0.7010169 8.221160e-03 8.221160e-03 0.290761900 #> 9863  4931.0 0.7010223 0.7010223 8.224111e-03 8.224111e-03 0.290753565 #> 9864  4931.5 0.7010277 0.7010277 8.227063e-03 8.227063e-03 0.290745283 #> 9865  4932.0 0.7010329 0.7010329 8.230018e-03 8.230018e-03 0.290737055 #> 9866  4932.5 0.7010381 0.7010381 8.232975e-03 8.232975e-03 0.290728880 #> 9867  4933.0 0.7010433 0.7010433 8.235934e-03 8.235934e-03 0.290720758 #> 9868  4933.5 0.7010484 0.7010484 8.238895e-03 8.238895e-03 0.290712690 #> 9869  4934.0 0.7010535 0.7010535 8.241858e-03 8.241858e-03 0.290704676 #> 9870  4934.5 0.7010585 0.7010585 8.244824e-03 8.244824e-03 0.290696714 #> 9871  4935.0 0.7010634 0.7010634 8.247791e-03 8.247791e-03 0.290688806 #> 9872  4935.5 0.7010683 0.7010683 8.250761e-03 8.250761e-03 0.290680952 #> 9873  4936.0 0.7010731 0.7010731 8.253733e-03 8.253733e-03 0.290673151 #> 9874  4936.5 0.7010779 0.7010779 8.256706e-03 8.256706e-03 0.290665403 #> 9875  4937.0 0.7010826 0.7010826 8.259682e-03 8.259682e-03 0.290657709 #> 9876  4937.5 0.7010873 0.7010873 8.262660e-03 8.262660e-03 0.290650068 #> 9877  4938.0 0.7010919 0.7010919 8.265640e-03 8.265640e-03 0.290642481 #> 9878  4938.5 0.7010964 0.7010964 8.268622e-03 8.268622e-03 0.290634947 #> 9879  4939.0 0.7011009 0.7011009 8.271605e-03 8.271605e-03 0.290627467 #> 9880  4939.5 0.7011054 0.7011054 8.274591e-03 8.274591e-03 0.290620040 #> 9881  4940.0 0.7011098 0.7011098 8.277579e-03 8.277579e-03 0.290612666 #> 9882  4940.5 0.7011141 0.7011141 8.280569e-03 8.280569e-03 0.290605346 #> 9883  4941.0 0.7011184 0.7011184 8.283561e-03 8.283561e-03 0.290598080 #> 9884  4941.5 0.7011226 0.7011226 8.286554e-03 8.286554e-03 0.290590867 #> 9885  4942.0 0.7011267 0.7011267 8.289550e-03 8.289550e-03 0.290583708 #> 9886  4942.5 0.7011309 0.7011309 8.292548e-03 8.292548e-03 0.290576602 #> 9887  4943.0 0.7011349 0.7011349 8.295547e-03 8.295547e-03 0.290569549 #> 9888  4943.5 0.7011389 0.7011389 8.298549e-03 8.298549e-03 0.290562550 #> 9889  4944.0 0.7011428 0.7011428 8.301552e-03 8.301552e-03 0.290555605 #> 9890  4944.5 0.7011467 0.7011467 8.304558e-03 8.304558e-03 0.290548713 #> 9891  4945.0 0.7011506 0.7011506 8.307565e-03 8.307565e-03 0.290541875 #> 9892  4945.5 0.7011543 0.7011543 8.310574e-03 8.310574e-03 0.290535090 #> 9893  4946.0 0.7011581 0.7011581 8.313585e-03 8.313585e-03 0.290528359 #> 9894  4946.5 0.7011617 0.7011617 8.316598e-03 8.316598e-03 0.290521681 #> 9895  4947.0 0.7011653 0.7011653 8.319612e-03 8.319612e-03 0.290515057 #> 9896  4947.5 0.7011689 0.7011689 8.322629e-03 8.322629e-03 0.290508487 #> 9897  4948.0 0.7011724 0.7011724 8.325647e-03 8.325647e-03 0.290501970 #> 9898  4948.5 0.7011758 0.7011758 8.328667e-03 8.328667e-03 0.290495506 #> 9899  4949.0 0.7011792 0.7011792 8.331689e-03 8.331689e-03 0.290489096 #> 9900  4949.5 0.7011825 0.7011825 8.334713e-03 8.334713e-03 0.290482740 #> 9901  4950.0 0.7011858 0.7011858 8.337739e-03 8.337739e-03 0.290476437 #> 9902  4950.5 0.7011890 0.7011890 8.340766e-03 8.340766e-03 0.290470188 #> 9903  4951.0 0.7011922 0.7011922 8.343795e-03 8.343795e-03 0.290463993 #> 9904  4951.5 0.7011953 0.7011953 8.346826e-03 8.346826e-03 0.290457851 #> 9905  4952.0 0.7011984 0.7011984 8.349858e-03 8.349858e-03 0.290451762 #> 9906  4952.5 0.7012014 0.7012014 8.352893e-03 8.352893e-03 0.290445728 #> 9907  4953.0 0.7012043 0.7012043 8.355929e-03 8.355929e-03 0.290439746 #> 9908  4953.5 0.7012072 0.7012072 8.358966e-03 8.358966e-03 0.290433819 #> 9909  4954.0 0.7012100 0.7012100 8.362006e-03 8.362006e-03 0.290427945 #> 9910  4954.5 0.7012128 0.7012128 8.365047e-03 8.365047e-03 0.290422124 #> 9911  4955.0 0.7012156 0.7012156 8.368090e-03 8.368090e-03 0.290416358 #> 9912  4955.5 0.7012182 0.7012182 8.371134e-03 8.371134e-03 0.290410644 #> 9913  4956.0 0.7012208 0.7012208 8.374181e-03 8.374181e-03 0.290404985 #> 9914  4956.5 0.7012234 0.7012234 8.377228e-03 8.377228e-03 0.290399379 #> 9915  4957.0 0.7012259 0.7012259 8.380278e-03 8.380278e-03 0.290393826 #> 9916  4957.5 0.7012283 0.7012283 8.383329e-03 8.383329e-03 0.290388327 #> 9917  4958.0 0.7012307 0.7012307 8.386382e-03 8.386382e-03 0.290382882 #> 9918  4958.5 0.7012331 0.7012331 8.389436e-03 8.389436e-03 0.290377491 #> 9919  4959.0 0.7012354 0.7012354 8.392492e-03 8.392492e-03 0.290372153 #> 9920  4959.5 0.7012376 0.7012376 8.395549e-03 8.395549e-03 0.290366868 #> 9921  4960.0 0.7012398 0.7012398 8.398608e-03 8.398608e-03 0.290361637 #> 9922  4960.5 0.7012419 0.7012419 8.401669e-03 8.401669e-03 0.290356460 #> 9923  4961.0 0.7012439 0.7012439 8.404731e-03 8.404731e-03 0.290351337 #> 9924  4961.5 0.7012459 0.7012459 8.407795e-03 8.407795e-03 0.290346267 #> 9925  4962.0 0.7012479 0.7012479 8.410860e-03 8.410860e-03 0.290341250 #> 9926  4962.5 0.7012498 0.7012498 8.413927e-03 8.413927e-03 0.290336287 #> 9927  4963.0 0.7012516 0.7012516 8.416995e-03 8.416995e-03 0.290331378 #> 9928  4963.5 0.7012534 0.7012534 8.420065e-03 8.420065e-03 0.290326522 #> 9929  4964.0 0.7012551 0.7012551 8.423136e-03 8.423136e-03 0.290321720 #> 9930  4964.5 0.7012568 0.7012568 8.426209e-03 8.426209e-03 0.290316972 #> 9931  4965.0 0.7012584 0.7012584 8.429283e-03 8.429283e-03 0.290312277 #> 9932  4965.5 0.7012600 0.7012600 8.432359e-03 8.432359e-03 0.290307636 #> 9933  4966.0 0.7012615 0.7012615 8.435436e-03 8.435436e-03 0.290303048 #> 9934  4966.5 0.7012630 0.7012630 8.438515e-03 8.438515e-03 0.290298514 #> 9935  4967.0 0.7012644 0.7012644 8.441595e-03 8.441595e-03 0.290294033 #> 9936  4967.5 0.7012657 0.7012657 8.444676e-03 8.444676e-03 0.290289606 #> 9937  4968.0 0.7012670 0.7012670 8.447759e-03 8.447759e-03 0.290285233 #> 9938  4968.5 0.7012682 0.7012682 8.450843e-03 8.450843e-03 0.290280913 #> 9939  4969.0 0.7012694 0.7012694 8.453929e-03 8.453929e-03 0.290276647 #> 9940  4969.5 0.7012706 0.7012706 8.457015e-03 8.457015e-03 0.290272434 #> 9941  4970.0 0.7012716 0.7012716 8.460104e-03 8.460104e-03 0.290268275 #> 9942  4970.5 0.7012726 0.7012726 8.463193e-03 8.463193e-03 0.290264169 #> 9943  4971.0 0.7012736 0.7012736 8.466284e-03 8.466284e-03 0.290260117 #> 9944  4971.5 0.7012745 0.7012745 8.469377e-03 8.469377e-03 0.290256119 #> 9945  4972.0 0.7012754 0.7012754 8.472470e-03 8.472470e-03 0.290252174 #> 9946  4972.5 0.7012762 0.7012762 8.475565e-03 8.475565e-03 0.290248282 #> 9947  4973.0 0.7012769 0.7012769 8.478662e-03 8.478662e-03 0.290244444 #> 9948  4973.5 0.7012776 0.7012776 8.481759e-03 8.481759e-03 0.290240660 #> 9949  4974.0 0.7012782 0.7012782 8.484858e-03 8.484858e-03 0.290236929 #> 9950  4974.5 0.7012788 0.7012788 8.487958e-03 8.487958e-03 0.290233251 #> 9951  4975.0 0.7012793 0.7012793 8.491059e-03 8.491059e-03 0.290229627 #> 9952  4975.5 0.7012798 0.7012798 8.494162e-03 8.494162e-03 0.290226057 #> 9953  4976.0 0.7012802 0.7012802 8.497266e-03 8.497266e-03 0.290222540 #> 9954  4976.5 0.7012806 0.7012806 8.500371e-03 8.500371e-03 0.290219077 #> 9955  4977.0 0.7012809 0.7012809 8.503477e-03 8.503477e-03 0.290215667 #> 9956  4977.5 0.7012811 0.7012811 8.506584e-03 8.506584e-03 0.290212310 #> 9957  4978.0 0.7012813 0.7012813 8.509693e-03 8.509693e-03 0.290209007 #> 9958  4978.5 0.7012814 0.7012814 8.512802e-03 8.512802e-03 0.290205758 #> 9959  4979.0 0.7012815 0.7012815 8.515913e-03 8.515913e-03 0.290202562 #> 9960  4979.5 0.7012816 0.7012816 8.519025e-03 8.519025e-03 0.290199419 #> 9961  4980.0 0.7012815 0.7012815 8.522139e-03 8.522139e-03 0.290196330 #> 9962  4980.5 0.7012815 0.7012815 8.525253e-03 8.525253e-03 0.290193294 #> 9963  4981.0 0.7012813 0.7012813 8.528368e-03 8.528368e-03 0.290190311 #> 9964  4981.5 0.7012811 0.7012811 8.531485e-03 8.531485e-03 0.290187382 #> 9965  4982.0 0.7012809 0.7012809 8.534602e-03 8.534602e-03 0.290184507 #> 9966  4982.5 0.7012806 0.7012806 8.537721e-03 8.537721e-03 0.290181685 #> 9967  4983.0 0.7012802 0.7012802 8.540841e-03 8.540841e-03 0.290178916 #> 9968  4983.5 0.7012798 0.7012798 8.543962e-03 8.543962e-03 0.290176200 #> 9969  4984.0 0.7012794 0.7012794 8.547084e-03 8.547084e-03 0.290173538 #> 9970  4984.5 0.7012789 0.7012789 8.550207e-03 8.550207e-03 0.290170930 #> 9971  4985.0 0.7012783 0.7012783 8.553330e-03 8.553330e-03 0.290168374 #> 9972  4985.5 0.7012777 0.7012777 8.556455e-03 8.556455e-03 0.290165872 #> 9973  4986.0 0.7012770 0.7012770 8.559581e-03 8.559581e-03 0.290163424 #> 9974  4986.5 0.7012763 0.7012763 8.562708e-03 8.562708e-03 0.290161028 #> 9975  4987.0 0.7012755 0.7012755 8.565836e-03 8.565836e-03 0.290158686 #> 9976  4987.5 0.7012746 0.7012746 8.568965e-03 8.568965e-03 0.290156397 #> 9977  4988.0 0.7012737 0.7012737 8.572095e-03 8.572095e-03 0.290154162 #> 9978  4988.5 0.7012728 0.7012728 8.575226e-03 8.575226e-03 0.290151980 #> 9979  4989.0 0.7012718 0.7012718 8.578358e-03 8.578358e-03 0.290149851 #> 9980  4989.5 0.7012707 0.7012707 8.581490e-03 8.581490e-03 0.290147775 #> 9981  4990.0 0.7012696 0.7012696 8.584624e-03 8.584624e-03 0.290145753 #> 9982  4990.5 0.7012685 0.7012685 8.587758e-03 8.587758e-03 0.290143784 #> 9983  4991.0 0.7012672 0.7012672 8.590894e-03 8.590894e-03 0.290141868 #> 9984  4991.5 0.7012660 0.7012660 8.594030e-03 8.594030e-03 0.290140005 #> 9985  4992.0 0.7012646 0.7012646 8.597167e-03 8.597167e-03 0.290138195 #> 9986  4992.5 0.7012633 0.7012633 8.600305e-03 8.600305e-03 0.290136439 #> 9987  4993.0 0.7012618 0.7012618 8.603444e-03 8.603444e-03 0.290134736 #> 9988  4993.5 0.7012603 0.7012603 8.606583e-03 8.606583e-03 0.290133086 #> 9989  4994.0 0.7012588 0.7012588 8.609724e-03 8.609724e-03 0.290131489 #> 9990  4994.5 0.7012572 0.7012572 8.612865e-03 8.612865e-03 0.290129945 #> 9991  4995.0 0.7012555 0.7012555 8.616007e-03 8.616007e-03 0.290128454 #> 9992  4995.5 0.7012538 0.7012538 8.619150e-03 8.619150e-03 0.290127017 #> 9993  4996.0 0.7012521 0.7012521 8.622294e-03 8.622294e-03 0.290125632 #> 9994  4996.5 0.7012503 0.7012503 8.625438e-03 8.625438e-03 0.290124301 #> 9995  4997.0 0.7012484 0.7012484 8.628583e-03 8.628583e-03 0.290123022 #> 9996  4997.5 0.7012465 0.7012465 8.631729e-03 8.631729e-03 0.290121797 #> 9997  4998.0 0.7012445 0.7012445 8.634875e-03 8.634875e-03 0.290120625 #> 9998  4998.5 0.7012425 0.7012425 8.638023e-03 8.638023e-03 0.290119506 #> 9999  4999.0 0.7012404 0.7012404 8.641171e-03 8.641171e-03 0.290118440 #> 10000 4999.5 0.7012383 0.7012383 8.644319e-03 8.644319e-03 0.290117426 #> 10001 5000.0 0.7012361 0.7012361 8.647469e-03 8.647469e-03 0.290116466 #> 10002 5000.5 0.7012338 0.7012338 8.650619e-03 8.650619e-03 0.290115559 #> 10003 5001.0 0.7012315 0.7012315 8.653770e-03 8.653770e-03 0.290114705 #> 10004 5001.5 0.7012292 0.7012292 8.656921e-03 8.656921e-03 0.290113903 #> 10005 5002.0 0.7012268 0.7012268 8.660073e-03 8.660073e-03 0.290113155 #> 10006 5002.5 0.7012243 0.7012243 8.663225e-03 8.663225e-03 0.290112459 #> 10007 5003.0 0.7012218 0.7012218 8.666379e-03 8.666379e-03 0.290111816 #> 10008 5003.5 0.7012192 0.7012192 8.669532e-03 8.669532e-03 0.290111227 #> 10009 5004.0 0.7012166 0.7012166 8.672687e-03 8.672687e-03 0.290110690 #> 10010 5004.5 0.7012140 0.7012140 8.675842e-03 8.675842e-03 0.290110205 #> 10011 5005.0 0.7012112 0.7012112 8.678997e-03 8.678997e-03 0.290109774 #> 10012 5005.5 0.7012085 0.7012085 8.682153e-03 8.682153e-03 0.290109396 #> 10013 5006.0 0.7012056 0.7012056 8.685310e-03 8.685310e-03 0.290109070 #> 10014 5006.5 0.7012027 0.7012027 8.688467e-03 8.688467e-03 0.290108797 #> 10015 5007.0 0.7011998 0.7011998 8.691625e-03 8.691625e-03 0.290108577 #> 10016 5007.5 0.7011968 0.7011968 8.694783e-03 8.694783e-03 0.290108409 #> 10017 5008.0 0.7011938 0.7011938 8.697942e-03 8.697942e-03 0.290108294 #> 10018 5008.5 0.7011907 0.7011907 8.701101e-03 8.701101e-03 0.290108232 #> 10019 5009.0 0.7011875 0.7011875 8.704260e-03 8.704260e-03 0.290108223 #> 10020 5009.5 0.7011843 0.7011843 8.707420e-03 8.707420e-03 0.290108266 #> 10021 5010.0 0.7011811 0.7011811 8.710581e-03 8.710581e-03 0.290108362 #> 10022 5010.5 0.7011777 0.7011777 8.713742e-03 8.713742e-03 0.290108510 #> 10023 5011.0 0.7011744 0.7011744 8.716903e-03 8.716903e-03 0.290108711 #> 10024 5011.5 0.7011710 0.7011710 8.720065e-03 8.720065e-03 0.290108965 #> 10025 5012.0 0.7011675 0.7011675 8.723227e-03 8.723227e-03 0.290109271 #> 10026 5012.5 0.7011640 0.7011640 8.726390e-03 8.726390e-03 0.290109630 #> 10027 5013.0 0.7011604 0.7011604 8.729553e-03 8.729553e-03 0.290110041 #> 10028 5013.5 0.7011568 0.7011568 8.732716e-03 8.732716e-03 0.290110505 #> 10029 5014.0 0.7011531 0.7011531 8.735880e-03 8.735880e-03 0.290111021 #> 10030 5014.5 0.7011494 0.7011494 8.739044e-03 8.739044e-03 0.290111590 #> 10031 5015.0 0.7011456 0.7011456 8.742208e-03 8.742208e-03 0.290112211 #> 10032 5015.5 0.7011417 0.7011417 8.745372e-03 8.745372e-03 0.290112885 #> 10033 5016.0 0.7011379 0.7011379 8.748537e-03 8.748537e-03 0.290113611 #> 10034 5016.5 0.7011339 0.7011339 8.751702e-03 8.751702e-03 0.290114389 #> 10035 5017.0 0.7011299 0.7011299 8.754868e-03 8.754868e-03 0.290115220 #> 10036 5017.5 0.7011259 0.7011259 8.758034e-03 8.758034e-03 0.290116103 #> 10037 5018.0 0.7011218 0.7011218 8.761200e-03 8.761200e-03 0.290117038 #> 10038 5018.5 0.7011176 0.7011176 8.764366e-03 8.764366e-03 0.290118026 #> 10039 5019.0 0.7011134 0.7011134 8.767532e-03 8.767532e-03 0.290119065 #> 10040 5019.5 0.7011091 0.7011091 8.770699e-03 8.770699e-03 0.290120158 #> 10041 5020.0 0.7011048 0.7011048 8.773866e-03 8.773866e-03 0.290121302 #> 10042 5020.5 0.7011005 0.7011005 8.777033e-03 8.777033e-03 0.290122498 #> 10043 5021.0 0.7010961 0.7010961 8.780200e-03 8.780200e-03 0.290123747 #> 10044 5021.5 0.7010916 0.7010916 8.783368e-03 8.783368e-03 0.290125048 #> 10045 5022.0 0.7010871 0.7010871 8.786535e-03 8.786535e-03 0.290126401 #> 10046 5022.5 0.7010825 0.7010825 8.789703e-03 8.789703e-03 0.290127806 #> 10047 5023.0 0.7010779 0.7010779 8.792871e-03 8.792871e-03 0.290129263 #> 10048 5023.5 0.7010732 0.7010732 8.796039e-03 8.796039e-03 0.290130773 #> 10049 5024.0 0.7010685 0.7010685 8.799207e-03 8.799207e-03 0.290132334 #> 10050 5024.5 0.7010637 0.7010637 8.802375e-03 8.802375e-03 0.290133947 #> 10051 5025.0 0.7010588 0.7010588 8.805543e-03 8.805543e-03 0.290135612 #> 10052 5025.5 0.7010540 0.7010540 8.808711e-03 8.808711e-03 0.290137330 #> 10053 5026.0 0.7010490 0.7010490 8.811880e-03 8.811880e-03 0.290139099 #> 10054 5026.5 0.7010440 0.7010440 8.815048e-03 8.815048e-03 0.290140920 #> 10055 5027.0 0.7010390 0.7010390 8.818217e-03 8.818217e-03 0.290142793 #> 10056 5027.5 0.7010339 0.7010339 8.821385e-03 8.821385e-03 0.290144718 #> 10057 5028.0 0.7010288 0.7010288 8.824553e-03 8.824553e-03 0.290146694 #> 10058 5028.5 0.7010236 0.7010236 8.827722e-03 8.827722e-03 0.290148723 #> 10059 5029.0 0.7010183 0.7010183 8.830890e-03 8.830890e-03 0.290150803 #> 10060 5029.5 0.7010130 0.7010130 8.834059e-03 8.834059e-03 0.290152935 #> 10061 5030.0 0.7010077 0.7010077 8.837227e-03 8.837227e-03 0.290155119 #> 10062 5030.5 0.7010023 0.7010023 8.840396e-03 8.840396e-03 0.290157354 #> 10063 5031.0 0.7009968 0.7009968 8.843564e-03 8.843564e-03 0.290159641 #> 10064 5031.5 0.7009913 0.7009913 8.846732e-03 8.846732e-03 0.290161980 #> 10065 5032.0 0.7009857 0.7009857 8.849900e-03 8.849900e-03 0.290164370 #> 10066 5032.5 0.7009801 0.7009801 8.853068e-03 8.853068e-03 0.290166812 #> 10067 5033.0 0.7009745 0.7009745 8.856236e-03 8.856236e-03 0.290169306 #> 10068 5033.5 0.7009687 0.7009687 8.859404e-03 8.859404e-03 0.290171851 #> 10069 5034.0 0.7009630 0.7009630 8.862571e-03 8.862571e-03 0.290174447 #> 10070 5034.5 0.7009572 0.7009572 8.865739e-03 8.865739e-03 0.290177095 #> 10071 5035.0 0.7009513 0.7009513 8.868906e-03 8.868906e-03 0.290179795 #> 10072 5035.5 0.7009454 0.7009454 8.872073e-03 8.872073e-03 0.290182546 #> 10073 5036.0 0.7009394 0.7009394 8.875240e-03 8.875240e-03 0.290185348 #> 10074 5036.5 0.7009334 0.7009334 8.878407e-03 8.878407e-03 0.290188202 #> 10075 5037.0 0.7009273 0.7009273 8.881574e-03 8.881574e-03 0.290191107 #> 10076 5037.5 0.7009212 0.7009212 8.884740e-03 8.884740e-03 0.290194063 #> 10077 5038.0 0.7009150 0.7009150 8.887906e-03 8.887906e-03 0.290197070 #> 10078 5038.5 0.7009088 0.7009088 8.891072e-03 8.891072e-03 0.290200129 #> 10079 5039.0 0.7009025 0.7009025 8.894237e-03 8.894237e-03 0.290203239 #> 10080 5039.5 0.7008962 0.7008962 8.897403e-03 8.897403e-03 0.290206401 #> 10081 5040.0 0.7008898 0.7008898 8.900568e-03 8.900568e-03 0.290209613 #> 10082 5040.5 0.7008834 0.7008834 8.903732e-03 8.903732e-03 0.290212877 #> 10083 5041.0 0.7008769 0.7008769 8.906897e-03 8.906897e-03 0.290216191 #> 10084 5041.5 0.7008704 0.7008704 8.910061e-03 8.910061e-03 0.290219557 #> 10085 5042.0 0.7008638 0.7008638 8.913225e-03 8.913225e-03 0.290222974 #> 10086 5042.5 0.7008572 0.7008572 8.916388e-03 8.916388e-03 0.290226441 #> 10087 5043.0 0.7008505 0.7008505 8.919551e-03 8.919551e-03 0.290229960 #> 10088 5043.5 0.7008438 0.7008438 8.922714e-03 8.922714e-03 0.290233530 #> 10089 5044.0 0.7008370 0.7008370 8.925876e-03 8.925876e-03 0.290237150 #> 10090 5044.5 0.7008301 0.7008301 8.929038e-03 8.929038e-03 0.290240822 #> 10091 5045.0 0.7008233 0.7008233 8.932199e-03 8.932199e-03 0.290244544 #> 10092 5045.5 0.7008163 0.7008163 8.935360e-03 8.935360e-03 0.290248317 #> 10093 5046.0 0.7008093 0.7008093 8.938521e-03 8.938521e-03 0.290252141 #> 10094 5046.5 0.7008023 0.7008023 8.941681e-03 8.941681e-03 0.290256016 #> 10095 5047.0 0.7007952 0.7007952 8.944841e-03 8.944841e-03 0.290259941 #> 10096 5047.5 0.7007881 0.7007881 8.948000e-03 8.948000e-03 0.290263917 #> 10097 5048.0 0.7007809 0.7007809 8.951158e-03 8.951158e-03 0.290267944 #> 10098 5048.5 0.7007737 0.7007737 8.954317e-03 8.954317e-03 0.290272021 #> 10099 5049.0 0.7007664 0.7007664 8.957474e-03 8.957474e-03 0.290276149 #> 10100 5049.5 0.7007590 0.7007590 8.960632e-03 8.960632e-03 0.290280328 #> 10101 5050.0 0.7007517 0.7007517 8.963788e-03 8.963788e-03 0.290284557 #> 10102 5050.5 0.7007442 0.7007442 8.966944e-03 8.966944e-03 0.290288836 #> 10103 5051.0 0.7007367 0.7007367 8.970100e-03 8.970100e-03 0.290293166 #> 10104 5051.5 0.7007292 0.7007292 8.973255e-03 8.973255e-03 0.290297546 #> 10105 5052.0 0.7007216 0.7007216 8.976409e-03 8.976409e-03 0.290301977 #> 10106 5052.5 0.7007140 0.7007140 8.979563e-03 8.979563e-03 0.290306458 #> 10107 5053.0 0.7007063 0.7007063 8.982716e-03 8.982716e-03 0.290310989 #> 10108 5053.5 0.7006986 0.7006986 8.985869e-03 8.985869e-03 0.290315571 #> 10109 5054.0 0.7006908 0.7006908 8.989021e-03 8.989021e-03 0.290320202 #> 10110 5054.5 0.7006829 0.7006829 8.992172e-03 8.992172e-03 0.290324884 #> 10111 5055.0 0.7006751 0.7006751 8.995322e-03 8.995322e-03 0.290329617 #> 10112 5055.5 0.7006671 0.7006671 8.998472e-03 8.998472e-03 0.290334399 #> 10113 5056.0 0.7006591 0.7006591 9.001622e-03 9.001622e-03 0.290339231 #> 10114 5056.5 0.7006511 0.7006511 9.004770e-03 9.004770e-03 0.290344114 #> 10115 5057.0 0.7006430 0.7006430 9.007918e-03 9.007918e-03 0.290349046 #> 10116 5057.5 0.7006349 0.7006349 9.011065e-03 9.011065e-03 0.290354029 #> 10117 5058.0 0.7006267 0.7006267 9.014211e-03 9.014211e-03 0.290359061 #> 10118 5058.5 0.7006185 0.7006185 9.017357e-03 9.017357e-03 0.290364143 #> 10119 5059.0 0.7006102 0.7006102 9.020502e-03 9.020502e-03 0.290369275 #> 10120 5059.5 0.7006019 0.7006019 9.023646e-03 9.023646e-03 0.290374457 #> 10121 5060.0 0.7005935 0.7005935 9.026789e-03 9.026789e-03 0.290379689 #> 10122 5060.5 0.7005851 0.7005851 9.029931e-03 9.029931e-03 0.290384971 #> 10123 5061.0 0.7005766 0.7005766 9.033073e-03 9.033073e-03 0.290390302 #> 10124 5061.5 0.7005681 0.7005681 9.036214e-03 9.036214e-03 0.290395683 #> 10125 5062.0 0.7005595 0.7005595 9.039354e-03 9.039354e-03 0.290401113 #> 10126 5062.5 0.7005509 0.7005509 9.042493e-03 9.042493e-03 0.290406593 #> 10127 5063.0 0.7005422 0.7005422 9.045631e-03 9.045631e-03 0.290412123 #> 10128 5063.5 0.7005335 0.7005335 9.048769e-03 9.048769e-03 0.290417702 #> 10129 5064.0 0.7005248 0.7005248 9.051905e-03 9.051905e-03 0.290423331 #> 10130 5064.5 0.7005160 0.7005160 9.055041e-03 9.055041e-03 0.290429009 #> 10131 5065.0 0.7005071 0.7005071 9.058176e-03 9.058176e-03 0.290434737 #> 10132 5065.5 0.7004982 0.7004982 9.061309e-03 9.061309e-03 0.290440514 #> 10133 5066.0 0.7004892 0.7004892 9.064442e-03 9.064442e-03 0.290446340 #> 10134 5066.5 0.7004802 0.7004802 9.067574e-03 9.067574e-03 0.290452215 #> 10135 5067.0 0.7004712 0.7004712 9.070705e-03 9.070705e-03 0.290458140 #> 10136 5067.5 0.7004621 0.7004621 9.073835e-03 9.073835e-03 0.290464114 #> 10137 5068.0 0.7004529 0.7004529 9.076964e-03 9.076964e-03 0.290470137 #> 10138 5068.5 0.7004437 0.7004437 9.080092e-03 9.080092e-03 0.290476209 #> 10139 5069.0 0.7004345 0.7004345 9.083219e-03 9.083219e-03 0.290482331 #> 10140 5069.5 0.7004252 0.7004252 9.086344e-03 9.086344e-03 0.290488501 #> 10141 5070.0 0.7004158 0.7004158 9.089469e-03 9.089469e-03 0.290494720 #> 10142 5070.5 0.7004064 0.7004064 9.092593e-03 9.092593e-03 0.290500988 #> 10143 5071.0 0.7003970 0.7003970 9.095716e-03 9.095716e-03 0.290507306 #> 10144 5071.5 0.7003875 0.7003875 9.098837e-03 9.098837e-03 0.290513672 #> 10145 5072.0 0.7003780 0.7003780 9.101958e-03 9.101958e-03 0.290520086 #> 10146 5072.5 0.7003684 0.7003684 9.105077e-03 9.105077e-03 0.290526550 #> 10147 5073.0 0.7003587 0.7003587 9.108196e-03 9.108196e-03 0.290533063 #> 10148 5073.5 0.7003491 0.7003491 9.111313e-03 9.111313e-03 0.290539624 #> 10149 5074.0 0.7003393 0.7003393 9.114429e-03 9.114429e-03 0.290546233 #> 10150 5074.5 0.7003296 0.7003296 9.117544e-03 9.117544e-03 0.290552892 #> 10151 5075.0 0.7003197 0.7003197 9.120658e-03 9.120658e-03 0.290559599 #> 10152 5075.5 0.7003099 0.7003099 9.123770e-03 9.123770e-03 0.290566354 #> 10153 5076.0 0.7003000 0.7003000 9.126881e-03 9.126881e-03 0.290573158 #> 10154 5076.5 0.7002900 0.7002900 9.129992e-03 9.129992e-03 0.290580010 #> 10155 5077.0 0.7002800 0.7002800 9.133100e-03 9.133100e-03 0.290586911 #> 10156 5077.5 0.7002699 0.7002699 9.136208e-03 9.136208e-03 0.290593860 #> 10157 5078.0 0.7002598 0.7002598 9.139315e-03 9.139315e-03 0.290600857 #> 10158 5078.5 0.7002497 0.7002497 9.142420e-03 9.142420e-03 0.290607903 #> 10159 5079.0 0.7002395 0.7002395 9.145524e-03 9.145524e-03 0.290614997 #> 10160 5079.5 0.7002292 0.7002292 9.148626e-03 9.148626e-03 0.290622139 #> 10161 5080.0 0.7002189 0.7002189 9.151728e-03 9.151728e-03 0.290629329 #> 10162 5080.5 0.7002086 0.7002086 9.154828e-03 9.154828e-03 0.290636567 #> 10163 5081.0 0.7001982 0.7001982 9.157926e-03 9.157926e-03 0.290643853 #> 10164 5081.5 0.7001878 0.7001878 9.161024e-03 9.161024e-03 0.290651187 #> 10165 5082.0 0.7001773 0.7001773 9.164120e-03 9.164120e-03 0.290658569 #> 10166 5082.5 0.7001668 0.7001668 9.167214e-03 9.167214e-03 0.290665999 #> 10167 5083.0 0.7001562 0.7001562 9.170308e-03 9.170308e-03 0.290673477 #> 10168 5083.5 0.7001456 0.7001456 9.173399e-03 9.173399e-03 0.290681003 #> 10169 5084.0 0.7001349 0.7001349 9.176490e-03 9.176490e-03 0.290688576 #> 10170 5084.5 0.7001242 0.7001242 9.179579e-03 9.179579e-03 0.290696197 #> 10171 5085.0 0.7001135 0.7001135 9.182667e-03 9.182667e-03 0.290703866 #> 10172 5085.5 0.7001027 0.7001027 9.185753e-03 9.185753e-03 0.290711582 #> 10173 5086.0 0.7000918 0.7000918 9.188838e-03 9.188838e-03 0.290719346 #> 10174 5086.5 0.7000809 0.7000809 9.191921e-03 9.191921e-03 0.290727157 #> 10175 5087.0 0.7000700 0.7000700 9.195003e-03 9.195003e-03 0.290735016 #> 10176 5087.5 0.7000590 0.7000590 9.198083e-03 9.198083e-03 0.290742922 #> 10177 5088.0 0.7000480 0.7000480 9.201162e-03 9.201162e-03 0.290750876 #> 10178 5088.5 0.7000369 0.7000369 9.204239e-03 9.204239e-03 0.290758877 #> 10179 5089.0 0.7000258 0.7000258 9.207315e-03 9.207315e-03 0.290766925 #> 10180 5089.5 0.7000146 0.7000146 9.210389e-03 9.210389e-03 0.290775020 #> 10181 5090.0 0.7000034 0.7000034 9.213462e-03 9.213462e-03 0.290783163 #> 10182 5090.5 0.6999921 0.6999921 9.216533e-03 9.216533e-03 0.290791353 #> 10183 5091.0 0.6999808 0.6999808 9.219603e-03 9.219603e-03 0.290799589 #> 10184 5091.5 0.6999695 0.6999695 9.222671e-03 9.222671e-03 0.290807873 #> 10185 5092.0 0.6999581 0.6999581 9.225737e-03 9.225737e-03 0.290816204 #> 10186 5092.5 0.6999466 0.6999466 9.228802e-03 9.228802e-03 0.290824582 #> 10187 5093.0 0.6999351 0.6999351 9.231865e-03 9.231865e-03 0.290833006 #> 10188 5093.5 0.6999236 0.6999236 9.234927e-03 9.234927e-03 0.290841477 #> 10189 5094.0 0.6999120 0.6999120 9.237986e-03 9.237986e-03 0.290849995 #> 10190 5094.5 0.6999004 0.6999004 9.241045e-03 9.241045e-03 0.290858560 #> 10191 5095.0 0.6998887 0.6998887 9.244101e-03 9.244101e-03 0.290867172 #> 10192 5095.5 0.6998770 0.6998770 9.247156e-03 9.247156e-03 0.290875830 #> 10193 5096.0 0.6998653 0.6998653 9.250209e-03 9.250209e-03 0.290884534 #> 10194 5096.5 0.6998535 0.6998535 9.253260e-03 9.253260e-03 0.290893285 #> 10195 5097.0 0.6998416 0.6998416 9.256310e-03 9.256310e-03 0.290902083 #> 10196 5097.5 0.6998297 0.6998297 9.259358e-03 9.259358e-03 0.290910927 #> 10197 5098.0 0.6998178 0.6998178 9.262404e-03 9.262404e-03 0.290919817 #> 10198 5098.5 0.6998058 0.6998058 9.265448e-03 9.265448e-03 0.290928754 #> 10199 5099.0 0.6997938 0.6997938 9.268491e-03 9.268491e-03 0.290937737 #> 10200 5099.5 0.6997817 0.6997817 9.271532e-03 9.271532e-03 0.290946766 #> 10201 5100.0 0.6997696 0.6997696 9.274571e-03 9.274571e-03 0.290955841 #> 10202 5100.5 0.6997574 0.6997574 9.277608e-03 9.277608e-03 0.290964962 #> 10203 5101.0 0.6997452 0.6997452 9.280643e-03 9.280643e-03 0.290974130 #> 10204 5101.5 0.6997330 0.6997330 9.283677e-03 9.283677e-03 0.290983343 #> 10205 5102.0 0.6997207 0.6997207 9.286709e-03 9.286709e-03 0.290992602 #> 10206 5102.5 0.6997084 0.6997084 9.289738e-03 9.289738e-03 0.291001907 #> 10207 5103.0 0.6996960 0.6996960 9.292766e-03 9.292766e-03 0.291011258 #> 10208 5103.5 0.6996836 0.6996836 9.295792e-03 9.295792e-03 0.291020655 #> 10209 5104.0 0.6996711 0.6996711 9.298816e-03 9.298816e-03 0.291030097 #> 10210 5104.5 0.6996586 0.6996586 9.301839e-03 9.301839e-03 0.291039585 #> 10211 5105.0 0.6996460 0.6996460 9.304859e-03 9.304859e-03 0.291049119 #> 10212 5105.5 0.6996334 0.6996334 9.307877e-03 9.307877e-03 0.291058698 #> 10213 5106.0 0.6996208 0.6996208 9.310893e-03 9.310893e-03 0.291068323 #> 10214 5106.5 0.6996081 0.6996081 9.313908e-03 9.313908e-03 0.291077993 #> 10215 5107.0 0.6995954 0.6995954 9.316920e-03 9.316920e-03 0.291087709 #> 10216 5107.5 0.6995826 0.6995826 9.319931e-03 9.319931e-03 0.291097469 #> 10217 5108.0 0.6995698 0.6995698 9.322939e-03 9.322939e-03 0.291107275 #> 10218 5108.5 0.6995569 0.6995569 9.325945e-03 9.325945e-03 0.291117127 #> 10219 5109.0 0.6995440 0.6995440 9.328950e-03 9.328950e-03 0.291127023 #> 10220 5109.5 0.6995311 0.6995311 9.331952e-03 9.331952e-03 0.291136965 #> 10221 5110.0 0.6995181 0.6995181 9.334952e-03 9.334952e-03 0.291146951 #> 10222 5110.5 0.6995051 0.6995051 9.337950e-03 9.337950e-03 0.291156983 #> 10223 5111.0 0.6994920 0.6994920 9.340946e-03 9.340946e-03 0.291167059 #> 10224 5111.5 0.6994789 0.6994789 9.343940e-03 9.343940e-03 0.291177180 #> 10225 5112.0 0.6994657 0.6994657 9.346932e-03 9.346932e-03 0.291187347 #> 10226 5112.5 0.6994525 0.6994525 9.349922e-03 9.349922e-03 0.291197558 #> 10227 5113.0 0.6994393 0.6994393 9.352910e-03 9.352910e-03 0.291207813 #> 10228 5113.5 0.6994260 0.6994260 9.355895e-03 9.355895e-03 0.291218113 #> 10229 5114.0 0.6994127 0.6994127 9.358878e-03 9.358878e-03 0.291228458 #> 10230 5114.5 0.6993993 0.6993993 9.361859e-03 9.361859e-03 0.291238847 #> 10231 5115.0 0.6993859 0.6993859 9.364838e-03 9.364838e-03 0.291249281 #> 10232 5115.5 0.6993724 0.6993724 9.367815e-03 9.367815e-03 0.291259759 #> 10233 5116.0 0.6993589 0.6993589 9.370790e-03 9.370790e-03 0.291270282 #> 10234 5116.5 0.6993454 0.6993454 9.373762e-03 9.373762e-03 0.291280849 #> 10235 5117.0 0.6993318 0.6993318 9.376732e-03 9.376732e-03 0.291291460 #> 10236 5117.5 0.6993182 0.6993182 9.379700e-03 9.379700e-03 0.291302115 #> 10237 5118.0 0.6993045 0.6993045 9.382665e-03 9.382665e-03 0.291312814 #> 10238 5118.5 0.6992908 0.6992908 9.385629e-03 9.385629e-03 0.291323558 #> 10239 5119.0 0.6992771 0.6992771 9.388590e-03 9.388590e-03 0.291334345 #> 10240 5119.5 0.6992633 0.6992633 9.391548e-03 9.391548e-03 0.291345176 #> 10241 5120.0 0.6992494 0.6992494 9.394505e-03 9.394505e-03 0.291356051 #> 10242 5120.5 0.6992356 0.6992356 9.397459e-03 9.397459e-03 0.291366970 #> 10243 5121.0 0.6992217 0.6992217 9.400410e-03 9.400410e-03 0.291377933 #> 10244 5121.5 0.6992077 0.6992077 9.403360e-03 9.403360e-03 0.291388939 #> 10245 5122.0 0.6991937 0.6991937 9.406307e-03 9.406307e-03 0.291399989 #> 10246 5122.5 0.6991797 0.6991797 9.409251e-03 9.409251e-03 0.291411083 #> 10247 5123.0 0.6991656 0.6991656 9.412194e-03 9.412194e-03 0.291422220 #> 10248 5123.5 0.6991515 0.6991515 9.415134e-03 9.415134e-03 0.291433401 #> 10249 5124.0 0.6991373 0.6991373 9.418071e-03 9.418071e-03 0.291444624 #> 10250 5124.5 0.6991231 0.6991231 9.421006e-03 9.421006e-03 0.291455892 #> 10251 5125.0 0.6991089 0.6991089 9.423939e-03 9.423939e-03 0.291467202 #> 10252 5125.5 0.6990946 0.6990946 9.426869e-03 9.426869e-03 0.291478556 #> 10253 5126.0 0.6990803 0.6990803 9.429796e-03 9.429796e-03 0.291489952 #> 10254 5126.5 0.6990659 0.6990659 9.432722e-03 9.432722e-03 0.291501392 #> 10255 5127.0 0.6990515 0.6990515 9.435644e-03 9.435644e-03 0.291512875 #> 10256 5127.5 0.6990370 0.6990370 9.438565e-03 9.438565e-03 0.291524401 #> 10257 5128.0 0.6990225 0.6990225 9.441482e-03 9.441482e-03 0.291535970 #> 10258 5128.5 0.6990080 0.6990080 9.444397e-03 9.444397e-03 0.291547581 #> 10259 5129.0 0.6989935 0.6989935 9.447310e-03 9.447310e-03 0.291559235 #> 10260 5129.5 0.6989788 0.6989788 9.450220e-03 9.450220e-03 0.291570932 #> 10261 5130.0 0.6989642 0.6989642 9.453128e-03 9.453128e-03 0.291582672 #> 10262 5130.5 0.6989495 0.6989495 9.456033e-03 9.456033e-03 0.291594454 #> 10263 5131.0 0.6989348 0.6989348 9.458935e-03 9.458935e-03 0.291606278 #> 10264 5131.5 0.6989200 0.6989200 9.461835e-03 9.461835e-03 0.291618145 #> 10265 5132.0 0.6989052 0.6989052 9.464732e-03 9.464732e-03 0.291630054 #> 10266 5132.5 0.6988904 0.6988904 9.467627e-03 9.467627e-03 0.291642006 #> 10267 5133.0 0.6988755 0.6988755 9.470519e-03 9.470519e-03 0.291654000 #> 10268 5133.5 0.6988606 0.6988606 9.473408e-03 9.473408e-03 0.291666036 #> 10269 5134.0 0.6988456 0.6988456 9.476294e-03 9.476294e-03 0.291678114 #> 10270 5134.5 0.6988306 0.6988306 9.479178e-03 9.479178e-03 0.291690234 #> 10271 5135.0 0.6988155 0.6988155 9.482060e-03 9.482060e-03 0.291702397 #> 10272 5135.5 0.6988005 0.6988005 9.484938e-03 9.484938e-03 0.291714601 #> 10273 5136.0 0.6987853 0.6987853 9.487814e-03 9.487814e-03 0.291726847 #> 10274 5136.5 0.6987702 0.6987702 9.490687e-03 9.490687e-03 0.291739134 #> 10275 5137.0 0.6987550 0.6987550 9.493558e-03 9.493558e-03 0.291751464 #> 10276 5137.5 0.6987397 0.6987397 9.496425e-03 9.496425e-03 0.291763835 #> 10277 5138.0 0.6987245 0.6987245 9.499290e-03 9.499290e-03 0.291776247 #> 10278 5138.5 0.6987091 0.6987091 9.502152e-03 9.502152e-03 0.291788701 #> 10279 5139.0 0.6986938 0.6986938 9.505012e-03 9.505012e-03 0.291801197 #> 10280 5139.5 0.6986784 0.6986784 9.507868e-03 9.507868e-03 0.291813734 #> 10281 5140.0 0.6986630 0.6986630 9.510722e-03 9.510722e-03 0.291826312 #> 10282 5140.5 0.6986475 0.6986475 9.513573e-03 9.513573e-03 0.291838932 #> 10283 5141.0 0.6986320 0.6986320 9.516421e-03 9.516421e-03 0.291851592 #> 10284 5141.5 0.6986164 0.6986164 9.519266e-03 9.519266e-03 0.291864294 #> 10285 5142.0 0.6986009 0.6986009 9.522109e-03 9.522109e-03 0.291877037 #> 10286 5142.5 0.6985852 0.6985852 9.524948e-03 9.524948e-03 0.291889821 #> 10287 5143.0 0.6985696 0.6985696 9.527785e-03 9.527785e-03 0.291902645 #> 10288 5143.5 0.6985539 0.6985539 9.530618e-03 9.530618e-03 0.291915511 #> 10289 5144.0 0.6985381 0.6985381 9.533449e-03 9.533449e-03 0.291928417 #> 10290 5144.5 0.6985224 0.6985224 9.536277e-03 9.536277e-03 0.291941364 #> 10291 5145.0 0.6985065 0.6985065 9.539102e-03 9.539102e-03 0.291954352 #> 10292 5145.5 0.6984907 0.6984907 9.541924e-03 9.541924e-03 0.291967380 #> 10293 5146.0 0.6984748 0.6984748 9.544743e-03 9.544743e-03 0.291980448 #> 10294 5146.5 0.6984589 0.6984589 9.547560e-03 9.547560e-03 0.291993557 #> 10295 5147.0 0.6984429 0.6984429 9.550373e-03 9.550373e-03 0.292006707 #> 10296 5147.5 0.6984269 0.6984269 9.553183e-03 9.553183e-03 0.292019896 #> 10297 5148.0 0.6984109 0.6984109 9.555990e-03 9.555990e-03 0.292033126 #> 10298 5148.5 0.6983948 0.6983948 9.558794e-03 9.558794e-03 0.292046396 #> 10299 5149.0 0.6983787 0.6983787 9.561596e-03 9.561596e-03 0.292059706 #> 10300 5149.5 0.6983626 0.6983626 9.564394e-03 9.564394e-03 0.292073056 #> 10301 5150.0 0.6983464 0.6983464 9.567189e-03 9.567189e-03 0.292086446 #> 10302 5150.5 0.6983301 0.6983301 9.569981e-03 9.569981e-03 0.292099876 #> 10303 5151.0 0.6983139 0.6983139 9.572770e-03 9.572770e-03 0.292113346 #> 10304 5151.5 0.6982976 0.6982976 9.575556e-03 9.575556e-03 0.292126855 #> 10305 5152.0 0.6982813 0.6982813 9.578338e-03 9.578338e-03 0.292140404 #> 10306 5152.5 0.6982649 0.6982649 9.581118e-03 9.581118e-03 0.292153993 #> 10307 5153.0 0.6982485 0.6982485 9.583894e-03 9.583894e-03 0.292167621 #> 10308 5153.5 0.6982320 0.6982320 9.586668e-03 9.586668e-03 0.292181288 #> 10309 5154.0 0.6982156 0.6982156 9.589438e-03 9.589438e-03 0.292194995 #> 10310 5154.5 0.6981991 0.6981991 9.592205e-03 9.592205e-03 0.292208741 #> 10311 5155.0 0.6981825 0.6981825 9.594969e-03 9.594969e-03 0.292222526 #> 10312 5155.5 0.6981659 0.6981659 9.597730e-03 9.597730e-03 0.292236351 #> 10313 5156.0 0.6981493 0.6981493 9.600488e-03 9.600488e-03 0.292250214 #> 10314 5156.5 0.6981326 0.6981326 9.603242e-03 9.603242e-03 0.292264117 #> 10315 5157.0 0.6981159 0.6981159 9.605993e-03 9.605993e-03 0.292278058 #> 10316 5157.5 0.6980992 0.6980992 9.608741e-03 9.608741e-03 0.292292038 #> 10317 5158.0 0.6980825 0.6980825 9.611486e-03 9.611486e-03 0.292306058 #> 10318 5158.5 0.6980657 0.6980657 9.614227e-03 9.614227e-03 0.292320115 #> 10319 5159.0 0.6980488 0.6980488 9.616965e-03 9.616965e-03 0.292334212 #> 10320 5159.5 0.6980320 0.6980320 9.619700e-03 9.619700e-03 0.292348347 #> 10321 5160.0 0.6980150 0.6980150 9.622432e-03 9.622432e-03 0.292362520 #> 10322 5160.5 0.6979981 0.6979981 9.625160e-03 9.625160e-03 0.292376732 #> 10323 5161.0 0.6979811 0.6979811 9.627885e-03 9.627885e-03 0.292390982 #> 10324 5161.5 0.6979641 0.6979641 9.630607e-03 9.630607e-03 0.292405270 #> 10325 5162.0 0.6979471 0.6979471 9.633325e-03 9.633325e-03 0.292419597 #> 10326 5162.5 0.6979300 0.6979300 9.636040e-03 9.636040e-03 0.292433961 #> 10327 5163.0 0.6979129 0.6979129 9.638752e-03 9.638752e-03 0.292448364 #> 10328 5163.5 0.6978957 0.6978957 9.641460e-03 9.641460e-03 0.292462805 #> 10329 5164.0 0.6978786 0.6978786 9.644165e-03 9.644165e-03 0.292477283 #> 10330 5164.5 0.6978613 0.6978613 9.646867e-03 9.646867e-03 0.292491800 #> 10331 5165.0 0.6978441 0.6978441 9.649565e-03 9.649565e-03 0.292506354 #> 10332 5165.5 0.6978268 0.6978268 9.652260e-03 9.652260e-03 0.292520945 #> 10333 5166.0 0.6978095 0.6978095 9.654951e-03 9.654951e-03 0.292535575 #> 10334 5166.5 0.6977921 0.6977921 9.657639e-03 9.657639e-03 0.292550242 #> 10335 5167.0 0.6977747 0.6977747 9.660323e-03 9.660323e-03 0.292564946 #> 10336 5167.5 0.6977573 0.6977573 9.663004e-03 9.663004e-03 0.292579687 #> 10337 5168.0 0.6977399 0.6977399 9.665682e-03 9.665682e-03 0.292594466 #> 10338 5168.5 0.6977224 0.6977224 9.668356e-03 9.668356e-03 0.292609282 #> 10339 5169.0 0.6977048 0.6977048 9.671026e-03 9.671026e-03 0.292624135 #> 10340 5169.5 0.6976873 0.6976873 9.673693e-03 9.673693e-03 0.292639026 #> 10341 5170.0 0.6976697 0.6976697 9.676357e-03 9.676357e-03 0.292653953 #> 10342 5170.5 0.6976521 0.6976521 9.679017e-03 9.679017e-03 0.292668917 #> 10343 5171.0 0.6976344 0.6976344 9.681673e-03 9.681673e-03 0.292683918 #> 10344 5171.5 0.6976167 0.6976167 9.684326e-03 9.684326e-03 0.292698955 #> 10345 5172.0 0.6975990 0.6975990 9.686975e-03 9.686975e-03 0.292714030 #> 10346 5172.5 0.6975812 0.6975812 9.689621e-03 9.689621e-03 0.292729140 #> 10347 5173.0 0.6975634 0.6975634 9.692263e-03 9.692263e-03 0.292744288 #> 10348 5173.5 0.6975456 0.6975456 9.694902e-03 9.694902e-03 0.292759472 #> 10349 5174.0 0.6975278 0.6975278 9.697537e-03 9.697537e-03 0.292774692 #> 10350 5174.5 0.6975099 0.6975099 9.700168e-03 9.700168e-03 0.292789948 #> 10351 5175.0 0.6974920 0.6974920 9.702796e-03 9.702796e-03 0.292805241 #> 10352 5175.5 0.6974740 0.6974740 9.705420e-03 9.705420e-03 0.292820569 #> 10353 5176.0 0.6974560 0.6974560 9.708040e-03 9.708040e-03 0.292835934 #> 10354 5176.5 0.6974380 0.6974380 9.710657e-03 9.710657e-03 0.292851335 #> 10355 5177.0 0.6974200 0.6974200 9.713270e-03 9.713270e-03 0.292866771 #> 10356 5177.5 0.6974019 0.6974019 9.715879e-03 9.715879e-03 0.292882244 #> 10357 5178.0 0.6973838 0.6973838 9.718485e-03 9.718485e-03 0.292897752 #> 10358 5178.5 0.6973656 0.6973656 9.721087e-03 9.721087e-03 0.292913295 #> 10359 5179.0 0.6973474 0.6973474 9.723685e-03 9.723685e-03 0.292928874 #> 10360 5179.5 0.6973292 0.6973292 9.726280e-03 9.726280e-03 0.292944489 #> 10361 5180.0 0.6973110 0.6973110 9.728871e-03 9.728871e-03 0.292960139 #> 10362 5180.5 0.6972927 0.6972927 9.731458e-03 9.731458e-03 0.292975825 #> 10363 5181.0 0.6972744 0.6972744 9.734041e-03 9.734041e-03 0.292991545 #> 10364 5181.5 0.6972561 0.6972561 9.736621e-03 9.736621e-03 0.293007301 #> 10365 5182.0 0.6972377 0.6972377 9.739196e-03 9.739196e-03 0.293023092 #> 10366 5182.5 0.6972193 0.6972193 9.741768e-03 9.741768e-03 0.293038918 #> 10367 5183.0 0.6972009 0.6972009 9.744336e-03 9.744336e-03 0.293054779 #> 10368 5183.5 0.6971824 0.6971824 9.746901e-03 9.746901e-03 0.293070674 #> 10369 5184.0 0.6971639 0.6971639 9.749461e-03 9.749461e-03 0.293086605 #> 10370 5184.5 0.6971454 0.6971454 9.752018e-03 9.752018e-03 0.293102570 #> 10371 5185.0 0.6971269 0.6971269 9.754571e-03 9.754571e-03 0.293118569 #> 10372 5185.5 0.6971083 0.6971083 9.757119e-03 9.757119e-03 0.293134604 #> 10373 5186.0 0.6970897 0.6970897 9.759665e-03 9.759665e-03 0.293150672 #> 10374 5186.5 0.6970710 0.6970710 9.762206e-03 9.762206e-03 0.293166775 #> 10375 5187.0 0.6970523 0.6970523 9.764743e-03 9.764743e-03 0.293182912 #> 10376 5187.5 0.6970336 0.6970336 9.767276e-03 9.767276e-03 0.293199083 #> 10377 5188.0 0.6970149 0.6970149 9.769806e-03 9.769806e-03 0.293215289 #> 10378 5188.5 0.6969961 0.6969961 9.772331e-03 9.772331e-03 0.293231528 #> 10379 5189.0 0.6969773 0.6969773 9.774853e-03 9.774853e-03 0.293247802 #> 10380 5189.5 0.6969585 0.6969585 9.777371e-03 9.777371e-03 0.293264109 #> 10381 5190.0 0.6969397 0.6969397 9.779884e-03 9.779884e-03 0.293280450 #> 10382 5190.5 0.6969208 0.6969208 9.782394e-03 9.782394e-03 0.293296825 #> 10383 5191.0 0.6969019 0.6969019 9.784900e-03 9.784900e-03 0.293313233 #> 10384 5191.5 0.6968829 0.6968829 9.787402e-03 9.787402e-03 0.293329675 #> 10385 5192.0 0.6968640 0.6968640 9.789899e-03 9.789899e-03 0.293346150 #> 10386 5192.5 0.6968449 0.6968449 9.792393e-03 9.792393e-03 0.293362658 #> 10387 5193.0 0.6968259 0.6968259 9.794883e-03 9.794883e-03 0.293379200 #> 10388 5193.5 0.6968069 0.6968069 9.797369e-03 9.797369e-03 0.293395775 #> 10389 5194.0 0.6967878 0.6967878 9.799850e-03 9.799850e-03 0.293412383 #> 10390 5194.5 0.6967686 0.6967686 9.802328e-03 9.802328e-03 0.293429025 #> 10391 5195.0 0.6967495 0.6967495 9.804801e-03 9.804801e-03 0.293445699 #> 10392 5195.5 0.6967303 0.6967303 9.807271e-03 9.807271e-03 0.293462406 #> 10393 5196.0 0.6967111 0.6967111 9.809736e-03 9.809736e-03 0.293479145 #> 10394 5196.5 0.6966919 0.6966919 9.812198e-03 9.812198e-03 0.293495918 #> 10395 5197.0 0.6966726 0.6966726 9.814655e-03 9.814655e-03 0.293512723 #> 10396 5197.5 0.6966533 0.6966533 9.817108e-03 9.817108e-03 0.293529560 #> 10397 5198.0 0.6966340 0.6966340 9.819557e-03 9.819557e-03 0.293546430 #> 10398 5198.5 0.6966147 0.6966147 9.822002e-03 9.822002e-03 0.293563332 #> 10399 5199.0 0.6965953 0.6965953 9.824442e-03 9.824442e-03 0.293580267 #> 10400 5199.5 0.6965759 0.6965759 9.826879e-03 9.826879e-03 0.293597233 #> 10401 5200.0 0.6965565 0.6965565 9.829311e-03 9.829311e-03 0.293614232 #> 10402 5200.5 0.6965370 0.6965370 9.831739e-03 9.831739e-03 0.293631263 #> 10403 5201.0 0.6965175 0.6965175 9.834163e-03 9.834163e-03 0.293648325 #> 10404 5201.5 0.6964980 0.6964980 9.836583e-03 9.836583e-03 0.293665420 #> 10405 5202.0 0.6964785 0.6964785 9.838998e-03 9.838998e-03 0.293682546 #> 10406 5202.5 0.6964589 0.6964589 9.841410e-03 9.841410e-03 0.293699704 #> 10407 5203.0 0.6964393 0.6964393 9.843817e-03 9.843817e-03 0.293716893 #> 10408 5203.5 0.6964197 0.6964197 9.846220e-03 9.846220e-03 0.293734114 #> 10409 5204.0 0.6964000 0.6964000 9.848618e-03 9.848618e-03 0.293751366 #> 10410 5204.5 0.6963803 0.6963803 9.851013e-03 9.851013e-03 0.293768650 #> 10411 5205.0 0.6963606 0.6963606 9.853403e-03 9.853403e-03 0.293785965 #> 10412 5205.5 0.6963409 0.6963409 9.855789e-03 9.855789e-03 0.293803311 #> 10413 5206.0 0.6963211 0.6963211 9.858170e-03 9.858170e-03 0.293820688 #> 10414 5206.5 0.6963014 0.6963014 9.860547e-03 9.860547e-03 0.293838096 #> 10415 5207.0 0.6962815 0.6962815 9.862920e-03 9.862920e-03 0.293855535 #> 10416 5207.5 0.6962617 0.6962617 9.865289e-03 9.865289e-03 0.293873004 #> 10417 5208.0 0.6962418 0.6962418 9.867653e-03 9.867653e-03 0.293890505 #> 10418 5208.5 0.6962220 0.6962220 9.870013e-03 9.870013e-03 0.293908035 #> 10419 5209.0 0.6962020 0.6962020 9.872369e-03 9.872369e-03 0.293925597 #> 10420 5209.5 0.6961821 0.6961821 9.874720e-03 9.874720e-03 0.293943189 #> 10421 5210.0 0.6961621 0.6961621 9.877067e-03 9.877067e-03 0.293960811 #> 10422 5210.5 0.6961421 0.6961421 9.879409e-03 9.879409e-03 0.293978463 #> 10423 5211.0 0.6961221 0.6961221 9.881748e-03 9.881748e-03 0.293996146 #> 10424 5211.5 0.6961021 0.6961021 9.884081e-03 9.884081e-03 0.294013859 #> 10425 5212.0 0.6960820 0.6960820 9.886411e-03 9.886411e-03 0.294031602 #> 10426 5212.5 0.6960619 0.6960619 9.888735e-03 9.888735e-03 0.294049374 #> 10427 5213.0 0.6960418 0.6960418 9.891056e-03 9.891056e-03 0.294067177 #> 10428 5213.5 0.6960216 0.6960216 9.893372e-03 9.893372e-03 0.294085009 #> 10429 5214.0 0.6960014 0.6960014 9.895683e-03 9.895683e-03 0.294102871 #> 10430 5214.5 0.6959812 0.6959812 9.897991e-03 9.897991e-03 0.294120762 #> 10431 5215.0 0.6959610 0.6959610 9.900293e-03 9.900293e-03 0.294138683 #> 10432 5215.5 0.6959408 0.6959408 9.902591e-03 9.902591e-03 0.294156633 #> 10433 5216.0 0.6959205 0.6959205 9.904885e-03 9.904885e-03 0.294174613 #> 10434 5216.5 0.6959002 0.6959002 9.907174e-03 9.907174e-03 0.294192621 #> 10435 5217.0 0.6958799 0.6958799 9.909459e-03 9.909459e-03 0.294210659 #> 10436 5217.5 0.6958595 0.6958595 9.911739e-03 9.911739e-03 0.294228726 #> 10437 5218.0 0.6958392 0.6958392 9.914015e-03 9.914015e-03 0.294246822 #> 10438 5218.5 0.6958188 0.6958188 9.916286e-03 9.916286e-03 0.294264946 #> 10439 5219.0 0.6957983 0.6957983 9.918553e-03 9.918553e-03 0.294283100 #> 10440 5219.5 0.6957779 0.6957779 9.920815e-03 9.920815e-03 0.294301282 #> 10441 5220.0 0.6957574 0.6957574 9.923072e-03 9.923072e-03 0.294319492 #> 10442 5220.5 0.6957369 0.6957369 9.925325e-03 9.925325e-03 0.294337731 #> 10443 5221.0 0.6957164 0.6957164 9.927573e-03 9.927573e-03 0.294355999 #> 10444 5221.5 0.6956959 0.6956959 9.929817e-03 9.929817e-03 0.294374294 #> 10445 5222.0 0.6956753 0.6956753 9.932056e-03 9.932056e-03 0.294392618 #> 10446 5222.5 0.6956547 0.6956547 9.934291e-03 9.934291e-03 0.294410970 #> 10447 5223.0 0.6956341 0.6956341 9.936521e-03 9.936521e-03 0.294429350 #> 10448 5223.5 0.6956135 0.6956135 9.938746e-03 9.938746e-03 0.294447758 #> 10449 5224.0 0.6955928 0.6955928 9.940967e-03 9.940967e-03 0.294466194 #> 10450 5224.5 0.6955722 0.6955722 9.943183e-03 9.943183e-03 0.294484657 #> 10451 5225.0 0.6955515 0.6955515 9.945394e-03 9.945394e-03 0.294503149 #> 10452 5225.5 0.6955307 0.6955307 9.947601e-03 9.947601e-03 0.294521667 #> 10453 5226.0 0.6955100 0.6955100 9.949803e-03 9.949803e-03 0.294540214 #> 10454 5226.5 0.6954892 0.6954892 9.952001e-03 9.952001e-03 0.294558787 #> 10455 5227.0 0.6954684 0.6954684 9.954193e-03 9.954193e-03 0.294577388 #> 10456 5227.5 0.6954476 0.6954476 9.956381e-03 9.956381e-03 0.294596016 #> 10457 5228.0 0.6954268 0.6954268 9.958565e-03 9.958565e-03 0.294614672 #> 10458 5228.5 0.6954059 0.6954059 9.960743e-03 9.960743e-03 0.294633354 #> 10459 5229.0 0.6953850 0.6953850 9.962917e-03 9.962917e-03 0.294652063 #> 10460 5229.5 0.6953641 0.6953641 9.965086e-03 9.965086e-03 0.294670799 #> 10461 5230.0 0.6953432 0.6953432 9.967251e-03 9.967251e-03 0.294689562 #> 10462 5230.5 0.6953222 0.6953222 9.969410e-03 9.969410e-03 0.294708351 #> 10463 5231.0 0.6953013 0.6953013 9.971565e-03 9.971565e-03 0.294727167 #> 10464 5231.5 0.6952803 0.6952803 9.973715e-03 9.973715e-03 0.294746009 #> 10465 5232.0 0.6952593 0.6952593 9.975861e-03 9.975861e-03 0.294764878 #> 10466 5232.5 0.6952382 0.6952382 9.978001e-03 9.978001e-03 0.294783773 #> 10467 5233.0 0.6952172 0.6952172 9.980137e-03 9.980137e-03 0.294802694 #> 10468 5233.5 0.6951961 0.6951961 9.982268e-03 9.982268e-03 0.294821642 #> 10469 5234.0 0.6951750 0.6951750 9.984394e-03 9.984394e-03 0.294840615 #> 10470 5234.5 0.6951539 0.6951539 9.986515e-03 9.986515e-03 0.294859614 #> 10471 5235.0 0.6951327 0.6951327 9.988632e-03 9.988632e-03 0.294878639 #> 10472 5235.5 0.6951116 0.6951116 9.990744e-03 9.990744e-03 0.294897690 #> 10473 5236.0 0.6950904 0.6950904 9.992850e-03 9.992850e-03 0.294916766 #> 10474 5236.5 0.6950692 0.6950692 9.994952e-03 9.994952e-03 0.294935868 #> 10475 5237.0 0.6950480 0.6950480 9.997050e-03 9.997050e-03 0.294954995 #> 10476 5237.5 0.6950267 0.6950267 9.999142e-03 9.999142e-03 0.294974148 #> 10477 5238.0 0.6950054 0.6950054 1.000123e-02 1.000123e-02 0.294993326 #> 10478 5238.5 0.6949842 0.6949842 1.000331e-02 1.000331e-02 0.295012529 #> 10479 5239.0 0.6949629 0.6949629 1.000539e-02 1.000539e-02 0.295031757 #> 10480 5239.5 0.6949415 0.6949415 1.000746e-02 1.000746e-02 0.295051010 #> 10481 5240.0 0.6949202 0.6949202 1.000953e-02 1.000953e-02 0.295070288 #> 10482 5240.5 0.6948988 0.6948988 1.001159e-02 1.001159e-02 0.295089591 #> 10483 5241.0 0.6948774 0.6948774 1.001365e-02 1.001365e-02 0.295108918 #> 10484 5241.5 0.6948560 0.6948560 1.001570e-02 1.001570e-02 0.295128270 #> 10485 5242.0 0.6948346 0.6948346 1.001775e-02 1.001775e-02 0.295147647 #> 10486 5242.5 0.6948132 0.6948132 1.001980e-02 1.001980e-02 0.295167048 #> 10487 5243.0 0.6947917 0.6947917 1.002183e-02 1.002183e-02 0.295186473 #> 10488 5243.5 0.6947702 0.6947702 1.002387e-02 1.002387e-02 0.295205922 #> 10489 5244.0 0.6947487 0.6947487 1.002589e-02 1.002589e-02 0.295225396 #> 10490 5244.5 0.6947272 0.6947272 1.002792e-02 1.002792e-02 0.295244893 #> 10491 5245.0 0.6947056 0.6947056 1.002994e-02 1.002994e-02 0.295264415 #> 10492 5245.5 0.6946841 0.6946841 1.003195e-02 1.003195e-02 0.295283960 #> 10493 5246.0 0.6946625 0.6946625 1.003396e-02 1.003396e-02 0.295303529 #> 10494 5246.5 0.6946409 0.6946409 1.003596e-02 1.003596e-02 0.295323122 #> 10495 5247.0 0.6946193 0.6946193 1.003796e-02 1.003796e-02 0.295342738 #> 10496 5247.5 0.6945977 0.6945977 1.003995e-02 1.003995e-02 0.295362378 #> 10497 5248.0 0.6945760 0.6945760 1.004194e-02 1.004194e-02 0.295382041 #> 10498 5248.5 0.6945543 0.6945543 1.004392e-02 1.004392e-02 0.295401727 #> 10499 5249.0 0.6945327 0.6945327 1.004590e-02 1.004590e-02 0.295421436 #> 10500 5249.5 0.6945110 0.6945110 1.004787e-02 1.004787e-02 0.295441169 #> 10501 5250.0 0.6944892 0.6944892 1.004984e-02 1.004984e-02 0.295460924 #> 10502 5250.5 0.6944675 0.6944675 1.005181e-02 1.005181e-02 0.295480703 #> 10503 5251.0 0.6944457 0.6944457 1.005376e-02 1.005376e-02 0.295500504 #> 10504 5251.5 0.6944240 0.6944240 1.005572e-02 1.005572e-02 0.295520327 #> 10505 5252.0 0.6944022 0.6944022 1.005766e-02 1.005766e-02 0.295540174 #> 10506 5252.5 0.6943804 0.6943804 1.005961e-02 1.005961e-02 0.295560043 #> 10507 5253.0 0.6943585 0.6943585 1.006154e-02 1.006154e-02 0.295579934 #> 10508 5253.5 0.6943367 0.6943367 1.006348e-02 1.006348e-02 0.295599848 #> 10509 5254.0 0.6943148 0.6943148 1.006540e-02 1.006540e-02 0.295619783 #> 10510 5254.5 0.6942929 0.6942929 1.006732e-02 1.006732e-02 0.295639741 #> 10511 5255.0 0.6942710 0.6942710 1.006924e-02 1.006924e-02 0.295659721 #> 10512 5255.5 0.6942491 0.6942491 1.007115e-02 1.007115e-02 0.295679723 #> 10513 5256.0 0.6942272 0.6942272 1.007306e-02 1.007306e-02 0.295699746 #> 10514 5256.5 0.6942052 0.6942052 1.007496e-02 1.007496e-02 0.295719792 #> 10515 5257.0 0.6941833 0.6941833 1.007686e-02 1.007686e-02 0.295739859 #> 10516 5257.5 0.6941613 0.6941613 1.007875e-02 1.007875e-02 0.295759947 #> 10517 5258.0 0.6941393 0.6941393 1.008063e-02 1.008063e-02 0.295780057 #> 10518 5258.5 0.6941173 0.6941173 1.008251e-02 1.008251e-02 0.295800188 #> 10519 5259.0 0.6940953 0.6940953 1.008439e-02 1.008439e-02 0.295820340 #> 10520 5259.5 0.6940732 0.6940732 1.008626e-02 1.008626e-02 0.295840514 #> 10521 5260.0 0.6940512 0.6940512 1.008813e-02 1.008813e-02 0.295860709 #> 10522 5260.5 0.6940291 0.6940291 1.008999e-02 1.008999e-02 0.295880924 #> 10523 5261.0 0.6940070 0.6940070 1.009184e-02 1.009184e-02 0.295901160 #> 10524 5261.5 0.6939849 0.6939849 1.009369e-02 1.009369e-02 0.295921418 #> 10525 5262.0 0.6939628 0.6939628 1.009553e-02 1.009553e-02 0.295941695 #> 10526 5262.5 0.6939406 0.6939406 1.009737e-02 1.009737e-02 0.295961994 #> 10527 5263.0 0.6939185 0.6939185 1.009921e-02 1.009921e-02 0.295982312 #> 10528 5263.5 0.6938963 0.6938963 1.010103e-02 1.010103e-02 0.296002651 #> 10529 5264.0 0.6938741 0.6938741 1.010286e-02 1.010286e-02 0.296023011 #> 10530 5264.5 0.6938519 0.6938519 1.010467e-02 1.010467e-02 0.296043390 #> 10531 5265.0 0.6938297 0.6938297 1.010649e-02 1.010649e-02 0.296063790 #> 10532 5265.5 0.6938075 0.6938075 1.010829e-02 1.010829e-02 0.296084209 #> 10533 5266.0 0.6937853 0.6937853 1.011010e-02 1.011010e-02 0.296104649 #> 10534 5266.5 0.6937630 0.6937630 1.011189e-02 1.011189e-02 0.296125108 #> 10535 5267.0 0.6937407 0.6937407 1.011368e-02 1.011368e-02 0.296145587 #> 10536 5267.5 0.6937184 0.6937184 1.011547e-02 1.011547e-02 0.296166085 #> 10537 5268.0 0.6936961 0.6936961 1.011725e-02 1.011725e-02 0.296186603 #> 10538 5268.5 0.6936738 0.6936738 1.011903e-02 1.011903e-02 0.296207140 #> 10539 5269.0 0.6936515 0.6936515 1.012080e-02 1.012080e-02 0.296227697 #> 10540 5269.5 0.6936292 0.6936292 1.012256e-02 1.012256e-02 0.296248272 #> 10541 5270.0 0.6936068 0.6936068 1.012432e-02 1.012432e-02 0.296268867 #> 10542 5270.5 0.6935844 0.6935844 1.012607e-02 1.012607e-02 0.296289481 #> 10543 5271.0 0.6935621 0.6935621 1.012782e-02 1.012782e-02 0.296310113 #> 10544 5271.5 0.6935397 0.6935397 1.012957e-02 1.012957e-02 0.296330765 #> 10545 5272.0 0.6935173 0.6935173 1.013130e-02 1.013130e-02 0.296351435 #> 10546 5272.5 0.6934948 0.6934948 1.013304e-02 1.013304e-02 0.296372123 #> 10547 5273.0 0.6934724 0.6934724 1.013476e-02 1.013476e-02 0.296392830 #> 10548 5273.5 0.6934500 0.6934500 1.013648e-02 1.013648e-02 0.296413556 #> 10549 5274.0 0.6934275 0.6934275 1.013820e-02 1.013820e-02 0.296434300 #> 10550 5274.5 0.6934050 0.6934050 1.013991e-02 1.013991e-02 0.296455061 #> 10551 5275.0 0.6933825 0.6933825 1.014162e-02 1.014162e-02 0.296475841 #> 10552 5275.5 0.6933600 0.6933600 1.014332e-02 1.014332e-02 0.296496639 #> 10553 5276.0 0.6933375 0.6933375 1.014501e-02 1.014501e-02 0.296517455 #> 10554 5276.5 0.6933150 0.6933150 1.014670e-02 1.014670e-02 0.296538289 #> 10555 5277.0 0.6932925 0.6932925 1.014838e-02 1.014838e-02 0.296559140 #> 10556 5277.5 0.6932699 0.6932699 1.015006e-02 1.015006e-02 0.296580009 #> 10557 5278.0 0.6932474 0.6932474 1.015174e-02 1.015174e-02 0.296600896 #> 10558 5278.5 0.6932248 0.6932248 1.015340e-02 1.015340e-02 0.296621800 #> 10559 5279.0 0.6932022 0.6932022 1.015507e-02 1.015507e-02 0.296642721 #> 10560 5279.5 0.6931796 0.6931796 1.015672e-02 1.015672e-02 0.296663659 #> 10561 5280.0 0.6931570 0.6931570 1.015837e-02 1.015837e-02 0.296684614 #> 10562 5280.5 0.6931344 0.6931344 1.016002e-02 1.016002e-02 0.296705587 #> 10563 5281.0 0.6931118 0.6931118 1.016166e-02 1.016166e-02 0.296726576 #> 10564 5281.5 0.6930891 0.6930891 1.016329e-02 1.016329e-02 0.296747582 #> 10565 5282.0 0.6930665 0.6930665 1.016492e-02 1.016492e-02 0.296768605 #> 10566 5282.5 0.6930438 0.6930438 1.016655e-02 1.016655e-02 0.296789644 #> 10567 5283.0 0.6930211 0.6930211 1.016816e-02 1.016816e-02 0.296810700 #> 10568 5283.5 0.6929985 0.6929985 1.016978e-02 1.016978e-02 0.296831772 #> 10569 5284.0 0.6929758 0.6929758 1.017138e-02 1.017138e-02 0.296852861 #> 10570 5284.5 0.6929530 0.6929530 1.017298e-02 1.017298e-02 0.296873965 #> 10571 5285.0 0.6929303 0.6929303 1.017458e-02 1.017458e-02 0.296895086 #> 10572 5285.5 0.6929076 0.6929076 1.017617e-02 1.017617e-02 0.296916223 #> 10573 5286.0 0.6928849 0.6928849 1.017776e-02 1.017776e-02 0.296937376 #> 10574 5286.5 0.6928621 0.6928621 1.017934e-02 1.017934e-02 0.296958544 #> 10575 5287.0 0.6928394 0.6928394 1.018091e-02 1.018091e-02 0.296979728 #> 10576 5287.5 0.6928166 0.6928166 1.018248e-02 1.018248e-02 0.297000928 #> 10577 5288.0 0.6927938 0.6927938 1.018404e-02 1.018404e-02 0.297022143 #> 10578 5288.5 0.6927710 0.6927710 1.018560e-02 1.018560e-02 0.297043374 #> 10579 5289.0 0.6927482 0.6927482 1.018715e-02 1.018715e-02 0.297064620 #> 10580 5289.5 0.6927254 0.6927254 1.018870e-02 1.018870e-02 0.297085881 #> 10581 5290.0 0.6927026 0.6927026 1.019024e-02 1.019024e-02 0.297107157 #> 10582 5290.5 0.6926798 0.6926798 1.019177e-02 1.019177e-02 0.297128448 #> 10583 5291.0 0.6926569 0.6926569 1.019330e-02 1.019330e-02 0.297149754 #> 10584 5291.5 0.6926341 0.6926341 1.019482e-02 1.019482e-02 0.297171075 #> 10585 5292.0 0.6926112 0.6926112 1.019634e-02 1.019634e-02 0.297192411 #> 10586 5292.5 0.6925884 0.6925884 1.019785e-02 1.019785e-02 0.297213761 #> 10587 5293.0 0.6925655 0.6925655 1.019936e-02 1.019936e-02 0.297235125 #> 10588 5293.5 0.6925426 0.6925426 1.020086e-02 1.020086e-02 0.297256504 #> 10589 5294.0 0.6925197 0.6925197 1.020236e-02 1.020236e-02 0.297277898 #> 10590 5294.5 0.6924968 0.6924968 1.020385e-02 1.020385e-02 0.297299305 #> 10591 5295.0 0.6924739 0.6924739 1.020533e-02 1.020533e-02 0.297320726 #> 10592 5295.5 0.6924510 0.6924510 1.020681e-02 1.020681e-02 0.297342162 #> 10593 5296.0 0.6924281 0.6924281 1.020828e-02 1.020828e-02 0.297363611 #> 10594 5296.5 0.6924052 0.6924052 1.020975e-02 1.020975e-02 0.297385074 #> 10595 5297.0 0.6923822 0.6923822 1.021121e-02 1.021121e-02 0.297406551 #> 10596 5297.5 0.6923593 0.6923593 1.021267e-02 1.021267e-02 0.297428042 #> 10597 5298.0 0.6923363 0.6923363 1.021412e-02 1.021412e-02 0.297449546 #> 10598 5298.5 0.6923134 0.6923134 1.021557e-02 1.021557e-02 0.297471063 #> 10599 5299.0 0.6922904 0.6922904 1.021701e-02 1.021701e-02 0.297492593 #> 10600 5299.5 0.6922674 0.6922674 1.021844e-02 1.021844e-02 0.297514137 #> 10601 5300.0 0.6922444 0.6922444 1.021987e-02 1.021987e-02 0.297535694 #> 10602 5300.5 0.6922214 0.6922214 1.022129e-02 1.022129e-02 0.297557264 #> 10603 5301.0 0.6921984 0.6921984 1.022271e-02 1.022271e-02 0.297578846 #> 10604 5301.5 0.6921754 0.6921754 1.022412e-02 1.022412e-02 0.297600442 #> 10605 5302.0 0.6921524 0.6921524 1.022552e-02 1.022552e-02 0.297622050 #> 10606 5302.5 0.6921294 0.6921294 1.022692e-02 1.022692e-02 0.297643670 #> 10607 5303.0 0.6921064 0.6921064 1.022832e-02 1.022832e-02 0.297665303 #> 10608 5303.5 0.6920833 0.6920833 1.022970e-02 1.022970e-02 0.297686949 #> 10609 5304.0 0.6920603 0.6920603 1.023109e-02 1.023109e-02 0.297708606 #> 10610 5304.5 0.6920373 0.6920373 1.023246e-02 1.023246e-02 0.297730276 #> 10611 5305.0 0.6920142 0.6920142 1.023383e-02 1.023383e-02 0.297751958 #> 10612 5305.5 0.6919911 0.6919911 1.023520e-02 1.023520e-02 0.297773652 #> 10613 5306.0 0.6919681 0.6919681 1.023656e-02 1.023656e-02 0.297795358 #> 10614 5306.5 0.6919450 0.6919450 1.023791e-02 1.023791e-02 0.297817075 #> 10615 5307.0 0.6919219 0.6919219 1.023926e-02 1.023926e-02 0.297838804 #> 10616 5307.5 0.6918989 0.6918989 1.024060e-02 1.024060e-02 0.297860545 #> 10617 5308.0 0.6918758 0.6918758 1.024194e-02 1.024194e-02 0.297882297 #> 10618 5308.5 0.6918527 0.6918527 1.024327e-02 1.024327e-02 0.297904061 #> 10619 5309.0 0.6918296 0.6918296 1.024460e-02 1.024460e-02 0.297925835 #> 10620 5309.5 0.6918065 0.6918065 1.024591e-02 1.024591e-02 0.297947621 #> 10621 5310.0 0.6917834 0.6917834 1.024723e-02 1.024723e-02 0.297969418 #> 10622 5310.5 0.6917602 0.6917602 1.024854e-02 1.024854e-02 0.297991226 #> 10623 5311.0 0.6917371 0.6917371 1.024984e-02 1.024984e-02 0.298013045 #> 10624 5311.5 0.6917140 0.6917140 1.025114e-02 1.025114e-02 0.298034874 #> 10625 5312.0 0.6916909 0.6916909 1.025243e-02 1.025243e-02 0.298056715 #> 10626 5312.5 0.6916677 0.6916677 1.025371e-02 1.025371e-02 0.298078565 #> 10627 5313.0 0.6916446 0.6916446 1.025499e-02 1.025499e-02 0.298100426 #> 10628 5313.5 0.6916214 0.6916214 1.025626e-02 1.025626e-02 0.298122298 #> 10629 5314.0 0.6915983 0.6915983 1.025753e-02 1.025753e-02 0.298144179 #> 10630 5314.5 0.6915751 0.6915751 1.025879e-02 1.025879e-02 0.298166071 #> 10631 5315.0 0.6915520 0.6915520 1.026005e-02 1.026005e-02 0.298187973 #> 10632 5315.5 0.6915288 0.6915288 1.026130e-02 1.026130e-02 0.298209885 #> 10633 5316.0 0.6915057 0.6915057 1.026254e-02 1.026254e-02 0.298231806 #> 10634 5316.5 0.6914825 0.6914825 1.026378e-02 1.026378e-02 0.298253738 #> 10635 5317.0 0.6914593 0.6914593 1.026501e-02 1.026501e-02 0.298275679 #> 10636 5317.5 0.6914361 0.6914361 1.026624e-02 1.026624e-02 0.298297629 #> 10637 5318.0 0.6914129 0.6914129 1.026746e-02 1.026746e-02 0.298319589 #> 10638 5318.5 0.6913898 0.6913898 1.026868e-02 1.026868e-02 0.298341558 #> 10639 5319.0 0.6913666 0.6913666 1.026989e-02 1.026989e-02 0.298363537 #> 10640 5319.5 0.6913434 0.6913434 1.027109e-02 1.027109e-02 0.298385524 #> 10641 5320.0 0.6913202 0.6913202 1.027229e-02 1.027229e-02 0.298407521 #> 10642 5320.5 0.6912970 0.6912970 1.027348e-02 1.027348e-02 0.298429526 #> 10643 5321.0 0.6912738 0.6912738 1.027467e-02 1.027467e-02 0.298451541 #> 10644 5321.5 0.6912506 0.6912506 1.027585e-02 1.027585e-02 0.298473564 #> 10645 5322.0 0.6912274 0.6912274 1.027702e-02 1.027702e-02 0.298495595 #> 10646 5322.5 0.6912042 0.6912042 1.027819e-02 1.027819e-02 0.298517636 #> 10647 5323.0 0.6911810 0.6911810 1.027935e-02 1.027935e-02 0.298539684 #> 10648 5323.5 0.6911577 0.6911577 1.028051e-02 1.028051e-02 0.298561741 #> 10649 5324.0 0.6911345 0.6911345 1.028166e-02 1.028166e-02 0.298583806 #> 10650 5324.5 0.6911113 0.6911113 1.028281e-02 1.028281e-02 0.298605879 #> 10651 5325.0 0.6910881 0.6910881 1.028395e-02 1.028395e-02 0.298627960 #> 10652 5325.5 0.6910649 0.6910649 1.028508e-02 1.028508e-02 0.298650050 #> 10653 5326.0 0.6910416 0.6910416 1.028621e-02 1.028621e-02 0.298672146 #> 10654 5326.5 0.6910184 0.6910184 1.028733e-02 1.028733e-02 0.298694251 #> 10655 5327.0 0.6909952 0.6909952 1.028845e-02 1.028845e-02 0.298716363 #> 10656 5327.5 0.6909720 0.6909720 1.028956e-02 1.028956e-02 0.298738483 #> 10657 5328.0 0.6909487 0.6909487 1.029066e-02 1.029066e-02 0.298760610 #> 10658 5328.5 0.6909255 0.6909255 1.029176e-02 1.029176e-02 0.298782744 #> 10659 5329.0 0.6909023 0.6909023 1.029286e-02 1.029286e-02 0.298804886 #> 10660 5329.5 0.6908790 0.6908790 1.029394e-02 1.029394e-02 0.298827035 #> 10661 5330.0 0.6908558 0.6908558 1.029502e-02 1.029502e-02 0.298849190 #> 10662 5330.5 0.6908325 0.6908325 1.029610e-02 1.029610e-02 0.298871353 #> 10663 5331.0 0.6908093 0.6908093 1.029717e-02 1.029717e-02 0.298893522 #> 10664 5331.5 0.6907861 0.6907861 1.029823e-02 1.029823e-02 0.298915698 #> 10665 5332.0 0.6907628 0.6907628 1.029929e-02 1.029929e-02 0.298937881 #> 10666 5332.5 0.6907396 0.6907396 1.030034e-02 1.030034e-02 0.298960070 #> 10667 5333.0 0.6907163 0.6907163 1.030139e-02 1.030139e-02 0.298982265 #> 10668 5333.5 0.6906931 0.6906931 1.030243e-02 1.030243e-02 0.299004467 #> 10669 5334.0 0.6906699 0.6906699 1.030346e-02 1.030346e-02 0.299026675 #> 10670 5334.5 0.6906466 0.6906466 1.030449e-02 1.030449e-02 0.299048889 #> 10671 5335.0 0.6906234 0.6906234 1.030551e-02 1.030551e-02 0.299071109 #> 10672 5335.5 0.6906001 0.6906001 1.030653e-02 1.030653e-02 0.299093335 #> 10673 5336.0 0.6905769 0.6905769 1.030754e-02 1.030754e-02 0.299115567 #> 10674 5336.5 0.6905537 0.6905537 1.030854e-02 1.030854e-02 0.299137804 #> 10675 5337.0 0.6905304 0.6905304 1.030954e-02 1.030954e-02 0.299160047 #> 10676 5337.5 0.6905072 0.6905072 1.031053e-02 1.031053e-02 0.299182295 #> 10677 5338.0 0.6904839 0.6904839 1.031152e-02 1.031152e-02 0.299204549 #> 10678 5338.5 0.6904607 0.6904607 1.031250e-02 1.031250e-02 0.299226808 #> 10679 5339.0 0.6904374 0.6904374 1.031348e-02 1.031348e-02 0.299249072 #> 10680 5339.5 0.6904142 0.6904142 1.031445e-02 1.031445e-02 0.299271342 #> 10681 5340.0 0.6903910 0.6903910 1.031541e-02 1.031541e-02 0.299293616 #> 10682 5340.5 0.6903677 0.6903677 1.031637e-02 1.031637e-02 0.299315895 #> 10683 5341.0 0.6903445 0.6903445 1.031732e-02 1.031732e-02 0.299338179 #> 10684 5341.5 0.6903213 0.6903213 1.031827e-02 1.031827e-02 0.299360468 #> 10685 5342.0 0.6902980 0.6902980 1.031921e-02 1.031921e-02 0.299382761 #> 10686 5342.5 0.6902748 0.6902748 1.032014e-02 1.032014e-02 0.299405058 #> 10687 5343.0 0.6902516 0.6902516 1.032107e-02 1.032107e-02 0.299427360 #> 10688 5343.5 0.6902283 0.6902283 1.032199e-02 1.032199e-02 0.299449666 #> 10689 5344.0 0.6902051 0.6902051 1.032291e-02 1.032291e-02 0.299471977 #> 10690 5344.5 0.6901819 0.6901819 1.032382e-02 1.032382e-02 0.299494291 #> 10691 5345.0 0.6901587 0.6901587 1.032472e-02 1.032472e-02 0.299516610 #> 10692 5345.5 0.6901354 0.6901354 1.032562e-02 1.032562e-02 0.299538932 #> 10693 5346.0 0.6901122 0.6901122 1.032651e-02 1.032651e-02 0.299561258 #> 10694 5346.5 0.6900890 0.6900890 1.032740e-02 1.032740e-02 0.299583588 #> 10695 5347.0 0.6900658 0.6900658 1.032828e-02 1.032828e-02 0.299605921 #> 10696 5347.5 0.6900426 0.6900426 1.032915e-02 1.032915e-02 0.299628258 #> 10697 5348.0 0.6900194 0.6900194 1.033002e-02 1.033002e-02 0.299650598 #> 10698 5348.5 0.6899962 0.6899962 1.033088e-02 1.033088e-02 0.299672941 #> 10699 5349.0 0.6899730 0.6899730 1.033174e-02 1.033174e-02 0.299695288 #> 10700 5349.5 0.6899498 0.6899498 1.033259e-02 1.033259e-02 0.299717637 #> 10701 5350.0 0.6899266 0.6899266 1.033344e-02 1.033344e-02 0.299739990 #> 10702 5350.5 0.6899034 0.6899034 1.033428e-02 1.033428e-02 0.299762345 #> 10703 5351.0 0.6898802 0.6898802 1.033511e-02 1.033511e-02 0.299784703 #> 10704 5351.5 0.6898570 0.6898570 1.033594e-02 1.033594e-02 0.299807064 #> 10705 5352.0 0.6898338 0.6898338 1.033676e-02 1.033676e-02 0.299829427 #> 10706 5352.5 0.6898106 0.6898106 1.033757e-02 1.033757e-02 0.299851793 #> 10707 5353.0 0.6897875 0.6897875 1.033838e-02 1.033838e-02 0.299874161 #> 10708 5353.5 0.6897643 0.6897643 1.033919e-02 1.033919e-02 0.299896532 #> 10709 5354.0 0.6897411 0.6897411 1.033998e-02 1.033998e-02 0.299918904 #> 10710 5354.5 0.6897179 0.6897179 1.034078e-02 1.034078e-02 0.299941279 #> 10711 5355.0 0.6896948 0.6896948 1.034156e-02 1.034156e-02 0.299963655 #> 10712 5355.5 0.6896716 0.6896716 1.034234e-02 1.034234e-02 0.299986034 #> 10713 5356.0 0.6896485 0.6896485 1.034312e-02 1.034312e-02 0.300008414 #> 10714 5356.5 0.6896253 0.6896253 1.034388e-02 1.034388e-02 0.300030796 #> 10715 5357.0 0.6896022 0.6896022 1.034465e-02 1.034465e-02 0.300053179 #> 10716 5357.5 0.6895790 0.6895790 1.034540e-02 1.034540e-02 0.300075564 #> 10717 5358.0 0.6895559 0.6895559 1.034615e-02 1.034615e-02 0.300097951 #> 10718 5358.5 0.6895328 0.6895328 1.034690e-02 1.034690e-02 0.300120338 #> 10719 5359.0 0.6895096 0.6895096 1.034764e-02 1.034764e-02 0.300142727 #> 10720 5359.5 0.6894865 0.6894865 1.034837e-02 1.034837e-02 0.300165117 #> 10721 5360.0 0.6894634 0.6894634 1.034909e-02 1.034909e-02 0.300187507 #> 10722 5360.5 0.6894403 0.6894403 1.034982e-02 1.034982e-02 0.300209899 #> 10723 5361.0 0.6894172 0.6894172 1.035053e-02 1.035053e-02 0.300232291 #> 10724 5361.5 0.6893941 0.6893941 1.035124e-02 1.035124e-02 0.300254684 #> 10725 5362.0 0.6893710 0.6893710 1.035194e-02 1.035194e-02 0.300277078 #> 10726 5362.5 0.6893479 0.6893479 1.035264e-02 1.035264e-02 0.300299472 #> 10727 5363.0 0.6893248 0.6893248 1.035333e-02 1.035333e-02 0.300321866 #> 10728 5363.5 0.6893017 0.6893017 1.035401e-02 1.035401e-02 0.300344261 #> 10729 5364.0 0.6892787 0.6892787 1.035469e-02 1.035469e-02 0.300366656 #> 10730 5364.5 0.6892556 0.6892556 1.035537e-02 1.035537e-02 0.300389051 #> 10731 5365.0 0.6892325 0.6892325 1.035603e-02 1.035603e-02 0.300411446 #> 10732 5365.5 0.6892095 0.6892095 1.035670e-02 1.035670e-02 0.300433840 #> 10733 5366.0 0.6891864 0.6891864 1.035735e-02 1.035735e-02 0.300456235 #> 10734 5366.5 0.6891634 0.6891634 1.035800e-02 1.035800e-02 0.300478629 #> 10735 5367.0 0.6891403 0.6891403 1.035864e-02 1.035864e-02 0.300501023 #> 10736 5367.5 0.6891173 0.6891173 1.035928e-02 1.035928e-02 0.300523416 #> 10737 5368.0 0.6890943 0.6890943 1.035991e-02 1.035991e-02 0.300545809 #> 10738 5368.5 0.6890713 0.6890713 1.036054e-02 1.036054e-02 0.300568201 #> 10739 5369.0 0.6890482 0.6890482 1.036116e-02 1.036116e-02 0.300590592 #> 10740 5369.5 0.6890252 0.6890252 1.036177e-02 1.036177e-02 0.300612982 #> 10741 5370.0 0.6890022 0.6890022 1.036238e-02 1.036238e-02 0.300635371 #> 10742 5370.5 0.6889793 0.6889793 1.036298e-02 1.036298e-02 0.300657759 #> 10743 5371.0 0.6889563 0.6889563 1.036358e-02 1.036358e-02 0.300680146 #> 10744 5371.5 0.6889333 0.6889333 1.036417e-02 1.036417e-02 0.300702532 #> 10745 5372.0 0.6889103 0.6889103 1.036475e-02 1.036475e-02 0.300724916 #> 10746 5372.5 0.6888874 0.6888874 1.036533e-02 1.036533e-02 0.300747299 #> 10747 5373.0 0.6888644 0.6888644 1.036590e-02 1.036590e-02 0.300769680 #> 10748 5373.5 0.6888415 0.6888415 1.036647e-02 1.036647e-02 0.300792059 #> 10749 5374.0 0.6888185 0.6888185 1.036703e-02 1.036703e-02 0.300814437 #> 10750 5374.5 0.6887956 0.6887956 1.036759e-02 1.036759e-02 0.300836813 #> 10751 5375.0 0.6887727 0.6887727 1.036814e-02 1.036814e-02 0.300859186 #> 10752 5375.5 0.6887498 0.6887498 1.036868e-02 1.036868e-02 0.300881558 #> 10753 5376.0 0.6887269 0.6887269 1.036922e-02 1.036922e-02 0.300903927 #> 10754 5376.5 0.6887040 0.6887040 1.036975e-02 1.036975e-02 0.300926294 #> 10755 5377.0 0.6886811 0.6886811 1.037027e-02 1.037027e-02 0.300948659 #> 10756 5377.5 0.6886582 0.6886582 1.037079e-02 1.037079e-02 0.300971021 #> 10757 5378.0 0.6886353 0.6886353 1.037130e-02 1.037130e-02 0.300993381 #> 10758 5378.5 0.6886124 0.6886124 1.037181e-02 1.037181e-02 0.301015738 #> 10759 5379.0 0.6885896 0.6885896 1.037231e-02 1.037231e-02 0.301038092 #> 10760 5379.5 0.6885667 0.6885667 1.037281e-02 1.037281e-02 0.301060444 #> 10761 5380.0 0.6885439 0.6885439 1.037330e-02 1.037330e-02 0.301082792 #> 10762 5380.5 0.6885211 0.6885211 1.037378e-02 1.037378e-02 0.301105138 #> 10763 5381.0 0.6884983 0.6884983 1.037426e-02 1.037426e-02 0.301127480 #> 10764 5381.5 0.6884754 0.6884754 1.037473e-02 1.037473e-02 0.301149819 #> 10765 5382.0 0.6884526 0.6884526 1.037520e-02 1.037520e-02 0.301172154 #> 10766 5382.5 0.6884299 0.6884299 1.037566e-02 1.037566e-02 0.301194487 #> 10767 5383.0 0.6884071 0.6884071 1.037611e-02 1.037611e-02 0.301216815 #> 10768 5383.5 0.6883843 0.6883843 1.037656e-02 1.037656e-02 0.301239140 #> 10769 5384.0 0.6883615 0.6883615 1.037701e-02 1.037701e-02 0.301261461 #> 10770 5384.5 0.6883388 0.6883388 1.037744e-02 1.037744e-02 0.301283779 #> 10771 5385.0 0.6883160 0.6883160 1.037787e-02 1.037787e-02 0.301306092 #> 10772 5385.5 0.6882933 0.6882933 1.037830e-02 1.037830e-02 0.301328402 #> 10773 5386.0 0.6882706 0.6882706 1.037872e-02 1.037872e-02 0.301350707 #> 10774 5386.5 0.6882479 0.6882479 1.037913e-02 1.037913e-02 0.301373008 #> 10775 5387.0 0.6882252 0.6882252 1.037954e-02 1.037954e-02 0.301395305 #> 10776 5387.5 0.6882025 0.6882025 1.037994e-02 1.037994e-02 0.301417598 #> 10777 5388.0 0.6881798 0.6881798 1.038033e-02 1.038033e-02 0.301439886 #> 10778 5388.5 0.6881571 0.6881571 1.038072e-02 1.038072e-02 0.301462169 #> 10779 5389.0 0.6881344 0.6881344 1.038111e-02 1.038111e-02 0.301484447 #> 10780 5389.5 0.6881118 0.6881118 1.038149e-02 1.038149e-02 0.301506721 #> 10781 5390.0 0.6880892 0.6880892 1.038186e-02 1.038186e-02 0.301528990 #> 10782 5390.5 0.6880665 0.6880665 1.038222e-02 1.038222e-02 0.301551254 #> 10783 5391.0 0.6880439 0.6880439 1.038258e-02 1.038258e-02 0.301573513 #> 10784 5391.5 0.6880213 0.6880213 1.038294e-02 1.038294e-02 0.301595767 #> 10785 5392.0 0.6879987 0.6879987 1.038329e-02 1.038329e-02 0.301618015 #> 10786 5392.5 0.6879761 0.6879761 1.038363e-02 1.038363e-02 0.301640258 #> 10787 5393.0 0.6879535 0.6879535 1.038397e-02 1.038397e-02 0.301662496 #> 10788 5393.5 0.6879310 0.6879310 1.038430e-02 1.038430e-02 0.301684728 #> 10789 5394.0 0.6879084 0.6879084 1.038462e-02 1.038462e-02 0.301706955 #> 10790 5394.5 0.6878859 0.6878859 1.038494e-02 1.038494e-02 0.301729175 #> 10791 5395.0 0.6878634 0.6878634 1.038526e-02 1.038526e-02 0.301751390 #> 10792 5395.5 0.6878408 0.6878408 1.038557e-02 1.038557e-02 0.301773599 #> 10793 5396.0 0.6878183 0.6878183 1.038587e-02 1.038587e-02 0.301795802 #> 10794 5396.5 0.6877958 0.6877958 1.038616e-02 1.038616e-02 0.301817999 #> 10795 5397.0 0.6877734 0.6877734 1.038645e-02 1.038645e-02 0.301840189 #> 10796 5397.5 0.6877509 0.6877509 1.038674e-02 1.038674e-02 0.301862374 #> 10797 5398.0 0.6877284 0.6877284 1.038702e-02 1.038702e-02 0.301884552 #> 10798 5398.5 0.6877060 0.6877060 1.038729e-02 1.038729e-02 0.301906723 #> 10799 5399.0 0.6876836 0.6876836 1.038756e-02 1.038756e-02 0.301928888 #> 10800 5399.5 0.6876611 0.6876611 1.038782e-02 1.038782e-02 0.301951046 #> 10801 5400.0 0.6876387 0.6876387 1.038807e-02 1.038807e-02 0.301973197 #> 10802 5400.5 0.6876163 0.6876163 1.038832e-02 1.038832e-02 0.301995342 #> 10803 5401.0 0.6875940 0.6875940 1.038857e-02 1.038857e-02 0.302017479 #> 10804 5401.5 0.6875716 0.6875716 1.038880e-02 1.038880e-02 0.302039610 #> 10805 5402.0 0.6875492 0.6875492 1.038904e-02 1.038904e-02 0.302061733 #> 10806 5402.5 0.6875269 0.6875269 1.038926e-02 1.038926e-02 0.302083849 #> 10807 5403.0 0.6875046 0.6875046 1.038948e-02 1.038948e-02 0.302105958 #> 10808 5403.5 0.6874822 0.6874822 1.038970e-02 1.038970e-02 0.302128060 #> 10809 5404.0 0.6874599 0.6874599 1.038991e-02 1.038991e-02 0.302150153 #> 10810 5404.5 0.6874377 0.6874377 1.039011e-02 1.039011e-02 0.302172240 #> 10811 5405.0 0.6874154 0.6874154 1.039031e-02 1.039031e-02 0.302194318 #> 10812 5405.5 0.6873931 0.6873931 1.039050e-02 1.039050e-02 0.302216389 #> 10813 5406.0 0.6873709 0.6873709 1.039068e-02 1.039068e-02 0.302238452 #> 10814 5406.5 0.6873486 0.6873486 1.039086e-02 1.039086e-02 0.302260506 #> 10815 5407.0 0.6873264 0.6873264 1.039104e-02 1.039104e-02 0.302282553 #> 10816 5407.5 0.6873042 0.6873042 1.039121e-02 1.039121e-02 0.302304592 #> 10817 5408.0 0.6872820 0.6872820 1.039137e-02 1.039137e-02 0.302326622 #> 10818 5408.5 0.6872598 0.6872598 1.039153e-02 1.039153e-02 0.302348644 #> 10819 5409.0 0.6872377 0.6872377 1.039168e-02 1.039168e-02 0.302370658 #> 10820 5409.5 0.6872155 0.6872155 1.039182e-02 1.039182e-02 0.302392663 #> 10821 5410.0 0.6871934 0.6871934 1.039196e-02 1.039196e-02 0.302414659 #> 10822 5410.5 0.6871713 0.6871713 1.039210e-02 1.039210e-02 0.302436647 #> 10823 5411.0 0.6871491 0.6871491 1.039223e-02 1.039223e-02 0.302458626 #> 10824 5411.5 0.6871271 0.6871271 1.039235e-02 1.039235e-02 0.302480596 #> 10825 5412.0 0.6871050 0.6871050 1.039247e-02 1.039247e-02 0.302502557 #> 10826 5412.5 0.6870829 0.6870829 1.039258e-02 1.039258e-02 0.302524509 #> 10827 5413.0 0.6870609 0.6870609 1.039268e-02 1.039268e-02 0.302546451 #> 10828 5413.5 0.6870388 0.6870388 1.039278e-02 1.039278e-02 0.302568385 #> 10829 5414.0 0.6870168 0.6870168 1.039287e-02 1.039287e-02 0.302590309 #> 10830 5414.5 0.6869948 0.6869948 1.039296e-02 1.039296e-02 0.302612224 #> 10831 5415.0 0.6869728 0.6869728 1.039305e-02 1.039305e-02 0.302634129 #> 10832 5415.5 0.6869509 0.6869509 1.039312e-02 1.039312e-02 0.302656024 #> 10833 5416.0 0.6869289 0.6869289 1.039319e-02 1.039319e-02 0.302677910 #> 10834 5416.5 0.6869070 0.6869070 1.039326e-02 1.039326e-02 0.302699786 #> 10835 5417.0 0.6868850 0.6868850 1.039332e-02 1.039332e-02 0.302721652 #> 10836 5417.5 0.6868631 0.6868631 1.039337e-02 1.039337e-02 0.302743509 #> 10837 5418.0 0.6868412 0.6868412 1.039342e-02 1.039342e-02 0.302765355 #> 10838 5418.5 0.6868193 0.6868193 1.039346e-02 1.039346e-02 0.302787190 #> 10839 5419.0 0.6867975 0.6867975 1.039350e-02 1.039350e-02 0.302809016 #> 10840 5419.5 0.6867756 0.6867756 1.039353e-02 1.039353e-02 0.302830831 #> 10841 5420.0 0.6867538 0.6867538 1.039356e-02 1.039356e-02 0.302852636 #> 10842 5420.5 0.6867320 0.6867320 1.039358e-02 1.039358e-02 0.302874431 #> 10843 5421.0 0.6867102 0.6867102 1.039359e-02 1.039359e-02 0.302896214 #> 10844 5421.5 0.6866884 0.6866884 1.039360e-02 1.039360e-02 0.302917987 #> 10845 5422.0 0.6866666 0.6866666 1.039360e-02 1.039360e-02 0.302939750 #> 10846 5422.5 0.6866449 0.6866449 1.039360e-02 1.039360e-02 0.302961501 #> 10847 5423.0 0.6866232 0.6866232 1.039359e-02 1.039359e-02 0.302983241 #> 10848 5423.5 0.6866015 0.6866015 1.039358e-02 1.039358e-02 0.303004971 #> 10849 5424.0 0.6865798 0.6865798 1.039356e-02 1.039356e-02 0.303026689 #> 10850 5424.5 0.6865581 0.6865581 1.039353e-02 1.039353e-02 0.303048396 #> 10851 5425.0 0.6865364 0.6865364 1.039350e-02 1.039350e-02 0.303070091 #> 10852 5425.5 0.6865148 0.6865148 1.039346e-02 1.039346e-02 0.303091776 #> 10853 5426.0 0.6864931 0.6864931 1.039342e-02 1.039342e-02 0.303113448 #> 10854 5426.5 0.6864715 0.6864715 1.039337e-02 1.039337e-02 0.303135110 #> 10855 5427.0 0.6864499 0.6864499 1.039332e-02 1.039332e-02 0.303156759 #> 10856 5427.5 0.6864283 0.6864283 1.039326e-02 1.039326e-02 0.303178397 #> 10857 5428.0 0.6864068 0.6864068 1.039320e-02 1.039320e-02 0.303200023 #> 10858 5428.5 0.6863852 0.6863852 1.039313e-02 1.039313e-02 0.303221637 #> 10859 5429.0 0.6863637 0.6863637 1.039305e-02 1.039305e-02 0.303243238 #> 10860 5429.5 0.6863422 0.6863422 1.039297e-02 1.039297e-02 0.303264828 #> 10861 5430.0 0.6863207 0.6863207 1.039288e-02 1.039288e-02 0.303286406 #> 10862 5430.5 0.6862992 0.6862992 1.039279e-02 1.039279e-02 0.303307971 #> 10863 5431.0 0.6862778 0.6862778 1.039269e-02 1.039269e-02 0.303329524 #> 10864 5431.5 0.6862563 0.6862563 1.039259e-02 1.039259e-02 0.303351065 #> 10865 5432.0 0.6862349 0.6862349 1.039248e-02 1.039248e-02 0.303372593 #> 10866 5432.5 0.6862135 0.6862135 1.039236e-02 1.039236e-02 0.303394108 #> 10867 5433.0 0.6861921 0.6861921 1.039224e-02 1.039224e-02 0.303415610 #> 10868 5433.5 0.6861708 0.6861708 1.039211e-02 1.039211e-02 0.303437100 #> 10869 5434.0 0.6861494 0.6861494 1.039198e-02 1.039198e-02 0.303458577 #> 10870 5434.5 0.6861281 0.6861281 1.039185e-02 1.039185e-02 0.303480041 #> 10871 5435.0 0.6861068 0.6861068 1.039170e-02 1.039170e-02 0.303501492 #> 10872 5435.5 0.6860855 0.6860855 1.039156e-02 1.039156e-02 0.303522930 #> 10873 5436.0 0.6860642 0.6860642 1.039140e-02 1.039140e-02 0.303544354 #> 10874 5436.5 0.6860430 0.6860430 1.039124e-02 1.039124e-02 0.303565765 #> 10875 5437.0 0.6860218 0.6860218 1.039108e-02 1.039108e-02 0.303587163 #> 10876 5437.5 0.6860005 0.6860005 1.039091e-02 1.039091e-02 0.303608547 #> 10877 5438.0 0.6859794 0.6859794 1.039073e-02 1.039073e-02 0.303629918 #> 10878 5438.5 0.6859582 0.6859582 1.039055e-02 1.039055e-02 0.303651275 #> 10879 5439.0 0.6859370 0.6859370 1.039036e-02 1.039036e-02 0.303672619 #> 10880 5439.5 0.6859159 0.6859159 1.039017e-02 1.039017e-02 0.303693948 #> 10881 5440.0 0.6858948 0.6858948 1.038997e-02 1.038997e-02 0.303715264 #> 10882 5440.5 0.6858737 0.6858737 1.038977e-02 1.038977e-02 0.303736565 #> 10883 5441.0 0.6858526 0.6858526 1.038956e-02 1.038956e-02 0.303757853 #> 10884 5441.5 0.6858315 0.6858315 1.038935e-02 1.038935e-02 0.303779126 #> 10885 5442.0 0.6858105 0.6858105 1.038913e-02 1.038913e-02 0.303800385 #> 10886 5442.5 0.6857895 0.6857895 1.038890e-02 1.038890e-02 0.303821630 #> 10887 5443.0 0.6857685 0.6857685 1.038867e-02 1.038867e-02 0.303842860 #> 10888 5443.5 0.6857475 0.6857475 1.038844e-02 1.038844e-02 0.303864076 #> 10889 5444.0 0.6857265 0.6857265 1.038819e-02 1.038819e-02 0.303885277 #> 10890 5444.5 0.6857056 0.6857056 1.038795e-02 1.038795e-02 0.303906464 #> 10891 5445.0 0.6856847 0.6856847 1.038770e-02 1.038770e-02 0.303927636 #> 10892 5445.5 0.6856638 0.6856638 1.038744e-02 1.038744e-02 0.303948793 #> 10893 5446.0 0.6856429 0.6856429 1.038717e-02 1.038717e-02 0.303969935 #> 10894 5446.5 0.6856220 0.6856220 1.038691e-02 1.038691e-02 0.303991062 #> 10895 5447.0 0.6856012 0.6856012 1.038663e-02 1.038663e-02 0.304012174 #> 10896 5447.5 0.6855804 0.6855804 1.038635e-02 1.038635e-02 0.304033271 #> 10897 5448.0 0.6855596 0.6855596 1.038607e-02 1.038607e-02 0.304054352 #> 10898 5448.5 0.6855388 0.6855388 1.038578e-02 1.038578e-02 0.304075419 #> 10899 5449.0 0.6855180 0.6855180 1.038548e-02 1.038548e-02 0.304096469 #> 10900 5449.5 0.6854973 0.6854973 1.038518e-02 1.038518e-02 0.304117505 #> 10901 5450.0 0.6854766 0.6854766 1.038487e-02 1.038487e-02 0.304138525 #> 10902 5450.5 0.6854559 0.6854559 1.038456e-02 1.038456e-02 0.304159529 #> 10903 5451.0 0.6854352 0.6854352 1.038425e-02 1.038425e-02 0.304180517 #> 10904 5451.5 0.6854146 0.6854146 1.038392e-02 1.038392e-02 0.304201490 #> 10905 5452.0 0.6853940 0.6853940 1.038360e-02 1.038360e-02 0.304222446 #> 10906 5452.5 0.6853733 0.6853733 1.038326e-02 1.038326e-02 0.304243387 #> 10907 5453.0 0.6853528 0.6853528 1.038292e-02 1.038292e-02 0.304264312 #> 10908 5453.5 0.6853322 0.6853322 1.038258e-02 1.038258e-02 0.304285220 #> 10909 5454.0 0.6853117 0.6853117 1.038223e-02 1.038223e-02 0.304306113 #> 10910 5454.5 0.6852911 0.6852911 1.038188e-02 1.038188e-02 0.304326989 #> 10911 5455.0 0.6852706 0.6852706 1.038152e-02 1.038152e-02 0.304347848 #> 10912 5455.5 0.6852502 0.6852502 1.038115e-02 1.038115e-02 0.304368691 #> 10913 5456.0 0.6852297 0.6852297 1.038078e-02 1.038078e-02 0.304389518 #> 10914 5456.5 0.6852093 0.6852093 1.038041e-02 1.038041e-02 0.304410328 #> 10915 5457.0 0.6851889 0.6851889 1.038003e-02 1.038003e-02 0.304431121 #> 10916 5457.5 0.6851685 0.6851685 1.037964e-02 1.037964e-02 0.304451898 #> 10917 5458.0 0.6851481 0.6851481 1.037925e-02 1.037925e-02 0.304472657 #> 10918 5458.5 0.6851277 0.6851277 1.037885e-02 1.037885e-02 0.304493400 #> 10919 5459.0 0.6851074 0.6851074 1.037845e-02 1.037845e-02 0.304514125 #> 10920 5459.5 0.6850871 0.6850871 1.037804e-02 1.037804e-02 0.304534834 #> 10921 5460.0 0.6850668 0.6850668 1.037763e-02 1.037763e-02 0.304555525 #> 10922 5460.5 0.6850466 0.6850466 1.037721e-02 1.037721e-02 0.304576199 #> 10923 5461.0 0.6850264 0.6850264 1.037679e-02 1.037679e-02 0.304596856 #> 10924 5461.5 0.6850061 0.6850061 1.037636e-02 1.037636e-02 0.304617496 #> 10925 5462.0 0.6849860 0.6849860 1.037593e-02 1.037593e-02 0.304638117 #> 10926 5462.5 0.6849658 0.6849658 1.037549e-02 1.037549e-02 0.304658722 #> 10927 5463.0 0.6849456 0.6849456 1.037505e-02 1.037505e-02 0.304679308 #> 10928 5463.5 0.6849255 0.6849255 1.037460e-02 1.037460e-02 0.304699877 #> 10929 5464.0 0.6849054 0.6849054 1.037414e-02 1.037414e-02 0.304720428 #> 10930 5464.5 0.6848854 0.6848854 1.037368e-02 1.037368e-02 0.304740962 #> 10931 5465.0 0.6848653 0.6848653 1.037322e-02 1.037322e-02 0.304761477 #> 10932 5465.5 0.6848453 0.6848453 1.037275e-02 1.037275e-02 0.304781974 #> 10933 5466.0 0.6848253 0.6848253 1.037227e-02 1.037227e-02 0.304802453 #> 10934 5466.5 0.6848053 0.6848053 1.037179e-02 1.037179e-02 0.304822914 #> 10935 5467.0 0.6847853 0.6847853 1.037131e-02 1.037131e-02 0.304843357 #> 10936 5467.5 0.6847654 0.6847654 1.037082e-02 1.037082e-02 0.304863781 #> 10937 5468.0 0.6847455 0.6847455 1.037032e-02 1.037032e-02 0.304884187 #> 10938 5468.5 0.6847256 0.6847256 1.036982e-02 1.036982e-02 0.304904574 #> 10939 5469.0 0.6847057 0.6847057 1.036932e-02 1.036932e-02 0.304924943 #> 10940 5469.5 0.6846859 0.6846859 1.036881e-02 1.036881e-02 0.304945293 #> 10941 5470.0 0.6846661 0.6846661 1.036829e-02 1.036829e-02 0.304965624 #> 10942 5470.5 0.6846463 0.6846463 1.036777e-02 1.036777e-02 0.304985937 #> 10943 5471.0 0.6846265 0.6846265 1.036724e-02 1.036724e-02 0.305006231 #> 10944 5471.5 0.6846068 0.6846068 1.036671e-02 1.036671e-02 0.305026506 #> 10945 5472.0 0.6845871 0.6845871 1.036617e-02 1.036617e-02 0.305046761 #> 10946 5472.5 0.6845674 0.6845674 1.036563e-02 1.036563e-02 0.305066998 #> 10947 5473.0 0.6845477 0.6845477 1.036509e-02 1.036509e-02 0.305087215 #> 10948 5473.5 0.6845281 0.6845281 1.036454e-02 1.036454e-02 0.305107414 #> 10949 5474.0 0.6845084 0.6845084 1.036398e-02 1.036398e-02 0.305127592 #> 10950 5474.5 0.6844888 0.6844888 1.036342e-02 1.036342e-02 0.305147752 #> 10951 5475.0 0.6844693 0.6844693 1.036285e-02 1.036285e-02 0.305167892 #> 10952 5475.5 0.6844497 0.6844497 1.036228e-02 1.036228e-02 0.305188012 #> 10953 5476.0 0.6844302 0.6844302 1.036170e-02 1.036170e-02 0.305208113 #> 10954 5476.5 0.6844107 0.6844107 1.036112e-02 1.036112e-02 0.305228194 #> 10955 5477.0 0.6843912 0.6843912 1.036053e-02 1.036053e-02 0.305248256 #> 10956 5477.5 0.6843718 0.6843718 1.035994e-02 1.035994e-02 0.305268297 #> 10957 5478.0 0.6843523 0.6843523 1.035934e-02 1.035934e-02 0.305288319 #> 10958 5478.5 0.6843329 0.6843329 1.035874e-02 1.035874e-02 0.305308320 #> 10959 5479.0 0.6843136 0.6843136 1.035814e-02 1.035814e-02 0.305328302 #> 10960 5479.5 0.6842942 0.6842942 1.035752e-02 1.035752e-02 0.305348263 #> 10961 5480.0 0.6842749 0.6842749 1.035691e-02 1.035691e-02 0.305368204 #> 10962 5480.5 0.6842556 0.6842556 1.035628e-02 1.035628e-02 0.305388125 #> 10963 5481.0 0.6842363 0.6842363 1.035566e-02 1.035566e-02 0.305408025 #> 10964 5481.5 0.6842171 0.6842171 1.035503e-02 1.035503e-02 0.305427906 #> 10965 5482.0 0.6841978 0.6841978 1.035439e-02 1.035439e-02 0.305447765 #> 10966 5482.5 0.6841786 0.6841786 1.035375e-02 1.035375e-02 0.305467604 #> 10967 5483.0 0.6841595 0.6841595 1.035310e-02 1.035310e-02 0.305487422 #> 10968 5483.5 0.6841403 0.6841403 1.035245e-02 1.035245e-02 0.305507220 #> 10969 5484.0 0.6841212 0.6841212 1.035179e-02 1.035179e-02 0.305526997 #> 10970 5484.5 0.6841021 0.6841021 1.035113e-02 1.035113e-02 0.305546753 #> 10971 5485.0 0.6840830 0.6840830 1.035047e-02 1.035047e-02 0.305566488 #> 10972 5485.5 0.6840640 0.6840640 1.034980e-02 1.034980e-02 0.305586202 #> 10973 5486.0 0.6840450 0.6840450 1.034912e-02 1.034912e-02 0.305605895 #> 10974 5486.5 0.6840260 0.6840260 1.034844e-02 1.034844e-02 0.305625567 #> 10975 5487.0 0.6840070 0.6840070 1.034775e-02 1.034775e-02 0.305645217 #> 10976 5487.5 0.6839881 0.6839881 1.034706e-02 1.034706e-02 0.305664846 #> 10977 5488.0 0.6839692 0.6839692 1.034637e-02 1.034637e-02 0.305684454 #> 10978 5488.5 0.6839503 0.6839503 1.034567e-02 1.034567e-02 0.305704041 #> 10979 5489.0 0.6839314 0.6839314 1.034496e-02 1.034496e-02 0.305723606 #> 10980 5489.5 0.6839126 0.6839126 1.034425e-02 1.034425e-02 0.305743149 #> 10981 5490.0 0.6838938 0.6838938 1.034354e-02 1.034354e-02 0.305762671 #> 10982 5490.5 0.6838750 0.6838750 1.034282e-02 1.034282e-02 0.305782171 #> 10983 5491.0 0.6838563 0.6838563 1.034209e-02 1.034209e-02 0.305801650 #> 10984 5491.5 0.6838375 0.6838375 1.034137e-02 1.034137e-02 0.305821106 #> 10985 5492.0 0.6838188 0.6838188 1.034063e-02 1.034063e-02 0.305840541 #> 10986 5492.5 0.6838002 0.6838002 1.033989e-02 1.033989e-02 0.305859953 #> 10987 5493.0 0.6837815 0.6837815 1.033915e-02 1.033915e-02 0.305879344 #> 10988 5493.5 0.6837629 0.6837629 1.033840e-02 1.033840e-02 0.305898712 #> 10989 5494.0 0.6837443 0.6837443 1.033765e-02 1.033765e-02 0.305918058 #> 10990 5494.5 0.6837257 0.6837257 1.033689e-02 1.033689e-02 0.305937382 #> 10991 5495.0 0.6837072 0.6837072 1.033613e-02 1.033613e-02 0.305956684 #> 10992 5495.5 0.6836887 0.6836887 1.033536e-02 1.033536e-02 0.305975963 #> 10993 5496.0 0.6836702 0.6836702 1.033459e-02 1.033459e-02 0.305995220 #> 10994 5496.5 0.6836517 0.6836517 1.033381e-02 1.033381e-02 0.306014454 #> 10995 5497.0 0.6836333 0.6836333 1.033303e-02 1.033303e-02 0.306033666 #> 10996 5497.5 0.6836149 0.6836149 1.033225e-02 1.033225e-02 0.306052855 #> 10997 5498.0 0.6835965 0.6835965 1.033146e-02 1.033146e-02 0.306072021 #> 10998 5498.5 0.6835782 0.6835782 1.033066e-02 1.033066e-02 0.306091165 #> 10999 5499.0 0.6835599 0.6835599 1.032986e-02 1.032986e-02 0.306110286 #> 11000 5499.5 0.6835416 0.6835416 1.032906e-02 1.032906e-02 0.306129383 #> 11001 5500.0 0.6835233 0.6835233 1.032825e-02 1.032825e-02 0.306148458 #> 11002 5500.5 0.6835051 0.6835051 1.032743e-02 1.032743e-02 0.306167510 #> 11003 5501.0 0.6834868 0.6834868 1.032661e-02 1.032661e-02 0.306186538 #> 11004 5501.5 0.6834687 0.6834687 1.032579e-02 1.032579e-02 0.306205544 #> 11005 5502.0 0.6834505 0.6834505 1.032496e-02 1.032496e-02 0.306224526 #> 11006 5502.5 0.6834324 0.6834324 1.032413e-02 1.032413e-02 0.306243484 #> 11007 5503.0 0.6834143 0.6834143 1.032329e-02 1.032329e-02 0.306262420 #> 11008 5503.5 0.6833962 0.6833962 1.032245e-02 1.032245e-02 0.306281332 #> 11009 5504.0 0.6833782 0.6833782 1.032160e-02 1.032160e-02 0.306300220 #> 11010 5504.5 0.6833602 0.6833602 1.032075e-02 1.032075e-02 0.306319085 #> 11011 5505.0 0.6833422 0.6833422 1.031990e-02 1.031990e-02 0.306337926 #> 11012 5505.5 0.6833242 0.6833242 1.031904e-02 1.031904e-02 0.306356744 #> 11013 5506.0 0.6833063 0.6833063 1.031817e-02 1.031817e-02 0.306375537 #> 11014 5506.5 0.6832884 0.6832884 1.031730e-02 1.031730e-02 0.306394307 #> 11015 5507.0 0.6832705 0.6832705 1.031643e-02 1.031643e-02 0.306413053 #> 11016 5507.5 0.6832527 0.6832527 1.031555e-02 1.031555e-02 0.306431775 #> 11017 5508.0 0.6832349 0.6832349 1.031467e-02 1.031467e-02 0.306450473 #> 11018 5508.5 0.6832171 0.6832171 1.031378e-02 1.031378e-02 0.306469147 #> 11019 5509.0 0.6831993 0.6831993 1.031289e-02 1.031289e-02 0.306487797 #> 11020 5509.5 0.6831816 0.6831816 1.031200e-02 1.031200e-02 0.306506422 #> 11021 5510.0 0.6831639 0.6831639 1.031109e-02 1.031109e-02 0.306525023 #> 11022 5510.5 0.6831462 0.6831462 1.031019e-02 1.031019e-02 0.306543600 #> 11023 5511.0 0.6831286 0.6831286 1.030928e-02 1.030928e-02 0.306562153 #> 11024 5511.5 0.6831110 0.6831110 1.030837e-02 1.030837e-02 0.306580681 #> 11025 5512.0 0.6830934 0.6830934 1.030745e-02 1.030745e-02 0.306599184 #> 11026 5512.5 0.6830758 0.6830758 1.030652e-02 1.030652e-02 0.306617663 #> 11027 5513.0 0.6830583 0.6830583 1.030560e-02 1.030560e-02 0.306636117 #> 11028 5513.5 0.6830408 0.6830408 1.030466e-02 1.030466e-02 0.306654547 #> 11029 5514.0 0.6830233 0.6830233 1.030373e-02 1.030373e-02 0.306672952 #> 11030 5514.5 0.6830059 0.6830059 1.030279e-02 1.030279e-02 0.306691332 #> 11031 5515.0 0.6829885 0.6829885 1.030184e-02 1.030184e-02 0.306709687 #> 11032 5515.5 0.6829711 0.6829711 1.030089e-02 1.030089e-02 0.306728017 #> 11033 5516.0 0.6829537 0.6829537 1.029994e-02 1.029994e-02 0.306746322 #> 11034 5516.5 0.6829364 0.6829364 1.029898e-02 1.029898e-02 0.306764602 #> 11035 5517.0 0.6829191 0.6829191 1.029802e-02 1.029802e-02 0.306782856 #> 11036 5517.5 0.6829019 0.6829019 1.029705e-02 1.029705e-02 0.306801086 #> 11037 5518.0 0.6828846 0.6828846 1.029608e-02 1.029608e-02 0.306819290 #> 11038 5518.5 0.6828674 0.6828674 1.029510e-02 1.029510e-02 0.306837469 #> 11039 5519.0 0.6828503 0.6828503 1.029412e-02 1.029412e-02 0.306855623 #> 11040 5519.5 0.6828331 0.6828331 1.029314e-02 1.029314e-02 0.306873751 #> 11041 5520.0 0.6828160 0.6828160 1.029215e-02 1.029215e-02 0.306891854 #> 11042 5520.5 0.6827989 0.6827989 1.029116e-02 1.029116e-02 0.306909931 #> 11043 5521.0 0.6827819 0.6827819 1.029016e-02 1.029016e-02 0.306927983 #> 11044 5521.5 0.6827648 0.6827648 1.028916e-02 1.028916e-02 0.306946008 #> 11045 5522.0 0.6827478 0.6827478 1.028815e-02 1.028815e-02 0.306964009 #> 11046 5522.5 0.6827309 0.6827309 1.028714e-02 1.028714e-02 0.306981983 #> 11047 5523.0 0.6827139 0.6827139 1.028613e-02 1.028613e-02 0.306999931 #> 11048 5523.5 0.6826970 0.6826970 1.028511e-02 1.028511e-02 0.307017854 #> 11049 5524.0 0.6826802 0.6826802 1.028409e-02 1.028409e-02 0.307035751 #> 11050 5524.5 0.6826633 0.6826633 1.028306e-02 1.028306e-02 0.307053621 #> 11051 5525.0 0.6826465 0.6826465 1.028203e-02 1.028203e-02 0.307071466 #> 11052 5525.5 0.6826297 0.6826297 1.028099e-02 1.028099e-02 0.307089284 #> 11053 5526.0 0.6826130 0.6826130 1.027995e-02 1.027995e-02 0.307107076 #> 11054 5526.5 0.6825962 0.6825962 1.027891e-02 1.027891e-02 0.307124842 #> 11055 5527.0 0.6825796 0.6825796 1.027786e-02 1.027786e-02 0.307142582 #> 11056 5527.5 0.6825629 0.6825629 1.027681e-02 1.027681e-02 0.307160295 #> 11057 5528.0 0.6825463 0.6825463 1.027575e-02 1.027575e-02 0.307177982 #> 11058 5528.5 0.6825297 0.6825297 1.027469e-02 1.027469e-02 0.307195642 #> 11059 5529.0 0.6825131 0.6825131 1.027363e-02 1.027363e-02 0.307213276 #> 11060 5529.5 0.6824966 0.6824966 1.027256e-02 1.027256e-02 0.307230883 #> 11061 5530.0 0.6824800 0.6824800 1.027149e-02 1.027149e-02 0.307248464 #> 11062 5530.5 0.6824636 0.6824636 1.027041e-02 1.027041e-02 0.307266017 #> 11063 5531.0 0.6824471 0.6824471 1.026933e-02 1.026933e-02 0.307283544 #> 11064 5531.5 0.6824307 0.6824307 1.026824e-02 1.026824e-02 0.307301045 #> 11065 5532.0 0.6824143 0.6824143 1.026715e-02 1.026715e-02 0.307318518 #> 11066 5532.5 0.6823980 0.6823980 1.026606e-02 1.026606e-02 0.307335965 #> 11067 5533.0 0.6823817 0.6823817 1.026496e-02 1.026496e-02 0.307353384 #> 11068 5533.5 0.6823654 0.6823654 1.026386e-02 1.026386e-02 0.307370777 #> 11069 5534.0 0.6823491 0.6823491 1.026276e-02 1.026276e-02 0.307388142 #> 11070 5534.5 0.6823329 0.6823329 1.026165e-02 1.026165e-02 0.307405480 #> 11071 5535.0 0.6823167 0.6823167 1.026053e-02 1.026053e-02 0.307422791 #> 11072 5535.5 0.6823005 0.6823005 1.025941e-02 1.025941e-02 0.307440075 #> 11073 5536.0 0.6822844 0.6822844 1.025829e-02 1.025829e-02 0.307457332 #> 11074 5536.5 0.6822683 0.6822683 1.025717e-02 1.025717e-02 0.307474561 #> 11075 5537.0 0.6822522 0.6822522 1.025604e-02 1.025604e-02 0.307491763 #> 11076 5537.5 0.6822362 0.6822362 1.025490e-02 1.025490e-02 0.307508937 #> 11077 5538.0 0.6822202 0.6822202 1.025376e-02 1.025376e-02 0.307526084 #> 11078 5538.5 0.6822042 0.6822042 1.025262e-02 1.025262e-02 0.307543203 #> 11079 5539.0 0.6821882 0.6821882 1.025148e-02 1.025148e-02 0.307560295 #> 11080 5539.5 0.6821723 0.6821723 1.025033e-02 1.025033e-02 0.307577359 #> 11081 5540.0 0.6821564 0.6821564 1.024917e-02 1.024917e-02 0.307594395 #> 11082 5540.5 0.6821406 0.6821406 1.024802e-02 1.024802e-02 0.307611403 #> 11083 5541.0 0.6821248 0.6821248 1.024685e-02 1.024685e-02 0.307628384 #> 11084 5541.5 0.6821090 0.6821090 1.024569e-02 1.024569e-02 0.307645337 #> 11085 5542.0 0.6820932 0.6820932 1.024452e-02 1.024452e-02 0.307662262 #> 11086 5542.5 0.6820775 0.6820775 1.024335e-02 1.024335e-02 0.307679158 #> 11087 5543.0 0.6820618 0.6820618 1.024217e-02 1.024217e-02 0.307696027 #> 11088 5543.5 0.6820461 0.6820461 1.024099e-02 1.024099e-02 0.307712868 #> 11089 5544.0 0.6820305 0.6820305 1.023980e-02 1.023980e-02 0.307729681 #> 11090 5544.5 0.6820149 0.6820149 1.023861e-02 1.023861e-02 0.307746465 #> 11091 5545.0 0.6819994 0.6819994 1.023742e-02 1.023742e-02 0.307763221 #> 11092 5545.5 0.6819838 0.6819838 1.023623e-02 1.023623e-02 0.307779949 #> 11093 5546.0 0.6819683 0.6819683 1.023502e-02 1.023502e-02 0.307796649 #> 11094 5546.5 0.6819529 0.6819529 1.023382e-02 1.023382e-02 0.307813320 #> 11095 5547.0 0.6819374 0.6819374 1.023261e-02 1.023261e-02 0.307829963 #> 11096 5547.5 0.6819220 0.6819220 1.023140e-02 1.023140e-02 0.307846577 #> 11097 5548.0 0.6819067 0.6819067 1.023019e-02 1.023019e-02 0.307863163 #> 11098 5548.5 0.6818913 0.6818913 1.022897e-02 1.022897e-02 0.307879720 #> 11099 5549.0 0.6818760 0.6818760 1.022774e-02 1.022774e-02 0.307896249 #> 11100 5549.5 0.6818607 0.6818607 1.022652e-02 1.022652e-02 0.307912749 #> 11101 5550.0 0.6818455 0.6818455 1.022529e-02 1.022529e-02 0.307929220 #> 11102 5550.5 0.6818303 0.6818303 1.022405e-02 1.022405e-02 0.307945662 #> 11103 5551.0 0.6818151 0.6818151 1.022281e-02 1.022281e-02 0.307962076 #> 11104 5551.5 0.6818000 0.6818000 1.022157e-02 1.022157e-02 0.307978461 #> 11105 5552.0 0.6817849 0.6817849 1.022033e-02 1.022033e-02 0.307994817 #> 11106 5552.5 0.6817698 0.6817698 1.021908e-02 1.021908e-02 0.308011143 #> 11107 5553.0 0.6817547 0.6817547 1.021782e-02 1.021782e-02 0.308027441 #> 11108 5553.5 0.6817397 0.6817397 1.021657e-02 1.021657e-02 0.308043710 #> 11109 5554.0 0.6817247 0.6817247 1.021531e-02 1.021531e-02 0.308059950 #> 11110 5554.5 0.6817098 0.6817098 1.021404e-02 1.021404e-02 0.308076160 #> 11111 5555.0 0.6816949 0.6816949 1.021278e-02 1.021278e-02 0.308092342 #> 11112 5555.5 0.6816800 0.6816800 1.021150e-02 1.021150e-02 0.308108494 #> 11113 5556.0 0.6816652 0.6816652 1.021023e-02 1.021023e-02 0.308124617 #> 11114 5556.5 0.6816503 0.6816503 1.020895e-02 1.020895e-02 0.308140710 #> 11115 5557.0 0.6816356 0.6816356 1.020767e-02 1.020767e-02 0.308156774 #> 11116 5557.5 0.6816208 0.6816208 1.020638e-02 1.020638e-02 0.308172809 #> 11117 5558.0 0.6816061 0.6816061 1.020509e-02 1.020509e-02 0.308188814 #> 11118 5558.5 0.6815914 0.6815914 1.020380e-02 1.020380e-02 0.308204790 #> 11119 5559.0 0.6815768 0.6815768 1.020250e-02 1.020250e-02 0.308220736 #> 11120 5559.5 0.6815621 0.6815621 1.020120e-02 1.020120e-02 0.308236653 #> 11121 5560.0 0.6815476 0.6815476 1.019990e-02 1.019990e-02 0.308252540 #> 11122 5560.5 0.6815330 0.6815330 1.019859e-02 1.019859e-02 0.308268397 #> 11123 5561.0 0.6815185 0.6815185 1.019728e-02 1.019728e-02 0.308284225 #> 11124 5561.5 0.6815040 0.6815040 1.019597e-02 1.019597e-02 0.308300022 #> 11125 5562.0 0.6814896 0.6814896 1.019465e-02 1.019465e-02 0.308315790 #> 11126 5562.5 0.6814751 0.6814751 1.019333e-02 1.019333e-02 0.308331528 #> 11127 5563.0 0.6814608 0.6814608 1.019201e-02 1.019201e-02 0.308347236 #> 11128 5563.5 0.6814464 0.6814464 1.019068e-02 1.019068e-02 0.308362914 #> 11129 5564.0 0.6814321 0.6814321 1.018935e-02 1.018935e-02 0.308378563 #> 11130 5564.5 0.6814178 0.6814178 1.018801e-02 1.018801e-02 0.308394181 #> 11131 5565.0 0.6814036 0.6814036 1.018667e-02 1.018667e-02 0.308409769 #> 11132 5565.5 0.6813893 0.6813893 1.018533e-02 1.018533e-02 0.308425327 #> 11133 5566.0 0.6813752 0.6813752 1.018398e-02 1.018398e-02 0.308440854 #> 11134 5566.5 0.6813610 0.6813610 1.018264e-02 1.018264e-02 0.308456352 #> 11135 5567.0 0.6813469 0.6813469 1.018128e-02 1.018128e-02 0.308471819 #> 11136 5567.5 0.6813328 0.6813328 1.017993e-02 1.017993e-02 0.308487256 #> 11137 5568.0 0.6813188 0.6813188 1.017857e-02 1.017857e-02 0.308502663 #> 11138 5568.5 0.6813048 0.6813048 1.017721e-02 1.017721e-02 0.308518039 #> 11139 5569.0 0.6812908 0.6812908 1.017584e-02 1.017584e-02 0.308533385 #> 11140 5569.5 0.6812768 0.6812768 1.017447e-02 1.017447e-02 0.308548700 #> 11141 5570.0 0.6812629 0.6812629 1.017310e-02 1.017310e-02 0.308563985 #> 11142 5570.5 0.6812490 0.6812490 1.017172e-02 1.017172e-02 0.308579240 #> 11143 5571.0 0.6812352 0.6812352 1.017034e-02 1.017034e-02 0.308594463 #> 11144 5571.5 0.6812214 0.6812214 1.016896e-02 1.016896e-02 0.308609657 #> 11145 5572.0 0.6812076 0.6812076 1.016757e-02 1.016757e-02 0.308624819 #> 11146 5572.5 0.6811939 0.6811939 1.016618e-02 1.016618e-02 0.308639951 #> 11147 5573.0 0.6811802 0.6811802 1.016479e-02 1.016479e-02 0.308655052 #> 11148 5573.5 0.6811665 0.6811665 1.016340e-02 1.016340e-02 0.308670122 #> 11149 5574.0 0.6811528 0.6811528 1.016200e-02 1.016200e-02 0.308685162 #> 11150 5574.5 0.6811392 0.6811392 1.016059e-02 1.016059e-02 0.308700170 #> 11151 5575.0 0.6811257 0.6811257 1.015919e-02 1.015919e-02 0.308715148 #> 11152 5575.5 0.6811121 0.6811121 1.015778e-02 1.015778e-02 0.308730095 #> 11153 5576.0 0.6810986 0.6810986 1.015637e-02 1.015637e-02 0.308745011 #> 11154 5576.5 0.6810852 0.6810852 1.015495e-02 1.015495e-02 0.308759895 #> 11155 5577.0 0.6810717 0.6810717 1.015353e-02 1.015353e-02 0.308774749 #> 11156 5577.5 0.6810583 0.6810583 1.015211e-02 1.015211e-02 0.308789572 #> 11157 5578.0 0.6810449 0.6810449 1.015069e-02 1.015069e-02 0.308804363 #> 11158 5578.5 0.6810316 0.6810316 1.014926e-02 1.014926e-02 0.308819124 #> 11159 5579.0 0.6810183 0.6810183 1.014783e-02 1.014783e-02 0.308833853 #> 11160 5579.5 0.6810051 0.6810051 1.014639e-02 1.014639e-02 0.308848551 #> 11161 5580.0 0.6809918 0.6809918 1.014496e-02 1.014496e-02 0.308863217 #> 11162 5580.5 0.6809786 0.6809786 1.014351e-02 1.014351e-02 0.308877853 #> 11163 5581.0 0.6809655 0.6809655 1.014207e-02 1.014207e-02 0.308892456 #> 11164 5581.5 0.6809523 0.6809523 1.014062e-02 1.014062e-02 0.308907029 #> 11165 5582.0 0.6809393 0.6809393 1.013917e-02 1.013917e-02 0.308921570 #> 11166 5582.5 0.6809262 0.6809262 1.013772e-02 1.013772e-02 0.308936080 #> 11167 5583.0 0.6809132 0.6809132 1.013626e-02 1.013626e-02 0.308950558 #> 11168 5583.5 0.6809002 0.6809002 1.013480e-02 1.013480e-02 0.308965005 #> 11169 5584.0 0.6808872 0.6808872 1.013334e-02 1.013334e-02 0.308979420 #> 11170 5584.5 0.6808743 0.6808743 1.013188e-02 1.013188e-02 0.308993803 #> 11171 5585.0 0.6808614 0.6808614 1.013041e-02 1.013041e-02 0.309008155 #> 11172 5585.5 0.6808486 0.6808486 1.012894e-02 1.012894e-02 0.309022475 #> 11173 5586.0 0.6808358 0.6808358 1.012746e-02 1.012746e-02 0.309036764 #> 11174 5586.5 0.6808230 0.6808230 1.012598e-02 1.012598e-02 0.309051021 #> 11175 5587.0 0.6808103 0.6808103 1.012450e-02 1.012450e-02 0.309065246 #> 11176 5587.5 0.6807975 0.6807975 1.012302e-02 1.012302e-02 0.309079439 #> 11177 5588.0 0.6807849 0.6807849 1.012153e-02 1.012153e-02 0.309093600 #> 11178 5588.5 0.6807722 0.6807722 1.012004e-02 1.012004e-02 0.309107730 #> 11179 5589.0 0.6807596 0.6807596 1.011855e-02 1.011855e-02 0.309121827 #> 11180 5589.5 0.6807471 0.6807471 1.011705e-02 1.011705e-02 0.309135893 #> 11181 5590.0 0.6807345 0.6807345 1.011555e-02 1.011555e-02 0.309149926 #> 11182 5590.5 0.6807220 0.6807220 1.011405e-02 1.011405e-02 0.309163928 #> 11183 5591.0 0.6807096 0.6807096 1.011255e-02 1.011255e-02 0.309177897 #> 11184 5591.5 0.6806971 0.6806971 1.011104e-02 1.011104e-02 0.309191835 #> 11185 5592.0 0.6806847 0.6806847 1.010953e-02 1.010953e-02 0.309205740 #> 11186 5592.5 0.6806724 0.6806724 1.010802e-02 1.010802e-02 0.309219613 #> 11187 5593.0 0.6806600 0.6806600 1.010650e-02 1.010650e-02 0.309233454 #> 11188 5593.5 0.6806478 0.6806478 1.010498e-02 1.010498e-02 0.309247263 #> 11189 5594.0 0.6806355 0.6806355 1.010346e-02 1.010346e-02 0.309261040 #> 11190 5594.5 0.6806233 0.6806233 1.010193e-02 1.010193e-02 0.309274784 #> 11191 5595.0 0.6806111 0.6806111 1.010041e-02 1.010041e-02 0.309288496 #> 11192 5595.5 0.6805989 0.6805989 1.009888e-02 1.009888e-02 0.309302176 #> 11193 5596.0 0.6805868 0.6805868 1.009734e-02 1.009734e-02 0.309315823 #> 11194 5596.5 0.6805748 0.6805748 1.009581e-02 1.009581e-02 0.309329438 #> 11195 5597.0 0.6805627 0.6805627 1.009427e-02 1.009427e-02 0.309343021 #> 11196 5597.5 0.6805507 0.6805507 1.009273e-02 1.009273e-02 0.309356571 #> 11197 5598.0 0.6805387 0.6805387 1.009118e-02 1.009118e-02 0.309370088 #> 11198 5598.5 0.6805268 0.6805268 1.008963e-02 1.008963e-02 0.309383573 #> 11199 5599.0 0.6805149 0.6805149 1.008808e-02 1.008808e-02 0.309397026 #> 11200 5599.5 0.6805030 0.6805030 1.008653e-02 1.008653e-02 0.309410446 #> 11201 5600.0 0.6804912 0.6804912 1.008498e-02 1.008498e-02 0.309423833 #> 11202 5600.5 0.6804794 0.6804794 1.008342e-02 1.008342e-02 0.309437188 #> 11203 5601.0 0.6804676 0.6804676 1.008186e-02 1.008186e-02 0.309450510 #> 11204 5601.5 0.6804559 0.6804559 1.008029e-02 1.008029e-02 0.309463799 #> 11205 5602.0 0.6804442 0.6804442 1.007873e-02 1.007873e-02 0.309477056 #> 11206 5602.5 0.6804326 0.6804326 1.007716e-02 1.007716e-02 0.309490280 #> 11207 5603.0 0.6804209 0.6804209 1.007558e-02 1.007558e-02 0.309503471 #> 11208 5603.5 0.6804094 0.6804094 1.007401e-02 1.007401e-02 0.309516629 #> 11209 5604.0 0.6803978 0.6803978 1.007243e-02 1.007243e-02 0.309529754 #> 11210 5604.5 0.6803863 0.6803863 1.007085e-02 1.007085e-02 0.309542847 #> 11211 5605.0 0.6803748 0.6803748 1.006927e-02 1.006927e-02 0.309555907 #> 11212 5605.5 0.6803634 0.6803634 1.006769e-02 1.006769e-02 0.309568933 #> 11213 5606.0 0.6803520 0.6803520 1.006610e-02 1.006610e-02 0.309581927 #> 11214 5606.5 0.6803406 0.6803406 1.006451e-02 1.006451e-02 0.309594888 #> 11215 5607.0 0.6803293 0.6803293 1.006291e-02 1.006291e-02 0.309607816 #> 11216 5607.5 0.6803180 0.6803180 1.006132e-02 1.006132e-02 0.309620711 #> 11217 5608.0 0.6803067 0.6803067 1.005972e-02 1.005972e-02 0.309633572 #> 11218 5608.5 0.6802955 0.6802955 1.005812e-02 1.005812e-02 0.309646401 #> 11219 5609.0 0.6802843 0.6802843 1.005652e-02 1.005652e-02 0.309659197 #> 11220 5609.5 0.6802731 0.6802731 1.005491e-02 1.005491e-02 0.309671959 #> 11221 5610.0 0.6802620 0.6802620 1.005330e-02 1.005330e-02 0.309684688 #> 11222 5610.5 0.6802509 0.6802509 1.005169e-02 1.005169e-02 0.309697384 #> 11223 5611.0 0.6802399 0.6802399 1.005008e-02 1.005008e-02 0.309710047 #> 11224 5611.5 0.6802289 0.6802289 1.004846e-02 1.004846e-02 0.309722677 #> 11225 5612.0 0.6802179 0.6802179 1.004684e-02 1.004684e-02 0.309735273 #> 11226 5612.5 0.6802069 0.6802069 1.004522e-02 1.004522e-02 0.309747836 #> 11227 5613.0 0.6801960 0.6801960 1.004360e-02 1.004360e-02 0.309760366 #> 11228 5613.5 0.6801852 0.6801852 1.004197e-02 1.004197e-02 0.309772863 #> 11229 5614.0 0.6801743 0.6801743 1.004034e-02 1.004034e-02 0.309785326 #> 11230 5614.5 0.6801635 0.6801635 1.003871e-02 1.003871e-02 0.309797756 #> 11231 5615.0 0.6801528 0.6801528 1.003708e-02 1.003708e-02 0.309810152 #> 11232 5615.5 0.6801420 0.6801420 1.003544e-02 1.003544e-02 0.309822515 #> 11233 5616.0 0.6801313 0.6801313 1.003381e-02 1.003381e-02 0.309834844 #> 11234 5616.5 0.6801207 0.6801207 1.003217e-02 1.003217e-02 0.309847140 #> 11235 5617.0 0.6801101 0.6801101 1.003052e-02 1.003052e-02 0.309859402 #> 11236 5617.5 0.6800995 0.6800995 1.002888e-02 1.002888e-02 0.309871631 #> 11237 5618.0 0.6800889 0.6800889 1.002723e-02 1.002723e-02 0.309883827 #> 11238 5618.5 0.6800784 0.6800784 1.002558e-02 1.002558e-02 0.309895988 #> 11239 5619.0 0.6800680 0.6800680 1.002393e-02 1.002393e-02 0.309908117 #> 11240 5619.5 0.6800575 0.6800575 1.002227e-02 1.002227e-02 0.309920211 #> 11241 5620.0 0.6800471 0.6800471 1.002061e-02 1.002061e-02 0.309932272 #> 11242 5620.5 0.6800367 0.6800367 1.001896e-02 1.001896e-02 0.309944299 #> 11243 5621.0 0.6800264 0.6800264 1.001729e-02 1.001729e-02 0.309956293 #> 11244 5621.5 0.6800161 0.6800161 1.001563e-02 1.001563e-02 0.309968253 #> 11245 5622.0 0.6800059 0.6800059 1.001396e-02 1.001396e-02 0.309980179 #> 11246 5622.5 0.6799956 0.6799956 1.001229e-02 1.001229e-02 0.309992072 #> 11247 5623.0 0.6799854 0.6799854 1.001062e-02 1.001062e-02 0.310003930 #> 11248 5623.5 0.6799753 0.6799753 1.000895e-02 1.000895e-02 0.310015755 #> 11249 5624.0 0.6799652 0.6799652 1.000727e-02 1.000727e-02 0.310027546 #> 11250 5624.5 0.6799551 0.6799551 1.000560e-02 1.000560e-02 0.310039303 #> 11251 5625.0 0.6799451 0.6799451 1.000392e-02 1.000392e-02 0.310051027 #> 11252 5625.5 0.6799350 0.6799350 1.000224e-02 1.000224e-02 0.310062716 #> 11253 5626.0 0.6799251 0.6799251 1.000055e-02 1.000055e-02 0.310074372 #> 11254 5626.5 0.6799151 0.6799151 9.998864e-03 9.998864e-03 0.310085994 #> 11255 5627.0 0.6799052 0.6799052 9.997175e-03 9.997175e-03 0.310097581 #> 11256 5627.5 0.6798954 0.6798954 9.995484e-03 9.995484e-03 0.310109135 #> 11257 5628.0 0.6798856 0.6798856 9.993791e-03 9.993791e-03 0.310120655 #> 11258 5628.5 0.6798758 0.6798758 9.992096e-03 9.992096e-03 0.310132141 #> 11259 5629.0 0.6798660 0.6798660 9.990398e-03 9.990398e-03 0.310143593 #> 11260 5629.5 0.6798563 0.6798563 9.988699e-03 9.988699e-03 0.310155011 #> 11261 5630.0 0.6798466 0.6798466 9.986997e-03 9.986997e-03 0.310166394 #> 11262 5630.5 0.6798370 0.6798370 9.985293e-03 9.985293e-03 0.310177744 #> 11263 5631.0 0.6798274 0.6798274 9.983587e-03 9.983587e-03 0.310189060 #> 11264 5631.5 0.6798178 0.6798178 9.981879e-03 9.981879e-03 0.310200341 #> 11265 5632.0 0.6798082 0.6798082 9.980169e-03 9.980169e-03 0.310211589 #> 11266 5632.5 0.6797987 0.6797987 9.978457e-03 9.978457e-03 0.310222802 #> 11267 5633.0 0.6797893 0.6797893 9.976742e-03 9.976742e-03 0.310233981 #> 11268 5633.5 0.6797798 0.6797798 9.975026e-03 9.975026e-03 0.310245126 #> 11269 5634.0 0.6797705 0.6797705 9.973308e-03 9.973308e-03 0.310256237 #> 11270 5634.5 0.6797611 0.6797611 9.971587e-03 9.971587e-03 0.310267313 #> 11271 5635.0 0.6797518 0.6797518 9.969865e-03 9.969865e-03 0.310278356 #> 11272 5635.5 0.6797425 0.6797425 9.968140e-03 9.968140e-03 0.310289364 #> 11273 5636.0 0.6797332 0.6797332 9.966414e-03 9.966414e-03 0.310300338 #> 11274 5636.5 0.6797240 0.6797240 9.964685e-03 9.964685e-03 0.310311277 #> 11275 5637.0 0.6797149 0.6797149 9.962955e-03 9.962955e-03 0.310322183 #> 11276 5637.5 0.6797057 0.6797057 9.961222e-03 9.961222e-03 0.310333054 #> 11277 5638.0 0.6796966 0.6796966 9.959488e-03 9.959488e-03 0.310343890 #> 11278 5638.5 0.6796876 0.6796876 9.957751e-03 9.957751e-03 0.310354693 #> 11279 5639.0 0.6796785 0.6796785 9.956013e-03 9.956013e-03 0.310365461 #> 11280 5639.5 0.6796695 0.6796695 9.954272e-03 9.954272e-03 0.310376194 #> 11281 5640.0 0.6796606 0.6796606 9.952530e-03 9.952530e-03 0.310386893 #> 11282 5640.5 0.6796517 0.6796517 9.950786e-03 9.950786e-03 0.310397558 #> 11283 5641.0 0.6796428 0.6796428 9.949040e-03 9.949040e-03 0.310408189 #> 11284 5641.5 0.6796339 0.6796339 9.947292e-03 9.947292e-03 0.310418785 #> 11285 5642.0 0.6796251 0.6796251 9.945542e-03 9.945542e-03 0.310429346 #> 11286 5642.5 0.6796163 0.6796163 9.943790e-03 9.943790e-03 0.310439874 #> 11287 5643.0 0.6796076 0.6796076 9.942037e-03 9.942037e-03 0.310450366 #> 11288 5643.5 0.6795989 0.6795989 9.940281e-03 9.940281e-03 0.310460824 #> 11289 5644.0 0.6795902 0.6795902 9.938524e-03 9.938524e-03 0.310471248 #> 11290 5644.5 0.6795816 0.6795816 9.936765e-03 9.936765e-03 0.310481637 #> 11291 5645.0 0.6795730 0.6795730 9.935004e-03 9.935004e-03 0.310491992 #> 11292 5645.5 0.6795644 0.6795644 9.933241e-03 9.933241e-03 0.310502312 #> 11293 5646.0 0.6795559 0.6795559 9.931477e-03 9.931477e-03 0.310512598 #> 11294 5646.5 0.6795474 0.6795474 9.929710e-03 9.929710e-03 0.310522849 #> 11295 5647.0 0.6795390 0.6795390 9.927942e-03 9.927942e-03 0.310533065 #> 11296 5647.5 0.6795306 0.6795306 9.926172e-03 9.926172e-03 0.310543247 #> 11297 5648.0 0.6795222 0.6795222 9.924400e-03 9.924400e-03 0.310553394 #> 11298 5648.5 0.6795139 0.6795139 9.922627e-03 9.922627e-03 0.310563507 #> 11299 5649.0 0.6795056 0.6795056 9.920851e-03 9.920851e-03 0.310573585 #> 11300 5649.5 0.6794973 0.6794973 9.919075e-03 9.919075e-03 0.310583628 #> 11301 5650.0 0.6794891 0.6794891 9.917296e-03 9.917296e-03 0.310593637 #> 11302 5650.5 0.6794809 0.6794809 9.915515e-03 9.915515e-03 0.310603611 #> 11303 5651.0 0.6794727 0.6794727 9.913733e-03 9.913733e-03 0.310613551 #> 11304 5651.5 0.6794646 0.6794646 9.911949e-03 9.911949e-03 0.310623456 #> 11305 5652.0 0.6794565 0.6794565 9.910164e-03 9.910164e-03 0.310633326 #> 11306 5652.5 0.6794485 0.6794485 9.908377e-03 9.908377e-03 0.310643161 #> 11307 5653.0 0.6794405 0.6794405 9.906588e-03 9.906588e-03 0.310652962 #> 11308 5653.5 0.6794325 0.6794325 9.904797e-03 9.904797e-03 0.310662728 #> 11309 5654.0 0.6794245 0.6794245 9.903005e-03 9.903005e-03 0.310672459 #> 11310 5654.5 0.6794166 0.6794166 9.901211e-03 9.901211e-03 0.310682155 #> 11311 5655.0 0.6794088 0.6794088 9.899416e-03 9.899416e-03 0.310691817 #> 11312 5655.5 0.6794009 0.6794009 9.897619e-03 9.897619e-03 0.310701444 #> 11313 5656.0 0.6793931 0.6793931 9.895820e-03 9.895820e-03 0.310711036 #> 11314 5656.5 0.6793854 0.6793854 9.894020e-03 9.894020e-03 0.310720594 #> 11315 5657.0 0.6793777 0.6793777 9.892218e-03 9.892218e-03 0.310730116 #> 11316 5657.5 0.6793700 0.6793700 9.890415e-03 9.890415e-03 0.310739604 #> 11317 5658.0 0.6793623 0.6793623 9.888610e-03 9.888610e-03 0.310749057 #> 11318 5658.5 0.6793547 0.6793547 9.886803e-03 9.886803e-03 0.310758475 #> 11319 5659.0 0.6793471 0.6793471 9.884995e-03 9.884995e-03 0.310767859 #> 11320 5659.5 0.6793396 0.6793396 9.883186e-03 9.883186e-03 0.310777207 #> 11321 5660.0 0.6793321 0.6793321 9.881375e-03 9.881375e-03 0.310786521 #> 11322 5660.5 0.6793246 0.6793246 9.879562e-03 9.879562e-03 0.310795800 #> 11323 5661.0 0.6793172 0.6793172 9.877748e-03 9.877748e-03 0.310805044 #> 11324 5661.5 0.6793098 0.6793098 9.875932e-03 9.875932e-03 0.310814253 #> 11325 5662.0 0.6793025 0.6793025 9.874115e-03 9.874115e-03 0.310823428 #> 11326 5662.5 0.6792951 0.6792951 9.872296e-03 9.872296e-03 0.310832567 #> 11327 5663.0 0.6792879 0.6792879 9.870476e-03 9.870476e-03 0.310841672 #> 11328 5663.5 0.6792806 0.6792806 9.868655e-03 9.868655e-03 0.310850741 #> 11329 5664.0 0.6792734 0.6792734 9.866832e-03 9.866832e-03 0.310859776 #> 11330 5664.5 0.6792662 0.6792662 9.865007e-03 9.865007e-03 0.310868776 #> 11331 5665.0 0.6792591 0.6792591 9.863182e-03 9.863182e-03 0.310877741 #> 11332 5665.5 0.6792520 0.6792520 9.861354e-03 9.861354e-03 0.310886671 #> 11333 5666.0 0.6792449 0.6792449 9.859526e-03 9.859526e-03 0.310895566 #> 11334 5666.5 0.6792379 0.6792379 9.857696e-03 9.857696e-03 0.310904426 #> 11335 5667.0 0.6792309 0.6792309 9.855864e-03 9.855864e-03 0.310913251 #> 11336 5667.5 0.6792239 0.6792239 9.854031e-03 9.854031e-03 0.310922042 #> 11337 5668.0 0.6792170 0.6792170 9.852197e-03 9.852197e-03 0.310930797 #> 11338 5668.5 0.6792101 0.6792101 9.850362e-03 9.850362e-03 0.310939517 #> 11339 5669.0 0.6792033 0.6792033 9.848525e-03 9.848525e-03 0.310948203 #> 11340 5669.5 0.6791965 0.6791965 9.846686e-03 9.846686e-03 0.310956853 #> 11341 5670.0 0.6791897 0.6791897 9.844847e-03 9.844847e-03 0.310965469 #> 11342 5670.5 0.6791829 0.6791829 9.843006e-03 9.843006e-03 0.310974049 #> 11343 5671.0 0.6791762 0.6791762 9.841164e-03 9.841164e-03 0.310982595 #> 11344 5671.5 0.6791696 0.6791696 9.839320e-03 9.839320e-03 0.310991106 #> 11345 5672.0 0.6791629 0.6791629 9.837475e-03 9.837475e-03 0.310999581 #> 11346 5672.5 0.6791563 0.6791563 9.835629e-03 9.835629e-03 0.311008022 #> 11347 5673.0 0.6791498 0.6791498 9.833782e-03 9.833782e-03 0.311016428 #> 11348 5673.5 0.6791433 0.6791433 9.831933e-03 9.831933e-03 0.311024798 #> 11349 5674.0 0.6791368 0.6791368 9.830083e-03 9.830083e-03 0.311033134 #> 11350 5674.5 0.6791303 0.6791303 9.828232e-03 9.828232e-03 0.311041435 #> 11351 5675.0 0.6791239 0.6791239 9.826380e-03 9.826380e-03 0.311049700 #> 11352 5675.5 0.6791175 0.6791175 9.824526e-03 9.824526e-03 0.311057931 #> 11353 5676.0 0.6791112 0.6791112 9.822672e-03 9.822672e-03 0.311066126 #> 11354 5676.5 0.6791049 0.6791049 9.820816e-03 9.820816e-03 0.311074287 #> 11355 5677.0 0.6790986 0.6790986 9.818959e-03 9.818959e-03 0.311082413 #> 11356 5677.5 0.6790924 0.6790924 9.817100e-03 9.817100e-03 0.311090503 #> 11357 5678.0 0.6790862 0.6790862 9.815241e-03 9.815241e-03 0.311098559 #> 11358 5678.5 0.6790800 0.6790800 9.813380e-03 9.813380e-03 0.311106579 #> 11359 5679.0 0.6790739 0.6790739 9.811518e-03 9.811518e-03 0.311114565 #> 11360 5679.5 0.6790678 0.6790678 9.809655e-03 9.809655e-03 0.311122515 #> 11361 5680.0 0.6790618 0.6790618 9.807791e-03 9.807791e-03 0.311130431 #> 11362 5680.5 0.6790558 0.6790558 9.805926e-03 9.805926e-03 0.311138311 #> 11363 5681.0 0.6790498 0.6790498 9.804059e-03 9.804059e-03 0.311146157 #> 11364 5681.5 0.6790438 0.6790438 9.802192e-03 9.802192e-03 0.311153967 #> 11365 5682.0 0.6790379 0.6790379 9.800323e-03 9.800323e-03 0.311161743 #> 11366 5682.5 0.6790321 0.6790321 9.798454e-03 9.798454e-03 0.311169483 #> 11367 5683.0 0.6790262 0.6790262 9.796583e-03 9.796583e-03 0.311177188 #> 11368 5683.5 0.6790204 0.6790204 9.794711e-03 9.794711e-03 0.311184859 #> 11369 5684.0 0.6790147 0.6790147 9.792838e-03 9.792838e-03 0.311192494 #> 11370 5684.5 0.6790089 0.6790089 9.790965e-03 9.790965e-03 0.311200094 #> 11371 5685.0 0.6790033 0.6790033 9.789090e-03 9.789090e-03 0.311207659 #> 11372 5685.5 0.6789976 0.6789976 9.787214e-03 9.787214e-03 0.311215189 #> 11373 5686.0 0.6789920 0.6789920 9.785337e-03 9.785337e-03 0.311222684 #> 11374 5686.5 0.6789864 0.6789864 9.783459e-03 9.783459e-03 0.311230145 #> 11375 5687.0 0.6789809 0.6789809 9.781580e-03 9.781580e-03 0.311237570 #> 11376 5687.5 0.6789753 0.6789753 9.779700e-03 9.779700e-03 0.311244960 #> 11377 5688.0 0.6789699 0.6789699 9.777819e-03 9.777819e-03 0.311252315 #> 11378 5688.5 0.6789644 0.6789644 9.775937e-03 9.775937e-03 0.311259635 #> 11379 5689.0 0.6789590 0.6789590 9.774054e-03 9.774054e-03 0.311266920 #> 11380 5689.5 0.6789537 0.6789537 9.772170e-03 9.772170e-03 0.311274169 #> 11381 5690.0 0.6789483 0.6789483 9.770285e-03 9.770285e-03 0.311281384 #> 11382 5690.5 0.6789430 0.6789430 9.768400e-03 9.768400e-03 0.311288564 #> 11383 5691.0 0.6789378 0.6789378 9.766513e-03 9.766513e-03 0.311295709 #> 11384 5691.5 0.6789326 0.6789326 9.764625e-03 9.764625e-03 0.311302819 #> 11385 5692.0 0.6789274 0.6789274 9.762737e-03 9.762737e-03 0.311309894 #> 11386 5692.5 0.6789222 0.6789222 9.760848e-03 9.760848e-03 0.311316934 #> 11387 5693.0 0.6789171 0.6789171 9.758957e-03 9.758957e-03 0.311323938 #> 11388 5693.5 0.6789120 0.6789120 9.757066e-03 9.757066e-03 0.311330908 #> 11389 5694.0 0.6789070 0.6789070 9.755174e-03 9.755174e-03 0.311337843 #> 11390 5694.5 0.6789020 0.6789020 9.753281e-03 9.753281e-03 0.311344743 #> 11391 5695.0 0.6788970 0.6788970 9.751388e-03 9.751388e-03 0.311351608 #> 11392 5695.5 0.6788921 0.6788921 9.749493e-03 9.749493e-03 0.311358438 #> 11393 5696.0 0.6788872 0.6788872 9.747598e-03 9.747598e-03 0.311365232 #> 11394 5696.5 0.6788823 0.6788823 9.745702e-03 9.745702e-03 0.311371992 #> 11395 5697.0 0.6788775 0.6788775 9.743804e-03 9.743804e-03 0.311378717 #> 11396 5697.5 0.6788727 0.6788727 9.741907e-03 9.741907e-03 0.311385407 #> 11397 5698.0 0.6788679 0.6788679 9.740008e-03 9.740008e-03 0.311392062 #> 11398 5698.5 0.6788632 0.6788632 9.738109e-03 9.738109e-03 0.311398682 #> 11399 5699.0 0.6788585 0.6788585 9.736209e-03 9.736209e-03 0.311405267 #> 11400 5699.5 0.6788539 0.6788539 9.734308e-03 9.734308e-03 0.311411817 #> 11401 5700.0 0.6788493 0.6788493 9.732406e-03 9.732406e-03 0.311418332 #> 11402 5700.5 0.6788447 0.6788447 9.730504e-03 9.730504e-03 0.311424812 #> 11403 5701.0 0.6788401 0.6788401 9.728600e-03 9.728600e-03 0.311431257 #> 11404 5701.5 0.6788356 0.6788356 9.726696e-03 9.726696e-03 0.311437668 #> 11405 5702.0 0.6788312 0.6788312 9.724792e-03 9.724792e-03 0.311444043 #> 11406 5702.5 0.6788267 0.6788267 9.722887e-03 9.722887e-03 0.311450383 #> 11407 5703.0 0.6788223 0.6788223 9.720981e-03 9.720981e-03 0.311456689 #> 11408 5703.5 0.6788180 0.6788180 9.719074e-03 9.719074e-03 0.311462959 #> 11409 5704.0 0.6788136 0.6788136 9.717166e-03 9.717166e-03 0.311469195 #> 11410 5704.5 0.6788093 0.6788093 9.715258e-03 9.715258e-03 0.311475396 #> 11411 5705.0 0.6788051 0.6788051 9.713350e-03 9.713350e-03 0.311481561 #> 11412 5705.5 0.6788009 0.6788009 9.711440e-03 9.711440e-03 0.311487692 #> 11413 5706.0 0.6787967 0.6787967 9.709530e-03 9.709530e-03 0.311493788 #> 11414 5706.5 0.6787925 0.6787925 9.707620e-03 9.707620e-03 0.311499850 #> 11415 5707.0 0.6787884 0.6787884 9.705708e-03 9.705708e-03 0.311505876 #> 11416 5707.5 0.6787843 0.6787843 9.703796e-03 9.703796e-03 0.311511867 #> 11417 5708.0 0.6787803 0.6787803 9.701884e-03 9.701884e-03 0.311517824 #> 11418 5708.5 0.6787763 0.6787763 9.699971e-03 9.699971e-03 0.311523746 #> 11419 5709.0 0.6787723 0.6787723 9.698057e-03 9.698057e-03 0.311529632 #> 11420 5709.5 0.6787684 0.6787684 9.696143e-03 9.696143e-03 0.311535485 #> 11421 5710.0 0.6787645 0.6787645 9.694228e-03 9.694228e-03 0.311541302 #> 11422 5710.5 0.6787606 0.6787606 9.692312e-03 9.692312e-03 0.311547084 #> 11423 5711.0 0.6787568 0.6787568 9.690396e-03 9.690396e-03 0.311552832 #> 11424 5711.5 0.6787530 0.6787530 9.688480e-03 9.688480e-03 0.311558545 #> 11425 5712.0 0.6787492 0.6787492 9.686563e-03 9.686563e-03 0.311564223 #> 11426 5712.5 0.6787455 0.6787455 9.684645e-03 9.684645e-03 0.311569866 #> 11427 5713.0 0.6787418 0.6787418 9.682727e-03 9.682727e-03 0.311575474 #> 11428 5713.5 0.6787381 0.6787381 9.680809e-03 9.680809e-03 0.311581048 #> 11429 5714.0 0.6787345 0.6787345 9.678889e-03 9.678889e-03 0.311586587 #> 11430 5714.5 0.6787309 0.6787309 9.676970e-03 9.676970e-03 0.311592091 #> 11431 5715.0 0.6787274 0.6787274 9.675050e-03 9.675050e-03 0.311597560 #> 11432 5715.5 0.6787239 0.6787239 9.673129e-03 9.673129e-03 0.311602995 #> 11433 5716.0 0.6787204 0.6787204 9.671208e-03 9.671208e-03 0.311608395 #> 11434 5716.5 0.6787170 0.6787170 9.669287e-03 9.669287e-03 0.311613760 #> 11435 5717.0 0.6787135 0.6787135 9.667365e-03 9.667365e-03 0.311619091 #> 11436 5717.5 0.6787102 0.6787102 9.665442e-03 9.665442e-03 0.311624387 #> 11437 5718.0 0.6787068 0.6787068 9.663520e-03 9.663520e-03 0.311629648 #> 11438 5718.5 0.6787035 0.6787035 9.661596e-03 9.661596e-03 0.311634874 #> 11439 5719.0 0.6787003 0.6787003 9.659673e-03 9.659673e-03 0.311640066 #> 11440 5719.5 0.6786970 0.6786970 9.657749e-03 9.657749e-03 0.311645223 #> 11441 5720.0 0.6786938 0.6786938 9.655824e-03 9.655824e-03 0.311650346 #> 11442 5720.5 0.6786907 0.6786907 9.653900e-03 9.653900e-03 0.311655434 #> 11443 5721.0 0.6786875 0.6786875 9.651974e-03 9.651974e-03 0.311660487 #> 11444 5721.5 0.6786844 0.6786844 9.650049e-03 9.650049e-03 0.311665506 #> 11445 5722.0 0.6786814 0.6786814 9.648123e-03 9.648123e-03 0.311670490 #> 11446 5722.5 0.6786784 0.6786784 9.646197e-03 9.646197e-03 0.311675439 #> 11447 5723.0 0.6786754 0.6786754 9.644270e-03 9.644270e-03 0.311680354 #> 11448 5723.5 0.6786724 0.6786724 9.642343e-03 9.642343e-03 0.311685235 #> 11449 5724.0 0.6786695 0.6786695 9.640416e-03 9.640416e-03 0.311690081 #> 11450 5724.5 0.6786666 0.6786666 9.638488e-03 9.638488e-03 0.311694892 #> 11451 5725.0 0.6786638 0.6786638 9.636561e-03 9.636561e-03 0.311699669 #> 11452 5725.5 0.6786610 0.6786610 9.634632e-03 9.634632e-03 0.311704411 #> 11453 5726.0 0.6786582 0.6786582 9.632704e-03 9.632704e-03 0.311709119 #> 11454 5726.5 0.6786554 0.6786554 9.630775e-03 9.630775e-03 0.311713792 #> 11455 5727.0 0.6786527 0.6786527 9.628846e-03 9.628846e-03 0.311718431 #> 11456 5727.5 0.6786500 0.6786500 9.626917e-03 9.626917e-03 0.311723035 #> 11457 5728.0 0.6786474 0.6786474 9.624988e-03 9.624988e-03 0.311727605 #> 11458 5728.5 0.6786448 0.6786448 9.623058e-03 9.623058e-03 0.311732140 #> 11459 5729.0 0.6786422 0.6786422 9.621128e-03 9.621128e-03 0.311736641 #> 11460 5729.5 0.6786397 0.6786397 9.619198e-03 9.619198e-03 0.311741108 #> 11461 5730.0 0.6786372 0.6786372 9.617267e-03 9.617267e-03 0.311745540 #> 11462 5730.5 0.6786347 0.6786347 9.615336e-03 9.615336e-03 0.311749938 #> 11463 5731.0 0.6786323 0.6786323 9.613406e-03 9.613406e-03 0.311754301 #> 11464 5731.5 0.6786299 0.6786299 9.611475e-03 9.611475e-03 0.311758631 #> 11465 5732.0 0.6786275 0.6786275 9.609543e-03 9.609543e-03 0.311762925 #> 11466 5732.5 0.6786252 0.6786252 9.607612e-03 9.607612e-03 0.311767186 #> 11467 5733.0 0.6786229 0.6786229 9.605680e-03 9.605680e-03 0.311771412 #> 11468 5733.5 0.6786206 0.6786206 9.603749e-03 9.603749e-03 0.311775604 #> 11469 5734.0 0.6786184 0.6786184 9.601817e-03 9.601817e-03 0.311779761 #> 11470 5734.5 0.6786162 0.6786162 9.599885e-03 9.599885e-03 0.311783885 #> 11471 5735.0 0.6786141 0.6786141 9.597953e-03 9.597953e-03 0.311787974 #> 11472 5735.5 0.6786120 0.6786120 9.596020e-03 9.596020e-03 0.311792028 #> 11473 5736.0 0.6786099 0.6786099 9.594088e-03 9.594088e-03 0.311796049 #> 11474 5736.5 0.6786078 0.6786078 9.592156e-03 9.592156e-03 0.311800035 #> 11475 5737.0 0.6786058 0.6786058 9.590223e-03 9.590223e-03 0.311803987 #> 11476 5737.5 0.6786038 0.6786038 9.588290e-03 9.588290e-03 0.311807905 #> 11477 5738.0 0.6786019 0.6786019 9.586357e-03 9.586357e-03 0.311811789 #> 11478 5738.5 0.6785999 0.6785999 9.584425e-03 9.584425e-03 0.311815639 #> 11479 5739.0 0.6785981 0.6785981 9.582492e-03 9.582492e-03 0.311819454 #> 11480 5739.5 0.6785962 0.6785962 9.580559e-03 9.580559e-03 0.311823236 #> 11481 5740.0 0.6785944 0.6785944 9.578626e-03 9.578626e-03 0.311826983 #> 11482 5740.5 0.6785926 0.6785926 9.576693e-03 9.576693e-03 0.311830696 #> 11483 5741.0 0.6785909 0.6785909 9.574760e-03 9.574760e-03 0.311834375 #> 11484 5741.5 0.6785892 0.6785892 9.572826e-03 9.572826e-03 0.311838021 #> 11485 5742.0 0.6785875 0.6785875 9.570893e-03 9.570893e-03 0.311841632 #> 11486 5742.5 0.6785858 0.6785858 9.568960e-03 9.568960e-03 0.311845209 #> 11487 5743.0 0.6785842 0.6785842 9.567027e-03 9.567027e-03 0.311848752 #> 11488 5743.5 0.6785826 0.6785826 9.565094e-03 9.565094e-03 0.311852261 #> 11489 5744.0 0.6785811 0.6785811 9.563161e-03 9.563161e-03 0.311855736 #> 11490 5744.5 0.6785796 0.6785796 9.561228e-03 9.561228e-03 0.311859177 #> 11491 5745.0 0.6785781 0.6785781 9.559295e-03 9.559295e-03 0.311862584 #> 11492 5745.5 0.6785767 0.6785767 9.557362e-03 9.557362e-03 0.311865958 #> 11493 5746.0 0.6785753 0.6785753 9.555429e-03 9.555429e-03 0.311869297 #> 11494 5746.5 0.6785739 0.6785739 9.553496e-03 9.553496e-03 0.311872603 #> 11495 5747.0 0.6785726 0.6785726 9.551563e-03 9.551563e-03 0.311875874 #> 11496 5747.5 0.6785713 0.6785713 9.549630e-03 9.549630e-03 0.311879112 #> 11497 5748.0 0.6785700 0.6785700 9.547698e-03 9.547698e-03 0.311882316 #> 11498 5748.5 0.6785687 0.6785687 9.545765e-03 9.545765e-03 0.311885487 #> 11499 5749.0 0.6785675 0.6785675 9.543833e-03 9.543833e-03 0.311888623 #> 11500 5749.5 0.6785664 0.6785664 9.541900e-03 9.541900e-03 0.311891726 #> 11501 5750.0 0.6785652 0.6785652 9.539968e-03 9.539968e-03 0.311894795 #> 11502 5750.5 0.6785641 0.6785641 9.538036e-03 9.538036e-03 0.311897830 #> 11503 5751.0 0.6785631 0.6785631 9.536104e-03 9.536104e-03 0.311900831 #> 11504 5751.5 0.6785620 0.6785620 9.534172e-03 9.534172e-03 0.311903799 #> 11505 5752.0 0.6785610 0.6785610 9.532240e-03 9.532240e-03 0.311906733 #> 11506 5752.5 0.6785601 0.6785601 9.530309e-03 9.530309e-03 0.311909634 #> 11507 5753.0 0.6785591 0.6785591 9.528378e-03 9.528378e-03 0.311912501 #> 11508 5753.5 0.6785582 0.6785582 9.526446e-03 9.526446e-03 0.311915334 #> 11509 5754.0 0.6785574 0.6785574 9.524515e-03 9.524515e-03 0.311918134 #> 11510 5754.5 0.6785565 0.6785565 9.522584e-03 9.522584e-03 0.311920900 #> 11511 5755.0 0.6785557 0.6785557 9.520654e-03 9.520654e-03 0.311923632 #> 11512 5755.5 0.6785549 0.6785549 9.518723e-03 9.518723e-03 0.311926331 #> 11513 5756.0 0.6785542 0.6785542 9.516793e-03 9.516793e-03 0.311928997 #> 11514 5756.5 0.6785535 0.6785535 9.514863e-03 9.514863e-03 0.311931629 #> 11515 5757.0 0.6785528 0.6785528 9.512933e-03 9.512933e-03 0.311934227 #> 11516 5757.5 0.6785522 0.6785522 9.511004e-03 9.511004e-03 0.311936792 #> 11517 5758.0 0.6785516 0.6785516 9.509075e-03 9.509075e-03 0.311939324 #> 11518 5758.5 0.6785510 0.6785510 9.507145e-03 9.507145e-03 0.311941822 #> 11519 5759.0 0.6785505 0.6785505 9.505217e-03 9.505217e-03 0.311944287 #> 11520 5759.5 0.6785500 0.6785500 9.503288e-03 9.503288e-03 0.311946718 #> 11521 5760.0 0.6785495 0.6785495 9.501360e-03 9.501360e-03 0.311949116 #> 11522 5760.5 0.6785491 0.6785491 9.499432e-03 9.499432e-03 0.311951481 #> 11523 5761.0 0.6785487 0.6785487 9.497504e-03 9.497504e-03 0.311953813 #> 11524 5761.5 0.6785483 0.6785483 9.495577e-03 9.495577e-03 0.311956111 #> 11525 5762.0 0.6785480 0.6785480 9.493650e-03 9.493650e-03 0.311958376 #> 11526 5762.5 0.6785477 0.6785477 9.491723e-03 9.491723e-03 0.311960607 #> 11527 5763.0 0.6785474 0.6785474 9.489797e-03 9.489797e-03 0.311962806 #> 11528 5763.5 0.6785472 0.6785472 9.487871e-03 9.487871e-03 0.311964971 #> 11529 5764.0 0.6785470 0.6785470 9.485945e-03 9.485945e-03 0.311967103 #> 11530 5764.5 0.6785468 0.6785468 9.484020e-03 9.484020e-03 0.311969202 #> 11531 5765.0 0.6785466 0.6785466 9.482094e-03 9.482094e-03 0.311971268 #> 11532 5765.5 0.6785465 0.6785465 9.480170e-03 9.480170e-03 0.311973301 #> 11533 5766.0 0.6785465 0.6785465 9.478245e-03 9.478245e-03 0.311975300 #> 11534 5766.5 0.6785464 0.6785464 9.476322e-03 9.476322e-03 0.311977267 #> 11535 5767.0 0.6785464 0.6785464 9.474398e-03 9.474398e-03 0.311979201 #> 11536 5767.5 0.6785464 0.6785464 9.472475e-03 9.472475e-03 0.311981101 #> 11537 5768.0 0.6785465 0.6785465 9.470552e-03 9.470552e-03 0.311982969 #> 11538 5768.5 0.6785466 0.6785466 9.468630e-03 9.468630e-03 0.311984803 #> 11539 5769.0 0.6785467 0.6785467 9.466708e-03 9.466708e-03 0.311986605 #> 11540 5769.5 0.6785468 0.6785468 9.464786e-03 9.464786e-03 0.311988374 #> 11541 5770.0 0.6785470 0.6785470 9.462865e-03 9.462865e-03 0.311990109 #> 11542 5770.5 0.6785472 0.6785472 9.460944e-03 9.460944e-03 0.311991812 #> 11543 5771.0 0.6785475 0.6785475 9.459024e-03 9.459024e-03 0.311993482 #> 11544 5771.5 0.6785478 0.6785478 9.457104e-03 9.457104e-03 0.311995120 #> 11545 5772.0 0.6785481 0.6785481 9.455185e-03 9.455185e-03 0.311996724 #> 11546 5772.5 0.6785484 0.6785484 9.453266e-03 9.453266e-03 0.311998296 #> 11547 5773.0 0.6785488 0.6785488 9.451348e-03 9.451348e-03 0.311999835 #> 11548 5773.5 0.6785492 0.6785492 9.449430e-03 9.449430e-03 0.312001341 #> 11549 5774.0 0.6785497 0.6785497 9.447513e-03 9.447513e-03 0.312002814 #> 11550 5774.5 0.6785501 0.6785501 9.445596e-03 9.445596e-03 0.312004255 #> 11551 5775.0 0.6785507 0.6785507 9.443679e-03 9.443679e-03 0.312005663 #> 11552 5775.5 0.6785512 0.6785512 9.441764e-03 9.441764e-03 0.312007039 #> 11553 5776.0 0.6785518 0.6785518 9.439848e-03 9.439848e-03 0.312008382 #> 11554 5776.5 0.6785524 0.6785524 9.437933e-03 9.437933e-03 0.312009692 #> 11555 5777.0 0.6785530 0.6785530 9.436019e-03 9.436019e-03 0.312010970 #> 11556 5777.5 0.6785537 0.6785537 9.434105e-03 9.434105e-03 0.312012215 #> 11557 5778.0 0.6785544 0.6785544 9.432192e-03 9.432192e-03 0.312013428 #> 11558 5778.5 0.6785551 0.6785551 9.430280e-03 9.430280e-03 0.312014608 #> 11559 5779.0 0.6785559 0.6785559 9.428367e-03 9.428367e-03 0.312015756 #> 11560 5779.5 0.6785567 0.6785567 9.426456e-03 9.426456e-03 0.312016871 #> 11561 5780.0 0.6785575 0.6785575 9.424545e-03 9.424545e-03 0.312017954 #> 11562 5780.5 0.6785584 0.6785584 9.422635e-03 9.422635e-03 0.312019005 #> 11563 5781.0 0.6785593 0.6785593 9.420725e-03 9.420725e-03 0.312020023 #> 11564 5781.5 0.6785602 0.6785602 9.418816e-03 9.418816e-03 0.312021009 #> 11565 5782.0 0.6785611 0.6785611 9.416907e-03 9.416907e-03 0.312021962 #> 11566 5782.5 0.6785621 0.6785621 9.414999e-03 9.414999e-03 0.312022884 #> 11567 5783.0 0.6785631 0.6785631 9.413092e-03 9.413092e-03 0.312023773 #> 11568 5783.5 0.6785642 0.6785642 9.411185e-03 9.411185e-03 0.312024630 #> 11569 5784.0 0.6785653 0.6785653 9.409279e-03 9.409279e-03 0.312025454 #> 11570 5784.5 0.6785664 0.6785664 9.407374e-03 9.407374e-03 0.312026247 #> 11571 5785.0 0.6785675 0.6785675 9.405469e-03 9.405469e-03 0.312027007 #> 11572 5785.5 0.6785687 0.6785687 9.403565e-03 9.403565e-03 0.312027736 #> 11573 5786.0 0.6785699 0.6785699 9.401662e-03 9.401662e-03 0.312028432 #> 11574 5786.5 0.6785711 0.6785711 9.399759e-03 9.399759e-03 0.312029096 #> 11575 5787.0 0.6785724 0.6785724 9.397857e-03 9.397857e-03 0.312029728 #> 11576 5787.5 0.6785737 0.6785737 9.395956e-03 9.395956e-03 0.312030328 #> 11577 5788.0 0.6785750 0.6785750 9.394055e-03 9.394055e-03 0.312030896 #> 11578 5788.5 0.6785764 0.6785764 9.392155e-03 9.392155e-03 0.312031432 #> 11579 5789.0 0.6785778 0.6785778 9.390256e-03 9.390256e-03 0.312031937 #> 11580 5789.5 0.6785792 0.6785792 9.388357e-03 9.388357e-03 0.312032409 #> 11581 5790.0 0.6785807 0.6785807 9.386459e-03 9.386459e-03 0.312032849 #> 11582 5790.5 0.6785822 0.6785822 9.384562e-03 9.384562e-03 0.312033258 #> 11583 5791.0 0.6785837 0.6785837 9.382666e-03 9.382666e-03 0.312033635 #> 11584 5791.5 0.6785852 0.6785852 9.380770e-03 9.380770e-03 0.312033980 #> 11585 5792.0 0.6785868 0.6785868 9.378875e-03 9.378875e-03 0.312034293 #> 11586 5792.5 0.6785884 0.6785884 9.376981e-03 9.376981e-03 0.312034575 #> 11587 5793.0 0.6785901 0.6785901 9.375088e-03 9.375088e-03 0.312034825 #> 11588 5793.5 0.6785918 0.6785918 9.373195e-03 9.373195e-03 0.312035043 #> 11589 5794.0 0.6785935 0.6785935 9.371304e-03 9.371304e-03 0.312035230 #> 11590 5794.5 0.6785952 0.6785952 9.369413e-03 9.369413e-03 0.312035385 #> 11591 5795.0 0.6785970 0.6785970 9.367522e-03 9.367522e-03 0.312035508 #> 11592 5795.5 0.6785988 0.6785988 9.365633e-03 9.365633e-03 0.312035600 #> 11593 5796.0 0.6786006 0.6786006 9.363744e-03 9.363744e-03 0.312035660 #> 11594 5796.5 0.6786025 0.6786025 9.361856e-03 9.361856e-03 0.312035689 #> 11595 5797.0 0.6786043 0.6786043 9.359969e-03 9.359969e-03 0.312035686 #> 11596 5797.5 0.6786063 0.6786063 9.358083e-03 9.358083e-03 0.312035652 #> 11597 5798.0 0.6786082 0.6786082 9.356198e-03 9.356198e-03 0.312035587 #> 11598 5798.5 0.6786102 0.6786102 9.354313e-03 9.354313e-03 0.312035490 #> 11599 5799.0 0.6786122 0.6786122 9.352430e-03 9.352430e-03 0.312035362 #> 11600 5799.5 0.6786143 0.6786143 9.350547e-03 9.350547e-03 0.312035202 #> 11601 5800.0 0.6786163 0.6786163 9.348665e-03 9.348665e-03 0.312035012 #> 11602 5800.5 0.6786184 0.6786184 9.346784e-03 9.346784e-03 0.312034789 #> 11603 5801.0 0.6786206 0.6786206 9.344904e-03 9.344904e-03 0.312034536 #> 11604 5801.5 0.6786227 0.6786227 9.343025e-03 9.343025e-03 0.312034252 #> 11605 5802.0 0.6786249 0.6786249 9.341146e-03 9.341146e-03 0.312033936 #> 11606 5802.5 0.6786271 0.6786271 9.339269e-03 9.339269e-03 0.312033589 #> 11607 5803.0 0.6786294 0.6786294 9.337392e-03 9.337392e-03 0.312033212 #> 11608 5803.5 0.6786317 0.6786317 9.335516e-03 9.335516e-03 0.312032803 #> 11609 5804.0 0.6786340 0.6786340 9.333641e-03 9.333641e-03 0.312032363 #> 11610 5804.5 0.6786363 0.6786363 9.331768e-03 9.331768e-03 0.312031892 #> 11611 5805.0 0.6786387 0.6786387 9.329895e-03 9.329895e-03 0.312031390 #> 11612 5805.5 0.6786411 0.6786411 9.328023e-03 9.328023e-03 0.312030857 #> 11613 5806.0 0.6786436 0.6786436 9.326151e-03 9.326151e-03 0.312030293 #> 11614 5806.5 0.6786460 0.6786460 9.324281e-03 9.324281e-03 0.312029698 #> 11615 5807.0 0.6786485 0.6786485 9.322412e-03 9.322412e-03 0.312029073 #> 11616 5807.5 0.6786510 0.6786510 9.320544e-03 9.320544e-03 0.312028416 #> 11617 5808.0 0.6786536 0.6786536 9.318677e-03 9.318677e-03 0.312027729 #> 11618 5808.5 0.6786562 0.6786562 9.316810e-03 9.316810e-03 0.312027011 #> 11619 5809.0 0.6786588 0.6786588 9.314945e-03 9.314945e-03 0.312026262 #> 11620 5809.5 0.6786614 0.6786614 9.313081e-03 9.313081e-03 0.312025483 #> 11621 5810.0 0.6786641 0.6786641 9.311217e-03 9.311217e-03 0.312024673 #> 11622 5810.5 0.6786668 0.6786668 9.309355e-03 9.309355e-03 0.312023832 #> 11623 5811.0 0.6786695 0.6786695 9.307494e-03 9.307494e-03 0.312022961 #> 11624 5811.5 0.6786723 0.6786723 9.305633e-03 9.305633e-03 0.312022059 #> 11625 5812.0 0.6786751 0.6786751 9.303774e-03 9.303774e-03 0.312021127 #> 11626 5812.5 0.6786779 0.6786779 9.301916e-03 9.301916e-03 0.312020164 #> 11627 5813.0 0.6786808 0.6786808 9.300058e-03 9.300058e-03 0.312019170 #> 11628 5813.5 0.6786837 0.6786837 9.298202e-03 9.298202e-03 0.312018146 #> 11629 5814.0 0.6786866 0.6786866 9.296347e-03 9.296347e-03 0.312017092 #> 11630 5814.5 0.6786895 0.6786895 9.294493e-03 9.294493e-03 0.312016008 #> 11631 5815.0 0.6786925 0.6786925 9.292640e-03 9.292640e-03 0.312014893 #> 11632 5815.5 0.6786955 0.6786955 9.290788e-03 9.290788e-03 0.312013747 #> 11633 5816.0 0.6786985 0.6786985 9.288937e-03 9.288937e-03 0.312012572 #> 11634 5816.5 0.6787015 0.6787015 9.287087e-03 9.287087e-03 0.312011366 #> 11635 5817.0 0.6787046 0.6787046 9.285238e-03 9.285238e-03 0.312010130 #> 11636 5817.5 0.6787077 0.6787077 9.283391e-03 9.283391e-03 0.312008864 #> 11637 5818.0 0.6787109 0.6787109 9.281544e-03 9.281544e-03 0.312007568 #> 11638 5818.5 0.6787141 0.6787141 9.279699e-03 9.279699e-03 0.312006241 #> 11639 5819.0 0.6787173 0.6787173 9.277854e-03 9.277854e-03 0.312004885 #> 11640 5819.5 0.6787205 0.6787205 9.276011e-03 9.276011e-03 0.312003498 #> 11641 5820.0 0.6787237 0.6787237 9.274169e-03 9.274169e-03 0.312002081 #> 11642 5820.5 0.6787270 0.6787270 9.272328e-03 9.272328e-03 0.312000635 #> 11643 5821.0 0.6787304 0.6787304 9.270488e-03 9.270488e-03 0.311999158 #> 11644 5821.5 0.6787337 0.6787337 9.268649e-03 9.268649e-03 0.311997652 #> 11645 5822.0 0.6787371 0.6787371 9.266811e-03 9.266811e-03 0.311996116 #> 11646 5822.5 0.6787405 0.6787405 9.264975e-03 9.264975e-03 0.311994550 #> 11647 5823.0 0.6787439 0.6787439 9.263140e-03 9.263140e-03 0.311992954 #> 11648 5823.5 0.6787474 0.6787474 9.261305e-03 9.261305e-03 0.311991328 #> 11649 5824.0 0.6787509 0.6787509 9.259472e-03 9.259472e-03 0.311989673 #> 11650 5824.5 0.6787544 0.6787544 9.257641e-03 9.257641e-03 0.311987987 #> 11651 5825.0 0.6787579 0.6787579 9.255810e-03 9.255810e-03 0.311986273 #> 11652 5825.5 0.6787615 0.6787615 9.253981e-03 9.253981e-03 0.311984528 #> 11653 5826.0 0.6787651 0.6787651 9.252152e-03 9.252152e-03 0.311982754 #> 11654 5826.5 0.6787687 0.6787687 9.250325e-03 9.250325e-03 0.311980951 #> 11655 5827.0 0.6787724 0.6787724 9.248499e-03 9.248499e-03 0.311979117 #> 11656 5827.5 0.6787761 0.6787761 9.246675e-03 9.246675e-03 0.311977255 #> 11657 5828.0 0.6787798 0.6787798 9.244851e-03 9.244851e-03 0.311975363 #> 11658 5828.5 0.6787835 0.6787835 9.243029e-03 9.243029e-03 0.311973441 #> 11659 5829.0 0.6787873 0.6787873 9.241208e-03 9.241208e-03 0.311971490 #> 11660 5829.5 0.6787911 0.6787911 9.239388e-03 9.239388e-03 0.311969510 #> 11661 5830.0 0.6787949 0.6787949 9.237570e-03 9.237570e-03 0.311967500 #> 11662 5830.5 0.6787988 0.6787988 9.235753e-03 9.235753e-03 0.311965461 #> 11663 5831.0 0.6788027 0.6788027 9.233937e-03 9.233937e-03 0.311963393 #> 11664 5831.5 0.6788066 0.6788066 9.232122e-03 9.232122e-03 0.311961296 #> 11665 5832.0 0.6788105 0.6788105 9.230308e-03 9.230308e-03 0.311959169 #> 11666 5832.5 0.6788145 0.6788145 9.228496e-03 9.228496e-03 0.311957014 #> 11667 5833.0 0.6788185 0.6788185 9.226685e-03 9.226685e-03 0.311954829 #> 11668 5833.5 0.6788225 0.6788225 9.224875e-03 9.224875e-03 0.311952615 #> 11669 5834.0 0.6788266 0.6788266 9.223067e-03 9.223067e-03 0.311950372 #> 11670 5834.5 0.6788306 0.6788306 9.221260e-03 9.221260e-03 0.311948100 #> 11671 5835.0 0.6788347 0.6788347 9.219454e-03 9.219454e-03 0.311945799 #> 11672 5835.5 0.6788389 0.6788389 9.217650e-03 9.217650e-03 0.311943470 #> 11673 5836.0 0.6788430 0.6788430 9.215846e-03 9.215846e-03 0.311941111 #> 11674 5836.5 0.6788472 0.6788472 9.214045e-03 9.214045e-03 0.311938723 #> 11675 5837.0 0.6788514 0.6788514 9.212244e-03 9.212244e-03 0.311936307 #> 11676 5837.5 0.6788557 0.6788557 9.210445e-03 9.210445e-03 0.311933862 #> 11677 5838.0 0.6788600 0.6788600 9.208647e-03 9.208647e-03 0.311931388 #> 11678 5838.5 0.6788643 0.6788643 9.206850e-03 9.206850e-03 0.311928885 #> 11679 5839.0 0.6788686 0.6788686 9.205055e-03 9.205055e-03 0.311926354 #> 11680 5839.5 0.6788729 0.6788729 9.203261e-03 9.203261e-03 0.311923794 #> 11681 5840.0 0.6788773 0.6788773 9.201469e-03 9.201469e-03 0.311921206 #> 11682 5840.5 0.6788817 0.6788817 9.199678e-03 9.199678e-03 0.311918589 #> 11683 5841.0 0.6788862 0.6788862 9.197888e-03 9.197888e-03 0.311915943 #> 11684 5841.5 0.6788906 0.6788906 9.196099e-03 9.196099e-03 0.311913269 #> 11685 5842.0 0.6788951 0.6788951 9.194312e-03 9.194312e-03 0.311910566 #> 11686 5842.5 0.6788996 0.6788996 9.192527e-03 9.192527e-03 0.311907836 #> 11687 5843.0 0.6789042 0.6789042 9.190742e-03 9.190742e-03 0.311905076 #> 11688 5843.5 0.6789088 0.6789088 9.188959e-03 9.188959e-03 0.311902289 #> 11689 5844.0 0.6789133 0.6789133 9.187178e-03 9.187178e-03 0.311899473 #> 11690 5844.5 0.6789180 0.6789180 9.185398e-03 9.185398e-03 0.311896628 #> 11691 5845.0 0.6789226 0.6789226 9.183619e-03 9.183619e-03 0.311893756 #> 11692 5845.5 0.6789273 0.6789273 9.181842e-03 9.181842e-03 0.311890855 #> 11693 5846.0 0.6789320 0.6789320 9.180066e-03 9.180066e-03 0.311887926 #> 11694 5846.5 0.6789367 0.6789367 9.178292e-03 9.178292e-03 0.311884970 #> 11695 5847.0 0.6789415 0.6789415 9.176519e-03 9.176519e-03 0.311881985 #> 11696 5847.5 0.6789463 0.6789463 9.174747e-03 9.174747e-03 0.311878972 #> 11697 5848.0 0.6789511 0.6789511 9.172977e-03 9.172977e-03 0.311875930 #> 11698 5848.5 0.6789559 0.6789559 9.171209e-03 9.171209e-03 0.311872861 #> 11699 5849.0 0.6789608 0.6789608 9.169441e-03 9.169441e-03 0.311869765 #> 11700 5849.5 0.6789657 0.6789657 9.167676e-03 9.167676e-03 0.311866640 #> 11701 5850.0 0.6789706 0.6789706 9.165911e-03 9.165911e-03 0.311863487 #> 11702 5850.5 0.6789755 0.6789755 9.164149e-03 9.164149e-03 0.311860307 #> 11703 5851.0 0.6789805 0.6789805 9.162387e-03 9.162387e-03 0.311857098 #> 11704 5851.5 0.6789855 0.6789855 9.160627e-03 9.160627e-03 0.311853863 #> 11705 5852.0 0.6789905 0.6789905 9.158869e-03 9.158869e-03 0.311850599 #> 11706 5852.5 0.6789956 0.6789956 9.157112e-03 9.157112e-03 0.311847308 #> 11707 5853.0 0.6790007 0.6790007 9.155357e-03 9.155357e-03 0.311843989 #> 11708 5853.5 0.6790058 0.6790058 9.153603e-03 9.153603e-03 0.311840642 #> 11709 5854.0 0.6790109 0.6790109 9.151851e-03 9.151851e-03 0.311837268 #> 11710 5854.5 0.6790160 0.6790160 9.150100e-03 9.150100e-03 0.311833867 #> 11711 5855.0 0.6790212 0.6790212 9.148350e-03 9.148350e-03 0.311830438 #> 11712 5855.5 0.6790264 0.6790264 9.146602e-03 9.146602e-03 0.311826981 #> 11713 5856.0 0.6790316 0.6790316 9.144856e-03 9.144856e-03 0.311823497 #> 11714 5856.5 0.6790369 0.6790369 9.143111e-03 9.143111e-03 0.311819986 #> 11715 5857.0 0.6790422 0.6790422 9.141368e-03 9.141368e-03 0.311816448 #> 11716 5857.5 0.6790475 0.6790475 9.139627e-03 9.139627e-03 0.311812882 #> 11717 5858.0 0.6790528 0.6790528 9.137886e-03 9.137886e-03 0.311809289 #> 11718 5858.5 0.6790582 0.6790582 9.136148e-03 9.136148e-03 0.311805669 #> 11719 5859.0 0.6790636 0.6790636 9.134411e-03 9.134411e-03 0.311802022 #> 11720 5859.5 0.6790690 0.6790690 9.132675e-03 9.132675e-03 0.311798347 #> 11721 5860.0 0.6790744 0.6790744 9.130941e-03 9.130941e-03 0.311794646 #> 11722 5860.5 0.6790799 0.6790799 9.129209e-03 9.129209e-03 0.311790917 #> 11723 5861.0 0.6790854 0.6790854 9.127478e-03 9.127478e-03 0.311787162 #> 11724 5861.5 0.6790909 0.6790909 9.125749e-03 9.125749e-03 0.311783380 #> 11725 5862.0 0.6790964 0.6790964 9.124022e-03 9.124022e-03 0.311779570 #> 11726 5862.5 0.6791020 0.6791020 9.122296e-03 9.122296e-03 0.311775734 #> 11727 5863.0 0.6791076 0.6791076 9.120571e-03 9.120571e-03 0.311771871 #> 11728 5863.5 0.6791132 0.6791132 9.118848e-03 9.118848e-03 0.311767981 #> 11729 5864.0 0.6791188 0.6791188 9.117127e-03 9.117127e-03 0.311764065 #> 11730 5864.5 0.6791245 0.6791245 9.115408e-03 9.115408e-03 0.311760121 #> 11731 5865.0 0.6791302 0.6791302 9.113690e-03 9.113690e-03 0.311756152 #> 11732 5865.5 0.6791359 0.6791359 9.111973e-03 9.111973e-03 0.311752155 #> 11733 5866.0 0.6791416 0.6791416 9.110259e-03 9.110259e-03 0.311748132 #> 11734 5866.5 0.6791474 0.6791474 9.108546e-03 9.108546e-03 0.311744082 #> 11735 5867.0 0.6791532 0.6791532 9.106834e-03 9.106834e-03 0.311740006 #> 11736 5867.5 0.6791590 0.6791590 9.105124e-03 9.105124e-03 0.311735903 #> 11737 5868.0 0.6791648 0.6791648 9.103416e-03 9.103416e-03 0.311731774 #> 11738 5868.5 0.6791707 0.6791707 9.101710e-03 9.101710e-03 0.311727619 #> 11739 5869.0 0.6791766 0.6791766 9.100005e-03 9.100005e-03 0.311723437 #> 11740 5869.5 0.6791825 0.6791825 9.098302e-03 9.098302e-03 0.311719229 #> 11741 5870.0 0.6791884 0.6791884 9.096600e-03 9.096600e-03 0.311714994 #> 11742 5870.5 0.6791944 0.6791944 9.094901e-03 9.094901e-03 0.311710734 #> 11743 5871.0 0.6792004 0.6792004 9.093202e-03 9.093202e-03 0.311706447 #> 11744 5871.5 0.6792064 0.6792064 9.091506e-03 9.091506e-03 0.311702134 #> 11745 5872.0 0.6792124 0.6792124 9.089811e-03 9.089811e-03 0.311697795 #> 11746 5872.5 0.6792185 0.6792185 9.088118e-03 9.088118e-03 0.311693430 #> 11747 5873.0 0.6792245 0.6792245 9.086427e-03 9.086427e-03 0.311689039 #> 11748 5873.5 0.6792306 0.6792306 9.084737e-03 9.084737e-03 0.311684622 #> 11749 5874.0 0.6792368 0.6792368 9.083049e-03 9.083049e-03 0.311680179 #> 11750 5874.5 0.6792429 0.6792429 9.081363e-03 9.081363e-03 0.311675710 #> 11751 5875.0 0.6792491 0.6792491 9.079678e-03 9.079678e-03 0.311671215 #> 11752 5875.5 0.6792553 0.6792553 9.077995e-03 9.077995e-03 0.311666695 #> 11753 5876.0 0.6792615 0.6792615 9.076314e-03 9.076314e-03 0.311662148 #> 11754 5876.5 0.6792678 0.6792678 9.074635e-03 9.074635e-03 0.311657576 #> 11755 5877.0 0.6792741 0.6792741 9.072957e-03 9.072957e-03 0.311652979 #> 11756 5877.5 0.6792804 0.6792804 9.071281e-03 9.071281e-03 0.311648355 #> 11757 5878.0 0.6792867 0.6792867 9.069607e-03 9.069607e-03 0.311643706 #> 11758 5878.5 0.6792930 0.6792930 9.067934e-03 9.067934e-03 0.311639032 #> 11759 5879.0 0.6792994 0.6792994 9.066264e-03 9.066264e-03 0.311634332 #> 11760 5879.5 0.6793058 0.6793058 9.064595e-03 9.064595e-03 0.311629606 #> 11761 5880.0 0.6793122 0.6793122 9.062927e-03 9.062927e-03 0.311624855 #> 11762 5880.5 0.6793187 0.6793187 9.061262e-03 9.061262e-03 0.311620079 #> 11763 5881.0 0.6793251 0.6793251 9.059598e-03 9.059598e-03 0.311615277 #> 11764 5881.5 0.6793316 0.6793316 9.057936e-03 9.057936e-03 0.311610450 #> 11765 5882.0 0.6793381 0.6793381 9.056276e-03 9.056276e-03 0.311605598 #> 11766 5882.5 0.6793447 0.6793447 9.054618e-03 9.054618e-03 0.311600721 #> 11767 5883.0 0.6793512 0.6793512 9.052961e-03 9.052961e-03 0.311595818 #> 11768 5883.5 0.6793578 0.6793578 9.051307e-03 9.051307e-03 0.311590890 #> 11769 5884.0 0.6793644 0.6793644 9.049654e-03 9.049654e-03 0.311585937 #> 11770 5884.5 0.6793710 0.6793710 9.048002e-03 9.048002e-03 0.311580960 #> 11771 5885.0 0.6793777 0.6793777 9.046353e-03 9.046353e-03 0.311575957 #> 11772 5885.5 0.6793844 0.6793844 9.044706e-03 9.044706e-03 0.311570929 #> 11773 5886.0 0.6793911 0.6793911 9.043060e-03 9.043060e-03 0.311565876 #> 11774 5886.5 0.6793978 0.6793978 9.041416e-03 9.041416e-03 0.311560798 #> 11775 5887.0 0.6794045 0.6794045 9.039774e-03 9.039774e-03 0.311555696 #> 11776 5887.5 0.6794113 0.6794113 9.038133e-03 9.038133e-03 0.311550568 #> 11777 5888.0 0.6794181 0.6794181 9.036495e-03 9.036495e-03 0.311545416 #> 11778 5888.5 0.6794249 0.6794249 9.034858e-03 9.034858e-03 0.311540239 #> 11779 5889.0 0.6794317 0.6794317 9.033224e-03 9.033224e-03 0.311535038 #> 11780 5889.5 0.6794386 0.6794386 9.031591e-03 9.031591e-03 0.311529812 #> 11781 5890.0 0.6794455 0.6794455 9.029960e-03 9.029960e-03 0.311524561 #> 11782 5890.5 0.6794524 0.6794524 9.028330e-03 9.028330e-03 0.311519286 #> 11783 5891.0 0.6794593 0.6794593 9.026703e-03 9.026703e-03 0.311513986 #> 11784 5891.5 0.6794663 0.6794663 9.025077e-03 9.025077e-03 0.311508662 #> 11785 5892.0 0.6794732 0.6794732 9.023454e-03 9.023454e-03 0.311503313 #> 11786 5892.5 0.6794802 0.6794802 9.021832e-03 9.021832e-03 0.311497941 #> 11787 5893.0 0.6794872 0.6794872 9.020212e-03 9.020212e-03 0.311492543 #> 11788 5893.5 0.6794943 0.6794943 9.018594e-03 9.018594e-03 0.311487122 #> 11789 5894.0 0.6795013 0.6795013 9.016978e-03 9.016978e-03 0.311481676 #> 11790 5894.5 0.6795084 0.6795084 9.015363e-03 9.015363e-03 0.311476206 #> 11791 5895.0 0.6795155 0.6795155 9.013751e-03 9.013751e-03 0.311470712 #> 11792 5895.5 0.6795227 0.6795227 9.012140e-03 9.012140e-03 0.311465194 #> 11793 5896.0 0.6795298 0.6795298 9.010532e-03 9.010532e-03 0.311459651 #> 11794 5896.5 0.6795370 0.6795370 9.008925e-03 9.008925e-03 0.311454085 #> 11795 5897.0 0.6795442 0.6795442 9.007320e-03 9.007320e-03 0.311448495 #> 11796 5897.5 0.6795514 0.6795514 9.005717e-03 9.005717e-03 0.311442881 #> 11797 5898.0 0.6795586 0.6795586 9.004116e-03 9.004116e-03 0.311437243 #> 11798 5898.5 0.6795659 0.6795659 9.002517e-03 9.002517e-03 0.311431581 #> 11799 5899.0 0.6795732 0.6795732 9.000920e-03 9.000920e-03 0.311425895 #> 11800 5899.5 0.6795805 0.6795805 8.999325e-03 8.999325e-03 0.311420185 #> 11801 5900.0 0.6795878 0.6795878 8.997732e-03 8.997732e-03 0.311414452 #> 11802 5900.5 0.6795952 0.6795952 8.996140e-03 8.996140e-03 0.311408695 #> 11803 5901.0 0.6796025 0.6796025 8.994551e-03 8.994551e-03 0.311402915 #> 11804 5901.5 0.6796099 0.6796099 8.992963e-03 8.992963e-03 0.311397111 #> 11805 5902.0 0.6796173 0.6796173 8.991378e-03 8.991378e-03 0.311391283 #> 11806 5902.5 0.6796248 0.6796248 8.989794e-03 8.989794e-03 0.311385432 #> 11807 5903.0 0.6796322 0.6796322 8.988213e-03 8.988213e-03 0.311379558 #> 11808 5903.5 0.6796397 0.6796397 8.986633e-03 8.986633e-03 0.311373660 #> 11809 5904.0 0.6796472 0.6796472 8.985055e-03 8.985055e-03 0.311367738 #> 11810 5904.5 0.6796547 0.6796547 8.983479e-03 8.983479e-03 0.311361794 #> 11811 5905.0 0.6796623 0.6796623 8.981905e-03 8.981905e-03 0.311355826 #> 11812 5905.5 0.6796698 0.6796698 8.980334e-03 8.980334e-03 0.311349835 #> 11813 5906.0 0.6796774 0.6796774 8.978764e-03 8.978764e-03 0.311343821 #> 11814 5906.5 0.6796850 0.6796850 8.977196e-03 8.977196e-03 0.311337783 #> 11815 5907.0 0.6796926 0.6796926 8.975630e-03 8.975630e-03 0.311331723 #> 11816 5907.5 0.6797003 0.6797003 8.974066e-03 8.974066e-03 0.311325639 #> 11817 5908.0 0.6797080 0.6797080 8.972504e-03 8.972504e-03 0.311319533 #> 11818 5908.5 0.6797157 0.6797157 8.970944e-03 8.970944e-03 0.311313403 #> 11819 5909.0 0.6797234 0.6797234 8.969386e-03 8.969386e-03 0.311307251 #> 11820 5909.5 0.6797311 0.6797311 8.967830e-03 8.967830e-03 0.311301075 #> 11821 5910.0 0.6797388 0.6797388 8.966276e-03 8.966276e-03 0.311294877 #> 11822 5910.5 0.6797466 0.6797466 8.964725e-03 8.964725e-03 0.311288656 #> 11823 5911.0 0.6797544 0.6797544 8.963175e-03 8.963175e-03 0.311282413 #> 11824 5911.5 0.6797622 0.6797622 8.961627e-03 8.961627e-03 0.311276146 #> 11825 5912.0 0.6797701 0.6797701 8.960081e-03 8.960081e-03 0.311269858 #> 11826 5912.5 0.6797779 0.6797779 8.958537e-03 8.958537e-03 0.311263546 #> 11827 5913.0 0.6797858 0.6797858 8.956995e-03 8.956995e-03 0.311257212 #> 11828 5913.5 0.6797937 0.6797937 8.955455e-03 8.955455e-03 0.311250855 #> 11829 5914.0 0.6798016 0.6798016 8.953918e-03 8.953918e-03 0.311244476 #> 11830 5914.5 0.6798095 0.6798095 8.952382e-03 8.952382e-03 0.311238075 #> 11831 5915.0 0.6798175 0.6798175 8.950848e-03 8.950848e-03 0.311231651 #> 11832 5915.5 0.6798255 0.6798255 8.949317e-03 8.949317e-03 0.311225205 #> 11833 5916.0 0.6798335 0.6798335 8.947787e-03 8.947787e-03 0.311218736 #> 11834 5916.5 0.6798415 0.6798415 8.946260e-03 8.946260e-03 0.311212246 #> 11835 5917.0 0.6798495 0.6798495 8.944734e-03 8.944734e-03 0.311205733 #> 11836 5917.5 0.6798576 0.6798576 8.943211e-03 8.943211e-03 0.311199198 #> 11837 5918.0 0.6798657 0.6798657 8.941689e-03 8.941689e-03 0.311192641 #> 11838 5918.5 0.6798738 0.6798738 8.940170e-03 8.940170e-03 0.311186061 #> 11839 5919.0 0.6798819 0.6798819 8.938653e-03 8.938653e-03 0.311179460 #> 11840 5919.5 0.6798900 0.6798900 8.937138e-03 8.937138e-03 0.311172837 #> 11841 5920.0 0.6798982 0.6798982 8.935625e-03 8.935625e-03 0.311166192 #> 11842 5920.5 0.6799064 0.6799064 8.934114e-03 8.934114e-03 0.311159525 #> 11843 5921.0 0.6799146 0.6799146 8.932605e-03 8.932605e-03 0.311152836 #> 11844 5921.5 0.6799228 0.6799228 8.931098e-03 8.931098e-03 0.311146126 #> 11845 5922.0 0.6799310 0.6799310 8.929593e-03 8.929593e-03 0.311139393 #> 11846 5922.5 0.6799393 0.6799393 8.928091e-03 8.928091e-03 0.311132639 #> 11847 5923.0 0.6799475 0.6799475 8.926590e-03 8.926590e-03 0.311125864 #> 11848 5923.5 0.6799558 0.6799558 8.925092e-03 8.925092e-03 0.311119067 #> 11849 5924.0 0.6799642 0.6799642 8.923595e-03 8.923595e-03 0.311112248 #> 11850 5924.5 0.6799725 0.6799725 8.922101e-03 8.922101e-03 0.311105408 #> 11851 5925.0 0.6799808 0.6799808 8.920609e-03 8.920609e-03 0.311098546 #> 11852 5925.5 0.6799892 0.6799892 8.919119e-03 8.919119e-03 0.311091663 #> 11853 5926.0 0.6799976 0.6799976 8.917631e-03 8.917631e-03 0.311084758 #> 11854 5926.5 0.6800060 0.6800060 8.916146e-03 8.916146e-03 0.311077832 #> 11855 5927.0 0.6800145 0.6800145 8.914662e-03 8.914662e-03 0.311070885 #> 11856 5927.5 0.6800229 0.6800229 8.913180e-03 8.913180e-03 0.311063917 #> 11857 5928.0 0.6800314 0.6800314 8.911701e-03 8.911701e-03 0.311056927 #> 11858 5928.5 0.6800399 0.6800399 8.910224e-03 8.910224e-03 0.311049917 #> 11859 5929.0 0.6800484 0.6800484 8.908749e-03 8.908749e-03 0.311042885 #> 11860 5929.5 0.6800569 0.6800569 8.907276e-03 8.907276e-03 0.311035832 #> 11861 5930.0 0.6800654 0.6800654 8.905805e-03 8.905805e-03 0.311028758 #> 11862 5930.5 0.6800740 0.6800740 8.904336e-03 8.904336e-03 0.311021663 #> 11863 5931.0 0.6800826 0.6800826 8.902870e-03 8.902870e-03 0.311014548 #> 11864 5931.5 0.6800912 0.6800912 8.901406e-03 8.901406e-03 0.311007411 #> 11865 5932.0 0.6800998 0.6800998 8.899943e-03 8.899943e-03 0.311000254 #> 11866 5932.5 0.6801084 0.6801084 8.898483e-03 8.898483e-03 0.310993076 #> 11867 5933.0 0.6801171 0.6801171 8.897025e-03 8.897025e-03 0.310985877 #> 11868 5933.5 0.6801258 0.6801258 8.895570e-03 8.895570e-03 0.310978657 #> 11869 5934.0 0.6801345 0.6801345 8.894116e-03 8.894116e-03 0.310971417 #> 11870 5934.5 0.6801432 0.6801432 8.892665e-03 8.892665e-03 0.310964157 #> 11871 5935.0 0.6801519 0.6801519 8.891216e-03 8.891216e-03 0.310956875 #> 11872 5935.5 0.6801607 0.6801607 8.889769e-03 8.889769e-03 0.310949574 #> 11873 5936.0 0.6801694 0.6801694 8.888324e-03 8.888324e-03 0.310942251 #> 11874 5936.5 0.6801782 0.6801782 8.886881e-03 8.886881e-03 0.310934909 #> 11875 5937.0 0.6801870 0.6801870 8.885441e-03 8.885441e-03 0.310927546 #> 11876 5937.5 0.6801958 0.6801958 8.884002e-03 8.884002e-03 0.310920163 #> 11877 5938.0 0.6802047 0.6802047 8.882566e-03 8.882566e-03 0.310912759 #> 11878 5938.5 0.6802135 0.6802135 8.881132e-03 8.881132e-03 0.310905335 #> 11879 5939.0 0.6802224 0.6802224 8.879701e-03 8.879701e-03 0.310897892 #> 11880 5939.5 0.6802313 0.6802313 8.878271e-03 8.878271e-03 0.310890428 #> 11881 5940.0 0.6802402 0.6802402 8.876844e-03 8.876844e-03 0.310882943 #> 11882 5940.5 0.6802491 0.6802491 8.875419e-03 8.875419e-03 0.310875439 #> 11883 5941.0 0.6802581 0.6802581 8.873996e-03 8.873996e-03 0.310867915 #> 11884 5941.5 0.6802671 0.6802671 8.872575e-03 8.872575e-03 0.310860371 #> 11885 5942.0 0.6802760 0.6802760 8.871157e-03 8.871157e-03 0.310852808 #> 11886 5942.5 0.6802850 0.6802850 8.869741e-03 8.869741e-03 0.310845224 #> 11887 5943.0 0.6802941 0.6802941 8.868327e-03 8.868327e-03 0.310837620 #> 11888 5943.5 0.6803031 0.6803031 8.866915e-03 8.866915e-03 0.310829997 #> 11889 5944.0 0.6803121 0.6803121 8.865506e-03 8.865506e-03 0.310822354 #> 11890 5944.5 0.6803212 0.6803212 8.864098e-03 8.864098e-03 0.310814692 #> 11891 5945.0 0.6803303 0.6803303 8.862693e-03 8.862693e-03 0.310807009 #> 11892 5945.5 0.6803394 0.6803394 8.861290e-03 8.861290e-03 0.310799308 #> 11893 5946.0 0.6803485 0.6803485 8.859890e-03 8.859890e-03 0.310791587 #> 11894 5946.5 0.6803577 0.6803577 8.858492e-03 8.858492e-03 0.310783846 #> 11895 5947.0 0.6803668 0.6803668 8.857095e-03 8.857095e-03 0.310776086 #> 11896 5947.5 0.6803760 0.6803760 8.855702e-03 8.855702e-03 0.310768306 #> 11897 5948.0 0.6803852 0.6803852 8.854310e-03 8.854310e-03 0.310760508 #> 11898 5948.5 0.6803944 0.6803944 8.852921e-03 8.852921e-03 0.310752689 #> 11899 5949.0 0.6804036 0.6804036 8.851534e-03 8.851534e-03 0.310744852 #> 11900 5949.5 0.6804129 0.6804129 8.850149e-03 8.850149e-03 0.310736996 #> 11901 5950.0 0.6804221 0.6804221 8.848766e-03 8.848766e-03 0.310729120 #> 11902 5950.5 0.6804314 0.6804314 8.847386e-03 8.847386e-03 0.310721226 #> 11903 5951.0 0.6804407 0.6804407 8.846008e-03 8.846008e-03 0.310713312 #> 11904 5951.5 0.6804500 0.6804500 8.844632e-03 8.844632e-03 0.310705379 #> 11905 5952.0 0.6804593 0.6804593 8.843259e-03 8.843259e-03 0.310697428 #> 11906 5952.5 0.6804687 0.6804687 8.841888e-03 8.841888e-03 0.310689457 #> 11907 5953.0 0.6804780 0.6804780 8.840519e-03 8.840519e-03 0.310681468 #> 11908 5953.5 0.6804874 0.6804874 8.839152e-03 8.839152e-03 0.310673460 #> 11909 5954.0 0.6804968 0.6804968 8.837788e-03 8.837788e-03 0.310665433 #> 11910 5954.5 0.6805062 0.6805062 8.836426e-03 8.836426e-03 0.310657387 #> 11911 5955.0 0.6805156 0.6805156 8.835066e-03 8.835066e-03 0.310649323 #> 11912 5955.5 0.6805251 0.6805251 8.833708e-03 8.833708e-03 0.310641240 #> 11913 5956.0 0.6805345 0.6805345 8.832353e-03 8.832353e-03 0.310633139 #> 11914 5956.5 0.6805440 0.6805440 8.831000e-03 8.831000e-03 0.310625019 #> 11915 5957.0 0.6805535 0.6805535 8.829650e-03 8.829650e-03 0.310616880 #> 11916 5957.5 0.6805630 0.6805630 8.828301e-03 8.828301e-03 0.310608723 #> 11917 5958.0 0.6805725 0.6805725 8.826956e-03 8.826956e-03 0.310600548 #> 11918 5958.5 0.6805820 0.6805820 8.825612e-03 8.825612e-03 0.310592355 #> 11919 5959.0 0.6805916 0.6805916 8.824270e-03 8.824270e-03 0.310584143 #> 11920 5959.5 0.6806012 0.6806012 8.822931e-03 8.822931e-03 0.310575913 #> 11921 5960.0 0.6806107 0.6806107 8.821595e-03 8.821595e-03 0.310567665 #> 11922 5960.5 0.6806203 0.6806203 8.820260e-03 8.820260e-03 0.310559398 #> 11923 5961.0 0.6806300 0.6806300 8.818928e-03 8.818928e-03 0.310551114 #> 11924 5961.5 0.6806396 0.6806396 8.817598e-03 8.817598e-03 0.310542812 #> 11925 5962.0 0.6806492 0.6806492 8.816271e-03 8.816271e-03 0.310534491 #> 11926 5962.5 0.6806589 0.6806589 8.814946e-03 8.814946e-03 0.310526153 #> 11927 5963.0 0.6806686 0.6806686 8.813623e-03 8.813623e-03 0.310517797 #> 11928 5963.5 0.6806783 0.6806783 8.812303e-03 8.812303e-03 0.310509423 #> 11929 5964.0 0.6806880 0.6806880 8.810985e-03 8.810985e-03 0.310501031 #> 11930 5964.5 0.6806977 0.6806977 8.809669e-03 8.809669e-03 0.310492621 #> 11931 5965.0 0.6807075 0.6807075 8.808355e-03 8.808355e-03 0.310484194 #> 11932 5965.5 0.6807172 0.6807172 8.807044e-03 8.807044e-03 0.310475749 #> 11933 5966.0 0.6807270 0.6807270 8.805735e-03 8.805735e-03 0.310467286 #> 11934 5966.5 0.6807368 0.6807368 8.804429e-03 8.804429e-03 0.310458806 #> 11935 5967.0 0.6807466 0.6807466 8.803125e-03 8.803125e-03 0.310450308 #> 11936 5967.5 0.6807564 0.6807564 8.801823e-03 8.801823e-03 0.310441793 #> 11937 5968.0 0.6807662 0.6807662 8.800524e-03 8.800524e-03 0.310433261 #> 11938 5968.5 0.6807761 0.6807761 8.799227e-03 8.799227e-03 0.310424711 #> 11939 5969.0 0.6807859 0.6807859 8.797932e-03 8.797932e-03 0.310416143 #> 11940 5969.5 0.6807958 0.6807958 8.796640e-03 8.796640e-03 0.310407559 #> 11941 5970.0 0.6808057 0.6808057 8.795350e-03 8.795350e-03 0.310398957 #> 11942 5970.5 0.6808156 0.6808156 8.794063e-03 8.794063e-03 0.310390338 #> 11943 5971.0 0.6808255 0.6808255 8.792777e-03 8.792777e-03 0.310381702 #> 11944 5971.5 0.6808355 0.6808355 8.791495e-03 8.791495e-03 0.310373049 #> 11945 5972.0 0.6808454 0.6808454 8.790214e-03 8.790214e-03 0.310364379 #> 11946 5972.5 0.6808554 0.6808554 8.788936e-03 8.788936e-03 0.310355692 #> 11947 5973.0 0.6808654 0.6808654 8.787660e-03 8.787660e-03 0.310346988 #> 11948 5973.5 0.6808753 0.6808753 8.786387e-03 8.786387e-03 0.310338267 #> 11949 5974.0 0.6808854 0.6808854 8.785116e-03 8.785116e-03 0.310329529 #> 11950 5974.5 0.6808954 0.6808954 8.783848e-03 8.783848e-03 0.310320775 #> 11951 5975.0 0.6809054 0.6809054 8.782581e-03 8.782581e-03 0.310312003 #> 11952 5975.5 0.6809155 0.6809155 8.781318e-03 8.781318e-03 0.310303215 #> 11953 5976.0 0.6809255 0.6809255 8.780056e-03 8.780056e-03 0.310294411 #> 11954 5976.5 0.6809356 0.6809356 8.778797e-03 8.778797e-03 0.310285590 #> 11955 5977.0 0.6809457 0.6809457 8.777541e-03 8.777541e-03 0.310276752 #> 11956 5977.5 0.6809558 0.6809558 8.776286e-03 8.776286e-03 0.310267898 #> 11957 5978.0 0.6809659 0.6809659 8.775035e-03 8.775035e-03 0.310259027 #> 11958 5978.5 0.6809761 0.6809761 8.773785e-03 8.773785e-03 0.310250140 #> 11959 5979.0 0.6809862 0.6809862 8.772538e-03 8.772538e-03 0.310241236 #> 11960 5979.5 0.6809964 0.6809964 8.771294e-03 8.771294e-03 0.310232316 #> 11961 5980.0 0.6810066 0.6810066 8.770051e-03 8.770051e-03 0.310223380 #> 11962 5980.5 0.6810168 0.6810168 8.768812e-03 8.768812e-03 0.310214428 #> 11963 5981.0 0.6810270 0.6810270 8.767574e-03 8.767574e-03 0.310205460 #> 11964 5981.5 0.6810372 0.6810372 8.766339e-03 8.766339e-03 0.310196475 #> 11965 5982.0 0.6810474 0.6810474 8.765107e-03 8.765107e-03 0.310187475 #> 11966 5982.5 0.6810577 0.6810577 8.763877e-03 8.763877e-03 0.310178458 #> 11967 5983.0 0.6810679 0.6810679 8.762649e-03 8.762649e-03 0.310169425 #> 11968 5983.5 0.6810782 0.6810782 8.761424e-03 8.761424e-03 0.310160377 #> 11969 5984.0 0.6810885 0.6810885 8.760201e-03 8.760201e-03 0.310151312 #> 11970 5984.5 0.6810988 0.6810988 8.758980e-03 8.758980e-03 0.310142232 #> 11971 5985.0 0.6811091 0.6811091 8.757762e-03 8.757762e-03 0.310133136 #> 11972 5985.5 0.6811194 0.6811194 8.756547e-03 8.756547e-03 0.310124024 #> 11973 5986.0 0.6811298 0.6811298 8.755334e-03 8.755334e-03 0.310114897 #> 11974 5986.5 0.6811401 0.6811401 8.754123e-03 8.754123e-03 0.310105754 #> 11975 5987.0 0.6811505 0.6811505 8.752915e-03 8.752915e-03 0.310096595 #> 11976 5987.5 0.6811609 0.6811609 8.751709e-03 8.751709e-03 0.310087421 #> 11977 5988.0 0.6811713 0.6811713 8.750505e-03 8.750505e-03 0.310078232 #> 11978 5988.5 0.6811817 0.6811817 8.749304e-03 8.749304e-03 0.310069027 #> 11979 5989.0 0.6811921 0.6811921 8.748106e-03 8.748106e-03 0.310059806 #> 11980 5989.5 0.6812025 0.6812025 8.746910e-03 8.746910e-03 0.310050570 #> 11981 5990.0 0.6812130 0.6812130 8.745716e-03 8.745716e-03 0.310041319 #> 11982 5990.5 0.6812234 0.6812234 8.744525e-03 8.744525e-03 0.310032053 #> 11983 5991.0 0.6812339 0.6812339 8.743336e-03 8.743336e-03 0.310022771 #> 11984 5991.5 0.6812444 0.6812444 8.742150e-03 8.742150e-03 0.310013475 #> 11985 5992.0 0.6812549 0.6812549 8.740966e-03 8.740966e-03 0.310004163 #> 11986 5992.5 0.6812654 0.6812654 8.739785e-03 8.739785e-03 0.309994836 #> 11987 5993.0 0.6812759 0.6812759 8.738606e-03 8.738606e-03 0.309985494 #> 11988 5993.5 0.6812864 0.6812864 8.737429e-03 8.737429e-03 0.309976137 #> 11989 5994.0 0.6812970 0.6812970 8.736255e-03 8.736255e-03 0.309966766 #> 11990 5994.5 0.6813075 0.6813075 8.735084e-03 8.735084e-03 0.309957379 #> 11991 5995.0 0.6813181 0.6813181 8.733915e-03 8.733915e-03 0.309947978 #> 11992 5995.5 0.6813287 0.6813287 8.732748e-03 8.732748e-03 0.309938562 #> 11993 5996.0 0.6813393 0.6813393 8.731584e-03 8.731584e-03 0.309929131 #> 11994 5996.5 0.6813499 0.6813499 8.730422e-03 8.730422e-03 0.309919685 #> 11995 5997.0 0.6813605 0.6813605 8.729263e-03 8.729263e-03 0.309910225 #> 11996 5997.5 0.6813711 0.6813711 8.728106e-03 8.728106e-03 0.309900751 #> 11997 5998.0 0.6813818 0.6813818 8.726952e-03 8.726952e-03 0.309891261 #> 11998 5998.5 0.6813924 0.6813924 8.725800e-03 8.725800e-03 0.309881758 #> 11999 5999.0 0.6814031 0.6814031 8.724651e-03 8.724651e-03 0.309872240 #> 12000 5999.5 0.6814138 0.6814138 8.723504e-03 8.723504e-03 0.309862707 #> 12001 6000.0 0.6814245 0.6814245 8.722360e-03 8.722360e-03 0.309853161 #> 12002 6000.5 0.6814352 0.6814352 8.721218e-03 8.721218e-03 0.309843599 #> 12003 6001.0 0.6814459 0.6814459 8.720079e-03 8.720079e-03 0.309834024 #> 12004 6001.5 0.6814566 0.6814566 8.718942e-03 8.718942e-03 0.309824435 #> 12005 6002.0 0.6814674 0.6814674 8.717807e-03 8.717807e-03 0.309814831 #> 12006 6002.5 0.6814781 0.6814781 8.716675e-03 8.716675e-03 0.309805214 #> 12007 6003.0 0.6814889 0.6814889 8.715546e-03 8.715546e-03 0.309795582 #> 12008 6003.5 0.6814996 0.6814996 8.714419e-03 8.714419e-03 0.309785936 #> 12009 6004.0 0.6815104 0.6815104 8.713295e-03 8.713295e-03 0.309776277 #> 12010 6004.5 0.6815212 0.6815212 8.712173e-03 8.712173e-03 0.309766603 #> 12011 6005.0 0.6815320 0.6815320 8.711053e-03 8.711053e-03 0.309756916 #> 12012 6005.5 0.6815428 0.6815428 8.709936e-03 8.709936e-03 0.309747215 #> 12013 6006.0 0.6815537 0.6815537 8.708822e-03 8.708822e-03 0.309737500 #> 12014 6006.5 0.6815645 0.6815645 8.707710e-03 8.707710e-03 0.309727771 #> 12015 6007.0 0.6815754 0.6815754 8.706600e-03 8.706600e-03 0.309718029 #> 12016 6007.5 0.6815862 0.6815862 8.705493e-03 8.705493e-03 0.309708273 #> 12017 6008.0 0.6815971 0.6815971 8.704389e-03 8.704389e-03 0.309698504 #> 12018 6008.5 0.6816080 0.6816080 8.703287e-03 8.703287e-03 0.309688721 #> 12019 6009.0 0.6816189 0.6816189 8.702188e-03 8.702188e-03 0.309678925 #> 12020 6009.5 0.6816298 0.6816298 8.701091e-03 8.701091e-03 0.309669115 #> 12021 6010.0 0.6816407 0.6816407 8.699996e-03 8.699996e-03 0.309659292 #> 12022 6010.5 0.6816516 0.6816516 8.698904e-03 8.698904e-03 0.309649456 #> 12023 6011.0 0.6816626 0.6816626 8.697815e-03 8.697815e-03 0.309639606 #> 12024 6011.5 0.6816735 0.6816735 8.696728e-03 8.696728e-03 0.309629743 #> 12025 6012.0 0.6816845 0.6816845 8.695644e-03 8.695644e-03 0.309619867 #> 12026 6012.5 0.6816955 0.6816955 8.694562e-03 8.694562e-03 0.309609978 #> 12027 6013.0 0.6817064 0.6817064 8.693483e-03 8.693483e-03 0.309600076 #> 12028 6013.5 0.6817174 0.6817174 8.692406e-03 8.692406e-03 0.309590161 #> 12029 6014.0 0.6817284 0.6817284 8.691332e-03 8.691332e-03 0.309580233 #> 12030 6014.5 0.6817394 0.6817394 8.690260e-03 8.690260e-03 0.309570292 #> 12031 6015.0 0.6817505 0.6817505 8.689191e-03 8.689191e-03 0.309560338 #> 12032 6015.5 0.6817615 0.6817615 8.688124e-03 8.688124e-03 0.309550371 #> 12033 6016.0 0.6817725 0.6817725 8.687060e-03 8.687060e-03 0.309540391 #> 12034 6016.5 0.6817836 0.6817836 8.685998e-03 8.685998e-03 0.309530399 #> 12035 6017.0 0.6817947 0.6817947 8.684939e-03 8.684939e-03 0.309520394 #> 12036 6017.5 0.6818057 0.6818057 8.683883e-03 8.683883e-03 0.309510377 #> 12037 6018.0 0.6818168 0.6818168 8.682829e-03 8.682829e-03 0.309500347 #> 12038 6018.5 0.6818279 0.6818279 8.681777e-03 8.681777e-03 0.309490304 #> 12039 6019.0 0.6818390 0.6818390 8.680728e-03 8.680728e-03 0.309480249 #> 12040 6019.5 0.6818501 0.6818501 8.679682e-03 8.679682e-03 0.309470182 #> 12041 6020.0 0.6818613 0.6818613 8.678638e-03 8.678638e-03 0.309460102 #> 12042 6020.5 0.6818724 0.6818724 8.677597e-03 8.677597e-03 0.309450009 #> 12043 6021.0 0.6818835 0.6818835 8.676558e-03 8.676558e-03 0.309439905 #> 12044 6021.5 0.6818947 0.6818947 8.675521e-03 8.675521e-03 0.309429788 #> 12045 6022.0 0.6819059 0.6819059 8.674488e-03 8.674488e-03 0.309419659 #> 12046 6022.5 0.6819170 0.6819170 8.673457e-03 8.673457e-03 0.309409518 #> 12047 6023.0 0.6819282 0.6819282 8.672428e-03 8.672428e-03 0.309399365 #> 12048 6023.5 0.6819394 0.6819394 8.671402e-03 8.671402e-03 0.309389200 #> 12049 6024.0 0.6819506 0.6819506 8.670378e-03 8.670378e-03 0.309379022 #> 12050 6024.5 0.6819618 0.6819618 8.669357e-03 8.669357e-03 0.309368833 #> 12051 6025.0 0.6819730 0.6819730 8.668339e-03 8.668339e-03 0.309358632 #> 12052 6025.5 0.6819843 0.6819843 8.667323e-03 8.667323e-03 0.309348419 #> 12053 6026.0 0.6819955 0.6819955 8.666310e-03 8.666310e-03 0.309338195 #> 12054 6026.5 0.6820067 0.6820067 8.665299e-03 8.665299e-03 0.309327958 #> 12055 6027.0 0.6820180 0.6820180 8.664291e-03 8.664291e-03 0.309317710 #> 12056 6027.5 0.6820293 0.6820293 8.663285e-03 8.663285e-03 0.309307450 #> 12057 6028.0 0.6820405 0.6820405 8.662282e-03 8.662282e-03 0.309297179 #> 12058 6028.5 0.6820518 0.6820518 8.661281e-03 8.661281e-03 0.309286896 #> 12059 6029.0 0.6820631 0.6820631 8.660284e-03 8.660284e-03 0.309276601 #> 12060 6029.5 0.6820744 0.6820744 8.659288e-03 8.659288e-03 0.309266295 #> 12061 6030.0 0.6820857 0.6820857 8.658295e-03 8.658295e-03 0.309255978 #> 12062 6030.5 0.6820970 0.6820970 8.657305e-03 8.657305e-03 0.309245649 #> 12063 6031.0 0.6821084 0.6821084 8.656317e-03 8.656317e-03 0.309235309 #> 12064 6031.5 0.6821197 0.6821197 8.655332e-03 8.655332e-03 0.309224958 #> 12065 6032.0 0.6821311 0.6821311 8.654349e-03 8.654349e-03 0.309214595 #> 12066 6032.5 0.6821424 0.6821424 8.653369e-03 8.653369e-03 0.309204221 #> 12067 6033.0 0.6821538 0.6821538 8.652392e-03 8.652392e-03 0.309193837 #> 12068 6033.5 0.6821651 0.6821651 8.651417e-03 8.651417e-03 0.309183441 #> 12069 6034.0 0.6821765 0.6821765 8.650445e-03 8.650445e-03 0.309173034 #> 12070 6034.5 0.6821879 0.6821879 8.649475e-03 8.649475e-03 0.309162616 #> 12071 6035.0 0.6821993 0.6821993 8.648508e-03 8.648508e-03 0.309152187 #> 12072 6035.5 0.6822107 0.6822107 8.647543e-03 8.647543e-03 0.309141747 #> 12073 6036.0 0.6822221 0.6822221 8.646581e-03 8.646581e-03 0.309131297 #> 12074 6036.5 0.6822335 0.6822335 8.645622e-03 8.645622e-03 0.309120835 #> 12075 6037.0 0.6822450 0.6822450 8.644665e-03 8.644665e-03 0.309110363 #> 12076 6037.5 0.6822564 0.6822564 8.643710e-03 8.643710e-03 0.309099880 #> 12077 6038.0 0.6822679 0.6822679 8.642759e-03 8.642759e-03 0.309089387 #> 12078 6038.5 0.6822793 0.6822793 8.641810e-03 8.641810e-03 0.309078883 #> 12079 6039.0 0.6822908 0.6822908 8.640863e-03 8.640863e-03 0.309068368 #> 12080 6039.5 0.6823022 0.6823022 8.639919e-03 8.639919e-03 0.309057843 #> 12081 6040.0 0.6823137 0.6823137 8.638978e-03 8.638978e-03 0.309047308 #> 12082 6040.5 0.6823252 0.6823252 8.638039e-03 8.638039e-03 0.309036762 #> 12083 6041.0 0.6823367 0.6823367 8.637102e-03 8.637102e-03 0.309026206 #> 12084 6041.5 0.6823482 0.6823482 8.636169e-03 8.636169e-03 0.309015639 #> 12085 6042.0 0.6823597 0.6823597 8.635238e-03 8.635238e-03 0.309005062 #> 12086 6042.5 0.6823712 0.6823712 8.634309e-03 8.634309e-03 0.308994475 #> 12087 6043.0 0.6823827 0.6823827 8.633383e-03 8.633383e-03 0.308983878 #> 12088 6043.5 0.6823943 0.6823943 8.632460e-03 8.632460e-03 0.308973271 #> 12089 6044.0 0.6824058 0.6824058 8.631539e-03 8.631539e-03 0.308962654 #> 12090 6044.5 0.6824174 0.6824174 8.630621e-03 8.630621e-03 0.308952026 #> 12091 6045.0 0.6824289 0.6824289 8.629706e-03 8.629706e-03 0.308941389 #> 12092 6045.5 0.6824405 0.6824405 8.628793e-03 8.628793e-03 0.308930742 #> 12093 6046.0 0.6824520 0.6824520 8.627882e-03 8.627882e-03 0.308920085 #> 12094 6046.5 0.6824636 0.6824636 8.626974e-03 8.626974e-03 0.308909418 #> 12095 6047.0 0.6824752 0.6824752 8.626069e-03 8.626069e-03 0.308898741 #> 12096 6047.5 0.6824868 0.6824868 8.625167e-03 8.625167e-03 0.308888055 #> 12097 6048.0 0.6824984 0.6824984 8.624267e-03 8.624267e-03 0.308877359 #> 12098 6048.5 0.6825100 0.6825100 8.623369e-03 8.623369e-03 0.308866653 #> 12099 6049.0 0.6825216 0.6825216 8.622474e-03 8.622474e-03 0.308855938 #> 12100 6049.5 0.6825332 0.6825332 8.621582e-03 8.621582e-03 0.308845213 #> 12101 6050.0 0.6825448 0.6825448 8.620692e-03 8.620692e-03 0.308834479 #> 12102 6050.5 0.6825565 0.6825565 8.619805e-03 8.619805e-03 0.308823735 #> 12103 6051.0 0.6825681 0.6825681 8.618921e-03 8.618921e-03 0.308812982 #> 12104 6051.5 0.6825797 0.6825797 8.618039e-03 8.618039e-03 0.308802219 #> 12105 6052.0 0.6825914 0.6825914 8.617160e-03 8.617160e-03 0.308791447 #> 12106 6052.5 0.6826031 0.6826031 8.616283e-03 8.616283e-03 0.308780666 #> 12107 6053.0 0.6826147 0.6826147 8.615409e-03 8.615409e-03 0.308769876 #> 12108 6053.5 0.6826264 0.6826264 8.614538e-03 8.614538e-03 0.308759077 #> 12109 6054.0 0.6826381 0.6826381 8.613669e-03 8.613669e-03 0.308748268 #> 12110 6054.5 0.6826497 0.6826497 8.612803e-03 8.612803e-03 0.308737451 #> 12111 6055.0 0.6826614 0.6826614 8.611939e-03 8.611939e-03 0.308726624 #> 12112 6055.5 0.6826731 0.6826731 8.611078e-03 8.611078e-03 0.308715789 #> 12113 6056.0 0.6826848 0.6826848 8.610220e-03 8.610220e-03 0.308704945 #> 12114 6056.5 0.6826965 0.6826965 8.609364e-03 8.609364e-03 0.308694091 #> 12115 6057.0 0.6827083 0.6827083 8.608511e-03 8.608511e-03 0.308683229 #> 12116 6057.5 0.6827200 0.6827200 8.607660e-03 8.607660e-03 0.308672359 #> 12117 6058.0 0.6827317 0.6827317 8.606812e-03 8.606812e-03 0.308661479 #> 12118 6058.5 0.6827434 0.6827434 8.605967e-03 8.605967e-03 0.308650591 #> 12119 6059.0 0.6827552 0.6827552 8.605124e-03 8.605124e-03 0.308639694 #> 12120 6059.5 0.6827669 0.6827669 8.604284e-03 8.604284e-03 0.308628789 #> 12121 6060.0 0.6827787 0.6827787 8.603446e-03 8.603446e-03 0.308617875 #> 12122 6060.5 0.6827904 0.6827904 8.602611e-03 8.602611e-03 0.308606952 #> 12123 6061.0 0.6828022 0.6828022 8.601779e-03 8.601779e-03 0.308596021 #> 12124 6061.5 0.6828140 0.6828140 8.600949e-03 8.600949e-03 0.308585082 #> 12125 6062.0 0.6828257 0.6828257 8.600122e-03 8.600122e-03 0.308574135 #> 12126 6062.5 0.6828375 0.6828375 8.599298e-03 8.599298e-03 0.308563179 #> 12127 6063.0 0.6828493 0.6828493 8.598476e-03 8.598476e-03 0.308552215 #> 12128 6063.5 0.6828611 0.6828611 8.597656e-03 8.597656e-03 0.308541243 #> 12129 6064.0 0.6828729 0.6828729 8.596840e-03 8.596840e-03 0.308530262 #> 12130 6064.5 0.6828847 0.6828847 8.596026e-03 8.596026e-03 0.308519274 #> 12131 6065.0 0.6828965 0.6828965 8.595214e-03 8.595214e-03 0.308508277 #> 12132 6065.5 0.6829083 0.6829083 8.594405e-03 8.594405e-03 0.308497272 #> 12133 6066.0 0.6829201 0.6829201 8.593599e-03 8.593599e-03 0.308486260 #> 12134 6066.5 0.6829320 0.6829320 8.592796e-03 8.592796e-03 0.308475240 #> 12135 6067.0 0.6829438 0.6829438 8.591995e-03 8.591995e-03 0.308464211 #> 12136 6067.5 0.6829556 0.6829556 8.591196e-03 8.591196e-03 0.308453175 #> 12137 6068.0 0.6829675 0.6829675 8.590401e-03 8.590401e-03 0.308442131 #> 12138 6068.5 0.6829793 0.6829793 8.589607e-03 8.589607e-03 0.308431080 #> 12139 6069.0 0.6829912 0.6829912 8.588817e-03 8.588817e-03 0.308420021 #> 12140 6069.5 0.6830030 0.6830030 8.588029e-03 8.588029e-03 0.308408954 #> 12141 6070.0 0.6830149 0.6830149 8.587244e-03 8.587244e-03 0.308397879 #> 12142 6070.5 0.6830267 0.6830267 8.586461e-03 8.586461e-03 0.308386797 #> 12143 6071.0 0.6830386 0.6830386 8.585681e-03 8.585681e-03 0.308375708 #> 12144 6071.5 0.6830505 0.6830505 8.584904e-03 8.584904e-03 0.308364611 #> 12145 6072.0 0.6830624 0.6830624 8.584129e-03 8.584129e-03 0.308353507 #> 12146 6072.5 0.6830742 0.6830742 8.583357e-03 8.583357e-03 0.308342395 #> 12147 6073.0 0.6830861 0.6830861 8.582587e-03 8.582587e-03 0.308331276 #> 12148 6073.5 0.6830980 0.6830980 8.581820e-03 8.581820e-03 0.308320150 #> 12149 6074.0 0.6831099 0.6831099 8.581056e-03 8.581056e-03 0.308309017 #> 12150 6074.5 0.6831218 0.6831218 8.580294e-03 8.580294e-03 0.308297876 #> 12151 6075.0 0.6831337 0.6831337 8.579535e-03 8.579535e-03 0.308286729 #> 12152 6075.5 0.6831456 0.6831456 8.578779e-03 8.578779e-03 0.308275574 #> 12153 6076.0 0.6831576 0.6831576 8.578025e-03 8.578025e-03 0.308264412 #> 12154 6076.5 0.6831695 0.6831695 8.577274e-03 8.577274e-03 0.308253244 #> 12155 6077.0 0.6831814 0.6831814 8.576525e-03 8.576525e-03 0.308242068 #> 12156 6077.5 0.6831933 0.6831933 8.575779e-03 8.575779e-03 0.308230886 #> 12157 6078.0 0.6832053 0.6832053 8.575036e-03 8.575036e-03 0.308219697 #> 12158 6078.5 0.6832172 0.6832172 8.574295e-03 8.574295e-03 0.308208501 #> 12159 6079.0 0.6832291 0.6832291 8.573557e-03 8.573557e-03 0.308197298 #> 12160 6079.5 0.6832411 0.6832411 8.572821e-03 8.572821e-03 0.308186089 #> 12161 6080.0 0.6832530 0.6832530 8.572088e-03 8.572088e-03 0.308174873 #> 12162 6080.5 0.6832650 0.6832650 8.571358e-03 8.571358e-03 0.308163650 #> 12163 6081.0 0.6832769 0.6832769 8.570631e-03 8.570631e-03 0.308152421 #> 12164 6081.5 0.6832889 0.6832889 8.569906e-03 8.569906e-03 0.308141185 #> 12165 6082.0 0.6833009 0.6833009 8.569183e-03 8.569183e-03 0.308129943 #> 12166 6082.5 0.6833128 0.6833128 8.568463e-03 8.568463e-03 0.308118695 #> 12167 6083.0 0.6833248 0.6833248 8.567746e-03 8.567746e-03 0.308107440 #> 12168 6083.5 0.6833368 0.6833368 8.567032e-03 8.567032e-03 0.308096179 #> 12169 6084.0 0.6833488 0.6833488 8.566320e-03 8.566320e-03 0.308084912 #> 12170 6084.5 0.6833608 0.6833608 8.565611e-03 8.565611e-03 0.308073638 #> 12171 6085.0 0.6833727 0.6833727 8.564904e-03 8.564904e-03 0.308062358 #> 12172 6085.5 0.6833847 0.6833847 8.564200e-03 8.564200e-03 0.308051073 #> 12173 6086.0 0.6833967 0.6833967 8.563499e-03 8.563499e-03 0.308039781 #> 12174 6086.5 0.6834087 0.6834087 8.562800e-03 8.562800e-03 0.308028483 #> 12175 6087.0 0.6834207 0.6834207 8.562104e-03 8.562104e-03 0.308017179 #> 12176 6087.5 0.6834327 0.6834327 8.561410e-03 8.561410e-03 0.308005869 #> 12177 6088.0 0.6834447 0.6834447 8.560720e-03 8.560720e-03 0.307994553 #> 12178 6088.5 0.6834567 0.6834567 8.560031e-03 8.560031e-03 0.307983232 #> 12179 6089.0 0.6834687 0.6834687 8.559346e-03 8.559346e-03 0.307971905 #> 12180 6089.5 0.6834808 0.6834808 8.558663e-03 8.558663e-03 0.307960571 #> 12181 6090.0 0.6834928 0.6834928 8.557982e-03 8.557982e-03 0.307949233 #> 12182 6090.5 0.6835048 0.6835048 8.557305e-03 8.557305e-03 0.307937888 #> 12183 6091.0 0.6835168 0.6835168 8.556630e-03 8.556630e-03 0.307926538 #> 12184 6091.5 0.6835289 0.6835289 8.555957e-03 8.555957e-03 0.307915183 #> 12185 6092.0 0.6835409 0.6835409 8.555287e-03 8.555287e-03 0.307903822 #> 12186 6092.5 0.6835529 0.6835529 8.554620e-03 8.554620e-03 0.307892455 #> 12187 6093.0 0.6835650 0.6835650 8.553955e-03 8.553955e-03 0.307881083 #> 12188 6093.5 0.6835770 0.6835770 8.553293e-03 8.553293e-03 0.307869706 #> 12189 6094.0 0.6835890 0.6835890 8.552634e-03 8.552634e-03 0.307858323 #> 12190 6094.5 0.6836011 0.6836011 8.551977e-03 8.551977e-03 0.307846936 #> 12191 6095.0 0.6836131 0.6836131 8.551323e-03 8.551323e-03 0.307835542 #> 12192 6095.5 0.6836252 0.6836252 8.550672e-03 8.550672e-03 0.307824144 #> 12193 6096.0 0.6836372 0.6836372 8.550023e-03 8.550023e-03 0.307812741 #> 12194 6096.5 0.6836493 0.6836493 8.549377e-03 8.549377e-03 0.307801332 #> 12195 6097.0 0.6836613 0.6836613 8.548733e-03 8.548733e-03 0.307789919 #> 12196 6097.5 0.6836734 0.6836734 8.548092e-03 8.548092e-03 0.307778500 #> 12197 6098.0 0.6836855 0.6836855 8.547454e-03 8.547454e-03 0.307767076 #> 12198 6098.5 0.6836975 0.6836975 8.546818e-03 8.546818e-03 0.307755648 #> 12199 6099.0 0.6837096 0.6837096 8.546185e-03 8.546185e-03 0.307744215 #> 12200 6099.5 0.6837217 0.6837217 8.545554e-03 8.545554e-03 0.307732777 #> 12201 6100.0 0.6837337 0.6837337 8.544926e-03 8.544926e-03 0.307721334 #> 12202 6100.5 0.6837458 0.6837458 8.544301e-03 8.544301e-03 0.307709886 #> 12203 6101.0 0.6837579 0.6837579 8.543679e-03 8.543679e-03 0.307698434 #> 12204 6101.5 0.6837700 0.6837700 8.543059e-03 8.543059e-03 0.307686977 #> 12205 6102.0 0.6837820 0.6837820 8.542441e-03 8.542441e-03 0.307675516 #> 12206 6102.5 0.6837941 0.6837941 8.541827e-03 8.541827e-03 0.307664050 #> 12207 6103.0 0.6838062 0.6838062 8.541214e-03 8.541214e-03 0.307652579 #> 12208 6103.5 0.6838183 0.6838183 8.540605e-03 8.540605e-03 0.307641104 #> 12209 6104.0 0.6838304 0.6838304 8.539998e-03 8.539998e-03 0.307629625 #> 12210 6104.5 0.6838425 0.6838425 8.539394e-03 8.539394e-03 0.307618141 #> 12211 6105.0 0.6838546 0.6838546 8.538792e-03 8.538792e-03 0.307606653 #> 12212 6105.5 0.6838666 0.6838666 8.538193e-03 8.538193e-03 0.307595161 #> 12213 6106.0 0.6838787 0.6838787 8.537597e-03 8.537597e-03 0.307583665 #> 12214 6106.5 0.6838908 0.6838908 8.537003e-03 8.537003e-03 0.307572164 #> 12215 6107.0 0.6839029 0.6839029 8.536412e-03 8.536412e-03 0.307560659 #> 12216 6107.5 0.6839150 0.6839150 8.535824e-03 8.535824e-03 0.307549150 #> 12217 6108.0 0.6839271 0.6839271 8.535238e-03 8.535238e-03 0.307537637 #> 12218 6108.5 0.6839392 0.6839392 8.534654e-03 8.534654e-03 0.307526121 #> 12219 6109.0 0.6839513 0.6839513 8.534074e-03 8.534074e-03 0.307514600 #> 12220 6109.5 0.6839634 0.6839634 8.533496e-03 8.533496e-03 0.307503075 #> 12221 6110.0 0.6839755 0.6839755 8.532920e-03 8.532920e-03 0.307491547 #> 12222 6110.5 0.6839876 0.6839876 8.532348e-03 8.532348e-03 0.307480014 #> 12223 6111.0 0.6839997 0.6839997 8.531777e-03 8.531777e-03 0.307468478 #> 12224 6111.5 0.6840119 0.6840119 8.531210e-03 8.531210e-03 0.307456938 #> 12225 6112.0 0.6840240 0.6840240 8.530645e-03 8.530645e-03 0.307445395 #> 12226 6112.5 0.6840361 0.6840361 8.530083e-03 8.530083e-03 0.307433848 #> 12227 6113.0 0.6840482 0.6840482 8.529523e-03 8.529523e-03 0.307422297 #> 12228 6113.5 0.6840603 0.6840603 8.528966e-03 8.528966e-03 0.307410743 #> 12229 6114.0 0.6840724 0.6840724 8.528412e-03 8.528412e-03 0.307399185 #> 12230 6114.5 0.6840845 0.6840845 8.527860e-03 8.527860e-03 0.307387624 #> 12231 6115.0 0.6840966 0.6840966 8.527311e-03 8.527311e-03 0.307376060 #> 12232 6115.5 0.6841087 0.6841087 8.526764e-03 8.526764e-03 0.307364492 #> 12233 6116.0 0.6841209 0.6841209 8.526220e-03 8.526220e-03 0.307352921 #> 12234 6116.5 0.6841330 0.6841330 8.525679e-03 8.525679e-03 0.307341346 #> 12235 6117.0 0.6841451 0.6841451 8.525140e-03 8.525140e-03 0.307329769 #> 12236 6117.5 0.6841572 0.6841572 8.524604e-03 8.524604e-03 0.307318188 #> 12237 6118.0 0.6841693 0.6841693 8.524070e-03 8.524070e-03 0.307306604 #> 12238 6118.5 0.6841814 0.6841814 8.523539e-03 8.523539e-03 0.307295017 #> 12239 6119.0 0.6841936 0.6841936 8.523011e-03 8.523011e-03 0.307283427 #> 12240 6119.5 0.6842057 0.6842057 8.522486e-03 8.522486e-03 0.307271834 #> 12241 6120.0 0.6842178 0.6842178 8.521963e-03 8.521963e-03 0.307260238 #> 12242 6120.5 0.6842299 0.6842299 8.521442e-03 8.521442e-03 0.307248639 #> 12243 6121.0 0.6842420 0.6842420 8.520924e-03 8.520924e-03 0.307237037 #> 12244 6121.5 0.6842542 0.6842542 8.520409e-03 8.520409e-03 0.307225433 #> 12245 6122.0 0.6842663 0.6842663 8.519897e-03 8.519897e-03 0.307213826 #> 12246 6122.5 0.6842784 0.6842784 8.519387e-03 8.519387e-03 0.307202216 #> 12247 6123.0 0.6842905 0.6842905 8.518879e-03 8.518879e-03 0.307190603 #> 12248 6123.5 0.6843026 0.6843026 8.518374e-03 8.518374e-03 0.307178988 #> 12249 6124.0 0.6843148 0.6843148 8.517872e-03 8.517872e-03 0.307167370 #> 12250 6124.5 0.6843269 0.6843269 8.517373e-03 8.517373e-03 0.307155749 #> 12251 6125.0 0.6843390 0.6843390 8.516876e-03 8.516876e-03 0.307144127 #> 12252 6125.5 0.6843511 0.6843511 8.516382e-03 8.516382e-03 0.307132501 #> 12253 6126.0 0.6843632 0.6843632 8.515890e-03 8.515890e-03 0.307120873 #> 12254 6126.5 0.6843754 0.6843754 8.515401e-03 8.515401e-03 0.307109243 #> 12255 6127.0 0.6843875 0.6843875 8.514914e-03 8.514914e-03 0.307097611 #> 12256 6127.5 0.6843996 0.6843996 8.514430e-03 8.514430e-03 0.307085976 #> 12257 6128.0 0.6844117 0.6844117 8.513949e-03 8.513949e-03 0.307074339 #> 12258 6128.5 0.6844238 0.6844238 8.513470e-03 8.513470e-03 0.307062700 #> 12259 6129.0 0.6844359 0.6844359 8.512994e-03 8.512994e-03 0.307051059 #> 12260 6129.5 0.6844481 0.6844481 8.512521e-03 8.512521e-03 0.307039416 #> 12261 6130.0 0.6844602 0.6844602 8.512050e-03 8.512050e-03 0.307027770 #> 12262 6130.5 0.6844723 0.6844723 8.511582e-03 8.511582e-03 0.307016123 #> 12263 6131.0 0.6844844 0.6844844 8.511116e-03 8.511116e-03 0.307004474 #> 12264 6131.5 0.6844965 0.6844965 8.510653e-03 8.510653e-03 0.306992822 #> 12265 6132.0 0.6845086 0.6845086 8.510193e-03 8.510193e-03 0.306981169 #> 12266 6132.5 0.6845208 0.6845208 8.509735e-03 8.509735e-03 0.306969514 #> 12267 6133.0 0.6845329 0.6845329 8.509280e-03 8.509280e-03 0.306957858 #> 12268 6133.5 0.6845450 0.6845450 8.508827e-03 8.508827e-03 0.306946199 #> 12269 6134.0 0.6845571 0.6845571 8.508377e-03 8.508377e-03 0.306934539 #> 12270 6134.5 0.6845692 0.6845692 8.507930e-03 8.507930e-03 0.306922877 #> 12271 6135.0 0.6845813 0.6845813 8.507485e-03 8.507485e-03 0.306911214 #> 12272 6135.5 0.6845934 0.6845934 8.507043e-03 8.507043e-03 0.306899549 #> 12273 6136.0 0.6846055 0.6846055 8.506603e-03 8.506603e-03 0.306887882 #> 12274 6136.5 0.6846176 0.6846176 8.506166e-03 8.506166e-03 0.306876214 #> 12275 6137.0 0.6846297 0.6846297 8.505731e-03 8.505731e-03 0.306864545 #> 12276 6137.5 0.6846418 0.6846418 8.505300e-03 8.505300e-03 0.306852874 #> 12277 6138.0 0.6846539 0.6846539 8.504870e-03 8.504870e-03 0.306841202 #> 12278 6138.5 0.6846660 0.6846660 8.504444e-03 8.504444e-03 0.306829529 #> 12279 6139.0 0.6846781 0.6846781 8.504020e-03 8.504020e-03 0.306817854 #> 12280 6139.5 0.6846902 0.6846902 8.503598e-03 8.503598e-03 0.306806178 #> 12281 6140.0 0.6847023 0.6847023 8.503179e-03 8.503179e-03 0.306794501 #> 12282 6140.5 0.6847144 0.6847144 8.502763e-03 8.502763e-03 0.306782823 #> 12283 6141.0 0.6847265 0.6847265 8.502349e-03 8.502349e-03 0.306771144 #> 12284 6141.5 0.6847386 0.6847386 8.501938e-03 8.501938e-03 0.306759463 #> 12285 6142.0 0.6847507 0.6847507 8.501530e-03 8.501530e-03 0.306747782 #> 12286 6142.5 0.6847628 0.6847628 8.501124e-03 8.501124e-03 0.306736100 #> 12287 6143.0 0.6847749 0.6847749 8.500720e-03 8.500720e-03 0.306724417 #> 12288 6143.5 0.6847869 0.6847869 8.500320e-03 8.500320e-03 0.306712733 #> 12289 6144.0 0.6847990 0.6847990 8.499922e-03 8.499922e-03 0.306701048 #> 12290 6144.5 0.6848111 0.6848111 8.499526e-03 8.499526e-03 0.306689363 #> 12291 6145.0 0.6848232 0.6848232 8.499133e-03 8.499133e-03 0.306677676 #> 12292 6145.5 0.6848353 0.6848353 8.498743e-03 8.498743e-03 0.306665989 #> 12293 6146.0 0.6848473 0.6848473 8.498355e-03 8.498355e-03 0.306654302 #> 12294 6146.5 0.6848594 0.6848594 8.497969e-03 8.497969e-03 0.306642614 #> 12295 6147.0 0.6848715 0.6848715 8.497587e-03 8.497587e-03 0.306630925 #> 12296 6147.5 0.6848836 0.6848836 8.497207e-03 8.497207e-03 0.306619236 #> 12297 6148.0 0.6848956 0.6848956 8.496829e-03 8.496829e-03 0.306607546 #> 12298 6148.5 0.6849077 0.6849077 8.496454e-03 8.496454e-03 0.306595856 #> 12299 6149.0 0.6849198 0.6849198 8.496082e-03 8.496082e-03 0.306584165 #> 12300 6149.5 0.6849318 0.6849318 8.495712e-03 8.495712e-03 0.306572474 #> 12301 6150.0 0.6849439 0.6849439 8.495345e-03 8.495345e-03 0.306560783 #> 12302 6150.5 0.6849559 0.6849559 8.494980e-03 8.494980e-03 0.306549092 #> 12303 6151.0 0.6849680 0.6849680 8.494618e-03 8.494618e-03 0.306537400 #> 12304 6151.5 0.6849800 0.6849800 8.494259e-03 8.494259e-03 0.306525708 #> 12305 6152.0 0.6849921 0.6849921 8.493902e-03 8.493902e-03 0.306514016 #> 12306 6152.5 0.6850041 0.6850041 8.493548e-03 8.493548e-03 0.306502324 #> 12307 6153.0 0.6850162 0.6850162 8.493196e-03 8.493196e-03 0.306490632 #> 12308 6153.5 0.6850282 0.6850282 8.492847e-03 8.492847e-03 0.306478940 #> 12309 6154.0 0.6850403 0.6850403 8.492500e-03 8.492500e-03 0.306467248 #> 12310 6154.5 0.6850523 0.6850523 8.492156e-03 8.492156e-03 0.306455556 #> 12311 6155.0 0.6850643 0.6850643 8.491815e-03 8.491815e-03 0.306443865 #> 12312 6155.5 0.6850764 0.6850764 8.491476e-03 8.491476e-03 0.306432173 #> 12313 6156.0 0.6850884 0.6850884 8.491140e-03 8.491140e-03 0.306420482 #> 12314 6156.5 0.6851004 0.6851004 8.490806e-03 8.490806e-03 0.306408791 #> 12315 6157.0 0.6851124 0.6851124 8.490475e-03 8.490475e-03 0.306397100 #> 12316 6157.5 0.6851244 0.6851244 8.490146e-03 8.490146e-03 0.306385409 #> 12317 6158.0 0.6851365 0.6851365 8.489820e-03 8.489820e-03 0.306373719 #> 12318 6158.5 0.6851485 0.6851485 8.489496e-03 8.489496e-03 0.306362030 #> 12319 6159.0 0.6851605 0.6851605 8.489176e-03 8.489176e-03 0.306350340 #> 12320 6159.5 0.6851725 0.6851725 8.488857e-03 8.488857e-03 0.306338652 #> 12321 6160.0 0.6851845 0.6851845 8.488541e-03 8.488541e-03 0.306326964 #> 12322 6160.5 0.6851965 0.6851965 8.488228e-03 8.488228e-03 0.306315276 #> 12323 6161.0 0.6852085 0.6852085 8.487917e-03 8.487917e-03 0.306303589 #> 12324 6161.5 0.6852205 0.6852205 8.487609e-03 8.487609e-03 0.306291903 #> 12325 6162.0 0.6852325 0.6852325 8.487304e-03 8.487304e-03 0.306280218 #> 12326 6162.5 0.6852445 0.6852445 8.487001e-03 8.487001e-03 0.306268533 #> 12327 6163.0 0.6852565 0.6852565 8.486700e-03 8.486700e-03 0.306256849 #> 12328 6163.5 0.6852684 0.6852684 8.486402e-03 8.486402e-03 0.306245166 #> 12329 6164.0 0.6852804 0.6852804 8.486107e-03 8.486107e-03 0.306233484 #> 12330 6164.5 0.6852924 0.6852924 8.485814e-03 8.485814e-03 0.306221803 #> 12331 6165.0 0.6853044 0.6853044 8.485524e-03 8.485524e-03 0.306210123 #> 12332 6165.5 0.6853163 0.6853163 8.485236e-03 8.485236e-03 0.306198444 #> 12333 6166.0 0.6853283 0.6853283 8.484951e-03 8.484951e-03 0.306186766 #> 12334 6166.5 0.6853402 0.6853402 8.484668e-03 8.484668e-03 0.306175089 #> 12335 6167.0 0.6853522 0.6853522 8.484388e-03 8.484388e-03 0.306163413 #> 12336 6167.5 0.6853642 0.6853642 8.484111e-03 8.484111e-03 0.306151738 #> 12337 6168.0 0.6853761 0.6853761 8.483836e-03 8.483836e-03 0.306140065 #> 12338 6168.5 0.6853880 0.6853880 8.483563e-03 8.483563e-03 0.306128393 #> 12339 6169.0 0.6854000 0.6854000 8.483293e-03 8.483293e-03 0.306116722 #> 12340 6169.5 0.6854119 0.6854119 8.483026e-03 8.483026e-03 0.306105052 #> 12341 6170.0 0.6854239 0.6854239 8.482761e-03 8.482761e-03 0.306093384 #> 12342 6170.5 0.6854358 0.6854358 8.482499e-03 8.482499e-03 0.306081718 #> 12343 6171.0 0.6854477 0.6854477 8.482239e-03 8.482239e-03 0.306070053 #> 12344 6171.5 0.6854596 0.6854596 8.481982e-03 8.481982e-03 0.306058389 #> 12345 6172.0 0.6854715 0.6854715 8.481727e-03 8.481727e-03 0.306046727 #> 12346 6172.5 0.6854835 0.6854835 8.481475e-03 8.481475e-03 0.306035066 #> 12347 6173.0 0.6854954 0.6854954 8.481225e-03 8.481225e-03 0.306023408 #> 12348 6173.5 0.6855073 0.6855073 8.480978e-03 8.480978e-03 0.306011751 #> 12349 6174.0 0.6855192 0.6855192 8.480734e-03 8.480734e-03 0.306000095 #> 12350 6174.5 0.6855311 0.6855311 8.480492e-03 8.480492e-03 0.305988442 #> 12351 6175.0 0.6855430 0.6855430 8.480252e-03 8.480252e-03 0.305976790 #> 12352 6175.5 0.6855548 0.6855548 8.480015e-03 8.480015e-03 0.305965140 #> 12353 6176.0 0.6855667 0.6855667 8.479781e-03 8.479781e-03 0.305953492 #> 12354 6176.5 0.6855786 0.6855786 8.479549e-03 8.479549e-03 0.305941846 #> 12355 6177.0 0.6855905 0.6855905 8.479319e-03 8.479319e-03 0.305930202 #> 12356 6177.5 0.6856023 0.6856023 8.479093e-03 8.479093e-03 0.305918560 #> 12357 6178.0 0.6856142 0.6856142 8.478868e-03 8.478868e-03 0.305906920 #> 12358 6178.5 0.6856261 0.6856261 8.478646e-03 8.478646e-03 0.305895282 #> 12359 6179.0 0.6856379 0.6856379 8.478427e-03 8.478427e-03 0.305883646 #> 12360 6179.5 0.6856498 0.6856498 8.478210e-03 8.478210e-03 0.305872012 #> 12361 6180.0 0.6856616 0.6856616 8.477996e-03 8.477996e-03 0.305860381 #> 12362 6180.5 0.6856735 0.6856735 8.477784e-03 8.477784e-03 0.305848752 #> 12363 6181.0 0.6856853 0.6856853 8.477575e-03 8.477575e-03 0.305837125 #> 12364 6181.5 0.6856971 0.6856971 8.477368e-03 8.477368e-03 0.305825500 #> 12365 6182.0 0.6857090 0.6857090 8.477164e-03 8.477164e-03 0.305813878 #> 12366 6182.5 0.6857208 0.6857208 8.476962e-03 8.476962e-03 0.305802259 #> 12367 6183.0 0.6857326 0.6857326 8.476763e-03 8.476763e-03 0.305790642 #> 12368 6183.5 0.6857444 0.6857444 8.476566e-03 8.476566e-03 0.305779027 #> 12369 6184.0 0.6857562 0.6857562 8.476372e-03 8.476372e-03 0.305767415 #> 12370 6184.5 0.6857680 0.6857680 8.476181e-03 8.476181e-03 0.305755805 #> 12371 6185.0 0.6857798 0.6857798 8.475991e-03 8.475991e-03 0.305744198 #> 12372 6185.5 0.6857916 0.6857916 8.475805e-03 8.475805e-03 0.305732594 #> 12373 6186.0 0.6858034 0.6858034 8.475621e-03 8.475621e-03 0.305720993 #> 12374 6186.5 0.6858152 0.6858152 8.475439e-03 8.475439e-03 0.305709394 #> 12375 6187.0 0.6858269 0.6858269 8.475260e-03 8.475260e-03 0.305697798 #> 12376 6187.5 0.6858387 0.6858387 8.475083e-03 8.475083e-03 0.305686205 #> 12377 6188.0 0.6858505 0.6858505 8.474909e-03 8.474909e-03 0.305674614 #> 12378 6188.5 0.6858622 0.6858622 8.474737e-03 8.474737e-03 0.305663027 #> 12379 6189.0 0.6858740 0.6858740 8.474568e-03 8.474568e-03 0.305651443 #> 12380 6189.5 0.6858857 0.6858857 8.474401e-03 8.474401e-03 0.305639861 #> 12381 6190.0 0.6858975 0.6858975 8.474237e-03 8.474237e-03 0.305628283 #> 12382 6190.5 0.6859092 0.6859092 8.474075e-03 8.474075e-03 0.305616708 #> 12383 6191.0 0.6859209 0.6859209 8.473916e-03 8.473916e-03 0.305605135 #> 12384 6191.5 0.6859327 0.6859327 8.473759e-03 8.473759e-03 0.305593566 #> 12385 6192.0 0.6859444 0.6859444 8.473605e-03 8.473605e-03 0.305582000 #> 12386 6192.5 0.6859561 0.6859561 8.473453e-03 8.473453e-03 0.305570438 #> 12387 6193.0 0.6859678 0.6859678 8.473304e-03 8.473304e-03 0.305558878 #> 12388 6193.5 0.6859795 0.6859795 8.473157e-03 8.473157e-03 0.305547322 #> 12389 6194.0 0.6859912 0.6859912 8.473013e-03 8.473013e-03 0.305535770 #> 12390 6194.5 0.6860029 0.6860029 8.472871e-03 8.472871e-03 0.305524220 #> 12391 6195.0 0.6860146 0.6860146 8.472732e-03 8.472732e-03 0.305512675 #> 12392 6195.5 0.6860263 0.6860263 8.472595e-03 8.472595e-03 0.305501132 #> 12393 6196.0 0.6860379 0.6860379 8.472461e-03 8.472461e-03 0.305489593 #> 12394 6196.5 0.6860496 0.6860496 8.472329e-03 8.472329e-03 0.305478058 #> 12395 6197.0 0.6860613 0.6860613 8.472199e-03 8.472199e-03 0.305466526 #> 12396 6197.5 0.6860729 0.6860729 8.472072e-03 8.472072e-03 0.305454998 #> 12397 6198.0 0.6860846 0.6860846 8.471948e-03 8.471948e-03 0.305443474 #> 12398 6198.5 0.6860962 0.6860962 8.471826e-03 8.471826e-03 0.305431953 #> 12399 6199.0 0.6861079 0.6861079 8.471706e-03 8.471706e-03 0.305420436 #> 12400 6199.5 0.6861195 0.6861195 8.471589e-03 8.471589e-03 0.305408923 #> 12401 6200.0 0.6861311 0.6861311 8.471474e-03 8.471474e-03 0.305397413 #> 12402 6200.5 0.6861427 0.6861427 8.471362e-03 8.471362e-03 0.305385908 #> 12403 6201.0 0.6861543 0.6861543 8.471252e-03 8.471252e-03 0.305374406 #> 12404 6201.5 0.6861659 0.6861659 8.471145e-03 8.471145e-03 0.305362908 #> 12405 6202.0 0.6861775 0.6861775 8.471040e-03 8.471040e-03 0.305351415 #> 12406 6202.5 0.6861891 0.6861891 8.470938e-03 8.470938e-03 0.305339925 #> 12407 6203.0 0.6862007 0.6862007 8.470838e-03 8.470838e-03 0.305328439 #> 12408 6203.5 0.6862123 0.6862123 8.470741e-03 8.470741e-03 0.305316958 #> 12409 6204.0 0.6862239 0.6862239 8.470646e-03 8.470646e-03 0.305305480 #> 12410 6204.5 0.6862354 0.6862354 8.470553e-03 8.470553e-03 0.305294007 #> 12411 6205.0 0.6862470 0.6862470 8.470463e-03 8.470463e-03 0.305282538 #> 12412 6205.5 0.6862586 0.6862586 8.470375e-03 8.470375e-03 0.305271073 #> 12413 6206.0 0.6862701 0.6862701 8.470290e-03 8.470290e-03 0.305259613 #> 12414 6206.5 0.6862816 0.6862816 8.470207e-03 8.470207e-03 0.305248157 #> 12415 6207.0 0.6862932 0.6862932 8.470127e-03 8.470127e-03 0.305236705 #> 12416 6207.5 0.6863047 0.6863047 8.470049e-03 8.470049e-03 0.305225258 #> 12417 6208.0 0.6863162 0.6863162 8.469974e-03 8.469974e-03 0.305213815 #> 12418 6208.5 0.6863277 0.6863277 8.469901e-03 8.469901e-03 0.305202376 #> 12419 6209.0 0.6863392 0.6863392 8.469830e-03 8.469830e-03 0.305190942 #> 12420 6209.5 0.6863507 0.6863507 8.469762e-03 8.469762e-03 0.305179513 #> 12421 6210.0 0.6863622 0.6863622 8.469696e-03 8.469696e-03 0.305168088 #> 12422 6210.5 0.6863737 0.6863737 8.469633e-03 8.469633e-03 0.305156668 #> 12423 6211.0 0.6863852 0.6863852 8.469572e-03 8.469572e-03 0.305145253 #> 12424 6211.5 0.6863966 0.6863966 8.469514e-03 8.469514e-03 0.305133842 #> 12425 6212.0 0.6864081 0.6864081 8.469458e-03 8.469458e-03 0.305122436 #> 12426 6212.5 0.6864196 0.6864196 8.469404e-03 8.469404e-03 0.305111035 #> 12427 6213.0 0.6864310 0.6864310 8.469353e-03 8.469353e-03 0.305099639 #> 12428 6213.5 0.6864424 0.6864424 8.469305e-03 8.469305e-03 0.305088247 #> 12429 6214.0 0.6864539 0.6864539 8.469258e-03 8.469258e-03 0.305076860 #> 12430 6214.5 0.6864653 0.6864653 8.469214e-03 8.469214e-03 0.305065479 #> 12431 6215.0 0.6864767 0.6864767 8.469173e-03 8.469173e-03 0.305054102 #> 12432 6215.5 0.6864881 0.6864881 8.469134e-03 8.469134e-03 0.305042730 #> 12433 6216.0 0.6864995 0.6864995 8.469097e-03 8.469097e-03 0.305031364 #> 12434 6216.5 0.6865109 0.6865109 8.469063e-03 8.469063e-03 0.305020002 #> 12435 6217.0 0.6865223 0.6865223 8.469032e-03 8.469032e-03 0.305008646 #> 12436 6217.5 0.6865337 0.6865337 8.469002e-03 8.469002e-03 0.304997295 #> 12437 6218.0 0.6865451 0.6865451 8.468975e-03 8.468975e-03 0.304985949 #> 12438 6218.5 0.6865564 0.6865564 8.468951e-03 8.468951e-03 0.304974608 #> 12439 6219.0 0.6865678 0.6865678 8.468929e-03 8.468929e-03 0.304963273 #> 12440 6219.5 0.6865791 0.6865791 8.468909e-03 8.468909e-03 0.304951942 #> 12441 6220.0 0.6865905 0.6865905 8.468891e-03 8.468891e-03 0.304940618 #> 12442 6220.5 0.6866018 0.6866018 8.468877e-03 8.468877e-03 0.304929298 #> 12443 6221.0 0.6866132 0.6866132 8.468864e-03 8.468864e-03 0.304917984 #> 12444 6221.5 0.6866245 0.6866245 8.468854e-03 8.468854e-03 0.304906676 #> 12445 6222.0 0.6866358 0.6866358 8.468846e-03 8.468846e-03 0.304895373 #> 12446 6222.5 0.6866471 0.6866471 8.468841e-03 8.468841e-03 0.304884075 #> 12447 6223.0 0.6866584 0.6866584 8.468838e-03 8.468838e-03 0.304872783 #> 12448 6223.5 0.6866697 0.6866697 8.468837e-03 8.468837e-03 0.304861497 #> 12449 6224.0 0.6866809 0.6866809 8.468839e-03 8.468839e-03 0.304850216 #> 12450 6224.5 0.6866922 0.6866922 8.468843e-03 8.468843e-03 0.304838941 #> 12451 6225.0 0.6867035 0.6867035 8.468850e-03 8.468850e-03 0.304827672 #> 12452 6225.5 0.6867147 0.6867147 8.468859e-03 8.468859e-03 0.304816409 #> 12453 6226.0 0.6867260 0.6867260 8.468870e-03 8.468870e-03 0.304805151 #> 12454 6226.5 0.6867372 0.6867372 8.468884e-03 8.468884e-03 0.304793899 #> 12455 6227.0 0.6867484 0.6867484 8.468900e-03 8.468900e-03 0.304782653 #> 12456 6227.5 0.6867597 0.6867597 8.468919e-03 8.468919e-03 0.304771413 #> 12457 6228.0 0.6867709 0.6867709 8.468940e-03 8.468940e-03 0.304760179 #> 12458 6228.5 0.6867821 0.6867821 8.468963e-03 8.468963e-03 0.304748951 #> 12459 6229.0 0.6867933 0.6867933 8.468988e-03 8.468988e-03 0.304737729 #> 12460 6229.5 0.6868045 0.6868045 8.469016e-03 8.469016e-03 0.304726513 #> 12461 6230.0 0.6868157 0.6868157 8.469047e-03 8.469047e-03 0.304715303 #> 12462 6230.5 0.6868268 0.6868268 8.469079e-03 8.469079e-03 0.304704099 #> 12463 6231.0 0.6868380 0.6868380 8.469115e-03 8.469115e-03 0.304692902 #> 12464 6231.5 0.6868491 0.6868491 8.469152e-03 8.469152e-03 0.304681710 #> 12465 6232.0 0.6868603 0.6868603 8.469192e-03 8.469192e-03 0.304670525 #> 12466 6232.5 0.6868714 0.6868714 8.469234e-03 8.469234e-03 0.304659346 #> 12467 6233.0 0.6868825 0.6868825 8.469279e-03 8.469279e-03 0.304648174 #> 12468 6233.5 0.6868937 0.6868937 8.469325e-03 8.469325e-03 0.304637007 #> 12469 6234.0 0.6869048 0.6869048 8.469375e-03 8.469375e-03 0.304625847 #> 12470 6234.5 0.6869159 0.6869159 8.469426e-03 8.469426e-03 0.304614694 #> 12471 6235.0 0.6869270 0.6869270 8.469480e-03 8.469480e-03 0.304603547 #> 12472 6235.5 0.6869381 0.6869381 8.469537e-03 8.469537e-03 0.304592406 #> 12473 6236.0 0.6869491 0.6869491 8.469595e-03 8.469595e-03 0.304581272 #> 12474 6236.5 0.6869602 0.6869602 8.469656e-03 8.469656e-03 0.304570145 #> 12475 6237.0 0.6869713 0.6869713 8.469719e-03 8.469719e-03 0.304559024 #> 12476 6237.5 0.6869823 0.6869823 8.469785e-03 8.469785e-03 0.304547910 #> 12477 6238.0 0.6869933 0.6869933 8.469853e-03 8.469853e-03 0.304536802 #> 12478 6238.5 0.6870044 0.6870044 8.469923e-03 8.469923e-03 0.304525702 #> 12479 6239.0 0.6870154 0.6870154 8.469996e-03 8.469996e-03 0.304514607 #> 12480 6239.5 0.6870264 0.6870264 8.470071e-03 8.470071e-03 0.304503520 #> 12481 6240.0 0.6870374 0.6870374 8.470148e-03 8.470148e-03 0.304492440 #> 12482 6240.5 0.6870484 0.6870484 8.470228e-03 8.470228e-03 0.304481366 #> 12483 6241.0 0.6870594 0.6870594 8.470310e-03 8.470310e-03 0.304470299 #> 12484 6241.5 0.6870704 0.6870704 8.470394e-03 8.470394e-03 0.304459239 #> 12485 6242.0 0.6870813 0.6870813 8.470481e-03 8.470481e-03 0.304448187 #> 12486 6242.5 0.6870923 0.6870923 8.470570e-03 8.470570e-03 0.304437141 #> 12487 6243.0 0.6871032 0.6871032 8.470661e-03 8.470661e-03 0.304426102 #> 12488 6243.5 0.6871142 0.6871142 8.470755e-03 8.470755e-03 0.304415070 #> 12489 6244.0 0.6871251 0.6871251 8.470851e-03 8.470851e-03 0.304404045 #> 12490 6244.5 0.6871360 0.6871360 8.470949e-03 8.470949e-03 0.304393028 #> 12491 6245.0 0.6871469 0.6871469 8.471050e-03 8.471050e-03 0.304382017 #> 12492 6245.5 0.6871578 0.6871578 8.471153e-03 8.471153e-03 0.304371014 #> 12493 6246.0 0.6871687 0.6871687 8.471258e-03 8.471258e-03 0.304360018 #> 12494 6246.5 0.6871796 0.6871796 8.471365e-03 8.471365e-03 0.304349029 #> 12495 6247.0 0.6871905 0.6871905 8.471475e-03 8.471475e-03 0.304338048 #> 12496 6247.5 0.6872013 0.6872013 8.471587e-03 8.471587e-03 0.304327074 #> 12497 6248.0 0.6872122 0.6872122 8.471701e-03 8.471701e-03 0.304316107 #> 12498 6248.5 0.6872230 0.6872230 8.471818e-03 8.471818e-03 0.304305148 #> 12499 6249.0 0.6872339 0.6872339 8.471937e-03 8.471937e-03 0.304294196 #> 12500 6249.5 0.6872447 0.6872447 8.472058e-03 8.472058e-03 0.304283251 #> 12501 6250.0 0.6872555 0.6872555 8.472182e-03 8.472182e-03 0.304272314 #> 12502 6250.5 0.6872663 0.6872663 8.472308e-03 8.472308e-03 0.304261385 #> 12503 6251.0 0.6872771 0.6872771 8.472436e-03 8.472436e-03 0.304250463 #> 12504 6251.5 0.6872879 0.6872879 8.472566e-03 8.472566e-03 0.304239549 #> 12505 6252.0 0.6872987 0.6872987 8.472699e-03 8.472699e-03 0.304228643 #> 12506 6252.5 0.6873094 0.6873094 8.472834e-03 8.472834e-03 0.304217744 #> 12507 6253.0 0.6873202 0.6873202 8.472971e-03 8.472971e-03 0.304206853 #> 12508 6253.5 0.6873309 0.6873309 8.473111e-03 8.473111e-03 0.304195969 #> 12509 6254.0 0.6873417 0.6873417 8.473253e-03 8.473253e-03 0.304185094 #> 12510 6254.5 0.6873524 0.6873524 8.473397e-03 8.473397e-03 0.304174226 #> 12511 6255.0 0.6873631 0.6873631 8.473543e-03 8.473543e-03 0.304163366 #> 12512 6255.5 0.6873738 0.6873738 8.473692e-03 8.473692e-03 0.304152514 #> 12513 6256.0 0.6873845 0.6873845 8.473843e-03 8.473843e-03 0.304141670 #> 12514 6256.5 0.6873952 0.6873952 8.473996e-03 8.473996e-03 0.304130834 #> 12515 6257.0 0.6874058 0.6874058 8.474152e-03 8.474152e-03 0.304120006 #> 12516 6257.5 0.6874165 0.6874165 8.474309e-03 8.474309e-03 0.304109186 #> 12517 6258.0 0.6874272 0.6874272 8.474469e-03 8.474469e-03 0.304098374 #> 12518 6258.5 0.6874378 0.6874378 8.474632e-03 8.474632e-03 0.304087570 #> 12519 6259.0 0.6874484 0.6874484 8.474796e-03 8.474796e-03 0.304076774 #> 12520 6259.5 0.6874591 0.6874591 8.474963e-03 8.474963e-03 0.304065986 #> 12521 6260.0 0.6874697 0.6874697 8.475132e-03 8.475132e-03 0.304055207 #> 12522 6260.5 0.6874803 0.6874803 8.475303e-03 8.475303e-03 0.304044435 #> 12523 6261.0 0.6874909 0.6874909 8.475477e-03 8.475477e-03 0.304033672 #> 12524 6261.5 0.6875014 0.6875014 8.475652e-03 8.475652e-03 0.304022917 #> 12525 6262.0 0.6875120 0.6875120 8.475830e-03 8.475830e-03 0.304012171 #> 12526 6262.5 0.6875226 0.6875226 8.476011e-03 8.476011e-03 0.304001433 #> 12527 6263.0 0.6875331 0.6875331 8.476193e-03 8.476193e-03 0.303990703 #> 12528 6263.5 0.6875436 0.6875436 8.476378e-03 8.476378e-03 0.303979982 #> 12529 6264.0 0.6875542 0.6875542 8.476565e-03 8.476565e-03 0.303969269 #> 12530 6264.5 0.6875647 0.6875647 8.476754e-03 8.476754e-03 0.303958565 #> 12531 6265.0 0.6875752 0.6875752 8.476945e-03 8.476945e-03 0.303947869 #> 12532 6265.5 0.6875857 0.6875857 8.477139e-03 8.477139e-03 0.303937182 #> 12533 6266.0 0.6875962 0.6875962 8.477335e-03 8.477335e-03 0.303926503 #> 12534 6266.5 0.6876066 0.6876066 8.477533e-03 8.477533e-03 0.303915833 #> 12535 6267.0 0.6876171 0.6876171 8.477733e-03 8.477733e-03 0.303905172 #> 12536 6267.5 0.6876275 0.6876275 8.477936e-03 8.477936e-03 0.303894519 #> 12537 6268.0 0.6876380 0.6876380 8.478141e-03 8.478141e-03 0.303883875 #> 12538 6268.5 0.6876484 0.6876484 8.478348e-03 8.478348e-03 0.303873240 #> 12539 6269.0 0.6876588 0.6876588 8.478557e-03 8.478557e-03 0.303862614 #> 12540 6269.5 0.6876692 0.6876692 8.478768e-03 8.478768e-03 0.303851996 #> 12541 6270.0 0.6876796 0.6876796 8.478982e-03 8.478982e-03 0.303841387 #> 12542 6270.5 0.6876900 0.6876900 8.479198e-03 8.479198e-03 0.303830787 #> 12543 6271.0 0.6877004 0.6877004 8.479416e-03 8.479416e-03 0.303820196 #> 12544 6271.5 0.6877107 0.6877107 8.479636e-03 8.479636e-03 0.303809614 #> 12545 6272.0 0.6877211 0.6877211 8.479858e-03 8.479858e-03 0.303799041 #> 12546 6272.5 0.6877314 0.6877314 8.480083e-03 8.480083e-03 0.303788477 #> 12547 6273.0 0.6877418 0.6877418 8.480310e-03 8.480310e-03 0.303777922 #> 12548 6273.5 0.6877521 0.6877521 8.480539e-03 8.480539e-03 0.303767376 #> 12549 6274.0 0.6877624 0.6877624 8.480770e-03 8.480770e-03 0.303756840 #> 12550 6274.5 0.6877727 0.6877727 8.481004e-03 8.481004e-03 0.303746312 #> 12551 6275.0 0.6877830 0.6877830 8.481239e-03 8.481239e-03 0.303735793 #> 12552 6275.5 0.6877932 0.6877932 8.481477e-03 8.481477e-03 0.303725284 #> 12553 6276.0 0.6878035 0.6878035 8.481717e-03 8.481717e-03 0.303714784 #> 12554 6276.5 0.6878137 0.6878137 8.481959e-03 8.481959e-03 0.303704293 #> 12555 6277.0 0.6878240 0.6878240 8.482203e-03 8.482203e-03 0.303693812 #> 12556 6277.5 0.6878342 0.6878342 8.482450e-03 8.482450e-03 0.303683340 #> 12557 6278.0 0.6878444 0.6878444 8.482699e-03 8.482699e-03 0.303672877 #> 12558 6278.5 0.6878546 0.6878546 8.482949e-03 8.482949e-03 0.303662423 #> 12559 6279.0 0.6878648 0.6878648 8.483202e-03 8.483202e-03 0.303651979 #> 12560 6279.5 0.6878750 0.6878750 8.483458e-03 8.483458e-03 0.303641545 #> 12561 6280.0 0.6878852 0.6878852 8.483715e-03 8.483715e-03 0.303631120 #> 12562 6280.5 0.6878953 0.6878953 8.483975e-03 8.483975e-03 0.303620704 #> 12563 6281.0 0.6879055 0.6879055 8.484236e-03 8.484236e-03 0.303610298 #> 12564 6281.5 0.6879156 0.6879156 8.484500e-03 8.484500e-03 0.303599902 #> 12565 6282.0 0.6879257 0.6879257 8.484766e-03 8.484766e-03 0.303589515 #> 12566 6282.5 0.6879358 0.6879358 8.485034e-03 8.485034e-03 0.303579138 #> 12567 6283.0 0.6879459 0.6879459 8.485304e-03 8.485304e-03 0.303568770 #> 12568 6283.5 0.6879560 0.6879560 8.485577e-03 8.485577e-03 0.303558412 #> 12569 6284.0 0.6879661 0.6879661 8.485852e-03 8.485852e-03 0.303548064 #> 12570 6284.5 0.6879761 0.6879761 8.486128e-03 8.486128e-03 0.303537726 #> 12571 6285.0 0.6879862 0.6879862 8.486407e-03 8.486407e-03 0.303527398 #> 12572 6285.5 0.6879962 0.6879962 8.486688e-03 8.486688e-03 0.303517079 #> 12573 6286.0 0.6880063 0.6880063 8.486971e-03 8.486971e-03 0.303506770 #> 12574 6286.5 0.6880163 0.6880163 8.487257e-03 8.487257e-03 0.303496471 #> 12575 6287.0 0.6880263 0.6880263 8.487544e-03 8.487544e-03 0.303486182 #> 12576 6287.5 0.6880363 0.6880363 8.487834e-03 8.487834e-03 0.303475903 #> 12577 6288.0 0.6880462 0.6880462 8.488125e-03 8.488125e-03 0.303465634 #> 12578 6288.5 0.6880562 0.6880562 8.488419e-03 8.488419e-03 0.303455375 #> 12579 6289.0 0.6880662 0.6880662 8.488715e-03 8.488715e-03 0.303445126 #> 12580 6289.5 0.6880761 0.6880761 8.489013e-03 8.489013e-03 0.303434887 #> 12581 6290.0 0.6880860 0.6880860 8.489313e-03 8.489313e-03 0.303424658 #> 12582 6290.5 0.6880959 0.6880959 8.489616e-03 8.489616e-03 0.303414440 #> 12583 6291.0 0.6881058 0.6881058 8.489920e-03 8.489920e-03 0.303404231 #> 12584 6291.5 0.6881157 0.6881157 8.490226e-03 8.490226e-03 0.303394033 #> 12585 6292.0 0.6881256 0.6881256 8.490535e-03 8.490535e-03 0.303383845 #> 12586 6292.5 0.6881355 0.6881355 8.490846e-03 8.490846e-03 0.303373667 #> 12587 6293.0 0.6881453 0.6881453 8.491159e-03 8.491159e-03 0.303363499 #> 12588 6293.5 0.6881552 0.6881552 8.491474e-03 8.491474e-03 0.303353342 #> 12589 6294.0 0.6881650 0.6881650 8.491791e-03 8.491791e-03 0.303343195 #> 12590 6294.5 0.6881748 0.6881748 8.492110e-03 8.492110e-03 0.303333058 #> 12591 6295.0 0.6881846 0.6881846 8.492431e-03 8.492431e-03 0.303322932 #> 12592 6295.5 0.6881944 0.6881944 8.492754e-03 8.492754e-03 0.303312816 #> 12593 6296.0 0.6882042 0.6882042 8.493080e-03 8.493080e-03 0.303302711 #> 12594 6296.5 0.6882140 0.6882140 8.493407e-03 8.493407e-03 0.303292616 #> 12595 6297.0 0.6882237 0.6882237 8.493737e-03 8.493737e-03 0.303282532 #> 12596 6297.5 0.6882335 0.6882335 8.494069e-03 8.494069e-03 0.303272458 #> 12597 6298.0 0.6882432 0.6882432 8.494402e-03 8.494402e-03 0.303262395 #> 12598 6298.5 0.6882529 0.6882529 8.494738e-03 8.494738e-03 0.303252343 #> 12599 6299.0 0.6882626 0.6882626 8.495076e-03 8.495076e-03 0.303242301 #> 12600 6299.5 0.6882723 0.6882723 8.495416e-03 8.495416e-03 0.303232270 #> 12601 6300.0 0.6882820 0.6882820 8.495758e-03 8.495758e-03 0.303222249 #> 12602 6300.5 0.6882917 0.6882917 8.496102e-03 8.496102e-03 0.303212239 #> 12603 6301.0 0.6883013 0.6883013 8.496448e-03 8.496448e-03 0.303202240 #> 12604 6301.5 0.6883110 0.6883110 8.496797e-03 8.496797e-03 0.303192252 #> 12605 6302.0 0.6883206 0.6883206 8.497147e-03 8.497147e-03 0.303182275 #> 12606 6302.5 0.6883302 0.6883302 8.497499e-03 8.497499e-03 0.303172308 #> 12607 6303.0 0.6883398 0.6883398 8.497854e-03 8.497854e-03 0.303162352 #> 12608 6303.5 0.6883494 0.6883494 8.498210e-03 8.498210e-03 0.303152408 #> 12609 6304.0 0.6883590 0.6883590 8.498569e-03 8.498569e-03 0.303142474 #> 12610 6304.5 0.6883685 0.6883685 8.498929e-03 8.498929e-03 0.303132551 #> 12611 6305.0 0.6883781 0.6883781 8.499292e-03 8.499292e-03 0.303122639 #> 12612 6305.5 0.6883876 0.6883876 8.499657e-03 8.499657e-03 0.303112738 #> 12613 6306.0 0.6883971 0.6883971 8.500023e-03 8.500023e-03 0.303102848 #> 12614 6306.5 0.6884066 0.6884066 8.500392e-03 8.500392e-03 0.303092969 #> 12615 6307.0 0.6884161 0.6884161 8.500763e-03 8.500763e-03 0.303083101 #> 12616 6307.5 0.6884256 0.6884256 8.501136e-03 8.501136e-03 0.303073245 #> 12617 6308.0 0.6884351 0.6884351 8.501510e-03 8.501510e-03 0.303063399 #> 12618 6308.5 0.6884445 0.6884445 8.501887e-03 8.501887e-03 0.303053565 #> 12619 6309.0 0.6884540 0.6884540 8.502266e-03 8.502266e-03 0.303043742 #> 12620 6309.5 0.6884634 0.6884634 8.502647e-03 8.502647e-03 0.303033930 #> 12621 6310.0 0.6884728 0.6884728 8.503030e-03 8.503030e-03 0.303024130 #> 12622 6310.5 0.6884822 0.6884822 8.503415e-03 8.503415e-03 0.303014340 #> 12623 6311.0 0.6884916 0.6884916 8.503802e-03 8.503802e-03 0.303004563 #> 12624 6311.5 0.6885010 0.6885010 8.504191e-03 8.504191e-03 0.302994796 #> 12625 6312.0 0.6885104 0.6885104 8.504582e-03 8.504582e-03 0.302985041 #> 12626 6312.5 0.6885197 0.6885197 8.504975e-03 8.504975e-03 0.302975297 #> 12627 6313.0 0.6885291 0.6885291 8.505370e-03 8.505370e-03 0.302965565 #> 12628 6313.5 0.6885384 0.6885384 8.505767e-03 8.505767e-03 0.302955844 #> 12629 6314.0 0.6885477 0.6885477 8.506166e-03 8.506166e-03 0.302946134 #> 12630 6314.5 0.6885570 0.6885570 8.506567e-03 8.506567e-03 0.302936437 #> 12631 6315.0 0.6885663 0.6885663 8.506970e-03 8.506970e-03 0.302926750 #> 12632 6315.5 0.6885755 0.6885755 8.507375e-03 8.507375e-03 0.302917075 #> 12633 6316.0 0.6885848 0.6885848 8.507782e-03 8.507782e-03 0.302907412 #> 12634 6316.5 0.6885940 0.6885940 8.508191e-03 8.508191e-03 0.302897761 #> 12635 6317.0 0.6886033 0.6886033 8.508602e-03 8.508602e-03 0.302888121 #> 12636 6317.5 0.6886125 0.6886125 8.509015e-03 8.509015e-03 0.302878493 #> 12637 6318.0 0.6886217 0.6886217 8.509430e-03 8.509430e-03 0.302868876 #> 12638 6318.5 0.6886309 0.6886309 8.509847e-03 8.509847e-03 0.302859272 #> 12639 6319.0 0.6886401 0.6886401 8.510266e-03 8.510266e-03 0.302849679 #> 12640 6319.5 0.6886492 0.6886492 8.510686e-03 8.510686e-03 0.302840097 #> 12641 6320.0 0.6886584 0.6886584 8.511109e-03 8.511109e-03 0.302830528 #> 12642 6320.5 0.6886675 0.6886675 8.511534e-03 8.511534e-03 0.302820970 #> 12643 6321.0 0.6886766 0.6886766 8.511961e-03 8.511961e-03 0.302811425 #> 12644 6321.5 0.6886857 0.6886857 8.512389e-03 8.512389e-03 0.302801891 #> 12645 6322.0 0.6886948 0.6886948 8.512820e-03 8.512820e-03 0.302792369 #> 12646 6322.5 0.6887039 0.6887039 8.513253e-03 8.513253e-03 0.302782859 #> 12647 6323.0 0.6887130 0.6887130 8.513687e-03 8.513687e-03 0.302773362 #> 12648 6323.5 0.6887220 0.6887220 8.514124e-03 8.514124e-03 0.302763876 #> 12649 6324.0 0.6887310 0.6887310 8.514562e-03 8.514562e-03 0.302754402 #> 12650 6324.5 0.6887401 0.6887401 8.515003e-03 8.515003e-03 0.302744940 #> 12651 6325.0 0.6887491 0.6887491 8.515445e-03 8.515445e-03 0.302735490 #> 12652 6325.5 0.6887581 0.6887581 8.515889e-03 8.515889e-03 0.302726053 #> 12653 6326.0 0.6887670 0.6887670 8.516335e-03 8.516335e-03 0.302716627 #> 12654 6326.5 0.6887760 0.6887760 8.516784e-03 8.516784e-03 0.302707214 #> 12655 6327.0 0.6887850 0.6887850 8.517234e-03 8.517234e-03 0.302697813 #> 12656 6327.5 0.6887939 0.6887939 8.517686e-03 8.517686e-03 0.302688424 #> 12657 6328.0 0.6888028 0.6888028 8.518140e-03 8.518140e-03 0.302679047 #> 12658 6328.5 0.6888117 0.6888117 8.518595e-03 8.518595e-03 0.302669683 #> 12659 6329.0 0.6888206 0.6888206 8.519053e-03 8.519053e-03 0.302660331 #> 12660 6329.5 0.6888295 0.6888295 8.519513e-03 8.519513e-03 0.302650991 #> 12661 6330.0 0.6888384 0.6888384 8.519975e-03 8.519975e-03 0.302641664 #> 12662 6330.5 0.6888472 0.6888472 8.520438e-03 8.520438e-03 0.302632348 #> 12663 6331.0 0.6888561 0.6888561 8.520904e-03 8.520904e-03 0.302623046 #> 12664 6331.5 0.6888649 0.6888649 8.521371e-03 8.521371e-03 0.302613756 #> 12665 6332.0 0.6888737 0.6888737 8.521840e-03 8.521840e-03 0.302604478 #> 12666 6332.5 0.6888825 0.6888825 8.522311e-03 8.522311e-03 0.302595212 #> 12667 6333.0 0.6888913 0.6888913 8.522784e-03 8.522784e-03 0.302585960 #> 12668 6333.5 0.6889000 0.6889000 8.523259e-03 8.523259e-03 0.302576719 #> 12669 6334.0 0.6889088 0.6889088 8.523736e-03 8.523736e-03 0.302567491 #> 12670 6334.5 0.6889175 0.6889175 8.524215e-03 8.524215e-03 0.302558276 #> 12671 6335.0 0.6889262 0.6889262 8.524695e-03 8.524695e-03 0.302549074 #> 12672 6335.5 0.6889349 0.6889349 8.525178e-03 8.525178e-03 0.302539884 #> 12673 6336.0 0.6889436 0.6889436 8.525662e-03 8.525662e-03 0.302530706 #> 12674 6336.5 0.6889523 0.6889523 8.526149e-03 8.526149e-03 0.302521542 #> 12675 6337.0 0.6889610 0.6889610 8.526637e-03 8.526637e-03 0.302512390 #> 12676 6337.5 0.6889696 0.6889696 8.527127e-03 8.527127e-03 0.302503251 #> 12677 6338.0 0.6889783 0.6889783 8.527619e-03 8.527619e-03 0.302494124 #> 12678 6338.5 0.6889869 0.6889869 8.528112e-03 8.528112e-03 0.302485010 #> 12679 6339.0 0.6889955 0.6889955 8.528608e-03 8.528608e-03 0.302475910 #> 12680 6339.5 0.6890041 0.6890041 8.529105e-03 8.529105e-03 0.302466822 #> 12681 6340.0 0.6890126 0.6890126 8.529605e-03 8.529605e-03 0.302457746 #> 12682 6340.5 0.6890212 0.6890212 8.530106e-03 8.530106e-03 0.302448684 #> 12683 6341.0 0.6890298 0.6890298 8.530609e-03 8.530609e-03 0.302439634 #> 12684 6341.5 0.6890383 0.6890383 8.531114e-03 8.531114e-03 0.302430598 #> 12685 6342.0 0.6890468 0.6890468 8.531621e-03 8.531621e-03 0.302421574 #> 12686 6342.5 0.6890553 0.6890553 8.532129e-03 8.532129e-03 0.302412564 #> 12687 6343.0 0.6890638 0.6890638 8.532640e-03 8.532640e-03 0.302403566 #> 12688 6343.5 0.6890723 0.6890723 8.533152e-03 8.533152e-03 0.302394581 #> 12689 6344.0 0.6890807 0.6890807 8.533666e-03 8.533666e-03 0.302385610 #> 12690 6344.5 0.6890892 0.6890892 8.534182e-03 8.534182e-03 0.302376651 #> 12691 6345.0 0.6890976 0.6890976 8.534700e-03 8.534700e-03 0.302367706 #> 12692 6345.5 0.6891060 0.6891060 8.535219e-03 8.535219e-03 0.302358774 #> 12693 6346.0 0.6891144 0.6891144 8.535741e-03 8.535741e-03 0.302349855 #> 12694 6346.5 0.6891228 0.6891228 8.536264e-03 8.536264e-03 0.302340949 #> 12695 6347.0 0.6891312 0.6891312 8.536789e-03 8.536789e-03 0.302332056 #> 12696 6347.5 0.6891395 0.6891395 8.537316e-03 8.537316e-03 0.302323176 #> 12697 6348.0 0.6891478 0.6891478 8.537845e-03 8.537845e-03 0.302314310 #> 12698 6348.5 0.6891562 0.6891562 8.538375e-03 8.538375e-03 0.302305457 #> 12699 6349.0 0.6891645 0.6891645 8.538907e-03 8.538907e-03 0.302296617 #> 12700 6349.5 0.6891728 0.6891728 8.539441e-03 8.539441e-03 0.302287790 #> 12701 6350.0 0.6891810 0.6891810 8.539977e-03 8.539977e-03 0.302278977 #> 12702 6350.5 0.6891893 0.6891893 8.540515e-03 8.540515e-03 0.302270177 #> 12703 6351.0 0.6891976 0.6891976 8.541055e-03 8.541055e-03 0.302261391 #> 12704 6351.5 0.6892058 0.6892058 8.541596e-03 8.541596e-03 0.302252618 #> 12705 6352.0 0.6892140 0.6892140 8.542139e-03 8.542139e-03 0.302243858 #> 12706 6352.5 0.6892222 0.6892222 8.542684e-03 8.542684e-03 0.302235112 #> 12707 6353.0 0.6892304 0.6892304 8.543231e-03 8.543231e-03 0.302226379 #> 12708 6353.5 0.6892386 0.6892386 8.543779e-03 8.543779e-03 0.302217660 #> 12709 6354.0 0.6892467 0.6892467 8.544329e-03 8.544329e-03 0.302208954 #> 12710 6354.5 0.6892549 0.6892549 8.544881e-03 8.544881e-03 0.302200262 #> 12711 6355.0 0.6892630 0.6892630 8.545435e-03 8.545435e-03 0.302191583 #> 12712 6355.5 0.6892711 0.6892711 8.545991e-03 8.545991e-03 0.302182918 #> 12713 6356.0 0.6892792 0.6892792 8.546548e-03 8.546548e-03 0.302174267 #> 12714 6356.5 0.6892873 0.6892873 8.547107e-03 8.547107e-03 0.302165629 #> 12715 6357.0 0.6892953 0.6892953 8.547668e-03 8.547668e-03 0.302157005 #> 12716 6357.5 0.6893034 0.6893034 8.548230e-03 8.548230e-03 0.302148394 #> 12717 6358.0 0.6893114 0.6893114 8.548795e-03 8.548795e-03 0.302139797 #> 12718 6358.5 0.6893194 0.6893194 8.549361e-03 8.549361e-03 0.302131214 #> 12719 6359.0 0.6893274 0.6893274 8.549929e-03 8.549929e-03 0.302122645 #> 12720 6359.5 0.6893354 0.6893354 8.550498e-03 8.550498e-03 0.302114089 #> 12721 6360.0 0.6893434 0.6893434 8.551070e-03 8.551070e-03 0.302105547 #> 12722 6360.5 0.6893513 0.6893513 8.551643e-03 8.551643e-03 0.302097019 #> 12723 6361.0 0.6893593 0.6893593 8.552218e-03 8.552218e-03 0.302088505 #> 12724 6361.5 0.6893672 0.6893672 8.552794e-03 8.552794e-03 0.302080005 #> 12725 6362.0 0.6893751 0.6893751 8.553372e-03 8.553372e-03 0.302071518 #> 12726 6362.5 0.6893830 0.6893830 8.553953e-03 8.553953e-03 0.302063045 #> 12727 6363.0 0.6893909 0.6893909 8.554534e-03 8.554534e-03 0.302054587 #> 12728 6363.5 0.6893987 0.6893987 8.555118e-03 8.555118e-03 0.302046142 #> 12729 6364.0 0.6894066 0.6894066 8.555703e-03 8.555703e-03 0.302037711 #> 12730 6364.5 0.6894144 0.6894144 8.556290e-03 8.556290e-03 0.302029294 #> 12731 6365.0 0.6894222 0.6894222 8.556878e-03 8.556878e-03 0.302020892 #> 12732 6365.5 0.6894300 0.6894300 8.557469e-03 8.557469e-03 0.302012503 #> 12733 6366.0 0.6894378 0.6894378 8.558061e-03 8.558061e-03 0.302004128 #> 12734 6366.5 0.6894456 0.6894456 8.558655e-03 8.558655e-03 0.301995767 #> 12735 6367.0 0.6894533 0.6894533 8.559250e-03 8.559250e-03 0.301987421 #> 12736 6367.5 0.6894611 0.6894611 8.559847e-03 8.559847e-03 0.301979088 #> 12737 6368.0 0.6894688 0.6894688 8.560446e-03 8.560446e-03 0.301970770 #> 12738 6368.5 0.6894765 0.6894765 8.561047e-03 8.561047e-03 0.301962466 #> 12739 6369.0 0.6894842 0.6894842 8.561649e-03 8.561649e-03 0.301954176 #> 12740 6369.5 0.6894918 0.6894918 8.562253e-03 8.562253e-03 0.301945900 #> 12741 6370.0 0.6894995 0.6894995 8.562858e-03 8.562858e-03 0.301937638 #> 12742 6370.5 0.6895071 0.6895071 8.563465e-03 8.563465e-03 0.301929391 #> 12743 6371.0 0.6895148 0.6895148 8.564074e-03 8.564074e-03 0.301921158 #> 12744 6371.5 0.6895224 0.6895224 8.564685e-03 8.564685e-03 0.301912939 #> 12745 6372.0 0.6895300 0.6895300 8.565297e-03 8.565297e-03 0.301904734 #> 12746 6372.5 0.6895375 0.6895375 8.565911e-03 8.565911e-03 0.301896544 #> 12747 6373.0 0.6895451 0.6895451 8.566527e-03 8.566527e-03 0.301888368 #> 12748 6373.5 0.6895526 0.6895526 8.567144e-03 8.567144e-03 0.301880207 #> 12749 6374.0 0.6895602 0.6895602 8.567763e-03 8.567763e-03 0.301872059 #> 12750 6374.5 0.6895677 0.6895677 8.568384e-03 8.568384e-03 0.301863927 #> 12751 6375.0 0.6895752 0.6895752 8.569006e-03 8.569006e-03 0.301855808 #> 12752 6375.5 0.6895827 0.6895827 8.569630e-03 8.569630e-03 0.301847704 #> 12753 6376.0 0.6895901 0.6895901 8.570255e-03 8.570255e-03 0.301839615 #> 12754 6376.5 0.6895976 0.6895976 8.570883e-03 8.570883e-03 0.301831540 #> 12755 6377.0 0.6896050 0.6896050 8.571511e-03 8.571511e-03 0.301823479 #> 12756 6377.5 0.6896124 0.6896124 8.572142e-03 8.572142e-03 0.301815433 #> 12757 6378.0 0.6896198 0.6896198 8.572774e-03 8.572774e-03 0.301807402 #> 12758 6378.5 0.6896272 0.6896272 8.573408e-03 8.573408e-03 0.301799385 #> 12759 6379.0 0.6896346 0.6896346 8.574043e-03 8.574043e-03 0.301791383 #> 12760 6379.5 0.6896419 0.6896419 8.574680e-03 8.574680e-03 0.301783395 #> 12761 6380.0 0.6896493 0.6896493 8.575319e-03 8.575319e-03 0.301775422 #> 12762 6380.5 0.6896566 0.6896566 8.575959e-03 8.575959e-03 0.301767464 #> 12763 6381.0 0.6896639 0.6896639 8.576601e-03 8.576601e-03 0.301759520 #> 12764 6381.5 0.6896712 0.6896712 8.577244e-03 8.577244e-03 0.301751591 #> 12765 6382.0 0.6896784 0.6896784 8.577889e-03 8.577889e-03 0.301743677 #> 12766 6382.5 0.6896857 0.6896857 8.578536e-03 8.578536e-03 0.301735777 #> 12767 6383.0 0.6896929 0.6896929 8.579184e-03 8.579184e-03 0.301727892 #> 12768 6383.5 0.6897001 0.6897001 8.579834e-03 8.579834e-03 0.301720022 #> 12769 6384.0 0.6897073 0.6897073 8.580486e-03 8.580486e-03 0.301712167 #> 12770 6384.5 0.6897145 0.6897145 8.581139e-03 8.581139e-03 0.301704326 #> 12771 6385.0 0.6897217 0.6897217 8.581793e-03 8.581793e-03 0.301696500 #> 12772 6385.5 0.6897289 0.6897289 8.582450e-03 8.582450e-03 0.301688689 #> 12773 6386.0 0.6897360 0.6897360 8.583107e-03 8.583107e-03 0.301680893 #> 12774 6386.5 0.6897431 0.6897431 8.583767e-03 8.583767e-03 0.301673112 #> 12775 6387.0 0.6897502 0.6897502 8.584428e-03 8.584428e-03 0.301665346 #> 12776 6387.5 0.6897573 0.6897573 8.585090e-03 8.585090e-03 0.301657594 #> 12777 6388.0 0.6897644 0.6897644 8.585754e-03 8.585754e-03 0.301649858 #> 12778 6388.5 0.6897714 0.6897714 8.586420e-03 8.586420e-03 0.301642136 #> 12779 6389.0 0.6897785 0.6897785 8.587087e-03 8.587087e-03 0.301634430 #> 12780 6389.5 0.6897855 0.6897855 8.587756e-03 8.587756e-03 0.301626738 #> 12781 6390.0 0.6897925 0.6897925 8.588427e-03 8.588427e-03 0.301619061 #> 12782 6390.5 0.6897995 0.6897995 8.589098e-03 8.589098e-03 0.301611400 #> 12783 6391.0 0.6898065 0.6898065 8.589772e-03 8.589772e-03 0.301603753 #> 12784 6391.5 0.6898134 0.6898134 8.590447e-03 8.590447e-03 0.301596122 #> 12785 6392.0 0.6898204 0.6898204 8.591124e-03 8.591124e-03 0.301588505 #> 12786 6392.5 0.6898273 0.6898273 8.591802e-03 8.591802e-03 0.301580904 #> 12787 6393.0 0.6898342 0.6898342 8.592481e-03 8.592481e-03 0.301573318 #> 12788 6393.5 0.6898411 0.6898411 8.593163e-03 8.593163e-03 0.301565747 #> 12789 6394.0 0.6898480 0.6898480 8.593845e-03 8.593845e-03 0.301558191 #> 12790 6394.5 0.6898548 0.6898548 8.594530e-03 8.594530e-03 0.301550650 #> 12791 6395.0 0.6898617 0.6898617 8.595215e-03 8.595215e-03 0.301543125 #> 12792 6395.5 0.6898685 0.6898685 8.595903e-03 8.595903e-03 0.301535614 #> 12793 6396.0 0.6898753 0.6898753 8.596591e-03 8.596591e-03 0.301528119 #> 12794 6396.5 0.6898821 0.6898821 8.597282e-03 8.597282e-03 0.301520639 #> 12795 6397.0 0.6898889 0.6898889 8.597974e-03 8.597974e-03 0.301513174 #> 12796 6397.5 0.6898956 0.6898956 8.598667e-03 8.598667e-03 0.301505725 #> 12797 6398.0 0.6899023 0.6899023 8.599362e-03 8.599362e-03 0.301498291 #> 12798 6398.5 0.6899091 0.6899091 8.600058e-03 8.600058e-03 0.301490872 #> 12799 6399.0 0.6899158 0.6899158 8.600756e-03 8.600756e-03 0.301483469 #> 12800 6399.5 0.6899225 0.6899225 8.601455e-03 8.601455e-03 0.301476081 #> 12801 6400.0 0.6899291 0.6899291 8.602156e-03 8.602156e-03 0.301468708 #> 12802 6400.5 0.6899358 0.6899358 8.602859e-03 8.602859e-03 0.301461350 #> 12803 6401.0 0.6899424 0.6899424 8.603562e-03 8.603562e-03 0.301454008 #> 12804 6401.5 0.6899491 0.6899491 8.604268e-03 8.604268e-03 0.301446682 #> 12805 6402.0 0.6899557 0.6899557 8.604975e-03 8.604975e-03 0.301439371 #> 12806 6402.5 0.6899622 0.6899622 8.605683e-03 8.605683e-03 0.301432075 #> 12807 6403.0 0.6899688 0.6899688 8.606393e-03 8.606393e-03 0.301424795 #> 12808 6403.5 0.6899754 0.6899754 8.607104e-03 8.607104e-03 0.301417530 #> 12809 6404.0 0.6899819 0.6899819 8.607817e-03 8.607817e-03 0.301410281 #> 12810 6404.5 0.6899884 0.6899884 8.608531e-03 8.608531e-03 0.301403047 #> 12811 6405.0 0.6899949 0.6899949 8.609246e-03 8.609246e-03 0.301395829 #> 12812 6405.5 0.6900014 0.6900014 8.609963e-03 8.609963e-03 0.301388626 #> 12813 6406.0 0.6900079 0.6900079 8.610682e-03 8.610682e-03 0.301381439 #> 12814 6406.5 0.6900143 0.6900143 8.611402e-03 8.611402e-03 0.301374267 #> 12815 6407.0 0.6900208 0.6900208 8.612123e-03 8.612123e-03 0.301367111 #> 12816 6407.5 0.6900272 0.6900272 8.612846e-03 8.612846e-03 0.301359971 #> 12817 6408.0 0.6900336 0.6900336 8.613571e-03 8.613571e-03 0.301352846 #> 12818 6408.5 0.6900400 0.6900400 8.614296e-03 8.614296e-03 0.301345737 #> 12819 6409.0 0.6900463 0.6900463 8.615023e-03 8.615023e-03 0.301338644 #> 12820 6409.5 0.6900527 0.6900527 8.615752e-03 8.615752e-03 0.301331566 #> 12821 6410.0 0.6900590 0.6900590 8.616482e-03 8.616482e-03 0.301324504 #> 12822 6410.5 0.6900653 0.6900653 8.617214e-03 8.617214e-03 0.301317457 #> 12823 6411.0 0.6900716 0.6900716 8.617946e-03 8.617946e-03 0.301310427 #> 12824 6411.5 0.6900779 0.6900779 8.618681e-03 8.618681e-03 0.301303412 #> 12825 6412.0 0.6900842 0.6900842 8.619416e-03 8.619416e-03 0.301296413 #> 12826 6412.5 0.6900904 0.6900904 8.620154e-03 8.620154e-03 0.301289429 #> 12827 6413.0 0.6900966 0.6900966 8.620892e-03 8.620892e-03 0.301282462 #> 12828 6413.5 0.6901029 0.6901029 8.621632e-03 8.621632e-03 0.301275510 #> 12829 6414.0 0.6901091 0.6901091 8.622373e-03 8.622373e-03 0.301268574 #> 12830 6414.5 0.6901152 0.6901152 8.623116e-03 8.623116e-03 0.301261654 #> 12831 6415.0 0.6901214 0.6901214 8.623860e-03 8.623860e-03 0.301254750 #> 12832 6415.5 0.6901275 0.6901275 8.624606e-03 8.624606e-03 0.301247861 #> 12833 6416.0 0.6901337 0.6901337 8.625353e-03 8.625353e-03 0.301240989 #> 12834 6416.5 0.6901398 0.6901398 8.626101e-03 8.626101e-03 0.301234132 #> 12835 6417.0 0.6901459 0.6901459 8.626851e-03 8.626851e-03 0.301227291 #> 12836 6417.5 0.6901519 0.6901519 8.627602e-03 8.627602e-03 0.301220466 #> 12837 6418.0 0.6901580 0.6901580 8.628354e-03 8.628354e-03 0.301213657 #> 12838 6418.5 0.6901640 0.6901640 8.629108e-03 8.629108e-03 0.301206864 #> 12839 6419.0 0.6901700 0.6901700 8.629863e-03 8.629863e-03 0.301200087 #> 12840 6419.5 0.6901761 0.6901761 8.630620e-03 8.630620e-03 0.301193326 #> 12841 6420.0 0.6901820 0.6901820 8.631378e-03 8.631378e-03 0.301186581 #> 12842 6420.5 0.6901880 0.6901880 8.632137e-03 8.632137e-03 0.301179852 #> 12843 6421.0 0.6901940 0.6901940 8.632897e-03 8.632897e-03 0.301173140 #> 12844 6421.5 0.6901999 0.6901999 8.633659e-03 8.633659e-03 0.301166443 #> 12845 6422.0 0.6902058 0.6902058 8.634423e-03 8.634423e-03 0.301159762 #> 12846 6422.5 0.6902117 0.6902117 8.635187e-03 8.635187e-03 0.301153097 #> 12847 6423.0 0.6902176 0.6902176 8.635953e-03 8.635953e-03 0.301146448 #> 12848 6423.5 0.6902235 0.6902235 8.636720e-03 8.636720e-03 0.301139816 #> 12849 6424.0 0.6902293 0.6902293 8.637489e-03 8.637489e-03 0.301133199 #> 12850 6424.5 0.6902351 0.6902351 8.638259e-03 8.638259e-03 0.301126599 #> 12851 6425.0 0.6902410 0.6902410 8.639030e-03 8.639030e-03 0.301120014 #> 12852 6425.5 0.6902468 0.6902468 8.639803e-03 8.639803e-03 0.301113446 #> 12853 6426.0 0.6902525 0.6902525 8.640577e-03 8.640577e-03 0.301106894 #> 12854 6426.5 0.6902583 0.6902583 8.641352e-03 8.641352e-03 0.301100359 #> 12855 6427.0 0.6902640 0.6902640 8.642129e-03 8.642129e-03 0.301093839 #> 12856 6427.5 0.6902698 0.6902698 8.642907e-03 8.642907e-03 0.301087336 #> 12857 6428.0 0.6902755 0.6902755 8.643686e-03 8.643686e-03 0.301080849 #> 12858 6428.5 0.6902812 0.6902812 8.644466e-03 8.644466e-03 0.301074378 #> 12859 6429.0 0.6902868 0.6902868 8.645248e-03 8.645248e-03 0.301067923 #> 12860 6429.5 0.6902925 0.6902925 8.646031e-03 8.646031e-03 0.301061485 #> 12861 6430.0 0.6902981 0.6902981 8.646816e-03 8.646816e-03 0.301055063 #> 12862 6430.5 0.6903037 0.6903037 8.647601e-03 8.647601e-03 0.301048657 #> 12863 6431.0 0.6903093 0.6903093 8.648388e-03 8.648388e-03 0.301042268 #> 12864 6431.5 0.6903149 0.6903149 8.649176e-03 8.649176e-03 0.301035894 #> 12865 6432.0 0.6903205 0.6903205 8.649966e-03 8.649966e-03 0.301029537 #> 12866 6432.5 0.6903260 0.6903260 8.650757e-03 8.650757e-03 0.301023197 #> 12867 6433.0 0.6903316 0.6903316 8.651549e-03 8.651549e-03 0.301016873 #> 12868 6433.5 0.6903371 0.6903371 8.652342e-03 8.652342e-03 0.301010565 #> 12869 6434.0 0.6903426 0.6903426 8.653137e-03 8.653137e-03 0.301004274 #> 12870 6434.5 0.6903481 0.6903481 8.653932e-03 8.653932e-03 0.300997999 #> 12871 6435.0 0.6903535 0.6903535 8.654730e-03 8.654730e-03 0.300991740 #> 12872 6435.5 0.6903590 0.6903590 8.655528e-03 8.655528e-03 0.300985498 #> 12873 6436.0 0.6903644 0.6903644 8.656327e-03 8.656327e-03 0.300979272 #> 12874 6436.5 0.6903698 0.6903698 8.657128e-03 8.657128e-03 0.300973063 #> 12875 6437.0 0.6903752 0.6903752 8.657930e-03 8.657930e-03 0.300966870 #> 12876 6437.5 0.6903806 0.6903806 8.658734e-03 8.658734e-03 0.300960694 #> 12877 6438.0 0.6903859 0.6903859 8.659538e-03 8.659538e-03 0.300954534 #> 12878 6438.5 0.6903913 0.6903913 8.660344e-03 8.660344e-03 0.300948390 #> 12879 6439.0 0.6903966 0.6903966 8.661151e-03 8.661151e-03 0.300942264 #> 12880 6439.5 0.6904019 0.6904019 8.661959e-03 8.661959e-03 0.300936153 #> 12881 6440.0 0.6904072 0.6904072 8.662769e-03 8.662769e-03 0.300930059 #> 12882 6440.5 0.6904124 0.6904124 8.663579e-03 8.663579e-03 0.300923982 #> 12883 6441.0 0.6904177 0.6904177 8.664391e-03 8.664391e-03 0.300917921 #> 12884 6441.5 0.6904229 0.6904229 8.665204e-03 8.665204e-03 0.300911877 #> 12885 6442.0 0.6904281 0.6904281 8.666019e-03 8.666019e-03 0.300905850 #> 12886 6442.5 0.6904333 0.6904333 8.666834e-03 8.666834e-03 0.300899839 #> 12887 6443.0 0.6904385 0.6904385 8.667651e-03 8.667651e-03 0.300893844 #> 12888 6443.5 0.6904437 0.6904437 8.668469e-03 8.668469e-03 0.300887867 #> 12889 6444.0 0.6904488 0.6904488 8.669288e-03 8.669288e-03 0.300881905 #> 12890 6444.5 0.6904539 0.6904539 8.670108e-03 8.670108e-03 0.300875961 #> 12891 6445.0 0.6904590 0.6904590 8.670930e-03 8.670930e-03 0.300870033 #> 12892 6445.5 0.6904641 0.6904641 8.671753e-03 8.671753e-03 0.300864122 #> 12893 6446.0 0.6904692 0.6904692 8.672576e-03 8.672576e-03 0.300858227 #> 12894 6446.5 0.6904742 0.6904742 8.673401e-03 8.673401e-03 0.300852350 #> 12895 6447.0 0.6904793 0.6904793 8.674228e-03 8.674228e-03 0.300846488 #> 12896 6447.5 0.6904843 0.6904843 8.675055e-03 8.675055e-03 0.300840644 #> 12897 6448.0 0.6904893 0.6904893 8.675884e-03 8.675884e-03 0.300834816 #> 12898 6448.5 0.6904943 0.6904943 8.676713e-03 8.676713e-03 0.300829005 #> 12899 6449.0 0.6904992 0.6904992 8.677544e-03 8.677544e-03 0.300823211 #> 12900 6449.5 0.6905042 0.6905042 8.678376e-03 8.678376e-03 0.300817434 #> 12901 6450.0 0.6905091 0.6905091 8.679209e-03 8.679209e-03 0.300811673 #> 12902 6450.5 0.6905140 0.6905140 8.680044e-03 8.680044e-03 0.300805929 #> 12903 6451.0 0.6905189 0.6905189 8.680879e-03 8.680879e-03 0.300800202 #> 12904 6451.5 0.6905238 0.6905238 8.681716e-03 8.681716e-03 0.300794491 #> 12905 6452.0 0.6905286 0.6905286 8.682553e-03 8.682553e-03 0.300788798 #> 12906 6452.5 0.6905335 0.6905335 8.683392e-03 8.683392e-03 0.300783121 #> 12907 6453.0 0.6905383 0.6905383 8.684232e-03 8.684232e-03 0.300777461 #> 12908 6453.5 0.6905431 0.6905431 8.685073e-03 8.685073e-03 0.300771818 #> 12909 6454.0 0.6905479 0.6905479 8.685916e-03 8.685916e-03 0.300766192 #> 12910 6454.5 0.6905527 0.6905527 8.686759e-03 8.686759e-03 0.300760582 #> 12911 6455.0 0.6905574 0.6905574 8.687603e-03 8.687603e-03 0.300754990 #> 12912 6455.5 0.6905621 0.6905621 8.688449e-03 8.688449e-03 0.300749414 #> 12913 6456.0 0.6905668 0.6905668 8.689296e-03 8.689296e-03 0.300743855 #> 12914 6456.5 0.6905715 0.6905715 8.690144e-03 8.690144e-03 0.300738313 #> 12915 6457.0 0.6905762 0.6905762 8.690992e-03 8.690992e-03 0.300732788 #> 12916 6457.5 0.6905809 0.6905809 8.691842e-03 8.691842e-03 0.300727280 #> 12917 6458.0 0.6905855 0.6905855 8.692693e-03 8.692693e-03 0.300721789 #> 12918 6458.5 0.6905901 0.6905901 8.693546e-03 8.693546e-03 0.300716315 #> 12919 6459.0 0.6905947 0.6905947 8.694399e-03 8.694399e-03 0.300710858 #> 12920 6459.5 0.6905993 0.6905993 8.695253e-03 8.695253e-03 0.300705417 #> 12921 6460.0 0.6906039 0.6906039 8.696109e-03 8.696109e-03 0.300699994 #> 12922 6460.5 0.6906084 0.6906084 8.696965e-03 8.696965e-03 0.300694588 #> 12923 6461.0 0.6906130 0.6906130 8.697823e-03 8.697823e-03 0.300689198 #> 12924 6461.5 0.6906175 0.6906175 8.698681e-03 8.698681e-03 0.300683826 #> 12925 6462.0 0.6906220 0.6906220 8.699541e-03 8.699541e-03 0.300678471 #> 12926 6462.5 0.6906265 0.6906265 8.700402e-03 8.700402e-03 0.300673132 #> 12927 6463.0 0.6906309 0.6906309 8.701264e-03 8.701264e-03 0.300667811 #> 12928 6463.5 0.6906354 0.6906354 8.702127e-03 8.702127e-03 0.300662507 #> 12929 6464.0 0.6906398 0.6906398 8.702991e-03 8.702991e-03 0.300657219 #> 12930 6464.5 0.6906442 0.6906442 8.703855e-03 8.703855e-03 0.300651949 #> 12931 6465.0 0.6906486 0.6906486 8.704722e-03 8.704722e-03 0.300646696 #> 12932 6465.5 0.6906530 0.6906530 8.705589e-03 8.705589e-03 0.300641460 #> 12933 6466.0 0.6906573 0.6906573 8.706457e-03 8.706457e-03 0.300636241 #> 12934 6466.5 0.6906616 0.6906616 8.707326e-03 8.707326e-03 0.300631039 #> 12935 6467.0 0.6906660 0.6906660 8.708196e-03 8.708196e-03 0.300625854 #> 12936 6467.5 0.6906702 0.6906702 8.709067e-03 8.709067e-03 0.300620686 #> 12937 6468.0 0.6906745 0.6906745 8.709939e-03 8.709939e-03 0.300615535 #> 12938 6468.5 0.6906788 0.6906788 8.710813e-03 8.710813e-03 0.300610402 #> 12939 6469.0 0.6906830 0.6906830 8.711687e-03 8.711687e-03 0.300605285 #> 12940 6469.5 0.6906873 0.6906873 8.712562e-03 8.712562e-03 0.300600186 #> 12941 6470.0 0.6906915 0.6906915 8.713438e-03 8.713438e-03 0.300595104 #> 12942 6470.5 0.6906956 0.6906956 8.714315e-03 8.714315e-03 0.300590039 #> 12943 6471.0 0.6906998 0.6906998 8.715194e-03 8.715194e-03 0.300584991 #> 12944 6471.5 0.6907040 0.6907040 8.716073e-03 8.716073e-03 0.300579960 #> 12945 6472.0 0.6907081 0.6907081 8.716953e-03 8.716953e-03 0.300574947 #> 12946 6472.5 0.6907122 0.6907122 8.717834e-03 8.717834e-03 0.300569951 #> 12947 6473.0 0.6907163 0.6907163 8.718717e-03 8.718717e-03 0.300564971 #> 12948 6473.5 0.6907204 0.6907204 8.719600e-03 8.719600e-03 0.300560010 #> 12949 6474.0 0.6907245 0.6907245 8.720484e-03 8.720484e-03 0.300555065 #> 12950 6474.5 0.6907285 0.6907285 8.721369e-03 8.721369e-03 0.300550137 #> 12951 6475.0 0.6907325 0.6907325 8.722255e-03 8.722255e-03 0.300545227 #> 12952 6475.5 0.6907365 0.6907365 8.723142e-03 8.723142e-03 0.300540334 #> 12953 6476.0 0.6907405 0.6907405 8.724030e-03 8.724030e-03 0.300535458 #> 12954 6476.5 0.6907445 0.6907445 8.724919e-03 8.724919e-03 0.300530599 #> 12955 6477.0 0.6907484 0.6907484 8.725809e-03 8.725809e-03 0.300525758 #> 12956 6477.5 0.6907524 0.6907524 8.726700e-03 8.726700e-03 0.300520934 #> 12957 6478.0 0.6907563 0.6907563 8.727592e-03 8.727592e-03 0.300516127 #> 12958 6478.5 0.6907602 0.6907602 8.728485e-03 8.728485e-03 0.300511338 #> 12959 6479.0 0.6907641 0.6907641 8.729378e-03 8.729378e-03 0.300506565 #> 12960 6479.5 0.6907679 0.6907679 8.730273e-03 8.730273e-03 0.300501810 #> 12961 6480.0 0.6907718 0.6907718 8.731169e-03 8.731169e-03 0.300497073 #> 12962 6480.5 0.6907756 0.6907756 8.732065e-03 8.732065e-03 0.300492352 #> 12963 6481.0 0.6907794 0.6907794 8.732963e-03 8.732963e-03 0.300487649 #> 12964 6481.5 0.6907832 0.6907832 8.733861e-03 8.733861e-03 0.300482963 #> 12965 6482.0 0.6907869 0.6907869 8.734760e-03 8.734760e-03 0.300478295 #> 12966 6482.5 0.6907907 0.6907907 8.735661e-03 8.735661e-03 0.300473644 #> 12967 6483.0 0.6907944 0.6907944 8.736562e-03 8.736562e-03 0.300469010 #> 12968 6483.5 0.6907981 0.6907981 8.737464e-03 8.737464e-03 0.300464393 #> 12969 6484.0 0.6908018 0.6908018 8.738367e-03 8.738367e-03 0.300459794 #> 12970 6484.5 0.6908055 0.6908055 8.739270e-03 8.739270e-03 0.300455212 #> 12971 6485.0 0.6908092 0.6908092 8.740175e-03 8.740175e-03 0.300450648 #> 12972 6485.5 0.6908128 0.6908128 8.741081e-03 8.741081e-03 0.300446101 #> 12973 6486.0 0.6908164 0.6908164 8.741987e-03 8.741987e-03 0.300441571 #> 12974 6486.5 0.6908200 0.6908200 8.742895e-03 8.742895e-03 0.300437059 #> 12975 6487.0 0.6908236 0.6908236 8.743803e-03 8.743803e-03 0.300432564 #> 12976 6487.5 0.6908272 0.6908272 8.744712e-03 8.744712e-03 0.300428087 #> 12977 6488.0 0.6908308 0.6908308 8.745622e-03 8.745622e-03 0.300423626 #> 12978 6488.5 0.6908343 0.6908343 8.746533e-03 8.746533e-03 0.300419184 #> 12979 6489.0 0.6908378 0.6908378 8.747445e-03 8.747445e-03 0.300414758 #> 12980 6489.5 0.6908413 0.6908413 8.748357e-03 8.748357e-03 0.300410350 #> 12981 6490.0 0.6908448 0.6908448 8.749271e-03 8.749271e-03 0.300405960 #> 12982 6490.5 0.6908482 0.6908482 8.750185e-03 8.750185e-03 0.300401587 #> 12983 6491.0 0.6908517 0.6908517 8.751100e-03 8.751100e-03 0.300397231 #> 12984 6491.5 0.6908551 0.6908551 8.752016e-03 8.752016e-03 0.300392893 #> 12985 6492.0 0.6908585 0.6908585 8.752933e-03 8.752933e-03 0.300388572 #> 12986 6492.5 0.6908619 0.6908619 8.753851e-03 8.753851e-03 0.300384269 #> 12987 6493.0 0.6908652 0.6908652 8.754769e-03 8.754769e-03 0.300379983 #> 12988 6493.5 0.6908686 0.6908686 8.755688e-03 8.755688e-03 0.300375714 #> 12989 6494.0 0.6908719 0.6908719 8.756609e-03 8.756609e-03 0.300371463 #> 12990 6494.5 0.6908752 0.6908752 8.757530e-03 8.757530e-03 0.300367230 #> 12991 6495.0 0.6908785 0.6908785 8.758451e-03 8.758451e-03 0.300363014 #> 12992 6495.5 0.6908818 0.6908818 8.759374e-03 8.759374e-03 0.300358815 #> 12993 6496.0 0.6908851 0.6908851 8.760298e-03 8.760298e-03 0.300354634 #> 12994 6496.5 0.6908883 0.6908883 8.761222e-03 8.761222e-03 0.300350471 #> 12995 6497.0 0.6908915 0.6908915 8.762147e-03 8.762147e-03 0.300346324 #> 12996 6497.5 0.6908947 0.6908947 8.763073e-03 8.763073e-03 0.300342196 #> 12997 6498.0 0.6908979 0.6908979 8.763999e-03 8.763999e-03 0.300338085 #> 12998 6498.5 0.6909011 0.6909011 8.764927e-03 8.764927e-03 0.300333991 #> 12999 6499.0 0.6909042 0.6909042 8.765855e-03 8.765855e-03 0.300329915 #> 13000 6499.5 0.6909074 0.6909074 8.766784e-03 8.766784e-03 0.300325856 #> 13001 6500.0 0.6909105 0.6909105 8.767714e-03 8.767714e-03 0.300321815 #> 13002 6500.5 0.6909136 0.6909136 8.768645e-03 8.768645e-03 0.300317792 #> 13003 6501.0 0.6909166 0.6909166 8.769576e-03 8.769576e-03 0.300313786 #> 13004 6501.5 0.6909197 0.6909197 8.770508e-03 8.770508e-03 0.300309797 #> 13005 6502.0 0.6909227 0.6909227 8.771441e-03 8.771441e-03 0.300305826 #> 13006 6502.5 0.6909258 0.6909258 8.772375e-03 8.772375e-03 0.300301873 #> 13007 6503.0 0.6909288 0.6909288 8.773309e-03 8.773309e-03 0.300297937 #> 13008 6503.5 0.6909317 0.6909317 8.774244e-03 8.774244e-03 0.300294018 #> 13009 6504.0 0.6909347 0.6909347 8.775180e-03 8.775180e-03 0.300290117 #> 13010 6504.5 0.6909376 0.6909376 8.776117e-03 8.776117e-03 0.300286234 #> 13011 6505.0 0.6909406 0.6909406 8.777054e-03 8.777054e-03 0.300282368 #> 13012 6505.5 0.6909435 0.6909435 8.777993e-03 8.777993e-03 0.300278520 #> 13013 6506.0 0.6909464 0.6909464 8.778932e-03 8.778932e-03 0.300274690 #> 13014 6506.5 0.6909493 0.6909493 8.779871e-03 8.779871e-03 0.300270877 #> 13015 6507.0 0.6909521 0.6909521 8.780812e-03 8.780812e-03 0.300267081 #> 13016 6507.5 0.6909549 0.6909549 8.781753e-03 8.781753e-03 0.300263303 #> 13017 6508.0 0.6909578 0.6909578 8.782695e-03 8.782695e-03 0.300259543 #> 13018 6508.5 0.6909606 0.6909606 8.783637e-03 8.783637e-03 0.300255800 #> 13019 6509.0 0.6909633 0.6909633 8.784580e-03 8.784580e-03 0.300252075 #> 13020 6509.5 0.6909661 0.6909661 8.785524e-03 8.785524e-03 0.300248367 #> 13021 6510.0 0.6909689 0.6909689 8.786469e-03 8.786469e-03 0.300244677 #> 13022 6510.5 0.6909716 0.6909716 8.787414e-03 8.787414e-03 0.300241005 #> 13023 6511.0 0.6909743 0.6909743 8.788360e-03 8.788360e-03 0.300237350 #> 13024 6511.5 0.6909770 0.6909770 8.789307e-03 8.789307e-03 0.300233713 #> 13025 6512.0 0.6909797 0.6909797 8.790255e-03 8.790255e-03 0.300230093 #> 13026 6512.5 0.6909823 0.6909823 8.791203e-03 8.791203e-03 0.300226491 #> 13027 6513.0 0.6909849 0.6909849 8.792152e-03 8.792152e-03 0.300222907 #> 13028 6513.5 0.6909876 0.6909876 8.793101e-03 8.793101e-03 0.300219340 #> 13029 6514.0 0.6909902 0.6909902 8.794051e-03 8.794051e-03 0.300215791 #> 13030 6514.5 0.6909927 0.6909927 8.795002e-03 8.795002e-03 0.300212260 #> 13031 6515.0 0.6909953 0.6909953 8.795954e-03 8.795954e-03 0.300208746 #> 13032 6515.5 0.6909978 0.6909978 8.796906e-03 8.796906e-03 0.300205249 #> 13033 6516.0 0.6910004 0.6910004 8.797859e-03 8.797859e-03 0.300201771 #> 13034 6516.5 0.6910029 0.6910029 8.798812e-03 8.798812e-03 0.300198309 #> 13035 6517.0 0.6910054 0.6910054 8.799766e-03 8.799766e-03 0.300194866 #> 13036 6517.5 0.6910078 0.6910078 8.800721e-03 8.800721e-03 0.300191440 #> 13037 6518.0 0.6910103 0.6910103 8.801677e-03 8.801677e-03 0.300188032 #> 13038 6518.5 0.6910127 0.6910127 8.802633e-03 8.802633e-03 0.300184641 #> 13039 6519.0 0.6910151 0.6910151 8.803590e-03 8.803590e-03 0.300181268 #> 13040 6519.5 0.6910175 0.6910175 8.804547e-03 8.804547e-03 0.300177913 #> 13041 6520.0 0.6910199 0.6910199 8.805505e-03 8.805505e-03 0.300174575 #> 13042 6520.5 0.6910223 0.6910223 8.806464e-03 8.806464e-03 0.300171255 #> 13043 6521.0 0.6910246 0.6910246 8.807423e-03 8.807423e-03 0.300167953 #> 13044 6521.5 0.6910269 0.6910269 8.808383e-03 8.808383e-03 0.300164668 #> 13045 6522.0 0.6910293 0.6910293 8.809343e-03 8.809343e-03 0.300161401 #> 13046 6522.5 0.6910315 0.6910315 8.810304e-03 8.810304e-03 0.300158151 #> 13047 6523.0 0.6910338 0.6910338 8.811266e-03 8.811266e-03 0.300154920 #> 13048 6523.5 0.6910361 0.6910361 8.812228e-03 8.812228e-03 0.300151705 #> 13049 6524.0 0.6910383 0.6910383 8.813191e-03 8.813191e-03 0.300148509 #> 13050 6524.5 0.6910405 0.6910405 8.814155e-03 8.814155e-03 0.300145330 #> 13051 6525.0 0.6910427 0.6910427 8.815119e-03 8.815119e-03 0.300142169 #> 13052 6525.5 0.6910449 0.6910449 8.816083e-03 8.816083e-03 0.300139025 #> 13053 6526.0 0.6910471 0.6910471 8.817049e-03 8.817049e-03 0.300135899 #> 13054 6526.5 0.6910492 0.6910492 8.818015e-03 8.818015e-03 0.300132791 #> 13055 6527.0 0.6910513 0.6910513 8.818981e-03 8.818981e-03 0.300129700 #> 13056 6527.5 0.6910534 0.6910534 8.819948e-03 8.819948e-03 0.300126627 #> 13057 6528.0 0.6910555 0.6910555 8.820915e-03 8.820915e-03 0.300123572 #> 13058 6528.5 0.6910576 0.6910576 8.821884e-03 8.821884e-03 0.300120534 #> 13059 6529.0 0.6910596 0.6910596 8.822852e-03 8.822852e-03 0.300117514 #> 13060 6529.5 0.6910617 0.6910617 8.823822e-03 8.823822e-03 0.300114512 #> 13061 6530.0 0.6910637 0.6910637 8.824791e-03 8.824791e-03 0.300111527 #> 13062 6530.5 0.6910657 0.6910657 8.825762e-03 8.825762e-03 0.300108560 #> 13063 6531.0 0.6910677 0.6910677 8.826733e-03 8.826733e-03 0.300105610 #> 13064 6531.5 0.6910696 0.6910696 8.827704e-03 8.827704e-03 0.300102679 #> 13065 6532.0 0.6910716 0.6910716 8.828676e-03 8.828676e-03 0.300099765 #> 13066 6532.5 0.6910735 0.6910735 8.829648e-03 8.829648e-03 0.300096868 #> 13067 6533.0 0.6910754 0.6910754 8.830621e-03 8.830621e-03 0.300093989 #> 13068 6533.5 0.6910773 0.6910773 8.831595e-03 8.831595e-03 0.300091128 #> 13069 6534.0 0.6910791 0.6910791 8.832569e-03 8.832569e-03 0.300088285 #> 13070 6534.5 0.6910810 0.6910810 8.833544e-03 8.833544e-03 0.300085459 #> 13071 6535.0 0.6910828 0.6910828 8.834519e-03 8.834519e-03 0.300082651 #> 13072 6535.5 0.6910846 0.6910846 8.835494e-03 8.835494e-03 0.300079860 #> 13073 6536.0 0.6910864 0.6910864 8.836471e-03 8.836471e-03 0.300077087 #> 13074 6536.5 0.6910882 0.6910882 8.837447e-03 8.837447e-03 0.300074332 #> 13075 6537.0 0.6910900 0.6910900 8.838424e-03 8.838424e-03 0.300071594 #> 13076 6537.5 0.6910917 0.6910917 8.839402e-03 8.839402e-03 0.300068875 #> 13077 6538.0 0.6910934 0.6910934 8.840380e-03 8.840380e-03 0.300066172 #> 13078 6538.5 0.6910952 0.6910952 8.841359e-03 8.841359e-03 0.300063488 #> 13079 6539.0 0.6910968 0.6910968 8.842338e-03 8.842338e-03 0.300060821 #> 13080 6539.5 0.6910985 0.6910985 8.843317e-03 8.843317e-03 0.300058172 #> 13081 6540.0 0.6911002 0.6911002 8.844297e-03 8.844297e-03 0.300055540 #> 13082 6540.5 0.6911018 0.6911018 8.845278e-03 8.845278e-03 0.300052926 #> 13083 6541.0 0.6911034 0.6911034 8.846259e-03 8.846259e-03 0.300050330 #> 13084 6541.5 0.6911050 0.6911050 8.847240e-03 8.847240e-03 0.300047751 #> 13085 6542.0 0.6911066 0.6911066 8.848222e-03 8.848222e-03 0.300045190 #> 13086 6542.5 0.6911081 0.6911081 8.849205e-03 8.849205e-03 0.300042646 #> 13087 6543.0 0.6911097 0.6911097 8.850187e-03 8.850187e-03 0.300040121 #> 13088 6543.5 0.6911112 0.6911112 8.851171e-03 8.851171e-03 0.300037613 #> 13089 6544.0 0.6911127 0.6911127 8.852154e-03 8.852154e-03 0.300035122 #> 13090 6544.5 0.6911142 0.6911142 8.853139e-03 8.853139e-03 0.300032649 #> 13091 6545.0 0.6911157 0.6911157 8.854123e-03 8.854123e-03 0.300030194 #> 13092 6545.5 0.6911171 0.6911171 8.855108e-03 8.855108e-03 0.300027756 #> 13093 6546.0 0.6911186 0.6911186 8.856094e-03 8.856094e-03 0.300025337 #> 13094 6546.5 0.6911200 0.6911200 8.857079e-03 8.857079e-03 0.300022934 #> 13095 6547.0 0.6911214 0.6911214 8.858066e-03 8.858066e-03 0.300020550 #> 13096 6547.5 0.6911228 0.6911228 8.859052e-03 8.859052e-03 0.300018183 #> 13097 6548.0 0.6911241 0.6911241 8.860040e-03 8.860040e-03 0.300015833 #> 13098 6548.5 0.6911255 0.6911255 8.861027e-03 8.861027e-03 0.300013501 #> 13099 6549.0 0.6911268 0.6911268 8.862015e-03 8.862015e-03 0.300011187 #> 13100 6549.5 0.6911281 0.6911281 8.863003e-03 8.863003e-03 0.300008891 #> 13101 6550.0 0.6911294 0.6911294 8.863992e-03 8.863992e-03 0.300006612 #> 13102 6550.5 0.6911307 0.6911307 8.864981e-03 8.864981e-03 0.300004350 #> 13103 6551.0 0.6911319 0.6911319 8.865971e-03 8.865971e-03 0.300002107 #> 13104 6551.5 0.6911332 0.6911332 8.866961e-03 8.866961e-03 0.299999881 #> 13105 6552.0 0.6911344 0.6911344 8.867951e-03 8.867951e-03 0.299997672 #> 13106 6552.5 0.6911356 0.6911356 8.868942e-03 8.868942e-03 0.299995481 #> 13107 6553.0 0.6911368 0.6911368 8.869933e-03 8.869933e-03 0.299993308 #> 13108 6553.5 0.6911379 0.6911379 8.870924e-03 8.870924e-03 0.299991153 #> 13109 6554.0 0.6911391 0.6911391 8.871916e-03 8.871916e-03 0.299989015 #> 13110 6554.5 0.6911402 0.6911402 8.872908e-03 8.872908e-03 0.299986894 #> 13111 6555.0 0.6911413 0.6911413 8.873901e-03 8.873901e-03 0.299984791 #> 13112 6555.5 0.6911424 0.6911424 8.874893e-03 8.874893e-03 0.299982706 #> 13113 6556.0 0.6911435 0.6911435 8.875887e-03 8.875887e-03 0.299980638 #> 13114 6556.5 0.6911445 0.6911445 8.876880e-03 8.876880e-03 0.299978588 #> 13115 6557.0 0.6911456 0.6911456 8.877874e-03 8.877874e-03 0.299976556 #> 13116 6557.5 0.6911466 0.6911466 8.878868e-03 8.878868e-03 0.299974541 #> 13117 6558.0 0.6911476 0.6911476 8.879863e-03 8.879863e-03 0.299972543 #> 13118 6558.5 0.6911486 0.6911486 8.880858e-03 8.880858e-03 0.299970564 #> 13119 6559.0 0.6911495 0.6911495 8.881853e-03 8.881853e-03 0.299968601 #> 13120 6559.5 0.6911505 0.6911505 8.882849e-03 8.882849e-03 0.299966657 #> 13121 6560.0 0.6911514 0.6911514 8.883845e-03 8.883845e-03 0.299964730 #> 13122 6560.5 0.6911523 0.6911523 8.884841e-03 8.884841e-03 0.299962820 #> 13123 6561.0 0.6911532 0.6911532 8.885837e-03 8.885837e-03 0.299960928 #> 13124 6561.5 0.6911541 0.6911541 8.886834e-03 8.886834e-03 0.299959054 #> 13125 6562.0 0.6911550 0.6911550 8.887831e-03 8.887831e-03 0.299957197 #> 13126 6562.5 0.6911558 0.6911558 8.888829e-03 8.888829e-03 0.299955358 #> 13127 6563.0 0.6911566 0.6911566 8.889826e-03 8.889826e-03 0.299953536 #> 13128 6563.5 0.6911574 0.6911574 8.890824e-03 8.890824e-03 0.299951732 #> 13129 6564.0 0.6911582 0.6911582 8.891823e-03 8.891823e-03 0.299949945 #> 13130 6564.5 0.6911590 0.6911590 8.892821e-03 8.892821e-03 0.299948176 #> 13131 6565.0 0.6911598 0.6911598 8.893820e-03 8.893820e-03 0.299946424 #> 13132 6565.5 0.6911605 0.6911605 8.894819e-03 8.894819e-03 0.299944690 #> 13133 6566.0 0.6911612 0.6911612 8.895819e-03 8.895819e-03 0.299942974 #> 13134 6566.5 0.6911619 0.6911619 8.896819e-03 8.896819e-03 0.299941274 #> 13135 6567.0 0.6911626 0.6911626 8.897819e-03 8.897819e-03 0.299939593 #> 13136 6567.5 0.6911633 0.6911633 8.898819e-03 8.898819e-03 0.299937929 #> 13137 6568.0 0.6911639 0.6911639 8.899819e-03 8.899819e-03 0.299936282 #> 13138 6568.5 0.6911645 0.6911645 8.900820e-03 8.900820e-03 0.299934653 #> 13139 6569.0 0.6911651 0.6911651 8.901821e-03 8.901821e-03 0.299933042 #> 13140 6569.5 0.6911657 0.6911657 8.902822e-03 8.902822e-03 0.299931448 #> 13141 6570.0 0.6911663 0.6911663 8.903824e-03 8.903824e-03 0.299929871 #> 13142 6570.5 0.6911669 0.6911669 8.904825e-03 8.904825e-03 0.299928312 #> 13143 6571.0 0.6911674 0.6911674 8.905827e-03 8.905827e-03 0.299926770 #> 13144 6571.5 0.6911679 0.6911679 8.906830e-03 8.906830e-03 0.299925246 #> 13145 6572.0 0.6911684 0.6911684 8.907832e-03 8.907832e-03 0.299923739 #> 13146 6572.5 0.6911689 0.6911689 8.908835e-03 8.908835e-03 0.299922250 #> 13147 6573.0 0.6911694 0.6911694 8.909837e-03 8.909837e-03 0.299920778 #> 13148 6573.5 0.6911698 0.6911698 8.910840e-03 8.910840e-03 0.299919324 #> 13149 6574.0 0.6911703 0.6911703 8.911844e-03 8.911844e-03 0.299917887 #> 13150 6574.5 0.6911707 0.6911707 8.912847e-03 8.912847e-03 0.299916467 #> 13151 6575.0 0.6911711 0.6911711 8.913851e-03 8.913851e-03 0.299915065 #> 13152 6575.5 0.6911715 0.6911715 8.914855e-03 8.914855e-03 0.299913681 #> 13153 6576.0 0.6911718 0.6911718 8.915859e-03 8.915859e-03 0.299912313 #> 13154 6576.5 0.6911722 0.6911722 8.916863e-03 8.916863e-03 0.299910964 #> 13155 6577.0 0.6911725 0.6911725 8.917868e-03 8.917868e-03 0.299909631 #> 13156 6577.5 0.6911728 0.6911728 8.918872e-03 8.918872e-03 0.299908316 #> 13157 6578.0 0.6911731 0.6911731 8.919877e-03 8.919877e-03 0.299907019 #> 13158 6578.5 0.6911734 0.6911734 8.920882e-03 8.920882e-03 0.299905738 #> 13159 6579.0 0.6911736 0.6911736 8.921887e-03 8.921887e-03 0.299904476 #> 13160 6579.5 0.6911739 0.6911739 8.922893e-03 8.922893e-03 0.299903230 #> 13161 6580.0 0.6911741 0.6911741 8.923898e-03 8.923898e-03 0.299902002 #> 13162 6580.5 0.6911743 0.6911743 8.924904e-03 8.924904e-03 0.299900791 #> 13163 6581.0 0.6911745 0.6911745 8.925910e-03 8.925910e-03 0.299899598 #> 13164 6581.5 0.6911747 0.6911747 8.926916e-03 8.926916e-03 0.299898422 #> 13165 6582.0 0.6911748 0.6911748 8.927922e-03 8.927922e-03 0.299897264 #> 13166 6582.5 0.6911749 0.6911749 8.928928e-03 8.928928e-03 0.299896122 #> 13167 6583.0 0.6911751 0.6911751 8.929935e-03 8.929935e-03 0.299894998 #> 13168 6583.5 0.6911752 0.6911752 8.930941e-03 8.930941e-03 0.299893892 #> 13169 6584.0 0.6911752 0.6911752 8.931948e-03 8.931948e-03 0.299892803 #> 13170 6584.5 0.6911753 0.6911753 8.932955e-03 8.932955e-03 0.299891731 #> 13171 6585.0 0.6911754 0.6911754 8.933962e-03 8.933962e-03 0.299890676 #> 13172 6585.5 0.6911754 0.6911754 8.934969e-03 8.934969e-03 0.299889639 #> 13173 6586.0 0.6911754 0.6911754 8.935976e-03 8.935976e-03 0.299888619 #> 13174 6586.5 0.6911754 0.6911754 8.936983e-03 8.936983e-03 0.299887616 #> 13175 6587.0 0.6911754 0.6911754 8.937991e-03 8.937991e-03 0.299886630 #> 13176 6587.5 0.6911753 0.6911753 8.938998e-03 8.938998e-03 0.299885662 #> 13177 6588.0 0.6911753 0.6911753 8.940006e-03 8.940006e-03 0.299884711 #> 13178 6588.5 0.6911752 0.6911752 8.941013e-03 8.941013e-03 0.299883778 #> 13179 6589.0 0.6911751 0.6911751 8.942021e-03 8.942021e-03 0.299882861 #> 13180 6589.5 0.6911750 0.6911750 8.943029e-03 8.943029e-03 0.299881962 #> 13181 6590.0 0.6911749 0.6911749 8.944037e-03 8.944037e-03 0.299881080 #> 13182 6590.5 0.6911747 0.6911747 8.945045e-03 8.945045e-03 0.299880216 #> 13183 6591.0 0.6911746 0.6911746 8.946053e-03 8.946053e-03 0.299879368 #> 13184 6591.5 0.6911744 0.6911744 8.947062e-03 8.947062e-03 0.299878538 #> 13185 6592.0 0.6911742 0.6911742 8.948070e-03 8.948070e-03 0.299877725 #> 13186 6592.5 0.6911740 0.6911740 8.949078e-03 8.949078e-03 0.299876929 #> 13187 6593.0 0.6911738 0.6911738 8.950087e-03 8.950087e-03 0.299876151 #> 13188 6593.5 0.6911735 0.6911735 8.951095e-03 8.951095e-03 0.299875389 #> 13189 6594.0 0.6911733 0.6911733 8.952104e-03 8.952104e-03 0.299874645 #> 13190 6594.5 0.6911730 0.6911730 8.953112e-03 8.953112e-03 0.299873918 #> 13191 6595.0 0.6911727 0.6911727 8.954121e-03 8.954121e-03 0.299873208 #> 13192 6595.5 0.6911724 0.6911724 8.955130e-03 8.955130e-03 0.299872516 #> 13193 6596.0 0.6911720 0.6911720 8.956139e-03 8.956139e-03 0.299871840 #> 13194 6596.5 0.6911717 0.6911717 8.957147e-03 8.957147e-03 0.299871182 #> 13195 6597.0 0.6911713 0.6911713 8.958156e-03 8.958156e-03 0.299870541 #> 13196 6597.5 0.6911709 0.6911709 8.959165e-03 8.959165e-03 0.299869917 #> 13197 6598.0 0.6911705 0.6911705 8.960174e-03 8.960174e-03 0.299869310 #> 13198 6598.5 0.6911701 0.6911701 8.961183e-03 8.961183e-03 0.299868720 #> 13199 6599.0 0.6911697 0.6911697 8.962192e-03 8.962192e-03 0.299868147 #> 13200 6599.5 0.6911692 0.6911692 8.963201e-03 8.963201e-03 0.299867591 #> 13201 6600.0 0.6911687 0.6911687 8.964210e-03 8.964210e-03 0.299867053 #> 13202 6600.5 0.6911683 0.6911683 8.965218e-03 8.965218e-03 0.299866531 #> 13203 6601.0 0.6911677 0.6911677 8.966227e-03 8.966227e-03 0.299866027 #> 13204 6601.5 0.6911672 0.6911672 8.967236e-03 8.967236e-03 0.299865539 #> 13205 6602.0 0.6911667 0.6911667 8.968245e-03 8.968245e-03 0.299865069 #> 13206 6602.5 0.6911661 0.6911661 8.969254e-03 8.969254e-03 0.299864616 #> 13207 6603.0 0.6911656 0.6911656 8.970263e-03 8.970263e-03 0.299864180 #> 13208 6603.5 0.6911650 0.6911650 8.971272e-03 8.971272e-03 0.299863760 #> 13209 6604.0 0.6911644 0.6911644 8.972281e-03 8.972281e-03 0.299863358 #> 13210 6604.5 0.6911637 0.6911637 8.973290e-03 8.973290e-03 0.299862973 #> 13211 6605.0 0.6911631 0.6911631 8.974299e-03 8.974299e-03 0.299862605 #> 13212 6605.5 0.6911624 0.6911624 8.975307e-03 8.975307e-03 0.299862254 #> 13213 6606.0 0.6911618 0.6911618 8.976316e-03 8.976316e-03 0.299861920 #> 13214 6606.5 0.6911611 0.6911611 8.977325e-03 8.977325e-03 0.299861602 #> 13215 6607.0 0.6911604 0.6911604 8.978334e-03 8.978334e-03 0.299861302 #> 13216 6607.5 0.6911596 0.6911596 8.979342e-03 8.979342e-03 0.299861019 #> 13217 6608.0 0.6911589 0.6911589 8.980351e-03 8.980351e-03 0.299860753 #> 13218 6608.5 0.6911581 0.6911581 8.981359e-03 8.981359e-03 0.299860503 #> 13219 6609.0 0.6911574 0.6911574 8.982368e-03 8.982368e-03 0.299860271 #> 13220 6609.5 0.6911566 0.6911566 8.983376e-03 8.983376e-03 0.299860055 #> 13221 6610.0 0.6911558 0.6911558 8.984384e-03 8.984384e-03 0.299859856 #> 13222 6610.5 0.6911549 0.6911549 8.985393e-03 8.985393e-03 0.299859675 #> 13223 6611.0 0.6911541 0.6911541 8.986401e-03 8.986401e-03 0.299859510 #> 13224 6611.5 0.6911532 0.6911532 8.987409e-03 8.987409e-03 0.299859362 #> 13225 6612.0 0.6911524 0.6911524 8.988417e-03 8.988417e-03 0.299859231 #> 13226 6612.5 0.6911515 0.6911515 8.989425e-03 8.989425e-03 0.299859117 #> 13227 6613.0 0.6911505 0.6911505 8.990433e-03 8.990433e-03 0.299859019 #> 13228 6613.5 0.6911496 0.6911496 8.991441e-03 8.991441e-03 0.299858939 #> 13229 6614.0 0.6911487 0.6911487 8.992448e-03 8.992448e-03 0.299858875 #> 13230 6614.5 0.6911477 0.6911477 8.993456e-03 8.993456e-03 0.299858828 #> 13231 6615.0 0.6911467 0.6911467 8.994463e-03 8.994463e-03 0.299858798 #> 13232 6615.5 0.6911457 0.6911457 8.995471e-03 8.995471e-03 0.299858785 #> 13233 6616.0 0.6911447 0.6911447 8.996478e-03 8.996478e-03 0.299858788 #> 13234 6616.5 0.6911437 0.6911437 8.997485e-03 8.997485e-03 0.299858809 #> 13235 6617.0 0.6911427 0.6911427 8.998492e-03 8.998492e-03 0.299858846 #> 13236 6617.5 0.6911416 0.6911416 8.999499e-03 8.999499e-03 0.299858899 #> 13237 6618.0 0.6911405 0.6911405 9.000505e-03 9.000505e-03 0.299858970 #> 13238 6618.5 0.6911394 0.6911394 9.001512e-03 9.001512e-03 0.299859057 #> 13239 6619.0 0.6911383 0.6911383 9.002519e-03 9.002519e-03 0.299859161 #> 13240 6619.5 0.6911372 0.6911372 9.003525e-03 9.003525e-03 0.299859282 #> 13241 6620.0 0.6911360 0.6911360 9.004531e-03 9.004531e-03 0.299859420 #> 13242 6620.5 0.6911349 0.6911349 9.005537e-03 9.005537e-03 0.299859574 #> 13243 6621.0 0.6911337 0.6911337 9.006543e-03 9.006543e-03 0.299859744 #> 13244 6621.5 0.6911325 0.6911325 9.007549e-03 9.007549e-03 0.299859932 #> 13245 6622.0 0.6911313 0.6911313 9.008554e-03 9.008554e-03 0.299860136 #> 13246 6622.5 0.6911301 0.6911301 9.009560e-03 9.009560e-03 0.299860357 #> 13247 6623.0 0.6911288 0.6911288 9.010565e-03 9.010565e-03 0.299860594 #> 13248 6623.5 0.6911276 0.6911276 9.011570e-03 9.011570e-03 0.299860849 #> 13249 6624.0 0.6911263 0.6911263 9.012575e-03 9.012575e-03 0.299861119 #> 13250 6624.5 0.6911250 0.6911250 9.013580e-03 9.013580e-03 0.299861407 #> 13251 6625.0 0.6911237 0.6911237 9.014584e-03 9.014584e-03 0.299861711 #> 13252 6625.5 0.6911224 0.6911224 9.015589e-03 9.015589e-03 0.299862031 #> 13253 6626.0 0.6911210 0.6911210 9.016593e-03 9.016593e-03 0.299862368 #> 13254 6626.5 0.6911197 0.6911197 9.017597e-03 9.017597e-03 0.299862722 #> 13255 6627.0 0.6911183 0.6911183 9.018601e-03 9.018601e-03 0.299863092 #> 13256 6627.5 0.6911169 0.6911169 9.019604e-03 9.019604e-03 0.299863479 #> 13257 6628.0 0.6911155 0.6911155 9.020608e-03 9.020608e-03 0.299863882 #> 13258 6628.5 0.6911141 0.6911141 9.021611e-03 9.021611e-03 0.299864302 #> 13259 6629.0 0.6911126 0.6911126 9.022614e-03 9.022614e-03 0.299864738 #> 13260 6629.5 0.6911112 0.6911112 9.023616e-03 9.023616e-03 0.299865191 #> 13261 6630.0 0.6911097 0.6911097 9.024619e-03 9.024619e-03 0.299865660 #> 13262 6630.5 0.6911082 0.6911082 9.025621e-03 9.025621e-03 0.299866146 #> 13263 6631.0 0.6911067 0.6911067 9.026623e-03 9.026623e-03 0.299866648 #> 13264 6631.5 0.6911052 0.6911052 9.027625e-03 9.027625e-03 0.299867167 #> 13265 6632.0 0.6911037 0.6911037 9.028627e-03 9.028627e-03 0.299867702 #> 13266 6632.5 0.6911021 0.6911021 9.029628e-03 9.029628e-03 0.299868253 #> 13267 6633.0 0.6911005 0.6911005 9.030629e-03 9.030629e-03 0.299868821 #> 13268 6633.5 0.6910990 0.6910990 9.031630e-03 9.031630e-03 0.299869405 #> 13269 6634.0 0.6910974 0.6910974 9.032631e-03 9.032631e-03 0.299870006 #> 13270 6634.5 0.6910957 0.6910957 9.033631e-03 9.033631e-03 0.299870623 #> 13271 6635.0 0.6910941 0.6910941 9.034631e-03 9.034631e-03 0.299871256 #> 13272 6635.5 0.6910925 0.6910925 9.035631e-03 9.035631e-03 0.299871906 #> 13273 6636.0 0.6910908 0.6910908 9.036631e-03 9.036631e-03 0.299872572 #> 13274 6636.5 0.6910891 0.6910891 9.037630e-03 9.037630e-03 0.299873254 #> 13275 6637.0 0.6910874 0.6910874 9.038629e-03 9.038629e-03 0.299873953 #> 13276 6637.5 0.6910857 0.6910857 9.039628e-03 9.039628e-03 0.299874668 #> 13277 6638.0 0.6910840 0.6910840 9.040627e-03 9.040627e-03 0.299875399 #> 13278 6638.5 0.6910822 0.6910822 9.041625e-03 9.041625e-03 0.299876147 #> 13279 6639.0 0.6910805 0.6910805 9.042623e-03 9.042623e-03 0.299876911 #> 13280 6639.5 0.6910787 0.6910787 9.043621e-03 9.043621e-03 0.299877691 #> 13281 6640.0 0.6910769 0.6910769 9.044618e-03 9.044618e-03 0.299878487 #> 13282 6640.5 0.6910751 0.6910751 9.045615e-03 9.045615e-03 0.299879300 #> 13283 6641.0 0.6910733 0.6910733 9.046612e-03 9.046612e-03 0.299880128 #> 13284 6641.5 0.6910714 0.6910714 9.047608e-03 9.047608e-03 0.299880973 #> 13285 6642.0 0.6910696 0.6910696 9.048604e-03 9.048604e-03 0.299881834 #> 13286 6642.5 0.6910677 0.6910677 9.049600e-03 9.049600e-03 0.299882712 #> 13287 6643.0 0.6910658 0.6910658 9.050596e-03 9.050596e-03 0.299883605 #> 13288 6643.5 0.6910639 0.6910639 9.051591e-03 9.051591e-03 0.299884514 #> 13289 6644.0 0.6910620 0.6910620 9.052586e-03 9.052586e-03 0.299885440 #> 13290 6644.5 0.6910600 0.6910600 9.053580e-03 9.053580e-03 0.299886382 #> 13291 6645.0 0.6910581 0.6910581 9.054575e-03 9.054575e-03 0.299887340 #> 13292 6645.5 0.6910561 0.6910561 9.055569e-03 9.055569e-03 0.299888314 #> 13293 6646.0 0.6910541 0.6910541 9.056562e-03 9.056562e-03 0.299889304 #> 13294 6646.5 0.6910521 0.6910521 9.057555e-03 9.057555e-03 0.299890310 #> 13295 6647.0 0.6910501 0.6910501 9.058548e-03 9.058548e-03 0.299891332 #> 13296 6647.5 0.6910481 0.6910481 9.059541e-03 9.059541e-03 0.299892370 #> 13297 6648.0 0.6910460 0.6910460 9.060533e-03 9.060533e-03 0.299893424 #> 13298 6648.5 0.6910440 0.6910440 9.061525e-03 9.061525e-03 0.299894494 #> 13299 6649.0 0.6910419 0.6910419 9.062516e-03 9.062516e-03 0.299895581 #> 13300 6649.5 0.6910398 0.6910398 9.063507e-03 9.063507e-03 0.299896683 #> 13301 6650.0 0.6910377 0.6910377 9.064498e-03 9.064498e-03 0.299897801 #> 13302 6650.5 0.6910356 0.6910356 9.065488e-03 9.065488e-03 0.299898935 #> 13303 6651.0 0.6910334 0.6910334 9.066478e-03 9.066478e-03 0.299900085 #> 13304 6651.5 0.6910313 0.6910313 9.067468e-03 9.067468e-03 0.299901251 #> 13305 6652.0 0.6910291 0.6910291 9.068457e-03 9.068457e-03 0.299902432 #> 13306 6652.5 0.6910269 0.6910269 9.069446e-03 9.069446e-03 0.299903630 #> 13307 6653.0 0.6910247 0.6910247 9.070434e-03 9.070434e-03 0.299904844 #> 13308 6653.5 0.6910225 0.6910225 9.071422e-03 9.071422e-03 0.299906073 #> 13309 6654.0 0.6910203 0.6910203 9.072410e-03 9.072410e-03 0.299907318 #> 13310 6654.5 0.6910180 0.6910180 9.073397e-03 9.073397e-03 0.299908579 #> 13311 6655.0 0.6910158 0.6910158 9.074384e-03 9.074384e-03 0.299909856 #> 13312 6655.5 0.6910135 0.6910135 9.075370e-03 9.075370e-03 0.299911149 #> 13313 6656.0 0.6910112 0.6910112 9.076356e-03 9.076356e-03 0.299912457 #> 13314 6656.5 0.6910089 0.6910089 9.077342e-03 9.077342e-03 0.299913782 #> 13315 6657.0 0.6910066 0.6910066 9.078327e-03 9.078327e-03 0.299915122 #> 13316 6657.5 0.6910042 0.6910042 9.079312e-03 9.079312e-03 0.299916478 #> 13317 6658.0 0.6910019 0.6910019 9.080296e-03 9.080296e-03 0.299917849 #> 13318 6658.5 0.6909995 0.6909995 9.081280e-03 9.081280e-03 0.299919236 #> 13319 6659.0 0.6909971 0.6909971 9.082264e-03 9.082264e-03 0.299920639 #> 13320 6659.5 0.6909947 0.6909947 9.083247e-03 9.083247e-03 0.299922058 #> 13321 6660.0 0.6909923 0.6909923 9.084229e-03 9.084229e-03 0.299923492 #> 13322 6660.5 0.6909898 0.6909898 9.085211e-03 9.085211e-03 0.299924942 #> 13323 6661.0 0.6909874 0.6909874 9.086193e-03 9.086193e-03 0.299926407 #> 13324 6661.5 0.6909849 0.6909849 9.087174e-03 9.087174e-03 0.299927889 #> 13325 6662.0 0.6909825 0.6909825 9.088155e-03 9.088155e-03 0.299929385 #> 13326 6662.5 0.6909800 0.6909800 9.089136e-03 9.089136e-03 0.299930898 #> 13327 6663.0 0.6909775 0.6909775 9.090115e-03 9.090115e-03 0.299932426 #> 13328 6663.5 0.6909749 0.6909749 9.091095e-03 9.091095e-03 0.299933969 #> 13329 6664.0 0.6909724 0.6909724 9.092074e-03 9.092074e-03 0.299935528 #> 13330 6664.5 0.6909698 0.6909698 9.093052e-03 9.093052e-03 0.299937103 #> 13331 6665.0 0.6909673 0.6909673 9.094030e-03 9.094030e-03 0.299938693 #> 13332 6665.5 0.6909647 0.6909647 9.095008e-03 9.095008e-03 0.299940298 #> 13333 6666.0 0.6909621 0.6909621 9.095985e-03 9.095985e-03 0.299941919 #> 13334 6666.5 0.6909595 0.6909595 9.096961e-03 9.096961e-03 0.299943556 #> 13335 6667.0 0.6909569 0.6909569 9.097937e-03 9.097937e-03 0.299945208 #> 13336 6667.5 0.6909542 0.6909542 9.098913e-03 9.098913e-03 0.299946875 #> 13337 6668.0 0.6909516 0.6909516 9.099888e-03 9.099888e-03 0.299948558 #> 13338 6668.5 0.6909489 0.6909489 9.100862e-03 9.100862e-03 0.299950256 #> 13339 6669.0 0.6909462 0.6909462 9.101837e-03 9.101837e-03 0.299951970 #> 13340 6669.5 0.6909435 0.6909435 9.102810e-03 9.102810e-03 0.299953699 #> 13341 6670.0 0.6909408 0.6909408 9.103783e-03 9.103783e-03 0.299955443 #> 13342 6670.5 0.6909380 0.6909380 9.104756e-03 9.104756e-03 0.299957203 #> 13343 6671.0 0.6909353 0.6909353 9.105728e-03 9.105728e-03 0.299958978 #> 13344 6671.5 0.6909325 0.6909325 9.106699e-03 9.106699e-03 0.299960768 #> 13345 6672.0 0.6909298 0.6909298 9.107670e-03 9.107670e-03 0.299962574 #> 13346 6672.5 0.6909270 0.6909270 9.108640e-03 9.108640e-03 0.299964395 #> 13347 6673.0 0.6909242 0.6909242 9.109610e-03 9.109610e-03 0.299966231 #> 13348 6673.5 0.6909213 0.6909213 9.110580e-03 9.110580e-03 0.299968082 #> 13349 6674.0 0.6909185 0.6909185 9.111548e-03 9.111548e-03 0.299969949 #> 13350 6674.5 0.6909157 0.6909157 9.112517e-03 9.112517e-03 0.299971830 #> 13351 6675.0 0.6909128 0.6909128 9.113484e-03 9.113484e-03 0.299973727 #> 13352 6675.5 0.6909099 0.6909099 9.114451e-03 9.114451e-03 0.299975639 #> 13353 6676.0 0.6909070 0.6909070 9.115418e-03 9.115418e-03 0.299977567 #> 13354 6676.5 0.6909041 0.6909041 9.116384e-03 9.116384e-03 0.299979509 #> 13355 6677.0 0.6909012 0.6909012 9.117350e-03 9.117350e-03 0.299981467 #> 13356 6677.5 0.6908982 0.6908982 9.118314e-03 9.118314e-03 0.299983439 #> 13357 6678.0 0.6908953 0.6908953 9.119279e-03 9.119279e-03 0.299985427 #> 13358 6678.5 0.6908923 0.6908923 9.120243e-03 9.120243e-03 0.299987430 #> 13359 6679.0 0.6908893 0.6908893 9.121206e-03 9.121206e-03 0.299989448 #> 13360 6679.5 0.6908864 0.6908864 9.122168e-03 9.122168e-03 0.299991481 #> 13361 6680.0 0.6908833 0.6908833 9.123130e-03 9.123130e-03 0.299993528 #> 13362 6680.5 0.6908803 0.6908803 9.124092e-03 9.124092e-03 0.299995591 #> 13363 6681.0 0.6908773 0.6908773 9.125053e-03 9.125053e-03 0.299997669 #> 13364 6681.5 0.6908742 0.6908742 9.126013e-03 9.126013e-03 0.299999762 #> 13365 6682.0 0.6908712 0.6908712 9.126973e-03 9.126973e-03 0.300001870 #> 13366 6682.5 0.6908681 0.6908681 9.127932e-03 9.127932e-03 0.300003993 #> 13367 6683.0 0.6908650 0.6908650 9.128890e-03 9.128890e-03 0.300006130 #> 13368 6683.5 0.6908619 0.6908619 9.129848e-03 9.129848e-03 0.300008283 #> 13369 6684.0 0.6908587 0.6908587 9.130805e-03 9.130805e-03 0.300010450 #> 13370 6684.5 0.6908556 0.6908556 9.131762e-03 9.131762e-03 0.300012633 #> 13371 6685.0 0.6908525 0.6908525 9.132718e-03 9.132718e-03 0.300014830 #> 13372 6685.5 0.6908493 0.6908493 9.133673e-03 9.133673e-03 0.300017042 #> 13373 6686.0 0.6908461 0.6908461 9.134628e-03 9.134628e-03 0.300019268 #> 13374 6686.5 0.6908429 0.6908429 9.135582e-03 9.135582e-03 0.300021510 #> 13375 6687.0 0.6908397 0.6908397 9.136536e-03 9.136536e-03 0.300023766 #> 13376 6687.5 0.6908365 0.6908365 9.137488e-03 9.137488e-03 0.300026037 #> 13377 6688.0 0.6908332 0.6908332 9.138441e-03 9.138441e-03 0.300028323 #> 13378 6688.5 0.6908300 0.6908300 9.139392e-03 9.139392e-03 0.300030624 #> 13379 6689.0 0.6908267 0.6908267 9.140343e-03 9.140343e-03 0.300032939 #> 13380 6689.5 0.6908234 0.6908234 9.141293e-03 9.141293e-03 0.300035269 #> 13381 6690.0 0.6908201 0.6908201 9.142243e-03 9.142243e-03 0.300037613 #> 13382 6690.5 0.6908168 0.6908168 9.143192e-03 9.143192e-03 0.300039973 #> 13383 6691.0 0.6908135 0.6908135 9.144140e-03 9.144140e-03 0.300042347 #> 13384 6691.5 0.6908102 0.6908102 9.145088e-03 9.145088e-03 0.300044735 #> 13385 6692.0 0.6908068 0.6908068 9.146035e-03 9.146035e-03 0.300047138 #> 13386 6692.5 0.6908035 0.6908035 9.146981e-03 9.146981e-03 0.300049556 #> 13387 6693.0 0.6908001 0.6908001 9.147927e-03 9.147927e-03 0.300051988 #> 13388 6693.5 0.6907967 0.6907967 9.148872e-03 9.148872e-03 0.300054435 #> 13389 6694.0 0.6907933 0.6907933 9.149816e-03 9.149816e-03 0.300056896 #> 13390 6694.5 0.6907899 0.6907899 9.150760e-03 9.150760e-03 0.300059372 #> 13391 6695.0 0.6907864 0.6907864 9.151703e-03 9.151703e-03 0.300061862 #> 13392 6695.5 0.6907830 0.6907830 9.152645e-03 9.152645e-03 0.300064366 #> 13393 6696.0 0.6907795 0.6907795 9.153586e-03 9.153586e-03 0.300066886 #> 13394 6696.5 0.6907761 0.6907761 9.154527e-03 9.154527e-03 0.300069419 #> 13395 6697.0 0.6907726 0.6907726 9.155467e-03 9.155467e-03 0.300071967 #> 13396 6697.5 0.6907691 0.6907691 9.156407e-03 9.156407e-03 0.300074529 #> 13397 6698.0 0.6907655 0.6907655 9.157346e-03 9.157346e-03 0.300077106 #> 13398 6698.5 0.6907620 0.6907620 9.158284e-03 9.158284e-03 0.300079697 #> 13399 6699.0 0.6907585 0.6907585 9.159221e-03 9.159221e-03 0.300082303 #> 13400 6699.5 0.6907549 0.6907549 9.160157e-03 9.160157e-03 0.300084922 #> 13401 6700.0 0.6907514 0.6907514 9.161093e-03 9.161093e-03 0.300087556 #> 13402 6700.5 0.6907478 0.6907478 9.162029e-03 9.162029e-03 0.300090204 #> 13403 6701.0 0.6907442 0.6907442 9.162963e-03 9.162963e-03 0.300092867 #> 13404 6701.5 0.6907406 0.6907406 9.163897e-03 9.163897e-03 0.300095544 #> 13405 6702.0 0.6907369 0.6907369 9.164830e-03 9.164830e-03 0.300098235 #> 13406 6702.5 0.6907333 0.6907333 9.165762e-03 9.165762e-03 0.300100940 #> 13407 6703.0 0.6907296 0.6907296 9.166693e-03 9.166693e-03 0.300103659 #> 13408 6703.5 0.6907260 0.6907260 9.167624e-03 9.167624e-03 0.300106392 #> 13409 6704.0 0.6907223 0.6907223 9.168554e-03 9.168554e-03 0.300109140 #> 13410 6704.5 0.6907186 0.6907186 9.169483e-03 9.169483e-03 0.300111902 #> 13411 6705.0 0.6907149 0.6907149 9.170412e-03 9.170412e-03 0.300114678 #> 13412 6705.5 0.6907112 0.6907112 9.171339e-03 9.171339e-03 0.300117468 #> 13413 6706.0 0.6907075 0.6907075 9.172266e-03 9.172266e-03 0.300120271 #> 13414 6706.5 0.6907037 0.6907037 9.173193e-03 9.173193e-03 0.300123089 #> 13415 6707.0 0.6907000 0.6907000 9.174118e-03 9.174118e-03 0.300125921 #> 13416 6707.5 0.6906962 0.6906962 9.175043e-03 9.175043e-03 0.300128767 #> 13417 6708.0 0.6906924 0.6906924 9.175967e-03 9.175967e-03 0.300131627 #> 13418 6708.5 0.6906886 0.6906886 9.176890e-03 9.176890e-03 0.300134501 #> 13419 6709.0 0.6906848 0.6906848 9.177812e-03 9.177812e-03 0.300137389 #> 13420 6709.5 0.6906810 0.6906810 9.178734e-03 9.178734e-03 0.300140291 #> 13421 6710.0 0.6906771 0.6906771 9.179654e-03 9.179654e-03 0.300143207 #> 13422 6710.5 0.6906733 0.6906733 9.180574e-03 9.180574e-03 0.300146136 #> 13423 6711.0 0.6906694 0.6906694 9.181493e-03 9.181493e-03 0.300149080 #> 13424 6711.5 0.6906656 0.6906656 9.182412e-03 9.182412e-03 0.300152037 #> 13425 6712.0 0.6906617 0.6906617 9.183329e-03 9.183329e-03 0.300155008 #> 13426 6712.5 0.6906578 0.6906578 9.184246e-03 9.184246e-03 0.300157993 #> 13427 6713.0 0.6906538 0.6906538 9.185162e-03 9.185162e-03 0.300160992 #> 13428 6713.5 0.6906499 0.6906499 9.186077e-03 9.186077e-03 0.300164004 #> 13429 6714.0 0.6906460 0.6906460 9.186992e-03 9.186992e-03 0.300167030 #> 13430 6714.5 0.6906420 0.6906420 9.187905e-03 9.187905e-03 0.300170070 #> 13431 6715.0 0.6906381 0.6906381 9.188818e-03 9.188818e-03 0.300173124 #> 13432 6715.5 0.6906341 0.6906341 9.189730e-03 9.189730e-03 0.300176191 #> 13433 6716.0 0.6906301 0.6906301 9.190641e-03 9.190641e-03 0.300179272 #> 13434 6716.5 0.6906261 0.6906261 9.191551e-03 9.191551e-03 0.300182366 #> 13435 6717.0 0.6906221 0.6906221 9.192461e-03 9.192461e-03 0.300185474 #> 13436 6717.5 0.6906180 0.6906180 9.193369e-03 9.193369e-03 0.300188596 #> 13437 6718.0 0.6906140 0.6906140 9.194277e-03 9.194277e-03 0.300191731 #> 13438 6718.5 0.6906099 0.6906099 9.195184e-03 9.195184e-03 0.300194880 #> 13439 6719.0 0.6906059 0.6906059 9.196090e-03 9.196090e-03 0.300198043 #> 13440 6719.5 0.6906018 0.6906018 9.196995e-03 9.196995e-03 0.300201219 #> 13441 6720.0 0.6905977 0.6905977 9.197899e-03 9.197899e-03 0.300204408 #> 13442 6720.5 0.6905936 0.6905936 9.198803e-03 9.198803e-03 0.300207611 #> 13443 6721.0 0.6905895 0.6905895 9.199706e-03 9.199706e-03 0.300210827 #> 13444 6721.5 0.6905853 0.6905853 9.200607e-03 9.200607e-03 0.300214057 #> 13445 6722.0 0.6905812 0.6905812 9.201508e-03 9.201508e-03 0.300217300 #> 13446 6722.5 0.6905770 0.6905770 9.202408e-03 9.202408e-03 0.300220556 #> 13447 6723.0 0.6905729 0.6905729 9.203308e-03 9.203308e-03 0.300223826 #> 13448 6723.5 0.6905687 0.6905687 9.204206e-03 9.204206e-03 0.300227110 #> 13449 6724.0 0.6905645 0.6905645 9.205103e-03 9.205103e-03 0.300230406 #> 13450 6724.5 0.6905603 0.6905603 9.206000e-03 9.206000e-03 0.300233716 #> 13451 6725.0 0.6905561 0.6905561 9.206895e-03 9.206895e-03 0.300237039 #> 13452 6725.5 0.6905518 0.6905518 9.207790e-03 9.207790e-03 0.300240375 #> 13453 6726.0 0.6905476 0.6905476 9.208684e-03 9.208684e-03 0.300243725 #> 13454 6726.5 0.6905433 0.6905433 9.209577e-03 9.209577e-03 0.300247088 #> 13455 6727.0 0.6905391 0.6905391 9.210469e-03 9.210469e-03 0.300250464 #> 13456 6727.5 0.6905348 0.6905348 9.211361e-03 9.211361e-03 0.300253853 #> 13457 6728.0 0.6905305 0.6905305 9.212251e-03 9.212251e-03 0.300257255 #> 13458 6728.5 0.6905262 0.6905262 9.213140e-03 9.213140e-03 0.300260671 #> 13459 6729.0 0.6905219 0.6905219 9.214029e-03 9.214029e-03 0.300264099 #> 13460 6729.5 0.6905175 0.6905175 9.214916e-03 9.214916e-03 0.300267541 #> 13461 6730.0 0.6905132 0.6905132 9.215803e-03 9.215803e-03 0.300270996 #> 13462 6730.5 0.6905088 0.6905088 9.216689e-03 9.216689e-03 0.300274463 #> 13463 6731.0 0.6905045 0.6905045 9.217574e-03 9.217574e-03 0.300277944 #> 13464 6731.5 0.6905001 0.6905001 9.218458e-03 9.218458e-03 0.300281438 #> 13465 6732.0 0.6904957 0.6904957 9.219341e-03 9.219341e-03 0.300284945 #> 13466 6732.5 0.6904913 0.6904913 9.220223e-03 9.220223e-03 0.300288464 #> 13467 6733.0 0.6904869 0.6904869 9.221104e-03 9.221104e-03 0.300291997 #> 13468 6733.5 0.6904825 0.6904825 9.221984e-03 9.221984e-03 0.300295542 #> 13469 6734.0 0.6904780 0.6904780 9.222863e-03 9.222863e-03 0.300299101 #> 13470 6734.5 0.6904736 0.6904736 9.223742e-03 9.223742e-03 0.300302672 #> 13471 6735.0 0.6904691 0.6904691 9.224619e-03 9.224619e-03 0.300306256 #> 13472 6735.5 0.6904647 0.6904647 9.225495e-03 9.225495e-03 0.300309853 #> 13473 6736.0 0.6904602 0.6904602 9.226371e-03 9.226371e-03 0.300313463 #> 13474 6736.5 0.6904557 0.6904557 9.227245e-03 9.227245e-03 0.300317085 #> 13475 6737.0 0.6904512 0.6904512 9.228119e-03 9.228119e-03 0.300320721 #> 13476 6737.5 0.6904466 0.6904466 9.228992e-03 9.228992e-03 0.300324369 #> 13477 6738.0 0.6904421 0.6904421 9.229863e-03 9.229863e-03 0.300328029 #> 13478 6738.5 0.6904376 0.6904376 9.230734e-03 9.230734e-03 0.300331703 #> 13479 6739.0 0.6904330 0.6904330 9.231604e-03 9.231604e-03 0.300335389 #> 13480 6739.5 0.6904284 0.6904284 9.232472e-03 9.232472e-03 0.300339087 #> 13481 6740.0 0.6904239 0.6904239 9.233340e-03 9.233340e-03 0.300342799 #> 13482 6740.5 0.6904193 0.6904193 9.234207e-03 9.234207e-03 0.300346523 #> 13483 6741.0 0.6904147 0.6904147 9.235073e-03 9.235073e-03 0.300350259 #> 13484 6741.5 0.6904101 0.6904101 9.235938e-03 9.235938e-03 0.300354008 #> 13485 6742.0 0.6904054 0.6904054 9.236801e-03 9.236801e-03 0.300357770 #> 13486 6742.5 0.6904008 0.6904008 9.237664e-03 9.237664e-03 0.300361544 #> 13487 6743.0 0.6903961 0.6903961 9.238526e-03 9.238526e-03 0.300365330 #> 13488 6743.5 0.6903915 0.6903915 9.239387e-03 9.239387e-03 0.300369129 #> 13489 6744.0 0.6903868 0.6903868 9.240247e-03 9.240247e-03 0.300372941 #> 13490 6744.5 0.6903821 0.6903821 9.241106e-03 9.241106e-03 0.300376764 #> 13491 6745.0 0.6903774 0.6903774 9.241964e-03 9.241964e-03 0.300380601 #> 13492 6745.5 0.6903727 0.6903727 9.242821e-03 9.242821e-03 0.300384449 #> 13493 6746.0 0.6903680 0.6903680 9.243676e-03 9.243676e-03 0.300388310 #> 13494 6746.5 0.6903633 0.6903633 9.244531e-03 9.244531e-03 0.300392184 #> 13495 6747.0 0.6903585 0.6903585 9.245385e-03 9.245385e-03 0.300396069 #> 13496 6747.5 0.6903538 0.6903538 9.246238e-03 9.246238e-03 0.300399967 #> 13497 6748.0 0.6903490 0.6903490 9.247090e-03 9.247090e-03 0.300403877 #> 13498 6748.5 0.6903443 0.6903443 9.247940e-03 9.247940e-03 0.300407799 #> 13499 6749.0 0.6903395 0.6903395 9.248790e-03 9.248790e-03 0.300411734 #> 13500 6749.5 0.6903347 0.6903347 9.249639e-03 9.249639e-03 0.300415681 #> 13501 6750.0 0.6903299 0.6903299 9.250487e-03 9.250487e-03 0.300419640 #> 13502 6750.5 0.6903251 0.6903251 9.251333e-03 9.251333e-03 0.300423611 #> 13503 6751.0 0.6903202 0.6903202 9.252179e-03 9.252179e-03 0.300427594 #> 13504 6751.5 0.6903154 0.6903154 9.253023e-03 9.253023e-03 0.300431589 #> 13505 6752.0 0.6903105 0.6903105 9.253867e-03 9.253867e-03 0.300435596 #> 13506 6752.5 0.6903057 0.6903057 9.254709e-03 9.254709e-03 0.300439616 #> 13507 6753.0 0.6903008 0.6903008 9.255551e-03 9.255551e-03 0.300443647 #> 13508 6753.5 0.6902959 0.6902959 9.256391e-03 9.256391e-03 0.300447690 #> 13509 6754.0 0.6902910 0.6902910 9.257230e-03 9.257230e-03 0.300451746 #> 13510 6754.5 0.6902861 0.6902861 9.258069e-03 9.258069e-03 0.300455813 #> 13511 6755.0 0.6902812 0.6902812 9.258906e-03 9.258906e-03 0.300459892 #> 13512 6755.5 0.6902763 0.6902763 9.259742e-03 9.259742e-03 0.300463983 #> 13513 6756.0 0.6902713 0.6902713 9.260577e-03 9.260577e-03 0.300468086 #> 13514 6756.5 0.6902664 0.6902664 9.261411e-03 9.261411e-03 0.300472201 #> 13515 6757.0 0.6902614 0.6902614 9.262244e-03 9.262244e-03 0.300476328 #> 13516 6757.5 0.6902565 0.6902565 9.263076e-03 9.263076e-03 0.300480466 #> 13517 6758.0 0.6902515 0.6902515 9.263907e-03 9.263907e-03 0.300484616 #> 13518 6758.5 0.6902465 0.6902465 9.264736e-03 9.264736e-03 0.300488778 #> 13519 6759.0 0.6902415 0.6902415 9.265565e-03 9.265565e-03 0.300492952 #> 13520 6759.5 0.6902365 0.6902365 9.266393e-03 9.266393e-03 0.300497138 #> 13521 6760.0 0.6902314 0.6902314 9.267219e-03 9.267219e-03 0.300501335 #> 13522 6760.5 0.6902264 0.6902264 9.268044e-03 9.268044e-03 0.300505543 #> 13523 6761.0 0.6902214 0.6902214 9.268869e-03 9.268869e-03 0.300509764 #> 13524 6761.5 0.6902163 0.6902163 9.269692e-03 9.269692e-03 0.300513996 #> 13525 6762.0 0.6902112 0.6902112 9.270514e-03 9.270514e-03 0.300518240 #> 13526 6762.5 0.6902062 0.6902062 9.271335e-03 9.271335e-03 0.300522495 #> 13527 6763.0 0.6902011 0.6902011 9.272155e-03 9.272155e-03 0.300526762 #> 13528 6763.5 0.6901960 0.6901960 9.272973e-03 9.272973e-03 0.300531040 #> 13529 6764.0 0.6901909 0.6901909 9.273791e-03 9.273791e-03 0.300535330 #> 13530 6764.5 0.6901858 0.6901858 9.274607e-03 9.274607e-03 0.300539631 #> 13531 6765.0 0.6901806 0.6901806 9.275423e-03 9.275423e-03 0.300543944 #> 13532 6765.5 0.6901755 0.6901755 9.276237e-03 9.276237e-03 0.300548268 #> 13533 6766.0 0.6901703 0.6901703 9.277050e-03 9.277050e-03 0.300552603 #> 13534 6766.5 0.6901652 0.6901652 9.277862e-03 9.277862e-03 0.300556950 #> 13535 6767.0 0.6901600 0.6901600 9.278673e-03 9.278673e-03 0.300561308 #> 13536 6767.5 0.6901548 0.6901548 9.279483e-03 9.279483e-03 0.300565678 #> 13537 6768.0 0.6901496 0.6901496 9.280292e-03 9.280292e-03 0.300570059 #> 13538 6768.5 0.6901444 0.6901444 9.281099e-03 9.281099e-03 0.300574451 #> 13539 6769.0 0.6901392 0.6901392 9.281905e-03 9.281905e-03 0.300578854 #> 13540 6769.5 0.6901340 0.6901340 9.282711e-03 9.282711e-03 0.300583269 #> 13541 6770.0 0.6901288 0.6901288 9.283515e-03 9.283515e-03 0.300587695 #> 13542 6770.5 0.6901236 0.6901236 9.284318e-03 9.284318e-03 0.300592132 #> 13543 6771.0 0.6901183 0.6901183 9.285120e-03 9.285120e-03 0.300596580 #> 13544 6771.5 0.6901130 0.6901130 9.285920e-03 9.285920e-03 0.300601039 #> 13545 6772.0 0.6901078 0.6901078 9.286720e-03 9.286720e-03 0.300605509 #> 13546 6772.5 0.6901025 0.6901025 9.287518e-03 9.287518e-03 0.300609991 #> 13547 6773.0 0.6900972 0.6900972 9.288315e-03 9.288315e-03 0.300614483 #> 13548 6773.5 0.6900919 0.6900919 9.289111e-03 9.289111e-03 0.300618987 #> 13549 6774.0 0.6900866 0.6900866 9.289906e-03 9.289906e-03 0.300623501 #> 13550 6774.5 0.6900813 0.6900813 9.290700e-03 9.290700e-03 0.300628027 #> 13551 6775.0 0.6900759 0.6900759 9.291492e-03 9.291492e-03 0.300632563 #> 13552 6775.5 0.6900706 0.6900706 9.292284e-03 9.292284e-03 0.300637111 #> 13553 6776.0 0.6900653 0.6900653 9.293074e-03 9.293074e-03 0.300641669 #> 13554 6776.5 0.6900599 0.6900599 9.293863e-03 9.293863e-03 0.300646238 #> 13555 6777.0 0.6900545 0.6900545 9.294651e-03 9.294651e-03 0.300650818 #> 13556 6777.5 0.6900492 0.6900492 9.295437e-03 9.295437e-03 0.300655409 #> 13557 6778.0 0.6900438 0.6900438 9.296223e-03 9.296223e-03 0.300660011 #> 13558 6778.5 0.6900384 0.6900384 9.297007e-03 9.297007e-03 0.300664623 #> 13559 6779.0 0.6900330 0.6900330 9.297790e-03 9.297790e-03 0.300669246 #> 13560 6779.5 0.6900275 0.6900275 9.298572e-03 9.298572e-03 0.300673880 #> 13561 6780.0 0.6900221 0.6900221 9.299353e-03 9.299353e-03 0.300678525 #> 13562 6780.5 0.6900167 0.6900167 9.300132e-03 9.300132e-03 0.300683180 #> 13563 6781.0 0.6900112 0.6900112 9.300910e-03 9.300910e-03 0.300687846 #> 13564 6781.5 0.6900058 0.6900058 9.301687e-03 9.301687e-03 0.300692523 #> 13565 6782.0 0.6900003 0.6900003 9.302463e-03 9.302463e-03 0.300697210 #> 13566 6782.5 0.6899949 0.6899949 9.303238e-03 9.303238e-03 0.300701908 #> 13567 6783.0 0.6899894 0.6899894 9.304011e-03 9.304011e-03 0.300706616 #> 13568 6783.5 0.6899839 0.6899839 9.304784e-03 9.304784e-03 0.300711335 #> 13569 6784.0 0.6899784 0.6899784 9.305555e-03 9.305555e-03 0.300716064 #> 13570 6784.5 0.6899729 0.6899729 9.306325e-03 9.306325e-03 0.300720804 #> 13571 6785.0 0.6899674 0.6899674 9.307093e-03 9.307093e-03 0.300725554 #> 13572 6785.5 0.6899618 0.6899618 9.307861e-03 9.307861e-03 0.300730315 #> 13573 6786.0 0.6899563 0.6899563 9.308627e-03 9.308627e-03 0.300735086 #> 13574 6786.5 0.6899507 0.6899507 9.309392e-03 9.309392e-03 0.300739867 #> 13575 6787.0 0.6899452 0.6899452 9.310155e-03 9.310155e-03 0.300744659 #> 13576 6787.5 0.6899396 0.6899396 9.310918e-03 9.310918e-03 0.300749461 #> 13577 6788.0 0.6899340 0.6899340 9.311679e-03 9.311679e-03 0.300754273 #> 13578 6788.5 0.6899285 0.6899285 9.312439e-03 9.312439e-03 0.300759096 #> 13579 6789.0 0.6899229 0.6899229 9.313198e-03 9.313198e-03 0.300763929 #> 13580 6789.5 0.6899173 0.6899173 9.313955e-03 9.313955e-03 0.300768772 #> 13581 6790.0 0.6899117 0.6899117 9.314712e-03 9.314712e-03 0.300773625 #> 13582 6790.5 0.6899060 0.6899060 9.315467e-03 9.315467e-03 0.300778489 #> 13583 6791.0 0.6899004 0.6899004 9.316220e-03 9.316220e-03 0.300783362 #> 13584 6791.5 0.6898948 0.6898948 9.316973e-03 9.316973e-03 0.300788246 #> 13585 6792.0 0.6898891 0.6898891 9.317724e-03 9.317724e-03 0.300793140 #> 13586 6792.5 0.6898835 0.6898835 9.318474e-03 9.318474e-03 0.300798044 #> 13587 6793.0 0.6898778 0.6898778 9.319223e-03 9.319223e-03 0.300802958 #> 13588 6793.5 0.6898721 0.6898721 9.319971e-03 9.319971e-03 0.300807881 #> 13589 6794.0 0.6898665 0.6898665 9.320717e-03 9.320717e-03 0.300812815 #> 13590 6794.5 0.6898608 0.6898608 9.321462e-03 9.321462e-03 0.300817759 #> 13591 6795.0 0.6898551 0.6898551 9.322206e-03 9.322206e-03 0.300822713 #> 13592 6795.5 0.6898494 0.6898494 9.322948e-03 9.322948e-03 0.300827677 #> 13593 6796.0 0.6898437 0.6898437 9.323689e-03 9.323689e-03 0.300832650 #> 13594 6796.5 0.6898379 0.6898379 9.324429e-03 9.324429e-03 0.300837634 #> 13595 6797.0 0.6898322 0.6898322 9.325168e-03 9.325168e-03 0.300842627 #> 13596 6797.5 0.6898265 0.6898265 9.325905e-03 9.325905e-03 0.300847630 #> 13597 6798.0 0.6898207 0.6898207 9.326641e-03 9.326641e-03 0.300852643 #> 13598 6798.5 0.6898150 0.6898150 9.327376e-03 9.327376e-03 0.300857665 #> 13599 6799.0 0.6898092 0.6898092 9.328109e-03 9.328109e-03 0.300862698 #> 13600 6799.5 0.6898034 0.6898034 9.328842e-03 9.328842e-03 0.300867740 #> 13601 6800.0 0.6897976 0.6897976 9.329572e-03 9.329572e-03 0.300872791 #> 13602 6800.5 0.6897918 0.6897918 9.330302e-03 9.330302e-03 0.300877852 #> 13603 6801.0 0.6897860 0.6897860 9.331030e-03 9.331030e-03 0.300882923 #> 13604 6801.5 0.6897802 0.6897802 9.331757e-03 9.331757e-03 0.300888004 #> 13605 6802.0 0.6897744 0.6897744 9.332483e-03 9.332483e-03 0.300893094 #> 13606 6802.5 0.6897686 0.6897686 9.333208e-03 9.333208e-03 0.300898194 #> 13607 6803.0 0.6897628 0.6897628 9.333931e-03 9.333931e-03 0.300903303 #> 13608 6803.5 0.6897569 0.6897569 9.334652e-03 9.334652e-03 0.300908421 #> 13609 6804.0 0.6897511 0.6897511 9.335373e-03 9.335373e-03 0.300913549 #> 13610 6804.5 0.6897452 0.6897452 9.336092e-03 9.336092e-03 0.300918687 #> 13611 6805.0 0.6897394 0.6897394 9.336810e-03 9.336810e-03 0.300923834 #> 13612 6805.5 0.6897335 0.6897335 9.337527e-03 9.337527e-03 0.300928990 #> 13613 6806.0 0.6897276 0.6897276 9.338242e-03 9.338242e-03 0.300934156 #> 13614 6806.5 0.6897217 0.6897217 9.338956e-03 9.338956e-03 0.300939331 #> 13615 6807.0 0.6897158 0.6897158 9.339668e-03 9.339668e-03 0.300944515 #> 13616 6807.5 0.6897099 0.6897099 9.340380e-03 9.340380e-03 0.300949708 #> 13617 6808.0 0.6897040 0.6897040 9.341090e-03 9.341090e-03 0.300954911 #> 13618 6808.5 0.6896981 0.6896981 9.341798e-03 9.341798e-03 0.300960123 #> 13619 6809.0 0.6896922 0.6896922 9.342506e-03 9.342506e-03 0.300965344 #> 13620 6809.5 0.6896862 0.6896862 9.343212e-03 9.343212e-03 0.300970575 #> 13621 6810.0 0.6896803 0.6896803 9.343916e-03 9.343916e-03 0.300975814 #> 13622 6810.5 0.6896743 0.6896743 9.344620e-03 9.344620e-03 0.300981063 #> 13623 6811.0 0.6896684 0.6896684 9.345322e-03 9.345322e-03 0.300986320 #> 13624 6811.5 0.6896624 0.6896624 9.346022e-03 9.346022e-03 0.300991587 #> 13625 6812.0 0.6896564 0.6896564 9.346721e-03 9.346721e-03 0.300996863 #> 13626 6812.5 0.6896504 0.6896504 9.347419e-03 9.347419e-03 0.301002147 #> 13627 6813.0 0.6896444 0.6896444 9.348116e-03 9.348116e-03 0.301007441 #> 13628 6813.5 0.6896384 0.6896384 9.348811e-03 9.348811e-03 0.301012744 #> 13629 6814.0 0.6896324 0.6896324 9.349505e-03 9.349505e-03 0.301018056 #> 13630 6814.5 0.6896264 0.6896264 9.350198e-03 9.350198e-03 0.301023376 #> 13631 6815.0 0.6896204 0.6896204 9.350889e-03 9.350889e-03 0.301028705 #> 13632 6815.5 0.6896144 0.6896144 9.351579e-03 9.351579e-03 0.301034044 #> 13633 6816.0 0.6896083 0.6896083 9.352267e-03 9.352267e-03 0.301039391 #> 13634 6816.5 0.6896023 0.6896023 9.352954e-03 9.352954e-03 0.301044746 #> 13635 6817.0 0.6895962 0.6895962 9.353640e-03 9.353640e-03 0.301050111 #> 13636 6817.5 0.6895902 0.6895902 9.354324e-03 9.354324e-03 0.301055484 #> 13637 6818.0 0.6895841 0.6895841 9.355007e-03 9.355007e-03 0.301060866 #> 13638 6818.5 0.6895781 0.6895781 9.355689e-03 9.355689e-03 0.301066257 #> 13639 6819.0 0.6895720 0.6895720 9.356369e-03 9.356369e-03 0.301071656 #> 13640 6819.5 0.6895659 0.6895659 9.357048e-03 9.357048e-03 0.301077064 #> 13641 6820.0 0.6895598 0.6895598 9.357726e-03 9.357726e-03 0.301082481 #> 13642 6820.5 0.6895537 0.6895537 9.358402e-03 9.358402e-03 0.301087906 #> 13643 6821.0 0.6895476 0.6895476 9.359076e-03 9.359076e-03 0.301093340 #> 13644 6821.5 0.6895415 0.6895415 9.359750e-03 9.359750e-03 0.301098782 #> 13645 6822.0 0.6895353 0.6895353 9.360422e-03 9.360422e-03 0.301104233 #> 13646 6822.5 0.6895292 0.6895292 9.361092e-03 9.361092e-03 0.301109692 #> 13647 6823.0 0.6895231 0.6895231 9.361762e-03 9.361762e-03 0.301115159 #> 13648 6823.5 0.6895169 0.6895169 9.362430e-03 9.362430e-03 0.301120635 #> 13649 6824.0 0.6895108 0.6895108 9.363096e-03 9.363096e-03 0.301126120 #> 13650 6824.5 0.6895046 0.6895046 9.363761e-03 9.363761e-03 0.301131612 #> 13651 6825.0 0.6894985 0.6894985 9.364425e-03 9.364425e-03 0.301137113 #> 13652 6825.5 0.6894923 0.6894923 9.365087e-03 9.365087e-03 0.301142623 #> 13653 6826.0 0.6894861 0.6894861 9.365748e-03 9.365748e-03 0.301148140 #> 13654 6826.5 0.6894799 0.6894799 9.366407e-03 9.366407e-03 0.301153666 #> 13655 6827.0 0.6894737 0.6894737 9.367065e-03 9.367065e-03 0.301159200 #> 13656 6827.5 0.6894675 0.6894675 9.367722e-03 9.367722e-03 0.301164742 #> 13657 6828.0 0.6894613 0.6894613 9.368377e-03 9.368377e-03 0.301170293 #> 13658 6828.5 0.6894551 0.6894551 9.369031e-03 9.369031e-03 0.301175851 #> 13659 6829.0 0.6894489 0.6894489 9.369683e-03 9.369683e-03 0.301181418 #> 13660 6829.5 0.6894427 0.6894427 9.370334e-03 9.370334e-03 0.301186993 #> 13661 6830.0 0.6894364 0.6894364 9.370984e-03 9.370984e-03 0.301192575 #> 13662 6830.5 0.6894302 0.6894302 9.371632e-03 9.371632e-03 0.301198166 #> 13663 6831.0 0.6894240 0.6894240 9.372279e-03 9.372279e-03 0.301203765 #> 13664 6831.5 0.6894177 0.6894177 9.372924e-03 9.372924e-03 0.301209372 #> 13665 6832.0 0.6894114 0.6894114 9.373568e-03 9.373568e-03 0.301214986 #> 13666 6832.5 0.6894052 0.6894052 9.374211e-03 9.374211e-03 0.301220609 #> 13667 6833.0 0.6893989 0.6893989 9.374852e-03 9.374852e-03 0.301226240 #> 13668 6833.5 0.6893926 0.6893926 9.375491e-03 9.375491e-03 0.301231878 #> 13669 6834.0 0.6893863 0.6893863 9.376130e-03 9.376130e-03 0.301237524 #> 13670 6834.5 0.6893801 0.6893801 9.376766e-03 9.376766e-03 0.301243178 #> 13671 6835.0 0.6893738 0.6893738 9.377402e-03 9.377402e-03 0.301248840 #> 13672 6835.5 0.6893675 0.6893675 9.378036e-03 9.378036e-03 0.301254509 #> 13673 6836.0 0.6893611 0.6893611 9.378668e-03 9.378668e-03 0.301260186 #> 13674 6836.5 0.6893548 0.6893548 9.379299e-03 9.379299e-03 0.301265871 #> 13675 6837.0 0.6893485 0.6893485 9.379929e-03 9.379929e-03 0.301271564 #> 13676 6837.5 0.6893422 0.6893422 9.380557e-03 9.380557e-03 0.301277264 #> 13677 6838.0 0.6893358 0.6893358 9.381184e-03 9.381184e-03 0.301282972 #> 13678 6838.5 0.6893295 0.6893295 9.381809e-03 9.381809e-03 0.301288687 #> 13679 6839.0 0.6893232 0.6893232 9.382433e-03 9.382433e-03 0.301294410 #> 13680 6839.5 0.6893168 0.6893168 9.383055e-03 9.383055e-03 0.301300141 #> 13681 6840.0 0.6893104 0.6893104 9.383676e-03 9.383676e-03 0.301305878 #> 13682 6840.5 0.6893041 0.6893041 9.384296e-03 9.384296e-03 0.301311624 #> 13683 6841.0 0.6892977 0.6892977 9.384914e-03 9.384914e-03 0.301317377 #> 13684 6841.5 0.6892913 0.6892913 9.385530e-03 9.385530e-03 0.301323137 #> 13685 6842.0 0.6892850 0.6892850 9.386145e-03 9.386145e-03 0.301328905 #> 13686 6842.5 0.6892786 0.6892786 9.386759e-03 9.386759e-03 0.301334679 #> 13687 6843.0 0.6892722 0.6892722 9.387371e-03 9.387371e-03 0.301340462 #> 13688 6843.5 0.6892658 0.6892658 9.387982e-03 9.387982e-03 0.301346251 #> 13689 6844.0 0.6892594 0.6892594 9.388591e-03 9.388591e-03 0.301352048 #> 13690 6844.5 0.6892529 0.6892529 9.389199e-03 9.389199e-03 0.301357852 #> 13691 6845.0 0.6892465 0.6892465 9.389805e-03 9.389805e-03 0.301363664 #> 13692 6845.5 0.6892401 0.6892401 9.390410e-03 9.390410e-03 0.301369482 #> 13693 6846.0 0.6892337 0.6892337 9.391014e-03 9.391014e-03 0.301375308 #> 13694 6846.5 0.6892272 0.6892272 9.391616e-03 9.391616e-03 0.301381141 #> 13695 6847.0 0.6892208 0.6892208 9.392216e-03 9.392216e-03 0.301386981 #> 13696 6847.5 0.6892144 0.6892144 9.392815e-03 9.392815e-03 0.301392828 #> 13697 6848.0 0.6892079 0.6892079 9.393413e-03 9.393413e-03 0.301398682 #> 13698 6848.5 0.6892014 0.6892014 9.394009e-03 9.394009e-03 0.301404543 #> 13699 6849.0 0.6891950 0.6891950 9.394603e-03 9.394603e-03 0.301410411 #> 13700 6849.5 0.6891885 0.6891885 9.395196e-03 9.395196e-03 0.301416286 #> 13701 6850.0 0.6891820 0.6891820 9.395788e-03 9.395788e-03 0.301422168 #> 13702 6850.5 0.6891756 0.6891756 9.396378e-03 9.396378e-03 0.301428056 #> 13703 6851.0 0.6891691 0.6891691 9.396967e-03 9.396967e-03 0.301433952 #> 13704 6851.5 0.6891626 0.6891626 9.397554e-03 9.397554e-03 0.301439855 #> 13705 6852.0 0.6891561 0.6891561 9.398139e-03 9.398139e-03 0.301445764 #> 13706 6852.5 0.6891496 0.6891496 9.398724e-03 9.398724e-03 0.301451680 #> 13707 6853.0 0.6891431 0.6891431 9.399306e-03 9.399306e-03 0.301457603 #> 13708 6853.5 0.6891366 0.6891366 9.399887e-03 9.399887e-03 0.301463533 #> 13709 6854.0 0.6891301 0.6891301 9.400467e-03 9.400467e-03 0.301469469 #> 13710 6854.5 0.6891235 0.6891235 9.401045e-03 9.401045e-03 0.301475412 #> 13711 6855.0 0.6891170 0.6891170 9.401622e-03 9.401622e-03 0.301481362 #> 13712 6855.5 0.6891105 0.6891105 9.402197e-03 9.402197e-03 0.301487318 #> 13713 6856.0 0.6891039 0.6891039 9.402771e-03 9.402771e-03 0.301493281 #> 13714 6856.5 0.6890974 0.6890974 9.403343e-03 9.403343e-03 0.301499251 #> 13715 6857.0 0.6890909 0.6890909 9.403914e-03 9.403914e-03 0.301505227 #> 13716 6857.5 0.6890843 0.6890843 9.404483e-03 9.404483e-03 0.301511209 #> 13717 6858.0 0.6890778 0.6890778 9.405050e-03 9.405050e-03 0.301517198 #> 13718 6858.5 0.6890712 0.6890712 9.405617e-03 9.405617e-03 0.301523194 #> 13719 6859.0 0.6890646 0.6890646 9.406181e-03 9.406181e-03 0.301529196 #> 13720 6859.5 0.6890581 0.6890581 9.406744e-03 9.406744e-03 0.301535204 #> 13721 6860.0 0.6890515 0.6890515 9.407306e-03 9.407306e-03 0.301541218 #> 13722 6860.5 0.6890449 0.6890449 9.407866e-03 9.407866e-03 0.301547239 #> 13723 6861.0 0.6890383 0.6890383 9.408425e-03 9.408425e-03 0.301553267 #> 13724 6861.5 0.6890317 0.6890317 9.408982e-03 9.408982e-03 0.301559300 #> 13725 6862.0 0.6890251 0.6890251 9.409537e-03 9.409537e-03 0.301565340 #> 13726 6862.5 0.6890185 0.6890185 9.410091e-03 9.410091e-03 0.301571386 #> 13727 6863.0 0.6890119 0.6890119 9.410644e-03 9.410644e-03 0.301577439 #> 13728 6863.5 0.6890053 0.6890053 9.411195e-03 9.411195e-03 0.301583497 #> 13729 6864.0 0.6889987 0.6889987 9.411744e-03 9.411744e-03 0.301589561 #> 13730 6864.5 0.6889921 0.6889921 9.412292e-03 9.412292e-03 0.301595632 #> 13731 6865.0 0.6889855 0.6889855 9.412839e-03 9.412839e-03 0.301601709 #> 13732 6865.5 0.6889788 0.6889788 9.413384e-03 9.413384e-03 0.301607792 #> 13733 6866.0 0.6889722 0.6889722 9.413927e-03 9.413927e-03 0.301613881 #> 13734 6866.5 0.6889656 0.6889656 9.414469e-03 9.414469e-03 0.301619975 #> 13735 6867.0 0.6889589 0.6889589 9.415009e-03 9.415009e-03 0.301626076 #> 13736 6867.5 0.6889523 0.6889523 9.415548e-03 9.415548e-03 0.301632183 #> 13737 6868.0 0.6889456 0.6889456 9.416085e-03 9.416085e-03 0.301638296 #> 13738 6868.5 0.6889390 0.6889390 9.416621e-03 9.416621e-03 0.301644414 #> 13739 6869.0 0.6889323 0.6889323 9.417155e-03 9.417155e-03 0.301650539 #> 13740 6869.5 0.6889256 0.6889256 9.417688e-03 9.417688e-03 0.301656669 #> 13741 6870.0 0.6889190 0.6889190 9.418219e-03 9.418219e-03 0.301662805 #> 13742 6870.5 0.6889123 0.6889123 9.418748e-03 9.418748e-03 0.301668947 #> 13743 6871.0 0.6889056 0.6889056 9.419276e-03 9.419276e-03 0.301675094 #> 13744 6871.5 0.6888989 0.6888989 9.419803e-03 9.419803e-03 0.301681247 #> 13745 6872.0 0.6888923 0.6888923 9.420328e-03 9.420328e-03 0.301687406 #> 13746 6872.5 0.6888856 0.6888856 9.420851e-03 9.420851e-03 0.301693571 #> 13747 6873.0 0.6888789 0.6888789 9.421373e-03 9.421373e-03 0.301699741 #> 13748 6873.5 0.6888722 0.6888722 9.421893e-03 9.421893e-03 0.301705917 #> 13749 6874.0 0.6888655 0.6888655 9.422412e-03 9.422412e-03 0.301712098 #> 13750 6874.5 0.6888588 0.6888588 9.422929e-03 9.422929e-03 0.301718285 #> 13751 6875.0 0.6888521 0.6888521 9.423445e-03 9.423445e-03 0.301724478 #> 13752 6875.5 0.6888454 0.6888454 9.423959e-03 9.423959e-03 0.301730676 #> 13753 6876.0 0.6888386 0.6888386 9.424471e-03 9.424471e-03 0.301736879 #> 13754 6876.5 0.6888319 0.6888319 9.424982e-03 9.424982e-03 0.301743088 #> 13755 6877.0 0.6888252 0.6888252 9.425492e-03 9.425492e-03 0.301749302 #> 13756 6877.5 0.6888185 0.6888185 9.426000e-03 9.426000e-03 0.301755521 #> 13757 6878.0 0.6888117 0.6888117 9.426506e-03 9.426506e-03 0.301761746 #> 13758 6878.5 0.6888050 0.6888050 9.427011e-03 9.427011e-03 0.301767977 #> 13759 6879.0 0.6887983 0.6887983 9.427514e-03 9.427514e-03 0.301774212 #> 13760 6879.5 0.6887915 0.6887915 9.428015e-03 9.428015e-03 0.301780453 #> 13761 6880.0 0.6887848 0.6887848 9.428515e-03 9.428515e-03 0.301786699 #> 13762 6880.5 0.6887780 0.6887780 9.429014e-03 9.429014e-03 0.301792950 #> 13763 6881.0 0.6887713 0.6887713 9.429511e-03 9.429511e-03 0.301799206 #> 13764 6881.5 0.6887645 0.6887645 9.430006e-03 9.430006e-03 0.301805468 #> 13765 6882.0 0.6887578 0.6887578 9.430500e-03 9.430500e-03 0.301811734 #> 13766 6882.5 0.6887510 0.6887510 9.430992e-03 9.430992e-03 0.301818006 #> 13767 6883.0 0.6887442 0.6887442 9.431483e-03 9.431483e-03 0.301824283 #> 13768 6883.5 0.6887375 0.6887375 9.431972e-03 9.431972e-03 0.301830564 #> 13769 6884.0 0.6887307 0.6887307 9.432459e-03 9.432459e-03 0.301836851 #> 13770 6884.5 0.6887239 0.6887239 9.432945e-03 9.432945e-03 0.301843143 #> 13771 6885.0 0.6887171 0.6887171 9.433430e-03 9.433430e-03 0.301849440 #> 13772 6885.5 0.6887103 0.6887103 9.433912e-03 9.433912e-03 0.301855741 #> 13773 6886.0 0.6887036 0.6887036 9.434394e-03 9.434394e-03 0.301862048 #> 13774 6886.5 0.6886968 0.6886968 9.434873e-03 9.434873e-03 0.301868359 #> 13775 6887.0 0.6886900 0.6886900 9.435351e-03 9.435351e-03 0.301874675 #> 13776 6887.5 0.6886832 0.6886832 9.435828e-03 9.435828e-03 0.301880996 #> 13777 6888.0 0.6886764 0.6886764 9.436303e-03 9.436303e-03 0.301887322 #> 13778 6888.5 0.6886696 0.6886696 9.436776e-03 9.436776e-03 0.301893652 #> 13779 6889.0 0.6886628 0.6886628 9.437248e-03 9.437248e-03 0.301899987 #> 13780 6889.5 0.6886560 0.6886560 9.437718e-03 9.437718e-03 0.301906327 #> 13781 6890.0 0.6886491 0.6886491 9.438186e-03 9.438186e-03 0.301912671 #> 13782 6890.5 0.6886423 0.6886423 9.438653e-03 9.438653e-03 0.301919020 #> 13783 6891.0 0.6886355 0.6886355 9.439119e-03 9.439119e-03 0.301925374 #> 13784 6891.5 0.6886287 0.6886287 9.439583e-03 9.439583e-03 0.301931732 #> 13785 6892.0 0.6886219 0.6886219 9.440045e-03 9.440045e-03 0.301938095 #> 13786 6892.5 0.6886150 0.6886150 9.440506e-03 9.440506e-03 0.301944462 #> 13787 6893.0 0.6886082 0.6886082 9.440965e-03 9.440965e-03 0.301950834 #> 13788 6893.5 0.6886014 0.6886014 9.441422e-03 9.441422e-03 0.301957210 #> 13789 6894.0 0.6885945 0.6885945 9.441878e-03 9.441878e-03 0.301963591 #> 13790 6894.5 0.6885877 0.6885877 9.442332e-03 9.442332e-03 0.301969976 #> 13791 6895.0 0.6885808 0.6885808 9.442785e-03 9.442785e-03 0.301976365 #> 13792 6895.5 0.6885740 0.6885740 9.443236e-03 9.443236e-03 0.301982759 #> 13793 6896.0 0.6885672 0.6885672 9.443685e-03 9.443685e-03 0.301989157 #> 13794 6896.5 0.6885603 0.6885603 9.444133e-03 9.444133e-03 0.301995559 #> 13795 6897.0 0.6885535 0.6885535 9.444580e-03 9.444580e-03 0.302001966 #> 13796 6897.5 0.6885466 0.6885466 9.445024e-03 9.445024e-03 0.302008377 #> 13797 6898.0 0.6885397 0.6885397 9.445467e-03 9.445467e-03 0.302014792 #> 13798 6898.5 0.6885329 0.6885329 9.445909e-03 9.445909e-03 0.302021211 #> 13799 6899.0 0.6885260 0.6885260 9.446349e-03 9.446349e-03 0.302027634 #> 13800 6899.5 0.6885192 0.6885192 9.446787e-03 9.446787e-03 0.302034062 #> 13801 6900.0 0.6885123 0.6885123 9.447224e-03 9.447224e-03 0.302040493 #> 13802 6900.5 0.6885054 0.6885054 9.447659e-03 9.447659e-03 0.302046929 #> 13803 6901.0 0.6884985 0.6884985 9.448092e-03 9.448092e-03 0.302053368 #> 13804 6901.5 0.6884917 0.6884917 9.448524e-03 9.448524e-03 0.302059812 #> 13805 6902.0 0.6884848 0.6884848 9.448955e-03 9.448955e-03 0.302066259 #> 13806 6902.5 0.6884779 0.6884779 9.449383e-03 9.449383e-03 0.302072710 #> 13807 6903.0 0.6884710 0.6884710 9.449810e-03 9.449810e-03 0.302079166 #> 13808 6903.5 0.6884641 0.6884641 9.450236e-03 9.450236e-03 0.302085625 #> 13809 6904.0 0.6884573 0.6884573 9.450660e-03 9.450660e-03 0.302092088 #> 13810 6904.5 0.6884504 0.6884504 9.451082e-03 9.451082e-03 0.302098555 #> 13811 6905.0 0.6884435 0.6884435 9.451503e-03 9.451503e-03 0.302105025 #> 13812 6905.5 0.6884366 0.6884366 9.451922e-03 9.451922e-03 0.302111500 #> 13813 6906.0 0.6884297 0.6884297 9.452339e-03 9.452339e-03 0.302117978 #> 13814 6906.5 0.6884228 0.6884228 9.452755e-03 9.452755e-03 0.302124460 #> 13815 6907.0 0.6884159 0.6884159 9.453169e-03 9.453169e-03 0.302130945 #> 13816 6907.5 0.6884090 0.6884090 9.453582e-03 9.453582e-03 0.302137435 #> 13817 6908.0 0.6884021 0.6884021 9.453993e-03 9.453993e-03 0.302143927 #> 13818 6908.5 0.6883952 0.6883952 9.454402e-03 9.454402e-03 0.302150424 #> 13819 6909.0 0.6883883 0.6883883 9.454810e-03 9.454810e-03 0.302156924 #> 13820 6909.5 0.6883814 0.6883814 9.455216e-03 9.455216e-03 0.302163427 #> 13821 6910.0 0.6883744 0.6883744 9.455620e-03 9.455620e-03 0.302169934 #> 13822 6910.5 0.6883675 0.6883675 9.456023e-03 9.456023e-03 0.302176444 #> 13823 6911.0 0.6883606 0.6883606 9.456424e-03 9.456424e-03 0.302182958 #> 13824 6911.5 0.6883537 0.6883537 9.456824e-03 9.456824e-03 0.302189476 #> 13825 6912.0 0.6883468 0.6883468 9.457222e-03 9.457222e-03 0.302195996 #> 13826 6912.5 0.6883399 0.6883399 9.457619e-03 9.457619e-03 0.302202520 #> 13827 6913.0 0.6883329 0.6883329 9.458013e-03 9.458013e-03 0.302209048 #> 13828 6913.5 0.6883260 0.6883260 9.458407e-03 9.458407e-03 0.302215578 #> 13829 6914.0 0.6883191 0.6883191 9.458798e-03 9.458798e-03 0.302222112 #> 13830 6914.5 0.6883122 0.6883122 9.459188e-03 9.459188e-03 0.302228650 #> 13831 6915.0 0.6883052 0.6883052 9.459576e-03 9.459576e-03 0.302235190 #> 13832 6915.5 0.6882983 0.6882983 9.459963e-03 9.459963e-03 0.302241734 #> 13833 6916.0 0.6882914 0.6882914 9.460348e-03 9.460348e-03 0.302248280 #> 13834 6916.5 0.6882844 0.6882844 9.460732e-03 9.460732e-03 0.302254830 #> 13835 6917.0 0.6882775 0.6882775 9.461113e-03 9.461113e-03 0.302261383 #> 13836 6917.5 0.6882706 0.6882706 9.461494e-03 9.461494e-03 0.302267939 #> 13837 6918.0 0.6882636 0.6882636 9.461872e-03 9.461872e-03 0.302274498 #> 13838 6918.5 0.6882567 0.6882567 9.462249e-03 9.462249e-03 0.302281061 #> 13839 6919.0 0.6882497 0.6882497 9.462624e-03 9.462624e-03 0.302287626 #> 13840 6919.5 0.6882428 0.6882428 9.462998e-03 9.462998e-03 0.302294194 #> 13841 6920.0 0.6882359 0.6882359 9.463370e-03 9.463370e-03 0.302300765 #> 13842 6920.5 0.6882289 0.6882289 9.463741e-03 9.463741e-03 0.302307339 #> 13843 6921.0 0.6882220 0.6882220 9.464109e-03 9.464109e-03 0.302313915 #> 13844 6921.5 0.6882150 0.6882150 9.464477e-03 9.464477e-03 0.302320495 #> 13845 6922.0 0.6882081 0.6882081 9.464842e-03 9.464842e-03 0.302327077 #> 13846 6922.5 0.6882011 0.6882011 9.465206e-03 9.465206e-03 0.302333663 #> 13847 6923.0 0.6881942 0.6881942 9.465568e-03 9.465568e-03 0.302340251 #> 13848 6923.5 0.6881872 0.6881872 9.465929e-03 9.465929e-03 0.302346841 #> 13849 6924.0 0.6881803 0.6881803 9.466288e-03 9.466288e-03 0.302353435 #> 13850 6924.5 0.6881733 0.6881733 9.466645e-03 9.466645e-03 0.302360031 #> 13851 6925.0 0.6881664 0.6881664 9.467001e-03 9.467001e-03 0.302366630 #> 13852 6925.5 0.6881594 0.6881594 9.467355e-03 9.467355e-03 0.302373231 #> 13853 6926.0 0.6881525 0.6881525 9.467708e-03 9.467708e-03 0.302379835 #> 13854 6926.5 0.6881455 0.6881455 9.468059e-03 9.468059e-03 0.302386441 #> 13855 6927.0 0.6881385 0.6881385 9.468408e-03 9.468408e-03 0.302393050 #> 13856 6927.5 0.6881316 0.6881316 9.468755e-03 9.468755e-03 0.302399662 #> 13857 6928.0 0.6881246 0.6881246 9.469101e-03 9.469101e-03 0.302406276 #> 13858 6928.5 0.6881177 0.6881177 9.469446e-03 9.469446e-03 0.302412892 #> 13859 6929.0 0.6881107 0.6881107 9.469788e-03 9.469788e-03 0.302419511 #> 13860 6929.5 0.6881037 0.6881037 9.470129e-03 9.470129e-03 0.302426132 #> 13861 6930.0 0.6880968 0.6880968 9.470469e-03 9.470469e-03 0.302432756 #> 13862 6930.5 0.6880898 0.6880898 9.470807e-03 9.470807e-03 0.302439382 #> 13863 6931.0 0.6880828 0.6880828 9.471143e-03 9.471143e-03 0.302446010 #> 13864 6931.5 0.6880759 0.6880759 9.471477e-03 9.471477e-03 0.302452641 #> 13865 6932.0 0.6880689 0.6880689 9.471810e-03 9.471810e-03 0.302459273 #> 13866 6932.5 0.6880620 0.6880620 9.472141e-03 9.472141e-03 0.302465908 #> 13867 6933.0 0.6880550 0.6880550 9.472471e-03 9.472471e-03 0.302472546 #> 13868 6933.5 0.6880480 0.6880480 9.472799e-03 9.472799e-03 0.302479185 #> 13869 6934.0 0.6880410 0.6880410 9.473125e-03 9.473125e-03 0.302485826 #> 13870 6934.5 0.6880341 0.6880341 9.473450e-03 9.473450e-03 0.302492470 #> 13871 6935.0 0.6880271 0.6880271 9.473773e-03 9.473773e-03 0.302499115 #> 13872 6935.5 0.6880201 0.6880201 9.474094e-03 9.474094e-03 0.302505763 #> 13873 6936.0 0.6880132 0.6880132 9.474414e-03 9.474414e-03 0.302512413 #> 13874 6936.5 0.6880062 0.6880062 9.474732e-03 9.474732e-03 0.302519065 #> 13875 6937.0 0.6879992 0.6879992 9.475049e-03 9.475049e-03 0.302525718 #> 13876 6937.5 0.6879923 0.6879923 9.475364e-03 9.475364e-03 0.302532374 #> 13877 6938.0 0.6879853 0.6879853 9.475677e-03 9.475677e-03 0.302539031 #> 13878 6938.5 0.6879783 0.6879783 9.475988e-03 9.475988e-03 0.302545691 #> 13879 6939.0 0.6879713 0.6879713 9.476298e-03 9.476298e-03 0.302552352 #> 13880 6939.5 0.6879644 0.6879644 9.476606e-03 9.476606e-03 0.302559015 #> 13881 6940.0 0.6879574 0.6879574 9.476913e-03 9.476913e-03 0.302565680 #> 13882 6940.5 0.6879504 0.6879504 9.477218e-03 9.477218e-03 0.302572346 #> 13883 6941.0 0.6879435 0.6879435 9.477521e-03 9.477521e-03 0.302579015 #> 13884 6941.5 0.6879365 0.6879365 9.477823e-03 9.477823e-03 0.302585685 #> 13885 6942.0 0.6879295 0.6879295 9.478123e-03 9.478123e-03 0.302592356 #> 13886 6942.5 0.6879225 0.6879225 9.478422e-03 9.478422e-03 0.302599030 #> 13887 6943.0 0.6879156 0.6879156 9.478718e-03 9.478718e-03 0.302605705 #> 13888 6943.5 0.6879086 0.6879086 9.479013e-03 9.479013e-03 0.302612381 #> 13889 6944.0 0.6879016 0.6879016 9.479307e-03 9.479307e-03 0.302619059 #> 13890 6944.5 0.6878947 0.6878947 9.479599e-03 9.479599e-03 0.302625739 #> 13891 6945.0 0.6878877 0.6878877 9.479889e-03 9.479889e-03 0.302632420 #> 13892 6945.5 0.6878807 0.6878807 9.480178e-03 9.480178e-03 0.302639103 #> 13893 6946.0 0.6878737 0.6878737 9.480464e-03 9.480464e-03 0.302645787 #> 13894 6946.5 0.6878668 0.6878668 9.480750e-03 9.480750e-03 0.302652473 #> 13895 6947.0 0.6878598 0.6878598 9.481033e-03 9.481033e-03 0.302659160 #> 13896 6947.5 0.6878528 0.6878528 9.481315e-03 9.481315e-03 0.302665848 #> 13897 6948.0 0.6878459 0.6878459 9.481596e-03 9.481596e-03 0.302672538 #> 13898 6948.5 0.6878389 0.6878389 9.481874e-03 9.481874e-03 0.302679229 #> 13899 6949.0 0.6878319 0.6878319 9.482151e-03 9.482151e-03 0.302685921 #> 13900 6949.5 0.6878250 0.6878250 9.482427e-03 9.482427e-03 0.302692614 #> 13901 6950.0 0.6878180 0.6878180 9.482701e-03 9.482701e-03 0.302699309 #> 13902 6950.5 0.6878110 0.6878110 9.482973e-03 9.482973e-03 0.302706005 #> 13903 6951.0 0.6878041 0.6878041 9.483243e-03 9.483243e-03 0.302712702 #> 13904 6951.5 0.6877971 0.6877971 9.483512e-03 9.483512e-03 0.302719400 #> 13905 6952.0 0.6877901 0.6877901 9.483779e-03 9.483779e-03 0.302726100 #> 13906 6952.5 0.6877832 0.6877832 9.484045e-03 9.484045e-03 0.302732800 #> 13907 6953.0 0.6877762 0.6877762 9.484309e-03 9.484309e-03 0.302739502 #> 13908 6953.5 0.6877692 0.6877692 9.484571e-03 9.484571e-03 0.302746204 #> 13909 6954.0 0.6877623 0.6877623 9.484832e-03 9.484832e-03 0.302752908 #> 13910 6954.5 0.6877553 0.6877553 9.485091e-03 9.485091e-03 0.302759613 #> 13911 6955.0 0.6877483 0.6877483 9.485348e-03 9.485348e-03 0.302766318 #> 13912 6955.5 0.6877414 0.6877414 9.485604e-03 9.485604e-03 0.302773025 #> 13913 6956.0 0.6877344 0.6877344 9.485858e-03 9.485858e-03 0.302779732 #> 13914 6956.5 0.6877275 0.6877275 9.486110e-03 9.486110e-03 0.302786440 #> 13915 6957.0 0.6877205 0.6877205 9.486361e-03 9.486361e-03 0.302793149 #> 13916 6957.5 0.6877135 0.6877135 9.486610e-03 9.486610e-03 0.302799859 #> 13917 6958.0 0.6877066 0.6877066 9.486857e-03 9.486857e-03 0.302806570 #> 13918 6958.5 0.6876996 0.6876996 9.487103e-03 9.487103e-03 0.302813281 #> 13919 6959.0 0.6876927 0.6876927 9.487347e-03 9.487347e-03 0.302819993 #> 13920 6959.5 0.6876857 0.6876857 9.487590e-03 9.487590e-03 0.302826706 #> 13921 6960.0 0.6876788 0.6876788 9.487831e-03 9.487831e-03 0.302833419 #> 13922 6960.5 0.6876718 0.6876718 9.488070e-03 9.488070e-03 0.302840133 #> 13923 6961.0 0.6876648 0.6876648 9.488307e-03 9.488307e-03 0.302846848 #> 13924 6961.5 0.6876579 0.6876579 9.488543e-03 9.488543e-03 0.302853563 #> 13925 6962.0 0.6876509 0.6876509 9.488778e-03 9.488778e-03 0.302860279 #> 13926 6962.5 0.6876440 0.6876440 9.489010e-03 9.489010e-03 0.302866996 #> 13927 6963.0 0.6876370 0.6876370 9.489241e-03 9.489241e-03 0.302873713 #> 13928 6963.5 0.6876301 0.6876301 9.489471e-03 9.489471e-03 0.302880430 #> 13929 6964.0 0.6876232 0.6876232 9.489699e-03 9.489699e-03 0.302887148 #> 13930 6964.5 0.6876162 0.6876162 9.489925e-03 9.489925e-03 0.302893866 #> 13931 6965.0 0.6876093 0.6876093 9.490149e-03 9.490149e-03 0.302900585 #> 13932 6965.5 0.6876023 0.6876023 9.490372e-03 9.490372e-03 0.302907304 #> 13933 6966.0 0.6875954 0.6875954 9.490593e-03 9.490593e-03 0.302914023 #> 13934 6966.5 0.6875884 0.6875884 9.490813e-03 9.490813e-03 0.302920743 #> 13935 6967.0 0.6875815 0.6875815 9.491031e-03 9.491031e-03 0.302927463 #> 13936 6967.5 0.6875746 0.6875746 9.491247e-03 9.491247e-03 0.302934183 #> 13937 6968.0 0.6875676 0.6875676 9.491461e-03 9.491461e-03 0.302940904 #> 13938 6968.5 0.6875607 0.6875607 9.491674e-03 9.491674e-03 0.302947624 #> 13939 6969.0 0.6875538 0.6875538 9.491886e-03 9.491886e-03 0.302954345 #> 13940 6969.5 0.6875468 0.6875468 9.492095e-03 9.492095e-03 0.302961066 #> 13941 6970.0 0.6875399 0.6875399 9.492303e-03 9.492303e-03 0.302967787 #> 13942 6970.5 0.6875330 0.6875330 9.492510e-03 9.492510e-03 0.302974508 #> 13943 6971.0 0.6875261 0.6875261 9.492715e-03 9.492715e-03 0.302981230 #> 13944 6971.5 0.6875191 0.6875191 9.492918e-03 9.492918e-03 0.302987951 #> 13945 6972.0 0.6875122 0.6875122 9.493119e-03 9.493119e-03 0.302994672 #> 13946 6972.5 0.6875053 0.6875053 9.493319e-03 9.493319e-03 0.303001394 #> 13947 6973.0 0.6874984 0.6874984 9.493517e-03 9.493517e-03 0.303008115 #> 13948 6973.5 0.6874915 0.6874915 9.493714e-03 9.493714e-03 0.303014836 #> 13949 6974.0 0.6874845 0.6874845 9.493909e-03 9.493909e-03 0.303021557 #> 13950 6974.5 0.6874776 0.6874776 9.494102e-03 9.494102e-03 0.303028278 #> 13951 6975.0 0.6874707 0.6874707 9.494294e-03 9.494294e-03 0.303034999 #> 13952 6975.5 0.6874638 0.6874638 9.494484e-03 9.494484e-03 0.303041720 #> 13953 6976.0 0.6874569 0.6874569 9.494672e-03 9.494672e-03 0.303048440 #> 13954 6976.5 0.6874500 0.6874500 9.494859e-03 9.494859e-03 0.303055160 #> 13955 6977.0 0.6874431 0.6874431 9.495044e-03 9.495044e-03 0.303061880 #> 13956 6977.5 0.6874362 0.6874362 9.495228e-03 9.495228e-03 0.303068600 #> 13957 6978.0 0.6874293 0.6874293 9.495409e-03 9.495409e-03 0.303075319 #> 13958 6978.5 0.6874224 0.6874224 9.495590e-03 9.495590e-03 0.303082038 #> 13959 6979.0 0.6874155 0.6874155 9.495768e-03 9.495768e-03 0.303088757 #> 13960 6979.5 0.6874086 0.6874086 9.495945e-03 9.495945e-03 0.303095475 #> 13961 6980.0 0.6874017 0.6874017 9.496120e-03 9.496120e-03 0.303102193 #> 13962 6980.5 0.6873948 0.6873948 9.496294e-03 9.496294e-03 0.303108910 #> 13963 6981.0 0.6873879 0.6873879 9.496466e-03 9.496466e-03 0.303115627 #> 13964 6981.5 0.6873810 0.6873810 9.496637e-03 9.496637e-03 0.303122343 #> 13965 6982.0 0.6873741 0.6873741 9.496805e-03 9.496805e-03 0.303129059 #> 13966 6982.5 0.6873673 0.6873673 9.496972e-03 9.496972e-03 0.303135774 #> 13967 6983.0 0.6873604 0.6873604 9.497138e-03 9.497138e-03 0.303142489 #> 13968 6983.5 0.6873535 0.6873535 9.497302e-03 9.497302e-03 0.303149203 #> 13969 6984.0 0.6873466 0.6873466 9.497464e-03 9.497464e-03 0.303155917 #> 13970 6984.5 0.6873397 0.6873397 9.497625e-03 9.497625e-03 0.303162629 #> 13971 6985.0 0.6873329 0.6873329 9.497784e-03 9.497784e-03 0.303169342 #> 13972 6985.5 0.6873260 0.6873260 9.497941e-03 9.497941e-03 0.303176053 #> 13973 6986.0 0.6873191 0.6873191 9.498097e-03 9.498097e-03 0.303182764 #> 13974 6986.5 0.6873123 0.6873123 9.498251e-03 9.498251e-03 0.303189473 #> 13975 6987.0 0.6873054 0.6873054 9.498404e-03 9.498404e-03 0.303196182 #> 13976 6987.5 0.6872986 0.6872986 9.498554e-03 9.498554e-03 0.303202891 #> 13977 6988.0 0.6872917 0.6872917 9.498704e-03 9.498704e-03 0.303209598 #> 13978 6988.5 0.6872848 0.6872848 9.498851e-03 9.498851e-03 0.303216304 #> 13979 6989.0 0.6872780 0.6872780 9.498997e-03 9.498997e-03 0.303223010 #> 13980 6989.5 0.6872711 0.6872711 9.499142e-03 9.499142e-03 0.303229715 #> 13981 6990.0 0.6872643 0.6872643 9.499284e-03 9.499284e-03 0.303236418 #> 13982 6990.5 0.6872575 0.6872575 9.499425e-03 9.499425e-03 0.303243121 #> 13983 6991.0 0.6872506 0.6872506 9.499565e-03 9.499565e-03 0.303249823 #> 13984 6991.5 0.6872438 0.6872438 9.499703e-03 9.499703e-03 0.303256523 #> 13985 6992.0 0.6872369 0.6872369 9.499839e-03 9.499839e-03 0.303263223 #> 13986 6992.5 0.6872301 0.6872301 9.499974e-03 9.499974e-03 0.303269921 #> 13987 6993.0 0.6872233 0.6872233 9.500107e-03 9.500107e-03 0.303276619 #> 13988 6993.5 0.6872164 0.6872164 9.500238e-03 9.500238e-03 0.303283315 #> 13989 6994.0 0.6872096 0.6872096 9.500368e-03 9.500368e-03 0.303290010 #> 13990 6994.5 0.6872028 0.6872028 9.500496e-03 9.500496e-03 0.303296704 #> 13991 6995.0 0.6871960 0.6871960 9.500622e-03 9.500622e-03 0.303303397 #> 13992 6995.5 0.6871892 0.6871892 9.500747e-03 9.500747e-03 0.303310088 #> 13993 6996.0 0.6871824 0.6871824 9.500871e-03 9.500871e-03 0.303316778 #> 13994 6996.5 0.6871755 0.6871755 9.500992e-03 9.500992e-03 0.303323467 #> 13995 6997.0 0.6871687 0.6871687 9.501112e-03 9.501112e-03 0.303330154 #> 13996 6997.5 0.6871619 0.6871619 9.501231e-03 9.501231e-03 0.303336840 #> 13997 6998.0 0.6871551 0.6871551 9.501348e-03 9.501348e-03 0.303343525 #> 13998 6998.5 0.6871483 0.6871483 9.501463e-03 9.501463e-03 0.303350209 #> 13999 6999.0 0.6871415 0.6871415 9.501577e-03 9.501577e-03 0.303356890 #> 14000 6999.5 0.6871347 0.6871347 9.501689e-03 9.501689e-03 0.303363571 #> 14001 7000.0 0.6871280 0.6871280 9.501799e-03 9.501799e-03 0.303370250 #> 14002 7000.5 0.6871212 0.6871212 9.501908e-03 9.501908e-03 0.303376927 #> 14003 7001.0 0.6871144 0.6871144 9.502015e-03 9.502015e-03 0.303383603 #> 14004 7001.5 0.6871076 0.6871076 9.502120e-03 9.502120e-03 0.303390277 #> 14005 7002.0 0.6871008 0.6871008 9.502224e-03 9.502224e-03 0.303396950 #> 14006 7002.5 0.6870941 0.6870941 9.502327e-03 9.502327e-03 0.303403621 #> 14007 7003.0 0.6870873 0.6870873 9.502427e-03 9.502427e-03 0.303410291 #> 14008 7003.5 0.6870805 0.6870805 9.502527e-03 9.502527e-03 0.303416959 #> 14009 7004.0 0.6870738 0.6870738 9.502624e-03 9.502624e-03 0.303423625 #> 14010 7004.5 0.6870670 0.6870670 9.502720e-03 9.502720e-03 0.303430289 #> 14011 7005.0 0.6870602 0.6870602 9.502814e-03 9.502814e-03 0.303436952 #> 14012 7005.5 0.6870535 0.6870535 9.502907e-03 9.502907e-03 0.303443613 #> 14013 7006.0 0.6870467 0.6870467 9.502998e-03 9.502998e-03 0.303450272 #> 14014 7006.5 0.6870400 0.6870400 9.503088e-03 9.503088e-03 0.303456929 #> 14015 7007.0 0.6870332 0.6870332 9.503175e-03 9.503175e-03 0.303463584 #> 14016 7007.5 0.6870265 0.6870265 9.503262e-03 9.503262e-03 0.303470238 #> 14017 7008.0 0.6870198 0.6870198 9.503346e-03 9.503346e-03 0.303476889 #> 14018 7008.5 0.6870130 0.6870130 9.503429e-03 9.503429e-03 0.303483539 #> 14019 7009.0 0.6870063 0.6870063 9.503511e-03 9.503511e-03 0.303490187 #> 14020 7009.5 0.6869996 0.6869996 9.503591e-03 9.503591e-03 0.303496833 #> 14021 7010.0 0.6869929 0.6869929 9.503669e-03 9.503669e-03 0.303503476 #> 14022 7010.5 0.6869861 0.6869861 9.503746e-03 9.503746e-03 0.303510118 #> 14023 7011.0 0.6869794 0.6869794 9.503821e-03 9.503821e-03 0.303516758 #> 14024 7011.5 0.6869727 0.6869727 9.503894e-03 9.503894e-03 0.303523395 #> 14025 7012.0 0.6869660 0.6869660 9.503966e-03 9.503966e-03 0.303530031 #> 14026 7012.5 0.6869593 0.6869593 9.504037e-03 9.504037e-03 0.303536664 #> 14027 7013.0 0.6869526 0.6869526 9.504105e-03 9.504105e-03 0.303543295 #> 14028 7013.5 0.6869459 0.6869459 9.504173e-03 9.504173e-03 0.303549924 #> 14029 7014.0 0.6869392 0.6869392 9.504238e-03 9.504238e-03 0.303556551 #> 14030 7014.5 0.6869325 0.6869325 9.504302e-03 9.504302e-03 0.303563176 #> 14031 7015.0 0.6869258 0.6869258 9.504365e-03 9.504365e-03 0.303569798 #> 14032 7015.5 0.6869192 0.6869192 9.504425e-03 9.504425e-03 0.303576418 #> 14033 7016.0 0.6869125 0.6869125 9.504484e-03 9.504484e-03 0.303583036 #> 14034 7016.5 0.6869058 0.6869058 9.504542e-03 9.504542e-03 0.303589651 #> 14035 7017.0 0.6868991 0.6868991 9.504598e-03 9.504598e-03 0.303596264 #> 14036 7017.5 0.6868925 0.6868925 9.504653e-03 9.504653e-03 0.303602875 #> 14037 7018.0 0.6868858 0.6868858 9.504705e-03 9.504705e-03 0.303609483 #> 14038 7018.5 0.6868792 0.6868792 9.504757e-03 9.504757e-03 0.303616089 #> 14039 7019.0 0.6868725 0.6868725 9.504806e-03 9.504806e-03 0.303622692 #> 14040 7019.5 0.6868659 0.6868659 9.504855e-03 9.504855e-03 0.303629293 #> 14041 7020.0 0.6868592 0.6868592 9.504901e-03 9.504901e-03 0.303635891 #> 14042 7020.5 0.6868526 0.6868526 9.504946e-03 9.504946e-03 0.303642487 #> 14043 7021.0 0.6868459 0.6868459 9.504989e-03 9.504989e-03 0.303649081 #> 14044 7021.5 0.6868393 0.6868393 9.505031e-03 9.505031e-03 0.303655671 #> 14045 7022.0 0.6868327 0.6868327 9.505071e-03 9.505071e-03 0.303662259 #> 14046 7022.5 0.6868260 0.6868260 9.505110e-03 9.505110e-03 0.303668845 #> 14047 7023.0 0.6868194 0.6868194 9.505147e-03 9.505147e-03 0.303675427 #> 14048 7023.5 0.6868128 0.6868128 9.505183e-03 9.505183e-03 0.303682007 #> 14049 7024.0 0.6868062 0.6868062 9.505217e-03 9.505217e-03 0.303688585 #> 14050 7024.5 0.6867996 0.6867996 9.505249e-03 9.505249e-03 0.303695159 #> 14051 7025.0 0.6867930 0.6867930 9.505280e-03 9.505280e-03 0.303701731 #> 14052 7025.5 0.6867864 0.6867864 9.505309e-03 9.505309e-03 0.303708300 #> 14053 7026.0 0.6867798 0.6867798 9.505337e-03 9.505337e-03 0.303714867 #> 14054 7026.5 0.6867732 0.6867732 9.505363e-03 9.505363e-03 0.303721430 #> 14055 7027.0 0.6867666 0.6867666 9.505387e-03 9.505387e-03 0.303727991 #> 14056 7027.5 0.6867600 0.6867600 9.505410e-03 9.505410e-03 0.303734548 #> 14057 7028.0 0.6867535 0.6867535 9.505432e-03 9.505432e-03 0.303741103 #> 14058 7028.5 0.6867469 0.6867469 9.505452e-03 9.505452e-03 0.303747655 #> 14059 7029.0 0.6867403 0.6867403 9.505470e-03 9.505470e-03 0.303754204 #> 14060 7029.5 0.6867338 0.6867338 9.505487e-03 9.505487e-03 0.303760750 #> 14061 7030.0 0.6867272 0.6867272 9.505502e-03 9.505502e-03 0.303767293 #> 14062 7030.5 0.6867207 0.6867207 9.505515e-03 9.505515e-03 0.303773832 #> 14063 7031.0 0.6867141 0.6867141 9.505527e-03 9.505527e-03 0.303780369 #> 14064 7031.5 0.6867076 0.6867076 9.505538e-03 9.505538e-03 0.303786903 #> 14065 7032.0 0.6867010 0.6867010 9.505547e-03 9.505547e-03 0.303793434 #> 14066 7032.5 0.6866945 0.6866945 9.505554e-03 9.505554e-03 0.303799961 #> 14067 7033.0 0.6866880 0.6866880 9.505560e-03 9.505560e-03 0.303806485 #> 14068 7033.5 0.6866814 0.6866814 9.505564e-03 9.505564e-03 0.303813007 #> 14069 7034.0 0.6866749 0.6866749 9.505567e-03 9.505567e-03 0.303819525 #> 14070 7034.5 0.6866684 0.6866684 9.505568e-03 9.505568e-03 0.303826039 #> 14071 7035.0 0.6866619 0.6866619 9.505568e-03 9.505568e-03 0.303832551 #> 14072 7035.5 0.6866554 0.6866554 9.505566e-03 9.505566e-03 0.303839059 #> 14073 7036.0 0.6866489 0.6866489 9.505563e-03 9.505563e-03 0.303845564 #> 14074 7036.5 0.6866424 0.6866424 9.505558e-03 9.505558e-03 0.303852066 #> 14075 7037.0 0.6866359 0.6866359 9.505551e-03 9.505551e-03 0.303858564 #> 14076 7037.5 0.6866294 0.6866294 9.505543e-03 9.505543e-03 0.303865059 #> 14077 7038.0 0.6866229 0.6866229 9.505533e-03 9.505533e-03 0.303871550 #> 14078 7038.5 0.6866164 0.6866164 9.505522e-03 9.505522e-03 0.303878038 #> 14079 7039.0 0.6866100 0.6866100 9.505509e-03 9.505509e-03 0.303884523 #> 14080 7039.5 0.6866035 0.6866035 9.505495e-03 9.505495e-03 0.303891004 #> 14081 7040.0 0.6865970 0.6865970 9.505479e-03 9.505479e-03 0.303897482 #> 14082 7040.5 0.6865906 0.6865906 9.505462e-03 9.505462e-03 0.303903956 #> 14083 7041.0 0.6865841 0.6865841 9.505443e-03 9.505443e-03 0.303910426 #> 14084 7041.5 0.6865777 0.6865777 9.505423e-03 9.505423e-03 0.303916893 #> 14085 7042.0 0.6865712 0.6865712 9.505401e-03 9.505401e-03 0.303923357 #> 14086 7042.5 0.6865648 0.6865648 9.505378e-03 9.505378e-03 0.303929817 #> 14087 7043.0 0.6865584 0.6865584 9.505353e-03 9.505353e-03 0.303936273 #> 14088 7043.5 0.6865519 0.6865519 9.505326e-03 9.505326e-03 0.303942726 #> 14089 7044.0 0.6865455 0.6865455 9.505298e-03 9.505298e-03 0.303949175 #> 14090 7044.5 0.6865391 0.6865391 9.505269e-03 9.505269e-03 0.303955620 #> 14091 7045.0 0.6865327 0.6865327 9.505237e-03 9.505237e-03 0.303962061 #> 14092 7045.5 0.6865263 0.6865263 9.505205e-03 9.505205e-03 0.303968499 #> 14093 7046.0 0.6865199 0.6865199 9.505171e-03 9.505171e-03 0.303974933 #> 14094 7046.5 0.6865135 0.6865135 9.505135e-03 9.505135e-03 0.303981363 #> 14095 7047.0 0.6865071 0.6865071 9.505098e-03 9.505098e-03 0.303987789 #> 14096 7047.5 0.6865007 0.6865007 9.505059e-03 9.505059e-03 0.303994212 #> 14097 7048.0 0.6864944 0.6864944 9.505019e-03 9.505019e-03 0.304000630 #> 14098 7048.5 0.6864880 0.6864880 9.504977e-03 9.504977e-03 0.304007045 #> 14099 7049.0 0.6864816 0.6864816 9.504934e-03 9.504934e-03 0.304013456 #> 14100 7049.5 0.6864752 0.6864752 9.504889e-03 9.504889e-03 0.304019863 #> 14101 7050.0 0.6864689 0.6864689 9.504843e-03 9.504843e-03 0.304026266 #> 14102 7050.5 0.6864625 0.6864625 9.504795e-03 9.504795e-03 0.304032665 #> 14103 7051.0 0.6864562 0.6864562 9.504746e-03 9.504746e-03 0.304039060 #> 14104 7051.5 0.6864499 0.6864499 9.504695e-03 9.504695e-03 0.304045450 #> 14105 7052.0 0.6864435 0.6864435 9.504643e-03 9.504643e-03 0.304051837 #> 14106 7052.5 0.6864372 0.6864372 9.504589e-03 9.504589e-03 0.304058220 #> 14107 7053.0 0.6864309 0.6864309 9.504534e-03 9.504534e-03 0.304064599 #> 14108 7053.5 0.6864245 0.6864245 9.504477e-03 9.504477e-03 0.304070973 #> 14109 7054.0 0.6864182 0.6864182 9.504419e-03 9.504419e-03 0.304077344 #> 14110 7054.5 0.6864119 0.6864119 9.504359e-03 9.504359e-03 0.304083710 #> 14111 7055.0 0.6864056 0.6864056 9.504298e-03 9.504298e-03 0.304090072 #> 14112 7055.5 0.6863993 0.6863993 9.504235e-03 9.504235e-03 0.304096430 #> 14113 7056.0 0.6863930 0.6863930 9.504171e-03 9.504171e-03 0.304102783 #> 14114 7056.5 0.6863868 0.6863868 9.504105e-03 9.504105e-03 0.304109133 #> 14115 7057.0 0.6863805 0.6863805 9.504038e-03 9.504038e-03 0.304115478 #> 14116 7057.5 0.6863742 0.6863742 9.503969e-03 9.503969e-03 0.304121818 #> 14117 7058.0 0.6863679 0.6863679 9.503899e-03 9.503899e-03 0.304128155 #> 14118 7058.5 0.6863617 0.6863617 9.503827e-03 9.503827e-03 0.304134487 #> 14119 7059.0 0.6863554 0.6863554 9.503754e-03 9.503754e-03 0.304140815 #> 14120 7059.5 0.6863492 0.6863492 9.503679e-03 9.503679e-03 0.304147138 #> 14121 7060.0 0.6863429 0.6863429 9.503603e-03 9.503603e-03 0.304153457 #> 14122 7060.5 0.6863367 0.6863367 9.503526e-03 9.503526e-03 0.304159771 #> 14123 7061.0 0.6863305 0.6863305 9.503446e-03 9.503446e-03 0.304166081 #> 14124 7061.5 0.6863242 0.6863242 9.503366e-03 9.503366e-03 0.304172387 #> 14125 7062.0 0.6863180 0.6863180 9.503284e-03 9.503284e-03 0.304178688 #> 14126 7062.5 0.6863118 0.6863118 9.503200e-03 9.503200e-03 0.304184984 #> 14127 7063.0 0.6863056 0.6863056 9.503115e-03 9.503115e-03 0.304191276 #> 14128 7063.5 0.6862994 0.6862994 9.503029e-03 9.503029e-03 0.304197563 #> 14129 7064.0 0.6862932 0.6862932 9.502941e-03 9.502941e-03 0.304203846 #> 14130 7064.5 0.6862870 0.6862870 9.502851e-03 9.502851e-03 0.304210124 #> 14131 7065.0 0.6862808 0.6862808 9.502760e-03 9.502760e-03 0.304216398 #> 14132 7065.5 0.6862747 0.6862747 9.502668e-03 9.502668e-03 0.304222666 #> 14133 7066.0 0.6862685 0.6862685 9.502574e-03 9.502574e-03 0.304228930 #> 14134 7066.5 0.6862623 0.6862623 9.502479e-03 9.502479e-03 0.304235190 #> 14135 7067.0 0.6862562 0.6862562 9.502382e-03 9.502382e-03 0.304241445 #> 14136 7067.5 0.6862500 0.6862500 9.502284e-03 9.502284e-03 0.304247694 #> 14137 7068.0 0.6862439 0.6862439 9.502184e-03 9.502184e-03 0.304253940 #> 14138 7068.5 0.6862377 0.6862377 9.502083e-03 9.502083e-03 0.304260180 #> 14139 7069.0 0.6862316 0.6862316 9.501980e-03 9.501980e-03 0.304266416 #> 14140 7069.5 0.6862255 0.6862255 9.501876e-03 9.501876e-03 0.304272646 #> 14141 7070.0 0.6862194 0.6862194 9.501770e-03 9.501770e-03 0.304278872 #> 14142 7070.5 0.6862132 0.6862132 9.501663e-03 9.501663e-03 0.304285093 #> 14143 7071.0 0.6862071 0.6862071 9.501555e-03 9.501555e-03 0.304291309 #> 14144 7071.5 0.6862010 0.6862010 9.501445e-03 9.501445e-03 0.304297520 #> 14145 7072.0 0.6861949 0.6861949 9.501334e-03 9.501334e-03 0.304303726 #> 14146 7072.5 0.6861889 0.6861889 9.501221e-03 9.501221e-03 0.304309928 #> 14147 7073.0 0.6861828 0.6861828 9.501107e-03 9.501107e-03 0.304316124 #> 14148 7073.5 0.6861767 0.6861767 9.500991e-03 9.500991e-03 0.304322315 #> 14149 7074.0 0.6861706 0.6861706 9.500874e-03 9.500874e-03 0.304328501 #> 14150 7074.5 0.6861646 0.6861646 9.500756e-03 9.500756e-03 0.304334682 #> 14151 7075.0 0.6861585 0.6861585 9.500636e-03 9.500636e-03 0.304340858 #> 14152 7075.5 0.6861525 0.6861525 9.500514e-03 9.500514e-03 0.304347029 #> 14153 7076.0 0.6861464 0.6861464 9.500391e-03 9.500391e-03 0.304353195 #> 14154 7076.5 0.6861404 0.6861404 9.500267e-03 9.500267e-03 0.304359356 #> 14155 7077.0 0.6861343 0.6861343 9.500141e-03 9.500141e-03 0.304365511 #> 14156 7077.5 0.6861283 0.6861283 9.500014e-03 9.500014e-03 0.304371662 #> 14157 7078.0 0.6861223 0.6861223 9.499886e-03 9.499886e-03 0.304377807 #> 14158 7078.5 0.6861163 0.6861163 9.499756e-03 9.499756e-03 0.304383947 #> 14159 7079.0 0.6861103 0.6861103 9.499624e-03 9.499624e-03 0.304390081 #> 14160 7079.5 0.6861043 0.6861043 9.499491e-03 9.499491e-03 0.304396211 #> 14161 7080.0 0.6860983 0.6860983 9.499357e-03 9.499357e-03 0.304402335 #> 14162 7080.5 0.6860923 0.6860923 9.499221e-03 9.499221e-03 0.304408454 #> 14163 7081.0 0.6860863 0.6860863 9.499084e-03 9.499084e-03 0.304414567 #> 14164 7081.5 0.6860804 0.6860804 9.498946e-03 9.498946e-03 0.304420675 #> 14165 7082.0 0.6860744 0.6860744 9.498806e-03 9.498806e-03 0.304426778 #> 14166 7082.5 0.6860685 0.6860685 9.498664e-03 9.498664e-03 0.304432875 #> 14167 7083.0 0.6860625 0.6860625 9.498522e-03 9.498522e-03 0.304438967 #> 14168 7083.5 0.6860566 0.6860566 9.498378e-03 9.498378e-03 0.304445054 #> 14169 7084.0 0.6860506 0.6860506 9.498232e-03 9.498232e-03 0.304451135 #> 14170 7084.5 0.6860447 0.6860447 9.498085e-03 9.498085e-03 0.304457210 #> 14171 7085.0 0.6860388 0.6860388 9.497937e-03 9.497937e-03 0.304463280 #> 14172 7085.5 0.6860329 0.6860329 9.497787e-03 9.497787e-03 0.304469345 #> 14173 7086.0 0.6860270 0.6860270 9.497636e-03 9.497636e-03 0.304475404 #> 14174 7086.5 0.6860211 0.6860211 9.497483e-03 9.497483e-03 0.304481457 #> 14175 7087.0 0.6860152 0.6860152 9.497329e-03 9.497329e-03 0.304487505 #> 14176 7087.5 0.6860093 0.6860093 9.497174e-03 9.497174e-03 0.304493547 #> 14177 7088.0 0.6860034 0.6860034 9.497017e-03 9.497017e-03 0.304499584 #> 14178 7088.5 0.6859975 0.6859975 9.496859e-03 9.496859e-03 0.304505615 #> 14179 7089.0 0.6859917 0.6859917 9.496699e-03 9.496699e-03 0.304511640 #> 14180 7089.5 0.6859858 0.6859858 9.496538e-03 9.496538e-03 0.304517660 #> 14181 7090.0 0.6859800 0.6859800 9.496376e-03 9.496376e-03 0.304523674 #> 14182 7090.5 0.6859741 0.6859741 9.496212e-03 9.496212e-03 0.304529682 #> 14183 7091.0 0.6859683 0.6859683 9.496047e-03 9.496047e-03 0.304535685 #> 14184 7091.5 0.6859624 0.6859624 9.495881e-03 9.495881e-03 0.304541681 #> 14185 7092.0 0.6859566 0.6859566 9.495713e-03 9.495713e-03 0.304547672 #> 14186 7092.5 0.6859508 0.6859508 9.495543e-03 9.495543e-03 0.304553658 #> 14187 7093.0 0.6859450 0.6859450 9.495373e-03 9.495373e-03 0.304559637 #> 14188 7093.5 0.6859392 0.6859392 9.495201e-03 9.495201e-03 0.304565610 #> 14189 7094.0 0.6859334 0.6859334 9.495027e-03 9.495027e-03 0.304571578 #> 14190 7094.5 0.6859276 0.6859276 9.494853e-03 9.494853e-03 0.304577540 #> 14191 7095.0 0.6859218 0.6859218 9.494677e-03 9.494677e-03 0.304583495 #> 14192 7095.5 0.6859161 0.6859161 9.494499e-03 9.494499e-03 0.304589445 #> 14193 7096.0 0.6859103 0.6859103 9.494320e-03 9.494320e-03 0.304595389 #> 14194 7096.5 0.6859045 0.6859045 9.494140e-03 9.494140e-03 0.304601327 #> 14195 7097.0 0.6858988 0.6858988 9.493958e-03 9.493958e-03 0.304607259 #> 14196 7097.5 0.6858930 0.6858930 9.493775e-03 9.493775e-03 0.304613185 #> 14197 7098.0 0.6858873 0.6858873 9.493591e-03 9.493591e-03 0.304619105 #> 14198 7098.5 0.6858816 0.6858816 9.493406e-03 9.493406e-03 0.304625019 #> 14199 7099.0 0.6858759 0.6858759 9.493219e-03 9.493219e-03 0.304630927 #> 14200 7099.5 0.6858701 0.6858701 9.493030e-03 9.493030e-03 0.304636829 #> 14201 7100.0 0.6858644 0.6858644 9.492840e-03 9.492840e-03 0.304642725 #> 14202 7100.5 0.6858587 0.6858587 9.492649e-03 9.492649e-03 0.304648614 #> 14203 7101.0 0.6858530 0.6858530 9.492457e-03 9.492457e-03 0.304654498 #> 14204 7101.5 0.6858474 0.6858474 9.492263e-03 9.492263e-03 0.304660375 #> 14205 7102.0 0.6858417 0.6858417 9.492068e-03 9.492068e-03 0.304666246 #> 14206 7102.5 0.6858360 0.6858360 9.491872e-03 9.491872e-03 0.304672111 #> 14207 7103.0 0.6858304 0.6858304 9.491674e-03 9.491674e-03 0.304677970 #> 14208 7103.5 0.6858247 0.6858247 9.491475e-03 9.491475e-03 0.304683822 #> 14209 7104.0 0.6858191 0.6858191 9.491274e-03 9.491274e-03 0.304689668 #> 14210 7104.5 0.6858134 0.6858134 9.491073e-03 9.491073e-03 0.304695508 #> 14211 7105.0 0.6858078 0.6858078 9.490869e-03 9.490869e-03 0.304701342 #> 14212 7105.5 0.6858022 0.6858022 9.490665e-03 9.490665e-03 0.304707169 #> 14213 7106.0 0.6857966 0.6857966 9.490459e-03 9.490459e-03 0.304712990 #> 14214 7106.5 0.6857909 0.6857909 9.490252e-03 9.490252e-03 0.304718805 #> 14215 7107.0 0.6857853 0.6857853 9.490044e-03 9.490044e-03 0.304724613 #> 14216 7107.5 0.6857798 0.6857798 9.489834e-03 9.489834e-03 0.304730415 #> 14217 7108.0 0.6857742 0.6857742 9.489623e-03 9.489623e-03 0.304736211 #> 14218 7108.5 0.6857686 0.6857686 9.489410e-03 9.489410e-03 0.304742000 #> 14219 7109.0 0.6857630 0.6857630 9.489197e-03 9.489197e-03 0.304747782 #> 14220 7109.5 0.6857575 0.6857575 9.488982e-03 9.488982e-03 0.304753558 #> 14221 7110.0 0.6857519 0.6857519 9.488765e-03 9.488765e-03 0.304759328 #> 14222 7110.5 0.6857464 0.6857464 9.488548e-03 9.488548e-03 0.304765091 #> 14223 7111.0 0.6857408 0.6857408 9.488329e-03 9.488329e-03 0.304770848 #> 14224 7111.5 0.6857353 0.6857353 9.488108e-03 9.488108e-03 0.304776598 #> 14225 7112.0 0.6857298 0.6857298 9.487887e-03 9.487887e-03 0.304782342 #> 14226 7112.5 0.6857243 0.6857243 9.487664e-03 9.487664e-03 0.304788079 #> 14227 7113.0 0.6857188 0.6857188 9.487440e-03 9.487440e-03 0.304793809 #> 14228 7113.5 0.6857133 0.6857133 9.487214e-03 9.487214e-03 0.304799533 #> 14229 7114.0 0.6857078 0.6857078 9.486988e-03 9.486988e-03 0.304805250 #> 14230 7114.5 0.6857023 0.6857023 9.486759e-03 9.486759e-03 0.304810960 #> 14231 7115.0 0.6856968 0.6856968 9.486530e-03 9.486530e-03 0.304816664 #> 14232 7115.5 0.6856913 0.6856913 9.486299e-03 9.486299e-03 0.304822361 #> 14233 7116.0 0.6856859 0.6856859 9.486067e-03 9.486067e-03 0.304828052 #> 14234 7116.5 0.6856804 0.6856804 9.485834e-03 9.485834e-03 0.304833736 #> 14235 7117.0 0.6856750 0.6856750 9.485600e-03 9.485600e-03 0.304839413 #> 14236 7117.5 0.6856696 0.6856696 9.485364e-03 9.485364e-03 0.304845083 #> 14237 7118.0 0.6856641 0.6856641 9.485127e-03 9.485127e-03 0.304850746 #> 14238 7118.5 0.6856587 0.6856587 9.484888e-03 9.484888e-03 0.304856403 #> 14239 7119.0 0.6856533 0.6856533 9.484649e-03 9.484649e-03 0.304862053 #> 14240 7119.5 0.6856479 0.6856479 9.484408e-03 9.484408e-03 0.304867696 #> 14241 7120.0 0.6856425 0.6856425 9.484166e-03 9.484166e-03 0.304873332 #> 14242 7120.5 0.6856371 0.6856371 9.483922e-03 9.483922e-03 0.304878961 #> 14243 7121.0 0.6856317 0.6856317 9.483677e-03 9.483677e-03 0.304884584 #> 14244 7121.5 0.6856264 0.6856264 9.483431e-03 9.483431e-03 0.304890199 #> 14245 7122.0 0.6856210 0.6856210 9.483184e-03 9.483184e-03 0.304895808 #> 14246 7122.5 0.6856157 0.6856157 9.482936e-03 9.482936e-03 0.304901410 #> 14247 7123.0 0.6856103 0.6856103 9.482686e-03 9.482686e-03 0.304907004 #> 14248 7123.5 0.6856050 0.6856050 9.482435e-03 9.482435e-03 0.304912592 #> 14249 7124.0 0.6855996 0.6855996 9.482182e-03 9.482182e-03 0.304918173 #> 14250 7124.5 0.6855943 0.6855943 9.481929e-03 9.481929e-03 0.304923747 #> 14251 7125.0 0.6855890 0.6855890 9.481674e-03 9.481674e-03 0.304929314 #> 14252 7125.5 0.6855837 0.6855837 9.481418e-03 9.481418e-03 0.304934873 #> 14253 7126.0 0.6855784 0.6855784 9.481161e-03 9.481161e-03 0.304940426 #> 14254 7126.5 0.6855731 0.6855731 9.480902e-03 9.480902e-03 0.304945972 #> 14255 7127.0 0.6855678 0.6855678 9.480642e-03 9.480642e-03 0.304951510 #> 14256 7127.5 0.6855626 0.6855626 9.480381e-03 9.480381e-03 0.304957041 #> 14257 7128.0 0.6855573 0.6855573 9.480119e-03 9.480119e-03 0.304962566 #> 14258 7128.5 0.6855521 0.6855521 9.479855e-03 9.479855e-03 0.304968083 #> 14259 7129.0 0.6855468 0.6855468 9.479591e-03 9.479591e-03 0.304973593 #> 14260 7129.5 0.6855416 0.6855416 9.479325e-03 9.479325e-03 0.304979096 #> 14261 7130.0 0.6855364 0.6855364 9.479057e-03 9.479057e-03 0.304984591 #> 14262 7130.5 0.6855311 0.6855311 9.478789e-03 9.478789e-03 0.304990080 #> 14263 7131.0 0.6855259 0.6855259 9.478519e-03 9.478519e-03 0.304995561 #> 14264 7131.5 0.6855207 0.6855207 9.478248e-03 9.478248e-03 0.305001035 #> 14265 7132.0 0.6855155 0.6855155 9.477976e-03 9.477976e-03 0.305006501 #> 14266 7132.5 0.6855103 0.6855103 9.477703e-03 9.477703e-03 0.305011961 #> 14267 7133.0 0.6855052 0.6855052 9.477428e-03 9.477428e-03 0.305017413 #> 14268 7133.5 0.6855000 0.6855000 9.477152e-03 9.477152e-03 0.305022857 #> 14269 7134.0 0.6854948 0.6854948 9.476875e-03 9.476875e-03 0.305028295 #> 14270 7134.5 0.6854897 0.6854897 9.476597e-03 9.476597e-03 0.305033725 #> 14271 7135.0 0.6854845 0.6854845 9.476318e-03 9.476318e-03 0.305039148 #> 14272 7135.5 0.6854794 0.6854794 9.476037e-03 9.476037e-03 0.305044563 #> 14273 7136.0 0.6854743 0.6854743 9.475755e-03 9.475755e-03 0.305049971 #> 14274 7136.5 0.6854692 0.6854692 9.475472e-03 9.475472e-03 0.305055372 #> 14275 7137.0 0.6854640 0.6854640 9.475188e-03 9.475188e-03 0.305060765 #> 14276 7137.5 0.6854589 0.6854589 9.474902e-03 9.474902e-03 0.305066150 #> 14277 7138.0 0.6854539 0.6854539 9.474616e-03 9.474616e-03 0.305071529 #> 14278 7138.5 0.6854488 0.6854488 9.474328e-03 9.474328e-03 0.305076899 #> 14279 7139.0 0.6854437 0.6854437 9.474039e-03 9.474039e-03 0.305082263 #> 14280 7139.5 0.6854386 0.6854386 9.473748e-03 9.473748e-03 0.305087618 #> 14281 7140.0 0.6854336 0.6854336 9.473457e-03 9.473457e-03 0.305092967 #> 14282 7140.5 0.6854285 0.6854285 9.473164e-03 9.473164e-03 0.305098307 #> 14283 7141.0 0.6854235 0.6854235 9.472871e-03 9.472871e-03 0.305103641 #> 14284 7141.5 0.6854185 0.6854185 9.472576e-03 9.472576e-03 0.305108966 #> 14285 7142.0 0.6854134 0.6854134 9.472279e-03 9.472279e-03 0.305114284 #>                r2 #> 1     0.000000000 #> 2     0.003337146 #> 3     0.006681767 #> 4     0.010033558 #> 5     0.013392251 #> 6     0.016757489 #> 7     0.020128975 #> 8     0.023506400 #> 9     0.026889453 #> 10    0.030277824 #> 11    0.033671200 #> 12    0.037069269 #> 13    0.040471717 #> 14    0.043878231 #> 15    0.047288499 #> 16    0.050702206 #> 17    0.054119038 #> 18    0.057538683 #> 19    0.060960827 #> 20    0.064385157 #> 21    0.067811362 #> 22    0.071239129 #> 23    0.074668149 #> 24    0.078098110 #> 25    0.081528705 #> 26    0.084959625 #> 27    0.088390563 #> 28    0.091821214 #> 29    0.095251274 #> 30    0.098680441 #> 31    0.102108412 #> 32    0.105534890 #> 33    0.108959577 #> 34    0.112382176 #> 35    0.115802395 #> 36    0.119219941 #> 37    0.122634526 #> 38    0.126045862 #> 39    0.129453664 #> 40    0.132857649 #> 41    0.136257538 #> 42    0.139653053 #> 43    0.143043919 #> 44    0.146429864 #> 45    0.149810618 #> 46    0.153185914 #> 47    0.156555490 #> 48    0.159919084 #> 49    0.163276437 #> 50    0.166627296 #> 51    0.169971409 #> 52    0.173308527 #> 53    0.176638404 #> 54    0.179960799 #> 55    0.183275473 #> 56    0.186582190 #> 57    0.189880719 #> 58    0.193170830 #> 59    0.196452298 #> 60    0.199724902 #> 61    0.202988423 #> 62    0.206242646 #> 63    0.209487361 #> 64    0.212722359 #> 65    0.215947437 #> 66    0.219162394 #> 67    0.222367034 #> 68    0.225561163 #> 69    0.228744592 #> 70    0.231917135 #> 71    0.235078610 #> 72    0.238228838 #> 73    0.241367645 #> 74    0.244494860 #> 75    0.247610314 #> 76    0.250713846 #> 77    0.253805294 #> 78    0.256884502 #> 79    0.259951318 #> 80    0.263005592 #> 81    0.266047180 #> 82    0.269075939 #> 83    0.272091732 #> 84    0.275094424 #> 85    0.278083884 #> 86    0.281059985 #> 87    0.284022604 #> 88    0.286971620 #> 89    0.289906917 #> 90    0.292828382 #> 91    0.295735905 #> 92    0.298629380 #> 93    0.301508705 #> 94    0.304373780 #> 95    0.307224510 #> 96    0.310060801 #> 97    0.312882565 #> 98    0.315689717 #> 99    0.318482173 #> 100   0.321259854 #> 101   0.324022684 #> 102   0.326770590 #> 103   0.329503503 #> 104   0.332221355 #> 105   0.334924084 #> 106   0.337611628 #> 107   0.340283931 #> 108   0.342940938 #> 109   0.345582596 #> 110   0.348208859 #> 111   0.350819678 #> 112   0.353415013 #> 113   0.355994822 #> 114   0.358559069 #> 115   0.361107718 #> 116   0.363640737 #> 117   0.366158098 #> 118   0.368659773 #> 119   0.371145738 #> 120   0.373615972 #> 121   0.376070455 #> 122   0.378509171 #> 123   0.380932105 #> 124   0.383339246 #> 125   0.385730583 #> 126   0.388106110 #> 127   0.390465822 #> 128   0.392809716 #> 129   0.395137790 #> 130   0.397450047 #> 131   0.399746490 #> 132   0.402027125 #> 133   0.404291960 #> 134   0.406541004 #> 135   0.408774268 #> 136   0.410991767 #> 137   0.413193516 #> 138   0.415379531 #> 139   0.417549833 #> 140   0.419704442 #> 141   0.421843379 #> 142   0.423966671 #> 143   0.426074342 #> 144   0.428166419 #> 145   0.430242933 #> 146   0.432303913 #> 147   0.434349392 #> 148   0.436379403 #> 149   0.438393982 #> 150   0.440393164 #> 151   0.442376988 #> 152   0.444345492 #> 153   0.446298718 #> 154   0.448236707 #> 155   0.450159501 #> 156   0.452067145 #> 157   0.453959685 #> 158   0.455837167 #> 159   0.457699638 #> 160   0.459547147 #> 161   0.461379744 #> 162   0.463197480 #> 163   0.465000407 #> 164   0.466788577 #> 165   0.468562043 #> 166   0.470320861 #> 167   0.472065086 #> 168   0.473794774 #> 169   0.475509983 #> 170   0.477210770 #> 171   0.478897194 #> 172   0.480569315 #> 173   0.482227192 #> 174   0.483870887 #> 175   0.485500461 #> 176   0.487115976 #> 177   0.488717496 #> 178   0.490305083 #> 179   0.491878801 #> 180   0.493438716 #> 181   0.494984892 #> 182   0.496517395 #> 183   0.498036291 #> 184   0.499541646 #> 185   0.501033528 #> 186   0.502512003 #> 187   0.503977140 #> 188   0.505429007 #> 189   0.506867673 #> 190   0.508293205 #> 191   0.509705675 #> 192   0.511105150 #> 193   0.512491702 #> 194   0.513865399 #> 195   0.515226314 #> 196   0.516574515 #> 197   0.517910074 #> 198   0.519233062 #> 199   0.520543550 #> 200   0.521841609 #> 201   0.523127312 #> 202   0.524400730 #> 203   0.525661934 #> 204   0.526910997 #> 205   0.528147990 #> 206   0.529372986 #> 207   0.530586057 #> 208   0.531787275 #> 209   0.532976713 #> 210   0.534154442 #> 211   0.535320536 #> 212   0.536475066 #> 213   0.537618105 #> 214   0.538749725 #> 215   0.539869999 #> 216   0.540978999 #> 217   0.542076796 #> 218   0.543163464 #> 219   0.544239075 #> 220   0.545303699 #> 221   0.546357411 #> 222   0.547400280 #> 223   0.548432379 #> 224   0.549453780 #> 225   0.550464554 #> 226   0.551464772 #> 227   0.552454506 #> 228   0.553433827 #> 229   0.554402805 #> 230   0.555361512 #> 231   0.556310018 #> 232   0.557248393 #> 233   0.558176708 #> 234   0.559095033 #> 235   0.560003438 #> 236   0.560901991 #> 237   0.561790763 #> 238   0.562669823 #> 239   0.563539240 #> 240   0.564399082 #> 241   0.565249419 #> 242   0.566090317 #> 243   0.566921846 #> 244   0.567744073 #> 245   0.568557066 #> 246   0.569360892 #> 247   0.570155618 #> 248   0.570941310 #> 249   0.571718036 #> 250   0.572485861 #> 251   0.573244851 #> 252   0.573995072 #> 253   0.574736589 #> 254   0.575469468 #> 255   0.576193772 #> 256   0.576909567 #> 257   0.577616917 #> 258   0.578315885 #> 259   0.579006535 #> 260   0.579688930 #> 261   0.580363134 #> 262   0.581029209 #> 263   0.581687216 #> 264   0.582337219 #> 265   0.582979278 #> 266   0.583613456 #> 267   0.584239813 #> 268   0.584858409 #> 269   0.585469306 #> 270   0.586072563 #> 271   0.586668240 #> 272   0.587256397 #> 273   0.587837092 #> 274   0.588410384 #> 275   0.588976331 #> 276   0.589534992 #> 277   0.590086425 #> 278   0.590630686 #> 279   0.591167832 #> 280   0.591697921 #> 281   0.592221009 #> 282   0.592737151 #> 283   0.593246404 #> 284   0.593748823 #> 285   0.594244463 #> 286   0.594733377 #> 287   0.595215622 #> 288   0.595691250 #> 289   0.596160316 #> 290   0.596622872 #> 291   0.597078972 #> 292   0.597528668 #> 293   0.597972013 #> 294   0.598409058 #> 295   0.598839854 #> 296   0.599264455 #> 297   0.599682909 #> 298   0.600095268 #> 299   0.600501582 #> 300   0.600901900 #> 301   0.601296274 #> 302   0.601684750 #> 303   0.602067379 #> 304   0.602444210 #> 305   0.602815289 #> 306   0.603180666 #> 307   0.603540387 #> 308   0.603894500 #> 309   0.604243052 #> 310   0.604586090 #> 311   0.604923659 #> 312   0.605255805 #> 313   0.605582574 #> 314   0.605904012 #> 315   0.606220163 #> 316   0.606531071 #> 317   0.606836782 #> 318   0.607137338 #> 319   0.607432784 #> 320   0.607723164 #> 321   0.608008519 #> 322   0.608288892 #> 323   0.608564327 #> 324   0.608834864 #> 325   0.609100547 #> 326   0.609361415 #> 327   0.609617511 #> 328   0.609868875 #> 329   0.610115547 #> 330   0.610357569 #> 331   0.610594978 #> 332   0.610827816 #> 333   0.611056122 #> 334   0.611279934 #> 335   0.611499291 #> 336   0.611714232 #> 337   0.611924794 #> 338   0.612131016 #> 339   0.612332935 #> 340   0.612530588 #> 341   0.612724012 #> 342   0.612913244 #> 343   0.613098320 #> 344   0.613279276 #> 345   0.613456148 #> 346   0.613628972 #> 347   0.613797782 #> 348   0.613962614 #> 349   0.614123502 #> 350   0.614280480 #> 351   0.614433584 #> 352   0.614582846 #> 353   0.614728300 #> 354   0.614869980 #> 355   0.615007918 #> 356   0.615142148 #> 357   0.615272702 #> 358   0.615399611 #> 359   0.615522909 #> 360   0.615642627 #> 361   0.615758796 #> 362   0.615871448 #> 363   0.615980613 #> 364   0.616086322 #> 365   0.616188606 #> 366   0.616287495 #> 367   0.616383019 #> 368   0.616475208 #> 369   0.616564090 #> 370   0.616649696 #> 371   0.616732054 #> 372   0.616811194 #> 373   0.616887143 #> 374   0.616959930 #> 375   0.617029583 #> 376   0.617096130 #> 377   0.617159598 #> 378   0.617220015 #> 379   0.617277407 #> 380   0.617331803 #> 381   0.617383228 #> 382   0.617431709 #> 383   0.617477272 #> 384   0.617519944 #> 385   0.617559750 #> 386   0.617596715 #> 387   0.617630865 #> 388   0.617662225 #> 389   0.617690821 #> 390   0.617716676 #> 391   0.617739815 #> 392   0.617760264 #> 393   0.617778045 #> 394   0.617793183 #> 395   0.617805701 #> 396   0.617815624 #> 397   0.617822973 #> 398   0.617827773 #> 399   0.617830047 #> 400   0.617829816 #> 401   0.617827104 #> 402   0.617821933 #> 403   0.617814325 #> 404   0.617804302 #> 405   0.617791886 #> 406   0.617777099 #> 407   0.617759961 #> 408   0.617740494 #> 409   0.617718719 #> 410   0.617694657 #> 411   0.617668329 #> 412   0.617639754 #> 413   0.617608955 #> 414   0.617575950 #> 415   0.617540759 #> 416   0.617503403 #> 417   0.617463901 #> 418   0.617422272 #> 419   0.617378536 #> 420   0.617332712 #> 421   0.617284819 #> 422   0.617234875 #> 423   0.617182900 #> 424   0.617128912 #> 425   0.617072929 #> 426   0.617014969 #> 427   0.616955051 #> 428   0.616893192 #> 429   0.616829409 #> 430   0.616763721 #> 431   0.616696145 #> 432   0.616626698 #> 433   0.616555397 #> 434   0.616482259 #> 435   0.616407301 #> 436   0.616330540 #> 437   0.616251991 #> 438   0.616171672 #> 439   0.616089599 #> 440   0.616005787 #> 441   0.615920253 #> 442   0.615833012 #> 443   0.615744081 #> 444   0.615653474 #> 445   0.615561207 #> 446   0.615467295 #> 447   0.615371754 #> 448   0.615274598 #> 449   0.615175843 #> 450   0.615075502 #> 451   0.614973591 #> 452   0.614870124 #> 453   0.614765115 #> 454   0.614658579 #> 455   0.614550530 #> 456   0.614440982 #> 457   0.614329948 #> 458   0.614217442 #> 459   0.614103479 #> 460   0.613988070 #> 461   0.613871231 #> 462   0.613752973 #> 463   0.613633311 #> 464   0.613512257 #> 465   0.613389824 #> 466   0.613266025 #> 467   0.613140872 #> 468   0.613014379 #> 469   0.612886557 #> 470   0.612757419 #> 471   0.612626978 #> 472   0.612495245 #> 473   0.612362232 #> 474   0.612227952 #> 475   0.612092416 #> 476   0.611955636 #> 477   0.611817623 #> 478   0.611678389 #> 479   0.611537946 #> 480   0.611396305 #> 481   0.611253477 #> 482   0.611109472 #> 483   0.610964303 #> 484   0.610817980 #> 485   0.610670514 #> 486   0.610521915 #> 487   0.610372195 #> 488   0.610221363 #> 489   0.610069431 #> 490   0.609916408 #> 491   0.609762305 #> 492   0.609607132 #> 493   0.609450900 #> 494   0.609293617 #> 495   0.609135295 #> 496   0.608975942 #> 497   0.608815569 #> 498   0.608654185 #> 499   0.608491801 #> 500   0.608328424 #> 501   0.608164065 #> 502   0.607998734 #> 503   0.607832438 #> 504   0.607665188 #> 505   0.607496993 #> 506   0.607327861 #> 507   0.607157802 #> 508   0.606986824 #> 509   0.606814936 #> 510   0.606642147 #> 511   0.606468465 #> 512   0.606293899 #> 513   0.606118457 #> 514   0.605942148 #> 515   0.605764980 #> 516   0.605586962 #> 517   0.605408101 #> 518   0.605228405 #> 519   0.605047883 #> 520   0.604866543 #> 521   0.604684392 #> 522   0.604501438 #> 523   0.604317689 #> 524   0.604133153 #> 525   0.603947837 #> 526   0.603761749 #> 527   0.603574896 #> 528   0.603387286 #> 529   0.603198926 #> 530   0.603009824 #> 531   0.602819986 #> 532   0.602629419 #> 533   0.602438132 #> 534   0.602246130 #> 535   0.602053421 #> 536   0.601860012 #> 537   0.601665910 #> 538   0.601471121 #> 539   0.601275652 #> 540   0.601079509 #> 541   0.600882701 #> 542   0.600685232 #> 543   0.600487109 #> 544   0.600288340 #> 545   0.600088929 #> 546   0.599888885 #> 547   0.599688212 #> 548   0.599486917 #> 549   0.599285007 #> 550   0.599082487 #> 551   0.598879363 #> 552   0.598675642 #> 553   0.598471330 #> 554   0.598266431 #> 555   0.598060953 #> 556   0.597854901 #> 557   0.597648280 #> 558   0.597441097 #> 559   0.597233357 #> 560   0.597025065 #> 561   0.596816228 #> 562   0.596606851 #> 563   0.596396938 #> 564   0.596186497 #> 565   0.595975531 #> 566   0.595764046 #> 567   0.595552048 #> 568   0.595339542 #> 569   0.595126532 #> 570   0.594913025 #> 571   0.594699025 #> 572   0.594484537 #> 573   0.594269566 #> 574   0.594054118 #> 575   0.593838196 #> 576   0.593621806 #> 577   0.593404953 #> 578   0.593187642 #> 579   0.592969877 #> 580   0.592751663 #> 581   0.592533005 #> 582   0.592313906 #> 583   0.592094373 #> 584   0.591874409 #> 585   0.591654020 #> 586   0.591433208 #> 587   0.591211979 #> 588   0.590990338 #> 589   0.590768288 #> 590   0.590545834 #> 591   0.590322980 #> 592   0.590099731 #> 593   0.589876090 #> 594   0.589652061 #> 595   0.589427650 #> 596   0.589202860 #> 597   0.588977695 #> 598   0.588752158 #> 599   0.588526255 #> 600   0.588299989 #> 601   0.588073364 #> 602   0.587846384 #> 603   0.587619052 #> 604   0.587391373 #> 605   0.587163350 #> 606   0.586934987 #> 607   0.586706288 #> 608   0.586477257 #> 609   0.586247896 #> 610   0.586018211 #> 611   0.585788203 #> 612   0.585557878 #> 613   0.585327238 #> 614   0.585096287 #> 615   0.584865028 #> 616   0.584633466 #> 617   0.584401602 #> 618   0.584169442 #> 619   0.583936987 #> 620   0.583704242 #> 621   0.583471209 #> 622   0.583237893 #> 623   0.583004295 #> 624   0.582770420 #> 625   0.582536271 #> 626   0.582301850 #> 627   0.582067162 #> 628   0.581832208 #> 629   0.581596992 #> 630   0.581361518 #> 631   0.581125788 #> 632   0.580889805 #> 633   0.580653572 #> 634   0.580417092 #> 635   0.580180368 #> 636   0.579943404 #> 637   0.579706201 #> 638   0.579468763 #> 639   0.579231092 #> 640   0.578993192 #> 641   0.578755064 #> 642   0.578516713 #> 643   0.578278140 #> 644   0.578039349 #> 645   0.577800341 #> 646   0.577561120 #> 647   0.577321689 #> 648   0.577082049 #> 649   0.576842205 #> 650   0.576602157 #> 651   0.576361908 #> 652   0.576121462 #> 653   0.575880821 #> 654   0.575639987 #> 655   0.575398963 #> 656   0.575157750 #> 657   0.574916353 #> 658   0.574674772 #> 659   0.574433010 #> 660   0.574191071 #> 661   0.573948955 #> 662   0.573706666 #> 663   0.573464205 #> 664   0.573221576 #> 665   0.572978779 #> 666   0.572735818 #> 667   0.572492695 #> 668   0.572249412 #> 669   0.572005971 #> 670   0.571762374 #> 671   0.571518624 #> 672   0.571274723 #> 673   0.571030672 #> 674   0.570786474 #> 675   0.570542131 #> 676   0.570297644 #> 677   0.570053017 #> 678   0.569808251 #> 679   0.569563348 #> 680   0.569318311 #> 681   0.569073140 #> 682   0.568827838 #> 683   0.568582407 #> 684   0.568336849 #> 685   0.568091166 #> 686   0.567845360 #> 687   0.567599432 #> 688   0.567353385 #> 689   0.567107221 #> 690   0.566860940 #> 691   0.566614545 #> 692   0.566368038 #> 693   0.566121421 #> 694   0.565874695 #> 695   0.565627863 #> 696   0.565380925 #> 697   0.565133884 #> 698   0.564886741 #> 699   0.564639498 #> 700   0.564392157 #> 701   0.564144720 #> 702   0.563897187 #> 703   0.563649561 #> 704   0.563401844 #> 705   0.563154036 #> 706   0.562906140 #> 707   0.562658158 #> 708   0.562410090 #> 709   0.562161938 #> 710   0.561913704 #> 711   0.561665390 #> 712   0.561416997 #> 713   0.561168526 #> 714   0.560919979 #> 715   0.560671358 #> 716   0.560422663 #> 717   0.560173897 #> 718   0.559925061 #> 719   0.559676156 #> 720   0.559427184 #> 721   0.559178146 #> 722   0.558929043 #> 723   0.558679878 #> 724   0.558430651 #> 725   0.558181363 #> 726   0.557932017 #> 727   0.557682613 #> 728   0.557433153 #> 729   0.557183638 #> 730   0.556934069 #> 731   0.556684448 #> 732   0.556434777 #> 733   0.556185055 #> 734   0.555935285 #> 735   0.555685468 #> 736   0.555435605 #> 737   0.555185697 #> 738   0.554935745 #> 739   0.554685752 #> 740   0.554435718 #> 741   0.554185643 #> 742   0.553935530 #> 743   0.553685380 #> 744   0.553435193 #> 745   0.553184972 #> 746   0.552934716 #> 747   0.552684428 #> 748   0.552434107 #> 749   0.552183757 #> 750   0.551933377 #> 751   0.551682968 #> 752   0.551432533 #> 753   0.551182071 #> 754   0.550931584 #> 755   0.550681073 #> 756   0.550430539 #> 757   0.550179984 #> 758   0.549929407 #> 759   0.549678811 #> 760   0.549428196 #> 761   0.549177563 #> 762   0.548926914 #> 763   0.548676249 #> 764   0.548425569 #> 765   0.548174875 #> 766   0.547924168 #> 767   0.547673450 #> 768   0.547422721 #> 769   0.547171982 #> 770   0.546921234 #> 771   0.546670478 #> 772   0.546419715 #> 773   0.546168945 #> 774   0.545918171 #> 775   0.545667392 #> 776   0.545416610 #> 777   0.545165825 #> 778   0.544915039 #> 779   0.544664251 #> 780   0.544413464 #> 781   0.544162678 #> 782   0.543911894 #> 783   0.543661112 #> 784   0.543410334 #> 785   0.543159560 #> 786   0.542908792 #> 787   0.542658029 #> 788   0.542407273 #> 789   0.542156525 #> 790   0.541905785 #> 791   0.541655055 #> 792   0.541404334 #> 793   0.541153624 #> 794   0.540902926 #> 795   0.540652240 #> 796   0.540401568 #> 797   0.540150909 #> 798   0.539900265 #> 799   0.539649636 #> 800   0.539399023 #> 801   0.539148427 #> 802   0.538897849 #> 803   0.538647289 #> 804   0.538396748 #> 805   0.538146226 #> 806   0.537895725 #> 807   0.537645246 #> 808   0.537394788 #> 809   0.537144352 #> 810   0.536893940 #> 811   0.536643551 #> 812   0.536393187 #> 813   0.536142848 #> 814   0.535892535 #> 815   0.535642249 #> 816   0.535391990 #> 817   0.535141758 #> 818   0.534891555 #> 819   0.534641381 #> 820   0.534391237 #> 821   0.534141123 #> 822   0.533891039 #> 823   0.533640988 #> 824   0.533390969 #> 825   0.533140982 #> 826   0.532891029 #> 827   0.532641109 #> 828   0.532391224 #> 829   0.532141375 #> 830   0.531891561 #> 831   0.531641783 #> 832   0.531392042 #> 833   0.531142338 #> 834   0.530892673 #> 835   0.530643046 #> 836   0.530393458 #> 837   0.530143909 #> 838   0.529894401 #> 839   0.529644933 #> 840   0.529395507 #> 841   0.529146122 #> 842   0.528896780 #> 843   0.528647481 #> 844   0.528398224 #> 845   0.528149012 #> 846   0.527899844 #> 847   0.527650721 #> 848   0.527401643 #> 849   0.527152611 #> 850   0.526903625 #> 851   0.526654686 #> 852   0.526405794 #> 853   0.526156951 #> 854   0.525908155 #> 855   0.525659408 #> 856   0.525410710 #> 857   0.525162062 #> 858   0.524913464 #> 859   0.524664917 #> 860   0.524416420 #> 861   0.524167975 #> 862   0.523919582 #> 863   0.523671241 #> 864   0.523422953 #> 865   0.523174719 #> 866   0.522926537 #> 867   0.522678410 #> 868   0.522430338 #> 869   0.522182320 #> 870   0.521934358 #> 871   0.521686451 #> 872   0.521438601 #> 873   0.521190807 #> 874   0.520943070 #> 875   0.520695391 #> 876   0.520447769 #> 877   0.520200206 #> 878   0.519952701 #> 879   0.519705254 #> 880   0.519457868 #> 881   0.519210541 #> 882   0.518963274 #> 883   0.518716067 #> 884   0.518468921 #> 885   0.518221837 #> 886   0.517974814 #> 887   0.517727852 #> 888   0.517480954 #> 889   0.517234117 #> 890   0.516987344 #> 891   0.516740634 #> 892   0.516493988 #> 893   0.516247406 #> 894   0.516000888 #> 895   0.515754435 #> 896   0.515508047 #> 897   0.515261724 #> 898   0.515015467 #> 899   0.514769276 #> 900   0.514523151 #> 901   0.514277093 #> 902   0.514031102 #> 903   0.513785179 #> 904   0.513539323 #> 905   0.513293535 #> 906   0.513047815 #> 907   0.512802163 #> 908   0.512556581 #> 909   0.512311068 #> 910   0.512065624 #> 911   0.511820250 #> 912   0.511574946 #> 913   0.511329712 #> 914   0.511084549 #> 915   0.510839457 #> 916   0.510594436 #> 917   0.510349486 #> 918   0.510104609 #> 919   0.509859803 #> 920   0.509615069 #> 921   0.509370409 #> 922   0.509125821 #> 923   0.508881306 #> 924   0.508636864 #> 925   0.508392497 #> 926   0.508148203 #> 927   0.507903983 #> 928   0.507659838 #> 929   0.507415767 #> 930   0.507171772 #> 931   0.506927851 #> 932   0.506684006 #> 933   0.506440237 #> 934   0.506196543 #> 935   0.505952926 #> 936   0.505709385 #> 937   0.505465921 #> 938   0.505222533 #> 939   0.504979223 #> 940   0.504735990 #> 941   0.504492835 #> 942   0.504249757 #> 943   0.504006758 #> 944   0.503763837 #> 945   0.503520994 #> 946   0.503278230 #> 947   0.503035545 #> 948   0.502792939 #> 949   0.502550412 #> 950   0.502307965 #> 951   0.502065597 #> 952   0.501823310 #> 953   0.501581103 #> 954   0.501338976 #> 955   0.501096930 #> 956   0.500854964 #> 957   0.500613080 #> 958   0.500371276 #> 959   0.500129555 #> 960   0.499887914 #> 961   0.499646356 #> 962   0.499404879 #> 963   0.499163485 #> 964   0.498922173 #> 965   0.498680943 #> 966   0.498439797 #> 967   0.498198733 #> 968   0.497957752 #> 969   0.497716854 #> 970   0.497476040 #> 971   0.497235310 #> 972   0.496994663 #> 973   0.496754100 #> 974   0.496513622 #> 975   0.496273228 #> 976   0.496032918 #> 977   0.495792693 #> 978   0.495552552 #> 979   0.495312497 #> 980   0.495072527 #> 981   0.494832642 #> 982   0.494592843 #> 983   0.494353129 #> 984   0.494113501 #> 985   0.493873959 #> 986   0.493634503 #> 987   0.493395133 #> 988   0.493155850 #> 989   0.492916653 #> 990   0.492677543 #> 991   0.492438520 #> 992   0.492199584 #> 993   0.491960734 #> 994   0.491721973 #> 995   0.491483298 #> 996   0.491244712 #> 997   0.491006212 #> 998   0.490767801 #> 999   0.490529478 #> 1000  0.490291243 #> 1001  0.490053096 #> 1002  0.489815037 #> 1003  0.489577068 #> 1004  0.489339186 #> 1005  0.489101394 #> 1006  0.488863690 #> 1007  0.488626076 #> 1008  0.488388550 #> 1009  0.488151114 #> 1010  0.487913767 #> 1011  0.487676510 #> 1012  0.487439343 #> 1013  0.487202265 #> 1014  0.486965277 #> 1015  0.486728379 #> 1016  0.486491572 #> 1017  0.486254854 #> 1018  0.486018227 #> 1019  0.485781691 #> 1020  0.485545245 #> 1021  0.485308889 #> 1022  0.485072625 #> 1023  0.484836451 #> 1024  0.484600369 #> 1025  0.484364378 #> 1026  0.484128478 #> 1027  0.483892669 #> 1028  0.483656952 #> 1029  0.483421326 #> 1030  0.483185792 #> 1031  0.482950350 #> 1032  0.482714999 #> 1033  0.482479741 #> 1034  0.482244574 #> 1035  0.482009500 #> 1036  0.481774518 #> 1037  0.481539628 #> 1038  0.481304831 #> 1039  0.481070127 #> 1040  0.480835515 #> 1041  0.480600995 #> 1042  0.480366569 #> 1043  0.480132235 #> 1044  0.479897995 #> 1045  0.479663847 #> 1046  0.479429793 #> 1047  0.479195832 #> 1048  0.478961964 #> 1049  0.478728190 #> 1050  0.478494509 #> 1051  0.478260922 #> 1052  0.478027429 #> 1053  0.477794029 #> 1054  0.477560723 #> 1055  0.477327511 #> 1056  0.477094393 #> 1057  0.476861369 #> 1058  0.476628439 #> 1059  0.476395604 #> 1060  0.476162862 #> 1061  0.475930215 #> 1062  0.475697663 #> 1063  0.475465205 #> 1064  0.475232842 #> 1065  0.475000573 #> 1066  0.474768399 #> 1067  0.474536320 #> 1068  0.474304336 #> 1069  0.474072446 #> 1070  0.473840652 #> 1071  0.473608953 #> 1072  0.473377349 #> 1073  0.473145840 #> 1074  0.472914426 #> 1075  0.472683108 #> 1076  0.472451885 #> 1077  0.472220758 #> 1078  0.471989726 #> 1079  0.471758789 #> 1080  0.471527949 #> 1081  0.471297204 #> 1082  0.471066554 #> 1083  0.470836001 #> 1084  0.470605543 #> 1085  0.470375182 #> 1086  0.470144916 #> 1087  0.469914747 #> 1088  0.469684673 #> 1089  0.469454696 #> 1090  0.469224814 #> 1091  0.468995030 #> 1092  0.468765341 #> 1093  0.468535749 #> 1094  0.468306253 #> 1095  0.468076854 #> 1096  0.467847551 #> 1097  0.467618344 #> 1098  0.467389235 #> 1099  0.467160222 #> 1100  0.466931305 #> 1101  0.466702486 #> 1102  0.466473763 #> 1103  0.466245137 #> 1104  0.466016608 #> 1105  0.465788176 #> 1106  0.465559841 #> 1107  0.465331602 #> 1108  0.465103461 #> 1109  0.464875417 #> 1110  0.464647470 #> 1111  0.464419621 #> 1112  0.464191868 #> 1113  0.463964213 #> 1114  0.463736655 #> 1115  0.463509195 #> 1116  0.463281831 #> 1117  0.463054566 #> 1118  0.462827397 #> 1119  0.462600326 #> 1120  0.462373353 #> 1121  0.462146477 #> 1122  0.461919699 #> 1123  0.461693018 #> 1124  0.461466436 #> 1125  0.461239950 #> 1126  0.461013563 #> 1127  0.460787273 #> 1128  0.460561081 #> 1129  0.460334987 #> 1130  0.460108991 #> 1131  0.459883092 #> 1132  0.459657292 #> 1133  0.459431589 #> 1134  0.459205984 #> 1135  0.458980478 #> 1136  0.458755069 #> 1137  0.458529759 #> 1138  0.458304546 #> 1139  0.458079432 #> 1140  0.457854415 #> 1141  0.457629497 #> 1142  0.457404677 #> 1143  0.457179956 #> 1144  0.456955332 #> 1145  0.456730807 #> 1146  0.456506380 #> 1147  0.456282051 #> 1148  0.456057821 #> 1149  0.455833689 #> 1150  0.455609655 #> 1151  0.455385720 #> 1152  0.455161883 #> 1153  0.454938145 #> 1154  0.454714505 #> 1155  0.454490963 #> 1156  0.454267520 #> 1157  0.454044175 #> 1158  0.453820929 #> 1159  0.453597782 #> 1160  0.453374733 #> 1161  0.453151782 #> 1162  0.452928931 #> 1163  0.452706177 #> 1164  0.452483523 #> 1165  0.452260967 #> 1166  0.452038510 #> 1167  0.451816151 #> 1168  0.451593891 #> 1169  0.451371730 #> 1170  0.451149667 #> 1171  0.450927703 #> 1172  0.450705838 #> 1173  0.450484072 #> 1174  0.450262404 #> 1175  0.450040835 #> 1176  0.449819365 #> 1177  0.449597994 #> 1178  0.449376721 #> 1179  0.449155547 #> 1180  0.448934473 #> 1181  0.448713496 #> 1182  0.448492619 #> 1183  0.448271841 #> 1184  0.448051161 #> 1185  0.447830581 #> 1186  0.447610099 #> 1187  0.447389716 #> 1188  0.447169432 #> 1189  0.446949247 #> 1190  0.446729160 #> 1191  0.446509173 #> 1192  0.446289285 #> 1193  0.446069495 #> 1194  0.445849805 #> 1195  0.445630213 #> 1196  0.445410720 #> 1197  0.445191326 #> 1198  0.444972031 #> 1199  0.444752836 #> 1200  0.444533739 #> 1201  0.444314740 #> 1202  0.444095841 #> 1203  0.443877041 #> 1204  0.443658340 #> 1205  0.443439738 #> 1206  0.443221235 #> 1207  0.443002830 #> 1208  0.442784525 #> 1209  0.442566319 #> 1210  0.442348211 #> 1211  0.442130203 #> 1212  0.441912293 #> 1213  0.441694483 #> 1214  0.441476771 #> 1215  0.441259159 #> 1216  0.441041645 #> 1217  0.440824230 #> 1218  0.440606915 #> 1219  0.440389698 #> 1220  0.440172580 #> 1221  0.439955561 #> 1222  0.439738641 #> 1223  0.439521820 #> 1224  0.439305098 #> 1225  0.439088475 #> 1226  0.438871951 #> 1227  0.438655526 #> 1228  0.438439199 #> 1229  0.438222972 #> 1230  0.438006844 #> 1231  0.437790814 #> 1232  0.437574883 #> 1233  0.437359052 #> 1234  0.437143319 #> 1235  0.436927685 #> 1236  0.436712150 #> 1237  0.436496714 #> 1238  0.436281376 #> 1239  0.436066138 #> 1240  0.435850998 #> 1241  0.435635958 #> 1242  0.435421016 #> 1243  0.435206173 #> 1244  0.434991428 #> 1245  0.434776783 #> 1246  0.434562236 #> 1247  0.434347788 #> 1248  0.434133439 #> 1249  0.433919189 #> 1250  0.433705038 #> 1251  0.433490985 #> 1252  0.433277031 #> 1253  0.433063176 #> 1254  0.432849419 #> 1255  0.432635761 #> 1256  0.432422202 #> 1257  0.432208742 #> 1258  0.431995380 #> 1259  0.431782117 #> 1260  0.431568953 #> 1261  0.431355887 #> 1262  0.431142920 #> 1263  0.430930052 #> 1264  0.430717282 #> 1265  0.430504611 #> 1266  0.430292038 #> 1267  0.430079564 #> 1268  0.429867189 #> 1269  0.429654912 #> 1270  0.429442734 #> 1271  0.429230654 #> 1272  0.429018673 #> 1273  0.428806790 #> 1274  0.428595006 #> 1275  0.428383320 #> 1276  0.428171733 #> 1277  0.427960244 #> 1278  0.427748853 #> 1279  0.427537561 #> 1280  0.427326367 #> 1281  0.427115272 #> 1282  0.426904275 #> 1283  0.426693377 #> 1284  0.426482576 #> 1285  0.426271875 #> 1286  0.426061271 #> 1287  0.425850766 #> 1288  0.425640359 #> 1289  0.425430050 #> 1290  0.425219839 #> 1291  0.425009727 #> 1292  0.424799713 #> 1293  0.424589797 #> 1294  0.424379979 #> 1295  0.424170260 #> 1296  0.423960638 #> 1297  0.423751115 #> 1298  0.423541690 #> 1299  0.423332363 #> 1300  0.423123134 #> 1301  0.422914002 #> 1302  0.422704969 #> 1303  0.422496034 #> 1304  0.422287197 #> 1305  0.422078458 #> 1306  0.421869817 #> 1307  0.421661274 #> 1308  0.421452829 #> 1309  0.421244482 #> 1310  0.421036232 #> 1311  0.420828080 #> 1312  0.420620027 #> 1313  0.420412071 #> 1314  0.420204212 #> 1315  0.419996452 #> 1316  0.419788789 #> 1317  0.419581224 #> 1318  0.419373757 #> 1319  0.419166388 #> 1320  0.418959116 #> 1321  0.418751941 #> 1322  0.418544865 #> 1323  0.418337886 #> 1324  0.418131004 #> 1325  0.417924220 #> 1326  0.417717534 #> 1327  0.417510945 #> 1328  0.417304454 #> 1329  0.417098060 #> 1330  0.416891764 #> 1331  0.416685565 #> 1332  0.416479463 #> 1333  0.416273459 #> 1334  0.416067552 #> 1335  0.415861742 #> 1336  0.415656030 #> 1337  0.415450415 #> 1338  0.415244898 #> 1339  0.415039477 #> 1340  0.414834154 #> 1341  0.414628928 #> 1342  0.414423799 #> 1343  0.414218768 #> 1344  0.414013833 #> 1345  0.413808996 #> 1346  0.413604255 #> 1347  0.413399612 #> 1348  0.413195066 #> 1349  0.412990616 #> 1350  0.412786264 #> 1351  0.412582008 #> 1352  0.412377850 #> 1353  0.412173788 #> 1354  0.411969824 #> 1355  0.411765956 #> 1356  0.411562185 #> 1357  0.411358511 #> 1358  0.411154933 #> 1359  0.410951453 #> 1360  0.410748069 #> 1361  0.410544781 #> 1362  0.410341591 #> 1363  0.410138497 #> 1364  0.409935499 #> 1365  0.409732599 #> 1366  0.409529795 #> 1367  0.409327087 #> 1368  0.409124476 #> 1369  0.408921961 #> 1370  0.408719543 #> 1371  0.408517221 #> 1372  0.408314996 #> 1373  0.408112867 #> 1374  0.407910834 #> 1375  0.407708898 #> 1376  0.407507058 #> 1377  0.407305314 #> 1378  0.407103667 #> 1379  0.406902116 #> 1380  0.406700661 #> 1381  0.406499302 #> 1382  0.406298039 #> 1383  0.406096872 #> 1384  0.405895802 #> 1385  0.405694827 #> 1386  0.405493949 #> 1387  0.405293166 #> 1388  0.405092480 #> 1389  0.404891889 #> 1390  0.404691395 #> 1391  0.404490996 #> 1392  0.404290693 #> 1393  0.404090486 #> 1394  0.403890374 #> 1395  0.403690359 #> 1396  0.403490439 #> 1397  0.403290615 #> 1398  0.403090886 #> 1399  0.402891254 #> 1400  0.402691716 #> 1401  0.402492275 #> 1402  0.402292929 #> 1403  0.402093678 #> 1404  0.401894523 #> 1405  0.401695464 #> 1406  0.401496500 #> 1407  0.401297631 #> 1408  0.401098858 #> 1409  0.400900180 #> 1410  0.400701597 #> 1411  0.400503110 #> 1412  0.400304718 #> 1413  0.400106421 #> 1414  0.399908220 #> 1415  0.399710113 #> 1416  0.399512102 #> 1417  0.399314186 #> 1418  0.399116365 #> 1419  0.398918639 #> 1420  0.398721008 #> 1421  0.398523472 #> 1422  0.398326031 #> 1423  0.398128685 #> 1424  0.397931434 #> 1425  0.397734277 #> 1426  0.397537216 #> 1427  0.397340249 #> 1428  0.397143377 #> 1429  0.396946600 #> 1430  0.396749918 #> 1431  0.396553330 #> 1432  0.396356837 #> 1433  0.396160439 #> 1434  0.395964135 #> 1435  0.395767925 #> 1436  0.395571811 #> 1437  0.395375790 #> 1438  0.395179864 #> 1439  0.394984033 #> 1440  0.394788296 #> 1441  0.394592653 #> 1442  0.394397105 #> 1443  0.394201651 #> 1444  0.394006292 #> 1445  0.393811026 #> 1446  0.393615855 #> 1447  0.393420778 #> 1448  0.393225795 #> 1449  0.393030906 #> 1450  0.392836111 #> 1451  0.392641411 #> 1452  0.392446804 #> 1453  0.392252291 #> 1454  0.392057873 #> 1455  0.391863548 #> 1456  0.391669317 #> 1457  0.391475180 #> 1458  0.391281137 #> 1459  0.391087187 #> 1460  0.390893331 #> 1461  0.390699569 #> 1462  0.390505901 #> 1463  0.390312327 #> 1464  0.390118846 #> 1465  0.389925458 #> 1466  0.389732164 #> 1467  0.389538964 #> 1468  0.389345857 #> 1469  0.389152844 #> 1470  0.388959924 #> 1471  0.388767098 #> 1472  0.388574364 #> 1473  0.388381725 #> 1474  0.388189178 #> 1475  0.387996725 #> 1476  0.387804365 #> 1477  0.387612098 #> 1478  0.387419924 #> 1479  0.387227844 #> 1480  0.387035856 #> 1481  0.386843962 #> 1482  0.386652160 #> 1483  0.386460452 #> 1484  0.386268836 #> 1485  0.386077314 #> 1486  0.385885884 #> 1487  0.385694548 #> 1488  0.385503304 #> 1489  0.385312153 #> 1490  0.385121094 #> 1491  0.384930128 #> 1492  0.384739256 #> 1493  0.384548475 #> 1494  0.384357788 #> 1495  0.384167192 #> 1496  0.383976690 #> 1497  0.383786280 #> 1498  0.383595962 #> 1499  0.383405737 #> 1500  0.383215605 #> 1501  0.383025564 #> 1502  0.382835616 #> 1503  0.382645761 #> 1504  0.382455997 #> 1505  0.382266326 #> 1506  0.382076747 #> 1507  0.381887261 #> 1508  0.381697866 #> 1509  0.381508564 #> 1510  0.381319353 #> 1511  0.381130235 #> 1512  0.380941208 #> 1513  0.380752274 #> 1514  0.380563432 #> 1515  0.380374681 #> 1516  0.380186022 #> 1517  0.379997455 #> 1518  0.379808980 #> 1519  0.379620597 #> 1520  0.379432305 #> 1521  0.379244105 #> 1522  0.379055997 #> 1523  0.378867981 #> 1524  0.378680056 #> 1525  0.378492222 #> 1526  0.378304480 #> 1527  0.378116830 #> 1528  0.377929271 #> 1529  0.377741803 #> 1530  0.377554427 #> 1531  0.377367142 #> 1532  0.377179948 #> 1533  0.376992846 #> 1534  0.376805835 #> 1535  0.376618915 #> 1536  0.376432086 #> 1537  0.376245348 #> 1538  0.376058702 #> 1539  0.375872146 #> 1540  0.375685682 #> 1541  0.375499308 #> 1542  0.375313026 #> 1543  0.375126834 #> 1544  0.374940733 #> 1545  0.374754724 #> 1546  0.374568804 #> 1547  0.374382976 #> 1548  0.374197239 #> 1549  0.374011592 #> 1550  0.373826036 #> 1551  0.373640570 #> 1552  0.373455195 #> 1553  0.373269911 #> 1554  0.373084717 #> 1555  0.372899613 #> 1556  0.372714601 #> 1557  0.372529678 #> 1558  0.372344846 #> 1559  0.372160104 #> 1560  0.371975453 #> 1561  0.371790891 #> 1562  0.371606420 #> 1563  0.371422040 #> 1564  0.371237749 #> 1565  0.371053549 #> 1566  0.370869438 #> 1567  0.370685418 #> 1568  0.370501488 #> 1569  0.370317647 #> 1570  0.370133897 #> 1571  0.369950237 #> 1572  0.369766666 #> 1573  0.369583185 #> 1574  0.369399795 #> 1575  0.369216494 #> 1576  0.369033282 #> 1577  0.368850160 #> 1578  0.368667128 #> 1579  0.368484186 #> 1580  0.368301333 #> 1581  0.368118570 #> 1582  0.367935896 #> 1583  0.367753312 #> 1584  0.367570817 #> 1585  0.367388412 #> 1586  0.367206096 #> 1587  0.367023869 #> 1588  0.366841732 #> 1589  0.366659684 #> 1590  0.366477725 #> 1591  0.366295855 #> 1592  0.366114075 #> 1593  0.365932384 #> 1594  0.365750781 #> 1595  0.365569268 #> 1596  0.365387844 #> 1597  0.365206508 #> 1598  0.365025262 #> 1599  0.364844105 #> 1600  0.364663036 #> 1601  0.364482057 #> 1602  0.364301166 #> 1603  0.364120363 #> 1604  0.363939650 #> 1605  0.363759025 #> 1606  0.363578489 #> 1607  0.363398042 #> 1608  0.363217683 #> 1609  0.363037412 #> 1610  0.362857231 #> 1611  0.362677137 #> 1612  0.362497132 #> 1613  0.362317216 #> 1614  0.362137387 #> 1615  0.361957648 #> 1616  0.361777996 #> 1617  0.361598433 #> 1618  0.361418958 #> 1619  0.361239571 #> 1620  0.361060272 #> 1621  0.360881061 #> 1622  0.360701938 #> 1623  0.360522904 #> 1624  0.360343957 #> 1625  0.360165098 #> 1626  0.359986328 #> 1627  0.359807645 #> 1628  0.359629050 #> 1629  0.359450543 #> 1630  0.359272123 #> 1631  0.359093792 #> 1632  0.358915548 #> 1633  0.358737391 #> 1634  0.358559323 #> 1635  0.358381341 #> 1636  0.358203448 #> 1637  0.358025642 #> 1638  0.357847923 #> 1639  0.357670292 #> 1640  0.357492748 #> 1641  0.357315292 #> 1642  0.357137923 #> 1643  0.356960641 #> 1644  0.356783447 #> 1645  0.356606340 #> 1646  0.356429320 #> 1647  0.356252387 #> 1648  0.356075541 #> 1649  0.355898783 #> 1650  0.355722111 #> 1651  0.355545526 #> 1652  0.355369029 #> 1653  0.355192618 #> 1654  0.355016294 #> 1655  0.354840057 #> 1656  0.354663907 #> 1657  0.354487844 #> 1658  0.354311867 #> 1659  0.354135977 #> 1660  0.353960174 #> 1661  0.353784457 #> 1662  0.353608827 #> 1663  0.353433284 #> 1664  0.353257827 #> 1665  0.353082457 #> 1666  0.352907173 #> 1667  0.352731975 #> 1668  0.352556864 #> 1669  0.352381839 #> 1670  0.352206901 #> 1671  0.352032048 #> 1672  0.351857282 #> 1673  0.351682603 #> 1674  0.351508009 #> 1675  0.351333501 #> 1676  0.351159080 #> 1677  0.350984745 #> 1678  0.350810495 #> 1679  0.350636332 #> 1680  0.350462254 #> 1681  0.350288263 #> 1682  0.350114357 #> 1683  0.349940537 #> 1684  0.349766803 #> 1685  0.349593155 #> 1686  0.349419593 #> 1687  0.349246116 #> 1688  0.349072724 #> 1689  0.348899419 #> 1690  0.348726199 #> 1691  0.348553064 #> 1692  0.348380015 #> 1693  0.348207052 #> 1694  0.348034174 #> 1695  0.347861381 #> 1696  0.347688674 #> 1697  0.347516052 #> 1698  0.347343515 #> 1699  0.347171063 #> 1700  0.346998697 #> 1701  0.346826416 #> 1702  0.346654220 #> 1703  0.346482109 #> 1704  0.346310083 #> 1705  0.346138142 #> 1706  0.345966286 #> 1707  0.345794515 #> 1708  0.345622829 #> 1709  0.345451228 #> 1710  0.345279712 #> 1711  0.345108281 #> 1712  0.344936934 #> 1713  0.344765672 #> 1714  0.344594495 #> 1715  0.344423402 #> 1716  0.344252394 #> 1717  0.344081471 #> 1718  0.343910632 #> 1719  0.343739877 #> 1720  0.343569207 #> 1721  0.343398622 #> 1722  0.343228121 #> 1723  0.343057704 #> 1724  0.342887372 #> 1725  0.342717124 #> 1726  0.342546960 #> 1727  0.342376880 #> 1728  0.342206885 #> 1729  0.342036974 #> 1730  0.341867147 #> 1731  0.341697404 #> 1732  0.341527745 #> 1733  0.341358169 #> 1734  0.341188678 #> 1735  0.341019271 #> 1736  0.340849948 #> 1737  0.340680709 #> 1738  0.340511553 #> 1739  0.340342481 #> 1740  0.340173493 #> 1741  0.340004589 #> 1742  0.339835768 #> 1743  0.339667031 #> 1744  0.339498377 #> 1745  0.339329807 #> 1746  0.339161321 #> 1747  0.338992918 #> 1748  0.338824599 #> 1749  0.338656363 #> 1750  0.338488210 #> 1751  0.338320141 #> 1752  0.338152155 #> 1753  0.337984252 #> 1754  0.337816432 #> 1755  0.337648696 #> 1756  0.337481043 #> 1757  0.337313473 #> 1758  0.337145986 #> 1759  0.336978582 #> 1760  0.336811261 #> 1761  0.336644023 #> 1762  0.336476868 #> 1763  0.336309796 #> 1764  0.336142807 #> 1765  0.335975900 #> 1766  0.335809077 #> 1767  0.335642336 #> 1768  0.335475678 #> 1769  0.335309102 #> 1770  0.335142610 #> 1771  0.334976200 #> 1772  0.334809872 #> 1773  0.334643627 #> 1774  0.334477464 #> 1775  0.334311384 #> 1776  0.334145387 #> 1777  0.333979471 #> 1778  0.333813639 #> 1779  0.333647888 #> 1780  0.333482220 #> 1781  0.333316634 #> 1782  0.333151130 #> 1783  0.332985708 #> 1784  0.332820369 #> 1785  0.332655111 #> 1786  0.332489936 #> 1787  0.332324843 #> 1788  0.332159832 #> 1789  0.331994902 #> 1790  0.331830055 #> 1791  0.331665289 #> 1792  0.331500606 #> 1793  0.331336004 #> 1794  0.331171484 #> 1795  0.331007046 #> 1796  0.330842689 #> 1797  0.330678414 #> 1798  0.330514221 #> 1799  0.330350109 #> 1800  0.330186079 #> 1801  0.330022130 #> 1802  0.329858263 #> 1803  0.329694478 #> 1804  0.329530773 #> 1805  0.329367150 #> 1806  0.329203609 #> 1807  0.329040149 #> 1808  0.328876770 #> 1809  0.328713472 #> 1810  0.328550256 #> 1811  0.328387120 #> 1812  0.328224066 #> 1813  0.328061093 #> 1814  0.327898201 #> 1815  0.327735390 #> 1816  0.327572660 #> 1817  0.327410011 #> 1818  0.327247443 #> 1819  0.327084956 #> 1820  0.326922549 #> 1821  0.326760224 #> 1822  0.326597979 #> 1823  0.326435815 #> 1824  0.326273731 #> 1825  0.326111729 #> 1826  0.325949807 #> 1827  0.325787965 #> 1828  0.325626204 #> 1829  0.325464524 #> 1830  0.325302924 #> 1831  0.325141404 #> 1832  0.324979965 #> 1833  0.324818607 #> 1834  0.324657328 #> 1835  0.324496130 #> 1836  0.324335012 #> 1837  0.324173975 #> 1838  0.324013018 #> 1839  0.323852140 #> 1840  0.323691343 #> 1841  0.323530627 #> 1842  0.323369990 #> 1843  0.323209433 #> 1844  0.323048956 #> 1845  0.322888559 #> 1846  0.322728242 #> 1847  0.322568005 #> 1848  0.322407848 #> 1849  0.322247770 #> 1850  0.322087773 #> 1851  0.321927855 #> 1852  0.321768016 #> 1853  0.321608258 #> 1854  0.321448579 #> 1855  0.321288980 #> 1856  0.321129460 #> 1857  0.320970020 #> 1858  0.320810659 #> 1859  0.320651378 #> 1860  0.320492176 #> 1861  0.320333053 #> 1862  0.320174010 #> 1863  0.320015046 #> 1864  0.319856162 #> 1865  0.319697356 #> 1866  0.319538630 #> 1867  0.319379983 #> 1868  0.319221416 #> 1869  0.319062927 #> 1870  0.318904517 #> 1871  0.318746187 #> 1872  0.318587935 #> 1873  0.318429763 #> 1874  0.318271669 #> 1875  0.318113654 #> 1876  0.317955718 #> 1877  0.317797861 #> 1878  0.317640083 #> 1879  0.317482383 #> 1880  0.317324762 #> 1881  0.317167220 #> 1882  0.317009757 #> 1883  0.316852372 #> 1884  0.316695065 #> 1885  0.316537837 #> 1886  0.316380688 #> 1887  0.316223617 #> 1888  0.316066625 #> 1889  0.315909711 #> 1890  0.315752875 #> 1891  0.315596118 #> 1892  0.315439439 #> 1893  0.315282838 #> 1894  0.315126316 #> 1895  0.314969871 #> 1896  0.314813505 #> 1897  0.314657217 #> 1898  0.314501007 #> 1899  0.314344875 #> 1900  0.314188822 #> 1901  0.314032846 #> 1902  0.313876948 #> 1903  0.313721128 #> 1904  0.313565385 #> 1905  0.313409721 #> 1906  0.313254135 #> 1907  0.313098626 #> 1908  0.312943195 #> 1909  0.312787842 #> 1910  0.312632566 #> 1911  0.312477368 #> 1912  0.312322248 #> 1913  0.312167205 #> 1914  0.312012239 #> 1915  0.311857352 #> 1916  0.311702541 #> 1917  0.311547808 #> 1918  0.311393153 #> 1919  0.311238575 #> 1920  0.311084074 #> 1921  0.310929650 #> 1922  0.310775304 #> 1923  0.310621035 #> 1924  0.310466843 #> 1925  0.310312729 #> 1926  0.310158691 #> 1927  0.310004730 #> 1928  0.309850847 #> 1929  0.309697040 #> 1930  0.309543311 #> 1931  0.309389658 #> 1932  0.309236083 #> 1933  0.309082584 #> 1934  0.308929162 #> 1935  0.308775817 #> 1936  0.308622549 #> 1937  0.308469357 #> 1938  0.308316242 #> 1939  0.308163204 #> 1940  0.308010243 #> 1941  0.307857358 #> 1942  0.307704549 #> 1943  0.307551817 #> 1944  0.307399162 #> 1945  0.307246583 #> 1946  0.307094081 #> 1947  0.306941655 #> 1948  0.306789305 #> 1949  0.306637032 #> 1950  0.306484835 #> 1951  0.306332714 #> 1952  0.306180669 #> 1953  0.306028701 #> 1954  0.305876809 #> 1955  0.305724993 #> 1956  0.305573253 #> 1957  0.305421589 #> 1958  0.305270001 #> 1959  0.305118489 #> 1960  0.304967053 #> 1961  0.304815693 #> 1962  0.304664409 #> 1963  0.304513201 #> 1964  0.304362068 #> 1965  0.304211012 #> 1966  0.304060031 #> 1967  0.303909126 #> 1968  0.303758296 #> 1969  0.303607542 #> 1970  0.303456864 #> 1971  0.303306261 #> 1972  0.303155734 #> 1973  0.303005283 #> 1974  0.302854907 #> 1975  0.302704606 #> 1976  0.302554381 #> 1977  0.302404231 #> 1978  0.302254157 #> 1979  0.302104157 #> 1980  0.301954234 #> 1981  0.301804385 #> 1982  0.301654612 #> 1983  0.301504913 #> 1984  0.301355290 #> 1985  0.301205742 #> 1986  0.301056269 #> 1987  0.300906872 #> 1988  0.300757549 #> 1989  0.300608301 #> 1990  0.300459128 #> 1991  0.300310030 #> 1992  0.300161007 #> 1993  0.300012059 #> 1994  0.299863186 #> 1995  0.299714387 #> 1996  0.299565663 #> 1997  0.299417014 #> 1998  0.299268440 #> 1999  0.299119940 #> 2000  0.298971515 #> 2001  0.298823164 #> 2002  0.298674888 #> 2003  0.298526687 #> 2004  0.298378560 #> 2005  0.298230507 #> 2006  0.298082529 #> 2007  0.297934625 #> 2008  0.297786796 #> 2009  0.297639041 #> 2010  0.297491360 #> 2011  0.297343753 #> 2012  0.297196221 #> 2013  0.297048763 #> 2014  0.296901379 #> 2015  0.296754069 #> 2016  0.296606834 #> 2017  0.296459672 #> 2018  0.296312584 #> 2019  0.296165571 #> 2020  0.296018631 #> 2021  0.295871765 #> 2022  0.295724973 #> 2023  0.295578255 #> 2024  0.295431611 #> 2025  0.295285041 #> 2026  0.295138544 #> 2027  0.294992121 #> 2028  0.294845772 #> 2029  0.294699497 #> 2030  0.294553295 #> 2031  0.294407166 #> 2032  0.294261112 #> 2033  0.294115130 #> 2034  0.293969223 #> 2035  0.293823389 #> 2036  0.293677628 #> 2037  0.293531941 #> 2038  0.293386327 #> 2039  0.293240786 #> 2040  0.293095319 #> 2041  0.292949925 #> 2042  0.292804604 #> 2043  0.292659356 #> 2044  0.292514182 #> 2045  0.292369081 #> 2046  0.292224052 #> 2047  0.292079097 #> 2048  0.291934215 #> 2049  0.291789407 #> 2050  0.291644671 #> 2051  0.291500008 #> 2052  0.291355417 #> 2053  0.291210900 #> 2054  0.291066456 #> 2055  0.290922084 #> 2056  0.290777786 #> 2057  0.290633560 #> 2058  0.290489406 #> 2059  0.290345326 #> 2060  0.290201318 #> 2061  0.290057383 #> 2062  0.289913520 #> 2063  0.289769730 #> 2064  0.289626013 #> 2065  0.289482368 #> 2066  0.289338795 #> 2067  0.289195295 #> 2068  0.289051868 #> 2069  0.288908512 #> 2070  0.288765229 #> 2071  0.288622019 #> 2072  0.288478880 #> 2073  0.288335814 #> 2074  0.288192821 #> 2075  0.288049899 #> 2076  0.287907050 #> 2077  0.287764272 #> 2078  0.287621567 #> 2079  0.287478934 #> 2080  0.287336373 #> 2081  0.287193883 #> 2082  0.287051466 #> 2083  0.286909121 #> 2084  0.286766848 #> 2085  0.286624646 #> 2086  0.286482517 #> 2087  0.286340459 #> 2088  0.286198473 #> 2089  0.286056559 #> 2090  0.285914716 #> 2091  0.285772945 #> 2092  0.285631246 #> 2093  0.285489619 #> 2094  0.285348063 #> 2095  0.285206578 #> 2096  0.285065165 #> 2097  0.284923824 #> 2098  0.284782554 #> 2099  0.284641356 #> 2100  0.284500229 #> 2101  0.284359173 #> 2102  0.284218189 #> 2103  0.284077276 #> 2104  0.283936434 #> 2105  0.283795664 #> 2106  0.283654965 #> 2107  0.283514336 #> 2108  0.283373780 #> 2109  0.283233294 #> 2110  0.283092879 #> 2111  0.282952536 #> 2112  0.282812263 #> 2113  0.282672062 #> 2114  0.282531931 #> 2115  0.282391872 #> 2116  0.282251883 #> 2117  0.282111965 #> 2118  0.281972118 #> 2119  0.281832342 #> 2120  0.281692637 #> 2121  0.281553002 #> 2122  0.281413438 #> 2123  0.281273945 #> 2124  0.281134523 #> 2125  0.280995171 #> 2126  0.280855890 #> 2127  0.280716679 #> 2128  0.280577539 #> 2129  0.280438470 #> 2130  0.280299471 #> 2131  0.280160542 #> 2132  0.280021684 #> 2133  0.279882896 #> 2134  0.279744179 #> 2135  0.279605532 #> 2136  0.279466955 #> 2137  0.279328449 #> 2138  0.279190012 #> 2139  0.279051646 #> 2140  0.278913351 #> 2141  0.278775125 #> 2142  0.278636969 #> 2143  0.278498884 #> 2144  0.278360869 #> 2145  0.278222923 #> 2146  0.278085048 #> 2147  0.277947243 #> 2148  0.277809507 #> 2149  0.277671842 #> 2150  0.277534246 #> 2151  0.277396720 #> 2152  0.277259265 #> 2153  0.277121878 #> 2154  0.276984562 #> 2155  0.276847315 #> 2156  0.276710139 #> 2157  0.276573031 #> 2158  0.276435994 #> 2159  0.276299026 #> 2160  0.276162128 #> 2161  0.276025299 #> 2162  0.275888540 #> 2163  0.275751850 #> 2164  0.275615229 #> 2165  0.275478679 #> 2166  0.275342197 #> 2167  0.275205785 #> 2168  0.275069442 #> 2169  0.274933169 #> 2170  0.274796965 #> 2171  0.274660830 #> 2172  0.274524765 #> 2173  0.274388768 #> 2174  0.274252841 #> 2175  0.274116983 #> 2176  0.273981194 #> 2177  0.273845474 #> 2178  0.273709824 #> 2179  0.273574242 #> 2180  0.273438729 #> 2181  0.273303286 #> 2182  0.273167911 #> 2183  0.273032605 #> 2184  0.272897368 #> 2185  0.272762200 #> 2186  0.272627100 #> 2187  0.272492070 #> 2188  0.272357108 #> 2189  0.272222215 #> 2190  0.272087391 #> 2191  0.271952635 #> 2192  0.271817948 #> 2193  0.271683330 #> 2194  0.271548780 #> 2195  0.271414299 #> 2196  0.271279887 #> 2197  0.271145543 #> 2198  0.271011267 #> 2199  0.270877060 #> 2200  0.270742921 #> 2201  0.270608851 #> 2202  0.270474849 #> 2203  0.270340915 #> 2204  0.270207050 #> 2205  0.270073253 #> 2206  0.269939524 #> 2207  0.269805864 #> 2208  0.269672271 #> 2209  0.269538747 #> 2210  0.269405291 #> 2211  0.269271903 #> 2212  0.269138584 #> 2213  0.269005332 #> 2214  0.268872148 #> 2215  0.268739033 #> 2216  0.268605985 #> 2217  0.268473005 #> 2218  0.268340093 #> 2219  0.268207249 #> 2220  0.268074473 #> 2221  0.267941765 #> 2222  0.267809124 #> 2223  0.267676552 #> 2224  0.267544047 #> 2225  0.267411609 #> 2226  0.267279240 #> 2227  0.267146938 #> 2228  0.267014704 #> 2229  0.266882537 #> 2230  0.266750438 #> 2231  0.266618407 #> 2232  0.266486443 #> 2233  0.266354546 #> 2234  0.266222717 #> 2235  0.266090956 #> 2236  0.265959262 #> 2237  0.265827635 #> 2238  0.265696076 #> 2239  0.265564584 #> 2240  0.265433159 #> 2241  0.265301801 #> 2242  0.265170511 #> 2243  0.265039288 #> 2244  0.264908132 #> 2245  0.264777044 #> 2246  0.264646022 #> 2247  0.264515068 #> 2248  0.264384181 #> 2249  0.264253361 #> 2250  0.264122608 #> 2251  0.263991921 #> 2252  0.263861302 #> 2253  0.263730750 #> 2254  0.263600265 #> 2255  0.263469846 #> 2256  0.263339495 #> 2257  0.263209210 #> 2258  0.263078992 #> 2259  0.262948841 #> 2260  0.262818757 #> 2261  0.262688739 #> 2262  0.262558789 #> 2263  0.262428905 #> 2264  0.262299087 #> 2265  0.262169336 #> 2266  0.262039652 #> 2267  0.261910034 #> 2268  0.261780483 #> 2269  0.261650998 #> 2270  0.261521580 #> 2271  0.261392229 #> 2272  0.261262944 #> 2273  0.261133725 #> 2274  0.261004572 #> 2275  0.260875486 #> 2276  0.260746467 #> 2277  0.260617513 #> 2278  0.260488626 #> 2279  0.260359806 #> 2280  0.260231051 #> 2281  0.260102363 #> 2282  0.259973740 #> 2283  0.259845184 #> 2284  0.259716695 #> 2285  0.259588271 #> 2286  0.259459913 #> 2287  0.259331621 #> 2288  0.259203396 #> 2289  0.259075236 #> 2290  0.258947142 #> 2291  0.258819115 #> 2292  0.258691153 #> 2293  0.258563257 #> 2294  0.258435427 #> 2295  0.258307663 #> 2296  0.258179964 #> 2297  0.258052332 #> 2298  0.257924765 #> 2299  0.257797264 #> 2300  0.257669829 #> 2301  0.257542459 #> 2302  0.257415155 #> 2303  0.257287917 #> 2304  0.257160744 #> 2305  0.257033637 #> 2306  0.256906595 #> 2307  0.256779619 #> 2308  0.256652709 #> 2309  0.256525864 #> 2310  0.256399084 #> 2311  0.256272370 #> 2312  0.256145721 #> 2313  0.256019138 #> 2314  0.255892620 #> 2315  0.255766167 #> 2316  0.255639780 #> 2317  0.255513458 #> 2318  0.255387201 #> 2319  0.255261010 #> 2320  0.255134884 #> 2321  0.255008822 #> 2322  0.254882826 #> 2323  0.254756896 #> 2324  0.254631030 #> 2325  0.254505229 #> 2326  0.254379494 #> 2327  0.254253823 #> 2328  0.254128218 #> 2329  0.254002677 #> 2330  0.253877202 #> 2331  0.253751791 #> 2332  0.253626445 #> 2333  0.253501165 #> 2334  0.253375949 #> 2335  0.253250798 #> 2336  0.253125711 #> 2337  0.253000690 #> 2338  0.252875733 #> 2339  0.252750841 #> 2340  0.252626014 #> 2341  0.252501251 #> 2342  0.252376554 #> 2343  0.252251920 #> 2344  0.252127352 #> 2345  0.252002848 #> 2346  0.251878408 #> 2347  0.251754034 #> 2348  0.251629723 #> 2349  0.251505477 #> 2350  0.251381296 #> 2351  0.251257179 #> 2352  0.251133127 #> 2353  0.251009139 #> 2354  0.250885215 #> 2355  0.250761356 #> 2356  0.250637561 #> 2357  0.250513830 #> 2358  0.250390164 #> 2359  0.250266562 #> 2360  0.250143024 #> 2361  0.250019550 #> 2362  0.249896141 #> 2363  0.249772796 #> 2364  0.249649515 #> 2365  0.249526298 #> 2366  0.249403145 #> 2367  0.249280056 #> 2368  0.249157032 #> 2369  0.249034071 #> 2370  0.248911174 #> 2371  0.248788342 #> 2372  0.248665573 #> 2373  0.248542868 #> 2374  0.248420228 #> 2375  0.248297651 #> 2376  0.248175138 #> 2377  0.248052688 #> 2378  0.247930303 #> 2379  0.247807982 #> 2380  0.247685724 #> 2381  0.247563530 #> 2382  0.247441400 #> 2383  0.247319333 #> 2384  0.247197330 #> 2385  0.247075391 #> 2386  0.246953516 #> 2387  0.246831704 #> 2388  0.246709955 #> 2389  0.246588271 #> 2390  0.246466650 #> 2391  0.246345092 #> 2392  0.246223598 #> 2393  0.246102167 #> 2394  0.245980800 #> 2395  0.245859497 #> 2396  0.245738256 #> 2397  0.245617079 #> 2398  0.245495966 #> 2399  0.245374916 #> 2400  0.245253929 #> 2401  0.245133006 #> 2402  0.245012146 #> 2403  0.244891349 #> 2404  0.244770615 #> 2405  0.244649945 #> 2406  0.244529338 #> 2407  0.244408794 #> 2408  0.244288313 #> 2409  0.244167895 #> 2410  0.244047541 #> 2411  0.243927249 #> 2412  0.243807021 #> 2413  0.243686856 #> 2414  0.243566753 #> 2415  0.243446714 #> 2416  0.243326738 #> 2417  0.243206825 #> 2418  0.243086974 #> 2419  0.242967187 #> 2420  0.242847463 #> 2421  0.242727801 #> 2422  0.242608202 #> 2423  0.242488667 #> 2424  0.242369194 #> 2425  0.242249783 #> 2426  0.242130436 #> 2427  0.242011151 #> 2428  0.241891929 #> 2429  0.241772770 #> 2430  0.241653674 #> 2431  0.241534640 #> 2432  0.241415669 #> 2433  0.241296760 #> 2434  0.241177915 #> 2435  0.241059131 #> 2436  0.240940411 #> 2437  0.240821753 #> 2438  0.240703157 #> 2439  0.240584624 #> 2440  0.240466153 #> 2441  0.240347745 #> 2442  0.240229400 #> 2443  0.240111117 #> 2444  0.239992896 #> 2445  0.239874738 #> 2446  0.239756642 #> 2447  0.239638608 #> 2448  0.239520637 #> 2449  0.239402728 #> 2450  0.239284881 #> 2451  0.239167097 #> 2452  0.239049375 #> 2453  0.238931715 #> 2454  0.238814118 #> 2455  0.238696582 #> 2456  0.238579109 #> 2457  0.238461698 #> 2458  0.238344349 #> 2459  0.238227063 #> 2460  0.238109838 #> 2461  0.237992675 #> 2462  0.237875575 #> 2463  0.237758537 #> 2464  0.237641560 #> 2465  0.237524646 #> 2466  0.237407793 #> 2467  0.237291003 #> 2468  0.237174275 #> 2469  0.237057608 #> 2470  0.236941004 #> 2471  0.236824461 #> 2472  0.236707980 #> 2473  0.236591561 #> 2474  0.236475204 #> 2475  0.236358909 #> 2476  0.236242676 #> 2477  0.236126504 #> 2478  0.236010394 #> 2479  0.235894346 #> 2480  0.235778360 #> 2481  0.235662435 #> 2482  0.235546572 #> 2483  0.235430771 #> 2484  0.235315032 #> 2485  0.235199354 #> 2486  0.235083737 #> 2487  0.234968183 #> 2488  0.234852690 #> 2489  0.234737258 #> 2490  0.234621888 #> 2491  0.234506580 #> 2492  0.234391333 #> 2493  0.234276148 #> 2494  0.234161024 #> 2495  0.234045961 #> 2496  0.233930960 #> 2497  0.233816021 #> 2498  0.233701143 #> 2499  0.233586326 #> 2500  0.233471571 #> 2501  0.233356877 #> 2502  0.233242245 #> 2503  0.233127673 #> 2504  0.233013164 #> 2505  0.232898715 #> 2506  0.232784328 #> 2507  0.232670002 #> 2508  0.232555737 #> 2509  0.232441533 #> 2510  0.232327391 #> 2511  0.232213310 #> 2512  0.232099290 #> 2513  0.231985331 #> 2514  0.231871434 #> 2515  0.231757597 #> 2516  0.231643822 #> 2517  0.231530108 #> 2518  0.231416455 #> 2519  0.231302863 #> 2520  0.231189332 #> 2521  0.231075862 #> 2522  0.230962453 #> 2523  0.230849105 #> 2524  0.230735818 #> 2525  0.230622592 #> 2526  0.230509427 #> 2527  0.230396323 #> 2528  0.230283280 #> 2529  0.230170298 #> 2530  0.230057377 #> 2531  0.229944516 #> 2532  0.229831717 #> 2533  0.229718978 #> 2534  0.229606300 #> 2535  0.229493683 #> 2536  0.229381127 #> 2537  0.229268632 #> 2538  0.229156197 #> 2539  0.229043824 #> 2540  0.228931511 #> 2541  0.228819258 #> 2542  0.228707067 #> 2543  0.228594936 #> 2544  0.228482866 #> 2545  0.228370856 #> 2546  0.228258908 #> 2547  0.228147019 #> 2548  0.228035192 #> 2549  0.227923425 #> 2550  0.227811719 #> 2551  0.227700073 #> 2552  0.227588488 #> 2553  0.227476964 #> 2554  0.227365500 #> 2555  0.227254096 #> 2556  0.227142753 #> 2557  0.227031471 #> 2558  0.226920249 #> 2559  0.226809088 #> 2560  0.226697987 #> 2561  0.226586946 #> 2562  0.226475966 #> 2563  0.226365047 #> 2564  0.226254188 #> 2565  0.226143389 #> 2566  0.226032651 #> 2567  0.225921973 #> 2568  0.225811355 #> 2569  0.225700798 #> 2570  0.225590301 #> 2571  0.225479865 #> 2572  0.225369488 #> 2573  0.225259173 #> 2574  0.225148917 #> 2575  0.225038722 #> 2576  0.224928587 #> 2577  0.224818512 #> 2578  0.224708497 #> 2579  0.224598543 #> 2580  0.224488649 #> 2581  0.224378815 #> 2582  0.224269041 #> 2583  0.224159328 #> 2584  0.224049674 #> 2585  0.223940081 #> 2586  0.223830548 #> 2587  0.223721075 #> 2588  0.223611663 #> 2589  0.223502310 #> 2590  0.223393018 #> 2591  0.223283785 #> 2592  0.223174613 #> 2593  0.223065501 #> 2594  0.222956448 #> 2595  0.222847456 #> 2596  0.222738524 #> 2597  0.222629652 #> 2598  0.222520840 #> 2599  0.222412088 #> 2600  0.222303396 #> 2601  0.222194764 #> 2602  0.222086192 #> 2603  0.221977680 #> 2604  0.221869227 #> 2605  0.221760835 #> 2606  0.221652503 #> 2607  0.221544231 #> 2608  0.221436018 #> 2609  0.221327866 #> 2610  0.221219773 #> 2611  0.221111740 #> 2612  0.221003767 #> 2613  0.220895854 #> 2614  0.220788001 #> 2615  0.220680208 #> 2616  0.220572475 #> 2617  0.220464801 #> 2618  0.220357187 #> 2619  0.220249633 #> 2620  0.220142139 #> 2621  0.220034705 #> 2622  0.219927330 #> 2623  0.219820015 #> 2624  0.219712760 #> 2625  0.219605565 #> 2626  0.219498429 #> 2627  0.219391354 #> 2628  0.219284338 #> 2629  0.219177381 #> 2630  0.219070485 #> 2631  0.218963648 #> 2632  0.218856870 #> 2633  0.218750153 #> 2634  0.218643495 #> 2635  0.218536897 #> 2636  0.218430359 #> 2637  0.218323880 #> 2638  0.218217461 #> 2639  0.218111101 #> 2640  0.218004801 #> 2641  0.217898561 #> 2642  0.217792381 #> 2643  0.217686260 #> 2644  0.217580198 #> 2645  0.217474197 #> 2646  0.217368254 #> 2647  0.217262372 #> 2648  0.217156549 #> 2649  0.217050786 #> 2650  0.216945082 #> 2651  0.216839437 #> 2652  0.216733853 #> 2653  0.216628328 #> 2654  0.216522862 #> 2655  0.216417456 #> 2656  0.216312110 #> 2657  0.216206823 #> 2658  0.216101595 #> 2659  0.215996427 #> 2660  0.215891319 #> 2661  0.215786270 #> 2662  0.215681281 #> 2663  0.215576351 #> 2664  0.215471481 #> 2665  0.215366670 #> 2666  0.215261918 #> 2667  0.215157226 #> 2668  0.215052594 #> 2669  0.214948021 #> 2670  0.214843508 #> 2671  0.214739054 #> 2672  0.214634659 #> 2673  0.214530324 #> 2674  0.214426048 #> 2675  0.214321832 #> 2676  0.214217675 #> 2677  0.214113578 #> 2678  0.214009540 #> 2679  0.213905562 #> 2680  0.213801643 #> 2681  0.213697783 #> 2682  0.213593983 #> 2683  0.213490242 #> 2684  0.213386561 #> 2685  0.213282939 #> 2686  0.213179376 #> 2687  0.213075873 #> 2688  0.212972429 #> 2689  0.212869045 #> 2690  0.212765720 #> 2691  0.212662454 #> 2692  0.212559248 #> 2693  0.212456102 #> 2694  0.212353014 #> 2695  0.212249986 #> 2696  0.212147018 #> 2697  0.212044108 #> 2698  0.211941259 #> 2699  0.211838468 #> 2700  0.211735737 #> 2701  0.211633066 #> 2702  0.211530453 #> 2703  0.211427900 #> 2704  0.211325407 #> 2705  0.211222973 #> 2706  0.211120598 #> 2707  0.211018282 #> 2708  0.210916026 #> 2709  0.210813830 #> 2710  0.210711692 #> 2711  0.210609614 #> 2712  0.210507596 #> 2713  0.210405637 #> 2714  0.210303737 #> 2715  0.210201896 #> 2716  0.210100115 #> 2717  0.209998394 #> 2718  0.209896731 #> 2719  0.209795128 #> 2720  0.209693585 #> 2721  0.209592100 #> 2722  0.209490676 #> 2723  0.209389310 #> 2724  0.209288004 #> 2725  0.209186757 #> 2726  0.209085570 #> 2727  0.208984442 #> 2728  0.208883374 #> 2729  0.208782364 #> 2730  0.208681415 #> 2731  0.208580524 #> 2732  0.208479693 #> 2733  0.208378922 #> 2734  0.208278210 #> 2735  0.208177557 #> 2736  0.208076963 #> 2737  0.207976429 #> 2738  0.207875955 #> 2739  0.207775540 #> 2740  0.207675184 #> 2741  0.207574888 #> 2742  0.207474651 #> 2743  0.207374473 #> 2744  0.207274355 #> 2745  0.207174297 #> 2746  0.207074298 #> 2747  0.206974358 #> 2748  0.206874478 #> 2749  0.206774657 #> 2750  0.206674895 #> 2751  0.206575194 #> 2752  0.206475551 #> 2753  0.206375968 #> 2754  0.206276445 #> 2755  0.206176981 #> 2756  0.206077576 #> 2757  0.205978231 #> 2758  0.205878946 #> 2759  0.205779720 #> 2760  0.205680553 #> 2761  0.205581446 #> 2762  0.205482399 #> 2763  0.205383411 #> 2764  0.205284482 #> 2765  0.205185613 #> 2766  0.205086804 #> 2767  0.204988054 #> 2768  0.204889364 #> 2769  0.204790733 #> 2770  0.204692162 #> 2771  0.204593651 #> 2772  0.204495199 #> 2773  0.204396807 #> 2774  0.204298474 #> 2775  0.204200201 #> 2776  0.204101987 #> 2777  0.204003834 #> 2778  0.203905739 #> 2779  0.203807705 #> 2780  0.203709730 #> 2781  0.203611815 #> 2782  0.203513959 #> 2783  0.203416163 #> 2784  0.203318427 #> 2785  0.203220751 #> 2786  0.203123134 #> 2787  0.203025577 #> 2788  0.202928080 #> 2789  0.202830642 #> 2790  0.202733264 #> 2791  0.202635946 #> 2792  0.202538688 #> 2793  0.202441490 #> 2794  0.202344351 #> 2795  0.202247272 #> 2796  0.202150253 #> 2797  0.202053294 #> 2798  0.201956394 #> 2799  0.201859555 #> 2800  0.201762775 #> 2801  0.201666055 #> 2802  0.201569395 #> 2803  0.201472795 #> 2804  0.201376255 #> 2805  0.201279775 #> 2806  0.201183354 #> 2807  0.201086994 #> 2808  0.200990694 #> 2809  0.200894453 #> 2810  0.200798273 #> 2811  0.200702153 #> 2812  0.200606092 #> 2813  0.200510092 #> 2814  0.200414151 #> 2815  0.200318271 #> 2816  0.200222451 #> 2817  0.200126691 #> 2818  0.200030991 #> 2819  0.199935351 #> 2820  0.199839771 #> 2821  0.199744252 #> 2822  0.199648792 #> 2823  0.199553393 #> 2824  0.199458054 #> 2825  0.199362775 #> 2826  0.199267556 #> 2827  0.199172398 #> 2828  0.199077300 #> 2829  0.198982262 #> 2830  0.198887284 #> 2831  0.198792367 #> 2832  0.198697510 #> 2833  0.198602714 #> 2834  0.198507977 #> 2835  0.198413301 #> 2836  0.198318686 #> 2837  0.198224131 #> 2838  0.198129636 #> 2839  0.198035202 #> 2840  0.197940828 #> 2841  0.197846515 #> 2842  0.197752262 #> 2843  0.197658070 #> 2844  0.197563939 #> 2845  0.197469867 #> 2846  0.197375857 #> 2847  0.197281907 #> 2848  0.197188018 #> 2849  0.197094189 #> 2850  0.197000421 #> 2851  0.196906714 #> 2852  0.196813067 #> 2853  0.196719482 #> 2854  0.196625956 #> 2855  0.196532492 #> 2856  0.196439089 #> 2857  0.196345746 #> 2858  0.196252464 #> 2859  0.196159243 #> 2860  0.196066083 #> 2861  0.195972984 #> 2862  0.195879945 #> 2863  0.195786968 #> 2864  0.195694052 #> 2865  0.195601196 #> 2866  0.195508402 #> 2867  0.195415669 #> 2868  0.195322996 #> 2869  0.195230385 #> 2870  0.195137835 #> 2871  0.195045346 #> 2872  0.194952918 #> 2873  0.194860552 #> 2874  0.194768247 #> 2875  0.194676002 #> 2876  0.194583820 #> 2877  0.194491698 #> 2878  0.194399638 #> 2879  0.194307639 #> 2880  0.194215702 #> 2881  0.194123826 #> 2882  0.194032011 #> 2883  0.193940258 #> 2884  0.193848566 #> 2885  0.193756936 #> 2886  0.193665367 #> 2887  0.193573860 #> 2888  0.193482415 #> 2889  0.193391031 #> 2890  0.193299709 #> 2891  0.193208448 #> 2892  0.193117249 #> 2893  0.193026112 #> 2894  0.192935037 #> 2895  0.192844023 #> 2896  0.192753072 #> 2897  0.192662182 #> 2898  0.192571354 #> 2899  0.192480588 #> 2900  0.192389884 #> 2901  0.192299242 #> 2902  0.192208662 #> 2903  0.192118144 #> 2904  0.192027688 #> 2905  0.191937294 #> 2906  0.191846962 #> 2907  0.191756693 #> 2908  0.191666486 #> 2909  0.191576341 #> 2910  0.191486258 #> 2911  0.191396238 #> 2912  0.191306280 #> 2913  0.191216384 #> 2914  0.191126551 #> 2915  0.191036780 #> 2916  0.190947072 #> 2917  0.190857426 #> 2918  0.190767843 #> 2919  0.190678322 #> 2920  0.190588864 #> 2921  0.190499469 #> 2922  0.190410136 #> 2923  0.190320866 #> 2924  0.190231659 #> 2925  0.190142515 #> 2926  0.190053433 #> 2927  0.189964415 #> 2928  0.189875459 #> 2929  0.189786566 #> 2930  0.189697737 #> 2931  0.189608970 #> 2932  0.189520267 #> 2933  0.189431626 #> 2934  0.189343049 #> 2935  0.189254535 #> 2936  0.189166084 #> 2937  0.189077697 #> 2938  0.188989373 #> 2939  0.188901112 #> 2940  0.188812915 #> 2941  0.188724781 #> 2942  0.188636710 #> 2943  0.188548703 #> 2944  0.188460760 #> 2945  0.188372880 #> 2946  0.188285064 #> 2947  0.188197312 #> 2948  0.188109623 #> 2949  0.188021999 #> 2950  0.187934438 #> 2951  0.187846941 #> 2952  0.187759508 #> 2953  0.187672138 #> 2954  0.187584833 #> 2955  0.187497592 #> 2956  0.187410415 #> 2957  0.187323303 #> 2958  0.187236254 #> 2959  0.187149270 #> 2960  0.187062350 #> 2961  0.186975494 #> 2962  0.186888703 #> 2963  0.186801976 #> 2964  0.186715314 #> 2965  0.186628716 #> 2966  0.186542183 #> 2967  0.186455714 #> 2968  0.186369310 #> 2969  0.186282971 #> 2970  0.186196697 #> 2971  0.186110487 #> 2972  0.186024343 #> 2973  0.185938263 #> 2974  0.185852249 #> 2975  0.185766299 #> 2976  0.185680414 #> 2977  0.185594595 #> 2978  0.185508841 #> 2979  0.185423152 #> 2980  0.185337529 #> 2981  0.185251970 #> 2982  0.185166478 #> 2983  0.185081050 #> 2984  0.184995689 #> 2985  0.184910392 #> 2986  0.184825162 #> 2987  0.184739997 #> 2988  0.184654898 #> 2989  0.184569865 #> 2990  0.184484898 #> 2991  0.184399996 #> 2992  0.184315161 #> 2993  0.184230391 #> 2994  0.184145688 #> 2995  0.184061051 #> 2996  0.183976480 #> 2997  0.183891975 #> 2998  0.183807537 #> 2999  0.183723165 #> 3000  0.183638859 #> 3001  0.183554621 #> 3002  0.183470448 #> 3003  0.183386343 #> 3004  0.183302304 #> 3005  0.183218331 #> 3006  0.183134426 #> 3007  0.183050587 #> 3008  0.182966816 #> 3009  0.182883111 #> 3010  0.182799474 #> 3011  0.182715904 #> 3012  0.182632401 #> 3013  0.182548965 #> 3014  0.182465596 #> 3015  0.182382295 #> 3016  0.182299062 #> 3017  0.182215896 #> 3018  0.182132797 #> 3019  0.182049767 #> 3020  0.181966804 #> 3021  0.181883909 #> 3022  0.181801081 #> 3023  0.181718322 #> 3024  0.181635631 #> 3025  0.181553008 #> 3026  0.181470453 #> 3027  0.181387966 #> 3028  0.181305548 #> 3029  0.181223198 #> 3030  0.181140916 #> 3031  0.181058703 #> 3032  0.180976558 #> 3033  0.180894483 #> 3034  0.180812475 #> 3035  0.180730537 #> 3036  0.180648668 #> 3037  0.180566868 #> 3038  0.180485136 #> 3039  0.180403474 #> 3040  0.180321881 #> 3041  0.180240357 #> 3042  0.180158903 #> 3043  0.180077518 #> 3044  0.179996202 #> 3045  0.179914957 #> 3046  0.179833780 #> 3047  0.179752674 #> 3048  0.179671637 #> 3049  0.179590671 #> 3050  0.179509774 #> 3051  0.179428947 #> 3052  0.179348191 #> 3053  0.179267504 #> 3054  0.179186888 #> 3055  0.179106343 #> 3056  0.179025868 #> 3057  0.178945463 #> 3058  0.178865129 #> 3059  0.178784866 #> 3060  0.178704674 #> 3061  0.178624552 #> 3062  0.178544502 #> 3063  0.178464522 #> 3064  0.178384614 #> 3065  0.178304777 #> 3066  0.178225011 #> 3067  0.178145317 #> 3068  0.178065694 #> 3069  0.177986143 #> 3070  0.177906664 #> 3071  0.177827256 #> 3072  0.177747920 #> 3073  0.177668656 #> 3074  0.177589465 #> 3075  0.177510345 #> 3076  0.177431298 #> 3077  0.177352323 #> 3078  0.177273420 #> 3079  0.177194590 #> 3080  0.177115833 #> 3081  0.177037148 #> 3082  0.176958536 #> 3083  0.176879997 #> 3084  0.176801531 #> 3085  0.176723138 #> 3086  0.176644818 #> 3087  0.176566572 #> 3088  0.176488399 #> 3089  0.176410299 #> 3090  0.176332273 #> 3091  0.176254321 #> 3092  0.176176443 #> 3093  0.176098638 #> 3094  0.176020908 #> 3095  0.175943251 #> 3096  0.175865669 #> 3097  0.175788161 #> 3098  0.175710727 #> 3099  0.175633368 #> 3100  0.175556084 #> 3101  0.175478874 #> 3102  0.175401740 #> 3103  0.175324680 #> 3104  0.175247695 #> 3105  0.175170785 #> 3106  0.175093951 #> 3107  0.175017192 #> 3108  0.174940508 #> 3109  0.174863901 #> 3110  0.174787368 #> 3111  0.174710912 #> 3112  0.174634531 #> 3113  0.174558227 #> 3114  0.174481999 #> 3115  0.174405847 #> 3116  0.174329771 #> 3117  0.174253772 #> 3118  0.174177850 #> 3119  0.174102004 #> 3120  0.174026235 #> 3121  0.173950543 #> 3122  0.173874928 #> 3123  0.173799391 #> 3124  0.173723930 #> 3125  0.173648548 #> 3126  0.173573242 #> 3127  0.173498015 #> 3128  0.173422865 #> 3129  0.173347793 #> 3130  0.173272799 #> 3131  0.173197884 #> 3132  0.173123046 #> 3133  0.173048287 #> 3134  0.172973607 #> 3135  0.172899005 #> 3136  0.172824482 #> 3137  0.172750038 #> 3138  0.172675674 #> 3139  0.172601388 #> 3140  0.172527181 #> 3141  0.172453055 #> 3142  0.172379007 #> 3143  0.172305040 #> 3144  0.172231152 #> 3145  0.172157344 #> 3146  0.172083616 #> 3147  0.172009969 #> 3148  0.171936402 #> 3149  0.171862915 #> 3150  0.171789509 #> 3151  0.171716184 #> 3152  0.171642940 #> 3153  0.171569776 #> 3154  0.171496694 #> 3155  0.171423694 #> 3156  0.171350774 #> 3157  0.171277937 #> 3158  0.171205181 #> 3159  0.171132507 #> 3160  0.171059915 #> 3161  0.170987406 #> 3162  0.170914978 #> 3163  0.170842633 #> 3164  0.170770371 #> 3165  0.170698192 #> 3166  0.170626095 #> 3167  0.170554082 #> 3168  0.170482151 #> 3169  0.170410304 #> 3170  0.170338541 #> 3171  0.170266861 #> 3172  0.170195265 #> 3173  0.170123753 #> 3174  0.170052326 #> 3175  0.169980982 #> 3176  0.169909723 #> 3177  0.169838548 #> 3178  0.169767458 #> 3179  0.169696453 #> 3180  0.169625534 #> 3181  0.169554699 #> 3182  0.169483950 #> 3183  0.169413286 #> 3184  0.169342708 #> 3185  0.169272216 #> 3186  0.169201810 #> 3187  0.169131490 #> 3188  0.169061256 #> 3189  0.168991109 #> 3190  0.168921048 #> 3191  0.168851075 #> 3192  0.168781188 #> 3193  0.168711389 #> 3194  0.168641676 #> 3195  0.168572052 #> 3196  0.168502515 #> 3197  0.168433066 #> 3198  0.168363705 #> 3199  0.168294432 #> 3200  0.168225248 #> 3201  0.168156152 #> 3202  0.168087145 #> 3203  0.168018226 #> 3204  0.167949397 #> 3205  0.167880657 #> 3206  0.167812007 #> 3207  0.167743446 #> 3208  0.167674975 #> 3209  0.167606594 #> 3210  0.167538303 #> 3211  0.167470102 #> 3212  0.167401992 #> 3213  0.167333973 #> 3214  0.167266044 #> 3215  0.167198207 #> 3216  0.167130461 #> 3217  0.167062806 #> 3218  0.166995243 #> 3219  0.166927772 #> 3220  0.166860393 #> 3221  0.166793106 #> 3222  0.166725911 #> 3223  0.166658809 #> 3224  0.166591800 #> 3225  0.166524884 #> 3226  0.166458061 #> 3227  0.166391332 #> 3228  0.166324696 #> 3229  0.166258154 #> 3230  0.166191705 #> 3231  0.166125352 #> 3232  0.166059092 #> 3233  0.165992927 #> 3234  0.165926857 #> 3235  0.165860882 #> 3236  0.165795002 #> 3237  0.165729218 #> 3238  0.165663529 #> 3239  0.165597936 #> 3240  0.165532439 #> 3241  0.165467038 #> 3242  0.165401734 #> 3243  0.165336526 #> 3244  0.165271416 #> 3245  0.165206402 #> 3246  0.165141486 #> 3247  0.165076667 #> 3248  0.165011947 #> 3249  0.164947324 #> 3250  0.164882799 #> 3251  0.164818373 #> 3252  0.164754045 #> 3253  0.164689817 #> 3254  0.164625687 #> 3255  0.164561657 #> 3256  0.164497726 #> 3257  0.164433895 #> 3258  0.164370164 #> 3259  0.164306534 #> 3260  0.164243003 #> 3261  0.164179574 #> 3262  0.164116245 #> 3263  0.164053018 #> 3264  0.163989892 #> 3265  0.163926867 #> 3266  0.163863945 #> 3267  0.163801125 #> 3268  0.163738407 #> 3269  0.163675791 #> 3270  0.163613279 #> 3271  0.163550869 #> 3272  0.163488563 #> 3273  0.163426360 #> 3274  0.163364261 #> 3275  0.163302267 #> 3276  0.163240376 #> 3277  0.163178590 #> 3278  0.163116909 #> 3279  0.163055333 #> 3280  0.162993863 #> 3281  0.162932497 #> 3282  0.162871238 #> 3283  0.162810085 #> 3284  0.162749038 #> 3285  0.162688098 #> 3286  0.162627264 #> 3287  0.162566538 #> 3288  0.162505919 #> 3289  0.162445408 #> 3290  0.162385005 #> 3291  0.162324710 #> 3292  0.162264523 #> 3293  0.162204445 #> 3294  0.162144476 #> 3295  0.162084616 #> 3296  0.162024866 #> 3297  0.161965225 #> 3298  0.161905695 #> 3299  0.161846275 #> 3300  0.161786966 #> 3301  0.161727767 #> 3302  0.161668680 #> 3303  0.161609704 #> 3304  0.161550840 #> 3305  0.161492088 #> 3306  0.161433449 #> 3307  0.161374922 #> 3308  0.161316507 #> 3309  0.161258206 #> 3310  0.161200019 #> 3311  0.161141945 #> 3312  0.161083985 #> 3313  0.161026140 #> 3314  0.160968409 #> 3315  0.160910794 #> 3316  0.160853293 #> 3317  0.160795908 #> 3318  0.160738639 #> 3319  0.160681486 #> 3320  0.160624450 #> 3321  0.160567530 #> 3322  0.160510727 #> 3323  0.160454042 #> 3324  0.160397474 #> 3325  0.160341025 #> 3326  0.160284693 #> 3327  0.160228481 #> 3328  0.160172387 #> 3329  0.160116412 #> 3330  0.160060557 #> 3331  0.160004822 #> 3332  0.159949207 #> 3333  0.159893713 #> 3334  0.159838339 #> 3335  0.159783087 #> 3336  0.159727956 #> 3337  0.159672947 #> 3338  0.159618060 #> 3339  0.159563296 #> 3340  0.159508654 #> 3341  0.159454135 #> 3342  0.159399741 #> 3343  0.159345469 #> 3344  0.159291322 #> 3345  0.159237300 #> 3346  0.159183402 #> 3347  0.159129630 #> 3348  0.159075983 #> 3349  0.159022462 #> 3350  0.158969067 #> 3351  0.158915799 #> 3352  0.158862657 #> 3353  0.158809643 #> 3354  0.158756757 #> 3355  0.158703998 #> 3356  0.158651368 #> 3357  0.158598867 #> 3358  0.158546494 #> 3359  0.158494251 #> 3360  0.158442138 #> 3361  0.158390155 #> 3362  0.158338302 #> 3363  0.158286581 #> 3364  0.158234990 #> 3365  0.158183531 #> 3366  0.158132204 #> 3367  0.158081010 #> 3368  0.158029948 #> 3369  0.157979019 #> 3370  0.157928224 #> 3371  0.157877563 #> 3372  0.157827036 #> 3373  0.157776643 #> 3374  0.157726386 #> 3375  0.157676264 #> 3376  0.157626277 #> 3377  0.157576427 #> 3378  0.157526714 #> 3379  0.157477137 #> 3380  0.157427698 #> 3381  0.157378397 #> 3382  0.157329234 #> 3383  0.157280209 #> 3384  0.157231323 #> 3385  0.157182577 #> 3386  0.157133970 #> 3387  0.157085504 #> 3388  0.157037178 #> 3389  0.156988994 #> 3390  0.156940950 #> 3391  0.156893049 #> 3392  0.156845290 #> 3393  0.156797673 #> 3394  0.156750199 #> 3395  0.156702869 #> 3396  0.156655683 #> 3397  0.156608642 #> 3398  0.156561745 #> 3399  0.156514993 #> 3400  0.156468387 #> 3401  0.156421927 #> 3402  0.156375614 #> 3403  0.156329447 #> 3404  0.156283428 #> 3405  0.156237557 #> 3406  0.156191834 #> 3407  0.156146259 #> 3408  0.156100834 #> 3409  0.156055559 #> 3410  0.156010433 #> 3411  0.155965458 #> 3412  0.155920634 #> 3413  0.155875962 #> 3414  0.155831441 #> 3415  0.155787073 #> 3416  0.155742857 #> 3417  0.155698795 #> 3418  0.155654886 #> 3419  0.155611132 #> 3420  0.155567532 #> 3421  0.155524088 #> 3422  0.155480799 #> 3423  0.155437666 #> 3424  0.155394689 #> 3425  0.155351870 #> 3426  0.155309208 #> 3427  0.155266704 #> 3428  0.155224359 #> 3429  0.155182173 #> 3430  0.155140146 #> 3431  0.155098279 #> 3432  0.155056573 #> 3433  0.155015027 #> 3434  0.154973643 #> 3435  0.154932421 #> 3436  0.154891361 #> 3437  0.154850464 #> 3438  0.154809731 #> 3439  0.154769162 #> 3440  0.154728757 #> 3441  0.154688517 #> 3442  0.154648442 #> 3443  0.154608534 #> 3444  0.154568792 #> 3445  0.154529217 #> 3446  0.154489810 #> 3447  0.154450571 #> 3448  0.154411500 #> 3449  0.154372599 #> 3450  0.154333867 #> 3451  0.154295306 #> 3452  0.154256915 #> 3453  0.154218695 #> 3454  0.154180648 #> 3455  0.154142772 #> 3456  0.154105070 #> 3457  0.154067541 #> 3458  0.154030186 #> 3459  0.153993006 #> 3460  0.153956001 #> 3461  0.153919171 #> 3462  0.153882518 #> 3463  0.153846041 #> 3464  0.153809742 #> 3465  0.153773621 #> 3466  0.153737678 #> 3467  0.153701915 #> 3468  0.153666330 #> 3469  0.153630927 #> 3470  0.153595703 #> 3471  0.153560662 #> 3472  0.153525802 #> 3473  0.153491124 #> 3474  0.153456630 #> 3475  0.153422320 #> 3476  0.153388193 #> 3477  0.153354252 #> 3478  0.153320496 #> 3479  0.153286926 #> 3480  0.153253543 #> 3481  0.153220347 #> 3482  0.153187339 #> 3483  0.153154519 #> 3484  0.153121889 #> 3485  0.153089448 #> 3486  0.153057197 #> 3487  0.153025138 #> 3488  0.152993270 #> 3489  0.152961594 #> 3490  0.152930111 #> 3491  0.152898821 #> 3492  0.152867725 #> 3493  0.152836824 #> 3494  0.152806119 #> 3495  0.152775609 #> 3496  0.152745296 #> 3497  0.152715180 #> 3498  0.152685262 #> 3499  0.152655542 #> 3500  0.152626022 #> 3501  0.152596701 #> 3502  0.152567581 #> 3503  0.152538662 #> 3504  0.152509945 #> 3505  0.152481430 #> 3506  0.152453119 #> 3507  0.152425011 #> 3508  0.152397107 #> 3509  0.152369409 #> 3510  0.152341916 #> 3511  0.152314630 #> 3512  0.152287551 #> 3513  0.152260680 #> 3514  0.152234018 #> 3515  0.152207564 #> 3516  0.152181321 #> 3517  0.152155288 #> 3518  0.152129466 #> 3519  0.152103856 #> 3520  0.152078459 #> 3521  0.152053275 #> 3522  0.152028306 #> 3523  0.152003551 #> 3524  0.151979011 #> 3525  0.151954688 #> 3526  0.151930582 #> 3527  0.151906693 #> 3528  0.151883023 #> 3529  0.151859572 #> 3530  0.151836341 #> 3531  0.151813330 #> 3532  0.151790540 #> 3533  0.151767973 #> 3534  0.151745628 #> 3535  0.151723507 #> 3536  0.151701610 #> 3537  0.151679938 #> 3538  0.151658491 #> 3539  0.151637272 #> 3540  0.151616279 #> 3541  0.151595514 #> 3542  0.151574979 #> 3543  0.151554672 #> 3544  0.151534596 #> 3545  0.151514751 #> 3546  0.151495138 #> 3547  0.151475757 #> 3548  0.151456610 #> 3549  0.151437697 #> 3550  0.151419019 #> 3551  0.151400576 #> 3552  0.151382370 #> 3553  0.151364401 #> 3554  0.151346671 #> 3555  0.151329179 #> 3556  0.151311927 #> 3557  0.151294915 #> 3558  0.151278145 #> 3559  0.151261617 #> 3560  0.151245332 #> 3561  0.151229290 #> 3562  0.151213493 #> 3563  0.151197941 #> 3564  0.151182636 #> 3565  0.151167577 #> 3566  0.151152767 #> 3567  0.151138205 #> 3568  0.151123892 #> 3569  0.151109830 #> 3570  0.151096019 #> 3571  0.151082460 #> 3572  0.151069154 #> 3573  0.151056101 #> 3574  0.151043304 #> 3575  0.151030761 #> 3576  0.151018475 #> 3577  0.151006446 #> 3578  0.150994675 #> 3579  0.150983163 #> 3580  0.150971910 #> 3581  0.150960918 #> 3582  0.150950188 #> 3583  0.150939720 #> 3584  0.150929515 #> 3585  0.150919575 #> 3586  0.150909899 #> 3587  0.150900489 #> 3588  0.150891346 #> 3589  0.150882471 #> 3590  0.150873864 #> 3591  0.150865527 #> 3592  0.150857460 #> 3593  0.150849665 #> 3594  0.150842141 #> 3595  0.150834891 #> 3596  0.150827915 #> 3597  0.150821214 #> 3598  0.150814788 #> 3599  0.150808640 #> 3600  0.150802769 #> 3601  0.150797177 #> 3602  0.150791865 #> 3603  0.150786833 #> 3604  0.150782082 #> 3605  0.150777614 #> 3606  0.150773429 #> 3607  0.150769529 #> 3608  0.150765914 #> 3609  0.150762585 #> 3610  0.150759544 #> 3611  0.150756790 #> 3612  0.150754326 #> 3613  0.150752152 #> 3614  0.150750269 #> 3615  0.150748678 #> 3616  0.150747380 #> 3617  0.150746375 #> 3618  0.150745666 #> 3619  0.150745253 #> 3620  0.150745137 #> 3621  0.150745319 #> 3622  0.150745800 #> 3623  0.150746580 #> 3624  0.150747662 #> 3625  0.150749046 #> 3626  0.150750732 #> 3627  0.150752723 #> 3628  0.150755018 #> 3629  0.150757620 #> 3630  0.150760528 #> 3631  0.150763745 #> 3632  0.150767271 #> 3633  0.150771107 #> 3634  0.150775254 #> 3635  0.150779713 #> 3636  0.150784485 #> 3637  0.150789572 #> 3638  0.150794974 #> 3639  0.150800692 #> 3640  0.150806728 #> 3641  0.150813082 #> 3642  0.150819756 #> 3643  0.150826750 #> 3644  0.150834066 #> 3645  0.150841704 #> 3646  0.150849667 #> 3647  0.150857954 #> 3648  0.150866567 #> 3649  0.150875508 #> 3650  0.150884776 #> 3651  0.150894373 #> 3652  0.150904301 #> 3653  0.150914560 #> 3654  0.150925152 #> 3655  0.150936077 #> 3656  0.150947336 #> 3657  0.150958932 #> 3658  0.150970864 #> 3659  0.150983135 #> 3660  0.150995744 #> 3661  0.151008693 #> 3662  0.151021984 #> 3663  0.151035618 #> 3664  0.151049594 #> 3665  0.151063916 #> 3666  0.151078583 #> 3667  0.151093598 #> 3668  0.151108960 #> 3669  0.151124671 #> 3670  0.151140733 #> 3671  0.151157147 #> 3672  0.151173913 #> 3673  0.151191033 #> 3674  0.151208507 #> 3675  0.151226338 #> 3676  0.151244527 #> 3677  0.151263073 #> 3678  0.151281979 #> 3679  0.151301246 #> 3680  0.151320875 #> 3681  0.151340867 #> 3682  0.151361224 #> 3683  0.151381945 #> 3684  0.151403034 #> 3685  0.151424490 #> 3686  0.151446315 #> 3687  0.151468511 #> 3688  0.151491078 #> 3689  0.151514017 #> 3690  0.151537331 #> 3691  0.151561019 #> 3692  0.151585083 #> 3693  0.151609525 #> 3694  0.151634346 #> 3695  0.151659546 #> 3696  0.151685128 #> 3697  0.151711091 #> 3698  0.151737439 #> 3699  0.151764171 #> 3700  0.151791289 #> 3701  0.151818794 #> 3702  0.151846687 #> 3703  0.151874970 #> 3704  0.151903644 #> 3705  0.151932710 #> 3706  0.151962170 #> 3707  0.151992024 #> 3708  0.152022274 #> 3709  0.152052921 #> 3710  0.152083966 #> 3711  0.152115411 #> 3712  0.152147257 #> 3713  0.152179505 #> 3714  0.152212156 #> 3715  0.152245212 #> 3716  0.152278674 #> 3717  0.152312543 #> 3718  0.152346821 #> 3719  0.152381508 #> 3720  0.152416607 #> 3721  0.152452117 #> 3722  0.152488041 #> 3723  0.152524381 #> 3724  0.152561136 #> 3725  0.152598309 #> 3726  0.152635900 #> 3727  0.152673912 #> 3728  0.152712344 #> 3729  0.152751200 #> 3730  0.152790479 #> 3731  0.152830184 #> 3732  0.152870315 #> 3733  0.152910874 #> 3734  0.152951862 #> 3735  0.152993281 #> 3736  0.153035131 #> 3737  0.153077415 #> 3738  0.153120132 #> 3739  0.153163286 #> 3740  0.153206877 #> 3741  0.153250906 #> 3742  0.153295375 #> 3743  0.153340284 #> 3744  0.153385637 #> 3745  0.153431432 #> 3746  0.153477673 #> 3747  0.153524361 #> 3748  0.153571496 #> 3749  0.153619080 #> 3750  0.153667114 #> 3751  0.153715600 #> 3752  0.153764539 #> 3753  0.153813933 #> 3754  0.153863782 #> 3755  0.153914089 #> 3756  0.153964854 #> 3757  0.154016078 #> 3758  0.154067764 #> 3759  0.154119913 #> 3760  0.154172525 #> 3761  0.154225603 #> 3762  0.154279147 #> 3763  0.154333159 #> 3764  0.154387641 #> 3765  0.154442593 #> 3766  0.154498018 #> 3767  0.154553916 #> 3768  0.154610288 #> 3769  0.154667137 #> 3770  0.154724463 #> 3771  0.154782269 #> 3772  0.154840554 #> 3773  0.154899321 #> 3774  0.154958572 #> 3775  0.155018306 #> 3776  0.155078527 #> 3777  0.155139235 #> 3778  0.155200431 #> 3779  0.155262117 #> 3780  0.155324295 #> 3781  0.155386965 #> 3782  0.155450130 #> 3783  0.155513790 #> 3784  0.155577947 #> 3785  0.155642602 #> 3786  0.155707756 #> 3787  0.155773412 #> 3788  0.155839570 #> 3789  0.155906232 #> 3790  0.155973400 #> 3791  0.156041074 #> 3792  0.156109255 #> 3793  0.156177947 #> 3794  0.156247149 #> 3795  0.156316863 #> 3796  0.156387091 #> 3797  0.156457834 #> 3798  0.156529093 #> 3799  0.156600870 #> 3800  0.156673166 #> 3801  0.156745982 #> 3802  0.156819321 #> 3803  0.156893182 #> 3804  0.156967569 #> 3805  0.157042482 #> 3806  0.157117922 #> 3807  0.157193891 #> 3808  0.157270390 #> 3809  0.157347421 #> 3810  0.157424985 #> 3811  0.157503084 #> 3812  0.157581719 #> 3813  0.157660891 #> 3814  0.157740601 #> 3815  0.157820852 #> 3816  0.157901644 #> 3817  0.157982978 #> 3818  0.158064857 #> 3819  0.158147282 #> 3820  0.158230254 #> 3821  0.158313774 #> 3822  0.158397844 #> 3823  0.158482465 #> 3824  0.158567639 #> 3825  0.158653366 #> 3826  0.158739649 #> 3827  0.158826489 #> 3828  0.158913887 #> 3829  0.159001844 #> 3830  0.159090362 #> 3831  0.159179442 #> 3832  0.159269086 #> 3833  0.159359295 #> 3834  0.159450070 #> 3835  0.159541413 #> 3836  0.159633326 #> 3837  0.159725808 #> 3838  0.159818863 #> 3839  0.159912490 #> 3840  0.160006693 #> 3841  0.160101471 #> 3842  0.160196826 #> 3843  0.160292761 #> 3844  0.160389275 #> 3845  0.160486370 #> 3846  0.160584049 #> 3847  0.160682311 #> 3848  0.160781159 #> 3849  0.160880594 #> 3850  0.160980616 #> 3851  0.161081229 #> 3852  0.161182432 #> 3853  0.161284227 #> 3854  0.161386615 #> 3855  0.161489599 #> 3856  0.161593178 #> 3857  0.161697355 #> 3858  0.161802131 #> 3859  0.161907507 #> 3860  0.162013485 #> 3861  0.162120065 #> 3862  0.162227249 #> 3863  0.162335039 #> 3864  0.162443435 #> 3865  0.162552439 #> 3866  0.162662053 #> 3867  0.162772277 #> 3868  0.162883113 #> 3869  0.162994562 #> 3870  0.163106625 #> 3871  0.163219305 #> 3872  0.163332601 #> 3873  0.163446515 #> 3874  0.163561049 #> 3875  0.163676204 #> 3876  0.163791981 #> 3877  0.163908381 #> 3878  0.164025406 #> 3879  0.164143057 #> 3880  0.164261335 #> 3881  0.164380241 #> 3882  0.164499776 #> 3883  0.164619943 #> 3884  0.164740741 #> 3885  0.164862173 #> 3886  0.164984239 #> 3887  0.165106941 #> 3888  0.165230280 #> 3889  0.165354257 #> 3890  0.165478873 #> 3891  0.165604129 #> 3892  0.165730028 #> 3893  0.165856569 #> 3894  0.165983754 #> 3895  0.166111584 #> 3896  0.166240061 #> 3897  0.166369185 #> 3898  0.166498958 #> 3899  0.166629381 #> 3900  0.166760455 #> 3901  0.166892181 #> 3902  0.167024560 #> 3903  0.167157593 #> 3904  0.167291282 #> 3905  0.167425628 #> 3906  0.167560632 #> 3907  0.167696294 #> 3908  0.167832616 #> 3909  0.167969599 #> 3910  0.168107244 #> 3911  0.168245553 #> 3912  0.168384526 #> 3913  0.168524164 #> 3914  0.168664468 #> 3915  0.168805440 #> 3916  0.168947080 #> 3917  0.169089390 #> 3918  0.169232370 #> 3919  0.169376022 #> 3920  0.169520347 #> 3921  0.169665345 #> 3922  0.169811017 #> 3923  0.169957365 #> 3924  0.170104390 #> 3925  0.170252092 #> 3926  0.170400473 #> 3927  0.170549533 #> 3928  0.170699273 #> 3929  0.170849695 #> 3930  0.171000799 #> 3931  0.171152586 #> 3932  0.171305057 #> 3933  0.171458214 #> 3934  0.171612056 #> 3935  0.171766585 #> 3936  0.171921802 #> 3937  0.172077707 #> 3938  0.172234302 #> 3939  0.172391587 #> 3940  0.172549563 #> 3941  0.172708231 #> 3942  0.172867592 #> 3943  0.173027647 #> 3944  0.173188396 #> 3945  0.173349840 #> 3946  0.173511980 #> 3947  0.173674817 #> 3948  0.173838352 #> 3949  0.174002585 #> 3950  0.174167517 #> 3951  0.174333149 #> 3952  0.174499482 #> 3953  0.174666516 #> 3954  0.174834251 #> 3955  0.175002690 #> 3956  0.175171832 #> 3957  0.175341678 #> 3958  0.175512229 #> 3959  0.175683485 #> 3960  0.175855447 #> 3961  0.176028116 #> 3962  0.176201493 #> 3963  0.176375577 #> 3964  0.176550370 #> 3965  0.176725871 #> 3966  0.176902083 #> 3967  0.177079005 #> 3968  0.177256638 #> 3969  0.177434982 #> 3970  0.177614038 #> 3971  0.177793806 #> 3972  0.177974288 #> 3973  0.178155483 #> 3974  0.178337391 #> 3975  0.178520015 #> 3976  0.178703353 #> 3977  0.178887406 #> 3978  0.179072175 #> 3979  0.179257661 #> 3980  0.179443863 #> 3981  0.179630782 #> 3982  0.179818418 #> 3983  0.180006772 #> 3984  0.180195845 #> 3985  0.180385635 #> 3986  0.180576145 #> 3987  0.180767374 #> 3988  0.180959322 #> 3989  0.181151990 #> 3990  0.181345378 #> 3991  0.181539486 #> 3992  0.181734315 #> 3993  0.181929864 #> 3994  0.182126135 #> 3995  0.182323126 #> 3996  0.182520839 #> 3997  0.182719274 #> 3998  0.182918431 #> 3999  0.183118309 #> 4000  0.183318910 #> 4001  0.183520232 #> 4002  0.183722277 #> 4003  0.183925045 #> 4004  0.184128535 #> 4005  0.184332747 #> 4006  0.184537683 #> 4007  0.184743340 #> 4008  0.184949721 #> 4009  0.185156824 #> 4010  0.185364650 #> 4011  0.185573198 #> 4012  0.185782470 #> 4013  0.185992463 #> 4014  0.186203180 #> 4015  0.186414618 #> 4016  0.186626779 #> 4017  0.186839663 #> 4018  0.187053268 #> 4019  0.187267595 #> 4020  0.187482644 #> 4021  0.187698415 #> 4022  0.187914907 #> 4023  0.188132120 #> 4024  0.188350055 #> 4025  0.188568710 #> 4026  0.188788085 #> 4027  0.189008181 #> 4028  0.189228996 #> 4029  0.189450531 #> 4030  0.189672786 #> 4031  0.189895759 #> 4032  0.190119451 #> 4033  0.190343861 #> 4034  0.190568989 #> 4035  0.190794835 #> 4036  0.191021397 #> 4037  0.191248676 #> 4038  0.191476671 #> 4039  0.191705381 #> 4040  0.191934807 #> 4041  0.192164947 #> 4042  0.192395801 #> 4043  0.192627369 #> 4044  0.192859649 #> 4045  0.193092642 #> 4046  0.193326347 #> 4047  0.193560763 #> 4048  0.193795889 #> 4049  0.194031726 #> 4050  0.194268271 #> 4051  0.194505525 #> 4052  0.194743486 #> 4053  0.194982155 #> 4054  0.195221530 #> 4055  0.195461610 #> 4056  0.195702396 #> 4057  0.195943885 #> 4058  0.196186077 #> 4059  0.196428972 #> 4060  0.196672568 #> 4061  0.196916864 #> 4062  0.197161860 #> 4063  0.197407555 #> 4064  0.197653948 #> 4065  0.197901038 #> 4066  0.198148824 #> 4067  0.198397304 #> 4068  0.198646479 #> 4069  0.198896346 #> 4070  0.199146905 #> 4071  0.199398155 #> 4072  0.199650095 #> 4073  0.199902723 #> 4074  0.200156039 #> 4075  0.200410041 #> 4076  0.200664729 #> 4077  0.200920100 #> 4078  0.201176154 #> 4079  0.201432890 #> 4080  0.201690306 #> 4081  0.201948402 #> 4082  0.202207175 #> 4083  0.202466624 #> 4084  0.202726749 #> 4085  0.202987548 #> 4086  0.203249019 #> 4087  0.203511162 #> 4088  0.203773974 #> 4089  0.204037455 #> 4090  0.204301602 #> 4091  0.204566415 #> 4092  0.204831893 #> 4093  0.205098032 #> 4094  0.205364833 #> 4095  0.205632293 #> 4096  0.205900411 #> 4097  0.206169186 #> 4098  0.206438615 #> 4099  0.206708697 #> 4100  0.206979431 #> 4101  0.207250815 #> 4102  0.207522847 #> 4103  0.207795526 #> 4104  0.208068850 #> 4105  0.208342816 #> 4106  0.208617424 #> 4107  0.208892672 #> 4108  0.209168557 #> 4109  0.209445079 #> 4110  0.209722235 #> 4111  0.210000023 #> 4112  0.210278441 #> 4113  0.210557488 #> 4114  0.210837162 #> 4115  0.211117461 #> 4116  0.211398382 #> 4117  0.211679924 #> 4118  0.211962085 #> 4119  0.212244863 #> 4120  0.212528256 #> 4121  0.212812262 #> 4122  0.213096878 #> 4123  0.213382103 #> 4124  0.213667934 #> 4125  0.213954370 #> 4126  0.214241409 #> 4127  0.214529047 #> 4128  0.214817283 #> 4129  0.215106115 #> 4130  0.215395541 #> 4131  0.215685558 #> 4132  0.215976164 #> 4133  0.216267356 #> 4134  0.216559133 #> 4135  0.216851493 #> 4136  0.217144432 #> 4137  0.217437948 #> 4138  0.217732040 #> 4139  0.218026704 #> 4140  0.218321938 #> 4141  0.218617740 #> 4142  0.218914108 #> 4143  0.219211039 #> 4144  0.219508530 #> 4145  0.219806578 #> 4146  0.220105183 #> 4147  0.220404340 #> 4148  0.220704047 #> 4149  0.221004302 #> 4150  0.221305102 #> 4151  0.221606444 #> 4152  0.221908326 #> 4153  0.222210746 #> 4154  0.222513700 #> 4155  0.222817185 #> 4156  0.223121200 #> 4157  0.223425741 #> 4158  0.223730805 #> 4159  0.224036391 #> 4160  0.224342494 #> 4161  0.224649113 #> 4162  0.224956244 #> 4163  0.225263884 #> 4164  0.225572031 #> 4165  0.225880682 #> 4166  0.226189834 #> 4167  0.226499484 #> 4168  0.226809629 #> 4169  0.227120266 #> 4170  0.227431392 #> 4171  0.227743004 #> 4172  0.228055100 #> 4173  0.228367675 #> 4174  0.228680728 #> 4175  0.228994255 #> 4176  0.229308253 #> 4177  0.229622718 #> 4178  0.229937649 #> 4179  0.230253041 #> 4180  0.230568892 #> 4181  0.230885198 #> 4182  0.231201956 #> 4183  0.231519163 #> 4184  0.231836816 #> 4185  0.232154912 #> 4186  0.232473447 #> 4187  0.232792418 #> 4188  0.233111822 #> 4189  0.233431656 #> 4190  0.233751916 #> 4191  0.234072599 #> 4192  0.234393702 #> 4193  0.234715221 #> 4194  0.235037152 #> 4195  0.235359494 #> 4196  0.235682241 #> 4197  0.236005392 #> 4198  0.236328941 #> 4199  0.236652887 #> 4200  0.236977225 #> 4201  0.237301952 #> 4202  0.237627065 #> 4203  0.237952560 #> 4204  0.238278433 #> 4205  0.238604681 #> 4206  0.238931301 #> 4207  0.239258289 #> 4208  0.239585641 #> 4209  0.239913354 #> 4210  0.240241424 #> 4211  0.240569848 #> 4212  0.240898622 #> 4213  0.241227742 #> 4214  0.241557205 #> 4215  0.241887007 #> 4216  0.242217144 #> 4217  0.242547613 #> 4218  0.242878411 #> 4219  0.243209532 #> 4220  0.243540975 #> 4221  0.243872734 #> 4222  0.244204806 #> 4223  0.244537188 #> 4224  0.244869876 #> 4225  0.245202865 #> 4226  0.245536153 #> 4227  0.245869734 #> 4228  0.246203607 #> 4229  0.246537766 #> 4230  0.246872208 #> 4231  0.247206929 #> 4232  0.247541925 #> 4233  0.247877193 #> 4234  0.248212728 #> 4235  0.248548527 #> 4236  0.248884585 #> 4237  0.249220899 #> 4238  0.249557465 #> 4239  0.249894280 #> 4240  0.250231338 #> 4241  0.250568636 #> 4242  0.250906170 #> 4243  0.251243937 #> 4244  0.251581932 #> 4245  0.251920151 #> 4246  0.252258591 #> 4247  0.252597247 #> 4248  0.252936115 #> 4249  0.253275192 #> 4250  0.253614473 #> 4251  0.253953954 #> 4252  0.254293632 #> 4253  0.254633501 #> 4254  0.254973559 #> 4255  0.255313802 #> 4256  0.255654224 #> 4257  0.255994822 #> 4258  0.256335593 #> 4259  0.256676531 #> 4260  0.257017633 #> 4261  0.257358895 #> 4262  0.257700313 #> 4263  0.258041882 #> 4264  0.258383599 #> 4265  0.258725459 #> 4266  0.259067458 #> 4267  0.259409593 #> 4268  0.259751859 #> 4269  0.260094251 #> 4270  0.260436766 #> 4271  0.260779400 #> 4272  0.261122148 #> 4273  0.261465007 #> 4274  0.261807972 #> 4275  0.262151039 #> 4276  0.262494204 #> 4277  0.262837463 #> 4278  0.263180811 #> 4279  0.263524245 #> 4280  0.263867760 #> 4281  0.264211352 #> 4282  0.264555017 #> 4283  0.264898750 #> 4284  0.265242549 #> 4285  0.265586407 #> 4286  0.265930322 #> 4287  0.266274289 #> 4288  0.266618304 #> 4289  0.266962363 #> 4290  0.267306460 #> 4291  0.267650594 #> 4292  0.267994758 #> 4293  0.268338949 #> 4294  0.268683164 #> 4295  0.269027396 #> 4296  0.269371643 #> 4297  0.269715900 #> 4298  0.270060163 #> 4299  0.270404428 #> 4300  0.270748691 #> 4301  0.271092947 #> 4302  0.271437192 #> 4303  0.271781422 #> 4304  0.272125633 #> 4305  0.272469821 #> 4306  0.272813982 #> 4307  0.273158110 #> 4308  0.273502203 #> 4309  0.273846256 #> 4310  0.274190264 #> 4311  0.274534224 #> 4312  0.274878132 #> 4313  0.275221983 #> 4314  0.275565773 #> 4315  0.275909498 #> 4316  0.276253154 #> 4317  0.276596736 #> 4318  0.276940241 #> 4319  0.277283665 #> 4320  0.277627002 #> 4321  0.277970250 #> 4322  0.278313403 #> 4323  0.278656459 #> 4324  0.278999412 #> 4325  0.279342258 #> 4326  0.279684994 #> 4327  0.280027616 #> 4328  0.280370118 #> 4329  0.280712498 #> 4330  0.281054750 #> 4331  0.281396872 #> 4332  0.281738858 #> 4333  0.282080705 #> 4334  0.282422409 #> 4335  0.282763966 #> 4336  0.283105371 #> 4337  0.283446620 #> 4338  0.283787710 #> 4339  0.284128636 #> 4340  0.284469395 #> 4341  0.284809982 #> 4342  0.285150393 #> 4343  0.285490625 #> 4344  0.285830673 #> 4345  0.286170533 #> 4346  0.286510201 #> 4347  0.286849674 #> 4348  0.287188947 #> 4349  0.287528017 #> 4350  0.287866879 #> 4351  0.288205529 #> 4352  0.288543964 #> 4353  0.288882179 #> 4354  0.289220171 #> 4355  0.289557936 #> 4356  0.289895469 #> 4357  0.290232768 #> 4358  0.290569827 #> 4359  0.290906644 #> 4360  0.291243214 #> 4361  0.291579533 #> 4362  0.291915598 #> 4363  0.292251405 #> 4364  0.292586949 #> 4365  0.292922228 #> 4366  0.293257237 #> 4367  0.293591972 #> 4368  0.293926430 #> 4369  0.294260607 #> 4370  0.294594499 #> 4371  0.294928103 #> 4372  0.295261414 #> 4373  0.295594429 #> 4374  0.295927144 #> 4375  0.296259555 #> 4376  0.296591660 #> 4377  0.296923453 #> 4378  0.297254932 #> 4379  0.297586093 #> 4380  0.297916931 #> 4381  0.298247444 #> 4382  0.298577628 #> 4383  0.298907479 #> 4384  0.299236994 #> 4385  0.299566169 #> 4386  0.299895000 #> 4387  0.300223484 #> 4388  0.300551617 #> 4389  0.300879397 #> 4390  0.301206818 #> 4391  0.301533878 #> 4392  0.301860573 #> 4393  0.302186901 #> 4394  0.302512856 #> 4395  0.302838436 #> 4396  0.303163638 #> 4397  0.303488457 #> 4398  0.303812891 #> 4399  0.304136937 #> 4400  0.304460590 #> 4401  0.304783847 #> 4402  0.305106705 #> 4403  0.305429162 #> 4404  0.305751212 #> 4405  0.306072853 #> 4406  0.306394082 #> 4407  0.306714896 #> 4408  0.307035291 #> 4409  0.307355263 #> 4410  0.307674811 #> 4411  0.307993929 #> 4412  0.308312616 #> 4413  0.308630868 #> 4414  0.308948682 #> 4415  0.309266055 #> 4416  0.309582983 #> 4417  0.309899463 #> 4418  0.310215493 #> 4419  0.310531069 #> 4420  0.310846188 #> 4421  0.311160847 #> 4422  0.311475042 #> 4423  0.311788772 #> 4424  0.312102033 #> 4425  0.312414822 #> 4426  0.312727135 #> 4427  0.313038970 #> 4428  0.313350325 #> 4429  0.313661195 #> 4430  0.313971578 #> 4431  0.314281472 #> 4432  0.314590873 #> 4433  0.314899778 #> 4434  0.315208185 #> 4435  0.315516091 #> 4436  0.315823492 #> 4437  0.316130386 #> 4438  0.316436771 #> 4439  0.316742643 #> 4440  0.317048000 #> 4441  0.317352839 #> 4442  0.317657157 #> 4443  0.317960952 #> 4444  0.318264220 #> 4445  0.318566960 #> 4446  0.318869168 #> 4447  0.319170842 #> 4448  0.319471979 #> 4449  0.319772577 #> 4450  0.320072633 #> 4451  0.320372145 #> 4452  0.320671109 #> 4453  0.320969524 #> 4454  0.321267387 #> 4455  0.321564695 #> 4456  0.321861446 #> 4457  0.322157637 #> 4458  0.322453267 #> 4459  0.322748332 #> 4460  0.323042830 #> 4461  0.323336758 #> 4462  0.323630115 #> 4463  0.323922899 #> 4464  0.324215105 #> 4465  0.324506733 #> 4466  0.324797780 #> 4467  0.325088244 #> 4468  0.325378122 #> 4469  0.325667413 #> 4470  0.325956113 #> 4471  0.326244221 #> 4472  0.326531735 #> 4473  0.326818652 #> 4474  0.327104970 #> 4475  0.327390688 #> 4476  0.327675802 #> 4477  0.327960311 #> 4478  0.328244212 #> 4479  0.328527505 #> 4480  0.328810185 #> 4481  0.329092253 #> 4482  0.329373705 #> 4483  0.329654539 #> 4484  0.329934754 #> 4485  0.330214347 #> 4486  0.330493317 #> 4487  0.330771662 #> 4488  0.331049380 #> 4489  0.331326468 #> 4490  0.331602925 #> 4491  0.331878749 #> 4492  0.332153939 #> 4493  0.332428492 #> 4494  0.332702407 #> 4495  0.332975681 #> 4496  0.333248314 #> 4497  0.333520303 #> 4498  0.333791646 #> 4499  0.334062343 #> 4500  0.334332390 #> 4501  0.334601787 #> 4502  0.334870532 #> 4503  0.335138623 #> 4504  0.335406059 #> 4505  0.335672837 #> 4506  0.335938957 #> 4507  0.336204416 #> 4508  0.336469214 #> 4509  0.336733348 #> 4510  0.336996818 #> 4511  0.337259621 #> 4512  0.337521756 #> 4513  0.337783222 #> 4514  0.338044017 #> 4515  0.338304140 #> 4516  0.338563589 #> 4517  0.338822363 #> 4518  0.339080461 #> 4519  0.339337881 #> 4520  0.339594622 #> 4521  0.339850682 #> 4522  0.340106061 #> 4523  0.340360756 #> 4524  0.340614767 #> 4525  0.340868093 #> 4526  0.341120731 #> 4527  0.341372682 #> 4528  0.341623943 #> 4529  0.341874513 #> 4530  0.342124392 #> 4531  0.342373578 #> 4532  0.342622070 #> 4533  0.342869867 #> 4534  0.343116968 #> 4535  0.343363372 #> 4536  0.343609077 #> 4537  0.343854082 #> 4538  0.344098387 #> 4539  0.344341991 #> 4540  0.344584892 #> 4541  0.344827089 #> 4542  0.345068582 #> 4543  0.345309370 #> 4544  0.345549451 #> 4545  0.345788825 #> 4546  0.346027490 #> 4547  0.346265447 #> 4548  0.346502693 #> 4549  0.346739229 #> 4550  0.346975052 #> 4551  0.347210164 #> 4552  0.347444562 #> 4553  0.347678245 #> 4554  0.347911214 #> 4555  0.348143467 #> 4556  0.348375004 #> 4557  0.348605823 #> 4558  0.348835925 #> 4559  0.349065308 #> 4560  0.349293971 #> 4561  0.349521915 #> 4562  0.349749138 #> 4563  0.349975640 #> 4564  0.350201420 #> 4565  0.350426477 #> 4566  0.350650812 #> 4567  0.350874422 #> 4568  0.351097309 #> 4569  0.351319471 #> 4570  0.351540907 #> 4571  0.351761618 #> 4572  0.351981602 #> 4573  0.352200859 #> 4574  0.352419389 #> 4575  0.352637192 #> 4576  0.352854266 #> 4577  0.353070611 #> 4578  0.353286228 #> 4579  0.353501115 #> 4580  0.353715272 #> 4581  0.353928700 #> 4582  0.354141396 #> 4583  0.354353362 #> 4584  0.354564596 #> 4585  0.354775099 #> 4586  0.354984870 #> 4587  0.355193909 #> 4588  0.355402216 #> 4589  0.355609790 #> 4590  0.355816631 #> 4591  0.356022739 #> 4592  0.356228114 #> 4593  0.356432755 #> 4594  0.356636662 #> 4595  0.356839836 #> 4596  0.357042276 #> 4597  0.357243981 #> 4598  0.357444952 #> 4599  0.357645188 #> 4600  0.357844690 #> 4601  0.358043458 #> 4602  0.358241490 #> 4603  0.358438788 #> 4604  0.358635351 #> 4605  0.358831179 #> 4606  0.359026272 #> 4607  0.359220630 #> 4608  0.359414254 #> 4609  0.359607142 #> 4610  0.359799295 #> 4611  0.359990714 #> 4612  0.360181397 #> 4613  0.360371346 #> 4614  0.360560560 #> 4615  0.360749040 #> 4616  0.360936785 #> 4617  0.361123795 #> 4618  0.361310072 #> 4619  0.361495614 #> 4620  0.361680422 #> 4621  0.361864496 #> 4622  0.362047837 #> 4623  0.362230443 #> 4624  0.362412317 #> 4625  0.362593458 #> 4626  0.362773865 #> 4627  0.362953540 #> 4628  0.363132482 #> 4629  0.363310693 #> 4630  0.363488171 #> 4631  0.363664918 #> 4632  0.363840933 #> 4633  0.364016217 #> 4634  0.364190770 #> 4635  0.364364593 #> 4636  0.364537685 #> 4637  0.364710048 #> 4638  0.364881681 #> 4639  0.365052586 #> 4640  0.365222761 #> 4641  0.365392208 #> 4642  0.365560927 #> 4643  0.365728918 #> 4644  0.365896183 #> 4645  0.366062720 #> 4646  0.366228531 #> 4647  0.366393617 #> 4648  0.366557976 #> 4649  0.366721611 #> 4650  0.366884521 #> 4651  0.367046708 #> 4652  0.367208170 #> 4653  0.367368910 #> 4654  0.367528927 #> 4655  0.367688222 #> 4656  0.367846796 #> 4657  0.368004648 #> 4658  0.368161780 #> 4659  0.368318193 #> 4660  0.368473886 #> 4661  0.368628860 #> 4662  0.368783116 #> 4663  0.368936654 #> 4664  0.369089476 #> 4665  0.369241581 #> 4666  0.369392971 #> 4667  0.369543645 #> 4668  0.369693605 #> 4669  0.369842851 #> 4670  0.369991384 #> 4671  0.370139205 #> 4672  0.370286313 #> 4673  0.370432710 #> 4674  0.370578397 #> 4675  0.370723374 #> 4676  0.370867643 #> 4677  0.371011202 #> 4678  0.371154054 #> 4679  0.371296199 #> 4680  0.371437638 #> 4681  0.371578371 #> 4682  0.371718400 #> 4683  0.371857725 #> 4684  0.371996346 #> 4685  0.372134265 #> 4686  0.372271483 #> 4687  0.372407999 #> 4688  0.372543816 #> 4689  0.372678933 #> 4690  0.372813352 #> 4691  0.372947073 #> 4692  0.373080097 #> 4693  0.373212426 #> 4694  0.373344059 #> 4695  0.373474998 #> 4696  0.373605243 #> 4697  0.373734796 #> 4698  0.373863657 #> 4699  0.373991827 #> 4700  0.374119308 #> 4701  0.374246099 #> 4702  0.374372202 #> 4703  0.374497617 #> 4704  0.374622346 #> 4705  0.374746390 #> 4706  0.374869749 #> 4707  0.374992424 #> 4708  0.375114417 #> 4709  0.375235728 #> 4710  0.375356357 #> 4711  0.375476307 #> 4712  0.375595578 #> 4713  0.375714171 #> 4714  0.375832087 #> 4715  0.375949327 #> 4716  0.376065891 #> 4717  0.376181782 #> 4718  0.376296999 #> 4719  0.376411544 #> 4720  0.376525418 #> 4721  0.376638622 #> 4722  0.376751157 #> 4723  0.376863023 #> 4724  0.376974222 #> 4725  0.377084755 #> 4726  0.377194623 #> 4727  0.377303827 #> 4728  0.377412368 #> 4729  0.377520247 #> 4730  0.377627465 #> 4731  0.377734023 #> 4732  0.377839923 #> 4733  0.377945164 #> 4734  0.378049749 #> 4735  0.378153678 #> 4736  0.378256952 #> 4737  0.378359573 #> 4738  0.378461542 #> 4739  0.378562859 #> 4740  0.378663526 #> 4741  0.378763544 #> 4742  0.378862914 #> 4743  0.378961637 #> 4744  0.379059714 #> 4745  0.379157146 #> 4746  0.379253935 #> 4747  0.379350081 #> 4748  0.379445586 #> 4749  0.379540450 #> 4750  0.379634676 #> 4751  0.379728263 #> 4752  0.379821214 #> 4753  0.379913528 #> 4754  0.380005209 #> 4755  0.380096256 #> 4756  0.380186670 #> 4757  0.380276454 #> 4758  0.380365608 #> 4759  0.380454132 #> 4760  0.380542030 #> 4761  0.380629300 #> 4762  0.380715946 #> 4763  0.380801967 #> 4764  0.380887366 #> 4765  0.380972142 #> 4766  0.381056299 #> 4767  0.381139835 #> 4768  0.381222754 #> 4769  0.381305055 #> 4770  0.381386741 #> 4771  0.381467812 #> 4772  0.381548270 #> 4773  0.381628115 #> 4774  0.381707350 #> 4775  0.381785975 #> 4776  0.381863991 #> 4777  0.381941400 #> 4778  0.382018202 #> 4779  0.382094400 #> 4780  0.382169994 #> 4781  0.382244985 #> 4782  0.382319376 #> 4783  0.382393166 #> 4784  0.382466357 #> 4785  0.382538951 #> 4786  0.382610948 #> 4787  0.382682351 #> 4788  0.382753159 #> 4789  0.382823375 #> 4790  0.382892999 #> 4791  0.382962033 #> 4792  0.383030479 #> 4793  0.383098336 #> 4794  0.383165607 #> 4795  0.383232294 #> 4796  0.383298396 #> 4797  0.383363915 #> 4798  0.383428853 #> 4799  0.383493211 #> 4800  0.383556990 #> 4801  0.383620191 #> 4802  0.383682816 #> 4803  0.383744866 #> 4804  0.383806342 #> 4805  0.383867246 #> 4806  0.383927578 #> 4807  0.383987340 #> 4808  0.384046533 #> 4809  0.384105159 #> 4810  0.384163219 #> 4811  0.384220713 #> 4812  0.384277644 #> 4813  0.384334013 #> 4814  0.384389820 #> 4815  0.384445068 #> 4816  0.384499756 #> 4817  0.384553888 #> 4818  0.384607464 #> 4819  0.384660484 #> 4820  0.384712952 #> 4821  0.384764867 #> 4822  0.384816231 #> 4823  0.384867045 #> 4824  0.384917312 #> 4825  0.384967031 #> 4826  0.385016204 #> 4827  0.385064833 #> 4828  0.385112919 #> 4829  0.385160462 #> 4830  0.385207466 #> 4831  0.385253929 #> 4832  0.385299855 #> 4833  0.385345244 #> 4834  0.385390098 #> 4835  0.385434417 #> 4836  0.385478203 #> 4837  0.385521458 #> 4838  0.385564183 #> 4839  0.385606378 #> 4840  0.385648046 #> 4841  0.385689187 #> 4842  0.385729803 #> 4843  0.385769895 #> 4844  0.385809465 #> 4845  0.385848513 #> 4846  0.385887042 #> 4847  0.385925051 #> 4848  0.385962543 #> 4849  0.385999520 #> 4850  0.386035981 #> 4851  0.386071929 #> 4852  0.386107364 #> 4853  0.386142289 #> 4854  0.386176703 #> 4855  0.386210610 #> 4856  0.386244009 #> 4857  0.386276903 #> 4858  0.386309292 #> 4859  0.386341178 #> 4860  0.386372562 #> 4861  0.386403445 #> 4862  0.386433829 #> 4863  0.386463715 #> 4864  0.386493104 #> 4865  0.386521997 #> 4866  0.386550396 #> 4867  0.386578303 #> 4868  0.386605717 #> 4869  0.386632642 #> 4870  0.386659077 #> 4871  0.386685025 #> 4872  0.386710486 #> 4873  0.386735461 #> 4874  0.386759953 #> 4875  0.386783962 #> 4876  0.386807490 #> 4877  0.386830538 #> 4878  0.386853107 #> 4879  0.386875198 #> 4880  0.386896813 #> 4881  0.386917953 #> 4882  0.386938619 #> 4883  0.386958813 #> 4884  0.386978536 #> 4885  0.386997788 #> 4886  0.387016573 #> 4887  0.387034889 #> 4888  0.387052740 #> 4889  0.387070126 #> 4890  0.387087048 #> 4891  0.387103508 #> 4892  0.387119507 #> 4893  0.387135047 #> 4894  0.387150127 #> 4895  0.387164751 #> 4896  0.387178919 #> 4897  0.387192632 #> 4898  0.387205891 #> 4899  0.387218698 #> 4900  0.387231055 #> 4901  0.387242961 #> 4902  0.387254420 #> 4903  0.387265431 #> 4904  0.387275996 #> 4905  0.387286116 #> 4906  0.387295794 #> 4907  0.387305029 #> 4908  0.387313823 #> 4909  0.387322177 #> 4910  0.387330093 #> 4911  0.387337572 #> 4912  0.387344615 #> 4913  0.387351223 #> 4914  0.387357398 #> 4915  0.387363140 #> 4916  0.387368452 #> 4917  0.387373334 #> 4918  0.387377787 #> 4919  0.387381813 #> 4920  0.387385413 #> 4921  0.387388588 #> 4922  0.387391340 #> 4923  0.387393669 #> 4924  0.387395577 #> 4925  0.387397066 #> 4926  0.387398135 #> 4927  0.387398787 #> 4928  0.387399023 #> 4929  0.387398844 #> 4930  0.387398251 #> 4931  0.387397245 #> 4932  0.387395828 #> 4933  0.387394001 #> 4934  0.387391765 #> 4935  0.387389121 #> 4936  0.387386071 #> 4937  0.387382615 #> 4938  0.387378756 #> 4939  0.387374493 #> 4940  0.387369828 #> 4941  0.387364763 #> 4942  0.387359299 #> 4943  0.387353436 #> 4944  0.387347177 #> 4945  0.387340522 #> 4946  0.387333472 #> 4947  0.387326029 #> 4948  0.387318193 #> 4949  0.387309967 #> 4950  0.387301350 #> 4951  0.387292346 #> 4952  0.387282953 #> 4953  0.387273175 #> 4954  0.387263011 #> 4955  0.387252463 #> 4956  0.387241533 #> 4957  0.387230220 #> 4958  0.387218528 #> 4959  0.387206456 #> 4960  0.387194006 #> 4961  0.387181179 #> 4962  0.387167977 #> 4963  0.387154400 #> 4964  0.387140449 #> 4965  0.387126126 #> 4966  0.387111432 #> 4967  0.387096367 #> 4968  0.387080934 #> 4969  0.387065134 #> 4970  0.387048966 #> 4971  0.387032433 #> 4972  0.387015536 #> 4973  0.386998276 #> 4974  0.386980654 #> 4975  0.386962671 #> 4976  0.386944328 #> 4977  0.386925627 #> 4978  0.386906568 #> 4979  0.386887153 #> 4980  0.386867382 #> 4981  0.386847258 #> 4982  0.386826780 #> 4983  0.386805951 #> 4984  0.386784771 #> 4985  0.386763241 #> 4986  0.386741363 #> 4987  0.386719137 #> 4988  0.386696565 #> 4989  0.386673648 #> 4990  0.386650386 #> 4991  0.386626782 #> 4992  0.386602836 #> 4993  0.386578549 #> 4994  0.386553923 #> 4995  0.386528958 #> 4996  0.386503655 #> 4997  0.386478016 #> 4998  0.386452041 #> 4999  0.386425733 #> 5000  0.386399091 #> 5001  0.386372117 #> 5002  0.386344813 #> 5003  0.386317178 #> 5004  0.386289214 #> 5005  0.386260923 #> 5006  0.386232305 #> 5007  0.386203361 #> 5008  0.386174093 #> 5009  0.386144502 #> 5010  0.386114588 #> 5011  0.386084352 #> 5012  0.386053797 #> 5013  0.386022922 #> 5014  0.385991729 #> 5015  0.385960218 #> 5016  0.385928392 #> 5017  0.385896251 #> 5018  0.385863795 #> 5019  0.385831027 #> 5020  0.385797947 #> 5021  0.385764555 #> 5022  0.385730855 #> 5023  0.385696845 #> 5024  0.385662527 #> 5025  0.385627903 #> 5026  0.385592973 #> 5027  0.385557738 #> 5028  0.385522200 #> 5029  0.385486359 #> 5030  0.385450217 #> 5031  0.385413774 #> 5032  0.385377031 #> 5033  0.385339990 #> 5034  0.385302651 #> 5035  0.385265016 #> 5036  0.385227085 #> 5037  0.385188859 #> 5038  0.385150340 #> 5039  0.385111529 #> 5040  0.385072426 #> 5041  0.385033032 #> 5042  0.384993349 #> 5043  0.384953377 #> 5044  0.384913118 #> 5045  0.384872572 #> 5046  0.384831741 #> 5047  0.384790625 #> 5048  0.384749225 #> 5049  0.384707543 #> 5050  0.384665579 #> 5051  0.384623334 #> 5052  0.384580810 #> 5053  0.384538006 #> 5054  0.384494925 #> 5055  0.384451568 #> 5056  0.384407934 #> 5057  0.384364025 #> 5058  0.384319843 #> 5059  0.384275387 #> 5060  0.384230660 #> 5061  0.384185661 #> 5062  0.384140392 #> 5063  0.384094854 #> 5064  0.384049048 #> 5065  0.384002974 #> 5066  0.383956635 #> 5067  0.383910029 #> 5068  0.383863159 #> 5069  0.383816026 #> 5070  0.383768630 #> 5071  0.383720972 #> 5072  0.383673054 #> 5073  0.383624876 #> 5074  0.383576438 #> 5075  0.383527743 #> 5076  0.383478791 #> 5077  0.383429582 #> 5078  0.383380119 #> 5079  0.383330401 #> 5080  0.383280429 #> 5081  0.383230205 #> 5082  0.383179730 #> 5083  0.383129004 #> 5084  0.383078028 #> 5085  0.383026803 #> 5086  0.382975330 #> 5087  0.382923610 #> 5088  0.382871643 #> 5089  0.382819432 #> 5090  0.382766975 #> 5091  0.382714276 #> 5092  0.382661334 #> 5093  0.382608149 #> 5094  0.382554724 #> 5095  0.382501059 #> 5096  0.382447155 #> 5097  0.382393013 #> 5098  0.382338633 #> 5099  0.382284016 #> 5100  0.382229164 #> 5101  0.382174077 #> 5102  0.382118756 #> 5103  0.382063202 #> 5104  0.382007416 #> 5105  0.381951399 #> 5106  0.381895150 #> 5107  0.381838673 #> 5108  0.381781966 #> 5109  0.381725031 #> 5110  0.381667870 #> 5111  0.381610482 #> 5112  0.381552868 #> 5113  0.381495030 #> 5114  0.381436968 #> 5115  0.381378684 #> 5116  0.381320177 #> 5117  0.381261449 #> 5118  0.381202500 #> 5119  0.381143332 #> 5120  0.381083945 #> 5121  0.381024340 #> 5122  0.380964518 #> 5123  0.380904479 #> 5124  0.380844225 #> 5125  0.380783757 #> 5126  0.380723074 #> 5127  0.380662178 #> 5128  0.380601071 #> 5129  0.380539751 #> 5130  0.380478221 #> 5131  0.380416481 #> 5132  0.380354532 #> 5133  0.380292375 #> 5134  0.380230010 #> 5135  0.380167439 #> 5136  0.380104661 #> 5137  0.380041679 #> 5138  0.379978492 #> 5139  0.379915102 #> 5140  0.379851509 #> 5141  0.379787714 #> 5142  0.379723717 #> 5143  0.379659521 #> 5144  0.379595124 #> 5145  0.379530529 #> 5146  0.379465736 #> 5147  0.379400745 #> 5148  0.379335558 #> 5149  0.379270175 #> 5150  0.379204597 #> 5151  0.379138825 #> 5152  0.379072859 #> 5153  0.379006701 #> 5154  0.378940350 #> 5155  0.378873808 #> 5156  0.378807076 #> 5157  0.378740154 #> 5158  0.378673043 #> 5159  0.378605743 #> 5160  0.378538256 #> 5161  0.378470583 #> 5162  0.378402723 #> 5163  0.378334678 #> 5164  0.378266448 #> 5165  0.378198035 #> 5166  0.378129438 #> 5167  0.378060659 #> 5168  0.377991698 #> 5169  0.377922557 #> 5170  0.377853235 #> 5171  0.377783733 #> 5172  0.377714053 #> 5173  0.377644195 #> 5174  0.377574160 #> 5175  0.377503947 #> 5176  0.377433559 #> 5177  0.377362996 #> 5178  0.377292258 #> 5179  0.377221346 #> 5180  0.377150262 #> 5181  0.377079005 #> 5182  0.377007576 #> 5183  0.376935976 #> 5184  0.376864206 #> 5185  0.376792266 #> 5186  0.376720157 #> 5187  0.376647881 #> 5188  0.376575436 #> 5189  0.376502825 #> 5190  0.376430047 #> 5191  0.376357104 #> 5192  0.376283997 #> 5193  0.376210725 #> 5194  0.376137289 #> 5195  0.376063691 #> 5196  0.375989931 #> 5197  0.375916009 #> 5198  0.375841927 #> 5199  0.375767684 #> 5200  0.375693282 #> 5201  0.375618721 #> 5202  0.375544002 #> 5203  0.375469126 #> 5204  0.375394093 #> 5205  0.375318904 #> 5206  0.375243559 #> 5207  0.375168059 #> 5208  0.375092405 #> 5209  0.375016598 #> 5210  0.374940638 #> 5211  0.374864526 #> 5212  0.374788262 #> 5213  0.374711847 #> 5214  0.374635281 #> 5215  0.374558566 #> 5216  0.374481702 #> 5217  0.374404690 #> 5218  0.374327530 #> 5219  0.374250223 #> 5220  0.374172769 #> 5221  0.374095169 #> 5222  0.374017424 #> 5223  0.373939535 #> 5224  0.373861501 #> 5225  0.373783324 #> 5226  0.373705004 #> 5227  0.373626542 #> 5228  0.373547939 #> 5229  0.373469195 #> 5230  0.373390310 #> 5231  0.373311286 #> 5232  0.373232122 #> 5233  0.373152820 #> 5234  0.373073381 #> 5235  0.372993804 #> 5236  0.372914090 #> 5237  0.372834240 #> 5238  0.372754255 #> 5239  0.372674135 #> 5240  0.372593880 #> 5241  0.372513492 #> 5242  0.372432971 #> 5243  0.372352317 #> 5244  0.372271531 #> 5245  0.372190614 #> 5246  0.372109567 #> 5247  0.372028389 #> 5248  0.371947081 #> 5249  0.371865645 #> 5250  0.371784080 #> 5251  0.371702387 #> 5252  0.371620567 #> 5253  0.371538620 #> 5254  0.371456547 #> 5255  0.371374349 #> 5256  0.371292025 #> 5257  0.371209578 #> 5258  0.371127006 #> 5259  0.371044311 #> 5260  0.370961493 #> 5261  0.370878553 #> 5262  0.370795492 #> 5263  0.370712309 #> 5264  0.370629006 #> 5265  0.370545583 #> 5266  0.370462041 #> 5267  0.370378379 #> 5268  0.370294600 #> 5269  0.370210703 #> 5270  0.370126688 #> 5271  0.370042557 #> 5272  0.369958310 #> 5273  0.369873947 #> 5274  0.369789469 #> 5275  0.369704877 #> 5276  0.369620171 #> 5277  0.369535351 #> 5278  0.369450418 #> 5279  0.369365374 #> 5280  0.369280217 #> 5281  0.369194949 #> 5282  0.369109570 #> 5283  0.369024081 #> 5284  0.368938482 #> 5285  0.368852774 #> 5286  0.368766958 #> 5287  0.368681033 #> 5288  0.368595001 #> 5289  0.368508861 #> 5290  0.368422615 #> 5291  0.368336263 #> 5292  0.368249805 #> 5293  0.368163243 #> 5294  0.368076575 #> 5295  0.367989804 #> 5296  0.367902929 #> 5297  0.367815951 #> 5298  0.367728871 #> 5299  0.367641688 #> 5300  0.367554405 #> 5301  0.367467020 #> 5302  0.367379534 #> 5303  0.367291949 #> 5304  0.367204264 #> 5305  0.367116480 #> 5306  0.367028597 #> 5307  0.366940616 #> 5308  0.366852538 #> 5309  0.366764363 #> 5310  0.366676091 #> 5311  0.366587723 #> 5312  0.366499260 #> 5313  0.366410701 #> 5314  0.366322048 #> 5315  0.366233300 #> 5316  0.366144459 #> 5317  0.366055525 #> 5318  0.365966498 #> 5319  0.365877378 #> 5320  0.365788167 #> 5321  0.365698865 #> 5322  0.365609471 #> 5323  0.365519988 #> 5324  0.365430414 #> 5325  0.365340751 #> 5326  0.365250999 #> 5327  0.365161158 #> 5328  0.365071230 #> 5329  0.364981214 #> 5330  0.364891110 #> 5331  0.364800920 #> 5332  0.364710644 #> 5333  0.364620282 #> 5334  0.364529835 #> 5335  0.364439303 #> 5336  0.364348686 #> 5337  0.364257986 #> 5338  0.364167202 #> 5339  0.364076334 #> 5340  0.363985385 #> 5341  0.363894353 #> 5342  0.363803239 #> 5343  0.363712045 #> 5344  0.363620769 #> 5345  0.363529413 #> 5346  0.363437977 #> 5347  0.363346461 #> 5348  0.363254867 #> 5349  0.363163194 #> 5350  0.363071442 #> 5351  0.362979613 #> 5352  0.362887706 #> 5353  0.362795723 #> 5354  0.362703663 #> 5355  0.362611527 #> 5356  0.362519315 #> 5357  0.362427028 #> 5358  0.362334667 #> 5359  0.362242230 #> 5360  0.362149720 #> 5361  0.362057137 #> 5362  0.361964480 #> 5363  0.361871751 #> 5364  0.361778949 #> 5365  0.361686076 #> 5366  0.361593131 #> 5367  0.361500115 #> 5368  0.361407029 #> 5369  0.361313872 #> 5370  0.361220645 #> 5371  0.361127350 #> 5372  0.361033985 #> 5373  0.360940551 #> 5374  0.360847050 #> 5375  0.360753480 #> 5376  0.360659843 #> 5377  0.360566140 #> 5378  0.360472369 #> 5379  0.360378533 #> 5380  0.360284631 #> 5381  0.360190663 #> 5382  0.360096631 #> 5383  0.360002534 #> 5384  0.359908372 #> 5385  0.359814147 #> 5386  0.359719859 #> 5387  0.359625507 #> 5388  0.359531093 #> 5389  0.359436617 #> 5390  0.359342078 #> 5391  0.359247479 #> 5392  0.359152818 #> 5393  0.359058096 #> 5394  0.358963315 #> 5395  0.358868473 #> 5396  0.358773572 #> 5397  0.358678611 #> 5398  0.358583592 #> 5399  0.358488514 #> 5400  0.358393378 #> 5401  0.358298185 #> 5402  0.358202934 #> 5403  0.358107627 #> 5404  0.358012262 #> 5405  0.357916842 #> 5406  0.357821366 #> 5407  0.357725834 #> 5408  0.357630248 #> 5409  0.357534606 #> 5410  0.357438910 #> 5411  0.357343161 #> 5412  0.357247357 #> 5413  0.357151501 #> 5414  0.357055591 #> 5415  0.356959629 #> 5416  0.356863615 #> 5417  0.356767549 #> 5418  0.356671431 #> 5419  0.356575262 #> 5420  0.356479043 #> 5421  0.356382773 #> 5422  0.356286453 #> 5423  0.356190083 #> 5424  0.356093665 #> 5425  0.355997197 #> 5426  0.355900680 #> 5427  0.355804115 #> 5428  0.355707502 #> 5429  0.355610842 #> 5430  0.355514134 #> 5431  0.355417379 #> 5432  0.355320578 #> 5433  0.355223730 #> 5434  0.355126837 #> 5435  0.355029898 #> 5436  0.354932914 #> 5437  0.354835885 #> 5438  0.354738811 #> 5439  0.354641693 #> 5440  0.354544532 #> 5441  0.354447326 #> 5442  0.354350078 #> 5443  0.354252787 #> 5444  0.354155453 #> 5445  0.354058077 #> 5446  0.353960659 #> 5447  0.353863199 #> 5448  0.353765698 #> 5449  0.353668156 #> 5450  0.353570574 #> 5451  0.353472952 #> 5452  0.353375289 #> 5453  0.353277587 #> 5454  0.353179846 #> 5455  0.353082065 #> 5456  0.352984246 #> 5457  0.352886389 #> 5458  0.352788494 #> 5459  0.352690561 #> 5460  0.352592590 #> 5461  0.352494583 #> 5462  0.352396539 #> 5463  0.352298458 #> 5464  0.352200341 #> 5465  0.352102189 #> 5466  0.352004001 #> 5467  0.351905778 #> 5468  0.351807519 #> 5469  0.351709227 #> 5470  0.351610900 #> 5471  0.351512539 #> 5472  0.351414144 #> 5473  0.351315716 #> 5474  0.351217256 #> 5475  0.351118762 #> 5476  0.351020236 #> 5477  0.350921678 #> 5478  0.350823087 #> 5479  0.350724466 #> 5480  0.350625813 #> 5481  0.350527129 #> 5482  0.350428415 #> 5483  0.350329670 #> 5484  0.350230895 #> 5485  0.350132091 #> 5486  0.350033257 #> 5487  0.349934394 #> 5488  0.349835502 #> 5489  0.349736581 #> 5490  0.349637632 #> 5491  0.349538655 #> 5492  0.349439651 #> 5493  0.349340619 #> 5494  0.349241560 #> 5495  0.349142474 #> 5496  0.349043361 #> 5497  0.348944222 #> 5498  0.348845057 #> 5499  0.348745867 #> 5500  0.348646651 #> 5501  0.348547410 #> 5502  0.348448144 #> 5503  0.348348853 #> 5504  0.348249539 #> 5505  0.348150200 #> 5506  0.348050837 #> 5507  0.347951451 #> 5508  0.347852042 #> 5509  0.347752610 #> 5510  0.347653155 #> 5511  0.347553678 #> 5512  0.347454179 #> 5513  0.347354658 #> 5514  0.347255115 #> 5515  0.347155551 #> 5516  0.347055966 #> 5517  0.346956361 #> 5518  0.346856735 #> 5519  0.346757088 #> 5520  0.346657422 #> 5521  0.346557736 #> 5522  0.346458031 #> 5523  0.346358307 #> 5524  0.346258564 #> 5525  0.346158802 #> 5526  0.346059022 #> 5527  0.345959223 #> 5528  0.345859407 #> 5529  0.345759574 #> 5530  0.345659723 #> 5531  0.345559855 #> 5532  0.345459970 #> 5533  0.345360069 #> 5534  0.345260152 #> 5535  0.345160218 #> 5536  0.345060269 #> 5537  0.344960304 #> 5538  0.344860324 #> 5539  0.344760330 #> 5540  0.344660320 #> 5541  0.344560296 #> 5542  0.344460258 #> 5543  0.344360205 #> 5544  0.344260139 #> 5545  0.344160060 #> 5546  0.344059967 #> 5547  0.343959862 #> 5548  0.343859743 #> 5549  0.343759613 #> 5550  0.343659470 #> 5551  0.343559315 #> 5552  0.343459148 #> 5553  0.343358970 #> 5554  0.343258780 #> 5555  0.343158580 #> 5556  0.343058369 #> 5557  0.342958147 #> 5558  0.342857915 #> 5559  0.342757673 #> 5560  0.342657421 #> 5561  0.342557160 #> 5562  0.342456889 #> 5563  0.342356609 #> 5564  0.342256321 #> 5565  0.342156023 #> 5566  0.342055718 #> 5567  0.341955404 #> 5568  0.341855083 #> 5569  0.341754753 #> 5570  0.341654417 #> 5571  0.341554073 #> 5572  0.341453722 #> 5573  0.341353364 #> 5574  0.341253000 #> 5575  0.341152630 #> 5576  0.341052254 #> 5577  0.340951872 #> 5578  0.340851484 #> 5579  0.340751091 #> 5580  0.340650693 #> 5581  0.340550290 #> 5582  0.340449882 #> 5583  0.340349470 #> 5584  0.340249054 #> 5585  0.340148633 #> 5586  0.340048209 #> 5587  0.339947782 #> 5588  0.339847351 #> 5589  0.339746917 #> 5590  0.339646480 #> 5591  0.339546040 #> 5592  0.339445598 #> 5593  0.339345154 #> 5594  0.339244708 #> 5595  0.339144260 #> 5596  0.339043811 #> 5597  0.338943360 #> 5598  0.338842908 #> 5599  0.338742455 #> 5600  0.338642002 #> 5601  0.338541548 #> 5602  0.338441094 #> 5603  0.338340639 #> 5604  0.338240185 #> 5605  0.338139732 #> 5606  0.338039279 #> 5607  0.337938827 #> 5608  0.337838375 #> 5609  0.337737926 #> 5610  0.337637477 #> 5611  0.337537030 #> 5612  0.337436585 #> 5613  0.337336143 #> 5614  0.337235702 #> 5615  0.337135264 #> 5616  0.337034829 #> 5617  0.336934397 #> 5618  0.336833967 #> 5619  0.336733541 #> 5620  0.336633119 #> 5621  0.336532700 #> 5622  0.336432286 #> 5623  0.336331875 #> 5624  0.336231469 #> 5625  0.336131068 #> 5626  0.336030671 #> 5627  0.335930279 #> 5628  0.335829892 #> 5629  0.335729511 #> 5630  0.335629135 #> 5631  0.335528765 #> 5632  0.335428401 #> 5633  0.335328043 #> 5634  0.335227692 #> 5635  0.335127347 #> 5636  0.335027009 #> 5637  0.334926678 #> 5638  0.334826354 #> 5639  0.334726037 #> 5640  0.334625728 #> 5641  0.334525427 #> 5642  0.334425133 #> 5643  0.334324848 #> 5644  0.334224571 #> 5645  0.334124303 #> 5646  0.334024043 #> 5647  0.333923792 #> 5648  0.333823551 #> 5649  0.333723318 #> 5650  0.333623095 #> 5651  0.333522882 #> 5652  0.333422679 #> 5653  0.333322486 #> 5654  0.333222303 #> 5655  0.333122130 #> 5656  0.333021968 #> 5657  0.332921817 #> 5658  0.332821677 #> 5659  0.332721548 #> 5660  0.332621430 #> 5661  0.332521324 #> 5662  0.332421230 #> 5663  0.332321148 #> 5664  0.332221077 #> 5665  0.332121019 #> 5666  0.332020974 #> 5667  0.331920941 #> 5668  0.331820921 #> 5669  0.331720914 #> 5670  0.331620921 #> 5671  0.331520940 #> 5672  0.331420974 #> 5673  0.331321021 #> 5674  0.331221082 #> 5675  0.331121157 #> 5676  0.331021247 #> 5677  0.330921350 #> 5678  0.330821469 #> 5679  0.330721603 #> 5680  0.330621751 #> 5681  0.330521915 #> 5682  0.330422094 #> 5683  0.330322288 #> 5684  0.330222499 #> 5685  0.330122725 #> 5686  0.330022967 #> 5687  0.329923226 #> 5688  0.329823501 #> 5689  0.329723792 #> 5690  0.329624101 #> 5691  0.329524426 #> 5692  0.329424768 #> 5693  0.329325128 #> 5694  0.329225505 #> 5695  0.329125900 #> 5696  0.329026313 #> 5697  0.328926744 #> 5698  0.328827193 #> 5699  0.328727660 #> 5700  0.328628145 #> 5701  0.328528650 #> 5702  0.328429173 #> 5703  0.328329715 #> 5704  0.328230277 #> 5705  0.328130857 #> 5706  0.328031457 #> 5707  0.327932077 #> 5708  0.327832717 #> 5709  0.327733377 #> 5710  0.327634057 #> 5711  0.327534757 #> 5712  0.327435478 #> 5713  0.327336220 #> 5714  0.327236982 #> 5715  0.327137765 #> 5716  0.327038570 #> 5717  0.326939396 #> 5718  0.326840243 #> 5719  0.326741112 #> 5720  0.326642003 #> 5721  0.326542916 #> 5722  0.326443851 #> 5723  0.326344809 #> 5724  0.326245788 #> 5725  0.326146791 #> 5726  0.326047816 #> 5727  0.325948865 #> 5728  0.325849936 #> 5729  0.325751031 #> 5730  0.325652149 #> 5731  0.325553290 #> 5732  0.325454456 #> 5733  0.325355645 #> 5734  0.325256859 #> 5735  0.325158096 #> 5736  0.325059359 #> 5737  0.324960645 #> 5738  0.324861957 #> 5739  0.324763293 #> 5740  0.324664654 #> 5741  0.324566041 #> 5742  0.324467452 #> 5743  0.324368890 #> 5744  0.324270353 #> 5745  0.324171841 #> 5746  0.324073356 #> 5747  0.323974897 #> 5748  0.323876464 #> 5749  0.323778057 #> 5750  0.323679677 #> 5751  0.323581324 #> 5752  0.323482998 #> 5753  0.323384698 #> 5754  0.323286426 #> 5755  0.323188181 #> 5756  0.323089964 #> 5757  0.322991774 #> 5758  0.322893612 #> 5759  0.322795478 #> 5760  0.322697372 #> 5761  0.322599295 #> 5762  0.322501245 #> 5763  0.322403224 #> 5764  0.322305232 #> 5765  0.322207269 #> 5766  0.322109335 #> 5767  0.322011430 #> 5768  0.321913554 #> 5769  0.321815707 #> 5770  0.321717890 #> 5771  0.321620103 #> 5772  0.321522346 #> 5773  0.321424619 #> 5774  0.321326922 #> 5775  0.321229255 #> 5776  0.321131618 #> 5777  0.321034013 #> 5778  0.320936438 #> 5779  0.320838894 #> 5780  0.320741380 #> 5781  0.320643899 #> 5782  0.320546448 #> 5783  0.320449029 #> 5784  0.320351641 #> 5785  0.320254285 #> 5786  0.320156962 #> 5787  0.320059670 #> 5788  0.319962410 #> 5789  0.319865183 #> 5790  0.319767988 #> 5791  0.319670825 #> 5792  0.319573696 #> 5793  0.319476599 #> 5794  0.319379536 #> 5795  0.319282505 #> 5796  0.319185508 #> 5797  0.319088544 #> 5798  0.318991614 #> 5799  0.318894718 #> 5800  0.318797855 #> 5801  0.318701027 #> 5802  0.318604232 #> 5803  0.318507472 #> 5804  0.318410747 #> 5805  0.318314055 #> 5806  0.318217399 #> 5807  0.318120777 #> 5808  0.318024191 #> 5809  0.317927639 #> 5810  0.317831123 #> 5811  0.317734642 #> 5812  0.317638197 #> 5813  0.317541787 #> 5814  0.317445414 #> 5815  0.317349076 #> 5816  0.317252774 #> 5817  0.317156508 #> 5818  0.317060279 #> 5819  0.316964086 #> 5820  0.316867930 #> 5821  0.316771810 #> 5822  0.316675728 #> 5823  0.316579682 #> 5824  0.316483674 #> 5825  0.316387702 #> 5826  0.316291769 #> 5827  0.316195872 #> 5828  0.316100014 #> 5829  0.316004193 #> 5830  0.315908410 #> 5831  0.315812666 #> 5832  0.315716959 #> 5833  0.315621291 #> 5834  0.315525661 #> 5835  0.315430070 #> 5836  0.315334518 #> 5837  0.315239004 #> 5838  0.315143530 #> 5839  0.315048095 #> 5840  0.314952699 #> 5841  0.314857342 #> 5842  0.314762025 #> 5843  0.314666747 #> 5844  0.314571509 #> 5845  0.314476312 #> 5846  0.314381154 #> 5847  0.314286036 #> 5848  0.314190959 #> 5849  0.314095922 #> 5850  0.314000925 #> 5851  0.313905970 #> 5852  0.313811055 #> 5853  0.313716181 #> 5854  0.313621348 #> 5855  0.313526556 #> 5856  0.313431806 #> 5857  0.313337097 #> 5858  0.313242430 #> 5859  0.313147804 #> 5860  0.313053220 #> 5861  0.312958679 #> 5862  0.312864179 #> 5863  0.312769721 #> 5864  0.312675306 #> 5865  0.312580933 #> 5866  0.312486603 #> 5867  0.312392316 #> 5868  0.312298071 #> 5869  0.312203870 #> 5870  0.312109711 #> 5871  0.312015596 #> 5872  0.311921524 #> 5873  0.311827495 #> 5874  0.311733510 #> 5875  0.311639569 #> 5876  0.311545671 #> 5877  0.311451818 #> 5878  0.311358009 #> 5879  0.311264243 #> 5880  0.311170522 #> 5881  0.311076846 #> 5882  0.310983214 #> 5883  0.310889627 #> 5884  0.310796085 #> 5885  0.310702587 #> 5886  0.310609135 #> 5887  0.310515728 #> 5888  0.310422366 #> 5889  0.310329050 #> 5890  0.310235779 #> 5891  0.310142554 #> 5892  0.310049374 #> 5893  0.309956241 #> 5894  0.309863153 #> 5895  0.309770112 #> 5896  0.309677117 #> 5897  0.309584169 #> 5898  0.309491267 #> 5899  0.309398411 #> 5900  0.309305603 #> 5901  0.309212841 #> 5902  0.309120126 #> 5903  0.309027458 #> 5904  0.308934838 #> 5905  0.308842265 #> 5906  0.308749739 #> 5907  0.308657261 #> 5908  0.308564831 #> 5909  0.308472449 #> 5910  0.308380114 #> 5911  0.308287828 #> 5912  0.308195590 #> 5913  0.308103400 #> 5914  0.308011258 #> 5915  0.307919165 #> 5916  0.307827121 #> 5917  0.307735126 #> 5918  0.307643179 #> 5919  0.307551281 #> 5920  0.307459433 #> 5921  0.307367634 #> 5922  0.307275884 #> 5923  0.307184184 #> 5924  0.307092533 #> 5925  0.307000932 #> 5926  0.306909381 #> 5927  0.306817880 #> 5928  0.306726429 #> 5929  0.306635028 #> 5930  0.306543677 #> 5931  0.306452377 #> 5932  0.306361127 #> 5933  0.306269928 #> 5934  0.306178780 #> 5935  0.306087683 #> 5936  0.305996637 #> 5937  0.305905641 #> 5938  0.305814697 #> 5939  0.305723805 #> 5940  0.305632964 #> 5941  0.305542174 #> 5942  0.305451436 #> 5943  0.305360750 #> 5944  0.305270116 #> 5945  0.305179534 #> 5946  0.305089004 #> 5947  0.304998526 #> 5948  0.304908101 #> 5949  0.304817728 #> 5950  0.304727408 #> 5951  0.304637140 #> 5952  0.304546925 #> 5953  0.304456764 #> 5954  0.304366655 #> 5955  0.304276599 #> 5956  0.304186597 #> 5957  0.304096648 #> 5958  0.304006753 #> 5959  0.303916911 #> 5960  0.303827124 #> 5961  0.303737390 #> 5962  0.303647709 #> 5963  0.303558083 #> 5964  0.303468512 #> 5965  0.303378994 #> 5966  0.303289531 #> 5967  0.303200123 #> 5968  0.303110769 #> 5969  0.303021470 #> 5970  0.302932225 #> 5971  0.302843036 #> 5972  0.302753902 #> 5973  0.302664823 #> 5974  0.302575799 #> 5975  0.302486831 #> 5976  0.302397918 #> 5977  0.302309061 #> 5978  0.302220260 #> 5979  0.302131515 #> 5980  0.302042825 #> 5981  0.301954192 #> 5982  0.301865615 #> 5983  0.301777094 #> 5984  0.301688630 #> 5985  0.301600222 #> 5986  0.301511871 #> 5987  0.301423576 #> 5988  0.301335339 #> 5989  0.301247158 #> 5990  0.301159035 #> 5991  0.301070969 #> 5992  0.300982960 #> 5993  0.300895008 #> 5994  0.300807114 #> 5995  0.300719278 #> 5996  0.300631500 #> 5997  0.300543779 #> 5998  0.300456116 #> 5999  0.300368512 #> 6000  0.300280965 #> 6001  0.300193477 #> 6002  0.300106048 #> 6003  0.300018677 #> 6004  0.299931364 #> 6005  0.299844110 #> 6006  0.299756916 #> 6007  0.299669780 #> 6008  0.299582703 #> 6009  0.299495685 #> 6010  0.299408727 #> 6011  0.299321828 #> 6012  0.299234989 #> 6013  0.299148209 #> 6014  0.299061489 #> 6015  0.298974829 #> 6016  0.298888228 #> 6017  0.298801688 #> 6018  0.298715208 #> 6019  0.298628788 #> 6020  0.298542429 #> 6021  0.298456130 #> 6022  0.298369892 #> 6023  0.298283714 #> 6024  0.298197598 #> 6025  0.298111542 #> 6026  0.298025547 #> 6027  0.297939614 #> 6028  0.297853741 #> 6029  0.297767930 #> 6030  0.297682181 #> 6031  0.297596493 #> 6032  0.297510867 #> 6033  0.297425302 #> 6034  0.297339800 #> 6035  0.297254359 #> 6036  0.297168981 #> 6037  0.297083665 #> 6038  0.296998411 #> 6039  0.296913219 #> 6040  0.296828091 #> 6041  0.296743024 #> 6042  0.296658021 #> 6043  0.296573080 #> 6044  0.296488203 #> 6045  0.296403388 #> 6046  0.296318637 #> 6047  0.296233949 #> 6048  0.296149325 #> 6049  0.296064764 #> 6050  0.295980266 #> 6051  0.295895833 #> 6052  0.295811463 #> 6053  0.295727157 #> 6054  0.295642915 #> 6055  0.295558738 #> 6056  0.295474625 #> 6057  0.295390576 #> 6058  0.295306591 #> 6059  0.295222671 #> 6060  0.295138816 #> 6061  0.295055026 #> 6062  0.294971300 #> 6063  0.294887640 #> 6064  0.294804045 #> 6065  0.294720515 #> 6066  0.294637050 #> 6067  0.294553651 #> 6068  0.294470317 #> 6069  0.294387049 #> 6070  0.294303847 #> 6071  0.294220711 #> 6072  0.294137640 #> 6073  0.294054636 #> 6074  0.293971698 #> 6075  0.293888826 #> 6076  0.293806021 #> 6077  0.293723282 #> 6078  0.293640610 #> 6079  0.293558005 #> 6080  0.293475466 #> 6081  0.293392995 #> 6082  0.293310590 #> 6083  0.293228253 #> 6084  0.293145983 #> 6085  0.293063780 #> 6086  0.292981645 #> 6087  0.292899577 #> 6088  0.292817577 #> 6089  0.292735645 #> 6090  0.292653781 #> 6091  0.292571984 #> 6092  0.292490256 #> 6093  0.292408596 #> 6094  0.292327005 #> 6095  0.292245482 #> 6096  0.292164027 #> 6097  0.292082641 #> 6098  0.292001324 #> 6099  0.291920076 #> 6100  0.291838897 #> 6101  0.291757787 #> 6102  0.291676746 #> 6103  0.291595774 #> 6104  0.291514872 #> 6105  0.291434039 #> 6106  0.291353276 #> 6107  0.291272583 #> 6108  0.291191959 #> 6109  0.291111405 #> 6110  0.291030922 #> 6111  0.290950508 #> 6112  0.290870165 #> 6113  0.290789892 #> 6114  0.290709690 #> 6115  0.290629558 #> 6116  0.290549497 #> 6117  0.290469507 #> 6118  0.290389588 #> 6119  0.290309739 #> 6120  0.290229962 #> 6121  0.290150256 #> 6122  0.290070621 #> 6123  0.289991058 #> 6124  0.289911566 #> 6125  0.289832146 #> 6126  0.289752798 #> 6127  0.289673521 #> 6128  0.289594317 #> 6129  0.289515184 #> 6130  0.289436124 #> 6131  0.289357136 #> 6132  0.289278220 #> 6133  0.289199377 #> 6134  0.289120607 #> 6135  0.289041909 #> 6136  0.288963284 #> 6137  0.288884732 #> 6138  0.288806253 #> 6139  0.288727847 #> 6140  0.288649515 #> 6141  0.288571255 #> 6142  0.288493070 #> 6143  0.288414957 #> 6144  0.288336919 #> 6145  0.288258954 #> 6146  0.288181063 #> 6147  0.288103246 #> 6148  0.288025504 #> 6149  0.287947835 #> 6150  0.287870241 #> 6151  0.287792721 #> 6152  0.287715276 #> 6153  0.287637905 #> 6154  0.287560609 #> 6155  0.287483388 #> 6156  0.287406242 #> 6157  0.287329171 #> 6158  0.287252175 #> 6159  0.287175254 #> 6160  0.287098409 #> 6161  0.287021640 #> 6162  0.286944946 #> 6163  0.286868327 #> 6164  0.286791785 #> 6165  0.286715318 #> 6166  0.286638927 #> 6167  0.286562613 #> 6168  0.286486375 #> 6169  0.286410213 #> 6170  0.286334127 #> 6171  0.286258119 #> 6172  0.286182186 #> 6173  0.286106331 #> 6174  0.286030553 #> 6175  0.285954851 #> 6176  0.285879227 #> 6177  0.285803680 #> 6178  0.285728210 #> 6179  0.285652818 #> 6180  0.285577503 #> 6181  0.285502266 #> 6182  0.285427106 #> 6183  0.285352025 #> 6184  0.285277021 #> 6185  0.285202095 #> 6186  0.285127248 #> 6187  0.285052479 #> 6188  0.284977788 #> 6189  0.284903176 #> 6190  0.284828643 #> 6191  0.284754188 #> 6192  0.284679812 #> 6193  0.284605515 #> 6194  0.284531297 #> 6195  0.284457158 #> 6196  0.284383099 #> 6197  0.284309119 #> 6198  0.284235218 #> 6199  0.284161397 #> 6200  0.284087656 #> 6201  0.284013994 #> 6202  0.283940413 #> 6203  0.283866911 #> 6204  0.283793490 #> 6205  0.283720148 #> 6206  0.283646888 #> 6207  0.283573707 #> 6208  0.283500607 #> 6209  0.283427588 #> 6210  0.283354650 #> 6211  0.283281793 #> 6212  0.283209016 #> 6213  0.283136321 #> 6214  0.283063707 #> 6215  0.282991174 #> 6216  0.282918723 #> 6217  0.282846353 #> 6218  0.282774065 #> 6219  0.282701859 #> 6220  0.282629734 #> 6221  0.282557692 #> 6222  0.282485732 #> 6223  0.282413854 #> 6224  0.282342058 #> 6225  0.282270344 #> 6226  0.282198714 #> 6227  0.282127166 #> 6228  0.282055700 #> 6229  0.281984318 #> 6230  0.281913018 #> 6231  0.281841802 #> 6232  0.281770668 #> 6233  0.281699619 #> 6234  0.281628652 #> 6235  0.281557769 #> 6236  0.281486970 #> 6237  0.281416254 #> 6238  0.281345622 #> 6239  0.281275075 #> 6240  0.281204611 #> 6241  0.281134232 #> 6242  0.281063936 #> 6243  0.280993726 #> 6244  0.280923599 #> 6245  0.280853558 #> 6246  0.280783601 #> 6247  0.280713729 #> 6248  0.280643942 #> 6249  0.280574240 #> 6250  0.280504623 #> 6251  0.280435092 #> 6252  0.280365646 #> 6253  0.280296285 #> 6254  0.280227010 #> 6255  0.280157821 #> 6256  0.280088718 #> 6257  0.280019701 #> 6258  0.279950770 #> 6259  0.279881924 #> 6260  0.279813166 #> 6261  0.279744493 #> 6262  0.279675908 #> 6263  0.279607408 #> 6264  0.279538996 #> 6265  0.279470670 #> 6266  0.279402432 #> 6267  0.279334280 #> 6268  0.279266216 #> 6269  0.279198239 #> 6270  0.279130350 #> 6271  0.279062548 #> 6272  0.278994833 #> 6273  0.278927207 #> 6274  0.278859668 #> 6275  0.278792217 #> 6276  0.278724854 #> 6277  0.278657580 #> 6278  0.278590394 #> 6279  0.278523296 #> 6280  0.278456287 #> 6281  0.278389366 #> 6282  0.278322534 #> 6283  0.278255792 #> 6284  0.278189138 #> 6285  0.278122573 #> 6286  0.278056097 #> 6287  0.277989711 #> 6288  0.277923414 #> 6289  0.277857207 #> 6290  0.277791090 #> 6291  0.277725062 #> 6292  0.277659124 #> 6293  0.277593276 #> 6294  0.277527519 #> 6295  0.277461851 #> 6296  0.277396274 #> 6297  0.277330788 #> 6298  0.277265392 #> 6299  0.277200086 #> 6300  0.277134872 #> 6301  0.277069748 #> 6302  0.277004716 #> 6303  0.276939774 #> 6304  0.276874924 #> 6305  0.276810165 #> 6306  0.276745498 #> 6307  0.276680922 #> 6308  0.276616438 #> 6309  0.276552046 #> 6310  0.276487746 #> 6311  0.276423538 #> 6312  0.276359422 #> 6313  0.276295399 #> 6314  0.276231468 #> 6315  0.276167629 #> 6316  0.276103883 #> 6317  0.276040230 #> 6318  0.275976670 #> 6319  0.275913202 #> 6320  0.275849828 #> 6321  0.275786547 #> 6322  0.275723359 #> 6323  0.275660265 #> 6324  0.275597264 #> 6325  0.275534357 #> 6326  0.275471544 #> 6327  0.275408825 #> 6328  0.275346200 #> 6329  0.275283669 #> 6330  0.275221232 #> 6331  0.275158889 #> 6332  0.275096641 #> 6333  0.275034488 #> 6334  0.274972429 #> 6335  0.274910466 #> 6336  0.274848597 #> 6337  0.274786823 #> 6338  0.274725145 #> 6339  0.274663561 #> 6340  0.274602074 #> 6341  0.274540681 #> 6342  0.274479385 #> 6343  0.274418184 #> 6344  0.274357079 #> 6345  0.274296070 #> 6346  0.274235157 #> 6347  0.274174341 #> 6348  0.274113620 #> 6349  0.274052997 #> 6350  0.273992470 #> 6351  0.273932039 #> 6352  0.273871706 #> 6353  0.273811469 #> 6354  0.273751329 #> 6355  0.273691287 #> 6356  0.273631342 #> 6357  0.273571494 #> 6358  0.273511744 #> 6359  0.273452092 #> 6360  0.273392537 #> 6361  0.273333080 #> 6362  0.273273721 #> 6363  0.273214461 #> 6364  0.273155298 #> 6365  0.273096234 #> 6366  0.273037269 #> 6367  0.272978402 #> 6368  0.272919633 #> 6369  0.272860964 #> 6370  0.272802394 #> 6371  0.272743922 #> 6372  0.272685550 #> 6373  0.272627277 #> 6374  0.272569104 #> 6375  0.272511030 #> 6376  0.272453056 #> 6377  0.272395181 #> 6378  0.272337407 #> 6379  0.272279732 #> 6380  0.272222158 #> 6381  0.272164684 #> 6382  0.272107310 #> 6383  0.272050037 #> 6384  0.271992865 #> 6385  0.271935793 #> 6386  0.271878822 #> 6387  0.271821952 #> 6388  0.271765183 #> 6389  0.271708515 #> 6390  0.271651949 #> 6391  0.271595484 #> 6392  0.271539121 #> 6393  0.271482860 #> 6394  0.271426700 #> 6395  0.271370642 #> 6396  0.271314687 #> 6397  0.271258833 #> 6398  0.271203082 #> 6399  0.271147433 #> 6400  0.271091887 #> 6401  0.271036444 #> 6402  0.270981103 #> 6403  0.270925865 #> 6404  0.270870730 #> 6405  0.270815699 #> 6406  0.270760770 #> 6407  0.270705946 #> 6408  0.270651224 #> 6409  0.270596606 #> 6410  0.270542092 #> 6411  0.270487682 #> 6412  0.270433376 #> 6413  0.270379174 #> 6414  0.270325076 #> 6415  0.270271083 #> 6416  0.270217194 #> 6417  0.270163410 #> 6418  0.270109730 #> 6419  0.270056156 #> 6420  0.270002686 #> 6421  0.269949321 #> 6422  0.269896062 #> 6423  0.269842908 #> 6424  0.269789860 #> 6425  0.269736917 #> 6426  0.269684079 #> 6427  0.269631348 #> 6428  0.269578722 #> 6429  0.269526203 #> 6430  0.269473790 #> 6431  0.269421483 #> 6432  0.269369283 #> 6433  0.269317189 #> 6434  0.269265202 #> 6435  0.269213321 #> 6436  0.269161548 #> 6437  0.269109882 #> 6438  0.269058323 #> 6439  0.269006871 #> 6440  0.268955527 #> 6441  0.268904290 #> 6442  0.268853161 #> 6443  0.268802139 #> 6444  0.268751226 #> 6445  0.268700420 #> 6446  0.268649723 #> 6447  0.268599134 #> 6448  0.268548654 #> 6449  0.268498282 #> 6450  0.268448019 #> 6451  0.268397864 #> 6452  0.268347819 #> 6453  0.268297882 #> 6454  0.268248055 #> 6455  0.268198337 #> 6456  0.268148728 #> 6457  0.268099229 #> 6458  0.268049840 #> 6459  0.268000560 #> 6460  0.267951390 #> 6461  0.267902331 #> 6462  0.267853381 #> 6463  0.267804542 #> 6464  0.267755813 #> 6465  0.267707195 #> 6466  0.267658687 #> 6467  0.267610290 #> 6468  0.267562004 #> 6469  0.267513829 #> 6470  0.267465766 #> 6471  0.267417813 #> 6472  0.267369972 #> 6473  0.267322242 #> 6474  0.267274624 #> 6475  0.267227118 #> 6476  0.267179724 #> 6477  0.267132442 #> 6478  0.267085272 #> 6479  0.267038214 #> 6480  0.266991269 #> 6481  0.266944436 #> 6482  0.266897716 #> 6483  0.266851108 #> 6484  0.266804614 #> 6485  0.266758232 #> 6486  0.266711964 #> 6487  0.266665809 #> 6488  0.266619768 #> 6489  0.266573840 #> 6490  0.266528025 #> 6491  0.266482325 #> 6492  0.266436738 #> 6493  0.266391265 #> 6494  0.266345907 #> 6495  0.266300662 #> 6496  0.266255533 #> 6497  0.266210517 #> 6498  0.266165617 #> 6499  0.266120831 #> 6500  0.266076160 #> 6501  0.266031604 #> 6502  0.265987164 #> 6503  0.265942838 #> 6504  0.265898629 #> 6505  0.265854534 #> 6506  0.265810555 #> 6507  0.265766693 #> 6508  0.265722946 #> 6509  0.265679315 #> 6510  0.265635800 #> 6511  0.265592402 #> 6512  0.265549120 #> 6513  0.265505954 #> 6514  0.265462905 #> 6515  0.265419973 #> 6516  0.265377159 #> 6517  0.265334461 #> 6518  0.265291880 #> 6519  0.265249416 #> 6520  0.265207071 #> 6521  0.265164842 #> 6522  0.265122731 #> 6523  0.265080739 #> 6524  0.265038864 #> 6525  0.264997107 #> 6526  0.264955468 #> 6527  0.264913948 #> 6528  0.264872546 #> 6529  0.264831263 #> 6530  0.264790098 #> 6531  0.264749053 #> 6532  0.264708126 #> 6533  0.264667318 #> 6534  0.264626630 #> 6535  0.264586061 #> 6536  0.264545611 #> 6537  0.264505281 #> 6538  0.264465071 #> 6539  0.264424981 #> 6540  0.264385010 #> 6541  0.264345160 #> 6542  0.264305430 #> 6543  0.264265820 #> 6544  0.264226331 #> 6545  0.264186962 #> 6546  0.264147714 #> 6547  0.264108587 #> 6548  0.264069581 #> 6549  0.264030696 #> 6550  0.263991933 #> 6551  0.263953290 #> 6552  0.263914770 #> 6553  0.263876371 #> 6554  0.263838093 #> 6555  0.263799938 #> 6556  0.263761904 #> 6557  0.263723993 #> 6558  0.263686204 #> 6559  0.263648537 #> 6560  0.263610993 #> 6561  0.263573571 #> 6562  0.263536273 #> 6563  0.263499097 #> 6564  0.263462044 #> 6565  0.263425114 #> 6566  0.263388308 #> 6567  0.263351625 #> 6568  0.263315065 #> 6569  0.263278630 #> 6570  0.263242318 #> 6571  0.263206129 #> 6572  0.263170065 #> 6573  0.263134125 #> 6574  0.263098310 #> 6575  0.263062619 #> 6576  0.263027052 #> 6577  0.262991610 #> 6578  0.262956293 #> 6579  0.262921101 #> 6580  0.262886033 #> 6581  0.262851091 #> 6582  0.262816275 #> 6583  0.262781583 #> 6584  0.262747018 #> 6585  0.262712578 #> 6586  0.262678263 #> 6587  0.262644075 #> 6588  0.262610013 #> 6589  0.262576077 #> 6590  0.262542267 #> 6591  0.262508584 #> 6592  0.262475027 #> 6593  0.262441597 #> 6594  0.262408294 #> 6595  0.262375118 #> 6596  0.262342068 #> 6597  0.262309146 #> 6598  0.262276352 #> 6599  0.262243685 #> 6600  0.262211145 #> 6601  0.262178733 #> 6602  0.262146449 #> 6603  0.262114293 #> 6604  0.262082265 #> 6605  0.262050365 #> 6606  0.262018593 #> 6607  0.261986950 #> 6608  0.261955435 #> 6609  0.261924050 #> 6610  0.261892793 #> 6611  0.261861665 #> 6612  0.261830666 #> 6613  0.261799796 #> 6614  0.261769055 #> 6615  0.261738444 #> 6616  0.261707963 #> 6617  0.261677611 #> 6618  0.261647390 #> 6619  0.261617298 #> 6620  0.261587336 #> 6621  0.261557504 #> 6622  0.261527803 #> 6623  0.261498232 #> 6624  0.261468792 #> 6625  0.261439482 #> 6626  0.261410304 #> 6627  0.261381256 #> 6628  0.261352339 #> 6629  0.261323554 #> 6630  0.261294899 #> 6631  0.261266377 #> 6632  0.261237985 #> 6633  0.261209726 #> 6634  0.261181598 #> 6635  0.261153602 #> 6636  0.261125739 #> 6637  0.261098007 #> 6638  0.261070408 #> 6639  0.261042941 #> 6640  0.261015607 #> 6641  0.260988406 #> 6642  0.260961337 #> 6643  0.260934401 #> 6644  0.260907598 #> 6645  0.260880929 #> 6646  0.260854393 #> 6647  0.260827990 #> 6648  0.260801720 #> 6649  0.260775585 #> 6650  0.260749583 #> 6651  0.260723715 #> 6652  0.260697981 #> 6653  0.260672381 #> 6654  0.260646916 #> 6655  0.260621584 #> 6656  0.260596388 #> 6657  0.260571326 #> 6658  0.260546398 #> 6659  0.260521606 #> 6660  0.260496949 #> 6661  0.260472426 #> 6662  0.260448039 #> 6663  0.260423788 #> 6664  0.260399671 #> 6665  0.260375691 #> 6666  0.260351846 #> 6667  0.260328137 #> 6668  0.260304564 #> 6669  0.260281127 #> 6670  0.260257826 #> 6671  0.260234661 #> 6672  0.260211633 #> 6673  0.260188742 #> 6674  0.260165987 #> 6675  0.260143369 #> 6676  0.260120887 #> 6677  0.260098543 #> 6678  0.260076336 #> 6679  0.260054267 #> 6680  0.260032334 #> 6681  0.260010539 #> 6682  0.259988882 #> 6683  0.259967363 #> 6684  0.259945981 #> 6685  0.259924737 #> 6686  0.259903632 #> 6687  0.259882665 #> 6688  0.259861836 #> 6689  0.259841145 #> 6690  0.259820593 #> 6691  0.259800180 #> 6692  0.259779905 #> 6693  0.259759769 #> 6694  0.259739773 #> 6695  0.259719915 #> 6696  0.259700197 #> 6697  0.259680618 #> 6698  0.259661179 #> 6699  0.259641879 #> 6700  0.259622719 #> 6701  0.259603699 #> 6702  0.259584818 #> 6703  0.259566078 #> 6704  0.259547478 #> 6705  0.259529018 #> 6706  0.259510699 #> 6707  0.259492520 #> 6708  0.259474482 #> 6709  0.259456584 #> 6710  0.259438827 #> 6711  0.259421212 #> 6712  0.259403737 #> 6713  0.259386404 #> 6714  0.259369212 #> 6715  0.259352161 #> 6716  0.259335252 #> 6717  0.259318484 #> 6718  0.259301858 #> 6719  0.259285374 #> 6720  0.259269032 #> 6721  0.259252833 #> 6722  0.259236775 #> 6723  0.259220860 #> 6724  0.259205087 #> 6725  0.259189456 #> 6726  0.259173968 #> 6727  0.259158623 #> 6728  0.259143421 #> 6729  0.259128362 #> 6730  0.259113445 #> 6731  0.259098672 #> 6732  0.259084043 #> 6733  0.259069556 #> 6734  0.259055213 #> 6735  0.259041014 #> 6736  0.259026959 #> 6737  0.259013047 #> 6738  0.258999279 #> 6739  0.258985655 #> 6740  0.258972176 #> 6741  0.258958840 #> 6742  0.258945649 #> 6743  0.258932603 #> 6744  0.258919701 #> 6745  0.258906944 #> 6746  0.258894331 #> 6747  0.258881863 #> 6748  0.258869541 #> 6749  0.258857363 #> 6750  0.258845331 #> 6751  0.258833444 #> 6752  0.258821702 #> 6753  0.258810106 #> 6754  0.258798655 #> 6755  0.258787350 #> 6756  0.258776191 #> 6757  0.258765178 #> 6758  0.258754310 #> 6759  0.258743589 #> 6760  0.258733014 #> 6761  0.258722586 #> 6762  0.258712304 #> 6763  0.258702168 #> 6764  0.258692179 #> 6765  0.258682337 #> 6766  0.258672641 #> 6767  0.258663092 #> 6768  0.258653691 #> 6769  0.258644436 #> 6770  0.258635329 #> 6771  0.258626369 #> 6772  0.258617556 #> 6773  0.258608891 #> 6774  0.258600373 #> 6775  0.258592003 #> 6776  0.258583781 #> 6777  0.258575707 #> 6778  0.258567781 #> 6779  0.258560003 #> 6780  0.258552373 #> 6781  0.258544891 #> 6782  0.258537557 #> 6783  0.258530372 #> 6784  0.258523336 #> 6785  0.258516448 #> 6786  0.258509709 #> 6787  0.258503119 #> 6788  0.258496677 #> 6789  0.258490385 #> 6790  0.258484241 #> 6791  0.258478247 #> 6792  0.258472402 #> 6793  0.258466707 #> 6794  0.258461161 #> 6795  0.258455764 #> 6796  0.258450517 #> 6797  0.258445420 #> 6798  0.258440473 #> 6799  0.258435675 #> 6800  0.258431028 #> 6801  0.258426530 #> 6802  0.258422183 #> 6803  0.258417986 #> 6804  0.258413939 #> 6805  0.258410042 #> 6806  0.258406297 #> 6807  0.258402701 #> 6808  0.258399257 #> 6809  0.258395963 #> 6810  0.258392820 #> 6811  0.258389827 #> 6812  0.258386986 #> 6813  0.258384296 #> 6814  0.258381757 #> 6815  0.258379369 #> 6816  0.258377133 #> 6817  0.258375048 #> 6818  0.258373115 #> 6819  0.258371333 #> 6820  0.258369702 #> 6821  0.258368224 #> 6822  0.258366897 #> 6823  0.258365722 #> 6824  0.258364699 #> 6825  0.258363828 #> 6826  0.258363109 #> 6827  0.258362543 #> 6828  0.258362128 #> 6829  0.258361866 #> 6830  0.258361756 #> 6831  0.258361799 #> 6832  0.258361995 #> 6833  0.258362343 #> 6834  0.258362843 #> 6835  0.258363497 #> 6836  0.258364303 #> 6837  0.258365263 #> 6838  0.258366375 #> 6839  0.258367640 #> 6840  0.258369059 #> 6841  0.258370630 #> 6842  0.258372355 #> 6843  0.258374234 #> 6844  0.258376265 #> 6845  0.258378451 #> 6846  0.258380789 #> 6847  0.258383282 #> 6848  0.258385928 #> 6849  0.258388727 #> 6850  0.258391681 #> 6851  0.258394789 #> 6852  0.258398050 #> 6853  0.258401465 #> 6854  0.258405035 #> 6855  0.258408758 #> 6856  0.258412636 #> 6857  0.258416668 #> 6858  0.258420855 #> 6859  0.258425195 #> 6860  0.258429691 #> 6861  0.258434340 #> 6862  0.258439145 #> 6863  0.258444104 #> 6864  0.258449217 #> 6865  0.258454485 #> 6866  0.258459909 #> 6867  0.258465486 #> 6868  0.258471219 #> 6869  0.258477107 #> 6870  0.258483150 #> 6871  0.258489348 #> 6872  0.258495701 #> 6873  0.258502209 #> 6874  0.258508872 #> 6875  0.258515691 #> 6876  0.258522665 #> 6877  0.258529795 #> 6878  0.258537080 #> 6879  0.258544520 #> 6880  0.258552116 #> 6881  0.258559867 #> 6882  0.258567774 #> 6883  0.258575837 #> 6884  0.258584056 #> 6885  0.258592430 #> 6886  0.258600960 #> 6887  0.258609646 #> 6888  0.258618488 #> 6889  0.258627486 #> 6890  0.258636640 #> 6891  0.258645950 #> 6892  0.258655416 #> 6893  0.258665038 #> 6894  0.258674816 #> 6895  0.258684751 #> 6896  0.258694842 #> 6897  0.258705089 #> 6898  0.258715492 #> 6899  0.258726052 #> 6900  0.258736768 #> 6901  0.258747641 #> 6902  0.258758670 #> 6903  0.258769856 #> 6904  0.258781198 #> 6905  0.258792697 #> 6906  0.258804352 #> 6907  0.258816164 #> 6908  0.258828133 #> 6909  0.258840258 #> 6910  0.258852540 #> 6911  0.258864979 #> 6912  0.258877575 #> 6913  0.258890328 #> 6914  0.258903237 #> 6915  0.258916304 #> 6916  0.258929527 #> 6917  0.258942907 #> 6918  0.258956445 #> 6919  0.258970139 #> 6920  0.258983990 #> 6921  0.258997998 #> 6922  0.259012164 #> 6923  0.259026486 #> 6924  0.259040966 #> 6925  0.259055603 #> 6926  0.259070397 #> 6927  0.259085348 #> 6928  0.259100456 #> 6929  0.259115722 #> 6930  0.259131144 #> 6931  0.259146725 #> 6932  0.259162462 #> 6933  0.259178356 #> 6934  0.259194408 #> 6935  0.259210618 #> 6936  0.259226984 #> 6937  0.259243508 #> 6938  0.259260189 #> 6939  0.259277028 #> 6940  0.259294024 #> 6941  0.259311177 #> 6942  0.259328488 #> 6943  0.259345956 #> 6944  0.259363582 #> 6945  0.259381365 #> 6946  0.259399305 #> 6947  0.259417403 #> 6948  0.259435659 #> 6949  0.259454072 #> 6950  0.259472642 #> 6951  0.259491369 #> 6952  0.259510255 #> 6953  0.259529297 #> 6954  0.259548497 #> 6955  0.259567855 #> 6956  0.259587370 #> 6957  0.259607042 #> 6958  0.259626872 #> 6959  0.259646859 #> 6960  0.259667004 #> 6961  0.259687306 #> 6962  0.259707766 #> 6963  0.259728383 #> 6964  0.259749158 #> 6965  0.259770089 #> 6966  0.259791179 #> 6967  0.259812425 #> 6968  0.259833829 #> 6969  0.259855391 #> 6970  0.259877110 #> 6971  0.259898986 #> 6972  0.259921019 #> 6973  0.259943210 #> 6974  0.259965558 #> 6975  0.259988064 #> 6976  0.260010726 #> 6977  0.260033546 #> 6978  0.260056523 #> 6979  0.260079658 #> 6980  0.260102949 #> 6981  0.260126398 #> 6982  0.260150004 #> 6983  0.260173767 #> 6984  0.260197687 #> 6985  0.260221764 #> 6986  0.260245998 #> 6987  0.260270390 #> 6988  0.260294938 #> 6989  0.260319643 #> 6990  0.260344506 #> 6991  0.260369525 #> 6992  0.260394701 #> 6993  0.260420034 #> 6994  0.260445523 #> 6995  0.260471170 #> 6996  0.260496973 #> 6997  0.260522933 #> 6998  0.260549050 #> 6999  0.260575323 #> 7000  0.260601753 #> 7001  0.260628340 #> 7002  0.260655083 #> 7003  0.260681983 #> 7004  0.260709039 #> 7005  0.260736251 #> 7006  0.260763620 #> 7007  0.260791145 #> 7008  0.260818827 #> 7009  0.260846664 #> 7010  0.260874658 #> 7011  0.260902809 #> 7012  0.260931115 #> 7013  0.260959577 #> 7014  0.260988195 #> 7015  0.261016970 #> 7016  0.261045900 #> 7017  0.261074986 #> 7018  0.261104228 #> 7019  0.261133625 #> 7020  0.261163178 #> 7021  0.261192887 #> 7022  0.261222752 #> 7023  0.261252772 #> 7024  0.261282947 #> 7025  0.261313278 #> 7026  0.261343764 #> 7027  0.261374406 #> 7028  0.261405203 #> 7029  0.261436155 #> 7030  0.261467262 #> 7031  0.261498524 #> 7032  0.261529941 #> 7033  0.261561513 #> 7034  0.261593239 #> 7035  0.261625121 #> 7036  0.261657157 #> 7037  0.261689348 #> 7038  0.261721693 #> 7039  0.261754193 #> 7040  0.261786848 #> 7041  0.261819656 #> 7042  0.261852619 #> 7043  0.261885736 #> 7044  0.261919007 #> 7045  0.261952432 #> 7046  0.261986011 #> 7047  0.262019744 #> 7048  0.262053631 #> 7049  0.262087672 #> 7050  0.262121866 #> 7051  0.262156213 #> 7052  0.262190714 #> 7053  0.262225369 #> 7054  0.262260177 #> 7055  0.262295137 #> 7056  0.262330251 #> 7057  0.262365518 #> 7058  0.262400938 #> 7059  0.262436511 #> 7060  0.262472237 #> 7061  0.262508115 #> 7062  0.262544145 #> 7063  0.262580328 #> 7064  0.262616664 #> 7065  0.262653152 #> 7066  0.262689792 #> 7067  0.262726583 #> 7068  0.262763527 #> 7069  0.262800623 #> 7070  0.262837871 #> 7071  0.262875270 #> 7072  0.262912820 #> 7073  0.262950522 #> 7074  0.262988376 #> 7075  0.263026381 #> 7076  0.263064536 #> 7077  0.263102843 #> 7078  0.263141301 #> 7079  0.263179909 #> 7080  0.263218668 #> 7081  0.263257578 #> 7082  0.263296638 #> 7083  0.263335848 #> 7084  0.263375209 #> 7085  0.263414720 #> 7086  0.263454380 #> 7087  0.263494191 #> 7088  0.263534151 #> 7089  0.263574261 #> 7090  0.263614520 #> 7091  0.263654929 #> 7092  0.263695487 #> 7093  0.263736193 #> 7094  0.263777049 #> 7095  0.263818054 #> 7096  0.263859208 #> 7097  0.263900510 #> 7098  0.263941960 #> 7099  0.263983559 #> 7100  0.264025306 #> 7101  0.264067201 #> 7102  0.264109244 #> 7103  0.264151435 #> 7104  0.264193773 #> 7105  0.264236259 #> 7106  0.264278892 #> 7107  0.264321673 #> 7108  0.264364600 #> 7109  0.264407675 #> 7110  0.264450896 #> 7111  0.264494264 #> 7112  0.264537778 #> 7113  0.264581439 #> 7114  0.264625246 #> 7115  0.264669199 #> 7116  0.264713297 #> 7117  0.264757542 #> 7118  0.264801932 #> 7119  0.264846468 #> 7120  0.264891149 #> 7121  0.264935975 #> 7122  0.264980945 #> 7123  0.265026061 #> 7124  0.265071322 #> 7125  0.265116727 #> 7126  0.265162276 #> 7127  0.265207969 #> 7128  0.265253806 #> 7129  0.265299788 #> 7130  0.265345913 #> 7131  0.265392181 #> 7132  0.265438593 #> 7133  0.265485147 #> 7134  0.265531845 #> 7135  0.265578686 #> 7136  0.265625669 #> 7137  0.265672795 #> 7138  0.265720063 #> 7139  0.265767473 #> 7140  0.265815025 #> 7141  0.265862719 #> 7142  0.265910555 #> 7143  0.265958532 #> 7144  0.266006650 #> 7145  0.266054909 #> 7146  0.266103309 #> 7147  0.266151849 #> 7148  0.266200531 #> 7149  0.266249352 #> 7150  0.266298314 #> 7151  0.266347415 #> 7152  0.266396656 #> 7153  0.266446037 #> 7154  0.266495557 #> 7155  0.266545216 #> 7156  0.266595015 #> 7157  0.266644952 #> 7158  0.266695027 #> 7159  0.266745241 #> 7160  0.266795593 #> 7161  0.266846083 #> 7162  0.266896711 #> 7163  0.266947476 #> 7164  0.266998379 #> 7165  0.267049419 #> 7166  0.267100596 #> 7167  0.267151910 #> 7168  0.267203360 #> 7169  0.267254946 #> 7170  0.267306669 #> 7171  0.267358527 #> 7172  0.267410521 #> 7173  0.267462651 #> 7174  0.267514916 #> 7175  0.267567316 #> 7176  0.267619851 #> 7177  0.267672520 #> 7178  0.267725324 #> 7179  0.267778262 #> 7180  0.267831334 #> 7181  0.267884539 #> 7182  0.267937878 #> 7183  0.267991351 #> 7184  0.268044956 #> 7185  0.268098695 #> 7186  0.268152566 #> 7187  0.268206569 #> 7188  0.268260704 #> 7189  0.268314972 #> 7190  0.268369371 #> 7191  0.268423901 #> 7192  0.268478563 #> 7193  0.268533356 #> 7194  0.268588279 #> 7195  0.268643333 #> 7196  0.268698517 #> 7197  0.268753831 #> 7198  0.268809275 #> 7199  0.268864849 #> 7200  0.268920551 #> 7201  0.268976383 #> 7202  0.269032344 #> 7203  0.269088433 #> 7204  0.269144651 #> 7205  0.269200996 #> 7206  0.269257470 #> 7207  0.269314071 #> 7208  0.269370799 #> 7209  0.269427654 #> 7210  0.269484636 #> 7211  0.269541745 #> 7212  0.269598980 #> 7213  0.269656341 #> 7214  0.269713828 #> 7215  0.269771440 #> 7216  0.269829177 #> 7217  0.269887040 #> 7218  0.269945027 #> 7219  0.270003139 #> 7220  0.270061375 #> 7221  0.270119734 #> 7222  0.270178218 #> 7223  0.270236825 #> 7224  0.270295555 #> 7225  0.270354408 #> 7226  0.270413383 #> 7227  0.270472481 #> 7228  0.270531701 #> 7229  0.270591042 #> 7230  0.270650505 #> 7231  0.270710089 #> 7232  0.270769794 #> 7233  0.270829620 #> 7234  0.270889566 #> 7235  0.270949633 #> 7236  0.271009819 #> 7237  0.271070124 #> 7238  0.271130549 #> 7239  0.271191093 #> 7240  0.271251755 #> 7241  0.271312536 #> 7242  0.271373435 #> 7243  0.271434452 #> 7244  0.271495586 #> 7245  0.271556837 #> 7246  0.271618206 #> 7247  0.271679690 #> 7248  0.271741292 #> 7249  0.271803009 #> 7250  0.271864842 #> 7251  0.271926791 #> 7252  0.271988854 #> 7253  0.272051033 #> 7254  0.272113326 #> 7255  0.272175733 #> 7256  0.272238254 #> 7257  0.272300889 #> 7258  0.272363637 #> 7259  0.272426498 #> 7260  0.272489472 #> 7261  0.272552558 #> 7262  0.272615757 #> 7263  0.272679067 #> 7264  0.272742488 #> 7265  0.272806021 #> 7266  0.272869665 #> 7267  0.272933419 #> 7268  0.272997283 #> 7269  0.273061257 #> 7270  0.273125341 #> 7271  0.273189534 #> 7272  0.273253836 #> 7273  0.273318247 #> 7274  0.273382765 #> 7275  0.273447392 #> 7276  0.273512127 #> 7277  0.273576968 #> 7278  0.273641917 #> 7279  0.273706973 #> 7280  0.273772134 #> 7281  0.273837402 #> 7282  0.273902776 #> 7283  0.273968255 #> 7284  0.274033838 #> 7285  0.274099527 #> 7286  0.274165320 #> 7287  0.274231217 #> 7288  0.274297217 #> 7289  0.274363321 #> 7290  0.274429528 #> 7291  0.274495838 #> 7292  0.274562250 #> 7293  0.274628763 #> 7294  0.274695379 #> 7295  0.274762096 #> 7296  0.274828914 #> 7297  0.274895832 #> 7298  0.274962851 #> 7299  0.275029969 #> 7300  0.275097187 #> 7301  0.275164505 #> 7302  0.275231921 #> 7303  0.275299436 #> 7304  0.275367049 #> 7305  0.275434760 #> 7306  0.275502568 #> 7307  0.275570473 #> 7308  0.275638475 #> 7309  0.275706574 #> 7310  0.275774769 #> 7311  0.275843059 #> 7312  0.275911445 #> 7313  0.275979925 #> 7314  0.276048500 #> 7315  0.276117170 #> 7316  0.276185933 #> 7317  0.276254790 #> 7318  0.276323740 #> 7319  0.276392783 #> 7320  0.276461918 #> 7321  0.276531146 #> 7322  0.276600465 #> 7323  0.276669875 #> 7324  0.276739377 #> 7325  0.276808969 #> 7326  0.276878651 #> 7327  0.276948423 #> 7328  0.277018284 #> 7329  0.277088235 #> 7330  0.277158274 #> 7331  0.277228402 #> 7332  0.277298618 #> 7333  0.277368921 #> 7334  0.277439312 #> 7335  0.277509789 #> 7336  0.277580353 #> 7337  0.277651003 #> 7338  0.277721738 #> 7339  0.277792559 #> 7340  0.277863465 #> 7341  0.277934456 #> 7342  0.278005530 #> 7343  0.278076689 #> 7344  0.278147931 #> 7345  0.278219256 #> 7346  0.278290663 #> 7347  0.278362153 #> 7348  0.278433725 #> 7349  0.278505378 #> 7350  0.278577112 #> 7351  0.278648927 #> 7352  0.278720822 #> 7353  0.278792797 #> 7354  0.278864851 #> 7355  0.278936985 #> 7356  0.279009197 #> 7357  0.279081488 #> 7358  0.279153857 #> 7359  0.279226303 #> 7360  0.279298826 #> 7361  0.279371426 #> 7362  0.279444103 #> 7363  0.279516855 #> 7364  0.279589683 #> 7365  0.279662586 #> 7366  0.279735563 #> 7367  0.279808615 #> 7368  0.279881741 #> 7369  0.279954941 #> 7370  0.280028213 #> 7371  0.280101558 #> 7372  0.280174976 #> 7373  0.280248465 #> 7374  0.280322026 #> 7375  0.280395658 #> 7376  0.280469361 #> 7377  0.280543134 #> 7378  0.280616977 #> 7379  0.280690889 #> 7380  0.280764870 #> 7381  0.280838920 #> 7382  0.280913038 #> 7383  0.280987223 #> 7384  0.281061477 #> 7385  0.281135797 #> 7386  0.281210183 #> 7387  0.281284636 #> 7388  0.281359155 #> 7389  0.281433738 #> 7390  0.281508387 #> 7391  0.281583100 #> 7392  0.281657877 #> 7393  0.281732718 #> 7394  0.281807622 #> 7395  0.281882588 #> 7396  0.281957617 #> 7397  0.282032708 #> 7398  0.282107860 #> 7399  0.282183074 #> 7400  0.282258348 #> 7401  0.282333682 #> 7402  0.282409076 #> 7403  0.282484530 #> 7404  0.282560042 #> 7405  0.282635613 #> 7406  0.282711242 #> 7407  0.282786929 #> 7408  0.282862673 #> 7409  0.282938474 #> 7410  0.283014331 #> 7411  0.283090244 #> 7412  0.283166213 #> 7413  0.283242236 #> 7414  0.283318315 #> 7415  0.283394447 #> 7416  0.283470634 #> 7417  0.283546873 #> 7418  0.283623166 #> 7419  0.283699511 #> 7420  0.283775908 #> 7421  0.283852357 #> 7422  0.283928857 #> 7423  0.284005408 #> 7424  0.284082009 #> 7425  0.284158660 #> 7426  0.284235360 #> 7427  0.284312109 #> 7428  0.284388907 #> 7429  0.284465753 #> 7430  0.284542647 #> 7431  0.284619588 #> 7432  0.284696576 #> 7433  0.284773610 #> 7434  0.284850690 #> 7435  0.284927815 #> 7436  0.285004986 #> 7437  0.285082201 #> 7438  0.285159460 #> 7439  0.285236763 #> 7440  0.285314109 #> 7441  0.285391498 #> 7442  0.285468930 #> 7443  0.285546403 #> 7444  0.285623918 #> 7445  0.285701474 #> 7446  0.285779070 #> 7447  0.285856707 #> 7448  0.285934383 #> 7449  0.286012099 #> 7450  0.286089853 #> 7451  0.286167646 #> 7452  0.286245477 #> 7453  0.286323345 #> 7454  0.286401251 #> 7455  0.286479192 #> 7456  0.286557170 #> 7457  0.286635184 #> 7458  0.286713233 #> 7459  0.286791316 #> 7460  0.286869434 #> 7461  0.286947586 #> 7462  0.287025771 #> 7463  0.287103990 #> 7464  0.287182241 #> 7465  0.287260523 #> 7466  0.287338838 #> 7467  0.287417184 #> 7468  0.287495560 #> 7469  0.287573967 #> 7470  0.287652403 #> 7471  0.287730869 #> 7472  0.287809364 #> 7473  0.287887887 #> 7474  0.287966439 #> 7475  0.288045017 #> 7476  0.288123623 #> 7477  0.288202256 #> 7478  0.288280915 #> 7479  0.288359599 #> 7480  0.288438309 #> 7481  0.288517044 #> 7482  0.288595803 #> 7483  0.288674586 #> 7484  0.288753392 #> 7485  0.288832222 #> 7486  0.288911074 #> 7487  0.288989948 #> 7488  0.289068844 #> 7489  0.289147761 #> 7490  0.289226699 #> 7491  0.289305657 #> 7492  0.289384634 #> 7493  0.289463632 #> 7494  0.289542648 #> 7495  0.289621683 #> 7496  0.289700735 #> 7497  0.289779805 #> 7498  0.289858893 #> 7499  0.289937997 #> 7500  0.290017117 #> 7501  0.290096253 #> 7502  0.290175404 #> 7503  0.290254570 #> 7504  0.290333750 #> 7505  0.290412944 #> 7506  0.290492152 #> 7507  0.290571373 #> 7508  0.290650606 #> 7509  0.290729851 #> 7510  0.290809108 #> 7511  0.290888376 #> 7512  0.290967655 #> 7513  0.291046944 #> 7514  0.291126243 #> 7515  0.291205551 #> 7516  0.291284868 #> 7517  0.291364194 #> 7518  0.291443527 #> 7519  0.291522868 #> 7520  0.291602216 #> 7521  0.291681571 #> 7522  0.291760932 #> 7523  0.291840298 #> 7524  0.291919670 #> 7525  0.291999046 #> 7526  0.292078427 #> 7527  0.292157812 #> 7528  0.292237200 #> 7529  0.292316591 #> 7530  0.292395985 #> 7531  0.292475380 #> 7532  0.292554778 #> 7533  0.292634176 #> 7534  0.292713575 #> 7535  0.292792974 #> 7536  0.292872373 #> 7537  0.292951772 #> 7538  0.293031169 #> 7539  0.293110564 #> 7540  0.293189958 #> 7541  0.293269349 #> 7542  0.293348737 #> 7543  0.293428122 #> 7544  0.293507503 #> 7545  0.293586879 #> 7546  0.293666251 #> 7547  0.293745618 #> 7548  0.293824979 #> 7549  0.293904334 #> 7550  0.293983682 #> 7551  0.294063023 #> 7552  0.294142357 #> 7553  0.294221682 #> 7554  0.294301000 #> 7555  0.294380308 #> 7556  0.294459608 #> 7557  0.294538897 #> 7558  0.294618177 #> 7559  0.294697445 #> 7560  0.294776703 #> 7561  0.294855949 #> 7562  0.294935183 #> 7563  0.295014405 #> 7564  0.295093614 #> 7565  0.295172809 #> 7566  0.295251991 #> 7567  0.295331159 #> 7568  0.295410311 #> 7569  0.295489449 #> 7570  0.295568571 #> 7571  0.295647677 #> 7572  0.295726767 #> 7573  0.295805840 #> 7574  0.295884895 #> 7575  0.295963933 #> 7576  0.296042952 #> 7577  0.296121953 #> 7578  0.296200935 #> 7579  0.296279897 #> 7580  0.296358839 #> 7581  0.296437760 #> 7582  0.296516661 #> 7583  0.296595540 #> 7584  0.296674397 #> 7585  0.296753233 #> 7586  0.296832045 #> 7587  0.296910835 #> 7588  0.296989601 #> 7589  0.297068343 #> 7590  0.297147060 #> 7591  0.297225753 #> 7592  0.297304420 #> 7593  0.297383062 #> 7594  0.297461677 #> 7595  0.297540266 #> 7596  0.297618828 #> 7597  0.297697362 #> 7598  0.297775869 #> 7599  0.297854347 #> 7600  0.297932796 #> 7601  0.298011216 #> 7602  0.298089606 #> 7603  0.298167966 #> 7604  0.298246296 #> 7605  0.298324595 #> 7606  0.298402862 #> 7607  0.298481098 #> 7608  0.298559301 #> 7609  0.298637471 #> 7610  0.298715609 #> 7611  0.298793713 #> 7612  0.298871783 #> 7613  0.298949818 #> 7614  0.299027819 #> 7615  0.299105784 #> 7616  0.299183714 #> 7617  0.299261607 #> 7618  0.299339464 #> 7619  0.299417284 #> 7620  0.299495067 #> 7621  0.299572812 #> 7622  0.299650519 #> 7623  0.299728187 #> 7624  0.299805815 #> 7625  0.299883405 #> 7626  0.299960954 #> 7627  0.300038463 #> 7628  0.300115932 #> 7629  0.300193359 #> 7630  0.300270744 #> 7631  0.300348088 #> 7632  0.300425389 #> 7633  0.300502647 #> 7634  0.300579862 #> 7635  0.300657033 #> 7636  0.300734161 #> 7637  0.300811243 #> 7638  0.300888281 #> 7639  0.300965273 #> 7640  0.301042220 #> 7641  0.301119120 #> 7642  0.301195974 #> 7643  0.301272781 #> 7644  0.301349540 #> 7645  0.301426252 #> 7646  0.301502915 #> 7647  0.301579530 #> 7648  0.301656096 #> 7649  0.301732612 #> 7650  0.301809079 #> 7651  0.301885495 #> 7652  0.301961861 #> 7653  0.302038175 #> 7654  0.302114438 #> 7655  0.302190649 #> 7656  0.302266808 #> 7657  0.302342915 #> 7658  0.302418968 #> 7659  0.302494968 #> 7660  0.302570913 #> 7661  0.302646805 #> 7662  0.302722642 #> 7663  0.302798424 #> 7664  0.302874150 #> 7665  0.302949821 #> 7666  0.303025435 #> 7667  0.303100993 #> 7668  0.303176494 #> 7669  0.303251937 #> 7670  0.303327323 #> 7671  0.303402650 #> 7672  0.303477919 #> 7673  0.303553128 #> 7674  0.303628279 #> 7675  0.303703369 #> 7676  0.303778400 #> 7677  0.303853370 #> 7678  0.303928279 #> 7679  0.304003127 #> 7680  0.304077913 #> 7681  0.304152637 #> 7682  0.304227299 #> 7683  0.304301897 #> 7684  0.304376433 #> 7685  0.304450905 #> 7686  0.304525313 #> 7687  0.304599657 #> 7688  0.304673936 #> 7689  0.304748150 #> 7690  0.304822298 #> 7691  0.304896381 #> 7692  0.304970398 #> 7693  0.305044347 #> 7694  0.305118230 #> 7695  0.305192046 #> 7696  0.305265794 #> 7697  0.305339474 #> 7698  0.305413085 #> 7699  0.305486628 #> 7700  0.305560102 #> 7701  0.305633506 #> 7702  0.305706840 #> 7703  0.305780104 #> 7704  0.305853297 #> 7705  0.305926419 #> 7706  0.305999470 #> 7707  0.306072449 #> 7708  0.306145357 #> 7709  0.306218191 #> 7710  0.306290953 #> 7711  0.306363642 #> 7712  0.306436258 #> 7713  0.306508799 #> 7714  0.306581267 #> 7715  0.306653660 #> 7716  0.306725978 #> 7717  0.306798221 #> 7718  0.306870388 #> 7719  0.306942479 #> 7720  0.307014494 #> 7721  0.307086432 #> 7722  0.307158294 #> 7723  0.307230078 #> 7724  0.307301784 #> 7725  0.307373412 #> 7726  0.307444962 #> 7727  0.307516433 #> 7728  0.307587826 #> 7729  0.307659139 #> 7730  0.307730372 #> 7731  0.307801525 #> 7732  0.307872598 #> 7733  0.307943590 #> 7734  0.308014501 #> 7735  0.308085330 #> 7736  0.308156078 #> 7737  0.308226744 #> 7738  0.308297327 #> 7739  0.308367828 #> 7740  0.308438246 #> 7741  0.308508580 #> 7742  0.308578830 #> 7743  0.308648997 #> 7744  0.308719079 #> 7745  0.308789077 #> 7746  0.308858989 #> 7747  0.308928817 #> 7748  0.308998558 #> 7749  0.309068214 #> 7750  0.309137784 #> 7751  0.309207266 #> 7752  0.309276662 #> 7753  0.309345971 #> 7754  0.309415192 #> 7755  0.309484326 #> 7756  0.309553371 #> 7757  0.309622327 #> 7758  0.309691195 #> 7759  0.309759974 #> 7760  0.309828664 #> 7761  0.309897263 #> 7762  0.309965773 #> 7763  0.310034193 #> 7764  0.310102521 #> 7765  0.310170759 #> 7766  0.310238906 #> 7767  0.310306961 #> 7768  0.310374925 #> 7769  0.310442796 #> 7770  0.310510575 #> 7771  0.310578261 #> 7772  0.310645854 #> 7773  0.310713353 #> 7774  0.310780760 #> 7775  0.310848072 #> 7776  0.310915290 #> 7777  0.310982414 #> 7778  0.311049442 #> 7779  0.311116376 #> 7780  0.311183215 #> 7781  0.311249957 #> 7782  0.311316604 #> 7783  0.311383155 #> 7784  0.311449609 #> 7785  0.311515967 #> 7786  0.311582227 #> 7787  0.311648391 #> 7788  0.311714456 #> 7789  0.311780424 #> 7790  0.311846293 #> 7791  0.311912065 #> 7792  0.311977737 #> 7793  0.312043310 #> 7794  0.312108785 #> 7795  0.312174159 #> 7796  0.312239434 #> 7797  0.312304609 #> 7798  0.312369684 #> 7799  0.312434658 #> 7800  0.312499531 #> 7801  0.312564303 #> 7802  0.312628974 #> 7803  0.312693543 #> 7804  0.312758010 #> 7805  0.312822375 #> 7806  0.312886637 #> 7807  0.312950797 #> 7808  0.313014854 #> 7809  0.313078808 #> 7810  0.313142658 #> 7811  0.313206405 #> 7812  0.313270047 #> 7813  0.313333586 #> 7814  0.313397020 #> 7815  0.313460349 #> 7816  0.313523574 #> 7817  0.313586693 #> 7818  0.313649706 #> 7819  0.313712615 #> 7820  0.313775417 #> 7821  0.313838113 #> 7822  0.313900702 #> 7823  0.313963185 #> 7824  0.314025561 #> 7825  0.314087830 #> 7826  0.314149992 #> 7827  0.314212046 #> 7828  0.314273992 #> 7829  0.314335830 #> 7830  0.314397560 #> 7831  0.314459182 #> 7832  0.314520694 #> 7833  0.314582098 #> 7834  0.314643392 #> 7835  0.314704577 #> 7836  0.314765652 #> 7837  0.314826618 #> 7838  0.314887473 #> 7839  0.314948218 #> 7840  0.315008853 #> 7841  0.315069376 #> 7842  0.315129789 #> 7843  0.315190090 #> 7844  0.315250281 #> 7845  0.315310359 #> 7846  0.315370326 #> 7847  0.315430180 #> 7848  0.315489922 #> 7849  0.315549552 #> 7850  0.315609069 #> 7851  0.315668474 #> 7852  0.315727765 #> 7853  0.315786942 #> 7854  0.315846007 #> 7855  0.315904957 #> 7856  0.315963794 #> 7857  0.316022516 #> 7858  0.316081125 #> 7859  0.316139618 #> 7860  0.316197997 #> 7861  0.316256261 #> 7862  0.316314410 #> 7863  0.316372444 #> 7864  0.316430362 #> 7865  0.316488164 #> 7866  0.316545851 #> 7867  0.316603421 #> 7868  0.316660876 #> 7869  0.316718213 #> 7870  0.316775435 #> 7871  0.316832539 #> 7872  0.316889526 #> 7873  0.316946396 #> 7874  0.317003149 #> 7875  0.317059784 #> 7876  0.317116302 #> 7877  0.317172701 #> 7878  0.317228982 #> 7879  0.317285146 #> 7880  0.317341190 #> 7881  0.317397116 #> 7882  0.317452924 #> 7883  0.317508612 #> 7884  0.317564181 #> 7885  0.317619631 #> 7886  0.317674962 #> 7887  0.317730173 #> 7888  0.317785264 #> 7889  0.317840235 #> 7890  0.317895086 #> 7891  0.317949816 #> 7892  0.318004426 #> 7893  0.318058916 #> 7894  0.318113285 #> 7895  0.318167532 #> 7896  0.318221659 #> 7897  0.318275665 #> 7898  0.318329549 #> 7899  0.318383311 #> 7900  0.318436952 #> 7901  0.318490471 #> 7902  0.318543868 #> 7903  0.318597143 #> 7904  0.318650295 #> 7905  0.318703325 #> 7906  0.318756232 #> 7907  0.318809017 #> 7908  0.318861679 #> 7909  0.318914217 #> 7910  0.318966633 #> 7911  0.319018925 #> 7912  0.319071094 #> 7913  0.319123139 #> 7914  0.319175060 #> 7915  0.319226857 #> 7916  0.319278531 #> 7917  0.319330080 #> 7918  0.319381505 #> 7919  0.319432806 #> 7920  0.319483982 #> 7921  0.319535034 #> 7922  0.319585960 #> 7923  0.319636762 #> 7924  0.319687439 #> 7925  0.319737990 #> 7926  0.319788416 #> 7927  0.319838717 #> 7928  0.319888892 #> 7929  0.319938942 #> 7930  0.319988865 #> 7931  0.320038663 #> 7932  0.320088335 #> 7933  0.320137881 #> 7934  0.320187300 #> 7935  0.320236593 #> 7936  0.320285760 #> 7937  0.320334800 #> 7938  0.320383713 #> 7939  0.320432499 #> 7940  0.320481159 #> 7941  0.320529691 #> 7942  0.320578097 #> 7943  0.320626375 #> 7944  0.320674526 #> 7945  0.320722549 #> 7946  0.320770445 #> 7947  0.320818213 #> 7948  0.320865853 #> 7949  0.320913365 #> 7950  0.320960750 #> 7951  0.321008006 #> 7952  0.321055135 #> 7953  0.321102135 #> 7954  0.321149006 #> 7955  0.321195750 #> 7956  0.321242364 #> 7957  0.321288850 #> 7958  0.321335208 #> 7959  0.321381436 #> 7960  0.321427536 #> 7961  0.321473507 #> 7962  0.321519348 #> 7963  0.321565061 #> 7964  0.321610644 #> 7965  0.321656098 #> 7966  0.321701423 #> 7967  0.321746618 #> 7968  0.321791684 #> 7969  0.321836620 #> 7970  0.321881426 #> 7971  0.321926102 #> 7972  0.321970649 #> 7973  0.322015066 #> 7974  0.322059352 #> 7975  0.322103509 #> 7976  0.322147535 #> 7977  0.322191432 #> 7978  0.322235198 #> 7979  0.322278833 #> 7980  0.322322338 #> 7981  0.322365713 #> 7982  0.322408957 #> 7983  0.322452070 #> 7984  0.322495053 #> 7985  0.322537905 #> 7986  0.322580627 #> 7987  0.322623217 #> 7988  0.322665676 #> 7989  0.322708005 #> 7990  0.322750202 #> 7991  0.322792269 #> 7992  0.322834204 #> 7993  0.322876008 #> 7994  0.322917680 #> 7995  0.322959222 #> 7996  0.323000632 #> 7997  0.323041911 #> 7998  0.323083058 #> 7999  0.323124073 #> 8000  0.323164957 #> 8001  0.323205710 #> 8002  0.323246331 #> 8003  0.323286820 #> 8004  0.323327177 #> 8005  0.323367403 #> 8006  0.323407497 #> 8007  0.323447458 #> 8008  0.323487288 #> 8009  0.323526986 #> 8010  0.323566553 #> 8011  0.323605987 #> 8012  0.323645289 #> 8013  0.323684459 #> 8014  0.323723496 #> 8015  0.323762402 #> 8016  0.323801175 #> 8017  0.323839817 #> 8018  0.323878326 #> 8019  0.323916702 #> 8020  0.323954947 #> 8021  0.323993059 #> 8022  0.324031039 #> 8023  0.324068886 #> 8024  0.324106601 #> 8025  0.324144183 #> 8026  0.324181634 #> 8027  0.324218951 #> 8028  0.324256136 #> 8029  0.324293189 #> 8030  0.324330109 #> 8031  0.324366896 #> 8032  0.324403551 #> 8033  0.324440073 #> 8034  0.324476463 #> 8035  0.324512720 #> 8036  0.324548845 #> 8037  0.324584837 #> 8038  0.324620696 #> 8039  0.324656422 #> 8040  0.324692016 #> 8041  0.324727477 #> 8042  0.324762806 #> 8043  0.324798001 #> 8044  0.324833064 #> 8045  0.324867995 #> 8046  0.324902792 #> 8047  0.324937457 #> 8048  0.324971989 #> 8049  0.325006389 #> 8050  0.325040656 #> 8051  0.325074790 #> 8052  0.325108791 #> 8053  0.325142659 #> 8054  0.325176395 #> 8055  0.325209998 #> 8056  0.325243469 #> 8057  0.325276807 #> 8058  0.325310011 #> 8059  0.325343084 #> 8060  0.325376023 #> 8061  0.325408830 #> 8062  0.325441505 #> 8063  0.325474046 #> 8064  0.325506455 #> 8065  0.325538731 #> 8066  0.325570875 #> 8067  0.325602886 #> 8068  0.325634765 #> 8069  0.325666511 #> 8070  0.325698124 #> 8071  0.325729605 #> 8072  0.325760953 #> 8073  0.325792169 #> 8074  0.325823252 #> 8075  0.325854203 #> 8076  0.325885021 #> 8077  0.325915707 #> 8078  0.325946260 #> 8079  0.325976681 #> 8080  0.326006970 #> 8081  0.326037126 #> 8082  0.326067150 #> 8083  0.326097042 #> 8084  0.326126802 #> 8085  0.326156429 #> 8086  0.326185924 #> 8087  0.326215287 #> 8088  0.326244518 #> 8089  0.326273616 #> 8090  0.326302583 #> 8091  0.326331418 #> 8092  0.326360120 #> 8093  0.326388691 #> 8094  0.326417129 #> 8095  0.326445436 #> 8096  0.326473611 #> 8097  0.326501654 #> 8098  0.326529565 #> 8099  0.326557345 #> 8100  0.326584992 #> 8101  0.326612508 #> 8102  0.326639893 #> 8103  0.326667146 #> 8104  0.326694267 #> 8105  0.326721257 #> 8106  0.326748115 #> 8107  0.326774842 #> 8108  0.326801437 #> 8109  0.326827902 #> 8110  0.326854234 #> 8111  0.326880436 #> 8112  0.326906507 #> 8113  0.326932446 #> 8114  0.326958254 #> 8115  0.326983932 #> 8116  0.327009478 #> 8117  0.327034893 #> 8118  0.327060178 #> 8119  0.327085331 #> 8120  0.327110354 #> 8121  0.327135246 #> 8122  0.327160008 #> 8123  0.327184639 #> 8124  0.327209139 #> 8125  0.327233509 #> 8126  0.327257749 #> 8127  0.327281858 #> 8128  0.327305836 #> 8129  0.327329685 #> 8130  0.327353403 #> 8131  0.327376992 #> 8132  0.327400450 #> 8133  0.327423778 #> 8134  0.327446976 #> 8135  0.327470044 #> 8136  0.327492983 #> 8137  0.327515792 #> 8138  0.327538471 #> 8139  0.327561021 #> 8140  0.327583441 #> 8141  0.327605731 #> 8142  0.327627892 #> 8143  0.327649924 #> 8144  0.327671827 #> 8145  0.327693600 #> 8146  0.327715244 #> 8147  0.327736759 #> 8148  0.327758146 #> 8149  0.327779403 #> 8150  0.327800531 #> 8151  0.327821531 #> 8152  0.327842402 #> 8153  0.327863145 #> 8154  0.327883759 #> 8155  0.327904244 #> 8156  0.327924601 #> 8157  0.327944830 #> 8158  0.327964931 #> 8159  0.327984903 #> 8160  0.328004748 #> 8161  0.328024464 #> 8162  0.328044053 #> 8163  0.328063514 #> 8164  0.328082847 #> 8165  0.328102053 #> 8166  0.328121131 #> 8167  0.328140081 #> 8168  0.328158904 #> 8169  0.328177600 #> 8170  0.328196169 #> 8171  0.328214610 #> 8172  0.328232925 #> 8173  0.328251112 #> 8174  0.328269173 #> 8175  0.328287107 #> 8176  0.328304914 #> 8177  0.328322595 #> 8178  0.328340149 #> 8179  0.328357577 #> 8180  0.328374878 #> 8181  0.328392053 #> 8182  0.328409102 #> 8183  0.328426025 #> 8184  0.328442823 #> 8185  0.328459494 #> 8186  0.328476039 #> 8187  0.328492459 #> 8188  0.328508754 #> 8189  0.328524923 #> 8190  0.328540966 #> 8191  0.328556885 #> 8192  0.328572678 #> 8193  0.328588346 #> 8194  0.328603889 #> 8195  0.328619307 #> 8196  0.328634601 #> 8197  0.328649770 #> 8198  0.328664814 #> 8199  0.328679734 #> 8200  0.328694529 #> 8201  0.328709201 #> 8202  0.328723748 #> 8203  0.328738171 #> 8204  0.328752470 #> 8205  0.328766645 #> 8206  0.328780697 #> 8207  0.328794625 #> 8208  0.328808430 #> 8209  0.328822111 #> 8210  0.328835669 #> 8211  0.328849103 #> 8212  0.328862415 #> 8213  0.328875603 #> 8214  0.328888669 #> 8215  0.328901612 #> 8216  0.328914433 #> 8217  0.328927131 #> 8218  0.328939706 #> 8219  0.328952159 #> 8220  0.328964490 #> 8221  0.328976699 #> 8222  0.328988786 #> 8223  0.329000751 #> 8224  0.329012595 #> 8225  0.329024316 #> 8226  0.329035917 #> 8227  0.329047396 #> 8228  0.329058753 #> 8229  0.329069990 #> 8230  0.329081105 #> 8231  0.329092100 #> 8232  0.329102974 #> 8233  0.329113727 #> 8234  0.329124359 #> 8235  0.329134872 #> 8236  0.329145263 #> 8237  0.329155535 #> 8238  0.329165687 #> 8239  0.329175718 #> 8240  0.329185630 #> 8241  0.329195422 #> 8242  0.329205094 #> 8243  0.329214647 #> 8244  0.329224081 #> 8245  0.329233396 #> 8246  0.329242591 #> 8247  0.329251667 #> 8248  0.329260625 #> 8249  0.329269464 #> 8250  0.329278184 #> 8251  0.329286786 #> 8252  0.329295270 #> 8253  0.329303635 #> 8254  0.329311882 #> 8255  0.329320011 #> 8256  0.329328023 #> 8257  0.329335916 #> 8258  0.329343693 #> 8259  0.329351351 #> 8260  0.329358893 #> 8261  0.329366317 #> 8262  0.329373624 #> 8263  0.329380815 #> 8264  0.329387888 #> 8265  0.329394845 #> 8266  0.329401685 #> 8267  0.329408409 #> 8268  0.329415017 #> 8269  0.329421508 #> 8270  0.329427884 #> 8271  0.329434144 #> 8272  0.329440288 #> 8273  0.329446316 #> 8274  0.329452229 #> 8275  0.329458027 #> 8276  0.329463709 #> 8277  0.329469277 #> 8278  0.329474729 #> 8279  0.329480067 #> 8280  0.329485290 #> 8281  0.329490399 #> 8282  0.329495393 #> 8283  0.329500273 #> 8284  0.329505039 #> 8285  0.329509691 #> 8286  0.329514230 #> 8287  0.329518654 #> 8288  0.329522965 #> 8289  0.329527163 #> 8290  0.329531247 #> 8291  0.329535219 #> 8292  0.329539077 #> 8293  0.329542822 #> 8294  0.329546455 #> 8295  0.329549976 #> 8296  0.329553384 #> 8297  0.329556679 #> 8298  0.329559863 #> 8299  0.329562934 #> 8300  0.329565894 #> 8301  0.329568742 #> 8302  0.329571479 #> 8303  0.329574104 #> 8304  0.329576618 #> 8305  0.329579021 #> 8306  0.329581313 #> 8307  0.329583494 #> 8308  0.329585564 #> 8309  0.329587524 #> 8310  0.329589373 #> 8311  0.329591113 #> 8312  0.329592742 #> 8313  0.329594261 #> 8314  0.329595670 #> 8315  0.329596970 #> 8316  0.329598161 #> 8317  0.329599242 #> 8318  0.329600213 #> 8319  0.329601076 #> 8320  0.329601830 #> 8321  0.329602475 #> 8322  0.329603012 #> 8323  0.329603440 #> 8324  0.329603759 #> 8325  0.329603971 #> 8326  0.329604075 #> 8327  0.329604070 #> 8328  0.329603959 #> 8329  0.329603739 #> 8330  0.329603412 #> 8331  0.329602978 #> 8332  0.329602437 #> 8333  0.329601789 #> 8334  0.329601034 #> 8335  0.329600172 #> 8336  0.329599204 #> 8337  0.329598130 #> 8338  0.329596950 #> 8339  0.329595663 #> 8340  0.329594271 #> 8341  0.329592773 #> 8342  0.329591169 #> 8343  0.329589460 #> 8344  0.329587646 #> 8345  0.329585726 #> 8346  0.329583702 #> 8347  0.329581573 #> 8348  0.329579339 #> 8349  0.329577001 #> 8350  0.329574558 #> 8351  0.329572012 #> 8352  0.329569361 #> 8353  0.329566607 #> 8354  0.329563748 #> 8355  0.329560787 #> 8356  0.329557722 #> 8357  0.329554553 #> 8358  0.329551282 #> 8359  0.329547908 #> 8360  0.329544431 #> 8361  0.329540851 #> 8362  0.329537169 #> 8363  0.329533385 #> 8364  0.329529498 #> 8365  0.329525510 #> 8366  0.329521420 #> 8367  0.329517228 #> 8368  0.329512935 #> 8369  0.329508540 #> 8370  0.329504045 #> 8371  0.329499448 #> 8372  0.329494750 #> 8373  0.329489952 #> 8374  0.329485054 #> 8375  0.329480054 #> 8376  0.329474955 #> 8377  0.329469756 #> 8378  0.329464457 #> 8379  0.329459058 #> 8380  0.329453560 #> 8381  0.329447962 #> 8382  0.329442265 #> 8383  0.329436469 #> 8384  0.329430574 #> 8385  0.329424580 #> 8386  0.329418488 #> 8387  0.329412297 #> 8388  0.329406008 #> 8389  0.329399621 #> 8390  0.329393136 #> 8391  0.329386554 #> 8392  0.329379873 #> 8393  0.329373096 #> 8394  0.329366221 #> 8395  0.329359249 #> 8396  0.329352180 #> 8397  0.329345014 #> 8398  0.329337752 #> 8399  0.329330393 #> 8400  0.329322938 #> 8401  0.329315386 #> 8402  0.329307739 #> 8403  0.329299996 #> 8404  0.329292158 #> 8405  0.329284223 #> 8406  0.329276194 #> 8407  0.329268070 #> 8408  0.329259850 #> 8409  0.329251536 #> 8410  0.329243127 #> 8411  0.329234624 #> 8412  0.329226026 #> 8413  0.329217334 #> 8414  0.329208548 #> 8415  0.329199669 #> 8416  0.329190696 #> 8417  0.329181629 #> 8418  0.329172469 #> 8419  0.329163216 #> 8420  0.329153870 #> 8421  0.329144431 #> 8422  0.329134899 #> 8423  0.329125275 #> 8424  0.329115559 #> 8425  0.329105750 #> 8426  0.329095850 #> 8427  0.329085858 #> 8428  0.329075774 #> 8429  0.329065599 #> 8430  0.329055332 #> 8431  0.329044974 #> 8432  0.329034526 #> 8433  0.329023986 #> 8434  0.329013356 #> 8435  0.329002635 #> 8436  0.328991824 #> 8437  0.328980923 #> 8438  0.328969932 #> 8439  0.328958852 #> 8440  0.328947681 #> 8441  0.328936421 #> 8442  0.328925072 #> 8443  0.328913634 #> 8444  0.328902107 #> 8445  0.328890491 #> 8446  0.328878786 #> 8447  0.328866993 #> 8448  0.328855112 #> 8449  0.328843143 #> 8450  0.328831085 #> 8451  0.328818940 #> 8452  0.328806708 #> 8453  0.328794388 #> 8454  0.328781980 #> 8455  0.328769486 #> 8456  0.328756905 #> 8457  0.328744237 #> 8458  0.328731482 #> 8459  0.328718641 #> 8460  0.328705714 #> 8461  0.328692700 #> 8462  0.328679601 #> 8463  0.328666416 #> 8464  0.328653145 #> 8465  0.328639789 #> 8466  0.328626348 #> 8467  0.328612822 #> 8468  0.328599211 #> 8469  0.328585515 #> 8470  0.328571735 #> 8471  0.328557870 #> 8472  0.328543921 #> 8473  0.328529888 #> 8474  0.328515772 #> 8475  0.328501571 #> 8476  0.328487287 #> 8477  0.328472920 #> 8478  0.328458469 #> 8479  0.328443936 #> 8480  0.328429319 #> 8481  0.328414620 #> 8482  0.328399839 #> 8483  0.328384975 #> 8484  0.328370029 #> 8485  0.328355001 #> 8486  0.328339891 #> 8487  0.328324700 #> 8488  0.328309427 #> 8489  0.328294073 #> 8490  0.328278638 #> 8491  0.328263121 #> 8492  0.328247524 #> 8493  0.328231847 #> 8494  0.328216088 #> 8495  0.328200250 #> 8496  0.328184332 #> 8497  0.328168333 #> 8498  0.328152255 #> 8499  0.328136097 #> 8500  0.328119860 #> 8501  0.328103543 #> 8502  0.328087148 #> 8503  0.328070673 #> 8504  0.328054120 #> 8505  0.328037488 #> 8506  0.328020778 #> 8507  0.328003990 #> 8508  0.327987123 #> 8509  0.327970179 #> 8510  0.327953157 #> 8511  0.327936057 #> 8512  0.327918880 #> 8513  0.327901626 #> 8514  0.327884295 #> 8515  0.327866887 #> 8516  0.327849402 #> 8517  0.327831841 #> 8518  0.327814203 #> 8519  0.327796490 #> 8520  0.327778700 #> 8521  0.327760834 #> 8522  0.327742893 #> 8523  0.327724876 #> 8524  0.327706784 #> 8525  0.327688617 #> 8526  0.327670375 #> 8527  0.327652057 #> 8528  0.327633666 #> 8529  0.327615200 #> 8530  0.327596659 #> 8531  0.327578045 #> 8532  0.327559356 #> 8533  0.327540594 #> 8534  0.327521758 #> 8535  0.327502848 #> 8536  0.327483866 #> 8537  0.327464810 #> 8538  0.327445681 #> 8539  0.327426480 #> 8540  0.327407206 #> 8541  0.327387859 #> 8542  0.327368440 #> 8543  0.327348950 #> 8544  0.327329387 #> 8545  0.327309752 #> 8546  0.327290046 #> 8547  0.327270269 #> 8548  0.327250420 #> 8549  0.327230500 #> 8550  0.327210510 #> 8551  0.327190448 #> 8552  0.327170317 #> 8553  0.327150114 #> 8554  0.327129842 #> 8555  0.327109499 #> 8556  0.327089087 #> 8557  0.327068605 #> 8558  0.327048053 #> 8559  0.327027432 #> 8560  0.327006742 #> 8561  0.326985983 #> 8562  0.326965154 #> 8563  0.326944258 #> 8564  0.326923292 #> 8565  0.326902259 #> 8566  0.326881157 #> 8567  0.326859987 #> 8568  0.326838750 #> 8569  0.326817444 #> 8570  0.326796071 #> 8571  0.326774631 #> 8572  0.326753124 #> 8573  0.326731550 #> 8574  0.326709909 #> 8575  0.326688201 #> 8576  0.326666427 #> 8577  0.326644586 #> 8578  0.326622679 #> 8579  0.326600707 #> 8580  0.326578668 #> 8581  0.326556564 #> 8582  0.326534394 #> 8583  0.326512159 #> 8584  0.326489859 #> 8585  0.326467494 #> 8586  0.326445065 #> 8587  0.326422570 #> 8588  0.326400011 #> 8589  0.326377388 #> 8590  0.326354701 #> 8591  0.326331949 #> 8592  0.326309134 #> 8593  0.326286256 #> 8594  0.326263314 #> 8595  0.326240308 #> 8596  0.326217240 #> 8597  0.326194108 #> 8598  0.326170914 #> 8599  0.326147657 #> 8600  0.326124338 #> 8601  0.326100956 #> 8602  0.326077512 #> 8603  0.326054007 #> 8604  0.326030439 #> 8605  0.326006810 #> 8606  0.325983120 #> 8607  0.325959368 #> 8608  0.325935555 #> 8609  0.325911681 #> 8610  0.325887746 #> 8611  0.325863751 #> 8612  0.325839696 #> 8613  0.325815580 #> 8614  0.325791404 #> 8615  0.325767168 #> 8616  0.325742872 #> 8617  0.325718516 #> 8618  0.325694102 #> 8619  0.325669627 #> 8620  0.325645094 #> 8621  0.325620502 #> 8622  0.325595851 #> 8623  0.325571141 #> 8624  0.325546373 #> 8625  0.325521547 #> 8626  0.325496663 #> 8627  0.325471720 #> 8628  0.325446720 #> 8629  0.325421662 #> 8630  0.325396547 #> 8631  0.325371375 #> 8632  0.325346145 #> 8633  0.325320858 #> 8634  0.325295515 #> 8635  0.325270115 #> 8636  0.325244658 #> 8637  0.325219145 #> 8638  0.325193576 #> 8639  0.325167951 #> 8640  0.325142271 #> 8641  0.325116534 #> 8642  0.325090742 #> 8643  0.325064895 #> 8644  0.325038993 #> 8645  0.325013035 #> 8646  0.324987023 #> 8647  0.324960956 #> 8648  0.324934835 #> 8649  0.324908659 #> 8650  0.324882429 #> 8651  0.324856145 #> 8652  0.324829808 #> 8653  0.324803416 #> 8654  0.324776971 #> 8655  0.324750473 #> 8656  0.324723921 #> 8657  0.324697317 #> 8658  0.324670659 #> 8659  0.324643949 #> 8660  0.324617187 #> 8661  0.324590372 #> 8662  0.324563504 #> 8663  0.324536585 #> 8664  0.324509614 #> 8665  0.324482591 #> 8666  0.324455516 #> 8667  0.324428390 #> 8668  0.324401213 #> 8669  0.324373985 #> 8670  0.324346706 #> 8671  0.324319376 #> 8672  0.324291995 #> 8673  0.324264564 #> 8674  0.324237083 #> 8675  0.324209551 #> 8676  0.324181970 #> 8677  0.324154339 #> 8678  0.324126658 #> 8679  0.324098928 #> 8680  0.324071148 #> 8681  0.324043319 #> 8682  0.324015441 #> 8683  0.323987515 #> 8684  0.323959539 #> 8685  0.323931516 #> 8686  0.323903443 #> 8687  0.323875323 #> 8688  0.323847155 #> 8689  0.323818938 #> 8690  0.323790674 #> 8691  0.323762363 #> 8692  0.323734004 #> 8693  0.323705598 #> 8694  0.323677144 #> 8695  0.323648644 #> 8696  0.323620097 #> 8697  0.323591503 #> 8698  0.323562863 #> 8699  0.323534177 #> 8700  0.323505444 #> 8701  0.323476666 #> 8702  0.323447842 #> 8703  0.323418972 #> 8704  0.323390056 #> 8705  0.323361095 #> 8706  0.323332089 #> 8707  0.323303038 #> 8708  0.323273943 #> 8709  0.323244802 #> 8710  0.323215617 #> 8711  0.323186387 #> 8712  0.323157113 #> 8713  0.323127795 #> 8714  0.323098434 #> 8715  0.323069028 #> 8716  0.323039579 #> 8717  0.323010086 #> 8718  0.322980550 #> 8719  0.322950971 #> 8720  0.322921349 #> 8721  0.322891684 #> 8722  0.322861976 #> 8723  0.322832226 #> 8724  0.322802433 #> 8725  0.322772599 #> 8726  0.322742722 #> 8727  0.322712803 #> 8728  0.322682843 #> 8729  0.322652841 #> 8730  0.322622797 #> 8731  0.322592712 #> 8732  0.322562586 #> 8733  0.322532419 #> 8734  0.322502212 #> 8735  0.322471963 #> 8736  0.322441674 #> 8737  0.322411345 #> 8738  0.322380975 #> 8739  0.322350566 #> 8740  0.322320116 #> 8741  0.322289627 #> 8742  0.322259098 #> 8743  0.322228530 #> 8744  0.322197923 #> 8745  0.322167276 #> 8746  0.322136590 #> 8747  0.322105866 #> 8748  0.322075103 #> 8749  0.322044301 #> 8750  0.322013461 #> 8751  0.321982583 #> 8752  0.321951666 #> 8753  0.321920712 #> 8754  0.321889720 #> 8755  0.321858690 #> 8756  0.321827623 #> 8757  0.321796519 #> 8758  0.321765377 #> 8759  0.321734199 #> 8760  0.321702983 #> 8761  0.321671731 #> 8762  0.321640442 #> 8763  0.321609117 #> 8764  0.321577756 #> 8765  0.321546359 #> 8766  0.321514925 #> 8767  0.321483456 #> 8768  0.321451951 #> 8769  0.321420411 #> 8770  0.321388835 #> 8771  0.321357224 #> 8772  0.321325578 #> 8773  0.321293897 #> 8774  0.321262182 #> 8775  0.321230431 #> 8776  0.321198647 #> 8777  0.321166828 #> 8778  0.321134974 #> 8779  0.321103087 #> 8780  0.321071166 #> 8781  0.321039211 #> 8782  0.321007223 #> 8783  0.320975201 #> 8784  0.320943146 #> 8785  0.320911058 #> 8786  0.320878937 #> 8787  0.320846783 #> 8788  0.320814597 #> 8789  0.320782377 #> 8790  0.320750126 #> 8791  0.320717842 #> 8792  0.320685526 #> 8793  0.320653179 #> 8794  0.320620799 #> 8795  0.320588388 #> 8796  0.320555945 #> 8797  0.320523471 #> 8798  0.320490966 #> 8799  0.320458429 #> 8800  0.320425862 #> 8801  0.320393264 #> 8802  0.320360635 #> 8803  0.320327976 #> 8804  0.320295286 #> 8805  0.320262567 #> 8806  0.320229817 #> 8807  0.320197037 #> 8808  0.320164228 #> 8809  0.320131389 #> 8810  0.320098520 #> 8811  0.320065623 #> 8812  0.320032696 #> 8813  0.319999740 #> 8814  0.319966755 #> 8815  0.319933741 #> 8816  0.319900699 #> 8817  0.319867628 #> 8818  0.319834529 #> 8819  0.319801402 #> 8820  0.319768247 #> 8821  0.319735063 #> 8822  0.319701853 #> 8823  0.319668614 #> 8824  0.319635348 #> 8825  0.319602055 #> 8826  0.319568735 #> 8827  0.319535387 #> 8828  0.319502013 #> 8829  0.319468612 #> 8830  0.319435184 #> 8831  0.319401730 #> 8832  0.319368249 #> 8833  0.319334743 #> 8834  0.319301210 #> 8835  0.319267652 #> 8836  0.319234067 #> 8837  0.319200457 #> 8838  0.319166822 #> 8839  0.319133161 #> 8840  0.319099475 #> 8841  0.319065764 #> 8842  0.319032028 #> 8843  0.318998268 #> 8844  0.318964482 #> 8845  0.318930673 #> 8846  0.318896839 #> 8847  0.318862980 #> 8848  0.318829098 #> 8849  0.318795191 #> 8850  0.318761261 #> 8851  0.318727308 #> 8852  0.318693330 #> 8853  0.318659330 #> 8854  0.318625306 #> 8855  0.318591259 #> 8856  0.318557189 #> 8857  0.318523096 #> 8858  0.318488980 #> 8859  0.318454842 #> 8860  0.318420682 #> 8861  0.318386499 #> 8862  0.318352294 #> 8863  0.318318068 #> 8864  0.318283819 #> 8865  0.318249549 #> 8866  0.318215257 #> 8867  0.318180943 #> 8868  0.318146608 #> 8869  0.318112253 #> 8870  0.318077876 #> 8871  0.318043478 #> 8872  0.318009059 #> 8873  0.317974620 #> 8874  0.317940160 #> 8875  0.317905680 #> 8876  0.317871180 #> 8877  0.317836659 #> 8878  0.317802119 #> 8879  0.317767559 #> 8880  0.317732979 #> 8881  0.317698380 #> 8882  0.317663761 #> 8883  0.317629123 #> 8884  0.317594466 #> 8885  0.317559790 #> 8886  0.317525095 #> 8887  0.317490381 #> 8888  0.317455649 #> 8889  0.317420898 #> 8890  0.317386129 #> 8891  0.317351341 #> 8892  0.317316536 #> 8893  0.317281712 #> 8894  0.317246871 #> 8895  0.317212012 #> 8896  0.317177136 #> 8897  0.317142242 #> 8898  0.317107331 #> 8899  0.317072403 #> 8900  0.317037458 #> 8901  0.317002496 #> 8902  0.316967517 #> 8903  0.316932522 #> 8904  0.316897510 #> 8905  0.316862482 #> 8906  0.316827437 #> 8907  0.316792377 #> 8908  0.316757301 #> 8909  0.316722208 #> 8910  0.316687101 #> 8911  0.316651977 #> 8912  0.316616838 #> 8913  0.316581684 #> 8914  0.316546515 #> 8915  0.316511330 #> 8916  0.316476131 #> 8917  0.316440917 #> 8918  0.316405688 #> 8919  0.316370445 #> 8920  0.316335188 #> 8921  0.316299916 #> 8922  0.316264630 #> 8923  0.316229330 #> 8924  0.316194016 #> 8925  0.316158689 #> 8926  0.316123348 #> 8927  0.316087993 #> 8928  0.316052625 #> 8929  0.316017244 #> 8930  0.315981850 #> 8931  0.315946443 #> 8932  0.315911023 #> 8933  0.315875590 #> 8934  0.315840145 #> 8935  0.315804687 #> 8936  0.315769217 #> 8937  0.315733735 #> 8938  0.315698240 #> 8939  0.315662734 #> 8940  0.315627216 #> 8941  0.315591686 #> 8942  0.315556145 #> 8943  0.315520592 #> 8944  0.315485028 #> 8945  0.315449453 #> 8946  0.315413867 #> 8947  0.315378270 #> 8948  0.315342662 #> 8949  0.315307043 #> 8950  0.315271414 #> 8951  0.315235774 #> 8952  0.315200124 #> 8953  0.315164464 #> 8954  0.315128794 #> 8955  0.315093114 #> 8956  0.315057424 #> 8957  0.315021725 #> 8958  0.314986016 #> 8959  0.314950297 #> 8960  0.314914570 #> 8961  0.314878833 #> 8962  0.314843087 #> 8963  0.314807332 #> 8964  0.314771568 #> 8965  0.314735796 #> 8966  0.314700015 #> 8967  0.314664225 #> 8968  0.314628428 #> 8969  0.314592622 #> 8970  0.314556808 #> 8971  0.314520986 #> 8972  0.314485156 #> 8973  0.314449319 #> 8974  0.314413474 #> 8975  0.314377622 #> 8976  0.314341762 #> 8977  0.314305895 #> 8978  0.314270021 #> 8979  0.314234140 #> 8980  0.314198253 #> 8981  0.314162358 #> 8982  0.314126457 #> 8983  0.314090550 #> 8984  0.314054636 #> 8985  0.314018716 #> 8986  0.313982789 #> 8987  0.313946857 #> 8988  0.313910919 #> 8989  0.313874975 #> 8990  0.313839026 #> 8991  0.313803071 #> 8992  0.313767111 #> 8993  0.313731145 #> 8994  0.313695175 #> 8995  0.313659199 #> 8996  0.313623218 #> 8997  0.313587233 #> 8998  0.313551243 #> 8999  0.313515248 #> 9000  0.313479249 #> 9001  0.313443246 #> 9002  0.313407238 #> 9003  0.313371227 #> 9004  0.313335211 #> 9005  0.313299192 #> 9006  0.313263168 #> 9007  0.313227142 #> 9008  0.313191112 #> 9009  0.313155078 #> 9010  0.313119041 #> 9011  0.313083001 #> 9012  0.313046959 #> 9013  0.313010913 #> 9014  0.312974864 #> 9015  0.312938813 #> 9016  0.312902759 #> 9017  0.312866703 #> 9018  0.312830645 #> 9019  0.312794584 #> 9020  0.312758521 #> 9021  0.312722456 #> 9022  0.312686390 #> 9023  0.312650322 #> 9024  0.312614252 #> 9025  0.312578181 #> 9026  0.312542108 #> 9027  0.312506034 #> 9028  0.312469959 #> 9029  0.312433883 #> 9030  0.312397806 #> 9031  0.312361728 #> 9032  0.312325649 #> 9033  0.312289570 #> 9034  0.312253491 #> 9035  0.312217411 #> 9036  0.312181331 #> 9037  0.312145250 #> 9038  0.312109170 #> 9039  0.312073090 #> 9040  0.312037010 #> 9041  0.312000931 #> 9042  0.311964852 #> 9043  0.311928773 #> 9044  0.311892696 #> 9045  0.311856619 #> 9046  0.311820543 #> 9047  0.311784468 #> 9048  0.311748394 #> 9049  0.311712321 #> 9050  0.311676250 #> 9051  0.311640180 #> 9052  0.311604112 #> 9053  0.311568045 #> 9054  0.311531981 #> 9055  0.311495918 #> 9056  0.311459857 #> 9057  0.311423799 #> 9058  0.311387743 #> 9059  0.311351689 #> 9060  0.311315637 #> 9061  0.311279589 #> 9062  0.311243542 #> 9063  0.311207499 #> 9064  0.311171459 #> 9065  0.311135422 #> 9066  0.311099387 #> 9067  0.311063357 #> 9068  0.311027329 #> 9069  0.310991305 #> 9070  0.310955285 #> 9071  0.310919268 #> 9072  0.310883255 #> 9073  0.310847246 #> 9074  0.310811241 #> 9075  0.310775240 #> 9076  0.310739244 #> 9077  0.310703252 #> 9078  0.310667264 #> 9079  0.310631281 #> 9080  0.310595302 #> 9081  0.310559329 #> 9082  0.310523360 #> 9083  0.310487396 #> 9084  0.310451438 #> 9085  0.310415484 #> 9086  0.310379536 #> 9087  0.310343594 #> 9088  0.310307657 #> 9089  0.310271726 #> 9090  0.310235800 #> 9091  0.310199880 #> 9092  0.310163967 #> 9093  0.310128059 #> 9094  0.310092158 #> 9095  0.310056263 #> 9096  0.310020374 #> 9097  0.309984492 #> 9098  0.309948616 #> 9099  0.309912747 #> 9100  0.309876886 #> 9101  0.309841031 #> 9102  0.309805183 #> 9103  0.309769342 #> 9104  0.309733508 #> 9105  0.309697682 #> 9106  0.309661864 #> 9107  0.309626052 #> 9108  0.309590249 #> 9109  0.309554453 #> 9110  0.309518666 #> 9111  0.309482886 #> 9112  0.309447115 #> 9113  0.309411351 #> 9114  0.309375596 #> 9115  0.309339850 #> 9116  0.309304112 #> 9117  0.309268382 #> 9118  0.309232661 #> 9119  0.309196949 #> 9120  0.309161247 #> 9121  0.309125553 #> 9122  0.309089868 #> 9123  0.309054193 #> 9124  0.309018526 #> 9125  0.308982870 #> 9126  0.308947223 #> 9127  0.308911585 #> 9128  0.308875958 #> 9129  0.308840340 #> 9130  0.308804732 #> 9131  0.308769134 #> 9132  0.308733547 #> 9133  0.308697970 #> 9134  0.308662403 #> 9135  0.308626847 #> 9136  0.308591301 #> 9137  0.308555766 #> 9138  0.308520241 #> 9139  0.308484728 #> 9140  0.308449226 #> 9141  0.308413734 #> 9142  0.308378254 #> 9143  0.308342786 #> 9144  0.308307328 #> 9145  0.308271882 #> 9146  0.308236448 #> 9147  0.308201026 #> 9148  0.308165615 #> 9149  0.308130216 #> 9150  0.308094829 #> 9151  0.308059455 #> 9152  0.308024092 #> 9153  0.307988742 #> 9154  0.307953404 #> 9155  0.307918079 #> 9156  0.307882766 #> 9157  0.307847466 #> 9158  0.307812179 #> 9159  0.307776905 #> 9160  0.307741644 #> 9161  0.307706396 #> 9162  0.307671161 #> 9163  0.307635939 #> 9164  0.307600731 #> 9165  0.307565536 #> 9166  0.307530355 #> 9167  0.307495188 #> 9168  0.307460034 #> 9169  0.307424894 #> 9170  0.307389768 #> 9171  0.307354657 #> 9172  0.307319559 #> 9173  0.307284476 #> 9174  0.307249407 #> 9175  0.307214353 #> 9176  0.307179313 #> 9177  0.307144288 #> 9178  0.307109278 #> 9179  0.307074282 #> 9180  0.307039302 #> 9181  0.307004336 #> 9182  0.306969386 #> 9183  0.306934451 #> 9184  0.306899531 #> 9185  0.306864627 #> 9186  0.306829739 #> 9187  0.306794865 #> 9188  0.306760008 #> 9189  0.306725167 #> 9190  0.306690341 #> 9191  0.306655532 #> 9192  0.306620738 #> 9193  0.306585961 #> 9194  0.306551200 #> 9195  0.306516456 #> 9196  0.306481727 #> 9197  0.306447016 #> 9198  0.306412321 #> 9199  0.306377643 #> 9200  0.306342982 #> 9201  0.306308338 #> 9202  0.306273711 #> 9203  0.306239101 #> 9204  0.306204509 #> 9205  0.306169933 #> 9206  0.306135375 #> 9207  0.306100835 #> 9208  0.306066312 #> 9209  0.306031807 #> 9210  0.305997320 #> 9211  0.305962851 #> 9212  0.305928400 #> 9213  0.305893967 #> 9214  0.305859552 #> 9215  0.305825155 #> 9216  0.305790776 #> 9217  0.305756417 #> 9218  0.305722075 #> 9219  0.305687753 #> 9220  0.305653449 #> 9221  0.305619163 #> 9222  0.305584897 #> 9223  0.305550650 #> 9224  0.305516422 #> 9225  0.305482213 #> 9226  0.305448024 #> 9227  0.305413853 #> 9228  0.305379703 #> 9229  0.305345571 #> 9230  0.305311460 #> 9231  0.305277368 #> 9232  0.305243296 #> 9233  0.305209244 #> 9234  0.305175212 #> 9235  0.305141200 #> 9236  0.305107209 #> 9237  0.305073237 #> 9238  0.305039286 #> 9239  0.305005356 #> 9240  0.304971445 #> 9241  0.304937556 #> 9242  0.304903687 #> 9243  0.304869840 #> 9244  0.304836013 #> 9245  0.304802207 #> 9246  0.304768422 #> 9247  0.304734658 #> 9248  0.304700916 #> 9249  0.304667195 #> 9250  0.304633496 #> 9251  0.304599818 #> 9252  0.304566161 #> 9253  0.304532527 #> 9254  0.304498914 #> 9255  0.304465323 #> 9256  0.304431754 #> 9257  0.304398207 #> 9258  0.304364682 #> 9259  0.304331179 #> 9260  0.304297699 #> 9261  0.304264241 #> 9262  0.304230806 #> 9263  0.304197393 #> 9264  0.304164003 #> 9265  0.304130636 #> 9266  0.304097292 #> 9267  0.304063970 #> 9268  0.304030672 #> 9269  0.303997397 #> 9270  0.303964144 #> 9271  0.303930916 #> 9272  0.303897710 #> 9273  0.303864528 #> 9274  0.303831370 #> 9275  0.303798235 #> 9276  0.303765124 #> 9277  0.303732036 #> 9278  0.303698973 #> 9279  0.303665933 #> 9280  0.303632918 #> 9281  0.303599927 #> 9282  0.303566959 #> 9283  0.303534017 #> 9284  0.303501098 #> 9285  0.303468204 #> 9286  0.303435335 #> 9287  0.303402490 #> 9288  0.303369671 #> 9289  0.303336875 #> 9290  0.303304105 #> 9291  0.303271360 #> 9292  0.303238640 #> 9293  0.303205945 #> 9294  0.303173275 #> 9295  0.303140631 #> 9296  0.303108011 #> 9297  0.303075418 #> 9298  0.303042850 #> 9299  0.303010308 #> 9300  0.302977791 #> 9301  0.302945300 #> 9302  0.302912835 #> 9303  0.302880396 #> 9304  0.302847984 #> 9305  0.302815597 #> 9306  0.302783236 #> 9307  0.302750902 #> 9308  0.302718595 #> 9309  0.302686313 #> 9310  0.302654059 #> 9311  0.302621831 #> 9312  0.302589629 #> 9313  0.302557455 #> 9314  0.302525307 #> 9315  0.302493186 #> 9316  0.302461093 #> 9317  0.302429026 #> 9318  0.302396987 #> 9319  0.302364975 #> 9320  0.302332990 #> 9321  0.302301033 #> 9322  0.302269104 #> 9323  0.302237202 #> 9324  0.302205327 #> 9325  0.302173481 #> 9326  0.302141662 #> 9327  0.302109871 #> 9328  0.302078109 #> 9329  0.302046374 #> 9330  0.302014668 #> 9331  0.301982989 #> 9332  0.301951340 #> 9333  0.301919718 #> 9334  0.301888125 #> 9335  0.301856561 #> 9336  0.301825025 #> 9337  0.301793518 #> 9338  0.301762040 #> 9339  0.301730590 #> 9340  0.301699170 #> 9341  0.301667779 #> 9342  0.301636417 #> 9343  0.301605084 #> 9344  0.301573780 #> 9345  0.301542505 #> 9346  0.301511261 #> 9347  0.301480045 #> 9348  0.301448859 #> 9349  0.301417703 #> 9350  0.301386577 #> 9351  0.301355480 #> 9352  0.301324413 #> 9353  0.301293377 #> 9354  0.301262370 #> 9355  0.301231394 #> 9356  0.301200447 #> 9357  0.301169531 #> 9358  0.301138645 #> 9359  0.301107790 #> 9360  0.301076965 #> 9361  0.301046171 #> 9362  0.301015408 #> 9363  0.300984675 #> 9364  0.300953973 #> 9365  0.300923302 #> 9366  0.300892662 #> 9367  0.300862053 #> 9368  0.300831475 #> 9369  0.300800928 #> 9370  0.300770413 #> 9371  0.300739929 #> 9372  0.300709476 #> 9373  0.300679055 #> 9374  0.300648665 #> 9375  0.300618307 #> 9376  0.300587981 #> 9377  0.300557687 #> 9378  0.300527424 #> 9379  0.300497193 #> 9380  0.300466995 #> 9381  0.300436828 #> 9382  0.300406694 #> 9383  0.300376592 #> 9384  0.300346522 #> 9385  0.300316484 #> 9386  0.300286479 #> 9387  0.300256507 #> 9388  0.300226567 #> 9389  0.300196660 #> 9390  0.300166785 #> 9391  0.300136944 #> 9392  0.300107135 #> 9393  0.300077359 #> 9394  0.300047616 #> 9395  0.300017907 #> 9396  0.299988230 #> 9397  0.299958587 #> 9398  0.299928977 #> 9399  0.299899401 #> 9400  0.299869858 #> 9401  0.299840348 #> 9402  0.299810873 #> 9403  0.299781430 #> 9404  0.299752022 #> 9405  0.299722647 #> 9406  0.299693307 #> 9407  0.299664000 #> 9408  0.299634728 #> 9409  0.299605489 #> 9410  0.299576285 #> 9411  0.299547115 #> 9412  0.299517979 #> 9413  0.299488877 #> 9414  0.299459811 #> 9415  0.299430778 #> 9416  0.299401780 #> 9417  0.299372817 #> 9418  0.299343889 #> 9419  0.299314996 #> 9420  0.299286137 #> 9421  0.299257313 #> 9422  0.299228525 #> 9423  0.299199771 #> 9424  0.299171053 #> 9425  0.299142369 #> 9426  0.299113722 #> 9427  0.299085109 #> 9428  0.299056532 #> 9429  0.299027990 #> 9430  0.298999484 #> 9431  0.298971014 #> 9432  0.298942579 #> 9433  0.298914181 #> 9434  0.298885818 #> 9435  0.298857490 #> 9436  0.298829199 #> 9437  0.298800944 #> 9438  0.298772725 #> 9439  0.298744543 #> 9440  0.298716396 #> 9441  0.298688286 #> 9442  0.298660212 #> 9443  0.298632175 #> 9444  0.298604174 #> 9445  0.298576210 #> 9446  0.298548282 #> 9447  0.298520391 #> 9448  0.298492537 #> 9449  0.298464720 #> 9450  0.298436940 #> 9451  0.298409196 #> 9452  0.298381490 #> 9453  0.298353821 #> 9454  0.298326189 #> 9455  0.298298594 #> 9456  0.298271037 #> 9457  0.298243517 #> 9458  0.298216034 #> 9459  0.298188589 #> 9460  0.298161182 #> 9461  0.298133812 #> 9462  0.298106480 #> 9463  0.298079185 #> 9464  0.298051929 #> 9465  0.298024710 #> 9466  0.297997529 #> 9467  0.297970387 #> 9468  0.297943282 #> 9469  0.297916216 #> 9470  0.297889187 #> 9471  0.297862197 #> 9472  0.297835246 #> 9473  0.297808333 #> 9474  0.297781458 #> 9475  0.297754622 #> 9476  0.297727824 #> 9477  0.297701065 #> 9478  0.297674345 #> 9479  0.297647664 #> 9480  0.297621021 #> 9481  0.297594418 #> 9482  0.297567853 #> 9483  0.297541327 #> 9484  0.297514841 #> 9485  0.297488393 #> 9486  0.297461985 #> 9487  0.297435616 #> 9488  0.297409287 #> 9489  0.297382997 #> 9490  0.297356746 #> 9491  0.297330535 #> 9492  0.297304364 #> 9493  0.297278232 #> 9494  0.297252140 #> 9495  0.297226087 #> 9496  0.297200075 #> 9497  0.297174102 #> 9498  0.297148170 #> 9499  0.297122277 #> 9500  0.297096424 #> 9501  0.297070612 #> 9502  0.297044840 #> 9503  0.297019108 #> 9504  0.296993416 #> 9505  0.296967765 #> 9506  0.296942154 #> 9507  0.296916584 #> 9508  0.296891054 #> 9509  0.296865565 #> 9510  0.296840117 #> 9511  0.296814709 #> 9512  0.296789342 #> 9513  0.296764016 #> 9514  0.296738731 #> 9515  0.296713487 #> 9516  0.296688284 #> 9517  0.296663121 #> 9518  0.296638001 #> 9519  0.296612921 #> 9520  0.296587882 #> 9521  0.296562885 #> 9522  0.296537930 #> 9523  0.296513015 #> 9524  0.296488142 #> 9525  0.296463311 #> 9526  0.296438522 #> 9527  0.296413774 #> 9528  0.296389067 #> 9529  0.296364403 #> 9530  0.296339780 #> 9531  0.296315200 #> 9532  0.296290661 #> 9533  0.296266164 #> 9534  0.296241709 #> 9535  0.296217297 #> 9536  0.296192926 #> 9537  0.296168598 #> 9538  0.296144312 #> 9539  0.296120069 #> 9540  0.296095867 #> 9541  0.296071709 #> 9542  0.296047592 #> 9543  0.296023519 #> 9544  0.295999488 #> 9545  0.295975499 #> 9546  0.295951554 #> 9547  0.295927651 #> 9548  0.295903791 #> 9549  0.295879974 #> 9550  0.295856200 #> 9551  0.295832469 #> 9552  0.295808781 #> 9553  0.295785136 #> 9554  0.295761534 #> 9555  0.295737975 #> 9556  0.295714460 #> 9557  0.295690988 #> 9558  0.295667559 #> 9559  0.295644174 #> 9560  0.295620832 #> 9561  0.295597534 #> 9562  0.295574280 #> 9563  0.295551069 #> 9564  0.295527901 #> 9565  0.295504778 #> 9566  0.295481698 #> 9567  0.295458663 #> 9568  0.295435671 #> 9569  0.295412723 #> 9570  0.295389819 #> 9571  0.295366959 #> 9572  0.295344143 #> 9573  0.295321372 #> 9574  0.295298644 #> 9575  0.295275961 #> 9576  0.295253322 #> 9577  0.295230728 #> 9578  0.295208178 #> 9579  0.295185672 #> 9580  0.295163211 #> 9581  0.295140795 #> 9582  0.295118423 #> 9583  0.295096096 #> 9584  0.295073814 #> 9585  0.295051576 #> 9586  0.295029383 #> 9587  0.295007235 #> 9588  0.294985132 #> 9589  0.294963074 #> 9590  0.294941061 #> 9591  0.294919093 #> 9592  0.294897171 #> 9593  0.294875293 #> 9594  0.294853461 #> 9595  0.294831673 #> 9596  0.294809932 #> 9597  0.294788235 #> 9598  0.294766584 #> 9599  0.294744979 #> 9600  0.294723419 #> 9601  0.294701904 #> 9602  0.294680435 #> 9603  0.294659012 #> 9604  0.294637634 #> 9605  0.294616303 #> 9606  0.294595017 #> 9607  0.294573777 #> 9608  0.294552582 #> 9609  0.294531434 #> 9610  0.294510332 #> 9611  0.294489276 #> 9612  0.294468265 #> 9613  0.294447301 #> 9614  0.294426383 #> 9615  0.294405512 #> 9616  0.294384686 #> 9617  0.294363907 #> 9618  0.294343175 #> 9619  0.294322488 #> 9620  0.294301848 #> 9621  0.294281255 #> 9622  0.294260708 #> 9623  0.294240208 #> 9624  0.294219754 #> 9625  0.294199347 #> 9626  0.294178987 #> 9627  0.294158674 #> 9628  0.294138407 #> 9629  0.294118187 #> 9630  0.294098014 #> 9631  0.294077888 #> 9632  0.294057809 #> 9633  0.294037777 #> 9634  0.294017793 #> 9635  0.293997855 #> 9636  0.293977964 #> 9637  0.293958121 #> 9638  0.293938325 #> 9639  0.293918576 #> 9640  0.293898875 #> 9641  0.293879220 #> 9642  0.293859614 #> 9643  0.293840054 #> 9644  0.293820543 #> 9645  0.293801079 #> 9646  0.293781662 #> 9647  0.293762293 #> 9648  0.293742972 #> 9649  0.293723698 #> 9650  0.293704472 #> 9651  0.293685294 #> 9652  0.293666164 #> 9653  0.293647082 #> 9654  0.293628048 #> 9655  0.293609061 #> 9656  0.293590123 #> 9657  0.293571233 #> 9658  0.293552390 #> 9659  0.293533596 #> 9660  0.293514850 #> 9661  0.293496152 #> 9662  0.293477503 #> 9663  0.293458902 #> 9664  0.293440349 #> 9665  0.293421844 #> 9666  0.293403388 #> 9667  0.293384981 #> 9668  0.293366621 #> 9669  0.293348311 #> 9670  0.293330049 #> 9671  0.293311835 #> 9672  0.293293670 #> 9673  0.293275554 #> 9674  0.293257487 #> 9675  0.293239468 #> 9676  0.293221498 #> 9677  0.293203577 #> 9678  0.293185705 #> 9679  0.293167882 #> 9680  0.293150107 #> 9681  0.293132382 #> 9682  0.293114706 #> 9683  0.293097078 #> 9684  0.293079500 #> 9685  0.293061971 #> 9686  0.293044491 #> 9687  0.293027061 #> 9688  0.293009679 #> 9689  0.292992347 #> 9690  0.292975064 #> 9691  0.292957831 #> 9692  0.292940647 #> 9693  0.292923512 #> 9694  0.292906427 #> 9695  0.292889391 #> 9696  0.292872405 #> 9697  0.292855468 #> 9698  0.292838581 #> 9699  0.292821744 #> 9700  0.292804956 #> 9701  0.292788218 #> 9702  0.292771530 #> 9703  0.292754891 #> 9704  0.292738302 #> 9705  0.292721764 #> 9706  0.292705275 #> 9707  0.292688836 #> 9708  0.292672446 #> 9709  0.292656107 #> 9710  0.292639818 #> 9711  0.292623579 #> 9712  0.292607390 #> 9713  0.292591251 #> 9714  0.292575162 #> 9715  0.292559124 #> 9716  0.292543135 #> 9717  0.292527197 #> 9718  0.292511309 #> 9719  0.292495472 #> 9720  0.292479684 #> 9721  0.292463948 #> 9722  0.292448261 #> 9723  0.292432625 #> 9724  0.292417039 #> 9725  0.292401504 #> 9726  0.292386020 #> 9727  0.292370586 #> 9728  0.292355202 #> 9729  0.292339870 #> 9730  0.292324587 #> 9731  0.292309356 #> 9732  0.292294175 #> 9733  0.292279045 #> 9734  0.292263966 #> 9735  0.292248937 #> 9736  0.292233960 #> 9737  0.292219033 #> 9738  0.292204157 #> 9739  0.292189332 #> 9740  0.292174558 #> 9741  0.292159835 #> 9742  0.292145163 #> 9743  0.292130542 #> 9744  0.292115972 #> 9745  0.292101453 #> 9746  0.292086985 #> 9747  0.292072569 #> 9748  0.292058203 #> 9749  0.292043889 #> 9750  0.292029626 #> 9751  0.292015414 #> 9752  0.292001254 #> 9753  0.291987145 #> 9754  0.291973087 #> 9755  0.291959080 #> 9756  0.291945125 #> 9757  0.291931221 #> 9758  0.291917369 #> 9759  0.291903568 #> 9760  0.291889819 #> 9761  0.291876121 #> 9762  0.291862475 #> 9763  0.291848881 #> 9764  0.291835338 #> 9765  0.291821846 #> 9766  0.291808406 #> 9767  0.291795018 #> 9768  0.291781682 #> 9769  0.291768397 #> 9770  0.291755164 #> 9771  0.291741983 #> 9772  0.291728854 #> 9773  0.291715776 #> 9774  0.291702751 #> 9775  0.291689777 #> 9776  0.291676855 #> 9777  0.291663985 #> 9778  0.291651167 #> 9779  0.291638401 #> 9780  0.291625686 #> 9781  0.291613024 #> 9782  0.291600414 #> 9783  0.291587856 #> 9784  0.291575350 #> 9785  0.291562896 #> 9786  0.291550494 #> 9787  0.291538145 #> 9788  0.291525847 #> 9789  0.291513602 #> 9790  0.291501409 #> 9791  0.291489268 #> 9792  0.291477180 #> 9793  0.291465143 #> 9794  0.291453159 #> 9795  0.291441227 #> 9796  0.291429348 #> 9797  0.291417521 #> 9798  0.291405746 #> 9799  0.291394024 #> 9800  0.291382354 #> 9801  0.291370737 #> 9802  0.291359172 #> 9803  0.291347659 #> 9804  0.291336199 #> 9805  0.291324792 #> 9806  0.291313437 #> 9807  0.291302134 #> 9808  0.291290884 #> 9809  0.291279687 #> 9810  0.291268542 #> 9811  0.291257450 #> 9812  0.291246411 #> 9813  0.291235424 #> 9814  0.291224490 #> 9815  0.291213608 #> 9816  0.291202780 #> 9817  0.291192004 #> 9818  0.291181280 #> 9819  0.291170610 #> 9820  0.291159992 #> 9821  0.291149427 #> 9822  0.291138915 #> 9823  0.291128456 #> 9824  0.291118049 #> 9825  0.291107696 #> 9826  0.291097395 #> 9827  0.291087147 #> 9828  0.291076952 #> 9829  0.291066810 #> 9830  0.291056721 #> 9831  0.291046685 #> 9832  0.291036701 #> 9833  0.291026771 #> 9834  0.291016894 #> 9835  0.291007070 #> 9836  0.290997299 #> 9837  0.290987580 #> 9838  0.290977915 #> 9839  0.290968303 #> 9840  0.290958744 #> 9841  0.290949238 #> 9842  0.290939786 #> 9843  0.290930386 #> 9844  0.290921039 #> 9845  0.290911746 #> 9846  0.290902506 #> 9847  0.290893319 #> 9848  0.290884185 #> 9849  0.290875104 #> 9850  0.290866076 #> 9851  0.290857102 #> 9852  0.290848181 #> 9853  0.290839313 #> 9854  0.290830498 #> 9855  0.290821737 #> 9856  0.290813029 #> 9857  0.290804374 #> 9858  0.290795773 #> 9859  0.290787224 #> 9860  0.290778730 #> 9861  0.290770288 #> 9862  0.290761900 #> 9863  0.290753565 #> 9864  0.290745283 #> 9865  0.290737055 #> 9866  0.290728880 #> 9867  0.290720758 #> 9868  0.290712690 #> 9869  0.290704676 #> 9870  0.290696714 #> 9871  0.290688806 #> 9872  0.290680952 #> 9873  0.290673151 #> 9874  0.290665403 #> 9875  0.290657709 #> 9876  0.290650068 #> 9877  0.290642481 #> 9878  0.290634947 #> 9879  0.290627467 #> 9880  0.290620040 #> 9881  0.290612666 #> 9882  0.290605346 #> 9883  0.290598080 #> 9884  0.290590867 #> 9885  0.290583708 #> 9886  0.290576602 #> 9887  0.290569549 #> 9888  0.290562550 #> 9889  0.290555605 #> 9890  0.290548713 #> 9891  0.290541875 #> 9892  0.290535090 #> 9893  0.290528359 #> 9894  0.290521681 #> 9895  0.290515057 #> 9896  0.290508487 #> 9897  0.290501970 #> 9898  0.290495506 #> 9899  0.290489096 #> 9900  0.290482740 #> 9901  0.290476437 #> 9902  0.290470188 #> 9903  0.290463993 #> 9904  0.290457851 #> 9905  0.290451762 #> 9906  0.290445728 #> 9907  0.290439746 #> 9908  0.290433819 #> 9909  0.290427945 #> 9910  0.290422124 #> 9911  0.290416358 #> 9912  0.290410644 #> 9913  0.290404985 #> 9914  0.290399379 #> 9915  0.290393826 #> 9916  0.290388327 #> 9917  0.290382882 #> 9918  0.290377491 #> 9919  0.290372153 #> 9920  0.290366868 #> 9921  0.290361637 #> 9922  0.290356460 #> 9923  0.290351337 #> 9924  0.290346267 #> 9925  0.290341250 #> 9926  0.290336287 #> 9927  0.290331378 #> 9928  0.290326522 #> 9929  0.290321720 #> 9930  0.290316972 #> 9931  0.290312277 #> 9932  0.290307636 #> 9933  0.290303048 #> 9934  0.290298514 #> 9935  0.290294033 #> 9936  0.290289606 #> 9937  0.290285233 #> 9938  0.290280913 #> 9939  0.290276647 #> 9940  0.290272434 #> 9941  0.290268275 #> 9942  0.290264169 #> 9943  0.290260117 #> 9944  0.290256119 #> 9945  0.290252174 #> 9946  0.290248282 #> 9947  0.290244444 #> 9948  0.290240660 #> 9949  0.290236929 #> 9950  0.290233251 #> 9951  0.290229627 #> 9952  0.290226057 #> 9953  0.290222540 #> 9954  0.290219077 #> 9955  0.290215667 #> 9956  0.290212310 #> 9957  0.290209007 #> 9958  0.290205758 #> 9959  0.290202562 #> 9960  0.290199419 #> 9961  0.290196330 #> 9962  0.290193294 #> 9963  0.290190311 #> 9964  0.290187382 #> 9965  0.290184507 #> 9966  0.290181685 #> 9967  0.290178916 #> 9968  0.290176200 #> 9969  0.290173538 #> 9970  0.290170930 #> 9971  0.290168374 #> 9972  0.290165872 #> 9973  0.290163424 #> 9974  0.290161028 #> 9975  0.290158686 #> 9976  0.290156397 #> 9977  0.290154162 #> 9978  0.290151980 #> 9979  0.290149851 #> 9980  0.290147775 #> 9981  0.290145753 #> 9982  0.290143784 #> 9983  0.290141868 #> 9984  0.290140005 #> 9985  0.290138195 #> 9986  0.290136439 #> 9987  0.290134736 #> 9988  0.290133086 #> 9989  0.290131489 #> 9990  0.290129945 #> 9991  0.290128454 #> 9992  0.290127017 #> 9993  0.290125632 #> 9994  0.290124301 #> 9995  0.290123022 #> 9996  0.290121797 #> 9997  0.290120625 #> 9998  0.290119506 #> 9999  0.290118440 #> 10000 0.290117426 #> 10001 0.290116466 #> 10002 0.290115559 #> 10003 0.290114705 #> 10004 0.290113903 #> 10005 0.290113155 #> 10006 0.290112459 #> 10007 0.290111816 #> 10008 0.290111227 #> 10009 0.290110690 #> 10010 0.290110205 #> 10011 0.290109774 #> 10012 0.290109396 #> 10013 0.290109070 #> 10014 0.290108797 #> 10015 0.290108577 #> 10016 0.290108409 #> 10017 0.290108294 #> 10018 0.290108232 #> 10019 0.290108223 #> 10020 0.290108266 #> 10021 0.290108362 #> 10022 0.290108510 #> 10023 0.290108711 #> 10024 0.290108965 #> 10025 0.290109271 #> 10026 0.290109630 #> 10027 0.290110041 #> 10028 0.290110505 #> 10029 0.290111021 #> 10030 0.290111590 #> 10031 0.290112211 #> 10032 0.290112885 #> 10033 0.290113611 #> 10034 0.290114389 #> 10035 0.290115220 #> 10036 0.290116103 #> 10037 0.290117038 #> 10038 0.290118026 #> 10039 0.290119065 #> 10040 0.290120158 #> 10041 0.290121302 #> 10042 0.290122498 #> 10043 0.290123747 #> 10044 0.290125048 #> 10045 0.290126401 #> 10046 0.290127806 #> 10047 0.290129263 #> 10048 0.290130773 #> 10049 0.290132334 #> 10050 0.290133947 #> 10051 0.290135612 #> 10052 0.290137330 #> 10053 0.290139099 #> 10054 0.290140920 #> 10055 0.290142793 #> 10056 0.290144718 #> 10057 0.290146694 #> 10058 0.290148723 #> 10059 0.290150803 #> 10060 0.290152935 #> 10061 0.290155119 #> 10062 0.290157354 #> 10063 0.290159641 #> 10064 0.290161980 #> 10065 0.290164370 #> 10066 0.290166812 #> 10067 0.290169306 #> 10068 0.290171851 #> 10069 0.290174447 #> 10070 0.290177095 #> 10071 0.290179795 #> 10072 0.290182546 #> 10073 0.290185348 #> 10074 0.290188202 #> 10075 0.290191107 #> 10076 0.290194063 #> 10077 0.290197070 #> 10078 0.290200129 #> 10079 0.290203239 #> 10080 0.290206401 #> 10081 0.290209613 #> 10082 0.290212877 #> 10083 0.290216191 #> 10084 0.290219557 #> 10085 0.290222974 #> 10086 0.290226441 #> 10087 0.290229960 #> 10088 0.290233530 #> 10089 0.290237150 #> 10090 0.290240822 #> 10091 0.290244544 #> 10092 0.290248317 #> 10093 0.290252141 #> 10094 0.290256016 #> 10095 0.290259941 #> 10096 0.290263917 #> 10097 0.290267944 #> 10098 0.290272021 #> 10099 0.290276149 #> 10100 0.290280328 #> 10101 0.290284557 #> 10102 0.290288836 #> 10103 0.290293166 #> 10104 0.290297546 #> 10105 0.290301977 #> 10106 0.290306458 #> 10107 0.290310989 #> 10108 0.290315571 #> 10109 0.290320202 #> 10110 0.290324884 #> 10111 0.290329617 #> 10112 0.290334399 #> 10113 0.290339231 #> 10114 0.290344114 #> 10115 0.290349046 #> 10116 0.290354029 #> 10117 0.290359061 #> 10118 0.290364143 #> 10119 0.290369275 #> 10120 0.290374457 #> 10121 0.290379689 #> 10122 0.290384971 #> 10123 0.290390302 #> 10124 0.290395683 #> 10125 0.290401113 #> 10126 0.290406593 #> 10127 0.290412123 #> 10128 0.290417702 #> 10129 0.290423331 #> 10130 0.290429009 #> 10131 0.290434737 #> 10132 0.290440514 #> 10133 0.290446340 #> 10134 0.290452215 #> 10135 0.290458140 #> 10136 0.290464114 #> 10137 0.290470137 #> 10138 0.290476209 #> 10139 0.290482331 #> 10140 0.290488501 #> 10141 0.290494720 #> 10142 0.290500988 #> 10143 0.290507306 #> 10144 0.290513672 #> 10145 0.290520086 #> 10146 0.290526550 #> 10147 0.290533063 #> 10148 0.290539624 #> 10149 0.290546233 #> 10150 0.290552892 #> 10151 0.290559599 #> 10152 0.290566354 #> 10153 0.290573158 #> 10154 0.290580010 #> 10155 0.290586911 #> 10156 0.290593860 #> 10157 0.290600857 #> 10158 0.290607903 #> 10159 0.290614997 #> 10160 0.290622139 #> 10161 0.290629329 #> 10162 0.290636567 #> 10163 0.290643853 #> 10164 0.290651187 #> 10165 0.290658569 #> 10166 0.290665999 #> 10167 0.290673477 #> 10168 0.290681003 #> 10169 0.290688576 #> 10170 0.290696197 #> 10171 0.290703866 #> 10172 0.290711582 #> 10173 0.290719346 #> 10174 0.290727157 #> 10175 0.290735016 #> 10176 0.290742922 #> 10177 0.290750876 #> 10178 0.290758877 #> 10179 0.290766925 #> 10180 0.290775020 #> 10181 0.290783163 #> 10182 0.290791353 #> 10183 0.290799589 #> 10184 0.290807873 #> 10185 0.290816204 #> 10186 0.290824582 #> 10187 0.290833006 #> 10188 0.290841477 #> 10189 0.290849995 #> 10190 0.290858560 #> 10191 0.290867172 #> 10192 0.290875830 #> 10193 0.290884534 #> 10194 0.290893285 #> 10195 0.290902083 #> 10196 0.290910927 #> 10197 0.290919817 #> 10198 0.290928754 #> 10199 0.290937737 #> 10200 0.290946766 #> 10201 0.290955841 #> 10202 0.290964962 #> 10203 0.290974130 #> 10204 0.290983343 #> 10205 0.290992602 #> 10206 0.291001907 #> 10207 0.291011258 #> 10208 0.291020655 #> 10209 0.291030097 #> 10210 0.291039585 #> 10211 0.291049119 #> 10212 0.291058698 #> 10213 0.291068323 #> 10214 0.291077993 #> 10215 0.291087709 #> 10216 0.291097469 #> 10217 0.291107275 #> 10218 0.291117127 #> 10219 0.291127023 #> 10220 0.291136965 #> 10221 0.291146951 #> 10222 0.291156983 #> 10223 0.291167059 #> 10224 0.291177180 #> 10225 0.291187347 #> 10226 0.291197558 #> 10227 0.291207813 #> 10228 0.291218113 #> 10229 0.291228458 #> 10230 0.291238847 #> 10231 0.291249281 #> 10232 0.291259759 #> 10233 0.291270282 #> 10234 0.291280849 #> 10235 0.291291460 #> 10236 0.291302115 #> 10237 0.291312814 #> 10238 0.291323558 #> 10239 0.291334345 #> 10240 0.291345176 #> 10241 0.291356051 #> 10242 0.291366970 #> 10243 0.291377933 #> 10244 0.291388939 #> 10245 0.291399989 #> 10246 0.291411083 #> 10247 0.291422220 #> 10248 0.291433401 #> 10249 0.291444624 #> 10250 0.291455892 #> 10251 0.291467202 #> 10252 0.291478556 #> 10253 0.291489952 #> 10254 0.291501392 #> 10255 0.291512875 #> 10256 0.291524401 #> 10257 0.291535970 #> 10258 0.291547581 #> 10259 0.291559235 #> 10260 0.291570932 #> 10261 0.291582672 #> 10262 0.291594454 #> 10263 0.291606278 #> 10264 0.291618145 #> 10265 0.291630054 #> 10266 0.291642006 #> 10267 0.291654000 #> 10268 0.291666036 #> 10269 0.291678114 #> 10270 0.291690234 #> 10271 0.291702397 #> 10272 0.291714601 #> 10273 0.291726847 #> 10274 0.291739134 #> 10275 0.291751464 #> 10276 0.291763835 #> 10277 0.291776247 #> 10278 0.291788701 #> 10279 0.291801197 #> 10280 0.291813734 #> 10281 0.291826312 #> 10282 0.291838932 #> 10283 0.291851592 #> 10284 0.291864294 #> 10285 0.291877037 #> 10286 0.291889821 #> 10287 0.291902645 #> 10288 0.291915511 #> 10289 0.291928417 #> 10290 0.291941364 #> 10291 0.291954352 #> 10292 0.291967380 #> 10293 0.291980448 #> 10294 0.291993557 #> 10295 0.292006707 #> 10296 0.292019896 #> 10297 0.292033126 #> 10298 0.292046396 #> 10299 0.292059706 #> 10300 0.292073056 #> 10301 0.292086446 #> 10302 0.292099876 #> 10303 0.292113346 #> 10304 0.292126855 #> 10305 0.292140404 #> 10306 0.292153993 #> 10307 0.292167621 #> 10308 0.292181288 #> 10309 0.292194995 #> 10310 0.292208741 #> 10311 0.292222526 #> 10312 0.292236351 #> 10313 0.292250214 #> 10314 0.292264117 #> 10315 0.292278058 #> 10316 0.292292038 #> 10317 0.292306058 #> 10318 0.292320115 #> 10319 0.292334212 #> 10320 0.292348347 #> 10321 0.292362520 #> 10322 0.292376732 #> 10323 0.292390982 #> 10324 0.292405270 #> 10325 0.292419597 #> 10326 0.292433961 #> 10327 0.292448364 #> 10328 0.292462805 #> 10329 0.292477283 #> 10330 0.292491800 #> 10331 0.292506354 #> 10332 0.292520945 #> 10333 0.292535575 #> 10334 0.292550242 #> 10335 0.292564946 #> 10336 0.292579687 #> 10337 0.292594466 #> 10338 0.292609282 #> 10339 0.292624135 #> 10340 0.292639026 #> 10341 0.292653953 #> 10342 0.292668917 #> 10343 0.292683918 #> 10344 0.292698955 #> 10345 0.292714030 #> 10346 0.292729140 #> 10347 0.292744288 #> 10348 0.292759472 #> 10349 0.292774692 #> 10350 0.292789948 #> 10351 0.292805241 #> 10352 0.292820569 #> 10353 0.292835934 #> 10354 0.292851335 #> 10355 0.292866771 #> 10356 0.292882244 #> 10357 0.292897752 #> 10358 0.292913295 #> 10359 0.292928874 #> 10360 0.292944489 #> 10361 0.292960139 #> 10362 0.292975825 #> 10363 0.292991545 #> 10364 0.293007301 #> 10365 0.293023092 #> 10366 0.293038918 #> 10367 0.293054779 #> 10368 0.293070674 #> 10369 0.293086605 #> 10370 0.293102570 #> 10371 0.293118569 #> 10372 0.293134604 #> 10373 0.293150672 #> 10374 0.293166775 #> 10375 0.293182912 #> 10376 0.293199083 #> 10377 0.293215289 #> 10378 0.293231528 #> 10379 0.293247802 #> 10380 0.293264109 #> 10381 0.293280450 #> 10382 0.293296825 #> 10383 0.293313233 #> 10384 0.293329675 #> 10385 0.293346150 #> 10386 0.293362658 #> 10387 0.293379200 #> 10388 0.293395775 #> 10389 0.293412383 #> 10390 0.293429025 #> 10391 0.293445699 #> 10392 0.293462406 #> 10393 0.293479145 #> 10394 0.293495918 #> 10395 0.293512723 #> 10396 0.293529560 #> 10397 0.293546430 #> 10398 0.293563332 #> 10399 0.293580267 #> 10400 0.293597233 #> 10401 0.293614232 #> 10402 0.293631263 #> 10403 0.293648325 #> 10404 0.293665420 #> 10405 0.293682546 #> 10406 0.293699704 #> 10407 0.293716893 #> 10408 0.293734114 #> 10409 0.293751366 #> 10410 0.293768650 #> 10411 0.293785965 #> 10412 0.293803311 #> 10413 0.293820688 #> 10414 0.293838096 #> 10415 0.293855535 #> 10416 0.293873004 #> 10417 0.293890505 #> 10418 0.293908035 #> 10419 0.293925597 #> 10420 0.293943189 #> 10421 0.293960811 #> 10422 0.293978463 #> 10423 0.293996146 #> 10424 0.294013859 #> 10425 0.294031602 #> 10426 0.294049374 #> 10427 0.294067177 #> 10428 0.294085009 #> 10429 0.294102871 #> 10430 0.294120762 #> 10431 0.294138683 #> 10432 0.294156633 #> 10433 0.294174613 #> 10434 0.294192621 #> 10435 0.294210659 #> 10436 0.294228726 #> 10437 0.294246822 #> 10438 0.294264946 #> 10439 0.294283100 #> 10440 0.294301282 #> 10441 0.294319492 #> 10442 0.294337731 #> 10443 0.294355999 #> 10444 0.294374294 #> 10445 0.294392618 #> 10446 0.294410970 #> 10447 0.294429350 #> 10448 0.294447758 #> 10449 0.294466194 #> 10450 0.294484657 #> 10451 0.294503149 #> 10452 0.294521667 #> 10453 0.294540214 #> 10454 0.294558787 #> 10455 0.294577388 #> 10456 0.294596016 #> 10457 0.294614672 #> 10458 0.294633354 #> 10459 0.294652063 #> 10460 0.294670799 #> 10461 0.294689562 #> 10462 0.294708351 #> 10463 0.294727167 #> 10464 0.294746009 #> 10465 0.294764878 #> 10466 0.294783773 #> 10467 0.294802694 #> 10468 0.294821642 #> 10469 0.294840615 #> 10470 0.294859614 #> 10471 0.294878639 #> 10472 0.294897690 #> 10473 0.294916766 #> 10474 0.294935868 #> 10475 0.294954995 #> 10476 0.294974148 #> 10477 0.294993326 #> 10478 0.295012529 #> 10479 0.295031757 #> 10480 0.295051010 #> 10481 0.295070288 #> 10482 0.295089591 #> 10483 0.295108918 #> 10484 0.295128270 #> 10485 0.295147647 #> 10486 0.295167048 #> 10487 0.295186473 #> 10488 0.295205922 #> 10489 0.295225396 #> 10490 0.295244893 #> 10491 0.295264415 #> 10492 0.295283960 #> 10493 0.295303529 #> 10494 0.295323122 #> 10495 0.295342738 #> 10496 0.295362378 #> 10497 0.295382041 #> 10498 0.295401727 #> 10499 0.295421436 #> 10500 0.295441169 #> 10501 0.295460924 #> 10502 0.295480703 #> 10503 0.295500504 #> 10504 0.295520327 #> 10505 0.295540174 #> 10506 0.295560043 #> 10507 0.295579934 #> 10508 0.295599848 #> 10509 0.295619783 #> 10510 0.295639741 #> 10511 0.295659721 #> 10512 0.295679723 #> 10513 0.295699746 #> 10514 0.295719792 #> 10515 0.295739859 #> 10516 0.295759947 #> 10517 0.295780057 #> 10518 0.295800188 #> 10519 0.295820340 #> 10520 0.295840514 #> 10521 0.295860709 #> 10522 0.295880924 #> 10523 0.295901160 #> 10524 0.295921418 #> 10525 0.295941695 #> 10526 0.295961994 #> 10527 0.295982312 #> 10528 0.296002651 #> 10529 0.296023011 #> 10530 0.296043390 #> 10531 0.296063790 #> 10532 0.296084209 #> 10533 0.296104649 #> 10534 0.296125108 #> 10535 0.296145587 #> 10536 0.296166085 #> 10537 0.296186603 #> 10538 0.296207140 #> 10539 0.296227697 #> 10540 0.296248272 #> 10541 0.296268867 #> 10542 0.296289481 #> 10543 0.296310113 #> 10544 0.296330765 #> 10545 0.296351435 #> 10546 0.296372123 #> 10547 0.296392830 #> 10548 0.296413556 #> 10549 0.296434300 #> 10550 0.296455061 #> 10551 0.296475841 #> 10552 0.296496639 #> 10553 0.296517455 #> 10554 0.296538289 #> 10555 0.296559140 #> 10556 0.296580009 #> 10557 0.296600896 #> 10558 0.296621800 #> 10559 0.296642721 #> 10560 0.296663659 #> 10561 0.296684614 #> 10562 0.296705587 #> 10563 0.296726576 #> 10564 0.296747582 #> 10565 0.296768605 #> 10566 0.296789644 #> 10567 0.296810700 #> 10568 0.296831772 #> 10569 0.296852861 #> 10570 0.296873965 #> 10571 0.296895086 #> 10572 0.296916223 #> 10573 0.296937376 #> 10574 0.296958544 #> 10575 0.296979728 #> 10576 0.297000928 #> 10577 0.297022143 #> 10578 0.297043374 #> 10579 0.297064620 #> 10580 0.297085881 #> 10581 0.297107157 #> 10582 0.297128448 #> 10583 0.297149754 #> 10584 0.297171075 #> 10585 0.297192411 #> 10586 0.297213761 #> 10587 0.297235125 #> 10588 0.297256504 #> 10589 0.297277898 #> 10590 0.297299305 #> 10591 0.297320726 #> 10592 0.297342162 #> 10593 0.297363611 #> 10594 0.297385074 #> 10595 0.297406551 #> 10596 0.297428042 #> 10597 0.297449546 #> 10598 0.297471063 #> 10599 0.297492593 #> 10600 0.297514137 #> 10601 0.297535694 #> 10602 0.297557264 #> 10603 0.297578846 #> 10604 0.297600442 #> 10605 0.297622050 #> 10606 0.297643670 #> 10607 0.297665303 #> 10608 0.297686949 #> 10609 0.297708606 #> 10610 0.297730276 #> 10611 0.297751958 #> 10612 0.297773652 #> 10613 0.297795358 #> 10614 0.297817075 #> 10615 0.297838804 #> 10616 0.297860545 #> 10617 0.297882297 #> 10618 0.297904061 #> 10619 0.297925835 #> 10620 0.297947621 #> 10621 0.297969418 #> 10622 0.297991226 #> 10623 0.298013045 #> 10624 0.298034874 #> 10625 0.298056715 #> 10626 0.298078565 #> 10627 0.298100426 #> 10628 0.298122298 #> 10629 0.298144179 #> 10630 0.298166071 #> 10631 0.298187973 #> 10632 0.298209885 #> 10633 0.298231806 #> 10634 0.298253738 #> 10635 0.298275679 #> 10636 0.298297629 #> 10637 0.298319589 #> 10638 0.298341558 #> 10639 0.298363537 #> 10640 0.298385524 #> 10641 0.298407521 #> 10642 0.298429526 #> 10643 0.298451541 #> 10644 0.298473564 #> 10645 0.298495595 #> 10646 0.298517636 #> 10647 0.298539684 #> 10648 0.298561741 #> 10649 0.298583806 #> 10650 0.298605879 #> 10651 0.298627960 #> 10652 0.298650050 #> 10653 0.298672146 #> 10654 0.298694251 #> 10655 0.298716363 #> 10656 0.298738483 #> 10657 0.298760610 #> 10658 0.298782744 #> 10659 0.298804886 #> 10660 0.298827035 #> 10661 0.298849190 #> 10662 0.298871353 #> 10663 0.298893522 #> 10664 0.298915698 #> 10665 0.298937881 #> 10666 0.298960070 #> 10667 0.298982265 #> 10668 0.299004467 #> 10669 0.299026675 #> 10670 0.299048889 #> 10671 0.299071109 #> 10672 0.299093335 #> 10673 0.299115567 #> 10674 0.299137804 #> 10675 0.299160047 #> 10676 0.299182295 #> 10677 0.299204549 #> 10678 0.299226808 #> 10679 0.299249072 #> 10680 0.299271342 #> 10681 0.299293616 #> 10682 0.299315895 #> 10683 0.299338179 #> 10684 0.299360468 #> 10685 0.299382761 #> 10686 0.299405058 #> 10687 0.299427360 #> 10688 0.299449666 #> 10689 0.299471977 #> 10690 0.299494291 #> 10691 0.299516610 #> 10692 0.299538932 #> 10693 0.299561258 #> 10694 0.299583588 #> 10695 0.299605921 #> 10696 0.299628258 #> 10697 0.299650598 #> 10698 0.299672941 #> 10699 0.299695288 #> 10700 0.299717637 #> 10701 0.299739990 #> 10702 0.299762345 #> 10703 0.299784703 #> 10704 0.299807064 #> 10705 0.299829427 #> 10706 0.299851793 #> 10707 0.299874161 #> 10708 0.299896532 #> 10709 0.299918904 #> 10710 0.299941279 #> 10711 0.299963655 #> 10712 0.299986034 #> 10713 0.300008414 #> 10714 0.300030796 #> 10715 0.300053179 #> 10716 0.300075564 #> 10717 0.300097951 #> 10718 0.300120338 #> 10719 0.300142727 #> 10720 0.300165117 #> 10721 0.300187507 #> 10722 0.300209899 #> 10723 0.300232291 #> 10724 0.300254684 #> 10725 0.300277078 #> 10726 0.300299472 #> 10727 0.300321866 #> 10728 0.300344261 #> 10729 0.300366656 #> 10730 0.300389051 #> 10731 0.300411446 #> 10732 0.300433840 #> 10733 0.300456235 #> 10734 0.300478629 #> 10735 0.300501023 #> 10736 0.300523416 #> 10737 0.300545809 #> 10738 0.300568201 #> 10739 0.300590592 #> 10740 0.300612982 #> 10741 0.300635371 #> 10742 0.300657759 #> 10743 0.300680146 #> 10744 0.300702532 #> 10745 0.300724916 #> 10746 0.300747299 #> 10747 0.300769680 #> 10748 0.300792059 #> 10749 0.300814437 #> 10750 0.300836813 #> 10751 0.300859186 #> 10752 0.300881558 #> 10753 0.300903927 #> 10754 0.300926294 #> 10755 0.300948659 #> 10756 0.300971021 #> 10757 0.300993381 #> 10758 0.301015738 #> 10759 0.301038092 #> 10760 0.301060444 #> 10761 0.301082792 #> 10762 0.301105138 #> 10763 0.301127480 #> 10764 0.301149819 #> 10765 0.301172154 #> 10766 0.301194487 #> 10767 0.301216815 #> 10768 0.301239140 #> 10769 0.301261461 #> 10770 0.301283779 #> 10771 0.301306092 #> 10772 0.301328402 #> 10773 0.301350707 #> 10774 0.301373008 #> 10775 0.301395305 #> 10776 0.301417598 #> 10777 0.301439886 #> 10778 0.301462169 #> 10779 0.301484447 #> 10780 0.301506721 #> 10781 0.301528990 #> 10782 0.301551254 #> 10783 0.301573513 #> 10784 0.301595767 #> 10785 0.301618015 #> 10786 0.301640258 #> 10787 0.301662496 #> 10788 0.301684728 #> 10789 0.301706955 #> 10790 0.301729175 #> 10791 0.301751390 #> 10792 0.301773599 #> 10793 0.301795802 #> 10794 0.301817999 #> 10795 0.301840189 #> 10796 0.301862374 #> 10797 0.301884552 #> 10798 0.301906723 #> 10799 0.301928888 #> 10800 0.301951046 #> 10801 0.301973197 #> 10802 0.301995342 #> 10803 0.302017479 #> 10804 0.302039610 #> 10805 0.302061733 #> 10806 0.302083849 #> 10807 0.302105958 #> 10808 0.302128060 #> 10809 0.302150153 #> 10810 0.302172240 #> 10811 0.302194318 #> 10812 0.302216389 #> 10813 0.302238452 #> 10814 0.302260506 #> 10815 0.302282553 #> 10816 0.302304592 #> 10817 0.302326622 #> 10818 0.302348644 #> 10819 0.302370658 #> 10820 0.302392663 #> 10821 0.302414659 #> 10822 0.302436647 #> 10823 0.302458626 #> 10824 0.302480596 #> 10825 0.302502557 #> 10826 0.302524509 #> 10827 0.302546451 #> 10828 0.302568385 #> 10829 0.302590309 #> 10830 0.302612224 #> 10831 0.302634129 #> 10832 0.302656024 #> 10833 0.302677910 #> 10834 0.302699786 #> 10835 0.302721652 #> 10836 0.302743509 #> 10837 0.302765355 #> 10838 0.302787190 #> 10839 0.302809016 #> 10840 0.302830831 #> 10841 0.302852636 #> 10842 0.302874431 #> 10843 0.302896214 #> 10844 0.302917987 #> 10845 0.302939750 #> 10846 0.302961501 #> 10847 0.302983241 #> 10848 0.303004971 #> 10849 0.303026689 #> 10850 0.303048396 #> 10851 0.303070091 #> 10852 0.303091776 #> 10853 0.303113448 #> 10854 0.303135110 #> 10855 0.303156759 #> 10856 0.303178397 #> 10857 0.303200023 #> 10858 0.303221637 #> 10859 0.303243238 #> 10860 0.303264828 #> 10861 0.303286406 #> 10862 0.303307971 #> 10863 0.303329524 #> 10864 0.303351065 #> 10865 0.303372593 #> 10866 0.303394108 #> 10867 0.303415610 #> 10868 0.303437100 #> 10869 0.303458577 #> 10870 0.303480041 #> 10871 0.303501492 #> 10872 0.303522930 #> 10873 0.303544354 #> 10874 0.303565765 #> 10875 0.303587163 #> 10876 0.303608547 #> 10877 0.303629918 #> 10878 0.303651275 #> 10879 0.303672619 #> 10880 0.303693948 #> 10881 0.303715264 #> 10882 0.303736565 #> 10883 0.303757853 #> 10884 0.303779126 #> 10885 0.303800385 #> 10886 0.303821630 #> 10887 0.303842860 #> 10888 0.303864076 #> 10889 0.303885277 #> 10890 0.303906464 #> 10891 0.303927636 #> 10892 0.303948793 #> 10893 0.303969935 #> 10894 0.303991062 #> 10895 0.304012174 #> 10896 0.304033271 #> 10897 0.304054352 #> 10898 0.304075419 #> 10899 0.304096469 #> 10900 0.304117505 #> 10901 0.304138525 #> 10902 0.304159529 #> 10903 0.304180517 #> 10904 0.304201490 #> 10905 0.304222446 #> 10906 0.304243387 #> 10907 0.304264312 #> 10908 0.304285220 #> 10909 0.304306113 #> 10910 0.304326989 #> 10911 0.304347848 #> 10912 0.304368691 #> 10913 0.304389518 #> 10914 0.304410328 #> 10915 0.304431121 #> 10916 0.304451898 #> 10917 0.304472657 #> 10918 0.304493400 #> 10919 0.304514125 #> 10920 0.304534834 #> 10921 0.304555525 #> 10922 0.304576199 #> 10923 0.304596856 #> 10924 0.304617496 #> 10925 0.304638117 #> 10926 0.304658722 #> 10927 0.304679308 #> 10928 0.304699877 #> 10929 0.304720428 #> 10930 0.304740962 #> 10931 0.304761477 #> 10932 0.304781974 #> 10933 0.304802453 #> 10934 0.304822914 #> 10935 0.304843357 #> 10936 0.304863781 #> 10937 0.304884187 #> 10938 0.304904574 #> 10939 0.304924943 #> 10940 0.304945293 #> 10941 0.304965624 #> 10942 0.304985937 #> 10943 0.305006231 #> 10944 0.305026506 #> 10945 0.305046761 #> 10946 0.305066998 #> 10947 0.305087215 #> 10948 0.305107414 #> 10949 0.305127592 #> 10950 0.305147752 #> 10951 0.305167892 #> 10952 0.305188012 #> 10953 0.305208113 #> 10954 0.305228194 #> 10955 0.305248256 #> 10956 0.305268297 #> 10957 0.305288319 #> 10958 0.305308320 #> 10959 0.305328302 #> 10960 0.305348263 #> 10961 0.305368204 #> 10962 0.305388125 #> 10963 0.305408025 #> 10964 0.305427906 #> 10965 0.305447765 #> 10966 0.305467604 #> 10967 0.305487422 #> 10968 0.305507220 #> 10969 0.305526997 #> 10970 0.305546753 #> 10971 0.305566488 #> 10972 0.305586202 #> 10973 0.305605895 #> 10974 0.305625567 #> 10975 0.305645217 #> 10976 0.305664846 #> 10977 0.305684454 #> 10978 0.305704041 #> 10979 0.305723606 #> 10980 0.305743149 #> 10981 0.305762671 #> 10982 0.305782171 #> 10983 0.305801650 #> 10984 0.305821106 #> 10985 0.305840541 #> 10986 0.305859953 #> 10987 0.305879344 #> 10988 0.305898712 #> 10989 0.305918058 #> 10990 0.305937382 #> 10991 0.305956684 #> 10992 0.305975963 #> 10993 0.305995220 #> 10994 0.306014454 #> 10995 0.306033666 #> 10996 0.306052855 #> 10997 0.306072021 #> 10998 0.306091165 #> 10999 0.306110286 #> 11000 0.306129383 #> 11001 0.306148458 #> 11002 0.306167510 #> 11003 0.306186538 #> 11004 0.306205544 #> 11005 0.306224526 #> 11006 0.306243484 #> 11007 0.306262420 #> 11008 0.306281332 #> 11009 0.306300220 #> 11010 0.306319085 #> 11011 0.306337926 #> 11012 0.306356744 #> 11013 0.306375537 #> 11014 0.306394307 #> 11015 0.306413053 #> 11016 0.306431775 #> 11017 0.306450473 #> 11018 0.306469147 #> 11019 0.306487797 #> 11020 0.306506422 #> 11021 0.306525023 #> 11022 0.306543600 #> 11023 0.306562153 #> 11024 0.306580681 #> 11025 0.306599184 #> 11026 0.306617663 #> 11027 0.306636117 #> 11028 0.306654547 #> 11029 0.306672952 #> 11030 0.306691332 #> 11031 0.306709687 #> 11032 0.306728017 #> 11033 0.306746322 #> 11034 0.306764602 #> 11035 0.306782856 #> 11036 0.306801086 #> 11037 0.306819290 #> 11038 0.306837469 #> 11039 0.306855623 #> 11040 0.306873751 #> 11041 0.306891854 #> 11042 0.306909931 #> 11043 0.306927983 #> 11044 0.306946008 #> 11045 0.306964009 #> 11046 0.306981983 #> 11047 0.306999931 #> 11048 0.307017854 #> 11049 0.307035751 #> 11050 0.307053621 #> 11051 0.307071466 #> 11052 0.307089284 #> 11053 0.307107076 #> 11054 0.307124842 #> 11055 0.307142582 #> 11056 0.307160295 #> 11057 0.307177982 #> 11058 0.307195642 #> 11059 0.307213276 #> 11060 0.307230883 #> 11061 0.307248464 #> 11062 0.307266017 #> 11063 0.307283544 #> 11064 0.307301045 #> 11065 0.307318518 #> 11066 0.307335965 #> 11067 0.307353384 #> 11068 0.307370777 #> 11069 0.307388142 #> 11070 0.307405480 #> 11071 0.307422791 #> 11072 0.307440075 #> 11073 0.307457332 #> 11074 0.307474561 #> 11075 0.307491763 #> 11076 0.307508937 #> 11077 0.307526084 #> 11078 0.307543203 #> 11079 0.307560295 #> 11080 0.307577359 #> 11081 0.307594395 #> 11082 0.307611403 #> 11083 0.307628384 #> 11084 0.307645337 #> 11085 0.307662262 #> 11086 0.307679158 #> 11087 0.307696027 #> 11088 0.307712868 #> 11089 0.307729681 #> 11090 0.307746465 #> 11091 0.307763221 #> 11092 0.307779949 #> 11093 0.307796649 #> 11094 0.307813320 #> 11095 0.307829963 #> 11096 0.307846577 #> 11097 0.307863163 #> 11098 0.307879720 #> 11099 0.307896249 #> 11100 0.307912749 #> 11101 0.307929220 #> 11102 0.307945662 #> 11103 0.307962076 #> 11104 0.307978461 #> 11105 0.307994817 #> 11106 0.308011143 #> 11107 0.308027441 #> 11108 0.308043710 #> 11109 0.308059950 #> 11110 0.308076160 #> 11111 0.308092342 #> 11112 0.308108494 #> 11113 0.308124617 #> 11114 0.308140710 #> 11115 0.308156774 #> 11116 0.308172809 #> 11117 0.308188814 #> 11118 0.308204790 #> 11119 0.308220736 #> 11120 0.308236653 #> 11121 0.308252540 #> 11122 0.308268397 #> 11123 0.308284225 #> 11124 0.308300022 #> 11125 0.308315790 #> 11126 0.308331528 #> 11127 0.308347236 #> 11128 0.308362914 #> 11129 0.308378563 #> 11130 0.308394181 #> 11131 0.308409769 #> 11132 0.308425327 #> 11133 0.308440854 #> 11134 0.308456352 #> 11135 0.308471819 #> 11136 0.308487256 #> 11137 0.308502663 #> 11138 0.308518039 #> 11139 0.308533385 #> 11140 0.308548700 #> 11141 0.308563985 #> 11142 0.308579240 #> 11143 0.308594463 #> 11144 0.308609657 #> 11145 0.308624819 #> 11146 0.308639951 #> 11147 0.308655052 #> 11148 0.308670122 #> 11149 0.308685162 #> 11150 0.308700170 #> 11151 0.308715148 #> 11152 0.308730095 #> 11153 0.308745011 #> 11154 0.308759895 #> 11155 0.308774749 #> 11156 0.308789572 #> 11157 0.308804363 #> 11158 0.308819124 #> 11159 0.308833853 #> 11160 0.308848551 #> 11161 0.308863217 #> 11162 0.308877853 #> 11163 0.308892456 #> 11164 0.308907029 #> 11165 0.308921570 #> 11166 0.308936080 #> 11167 0.308950558 #> 11168 0.308965005 #> 11169 0.308979420 #> 11170 0.308993803 #> 11171 0.309008155 #> 11172 0.309022475 #> 11173 0.309036764 #> 11174 0.309051021 #> 11175 0.309065246 #> 11176 0.309079439 #> 11177 0.309093600 #> 11178 0.309107730 #> 11179 0.309121827 #> 11180 0.309135893 #> 11181 0.309149926 #> 11182 0.309163928 #> 11183 0.309177897 #> 11184 0.309191835 #> 11185 0.309205740 #> 11186 0.309219613 #> 11187 0.309233454 #> 11188 0.309247263 #> 11189 0.309261040 #> 11190 0.309274784 #> 11191 0.309288496 #> 11192 0.309302176 #> 11193 0.309315823 #> 11194 0.309329438 #> 11195 0.309343021 #> 11196 0.309356571 #> 11197 0.309370088 #> 11198 0.309383573 #> 11199 0.309397026 #> 11200 0.309410446 #> 11201 0.309423833 #> 11202 0.309437188 #> 11203 0.309450510 #> 11204 0.309463799 #> 11205 0.309477056 #> 11206 0.309490280 #> 11207 0.309503471 #> 11208 0.309516629 #> 11209 0.309529754 #> 11210 0.309542847 #> 11211 0.309555907 #> 11212 0.309568933 #> 11213 0.309581927 #> 11214 0.309594888 #> 11215 0.309607816 #> 11216 0.309620711 #> 11217 0.309633572 #> 11218 0.309646401 #> 11219 0.309659197 #> 11220 0.309671959 #> 11221 0.309684688 #> 11222 0.309697384 #> 11223 0.309710047 #> 11224 0.309722677 #> 11225 0.309735273 #> 11226 0.309747836 #> 11227 0.309760366 #> 11228 0.309772863 #> 11229 0.309785326 #> 11230 0.309797756 #> 11231 0.309810152 #> 11232 0.309822515 #> 11233 0.309834844 #> 11234 0.309847140 #> 11235 0.309859402 #> 11236 0.309871631 #> 11237 0.309883827 #> 11238 0.309895988 #> 11239 0.309908117 #> 11240 0.309920211 #> 11241 0.309932272 #> 11242 0.309944299 #> 11243 0.309956293 #> 11244 0.309968253 #> 11245 0.309980179 #> 11246 0.309992072 #> 11247 0.310003930 #> 11248 0.310015755 #> 11249 0.310027546 #> 11250 0.310039303 #> 11251 0.310051027 #> 11252 0.310062716 #> 11253 0.310074372 #> 11254 0.310085994 #> 11255 0.310097581 #> 11256 0.310109135 #> 11257 0.310120655 #> 11258 0.310132141 #> 11259 0.310143593 #> 11260 0.310155011 #> 11261 0.310166394 #> 11262 0.310177744 #> 11263 0.310189060 #> 11264 0.310200341 #> 11265 0.310211589 #> 11266 0.310222802 #> 11267 0.310233981 #> 11268 0.310245126 #> 11269 0.310256237 #> 11270 0.310267313 #> 11271 0.310278356 #> 11272 0.310289364 #> 11273 0.310300338 #> 11274 0.310311277 #> 11275 0.310322183 #> 11276 0.310333054 #> 11277 0.310343890 #> 11278 0.310354693 #> 11279 0.310365461 #> 11280 0.310376194 #> 11281 0.310386893 #> 11282 0.310397558 #> 11283 0.310408189 #> 11284 0.310418785 #> 11285 0.310429346 #> 11286 0.310439874 #> 11287 0.310450366 #> 11288 0.310460824 #> 11289 0.310471248 #> 11290 0.310481637 #> 11291 0.310491992 #> 11292 0.310502312 #> 11293 0.310512598 #> 11294 0.310522849 #> 11295 0.310533065 #> 11296 0.310543247 #> 11297 0.310553394 #> 11298 0.310563507 #> 11299 0.310573585 #> 11300 0.310583628 #> 11301 0.310593637 #> 11302 0.310603611 #> 11303 0.310613551 #> 11304 0.310623456 #> 11305 0.310633326 #> 11306 0.310643161 #> 11307 0.310652962 #> 11308 0.310662728 #> 11309 0.310672459 #> 11310 0.310682155 #> 11311 0.310691817 #> 11312 0.310701444 #> 11313 0.310711036 #> 11314 0.310720594 #> 11315 0.310730116 #> 11316 0.310739604 #> 11317 0.310749057 #> 11318 0.310758475 #> 11319 0.310767859 #> 11320 0.310777207 #> 11321 0.310786521 #> 11322 0.310795800 #> 11323 0.310805044 #> 11324 0.310814253 #> 11325 0.310823428 #> 11326 0.310832567 #> 11327 0.310841672 #> 11328 0.310850741 #> 11329 0.310859776 #> 11330 0.310868776 #> 11331 0.310877741 #> 11332 0.310886671 #> 11333 0.310895566 #> 11334 0.310904426 #> 11335 0.310913251 #> 11336 0.310922042 #> 11337 0.310930797 #> 11338 0.310939517 #> 11339 0.310948203 #> 11340 0.310956853 #> 11341 0.310965469 #> 11342 0.310974049 #> 11343 0.310982595 #> 11344 0.310991106 #> 11345 0.310999581 #> 11346 0.311008022 #> 11347 0.311016428 #> 11348 0.311024798 #> 11349 0.311033134 #> 11350 0.311041435 #> 11351 0.311049700 #> 11352 0.311057931 #> 11353 0.311066126 #> 11354 0.311074287 #> 11355 0.311082413 #> 11356 0.311090503 #> 11357 0.311098559 #> 11358 0.311106579 #> 11359 0.311114565 #> 11360 0.311122515 #> 11361 0.311130431 #> 11362 0.311138311 #> 11363 0.311146157 #> 11364 0.311153967 #> 11365 0.311161743 #> 11366 0.311169483 #> 11367 0.311177188 #> 11368 0.311184859 #> 11369 0.311192494 #> 11370 0.311200094 #> 11371 0.311207659 #> 11372 0.311215189 #> 11373 0.311222684 #> 11374 0.311230145 #> 11375 0.311237570 #> 11376 0.311244960 #> 11377 0.311252315 #> 11378 0.311259635 #> 11379 0.311266920 #> 11380 0.311274169 #> 11381 0.311281384 #> 11382 0.311288564 #> 11383 0.311295709 #> 11384 0.311302819 #> 11385 0.311309894 #> 11386 0.311316934 #> 11387 0.311323938 #> 11388 0.311330908 #> 11389 0.311337843 #> 11390 0.311344743 #> 11391 0.311351608 #> 11392 0.311358438 #> 11393 0.311365232 #> 11394 0.311371992 #> 11395 0.311378717 #> 11396 0.311385407 #> 11397 0.311392062 #> 11398 0.311398682 #> 11399 0.311405267 #> 11400 0.311411817 #> 11401 0.311418332 #> 11402 0.311424812 #> 11403 0.311431257 #> 11404 0.311437668 #> 11405 0.311444043 #> 11406 0.311450383 #> 11407 0.311456689 #> 11408 0.311462959 #> 11409 0.311469195 #> 11410 0.311475396 #> 11411 0.311481561 #> 11412 0.311487692 #> 11413 0.311493788 #> 11414 0.311499850 #> 11415 0.311505876 #> 11416 0.311511867 #> 11417 0.311517824 #> 11418 0.311523746 #> 11419 0.311529632 #> 11420 0.311535485 #> 11421 0.311541302 #> 11422 0.311547084 #> 11423 0.311552832 #> 11424 0.311558545 #> 11425 0.311564223 #> 11426 0.311569866 #> 11427 0.311575474 #> 11428 0.311581048 #> 11429 0.311586587 #> 11430 0.311592091 #> 11431 0.311597560 #> 11432 0.311602995 #> 11433 0.311608395 #> 11434 0.311613760 #> 11435 0.311619091 #> 11436 0.311624387 #> 11437 0.311629648 #> 11438 0.311634874 #> 11439 0.311640066 #> 11440 0.311645223 #> 11441 0.311650346 #> 11442 0.311655434 #> 11443 0.311660487 #> 11444 0.311665506 #> 11445 0.311670490 #> 11446 0.311675439 #> 11447 0.311680354 #> 11448 0.311685235 #> 11449 0.311690081 #> 11450 0.311694892 #> 11451 0.311699669 #> 11452 0.311704411 #> 11453 0.311709119 #> 11454 0.311713792 #> 11455 0.311718431 #> 11456 0.311723035 #> 11457 0.311727605 #> 11458 0.311732140 #> 11459 0.311736641 #> 11460 0.311741108 #> 11461 0.311745540 #> 11462 0.311749938 #> 11463 0.311754301 #> 11464 0.311758631 #> 11465 0.311762925 #> 11466 0.311767186 #> 11467 0.311771412 #> 11468 0.311775604 #> 11469 0.311779761 #> 11470 0.311783885 #> 11471 0.311787974 #> 11472 0.311792028 #> 11473 0.311796049 #> 11474 0.311800035 #> 11475 0.311803987 #> 11476 0.311807905 #> 11477 0.311811789 #> 11478 0.311815639 #> 11479 0.311819454 #> 11480 0.311823236 #> 11481 0.311826983 #> 11482 0.311830696 #> 11483 0.311834375 #> 11484 0.311838021 #> 11485 0.311841632 #> 11486 0.311845209 #> 11487 0.311848752 #> 11488 0.311852261 #> 11489 0.311855736 #> 11490 0.311859177 #> 11491 0.311862584 #> 11492 0.311865958 #> 11493 0.311869297 #> 11494 0.311872603 #> 11495 0.311875874 #> 11496 0.311879112 #> 11497 0.311882316 #> 11498 0.311885487 #> 11499 0.311888623 #> 11500 0.311891726 #> 11501 0.311894795 #> 11502 0.311897830 #> 11503 0.311900831 #> 11504 0.311903799 #> 11505 0.311906733 #> 11506 0.311909634 #> 11507 0.311912501 #> 11508 0.311915334 #> 11509 0.311918134 #> 11510 0.311920900 #> 11511 0.311923632 #> 11512 0.311926331 #> 11513 0.311928997 #> 11514 0.311931629 #> 11515 0.311934227 #> 11516 0.311936792 #> 11517 0.311939324 #> 11518 0.311941822 #> 11519 0.311944287 #> 11520 0.311946718 #> 11521 0.311949116 #> 11522 0.311951481 #> 11523 0.311953813 #> 11524 0.311956111 #> 11525 0.311958376 #> 11526 0.311960607 #> 11527 0.311962806 #> 11528 0.311964971 #> 11529 0.311967103 #> 11530 0.311969202 #> 11531 0.311971268 #> 11532 0.311973301 #> 11533 0.311975300 #> 11534 0.311977267 #> 11535 0.311979201 #> 11536 0.311981101 #> 11537 0.311982969 #> 11538 0.311984803 #> 11539 0.311986605 #> 11540 0.311988374 #> 11541 0.311990109 #> 11542 0.311991812 #> 11543 0.311993482 #> 11544 0.311995120 #> 11545 0.311996724 #> 11546 0.311998296 #> 11547 0.311999835 #> 11548 0.312001341 #> 11549 0.312002814 #> 11550 0.312004255 #> 11551 0.312005663 #> 11552 0.312007039 #> 11553 0.312008382 #> 11554 0.312009692 #> 11555 0.312010970 #> 11556 0.312012215 #> 11557 0.312013428 #> 11558 0.312014608 #> 11559 0.312015756 #> 11560 0.312016871 #> 11561 0.312017954 #> 11562 0.312019005 #> 11563 0.312020023 #> 11564 0.312021009 #> 11565 0.312021962 #> 11566 0.312022884 #> 11567 0.312023773 #> 11568 0.312024630 #> 11569 0.312025454 #> 11570 0.312026247 #> 11571 0.312027007 #> 11572 0.312027736 #> 11573 0.312028432 #> 11574 0.312029096 #> 11575 0.312029728 #> 11576 0.312030328 #> 11577 0.312030896 #> 11578 0.312031432 #> 11579 0.312031937 #> 11580 0.312032409 #> 11581 0.312032849 #> 11582 0.312033258 #> 11583 0.312033635 #> 11584 0.312033980 #> 11585 0.312034293 #> 11586 0.312034575 #> 11587 0.312034825 #> 11588 0.312035043 #> 11589 0.312035230 #> 11590 0.312035385 #> 11591 0.312035508 #> 11592 0.312035600 #> 11593 0.312035660 #> 11594 0.312035689 #> 11595 0.312035686 #> 11596 0.312035652 #> 11597 0.312035587 #> 11598 0.312035490 #> 11599 0.312035362 #> 11600 0.312035202 #> 11601 0.312035012 #> 11602 0.312034789 #> 11603 0.312034536 #> 11604 0.312034252 #> 11605 0.312033936 #> 11606 0.312033589 #> 11607 0.312033212 #> 11608 0.312032803 #> 11609 0.312032363 #> 11610 0.312031892 #> 11611 0.312031390 #> 11612 0.312030857 #> 11613 0.312030293 #> 11614 0.312029698 #> 11615 0.312029073 #> 11616 0.312028416 #> 11617 0.312027729 #> 11618 0.312027011 #> 11619 0.312026262 #> 11620 0.312025483 #> 11621 0.312024673 #> 11622 0.312023832 #> 11623 0.312022961 #> 11624 0.312022059 #> 11625 0.312021127 #> 11626 0.312020164 #> 11627 0.312019170 #> 11628 0.312018146 #> 11629 0.312017092 #> 11630 0.312016008 #> 11631 0.312014893 #> 11632 0.312013747 #> 11633 0.312012572 #> 11634 0.312011366 #> 11635 0.312010130 #> 11636 0.312008864 #> 11637 0.312007568 #> 11638 0.312006241 #> 11639 0.312004885 #> 11640 0.312003498 #> 11641 0.312002081 #> 11642 0.312000635 #> 11643 0.311999158 #> 11644 0.311997652 #> 11645 0.311996116 #> 11646 0.311994550 #> 11647 0.311992954 #> 11648 0.311991328 #> 11649 0.311989673 #> 11650 0.311987987 #> 11651 0.311986273 #> 11652 0.311984528 #> 11653 0.311982754 #> 11654 0.311980951 #> 11655 0.311979117 #> 11656 0.311977255 #> 11657 0.311975363 #> 11658 0.311973441 #> 11659 0.311971490 #> 11660 0.311969510 #> 11661 0.311967500 #> 11662 0.311965461 #> 11663 0.311963393 #> 11664 0.311961296 #> 11665 0.311959169 #> 11666 0.311957014 #> 11667 0.311954829 #> 11668 0.311952615 #> 11669 0.311950372 #> 11670 0.311948100 #> 11671 0.311945799 #> 11672 0.311943470 #> 11673 0.311941111 #> 11674 0.311938723 #> 11675 0.311936307 #> 11676 0.311933862 #> 11677 0.311931388 #> 11678 0.311928885 #> 11679 0.311926354 #> 11680 0.311923794 #> 11681 0.311921206 #> 11682 0.311918589 #> 11683 0.311915943 #> 11684 0.311913269 #> 11685 0.311910566 #> 11686 0.311907836 #> 11687 0.311905076 #> 11688 0.311902289 #> 11689 0.311899473 #> 11690 0.311896628 #> 11691 0.311893756 #> 11692 0.311890855 #> 11693 0.311887926 #> 11694 0.311884970 #> 11695 0.311881985 #> 11696 0.311878972 #> 11697 0.311875930 #> 11698 0.311872861 #> 11699 0.311869765 #> 11700 0.311866640 #> 11701 0.311863487 #> 11702 0.311860307 #> 11703 0.311857098 #> 11704 0.311853863 #> 11705 0.311850599 #> 11706 0.311847308 #> 11707 0.311843989 #> 11708 0.311840642 #> 11709 0.311837268 #> 11710 0.311833867 #> 11711 0.311830438 #> 11712 0.311826981 #> 11713 0.311823497 #> 11714 0.311819986 #> 11715 0.311816448 #> 11716 0.311812882 #> 11717 0.311809289 #> 11718 0.311805669 #> 11719 0.311802022 #> 11720 0.311798347 #> 11721 0.311794646 #> 11722 0.311790917 #> 11723 0.311787162 #> 11724 0.311783380 #> 11725 0.311779570 #> 11726 0.311775734 #> 11727 0.311771871 #> 11728 0.311767981 #> 11729 0.311764065 #> 11730 0.311760121 #> 11731 0.311756152 #> 11732 0.311752155 #> 11733 0.311748132 #> 11734 0.311744082 #> 11735 0.311740006 #> 11736 0.311735903 #> 11737 0.311731774 #> 11738 0.311727619 #> 11739 0.311723437 #> 11740 0.311719229 #> 11741 0.311714994 #> 11742 0.311710734 #> 11743 0.311706447 #> 11744 0.311702134 #> 11745 0.311697795 #> 11746 0.311693430 #> 11747 0.311689039 #> 11748 0.311684622 #> 11749 0.311680179 #> 11750 0.311675710 #> 11751 0.311671215 #> 11752 0.311666695 #> 11753 0.311662148 #> 11754 0.311657576 #> 11755 0.311652979 #> 11756 0.311648355 #> 11757 0.311643706 #> 11758 0.311639032 #> 11759 0.311634332 #> 11760 0.311629606 #> 11761 0.311624855 #> 11762 0.311620079 #> 11763 0.311615277 #> 11764 0.311610450 #> 11765 0.311605598 #> 11766 0.311600721 #> 11767 0.311595818 #> 11768 0.311590890 #> 11769 0.311585937 #> 11770 0.311580960 #> 11771 0.311575957 #> 11772 0.311570929 #> 11773 0.311565876 #> 11774 0.311560798 #> 11775 0.311555696 #> 11776 0.311550568 #> 11777 0.311545416 #> 11778 0.311540239 #> 11779 0.311535038 #> 11780 0.311529812 #> 11781 0.311524561 #> 11782 0.311519286 #> 11783 0.311513986 #> 11784 0.311508662 #> 11785 0.311503313 #> 11786 0.311497941 #> 11787 0.311492543 #> 11788 0.311487122 #> 11789 0.311481676 #> 11790 0.311476206 #> 11791 0.311470712 #> 11792 0.311465194 #> 11793 0.311459651 #> 11794 0.311454085 #> 11795 0.311448495 #> 11796 0.311442881 #> 11797 0.311437243 #> 11798 0.311431581 #> 11799 0.311425895 #> 11800 0.311420185 #> 11801 0.311414452 #> 11802 0.311408695 #> 11803 0.311402915 #> 11804 0.311397111 #> 11805 0.311391283 #> 11806 0.311385432 #> 11807 0.311379558 #> 11808 0.311373660 #> 11809 0.311367738 #> 11810 0.311361794 #> 11811 0.311355826 #> 11812 0.311349835 #> 11813 0.311343821 #> 11814 0.311337783 #> 11815 0.311331723 #> 11816 0.311325639 #> 11817 0.311319533 #> 11818 0.311313403 #> 11819 0.311307251 #> 11820 0.311301075 #> 11821 0.311294877 #> 11822 0.311288656 #> 11823 0.311282413 #> 11824 0.311276146 #> 11825 0.311269858 #> 11826 0.311263546 #> 11827 0.311257212 #> 11828 0.311250855 #> 11829 0.311244476 #> 11830 0.311238075 #> 11831 0.311231651 #> 11832 0.311225205 #> 11833 0.311218736 #> 11834 0.311212246 #> 11835 0.311205733 #> 11836 0.311199198 #> 11837 0.311192641 #> 11838 0.311186061 #> 11839 0.311179460 #> 11840 0.311172837 #> 11841 0.311166192 #> 11842 0.311159525 #> 11843 0.311152836 #> 11844 0.311146126 #> 11845 0.311139393 #> 11846 0.311132639 #> 11847 0.311125864 #> 11848 0.311119067 #> 11849 0.311112248 #> 11850 0.311105408 #> 11851 0.311098546 #> 11852 0.311091663 #> 11853 0.311084758 #> 11854 0.311077832 #> 11855 0.311070885 #> 11856 0.311063917 #> 11857 0.311056927 #> 11858 0.311049917 #> 11859 0.311042885 #> 11860 0.311035832 #> 11861 0.311028758 #> 11862 0.311021663 #> 11863 0.311014548 #> 11864 0.311007411 #> 11865 0.311000254 #> 11866 0.310993076 #> 11867 0.310985877 #> 11868 0.310978657 #> 11869 0.310971417 #> 11870 0.310964157 #> 11871 0.310956875 #> 11872 0.310949574 #> 11873 0.310942251 #> 11874 0.310934909 #> 11875 0.310927546 #> 11876 0.310920163 #> 11877 0.310912759 #> 11878 0.310905335 #> 11879 0.310897892 #> 11880 0.310890428 #> 11881 0.310882943 #> 11882 0.310875439 #> 11883 0.310867915 #> 11884 0.310860371 #> 11885 0.310852808 #> 11886 0.310845224 #> 11887 0.310837620 #> 11888 0.310829997 #> 11889 0.310822354 #> 11890 0.310814692 #> 11891 0.310807009 #> 11892 0.310799308 #> 11893 0.310791587 #> 11894 0.310783846 #> 11895 0.310776086 #> 11896 0.310768306 #> 11897 0.310760508 #> 11898 0.310752689 #> 11899 0.310744852 #> 11900 0.310736996 #> 11901 0.310729120 #> 11902 0.310721226 #> 11903 0.310713312 #> 11904 0.310705379 #> 11905 0.310697428 #> 11906 0.310689457 #> 11907 0.310681468 #> 11908 0.310673460 #> 11909 0.310665433 #> 11910 0.310657387 #> 11911 0.310649323 #> 11912 0.310641240 #> 11913 0.310633139 #> 11914 0.310625019 #> 11915 0.310616880 #> 11916 0.310608723 #> 11917 0.310600548 #> 11918 0.310592355 #> 11919 0.310584143 #> 11920 0.310575913 #> 11921 0.310567665 #> 11922 0.310559398 #> 11923 0.310551114 #> 11924 0.310542812 #> 11925 0.310534491 #> 11926 0.310526153 #> 11927 0.310517797 #> 11928 0.310509423 #> 11929 0.310501031 #> 11930 0.310492621 #> 11931 0.310484194 #> 11932 0.310475749 #> 11933 0.310467286 #> 11934 0.310458806 #> 11935 0.310450308 #> 11936 0.310441793 #> 11937 0.310433261 #> 11938 0.310424711 #> 11939 0.310416143 #> 11940 0.310407559 #> 11941 0.310398957 #> 11942 0.310390338 #> 11943 0.310381702 #> 11944 0.310373049 #> 11945 0.310364379 #> 11946 0.310355692 #> 11947 0.310346988 #> 11948 0.310338267 #> 11949 0.310329529 #> 11950 0.310320775 #> 11951 0.310312003 #> 11952 0.310303215 #> 11953 0.310294411 #> 11954 0.310285590 #> 11955 0.310276752 #> 11956 0.310267898 #> 11957 0.310259027 #> 11958 0.310250140 #> 11959 0.310241236 #> 11960 0.310232316 #> 11961 0.310223380 #> 11962 0.310214428 #> 11963 0.310205460 #> 11964 0.310196475 #> 11965 0.310187475 #> 11966 0.310178458 #> 11967 0.310169425 #> 11968 0.310160377 #> 11969 0.310151312 #> 11970 0.310142232 #> 11971 0.310133136 #> 11972 0.310124024 #> 11973 0.310114897 #> 11974 0.310105754 #> 11975 0.310096595 #> 11976 0.310087421 #> 11977 0.310078232 #> 11978 0.310069027 #> 11979 0.310059806 #> 11980 0.310050570 #> 11981 0.310041319 #> 11982 0.310032053 #> 11983 0.310022771 #> 11984 0.310013475 #> 11985 0.310004163 #> 11986 0.309994836 #> 11987 0.309985494 #> 11988 0.309976137 #> 11989 0.309966766 #> 11990 0.309957379 #> 11991 0.309947978 #> 11992 0.309938562 #> 11993 0.309929131 #> 11994 0.309919685 #> 11995 0.309910225 #> 11996 0.309900751 #> 11997 0.309891261 #> 11998 0.309881758 #> 11999 0.309872240 #> 12000 0.309862707 #> 12001 0.309853161 #> 12002 0.309843599 #> 12003 0.309834024 #> 12004 0.309824435 #> 12005 0.309814831 #> 12006 0.309805214 #> 12007 0.309795582 #> 12008 0.309785936 #> 12009 0.309776277 #> 12010 0.309766603 #> 12011 0.309756916 #> 12012 0.309747215 #> 12013 0.309737500 #> 12014 0.309727771 #> 12015 0.309718029 #> 12016 0.309708273 #> 12017 0.309698504 #> 12018 0.309688721 #> 12019 0.309678925 #> 12020 0.309669115 #> 12021 0.309659292 #> 12022 0.309649456 #> 12023 0.309639606 #> 12024 0.309629743 #> 12025 0.309619867 #> 12026 0.309609978 #> 12027 0.309600076 #> 12028 0.309590161 #> 12029 0.309580233 #> 12030 0.309570292 #> 12031 0.309560338 #> 12032 0.309550371 #> 12033 0.309540391 #> 12034 0.309530399 #> 12035 0.309520394 #> 12036 0.309510377 #> 12037 0.309500347 #> 12038 0.309490304 #> 12039 0.309480249 #> 12040 0.309470182 #> 12041 0.309460102 #> 12042 0.309450009 #> 12043 0.309439905 #> 12044 0.309429788 #> 12045 0.309419659 #> 12046 0.309409518 #> 12047 0.309399365 #> 12048 0.309389200 #> 12049 0.309379022 #> 12050 0.309368833 #> 12051 0.309358632 #> 12052 0.309348419 #> 12053 0.309338195 #> 12054 0.309327958 #> 12055 0.309317710 #> 12056 0.309307450 #> 12057 0.309297179 #> 12058 0.309286896 #> 12059 0.309276601 #> 12060 0.309266295 #> 12061 0.309255978 #> 12062 0.309245649 #> 12063 0.309235309 #> 12064 0.309224958 #> 12065 0.309214595 #> 12066 0.309204221 #> 12067 0.309193837 #> 12068 0.309183441 #> 12069 0.309173034 #> 12070 0.309162616 #> 12071 0.309152187 #> 12072 0.309141747 #> 12073 0.309131297 #> 12074 0.309120835 #> 12075 0.309110363 #> 12076 0.309099880 #> 12077 0.309089387 #> 12078 0.309078883 #> 12079 0.309068368 #> 12080 0.309057843 #> 12081 0.309047308 #> 12082 0.309036762 #> 12083 0.309026206 #> 12084 0.309015639 #> 12085 0.309005062 #> 12086 0.308994475 #> 12087 0.308983878 #> 12088 0.308973271 #> 12089 0.308962654 #> 12090 0.308952026 #> 12091 0.308941389 #> 12092 0.308930742 #> 12093 0.308920085 #> 12094 0.308909418 #> 12095 0.308898741 #> 12096 0.308888055 #> 12097 0.308877359 #> 12098 0.308866653 #> 12099 0.308855938 #> 12100 0.308845213 #> 12101 0.308834479 #> 12102 0.308823735 #> 12103 0.308812982 #> 12104 0.308802219 #> 12105 0.308791447 #> 12106 0.308780666 #> 12107 0.308769876 #> 12108 0.308759077 #> 12109 0.308748268 #> 12110 0.308737451 #> 12111 0.308726624 #> 12112 0.308715789 #> 12113 0.308704945 #> 12114 0.308694091 #> 12115 0.308683229 #> 12116 0.308672359 #> 12117 0.308661479 #> 12118 0.308650591 #> 12119 0.308639694 #> 12120 0.308628789 #> 12121 0.308617875 #> 12122 0.308606952 #> 12123 0.308596021 #> 12124 0.308585082 #> 12125 0.308574135 #> 12126 0.308563179 #> 12127 0.308552215 #> 12128 0.308541243 #> 12129 0.308530262 #> 12130 0.308519274 #> 12131 0.308508277 #> 12132 0.308497272 #> 12133 0.308486260 #> 12134 0.308475240 #> 12135 0.308464211 #> 12136 0.308453175 #> 12137 0.308442131 #> 12138 0.308431080 #> 12139 0.308420021 #> 12140 0.308408954 #> 12141 0.308397879 #> 12142 0.308386797 #> 12143 0.308375708 #> 12144 0.308364611 #> 12145 0.308353507 #> 12146 0.308342395 #> 12147 0.308331276 #> 12148 0.308320150 #> 12149 0.308309017 #> 12150 0.308297876 #> 12151 0.308286729 #> 12152 0.308275574 #> 12153 0.308264412 #> 12154 0.308253244 #> 12155 0.308242068 #> 12156 0.308230886 #> 12157 0.308219697 #> 12158 0.308208501 #> 12159 0.308197298 #> 12160 0.308186089 #> 12161 0.308174873 #> 12162 0.308163650 #> 12163 0.308152421 #> 12164 0.308141185 #> 12165 0.308129943 #> 12166 0.308118695 #> 12167 0.308107440 #> 12168 0.308096179 #> 12169 0.308084912 #> 12170 0.308073638 #> 12171 0.308062358 #> 12172 0.308051073 #> 12173 0.308039781 #> 12174 0.308028483 #> 12175 0.308017179 #> 12176 0.308005869 #> 12177 0.307994553 #> 12178 0.307983232 #> 12179 0.307971905 #> 12180 0.307960571 #> 12181 0.307949233 #> 12182 0.307937888 #> 12183 0.307926538 #> 12184 0.307915183 #> 12185 0.307903822 #> 12186 0.307892455 #> 12187 0.307881083 #> 12188 0.307869706 #> 12189 0.307858323 #> 12190 0.307846936 #> 12191 0.307835542 #> 12192 0.307824144 #> 12193 0.307812741 #> 12194 0.307801332 #> 12195 0.307789919 #> 12196 0.307778500 #> 12197 0.307767076 #> 12198 0.307755648 #> 12199 0.307744215 #> 12200 0.307732777 #> 12201 0.307721334 #> 12202 0.307709886 #> 12203 0.307698434 #> 12204 0.307686977 #> 12205 0.307675516 #> 12206 0.307664050 #> 12207 0.307652579 #> 12208 0.307641104 #> 12209 0.307629625 #> 12210 0.307618141 #> 12211 0.307606653 #> 12212 0.307595161 #> 12213 0.307583665 #> 12214 0.307572164 #> 12215 0.307560659 #> 12216 0.307549150 #> 12217 0.307537637 #> 12218 0.307526121 #> 12219 0.307514600 #> 12220 0.307503075 #> 12221 0.307491547 #> 12222 0.307480014 #> 12223 0.307468478 #> 12224 0.307456938 #> 12225 0.307445395 #> 12226 0.307433848 #> 12227 0.307422297 #> 12228 0.307410743 #> 12229 0.307399185 #> 12230 0.307387624 #> 12231 0.307376060 #> 12232 0.307364492 #> 12233 0.307352921 #> 12234 0.307341346 #> 12235 0.307329769 #> 12236 0.307318188 #> 12237 0.307306604 #> 12238 0.307295017 #> 12239 0.307283427 #> 12240 0.307271834 #> 12241 0.307260238 #> 12242 0.307248639 #> 12243 0.307237037 #> 12244 0.307225433 #> 12245 0.307213826 #> 12246 0.307202216 #> 12247 0.307190603 #> 12248 0.307178988 #> 12249 0.307167370 #> 12250 0.307155749 #> 12251 0.307144127 #> 12252 0.307132501 #> 12253 0.307120873 #> 12254 0.307109243 #> 12255 0.307097611 #> 12256 0.307085976 #> 12257 0.307074339 #> 12258 0.307062700 #> 12259 0.307051059 #> 12260 0.307039416 #> 12261 0.307027770 #> 12262 0.307016123 #> 12263 0.307004474 #> 12264 0.306992822 #> 12265 0.306981169 #> 12266 0.306969514 #> 12267 0.306957858 #> 12268 0.306946199 #> 12269 0.306934539 #> 12270 0.306922877 #> 12271 0.306911214 #> 12272 0.306899549 #> 12273 0.306887882 #> 12274 0.306876214 #> 12275 0.306864545 #> 12276 0.306852874 #> 12277 0.306841202 #> 12278 0.306829529 #> 12279 0.306817854 #> 12280 0.306806178 #> 12281 0.306794501 #> 12282 0.306782823 #> 12283 0.306771144 #> 12284 0.306759463 #> 12285 0.306747782 #> 12286 0.306736100 #> 12287 0.306724417 #> 12288 0.306712733 #> 12289 0.306701048 #> 12290 0.306689363 #> 12291 0.306677676 #> 12292 0.306665989 #> 12293 0.306654302 #> 12294 0.306642614 #> 12295 0.306630925 #> 12296 0.306619236 #> 12297 0.306607546 #> 12298 0.306595856 #> 12299 0.306584165 #> 12300 0.306572474 #> 12301 0.306560783 #> 12302 0.306549092 #> 12303 0.306537400 #> 12304 0.306525708 #> 12305 0.306514016 #> 12306 0.306502324 #> 12307 0.306490632 #> 12308 0.306478940 #> 12309 0.306467248 #> 12310 0.306455556 #> 12311 0.306443865 #> 12312 0.306432173 #> 12313 0.306420482 #> 12314 0.306408791 #> 12315 0.306397100 #> 12316 0.306385409 #> 12317 0.306373719 #> 12318 0.306362030 #> 12319 0.306350340 #> 12320 0.306338652 #> 12321 0.306326964 #> 12322 0.306315276 #> 12323 0.306303589 #> 12324 0.306291903 #> 12325 0.306280218 #> 12326 0.306268533 #> 12327 0.306256849 #> 12328 0.306245166 #> 12329 0.306233484 #> 12330 0.306221803 #> 12331 0.306210123 #> 12332 0.306198444 #> 12333 0.306186766 #> 12334 0.306175089 #> 12335 0.306163413 #> 12336 0.306151738 #> 12337 0.306140065 #> 12338 0.306128393 #> 12339 0.306116722 #> 12340 0.306105052 #> 12341 0.306093384 #> 12342 0.306081718 #> 12343 0.306070053 #> 12344 0.306058389 #> 12345 0.306046727 #> 12346 0.306035066 #> 12347 0.306023408 #> 12348 0.306011751 #> 12349 0.306000095 #> 12350 0.305988442 #> 12351 0.305976790 #> 12352 0.305965140 #> 12353 0.305953492 #> 12354 0.305941846 #> 12355 0.305930202 #> 12356 0.305918560 #> 12357 0.305906920 #> 12358 0.305895282 #> 12359 0.305883646 #> 12360 0.305872012 #> 12361 0.305860381 #> 12362 0.305848752 #> 12363 0.305837125 #> 12364 0.305825500 #> 12365 0.305813878 #> 12366 0.305802259 #> 12367 0.305790642 #> 12368 0.305779027 #> 12369 0.305767415 #> 12370 0.305755805 #> 12371 0.305744198 #> 12372 0.305732594 #> 12373 0.305720993 #> 12374 0.305709394 #> 12375 0.305697798 #> 12376 0.305686205 #> 12377 0.305674614 #> 12378 0.305663027 #> 12379 0.305651443 #> 12380 0.305639861 #> 12381 0.305628283 #> 12382 0.305616708 #> 12383 0.305605135 #> 12384 0.305593566 #> 12385 0.305582000 #> 12386 0.305570438 #> 12387 0.305558878 #> 12388 0.305547322 #> 12389 0.305535770 #> 12390 0.305524220 #> 12391 0.305512675 #> 12392 0.305501132 #> 12393 0.305489593 #> 12394 0.305478058 #> 12395 0.305466526 #> 12396 0.305454998 #> 12397 0.305443474 #> 12398 0.305431953 #> 12399 0.305420436 #> 12400 0.305408923 #> 12401 0.305397413 #> 12402 0.305385908 #> 12403 0.305374406 #> 12404 0.305362908 #> 12405 0.305351415 #> 12406 0.305339925 #> 12407 0.305328439 #> 12408 0.305316958 #> 12409 0.305305480 #> 12410 0.305294007 #> 12411 0.305282538 #> 12412 0.305271073 #> 12413 0.305259613 #> 12414 0.305248157 #> 12415 0.305236705 #> 12416 0.305225258 #> 12417 0.305213815 #> 12418 0.305202376 #> 12419 0.305190942 #> 12420 0.305179513 #> 12421 0.305168088 #> 12422 0.305156668 #> 12423 0.305145253 #> 12424 0.305133842 #> 12425 0.305122436 #> 12426 0.305111035 #> 12427 0.305099639 #> 12428 0.305088247 #> 12429 0.305076860 #> 12430 0.305065479 #> 12431 0.305054102 #> 12432 0.305042730 #> 12433 0.305031364 #> 12434 0.305020002 #> 12435 0.305008646 #> 12436 0.304997295 #> 12437 0.304985949 #> 12438 0.304974608 #> 12439 0.304963273 #> 12440 0.304951942 #> 12441 0.304940618 #> 12442 0.304929298 #> 12443 0.304917984 #> 12444 0.304906676 #> 12445 0.304895373 #> 12446 0.304884075 #> 12447 0.304872783 #> 12448 0.304861497 #> 12449 0.304850216 #> 12450 0.304838941 #> 12451 0.304827672 #> 12452 0.304816409 #> 12453 0.304805151 #> 12454 0.304793899 #> 12455 0.304782653 #> 12456 0.304771413 #> 12457 0.304760179 #> 12458 0.304748951 #> 12459 0.304737729 #> 12460 0.304726513 #> 12461 0.304715303 #> 12462 0.304704099 #> 12463 0.304692902 #> 12464 0.304681710 #> 12465 0.304670525 #> 12466 0.304659346 #> 12467 0.304648174 #> 12468 0.304637007 #> 12469 0.304625847 #> 12470 0.304614694 #> 12471 0.304603547 #> 12472 0.304592406 #> 12473 0.304581272 #> 12474 0.304570145 #> 12475 0.304559024 #> 12476 0.304547910 #> 12477 0.304536802 #> 12478 0.304525702 #> 12479 0.304514607 #> 12480 0.304503520 #> 12481 0.304492440 #> 12482 0.304481366 #> 12483 0.304470299 #> 12484 0.304459239 #> 12485 0.304448187 #> 12486 0.304437141 #> 12487 0.304426102 #> 12488 0.304415070 #> 12489 0.304404045 #> 12490 0.304393028 #> 12491 0.304382017 #> 12492 0.304371014 #> 12493 0.304360018 #> 12494 0.304349029 #> 12495 0.304338048 #> 12496 0.304327074 #> 12497 0.304316107 #> 12498 0.304305148 #> 12499 0.304294196 #> 12500 0.304283251 #> 12501 0.304272314 #> 12502 0.304261385 #> 12503 0.304250463 #> 12504 0.304239549 #> 12505 0.304228643 #> 12506 0.304217744 #> 12507 0.304206853 #> 12508 0.304195969 #> 12509 0.304185094 #> 12510 0.304174226 #> 12511 0.304163366 #> 12512 0.304152514 #> 12513 0.304141670 #> 12514 0.304130834 #> 12515 0.304120006 #> 12516 0.304109186 #> 12517 0.304098374 #> 12518 0.304087570 #> 12519 0.304076774 #> 12520 0.304065986 #> 12521 0.304055207 #> 12522 0.304044435 #> 12523 0.304033672 #> 12524 0.304022917 #> 12525 0.304012171 #> 12526 0.304001433 #> 12527 0.303990703 #> 12528 0.303979982 #> 12529 0.303969269 #> 12530 0.303958565 #> 12531 0.303947869 #> 12532 0.303937182 #> 12533 0.303926503 #> 12534 0.303915833 #> 12535 0.303905172 #> 12536 0.303894519 #> 12537 0.303883875 #> 12538 0.303873240 #> 12539 0.303862614 #> 12540 0.303851996 #> 12541 0.303841387 #> 12542 0.303830787 #> 12543 0.303820196 #> 12544 0.303809614 #> 12545 0.303799041 #> 12546 0.303788477 #> 12547 0.303777922 #> 12548 0.303767376 #> 12549 0.303756840 #> 12550 0.303746312 #> 12551 0.303735793 #> 12552 0.303725284 #> 12553 0.303714784 #> 12554 0.303704293 #> 12555 0.303693812 #> 12556 0.303683340 #> 12557 0.303672877 #> 12558 0.303662423 #> 12559 0.303651979 #> 12560 0.303641545 #> 12561 0.303631120 #> 12562 0.303620704 #> 12563 0.303610298 #> 12564 0.303599902 #> 12565 0.303589515 #> 12566 0.303579138 #> 12567 0.303568770 #> 12568 0.303558412 #> 12569 0.303548064 #> 12570 0.303537726 #> 12571 0.303527398 #> 12572 0.303517079 #> 12573 0.303506770 #> 12574 0.303496471 #> 12575 0.303486182 #> 12576 0.303475903 #> 12577 0.303465634 #> 12578 0.303455375 #> 12579 0.303445126 #> 12580 0.303434887 #> 12581 0.303424658 #> 12582 0.303414440 #> 12583 0.303404231 #> 12584 0.303394033 #> 12585 0.303383845 #> 12586 0.303373667 #> 12587 0.303363499 #> 12588 0.303353342 #> 12589 0.303343195 #> 12590 0.303333058 #> 12591 0.303322932 #> 12592 0.303312816 #> 12593 0.303302711 #> 12594 0.303292616 #> 12595 0.303282532 #> 12596 0.303272458 #> 12597 0.303262395 #> 12598 0.303252343 #> 12599 0.303242301 #> 12600 0.303232270 #> 12601 0.303222249 #> 12602 0.303212239 #> 12603 0.303202240 #> 12604 0.303192252 #> 12605 0.303182275 #> 12606 0.303172308 #> 12607 0.303162352 #> 12608 0.303152408 #> 12609 0.303142474 #> 12610 0.303132551 #> 12611 0.303122639 #> 12612 0.303112738 #> 12613 0.303102848 #> 12614 0.303092969 #> 12615 0.303083101 #> 12616 0.303073245 #> 12617 0.303063399 #> 12618 0.303053565 #> 12619 0.303043742 #> 12620 0.303033930 #> 12621 0.303024130 #> 12622 0.303014340 #> 12623 0.303004563 #> 12624 0.302994796 #> 12625 0.302985041 #> 12626 0.302975297 #> 12627 0.302965565 #> 12628 0.302955844 #> 12629 0.302946134 #> 12630 0.302936437 #> 12631 0.302926750 #> 12632 0.302917075 #> 12633 0.302907412 #> 12634 0.302897761 #> 12635 0.302888121 #> 12636 0.302878493 #> 12637 0.302868876 #> 12638 0.302859272 #> 12639 0.302849679 #> 12640 0.302840097 #> 12641 0.302830528 #> 12642 0.302820970 #> 12643 0.302811425 #> 12644 0.302801891 #> 12645 0.302792369 #> 12646 0.302782859 #> 12647 0.302773362 #> 12648 0.302763876 #> 12649 0.302754402 #> 12650 0.302744940 #> 12651 0.302735490 #> 12652 0.302726053 #> 12653 0.302716627 #> 12654 0.302707214 #> 12655 0.302697813 #> 12656 0.302688424 #> 12657 0.302679047 #> 12658 0.302669683 #> 12659 0.302660331 #> 12660 0.302650991 #> 12661 0.302641664 #> 12662 0.302632348 #> 12663 0.302623046 #> 12664 0.302613756 #> 12665 0.302604478 #> 12666 0.302595212 #> 12667 0.302585960 #> 12668 0.302576719 #> 12669 0.302567491 #> 12670 0.302558276 #> 12671 0.302549074 #> 12672 0.302539884 #> 12673 0.302530706 #> 12674 0.302521542 #> 12675 0.302512390 #> 12676 0.302503251 #> 12677 0.302494124 #> 12678 0.302485010 #> 12679 0.302475910 #> 12680 0.302466822 #> 12681 0.302457746 #> 12682 0.302448684 #> 12683 0.302439634 #> 12684 0.302430598 #> 12685 0.302421574 #> 12686 0.302412564 #> 12687 0.302403566 #> 12688 0.302394581 #> 12689 0.302385610 #> 12690 0.302376651 #> 12691 0.302367706 #> 12692 0.302358774 #> 12693 0.302349855 #> 12694 0.302340949 #> 12695 0.302332056 #> 12696 0.302323176 #> 12697 0.302314310 #> 12698 0.302305457 #> 12699 0.302296617 #> 12700 0.302287790 #> 12701 0.302278977 #> 12702 0.302270177 #> 12703 0.302261391 #> 12704 0.302252618 #> 12705 0.302243858 #> 12706 0.302235112 #> 12707 0.302226379 #> 12708 0.302217660 #> 12709 0.302208954 #> 12710 0.302200262 #> 12711 0.302191583 #> 12712 0.302182918 #> 12713 0.302174267 #> 12714 0.302165629 #> 12715 0.302157005 #> 12716 0.302148394 #> 12717 0.302139797 #> 12718 0.302131214 #> 12719 0.302122645 #> 12720 0.302114089 #> 12721 0.302105547 #> 12722 0.302097019 #> 12723 0.302088505 #> 12724 0.302080005 #> 12725 0.302071518 #> 12726 0.302063045 #> 12727 0.302054587 #> 12728 0.302046142 #> 12729 0.302037711 #> 12730 0.302029294 #> 12731 0.302020892 #> 12732 0.302012503 #> 12733 0.302004128 #> 12734 0.301995767 #> 12735 0.301987421 #> 12736 0.301979088 #> 12737 0.301970770 #> 12738 0.301962466 #> 12739 0.301954176 #> 12740 0.301945900 #> 12741 0.301937638 #> 12742 0.301929391 #> 12743 0.301921158 #> 12744 0.301912939 #> 12745 0.301904734 #> 12746 0.301896544 #> 12747 0.301888368 #> 12748 0.301880207 #> 12749 0.301872059 #> 12750 0.301863927 #> 12751 0.301855808 #> 12752 0.301847704 #> 12753 0.301839615 #> 12754 0.301831540 #> 12755 0.301823479 #> 12756 0.301815433 #> 12757 0.301807402 #> 12758 0.301799385 #> 12759 0.301791383 #> 12760 0.301783395 #> 12761 0.301775422 #> 12762 0.301767464 #> 12763 0.301759520 #> 12764 0.301751591 #> 12765 0.301743677 #> 12766 0.301735777 #> 12767 0.301727892 #> 12768 0.301720022 #> 12769 0.301712167 #> 12770 0.301704326 #> 12771 0.301696500 #> 12772 0.301688689 #> 12773 0.301680893 #> 12774 0.301673112 #> 12775 0.301665346 #> 12776 0.301657594 #> 12777 0.301649858 #> 12778 0.301642136 #> 12779 0.301634430 #> 12780 0.301626738 #> 12781 0.301619061 #> 12782 0.301611400 #> 12783 0.301603753 #> 12784 0.301596122 #> 12785 0.301588505 #> 12786 0.301580904 #> 12787 0.301573318 #> 12788 0.301565747 #> 12789 0.301558191 #> 12790 0.301550650 #> 12791 0.301543125 #> 12792 0.301535614 #> 12793 0.301528119 #> 12794 0.301520639 #> 12795 0.301513174 #> 12796 0.301505725 #> 12797 0.301498291 #> 12798 0.301490872 #> 12799 0.301483469 #> 12800 0.301476081 #> 12801 0.301468708 #> 12802 0.301461350 #> 12803 0.301454008 #> 12804 0.301446682 #> 12805 0.301439371 #> 12806 0.301432075 #> 12807 0.301424795 #> 12808 0.301417530 #> 12809 0.301410281 #> 12810 0.301403047 #> 12811 0.301395829 #> 12812 0.301388626 #> 12813 0.301381439 #> 12814 0.301374267 #> 12815 0.301367111 #> 12816 0.301359971 #> 12817 0.301352846 #> 12818 0.301345737 #> 12819 0.301338644 #> 12820 0.301331566 #> 12821 0.301324504 #> 12822 0.301317457 #> 12823 0.301310427 #> 12824 0.301303412 #> 12825 0.301296413 #> 12826 0.301289429 #> 12827 0.301282462 #> 12828 0.301275510 #> 12829 0.301268574 #> 12830 0.301261654 #> 12831 0.301254750 #> 12832 0.301247861 #> 12833 0.301240989 #> 12834 0.301234132 #> 12835 0.301227291 #> 12836 0.301220466 #> 12837 0.301213657 #> 12838 0.301206864 #> 12839 0.301200087 #> 12840 0.301193326 #> 12841 0.301186581 #> 12842 0.301179852 #> 12843 0.301173140 #> 12844 0.301166443 #> 12845 0.301159762 #> 12846 0.301153097 #> 12847 0.301146448 #> 12848 0.301139816 #> 12849 0.301133199 #> 12850 0.301126599 #> 12851 0.301120014 #> 12852 0.301113446 #> 12853 0.301106894 #> 12854 0.301100359 #> 12855 0.301093839 #> 12856 0.301087336 #> 12857 0.301080849 #> 12858 0.301074378 #> 12859 0.301067923 #> 12860 0.301061485 #> 12861 0.301055063 #> 12862 0.301048657 #> 12863 0.301042268 #> 12864 0.301035894 #> 12865 0.301029537 #> 12866 0.301023197 #> 12867 0.301016873 #> 12868 0.301010565 #> 12869 0.301004274 #> 12870 0.300997999 #> 12871 0.300991740 #> 12872 0.300985498 #> 12873 0.300979272 #> 12874 0.300973063 #> 12875 0.300966870 #> 12876 0.300960694 #> 12877 0.300954534 #> 12878 0.300948390 #> 12879 0.300942264 #> 12880 0.300936153 #> 12881 0.300930059 #> 12882 0.300923982 #> 12883 0.300917921 #> 12884 0.300911877 #> 12885 0.300905850 #> 12886 0.300899839 #> 12887 0.300893844 #> 12888 0.300887867 #> 12889 0.300881905 #> 12890 0.300875961 #> 12891 0.300870033 #> 12892 0.300864122 #> 12893 0.300858227 #> 12894 0.300852350 #> 12895 0.300846488 #> 12896 0.300840644 #> 12897 0.300834816 #> 12898 0.300829005 #> 12899 0.300823211 #> 12900 0.300817434 #> 12901 0.300811673 #> 12902 0.300805929 #> 12903 0.300800202 #> 12904 0.300794491 #> 12905 0.300788798 #> 12906 0.300783121 #> 12907 0.300777461 #> 12908 0.300771818 #> 12909 0.300766192 #> 12910 0.300760582 #> 12911 0.300754990 #> 12912 0.300749414 #> 12913 0.300743855 #> 12914 0.300738313 #> 12915 0.300732788 #> 12916 0.300727280 #> 12917 0.300721789 #> 12918 0.300716315 #> 12919 0.300710858 #> 12920 0.300705417 #> 12921 0.300699994 #> 12922 0.300694588 #> 12923 0.300689198 #> 12924 0.300683826 #> 12925 0.300678471 #> 12926 0.300673132 #> 12927 0.300667811 #> 12928 0.300662507 #> 12929 0.300657219 #> 12930 0.300651949 #> 12931 0.300646696 #> 12932 0.300641460 #> 12933 0.300636241 #> 12934 0.300631039 #> 12935 0.300625854 #> 12936 0.300620686 #> 12937 0.300615535 #> 12938 0.300610402 #> 12939 0.300605285 #> 12940 0.300600186 #> 12941 0.300595104 #> 12942 0.300590039 #> 12943 0.300584991 #> 12944 0.300579960 #> 12945 0.300574947 #> 12946 0.300569951 #> 12947 0.300564971 #> 12948 0.300560010 #> 12949 0.300555065 #> 12950 0.300550137 #> 12951 0.300545227 #> 12952 0.300540334 #> 12953 0.300535458 #> 12954 0.300530599 #> 12955 0.300525758 #> 12956 0.300520934 #> 12957 0.300516127 #> 12958 0.300511338 #> 12959 0.300506565 #> 12960 0.300501810 #> 12961 0.300497073 #> 12962 0.300492352 #> 12963 0.300487649 #> 12964 0.300482963 #> 12965 0.300478295 #> 12966 0.300473644 #> 12967 0.300469010 #> 12968 0.300464393 #> 12969 0.300459794 #> 12970 0.300455212 #> 12971 0.300450648 #> 12972 0.300446101 #> 12973 0.300441571 #> 12974 0.300437059 #> 12975 0.300432564 #> 12976 0.300428087 #> 12977 0.300423626 #> 12978 0.300419184 #> 12979 0.300414758 #> 12980 0.300410350 #> 12981 0.300405960 #> 12982 0.300401587 #> 12983 0.300397231 #> 12984 0.300392893 #> 12985 0.300388572 #> 12986 0.300384269 #> 12987 0.300379983 #> 12988 0.300375714 #> 12989 0.300371463 #> 12990 0.300367230 #> 12991 0.300363014 #> 12992 0.300358815 #> 12993 0.300354634 #> 12994 0.300350471 #> 12995 0.300346324 #> 12996 0.300342196 #> 12997 0.300338085 #> 12998 0.300333991 #> 12999 0.300329915 #> 13000 0.300325856 #> 13001 0.300321815 #> 13002 0.300317792 #> 13003 0.300313786 #> 13004 0.300309797 #> 13005 0.300305826 #> 13006 0.300301873 #> 13007 0.300297937 #> 13008 0.300294018 #> 13009 0.300290117 #> 13010 0.300286234 #> 13011 0.300282368 #> 13012 0.300278520 #> 13013 0.300274690 #> 13014 0.300270877 #> 13015 0.300267081 #> 13016 0.300263303 #> 13017 0.300259543 #> 13018 0.300255800 #> 13019 0.300252075 #> 13020 0.300248367 #> 13021 0.300244677 #> 13022 0.300241005 #> 13023 0.300237350 #> 13024 0.300233713 #> 13025 0.300230093 #> 13026 0.300226491 #> 13027 0.300222907 #> 13028 0.300219340 #> 13029 0.300215791 #> 13030 0.300212260 #> 13031 0.300208746 #> 13032 0.300205249 #> 13033 0.300201771 #> 13034 0.300198309 #> 13035 0.300194866 #> 13036 0.300191440 #> 13037 0.300188032 #> 13038 0.300184641 #> 13039 0.300181268 #> 13040 0.300177913 #> 13041 0.300174575 #> 13042 0.300171255 #> 13043 0.300167953 #> 13044 0.300164668 #> 13045 0.300161401 #> 13046 0.300158151 #> 13047 0.300154920 #> 13048 0.300151705 #> 13049 0.300148509 #> 13050 0.300145330 #> 13051 0.300142169 #> 13052 0.300139025 #> 13053 0.300135899 #> 13054 0.300132791 #> 13055 0.300129700 #> 13056 0.300126627 #> 13057 0.300123572 #> 13058 0.300120534 #> 13059 0.300117514 #> 13060 0.300114512 #> 13061 0.300111527 #> 13062 0.300108560 #> 13063 0.300105610 #> 13064 0.300102679 #> 13065 0.300099765 #> 13066 0.300096868 #> 13067 0.300093989 #> 13068 0.300091128 #> 13069 0.300088285 #> 13070 0.300085459 #> 13071 0.300082651 #> 13072 0.300079860 #> 13073 0.300077087 #> 13074 0.300074332 #> 13075 0.300071594 #> 13076 0.300068875 #> 13077 0.300066172 #> 13078 0.300063488 #> 13079 0.300060821 #> 13080 0.300058172 #> 13081 0.300055540 #> 13082 0.300052926 #> 13083 0.300050330 #> 13084 0.300047751 #> 13085 0.300045190 #> 13086 0.300042646 #> 13087 0.300040121 #> 13088 0.300037613 #> 13089 0.300035122 #> 13090 0.300032649 #> 13091 0.300030194 #> 13092 0.300027756 #> 13093 0.300025337 #> 13094 0.300022934 #> 13095 0.300020550 #> 13096 0.300018183 #> 13097 0.300015833 #> 13098 0.300013501 #> 13099 0.300011187 #> 13100 0.300008891 #> 13101 0.300006612 #> 13102 0.300004350 #> 13103 0.300002107 #> 13104 0.299999881 #> 13105 0.299997672 #> 13106 0.299995481 #> 13107 0.299993308 #> 13108 0.299991153 #> 13109 0.299989015 #> 13110 0.299986894 #> 13111 0.299984791 #> 13112 0.299982706 #> 13113 0.299980638 #> 13114 0.299978588 #> 13115 0.299976556 #> 13116 0.299974541 #> 13117 0.299972543 #> 13118 0.299970564 #> 13119 0.299968601 #> 13120 0.299966657 #> 13121 0.299964730 #> 13122 0.299962820 #> 13123 0.299960928 #> 13124 0.299959054 #> 13125 0.299957197 #> 13126 0.299955358 #> 13127 0.299953536 #> 13128 0.299951732 #> 13129 0.299949945 #> 13130 0.299948176 #> 13131 0.299946424 #> 13132 0.299944690 #> 13133 0.299942974 #> 13134 0.299941274 #> 13135 0.299939593 #> 13136 0.299937929 #> 13137 0.299936282 #> 13138 0.299934653 #> 13139 0.299933042 #> 13140 0.299931448 #> 13141 0.299929871 #> 13142 0.299928312 #> 13143 0.299926770 #> 13144 0.299925246 #> 13145 0.299923739 #> 13146 0.299922250 #> 13147 0.299920778 #> 13148 0.299919324 #> 13149 0.299917887 #> 13150 0.299916467 #> 13151 0.299915065 #> 13152 0.299913681 #> 13153 0.299912313 #> 13154 0.299910964 #> 13155 0.299909631 #> 13156 0.299908316 #> 13157 0.299907019 #> 13158 0.299905738 #> 13159 0.299904476 #> 13160 0.299903230 #> 13161 0.299902002 #> 13162 0.299900791 #> 13163 0.299899598 #> 13164 0.299898422 #> 13165 0.299897264 #> 13166 0.299896122 #> 13167 0.299894998 #> 13168 0.299893892 #> 13169 0.299892803 #> 13170 0.299891731 #> 13171 0.299890676 #> 13172 0.299889639 #> 13173 0.299888619 #> 13174 0.299887616 #> 13175 0.299886630 #> 13176 0.299885662 #> 13177 0.299884711 #> 13178 0.299883778 #> 13179 0.299882861 #> 13180 0.299881962 #> 13181 0.299881080 #> 13182 0.299880216 #> 13183 0.299879368 #> 13184 0.299878538 #> 13185 0.299877725 #> 13186 0.299876929 #> 13187 0.299876151 #> 13188 0.299875389 #> 13189 0.299874645 #> 13190 0.299873918 #> 13191 0.299873208 #> 13192 0.299872516 #> 13193 0.299871840 #> 13194 0.299871182 #> 13195 0.299870541 #> 13196 0.299869917 #> 13197 0.299869310 #> 13198 0.299868720 #> 13199 0.299868147 #> 13200 0.299867591 #> 13201 0.299867053 #> 13202 0.299866531 #> 13203 0.299866027 #> 13204 0.299865539 #> 13205 0.299865069 #> 13206 0.299864616 #> 13207 0.299864180 #> 13208 0.299863760 #> 13209 0.299863358 #> 13210 0.299862973 #> 13211 0.299862605 #> 13212 0.299862254 #> 13213 0.299861920 #> 13214 0.299861602 #> 13215 0.299861302 #> 13216 0.299861019 #> 13217 0.299860753 #> 13218 0.299860503 #> 13219 0.299860271 #> 13220 0.299860055 #> 13221 0.299859856 #> 13222 0.299859675 #> 13223 0.299859510 #> 13224 0.299859362 #> 13225 0.299859231 #> 13226 0.299859117 #> 13227 0.299859019 #> 13228 0.299858939 #> 13229 0.299858875 #> 13230 0.299858828 #> 13231 0.299858798 #> 13232 0.299858785 #> 13233 0.299858788 #> 13234 0.299858809 #> 13235 0.299858846 #> 13236 0.299858899 #> 13237 0.299858970 #> 13238 0.299859057 #> 13239 0.299859161 #> 13240 0.299859282 #> 13241 0.299859420 #> 13242 0.299859574 #> 13243 0.299859744 #> 13244 0.299859932 #> 13245 0.299860136 #> 13246 0.299860357 #> 13247 0.299860594 #> 13248 0.299860849 #> 13249 0.299861119 #> 13250 0.299861407 #> 13251 0.299861711 #> 13252 0.299862031 #> 13253 0.299862368 #> 13254 0.299862722 #> 13255 0.299863092 #> 13256 0.299863479 #> 13257 0.299863882 #> 13258 0.299864302 #> 13259 0.299864738 #> 13260 0.299865191 #> 13261 0.299865660 #> 13262 0.299866146 #> 13263 0.299866648 #> 13264 0.299867167 #> 13265 0.299867702 #> 13266 0.299868253 #> 13267 0.299868821 #> 13268 0.299869405 #> 13269 0.299870006 #> 13270 0.299870623 #> 13271 0.299871256 #> 13272 0.299871906 #> 13273 0.299872572 #> 13274 0.299873254 #> 13275 0.299873953 #> 13276 0.299874668 #> 13277 0.299875399 #> 13278 0.299876147 #> 13279 0.299876911 #> 13280 0.299877691 #> 13281 0.299878487 #> 13282 0.299879300 #> 13283 0.299880128 #> 13284 0.299880973 #> 13285 0.299881834 #> 13286 0.299882712 #> 13287 0.299883605 #> 13288 0.299884514 #> 13289 0.299885440 #> 13290 0.299886382 #> 13291 0.299887340 #> 13292 0.299888314 #> 13293 0.299889304 #> 13294 0.299890310 #> 13295 0.299891332 #> 13296 0.299892370 #> 13297 0.299893424 #> 13298 0.299894494 #> 13299 0.299895581 #> 13300 0.299896683 #> 13301 0.299897801 #> 13302 0.299898935 #> 13303 0.299900085 #> 13304 0.299901251 #> 13305 0.299902432 #> 13306 0.299903630 #> 13307 0.299904844 #> 13308 0.299906073 #> 13309 0.299907318 #> 13310 0.299908579 #> 13311 0.299909856 #> 13312 0.299911149 #> 13313 0.299912457 #> 13314 0.299913782 #> 13315 0.299915122 #> 13316 0.299916478 #> 13317 0.299917849 #> 13318 0.299919236 #> 13319 0.299920639 #> 13320 0.299922058 #> 13321 0.299923492 #> 13322 0.299924942 #> 13323 0.299926407 #> 13324 0.299927889 #> 13325 0.299929385 #> 13326 0.299930898 #> 13327 0.299932426 #> 13328 0.299933969 #> 13329 0.299935528 #> 13330 0.299937103 #> 13331 0.299938693 #> 13332 0.299940298 #> 13333 0.299941919 #> 13334 0.299943556 #> 13335 0.299945208 #> 13336 0.299946875 #> 13337 0.299948558 #> 13338 0.299950256 #> 13339 0.299951970 #> 13340 0.299953699 #> 13341 0.299955443 #> 13342 0.299957203 #> 13343 0.299958978 #> 13344 0.299960768 #> 13345 0.299962574 #> 13346 0.299964395 #> 13347 0.299966231 #> 13348 0.299968082 #> 13349 0.299969949 #> 13350 0.299971830 #> 13351 0.299973727 #> 13352 0.299975639 #> 13353 0.299977567 #> 13354 0.299979509 #> 13355 0.299981467 #> 13356 0.299983439 #> 13357 0.299985427 #> 13358 0.299987430 #> 13359 0.299989448 #> 13360 0.299991481 #> 13361 0.299993528 #> 13362 0.299995591 #> 13363 0.299997669 #> 13364 0.299999762 #> 13365 0.300001870 #> 13366 0.300003993 #> 13367 0.300006130 #> 13368 0.300008283 #> 13369 0.300010450 #> 13370 0.300012633 #> 13371 0.300014830 #> 13372 0.300017042 #> 13373 0.300019268 #> 13374 0.300021510 #> 13375 0.300023766 #> 13376 0.300026037 #> 13377 0.300028323 #> 13378 0.300030624 #> 13379 0.300032939 #> 13380 0.300035269 #> 13381 0.300037613 #> 13382 0.300039973 #> 13383 0.300042347 #> 13384 0.300044735 #> 13385 0.300047138 #> 13386 0.300049556 #> 13387 0.300051988 #> 13388 0.300054435 #> 13389 0.300056896 #> 13390 0.300059372 #> 13391 0.300061862 #> 13392 0.300064366 #> 13393 0.300066886 #> 13394 0.300069419 #> 13395 0.300071967 #> 13396 0.300074529 #> 13397 0.300077106 #> 13398 0.300079697 #> 13399 0.300082303 #> 13400 0.300084922 #> 13401 0.300087556 #> 13402 0.300090204 #> 13403 0.300092867 #> 13404 0.300095544 #> 13405 0.300098235 #> 13406 0.300100940 #> 13407 0.300103659 #> 13408 0.300106392 #> 13409 0.300109140 #> 13410 0.300111902 #> 13411 0.300114678 #> 13412 0.300117468 #> 13413 0.300120271 #> 13414 0.300123089 #> 13415 0.300125921 #> 13416 0.300128767 #> 13417 0.300131627 #> 13418 0.300134501 #> 13419 0.300137389 #> 13420 0.300140291 #> 13421 0.300143207 #> 13422 0.300146136 #> 13423 0.300149080 #> 13424 0.300152037 #> 13425 0.300155008 #> 13426 0.300157993 #> 13427 0.300160992 #> 13428 0.300164004 #> 13429 0.300167030 #> 13430 0.300170070 #> 13431 0.300173124 #> 13432 0.300176191 #> 13433 0.300179272 #> 13434 0.300182366 #> 13435 0.300185474 #> 13436 0.300188596 #> 13437 0.300191731 #> 13438 0.300194880 #> 13439 0.300198043 #> 13440 0.300201219 #> 13441 0.300204408 #> 13442 0.300207611 #> 13443 0.300210827 #> 13444 0.300214057 #> 13445 0.300217300 #> 13446 0.300220556 #> 13447 0.300223826 #> 13448 0.300227110 #> 13449 0.300230406 #> 13450 0.300233716 #> 13451 0.300237039 #> 13452 0.300240375 #> 13453 0.300243725 #> 13454 0.300247088 #> 13455 0.300250464 #> 13456 0.300253853 #> 13457 0.300257255 #> 13458 0.300260671 #> 13459 0.300264099 #> 13460 0.300267541 #> 13461 0.300270996 #> 13462 0.300274463 #> 13463 0.300277944 #> 13464 0.300281438 #> 13465 0.300284945 #> 13466 0.300288464 #> 13467 0.300291997 #> 13468 0.300295542 #> 13469 0.300299101 #> 13470 0.300302672 #> 13471 0.300306256 #> 13472 0.300309853 #> 13473 0.300313463 #> 13474 0.300317085 #> 13475 0.300320721 #> 13476 0.300324369 #> 13477 0.300328029 #> 13478 0.300331703 #> 13479 0.300335389 #> 13480 0.300339087 #> 13481 0.300342799 #> 13482 0.300346523 #> 13483 0.300350259 #> 13484 0.300354008 #> 13485 0.300357770 #> 13486 0.300361544 #> 13487 0.300365330 #> 13488 0.300369129 #> 13489 0.300372941 #> 13490 0.300376764 #> 13491 0.300380601 #> 13492 0.300384449 #> 13493 0.300388310 #> 13494 0.300392184 #> 13495 0.300396069 #> 13496 0.300399967 #> 13497 0.300403877 #> 13498 0.300407799 #> 13499 0.300411734 #> 13500 0.300415681 #> 13501 0.300419640 #> 13502 0.300423611 #> 13503 0.300427594 #> 13504 0.300431589 #> 13505 0.300435596 #> 13506 0.300439616 #> 13507 0.300443647 #> 13508 0.300447690 #> 13509 0.300451746 #> 13510 0.300455813 #> 13511 0.300459892 #> 13512 0.300463983 #> 13513 0.300468086 #> 13514 0.300472201 #> 13515 0.300476328 #> 13516 0.300480466 #> 13517 0.300484616 #> 13518 0.300488778 #> 13519 0.300492952 #> 13520 0.300497138 #> 13521 0.300501335 #> 13522 0.300505543 #> 13523 0.300509764 #> 13524 0.300513996 #> 13525 0.300518240 #> 13526 0.300522495 #> 13527 0.300526762 #> 13528 0.300531040 #> 13529 0.300535330 #> 13530 0.300539631 #> 13531 0.300543944 #> 13532 0.300548268 #> 13533 0.300552603 #> 13534 0.300556950 #> 13535 0.300561308 #> 13536 0.300565678 #> 13537 0.300570059 #> 13538 0.300574451 #> 13539 0.300578854 #> 13540 0.300583269 #> 13541 0.300587695 #> 13542 0.300592132 #> 13543 0.300596580 #> 13544 0.300601039 #> 13545 0.300605509 #> 13546 0.300609991 #> 13547 0.300614483 #> 13548 0.300618987 #> 13549 0.300623501 #> 13550 0.300628027 #> 13551 0.300632563 #> 13552 0.300637111 #> 13553 0.300641669 #> 13554 0.300646238 #> 13555 0.300650818 #> 13556 0.300655409 #> 13557 0.300660011 #> 13558 0.300664623 #> 13559 0.300669246 #> 13560 0.300673880 #> 13561 0.300678525 #> 13562 0.300683180 #> 13563 0.300687846 #> 13564 0.300692523 #> 13565 0.300697210 #> 13566 0.300701908 #> 13567 0.300706616 #> 13568 0.300711335 #> 13569 0.300716064 #> 13570 0.300720804 #> 13571 0.300725554 #> 13572 0.300730315 #> 13573 0.300735086 #> 13574 0.300739867 #> 13575 0.300744659 #> 13576 0.300749461 #> 13577 0.300754273 #> 13578 0.300759096 #> 13579 0.300763929 #> 13580 0.300768772 #> 13581 0.300773625 #> 13582 0.300778489 #> 13583 0.300783362 #> 13584 0.300788246 #> 13585 0.300793140 #> 13586 0.300798044 #> 13587 0.300802958 #> 13588 0.300807881 #> 13589 0.300812815 #> 13590 0.300817759 #> 13591 0.300822713 #> 13592 0.300827677 #> 13593 0.300832650 #> 13594 0.300837634 #> 13595 0.300842627 #> 13596 0.300847630 #> 13597 0.300852643 #> 13598 0.300857665 #> 13599 0.300862698 #> 13600 0.300867740 #> 13601 0.300872791 #> 13602 0.300877852 #> 13603 0.300882923 #> 13604 0.300888004 #> 13605 0.300893094 #> 13606 0.300898194 #> 13607 0.300903303 #> 13608 0.300908421 #> 13609 0.300913549 #> 13610 0.300918687 #> 13611 0.300923834 #> 13612 0.300928990 #> 13613 0.300934156 #> 13614 0.300939331 #> 13615 0.300944515 #> 13616 0.300949708 #> 13617 0.300954911 #> 13618 0.300960123 #> 13619 0.300965344 #> 13620 0.300970575 #> 13621 0.300975814 #> 13622 0.300981063 #> 13623 0.300986320 #> 13624 0.300991587 #> 13625 0.300996863 #> 13626 0.301002147 #> 13627 0.301007441 #> 13628 0.301012744 #> 13629 0.301018056 #> 13630 0.301023376 #> 13631 0.301028705 #> 13632 0.301034044 #> 13633 0.301039391 #> 13634 0.301044746 #> 13635 0.301050111 #> 13636 0.301055484 #> 13637 0.301060866 #> 13638 0.301066257 #> 13639 0.301071656 #> 13640 0.301077064 #> 13641 0.301082481 #> 13642 0.301087906 #> 13643 0.301093340 #> 13644 0.301098782 #> 13645 0.301104233 #> 13646 0.301109692 #> 13647 0.301115159 #> 13648 0.301120635 #> 13649 0.301126120 #> 13650 0.301131612 #> 13651 0.301137113 #> 13652 0.301142623 #> 13653 0.301148140 #> 13654 0.301153666 #> 13655 0.301159200 #> 13656 0.301164742 #> 13657 0.301170293 #> 13658 0.301175851 #> 13659 0.301181418 #> 13660 0.301186993 #> 13661 0.301192575 #> 13662 0.301198166 #> 13663 0.301203765 #> 13664 0.301209372 #> 13665 0.301214986 #> 13666 0.301220609 #> 13667 0.301226240 #> 13668 0.301231878 #> 13669 0.301237524 #> 13670 0.301243178 #> 13671 0.301248840 #> 13672 0.301254509 #> 13673 0.301260186 #> 13674 0.301265871 #> 13675 0.301271564 #> 13676 0.301277264 #> 13677 0.301282972 #> 13678 0.301288687 #> 13679 0.301294410 #> 13680 0.301300141 #> 13681 0.301305878 #> 13682 0.301311624 #> 13683 0.301317377 #> 13684 0.301323137 #> 13685 0.301328905 #> 13686 0.301334679 #> 13687 0.301340462 #> 13688 0.301346251 #> 13689 0.301352048 #> 13690 0.301357852 #> 13691 0.301363664 #> 13692 0.301369482 #> 13693 0.301375308 #> 13694 0.301381141 #> 13695 0.301386981 #> 13696 0.301392828 #> 13697 0.301398682 #> 13698 0.301404543 #> 13699 0.301410411 #> 13700 0.301416286 #> 13701 0.301422168 #> 13702 0.301428056 #> 13703 0.301433952 #> 13704 0.301439855 #> 13705 0.301445764 #> 13706 0.301451680 #> 13707 0.301457603 #> 13708 0.301463533 #> 13709 0.301469469 #> 13710 0.301475412 #> 13711 0.301481362 #> 13712 0.301487318 #> 13713 0.301493281 #> 13714 0.301499251 #> 13715 0.301505227 #> 13716 0.301511209 #> 13717 0.301517198 #> 13718 0.301523194 #> 13719 0.301529196 #> 13720 0.301535204 #> 13721 0.301541218 #> 13722 0.301547239 #> 13723 0.301553267 #> 13724 0.301559300 #> 13725 0.301565340 #> 13726 0.301571386 #> 13727 0.301577439 #> 13728 0.301583497 #> 13729 0.301589561 #> 13730 0.301595632 #> 13731 0.301601709 #> 13732 0.301607792 #> 13733 0.301613881 #> 13734 0.301619975 #> 13735 0.301626076 #> 13736 0.301632183 #> 13737 0.301638296 #> 13738 0.301644414 #> 13739 0.301650539 #> 13740 0.301656669 #> 13741 0.301662805 #> 13742 0.301668947 #> 13743 0.301675094 #> 13744 0.301681247 #> 13745 0.301687406 #> 13746 0.301693571 #> 13747 0.301699741 #> 13748 0.301705917 #> 13749 0.301712098 #> 13750 0.301718285 #> 13751 0.301724478 #> 13752 0.301730676 #> 13753 0.301736879 #> 13754 0.301743088 #> 13755 0.301749302 #> 13756 0.301755521 #> 13757 0.301761746 #> 13758 0.301767977 #> 13759 0.301774212 #> 13760 0.301780453 #> 13761 0.301786699 #> 13762 0.301792950 #> 13763 0.301799206 #> 13764 0.301805468 #> 13765 0.301811734 #> 13766 0.301818006 #> 13767 0.301824283 #> 13768 0.301830564 #> 13769 0.301836851 #> 13770 0.301843143 #> 13771 0.301849440 #> 13772 0.301855741 #> 13773 0.301862048 #> 13774 0.301868359 #> 13775 0.301874675 #> 13776 0.301880996 #> 13777 0.301887322 #> 13778 0.301893652 #> 13779 0.301899987 #> 13780 0.301906327 #> 13781 0.301912671 #> 13782 0.301919020 #> 13783 0.301925374 #> 13784 0.301931732 #> 13785 0.301938095 #> 13786 0.301944462 #> 13787 0.301950834 #> 13788 0.301957210 #> 13789 0.301963591 #> 13790 0.301969976 #> 13791 0.301976365 #> 13792 0.301982759 #> 13793 0.301989157 #> 13794 0.301995559 #> 13795 0.302001966 #> 13796 0.302008377 #> 13797 0.302014792 #> 13798 0.302021211 #> 13799 0.302027634 #> 13800 0.302034062 #> 13801 0.302040493 #> 13802 0.302046929 #> 13803 0.302053368 #> 13804 0.302059812 #> 13805 0.302066259 #> 13806 0.302072710 #> 13807 0.302079166 #> 13808 0.302085625 #> 13809 0.302092088 #> 13810 0.302098555 #> 13811 0.302105025 #> 13812 0.302111500 #> 13813 0.302117978 #> 13814 0.302124460 #> 13815 0.302130945 #> 13816 0.302137435 #> 13817 0.302143927 #> 13818 0.302150424 #> 13819 0.302156924 #> 13820 0.302163427 #> 13821 0.302169934 #> 13822 0.302176444 #> 13823 0.302182958 #> 13824 0.302189476 #> 13825 0.302195996 #> 13826 0.302202520 #> 13827 0.302209048 #> 13828 0.302215578 #> 13829 0.302222112 #> 13830 0.302228650 #> 13831 0.302235190 #> 13832 0.302241734 #> 13833 0.302248280 #> 13834 0.302254830 #> 13835 0.302261383 #> 13836 0.302267939 #> 13837 0.302274498 #> 13838 0.302281061 #> 13839 0.302287626 #> 13840 0.302294194 #> 13841 0.302300765 #> 13842 0.302307339 #> 13843 0.302313915 #> 13844 0.302320495 #> 13845 0.302327077 #> 13846 0.302333663 #> 13847 0.302340251 #> 13848 0.302346841 #> 13849 0.302353435 #> 13850 0.302360031 #> 13851 0.302366630 #> 13852 0.302373231 #> 13853 0.302379835 #> 13854 0.302386441 #> 13855 0.302393050 #> 13856 0.302399662 #> 13857 0.302406276 #> 13858 0.302412892 #> 13859 0.302419511 #> 13860 0.302426132 #> 13861 0.302432756 #> 13862 0.302439382 #> 13863 0.302446010 #> 13864 0.302452641 #> 13865 0.302459273 #> 13866 0.302465908 #> 13867 0.302472546 #> 13868 0.302479185 #> 13869 0.302485826 #> 13870 0.302492470 #> 13871 0.302499115 #> 13872 0.302505763 #> 13873 0.302512413 #> 13874 0.302519065 #> 13875 0.302525718 #> 13876 0.302532374 #> 13877 0.302539031 #> 13878 0.302545691 #> 13879 0.302552352 #> 13880 0.302559015 #> 13881 0.302565680 #> 13882 0.302572346 #> 13883 0.302579015 #> 13884 0.302585685 #> 13885 0.302592356 #> 13886 0.302599030 #> 13887 0.302605705 #> 13888 0.302612381 #> 13889 0.302619059 #> 13890 0.302625739 #> 13891 0.302632420 #> 13892 0.302639103 #> 13893 0.302645787 #> 13894 0.302652473 #> 13895 0.302659160 #> 13896 0.302665848 #> 13897 0.302672538 #> 13898 0.302679229 #> 13899 0.302685921 #> 13900 0.302692614 #> 13901 0.302699309 #> 13902 0.302706005 #> 13903 0.302712702 #> 13904 0.302719400 #> 13905 0.302726100 #> 13906 0.302732800 #> 13907 0.302739502 #> 13908 0.302746204 #> 13909 0.302752908 #> 13910 0.302759613 #> 13911 0.302766318 #> 13912 0.302773025 #> 13913 0.302779732 #> 13914 0.302786440 #> 13915 0.302793149 #> 13916 0.302799859 #> 13917 0.302806570 #> 13918 0.302813281 #> 13919 0.302819993 #> 13920 0.302826706 #> 13921 0.302833419 #> 13922 0.302840133 #> 13923 0.302846848 #> 13924 0.302853563 #> 13925 0.302860279 #> 13926 0.302866996 #> 13927 0.302873713 #> 13928 0.302880430 #> 13929 0.302887148 #> 13930 0.302893866 #> 13931 0.302900585 #> 13932 0.302907304 #> 13933 0.302914023 #> 13934 0.302920743 #> 13935 0.302927463 #> 13936 0.302934183 #> 13937 0.302940904 #> 13938 0.302947624 #> 13939 0.302954345 #> 13940 0.302961066 #> 13941 0.302967787 #> 13942 0.302974508 #> 13943 0.302981230 #> 13944 0.302987951 #> 13945 0.302994672 #> 13946 0.303001394 #> 13947 0.303008115 #> 13948 0.303014836 #> 13949 0.303021557 #> 13950 0.303028278 #> 13951 0.303034999 #> 13952 0.303041720 #> 13953 0.303048440 #> 13954 0.303055160 #> 13955 0.303061880 #> 13956 0.303068600 #> 13957 0.303075319 #> 13958 0.303082038 #> 13959 0.303088757 #> 13960 0.303095475 #> 13961 0.303102193 #> 13962 0.303108910 #> 13963 0.303115627 #> 13964 0.303122343 #> 13965 0.303129059 #> 13966 0.303135774 #> 13967 0.303142489 #> 13968 0.303149203 #> 13969 0.303155917 #> 13970 0.303162629 #> 13971 0.303169342 #> 13972 0.303176053 #> 13973 0.303182764 #> 13974 0.303189473 #> 13975 0.303196182 #> 13976 0.303202891 #> 13977 0.303209598 #> 13978 0.303216304 #> 13979 0.303223010 #> 13980 0.303229715 #> 13981 0.303236418 #> 13982 0.303243121 #> 13983 0.303249823 #> 13984 0.303256523 #> 13985 0.303263223 #> 13986 0.303269921 #> 13987 0.303276619 #> 13988 0.303283315 #> 13989 0.303290010 #> 13990 0.303296704 #> 13991 0.303303397 #> 13992 0.303310088 #> 13993 0.303316778 #> 13994 0.303323467 #> 13995 0.303330154 #> 13996 0.303336840 #> 13997 0.303343525 #> 13998 0.303350209 #> 13999 0.303356890 #> 14000 0.303363571 #> 14001 0.303370250 #> 14002 0.303376927 #> 14003 0.303383603 #> 14004 0.303390277 #> 14005 0.303396950 #> 14006 0.303403621 #> 14007 0.303410291 #> 14008 0.303416959 #> 14009 0.303423625 #> 14010 0.303430289 #> 14011 0.303436952 #> 14012 0.303443613 #> 14013 0.303450272 #> 14014 0.303456929 #> 14015 0.303463584 #> 14016 0.303470238 #> 14017 0.303476889 #> 14018 0.303483539 #> 14019 0.303490187 #> 14020 0.303496833 #> 14021 0.303503476 #> 14022 0.303510118 #> 14023 0.303516758 #> 14024 0.303523395 #> 14025 0.303530031 #> 14026 0.303536664 #> 14027 0.303543295 #> 14028 0.303549924 #> 14029 0.303556551 #> 14030 0.303563176 #> 14031 0.303569798 #> 14032 0.303576418 #> 14033 0.303583036 #> 14034 0.303589651 #> 14035 0.303596264 #> 14036 0.303602875 #> 14037 0.303609483 #> 14038 0.303616089 #> 14039 0.303622692 #> 14040 0.303629293 #> 14041 0.303635891 #> 14042 0.303642487 #> 14043 0.303649081 #> 14044 0.303655671 #> 14045 0.303662259 #> 14046 0.303668845 #> 14047 0.303675427 #> 14048 0.303682007 #> 14049 0.303688585 #> 14050 0.303695159 #> 14051 0.303701731 #> 14052 0.303708300 #> 14053 0.303714867 #> 14054 0.303721430 #> 14055 0.303727991 #> 14056 0.303734548 #> 14057 0.303741103 #> 14058 0.303747655 #> 14059 0.303754204 #> 14060 0.303760750 #> 14061 0.303767293 #> 14062 0.303773832 #> 14063 0.303780369 #> 14064 0.303786903 #> 14065 0.303793434 #> 14066 0.303799961 #> 14067 0.303806485 #> 14068 0.303813007 #> 14069 0.303819525 #> 14070 0.303826039 #> 14071 0.303832551 #> 14072 0.303839059 #> 14073 0.303845564 #> 14074 0.303852066 #> 14075 0.303858564 #> 14076 0.303865059 #> 14077 0.303871550 #> 14078 0.303878038 #> 14079 0.303884523 #> 14080 0.303891004 #> 14081 0.303897482 #> 14082 0.303903956 #> 14083 0.303910426 #> 14084 0.303916893 #> 14085 0.303923357 #> 14086 0.303929817 #> 14087 0.303936273 #> 14088 0.303942726 #> 14089 0.303949175 #> 14090 0.303955620 #> 14091 0.303962061 #> 14092 0.303968499 #> 14093 0.303974933 #> 14094 0.303981363 #> 14095 0.303987789 #> 14096 0.303994212 #> 14097 0.304000630 #> 14098 0.304007045 #> 14099 0.304013456 #> 14100 0.304019863 #> 14101 0.304026266 #> 14102 0.304032665 #> 14103 0.304039060 #> 14104 0.304045450 #> 14105 0.304051837 #> 14106 0.304058220 #> 14107 0.304064599 #> 14108 0.304070973 #> 14109 0.304077344 #> 14110 0.304083710 #> 14111 0.304090072 #> 14112 0.304096430 #> 14113 0.304102783 #> 14114 0.304109133 #> 14115 0.304115478 #> 14116 0.304121818 #> 14117 0.304128155 #> 14118 0.304134487 #> 14119 0.304140815 #> 14120 0.304147138 #> 14121 0.304153457 #> 14122 0.304159771 #> 14123 0.304166081 #> 14124 0.304172387 #> 14125 0.304178688 #> 14126 0.304184984 #> 14127 0.304191276 #> 14128 0.304197563 #> 14129 0.304203846 #> 14130 0.304210124 #> 14131 0.304216398 #> 14132 0.304222666 #> 14133 0.304228930 #> 14134 0.304235190 #> 14135 0.304241445 #> 14136 0.304247694 #> 14137 0.304253940 #> 14138 0.304260180 #> 14139 0.304266416 #> 14140 0.304272646 #> 14141 0.304278872 #> 14142 0.304285093 #> 14143 0.304291309 #> 14144 0.304297520 #> 14145 0.304303726 #> 14146 0.304309928 #> 14147 0.304316124 #> 14148 0.304322315 #> 14149 0.304328501 #> 14150 0.304334682 #> 14151 0.304340858 #> 14152 0.304347029 #> 14153 0.304353195 #> 14154 0.304359356 #> 14155 0.304365511 #> 14156 0.304371662 #> 14157 0.304377807 #> 14158 0.304383947 #> 14159 0.304390081 #> 14160 0.304396211 #> 14161 0.304402335 #> 14162 0.304408454 #> 14163 0.304414567 #> 14164 0.304420675 #> 14165 0.304426778 #> 14166 0.304432875 #> 14167 0.304438967 #> 14168 0.304445054 #> 14169 0.304451135 #> 14170 0.304457210 #> 14171 0.304463280 #> 14172 0.304469345 #> 14173 0.304475404 #> 14174 0.304481457 #> 14175 0.304487505 #> 14176 0.304493547 #> 14177 0.304499584 #> 14178 0.304505615 #> 14179 0.304511640 #> 14180 0.304517660 #> 14181 0.304523674 #> 14182 0.304529682 #> 14183 0.304535685 #> 14184 0.304541681 #> 14185 0.304547672 #> 14186 0.304553658 #> 14187 0.304559637 #> 14188 0.304565610 #> 14189 0.304571578 #> 14190 0.304577540 #> 14191 0.304583495 #> 14192 0.304589445 #> 14193 0.304595389 #> 14194 0.304601327 #> 14195 0.304607259 #> 14196 0.304613185 #> 14197 0.304619105 #> 14198 0.304625019 #> 14199 0.304630927 #> 14200 0.304636829 #> 14201 0.304642725 #> 14202 0.304648614 #> 14203 0.304654498 #> 14204 0.304660375 #> 14205 0.304666246 #> 14206 0.304672111 #> 14207 0.304677970 #> 14208 0.304683822 #> 14209 0.304689668 #> 14210 0.304695508 #> 14211 0.304701342 #> 14212 0.304707169 #> 14213 0.304712990 #> 14214 0.304718805 #> 14215 0.304724613 #> 14216 0.304730415 #> 14217 0.304736211 #> 14218 0.304742000 #> 14219 0.304747782 #> 14220 0.304753558 #> 14221 0.304759328 #> 14222 0.304765091 #> 14223 0.304770848 #> 14224 0.304776598 #> 14225 0.304782342 #> 14226 0.304788079 #> 14227 0.304793809 #> 14228 0.304799533 #> 14229 0.304805250 #> 14230 0.304810960 #> 14231 0.304816664 #> 14232 0.304822361 #> 14233 0.304828052 #> 14234 0.304833736 #> 14235 0.304839413 #> 14236 0.304845083 #> 14237 0.304850746 #> 14238 0.304856403 #> 14239 0.304862053 #> 14240 0.304867696 #> 14241 0.304873332 #> 14242 0.304878961 #> 14243 0.304884584 #> 14244 0.304890199 #> 14245 0.304895808 #> 14246 0.304901410 #> 14247 0.304907004 #> 14248 0.304912592 #> 14249 0.304918173 #> 14250 0.304923747 #> 14251 0.304929314 #> 14252 0.304934873 #> 14253 0.304940426 #> 14254 0.304945972 #> 14255 0.304951510 #> 14256 0.304957041 #> 14257 0.304962566 #> 14258 0.304968083 #> 14259 0.304973593 #> 14260 0.304979096 #> 14261 0.304984591 #> 14262 0.304990080 #> 14263 0.304995561 #> 14264 0.305001035 #> 14265 0.305006501 #> 14266 0.305011961 #> 14267 0.305017413 #> 14268 0.305022857 #> 14269 0.305028295 #> 14270 0.305033725 #> 14271 0.305039148 #> 14272 0.305044563 #> 14273 0.305049971 #> 14274 0.305055372 #> 14275 0.305060765 #> 14276 0.305066150 #> 14277 0.305071529 #> 14278 0.305076899 #> 14279 0.305082263 #> 14280 0.305087618 #> 14281 0.305092967 #> 14282 0.305098307 #> 14283 0.305103641 #> 14284 0.305108966 #> 14285 0.305114284 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 5716 rows ] #>  #> attr(,\"class\") #> [1] \"sir_subpops_model\" # }"},{"path":"https://oucru-modelling.github.io/serosv/reference/standardize_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocess data — standardize_data","title":"Preprocess data — standardize_data","text":"Preprocess data","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/standardize_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocess data — standardize_data","text":"","code":"standardize_data(   df,   plate_id_col = \"PLATE_ID\",   id_col = \"SAMPLE_ID\",   result_col = \"RESULT\",   dilution_fct_col = \"DILUTION_FACTORS\",   antitoxin_label = \"Anti_toxin\",   negative_col = \"^NEGATIVE_*\" )"},{"path":"https://oucru-modelling.github.io/serosv/reference/standardize_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocess data — standardize_data","text":"df - data.frame columns plate id, sample id, result, dilution factor, (optionally) negative controls plate_id_col - name column storing plates id id_col - name column storing sample id result_col - name column storing result dilution_fct_col - name column storing dilution factors antitoxin_label - antitoxin label sample id column negative_col - regex columns negative controls, assumed label followed dilution factor (e.g. NEGATIVE_50, NEGATIVE_100)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/standardize_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocess data — standardize_data","text":"standardized data.frame can passed `to_titer()`","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/tb_nl_1966_1973.html","id":null,"dir":"Reference","previous_headings":"","what":"Tuberculosis serological data from the Netherlands 1966-1973 (aggregated) — tb_nl_1966_1973","title":"Tuberculosis serological data from the Netherlands 1966-1973 (aggregated) — tb_nl_1966_1973","text":"study tuberculosis conducted Netherlands. Schoolchildren, aged 6 18 years, tested using tuberculin skin test.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/tb_nl_1966_1973.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tuberculosis serological data from the Netherlands 1966-1973 (aggregated) — tb_nl_1966_1973","text":"","code":"tb_nl_1966_1973"},{"path":"https://oucru-modelling.github.io/serosv/reference/tb_nl_1966_1973.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Tuberculosis serological data from the Netherlands 1966-1973 (aggregated) — tb_nl_1966_1973","text":"data frame 5 variables: age Age group pos Number seropositive individuals tot Total number individuals surveyed gender Gender cohort (unsure 0 1 means) birthyr Birth year cohort","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/tb_nl_1966_1973.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Tuberculosis serological data from the Netherlands 1966-1973 (aggregated) — tb_nl_1966_1973","text":"Nagelkerke, N., Heisterkamp, S., Borgdorff, M., Broekmans, J. Van Houwelingen, H. (1999), Semi-parametric estimation age-time specific infection incidence serial prevalence data. Statist. Med., 18: 307-320. doi:10.1002/(SICI)1097-0258(19990215)18:3<307::AID-SIM15>3.0.CO;2-Z","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/tb_nl_1966_1973.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tuberculosis serological data from the Netherlands 1966-1973 (aggregated) — tb_nl_1966_1973","text":"","code":"# Reproduce Fig 4.6, p.70 age <- tb_nl_1966_1973$age birthyr <- tb_nl_1966_1973$birthyr pos <- tb_nl_1966_1973$pos tot <- tb_nl_1966_1973$tot # left panel plot(age, pos / tot,   pch = 16, cex = 0.00005 * tot, xlab = \"age\",   ylab = \"prevalence\", xlim = c(6, 18) )  # right panel plot(birthyr, pos / tot,   pch = 16, cex = 0.00005 * tot, xlab = \"year\", ylab = \"prevalence\" )"},{"path":"https://oucru-modelling.github.io/serosv/reference/to_titer.html","id":null,"dir":"Reference","previous_headings":"","what":"Process assay test result to titer — to_titer","title":"Process assay test result to titer — to_titer","text":"Process assay test result titer","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/to_titer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process assay test result to titer — to_titer","text":"","code":"to_titer(   df,   model = \"4PL\",   positive_threshold = NULL,   ci = 0.95,   negative_control = TRUE )"},{"path":"https://oucru-modelling.github.io/serosv/reference/to_titer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process assay test result to titer — to_titer","text":"df - standardized data.frame generated `standardize_data()` model - either name built-model fit standard curve named list 2 functions \"mod\" \"quantify_ci\" positive_threshold - NULL, processed_data serostatus labeled ci - confidence interval titer estimates negative_control - TRUE, output tibble include result negative controls","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/to_titer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process assay test result to titer — to_titer","text":"data.frame 8 columns plate_id id plate data list `data.frame`s containing results plate antitoxin_df list `data.frame`s containing results antitoxins plate standard_curve_func list functions mapping OD titer plate std_crv_midpoint midpoint standard curve, qualitative analysis processed_data list `tibble`s containing samples titer estimates (lower, median, upper) negative_control list `tibble`s containing negative control check results (`negative_control=TRUE`)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/transform.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a dataframe with `t`, `pos` and `tot` columns from\n`t` and `seropositive` vectors. — transform","title":"Generate a dataframe with `t`, `pos` and `tot` columns from\n`t` and `seropositive` vectors. — transform","text":"Generate dataframe `t`, `pos` `tot` columns `t` `seropositive` vectors.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a dataframe with `t`, `pos` and `tot` columns from\n`t` and `seropositive` vectors. — transform","text":"","code":"transform(t, spos)"},{"path":"https://oucru-modelling.github.io/serosv/reference/transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a dataframe with `t`, `pos` and `tot` columns from\n`t` and `seropositive` vectors. — transform","text":"t time vector. spos seropositive vector.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a dataframe with `t`, `pos` and `tot` columns from\n`t` and `seropositive` vectors. — transform","text":"","code":"df <- hcv_be_2006 hcv_df <- transform(df$dur, df$seropositive) hcv_df #> # A tibble: 116 × 3 #>        t   pos   tot #>    <dbl> <int> <int> #>  1   0.1     0     1 #>  2   0.3     0     1 #>  3   0.4     0     3 #>  4   0.5     0     1 #>  5   0.7     2     5 #>  6   0.8     3     4 #>  7   0.9     3     4 #>  8   1       5     7 #>  9   1.1     1     2 #> 10   1.2     3     4 #> # ℹ 106 more rows"},{"path":"https://oucru-modelling.github.io/serosv/reference/transform_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Aggregate data — transform_data","title":"Aggregate data — transform_data","text":"Generate dataframe `t`, `pos` `tot` columns `t` `seropositive` vectors.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/transform_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregate data — transform_data","text":"","code":"transform_data(t, spos, stratum_col = \"t\")"},{"path":"https://oucru-modelling.github.io/serosv/reference/transform_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aggregate data — transform_data","text":"t time vector (stratification). spos seropositive vector. stratum_col new name time vector (default \"t\")","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/transform_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aggregate data — transform_data","text":"dataframe aggregated format","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/transform_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aggregate data — transform_data","text":"","code":"df <- hcv_be_2006 hcv_df <- transform_data(df$dur, df$seropositive) hcv_df #> # A tibble: 116 × 3 #>        t   pos   tot #>    <dbl> <int> <int> #>  1   0.1     0     1 #>  2   0.3     0     1 #>  3   0.4     0     3 #>  4   0.5     0     1 #>  5   0.7     2     5 #>  6   0.8     3     4 #>  7   0.9     3     4 #>  8   1       5     7 #>  9   1.1     1     2 #> 10   1.2     3     4 #> # ℹ 106 more rows"},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_1999_2000.html","id":null,"dir":"Reference","previous_headings":"","what":"VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated) — vzv_be_1999_2000","title":"VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated) — vzv_be_1999_2000","text":"Age-specific seroprevalence VZV antibodies, assessed Flanders (Belgium) October 1999 April 2000. population stratified age order obtain approximately 100 observations per age group.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_1999_2000.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated) — vzv_be_1999_2000","text":"","code":"vzv_be_1999_2000"},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_1999_2000.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated) — vzv_be_1999_2000","text":"data frame 3 variables: age Age group pos Number seropositive individuals tot Total number individuals surveyed","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_1999_2000.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated) — vzv_be_1999_2000","text":"Thiry, N., Beutels, P., Shkedy, Z. et al. seroepidemiology primary varicella-zoster virus infection Flanders (Belgium). Eur J Pediatr 161, 588-593 (2002). doi:10.1007/s00431-002-1053-2","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_1999_2000.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated) — vzv_be_1999_2000","text":"","code":"# Reproduce Fig 4.7 (left panel), p.71 age <- vzv_be_1999_2000$age pos <- vzv_be_1999_2000$pos tot <- vzv_be_1999_2000$tot plot(age, pos / tot,   cex = 0.036 * tot, pch = 19, xlab = \"age\", ylab = \"seroprevalence\",   xlim = c(0, 45), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_2001_2003.html","id":null,"dir":"Reference","previous_headings":"","what":"VZV serological data from Belgium from 2001-2003 (line listing) — vzv_be_2001_2003","title":"VZV serological data from Belgium from 2001-2003 (line listing) — vzv_be_2001_2003","text":"survey one used study seroprevalence parvovirus B19 Belgium, described .","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_2001_2003.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"VZV serological data from Belgium from 2001-2003 (line listing) — vzv_be_2001_2003","text":"","code":"vzv_be_2001_2003"},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_2001_2003.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"VZV serological data from Belgium from 2001-2003 (line listing) — vzv_be_2001_2003","text":"data frame 4 variables: age Age individual seropositive individual seropositive gender Gender individual VZVmIUml VZV milli international units per ml","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_2001_2003.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"VZV serological data from Belgium from 2001-2003 (line listing) — vzv_be_2001_2003","text":"MOSSONG, J., N. HENS, V. FRIEDERICHS, . DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. \"Parvovirus B19 Infection Five European Countries: Seroepidemiology, Force Infection Maternal Risk Infection.\" Epidemiology Infection 136, . 8 (2008): 1059-68. doi:10.1017/S0950268807009661","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_2001_2003.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"VZV serological data from Belgium from 2001-2003 (line listing) — vzv_be_2001_2003","text":"","code":"# Reproduce Fig 4.7 (right panel), p.71 library(dplyr) df <- vzv_be_2001_2003 %>%   mutate(age = round(age)) %>%   group_by(age) %>%   summarise(pos = sum(seropositive), tot = n()) plot(df$age, df$pos / df$tot,   cex = 0.036 * df$tot, pch = 19, xlab = \"age\", ylab = \"seroprevalence\",   xlim = c(0, 45), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_parvo_be.html","id":null,"dir":"Reference","previous_headings":"","what":"VZV and Parvovirus B19 serological data in Belgium (line listing) — vzv_parvo_be","title":"VZV and Parvovirus B19 serological data in Belgium (line listing) — vzv_parvo_be","text":"VZV Parvovirus B19 serological data Belgium (line listing)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_parvo_be.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"VZV and Parvovirus B19 serological data in Belgium (line listing) — vzv_parvo_be","text":"","code":"vzv_parvo_be"},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_parvo_be.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"VZV and Parvovirus B19 serological data in Belgium (line listing) — vzv_parvo_be","text":"data frame 7 variables: id ID individual age Age individual gender Gender individual parvouml Parvo B19 antibody units per ml parvo_res individual positive parvovirus B19 VZVmUIml VZV milli international units per ml vzv_res individual positive VZV","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_parvo_be.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"VZV and Parvovirus B19 serological data in Belgium (line listing) — vzv_parvo_be","text":"MOSSONG, J., N. HENS, V. FRIEDERICHS, . DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. \"Parvovirus B19 Infection Five European Countries: Seroepidemiology, Force Infection Maternal Risk Infection.\" Epidemiology Infection 136, . 8 (2008): 1059-68. doi:10.1017/S0950268807009661","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/weibull_model.html","id":null,"dir":"Reference","previous_headings":"","what":"The Weibull model. — weibull_model","title":"The Weibull model. — weibull_model","text":"Refers section 6.1.2.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/weibull_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Weibull model. — weibull_model","text":"","code":"weibull_model(data)"},{"path":"https://oucru-modelling.github.io/serosv/reference/weibull_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Weibull model. — weibull_model","text":"data input data frame, must either `t`, `pos`, `tot` column aggregated data `t`, `status` linelisting data","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/weibull_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Weibull model. — weibull_model","text":"list class weibull_model following items datatype type datatype used model fitting (aggregated linelisting) df dataframe used fitting model info fitted \"glm\" object sp seroprevalence foi force infection","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/weibull_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Weibull model. — weibull_model","text":"","code":"df <- hcv_be_2006[order(hcv_be_2006$dur), ] df$t <- df$dur df$status <- df$seropositive model <- weibull_model(df) plot(model)"},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/news/index.html","id":"serosv-110","dir":"Changelog","previous_headings":"","what":"serosv 1.1.0","title":"serosv 1.1.0","text":"CRAN release: 2025-04-09 add correct_prevalence() function estimate real prevalence imperfect test add compare_models() function model selection update API takes data.frame input","code":""},{"path":"https://oucru-modelling.github.io/serosv/news/index.html","id":"serosv-101","dir":"Changelog","previous_headings":"","what":"serosv 1.0.1","title":"serosv 1.0.1","text":"CRAN release: 2024-10-18","code":""},{"path":"https://oucru-modelling.github.io/serosv/news/index.html","id":"serosv-100","dir":"Changelog","previous_headings":"","what":"serosv 1.0.0","title":"serosv 1.0.0","text":"CRAN release: 2024-10-07 Initial CRAN submission.","code":""}]
+[{"path":"https://oucru-modelling.github.io/serosv/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2024 serosv authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://oucru-modelling.github.io/serosv/articles/data_transformation.html","id":"aggregate-data","dir":"Articles","previous_headings":"","what":"Aggregate data","title":"Data transformation","text":"Use function transform_data() convert line listing data (data t, seropositive columns) aggregated data (data t, pos, tot columns) Arguments: t time vector spos seropositive vector (TRUE/FALSE 1/0) stratum_col new name time vector, default stratum_col = \"t\"","code":"linelisting_data <- hcv_be_2006 aggregated_data <- transform_data(linelisting_data, stratum_col = \"dur\", status_col=\"seropositive\") aggregated_data #> # A tibble: 116 × 3 #>      age   pos   tot #>    <dbl> <int> <int> #>  1   0.1     0     1 #>  2   0.3     0     1 #>  3   0.4     0     3 #>  4   0.5     0     1 #>  5   0.7     2     5 #>  6   0.8     3     4 #>  7   0.9     3     4 #>  8   1       5     7 #>  9   1.1     1     2 #> 10   1.2     3     4 #> # ℹ 106 more rows"},{"path":"https://oucru-modelling.github.io/serosv/articles/hierarchical_model.html","id":"parametric-bayesian-framework","dir":"Articles","previous_headings":"","what":"Parametric Bayesian framework","title":"Hierarchical Bayesian models","text":"Currently, serosv models parametric Bayesian framework Proposed approach Prevalence parametric form π(ai,α)\\pi(a_i, \\alpha) α\\alpha parameter vector One can constraint parameter space prior distribution P(α)P(\\alpha) order achieve desired monotonicity posterior distribution P(π1,π2,...,πm|y,n)P(\\pi_1, \\pi_2, ..., \\pi_m|y,n) : n=(n1,n2,...,nm)n = (n_1, n_2, ..., n_m) nin_i sample size age aia_i y=(y1,y2,...,ym)y = (y_1, y_2, ..., y_m) yiy_i number infected individual nin_i sampled subjects","code":""},{"path":"https://oucru-modelling.github.io/serosv/articles/hierarchical_model.html","id":"farrington","dir":"Articles","previous_headings":"Parametric Bayesian framework","what":"Farrington","title":"Hierarchical Bayesian models","text":"Refer Chapter 10.3.1 Proposed model model prevalence followed π()=1−exp{α1α2ae−α2a+1α2(α1α2−α3)(e−α2a−1)−α3a} \\pi () = 1 - exp\\{ \\frac{\\alpha_1}{\\alpha_2}ae^{-\\alpha_2 } + \\frac{1}{\\alpha_2}(\\frac{\\alpha_1}{\\alpha_2} - \\alpha_3)(e^{-\\alpha_2 } - 1) -\\alpha_3 \\} likelihood model, independent binomial distribution assumed number infected individuals age aia_i yi∼Bin(ni,πi), =1,2,3,...m y_i \\sim Bin(n_i, \\pi_i), \\text{  } = 1,2,3,...m constraint parameter space can incorporated assuming truncated normal distribution components α\\alpha, α=(α1,α2,α3)\\alpha = (\\alpha_1, \\alpha_2, \\alpha_3) πi=π(ai,α)\\pi_i = \\pi(a_i,\\alpha) αj∼truncated 𝒩(μj,τj),j=1,2,3 \\alpha_j \\sim \\text{truncated  } \\mathcal{N}(\\mu_j, \\tau_j), \\text{ } j = 1,2,3 joint posterior distribution α\\alpha can derived combining likelihood prior followed P(α|y)∝∏=1mBin(yi|ni,π(ai,α))∏=13−1τjexp(12τj2(αj−μj)2) P(\\alpha|y) \\propto \\prod^m_{=1} \\text{Bin}(y_i|n_i, \\pi(a_i, \\alpha)) \\prod^3_{=1}-\\frac{1}{\\tau_j}\\text{exp}(\\frac{1}{2\\tau^2_j} (\\alpha_j - \\mu_j)^2) flat hyperprior distribution defined followed: μj∼𝒩(0,10000)\\mu_j \\sim \\mathcal{N}(0, 10000) τj−2∼Γ(100,100)\\tau^{-2}_j \\sim \\Gamma(100,100) full conditional distribution αi\\alpha_i thus P(αi|αj,αk,k,j≠)∝−1τiexp(12τi2(αi−μi)2)∏=1mBin(yi|ni,π(ai,α)) P(\\alpha_i|\\alpha_j,\\alpha_k, k, j \\neq ) \\propto  -\\frac{1}{\\tau_i}\\text{exp}(\\frac{1}{2\\tau^2_i} (\\alpha_i - \\mu_i)^2) \\prod^m_{=1} \\text{Bin}(y_i|n_i, \\pi(a_i, \\alpha)) Fitting data fit Farrington model, use hierarchical_bayesian_model() define type = \"far2\" type = \"far3\" type = \"far2\" refers Farrington model 2 parameters (α3=0\\alpha_3 = 0) type = \"far3\" refers Farrington model 3 parameters (α3>0\\alpha_3 > 0)","code":"df <- mumps_uk_1986_1987 model <- hierarchical_bayesian_model(df, type=\"far3\") #>  #> SAMPLING FOR MODEL 'fra_3' NOW (CHAIN 1). #> Chain 1: Rejecting initial value: #> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity. #> Chain 1:   Stan can't start sampling from this initial value. #> Chain 1:  #> Chain 1: Gradient evaluation took 0.000154 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.54 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1:  #> Chain 1:  #> Chain 1: Iteration:    1 / 5000 [  0%]  (Warmup) #> Chain 1: Iteration:  500 / 5000 [ 10%]  (Warmup) #> Chain 1: Iteration: 1000 / 5000 [ 20%]  (Warmup) #> Chain 1: Iteration: 1500 / 5000 [ 30%]  (Warmup) #> Chain 1: Iteration: 1501 / 5000 [ 30%]  (Sampling) #> Chain 1: Iteration: 2000 / 5000 [ 40%]  (Sampling) #> Chain 1: Iteration: 2500 / 5000 [ 50%]  (Sampling) #> Chain 1: Iteration: 3000 / 5000 [ 60%]  (Sampling) #> Chain 1: Iteration: 3500 / 5000 [ 70%]  (Sampling) #> Chain 1: Iteration: 4000 / 5000 [ 80%]  (Sampling) #> Chain 1: Iteration: 4500 / 5000 [ 90%]  (Sampling) #> Chain 1: Iteration: 5000 / 5000 [100%]  (Sampling) #> Chain 1:  #> Chain 1:  Elapsed Time: 16.591 seconds (Warm-up) #> Chain 1:                96.031 seconds (Sampling) #> Chain 1:                112.622 seconds (Total) #> Chain 1: #> Warning: There were 288 divergent transitions after warmup. See #> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup #> to find out why this is a problem and how to eliminate them. #> Warning: There were 11 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See #> https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded #> Warning: Examine the pairs() plot to diagnose sampling problems #> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable. #> Running the chains for more iterations may help. See #> https://mc-stan.org/misc/warnings.html#tail-ess  model$info #>                       mean      se_mean           sd          2.5% #> alpha1        1.396732e-01 2.328172e-04 5.926643e-03  1.290067e-01 #> alpha2        1.989632e-01 3.342249e-04 8.454176e-03  1.847781e-01 #> alpha3        9.017286e-03 2.957517e-04 7.503320e-03  2.519932e-04 #> tau_alpha1    2.051039e+00 2.925237e-01 5.992958e+00  1.814318e-06 #> tau_alpha2    4.280878e+00 1.545375e+00 1.261621e+01  5.514011e-06 #> tau_alpha3    1.594944e+00 2.713658e-01 4.513571e+00  1.717967e-06 #> mu_alpha1    -2.434489e+00 4.143249e+00 4.467754e+01 -1.656101e+02 #> mu_alpha2    -9.043922e-01 1.406475e+00 3.373188e+01 -9.031034e+01 #> mu_alpha3     2.147579e+00 1.666808e+00 4.272829e+01 -9.818932e+01 #> sigma_alpha1  9.673678e+03 9.523462e+03 2.012224e+05  2.027127e-01 #> sigma_alpha2  8.037575e+01 1.664371e+01 5.816331e+02  1.342710e-01 #> sigma_alpha3  1.665419e+02 4.235920e+01 1.470059e+03  2.354869e-01 #> lp__         -2.534311e+03 2.684310e-01 4.176499e+00 -2.542650e+03 #>                        25%           50%           75%         97.5%      n_eff #> alpha1        1.353546e-01  1.393536e-01  1.435425e-01  1.520502e-01  648.01829 #> alpha2        1.931939e-01  1.981250e-01  2.037919e-01  2.181404e-01  639.83066 #> alpha3        3.301399e-03  6.948630e-03  1.310897e-02  2.798314e-02  643.65402 #> tau_alpha1    4.699710e-04  1.339575e-02  4.377299e-01  2.433537e+01  419.72073 #> tau_alpha2    1.099782e-03  3.547442e-02  9.730938e-01  5.546799e+01   66.64842 #> tau_alpha3    3.847899e-04  1.424805e-02  4.511886e-01  1.803311e+01  276.64990 #> mu_alpha1    -4.777740e+00  1.761566e-01  4.990839e+00  8.616947e+01  116.27771 #> mu_alpha2    -3.043304e+00  1.912986e-01  2.809284e+00  6.930927e+01  575.19790 #> mu_alpha3    -4.356601e+00  8.834931e-02  7.109717e+00  1.097394e+02  657.14323 #> sigma_alpha1  1.511461e+00  8.640059e+00  4.612799e+01  7.424105e+02  446.43988 #> sigma_alpha2  1.013731e+00  5.309408e+00  3.015428e+01  4.258861e+02 1221.23065 #> sigma_alpha3  1.488748e+00  8.377652e+00  5.097869e+01  7.629637e+02 1204.40906 #> lp__         -2.537035e+03 -2.534291e+03 -2.531361e+03 -2.526569e+03  242.08025 #>                   Rhat #> alpha1       1.0006390 #> alpha2       0.9997486 #> alpha3       0.9999655 #> tau_alpha1   1.0023789 #> tau_alpha2   1.0060590 #> tau_alpha3   1.0005442 #> mu_alpha1    1.0039620 #> mu_alpha2    0.9997144 #> mu_alpha3    1.0015442 #> sigma_alpha1 1.0019918 #> sigma_alpha2 1.0000931 #> sigma_alpha3 0.9997146 #> lp__         1.0215691 plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/hierarchical_model.html","id":"log-logistic","dir":"Articles","previous_headings":"Parametric Bayesian framework","what":"Log-logistic","title":"Hierarchical Bayesian models","text":"Proposed approach model seroprevalence followed π()=βaα1+βaα,α,β>0 \\pi() = \\frac{\\beta ^\\alpha}{1 + \\beta ^\\alpha}, \\text{ } \\alpha, \\beta > 0 likelihood specified Farrington model (yi∼Bin(ni,πi)y_i \\sim Bin(n_i, \\pi_i)) logit(π())=α2+α1log() \\text{logit}(\\pi()) = \\alpha_2 + \\alpha_1\\log() α2=log(β)\\alpha_2 = \\text{log}(\\beta) prior model α1\\alpha_1 specified α1∼truncated 𝒩(μ1,τ1)\\alpha_1 \\sim \\text{truncated  } \\mathcal{N}(\\mu_1, \\tau_1) flat hyperprior Farrington model β\\beta constrained positive specifying α2∼𝒩(μ2,τ2)\\alpha_2 \\sim \\mathcal{N}(\\mu_2, \\tau_2) full conditional distribution α1\\alpha_1 thus P(α1|α2)∝−1τ1exp(12τ12(α1−μ1)2)∏=1mBin(yi|ni,π(ai,α1,α2)) P(\\alpha_1|\\alpha_2) \\propto -\\frac{1}{\\tau_1} \\text{exp} (\\frac{1}{2 \\tau_1^2} (\\alpha_1 - \\mu_1)^2) \\prod_{=1}^m \\text{Bin}(y_i|n_i,\\pi(a_i, \\alpha_1, \\alpha_2) ) α2\\alpha_2 can derived way Fitting data fit Log-logistic model, use hierarchical_bayesian_model() define type = \"log_logistic\"","code":"df <- rubella_uk_1986_1987 model <- hierarchical_bayesian_model(df, type=\"log_logistic\") #>  #> SAMPLING FOR MODEL 'log_logistic' NOW (CHAIN 1). #> Chain 1:  #> Chain 1: Gradient evaluation took 6.4e-05 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.64 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1:  #> Chain 1:  #> Chain 1: Iteration:    1 / 5000 [  0%]  (Warmup) #> Chain 1: Iteration:  500 / 5000 [ 10%]  (Warmup) #> Chain 1: Iteration: 1000 / 5000 [ 20%]  (Warmup) #> Chain 1: Iteration: 1500 / 5000 [ 30%]  (Warmup) #> Chain 1: Iteration: 1501 / 5000 [ 30%]  (Sampling) #> Chain 1: Iteration: 2000 / 5000 [ 40%]  (Sampling) #> Chain 1: Iteration: 2500 / 5000 [ 50%]  (Sampling) #> Chain 1: Iteration: 3000 / 5000 [ 60%]  (Sampling) #> Chain 1: Iteration: 3500 / 5000 [ 70%]  (Sampling) #> Chain 1: Iteration: 4000 / 5000 [ 80%]  (Sampling) #> Chain 1: Iteration: 4500 / 5000 [ 90%]  (Sampling) #> Chain 1: Iteration: 5000 / 5000 [100%]  (Sampling) #> Chain 1:  #> Chain 1:  Elapsed Time: 4.15 seconds (Warm-up) #> Chain 1:                5.77 seconds (Sampling) #> Chain 1:                9.92 seconds (Total) #> Chain 1: #> Warning: There were 583 divergent transitions after warmup. See #> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup #> to find out why this is a problem and how to eliminate them. #> Warning: Examine the pairs() plot to diagnose sampling problems #> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable. #> Running the chains for more iterations may help. See #> https://mc-stan.org/misc/warnings.html#bulk-ess #> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable. #> Running the chains for more iterations may help. See #> https://mc-stan.org/misc/warnings.html#tail-ess  model$type #> [1] \"log_logistic\" plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/imperfect_test.html","id":"imperfect-test","dir":"Articles","previous_headings":"","what":"Imperfect test","title":"Imperfect serological test","text":"Function correct_prevalence() used estimating true prevalence serological test used imperfect Arguments: data input data frame, must either : age, pos, tot columns (aggregated data) age, status columns (linelisting data) Users can specifiy name columns input data frame using arguments age_col, pos_col, tot_color age_col, status_col respectively bayesian whether adjust sero-prevalence using Bayesian frequentist approach. set TRUE, true sero-prevalence estimated using MCMC. init_se sensitivity serological test (default value 0.95) init_sp specificity serological test (default value 0.8) study_size_se (applicable bayesian=TRUE) sample size sensitivity validation study (default value 1000) study_size_sp (applicable bayesian=TRUE) sample size specificity validation study (default value 1000) chains (applicable bayesian=TRUE) number Markov chains (default 1) warmup (applicable bayesian=TRUE) number warm runs (default value 1000) iter (applicable bayesian=TRUE) number iterations (default value 2000) function return list 2 items: info bayesian = TRUE contains estimated values se, sp corrected seroprevalence else return formula computing corrected seroprevalence corrected_sero return data.frame age, sero (corrected sero) pos, tot (adjusted based corrected prevalence) User can visualize output using plot_corrected_prev() function   compare correction methods single plot, provide output second method optional y argument plot_corrected_prev()","code":"# ---- estimate real prevalence using Bayesian approach ---- data <- rubella_uk_1986_1987 output <- correct_prevalence(data, warmup = 1000, iter = 4000, init_se=0.9, init_sp = 0.8, study_size_se=1000, study_size_sp=3000) #>  #> SAMPLING FOR MODEL 'prevalence_correction' NOW (CHAIN 1). #> Chain 1:  #> Chain 1: Gradient evaluation took 9.4e-05 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.94 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1:  #> Chain 1:  #> Chain 1: Iteration:    1 / 4000 [  0%]  (Warmup) #> Chain 1: Iteration:  400 / 4000 [ 10%]  (Warmup) #> Chain 1: Iteration:  800 / 4000 [ 20%]  (Warmup) #> Chain 1: Iteration: 1001 / 4000 [ 25%]  (Sampling) #> Chain 1: Iteration: 1400 / 4000 [ 35%]  (Sampling) #> Chain 1: Iteration: 1800 / 4000 [ 45%]  (Sampling) #> Chain 1: Iteration: 2200 / 4000 [ 55%]  (Sampling) #> Chain 1: Iteration: 2600 / 4000 [ 65%]  (Sampling) #> Chain 1: Iteration: 3000 / 4000 [ 75%]  (Sampling) #> Chain 1: Iteration: 3400 / 4000 [ 85%]  (Sampling) #> Chain 1: Iteration: 3800 / 4000 [ 95%]  (Sampling) #> Chain 1: Iteration: 4000 / 4000 [100%]  (Sampling) #> Chain 1:  #> Chain 1:  Elapsed Time: 2.057 seconds (Warm-up) #> Chain 1:                4.107 seconds (Sampling) #> Chain 1:                6.164 seconds (Total) #> Chain 1:  # check fitted value  output$info[1:2, ] #>             mean      se_mean          sd      2.5%       25%       50% #> est_se 0.9278179 0.0001098350 0.006095913 0.9151759 0.9239329 0.9279511 #> est_sp 0.8028005 0.0001011728 0.006973970 0.7888135 0.7981642 0.8028934 #>              75%     97.5%    n_eff      Rhat #> est_se 0.9318726 0.9390499 3080.323 0.9998417 #> est_sp 0.8075049 0.8167427 4751.518 0.9996668  # ---- estimate real prevalence using frequentist approach ---- freq_output <- correct_prevalence(data, bayesian = FALSE, init_se=0.9, init_sp = 0.8)  # check info freq_output$info #> [1] \"Formula: real_sero = (observed_sero + sp - 1) / (se + sp -1)\" # Plot output of the frequentist approach plot_corrected_prev(freq_output) # Plot output of the bayesian approach  plot_corrected_prev(output) plot_corrected_prev(output, freq_output) # set facet = TRUE to display the confidence or credible intervals for each method plot_corrected_prev(output, freq_output, facet = TRUE)"},{"path":"https://oucru-modelling.github.io/serosv/articles/imperfect_test.html","id":"fitting-corrected-data","dir":"Articles","previous_headings":"Imperfect test","what":"Fitting corrected data","title":"Imperfect serological test","text":"Data seroprevalence correction Bayesian approach  Frequentist approach  Original data","code":"suppressWarnings(   corrected_data <- farrington_model(   output$corrected_se,   start=list(alpha=0.07,beta=0.1,gamma=0.03)) )  plot(corrected_data) #> Warning: No shared levels found between `names(values)` of the manual scale and the #> data's fill values. suppressWarnings(   corrected_data <- farrington_model(   freq_output$corrected_se,   start=list(alpha=0.07,beta=0.1,gamma=0.03)) )  plot(corrected_data) #> Warning: No shared levels found between `names(values)` of the manual scale and the #> data's fill values. suppressWarnings(   original_data <- farrington_model(   data,   start=list(alpha=0.07,beta=0.1,gamma=0.03)) ) plot(original_data) #> Warning: No shared levels found between `names(values)` of the manual scale and the #> data's fill values."},{"path":"https://oucru-modelling.github.io/serosv/articles/linelisting_vs_aggregated.html","id":"input-data-format","dir":"Articles","previous_headings":"","what":"Input data format","title":"Input data","text":"*_model() functions serosv require data argument input data fitted. package can handle linelisting aggregated data, infers format structure input data frame. means input data expected follow specific format. linelisting data: data must 3 columns follows age vector (column name specified via age_col parameter) vector counts sero positives age group (column name specified via pos_col parameter) vector total population age group (column name specified via tot_col parameter) aggregated data: data must 2 columns follows age vector individuals vector indicating serostatus individual (column name specified via status_col parameter) Example: Fitting linelisting aggregated data using polynomial_model()","code":"linelisting <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age), ] aggregated <- hav_bg_1964  # View the 2 different data format head(linelisting) #>     age seropositive year gender parvouml #> 1     1            0 2001      m        8 #> 2     1            0 2001      m        7 #> 3     1            0 2001      m        6 #> 406   1            0 2001      f       11 #> 407   1            0 2001      f        7 #> 408   1            0 2001      f        5 head(aggregated) #>   age pos tot #> 1   1   3  16 #> 2   2   3  15 #> 3   3   3  16 #> 4   4   4  13 #> 5   5   7  12 #> 6   6   4  15  # fit with aggregated data model1 <- polynomial_model(aggregated, k=1) plot(model1) # fit with linelisting data model2 <- polynomial_model(linelisting, k=1, status_col = \"seropositive\") plot(model2)"},{"path":"https://oucru-modelling.github.io/serosv/articles/linelisting_vs_aggregated.html","id":"data-transformation","dir":"Articles","previous_headings":"","what":"Data transformation","title":"Input data","text":"serosv also offers function transform_data() convert linelisting aggregated data. information, refer Data transformation","code":"transform_data(   linelisting,   stratum_col=\"age\",    status_col=\"seropositive\") %>%    polynomial_model(k=1) %>%    plot()"},{"path":"https://oucru-modelling.github.io/serosv/articles/model_multivariate_data.html","id":"bivariate-dale-model","dir":"Articles","previous_headings":"","what":"Bivariate Dale model","title":"Model multivariate serological data","text":"Fitting bivariate Dale model using bivariate_dale_model() use rubella_mumps_uk data example plot function bivariate_dale_model object takes 3 additional parameters: y1 y2 labels 2 diseases modeled plot_type type plot returned \"ci\" returns fitted line marginal prevalence 95% confidence interval \"sp\" returns conditional joint seroprevalence FOI inputs returns individual plots  line listing data vzv_parvo_be , use generate_quad_matrix() function create (NN, NP, PN, PP) fitting model","code":"data <- rubella_mumps_uk model <- bivariate_dale_model(age = data$age, y = data[, c(\"NN\", \"NP\", \"PN\", \"PP\")], monotonized=TRUE) plot(model, y1 = \"Rubella\", y2 = \"Mumps\", plot_type = \"sp\") generate_quad_matrix(vzv_parvo_be, vzv_res, parvo_res, age, discrete_age = F) #> # A tibble: 1,751 × 6 #>       age    NN    NP    PN    PP total #>     <dbl> <int> <int> <int> <int> <int> #>  1 0.0329     0     0     1     0     1 #>  2 0.0521     1     0     0     0     1 #>  3 0.266      0     0     1     0     1 #>  4 0.403      1     0     0     0     1 #>  5 0.493      0     0     0     1     1 #>  6 0.663      1     0     0     0     1 #>  7 0.811      1     0     0     0     1 #>  8 1.05       0     0     0     1     1 #>  9 1.07       1     0     1     0     2 #> 10 1.10       0     0     1     0     1 #> # ℹ 1,741 more rows"},{"path":"https://oucru-modelling.github.io/serosv/articles/model_quantitative_data.html","id":"mixture-model","dir":"Articles","previous_headings":"","what":"Mixture model","title":"Modeling directly from antibody levels","text":"Proposed model Consider two-component Gaussian mixture model antibody level \\(Z\\), component \\(Z_j\\) represent antibody level arising 2 latent sub-populations \\(j \\\\{, S\\}\\) (.e., Infected Susceptible groups). Let \\(f_j(z_j|\\theta_j)\\) denotes density component \\(Z_j\\), \\(\\theta_I\\) \\(\\theta_S\\) parameters Susceptible Infected components respectively. \\(\\pi_{\\text{TRUE}}()\\) age-dependent mixing probability (.e., true prevalence), density mixture formulated \\[ f(z|z_I, z_S,) = (1-\\pi_{\\text{TRUE}}())f_S(z_S|\\theta_S)+\\pi_{\\text{TRUE}}()f_I(z_I|\\theta_I) \\] age-specific mean antibody level \\(E(Z|)\\) thus equals \\[ \\mu() = (1-\\pi_{\\text{TRUE}}())\\mu_S+\\pi_{\\text{TRUE}}()\\mu_I\\] true prevalence can calculated \\[ \\pi_{\\text{TRUE}}() = \\frac{\\mu() - \\mu_S}{\\mu_I - \\mu_S} \\] Force infection can inferred \\[ \\lambda_{TRUE} = \\frac{\\mu'()}{\\mu_I - \\mu()} \\] Refer Chapter 11.3 book Hens et al. (2012) detailed explanation method. Fitting data General workflow: Step 1: Fit antibody level data 2-component mixture model Step 2: fitted mixture model, estimate seroprevalence FOI fit antibody data, use mixture_model function  sero-prevalence FOI can esimated using function estimate_from_mixture","code":"df <- vzv_be_2001_2003[vzv_be_2001_2003$age < 40.5,] df <- df[order(df$age),] data <- df$VZVmIUml model <- mixture_model(antibody_level = data) print(model) #> Mixture model  #>  #> Estimated proportion: #>  Susceptible=0.1088, Infected=0.8912  #>  #> Estimated mean Log(Antibody): #>  Susceptible=2.349, Infected=6.439 plot(model) est_mixture <- estimate_from_mixture(df$age, data, mixture_model = model, threshold_status = df$seropositive, sp=83, monotonize = FALSE) est_mixture #> Age-varying seroprevalence estimated from mixture model  #>  #> Monotonized seroprevalence:  FALSE #> Family: gaussian  #> Link function: identity  #>  #> Formula: #> log_antibody ~ s(age, bs = \"ps\", sp = 83) #>  #> Estimated degrees of freedom: #> 5.13  total = 6.13  #>  #> GCV score: 2.056333 plot(est_mixture) #> Warning: No shared levels found between `names(values)` of the manual scale and the #> data's fill values."},{"path":"https://oucru-modelling.github.io/serosv/articles/model_selection.html","id":"generate-models-comparison-data-frame","dir":"Articles","previous_headings":"","what":"Generate models comparison data.frame","title":"Model selection","text":"Function compare_models() used quickly computing comparison metrics set models given dataset. function takes following arguments: data dataset fitted models method method generate comparison metrics. can name one built-methods, user-defined function. … functions take data return fitted serosv model. return data.frame following columns label model identifier. Either user defined name index based order provided. type type model (serosv model class) metrics depending method selected Built-method serosv currently provide 2 built-metrics generating methods \"AIC/BIC\" returns fitted model’s AIC, BIC Log-likelihood (applicable) \"CV\" split data “train” “test” set return logloss AUC model’s prediction test set Sample usage aggregated data","code":"# ----- Return AIC, BIC -----  aic_bic_out <- compare_models(   data = data,   method = \"AIC/BIC\",   griffith = ~polynomial_model(.x, k=2),   penalized_spline = penalized_spline_model,   farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),   local_polynomial = lp_model # expect to not return any values   ) %>% suppressWarnings()  # ----- Return Cross-validation metrics -----  cv_out <- compare_models(     data,     method = \"CV\",     griffith = ~polynomial_model(.x, k=2),     penalized_spline = penalized_spline_model,     farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),     local_polynomial = lp_model    ) %>% suppressWarnings()  aic_bic_out ## # A tibble: 4 × 8 ##   label            type                  AIC   BIC logLik    df mod_out    plots ##   <chr>            <chr>               <dbl> <dbl>  <dbl> <dbl> <list>     <lis> ## 1 griffith         polynomial_model    1353. 1363.  -674.  2    <plynml_m> <gg>  ## 2 penalized_spline penalized_spline_m… 1330. 1365.  -658.  6.88 <pnlzd_s_> <gg>  ## 3 farrington       farrington_model    1337.   NA   -665.  3    <frrngtn_> <gg>  ## 4 local_polynomial lp_model              NA    NA     NA  NA    <lp_model> <gg> cv_out ## # A tibble: 4 × 6 ##   label            logloss   auc type                   mod_out    plots  ##   <chr>              <dbl> <dbl> <chr>                  <list>     <list> ## 1 griffith            277. 0.594 polynomial_model       <plynml_m> <gg>   ## 2 penalized_spline    197. 0.601 penalized_spline_model <pnlzd_s_> <gg>   ## 3 farrington          356. 0.600 farrington_model       <frrngtn_> <gg>   ## 4 local_polynomial    237. 0.588 lp_model               <lp_model> <gg> # ----- Return AIC, BIC -----  aic_bic_out <- compare_models(   data = aggregated,   method = \"AIC/BIC\",   griffith = ~polynomial_model(.x, k=2),   penalized_spline = penalized_spline_model,   farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),   local_polynomial = lp_model # expect to not return any values   ) %>% suppressWarnings()  # ----- Return Cross-validation metrics (default with 4 folds) -----  cv_out <- compare_models(     data = aggregated,     method = \"CV\",     griffith = ~polynomial_model(.x, k=2),     penalized_spline = penalized_spline_model,     farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),     local_polynomial = lp_model    ) %>% suppressWarnings()   aic_bic_out ## # A tibble: 4 × 8 ##   label            type                  AIC   BIC logLik    df mod_out    plots ##   <chr>            <chr>               <dbl> <dbl>  <dbl> <dbl> <list>     <lis> ## 1 griffith         polynomial_model     489.  496.  -243.  2    <plynml_m> <gg>  ## 2 penalized_spline penalized_spline_m…  257.  271.  -124.  4.37 <pnlzd_s_> <gg>  ## 3 farrington       farrington_model    1337.   NA   -665.  3    <frrngtn_> <gg>  ## 4 local_polynomial lp_model              NA    NA     NA  NA    <lp_model> <gg> cv_out ## # A tibble: 4 × 6 ##   label              mse logloss type                   mod_out    plots  ##   <chr>            <dbl>   <dbl> <chr>                  <list>     <list> ## 1 griffith         0.217   178.  polynomial_model       <plynml_m> <gg>   ## 2 penalized_spline 0.123    79.7 penalized_spline_model <pnlzd_s_> <gg>   ## 3 farrington       0.117    65.5 farrington_model       <frrngtn_> <gg>   ## 4 local_polynomial 0.117    71.3 lp_model               <lp_model> <gg>"},{"path":"https://oucru-modelling.github.io/serosv/articles/model_selection.html","id":"generate-custom-metrics","dir":"Articles","previous_headings":"","what":"Generate custom metrics","title":"Model selection","text":"users can also provide custom function generate comparison metrics. function must accepts 2 parameters: dat input data model_func function takes input data returns serosv model must returns data frame 1 row column represents one metric. Example: following implements holdout validation returns MAE: can run compare_models custom metrics function","code":"generate_mae <- function(dat, model_func){   n_train <- round(nrow(dat)*0.8)   train <- dat[1:n_train,]   test <- dat[n_train:nrow(dat),]      fit <- model_func(dat)   pred <- predict(fit, test[, 1, drop=FALSE])      # handle error differently depending on datatype   mae <- if(fit$datatype == \"linelisting\"){     sum(abs(test$status - pred), na.rm=TRUE)/nrow(test)   }else{     sum(abs(test$pos/test$tot - pred), na.rm=TRUE)/nrow(test)   }      data.frame(     mae = mae   ) } compare_models(     data = aggregated,     method = generate_mae,     griffith = ~polynomial_model(.x, k=2),     penalized_spline = penalized_spline_model,     farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),     local_polynomial = lp_model    ) %>% suppressWarnings() ##              label       mae ## 1         griffith 1.9127037 ## 2 penalized_spline 0.4801265 ## 3       farrington 0.3710685 ## 4 local_polynomial 0.3285755 compare_models(     data = data,     method = generate_mae,     griffith = ~polynomial_model(.x, k=2),     penalized_spline = penalized_spline_model,     farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),     local_polynomial = lp_model    ) %>% suppressWarnings() ##              label       mae ## 1         griffith 1.6883332 ## 2 penalized_spline 0.5215807 ## 3       farrington 0.4636040 ## 4 local_polynomial 0.4536016"},{"path":"https://oucru-modelling.github.io/serosv/articles/nonparametric_model.html","id":"local-estimation-by-polynomial","dir":"Articles","previous_headings":"","what":"Local estimation by polynomial","title":"Nonparametric model","text":"Proposed model Within local polynomial framework, linear predictor \\(\\eta()\\) approximated locally one particular value \\(a_0\\) age line (local linear, degree \\(p=1\\)) parabola (local quadratic, degree \\(p=2\\)). general degree \\(p\\), linear predictor neighbor \\(a_0\\), labeled \\(a_i\\) equivalent Taylor approximation \\[ \\eta(a_i) = \\eta(a_0) + \\eta^{(1)}(a_0)(a_i - a_0) + \\frac{\\eta^{(2)}(a_0)}{2}(a_i - a_0)^2 + ... + \\frac{\\eta^{(p)}(a_0)}{p!}(a_i - a_0)^p \\] \\(\\eta(a_i)\\) can estimated maximizing \\[ \\Sigma_{=1}^{N} \\ell_i \\{Y_i, g^{-1} (\\beta_0 + \\beta_1(a_i-a_0)+ \\beta_2(a_i-a_0)^2 ... + \\beta_p(a_i-a_0)^p) \\} K_h(a_i - a_0) \\] : \\(\\ell_i\\) binomial log-likelihood \\(\\ell_i\\{Y_i, \\pi\\} = Y_ilog\\{\\pi\\} + (1-Y_i)log\\{1-\\pi\\}\\) \\(K_h\\) kernel function specified smoothing parameter \\(h\\), also called “bandwidth” “window width” estimator \\(k\\)-th derivative \\(\\eta(a_0)\\), \\(k = 0,1,…,p\\) (degree local polynomial) thus \\[ \\hat{\\eta}^{(k)}(a_0) = k!\\hat{\\beta}_k(a_0) \\] estimator prevalence age \\(a_0\\) given \\[ \\hat{\\pi}(a_0) = g^{-1}\\{ \\hat{\\beta}_0(a_0) \\} \\] \\(g\\) link function estimator force infection age \\(a_0\\) assuming \\(p \\ge 1\\) followed \\[ \\hat{\\lambda}(a_0) = \\hat{\\beta}_1(a_0) \\delta \\{ \\hat{\\beta}_0 (a_0) \\} \\] \\(\\delta \\{ \\hat{\\beta}_0(a_0) \\} = \\frac{dg^{-1} \\{ \\hat{\\beta}_0(a_0) \\} } {d\\hat{\\beta}_0(a_0)}\\) Refer Chapter 7.1 book Hens et al. (2012) detailed explanation method. Fitting data fit local estimation polynomials, use lp_model() function model parameters include: nn number nearest neighbors included approximation (.e., nearest neighbor component smoothing parameter) h constant component smoothing parameter deg degree polynomial kern type kernel  users can also provide h nn numeric vector, case package use parameter value minimize generalized cross-validation (GCV) criterion.   Alternatively, use plot_gcv() show GCV curves nearest neighbor method (left) constant bandwidth (right) manual parameter selection.  Based plot, can fit model using parameter value give lowest GCV (nn = 0.3 h = 14)","code":"mump <- mumps_uk_1986_1987 lp <- lp_model(mump, kern=\"tcub\", nn = 0.5, deg=2) lp #> Local polynomial model  #>  #> Input type:  aggregated  #> Configs:  nn=0.5, bandwidth(h)=0, degree=2, kernel=tcub  #>  #> Call: #> locfit(formula = y ~ lp(age, deg = deg, nn = nn, h = h), family = \"binomial\",  #>     kern = kern) #>  #> Number of observations:          44  #> Family:  Logistic  #> Fitted Degrees of freedom:       6.463  #> Residual scale:                  1 plot(lp) # ----- Tune nearest neighbor (nn) parameter lp_model(mump, kern=\"tcub\", nn = seq(0.2, 0.8, 0.1), deg=2) %>%    plot() # ----- Tune bandwidth (h) parameter lp_model(mump, kern=\"tcub\", h = seq(5,25), deg=2) %>%    plot() plot_gcv(    mump$age, mump$pos, mump$tot,    nn_seq = seq(0.2, 0.8, by=0.1),    h_seq = seq(5, 25, by=1)  ) lp_model(mump, kern=\"tcub\", nn = 0.3, deg=2) %>%    plot() lp_model(mump, kern=\"tcub\", h = 14, deg=2)  %>%    plot()"},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/articles/parametric_model.html","id":"polynomial-models","dir":"Articles","previous_headings":"Frequentist methods","what":"Polynomial models","title":"Parametric models","text":"Seroprevalence modeled using following serocatalytic model \\[ \\pi()  = 1 - \\text{exp}({-\\Sigma_{=1}^k \\beta_i ^}) \\] : \\(\\) variable age \\(\\pi\\) seroprevalence population age \\(\\) \\(k\\) degree polynomial \\(\\beta_i\\) model parameters implies force infection \\(\\lambda() = \\Sigma_{=1}^k \\beta_i ^{-1}\\) generalization encompasses several classical serocatalytic model including Muench model (assuming \\(k=1\\)) (Muench 1934) Griffith model (assuming \\(k = 2\\)) Grenfell Anderson model (assuming higher degree \\(k\\)) (Grenfell Anderson 1985) Refer Chapter 6.1.1 book Hens et al. (2012) detailed explanation methods. Fitting data use Parvo B19 data Finland 1997–1998 example. fit polynomial model, use polynomial_model() function.  users can also choose provide range values \\(k\\) case package try find best \\(k\\) parameter determined Loglikelihood Ratio test (LRT)","code":"data <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age), ] # Fit a Muench model muench <- polynomial_model(data, k = 1, status_col=\"seropositive\") summary(muench$info) #>  #> Call: #> glm(formula = Age(k), family = binomial(link = link), data = df) #>  #> Coefficients: #>      Estimate Std. Error z value Pr(>|z|)     #> age -0.029088   0.001375  -21.15   <2e-16 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance:    Inf  on 1117  degrees of freedom #> Residual deviance: 1366.9  on 1116  degrees of freedom #> AIC: 1368.9 #>  #> Number of Fisher Scoring iterations: 6 plot(muench) # Provide a range of values for k best_param <- polynomial_model(data, k = 1:5, status_col = \"seropositive\") plot(best_param) # View the best model here which suggests k = 4 is the best parameter value best_param #> Polynomial model #>  #> Input type:  linelisting  #> Degree (k):  4  #>  #> Call:  glm(formula = Age(k), family = binomial(link = link), data = df) #>  #> Coefficients: #>        age    I(age^2)    I(age^3)    I(age^4)   #> -3.381e-02  -9.950e-04   5.551e-05  -5.737e-07   #>  #> Degrees of Freedom: 1117 Total (i.e. Null);  1113 Residual #> Null Deviance:       Inf  #> Residual Deviance: 1336  AIC: 1344"},{"path":"https://oucru-modelling.github.io/serosv/articles/parametric_model.html","id":"fractional-polynomial-model","dir":"Articles","previous_headings":"Frequentist methods","what":"Fractional polynomial model","title":"Parametric models","text":"Proposed model Fractional polynomial model generalize conventional polynomial class functions. context binary responses, fractional polynomial degree \\(m\\) linear predictor defined followed \\[ \\eta_m(, \\beta, p_1, p_2, ...,p_m) = \\Sigma^m_{=0} \\beta_i H_i() \\] \\(m\\) integer, \\(p_1 \\le p_2 \\le... \\le p_m\\) sequence powers, \\(H_i()\\) transformation given \\[ H_i = \\begin{cases} ^{p_i} & \\text{ } p_i \\neq p_{-1}, \\\\ H_{-1}() \\times log()  & \\text{ } p_i = p_{-1}, \\end{cases} \\] Refer Chapter 6.2 book Hens et al. (2012) detailed explanation methods. Fitting data Use fp_model() fit fractional polynomial model. parameter p specifies powers polynomial term (length p thus model’s degree)  users can also tell package perform parameter selection providing p named list 2 elements: degree maximum number terms search p_range possible powers term  restrict parameters search predictions monotonic (thus ensuring FOI \\(\\lambda \\geq 0\\)) set monotonic=TRUE","code":"hav <- hav_be_1993_1994 model <- fp_model(hav, p=c(1, 1.5), link=\"cloglog\") model #> Fractional polynomial model  #>  #> Input type:  aggregated  #> Powers:  1, 1.5  #>  #> Call:  glm(formula = as.formula(formulate(p)), family = binomial(link = link)) #>  #> Coefficients: #> (Intercept)     I(age^1)   I(age^1.5)   #>    -4.56685      0.22340     -0.01775   #>  #> Degrees of Freedom: 85 Total (i.e. Null);  83 Residual #> Null Deviance:       1320  #> Residual Deviance: 85.81     AIC: 365.4 plot(model) model <- fp_model(hav,                    p=list(                     p_range=seq(-2,3,0.1),                     degree=2                   ),                    monotonic=FALSE,                   link=\"cloglog\") plot(model) # the best set of powers for this dataset is 1.5 and 1.6 model #> Fractional polynomial model  #>  #> Input type:  aggregated  #> Powers:  1.5, 1.6  #>  #> Call:  glm(formula = as.formula(formulate(curr_p)), family = binomial(link = link)) #>  #> Coefficients: #> (Intercept)   I(age^1.5)   I(age^1.6)   #>    -3.61083      0.12443     -0.07656   #>  #> Degrees of Freedom: 85 Total (i.e. Null);  83 Residual #> Null Deviance:       1320  #> Residual Deviance: 81.6  AIC: 361.2 # ---- Best model with the monotonic constraint ----- model <- fp_model(hav,                    p=list(                     p_range=seq(-2,3,0.1),                     degree=2                   ),                    monotonic=TRUE,                   link=\"cloglog\") plot(model) # the best set of powers with the monotonic constraint is 0.5 and 1.1 model #> Fractional polynomial model  #>  #> Input type:  aggregated  #> Powers:  0.5, 1.1  #>  #> Call:  glm(formula = as.formula(formulate(curr_p)), family = binomial(link = link)) #>  #> Coefficients: #> (Intercept)   I(age^0.5)   I(age^1.1)   #>    -7.64170      1.67492     -0.05304   #>  #> Degrees of Freedom: 85 Total (i.e. Null);  83 Residual #> Null Deviance:       1320  #> Residual Deviance: 106   AIC: 385.5"},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/articles/parametric_model.html","id":"farrington-model","dir":"Articles","previous_headings":"Frequentist methods > Nonlinear models","what":"Farrington model","title":"Parametric models","text":"Proposed model Farrington’s model, force infection defined non-negative \\(\\lambda() \\geq 0\\) increases peak linear fashion followed exponential decrease \\[ \\lambda() = (\\alpha - \\gamma)e^{-\\beta } + \\gamma \\] \\(\\gamma\\) called long term residual FOI, \\(\\rightarrow \\infty\\) , \\(\\lambda () \\rightarrow \\gamma\\) Integrating \\(\\lambda()\\) results following non-linear model prevalence \\[ \\pi () = 1 - e^{-\\int_0^\\lambda(s) ds} \\\\ = 1 - exp\\{ \\frac{\\alpha}{\\beta}ae^{-\\beta } + \\frac{1}{\\beta}(\\frac{\\alpha}{\\beta} - \\gamma)(e^{-\\beta } - 1) -\\gamma \\} \\] Refer Chapter 6.1.2 book Hens et al. (2012) detailed explanation methods. Fitting data Use farrington_model() fit Farrington’s model.","code":"farrington_md <- farrington_model(    rubella_uk_1986_1987,    start=list(alpha=0.07,beta=0.1,gamma=0.03)    ) farrington_md #> Farrington model  #>  #> Input type:  aggregated  #>  #> Call: #> mle(minuslogl = farrington, start = start, fixed = fixed) #>  #> Coefficients: #>      alpha       beta      gamma  #> 0.07034904 0.20243950 0.03665599 plot(farrington_md)"},{"path":"https://oucru-modelling.github.io/serosv/articles/parametric_model.html","id":"weibull-model","dir":"Articles","previous_headings":"Frequentist methods > Nonlinear models","what":"Weibull model","title":"Parametric models","text":"Proposed model Weibull model, prevalence given \\[ \\pi (d) = 1 - e^{ - \\beta_0 d ^ {\\beta_1}} \\] \\(d\\) exposure time (difference age injection age test) model reformulated GLM model log - log link linear predictor using log(d) \\[\\eta(d) = log(\\beta_0) + \\beta_1 log(d)\\] Thus implies force infection monotone function exposure time followed \\[ \\lambda(d) = \\beta_0 \\beta_1 d^{\\beta_1 - 1} \\] Refer Chapter 6.1.2 book Hens et al. (2012) detailed explanation methods. Fitting data Use weibull_model() fit Weibull model.","code":"hcv <- hcv_be_2006[order(hcv_be_2006$dur), ]  wb_md <- hcv %>% weibull_model(t_lab = \"dur\", status_col=\"seropositive\") wb_md #> Weibull model  #>  #> Input type:  linelisting  #> b0=-0.276, b1=0.3807  #>  #> Call:  glm(formula = spos ~ log(t), family = binomial(link = \"cloglog\")) #>  #> Coefficients: #> (Intercept)       log(t)   #>     -0.2760       0.3807   #>  #> Degrees of Freedom: 420 Total (i.e. Null);  419 Residual #> Null Deviance:       452.1  #> Residual Deviance: 419.4     AIC: 423.4 plot(wb_md)"},{"path":"https://oucru-modelling.github.io/serosv/articles/parametric_model.html","id":"bayesian-methods","dir":"Articles","previous_headings":"","what":"Bayesian methods","title":"Parametric models","text":"Proposed approach Consider model prevalence parametric form \\(\\pi(a_i, \\alpha)\\) \\(\\alpha\\) parameter vector One can constraint parameter space prior distribution \\(P(\\alpha)\\) order achieve desired monotonicity posterior distribution \\(P(\\pi_1, \\pi_2, ..., \\pi_m|y,n)\\) : \\(n = (n_1, n_2, ..., n_m)\\) \\(n_i\\) sample size age \\(a_i\\) \\(y = (y_1, y_2, ..., y_m)\\) \\(y_i\\) number infected individual \\(n_i\\) sampled subjects","code":""},{"path":"https://oucru-modelling.github.io/serosv/articles/parametric_model.html","id":"farrington","dir":"Articles","previous_headings":"Bayesian methods","what":"Farrington","title":"Parametric models","text":"Proposed model model prevalence followed \\[ \\pi () = 1 - exp\\{ \\frac{\\alpha_1}{\\alpha_2}ae^{-\\alpha_2 } + \\frac{1}{\\alpha_2}(\\frac{\\alpha_1}{\\alpha_2} - \\alpha_3)(e^{-\\alpha_2 } - 1) -\\alpha_3 \\} \\] likelihood model, independent binomial distribution assumed number infected individuals age \\(a_i\\) \\[ y_i \\sim Bin(n_i, \\pi_i), \\text{  } = 1,2,3,...m \\] constraint parameter space can incorporated assuming truncated normal distribution components \\(\\alpha\\), \\(\\alpha = (\\alpha_1, \\alpha_2, \\alpha_3)\\) \\(\\pi_i = \\pi(a_i,\\alpha)\\) \\[ \\alpha_j \\sim \\text{truncated  } \\mathcal{N}(\\mu_j, \\tau_j), \\text{ } j = 1,2,3 \\] joint posterior distribution \\(\\alpha\\) can derived combining likelihood prior followed \\[ P(\\alpha|y) \\propto \\prod^m_{=1} \\text{Bin}(y_i|n_i, \\pi(a_i, \\alpha)) \\prod^3_{=1}-\\frac{1}{\\tau_j}\\text{exp}(\\frac{1}{2\\tau^2_j} (\\alpha_j - \\mu_j)^2) \\] flat hyperprior distribution defined followed: \\(\\mu_j \\sim \\mathcal{N}(0, 10000)\\) \\(\\tau^{-2}_j \\sim \\Gamma(100,100)\\) full conditional distribution \\(\\alpha_i\\) thus \\[ P(\\alpha_i|\\alpha_j,\\alpha_k, k, j \\neq ) \\propto  -\\frac{1}{\\tau_i}\\text{exp}(\\frac{1}{2\\tau^2_i} (\\alpha_i - \\mu_i)^2) \\prod^m_{=1} \\text{Bin}(y_i|n_i, \\pi(a_i, \\alpha)) \\] Refer Chapter 10.3.1 book Hens et al. (2012) detailed explanation method. Fitting data fit Farrington model, use hierarchical_bayesian_model() define type = \"far2\" type = \"far3\" type = \"far2\" refers Farrington model 2 parameters (\\(\\alpha_3 = 0\\)) type = \"far3\" refers Farrington model 3 parameters (\\(\\alpha_3 > 0\\))","code":"df <- mumps_uk_1986_1987 model <- hierarchical_bayesian_model(df, type=\"far3\") #>  #> SAMPLING FOR MODEL 'fra_3' NOW (CHAIN 1). #> Chain 1: Rejecting initial value: #> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity. #> Chain 1:   Stan can't start sampling from this initial value. #> Chain 1:  #> Chain 1: Gradient evaluation took 0.000165 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.65 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1:  #> Chain 1:  #> Chain 1: Iteration:    1 / 5000 [  0%]  (Warmup) #> Chain 1: Iteration:  500 / 5000 [ 10%]  (Warmup) #> Chain 1: Iteration: 1000 / 5000 [ 20%]  (Warmup) #> Chain 1: Iteration: 1500 / 5000 [ 30%]  (Warmup) #> Chain 1: Iteration: 1501 / 5000 [ 30%]  (Sampling) #> Chain 1: Iteration: 2000 / 5000 [ 40%]  (Sampling) #> Chain 1: Iteration: 2500 / 5000 [ 50%]  (Sampling) #> Chain 1: Iteration: 3000 / 5000 [ 60%]  (Sampling) #> Chain 1: Iteration: 3500 / 5000 [ 70%]  (Sampling) #> Chain 1: Iteration: 4000 / 5000 [ 80%]  (Sampling) #> Chain 1: Iteration: 4500 / 5000 [ 90%]  (Sampling) #> Chain 1: Iteration: 5000 / 5000 [100%]  (Sampling) #> Chain 1:  #> Chain 1:  Elapsed Time: 17.494 seconds (Warm-up) #> Chain 1:                100.35 seconds (Sampling) #> Chain 1:                117.844 seconds (Total) #> Chain 1: model #> Hierarchical Bayesian model  #>  #> Input type:  aggregated  #> Model:  Farrington model with 3 parameters  #>  #> Fitted parameters: #>  alpha1 = 0.1397 (95% CrI [0.129, 0.1521], sd = 0.005927) #>  alpha2 = 0.199 (95% CrI [0.1848, 0.2181], sd = 0.008454) #>  alpha3 = 0.009017 (95% CrI [0.000252, 0.02798], sd = 0.007503) plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/parametric_model.html","id":"log-logistic","dir":"Articles","previous_headings":"Bayesian methods","what":"Log-logistic","title":"Parametric models","text":"Proposed approach model seroprevalence followed \\[ \\pi() = \\frac{\\beta ^\\alpha}{1 + \\beta ^\\alpha}, \\text{ } \\alpha, \\beta > 0 \\] likelihood specified Farrington model (\\(y_i \\sim Bin(n_i, \\pi_i)\\)) \\[ \\text{logit}(\\pi()) = \\alpha_2 + \\alpha_1\\log() \\] \\(\\alpha_2 = \\text{log}(\\beta)\\) prior model \\(\\alpha_1\\) specified \\(\\alpha_1 \\sim \\text{truncated  } \\mathcal{N}(\\mu_1, \\tau_1)\\) flat hyperprior Farrington model \\(\\beta\\) constrained positive specifying \\(\\alpha_2 \\sim \\mathcal{N}(\\mu_2, \\tau_2)\\) full conditional distribution \\(\\alpha_1\\) thus \\[ P(\\alpha_1|\\alpha_2) \\propto -\\frac{1}{\\tau_1} \\text{exp} (\\frac{1}{2 \\tau_1^2} (\\alpha_1 - \\mu_1)^2) \\prod_{=1}^m \\text{Bin}(y_i|n_i,\\pi(a_i, \\alpha_1, \\alpha_2) ) \\] \\(\\alpha_2\\) can derived way Refer Chapter 10.3.3 book Hens et al. (2012) detailed explanation method. Fitting data fit Log-logistic model, use hierarchical_bayesian_model() define type = \"log_logistic\"","code":"df <- rubella_uk_1986_1987 model <- hierarchical_bayesian_model(df, type=\"log_logistic\") #>  #> SAMPLING FOR MODEL 'log_logistic' NOW (CHAIN 1). #> Chain 1:  #> Chain 1: Gradient evaluation took 6.5e-05 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.65 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1:  #> Chain 1:  #> Chain 1: Iteration:    1 / 5000 [  0%]  (Warmup) #> Chain 1: Iteration:  500 / 5000 [ 10%]  (Warmup) #> Chain 1: Iteration: 1000 / 5000 [ 20%]  (Warmup) #> Chain 1: Iteration: 1500 / 5000 [ 30%]  (Warmup) #> Chain 1: Iteration: 1501 / 5000 [ 30%]  (Sampling) #> Chain 1: Iteration: 2000 / 5000 [ 40%]  (Sampling) #> Chain 1: Iteration: 2500 / 5000 [ 50%]  (Sampling) #> Chain 1: Iteration: 3000 / 5000 [ 60%]  (Sampling) #> Chain 1: Iteration: 3500 / 5000 [ 70%]  (Sampling) #> Chain 1: Iteration: 4000 / 5000 [ 80%]  (Sampling) #> Chain 1: Iteration: 4500 / 5000 [ 90%]  (Sampling) #> Chain 1: Iteration: 5000 / 5000 [100%]  (Sampling) #> Chain 1:  #> Chain 1:  Elapsed Time: 4.337 seconds (Warm-up) #> Chain 1:                6.096 seconds (Sampling) #> Chain 1:                10.433 seconds (Total) #> Chain 1: model #> Hierarchical Bayesian model  #>  #> Input type:  aggregated  #> Model:  Log-logistic model  #>  #> Fitted parameters: #>  alpha1 = 1.642 (95% CrI [1.539, 1.749], sd = 0.05377) #>  alpha2 = -2.958 (95% CrI [-3.223, -2.722], sd = 0.1275) plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/reading_to_titer.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Convert assay readings to titer","text":"Serological assays often produce raw signals optical density (OD) fluorescence intensity. measurements directly interpretable must converted titers (e.g., IU/ml) using calibration model. serosv provides function to_titer() perform conversion fitting standard curve (per plate) mapping assay readings estimated titers","code":""},{"path":"https://oucru-modelling.github.io/serosv/articles/reading_to_titer.html","id":"convert-to-titer-workflow","dir":"Articles","previous_headings":"","what":"Convert to titer workflow","title":"Convert assay readings to titer","text":"input data expected following information Sample id containing id samples label antitoxins Plate id id plate (standard curve fitted plate) Dilution level (e.g., 50, 100, 200) (Optionally) columns result negative control different dilution level (e.g., negative_50, negative_100, negative_200) demonstrate use function, use simulated dataset Standardize data fitting standard curve, input data must standardized required format. handled function standardize_data(). Fitting data function to_titer() fits standard curve plate converts assay readings titer estimates. users can configure following: model either name built-model (\"4PL\" 4 parameters log-logistic model) named list 2 items \"mod\" \"quantify_ci\". Section Custom models provide details functions. ci confidence interval titer estimate (default .95 95% CI) negative_control whether include result negative controls (optionally) positive_threshold threshold titer sample considered positive. provided, output include serostatus Output format output nested tibble following columns plate_id ids plate data list data.frames containing input samples data corresponding plate antitoxin_df list data.frames containing input reference data corresponding plate standard_curve_func list functions mapping assay reading titer plate std_crv_midpoint midpoint standard curve, qualitative analysis processed_data list tibbles containing samples titer estimates (lower, median, upper) negative_control list tibbles containing negative control check results (negative_control=TRUE) access estimated titers, simply unnest processed_data column. columns titer estimates lower lower bound confidence interval titer estimate. median median (predicted) titer value. upper upper bound confidence interval titer estimates Visualize standard curves Standard curves can visualized using function plot_standard_curve()   Positive threshold different dilutions also added plot using add_threshold() function, note works facet=FALSE  Visualizing estimate availability function plot_titer_qc() visualizes whether titer estimates can computed sample across tested dilutions. sample represented grid size n_estimates × n_dilutions, cell color indicates estimate availability: Green: estimate available Orange: assay reading low estimate Red: assay reading high estimate sample grids arranged plate, column corresponding single plate containing samples plate.","code":"set.seed(1) # Config n_samples <- 50 n_plates  <- 5 dilutions <- c(50, 100, 200) ref_conc_antitoxin <- 10  # 4PL function: OD = D + (A - D) / (1 + 10^((log10(conc) - c) * b)) mock_4pl <- function(conc, A = 0, B = 1.8, C = -2.5, D = 4) {   D + (A - D) / (1 + 10^(( log10(conc) - C)*B)) }  # Assign each sample a \"true\" concentration (UI/ml) # ~60% positive (conc > 0.1), ~40% negative sample_meta <- tibble(   SAMPLE_ID    = sprintf(\"S%03d\", 1:n_samples),   PLATE_ID     = rep(1:n_plates, length.out = n_samples),   true_conc    = c(     runif(round(n_samples * 0.8), 0.15, 100),   # positives     runif(round(n_samples * 0.2), 0.001, 0.09) # negatives   ) %>% sample()                               # shuffle )  # Negative control concentrations per plate (one fixed OD per plate per dilution) neg_ctrl_conc <- tibble(   PLATE_ID      = 1:n_plates,   true_neg_conc = runif(n_plates, 0.001, 0.04),   neg_conc_50   = true_neg_conc/50,   neg_conc_100  = true_neg_conc/100,   neg_conc_200  = true_neg_conc/200 ) neg_ctrl <- neg_ctrl_conc %>%   mutate(     NEGATIVE_50  = round(mock_4pl(neg_conc_50)  + rnorm(n_plates, 0, 0.003), 4),     NEGATIVE_100 = round(mock_4pl(neg_conc_100) + rnorm(n_plates, 0, 0.003), 4),     NEGATIVE_200 = round(mock_4pl(neg_conc_200) + rnorm(n_plates, 0, 0.003), 4)   ) %>%   select(PLATE_ID, NEGATIVE_50, NEGATIVE_100, NEGATIVE_200)  # Antitoxin  antitoxin_conc <-  c(50, 100, 200, 400, 800, 1600, 3200, 6400, 12800, 25600, 51200, 102400) antitoxin <- tibble(   SAMPLE_ID    = rep(\"Antitoxin\", n_plates),   true_conc    = rep(ref_conc_antitoxin, n_plates),   PLATE_ID = 1:n_plates ) %>%    crossing(     DILUTION = antitoxin_conc   ) %>%    mutate(     eff_conc = true_conc/DILUTION   )  # Generate one row per sample × dilution, compute OD via 4PL + noise simulated_data <- sample_meta %>%   crossing(DILUTION = dilutions) %>%   mutate(     eff_conc  = true_conc / DILUTION   ) %>%    bind_rows(     antitoxin   ) %>%    mutate(     # Effective concentration decreases with dilution     od_mean   = mock_4pl(eff_conc,                          B = runif(n(), 1.7, 1.9),                          C = runif(n(), -2.7, -2.4)),     noise     = rnorm(n(), 0, 0.008),     RESULT    = round(pmax(od_mean + noise, 0.02), 4),      # Blanks: clearly below result (reagent background only)     BLANK_1   = round(runif(n(), 0.010, pmin(RESULT - 0.01, 0.045)), 4),     BLANK_2   = round(runif(n(), 0.010, pmin(RESULT - 0.01, 0.045)), 4),     BLANK_3   = round(runif(n(), 0.010, pmin(RESULT - 0.01, 0.045)), 4)   ) %>%   left_join(neg_ctrl, by = \"PLATE_ID\") %>%   select(     SAMPLE_ID, PLATE_ID,      DILUTION, RESULT,     # BLANK_1, BLANK_2, BLANK_3,     NEGATIVE_50, NEGATIVE_100, NEGATIVE_200   ) %>%   arrange(PLATE_ID, SAMPLE_ID, DILUTION) simulated_data ## # A tibble: 210 × 7 ##    SAMPLE_ID PLATE_ID DILUTION RESULT NEGATIVE_50 NEGATIVE_100 NEGATIVE_200 ##    <chr>        <int>    <dbl>  <dbl>       <dbl>        <dbl>        <dbl> ##  1 Antitoxin        1       50  3.98        0.306       0.0939       0.0269 ##  2 Antitoxin        1      100  4.00        0.306       0.0939       0.0269 ##  3 Antitoxin        1      200  3.99        0.306       0.0939       0.0269 ##  4 Antitoxin        1      400  3.96        0.306       0.0939       0.0269 ##  5 Antitoxin        1      800  3.79        0.306       0.0939       0.0269 ##  6 Antitoxin        1     1600  3.51        0.306       0.0939       0.0269 ##  7 Antitoxin        1     3200  2.57        0.306       0.0939       0.0269 ##  8 Antitoxin        1     6400  0.750       0.306       0.0939       0.0269 ##  9 Antitoxin        1    12800  0.298       0.306       0.0939       0.0269 ## 10 Antitoxin        1    25600  0.135       0.306       0.0939       0.0269 ## # ℹ 200 more rows standardized_dat <- standardize_data(   simulated_data,   plate_id_col = \"PLATE_ID\", # specify the column for plate id   id_col = \"SAMPLE_ID\", # specify the column for sample id   result_col = \"RESULT\", # specify the column for raw assay readings   dilution_fct_col= \"DILUTION\", # specify the column for dilution factor   antitoxin_label = \"Antitoxin\", # specify the label for antitoxin (in the sample id column)   negative_col = \"^NEGATIVE_*\" # (optionally) specify the regex (i.e., pattern) for columns for negative controls )  standardized_dat ## # A tibble: 210 × 7 ##    sample_id plate_id dilution_factors result negative_50 negative_100 ##    <chr>        <int>            <dbl>  <dbl>       <dbl>        <dbl> ##  1 antitoxin        1               50  3.98        0.306       0.0939 ##  2 antitoxin        1              100  4.00        0.306       0.0939 ##  3 antitoxin        1              200  3.99        0.306       0.0939 ##  4 antitoxin        1              400  3.96        0.306       0.0939 ##  5 antitoxin        1              800  3.79        0.306       0.0939 ##  6 antitoxin        1             1600  3.51        0.306       0.0939 ##  7 antitoxin        1             3200  2.57        0.306       0.0939 ##  8 antitoxin        1             6400  0.750       0.306       0.0939 ##  9 antitoxin        1            12800  0.298       0.306       0.0939 ## 10 antitoxin        1            25600  0.135       0.306       0.0939 ## # ℹ 200 more rows ## # ℹ 1 more variable: negative_200 <dbl> out <- to_titer(   standardized_dat,   model = \"4PL\",   ci = 0.95,   positive_threshold = 0.1,   negative_control = TRUE ) out ## # A tibble: 5 × 8 ## # Groups:   plate_id [5] ##   plate_id data              antitoxin_df standard_curve_df standard_curve_func ##      <int> <list>            <list>       <list>            <list>              ## 1        1 <tibble [42 × 6]> <tibble>     <df [512 × 4]>    <fn>                ## 2        2 <tibble [42 × 6]> <tibble>     <df [512 × 4]>    <fn>                ## 3        3 <tibble [42 × 6]> <tibble>     <df [512 × 4]>    <fn>                ## 4        4 <tibble [42 × 6]> <tibble>     <df [512 × 4]>    <fn>                ## 5        5 <tibble [42 × 6]> <tibble>     <df [512 × 4]>    <fn>                ## # ℹ 3 more variables: std_crv_midpoint <dbl>, processed_data <list>, ## #   negative_control <list> out %>%    select(plate_id, processed_data) %>%    unnest(processed_data) %>%    select(plate_id, sample_id, result, lower, median, upper, positive) ## # A tibble: 150 × 7 ## # Groups:   plate_id [5] ##    plate_id sample_id result lower median upper positive ##       <int> <chr>      <dbl> <dbl>  <dbl> <dbl> <lgl>    ##  1        1 S001        4.00 -1.71     NA    NA TRUE     ##  2        1 S001        4.00 -1.73     NA    NA TRUE     ##  3        1 S001        4.00 -1.68     NA    NA TRUE     ##  4        1 S006        4.02 -1.54     NA    NA TRUE     ##  5        1 S006        4.00 -1.73     NA    NA TRUE     ##  6        1 S006        3.99 -1.78     NA    NA TRUE     ##  7        1 S011        4.00 -1.74     NA    NA TRUE     ##  8        1 S011        3.99 -1.79     NA    NA TRUE     ##  9        1 S011        4.00 -1.71     NA    NA TRUE     ## 10        1 S016        4.00 -1.70     NA    NA TRUE     ## # ℹ 140 more rows # visualize standard curves with datapoints for antitoxin plot_standard_curve(out, facet=TRUE) # set facet to FALSE to view all the curves together plot_standard_curve(out, facet=FALSE) # set facet to FALSE to view all the curves together plot_standard_curve(out, facet=FALSE) +   add_thresholds(     dilution_factors = c(50, 100, 200),     positive_threshold = 0.1   ) plot_titer_qc(   out,   n_plates = NULL, # maximum number of plates to visualize, if NULL then plot all   n_samples = 10, # maximum number of samples per plate to visualize   n_dilutions = 3 # number of dilutions used for testing )"},{"path":"https://oucru-modelling.github.io/serosv/articles/reading_to_titer.html","id":"custom-mod","dir":"Articles","previous_headings":"","what":"Custom models","title":"Convert assay readings to titer","text":"users can also define model fitting standard curve function quantifying confidence intervals. , define model argument serosv::to_titer() named list 2 items : mod function takes df input return fitted standard curve model quantify_ci function compute confidence intervals, following required arguments object fitted model newdata new predictor values predictions made nb number samples (required bootstrapping, may unused otherwise) alpha significance level quantify_ci must return data.frame/tibble following columns: lower lower bound confidence interval titer estimate. median median (predicted) titer value. upper upper bound confidence interval titer estimate. Example demonstration purpose, example mod quantify_ci functions 4PL model custom model can provided followed Visualize standard curves","code":"library(mvtnorm) library(purrr) # custom model function custom_4PL <- function (df){   good_guess4PL <- function(x, y, eps = 0.3) {     nb_rep <- unique(table(x))     the_order <- order(x)     x <- x[the_order]     y <- y[the_order]     a <- min(y)     d <- max(y)     c <- approx(y, x, (d - a)/2, ties = \"ordered\")$y     list(a = a, c = c, d = d, b = (approx(x, y, c + eps, ties = \"ordered\")$y -          approx(x, y, c - eps, ties = \"ordered\")$y)/(2 * eps))   }      nls(     result ~ d + (a - d) / (1 + 10 ^ ((log10(concentration) - c) * b)),     data = df,     start = with(df, good_guess4PL(log10(concentration), result))   ) }  # custom quantify CI function for the model custom_quantify_ci <- function(object, newdata, nb = 9999, alpha = 0.025){   rowsplit <- function(df) split(df, 1:nrow(df))    nb |>     rmvnorm(mean = coef(object), sigma = vcov(object)) |>     as.data.frame() |>     rowsplit() |>     map(as.list) |>     map(~ c(.x, newdata)) |>     map_dfc(eval, expr = parse(text = as.character(formula(object))[3])) %>%     apply(1, quantile, c(alpha, .5, 1 - alpha))  %>%     t() %>%  as.data.frame() %>%     setNames(c(\"lower\", \"median\", \"upper\")) } # use the custom model and quantify ci function custom_mod <- list(   \"mod\" = custom_4PL,   \"quantify_ci\" = custom_quantify_ci ) custom_mod_out <- to_titer(   standardized_dat,   model = custom_mod,   positive_threshold = 0.1, ci = 0.95,   negative_control = TRUE) plot_standard_curve(custom_mod_out, facet=TRUE)"},{"path":"https://oucru-modelling.github.io/serosv/articles/repeated_cross_sectional.html","id":"age-time-varying-model","dir":"Articles","previous_headings":"","what":"Age-time varying model","title":"Model repeated cross-sectional data","text":"monitor changes population’s seroprevalence time, modelers often conduct multiple cross-sectional surveys different time points, using new representative sample. resulting data known repeated cross-sectional data. Proposed approach model repeated cross-sectional serological data, serosv offers age_time_model() function implements following workflow: Fit age-specific seroprevalence survey period Monotonize age-specific birth-cohort-specific prevalence time Fit monotonized age-specific seroprevalence survey period Fitting data function expects input data following columns: Either age status (linelisting) age pos + tot (aggregated) column date survey (specified via time_col argument) column id survey period (specified via grouping_col argument) monotonization method can specified via monotonize_method argument, serosv currently supports 2 options: Pooled adjacent violators average (monotonize_method = \"pava\") Shape constrained additive model (monotonize_method = \"scam\") users can also configure monotonize either: Age-specific seroprevalence time (age_correct = FALSE) birth cohort specific seroprevalence time (age_correct=TRUE) output data.frame dimension [number survey, 9], row corresponds single survey period. columns : column id survey period df - input data.frame corresponding survey period info - model seroprevalence monotonized_info - model monotonized seroprevalence monotonized_ci_mod - model monotonized confidence interval sp - predicted seroprevalence given input data foi - estimated force infection sp monotonized_sp - predicted monotonized seroprevalence given input data monotonized_foi - estimated force infection monotonized_sp visualization, plot function age_time_model offers following configurations facet whether visualize result survey period separately (facet=TRUE) single plot (facet=FALSE) modtype choose model visualize, model fitted input data (modtype=\"non-monotonized\") monotonized data (modtype=\"monotonized\") Example: output model PAVA monotonization    Example: output model SCAM monotonization","code":"# Prepare data  tb_nl <- tb_nl_1966_1973 %>%    mutate(     survey_year = age + birthyr,     survey_time = as.Date(paste0(survey_year, \"-01-01\"))   ) %>% select(-birthyr) %>%    filter(survey_year > 1966) %>%    group_by(age, survey_year, survey_time) %>%    summarize(pos = sum(pos), tot = sum(tot), .groups = \"drop\")  head(tb_nl) #> # A tibble: 6 × 5 #>     age survey_year survey_time   pos    tot #>   <int>       <dbl> <date>      <int>  <int> #> 1     6        1970 1970-01-01    140  40868 #> 2     6        1971 1971-01-01     55  17874 #> 3     6        1972 1972-01-01      4   2163 #> 4     7        1970 1970-01-01    328 105960 #> 5     7        1971 1971-01-01    308  96326 #> 6     7        1972 1972-01-01     11   2807 out_pava <- tb_nl %>%    age_time_model(     time_col = \"survey_time\",      grouping_col = \"survey_year\",     age_correct = F,     monotonize_method = \"pava\"   ) %>%    suppressWarnings()  out_scam <- tb_nl %>%    age_time_model(     time_col = \"survey_time\",      grouping_col = \"survey_year\",     age_correct = T,     monotonize_method = \"scam\"   ) %>%    suppressWarnings() out_pava #> Age-time varying seroprevalence model  #>  #> Input type:  aggregated  #> Grouping variable:  survey_year  #> Monotonization method:  pava  #> Monotonize across:  age group  #> # A tibble: 7 × 9 #>   survey_year monotonized_info monotonized_ci_mod df       info   sp         #>         <dbl> <list>           <list>             <list>   <list> <list>     #> 1        1967 <gam>            <named list [2]>   <tibble> <gam>  <dbl [5]>  #> 2        1968 <gam>            <named list [2]>   <tibble> <gam>  <dbl [5]>  #> 3        1969 <gam>            <named list [2]>   <tibble> <gam>  <dbl [6]>  #> 4        1970 <gam>            <named list [2]>   <tibble> <gam>  <dbl [13]> #> 5        1971 <gam>            <named list [2]>   <tibble> <gam>  <dbl [8]>  #> 6        1972 <gam>            <named list [2]>   <tibble> <gam>  <dbl [8]>  #> 7        1973 <gam>            <named list [2]>   <tibble> <gam>  <dbl [5]>  #> # ℹ 3 more variables: foi <list>, monotonized_sp <list>, monotonized_foi <list> out_scam #> Age-time varying seroprevalence model  #>  #> Input type:  aggregated  #> Grouping variable:  survey_year  #> Monotonization method:  scam  #> Monotonize across:  birth cohort  #> # A tibble: 7 × 9 #>   survey_year monotonized_info monotonized_ci_mod df       info   sp         #>         <dbl> <list>           <list>             <list>   <list> <list>     #> 1        1967 <gam>            <named list [2]>   <tibble> <gam>  <dbl [5]>  #> 2        1968 <gam>            <named list [2]>   <tibble> <gam>  <dbl [5]>  #> 3        1969 <gam>            <named list [2]>   <tibble> <gam>  <dbl [6]>  #> 4        1970 <gam>            <named list [2]>   <tibble> <gam>  <dbl [13]> #> 5        1971 <gam>            <named list [2]>   <tibble> <gam>  <dbl [8]>  #> 6        1972 <gam>            <named list [2]>   <tibble> <gam>  <dbl [8]>  #> 7        1973 <gam>            <named list [2]>   <tibble> <gam>  <dbl [5]>  #> # ℹ 3 more variables: foi <list>, monotonized_sp <list>, monotonized_foi <list> plot(out_pava, facet = TRUE, modtype = \"non-monotonized\") + ylim(c(0, 0.07)) plot(out_pava, facet = TRUE, modtype = \"monotonized\") + ylim(c(0, 0.07)) plot(out_pava, facet = FALSE, modtype = \"monotonized\") + ylim(c(0, 0.07)) plot(out_scam, facet = TRUE, modtype = \"non-monotonized\") + ylim(c(0, 0.07)) plot(out_scam, facet = TRUE, modtype = \"monotonized\") + ylim(c(0, 0.07))"},{"path":"https://oucru-modelling.github.io/serosv/articles/semiparametric_model.html","id":"sec-penalized-splines","dir":"Articles","previous_headings":"","what":"Penalized splines","title":"Semiparametric model","text":"Proposed model general model relating prevalence age can written GLM \\[ g(P(Y_i = 1| _i)) = g(\\pi(a_i)) = \\eta(a_i) \\] \\(g\\) link function \\(\\eta\\) linear predictor linear predictor can estimated semi-parametrically using penalized spline truncated power basis functions degree \\(p\\) fixed knots \\(\\kappa_1,..., \\kappa_k\\) followed \\[ \\eta(a_i) = \\beta_0 + \\beta_1a_i + ... + \\beta_p a_i^p + \\Sigma_{k=1}^ku_k(a_i - \\kappa_k)^p_+ \\] \\[ (a_i - \\kappa_k)^p_+ = \\begin{cases} 0, & a_i \\le \\kappa_k \\\\ (a_i - \\kappa_k)^p, & a_i > \\kappa_k \\end{cases} \\] matrix notation, mean structure model \\(\\eta(a_i)\\) becomes \\[ \\eta = \\textbf{X}\\beta + \\textbf{Zu} \\] \\(\\eta = [\\eta(a_i) ... \\eta(a_N) ]^T\\), \\(\\beta = [\\beta_0 \\beta_1 .... \\beta_p]^T\\), \\(\\textbf{u} = [u_1 u_2 ... u_k]^T\\) regression corresponding design matrices \\[ \\textbf{X} = \\begin{bmatrix} 1 & a_1 & a_1^2 & ... & a_1^p \\\\ 1 & a_2 & a_2^2 & ... & a_2^p \\\\ \\vdots & \\vdots & \\vdots & \\dots & \\vdots \\\\ 1 & a_N & a_N^2 & ... & a_N^p \\end{bmatrix}, \\textbf{Z} = \\begin{bmatrix} (a_1 - \\kappa_1 )_+^p & (a_1 - \\kappa_2 )_+^p & \\dots & (a_1 - \\kappa_k)_+^p \\\\ (a_2 - \\kappa_1 )_+^p & (a_2 - \\kappa_2 )_+^p & \\dots & (a_2 - \\kappa_k)_+^p \\\\ \\vdots & \\vdots & \\dots & \\vdots \\\\ (a_N - \\kappa_1 )_+^p & (a_N - \\kappa_2 )_+^p & \\dots & (a_N - \\kappa_k)_+^p \\end{bmatrix} \\] FOI can derived \\[ \\hat{\\lambda}(a_i) = [\\hat{\\beta_1} , 2\\hat{\\beta_2}a_i, ..., p \\hat{\\beta} a_i ^{p-1} + \\Sigma^k_{k=1} p \\hat{u}_k(a_i - \\kappa_k)^{p-1}_+] \\delta(\\hat{\\eta}(a_i)) \\] \\(\\delta(.)\\) determined link function used model","code":""},{"path":"https://oucru-modelling.github.io/serosv/articles/semiparametric_model.html","id":"penalized-likelihood-framework","dir":"Articles","previous_headings":"Penalized splines","what":"Penalized likelihood framework","title":"Semiparametric model","text":"Proposed approach first approach fit model maximizing following penalized likelihood \\[\\begin{equation} \\phi^{-1}[y^T(\\textbf{X}\\beta + \\textbf{Zu} ) -  \\textbf{1}^Tc(\\textbf{X}\\beta + \\textbf{Zu} )] - \\frac{1}{2}\\lambda^2 \\begin{bmatrix} \\beta \\\\ \\textbf{u} \\end{bmatrix}^T D\\begin{bmatrix} \\beta \\\\ \\textbf{u} \\end{bmatrix}  \\tag{1} \\end{equation}\\] : \\(X\\beta + Zu\\) predictor \\(D\\) known semi-definite penalty matrix (Wahba 1978), (Green Silverman 1993) \\(y\\) response vector \\(\\textbf{1}\\) unit vector, \\(c(.)\\) determined link function used \\(\\lambda\\) smoothing parameter (larger values –> smoother curves) \\(\\phi\\) overdispersion parameter equals 1 overdispersion Refer Chapter 8.2.1 book Hens et al. (2012) detailed explanation method. Fitting data fit data using penalized likelihood framework, specify framework = \"pl\" Basis function can defined via s parameter, values s includes: \"tp\" thin plate regression splines \"cr\" cubic regression splines \"ps\" P-splines proposed (Eilers Marx 1996) \"ad\" Adaptive smoothers options, refer mgcv documentation (Wood 2017)","code":"pl <- parvob19_be_2001_2003 %>%    penalized_spline_model(status_col = \"seropositive\", s = \"tp\", framework = \"pl\")  pl #> Penalized spline model  #>  #> Input type:  linelisting  #> Framework:  Penalized likelihood  #>  #> Family: binomial  #> Link function: logit  #>  #> Formula: #> spos ~ s(age, bs = s, sp = sp) #>  #> Estimated degrees of freedom: #> 6.16  total = 7.16  #>  #> UBRE score: 0.1206458 plot(pl)"},{"path":"https://oucru-modelling.github.io/serosv/articles/semiparametric_model.html","id":"generalized-linear-mixed-model-framework","dir":"Articles","previous_headings":"Penalized splines","what":"Generalized Linear Mixed Model framework","title":"Semiparametric model","text":"Proposed approach Looking back (1), constraint \\(\\textbf{u}\\) \\(\\Sigma_ku_k^2 < C\\) positive value \\(C\\) equivalent choosing \\((\\beta, \\textbf{u})\\) maximise (1) \\(D = diag(\\textbf{0}, \\textbf{1})\\) \\(\\textbf{0}\\) denotes zero vector length \\(p+1\\) \\(\\textbf{1}\\) denotes unit vector length \\(K\\) fixed value \\(\\lambda\\) equivalent fitting following generalized linear mixed model Ngo Wand (2004) \\[ f(y|u) = exp\\{ \\phi^{-1} [y^T(X\\beta + Zu) - c(X\\beta + Zu)] + 1^Tc(y)\\},\\\\ u \\sim N(0, G) \\] similar notations \\(G = \\sigma^2_uI_{K \\times K}\\) Thus \\(Z\\) penalized assuming corresponding coefficients \\(\\textbf{u}\\) random effect \\(\\textbf{u} \\sim N(\\textbf{0}, \\mathbf{\\sigma}^2_u \\textbf{})\\). Refer Chapter 8.2.2 book Hens et al. (2012) detailed explanation method. Fitting data fit data using penalized likelihood framework, specify framework = \"glmm\"","code":"glmm <- parvob19_be_2001_2003 %>%    penalized_spline_model(status_col = \"seropositive\", s = \"tp\", framework = \"glmm\")  #>  #>  Maximum number of PQL iterations:  20 #> iteration 1 #> iteration 2 #> iteration 3 #> iteration 4 glmm #> Penalized spline model  #>  #> Input type:  linelisting  #> Framework:  Mixed model  #> $lme #> Linear mixed-effects model fit by maximum likelihood #>   Data: data  #>   Log-likelihood: -6977.429 #>   Fixed: fixed  #> X(Intercept)   Xs(age)Fx1  #>    0.7122306    3.6123783  #>  #> Random effects: #>  Formula: ~Xr - 1 | g #>  Structure: pdIdnot #>              Xr1      Xr2      Xr3      Xr4      Xr5      Xr6      Xr7      Xr8 #> StdDev: 6.020273 6.020273 6.020273 6.020273 6.020273 6.020273 6.020273 6.020273 #>         Residual #> StdDev:        1 #>  #> Variance function: #>  Structure: fixed weights #>  Formula: ~invwt  #> Number of Observations: 3080 #> Number of Groups: 1  #>  #> $gam #>  #> Family: binomial  #> Link function: logit  #>  #> Formula: #> spos ~ s(age, bs = s, sp = sp) #>  #> Estimated degrees of freedom: #> 6.45  total = 7.45  #>  #>  #> attr(,\"class\") #> [1] \"gamm\" \"list\" plot(glmm)"},{"path":"https://oucru-modelling.github.io/serosv/articles/sir_model.html","id":"basic-sir-model","dir":"Articles","previous_headings":"","what":"Basic SIR model","title":"SIR model","text":"Proposed model Let’s consider transmission model consists 3 compartments: susceptible (S), infected (), recovered (R) following assumptions: Individuals born susceptible group (exposure time age individual) transfer infected class recovered class Recovered individuals gained lifelong immunity Age homogeneity described system 3 differential equations \\[ \\begin{cases} \\frac{dS(t)}{dt} = B(t) (1-p) - \\lambda(t)S(t) - \\mu S(t) \\\\ \\frac{dI(t)}{dt} = \\lambda(t)S(t) - \\nu (t) - \\mu (t) - \\alpha (t)  \\\\ \\frac{dR(t)}{dt} =  B(t) p + \\nu (t) - \\mu R(t) \\end{cases} \\] : \\(B(t) = \\mu N(t)\\) \\(\\lambda(t) = \\beta (t)\\) \\(\\beta\\) transmission rate \\(\\mu\\) natural death rate \\(\\nu\\) recovery rate \\(\\alpha\\) disease related death rate \\(p\\) proportion newborn vaccinated moved directly recovered compartment Simulating data simulate basic SIR model, use sir_basic_model() specify following parameters state - initial population compartment times - time sequence parameters - parameters SIR model","code":"state <- c(S=4999, I=1, R=0) parameters <- c(   mu=1/75, # 1 divided by life expectancy (75 years old)   alpha=0, # no disease-related death   beta=0.0005, # transmission rate   nu=1, # 1 year for infected to recover   p=0 # no vaccination at birth ) times <- seq(0, 250, by=0.1) model <- sir_basic_model(times, state, parameters) model$parameters #>         mu      alpha       beta         nu          p  #> 0.01333333 0.00000000 0.00050000 1.00000000 0.00000000 plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/sir_model.html","id":"sir-model-with-constant-force-of-infection-at-endemic-state","dir":"Articles","previous_headings":"","what":"SIR model with constant Force of Infection at Endemic state","title":"SIR model","text":"Proposed model Let’s consider transmission model consists 3 compartments: susceptible (S), infected (), recovered (R) following assumptions: Time homogeneity Age heterogeneity Described system 3 differential equations \\[ \\begin{cases} \\frac{ds()}{da} = -\\lambda s() \\\\ \\frac{di()}{da} = \\lambda s() - \\nu ()  \\\\ \\frac{dr()}{da} =  \\nu () \\end{cases} \\] : \\(s(), (), r()\\) proportion susceptible, infected, recovered population age group \\(\\) respectively \\(\\lambda\\) force infection \\(\\nu\\) recovery rate Simulating data simulate SIR model constant FOI, use sir_static_model() specify following parameters state - initial proportion compartment ages - age sequence parameters - parameters model","code":"state <- c(s=0.99,i=0.01,r=0) parameters <- c(   lambda = 0.05,   nu=1/(14/365) # 2 weeks to recover ) ages<-seq(0, 90, by=0.01) model <- sir_static_model(ages, state, parameters) model$parameters #>   lambda       nu  #>  0.05000 26.07143 plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/sir_model.html","id":"sir-model-with-sub-populations","dir":"Articles","previous_headings":"","what":"SIR model with sub populations","title":"SIR model","text":"Proposed model Extends SIR model interacting sub-populations (different age groups) K subpopulations, WAIFW matrix mixing matrix given \\[ C = \\begin{bmatrix} \\beta_{11} & \\beta_{12}  & ... & \\beta_{1K} \\\\ \\beta_{21} & \\beta_{22}  & ... & \\beta_{2K} \\\\ \\vdots & \\vdots  & ... & \\vdots \\\\ \\beta_{K1} & \\beta_{K2}  & ... & \\beta_{KK} \\\\ \\end{bmatrix} \\] system differential equations \\(th\\) subpopulation given \\[ \\begin{cases} \\frac{dS_i(t)}{dt} = -(\\sum^K_{j=1}\\beta_{ij}I_j(t)) S_i(t) + N_i\\mu_i - \\mu_i S_i(t) \\\\ \\frac{dI_i(t)}{dt} = (\\sum^K_{j=1}\\beta_{ij}I_j(t)) S_i(t)  - (\\nu_i + \\mu_i) I_i(t)  \\\\ \\frac{dR_i(t)}{dt} = \\nu_i I_i(t)  - \\mu_i R_i(t) \\end{cases} \\] Simulating data simulate SIR model subpopulations, use sir_subpops_model() specify following parameters state - initial proportion compartment population beta_matrix - WAIFW matrix, dimensions [K, K] times - time sequence parameters - parameters model","code":"state <- c(   # proportion of each compartment for each population    s = c(0.8, 0.6),    i = c(0.2, 0.4),   r = c(  0,   0) ) beta_matrix <- matrix(   c(0.05, 0.00,     0.00, 0.05),   2 ) parameters <- list(   beta = beta_matrix,   nu = c(1/30, 1/30),   mu = 0.001 )  times<-seq(0,10000,by=0.5) model <- sir_subpops_model(times, state, parameters) model$parameters #> $beta #>      [,1] [,2] #> [1,] 0.05 0.00 #> [2,] 0.00 0.05 #>  #> $nu #> [1] 0.03333333 0.03333333 #>  #> $mu #> [1] 0.001 #>  #> $k #> [1] 2 plot(model) # returns plot for each population #> $subpop_1 #>  #> $subpop_2"},{"path":"https://oucru-modelling.github.io/serosv/articles/sir_model.html","id":"mseir-model","dir":"Articles","previous_headings":"","what":"MSEIR model","title":"SIR model","text":"Proposed model Extends SIR model 2 additional compartments: maternal immunity (M) exposed period (E) described following system ordinary differential equation \\[ \\begin{cases} \\frac{dM()}{da} = -(\\gamma + \\mu())M() \\\\ \\frac{dS()}{da} = \\gamma M() - (\\lambda() + \\mu()) S() \\\\ \\frac{dE()}{da} = \\lambda() S()  - (\\sigma + \\mu()) E() \\\\ \\frac{dI()}{da} = \\sigma() E()  - (\\nu + \\mu()) ()  \\\\ \\frac{dR()}{da} = \\nu ()  - \\mu() R() \\end{cases} \\] \\(M(0)\\) = B, number births population \\(\\gamma\\) rate antibody decaying \\(\\lambda()\\) force infection age \\(\\) \\(\\mu()\\) natural death rate age \\(\\) \\(\\sigma\\) rate becoming infected exposed \\(\\nu\\) recovery rate Simulating data simulate MSEIR model, use mseir_model() specify following parameters - age sequence model parameters including gamma, lambda, sigma, nu","code":"model <- mseir_model(   a=seq(from=1,to=20,length=500), # age range from 0 -> 20 yo   gamma=1/0.5, # 6 months in the maternal antibodies   lambda=0.2,  # 5 years in the susceptible class   sigma=26.07, # 14 days in the latent class   nu=36.5      # 10 days in the infected class ) model$parameters #> $gamma #> [1] 2 #>  #> $lambda #> [1] 0.2 #>  #> $sigma #> [1] 26.07 #>  #> $nu #> [1] 36.5 plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/visualizing_model.html","id":"visualize-model","dir":"Articles","previous_headings":"","what":"Visualize model","title":"Model visualization","text":"visualize model, user can simply use function plot  implementation confidence interval calculation yet available, plot function simply visualize seroprevalence line","code":"hav <- hav_be_1993_1994 model <- fp_model(   hav,   p=c(1.5, 1.6), link=\"cloglog\") plot(model) rubella <- rubella_uk_1986_1987  farrington_md <- farrington_model(    rubella,    start=list(alpha=0.07,beta=0.1,gamma=0.03)    ) plot(farrington_md)"},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/articles/visualizing_model.html","id":"built-in-function","dir":"Articles","previous_headings":"Customize the plot","what":"Built-in function","title":"Model visualization","text":"serosv offers function set_plot_style() customize key attributes plot. Current modifiable attributes include color, linetype seroprevalence, foi fill color confidence interval","code":"hav_mod <- polynomial_model(hav_bg_1964, k=3)  # customize plot plot(hav_mod) +   set_plot_style(     sero = \"#3de071\",     foi = \"#2f22e0\",     ci = \"#aaf2b2\",     foi_line = \"dotted\",      sero_line = \"dotdash\"   ) #> Scale for colour is already present. #> Adding another scale for colour, which will replace the existing scale. #> Scale for linetype is already present. #> Adding another scale for linetype, which will replace the existing scale. #> Scale for fill is already present. #> Adding another scale for fill, which will replace the existing scale."},{"path":"https://oucru-modelling.github.io/serosv/articles/visualizing_model.html","id":"ggplot2-functions","dir":"Articles","previous_headings":"Customize the plot","what":"ggplot2 functions","title":"Model visualization","text":"Since serosv uses ggplot2 plotting, returned plot gg object meaning standard ggplot2 layer can appended + configurations. Examples","code":"# return gg object class(plot(hav_mod)) #> [1] \"gg\"     \"ggplot\" library(ggplot2)  # Set x and y limits plot(hav_mod) +   coord_cartesian(xlim = c(0, 50), ylim = c(0, 1.5)) #> Coordinate system already present. Adding new coordinate system, which will #> replace the existing one. # Configure the plot theme plot(hav_mod) +   theme_bw() # Set titles and omit legends plot(hav_mod) +   theme_bw() +   ggtitle(\"Age-stratified Hepatitis A prevalence in Bulgaria (1964)\") +   guides(     colour = \"none\",     linetype = \"none\",     fill = \"none\"   )"},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"polynomial-models","dir":"Articles > Web-only","previous_headings":"","what":"Polynomial models","title":"serosv: model infectious disease parameters from serosurveys","text":"Refer Chapter 6.1.1 Use polynomial_model() fit polynomial model. use Hepatitis data Belgium 1993–1994 example.","code":"a <- hav_bg_1964 neg <- a$tot -a$pos pos <- a$pos age <- a$age tot <- a$tot"},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"muench-model","dir":"Articles > Web-only","previous_headings":"Polynomial models","what":"Muench model","title":"serosv: model infectious disease parameters from serosurveys","text":"Muench’s model can estimated either defining k = 1 (degree one linear predictor) setting type = \"Muench\". can plot model plot() function.","code":"muench1 <- polynomial_model(age, pos, neg, k = 1) summary(muench1$info) #>  #> Call: #> glm(formula = age(k), family = binomial(link = \"log\"), data = df) #>  #> Coefficients: #>      Estimate Std. Error z value Pr(>|z|)     #> Age -0.050500   0.002457  -20.55   <2e-16 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance:    Inf  on 83  degrees of freedom #> Residual deviance: 97.275  on 82  degrees of freedom #> AIC: 219.19 #>  #> Number of Fisher Scoring iterations: 5  muench2 <- polynomial_model(age, pos, neg, type = \"Muench\") summary(muench2$info) #>  #> Call: #> glm(formula = age(k), family = binomial(link = \"log\"), data = df) #>  #> Coefficients: #>      Estimate Std. Error z value Pr(>|z|)     #> Age -0.050500   0.002457  -20.55   <2e-16 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance:    Inf  on 83  degrees of freedom #> Residual deviance: 97.275  on 82  degrees of freedom #> AIC: 219.19 #>  #> Number of Fisher Scoring iterations: 5 plot(muench2)"},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"griffith-model","dir":"Articles > Web-only","previous_headings":"Polynomial models","what":"Griffith model","title":"serosv: model infectious disease parameters from serosurveys","text":"Similarly, can estimate Griffith’s model.","code":"gf_model <- polynomial_model(age, pos, neg, type = \"Griffith\") plot(gf_model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"grenfell-and-anderson-model","dir":"Articles > Web-only","previous_headings":"Polynomial models","what":"Grenfell and Anderson model","title":"serosv: model infectious disease parameters from serosurveys","text":"Grenfell Anderson’s model.","code":"grf_model <- polynomial_model(age,pos,neg,type = \"Grenfell\") plot(grf_model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"nonlinear-models","dir":"Articles > Web-only","previous_headings":"","what":"Nonlinear models","title":"serosv: model infectious disease parameters from serosurveys","text":"Refer Chapter 6.1.2","code":""},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"farrington-model","dir":"Articles > Web-only","previous_headings":"Nonlinear models","what":"Farrington model","title":"serosv: model infectious disease parameters from serosurveys","text":"Use farrington_model() fit Farrington’s model.","code":"rubella <- rubella_uk_1986_1987 rubella$neg <- rubella$tot - rubella$pos  farrington_md <- farrington_model(    rubella$age, rubella$pos, rubella$tot,    start=list(alpha=0.07,beta=0.1,gamma=0.03)    ) plot(farrington_md)"},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"weibull-model","dir":"Articles > Web-only","previous_headings":"Nonlinear models","what":"Weibull model","title":"serosv: model infectious disease parameters from serosurveys","text":"Use weibull_model() fit Weibull model.","code":"hcv <- hcv_be_2006[order(hcv_be_2006$dur), ] dur <- hcv$dur infected <- hcv$seropositive  wb_md <- weibull_model(    t = dur,    spos = infected    ) plot(wb_md)"},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"fractional-polynomial-model","dir":"Articles > Web-only","previous_headings":"","what":"Fractional polynomial model","title":"serosv: model infectious disease parameters from serosurveys","text":"Refer Chapter 6.2 Use find_best_fp_powers() find powers gives lowest deviance score Use fp_model() fit fractional polynomial model","code":"hav <- hav_be_1993_1994 best_p <- find_best_fp_powers(   hav$age, hav$pos, hav$tot,   p=seq(-2,3,0.1), mc=FALSE, degree=2, link=\"cloglog\" ) best_p #> $p #> [1] 1.5 1.6 #>  #> $deviance #> [1] 81.60333 #>  #> $model #>  #> Call:  glm(formula = as.formula(formulate(p_cur)), family = binomial(link = link)) #>  #> Coefficients: #> (Intercept)   I(age^1.5)   I(age^1.6)   #>    -3.61083      0.12443     -0.07656   #>  #> Degrees of Freedom: 85 Total (i.e. Null);  83 Residual #> Null Deviance:       1320  #> Residual Deviance: 81.6  AIC: 361.2 model <- fp_model(   hav$age, hav$pos, hav$tot,   p=c(1.5, 1.6), link=\"cloglog\") plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"nonparametric-model","dir":"Articles > Web-only","previous_headings":"","what":"Nonparametric model","title":"serosv: model infectious disease parameters from serosurveys","text":"Refer Chapter 7.1","code":""},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"local-estimation-by-polynomial","dir":"Articles > Web-only","previous_headings":"Nonparametric model","what":"Local estimation by polynomial","title":"serosv: model infectious disease parameters from serosurveys","text":"Use plot_gcv() show GCV curves nearest neighbor method (left) constant bandwidth (right).  Use lp_model() fit local estimation polynomials.","code":"mump <- mumps_uk_1986_1987 age <- mump$age pos <- mump$pos tot <- mump$tot y <- pos/tot plot_gcv(    age, pos, tot,    nn_seq = seq(0.2, 0.8, by=0.1),    h_seq = seq(5, 25, by=1)  ) lp <- lp_model(age, pos, tot, kern=\"tcub\", nn=0.7, deg=2) plot(lp)"},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/articles/web-only/demo.html","id":"modeling-multivariate-serological-data","dir":"Articles > Web-only","previous_headings":"","what":"Modeling Multivariate serological data","title":"serosv: model infectious disease parameters from serosurveys","text":"Fitting bivariate Dale model using bivariate_dale_model() use rubella_mumps_uk data example plot function bivariate_dale_model object takes 3 additional parameters: y1 y2 labels 2 diseases modeled plot_type type plot returned \"ci\" returns fitted line marginal prevalence 95% confidence interval \"sp\" returns conditional joint seroprevalence FOI inputs returns individual plots  line listing data vzv_parvo_be , use generate_quad_matrix() function create (NN, NP, PN, PP) fitting model","code":"data <- rubella_mumps_uk model <- bivariate_dale_model(age = data$age, y = data[, c(\"NN\", \"NP\", \"PN\", \"PP\")], monotonized=TRUE) plot(model, y1 = \"Rubella\", y2 = \"Mumps\", plot_type = \"ci\") generate_quad_matrix(vzv_parvo_be, vzv_res, parvo_res, age, discrete_age = F)"},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Anh Phan Truong Quynh. Author, maintainer. Nguyen Pham Nguyen . Author. Long Bui Thanh. Author. Tuyen Huynh. Author. Thinh Ong. Author. Marc Choisy. Author.","code":""},{"path":"https://oucru-modelling.github.io/serosv/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Phan Truong Quynh , Pham Nguyen N, Bui Thanh L, Huynh T, Ong T, Choisy M (2026). serosv: Model Infectious Disease Parameters Serosurveys. https://oucru-modelling.github.io/serosv/, https://github.com/OUCRU-Modelling/serosv.","code":"@Manual{,   title = {serosv: Model Infectious Disease Parameters from Serosurveys},   author = {Anh {Phan Truong Quynh} and Nguyen {Pham Nguyen The} and Long {Bui Thanh} and Tuyen Huynh and Thinh Ong and Marc Choisy},   year = {2026},   note = {https://oucru-modelling.github.io/serosv/, https://github.com/OUCRU-Modelling/serosv}, }"},{"path":"https://oucru-modelling.github.io/serosv/index.html","id":"serosv-","dir":"","previous_headings":"","what":"Model Infectious Disease Parameters from Serosurveys","title":"Model Infectious Disease Parameters from Serosurveys","text":"serosv easy--use efficient tool estimate infectious diseases parameters (seroprevalence force infection) using serological data. current version mostly based book “Modeling Infectious Disease Parameters Based Serological Social Contact Data – Modern Statistical Perspective” Hens et al., 2012 Springer.","code":""},{"path":"https://oucru-modelling.github.io/serosv/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Model Infectious Disease Parameters from Serosurveys","text":"can install development version serosv :","code":"# install.packages(\"devtools\") devtools::install_github(\"OUCRU-Modelling/serosv\")"},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/index.html","id":"datasets","dir":"","previous_headings":"Feature overview","what":"Datasets","title":"Model Infectious Disease Parameters from Serosurveys","text":"serosv contains 15 built-serological datasets provided Hens et al., 2012 Springer. Simply call name load dataset, example:","code":"rubella <- rubella_uk_1986_1987"},{"path":"https://oucru-modelling.github.io/serosv/index.html","id":"methods","dir":"","previous_headings":"Feature overview","what":"Methods","title":"Model Infectious Disease Parameters from Serosurveys","text":"following methods available estimate seroprevalence force infection. Parametric approaches: Frequentist methods: Polynomial models: Muench’s model Griffiths’ model Grenfell Anderson’s model Nonlinear models: Farrington’s model Weibull model Fractional polynomial models Bayesian methods: Hierarchical Farrington model Hierarchical log-logistic model Nonparametric approaches: Local estimation polynomials Semiparametric approaches: Penalized splines: Penalized likelihood framework Generalized Linear Mixed Model framework","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/index.html","id":"fitting-rubella-data-from-the-uk","dir":"","previous_headings":"Demo","what":"Fitting rubella data from the UK","title":"Model Infectious Disease Parameters from Serosurveys","text":"Load rubella UK dataset. Fit data using fractional polynomial model via fp_model(). example, model searches best combination powers within specified range. Visualize model","code":"library(serosv) rubella <- rubella_uk_1986_1987 rubella_mod <- fp_model(   rubella,   p=list(     p_range=seq(-2,3,0.1), # range of powers to search over     degree=2 # maximum degree for the search   ),    monotonic = T, # enforce model to be monotonic   link=\"logit\" ) rubella_mod #> Fractional polynomial model  #>  #> Input type:  aggregated  #> Powers:  -0.9, -0.9  #>  #> Call:  glm(formula = as.formula(formulate(curr_p)), family = binomial(link = link)) #>  #> Coefficients: #>               (Intercept)                I(age^-0.9)   #>                     4.342                     -4.696   #> I(I(age^-0.9) * log(age))   #>                    -9.845   #>  #> Degrees of Freedom: 43 Total (i.e. Null);  41 Residual #> Null Deviance:       1369  #> Residual Deviance: 37.58     AIC: 210.1 plot(rubella_mod)"},{"path":"https://oucru-modelling.github.io/serosv/index.html","id":"fitting-parvo-b19-data-from-finland","dir":"","previous_headings":"Demo","what":"Fitting Parvo B19 data from Finland","title":"Model Infectious Disease Parameters from Serosurveys","text":"","code":"library(dplyr) parvob19 <- parvob19_fi_1997_1998  # for linelisting data, either transform it to aggregated transform_data(   parvob19$age,    parvob19$seropositive,   stratum_col = \"age\") |>   polynomial_model(k = 1) |>   plot() # or fit data as is parvob19 |>   rename(status = seropositive) |>   polynomial_model(k = 1) |>   plot()"},{"path":"https://oucru-modelling.github.io/serosv/reference/add_thresholds.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize positive threshold at different dilution factors — add_thresholds","title":"Visualize positive threshold at different dilution factors — add_thresholds","text":"Visualize positive threshold different dilution factors","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/add_thresholds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize positive threshold at different dilution factors — add_thresholds","text":"","code":"add_thresholds(dilution_factors, positive_threshold = 0.1, shift_text = 0.15)"},{"path":"https://oucru-modelling.github.io/serosv/reference/add_thresholds.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize positive threshold at different dilution factors — add_thresholds","text":"dilution_factors dilution factors visualized positive_threshold titer threshold sample considered positive shift_text adjust much text shifted along x-axis (relative threshold line)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/age_time_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Age-time varying seroprevalence — age_time_model","title":"Age-time varying seroprevalence — age_time_model","text":"Fit age-stratified seroprevalence across multiple time points. Also try monotonize age (birth cohort) - specific seroprevalence.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/age_time_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Age-time varying seroprevalence — age_time_model","text":"","code":"age_time_model(   data,   age_col = \"age\",   status_col = \"status\",   pos_col = \"pos\",   tot_col = \"tot\",   time_col = \"date\",   grouping_col = \"group\",   age_correct = F,   le = 512,   ci = 0.95,   monotonize_method = \"pava\" )"},{"path":"https://oucru-modelling.github.io/serosv/reference/age_time_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Age-time varying seroprevalence — age_time_model","text":"data input data, must age, status, time, group columns, group column determines data aggregated age_col name `age` column (default age_col=\"age\"). status_col name `status` column (default status_col=\"status\"). pos_col name `pos` column (default pos_col=\"pos\"). tot_col name `tot` column (default tot_col=\"tot\"). time_col name column time (default \"date\") grouping_col name column time (default \"group\") age_correct boolean, `TRUE`, monotonize age-specific prevalence. Monotonize birth cohort-specific seroprevalence otherwise. le number bins generate age grid, used monotonizing data ci confidence interval smoothing monotonize_method either \"pava\" \"scam\"","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/age_time_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Age-time varying seroprevalence — age_time_model","text":"list class time_age_model 4 items data.frame dimension n_group x 9, columns `info`, `sp`, `foi` store output non-monotonized data `monotonized_info`, `monotonized_sp`,  `monotonized_foi`,  `monotonized_ci_mod` store output monotonized data grouping_col name column grouping age_correct boolean indicating whether data monotonized across age cohort datatype whether input data aggregated line-listing data","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/bivariate_dale_model.html","id":null,"dir":"Reference","previous_headings":"","what":"A bivariate Dale model — bivariate_dale_model","title":"A bivariate Dale model — bivariate_dale_model","text":"Refers section 12.2","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/bivariate_dale_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A bivariate Dale model — bivariate_dale_model","text":"","code":"bivariate_dale_model(age, y, constant_or = F, monotonized = T, sparopt = 0)"},{"path":"https://oucru-modelling.github.io/serosv/reference/bivariate_dale_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A bivariate Dale model — bivariate_dale_model","text":"age age vector y dataframe predictors (NN, NP, PN, PP), order constant_or whether set constant restriction monotonized whether monotonize seroprevalence sparopt smoothing parameter VGAM model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/bivariate_dale_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A bivariate Dale model — bivariate_dale_model","text":"","code":"data <- rubella_mumps_uk model <- bivariate_dale_model(age = data$age,         y = data[, c(\"NN\", \"NP\", \"PN\", \"PP\")], monotonized=TRUE) plot(model, y1 = \"Rubella\", y2 = \"Mumps\", plot_type = \"sp\")"},{"path":"https://oucru-modelling.github.io/serosv/reference/compare_models.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare models — compare_models","title":"Compare models — compare_models","text":"Compare models","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compare_models.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare models — compare_models","text":"","code":"compare_models(data, method = \"AIC/BIC\", ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compare_models.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare models — compare_models","text":"data input data fit models method method compare models. Can one built-methods function compute returned metrics (see Details). ... models compared. Must models created serosv. models' names provided, indices used instead `model` column returned data.frame.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compare_models.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare models — compare_models","text":"data.frame 4 columns label name index model type model type given model (serosv model name) AIC AIC value model (lower value indicates better fit) BIC BIC value model (lower value indicates better fit)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compare_models.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compare models — compare_models","text":"Built-comparison methods include: - computing AIC BIC, returns AIC, BIC values model available - cross validation, reutns","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.age_time_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute confidence interval for time age model — compute_ci.age_time_model","title":"Compute confidence interval for time age model — compute_ci.age_time_model","text":"Compute confidence interval time age model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.age_time_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute confidence interval for time age model — compute_ci.age_time_model","text":"","code":"# S3 method for class 'age_time_model' compute_ci(x, ci = 0.95, le = 100, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.age_time_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute confidence interval for time age model — compute_ci.age_time_model","text":"x serosv models ci confidence interval le number data computing confidence interval ... arbitrary argument","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.age_time_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute confidence interval for time age model — compute_ci.age_time_model","text":"confidence interval dataframe n_group x 3 cols, columns `group`, `sp_df`, `foi_df`","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.default.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute confidence interval for a model of serosv — compute_ci.default","title":"Compute confidence interval for a model of serosv — compute_ci.default","text":"Compute confidence interval model serosv","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.default.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute confidence interval for a model of serosv — compute_ci.default","text":"","code":"# Default S3 method compute_ci(x, ci = 0.95, le = 100, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.default.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute confidence interval for a model of serosv — compute_ci.default","text":"x serosv models ci confidence interval le number data computing confidence interval ... arbitrary argument","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.default.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute confidence interval for a model of serosv — compute_ci.default","text":"confidence interval dataframe 4 variables, x y fitted values ymin ymax confidence interval","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.fp_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute confidence interval for fractional polynomial model — compute_ci.fp_model","title":"Compute confidence interval for fractional polynomial model — compute_ci.fp_model","text":"Compute confidence interval fractional polynomial model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.fp_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute confidence interval for fractional polynomial model — compute_ci.fp_model","text":"","code":"# S3 method for class 'fp_model' compute_ci(x, ci = 0.95, le = 100, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.fp_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute confidence interval for fractional polynomial model — compute_ci.fp_model","text":"x serosv models ci confidence interval le number data computing confidence interval ... arbitrary argument","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.fp_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute confidence interval for fractional polynomial model — compute_ci.fp_model","text":"confidence interval dataframe 4 variables, x y fitted values ymin ymax confidence interval","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.glmm_ps_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute confidence interval for GLMM — compute_ci.glmm_ps_model","title":"Compute confidence interval for GLMM — compute_ci.glmm_ps_model","text":"Compute confidence interval GLMM","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.glmm_ps_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute confidence interval for GLMM — compute_ci.glmm_ps_model","text":"","code":"compute_ci.glmm_ps_model(x, ci = 0.95, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.glmm_ps_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute confidence interval for GLMM — compute_ci.glmm_ps_model","text":"x - serosv models ci - confidence interval ... - arbitrary arguments","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.hierarchical_bayesian_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute 95% credible interval for hierarchical Bayesian model — compute_ci.hierarchical_bayesian_model","title":"Compute 95% credible interval for hierarchical Bayesian model — compute_ci.hierarchical_bayesian_model","text":"Compute 95% credible interval hierarchical Bayesian model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.hierarchical_bayesian_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute 95% credible interval for hierarchical Bayesian model — compute_ci.hierarchical_bayesian_model","text":"","code":"# S3 method for class 'hierarchical_bayesian_model' compute_ci(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.hierarchical_bayesian_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute 95% credible interval for hierarchical Bayesian model — compute_ci.hierarchical_bayesian_model","text":"x serosv models ... arbitrary arguments","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.hierarchical_bayesian_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute 95% credible interval for hierarchical Bayesian model — compute_ci.hierarchical_bayesian_model","text":"list confidence interval seroprevalence foi. confidence interval dataframe 4 variables, x y fitted values ymin ymax confidence interval","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute confidence interval — compute_ci","title":"Compute confidence interval — compute_ci","text":"Compute confidence interval","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute confidence interval — compute_ci","text":"","code":"compute_ci(x, ci = 0.95, le = 100, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute confidence interval — compute_ci","text":"x - serosv models ci - confidence interval le - number data computing confidence interval ... - arbitrary argument","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute confidence interval — compute_ci","text":"confidence interval dataframe 4 variables, x y fitted values ymin ymax confidence interval","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.lp_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute confidence interval for local polynomial model — compute_ci.lp_model","title":"Compute confidence interval for local polynomial model — compute_ci.lp_model","text":"Compute confidence interval local polynomial model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.lp_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute confidence interval for local polynomial model — compute_ci.lp_model","text":"","code":"# S3 method for class 'lp_model' compute_ci(x, ci = 0.95, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.lp_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute confidence interval for local polynomial model — compute_ci.lp_model","text":"x serosv models ci confidence interval ... arbitrary arguments","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.lp_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute confidence interval for local polynomial model — compute_ci.lp_model","text":"confidence interval dataframe 4 variables, x y fitted values ymin ymax confidence interval","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.mixture_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute confidence interval for mixture model — compute_ci.mixture_model","title":"Compute confidence interval for mixture model — compute_ci.mixture_model","text":"Compute confidence interval mixture model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.mixture_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute confidence interval for mixture model — compute_ci.mixture_model","text":"","code":"# S3 method for class 'mixture_model' compute_ci(x, ci = 0.95, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.mixture_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute confidence interval for mixture model — compute_ci.mixture_model","text":"x serosv mixture_model object ci confidence interval ... arbitrary arguments","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.mixture_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute confidence interval for mixture model — compute_ci.mixture_model","text":"list confidence interval susceptible infected. confidence interval list 2 items lower upper bound interval.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.penalized_spline_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute confidence interval for penalized_spline_model — compute_ci.penalized_spline_model","title":"Compute confidence interval for penalized_spline_model — compute_ci.penalized_spline_model","text":"Compute confidence interval penalized_spline_model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.penalized_spline_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute confidence interval for penalized_spline_model — compute_ci.penalized_spline_model","text":"","code":"# S3 method for class 'penalized_spline_model' compute_ci(x, ci = 0.95, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.penalized_spline_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute confidence interval for penalized_spline_model — compute_ci.penalized_spline_model","text":"x serosv models ci confidence interval ... arbitrary arguments","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.penalized_spline_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute confidence interval for penalized_spline_model — compute_ci.penalized_spline_model","text":"list confidence interval seroprevalence foi confidence interval dataframe 4 variables, x y fitted values ymin ymax confidence interval","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.weibull_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute confidence interval for Weibull model — compute_ci.weibull_model","title":"Compute confidence interval for Weibull model — compute_ci.weibull_model","text":"Compute confidence interval Weibull model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.weibull_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute confidence interval for Weibull model — compute_ci.weibull_model","text":"","code":"# S3 method for class 'weibull_model' compute_ci(x, ci = 0.95, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.weibull_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute confidence interval for Weibull model — compute_ci.weibull_model","text":"x serosv models ci confidence interval ... arbitrary argument","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/compute_ci.weibull_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute confidence interval for Weibull model — compute_ci.weibull_model","text":"confidence interval dataframe 4 variables, x y fitted values ymin ymax confidence interval","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/correct_prevalence.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the true sero prevalence using Frequentist/Bayesian estimation — correct_prevalence","title":"Estimate the true sero prevalence using Frequentist/Bayesian estimation — correct_prevalence","text":"Estimate true sero prevalence using Frequentist/Bayesian estimation","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/correct_prevalence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the true sero prevalence using Frequentist/Bayesian estimation — correct_prevalence","text":"","code":"correct_prevalence(   data,   bayesian = TRUE,   age_col = \"age\",   pos_col = \"pos\",   tot_col = \"tot\",   status_col = \"status\",   init_se = 0.95,   init_sp = 0.8,   study_size_se = 1000,   study_size_sp = 1000,   chains = 1,   warmup = 1000,   iter = 2000 )"},{"path":"https://oucru-modelling.github.io/serosv/reference/correct_prevalence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the true sero prevalence using Frequentist/Bayesian estimation — correct_prevalence","text":"data input data frame, must either columns `age`, `pos`, `tot` (aggregated data) `age`, `status` (linelisting data) bayesian whether adjust sero-prevalence using Bayesian frequentist approach. set `TRUE`, true sero-prevalence estimated using MCMC. age_col name `age` column (default age_col=\"age\"). pos_col name `pos` column (default pos_col=\"pos\"). tot_col name `tot` column (default tot_col=\"tot\"). status_col name `status` column (default status_col=\"status\"). init_se sensitivity serological test init_sp specificity serological test study_size_se (applicable `bayesian=TRUE`) study size sensitivity validation study (.e., number confirmed infected patients study) study_size_sp (applicable `bayesian=TRUE`) study size specificity validation study (.e., number confirmed non-infected patients study) chains (applicable `bayesian=TRUE`) number Markov chains warmup (applicable `bayesian=TRUE`) number warm runs iter (applicable `bayesian=TRUE`) number iterations","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/correct_prevalence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the true sero prevalence using Frequentist/Bayesian estimation — correct_prevalence","text":"list 3 items info estimated parameters (`bayesian = TRUE`) formula compute corrected prevalence (`bayesian = FALSE`) df data.frame input data (aggregated form) 95% confidence interval apparent (.e. observed) seroprevalence corrected_sero data.frame containing age, corresponding estimated seroprevalance 95% confidence/credible interval, adjusted tot pos","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/correct_prevalence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the true sero prevalence using Frequentist/Bayesian estimation — correct_prevalence","text":"","code":"data <- rubella_uk_1986_1987 correct_prevalence(data) #>  #> SAMPLING FOR MODEL 'prevalence_correction' NOW (CHAIN 1). #> Chain 1:  #> Chain 1: Gradient evaluation took 0.000162 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.62 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1:  #> Chain 1:  #> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup) #> Chain 1: Iteration:  200 / 2000 [ 10%]  (Warmup) #> Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup) #> Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup) #> Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup) #> Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup) #> Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling) #> Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling) #> Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling) #> Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling) #> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling) #> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling) #> Chain 1:  #> Chain 1:  Elapsed Time: 1.64 seconds (Warm-up) #> Chain 1:                1.235 seconds (Sampling) #> Chain 1:                2.875 seconds (Total) #> Chain 1:  #> $info #>                    mean      se_mean          sd          2.5%           25% #> est_se     9.579691e-01 0.0001720772 0.005234571  9.468221e-01  9.545560e-01 #> est_sp     8.070363e-01 0.0003593900 0.010998038  7.851243e-01  8.001180e-01 #> theta[1]   1.747732e-02 0.0004012933 0.015518658  3.397982e-04  5.556424e-03 #> theta[2]   4.521156e-02 0.0009719303 0.032103002  3.497362e-03  1.967008e-02 #> theta[3]   3.934172e-02 0.0008073821 0.027757712  1.900997e-03  1.793613e-02 #> theta[4]   1.439986e-01 0.0014055351 0.046463549  5.425350e-02  1.123683e-01 #> theta[5]   3.158280e-01 0.0011278041 0.045792461  2.305850e-01  2.838526e-01 #> theta[6]   4.452249e-01 0.0012812842 0.047140675  3.545909e-01  4.120292e-01 #> theta[7]   4.636578e-01 0.0013137642 0.049670685  3.671684e-01  4.296514e-01 #> theta[8]   6.077679e-01 0.0012322838 0.051758945  5.034081e-01  5.746207e-01 #> theta[9]   7.173174e-01 0.0011569343 0.042494430  6.248539e-01  6.900703e-01 #> theta[10]  6.414107e-01 0.0013163957 0.050844687  5.427184e-01  6.076674e-01 #> theta[11]  7.251551e-01 0.0012041984 0.046902001  6.257779e-01  6.937983e-01 #> theta[12]  8.200245e-01 0.0010676660 0.039122057  7.334985e-01  7.953583e-01 #> theta[13]  7.607357e-01 0.0010292102 0.041323713  6.779961e-01  7.336668e-01 #> theta[14]  8.589639e-01 0.0008862776 0.033589290  7.896023e-01  8.380959e-01 #> theta[15]  7.716052e-01 0.0017929505 0.065516404  6.349394e-01  7.285860e-01 #> theta[16]  8.381310e-01 0.0017053329 0.066149986  7.006723e-01  7.966934e-01 #> theta[17]  9.133985e-01 0.0014411494 0.044158724  8.153283e-01  8.879187e-01 #> theta[18]  8.803590e-01 0.0013704992 0.048219400  7.815409e-01  8.477237e-01 #> theta[19]  8.801362e-01 0.0009873217 0.041440481  7.940748e-01  8.510319e-01 #> theta[20]  8.516456e-01 0.0013062293 0.051569875  7.386624e-01  8.196764e-01 #> theta[21]  9.149110e-01 0.0014600247 0.046220606  8.144062e-01  8.874402e-01 #> theta[22]  8.248331e-01 0.0012972922 0.049342001  7.226267e-01  7.924436e-01 #> theta[23]  9.461869e-01 0.0012688504 0.035172720  8.654861e-01  9.237220e-01 #> theta[24]  9.458786e-01 0.0010783110 0.033650049  8.708283e-01  9.239385e-01 #> theta[25]  9.706552e-01 0.0006278715 0.023829751  9.115230e-01  9.589298e-01 #> theta[26]  9.400029e-01 0.0010821960 0.033457664  8.681502e-01  9.181518e-01 #> theta[27]  9.251679e-01 0.0012477057 0.043365359  8.198036e-01  9.011331e-01 #> theta[28]  9.578219e-01 0.0008736670 0.032194552  8.824005e-01  9.384708e-01 #> theta[29]  9.064031e-01 0.0012458483 0.043045202  8.166192e-01  8.788169e-01 #> theta[30]  9.164445e-01 0.0013366119 0.049352158  8.018241e-01  8.855027e-01 #> theta[31]  8.607109e-01 0.0017207072 0.062663082  7.114398e-01  8.265632e-01 #> theta[32]  9.407438e-01 0.0012121067 0.045559916  8.244369e-01  9.173058e-01 #> theta[33]  9.357270e-01 0.0013794554 0.054278925  7.888680e-01  9.085377e-01 #> theta[34]  9.458862e-01 0.0012548097 0.043638644  8.334385e-01  9.249963e-01 #> theta[35]  8.961572e-01 0.0018858060 0.062735450  7.444533e-01  8.625101e-01 #> theta[36]  9.553792e-01 0.0009240727 0.037492222  8.613046e-01  9.363582e-01 #> theta[37]  9.389160e-01 0.0014208188 0.049938528  8.041306e-01  9.158440e-01 #> theta[38]  8.990585e-01 0.0018333159 0.066536570  7.442794e-01  8.581524e-01 #> theta[39]  9.168747e-01 0.0018935154 0.062687616  7.562417e-01  8.885957e-01 #> theta[40]  9.371065e-01 0.0015140064 0.056832194  7.920169e-01  9.115977e-01 #> theta[41]  9.528511e-01 0.0010380880 0.045128543  8.275254e-01  9.341298e-01 #> theta[42]  9.173219e-01 0.0017494957 0.065112154  7.639822e-01  8.834364e-01 #> theta[43]  9.080154e-01 0.0019124502 0.072102410  7.300474e-01  8.720032e-01 #> theta[44]  9.386586e-01 0.0014204252 0.055390859  8.014757e-01  9.138788e-01 #> lp__      -2.763145e+03 0.3011237007 5.715746786 -2.775866e+03 -2.766777e+03 #>                     50%           75%         97.5%     n_eff      Rhat #> est_se     9.582485e-01  9.613921e-01  9.678490e-01  925.3698 0.9990461 #> est_sp     8.070742e-01  8.144335e-01  8.281304e-01  936.4798 1.0003010 #> theta[1]   1.325944e-02  2.580038e-02  5.764662e-02 1495.4931 0.9990388 #> theta[2]   3.926800e-02  6.313427e-02  1.179769e-01 1090.9906 0.9991753 #> theta[3]   3.510345e-02  5.608013e-02  1.059055e-01 1181.9772 0.9992290 #> theta[4]   1.436227e-01  1.740261e-01  2.363213e-01 1092.8017 1.0041265 #> theta[5]   3.135946e-01  3.461306e-01  4.051240e-01 1648.6201 1.0032065 #> theta[6]   4.451716e-01  4.781418e-01  5.345672e-01 1353.6321 1.0045891 #> theta[7]   4.650392e-01  4.964724e-01  5.600045e-01 1429.4384 0.9991162 #> theta[8]   6.071917e-01  6.426559e-01  7.083871e-01 1764.2062 0.9996147 #> theta[9]   7.187469e-01  7.459264e-01  7.981012e-01 1349.1078 0.9994776 #> theta[10]  6.413189e-01  6.777565e-01  7.376091e-01 1491.8264 0.9999393 #> theta[11]  7.275840e-01  7.571155e-01  8.113803e-01 1517.0036 0.9990510 #> theta[12]  8.232098e-01  8.466952e-01  8.914191e-01 1342.6802 0.9990800 #> theta[13]  7.630979e-01  7.894624e-01  8.355361e-01 1612.0945 0.9994269 #> theta[14]  8.596282e-01  8.830362e-01  9.199369e-01 1436.3561 1.0028516 #> theta[15]  7.768558e-01  8.169429e-01  8.886224e-01 1335.2529 0.9990301 #> theta[16]  8.440784e-01  8.872369e-01  9.488487e-01 1504.6697 0.9990161 #> theta[17]  9.172955e-01  9.451696e-01  9.910868e-01  938.8907 0.9990891 #> theta[18]  8.832052e-01  9.150528e-01  9.691902e-01 1237.9014 1.0004986 #> theta[19]  8.835078e-01  9.116064e-01  9.480115e-01 1761.7010 0.9998162 #> theta[20]  8.551365e-01  8.881845e-01  9.406861e-01 1558.6669 1.0006793 #> theta[21]  9.202015e-01  9.487129e-01  9.903668e-01 1002.1918 1.0008532 #> theta[22]  8.264882e-01  8.589395e-01  9.131122e-01 1446.6315 0.9993236 #> theta[23]  9.535076e-01  9.739242e-01  9.950822e-01  768.4065 0.9994326 #> theta[24]  9.497731e-01  9.711970e-01  9.966858e-01  973.8305 0.9995677 #> theta[25]  9.762265e-01  9.886092e-01  9.990507e-01 1440.4476 1.0010816 #> theta[26]  9.439230e-01  9.641349e-01  9.964088e-01  955.8272 1.0003354 #> theta[27]  9.305549e-01  9.561449e-01  9.929416e-01 1207.9850 0.9998152 #> theta[28]  9.653058e-01  9.828475e-01  9.980652e-01 1357.9160 0.9997075 #> theta[29]  9.094284e-01  9.377292e-01  9.803109e-01 1193.7659 0.9990008 #> theta[30]  9.239625e-01  9.526448e-01  9.917734e-01 1363.3321 0.9990114 #> theta[31]  8.645073e-01  9.066013e-01  9.615028e-01 1326.2016 0.9995796 #> theta[32]  9.514552e-01  9.741705e-01  9.978432e-01 1412.8111 0.9994082 #> theta[33]  9.493927e-01  9.758949e-01  9.982326e-01 1548.2716 1.0000931 #> theta[34]  9.557844e-01  9.797785e-01  9.973173e-01 1209.4468 0.9998171 #> theta[35]  9.057973e-01  9.406812e-01  9.882563e-01 1106.7053 0.9990242 #> theta[36]  9.663073e-01  9.840566e-01  9.982648e-01 1646.1524 0.9992741 #> theta[37]  9.502159e-01  9.773138e-01  9.976396e-01 1235.3616 0.9992080 #> theta[38]  9.099911e-01  9.501315e-01  9.910139e-01 1317.1832 0.9990001 #> theta[39]  9.280455e-01  9.629734e-01  9.939965e-01 1096.0386 0.9992802 #> theta[40]  9.530193e-01  9.777640e-01  9.975286e-01 1409.0728 0.9993224 #> theta[41]  9.646571e-01  9.854371e-01  9.991666e-01 1889.8803 0.9991952 #> theta[42]  9.328743e-01  9.681655e-01  9.964167e-01 1385.1550 0.9990917 #> theta[43]  9.253118e-01  9.602544e-01  9.959770e-01 1421.4097 0.9991980 #> theta[44]  9.546272e-01  9.817446e-01  9.978124e-01 1520.6858 0.9991256 #> lp__      -2.762797e+03 -2.759032e+03 -2.753133e+03  360.2932 1.0126026 #>  #> $df #>     age pos tot      sero  sero_lwr  sero_upr #> 1   1.5  31 206 0.1504854 0.1060177 0.2083748 #> 2   2.5  30 146 0.2054795 0.1449496 0.2818842 #> 3   3.5  34 168 0.2023810 0.1460087 0.2727002 #> 4   4.5  57 189 0.3015873 0.2382102 0.3731851 #> 5   5.5  95 219 0.4337900 0.3676479 0.5022934 #> 6   6.5 104 195 0.5333333 0.4608072 0.6045248 #> 7   7.5  90 164 0.5487805 0.4693519 0.6258878 #> 8   8.5  96 145 0.6620690 0.5782402 0.7371628 #> 9   9.5 134 180 0.7444444 0.6731424 0.8050917 #> 10 10.5 110 160 0.6875000 0.6087555 0.7570748 #> 11 11.5 111 148 0.7500000 0.6709203 0.8158278 #> 12 12.5 147 178 0.8258427 0.7603216 0.8769152 #> 13 13.5 138 177 0.7796610 0.7099871 0.8369125 #> 14 14.5 141 165 0.8545455 0.7892898 0.9027796 #> 15 15.5  53  67 0.7910448 0.6710850 0.8771184 #> 16 16.5  49  58 0.8448276 0.7207490 0.9223300 #> 17 17.5  73  81 0.9012346 0.8095518 0.9533386 #> 18 18.5  69  79 0.8734177 0.7750073 0.9343581 #> 19 19.5  97 111 0.8738739 0.7941533 0.9268253 #> 20 20.5  65  76 0.8552632 0.7515326 0.9220985 #> 21 21.5  74  82 0.9024390 0.8117280 0.9539169 #> 22 22.5  84 101 0.8316832 0.7413312 0.8961330 #> 23 23.5  82  88 0.9318182 0.8519298 0.9719801 #> 24 24.5  79  85 0.9294118 0.8470005 0.9709798 #> 25 25.5  90  94 0.9574468 0.8884991 0.9862810 #> 26 26.5  84  91 0.9230769 0.8427965 0.9658735 #> 27 27.5  81  89 0.9101124 0.8256740 0.9575953 #> 28 28.5  72  76 0.9473684 0.8636358 0.9830057 #> 29 29.5  71  79 0.8987342 0.8050453 0.9521374 #> 30 30.5  51  56 0.9107143 0.7963017 0.9666611 #> 31 31.5  45  52 0.8653846 0.7359542 0.9396995 #> 32 32.5  45  48 0.9375000 0.8179078 0.9837187 #> 33 33.5  35  37 0.9459459 0.8046945 0.9905819 #> 34 34.5  39  41 0.9512195 0.8219181 0.9915057 #> 35 35.5  36  40 0.9000000 0.7540414 0.9674737 #> 36 36.5  37  38 0.9736842 0.8456597 0.9986248 #> 37 37.5  37  39 0.9487179 0.8137029 0.9910676 #> 38 38.5  37  41 0.9024390 0.7594053 0.9682791 #> 39 39.5  28  30 0.9333333 0.7649271 0.9883682 #> 40 40.5  26  27 0.9629630 0.7911082 0.9980636 #> 41 41.5  25  25 1.0000000 0.8342270 1.0000000 #> 42 42.5  21  22 0.9545455 0.7511570 0.9976227 #> 43 43.5  18  19 0.9473684 0.7189259 0.9972465 #> 44 44.5  18  18 1.0000000 0.7812431 1.0000000 #>  #> $corrected_se #>     age        pos tot       sero     sero_lwr   sero_upr #> 1   1.5   2.731444 206 0.01325944 0.0003397982 0.05764662 #> 2   2.5   5.733129 146 0.03926800 0.0034973616 0.11797694 #> 3   3.5   5.897379 168 0.03510345 0.0019009974 0.10590546 #> 4   4.5  27.144698 189 0.14362274 0.0542534972 0.23632128 #> 5   5.5  68.677211 219 0.31359457 0.2305849615 0.40512397 #> 6   6.5  86.808453 195 0.44517156 0.3545909437 0.53456725 #> 7   7.5  76.266421 164 0.46503915 0.3671684261 0.56000455 #> 8   8.5  88.042801 145 0.60719173 0.5034081474 0.70838708 #> 9   9.5 129.374436 180 0.71874687 0.6248538646 0.79810121 #> 10 10.5 102.611026 160 0.64131891 0.5427183649 0.73760906 #> 11 11.5 107.682425 148 0.72758395 0.6257778650 0.81138031 #> 12 12.5 146.531341 178 0.82320978 0.7334985417 0.89141914 #> 13 13.5 135.068334 177 0.76309793 0.6779961210 0.83553609 #> 14 14.5 141.838659 165 0.85962824 0.7896023026 0.91993691 #> 15 15.5  52.049338  67 0.77685579 0.6349393644 0.88862239 #> 16 16.5  48.956545  58 0.84407836 0.7006722751 0.94884869 #> 17 17.5  74.300939  81 0.91729554 0.8153282665 0.99108683 #> 18 18.5  69.773211  79 0.88320521 0.7815408701 0.96919022 #> 19 19.5  98.069368 111 0.88350782 0.7940747981 0.94801151 #> 20 20.5  64.990373  76 0.85513649 0.7386624226 0.94068610 #> 21 21.5  75.456521  82 0.92020148 0.8144062288 0.99036682 #> 22 22.5  83.475309 101 0.82648821 0.7226266670 0.91311217 #> 23 23.5  83.908671  88 0.95350762 0.8654860615 0.99508220 #> 24 24.5  80.730713  85 0.94977310 0.8708282569 0.99668585 #> 25 25.5  91.765295  94 0.97622654 0.9115229908 0.99905070 #> 26 26.5  85.896995  91 0.94392302 0.8681502083 0.99640880 #> 27 27.5  82.819389  89 0.93055493 0.8198035939 0.99294163 #> 28 28.5  73.363239  76 0.96530578 0.8824004970 0.99806516 #> 29 29.5  71.844843  79 0.90942839 0.8166192321 0.98031092 #> 30 30.5  51.741898  56 0.92396246 0.8018241060 0.99177343 #> 31 31.5  44.954381  52 0.86450734 0.7114398199 0.96150283 #> 32 32.5  45.669850  48 0.95145521 0.8244368835 0.99784321 #> 33 33.5  35.127529  37 0.94939266 0.7888680098 0.99823258 #> 34 34.5  39.187161  41 0.95578440 0.8334385481 0.99731726 #> 35 35.5  36.231892  40 0.90579729 0.7444533435 0.98825635 #> 36 36.5  36.719678  38 0.96630732 0.8613046445 0.99826479 #> 37 37.5  37.058419  39 0.95021588 0.8041305633 0.99763957 #> 38 38.5  37.309634  41 0.90999106 0.7442793553 0.99101392 #> 39 39.5  27.841364  30 0.92804546 0.7562417314 0.99399646 #> 40 40.5  25.731521  27 0.95301930 0.7920169109 0.99752857 #> 41 41.5  24.116427  25 0.96465707 0.8275253759 0.99916657 #> 42 42.5  20.523235  22 0.93287433 0.7639822308 0.99641675 #> 43 43.5  17.580925  19 0.92531182 0.7300474394 0.99597705 #> 44 44.5  17.183289  18 0.95462715 0.8014756977 0.99781240 #>  #> $method #> [1] \"bayesian\" #>"},{"path":"https://oucru-modelling.github.io/serosv/reference/est_foi.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate force of infection — est_foi","title":"Estimate force of infection — est_foi","text":"Estimate force infection","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/est_foi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate force of infection — est_foi","text":"","code":"est_foi(t, sp)"},{"path":"https://oucru-modelling.github.io/serosv/reference/est_foi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate force of infection — est_foi","text":"t - time (case age) vector sp - seroprevalence vector","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/est_foi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate force of infection — est_foi","text":"computed foi vector","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/estimate_from_mixture.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate seroprevalence and FOI from a fixed mixture model — estimate_from_mixture","title":"Estimate seroprevalence and FOI from a fixed mixture model — estimate_from_mixture","text":"Estimate age-specific seroprevalence FOI given fitted mixture model (generated [serosv::mixture_model()])","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/estimate_from_mixture.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate seroprevalence and FOI from a fixed mixture model — estimate_from_mixture","text":"","code":"estimate_from_mixture(   age,   antibody_level,   threshold_status = NULL,   mixture_model,   s = \"ps\",   sp = 83,   monotonize = TRUE )"},{"path":"https://oucru-modelling.github.io/serosv/reference/estimate_from_mixture.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate seroprevalence and FOI from a fixed mixture model — estimate_from_mixture","text":"age vector age antibody_level vector corresponding raw antibody level threshold_status sero status using threshold approach line listing (optional, visualization comparison ) mixture_model mixture_model object generated serosv::mixture_model() s smoothing basis used fit antibody level sp smoothing parameter monotonize whether monotonize seroprevalence (default TRUE)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/estimate_from_mixture.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate seroprevalence and FOI from a fixed mixture model — estimate_from_mixture","text":"list class estimated_from_mixture following items df dataframe used fitting model info fitted \"gam\" model mu() sp seroprevalence foi force infection threshold_status serostatus using threshold method provided","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/estimate_from_mixture.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate seroprevalence and FOI from a fixed mixture model — estimate_from_mixture","text":"Antibody level (denoted \\(Z\\)) modeled using 2-component Gaussian mixture model. component \\(Z_j\\) (\\(j \\\\{, S\\}\\)) represents antibody level latent Infected Susceptible sub-populations, following density \\(f_j(z_j|\\theta_j)\\) Let \\(\\pi_{\\text{TRUE}}()\\) denotes age-dependent mixing probability (.e., true prevalence), density mixture formulated $$f(z|z_I, z_S,) = (1-\\pi_{\\text{TRUE}}())f_S(z_S|\\theta_S)+\\pi_{\\text{TRUE}}()f_I(z_I|\\theta_I)$$ mean \\(E(Z|)\\) thus equals $$\\mu() = (1-\\pi_{\\text{TRUE}}())\\mu_S+\\pi_{\\text{TRUE}}()\\mu_I$$ true prevalence can computed $$\\pi_{\\text{TRUE}}() = \\frac{\\mu() - \\mu_S}{\\mu_I - \\mu_S}$$ FOI can inferred $$\\lambda_{TRUE} = \\frac{\\mu'()}{\\mu_I - \\mu()}$$ Function [serosv::mixture_model()] fits antibody level data \\(f_S(z_S|\\theta_S)\\) \\(f_I(z_I|\\theta_I)\\) Function [serosv::estimate_mixture()] estimate age-specific antibody level \\(\\mu()\\) infer estimation \\(\\pi_{\\text{TRUE}}()\\) \\(\\lambda_{TRUE}\\) Refer section 11.3. book Hens et al. (2012) details.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/estimate_from_mixture.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Estimate seroprevalence and FOI from a fixed mixture model — estimate_from_mixture","text":"Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based Serological Social Contact Data: Modern Statistical Perspective. tatistics Biology Health. Springer New York. doi:10.1007/978-1-4614-4072-7 .","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/farrington_model.html","id":null,"dir":"Reference","previous_headings":"","what":"The Farrington (1990) model. — farrington_model","title":"The Farrington (1990) model. — farrington_model","text":"Fit age-stratified seroprevalence data using Farrington (1990) model, assumes force infection increases linearly age subsequently decreases exponentially.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/farrington_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Farrington (1990) model. — farrington_model","text":"","code":"farrington_model(   data,   start,   fixed = list(),   age_col = \"age\",   pos_col = \"pos\",   tot_col = \"tot\",   status_col = \"status\" )"},{"path":"https://oucru-modelling.github.io/serosv/reference/farrington_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Farrington (1990) model. — farrington_model","text":"data input data frame, must either columns `age`, `pos`, `tot` (aggregated data) `age`, `status` (linelisting data) start Named list vectors single vector. Initial values optimizer. fixed Named list vectors single vector. Parameter values keep fixed optimization. age_col name `age` column (default age_col=\"age\"). pos_col name `pos` column (default pos_col=\"pos\"). tot_col name `tot` column (default tot_col=\"tot\"). status_col name `status` column (default status_col=\"status\").","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/farrington_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Farrington (1990) model. — farrington_model","text":"list class farrington_model 5 items datatype type datatype used model fitting (aggregated linelisting) df dataframe used fitting model info fitted \"mle\" object sp seroprevalence foi force infection","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/farrington_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The Farrington (1990) model. — farrington_model","text":"force infection defined followed $$ \\lambda() = (\\alpha - \\gamma)e^{-\\beta } + \\gamma $$ \\(\\gamma\\) called long term residual FOI, \\(\\rightarrow \\infty\\) , \\(\\lambda () \\rightarrow \\gamma\\) seroprevalence can thus estimated using non-linear model $$  \\pi() = 1 - exp\\{ \\frac{\\alpha}{\\beta}ae^{-\\beta } +  \\frac{1}{\\beta}(\\frac{\\alpha}{\\beta} -  \\gamma)(e^{-\\beta } - 1) -\\gamma \\} $$ Refer section 6.1.2. book Hens et al. (2012) details.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/farrington_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The Farrington (1990) model. — farrington_model","text":"Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based Serological Social Contact Data: Modern Statistical Perspective. tatistics Biology Health. Springer New York. doi:10.1007/978-1-4614-4072-7 .","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/farrington_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Farrington (1990) model. — farrington_model","text":"","code":"df <- rubella_uk_1986_1987 model <- farrington_model(   df,   start=list(alpha=0.07,beta=0.1,gamma=0.03)   ) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced plot(model) #> Warning: No shared levels found between `names(values)` of the manual scale and the #> data's fill values."},{"path":"https://oucru-modelling.github.io/serosv/reference/find_best_bdm_param.html","id":null,"dir":"Reference","previous_headings":"","what":"Find best smoothing parameter for Bivariate Dale model — find_best_bdm_param","title":"Find best smoothing parameter for Bivariate Dale model — find_best_bdm_param","text":"Find best smoothing parameter Bivariate Dale model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/find_best_bdm_param.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find best smoothing parameter for Bivariate Dale model — find_best_bdm_param","text":"","code":"find_best_bdm_param(age, y, spar_seq = seq(0, 0.1, 0.001))"},{"path":"https://oucru-modelling.github.io/serosv/reference/find_best_bdm_param.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find best smoothing parameter for Bivariate Dale model — find_best_bdm_param","text":"age age vector y dataframe predictors (NN, NP, PN, PP), order spar_seq sequence smoothing parameters evaluated","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/find_best_bdm_param.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find best smoothing parameter for Bivariate Dale model — find_best_bdm_param","text":"data frame optimal parameter different criteria","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/find_best_fp_powers.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the powers of the fractional polynomial model which has the lowest deviance score. — find_best_fp_powers","title":"Returns the powers of the fractional polynomial model which has the lowest deviance score. — find_best_fp_powers","text":"Return best powers given degree","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/find_best_fp_powers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the powers of the fractional polynomial model which has the lowest deviance score. — find_best_fp_powers","text":"","code":"find_best_fp_powers(data, p, mc, degree, link = \"logit\")"},{"path":"https://oucru-modelling.github.io/serosv/reference/find_best_fp_powers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the powers of the fractional polynomial model which has the lowest deviance score. — find_best_fp_powers","text":"data input data frame, must either columns `age`, `pos`, `tot` (aggregated data) `age`, `status` (linelisting data) p powers sequence tested. mc indicates returned model monotonic. degree maximum degree (.e. number power terms) search best model. Recommended <= 2. link link function. Defaulted \"logit\".","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/find_best_fp_powers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns the powers of the fractional polynomial model which has the lowest deviance score. — find_best_fp_powers","text":"list 3 elements: p best power fp model. deviance Deviance best fitted model. model best model fitted","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/fp_model.html","id":null,"dir":"Reference","previous_headings":"","what":"A fractional polynomial model. — fp_model","title":"A fractional polynomial model. — fp_model","text":"Fractional polynomial model generalization polynomial models power terms can fractions, allowing flexibility better fit data asymptotic behavior expected.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/fp_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A fractional polynomial model. — fp_model","text":"","code":"fp_model(   data,   p,   monotonic = FALSE,   link = \"logit\",   age_col = \"age\",   pos_col = \"pos\",   tot_col = \"tot\",   status_col = \"status\" )"},{"path":"https://oucru-modelling.github.io/serosv/reference/fp_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A fractional polynomial model. — fp_model","text":"data input data frame, must either `age`, `pos`, `tot` columns (aggregated data) `age`, `status` (linelisting data) p either: numeric vector specifying powers apply predictors named list two elements, \"p_range\" \"degree\". \"p_range\"    sequence powers \"degree\" maximum degree.    case package search best degree power combinations monotonic whether returned model monotonic (search specified) link link function model. Defaulted \"logit\". age_col name `age` column (default age_col=\"age\"). pos_col name `pos` column (default pos_col=\"pos\"). tot_col name `tot` column (default tot_col=\"tot\"). status_col name `status` column (default status_col=\"status\").","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/fp_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A fractional polynomial model. — fp_model","text":"list class fp_model 5 items datatype type data used fitting model (aggregated linelisting) df dataframe used fitting model info fitted glm model sp seroprevalence foi force infection","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/fp_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"A fractional polynomial model. — fp_model","text":"Instead polynomial, linear predictor now defined $$  \\eta_m(, \\beta, p_1, p_2, ...,p_m) = \\Sigma^m_{=0} \\beta_i H_i() $$ \\(m\\) integer, \\(p_1 \\le p_2 \\le... \\le p_m\\) sequence powers, \\(H_i()\\) transformation given $$ H_i = \\begin{cases} ^{p_i} & \\text{ } p_i \\neq p_{-1}, \\\\ H_{-1}() \\times log()  & \\text{ } p_i = p_{-1}, \\end{cases} $$ Refers section 6.2. book Hens et al. (2012) details.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/fp_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"A fractional polynomial model. — fp_model","text":"Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based Serological Social Contact Data: Modern Statistical Perspective. tatistics Biology Health. Springer New York. doi:10.1007/978-1-4614-4072-7 .","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/fp_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A fractional polynomial model. — fp_model","text":"","code":"df <- hav_be_1993_1994 model <- fp_model(   df,   p=c(1.5, 1.6), link=\"cloglog\") plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/reference/generate_quad_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate the (NN, NP, PN, PP) matrix from line-listing data — generate_quad_matrix","title":"Generate the (NN, NP, PN, PP) matrix from line-listing data — generate_quad_matrix","text":"Generate (NN, NP, PN, PP) matrix line-listing data","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/generate_quad_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate the (NN, NP, PN, PP) matrix from line-listing data — generate_quad_matrix","text":"","code":"generate_quad_matrix(data, y1, y2, age, discrete_age = F)"},{"path":"https://oucru-modelling.github.io/serosv/reference/generate_quad_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate the (NN, NP, PN, PP) matrix from line-listing data — generate_quad_matrix","text":"data - dataframe transformed y1 - serostatus y1 y2 - serostatus y2 age - individual's age discrete_age - round age discrete","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/generate_quad_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate the (NN, NP, PN, PP) matrix from line-listing data — generate_quad_matrix","text":"transformed matrix","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/generate_quad_matrix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate the (NN, NP, PN, PP) matrix from line-listing data — generate_quad_matrix","text":"","code":"data <- vzv_parvo_be data <- generate_quad_matrix(data, parvo_res, vzv_res, age)"},{"path":"https://oucru-modelling.github.io/serosv/reference/glmm_ps_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Fitting penalized spline model with Generalized Linear Mixture Model framework — glmm_ps_model","title":"Fitting penalized spline model with Generalized Linear Mixture Model framework — glmm_ps_model","text":"Fitting penalized spline model Generalized Linear Mixture Model framework","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/glmm_ps_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fitting penalized spline model with Generalized Linear Mixture Model framework — glmm_ps_model","text":"","code":"glmm_ps_model(age, res, s = \"bs\", link = \"logit\")"},{"path":"https://oucru-modelling.github.io/serosv/reference/glmm_ps_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fitting penalized spline model with Generalized Linear Mixture Model framework — glmm_ps_model","text":"age age vector. res serostatus vector. s smoothing basis use. link link function use.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/glmm_ps_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fitting penalized spline model with Generalized Linear Mixture Model framework — glmm_ps_model","text":"","code":"data <- parvob19_be_2001_2003 mod <- glmm_ps_model(data$age, data$seropositive) #>  #>  Maximum number of PQL iterations:  20  #> iteration 1 #> iteration 2 #> iteration 3 #> iteration 4 mod$gam$info #> NULL"},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_1993_1994.html","id":null,"dir":"Reference","previous_headings":"","what":"Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated) — hav_be_1993_1994","title":"Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated) — hav_be_1993_1994","text":"study prevalence HAV antibodies conducted Flemish Community Belgium 1993 early 1994","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_1993_1994.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated) — hav_be_1993_1994","text":"","code":"hav_be_1993_1994"},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_1993_1994.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated) — hav_be_1993_1994","text":"data frame 3 variables: age Age group pos Number seropositive individuals tot Total number individuals surveyed","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_1993_1994.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated) — hav_be_1993_1994","text":"Beutels, M., Van Damme, P., Aelvoet, W. et al. Prevalence hepatitis , B C Flemish population. Eur J Epidemiol 13, 275-280 (1997). doi:10.1023/:1007393405966","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_1993_1994.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated) — hav_be_1993_1994","text":"","code":"with(hav_be_1993_1994,   plot(     age, pos / tot,     pty = \"s\", cex = 0.34 * sqrt(tot), pch = 16, xlab = \"age\",     ylab = \"seroprevalence\", xlim = c(0, 86), ylim = c(0, 1)   ) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_2002.html","id":null,"dir":"Reference","previous_headings":"","what":"Hepatitis A serological data from Belgium in 2002 (line listing) — hav_be_2002","title":"Hepatitis A serological data from Belgium in 2002 (line listing) — hav_be_2002","text":"subset serological dataset Varicella-Zoster Virus (VZV) Parvovirus B19 Belgium individuals living Flanders selected","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_2002.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hepatitis A serological data from Belgium in 2002 (line listing) — hav_be_2002","text":"","code":"hav_be_2002"},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_2002.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hepatitis A serological data from Belgium in 2002 (line listing) — hav_be_2002","text":"data frame 2 variables: age Age individual seropositive individual seropositive ","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_2002.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Hepatitis A serological data from Belgium in 2002 (line listing) — hav_be_2002","text":"Thiry, N., Beutels, P., Shkedy, Z. et al. seroepidemiology primary varicella-zoster virus infection Flanders (Belgium). Eur J Pediatr 161, 588-593 (2002). doi:10.1007/s00431-002-1053-2","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_be_2002.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hepatitis A serological data from Belgium in 2002 (line listing) — hav_be_2002","text":"","code":"library(dplyr) #> Warning: package ‘dplyr’ was built under R version 4.3.1 #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union df <- hav_be_2002 %>%   group_by(age) %>%   summarise(pos = sum(seropositive), tot = n())  with(   df,   plot(     age, pos / tot,     pty = \"s\", cex = 0.34 * sqrt(tot), pch = 16, xlab = \"age\",     ylab = \"seroprevalence\", xlim = c(0, 86), ylim = c(0, 1)   ) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_bg_1964.html","id":null,"dir":"Reference","previous_headings":"","what":"Hepatitis A serological data from Bulgaria in 1964 (aggregated) — hav_bg_1964","title":"Hepatitis A serological data from Bulgaria in 1964 (aggregated) — hav_bg_1964","text":"cross-sectional survey conducted 1964 Bulgaria. Samples collected schoolchildren blood donors.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_bg_1964.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hepatitis A serological data from Bulgaria in 1964 (aggregated) — hav_bg_1964","text":"","code":"hav_bg_1964"},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_bg_1964.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hepatitis A serological data from Bulgaria in 1964 (aggregated) — hav_bg_1964","text":"data frame 3 variables: age Age group pos Number seropositive individuals tot Total number individuals surveyed","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_bg_1964.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Hepatitis A serological data from Bulgaria in 1964 (aggregated) — hav_bg_1964","text":"Keiding, Niels. \"Age-Specific Incidence Prevalence: Statistical Perspective.\" Journal Royal Statistical Society. Series (Statistics Society) 154, . 3 (1991): 371-412. doi:10.2307/2983150","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hav_bg_1964.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hepatitis A serological data from Bulgaria in 1964 (aggregated) — hav_bg_1964","text":"","code":"with(   hav_bg_1964,   plot(     age, pos / tot,     pty = \"s\", cex = 0.6 * sqrt(tot), pch = 16, xlab = \"age\",     ylab = \"seroprevalence\", xlim = c(0, 86), ylim = c(0, 1)   ) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/hbv_ru_1999.html","id":null,"dir":"Reference","previous_headings":"","what":"Hepatitis B serological data from Russia in 1999 (aggregated) — hbv_ru_1999","title":"Hepatitis B serological data from Russia in 1999 (aggregated) — hbv_ru_1999","text":"seroprevalence study conducted St. Petersburg (information book)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hbv_ru_1999.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hepatitis B serological data from Russia in 1999 (aggregated) — hbv_ru_1999","text":"","code":"hbv_ru_1999"},{"path":"https://oucru-modelling.github.io/serosv/reference/hbv_ru_1999.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hepatitis B serological data from Russia in 1999 (aggregated) — hbv_ru_1999","text":"data frame 4 variables: age Age group pos Number seropositive individuals tot Total number individuals surveyed gender Gender cohort (unsure 1 2 means)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hbv_ru_1999.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Hepatitis B serological data from Russia in 1999 (aggregated) — hbv_ru_1999","text":"Mukomolov, S., L. Shliakhtenko, . Levakova, E. Shargorodskaya. Viral hepatitis Russian federation. analytical overview. Technical Report 213 (3), 3rd edn. St Petersburg Pasteur Institute, St Petersburg, 2000.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hbv_ru_1999.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hepatitis B serological data from Russia in 1999 (aggregated) — hbv_ru_1999","text":"","code":"library(dplyr) hbv_ru_1999$age <- trunc(hbv_ru_1999$age / 1) * 1 hbv_ru_1999$age[hbv_ru_1999$age > 40] <- trunc(   hbv_ru_1999$age[hbv_ru_1999$age > 40] / 5 ) * 5 df <- hbv_ru_1999 %>%   group_by(age) %>%   summarise(pos = sum(pos), tot = sum(tot))  plot(   df$age, df$pos / df$tot,   cex = 0.32 * sqrt(df$tot), pch = 16, xlab = \"age\",   ylab = \"seroprevalence\", xlim = c(0, 72) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/hcv_be_2006.html","id":null,"dir":"Reference","previous_headings":"","what":"Hepatitis C serological data from Belgium in 2006 (line listing) — hcv_be_2006","title":"Hepatitis C serological data from Belgium in 2006 (line listing) — hcv_be_2006","text":"study HCV infection among injecting drug users. injecting drug users interviewed means standardized face--face interview information socio-demographic status, drug use history, drug use, related risk behavior recorded","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hcv_be_2006.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hepatitis C serological data from Belgium in 2006 (line listing) — hcv_be_2006","text":"","code":"hcv_be_2006"},{"path":"https://oucru-modelling.github.io/serosv/reference/hcv_be_2006.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hepatitis C serological data from Belgium in 2006 (line listing) — hcv_be_2006","text":"data frame 3 variables: dur Duration injection/Exposure time (years) seropositive individual seropositive ","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hcv_be_2006.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Hepatitis C serological data from Belgium in 2006 (line listing) — hcv_be_2006","text":"Mathei, C., Shkedy, Z., Denis, B., Kabali, C., Aerts, M., Molenberghs, G., Van Damme, P. Buntinx, F. (2006), Evidence substantial role sharing injecting paraphernalia syringes/needles spread hepatitis C among injecting drug users. Journal Viral Hepatitis, 13: 560-570. doi:10.1111/j.1365-2893.2006.00725.x","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hcv_be_2006.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hepatitis C serological data from Belgium in 2006 (line listing) — hcv_be_2006","text":"","code":"library(dplyr) # snapping age to aggregated age group # (credit: https://stackoverflow.com/a/12861810) groups <- c(0.5:24.5) range <- 0.5 low <- findInterval(hcv_be_2006$dur, groups) high <- low + 1 low_diff <- hcv_be_2006$dur - groups[ifelse(low == 0, NA, low)] high_diff <- groups[ifelse(high == 0, NA, high)] - hcv_be_2006$dur mins <- pmin(low_diff, high_diff, na.rm = TRUE) pick <- ifelse(!is.na(low_diff) & mins == low_diff, low, high) hcv_be_2006$dur <- ifelse(   mins <= range + .Machine$double.eps, groups[pick], hcv_be_2006$dur ) hcv_be_2006 <- hcv_be_2006 %>%   group_by(dur) %>%   summarise(tot = n(), pos = sum(seropositive))  with(hcv_be_2006,   plot(     dur, pos / tot,     cex = 0.42 * sqrt(tot), pch = 16,     xlab = \"duration of injection (years)\",     ylab = \"seroprevalence\", xlim = c(0, 25), ylim = c(0, 1)   ) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/hierarchical_bayesian_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Hierarchical Bayesian Model — hierarchical_bayesian_model","title":"Hierarchical Bayesian Model — hierarchical_bayesian_model","text":"Fit age-stratified seroprevalence parametric hierarchical Bayesian models. Supported models including Farrington model (2 3 parameters variants) Log Logistic model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hierarchical_bayesian_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hierarchical Bayesian Model — hierarchical_bayesian_model","text":"","code":"hierarchical_bayesian_model(   data,   age_col = \"age\",   pos_col = \"pos\",   tot_col = \"tot\",   status_col = \"status\",   type = \"far3\",   chains = 1,   warmup = 1500,   iter = 5000 )"},{"path":"https://oucru-modelling.github.io/serosv/reference/hierarchical_bayesian_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hierarchical Bayesian Model — hierarchical_bayesian_model","text":"data input data frame, must either columns `age`, `pos`, `tot` (aggregated data) `age`, `status` (linelisting data) age_col name `age` column (default age_col=\"age\"). pos_col name `pos` column (default pos_col=\"pos\"). tot_col name `tot` column (default tot_col=\"tot\"). status_col name `status` column (default status_col=\"status\"). type type model (\"far2\", \"far3\" \"log_logistic\") chains number Markov chains warmup number warmup runs iter number iterations","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hierarchical_bayesian_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hierarchical Bayesian Model — hierarchical_bayesian_model","text":"list class hierarchical_bayesian_model 6 items datatype type datatype used model fitting (aggregated linelisting) df dataframe used fitting model type type bayesian model far2, far3 log_logistic info parameters fitted model sp seroprevalence foi force infection sp_func function compute seroprevalence given age model parameters foi function compute force infection given age model parameters","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hierarchical_bayesian_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hierarchical Bayesian Model — hierarchical_bayesian_model","text":"Consider model prevalence parametric form \\(\\pi(a_i, \\alpha)\\) \\(\\alpha\\) parameter vector Bayesian framework, can constraint parameter space prior distribution \\(P(\\alpha)\\) achieve monotonicity posterior distribution \\(P(\\pi_1, \\pi_2, ..., \\pi_m|y,n)\\) : - \\(n = (n_1, n_2, ..., n_m)\\) \\(n_i\\) sample size age \\(a_i\\) - \\(y = (y_1, y_2, ..., y_m)\\) \\(y_i\\) number infected individual \\(n_i\\) sampled subjects Farrington model 3 parameters, prevalence formulated follow $$ \\pi () = 1 - exp\\{ \\frac{\\alpha_1}{\\alpha_2}ae^{-\\alpha_2 } + \\frac{1}{\\alpha_2}(\\frac{\\alpha_1}{\\alpha_2} - \\alpha_3)(e^{-\\alpha_2 } - 1) -\\alpha_3 \\} $$ likelihood model defined \\(y_i \\sim Bin(n_i, \\pi_i), \\text{  } = 1,2,3,...m\\) constraint parameter space can incorporated assuming  truncated normal distribution components \\(\\alpha\\), \\(\\alpha = (\\alpha_1, \\alpha_2, \\alpha_3)\\) \\(\\pi_i = \\pi(a_i,\\alpha)\\) flat hyperpriors defined \\(\\mu_j \\sim \\mathcal{N}(0, 10000)\\) \\(\\tau^{-2}_j \\sim \\Gamma(100,100)\\) Farrington model 2 parameters, equivalent previous model \\(\\alpha_3 = 0\\) Log logistic model, seroprevalence instead defined $$\\pi() = \\frac{\\beta ^\\alpha}{1 + \\beta ^\\alpha}, \\text{ } \\alpha, \\beta > 0$$ likelihood similarly defined \\(y_i \\sim Bin(n_i, \\pi_i))\\) prior model \\(\\alpha_1\\) specified \\(\\alpha_1 \\sim \\text{truncated  } \\mathcal{N}(\\mu_1, \\tau_1)\\) flat hyperpriors Farrington model \\(\\beta\\) constrained positive specifying \\(\\alpha_2 \\sim \\mathcal{N}(\\mu_2, \\tau_2)\\) Refer section Chapter 10.3 book Hens et al. (2012) details.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hierarchical_bayesian_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hierarchical Bayesian Model — hierarchical_bayesian_model","text":"Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based Serological Social Contact Data: Modern Statistical Perspective. tatistics Biology Health. Springer New York. doi:10.1007/978-1-4614-4072-7 .","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/hierarchical_bayesian_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hierarchical Bayesian Model — hierarchical_bayesian_model","text":"","code":"# \\donttest{ df <- mumps_uk_1986_1987 model <- hierarchical_bayesian_model(df, type=\"far3\") #>  #> SAMPLING FOR MODEL 'fra_3' NOW (CHAIN 1). #> Chain 1: Rejecting initial value: #> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity. #> Chain 1:   Stan can't start sampling from this initial value. #> Chain 1: Rejecting initial value: #> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity. #> Chain 1:   Stan can't start sampling from this initial value. #> Chain 1: Rejecting initial value: #> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity. #> Chain 1:   Stan can't start sampling from this initial value. #> Chain 1: Rejecting initial value: #> Chain 1:   Log probability evaluates to log(0), i.e. negative infinity. #> Chain 1:   Stan can't start sampling from this initial value. #> Chain 1:  #> Chain 1: Gradient evaluation took 0.00013 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.3 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1:  #> Chain 1:  #> Chain 1: Iteration:    1 / 5000 [  0%]  (Warmup) #> Chain 1: Iteration:  500 / 5000 [ 10%]  (Warmup) #> Chain 1: Iteration: 1000 / 5000 [ 20%]  (Warmup) #> Chain 1: Iteration: 1500 / 5000 [ 30%]  (Warmup) #> Chain 1: Iteration: 1501 / 5000 [ 30%]  (Sampling) #> Chain 1: Iteration: 2000 / 5000 [ 40%]  (Sampling) #> Chain 1: Iteration: 2500 / 5000 [ 50%]  (Sampling) #> Chain 1: Iteration: 3000 / 5000 [ 60%]  (Sampling) #> Chain 1: Iteration: 3500 / 5000 [ 70%]  (Sampling) #> Chain 1: Iteration: 4000 / 5000 [ 80%]  (Sampling) #> Chain 1: Iteration: 4500 / 5000 [ 90%]  (Sampling) #> Chain 1: Iteration: 5000 / 5000 [100%]  (Sampling) #> Chain 1:  #> Chain 1:  Elapsed Time: 15.235 seconds (Warm-up) #> Chain 1:                139.842 seconds (Sampling) #> Chain 1:                155.077 seconds (Total) #> Chain 1:  #> Warning: There were 324 divergent transitions after warmup. See #> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup #> to find out why this is a problem and how to eliminate them. #> Warning: There were 143 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See #> https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded #> Warning: Examine the pairs() plot to diagnose sampling problems #> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable. #> Running the chains for more iterations may help. See #> https://mc-stan.org/misc/warnings.html#bulk-ess #> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable. #> Running the chains for more iterations may help. See #> https://mc-stan.org/misc/warnings.html#tail-ess model$info #>                       mean      se_mean           sd          2.5% #> alpha1        1.394994e-01 2.208144e-04 5.686602e-03  1.290184e-01 #> alpha2        1.984613e-01 3.072933e-04 7.829686e-03  1.844645e-01 #> alpha3        8.289398e-03 2.217734e-04 6.839120e-03  3.133171e-04 #> tau_alpha1    6.381566e+00 1.529942e+00 1.636028e+01  6.597716e-06 #> tau_alpha2    1.371545e+01 8.454139e+00 4.030987e+01  5.871040e-06 #> tau_alpha3    5.780803e+00 1.085321e+00 1.356397e+01  7.111779e-06 #> mu_alpha1     8.127640e-01 1.294292e+00 2.957688e+01 -7.133897e+01 #> mu_alpha2     3.575058e+00 1.799463e+00 3.726214e+01 -7.253911e+01 #> mu_alpha3     3.883757e+00 1.307763e+00 3.405219e+01 -6.454653e+01 #> sigma_alpha1  6.352800e+01 1.682171e+01 7.004084e+02  1.213045e-01 #> sigma_alpha2  7.855927e+01 1.684436e+01 6.470535e+02  7.736001e-02 #> sigma_alpha3  1.873854e+02 1.323037e+02 3.877173e+03  1.431469e-01 #> lp__         -2.532504e+03 3.710684e-01 4.655392e+00 -2.542452e+03 #>                        25%           50%           75%         97.5%      n_eff #> alpha1        1.357106e-01  1.390419e-01  1.430492e-01  1.511064e-01  663.20993 #> alpha2        1.931963e-01  1.983732e-01  2.027147e-01  2.165249e-01  649.20593 #> alpha3        2.841082e-03  6.688870e-03  1.163816e-02  2.583495e-02  951.00250 #> tau_alpha1    1.733407e-03  9.495067e-02  2.687740e+00  6.795883e+01  114.34889 #> tau_alpha2    1.342653e-03  6.464717e-02  2.511530e+00  1.670964e+02   22.73443 #> tau_alpha3    1.111049e-03  5.337217e-02  2.822276e+00  4.880180e+01  156.19150 #> mu_alpha1    -1.276472e+00  1.729980e-01  2.532245e+00  5.703259e+01  522.20403 #> mu_alpha2    -1.528020e+00  2.032933e-01  2.920524e+00  1.045006e+02  428.79515 #> mu_alpha3    -1.524451e+00  3.939297e-02  3.640311e+00  9.311625e+01  678.00364 #> sigma_alpha1  6.099697e-01  3.245271e+00  2.401891e+01  3.893194e+02 1733.65511 #> sigma_alpha2  6.310022e-01  3.933036e+00  2.729092e+01  4.127492e+02 1475.60823 #> sigma_alpha3  5.952566e-01  4.328557e+00  3.000085e+01  3.749841e+02  858.78884 #> lp__         -2.535634e+03 -2.532144e+03 -2.528948e+03 -2.524553e+03  157.39993 #>                   Rhat #> alpha1       1.0002098 #> alpha2       0.9998023 #> alpha3       0.9997596 #> tau_alpha1   1.0003182 #> tau_alpha2   1.0444726 #> tau_alpha3   1.0101265 #> mu_alpha1    1.0073358 #> mu_alpha2    1.0048162 #> mu_alpha3    1.0031755 #> sigma_alpha1 0.9997210 #> sigma_alpha2 1.0000959 #> sigma_alpha3 1.0009114 #> lp__         0.9998177 plot(model)  # }"},{"path":"https://oucru-modelling.github.io/serosv/reference/lp_model.html","id":null,"dir":"Reference","previous_headings":"","what":"A local polynomial model. — lp_model","title":"A local polynomial model. — lp_model","text":"Fit age-specific seroprevalence local polynomial model, linear predictor approximated locally one particular age.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/lp_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A local polynomial model. — lp_model","text":"","code":"lp_model(   data,   kern = \"tcub\",   nn = 0,   h = 0,   deg = 2,   age_col = \"age\",   pos_col = \"pos\",   tot_col = \"tot\",   status_col = \"status\" )"},{"path":"https://oucru-modelling.github.io/serosv/reference/lp_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A local polynomial model. — lp_model","text":"data input data frame, must either columns `age`, `pos`, `tot` (aggregated data) `age`, `status` (linelisting data) kern Weight function, default = \"tcub\". choices \"rect\", \"trwt\", \"tria\", \"epan\", \"bisq\" \"gauss\". Choices may restricted derivatives required; e.g. confidence bands bandwidth selectors. nn Nearest neighbor component smoothing parameter. Default value 0.7, unless either h provided, case default 0. h constant bandwidth smoothing parameter. Default: 0. deg Degree polynomial use. Default: 2. age_col name `age` column (default age_col=\"age\"). pos_col name `pos` column (default pos_col=\"pos\"). tot_col name `tot` column (default tot_col=\"tot\"). status_col name `status` column (default status_col=\"status\").","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/lp_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A local polynomial model. — lp_model","text":"list class lp_model 6 items datatype type datatype used model fitting (aggregated linelisting) df dataframe used fitting model pi fitted locfit object pi eta fitted locfit object eta sp seroprevalence foi force infection","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/lp_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"A local polynomial model. — lp_model","text":"Consider linear predictor \\(\\eta()\\) approximated locally one particular value \\(a_0\\). general degree \\(p\\), linear predictor neighbor \\(a_0\\), labeled \\(a_i\\) equivalent Taylor approximation $$ \\eta(a_i) = \\eta(a_0) + \\eta^{(1)}(a_0)(a_i - a_0) + \\frac{\\eta^{(2)}(a_0)}{2}(a_i - a_0)^2 + ... + \\frac{\\eta^{(p)}(a_0)}{p!}(a_i - a_0)^p $$ \\(\\eta(a_i)\\) can estimated maximizing $$  \\Sigma_{=1}^{N} \\ell_i \\{Y_i, g^{-1} (\\beta_0 + \\beta_1(a_i-a_0)+ \\beta_2(a_i-a_0)^2 ... +  \\beta_p(a_i-a_0)^p) \\} K_h(a_i - a_0) $$ estimator \\(k\\)-th derivative \\(\\eta(a_0)\\), \\(k = 0,1,…,p\\) (degree local polynomial) thus: $$  \\hat{\\eta}^{(k)}(a_0) = k!\\hat{\\beta}_k(a_0) $$ estimator prevalence age \\(a_0\\) given $$ \\hat{\\pi}(a_0) = g^{-1}\\{ \\hat{\\beta}_0(a_0) \\} $$ \\(g\\) link function estimator force infection age \\(a_0\\) assuming \\(p \\ge 1\\) followed $$ \\hat{\\lambda}(a_0) = \\hat{\\beta}_1(a_0) \\delta \\{ \\hat{\\beta}_0 (a_0) \\} $$ \\(\\delta \\{ \\hat{\\beta}_0(a_0) \\} = \\frac{dg^{-1} \\{ \\hat{\\beta}_0(a_0) \\} } {d\\hat{\\beta}_0(a_0)}\\) Refer section 7.1 7.2. book Hens et al. (2012) details.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/lp_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"A local polynomial model. — lp_model","text":"Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based Serological Social Contact Data: Modern Statistical Perspective. tatistics Biology Health. Springer New York. doi:10.1007/978-1-4614-4072-7 .","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/lp_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A local polynomial model. — lp_model","text":"","code":"df <- mumps_uk_1986_1987 model <- lp_model(   df,   nn=0.7, kern=\"tcub\"   ) plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/reference/mixture_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a mixture model to classify serostatus — mixture_model","title":"Fit a mixture model to classify serostatus — mixture_model","text":"Fit antibody level data 2-component Gaussian mixture model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mixture_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a mixture model to classify serostatus — mixture_model","text":"","code":"mixture_model(   antibody_level,   breaks = 40,   pi = c(0.2, 0.8),   mu = c(2, 6),   sigma = c(0.5, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/mixture_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a mixture model to classify serostatus — mixture_model","text":"antibody_level vector corresponding raw antibody level breaks number intervals antibody_level grouped pi proportion susceptible, infected mu vector means component distributions (vector 2 numbers ascending order) sigma vector standard deviations component distributions (vector 2 number)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mixture_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit a mixture model to classify serostatus — mixture_model","text":"list class mixture_model following items df dataframe used fitting model info list 3 items parameters, distribution constraints fitted model susceptible fitted distribution susceptible infected fitted distribution infected","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mixture_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit a mixture model to classify serostatus — mixture_model","text":"Antibody level (denoted \\(Z\\)) modeled using 2-component Gaussian mixture model. component \\(Z_j\\) (\\(j \\\\{, S\\}\\)) represents antibody level latent Infected Susceptible sub-populations, following density \\(f_j(z_j|\\theta_j)\\) Let \\(\\pi_{\\text{TRUE}}()\\) denotes age-dependent mixing probability (.e., true prevalence), density mixture formulated $$f(z|z_I, z_S,) = (1-\\pi_{\\text{TRUE}}())f_S(z_S|\\theta_S)+\\pi_{\\text{TRUE}}()f_I(z_I|\\theta_I)$$ mean \\(E(Z|)\\) thus equals $$\\mu() = (1-\\pi_{\\text{TRUE}}())\\mu_S+\\pi_{\\text{TRUE}}()\\mu_I$$ true prevalence can computed $$\\pi_{\\text{TRUE}}() = \\frac{\\mu() - \\mu_S}{\\mu_I - \\mu_S}$$ FOI can inferred $$\\lambda_{TRUE} = \\frac{\\mu'()}{\\mu_I - \\mu()}$$ Function [serosv::mixture_model()] fits antibody level data \\(f_S(z_S|\\theta_S)\\) \\(f_I(z_I|\\theta_I)\\) Function [serosv::estimate_mixture()] estimate age-specific antibody level \\(\\mu()\\) infer estimation \\(\\pi_{\\text{TRUE}}()\\) \\(\\lambda_{TRUE}\\) Refer section 11.3. book Hens et al. (2012) details.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mixture_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit a mixture model to classify serostatus — mixture_model","text":"Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based Serological Social Contact Data: Modern Statistical Perspective. tatistics Biology Health. Springer New York. doi:10.1007/978-1-4614-4072-7 .","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mixture_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit a mixture model to classify serostatus — mixture_model","text":"","code":"df <- vzv_be_2001_2003[vzv_be_2001_2003$age < 40.5,] data <- df$VZVmIUml[order(df$age)] model <- mixture_model(antibody_level = data) model$info #>  #> Parameters: #>       pi    mu  sigma #> 1 0.1088 2.349 0.6804 #> 2 0.8912 6.439 0.9437 #>  #> Distribution: #> [1] \"norm\" #>  #> Constraints: #>    conpi    conmu consigma  #>   \"NONE\"   \"NONE\"   \"NONE\"  #>  plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/reference/mseir_model.html","id":null,"dir":"Reference","previous_headings":"","what":"MSEIR model — mseir_model","title":"MSEIR model — mseir_model","text":"Simulate transmission model 5 compartments: maternal immunity (M), susceptible (S), exposed (E), infected (), recovered (R)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mseir_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MSEIR model — mseir_model","text":"","code":"mseir_model(a, gamma, lambda, sigma, nu)"},{"path":"https://oucru-modelling.github.io/serosv/reference/mseir_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MSEIR model — mseir_model","text":"age sequence gamma time maternal class. lambda time susceptible class. sigma time latent class. nu time infected class.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mseir_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MSEIR model — mseir_model","text":"list class mseir_model following parameters parameters list parameters used fitting model output matrix proportion compartment time","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mseir_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MSEIR model — mseir_model","text":"Follow MSEIR described book Hens et al. (section 3.5.1.) model described following system equations $$  \\begin{cases}    \\frac{dM()}{da} = -(\\gamma + \\mu())M() \\\\    \\frac{dS()}{da} = \\gamma M() - (\\lambda() + \\mu()) S() \\\\    \\frac{dE()}{da} = \\lambda() S()  - (\\sigma + \\mu()) E() \\\\    \\frac{dI()}{da} = \\sigma() E()  - (\\nu + \\mu()) ()  \\\\    \\frac{dR()}{da} = \\nu ()  - \\mu() R()    \\end{cases} $$ : -   \\(M(0)\\) = B, number births population -   \\(\\gamma\\) rate antibody decaying -   \\(\\lambda()\\) force infection age \\(\\) -   \\(\\mu()\\) natural death rate age \\(\\) -   \\(\\sigma\\) rate becoming infected exposed -   \\(\\nu\\) recovery rate","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mseir_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"MSEIR model — mseir_model","text":"Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based Serological Social Contact Data: Modern Statistical Perspective. tatistics Biology Health. Springer New York. doi:10.1007/978-1-4614-4072-7 .","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mseir_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MSEIR model — mseir_model","text":"","code":"model <- mseir_model(   a=seq(from=1,to=20,length=500), # age range from 0 -> 20 yo   gamma=1/0.5, # 6 months in the maternal antibodies   lambda=0.2,  # 5 years in the susceptible class   sigma=26.07, # 14 days in the latent class   nu=36.5      # 10 days in the infected class ) model #> $parameters #> $parameters$gamma #> [1] 2 #>  #> $parameters$lambda #> [1] 0.2 #>  #> $parameters$sigma #> [1] 26.07 #>  #> $parameters$nu #> [1] 36.5 #>  #>  #> $output #>             a            m          s            e            i         r #> 1    0.000000 1.000000e+00 0.00000000 0.0000000000 0.0000000000 0.0000000 #> 2    1.000000 1.353353e-01 0.75932830 0.0057834028 0.0041067171 0.0954463 #> 3    1.038076 1.254118e-01 0.76345309 0.0058216660 0.0041376298 0.1011758 #> 4    1.076152 1.162160e-01 0.76682174 0.0058536161 0.0041637570 0.1069449 #> 5    1.114228 1.076945e-01 0.76949316 0.0058797427 0.0041854686 0.1127471 #> 6    1.152305 9.979779e-02 0.77152186 0.0059004992 0.0042031073 0.1185767 #> 7    1.190381 9.248012e-02 0.77295838 0.0059163055 0.0042169906 0.1244282 #> 8    1.228457 8.569903e-02 0.77384950 0.0059275507 0.0042274125 0.1302965 #> 9    1.266533 7.941515e-02 0.77423855 0.0059345950 0.0042346455 0.1361771 #> 10   1.304609 7.359204e-02 0.77416565 0.0059377721 0.0042389416 0.1420656 #> 11   1.342685 6.819591e-02 0.77366797 0.0059373908 0.0042405345 0.1479582 #> 12   1.380762 6.319545e-02 0.77277991 0.0059337375 0.0042396406 0.1538513 #> 13   1.418838 5.856164e-02 0.77153335 0.0059270770 0.0042364602 0.1597415 #> 14   1.456914 5.426761e-02 0.76995777 0.0059176546 0.0042311787 0.1656258 #> 15   1.494990 5.028844e-02 0.76808050 0.0059056976 0.0042239679 0.1715014 #> 16   1.533066 4.660105e-02 0.76592682 0.0058914163 0.0042149869 0.1773657 #> 17   1.571142 4.318403e-02 0.76352014 0.0058750053 0.0042043830 0.1832164 #> 18   1.609218 4.001756e-02 0.76088213 0.0058566450 0.0041922923 0.1890514 #> 19   1.647295 3.708328e-02 0.75803284 0.0058365022 0.0041788412 0.1948685 #> 20   1.685371 3.436415e-02 0.75499084 0.0058147313 0.0041641464 0.2006661 #> 21   1.723447 3.184440e-02 0.75177331 0.0057914753 0.0041483160 0.2064425 #> 22   1.761523 2.950941e-02 0.74839613 0.0057668664 0.0041314501 0.2121961 #> 23   1.799599 2.734563e-02 0.74487402 0.0057410270 0.0041136413 0.2179257 #> 24   1.837675 2.534052e-02 0.74122058 0.0057140705 0.0040949753 0.2236299 #> 25   1.875752 2.348243e-02 0.73744840 0.0056861015 0.0040755312 0.2293075 #> 26   1.913828 2.176058e-02 0.73356912 0.0056572169 0.0040553825 0.2349577 #> 27   1.951904 2.016498e-02 0.72959349 0.0056275065 0.0040345970 0.2405794 #> 28   1.989980 1.868639e-02 0.72553148 0.0055970530 0.0040132374 0.2461718 #> 29   2.028056 1.731621e-02 0.72139228 0.0055659332 0.0039913617 0.2517342 #> 30   2.066132 1.604650e-02 0.71718441 0.0055342179 0.0039690236 0.2572659 #> 31   2.104208 1.486989e-02 0.71291571 0.0055019726 0.0039462726 0.2627662 #> 32   2.142285 1.377956e-02 0.70859345 0.0054692578 0.0039231546 0.2682346 #> 33   2.180361 1.276917e-02 0.70422434 0.0054361294 0.0038997119 0.2736706 #> 34   2.218437 1.183287e-02 0.69981457 0.0054026391 0.0038759837 0.2790739 #> 35   2.256513 1.096523e-02 0.69536987 0.0053688346 0.0038520061 0.2844441 #> 36   2.294589 1.016120e-02 0.69089552 0.0053347600 0.0038278125 0.2897807 #> 37   2.332665 9.416134e-03 0.68639637 0.0053004560 0.0038034337 0.2950836 #> 38   2.370741 8.725697e-03 0.68187694 0.0052659599 0.0037788981 0.3003525 #> 39   2.408818 8.085885e-03 0.67734136 0.0052313065 0.0037542320 0.3055872 #> 40   2.446894 7.492988e-03 0.67279346 0.0051965277 0.0037294596 0.3107876 #> 41   2.484970 6.943565e-03 0.66823675 0.0051616529 0.0037046032 0.3159534 #> 42   2.523046 6.434429e-03 0.66367448 0.0051267092 0.0036796834 0.3210847 #> 43   2.561122 5.962625e-03 0.65910963 0.0050917216 0.0036547192 0.3261813 #> 44   2.599198 5.525416e-03 0.65454495 0.0050567131 0.0036297279 0.3312432 #> 45   2.637275 5.120265e-03 0.64998296 0.0050217048 0.0036047258 0.3362703 #> 46   2.675351 4.744822e-03 0.64542599 0.0049867162 0.0035797275 0.3412627 #> 47   2.713427 4.396908e-03 0.64087617 0.0049517651 0.0035547466 0.3462204 #> 48   2.751503 4.074505e-03 0.63633544 0.0049168681 0.0035297957 0.3511434 #> 49   2.789579 3.775742e-03 0.63180562 0.0048820401 0.0035048863 0.3560317 #> 50   2.827655 3.498886e-03 0.62728834 0.0048472950 0.0034800288 0.3608854 #> 51   2.865731 3.242330e-03 0.62278511 0.0048126455 0.0034552329 0.3657047 #> 52   2.903808 3.004587e-03 0.61829730 0.0047781031 0.0034305074 0.3704895 #> 53   2.941884 2.784276e-03 0.61382617 0.0047436785 0.0034058605 0.3752400 #> 54   2.979960 2.580119e-03 0.60937286 0.0047093814 0.0033812995 0.3799563 #> 55   3.018036 2.390932e-03 0.60493843 0.0046752205 0.0033568312 0.3846386 #> 56   3.056112 2.215617e-03 0.60052382 0.0046412039 0.0033324618 0.3892869 #> 57   3.094188 2.053157e-03 0.59612990 0.0046073390 0.0033081968 0.3939014 #> 58   3.132265 1.902609e-03 0.59175744 0.0045736324 0.0032840415 0.3984823 #> 59   3.170341 1.763101e-03 0.58740716 0.0045400902 0.0032600003 0.4030296 #> 60   3.208417 1.633821e-03 0.58307969 0.0045067177 0.0032360775 0.4075437 #> 61   3.246493 1.514021e-03 0.57877561 0.0044735200 0.0032122769 0.4120246 #> 62   3.284569 1.403006e-03 0.57449543 0.0044405014 0.0031886020 0.4164725 #> 63   3.322645 1.300131e-03 0.57023961 0.0044076659 0.0031650557 0.4208875 #> 64   3.360721 1.204799e-03 0.56600856 0.0043750170 0.0031416409 0.4252700 #> 65   3.398798 1.116457e-03 0.56180265 0.0043425580 0.0031183600 0.4296200 #> 66   3.436874 1.034593e-03 0.55762219 0.0043102916 0.0030952152 0.4339377 #> 67   3.474950 9.587312e-04 0.55346746 0.0042782203 0.0030722084 0.4382234 #> 68   3.513026 8.884323e-04 0.54933872 0.0042463463 0.0030493414 0.4424772 #> 69   3.551102 8.232881e-04 0.54523616 0.0042146714 0.0030266156 0.4466993 #> 70   3.589178 7.629205e-04 0.54115996 0.0041831973 0.0030040323 0.4508899 #> 71   3.627255 7.069794e-04 0.53711028 0.0041519253 0.0029815927 0.4550492 #> 72   3.665331 6.551402e-04 0.53308724 0.0041208566 0.0029592977 0.4591775 #> 73   3.703407 6.071021e-04 0.52909093 0.0040899922 0.0029371480 0.4632748 #> 74   3.741483 5.625863e-04 0.52512143 0.0040593327 0.0029151443 0.4673415 #> 75   3.779559 5.213347e-04 0.52117879 0.0040288788 0.0028932871 0.4713777 #> 76   3.817635 4.831079e-04 0.51726304 0.0039986309 0.0028715769 0.4753836 #> 77   3.855711 4.476840e-04 0.51337422 0.0039685892 0.0028500138 0.4793595 #> 78   3.893788 4.148576e-04 0.50951230 0.0039387540 0.0028285980 0.4833055 #> 79   3.931864 3.844382e-04 0.50567729 0.0039091252 0.0028073296 0.4872218 #> 80   3.969940 3.562493e-04 0.50186914 0.0038797027 0.0027862087 0.4911087 #> 81   4.008016 3.301274e-04 0.49808783 0.0038504863 0.0027652350 0.4949663 #> 82   4.046092 3.059208e-04 0.49433330 0.0038214757 0.0027444085 0.4987949 #> 83   4.084168 2.834892e-04 0.49060548 0.0037926704 0.0027237289 0.5025946 #> 84   4.122244 2.627023e-04 0.48690430 0.0037640701 0.0027031958 0.5063657 #> 85   4.160321 2.434397e-04 0.48322968 0.0037356742 0.0026828091 0.5101084 #> 86   4.198397 2.255895e-04 0.47958153 0.0037074819 0.0026625681 0.5138228 #> 87   4.236473 2.090482e-04 0.47595976 0.0036794928 0.0026424725 0.5175092 #> 88   4.274549 1.937197e-04 0.47236425 0.0036517059 0.0026225217 0.5211678 #> 89   4.312625 1.795152e-04 0.46879490 0.0036241205 0.0026027153 0.5247987 #> 90   4.350701 1.663523e-04 0.46525159 0.0035967357 0.0025830527 0.5284023 #> 91   4.388778 1.541545e-04 0.46173420 0.0035695507 0.0025635331 0.5319786 #> 92   4.426854 1.428511e-04 0.45824260 0.0035425646 0.0025441561 0.5355278 #> 93   4.464930 1.323766e-04 0.45477666 0.0035157763 0.0025249208 0.5390503 #> 94   4.503006 1.226701e-04 0.45133626 0.0034891849 0.0025058267 0.5425461 #> 95   4.541082 1.136753e-04 0.44792124 0.0034627893 0.0024868730 0.5460154 #> 96   4.579158 1.053401e-04 0.44453148 0.0034365885 0.0024680589 0.5494585 #> 97   4.617234 9.761602e-05 0.44116682 0.0034105814 0.0024493838 0.5528756 #> 98   4.655311 9.045833e-05 0.43782712 0.0033847670 0.0024308468 0.5562668 #> 99   4.693387 8.382548e-05 0.43451224 0.0033591440 0.0024124471 0.5596323 #> 100  4.731463 7.767898e-05 0.43122202 0.0033337113 0.0023941840 0.5629724 #> 101  4.769539 7.198317e-05 0.42795631 0.0033084679 0.0023760566 0.5662872 #> 102  4.807615 6.670501e-05 0.42471496 0.0032834126 0.0023580641 0.5695769 #> 103  4.845691 6.181387e-05 0.42149781 0.0032585441 0.0023402057 0.5728416 #> 104  4.883768 5.728137e-05 0.41830471 0.0032338613 0.0023224805 0.5760817 #> 105  4.921844 5.308122e-05 0.41513551 0.0032093630 0.0023048878 0.5792972 #> 106  4.959920 4.918904e-05 0.41199004 0.0031850480 0.0022874265 0.5824883 #> 107  4.997996 4.558226e-05 0.40886815 0.0031609151 0.0022700960 0.5856553 #> 108  5.036072 4.223994e-05 0.40576968 0.0031369630 0.0022528952 0.5887982 #> 109  5.074148 3.914270e-05 0.40269447 0.0031131907 0.0022358234 0.5919174 #> 110  5.112224 3.627256e-05 0.39964237 0.0030895968 0.0022188797 0.5950129 #> 111  5.150301 3.361288e-05 0.39661321 0.0030661802 0.0022020633 0.5980849 #> 112  5.188377 3.114822e-05 0.39360683 0.0030429396 0.0021853731 0.6011337 #> 113  5.226453 2.886428e-05 0.39062308 0.0030198739 0.0021688085 0.6041594 #> 114  5.264529 2.674781e-05 0.38766180 0.0029969817 0.0021523685 0.6071621 #> 115  5.302605 2.478652e-05 0.38472284 0.0029742619 0.0021360522 0.6101421 #> 116  5.340681 2.296905e-05 0.38180602 0.0029517133 0.0021198589 0.6130994 #> 117  5.378758 2.128485e-05 0.37891120 0.0029293346 0.0021037875 0.6160344 #> 118  5.416834 1.972414e-05 0.37603822 0.0029071247 0.0020878373 0.6189471 #> 119  5.454910 1.827787e-05 0.37318692 0.0028850823 0.0020720073 0.6218377 #> 120  5.492986 1.693764e-05 0.37035714 0.0028632063 0.0020562968 0.6247064 #> 121  5.531062 1.569569e-05 0.36754874 0.0028414954 0.0020407049 0.6275534 #> 122  5.569138 1.454481e-05 0.36476154 0.0028199485 0.0020252307 0.6303787 #> 123  5.607214 1.347831e-05 0.36199541 0.0027985643 0.0020098733 0.6331827 #> 124  5.645291 1.249001e-05 0.35925019 0.0027773417 0.0019946320 0.6359653 #> 125  5.683367 1.157418e-05 0.35652572 0.0027562795 0.0019795058 0.6387269 #> 126  5.721443 1.072551e-05 0.35382185 0.0027353766 0.0019644940 0.6414676 #> 127  5.759519 9.939060e-06 0.35113843 0.0027146317 0.0019495957 0.6441874 #> 128  5.797595 9.210279e-06 0.34847531 0.0026940437 0.0019348100 0.6468866 #> 129  5.835671 8.534936e-06 0.34583234 0.0026736114 0.0019201361 0.6495654 #> 130  5.873747 7.909112e-06 0.34320938 0.0026533337 0.0019055733 0.6522238 #> 131  5.911824 7.329177e-06 0.34060626 0.0026332095 0.0018911207 0.6548621 #> 132  5.949900 6.791766e-06 0.33802285 0.0026132377 0.0018767774 0.6574803 #> 133  5.987976 6.293760e-06 0.33545901 0.0025934169 0.0018625427 0.6600787 #> 134  6.026052 5.832270e-06 0.33291457 0.0025737463 0.0018484158 0.6626574 #> 135  6.064128 5.404619e-06 0.33038940 0.0025542246 0.0018343958 0.6652166 #> 136  6.102204 5.008326e-06 0.32788336 0.0025348508 0.0018204820 0.6677563 #> 137  6.140281 4.641091e-06 0.32539630 0.0025156236 0.0018066736 0.6702768 #> 138  6.178357 4.300783e-06 0.32292809 0.0024965422 0.0017929697 0.6727781 #> 139  6.216433 3.985428e-06 0.32047857 0.0024776052 0.0017793697 0.6752605 #> 140  6.254509 3.693197e-06 0.31804761 0.0024588118 0.0017658726 0.6777240 #> 141  6.292585 3.422394e-06 0.31563507 0.0024401607 0.0017524779 0.6801689 #> 142  6.330661 3.171447e-06 0.31324082 0.0024216510 0.0017391846 0.6825952 #> 143  6.368737 2.938901e-06 0.31086471 0.0024032815 0.0017259921 0.6850031 #> 144  6.406814 2.723406e-06 0.30850661 0.0023850513 0.0017128995 0.6873927 #> 145  6.444890 2.523712e-06 0.30616638 0.0023669592 0.0016999062 0.6897642 #> 146  6.482966 2.338661e-06 0.30384389 0.0023490042 0.0016870113 0.6921178 #> 147  6.521042 2.167179e-06 0.30153901 0.0023311854 0.0016742142 0.6944534 #> 148  6.559118 2.008271e-06 0.29925160 0.0023135016 0.0016615141 0.6967714 #> 149  6.597194 1.861015e-06 0.29698153 0.0022959519 0.0016489103 0.6990717 #> 150  6.635271 1.724556e-06 0.29472867 0.0022785352 0.0016364020 0.7013547 #> 151  6.673347 1.598103e-06 0.29249290 0.0022612506 0.0016239885 0.7036203 #> 152  6.711423 1.480922e-06 0.29027407 0.0022440970 0.0016116692 0.7058687 #> 153  6.749499 1.372333e-06 0.28807207 0.0022270735 0.0015994432 0.7081000 #> 154  6.787575 1.271707e-06 0.28588677 0.0022101791 0.0015873100 0.7103145 #> 155  6.825651 1.178459e-06 0.28371804 0.0021934128 0.0015752688 0.7125121 #> 156  6.863727 1.092049e-06 0.28156575 0.0021767736 0.0015633188 0.7146931 #> 157  6.901804 1.011974e-06 0.27942979 0.0021602606 0.0015514595 0.7168575 #> 158  6.939880 9.377715e-07 0.27731002 0.0021438728 0.0015396901 0.7190055 #> 159  6.977956 8.690095e-07 0.27520633 0.0021276093 0.0015280100 0.7211372 #> 160  7.016032 8.052894e-07 0.27311859 0.0021114691 0.0015164184 0.7232527 #> 161  7.054108 7.462416e-07 0.27104669 0.0020954513 0.0015049148 0.7253522 #> 162  7.092184 6.915235e-07 0.26899050 0.0020795550 0.0014934984 0.7274358 #> 163  7.130261 6.408176e-07 0.26694991 0.0020637793 0.0014821686 0.7295035 #> 164  7.168337 5.938296e-07 0.26492479 0.0020481232 0.0014709247 0.7315556 #> 165  7.206413 5.502871e-07 0.26291503 0.0020325859 0.0014597660 0.7335921 #> 166  7.244489 5.099374e-07 0.26092052 0.0020171664 0.0014486921 0.7356131 #> 167  7.282565 4.725462e-07 0.25894113 0.0020018639 0.0014377021 0.7376188 #> 168  7.320641 4.378968e-07 0.25697676 0.0019866774 0.0014267954 0.7396093 #> 169  7.358717 4.057880e-07 0.25502729 0.0019716061 0.0014159715 0.7415847 #> 170  7.396794 3.760337e-07 0.25309260 0.0019566492 0.0014052297 0.7435451 #> 171  7.434870 3.484610e-07 0.25117259 0.0019418056 0.0013945694 0.7454907 #> 172  7.472946 3.229101e-07 0.24926714 0.0019270747 0.0013839899 0.7474215 #> 173  7.511022 2.992328e-07 0.24737615 0.0019124555 0.0013734907 0.7493376 #> 174  7.549098 2.772915e-07 0.24549950 0.0018979472 0.0013630711 0.7512392 #> 175  7.587174 2.569592e-07 0.24363708 0.0018835490 0.0013527305 0.7531264 #> 176  7.625251 2.381176e-07 0.24178880 0.0018692600 0.0013424684 0.7549992 #> 177  7.663327 2.206577e-07 0.23995453 0.0018550793 0.0013322842 0.7568579 #> 178  7.701403 2.044780e-07 0.23813418 0.0018410063 0.0013221771 0.7587024 #> 179  7.739479 1.894846e-07 0.23632763 0.0018270399 0.0013121468 0.7605330 #> 180  7.777555 1.755907e-07 0.23453479 0.0018131796 0.0013021925 0.7623497 #> 181  7.815631 1.627155e-07 0.23275555 0.0017994243 0.0012923138 0.7641525 #> 182  7.853707 1.507844e-07 0.23098981 0.0017857734 0.0012825100 0.7659418 #> 183  7.891784 1.397281e-07 0.22923746 0.0017722261 0.0012727805 0.7677174 #> 184  7.929860 1.294826e-07 0.22749840 0.0017587815 0.0012631249 0.7694796 #> 185  7.967936 1.199883e-07 0.22577254 0.0017454390 0.0012535425 0.7712284 #> 186  8.006012 1.111901e-07 0.22405977 0.0017321976 0.0012440328 0.7729639 #> 187  8.044088 1.030371e-07 0.22235999 0.0017190567 0.0012345952 0.7746863 #> 188  8.082164 9.548195e-08 0.22067311 0.0017060154 0.0012252292 0.7763956 #> 189  8.120240 8.848074e-08 0.21899902 0.0016930731 0.0012159343 0.7780919 #> 190  8.158317 8.199289e-08 0.21733763 0.0016802290 0.0012067099 0.7797753 #> 191  8.196393 7.598077e-08 0.21568885 0.0016674823 0.0011975555 0.7814460 #> 192  8.234469 7.040948e-08 0.21405257 0.0016548324 0.0011884705 0.7831041 #> 193  8.272545 6.524671e-08 0.21242871 0.0016422783 0.0011794544 0.7847495 #> 194  8.310621 6.046250e-08 0.21081716 0.0016298195 0.0011705067 0.7863825 #> 195  8.348697 5.602909e-08 0.20921784 0.0016174553 0.0011616269 0.7880030 #> 196  8.386774 5.192076e-08 0.20763066 0.0016051848 0.0011528145 0.7896113 #> 197  8.424850 4.811367e-08 0.20605551 0.0015930074 0.0011440689 0.7912074 #> 198  8.462926 4.458574e-08 0.20449231 0.0015809224 0.0011353897 0.7927913 #> 199  8.501002 4.131650e-08 0.20294097 0.0015689290 0.0011267763 0.7943633 #> 200  8.539078 3.828697e-08 0.20140140 0.0015570267 0.0011182282 0.7959233 #> 201  8.577154 3.547958e-08 0.19987351 0.0015452146 0.0011097450 0.7974715 #> 202  8.615230 3.287804e-08 0.19835721 0.0015334922 0.0011013262 0.7990079 #> 203  8.653307 3.046726e-08 0.19685242 0.0015218586 0.0010929712 0.8005327 #> 204  8.691383 2.823325e-08 0.19535904 0.0015103134 0.0010846796 0.8020459 #> 205  8.729459 2.616305e-08 0.19387699 0.0014988557 0.0010764509 0.8035477 #> 206  8.767535 2.424464e-08 0.19240618 0.0014874849 0.0010682846 0.8050380 #> 207  8.805611 2.246691e-08 0.19094653 0.0014762004 0.0010601803 0.8065171 #> 208  8.843687 2.081952e-08 0.18949795 0.0014650015 0.0010521374 0.8079849 #> 209  8.881764 1.929293e-08 0.18806036 0.0014538876 0.0010441556 0.8094416 #> 210  8.919840 1.787828e-08 0.18663368 0.0014428580 0.0010362343 0.8108872 #> 211  8.957916 1.656735e-08 0.18521782 0.0014319120 0.0010283732 0.8123219 #> 212  8.995992 1.535255e-08 0.18381270 0.0014210491 0.0010205716 0.8137457 #> 213  9.034068 1.422683e-08 0.18241824 0.0014102686 0.0010128292 0.8151586 #> 214  9.072144 1.318365e-08 0.18103436 0.0013995699 0.0010051456 0.8165609 #> 215  9.110220 1.221696e-08 0.17966098 0.0013889523 0.0009975203 0.8179525 #> 216  9.148297 1.132115e-08 0.17829802 0.0013784153 0.0009899528 0.8193336 #> 217  9.186373 1.049103e-08 0.17694539 0.0013679582 0.0009824427 0.8207042 #> 218  9.224449 9.721773e-09 0.17560303 0.0013575805 0.0009749896 0.8220644 #> 219  9.262525 9.008925e-09 0.17427086 0.0013472814 0.0009675930 0.8234143 #> 220  9.300601 8.348346e-09 0.17294878 0.0013370606 0.0009602526 0.8247539 #> 221  9.338677 7.736204e-09 0.17163674 0.0013269172 0.0009529678 0.8260834 #> 222  9.376754 7.168947e-09 0.17033465 0.0013168508 0.0009457383 0.8274028 #> 223  9.414830 6.643285e-09 0.16904244 0.0013068608 0.0009385636 0.8287121 #> 224  9.452906 6.156166e-09 0.16776003 0.0012969465 0.0009314434 0.8300116 #> 225  9.490982 5.704766e-09 0.16648736 0.0012871075 0.0009243772 0.8313012 #> 226  9.529058 5.286464e-09 0.16522433 0.0012773431 0.0009173646 0.8325810 #> 227  9.567134 4.898834e-09 0.16397089 0.0012676528 0.0009104052 0.8338510 #> 228  9.605210 4.539628e-09 0.16272696 0.0012580360 0.0009034986 0.8351115 #> 229  9.643287 4.206760e-09 0.16149246 0.0012484921 0.0008966444 0.8363624 #> 230  9.681363 3.898299e-09 0.16026733 0.0012390207 0.0008898421 0.8376038 #> 231  9.719439 3.612457e-09 0.15905149 0.0012296211 0.0008830915 0.8388358 #> 232  9.757515 3.347574e-09 0.15784488 0.0012202928 0.0008763921 0.8400584 #> 233  9.795591 3.102113e-09 0.15664742 0.0012110353 0.0008697435 0.8412718 #> 234  9.833667 2.874651e-09 0.15545904 0.0012018480 0.0008631454 0.8424760 #> 235  9.871743 2.663867e-09 0.15427968 0.0011927304 0.0008565973 0.8436710 #> 236  9.909820 2.468539e-09 0.15310927 0.0011836820 0.0008500989 0.8448569 #> 237  9.947896 2.287534e-09 0.15194774 0.0011747022 0.0008436498 0.8460339 #> 238  9.985972 2.119800e-09 0.15079501 0.0011657906 0.0008372496 0.8472019 #> 239 10.024048 1.964366e-09 0.14965104 0.0011569466 0.0008308980 0.8483611 #> 240 10.062124 1.820329e-09 0.14851574 0.0011481696 0.0008245945 0.8495115 #> 241 10.100200 1.686854e-09 0.14738905 0.0011394592 0.0008183389 0.8506531 #> 242 10.138277 1.563165e-09 0.14627091 0.0011308149 0.0008121307 0.8517861 #> 243 10.176353 1.448546e-09 0.14516126 0.0011222362 0.0008059697 0.8529105 #> 244 10.214429 1.342332e-09 0.14406002 0.0011137226 0.0007998553 0.8540264 #> 245 10.252505 1.243905e-09 0.14296714 0.0011052736 0.0007937874 0.8551338 #> 246 10.290581 1.152696e-09 0.14188254 0.0010968886 0.0007877655 0.8562328 #> 247 10.328657 1.068175e-09 0.14080618 0.0010885673 0.0007817892 0.8573235 #> 248 10.366733 9.898508e-10 0.13973798 0.0010803091 0.0007758583 0.8584059 #> 249 10.404810 9.172700e-10 0.13867788 0.0010721135 0.0007699724 0.8594800 #> 250 10.442886 8.500112e-10 0.13762583 0.0010639801 0.0007641312 0.8605461 #> 251 10.480962 7.876842e-10 0.13658176 0.0010559085 0.0007583343 0.8616040 #> 252 10.519038 7.299273e-10 0.13554561 0.0010478980 0.0007525813 0.8626539 #> 253 10.557114 6.764054e-10 0.13451732 0.0010399483 0.0007468720 0.8636959 #> 254 10.595190 6.268081e-10 0.13349683 0.0010320590 0.0007412060 0.8647299 #> 255 10.633267 5.808474e-10 0.13248408 0.0010242295 0.0007355830 0.8657561 #> 256 10.671343 5.382568e-10 0.13147901 0.0010164593 0.0007300026 0.8667745 #> 257 10.709419 4.987891e-10 0.13048157 0.0010087482 0.0007244646 0.8677852 #> 258 10.747495 4.622155e-10 0.12949170 0.0010010955 0.0007189686 0.8687882 #> 259 10.785571 4.283235e-10 0.12850934 0.0009935009 0.0007135143 0.8697836 #> 260 10.823647 3.969167e-10 0.12753443 0.0009859639 0.0007081013 0.8707715 #> 261 10.861723 3.678128e-10 0.12656691 0.0009784840 0.0007027294 0.8717519 #> 262 10.899800 3.408430e-10 0.12560673 0.0009710610 0.0006973983 0.8727248 #> 263 10.937876 3.158507e-10 0.12465384 0.0009636942 0.0006921076 0.8736904 #> 264 10.975952 2.926910e-10 0.12370818 0.0009563833 0.0006868571 0.8746486 #> 265 11.014028 2.712294e-10 0.12276969 0.0009491279 0.0006816464 0.8755995 #> 266 11.052104 2.513415e-10 0.12183832 0.0009419275 0.0006764752 0.8765433 #> 267 11.090180 2.329119e-10 0.12091402 0.0009347818 0.0006713433 0.8774799 #> 268 11.128257 2.158337e-10 0.11999673 0.0009276902 0.0006662502 0.8784093 #> 269 11.166333 2.000077e-10 0.11908640 0.0009206525 0.0006611959 0.8793318 #> 270 11.204409 1.853421e-10 0.11818297 0.0009136681 0.0006561798 0.8802472 #> 271 11.242485 1.717519e-10 0.11728640 0.0009067368 0.0006512019 0.8811557 #> 272 11.280561 1.591582e-10 0.11639663 0.0008998580 0.0006462616 0.8820573 #> 273 11.318637 1.474880e-10 0.11551361 0.0008930314 0.0006413589 0.8829520 #> 274 11.356713 1.366734e-10 0.11463729 0.0008862566 0.0006364933 0.8838400 #> 275 11.394790 1.266518e-10 0.11376761 0.0008795331 0.0006316647 0.8847212 #> 276 11.432866 1.173651e-10 0.11290454 0.0008728607 0.0006268727 0.8855957 #> 277 11.470942 1.087593e-10 0.11204801 0.0008662389 0.0006221171 0.8864636 #> 278 11.509018 1.007845e-10 0.11119798 0.0008596674 0.0006173975 0.8873250 #> 279 11.547094 9.339453e-11 0.11035439 0.0008531457 0.0006127137 0.8881797 #> 280 11.585170 8.654638e-11 0.10951721 0.0008466735 0.0006080655 0.8890280 #> 281 11.623246 8.020037e-11 0.10868638 0.0008402503 0.0006034525 0.8898699 #> 282 11.661323 7.431968e-11 0.10786185 0.0008338759 0.0005988745 0.8907054 #> 283 11.699399 6.887020e-11 0.10704358 0.0008275499 0.0005943313 0.8915345 #> 284 11.737475 6.382029e-11 0.10623151 0.0008212718 0.0005898225 0.8923574 #> 285 11.775551 5.914067e-11 0.10542561 0.0008150414 0.0005853479 0.8931740 #> 286 11.813627 5.480419e-11 0.10462582 0.0008088583 0.0005809073 0.8939844 #> 287 11.851703 5.078567e-11 0.10383209 0.0008027220 0.0005765004 0.8947887 #> 288 11.889780 4.706182e-11 0.10304439 0.0007966323 0.0005721269 0.8955868 #> 289 11.927856 4.361101e-11 0.10226267 0.0007905888 0.0005677865 0.8963790 #> 290 11.965932 4.041324e-11 0.10148687 0.0007845912 0.0005634791 0.8971651 #> 291 12.004008 3.744994e-11 0.10071696 0.0007786390 0.0005592044 0.8979452 #> 292 12.042084 3.470392e-11 0.09995289 0.0007727320 0.0005549621 0.8987194 #> 293 12.080160 3.215926e-11 0.09919462 0.0007668699 0.0005507520 0.8994878 #> 294 12.118236 2.980119e-11 0.09844209 0.0007610521 0.0005465738 0.9002503 #> 295 12.156313 2.761602e-11 0.09769528 0.0007552786 0.0005424273 0.9010070 #> 296 12.194389 2.559107e-11 0.09695414 0.0007495488 0.0005383123 0.9017580 #> 297 12.232465 2.371461e-11 0.09621861 0.0007438625 0.0005342285 0.9025033 #> 298 12.270541 2.197574e-11 0.09548867 0.0007382193 0.0005301757 0.9032429 #> 299 12.308617 2.036437e-11 0.09476426 0.0007326190 0.0005261536 0.9039770 #> 300 12.346693 1.887115e-11 0.09404535 0.0007270611 0.0005221620 0.9047054 #> 301 12.384770 1.748742e-11 0.09333189 0.0007215454 0.0005182008 0.9054284 #> 302 12.422846 1.620516e-11 0.09262385 0.0007160715 0.0005142695 0.9061458 #> 303 12.460922 1.501692e-11 0.09192118 0.0007106392 0.0005103681 0.9068578 #> 304 12.498998 1.391580e-11 0.09122383 0.0007052480 0.0005064963 0.9075644 #> 305 12.537074 1.289543e-11 0.09053178 0.0006998978 0.0005026539 0.9082657 #> 306 12.575150 1.194987e-11 0.08984498 0.0006945882 0.0004988406 0.9089616 #> 307 12.613226 1.107365e-11 0.08916339 0.0006893188 0.0004950562 0.9096522 #> 308 12.651303 1.026167e-11 0.08848697 0.0006840894 0.0004913006 0.9103376 #> 309 12.689379 9.509237e-12 0.08781568 0.0006788997 0.0004875734 0.9110178 #> 310 12.727455 8.811972e-12 0.08714948 0.0006737494 0.0004838745 0.9116929 #> 311 12.765531 8.165835e-12 0.08648834 0.0006686381 0.0004802037 0.9123628 #> 312 12.803607 7.567076e-12 0.08583221 0.0006635656 0.0004765608 0.9130277 #> 313 12.841683 7.012220e-12 0.08518106 0.0006585316 0.0004729454 0.9136875 #> 314 12.879760 6.498050e-12 0.08453485 0.0006535358 0.0004693575 0.9143423 #> 315 12.917836 6.021581e-12 0.08389354 0.0006485778 0.0004657968 0.9149921 #> 316 12.955912 5.580049e-12 0.08325710 0.0006436575 0.0004622631 0.9156370 #> 317 12.993988 5.170892e-12 0.08262549 0.0006387745 0.0004587563 0.9162770 #> 318 13.032064 4.791736e-12 0.08199867 0.0006339286 0.0004552760 0.9169121 #> 319 13.070140 4.440383e-12 0.08137660 0.0006291194 0.0004518221 0.9175425 #> 320 13.108216 4.114792e-12 0.08075925 0.0006243467 0.0004483945 0.9181680 #> 321 13.146293 3.813075e-12 0.08014659 0.0006196102 0.0004449928 0.9187888 #> 322 13.184369 3.533481e-12 0.07953857 0.0006149097 0.0004416170 0.9194049 #> 323 13.222445 3.274389e-12 0.07893516 0.0006102448 0.0004382667 0.9200163 #> 324 13.260521 3.034295e-12 0.07833634 0.0006056153 0.0004349419 0.9206231 #> 325 13.298597 2.811805e-12 0.07774205 0.0006010209 0.0004316423 0.9212253 #> 326 13.336673 2.605630e-12 0.07715228 0.0005964614 0.0004283677 0.9218229 #> 327 13.374749 2.414572e-12 0.07656698 0.0005919364 0.0004251180 0.9224160 #> 328 13.412826 2.237524e-12 0.07598612 0.0005874458 0.0004218929 0.9230045 #> 329 13.450902 2.073458e-12 0.07540966 0.0005829893 0.0004186923 0.9235887 #> 330 13.488978 1.921421e-12 0.07483758 0.0005785665 0.0004155160 0.9241683 #> 331 13.527054 1.780533e-12 0.07426984 0.0005741774 0.0004123637 0.9247436 #> 332 13.565130 1.649976e-12 0.07370641 0.0005698215 0.0004092354 0.9253145 #> 333 13.603206 1.528991e-12 0.07314725 0.0005654986 0.0004061308 0.9258811 #> 334 13.641283 1.416878e-12 0.07259233 0.0005612086 0.0004030498 0.9264434 #> 335 13.679359 1.312986e-12 0.07204162 0.0005569511 0.0003999921 0.9270014 #> 336 13.717435 1.216711e-12 0.07149509 0.0005527259 0.0003969577 0.9275552 #> 337 13.755511 1.127496e-12 0.07095271 0.0005485327 0.0003939462 0.9281048 #> 338 13.793587 1.044822e-12 0.07041444 0.0005443714 0.0003909576 0.9286502 #> 339 13.831663 9.682107e-13 0.06988025 0.0005402416 0.0003879917 0.9291915 #> 340 13.869739 8.972167e-13 0.06935012 0.0005361432 0.0003850483 0.9297287 #> 341 13.907816 8.314284e-13 0.06882401 0.0005320758 0.0003821272 0.9302618 #> 342 13.945892 7.704639e-13 0.06830189 0.0005280394 0.0003792283 0.9307908 #> 343 13.983968 7.139697e-13 0.06778373 0.0005240335 0.0003763513 0.9313159 #> 344 14.022044 6.616179e-13 0.06726950 0.0005200580 0.0003734962 0.9318369 #> 345 14.060120 6.131048e-13 0.06675918 0.0005161127 0.0003706627 0.9323540 #> 346 14.098196 5.681489e-13 0.06625272 0.0005121973 0.0003678508 0.9328672 #> 347 14.136273 5.264895e-13 0.06575011 0.0005083116 0.0003650602 0.9333765 #> 348 14.174349 4.878846e-13 0.06525131 0.0005044554 0.0003622907 0.9338819 #> 349 14.212425 4.521105e-13 0.06475629 0.0005006284 0.0003595422 0.9343835 #> 350 14.250501 4.189596e-13 0.06426503 0.0004968305 0.0003568146 0.9348813 #> 351 14.288577 3.882394e-13 0.06377749 0.0004930614 0.0003541077 0.9353753 #> 352 14.326653 3.597717e-13 0.06329366 0.0004893209 0.0003514214 0.9358656 #> 353 14.364729 3.333915e-13 0.06281349 0.0004856088 0.0003487554 0.9363521 #> 354 14.402806 3.089456e-13 0.06233697 0.0004819248 0.0003461096 0.9368350 #> 355 14.440882 2.862922e-13 0.06186406 0.0004782687 0.0003434839 0.9373142 #> 356 14.478958 2.652998e-13 0.06139474 0.0004746405 0.0003408781 0.9377897 #> 357 14.517034 2.458467e-13 0.06092898 0.0004710397 0.0003382921 0.9382617 #> 358 14.555110 2.278200e-13 0.06046676 0.0004674662 0.0003357258 0.9387301 #> 359 14.593186 2.111151e-13 0.06000804 0.0004639199 0.0003331788 0.9391949 #> 360 14.631263 1.956351e-13 0.05955280 0.0004604005 0.0003306512 0.9396561 #> 361 14.669339 1.812902e-13 0.05910101 0.0004569077 0.0003281428 0.9401139 #> 362 14.707415 1.679971e-13 0.05865265 0.0004534415 0.0003256534 0.9405683 #> 363 14.745491 1.556787e-13 0.05820770 0.0004500015 0.0003231829 0.9410191 #> 364 14.783567 1.442636e-13 0.05776612 0.0004465877 0.0003207311 0.9414666 #> 365 14.821643 1.336855e-13 0.05732788 0.0004431997 0.0003182980 0.9419106 #> 366 14.859719 1.238830e-13 0.05689298 0.0004398375 0.0003158833 0.9423513 #> 367 14.897796 1.147993e-13 0.05646137 0.0004365007 0.0003134869 0.9427886 #> 368 14.935872 1.063816e-13 0.05603304 0.0004331893 0.0003111087 0.9432227 #> 369 14.973948 9.858120e-14 0.05560795 0.0004299030 0.0003087485 0.9436534 #> 370 15.012024 9.135274e-14 0.05518609 0.0004266416 0.0003064063 0.9440809 #> 371 15.050100 8.465431e-14 0.05476743 0.0004234050 0.0003040818 0.9445051 #> 372 15.088176 7.844704e-14 0.05435195 0.0004201929 0.0003017749 0.9449261 #> 373 15.126253 7.269491e-14 0.05393962 0.0004170052 0.0002994855 0.9453439 #> 374 15.164329 6.736456e-14 0.05353042 0.0004138417 0.0002972136 0.9457585 #> 375 15.202405 6.242506e-14 0.05312432 0.0004107021 0.0002949588 0.9461700 #> 376 15.240481 5.784774e-14 0.05272130 0.0004075864 0.0002927212 0.9465784 #> 377 15.278557 5.360606e-14 0.05232134 0.0004044943 0.0002905005 0.9469837 #> 378 15.316633 4.967540e-14 0.05192442 0.0004014257 0.0002882967 0.9473859 #> 379 15.354709 4.603296e-14 0.05153050 0.0003983804 0.0002861095 0.9477850 #> 380 15.392786 4.265759e-14 0.05113958 0.0003953581 0.0002839390 0.9481811 #> 381 15.430862 3.952973e-14 0.05075162 0.0003923588 0.0002817850 0.9485742 #> 382 15.468938 3.663121e-14 0.05036660 0.0003893823 0.0002796473 0.9489644 #> 383 15.507014 3.394523e-14 0.04998450 0.0003864283 0.0002775258 0.9493515 #> 384 15.545090 3.145620e-14 0.04960530 0.0003834967 0.0002754204 0.9497358 #> 385 15.583166 2.914967e-14 0.04922898 0.0003805874 0.0002733310 0.9501171 #> 386 15.621242 2.701228e-14 0.04885552 0.0003777002 0.0002712574 0.9504955 #> 387 15.659319 2.503160e-14 0.04848488 0.0003748348 0.0002691995 0.9508711 #> 388 15.697395 2.319616e-14 0.04811706 0.0003719912 0.0002671573 0.9512438 #> 389 15.735471 2.149530e-14 0.04775203 0.0003691692 0.0002651306 0.9516137 #> 390 15.773547 1.991916e-14 0.04738977 0.0003663685 0.0002631192 0.9519807 #> 391 15.811623 1.845859e-14 0.04703026 0.0003635891 0.0002611231 0.9523450 #> 392 15.849699 1.710512e-14 0.04667347 0.0003608308 0.0002591422 0.9527066 #> 393 15.887776 1.585088e-14 0.04631939 0.0003580935 0.0002571762 0.9530653 #> 394 15.925852 1.468862e-14 0.04596800 0.0003553769 0.0002552252 0.9534214 #> 395 15.963928 1.361158e-14 0.04561927 0.0003526809 0.0002532890 0.9537748 #> 396 16.002004 1.261351e-14 0.04527319 0.0003500053 0.0002513675 0.9541254 #> 397 16.040080 1.168862e-14 0.04492973 0.0003473501 0.0002494605 0.9544735 #> 398 16.078156 1.083156e-14 0.04458888 0.0003447150 0.0002475680 0.9548188 #> 399 16.116232 1.003733e-14 0.04425062 0.0003420999 0.0002456899 0.9551616 #> 400 16.154309 9.301346e-15 0.04391492 0.0003395046 0.0002438260 0.9555018 #> 401 16.192385 8.619326e-15 0.04358177 0.0003369290 0.0002419763 0.9558393 #> 402 16.230461 7.987314e-15 0.04325114 0.0003343729 0.0002401406 0.9561743 #> 403 16.268537 7.401645e-15 0.04292302 0.0003318363 0.0002383188 0.9565068 #> 404 16.306613 6.858920e-15 0.04259740 0.0003293189 0.0002365108 0.9568368 #> 405 16.344689 6.355990e-15 0.04227424 0.0003268206 0.0002347166 0.9571642 #> 406 16.382766 5.889937e-15 0.04195353 0.0003243412 0.0002329360 0.9574892 #> 407 16.420842 5.458058e-15 0.04163526 0.0003218807 0.0002311688 0.9578117 #> 408 16.458918 5.057846e-15 0.04131940 0.0003194388 0.0002294151 0.9581317 #> 409 16.496994 4.686980e-15 0.04100594 0.0003170154 0.0002276747 0.9584494 #> 410 16.535070 4.343307e-15 0.04069486 0.0003146104 0.0002259475 0.9587646 #> 411 16.573146 4.024835e-15 0.04038614 0.0003122237 0.0002242334 0.9590774 #> 412 16.611222 3.729714e-15 0.04007975 0.0003098551 0.0002225323 0.9593879 #> 413 16.649299 3.456233e-15 0.03977570 0.0003075044 0.0002208441 0.9596960 #> 414 16.687375 3.202805e-15 0.03947395 0.0003051716 0.0002191687 0.9600017 #> 415 16.725451 2.967959e-15 0.03917448 0.0003028565 0.0002175060 0.9603052 #> 416 16.763527 2.750334e-15 0.03887729 0.0003005589 0.0002158559 0.9606063 #> 417 16.801603 2.548666e-15 0.03858236 0.0002982788 0.0002142184 0.9609051 #> 418 16.839679 2.361785e-15 0.03828966 0.0002960159 0.0002125933 0.9612017 #> 419 16.877756 2.188607e-15 0.03799918 0.0002937703 0.0002109805 0.9614961 #> 420 16.915832 2.028128e-15 0.03771091 0.0002915416 0.0002093799 0.9617882 #> 421 16.953908 1.879415e-15 0.03742482 0.0002893299 0.0002077915 0.9620781 #> 422 16.991984 1.741607e-15 0.03714091 0.0002871350 0.0002062151 0.9623657 #> 423 17.030060 1.613904e-15 0.03685915 0.0002849567 0.0002046507 0.9626512 #> 424 17.068136 1.495565e-15 0.03657952 0.0002827949 0.0002030982 0.9629346 #> 425 17.106212 1.385902e-15 0.03630202 0.0002806495 0.0002015574 0.9632158 #> 426 17.144289 1.284281e-15 0.03602662 0.0002785204 0.0002000283 0.9634948 #> 427 17.182365 1.190111e-15 0.03575331 0.0002764075 0.0001985108 0.9637718 #> 428 17.220441 1.102847e-15 0.03548207 0.0002743106 0.0001970049 0.9640466 #> 429 17.258517 1.021980e-15 0.03521290 0.0002722296 0.0001955103 0.9643194 #> 430 17.296593 9.470438e-16 0.03494576 0.0002701644 0.0001940271 0.9645900 #> 431 17.334669 8.776018e-16 0.03468065 0.0002681148 0.0001925552 0.9648587 #> 432 17.372745 8.132517e-16 0.03441755 0.0002660808 0.0001910944 0.9651253 #> 433 17.410822 7.536201e-16 0.03415645 0.0002640622 0.0001896447 0.9653898 #> 434 17.448898 6.983609e-16 0.03389733 0.0002620590 0.0001882060 0.9656524 #> 435 17.486974 6.471537e-16 0.03364017 0.0002600709 0.0001867782 0.9659130 #> 436 17.525050 5.997012e-16 0.03338497 0.0002580979 0.0001853613 0.9661716 #> 437 17.563126 5.557281e-16 0.03313170 0.0002561399 0.0001839550 0.9664282 #> 438 17.601202 5.149794e-16 0.03288035 0.0002541968 0.0001825595 0.9666829 #> 439 17.639279 4.772185e-16 0.03263091 0.0002522684 0.0001811746 0.9669356 #> 440 17.677355 4.422265e-16 0.03238336 0.0002503546 0.0001798001 0.9671865 #> 441 17.715431 4.098003e-16 0.03213769 0.0002484553 0.0001784361 0.9674354 #> 442 17.753507 3.797517e-16 0.03189389 0.0002465705 0.0001770824 0.9676825 #> 443 17.791583 3.519064e-16 0.03165193 0.0002446999 0.0001757390 0.9679276 #> 444 17.829659 3.261029e-16 0.03141181 0.0002428435 0.0001744058 0.9681709 #> 445 17.867735 3.021914e-16 0.03117351 0.0002410012 0.0001730827 0.9684124 #> 446 17.905812 2.800333e-16 0.03093702 0.0002391729 0.0001717696 0.9686520 #> 447 17.943888 2.594998e-16 0.03070232 0.0002373585 0.0001704666 0.9688899 #> 448 17.981964 2.404720e-16 0.03046940 0.0002355578 0.0001691733 0.9691259 #> 449 18.020040 2.228394e-16 0.03023825 0.0002337708 0.0001678899 0.9693601 #> 450 18.058116 2.064998e-16 0.03000886 0.0002319973 0.0001666163 0.9695925 #> 451 18.096192 1.913582e-16 0.02978120 0.0002302373 0.0001653523 0.9698232 #> 452 18.134269 1.773268e-16 0.02955527 0.0002284907 0.0001640979 0.9700521 #> 453 18.172345 1.643244e-16 0.02933106 0.0002267573 0.0001628530 0.9702793 #> 454 18.210421 1.522753e-16 0.02910854 0.0002250370 0.0001616175 0.9705048 #> 455 18.248497 1.411097e-16 0.02888771 0.0002233298 0.0001603914 0.9707286 #> 456 18.286573 1.307629e-16 0.02866856 0.0002216356 0.0001591746 0.9709506 #> 457 18.324649 1.211747e-16 0.02845107 0.0002199542 0.0001579671 0.9711710 #> 458 18.362725 1.122895e-16 0.02823524 0.0002182856 0.0001567687 0.9713897 #> 459 18.400802 1.040559e-16 0.02802103 0.0002166296 0.0001555794 0.9716068 #> 460 18.438878 9.642603e-17 0.02780846 0.0002149862 0.0001543991 0.9718222 #> 461 18.476954 8.935559e-17 0.02759750 0.0002133552 0.0001532278 0.9720359 #> 462 18.515030 8.280360e-17 0.02738813 0.0002117366 0.0001520654 0.9722481 #> 463 18.553106 7.673203e-17 0.02718036 0.0002101303 0.0001509118 0.9724586 #> 464 18.591182 7.110566e-17 0.02697416 0.0002085362 0.0001497669 0.9726675 #> 465 18.629259 6.589184e-17 0.02676952 0.0002069542 0.0001486307 0.9728749 #> 466 18.667335 6.106033e-17 0.02656644 0.0002053842 0.0001475032 0.9730807 #> 467 18.705411 5.658308e-17 0.02636490 0.0002038261 0.0001463842 0.9732849 #> 468 18.743487 5.243413e-17 0.02616489 0.0002022798 0.0001452737 0.9734876 #> 469 18.781563 4.858940e-17 0.02596639 0.0002007452 0.0001441716 0.9736887 #> 470 18.819639 4.502659e-17 0.02576941 0.0001992223 0.0001430778 0.9738883 #> 471 18.857715 4.172501e-17 0.02557391 0.0001977109 0.0001419924 0.9740864 #> 472 18.895792 3.866553e-17 0.02537990 0.0001962110 0.0001409152 0.9742830 #> 473 18.933868 3.583038e-17 0.02518736 0.0001947225 0.0001398462 0.9744781 #> 474 18.971944 3.320312e-17 0.02499628 0.0001932453 0.0001387853 0.9746717 #> 475 19.010020 3.076850e-17 0.02480665 0.0001917793 0.0001377324 0.9748638 #> 476 19.048096 2.851241e-17 0.02461846 0.0001903244 0.0001366875 0.9750545 #> 477 19.086172 2.642174e-17 0.02443170 0.0001888805 0.0001356506 0.9752438 #> 478 19.124248 2.448436e-17 0.02424635 0.0001874476 0.0001346215 0.9754316 #> 479 19.162325 2.268905e-17 0.02406241 0.0001860256 0.0001336002 0.9756180 #> 480 19.200401 2.102538e-17 0.02387986 0.0001846143 0.0001325867 0.9758029 #> 481 19.238477 1.948369e-17 0.02369870 0.0001832138 0.0001315808 0.9759865 #> 482 19.276553 1.805505e-17 0.02351892 0.0001818239 0.0001305826 0.9761687 #> 483 19.314629 1.673117e-17 0.02334050 0.0001804445 0.0001295920 0.9763495 #> 484 19.352705 1.550435e-17 0.02316343 0.0001790756 0.0001286088 0.9765289 #> 485 19.390782 1.436750e-17 0.02298770 0.0001777171 0.0001276332 0.9767069 #> 486 19.428858 1.331400e-17 0.02281331 0.0001763689 0.0001266649 0.9768837 #> 487 19.466934 1.233775e-17 0.02264024 0.0001750309 0.0001257040 0.9770590 #> 488 19.505010 1.143309e-17 0.02246849 0.0001737030 0.0001247504 0.9772331 #> 489 19.543086 1.059476e-17 0.02229804 0.0001723853 0.0001238040 0.9774058 #> 490 19.581162 9.817898e-18 0.02212888 0.0001710775 0.0001228648 0.9775772 #> 491 19.619238 9.098001e-18 0.02196100 0.0001697797 0.0001219327 0.9777473 #> 492 19.657315 8.430891e-18 0.02179440 0.0001684917 0.0001210076 0.9779161 #> 493 19.695391 7.812696e-18 0.02162906 0.0001672134 0.0001200896 0.9780836 #> 494 19.733467 7.239831e-18 0.02146497 0.0001659449 0.0001191786 0.9782499 #> 495 19.771543 6.708970e-18 0.02130213 0.0001646860 0.0001182745 0.9784149 #> 496 19.809619 6.217036e-18 0.02114053 0.0001634366 0.0001173772 0.9785787 #> 497 19.847695 5.761172e-18 0.02098015 0.0001621967 0.0001164868 0.9787412 #> 498 19.885772 5.338734e-18 0.02082099 0.0001609663 0.0001156031 0.9789024 #> 499 19.923848 4.947272e-18 0.02066303 0.0001597451 0.0001147261 0.9790625 #> 500 19.961924 4.584513e-18 0.02050628 0.0001585333 0.0001138557 0.9792213 #> 501 20.000000 4.248354e-18 0.02035071 0.0001573306 0.0001129920 0.9793790 #>  #> attr(,\"class\") #> [1] \"mseir_model\""},{"path":"https://oucru-modelling.github.io/serosv/reference/mumps_uk_1986_1987.html","id":null,"dir":"Reference","previous_headings":"","what":"Mumps serological data from the UK in 1986 and 1987 (aggregated) — mumps_uk_1986_1987","title":"Mumps serological data from the UK in 1986 and 1987 (aggregated) — mumps_uk_1986_1987","text":"large survey prevalence antibodies mumps rubella viruses UK. survey, covering subjects 1 65 years age, provides information prevalence antibody age","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mumps_uk_1986_1987.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mumps serological data from the UK in 1986 and 1987 (aggregated) — mumps_uk_1986_1987","text":"","code":"mumps_uk_1986_1987"},{"path":"https://oucru-modelling.github.io/serosv/reference/mumps_uk_1986_1987.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Mumps serological data from the UK in 1986 and 1987 (aggregated) — mumps_uk_1986_1987","text":"data frame 3 variables: age Midpoint age group (e.g. 1.5 = 1-2 years old, 2.5 = 2-3 years old) pos Number seropositive individuals tot Total number individuals surveyed","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mumps_uk_1986_1987.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Mumps serological data from the UK in 1986 and 1987 (aggregated) — mumps_uk_1986_1987","text":"Morgan-Capner P, Wright J, Miller C L, Miller E. Surveillance antibody measles, mumps, rubella age. British Medical Journal 1988; 297 :770 doi:10.1136/bmj.297.6651.770","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/mumps_uk_1986_1987.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mumps serological data from the UK in 1986 and 1987 (aggregated) — mumps_uk_1986_1987","text":"","code":"with(mumps_uk_1986_1987,   plot(age, pos / tot,     cex = 0.1 * sqrt(tot), pch = 16, xlab = \"age\", ylab = \"seroprevalence\",     xlim = c(0, 45), ylim = c(0, 1)   ) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_be_2001_2003.html","id":null,"dir":"Reference","previous_headings":"","what":"Parvo B19 serological data from Belgium from 2001-2003 (line listing) — parvob19_be_2001_2003","title":"Parvo B19 serological data from Belgium from 2001-2003 (line listing) — parvob19_be_2001_2003","text":"seroprevalence survey testing parvovirus B19 IgG antibody, performed large representative national serum banks Belgium, England Wales, Finland, Italy, Poland. sera collected 1995 2004 obtained residual sera submitted routine laboratory testing.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_be_2001_2003.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parvo B19 serological data from Belgium from 2001-2003 (line listing) — parvob19_be_2001_2003","text":"","code":"parvob19_be_2001_2003"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_be_2001_2003.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Parvo B19 serological data from Belgium from 2001-2003 (line listing) — parvob19_be_2001_2003","text":"data frame 5 variables: age Age individual seropositive individual seropositive year Year surveyed gender Gender individual parvouml Parvo B19 antibody units per ml","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_be_2001_2003.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Parvo B19 serological data from Belgium from 2001-2003 (line listing) — parvob19_be_2001_2003","text":"MOSSONG, J., N. HENS, V. FRIEDERICHS, . DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. \"Parvovirus B19 Infection Five European Countries: Seroepidemiology, Force Infection Maternal Risk Infection.\" Epidemiology Infection 136, . 8 (2008): 1059-68. doi:10.1017/S0950268807009661","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_be_2001_2003.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parvo B19 serological data from Belgium from 2001-2003 (line listing) — parvob19_be_2001_2003","text":"","code":"library(dplyr) df <- parvob19_be_2001_2003 %>%   group_by(age) %>%   summarise(pos = sum(seropositive), tot = n()) plot(df$age, df$pos / df$tot,   cex = 0.3 * sqrt(df$tot), pch = 16, xlab = \"age\", ylab = \"seroprevalence\",   xlim = c(0, 82), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_ew_1996.html","id":null,"dir":"Reference","previous_headings":"","what":"Parvo B19 serological data from England and Wales in 1996 (line listing) — parvob19_ew_1996","title":"Parvo B19 serological data from England and Wales in 1996 (line listing) — parvob19_ew_1996","text":"seroprevalence survey testing parvovirus B19 IgG antibody, performed large representative national serum banks Belgium, England Wales, Finland, Italy, Poland. sera collected 1995 2004 obtained residual sera submitted routine laboratory testing.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_ew_1996.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parvo B19 serological data from England and Wales in 1996 (line listing) — parvob19_ew_1996","text":"","code":"parvob19_ew_1996"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_ew_1996.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Parvo B19 serological data from England and Wales in 1996 (line listing) — parvob19_ew_1996","text":"data frame 5 variables: age Age individual seropositive individual seropositive year Year surveyed gender Gender individual parvouml Parvo B19 antibody units per ml","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_ew_1996.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Parvo B19 serological data from England and Wales in 1996 (line listing) — parvob19_ew_1996","text":"MOSSONG, J., N. HENS, V. FRIEDERICHS, . DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. \"Parvovirus B19 Infection Five European Countries: Seroepidemiology, Force Infection Maternal Risk Infection.\" Epidemiology Infection 136, . 8 (2008): 1059-68. doi:10.1017/S0950268807009661","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_ew_1996.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parvo B19 serological data from England and Wales in 1996 (line listing) — parvob19_ew_1996","text":"","code":"# Note: This figure will look different to that of in the book, since we # believe that the original authors has made some errors in specifying # the sample size of the dots.  library(dplyr) df <- parvob19_ew_1996 %>%   mutate(age = round(age)) %>%   group_by(age) %>%   summarise(pos = sum(seropositive), tot = n()) plot(df$age, df$pos / df$tot,   cex = 0.3 * sqrt(df$tot), pch = 16, xlab = \"age\", ylab = \"seroprevalence\",   xlim = c(0, 82), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_fi_1997_1998.html","id":null,"dir":"Reference","previous_headings":"","what":"Parvo B19 serological data from Finland from 1997-1998 (line listing) — parvob19_fi_1997_1998","title":"Parvo B19 serological data from Finland from 1997-1998 (line listing) — parvob19_fi_1997_1998","text":"seroprevalence survey testing parvovirus B19 IgG antibody, performed large representative national serum banks Belgium, England Wales, Finland, Italy, Poland. sera collected 1995 2004 obtained residual sera submitted routine laboratory testing.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_fi_1997_1998.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parvo B19 serological data from Finland from 1997-1998 (line listing) — parvob19_fi_1997_1998","text":"","code":"parvob19_fi_1997_1998"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_fi_1997_1998.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Parvo B19 serological data from Finland from 1997-1998 (line listing) — parvob19_fi_1997_1998","text":"data frame 5 variables: age Age individual seropositive individual seropositive year Year surveyed gender Gender individual parvouml Parvo B19 antibody units per ml","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_fi_1997_1998.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Parvo B19 serological data from Finland from 1997-1998 (line listing) — parvob19_fi_1997_1998","text":"MOSSONG, J., N. HENS, V. FRIEDERICHS, . DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. \"Parvovirus B19 Infection Five European Countries: Seroepidemiology, Force Infection Maternal Risk Infection.\" Epidemiology Infection 136, . 8 (2008): 1059-68. doi:10.1017/S0950268807009661","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_fi_1997_1998.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parvo B19 serological data from Finland from 1997-1998 (line listing) — parvob19_fi_1997_1998","text":"","code":"# Note: This figure will look different to that of in the book, since we # believe that the original authors has made some errors in specifying # the sample size of the dots.  library(dplyr) df <- parvob19_fi_1997_1998 %>%   mutate(age = round(age)) %>%   group_by(age) %>%   summarise(pos = sum(seropositive), tot = n()) plot(df$age, df$pos / df$tot,   cex = 0.4 * sqrt(df$tot), pch = 16, xlab = \"age\", ylab = \"seroprevalence\",   xlim = c(0, 82), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_it_2003_2004.html","id":null,"dir":"Reference","previous_headings":"","what":"Parvo B19 serological data from Italy from 2003-2004 (line listing) — parvob19_it_2003_2004","title":"Parvo B19 serological data from Italy from 2003-2004 (line listing) — parvob19_it_2003_2004","text":"seroprevalence survey testing parvovirus B19 IgG antibody, performed large representative national serum banks Belgium, England Wales, Finland, Italy, Poland. sera collected 1995 2004 obtained residual sera submitted routine laboratory testing.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_it_2003_2004.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parvo B19 serological data from Italy from 2003-2004 (line listing) — parvob19_it_2003_2004","text":"","code":"parvob19_it_2003_2004"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_it_2003_2004.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Parvo B19 serological data from Italy from 2003-2004 (line listing) — parvob19_it_2003_2004","text":"data frame 5 variables: age Age individual seropositive individual seropositive year Year surveyed gender Gender individual parvouml Parvo B19 antibody units per ml","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_it_2003_2004.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Parvo B19 serological data from Italy from 2003-2004 (line listing) — parvob19_it_2003_2004","text":"MOSSONG, J., N. HENS, V. FRIEDERICHS, . DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. \"Parvovirus B19 Infection Five European Countries: Seroepidemiology, Force Infection Maternal Risk Infection.\" Epidemiology Infection 136, . 8 (2008): 1059-68. doi:10.1017/S0950268807009661","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_it_2003_2004.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parvo B19 serological data from Italy from 2003-2004 (line listing) — parvob19_it_2003_2004","text":"","code":"library(dplyr) df <- parvob19_it_2003_2004 %>%   group_by(age) %>%   summarise(pos = sum(seropositive), tot = n()) plot(df$age, df$pos / df$tot,   cex = 0.32 * sqrt(df$tot), pch = 16, xlab = \"age\", ylab = \"seroprevalence\",   xlim = c(0, 82), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_pl_1995_2004.html","id":null,"dir":"Reference","previous_headings":"","what":"Parvo B19 serological data from Poland from 1995-2004 (line listing) — parvob19_pl_1995_2004","title":"Parvo B19 serological data from Poland from 1995-2004 (line listing) — parvob19_pl_1995_2004","text":"seroprevalence survey testing parvovirus B19 IgG antibody, performed large representative national serum banks Belgium, England Wales, Finland, Italy, Poland. sera collected 1995 2004 obtained residual sera submitted routine laboratory testing.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_pl_1995_2004.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parvo B19 serological data from Poland from 1995-2004 (line listing) — parvob19_pl_1995_2004","text":"","code":"parvob19_pl_1995_2004"},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_pl_1995_2004.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Parvo B19 serological data from Poland from 1995-2004 (line listing) — parvob19_pl_1995_2004","text":"data frame 5 variables: age Age individual seropositive individual seropositive year Year surveyed gender Gender individual parvouml Parvo B19 antibody units per ml","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_pl_1995_2004.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Parvo B19 serological data from Poland from 1995-2004 (line listing) — parvob19_pl_1995_2004","text":"MOSSONG, J., N. HENS, V. FRIEDERICHS, . DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. \"Parvovirus B19 Infection Five European Countries: Seroepidemiology, Force Infection Maternal Risk Infection.\" Epidemiology Infection 136, . 8 (2008): 1059-68. doi:10.1017/S0950268807009661","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/parvob19_pl_1995_2004.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parvo B19 serological data from Poland from 1995-2004 (line listing) — parvob19_pl_1995_2004","text":"","code":"# Note: This figure will look different to that of in the book, since we # believe that the original authors has made some errors in specifying # the sample size of the dots.  library(dplyr) df <- parvob19_pl_1995_2004 %>%   mutate(age = round(age)) %>%   group_by(age) %>%   summarise(pos = sum(seropositive), tot = n()) plot(df$age, df$pos / df$tot,   cex = 0.32 * sqrt(df$tot), pch = 16, xlab = \"age\", ylab = \"seroprevalence\",   xlim = c(0, 82), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/pava.html","id":null,"dir":"Reference","previous_headings":"","what":"Monotonize seroprevalence — pava","title":"Monotonize seroprevalence — pava","text":"Monotonize seroprevalence","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/pava.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Monotonize seroprevalence — pava","text":"","code":"pava(pos = pos, tot = rep(1, length(pos)))"},{"path":"https://oucru-modelling.github.io/serosv/reference/pava.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Monotonize seroprevalence — pava","text":"pos positive count vector. tot total count vector.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/pava.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Monotonize seroprevalence — pava","text":"computed list 2 items pai1 original values pai2 monotonized value","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/penalized_spline_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Penalized Spline model — penalized_spline_model","title":"Penalized Spline model — penalized_spline_model","text":"Fit age-specific seroprevalence semi-parametric model predictor modeled penalized splines. penalized splines can estimated either (1) penalized likelihood framework (2) mixed model framework","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/penalized_spline_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Penalized Spline model — penalized_spline_model","text":"","code":"penalized_spline_model(   data,   age_col = \"age\",   pos_col = \"pos\",   tot_col = \"tot\",   status_col = \"status\",   s = \"bs\",   link = \"logit\",   framework = \"pl\",   sp = NULL )"},{"path":"https://oucru-modelling.github.io/serosv/reference/penalized_spline_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Penalized Spline model — penalized_spline_model","text":"data input data frame, must either columns `age`, `pos`, `tot` (aggregated data) columns `age`, `status` (linelisting data) age_col name `age` column (default age_col=\"age\"). pos_col name `pos` column (default pos_col=\"pos\"). tot_col name `tot` column (default tot_col=\"tot\"). status_col name `status` column (default status_col=\"status\"). s smoothing basis use link link function use framework approach fit model (\"pl\" penalized likelihood framework, \"glmm\" generalized linear mixed model framework) sp smoothing parameter","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/penalized_spline_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Penalized Spline model — penalized_spline_model","text":"list class penalized_spline_model 6 attributes datatype type datatype used model fitting (aggregated linelisting) df dataframe used fitting model framework either pl glmm info fitted \"gam\" model framework pl \"gamm\" model framework glmm sp seroprevalence foi force infection","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/penalized_spline_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Penalized Spline model — penalized_spline_model","text":"semi-parametric model, predictor formulated penalized spline truncated power basis functions degree \\(p\\) fixed knots \\(\\kappa_1,..., \\kappa_k\\) followed $$ \\eta(a_i) = \\beta_0 + \\beta_1a_i + ... + \\beta_p a_i^p + \\Sigma_{k=1}^ku_k(a_i - \\kappa_k)^p_+ $$ - : $$ (a_i - \\kappa_k)^p_+ = \\begin{cases} 0, & a_i \\le \\kappa_k \\\\ (a_i - \\kappa_k)^p, & a_i > \\kappa_k \\end{cases} $$ FOI can derived $$\\hat{\\lambda}(a_i) = [\\hat{\\beta_1} , 2\\hat{\\beta_2}a_i, ..., p \\hat{\\beta} a_i ^{p-1} + \\Sigma^k_{k=1} p \\hat{u}_k(a_i - \\kappa_k)^{p-1}_+] \\delta(\\hat{\\eta}(a_i)) $$ \\(\\delta(.)\\) determined link function used model matrix annotation, mean structure model \\(\\eta(a_i)\\) becomes $$\\eta = \\textbf{X}\\beta + \\textbf{Zu}$$ \\(\\eta = [\\eta(a_i) ... \\eta(a_N) ]^T\\), \\(\\beta = [\\beta_0 \\beta_1 .... \\beta_p]^T\\), \\(\\textbf{u} = [u_1 u_2 ... u_k]^T\\) regression corresponding design matrices $$\\textbf{X} = \\begin{bmatrix} 1 & a_1 & a_1^2 & ... & a_1^p \\\\ 1 & a_2 & a_2^2 & ... & a_2^p \\\\ \\vdots & \\vdots & \\vdots & \\dots & \\vdots \\\\ 1 & a_N & a_N^2 & ... & a_N^p \\end{bmatrix}, \\textbf{Z} = \\begin{bmatrix} (a_1 - \\kappa_1 )_+^p & (a_1 - \\kappa_2 )_+^p & \\dots & (a_1 - \\kappa_k)_+^p \\\\ (a_2 - \\kappa_1 )_+^p & (a_2 - \\kappa_2 )_+^p & \\dots & (a_2 - \\kappa_k)_+^p \\\\ \\vdots & \\vdots & \\dots & \\vdots \\\\ (a_N - \\kappa_1 )_+^p & (a_N - \\kappa_2 )_+^p & \\dots & (a_N - \\kappa_k)_+^p \\end{bmatrix} $$ penalized likelihood framework, model fitted maximizing following likelihood $$ \\phi^{-1}[y^T(\\textbf{X}\\beta + \\textbf{Zu} ) -  \\textbf{1}^Tc(\\textbf{X}\\beta + \\textbf{Zu} )] - \\frac{1}{2}\\lambda^2 \\begin{bmatrix} \\beta \\\\ \\textbf{u} \\end{bmatrix}^T D\\begin{bmatrix} \\beta \\\\ \\textbf{u} \\end{bmatrix} $$ : - \\(X\\beta + Zu\\) predictor - \\(D\\) known semi-definite penalty matrix [@Wahba1978], [@Green1993] - \\(y\\) response vector - \\(\\textbf{1}\\) unit vector, \\(c(.)\\) determined link function used - \\(\\lambda\\) smoothing parameter (larger values –> smoother curves) - \\(\\phi\\) overdispersion parameter equals 1 overdispersion mixed model framework, model instead treats coefficients \\(\\textbf{u}\\) likelihood formulation random effects \\(\\textbf{u} \\sim N(\\textbf{0}, \\mathbf{\\sigma}^2_u \\textbf{})\\) Refer section 8.1 8.2 book Hens et al. (2012) details.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/penalized_spline_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Penalized Spline model — penalized_spline_model","text":"Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based Serological Social Contact Data: Modern Statistical Perspective. tatistics Biology Health. Springer New York. doi:10.1007/978-1-4614-4072-7 .","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/penalized_spline_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Penalized Spline model — penalized_spline_model","text":"","code":"data <- parvob19_be_2001_2003 data$status <- data$seropositive model <- penalized_spline_model(data, framework=\"glmm\") #>  #>  Maximum number of PQL iterations:  20  #> iteration 1 #> iteration 2 #> iteration 3 #> iteration 4 model$info$gam #>  #> Family: binomial  #> Link function: logit  #>  #> Formula: #> spos ~ s(age, bs = s, sp = sp) #>  #> Estimated degrees of freedom: #> 5.8  total = 6.8  #>  plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.age_time_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot output for age_time_model — plot.age_time_model","title":"Plot output for age_time_model — plot.age_time_model","text":"Plot output age_time_model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.age_time_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot output for age_time_model — plot.age_time_model","text":"","code":"# S3 method for class 'age_time_model' plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.age_time_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot output for age_time_model — plot.age_time_model","text":"x - `age_time_model` object ... arbitrary params. Supported options include: facet: Whether facet plot group. modtype: model plot, either \"monotonized\" \"non-monotonized\". le: Number bins used generate x-axis; higher values produce smoother curves. cex: Adjusts size data points (facet = TRUE).","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.age_time_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot output for age_time_model — plot.age_time_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.bivariate_dale_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for bivariate_dale_model — plot.bivariate_dale_model","title":"plot() overloading for bivariate_dale_model — plot.bivariate_dale_model","text":"plot() overloading bivariate_dale_model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.bivariate_dale_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for bivariate_dale_model — plot.bivariate_dale_model","text":"","code":"# S3 method for bivariate_dale_model plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.bivariate_dale_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for bivariate_dale_model — plot.bivariate_dale_model","text":"x bivariate_dale_model object ... arbitrary params including y1, y2 (label predictors), plot_type (\"ci\" \"sp\")","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.bivariate_dale_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for bivariate_dale_model — plot.bivariate_dale_model","text":"patchwork object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.estimate_from_mixture.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for result of estimate_from_mixture — plot.estimate_from_mixture","title":"plot() overloading for result of estimate_from_mixture — plot.estimate_from_mixture","text":"plot() overloading result estimate_from_mixture","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.estimate_from_mixture.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for result of estimate_from_mixture — plot.estimate_from_mixture","text":"","code":"# S3 method for class 'estimate_from_mixture' plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.estimate_from_mixture.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for result of estimate_from_mixture — plot.estimate_from_mixture","text":"x mixture_model ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.estimate_from_mixture.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for result of estimate_from_mixture — plot.estimate_from_mixture","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.farrington_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for Farrington model — plot.farrington_model","title":"plot() overloading for Farrington model — plot.farrington_model","text":"plot() overloading Farrington model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.farrington_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for Farrington model — plot.farrington_model","text":"","code":"# S3 method for class 'farrington_model' plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.farrington_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for Farrington model — plot.farrington_model","text":"x Farrington model object. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.farrington_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for Farrington model — plot.farrington_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.fp_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for fractional polynomial model — plot.fp_model","title":"plot() overloading for fractional polynomial model — plot.fp_model","text":"plot() overloading fractional polynomial model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.fp_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for fractional polynomial model — plot.fp_model","text":"","code":"# S3 method for class 'fp_model' plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.fp_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for fractional polynomial model — plot.fp_model","text":"x fractional polynomial model object. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.fp_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for fractional polynomial model — plot.fp_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.glmm_ps_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for penalized spline fitted with GLMM — plot.glmm_ps_model","title":"plot() overloading for penalized spline fitted with GLMM — plot.glmm_ps_model","text":"plot() overloading penalized spline fitted GLMM","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.glmm_ps_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for penalized spline fitted with GLMM — plot.glmm_ps_model","text":"","code":"# S3 method for glmm_ps_model plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.glmm_ps_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for penalized spline fitted with GLMM — plot.glmm_ps_model","text":"x glmm_ps_model object ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.hierarchical_bayesian_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for hierarchical_bayesian_model — plot.hierarchical_bayesian_model","title":"plot() overloading for hierarchical_bayesian_model — plot.hierarchical_bayesian_model","text":"plot() overloading hierarchical_bayesian_model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.hierarchical_bayesian_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for hierarchical_bayesian_model — plot.hierarchical_bayesian_model","text":"","code":"# S3 method for class 'hierarchical_bayesian_model' plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.hierarchical_bayesian_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for hierarchical_bayesian_model — plot.hierarchical_bayesian_model","text":"x hierarchical_bayesian_model object created serosv. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.hierarchical_bayesian_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for hierarchical_bayesian_model — plot.hierarchical_bayesian_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.lp_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for local polynomial model — plot.lp_model","title":"plot() overloading for local polynomial model — plot.lp_model","text":"plot() overloading local polynomial model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.lp_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for local polynomial model — plot.lp_model","text":"","code":"# S3 method for class 'lp_model' plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.lp_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for local polynomial model — plot.lp_model","text":"x local polynomial model object. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.lp_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for local polynomial model — plot.lp_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.mixture_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for mixture model — plot.mixture_model","title":"plot() overloading for mixture model — plot.mixture_model","text":"plot() overloading mixture model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.mixture_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for mixture model — plot.mixture_model","text":"","code":"# S3 method for class 'mixture_model' plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.mixture_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for mixture model — plot.mixture_model","text":"x mixture_model ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.mixture_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for mixture model — plot.mixture_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.mseir_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for MSEIR model — plot.mseir_model","title":"plot() overloading for MSEIR model — plot.mseir_model","text":"plot() overloading MSEIR model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.mseir_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for MSEIR model — plot.mseir_model","text":"","code":"# S3 method for class 'mseir_model' plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.mseir_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for MSEIR model — plot.mseir_model","text":"x mseir_model object. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.mseir_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for MSEIR model — plot.mseir_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.penalized_spline_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for penalized spline — plot.penalized_spline_model","title":"plot() overloading for penalized spline — plot.penalized_spline_model","text":"plot() overloading penalized spline","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.penalized_spline_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for penalized spline — plot.penalized_spline_model","text":"","code":"# S3 method for class 'penalized_spline_model' plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.penalized_spline_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for penalized spline — plot.penalized_spline_model","text":"x penalized_spline_model object ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.penalized_spline_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for penalized spline — plot.penalized_spline_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.polynomial_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for polynomial model — plot.polynomial_model","title":"plot() overloading for polynomial model — plot.polynomial_model","text":"plot() overloading polynomial model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.polynomial_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for polynomial model — plot.polynomial_model","text":"","code":"# S3 method for class 'polynomial_model' plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.polynomial_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for polynomial model — plot.polynomial_model","text":"x polynomial model object ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.polynomial_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for polynomial model — plot.polynomial_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_basic_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for SIR model — plot.sir_basic_model","title":"plot() overloading for SIR model — plot.sir_basic_model","text":"plot() overloading SIR model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_basic_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for SIR model — plot.sir_basic_model","text":"","code":"# S3 method for class 'sir_basic_model' plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_basic_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for SIR model — plot.sir_basic_model","text":"x sir_basic_model object. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_basic_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for SIR model — plot.sir_basic_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_static_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for SIR static model — plot.sir_static_model","title":"plot() overloading for SIR static model — plot.sir_static_model","text":"plot() overloading SIR static model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_static_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for SIR static model — plot.sir_static_model","text":"","code":"# S3 method for class 'sir_static_model' plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_static_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for SIR static model — plot.sir_static_model","text":"x sir_static_model object. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_static_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for SIR static model — plot.sir_static_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_subpops_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for SIR sub populations model — plot.sir_subpops_model","title":"plot() overloading for SIR sub populations model — plot.sir_subpops_model","text":"plot() overloading SIR sub populations model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_subpops_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for SIR sub populations model — plot.sir_subpops_model","text":"","code":"# S3 method for class 'sir_subpops_model' plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_subpops_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for SIR sub populations model — plot.sir_subpops_model","text":"x sir_subpops_models object. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.sir_subpops_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for SIR sub populations model — plot.sir_subpops_model","text":"list ggplot objects, object plot corresponding subpopulation","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.weibull_model.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() overloading for Weibull model — plot.weibull_model","title":"plot() overloading for Weibull model — plot.weibull_model","text":"plot() overloading Weibull model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.weibull_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() overloading for Weibull model — plot.weibull_model","text":"","code":"# S3 method for class 'weibull_model' plot(x, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.weibull_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() overloading for Weibull model — plot.weibull_model","text":"x Weibull model object. ... arbitrary params.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot.weibull_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot() overloading for Weibull model — plot.weibull_model","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_corrected_prev.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot output for corrected_prevalence — plot_corrected_prev","title":"Plot output for corrected_prevalence — plot_corrected_prev","text":"Plot output corrected_prevalence","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_corrected_prev.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot output for corrected_prevalence — plot_corrected_prev","text":"","code":"plot_corrected_prev(x, y = NULL, facet = FALSE)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_corrected_prev.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot output for corrected_prevalence — plot_corrected_prev","text":"x - output `correct_prevalence()` function y - another output `correct_prevalence()` function (optional, comparison ) facet - whether plot facets plot (y provided)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_corrected_prev.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot output for corrected_prevalence — plot_corrected_prev","text":"ggplot object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_gcv.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting GCV values with respect to different nn-s and h-s parameters. — plot_gcv","title":"Plotting GCV values with respect to different nn-s and h-s parameters. — plot_gcv","text":"Refers section 7.2.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_gcv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting GCV values with respect to different nn-s and h-s parameters. — plot_gcv","text":"","code":"plot_gcv(age, pos, tot, nn_seq, h_seq, kern = \"tcub\", deg = 2)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_gcv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting GCV values with respect to different nn-s and h-s parameters. — plot_gcv","text":"age age vector. pos pos vector. tot tot vector.#' nn_seq Nearest neighbor sequence. h_seq Smoothing parameter sequence. kern Weight function, default = \"tcub\". choices \"rect\", \"trwt\", \"tria\", \"epan\", \"bisq\" \"gauss\". Choices may restricted derivatives required; e.g. confidence bands bandwidth selectors. deg Degree polynomial use. Default: 2.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_gcv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting GCV values with respect to different nn-s and h-s parameters. — plot_gcv","text":"plot gcv value","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_gcv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting GCV values with respect to different nn-s and h-s parameters. — plot_gcv","text":"","code":"df <- mumps_uk_1986_1987 plot_gcv(   df$age, df$pos, df$tot,   nn_seq = seq(0.2, 0.8, by=0.1),   h_seq = seq(5, 25, by=1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_standard_curve.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize standard curves for each plate — plot_standard_curve","title":"Visualize standard curves for each plate — plot_standard_curve","text":"Visualize standard curves plate","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_standard_curve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize standard curves for each plate — plot_standard_curve","text":"","code":"plot_standard_curve(   x,   facet = TRUE,   xlab = \"log10(concentration)\",   ylab = \"Optical density\",   datapoint_size = 2 )"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_standard_curve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize standard curves for each plate — plot_standard_curve","text":"x output `to_titer()` facet whether faceted plates plot standard curves single plot xlab label x axis ylab label y axis datapoint_size size data point (applicable `facet=TRUE`)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_titer_qc.html","id":null,"dir":"Reference","previous_headings":"","what":"Quality control plot — plot_titer_qc","title":"Quality control plot — plot_titer_qc","text":"Visualize sample, whether titer estimates can computed tested dilutions.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_titer_qc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quality control plot — plot_titer_qc","text":"","code":"plot_titer_qc(x, n_plates = 18, n_samples = 22, n_dilutions = 3)"},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_titer_qc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quality control plot — plot_titer_qc","text":"x - output `to_titer()` n_plates - maximum number plates plot n_samples - maximum number samples per plate plot n_dilutions - number dilutions used testing","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/plot_titer_qc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Quality control plot — plot_titer_qc","text":"sample represented `n_estimates x n_dilutions` grid cell color indicate estimate availability (green = estimate available, orange = result low, red = result high) sample grids arranged columns column represent samples plate figure demonstrates interpretation plot.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/polynomial_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Polynomial models — polynomial_model","title":"Polynomial models — polynomial_model","text":"Fit age-stratified seroprevalence data serocatalytic models formulated polynomials.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/polynomial_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polynomial models — polynomial_model","text":"","code":"polynomial_model(   data,   k,   link = \"log\",   age_col = \"age\",   pos_col = \"pos\",   tot_col = \"tot\",   status_col = \"status\" )"},{"path":"https://oucru-modelling.github.io/serosv/reference/polynomial_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Polynomial models — polynomial_model","text":"data input data frame, must either columns `age`, `pos`, `tot` (aggregated data) `age`, `status` (linelisting data) k degree polynomial. (k=1 Muench model, k=2 Griffith model, k=3 Grenfell model). link link function (default link=\"log\"). age_col name `age` column (default age_col=\"age\"). pos_col name `pos` column (default pos_col=\"pos\"). tot_col name `tot` column (default tot_col=\"tot\"). status_col name `status` column (default status_col=\"status\").","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/polynomial_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Polynomial models — polynomial_model","text":"list class polynomial_model 5 items datatype type datatype used model fitting (aggregated linelisting) df dataframe used fitting model info fitted \"glm\" object sp seroprevalence foi force infection","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/polynomial_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Polynomial models — polynomial_model","text":"seroprevalence assumed follow general format $$ \\pi()  = 1 - e^{-\\Sigma_{=1}^k \\beta_i ^} $$ implies force infection \\(\\lambda() = \\Sigma_{=1}^k \\beta_i ^{-1}\\) : - \\(\\pi\\) seroprevalence age \\(\\) - \\(\\) variable age - \\(k\\) degree polynomial seroprevalence \\(\\pi()\\) fitted using GLM log link linear predictor \\(\\eta() = \\Sigma_{=1}^k \\beta_i ^{}\\) Muench (1934) model equivalent degree 1 (\\(k=1\\)) linear predictor Griffith model equivalent degree 2 (\\(k=2\\)) linear predictor Grenfell & Anderson (1985) suggested higher order polynomials (\\(k \\geq 3\\)) Refer section 6.1.1. book Hens et al. (2012) details.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/polynomial_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Polynomial models — polynomial_model","text":"Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based Serological Social Contact Data: Modern Statistical Perspective. tatistics Biology Health. Springer New York. doi:10.1007/978-1-4614-4072-7 . Grenfell, B. T., R. M. Anderson. 1985. “Estimation Age-Related Rates Infection Case Notifications Serological Data.” Journal Hygiene 95 (2): 419–36. doi:10.1017/s0022172400062859 . Muench, Hugo. 1934. “Derivation Rates Summation Data Catalytic Curve.” Journal American Statistical Association 29 (185): 25–38. doi:10.1080/01621459.1934.10502684 .","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/polynomial_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Polynomial models — polynomial_model","text":"","code":"data <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age), ] aggregated <- transform_data(data, stratum_col = \"age\", status_col=\"seropositive\")  # fit with aggregated data model <- polynomial_model(aggregated, k = 1) # fit with linelisting data model <- polynomial_model(data,     status_col = \"seropositive\",     k = 1) plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.age_time_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict from the age_time_mdoel — predict.age_time_model","title":"Predict from the age_time_mdoel — predict.age_time_model","text":"Predict age_time_mdoel","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.age_time_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict from the age_time_mdoel — predict.age_time_model","text":"","code":"# S3 method for class 'age_time_model' predict(object, newdata, modtype = \"monotonized\", ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.age_time_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict from the age_time_mdoel — predict.age_time_model","text":"object serosv models newdata data.frame age column generate prediction modtype either \"monotonized\" (predict using monotonized model) \"non-monotonized\" ... arbitrary argument","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.age_time_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict from the age_time_mdoel — predict.age_time_model","text":"confidence interval dataframe n_group x 3 cols, columns `group`, `sp_df`, `foi_df`","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.farrington_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction for serosv Farrington model — predict.farrington_model","title":"Prediction for serosv Farrington model — predict.farrington_model","text":"Prediction serosv Farrington model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.farrington_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction for serosv Farrington model — predict.farrington_model","text":"","code":"# S3 method for class 'farrington_model' predict(object, newdata = NULL, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.farrington_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction for serosv Farrington model — predict.farrington_model","text":"object serosv models newdata data.frame age column generate prediction ... arbitrary argument","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.farrington_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction for serosv Farrington model — predict.farrington_model","text":"prediction output","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.fp_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction for serosv fractional polynomial model — predict.fp_model","title":"Prediction for serosv fractional polynomial model — predict.fp_model","text":"Prediction serosv fractional polynomial model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.fp_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction for serosv fractional polynomial model — predict.fp_model","text":"","code":"# S3 method for class 'fp_model' predict(object, newdata = NULL, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.fp_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction for serosv fractional polynomial model — predict.fp_model","text":"object serosv models newdata data.frame age column generate prediction ... arbitrary argument","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.fp_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction for serosv fractional polynomial model — predict.fp_model","text":"prediction output","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.hierarchical_bayesian_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict from an hierarchical bayesian model — predict.hierarchical_bayesian_model","title":"Predict from an hierarchical bayesian model — predict.hierarchical_bayesian_model","text":"Predict hierarchical bayesian model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.hierarchical_bayesian_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict from an hierarchical bayesian model — predict.hierarchical_bayesian_model","text":"","code":"# S3 method for class 'hierarchical_bayesian_model' predict(object, newdata = NULL, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.hierarchical_bayesian_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict from an hierarchical bayesian model — predict.hierarchical_bayesian_model","text":"object serosv models newdata data.frame age column generate prediction ... arbitrary arguments","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.hierarchical_bayesian_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict from an hierarchical bayesian model — predict.hierarchical_bayesian_model","text":"list confidence interval seroprevalence foi. confidence interval dataframe 4 variables, x y fitted values ymin ymax confidence interval","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.lp_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction for serosv local polynomial model — predict.lp_model","title":"Prediction for serosv local polynomial model — predict.lp_model","text":"Prediction serosv local polynomial model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.lp_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction for serosv local polynomial model — predict.lp_model","text":"","code":"# S3 method for class 'lp_model' predict(object, newdata = NULL, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.lp_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction for serosv local polynomial model — predict.lp_model","text":"object serosv models newdata data.frame age column generate prediction ... arbitrary argument","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.lp_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction for serosv local polynomial model — predict.lp_model","text":"prediction output","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.penalized_spline_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction for serosv penalized spline model — predict.penalized_spline_model","title":"Prediction for serosv penalized spline model — predict.penalized_spline_model","text":"Prediction serosv penalized spline model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.penalized_spline_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction for serosv penalized spline model — predict.penalized_spline_model","text":"","code":"# S3 method for class 'penalized_spline_model' predict(object, newdata = NULL, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.penalized_spline_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction for serosv penalized spline model — predict.penalized_spline_model","text":"object serosv models newdata data.frame age column generate prediction ... arbitrary argument","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.penalized_spline_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction for serosv penalized spline model — predict.penalized_spline_model","text":"prediction output","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.polynomial_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction for serosv polynomial model — predict.polynomial_model","title":"Prediction for serosv polynomial model — predict.polynomial_model","text":"wrapper predict.glm direct prediction polynomial_model object","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.polynomial_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction for serosv polynomial model — predict.polynomial_model","text":"","code":"# S3 method for class 'polynomial_model' predict(object, newdata = NULL, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.polynomial_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction for serosv polynomial model — predict.polynomial_model","text":"object serosv models newdata data.frame age column generate prediction ... arbitrary argument","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.polynomial_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction for serosv polynomial model — predict.polynomial_model","text":"prediction output","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.weibull_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Prediction for serosv Weibull model — predict.weibull_model","title":"Prediction for serosv Weibull model — predict.weibull_model","text":"Prediction serosv Weibull model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.weibull_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prediction for serosv Weibull model — predict.weibull_model","text":"","code":"# S3 method for class 'weibull_model' predict(object, newdata = NULL, ...)"},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.weibull_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prediction for serosv Weibull model — predict.weibull_model","text":"object serosv models newdata data.frame age column generate prediction ... arbitrary argument","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/predict.weibull_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prediction for serosv Weibull model — predict.weibull_model","text":"prediction output","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_mumps_uk.html","id":null,"dir":"Reference","previous_headings":"","what":"Rubella - Mumps data from the UK (aggregated) — rubella_mumps_uk","title":"Rubella - Mumps data from the UK (aggregated) — rubella_mumps_uk","text":"Rubella - Mumps data UK (aggregated)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_mumps_uk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rubella - Mumps data from the UK (aggregated) — rubella_mumps_uk","text":"","code":"rubella_mumps_uk"},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_mumps_uk.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Rubella - Mumps data from the UK (aggregated) — rubella_mumps_uk","text":"data frame 5 variables: age Age group NN Number individuals negative rubella mumps NP Number individuals negative rubella positive mumps PN Number individuals positive rubella negative mumps PP Number individuals positive rubella mumps","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_mumps_uk.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Rubella - Mumps data from the UK (aggregated) — rubella_mumps_uk","text":"Morgan-Capner P, Wright J, Miller C L, Miller E. Surveillance antibody measles, mumps, rubella age. British Medical Journal 1988; 297 :770 doi:10.1136/bmj.297.6651.770","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_uk_1986_1987.html","id":null,"dir":"Reference","previous_headings":"","what":"Rubella serological data from the UK in 1986 and 1987 (aggregated) — rubella_uk_1986_1987","title":"Rubella serological data from the UK in 1986 and 1987 (aggregated) — rubella_uk_1986_1987","text":"Prevalence rubella UK, obtained large survey prevalence antibodies mumps rubella viruses.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_uk_1986_1987.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rubella serological data from the UK in 1986 and 1987 (aggregated) — rubella_uk_1986_1987","text":"","code":"rubella_uk_1986_1987"},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_uk_1986_1987.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Rubella serological data from the UK in 1986 and 1987 (aggregated) — rubella_uk_1986_1987","text":"data frame 3 variables: age Midpoint age group (e.g. 1.5 = 1-2 years old, 2.5 = 2-3 years old) pos Number seropositive individuals tot Total number individuals surveyed","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_uk_1986_1987.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Rubella serological data from the UK in 1986 and 1987 (aggregated) — rubella_uk_1986_1987","text":"Morgan-Capner P, Wright J, Miller C L, Miller E. Surveillance antibody measles, mumps, rubella age. British Medical Journal 1988; 297 :770 doi:10.1136/bmj.297.6651.770","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/rubella_uk_1986_1987.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rubella serological data from the UK in 1986 and 1987 (aggregated) — rubella_uk_1986_1987","text":"","code":"with(rubella_uk_1986_1987,   plot(age, pos / tot,     cex = 0.2 * sqrt(tot), pch = 16, xlab = \"age\", ylab = \"seroprevalence\",     xlim = c(0, 45), ylim = c(0, 1)   ) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/serosv-package.html","id":null,"dir":"Reference","previous_headings":"","what":"serosv: model infectious disease parameters — serosv-package","title":"serosv: model infectious disease parameters — serosv-package","text":"easy--use efficient tool estimate infectious diseases parameters using serological data. Implemented models include SIR models (basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()), parametric models (polynomial_model(), fp_model()), nonparametric models (lp_model()), semiparametric models (penalized_splines_model()), hierarchical models (hierarchical_bayesian_model()). package based book \"Modeling Infectious Disease Parameters Based Serological Social Contact Data: Modern Statistical Perspective\" (Hens, Niel & Shkedy, Ziv & Aerts, Marc & Faes, Christel & Damme, Pierre & Beutels, Philippe., 2013) doi:10.1007/978-1-4614-4072-7 .","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/serosv-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"serosv: model infectious disease parameters — serosv-package","text":"Maintainer: Anh Phan Truong Quynh anhptq@oucru.org (ORCID) Authors: Nguyen Pham Nguyen nguyenpnt@oucru.org (ORCID) Long Bui Thanh Tuyen Huynh tuyenhn@oucru.org Thinh Ong thinhop@oucru.org (ORCID) Marc Choisy mchoisy@oucru.org (ORCID)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/set_plot_style.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper to adjust styling of a plot — set_plot_style","title":"Helper to adjust styling of a plot — set_plot_style","text":"Helper adjust styling plot","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/set_plot_style.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper to adjust styling of a plot — set_plot_style","text":"","code":"set_plot_style(   sero = \"blueviolet\",   ci = \"royalblue1\",   foi = \"#fc0328\",   sero_line = \"solid\",   foi_line = \"dashed\",   xlabel = \"Age\" )"},{"path":"https://oucru-modelling.github.io/serosv/reference/set_plot_style.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper to adjust styling of a plot — set_plot_style","text":"sero - color seroprevalence line ci - color confidence interval foi - color force infection line sero_line - linetype seroprevalence line foi_line - linetype force infection line xlabel - x label","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/set_plot_style.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper to adjust styling of a plot — set_plot_style","text":"list updated aesthetic values","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_basic_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Basic SIR model — sir_basic_model","title":"Basic SIR model — sir_basic_model","text":"Simulate transmission model 3 compartments: susceptible (S), infected (), recovered (R)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_basic_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basic SIR model — sir_basic_model","text":"","code":"sir_basic_model(times, state, parameters)"},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_basic_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Basic SIR model — sir_basic_model","text":"times time sequence. state initial state model. named vector following S: initial susceptible population : initial infected population R: initial recovered population parameters parameters model. named vector following alpha: disease-related death rate mu: natural death rate (= 1/life expectancy) beta: transmission rate nu: recovery rate p: proportion vaccinated birth","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_basic_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Basic SIR model — sir_basic_model","text":"list class sir_basic_model following items parameters list parameters used fitting model output matrix population compartment time","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_basic_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Basic SIR model — sir_basic_model","text":"Follow SIR model described book Hens et al. (section 3.1.3.) Assumptions: - Individuals born susceptible group (exposure time age individual) transfer infected class recovered class - Recovered individuals gained lifelong immunity - Age homogeneity model described system 3 differential equations $$ \\begin{cases} \\frac{dS(t)}{dt} = B(t) (1-p) - \\lambda(t)S(t) - \\mu S(t) \\\\ \\frac{dI(t)}{dt} = \\lambda(t)S(t) - \\nu (t) - \\mu (t) - \\alpha (t)  \\\\ \\frac{dR(t)}{dt} =  B(t) p + \\nu (t) - \\mu R(t) \\end{cases} $$ : -   \\(B(t) = \\mu N(t)\\) -   \\(\\lambda(t) = \\beta (t)\\) \\(\\beta\\) transmission rate -   \\(\\mu\\) natural death rate -   \\(\\nu\\) recovery rate -   \\(\\alpha\\) disease related death rate -   \\(p\\) proportion newborn vaccinated moved directly recovered compartment","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_basic_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Basic SIR model — sir_basic_model","text":"Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based Serological Social Contact Data: Modern Statistical Perspective. tatistics Biology Health. Springer New York. doi:10.1007/978-1-4614-4072-7 .","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_basic_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Basic SIR model — sir_basic_model","text":"","code":"state <- c(S=4999, I=1, R=0) parameters <- c(   mu=1/75, # 1 divided by life expectancy (75 years old)   alpha=0, # no disease-related death   beta=0.0005, # transmission rate   nu=1, # 1 year for infected to recover   p=0 # no vaccination at birth ) times <- seq(0, 250, by=0.1) model <- sir_basic_model(times, state, parameters) model #> $parameters #>         mu      alpha       beta         nu          p  #> 0.01333333 0.00000000 0.00050000 1.00000000 0.00000000  #>  #> $output #>       time         S            I            R #> 1      0.0 4999.0000 1.000000e+00    0.0000000 #> 2      0.1 4998.7320 1.160221e+00    0.1077427 #> 3      0.2 4998.4213 1.346094e+00    0.2326044 #> 4      0.3 4998.0610 1.561716e+00    0.3773237 #> 5      0.4 4997.6431 1.811844e+00    0.5450805 #> 6      0.5 4997.1585 2.101986e+00    0.7395605 #> 7      0.6 4996.5964 2.438526e+00    0.9650385 #> 8      0.7 4995.9447 2.828861e+00    1.2264683 #> 9      0.8 4995.1888 3.281562e+00    1.5295984 #> 10     0.9 4994.3124 3.806555e+00    1.8810889 #> 11     1.0 4993.2960 4.415328e+00    2.2886597 #> 12     1.1 4992.1176 5.121181e+00    2.7612570 #> 13     1.2 4990.7513 5.939496e+00    3.3092454 #> 14     1.3 4989.1673 6.888062e+00    3.9446311 #> 15     1.4 4987.3312 7.987438e+00    4.6813171 #> 16     1.5 4985.2032 9.261364e+00    5.5353974 #> 17     1.6 4982.7373 1.073724e+01    6.5254956 #> 18     1.7 4979.8802 1.244666e+01    7.6731536 #> 19     1.8 4976.5707 1.442600e+01    9.0032756 #> 20     1.9 4972.7382 1.671712e+01   10.5446365 #> 21     2.0 4968.3014 1.936813e+01   12.3304612 #> 22     2.1 4963.1667 2.243418e+01   14.3990819 #> 23     2.2 4957.2269 2.597840e+01   16.7946833 #> 24     2.3 4950.3589 3.007295e+01   19.5681420 #> 25     2.4 4942.4220 3.479999e+01   22.7779683 #> 26     2.5 4933.2558 4.025286e+01   26.4913554 #> 27     2.6 4922.6774 4.653722e+01   30.7853403 #> 28     2.7 4910.4798 5.377212e+01   35.7480765 #> 29     2.8 4896.4287 6.209111e+01   41.4802135 #> 30     2.9 4880.2606 7.164304e+01   48.0963714 #> 31     3.0 4861.6806 8.259275e+01   55.7266884 #> 32     3.1 4840.3604 9.512119e+01   64.5184070 #> 33     3.2 4815.9375 1.094250e+02   74.6374493 #> 34     3.3 4788.0147 1.257154e+02   86.2699071 #> 35     3.4 4756.1611 1.442155e+02   99.6233541 #> 36     3.5 4719.9151 1.651570e+02  114.9278525 #> 37     3.6 4678.7892 1.887743e+02  132.4364981 #> 38     3.7 4632.2776 2.152971e+02  152.4253126 #> 39     3.8 4579.8673 2.449404e+02  175.1922628 #> 40     3.9 4521.0529 2.778920e+02  201.0551572 #> 41     4.0 4455.3547 3.142972e+02  230.3481645 #> 42     4.1 4382.3416 3.542416e+02  263.4167064 #> 43     4.2 4301.6574 3.977321e+02  300.6105180 #> 44     4.3 4213.0491 4.446762e+02  342.2747557 #> 45     4.4 4116.3969 4.948639e+02  388.7391584 #> 46     4.5 4011.7435 5.479511e+02  440.3054474 #> 47     4.6 3899.3180 6.034486e+02  497.2333690 #> 48     4.7 3779.5549 6.607191e+02  559.7260108 #> 49     4.8 3653.1014 7.189833e+02  627.9153170 #> 50     4.9 3520.8135 7.773377e+02  701.8487820 #> 51     5.0 3383.7376 8.347838e+02  781.4785087 #> 52     5.1 3243.0786 8.902676e+02  866.6537354 #> 53     5.2 3100.1558 9.427265e+02  957.1177216 #> 54     5.3 2956.3499 9.911405e+02 1052.5095881 #> 55     5.4 2813.0466 1.034582e+03 1152.3712323 #> 56     5.5 2671.5798 1.072261e+03 1256.1589966 #> 57     5.6 2533.1819 1.103559e+03 1363.2593013 #> 58     5.7 2398.9427 1.128050e+03 1473.0071156 #> 59     5.8 2269.7810 1.145513e+03 1584.7059460 #> 60     5.9 2146.4291 1.155923e+03 1697.6479867 #> 61     6.0 2029.4295 1.159437e+03 1811.1332030 #> 62     6.1 1919.1419 1.156372e+03 1924.4863558 #> 63     6.2 1815.7593 1.147169e+03 2037.0712745 #> 64     6.3 1719.3283 1.132370e+03 2148.3020058 #> 65     6.4 1629.7729 1.112576e+03 2257.6507525 #> 66     6.5 1546.9188 1.088428e+03 2364.6527513 #> 67     6.6 1470.5167 1.060575e+03 2468.9084084 #> 68     6.7 1400.2634 1.029653e+03 2570.0831096 #> 69     6.8 1335.8203 9.962746e+02 2667.9051667 #> 70     6.9 1276.8286 9.610090e+02 2762.1623582 #> 71     7.0 1222.9220 9.243805e+02 2852.6974870 #> 72     7.1 1173.7359 8.868608e+02 2939.4033246 #> 73     7.2 1128.9149 8.488678e+02 3022.2172493 #> 74     7.3 1088.1178 8.107663e+02 3101.1158224 #> 75     7.4 1051.0210 7.728695e+02 3176.1094867 #> 76     7.5 1017.3203 7.354422e+02 3247.2375209 #> 77     7.6  986.7324 6.987042e+02 3314.5633374 #> 78     7.7  958.9949 6.628349e+02 3378.1701781 #> 79     7.8  933.8656 6.279772e+02 3438.1572322 #> 80     7.9  911.1224 5.942414e+02 3494.6361813 #> 81     8.0  890.5618 5.617100e+02 3547.7281617 #> 82     8.1  871.9980 5.304408e+02 3597.5611217 #> 83     8.2  855.2616 5.004708e+02 3644.2675485 #> 84     8.3  840.1983 4.718192e+02 3687.9825323 #> 85     8.4  826.6677 4.444902e+02 3728.8421358 #> 86     8.5  814.5421 4.184759e+02 3766.9820366 #> 87     8.6  803.7054 3.937582e+02 3802.5364101 #> 88     8.7  794.0521 3.703109e+02 3835.6370249 #> 89     8.8  785.4861 3.481014e+02 3866.4125218 #> 90     8.9  777.9201 3.270920e+02 3894.9878513 #> 91     9.0  771.2746 3.072416e+02 3921.4838479 #> 92     9.1  765.4769 2.885062e+02 3946.0169197 #> 93     9.2  760.4611 2.708401e+02 3968.6988359 #> 94     9.3  756.1668 2.541966e+02 3989.6365965 #> 95     9.4  752.5390 2.385287e+02 4008.9323696 #> 96     9.5  749.5271 2.237894e+02 4026.6834856 #> 97     9.6  747.0852 2.099323e+02 4042.9824762 #> 98     9.7  745.1710 1.969118e+02 4057.9171510 #> 99     9.8  743.7458 1.846835e+02 4071.5707024 #> 100    9.9  742.7739 1.732042e+02 4084.0218338 #> 101   10.0  742.2228 1.624323e+02 4095.3449045 #> 102   10.1  742.0623 1.523276e+02 4105.6100871 #> 103   10.2  742.2648 1.428517e+02 4114.8835338 #> 104   10.3  742.8047 1.339677e+02 4123.2275477 #> 105   10.4  743.6586 1.256407e+02 4130.7007573 #> 106   10.5  744.8046 1.178371e+02 4137.3582912 #> 107   10.6  746.2228 1.105253e+02 4143.2519513 #> 108   10.7  747.8944 1.036752e+02 4148.4303843 #> 109   10.8  749.8024 9.725838e+01 4152.9392487 #> 110   10.9  751.9307 9.124792e+01 4156.8213773 #> 111   11.0  754.2646 8.561846e+01 4160.1169347 #> 112   11.1  756.7904 8.034607e+01 4162.8635691 #> 113   11.2  759.4952 7.540822e+01 4165.0965576 #> 114   11.3  762.3673 7.078371e+01 4166.8489457 #> 115   11.4  765.3957 6.645260e+01 4168.1516805 #> 116   11.5  768.5701 6.239619e+01 4169.0337371 #> 117   11.6  771.8809 5.859689e+01 4169.5222389 #> 118   11.7  775.3192 5.503821e+01 4169.6425718 #> 119   11.8  778.8768 5.170471e+01 4169.4184920 #> 120   11.9  782.5459 4.858188e+01 4168.8722284 #> 121   12.0  786.3193 4.565616e+01 4168.0245782 #> 122   12.1  790.1902 4.291483e+01 4166.8949983 #> 123   12.2  794.1523 4.034600e+01 4165.5016907 #> 124   12.3  798.1998 3.793854e+01 4163.8616822 #> 125   12.4  802.3271 3.568202e+01 4161.9909009 #> 126   12.5  806.5290 3.356671e+01 4159.9042463 #> 127   12.6  810.8009 3.158349e+01 4157.6156567 #> 128   12.7  815.1380 2.972384e+01 4155.1381709 #> 129   12.8  819.5362 2.797979e+01 4152.4839869 #> 130   12.9  823.9916 2.634391e+01 4149.6645170 #> 131   13.0  828.5003 2.480923e+01 4146.6904387 #> 132   13.1  833.0590 2.336926e+01 4143.5717431 #> 133   13.2  837.6643 2.201790e+01 4140.3177793 #> 134   13.3  842.3132 2.074949e+01 4136.9372970 #> 135   13.4  847.0028 1.955872e+01 4133.4384854 #> 136   13.5  851.7304 1.844062e+01 4129.8290100 #> 137   13.6  856.4934 1.739057e+01 4126.1160468 #> 138   13.7  861.2895 1.640423e+01 4122.3063147 #> 139   13.8  866.1163 1.547756e+01 4118.4061050 #> 140   13.9  870.9719 1.460676e+01 4114.4213098 #> 141   14.0  875.8542 1.378832e+01 4110.3574477 #> 142   14.1  880.7614 1.301892e+01 4106.2196884 #> 143   14.2  885.6916 1.229548e+01 4102.0128756 #> 144   14.3  890.6433 1.161511e+01 4097.7415479 #> 145   14.4  895.6149 1.097511e+01 4093.4099590 #> 146   14.5  900.6049 1.037295e+01 4089.0220961 #> 147   14.6  905.6120 9.806289e+00 4084.5816972 #> 148   14.7  910.6348 9.272905e+00 4080.0922671 #> 149   14.8  915.6722 8.770739e+00 4075.5570929 #> 150   14.9  920.7229 8.297859e+00 4070.9792576 #> 151   15.0  925.7859 7.852460e+00 4066.3616536 #> 152   15.1  930.8602 7.432852e+00 4061.7069946 #> 153   15.2  935.9447 7.037453e+00 4057.0178275 #> 154   15.3  941.0387 6.664784e+00 4052.2965428 #> 155   15.4  946.1412 6.313459e+00 4047.5453848 #> 156   15.5  951.2514 5.982181e+00 4042.7664607 #> 157   15.6  956.3685 5.669734e+00 4037.9617495 #> 158   15.7  961.4919 5.374983e+00 4033.1331100 #> 159   15.8  966.6209 5.096861e+00 4028.2822886 #> 160   15.9  971.7547 4.834371e+00 4023.4109263 #> 161   16.0  976.8929 4.586576e+00 4018.5205652 #> 162   16.1  982.0347 4.352601e+00 4013.6126550 #> 163   16.2  987.1798 4.131624e+00 4008.6885587 #> 164   16.3  992.3276 3.922876e+00 4003.7495579 #> 165   16.4  997.4775 3.725633e+00 3998.7968580 #> 166   16.5 1002.6292 3.539219e+00 3993.8315927 #> 167   16.6 1007.7822 3.362999e+00 3988.8548290 #> 168   16.7 1012.9361 3.196376e+00 3983.8675708 #> 169   16.8 1018.0904 3.038792e+00 3978.8707629 #> 170   16.9 1023.2450 2.889721e+00 3973.8652947 #> 171   17.0 1028.3993 2.748672e+00 3968.8520038 #> 172   17.1 1033.5531 2.615181e+00 3963.8316788 #> 173   17.2 1038.7061 2.488814e+00 3958.8050624 #> 174   17.3 1043.8580 2.369164e+00 3953.7728545 #> 175   17.4 1049.0084 2.255847e+00 3948.7357143 #> 176   17.5 1054.1572 2.148503e+00 3943.6942630 #> 177   17.6 1059.3041 2.046794e+00 3938.6490863 #> 178   17.7 1064.4489 1.950401e+00 3933.6007362 #> 179   17.8 1069.5912 1.859026e+00 3928.5497330 #> 180   17.9 1074.7310 1.772387e+00 3923.4965676 #> 181   18.0 1079.8681 1.690221e+00 3918.4417028 #> 182   18.1 1085.0021 1.612277e+00 3913.3855752 #> 183   18.2 1090.1331 1.538323e+00 3908.3285967 #> 184   18.3 1095.2607 1.468137e+00 3903.2711559 #> 185   18.4 1100.3849 1.401512e+00 3898.2136197 #> 186   18.5 1105.5054 1.338254e+00 3893.1563342 #> 187   18.6 1110.6222 1.278178e+00 3888.0996261 #> 188   18.7 1115.7351 1.221111e+00 3883.0438041 #> 189   18.8 1120.8440 1.166891e+00 3877.9891593 #> 190   18.9 1125.9487 1.115362e+00 3872.9359668 #> 191   19.0 1131.0491 1.066381e+00 3867.8844866 #> 192   19.1 1136.1452 1.019811e+00 3862.8349639 #> 193   19.2 1141.2368 9.755230e-01 3857.7876306 #> 194   19.3 1146.3239 9.333959e-01 3852.7427058 #> 195   19.4 1151.4063 8.933151e-01 3847.7003963 #> 196   19.5 1156.4839 8.551726e-01 3842.6608978 #> 197   19.6 1161.5567 8.188665e-01 3837.6243950 #> 198   19.7 1166.6246 7.843005e-01 3832.5910626 #> 199   19.8 1171.6876 7.513840e-01 3827.5610658 #> 200   19.9 1176.7454 7.200310e-01 3822.5345605 #> 201   20.0 1181.7981 6.901608e-01 3817.5116943 #> 202   20.1 1186.8457 6.616967e-01 3812.4926066 #> 203   20.2 1191.8880 6.345667e-01 3807.4774294 #> 204   20.3 1196.9250 6.087024e-01 3802.4662872 #> 205   20.4 1201.9567 5.840392e-01 3797.4592979 #> 206   20.5 1206.9829 5.605163e-01 3792.4565730 #> 207   20.6 1212.0037 5.380759e-01 3787.4582178 #> 208   20.7 1217.0190 5.166636e-01 3782.4643319 #> 209   20.8 1222.0288 4.962277e-01 3777.4750095 #> 210   20.9 1227.0329 4.767194e-01 3772.4903396 #> 211   21.0 1232.0315 4.580926e-01 3767.5104064 #> 212   21.1 1237.0244 4.403036e-01 3762.5352895 #> 213   21.2 1242.0116 4.233110e-01 3757.5650641 #> 214   21.3 1246.9931 4.070756e-01 3752.5998012 #> 215   21.4 1251.9689 3.915603e-01 3747.6395681 #> 216   21.5 1256.9388 3.767301e-01 3742.6844283 #> 217   21.6 1261.9030 3.625516e-01 3737.7344418 #> 218   21.7 1266.8613 3.489932e-01 3732.7896651 #> 219   21.8 1271.8138 3.360252e-01 3727.8501520 #> 220   21.9 1276.7604 3.236191e-01 3722.9159528 #> 221   22.0 1281.7011 3.117481e-01 3717.9871154 #> 222   22.1 1286.6359 3.003866e-01 3713.0636847 #> 223   22.2 1291.5648 2.895107e-01 3708.1457033 #> 224   22.3 1296.4877 2.790972e-01 3703.2332113 #> 225   22.4 1301.4046 2.691245e-01 3698.3262464 #> 226   22.5 1306.3156 2.595719e-01 3693.4248444 #> 227   22.6 1311.2205 2.504199e-01 3688.5290387 #> 228   22.7 1316.1195 2.416497e-01 3683.6388611 #> 229   22.8 1321.0124 2.332438e-01 3678.7543413 #> 230   22.9 1325.8993 2.251853e-01 3673.8755074 #> 231   23.0 1330.7802 2.174583e-01 3669.0023856 #> 232   23.1 1335.6550 2.100477e-01 3664.1350007 #> 233   23.2 1340.5237 2.029391e-01 3659.2733759 #> 234   23.3 1345.3863 1.961188e-01 3654.4175332 #> 235   23.4 1350.2429 1.895737e-01 3649.5674929 #> 236   23.5 1355.0934 1.832915e-01 3644.7232741 #> 237   23.6 1359.9378 1.772605e-01 3639.8848949 #> 238   23.7 1364.7762 1.714694e-01 3635.0523719 #> 239   23.8 1369.6084 1.659077e-01 3630.2257208 #> 240   23.9 1374.4345 1.605650e-01 3625.4049562 #> 241   24.0 1379.2545 1.554319e-01 3620.5900916 #> 242   24.1 1384.0684 1.504992e-01 3615.7811397 #> 243   24.2 1388.8761 1.457581e-01 3610.9781122 #> 244   24.3 1393.6778 1.412002e-01 3606.1810198 #> 245   24.4 1398.4733 1.368177e-01 3601.3898727 #> 246   24.5 1403.2627 1.326029e-01 3596.6046800 #> 247   24.6 1408.0460 1.285488e-01 3591.8254502 #> 248   24.7 1412.8232 1.246484e-01 3587.0521910 #> 249   24.8 1417.5942 1.208952e-01 3582.2849096 #> 250   24.9 1422.3591 1.172830e-01 3577.5236123 #> 251   25.0 1427.1179 1.138058e-01 3572.7683051 #> 252   25.1 1431.8705 1.104579e-01 3568.0189930 #> 253   25.2 1436.6171 1.072340e-01 3563.2756808 #> 254   25.3 1441.3575 1.041289e-01 3558.5383726 #> 255   25.4 1446.0918 1.011377e-01 3553.8070721 #> 256   25.5 1450.8200 9.825558e-02 3549.0817823 #> 257   25.6 1455.5420 9.547818e-02 3544.3625059 #> 258   25.7 1460.2580 9.280119e-02 3539.6492452 #> 259   25.8 1464.9678 9.022051e-02 3534.9420020 #> 260   25.9 1469.6715 8.773224e-02 3530.2407777 #> 261   26.0 1474.3691 8.533265e-02 3525.5455733 #> 262   26.1 1479.0606 8.301818e-02 3520.8563895 #> 263   26.2 1483.7460 8.078542e-02 3516.1732267 #> 264   26.3 1488.4253 7.863111e-02 3511.4960846 #> 265   26.4 1493.0985 7.655215e-02 3506.8249631 #> 266   26.5 1497.7656 7.454556e-02 3502.1598616 #> 267   26.6 1502.4266 7.260850e-02 3497.5007789 #> 268   26.7 1507.0815 7.073825e-02 3492.8477141 #> 269   26.8 1511.7304 6.893220e-02 3488.2006655 #> 270   26.9 1516.3732 6.718787e-02 3483.5596315 #> 271   27.0 1521.0099 6.550287e-02 3478.9246100 #> 272   27.1 1525.6405 6.387493e-02 3474.2955990 #> 273   27.2 1530.2651 6.230186e-02 3469.6725959 #> 274   27.3 1534.8836 6.078157e-02 3465.0555982 #> 275   27.4 1539.4961 5.931207e-02 3460.4446030 #> 276   27.5 1544.1025 5.789144e-02 3455.8396073 #> 277   27.6 1548.7029 5.651784e-02 3451.2406079 #> 278   27.7 1553.2972 5.518952e-02 3446.6476014 #> 279   27.8 1557.8855 5.390479e-02 3442.0605843 #> 280   27.9 1562.4678 5.266203e-02 3437.4795529 #> 281   28.0 1567.0440 5.145972e-02 3432.9045033 #> 282   28.1 1571.6143 5.029635e-02 3428.3354314 #> 283   28.2 1576.1785 4.917050e-02 3423.7723333 #> 284   28.3 1580.7367 4.808083e-02 3419.2152045 #> 285   28.4 1585.2889 4.702601e-02 3414.6640408 #> 286   28.5 1589.8352 4.600480e-02 3410.1188374 #> 287   28.6 1594.3754 4.501598e-02 3405.5795899 #> 288   28.7 1598.9096 4.405842e-02 3401.0462935 #> 289   28.8 1603.4379 4.313099e-02 3396.5189432 #> 290   28.9 1607.9602 4.223264e-02 3391.9975341 #> 291   29.0 1612.4766 4.136235e-02 3387.4820613 #> 292   29.1 1616.9870 4.051913e-02 3382.9725194 #> 293   29.2 1621.4914 3.970206e-02 3378.4689033 #> 294   29.3 1625.9899 3.891021e-02 3373.9712076 #> 295   29.4 1630.4824 3.814273e-02 3369.4794269 #> 296   29.5 1634.9690 3.739878e-02 3364.9935558 #> 297   29.6 1639.4497 3.667757e-02 3360.5135887 #> 298   29.7 1643.9245 3.597831e-02 3356.0395199 #> 299   29.8 1648.3934 3.530028e-02 3351.5713437 #> 300   29.9 1652.8563 3.464277e-02 3347.1090544 #> 301   30.0 1657.3133 3.400508e-02 3342.6526462 #> 302   30.1 1661.7645 3.338656e-02 3338.2021131 #> 303   30.2 1666.2098 3.278659e-02 3333.7574492 #> 304   30.3 1670.6491 3.220455e-02 3329.3186485 #> 305   30.4 1675.0827 3.163986e-02 3324.8857050 #> 306   30.5 1679.5103 3.109196e-02 3320.4586126 #> 307   30.6 1683.9321 3.056031e-02 3316.0373650 #> 308   30.7 1688.3480 3.004438e-02 3311.6219563 #> 309   30.8 1692.7581 2.954369e-02 3307.2123800 #> 310   30.9 1697.1623 2.905774e-02 3302.8086299 #> 311   31.0 1701.5607 2.858607e-02 3298.4106997 #> 312   31.1 1705.9533 2.812824e-02 3294.0185831 #> 313   31.2 1710.3400 2.768382e-02 3289.6322737 #> 314   31.3 1714.7210 2.725239e-02 3285.2517650 #> 315   31.4 1719.0961 2.683356e-02 3280.8770506 #> 316   31.5 1723.4654 2.642694e-02 3276.5081241 #> 317   31.6 1727.8290 2.603217e-02 3272.1449788 #> 318   31.7 1732.1867 2.564889e-02 3267.7876084 #> 319   31.8 1736.5387 2.527675e-02 3263.4360061 #> 320   31.9 1740.8849 2.491543e-02 3259.0901655 #> 321   32.0 1745.2254 2.456461e-02 3254.7500799 #> 322   32.1 1749.5600 2.422398e-02 3250.4157427 #> 323   32.2 1753.8890 2.389325e-02 3246.0871472 #> 324   32.3 1758.2121 2.357213e-02 3241.7642867 #> 325   32.4 1762.5296 2.326036e-02 3237.4471546 #> 326   32.5 1766.8413 2.295766e-02 3233.1357441 #> 327   32.6 1771.1473 2.266378e-02 3228.8300486 #> 328   32.7 1775.4476 2.237847e-02 3224.5300612 #> 329   32.8 1779.7421 2.210151e-02 3220.2357752 #> 330   32.9 1784.0310 2.183266e-02 3215.9471838 #> 331   33.0 1788.3141 2.157170e-02 3211.6642803 #> 332   33.1 1792.5916 2.131842e-02 3207.3870578 #> 333   33.2 1796.8634 2.107262e-02 3203.1155095 #> 334   33.3 1801.1295 2.083410e-02 3198.8496287 #> 335   33.4 1805.3900 2.060267e-02 3194.5894084 #> 336   33.5 1809.6448 2.037815e-02 3190.3348418 #> 337   33.6 1813.8939 2.016036e-02 3186.0859222 #> 338   33.7 1818.1374 1.994914e-02 3181.8426425 #> 339   33.8 1822.3753 1.974431e-02 3177.6049960 #> 340   33.9 1826.6075 1.954573e-02 3173.3729758 #> 341   34.0 1830.8341 1.935323e-02 3169.1465749 #> 342   34.1 1835.0550 1.916668e-02 3164.9257866 #> 343   34.2 1839.2704 1.898593e-02 3160.7106038 #> 344   34.3 1843.4802 1.881085e-02 3156.5010198 #> 345   34.4 1847.6843 1.864130e-02 3152.2970277 #> 346   34.5 1851.8829 1.847716e-02 3148.0986204 #> 347   34.6 1856.0759 1.831831e-02 3143.9057911 #> 348   34.7 1860.2633 1.816463e-02 3139.7185330 #> 349   34.8 1864.4451 1.801601e-02 3135.5368390 #> 350   34.9 1868.6214 1.787233e-02 3131.3607023 #> 351   35.0 1872.7922 1.773351e-02 3127.1901160 #> 352   35.1 1876.9573 1.759943e-02 3123.0250731 #> 353   35.2 1881.1170 1.747000e-02 3118.8655667 #> 354   35.3 1885.2711 1.734512e-02 3114.7115899 #> 355   35.4 1889.4196 1.722472e-02 3110.5631358 #> 356   35.5 1893.5627 1.710869e-02 3106.4201975 #> 357   35.6 1897.7002 1.699697e-02 3102.2827681 #> 358   35.7 1901.8323 1.688946e-02 3098.1508406 #> 359   35.8 1905.9588 1.678610e-02 3094.0244081 #> 360   35.9 1910.0798 1.668682e-02 3089.9034637 #> 361   36.0 1914.1954 1.659153e-02 3085.7880006 #> 362   36.1 1918.3055 1.650019e-02 3081.6780117 #> 363   36.2 1922.4101 1.641271e-02 3077.5734903 #> 364   36.3 1926.5092 1.632905e-02 3073.4744293 #> 365   36.4 1930.6029 1.624915e-02 3069.3808220 #> 366   36.5 1934.6912 1.617294e-02 3065.2926613 #> 367   36.6 1938.7740 1.610038e-02 3061.2099405 #> 368   36.7 1942.8513 1.603141e-02 3057.1326526 #> 369   36.8 1946.9232 1.596600e-02 3053.0607908 #> 370   36.9 1950.9897 1.590408e-02 3048.9943481 #> 371   37.0 1955.0508 1.584562e-02 3044.9333177 #> 372   37.1 1959.1065 1.579059e-02 3040.8776927 #> 373   37.2 1963.1568 1.573893e-02 3036.8274663 #> 374   37.3 1967.2017 1.569062e-02 3032.7826316 #> 375   37.4 1971.2412 1.564562e-02 3028.7431818 #> 376   37.5 1975.2753 1.560389e-02 3024.7091100 #> 377   37.6 1979.3040 1.556542e-02 3020.6804094 #> 378   37.7 1983.3274 1.553016e-02 3016.6570731 #> 379   37.8 1987.3454 1.549810e-02 3012.6390943 #> 380   37.9 1991.3581 1.546921e-02 3008.6264662 #> 381   38.0 1995.3654 1.544347e-02 3004.6191819 #> 382   38.1 1999.3673 1.542087e-02 3000.6172348 #> 383   38.2 2003.3640 1.540137e-02 2996.6206179 #> 384   38.3 2007.3553 1.538497e-02 2992.6293245 #> 385   38.4 2011.3413 1.537166e-02 2988.6433477 #> 386   38.5 2015.3220 1.536141e-02 2984.6626809 #> 387   38.6 2019.2973 1.535423e-02 2980.6873173 #> 388   38.7 2023.2674 1.535009e-02 2976.7172500 #> 389   38.8 2027.2322 1.534901e-02 2972.7524724 #> 390   38.9 2031.1917 1.535096e-02 2968.7929776 #> 391   39.0 2035.1459 1.535595e-02 2964.8387591 #> 392   39.1 2039.0948 1.536398e-02 2960.8898099 #> 393   39.2 2043.0385 1.537505e-02 2956.9461235 #> 394   39.3 2046.9769 1.538915e-02 2953.0076930 #> 395   39.4 2050.9101 1.540630e-02 2949.0745119 #> 396   39.5 2054.8380 1.542651e-02 2945.1465733 #> 397   39.6 2058.7607 1.544977e-02 2941.2238708 #> 398   39.7 2062.6781 1.547609e-02 2937.3063974 #> 399   39.8 2066.5903 1.550550e-02 2933.3941467 #> 400   39.9 2070.4974 1.553800e-02 2929.4871119 #> 401   40.0 2074.3991 1.557361e-02 2925.5852865 #> 402   40.1 2078.2957 1.561234e-02 2921.6886637 #> 403   40.2 2082.1871 1.565422e-02 2917.7972369 #> 404   40.3 2086.0733 1.569926e-02 2913.9109997 #> 405   40.4 2089.9543 1.574749e-02 2910.0299453 #> 406   40.5 2093.8301 1.579894e-02 2906.1540671 #> 407   40.6 2097.7008 1.585361e-02 2902.2833587 #> 408   40.7 2101.5663 1.591156e-02 2898.4178134 #> 409   40.8 2105.4266 1.597280e-02 2894.5574247 #> 410   40.9 2109.2818 1.603737e-02 2890.7021860 #> 411   41.0 2113.1318 1.610531e-02 2886.8520908 #> 412   41.1 2116.9767 1.617664e-02 2883.0071326 #> 413   41.2 2120.8164 1.625141e-02 2879.1673049 #> 414   41.3 2124.6511 1.632966e-02 2875.3326013 #> 415   41.4 2128.4806 1.641143e-02 2871.5030151 #> 416   41.5 2132.3050 1.649677e-02 2867.6785400 #> 417   41.6 2136.1242 1.658572e-02 2863.8591695 #> 418   41.7 2139.9384 1.667833e-02 2860.0448972 #> 419   41.8 2143.7475 1.677465e-02 2856.2357167 #> 420   41.9 2147.5515 1.687474e-02 2852.4316214 #> 421   42.0 2151.3504 1.697866e-02 2848.6326052 #> 422   42.1 2155.1443 1.708646e-02 2844.8386614 #> 423   42.2 2158.9330 1.719820e-02 2841.0497839 #> 424   42.3 2162.7167 1.731396e-02 2837.2659662 #> 425   42.4 2166.4954 1.743378e-02 2833.4872020 #> 426   42.5 2170.2690 1.755775e-02 2829.7134850 #> 427   42.6 2174.0375 1.768594e-02 2825.9448088 #> 428   42.7 2177.8010 1.781842e-02 2822.1811673 #> 429   42.8 2181.5595 1.795527e-02 2818.4225540 #> 430   42.9 2185.3129 1.809656e-02 2814.6689627 #> 431   43.0 2189.0614 1.824239e-02 2810.9203873 #> 432   43.1 2192.8048 1.839284e-02 2807.1768215 #> 433   43.2 2196.5432 1.854799e-02 2803.4382590 #> 434   43.3 2200.2766 1.870796e-02 2799.7046936 #> 435   43.4 2204.0050 1.887282e-02 2795.9761193 #> 436   43.5 2207.7284 1.904268e-02 2792.2525298 #> 437   43.6 2211.4469 1.921764e-02 2788.5339191 #> 438   43.7 2215.1603 1.939782e-02 2784.8202809 #> 439   43.8 2218.8688 1.958332e-02 2781.1116092 #> 440   43.9 2222.5723 1.977425e-02 2777.4078979 #> 441   44.0 2226.2709 1.997075e-02 2773.7091409 #> 442   44.1 2229.9645 2.017292e-02 2770.0153323 #> 443   44.2 2233.6532 2.038090e-02 2766.3264660 #> 444   44.3 2237.3369 2.059482e-02 2762.6425359 #> 445   44.4 2241.0156 2.081482e-02 2758.9635362 #> 446   44.5 2244.6895 2.104103e-02 2755.2894608 #> 447   44.6 2248.3584 2.127361e-02 2751.6203039 #> 448   44.7 2252.0224 2.151270e-02 2747.9560594 #> 449   44.8 2255.6815 2.175846e-02 2744.2967216 #> 450   44.9 2259.3357 2.201106e-02 2740.6422846 #> 451   45.0 2262.9850 2.227065e-02 2736.9927425 #> 452   45.1 2266.6294 2.253741e-02 2733.3480895 #> 453   45.2 2270.2689 2.281152e-02 2729.7083198 #> 454   45.3 2273.9035 2.309317e-02 2726.0734277 #> 455   45.4 2277.5332 2.338254e-02 2722.4434074 #> 456   45.5 2281.1581 2.367982e-02 2718.8182532 #> 457   45.6 2284.7781 2.398524e-02 2715.1979594 #> 458   45.7 2288.3932 2.429898e-02 2711.5825205 #> 459   45.8 2292.0034 2.462128e-02 2707.9719306 #> 460   45.9 2295.6089 2.495235e-02 2704.3661844 #> 461   46.0 2299.2094 2.529243e-02 2700.7652761 #> 462   46.1 2302.8052 2.564176e-02 2697.1692003 #> 463   46.2 2306.3960 2.600058e-02 2693.5779514 #> 464   46.3 2309.9821 2.636916e-02 2689.9915240 #> 465   46.4 2313.5633 2.674775e-02 2686.4099127 #> 466   46.5 2317.1398 2.713664e-02 2682.8331120 #> 467   46.6 2320.7113 2.753609e-02 2679.2611166 #> 468   46.7 2324.2781 2.794642e-02 2675.6939212 #> 469   46.8 2327.8401 2.836791e-02 2672.1315204 #> 470   46.9 2331.3973 2.880089e-02 2668.5739090 #> 471   47.0 2334.9497 2.924567e-02 2665.0210817 #> 472   47.1 2338.4973 2.970260e-02 2661.4730335 #> 473   47.2 2342.0401 3.017201e-02 2657.9297591 #> 474   47.3 2345.5781 3.065426e-02 2654.3912535 #> 475   47.4 2349.1113 3.114973e-02 2650.8575116 #> 476   47.5 2352.6398 3.165880e-02 2647.3285284 #> 477   47.6 2356.1635 3.218186e-02 2643.8042990 #> 478   47.7 2359.6825 3.271932e-02 2640.2848184 #> 479   47.8 2363.1966 3.327161e-02 2636.7700817 #> 480   47.9 2366.7061 3.383916e-02 2633.2600842 #> 481   48.0 2370.2108 3.442243e-02 2629.7548211 #> 482   48.1 2373.7107 3.502188e-02 2626.2542876 #> 483   48.2 2377.2059 3.563801e-02 2622.7584791 #> 484   48.3 2380.6963 3.627130e-02 2619.2673911 #> 485   48.4 2384.1821 3.692229e-02 2615.7810189 #> 486   48.5 2387.6631 3.759151e-02 2612.2993580 #> 487   48.6 2391.1393 3.827952e-02 2608.8224041 #> 488   48.7 2394.6109 3.898689e-02 2605.3501527 #> 489   48.8 2398.0777 3.971422e-02 2601.8825996 #> 490   48.9 2401.5398 4.046213e-02 2598.4197406 #> 491   49.0 2404.9972 4.123126e-02 2594.9615714 #> 492   49.1 2408.4499 4.202226e-02 2591.5080879 #> 493   49.2 2411.8979 4.283583e-02 2588.0592862 #> 494   49.3 2415.3412 4.367267e-02 2584.6151624 #> 495   49.4 2418.7798 4.453353e-02 2581.1757124 #> 496   49.5 2422.2136 4.541915e-02 2577.7409326 #> 497   49.6 2425.6429 4.633033e-02 2574.3108192 #> 498   49.7 2429.0674 4.726790e-02 2570.8853686 #> 499   49.8 2432.4872 4.823268e-02 2567.4645773 #> 500   49.9 2435.9023 4.922558e-02 2564.0484418 #> 501   50.0 2439.3128 5.024748e-02 2560.6369588 #> 502   50.1 2442.7186 5.129934e-02 2557.2301250 #> 503   50.2 2446.1197 5.238213e-02 2553.8279373 #> 504   50.3 2449.5161 5.349687e-02 2550.4303926 #> 505   50.4 2452.9079 5.464460e-02 2547.0374880 #> 506   50.5 2456.2950 5.582641e-02 2543.6492206 #> 507   50.6 2459.6774 5.704344e-02 2540.2655878 #> 508   50.7 2463.0551 5.829685e-02 2536.8865869 #> 509   50.8 2466.4282 5.958786e-02 2533.5122155 #> 510   50.9 2469.7966 6.091773e-02 2530.1424711 #> 511   51.0 2473.1604 6.228775e-02 2526.7773517 #> 512   51.1 2476.5194 6.369929e-02 2523.4168551 #> 513   51.2 2479.8739 6.515376e-02 2520.0609793 #> 514   51.3 2483.2236 6.665260e-02 2516.7097227 #> 515   51.4 2486.5687 6.819734e-02 2513.3630834 #> 516   51.5 2489.9092 6.978955e-02 2510.0210601 #> 517   51.6 2493.2449 7.143085e-02 2506.6836514 #> 518   51.7 2496.5760 7.312293e-02 2503.3508562 #> 519   51.8 2499.9025 7.486756e-02 2500.0226734 #> 520   51.9 2503.2242 7.666655e-02 2496.6991023 #> 521   52.0 2506.5413 7.852180e-02 2493.3801423 #> 522   52.1 2509.8538 8.043528e-02 2490.0657929 #> 523   52.2 2513.1615 8.240902e-02 2486.7560538 #> 524   52.3 2516.4646 8.444516e-02 2483.4509252 #> 525   52.4 2519.7630 8.654588e-02 2480.1504072 #> 526   52.5 2523.0568 8.871348e-02 2476.8545001 #> 527   52.6 2526.3458 9.095034e-02 2473.5632048 #> 528   52.7 2529.6302 9.325892e-02 2470.2765220 #> 529   52.8 2532.9099 9.564179e-02 2466.9944529 #> 530   52.9 2536.1849 9.810163e-02 2463.7169990 #> 531   53.0 2539.4552 1.006412e-01 2460.4441619 #> 532   53.1 2542.7208 1.032634e-01 2457.1759435 #> 533   53.2 2545.9817 1.059712e-01 2453.9123462 #> 534   53.3 2549.2379 1.087677e-01 2450.6533724 #> 535   53.4 2552.4893 1.116561e-01 2447.3990251 #> 536   53.5 2555.7361 1.146399e-01 2444.1493073 #> 537   53.6 2558.9781 1.177226e-01 2440.9042228 #> 538   53.7 2562.2153 1.209077e-01 2437.6637753 #> 539   53.8 2565.4478 1.241991e-01 2434.4279691 #> 540   53.9 2568.6756 1.276006e-01 2431.1968088 #> 541   54.0 2571.8986 1.311165e-01 2427.9702995 #> 542   54.1 2575.1168 1.347510e-01 2424.7484465 #> 543   54.2 2578.3302 1.385085e-01 2421.5312558 #> 544   54.3 2581.5389 1.423936e-01 2418.3187337 #> 545   54.4 2584.7427 1.464111e-01 2415.1108869 #> 546   54.5 2587.9417 1.505661e-01 2411.9077227 #> 547   54.6 2591.1359 1.548638e-01 2408.7092489 #> 548   54.7 2594.3252 1.593096e-01 2405.5154737 #> 549   54.8 2597.5097 1.639091e-01 2402.3264059 #> 550   54.9 2600.6893 1.686682e-01 2399.1420550 #> 551   55.0 2603.8640 1.735931e-01 2395.9624308 #> 552   55.1 2607.0338 1.786901e-01 2392.7875441 #> 553   55.2 2610.1986 1.839659e-01 2389.6174059 #> 554   55.3 2613.3585 1.894275e-01 2386.4520282 #> 555   55.4 2616.5135 1.950819e-01 2383.2914236 #> 556   55.5 2619.6635 2.009369e-01 2380.1356054 #> 557   55.6 2622.8084 2.070001e-01 2376.9845875 #> 558   55.7 2625.9483 2.132798e-01 2373.8383849 #> 559   55.8 2629.0832 2.197845e-01 2370.6970131 #> 560   55.9 2632.2130 2.265230e-01 2367.5604887 #> 561   56.0 2635.3377 2.335046e-01 2364.4288290 #> 562   56.1 2638.4572 2.407390e-01 2361.3020524 #> 563   56.2 2641.5716 2.482362e-01 2358.1801780 #> 564   56.3 2644.6808 2.560068e-01 2355.0632261 #> 565   56.4 2647.7847 2.640615e-01 2351.9512180 #> 566   56.5 2650.8834 2.724120e-01 2348.8441761 #> 567   56.6 2653.9768 2.810700e-01 2345.7421239 #> 568   56.7 2657.0649 2.900480e-01 2342.6450862 #> 569   56.8 2660.1476 2.993590e-01 2339.5530888 #> 570   56.9 2663.2248 3.090164e-01 2336.4661591 #> 571   57.0 2666.2966 3.190345e-01 2333.3843255 #> 572   57.1 2669.3630 3.294279e-01 2330.3076180 #> 573   57.2 2672.4237 3.402119e-01 2327.2360681 #> 574   57.3 2675.4789 3.514027e-01 2324.1697088 #> 575   57.4 2678.5284 3.630171e-01 2321.1085746 #> 576   57.5 2681.5722 3.750724e-01 2318.0527017 #> 577   57.6 2684.6103 3.875870e-01 2315.0021281 #> 578   57.7 2687.6425 4.005799e-01 2311.9568937 #> 579   57.8 2690.6689 4.140712e-01 2308.9170400 #> 580   57.9 2693.6893 4.280814e-01 2305.8826107 #> 581   58.0 2696.7037 4.426326e-01 2302.8536515 #> 582   58.1 2699.7120 4.577472e-01 2299.8302104 #> 583   58.2 2702.7142 4.734491e-01 2296.8123375 #> 584   58.3 2705.7102 4.897631e-01 2293.8000853 #> 585   58.4 2708.6998 5.067150e-01 2290.7935086 #> 586   58.5 2711.6830 5.243319e-01 2287.7926652 #> 587   58.6 2714.6597 5.426422e-01 2284.7976151 #> 588   58.7 2717.6299 5.616754e-01 2281.8084215 #> 589   58.8 2720.5934 5.814625e-01 2278.8251502 #> 590   58.9 2723.5501 6.020357e-01 2275.8478704 #> 591   59.0 2726.4999 6.234288e-01 2272.8766544 #> 592   59.1 2729.4427 6.456773e-01 2269.9115776 #> 593   59.2 2732.3785 6.688181e-01 2266.9527193 #> 594   59.3 2735.3069 6.928898e-01 2264.0001622 #> 595   59.4 2738.2281 7.179328e-01 2261.0539930 #> 596   59.5 2741.1417 7.439895e-01 2258.1143023 #> 597   59.6 2744.0477 7.711041e-01 2255.1811851 #> 598   59.7 2746.9459 7.993229e-01 2252.2547405 #> 599   59.8 2749.8362 8.286942e-01 2249.3350724 #> 600   59.9 2752.7184 8.592688e-01 2246.4222895 #> 601   60.0 2755.5924 8.910996e-01 2243.5165055 #> 602   60.1 2758.4579 9.242422e-01 2240.6178393 #> 603   60.2 2761.3148 9.587547e-01 2237.7264155 #> 604   60.3 2764.1629 9.946977e-01 2234.8423642 #> 605   60.4 2767.0020 1.032135e+00 2231.9658219 #> 606   60.5 2769.8319 1.071133e+00 2229.0969310 #> 607   60.6 2772.6524 1.111762e+00 2226.2358409 #> 608   60.7 2775.4632 1.154094e+00 2223.3827076 #> 609   60.8 2778.2641 1.198206e+00 2220.5376948 #> 610   60.9 2781.0548 1.244178e+00 2217.7009733 #> 611   61.0 2783.8352 1.292094e+00 2214.8727221 #> 612   61.1 2786.6048 1.342041e+00 2212.0531286 #> 613   61.2 2789.3635 1.394112e+00 2209.2423888 #> 614   61.3 2792.1109 1.448402e+00 2206.4407079 #> 615   61.4 2794.8467 1.505013e+00 2203.6483006 #> 616   61.5 2797.5706 1.564050e+00 2200.8653917 #> 617   61.6 2800.2822 1.625624e+00 2198.0922166 #> 618   61.7 2802.9811 1.689851e+00 2195.3290213 #> 619   61.8 2805.6671 1.756851e+00 2192.5760639 #> 620   61.9 2808.3396 1.826753e+00 2189.8336141 #> 621   62.0 2810.9984 1.899689e+00 2187.1019546 #> 622   62.1 2813.6428 1.975799e+00 2184.3813810 #> 623   62.2 2816.2726 2.055230e+00 2181.6722030 #> 624   62.3 2818.8871 2.138134e+00 2178.9747449 #> 625   62.4 2821.4860 2.224672e+00 2176.2893462 #> 626   62.5 2824.0686 2.315012e+00 2173.6163623 #> 627   62.6 2826.6345 2.409332e+00 2170.9561653 #> 628   62.7 2829.1830 2.507814e+00 2168.3091450 #> 629   62.8 2831.7136 2.610654e+00 2165.6757096 #> 630   62.9 2834.2257 2.718053e+00 2163.0562863 #> 631   63.0 2836.7185 2.830225e+00 2160.4513228 #> 632   63.1 2839.1913 2.947392e+00 2157.8612877 #> 633   63.2 2841.6435 3.069788e+00 2155.2866718 #> 634   63.3 2844.0744 3.197656e+00 2152.7279894 #> 635   63.4 2846.4830 3.331254e+00 2150.1857787 #> 636   63.5 2848.8685 3.470849e+00 2147.6606038 #> 637   63.6 2851.2302 3.616724e+00 2145.1530551 #> 638   63.7 2853.5671 3.769172e+00 2142.6637513 #> 639   63.8 2855.8782 3.928502e+00 2140.1933404 #> 640   63.9 2858.1625 4.095038e+00 2137.7425010 #> 641   64.0 2860.4189 4.269118e+00 2135.3119438 #> 642   64.1 2862.6465 4.451098e+00 2132.9024134 #> 643   64.2 2864.8440 4.641348e+00 2130.5146895 #> 644   64.3 2867.0102 4.840257e+00 2128.1495890 #> 645   64.4 2869.1438 5.048234e+00 2125.8079672 #> 646   64.5 2871.2436 5.265705e+00 2123.4907202 #> 647   64.6 2873.3081 5.493116e+00 2121.1987864 #> 648   64.7 2875.3359 5.730934e+00 2118.9331485 #> 649   64.8 2877.3255 5.979650e+00 2116.6948359 #> 650   64.9 2879.2753 6.239773e+00 2114.4849265 #> 651   65.0 2881.1836 6.511840e+00 2112.3045491 #> 652   65.1 2883.0487 6.796412e+00 2110.1548856 #> 653   65.2 2884.8688 7.094073e+00 2108.0371738 #> 654   65.3 2886.6419 7.405435e+00 2105.9527096 #> 655   65.4 2888.3660 7.731140e+00 2103.9028496 #> 656   65.5 2890.0391 8.071855e+00 2101.8890142 #> 657   65.6 2891.6590 8.428279e+00 2099.9126901 #> 658   65.7 2893.2234 8.801143e+00 2097.9754335 #> 659   65.8 2894.7299 9.191208e+00 2096.0788729 #> 660   65.9 2896.1760 9.599269e+00 2094.2247124 #> 661   66.0 2897.5591 1.002616e+01 2092.4147353 #> 662   66.1 2898.8765 1.047273e+01 2090.6508068 #> 663   66.2 2900.1252 1.093990e+01 2088.9348782 #> 664   66.3 2901.3024 1.142861e+01 2087.2689902 #> 665   66.4 2902.4049 1.193982e+01 2085.6552765 #> 666   66.5 2903.4295 1.247457e+01 2084.0959678 #> 667   66.6 2904.3727 1.303391e+01 2082.5933959 #> 668   66.7 2905.2311 1.361894e+01 2081.1499973 #> 669   66.8 2906.0009 1.423081e+01 2079.7683177 #> 670   66.9 2906.6783 1.487071e+01 2078.4510160 #> 671   67.0 2907.2593 1.553987e+01 2077.2008687 #> 672   67.1 2907.7397 1.623957e+01 2076.0207742 #> 673   67.2 2908.1151 1.697114e+01 2074.9137573 #> 674   67.3 2908.3811 1.773596e+01 2073.8829734 #> 675   67.4 2908.5329 1.853543e+01 2072.9317136 #> 676   67.5 2908.5656 1.937103e+01 2072.0634085 #> 677   67.6 2908.4741 2.024427e+01 2071.2816331 #> 678   67.7 2908.2532 2.115671e+01 2070.5901112 #> 679   67.8 2907.8973 2.210996e+01 2069.9927200 #> 680   67.9 2907.4008 2.310567e+01 2069.4934942 #> 681   68.0 2906.7578 2.414553e+01 2069.0966304 #> 682   68.1 2905.9622 2.523128e+01 2068.8064910 #> 683   68.2 2905.0077 2.636470e+01 2068.6276086 #> 684   68.3 2903.8877 2.754761e+01 2068.5646891 #> 685   68.4 2902.5955 2.878186e+01 2068.6226158 #> 686   68.5 2901.1242 3.006933e+01 2068.8064520 #> 687   68.6 2899.4666 3.141193e+01 2069.1214441 #> 688   68.7 2897.6154 3.281161e+01 2069.5730235 #> 689   68.8 2895.5629 3.427031e+01 2070.1668091 #> 690   68.9 2893.3014 3.579000e+01 2070.9086081 #> 691   69.0 2890.8229 3.737265e+01 2071.8044168 #> 692   69.1 2888.1193 3.902024e+01 2072.8604204 #> 693   69.2 2885.1823 4.073471e+01 2074.0829924 #> 694   69.3 2882.0033 4.251802e+01 2075.4786926 #> 695   69.4 2878.5737 4.437207e+01 2077.0542646 #> 696   69.5 2874.8846 4.629872e+01 2078.8166319 #> 697   69.6 2870.9273 4.829980e+01 2080.7728928 #> 698   69.7 2866.6926 5.037705e+01 2082.9303143 #> 699   69.8 2862.1715 5.253215e+01 2085.2963248 #> 700   69.9 2857.3548 5.476665e+01 2087.8785043 #> 701   70.0 2852.2334 5.708201e+01 2090.6845742 #> 702   70.1 2846.7981 5.947956e+01 2093.7223845 #> 703   70.2 2841.0396 6.196048e+01 2096.9998998 #> 704   70.3 2834.9491 6.452576e+01 2100.5251831 #> 705   70.4 2828.5174 6.717622e+01 2104.3063775 #> 706   70.5 2821.7359 6.991245e+01 2108.3516856 #> 707   70.6 2814.5958 7.273482e+01 2112.6693475 #> 708   70.7 2807.0890 7.564343e+01 2117.2676155 #> 709   70.8 2799.2072 7.863810e+01 2122.1547272 #> 710   70.9 2790.9428 8.171833e+01 2127.3388758 #> 711   71.0 2782.2885 8.488330e+01 2132.8281782 #> 712   71.1 2773.2375 8.813184e+01 2138.6306406 #> 713   71.2 2763.7835 9.146238e+01 2144.7541219 #> 714   71.3 2753.9207 9.487296e+01 2151.2062944 #> 715   71.4 2743.6442 9.836118e+01 2157.9946028 #> 716   71.5 2732.9496 1.019242e+02 2165.1262201 #> 717   71.6 2721.8333 1.055587e+02 2172.6080031 #> 718   71.7 2710.2926 1.092610e+02 2180.4464441 #> 719   71.8 2698.3258 1.130266e+02 2188.6476229 #> 720   71.9 2685.9320 1.168508e+02 2197.2171562 #> 721   72.0 2673.1116 1.207283e+02 2206.1601469 #> 722   72.1 2659.8657 1.246532e+02 2215.4811321 #> 723   72.2 2646.1969 1.286191e+02 2225.1840310 #> 724   72.3 2632.1088 1.326191e+02 2235.2720936 #> 725   72.4 2617.6063 1.366459e+02 2245.7478493 #> 726   72.5 2602.6956 1.406914e+02 2256.6130570 #> 727   72.6 2587.3841 1.447472e+02 2267.8686569 #> 728   72.7 2571.6807 1.488046e+02 2279.5147247 #> 729   72.8 2555.5955 1.528541e+02 2291.5504283 #> 730   72.9 2539.1399 1.568862e+02 2303.9739879 #> 731   73.0 2522.3267 1.608907e+02 2316.7826403 #> 732   73.1 2505.1700 1.648574e+02 2329.9726081 #> 733   73.2 2487.6853 1.687756e+02 2343.5390725 #> 734   73.3 2469.8892 1.726346e+02 2357.4761536 #> 735   73.4 2451.7996 1.764235e+02 2371.7768953 #> 736   73.5 2433.4354 1.801313e+02 2386.4332570 #> 737   73.6 2414.8168 1.837470e+02 2401.4361126 #> 738   73.7 2395.9649 1.872598e+02 2416.7752561 #> 739   73.8 2376.9016 1.906590e+02 2432.4394145 #> 740   73.9 2357.6498 1.939339e+02 2448.4162682 #> 741   74.0 2338.2330 1.970745e+02 2464.6924790 #> 742   74.1 2318.6754 2.000709e+02 2481.2537254 #> 743   74.2 2299.0015 2.029137e+02 2498.0847446 #> 744   74.3 2279.2365 2.055941e+02 2515.1693824 #> 745   74.4 2259.4056 2.081037e+02 2532.4906488 #> 746   74.5 2239.5342 2.104350e+02 2550.0307807 #> 747   74.6 2219.6477 2.125810e+02 2567.7713090 #> 748   74.7 2199.7714 2.145355e+02 2585.6931323 #> 749   74.8 2179.9303 2.162931e+02 2603.7765936 #> 750   74.9 2160.1493 2.178491e+02 2622.0015614 #> 751   75.0 2140.4526 2.191999e+02 2640.3475140 #> 752   75.1 2120.8639 2.203425e+02 2658.7936251 #> 753   75.2 2101.4062 2.212749e+02 2677.3188515 #> 754   75.3 2082.1019 2.219960e+02 2695.9020208 #> 755   75.4 2062.9725 2.225056e+02 2714.5219191 #> 756   75.5 2044.0384 2.228042e+02 2733.1573780 #> 757   75.6 2025.3194 2.228933e+02 2751.7873587 #> 758   75.7 2006.8339 2.227751e+02 2770.3910353 #> 759   75.8 1988.5995 2.224526e+02 2788.9478731 #> 760   75.9 1970.6327 2.219296e+02 2807.4377046 #> 761   76.0 1952.9486 2.212106e+02 2825.8408005 #> 762   76.1 1935.5614 2.203007e+02 2844.1379366 #> 763   76.2 1918.4840 2.192056e+02 2862.3104546 #> 764   76.3 1901.7282 2.179314e+02 2880.3403186 #> 765   76.4 1885.3048 2.164851e+02 2898.2101655 #> 766   76.5 1869.2230 2.148736e+02 2915.9033494 #> 767   76.6 1853.4914 2.131046e+02 2933.4039807 #> 768   76.7 1838.1172 2.111858e+02 2950.6969590 #> 769   76.8 1823.1066 2.091254e+02 2967.7679997 #> 770   76.9 1808.4647 2.069317e+02 2984.6036563 #> 771   77.0 1794.1957 2.046130e+02 3001.1913356 #> 772   77.1 1780.3028 2.021779e+02 3017.5193082 #> 773   77.2 1766.7884 1.996349e+02 3033.5767142 #> 774   77.3 1753.6538 1.969926e+02 3049.3535636 #> 775   77.4 1740.8997 1.942596e+02 3064.8407323 #> 776   77.5 1728.5259 1.914441e+02 3080.0299540 #> 777   77.6 1716.5316 1.885546e+02 3094.9138082 #> 778   77.7 1704.9152 1.855991e+02 3109.4857047 #> 779   77.8 1693.6746 1.825855e+02 3123.7398646 #> 780   77.9 1682.8071 1.795216e+02 3137.6712994 #> 781   78.0 1672.3093 1.764149e+02 3151.2757866 #> 782   78.1 1662.1775 1.732726e+02 3164.5498437 #> 783   78.2 1652.4077 1.701016e+02 3177.4907007 #> 784   78.3 1642.9952 1.669086e+02 3190.0962706 #> 785   78.4 1633.9350 1.636999e+02 3202.3651186 #> 786   78.5 1625.2220 1.604815e+02 3214.2964317 #> 787   78.6 1616.8507 1.572593e+02 3225.8899858 #> 788   78.7 1608.8154 1.540385e+02 3237.1461137 #> 789   78.8 1601.1099 1.508244e+02 3248.0656732 #> 790   78.9 1593.7283 1.476216e+02 3258.6500137 #> 791   79.0 1586.6643 1.444347e+02 3268.9009447 #> 792   79.1 1579.9115 1.412678e+02 3278.8207042 #> 793   79.2 1573.4633 1.381247e+02 3288.4119269 #> 794   79.3 1567.3133 1.350091e+02 3297.6776141 #> 795   79.4 1561.4548 1.319241e+02 3306.6211039 #> 796   79.5 1555.8812 1.288728e+02 3315.2460423 #> 797   79.6 1550.5858 1.258578e+02 3323.5563553 #> 798   79.7 1545.5621 1.228817e+02 3331.5562223 #> 799   79.8 1540.8033 1.199466e+02 3339.2500499 #> 800   79.9 1536.3030 1.170546e+02 3346.6424477 #> 801   80.0 1532.0546 1.142072e+02 3353.7382044 #> 802   80.1 1528.0515 1.114062e+02 3360.5422655 #> 803   80.2 1524.2875 1.086528e+02 3367.0597123 #> 804   80.3 1520.7562 1.059481e+02 3373.2957416 #> 805   80.4 1517.4513 1.032930e+02 3379.2556469 #> 806   80.5 1514.3668 1.006884e+02 3384.9448005 #> 807   80.6 1511.4964 9.813494e+01 3390.3686372 #> 808   80.7 1508.8344 9.563296e+01 3395.5326382 #> 809   80.8 1506.3748 9.318284e+01 3400.4423170 #> 810   80.9 1504.1120 9.078478e+01 3405.1032058 #> 811   81.0 1502.0403 8.843885e+01 3409.5208431 #> 812   81.1 1500.1542 8.614501e+01 3413.7007617 #> 813   81.2 1498.4484 8.390314e+01 3417.6484790 #> 814   81.3 1496.9175 8.171300e+01 3421.3694861 #> 815   81.4 1495.5565 7.957428e+01 3424.8692402 #> 816   81.5 1494.3603 7.748659e+01 3428.1531552 #> 817   81.6 1493.3239 7.544946e+01 3431.2265954 #> 818   81.7 1492.4428 7.346236e+01 3434.0948683 #> 819   81.8 1491.7121 7.152472e+01 3436.7632189 #> 820   81.9 1491.1273 6.963590e+01 3439.2368242 #> 821   82.0 1490.6840 6.779521e+01 3441.5207887 #> 822   82.1 1490.3779 6.600194e+01 3443.6201401 #> 823   82.2 1490.2048 6.425534e+01 3445.5398258 #> 824   82.3 1490.1607 6.255462e+01 3447.2847096 #> 825   82.4 1490.2415 6.089898e+01 3448.8595693 #> 826   82.5 1490.4433 5.928757e+01 3450.2690942 #> 827   82.6 1490.7626 5.771955e+01 3451.5178833 #> 828   82.7 1491.1955 5.619406e+01 3452.6104441 #> 829   82.8 1491.7386 5.471023e+01 3453.5511910 #> 830   82.9 1492.3884 5.326717e+01 3454.3444447 #> 831   83.0 1493.1416 5.186398e+01 3454.9944319 #> 832   83.1 1493.9949 5.049979e+01 3455.5052846 #> 833   83.2 1494.9453 4.917371e+01 3455.8810402 #> 834   83.3 1495.9895 4.788483e+01 3456.1256416 #> 835   83.4 1497.1248 4.663227e+01 3456.2429379 #> 836   83.5 1498.3482 4.541516e+01 3456.2366846 #> 837   83.6 1499.6568 4.423262e+01 3456.1105443 #> 838   83.7 1501.0481 4.308377e+01 3455.8680878 #> 839   83.8 1502.5194 4.196777e+01 3455.5127950 #> 840   83.9 1504.0682 4.088376e+01 3455.0480558 #> 841   84.0 1505.6919 3.983091e+01 3454.4771714 #> 842   84.1 1507.3882 3.880840e+01 3453.8033559 #> 843   84.2 1509.1549 3.781541e+01 3453.0297372 #> 844   84.3 1510.9895 3.685114e+01 3452.1593587 #> 845   84.4 1512.8900 3.591482e+01 3451.1951805 #> 846   84.5 1514.8542 3.500567e+01 3450.1400815 #> 847   84.6 1516.8802 3.412294e+01 3448.9968605 #> 848   84.7 1518.9659 3.326588e+01 3447.7682379 #> 849   84.8 1521.1094 3.243379e+01 3446.4568573 #> 850   84.9 1523.3088 3.162594e+01 3445.0652875 #> 851   85.0 1525.5623 3.084164e+01 3443.5960236 #> 852   85.1 1527.8683 3.008023e+01 3442.0514892 #> 853   85.2 1530.2249 2.934103e+01 3440.4340378 #> 854   85.3 1532.6306 2.862340e+01 3438.7459544 #> 855   85.4 1535.0838 2.792672e+01 3436.9894576 #> 856   85.5 1537.5829 2.725037e+01 3435.1667006 #> 857   85.6 1540.1265 2.659375e+01 3433.2797735 #> 858   85.7 1542.7130 2.595629e+01 3431.3307045 #> 859   85.8 1545.3411 2.533740e+01 3429.3214618 #> 860   85.9 1548.0095 2.473655e+01 3427.2539548 #> 861   86.0 1550.7168 2.415319e+01 3425.1300363 #> 862   86.1 1553.4617 2.358681e+01 3422.9515034 #> 863   86.2 1556.2430 2.303689e+01 3420.7200996 #> 864   86.3 1559.0595 2.250294e+01 3418.4375157 #> 865   86.4 1561.9101 2.198448e+01 3416.1053918 #> 866   86.5 1564.7936 2.148104e+01 3413.7253186 #> 867   86.6 1567.7090 2.099218e+01 3411.2988387 #> 868   86.7 1570.6551 2.051744e+01 3408.8274480 #> 869   86.8 1573.6310 2.005642e+01 3406.3125971 #> 870   86.9 1576.6356 1.960868e+01 3403.7556928 #> 871   87.0 1579.6681 1.917383e+01 3401.1580991 #> 872   87.1 1582.7274 1.875148e+01 3398.5211386 #> 873   87.2 1585.8126 1.834126e+01 3395.8460940 #> 874   87.3 1588.9230 1.794278e+01 3393.1342086 #> 875   87.4 1592.0576 1.755571e+01 3390.3866884 #> 876   87.5 1595.2156 1.717968e+01 3387.6047025 #> 877   87.6 1598.3962 1.681438e+01 3384.7893845 #> 878   87.7 1601.5987 1.645947e+01 3381.9418337 #> 879   87.8 1604.8222 1.611464e+01 3379.0631160 #> 880   87.9 1608.0662 1.577958e+01 3376.1542650 #> 881   88.0 1611.3297 1.545400e+01 3373.2162828 #> 882   88.1 1614.6122 1.513762e+01 3370.2501414 #> 883   88.2 1617.9131 1.483016e+01 3367.2567832 #> 884   88.3 1621.2315 1.453135e+01 3364.2371222 #> 885   88.4 1624.5670 1.424092e+01 3361.1920447 #> 886   88.5 1627.9190 1.395864e+01 3358.1224103 #> 887   88.6 1631.2867 1.368425e+01 3355.0290529 #> 888   88.7 1634.6697 1.341751e+01 3351.9127809 #> 889   88.8 1638.0674 1.315821e+01 3348.7743789 #> 890   88.9 1641.4793 1.290611e+01 3345.6146075 #> 891   89.0 1644.9048 1.266101e+01 3342.4342049 #> 892   89.1 1648.3434 1.242270e+01 3339.2338870 #> 893   89.2 1651.7947 1.219097e+01 3336.0143484 #> 894   89.3 1655.2581 1.196563e+01 3332.7762632 #> 895   89.4 1658.7332 1.174649e+01 3329.5202852 #> 896   89.5 1662.2196 1.153337e+01 3326.2470489 #> 897   89.6 1665.7167 1.132610e+01 3322.9571702 #> 898   89.7 1669.2243 1.112450e+01 3319.6512465 #> 899   89.8 1672.7417 1.092841e+01 3316.3298579 #> 900   89.9 1676.2688 1.073767e+01 3312.9935673 #> 901   90.0 1679.8050 1.055211e+01 3309.6429212 #> 902   90.1 1683.3499 1.037161e+01 3306.2784500 #> 903   90.2 1686.9033 1.019599e+01 3302.9006688 #> 904   90.3 1690.4648 1.002514e+01 3299.5100777 #> 905   90.4 1694.0339 9.858901e+00 3296.1071621 #> 906   90.5 1697.6105 9.697154e+00 3292.6923937 #> 907   90.6 1701.1940 9.539768e+00 3289.2662303 #> 908   90.7 1704.7843 9.386620e+00 3285.8291168 #> 909   90.8 1708.3809 9.237590e+00 3282.3814853 #> 910   90.9 1711.9837 9.092563e+00 3278.9237555 #> 911   91.0 1715.5922 8.951426e+00 3275.4563352 #> 912   91.1 1719.2063 8.814071e+00 3271.9796207 #> 913   91.2 1722.8256 8.680394e+00 3268.4939970 #> 914   91.3 1726.4499 8.550292e+00 3264.9998385 #> 915   91.4 1730.0788 8.423668e+00 3261.4975087 #> 916   91.5 1733.7122 8.300425e+00 3257.9873615 #> 917   91.6 1737.3498 8.180473e+00 3254.4697405 #> 918   91.7 1740.9913 8.063721e+00 3250.9449799 #> 919   91.8 1744.6365 7.950084e+00 3247.4134049 #> 920   91.9 1748.2852 7.839477e+00 3243.8753315 #> 921   92.0 1751.9371 7.731821e+00 3240.3310672 #> 922   92.1 1755.5921 7.627036e+00 3236.7809111 #> 923   92.2 1759.2498 7.525046e+00 3233.2251542 #> 924   92.3 1762.9101 7.425779e+00 3229.6640797 #> 925   92.4 1766.5729 7.329163e+00 3226.0979631 #> 926   92.5 1770.2378 7.235130e+00 3222.5270726 #> 927   92.6 1773.9047 7.143612e+00 3218.9516694 #> 928   92.7 1777.5734 7.054545e+00 3215.3720075 #> 929   92.8 1781.2438 6.967867e+00 3211.7883344 #> 930   92.9 1784.9156 6.883518e+00 3208.2008913 #> 931   93.0 1788.5886 6.801438e+00 3204.6099129 #> 932   93.1 1792.2628 6.721572e+00 3201.0156278 #> 933   93.2 1795.9379 6.643864e+00 3197.4182589 #> 934   93.3 1799.6137 6.568262e+00 3193.8180233 #> 935   93.4 1803.2902 6.494713e+00 3190.2151328 #> 936   93.5 1806.9670 6.423169e+00 3186.6097935 #> 937   93.6 1810.6442 6.353580e+00 3183.0022066 #> 938   93.7 1814.3215 6.285902e+00 3179.3925682 #> 939   93.8 1817.9988 6.220088e+00 3175.7810697 #> 940   93.9 1821.6760 6.156094e+00 3172.1678976 #> 941   94.0 1825.3529 6.093879e+00 3168.5532341 #> 942   94.1 1829.0293 6.033403e+00 3164.9372568 #> 943   94.2 1832.7052 5.974624e+00 3161.3201391 #> 944   94.3 1836.3804 5.917505e+00 3157.7020503 #> 945   94.4 1840.0548 5.862010e+00 3154.0831558 #> 946   94.5 1843.7283 5.808102e+00 3150.4636170 #> 947   94.6 1847.4007 5.755746e+00 3146.8435918 #> 948   94.7 1851.0719 5.704910e+00 3143.2232341 #> 949   94.8 1854.7417 5.655560e+00 3139.6026947 #> 950   94.9 1858.4102 5.607666e+00 3135.9821209 #> 951   95.0 1862.0771 5.561198e+00 3132.3616565 #> 952   95.1 1865.7424 5.516125e+00 3128.7414424 #> 953   95.2 1869.4060 5.472421e+00 3125.1216164 #> 954   95.3 1873.0676 5.430057e+00 3121.5023133 #> 955   95.4 1876.7273 5.389007e+00 3117.8836651 #> 956   95.5 1880.3850 5.349246e+00 3114.2658009 #> 957   95.6 1884.0404 5.310749e+00 3110.6488472 #> 958   95.7 1887.6936 5.273493e+00 3107.0329280 #> 959   95.8 1891.3444 5.237454e+00 3103.4181647 #> 960   95.9 1894.9927 5.202611e+00 3099.8046764 #> 961   96.0 1898.6385 5.168942e+00 3096.1925797 #> 962   96.1 1902.2816 5.136427e+00 3092.5819891 #> 963   96.2 1905.9219 5.105047e+00 3088.9730169 #> 964   96.3 1909.5594 5.074781e+00 3085.3657734 #> 965   96.4 1913.1940 5.045611e+00 3081.7603667 #> 966   96.5 1916.8256 5.017521e+00 3078.1569031 #> 967   96.6 1920.4540 4.990493e+00 3074.5554870 #> 968   96.7 1924.0793 4.964510e+00 3070.9562209 #> 969   96.8 1927.7012 4.939558e+00 3067.3592058 #> 970   96.9 1931.3198 4.915621e+00 3063.7645407 #> 971   97.0 1934.9350 4.892684e+00 3060.1723234 #> 972   97.1 1938.5466 4.870735e+00 3056.5826498 #> 973   97.2 1942.1546 4.849759e+00 3052.9956146 #> 974   97.3 1945.7589 4.829744e+00 3049.4113108 #> 975   97.4 1949.3595 4.810679e+00 3045.8298304 #> 976   97.5 1952.9562 4.792551e+00 3042.2512637 #> 977   97.6 1956.5490 4.775349e+00 3038.6757000 #> 978   97.7 1960.1377 4.759064e+00 3035.1032274 #> 979   97.8 1963.7224 4.743684e+00 3031.5339329 #> 980   97.9 1967.3029 4.729202e+00 3027.9679023 #> 981   98.0 1970.8792 4.715607e+00 3024.4052203 #> 982   98.1 1974.4511 4.702892e+00 3020.8459709 #> 983   98.2 1978.0187 4.691048e+00 3017.2902369 #> 984   98.3 1981.5818 4.680068e+00 3013.7381004 #> 985   98.4 1985.1404 4.669946e+00 3010.1896426 #> 986   98.5 1988.6944 4.660674e+00 3006.6449439 #> 987   98.6 1992.2437 4.652246e+00 3003.1040840 #> 988   98.7 1995.7882 4.644658e+00 2999.5671418 #> 989   98.8 1999.3279 4.637903e+00 2996.0341958 #> 990   98.9 2002.8627 4.631977e+00 2992.5053237 #> 991   99.0 2006.3925 4.626875e+00 2988.9806026 #> 992   99.1 2009.9173 4.622595e+00 2985.4601093 #> 993   99.2 2013.4369 4.619132e+00 2981.9439199 #> 994   99.3 2016.9514 4.616483e+00 2978.4321104 #> 995   99.4 2020.4606 4.614646e+00 2974.9247560 #> 996   99.5 2023.9644 4.613618e+00 2971.4219318 #> 997   99.6 2027.4629 4.613399e+00 2967.9237127 #> 998   99.7 2030.9558 4.613985e+00 2964.4301731 #> 999   99.8 2034.4432 4.615377e+00 2960.9413874 #> 1000  99.9 2037.9250 4.617574e+00 2957.4574296 #> 1001 100.0 2041.4011 4.620576e+00 2953.9783737 #> 1002 100.1 2044.8713 4.624383e+00 2950.5042938 #> 1003 100.2 2048.3357 4.628995e+00 2947.0352634 #> 1004 100.3 2051.7942 4.634414e+00 2943.5713566 #> 1005 100.4 2055.2467 4.640641e+00 2940.1126472 #> 1006 100.5 2058.6931 4.647677e+00 2936.6592090 #> 1007 100.6 2062.1334 4.655526e+00 2933.2111161 #> 1008 100.7 2065.5674 4.664190e+00 2929.7684427 #> 1009 100.8 2068.9951 4.673671e+00 2926.3312631 #> 1010 100.9 2072.4164 4.683974e+00 2922.8996518 #> 1011 101.0 2075.8312 4.695102e+00 2919.4736838 #> 1012 101.1 2079.2395 4.707059e+00 2916.0534341 #> 1013 101.2 2082.6412 4.719850e+00 2912.6389783 #> 1014 101.3 2086.0361 4.733480e+00 2909.2303921 #> 1015 101.4 2089.4243 4.747954e+00 2905.8277520 #> 1016 101.5 2092.8056 4.763279e+00 2902.4311346 #> 1017 101.6 2096.1799 4.779460e+00 2899.0406172 #> 1018 101.7 2099.5472 4.796505e+00 2895.6562776 #> 1019 101.8 2102.9074 4.814420e+00 2892.2781943 #> 1020 101.9 2106.2603 4.833213e+00 2888.9064462 #> 1021 102.0 2109.6060 4.852892e+00 2885.5411130 #> 1022 102.1 2112.9443 4.873465e+00 2882.1822751 #> 1023 102.2 2116.2750 4.894942e+00 2878.8300138 #> 1024 102.3 2119.5983 4.917331e+00 2875.4844109 #> 1025 102.4 2122.9138 4.940643e+00 2872.1455493 #> 1026 102.5 2126.2216 4.964887e+00 2868.8135127 #> 1027 102.6 2129.5215 4.990074e+00 2865.4883856 #> 1028 102.7 2132.8135 5.016216e+00 2862.1702538 #> 1029 102.8 2136.0975 5.043324e+00 2858.8592039 #> 1030 102.9 2139.3733 5.071410e+00 2855.5553234 #> 1031 103.0 2142.6408 5.100486e+00 2852.2587014 #> 1032 103.1 2145.9000 5.130566e+00 2848.9694277 #> 1033 103.2 2149.1507 5.161664e+00 2845.6875935 #> 1034 103.3 2152.3929 5.193793e+00 2842.4132913 #> 1035 103.4 2155.6264 5.226969e+00 2839.1466149 #> 1036 103.5 2158.8511 5.261205e+00 2835.8876593 #> 1037 103.6 2162.0670 5.296519e+00 2832.6365213 #> 1038 103.7 2165.2738 5.332926e+00 2829.3932987 #> 1039 103.8 2168.4715 5.370443e+00 2826.1580911 #> 1040 103.9 2171.6599 5.409087e+00 2822.9309996 #> 1041 104.0 2174.8390 5.448877e+00 2819.7121271 #> 1042 104.1 2178.0086 5.489831e+00 2816.5015778 #> 1043 104.2 2181.1686 5.531968e+00 2813.2994581 #> 1044 104.3 2184.3188 5.575307e+00 2810.1058758 #> 1045 104.4 2187.4592 5.619871e+00 2806.9209408 #> 1046 104.5 2190.5896 5.665678e+00 2803.7447650 #> 1047 104.6 2193.7098 5.712751e+00 2800.5774620 #> 1048 104.7 2196.8197 5.761113e+00 2797.4191476 #> 1049 104.8 2199.9193 5.810786e+00 2794.2699398 #> 1050 104.9 2203.0082 5.861794e+00 2791.1299587 #> 1051 105.0 2206.0865 5.914162e+00 2787.9993267 #> 1052 105.1 2209.1539 5.967915e+00 2784.8781683 #> 1053 105.2 2212.2103 6.023078e+00 2781.7666106 #> 1054 105.3 2215.2555 6.079678e+00 2778.6647831 #> 1055 105.4 2218.2894 6.137743e+00 2775.5728179 #> 1056 105.5 2221.3118 6.197301e+00 2772.4908496 #> 1057 105.6 2224.3226 6.258380e+00 2769.4190154 #> 1058 105.7 2227.3215 6.321012e+00 2766.3574554 #> 1059 105.8 2230.3085 6.385225e+00 2763.3063126 #> 1060 105.9 2233.2832 6.451052e+00 2760.2657327 #> 1061 106.0 2236.2456 6.518525e+00 2757.2358645 #> 1062 106.1 2239.1955 6.587678e+00 2754.2168599 #> 1063 106.2 2242.1326 6.658544e+00 2751.2088740 #> 1064 106.3 2245.0568 6.731159e+00 2748.2120650 #> 1065 106.4 2247.9678 6.805558e+00 2745.2265946 #> 1066 106.5 2250.8656 6.881779e+00 2742.2526279 #> 1067 106.6 2253.7498 6.959860e+00 2739.2903335 #> 1068 106.7 2256.6203 7.039839e+00 2736.3398836 #> 1069 106.8 2259.4768 7.121757e+00 2733.4014542 #> 1070 106.9 2262.3191 7.205654e+00 2730.4752250 #> 1071 107.0 2265.1470 7.291574e+00 2727.5613797 #> 1072 107.1 2267.9603 7.379559e+00 2724.6601060 #> 1073 107.2 2270.7588 7.469653e+00 2721.7715958 #> 1074 107.3 2273.5421 7.561902e+00 2718.8960451 #> 1075 107.4 2276.3100 7.656353e+00 2716.0336544 #> 1076 107.5 2279.0623 7.753054e+00 2713.1846284 #> 1077 107.6 2281.7988 7.852054e+00 2710.3491768 #> 1078 107.7 2284.5191 7.953402e+00 2707.5275136 #> 1079 107.8 2287.2230 8.057152e+00 2704.7198577 #> 1080 107.9 2289.9102 8.163354e+00 2701.9264330 #> 1081 108.0 2292.5805 8.272065e+00 2699.1474686 #> 1082 108.1 2295.2335 8.383339e+00 2696.3831984 #> 1083 108.2 2297.8689 8.497233e+00 2693.6338618 #> 1084 108.3 2300.4865 8.613805e+00 2690.8997038 #> 1085 108.4 2303.0859 8.733116e+00 2688.1809747 #> 1086 108.5 2305.6668 8.855226e+00 2685.4779307 #> 1087 108.6 2308.2290 8.980198e+00 2682.7908338 #> 1088 108.7 2310.7720 9.108096e+00 2680.1199518 #> 1089 108.8 2313.2955 9.238986e+00 2677.4655589 #> 1090 108.9 2315.7991 9.372935e+00 2674.8279353 #> 1091 109.0 2318.2826 9.510011e+00 2672.2073680 #> 1092 109.1 2320.7456 9.650286e+00 2669.6041501 #> 1093 109.2 2323.1876 9.793830e+00 2667.0185819 #> 1094 109.3 2325.6083 9.940718e+00 2664.4509701 #> 1095 109.4 2328.0073 1.009102e+01 2661.9016287 #> 1096 109.5 2330.3843 1.024483e+01 2659.3708789 #> 1097 109.6 2332.7387 1.040220e+01 2656.8590493 #> 1098 109.7 2335.0703 1.056324e+01 2654.3664757 #> 1099 109.8 2337.3785 1.072801e+01 2651.8935019 #> 1100 109.9 2339.6629 1.089660e+01 2649.4404795 #> 1101 110.0 2341.9231 1.106909e+01 2647.0077680 #> 1102 110.1 2344.1587 1.124559e+01 2644.5957350 #> 1103 110.2 2346.3691 1.142616e+01 2642.2047569 #> 1104 110.3 2348.5539 1.161092e+01 2639.8352180 #> 1105 110.4 2350.7126 1.179994e+01 2637.4875119 #> 1106 110.5 2352.8446 1.199332e+01 2635.1620406 #> 1107 110.6 2354.9496 1.219116e+01 2632.8592155 #> 1108 110.7 2357.0270 1.239357e+01 2630.5794571 #> 1109 110.8 2359.0762 1.260063e+01 2628.3231954 #> 1110 110.9 2361.0967 1.281246e+01 2626.0908698 #> 1111 111.0 2363.0879 1.302915e+01 2623.8829296 #> 1112 111.1 2365.0493 1.325082e+01 2621.6998342 #> 1113 111.2 2366.9804 1.347758e+01 2619.5420529 #> 1114 111.3 2368.8804 1.370952e+01 2617.4100655 #> 1115 111.4 2370.7489 1.394677e+01 2615.3043621 #> 1116 111.5 2372.5851 1.418944e+01 2613.2254436 #> 1117 111.6 2374.3885 1.443765e+01 2611.1738219 #> 1118 111.7 2376.1585 1.469151e+01 2609.1500195 #> 1119 111.8 2377.8943 1.495115e+01 2607.1545706 #> 1120 111.9 2379.5953 1.521668e+01 2605.1880203 #> 1121 112.0 2381.2608 1.548823e+01 2603.2509256 #> 1122 112.1 2382.8902 1.576592e+01 2601.3438549 #> 1123 112.2 2384.4827 1.604989e+01 2599.4673887 #> 1124 112.3 2386.0376 1.634026e+01 2597.6221194 #> 1125 112.4 2387.5542 1.663716e+01 2595.8086514 #> 1126 112.5 2389.0317 1.694072e+01 2594.0276017 #> 1127 112.6 2390.4693 1.725107e+01 2592.2795994 #> 1128 112.7 2391.8664 1.756836e+01 2590.5652866 #> 1129 112.8 2393.2220 1.789271e+01 2588.8853175 #> 1130 112.9 2394.5354 1.822427e+01 2587.2403597 #> 1131 113.0 2395.8057 1.856317e+01 2585.6310932 #> 1132 113.1 2397.0322 1.890956e+01 2584.0582115 #> 1133 113.2 2398.2140 1.926357e+01 2582.5224206 #> 1134 113.3 2399.3502 1.962534e+01 2581.0244402 #> 1135 113.4 2400.4400 1.999502e+01 2579.5650028 #> 1136 113.5 2401.4824 2.037275e+01 2578.1448545 #> 1137 113.6 2402.4766 2.075867e+01 2576.7647544 #> 1138 113.7 2403.4216 2.115293e+01 2575.4254751 #> 1139 113.8 2404.3165 2.155566e+01 2574.1278024 #> 1140 113.9 2405.1604 2.196702e+01 2572.8725354 #> 1141 114.0 2405.9524 2.238715e+01 2571.6604864 #> 1142 114.1 2406.6913 2.281618e+01 2570.4924811 #> 1143 114.2 2407.3764 2.325427e+01 2569.3693580 #> 1144 114.3 2408.0065 2.370154e+01 2568.2919688 #> 1145 114.4 2408.5807 2.415815e+01 2567.2611780 #> 1146 114.5 2409.0979 2.462422e+01 2566.2778629 #> 1147 114.6 2409.5572 2.509990e+01 2565.3429132 #> 1148 114.7 2409.9575 2.558532e+01 2564.4572310 #> 1149 114.8 2410.2977 2.608060e+01 2563.6217306 #> 1150 114.9 2410.5768 2.658589e+01 2562.8373379 #> 1151 115.0 2410.7937 2.710130e+01 2562.1049904 #> 1152 115.1 2410.9474 2.762697e+01 2561.4256369 #> 1153 115.2 2411.0368 2.816299e+01 2560.8002369 #> 1154 115.3 2411.0607 2.870951e+01 2560.2297603 #> 1155 115.4 2411.0182 2.926661e+01 2559.7151868 #> 1156 115.5 2410.9081 2.983441e+01 2559.2575058 #> 1157 115.6 2410.7293 3.041300e+01 2558.8577155 #> 1158 115.7 2410.4807 3.100249e+01 2558.5168223 #> 1159 115.8 2410.1612 3.160295e+01 2558.2358404 #> 1160 115.9 2409.7697 3.221447e+01 2558.0157911 #> 1161 116.0 2409.3052 3.283712e+01 2557.8577018 #> 1162 116.1 2408.7664 3.347096e+01 2557.7626058 #> 1163 116.2 2408.1524 3.411606e+01 2557.7315406 #> 1164 116.3 2407.4620 3.477246e+01 2557.7655478 #> 1165 116.4 2406.6941 3.544019e+01 2557.8656720 #> 1166 116.5 2405.8478 3.611929e+01 2558.0329595 #> 1167 116.6 2404.9218 3.680977e+01 2558.2684573 #> 1168 116.7 2403.9152 3.751164e+01 2558.5732126 #> 1169 116.8 2402.8268 3.822488e+01 2558.9482707 #> 1170 116.9 2401.6558 3.894949e+01 2559.3946744 #> 1171 117.0 2400.4011 3.968543e+01 2559.9134626 #> 1172 117.1 2399.0617 4.043266e+01 2560.5056690 #> 1173 117.2 2397.6366 4.119110e+01 2561.1723202 #> 1174 117.3 2396.1249 4.196070e+01 2561.9144350 #> 1175 117.4 2394.5256 4.274134e+01 2562.7330222 #> 1176 117.5 2392.8380 4.353294e+01 2563.6290793 #> 1177 117.6 2391.0611 4.433535e+01 2564.6035910 #> 1178 117.7 2389.1940 4.514844e+01 2565.6575270 #> 1179 117.8 2387.2361 4.597205e+01 2566.7918405 #> 1180 117.9 2385.1865 4.680599e+01 2568.0074666 #> 1181 118.0 2383.0446 4.765006e+01 2569.3053197 #> 1182 118.1 2380.8097 4.850405e+01 2570.6862922 #> 1183 118.2 2378.4810 4.936772e+01 2572.1512523 #> 1184 118.3 2376.0582 5.024079e+01 2573.7010418 #> 1185 118.4 2373.5405 5.112299e+01 2575.3364739 #> 1186 118.5 2370.9277 5.201401e+01 2577.0583315 #> 1187 118.6 2368.2191 5.291352e+01 2578.8673643 #> 1188 118.7 2365.4146 5.382116e+01 2580.7642872 #> 1189 118.8 2362.5137 5.473657e+01 2582.7497777 #> 1190 118.9 2359.5162 5.565933e+01 2584.8244736 #> 1191 119.0 2356.4220 5.658904e+01 2586.9889707 #> 1192 119.1 2353.2309 5.752523e+01 2589.2438205 #> 1193 119.2 2349.9430 5.846745e+01 2591.5895277 #> 1194 119.3 2346.5583 5.941518e+01 2594.0265480 #> 1195 119.4 2343.0768 6.036791e+01 2596.5552854 #> 1196 119.5 2339.4988 6.132510e+01 2599.1760903 #> 1197 119.6 2335.8246 6.228616e+01 2601.8892567 #> 1198 119.7 2332.0545 6.325052e+01 2604.6950200 #> 1199 119.8 2328.1889 6.421755e+01 2607.5935549 #> 1200 119.9 2324.2284 6.518660e+01 2610.5849728 #> 1201 120.0 2320.1737 6.615703e+01 2613.6693198 #> 1202 120.1 2316.0253 6.712813e+01 2616.8465743 #> 1203 120.2 2311.7842 6.809920e+01 2620.1166451 #> 1204 120.3 2307.4511 6.906951e+01 2623.4793693 #> 1205 120.4 2303.0272 7.003831e+01 2626.9345100 #> 1206 120.5 2298.5134 7.100483e+01 2630.4817551 #> 1207 120.6 2293.9110 7.196829e+01 2634.1207149 #> 1208 120.7 2289.2212 7.292787e+01 2637.8509207 #> 1209 120.8 2284.4454 7.388276e+01 2641.6718231 #> 1210 120.9 2279.5851 7.483213e+01 2645.5827909 #> 1211 121.0 2274.6418 7.577512e+01 2649.5831095 #> 1212 121.1 2269.6171 7.671088e+01 2653.6719796 #> 1213 121.2 2264.5130 7.763852e+01 2657.8485168 #> 1214 121.3 2259.3311 7.855718e+01 2662.1117502 #> 1215 121.4 2254.0734 7.946597e+01 2666.4606219 #> 1216 121.5 2248.7420 8.036400e+01 2670.8939867 #> 1217 121.6 2243.3390 8.125036e+01 2675.4106116 #> 1218 121.7 2237.8667 8.212417e+01 2680.0091756 #> 1219 121.8 2232.3272 8.298452e+01 2684.6882700 #> 1220 121.9 2226.7231 8.383054e+01 2689.4463985 #> 1221 122.0 2221.0567 8.466131e+01 2694.2819778 #> 1222 122.1 2215.3307 8.547597e+01 2699.1933382 #> 1223 122.2 2209.5476 8.627365e+01 2704.1787246 #> 1224 122.3 2203.7102 8.705347e+01 2709.2362974 #> 1225 122.4 2197.8213 8.781459e+01 2714.3641339 #> 1226 122.5 2191.8836 8.855618e+01 2719.5602301 #> 1227 122.6 2185.9001 8.927742e+01 2724.8225019 #> 1228 122.7 2179.8737 8.997751e+01 2730.1487877 #> 1229 122.8 2173.8075 9.065569e+01 2735.5368499 #> 1230 122.9 2167.7044 9.131118e+01 2740.9843777 #> 1231 123.0 2161.5677 9.194328e+01 2746.4889895 #> 1232 123.1 2155.4005 9.255128e+01 2752.0482356 #> 1233 123.2 2149.2059 9.313451e+01 2757.6596013 #> 1234 123.3 2142.9872 9.369234e+01 2763.3205099 #> 1235 123.4 2136.7475 9.422415e+01 2769.0283258 #> 1236 123.5 2130.4903 9.472938e+01 2774.7803582 #> 1237 123.6 2124.2186 9.520749e+01 2780.5738648 #> 1238 123.7 2117.9360 9.565799e+01 2786.4060549 #> 1239 123.8 2111.6455 9.608041e+01 2792.2740941 #> 1240 123.9 2105.3506 9.647433e+01 2798.1751077 #> 1241 124.0 2099.0544 9.683939e+01 2804.1061847 #> 1242 124.1 2092.7604 9.717523e+01 2810.0643825 #> 1243 124.2 2086.4717 9.748156e+01 2816.0467307 #> 1244 124.3 2080.1916 9.775814e+01 2822.0502356 #> 1245 124.4 2073.9234 9.800475e+01 2828.0718842 #> 1246 124.5 2067.6701 9.822123e+01 2834.1086493 #> 1247 124.6 2061.4350 9.840746e+01 2840.1574933 #> 1248 124.7 2055.2213 9.856336e+01 2846.2153726 #> 1249 124.8 2049.0319 9.868890e+01 2852.2792425 #> 1250 124.9 2042.8698 9.878409e+01 2858.3460612 #> 1251 125.0 2036.7382 9.884899e+01 2864.4127943 #> 1252 125.1 2030.6399 9.888368e+01 2870.4764189 #> 1253 125.2 2024.5777 9.888833e+01 2876.5339283 #> 1254 125.3 2018.5546 9.886310e+01 2882.5823355 #> 1255 125.4 2012.5731 9.880821e+01 2888.6186779 #> 1256 125.5 2006.6360 9.872393e+01 2894.6400212 #> 1257 125.6 2000.7460 9.861057e+01 2900.6434627 #> 1258 125.7 1994.9054 9.846845e+01 2906.6261359 #> 1259 125.8 1989.1168 9.829795e+01 2912.5852135 #> 1260 125.9 1983.3826 9.809948e+01 2918.5179111 #> 1261 126.0 1977.7050 9.787349e+01 2924.4214906 #> 1262 126.1 1972.0863 9.762044e+01 2930.2932635 #> 1263 126.2 1966.5286 9.734084e+01 2936.1305936 #> 1264 126.3 1961.0339 9.703523e+01 2941.9309000 #> 1265 126.4 1955.6042 9.670417e+01 2947.6916600 #> 1266 126.5 1950.2414 9.634823e+01 2953.4104116 #> 1267 126.6 1944.9472 9.596803e+01 2959.0847555 #> 1268 126.7 1939.7234 9.556420e+01 2964.7123575 #> 1269 126.8 1934.5717 9.513738e+01 2970.2909507 #> 1270 126.9 1929.4934 9.468825e+01 2975.8183369 #> 1271 127.0 1924.4901 9.421750e+01 2981.2923886 #> 1272 127.1 1919.5631 9.372581e+01 2986.7110500 #> 1273 127.2 1914.7138 9.321390e+01 2992.0723387 #> 1274 127.3 1909.9432 9.268249e+01 2997.3743465 #> 1275 127.4 1905.2524 9.213232e+01 3002.6152405 #> 1276 127.5 1900.6426 9.156412e+01 3007.7932638 #> 1277 127.6 1896.1146 9.097864e+01 3012.9067356 #> 1278 127.7 1891.6693 9.037663e+01 3017.9540525 #> 1279 127.8 1887.3075 8.975883e+01 3022.9336878 #> 1280 127.9 1883.0298 8.912601e+01 3027.8441922 #> 1281 128.0 1878.8369 8.847890e+01 3032.6841934 #> 1282 128.1 1874.7293 8.781828e+01 3037.4523962 #> 1283 128.2 1870.7076 8.714487e+01 3042.1475818 #> 1284 128.3 1866.7720 8.645942e+01 3046.7686075 #> 1285 128.4 1862.9229 8.576267e+01 3051.3144059 #> 1286 128.5 1859.1607 8.505535e+01 3055.7839845 #> 1287 128.6 1855.4854 8.433818e+01 3060.1764246 #> 1288 128.7 1851.8972 8.361187e+01 3064.4908803 #> 1289 128.8 1848.3963 8.287713e+01 3068.7265776 #> 1290 128.9 1844.9825 8.213465e+01 3072.8828133 #> 1291 129.0 1841.6559 8.138510e+01 3076.9589534 #> 1292 129.1 1838.4164 8.062916e+01 3080.9544323 #> 1293 129.2 1835.2638 7.986747e+01 3084.8687511 #> 1294 129.3 1832.1978 7.910068e+01 3088.7014761 #> 1295 129.4 1829.2183 7.832942e+01 3092.4522374 #> 1296 129.5 1826.3250 7.755428e+01 3096.1207275 #> 1297 129.6 1823.5174 7.677588e+01 3099.7066994 #> 1298 129.7 1820.7953 7.599478e+01 3103.2099651 #> 1299 129.8 1818.1581 7.521155e+01 3106.6303938 #> 1300 129.9 1815.6054 7.442673e+01 3109.9679105 #> 1301 130.0 1813.1366 7.364086e+01 3113.2224937 #> 1302 130.1 1810.7514 7.285444e+01 3116.3941743 #> 1303 130.2 1808.4490 7.206798e+01 3119.4830333 #> 1304 130.3 1806.2288 7.128195e+01 3122.4892004 #> 1305 130.4 1804.0903 7.049681e+01 3125.4128518 #> 1306 130.5 1802.0328 6.971300e+01 3128.2542090 #> 1307 130.6 1800.0555 6.893096e+01 3131.0135364 #> 1308 130.7 1798.1578 6.815108e+01 3133.6911399 #> 1309 130.8 1796.3389 6.737377e+01 3136.2873651 #> 1310 130.9 1794.5980 6.659940e+01 3138.8025955 #> 1311 131.0 1792.9344 6.582833e+01 3141.2372508 #> 1312 131.1 1791.3473 6.506089e+01 3143.5917852 #> 1313 131.2 1789.8359 6.429742e+01 3145.8666855 #> 1314 131.3 1788.3993 6.353823e+01 3148.0624699 #> 1315 131.4 1787.0367 6.278361e+01 3150.1796859 #> 1316 131.5 1785.7473 6.203384e+01 3152.2189092 #> 1317 131.6 1784.5301 6.128918e+01 3154.1807415 #> 1318 131.7 1783.3843 6.054988e+01 3156.0658096 #> 1319 131.8 1782.3091 5.981618e+01 3157.8747633 #> 1320 131.9 1781.3034 5.908830e+01 3159.6082745 #> 1321 132.0 1780.3665 5.836644e+01 3161.2670355 #> 1322 132.1 1779.4974 5.765080e+01 3162.8517575 #> 1323 132.2 1778.6953 5.694155e+01 3164.3631694 #> 1324 132.3 1777.9591 5.623886e+01 3165.8020165 #> 1325 132.4 1777.2880 5.554290e+01 3167.1690592 #> 1326 132.5 1776.6811 5.485379e+01 3168.4650717 #> 1327 132.6 1776.1375 5.417167e+01 3169.6908408 #> 1328 132.7 1775.6562 5.349667e+01 3170.8471651 #> 1329 132.8 1775.2363 5.282888e+01 3171.9348531 #> 1330 132.9 1774.8769 5.216842e+01 3172.9547231 #> 1331 133.0 1774.5770 5.151536e+01 3173.9076013 #> 1332 133.1 1774.3359 5.086980e+01 3174.7943213 #> 1333 133.2 1774.1525 5.023178e+01 3175.6157232 #> 1334 133.3 1774.0260 4.960139e+01 3176.3726522 #> 1335 133.4 1773.9554 4.897867e+01 3177.0659583 #> 1336 133.5 1773.9398 4.836366e+01 3177.6964949 #> 1337 133.6 1773.9785 4.775640e+01 3178.2651185 #> 1338 133.7 1774.0704 4.715692e+01 3178.7726875 #> 1339 133.8 1774.2147 4.656524e+01 3179.2200618 #> 1340 133.9 1774.4105 4.598137e+01 3179.6081019 #> 1341 134.0 1774.6570 4.540533e+01 3179.9376684 #> 1342 134.1 1774.9533 4.483712e+01 3180.2096210 #> 1343 134.2 1775.2985 4.427672e+01 3180.4248183 #> 1344 134.3 1775.6917 4.372414e+01 3180.5841172 #> 1345 134.4 1776.1323 4.317935e+01 3180.6883721 #> 1346 134.5 1776.6192 4.264234e+01 3180.7384344 #> 1347 134.6 1777.1518 4.211308e+01 3180.7351524 #> 1348 134.7 1777.7291 4.159154e+01 3180.6793703 #> 1349 134.8 1778.3504 4.107770e+01 3180.5719284 #> 1350 134.9 1779.0148 4.057150e+01 3180.4136621 #> 1351 135.0 1779.7217 4.007292e+01 3180.2054019 #> 1352 135.1 1780.4701 3.958191e+01 3179.9479727 #> 1353 135.2 1781.2594 3.909841e+01 3179.6421941 #> 1354 135.3 1782.0887 3.862239e+01 3179.2888794 #> 1355 135.4 1782.9574 3.815378e+01 3178.8888356 #> 1356 135.5 1783.8646 3.769252e+01 3178.4428633 #> 1357 135.6 1784.8097 3.723857e+01 3177.9517562 #> 1358 135.7 1785.7918 3.679186e+01 3177.4163010 #> 1359 135.8 1786.8104 3.635233e+01 3176.8372771 #> 1360 135.9 1787.8646 3.591991e+01 3176.2154567 #> 1361 136.0 1788.9539 3.549453e+01 3175.5516042 #> 1362 136.1 1790.0774 3.507613e+01 3174.8464764 #> 1363 136.2 1791.2345 3.466464e+01 3174.1008220 #> 1364 136.3 1792.4246 3.425999e+01 3173.3153820 #> 1365 136.4 1793.6470 3.386211e+01 3172.4908891 #> 1366 136.5 1794.9010 3.347091e+01 3171.6280679 #> 1367 136.6 1796.1860 3.308634e+01 3170.7276346 #> 1368 136.7 1797.5014 3.270831e+01 3169.7902972 #> 1369 136.8 1798.8465 3.233676e+01 3168.8167550 #> 1370 136.9 1800.2207 3.197159e+01 3167.8076992 #> 1371 137.0 1801.6234 3.161275e+01 3166.7638123 #> 1372 137.1 1803.0541 3.126014e+01 3165.6857683 #> 1373 137.2 1804.5121 3.091370e+01 3164.5742326 #> 1374 137.3 1805.9968 3.057336e+01 3163.4298621 #> 1375 137.4 1807.5077 3.023902e+01 3162.2533053 #> 1376 137.5 1809.0442 2.991061e+01 3161.0452020 #> 1377 137.6 1810.6057 2.958807e+01 3159.8061833 #> 1378 137.7 1812.1918 2.927131e+01 3158.5368722 #> 1379 137.8 1813.8019 2.896025e+01 3157.2378830 #> 1380 137.9 1815.4354 2.865482e+01 3155.9098214 #> 1381 138.0 1817.0918 2.835494e+01 3154.5532851 #> 1382 138.1 1818.7706 2.806055e+01 3153.1688630 #> 1383 138.2 1820.4713 2.777155e+01 3151.7571361 #> 1384 138.3 1822.1934 2.748788e+01 3150.3186770 #> 1385 138.4 1823.9365 2.720947e+01 3148.8540500 #> 1386 138.5 1825.6999 2.693624e+01 3147.3638115 #> 1387 138.6 1827.4834 2.666812e+01 3145.8485097 #> 1388 138.7 1829.2863 2.640503e+01 3144.3086849 #> 1389 138.8 1831.1082 2.614691e+01 3142.7448694 #> 1390 138.9 1832.9487 2.589369e+01 3141.1575879 #> 1391 139.0 1834.8074 2.564528e+01 3139.5473572 #> 1392 139.1 1836.6837 2.540163e+01 3137.9146865 #> 1393 139.2 1838.5773 2.516267e+01 3136.2600774 #> 1394 139.3 1840.4876 2.492833e+01 3134.5840241 #> 1395 139.4 1842.4145 2.469854e+01 3132.8870134 #> 1396 139.5 1844.3572 2.447323e+01 3131.1695246 #> 1397 139.6 1846.3156 2.425234e+01 3129.4320302 #> 1398 139.7 1848.2892 2.403582e+01 3127.6749954 #> 1399 139.8 1850.2775 2.382358e+01 3125.8988782 #> 1400 139.9 1852.2803 2.361557e+01 3124.1041301 #> 1401 140.0 1854.2971 2.341174e+01 3122.2911954 #> 1402 140.1 1856.3275 2.321201e+01 3120.4605121 #> 1403 140.2 1858.3712 2.301632e+01 3118.6125111 #> 1404 140.3 1860.4278 2.282463e+01 3116.7476173 #> 1405 140.4 1862.4969 2.263687e+01 3114.8662488 #> 1406 140.5 1864.5782 2.245298e+01 3112.9688177 #> 1407 140.6 1866.6714 2.227291e+01 3111.0557296 #> 1408 140.7 1868.7760 2.209661e+01 3109.1273843 #> 1409 140.8 1870.8918 2.192401e+01 3107.1841753 #> 1410 140.9 1873.0184 2.175507e+01 3105.2264905 #> 1411 141.0 1875.1556 2.158973e+01 3103.2547118 #> 1412 141.1 1877.3028 2.142795e+01 3101.2692155 #> 1413 141.2 1879.4600 2.126966e+01 3099.2703723 #> 1414 141.3 1881.6266 2.111483e+01 3097.2585472 #> 1415 141.4 1883.8025 2.096339e+01 3095.2341002 #> 1416 141.5 1885.9873 2.081532e+01 3093.1973857 #> 1417 141.6 1888.1807 2.067055e+01 3091.1487530 #> 1418 141.7 1890.3824 2.052904e+01 3089.0885462 #> 1419 141.8 1892.5921 2.039076e+01 3087.0171046 #> 1420 141.9 1894.8096 2.025564e+01 3084.9347625 #> 1421 142.0 1897.0345 2.012366e+01 3082.8418493 #> 1422 142.1 1899.2665 1.999476e+01 3080.7386898 #> 1423 142.2 1901.5055 1.986891e+01 3078.6256042 #> 1424 142.3 1903.7510 1.974607e+01 3076.5029080 #> 1425 142.4 1906.0029 1.962619e+01 3074.3709126 #> 1426 142.5 1908.2608 1.950924e+01 3072.2299246 #> 1427 142.6 1910.5246 1.939517e+01 3070.0802467 #> 1428 142.7 1912.7939 1.928396e+01 3067.9221775 #> 1429 142.8 1915.0684 1.917557e+01 3065.7560112 #> 1430 142.9 1917.3480 1.906996e+01 3063.5820382 #> 1431 143.0 1919.6324 1.896709e+01 3061.4005452 #> 1432 143.1 1921.9213 1.886693e+01 3059.2118147 #> 1433 143.2 1924.2144 1.876945e+01 3057.0161258 #> 1434 143.3 1926.5116 1.867462e+01 3054.8137539 #> 1435 143.4 1928.8126 1.858241e+01 3052.6049707 #> 1436 143.5 1931.1172 1.849278e+01 3050.3900446 #> 1437 143.6 1933.4251 1.840570e+01 3048.1692405 #> 1438 143.7 1935.7360 1.832115e+01 3045.9428202 #> 1439 143.8 1938.0499 1.823910e+01 3043.7110420 #> 1440 143.9 1940.3663 1.815951e+01 3041.4741613 #> 1441 144.0 1942.6852 1.808237e+01 3039.2324303 #> 1442 144.1 1945.0063 1.800765e+01 3036.9860982 #> 1443 144.2 1947.3293 1.793531e+01 3034.7354114 #> 1444 144.3 1949.6540 1.786534e+01 3032.4806134 #> 1445 144.4 1951.9803 1.779772e+01 3030.2219449 #> 1446 144.5 1954.3079 1.773241e+01 3027.9596440 #> 1447 144.6 1956.6367 1.766940e+01 3025.6939461 #> 1448 144.7 1958.9663 1.760866e+01 3023.4250841 #> 1449 144.8 1961.2965 1.755018e+01 3021.1532885 #> 1450 144.9 1963.6273 1.749393e+01 3018.8787874 #> 1451 145.0 1965.9583 1.743990e+01 3016.6018064 #> 1452 145.1 1968.2894 1.738805e+01 3014.3225691 #> 1453 145.2 1970.6203 1.733838e+01 3012.0412966 #> 1454 145.3 1972.9509 1.729087e+01 3009.7582081 #> 1455 145.4 1975.2810 1.724550e+01 3007.4735207 #> 1456 145.5 1977.6103 1.720225e+01 3005.1874495 #> 1457 145.6 1979.9387 1.716110e+01 3002.9002077 #> 1458 145.7 1982.2659 1.712205e+01 3000.6120066 #> 1459 145.8 1984.5919 1.708508e+01 2998.3230556 #> 1460 145.9 1986.9163 1.705016e+01 2996.0335626 #> 1461 146.0 1989.2390 1.701730e+01 2993.7437337 #> 1462 146.1 1991.5598 1.698647e+01 2991.4537732 #> 1463 146.2 1993.8785 1.695766e+01 2989.1638842 #> 1464 146.3 1996.1949 1.693086e+01 2986.8742681 #> 1465 146.4 1998.5088 1.690606e+01 2984.5851248 #> 1466 146.5 2000.8201 1.688325e+01 2982.2966530 #> 1467 146.6 2003.1285 1.686242e+01 2980.0090500 #> 1468 146.7 2005.4339 1.684356e+01 2977.7225119 #> 1469 146.8 2007.7361 1.682666e+01 2975.4372333 #> 1470 146.9 2010.0349 1.681170e+01 2973.1534081 #> 1471 147.0 2012.3301 1.679869e+01 2970.8712289 #> 1472 147.1 2014.6215 1.678762e+01 2968.5908870 #> 1473 147.2 2016.9090 1.677847e+01 2966.3125732 #> 1474 147.3 2019.1923 1.677124e+01 2964.0364771 #> 1475 147.4 2021.4713 1.676593e+01 2961.7627873 #> 1476 147.5 2023.7458 1.676253e+01 2959.4916919 #> 1477 147.6 2026.0156 1.676103e+01 2957.2233779 #> 1478 147.7 2028.2805 1.676144e+01 2954.9580317 #> 1479 147.8 2030.5404 1.676374e+01 2952.6958392 #> 1480 147.9 2032.7951 1.676793e+01 2950.4369853 #> 1481 148.0 2035.0443 1.677401e+01 2948.1816548 #> 1482 148.1 2037.2880 1.678198e+01 2945.9300315 #> 1483 148.2 2039.5259 1.679184e+01 2943.6822990 #> 1484 148.3 2041.7578 1.680358e+01 2941.4386404 #> 1485 148.4 2043.9836 1.681720e+01 2939.1992385 #> 1486 148.5 2046.2030 1.683270e+01 2936.9642756 #> 1487 148.6 2048.4160 1.685008e+01 2934.7339338 #> 1488 148.7 2050.6223 1.686935e+01 2932.5083950 #> 1489 148.8 2052.8217 1.689049e+01 2930.2878410 #> 1490 148.9 2055.0140 1.691352e+01 2928.0724530 #> 1491 149.0 2057.1991 1.693844e+01 2925.8624127 #> 1492 149.1 2059.3769 1.696524e+01 2923.6579013 #> 1493 149.2 2061.5470 1.699393e+01 2921.4591002 #> 1494 149.3 2063.7093 1.702452e+01 2919.2661907 #> 1495 149.4 2065.8636 1.705700e+01 2917.0793543 #> 1496 149.5 2068.0099 1.709138e+01 2914.8987724 #> 1497 149.6 2070.1477 1.712766e+01 2912.7246268 #> 1498 149.7 2072.2770 1.716585e+01 2910.5570993 #> 1499 149.8 2074.3977 1.720596e+01 2908.3963721 #> 1500 149.9 2076.5094 1.724798e+01 2906.2426276 #> 1501 150.0 2078.6120 1.729193e+01 2904.0960485 #> 1502 150.1 2080.7054 1.733780e+01 2901.9568178 #> 1503 150.2 2082.7893 1.738562e+01 2899.8251191 #> 1504 150.3 2084.8635 1.743538e+01 2897.7011361 #> 1505 150.4 2086.9279 1.748709e+01 2895.5850534 #> 1506 150.5 2088.9822 1.754076e+01 2893.4770558 #> 1507 150.6 2091.0263 1.759640e+01 2891.3773288 #> 1508 150.7 2093.0599 1.765401e+01 2889.2860583 #> 1509 150.8 2095.0830 1.771361e+01 2887.2034311 #> 1510 150.9 2097.0952 1.777521e+01 2885.1296344 #> 1511 151.0 2099.0963 1.783880e+01 2883.0648562 #> 1512 151.1 2101.0863 1.790442e+01 2881.0092854 #> 1513 151.2 2103.0648 1.797205e+01 2878.9631114 #> 1514 151.3 2105.0318 1.804172e+01 2876.9265245 #> 1515 151.4 2106.9868 1.811344e+01 2874.8997158 #> 1516 151.5 2108.9299 1.818721e+01 2872.8828774 #> 1517 151.6 2110.8607 1.826306e+01 2870.8762021 #> 1518 151.7 2112.7791 1.834098e+01 2868.8798839 #> 1519 151.8 2114.6849 1.842100e+01 2866.8941174 #> 1520 151.9 2116.5778 1.850313e+01 2864.9190985 #> 1521 152.0 2118.4576 1.858737e+01 2862.9550240 #> 1522 152.1 2120.3242 1.867374e+01 2861.0020917 #> 1523 152.2 2122.1772 1.876227e+01 2859.0605005 #> 1524 152.3 2124.0166 1.885295e+01 2857.1304506 #> 1525 152.4 2125.8421 1.894581e+01 2855.2121431 #> 1526 152.5 2127.6534 1.904085e+01 2853.3057802 #> 1527 152.6 2129.4503 1.913810e+01 2851.4115656 #> 1528 152.7 2131.2327 1.923756e+01 2849.5297040 #> 1529 152.8 2133.0003 1.933926e+01 2847.6604012 #> 1530 152.9 2134.7529 1.944321e+01 2845.8038646 #> 1531 153.0 2136.4903 1.954942e+01 2843.9603026 #> 1532 153.1 2138.2122 1.965791e+01 2842.1299250 #> 1533 153.2 2139.9184 1.976870e+01 2840.3129428 #> 1534 153.3 2141.6086 1.988180e+01 2838.5095686 #> 1535 153.4 2143.2828 1.999723e+01 2836.7200161 #> 1536 153.5 2144.9405 2.011501e+01 2834.9445005 #> 1537 153.6 2146.5816 2.023515e+01 2833.1832383 #> 1538 153.7 2148.2059 2.035766e+01 2831.4364474 #> 1539 153.8 2149.8131 2.048258e+01 2829.7043472 #> 1540 153.9 2151.4029 2.060991e+01 2827.9871585 #> 1541 154.0 2152.9752 2.073966e+01 2826.2851035 #> 1542 154.1 2154.5297 2.087187e+01 2824.5984058 #> 1543 154.2 2156.0662 2.100654e+01 2822.9272906 #> 1544 154.3 2157.5843 2.114370e+01 2821.2719843 #> 1545 154.4 2159.0839 2.128336e+01 2819.6327150 #> 1546 154.5 2160.5648 2.142553e+01 2818.0097121 #> 1547 154.6 2162.0265 2.157025e+01 2816.4032066 #> 1548 154.7 2163.4691 2.171752e+01 2814.8134308 #> 1549 154.8 2164.8920 2.186735e+01 2813.2406186 #> 1550 154.9 2166.2952 2.201978e+01 2811.6850054 #> 1551 155.0 2167.6784 2.217482e+01 2810.1468278 #> 1552 155.1 2169.0412 2.233248e+01 2808.6263240 #> 1553 155.2 2170.3835 2.249278e+01 2807.1237337 #> 1554 155.3 2171.7050 2.265574e+01 2805.6392979 #> 1555 155.4 2173.0054 2.282138e+01 2804.1732590 #> 1556 155.5 2174.2844 2.298972e+01 2802.7258608 #> 1557 155.6 2175.5419 2.316076e+01 2801.2973484 #> 1558 155.7 2176.7775 2.333453e+01 2799.8879685 #> 1559 155.8 2177.9910 2.351104e+01 2798.4979688 #> 1560 155.9 2179.1821 2.369031e+01 2797.1275985 #> 1561 156.0 2180.3505 2.387236e+01 2795.7771080 #> 1562 156.1 2181.4961 2.405720e+01 2794.4467488 #> 1563 156.2 2182.6184 2.424484e+01 2793.1367738 #> 1564 156.3 2183.7173 2.443530e+01 2791.8474370 #> 1565 156.4 2184.7924 2.462860e+01 2790.5789934 #> 1566 156.5 2185.8436 2.482475e+01 2789.3316991 #> 1567 156.6 2186.8704 2.502376e+01 2788.1058115 #> 1568 156.7 2187.8728 2.522564e+01 2786.9015885 #> 1569 156.8 2188.8503 2.543042e+01 2785.7192893 #> 1570 156.9 2189.8027 2.563809e+01 2784.5591738 #> 1571 157.0 2190.7298 2.584867e+01 2783.4215028 #> 1572 157.1 2191.6313 2.606217e+01 2782.3065378 #> 1573 157.2 2192.5069 2.627861e+01 2781.2145408 #> 1574 157.3 2193.3562 2.649798e+01 2780.1457747 #> 1575 157.4 2194.1792 2.672030e+01 2779.1005027 #> 1576 157.5 2194.9754 2.694558e+01 2778.0789887 #> 1577 157.6 2195.7447 2.717382e+01 2777.0814965 #> 1578 157.7 2196.4867 2.740503e+01 2776.1082908 #> 1579 157.8 2197.2011 2.763922e+01 2775.1596359 #> 1580 157.9 2197.8878 2.787638e+01 2774.2357967 #> 1581 158.0 2198.5464 2.811652e+01 2773.3370377 #> 1582 158.1 2199.1767 2.835964e+01 2772.4636235 #> 1583 158.2 2199.7784 2.860575e+01 2771.6158184 #> 1584 158.3 2200.3513 2.885484e+01 2770.7938864 #> 1585 158.4 2200.8950 2.910692e+01 2769.9980912 #> 1586 158.5 2201.4093 2.936197e+01 2769.2286957 #> 1587 158.6 2201.8940 2.961999e+01 2768.4859622 #> 1588 158.7 2202.3489 2.988099e+01 2767.7701522 #> 1589 158.8 2202.7735 3.014495e+01 2767.0815262 #> 1590 158.9 2203.1678 3.041186e+01 2766.4203438 #> 1591 159.0 2203.5314 3.068172e+01 2765.7868630 #> 1592 159.1 2203.8642 3.095451e+01 2765.1813407 #> 1593 159.2 2204.1658 3.123022e+01 2764.6040322 #> 1594 159.3 2204.4360 3.150883e+01 2764.0551910 #> 1595 159.4 2204.6746 3.179034e+01 2763.5350689 #> 1596 159.5 2204.8814 3.207472e+01 2763.0439155 #> 1597 159.6 2205.0561 3.236195e+01 2762.5819783 #> 1598 159.7 2205.1985 3.265202e+01 2762.1495024 #> 1599 159.8 2205.3084 3.294489e+01 2761.7467302 #> 1600 159.9 2205.3856 3.324054e+01 2761.3739016 #> 1601 160.0 2205.4298 3.353895e+01 2761.0312533 #> 1602 160.1 2205.4409 3.384008e+01 2760.7190190 #> 1603 160.2 2205.4187 3.414391e+01 2760.4374290 #> 1604 160.3 2205.3629 3.445040e+01 2760.1867101 #> 1605 160.4 2205.2734 3.475952e+01 2759.9670852 #> 1606 160.5 2205.1500 3.507122e+01 2759.7787734 #> 1607 160.6 2204.9925 3.538547e+01 2759.6219895 #> 1608 160.7 2204.8008 3.570222e+01 2759.4969439 #> 1609 160.8 2204.5747 3.602143e+01 2759.4038423 #> 1610 160.9 2204.3141 3.634305e+01 2759.3428855 #> 1611 161.0 2204.0187 3.666704e+01 2759.3142692 #> 1612 161.1 2203.6885 3.699333e+01 2759.3181839 #> 1613 161.2 2203.3233 3.732188e+01 2759.3548142 #> 1614 161.3 2202.9230 3.765263e+01 2759.4243390 #> 1615 161.4 2202.4876 3.798551e+01 2759.5269312 #> 1616 161.5 2202.0168 3.832047e+01 2759.6627572 #> 1617 161.6 2201.5106 3.865744e+01 2759.8319768 #> 1618 161.7 2200.9689 3.899635e+01 2760.0347429 #> 1619 161.8 2200.3917 3.933713e+01 2760.2712014 #> 1620 161.9 2199.7788 3.967971e+01 2760.5414906 #> 1621 162.0 2199.1303 4.002401e+01 2760.8457414 #> 1622 162.1 2198.4460 4.036995e+01 2761.1840765 #> 1623 162.2 2197.7259 4.071745e+01 2761.5566107 #> 1624 162.3 2196.9701 4.106643e+01 2761.9634500 #> 1625 162.4 2196.1785 4.141680e+01 2762.4046921 #> 1626 162.5 2195.3511 4.176846e+01 2762.8804252 #> 1627 162.6 2194.4879 4.212134e+01 2763.3907287 #> 1628 162.7 2193.5890 4.247532e+01 2763.9356721 #> 1629 162.8 2192.6544 4.283031e+01 2764.5153154 #> 1630 162.9 2191.6841 4.318621e+01 2765.1297083 #> 1631 163.0 2190.6782 4.354292e+01 2765.7788903 #> 1632 163.1 2189.6368 4.390033e+01 2766.4628904 #> 1633 163.2 2188.5599 4.425833e+01 2767.1817266 #> 1634 163.3 2187.4478 4.461681e+01 2767.9354057 #> 1635 163.4 2186.3004 4.497565e+01 2768.7239235 #> 1636 163.5 2185.1180 4.533473e+01 2769.5472638 #> 1637 163.6 2183.9007 4.569394e+01 2770.4053989 #> 1638 163.7 2182.6486 4.605315e+01 2771.2982888 #> 1639 163.8 2181.3619 4.641224e+01 2772.2258813 #> 1640 163.9 2180.0408 4.677109e+01 2773.1881116 #> 1641 164.0 2178.6855 4.712955e+01 2774.1849021 #> 1642 164.1 2177.2963 4.748750e+01 2775.2161624 #> 1643 164.2 2175.8734 4.784481e+01 2776.2817888 #> 1644 164.3 2174.4170 4.820133e+01 2777.3816641 #> 1645 164.4 2172.9274 4.855694e+01 2778.5156579 #> 1646 164.5 2171.4049 4.891148e+01 2779.6836257 #> 1647 164.6 2169.8498 4.926482e+01 2780.8854093 #> 1648 164.7 2168.2623 4.961682e+01 2782.1208363 #> 1649 164.8 2166.6430 4.996733e+01 2783.3897201 #> 1650 164.9 2164.9919 5.031620e+01 2784.6918599 #> 1651 165.0 2163.3097 5.066328e+01 2786.0270402 #> 1652 165.1 2161.5965 5.100842e+01 2787.3950310 #> 1653 165.2 2159.8529 5.135148e+01 2788.7955877 #> 1654 165.3 2158.0792 5.169231e+01 2790.2284506 #> 1655 165.4 2156.2759 5.203073e+01 2791.6933456 #> 1656 165.5 2154.4434 5.236662e+01 2793.1899831 #> 1657 165.6 2152.5821 5.269981e+01 2794.7180591 #> 1658 165.7 2150.6926 5.303014e+01 2796.2772541 #> 1659 165.8 2148.7753 5.335747e+01 2797.8672340 #> 1660 165.9 2146.8307 5.368163e+01 2799.4876492 #> 1661 166.0 2144.8594 5.400248e+01 2801.1381356 #> 1662 166.1 2142.8618 5.431985e+01 2802.8183137 #> 1663 166.2 2140.8386 5.463359e+01 2804.5277892 #> 1664 166.3 2138.7903 5.494356e+01 2806.2661529 #> 1665 166.4 2136.7174 5.524959e+01 2808.0329809 #> 1666 166.5 2134.6206 5.555153e+01 2809.8278344 #> 1667 166.6 2132.5005 5.584924e+01 2811.6502601 #> 1668 166.7 2130.3577 5.614256e+01 2813.4997902 #> 1669 166.8 2128.1927 5.643134e+01 2815.3759427 #> 1670 166.9 2126.0063 5.671544e+01 2817.2782211 #> 1671 167.0 2123.7992 5.699470e+01 2819.2061154 #> 1672 167.1 2121.5719 5.726899e+01 2821.1591013 #> 1673 167.2 2119.3252 5.753817e+01 2823.1366412 #> 1674 167.3 2117.0597 5.780209e+01 2825.1381843 #> 1675 167.4 2114.7762 5.806061e+01 2827.1631663 #> 1676 167.5 2112.4754 5.831361e+01 2829.2110104 #> 1677 167.6 2110.1579 5.856095e+01 2831.2811272 #> 1678 167.7 2107.8246 5.880250e+01 2833.3729148 #> 1679 167.8 2105.4761 5.903814e+01 2835.4857597 #> 1680 167.9 2103.1132 5.926774e+01 2837.6190365 #> 1681 168.0 2100.7367 5.949118e+01 2839.7721089 #> 1682 168.1 2098.3473 5.970835e+01 2841.9443292 #> 1683 168.2 2095.9458 5.991914e+01 2844.1350396 #> 1684 168.3 2093.5330 6.012343e+01 2846.3435719 #> 1685 168.4 2091.1096 6.032113e+01 2848.5692484 #> 1686 168.5 2088.6765 6.051212e+01 2850.8113817 #> 1687 168.6 2086.2344 6.069633e+01 2853.0692759 #> 1688 168.7 2083.7841 6.087365e+01 2855.3422265 #> 1689 168.8 2081.3265 6.104399e+01 2857.6295208 #> 1690 168.9 2078.8623 6.120729e+01 2859.9304389 #> 1691 169.0 2076.3923 6.136344e+01 2862.2442535 #> 1692 169.1 2073.9174 6.151240e+01 2864.5702309 #> 1693 169.2 2071.4383 6.165407e+01 2866.9076312 #> 1694 169.3 2068.9559 6.178841e+01 2869.2557091 #> 1695 169.4 2066.4709 6.191535e+01 2871.6137140 #> 1696 169.5 2063.9843 6.203484e+01 2873.9808908 #> 1697 169.6 2061.4967 6.214684e+01 2876.3564801 #> 1698 169.7 2059.0090 6.225129e+01 2878.7397194 #> 1699 169.8 2056.5220 6.234816e+01 2881.1298428 #> 1700 169.9 2054.0365 6.243742e+01 2883.5260821 #> 1701 170.0 2051.5533 6.251904e+01 2885.9276671 #> 1702 170.1 2049.0732 6.259300e+01 2888.3338261 #> 1703 170.2 2046.5969 6.265929e+01 2890.7437866 #> 1704 170.3 2044.1253 6.271788e+01 2893.1567757 #> 1705 170.4 2041.6592 6.276878e+01 2895.5720208 #> 1706 170.5 2039.1993 6.281199e+01 2897.9887498 #> 1707 170.6 2036.7463 6.284751e+01 2900.4061919 #> 1708 170.7 2034.3011 6.287534e+01 2902.8235783 #> 1709 170.8 2031.8643 6.289551e+01 2905.2401422 #> 1710 170.9 2029.4368 6.290804e+01 2907.6551197 #> 1711 171.0 2027.0193 6.291295e+01 2910.0677502 #> 1712 171.1 2024.6125 6.291027e+01 2912.4772772 #> 1713 171.2 2022.2170 6.290004e+01 2914.8829480 #> 1714 171.3 2019.8337 6.288230e+01 2917.2840153 #> 1715 171.4 2017.4632 6.285709e+01 2919.6797366 #> 1716 171.5 2015.1062 6.282446e+01 2922.0693756 #> 1717 171.6 2012.7633 6.278448e+01 2924.4522018 #> 1718 171.7 2010.4353 6.273719e+01 2926.8274919 #> 1719 171.8 2008.1228 6.268267e+01 2929.1945293 #> 1720 171.9 2005.8264 6.262098e+01 2931.5526053 #> 1721 172.0 2003.5468 6.255219e+01 2933.9010190 #> 1722 172.1 2001.2845 6.247638e+01 2936.2390779 #> 1723 172.2 1999.0403 6.239364e+01 2938.5660985 #> 1724 172.3 1996.8146 6.230404e+01 2940.8814063 #> 1725 172.4 1994.6080 6.220768e+01 2943.1843364 #> 1726 172.5 1992.4211 6.210464e+01 2945.4742339 #> 1727 172.6 1990.2545 6.199503e+01 2947.7504539 #> 1728 172.7 1988.1087 6.187894e+01 2950.0123625 #> 1729 172.8 1985.9842 6.175647e+01 2952.2593363 #> 1730 172.9 1983.8815 6.162773e+01 2954.4907633 #> 1731 173.0 1981.8011 6.149283e+01 2956.7060429 #> 1732 173.1 1979.7435 6.135188e+01 2958.9045863 #> 1733 173.2 1977.7092 6.120499e+01 2961.0858166 #> 1734 173.3 1975.6986 6.105228e+01 2963.2491693 #> 1735 173.4 1973.7121 6.089386e+01 2965.3940920 #> 1736 173.5 1971.7501 6.072986e+01 2967.5200455 #> 1737 173.6 1969.8131 6.056039e+01 2969.6265029 #> 1738 173.7 1967.9015 6.038559e+01 2971.7129507 #> 1739 173.8 1966.0155 6.020558e+01 2973.7788883 #> 1740 173.9 1964.1557 6.002048e+01 2975.8238287 #> 1741 174.0 1962.3223 5.983043e+01 2977.8472982 #> 1742 174.1 1960.5156 5.963555e+01 2979.8488366 #> 1743 174.2 1958.7360 5.943597e+01 2981.8279975 #> 1744 174.3 1956.9838 5.923184e+01 2983.7843481 #> 1745 174.4 1955.2593 5.902327e+01 2985.7174696 #> 1746 174.5 1953.5626 5.881041e+01 2987.6269569 #> 1747 174.6 1951.8942 5.859339e+01 2989.5124190 #> 1748 174.7 1950.2542 5.837234e+01 2991.3734786 #> 1749 174.8 1948.6428 5.814740e+01 2993.2097725 #> 1750 174.9 1947.0604 5.791870e+01 2995.0209514 #> 1751 175.0 1945.5069 5.768637e+01 2996.8066800 #> 1752 175.1 1943.9828 5.745056e+01 2998.5666368 #> 1753 175.2 1942.4881 5.721140e+01 3000.3005144 #> 1754 175.3 1941.0230 5.696901e+01 3002.0080189 #> 1755 175.4 1939.5876 5.672354e+01 3003.6888704 #> 1756 175.5 1938.1821 5.647511e+01 3005.3428026 #> 1757 175.6 1936.8066 5.622386e+01 3006.9695628 #> 1758 175.7 1935.4612 5.596993e+01 3008.5689120 #> 1759 175.8 1934.1459 5.571343e+01 3010.1406244 #> 1760 175.9 1932.8610 5.545450e+01 3011.6844876 #> 1761 176.0 1931.6064 5.519328e+01 3013.2003023 #> 1762 176.1 1930.3822 5.492988e+01 3014.6878823 #> 1763 176.2 1929.1885 5.466443e+01 3016.1470544 #> 1764 176.3 1928.0253 5.439706e+01 3017.5776580 #> 1765 176.4 1926.8926 5.412789e+01 3018.9795453 #> 1766 176.5 1925.7904 5.385704e+01 3020.3525809 #> 1767 176.6 1924.7187 5.358464e+01 3021.6966416 #> 1768 176.7 1923.6776 5.331080e+01 3023.0116163 #> 1769 176.8 1922.6670 5.303564e+01 3024.2974060 #> 1770 176.9 1921.6868 5.275927e+01 3025.5539233 #> 1771 177.0 1920.7371 5.248181e+01 3026.7810924 #> 1772 177.1 1919.8178 5.220337e+01 3027.9788490 #> 1773 177.2 1918.9288 5.192406e+01 3029.1471397 #> 1774 177.3 1918.0701 5.164398e+01 3030.2859224 #> 1775 177.4 1917.2416 5.136326e+01 3031.3951654 #> 1776 177.5 1916.4432 5.108198e+01 3032.4748479 #> 1777 177.6 1915.6748 5.080025e+01 3033.5249592 #> 1778 177.7 1914.9363 5.051817e+01 3034.5454988 #> 1779 177.8 1914.2277 5.023584e+01 3035.5364761 #> 1780 177.9 1913.5487 4.995335e+01 3036.4979103 #> 1781 178.0 1912.8994 4.967081e+01 3037.4298300 #> 1782 178.1 1912.2794 4.938829e+01 3038.3322729 #> 1783 178.2 1911.6888 4.910590e+01 3039.2052860 #> 1784 178.3 1911.1274 4.882371e+01 3040.0489249 #> 1785 178.4 1910.5949 4.854182e+01 3040.8632540 #> 1786 178.5 1910.0913 4.826031e+01 3041.6483457 #> 1787 178.6 1909.6165 4.797925e+01 3042.4042811 #> 1788 178.7 1909.1701 4.769873e+01 3043.1311486 #> 1789 178.8 1908.7521 4.741883e+01 3043.8290448 #> 1790 178.9 1908.3623 4.713962e+01 3044.4980734 #> 1791 179.0 1908.0005 4.686117e+01 3045.1383457 #> 1792 179.1 1907.6665 4.658356e+01 3045.7499798 #> 1793 179.2 1907.3600 4.630685e+01 3046.3331007 #> 1794 179.3 1907.0810 4.603111e+01 3046.8878400 #> 1795 179.4 1906.8293 4.575641e+01 3047.4143358 #> 1796 179.5 1906.6045 4.548280e+01 3047.9127322 #> 1797 179.6 1906.4065 4.521035e+01 3048.3831793 #> 1798 179.7 1906.2350 4.493912e+01 3048.8258332 #> 1799 179.8 1906.0900 4.466916e+01 3049.2408554 #> 1800 179.9 1905.9711 4.440053e+01 3049.6284127 #> 1801 180.0 1905.8780 4.413328e+01 3049.9886771 #> 1802 180.1 1905.8107 4.386747e+01 3050.3218257 #> 1803 180.2 1905.7688 4.360313e+01 3050.6280403 #> 1804 180.3 1905.7522 4.334033e+01 3050.9075072 #> 1805 180.4 1905.7605 4.307910e+01 3051.1604172 #> 1806 180.5 1905.7935 4.281949e+01 3051.3869653 #> 1807 180.6 1905.8511 4.256154e+01 3051.5873505 #> 1808 180.7 1905.9329 4.230529e+01 3051.7617757 #> 1809 180.8 1906.0388 4.205079e+01 3051.9104474 #> 1810 180.9 1906.1684 4.179806e+01 3052.0335758 #> 1811 181.0 1906.3215 4.154714e+01 3052.1313743 #> 1812 181.1 1906.4979 4.129807e+01 3052.2040594 #> 1813 181.2 1906.6973 4.105088e+01 3052.2518508 #> 1814 181.3 1906.9194 4.080559e+01 3052.2749709 #> 1815 181.4 1907.1641 4.056225e+01 3052.2736450 #> 1816 181.5 1907.4310 4.032087e+01 3052.2481008 #> 1817 181.6 1907.7199 4.008149e+01 3052.1985684 #> 1818 181.7 1908.0306 3.984412e+01 3052.1252804 #> 1819 181.8 1908.3627 3.960880e+01 3052.0284712 #> 1820 181.9 1908.7161 3.937554e+01 3051.9083774 #> 1821 182.0 1909.0904 3.914437e+01 3051.7652375 #> 1822 182.1 1909.4854 3.891531e+01 3051.5992916 #> 1823 182.2 1909.9009 3.868836e+01 3051.4107815 #> 1824 182.3 1910.3365 3.846356e+01 3051.1999503 #> 1825 182.4 1910.7920 3.824092e+01 3050.9670428 #> 1826 182.5 1911.2672 3.802045e+01 3050.7123047 #> 1827 182.6 1911.7618 3.780217e+01 3050.4359831 #> 1828 182.7 1912.2756 3.758609e+01 3050.1383261 #> 1829 182.8 1912.8082 3.737223e+01 3049.8195826 #> 1830 182.9 1913.3594 3.716058e+01 3049.4800023 #> 1831 183.0 1913.9290 3.695117e+01 3049.1198358 #> 1832 183.1 1914.5167 3.674401e+01 3048.7393342 #> 1833 183.2 1915.1222 3.653909e+01 3048.3387493 #> 1834 183.3 1915.7452 3.633643e+01 3047.9183331 #> 1835 183.4 1916.3856 3.613604e+01 3047.4783382 #> 1836 183.5 1917.0431 3.593792e+01 3047.0190172 #> 1837 183.6 1917.7173 3.574208e+01 3046.5406233 #> 1838 183.7 1918.4081 3.554852e+01 3046.0434094 #> 1839 183.8 1919.1151 3.535724e+01 3045.5276287 #> 1840 183.9 1919.8382 3.516825e+01 3044.9935344 #> 1841 184.0 1920.5771 3.498154e+01 3044.4413795 #> 1842 184.1 1921.3315 3.479713e+01 3043.8714168 #> 1843 184.2 1922.1011 3.461501e+01 3043.2838989 #> 1844 184.3 1922.8857 3.443517e+01 3042.6790783 #> 1845 184.4 1923.6852 3.425763e+01 3042.0572069 #> 1846 184.5 1924.4991 3.408238e+01 3041.4185364 #> 1847 184.6 1925.3273 3.390942e+01 3040.7633179 #> 1848 184.7 1926.1695 3.373875e+01 3040.0918022 #> 1849 184.8 1927.0254 3.357036e+01 3039.4042392 #> 1850 184.9 1927.8949 3.340425e+01 3038.7008785 #> 1851 185.0 1928.7776 3.324042e+01 3037.9819691 #> 1852 185.1 1929.6734 3.307886e+01 3037.2477590 #> 1853 185.2 1930.5819 3.291957e+01 3036.4984958 #> 1854 185.3 1931.5030 3.276255e+01 3035.7344260 #> 1855 185.4 1932.4364 3.260779e+01 3034.9557958 #> 1856 185.5 1933.3819 3.245528e+01 3034.1628500 #> 1857 185.6 1934.3391 3.230503e+01 3033.3558329 #> 1858 185.7 1935.3080 3.215702e+01 3032.5349878 #> 1859 185.8 1936.2882 3.201124e+01 3031.7005571 #> 1860 185.9 1937.2795 3.186770e+01 3030.8527823 #> 1861 186.0 1938.2817 3.172639e+01 3029.9919036 #> 1862 186.1 1939.2946 3.158729e+01 3029.1181607 #> 1863 186.2 1940.3178 3.145040e+01 3028.2317919 #> 1864 186.3 1941.3513 3.131571e+01 3027.3330346 #> 1865 186.4 1942.3946 3.118322e+01 3026.4221252 #> 1866 186.5 1943.4478 3.105292e+01 3025.4992988 #> 1867 186.6 1944.5104 3.092480e+01 3024.5647897 #> 1868 186.7 1945.5823 3.079885e+01 3023.6188309 #> 1869 186.8 1946.6633 3.067507e+01 3022.6616542 #> 1870 186.9 1947.7531 3.055344e+01 3021.6934905 #> 1871 187.0 1948.8515 3.043396e+01 3020.7145694 #> 1872 187.1 1949.9583 3.031662e+01 3019.7251194 #> 1873 187.2 1951.0732 3.020140e+01 3018.7253678 #> 1874 187.3 1952.1961 3.008831e+01 3017.7155407 #> 1875 187.4 1953.3268 2.997733e+01 3016.6958631 #> 1876 187.5 1954.4650 2.986845e+01 3015.6665588 #> 1877 187.6 1955.6105 2.976167e+01 3014.6278503 #> 1878 187.7 1956.7631 2.965698e+01 3013.5799592 #> 1879 187.8 1957.9225 2.955436e+01 3012.5231054 #> 1880 187.9 1959.0887 2.945380e+01 3011.4575082 #> 1881 188.0 1960.2613 2.935531e+01 3010.3833852 #> 1882 188.1 1961.4402 2.925887e+01 3009.3009531 #> 1883 188.2 1962.6251 2.916446e+01 3008.2104274 #> 1884 188.3 1963.8159 2.907209e+01 3007.1120222 #> 1885 188.4 1965.0123 2.898174e+01 3006.0059506 #> 1886 188.5 1966.2142 2.889340e+01 3004.8924244 #> 1887 188.6 1967.4213 2.880707e+01 3003.7716543 #> 1888 188.7 1968.6334 2.872273e+01 3002.6438498 #> 1889 188.8 1969.8504 2.864038e+01 3001.5092191 #> 1890 188.9 1971.0720 2.856000e+01 3000.3679694 #> 1891 189.0 1972.2981 2.848160e+01 2999.2203068 #> 1892 189.1 1973.5284 2.840515e+01 2998.0664359 #> 1893 189.2 1974.7628 2.833065e+01 2996.9065604 #> 1894 189.3 1976.0010 2.825810e+01 2995.7408830 #> 1895 189.4 1977.2429 2.818748e+01 2994.5696048 #> 1896 189.5 1978.4883 2.811878e+01 2993.3929263 #> 1897 189.6 1979.7369 2.805200e+01 2992.2110465 #> 1898 189.7 1980.9887 2.798713e+01 2991.0241635 #> 1899 189.8 1982.2434 2.792416e+01 2989.8324742 #> 1900 189.9 1983.5007 2.786308e+01 2988.6361744 #> 1901 190.0 1984.7607 2.780389e+01 2987.4354588 #> 1902 190.1 1986.0229 2.774656e+01 2986.2305213 #> 1903 190.2 1987.2873 2.769111e+01 2985.0215544 #> 1904 190.3 1988.5537 2.763752e+01 2983.8087496 #> 1905 190.4 1989.8219 2.758577e+01 2982.5922976 #> 1906 190.5 1991.0917 2.753587e+01 2981.3723878 #> 1907 190.6 1992.3630 2.748781e+01 2980.1492087 #> 1908 190.7 1993.6355 2.744158e+01 2978.9229480 #> 1909 190.8 1994.9090 2.739717e+01 2977.6937919 #> 1910 190.9 1996.1835 2.735457e+01 2976.4619262 #> 1911 191.0 1997.4587 2.731378e+01 2975.2275353 #> 1912 191.1 1998.7344 2.727479e+01 2973.9908030 #> 1913 191.2 2000.0105 2.723759e+01 2972.7519117 #> 1914 191.3 2001.2868 2.720218e+01 2971.5110434 #> 1915 191.4 2002.5631 2.716855e+01 2970.2683787 #> 1916 191.5 2003.8392 2.713669e+01 2969.0240977 #> 1917 191.6 2005.1150 2.710660e+01 2967.7783794 #> 1918 191.7 2006.3903 2.707827e+01 2966.5314018 #> 1919 191.8 2007.6650 2.705170e+01 2965.2833424 #> 1920 191.9 2008.9388 2.702687e+01 2964.0343774 #> 1921 192.0 2010.2115 2.700378e+01 2962.7846826 #> 1922 192.1 2011.4831 2.698243e+01 2961.5344326 #> 1923 192.2 2012.7534 2.696281e+01 2960.2838013 #> 1924 192.3 2014.0221 2.694491e+01 2959.0329620 #> 1925 192.4 2015.2892 2.692874e+01 2957.7820869 #> 1926 192.5 2016.5544 2.691427e+01 2956.5313477 #> 1927 192.6 2017.8176 2.690152e+01 2955.2809150 #> 1928 192.7 2019.0786 2.689047e+01 2954.0309590 #> 1929 192.8 2020.3372 2.688111e+01 2952.7816489 #> 1930 192.9 2021.5934 2.687345e+01 2951.5331533 #> 1931 193.0 2022.8469 2.686748e+01 2950.2856399 #> 1932 193.1 2024.0975 2.686319e+01 2949.0392760 #> 1933 193.2 2025.3452 2.686057e+01 2947.7942280 #> 1934 193.3 2026.5897 2.685964e+01 2946.5506615 #> 1935 193.4 2027.8309 2.686037e+01 2945.3087416 #> 1936 193.5 2029.0686 2.686276e+01 2944.0686328 #> 1937 193.6 2030.3027 2.686682e+01 2942.8304987 #> 1938 193.7 2031.5330 2.687253e+01 2941.5945025 #> 1939 193.8 2032.7593 2.687989e+01 2940.3608066 #> 1940 193.9 2033.9815 2.688890e+01 2939.1295728 #> 1941 194.0 2035.1995 2.689956e+01 2937.9009623 #> 1942 194.1 2036.4130 2.691186e+01 2936.6751357 #> 1943 194.2 2037.6220 2.692579e+01 2935.4522531 #> 1944 194.3 2038.8262 2.694135e+01 2934.2324738 #> 1945 194.4 2040.0255 2.695855e+01 2933.0159566 #> 1946 194.5 2041.2198 2.697737e+01 2931.8028598 #> 1947 194.6 2042.4089 2.699781e+01 2930.5933412 #> 1948 194.7 2043.5926 2.701986e+01 2929.3875577 #> 1949 194.8 2044.7708 2.704354e+01 2928.1856660 #> 1950 194.9 2045.9434 2.706882e+01 2926.9878222 #> 1951 195.0 2047.1101 2.709572e+01 2925.7941816 #> 1952 195.1 2048.2709 2.712422e+01 2924.6048993 #> 1953 195.2 2049.4256 2.715432e+01 2923.4201297 #> 1954 195.3 2050.5740 2.718601e+01 2922.2400266 #> 1955 195.4 2051.7159 2.721931e+01 2921.0647435 #> 1956 195.5 2052.8514 2.725420e+01 2919.8944333 #> 1957 195.6 2053.9801 2.729067e+01 2918.7292483 #> 1958 195.7 2055.1019 2.732874e+01 2917.5693403 #> 1959 195.8 2056.2168 2.736838e+01 2916.4148607 #> 1960 195.9 2057.3244 2.740961e+01 2915.2659604 #> 1961 196.0 2058.4248 2.745241e+01 2914.1227898 #> 1962 196.1 2059.5177 2.749679e+01 2912.9854986 #> 1963 196.2 2060.6030 2.754274e+01 2911.8542363 #> 1964 196.3 2061.6806 2.759026e+01 2910.7291517 #> 1965 196.4 2062.7503 2.763934e+01 2909.6103932 #> 1966 196.5 2063.8119 2.768999e+01 2908.4981087 #> 1967 196.6 2064.8654 2.774220e+01 2907.3924455 #> 1968 196.7 2065.9105 2.779596e+01 2906.2935507 #> 1969 196.8 2066.9472 2.785128e+01 2905.2015705 #> 1970 196.9 2067.9752 2.790815e+01 2904.1166509 #> 1971 197.0 2068.9945 2.796656e+01 2903.0389372 #> 1972 197.1 2070.0049 2.802652e+01 2901.9685745 #> 1973 197.2 2071.0063 2.808802e+01 2900.9057069 #> 1974 197.3 2071.9985 2.815106e+01 2899.8504785 #> 1975 197.4 2072.9813 2.821564e+01 2898.8030325 #> 1976 197.5 2073.9547 2.828174e+01 2897.7635119 #> 1977 197.6 2074.9186 2.834937e+01 2896.7320588 #> 1978 197.7 2075.8727 2.841853e+01 2895.7088150 #> 1979 197.8 2076.8169 2.848921e+01 2894.6939218 #> 1980 197.9 2077.7511 2.856140e+01 2893.6875198 #> 1981 198.0 2078.6751 2.863511e+01 2892.6897491 #> 1982 198.1 2079.5889 2.871033e+01 2891.7007493 #> 1983 198.2 2080.4923 2.878705e+01 2890.7206592 #> 1984 198.3 2081.3851 2.886528e+01 2889.7496173 #> 1985 198.4 2082.2672 2.894500e+01 2888.7877613 #> 1986 198.5 2083.1386 2.902621e+01 2887.8352283 #> 1987 198.6 2083.9989 2.910891e+01 2886.8921549 #> 1988 198.7 2084.8482 2.919310e+01 2885.9586768 #> 1989 198.8 2085.6863 2.927876e+01 2885.0349294 #> 1990 198.9 2086.5131 2.936590e+01 2884.1210471 #> 1991 199.0 2087.3283 2.945450e+01 2883.2171639 #> 1992 199.1 2088.1320 2.954457e+01 2882.3234129 #> 1993 199.2 2088.9240 2.963610e+01 2881.4399265 #> 1994 199.3 2089.7041 2.972908e+01 2880.5668364 #> 1995 199.4 2090.4722 2.982350e+01 2879.7042736 #> 1996 199.5 2091.2283 2.991936e+01 2878.8523683 #> 1997 199.6 2091.9721 3.001666e+01 2878.0112499 #> 1998 199.7 2092.7036 3.011539e+01 2877.1810468 #> 1999 199.8 2093.4226 3.021553e+01 2876.3618870 #> 2000 199.9 2094.1290 3.031709e+01 2875.5538972 #> 2001 200.0 2094.8227 3.042006e+01 2874.7572034 #> 2002 200.1 2095.5036 3.052442e+01 2873.9719308 #> 2003 200.2 2096.1716 3.063017e+01 2873.1982035 #> 2004 200.3 2096.8265 3.073731e+01 2872.4361448 #> 2005 200.4 2097.4683 3.084582e+01 2871.6858768 #> 2006 200.5 2098.0968 3.095570e+01 2870.9475209 #> 2007 200.6 2098.7119 3.106694e+01 2870.2211972 #> 2008 200.7 2099.3135 3.117952e+01 2869.5070249 #> 2009 200.8 2099.9014 3.129344e+01 2868.8051221 #> 2010 200.9 2100.4757 3.140870e+01 2868.1156056 #> 2011 201.0 2101.0361 3.152527e+01 2867.4385913 #> 2012 201.1 2101.5827 3.164314e+01 2866.7741938 #> 2013 201.2 2102.1152 3.176232e+01 2866.1225265 #> 2014 201.3 2102.6335 3.188278e+01 2865.4837015 #> 2015 201.4 2103.1377 3.200452e+01 2864.8578298 #> 2016 201.5 2103.6275 3.212752e+01 2864.2450208 #> 2017 201.6 2104.1028 3.225177e+01 2863.6453828 #> 2018 201.7 2104.5637 3.237726e+01 2863.0590227 #> 2019 201.8 2105.0100 3.250398e+01 2862.4860458 #> 2020 201.9 2105.4415 3.263190e+01 2861.9265562 #> 2021 202.0 2105.8583 3.276103e+01 2861.3806562 #> 2022 202.1 2106.2602 3.289134e+01 2860.8484469 #> 2023 202.2 2106.6472 3.302282e+01 2860.3300276 #> 2024 202.3 2107.0191 3.315545e+01 2859.8254960 #> 2025 202.4 2107.3758 3.328922e+01 2859.3349483 #> 2026 202.5 2107.7174 3.342412e+01 2858.8584788 #> 2027 202.6 2108.0437 3.356012e+01 2858.3961804 #> 2028 202.7 2108.3546 3.369722e+01 2857.9481438 #> 2029 202.8 2108.6502 3.383538e+01 2857.5144582 #> 2030 202.9 2108.9302 3.397461e+01 2857.0952108 #> 2031 203.0 2109.1946 3.411486e+01 2856.6904869 #> 2032 203.1 2109.4435 3.425614e+01 2856.3003699 #> 2033 203.2 2109.6766 3.439842e+01 2855.9249411 #> 2034 203.3 2109.8940 3.454168e+01 2855.5642801 #> 2035 203.4 2110.0956 3.468589e+01 2855.2184639 #> 2036 203.5 2110.2814 3.483105e+01 2854.8875677 #> 2037 203.6 2110.4512 3.497713e+01 2854.5716645 #> 2038 203.7 2110.6051 3.512410e+01 2854.2708250 #> 2039 203.8 2110.7429 3.527194e+01 2853.9851176 #> 2040 203.9 2110.8647 3.542064e+01 2853.7146086 #> 2041 204.0 2110.9705 3.557017e+01 2853.4593616 #> 2042 204.1 2111.0601 3.572051e+01 2853.2194380 #> 2043 204.2 2111.1335 3.587162e+01 2852.9948968 #> 2044 204.3 2111.1907 3.602349e+01 2852.7857943 #> 2045 204.4 2111.2317 3.617610e+01 2852.5921843 #> 2046 204.5 2111.2565 3.632941e+01 2852.4141180 #> 2047 204.6 2111.2650 3.648340e+01 2852.2516440 #> 2048 204.7 2111.2571 3.663804e+01 2852.1048082 #> 2049 204.8 2111.2330 3.679331e+01 2851.9736537 #> 2050 204.9 2111.1926 3.694918e+01 2851.8582206 #> 2051 205.0 2111.1358 3.710562e+01 2851.7585464 #> 2052 205.1 2111.0627 3.726260e+01 2851.6746658 #> 2053 205.2 2110.9733 3.742009e+01 2851.6066103 #> 2054 205.3 2110.8675 3.757807e+01 2851.5544087 #> 2055 205.4 2110.7454 3.773649e+01 2851.5180864 #> 2056 205.5 2110.6070 3.789534e+01 2851.4976658 #> 2057 205.6 2110.4523 3.805458e+01 2851.4931663 #> 2058 205.7 2110.2812 3.821417e+01 2851.5046041 #> 2059 205.8 2110.0939 3.837409e+01 2851.5319920 #> 2060 205.9 2109.8904 3.853430e+01 2851.5753397 #> 2061 206.0 2109.6706 3.869478e+01 2851.6346535 #> 2062 206.1 2109.4346 3.885547e+01 2851.7099363 #> 2063 206.2 2109.1824 3.901636e+01 2851.8011878 #> 2064 206.3 2108.9142 3.917741e+01 2851.9084040 #> 2065 206.4 2108.6298 3.933858e+01 2852.0315776 #> 2066 206.5 2108.3295 3.949983e+01 2852.1706976 #> 2067 206.6 2108.0131 3.966113e+01 2852.3257494 #> 2068 206.7 2107.6808 3.982245e+01 2852.4967151 #> 2069 206.8 2107.3327 3.998374e+01 2852.6835729 #> 2070 206.9 2106.9687 4.014497e+01 2852.8862972 #> 2071 207.0 2106.5890 4.030610e+01 2853.1048590 #> 2072 207.1 2106.1937 4.046709e+01 2853.3392253 #> 2073 207.2 2105.7827 4.062791e+01 2853.5893594 #> 2074 207.3 2105.3563 4.078851e+01 2853.8552208 #> 2075 207.4 2104.9144 4.094886e+01 2854.1367651 #> 2076 207.5 2104.4571 4.110891e+01 2854.4339440 #> 2077 207.6 2103.9847 4.126863e+01 2854.7467053 #> 2078 207.7 2103.4970 4.142798e+01 2855.0749930 #> 2079 207.8 2102.9943 4.158691e+01 2855.4187469 #> 2080 207.9 2102.4767 4.174539e+01 2855.7779031 #> 2081 208.0 2101.9442 4.190337e+01 2856.1523934 #> 2082 208.1 2101.3970 4.206081e+01 2856.5421457 #> 2083 208.2 2100.8352 4.221768e+01 2856.9470840 #> 2084 208.3 2100.2590 4.237392e+01 2857.3671281 #> 2085 208.4 2099.6683 4.252950e+01 2857.8021937 #> 2086 208.5 2099.0634 4.268438e+01 2858.2521926 #> 2087 208.6 2098.4445 4.283851e+01 2858.7170322 #> 2088 208.7 2097.8115 4.299185e+01 2859.1966161 #> 2089 208.8 2097.1648 4.314436e+01 2859.6908437 #> 2090 208.9 2096.5044 4.329600e+01 2860.1996103 #> 2091 209.0 2095.8305 4.344672e+01 2860.7228070 #> 2092 209.1 2095.1432 4.359648e+01 2861.2603208 #> 2093 209.2 2094.4427 4.374523e+01 2861.8120349 #> 2094 209.3 2093.7292 4.389295e+01 2862.3778279 #> 2095 209.4 2093.0028 4.403958e+01 2862.9575748 #> 2096 209.5 2092.2638 4.418508e+01 2863.5511462 #> 2097 209.6 2091.5122 4.432940e+01 2864.1584087 #> 2098 209.7 2090.7483 4.447252e+01 2864.7792249 #> 2099 209.8 2089.9722 4.461438e+01 2865.4134534 #> 2100 209.9 2089.1841 4.475494e+01 2866.0609488 #> 2101 210.0 2088.3843 4.489416e+01 2866.7215616 #> 2102 210.1 2087.5729 4.503200e+01 2867.3951383 #> 2103 210.2 2086.7501 4.516842e+01 2868.0815216 #> 2104 210.3 2085.9161 4.530337e+01 2868.7805504 #> 2105 210.4 2085.0711 4.543683e+01 2869.4920594 #> 2106 210.5 2084.2154 4.556873e+01 2870.2158798 #> 2107 210.6 2083.3491 4.569905e+01 2870.9518387 #> 2108 210.7 2082.4725 4.582775e+01 2871.6997597 #> 2109 210.8 2081.5858 4.595479e+01 2872.4594626 #> 2110 210.9 2080.6891 4.608012e+01 2873.2307634 #> 2111 211.0 2079.7828 4.620371e+01 2874.0134746 #> 2112 211.1 2078.8671 4.632552e+01 2874.8074053 #> 2113 211.2 2077.9421 4.644552e+01 2875.6123607 #> 2114 211.3 2077.0082 4.656366e+01 2876.4281429 #> 2115 211.4 2076.0655 4.667992e+01 2877.2545505 #> 2116 211.5 2075.1144 4.679424e+01 2878.0913785 #> 2117 211.6 2074.1550 4.690661e+01 2878.9384190 #> 2118 211.7 2073.1876 4.701698e+01 2879.7954608 #> 2119 211.8 2072.2124 4.712533e+01 2880.6622893 #> 2120 211.9 2071.2297 4.723161e+01 2881.5386872 #> 2121 212.0 2070.2398 4.733580e+01 2882.4244340 #> 2122 212.1 2069.2428 4.743786e+01 2883.3193062 #> 2123 212.2 2068.2392 4.753776e+01 2884.2230776 #> 2124 212.3 2067.2290 4.763547e+01 2885.1355193 #> 2125 212.4 2066.2126 4.773097e+01 2886.0563996 #> 2126 212.5 2065.1903 4.782422e+01 2886.9854841 #> 2127 212.6 2064.1623 4.791520e+01 2887.9225360 #> 2128 212.7 2063.1288 4.800387e+01 2888.8673162 #> 2129 212.8 2062.0902 4.809022e+01 2889.8195831 #> 2130 212.9 2061.0467 4.817422e+01 2890.7790929 #> 2131 213.0 2059.9986 4.825583e+01 2891.7455996 #> 2132 213.1 2058.9461 4.833505e+01 2892.7188551 #> 2133 213.2 2057.8895 4.841185e+01 2893.6986096 #> 2134 213.3 2056.8292 4.848620e+01 2894.6846112 #> 2135 213.4 2055.7653 4.855809e+01 2895.6766063 #> 2136 213.5 2054.6982 4.862749e+01 2896.6743395 #> 2137 213.6 2053.6281 4.869439e+01 2897.6775541 #> 2138 213.7 2052.5552 4.875877e+01 2898.6859918 #> 2139 213.8 2051.4800 4.882062e+01 2899.6993929 #> 2140 213.9 2050.4026 4.887991e+01 2900.7174965 #> 2141 214.0 2049.3233 4.893663e+01 2901.7400406 #> 2142 214.1 2048.2425 4.899078e+01 2902.7667621 #> 2143 214.2 2047.1603 4.904233e+01 2903.7973970 #> 2144 214.3 2046.0770 4.909128e+01 2904.8316804 #> 2145 214.4 2044.9930 4.913762e+01 2905.8693468 #> 2146 214.5 2043.9085 4.918133e+01 2906.9101300 #> 2147 214.6 2042.8238 4.922241e+01 2907.9537634 #> 2148 214.7 2041.7392 4.926086e+01 2908.9999800 #> 2149 214.8 2040.6548 4.929666e+01 2910.0485124 #> 2150 214.9 2039.5711 4.932981e+01 2911.0990932 #> 2151 215.0 2038.4882 4.936032e+01 2912.1514549 #> 2152 215.1 2037.4065 4.938816e+01 2913.2053299 #> 2153 215.2 2036.3262 4.941336e+01 2914.2604510 #> 2154 215.3 2035.2476 4.943589e+01 2915.3165511 #> 2155 215.4 2034.1709 4.945578e+01 2916.3733636 #> 2156 215.5 2033.0964 4.947301e+01 2917.4306224 #> 2157 215.6 2032.0243 4.948759e+01 2918.4880618 #> 2158 215.7 2030.9551 4.949952e+01 2919.5454171 #> 2159 215.8 2029.8888 4.950882e+01 2920.6024242 #> 2160 215.9 2028.8257 4.951548e+01 2921.6588200 #> 2161 216.0 2027.7661 4.951951e+01 2922.7143424 #> 2162 216.1 2026.7103 4.952093e+01 2923.7687305 #> 2163 216.2 2025.6585 4.951973e+01 2924.8217246 #> 2164 216.3 2024.6110 4.951594e+01 2925.8730662 #> 2165 216.4 2023.5679 4.950956e+01 2926.9224984 #> 2166 216.5 2022.5296 4.950060e+01 2927.9697658 #> 2167 216.6 2021.4963 4.948908e+01 2929.0146146 #> 2168 216.7 2020.4682 4.947502e+01 2930.0567928 #> 2169 216.8 2019.4455 4.945842e+01 2931.0960501 #> 2170 216.9 2018.4286 4.943931e+01 2932.1321382 #> 2171 217.0 2017.4175 4.941770e+01 2933.1648109 #> 2172 217.1 2016.4126 4.939361e+01 2934.1938238 #> 2173 217.2 2015.4140 4.936706e+01 2935.2189351 #> 2174 217.3 2014.4220 4.933806e+01 2936.2399049 #> 2175 217.4 2013.4369 4.930665e+01 2937.2564958 #> 2176 217.5 2012.4587 4.927284e+01 2938.2684729 #> 2177 217.6 2011.4877 4.923665e+01 2939.2756038 #> 2178 217.7 2010.5242 4.919811e+01 2940.2776585 #> 2179 217.8 2009.5683 4.915724e+01 2941.2744101 #> 2180 217.9 2008.6203 4.911407e+01 2942.2656340 #> 2181 218.0 2007.6803 4.906862e+01 2943.2511086 #> 2182 218.1 2006.7485 4.902091e+01 2944.2306152 #> 2183 218.2 2005.8251 4.897098e+01 2945.2039382 #> 2184 218.3 2004.9103 4.891886e+01 2946.1708646 #> 2185 218.4 2004.0043 4.886456e+01 2947.1311850 #> 2186 218.5 2003.1072 4.880813e+01 2948.0846928 #> 2187 218.6 2002.2192 4.874959e+01 2949.0311846 #> 2188 218.7 2001.3406 4.868896e+01 2949.9704605 #> 2189 218.8 2000.4714 4.862629e+01 2950.9023237 #> 2190 218.9 1999.6118 4.856159e+01 2951.8265808 #> 2191 219.0 1998.7620 4.849491e+01 2952.7430419 #> 2192 219.1 1997.9222 4.842628e+01 2953.6515205 #> 2193 219.2 1997.0924 4.835573e+01 2954.5518335 #> 2194 219.3 1996.2729 4.828328e+01 2955.4438015 #> 2195 219.4 1995.4638 4.820899e+01 2956.3272486 #> 2196 219.5 1994.6651 4.813287e+01 2957.2020025 #> 2197 219.6 1993.8771 4.805496e+01 2958.0678947 #> 2198 219.7 1993.0999 4.797531e+01 2958.9247601 #> 2199 219.8 1992.3336 4.789394e+01 2959.7724376 #> 2200 219.9 1991.5783 4.781089e+01 2960.6107696 #> 2201 220.0 1990.8342 4.772619e+01 2961.4396024 #> 2202 220.1 1990.1013 4.763988e+01 2962.2587861 #> 2203 220.2 1989.3798 4.755201e+01 2963.0681745 #> 2204 220.3 1988.6698 4.746259e+01 2963.8676254 #> 2205 220.4 1987.9713 4.737168e+01 2964.6570001 #> 2206 220.5 1987.2845 4.727930e+01 2965.4361642 #> 2207 220.6 1986.6095 4.718549e+01 2966.2049867 #> 2208 220.7 1985.9464 4.709030e+01 2966.9633408 #> 2209 220.8 1985.2951 4.699375e+01 2967.7111036 #> 2210 220.9 1984.6560 4.689589e+01 2968.4481557 #> 2211 221.0 1984.0289 4.679675e+01 2969.1743819 #> 2212 221.1 1983.4140 4.669637e+01 2969.8896709 #> 2213 221.2 1982.8113 4.659479e+01 2970.5939151 #> 2214 221.3 1982.2209 4.649204e+01 2971.2870109 #> 2215 221.4 1981.6430 4.638816e+01 2971.9688585 #> 2216 221.5 1981.0774 4.628320e+01 2972.6393620 #> 2217 221.6 1980.5244 4.617717e+01 2973.2984293 #> 2218 221.7 1979.9839 4.607014e+01 2973.9459723 #> 2219 221.8 1979.4560 4.596212e+01 2974.5819065 #> 2220 221.9 1978.9407 4.585316e+01 2975.2061513 #> 2221 222.0 1978.4381 4.574329e+01 2975.8186301 #> 2222 222.1 1977.9482 4.563256e+01 2976.4192697 #> 2223 222.2 1977.4710 4.552099e+01 2977.0080010 #> 2224 222.3 1977.0066 4.540863e+01 2977.5847584 #> 2225 222.4 1976.5550 4.529550e+01 2978.1494801 #> 2226 222.5 1976.1162 4.518165e+01 2978.7021081 #> 2227 222.6 1975.6903 4.506712e+01 2979.2425878 #> 2228 222.7 1975.2772 4.495193e+01 2979.7708686 #> 2229 222.8 1974.8770 4.483612e+01 2980.2869031 #> 2230 222.9 1974.4896 4.471974e+01 2980.7906478 #> 2231 223.0 1974.1151 4.460280e+01 2981.2820627 #> 2232 223.1 1973.7535 4.448535e+01 2981.7611111 #> 2233 223.2 1973.4048 4.436743e+01 2982.2277601 #> 2234 223.3 1973.0690 4.424906e+01 2982.6819800 #> 2235 223.4 1972.7460 4.413027e+01 2983.1237445 #> 2236 223.5 1972.4359 4.401111e+01 2983.5530310 #> 2237 223.6 1972.1386 4.389161e+01 2983.9698199 #> 2238 223.7 1971.8541 4.377179e+01 2984.3740951 #> 2239 223.8 1971.5825 4.365170e+01 2984.7658436 #> 2240 223.9 1971.3236 4.353135e+01 2985.1450559 #> 2241 224.0 1971.0775 4.341079e+01 2985.5117253 #> 2242 224.1 1970.8441 4.329004e+01 2985.8658486 #> 2243 224.2 1970.6234 4.316914e+01 2986.2074256 #> 2244 224.3 1970.4154 4.304812e+01 2986.5364591 #> 2245 224.4 1970.2200 4.292700e+01 2986.8529549 #> 2246 224.5 1970.0373 4.280582e+01 2987.1569219 #> 2247 224.6 1969.8670 4.268461e+01 2987.4483718 #> 2248 224.7 1969.7093 4.256338e+01 2987.7273194 #> 2249 224.8 1969.5640 4.244219e+01 2987.9937821 #> 2250 224.9 1969.4312 4.232104e+01 2988.2477802 #> 2251 225.0 1969.3107 4.219997e+01 2988.4893370 #> 2252 225.1 1969.2025 4.207900e+01 2988.7184780 #> 2253 225.2 1969.1066 4.195817e+01 2988.9352318 #> 2254 225.3 1969.0229 4.183750e+01 2989.1396295 #> 2255 225.4 1968.9513 4.171701e+01 2989.3317047 #> 2256 225.5 1968.8918 4.159674e+01 2989.5114937 #> 2257 225.6 1968.8443 4.147670e+01 2989.6790351 #> 2258 225.7 1968.8087 4.135692e+01 2989.8343701 #> 2259 225.8 1968.7850 4.123742e+01 2989.9775421 #> 2260 225.9 1968.7732 4.111824e+01 2990.1085970 #> 2261 226.0 1968.7730 4.099938e+01 2990.2275831 #> 2262 226.1 1968.7846 4.088088e+01 2990.3345507 #> 2263 226.2 1968.8077 4.076276e+01 2990.4295525 #> 2264 226.3 1968.8423 4.064504e+01 2990.5126433 #> 2265 226.4 1968.8884 4.052774e+01 2990.5838800 #> 2266 226.5 1968.9458 4.041089e+01 2990.6433217 #> 2267 226.6 1969.0145 4.029450e+01 2990.6910294 #> 2268 226.7 1969.0943 4.017859e+01 2990.7270661 #> 2269 226.8 1969.1853 4.006318e+01 2990.7514967 #> 2270 226.9 1969.2873 3.994831e+01 2990.7643883 #> 2271 227.0 1969.4002 3.983397e+01 2990.7658094 #> 2272 227.1 1969.5240 3.972020e+01 2990.7558306 #> 2273 227.2 1969.6585 3.960700e+01 2990.7345243 #> 2274 227.3 1969.8036 3.949441e+01 2990.7019644 #> 2275 227.4 1969.9593 3.938243e+01 2990.6582266 #> 2276 227.5 1970.1255 3.927109e+01 2990.6033884 #> 2277 227.6 1970.3021 3.916040e+01 2990.5375286 #> 2278 227.7 1970.4889 3.905037e+01 2990.4607278 #> 2279 227.8 1970.6859 3.894102e+01 2990.3730679 #> 2280 227.9 1970.8930 3.883238e+01 2990.2746325 #> 2281 228.0 1971.1100 3.872445e+01 2990.1655065 #> 2282 228.1 1971.3370 3.861724e+01 2990.0457762 #> 2283 228.2 1971.5737 3.851078e+01 2989.9155292 #> 2284 228.3 1971.8201 3.840508e+01 2989.7748547 #> 2285 228.4 1972.0760 3.830015e+01 2989.6238428 #> 2286 228.5 1972.3414 3.819600e+01 2989.4625850 #> 2287 228.6 1972.6162 3.809265e+01 2989.2911741 #> 2288 228.7 1972.9002 3.799011e+01 2989.1097039 #> 2289 228.8 1973.1933 3.788840e+01 2988.9182695 #> 2290 228.9 1973.4955 3.778752e+01 2988.7169669 #> 2291 229.0 1973.8066 3.768748e+01 2988.5058933 #> 2292 229.1 1974.1265 3.758830e+01 2988.2851469 #> 2293 229.2 1974.4552 3.748999e+01 2988.0548268 #> 2294 229.3 1974.7924 3.739257e+01 2987.8150332 #> 2295 229.4 1975.1381 3.729603e+01 2987.5658671 #> 2296 229.5 1975.4922 3.720039e+01 2987.3074305 #> 2297 229.6 1975.8545 3.710566e+01 2987.0398260 #> 2298 229.7 1976.2250 3.701185e+01 2986.7631574 #> 2299 229.8 1976.6035 3.691897e+01 2986.4775291 #> 2300 229.9 1976.9899 3.682702e+01 2986.1830462 #> 2301 230.0 1977.3842 3.673603e+01 2985.8798146 #> 2302 230.1 1977.7861 3.664598e+01 2985.5679410 #> 2303 230.2 1978.1956 3.655690e+01 2985.2475326 #> 2304 230.3 1978.6125 3.646879e+01 2984.9186973 #> 2305 230.4 1979.0368 3.638166e+01 2984.5815437 #> 2306 230.5 1979.4683 3.629551e+01 2984.2361809 #> 2307 230.6 1979.9069 3.621035e+01 2983.8827186 #> 2308 230.7 1980.3525 3.612619e+01 2983.5212670 #> 2309 230.8 1980.8050 3.604304e+01 2983.1519370 #> 2310 230.9 1981.2643 3.596089e+01 2982.7748397 #> 2311 231.0 1981.7301 3.587977e+01 2982.3900867 #> 2312 231.1 1982.2025 3.579966e+01 2981.9977903 #> 2313 231.2 1982.6813 3.572059e+01 2981.5980630 #> 2314 231.3 1983.1664 3.564255e+01 2981.1910177 #> 2315 231.4 1983.6577 3.556554e+01 2980.7767677 #> 2316 231.5 1984.1550 3.548959e+01 2980.3554265 #> 2317 231.6 1984.6582 3.541467e+01 2979.9271083 #> 2318 231.7 1985.1673 3.534082e+01 2979.4919272 #> 2319 231.8 1985.6820 3.526801e+01 2979.0499978 #> 2320 231.9 1986.2023 3.519627e+01 2978.6014348 #> 2321 232.0 1986.7281 3.512559e+01 2978.1463534 #> 2322 232.1 1987.2591 3.505599e+01 2977.6848687 #> 2323 232.2 1987.7955 3.498745e+01 2977.2170961 #> 2324 232.3 1988.3369 3.491999e+01 2976.7431515 #> 2325 232.4 1988.8832 3.485360e+01 2976.2631504 #> 2326 232.5 1989.4345 3.478830e+01 2975.7772089 #> 2327 232.6 1989.9905 3.472408e+01 2975.2854430 #> 2328 232.7 1990.5511 3.466095e+01 2974.7879689 #> 2329 232.8 1991.1162 3.459890e+01 2974.2849029 #> 2330 232.9 1991.6857 3.453795e+01 2973.7763612 #> 2331 233.0 1992.2595 3.447808e+01 2973.2624604 #> 2332 233.1 1992.8374 3.441932e+01 2972.7433168 #> 2333 233.2 1993.4193 3.436165e+01 2972.2190470 #> 2334 233.3 1994.0052 3.430507e+01 2971.6897673 #> 2335 233.4 1994.5948 3.424960e+01 2971.1555944 #> 2336 233.5 1995.1881 3.419523e+01 2970.6166447 #> 2337 233.6 1995.7850 3.414196e+01 2970.0730346 #> 2338 233.7 1996.3853 3.408979e+01 2969.5248806 #> 2339 233.8 1996.9890 3.403873e+01 2968.9722990 #> 2340 233.9 1997.5958 3.398877e+01 2968.4154062 #> 2341 234.0 1998.2058 3.393992e+01 2967.8543182 #> 2342 234.1 1998.8187 3.389218e+01 2967.2891513 #> 2343 234.2 1999.4344 3.384554e+01 2966.7200215 #> 2344 234.3 2000.0529 3.380001e+01 2966.1470446 #> 2345 234.4 2000.6741 3.375559e+01 2965.5703365 #> 2346 234.5 2001.2977 3.371227e+01 2964.9900128 #> 2347 234.6 2001.9237 3.367006e+01 2964.4061890 #> 2348 234.7 2002.5521 3.362896e+01 2963.8189804 #> 2349 234.8 2003.1825 3.358897e+01 2963.2285021 #> 2350 234.9 2003.8151 3.355008e+01 2962.6348693 #> 2351 235.0 2004.4495 3.351230e+01 2962.0381966 #> 2352 235.1 2005.0858 3.347562e+01 2961.4385988 #> 2353 235.2 2005.7238 3.344006e+01 2960.8361901 #> 2354 235.3 2006.3633 3.340559e+01 2960.2310847 #> 2355 235.4 2007.0044 3.337223e+01 2959.6233967 #> 2356 235.5 2007.6468 3.333997e+01 2959.0132397 #> 2357 235.6 2008.2905 3.330882e+01 2958.4007272 #> 2358 235.7 2008.9353 3.327876e+01 2957.7859724 #> 2359 235.8 2009.5811 3.324981e+01 2957.1690883 #> 2360 235.9 2010.2279 3.322195e+01 2956.5501876 #> 2361 236.0 2010.8754 3.319520e+01 2955.9293828 #> 2362 236.1 2011.5237 3.316953e+01 2955.3067859 #> 2363 236.2 2012.1725 3.314497e+01 2954.6825088 #> 2364 236.3 2012.8218 3.312149e+01 2954.0566632 #> 2365 236.4 2013.4715 3.309911e+01 2953.4293601 #> 2366 236.5 2014.1215 3.307782e+01 2952.8007106 #> 2367 236.6 2014.7716 3.305761e+01 2952.1708253 #> 2368 236.7 2015.4217 3.303849e+01 2951.5398144 #> 2369 236.8 2016.0718 3.302046e+01 2950.9077880 #> 2370 236.9 2016.7216 3.300351e+01 2950.2748557 #> 2371 237.0 2017.3712 3.298764e+01 2949.6411267 #> 2372 237.1 2018.0204 3.297285e+01 2949.0067099 #> 2373 237.2 2018.6692 3.295913e+01 2948.3717140 #> 2374 237.3 2019.3173 3.294649e+01 2947.7362470 #> 2375 237.4 2019.9647 3.293491e+01 2947.1004169 #> 2376 237.5 2020.6113 3.292441e+01 2946.4643311 #> 2377 237.6 2021.2569 3.291498e+01 2945.8280967 #> 2378 237.7 2021.9016 3.290660e+01 2945.1918202 #> 2379 237.8 2022.5451 3.289929e+01 2944.5556081 #> 2380 237.9 2023.1874 3.289304e+01 2943.9195661 #> 2381 238.0 2023.8284 3.288785e+01 2943.2837998 #> 2382 238.1 2024.4679 3.288371e+01 2942.6484142 #> 2383 238.2 2025.1059 3.288062e+01 2942.0135139 #> 2384 238.3 2025.7422 3.287858e+01 2941.3792031 #> 2385 238.4 2026.3768 3.287758e+01 2940.7455857 #> 2386 238.5 2027.0096 3.287762e+01 2940.1127648 #> 2387 238.6 2027.6405 3.287870e+01 2939.4808435 #> 2388 238.7 2028.2693 3.288082e+01 2938.8499242 #> 2389 238.8 2028.8959 3.288397e+01 2938.2201087 #> 2390 238.9 2029.5203 3.288815e+01 2937.5914988 #> 2391 239.0 2030.1424 3.289336e+01 2936.9641954 #> 2392 239.1 2030.7621 3.289958e+01 2936.3382991 #> 2393 239.2 2031.3793 3.290683e+01 2935.7139099 #> 2394 239.3 2031.9938 3.291509e+01 2935.0911276 #> 2395 239.4 2032.6056 3.292436e+01 2934.4700512 #> 2396 239.5 2033.2146 3.293464e+01 2933.8507794 #> 2397 239.6 2033.8207 3.294593e+01 2933.2334103 #> 2398 239.7 2034.4237 3.295821e+01 2932.6180414 #> 2399 239.8 2035.0237 3.297149e+01 2932.0047699 #> 2400 239.9 2035.6205 3.298576e+01 2931.3936923 #> 2401 240.0 2036.2141 3.300102e+01 2930.7849047 #> 2402 240.1 2036.8042 3.301727e+01 2930.1785026 #> 2403 240.2 2037.3909 3.303449e+01 2929.5745809 #> 2404 240.3 2037.9741 3.305270e+01 2928.9732341 #> 2405 240.4 2038.5536 3.307187e+01 2928.3745559 #> 2406 240.5 2039.1294 3.309201e+01 2927.7786397 #> 2407 240.6 2039.7013 3.311311e+01 2927.1855782 #> 2408 240.7 2040.2694 3.313517e+01 2926.5954636 #> 2409 240.8 2040.8334 3.315818e+01 2926.0083874 #> 2410 240.9 2041.3934 3.318214e+01 2925.4244405 #> 2411 241.0 2041.9492 3.320704e+01 2924.8437134 #> 2412 241.1 2042.5008 3.323289e+01 2924.2662958 #> 2413 241.2 2043.0481 3.325966e+01 2923.6922768 #> 2414 241.3 2043.5909 3.328737e+01 2923.1217451 #> 2415 241.4 2044.1292 3.331600e+01 2922.5547885 #> 2416 241.5 2044.6630 3.334555e+01 2921.9914942 #> 2417 241.6 2045.1920 3.337601e+01 2921.4319490 #> 2418 241.7 2045.7164 3.340737e+01 2920.8762387 #> 2419 241.8 2046.2359 3.343964e+01 2920.3244487 #> 2420 241.9 2046.7505 3.347281e+01 2919.7766637 #> 2421 242.0 2047.2602 3.350687e+01 2919.2329677 #> 2422 242.1 2047.7647 3.354181e+01 2918.6934438 #> 2423 242.2 2048.2642 3.357763e+01 2918.1581748 #> 2424 242.3 2048.7584 3.361433e+01 2917.6272425 #> 2425 242.4 2049.2474 3.365189e+01 2917.1007281 #> 2426 242.5 2049.7310 3.369031e+01 2916.5787121 #> 2427 242.6 2050.2091 3.372959e+01 2916.0612743 #> 2428 242.7 2050.6818 3.376972e+01 2915.5484936 #> 2429 242.8 2051.1489 3.381069e+01 2915.0404484 #> 2430 242.9 2051.6103 3.385249e+01 2914.5372161 #> 2431 243.0 2052.0660 3.389513e+01 2914.0388734 #> 2432 243.1 2052.5159 3.393858e+01 2913.5454965 #> 2433 243.2 2052.9600 3.398285e+01 2913.0571604 #> 2434 243.3 2053.3981 3.402793e+01 2912.5739395 #> 2435 243.4 2053.8303 3.407381e+01 2912.0959076 #> 2436 243.5 2054.2564 3.412049e+01 2911.6231372 #> 2437 243.6 2054.6764 3.416795e+01 2911.1557006 #> 2438 243.7 2055.0901 3.421619e+01 2910.6936686 #> 2439 243.8 2055.4977 3.426520e+01 2910.2371117 #> 2440 243.9 2055.8989 3.431498e+01 2909.7860994 #> 2441 244.0 2056.2938 3.436551e+01 2909.3407000 #> 2442 244.1 2056.6822 3.441679e+01 2908.9009815 #> 2443 244.2 2057.0642 3.446881e+01 2908.4670106 #> 2444 244.3 2057.4396 3.452156e+01 2908.0388531 #> 2445 244.4 2057.8084 3.457504e+01 2907.6165743 #> 2446 244.5 2058.1705 3.462923e+01 2907.2002380 #> 2447 244.6 2058.5260 3.468413e+01 2906.7899076 #> 2448 244.7 2058.8746 3.473973e+01 2906.3856452 #> 2449 244.8 2059.2165 3.479602e+01 2905.9875122 #> 2450 244.9 2059.5514 3.485299e+01 2905.5955689 #> 2451 245.0 2059.8795 3.491063e+01 2905.2098745 #> 2452 245.1 2060.2006 3.496893e+01 2904.8304876 #> 2453 245.2 2060.5146 3.502789e+01 2904.4574654 #> 2454 245.3 2060.8216 3.508749e+01 2904.0908644 #> 2455 245.4 2061.1215 3.514773e+01 2903.7307398 #> 2456 245.5 2061.4143 3.520859e+01 2903.3771460 #> 2457 245.6 2061.6998 3.527007e+01 2903.0301363 #> 2458 245.7 2061.9781 3.533215e+01 2902.6897628 #> 2459 245.8 2062.2491 3.539483e+01 2902.3560768 #> 2460 245.9 2062.5128 3.545809e+01 2902.0291283 #> 2461 246.0 2062.7691 3.552193e+01 2901.7089663 #> 2462 246.1 2063.0180 3.558633e+01 2901.3956387 #> 2463 246.2 2063.2595 3.565128e+01 2901.0891922 #> 2464 246.3 2063.4935 3.571678e+01 2900.7896726 #> 2465 246.4 2063.7201 3.578281e+01 2900.4971242 #> 2466 246.5 2063.9390 3.584937e+01 2900.2115906 #> 2467 246.6 2064.1505 3.591643e+01 2899.9331138 #> 2468 246.7 2064.3543 3.598399e+01 2899.6617349 #> 2469 246.8 2064.5505 3.605204e+01 2899.3974937 #> 2470 246.9 2064.7390 3.612057e+01 2899.1404290 #> 2471 247.0 2064.9199 3.618956e+01 2898.8905780 #> 2472 247.1 2065.0930 3.625900e+01 2898.6479771 #> 2473 247.2 2065.2585 3.632888e+01 2898.4126612 #> 2474 247.3 2065.4161 3.639919e+01 2898.1846641 #> 2475 247.4 2065.5661 3.646992e+01 2897.9640182 #> 2476 247.5 2065.7082 3.654106e+01 2897.7507548 #> 2477 247.6 2065.8425 3.661258e+01 2897.5449038 #> 2478 247.7 2065.9690 3.668448e+01 2897.3464939 #> 2479 247.8 2066.0877 3.675675e+01 2897.1555523 #> 2480 247.9 2066.1985 3.682938e+01 2896.9721052 #> 2481 248.0 2066.3015 3.690234e+01 2896.7961772 #> 2482 248.1 2066.3966 3.697564e+01 2896.6277917 #> 2483 248.2 2066.4838 3.704924e+01 2896.4669708 #> 2484 248.3 2066.5631 3.712315e+01 2896.3137351 #> 2485 248.4 2066.6345 3.719735e+01 2896.1681040 #> 2486 248.5 2066.6981 3.727182e+01 2896.0300953 #> 2487 248.6 2066.7537 3.734655e+01 2895.8997257 #> 2488 248.7 2066.8015 3.742152e+01 2895.7770103 #> 2489 248.8 2066.8413 3.749673e+01 2895.6619627 #> 2490 248.9 2066.8732 3.757216e+01 2895.5545954 #> 2491 249.0 2066.8973 3.764779e+01 2895.4549193 #> 2492 249.1 2066.9134 3.772362e+01 2895.3629438 #> 2493 249.2 2066.9217 3.779961e+01 2895.2786769 #> 2494 249.3 2066.9221 3.787577e+01 2895.2021253 #> 2495 249.4 2066.9146 3.795208e+01 2895.1332939 #> 2496 249.5 2066.8993 3.802852e+01 2895.0721866 #> 2497 249.6 2066.8761 3.810507e+01 2895.0188054 #> 2498 249.7 2066.8451 3.818173e+01 2894.9731510 #> 2499 249.8 2066.8063 3.825848e+01 2894.9352226 #> 2500 249.9 2066.7597 3.833529e+01 2894.9050179 #> 2501 250.0 2066.7053 3.841217e+01 2894.8825331 #>  #> attr(,\"class\") #> [1] \"sir_basic_model\""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_static_model.html","id":null,"dir":"Reference","previous_headings":"","what":"SIR static model (age-heterogeneous, endemic equilibrium) — sir_static_model","title":"SIR static model (age-heterogeneous, endemic equilibrium) — sir_static_model","text":"Simulate transmission model constant force infection consists 3 compartments: susceptible (S), infected (), recovered (R)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_static_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SIR static model (age-heterogeneous, endemic equilibrium) — sir_static_model","text":"","code":"sir_static_model(a, state, parameters)"},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_static_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SIR static model (age-heterogeneous, endemic equilibrium) — sir_static_model","text":"age sequence. state initial state system. named vector following s: initial susceptible proportion : initial infected proportion r: initial recovered proportion parameters model's parameter. named vector following lambda: natural death rate nu: recovery rate","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_static_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SIR static model (age-heterogeneous, endemic equilibrium) — sir_static_model","text":"list class sir_static_model following items parameters list parameters used fitting model output matrix proportion compartment time","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_static_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SIR static model (age-heterogeneous, endemic equilibrium) — sir_static_model","text":"Follow SIR model endemic state described book Hens et al. (section 3.2.2.) Assumptions: - Time homogeneity - Age heterogeneity - Constant force infection model described system 3 differential equations $$ \\begin{cases} \\frac{ds()}{da} = -\\lambda s() \\\\ \\frac{di()}{da} = \\lambda s() - \\nu ()  \\\\ \\frac{dr()}{da} =  \\nu () \\end{cases} $$ : -   \\(s(), (), r()\\) proportion susceptible, infected, recovered population age group \\(\\) respectively -   \\(\\lambda\\) force infection -   \\(\\nu\\) recovery rate","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_static_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SIR static model (age-heterogeneous, endemic equilibrium) — sir_static_model","text":"Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based Serological Social Contact Data: Modern Statistical Perspective. tatistics Biology Health. Springer New York. doi:10.1007/978-1-4614-4072-7 .","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_static_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SIR static model (age-heterogeneous, endemic equilibrium) — sir_static_model","text":"","code":"state <- c(s=0.99,i=0.01,r=0) parameters <- c(   lambda = 0.05,   nu=1/(14/365) # 2 weeks to recover ) ages<-seq(0, 90, by=0.01) model = sir_static_model(ages, state, parameters) model #> $parameters #>   lambda       nu  #>  0.05000 26.07143  #>  #> $output #>       time          s            i           r #> 1     0.00 0.99000000 1.000000e-02 0.000000000 #> 2     0.01 0.98950512 8.139822e-03 0.002355054 #> 3     0.02 0.98901049 6.708036e-03 0.004281469 #> 4     0.03 0.98851611 5.603950e-03 0.005879937 #> 5     0.04 0.98802198 4.752794e-03 0.007225227 #> 6     0.05 0.98752809 4.096558e-03 0.008375351 #> 7     0.06 0.98703445 3.590872e-03 0.009374678 #> 8     0.07 0.98654106 3.201102e-03 0.010257841 #> 9     0.08 0.98604791 2.900525e-03 0.011051565 #> 10    0.09 0.98555501 2.668724e-03 0.011776268 #> 11    0.10 0.98506235 2.489909e-03 0.012447737 #> 12    0.11 0.98456995 2.351916e-03 0.013078137 #> 13    0.12 0.98407778 2.245378e-03 0.013676837 #> 14    0.13 0.98358587 2.163076e-03 0.014251056 #> 15    0.14 0.98309420 2.099446e-03 0.014806355 #> 16    0.15 0.98260277 2.050204e-03 0.015347022 #> 17    0.16 0.98211160 2.012047e-03 0.015876357 #> 18    0.17 0.98162066 1.982432e-03 0.016396906 #> 19    0.18 0.98112997 1.959397e-03 0.016910628 #> 20    0.19 0.98063953 1.941438e-03 0.017419030 #> 21    0.20 0.98014934 1.927389e-03 0.017923276 #> 22    0.21 0.97965938 1.916345e-03 0.018424271 #> 23    0.22 0.97916968 1.907612e-03 0.018922712 #> 24    0.23 0.97868021 1.900660e-03 0.019419127 #> 25    0.24 0.97819100 1.895079e-03 0.019913926 #> 26    0.25 0.97770202 1.890571e-03 0.020407406 #> 27    0.26 0.97721329 1.886886e-03 0.020899820 #> 28    0.27 0.97672481 1.883834e-03 0.021391357 #> 29    0.28 0.97623657 1.881269e-03 0.021882162 #> 30    0.29 0.97574857 1.879079e-03 0.022372348 #> 31    0.30 0.97526082 1.877178e-03 0.022862001 #> 32    0.31 0.97477331 1.875500e-03 0.023351189 #> 33    0.32 0.97428605 1.873993e-03 0.023839961 #> 34    0.33 0.97379903 1.872617e-03 0.024328357 #> 35    0.34 0.97331225 1.871344e-03 0.024816409 #> 36    0.35 0.97282571 1.870148e-03 0.025304138 #> 37    0.36 0.97233942 1.869014e-03 0.025791565 #> 38    0.37 0.97185337 1.867925e-03 0.026278701 #> 39    0.38 0.97136757 1.866873e-03 0.026765558 #> 40    0.39 0.97088201 1.865849e-03 0.027252145 #> 41    0.40 0.97039669 1.864846e-03 0.027738468 #> 42    0.41 0.96991161 1.863860e-03 0.028224531 #> 43    0.42 0.96942677 1.862886e-03 0.028710339 #> 44    0.43 0.96894218 1.861923e-03 0.029195894 #> 45    0.44 0.96845783 1.860968e-03 0.029681200 #> 46    0.45 0.96797372 1.860019e-03 0.030166257 #> 47    0.46 0.96748986 1.859074e-03 0.030651067 #> 48    0.47 0.96700623 1.858134e-03 0.031135632 #> 49    0.48 0.96652285 1.857196e-03 0.031619952 #> 50    0.49 0.96603971 1.856261e-03 0.032104028 #> 51    0.50 0.96555681 1.855328e-03 0.032587860 #> 52    0.51 0.96507415 1.854396e-03 0.033071449 #> 53    0.52 0.96459174 1.853466e-03 0.033554795 #> 54    0.53 0.96410956 1.852538e-03 0.034037900 #> 55    0.54 0.96362763 1.851610e-03 0.034520762 #> 56    0.55 0.96314594 1.850683e-03 0.035003382 #> 57    0.56 0.96266448 1.849757e-03 0.035485761 #> 58    0.57 0.96218327 1.848831e-03 0.035967898 #> 59    0.58 0.96170230 1.847906e-03 0.036449794 #> 60    0.59 0.96122157 1.846982e-03 0.036931450 #> 61    0.60 0.96074108 1.846059e-03 0.037412864 #> 62    0.61 0.96026083 1.845136e-03 0.037894037 #> 63    0.62 0.95978082 1.844213e-03 0.038374970 #> 64    0.63 0.95930105 1.843291e-03 0.038855663 #> 65    0.64 0.95882152 1.842369e-03 0.039336115 #> 66    0.65 0.95834222 1.841448e-03 0.039816327 #> 67    0.66 0.95786317 1.840528e-03 0.040296299 #> 68    0.67 0.95738436 1.839608e-03 0.040776031 #> 69    0.68 0.95690579 1.838688e-03 0.041255523 #> 70    0.69 0.95642746 1.837769e-03 0.041734776 #> 71    0.70 0.95594936 1.836850e-03 0.042213789 #> 72    0.71 0.95547151 1.835932e-03 0.042692562 #> 73    0.72 0.95499389 1.835014e-03 0.043171096 #> 74    0.73 0.95451651 1.834097e-03 0.043649391 #> 75    0.74 0.95403937 1.833180e-03 0.044127447 #> 76    0.75 0.95356247 1.832264e-03 0.044605264 #> 77    0.76 0.95308581 1.831348e-03 0.045082842 #> 78    0.77 0.95260939 1.830433e-03 0.045560181 #> 79    0.78 0.95213320 1.829518e-03 0.046037282 #> 80    0.79 0.95165725 1.828603e-03 0.046514144 #> 81    0.80 0.95118154 1.827689e-03 0.046990768 #> 82    0.81 0.95070607 1.826775e-03 0.047467153 #> 83    0.82 0.95023084 1.825862e-03 0.047943300 #> 84    0.83 0.94975584 1.824949e-03 0.048419210 #> 85    0.84 0.94928108 1.824037e-03 0.048894881 #> 86    0.85 0.94880656 1.823125e-03 0.049370315 #> 87    0.86 0.94833227 1.822214e-03 0.049845511 #> 88    0.87 0.94785823 1.821303e-03 0.050320470 #> 89    0.88 0.94738442 1.820393e-03 0.050795191 #> 90    0.89 0.94691084 1.819483e-03 0.051269675 #> 91    0.90 0.94643751 1.818573e-03 0.051743921 #> 92    0.91 0.94596441 1.817664e-03 0.052217931 #> 93    0.92 0.94549154 1.816756e-03 0.052691703 #> 94    0.93 0.94501891 1.815847e-03 0.053165239 #> 95    0.94 0.94454652 1.814940e-03 0.053638538 #> 96    0.95 0.94407437 1.814033e-03 0.054111601 #> 97    0.96 0.94360245 1.813126e-03 0.054584427 #> 98    0.97 0.94313076 1.812219e-03 0.055057016 #> 99    0.98 0.94265932 1.811314e-03 0.055529370 #> 100   0.99 0.94218810 1.810408e-03 0.056001487 #> 101   1.00 0.94171713 1.809503e-03 0.056473368 #> 102   1.01 0.94124639 1.808599e-03 0.056945014 #> 103   1.02 0.94077588 1.807695e-03 0.057416423 #> 104   1.03 0.94030561 1.806791e-03 0.057887597 #> 105   1.04 0.93983558 1.805888e-03 0.058358536 #> 106   1.05 0.93936578 1.804985e-03 0.058829239 #> 107   1.06 0.93889621 1.804083e-03 0.059299707 #> 108   1.07 0.93842688 1.803181e-03 0.059769939 #> 109   1.08 0.93795778 1.802280e-03 0.060239937 #> 110   1.09 0.93748892 1.801379e-03 0.060709699 #> 111   1.10 0.93702029 1.800478e-03 0.061179227 #> 112   1.11 0.93655190 1.799578e-03 0.061648520 #> 113   1.12 0.93608374 1.798679e-03 0.062117579 #> 114   1.13 0.93561582 1.797780e-03 0.062586403 #> 115   1.14 0.93514813 1.796881e-03 0.063054992 #> 116   1.15 0.93468067 1.795983e-03 0.063523348 #> 117   1.16 0.93421345 1.795085e-03 0.063991469 #> 118   1.17 0.93374646 1.794188e-03 0.064459356 #> 119   1.18 0.93327970 1.793291e-03 0.064927010 #> 120   1.19 0.93281318 1.792394e-03 0.065394430 #> 121   1.20 0.93234689 1.791498e-03 0.065861615 #> 122   1.21 0.93188083 1.790603e-03 0.066328568 #> 123   1.22 0.93141501 1.789708e-03 0.066795287 #> 124   1.23 0.93094941 1.788813e-03 0.067261773 #> 125   1.24 0.93048406 1.787919e-03 0.067728025 #> 126   1.25 0.93001893 1.787025e-03 0.068194045 #> 127   1.26 0.92955404 1.786132e-03 0.068659831 #> 128   1.27 0.92908938 1.785239e-03 0.069125385 #> 129   1.28 0.92862495 1.784347e-03 0.069590706 #> 130   1.29 0.92816075 1.783455e-03 0.070055794 #> 131   1.30 0.92769679 1.782563e-03 0.070520650 #> 132   1.31 0.92723305 1.781672e-03 0.070985274 #> 133   1.32 0.92676955 1.780781e-03 0.071449665 #> 134   1.33 0.92630628 1.779891e-03 0.071913824 #> 135   1.34 0.92584325 1.779002e-03 0.072377751 #> 136   1.35 0.92538044 1.778112e-03 0.072841447 #> 137   1.36 0.92491787 1.777223e-03 0.073304910 #> 138   1.37 0.92445552 1.776335e-03 0.073768142 #> 139   1.38 0.92399341 1.775447e-03 0.074231142 #> 140   1.39 0.92353153 1.774560e-03 0.074693911 #> 141   1.40 0.92306988 1.773673e-03 0.075156448 #> 142   1.41 0.92260846 1.772786e-03 0.075618754 #> 143   1.42 0.92214727 1.771900e-03 0.076080829 #> 144   1.43 0.92168631 1.771014e-03 0.076542674 #> 145   1.44 0.92122558 1.770129e-03 0.077004287 #> 146   1.45 0.92076509 1.769244e-03 0.077465669 #> 147   1.46 0.92030482 1.768360e-03 0.077926821 #> 148   1.47 0.91984478 1.767476e-03 0.078387743 #> 149   1.48 0.91938497 1.766592e-03 0.078848434 #> 150   1.49 0.91892540 1.765709e-03 0.079308894 #> 151   1.50 0.91846605 1.764826e-03 0.079769125 #> 152   1.51 0.91800693 1.763944e-03 0.080229125 #> 153   1.52 0.91754804 1.763062e-03 0.080688896 #> 154   1.53 0.91708938 1.762181e-03 0.081148436 #> 155   1.54 0.91663095 1.761300e-03 0.081607747 #> 156   1.55 0.91617275 1.760420e-03 0.082066829 #> 157   1.56 0.91571478 1.759540e-03 0.082525681 #> 158   1.57 0.91525704 1.758660e-03 0.082984303 #> 159   1.58 0.91479952 1.757781e-03 0.083442696 #> 160   1.59 0.91434224 1.756902e-03 0.083900860 #> 161   1.60 0.91388518 1.756024e-03 0.084358796 #> 162   1.61 0.91342835 1.755146e-03 0.084816502 #> 163   1.62 0.91297175 1.754269e-03 0.085273979 #> 164   1.63 0.91251538 1.753392e-03 0.085731228 #> 165   1.64 0.91205924 1.752516e-03 0.086188248 #> 166   1.65 0.91160332 1.751640e-03 0.086645040 #> 167   1.66 0.91114763 1.750764e-03 0.087101603 #> 168   1.67 0.91069217 1.749889e-03 0.087557938 #> 169   1.68 0.91023694 1.749014e-03 0.088014045 #> 170   1.69 0.90978194 1.748140e-03 0.088469924 #> 171   1.70 0.90932716 1.747266e-03 0.088925575 #> 172   1.71 0.90887261 1.746393e-03 0.089380999 #> 173   1.72 0.90841829 1.745520e-03 0.089836194 #> 174   1.73 0.90796419 1.744647e-03 0.090291163 #> 175   1.74 0.90751032 1.743775e-03 0.090745903 #> 176   1.75 0.90705668 1.742903e-03 0.091200417 #> 177   1.76 0.90660326 1.742032e-03 0.091654703 #> 178   1.77 0.90615008 1.741161e-03 0.092108762 #> 179   1.78 0.90569711 1.740291e-03 0.092562594 #> 180   1.79 0.90524438 1.739421e-03 0.093016200 #> 181   1.80 0.90479187 1.738551e-03 0.093469578 #> 182   1.81 0.90433959 1.737682e-03 0.093922730 #> 183   1.82 0.90388753 1.736814e-03 0.094375655 #> 184   1.83 0.90343570 1.735946e-03 0.094828354 #> 185   1.84 0.90298409 1.735078e-03 0.095280827 #> 186   1.85 0.90253272 1.734211e-03 0.095733074 #> 187   1.86 0.90208156 1.733344e-03 0.096185094 #> 188   1.87 0.90163063 1.732477e-03 0.096636889 #> 189   1.88 0.90117993 1.731611e-03 0.097088457 #> 190   1.89 0.90072945 1.730746e-03 0.097539800 #> 191   1.90 0.90027920 1.729880e-03 0.097990918 #> 192   1.91 0.89982917 1.729016e-03 0.098441809 #> 193   1.92 0.89937937 1.728151e-03 0.098892476 #> 194   1.93 0.89892980 1.727288e-03 0.099342917 #> 195   1.94 0.89848044 1.726424e-03 0.099793133 #> 196   1.95 0.89803131 1.725561e-03 0.100243124 #> 197   1.96 0.89758241 1.724699e-03 0.100692890 #> 198   1.97 0.89713373 1.723836e-03 0.101142431 #> 199   1.98 0.89668528 1.722975e-03 0.101591748 #> 200   1.99 0.89623705 1.722113e-03 0.102040840 #> 201   2.00 0.89578904 1.721253e-03 0.102489707 #> 202   2.01 0.89534126 1.720392e-03 0.102938350 #> 203   2.02 0.89489370 1.719532e-03 0.103386769 #> 204   2.03 0.89444636 1.718673e-03 0.103834963 #> 205   2.04 0.89399925 1.717814e-03 0.104282934 #> 206   2.05 0.89355236 1.716955e-03 0.104730680 #> 207   2.06 0.89310570 1.716097e-03 0.105178203 #> 208   2.07 0.89265926 1.715239e-03 0.105625502 #> 209   2.08 0.89221304 1.714381e-03 0.106072578 #> 210   2.09 0.89176705 1.713524e-03 0.106519430 #> 211   2.10 0.89132127 1.712668e-03 0.106966058 #> 212   2.11 0.89087572 1.711812e-03 0.107412464 #> 213   2.12 0.89043040 1.710956e-03 0.107858646 #> 214   2.13 0.88998529 1.710101e-03 0.108304605 #> 215   2.14 0.88954041 1.709246e-03 0.108750341 #> 216   2.15 0.88909575 1.708392e-03 0.109195855 #> 217   2.16 0.88865132 1.707538e-03 0.109641146 #> 218   2.17 0.88820710 1.706684e-03 0.110086214 #> 219   2.18 0.88776311 1.705831e-03 0.110531060 #> 220   2.19 0.88731934 1.704978e-03 0.110975683 #> 221   2.20 0.88687579 1.704126e-03 0.111420084 #> 222   2.21 0.88643246 1.703274e-03 0.111864263 #> 223   2.22 0.88598936 1.702423e-03 0.112308220 #> 224   2.23 0.88554647 1.701572e-03 0.112751955 #> 225   2.24 0.88510381 1.700721e-03 0.113195468 #> 226   2.25 0.88466137 1.699871e-03 0.113638759 #> 227   2.26 0.88421915 1.699021e-03 0.114081829 #> 228   2.27 0.88377715 1.698172e-03 0.114524678 #> 229   2.28 0.88333537 1.697323e-03 0.114967305 #> 230   2.29 0.88289382 1.696475e-03 0.115409710 #> 231   2.30 0.88245248 1.695627e-03 0.115851895 #> 232   2.31 0.88201136 1.694779e-03 0.116293858 #> 233   2.32 0.88157047 1.693932e-03 0.116735601 #> 234   2.33 0.88112979 1.693085e-03 0.117177123 #> 235   2.34 0.88068934 1.692239e-03 0.117618424 #> 236   2.35 0.88024910 1.691393e-03 0.118059505 #> 237   2.36 0.87980909 1.690547e-03 0.118500365 #> 238   2.37 0.87936929 1.689702e-03 0.118941004 #> 239   2.38 0.87892972 1.688858e-03 0.119381424 #> 240   2.39 0.87849036 1.688013e-03 0.119821623 #> 241   2.40 0.87805123 1.687170e-03 0.120261602 #> 242   2.41 0.87761231 1.686326e-03 0.120701361 #> 243   2.42 0.87717362 1.685483e-03 0.121140901 #> 244   2.43 0.87673514 1.684641e-03 0.121580221 #> 245   2.44 0.87629688 1.683799e-03 0.122019321 #> 246   2.45 0.87585884 1.682957e-03 0.122458201 #> 247   2.46 0.87542102 1.682116e-03 0.122896863 #> 248   2.47 0.87498342 1.681275e-03 0.123335305 #> 249   2.48 0.87454604 1.680434e-03 0.123773527 #> 250   2.49 0.87410887 1.679594e-03 0.124211531 #> 251   2.50 0.87367193 1.678755e-03 0.124649316 #> 252   2.51 0.87323520 1.677916e-03 0.125086882 #> 253   2.52 0.87279869 1.677077e-03 0.125524229 #> 254   2.53 0.87236240 1.676238e-03 0.125961358 #> 255   2.54 0.87192633 1.675401e-03 0.126398268 #> 256   2.55 0.87149048 1.674563e-03 0.126834960 #> 257   2.56 0.87105484 1.673726e-03 0.127271433 #> 258   2.57 0.87061942 1.672889e-03 0.127707688 #> 259   2.58 0.87018422 1.672053e-03 0.128143725 #> 260   2.59 0.86974924 1.671217e-03 0.128579545 #> 261   2.60 0.86931447 1.670382e-03 0.129015146 #> 262   2.61 0.86887992 1.669547e-03 0.129450529 #> 263   2.62 0.86844559 1.668712e-03 0.129885695 #> 264   2.63 0.86801148 1.667878e-03 0.130320644 #> 265   2.64 0.86757758 1.667044e-03 0.130755375 #> 266   2.65 0.86714390 1.666211e-03 0.131189889 #> 267   2.66 0.86671044 1.665378e-03 0.131624185 #> 268   2.67 0.86627719 1.664546e-03 0.132058264 #> 269   2.68 0.86584416 1.663714e-03 0.132492127 #> 270   2.69 0.86541135 1.662882e-03 0.132925772 #> 271   2.70 0.86497875 1.662051e-03 0.133359201 #> 272   2.71 0.86454637 1.661220e-03 0.133792413 #> 273   2.72 0.86411420 1.660390e-03 0.134225409 #> 274   2.73 0.86368225 1.659560e-03 0.134658188 #> 275   2.74 0.86325052 1.658730e-03 0.135090751 #> 276   2.75 0.86281900 1.657901e-03 0.135523097 #> 277   2.76 0.86238770 1.657072e-03 0.135955228 #> 278   2.77 0.86195661 1.656244e-03 0.136387142 #> 279   2.78 0.86152574 1.655416e-03 0.136818841 #> 280   2.79 0.86109509 1.654588e-03 0.137250323 #> 281   2.80 0.86066465 1.653761e-03 0.137681590 #> 282   2.81 0.86023442 1.652935e-03 0.138112642 #> 283   2.82 0.85980441 1.652108e-03 0.138543478 #> 284   2.83 0.85937462 1.651283e-03 0.138974098 #> 285   2.84 0.85894504 1.650457e-03 0.139404504 #> 286   2.85 0.85851567 1.649632e-03 0.139834694 #> 287   2.86 0.85808652 1.648807e-03 0.140264669 #> 288   2.87 0.85765759 1.647983e-03 0.140694429 #> 289   2.88 0.85722887 1.647160e-03 0.141123975 #> 290   2.89 0.85680036 1.646336e-03 0.141553305 #> 291   2.90 0.85637207 1.645513e-03 0.141982422 #> 292   2.91 0.85594399 1.644691e-03 0.142411323 #> 293   2.92 0.85551612 1.643868e-03 0.142840010 #> 294   2.93 0.85508847 1.643047e-03 0.143268483 #> 295   2.94 0.85466103 1.642225e-03 0.143696742 #> 296   2.95 0.85423381 1.641405e-03 0.144124786 #> 297   2.96 0.85380680 1.640584e-03 0.144552617 #> 298   2.97 0.85338000 1.639764e-03 0.144980234 #> 299   2.98 0.85295342 1.638944e-03 0.145407637 #> 300   2.99 0.85252705 1.638125e-03 0.145834826 #> 301   3.00 0.85210089 1.637306e-03 0.146261802 #> 302   3.01 0.85167495 1.636488e-03 0.146688565 #> 303   3.02 0.85124922 1.635670e-03 0.147115114 #> 304   3.03 0.85082370 1.634852e-03 0.147541450 #> 305   3.04 0.85039839 1.634035e-03 0.147967572 #> 306   3.05 0.84997330 1.633218e-03 0.148393482 #> 307   3.06 0.84954842 1.632402e-03 0.148819179 #> 308   3.07 0.84912375 1.631586e-03 0.149244663 #> 309   3.08 0.84869930 1.630770e-03 0.149669934 #> 310   3.09 0.84827505 1.629955e-03 0.150094993 #> 311   3.10 0.84785102 1.629140e-03 0.150519839 #> 312   3.11 0.84742720 1.628326e-03 0.150944473 #> 313   3.12 0.84700359 1.627512e-03 0.151368895 #> 314   3.13 0.84658020 1.626698e-03 0.151793104 #> 315   3.14 0.84615701 1.625885e-03 0.152217102 #> 316   3.15 0.84573404 1.625072e-03 0.152640887 #> 317   3.16 0.84531128 1.624260e-03 0.153064461 #> 318   3.17 0.84488873 1.623448e-03 0.153487823 #> 319   3.18 0.84446639 1.622636e-03 0.153910973 #> 320   3.19 0.84404426 1.621825e-03 0.154333912 #> 321   3.20 0.84362235 1.621015e-03 0.154756640 #> 322   3.21 0.84320064 1.620204e-03 0.155179156 #> 323   3.22 0.84277915 1.619394e-03 0.155601460 #> 324   3.23 0.84235786 1.618585e-03 0.156023554 #> 325   3.24 0.84193679 1.617776e-03 0.156445437 #> 326   3.25 0.84151592 1.616967e-03 0.156867109 #> 327   3.26 0.84109527 1.616159e-03 0.157288570 #> 328   3.27 0.84067483 1.615351e-03 0.157709820 #> 329   3.28 0.84025460 1.614544e-03 0.158130860 #> 330   3.29 0.83983457 1.613736e-03 0.158551690 #> 331   3.30 0.83941476 1.612930e-03 0.158972309 #> 332   3.31 0.83899516 1.612124e-03 0.159392717 #> 333   3.32 0.83857577 1.611318e-03 0.159812916 #> 334   3.33 0.83815658 1.610512e-03 0.160232904 #> 335   3.34 0.83773761 1.609707e-03 0.160652683 #> 336   3.35 0.83731885 1.608903e-03 0.161072252 #> 337   3.36 0.83690029 1.608098e-03 0.161491611 #> 338   3.37 0.83648195 1.607294e-03 0.161910760 #> 339   3.38 0.83606381 1.606491e-03 0.162329700 #> 340   3.39 0.83564588 1.605688e-03 0.162748431 #> 341   3.40 0.83522816 1.604885e-03 0.163166952 #> 342   3.41 0.83481065 1.604083e-03 0.163585264 #> 343   3.42 0.83439335 1.603281e-03 0.164003367 #> 344   3.43 0.83397626 1.602480e-03 0.164421261 #> 345   3.44 0.83355938 1.601679e-03 0.164838945 #> 346   3.45 0.83314270 1.600878e-03 0.165256422 #> 347   3.46 0.83272623 1.600078e-03 0.165673689 #> 348   3.47 0.83230997 1.599278e-03 0.166090748 #> 349   3.48 0.83189392 1.598479e-03 0.166507598 #> 350   3.49 0.83147808 1.597680e-03 0.166924241 #> 351   3.50 0.83106244 1.596881e-03 0.167340674 #> 352   3.51 0.83064702 1.596083e-03 0.167756900 #> 353   3.52 0.83023180 1.595285e-03 0.168172917 #> 354   3.53 0.82981679 1.594487e-03 0.168588727 #> 355   3.54 0.82940198 1.593690e-03 0.169004329 #> 356   3.55 0.82898738 1.592894e-03 0.169419723 #> 357   3.56 0.82857299 1.592097e-03 0.169834909 #> 358   3.57 0.82815881 1.591302e-03 0.170249888 #> 359   3.58 0.82774483 1.590506e-03 0.170664659 #> 360   3.59 0.82733107 1.589711e-03 0.171079223 #> 361   3.60 0.82691750 1.588916e-03 0.171493580 #> 362   3.61 0.82650415 1.588122e-03 0.171907730 #> 363   3.62 0.82609100 1.587328e-03 0.172321672 #> 364   3.63 0.82567806 1.586535e-03 0.172735408 #> 365   3.64 0.82526532 1.585742e-03 0.173148937 #> 366   3.65 0.82485279 1.584949e-03 0.173562259 #> 367   3.66 0.82444047 1.584157e-03 0.173975375 #> 368   3.67 0.82402835 1.583365e-03 0.174388284 #> 369   3.68 0.82361644 1.582573e-03 0.174800987 #> 370   3.69 0.82320473 1.581782e-03 0.175213483 #> 371   3.70 0.82279323 1.580992e-03 0.175625773 #> 372   3.71 0.82238194 1.580201e-03 0.176037857 #> 373   3.72 0.82197085 1.579411e-03 0.176449736 #> 374   3.73 0.82155997 1.578622e-03 0.176861408 #> 375   3.74 0.82114929 1.577833e-03 0.177272874 #> 376   3.75 0.82073882 1.577044e-03 0.177684135 #> 377   3.76 0.82032855 1.576256e-03 0.178095190 #> 378   3.77 0.81991849 1.575468e-03 0.178506040 #> 379   3.78 0.81950864 1.574680e-03 0.178916684 #> 380   3.79 0.81909898 1.573893e-03 0.179327123 #> 381   3.80 0.81868954 1.573106e-03 0.179737357 #> 382   3.81 0.81828029 1.572320e-03 0.180147386 #> 383   3.82 0.81787126 1.571534e-03 0.180557210 #> 384   3.83 0.81746242 1.570749e-03 0.180966829 #> 385   3.84 0.81705379 1.569963e-03 0.181376243 #> 386   3.85 0.81664537 1.569179e-03 0.181785453 #> 387   3.86 0.81623715 1.568394e-03 0.182194458 #> 388   3.87 0.81582913 1.567610e-03 0.182603258 #> 389   3.88 0.81542132 1.566827e-03 0.183011855 #> 390   3.89 0.81501371 1.566043e-03 0.183420246 #> 391   3.90 0.81460631 1.565261e-03 0.183828434 #> 392   3.91 0.81419910 1.564478e-03 0.184236418 #> 393   3.92 0.81379211 1.563696e-03 0.184644198 #> 394   3.93 0.81338531 1.562914e-03 0.185051774 #> 395   3.94 0.81297872 1.562133e-03 0.185459146 #> 396   3.95 0.81257233 1.561352e-03 0.185866315 #> 397   3.96 0.81216615 1.560572e-03 0.186273280 #> 398   3.97 0.81176017 1.559792e-03 0.186680042 #> 399   3.98 0.81135439 1.559012e-03 0.187086600 #> 400   3.99 0.81094881 1.558233e-03 0.187492955 #> 401   4.00 0.81054344 1.557454e-03 0.187899107 #> 402   4.01 0.81013827 1.556675e-03 0.188305056 #> 403   4.02 0.80973330 1.555897e-03 0.188710802 #> 404   4.03 0.80932854 1.555119e-03 0.189116345 #> 405   4.04 0.80892397 1.554342e-03 0.189521686 #> 406   4.05 0.80851961 1.553565e-03 0.189926824 #> 407   4.06 0.80811545 1.552788e-03 0.190331759 #> 408   4.07 0.80771150 1.552012e-03 0.190736492 #> 409   4.08 0.80730774 1.551236e-03 0.191141023 #> 410   4.09 0.80690419 1.550461e-03 0.191545351 #> 411   4.10 0.80650084 1.549686e-03 0.191949477 #> 412   4.11 0.80609769 1.548911e-03 0.192353402 #> 413   4.12 0.80569474 1.548137e-03 0.192757124 #> 414   4.13 0.80529199 1.547363e-03 0.193160645 #> 415   4.14 0.80488945 1.546590e-03 0.193563963 #> 416   4.15 0.80448710 1.545817e-03 0.193967081 #> 417   4.16 0.80408496 1.545044e-03 0.194369996 #> 418   4.17 0.80368302 1.544272e-03 0.194772711 #> 419   4.18 0.80328128 1.543500e-03 0.195175224 #> 420   4.19 0.80287974 1.542728e-03 0.195577536 #> 421   4.20 0.80247840 1.541957e-03 0.195979646 #> 422   4.21 0.80207726 1.541186e-03 0.196381556 #> 423   4.22 0.80167632 1.540416e-03 0.196783265 #> 424   4.23 0.80127558 1.539646e-03 0.197184773 #> 425   4.24 0.80087504 1.538876e-03 0.197586080 #> 426   4.25 0.80047471 1.538107e-03 0.197987187 #> 427   4.26 0.80007457 1.537338e-03 0.198388093 #> 428   4.27 0.79967463 1.536569e-03 0.198788799 #> 429   4.28 0.79927489 1.535801e-03 0.199189304 #> 430   4.29 0.79887536 1.535034e-03 0.199589610 #> 431   4.30 0.79847602 1.534266e-03 0.199989715 #> 432   4.31 0.79807688 1.533499e-03 0.200389620 #> 433   4.32 0.79767794 1.532733e-03 0.200789325 #> 434   4.33 0.79727920 1.531967e-03 0.201188831 #> 435   4.34 0.79688066 1.531201e-03 0.201588136 #> 436   4.35 0.79648232 1.530435e-03 0.201987243 #> 437   4.36 0.79608418 1.529670e-03 0.202386149 #> 438   4.37 0.79568624 1.528906e-03 0.202784857 #> 439   4.38 0.79528849 1.528141e-03 0.203183365 #> 440   4.39 0.79489095 1.527378e-03 0.203581673 #> 441   4.40 0.79449360 1.526614e-03 0.203979783 #> 442   4.41 0.79409646 1.525851e-03 0.204377693 #> 443   4.42 0.79369951 1.525088e-03 0.204775405 #> 444   4.43 0.79330276 1.524326e-03 0.205172918 #> 445   4.44 0.79290620 1.523564e-03 0.205570232 #> 446   4.45 0.79250985 1.522802e-03 0.205967348 #> 447   4.46 0.79211369 1.522041e-03 0.206364265 #> 448   4.47 0.79171774 1.521280e-03 0.206760984 #> 449   4.48 0.79132198 1.520520e-03 0.207157504 #> 450   4.49 0.79092641 1.519760e-03 0.207553826 #> 451   4.50 0.79053105 1.519000e-03 0.207949950 #> 452   4.51 0.79013588 1.518241e-03 0.208345876 #> 453   4.52 0.78974091 1.517482e-03 0.208741605 #> 454   4.53 0.78934614 1.516723e-03 0.209137135 #> 455   4.54 0.78895157 1.515965e-03 0.209532468 #> 456   4.55 0.78855719 1.515207e-03 0.209927603 #> 457   4.56 0.78816301 1.514450e-03 0.210322540 #> 458   4.57 0.78776903 1.513693e-03 0.210717280 #> 459   4.58 0.78737524 1.512936e-03 0.211111823 #> 460   4.59 0.78698165 1.512180e-03 0.211506168 #> 461   4.60 0.78658826 1.511424e-03 0.211900317 #> 462   4.61 0.78619506 1.510669e-03 0.212294268 #> 463   4.62 0.78580206 1.509913e-03 0.212688022 #> 464   4.63 0.78540926 1.509159e-03 0.213081580 #> 465   4.64 0.78501665 1.508404e-03 0.213474941 #> 466   4.65 0.78462424 1.507650e-03 0.213868105 #> 467   4.66 0.78423203 1.506897e-03 0.214261073 #> 468   4.67 0.78384001 1.506143e-03 0.214653844 #> 469   4.68 0.78344819 1.505390e-03 0.215046419 #> 470   4.69 0.78305656 1.504638e-03 0.215438798 #> 471   4.70 0.78266513 1.503886e-03 0.215830980 #> 472   4.71 0.78227390 1.503134e-03 0.216222967 #> 473   4.72 0.78188286 1.502383e-03 0.216614757 #> 474   4.73 0.78149202 1.501632e-03 0.217006352 #> 475   4.74 0.78110137 1.500881e-03 0.217397751 #> 476   4.75 0.78071091 1.500131e-03 0.217788955 #> 477   4.76 0.78032066 1.499381e-03 0.218179962 #> 478   4.77 0.77993059 1.498631e-03 0.218570775 #> 479   4.78 0.77954073 1.497882e-03 0.218961392 #> 480   4.79 0.77915105 1.497134e-03 0.219351813 #> 481   4.80 0.77876157 1.496385e-03 0.219742040 #> 482   4.81 0.77837229 1.495637e-03 0.220132071 #> 483   4.82 0.77798320 1.494889e-03 0.220521908 #> 484   4.83 0.77759431 1.494142e-03 0.220911550 #> 485   4.84 0.77720561 1.493395e-03 0.221300996 #> 486   4.85 0.77681710 1.492649e-03 0.221690249 #> 487   4.86 0.77642879 1.491903e-03 0.222079306 #> 488   4.87 0.77604067 1.491157e-03 0.222468169 #> 489   4.88 0.77565275 1.490412e-03 0.222856838 #> 490   4.89 0.77526502 1.489667e-03 0.223245313 #> 491   4.90 0.77487749 1.488922e-03 0.223633593 #> 492   4.91 0.77449014 1.488178e-03 0.224021679 #> 493   4.92 0.77410300 1.487434e-03 0.224409571 #> 494   4.93 0.77371604 1.486690e-03 0.224797270 #> 495   4.94 0.77332928 1.485947e-03 0.225184774 #> 496   4.95 0.77294271 1.485204e-03 0.225572085 #> 497   4.96 0.77255634 1.484462e-03 0.225959202 #> 498   4.97 0.77217015 1.483720e-03 0.226346126 #> 499   4.98 0.77178417 1.482978e-03 0.226732856 #> 500   4.99 0.77139837 1.482237e-03 0.227119393 #> 501   5.00 0.77101277 1.481496e-03 0.227505737 #> 502   5.01 0.77062736 1.480755e-03 0.227891887 #> 503   5.02 0.77024214 1.480015e-03 0.228277845 #> 504   5.03 0.76985712 1.479275e-03 0.228663609 #> 505   5.04 0.76947228 1.478536e-03 0.229049181 #> 506   5.05 0.76908764 1.477797e-03 0.229434560 #> 507   5.06 0.76870320 1.477058e-03 0.229819747 #> 508   5.07 0.76831894 1.476320e-03 0.230204741 #> 509   5.08 0.76793488 1.475582e-03 0.230589542 #> 510   5.09 0.76755100 1.474844e-03 0.230974151 #> 511   5.10 0.76716733 1.474107e-03 0.231358568 #> 512   5.11 0.76678384 1.473370e-03 0.231742793 #> 513   5.12 0.76640054 1.472633e-03 0.232126825 #> 514   5.13 0.76601744 1.471897e-03 0.232510666 #> 515   5.14 0.76563452 1.471162e-03 0.232894315 #> 516   5.15 0.76525180 1.470426e-03 0.233277772 #> 517   5.16 0.76486927 1.469691e-03 0.233661037 #> 518   5.17 0.76448693 1.468956e-03 0.234044111 #> 519   5.18 0.76410478 1.468222e-03 0.234426993 #> 520   5.19 0.76372283 1.467488e-03 0.234809684 #> 521   5.20 0.76334106 1.466755e-03 0.235192183 #> 522   5.21 0.76295949 1.466022e-03 0.235574492 #> 523   5.22 0.76257810 1.465289e-03 0.235956609 #> 524   5.23 0.76219691 1.464556e-03 0.236338535 #> 525   5.24 0.76181591 1.463824e-03 0.236720270 #> 526   5.25 0.76143509 1.463092e-03 0.237101815 #> 527   5.26 0.76105447 1.462361e-03 0.237483169 #> 528   5.27 0.76067404 1.461630e-03 0.237864332 #> 529   5.28 0.76029380 1.460899e-03 0.238245304 #> 530   5.29 0.75991374 1.460169e-03 0.238626087 #> 531   5.30 0.75953388 1.459439e-03 0.239006678 #> 532   5.31 0.75915421 1.458710e-03 0.239387080 #> 533   5.32 0.75877473 1.457981e-03 0.239767291 #> 534   5.33 0.75839544 1.457252e-03 0.240147313 #> 535   5.34 0.75801633 1.456523e-03 0.240527144 #> 536   5.35 0.75763742 1.455795e-03 0.240906786 #> 537   5.36 0.75725870 1.455067e-03 0.241286237 #> 538   5.37 0.75688016 1.454340e-03 0.241665499 #> 539   5.38 0.75650181 1.453613e-03 0.242044572 #> 540   5.39 0.75612366 1.452887e-03 0.242423455 #> 541   5.40 0.75574569 1.452160e-03 0.242802149 #> 542   5.41 0.75536791 1.451434e-03 0.243180653 #> 543   5.42 0.75499032 1.450709e-03 0.243558968 #> 544   5.43 0.75461292 1.449984e-03 0.243937094 #> 545   5.44 0.75423571 1.449259e-03 0.244315031 #> 546   5.45 0.75385869 1.448534e-03 0.244692779 #> 547   5.46 0.75348185 1.447810e-03 0.245070338 #> 548   5.47 0.75310520 1.447087e-03 0.245447709 #> 549   5.48 0.75272875 1.446363e-03 0.245824891 #> 550   5.49 0.75235248 1.445640e-03 0.246201884 #> 551   5.50 0.75197639 1.444918e-03 0.246578689 #> 552   5.51 0.75160050 1.444195e-03 0.246955305 #> 553   5.52 0.75122479 1.443473e-03 0.247331733 #> 554   5.53 0.75084927 1.442752e-03 0.247707974 #> 555   5.54 0.75047394 1.442031e-03 0.248084026 #> 556   5.55 0.75009880 1.441310e-03 0.248459890 #> 557   5.56 0.74972384 1.440589e-03 0.248835566 #> 558   5.57 0.74934908 1.439869e-03 0.249211054 #> 559   5.58 0.74897450 1.439149e-03 0.249586355 #> 560   5.59 0.74860010 1.438430e-03 0.249961468 #> 561   5.60 0.74822590 1.437711e-03 0.250336393 #> 562   5.61 0.74785188 1.436992e-03 0.250711131 #> 563   5.62 0.74747804 1.436274e-03 0.251085682 #> 564   5.63 0.74710440 1.435556e-03 0.251460046 #> 565   5.64 0.74673094 1.434838e-03 0.251834222 #> 566   5.65 0.74635767 1.434121e-03 0.252208212 #> 567   5.66 0.74598458 1.433404e-03 0.252582014 #> 568   5.67 0.74561168 1.432688e-03 0.252955630 #> 569   5.68 0.74523897 1.431972e-03 0.253329059 #> 570   5.69 0.74486644 1.431256e-03 0.253702301 #> 571   5.70 0.74449410 1.430540e-03 0.254075356 #> 572   5.71 0.74412195 1.429825e-03 0.254448225 #> 573   5.72 0.74374998 1.429111e-03 0.254820908 #> 574   5.73 0.74337820 1.428396e-03 0.255193405 #> 575   5.74 0.74300660 1.427682e-03 0.255565715 #> 576   5.75 0.74263519 1.426969e-03 0.255937839 #> 577   5.76 0.74226397 1.426255e-03 0.256309777 #> 578   5.77 0.74189293 1.425542e-03 0.256681529 #> 579   5.78 0.74152207 1.424830e-03 0.257053096 #> 580   5.79 0.74115141 1.424117e-03 0.257424476 #> 581   5.80 0.74078092 1.423406e-03 0.257795671 #> 582   5.81 0.74041063 1.422694e-03 0.258166681 #> 583   5.82 0.74004051 1.421983e-03 0.258537505 #> 584   5.83 0.73967058 1.421272e-03 0.258908143 #> 585   5.84 0.73930084 1.420562e-03 0.259278596 #> 586   5.85 0.73893128 1.419851e-03 0.259648865 #> 587   5.86 0.73856191 1.419142e-03 0.260018948 #> 588   5.87 0.73819272 1.418432e-03 0.260388846 #> 589   5.88 0.73782372 1.417723e-03 0.260758559 #> 590   5.89 0.73745490 1.417015e-03 0.261128087 #> 591   5.90 0.73708626 1.416306e-03 0.261497431 #> 592   5.91 0.73671781 1.415598e-03 0.261866590 #> 593   5.92 0.73634954 1.414891e-03 0.262235564 #> 594   5.93 0.73598146 1.414183e-03 0.262604354 #> 595   5.94 0.73561356 1.413477e-03 0.262972960 #> 596   5.95 0.73524585 1.412770e-03 0.263341381 #> 597   5.96 0.73487832 1.412064e-03 0.263709619 #> 598   5.97 0.73451097 1.411358e-03 0.264077672 #> 599   5.98 0.73414381 1.410652e-03 0.264445541 #> 600   5.99 0.73377683 1.409947e-03 0.264813226 #> 601   6.00 0.73341003 1.409242e-03 0.265180728 #> 602   6.01 0.73304342 1.408538e-03 0.265548046 #> 603   6.02 0.73267699 1.407834e-03 0.265915180 #> 604   6.03 0.73231074 1.407130e-03 0.266282131 #> 605   6.04 0.73194468 1.406427e-03 0.266648898 #> 606   6.05 0.73157879 1.405724e-03 0.267015482 #> 607   6.06 0.73121310 1.405021e-03 0.267381882 #> 608   6.07 0.73084758 1.404319e-03 0.267748100 #> 609   6.08 0.73048225 1.403617e-03 0.268114134 #> 610   6.09 0.73011710 1.402915e-03 0.268479986 #> 611   6.10 0.72975213 1.402214e-03 0.268845655 #> 612   6.11 0.72938735 1.401513e-03 0.269211140 #> 613   6.12 0.72902274 1.400812e-03 0.269576443 #> 614   6.13 0.72865832 1.400112e-03 0.269941564 #> 615   6.14 0.72829409 1.399412e-03 0.270306502 #> 616   6.15 0.72793003 1.398713e-03 0.270671258 #> 617   6.16 0.72756616 1.398013e-03 0.271035831 #> 618   6.17 0.72720246 1.397315e-03 0.271400222 #> 619   6.18 0.72683895 1.396616e-03 0.271764431 #> 620   6.19 0.72647562 1.395918e-03 0.272128457 #> 621   6.20 0.72611248 1.395220e-03 0.272492302 #> 622   6.21 0.72574951 1.394523e-03 0.272855965 #> 623   6.22 0.72538673 1.393826e-03 0.273219446 #> 624   6.23 0.72502413 1.393129e-03 0.273582746 #> 625   6.24 0.72466170 1.392433e-03 0.273945864 #> 626   6.25 0.72429946 1.391737e-03 0.274308800 #> 627   6.26 0.72393740 1.391041e-03 0.274671555 #> 628   6.27 0.72357553 1.390346e-03 0.275034128 #> 629   6.28 0.72321383 1.389651e-03 0.275396521 #> 630   6.29 0.72285231 1.388956e-03 0.275758732 #> 631   6.30 0.72249098 1.388262e-03 0.276120762 #> 632   6.31 0.72212982 1.387568e-03 0.276482611 #> 633   6.32 0.72176885 1.386874e-03 0.276844280 #> 634   6.33 0.72140805 1.386181e-03 0.277205767 #> 635   6.34 0.72104744 1.385488e-03 0.277567074 #> 636   6.35 0.72068700 1.384795e-03 0.277928200 #> 637   6.36 0.72032675 1.384103e-03 0.278289146 #> 638   6.37 0.71996668 1.383411e-03 0.278649911 #> 639   6.38 0.71960678 1.382720e-03 0.279010496 #> 640   6.39 0.71924707 1.382028e-03 0.279370901 #> 641   6.40 0.71888754 1.381338e-03 0.279731125 #> 642   6.41 0.71852818 1.380647e-03 0.280091169 #> 643   6.42 0.71816901 1.379957e-03 0.280451034 #> 644   6.43 0.71781001 1.379267e-03 0.280810718 #> 645   6.44 0.71745120 1.378578e-03 0.281170223 #> 646   6.45 0.71709256 1.377889e-03 0.281529548 #> 647   6.46 0.71673411 1.377200e-03 0.281888694 #> 648   6.47 0.71637583 1.376511e-03 0.282247660 #> 649   6.48 0.71601773 1.375823e-03 0.282606446 #> 650   6.49 0.71565981 1.375136e-03 0.282965053 #> 651   6.50 0.71530207 1.374448e-03 0.283323481 #> 652   6.51 0.71494451 1.373761e-03 0.283681730 #> 653   6.52 0.71458713 1.373074e-03 0.284039799 #> 654   6.53 0.71422992 1.372388e-03 0.284397690 #> 655   6.54 0.71387290 1.371702e-03 0.284755402 #> 656   6.55 0.71351605 1.371016e-03 0.285112935 #> 657   6.56 0.71315938 1.370331e-03 0.285470289 #> 658   6.57 0.71280289 1.369646e-03 0.285827465 #> 659   6.58 0.71244658 1.368961e-03 0.286184462 #> 660   6.59 0.71209044 1.368277e-03 0.286541280 #> 661   6.60 0.71173449 1.367593e-03 0.286897920 #> 662   6.61 0.71137871 1.366909e-03 0.287254382 #> 663   6.62 0.71102311 1.366226e-03 0.287610666 #> 664   6.63 0.71066769 1.365543e-03 0.287966772 #> 665   6.64 0.71031244 1.364861e-03 0.288322699 #> 666   6.65 0.70995737 1.364178e-03 0.288678449 #> 667   6.66 0.70960248 1.363496e-03 0.289034021 #> 668   6.67 0.70924777 1.362815e-03 0.289389415 #> 669   6.68 0.70889323 1.362134e-03 0.289744632 #> 670   6.69 0.70853888 1.361453e-03 0.290099671 #> 671   6.70 0.70818470 1.360772e-03 0.290454532 #> 672   6.71 0.70783069 1.360092e-03 0.290809216 #> 673   6.72 0.70747687 1.359412e-03 0.291163723 #> 674   6.73 0.70712322 1.358733e-03 0.291518052 #> 675   6.74 0.70676974 1.358053e-03 0.291872205 #> 676   6.75 0.70641645 1.357374e-03 0.292226180 #> 677   6.76 0.70606333 1.356696e-03 0.292579979 #> 678   6.77 0.70571038 1.356018e-03 0.292933600 #> 679   6.78 0.70535761 1.355340e-03 0.293287045 #> 680   6.79 0.70500502 1.354662e-03 0.293640313 #> 681   6.80 0.70465261 1.353985e-03 0.293993405 #> 682   6.81 0.70430037 1.353308e-03 0.294346320 #> 683   6.82 0.70394831 1.352632e-03 0.294699059 #> 684   6.83 0.70359642 1.351956e-03 0.295051621 #> 685   6.84 0.70324471 1.351280e-03 0.295404007 #> 686   6.85 0.70289318 1.350605e-03 0.295756217 #> 687   6.86 0.70254182 1.349929e-03 0.296108251 #> 688   6.87 0.70219064 1.349255e-03 0.296460109 #> 689   6.88 0.70183963 1.348580e-03 0.296811791 #> 690   6.89 0.70148880 1.347906e-03 0.297163297 #> 691   6.90 0.70113814 1.347232e-03 0.297514628 #> 692   6.91 0.70078766 1.346559e-03 0.297865783 #> 693   6.92 0.70043735 1.345886e-03 0.298216762 #> 694   6.93 0.70008722 1.345213e-03 0.298567566 #> 695   6.94 0.69973727 1.344540e-03 0.298918194 #> 696   6.95 0.69938748 1.343868e-03 0.299268648 #> 697   6.96 0.69903788 1.343197e-03 0.299618926 #> 698   6.97 0.69868845 1.342525e-03 0.299969029 #> 699   6.98 0.69833919 1.341854e-03 0.300318957 #> 700   6.99 0.69799011 1.341183e-03 0.300668710 #> 701   7.00 0.69764120 1.340513e-03 0.301018288 #> 702   7.01 0.69729247 1.339843e-03 0.301367692 #> 703   7.02 0.69694391 1.339173e-03 0.301716921 #> 704   7.03 0.69659552 1.338504e-03 0.302065975 #> 705   7.04 0.69624731 1.337835e-03 0.302414855 #> 706   7.05 0.69589927 1.337166e-03 0.302763560 #> 707   7.06 0.69555141 1.336497e-03 0.303112091 #> 708   7.07 0.69520372 1.335829e-03 0.303460448 #> 709   7.08 0.69485621 1.335162e-03 0.303808631 #> 710   7.09 0.69450887 1.334494e-03 0.304156639 #> 711   7.10 0.69416170 1.333827e-03 0.304504474 #> 712   7.11 0.69381470 1.333160e-03 0.304852135 #> 713   7.12 0.69346788 1.332494e-03 0.305199622 #> 714   7.13 0.69312124 1.331828e-03 0.305546935 #> 715   7.14 0.69277476 1.331162e-03 0.305894075 #> 716   7.15 0.69242846 1.330497e-03 0.306241041 #> 717   7.16 0.69208233 1.329832e-03 0.306587834 #> 718   7.17 0.69173638 1.329167e-03 0.306934454 #> 719   7.18 0.69139060 1.328502e-03 0.307280900 #> 720   7.19 0.69104499 1.327838e-03 0.307627173 #> 721   7.20 0.69069955 1.327175e-03 0.307973273 #> 722   7.21 0.69035429 1.326511e-03 0.308319200 #> 723   7.22 0.69000920 1.325848e-03 0.308664954 #> 724   7.23 0.68966428 1.325185e-03 0.309010535 #> 725   7.24 0.68931953 1.324523e-03 0.309355943 #> 726   7.25 0.68897496 1.323861e-03 0.309701179 #> 727   7.26 0.68863056 1.323199e-03 0.310046242 #> 728   7.27 0.68828633 1.322538e-03 0.310391133 #> 729   7.28 0.68794227 1.321876e-03 0.310735851 #> 730   7.29 0.68759839 1.321216e-03 0.311080397 #> 731   7.30 0.68725467 1.320555e-03 0.311424771 #> 732   7.31 0.68691113 1.319895e-03 0.311768972 #> 733   7.32 0.68656776 1.319235e-03 0.312113002 #> 734   7.33 0.68622456 1.318576e-03 0.312456859 #> 735   7.34 0.68588154 1.317917e-03 0.312800545 #> 736   7.35 0.68553868 1.317258e-03 0.313144059 #> 737   7.36 0.68519600 1.316600e-03 0.313487401 #> 738   7.37 0.68485349 1.315941e-03 0.313830571 #> 739   7.38 0.68451115 1.315284e-03 0.314173570 #> 740   7.39 0.68416898 1.314626e-03 0.314516398 #> 741   7.40 0.68382698 1.313969e-03 0.314859054 #> 742   7.41 0.68348515 1.313312e-03 0.315201539 #> 743   7.42 0.68314349 1.312656e-03 0.315543852 #> 744   7.43 0.68280201 1.311999e-03 0.315885995 #> 745   7.44 0.68246069 1.311344e-03 0.316227967 #> 746   7.45 0.68211954 1.310688e-03 0.316569767 #> 747   7.46 0.68177857 1.310033e-03 0.316911397 #> 748   7.47 0.68143777 1.309378e-03 0.317252856 #> 749   7.48 0.68109713 1.308724e-03 0.317594144 #> 750   7.49 0.68075667 1.308069e-03 0.317935262 #> 751   7.50 0.68041638 1.307415e-03 0.318276209 #> 752   7.51 0.68007625 1.306762e-03 0.318616986 #> 753   7.52 0.67973630 1.306109e-03 0.318957592 #> 754   7.53 0.67939652 1.305456e-03 0.319298028 #> 755   7.54 0.67905690 1.304803e-03 0.319638294 #> 756   7.55 0.67871746 1.304151e-03 0.319978390 #> 757   7.56 0.67837819 1.303499e-03 0.320318316 #> 758   7.57 0.67803908 1.302848e-03 0.320658072 #> 759   7.58 0.67770015 1.302196e-03 0.320997658 #> 760   7.59 0.67736138 1.301545e-03 0.321337074 #> 761   7.60 0.67702278 1.300895e-03 0.321676321 #> 762   7.61 0.67668436 1.300244e-03 0.322015398 #> 763   7.62 0.67634610 1.299594e-03 0.322354305 #> 764   7.63 0.67600801 1.298945e-03 0.322693043 #> 765   7.64 0.67567009 1.298296e-03 0.323031612 #> 766   7.65 0.67533234 1.297647e-03 0.323370012 #> 767   7.66 0.67499476 1.296998e-03 0.323708242 #> 768   7.67 0.67465735 1.296350e-03 0.324046304 #> 769   7.68 0.67432010 1.295702e-03 0.324384196 #> 770   7.69 0.67398303 1.295054e-03 0.324721920 #> 771   7.70 0.67364612 1.294406e-03 0.325059474 #> 772   7.71 0.67330938 1.293759e-03 0.325396860 #> 773   7.72 0.67297281 1.293113e-03 0.325734077 #> 774   7.73 0.67263641 1.292466e-03 0.326071126 #> 775   7.74 0.67230017 1.291820e-03 0.326408006 #> 776   7.75 0.67196411 1.291175e-03 0.326744718 #> 777   7.76 0.67162821 1.290529e-03 0.327081262 #> 778   7.77 0.67129248 1.289884e-03 0.327417637 #> 779   7.78 0.67095692 1.289239e-03 0.327753844 #> 780   7.79 0.67062152 1.288595e-03 0.328089883 #> 781   7.80 0.67028630 1.287951e-03 0.328425754 #> 782   7.81 0.66995124 1.287307e-03 0.328761457 #> 783   7.82 0.66961634 1.286663e-03 0.329096993 #> 784   7.83 0.66928162 1.286020e-03 0.329432360 #> 785   7.84 0.66894706 1.285377e-03 0.329767561 #> 786   7.85 0.66861267 1.284735e-03 0.330102593 #> 787   7.86 0.66827845 1.284093e-03 0.330437458 #> 788   7.87 0.66794439 1.283451e-03 0.330772156 #> 789   7.88 0.66761050 1.282809e-03 0.331106686 #> 790   7.89 0.66727678 1.282168e-03 0.331441049 #> 791   7.90 0.66694323 1.281527e-03 0.331775245 #> 792   7.91 0.66660984 1.280886e-03 0.332109274 #> 793   7.92 0.66627662 1.280246e-03 0.332443136 #> 794   7.93 0.66594356 1.279606e-03 0.332776831 #> 795   7.94 0.66561067 1.278966e-03 0.333110359 #> 796   7.95 0.66527795 1.278327e-03 0.333443720 #> 797   7.96 0.66494540 1.277688e-03 0.333776915 #> 798   7.97 0.66461301 1.277049e-03 0.334109944 #> 799   7.98 0.66428078 1.276411e-03 0.334442805 #> 800   7.99 0.66394873 1.275773e-03 0.334775501 #> 801   8.00 0.66361683 1.275135e-03 0.335108030 #> 802   8.01 0.66328511 1.274498e-03 0.335440393 #> 803   8.02 0.66295355 1.273861e-03 0.335772590 #> 804   8.03 0.66262216 1.273224e-03 0.336104620 #> 805   8.04 0.66229093 1.272588e-03 0.336436485 #> 806   8.05 0.66195986 1.271951e-03 0.336768184 #> 807   8.06 0.66162897 1.271316e-03 0.337099717 #> 808   8.07 0.66129824 1.270680e-03 0.337431084 #> 809   8.08 0.66096767 1.270045e-03 0.337762286 #> 810   8.09 0.66063727 1.269410e-03 0.338093322 #> 811   8.10 0.66030703 1.268776e-03 0.338424193 #> 812   8.11 0.65997696 1.268141e-03 0.338754898 #> 813   8.12 0.65964705 1.267507e-03 0.339085438 #> 814   8.13 0.65931731 1.266874e-03 0.339415812 #> 815   8.14 0.65898774 1.266241e-03 0.339746022 #> 816   8.15 0.65865833 1.265608e-03 0.340076066 #> 817   8.16 0.65832908 1.264975e-03 0.340405946 #> 818   8.17 0.65800000 1.264343e-03 0.340735661 #> 819   8.18 0.65767108 1.263711e-03 0.341065210 #> 820   8.19 0.65734233 1.263079e-03 0.341394595 #> 821   8.20 0.65701374 1.262447e-03 0.341723816 #> 822   8.21 0.65668531 1.261816e-03 0.342052872 #> 823   8.22 0.65635705 1.261186e-03 0.342381763 #> 824   8.23 0.65602895 1.260555e-03 0.342710490 #> 825   8.24 0.65570102 1.259925e-03 0.343039053 #> 826   8.25 0.65537325 1.259295e-03 0.343367451 #> 827   8.26 0.65504565 1.258666e-03 0.343695685 #> 828   8.27 0.65471821 1.258037e-03 0.344023755 #> 829   8.28 0.65439093 1.257408e-03 0.344351662 #> 830   8.29 0.65406382 1.256779e-03 0.344679404 #> 831   8.30 0.65373687 1.256151e-03 0.345006982 #> 832   8.31 0.65341008 1.255523e-03 0.345334397 #> 833   8.32 0.65308346 1.254895e-03 0.345661648 #> 834   8.33 0.65275700 1.254268e-03 0.345988735 #> 835   8.34 0.65243070 1.253641e-03 0.346315659 #> 836   8.35 0.65210457 1.253015e-03 0.346642420 #> 837   8.36 0.65177860 1.252388e-03 0.346969017 #> 838   8.37 0.65145279 1.251762e-03 0.347295451 #> 839   8.38 0.65112714 1.251136e-03 0.347621721 #> 840   8.39 0.65080166 1.250511e-03 0.347947829 #> 841   8.40 0.65047634 1.249886e-03 0.348273774 #> 842   8.41 0.65015118 1.249261e-03 0.348599555 #> 843   8.42 0.64982619 1.248637e-03 0.348925174 #> 844   8.43 0.64950136 1.248012e-03 0.349250630 #> 845   8.44 0.64917669 1.247389e-03 0.349575923 #> 846   8.45 0.64885218 1.246765e-03 0.349901054 #> 847   8.46 0.64852784 1.246142e-03 0.350226023 #> 848   8.47 0.64820365 1.245519e-03 0.350550828 #> 849   8.48 0.64787963 1.244896e-03 0.350875472 #> 850   8.49 0.64755577 1.244274e-03 0.351199953 #> 851   8.50 0.64723208 1.243652e-03 0.351524272 #> 852   8.51 0.64690854 1.243030e-03 0.351848429 #> 853   8.52 0.64658517 1.242409e-03 0.352172423 #> 854   8.53 0.64626196 1.241788e-03 0.352496256 #> 855   8.54 0.64593891 1.241167e-03 0.352819927 #> 856   8.55 0.64561602 1.240547e-03 0.353143436 #> 857   8.56 0.64529329 1.239927e-03 0.353466784 #> 858   8.57 0.64497072 1.239307e-03 0.353789970 #> 859   8.58 0.64464832 1.238687e-03 0.354112994 #> 860   8.59 0.64432608 1.238068e-03 0.354435857 #> 861   8.60 0.64400399 1.237449e-03 0.354758558 #> 862   8.61 0.64368207 1.236831e-03 0.355081098 #> 863   8.62 0.64336031 1.236213e-03 0.355403477 #> 864   8.63 0.64303871 1.235595e-03 0.355725695 #> 865   8.64 0.64271727 1.234977e-03 0.356047751 #> 866   8.65 0.64239599 1.234360e-03 0.356369647 #> 867   8.66 0.64207488 1.233743e-03 0.356691382 #> 868   8.67 0.64175392 1.233126e-03 0.357012956 #> 869   8.68 0.64143312 1.232509e-03 0.357334369 #> 870   8.69 0.64111249 1.231893e-03 0.357655621 #> 871   8.70 0.64079201 1.231278e-03 0.357976713 #> 872   8.71 0.64047169 1.230662e-03 0.358297645 #> 873   8.72 0.64015154 1.230047e-03 0.358618416 #> 874   8.73 0.63983154 1.229432e-03 0.358939026 #> 875   8.74 0.63951171 1.228817e-03 0.359259477 #> 876   8.75 0.63919203 1.228203e-03 0.359579767 #> 877   8.76 0.63887251 1.227589e-03 0.359899897 #> 878   8.77 0.63855316 1.226976e-03 0.360219867 #> 879   8.78 0.63823396 1.226362e-03 0.360539677 #> 880   8.79 0.63791492 1.225749e-03 0.360859328 #> 881   8.80 0.63759605 1.225137e-03 0.361178818 #> 882   8.81 0.63727733 1.224524e-03 0.361498149 #> 883   8.82 0.63695877 1.223912e-03 0.361817320 #> 884   8.83 0.63664037 1.223300e-03 0.362136331 #> 885   8.84 0.63632213 1.222689e-03 0.362455184 #> 886   8.85 0.63600405 1.222077e-03 0.362773876 #> 887   8.86 0.63568612 1.221467e-03 0.363092410 #> 888   8.87 0.63536836 1.220856e-03 0.363410784 #> 889   8.88 0.63505076 1.220246e-03 0.363728999 #> 890   8.89 0.63473331 1.219636e-03 0.364047055 #> 891   8.90 0.63441602 1.219026e-03 0.364364952 #> 892   8.91 0.63409889 1.218417e-03 0.364682690 #> 893   8.92 0.63378192 1.217808e-03 0.365000269 #> 894   8.93 0.63346511 1.217199e-03 0.365317690 #> 895   8.94 0.63314846 1.216591e-03 0.365634952 #> 896   8.95 0.63283196 1.215982e-03 0.365952055 #> 897   8.96 0.63251563 1.215375e-03 0.366269000 #> 898   8.97 0.63219945 1.214767e-03 0.366585786 #> 899   8.98 0.63188343 1.214160e-03 0.366902414 #> 900   8.99 0.63156756 1.213553e-03 0.367218884 #> 901   9.00 0.63125186 1.212946e-03 0.367535195 #> 902   9.01 0.63093631 1.212340e-03 0.367851349 #> 903   9.02 0.63062092 1.211734e-03 0.368167344 #> 904   9.03 0.63030569 1.211128e-03 0.368483181 #> 905   9.04 0.62999062 1.210523e-03 0.368798861 #> 906   9.05 0.62967570 1.209918e-03 0.369114383 #> 907   9.06 0.62936094 1.209313e-03 0.369429747 #> 908   9.07 0.62904634 1.208708e-03 0.369744953 #> 909   9.08 0.62873189 1.208104e-03 0.370060002 #> 910   9.09 0.62841761 1.207500e-03 0.370374893 #> 911   9.10 0.62810348 1.206897e-03 0.370689627 #> 912   9.11 0.62778950 1.206293e-03 0.371004203 #> 913   9.12 0.62747569 1.205690e-03 0.371318623 #> 914   9.13 0.62716203 1.205088e-03 0.371632885 #> 915   9.14 0.62684853 1.204485e-03 0.371946990 #> 916   9.15 0.62653518 1.203883e-03 0.372260938 #> 917   9.16 0.62622199 1.203281e-03 0.372574729 #> 918   9.17 0.62590896 1.202680e-03 0.372888363 #> 919   9.18 0.62559608 1.202079e-03 0.373201841 #> 920   9.19 0.62528336 1.201478e-03 0.373515161 #> 921   9.20 0.62497080 1.200877e-03 0.373828325 #> 922   9.21 0.62465839 1.200277e-03 0.374141333 #> 923   9.22 0.62434614 1.199677e-03 0.374454184 #> 924   9.23 0.62403404 1.199077e-03 0.374766879 #> 925   9.24 0.62372210 1.198478e-03 0.375079417 #> 926   9.25 0.62341032 1.197879e-03 0.375391800 #> 927   9.26 0.62309869 1.197280e-03 0.375704026 #> 928   9.27 0.62278722 1.196681e-03 0.376016096 #> 929   9.28 0.62247591 1.196083e-03 0.376328010 #> 930   9.29 0.62216475 1.195485e-03 0.376639768 #> 931   9.30 0.62185374 1.194888e-03 0.376951370 #> 932   9.31 0.62154289 1.194290e-03 0.377262816 #> 933   9.32 0.62123220 1.193693e-03 0.377574107 #> 934   9.33 0.62092166 1.193097e-03 0.377885242 #> 935   9.34 0.62061128 1.192500e-03 0.378196222 #> 936   9.35 0.62030105 1.191904e-03 0.378507046 #> 937   9.36 0.61999098 1.191308e-03 0.378817715 #> 938   9.37 0.61968106 1.190713e-03 0.379128228 #> 939   9.38 0.61937130 1.190118e-03 0.379438587 #> 940   9.39 0.61906169 1.189523e-03 0.379748790 #> 941   9.40 0.61875223 1.188928e-03 0.380058838 #> 942   9.41 0.61844293 1.188334e-03 0.380368731 #> 943   9.42 0.61813379 1.187740e-03 0.380678469 #> 944   9.43 0.61782480 1.187146e-03 0.380988053 #> 945   9.44 0.61751597 1.186553e-03 0.381297481 #> 946   9.45 0.61720729 1.185960e-03 0.381606755 #> 947   9.46 0.61689876 1.185367e-03 0.381915875 #> 948   9.47 0.61659039 1.184774e-03 0.382224839 #> 949   9.48 0.61628217 1.184182e-03 0.382533650 #> 950   9.49 0.61597410 1.183590e-03 0.382842306 #> 951   9.50 0.61566619 1.182998e-03 0.383150808 #> 952   9.51 0.61535844 1.182407e-03 0.383459155 #> 953   9.52 0.61505084 1.181816e-03 0.383767348 #> 954   9.53 0.61474339 1.181225e-03 0.384075388 #> 955   9.54 0.61443609 1.180635e-03 0.384383273 #> 956   9.55 0.61412895 1.180045e-03 0.384691005 #> 957   9.56 0.61382196 1.179455e-03 0.384998582 #> 958   9.57 0.61351513 1.178865e-03 0.385306006 #> 959   9.58 0.61320845 1.178276e-03 0.385613276 #> 960   9.59 0.61290192 1.177687e-03 0.385920393 #> 961   9.60 0.61259555 1.177098e-03 0.386227356 #> 962   9.61 0.61228932 1.176510e-03 0.386534165 #> 963   9.62 0.61198326 1.175922e-03 0.386840822 #> 964   9.63 0.61167734 1.175334e-03 0.387147325 #> 965   9.64 0.61137158 1.174746e-03 0.387453674 #> 966   9.65 0.61106597 1.174159e-03 0.387759871 #> 967   9.66 0.61076051 1.173572e-03 0.388065915 #> 968   9.67 0.61045521 1.172986e-03 0.388371805 #> 969   9.68 0.61015006 1.172399e-03 0.388677543 #> 970   9.69 0.60984506 1.171813e-03 0.388983128 #> 971   9.70 0.60954021 1.171227e-03 0.389288560 #> 972   9.71 0.60923552 1.170642e-03 0.389593839 #> 973   9.72 0.60893098 1.170057e-03 0.389898966 #> 974   9.73 0.60862659 1.169472e-03 0.390203940 #> 975   9.74 0.60832235 1.168887e-03 0.390508762 #> 976   9.75 0.60801827 1.168303e-03 0.390813432 #> 977   9.76 0.60771433 1.167719e-03 0.391117949 #> 978   9.77 0.60741055 1.167135e-03 0.391422314 #> 979   9.78 0.60710692 1.166552e-03 0.391726527 #> 980   9.79 0.60680344 1.165969e-03 0.392030587 #> 981   9.80 0.60650012 1.165386e-03 0.392334496 #> 982   9.81 0.60619694 1.164803e-03 0.392638253 #> 983   9.82 0.60589392 1.164221e-03 0.392941858 #> 984   9.83 0.60559105 1.163639e-03 0.393245311 #> 985   9.84 0.60528833 1.163057e-03 0.393548612 #> 986   9.85 0.60498576 1.162476e-03 0.393851762 #> 987   9.86 0.60468334 1.161895e-03 0.394154761 #> 988   9.87 0.60438108 1.161314e-03 0.394457608 #> 989   9.88 0.60407896 1.160734e-03 0.394760303 #> 990   9.89 0.60377700 1.160153e-03 0.395062847 #> 991   9.90 0.60347519 1.159574e-03 0.395365240 #> 992   9.91 0.60317352 1.158994e-03 0.395667482 #> 993   9.92 0.60287201 1.158415e-03 0.395969573 #> 994   9.93 0.60257065 1.157835e-03 0.396271513 #> 995   9.94 0.60226944 1.157257e-03 0.396573302 #> 996   9.95 0.60196838 1.156678e-03 0.396874939 #> 997   9.96 0.60166747 1.156100e-03 0.397176427 #> 998   9.97 0.60136671 1.155522e-03 0.397477763 #> 999   9.98 0.60106611 1.154944e-03 0.397778949 #> 1000  9.99 0.60076565 1.154367e-03 0.398079984 #> 1001 10.00 0.60046534 1.153790e-03 0.398380869 #> 1002 10.01 0.60016518 1.153213e-03 0.398681603 #> 1003 10.02 0.59986518 1.152637e-03 0.398982187 #> 1004 10.03 0.59956532 1.152061e-03 0.399282621 #> 1005 10.04 0.59926561 1.151485e-03 0.399582905 #> 1006 10.05 0.59896605 1.150909e-03 0.399883038 #> 1007 10.06 0.59866664 1.150334e-03 0.400183022 #> 1008 10.07 0.59836739 1.149759e-03 0.400482855 #> 1009 10.08 0.59806828 1.149184e-03 0.400782539 #> 1010 10.09 0.59776932 1.148610e-03 0.401082073 #> 1011 10.10 0.59747051 1.148036e-03 0.401381457 #> 1012 10.11 0.59717185 1.147462e-03 0.401680692 #> 1013 10.12 0.59687334 1.146888e-03 0.401979776 #> 1014 10.13 0.59657497 1.146315e-03 0.402278712 #> 1015 10.14 0.59627676 1.145742e-03 0.402577498 #> 1016 10.15 0.59597870 1.145169e-03 0.402876134 #> 1017 10.16 0.59568078 1.144597e-03 0.403174622 #> 1018 10.17 0.59538302 1.144024e-03 0.403472960 #> 1019 10.18 0.59508540 1.143453e-03 0.403771149 #> 1020 10.19 0.59478793 1.142881e-03 0.404069189 #> 1021 10.20 0.59449061 1.142310e-03 0.404367080 #> 1022 10.21 0.59419344 1.141739e-03 0.404664822 #> 1023 10.22 0.59389642 1.141168e-03 0.404962415 #> 1024 10.23 0.59359954 1.140598e-03 0.405259859 #> 1025 10.24 0.59330282 1.140027e-03 0.405557155 #> 1026 10.25 0.59300624 1.139458e-03 0.405854302 #> 1027 10.26 0.59270981 1.138888e-03 0.406151301 #> 1028 10.27 0.59241353 1.138319e-03 0.406448151 #> 1029 10.28 0.59211740 1.137750e-03 0.406744853 #> 1030 10.29 0.59182141 1.137181e-03 0.407041406 #> 1031 10.30 0.59152558 1.136612e-03 0.407337811 #> 1032 10.31 0.59122989 1.136044e-03 0.407634068 #> 1033 10.32 0.59093435 1.135476e-03 0.407930177 #> 1034 10.33 0.59063895 1.134909e-03 0.408226138 #> 1035 10.34 0.59034371 1.134341e-03 0.408521951 #> 1036 10.35 0.59004861 1.133774e-03 0.408817616 #> 1037 10.36 0.58975366 1.133208e-03 0.409113134 #> 1038 10.37 0.58945886 1.132641e-03 0.409408503 #> 1039 10.38 0.58916420 1.132075e-03 0.409703725 #> 1040 10.39 0.58886969 1.131509e-03 0.409998800 #> 1041 10.40 0.58857533 1.130944e-03 0.410293726 #> 1042 10.41 0.58828112 1.130378e-03 0.410588506 #> 1043 10.42 0.58798705 1.129813e-03 0.410883138 #> 1044 10.43 0.58769313 1.129248e-03 0.411177623 #> 1045 10.44 0.58739936 1.128684e-03 0.411471960 #> 1046 10.45 0.58710573 1.128120e-03 0.411766151 #> 1047 10.46 0.58681225 1.127556e-03 0.412060194 #> 1048 10.47 0.58651892 1.126992e-03 0.412354091 #> 1049 10.48 0.58622573 1.126429e-03 0.412647840 #> 1050 10.49 0.58593269 1.125866e-03 0.412941443 #> 1051 10.50 0.58563980 1.125303e-03 0.413234899 #> 1052 10.51 0.58534705 1.124740e-03 0.413528208 #> 1053 10.52 0.58505445 1.124178e-03 0.413821371 #> 1054 10.53 0.58476200 1.123616e-03 0.414114387 #> 1055 10.54 0.58446969 1.123055e-03 0.414407256 #> 1056 10.55 0.58417753 1.122493e-03 0.414699980 #> 1057 10.56 0.58388551 1.121932e-03 0.414992556 #> 1058 10.57 0.58359364 1.121371e-03 0.415284987 #> 1059 10.58 0.58330192 1.120811e-03 0.415577272 #> 1060 10.59 0.58301034 1.120250e-03 0.415869410 #> 1061 10.60 0.58271891 1.119690e-03 0.416161402 #> 1062 10.61 0.58242762 1.119131e-03 0.416453248 #> 1063 10.62 0.58213648 1.118571e-03 0.416744949 #> 1064 10.63 0.58184548 1.118012e-03 0.417036504 #> 1065 10.64 0.58155463 1.117453e-03 0.417327912 #> 1066 10.65 0.58126393 1.116895e-03 0.417619176 #> 1067 10.66 0.58097337 1.116336e-03 0.417910293 #> 1068 10.67 0.58068296 1.115778e-03 0.418201266 #> 1069 10.68 0.58039269 1.115221e-03 0.418492092 #> 1070 10.69 0.58010256 1.114663e-03 0.418782773 #> 1071 10.70 0.57981258 1.114106e-03 0.419073309 #> 1072 10.71 0.57952275 1.113549e-03 0.419363700 #> 1073 10.72 0.57923306 1.112992e-03 0.419653946 #> 1074 10.73 0.57894352 1.112436e-03 0.419944046 #> 1075 10.74 0.57865412 1.111880e-03 0.420234002 #> 1076 10.75 0.57836486 1.111324e-03 0.420523812 #> 1077 10.76 0.57807575 1.110769e-03 0.420813478 #> 1078 10.77 0.57778679 1.110213e-03 0.421102999 #> 1079 10.78 0.57749797 1.109658e-03 0.421392375 #> 1080 10.79 0.57720929 1.109104e-03 0.421681607 #> 1081 10.80 0.57692076 1.108549e-03 0.421970693 #> 1082 10.81 0.57663237 1.107995e-03 0.422259636 #> 1083 10.82 0.57634412 1.107441e-03 0.422548434 #> 1084 10.83 0.57605602 1.106888e-03 0.422837088 #> 1085 10.84 0.57576807 1.106334e-03 0.423125597 #> 1086 10.85 0.57548026 1.105781e-03 0.423413962 #> 1087 10.86 0.57519259 1.105229e-03 0.423702183 #> 1088 10.87 0.57490506 1.104676e-03 0.423990260 #> 1089 10.88 0.57461768 1.104124e-03 0.424278193 #> 1090 10.89 0.57433045 1.103572e-03 0.424565982 #> 1091 10.90 0.57404335 1.103020e-03 0.424853627 #> 1092 10.91 0.57375640 1.102469e-03 0.425141128 #> 1093 10.92 0.57346960 1.101918e-03 0.425428486 #> 1094 10.93 0.57318293 1.101367e-03 0.425715700 #> 1095 10.94 0.57289641 1.100817e-03 0.426002770 #> 1096 10.95 0.57261004 1.100266e-03 0.426289697 #> 1097 10.96 0.57232380 1.099716e-03 0.426576480 #> 1098 10.97 0.57203771 1.099167e-03 0.426863121 #> 1099 10.98 0.57175177 1.098617e-03 0.427149617 #> 1100 10.99 0.57146596 1.098068e-03 0.427435971 #> 1101 11.00 0.57118030 1.097519e-03 0.427722181 #> 1102 11.01 0.57089478 1.096970e-03 0.428008249 #> 1103 11.02 0.57060940 1.096422e-03 0.428294173 #> 1104 11.03 0.57032417 1.095874e-03 0.428579955 #> 1105 11.04 0.57003908 1.095326e-03 0.428865593 #> 1106 11.05 0.56975413 1.094779e-03 0.429151089 #> 1107 11.06 0.56946933 1.094231e-03 0.429436442 #> 1108 11.07 0.56918466 1.093685e-03 0.429721653 #> 1109 11.08 0.56890014 1.093138e-03 0.430006721 #> 1110 11.09 0.56861576 1.092591e-03 0.430291646 #> 1111 11.10 0.56833153 1.092045e-03 0.430576429 #> 1112 11.11 0.56804743 1.091499e-03 0.430861070 #> 1113 11.12 0.56776348 1.090954e-03 0.431145568 #> 1114 11.13 0.56747967 1.090408e-03 0.431429924 #> 1115 11.14 0.56719600 1.089863e-03 0.431714138 #> 1116 11.15 0.56691247 1.089319e-03 0.431998210 #> 1117 11.16 0.56662909 1.088774e-03 0.432282140 #> 1118 11.17 0.56634584 1.088230e-03 0.432565928 #> 1119 11.18 0.56606274 1.087686e-03 0.432849574 #> 1120 11.19 0.56577978 1.087142e-03 0.433133078 #> 1121 11.20 0.56549696 1.086599e-03 0.433416441 #> 1122 11.21 0.56521428 1.086055e-03 0.433699662 #> 1123 11.22 0.56493175 1.085513e-03 0.433982741 #> 1124 11.23 0.56464935 1.084970e-03 0.434265679 #> 1125 11.24 0.56436710 1.084428e-03 0.434548476 #> 1126 11.25 0.56408498 1.083886e-03 0.434831131 #> 1127 11.26 0.56380301 1.083344e-03 0.435113645 #> 1128 11.27 0.56352118 1.082802e-03 0.435396017 #> 1129 11.28 0.56323949 1.082261e-03 0.435678249 #> 1130 11.29 0.56295794 1.081720e-03 0.435960339 #> 1131 11.30 0.56267653 1.081179e-03 0.436242289 #> 1132 11.31 0.56239526 1.080639e-03 0.436524097 #> 1133 11.32 0.56211414 1.080099e-03 0.436805764 #> 1134 11.33 0.56183315 1.079559e-03 0.437087291 #> 1135 11.34 0.56155230 1.079019e-03 0.437368677 #> 1136 11.35 0.56127160 1.078480e-03 0.437649923 #> 1137 11.36 0.56099103 1.077940e-03 0.437931027 #> 1138 11.37 0.56071061 1.077402e-03 0.438211992 #> 1139 11.38 0.56043032 1.076863e-03 0.438492815 #> 1140 11.39 0.56015018 1.076325e-03 0.438773499 #> 1141 11.40 0.55987017 1.075787e-03 0.439054042 #> 1142 11.41 0.55959031 1.075249e-03 0.439334445 #> 1143 11.42 0.55931058 1.074712e-03 0.439614708 #> 1144 11.43 0.55903100 1.074174e-03 0.439894830 #> 1145 11.44 0.55875155 1.073637e-03 0.440174813 #> 1146 11.45 0.55847224 1.073101e-03 0.440454655 #> 1147 11.46 0.55819308 1.072564e-03 0.440734358 #> 1148 11.47 0.55791405 1.072028e-03 0.441013921 #> 1149 11.48 0.55763516 1.071492e-03 0.441293344 #> 1150 11.49 0.55735642 1.070957e-03 0.441572628 #> 1151 11.50 0.55707781 1.070421e-03 0.441851772 #> 1152 11.51 0.55679934 1.069886e-03 0.442130776 #> 1153 11.52 0.55652101 1.069351e-03 0.442409641 #> 1154 11.53 0.55624282 1.068817e-03 0.442688366 #> 1155 11.54 0.55596476 1.068283e-03 0.442966953 #> 1156 11.55 0.55568685 1.067749e-03 0.443245400 #> 1157 11.56 0.55540908 1.067215e-03 0.443523707 #> 1158 11.57 0.55513144 1.066681e-03 0.443801876 #> 1159 11.58 0.55485395 1.066148e-03 0.444079905 #> 1160 11.59 0.55457659 1.065615e-03 0.444357796 #> 1161 11.60 0.55429937 1.065083e-03 0.444635548 #> 1162 11.61 0.55402229 1.064550e-03 0.444913161 #> 1163 11.62 0.55374535 1.064018e-03 0.445190635 #> 1164 11.63 0.55346854 1.063486e-03 0.445467970 #> 1165 11.64 0.55319188 1.062954e-03 0.445745167 #> 1166 11.65 0.55291535 1.062423e-03 0.446022225 #> 1167 11.66 0.55263896 1.061892e-03 0.446299145 #> 1168 11.67 0.55236271 1.061361e-03 0.446575926 #> 1169 11.68 0.55208660 1.060831e-03 0.446852569 #> 1170 11.69 0.55181063 1.060300e-03 0.447129073 #> 1171 11.70 0.55153479 1.059770e-03 0.447405440 #> 1172 11.71 0.55125909 1.059241e-03 0.447681668 #> 1173 11.72 0.55098353 1.058711e-03 0.447957758 #> 1174 11.73 0.55070811 1.058182e-03 0.448233710 #> 1175 11.74 0.55043282 1.057653e-03 0.448509524 #> 1176 11.75 0.55015768 1.057124e-03 0.448785201 #> 1177 11.76 0.54988266 1.056596e-03 0.449060739 #> 1178 11.77 0.54960779 1.056068e-03 0.449336140 #> 1179 11.78 0.54933306 1.055540e-03 0.449611403 #> 1180 11.79 0.54905846 1.055012e-03 0.449886529 #> 1181 11.80 0.54878400 1.054485e-03 0.450161517 #> 1182 11.81 0.54850968 1.053958e-03 0.450436367 #> 1183 11.82 0.54823549 1.053431e-03 0.450711080 #> 1184 11.83 0.54796144 1.052904e-03 0.450985656 #> 1185 11.84 0.54768753 1.052378e-03 0.451260095 #> 1186 11.85 0.54741375 1.051852e-03 0.451534396 #> 1187 11.86 0.54714011 1.051326e-03 0.451808560 #> 1188 11.87 0.54686661 1.050801e-03 0.452082588 #> 1189 11.88 0.54659325 1.050275e-03 0.452356478 #> 1190 11.89 0.54632002 1.049750e-03 0.452630231 #> 1191 11.90 0.54604693 1.049225e-03 0.452903848 #> 1192 11.91 0.54577397 1.048701e-03 0.453177327 #> 1193 11.92 0.54550115 1.048177e-03 0.453450670 #> 1194 11.93 0.54522847 1.047653e-03 0.453723877 #> 1195 11.94 0.54495592 1.047129e-03 0.453996946 #> 1196 11.95 0.54468351 1.046606e-03 0.454269880 #> 1197 11.96 0.54441124 1.046083e-03 0.454542677 #> 1198 11.97 0.54413910 1.045560e-03 0.454815337 #> 1199 11.98 0.54386710 1.045037e-03 0.455087861 #> 1200 11.99 0.54359524 1.044515e-03 0.455360249 #> 1201 12.00 0.54332351 1.043992e-03 0.455632501 #> 1202 12.01 0.54305191 1.043471e-03 0.455904617 #> 1203 12.02 0.54278045 1.042949e-03 0.456176597 #> 1204 12.03 0.54250913 1.042428e-03 0.456448440 #> 1205 12.04 0.54223795 1.041907e-03 0.456720148 #> 1206 12.05 0.54196689 1.041386e-03 0.456991720 #> 1207 12.06 0.54169598 1.040865e-03 0.457263156 #> 1208 12.07 0.54142520 1.040345e-03 0.457534457 #> 1209 12.08 0.54115455 1.039825e-03 0.457805622 #> 1210 12.09 0.54088404 1.039305e-03 0.458076652 #> 1211 12.10 0.54061367 1.038786e-03 0.458347545 #> 1212 12.11 0.54034343 1.038266e-03 0.458618304 #> 1213 12.12 0.54007333 1.037747e-03 0.458888927 #> 1214 12.13 0.53980336 1.037229e-03 0.459159415 #> 1215 12.14 0.53953352 1.036710e-03 0.459429768 #> 1216 12.15 0.53926382 1.036192e-03 0.459699985 #> 1217 12.16 0.53899426 1.035674e-03 0.459970068 #> 1218 12.17 0.53872483 1.035156e-03 0.460240015 #> 1219 12.18 0.53845553 1.034639e-03 0.460509828 #> 1220 12.19 0.53818637 1.034121e-03 0.460779506 #> 1221 12.20 0.53791735 1.033605e-03 0.461049048 #> 1222 12.21 0.53764846 1.033088e-03 0.461318457 #> 1223 12.22 0.53737970 1.032571e-03 0.461587730 #> 1224 12.23 0.53711108 1.032055e-03 0.461856869 #> 1225 12.24 0.53684259 1.031539e-03 0.462125873 #> 1226 12.25 0.53657423 1.031024e-03 0.462394743 #> 1227 12.26 0.53630601 1.030508e-03 0.462663479 #> 1228 12.27 0.53603793 1.029993e-03 0.462932080 #> 1229 12.28 0.53576998 1.029478e-03 0.463200547 #> 1230 12.29 0.53550216 1.028964e-03 0.463468879 #> 1231 12.30 0.53523447 1.028449e-03 0.463737078 #> 1232 12.31 0.53496692 1.027935e-03 0.464005142 #> 1233 12.32 0.53469951 1.027422e-03 0.464273073 #> 1234 12.33 0.53443222 1.026908e-03 0.464540869 #> 1235 12.34 0.53416507 1.026395e-03 0.464808532 #> 1236 12.35 0.53389806 1.025882e-03 0.465076061 #> 1237 12.36 0.53363118 1.025369e-03 0.465343456 #> 1238 12.37 0.53336443 1.024856e-03 0.465610717 #> 1239 12.38 0.53309781 1.024344e-03 0.465877845 #> 1240 12.39 0.53283133 1.023832e-03 0.466144839 #> 1241 12.40 0.53256498 1.023320e-03 0.466411700 #> 1242 12.41 0.53229876 1.022808e-03 0.466678428 #> 1243 12.42 0.53203268 1.022297e-03 0.466945022 #> 1244 12.43 0.53176673 1.021786e-03 0.467211483 #> 1245 12.44 0.53150091 1.021275e-03 0.467477810 #> 1246 12.45 0.53123523 1.020765e-03 0.467744005 #> 1247 12.46 0.53096968 1.020255e-03 0.468010066 #> 1248 12.47 0.53070426 1.019745e-03 0.468275995 #> 1249 12.48 0.53043897 1.019235e-03 0.468541791 #> 1250 12.49 0.53017382 1.018725e-03 0.468807453 #> 1251 12.50 0.52990880 1.018216e-03 0.469072983 #> 1252 12.51 0.52964391 1.017707e-03 0.469338380 #> 1253 12.52 0.52937916 1.017198e-03 0.469603645 #> 1254 12.53 0.52911453 1.016690e-03 0.469868777 #> 1255 12.54 0.52885004 1.016182e-03 0.470133776 #> 1256 12.55 0.52858568 1.015674e-03 0.470398643 #> 1257 12.56 0.52832146 1.015166e-03 0.470663377 #> 1258 12.57 0.52805736 1.014659e-03 0.470927980 #> 1259 12.58 0.52779340 1.014151e-03 0.471192450 #> 1260 12.59 0.52752957 1.013645e-03 0.471456787 #> 1261 12.60 0.52726587 1.013138e-03 0.471720993 #> 1262 12.61 0.52700230 1.012631e-03 0.471985066 #> 1263 12.62 0.52673887 1.012125e-03 0.472249008 #> 1264 12.63 0.52647556 1.011619e-03 0.472512817 #> 1265 12.64 0.52621239 1.011114e-03 0.472776495 #> 1266 12.65 0.52594935 1.010608e-03 0.473040041 #> 1267 12.66 0.52568644 1.010103e-03 0.473303455 #> 1268 12.67 0.52542366 1.009598e-03 0.473566737 #> 1269 12.68 0.52516102 1.009093e-03 0.473829888 #> 1270 12.69 0.52489850 1.008589e-03 0.474092908 #> 1271 12.70 0.52463612 1.008085e-03 0.474355795 #> 1272 12.71 0.52437387 1.007581e-03 0.474618552 #> 1273 12.72 0.52411175 1.007077e-03 0.474881177 #> 1274 12.73 0.52384976 1.006574e-03 0.475143671 #> 1275 12.74 0.52358790 1.006071e-03 0.475406033 #> 1276 12.75 0.52332617 1.005568e-03 0.475668265 #> 1277 12.76 0.52306457 1.005065e-03 0.475930365 #> 1278 12.77 0.52280310 1.004563e-03 0.476192334 #> 1279 12.78 0.52254177 1.004060e-03 0.476454173 #> 1280 12.79 0.52228056 1.003559e-03 0.476715880 #> 1281 12.80 0.52201949 1.003057e-03 0.476977457 #> 1282 12.81 0.52175854 1.002556e-03 0.477238903 #> 1283 12.82 0.52149773 1.002054e-03 0.477500218 #> 1284 12.83 0.52123704 1.001553e-03 0.477761403 #> 1285 12.84 0.52097649 1.001053e-03 0.478022457 #> 1286 12.85 0.52071607 1.000552e-03 0.478283380 #> 1287 12.86 0.52045577 1.000052e-03 0.478544173 #> 1288 12.87 0.52019561 9.995524e-04 0.478804836 #> 1289 12.88 0.51993558 9.990527e-04 0.479065369 #> 1290 12.89 0.51967568 9.985533e-04 0.479325771 #> 1291 12.90 0.51941590 9.980542e-04 0.479586043 #> 1292 12.91 0.51915626 9.975553e-04 0.479846185 #> 1293 12.92 0.51889675 9.970566e-04 0.480106197 #> 1294 12.93 0.51863736 9.965582e-04 0.480366079 #> 1295 12.94 0.51837811 9.960601e-04 0.480625831 #> 1296 12.95 0.51811898 9.955621e-04 0.480885453 #> 1297 12.96 0.51785999 9.950645e-04 0.481144945 #> 1298 12.97 0.51760112 9.945671e-04 0.481404308 #> 1299 12.98 0.51734239 9.940699e-04 0.481663541 #> 1300 12.99 0.51708378 9.935730e-04 0.481922644 #> 1301 13.00 0.51682531 9.930763e-04 0.482181618 #> 1302 13.01 0.51656696 9.925799e-04 0.482440463 #> 1303 13.02 0.51630874 9.920838e-04 0.482699178 #> 1304 13.03 0.51605065 9.915879e-04 0.482957764 #> 1305 13.04 0.51579269 9.910922e-04 0.483216220 #> 1306 13.05 0.51553486 9.905968e-04 0.483474548 #> 1307 13.06 0.51527715 9.901016e-04 0.483732746 #> 1308 13.07 0.51501958 9.896067e-04 0.483990815 #> 1309 13.08 0.51476213 9.891120e-04 0.484248755 #> 1310 13.09 0.51450482 9.886175e-04 0.484506566 #> 1311 13.10 0.51424763 9.881234e-04 0.484764248 #> 1312 13.11 0.51399057 9.876294e-04 0.485021802 #> 1313 13.12 0.51373364 9.871357e-04 0.485279227 #> 1314 13.13 0.51347683 9.866423e-04 0.485536523 #> 1315 13.14 0.51322016 9.861491e-04 0.485793690 #> 1316 13.15 0.51296361 9.856561e-04 0.486050729 #> 1317 13.16 0.51270720 9.851634e-04 0.486307640 #> 1318 13.17 0.51245091 9.846710e-04 0.486564422 #> 1319 13.18 0.51219475 9.841788e-04 0.486821075 #> 1320 13.19 0.51193871 9.836868e-04 0.487077600 #> 1321 13.20 0.51168281 9.831951e-04 0.487333998 #> 1322 13.21 0.51142703 9.827036e-04 0.487590267 #> 1323 13.22 0.51117138 9.822124e-04 0.487846407 #> 1324 13.23 0.51091586 9.817214e-04 0.488102420 #> 1325 13.24 0.51066046 9.812306e-04 0.488358305 #> 1326 13.25 0.51040520 9.807402e-04 0.488614062 #> 1327 13.26 0.51015006 9.802499e-04 0.488869691 #> 1328 13.27 0.50989505 9.797599e-04 0.489125192 #> 1329 13.28 0.50964016 9.792701e-04 0.489380566 #> 1330 13.29 0.50938541 9.787806e-04 0.489635812 #> 1331 13.30 0.50913078 9.782914e-04 0.489890930 #> 1332 13.31 0.50887628 9.778023e-04 0.490145921 #> 1333 13.32 0.50862190 9.773136e-04 0.490400784 #> 1334 13.33 0.50836765 9.768250e-04 0.490655520 #> 1335 13.34 0.50811353 9.763367e-04 0.490910129 #> 1336 13.35 0.50785954 9.758487e-04 0.491164610 #> 1337 13.36 0.50760567 9.753609e-04 0.491418964 #> 1338 13.37 0.50735194 9.748733e-04 0.491673191 #> 1339 13.38 0.50709832 9.743860e-04 0.491927291 #> 1340 13.39 0.50684484 9.738989e-04 0.492181264 #> 1341 13.40 0.50659148 9.734121e-04 0.492435110 #> 1342 13.41 0.50633825 9.729255e-04 0.492688829 #> 1343 13.42 0.50608514 9.724392e-04 0.492942421 #> 1344 13.43 0.50583216 9.719531e-04 0.493195886 #> 1345 13.44 0.50557931 9.714672e-04 0.493449225 #> 1346 13.45 0.50532658 9.709816e-04 0.493702437 #> 1347 13.46 0.50507398 9.704963e-04 0.493955523 #> 1348 13.47 0.50482151 9.700111e-04 0.494208482 #> 1349 13.48 0.50456916 9.695262e-04 0.494461314 #> 1350 13.49 0.50431694 9.690416e-04 0.494714020 #> 1351 13.50 0.50406484 9.685572e-04 0.494966600 #> 1352 13.51 0.50381287 9.680730e-04 0.495219054 #> 1353 13.52 0.50356103 9.675891e-04 0.495471381 #> 1354 13.53 0.50330931 9.671055e-04 0.495723583 #> 1355 13.54 0.50305772 9.666220e-04 0.495975658 #> 1356 13.55 0.50280625 9.661388e-04 0.496227607 #> 1357 13.56 0.50255491 9.656559e-04 0.496479430 #> 1358 13.57 0.50230370 9.651732e-04 0.496731128 #> 1359 13.58 0.50205261 9.646907e-04 0.496982699 #> 1360 13.59 0.50180165 9.642085e-04 0.497234145 #> 1361 13.60 0.50155081 9.637265e-04 0.497485465 #> 1362 13.61 0.50130010 9.632448e-04 0.497736659 #> 1363 13.62 0.50104951 9.627633e-04 0.497987728 #> 1364 13.63 0.50079905 9.622820e-04 0.498238672 #> 1365 13.64 0.50054871 9.618010e-04 0.498489490 #> 1366 13.65 0.50029850 9.613202e-04 0.498740182 #> 1367 13.66 0.50004841 9.608397e-04 0.498990750 #> 1368 13.67 0.49979845 9.603594e-04 0.499241192 #> 1369 13.68 0.49954861 9.598793e-04 0.499491508 #> 1370 13.69 0.49929890 9.593995e-04 0.499741700 #> 1371 13.70 0.49904931 9.589199e-04 0.499991767 #> 1372 13.71 0.49879985 9.584406e-04 0.500241708 #> 1373 13.72 0.49855051 9.579615e-04 0.500491525 #> 1374 13.73 0.49830130 9.574826e-04 0.500741217 #> 1375 13.74 0.49805221 9.570040e-04 0.500990784 #> 1376 13.75 0.49780325 9.565256e-04 0.501240226 #> 1377 13.76 0.49755441 9.560475e-04 0.501489544 #> 1378 13.77 0.49730569 9.555695e-04 0.501738737 #> 1379 13.78 0.49705710 9.550919e-04 0.501987805 #> 1380 13.79 0.49680864 9.546145e-04 0.502236749 #> 1381 13.80 0.49656029 9.541373e-04 0.502485568 #> 1382 13.81 0.49631208 9.536603e-04 0.502734263 #> 1383 13.82 0.49606398 9.531836e-04 0.502982834 #> 1384 13.83 0.49581601 9.527071e-04 0.503231281 #> 1385 13.84 0.49556817 9.522309e-04 0.503479603 #> 1386 13.85 0.49532044 9.517549e-04 0.503727801 #> 1387 13.86 0.49507285 9.512791e-04 0.503975875 #> 1388 13.87 0.49482537 9.508036e-04 0.504223825 #> 1389 13.88 0.49457802 9.503283e-04 0.504471651 #> 1390 13.89 0.49433079 9.498533e-04 0.504719354 #> 1391 13.90 0.49408369 9.493785e-04 0.504966932 #> 1392 13.91 0.49383671 9.489039e-04 0.505214387 #> 1393 13.92 0.49358985 9.484296e-04 0.505461718 #> 1394 13.93 0.49334312 9.479555e-04 0.505708925 #> 1395 13.94 0.49309651 9.474816e-04 0.505956009 #> 1396 13.95 0.49285002 9.470080e-04 0.506202969 #> 1397 13.96 0.49260366 9.465346e-04 0.506449806 #> 1398 13.97 0.49235742 9.460615e-04 0.506696519 #> 1399 13.98 0.49211130 9.455886e-04 0.506943109 #> 1400 13.99 0.49186531 9.451159e-04 0.507189576 #> 1401 14.00 0.49161944 9.446434e-04 0.507435920 #> 1402 14.01 0.49137369 9.441712e-04 0.507682140 #> 1403 14.02 0.49112806 9.436993e-04 0.507928238 #> 1404 14.03 0.49088256 9.432275e-04 0.508174212 #> 1405 14.04 0.49063718 9.427560e-04 0.508420064 #> 1406 14.05 0.49039192 9.422848e-04 0.508665792 #> 1407 14.06 0.49014679 9.418138e-04 0.508911398 #> 1408 14.07 0.48990178 9.413430e-04 0.509156881 #> 1409 14.08 0.48965689 9.408724e-04 0.509402241 #> 1410 14.09 0.48941212 9.404021e-04 0.509647479 #> 1411 14.10 0.48916747 9.399320e-04 0.509892594 #> 1412 14.11 0.48892295 9.394622e-04 0.510137586 #> 1413 14.12 0.48867855 9.389926e-04 0.510382456 #> 1414 14.13 0.48843427 9.385232e-04 0.510627204 #> 1415 14.14 0.48819012 9.380540e-04 0.510871829 #> 1416 14.15 0.48794608 9.375851e-04 0.511116332 #> 1417 14.16 0.48770217 9.371164e-04 0.511360713 #> 1418 14.17 0.48745838 9.366480e-04 0.511604971 #> 1419 14.18 0.48721471 9.361798e-04 0.511849108 #> 1420 14.19 0.48697117 9.357118e-04 0.512093122 #> 1421 14.20 0.48672774 9.352441e-04 0.512337015 #> 1422 14.21 0.48648444 9.347766e-04 0.512580785 #> 1423 14.22 0.48624126 9.343093e-04 0.512824434 #> 1424 14.23 0.48599820 9.338423e-04 0.513067961 #> 1425 14.24 0.48575526 9.333755e-04 0.513311366 #> 1426 14.25 0.48551244 9.329089e-04 0.513554649 #> 1427 14.26 0.48526975 9.324426e-04 0.513797811 #> 1428 14.27 0.48502717 9.319764e-04 0.514040852 #> 1429 14.28 0.48478472 9.315106e-04 0.514283771 #> 1430 14.29 0.48454239 9.310449e-04 0.514526568 #> 1431 14.30 0.48430018 9.305795e-04 0.514769244 #> 1432 14.31 0.48405809 9.301144e-04 0.515011799 #> 1433 14.32 0.48381612 9.296494e-04 0.515254232 #> 1434 14.33 0.48357427 9.291847e-04 0.515496545 #> 1435 14.34 0.48333254 9.287202e-04 0.515738736 #> 1436 14.35 0.48309094 9.282560e-04 0.515980806 #> 1437 14.36 0.48284945 9.277920e-04 0.516222755 #> 1438 14.37 0.48260809 9.273282e-04 0.516464583 #> 1439 14.38 0.48236684 9.268646e-04 0.516706290 #> 1440 14.39 0.48212572 9.264013e-04 0.516947877 #> 1441 14.40 0.48188472 9.259382e-04 0.517189343 #> 1442 14.41 0.48164384 9.254754e-04 0.517430688 #> 1443 14.42 0.48140308 9.250128e-04 0.517671912 #> 1444 14.43 0.48116243 9.245504e-04 0.517913016 #> 1445 14.44 0.48092191 9.240882e-04 0.518153999 #> 1446 14.45 0.48068151 9.236263e-04 0.518394862 #> 1447 14.46 0.48044123 9.231646e-04 0.518635604 #> 1448 14.47 0.48020107 9.227031e-04 0.518876226 #> 1449 14.48 0.47996103 9.222419e-04 0.519116728 #> 1450 14.49 0.47972111 9.217809e-04 0.519357109 #> 1451 14.50 0.47948131 9.213201e-04 0.519597371 #> 1452 14.51 0.47924163 9.208596e-04 0.519837512 #> 1453 14.52 0.47900207 9.203993e-04 0.520077533 #> 1454 14.53 0.47876263 9.199392e-04 0.520317435 #> 1455 14.54 0.47852330 9.194793e-04 0.520557216 #> 1456 14.55 0.47828410 9.190197e-04 0.520796877 #> 1457 14.56 0.47804502 9.185603e-04 0.521036419 #> 1458 14.57 0.47780606 9.181011e-04 0.521275841 #> 1459 14.58 0.47756721 9.176422e-04 0.521515143 #> 1460 14.59 0.47732849 9.171835e-04 0.521754326 #> 1461 14.60 0.47708989 9.167250e-04 0.521993389 #> 1462 14.61 0.47685140 9.162668e-04 0.522232333 #> 1463 14.62 0.47661303 9.158087e-04 0.522471157 #> 1464 14.63 0.47637479 9.153510e-04 0.522709862 #> 1465 14.64 0.47613666 9.148934e-04 0.522948447 #> 1466 14.65 0.47589865 9.144361e-04 0.523186913 #> 1467 14.66 0.47566076 9.139790e-04 0.523425260 #> 1468 14.67 0.47542299 9.135221e-04 0.523663488 #> 1469 14.68 0.47518534 9.130654e-04 0.523901597 #> 1470 14.69 0.47494780 9.126090e-04 0.524139586 #> 1471 14.70 0.47471039 9.121528e-04 0.524377457 #> 1472 14.71 0.47447309 9.116969e-04 0.524615209 #> 1473 14.72 0.47423592 9.112411e-04 0.524852842 #> 1474 14.73 0.47399886 9.107856e-04 0.525090356 #> 1475 14.74 0.47376192 9.103303e-04 0.525327752 #> 1476 14.75 0.47352510 9.098753e-04 0.525565028 #> 1477 14.76 0.47328839 9.094205e-04 0.525802187 #> 1478 14.77 0.47305181 9.089659e-04 0.526039226 #> 1479 14.78 0.47281534 9.085115e-04 0.526276147 #> 1480 14.79 0.47257899 9.080574e-04 0.526512950 #> 1481 14.80 0.47234276 9.076034e-04 0.526749635 #> 1482 14.81 0.47210665 9.071498e-04 0.526986201 #> 1483 14.82 0.47187066 9.066963e-04 0.527222648 #> 1484 14.83 0.47163478 9.062431e-04 0.527458978 #> 1485 14.84 0.47139902 9.057900e-04 0.527695189 #> 1486 14.85 0.47116338 9.053373e-04 0.527931283 #> 1487 14.86 0.47092786 9.048847e-04 0.528167258 #> 1488 14.87 0.47069245 9.044324e-04 0.528403116 #> 1489 14.88 0.47045716 9.039803e-04 0.528638855 #> 1490 14.89 0.47022199 9.035284e-04 0.528874477 #> 1491 14.90 0.46998694 9.030767e-04 0.529109981 #> 1492 14.91 0.46975201 9.026253e-04 0.529345367 #> 1493 14.92 0.46951719 9.021741e-04 0.529580635 #> 1494 14.93 0.46928249 9.017231e-04 0.529815786 #> 1495 14.94 0.46904791 9.012724e-04 0.530050820 #> 1496 14.95 0.46881344 9.008219e-04 0.530285735 #> 1497 14.96 0.46857909 9.003716e-04 0.530520534 #> 1498 14.97 0.46834486 8.999215e-04 0.530755215 #> 1499 14.98 0.46811075 8.994717e-04 0.530989779 #> 1500 14.99 0.46787675 8.990220e-04 0.531224225 #> 1501 15.00 0.46764287 8.985726e-04 0.531458555 #> 1502 15.01 0.46740911 8.981235e-04 0.531692767 #> 1503 15.02 0.46717546 8.976745e-04 0.531926862 #> 1504 15.03 0.46694193 8.972258e-04 0.532160840 #> 1505 15.04 0.46670852 8.967773e-04 0.532394701 #> 1506 15.05 0.46647523 8.963290e-04 0.532628445 #> 1507 15.06 0.46624205 8.958810e-04 0.532862073 #> 1508 15.07 0.46600898 8.954331e-04 0.533095583 #> 1509 15.08 0.46577604 8.949855e-04 0.533328977 #> 1510 15.09 0.46554321 8.945381e-04 0.533562254 #> 1511 15.10 0.46531049 8.940910e-04 0.533795415 #> 1512 15.11 0.46507790 8.936441e-04 0.534028459 #> 1513 15.12 0.46484542 8.931973e-04 0.534261386 #> 1514 15.13 0.46461305 8.927509e-04 0.534494198 #> 1515 15.14 0.46438080 8.923046e-04 0.534726892 #> 1516 15.15 0.46414867 8.918585e-04 0.534959471 #> 1517 15.16 0.46391665 8.914127e-04 0.535191933 #> 1518 15.17 0.46368475 8.909671e-04 0.535424279 #> 1519 15.18 0.46345297 8.905218e-04 0.535656509 #> 1520 15.19 0.46322130 8.900766e-04 0.535888622 #> 1521 15.20 0.46298975 8.896317e-04 0.536120620 #> 1522 15.21 0.46275831 8.891870e-04 0.536352502 #> 1523 15.22 0.46252699 8.887425e-04 0.536584268 #> 1524 15.23 0.46229578 8.882982e-04 0.536815917 #> 1525 15.24 0.46206469 8.878542e-04 0.537047452 #> 1526 15.25 0.46183372 8.874104e-04 0.537278870 #> 1527 15.26 0.46160286 8.869668e-04 0.537510173 #> 1528 15.27 0.46137212 8.865234e-04 0.537741360 #> 1529 15.28 0.46114149 8.860803e-04 0.537972432 #> 1530 15.29 0.46091098 8.856373e-04 0.538203388 #> 1531 15.30 0.46068058 8.851946e-04 0.538434228 #> 1532 15.31 0.46045029 8.847521e-04 0.538664953 #> 1533 15.32 0.46022013 8.843099e-04 0.538895563 #> 1534 15.33 0.45999007 8.838678e-04 0.539126058 #> 1535 15.34 0.45976014 8.834260e-04 0.539356437 #> 1536 15.35 0.45953031 8.829844e-04 0.539586701 #> 1537 15.36 0.45930061 8.825430e-04 0.539816851 #> 1538 15.37 0.45907101 8.821019e-04 0.540046885 #> 1539 15.38 0.45884154 8.816609e-04 0.540276804 #> 1540 15.39 0.45861217 8.812202e-04 0.540506608 #> 1541 15.40 0.45838292 8.807797e-04 0.540736297 #> 1542 15.41 0.45815379 8.803394e-04 0.540965872 #> 1543 15.42 0.45792477 8.798994e-04 0.541195331 #> 1544 15.43 0.45769586 8.794595e-04 0.541424676 #> 1545 15.44 0.45746707 8.790199e-04 0.541653907 #> 1546 15.45 0.45723840 8.785805e-04 0.541883022 #> 1547 15.46 0.45700983 8.781413e-04 0.542112024 #> 1548 15.47 0.45678139 8.777024e-04 0.542340910 #> 1549 15.48 0.45655305 8.772636e-04 0.542569683 #> 1550 15.49 0.45632483 8.768251e-04 0.542798341 #> 1551 15.50 0.45609673 8.763868e-04 0.543026884 #> 1552 15.51 0.45586874 8.759487e-04 0.543255314 #> 1553 15.52 0.45564086 8.755108e-04 0.543483629 #> 1554 15.53 0.45541310 8.750732e-04 0.543711830 #> 1555 15.54 0.45518545 8.746358e-04 0.543939917 #> 1556 15.55 0.45495791 8.741986e-04 0.544167890 #> 1557 15.56 0.45473049 8.737616e-04 0.544395750 #> 1558 15.57 0.45450318 8.733248e-04 0.544623495 #> 1559 15.58 0.45427599 8.728883e-04 0.544851126 #> 1560 15.59 0.45404890 8.724519e-04 0.545078644 #> 1561 15.60 0.45382194 8.720158e-04 0.545306048 #> 1562 15.61 0.45359508 8.715799e-04 0.545533338 #> 1563 15.62 0.45336834 8.711442e-04 0.545760514 #> 1564 15.63 0.45314171 8.707088e-04 0.545987577 #> 1565 15.64 0.45291520 8.702735e-04 0.546214527 #> 1566 15.65 0.45268880 8.698385e-04 0.546441363 #> 1567 15.66 0.45246251 8.694037e-04 0.546668085 #> 1568 15.67 0.45223634 8.689691e-04 0.546894695 #> 1569 15.68 0.45201027 8.685347e-04 0.547121191 #> 1570 15.69 0.45178433 8.681005e-04 0.547347574 #> 1571 15.70 0.45155849 8.676666e-04 0.547573843 #> 1572 15.71 0.45133277 8.672329e-04 0.547800000 #> 1573 15.72 0.45110716 8.667994e-04 0.548026043 #> 1574 15.73 0.45088166 8.663661e-04 0.548251974 #> 1575 15.74 0.45065628 8.659330e-04 0.548477791 #> 1576 15.75 0.45043100 8.655001e-04 0.548703496 #> 1577 15.76 0.45020584 8.650675e-04 0.548929088 #> 1578 15.77 0.44998080 8.646351e-04 0.549154567 #> 1579 15.78 0.44975586 8.642029e-04 0.549379933 #> 1580 15.79 0.44953104 8.637709e-04 0.549605187 #> 1581 15.80 0.44930633 8.633391e-04 0.549830328 #> 1582 15.81 0.44908174 8.629075e-04 0.550055357 #> 1583 15.82 0.44885725 8.624762e-04 0.550280273 #> 1584 15.83 0.44863288 8.620451e-04 0.550505076 #> 1585 15.84 0.44840862 8.616141e-04 0.550729768 #> 1586 15.85 0.44818447 8.611834e-04 0.550954347 #> 1587 15.86 0.44796043 8.607530e-04 0.551178813 #> 1588 15.87 0.44773651 8.603227e-04 0.551403168 #> 1589 15.88 0.44751270 8.598926e-04 0.551627410 #> 1590 15.89 0.44728900 8.594628e-04 0.551851541 #> 1591 15.90 0.44706541 8.590332e-04 0.552075559 #> 1592 15.91 0.44684193 8.586038e-04 0.552299465 #> 1593 15.92 0.44661857 8.581746e-04 0.552523259 #> 1594 15.93 0.44639531 8.577456e-04 0.552746942 #> 1595 15.94 0.44617217 8.573168e-04 0.552970512 #> 1596 15.95 0.44594914 8.568883e-04 0.553193971 #> 1597 15.96 0.44572622 8.564599e-04 0.553417319 #> 1598 15.97 0.44550341 8.560318e-04 0.553640554 #> 1599 15.98 0.44528072 8.556039e-04 0.553863678 #> 1600 15.99 0.44505813 8.551762e-04 0.554086690 #> 1601 16.00 0.44483566 8.547487e-04 0.554309591 #> 1602 16.01 0.44461330 8.543215e-04 0.554532381 #> 1603 16.02 0.44439105 8.538944e-04 0.554755059 #> 1604 16.03 0.44416891 8.534676e-04 0.554977626 #> 1605 16.04 0.44394688 8.530409e-04 0.555200081 #> 1606 16.05 0.44372496 8.526145e-04 0.555422426 #> 1607 16.06 0.44350315 8.521883e-04 0.555644659 #> 1608 16.07 0.44328146 8.517623e-04 0.555866781 #> 1609 16.08 0.44305987 8.513366e-04 0.556088792 #> 1610 16.09 0.44283840 8.509110e-04 0.556310692 #> 1611 16.10 0.44261703 8.504857e-04 0.556532482 #> 1612 16.11 0.44239578 8.500605e-04 0.556754160 #> 1613 16.12 0.44217464 8.496356e-04 0.556975728 #> 1614 16.13 0.44195360 8.492109e-04 0.557197184 #> 1615 16.14 0.44173268 8.487864e-04 0.557418530 #> 1616 16.15 0.44151187 8.483621e-04 0.557639766 #> 1617 16.16 0.44129117 8.479380e-04 0.557860891 #> 1618 16.17 0.44107058 8.475142e-04 0.558081905 #> 1619 16.18 0.44085010 8.470905e-04 0.558302809 #> 1620 16.19 0.44062973 8.466671e-04 0.558523602 #> 1621 16.20 0.44040947 8.462438e-04 0.558744285 #> 1622 16.21 0.44018932 8.458208e-04 0.558964858 #> 1623 16.22 0.43996928 8.453980e-04 0.559185320 #> 1624 16.23 0.43974935 8.449754e-04 0.559405673 #> 1625 16.24 0.43952953 8.445530e-04 0.559625915 #> 1626 16.25 0.43930982 8.441309e-04 0.559846047 #> 1627 16.26 0.43909022 8.437089e-04 0.560066069 #> 1628 16.27 0.43887073 8.432872e-04 0.560285981 #> 1629 16.28 0.43865135 8.428656e-04 0.560505783 #> 1630 16.29 0.43843208 8.424443e-04 0.560725475 #> 1631 16.30 0.43821292 8.420232e-04 0.560945057 #> 1632 16.31 0.43799387 8.416023e-04 0.561164530 #> 1633 16.32 0.43777493 8.411816e-04 0.561383893 #> 1634 16.33 0.43755609 8.407611e-04 0.561603146 #> 1635 16.34 0.43733737 8.403408e-04 0.561822290 #> 1636 16.35 0.43711876 8.399207e-04 0.562041324 #> 1637 16.36 0.43690025 8.395009e-04 0.562260249 #> 1638 16.37 0.43668186 8.390812e-04 0.562479064 #> 1639 16.38 0.43646357 8.386618e-04 0.562697769 #> 1640 16.39 0.43624539 8.382426e-04 0.562916366 #> 1641 16.40 0.43602732 8.378236e-04 0.563134853 #> 1642 16.41 0.43580936 8.374048e-04 0.563353231 #> 1643 16.42 0.43559151 8.369862e-04 0.563571500 #> 1644 16.43 0.43537377 8.365678e-04 0.563789660 #> 1645 16.44 0.43515614 8.361496e-04 0.564007710 #> 1646 16.45 0.43493862 8.357316e-04 0.564225652 #> 1647 16.46 0.43472120 8.353139e-04 0.564443485 #> 1648 16.47 0.43450390 8.348963e-04 0.564661209 #> 1649 16.48 0.43428670 8.344790e-04 0.564878824 #> 1650 16.49 0.43406961 8.340618e-04 0.565096330 #> 1651 16.50 0.43385263 8.336449e-04 0.565313727 #> 1652 16.51 0.43363576 8.332282e-04 0.565531016 #> 1653 16.52 0.43341899 8.328117e-04 0.565748196 #> 1654 16.53 0.43320234 8.323954e-04 0.565965268 #> 1655 16.54 0.43298579 8.319793e-04 0.566182231 #> 1656 16.55 0.43276935 8.315634e-04 0.566399086 #> 1657 16.56 0.43255302 8.311477e-04 0.566615832 #> 1658 16.57 0.43233680 8.307322e-04 0.566832470 #> 1659 16.58 0.43212068 8.303170e-04 0.567049000 #> 1660 16.59 0.43190468 8.299019e-04 0.567265421 #> 1661 16.60 0.43168878 8.294871e-04 0.567481734 #> 1662 16.61 0.43147299 8.290724e-04 0.567697939 #> 1663 16.62 0.43125731 8.286580e-04 0.567914036 #> 1664 16.63 0.43104173 8.282438e-04 0.568130025 #> 1665 16.64 0.43082626 8.278298e-04 0.568345906 #> 1666 16.65 0.43061090 8.274160e-04 0.568561679 #> 1667 16.66 0.43039565 8.270024e-04 0.568777345 #> 1668 16.67 0.43018051 8.265890e-04 0.568992902 #> 1669 16.68 0.42996547 8.261758e-04 0.569208352 #> 1670 16.69 0.42975054 8.257628e-04 0.569423694 #> 1671 16.70 0.42953572 8.253500e-04 0.569638928 #> 1672 16.71 0.42932101 8.249374e-04 0.569854055 #> 1673 16.72 0.42910640 8.245251e-04 0.570069074 #> 1674 16.73 0.42889190 8.241129e-04 0.570283986 #> 1675 16.74 0.42867751 8.237010e-04 0.570498790 #> 1676 16.75 0.42846322 8.232892e-04 0.570713487 #> 1677 16.76 0.42824905 8.228777e-04 0.570928077 #> 1678 16.77 0.42803497 8.224663e-04 0.571142559 #> 1679 16.78 0.42782101 8.220552e-04 0.571356934 #> 1680 16.79 0.42760715 8.216443e-04 0.571571202 #> 1681 16.80 0.42739340 8.212336e-04 0.571785363 #> 1682 16.81 0.42717976 8.208230e-04 0.571999417 #> 1683 16.82 0.42696622 8.204127e-04 0.572213364 #> 1684 16.83 0.42675279 8.200026e-04 0.572427203 #> 1685 16.84 0.42653947 8.195927e-04 0.572640936 #> 1686 16.85 0.42632625 8.191830e-04 0.572854563 #> 1687 16.86 0.42611314 8.187735e-04 0.573068082 #> 1688 16.87 0.42590014 8.183643e-04 0.573281495 #> 1689 16.88 0.42568724 8.179552e-04 0.573494800 #> 1690 16.89 0.42547445 8.175463e-04 0.573708000 #> 1691 16.90 0.42526177 8.171376e-04 0.573921092 #> 1692 16.91 0.42504919 8.167292e-04 0.574134079 #> 1693 16.92 0.42483672 8.163209e-04 0.574346958 #> 1694 16.93 0.42462436 8.159128e-04 0.574559732 #> 1695 16.94 0.42441210 8.155050e-04 0.574772399 #> 1696 16.95 0.42419994 8.150973e-04 0.574984959 #> 1697 16.96 0.42398790 8.146899e-04 0.575197414 #> 1698 16.97 0.42377596 8.142826e-04 0.575409762 #> 1699 16.98 0.42356412 8.138756e-04 0.575622004 #> 1700 16.99 0.42335239 8.134688e-04 0.575834140 #> 1701 17.00 0.42314077 8.130621e-04 0.576046170 #> 1702 17.01 0.42292925 8.126557e-04 0.576258094 #> 1703 17.02 0.42271784 8.122495e-04 0.576469912 #> 1704 17.03 0.42250653 8.118435e-04 0.576681624 #> 1705 17.04 0.42229533 8.114376e-04 0.576893230 #> 1706 17.05 0.42208424 8.110320e-04 0.577104731 #> 1707 17.06 0.42187325 8.106266e-04 0.577316126 #> 1708 17.07 0.42166236 8.102214e-04 0.577527415 #> 1709 17.08 0.42145159 8.098164e-04 0.577738598 #> 1710 17.09 0.42124091 8.094116e-04 0.577949676 #> 1711 17.10 0.42103034 8.090070e-04 0.578160649 #> 1712 17.11 0.42081988 8.086026e-04 0.578371516 #> 1713 17.12 0.42060952 8.081984e-04 0.578582277 #> 1714 17.13 0.42039927 8.077944e-04 0.578792933 #> 1715 17.14 0.42018913 8.073906e-04 0.579003484 #> 1716 17.15 0.41997908 8.069870e-04 0.579213930 #> 1717 17.16 0.41976915 8.065836e-04 0.579424270 #> 1718 17.17 0.41955931 8.061804e-04 0.579634506 #> 1719 17.18 0.41934959 8.057774e-04 0.579844636 #> 1720 17.19 0.41913996 8.053746e-04 0.580054661 #> 1721 17.20 0.41893045 8.049720e-04 0.580264581 #> 1722 17.21 0.41872103 8.045697e-04 0.580474396 #> 1723 17.22 0.41851173 8.041675e-04 0.580684107 #> 1724 17.23 0.41830252 8.037655e-04 0.580893712 #> 1725 17.24 0.41809342 8.033637e-04 0.581103213 #> 1726 17.25 0.41788443 8.029621e-04 0.581312609 #> 1727 17.26 0.41767554 8.025607e-04 0.581521901 #> 1728 17.27 0.41746675 8.021596e-04 0.581731087 #> 1729 17.28 0.41725807 8.017586e-04 0.581940170 #> 1730 17.29 0.41704949 8.013578e-04 0.582149147 #> 1731 17.30 0.41684102 8.009572e-04 0.582358020 #> 1732 17.31 0.41663265 8.005568e-04 0.582566789 #> 1733 17.32 0.41642439 8.001567e-04 0.582775454 #> 1734 17.33 0.41621623 7.997567e-04 0.582984014 #> 1735 17.34 0.41600817 7.993569e-04 0.583192470 #> 1736 17.35 0.41580022 7.989573e-04 0.583400821 #> 1737 17.36 0.41559237 7.985579e-04 0.583609069 #> 1738 17.37 0.41538463 7.981588e-04 0.583817212 #> 1739 17.38 0.41517699 7.977598e-04 0.584025252 #> 1740 17.39 0.41496945 7.973610e-04 0.584233187 #> 1741 17.40 0.41476202 7.969624e-04 0.584441019 #> 1742 17.41 0.41455469 7.965640e-04 0.584648746 #> 1743 17.42 0.41434746 7.961659e-04 0.584856370 #> 1744 17.43 0.41414034 7.957679e-04 0.585063890 #> 1745 17.44 0.41393332 7.953701e-04 0.585271306 #> 1746 17.45 0.41372641 7.949725e-04 0.585478619 #> 1747 17.46 0.41351960 7.945751e-04 0.585685827 #> 1748 17.47 0.41331289 7.941779e-04 0.585892933 #> 1749 17.48 0.41310628 7.937809e-04 0.586099935 #> 1750 17.49 0.41289978 7.933842e-04 0.586306833 #> 1751 17.50 0.41269338 7.929876e-04 0.586513628 #> 1752 17.51 0.41248709 7.925912e-04 0.586720319 #> 1753 17.52 0.41228090 7.921950e-04 0.586926907 #> 1754 17.53 0.41207481 7.917990e-04 0.587133392 #> 1755 17.54 0.41186882 7.914032e-04 0.587339774 #> 1756 17.55 0.41166294 7.910076e-04 0.587546053 #> 1757 17.56 0.41145716 7.906122e-04 0.587752228 #> 1758 17.57 0.41125148 7.902170e-04 0.587958301 #> 1759 17.58 0.41104591 7.898219e-04 0.588164270 #> 1760 17.59 0.41084044 7.894271e-04 0.588370136 #> 1761 17.60 0.41063507 7.890325e-04 0.588575900 #> 1762 17.61 0.41042980 7.886381e-04 0.588781560 #> 1763 17.62 0.41022464 7.882439e-04 0.588987118 #> 1764 17.63 0.41001958 7.878499e-04 0.589192573 #> 1765 17.64 0.40981462 7.874560e-04 0.589397926 #> 1766 17.65 0.40960976 7.870624e-04 0.589603175 #> 1767 17.66 0.40940501 7.866690e-04 0.589808323 #> 1768 17.67 0.40920036 7.862757e-04 0.590013367 #> 1769 17.68 0.40899581 7.858827e-04 0.590218309 #> 1770 17.69 0.40879136 7.854899e-04 0.590423149 #> 1771 17.70 0.40858702 7.850972e-04 0.590627886 #> 1772 17.71 0.40838277 7.847048e-04 0.590832521 #> 1773 17.72 0.40817863 7.843125e-04 0.591037054 #> 1774 17.73 0.40797460 7.839204e-04 0.591241484 #> 1775 17.74 0.40777066 7.835286e-04 0.591445812 #> 1776 17.75 0.40756682 7.831369e-04 0.591650038 #> 1777 17.76 0.40736309 7.827454e-04 0.591854162 #> 1778 17.77 0.40715946 7.823542e-04 0.592058184 #> 1779 17.78 0.40695593 7.819631e-04 0.592262104 #> 1780 17.79 0.40675251 7.815722e-04 0.592465922 #> 1781 17.80 0.40654918 7.811815e-04 0.592669638 #> 1782 17.81 0.40634596 7.807910e-04 0.592873252 #> 1783 17.82 0.40614283 7.804007e-04 0.593076765 #> 1784 17.83 0.40593981 7.800106e-04 0.593280176 #> 1785 17.84 0.40573689 7.796207e-04 0.593483485 #> 1786 17.85 0.40553408 7.792310e-04 0.593686692 #> 1787 17.86 0.40533136 7.788415e-04 0.593889798 #> 1788 17.87 0.40512875 7.784522e-04 0.594092802 #> 1789 17.88 0.40492623 7.780630e-04 0.594295705 #> 1790 17.89 0.40472382 7.776741e-04 0.594498507 #> 1791 17.90 0.40452151 7.772854e-04 0.594701207 #> 1792 17.91 0.40431930 7.768968e-04 0.594903806 #> 1793 17.92 0.40411719 7.765085e-04 0.595106303 #> 1794 17.93 0.40391518 7.761203e-04 0.595308699 #> 1795 17.94 0.40371327 7.757323e-04 0.595510994 #> 1796 17.95 0.40351147 7.753446e-04 0.595713188 #> 1797 17.96 0.40330976 7.749570e-04 0.595915281 #> 1798 17.97 0.40310816 7.745696e-04 0.596117273 #> 1799 17.98 0.40290665 7.741824e-04 0.596319164 #> 1800 17.99 0.40270525 7.737954e-04 0.596520954 #> 1801 18.00 0.40250395 7.734086e-04 0.596722643 #> 1802 18.01 0.40230275 7.730220e-04 0.596924231 #> 1803 18.02 0.40210165 7.726356e-04 0.597125719 #> 1804 18.03 0.40190064 7.722494e-04 0.597327106 #> 1805 18.04 0.40169974 7.718634e-04 0.597528392 #> 1806 18.05 0.40149895 7.714775e-04 0.597729577 #> 1807 18.06 0.40129825 7.710919e-04 0.597930662 #> 1808 18.07 0.40109765 7.707064e-04 0.598131647 #> 1809 18.08 0.40089715 7.703212e-04 0.598332531 #> 1810 18.09 0.40069675 7.699361e-04 0.598533314 #> 1811 18.10 0.40049645 7.695512e-04 0.598733997 #> 1812 18.11 0.40029625 7.691666e-04 0.598934580 #> 1813 18.12 0.40009616 7.687821e-04 0.599135063 #> 1814 18.13 0.39989616 7.683978e-04 0.599335445 #> 1815 18.14 0.39969626 7.680137e-04 0.599535727 #> 1816 18.15 0.39949646 7.676298e-04 0.599735910 #> 1817 18.16 0.39929676 7.672460e-04 0.599935992 #> 1818 18.17 0.39909716 7.668625e-04 0.600135974 #> 1819 18.18 0.39889767 7.664792e-04 0.600335856 #> 1820 18.19 0.39869827 7.660960e-04 0.600535638 #> 1821 18.20 0.39849897 7.657131e-04 0.600735320 #> 1822 18.21 0.39829977 7.653303e-04 0.600934902 #> 1823 18.22 0.39810067 7.649478e-04 0.601134385 #> 1824 18.23 0.39790167 7.645654e-04 0.601333768 #> 1825 18.24 0.39770277 7.641832e-04 0.601533051 #> 1826 18.25 0.39750396 7.638012e-04 0.601732235 #> 1827 18.26 0.39730526 7.634194e-04 0.601931319 #> 1828 18.27 0.39710666 7.630378e-04 0.602130304 #> 1829 18.28 0.39690815 7.626563e-04 0.602329189 #> 1830 18.29 0.39670975 7.622751e-04 0.602527975 #> 1831 18.30 0.39651144 7.618941e-04 0.602726661 #> 1832 18.31 0.39631324 7.615132e-04 0.602925248 #> 1833 18.32 0.39611513 7.611326e-04 0.603123736 #> 1834 18.33 0.39591712 7.607521e-04 0.603322124 #> 1835 18.34 0.39571921 7.603718e-04 0.603520414 #> 1836 18.35 0.39552140 7.599917e-04 0.603718604 #> 1837 18.36 0.39532369 7.596118e-04 0.603916695 #> 1838 18.37 0.39512608 7.592321e-04 0.604114687 #> 1839 18.38 0.39492857 7.588526e-04 0.604312580 #> 1840 18.39 0.39473115 7.584733e-04 0.604510375 #> 1841 18.40 0.39453384 7.580941e-04 0.604708070 #> 1842 18.41 0.39433662 7.577152e-04 0.604905667 #> 1843 18.42 0.39413950 7.573364e-04 0.605103164 #> 1844 18.43 0.39394248 7.569578e-04 0.605300564 #> 1845 18.44 0.39374556 7.565794e-04 0.605497864 #> 1846 18.45 0.39354873 7.562012e-04 0.605695066 #> 1847 18.46 0.39335201 7.558232e-04 0.605892169 #> 1848 18.47 0.39315538 7.554454e-04 0.606089174 #> 1849 18.48 0.39295885 7.550678e-04 0.606286080 #> 1850 18.49 0.39276242 7.546904e-04 0.606482888 #> 1851 18.50 0.39256609 7.543131e-04 0.606679597 #> 1852 18.51 0.39236986 7.539360e-04 0.606876208 #> 1853 18.52 0.39217372 7.535592e-04 0.607072721 #> 1854 18.53 0.39197768 7.531825e-04 0.607269135 #> 1855 18.54 0.39178174 7.528060e-04 0.607465452 #> 1856 18.55 0.39158590 7.524297e-04 0.607661670 #> 1857 18.56 0.39139016 7.520536e-04 0.607857790 #> 1858 18.57 0.39119451 7.516776e-04 0.608053812 #> 1859 18.58 0.39099896 7.513019e-04 0.608249736 #> 1860 18.59 0.39080351 7.509263e-04 0.608445562 #> 1861 18.60 0.39060816 7.505509e-04 0.608641291 #> 1862 18.61 0.39041290 7.501758e-04 0.608836921 #> 1863 18.62 0.39021775 7.498008e-04 0.609032454 #> 1864 18.63 0.39002269 7.494260e-04 0.609227889 #> 1865 18.64 0.38982772 7.490513e-04 0.609423226 #> 1866 18.65 0.38963286 7.486769e-04 0.609618466 #> 1867 18.66 0.38943809 7.483027e-04 0.609813608 #> 1868 18.67 0.38924342 7.479286e-04 0.610008652 #> 1869 18.68 0.38904885 7.475547e-04 0.610203599 #> 1870 18.69 0.38885437 7.471811e-04 0.610398448 #> 1871 18.70 0.38865999 7.468076e-04 0.610593200 #> 1872 18.71 0.38846571 7.464343e-04 0.610787855 #> 1873 18.72 0.38827153 7.460611e-04 0.610982413 #> 1874 18.73 0.38807744 7.456882e-04 0.611176873 #> 1875 18.74 0.38788345 7.453154e-04 0.611371236 #> 1876 18.75 0.38768956 7.449429e-04 0.611565502 #> 1877 18.76 0.38749576 7.445705e-04 0.611759670 #> 1878 18.77 0.38730206 7.441983e-04 0.611953742 #> 1879 18.78 0.38710846 7.438263e-04 0.612147717 #> 1880 18.79 0.38691495 7.434545e-04 0.612341594 #> 1881 18.80 0.38672154 7.430828e-04 0.612535375 #> 1882 18.81 0.38652823 7.427114e-04 0.612729059 #> 1883 18.82 0.38633501 7.423401e-04 0.612922646 #> 1884 18.83 0.38614189 7.419691e-04 0.613116136 #> 1885 18.84 0.38594887 7.415982e-04 0.613309530 #> 1886 18.85 0.38575595 7.412275e-04 0.613502827 #> 1887 18.86 0.38556312 7.408569e-04 0.613696027 #> 1888 18.87 0.38537038 7.404866e-04 0.613889131 #> 1889 18.88 0.38517775 7.401164e-04 0.614082138 #> 1890 18.89 0.38498520 7.397465e-04 0.614275049 #> 1891 18.90 0.38479276 7.393767e-04 0.614467863 #> 1892 18.91 0.38460041 7.390071e-04 0.614660581 #> 1893 18.92 0.38440816 7.386377e-04 0.614853202 #> 1894 18.93 0.38421600 7.382685e-04 0.615045728 #> 1895 18.94 0.38402394 7.378994e-04 0.615238157 #> 1896 18.95 0.38383198 7.375306e-04 0.615430490 #> 1897 18.96 0.38364011 7.371619e-04 0.615622726 #> 1898 18.97 0.38344834 7.367934e-04 0.615814867 #> 1899 18.98 0.38325666 7.364251e-04 0.616006912 #> 1900 18.99 0.38306508 7.360570e-04 0.616198860 #> 1901 19.00 0.38287360 7.356890e-04 0.616390713 #> 1902 19.01 0.38268221 7.353213e-04 0.616582469 #> 1903 19.02 0.38249092 7.349537e-04 0.616774130 #> 1904 19.03 0.38229972 7.345863e-04 0.616965695 #> 1905 19.04 0.38210862 7.342191e-04 0.617157165 #> 1906 19.05 0.38191761 7.338521e-04 0.617348538 #> 1907 19.06 0.38172670 7.334853e-04 0.617539816 #> 1908 19.07 0.38153588 7.331186e-04 0.617730998 #> 1909 19.08 0.38134516 7.327522e-04 0.617922085 #> 1910 19.09 0.38115454 7.323859e-04 0.618113076 #> 1911 19.10 0.38096401 7.320198e-04 0.618303972 #> 1912 19.11 0.38077357 7.316539e-04 0.618494772 #> 1913 19.12 0.38058323 7.312881e-04 0.618685477 #> 1914 19.13 0.38039299 7.309226e-04 0.618876087 #> 1915 19.14 0.38020284 7.305572e-04 0.619066601 #> 1916 19.15 0.38001279 7.301920e-04 0.619257020 #> 1917 19.16 0.37982283 7.298270e-04 0.619447344 #> 1918 19.17 0.37963296 7.294622e-04 0.619637573 #> 1919 19.18 0.37944320 7.290975e-04 0.619827707 #> 1920 19.19 0.37925352 7.287331e-04 0.620017745 #> 1921 19.20 0.37906394 7.283688e-04 0.620207689 #> 1922 19.21 0.37887446 7.280047e-04 0.620397538 #> 1923 19.22 0.37868507 7.276408e-04 0.620587291 #> 1924 19.23 0.37849577 7.272771e-04 0.620776950 #> 1925 19.24 0.37830657 7.269135e-04 0.620966515 #> 1926 19.25 0.37811747 7.265502e-04 0.621155984 #> 1927 19.26 0.37792845 7.261870e-04 0.621345359 #> 1928 19.27 0.37773954 7.258240e-04 0.621534639 #> 1929 19.28 0.37755071 7.254612e-04 0.621723824 #> 1930 19.29 0.37736199 7.250985e-04 0.621912915 #> 1931 19.30 0.37717335 7.247361e-04 0.622101911 #> 1932 19.31 0.37698481 7.243738e-04 0.622290813 #> 1933 19.32 0.37679637 7.240117e-04 0.622479620 #> 1934 19.33 0.37660802 7.236498e-04 0.622668333 #> 1935 19.34 0.37641976 7.232880e-04 0.622856952 #> 1936 19.35 0.37623160 7.229265e-04 0.623045476 #> 1937 19.36 0.37604353 7.225651e-04 0.623233907 #> 1938 19.37 0.37585555 7.222039e-04 0.623422243 #> 1939 19.38 0.37566767 7.218429e-04 0.623610484 #> 1940 19.39 0.37547989 7.214821e-04 0.623798632 #> 1941 19.40 0.37529219 7.211214e-04 0.623986686 #> 1942 19.41 0.37510459 7.207610e-04 0.624174645 #> 1943 19.42 0.37491709 7.204007e-04 0.624362511 #> 1944 19.43 0.37472968 7.200406e-04 0.624550283 #> 1945 19.44 0.37454236 7.196806e-04 0.624737961 #> 1946 19.45 0.37435513 7.193209e-04 0.624925545 #> 1947 19.46 0.37416800 7.189613e-04 0.625113035 #> 1948 19.47 0.37398097 7.186019e-04 0.625300432 #> 1949 19.48 0.37379402 7.182427e-04 0.625487735 #> 1950 19.49 0.37360717 7.178837e-04 0.625674944 #> 1951 19.50 0.37342042 7.175248e-04 0.625862060 #> 1952 19.51 0.37323375 7.171661e-04 0.626049082 #> 1953 19.52 0.37304718 7.168077e-04 0.626236011 #> 1954 19.53 0.37286070 7.164493e-04 0.626422846 #> 1955 19.54 0.37267432 7.160912e-04 0.626609588 #> 1956 19.55 0.37248803 7.157332e-04 0.626796237 #> 1957 19.56 0.37230183 7.153755e-04 0.626982792 #> 1958 19.57 0.37211573 7.150179e-04 0.627169254 #> 1959 19.58 0.37192972 7.146604e-04 0.627355623 #> 1960 19.59 0.37174380 7.143032e-04 0.627541898 #> 1961 19.60 0.37155797 7.139461e-04 0.627728081 #> 1962 19.61 0.37137224 7.135893e-04 0.627914170 #> 1963 19.62 0.37118660 7.132326e-04 0.628100167 #> 1964 19.63 0.37100105 7.128760e-04 0.628286070 #> 1965 19.64 0.37081560 7.125197e-04 0.628471881 #> 1966 19.65 0.37063024 7.121635e-04 0.628657598 #> 1967 19.66 0.37044497 7.118075e-04 0.628843223 #> 1968 19.67 0.37025979 7.114517e-04 0.629028755 #> 1969 19.68 0.37007471 7.110961e-04 0.629214194 #> 1970 19.69 0.36988972 7.107406e-04 0.629399541 #> 1971 19.70 0.36970482 7.103853e-04 0.629584795 #> 1972 19.71 0.36952001 7.100302e-04 0.629769956 #> 1973 19.72 0.36933530 7.096753e-04 0.629955025 #> 1974 19.73 0.36915068 7.093205e-04 0.630140001 #> 1975 19.74 0.36896615 7.089660e-04 0.630324885 #> 1976 19.75 0.36878171 7.086116e-04 0.630509676 #> 1977 19.76 0.36859737 7.082574e-04 0.630694375 #> 1978 19.77 0.36841311 7.079033e-04 0.630878982 #> 1979 19.78 0.36822895 7.075495e-04 0.631063496 #> 1980 19.79 0.36804489 7.071958e-04 0.631247919 #> 1981 19.80 0.36786091 7.068423e-04 0.631432249 #> 1982 19.81 0.36767702 7.064889e-04 0.631616486 #> 1983 19.82 0.36749323 7.061358e-04 0.631800632 #> 1984 19.83 0.36730953 7.057828e-04 0.631984686 #> 1985 19.84 0.36712592 7.054300e-04 0.632168647 #> 1986 19.85 0.36694241 7.050774e-04 0.632352517 #> 1987 19.86 0.36675898 7.047249e-04 0.632536295 #> 1988 19.87 0.36657565 7.043726e-04 0.632719981 #> 1989 19.88 0.36639240 7.040205e-04 0.632903575 #> 1990 19.89 0.36620925 7.036686e-04 0.633087077 #> 1991 19.90 0.36602620 7.033169e-04 0.633270488 #> 1992 19.91 0.36584323 7.029653e-04 0.633453807 #> 1993 19.92 0.36566035 7.026139e-04 0.633637034 #> 1994 19.93 0.36547757 7.022627e-04 0.633820170 #> 1995 19.94 0.36529487 7.019116e-04 0.634003214 #> 1996 19.95 0.36511227 7.015608e-04 0.634186167 #> 1997 19.96 0.36492976 7.012101e-04 0.634369028 #> 1998 19.97 0.36474734 7.008596e-04 0.634551798 #> 1999 19.98 0.36456501 7.005092e-04 0.634734476 #> 2000 19.99 0.36438278 7.001591e-04 0.634917063 #> 2001 20.00 0.36420063 6.998091e-04 0.635099559 #> 2002 20.01 0.36401858 6.994592e-04 0.635281964 #> 2003 20.02 0.36383661 6.991096e-04 0.635464277 #> 2004 20.03 0.36365474 6.987601e-04 0.635646500 #> 2005 20.04 0.36347296 6.984108e-04 0.635828631 #> 2006 20.05 0.36329127 6.980617e-04 0.636010671 #> 2007 20.06 0.36310967 6.977128e-04 0.636192620 #> 2008 20.07 0.36292816 6.973640e-04 0.636374478 #> 2009 20.08 0.36274674 6.970154e-04 0.636556246 #> 2010 20.09 0.36256541 6.966670e-04 0.636737922 #> 2011 20.10 0.36238417 6.963188e-04 0.636919508 #> 2012 20.11 0.36220303 6.959707e-04 0.637101003 #> 2013 20.12 0.36202197 6.956228e-04 0.637282407 #> 2014 20.13 0.36184100 6.952751e-04 0.637463720 #> 2015 20.14 0.36166013 6.949275e-04 0.637644943 #> 2016 20.15 0.36147934 6.945801e-04 0.637826075 #> 2017 20.16 0.36129865 6.942329e-04 0.638007117 #> 2018 20.17 0.36111805 6.938859e-04 0.638188068 #> 2019 20.18 0.36093753 6.935390e-04 0.638368929 #> 2020 20.19 0.36075711 6.931924e-04 0.638549699 #> 2021 20.20 0.36057677 6.928458e-04 0.638730379 #> 2022 20.21 0.36039653 6.924995e-04 0.638910969 #> 2023 20.22 0.36021638 6.921533e-04 0.639091468 #> 2024 20.23 0.36003632 6.918074e-04 0.639271878 #> 2025 20.24 0.35985634 6.914615e-04 0.639452197 #> 2026 20.25 0.35967646 6.911159e-04 0.639632425 #> 2027 20.26 0.35949667 6.907704e-04 0.639812564 #> 2028 20.27 0.35931696 6.904251e-04 0.639992613 #> 2029 20.28 0.35913735 6.900800e-04 0.640172572 #> 2030 20.29 0.35895782 6.897350e-04 0.640352440 #> 2031 20.30 0.35877839 6.893903e-04 0.640532219 #> 2032 20.31 0.35859905 6.890457e-04 0.640711908 #> 2033 20.32 0.35841979 6.887012e-04 0.640891507 #> 2034 20.33 0.35824063 6.883570e-04 0.641071017 #> 2035 20.34 0.35806155 6.880129e-04 0.641250436 #> 2036 20.35 0.35788256 6.876689e-04 0.641429766 #> 2037 20.36 0.35770367 6.873252e-04 0.641609007 #> 2038 20.37 0.35752486 6.869816e-04 0.641788157 #> 2039 20.38 0.35734614 6.866382e-04 0.641967218 #> 2040 20.39 0.35716751 6.862950e-04 0.642146190 #> 2041 20.40 0.35698898 6.859519e-04 0.642325072 #> 2042 20.41 0.35681053 6.856090e-04 0.642503865 #> 2043 20.42 0.35663217 6.852663e-04 0.642682568 #> 2044 20.43 0.35645389 6.849238e-04 0.642861182 #> 2045 20.44 0.35627571 6.845814e-04 0.643039707 #> 2046 20.45 0.35609762 6.842392e-04 0.643218143 #> 2047 20.46 0.35591961 6.838971e-04 0.643396489 #> 2048 20.47 0.35574170 6.835553e-04 0.643574746 #> 2049 20.48 0.35556387 6.832136e-04 0.643752914 #> 2050 20.49 0.35538613 6.828721e-04 0.643930993 #> 2051 20.50 0.35520849 6.825307e-04 0.644108983 #> 2052 20.51 0.35503093 6.821895e-04 0.644286885 #> 2053 20.52 0.35485345 6.818485e-04 0.644464697 #> 2054 20.53 0.35467607 6.815077e-04 0.644642420 #> 2055 20.54 0.35449878 6.811670e-04 0.644820054 #> 2056 20.55 0.35432157 6.808265e-04 0.644997600 #> 2057 20.56 0.35414446 6.804862e-04 0.645175057 #> 2058 20.57 0.35396743 6.801460e-04 0.645352425 #> 2059 20.58 0.35379049 6.798060e-04 0.645529704 #> 2060 20.59 0.35361364 6.794662e-04 0.645706895 #> 2061 20.60 0.35343688 6.791266e-04 0.645883997 #> 2062 20.61 0.35326020 6.787871e-04 0.646061011 #> 2063 20.62 0.35308362 6.784478e-04 0.646237936 #> 2064 20.63 0.35290712 6.781087e-04 0.646414773 #> 2065 20.64 0.35273071 6.777697e-04 0.646591522 #> 2066 20.65 0.35255439 6.774309e-04 0.646768182 #> 2067 20.66 0.35237815 6.770923e-04 0.646944754 #> 2068 20.67 0.35220201 6.767538e-04 0.647121237 #> 2069 20.68 0.35202595 6.764155e-04 0.647297632 #> 2070 20.69 0.35184998 6.760774e-04 0.647473939 #> 2071 20.70 0.35167410 6.757394e-04 0.647650158 #> 2072 20.71 0.35149831 6.754016e-04 0.647826289 #> 2073 20.72 0.35132260 6.750640e-04 0.648002332 #> 2074 20.73 0.35114699 6.747266e-04 0.648178287 #> 2075 20.74 0.35097146 6.743893e-04 0.648354154 #> 2076 20.75 0.35079601 6.740522e-04 0.648529933 #> 2077 20.76 0.35062066 6.737152e-04 0.648705624 #> 2078 20.77 0.35044539 6.733785e-04 0.648881227 #> 2079 20.78 0.35027022 6.730419e-04 0.649056743 #> 2080 20.79 0.35009512 6.727054e-04 0.649232171 #> 2081 20.80 0.34992012 6.723692e-04 0.649407511 #> 2082 20.81 0.34974520 6.720331e-04 0.649582763 #> 2083 20.82 0.34957037 6.716971e-04 0.649757928 #> 2084 20.83 0.34939563 6.713614e-04 0.649933005 #> 2085 20.84 0.34922098 6.710258e-04 0.650107995 #> 2086 20.85 0.34904641 6.706903e-04 0.650282897 #> 2087 20.86 0.34887193 6.703551e-04 0.650457712 #> 2088 20.87 0.34869754 6.700200e-04 0.650632440 #> 2089 20.88 0.34852324 6.696851e-04 0.650807080 #> 2090 20.89 0.34834902 6.693503e-04 0.650981633 #> 2091 20.90 0.34817489 6.690157e-04 0.651156098 #> 2092 20.91 0.34800084 6.686813e-04 0.651330476 #> 2093 20.92 0.34782689 6.683470e-04 0.651504768 #> 2094 20.93 0.34765302 6.680129e-04 0.651678972 #> 2095 20.94 0.34747923 6.676790e-04 0.651853089 #> 2096 20.95 0.34730554 6.673453e-04 0.652027119 #> 2097 20.96 0.34713193 6.670117e-04 0.652201062 #> 2098 20.97 0.34695840 6.666782e-04 0.652374918 #> 2099 20.98 0.34678497 6.663450e-04 0.652548687 #> 2100 20.99 0.34661162 6.660119e-04 0.652722369 #> 2101 21.00 0.34643836 6.656790e-04 0.652895964 #> 2102 21.01 0.34626518 6.653462e-04 0.653069473 #> 2103 21.02 0.34609209 6.650136e-04 0.653242895 #> 2104 21.03 0.34591909 6.646812e-04 0.653416230 #> 2105 21.04 0.34574617 6.643489e-04 0.653589479 #> 2106 21.05 0.34557334 6.640169e-04 0.653762641 #> 2107 21.06 0.34540060 6.636849e-04 0.653935716 #> 2108 21.07 0.34522794 6.633532e-04 0.654108705 #> 2109 21.08 0.34505537 6.630216e-04 0.654281607 #> 2110 21.09 0.34488289 6.626901e-04 0.654454423 #> 2111 21.10 0.34471049 6.623589e-04 0.654627153 #> 2112 21.11 0.34453818 6.620278e-04 0.654799796 #> 2113 21.12 0.34436595 6.616969e-04 0.654972353 #> 2114 21.13 0.34419381 6.613661e-04 0.655144824 #> 2115 21.14 0.34402176 6.610355e-04 0.655317208 #> 2116 21.15 0.34384979 6.607051e-04 0.655489507 #> 2117 21.16 0.34367791 6.603748e-04 0.655661719 #> 2118 21.17 0.34350611 6.600447e-04 0.655833845 #> 2119 21.18 0.34333440 6.597147e-04 0.656005885 #> 2120 21.19 0.34316278 6.593850e-04 0.656177839 #> 2121 21.20 0.34299124 6.590554e-04 0.656349707 #> 2122 21.21 0.34281978 6.587259e-04 0.656521490 #> 2123 21.22 0.34264842 6.583966e-04 0.656693186 #> 2124 21.23 0.34247714 6.580675e-04 0.656864796 #> 2125 21.24 0.34230594 6.577386e-04 0.657036321 #> 2126 21.25 0.34213483 6.574098e-04 0.657207760 #> 2127 21.26 0.34196381 6.570812e-04 0.657379113 #> 2128 21.27 0.34179287 6.567527e-04 0.657550381 #> 2129 21.28 0.34162201 6.564244e-04 0.657721563 #> 2130 21.29 0.34145124 6.560963e-04 0.657892660 #> 2131 21.30 0.34128056 6.557683e-04 0.658063670 #> 2132 21.31 0.34110996 6.554405e-04 0.658234596 #> 2133 21.32 0.34093945 6.551129e-04 0.658405436 #> 2134 21.33 0.34076902 6.547854e-04 0.658576190 #> 2135 21.34 0.34059868 6.544581e-04 0.658746860 #> 2136 21.35 0.34042843 6.541309e-04 0.658917444 #> 2137 21.36 0.34025825 6.538039e-04 0.659087942 #> 2138 21.37 0.34008817 6.534771e-04 0.659258356 #> 2139 21.38 0.33991817 6.531505e-04 0.659428684 #> 2140 21.39 0.33974825 6.528240e-04 0.659598927 #> 2141 21.40 0.33957842 6.524976e-04 0.659769085 #> 2142 21.41 0.33940867 6.521715e-04 0.659939158 #> 2143 21.42 0.33923901 6.518455e-04 0.660109146 #> 2144 21.43 0.33906943 6.515196e-04 0.660279049 #> 2145 21.44 0.33889994 6.511940e-04 0.660448867 #> 2146 21.45 0.33873053 6.508684e-04 0.660618600 #> 2147 21.46 0.33856121 6.505431e-04 0.660788248 #> 2148 21.47 0.33839197 6.502179e-04 0.660957812 #> 2149 21.48 0.33822282 6.498929e-04 0.661127291 #> 2150 21.49 0.33805375 6.495680e-04 0.661296685 #> 2151 21.50 0.33788476 6.492433e-04 0.661465994 #> 2152 21.51 0.33771586 6.489188e-04 0.661635219 #> 2153 21.52 0.33754705 6.485944e-04 0.661804359 #> 2154 21.53 0.33737832 6.482702e-04 0.661973414 #> 2155 21.54 0.33720967 6.479461e-04 0.662142385 #> 2156 21.55 0.33704111 6.476222e-04 0.662311272 #> 2157 21.56 0.33687263 6.472985e-04 0.662480074 #> 2158 21.57 0.33670423 6.469749e-04 0.662648792 #> 2159 21.58 0.33653592 6.466515e-04 0.662817425 #> 2160 21.59 0.33636770 6.463283e-04 0.662985974 #> 2161 21.60 0.33619956 6.460052e-04 0.663154439 #> 2162 21.61 0.33603150 6.456823e-04 0.663322820 #> 2163 21.62 0.33586352 6.453595e-04 0.663491117 #> 2164 21.63 0.33569563 6.450369e-04 0.663659329 #> 2165 21.64 0.33552783 6.447145e-04 0.663827457 #> 2166 21.65 0.33536011 6.443922e-04 0.663995502 #> 2167 21.66 0.33519247 6.440701e-04 0.664163462 #> 2168 21.67 0.33502491 6.437481e-04 0.664331338 #> 2169 21.68 0.33485744 6.434263e-04 0.664499130 #> 2170 21.69 0.33469006 6.431047e-04 0.664666839 #> 2171 21.70 0.33452275 6.427832e-04 0.664834464 #> 2172 21.71 0.33435553 6.424619e-04 0.665002005 #> 2173 21.72 0.33418840 6.421408e-04 0.665169462 #> 2174 21.73 0.33402135 6.418198e-04 0.665336835 #> 2175 21.74 0.33385438 6.414989e-04 0.665504125 #> 2176 21.75 0.33368749 6.411783e-04 0.665671331 #> 2177 21.76 0.33352069 6.408578e-04 0.665838454 #> 2178 21.77 0.33335397 6.405374e-04 0.666005493 #> 2179 21.78 0.33318733 6.402172e-04 0.666172448 #> 2180 21.79 0.33302078 6.398972e-04 0.666339320 #> 2181 21.80 0.33285431 6.395773e-04 0.666506109 #> 2182 21.81 0.33268793 6.392576e-04 0.666672814 #> 2183 21.82 0.33252163 6.389381e-04 0.666839436 #> 2184 21.83 0.33235541 6.386187e-04 0.667005975 #> 2185 21.84 0.33218927 6.382994e-04 0.667172430 #> 2186 21.85 0.33202322 6.379804e-04 0.667338802 #> 2187 21.86 0.33185725 6.376615e-04 0.667505091 #> 2188 21.87 0.33169136 6.373427e-04 0.667671297 #> 2189 21.88 0.33152556 6.370241e-04 0.667837420 #> 2190 21.89 0.33135983 6.367057e-04 0.668003460 #> 2191 21.90 0.33119420 6.363874e-04 0.668169417 #> 2192 21.91 0.33102864 6.360693e-04 0.668335290 #> 2193 21.92 0.33086317 6.357514e-04 0.668501081 #> 2194 21.93 0.33069778 6.354336e-04 0.668666789 #> 2195 21.94 0.33053247 6.351159e-04 0.668832415 #> 2196 21.95 0.33036724 6.347984e-04 0.668997957 #> 2197 21.96 0.33020210 6.344811e-04 0.669163417 #> 2198 21.97 0.33003704 6.341640e-04 0.669328794 #> 2199 21.98 0.32987207 6.338470e-04 0.669494088 #> 2200 21.99 0.32970717 6.335301e-04 0.669659300 #> 2201 22.00 0.32954236 6.332134e-04 0.669824429 #> 2202 22.01 0.32937763 6.328969e-04 0.669989475 #> 2203 22.02 0.32921298 6.325805e-04 0.670154439 #> 2204 22.03 0.32904841 6.322643e-04 0.670319321 #> 2205 22.04 0.32888393 6.319483e-04 0.670484120 #> 2206 22.05 0.32871953 6.316324e-04 0.670648837 #> 2207 22.06 0.32855521 6.313166e-04 0.670813471 #> 2208 22.07 0.32839098 6.310011e-04 0.670978023 #> 2209 22.08 0.32822682 6.306856e-04 0.671142493 #> 2210 22.09 0.32806275 6.303704e-04 0.671306881 #> 2211 22.10 0.32789876 6.300553e-04 0.671471186 #> 2212 22.11 0.32773485 6.297403e-04 0.671635410 #> 2213 22.12 0.32757102 6.294255e-04 0.671799551 #> 2214 22.13 0.32740728 6.291109e-04 0.671963610 #> 2215 22.14 0.32724362 6.287964e-04 0.672127587 #> 2216 22.15 0.32708004 6.284821e-04 0.672291482 #> 2217 22.16 0.32691654 6.281679e-04 0.672455296 #> 2218 22.17 0.32675312 6.278539e-04 0.672619027 #> 2219 22.18 0.32658978 6.275401e-04 0.672782677 #> 2220 22.19 0.32642653 6.272264e-04 0.672946245 #> 2221 22.20 0.32626336 6.269128e-04 0.673109731 #> 2222 22.21 0.32610027 6.265995e-04 0.673273135 #> 2223 22.22 0.32593726 6.262862e-04 0.673436457 #> 2224 22.23 0.32577433 6.259732e-04 0.673599698 #> 2225 22.24 0.32561148 6.256603e-04 0.673762858 #> 2226 22.25 0.32544872 6.253475e-04 0.673925936 #> 2227 22.26 0.32528603 6.250349e-04 0.674088932 #> 2228 22.27 0.32512343 6.247225e-04 0.674251847 #> 2229 22.28 0.32496091 6.244102e-04 0.674414680 #> 2230 22.29 0.32479847 6.240981e-04 0.674577432 #> 2231 22.30 0.32463611 6.237861e-04 0.674740103 #> 2232 22.31 0.32447383 6.234743e-04 0.674902692 #> 2233 22.32 0.32431164 6.231626e-04 0.675065200 #> 2234 22.33 0.32414952 6.228511e-04 0.675227627 #> 2235 22.34 0.32398749 6.225398e-04 0.675389972 #> 2236 22.35 0.32382553 6.222286e-04 0.675552237 #> 2237 22.36 0.32366366 6.219176e-04 0.675714420 #> 2238 22.37 0.32350187 6.216067e-04 0.675876522 #> 2239 22.38 0.32334016 6.212959e-04 0.676038544 #> 2240 22.39 0.32317853 6.209854e-04 0.676200484 #> 2241 22.40 0.32301698 6.206750e-04 0.676362343 #> 2242 22.41 0.32285551 6.203647e-04 0.676524122 #> 2243 22.42 0.32269413 6.200546e-04 0.676685819 #> 2244 22.43 0.32253282 6.197446e-04 0.676847436 #> 2245 22.44 0.32237159 6.194348e-04 0.677008972 #> 2246 22.45 0.32221045 6.191252e-04 0.677170427 #> 2247 22.46 0.32204938 6.188157e-04 0.677331801 #> 2248 22.47 0.32188840 6.185064e-04 0.677493095 #> 2249 22.48 0.32172749 6.181972e-04 0.677654308 #> 2250 22.49 0.32156667 6.178882e-04 0.677815441 #> 2251 22.50 0.32140593 6.175793e-04 0.677976493 #> 2252 22.51 0.32124527 6.172706e-04 0.678137464 #> 2253 22.52 0.32108468 6.169621e-04 0.678298355 #> 2254 22.53 0.32092418 6.166537e-04 0.678459166 #> 2255 22.54 0.32076376 6.163454e-04 0.678619896 #> 2256 22.55 0.32060342 6.160373e-04 0.678780546 #> 2257 22.56 0.32044315 6.157294e-04 0.678941116 #> 2258 22.57 0.32028297 6.154216e-04 0.679101605 #> 2259 22.58 0.32012287 6.151139e-04 0.679262014 #> 2260 22.59 0.31996285 6.148065e-04 0.679422343 #> 2261 22.60 0.31980291 6.144991e-04 0.679582592 #> 2262 22.61 0.31964305 6.141920e-04 0.679742761 #> 2263 22.62 0.31948327 6.138849e-04 0.679902849 #> 2264 22.63 0.31932356 6.135781e-04 0.680062858 #> 2265 22.64 0.31916394 6.132714e-04 0.680222786 #> 2266 22.65 0.31900440 6.129648e-04 0.680382635 #> 2267 22.66 0.31884494 6.126584e-04 0.680542404 #> 2268 22.67 0.31868556 6.123522e-04 0.680702093 #> 2269 22.68 0.31852625 6.120461e-04 0.680861702 #> 2270 22.69 0.31836703 6.117401e-04 0.681021231 #> 2271 22.70 0.31820789 6.114343e-04 0.681180680 #> 2272 22.71 0.31804882 6.111287e-04 0.681340050 #> 2273 22.72 0.31788984 6.108232e-04 0.681499340 #> 2274 22.73 0.31773093 6.105178e-04 0.681658551 #> 2275 22.74 0.31757211 6.102127e-04 0.681817682 #> 2276 22.75 0.31741336 6.099076e-04 0.681976733 #> 2277 22.76 0.31725469 6.096028e-04 0.682135705 #> 2278 22.77 0.31709610 6.092980e-04 0.682294598 #> 2279 22.78 0.31693760 6.089935e-04 0.682453411 #> 2280 22.79 0.31677917 6.086890e-04 0.682612144 #> 2281 22.80 0.31662082 6.083848e-04 0.682770798 #> 2282 22.81 0.31646255 6.080807e-04 0.682929373 #> 2283 22.82 0.31630435 6.077767e-04 0.683087869 #> 2284 22.83 0.31614624 6.074729e-04 0.683246286 #> 2285 22.84 0.31598821 6.071692e-04 0.683404623 #> 2286 22.85 0.31583025 6.068657e-04 0.683562881 #> 2287 22.86 0.31567238 6.065624e-04 0.683721060 #> 2288 22.87 0.31551458 6.062591e-04 0.683879160 #> 2289 22.88 0.31535686 6.059561e-04 0.684037181 #> 2290 22.89 0.31519922 6.056532e-04 0.684195123 #> 2291 22.90 0.31504166 6.053504e-04 0.684352986 #> 2292 22.91 0.31488418 6.050478e-04 0.684510770 #> 2293 22.92 0.31472678 6.047454e-04 0.684668475 #> 2294 22.93 0.31456946 6.044431e-04 0.684826101 #> 2295 22.94 0.31441221 6.041409e-04 0.684983649 #> 2296 22.95 0.31425504 6.038390e-04 0.685141118 #> 2297 22.96 0.31409796 6.035371e-04 0.685298508 #> 2298 22.97 0.31394095 6.032354e-04 0.685455819 #> 2299 22.98 0.31378401 6.029339e-04 0.685613052 #> 2300 22.99 0.31362716 6.026325e-04 0.685770206 #> 2301 23.00 0.31347039 6.023312e-04 0.685927282 #> 2302 23.01 0.31331369 6.020301e-04 0.686084279 #> 2303 23.02 0.31315707 6.017292e-04 0.686241198 #> 2304 23.03 0.31300053 6.014284e-04 0.686398038 #> 2305 23.04 0.31284407 6.011278e-04 0.686554800 #> 2306 23.05 0.31268769 6.008273e-04 0.686711483 #> 2307 23.06 0.31253138 6.005270e-04 0.686868088 #> 2308 23.07 0.31237516 6.002268e-04 0.687024615 #> 2309 23.08 0.31221901 5.999267e-04 0.687181064 #> 2310 23.09 0.31206294 5.996268e-04 0.687337434 #> 2311 23.10 0.31190695 5.993271e-04 0.687493726 #> 2312 23.11 0.31175103 5.990275e-04 0.687649940 #> 2313 23.12 0.31159520 5.987281e-04 0.687806076 #> 2314 23.13 0.31143944 5.984288e-04 0.687962134 #> 2315 23.14 0.31128376 5.981296e-04 0.688118114 #> 2316 23.15 0.31112815 5.978307e-04 0.688274016 #> 2317 23.16 0.31097263 5.975318e-04 0.688429840 #> 2318 23.17 0.31081718 5.972331e-04 0.688585586 #> 2319 23.18 0.31066181 5.969346e-04 0.688741255 #> 2320 23.19 0.31050652 5.966362e-04 0.688896845 #> 2321 23.20 0.31035130 5.963379e-04 0.689052358 #> 2322 23.21 0.31019617 5.960398e-04 0.689207793 #> 2323 23.22 0.31004111 5.957419e-04 0.689363150 #> 2324 23.23 0.30988613 5.954441e-04 0.689518430 #> 2325 23.24 0.30973122 5.951465e-04 0.689673632 #> 2326 23.25 0.30957640 5.948490e-04 0.689828756 #> 2327 23.26 0.30942165 5.945516e-04 0.689983803 #> 2328 23.27 0.30926697 5.942544e-04 0.690138772 #> 2329 23.28 0.30911238 5.939574e-04 0.690293664 #> 2330 23.29 0.30895786 5.936605e-04 0.690448479 #> 2331 23.30 0.30880342 5.933637e-04 0.690603216 #> 2332 23.31 0.30864906 5.930671e-04 0.690757875 #> 2333 23.32 0.30849477 5.927706e-04 0.690912458 #> 2334 23.33 0.30834056 5.924743e-04 0.691066963 #> 2335 23.34 0.30818643 5.921782e-04 0.691221391 #> 2336 23.35 0.30803238 5.918821e-04 0.691375742 #> 2337 23.36 0.30787840 5.915863e-04 0.691530015 #> 2338 23.37 0.30772450 5.912906e-04 0.691684212 #> 2339 23.38 0.30757067 5.909950e-04 0.691838331 #> 2340 23.39 0.30741693 5.906996e-04 0.691992373 #> 2341 23.40 0.30726326 5.904043e-04 0.692146339 #> 2342 23.41 0.30710966 5.901092e-04 0.692300227 #> 2343 23.42 0.30695615 5.898142e-04 0.692454038 #> 2344 23.43 0.30680271 5.895193e-04 0.692607773 #> 2345 23.44 0.30664934 5.892247e-04 0.692761431 #> 2346 23.45 0.30649606 5.889301e-04 0.692915012 #> 2347 23.46 0.30634285 5.886357e-04 0.693068516 #> 2348 23.47 0.30618972 5.883415e-04 0.693221943 #> 2349 23.48 0.30603666 5.880474e-04 0.693375294 #> 2350 23.49 0.30588368 5.877534e-04 0.693528568 #> 2351 23.50 0.30573078 5.874596e-04 0.693681765 #> 2352 23.51 0.30557795 5.871660e-04 0.693834886 #> 2353 23.52 0.30542520 5.868725e-04 0.693987930 #> 2354 23.53 0.30527252 5.865791e-04 0.694140898 #> 2355 23.54 0.30511992 5.862859e-04 0.694293790 #> 2356 23.55 0.30496740 5.859928e-04 0.694446604 #> 2357 23.56 0.30481496 5.856999e-04 0.694599343 #> 2358 23.57 0.30466259 5.854071e-04 0.694752005 #> 2359 23.58 0.30451029 5.851145e-04 0.694904591 #> 2360 23.59 0.30435808 5.848220e-04 0.695057101 #> 2361 23.60 0.30420594 5.845297e-04 0.695209534 #> 2362 23.61 0.30405387 5.842375e-04 0.695361891 #> 2363 23.62 0.30390188 5.839454e-04 0.695514172 #> 2364 23.63 0.30374997 5.836535e-04 0.695666377 #> 2365 23.64 0.30359813 5.833618e-04 0.695818506 #> 2366 23.65 0.30344637 5.830702e-04 0.695970558 #> 2367 23.66 0.30329469 5.827787e-04 0.696122535 #> 2368 23.67 0.30314308 5.824874e-04 0.696274436 #> 2369 23.68 0.30299154 5.821962e-04 0.696426261 #> 2370 23.69 0.30284009 5.819052e-04 0.696578010 #> 2371 23.70 0.30268870 5.816143e-04 0.696729683 #> 2372 23.71 0.30253740 5.813236e-04 0.696881280 #> 2373 23.72 0.30238617 5.810330e-04 0.697032801 #> 2374 23.73 0.30223501 5.807425e-04 0.697184247 #> 2375 23.74 0.30208393 5.804522e-04 0.697335617 #> 2376 23.75 0.30193293 5.801621e-04 0.697486912 #> 2377 23.76 0.30178200 5.798721e-04 0.697638130 #> 2378 23.77 0.30163114 5.795822e-04 0.697789274 #> 2379 23.78 0.30148037 5.792925e-04 0.697940341 #> 2380 23.79 0.30132966 5.790029e-04 0.698091333 #> 2381 23.80 0.30117904 5.787135e-04 0.698242250 #> 2382 23.81 0.30102848 5.784242e-04 0.698393091 #> 2383 23.82 0.30087801 5.781351e-04 0.698543857 #> 2384 23.83 0.30072761 5.778461e-04 0.698694547 #> 2385 23.84 0.30057728 5.775572e-04 0.698845162 #> 2386 23.85 0.30042703 5.772685e-04 0.698995702 #> 2387 23.86 0.30027685 5.769800e-04 0.699146167 #> 2388 23.87 0.30012675 5.766915e-04 0.699296556 #> 2389 23.88 0.29997673 5.764033e-04 0.699446870 #> 2390 23.89 0.29982678 5.761151e-04 0.699597109 #> 2391 23.90 0.29967690 5.758271e-04 0.699747273 #> 2392 23.91 0.29952710 5.755393e-04 0.699897362 #> 2393 23.92 0.29937737 5.752516e-04 0.700047376 #> 2394 23.93 0.29922772 5.749641e-04 0.700197314 #> 2395 23.94 0.29907815 5.746766e-04 0.700347178 #> 2396 23.95 0.29892864 5.743894e-04 0.700496967 #> 2397 23.96 0.29877922 5.741023e-04 0.700646681 #> 2398 23.97 0.29862986 5.738153e-04 0.700796321 #> 2399 23.98 0.29848059 5.735284e-04 0.700945885 #> 2400 23.99 0.29833138 5.732417e-04 0.701095375 #> 2401 24.00 0.29818226 5.729552e-04 0.701244790 #> 2402 24.01 0.29803320 5.726688e-04 0.701394130 #> 2403 24.02 0.29788422 5.723825e-04 0.701543396 #> 2404 24.03 0.29773532 5.720964e-04 0.701692587 #> 2405 24.04 0.29758649 5.718104e-04 0.701841703 #> 2406 24.05 0.29743773 5.715246e-04 0.701990745 #> 2407 24.06 0.29728905 5.712389e-04 0.702139712 #> 2408 24.07 0.29714044 5.709534e-04 0.702288605 #> 2409 24.08 0.29699191 5.706680e-04 0.702437424 #> 2410 24.09 0.29684345 5.703827e-04 0.702586168 #> 2411 24.10 0.29669506 5.700976e-04 0.702734838 #> 2412 24.11 0.29654675 5.698126e-04 0.702883433 #> 2413 24.12 0.29639852 5.695278e-04 0.703031954 #> 2414 24.13 0.29625036 5.692431e-04 0.703180401 #> 2415 24.14 0.29610227 5.689585e-04 0.703328774 #> 2416 24.15 0.29595425 5.686741e-04 0.703477072 #> 2417 24.16 0.29580631 5.683898e-04 0.703625297 #> 2418 24.17 0.29565845 5.681057e-04 0.703773447 #> 2419 24.18 0.29551065 5.678217e-04 0.703921523 #> 2420 24.19 0.29536294 5.675379e-04 0.704069526 #> 2421 24.20 0.29521529 5.672542e-04 0.704217454 #> 2422 24.21 0.29506772 5.669706e-04 0.704365308 #> 2423 24.22 0.29492022 5.666872e-04 0.704513089 #> 2424 24.23 0.29477280 5.664040e-04 0.704660795 #> 2425 24.24 0.29462545 5.661208e-04 0.704808428 #> 2426 24.25 0.29447818 5.658378e-04 0.704955987 #> 2427 24.26 0.29433097 5.655550e-04 0.705103472 #> 2428 24.27 0.29418384 5.652723e-04 0.705250883 #> 2429 24.28 0.29403679 5.649897e-04 0.705398221 #> 2430 24.29 0.29388981 5.647073e-04 0.705545485 #> 2431 24.30 0.29374290 5.644250e-04 0.705692676 #> 2432 24.31 0.29359606 5.641429e-04 0.705839792 #> 2433 24.32 0.29344930 5.638609e-04 0.705986836 #> 2434 24.33 0.29330262 5.635790e-04 0.706133806 #> 2435 24.34 0.29315600 5.632973e-04 0.706280702 #> 2436 24.35 0.29300946 5.630157e-04 0.706427525 #> 2437 24.36 0.29286299 5.627343e-04 0.706574274 #> 2438 24.37 0.29271660 5.624530e-04 0.706720951 #> 2439 24.38 0.29257027 5.621718e-04 0.706867554 #> 2440 24.39 0.29242403 5.618908e-04 0.707014083 #> 2441 24.40 0.29227785 5.616099e-04 0.707160539 #> 2442 24.41 0.29213175 5.613292e-04 0.707306923 #> 2443 24.42 0.29198572 5.610486e-04 0.707453233 #> 2444 24.43 0.29183976 5.607681e-04 0.707599469 #> 2445 24.44 0.29169388 5.604878e-04 0.707745633 #> 2446 24.45 0.29154807 5.602077e-04 0.707891724 #> 2447 24.46 0.29140233 5.599276e-04 0.708037741 #> 2448 24.47 0.29125667 5.596477e-04 0.708183686 #> 2449 24.48 0.29111107 5.593680e-04 0.708329558 #> 2450 24.49 0.29096556 5.590884e-04 0.708475357 #> 2451 24.50 0.29082011 5.588089e-04 0.708621082 #> 2452 24.51 0.29067473 5.585295e-04 0.708766735 #> 2453 24.52 0.29052943 5.582504e-04 0.708912316 #> 2454 24.53 0.29038421 5.579713e-04 0.709057823 #> 2455 24.54 0.29023905 5.576924e-04 0.709203258 #> 2456 24.55 0.29009397 5.574136e-04 0.709348620 #> 2457 24.56 0.28994896 5.571350e-04 0.709493909 #> 2458 24.57 0.28980402 5.568565e-04 0.709639126 #> 2459 24.58 0.28965915 5.565781e-04 0.709784270 #> 2460 24.59 0.28951436 5.562999e-04 0.709929342 #> 2461 24.60 0.28936964 5.560218e-04 0.710074341 #> 2462 24.61 0.28922499 5.557439e-04 0.710219268 #> 2463 24.62 0.28908041 5.554661e-04 0.710364122 #> 2464 24.63 0.28893591 5.551884e-04 0.710508903 #> 2465 24.64 0.28879148 5.549109e-04 0.710653613 #> 2466 24.65 0.28864712 5.546335e-04 0.710798250 #> 2467 24.66 0.28850283 5.543562e-04 0.710942815 #> 2468 24.67 0.28835861 5.540791e-04 0.711087307 #> 2469 24.68 0.28821447 5.538022e-04 0.711231727 #> 2470 24.69 0.28807040 5.535253e-04 0.711376075 #> 2471 24.70 0.28792640 5.532486e-04 0.711520351 #> 2472 24.71 0.28778247 5.529721e-04 0.711664555 #> 2473 24.72 0.28763862 5.526957e-04 0.711808687 #> 2474 24.73 0.28749483 5.524194e-04 0.711952746 #> 2475 24.74 0.28735112 5.521432e-04 0.712096734 #> 2476 24.75 0.28720748 5.518672e-04 0.712240650 #> 2477 24.76 0.28706392 5.515914e-04 0.712384493 #> 2478 24.77 0.28692042 5.513157e-04 0.712528265 #> 2479 24.78 0.28677699 5.510401e-04 0.712671965 #> 2480 24.79 0.28663364 5.507646e-04 0.712815593 #> 2481 24.80 0.28649036 5.504893e-04 0.712959150 #> 2482 24.81 0.28634715 5.502141e-04 0.713102634 #> 2483 24.82 0.28620401 5.499391e-04 0.713246047 #> 2484 24.83 0.28606095 5.496642e-04 0.713389388 #> 2485 24.84 0.28591795 5.493894e-04 0.713532658 #> 2486 24.85 0.28577503 5.491148e-04 0.713675855 #> 2487 24.86 0.28563218 5.488403e-04 0.713818982 #> 2488 24.87 0.28548940 5.485660e-04 0.713962037 #> 2489 24.88 0.28534669 5.482917e-04 0.714105020 #> 2490 24.89 0.28520405 5.480177e-04 0.714247932 #> 2491 24.90 0.28506148 5.477437e-04 0.714390772 #> 2492 24.91 0.28491899 5.474699e-04 0.714533541 #> 2493 24.92 0.28477657 5.471963e-04 0.714676238 #> 2494 24.93 0.28463421 5.469227e-04 0.714818865 #> 2495 24.94 0.28449193 5.466493e-04 0.714961419 #> 2496 24.95 0.28434972 5.463761e-04 0.715103903 #> 2497 24.96 0.28420758 5.461030e-04 0.715246316 #> 2498 24.97 0.28406551 5.458300e-04 0.715388657 #> 2499 24.98 0.28392352 5.455571e-04 0.715530927 #> 2500 24.99 0.28378159 5.452844e-04 0.715673126 #> 2501 25.00 0.28363973 5.450118e-04 0.715815254 #> 2502 25.01 0.28349795 5.447394e-04 0.715957311 #> 2503 25.02 0.28335624 5.444671e-04 0.716099297 #> 2504 25.03 0.28321459 5.441949e-04 0.716241211 #> 2505 25.04 0.28307302 5.439229e-04 0.716383055 #> 2506 25.05 0.28293152 5.436510e-04 0.716524828 #> 2507 25.06 0.28279009 5.433793e-04 0.716666531 #> 2508 25.07 0.28264873 5.431076e-04 0.716808162 #> 2509 25.08 0.28250744 5.428361e-04 0.716949722 #> 2510 25.09 0.28236622 5.425648e-04 0.717091212 #> 2511 25.10 0.28222508 5.422936e-04 0.717232631 #> 2512 25.11 0.28208400 5.420225e-04 0.717373980 #> 2513 25.12 0.28194299 5.417516e-04 0.717515257 #> 2514 25.13 0.28180205 5.414808e-04 0.717656464 #> 2515 25.14 0.28166119 5.412101e-04 0.717797601 #> 2516 25.15 0.28152039 5.409395e-04 0.717938667 #> 2517 25.16 0.28137967 5.406691e-04 0.718079662 #> 2518 25.17 0.28123901 5.403989e-04 0.718220587 #> 2519 25.18 0.28109843 5.401287e-04 0.718361442 #> 2520 25.19 0.28095792 5.398587e-04 0.718502226 #> 2521 25.20 0.28081747 5.395889e-04 0.718642939 #> 2522 25.21 0.28067710 5.393192e-04 0.718783583 #> 2523 25.22 0.28053679 5.390496e-04 0.718924156 #> 2524 25.23 0.28039656 5.387801e-04 0.719064659 #> 2525 25.24 0.28025640 5.385108e-04 0.719205091 #> 2526 25.25 0.28011630 5.382416e-04 0.719345454 #> 2527 25.26 0.27997628 5.379725e-04 0.719485746 #> 2528 25.27 0.27983633 5.377036e-04 0.719625968 #> 2529 25.28 0.27969645 5.374348e-04 0.719766120 #> 2530 25.29 0.27955663 5.371662e-04 0.719906202 #> 2531 25.30 0.27941689 5.368977e-04 0.720046214 #> 2532 25.31 0.27927722 5.366293e-04 0.720186156 #> 2533 25.32 0.27913761 5.363610e-04 0.720326028 #> 2534 25.33 0.27899808 5.360929e-04 0.720465830 #> 2535 25.34 0.27885861 5.358250e-04 0.720605562 #> 2536 25.35 0.27871922 5.355571e-04 0.720745224 #> 2537 25.36 0.27857989 5.352894e-04 0.720884817 #> 2538 25.37 0.27844064 5.350218e-04 0.721024339 #> 2539 25.38 0.27830145 5.347544e-04 0.721163792 #> 2540 25.39 0.27816234 5.344871e-04 0.721303176 #> 2541 25.40 0.27802329 5.342199e-04 0.721442489 #> 2542 25.41 0.27788431 5.339528e-04 0.721581733 #> 2543 25.42 0.27774541 5.336859e-04 0.721720907 #> 2544 25.43 0.27760657 5.334192e-04 0.721860012 #> 2545 25.44 0.27746780 5.331525e-04 0.721999047 #> 2546 25.45 0.27732910 5.328860e-04 0.722138013 #> 2547 25.46 0.27719047 5.326196e-04 0.722276909 #> 2548 25.47 0.27705191 5.323534e-04 0.722415736 #> 2549 25.48 0.27691342 5.320873e-04 0.722554494 #> 2550 25.49 0.27677500 5.318213e-04 0.722693182 #> 2551 25.50 0.27663664 5.315555e-04 0.722831801 #> 2552 25.51 0.27649836 5.312897e-04 0.722970350 #> 2553 25.52 0.27636015 5.310242e-04 0.723108830 #> 2554 25.53 0.27622200 5.307587e-04 0.723247241 #> 2555 25.54 0.27608392 5.304934e-04 0.723385583 #> 2556 25.55 0.27594592 5.302282e-04 0.723523856 #> 2557 25.56 0.27580798 5.299632e-04 0.723662059 #> 2558 25.57 0.27567011 5.296983e-04 0.723800194 #> 2559 25.58 0.27553231 5.294335e-04 0.723938259 #> 2560 25.59 0.27539458 5.291688e-04 0.724076255 #> 2561 25.60 0.27525691 5.289043e-04 0.724214183 #> 2562 25.61 0.27511932 5.286399e-04 0.724352041 #> 2563 25.62 0.27498179 5.283757e-04 0.724489831 #> 2564 25.63 0.27484434 5.281115e-04 0.724627551 #> 2565 25.64 0.27470695 5.278476e-04 0.724765203 #> 2566 25.65 0.27456963 5.275837e-04 0.724902786 #> 2567 25.66 0.27443238 5.273200e-04 0.725040300 #> 2568 25.67 0.27429520 5.270564e-04 0.725177746 #> 2569 25.68 0.27415808 5.267929e-04 0.725315123 #> 2570 25.69 0.27402104 5.265296e-04 0.725452431 #> 2571 25.70 0.27388406 5.262664e-04 0.725589670 #> 2572 25.71 0.27374716 5.260033e-04 0.725726841 #> 2573 25.72 0.27361032 5.257404e-04 0.725863943 #> 2574 25.73 0.27347355 5.254776e-04 0.726000977 #> 2575 25.74 0.27333684 5.252149e-04 0.726137943 #> 2576 25.75 0.27320021 5.249524e-04 0.726274839 #> 2577 25.76 0.27306364 5.246900e-04 0.726411668 #> 2578 25.77 0.27292714 5.244277e-04 0.726548428 #> 2579 25.78 0.27279072 5.241655e-04 0.726685119 #> 2580 25.79 0.27265435 5.239035e-04 0.726821743 #> 2581 25.80 0.27251806 5.236416e-04 0.726958298 #> 2582 25.81 0.27238184 5.233799e-04 0.727094784 #> 2583 25.82 0.27224568 5.231182e-04 0.727231203 #> 2584 25.83 0.27210959 5.228567e-04 0.727367553 #> 2585 25.84 0.27197357 5.225954e-04 0.727503835 #> 2586 25.85 0.27183762 5.223342e-04 0.727640049 #> 2587 25.86 0.27170173 5.220731e-04 0.727776195 #> 2588 25.87 0.27156591 5.218121e-04 0.727912273 #> 2589 25.88 0.27143017 5.215512e-04 0.728048283 #> 2590 25.89 0.27129448 5.212905e-04 0.728184225 #> 2591 25.90 0.27115887 5.210299e-04 0.728320099 #> 2592 25.91 0.27102333 5.207695e-04 0.728455905 #> 2593 25.92 0.27088785 5.205092e-04 0.728591643 #> 2594 25.93 0.27075244 5.202490e-04 0.728727313 #> 2595 25.94 0.27061710 5.199889e-04 0.728862916 #> 2596 25.95 0.27048182 5.197290e-04 0.728998450 #> 2597 25.96 0.27034661 5.194692e-04 0.729133917 #> 2598 25.97 0.27021147 5.192095e-04 0.729269316 #> 2599 25.98 0.27007640 5.189500e-04 0.729404648 #> 2600 25.99 0.26994140 5.186906e-04 0.729539912 #> 2601 26.00 0.26980646 5.184313e-04 0.729675108 #> 2602 26.01 0.26967159 5.181721e-04 0.729810237 #> 2603 26.02 0.26953679 5.179131e-04 0.729945298 #> 2604 26.03 0.26940205 5.176542e-04 0.730080291 #> 2605 26.04 0.26926739 5.173955e-04 0.730215218 #> 2606 26.05 0.26913279 5.171368e-04 0.730350076 #> 2607 26.06 0.26899825 5.168783e-04 0.730484868 #> 2608 26.07 0.26886379 5.166200e-04 0.730619591 #> 2609 26.08 0.26872939 5.163617e-04 0.730754248 #> 2610 26.09 0.26859506 5.161036e-04 0.730888837 #> 2611 26.10 0.26846080 5.158456e-04 0.731023359 #> 2612 26.11 0.26832660 5.155878e-04 0.731157814 #> 2613 26.12 0.26819247 5.153300e-04 0.731292201 #> 2614 26.13 0.26805841 5.150724e-04 0.731426522 #> 2615 26.14 0.26792441 5.148150e-04 0.731560775 #> 2616 26.15 0.26779048 5.145576e-04 0.731694961 #> 2617 26.16 0.26765662 5.143004e-04 0.731829080 #> 2618 26.17 0.26752282 5.140433e-04 0.731963132 #> 2619 26.18 0.26738910 5.137864e-04 0.732097117 #> 2620 26.19 0.26725544 5.135295e-04 0.732231035 #> 2621 26.20 0.26712184 5.132728e-04 0.732364886 #> 2622 26.21 0.26698831 5.130162e-04 0.732498670 #> 2623 26.22 0.26685485 5.127598e-04 0.732632387 #> 2624 26.23 0.26672146 5.125035e-04 0.732766038 #> 2625 26.24 0.26658813 5.122473e-04 0.732899621 #> 2626 26.25 0.26645487 5.119912e-04 0.733033138 #> 2627 26.26 0.26632168 5.117353e-04 0.733166588 #> 2628 26.27 0.26618855 5.114795e-04 0.733299971 #> 2629 26.28 0.26605549 5.112238e-04 0.733433288 #> 2630 26.29 0.26592249 5.109683e-04 0.733566538 #> 2631 26.30 0.26578957 5.107129e-04 0.733699722 #> 2632 26.31 0.26565670 5.104576e-04 0.733832838 #> 2633 26.32 0.26552391 5.102024e-04 0.733965889 #> 2634 26.33 0.26539118 5.099474e-04 0.734098873 #> 2635 26.34 0.26525852 5.096925e-04 0.734231790 #> 2636 26.35 0.26512592 5.094377e-04 0.734364641 #> 2637 26.36 0.26499339 5.091830e-04 0.734497425 #> 2638 26.37 0.26486093 5.089285e-04 0.734630143 #> 2639 26.38 0.26472853 5.086741e-04 0.734762795 #> 2640 26.39 0.26459620 5.084198e-04 0.734895381 #> 2641 26.40 0.26446393 5.081657e-04 0.735027900 #> 2642 26.41 0.26433174 5.079117e-04 0.735160353 #> 2643 26.42 0.26419960 5.076578e-04 0.735292739 #> 2644 26.43 0.26406754 5.074040e-04 0.735425060 #> 2645 26.44 0.26393554 5.071504e-04 0.735557314 #> 2646 26.45 0.26380360 5.068968e-04 0.735689503 #> 2647 26.46 0.26367173 5.066435e-04 0.735821625 #> 2648 26.47 0.26353993 5.063902e-04 0.735953681 #> 2649 26.48 0.26340819 5.061371e-04 0.736085671 #> 2650 26.49 0.26327652 5.058841e-04 0.736217595 #> 2651 26.50 0.26314492 5.056312e-04 0.736349454 #> 2652 26.51 0.26301338 5.053784e-04 0.736481246 #> 2653 26.52 0.26288190 5.051258e-04 0.736612972 #> 2654 26.53 0.26275049 5.048733e-04 0.736744633 #> 2655 26.54 0.26261915 5.046209e-04 0.736876228 #> 2656 26.55 0.26248787 5.043687e-04 0.737007757 #> 2657 26.56 0.26235666 5.041166e-04 0.737139220 #> 2658 26.57 0.26222552 5.038646e-04 0.737270618 #> 2659 26.58 0.26209444 5.036127e-04 0.737401950 #> 2660 26.59 0.26196342 5.033610e-04 0.737533216 #> 2661 26.60 0.26183247 5.031093e-04 0.737664416 #> 2662 26.61 0.26170159 5.028578e-04 0.737795551 #> 2663 26.62 0.26157077 5.026065e-04 0.737926621 #> 2664 26.63 0.26144002 5.023552e-04 0.738057625 #> 2665 26.64 0.26130933 5.021041e-04 0.738188563 #> 2666 26.65 0.26117871 5.018531e-04 0.738319436 #> 2667 26.66 0.26104815 5.016023e-04 0.738450244 #> 2668 26.67 0.26091766 5.013515e-04 0.738580986 #> 2669 26.68 0.26078724 5.011009e-04 0.738711663 #> 2670 26.69 0.26065688 5.008504e-04 0.738842274 #> 2671 26.70 0.26052658 5.006001e-04 0.738972821 #> 2672 26.71 0.26039635 5.003498e-04 0.739103302 #> 2673 26.72 0.26026618 5.000997e-04 0.739233717 #> 2674 26.73 0.26013608 4.998497e-04 0.739364068 #> 2675 26.74 0.26000605 4.995999e-04 0.739494353 #> 2676 26.75 0.25987608 4.993501e-04 0.739624573 #> 2677 26.76 0.25974617 4.991005e-04 0.739754729 #> 2678 26.77 0.25961633 4.988510e-04 0.739884819 #> 2679 26.78 0.25948655 4.986017e-04 0.740014844 #> 2680 26.79 0.25935684 4.983524e-04 0.740144804 #> 2681 26.80 0.25922720 4.981033e-04 0.740274699 #> 2682 26.81 0.25909762 4.978543e-04 0.740404529 #> 2683 26.82 0.25896810 4.976055e-04 0.740534295 #> 2684 26.83 0.25883865 4.973567e-04 0.740663995 #> 2685 26.84 0.25870926 4.971081e-04 0.740793631 #> 2686 26.85 0.25857994 4.968596e-04 0.740923201 #> 2687 26.86 0.25845068 4.966112e-04 0.741052707 #> 2688 26.87 0.25832149 4.963630e-04 0.741182149 #> 2689 26.88 0.25819236 4.961149e-04 0.741311525 #> 2690 26.89 0.25806330 4.958669e-04 0.741440837 #> 2691 26.90 0.25793430 4.956190e-04 0.741570084 #> 2692 26.91 0.25780536 4.953713e-04 0.741699267 #> 2693 26.92 0.25767649 4.951236e-04 0.741828385 #> 2694 26.93 0.25754769 4.948761e-04 0.741957439 #> 2695 26.94 0.25741894 4.946288e-04 0.742086428 #> 2696 26.95 0.25729027 4.943815e-04 0.742215352 #> 2697 26.96 0.25716165 4.941344e-04 0.742344212 #> 2698 26.97 0.25703310 4.938874e-04 0.742473008 #> 2699 26.98 0.25690462 4.936405e-04 0.742601740 #> 2700 26.99 0.25677620 4.933937e-04 0.742730406 #> 2701 27.00 0.25664784 4.931471e-04 0.742859009 #> 2702 27.01 0.25651955 4.929006e-04 0.742987547 #> 2703 27.02 0.25639132 4.926542e-04 0.743116022 #> 2704 27.03 0.25626316 4.924079e-04 0.743244431 #> 2705 27.04 0.25613506 4.921618e-04 0.743372777 #> 2706 27.05 0.25600703 4.919158e-04 0.743501059 #> 2707 27.06 0.25587905 4.916699e-04 0.743629276 #> 2708 27.07 0.25575115 4.914241e-04 0.743757429 #> 2709 27.08 0.25562330 4.911785e-04 0.743885519 #> 2710 27.09 0.25549552 4.909329e-04 0.744013544 #> 2711 27.10 0.25536781 4.906875e-04 0.744141505 #> 2712 27.11 0.25524016 4.904422e-04 0.744269402 #> 2713 27.12 0.25511257 4.901971e-04 0.744397236 #> 2714 27.13 0.25498504 4.899520e-04 0.744525005 #> 2715 27.14 0.25485758 4.897071e-04 0.744652711 #> 2716 27.15 0.25473019 4.894623e-04 0.744780353 #> 2717 27.16 0.25460285 4.892177e-04 0.744907931 #> 2718 27.17 0.25447558 4.889731e-04 0.745035445 #> 2719 27.18 0.25434838 4.887287e-04 0.745162895 #> 2720 27.19 0.25422123 4.884844e-04 0.745290282 #> 2721 27.20 0.25409415 4.882402e-04 0.745417605 #> 2722 27.21 0.25396714 4.879962e-04 0.745544864 #> 2723 27.22 0.25384019 4.877522e-04 0.745672060 #> 2724 27.23 0.25371330 4.875084e-04 0.745799192 #> 2725 27.24 0.25358647 4.872647e-04 0.745926261 #> 2726 27.25 0.25345971 4.870211e-04 0.746053266 #> 2727 27.26 0.25333301 4.867777e-04 0.746180208 #> 2728 27.27 0.25320638 4.865344e-04 0.746307086 #> 2729 27.28 0.25307981 4.862911e-04 0.746433901 #> 2730 27.29 0.25295330 4.860481e-04 0.746560652 #> 2731 27.30 0.25282686 4.858051e-04 0.746687340 #> 2732 27.31 0.25270047 4.855623e-04 0.746813965 #> 2733 27.32 0.25257415 4.853195e-04 0.746940526 #> 2734 27.33 0.25244790 4.850769e-04 0.747067024 #> 2735 27.34 0.25232171 4.848345e-04 0.747193459 #> 2736 27.35 0.25219558 4.845921e-04 0.747319831 #> 2737 27.36 0.25206951 4.843499e-04 0.747446139 #> 2738 27.37 0.25194351 4.841078e-04 0.747572385 #> 2739 27.38 0.25181757 4.838658e-04 0.747698567 #> 2740 27.39 0.25169169 4.836239e-04 0.747824686 #> 2741 27.40 0.25156588 4.833821e-04 0.747950742 #> 2742 27.41 0.25144012 4.831405e-04 0.748076735 #> 2743 27.42 0.25131444 4.828990e-04 0.748202665 #> 2744 27.43 0.25118881 4.826576e-04 0.748328533 #> 2745 27.44 0.25106325 4.824163e-04 0.748454337 #> 2746 27.45 0.25093775 4.821752e-04 0.748580078 #> 2747 27.46 0.25081231 4.819342e-04 0.748705757 #> 2748 27.47 0.25068693 4.816933e-04 0.748831373 #> 2749 27.48 0.25056162 4.814525e-04 0.748956925 #> 2750 27.49 0.25043637 4.812118e-04 0.749082416 #> 2751 27.50 0.25031119 4.809713e-04 0.749207843 #> 2752 27.51 0.25018606 4.807308e-04 0.749333208 #> 2753 27.52 0.25006100 4.804905e-04 0.749458510 #> 2754 27.53 0.24993600 4.802503e-04 0.749583749 #> 2755 27.54 0.24981106 4.800103e-04 0.749708926 #> 2756 27.55 0.24968619 4.797703e-04 0.749834040 #> 2757 27.56 0.24956138 4.795305e-04 0.749959092 #> 2758 27.57 0.24943663 4.792908e-04 0.750084081 #> 2759 27.58 0.24931194 4.790512e-04 0.750209008 #> 2760 27.59 0.24918732 4.788118e-04 0.750333872 #> 2761 27.60 0.24906275 4.785724e-04 0.750458674 #> 2762 27.61 0.24893825 4.783332e-04 0.750583414 #> 2763 27.62 0.24881382 4.780941e-04 0.750708091 #> 2764 27.63 0.24868944 4.778551e-04 0.750832706 #> 2765 27.64 0.24856513 4.776162e-04 0.750957258 #> 2766 27.65 0.24844087 4.773775e-04 0.751081748 #> 2767 27.66 0.24831668 4.771388e-04 0.751206176 #> 2768 27.67 0.24819256 4.769003e-04 0.751330542 #> 2769 27.68 0.24806849 4.766619e-04 0.751454846 #> 2770 27.69 0.24794449 4.764237e-04 0.751579087 #> 2771 27.70 0.24782055 4.761855e-04 0.751703267 #> 2772 27.71 0.24769667 4.759475e-04 0.751827384 #> 2773 27.72 0.24757285 4.757096e-04 0.751951439 #> 2774 27.73 0.24744910 4.754718e-04 0.752075433 #> 2775 27.74 0.24732540 4.752341e-04 0.752199364 #> 2776 27.75 0.24720177 4.749965e-04 0.752323233 #> 2777 27.76 0.24707820 4.747591e-04 0.752447041 #> 2778 27.77 0.24695469 4.745218e-04 0.752570786 #> 2779 27.78 0.24683125 4.742846e-04 0.752694470 #> 2780 27.79 0.24670786 4.740475e-04 0.752818092 #> 2781 27.80 0.24658454 4.738105e-04 0.752941652 #> 2782 27.81 0.24646128 4.735737e-04 0.753065150 #> 2783 27.82 0.24633808 4.733370e-04 0.753188587 #> 2784 27.83 0.24621494 4.731003e-04 0.753311962 #> 2785 27.84 0.24609186 4.728639e-04 0.753435275 #> 2786 27.85 0.24596885 4.726275e-04 0.753558526 #> 2787 27.86 0.24584589 4.723912e-04 0.753681716 #> 2788 27.87 0.24572300 4.721551e-04 0.753804845 #> 2789 27.88 0.24560017 4.719191e-04 0.753927912 #> 2790 27.89 0.24547740 4.716832e-04 0.754050917 #> 2791 27.90 0.24535469 4.714474e-04 0.754173861 #> 2792 27.91 0.24523205 4.712117e-04 0.754296743 #> 2793 27.92 0.24510946 4.709762e-04 0.754419564 #> 2794 27.93 0.24498694 4.707407e-04 0.754542324 #> 2795 27.94 0.24486447 4.705054e-04 0.754665022 #> 2796 27.95 0.24474207 4.702702e-04 0.754787659 #> 2797 27.96 0.24461973 4.700352e-04 0.754910234 #> 2798 27.97 0.24449745 4.698002e-04 0.755032748 #> 2799 27.98 0.24437523 4.695654e-04 0.755155201 #> 2800 27.99 0.24425308 4.693306e-04 0.755277593 #> 2801 28.00 0.24413098 4.690960e-04 0.755399924 #> 2802 28.01 0.24400895 4.688615e-04 0.755522193 #> 2803 28.02 0.24388697 4.686272e-04 0.755644402 #> 2804 28.03 0.24376506 4.683929e-04 0.755766549 #> 2805 28.04 0.24364321 4.681588e-04 0.755888635 #> 2806 28.05 0.24352142 4.679248e-04 0.756010660 #> 2807 28.06 0.24339968 4.676909e-04 0.756132624 #> 2808 28.07 0.24327802 4.674571e-04 0.756254528 #> 2809 28.08 0.24315641 4.672234e-04 0.756376370 #> 2810 28.09 0.24303486 4.669898e-04 0.756498151 #> 2811 28.10 0.24291337 4.667564e-04 0.756619872 #> 2812 28.11 0.24279195 4.665231e-04 0.756741531 #> 2813 28.12 0.24267058 4.662899e-04 0.756863130 #> 2814 28.13 0.24254927 4.660568e-04 0.756984668 #> 2815 28.14 0.24242803 4.658238e-04 0.757106146 #> 2816 28.15 0.24230685 4.655910e-04 0.757227562 #> 2817 28.16 0.24218572 4.653582e-04 0.757348918 #> 2818 28.17 0.24206466 4.651256e-04 0.757470213 #> 2819 28.18 0.24194366 4.648931e-04 0.757591448 #> 2820 28.19 0.24182272 4.646607e-04 0.757712622 #> 2821 28.20 0.24170184 4.644285e-04 0.757833735 #> 2822 28.21 0.24158102 4.641963e-04 0.757954788 #> 2823 28.22 0.24146026 4.639643e-04 0.758075780 #> 2824 28.23 0.24133956 4.637323e-04 0.758196712 #> 2825 28.24 0.24121892 4.635005e-04 0.758317584 #> 2826 28.25 0.24109834 4.632688e-04 0.758438395 #> 2827 28.26 0.24097782 4.630373e-04 0.758559145 #> 2828 28.27 0.24085736 4.628058e-04 0.758679836 #> 2829 28.28 0.24073696 4.625744e-04 0.758800466 #> 2830 28.29 0.24061662 4.623432e-04 0.758921035 #> 2831 28.30 0.24049634 4.621121e-04 0.759041545 #> 2832 28.31 0.24037613 4.618811e-04 0.759161994 #> 2833 28.32 0.24025597 4.616502e-04 0.759282383 #> 2834 28.33 0.24013587 4.614195e-04 0.759402711 #> 2835 28.34 0.24001583 4.611888e-04 0.759522980 #> 2836 28.35 0.23989585 4.609583e-04 0.759643188 #> 2837 28.36 0.23977594 4.607278e-04 0.759763337 #> 2838 28.37 0.23965608 4.604975e-04 0.759883425 #> 2839 28.38 0.23953628 4.602673e-04 0.760003453 #> 2840 28.39 0.23941654 4.600373e-04 0.760123422 #> 2841 28.40 0.23929686 4.598073e-04 0.760243330 #> 2842 28.41 0.23917724 4.595775e-04 0.760363178 #> 2843 28.42 0.23905769 4.593477e-04 0.760482967 #> 2844 28.43 0.23893819 4.591181e-04 0.760602695 #> 2845 28.44 0.23881875 4.588886e-04 0.760722364 #> 2846 28.45 0.23869937 4.586592e-04 0.760841973 #> 2847 28.46 0.23858005 4.584300e-04 0.760961522 #> 2848 28.47 0.23846079 4.582008e-04 0.761081012 #> 2849 28.48 0.23834159 4.579718e-04 0.761200441 #> 2850 28.49 0.23822245 4.577428e-04 0.761319811 #> 2851 28.50 0.23810336 4.575140e-04 0.761439121 #> 2852 28.51 0.23798434 4.572853e-04 0.761558372 #> 2853 28.52 0.23786538 4.570567e-04 0.761677563 #> 2854 28.53 0.23774648 4.568283e-04 0.761796694 #> 2855 28.54 0.23762763 4.565999e-04 0.761915766 #> 2856 28.55 0.23750885 4.563717e-04 0.762034779 #> 2857 28.56 0.23739012 4.561435e-04 0.762153732 #> 2858 28.57 0.23727146 4.559155e-04 0.762272625 #> 2859 28.58 0.23715285 4.556876e-04 0.762391459 #> 2860 28.59 0.23703431 4.554598e-04 0.762510234 #> 2861 28.60 0.23691582 4.552322e-04 0.762628949 #> 2862 28.61 0.23679739 4.550046e-04 0.762747605 #> 2863 28.62 0.23667902 4.547771e-04 0.762866201 #> 2864 28.63 0.23656071 4.545498e-04 0.762984738 #> 2865 28.64 0.23644246 4.543226e-04 0.763103216 #> 2866 28.65 0.23632427 4.540955e-04 0.763221635 #> 2867 28.66 0.23620614 4.538685e-04 0.763339995 #> 2868 28.67 0.23608806 4.536416e-04 0.763458295 #> 2869 28.68 0.23597005 4.534149e-04 0.763576537 #> 2870 28.69 0.23585209 4.531882e-04 0.763694719 #> 2871 28.70 0.23573420 4.529617e-04 0.763812842 #> 2872 28.71 0.23561636 4.527352e-04 0.763930906 #> 2873 28.72 0.23549858 4.525089e-04 0.764048911 #> 2874 28.73 0.23538086 4.522827e-04 0.764166857 #> 2875 28.74 0.23526320 4.520567e-04 0.764284744 #> 2876 28.75 0.23514560 4.518307e-04 0.764402572 #> 2877 28.76 0.23502805 4.516048e-04 0.764520342 #> 2878 28.77 0.23491057 4.513791e-04 0.764638052 #> 2879 28.78 0.23479314 4.511534e-04 0.764755704 #> 2880 28.79 0.23467578 4.509279e-04 0.764873296 #> 2881 28.80 0.23455847 4.507025e-04 0.764990830 #> 2882 28.81 0.23444122 4.504772e-04 0.765108306 #> 2883 28.82 0.23432403 4.502520e-04 0.765225722 #> 2884 28.83 0.23420689 4.500270e-04 0.765343080 #> 2885 28.84 0.23408982 4.498020e-04 0.765460379 #> 2886 28.85 0.23397280 4.495772e-04 0.765577619 #> 2887 28.86 0.23385585 4.493524e-04 0.765694801 #> 2888 28.87 0.23373895 4.491278e-04 0.765811925 #> 2889 28.88 0.23362211 4.489033e-04 0.765928989 #> 2890 28.89 0.23350533 4.486789e-04 0.766045996 #> 2891 28.90 0.23338860 4.484546e-04 0.766162943 #> 2892 28.91 0.23327194 4.482305e-04 0.766279833 #> 2893 28.92 0.23315533 4.480064e-04 0.766396664 #> 2894 28.93 0.23303878 4.477825e-04 0.766513436 #> 2895 28.94 0.23292229 4.475586e-04 0.766630150 #> 2896 28.95 0.23280586 4.473349e-04 0.766746806 #> 2897 28.96 0.23268949 4.471113e-04 0.766863403 #> 2898 28.97 0.23257317 4.468878e-04 0.766979943 #> 2899 28.98 0.23245691 4.466644e-04 0.767096424 #> 2900 28.99 0.23234071 4.464411e-04 0.767212846 #> 2901 29.00 0.23222457 4.462180e-04 0.767329211 #> 2902 29.01 0.23210849 4.459949e-04 0.767445517 #> 2903 29.02 0.23199246 4.457720e-04 0.767561765 #> 2904 29.03 0.23187650 4.455491e-04 0.767677955 #> 2905 29.04 0.23176059 4.453264e-04 0.767794087 #> 2906 29.05 0.23164473 4.451038e-04 0.767910161 #> 2907 29.06 0.23152894 4.448813e-04 0.768026177 #> 2908 29.07 0.23141321 4.446589e-04 0.768142135 #> 2909 29.08 0.23129753 4.444366e-04 0.768258035 #> 2910 29.09 0.23118191 4.442145e-04 0.768373877 #> 2911 29.10 0.23106635 4.439924e-04 0.768489661 #> 2912 29.11 0.23095084 4.437705e-04 0.768605387 #> 2913 29.12 0.23083540 4.435487e-04 0.768721056 #> 2914 29.13 0.23072001 4.433269e-04 0.768836666 #> 2915 29.14 0.23060468 4.431053e-04 0.768952219 #> 2916 29.15 0.23048940 4.428838e-04 0.769067714 #> 2917 29.16 0.23037419 4.426624e-04 0.769183151 #> 2918 29.17 0.23025903 4.424412e-04 0.769298531 #> 2919 29.18 0.23014393 4.422200e-04 0.769413853 #> 2920 29.19 0.23002888 4.419990e-04 0.769529117 #> 2921 29.20 0.22991390 4.417780e-04 0.769644324 #> 2922 29.21 0.22979897 4.415572e-04 0.769759473 #> 2923 29.22 0.22968410 4.413365e-04 0.769874564 #> 2924 29.23 0.22956929 4.411158e-04 0.769989598 #> 2925 29.24 0.22945453 4.408953e-04 0.770104575 #> 2926 29.25 0.22933983 4.406749e-04 0.770219494 #> 2927 29.26 0.22922519 4.404547e-04 0.770334355 #> 2928 29.27 0.22911061 4.402345e-04 0.770449159 #> 2929 29.28 0.22899608 4.400144e-04 0.770563906 #> 2930 29.29 0.22888161 4.397945e-04 0.770678596 #> 2931 29.30 0.22876720 4.395746e-04 0.770793228 #> 2932 29.31 0.22865284 4.393549e-04 0.770907802 #> 2933 29.32 0.22853854 4.391353e-04 0.771022320 #> 2934 29.33 0.22842430 4.389158e-04 0.771136780 #> 2935 29.34 0.22831012 4.386964e-04 0.771251183 #> 2936 29.35 0.22819599 4.384771e-04 0.771365529 #> 2937 29.36 0.22808192 4.382579e-04 0.771479818 #> 2938 29.37 0.22796791 4.380388e-04 0.771594049 #> 2939 29.38 0.22785396 4.378198e-04 0.771708224 #> 2940 29.39 0.22774006 4.376010e-04 0.771822341 #> 2941 29.40 0.22762622 4.373822e-04 0.771936401 #> 2942 29.41 0.22751243 4.371636e-04 0.772050404 #> 2943 29.42 0.22739870 4.369451e-04 0.772164351 #> 2944 29.43 0.22728503 4.367267e-04 0.772278240 #> 2945 29.44 0.22717142 4.365084e-04 0.772392073 #> 2946 29.45 0.22705786 4.362902e-04 0.772505848 #> 2947 29.46 0.22694436 4.360721e-04 0.772619567 #> 2948 29.47 0.22683092 4.358541e-04 0.772733229 #> 2949 29.48 0.22671753 4.356362e-04 0.772846834 #> 2950 29.49 0.22660420 4.354184e-04 0.772960382 #> 2951 29.50 0.22649093 4.352008e-04 0.773073873 #> 2952 29.51 0.22637771 4.349832e-04 0.773187308 #> 2953 29.52 0.22626455 4.347658e-04 0.773300686 #> 2954 29.53 0.22615144 4.345485e-04 0.773414007 #> 2955 29.54 0.22603840 4.343313e-04 0.773527272 #> 2956 29.55 0.22592541 4.341141e-04 0.773640480 #> 2957 29.56 0.22581247 4.338971e-04 0.773753631 #> 2958 29.57 0.22569959 4.336803e-04 0.773866726 #> 2959 29.58 0.22558677 4.334635e-04 0.773979765 #> 2960 29.59 0.22547401 4.332468e-04 0.774092747 #> 2961 29.60 0.22536130 4.330302e-04 0.774205672 #> 2962 29.61 0.22524865 4.328138e-04 0.774318541 #> 2963 29.62 0.22513605 4.325974e-04 0.774431353 #> 2964 29.63 0.22502351 4.323812e-04 0.774544110 #> 2965 29.64 0.22491103 4.321650e-04 0.774656809 #> 2966 29.65 0.22479860 4.319490e-04 0.774769453 #> 2967 29.66 0.22468623 4.317331e-04 0.774882040 #> 2968 29.67 0.22457391 4.315173e-04 0.774994571 #> 2969 29.68 0.22446165 4.313016e-04 0.775107045 #> 2970 29.69 0.22434945 4.310860e-04 0.775219464 #> 2971 29.70 0.22423730 4.308705e-04 0.775331826 #> 2972 29.71 0.22412521 4.306551e-04 0.775444132 #> 2973 29.72 0.22401318 4.304398e-04 0.775556382 #> 2974 29.73 0.22390120 4.302246e-04 0.775668576 #> 2975 29.74 0.22378928 4.300096e-04 0.775780713 #> 2976 29.75 0.22367741 4.297946e-04 0.775892795 #> 2977 29.76 0.22356560 4.295798e-04 0.776004821 #> 2978 29.77 0.22345384 4.293651e-04 0.776116790 #> 2979 29.78 0.22334215 4.291504e-04 0.776228704 #> 2980 29.79 0.22323050 4.289359e-04 0.776340561 #> 2981 29.80 0.22311892 4.287215e-04 0.776452363 #> 2982 29.81 0.22300738 4.285072e-04 0.776564109 #> 2983 29.82 0.22289591 4.282930e-04 0.776675799 #> 2984 29.83 0.22278449 4.280789e-04 0.776787433 #> 2985 29.84 0.22267312 4.278649e-04 0.776899012 #> 2986 29.85 0.22256181 4.276510e-04 0.777010534 #> 2987 29.86 0.22245056 4.274373e-04 0.777122001 #> 2988 29.87 0.22233936 4.272236e-04 0.777233412 #> 2989 29.88 0.22222822 4.270100e-04 0.777344768 #> 2990 29.89 0.22211714 4.267966e-04 0.777456068 #> 2991 29.90 0.22200611 4.265832e-04 0.777567312 #> 2992 29.91 0.22189513 4.263700e-04 0.777678500 #> 2993 29.92 0.22178421 4.261569e-04 0.777789633 #> 2994 29.93 0.22167335 4.259438e-04 0.777900711 #> 2995 29.94 0.22156254 4.257309e-04 0.778011733 #> 2996 29.95 0.22145178 4.255181e-04 0.778122699 #> 2997 29.96 0.22134108 4.253054e-04 0.778233610 #> 2998 29.97 0.22123044 4.250928e-04 0.778344465 #> 2999 29.98 0.22111985 4.248803e-04 0.778455265 #> 3000 29.99 0.22100932 4.246679e-04 0.778566010 #> 3001 30.00 0.22089884 4.244556e-04 0.778676699 #> 3002 30.01 0.22078842 4.242435e-04 0.778787333 #> 3003 30.02 0.22067806 4.240314e-04 0.778897912 #> 3004 30.03 0.22056775 4.238194e-04 0.779008436 #> 3005 30.04 0.22045749 4.236076e-04 0.779118904 #> 3006 30.05 0.22034729 4.233958e-04 0.779229317 #> 3007 30.06 0.22023714 4.231842e-04 0.779339674 #> 3008 30.07 0.22012705 4.229726e-04 0.779449977 #> 3009 30.08 0.22001701 4.227612e-04 0.779560224 #> 3010 30.09 0.21990703 4.225499e-04 0.779670417 #> 3011 30.10 0.21979711 4.223387e-04 0.779780554 #> 3012 30.11 0.21968724 4.221275e-04 0.779890636 #> 3013 30.12 0.21957742 4.219165e-04 0.780000663 #> 3014 30.13 0.21946766 4.217056e-04 0.780110636 #> 3015 30.14 0.21935795 4.214948e-04 0.780220553 #> 3016 30.15 0.21924830 4.212841e-04 0.780330415 #> 3017 30.16 0.21913870 4.210735e-04 0.780440222 #> 3018 30.17 0.21902916 4.208631e-04 0.780549975 #> 3019 30.18 0.21891967 4.206527e-04 0.780659672 #> 3020 30.19 0.21881024 4.204424e-04 0.780769315 #> 3021 30.20 0.21870086 4.202322e-04 0.780878903 #> 3022 30.21 0.21859154 4.200222e-04 0.780988436 #> 3023 30.22 0.21848227 4.198122e-04 0.781097915 #> 3024 30.23 0.21837306 4.196024e-04 0.781207338 #> 3025 30.24 0.21826390 4.193926e-04 0.781316707 #> 3026 30.25 0.21815480 4.191830e-04 0.781426022 #> 3027 30.26 0.21804575 4.189734e-04 0.781535281 #> 3028 30.27 0.21793675 4.187640e-04 0.781644487 #> 3029 30.28 0.21782781 4.185547e-04 0.781753637 #> 3030 30.29 0.21771892 4.183454e-04 0.781862733 #> 3031 30.30 0.21761009 4.181363e-04 0.781971774 #> 3032 30.31 0.21750131 4.179273e-04 0.782080761 #> 3033 30.32 0.21739259 4.177184e-04 0.782189694 #> 3034 30.33 0.21728392 4.175096e-04 0.782298571 #> 3035 30.34 0.21717530 4.173009e-04 0.782407395 #> 3036 30.35 0.21706674 4.170923e-04 0.782516164 #> 3037 30.36 0.21695824 4.168838e-04 0.782624879 #> 3038 30.37 0.21684979 4.166754e-04 0.782733539 #> 3039 30.38 0.21674139 4.164671e-04 0.782842145 #> 3040 30.39 0.21663304 4.162589e-04 0.782950697 #> 3041 30.40 0.21652475 4.160509e-04 0.783059195 #> 3042 30.41 0.21641652 4.158429e-04 0.783167638 #> 3043 30.42 0.21630834 4.156350e-04 0.783276027 #> 3044 30.43 0.21620021 4.154273e-04 0.783384362 #> 3045 30.44 0.21609214 4.152196e-04 0.783492643 #> 3046 30.45 0.21598412 4.150120e-04 0.783600869 #> 3047 30.46 0.21587615 4.148046e-04 0.783709042 #> 3048 30.47 0.21576824 4.145972e-04 0.783817160 #> 3049 30.48 0.21566039 4.143900e-04 0.783925225 #> 3050 30.49 0.21555258 4.141828e-04 0.784033235 #> 3051 30.50 0.21544483 4.139758e-04 0.784141191 #> 3052 30.51 0.21533714 4.137689e-04 0.784249094 #> 3053 30.52 0.21522950 4.135620e-04 0.784356942 #> 3054 30.53 0.21512191 4.133553e-04 0.784464737 #> 3055 30.54 0.21501437 4.131487e-04 0.784572478 #> 3056 30.55 0.21490689 4.129422e-04 0.784680164 #> 3057 30.56 0.21479947 4.127357e-04 0.784787798 #> 3058 30.57 0.21469209 4.125294e-04 0.784895377 #> 3059 30.58 0.21458477 4.123232e-04 0.785002902 #> 3060 30.59 0.21447751 4.121171e-04 0.785110374 #> 3061 30.60 0.21437030 4.119111e-04 0.785217792 #> 3062 30.61 0.21426314 4.117052e-04 0.785325156 #> 3063 30.62 0.21415603 4.114994e-04 0.785432467 #> 3064 30.63 0.21404898 4.112937e-04 0.785539724 #> 3065 30.64 0.21394198 4.110881e-04 0.785646927 #> 3066 30.65 0.21383504 4.108826e-04 0.785754077 #> 3067 30.66 0.21372815 4.106772e-04 0.785861173 #> 3068 30.67 0.21362131 4.104719e-04 0.785968216 #> 3069 30.68 0.21351453 4.102667e-04 0.786075205 #> 3070 30.69 0.21340780 4.100616e-04 0.786182140 #> 3071 30.70 0.21330112 4.098567e-04 0.786289023 #> 3072 30.71 0.21319450 4.096518e-04 0.786395851 #> 3073 30.72 0.21308793 4.094470e-04 0.786502627 #> 3074 30.73 0.21298141 4.092423e-04 0.786609349 #> 3075 30.74 0.21287494 4.090378e-04 0.786716017 #> 3076 30.75 0.21276853 4.088333e-04 0.786822633 #> 3077 30.76 0.21266218 4.086289e-04 0.786929195 #> 3078 30.77 0.21255587 4.084247e-04 0.787035704 #> 3079 30.78 0.21244962 4.082205e-04 0.787142159 #> 3080 30.79 0.21234342 4.080165e-04 0.787248561 #> 3081 30.80 0.21223728 4.078125e-04 0.787354911 #> 3082 30.81 0.21213118 4.076086e-04 0.787461207 #> 3083 30.82 0.21202515 4.074049e-04 0.787567449 #> 3084 30.83 0.21191916 4.072012e-04 0.787673639 #> 3085 30.84 0.21181323 4.069977e-04 0.787779776 #> 3086 30.85 0.21170735 4.067942e-04 0.787885859 #> 3087 30.86 0.21160152 4.065909e-04 0.787991890 #> 3088 30.87 0.21149574 4.063877e-04 0.788097867 #> 3089 30.88 0.21139002 4.061845e-04 0.788203792 #> 3090 30.89 0.21128435 4.059815e-04 0.788309664 #> 3091 30.90 0.21117874 4.057785e-04 0.788415482 #> 3092 30.91 0.21107318 4.055757e-04 0.788521248 #> 3093 30.92 0.21096767 4.053730e-04 0.788626961 #> 3094 30.93 0.21086221 4.051703e-04 0.788732621 #> 3095 30.94 0.21075680 4.049678e-04 0.788838229 #> 3096 30.95 0.21065145 4.047653e-04 0.788943783 #> 3097 30.96 0.21054615 4.045630e-04 0.789049285 #> 3098 30.97 0.21044091 4.043608e-04 0.789154734 #> 3099 30.98 0.21033571 4.041587e-04 0.789260130 #> 3100 30.99 0.21023057 4.039566e-04 0.789365474 #> 3101 31.00 0.21012548 4.037547e-04 0.789470765 #> 3102 31.01 0.21002044 4.035529e-04 0.789576003 #> 3103 31.02 0.20991546 4.033511e-04 0.789681189 #> 3104 31.03 0.20981053 4.031495e-04 0.789786322 #> 3105 31.04 0.20970565 4.029480e-04 0.789891402 #> 3106 31.05 0.20960082 4.027466e-04 0.789996430 #> 3107 31.06 0.20949605 4.025452e-04 0.790101406 #> 3108 31.07 0.20939133 4.023440e-04 0.790206329 #> 3109 31.08 0.20928666 4.021429e-04 0.790311200 #> 3110 31.09 0.20918204 4.019419e-04 0.790416018 #> 3111 31.10 0.20907748 4.017410e-04 0.790520784 #> 3112 31.11 0.20897296 4.015401e-04 0.790625497 #> 3113 31.12 0.20886850 4.013394e-04 0.790730158 #> 3114 31.13 0.20876409 4.011388e-04 0.790834767 #> 3115 31.14 0.20865974 4.009383e-04 0.790939323 #> 3116 31.15 0.20855543 4.007379e-04 0.791043828 #> 3117 31.16 0.20845118 4.005375e-04 0.791148280 #> 3118 31.17 0.20834698 4.003373e-04 0.791252679 #> 3119 31.18 0.20824284 4.001372e-04 0.791357027 #> 3120 31.19 0.20813874 3.999372e-04 0.791461322 #> 3121 31.20 0.20803470 3.997373e-04 0.791565566 #> 3122 31.21 0.20793071 3.995375e-04 0.791669757 #> 3123 31.22 0.20782677 3.993377e-04 0.791773896 #> 3124 31.23 0.20772288 3.991381e-04 0.791877983 #> 3125 31.24 0.20761904 3.989386e-04 0.791982018 #> 3126 31.25 0.20751526 3.987392e-04 0.792086001 #> 3127 31.26 0.20741153 3.985399e-04 0.792189932 #> 3128 31.27 0.20730785 3.983406e-04 0.792293811 #> 3129 31.28 0.20720422 3.981415e-04 0.792397638 #> 3130 31.29 0.20710064 3.979425e-04 0.792501413 #> 3131 31.30 0.20699712 3.977436e-04 0.792605137 #> 3132 31.31 0.20689365 3.975448e-04 0.792708808 #> 3133 31.32 0.20679023 3.973460e-04 0.792812428 #> 3134 31.33 0.20668686 3.971474e-04 0.792915996 #> 3135 31.34 0.20658354 3.969489e-04 0.793019512 #> 3136 31.35 0.20648027 3.967505e-04 0.793122976 #> 3137 31.36 0.20637706 3.965521e-04 0.793226389 #> 3138 31.37 0.20627390 3.963539e-04 0.793329750 #> 3139 31.38 0.20617078 3.961558e-04 0.793433059 #> 3140 31.39 0.20606773 3.959577e-04 0.793536317 #> 3141 31.40 0.20596472 3.957598e-04 0.793639523 #> 3142 31.41 0.20586176 3.955620e-04 0.793742677 #> 3143 31.42 0.20575886 3.953643e-04 0.793845780 #> 3144 31.43 0.20565600 3.951666e-04 0.793948832 #> 3145 31.44 0.20555320 3.949691e-04 0.794051831 #> 3146 31.45 0.20545045 3.947717e-04 0.794154780 #> 3147 31.46 0.20534775 3.945743e-04 0.794257677 #> 3148 31.47 0.20524510 3.943771e-04 0.794360522 #> 3149 31.48 0.20514250 3.941799e-04 0.794463316 #> 3150 31.49 0.20503996 3.939829e-04 0.794566059 #> 3151 31.50 0.20493746 3.937860e-04 0.794668750 #> 3152 31.51 0.20483502 3.935891e-04 0.794771390 #> 3153 31.52 0.20473263 3.933924e-04 0.794873979 #> 3154 31.53 0.20463029 3.931957e-04 0.794976516 #> 3155 31.54 0.20452800 3.929992e-04 0.795079002 #> 3156 31.55 0.20442576 3.928027e-04 0.795181437 #> 3157 31.56 0.20432357 3.926064e-04 0.795283821 #> 3158 31.57 0.20422144 3.924101e-04 0.795386153 #> 3159 31.58 0.20411935 3.922140e-04 0.795488435 #> 3160 31.59 0.20401732 3.920179e-04 0.795590665 #> 3161 31.60 0.20391533 3.918219e-04 0.795692844 #> 3162 31.61 0.20381340 3.916261e-04 0.795794972 #> 3163 31.62 0.20371152 3.914303e-04 0.795897049 #> 3164 31.63 0.20360969 3.912347e-04 0.795999075 #> 3165 31.64 0.20350791 3.910391e-04 0.796101050 #> 3166 31.65 0.20340618 3.908436e-04 0.796202974 #> 3167 31.66 0.20330450 3.906482e-04 0.796304847 #> 3168 31.67 0.20320288 3.904530e-04 0.796406669 #> 3169 31.68 0.20310130 3.902578e-04 0.796508440 #> 3170 31.69 0.20299978 3.900627e-04 0.796610161 #> 3171 31.70 0.20289830 3.898677e-04 0.796711830 #> 3172 31.71 0.20279688 3.896728e-04 0.796813449 #> 3173 31.72 0.20269551 3.894780e-04 0.796915017 #> 3174 31.73 0.20259418 3.892834e-04 0.797016534 #> 3175 31.74 0.20249291 3.890888e-04 0.797118000 #> 3176 31.75 0.20239169 3.888943e-04 0.797219416 #> 3177 31.76 0.20229052 3.886999e-04 0.797320781 #> 3178 31.77 0.20218940 3.885056e-04 0.797422095 #> 3179 31.78 0.20208833 3.883114e-04 0.797523359 #> 3180 31.79 0.20198731 3.881173e-04 0.797624572 #> 3181 31.80 0.20188634 3.879232e-04 0.797725734 #> 3182 31.81 0.20178542 3.877293e-04 0.797826846 #> 3183 31.82 0.20168456 3.875355e-04 0.797927908 #> 3184 31.83 0.20158374 3.873418e-04 0.798028918 #> 3185 31.84 0.20148297 3.871482e-04 0.798129879 #> 3186 31.85 0.20138226 3.869547e-04 0.798230788 #> 3187 31.86 0.20128159 3.867612e-04 0.798331648 #> 3188 31.87 0.20118098 3.865679e-04 0.798432457 #> 3189 31.88 0.20108041 3.863747e-04 0.798533215 #> 3190 31.89 0.20097989 3.861815e-04 0.798633924 #> 3191 31.90 0.20087943 3.859885e-04 0.798734582 #> 3192 31.91 0.20077902 3.857955e-04 0.798835189 #> 3193 31.92 0.20067865 3.856027e-04 0.798935746 #> 3194 31.93 0.20057834 3.854099e-04 0.799036253 #> 3195 31.94 0.20047807 3.852173e-04 0.799136710 #> 3196 31.95 0.20037786 3.850247e-04 0.799237117 #> 3197 31.96 0.20027769 3.848322e-04 0.799337473 #> 3198 31.97 0.20017758 3.846399e-04 0.799437779 #> 3199 31.98 0.20007752 3.844476e-04 0.799538035 #> 3200 31.99 0.19997750 3.842554e-04 0.799638241 #> 3201 32.00 0.19987754 3.840633e-04 0.799738397 #> 3202 32.01 0.19977763 3.838714e-04 0.799838503 #> 3203 32.02 0.19967776 3.836795e-04 0.799938559 #> 3204 32.03 0.19957795 3.834877e-04 0.800038564 #> 3205 32.04 0.19947818 3.832960e-04 0.800138520 #> 3206 32.05 0.19937847 3.831044e-04 0.800238426 #> 3207 32.06 0.19927881 3.829129e-04 0.800338282 #> 3208 32.07 0.19917919 3.827215e-04 0.800438087 #> 3209 32.08 0.19907963 3.825302e-04 0.800537844 #> 3210 32.09 0.19898011 3.823389e-04 0.800637550 #> 3211 32.10 0.19888065 3.821478e-04 0.800737206 #> 3212 32.11 0.19878123 3.819568e-04 0.800836812 #> 3213 32.12 0.19868186 3.817659e-04 0.800936369 #> 3214 32.13 0.19858255 3.815750e-04 0.801035876 #> 3215 32.14 0.19848328 3.813843e-04 0.801135333 #> 3216 32.15 0.19838407 3.811936e-04 0.801234741 #> 3217 32.16 0.19828490 3.810031e-04 0.801334099 #> 3218 32.17 0.19818578 3.808126e-04 0.801433407 #> 3219 32.18 0.19808671 3.806223e-04 0.801532665 #> 3220 32.19 0.19798769 3.804320e-04 0.801631874 #> 3221 32.20 0.19788872 3.802419e-04 0.801731033 #> 3222 32.21 0.19778981 3.800518e-04 0.801830143 #> 3223 32.22 0.19769093 3.798618e-04 0.801929203 #> 3224 32.23 0.19759211 3.796719e-04 0.802028214 #> 3225 32.24 0.19749334 3.794821e-04 0.802127175 #> 3226 32.25 0.19739462 3.792924e-04 0.802226087 #> 3227 32.26 0.19729595 3.791028e-04 0.802324949 #> 3228 32.27 0.19719732 3.789133e-04 0.802423762 #> 3229 32.28 0.19709875 3.787239e-04 0.802522525 #> 3230 32.29 0.19700023 3.785346e-04 0.802621239 #> 3231 32.30 0.19690175 3.783454e-04 0.802719904 #> 3232 32.31 0.19680332 3.781563e-04 0.802818519 #> 3233 32.32 0.19670495 3.779672e-04 0.802917085 #> 3234 32.33 0.19660662 3.777783e-04 0.803015602 #> 3235 32.34 0.19650834 3.775895e-04 0.803114070 #> 3236 32.35 0.19641011 3.774007e-04 0.803212488 #> 3237 32.36 0.19631193 3.772121e-04 0.803310857 #> 3238 32.37 0.19621380 3.770235e-04 0.803409177 #> 3239 32.38 0.19611572 3.768350e-04 0.803507448 #> 3240 32.39 0.19601768 3.766467e-04 0.803605670 #> 3241 32.40 0.19591970 3.764584e-04 0.803703843 #> 3242 32.41 0.19582176 3.762702e-04 0.803801966 #> 3243 32.42 0.19572388 3.760821e-04 0.803900041 #> 3244 32.43 0.19562604 3.758941e-04 0.803998066 #> 3245 32.44 0.19552825 3.757062e-04 0.804096043 #> 3246 32.45 0.19543051 3.755184e-04 0.804193970 #> 3247 32.46 0.19533282 3.753307e-04 0.804291849 #> 3248 32.47 0.19523518 3.751431e-04 0.804389678 #> 3249 32.48 0.19513759 3.749556e-04 0.804487459 #> 3250 32.49 0.19504004 3.747681e-04 0.804585191 #> 3251 32.50 0.19494255 3.745808e-04 0.804682874 #> 3252 32.51 0.19484510 3.743935e-04 0.804780508 #> 3253 32.52 0.19474770 3.742064e-04 0.804878093 #> 3254 32.53 0.19465035 3.740193e-04 0.804975630 #> 3255 32.54 0.19455305 3.738324e-04 0.805073118 #> 3256 32.55 0.19445580 3.736455e-04 0.805170557 #> 3257 32.56 0.19435859 3.734587e-04 0.805267947 #> 3258 32.57 0.19426144 3.732720e-04 0.805365289 #> 3259 32.58 0.19416433 3.730855e-04 0.805462582 #> 3260 32.59 0.19406727 3.728990e-04 0.805559826 #> 3261 32.60 0.19397027 3.727126e-04 0.805657022 #> 3262 32.61 0.19387330 3.725263e-04 0.805754169 #> 3263 32.62 0.19377639 3.723400e-04 0.805851268 #> 3264 32.63 0.19367953 3.721539e-04 0.805948318 #> 3265 32.64 0.19358271 3.719679e-04 0.806045320 #> 3266 32.65 0.19348595 3.717819e-04 0.806142273 #> 3267 32.66 0.19338923 3.715961e-04 0.806239177 #> 3268 32.67 0.19329256 3.714103e-04 0.806336034 #> 3269 32.68 0.19319593 3.712247e-04 0.806432841 #> 3270 32.69 0.19309936 3.710391e-04 0.806529601 #> 3271 32.70 0.19300283 3.708536e-04 0.806626312 #> 3272 32.71 0.19290636 3.706683e-04 0.806722974 #> 3273 32.72 0.19280993 3.704830e-04 0.806819589 #> 3274 32.73 0.19271355 3.702978e-04 0.806916155 #> 3275 32.74 0.19261721 3.701127e-04 0.807012673 #> 3276 32.75 0.19252093 3.699277e-04 0.807109142 #> 3277 32.76 0.19242469 3.697428e-04 0.807205564 #> 3278 32.77 0.19232851 3.695579e-04 0.807301937 #> 3279 32.78 0.19223237 3.693732e-04 0.807398262 #> 3280 32.79 0.19213627 3.691886e-04 0.807494538 #> 3281 32.80 0.19204023 3.690040e-04 0.807590767 #> 3282 32.81 0.19194423 3.688196e-04 0.807686948 #> 3283 32.82 0.19184828 3.686352e-04 0.807783080 #> 3284 32.83 0.19175238 3.684509e-04 0.807879165 #> 3285 32.84 0.19165653 3.682667e-04 0.807975201 #> 3286 32.85 0.19156073 3.680827e-04 0.808071189 #> 3287 32.86 0.19146497 3.678987e-04 0.808167130 #> 3288 32.87 0.19136926 3.677148e-04 0.808263022 #> 3289 32.88 0.19127360 3.675309e-04 0.808358867 #> 3290 32.89 0.19117799 3.673472e-04 0.808454663 #> 3291 32.90 0.19108242 3.671636e-04 0.808550412 #> 3292 32.91 0.19098691 3.669801e-04 0.808646113 #> 3293 32.92 0.19089144 3.667966e-04 0.808741766 #> 3294 32.93 0.19079602 3.666133e-04 0.808837371 #> 3295 32.94 0.19070064 3.664300e-04 0.808932929 #> 3296 32.95 0.19060531 3.662468e-04 0.809028438 #> 3297 32.96 0.19051004 3.660638e-04 0.809123900 #> 3298 32.97 0.19041480 3.658808e-04 0.809219315 #> 3299 32.98 0.19031962 3.656979e-04 0.809314681 #> 3300 32.99 0.19022449 3.655151e-04 0.809410000 #> 3301 33.00 0.19012940 3.653324e-04 0.809505271 #> 3302 33.01 0.19003436 3.651497e-04 0.809600495 #> 3303 33.02 0.18993936 3.649672e-04 0.809695671 #> 3304 33.03 0.18984442 3.647848e-04 0.809790799 #> 3305 33.04 0.18974952 3.646024e-04 0.809885880 #> 3306 33.05 0.18965467 3.644202e-04 0.809980913 #> 3307 33.06 0.18955986 3.642380e-04 0.810075899 #> 3308 33.07 0.18946511 3.640559e-04 0.810170837 #> 3309 33.08 0.18937040 3.638739e-04 0.810265728 #> 3310 33.09 0.18927574 3.636921e-04 0.810360571 #> 3311 33.10 0.18918112 3.635103e-04 0.810455367 #> 3312 33.11 0.18908656 3.633285e-04 0.810550116 #> 3313 33.12 0.18899204 3.631469e-04 0.810644817 #> 3314 33.13 0.18889756 3.629654e-04 0.810739471 #> 3315 33.14 0.18880314 3.627840e-04 0.810834078 #> 3316 33.15 0.18870876 3.626026e-04 0.810928637 #> 3317 33.16 0.18861443 3.624214e-04 0.811023149 #> 3318 33.17 0.18852015 3.622402e-04 0.811117614 #> 3319 33.18 0.18842591 3.620591e-04 0.811212032 #> 3320 33.19 0.18833172 3.618781e-04 0.811306402 #> 3321 33.20 0.18823758 3.616972e-04 0.811400725 #> 3322 33.21 0.18814348 3.615164e-04 0.811495001 #> 3323 33.22 0.18804943 3.613357e-04 0.811589230 #> 3324 33.23 0.18795543 3.611551e-04 0.811683412 #> 3325 33.24 0.18786148 3.609746e-04 0.811777547 #> 3326 33.25 0.18776757 3.607941e-04 0.811871635 #> 3327 33.26 0.18767371 3.606138e-04 0.811965675 #> 3328 33.27 0.18757990 3.604335e-04 0.812059669 #> 3329 33.28 0.18748613 3.602533e-04 0.812153616 #> 3330 33.29 0.18739241 3.600733e-04 0.812247515 #> 3331 33.30 0.18729874 3.598933e-04 0.812341368 #> 3332 33.31 0.18720511 3.597134e-04 0.812435174 #> 3333 33.32 0.18711153 3.595336e-04 0.812528933 #> 3334 33.33 0.18701800 3.593538e-04 0.812622645 #> 3335 33.34 0.18692452 3.591742e-04 0.812716310 #> 3336 33.35 0.18683108 3.589947e-04 0.812809929 #> 3337 33.36 0.18673768 3.588152e-04 0.812903500 #> 3338 33.37 0.18664434 3.586358e-04 0.812997025 #> 3339 33.38 0.18655104 3.584566e-04 0.813090503 #> 3340 33.39 0.18645779 3.582774e-04 0.813183935 #> 3341 33.40 0.18636458 3.580983e-04 0.813277320 #> 3342 33.41 0.18627142 3.579193e-04 0.813370658 #> 3343 33.42 0.18617831 3.577404e-04 0.813463949 #> 3344 33.43 0.18608524 3.575615e-04 0.813557194 #> 3345 33.44 0.18599223 3.573828e-04 0.813650392 #> 3346 33.45 0.18589925 3.572042e-04 0.813743543 #> 3347 33.46 0.18580633 3.570256e-04 0.813836648 #> 3348 33.47 0.18571345 3.568471e-04 0.813929707 #> 3349 33.48 0.18562061 3.566688e-04 0.814022719 #> 3350 33.49 0.18552783 3.564905e-04 0.814115684 #> 3351 33.50 0.18543508 3.563123e-04 0.814208603 #> 3352 33.51 0.18534239 3.561342e-04 0.814301475 #> 3353 33.52 0.18524974 3.559561e-04 0.814394301 #> 3354 33.53 0.18515714 3.557782e-04 0.814487081 #> 3355 33.54 0.18506459 3.556004e-04 0.814579814 #> 3356 33.55 0.18497208 3.554226e-04 0.814672501 #> 3357 33.56 0.18487961 3.552449e-04 0.814765142 #> 3358 33.57 0.18478720 3.550674e-04 0.814857736 #> 3359 33.58 0.18469483 3.548899e-04 0.814950284 #> 3360 33.59 0.18460250 3.547125e-04 0.815042786 #> 3361 33.60 0.18451022 3.545352e-04 0.815135241 #> 3362 33.61 0.18441799 3.543579e-04 0.815227651 #> 3363 33.62 0.18432581 3.541808e-04 0.815320014 #> 3364 33.63 0.18423367 3.540037e-04 0.815412331 #> 3365 33.64 0.18414157 3.538268e-04 0.815504601 #> 3366 33.65 0.18404952 3.536499e-04 0.815596826 #> 3367 33.66 0.18395752 3.534731e-04 0.815689005 #> 3368 33.67 0.18386557 3.532964e-04 0.815781137 #> 3369 33.68 0.18377366 3.531198e-04 0.815873223 #> 3370 33.69 0.18368179 3.529433e-04 0.815965264 #> 3371 33.70 0.18358997 3.527669e-04 0.816057258 #> 3372 33.71 0.18349820 3.525906e-04 0.816149207 #> 3373 33.72 0.18340648 3.524143e-04 0.816241109 #> 3374 33.73 0.18331480 3.522381e-04 0.816332965 #> 3375 33.74 0.18322316 3.520621e-04 0.816424776 #> 3376 33.75 0.18313157 3.518861e-04 0.816516541 #> 3377 33.76 0.18304003 3.517102e-04 0.816608260 #> 3378 33.77 0.18294853 3.515344e-04 0.816699933 #> 3379 33.78 0.18285708 3.513587e-04 0.816791560 #> 3380 33.79 0.18276568 3.511830e-04 0.816883141 #> 3381 33.80 0.18267432 3.510075e-04 0.816974677 #> 3382 33.81 0.18258300 3.508320e-04 0.817066166 #> 3383 33.82 0.18249173 3.506566e-04 0.817157610 #> 3384 33.83 0.18240051 3.504814e-04 0.817249009 #> 3385 33.84 0.18230933 3.503062e-04 0.817340361 #> 3386 33.85 0.18221820 3.501310e-04 0.817431668 #> 3387 33.86 0.18212711 3.499560e-04 0.817522930 #> 3388 33.87 0.18203607 3.497811e-04 0.817614145 #> 3389 33.88 0.18194508 3.496062e-04 0.817705316 #> 3390 33.89 0.18185413 3.494315e-04 0.817796440 #> 3391 33.90 0.18176322 3.492568e-04 0.817887519 #> 3392 33.91 0.18167237 3.490822e-04 0.817978553 #> 3393 33.92 0.18158155 3.489077e-04 0.818069541 #> 3394 33.93 0.18149078 3.487333e-04 0.818160483 #> 3395 33.94 0.18140006 3.485590e-04 0.818251380 #> 3396 33.95 0.18130938 3.483848e-04 0.818342232 #> 3397 33.96 0.18121875 3.482106e-04 0.818433038 #> 3398 33.97 0.18112816 3.480366e-04 0.818523799 #> 3399 33.98 0.18103762 3.478626e-04 0.818614514 #> 3400 33.99 0.18094713 3.476887e-04 0.818705184 #> 3401 34.00 0.18085668 3.475149e-04 0.818795809 #> 3402 34.01 0.18076627 3.473412e-04 0.818886388 #> 3403 34.02 0.18067591 3.471675e-04 0.818976923 #> 3404 34.03 0.18058559 3.469940e-04 0.819067412 #> 3405 34.04 0.18049532 3.468206e-04 0.819157855 #> 3406 34.05 0.18040510 3.466472e-04 0.819248254 #> 3407 34.06 0.18031492 3.464739e-04 0.819338607 #> 3408 34.07 0.18022478 3.463007e-04 0.819428915 #> 3409 34.08 0.18013469 3.461276e-04 0.819519178 #> 3410 34.09 0.18004465 3.459546e-04 0.819609396 #> 3411 34.10 0.17995465 3.457816e-04 0.819699569 #> 3412 34.11 0.17986469 3.456088e-04 0.819789696 #> 3413 34.12 0.17977478 3.454360e-04 0.819879779 #> 3414 34.13 0.17968492 3.452634e-04 0.819969817 #> 3415 34.14 0.17959510 3.450908e-04 0.820059809 #> 3416 34.15 0.17950532 3.449183e-04 0.820149757 #> 3417 34.16 0.17941559 3.447459e-04 0.820239659 #> 3418 34.17 0.17932591 3.445735e-04 0.820329517 #> 3419 34.18 0.17923627 3.444013e-04 0.820419330 #> 3420 34.19 0.17914667 3.442291e-04 0.820509098 #> 3421 34.20 0.17905712 3.440571e-04 0.820598821 #> 3422 34.21 0.17896762 3.438851e-04 0.820688499 #> 3423 34.22 0.17887815 3.437132e-04 0.820778132 #> 3424 34.23 0.17878874 3.435414e-04 0.820867721 #> 3425 34.24 0.17869937 3.433696e-04 0.820957265 #> 3426 34.25 0.17861004 3.431980e-04 0.821046764 #> 3427 34.26 0.17852076 3.430264e-04 0.821136218 #> 3428 34.27 0.17843152 3.428550e-04 0.821225627 #> 3429 34.28 0.17834232 3.426836e-04 0.821314992 #> 3430 34.29 0.17825318 3.425123e-04 0.821404313 #> 3431 34.30 0.17816407 3.423411e-04 0.821493588 #> 3432 34.31 0.17807501 3.421699e-04 0.821582819 #> 3433 34.32 0.17798600 3.419989e-04 0.821672005 #> 3434 34.33 0.17789703 3.418279e-04 0.821761147 #> 3435 34.34 0.17780810 3.416571e-04 0.821850244 #> 3436 34.35 0.17771922 3.414863e-04 0.821939297 #> 3437 34.36 0.17763038 3.413156e-04 0.822028305 #> 3438 34.37 0.17754159 3.411450e-04 0.822117268 #> 3439 34.38 0.17745284 3.409744e-04 0.822206188 #> 3440 34.39 0.17736413 3.408040e-04 0.822295062 #> 3441 34.40 0.17727547 3.406336e-04 0.822383893 #> 3442 34.41 0.17718686 3.404634e-04 0.822472678 #> 3443 34.42 0.17709829 3.402932e-04 0.822561420 #> 3444 34.43 0.17700976 3.401231e-04 0.822650117 #> 3445 34.44 0.17692128 3.399530e-04 0.822738770 #> 3446 34.45 0.17683284 3.397831e-04 0.822827378 #> 3447 34.46 0.17674444 3.396133e-04 0.822915942 #> 3448 34.47 0.17665609 3.394435e-04 0.823004462 #> 3449 34.48 0.17656779 3.392738e-04 0.823092938 #> 3450 34.49 0.17647953 3.391042e-04 0.823181369 #> 3451 34.50 0.17639131 3.389347e-04 0.823269757 #> 3452 34.51 0.17630314 3.387653e-04 0.823358100 #> 3453 34.52 0.17621501 3.385959e-04 0.823446398 #> 3454 34.53 0.17612692 3.384267e-04 0.823534653 #> 3455 34.54 0.17603888 3.382575e-04 0.823622864 #> 3456 34.55 0.17595088 3.380884e-04 0.823711030 #> 3457 34.56 0.17586293 3.379194e-04 0.823799153 #> 3458 34.57 0.17577502 3.377505e-04 0.823887231 #> 3459 34.58 0.17568715 3.375817e-04 0.823975266 #> 3460 34.59 0.17559933 3.374129e-04 0.824063256 #> 3461 34.60 0.17551155 3.372443e-04 0.824151202 #> 3462 34.61 0.17542382 3.370757e-04 0.824239105 #> 3463 34.62 0.17533613 3.369072e-04 0.824326963 #> 3464 34.63 0.17524848 3.367388e-04 0.824414778 #> 3465 34.64 0.17516088 3.365705e-04 0.824502549 #> 3466 34.65 0.17507332 3.364022e-04 0.824590275 #> 3467 34.66 0.17498581 3.362341e-04 0.824677958 #> 3468 34.67 0.17489834 3.360660e-04 0.824765597 #> 3469 34.68 0.17481091 3.358980e-04 0.824853193 #> 3470 34.69 0.17472353 3.357301e-04 0.824940744 #> 3471 34.70 0.17463619 3.355623e-04 0.825028252 #> 3472 34.71 0.17454889 3.353945e-04 0.825115716 #> 3473 34.72 0.17446164 3.352269e-04 0.825203136 #> 3474 34.73 0.17437443 3.350593e-04 0.825290513 #> 3475 34.74 0.17428726 3.348918e-04 0.825377846 #> 3476 34.75 0.17420014 3.347244e-04 0.825465135 #> 3477 34.76 0.17411306 3.345571e-04 0.825552381 #> 3478 34.77 0.17402603 3.343898e-04 0.825639583 #> 3479 34.78 0.17393904 3.342227e-04 0.825726741 #> 3480 34.79 0.17385209 3.340556e-04 0.825813856 #> 3481 34.80 0.17376518 3.338886e-04 0.825900927 #> 3482 34.81 0.17367832 3.337217e-04 0.825987955 #> 3483 34.82 0.17359151 3.335549e-04 0.826074939 #> 3484 34.83 0.17350473 3.333882e-04 0.826161880 #> 3485 34.84 0.17341800 3.332215e-04 0.826248777 #> 3486 34.85 0.17333131 3.330550e-04 0.826335631 #> 3487 34.86 0.17324467 3.328885e-04 0.826422442 #> 3488 34.87 0.17315807 3.327221e-04 0.826509209 #> 3489 34.88 0.17307151 3.325557e-04 0.826595933 #> 3490 34.89 0.17298500 3.323895e-04 0.826682613 #> 3491 34.90 0.17289853 3.322234e-04 0.826769250 #> 3492 34.91 0.17281210 3.320573e-04 0.826855844 #> 3493 34.92 0.17272571 3.318913e-04 0.826942394 #> 3494 34.93 0.17263937 3.317254e-04 0.827028901 #> 3495 34.94 0.17255308 3.315596e-04 0.827115365 #> 3496 34.95 0.17246682 3.313938e-04 0.827201786 #> 3497 34.96 0.17238061 3.312282e-04 0.827288164 #> 3498 34.97 0.17229444 3.310626e-04 0.827374498 #> 3499 34.98 0.17220831 3.308971e-04 0.827460789 #> 3500 34.99 0.17212223 3.307317e-04 0.827547037 #> 3501 35.00 0.17203619 3.305664e-04 0.827633242 #> 3502 35.01 0.17195020 3.304011e-04 0.827719404 #> 3503 35.02 0.17186424 3.302360e-04 0.827805523 #> 3504 35.03 0.17177833 3.300709e-04 0.827891598 #> 3505 35.04 0.17169246 3.299059e-04 0.827977631 #> 3506 35.05 0.17160664 3.297410e-04 0.828063621 #> 3507 35.06 0.17152086 3.295762e-04 0.828149567 #> 3508 35.07 0.17143512 3.294114e-04 0.828235471 #> 3509 35.08 0.17134942 3.292468e-04 0.828321332 #> 3510 35.09 0.17126377 3.290822e-04 0.828407150 #> 3511 35.10 0.17117816 3.289177e-04 0.828492925 #> 3512 35.11 0.17109259 3.287533e-04 0.828578657 #> 3513 35.12 0.17100706 3.285889e-04 0.828664346 #> 3514 35.13 0.17092158 3.284247e-04 0.828749993 #> 3515 35.14 0.17083614 3.282605e-04 0.828835596 #> 3516 35.15 0.17075075 3.280964e-04 0.828921157 #> 3517 35.16 0.17066539 3.279324e-04 0.829006675 #> 3518 35.17 0.17058008 3.277685e-04 0.829092150 #> 3519 35.18 0.17049481 3.276046e-04 0.829177583 #> 3520 35.19 0.17040959 3.274409e-04 0.829262973 #> 3521 35.20 0.17032440 3.272772e-04 0.829348320 #> 3522 35.21 0.17023926 3.271136e-04 0.829433624 #> 3523 35.22 0.17015416 3.269501e-04 0.829518886 #> 3524 35.23 0.17006911 3.267866e-04 0.829604106 #> 3525 35.24 0.16998409 3.266233e-04 0.829689282 #> 3526 35.25 0.16989912 3.264600e-04 0.829774416 #> 3527 35.26 0.16981420 3.262968e-04 0.829859508 #> 3528 35.27 0.16972931 3.261337e-04 0.829944557 #> 3529 35.28 0.16964447 3.259707e-04 0.830029563 #> 3530 35.29 0.16955966 3.258078e-04 0.830114527 #> 3531 35.30 0.16947491 3.256449e-04 0.830199449 #> 3532 35.31 0.16939019 3.254821e-04 0.830284328 #> 3533 35.32 0.16930552 3.253194e-04 0.830369164 #> 3534 35.33 0.16922088 3.251568e-04 0.830453959 #> 3535 35.34 0.16913630 3.249943e-04 0.830538711 #> 3536 35.35 0.16905175 3.248318e-04 0.830623420 #> 3537 35.36 0.16896724 3.246694e-04 0.830708087 #> 3538 35.37 0.16888278 3.245071e-04 0.830792712 #> 3539 35.38 0.16879836 3.243449e-04 0.830877294 #> 3540 35.39 0.16871398 3.241828e-04 0.830961835 #> 3541 35.40 0.16862965 3.240207e-04 0.831046333 #> 3542 35.41 0.16854535 3.238588e-04 0.831130788 #> 3543 35.42 0.16846110 3.236969e-04 0.831215202 #> 3544 35.43 0.16837689 3.235351e-04 0.831299573 #> 3545 35.44 0.16829272 3.233733e-04 0.831383902 #> 3546 35.45 0.16820860 3.232117e-04 0.831468189 #> 3547 35.46 0.16812452 3.230501e-04 0.831552434 #> 3548 35.47 0.16804047 3.228886e-04 0.831636637 #> 3549 35.48 0.16795648 3.227272e-04 0.831720798 #> 3550 35.49 0.16787252 3.225659e-04 0.831804916 #> 3551 35.50 0.16778860 3.224047e-04 0.831888993 #> 3552 35.51 0.16770473 3.222435e-04 0.831973027 #> 3553 35.52 0.16762090 3.220824e-04 0.832057020 #> 3554 35.53 0.16753711 3.219214e-04 0.832140970 #> 3555 35.54 0.16745336 3.217605e-04 0.832224879 #> 3556 35.55 0.16736966 3.215997e-04 0.832308745 #> 3557 35.56 0.16728599 3.214389e-04 0.832392570 #> 3558 35.57 0.16720237 3.212782e-04 0.832476353 #> 3559 35.58 0.16711879 3.211176e-04 0.832560094 #> 3560 35.59 0.16703525 3.209571e-04 0.832643793 #> 3561 35.60 0.16695175 3.207967e-04 0.832727450 #> 3562 35.61 0.16686830 3.206363e-04 0.832811065 #> 3563 35.62 0.16678489 3.204760e-04 0.832894639 #> 3564 35.63 0.16670151 3.203158e-04 0.832978171 #> 3565 35.64 0.16661818 3.201557e-04 0.833061661 #> 3566 35.65 0.16653490 3.199957e-04 0.833145109 #> 3567 35.66 0.16645165 3.198357e-04 0.833228515 #> 3568 35.67 0.16636844 3.196758e-04 0.833311880 #> 3569 35.68 0.16628528 3.195160e-04 0.833395204 #> 3570 35.69 0.16620216 3.193563e-04 0.833478485 #> 3571 35.70 0.16611908 3.191967e-04 0.833561725 #> 3572 35.71 0.16603604 3.190371e-04 0.833644923 #> 3573 35.72 0.16595304 3.188777e-04 0.833728080 #> 3574 35.73 0.16587009 3.187183e-04 0.833811195 #> 3575 35.74 0.16578717 3.185589e-04 0.833894269 #> 3576 35.75 0.16570430 3.183997e-04 0.833977301 #> 3577 35.76 0.16562147 3.182405e-04 0.834060292 #> 3578 35.77 0.16553868 3.180815e-04 0.834143241 #> 3579 35.78 0.16545593 3.179225e-04 0.834226149 #> 3580 35.79 0.16537322 3.177635e-04 0.834309015 #> 3581 35.80 0.16529056 3.176047e-04 0.834391840 #> 3582 35.81 0.16520793 3.174459e-04 0.834474623 #> 3583 35.82 0.16512535 3.172872e-04 0.834557365 #> 3584 35.83 0.16504281 3.171286e-04 0.834640066 #> 3585 35.84 0.16496031 3.169701e-04 0.834722725 #> 3586 35.85 0.16487785 3.168117e-04 0.834805343 #> 3587 35.86 0.16479543 3.166533e-04 0.834887920 #> 3588 35.87 0.16471305 3.164950e-04 0.834970455 #> 3589 35.88 0.16463071 3.163368e-04 0.835052949 #> 3590 35.89 0.16454842 3.161787e-04 0.835135402 #> 3591 35.90 0.16446617 3.160206e-04 0.835217814 #> 3592 35.91 0.16438395 3.158627e-04 0.835300184 #> 3593 35.92 0.16430178 3.157048e-04 0.835382514 #> 3594 35.93 0.16421965 3.155470e-04 0.835464802 #> 3595 35.94 0.16413756 3.153892e-04 0.835547049 #> 3596 35.95 0.16405551 3.152316e-04 0.835629255 #> 3597 35.96 0.16397351 3.150740e-04 0.835711420 #> 3598 35.97 0.16389154 3.149165e-04 0.835793543 #> 3599 35.98 0.16380961 3.147591e-04 0.835875626 #> 3600 35.99 0.16372773 3.146017e-04 0.835957668 #> 3601 36.00 0.16364589 3.144445e-04 0.836039668 #> 3602 36.01 0.16356408 3.142873e-04 0.836121628 #> 3603 36.02 0.16348232 3.141302e-04 0.836203547 #> 3604 36.03 0.16340060 3.139732e-04 0.836285424 #> 3605 36.04 0.16331892 3.138162e-04 0.836367261 #> 3606 36.05 0.16323728 3.136593e-04 0.836449057 #> 3607 36.06 0.16315569 3.135026e-04 0.836530812 #> 3608 36.07 0.16307413 3.133458e-04 0.836612526 #> 3609 36.08 0.16299261 3.131892e-04 0.836694200 #> 3610 36.09 0.16291114 3.130327e-04 0.836775832 #> 3611 36.10 0.16282970 3.128762e-04 0.836857424 #> 3612 36.11 0.16274831 3.127198e-04 0.836938975 #> 3613 36.12 0.16266695 3.125635e-04 0.837020485 #> 3614 36.13 0.16258564 3.124072e-04 0.837101954 #> 3615 36.14 0.16250437 3.122510e-04 0.837183383 #> 3616 36.15 0.16242313 3.120950e-04 0.837264771 #> 3617 36.16 0.16234194 3.119390e-04 0.837346118 #> 3618 36.17 0.16226079 3.117830e-04 0.837427425 #> 3619 36.18 0.16217968 3.116272e-04 0.837508691 #> 3620 36.19 0.16209861 3.114714e-04 0.837589916 #> 3621 36.20 0.16201758 3.113157e-04 0.837671101 #> 3622 36.21 0.16193659 3.111601e-04 0.837752245 #> 3623 36.22 0.16185565 3.110045e-04 0.837833349 #> 3624 36.23 0.16177474 3.108491e-04 0.837914412 #> 3625 36.24 0.16169387 3.106937e-04 0.837995434 #> 3626 36.25 0.16161305 3.105384e-04 0.838076416 #> 3627 36.26 0.16153226 3.103831e-04 0.838157358 #> 3628 36.27 0.16145151 3.102280e-04 0.838238259 #> 3629 36.28 0.16137081 3.100729e-04 0.838319120 #> 3630 36.29 0.16129014 3.099179e-04 0.838399940 #> 3631 36.30 0.16120952 3.097630e-04 0.838480720 #> 3632 36.31 0.16112893 3.096082e-04 0.838561459 #> 3633 36.32 0.16104839 3.094534e-04 0.838642158 #> 3634 36.33 0.16096788 3.092987e-04 0.838722817 #> 3635 36.34 0.16088742 3.091441e-04 0.838803436 #> 3636 36.35 0.16080700 3.089896e-04 0.838884014 #> 3637 36.36 0.16072661 3.088351e-04 0.838964552 #> 3638 36.37 0.16064627 3.086807e-04 0.839045049 #> 3639 36.38 0.16056597 3.085264e-04 0.839125506 #> 3640 36.39 0.16048570 3.083722e-04 0.839205924 #> 3641 36.40 0.16040548 3.082181e-04 0.839286301 #> 3642 36.41 0.16032530 3.080640e-04 0.839366637 #> 3643 36.42 0.16024516 3.079100e-04 0.839446934 #> 3644 36.43 0.16016505 3.077561e-04 0.839527190 #> 3645 36.44 0.16008499 3.076022e-04 0.839607407 #> 3646 36.45 0.16000497 3.074485e-04 0.839687583 #> 3647 36.46 0.15992499 3.072948e-04 0.839767719 #> 3648 36.47 0.15984504 3.071412e-04 0.839847815 #> 3649 36.48 0.15976514 3.069876e-04 0.839927871 #> 3650 36.49 0.15968528 3.068342e-04 0.840007887 #> 3651 36.50 0.15960546 3.066808e-04 0.840087864 #> 3652 36.51 0.15952567 3.065275e-04 0.840167800 #> 3653 36.52 0.15944593 3.063743e-04 0.840247696 #> 3654 36.53 0.15936623 3.062211e-04 0.840327552 #> 3655 36.54 0.15928656 3.060681e-04 0.840407368 #> 3656 36.55 0.15920694 3.059151e-04 0.840487145 #> 3657 36.56 0.15912736 3.057621e-04 0.840566881 #> 3658 36.57 0.15904781 3.056093e-04 0.840646578 #> 3659 36.58 0.15896831 3.054565e-04 0.840726234 #> 3660 36.59 0.15888884 3.053038e-04 0.840805851 #> 3661 36.60 0.15880942 3.051512e-04 0.840885429 #> 3662 36.61 0.15873004 3.049987e-04 0.840964966 #> 3663 36.62 0.15865069 3.048462e-04 0.841044464 #> 3664 36.63 0.15857138 3.046938e-04 0.841123922 #> 3665 36.64 0.15849212 3.045415e-04 0.841203340 #> 3666 36.65 0.15841289 3.043893e-04 0.841282718 #> 3667 36.66 0.15833371 3.042372e-04 0.841362057 #> 3668 36.67 0.15825456 3.040851e-04 0.841441356 #> 3669 36.68 0.15817545 3.039331e-04 0.841520616 #> 3670 36.69 0.15809638 3.037811e-04 0.841599836 #> 3671 36.70 0.15801735 3.036293e-04 0.841679016 #> 3672 36.71 0.15793837 3.034775e-04 0.841758157 #> 3673 36.72 0.15785942 3.033258e-04 0.841837258 #> 3674 36.73 0.15778051 3.031742e-04 0.841916319 #> 3675 36.74 0.15770164 3.030226e-04 0.841995341 #> 3676 36.75 0.15762280 3.028712e-04 0.842074324 #> 3677 36.76 0.15754401 3.027198e-04 0.842153267 #> 3678 36.77 0.15746526 3.025684e-04 0.842232171 #> 3679 36.78 0.15738655 3.024172e-04 0.842311035 #> 3680 36.79 0.15730787 3.022660e-04 0.842389860 #> 3681 36.80 0.15722924 3.021149e-04 0.842468645 #> 3682 36.81 0.15715064 3.019639e-04 0.842547391 #> 3683 36.82 0.15707209 3.018130e-04 0.842626098 #> 3684 36.83 0.15699357 3.016621e-04 0.842704765 #> 3685 36.84 0.15691510 3.015113e-04 0.842783393 #> 3686 36.85 0.15683666 3.013606e-04 0.842861982 #> 3687 36.86 0.15675826 3.012099e-04 0.842940531 #> 3688 36.87 0.15667990 3.010594e-04 0.843019041 #> 3689 36.88 0.15660158 3.009089e-04 0.843097512 #> 3690 36.89 0.15652330 3.007585e-04 0.843175944 #> 3691 36.90 0.15644506 3.006081e-04 0.843254336 #> 3692 36.91 0.15636685 3.004579e-04 0.843332689 #> 3693 36.92 0.15628869 3.003077e-04 0.843411003 #> 3694 36.93 0.15621056 3.001575e-04 0.843489278 #> 3695 36.94 0.15613248 3.000075e-04 0.843567514 #> 3696 36.95 0.15605443 2.998575e-04 0.843645711 #> 3697 36.96 0.15597642 2.997076e-04 0.843723868 #> 3698 36.97 0.15589846 2.995578e-04 0.843801987 #> 3699 36.98 0.15582053 2.994081e-04 0.843880066 #> 3700 36.99 0.15574263 2.992584e-04 0.843958107 #> 3701 37.00 0.15566478 2.991088e-04 0.844036108 #> 3702 37.01 0.15558697 2.989593e-04 0.844114071 #> 3703 37.02 0.15550920 2.988099e-04 0.844191994 #> 3704 37.03 0.15543146 2.986605e-04 0.844269879 #> 3705 37.04 0.15535376 2.985112e-04 0.844347724 #> 3706 37.05 0.15527611 2.983620e-04 0.844425531 #> 3707 37.06 0.15519849 2.982129e-04 0.844503299 #> 3708 37.07 0.15512091 2.980638e-04 0.844581028 #> 3709 37.08 0.15504337 2.979148e-04 0.844658718 #> 3710 37.09 0.15496586 2.977659e-04 0.844736369 #> 3711 37.10 0.15488840 2.976170e-04 0.844813982 #> 3712 37.11 0.15481098 2.974683e-04 0.844891555 #> 3713 37.12 0.15473359 2.973196e-04 0.844969090 #> 3714 37.13 0.15465624 2.971709e-04 0.845046586 #> 3715 37.14 0.15457893 2.970224e-04 0.845124043 #> 3716 37.15 0.15450166 2.968739e-04 0.845201462 #> 3717 37.16 0.15442443 2.967255e-04 0.845278842 #> 3718 37.17 0.15434724 2.965772e-04 0.845356183 #> 3719 37.18 0.15427009 2.964289e-04 0.845433486 #> 3720 37.19 0.15419297 2.962808e-04 0.845510750 #> 3721 37.20 0.15411589 2.961327e-04 0.845587975 #> 3722 37.21 0.15403885 2.959846e-04 0.845665162 #> 3723 37.22 0.15396185 2.958367e-04 0.845742310 #> 3724 37.23 0.15388489 2.956888e-04 0.845819420 #> 3725 37.24 0.15380797 2.955410e-04 0.845896491 #> 3726 37.25 0.15373108 2.953932e-04 0.845973523 #> 3727 37.26 0.15365424 2.952456e-04 0.846050517 #> 3728 37.27 0.15357743 2.950980e-04 0.846127473 #> 3729 37.28 0.15350066 2.949505e-04 0.846204390 #> 3730 37.29 0.15342393 2.948030e-04 0.846281268 #> 3731 37.30 0.15334724 2.946557e-04 0.846358108 #> 3732 37.31 0.15327058 2.945084e-04 0.846434910 #> 3733 37.32 0.15319397 2.943612e-04 0.846511673 #> 3734 37.33 0.15311739 2.942140e-04 0.846588398 #> 3735 37.34 0.15304085 2.940670e-04 0.846665085 #> 3736 37.35 0.15296435 2.939200e-04 0.846741733 #> 3737 37.36 0.15288788 2.937730e-04 0.846818343 #> 3738 37.37 0.15281146 2.936262e-04 0.846894915 #> 3739 37.38 0.15273507 2.934794e-04 0.846971448 #> 3740 37.39 0.15265872 2.933327e-04 0.847047944 #> 3741 37.40 0.15258241 2.931861e-04 0.847124400 #> 3742 37.41 0.15250614 2.930395e-04 0.847200819 #> 3743 37.42 0.15242991 2.928930e-04 0.847277200 #> 3744 37.43 0.15235371 2.927466e-04 0.847353542 #> 3745 37.44 0.15227755 2.926003e-04 0.847429846 #> 3746 37.45 0.15220143 2.924540e-04 0.847506112 #> 3747 37.46 0.15212535 2.923078e-04 0.847582340 #> 3748 37.47 0.15204931 2.921617e-04 0.847658530 #> 3749 37.48 0.15197330 2.920157e-04 0.847734682 #> 3750 37.49 0.15189734 2.918697e-04 0.847810795 #> 3751 37.50 0.15182141 2.917238e-04 0.847886871 #> 3752 37.51 0.15174551 2.915780e-04 0.847962908 #> 3753 37.52 0.15166966 2.914322e-04 0.848038908 #> 3754 37.53 0.15159384 2.912866e-04 0.848114869 #> 3755 37.54 0.15151807 2.911409e-04 0.848190793 #> 3756 37.55 0.15144233 2.909954e-04 0.848266679 #> 3757 37.56 0.15136662 2.908499e-04 0.848342526 #> 3758 37.57 0.15129096 2.907046e-04 0.848418336 #> 3759 37.58 0.15121533 2.905592e-04 0.848494108 #> 3760 37.59 0.15113974 2.904140e-04 0.848569842 #> 3761 37.60 0.15106419 2.902688e-04 0.848645538 #> 3762 37.61 0.15098868 2.901237e-04 0.848721196 #> 3763 37.62 0.15091320 2.899787e-04 0.848796817 #> 3764 37.63 0.15083777 2.898338e-04 0.848872400 #> 3765 37.64 0.15076237 2.896889e-04 0.848947945 #> 3766 37.65 0.15068700 2.895441e-04 0.849023452 #> 3767 37.66 0.15061168 2.893993e-04 0.849098921 #> 3768 37.67 0.15053639 2.892547e-04 0.849174353 #> 3769 37.68 0.15046114 2.891101e-04 0.849249747 #> 3770 37.69 0.15038593 2.889656e-04 0.849325103 #> 3771 37.70 0.15031076 2.888211e-04 0.849400422 #> 3772 37.71 0.15023562 2.886767e-04 0.849475703 #> 3773 37.72 0.15016052 2.885324e-04 0.849550946 #> 3774 37.73 0.15008546 2.883882e-04 0.849626152 #> 3775 37.74 0.15001044 2.882440e-04 0.849701320 #> 3776 37.75 0.14993545 2.881000e-04 0.849776450 #> 3777 37.76 0.14986050 2.879559e-04 0.849851543 #> 3778 37.77 0.14978559 2.878120e-04 0.849926599 #> 3779 37.78 0.14971071 2.876681e-04 0.850001617 #> 3780 37.79 0.14963588 2.875243e-04 0.850076597 #> 3781 37.80 0.14956108 2.873806e-04 0.850151540 #> 3782 37.81 0.14948632 2.872370e-04 0.850226446 #> 3783 37.82 0.14941159 2.870934e-04 0.850301314 #> 3784 37.83 0.14933691 2.869499e-04 0.850376145 #> 3785 37.84 0.14926226 2.868064e-04 0.850450938 #> 3786 37.85 0.14918764 2.866631e-04 0.850525694 #> 3787 37.86 0.14911307 2.865198e-04 0.850600412 #> 3788 37.87 0.14903853 2.863765e-04 0.850675093 #> 3789 37.88 0.14896403 2.862334e-04 0.850749737 #> 3790 37.89 0.14888957 2.860903e-04 0.850824343 #> 3791 37.90 0.14881514 2.859473e-04 0.850898913 #> 3792 37.91 0.14874075 2.858044e-04 0.850973445 #> 3793 37.92 0.14866640 2.856615e-04 0.851047939 #> 3794 37.93 0.14859208 2.855187e-04 0.851122397 #> 3795 37.94 0.14851781 2.853760e-04 0.851196817 #> 3796 37.95 0.14844357 2.852333e-04 0.851271200 #> 3797 37.96 0.14836936 2.850907e-04 0.851345546 #> 3798 37.97 0.14829520 2.849482e-04 0.851419854 #> 3799 37.98 0.14822107 2.848058e-04 0.851494126 #> 3800 37.99 0.14814698 2.846634e-04 0.851568360 #> 3801 38.00 0.14807292 2.845211e-04 0.851642557 #> 3802 38.01 0.14799890 2.843789e-04 0.851716718 #> 3803 38.02 0.14792492 2.842367e-04 0.851790841 #> 3804 38.03 0.14785098 2.840947e-04 0.851864927 #> 3805 38.04 0.14777707 2.839526e-04 0.851938976 #> 3806 38.05 0.14770320 2.838107e-04 0.852012988 #> 3807 38.06 0.14762937 2.836688e-04 0.852086963 #> 3808 38.07 0.14755557 2.835270e-04 0.852160901 #> 3809 38.08 0.14748181 2.833853e-04 0.852234802 #> 3810 38.09 0.14740809 2.832437e-04 0.852308666 #> 3811 38.10 0.14733440 2.831021e-04 0.852382493 #> 3812 38.11 0.14726076 2.829606e-04 0.852456284 #> 3813 38.12 0.14718714 2.828191e-04 0.852530037 #> 3814 38.13 0.14711357 2.826777e-04 0.852603754 #> 3815 38.14 0.14704003 2.825364e-04 0.852677433 #> 3816 38.15 0.14696653 2.823952e-04 0.852751076 #> 3817 38.16 0.14689306 2.822540e-04 0.852824682 #> 3818 38.17 0.14681964 2.821129e-04 0.852898252 #> 3819 38.18 0.14674624 2.819719e-04 0.852971784 #> 3820 38.19 0.14667289 2.818310e-04 0.853045280 #> 3821 38.20 0.14659957 2.816901e-04 0.853118739 #> 3822 38.21 0.14652629 2.815493e-04 0.853192161 #> 3823 38.22 0.14645304 2.814085e-04 0.853265547 #> 3824 38.23 0.14637984 2.812679e-04 0.853338895 #> 3825 38.24 0.14630667 2.811273e-04 0.853412208 #> 3826 38.25 0.14623353 2.809867e-04 0.853485483 #> 3827 38.26 0.14616043 2.808463e-04 0.853558722 #> 3828 38.27 0.14608737 2.807059e-04 0.853631925 #> 3829 38.28 0.14601434 2.805656e-04 0.853705090 #> 3830 38.29 0.14594136 2.804253e-04 0.853778219 #> 3831 38.30 0.14586840 2.802852e-04 0.853851312 #> 3832 38.31 0.14579549 2.801450e-04 0.853924368 #> 3833 38.32 0.14572261 2.800050e-04 0.853997388 #> 3834 38.33 0.14564976 2.798650e-04 0.854070371 #> 3835 38.34 0.14557696 2.797251e-04 0.854143317 #> 3836 38.35 0.14550419 2.795853e-04 0.854216228 #> 3837 38.36 0.14543145 2.794456e-04 0.854289101 #> 3838 38.37 0.14535876 2.793059e-04 0.854361938 #> 3839 38.38 0.14528609 2.791663e-04 0.854434739 #> 3840 38.39 0.14521347 2.790267e-04 0.854507504 #> 3841 38.40 0.14514088 2.788872e-04 0.854580232 #> 3842 38.41 0.14506833 2.787478e-04 0.854652923 #> 3843 38.42 0.14499581 2.786085e-04 0.854725579 #> 3844 38.43 0.14492333 2.784692e-04 0.854798198 #> 3845 38.44 0.14485089 2.783300e-04 0.854870781 #> 3846 38.45 0.14477848 2.781909e-04 0.854943327 #> 3847 38.46 0.14470611 2.780518e-04 0.855015837 #> 3848 38.47 0.14463378 2.779128e-04 0.855088311 #> 3849 38.48 0.14456148 2.777739e-04 0.855160749 #> 3850 38.49 0.14448921 2.776351e-04 0.855233151 #> 3851 38.50 0.14441699 2.774963e-04 0.855305516 #> 3852 38.51 0.14434480 2.773576e-04 0.855377845 #> 3853 38.52 0.14427264 2.772189e-04 0.855450138 #> 3854 38.53 0.14420052 2.770803e-04 0.855522395 #> 3855 38.54 0.14412844 2.769418e-04 0.855594616 #> 3856 38.55 0.14405640 2.768034e-04 0.855666800 #> 3857 38.56 0.14398439 2.766650e-04 0.855738949 #> 3858 38.57 0.14391241 2.765267e-04 0.855811061 #> 3859 38.58 0.14384047 2.763885e-04 0.855883138 #> 3860 38.59 0.14376857 2.762503e-04 0.855955178 #> 3861 38.60 0.14369671 2.761123e-04 0.856027183 #> 3862 38.61 0.14362487 2.759742e-04 0.856099151 #> 3863 38.62 0.14355308 2.758363e-04 0.856171084 #> 3864 38.63 0.14348132 2.756984e-04 0.856242980 #> 3865 38.64 0.14340960 2.755606e-04 0.856314841 #> 3866 38.65 0.14333791 2.754228e-04 0.856386665 #> 3867 38.66 0.14326626 2.752852e-04 0.856458454 #> 3868 38.67 0.14319465 2.751475e-04 0.856530207 #> 3869 38.68 0.14312307 2.750100e-04 0.856601924 #> 3870 38.69 0.14305152 2.748725e-04 0.856673605 #> 3871 38.70 0.14298001 2.747351e-04 0.856745250 #> 3872 38.71 0.14290854 2.745978e-04 0.856816860 #> 3873 38.72 0.14283711 2.744605e-04 0.856888433 #> 3874 38.73 0.14276571 2.743233e-04 0.856959971 #> 3875 38.74 0.14269434 2.741862e-04 0.857031473 #> 3876 38.75 0.14262301 2.740492e-04 0.857102940 #> 3877 38.76 0.14255172 2.739122e-04 0.857174370 #> 3878 38.77 0.14248046 2.737752e-04 0.857245765 #> 3879 38.78 0.14240924 2.736384e-04 0.857317125 #> 3880 38.79 0.14233805 2.735016e-04 0.857388448 #> 3881 38.80 0.14226690 2.733649e-04 0.857459736 #> 3882 38.81 0.14219578 2.732282e-04 0.857530989 #> 3883 38.82 0.14212470 2.730917e-04 0.857602205 #> 3884 38.83 0.14205366 2.729551e-04 0.857673386 #> 3885 38.84 0.14198265 2.728187e-04 0.857744532 #> 3886 38.85 0.14191168 2.726823e-04 0.857815642 #> 3887 38.86 0.14184074 2.725460e-04 0.857886716 #> 3888 38.87 0.14176984 2.724098e-04 0.857957755 #> 3889 38.88 0.14169897 2.722736e-04 0.858028758 #> 3890 38.89 0.14162814 2.721375e-04 0.858099726 #> 3891 38.90 0.14155734 2.720015e-04 0.858170659 #> 3892 38.91 0.14148658 2.718655e-04 0.858241556 #> 3893 38.92 0.14141585 2.717296e-04 0.858312417 #> 3894 38.93 0.14134516 2.715938e-04 0.858383243 #> 3895 38.94 0.14127451 2.714580e-04 0.858454034 #> 3896 38.95 0.14120389 2.713223e-04 0.858524789 #> 3897 38.96 0.14113330 2.711867e-04 0.858595509 #> 3898 38.97 0.14106276 2.710511e-04 0.858666194 #> 3899 38.98 0.14099224 2.709156e-04 0.858736843 #> 3900 38.99 0.14092176 2.707802e-04 0.858807457 #> 3901 39.00 0.14085132 2.706449e-04 0.858878036 #> 3902 39.01 0.14078091 2.705096e-04 0.858948579 #> 3903 39.02 0.14071054 2.703744e-04 0.859019087 #> 3904 39.03 0.14064020 2.702392e-04 0.859089560 #> 3905 39.04 0.14056990 2.701041e-04 0.859159997 #> 3906 39.05 0.14049963 2.699691e-04 0.859230400 #> 3907 39.06 0.14042940 2.698341e-04 0.859300767 #> 3908 39.07 0.14035920 2.696993e-04 0.859371099 #> 3909 39.08 0.14028904 2.695644e-04 0.859441396 #> 3910 39.09 0.14021891 2.694297e-04 0.859511658 #> 3911 39.10 0.14014882 2.692950e-04 0.859581884 #> 3912 39.11 0.14007876 2.691604e-04 0.859652076 #> 3913 39.12 0.14000874 2.690259e-04 0.859722232 #> 3914 39.13 0.13993875 2.688914e-04 0.859792354 #> 3915 39.14 0.13986880 2.687570e-04 0.859862440 #> 3916 39.15 0.13979889 2.686226e-04 0.859932491 #> 3917 39.16 0.13972900 2.684883e-04 0.860002507 #> 3918 39.17 0.13965916 2.683541e-04 0.860072489 #> 3919 39.18 0.13958935 2.682200e-04 0.860142435 #> 3920 39.19 0.13951957 2.680859e-04 0.860212346 #> 3921 39.20 0.13944983 2.679519e-04 0.860282223 #> 3922 39.21 0.13938012 2.678180e-04 0.860352064 #> 3923 39.22 0.13931045 2.676841e-04 0.860421871 #> 3924 39.23 0.13924081 2.675503e-04 0.860491642 #> 3925 39.24 0.13917120 2.674165e-04 0.860561379 #> 3926 39.25 0.13910164 2.672829e-04 0.860631081 #> 3927 39.26 0.13903210 2.671493e-04 0.860700748 #> 3928 39.27 0.13896260 2.670157e-04 0.860770380 #> 3929 39.28 0.13889314 2.668822e-04 0.860839978 #> 3930 39.29 0.13882371 2.667488e-04 0.860909540 #> 3931 39.30 0.13875432 2.666155e-04 0.860979068 #> 3932 39.31 0.13868496 2.664822e-04 0.861048561 #> 3933 39.32 0.13861563 2.663490e-04 0.861118019 #> 3934 39.33 0.13854634 2.662159e-04 0.861187443 #> 3935 39.34 0.13847709 2.660828e-04 0.861256832 #> 3936 39.35 0.13840786 2.659498e-04 0.861326186 #> 3937 39.36 0.13833868 2.658168e-04 0.861395506 #> 3938 39.37 0.13826953 2.656840e-04 0.861464791 #> 3939 39.38 0.13820041 2.655512e-04 0.861534041 #> 3940 39.39 0.13813132 2.654184e-04 0.861603257 #> 3941 39.40 0.13806228 2.652857e-04 0.861672438 #> 3942 39.41 0.13799326 2.651531e-04 0.861741584 #> 3943 39.42 0.13792428 2.650206e-04 0.861810696 #> 3944 39.43 0.13785534 2.648881e-04 0.861879774 #> 3945 39.44 0.13778643 2.647557e-04 0.861948816 #> 3946 39.45 0.13771755 2.646233e-04 0.862017825 #> 3947 39.46 0.13764871 2.644911e-04 0.862086799 #> 3948 39.47 0.13757990 2.643589e-04 0.862155738 #> 3949 39.48 0.13751113 2.642267e-04 0.862224643 #> 3950 39.49 0.13744239 2.640946e-04 0.862293513 #> 3951 39.50 0.13737369 2.639626e-04 0.862362349 #> 3952 39.51 0.13730502 2.638307e-04 0.862431151 #> 3953 39.52 0.13723638 2.636988e-04 0.862499918 #> 3954 39.53 0.13716778 2.635670e-04 0.862568651 #> 3955 39.54 0.13709922 2.634352e-04 0.862637350 #> 3956 39.55 0.13703068 2.633035e-04 0.862706014 #> 3957 39.56 0.13696218 2.631719e-04 0.862774644 #> 3958 39.57 0.13689372 2.630404e-04 0.862843239 #> 3959 39.58 0.13682529 2.629089e-04 0.862911800 #> 3960 39.59 0.13675690 2.627775e-04 0.862980327 #> 3961 39.60 0.13668853 2.626461e-04 0.863048820 #> 3962 39.61 0.13662021 2.625148e-04 0.863117279 #> 3963 39.62 0.13655191 2.623836e-04 0.863185703 #> 3964 39.63 0.13648365 2.622524e-04 0.863254093 #> 3965 39.64 0.13641543 2.621213e-04 0.863322449 #> 3966 39.65 0.13634724 2.619903e-04 0.863390770 #> 3967 39.66 0.13627908 2.618593e-04 0.863459058 #> 3968 39.67 0.13621096 2.617284e-04 0.863527311 #> 3969 39.68 0.13614287 2.615976e-04 0.863595531 #> 3970 39.69 0.13607482 2.614668e-04 0.863663716 #> 3971 39.70 0.13600680 2.613361e-04 0.863731867 #> 3972 39.71 0.13593881 2.612055e-04 0.863799984 #> 3973 39.72 0.13587086 2.610749e-04 0.863868067 #> 3974 39.73 0.13580294 2.609444e-04 0.863936116 #> 3975 39.74 0.13573506 2.608140e-04 0.864004131 #> 3976 39.75 0.13566720 2.606836e-04 0.864072112 #> 3977 39.76 0.13559939 2.605533e-04 0.864140059 #> 3978 39.77 0.13553161 2.604231e-04 0.864207972 #> 3979 39.78 0.13546386 2.602929e-04 0.864275851 #> 3980 39.79 0.13539614 2.601628e-04 0.864343696 #> 3981 39.80 0.13532846 2.600327e-04 0.864411507 #> 3982 39.81 0.13526081 2.599027e-04 0.864479284 #> 3983 39.82 0.13519320 2.597728e-04 0.864547028 #> 3984 39.83 0.13512562 2.596430e-04 0.864614737 #> 3985 39.84 0.13505807 2.595132e-04 0.864682413 #> 3986 39.85 0.13499056 2.593835e-04 0.864750055 #> 3987 39.86 0.13492308 2.592538e-04 0.864817663 #> 3988 39.87 0.13485564 2.591242e-04 0.864885237 #> 3989 39.88 0.13478823 2.589947e-04 0.864952778 #> 3990 39.89 0.13472085 2.588652e-04 0.865020285 #> 3991 39.90 0.13465351 2.587358e-04 0.865087758 #> 3992 39.91 0.13458620 2.586065e-04 0.865155197 #> 3993 39.92 0.13451892 2.584772e-04 0.865222602 #> 3994 39.93 0.13445168 2.583480e-04 0.865289974 #> 3995 39.94 0.13438447 2.582189e-04 0.865357312 #> 3996 39.95 0.13431729 2.580898e-04 0.865424617 #> 3997 39.96 0.13425015 2.579608e-04 0.865491888 #> 3998 39.97 0.13418304 2.578318e-04 0.865559125 #> 3999 39.98 0.13411597 2.577029e-04 0.865626329 #> 4000 39.99 0.13404893 2.575741e-04 0.865693499 #> 4001 40.00 0.13398192 2.574454e-04 0.865760635 #> 4002 40.01 0.13391495 2.573167e-04 0.865827738 #> 4003 40.02 0.13384800 2.571880e-04 0.865894808 #> 4004 40.03 0.13378110 2.570595e-04 0.865961843 #> 4005 40.04 0.13371422 2.569310e-04 0.866028846 #> 4006 40.05 0.13364738 2.568025e-04 0.866095815 #> 4007 40.06 0.13358058 2.566742e-04 0.866162750 #> 4008 40.07 0.13351380 2.565459e-04 0.866229652 #> 4009 40.08 0.13344706 2.564176e-04 0.866296520 #> 4010 40.09 0.13338036 2.562895e-04 0.866363355 #> 4011 40.10 0.13331368 2.561613e-04 0.866430157 #> 4012 40.11 0.13324704 2.560333e-04 0.866496925 #> 4013 40.12 0.13318043 2.559053e-04 0.866563660 #> 4014 40.13 0.13311386 2.557774e-04 0.866630362 #> 4015 40.14 0.13304732 2.556495e-04 0.866697030 #> 4016 40.15 0.13298081 2.555217e-04 0.866763665 #> 4017 40.16 0.13291434 2.553940e-04 0.866830266 #> 4018 40.17 0.13284790 2.552663e-04 0.866896834 #> 4019 40.18 0.13278149 2.551387e-04 0.866963369 #> 4020 40.19 0.13271512 2.550112e-04 0.867029871 #> 4021 40.20 0.13264878 2.548837e-04 0.867096339 #> 4022 40.21 0.13258247 2.547563e-04 0.867162775 #> 4023 40.22 0.13251619 2.546290e-04 0.867229177 #> 4024 40.23 0.13244995 2.545017e-04 0.867295545 #> 4025 40.24 0.13238374 2.543745e-04 0.867361881 #> 4026 40.25 0.13231757 2.542473e-04 0.867428184 #> 4027 40.26 0.13225143 2.541202e-04 0.867494453 #> 4028 40.27 0.13218532 2.539932e-04 0.867560689 #> 4029 40.28 0.13211924 2.538662e-04 0.867626892 #> 4030 40.29 0.13205320 2.537393e-04 0.867693062 #> 4031 40.30 0.13198719 2.536125e-04 0.867759199 #> 4032 40.31 0.13192121 2.534857e-04 0.867825303 #> 4033 40.32 0.13185527 2.533590e-04 0.867891374 #> 4034 40.33 0.13178936 2.532324e-04 0.867957412 #> 4035 40.34 0.13172348 2.531058e-04 0.868023416 #> 4036 40.35 0.13165763 2.529793e-04 0.868089388 #> 4037 40.36 0.13159182 2.528528e-04 0.868155327 #> 4038 40.37 0.13152604 2.527264e-04 0.868221233 #> 4039 40.38 0.13146029 2.526001e-04 0.868287106 #> 4040 40.39 0.13139458 2.524738e-04 0.868352946 #> 4041 40.40 0.13132890 2.523476e-04 0.868418753 #> 4042 40.41 0.13126325 2.522215e-04 0.868484527 #> 4043 40.42 0.13119764 2.520954e-04 0.868550268 #> 4044 40.43 0.13113205 2.519694e-04 0.868615977 #> 4045 40.44 0.13106650 2.518434e-04 0.868681652 #> 4046 40.45 0.13100099 2.517175e-04 0.868747295 #> 4047 40.46 0.13093550 2.515917e-04 0.868812905 #> 4048 40.47 0.13087005 2.514659e-04 0.868878482 #> 4049 40.48 0.13080463 2.513402e-04 0.868944027 #> 4050 40.49 0.13073925 2.512146e-04 0.869009538 #> 4051 40.50 0.13067389 2.510890e-04 0.869075017 #> 4052 40.51 0.13060857 2.509635e-04 0.869140463 #> 4053 40.52 0.13054329 2.508380e-04 0.869205877 #> 4054 40.53 0.13047803 2.507127e-04 0.869271257 #> 4055 40.54 0.13041281 2.505873e-04 0.869336605 #> 4056 40.55 0.13034762 2.504621e-04 0.869401921 #> 4057 40.56 0.13028246 2.503369e-04 0.869467204 #> 4058 40.57 0.13021733 2.502117e-04 0.869532454 #> 4059 40.58 0.13015224 2.500867e-04 0.869597671 #> 4060 40.59 0.13008718 2.499616e-04 0.869662856 #> 4061 40.60 0.13002216 2.498367e-04 0.869728008 #> 4062 40.61 0.12995716 2.497118e-04 0.869793128 #> 4063 40.62 0.12989220 2.495870e-04 0.869858215 #> 4064 40.63 0.12982727 2.494622e-04 0.869923270 #> 4065 40.64 0.12976237 2.493375e-04 0.869988292 #> 4066 40.65 0.12969751 2.492129e-04 0.870053281 #> 4067 40.66 0.12963267 2.490883e-04 0.870118239 #> 4068 40.67 0.12956787 2.489638e-04 0.870183163 #> 4069 40.68 0.12950311 2.488393e-04 0.870248055 #> 4070 40.69 0.12943837 2.487150e-04 0.870312915 #> 4071 40.70 0.12937367 2.485906e-04 0.870377743 #> 4072 40.71 0.12930900 2.484664e-04 0.870442537 #> 4073 40.72 0.12924436 2.483422e-04 0.870507300 #> 4074 40.73 0.12917975 2.482180e-04 0.870572030 #> 4075 40.74 0.12911518 2.480939e-04 0.870636728 #> 4076 40.75 0.12905064 2.479699e-04 0.870701393 #> 4077 40.76 0.12898613 2.478460e-04 0.870766027 #> 4078 40.77 0.12892165 2.477221e-04 0.870830627 #> 4079 40.78 0.12885721 2.475983e-04 0.870895196 #> 4080 40.79 0.12879279 2.474745e-04 0.870959732 #> 4081 40.80 0.12872841 2.473508e-04 0.871024236 #> 4082 40.81 0.12866406 2.472271e-04 0.871088708 #> 4083 40.82 0.12859975 2.471036e-04 0.871153148 #> 4084 40.83 0.12853547 2.469800e-04 0.871217555 #> 4085 40.84 0.12847121 2.468566e-04 0.871281930 #> 4086 40.85 0.12840699 2.467332e-04 0.871346273 #> 4087 40.86 0.12834281 2.466098e-04 0.871410584 #> 4088 40.87 0.12827865 2.464866e-04 0.871474862 #> 4089 40.88 0.12821453 2.463634e-04 0.871539109 #> 4090 40.89 0.12815044 2.462402e-04 0.871603323 #> 4091 40.90 0.12808638 2.461171e-04 0.871667506 #> 4092 40.91 0.12802235 2.459941e-04 0.871731656 #> 4093 40.92 0.12795835 2.458711e-04 0.871795774 #> 4094 40.93 0.12789439 2.457482e-04 0.871859860 #> 4095 40.94 0.12783046 2.456254e-04 0.871923914 #> 4096 40.95 0.12776656 2.455026e-04 0.871987936 #> 4097 40.96 0.12770269 2.453799e-04 0.872051926 #> 4098 40.97 0.12763886 2.452572e-04 0.872115884 #> 4099 40.98 0.12757506 2.451346e-04 0.872179810 #> 4100 40.99 0.12751128 2.450121e-04 0.872243704 #> 4101 41.00 0.12744754 2.448896e-04 0.872307567 #> 4102 41.01 0.12738384 2.447672e-04 0.872371397 #> 4103 41.02 0.12732016 2.446448e-04 0.872435195 #> 4104 41.03 0.12725652 2.445225e-04 0.872498962 #> 4105 41.04 0.12719290 2.444003e-04 0.872562696 #> 4106 41.05 0.12712932 2.442781e-04 0.872626399 #> 4107 41.06 0.12706577 2.441560e-04 0.872690070 #> 4108 41.07 0.12700226 2.440340e-04 0.872753709 #> 4109 41.08 0.12693877 2.439120e-04 0.872817316 #> 4110 41.09 0.12687532 2.437901e-04 0.872880892 #> 4111 41.10 0.12681190 2.436682e-04 0.872944435 #> 4112 41.11 0.12674851 2.435464e-04 0.873007947 #> 4113 41.12 0.12668515 2.434247e-04 0.873071427 #> 4114 41.13 0.12662182 2.433030e-04 0.873134876 #> 4115 41.14 0.12655853 2.431814e-04 0.873198293 #> 4116 41.15 0.12649526 2.430598e-04 0.873261678 #> 4117 41.16 0.12643203 2.429383e-04 0.873325031 #> 4118 41.17 0.12636883 2.428169e-04 0.873388353 #> 4119 41.18 0.12630566 2.426955e-04 0.873451643 #> 4120 41.19 0.12624252 2.425742e-04 0.873514901 #> 4121 41.20 0.12617942 2.424529e-04 0.873578128 #> 4122 41.21 0.12611635 2.423317e-04 0.873641323 #> 4123 41.22 0.12605330 2.422106e-04 0.873704486 #> 4124 41.23 0.12599029 2.420895e-04 0.873767618 #> 4125 41.24 0.12592731 2.419685e-04 0.873830719 #> 4126 41.25 0.12586436 2.418475e-04 0.873893788 #> 4127 41.26 0.12580145 2.417266e-04 0.873956825 #> 4128 41.27 0.12573856 2.416058e-04 0.874019831 #> 4129 41.28 0.12567571 2.414850e-04 0.874082805 #> 4130 41.29 0.12561289 2.413643e-04 0.874145748 #> 4131 41.30 0.12555010 2.412437e-04 0.874208659 #> 4132 41.31 0.12548734 2.411231e-04 0.874271539 #> 4133 41.32 0.12542461 2.410025e-04 0.874334388 #> 4134 41.33 0.12536191 2.408821e-04 0.874397205 #> 4135 41.34 0.12529925 2.407617e-04 0.874459991 #> 4136 41.35 0.12523661 2.406413e-04 0.874522745 #> 4137 41.36 0.12517401 2.405210e-04 0.874585468 #> 4138 41.37 0.12511144 2.404008e-04 0.874648160 #> 4139 41.38 0.12504890 2.402806e-04 0.874710820 #> 4140 41.39 0.12498639 2.401605e-04 0.874773449 #> 4141 41.40 0.12492391 2.400405e-04 0.874836046 #> 4142 41.41 0.12486147 2.399205e-04 0.874898613 #> 4143 41.42 0.12479905 2.398005e-04 0.874961148 #> 4144 41.43 0.12473667 2.396807e-04 0.875023652 #> 4145 41.44 0.12467432 2.395609e-04 0.875086124 #> 4146 41.45 0.12461199 2.394411e-04 0.875148565 #> 4147 41.46 0.12454970 2.393214e-04 0.875210976 #> 4148 41.47 0.12448744 2.392018e-04 0.875273354 #> 4149 41.48 0.12442522 2.390822e-04 0.875335702 #> 4150 41.49 0.12436302 2.389627e-04 0.875398019 #> 4151 41.50 0.12430085 2.388433e-04 0.875460304 #> 4152 41.51 0.12423872 2.387239e-04 0.875522558 #> 4153 41.52 0.12417661 2.386045e-04 0.875584782 #> 4154 41.53 0.12411454 2.384853e-04 0.875646974 #> 4155 41.54 0.12405250 2.383660e-04 0.875709135 #> 4156 41.55 0.12399049 2.382469e-04 0.875771265 #> 4157 41.56 0.12392851 2.381278e-04 0.875833363 #> 4158 41.57 0.12386656 2.380088e-04 0.875895431 #> 4159 41.58 0.12380464 2.378898e-04 0.875957468 #> 4160 41.59 0.12374276 2.377709e-04 0.876019474 #> 4161 41.60 0.12368090 2.376520e-04 0.876081449 #> 4162 41.61 0.12361907 2.375332e-04 0.876143392 #> 4163 41.62 0.12355728 2.374145e-04 0.876205305 #> 4164 41.63 0.12349552 2.372958e-04 0.876267187 #> 4165 41.64 0.12343378 2.371772e-04 0.876329038 #> 4166 41.65 0.12337208 2.370586e-04 0.876390858 #> 4167 41.66 0.12331041 2.369401e-04 0.876452647 #> 4168 41.67 0.12324877 2.368217e-04 0.876514405 #> 4169 41.68 0.12318716 2.367033e-04 0.876576133 #> 4170 41.69 0.12312559 2.365850e-04 0.876637829 #> 4171 41.70 0.12306404 2.364667e-04 0.876699495 #> 4172 41.71 0.12300252 2.363485e-04 0.876761130 #> 4173 41.72 0.12294104 2.362304e-04 0.876822734 #> 4174 41.73 0.12287958 2.361123e-04 0.876884307 #> 4175 41.74 0.12281816 2.359943e-04 0.876945849 #> 4176 41.75 0.12275676 2.358763e-04 0.877007361 #> 4177 41.76 0.12269540 2.357584e-04 0.877068842 #> 4178 41.77 0.12263407 2.356405e-04 0.877130292 #> 4179 41.78 0.12257277 2.355227e-04 0.877191712 #> 4180 41.79 0.12251149 2.354050e-04 0.877253101 #> 4181 41.80 0.12245025 2.352873e-04 0.877314459 #> 4182 41.81 0.12238904 2.351697e-04 0.877375786 #> 4183 41.82 0.12232786 2.350522e-04 0.877437083 #> 4184 41.83 0.12226672 2.349347e-04 0.877498349 #> 4185 41.84 0.12220560 2.348172e-04 0.877559585 #> 4186 41.85 0.12214451 2.346999e-04 0.877620790 #> 4187 41.86 0.12208345 2.345825e-04 0.877681964 #> 4188 41.87 0.12202243 2.344653e-04 0.877743108 #> 4189 41.88 0.12196143 2.343481e-04 0.877804221 #> 4190 41.89 0.12190047 2.342309e-04 0.877865303 #> 4191 41.90 0.12183953 2.341138e-04 0.877926355 #> 4192 41.91 0.12177863 2.339968e-04 0.877987377 #> 4193 41.92 0.12171775 2.338798e-04 0.878048368 #> 4194 41.93 0.12165691 2.337629e-04 0.878109329 #> 4195 41.94 0.12159610 2.336461e-04 0.878170259 #> 4196 41.95 0.12153531 2.335293e-04 0.878231158 #> 4197 41.96 0.12147456 2.334125e-04 0.878292028 #> 4198 41.97 0.12141384 2.332959e-04 0.878352866 #> 4199 41.98 0.12135315 2.331793e-04 0.878413675 #> 4200 41.99 0.12129248 2.330627e-04 0.878474453 #> 4201 42.00 0.12123185 2.329462e-04 0.878535200 #> 4202 42.01 0.12117125 2.328297e-04 0.878595918 #> 4203 42.02 0.12111068 2.327134e-04 0.878656604 #> 4204 42.03 0.12105014 2.325970e-04 0.878717261 #> 4205 42.04 0.12098963 2.324808e-04 0.878777887 #> 4206 42.05 0.12092915 2.323646e-04 0.878838483 #> 4207 42.06 0.12086870 2.322484e-04 0.878899049 #> 4208 42.07 0.12080828 2.321323e-04 0.878959584 #> 4209 42.08 0.12074789 2.320163e-04 0.879020089 #> 4210 42.09 0.12068754 2.319003e-04 0.879080564 #> 4211 42.10 0.12062721 2.317844e-04 0.879141009 #> 4212 42.11 0.12056691 2.316685e-04 0.879201423 #> 4213 42.12 0.12050664 2.315527e-04 0.879261807 #> 4214 42.13 0.12044640 2.314370e-04 0.879322161 #> 4215 42.14 0.12038619 2.313213e-04 0.879382485 #> 4216 42.15 0.12032602 2.312056e-04 0.879442779 #> 4217 42.16 0.12026587 2.310901e-04 0.879503042 #> 4218 42.17 0.12020575 2.309745e-04 0.879563276 #> 4219 42.18 0.12014566 2.308591e-04 0.879623479 #> 4220 42.19 0.12008560 2.307437e-04 0.879683652 #> 4221 42.20 0.12002558 2.306283e-04 0.879743795 #> 4222 42.21 0.11996558 2.305131e-04 0.879803908 #> 4223 42.22 0.11990561 2.303978e-04 0.879863991 #> 4224 42.23 0.11984567 2.302827e-04 0.879924044 #> 4225 42.24 0.11978577 2.301675e-04 0.879984067 #> 4226 42.25 0.11972589 2.300525e-04 0.880044060 #> 4227 42.26 0.11966604 2.299375e-04 0.880104023 #> 4228 42.27 0.11960622 2.298225e-04 0.880163956 #> 4229 42.28 0.11954643 2.297077e-04 0.880223859 #> 4230 42.29 0.11948667 2.295928e-04 0.880283733 #> 4231 42.30 0.11942695 2.294781e-04 0.880343576 #> 4232 42.31 0.11936725 2.293634e-04 0.880403389 #> 4233 42.32 0.11930758 2.292487e-04 0.880463172 #> 4234 42.33 0.11924794 2.291341e-04 0.880522926 #> 4235 42.34 0.11918833 2.290196e-04 0.880582649 #> 4236 42.35 0.11912875 2.289051e-04 0.880642343 #> 4237 42.36 0.11906920 2.287907e-04 0.880702007 #> 4238 42.37 0.11900968 2.286763e-04 0.880761641 #> 4239 42.38 0.11895019 2.285620e-04 0.880821245 #> 4240 42.39 0.11889073 2.284477e-04 0.880880820 #> 4241 42.40 0.11883130 2.283335e-04 0.880940365 #> 4242 42.41 0.11877190 2.282194e-04 0.880999880 #> 4243 42.42 0.11871253 2.281053e-04 0.881059365 #> 4244 42.43 0.11865319 2.279913e-04 0.881118820 #> 4245 42.44 0.11859388 2.278773e-04 0.881178246 #> 4246 42.45 0.11853459 2.277634e-04 0.881237642 #> 4247 42.46 0.11847534 2.276496e-04 0.881297008 #> 4248 42.47 0.11841612 2.275358e-04 0.881356345 #> 4249 42.48 0.11835693 2.274220e-04 0.881415652 #> 4250 42.49 0.11829776 2.273084e-04 0.881474929 #> 4251 42.50 0.11823863 2.271947e-04 0.881534177 #> 4252 42.51 0.11817952 2.270812e-04 0.881593395 #> 4253 42.52 0.11812045 2.269676e-04 0.881652584 #> 4254 42.53 0.11806140 2.268542e-04 0.881711743 #> 4255 42.54 0.11800239 2.267408e-04 0.881770872 #> 4256 42.55 0.11794340 2.266275e-04 0.881829972 #> 4257 42.56 0.11788444 2.265142e-04 0.881889042 #> 4258 42.57 0.11782552 2.264009e-04 0.881948083 #> 4259 42.58 0.11776662 2.262878e-04 0.882007094 #> 4260 42.59 0.11770775 2.261746e-04 0.882066076 #> 4261 42.60 0.11764891 2.260616e-04 0.882125028 #> 4262 42.61 0.11759010 2.259486e-04 0.882183951 #> 4263 42.62 0.11753132 2.258356e-04 0.882242844 #> 4264 42.63 0.11747257 2.257228e-04 0.882301708 #> 4265 42.64 0.11741385 2.256099e-04 0.882360542 #> 4266 42.65 0.11735516 2.254971e-04 0.882419347 #> 4267 42.66 0.11729649 2.253844e-04 0.882478123 #> 4268 42.67 0.11723786 2.252718e-04 0.882536869 #> 4269 42.68 0.11717925 2.251592e-04 0.882595586 #> 4270 42.69 0.11712068 2.250466e-04 0.882654274 #> 4271 42.70 0.11706213 2.249341e-04 0.882712932 #> 4272 42.71 0.11700362 2.248217e-04 0.882771561 #> 4273 42.72 0.11694513 2.247093e-04 0.882830160 #> 4274 42.73 0.11688667 2.245970e-04 0.882888730 #> 4275 42.74 0.11682824 2.244847e-04 0.882947271 #> 4276 42.75 0.11676984 2.243725e-04 0.883005783 #> 4277 42.76 0.11671147 2.242603e-04 0.883064266 #> 4278 42.77 0.11665313 2.241482e-04 0.883122719 #> 4279 42.78 0.11659482 2.240362e-04 0.883181143 #> 4280 42.79 0.11653654 2.239242e-04 0.883239538 #> 4281 42.80 0.11647828 2.238122e-04 0.883297903 #> 4282 42.81 0.11642006 2.237004e-04 0.883356240 #> 4283 42.82 0.11636186 2.235885e-04 0.883414547 #> 4284 42.83 0.11630370 2.234768e-04 0.883472825 #> 4285 42.84 0.11624556 2.233651e-04 0.883531074 #> 4286 42.85 0.11618745 2.232534e-04 0.883589294 #> 4287 42.86 0.11612937 2.231418e-04 0.883647485 #> 4288 42.87 0.11607132 2.230303e-04 0.883705647 #> 4289 42.88 0.11601330 2.229188e-04 0.883763779 #> 4290 42.89 0.11595531 2.228073e-04 0.883821883 #> 4291 42.90 0.11589735 2.226960e-04 0.883879958 #> 4292 42.91 0.11583941 2.225847e-04 0.883938003 #> 4293 42.92 0.11578151 2.224734e-04 0.883996020 #> 4294 42.93 0.11572363 2.223622e-04 0.884054007 #> 4295 42.94 0.11566578 2.222510e-04 0.884111966 #> 4296 42.95 0.11560796 2.221399e-04 0.884169895 #> 4297 42.96 0.11555018 2.220289e-04 0.884227796 #> 4298 42.97 0.11549241 2.219179e-04 0.884285667 #> 4299 42.98 0.11543468 2.218070e-04 0.884343510 #> 4300 42.99 0.11537698 2.216961e-04 0.884401324 #> 4301 43.00 0.11531931 2.215853e-04 0.884459109 #> 4302 43.01 0.11526166 2.214745e-04 0.884516865 #> 4303 43.02 0.11520404 2.213638e-04 0.884574592 #> 4304 43.03 0.11514646 2.212531e-04 0.884632290 #> 4305 43.04 0.11508890 2.211425e-04 0.884689960 #> 4306 43.05 0.11503137 2.210320e-04 0.884747600 #> 4307 43.06 0.11497387 2.209215e-04 0.884805212 #> 4308 43.07 0.11491639 2.208111e-04 0.884862795 #> 4309 43.08 0.11485895 2.207007e-04 0.884920349 #> 4310 43.09 0.11480153 2.205904e-04 0.884977875 #> 4311 43.10 0.11474415 2.204801e-04 0.885035371 #> 4312 43.11 0.11468679 2.203699e-04 0.885092839 #> 4313 43.12 0.11462946 2.202597e-04 0.885150279 #> 4314 43.13 0.11457216 2.201496e-04 0.885207689 #> 4315 43.14 0.11451489 2.200396e-04 0.885265071 #> 4316 43.15 0.11445765 2.199296e-04 0.885322424 #> 4317 43.16 0.11440043 2.198197e-04 0.885379748 #> 4318 43.17 0.11434325 2.197098e-04 0.885437044 #> 4319 43.18 0.11428609 2.196000e-04 0.885494311 #> 4320 43.19 0.11422896 2.194902e-04 0.885551550 #> 4321 43.20 0.11417186 2.193805e-04 0.885608760 #> 4322 43.21 0.11411479 2.192708e-04 0.885665941 #> 4323 43.22 0.11405774 2.191612e-04 0.885723094 #> 4324 43.23 0.11400073 2.190516e-04 0.885780218 #> 4325 43.24 0.11394374 2.189421e-04 0.885837314 #> 4326 43.25 0.11388679 2.188327e-04 0.885894381 #> 4327 43.26 0.11382986 2.187233e-04 0.885951419 #> 4328 43.27 0.11377296 2.186140e-04 0.886008429 #> 4329 43.28 0.11371608 2.185047e-04 0.886065411 #> 4330 43.29 0.11365924 2.183955e-04 0.886122364 #> 4331 43.30 0.11360243 2.182863e-04 0.886179289 #> 4332 43.31 0.11354564 2.181772e-04 0.886236185 #> 4333 43.32 0.11348888 2.180681e-04 0.886293052 #> 4334 43.33 0.11343215 2.179591e-04 0.886349892 #> 4335 43.34 0.11337545 2.178502e-04 0.886406703 #> 4336 43.35 0.11331877 2.177413e-04 0.886463485 #> 4337 43.36 0.11326213 2.176324e-04 0.886520239 #> 4338 43.37 0.11320551 2.175236e-04 0.886576965 #> 4339 43.38 0.11314892 2.174149e-04 0.886633662 #> 4340 43.39 0.11309236 2.173062e-04 0.886690331 #> 4341 43.40 0.11303583 2.171976e-04 0.886746972 #> 4342 43.41 0.11297933 2.170890e-04 0.886803584 #> 4343 43.42 0.11292285 2.169805e-04 0.886860168 #> 4344 43.43 0.11286640 2.168720e-04 0.886916724 #> 4345 43.44 0.11280998 2.167636e-04 0.886973252 #> 4346 43.45 0.11275359 2.166553e-04 0.887029751 #> 4347 43.46 0.11269723 2.165470e-04 0.887086222 #> 4348 43.47 0.11264090 2.164387e-04 0.887142665 #> 4349 43.48 0.11258459 2.163305e-04 0.887199079 #> 4350 43.49 0.11252831 2.162224e-04 0.887255465 #> 4351 43.50 0.11247206 2.161143e-04 0.887311824 #> 4352 43.51 0.11241584 2.160063e-04 0.887368154 #> 4353 43.52 0.11235965 2.158983e-04 0.887424456 #> 4354 43.53 0.11230348 2.157904e-04 0.887480729 #> 4355 43.54 0.11224734 2.156825e-04 0.887536975 #> 4356 43.55 0.11219123 2.155747e-04 0.887593192 #> 4357 43.56 0.11213515 2.154669e-04 0.887649382 #> 4358 43.57 0.11207910 2.153592e-04 0.887705543 #> 4359 43.58 0.11202307 2.152516e-04 0.887761676 #> 4360 43.59 0.11196707 2.151440e-04 0.887817781 #> 4361 43.60 0.11191111 2.150364e-04 0.887873858 #> 4362 43.61 0.11185516 2.149289e-04 0.887929907 #> 4363 43.62 0.11179925 2.148215e-04 0.887985928 #> 4364 43.63 0.11174336 2.147141e-04 0.888041921 #> 4365 43.64 0.11168751 2.146068e-04 0.888097886 #> 4366 43.65 0.11163168 2.144995e-04 0.888153824 #> 4367 43.66 0.11157588 2.143923e-04 0.888209733 #> 4368 43.67 0.11152010 2.142851e-04 0.888265614 #> 4369 43.68 0.11146435 2.141780e-04 0.888321467 #> 4370 43.69 0.11140864 2.140709e-04 0.888377292 #> 4371 43.70 0.11135295 2.139639e-04 0.888433090 #> 4372 43.71 0.11129728 2.138570e-04 0.888488859 #> 4373 43.72 0.11124165 2.137501e-04 0.888544601 #> 4374 43.73 0.11118604 2.136432e-04 0.888600315 #> 4375 43.74 0.11113046 2.135364e-04 0.888656001 #> 4376 43.75 0.11107491 2.134297e-04 0.888711659 #> 4377 43.76 0.11101939 2.133230e-04 0.888767289 #> 4378 43.77 0.11096389 2.132164e-04 0.888822891 #> 4379 43.78 0.11090842 2.131098e-04 0.888878466 #> 4380 43.79 0.11085298 2.130033e-04 0.888934013 #> 4381 43.80 0.11079757 2.128968e-04 0.888989532 #> 4382 43.81 0.11074219 2.127904e-04 0.889045023 #> 4383 43.82 0.11068683 2.126840e-04 0.889100487 #> 4384 43.83 0.11063150 2.125777e-04 0.889155923 #> 4385 43.84 0.11057620 2.124714e-04 0.889211331 #> 4386 43.85 0.11052092 2.123652e-04 0.889266712 #> 4387 43.86 0.11046568 2.122591e-04 0.889322064 #> 4388 43.87 0.11041046 2.121530e-04 0.889377390 #> 4389 43.88 0.11035527 2.120469e-04 0.889432687 #> 4390 43.89 0.11030010 2.119409e-04 0.889487957 #> 4391 43.90 0.11024497 2.118350e-04 0.889543199 #> 4392 43.91 0.11018986 2.117291e-04 0.889598414 #> 4393 43.92 0.11013478 2.116232e-04 0.889653601 #> 4394 43.93 0.11007972 2.115174e-04 0.889708760 #> 4395 43.94 0.11002470 2.114117e-04 0.889763892 #> 4396 43.95 0.10996970 2.113060e-04 0.889818996 #> 4397 43.96 0.10991473 2.112004e-04 0.889874073 #> 4398 43.97 0.10985978 2.110948e-04 0.889929122 #> 4399 43.98 0.10980487 2.109893e-04 0.889984144 #> 4400 43.99 0.10974998 2.108838e-04 0.890039138 #> 4401 44.00 0.10969512 2.107784e-04 0.890094105 #> 4402 44.01 0.10964028 2.106731e-04 0.890149044 #> 4403 44.02 0.10958548 2.105678e-04 0.890203956 #> 4404 44.03 0.10953070 2.104625e-04 0.890258840 #> 4405 44.04 0.10947595 2.103573e-04 0.890313697 #> 4406 44.05 0.10942122 2.102521e-04 0.890368526 #> 4407 44.06 0.10936652 2.101470e-04 0.890423328 #> 4408 44.07 0.10931185 2.100420e-04 0.890478103 #> 4409 44.08 0.10925721 2.099370e-04 0.890532850 #> 4410 44.09 0.10920260 2.098321e-04 0.890587570 #> 4411 44.10 0.10914801 2.097272e-04 0.890642263 #> 4412 44.11 0.10909345 2.096223e-04 0.890696928 #> 4413 44.12 0.10903892 2.095175e-04 0.890751566 #> 4414 44.13 0.10898441 2.094128e-04 0.890806176 #> 4415 44.14 0.10892993 2.093081e-04 0.890860760 #> 4416 44.15 0.10887548 2.092035e-04 0.890915316 #> 4417 44.16 0.10882106 2.090989e-04 0.890969844 #> 4418 44.17 0.10876666 2.089944e-04 0.891024346 #> 4419 44.18 0.10871229 2.088899e-04 0.891078820 #> 4420 44.19 0.10865795 2.087855e-04 0.891133267 #> 4421 44.20 0.10860363 2.086811e-04 0.891187687 #> 4422 44.21 0.10854934 2.085768e-04 0.891242079 #> 4423 44.22 0.10849508 2.084726e-04 0.891296445 #> 4424 44.23 0.10844085 2.083684e-04 0.891350783 #> 4425 44.24 0.10838664 2.082642e-04 0.891405094 #> 4426 44.25 0.10833246 2.081601e-04 0.891459378 #> 4427 44.26 0.10827831 2.080560e-04 0.891513635 #> 4428 44.27 0.10822418 2.079520e-04 0.891567864 #> 4429 44.28 0.10817009 2.078481e-04 0.891622067 #> 4430 44.29 0.10811601 2.077442e-04 0.891676242 #> 4431 44.30 0.10806197 2.076403e-04 0.891730390 #> 4432 44.31 0.10800795 2.075366e-04 0.891784512 #> 4433 44.32 0.10795396 2.074328e-04 0.891838606 #> 4434 44.33 0.10790000 2.073291e-04 0.891892673 #> 4435 44.34 0.10784606 2.072255e-04 0.891946713 #> 4436 44.35 0.10779215 2.071219e-04 0.892000726 #> 4437 44.36 0.10773827 2.070184e-04 0.892054713 #> 4438 44.37 0.10768441 2.069149e-04 0.892108672 #> 4439 44.38 0.10763058 2.068114e-04 0.892162604 #> 4440 44.39 0.10757678 2.067081e-04 0.892216509 #> 4441 44.40 0.10752301 2.066047e-04 0.892270387 #> 4442 44.41 0.10746926 2.065015e-04 0.892324239 #> 4443 44.42 0.10741554 2.063982e-04 0.892378063 #> 4444 44.43 0.10736184 2.062951e-04 0.892431861 #> 4445 44.44 0.10730818 2.061919e-04 0.892485631 #> 4446 44.45 0.10725454 2.060889e-04 0.892539375 #> 4447 44.46 0.10720092 2.059859e-04 0.892593092 #> 4448 44.47 0.10714734 2.058829e-04 0.892646782 #> 4449 44.48 0.10709377 2.057800e-04 0.892700445 #> 4450 44.49 0.10704024 2.056771e-04 0.892754082 #> 4451 44.50 0.10698673 2.055743e-04 0.892807691 #> 4452 44.51 0.10693325 2.054715e-04 0.892861274 #> 4453 44.52 0.10687980 2.053688e-04 0.892914830 #> 4454 44.53 0.10682637 2.052662e-04 0.892968359 #> 4455 44.54 0.10677297 2.051636e-04 0.893021861 #> 4456 44.55 0.10671960 2.050610e-04 0.893075337 #> 4457 44.56 0.10666626 2.049585e-04 0.893128786 #> 4458 44.57 0.10661294 2.048560e-04 0.893182208 #> 4459 44.58 0.10655964 2.047536e-04 0.893235604 #> 4460 44.59 0.10650638 2.046513e-04 0.893288973 #> 4461 44.60 0.10645314 2.045490e-04 0.893342315 #> 4462 44.61 0.10639992 2.044467e-04 0.893395630 #> 4463 44.62 0.10634674 2.043445e-04 0.893448919 #> 4464 44.63 0.10629358 2.042424e-04 0.893502182 #> 4465 44.64 0.10624044 2.041403e-04 0.893555417 #> 4466 44.65 0.10618734 2.040383e-04 0.893608626 #> 4467 44.66 0.10613426 2.039363e-04 0.893661809 #> 4468 44.67 0.10608120 2.038343e-04 0.893714964 #> 4469 44.68 0.10602817 2.037324e-04 0.893768094 #> 4470 44.69 0.10597517 2.036306e-04 0.893821196 #> 4471 44.70 0.10592220 2.035288e-04 0.893874272 #> 4472 44.71 0.10586925 2.034271e-04 0.893927322 #> 4473 44.72 0.10581633 2.033254e-04 0.893980345 #> 4474 44.73 0.10576343 2.032237e-04 0.894033342 #> 4475 44.74 0.10571057 2.031221e-04 0.894086312 #> 4476 44.75 0.10565772 2.030206e-04 0.894139255 #> 4477 44.76 0.10560491 2.029191e-04 0.894192173 #> 4478 44.77 0.10555212 2.028177e-04 0.894245063 #> 4479 44.78 0.10549936 2.027163e-04 0.894297927 #> 4480 44.79 0.10544662 2.026150e-04 0.894350765 #> 4481 44.80 0.10539391 2.025137e-04 0.894403577 #> 4482 44.81 0.10534123 2.024125e-04 0.894456362 #> 4483 44.82 0.10528857 2.023113e-04 0.894509120 #> 4484 44.83 0.10523594 2.022101e-04 0.894561853 #> 4485 44.84 0.10518333 2.021091e-04 0.894614559 #> 4486 44.85 0.10513075 2.020080e-04 0.894667238 #> 4487 44.86 0.10507820 2.019071e-04 0.894719891 #> 4488 44.87 0.10502568 2.018061e-04 0.894772518 #> 4489 44.88 0.10497318 2.017053e-04 0.894825119 #> 4490 44.89 0.10492070 2.016044e-04 0.894877693 #> 4491 44.90 0.10486826 2.015036e-04 0.894930241 #> 4492 44.91 0.10481583 2.014029e-04 0.894982763 #> 4493 44.92 0.10476344 2.013022e-04 0.895035258 #> 4494 44.93 0.10471107 2.012016e-04 0.895087728 #> 4495 44.94 0.10465873 2.011010e-04 0.895140171 #> 4496 44.95 0.10460641 2.010005e-04 0.895192587 #> 4497 44.96 0.10455412 2.009000e-04 0.895244978 #> 4498 44.97 0.10450186 2.007996e-04 0.895297342 #> 4499 44.98 0.10444962 2.006992e-04 0.895349681 #> 4500 44.99 0.10439741 2.005989e-04 0.895401993 #> 4501 45.00 0.10434522 2.004986e-04 0.895454279 #> 4502 45.01 0.10429306 2.003984e-04 0.895506539 #> 4503 45.02 0.10424093 2.002982e-04 0.895558772 #> 4504 45.03 0.10418882 2.001981e-04 0.895610980 #> 4505 45.04 0.10413674 2.000980e-04 0.895663161 #> 4506 45.05 0.10408469 1.999980e-04 0.895715317 #> 4507 45.06 0.10403266 1.998980e-04 0.895767446 #> 4508 45.07 0.10398065 1.997981e-04 0.895819549 #> 4509 45.08 0.10392868 1.996983e-04 0.895871626 #> 4510 45.09 0.10387672 1.995984e-04 0.895923678 #> 4511 45.10 0.10382480 1.994987e-04 0.895975703 #> 4512 45.11 0.10377290 1.993989e-04 0.896027702 #> 4513 45.12 0.10372103 1.992993e-04 0.896079675 #> 4514 45.13 0.10366918 1.991996e-04 0.896131622 #> 4515 45.14 0.10361736 1.991001e-04 0.896183543 #> 4516 45.15 0.10356556 1.990005e-04 0.896235439 #> 4517 45.16 0.10351379 1.989011e-04 0.896287308 #> 4518 45.17 0.10346205 1.988016e-04 0.896339151 #> 4519 45.18 0.10341033 1.987023e-04 0.896390969 #> 4520 45.19 0.10335864 1.986029e-04 0.896442760 #> 4521 45.20 0.10330697 1.985036e-04 0.896494526 #> 4522 45.21 0.10325533 1.984044e-04 0.896546266 #> 4523 45.22 0.10320371 1.983052e-04 0.896597980 #> 4524 45.23 0.10315213 1.982061e-04 0.896649668 #> 4525 45.24 0.10310056 1.981070e-04 0.896701330 #> 4526 45.25 0.10304903 1.980080e-04 0.896752967 #> 4527 45.26 0.10299751 1.979090e-04 0.896804577 #> 4528 45.27 0.10294603 1.978101e-04 0.896856162 #> 4529 45.28 0.10289457 1.977112e-04 0.896907721 #> 4530 45.29 0.10284313 1.976124e-04 0.896959254 #> 4531 45.30 0.10279172 1.975136e-04 0.897010762 #> 4532 45.31 0.10274034 1.974149e-04 0.897062244 #> 4533 45.32 0.10268898 1.973162e-04 0.897113700 #> 4534 45.33 0.10263765 1.972176e-04 0.897165130 #> 4535 45.34 0.10258635 1.971190e-04 0.897216534 #> 4536 45.35 0.10253507 1.970204e-04 0.897267913 #> 4537 45.36 0.10248381 1.969220e-04 0.897319267 #> 4538 45.37 0.10243258 1.968235e-04 0.897370594 #> 4539 45.38 0.10238138 1.967251e-04 0.897421896 #> 4540 45.39 0.10233020 1.966268e-04 0.897473172 #> 4541 45.40 0.10227905 1.965285e-04 0.897524423 #> 4542 45.41 0.10222792 1.964303e-04 0.897575648 #> 4543 45.42 0.10217682 1.963321e-04 0.897626847 #> 4544 45.43 0.10212575 1.962339e-04 0.897678021 #> 4545 45.44 0.10207470 1.961358e-04 0.897729169 #> 4546 45.45 0.10202367 1.960378e-04 0.897780292 #> 4547 45.46 0.10197267 1.959398e-04 0.897831389 #> 4548 45.47 0.10192170 1.958419e-04 0.897882460 #> 4549 45.48 0.10187075 1.957440e-04 0.897933506 #> 4550 45.49 0.10181983 1.956461e-04 0.897984527 #> 4551 45.50 0.10176893 1.955483e-04 0.898035522 #> 4552 45.51 0.10171806 1.954506e-04 0.898086491 #> 4553 45.52 0.10166721 1.953529e-04 0.898137435 #> 4554 45.53 0.10161639 1.952552e-04 0.898188354 #> 4555 45.54 0.10156560 1.951576e-04 0.898239247 #> 4556 45.55 0.10151483 1.950601e-04 0.898290115 #> 4557 45.56 0.10146408 1.949625e-04 0.898340957 #> 4558 45.57 0.10141336 1.948651e-04 0.898391774 #> 4559 45.58 0.10136267 1.947677e-04 0.898442565 #> 4560 45.59 0.10131200 1.946703e-04 0.898493331 #> 4561 45.60 0.10126136 1.945730e-04 0.898544072 #> 4562 45.61 0.10121074 1.944758e-04 0.898594787 #> 4563 45.62 0.10116014 1.943785e-04 0.898645477 #> 4564 45.63 0.10110958 1.942814e-04 0.898696142 #> 4565 45.64 0.10105903 1.941843e-04 0.898746781 #> 4566 45.65 0.10100852 1.940872e-04 0.898797395 #> 4567 45.66 0.10095803 1.939902e-04 0.898847984 #> 4568 45.67 0.10090756 1.938932e-04 0.898898547 #> 4569 45.68 0.10085712 1.937963e-04 0.898949085 #> 4570 45.69 0.10080670 1.936994e-04 0.898999598 #> 4571 45.70 0.10075631 1.936026e-04 0.899050085 #> 4572 45.71 0.10070595 1.935058e-04 0.899100548 #> 4573 45.72 0.10065561 1.934091e-04 0.899150985 #> 4574 45.73 0.10060529 1.933124e-04 0.899201397 #> 4575 45.74 0.10055500 1.932158e-04 0.899251784 #> 4576 45.75 0.10050474 1.931192e-04 0.899302145 #> 4577 45.76 0.10045450 1.930226e-04 0.899352481 #> 4578 45.77 0.10040428 1.929262e-04 0.899402793 #> 4579 45.78 0.10035409 1.928297e-04 0.899453079 #> 4580 45.79 0.10030393 1.927333e-04 0.899503339 #> 4581 45.80 0.10025379 1.926370e-04 0.899553575 #> 4582 45.81 0.10020367 1.925407e-04 0.899603786 #> 4583 45.82 0.10015358 1.924444e-04 0.899653971 #> 4584 45.83 0.10010352 1.923482e-04 0.899704132 #> 4585 45.84 0.10005348 1.922521e-04 0.899754267 #> 4586 45.85 0.10000347 1.921560e-04 0.899804378 #> 4587 45.86 0.09995348 1.920599e-04 0.899854463 #> 4588 45.87 0.09990351 1.919639e-04 0.899904523 #> 4589 45.88 0.09985357 1.918680e-04 0.899954558 #> 4590 45.89 0.09980366 1.917721e-04 0.900004569 #> 4591 45.90 0.09975377 1.916762e-04 0.900054554 #> 4592 45.91 0.09970391 1.915804e-04 0.900104514 #> 4593 45.92 0.09965407 1.914846e-04 0.900154449 #> 4594 45.93 0.09960425 1.913889e-04 0.900204360 #> 4595 45.94 0.09955446 1.912932e-04 0.900254245 #> 4596 45.95 0.09950470 1.911976e-04 0.900304105 #> 4597 45.96 0.09945496 1.911020e-04 0.900353941 #> 4598 45.97 0.09940524 1.910065e-04 0.900403752 #> 4599 45.98 0.09935555 1.909110e-04 0.900453537 #> 4600 45.99 0.09930589 1.908156e-04 0.900503298 #> 4601 46.00 0.09925625 1.907202e-04 0.900553034 #> 4602 46.01 0.09920663 1.906249e-04 0.900602745 #> 4603 46.02 0.09915704 1.905296e-04 0.900652431 #> 4604 46.03 0.09910747 1.904343e-04 0.900702093 #> 4605 46.04 0.09905793 1.903391e-04 0.900751729 #> 4606 46.05 0.09900842 1.902440e-04 0.900801341 #> 4607 46.06 0.09895892 1.901489e-04 0.900850928 #> 4608 46.07 0.09890946 1.900539e-04 0.900900490 #> 4609 46.08 0.09886001 1.899589e-04 0.900950027 #> 4610 46.09 0.09881060 1.898639e-04 0.900999540 #> 4611 46.10 0.09876120 1.897690e-04 0.901049028 #> 4612 46.11 0.09871183 1.896741e-04 0.901098491 #> 4613 46.12 0.09866249 1.895793e-04 0.901147929 #> 4614 46.13 0.09861317 1.894845e-04 0.901197343 #> 4615 46.14 0.09856388 1.893898e-04 0.901246732 #> 4616 46.15 0.09851461 1.892952e-04 0.901296096 #> 4617 46.16 0.09846536 1.892005e-04 0.901345436 #> 4618 46.17 0.09841614 1.891060e-04 0.901394751 #> 4619 46.18 0.09836695 1.890114e-04 0.901444041 #> 4620 46.19 0.09831778 1.889169e-04 0.901493307 #> 4621 46.20 0.09826863 1.888225e-04 0.901542548 #> 4622 46.21 0.09821951 1.887281e-04 0.901591764 #> 4623 46.22 0.09817041 1.886338e-04 0.901640956 #> 4624 46.23 0.09812134 1.885395e-04 0.901690123 #> 4625 46.24 0.09807229 1.884452e-04 0.901739266 #> 4626 46.25 0.09802326 1.883510e-04 0.901788384 #> 4627 46.26 0.09797427 1.882569e-04 0.901837478 #> 4628 46.27 0.09792529 1.881628e-04 0.901886547 #> 4629 46.28 0.09787634 1.880687e-04 0.901935591 #> 4630 46.29 0.09782741 1.879747e-04 0.901984611 #> 4631 46.30 0.09777851 1.878808e-04 0.902033606 #> 4632 46.31 0.09772964 1.877868e-04 0.902082577 #> 4633 46.32 0.09768078 1.876930e-04 0.902131524 #> 4634 46.33 0.09763195 1.875991e-04 0.902180446 #> 4635 46.34 0.09758315 1.875054e-04 0.902229343 #> 4636 46.35 0.09753437 1.874116e-04 0.902278217 #> 4637 46.36 0.09748562 1.873180e-04 0.902327065 #> 4638 46.37 0.09743689 1.872243e-04 0.902375889 #> 4639 46.38 0.09738818 1.871307e-04 0.902424689 #> 4640 46.39 0.09733950 1.870372e-04 0.902473465 #> 4641 46.40 0.09729084 1.869437e-04 0.902522216 #> 4642 46.41 0.09724221 1.868502e-04 0.902570943 #> 4643 46.42 0.09719360 1.867568e-04 0.902619645 #> 4644 46.43 0.09714501 1.866635e-04 0.902668323 #> 4645 46.44 0.09709645 1.865702e-04 0.902716977 #> 4646 46.45 0.09704792 1.864769e-04 0.902765606 #> 4647 46.46 0.09699941 1.863837e-04 0.902814211 #> 4648 46.47 0.09695092 1.862905e-04 0.902862792 #> 4649 46.48 0.09690245 1.861974e-04 0.902911348 #> 4650 46.49 0.09685402 1.861043e-04 0.902959880 #> 4651 46.50 0.09680560 1.860113e-04 0.903008388 #> 4652 46.51 0.09675721 1.859183e-04 0.903056872 #> 4653 46.52 0.09670884 1.858254e-04 0.903105332 #> 4654 46.53 0.09666050 1.857325e-04 0.903153767 #> 4655 46.54 0.09661218 1.856397e-04 0.903202178 #> 4656 46.55 0.09656389 1.855469e-04 0.903250565 #> 4657 46.56 0.09651562 1.854541e-04 0.903298927 #> 4658 46.57 0.09646737 1.853614e-04 0.903347266 #> 4659 46.58 0.09641915 1.852688e-04 0.903395580 #> 4660 46.59 0.09637095 1.851761e-04 0.903443870 #> 4661 46.60 0.09632278 1.850836e-04 0.903492136 #> 4662 46.61 0.09627463 1.849911e-04 0.903540378 #> 4663 46.62 0.09622651 1.848986e-04 0.903588596 #> 4664 46.63 0.09617840 1.848062e-04 0.903636789 #> 4665 46.64 0.09613033 1.847138e-04 0.903684959 #> 4666 46.65 0.09608227 1.846214e-04 0.903733104 #> 4667 46.66 0.09603425 1.845292e-04 0.903781226 #> 4668 46.67 0.09598624 1.844369e-04 0.903829323 #> 4669 46.68 0.09593826 1.843447e-04 0.903877397 #> 4670 46.69 0.09589030 1.842526e-04 0.903925446 #> 4671 46.70 0.09584237 1.841605e-04 0.903973471 #> 4672 46.71 0.09579446 1.840684e-04 0.904021472 #> 4673 46.72 0.09574657 1.839764e-04 0.904069450 #> 4674 46.73 0.09569871 1.838844e-04 0.904117403 #> 4675 46.74 0.09565088 1.837925e-04 0.904165332 #> 4676 46.75 0.09560306 1.837006e-04 0.904213238 #> 4677 46.76 0.09555527 1.836088e-04 0.904261119 #> 4678 46.77 0.09550751 1.835170e-04 0.904308976 #> 4679 46.78 0.09545976 1.834253e-04 0.904356810 #> 4680 46.79 0.09541205 1.833336e-04 0.904404620 #> 4681 46.80 0.09536435 1.832420e-04 0.904452405 #> 4682 46.81 0.09531668 1.831504e-04 0.904500167 #> 4683 46.82 0.09526904 1.830588e-04 0.904547905 #> 4684 46.83 0.09522141 1.829673e-04 0.904595619 #> 4685 46.84 0.09517381 1.828758e-04 0.904643310 #> 4686 46.85 0.09512624 1.827844e-04 0.904690976 #> 4687 46.86 0.09507869 1.826931e-04 0.904738619 #> 4688 46.87 0.09503116 1.826017e-04 0.904786237 #> 4689 46.88 0.09498366 1.825105e-04 0.904833832 #> 4690 46.89 0.09493618 1.824192e-04 0.904881404 #> 4691 46.90 0.09488872 1.823280e-04 0.904928951 #> 4692 46.91 0.09484129 1.822369e-04 0.904976475 #> 4693 46.92 0.09479388 1.821458e-04 0.905023975 #> 4694 46.93 0.09474649 1.820548e-04 0.905071451 #> 4695 46.94 0.09469913 1.819637e-04 0.905118903 #> 4696 46.95 0.09465180 1.818728e-04 0.905166332 #> 4697 46.96 0.09460448 1.817819e-04 0.905213737 #> 4698 46.97 0.09455719 1.816910e-04 0.905261118 #> 4699 46.98 0.09450992 1.816002e-04 0.905308476 #> 4700 46.99 0.09446268 1.815094e-04 0.905355810 #> 4701 47.00 0.09441546 1.814187e-04 0.905403120 #> 4702 47.01 0.09436827 1.813280e-04 0.905450407 #> 4703 47.02 0.09432109 1.812373e-04 0.905497669 #> 4704 47.03 0.09427394 1.811468e-04 0.905544909 #> 4705 47.04 0.09422682 1.810562e-04 0.905592125 #> 4706 47.05 0.09417972 1.809657e-04 0.905639317 #> 4707 47.06 0.09413264 1.808752e-04 0.905686485 #> 4708 47.07 0.09408558 1.807848e-04 0.905733630 #> 4709 47.08 0.09403855 1.806944e-04 0.905780752 #> 4710 47.09 0.09399155 1.806041e-04 0.905827850 #> 4711 47.10 0.09394456 1.805138e-04 0.905874924 #> 4712 47.11 0.09389760 1.804236e-04 0.905921975 #> 4713 47.12 0.09385066 1.803334e-04 0.905969002 #> 4714 47.13 0.09380375 1.802433e-04 0.906016006 #> 4715 47.14 0.09375686 1.801532e-04 0.906062986 #> 4716 47.15 0.09370999 1.800631e-04 0.906109943 #> 4717 47.16 0.09366315 1.799731e-04 0.906156876 #> 4718 47.17 0.09361633 1.798832e-04 0.906203786 #> 4719 47.18 0.09356953 1.797932e-04 0.906250672 #> 4720 47.19 0.09352276 1.797034e-04 0.906297535 #> 4721 47.20 0.09347601 1.796135e-04 0.906344375 #> 4722 47.21 0.09342929 1.795237e-04 0.906391191 #> 4723 47.22 0.09338258 1.794340e-04 0.906437983 #> 4724 47.23 0.09333590 1.793443e-04 0.906484753 #> 4725 47.24 0.09328925 1.792547e-04 0.906531499 #> 4726 47.25 0.09324261 1.791651e-04 0.906578221 #> 4727 47.26 0.09319600 1.790755e-04 0.906624920 #> 4728 47.27 0.09314942 1.789860e-04 0.906671596 #> 4729 47.28 0.09310285 1.788965e-04 0.906718249 #> 4730 47.29 0.09305631 1.788071e-04 0.906764878 #> 4731 47.30 0.09300980 1.787177e-04 0.906811484 #> 4732 47.31 0.09296331 1.786284e-04 0.906858067 #> 4733 47.32 0.09291683 1.785391e-04 0.906904626 #> 4734 47.33 0.09287039 1.784498e-04 0.906951162 #> 4735 47.34 0.09282396 1.783606e-04 0.906997675 #> 4736 47.35 0.09277756 1.782715e-04 0.907044164 #> 4737 47.36 0.09273119 1.781824e-04 0.907090631 #> 4738 47.37 0.09268483 1.780933e-04 0.907137074 #> 4739 47.38 0.09263850 1.780043e-04 0.907183494 #> 4740 47.39 0.09259219 1.779153e-04 0.907229890 #> 4741 47.40 0.09254591 1.778263e-04 0.907276264 #> 4742 47.41 0.09249965 1.777375e-04 0.907322614 #> 4743 47.42 0.09245341 1.776486e-04 0.907368941 #> 4744 47.43 0.09240720 1.775598e-04 0.907415245 #> 4745 47.44 0.09236100 1.774710e-04 0.907461526 #> 4746 47.45 0.09231483 1.773823e-04 0.907507784 #> 4747 47.46 0.09226869 1.772937e-04 0.907554018 #> 4748 47.47 0.09222257 1.772050e-04 0.907600230 #> 4749 47.48 0.09217647 1.771165e-04 0.907646418 #> 4750 47.49 0.09213039 1.770279e-04 0.907692583 #> 4751 47.50 0.09208434 1.769394e-04 0.907738725 #> 4752 47.51 0.09203830 1.768510e-04 0.907784844 #> 4753 47.52 0.09199230 1.767626e-04 0.907830940 #> 4754 47.53 0.09194631 1.766742e-04 0.907877013 #> 4755 47.54 0.09190035 1.765859e-04 0.907923063 #> 4756 47.55 0.09185441 1.764976e-04 0.907969090 #> 4757 47.56 0.09180850 1.764094e-04 0.908015094 #> 4758 47.57 0.09176260 1.763212e-04 0.908061075 #> 4759 47.58 0.09171673 1.762331e-04 0.908107033 #> 4760 47.59 0.09167089 1.761450e-04 0.908152968 #> 4761 47.60 0.09162506 1.760569e-04 0.908198880 #> 4762 47.61 0.09157926 1.759689e-04 0.908244769 #> 4763 47.62 0.09153348 1.758810e-04 0.908290636 #> 4764 47.63 0.09148773 1.757931e-04 0.908336479 #> 4765 47.64 0.09144200 1.757052e-04 0.908382299 #> 4766 47.65 0.09139629 1.756173e-04 0.908428096 #> 4767 47.66 0.09135060 1.755296e-04 0.908473871 #> 4768 47.67 0.09130494 1.754418e-04 0.908519623 #> 4769 47.68 0.09125929 1.753541e-04 0.908565351 #> 4770 47.69 0.09121368 1.752665e-04 0.908611057 #> 4771 47.70 0.09116808 1.751789e-04 0.908656740 #> 4772 47.71 0.09112251 1.750913e-04 0.908702401 #> 4773 47.72 0.09107696 1.750038e-04 0.908748038 #> 4774 47.73 0.09103143 1.749163e-04 0.908793653 #> 4775 47.74 0.09098593 1.748288e-04 0.908839244 #> 4776 47.75 0.09094045 1.747415e-04 0.908884813 #> 4777 47.76 0.09089499 1.746541e-04 0.908930359 #> 4778 47.77 0.09084955 1.745668e-04 0.908975883 #> 4779 47.78 0.09080414 1.744795e-04 0.909021384 #> 4780 47.79 0.09075875 1.743923e-04 0.909066862 #> 4781 47.80 0.09071338 1.743051e-04 0.909112317 #> 4782 47.81 0.09066803 1.742180e-04 0.909157749 #> 4783 47.82 0.09062271 1.741309e-04 0.909203159 #> 4784 47.83 0.09057741 1.740439e-04 0.909248546 #> 4785 47.84 0.09053213 1.739569e-04 0.909293910 #> 4786 47.85 0.09048688 1.738699e-04 0.909339252 #> 4787 47.86 0.09044165 1.737830e-04 0.909384571 #> 4788 47.87 0.09039644 1.736961e-04 0.909429868 #> 4789 47.88 0.09035125 1.736093e-04 0.909475141 #> 4790 47.89 0.09030608 1.735225e-04 0.909520392 #> 4791 47.90 0.09026094 1.734358e-04 0.909565621 #> 4792 47.91 0.09021582 1.733491e-04 0.909610827 #> 4793 47.92 0.09017073 1.732624e-04 0.909656010 #> 4794 47.93 0.09012565 1.731758e-04 0.909701171 #> 4795 47.94 0.09008060 1.730893e-04 0.909746309 #> 4796 47.95 0.09003557 1.730027e-04 0.909791425 #> 4797 47.96 0.08999057 1.729163e-04 0.909836518 #> 4798 47.97 0.08994558 1.728298e-04 0.909881588 #> 4799 47.98 0.08990062 1.727434e-04 0.909926636 #> 4800 47.99 0.08985568 1.726571e-04 0.909971661 #> 4801 48.00 0.08981076 1.725708e-04 0.910016664 #> 4802 48.01 0.08976587 1.724845e-04 0.910061645 #> 4803 48.02 0.08972100 1.723983e-04 0.910106603 #> 4804 48.03 0.08967615 1.723121e-04 0.910151538 #> 4805 48.04 0.08963132 1.722260e-04 0.910196451 #> 4806 48.05 0.08958652 1.721399e-04 0.910241342 #> 4807 48.06 0.08954174 1.720538e-04 0.910286210 #> 4808 48.07 0.08949698 1.719678e-04 0.910331056 #> 4809 48.08 0.08945224 1.718819e-04 0.910375879 #> 4810 48.09 0.08940752 1.717960e-04 0.910420680 #> 4811 48.10 0.08936283 1.717101e-04 0.910465458 #> 4812 48.11 0.08931816 1.716242e-04 0.910510214 #> 4813 48.12 0.08927351 1.715385e-04 0.910554948 #> 4814 48.13 0.08922889 1.714527e-04 0.910599659 #> 4815 48.14 0.08918428 1.713670e-04 0.910644348 #> 4816 48.15 0.08913970 1.712813e-04 0.910689015 #> 4817 48.16 0.08909515 1.711957e-04 0.910733659 #> 4818 48.17 0.08905061 1.711101e-04 0.910778281 #> 4819 48.18 0.08900609 1.710246e-04 0.910822881 #> 4820 48.19 0.08896160 1.709391e-04 0.910867458 #> 4821 48.20 0.08891713 1.708537e-04 0.910912014 #> 4822 48.21 0.08887269 1.707683e-04 0.910956546 #> 4823 48.22 0.08882826 1.706829e-04 0.911001057 #> 4824 48.23 0.08878386 1.705976e-04 0.911045545 #> 4825 48.24 0.08873948 1.705123e-04 0.911090011 #> 4826 48.25 0.08869512 1.704271e-04 0.911134455 #> 4827 48.26 0.08865078 1.703419e-04 0.911178877 #> 4828 48.27 0.08860647 1.702567e-04 0.911223276 #> 4829 48.28 0.08856217 1.701716e-04 0.911267654 #> 4830 48.29 0.08851790 1.700866e-04 0.911312009 #> 4831 48.30 0.08847366 1.700015e-04 0.911356342 #> 4832 48.31 0.08842943 1.699166e-04 0.911400653 #> 4833 48.32 0.08838523 1.698316e-04 0.911444941 #> 4834 48.33 0.08834105 1.697467e-04 0.911489208 #> 4835 48.34 0.08829689 1.696619e-04 0.911533452 #> 4836 48.35 0.08825275 1.695771e-04 0.911577674 #> 4837 48.36 0.08820863 1.694923e-04 0.911621874 #> 4838 48.37 0.08816454 1.694076e-04 0.911666052 #> 4839 48.38 0.08812047 1.693229e-04 0.911710208 #> 4840 48.39 0.08807642 1.692382e-04 0.911754342 #> 4841 48.40 0.08803239 1.691537e-04 0.911798454 #> 4842 48.41 0.08798839 1.690691e-04 0.911842544 #> 4843 48.42 0.08794440 1.689846e-04 0.911886611 #> 4844 48.43 0.08790044 1.689001e-04 0.911930657 #> 4845 48.44 0.08785650 1.688157e-04 0.911974681 #> 4846 48.45 0.08781259 1.687313e-04 0.912018682 #> 4847 48.46 0.08776869 1.686469e-04 0.912062662 #> 4848 48.47 0.08772482 1.685626e-04 0.912106620 #> 4849 48.48 0.08768097 1.684784e-04 0.912150555 #> 4850 48.49 0.08763714 1.683942e-04 0.912194469 #> 4851 48.50 0.08759333 1.683100e-04 0.912238361 #> 4852 48.51 0.08754954 1.682259e-04 0.912282231 #> 4853 48.52 0.08750578 1.681418e-04 0.912326079 #> 4854 48.53 0.08746204 1.680577e-04 0.912369905 #> 4855 48.54 0.08741832 1.679737e-04 0.912413709 #> 4856 48.55 0.08737462 1.678897e-04 0.912457491 #> 4857 48.56 0.08733094 1.678058e-04 0.912501251 #> 4858 48.57 0.08728729 1.677219e-04 0.912544990 #> 4859 48.58 0.08724366 1.676381e-04 0.912588706 #> 4860 48.59 0.08720004 1.675543e-04 0.912632401 #> 4861 48.60 0.08715646 1.674705e-04 0.912676074 #> 4862 48.61 0.08711289 1.673868e-04 0.912719725 #> 4863 48.62 0.08706934 1.673032e-04 0.912763354 #> 4864 48.63 0.08702582 1.672195e-04 0.912806962 #> 4865 48.64 0.08698232 1.671359e-04 0.912850547 #> 4866 48.65 0.08693884 1.670524e-04 0.912894111 #> 4867 48.66 0.08689538 1.669689e-04 0.912937653 #> 4868 48.67 0.08685194 1.668854e-04 0.912981173 #> 4869 48.68 0.08680853 1.668020e-04 0.913024672 #> 4870 48.69 0.08676513 1.667186e-04 0.913068149 #> 4871 48.70 0.08672176 1.666353e-04 0.913111604 #> 4872 48.71 0.08667841 1.665520e-04 0.913155037 #> 4873 48.72 0.08663508 1.664687e-04 0.913198449 #> 4874 48.73 0.08659178 1.663855e-04 0.913241839 #> 4875 48.74 0.08654849 1.663023e-04 0.913285207 #> 4876 48.75 0.08650523 1.662192e-04 0.913328554 #> 4877 48.76 0.08646199 1.661361e-04 0.913371878 #> 4878 48.77 0.08641877 1.660531e-04 0.913415182 #> 4879 48.78 0.08637557 1.659701e-04 0.913458463 #> 4880 48.79 0.08633239 1.658871e-04 0.913501723 #> 4881 48.80 0.08628923 1.658042e-04 0.913544962 #> 4882 48.81 0.08624610 1.657213e-04 0.913588178 #> 4883 48.82 0.08620299 1.656385e-04 0.913631373 #> 4884 48.83 0.08615990 1.655557e-04 0.913674547 #> 4885 48.84 0.08611683 1.654729e-04 0.913717699 #> 4886 48.85 0.08607378 1.653902e-04 0.913760829 #> 4887 48.86 0.08603075 1.653075e-04 0.913803938 #> 4888 48.87 0.08598775 1.652249e-04 0.913847025 #> 4889 48.88 0.08594477 1.651423e-04 0.913890091 #> 4890 48.89 0.08590181 1.650597e-04 0.913933135 #> 4891 48.90 0.08585886 1.649772e-04 0.913976158 #> 4892 48.91 0.08581595 1.648948e-04 0.914019159 #> 4893 48.92 0.08577305 1.648123e-04 0.914062139 #> 4894 48.93 0.08573017 1.647300e-04 0.914105097 #> 4895 48.94 0.08568732 1.646476e-04 0.914148034 #> 4896 48.95 0.08564449 1.645653e-04 0.914190949 #> 4897 48.96 0.08560167 1.644830e-04 0.914233843 #> 4898 48.97 0.08555888 1.644008e-04 0.914276715 #> 4899 48.98 0.08551612 1.643186e-04 0.914319566 #> 4900 48.99 0.08547337 1.642365e-04 0.914362396 #> 4901 49.00 0.08543064 1.641544e-04 0.914405204 #> 4902 49.01 0.08538794 1.640723e-04 0.914447990 #> 4903 49.02 0.08534525 1.639903e-04 0.914490756 #> 4904 49.03 0.08530259 1.639084e-04 0.914533500 #> 4905 49.04 0.08525995 1.638264e-04 0.914576222 #> 4906 49.05 0.08521733 1.637445e-04 0.914618923 #> 4907 49.06 0.08517473 1.636627e-04 0.914661603 #> 4908 49.07 0.08513216 1.635809e-04 0.914704262 #> 4909 49.08 0.08508960 1.634991e-04 0.914746899 #> 4910 49.09 0.08504707 1.634174e-04 0.914789515 #> 4911 49.10 0.08500455 1.633357e-04 0.914832109 #> 4912 49.11 0.08496206 1.632540e-04 0.914874683 #> 4913 49.12 0.08491959 1.631724e-04 0.914917235 #> 4914 49.13 0.08487714 1.630909e-04 0.914959766 #> 4915 49.14 0.08483472 1.630093e-04 0.915002275 #> 4916 49.15 0.08479231 1.629279e-04 0.915044763 #> 4917 49.16 0.08474992 1.628464e-04 0.915087230 #> 4918 49.17 0.08470756 1.627650e-04 0.915129676 #> 4919 49.18 0.08466522 1.626836e-04 0.915172101 #> 4920 49.19 0.08462289 1.626023e-04 0.915214504 #> 4921 49.20 0.08458059 1.625210e-04 0.915256886 #> 4922 49.21 0.08453831 1.624398e-04 0.915299247 #> 4923 49.22 0.08449605 1.623586e-04 0.915341587 #> 4924 49.23 0.08445382 1.622774e-04 0.915383905 #> 4925 49.24 0.08441160 1.621963e-04 0.915426203 #> 4926 49.25 0.08436941 1.621152e-04 0.915468479 #> 4927 49.26 0.08432723 1.620342e-04 0.915510734 #> 4928 49.27 0.08428508 1.619532e-04 0.915552969 #> 4929 49.28 0.08424295 1.618723e-04 0.915595182 #> 4930 49.29 0.08420084 1.617913e-04 0.915637373 #> 4931 49.30 0.08415875 1.617105e-04 0.915679544 #> 4932 49.31 0.08411668 1.616296e-04 0.915721694 #> 4933 49.32 0.08407463 1.615488e-04 0.915763822 #> 4934 49.33 0.08403260 1.614681e-04 0.915805930 #> 4935 49.34 0.08399060 1.613874e-04 0.915848017 #> 4936 49.35 0.08394861 1.613067e-04 0.915890082 #> 4937 49.36 0.08390665 1.612261e-04 0.915932126 #> 4938 49.37 0.08386470 1.611455e-04 0.915974150 #> 4939 49.38 0.08382278 1.610649e-04 0.916016152 #> 4940 49.39 0.08378088 1.609844e-04 0.916058134 #> 4941 49.40 0.08373900 1.609039e-04 0.916100094 #> 4942 49.41 0.08369714 1.608235e-04 0.916142034 #> 4943 49.42 0.08365530 1.607431e-04 0.916183952 #> 4944 49.43 0.08361349 1.606628e-04 0.916225850 #> 4945 49.44 0.08357169 1.605824e-04 0.916267726 #> 4946 49.45 0.08352992 1.605022e-04 0.916309582 #> 4947 49.46 0.08348816 1.604219e-04 0.916351417 #> 4948 49.47 0.08344643 1.603417e-04 0.916393231 #> 4949 49.48 0.08340471 1.602616e-04 0.916435024 #> 4950 49.49 0.08336302 1.601815e-04 0.916476796 #> 4951 49.50 0.08332135 1.601014e-04 0.916518547 #> 4952 49.51 0.08327970 1.600214e-04 0.916560277 #> 4953 49.52 0.08323807 1.599414e-04 0.916601986 #> 4954 49.53 0.08319646 1.598614e-04 0.916643675 #> 4955 49.54 0.08315488 1.597815e-04 0.916685343 #> 4956 49.55 0.08311331 1.597017e-04 0.916726990 #> 4957 49.56 0.08307176 1.596218e-04 0.916768616 #> 4958 49.57 0.08303024 1.595420e-04 0.916810221 #> 4959 49.58 0.08298873 1.594623e-04 0.916851806 #> 4960 49.59 0.08294725 1.593826e-04 0.916893369 #> 4961 49.60 0.08290578 1.593029e-04 0.916934912 #> 4962 49.61 0.08286434 1.592233e-04 0.916976434 #> 4963 49.62 0.08282292 1.591437e-04 0.917017936 #> 4964 49.63 0.08278152 1.590641e-04 0.917059416 #> 4965 49.64 0.08274014 1.589846e-04 0.917100876 #> 4966 49.65 0.08269878 1.589051e-04 0.917142316 #> 4967 49.66 0.08265744 1.588257e-04 0.917183734 #> 4968 49.67 0.08261612 1.587463e-04 0.917225132 #> 4969 49.68 0.08257482 1.586670e-04 0.917266509 #> 4970 49.69 0.08253355 1.585877e-04 0.917307865 #> 4971 49.70 0.08249229 1.585084e-04 0.917349201 #> 4972 49.71 0.08245105 1.584291e-04 0.917390516 #> 4973 49.72 0.08240984 1.583500e-04 0.917431811 #> 4974 49.73 0.08236864 1.582708e-04 0.917473084 #> 4975 49.74 0.08232747 1.581917e-04 0.917514338 #> 4976 49.75 0.08228632 1.581126e-04 0.917555570 #> 4977 49.76 0.08224518 1.580336e-04 0.917596782 #> 4978 49.77 0.08220407 1.579546e-04 0.917637973 #> 4979 49.78 0.08216298 1.578756e-04 0.917679144 #> 4980 49.79 0.08212191 1.577967e-04 0.917720294 #> 4981 49.80 0.08208086 1.577178e-04 0.917761424 #> 4982 49.81 0.08203983 1.576390e-04 0.917802533 #> 4983 49.82 0.08199882 1.575602e-04 0.917843621 #> 4984 49.83 0.08195783 1.574814e-04 0.917884689 #> 4985 49.84 0.08191686 1.574027e-04 0.917925737 #> 4986 49.85 0.08187591 1.573240e-04 0.917966763 #> 4987 49.86 0.08183498 1.572454e-04 0.918007770 #> 4988 49.87 0.08179408 1.571668e-04 0.918048756 #> 4989 49.88 0.08175319 1.570882e-04 0.918089721 #> 4990 49.89 0.08171232 1.570097e-04 0.918130666 #> 4991 49.90 0.08167148 1.569312e-04 0.918171590 #> 4992 49.91 0.08163065 1.568528e-04 0.918212494 #> 4993 49.92 0.08158985 1.567743e-04 0.918253378 #> 4994 49.93 0.08154906 1.566960e-04 0.918294241 #> 4995 49.94 0.08150830 1.566176e-04 0.918335084 #> 4996 49.95 0.08146755 1.565394e-04 0.918375906 #> 4997 49.96 0.08142683 1.564611e-04 0.918416708 #> 4998 49.97 0.08138613 1.563829e-04 0.918457489 #> 4999 49.98 0.08134545 1.563047e-04 0.918498250 #> 5000 49.99 0.08130478 1.562266e-04 0.918538991 #> 5001 50.00 0.08126414 1.561485e-04 0.918579711 #> 5002 50.01 0.08122352 1.560704e-04 0.918620411 #> 5003 50.02 0.08118292 1.559924e-04 0.918661091 #> 5004 50.03 0.08114234 1.559145e-04 0.918701750 #> 5005 50.04 0.08110177 1.558365e-04 0.918742389 #> 5006 50.05 0.08106123 1.557586e-04 0.918783008 #> 5007 50.06 0.08102071 1.556808e-04 0.918823606 #> 5008 50.07 0.08098021 1.556029e-04 0.918864184 #> 5009 50.08 0.08093973 1.555252e-04 0.918904742 #> 5010 50.09 0.08089927 1.554474e-04 0.918945279 #> 5011 50.10 0.08085883 1.553697e-04 0.918985797 #> 5012 50.11 0.08081841 1.552920e-04 0.919026294 #> 5013 50.12 0.08077802 1.552144e-04 0.919066770 #> 5014 50.13 0.08073764 1.551368e-04 0.919107227 #> 5015 50.14 0.08069728 1.550593e-04 0.919147663 #> 5016 50.15 0.08065694 1.549818e-04 0.919188079 #> 5017 50.16 0.08061662 1.549043e-04 0.919228475 #> 5018 50.17 0.08057632 1.548269e-04 0.919268851 #> 5019 50.18 0.08053604 1.547495e-04 0.919309206 #> 5020 50.19 0.08049579 1.546721e-04 0.919349542 #> 5021 50.20 0.08045555 1.545948e-04 0.919389857 #> 5022 50.21 0.08041533 1.545175e-04 0.919430152 #> 5023 50.22 0.08037513 1.544403e-04 0.919470427 #> 5024 50.23 0.08033496 1.543631e-04 0.919510681 #> 5025 50.24 0.08029480 1.542859e-04 0.919550916 #> 5026 50.25 0.08025466 1.542088e-04 0.919591130 #> 5027 50.26 0.08021454 1.541317e-04 0.919631325 #> 5028 50.27 0.08017445 1.540547e-04 0.919671499 #> 5029 50.28 0.08013437 1.539777e-04 0.919711653 #> 5030 50.29 0.08009431 1.539007e-04 0.919751787 #> 5031 50.30 0.08005427 1.538238e-04 0.919791901 #> 5032 50.31 0.08001426 1.537469e-04 0.919831996 #> 5033 50.32 0.07997426 1.536700e-04 0.919872069 #> 5034 50.33 0.07993428 1.535932e-04 0.919912123 #> 5035 50.34 0.07989433 1.535164e-04 0.919952157 #> 5036 50.35 0.07985439 1.534397e-04 0.919992171 #> 5037 50.36 0.07981447 1.533630e-04 0.920032165 #> 5038 50.37 0.07977457 1.532863e-04 0.920072139 #> 5039 50.38 0.07973470 1.532097e-04 0.920112093 #> 5040 50.39 0.07969484 1.531331e-04 0.920152027 #> 5041 50.40 0.07965500 1.530566e-04 0.920191941 #> 5042 50.41 0.07961518 1.529800e-04 0.920231835 #> 5043 50.42 0.07957539 1.529036e-04 0.920271709 #> 5044 50.43 0.07953561 1.528271e-04 0.920311563 #> 5045 50.44 0.07949585 1.527507e-04 0.920351398 #> 5046 50.45 0.07945611 1.526744e-04 0.920391212 #> 5047 50.46 0.07941640 1.525981e-04 0.920431006 #> 5048 50.47 0.07937670 1.525218e-04 0.920470781 #> 5049 50.48 0.07933702 1.524455e-04 0.920510536 #> 5050 50.49 0.07929736 1.523693e-04 0.920550270 #> 5051 50.50 0.07925772 1.522932e-04 0.920589985 #> 5052 50.51 0.07921810 1.522171e-04 0.920629681 #> 5053 50.52 0.07917850 1.521410e-04 0.920669356 #> 5054 50.53 0.07913892 1.520649e-04 0.920709011 #> 5055 50.54 0.07909936 1.519889e-04 0.920748647 #> 5056 50.55 0.07905982 1.519129e-04 0.920788263 #> 5057 50.56 0.07902030 1.518370e-04 0.920827858 #> 5058 50.57 0.07898080 1.517611e-04 0.920867435 #> 5059 50.58 0.07894132 1.516852e-04 0.920906991 #> 5060 50.59 0.07890186 1.516094e-04 0.920946528 #> 5061 50.60 0.07886242 1.515336e-04 0.920986045 #> 5062 50.61 0.07882300 1.514579e-04 0.921025542 #> 5063 50.62 0.07878360 1.513822e-04 0.921065019 #> 5064 50.63 0.07874422 1.513065e-04 0.921104477 #> 5065 50.64 0.07870485 1.512308e-04 0.921143915 #> 5066 50.65 0.07866551 1.511553e-04 0.921183333 #> 5067 50.66 0.07862619 1.510797e-04 0.921222731 #> 5068 50.67 0.07858689 1.510042e-04 0.921262110 #> 5069 50.68 0.07854760 1.509287e-04 0.921301469 #> 5070 50.69 0.07850834 1.508532e-04 0.921340809 #> 5071 50.70 0.07846909 1.507778e-04 0.921380128 #> 5072 50.71 0.07842987 1.507025e-04 0.921419428 #> 5073 50.72 0.07839066 1.506271e-04 0.921458709 #> 5074 50.73 0.07835148 1.505518e-04 0.921497970 #> 5075 50.74 0.07831231 1.504766e-04 0.921537211 #> 5076 50.75 0.07827317 1.504014e-04 0.921576433 #> 5077 50.76 0.07823404 1.503262e-04 0.921615634 #> 5078 50.77 0.07819493 1.502510e-04 0.921654817 #> 5079 50.78 0.07815584 1.501759e-04 0.921693980 #> 5080 50.79 0.07811678 1.501009e-04 0.921733123 #> 5081 50.80 0.07807773 1.500258e-04 0.921772247 #> 5082 50.81 0.07803870 1.499508e-04 0.921811351 #> 5083 50.82 0.07799969 1.498759e-04 0.921850435 #> 5084 50.83 0.07796070 1.498010e-04 0.921889500 #> 5085 50.84 0.07792173 1.497261e-04 0.921928546 #> 5086 50.85 0.07788278 1.496512e-04 0.921967572 #> 5087 50.86 0.07784385 1.495764e-04 0.922006578 #> 5088 50.87 0.07780493 1.495017e-04 0.922045565 #> 5089 50.88 0.07776604 1.494269e-04 0.922084533 #> 5090 50.89 0.07772717 1.493522e-04 0.922123481 #> 5091 50.90 0.07768831 1.492776e-04 0.922162409 #> 5092 50.91 0.07764948 1.492030e-04 0.922201318 #> 5093 50.92 0.07761066 1.491284e-04 0.922240208 #> 5094 50.93 0.07757187 1.490538e-04 0.922279078 #> 5095 50.94 0.07753309 1.489793e-04 0.922317929 #> 5096 50.95 0.07749434 1.489048e-04 0.922356760 #> 5097 50.96 0.07745560 1.488304e-04 0.922395572 #> 5098 50.97 0.07741688 1.487560e-04 0.922434365 #> 5099 50.98 0.07737818 1.486817e-04 0.922473138 #> 5100 50.99 0.07733950 1.486073e-04 0.922511891 #> 5101 51.00 0.07730084 1.485330e-04 0.922550626 #> 5102 51.01 0.07726220 1.484588e-04 0.922589341 #> 5103 51.02 0.07722358 1.483846e-04 0.922628036 #> 5104 51.03 0.07718498 1.483104e-04 0.922666713 #> 5105 51.04 0.07714639 1.482363e-04 0.922705370 #> 5106 51.05 0.07710783 1.481622e-04 0.922744007 #> 5107 51.06 0.07706929 1.480881e-04 0.922782626 #> 5108 51.07 0.07703076 1.480141e-04 0.922821225 #> 5109 51.08 0.07699226 1.479401e-04 0.922859804 #> 5110 51.09 0.07695377 1.478661e-04 0.922898365 #> 5111 51.10 0.07691530 1.477922e-04 0.922936906 #> 5112 51.11 0.07687685 1.477184e-04 0.922975428 #> 5113 51.12 0.07683842 1.476445e-04 0.923013931 #> 5114 51.13 0.07680002 1.475707e-04 0.923052414 #> 5115 51.14 0.07676162 1.474969e-04 0.923090878 #> 5116 51.15 0.07672325 1.474232e-04 0.923129323 #> 5117 51.16 0.07668490 1.473495e-04 0.923167749 #> 5118 51.17 0.07664657 1.472759e-04 0.923206156 #> 5119 51.18 0.07660825 1.472022e-04 0.923244543 #> 5120 51.19 0.07656996 1.471287e-04 0.923282911 #> 5121 51.20 0.07653168 1.470551e-04 0.923321260 #> 5122 51.21 0.07649343 1.469816e-04 0.923359590 #> 5123 51.22 0.07645519 1.469081e-04 0.923397900 #> 5124 51.23 0.07641697 1.468347e-04 0.923436192 #> 5125 51.24 0.07637877 1.467613e-04 0.923474464 #> 5126 51.25 0.07634059 1.466879e-04 0.923512717 #> 5127 51.26 0.07630243 1.466146e-04 0.923550951 #> 5128 51.27 0.07626429 1.465413e-04 0.923589166 #> 5129 51.28 0.07622617 1.464681e-04 0.923627362 #> 5130 51.29 0.07618807 1.463949e-04 0.923665539 #> 5131 51.30 0.07614998 1.463217e-04 0.923703697 #> 5132 51.31 0.07611192 1.462485e-04 0.923741835 #> 5133 51.32 0.07607387 1.461754e-04 0.923779955 #> 5134 51.33 0.07603584 1.461024e-04 0.923818055 #> 5135 51.34 0.07599783 1.460293e-04 0.923856137 #> 5136 51.35 0.07595984 1.459563e-04 0.923894199 #> 5137 51.36 0.07592187 1.458834e-04 0.923932243 #> 5138 51.37 0.07588392 1.458104e-04 0.923970267 #> 5139 51.38 0.07584599 1.457376e-04 0.924008272 #> 5140 51.39 0.07580808 1.456647e-04 0.924046259 #> 5141 51.40 0.07577018 1.455919e-04 0.924084226 #> 5142 51.41 0.07573231 1.455191e-04 0.924122175 #> 5143 51.42 0.07569445 1.454464e-04 0.924160104 #> 5144 51.43 0.07565661 1.453737e-04 0.924198014 #> 5145 51.44 0.07561879 1.453010e-04 0.924235906 #> 5146 51.45 0.07558099 1.452284e-04 0.924273779 #> 5147 51.46 0.07554321 1.451558e-04 0.924311632 #> 5148 51.47 0.07550545 1.450832e-04 0.924349467 #> 5149 51.48 0.07546771 1.450107e-04 0.924387283 #> 5150 51.49 0.07542998 1.449382e-04 0.924425080 #> 5151 51.50 0.07539228 1.448658e-04 0.924462858 #> 5152 51.51 0.07535459 1.447933e-04 0.924500617 #> 5153 51.52 0.07531692 1.447210e-04 0.924538357 #> 5154 51.53 0.07527927 1.446486e-04 0.924576078 #> 5155 51.54 0.07524164 1.445763e-04 0.924613781 #> 5156 51.55 0.07520403 1.445040e-04 0.924651465 #> 5157 51.56 0.07516644 1.444318e-04 0.924689130 #> 5158 51.57 0.07512886 1.443596e-04 0.924726776 #> 5159 51.58 0.07509131 1.442874e-04 0.924764403 #> 5160 51.59 0.07505377 1.442153e-04 0.924802011 #> 5161 51.60 0.07501626 1.441432e-04 0.924839601 #> 5162 51.61 0.07497876 1.440712e-04 0.924877172 #> 5163 51.62 0.07494128 1.439992e-04 0.924914724 #> 5164 51.63 0.07490382 1.439272e-04 0.924952257 #> 5165 51.64 0.07486637 1.438552e-04 0.924989771 #> 5166 51.65 0.07482895 1.437833e-04 0.925027267 #> 5167 51.66 0.07479154 1.437114e-04 0.925064744 #> 5168 51.67 0.07475416 1.436396e-04 0.925102202 #> 5169 51.68 0.07471679 1.435678e-04 0.925139642 #> 5170 51.69 0.07467944 1.434960e-04 0.925177063 #> 5171 51.70 0.07464211 1.434243e-04 0.925214465 #> 5172 51.71 0.07460480 1.433526e-04 0.925251848 #> 5173 51.72 0.07456751 1.432810e-04 0.925289213 #> 5174 51.73 0.07453023 1.432093e-04 0.925326559 #> 5175 51.74 0.07449298 1.431378e-04 0.925363886 #> 5176 51.75 0.07445574 1.430662e-04 0.925401195 #> 5177 51.76 0.07441852 1.429947e-04 0.925438485 #> 5178 51.77 0.07438132 1.429232e-04 0.925475757 #> 5179 51.78 0.07434414 1.428518e-04 0.925513010 #> 5180 51.79 0.07430698 1.427804e-04 0.925550244 #> 5181 51.80 0.07426983 1.427090e-04 0.925587459 #> 5182 51.81 0.07423271 1.426376e-04 0.925624656 #> 5183 51.82 0.07419560 1.425663e-04 0.925661835 #> 5184 51.83 0.07415851 1.424951e-04 0.925698994 #> 5185 51.84 0.07412144 1.424238e-04 0.925736136 #> 5186 51.85 0.07408439 1.423527e-04 0.925773258 #> 5187 51.86 0.07404736 1.422815e-04 0.925810362 #> 5188 51.87 0.07401034 1.422104e-04 0.925847448 #> 5189 51.88 0.07397335 1.421393e-04 0.925884515 #> 5190 51.89 0.07393637 1.420682e-04 0.925921563 #> 5191 51.90 0.07389941 1.419972e-04 0.925958593 #> 5192 51.91 0.07386247 1.419262e-04 0.925995605 #> 5193 51.92 0.07382555 1.418553e-04 0.926032598 #> 5194 51.93 0.07378864 1.417844e-04 0.926069572 #> 5195 51.94 0.07375176 1.417135e-04 0.926106528 #> 5196 51.95 0.07371489 1.416427e-04 0.926143466 #> 5197 51.96 0.07367804 1.415719e-04 0.926180385 #> 5198 51.97 0.07364121 1.415011e-04 0.926217285 #> 5199 51.98 0.07360440 1.414304e-04 0.926254167 #> 5200 51.99 0.07356761 1.413597e-04 0.926291031 #> 5201 52.00 0.07353083 1.412890e-04 0.926327876 #> 5202 52.01 0.07349408 1.412184e-04 0.926364703 #> 5203 52.02 0.07345734 1.411478e-04 0.926401512 #> 5204 52.03 0.07342062 1.410772e-04 0.926438302 #> 5205 52.04 0.07338392 1.410067e-04 0.926475073 #> 5206 52.05 0.07334724 1.409362e-04 0.926511827 #> 5207 52.06 0.07331057 1.408658e-04 0.926548562 #> 5208 52.07 0.07327393 1.407954e-04 0.926585278 #> 5209 52.08 0.07323730 1.407250e-04 0.926621976 #> 5210 52.09 0.07320069 1.406546e-04 0.926658656 #> 5211 52.10 0.07316410 1.405843e-04 0.926695318 #> 5212 52.11 0.07312753 1.405140e-04 0.926731961 #> 5213 52.12 0.07309097 1.404438e-04 0.926768586 #> 5214 52.13 0.07305443 1.403736e-04 0.926805192 #> 5215 52.14 0.07301792 1.403034e-04 0.926841781 #> 5216 52.15 0.07298142 1.402333e-04 0.926878351 #> 5217 52.16 0.07294493 1.401632e-04 0.926914902 #> 5218 52.17 0.07290847 1.400931e-04 0.926951436 #> 5219 52.18 0.07287203 1.400231e-04 0.926987951 #> 5220 52.19 0.07283560 1.399531e-04 0.927024448 #> 5221 52.20 0.07279919 1.398832e-04 0.927060926 #> 5222 52.21 0.07276280 1.398132e-04 0.927097387 #> 5223 52.22 0.07272643 1.397433e-04 0.927133829 #> 5224 52.23 0.07269007 1.396735e-04 0.927170253 #> 5225 52.24 0.07265374 1.396037e-04 0.927206659 #> 5226 52.25 0.07261742 1.395339e-04 0.927243046 #> 5227 52.26 0.07258112 1.394641e-04 0.927279416 #> 5228 52.27 0.07254484 1.393944e-04 0.927315767 #> 5229 52.28 0.07250858 1.393247e-04 0.927352100 #> 5230 52.29 0.07247233 1.392551e-04 0.927388415 #> 5231 52.30 0.07243610 1.391855e-04 0.927424712 #> 5232 52.31 0.07239989 1.391159e-04 0.927460990 #> 5233 52.32 0.07236370 1.390464e-04 0.927497251 #> 5234 52.33 0.07232753 1.389769e-04 0.927533493 #> 5235 52.34 0.07229138 1.389074e-04 0.927569717 #> 5236 52.35 0.07225524 1.388380e-04 0.927605923 #> 5237 52.36 0.07221912 1.387686e-04 0.927642111 #> 5238 52.37 0.07218302 1.386992e-04 0.927678281 #> 5239 52.38 0.07214694 1.386299e-04 0.927714433 #> 5240 52.39 0.07211087 1.385606e-04 0.927750567 #> 5241 52.40 0.07207483 1.384913e-04 0.927786682 #> 5242 52.41 0.07203880 1.384221e-04 0.927822780 #> 5243 52.42 0.07200279 1.383529e-04 0.927858860 #> 5244 52.43 0.07196680 1.382837e-04 0.927894921 #> 5245 52.44 0.07193082 1.382146e-04 0.927930965 #> 5246 52.45 0.07189486 1.381455e-04 0.927966990 #> 5247 52.46 0.07185893 1.380764e-04 0.928002998 #> 5248 52.47 0.07182301 1.380074e-04 0.928038987 #> 5249 52.48 0.07178710 1.379384e-04 0.928074959 #> 5250 52.49 0.07175122 1.378695e-04 0.928110912 #> 5251 52.50 0.07171535 1.378006e-04 0.928146848 #> 5252 52.51 0.07167950 1.377317e-04 0.928182765 #> 5253 52.52 0.07164367 1.376628e-04 0.928218665 #> 5254 52.53 0.07160786 1.375940e-04 0.928254547 #> 5255 52.54 0.07157206 1.375252e-04 0.928290410 #> 5256 52.55 0.07153629 1.374565e-04 0.928326256 #> 5257 52.56 0.07150053 1.373878e-04 0.928362084 #> 5258 52.57 0.07146479 1.373191e-04 0.928397894 #> 5259 52.58 0.07142906 1.372505e-04 0.928433686 #> 5260 52.59 0.07139336 1.371819e-04 0.928469461 #> 5261 52.60 0.07135767 1.371133e-04 0.928505217 #> 5262 52.61 0.07132200 1.370447e-04 0.928540955 #> 5263 52.62 0.07128635 1.369762e-04 0.928576676 #> 5264 52.63 0.07125071 1.369078e-04 0.928612379 #> 5265 52.64 0.07121510 1.368393e-04 0.928648064 #> 5266 52.65 0.07117950 1.367709e-04 0.928683731 #> 5267 52.66 0.07114392 1.367026e-04 0.928719380 #> 5268 52.67 0.07110835 1.366342e-04 0.928755011 #> 5269 52.68 0.07107281 1.365659e-04 0.928790625 #> 5270 52.69 0.07103728 1.364977e-04 0.928826221 #> 5271 52.70 0.07100177 1.364294e-04 0.928861799 #> 5272 52.71 0.07096628 1.363612e-04 0.928897359 #> 5273 52.72 0.07093081 1.362931e-04 0.928932901 #> 5274 52.73 0.07089535 1.362249e-04 0.928968426 #> 5275 52.74 0.07085991 1.361568e-04 0.929003933 #> 5276 52.75 0.07082449 1.360888e-04 0.929039422 #> 5277 52.76 0.07078909 1.360208e-04 0.929074893 #> 5278 52.77 0.07075370 1.359528e-04 0.929110347 #> 5279 52.78 0.07071833 1.358848e-04 0.929145783 #> 5280 52.79 0.07068298 1.358169e-04 0.929181201 #> 5281 52.80 0.07064765 1.357490e-04 0.929216602 #> 5282 52.81 0.07061233 1.356811e-04 0.929251985 #> 5283 52.82 0.07057704 1.356133e-04 0.929287350 #> 5284 52.83 0.07054176 1.355455e-04 0.929322697 #> 5285 52.84 0.07050650 1.354778e-04 0.929358027 #> 5286 52.85 0.07047125 1.354100e-04 0.929393339 #> 5287 52.86 0.07043602 1.353423e-04 0.929428634 #> 5288 52.87 0.07040081 1.352747e-04 0.929463911 #> 5289 52.88 0.07036562 1.352071e-04 0.929499170 #> 5290 52.89 0.07033045 1.351395e-04 0.929534412 #> 5291 52.90 0.07029529 1.350719e-04 0.929569636 #> 5292 52.91 0.07026015 1.350044e-04 0.929604842 #> 5293 52.92 0.07022503 1.349369e-04 0.929640031 #> 5294 52.93 0.07018993 1.348695e-04 0.929675202 #> 5295 52.94 0.07015484 1.348021e-04 0.929710356 #> 5296 52.95 0.07011977 1.347347e-04 0.929745492 #> 5297 52.96 0.07008472 1.346673e-04 0.929780610 #> 5298 52.97 0.07004969 1.346000e-04 0.929815711 #> 5299 52.98 0.07001467 1.345327e-04 0.929850794 #> 5300 52.99 0.06997967 1.344655e-04 0.929885860 #> 5301 53.00 0.06994469 1.343983e-04 0.929920908 #> 5302 53.01 0.06990973 1.343311e-04 0.929955939 #> 5303 53.02 0.06987478 1.342639e-04 0.929990953 #> 5304 53.03 0.06983985 1.341968e-04 0.930025948 #> 5305 53.04 0.06980494 1.341297e-04 0.930060927 #> 5306 53.05 0.06977005 1.340627e-04 0.930095887 #> 5307 53.06 0.06973517 1.339957e-04 0.930130831 #> 5308 53.07 0.06970031 1.339287e-04 0.930165757 #> 5309 53.08 0.06966547 1.338617e-04 0.930200665 #> 5310 53.09 0.06963065 1.337948e-04 0.930235556 #> 5311 53.10 0.06959584 1.337279e-04 0.930270429 #> 5312 53.11 0.06956105 1.336611e-04 0.930305285 #> 5313 53.12 0.06952628 1.335943e-04 0.930340124 #> 5314 53.13 0.06949153 1.335275e-04 0.930374945 #> 5315 53.14 0.06945679 1.334608e-04 0.930409749 #> 5316 53.15 0.06942207 1.333940e-04 0.930444536 #> 5317 53.16 0.06938737 1.333274e-04 0.930479305 #> 5318 53.17 0.06935268 1.332607e-04 0.930514056 #> 5319 53.18 0.06931802 1.331941e-04 0.930548791 #> 5320 53.19 0.06928336 1.331275e-04 0.930583508 #> 5321 53.20 0.06924873 1.330610e-04 0.930618207 #> 5322 53.21 0.06921412 1.329945e-04 0.930652889 #> 5323 53.22 0.06917952 1.329280e-04 0.930687554 #> 5324 53.23 0.06914494 1.328615e-04 0.930722202 #> 5325 53.24 0.06911037 1.327951e-04 0.930756832 #> 5326 53.25 0.06907583 1.327287e-04 0.930791445 #> 5327 53.26 0.06904130 1.326624e-04 0.930826041 #> 5328 53.27 0.06900679 1.325961e-04 0.930860619 #> 5329 53.28 0.06897229 1.325298e-04 0.930895180 #> 5330 53.29 0.06893781 1.324635e-04 0.930929724 #> 5331 53.30 0.06890335 1.323973e-04 0.930964250 #> 5332 53.31 0.06886891 1.323311e-04 0.930998760 #> 5333 53.32 0.06883448 1.322650e-04 0.931033251 #> 5334 53.33 0.06880007 1.321989e-04 0.931067726 #> 5335 53.34 0.06876568 1.321328e-04 0.931102184 #> 5336 53.35 0.06873131 1.320667e-04 0.931136624 #> 5337 53.36 0.06869695 1.320007e-04 0.931171047 #> 5338 53.37 0.06866261 1.319347e-04 0.931205453 #> 5339 53.38 0.06862829 1.318688e-04 0.931239842 #> 5340 53.39 0.06859398 1.318029e-04 0.931274213 #> 5341 53.40 0.06855970 1.317370e-04 0.931308568 #> 5342 53.41 0.06852542 1.316711e-04 0.931342905 #> 5343 53.42 0.06849117 1.316053e-04 0.931377225 #> 5344 53.43 0.06845693 1.315395e-04 0.931411527 #> 5345 53.44 0.06842271 1.314738e-04 0.931445813 #> 5346 53.45 0.06838851 1.314081e-04 0.931480082 #> 5347 53.46 0.06835432 1.313424e-04 0.931514333 #> 5348 53.47 0.06832016 1.312767e-04 0.931548567 #> 5349 53.48 0.06828600 1.312111e-04 0.931582784 #> 5350 53.49 0.06825187 1.311455e-04 0.931616985 #> 5351 53.50 0.06821775 1.310800e-04 0.931651167 #> 5352 53.51 0.06818365 1.310144e-04 0.931685333 #> 5353 53.52 0.06814957 1.309489e-04 0.931719482 #> 5354 53.53 0.06811550 1.308835e-04 0.931753614 #> 5355 53.54 0.06808145 1.308181e-04 0.931787729 #> 5356 53.55 0.06804742 1.307527e-04 0.931821826 #> 5357 53.56 0.06801341 1.306873e-04 0.931855907 #> 5358 53.57 0.06797941 1.306220e-04 0.931889970 #> 5359 53.58 0.06794543 1.305567e-04 0.931924017 #> 5360 53.59 0.06791146 1.304914e-04 0.931958046 #> 5361 53.60 0.06787752 1.304262e-04 0.931992059 #> 5362 53.61 0.06784358 1.303610e-04 0.932026054 #> 5363 53.62 0.06780967 1.302958e-04 0.932060033 #> 5364 53.63 0.06777578 1.302307e-04 0.932093994 #> 5365 53.64 0.06774190 1.301656e-04 0.932127939 #> 5366 53.65 0.06770803 1.301005e-04 0.932161866 #> 5367 53.66 0.06767419 1.300355e-04 0.932195777 #> 5368 53.67 0.06764036 1.299705e-04 0.932229670 #> 5369 53.68 0.06760655 1.299055e-04 0.932263547 #> 5370 53.69 0.06757275 1.298406e-04 0.932297407 #> 5371 53.70 0.06753897 1.297757e-04 0.932331250 #> 5372 53.71 0.06750521 1.297108e-04 0.932365076 #> 5373 53.72 0.06747147 1.296460e-04 0.932398885 #> 5374 53.73 0.06743774 1.295812e-04 0.932432677 #> 5375 53.74 0.06740403 1.295164e-04 0.932466452 #> 5376 53.75 0.06737034 1.294517e-04 0.932500210 #> 5377 53.76 0.06733666 1.293869e-04 0.932533952 #> 5378 53.77 0.06730300 1.293223e-04 0.932567676 #> 5379 53.78 0.06726936 1.292576e-04 0.932601384 #> 5380 53.79 0.06723573 1.291930e-04 0.932635075 #> 5381 53.80 0.06720212 1.291284e-04 0.932668749 #> 5382 53.81 0.06716853 1.290639e-04 0.932702406 #> 5383 53.82 0.06713495 1.289994e-04 0.932736047 #> 5384 53.83 0.06710139 1.289349e-04 0.932769670 #> 5385 53.84 0.06706785 1.288704e-04 0.932803277 #> 5386 53.85 0.06703433 1.288060e-04 0.932836867 #> 5387 53.86 0.06700082 1.287416e-04 0.932870440 #> 5388 53.87 0.06696733 1.286773e-04 0.932903997 #> 5389 53.88 0.06693385 1.286129e-04 0.932937536 #> 5390 53.89 0.06690039 1.285487e-04 0.932971059 #> 5391 53.90 0.06686695 1.284844e-04 0.933004565 #> 5392 53.91 0.06683353 1.284202e-04 0.933038054 #> 5393 53.92 0.06680012 1.283560e-04 0.933071527 #> 5394 53.93 0.06676673 1.282918e-04 0.933104983 #> 5395 53.94 0.06673335 1.282277e-04 0.933138422 #> 5396 53.95 0.06669999 1.281636e-04 0.933171844 #> 5397 53.96 0.06666665 1.280995e-04 0.933205250 #> 5398 53.97 0.06663333 1.280355e-04 0.933238639 #> 5399 53.98 0.06660002 1.279715e-04 0.933272012 #> 5400 53.99 0.06656673 1.279075e-04 0.933305367 #> 5401 54.00 0.06653345 1.278436e-04 0.933338706 #> 5402 54.01 0.06650019 1.277797e-04 0.933372029 #> 5403 54.02 0.06646695 1.277158e-04 0.933405334 #> 5404 54.03 0.06643372 1.276520e-04 0.933438623 #> 5405 54.04 0.06640052 1.275881e-04 0.933471896 #> 5406 54.05 0.06636732 1.275244e-04 0.933505151 #> 5407 54.06 0.06633415 1.274606e-04 0.933538390 #> 5408 54.07 0.06630099 1.273969e-04 0.933571613 #> 5409 54.08 0.06626785 1.273332e-04 0.933604819 #> 5410 54.09 0.06623472 1.272696e-04 0.933638008 #> 5411 54.10 0.06620161 1.272060e-04 0.933671181 #> 5412 54.11 0.06616852 1.271424e-04 0.933704337 #> 5413 54.12 0.06613544 1.270788e-04 0.933737476 #> 5414 54.13 0.06610239 1.270153e-04 0.933770599 #> 5415 54.14 0.06606934 1.269518e-04 0.933803706 #> 5416 54.15 0.06603632 1.268883e-04 0.933836796 #> 5417 54.16 0.06600331 1.268249e-04 0.933869869 #> 5418 54.17 0.06597031 1.267615e-04 0.933902926 #> 5419 54.18 0.06593734 1.266981e-04 0.933935966 #> 5420 54.19 0.06590438 1.266348e-04 0.933968990 #> 5421 54.20 0.06587143 1.265715e-04 0.934001997 #> 5422 54.21 0.06583850 1.265082e-04 0.934034988 #> 5423 54.22 0.06580559 1.264450e-04 0.934067962 #> 5424 54.23 0.06577270 1.263818e-04 0.934100920 #> 5425 54.24 0.06573982 1.263186e-04 0.934133861 #> 5426 54.25 0.06570696 1.262555e-04 0.934166786 #> 5427 54.26 0.06567411 1.261924e-04 0.934199695 #> 5428 54.27 0.06564128 1.261293e-04 0.934232586 #> 5429 54.28 0.06560847 1.260662e-04 0.934265462 #> 5430 54.29 0.06557568 1.260032e-04 0.934298321 #> 5431 54.30 0.06554290 1.259402e-04 0.934331164 #> 5432 54.31 0.06551013 1.258773e-04 0.934363990 #> 5433 54.32 0.06547739 1.258144e-04 0.934396800 #> 5434 54.33 0.06544466 1.257515e-04 0.934429593 #> 5435 54.34 0.06541194 1.256886e-04 0.934462370 #> 5436 54.35 0.06537924 1.256258e-04 0.934495131 #> 5437 54.36 0.06534656 1.255630e-04 0.934527875 #> 5438 54.37 0.06531390 1.255002e-04 0.934560603 #> 5439 54.38 0.06528125 1.254375e-04 0.934593314 #> 5440 54.39 0.06524862 1.253748e-04 0.934626010 #> 5441 54.40 0.06521600 1.253121e-04 0.934658688 #> 5442 54.41 0.06518340 1.252495e-04 0.934691351 #> 5443 54.42 0.06515082 1.251869e-04 0.934723997 #> 5444 54.43 0.06511825 1.251243e-04 0.934756627 #> 5445 54.44 0.06508570 1.250617e-04 0.934789240 #> 5446 54.45 0.06505316 1.249992e-04 0.934821838 #> 5447 54.46 0.06502064 1.249367e-04 0.934854419 #> 5448 54.47 0.06498814 1.248743e-04 0.934886983 #> 5449 54.48 0.06495566 1.248119e-04 0.934919532 #> 5450 54.49 0.06492319 1.247495e-04 0.934952064 #> 5451 54.50 0.06489073 1.246871e-04 0.934984580 #> 5452 54.51 0.06485830 1.246248e-04 0.935017079 #> 5453 54.52 0.06482588 1.245625e-04 0.935049562 #> 5454 54.53 0.06479347 1.245002e-04 0.935082030 #> 5455 54.54 0.06476108 1.244380e-04 0.935114480 #> 5456 54.55 0.06472871 1.243758e-04 0.935146915 #> 5457 54.56 0.06469635 1.243136e-04 0.935179333 #> 5458 54.57 0.06466401 1.242515e-04 0.935211736 #> 5459 54.58 0.06463169 1.241894e-04 0.935244122 #> 5460 54.59 0.06459938 1.241273e-04 0.935276492 #> 5461 54.60 0.06456709 1.240652e-04 0.935308845 #> 5462 54.61 0.06453481 1.240032e-04 0.935341183 #> 5463 54.62 0.06450255 1.239412e-04 0.935373504 #> 5464 54.63 0.06447031 1.238793e-04 0.935405809 #> 5465 54.64 0.06443808 1.238173e-04 0.935438098 #> 5466 54.65 0.06440587 1.237555e-04 0.935470371 #> 5467 54.66 0.06437368 1.236936e-04 0.935502628 #> 5468 54.67 0.06434150 1.236318e-04 0.935534869 #> 5469 54.68 0.06430934 1.235700e-04 0.935567093 #> 5470 54.69 0.06427719 1.235082e-04 0.935599301 #> 5471 54.70 0.06424506 1.234465e-04 0.935631494 #> 5472 54.71 0.06421295 1.233847e-04 0.935663670 #> 5473 54.72 0.06418085 1.233231e-04 0.935695830 #> 5474 54.73 0.06414876 1.232614e-04 0.935727974 #> 5475 54.74 0.06411670 1.231998e-04 0.935760102 #> 5476 54.75 0.06408465 1.231382e-04 0.935792214 #> 5477 54.76 0.06405261 1.230767e-04 0.935824310 #> 5478 54.77 0.06402060 1.230151e-04 0.935856390 #> 5479 54.78 0.06398859 1.229537e-04 0.935888454 #> 5480 54.79 0.06395661 1.228922e-04 0.935920501 #> 5481 54.80 0.06392464 1.228308e-04 0.935952533 #> 5482 54.81 0.06389268 1.227694e-04 0.935984549 #> 5483 54.82 0.06386074 1.227080e-04 0.936016549 #> 5484 54.83 0.06382882 1.226467e-04 0.936048532 #> 5485 54.84 0.06379691 1.225853e-04 0.936080500 #> 5486 54.85 0.06376502 1.225241e-04 0.936112452 #> 5487 54.86 0.06373315 1.224628e-04 0.936144388 #> 5488 54.87 0.06370129 1.224016e-04 0.936176307 #> 5489 54.88 0.06366945 1.223404e-04 0.936208211 #> 5490 54.89 0.06363762 1.222793e-04 0.936240099 #> 5491 54.90 0.06360581 1.222181e-04 0.936271971 #> 5492 54.91 0.06357402 1.221570e-04 0.936303827 #> 5493 54.92 0.06354224 1.220960e-04 0.936335667 #> 5494 54.93 0.06351047 1.220349e-04 0.936367492 #> 5495 54.94 0.06347873 1.219739e-04 0.936399300 #> 5496 54.95 0.06344699 1.219130e-04 0.936431092 #> 5497 54.96 0.06341528 1.218520e-04 0.936462869 #> 5498 54.97 0.06338358 1.217911e-04 0.936494629 #> 5499 54.98 0.06335190 1.217302e-04 0.936526374 #> 5500 54.99 0.06332023 1.216694e-04 0.936558103 #> 5501 55.00 0.06328858 1.216086e-04 0.936589816 #> 5502 55.01 0.06325694 1.215478e-04 0.936621513 #> 5503 55.02 0.06322532 1.214870e-04 0.936653195 #> 5504 55.03 0.06319371 1.214263e-04 0.936684860 #> 5505 55.04 0.06316212 1.213656e-04 0.936716510 #> 5506 55.05 0.06313055 1.213049e-04 0.936748143 #> 5507 55.06 0.06309899 1.212443e-04 0.936779762 #> 5508 55.07 0.06306745 1.211837e-04 0.936811364 #> 5509 55.08 0.06303593 1.211231e-04 0.936842950 #> 5510 55.09 0.06300442 1.210626e-04 0.936874521 #> 5511 55.10 0.06297292 1.210020e-04 0.936906076 #> 5512 55.11 0.06294144 1.209416e-04 0.936937615 #> 5513 55.12 0.06290998 1.208811e-04 0.936969138 #> 5514 55.13 0.06287853 1.208207e-04 0.937000646 #> 5515 55.14 0.06284710 1.207603e-04 0.937032137 #> 5516 55.15 0.06281569 1.206999e-04 0.937063613 #> 5517 55.16 0.06278429 1.206396e-04 0.937095074 #> 5518 55.17 0.06275290 1.205793e-04 0.937126518 #> 5519 55.18 0.06272153 1.205190e-04 0.937157947 #> 5520 55.19 0.06269018 1.204588e-04 0.937189360 #> 5521 55.20 0.06265884 1.203985e-04 0.937220758 #> 5522 55.21 0.06262752 1.203384e-04 0.937252140 #> 5523 55.22 0.06259622 1.202782e-04 0.937283506 #> 5524 55.23 0.06256493 1.202181e-04 0.937314856 #> 5525 55.24 0.06253365 1.201580e-04 0.937346191 #> 5526 55.25 0.06250239 1.200979e-04 0.937377510 #> 5527 55.26 0.06247115 1.200379e-04 0.937408813 #> 5528 55.27 0.06243992 1.199779e-04 0.937440101 #> 5529 55.28 0.06240871 1.199179e-04 0.937471373 #> 5530 55.29 0.06237751 1.198580e-04 0.937502630 #> 5531 55.30 0.06234633 1.197981e-04 0.937533871 #> 5532 55.31 0.06231517 1.197382e-04 0.937565096 #> 5533 55.32 0.06228402 1.196783e-04 0.937596306 #> 5534 55.33 0.06225288 1.196185e-04 0.937627500 #> 5535 55.34 0.06222176 1.195587e-04 0.937658678 #> 5536 55.35 0.06219066 1.194989e-04 0.937689841 #> 5537 55.36 0.06215957 1.194392e-04 0.937720988 #> 5538 55.37 0.06212850 1.193795e-04 0.937752120 #> 5539 55.38 0.06209744 1.193198e-04 0.937783236 #> 5540 55.39 0.06206640 1.192602e-04 0.937814337 #> 5541 55.40 0.06203538 1.192006e-04 0.937845422 #> 5542 55.41 0.06200437 1.191410e-04 0.937876491 #> 5543 55.42 0.06197337 1.190814e-04 0.937907545 #> 5544 55.43 0.06194239 1.190219e-04 0.937938584 #> 5545 55.44 0.06191143 1.189624e-04 0.937969607 #> 5546 55.45 0.06188048 1.189029e-04 0.938000614 #> 5547 55.46 0.06184955 1.188435e-04 0.938031606 #> 5548 55.47 0.06181863 1.187841e-04 0.938062583 #> 5549 55.48 0.06178773 1.187247e-04 0.938093544 #> 5550 55.49 0.06175685 1.186654e-04 0.938124489 #> 5551 55.50 0.06172597 1.186060e-04 0.938155419 #> 5552 55.51 0.06169512 1.185468e-04 0.938186334 #> 5553 55.52 0.06166428 1.184875e-04 0.938217233 #> 5554 55.53 0.06163346 1.184283e-04 0.938248116 #> 5555 55.54 0.06160265 1.183691e-04 0.938278985 #> 5556 55.55 0.06157185 1.183099e-04 0.938309837 #> 5557 55.56 0.06154107 1.182508e-04 0.938340675 #> 5558 55.57 0.06151031 1.181916e-04 0.938371497 #> 5559 55.58 0.06147956 1.181326e-04 0.938402303 #> 5560 55.59 0.06144883 1.180735e-04 0.938433094 #> 5561 55.60 0.06141812 1.180145e-04 0.938463870 #> 5562 55.61 0.06138741 1.179555e-04 0.938494631 #> 5563 55.62 0.06135673 1.178965e-04 0.938525376 #> 5564 55.63 0.06132606 1.178376e-04 0.938556105 #> 5565 55.64 0.06129540 1.177787e-04 0.938586820 #> 5566 55.65 0.06126476 1.177198e-04 0.938617518 #> 5567 55.66 0.06123414 1.176610e-04 0.938648202 #> 5568 55.67 0.06120353 1.176022e-04 0.938678870 #> 5569 55.68 0.06117293 1.175434e-04 0.938709523 #> 5570 55.69 0.06114235 1.174846e-04 0.938740161 #> 5571 55.70 0.06111179 1.174259e-04 0.938770783 #> 5572 55.71 0.06108124 1.173672e-04 0.938801390 #> 5573 55.72 0.06105071 1.173085e-04 0.938831982 #> 5574 55.73 0.06102019 1.172499e-04 0.938862558 #> 5575 55.74 0.06098969 1.171913e-04 0.938893119 #> 5576 55.75 0.06095920 1.171327e-04 0.938923665 #> 5577 55.76 0.06092873 1.170741e-04 0.938954195 #> 5578 55.77 0.06089827 1.170156e-04 0.938984711 #> 5579 55.78 0.06086783 1.169571e-04 0.939015211 #> 5580 55.79 0.06083741 1.168987e-04 0.939045695 #> 5581 55.80 0.06080699 1.168402e-04 0.939076165 #> 5582 55.81 0.06077660 1.167818e-04 0.939106619 #> 5583 55.82 0.06074622 1.167234e-04 0.939137058 #> 5584 55.83 0.06071585 1.166651e-04 0.939167482 #> 5585 55.84 0.06068550 1.166068e-04 0.939197891 #> 5586 55.85 0.06065517 1.165485e-04 0.939228284 #> 5587 55.86 0.06062485 1.164902e-04 0.939258663 #> 5588 55.87 0.06059454 1.164320e-04 0.939289026 #> 5589 55.88 0.06056425 1.163738e-04 0.939319374 #> 5590 55.89 0.06053398 1.163156e-04 0.939349706 #> 5591 55.90 0.06050372 1.162575e-04 0.939380024 #> 5592 55.91 0.06047347 1.161994e-04 0.939410326 #> 5593 55.92 0.06044325 1.161413e-04 0.939440614 #> 5594 55.93 0.06041303 1.160832e-04 0.939470886 #> 5595 55.94 0.06038283 1.160252e-04 0.939501143 #> 5596 55.95 0.06035265 1.159672e-04 0.939531385 #> 5597 55.96 0.06032248 1.159092e-04 0.939561611 #> 5598 55.97 0.06029233 1.158513e-04 0.939591823 #> 5599 55.98 0.06026219 1.157934e-04 0.939622020 #> 5600 55.99 0.06023206 1.157355e-04 0.939652201 #> 5601 56.00 0.06020196 1.156777e-04 0.939682367 #> 5602 56.01 0.06017186 1.156198e-04 0.939712519 #> 5603 56.02 0.06014178 1.155620e-04 0.939742655 #> 5604 56.03 0.06011172 1.155043e-04 0.939772776 #> 5605 56.04 0.06008167 1.154465e-04 0.939802882 #> 5606 56.05 0.06005164 1.153888e-04 0.939832973 #> 5607 56.06 0.06002162 1.153311e-04 0.939863049 #> 5608 56.07 0.05999162 1.152735e-04 0.939893110 #> 5609 56.08 0.05996163 1.152159e-04 0.939923156 #> 5610 56.09 0.05993165 1.151583e-04 0.939953187 #> 5611 56.10 0.05990170 1.151007e-04 0.939983203 #> 5612 56.11 0.05987175 1.150432e-04 0.940013204 #> 5613 56.12 0.05984182 1.149857e-04 0.940043190 #> 5614 56.13 0.05981191 1.149282e-04 0.940073160 #> 5615 56.14 0.05978201 1.148707e-04 0.940103116 #> 5616 56.15 0.05975213 1.148133e-04 0.940133057 #> 5617 56.16 0.05972226 1.147559e-04 0.940162983 #> 5618 56.17 0.05969241 1.146986e-04 0.940192894 #> 5619 56.18 0.05966257 1.146412e-04 0.940222790 #> 5620 56.19 0.05963274 1.145839e-04 0.940252672 #> 5621 56.20 0.05960294 1.145266e-04 0.940282538 #> 5622 56.21 0.05957314 1.144694e-04 0.940312389 #> 5623 56.22 0.05954336 1.144122e-04 0.940342225 #> 5624 56.23 0.05951360 1.143550e-04 0.940372047 #> 5625 56.24 0.05948385 1.142978e-04 0.940401853 #> 5626 56.25 0.05945411 1.142407e-04 0.940431645 #> 5627 56.26 0.05942439 1.141836e-04 0.940461422 #> 5628 56.27 0.05939469 1.141265e-04 0.940491184 #> 5629 56.28 0.05936500 1.140694e-04 0.940520931 #> 5630 56.29 0.05933532 1.140124e-04 0.940550663 #> 5631 56.30 0.05930566 1.139554e-04 0.940580380 #> 5632 56.31 0.05927602 1.138985e-04 0.940610082 #> 5633 56.32 0.05924639 1.138415e-04 0.940639770 #> 5634 56.33 0.05921677 1.137846e-04 0.940669443 #> 5635 56.34 0.05918717 1.137278e-04 0.940699100 #> 5636 56.35 0.05915759 1.136709e-04 0.940728743 #> 5637 56.36 0.05912801 1.136141e-04 0.940758372 #> 5638 56.37 0.05909846 1.135573e-04 0.940787985 #> 5639 56.38 0.05906892 1.135005e-04 0.940817584 #> 5640 56.39 0.05903939 1.134438e-04 0.940847167 #> 5641 56.40 0.05900988 1.133871e-04 0.940876737 #> 5642 56.41 0.05898038 1.133304e-04 0.940906291 #> 5643 56.42 0.05895090 1.132738e-04 0.940935830 #> 5644 56.43 0.05892143 1.132171e-04 0.940965355 #> 5645 56.44 0.05889197 1.131605e-04 0.940994865 #> 5646 56.45 0.05886254 1.131040e-04 0.941024360 #> 5647 56.46 0.05883311 1.130474e-04 0.941053841 #> 5648 56.47 0.05880370 1.129909e-04 0.941083306 #> 5649 56.48 0.05877431 1.129344e-04 0.941112757 #> 5650 56.49 0.05874493 1.128780e-04 0.941142193 #> 5651 56.50 0.05871556 1.128216e-04 0.941171615 #> 5652 56.51 0.05868621 1.127652e-04 0.941201022 #> 5653 56.52 0.05865688 1.127088e-04 0.941230414 #> 5654 56.53 0.05862756 1.126525e-04 0.941259791 #> 5655 56.54 0.05859825 1.125961e-04 0.941289154 #> 5656 56.55 0.05856896 1.125399e-04 0.941318502 #> 5657 56.56 0.05853968 1.124836e-04 0.941347836 #> 5658 56.57 0.05851042 1.124274e-04 0.941377154 #> 5659 56.58 0.05848117 1.123712e-04 0.941406459 #> 5660 56.59 0.05845194 1.123150e-04 0.941435748 #> 5661 56.60 0.05842272 1.122589e-04 0.941465023 #> 5662 56.61 0.05839351 1.122027e-04 0.941494283 #> 5663 56.62 0.05836432 1.121467e-04 0.941523529 #> 5664 56.63 0.05833515 1.120906e-04 0.941552759 #> 5665 56.64 0.05830599 1.120346e-04 0.941581976 #> 5666 56.65 0.05827684 1.119786e-04 0.941611178 #> 5667 56.66 0.05824771 1.119226e-04 0.941640365 #> 5668 56.67 0.05821860 1.118666e-04 0.941669537 #> 5669 56.68 0.05818949 1.118107e-04 0.941698695 #> 5670 56.69 0.05816041 1.117548e-04 0.941727838 #> 5671 56.70 0.05813133 1.116990e-04 0.941756967 #> 5672 56.71 0.05810228 1.116431e-04 0.941786082 #> 5673 56.72 0.05807323 1.115873e-04 0.941815181 #> 5674 56.73 0.05804420 1.115315e-04 0.941844266 #> 5675 56.74 0.05801519 1.114758e-04 0.941873337 #> 5676 56.75 0.05798619 1.114201e-04 0.941902393 #> 5677 56.76 0.05795720 1.113644e-04 0.941931435 #> 5678 56.77 0.05792823 1.113087e-04 0.941960462 #> 5679 56.78 0.05789927 1.112531e-04 0.941989474 #> 5680 56.79 0.05787033 1.111975e-04 0.942018472 #> 5681 56.80 0.05784140 1.111419e-04 0.942047456 #> 5682 56.81 0.05781249 1.110863e-04 0.942076425 #> 5683 56.82 0.05778359 1.110308e-04 0.942105379 #> 5684 56.83 0.05775471 1.109753e-04 0.942134319 #> 5685 56.84 0.05772584 1.109198e-04 0.942163245 #> 5686 56.85 0.05769698 1.108644e-04 0.942192156 #> 5687 56.86 0.05766814 1.108089e-04 0.942221053 #> 5688 56.87 0.05763931 1.107535e-04 0.942249935 #> 5689 56.88 0.05761050 1.106982e-04 0.942278803 #> 5690 56.89 0.05758170 1.106429e-04 0.942307656 #> 5691 56.90 0.05755292 1.105875e-04 0.942336495 #> 5692 56.91 0.05752415 1.105323e-04 0.942365320 #> 5693 56.92 0.05749539 1.104770e-04 0.942394130 #> 5694 56.93 0.05746665 1.104218e-04 0.942422926 #> 5695 56.94 0.05743793 1.103666e-04 0.942451707 #> 5696 56.95 0.05740921 1.103114e-04 0.942480474 #> 5697 56.96 0.05738052 1.102563e-04 0.942509226 #> 5698 56.97 0.05735183 1.102012e-04 0.942537965 #> 5699 56.98 0.05732317 1.101461e-04 0.942566688 #> 5700 56.99 0.05729451 1.100910e-04 0.942595398 #> 5701 57.00 0.05726587 1.100360e-04 0.942624093 #> 5702 57.01 0.05723725 1.099810e-04 0.942652774 #> 5703 57.02 0.05720863 1.099260e-04 0.942681440 #> 5704 57.03 0.05718004 1.098711e-04 0.942710092 #> 5705 57.04 0.05715145 1.098161e-04 0.942738730 #> 5706 57.05 0.05712289 1.097612e-04 0.942767354 #> 5707 57.06 0.05709433 1.097064e-04 0.942795963 #> 5708 57.07 0.05706579 1.096515e-04 0.942824558 #> 5709 57.08 0.05703726 1.095967e-04 0.942853138 #> 5710 57.09 0.05700875 1.095419e-04 0.942881705 #> 5711 57.10 0.05698026 1.094872e-04 0.942910257 #> 5712 57.11 0.05695177 1.094324e-04 0.942938794 #> 5713 57.12 0.05692330 1.093777e-04 0.942967318 #> 5714 57.13 0.05689485 1.093231e-04 0.942995827 #> 5715 57.14 0.05686641 1.092684e-04 0.943024322 #> 5716 57.15 0.05683798 1.092138e-04 0.943052803 #> 5717 57.16 0.05680957 1.091592e-04 0.943081269 #> 5718 57.17 0.05678117 1.091046e-04 0.943109721 #> 5719 57.18 0.05675279 1.090501e-04 0.943138160 #> 5720 57.19 0.05672442 1.089956e-04 0.943166583 #> 5721 57.20 0.05669607 1.089411e-04 0.943194993 #> 5722 57.21 0.05666773 1.088867e-04 0.943223388 #> 5723 57.22 0.05663940 1.088322e-04 0.943251770 #> 5724 57.23 0.05661109 1.087778e-04 0.943280137 #> 5725 57.24 0.05658279 1.087234e-04 0.943308489 #> 5726 57.25 0.05655450 1.086691e-04 0.943336828 #> 5727 57.26 0.05652623 1.086148e-04 0.943365153 #> 5728 57.27 0.05649798 1.085605e-04 0.943393463 #> 5729 57.28 0.05646973 1.085062e-04 0.943421759 #> 5730 57.29 0.05644151 1.084520e-04 0.943450041 #> 5731 57.30 0.05641329 1.083978e-04 0.943478309 #> 5732 57.31 0.05638509 1.083436e-04 0.943506563 #> 5733 57.32 0.05635691 1.082894e-04 0.943534803 #> 5734 57.33 0.05632874 1.082353e-04 0.943563028 #> 5735 57.34 0.05630058 1.081812e-04 0.943591240 #> 5736 57.35 0.05627244 1.081271e-04 0.943619437 #> 5737 57.36 0.05624431 1.080731e-04 0.943647620 #> 5738 57.37 0.05621619 1.080190e-04 0.943675789 #> 5739 57.38 0.05618809 1.079650e-04 0.943703944 #> 5740 57.39 0.05616000 1.079111e-04 0.943732085 #> 5741 57.40 0.05613193 1.078571e-04 0.943760212 #> 5742 57.41 0.05610387 1.078032e-04 0.943788325 #> 5743 57.42 0.05607583 1.077493e-04 0.943816424 #> 5744 57.43 0.05604780 1.076955e-04 0.943844509 #> 5745 57.44 0.05601978 1.076416e-04 0.943872579 #> 5746 57.45 0.05599178 1.075878e-04 0.943900636 #> 5747 57.46 0.05596379 1.075340e-04 0.943928679 #> 5748 57.47 0.05593581 1.074803e-04 0.943956707 #> 5749 57.48 0.05590785 1.074266e-04 0.943984722 #> 5750 57.49 0.05587990 1.073729e-04 0.944012723 #> 5751 57.50 0.05585197 1.073192e-04 0.944040709 #> 5752 57.51 0.05582405 1.072655e-04 0.944068682 #> 5753 57.52 0.05579615 1.072119e-04 0.944096641 #> 5754 57.53 0.05576826 1.071583e-04 0.944124585 #> 5755 57.54 0.05574038 1.071048e-04 0.944152516 #> 5756 57.55 0.05571252 1.070512e-04 0.944180433 #> 5757 57.56 0.05568467 1.069977e-04 0.944208336 #> 5758 57.57 0.05565683 1.069442e-04 0.944236225 #> 5759 57.58 0.05562901 1.068908e-04 0.944264099 #> 5760 57.59 0.05560120 1.068373e-04 0.944291960 #> 5761 57.60 0.05557341 1.067839e-04 0.944319807 #> 5762 57.61 0.05554563 1.067306e-04 0.944347641 #> 5763 57.62 0.05551786 1.066772e-04 0.944375460 #> 5764 57.63 0.05549011 1.066239e-04 0.944403265 #> 5765 57.64 0.05546237 1.065706e-04 0.944431057 #> 5766 57.65 0.05543465 1.065173e-04 0.944458834 #> 5767 57.66 0.05540694 1.064641e-04 0.944486598 #> 5768 57.67 0.05537924 1.064108e-04 0.944514348 #> 5769 57.68 0.05535156 1.063576e-04 0.944542083 #> 5770 57.69 0.05532389 1.063045e-04 0.944569805 #> 5771 57.70 0.05529624 1.062513e-04 0.944597514 #> 5772 57.71 0.05526859 1.061982e-04 0.944625208 #> 5773 57.72 0.05524097 1.061451e-04 0.944652888 #> 5774 57.73 0.05521335 1.060921e-04 0.944680555 #> 5775 57.74 0.05518575 1.060391e-04 0.944708208 #> 5776 57.75 0.05515817 1.059860e-04 0.944735847 #> 5777 57.76 0.05513059 1.059331e-04 0.944763472 #> 5778 57.77 0.05510304 1.058801e-04 0.944791083 #> 5779 57.78 0.05507549 1.058272e-04 0.944818681 #> 5780 57.79 0.05504796 1.057743e-04 0.944846265 #> 5781 57.80 0.05502044 1.057214e-04 0.944873835 #> 5782 57.81 0.05499294 1.056686e-04 0.944901391 #> 5783 57.82 0.05496545 1.056157e-04 0.944928933 #> 5784 57.83 0.05493798 1.055629e-04 0.944956462 #> 5785 57.84 0.05491051 1.055102e-04 0.944983977 #> 5786 57.85 0.05488306 1.054574e-04 0.945011478 #> 5787 57.86 0.05485563 1.054047e-04 0.945038965 #> 5788 57.87 0.05482821 1.053520e-04 0.945066439 #> 5789 57.88 0.05480080 1.052994e-04 0.945093899 #> 5790 57.89 0.05477341 1.052467e-04 0.945121345 #> 5791 57.90 0.05474603 1.051941e-04 0.945148778 #> 5792 57.91 0.05471866 1.051415e-04 0.945176196 #> 5793 57.92 0.05469131 1.050890e-04 0.945203601 #> 5794 57.93 0.05466397 1.050365e-04 0.945230993 #> 5795 57.94 0.05463665 1.049839e-04 0.945258370 #> 5796 57.95 0.05460933 1.049315e-04 0.945285734 #> 5797 57.96 0.05458204 1.048790e-04 0.945313085 #> 5798 57.97 0.05455475 1.048266e-04 0.945340421 #> 5799 57.98 0.05452748 1.047742e-04 0.945367744 #> 5800 57.99 0.05450022 1.047218e-04 0.945395054 #> 5801 58.00 0.05447298 1.046695e-04 0.945422349 #> 5802 58.01 0.05444575 1.046171e-04 0.945449631 #> 5803 58.02 0.05441854 1.045649e-04 0.945476900 #> 5804 58.03 0.05439133 1.045126e-04 0.945504154 #> 5805 58.04 0.05436414 1.044603e-04 0.945531395 #> 5806 58.05 0.05433697 1.044081e-04 0.945558623 #> 5807 58.06 0.05430981 1.043559e-04 0.945585837 #> 5808 58.07 0.05428266 1.043038e-04 0.945613037 #> 5809 58.08 0.05425552 1.042516e-04 0.945640224 #> 5810 58.09 0.05422840 1.041995e-04 0.945667397 #> 5811 58.10 0.05420130 1.041474e-04 0.945694556 #> 5812 58.11 0.05417420 1.040954e-04 0.945721702 #> 5813 58.12 0.05414712 1.040433e-04 0.945748835 #> 5814 58.13 0.05412006 1.039913e-04 0.945775953 #> 5815 58.14 0.05409300 1.039393e-04 0.945803059 #> 5816 58.15 0.05406596 1.038874e-04 0.945830150 #> 5817 58.16 0.05403894 1.038355e-04 0.945857229 #> 5818 58.17 0.05401192 1.037835e-04 0.945884293 #> 5819 58.18 0.05398492 1.037317e-04 0.945911344 #> 5820 58.19 0.05395794 1.036798e-04 0.945938382 #> 5821 58.20 0.05393097 1.036280e-04 0.945965406 #> 5822 58.21 0.05390401 1.035762e-04 0.945992416 #> 5823 58.22 0.05387706 1.035244e-04 0.946019414 #> 5824 58.23 0.05385013 1.034727e-04 0.946046397 #> 5825 58.24 0.05382321 1.034209e-04 0.946073367 #> 5826 58.25 0.05379631 1.033692e-04 0.946100324 #> 5827 58.26 0.05376942 1.033176e-04 0.946127267 #> 5828 58.27 0.05374254 1.032659e-04 0.946154196 #> 5829 58.28 0.05371567 1.032143e-04 0.946181113 #> 5830 58.29 0.05368882 1.031627e-04 0.946208015 #> 5831 58.30 0.05366198 1.031111e-04 0.946234905 #> 5832 58.31 0.05363516 1.030596e-04 0.946261780 #> 5833 58.32 0.05360835 1.030081e-04 0.946288643 #> 5834 58.33 0.05358155 1.029566e-04 0.946315492 #> 5835 58.34 0.05355477 1.029051e-04 0.946342327 #> 5836 58.35 0.05352800 1.028537e-04 0.946369149 #> 5837 58.36 0.05350124 1.028023e-04 0.946395958 #> 5838 58.37 0.05347450 1.027509e-04 0.946422754 #> 5839 58.38 0.05344777 1.026995e-04 0.946449535 #> 5840 58.39 0.05342105 1.026482e-04 0.946476304 #> 5841 58.40 0.05339434 1.025969e-04 0.946503059 #> 5842 58.41 0.05336765 1.025456e-04 0.946529801 #> 5843 58.42 0.05334098 1.024943e-04 0.946556529 #> 5844 58.43 0.05331431 1.024431e-04 0.946583244 #> 5845 58.44 0.05328766 1.023919e-04 0.946609946 #> 5846 58.45 0.05326102 1.023407e-04 0.946636634 #> 5847 58.46 0.05323440 1.022895e-04 0.946663309 #> 5848 58.47 0.05320779 1.022384e-04 0.946689971 #> 5849 58.48 0.05318119 1.021873e-04 0.946716620 #> 5850 58.49 0.05315461 1.021362e-04 0.946743255 #> 5851 58.50 0.05312804 1.020852e-04 0.946769876 #> 5852 58.51 0.05310148 1.020341e-04 0.946796485 #> 5853 58.52 0.05307494 1.019831e-04 0.946823080 #> 5854 58.53 0.05304841 1.019322e-04 0.946849662 #> 5855 58.54 0.05302189 1.018812e-04 0.946876230 #> 5856 58.55 0.05299538 1.018303e-04 0.946902785 #> 5857 58.56 0.05296889 1.017794e-04 0.946929327 #> 5858 58.57 0.05294242 1.017285e-04 0.946955856 #> 5859 58.58 0.05291595 1.016776e-04 0.946982371 #> 5860 58.59 0.05288950 1.016268e-04 0.947008874 #> 5861 58.60 0.05286306 1.015760e-04 0.947035363 #> 5862 58.61 0.05283664 1.015252e-04 0.947061838 #> 5863 58.62 0.05281022 1.014745e-04 0.947088301 #> 5864 58.63 0.05278383 1.014238e-04 0.947114750 #> 5865 58.64 0.05275744 1.013731e-04 0.947141186 #> 5866 58.65 0.05273107 1.013224e-04 0.947167609 #> 5867 58.66 0.05270471 1.012717e-04 0.947194018 #> 5868 58.67 0.05267836 1.012211e-04 0.947220415 #> 5869 58.68 0.05265203 1.011705e-04 0.947246798 #> 5870 58.69 0.05262571 1.011200e-04 0.947273168 #> 5871 58.70 0.05259941 1.010694e-04 0.947299525 #> 5872 58.71 0.05257311 1.010189e-04 0.947325869 #> 5873 58.72 0.05254683 1.009684e-04 0.947352199 #> 5874 58.73 0.05252057 1.009179e-04 0.947378516 #> 5875 58.74 0.05249431 1.008675e-04 0.947404820 #> 5876 58.75 0.05246807 1.008170e-04 0.947431112 #> 5877 58.76 0.05244184 1.007667e-04 0.947457389 #> 5878 58.77 0.05241563 1.007163e-04 0.947483654 #> 5879 58.78 0.05238943 1.006659e-04 0.947509906 #> 5880 58.79 0.05236324 1.006156e-04 0.947536144 #> 5881 58.80 0.05233707 1.005653e-04 0.947562370 #> 5882 58.81 0.05231090 1.005150e-04 0.947588582 #> 5883 58.82 0.05228475 1.004648e-04 0.947614781 #> 5884 58.83 0.05225862 1.004146e-04 0.947640967 #> 5885 58.84 0.05223250 1.003644e-04 0.947667140 #> 5886 58.85 0.05220639 1.003142e-04 0.947693300 #> 5887 58.86 0.05218029 1.002641e-04 0.947719447 #> 5888 58.87 0.05215421 1.002140e-04 0.947745580 #> 5889 58.88 0.05212813 1.001639e-04 0.947771701 #> 5890 58.89 0.05210208 1.001138e-04 0.947797809 #> 5891 58.90 0.05207603 1.000637e-04 0.947823903 #> 5892 58.91 0.05205000 1.000137e-04 0.947849985 #> 5893 58.92 0.05202398 9.996373e-05 0.947876053 #> 5894 58.93 0.05199798 9.991376e-05 0.947902109 #> 5895 58.94 0.05197198 9.986382e-05 0.947928151 #> 5896 58.95 0.05194601 9.981390e-05 0.947954181 #> 5897 58.96 0.05192004 9.976401e-05 0.947980197 #> 5898 58.97 0.05189409 9.971414e-05 0.948006201 #> 5899 58.98 0.05186814 9.966429e-05 0.948032191 #> 5900 58.99 0.05184222 9.961447e-05 0.948058168 #> 5901 59.00 0.05181630 9.956468e-05 0.948084133 #> 5902 59.01 0.05179040 9.951491e-05 0.948110084 #> 5903 59.02 0.05176451 9.946516e-05 0.948136023 #> 5904 59.03 0.05173864 9.941544e-05 0.948161948 #> 5905 59.04 0.05171277 9.936575e-05 0.948187861 #> 5906 59.05 0.05168692 9.931608e-05 0.948213760 #> 5907 59.06 0.05166109 9.926643e-05 0.948239647 #> 5908 59.07 0.05163526 9.921681e-05 0.948265521 #> 5909 59.08 0.05160945 9.916721e-05 0.948291381 #> 5910 59.09 0.05158365 9.911764e-05 0.948317229 #> 5911 59.10 0.05155787 9.906810e-05 0.948343064 #> 5912 59.11 0.05153210 9.901857e-05 0.948368886 #> 5913 59.12 0.05150634 9.896908e-05 0.948394695 #> 5914 59.13 0.05148059 9.891961e-05 0.948420492 #> 5915 59.14 0.05145485 9.887016e-05 0.948446275 #> 5916 59.15 0.05142913 9.882074e-05 0.948472045 #> 5917 59.16 0.05140343 9.877134e-05 0.948497803 #> 5918 59.17 0.05137773 9.872196e-05 0.948523547 #> 5919 59.18 0.05135205 9.867262e-05 0.948549279 #> 5920 59.19 0.05132638 9.862329e-05 0.948574998 #> 5921 59.20 0.05130072 9.857399e-05 0.948600704 #> 5922 59.21 0.05127508 9.852472e-05 0.948626398 #> 5923 59.22 0.05124945 9.847547e-05 0.948652078 #> 5924 59.23 0.05122383 9.842624e-05 0.948677745 #> 5925 59.24 0.05119822 9.837704e-05 0.948703400 #> 5926 59.25 0.05117263 9.832786e-05 0.948729042 #> 5927 59.26 0.05114705 9.827871e-05 0.948754671 #> 5928 59.27 0.05112148 9.822959e-05 0.948780287 #> 5929 59.28 0.05109593 9.818048e-05 0.948805891 #> 5930 59.29 0.05107039 9.813141e-05 0.948831481 #> 5931 59.30 0.05104486 9.808235e-05 0.948857059 #> 5932 59.31 0.05101934 9.803332e-05 0.948882624 #> 5933 59.32 0.05099384 9.798432e-05 0.948908177 #> 5934 59.33 0.05096835 9.793534e-05 0.948933716 #> 5935 59.34 0.05094287 9.788638e-05 0.948959243 #> 5936 59.35 0.05091741 9.783745e-05 0.948984757 #> 5937 59.36 0.05089195 9.778855e-05 0.949010258 #> 5938 59.37 0.05086651 9.773966e-05 0.949035747 #> 5939 59.38 0.05084109 9.769081e-05 0.949061223 #> 5940 59.39 0.05081567 9.764197e-05 0.949086686 #> 5941 59.40 0.05079027 9.759316e-05 0.949112136 #> 5942 59.41 0.05076488 9.754438e-05 0.949137573 #> 5943 59.42 0.05073951 9.749562e-05 0.949162998 #> 5944 59.43 0.05071414 9.744688e-05 0.949188410 #> 5945 59.44 0.05068879 9.739817e-05 0.949213810 #> 5946 59.45 0.05066345 9.734949e-05 0.949239197 #> 5947 59.46 0.05063813 9.730082e-05 0.949264571 #> 5948 59.47 0.05061282 9.725219e-05 0.949289932 #> 5949 59.48 0.05058752 9.720357e-05 0.949315281 #> 5950 59.49 0.05056223 9.715498e-05 0.949340617 #> 5951 59.50 0.05053695 9.710642e-05 0.949365940 #> 5952 59.51 0.05051169 9.705788e-05 0.949391251 #> 5953 59.52 0.05048644 9.700936e-05 0.949416549 #> 5954 59.53 0.05046120 9.696087e-05 0.949441834 #> 5955 59.54 0.05043598 9.691240e-05 0.949467107 #> 5956 59.55 0.05041077 9.686395e-05 0.949492367 #> 5957 59.56 0.05038557 9.681553e-05 0.949517615 #> 5958 59.57 0.05036038 9.676714e-05 0.949542850 #> 5959 59.58 0.05033521 9.671877e-05 0.949568072 #> 5960 59.59 0.05031005 9.667042e-05 0.949593282 #> 5961 59.60 0.05028490 9.662210e-05 0.949618479 #> 5962 59.61 0.05025976 9.657380e-05 0.949643663 #> 5963 59.62 0.05023464 9.652552e-05 0.949668835 #> 5964 59.63 0.05020953 9.647727e-05 0.949693994 #> 5965 59.64 0.05018443 9.642904e-05 0.949719141 #> 5966 59.65 0.05015934 9.638084e-05 0.949744275 #> 5967 59.66 0.05013427 9.633266e-05 0.949769397 #> 5968 59.67 0.05010921 9.628451e-05 0.949794506 #> 5969 59.68 0.05008416 9.623638e-05 0.949819602 #> 5970 59.69 0.05005913 9.618827e-05 0.949844686 #> 5971 59.70 0.05003410 9.614019e-05 0.949869757 #> 5972 59.71 0.05000909 9.609213e-05 0.949894816 #> 5973 59.72 0.04998409 9.604410e-05 0.949919863 #> 5974 59.73 0.04995911 9.599609e-05 0.949944896 #> 5975 59.74 0.04993413 9.594810e-05 0.949969918 #> 5976 59.75 0.04990917 9.590014e-05 0.949994927 #> 5977 59.76 0.04988422 9.585220e-05 0.950019923 #> 5978 59.77 0.04985929 9.580429e-05 0.950044907 #> 5979 59.78 0.04983437 9.575640e-05 0.950069878 #> 5980 59.79 0.04980945 9.570853e-05 0.950094837 #> 5981 59.80 0.04978456 9.566069e-05 0.950119783 #> 5982 59.81 0.04975967 9.561287e-05 0.950144717 #> 5983 59.82 0.04973480 9.556508e-05 0.950169638 #> 5984 59.83 0.04970994 9.551731e-05 0.950194547 #> 5985 59.84 0.04968509 9.546956e-05 0.950219444 #> 5986 59.85 0.04966025 9.542184e-05 0.950244328 #> 5987 59.86 0.04963543 9.537414e-05 0.950269200 #> 5988 59.87 0.04961061 9.532646e-05 0.950294059 #> 5989 59.88 0.04958582 9.527881e-05 0.950318905 #> 5990 59.89 0.04956103 9.523118e-05 0.950343740 #> 5991 59.90 0.04953625 9.518358e-05 0.950368562 #> 5992 59.91 0.04951149 9.513600e-05 0.950393371 #> 5993 59.92 0.04948674 9.508844e-05 0.950418168 #> 5994 59.93 0.04946201 9.504091e-05 0.950442953 #> 5995 59.94 0.04943728 9.499340e-05 0.950467725 #> 5996 59.95 0.04941257 9.494592e-05 0.950492485 #> 5997 59.96 0.04938787 9.489846e-05 0.950517233 #> 5998 59.97 0.04936318 9.485102e-05 0.950541968 #> 5999 59.98 0.04933851 9.480361e-05 0.950566691 #> 6000 59.99 0.04931384 9.475622e-05 0.950591401 #> 6001 60.00 0.04928919 9.470885e-05 0.950616100 #> 6002 60.01 0.04926455 9.466151e-05 0.950640785 #> 6003 60.02 0.04923993 9.461419e-05 0.950665459 #> 6004 60.03 0.04921531 9.456689e-05 0.950690120 #> 6005 60.04 0.04919071 9.451962e-05 0.950714769 #> 6006 60.05 0.04916612 9.447237e-05 0.950739405 #> 6007 60.06 0.04914155 9.442515e-05 0.950764029 #> 6008 60.07 0.04911698 9.437795e-05 0.950788641 #> 6009 60.08 0.04909243 9.433077e-05 0.950813241 #> 6010 60.09 0.04906789 9.428362e-05 0.950837828 #> 6011 60.10 0.04904336 9.423649e-05 0.950862403 #> 6012 60.11 0.04901885 9.418938e-05 0.950886965 #> 6013 60.12 0.04899434 9.414230e-05 0.950911516 #> 6014 60.13 0.04896985 9.409524e-05 0.950936054 #> 6015 60.14 0.04894537 9.404820e-05 0.950960580 #> 6016 60.15 0.04892091 9.400119e-05 0.950985093 #> 6017 60.16 0.04889645 9.395420e-05 0.951009595 #> 6018 60.17 0.04887201 9.390724e-05 0.951034084 #> 6019 60.18 0.04884758 9.386029e-05 0.951058561 #> 6020 60.19 0.04882316 9.381338e-05 0.951083025 #> 6021 60.20 0.04879876 9.376648e-05 0.951107478 #> 6022 60.21 0.04877436 9.371961e-05 0.951131918 #> 6023 60.22 0.04874998 9.367276e-05 0.951156346 #> 6024 60.23 0.04872561 9.362594e-05 0.951180761 #> 6025 60.24 0.04870126 9.357914e-05 0.951205165 #> 6026 60.25 0.04867691 9.353236e-05 0.951229556 #> 6027 60.26 0.04865258 9.348560e-05 0.951253935 #> 6028 60.27 0.04862826 9.343887e-05 0.951278302 #> 6029 60.28 0.04860395 9.339216e-05 0.951302657 #> 6030 60.29 0.04857965 9.334548e-05 0.951327000 #> 6031 60.30 0.04855537 9.329882e-05 0.951351330 #> 6032 60.31 0.04853110 9.325218e-05 0.951375648 #> 6033 60.32 0.04850684 9.320557e-05 0.951399954 #> 6034 60.33 0.04848259 9.315898e-05 0.951424248 #> 6035 60.34 0.04845836 9.311241e-05 0.951448530 #> 6036 60.35 0.04843413 9.306586e-05 0.951472800 #> 6037 60.36 0.04840992 9.301934e-05 0.951497057 #> 6038 60.37 0.04838572 9.297284e-05 0.951521303 #> 6039 60.38 0.04836154 9.292637e-05 0.951545536 #> 6040 60.39 0.04833736 9.287992e-05 0.951569757 #> 6041 60.40 0.04831320 9.283349e-05 0.951593966 #> 6042 60.41 0.04828905 9.278708e-05 0.951618163 #> 6043 60.42 0.04826491 9.274070e-05 0.951642348 #> 6044 60.43 0.04824078 9.269434e-05 0.951666521 #> 6045 60.44 0.04821667 9.264801e-05 0.951690682 #> 6046 60.45 0.04819257 9.260170e-05 0.951714830 #> 6047 60.46 0.04816848 9.255541e-05 0.951738967 #> 6048 60.47 0.04814440 9.250914e-05 0.951763091 #> 6049 60.48 0.04812033 9.246290e-05 0.951787204 #> 6050 60.49 0.04809628 9.241668e-05 0.951811304 #> 6051 60.50 0.04807224 9.237048e-05 0.951835392 #> 6052 60.51 0.04804821 9.232431e-05 0.951859469 #> 6053 60.52 0.04802419 9.227816e-05 0.951883533 #> 6054 60.53 0.04800018 9.223203e-05 0.951907585 #> 6055 60.54 0.04797619 9.218592e-05 0.951931625 #> 6056 60.55 0.04795221 9.213984e-05 0.951955654 #> 6057 60.56 0.04792824 9.209378e-05 0.951979670 #> 6058 60.57 0.04790428 9.204775e-05 0.952003674 #> 6059 60.58 0.04788033 9.200174e-05 0.952027666 #> 6060 60.59 0.04785640 9.195575e-05 0.952051646 #> 6061 60.60 0.04783248 9.190978e-05 0.952075614 #> 6062 60.61 0.04780857 9.186384e-05 0.952099571 #> 6063 60.62 0.04778467 9.181792e-05 0.952123515 #> 6064 60.63 0.04776078 9.177202e-05 0.952147447 #> 6065 60.64 0.04773691 9.172614e-05 0.952171367 #> 6066 60.65 0.04771304 9.168029e-05 0.952195276 #> 6067 60.66 0.04768919 9.163446e-05 0.952219172 #> 6068 60.67 0.04766535 9.158866e-05 0.952243057 #> 6069 60.68 0.04764153 9.154288e-05 0.952266929 #> 6070 60.69 0.04761771 9.149712e-05 0.952290790 #> 6071 60.70 0.04759391 9.145138e-05 0.952314638 #> 6072 60.71 0.04757012 9.140566e-05 0.952338475 #> 6073 60.72 0.04754634 9.135997e-05 0.952362300 #> 6074 60.73 0.04752257 9.131430e-05 0.952386113 #> 6075 60.74 0.04749882 9.126866e-05 0.952409914 #> 6076 60.75 0.04747507 9.122304e-05 0.952433703 #> 6077 60.76 0.04745134 9.117744e-05 0.952457480 #> 6078 60.77 0.04742762 9.113186e-05 0.952481245 #> 6079 60.78 0.04740391 9.108630e-05 0.952504999 #> 6080 60.79 0.04738022 9.104077e-05 0.952528740 #> 6081 60.80 0.04735653 9.099526e-05 0.952552470 #> 6082 60.81 0.04733286 9.094978e-05 0.952576188 #> 6083 60.82 0.04730920 9.090431e-05 0.952599894 #> 6084 60.83 0.04728555 9.085887e-05 0.952623588 #> 6085 60.84 0.04726192 9.081345e-05 0.952647270 #> 6086 60.85 0.04723829 9.076806e-05 0.952670941 #> 6087 60.86 0.04721468 9.072269e-05 0.952694599 #> 6088 60.87 0.04719108 9.067734e-05 0.952718246 #> 6089 60.88 0.04716749 9.063201e-05 0.952741881 #> 6090 60.89 0.04714391 9.058670e-05 0.952765504 #> 6091 60.90 0.04712034 9.054142e-05 0.952789116 #> 6092 60.91 0.04709679 9.049616e-05 0.952812715 #> 6093 60.92 0.04707325 9.045093e-05 0.952836303 #> 6094 60.93 0.04704972 9.040571e-05 0.952859879 #> 6095 60.94 0.04702620 9.036052e-05 0.952883443 #> 6096 60.95 0.04700269 9.031535e-05 0.952906995 #> 6097 60.96 0.04697919 9.027020e-05 0.952930536 #> 6098 60.97 0.04695571 9.022508e-05 0.952954065 #> 6099 60.98 0.04693224 9.017998e-05 0.952977582 #> 6100 60.99 0.04690878 9.013490e-05 0.953001087 #> 6101 61.00 0.04688533 9.008984e-05 0.953024581 #> 6102 61.01 0.04686189 9.004481e-05 0.953048063 #> 6103 61.02 0.04683847 8.999980e-05 0.953071533 #> 6104 61.03 0.04681505 8.995481e-05 0.953094991 #> 6105 61.04 0.04679165 8.990985e-05 0.953118438 #> 6106 61.05 0.04676826 8.986490e-05 0.953141873 #> 6107 61.06 0.04674488 8.981998e-05 0.953165296 #> 6108 61.07 0.04672152 8.977508e-05 0.953188708 #> 6109 61.08 0.04669816 8.973021e-05 0.953212107 #> 6110 61.09 0.04667482 8.968535e-05 0.953235495 #> 6111 61.10 0.04665149 8.964052e-05 0.953258872 #> 6112 61.11 0.04662817 8.959571e-05 0.953282237 #> 6113 61.12 0.04660486 8.955092e-05 0.953305590 #> 6114 61.13 0.04658156 8.950616e-05 0.953328931 #> 6115 61.14 0.04655828 8.946142e-05 0.953352261 #> 6116 61.15 0.04653500 8.941670e-05 0.953375579 #> 6117 61.16 0.04651174 8.937200e-05 0.953398885 #> 6118 61.17 0.04648849 8.932733e-05 0.953422180 #> 6119 61.18 0.04646525 8.928267e-05 0.953445463 #> 6120 61.19 0.04644203 8.923804e-05 0.953468734 #> 6121 61.20 0.04641881 8.919344e-05 0.953491994 #> 6122 61.21 0.04639561 8.914885e-05 0.953515242 #> 6123 61.22 0.04637242 8.910429e-05 0.953538479 #> 6124 61.23 0.04634924 8.905975e-05 0.953561704 #> 6125 61.24 0.04632607 8.901523e-05 0.953584917 #> 6126 61.25 0.04630291 8.897073e-05 0.953608119 #> 6127 61.26 0.04627976 8.892626e-05 0.953631309 #> 6128 61.27 0.04625663 8.888180e-05 0.953654488 #> 6129 61.28 0.04623351 8.883737e-05 0.953677655 #> 6130 61.29 0.04621040 8.879297e-05 0.953700810 #> 6131 61.30 0.04618730 8.874858e-05 0.953723954 #> 6132 61.31 0.04616421 8.870422e-05 0.953747086 #> 6133 61.32 0.04614113 8.865988e-05 0.953770207 #> 6134 61.33 0.04611807 8.861556e-05 0.953793316 #> 6135 61.34 0.04609502 8.857126e-05 0.953816413 #> 6136 61.35 0.04607197 8.852699e-05 0.953839499 #> 6137 61.36 0.04604894 8.848273e-05 0.953862574 #> 6138 61.37 0.04602592 8.843850e-05 0.953885637 #> 6139 61.38 0.04600292 8.839430e-05 0.953908688 #> 6140 61.39 0.04597992 8.835011e-05 0.953931728 #> 6141 61.40 0.04595694 8.830595e-05 0.953954757 #> 6142 61.41 0.04593396 8.826180e-05 0.953977773 #> 6143 61.42 0.04591100 8.821768e-05 0.954000779 #> 6144 61.43 0.04588805 8.817359e-05 0.954023773 #> 6145 61.44 0.04586512 8.812951e-05 0.954046755 #> 6146 61.45 0.04584219 8.808546e-05 0.954069726 #> 6147 61.46 0.04581927 8.804143e-05 0.954092685 #> 6148 61.47 0.04579637 8.799742e-05 0.954115633 #> 6149 61.48 0.04577348 8.795343e-05 0.954138570 #> 6150 61.49 0.04575060 8.790946e-05 0.954161495 #> 6151 61.50 0.04572773 8.786552e-05 0.954184408 #> 6152 61.51 0.04570487 8.782160e-05 0.954207310 #> 6153 61.52 0.04568202 8.777770e-05 0.954230201 #> 6154 61.53 0.04565919 8.773382e-05 0.954253080 #> 6155 61.54 0.04563636 8.768996e-05 0.954275948 #> 6156 61.55 0.04561355 8.764613e-05 0.954298804 #> 6157 61.56 0.04559075 8.760232e-05 0.954321649 #> 6158 61.57 0.04556796 8.755853e-05 0.954344482 #> 6159 61.58 0.04554518 8.751476e-05 0.954367305 #> 6160 61.59 0.04552241 8.747101e-05 0.954390115 #> 6161 61.60 0.04549966 8.742729e-05 0.954412914 #> 6162 61.61 0.04547691 8.738358e-05 0.954435702 #> 6163 61.62 0.04545418 8.733990e-05 0.954458479 #> 6164 61.63 0.04543146 8.729624e-05 0.954481244 #> 6165 61.64 0.04540875 8.725261e-05 0.954503997 #> 6166 61.65 0.04538605 8.720899e-05 0.954526740 #> 6167 61.66 0.04536336 8.716540e-05 0.954549471 #> 6168 61.67 0.04534069 8.712183e-05 0.954572190 #> 6169 61.68 0.04531802 8.707828e-05 0.954594899 #> 6170 61.69 0.04529537 8.703475e-05 0.954617595 #> 6171 61.70 0.04527273 8.699124e-05 0.954640281 #> 6172 61.71 0.04525010 8.694776e-05 0.954662955 #> 6173 61.72 0.04522748 8.690429e-05 0.954685618 #> 6174 61.73 0.04520487 8.686085e-05 0.954708270 #> 6175 61.74 0.04518227 8.681743e-05 0.954730910 #> 6176 61.75 0.04515969 8.677404e-05 0.954753539 #> 6177 61.76 0.04513711 8.673066e-05 0.954776156 #> 6178 61.77 0.04511455 8.668730e-05 0.954798762 #> 6179 61.78 0.04509200 8.664397e-05 0.954821357 #> 6180 61.79 0.04506946 8.660066e-05 0.954843941 #> 6181 61.80 0.04504693 8.655737e-05 0.954866514 #> 6182 61.81 0.04502441 8.651410e-05 0.954889075 #> 6183 61.82 0.04500190 8.647086e-05 0.954911624 #> 6184 61.83 0.04497941 8.642763e-05 0.954934163 #> 6185 61.84 0.04495693 8.638443e-05 0.954956690 #> 6186 61.85 0.04493445 8.634125e-05 0.954979206 #> 6187 61.86 0.04491199 8.629809e-05 0.955001711 #> 6188 61.87 0.04488954 8.625495e-05 0.955024205 #> 6189 61.88 0.04486710 8.621183e-05 0.955046687 #> 6190 61.89 0.04484467 8.616874e-05 0.955069158 #> 6191 61.90 0.04482226 8.612566e-05 0.955091618 #> 6192 61.91 0.04479985 8.608261e-05 0.955114066 #> 6193 61.92 0.04477746 8.603958e-05 0.955136504 #> 6194 61.93 0.04475507 8.599657e-05 0.955158930 #> 6195 61.94 0.04473270 8.595359e-05 0.955181345 #> 6196 61.95 0.04471034 8.591062e-05 0.955203748 #> 6197 61.96 0.04468799 8.586767e-05 0.955226141 #> 6198 61.97 0.04466565 8.582475e-05 0.955248522 #> 6199 61.98 0.04464333 8.578185e-05 0.955270892 #> 6200 61.99 0.04462101 8.573897e-05 0.955293251 #> 6201 62.00 0.04459870 8.569611e-05 0.955315599 #> 6202 62.01 0.04457641 8.565327e-05 0.955337936 #> 6203 62.02 0.04455413 8.561046e-05 0.955360261 #> 6204 62.03 0.04453186 8.556766e-05 0.955382576 #> 6205 62.04 0.04450960 8.552489e-05 0.955404879 #> 6206 62.05 0.04448735 8.548214e-05 0.955427171 #> 6207 62.06 0.04446511 8.543941e-05 0.955449452 #> 6208 62.07 0.04444288 8.539670e-05 0.955471721 #> 6209 62.08 0.04442067 8.535401e-05 0.955493980 #> 6210 62.09 0.04439846 8.531134e-05 0.955516227 #> 6211 62.10 0.04437627 8.526870e-05 0.955538464 #> 6212 62.11 0.04435409 8.522608e-05 0.955560689 #> 6213 62.12 0.04433191 8.518347e-05 0.955582903 #> 6214 62.13 0.04430975 8.514089e-05 0.955605106 #> 6215 62.14 0.04428760 8.509833e-05 0.955627298 #> 6216 62.15 0.04426547 8.505579e-05 0.955649479 #> 6217 62.16 0.04424334 8.501328e-05 0.955671648 #> 6218 62.17 0.04422122 8.497078e-05 0.955693807 #> 6219 62.18 0.04419912 8.492831e-05 0.955715955 #> 6220 62.19 0.04417702 8.488585e-05 0.955738091 #> 6221 62.20 0.04415494 8.484342e-05 0.955760216 #> 6222 62.21 0.04413287 8.480101e-05 0.955782331 #> 6223 62.22 0.04411081 8.475862e-05 0.955804434 #> 6224 62.23 0.04408876 8.471625e-05 0.955826526 #> 6225 62.24 0.04406672 8.467390e-05 0.955848608 #> 6226 62.25 0.04404469 8.463158e-05 0.955870678 #> 6227 62.26 0.04402267 8.458927e-05 0.955892737 #> 6228 62.27 0.04400067 8.454699e-05 0.955914785 #> 6229 62.28 0.04397867 8.450472e-05 0.955936822 #> 6230 62.29 0.04395669 8.446248e-05 0.955958848 #> 6231 62.30 0.04393472 8.442026e-05 0.955980863 #> 6232 62.31 0.04391275 8.437806e-05 0.956002867 #> 6233 62.32 0.04389080 8.433588e-05 0.956024860 #> 6234 62.33 0.04386886 8.429373e-05 0.956046843 #> 6235 62.34 0.04384693 8.425159e-05 0.956068814 #> 6236 62.35 0.04382502 8.420948e-05 0.956090774 #> 6237 62.36 0.04380311 8.416738e-05 0.956112723 #> 6238 62.37 0.04378121 8.412531e-05 0.956134661 #> 6239 62.38 0.04375933 8.408326e-05 0.956156588 #> 6240 62.39 0.04373745 8.404122e-05 0.956178504 #> 6241 62.40 0.04371559 8.399921e-05 0.956200410 #> 6242 62.41 0.04369374 8.395723e-05 0.956222304 #> 6243 62.42 0.04367190 8.391526e-05 0.956244187 #> 6244 62.43 0.04365007 8.387331e-05 0.956266060 #> 6245 62.44 0.04362825 8.383138e-05 0.956287921 #> 6246 62.45 0.04360644 8.378948e-05 0.956309772 #> 6247 62.46 0.04358464 8.374759e-05 0.956331612 #> 6248 62.47 0.04356285 8.370573e-05 0.956353440 #> 6249 62.48 0.04354108 8.366389e-05 0.956375258 #> 6250 62.49 0.04351931 8.362207e-05 0.956397065 #> 6251 62.50 0.04349756 8.358027e-05 0.956418861 #> 6252 62.51 0.04347582 8.353849e-05 0.956440646 #> 6253 62.52 0.04345408 8.349673e-05 0.956462420 #> 6254 62.53 0.04343236 8.345499e-05 0.956484184 #> 6255 62.54 0.04341065 8.341327e-05 0.956505936 #> 6256 62.55 0.04338895 8.337158e-05 0.956527678 #> 6257 62.56 0.04336726 8.332990e-05 0.956549409 #> 6258 62.57 0.04334558 8.328825e-05 0.956571128 #> 6259 62.58 0.04332392 8.324661e-05 0.956592837 #> 6260 62.59 0.04330226 8.320500e-05 0.956614536 #> 6261 62.60 0.04328061 8.316341e-05 0.956636223 #> 6262 62.61 0.04325898 8.312184e-05 0.956657899 #> 6263 62.62 0.04323735 8.308029e-05 0.956679565 #> 6264 62.63 0.04321574 8.303876e-05 0.956701220 #> 6265 62.64 0.04319414 8.299725e-05 0.956722864 #> 6266 62.65 0.04317255 8.295576e-05 0.956744497 #> 6267 62.66 0.04315097 8.291429e-05 0.956766119 #> 6268 62.67 0.04312940 8.287284e-05 0.956787731 #> 6269 62.68 0.04310784 8.283142e-05 0.956809332 #> 6270 62.69 0.04308629 8.279001e-05 0.956830922 #> 6271 62.70 0.04306475 8.274863e-05 0.956852501 #> 6272 62.71 0.04304322 8.270726e-05 0.956874069 #> 6273 62.72 0.04302171 8.266592e-05 0.956895627 #> 6274 62.73 0.04300020 8.262460e-05 0.956917173 #> 6275 62.74 0.04297871 8.258330e-05 0.956938709 #> 6276 62.75 0.04295722 8.254202e-05 0.956960235 #> 6277 62.76 0.04293575 8.250075e-05 0.956981749 #> 6278 62.77 0.04291429 8.245951e-05 0.957003253 #> 6279 62.78 0.04289284 8.241830e-05 0.957024746 #> 6280 62.79 0.04287139 8.237710e-05 0.957046228 #> 6281 62.80 0.04284996 8.233592e-05 0.957067700 #> 6282 62.81 0.04282854 8.229476e-05 0.957089161 #> 6283 62.82 0.04280714 8.225362e-05 0.957110611 #> 6284 62.83 0.04278574 8.221251e-05 0.957132050 #> 6285 62.84 0.04276435 8.217141e-05 0.957153479 #> 6286 62.85 0.04274297 8.213034e-05 0.957174896 #> 6287 62.86 0.04272161 8.208928e-05 0.957196304 #> 6288 62.87 0.04270025 8.204825e-05 0.957217700 #> 6289 62.88 0.04267891 8.200723e-05 0.957239086 #> 6290 62.89 0.04265757 8.196624e-05 0.957260461 #> 6291 62.90 0.04263625 8.192527e-05 0.957281825 #> 6292 62.91 0.04261494 8.188431e-05 0.957303179 #> 6293 62.92 0.04259363 8.184338e-05 0.957324522 #> 6294 62.93 0.04257234 8.180247e-05 0.957345855 #> 6295 62.94 0.04255106 8.176158e-05 0.957367176 #> 6296 62.95 0.04252979 8.172071e-05 0.957388488 #> 6297 62.96 0.04250853 8.167986e-05 0.957409788 #> 6298 62.97 0.04248728 8.163903e-05 0.957431078 #> 6299 62.98 0.04246604 8.159822e-05 0.957452357 #> 6300 62.99 0.04244482 8.155743e-05 0.957473625 #> 6301 63.00 0.04242360 8.151666e-05 0.957494883 #> 6302 63.01 0.04240239 8.147591e-05 0.957516131 #> 6303 63.02 0.04238120 8.143519e-05 0.957537367 #> 6304 63.03 0.04236001 8.139448e-05 0.957558593 #> 6305 63.04 0.04233884 8.135379e-05 0.957579809 #> 6306 63.05 0.04231767 8.131313e-05 0.957601013 #> 6307 63.06 0.04229652 8.127248e-05 0.957622208 #> 6308 63.07 0.04227538 8.123185e-05 0.957643391 #> 6309 63.08 0.04225424 8.119125e-05 0.957664564 #> 6310 63.09 0.04223312 8.115066e-05 0.957685727 #> 6311 63.10 0.04221201 8.111010e-05 0.957706878 #> 6312 63.11 0.04219091 8.106955e-05 0.957728020 #> 6313 63.12 0.04216982 8.102903e-05 0.957749150 #> 6314 63.13 0.04214874 8.098852e-05 0.957770271 #> 6315 63.14 0.04212767 8.094804e-05 0.957791380 #> 6316 63.15 0.04210661 8.090757e-05 0.957812479 #> 6317 63.16 0.04208557 8.086713e-05 0.957833568 #> 6318 63.17 0.04206453 8.082671e-05 0.957854646 #> 6319 63.18 0.04204350 8.078630e-05 0.957875713 #> 6320 63.19 0.04202248 8.074592e-05 0.957896770 #> 6321 63.20 0.04200148 8.070556e-05 0.957917816 #> 6322 63.21 0.04198048 8.066522e-05 0.957938852 #> 6323 63.22 0.04195950 8.062489e-05 0.957959877 #> 6324 63.23 0.04193852 8.058459e-05 0.957980892 #> 6325 63.24 0.04191756 8.054431e-05 0.958001897 #> 6326 63.25 0.04189661 8.050405e-05 0.958022890 #> 6327 63.26 0.04187566 8.046380e-05 0.958043874 #> 6328 63.27 0.04185473 8.042358e-05 0.958064846 #> 6329 63.28 0.04183381 8.038338e-05 0.958085809 #> 6330 63.29 0.04181290 8.034320e-05 0.958106761 #> 6331 63.30 0.04179199 8.030304e-05 0.958127702 #> 6332 63.31 0.04177110 8.026290e-05 0.958148633 #> 6333 63.32 0.04175022 8.022277e-05 0.958169553 #> 6334 63.33 0.04172935 8.018267e-05 0.958190463 #> 6335 63.34 0.04170849 8.014259e-05 0.958211363 #> 6336 63.35 0.04168765 8.010253e-05 0.958232252 #> 6337 63.36 0.04166681 8.006249e-05 0.958253131 #> 6338 63.37 0.04164598 8.002247e-05 0.958273999 #> 6339 63.38 0.04162516 7.998247e-05 0.958294857 #> 6340 63.39 0.04160435 7.994249e-05 0.958315704 #> 6341 63.40 0.04158356 7.990252e-05 0.958336541 #> 6342 63.41 0.04156277 7.986258e-05 0.958357368 #> 6343 63.42 0.04154199 7.982266e-05 0.958378184 #> 6344 63.43 0.04152123 7.978276e-05 0.958398989 #> 6345 63.44 0.04150047 7.974288e-05 0.958419785 #> 6346 63.45 0.04147973 7.970302e-05 0.958440570 #> 6347 63.46 0.04145899 7.966318e-05 0.958461344 #> 6348 63.47 0.04143827 7.962335e-05 0.958482108 #> 6349 63.48 0.04141755 7.958355e-05 0.958502862 #> 6350 63.49 0.04139685 7.954377e-05 0.958523605 #> 6351 63.50 0.04137616 7.950401e-05 0.958544338 #> 6352 63.51 0.04135547 7.946427e-05 0.958565061 #> 6353 63.52 0.04133480 7.942454e-05 0.958585773 #> 6354 63.53 0.04131414 7.938484e-05 0.958606475 #> 6355 63.54 0.04129349 7.934516e-05 0.958627167 #> 6356 63.55 0.04127285 7.930550e-05 0.958647848 #> 6357 63.56 0.04125222 7.926585e-05 0.958668519 #> 6358 63.57 0.04123159 7.922623e-05 0.958689180 #> 6359 63.58 0.04121098 7.918663e-05 0.958709830 #> 6360 63.59 0.04119038 7.914704e-05 0.958730470 #> 6361 63.60 0.04116979 7.910748e-05 0.958751099 #> 6362 63.61 0.04114921 7.906794e-05 0.958771719 #> 6363 63.62 0.04112864 7.902841e-05 0.958792328 #> 6364 63.63 0.04110808 7.898891e-05 0.958812926 #> 6365 63.64 0.04108754 7.894942e-05 0.958833515 #> 6366 63.65 0.04106700 7.890996e-05 0.958854093 #> 6367 63.66 0.04104647 7.887051e-05 0.958874661 #> 6368 63.67 0.04102595 7.883109e-05 0.958895218 #> 6369 63.68 0.04100544 7.879168e-05 0.958915765 #> 6370 63.69 0.04098495 7.875230e-05 0.958936302 #> 6371 63.70 0.04096446 7.871293e-05 0.958956829 #> 6372 63.71 0.04094398 7.867358e-05 0.958977346 #> 6373 63.72 0.04092351 7.863426e-05 0.958997852 #> 6374 63.73 0.04090306 7.859495e-05 0.959018348 #> 6375 63.74 0.04088261 7.855566e-05 0.959038833 #> 6376 63.75 0.04086217 7.851639e-05 0.959059309 #> 6377 63.76 0.04084175 7.847715e-05 0.959079774 #> 6378 63.77 0.04082133 7.843792e-05 0.959100229 #> 6379 63.78 0.04080093 7.839871e-05 0.959120674 #> 6380 63.79 0.04078053 7.835952e-05 0.959141108 #> 6381 63.80 0.04076015 7.832035e-05 0.959161533 #> 6382 63.81 0.04073977 7.828120e-05 0.959181947 #> 6383 63.82 0.04071941 7.824207e-05 0.959202351 #> 6384 63.83 0.04069905 7.820296e-05 0.959222745 #> 6385 63.84 0.04067871 7.816386e-05 0.959243128 #> 6386 63.85 0.04065837 7.812479e-05 0.959263501 #> 6387 63.86 0.04063805 7.808574e-05 0.959283865 #> 6388 63.87 0.04061774 7.804671e-05 0.959304218 #> 6389 63.88 0.04059743 7.800769e-05 0.959324560 #> 6390 63.89 0.04057714 7.796870e-05 0.959344893 #> 6391 63.90 0.04055685 7.792972e-05 0.959365216 #> 6392 63.91 0.04053658 7.789077e-05 0.959385528 #> 6393 63.92 0.04051632 7.785183e-05 0.959405830 #> 6394 63.93 0.04049607 7.781292e-05 0.959426122 #> 6395 63.94 0.04047582 7.777402e-05 0.959446404 #> 6396 63.95 0.04045559 7.773514e-05 0.959466676 #> 6397 63.96 0.04043537 7.769628e-05 0.959486937 #> 6398 63.97 0.04041515 7.765745e-05 0.959507189 #> 6399 63.98 0.04039495 7.761863e-05 0.959527430 #> 6400 63.99 0.04037476 7.757983e-05 0.959547661 #> 6401 64.00 0.04035458 7.754105e-05 0.959567882 #> 6402 64.01 0.04033440 7.750229e-05 0.959588093 #> 6403 64.02 0.04031424 7.746355e-05 0.959608294 #> 6404 64.03 0.04029409 7.742482e-05 0.959628485 #> 6405 64.04 0.04027395 7.738612e-05 0.959648666 #> 6406 64.05 0.04025382 7.734744e-05 0.959668836 #> 6407 64.06 0.04023369 7.730877e-05 0.959688997 #> 6408 64.07 0.04021358 7.727013e-05 0.959709147 #> 6409 64.08 0.04019348 7.723150e-05 0.959729288 #> 6410 64.09 0.04017339 7.719290e-05 0.959749418 #> 6411 64.10 0.04015331 7.715431e-05 0.959769538 #> 6412 64.11 0.04013324 7.711574e-05 0.959789649 #> 6413 64.12 0.04011317 7.707719e-05 0.959809749 #> 6414 64.13 0.04009312 7.703867e-05 0.959829839 #> 6415 64.14 0.04007308 7.700016e-05 0.959849919 #> 6416 64.15 0.04005305 7.696167e-05 0.959869989 #> 6417 64.16 0.04003303 7.692319e-05 0.959890049 #> 6418 64.17 0.04001302 7.688474e-05 0.959910099 #> 6419 64.18 0.03999301 7.684631e-05 0.959930139 #> 6420 64.19 0.03997302 7.680790e-05 0.959950169 #> 6421 64.20 0.03995304 7.676950e-05 0.959970189 #> 6422 64.21 0.03993307 7.673113e-05 0.959990199 #> 6423 64.22 0.03991311 7.669277e-05 0.960010198 #> 6424 64.23 0.03989316 7.665443e-05 0.960030188 #> 6425 64.24 0.03987322 7.661612e-05 0.960050168 #> 6426 64.25 0.03985328 7.657782e-05 0.960070138 #> 6427 64.26 0.03983336 7.653954e-05 0.960090098 #> 6428 64.27 0.03981345 7.650128e-05 0.960110048 #> 6429 64.28 0.03979355 7.646304e-05 0.960129988 #> 6430 64.29 0.03977366 7.642481e-05 0.960149918 #> 6431 64.30 0.03975378 7.638661e-05 0.960169838 #> 6432 64.31 0.03973390 7.634843e-05 0.960189748 #> 6433 64.32 0.03971404 7.631026e-05 0.960209648 #> 6434 64.33 0.03969419 7.627212e-05 0.960229539 #> 6435 64.34 0.03967435 7.623399e-05 0.960249419 #> 6436 64.35 0.03965451 7.619588e-05 0.960269289 #> 6437 64.36 0.03963469 7.615780e-05 0.960289150 #> 6438 64.37 0.03961488 7.611973e-05 0.960309000 #> 6439 64.38 0.03959508 7.608168e-05 0.960328841 #> 6440 64.39 0.03957529 7.604364e-05 0.960348671 #> 6441 64.40 0.03955550 7.600563e-05 0.960368492 #> 6442 64.41 0.03953573 7.596764e-05 0.960388303 #> 6443 64.42 0.03951597 7.592966e-05 0.960408104 #> 6444 64.43 0.03949621 7.589171e-05 0.960427895 #> 6445 64.44 0.03947647 7.585377e-05 0.960447676 #> 6446 64.45 0.03945674 7.581586e-05 0.960467447 #> 6447 64.46 0.03943701 7.577796e-05 0.960487208 #> 6448 64.47 0.03941730 7.574008e-05 0.960506960 #> 6449 64.48 0.03939760 7.570222e-05 0.960526701 #> 6450 64.49 0.03937790 7.566437e-05 0.960546433 #> 6451 64.50 0.03935822 7.562655e-05 0.960566155 #> 6452 64.51 0.03933854 7.558875e-05 0.960585867 #> 6453 64.52 0.03931888 7.555096e-05 0.960605569 #> 6454 64.53 0.03929923 7.551320e-05 0.960625261 #> 6455 64.54 0.03927958 7.547545e-05 0.960644944 #> 6456 64.55 0.03925995 7.543772e-05 0.960664616 #> 6457 64.56 0.03924032 7.540001e-05 0.960684279 #> 6458 64.57 0.03922071 7.536232e-05 0.960703932 #> 6459 64.58 0.03920110 7.532465e-05 0.960723575 #> 6460 64.59 0.03918150 7.528700e-05 0.960743209 #> 6461 64.60 0.03916192 7.524936e-05 0.960762832 #> 6462 64.61 0.03914234 7.521175e-05 0.960782446 #> 6463 64.62 0.03912278 7.517415e-05 0.960802050 #> 6464 64.63 0.03910322 7.513657e-05 0.960821644 #> 6465 64.64 0.03908367 7.509901e-05 0.960841228 #> 6466 64.65 0.03906414 7.506147e-05 0.960860802 #> 6467 64.66 0.03904461 7.502395e-05 0.960880367 #> 6468 64.67 0.03902509 7.498645e-05 0.960899922 #> 6469 64.68 0.03900558 7.494897e-05 0.960919467 #> 6470 64.69 0.03898609 7.491150e-05 0.960939003 #> 6471 64.70 0.03896660 7.487406e-05 0.960958528 #> 6472 64.71 0.03894712 7.483663e-05 0.960978044 #> 6473 64.72 0.03892765 7.479922e-05 0.960997550 #> 6474 64.73 0.03890819 7.476183e-05 0.961017047 #> 6475 64.74 0.03888874 7.472446e-05 0.961036533 #> 6476 64.75 0.03886930 7.468710e-05 0.961056010 #> 6477 64.76 0.03884987 7.464977e-05 0.961075477 #> 6478 64.77 0.03883045 7.461245e-05 0.961094935 #> 6479 64.78 0.03881104 7.457516e-05 0.961114382 #> 6480 64.79 0.03879164 7.453788e-05 0.961133820 #> 6481 64.80 0.03877225 7.450062e-05 0.961153248 #> 6482 64.81 0.03875287 7.446338e-05 0.961172667 #> 6483 64.82 0.03873350 7.442616e-05 0.961192076 #> 6484 64.83 0.03871414 7.438895e-05 0.961211475 #> 6485 64.84 0.03869478 7.435177e-05 0.961230864 #> 6486 64.85 0.03867544 7.431460e-05 0.961250244 #> 6487 64.86 0.03865611 7.427745e-05 0.961269614 #> 6488 64.87 0.03863679 7.424032e-05 0.961288974 #> 6489 64.88 0.03861747 7.420321e-05 0.961308325 #> 6490 64.89 0.03859817 7.416612e-05 0.961327666 #> 6491 64.90 0.03857887 7.412905e-05 0.961346997 #> 6492 64.91 0.03855959 7.409199e-05 0.961366319 #> 6493 64.92 0.03854031 7.405495e-05 0.961385631 #> 6494 64.93 0.03852105 7.401794e-05 0.961404933 #> 6495 64.94 0.03850179 7.398094e-05 0.961424226 #> 6496 64.95 0.03848255 7.394395e-05 0.961443509 #> 6497 64.96 0.03846331 7.390699e-05 0.961462783 #> 6498 64.97 0.03844408 7.387005e-05 0.961482046 #> 6499 64.98 0.03842487 7.383312e-05 0.961501301 #> 6500 64.99 0.03840566 7.379621e-05 0.961520545 #> 6501 65.00 0.03838646 7.375933e-05 0.961539780 #> 6502 65.01 0.03836727 7.372246e-05 0.961559005 #> 6503 65.02 0.03834809 7.368560e-05 0.961578221 #> 6504 65.03 0.03832892 7.364877e-05 0.961597427 #> 6505 65.04 0.03830976 7.361195e-05 0.961616624 #> 6506 65.05 0.03829061 7.357516e-05 0.961635811 #> 6507 65.06 0.03827147 7.353838e-05 0.961654988 #> 6508 65.07 0.03825234 7.350162e-05 0.961674156 #> 6509 65.08 0.03823322 7.346488e-05 0.961693314 #> 6510 65.09 0.03821411 7.342815e-05 0.961712462 #> 6511 65.10 0.03819501 7.339145e-05 0.961731601 #> 6512 65.11 0.03817591 7.335476e-05 0.961750731 #> 6513 65.12 0.03815683 7.331809e-05 0.961769851 #> 6514 65.13 0.03813776 7.328145e-05 0.961788961 #> 6515 65.14 0.03811869 7.324481e-05 0.961808062 #> 6516 65.15 0.03809964 7.320820e-05 0.961827153 #> 6517 65.16 0.03808059 7.317161e-05 0.961846234 #> 6518 65.17 0.03806156 7.313503e-05 0.961865307 #> 6519 65.18 0.03804253 7.309847e-05 0.961884369 #> 6520 65.19 0.03802352 7.306193e-05 0.961903422 #> 6521 65.20 0.03800451 7.302541e-05 0.961922466 #> 6522 65.21 0.03798551 7.298890e-05 0.961941500 #> 6523 65.22 0.03796652 7.295242e-05 0.961960524 #> 6524 65.23 0.03794754 7.291595e-05 0.961979539 #> 6525 65.24 0.03792858 7.287950e-05 0.961998545 #> 6526 65.25 0.03790962 7.284307e-05 0.962017541 #> 6527 65.26 0.03789067 7.280666e-05 0.962036527 #> 6528 65.27 0.03787173 7.277027e-05 0.962055504 #> 6529 65.28 0.03785279 7.273389e-05 0.962074472 #> 6530 65.29 0.03783387 7.269753e-05 0.962093430 #> 6531 65.30 0.03781496 7.266119e-05 0.962112378 #> 6532 65.31 0.03779606 7.262487e-05 0.962131317 #> 6533 65.32 0.03777716 7.258857e-05 0.962150247 #> 6534 65.33 0.03775828 7.255228e-05 0.962169167 #> 6535 65.34 0.03773941 7.251602e-05 0.962188078 #> 6536 65.35 0.03772054 7.247977e-05 0.962206979 #> 6537 65.36 0.03770169 7.244354e-05 0.962225871 #> 6538 65.37 0.03768284 7.240732e-05 0.962244753 #> 6539 65.38 0.03766400 7.237113e-05 0.962263626 #> 6540 65.39 0.03764518 7.233495e-05 0.962282489 #> 6541 65.40 0.03762636 7.229879e-05 0.962301343 #> 6542 65.41 0.03760755 7.226265e-05 0.962320188 #> 6543 65.42 0.03758875 7.222653e-05 0.962339023 #> 6544 65.43 0.03756996 7.219043e-05 0.962357849 #> 6545 65.44 0.03755118 7.215434e-05 0.962376665 #> 6546 65.45 0.03753241 7.211827e-05 0.962395472 #> 6547 65.46 0.03751365 7.208222e-05 0.962414270 #> 6548 65.47 0.03749490 7.204619e-05 0.962433058 #> 6549 65.48 0.03747615 7.201018e-05 0.962451837 #> 6550 65.49 0.03745742 7.197418e-05 0.962470606 #> 6551 65.50 0.03743870 7.193820e-05 0.962489366 #> 6552 65.51 0.03741998 7.190224e-05 0.962508117 #> 6553 65.52 0.03740128 7.186630e-05 0.962526858 #> 6554 65.53 0.03738258 7.183038e-05 0.962545590 #> 6555 65.54 0.03736389 7.179447e-05 0.962564313 #> 6556 65.55 0.03734522 7.175858e-05 0.962583026 #> 6557 65.56 0.03732655 7.172271e-05 0.962601730 #> 6558 65.57 0.03730789 7.168686e-05 0.962620424 #> 6559 65.58 0.03728924 7.165102e-05 0.962639109 #> 6560 65.59 0.03727060 7.161521e-05 0.962657785 #> 6561 65.60 0.03725197 7.157941e-05 0.962676451 #> 6562 65.61 0.03723335 7.154363e-05 0.962695109 #> 6563 65.62 0.03721474 7.150786e-05 0.962713756 #> 6564 65.63 0.03719613 7.147212e-05 0.962732395 #> 6565 65.64 0.03717754 7.143639e-05 0.962751024 #> 6566 65.65 0.03715896 7.140068e-05 0.962769644 #> 6567 65.66 0.03714038 7.136499e-05 0.962788254 #> 6568 65.67 0.03712182 7.132932e-05 0.962806855 #> 6569 65.68 0.03710326 7.129366e-05 0.962825447 #> 6570 65.69 0.03708471 7.125802e-05 0.962844030 #> 6571 65.70 0.03706617 7.122240e-05 0.962862603 #> 6572 65.71 0.03704765 7.118680e-05 0.962881167 #> 6573 65.72 0.03702913 7.115122e-05 0.962899722 #> 6574 65.73 0.03701062 7.111565e-05 0.962918268 #> 6575 65.74 0.03699212 7.108010e-05 0.962936804 #> 6576 65.75 0.03697362 7.104457e-05 0.962955331 #> 6577 65.76 0.03695514 7.100906e-05 0.962973849 #> 6578 65.77 0.03693667 7.097356e-05 0.962992357 #> 6579 65.78 0.03691821 7.093808e-05 0.963010856 #> 6580 65.79 0.03689975 7.090262e-05 0.963029346 #> 6581 65.80 0.03688131 7.086718e-05 0.963047827 #> 6582 65.81 0.03686287 7.083176e-05 0.963066298 #> 6583 65.82 0.03684444 7.079635e-05 0.963084761 #> 6584 65.83 0.03682603 7.076096e-05 0.963103214 #> 6585 65.84 0.03680762 7.072559e-05 0.963121657 #> 6586 65.85 0.03678922 7.069023e-05 0.963140092 #> 6587 65.86 0.03677083 7.065490e-05 0.963158517 #> 6588 65.87 0.03675245 7.061958e-05 0.963176933 #> 6589 65.88 0.03673408 7.058428e-05 0.963195340 #> 6590 65.89 0.03671571 7.054900e-05 0.963213738 #> 6591 65.90 0.03669736 7.051373e-05 0.963232127 #> 6592 65.91 0.03667902 7.047848e-05 0.963250506 #> 6593 65.92 0.03666068 7.044325e-05 0.963268876 #> 6594 65.93 0.03664235 7.040804e-05 0.963287237 #> 6595 65.94 0.03662404 7.037284e-05 0.963305589 #> 6596 65.95 0.03660573 7.033767e-05 0.963323932 #> 6597 65.96 0.03658743 7.030251e-05 0.963342265 #> 6598 65.97 0.03656914 7.026736e-05 0.963360589 #> 6599 65.98 0.03655086 7.023224e-05 0.963378904 #> 6600 65.99 0.03653259 7.019713e-05 0.963397210 #> 6601 66.00 0.03651433 7.016204e-05 0.963415507 #> 6602 66.01 0.03649608 7.012697e-05 0.963433795 #> 6603 66.02 0.03647783 7.009191e-05 0.963452073 #> 6604 66.03 0.03645960 7.005688e-05 0.963470343 #> 6605 66.04 0.03644138 7.002186e-05 0.963488603 #> 6606 66.05 0.03642316 6.998685e-05 0.963506854 #> 6607 66.06 0.03640495 6.995187e-05 0.963525096 #> 6608 66.07 0.03638675 6.991690e-05 0.963543329 #> 6609 66.08 0.03636857 6.988195e-05 0.963561553 #> 6610 66.09 0.03635039 6.984702e-05 0.963579768 #> 6611 66.10 0.03633221 6.981211e-05 0.963597973 #> 6612 66.11 0.03631405 6.977721e-05 0.963616170 #> 6613 66.12 0.03629590 6.974233e-05 0.963634357 #> 6614 66.13 0.03627776 6.970747e-05 0.963652535 #> 6615 66.14 0.03625962 6.967262e-05 0.963670704 #> 6616 66.15 0.03624150 6.963779e-05 0.963688865 #> 6617 66.16 0.03622338 6.960298e-05 0.963707016 #> 6618 66.17 0.03620527 6.956819e-05 0.963725158 #> 6619 66.18 0.03618718 6.953342e-05 0.963743290 #> 6620 66.19 0.03616909 6.949866e-05 0.963761414 #> 6621 66.20 0.03615101 6.946392e-05 0.963779529 #> 6622 66.21 0.03613294 6.942919e-05 0.963797635 #> 6623 66.22 0.03611487 6.939449e-05 0.963815731 #> 6624 66.23 0.03609682 6.935980e-05 0.963833819 #> 6625 66.24 0.03607878 6.932513e-05 0.963851898 #> 6626 66.25 0.03606074 6.929047e-05 0.963869967 #> 6627 66.26 0.03604272 6.925584e-05 0.963888028 #> 6628 66.27 0.03602470 6.922122e-05 0.963906079 #> 6629 66.28 0.03600669 6.918662e-05 0.963924122 #> 6630 66.29 0.03598869 6.915203e-05 0.963942155 #> 6631 66.30 0.03597070 6.911746e-05 0.963960179 #> 6632 66.31 0.03595272 6.908291e-05 0.963978195 #> 6633 66.32 0.03593475 6.904838e-05 0.963996201 #> 6634 66.33 0.03591679 6.901387e-05 0.964014199 #> 6635 66.34 0.03589883 6.897937e-05 0.964032187 #> 6636 66.35 0.03588089 6.894489e-05 0.964050166 #> 6637 66.36 0.03586295 6.891042e-05 0.964068137 #> 6638 66.37 0.03584503 6.887598e-05 0.964086098 #> 6639 66.38 0.03582711 6.884155e-05 0.964104051 #> 6640 66.39 0.03580920 6.880713e-05 0.964121994 #> 6641 66.40 0.03579130 6.877274e-05 0.964139929 #> 6642 66.41 0.03577341 6.873836e-05 0.964157854 #> 6643 66.42 0.03575553 6.870400e-05 0.964175771 #> 6644 66.43 0.03573765 6.866966e-05 0.964193679 #> 6645 66.44 0.03571979 6.863533e-05 0.964211577 #> 6646 66.45 0.03570193 6.860102e-05 0.964229467 #> 6647 66.46 0.03568409 6.856673e-05 0.964247348 #> 6648 66.47 0.03566625 6.853246e-05 0.964265220 #> 6649 66.48 0.03564842 6.849820e-05 0.964283083 #> 6650 66.49 0.03563060 6.846396e-05 0.964300937 #> 6651 66.50 0.03561279 6.842973e-05 0.964318782 #> 6652 66.51 0.03559499 6.839553e-05 0.964336618 #> 6653 66.52 0.03557719 6.836134e-05 0.964354445 #> 6654 66.53 0.03555941 6.832717e-05 0.964372263 #> 6655 66.54 0.03554163 6.829301e-05 0.964390073 #> 6656 66.55 0.03552387 6.825887e-05 0.964407873 #> 6657 66.56 0.03550611 6.822475e-05 0.964425665 #> 6658 66.57 0.03548836 6.819065e-05 0.964443448 #> 6659 66.58 0.03547062 6.815656e-05 0.964461221 #> 6660 66.59 0.03545289 6.812249e-05 0.964478986 #> 6661 66.60 0.03543517 6.808844e-05 0.964496742 #> 6662 66.61 0.03541746 6.805440e-05 0.964514490 #> 6663 66.62 0.03539975 6.802038e-05 0.964532228 #> 6664 66.63 0.03538206 6.798638e-05 0.964549957 #> 6665 66.64 0.03536437 6.795240e-05 0.964567678 #> 6666 66.65 0.03534669 6.791843e-05 0.964585390 #> 6667 66.66 0.03532902 6.788448e-05 0.964603093 #> 6668 66.67 0.03531136 6.785055e-05 0.964620787 #> 6669 66.68 0.03529371 6.781663e-05 0.964638472 #> 6670 66.69 0.03527607 6.778273e-05 0.964656148 #> 6671 66.70 0.03525844 6.774885e-05 0.964673816 #> 6672 66.71 0.03524081 6.771498e-05 0.964691474 #> 6673 66.72 0.03522319 6.768113e-05 0.964709124 #> 6674 66.73 0.03520559 6.764730e-05 0.964726765 #> 6675 66.74 0.03518799 6.761348e-05 0.964744397 #> 6676 66.75 0.03517040 6.757969e-05 0.964762021 #> 6677 66.76 0.03515282 6.754590e-05 0.964779635 #> 6678 66.77 0.03513525 6.751214e-05 0.964797241 #> 6679 66.78 0.03511768 6.747839e-05 0.964814838 #> 6680 66.79 0.03510013 6.744466e-05 0.964832426 #> 6681 66.80 0.03508258 6.741095e-05 0.964850006 #> 6682 66.81 0.03506505 6.737725e-05 0.964867576 #> 6683 66.82 0.03504752 6.734357e-05 0.964885138 #> 6684 66.83 0.03503000 6.730991e-05 0.964902691 #> 6685 66.84 0.03501249 6.727626e-05 0.964920235 #> 6686 66.85 0.03499499 6.724263e-05 0.964937771 #> 6687 66.86 0.03497749 6.720902e-05 0.964955298 #> 6688 66.87 0.03496001 6.717542e-05 0.964972816 #> 6689 66.88 0.03494253 6.714184e-05 0.964990325 #> 6690 66.89 0.03492507 6.710828e-05 0.965007825 #> 6691 66.90 0.03490761 6.707473e-05 0.965025317 #> 6692 66.91 0.03489016 6.704121e-05 0.965042800 #> 6693 66.92 0.03487272 6.700769e-05 0.965060274 #> 6694 66.93 0.03485529 6.697420e-05 0.965077740 #> 6695 66.94 0.03483786 6.694072e-05 0.965095197 #> 6696 66.95 0.03482045 6.690726e-05 0.965112645 #> 6697 66.96 0.03480304 6.687381e-05 0.965130084 #> 6698 66.97 0.03478565 6.684038e-05 0.965147515 #> 6699 66.98 0.03476826 6.680697e-05 0.965164936 #> 6700 66.99 0.03475088 6.677358e-05 0.965182350 #> 6701 67.00 0.03473351 6.674020e-05 0.965199754 #> 6702 67.01 0.03471614 6.670684e-05 0.965217150 #> 6703 67.02 0.03469879 6.667349e-05 0.965234537 #> 6704 67.03 0.03468144 6.664016e-05 0.965251915 #> 6705 67.04 0.03466411 6.660685e-05 0.965269285 #> 6706 67.05 0.03464678 6.657356e-05 0.965286646 #> 6707 67.06 0.03462946 6.654028e-05 0.965303998 #> 6708 67.07 0.03461215 6.650702e-05 0.965321342 #> 6709 67.08 0.03459485 6.647377e-05 0.965338677 #> 6710 67.09 0.03457756 6.644054e-05 0.965356003 #> 6711 67.10 0.03456027 6.640733e-05 0.965373321 #> 6712 67.11 0.03454300 6.637413e-05 0.965390630 #> 6713 67.12 0.03452573 6.634096e-05 0.965407930 #> 6714 67.13 0.03450847 6.630779e-05 0.965425222 #> 6715 67.14 0.03449122 6.627465e-05 0.965442505 #> 6716 67.15 0.03447398 6.624152e-05 0.965459780 #> 6717 67.16 0.03445675 6.620841e-05 0.965477045 #> 6718 67.17 0.03443952 6.617531e-05 0.965494303 #> 6719 67.18 0.03442231 6.614223e-05 0.965511551 #> 6720 67.19 0.03440510 6.610917e-05 0.965528791 #> 6721 67.20 0.03438790 6.607612e-05 0.965546022 #> 6722 67.21 0.03437071 6.604309e-05 0.965563245 #> 6723 67.22 0.03435353 6.601008e-05 0.965580459 #> 6724 67.23 0.03433636 6.597708e-05 0.965597665 #> 6725 67.24 0.03431919 6.594410e-05 0.965614861 #> 6726 67.25 0.03430204 6.591114e-05 0.965632050 #> 6727 67.26 0.03428489 6.587819e-05 0.965649229 #> 6728 67.27 0.03426775 6.584526e-05 0.965666400 #> 6729 67.28 0.03425062 6.581235e-05 0.965683563 #> 6730 67.29 0.03423350 6.577945e-05 0.965700717 #> 6731 67.30 0.03421639 6.574657e-05 0.965717862 #> 6732 67.31 0.03419929 6.571370e-05 0.965734999 #> 6733 67.32 0.03418219 6.568085e-05 0.965752127 #> 6734 67.33 0.03416511 6.564802e-05 0.965769247 #> 6735 67.34 0.03414803 6.561520e-05 0.965786358 #> 6736 67.35 0.03413096 6.558240e-05 0.965803461 #> 6737 67.36 0.03411390 6.554962e-05 0.965820555 #> 6738 67.37 0.03409684 6.551685e-05 0.965837640 #> 6739 67.38 0.03407980 6.548410e-05 0.965854717 #> 6740 67.39 0.03406276 6.545137e-05 0.965871785 #> 6741 67.40 0.03404574 6.541865e-05 0.965888845 #> 6742 67.41 0.03402872 6.538595e-05 0.965905896 #> 6743 67.42 0.03401171 6.535327e-05 0.965922939 #> 6744 67.43 0.03399471 6.532060e-05 0.965939973 #> 6745 67.44 0.03397771 6.528795e-05 0.965956999 #> 6746 67.45 0.03396073 6.525531e-05 0.965974016 #> 6747 67.46 0.03394375 6.522269e-05 0.965991025 #> 6748 67.47 0.03392678 6.519009e-05 0.966008025 #> 6749 67.48 0.03390983 6.515750e-05 0.966025017 #> 6750 67.49 0.03389287 6.512493e-05 0.966042000 #> 6751 67.50 0.03387593 6.509238e-05 0.966058975 #> 6752 67.51 0.03385900 6.505984e-05 0.966075941 #> 6753 67.52 0.03384207 6.502732e-05 0.966092899 #> 6754 67.53 0.03382516 6.499481e-05 0.966109849 #> 6755 67.54 0.03380825 6.496232e-05 0.966126789 #> 6756 67.55 0.03379135 6.492985e-05 0.966143722 #> 6757 67.56 0.03377446 6.489739e-05 0.966160646 #> 6758 67.57 0.03375757 6.486495e-05 0.966177561 #> 6759 67.58 0.03374070 6.483253e-05 0.966194468 #> 6760 67.59 0.03372383 6.480012e-05 0.966211367 #> 6761 67.60 0.03370698 6.476773e-05 0.966228257 #> 6762 67.61 0.03369013 6.473535e-05 0.966245138 #> 6763 67.62 0.03367329 6.470299e-05 0.966262012 #> 6764 67.63 0.03365645 6.467065e-05 0.966278876 #> 6765 67.64 0.03363963 6.463832e-05 0.966295733 #> 6766 67.65 0.03362281 6.460601e-05 0.966312581 #> 6767 67.66 0.03360601 6.457371e-05 0.966329420 #> 6768 67.67 0.03358921 6.454144e-05 0.966346251 #> 6769 67.68 0.03357242 6.450917e-05 0.966363074 #> 6770 67.69 0.03355563 6.447693e-05 0.966379888 #> 6771 67.70 0.03353886 6.444470e-05 0.966396694 #> 6772 67.71 0.03352210 6.441248e-05 0.966413492 #> 6773 67.72 0.03350534 6.438028e-05 0.966430281 #> 6774 67.73 0.03348859 6.434810e-05 0.966447061 #> 6775 67.74 0.03347185 6.431594e-05 0.966463834 #> 6776 67.75 0.03345512 6.428379e-05 0.966480597 #> 6777 67.76 0.03343840 6.425165e-05 0.966497353 #> 6778 67.77 0.03342168 6.421953e-05 0.966514100 #> 6779 67.78 0.03340497 6.418743e-05 0.966530839 #> 6780 67.79 0.03338828 6.415535e-05 0.966547569 #> 6781 67.80 0.03337159 6.412328e-05 0.966564291 #> 6782 67.81 0.03335490 6.409122e-05 0.966581005 #> 6783 67.82 0.03333823 6.405919e-05 0.966597710 #> 6784 67.83 0.03332157 6.402716e-05 0.966614407 #> 6785 67.84 0.03330491 6.399516e-05 0.966631096 #> 6786 67.85 0.03328826 6.396317e-05 0.966647776 #> 6787 67.86 0.03327162 6.393120e-05 0.966664448 #> 6788 67.87 0.03325499 6.389924e-05 0.966681112 #> 6789 67.88 0.03323837 6.386730e-05 0.966697767 #> 6790 67.89 0.03322175 6.383537e-05 0.966714414 #> 6791 67.90 0.03320514 6.380346e-05 0.966731053 #> 6792 67.91 0.03318855 6.377157e-05 0.966747683 #> 6793 67.92 0.03317196 6.373969e-05 0.966764305 #> 6794 67.93 0.03315537 6.370783e-05 0.966780919 #> 6795 67.94 0.03313880 6.367598e-05 0.966797524 #> 6796 67.95 0.03312223 6.364415e-05 0.966814121 #> 6797 67.96 0.03310568 6.361234e-05 0.966830710 #> 6798 67.97 0.03308913 6.358054e-05 0.966847290 #> 6799 67.98 0.03307259 6.354876e-05 0.966863863 #> 6800 67.99 0.03305606 6.351699e-05 0.966880426 #> 6801 68.00 0.03303953 6.348524e-05 0.966896982 #> 6802 68.01 0.03302302 6.345351e-05 0.966913530 #> 6803 68.02 0.03300651 6.342179e-05 0.966930069 #> 6804 68.03 0.03299001 6.339008e-05 0.966946599 #> 6805 68.04 0.03297352 6.335840e-05 0.966963122 #> 6806 68.05 0.03295704 6.332672e-05 0.966979636 #> 6807 68.06 0.03294056 6.329507e-05 0.966996142 #> 6808 68.07 0.03292410 6.326343e-05 0.967012640 #> 6809 68.08 0.03290764 6.323181e-05 0.967029130 #> 6810 68.09 0.03289119 6.320020e-05 0.967045611 #> 6811 68.10 0.03287475 6.316861e-05 0.967062084 #> 6812 68.11 0.03285831 6.313703e-05 0.967078549 #> 6813 68.12 0.03284189 6.310547e-05 0.967095006 #> 6814 68.13 0.03282547 6.307392e-05 0.967111454 #> 6815 68.14 0.03280906 6.304239e-05 0.967127894 #> 6816 68.15 0.03279266 6.301088e-05 0.967144326 #> 6817 68.16 0.03277627 6.297938e-05 0.967160750 #> 6818 68.17 0.03275989 6.294790e-05 0.967177165 #> 6819 68.18 0.03274351 6.291644e-05 0.967193573 #> 6820 68.19 0.03272714 6.288499e-05 0.967209972 #> 6821 68.20 0.03271078 6.285355e-05 0.967226363 #> 6822 68.21 0.03269443 6.282213e-05 0.967242745 #> 6823 68.22 0.03267809 6.279073e-05 0.967259120 #> 6824 68.23 0.03266175 6.275934e-05 0.967275486 #> 6825 68.24 0.03264543 6.272797e-05 0.967291844 #> 6826 68.25 0.03262911 6.269661e-05 0.967308194 #> 6827 68.26 0.03261280 6.266527e-05 0.967324536 #> 6828 68.27 0.03259650 6.263395e-05 0.967340870 #> 6829 68.28 0.03258020 6.260264e-05 0.967357195 #> 6830 68.29 0.03256392 6.257135e-05 0.967373513 #> 6831 68.30 0.03254764 6.254007e-05 0.967389822 #> 6832 68.31 0.03253137 6.250881e-05 0.967406123 #> 6833 68.32 0.03251511 6.247756e-05 0.967422416 #> 6834 68.33 0.03249885 6.244633e-05 0.967438700 #> 6835 68.34 0.03248261 6.241511e-05 0.967454977 #> 6836 68.35 0.03246637 6.238391e-05 0.967471246 #> 6837 68.36 0.03245014 6.235273e-05 0.967487506 #> 6838 68.37 0.03243392 6.232156e-05 0.967503758 #> 6839 68.38 0.03241771 6.229041e-05 0.967520002 #> 6840 68.39 0.03240150 6.225927e-05 0.967536238 #> 6841 68.40 0.03238531 6.222815e-05 0.967552466 #> 6842 68.41 0.03236912 6.219704e-05 0.967568686 #> 6843 68.42 0.03235294 6.216595e-05 0.967584897 #> 6844 68.43 0.03233676 6.213488e-05 0.967601101 #> 6845 68.44 0.03232060 6.210382e-05 0.967617296 #> 6846 68.45 0.03230444 6.207277e-05 0.967633483 #> 6847 68.46 0.03228830 6.204174e-05 0.967649663 #> 6848 68.47 0.03227216 6.201073e-05 0.967665834 #> 6849 68.48 0.03225602 6.197973e-05 0.967681997 #> 6850 68.49 0.03223990 6.194875e-05 0.967698152 #> 6851 68.50 0.03222378 6.191778e-05 0.967714299 #> 6852 68.51 0.03220768 6.188683e-05 0.967730437 #> 6853 68.52 0.03219158 6.185590e-05 0.967746568 #> 6854 68.53 0.03217548 6.182498e-05 0.967762691 #> 6855 68.54 0.03215940 6.179407e-05 0.967778805 #> 6856 68.55 0.03214332 6.176318e-05 0.967794912 #> 6857 68.56 0.03212726 6.173231e-05 0.967811011 #> 6858 68.57 0.03211120 6.170145e-05 0.967827101 #> 6859 68.58 0.03209515 6.167061e-05 0.967843183 #> 6860 68.59 0.03207910 6.163978e-05 0.967859258 #> 6861 68.60 0.03206307 6.160897e-05 0.967875324 #> 6862 68.61 0.03204704 6.157817e-05 0.967891383 #> 6863 68.62 0.03203102 6.154739e-05 0.967907433 #> 6864 68.63 0.03201501 6.151662e-05 0.967923475 #> 6865 68.64 0.03199900 6.148587e-05 0.967939509 #> 6866 68.65 0.03198301 6.145514e-05 0.967955536 #> 6867 68.66 0.03196702 6.142442e-05 0.967971554 #> 6868 68.67 0.03195104 6.139371e-05 0.967987564 #> 6869 68.68 0.03193507 6.136302e-05 0.968003566 #> 6870 68.69 0.03191911 6.133235e-05 0.968019560 #> 6871 68.70 0.03190315 6.130169e-05 0.968035547 #> 6872 68.71 0.03188720 6.127105e-05 0.968051525 #> 6873 68.72 0.03187126 6.124042e-05 0.968067495 #> 6874 68.73 0.03185533 6.120981e-05 0.968083457 #> 6875 68.74 0.03183941 6.117921e-05 0.968099412 #> 6876 68.75 0.03182349 6.114863e-05 0.968115358 #> 6877 68.76 0.03180759 6.111806e-05 0.968131296 #> 6878 68.77 0.03179169 6.108751e-05 0.968147227 #> 6879 68.78 0.03177579 6.105697e-05 0.968163149 #> 6880 68.79 0.03175991 6.102645e-05 0.968179064 #> 6881 68.80 0.03174403 6.099595e-05 0.968194970 #> 6882 68.81 0.03172817 6.096546e-05 0.968210869 #> 6883 68.82 0.03171231 6.093498e-05 0.968226759 #> 6884 68.83 0.03169645 6.090452e-05 0.968242642 #> 6885 68.84 0.03168061 6.087408e-05 0.968258517 #> 6886 68.85 0.03166477 6.084365e-05 0.968274383 #> 6887 68.86 0.03164894 6.081323e-05 0.968290242 #> 6888 68.87 0.03163312 6.078284e-05 0.968306093 #> 6889 68.88 0.03161731 6.075245e-05 0.968321936 #> 6890 68.89 0.03160151 6.072208e-05 0.968337771 #> 6891 68.90 0.03158571 6.069173e-05 0.968353598 #> 6892 68.91 0.03156992 6.066139e-05 0.968369418 #> 6893 68.92 0.03155414 6.063107e-05 0.968385229 #> 6894 68.93 0.03153837 6.060076e-05 0.968401032 #> 6895 68.94 0.03152260 6.057047e-05 0.968416828 #> 6896 68.95 0.03150684 6.054019e-05 0.968432616 #> 6897 68.96 0.03149109 6.050993e-05 0.968448395 #> 6898 68.97 0.03147535 6.047968e-05 0.968464167 #> 6899 68.98 0.03145962 6.044945e-05 0.968479931 #> 6900 68.99 0.03144389 6.041923e-05 0.968495687 #> 6901 69.00 0.03142818 6.038903e-05 0.968511435 #> 6902 69.01 0.03141247 6.035884e-05 0.968527176 #> 6903 69.02 0.03139676 6.032867e-05 0.968542908 #> 6904 69.03 0.03138107 6.029851e-05 0.968558633 #> 6905 69.04 0.03136538 6.026837e-05 0.968574350 #> 6906 69.05 0.03134970 6.023824e-05 0.968590059 #> 6907 69.06 0.03133403 6.020813e-05 0.968605760 #> 6908 69.07 0.03131837 6.017804e-05 0.968621453 #> 6909 69.08 0.03130271 6.014795e-05 0.968637138 #> 6910 69.09 0.03128707 6.011789e-05 0.968652816 #> 6911 69.10 0.03127143 6.008784e-05 0.968668485 #> 6912 69.11 0.03125580 6.005780e-05 0.968684147 #> 6913 69.12 0.03124017 6.002778e-05 0.968699801 #> 6914 69.13 0.03122455 5.999777e-05 0.968715447 #> 6915 69.14 0.03120895 5.996778e-05 0.968731086 #> 6916 69.15 0.03119335 5.993780e-05 0.968746716 #> 6917 69.16 0.03117775 5.990784e-05 0.968762339 #> 6918 69.17 0.03116217 5.987790e-05 0.968777954 #> 6919 69.18 0.03114659 5.984797e-05 0.968793561 #> 6920 69.19 0.03113102 5.981805e-05 0.968809160 #> 6921 69.20 0.03111546 5.978815e-05 0.968824752 #> 6922 69.21 0.03109991 5.975826e-05 0.968840336 #> 6923 69.22 0.03108436 5.972839e-05 0.968855912 #> 6924 69.23 0.03106882 5.969853e-05 0.968871480 #> 6925 69.24 0.03105329 5.966869e-05 0.968887040 #> 6926 69.25 0.03103777 5.963886e-05 0.968902593 #> 6927 69.26 0.03102225 5.960905e-05 0.968918138 #> 6928 69.27 0.03100675 5.957925e-05 0.968933675 #> 6929 69.28 0.03099125 5.954947e-05 0.968949204 #> 6930 69.29 0.03097575 5.951970e-05 0.968964725 #> 6931 69.30 0.03096027 5.948995e-05 0.968980239 #> 6932 69.31 0.03094479 5.946021e-05 0.968995745 #> 6933 69.32 0.03092933 5.943049e-05 0.969011243 #> 6934 69.33 0.03091387 5.940078e-05 0.969026734 #> 6935 69.34 0.03089841 5.937109e-05 0.969042217 #> 6936 69.35 0.03088297 5.934141e-05 0.969057692 #> 6937 69.36 0.03086753 5.931175e-05 0.969073159 #> 6938 69.37 0.03085210 5.928210e-05 0.969088619 #> 6939 69.38 0.03083668 5.925247e-05 0.969104070 #> 6940 69.39 0.03082126 5.922285e-05 0.969119514 #> 6941 69.40 0.03080586 5.919325e-05 0.969134951 #> 6942 69.41 0.03079046 5.916366e-05 0.969150379 #> 6943 69.42 0.03077507 5.913408e-05 0.969165800 #> 6944 69.43 0.03075968 5.910452e-05 0.969181214 #> 6945 69.44 0.03074431 5.907498e-05 0.969196619 #> 6946 69.45 0.03072894 5.904545e-05 0.969212017 #> 6947 69.46 0.03071358 5.901593e-05 0.969227407 #> 6948 69.47 0.03069822 5.898643e-05 0.969242790 #> 6949 69.48 0.03068288 5.895694e-05 0.969258164 #> 6950 69.49 0.03066754 5.892747e-05 0.969273532 #> 6951 69.50 0.03065221 5.889802e-05 0.969288891 #> 6952 69.51 0.03063689 5.886858e-05 0.969304243 #> 6953 69.52 0.03062157 5.883915e-05 0.969319587 #> 6954 69.53 0.03060627 5.880974e-05 0.969334923 #> 6955 69.54 0.03059097 5.878034e-05 0.969350252 #> 6956 69.55 0.03057568 5.875096e-05 0.969365573 #> 6957 69.56 0.03056039 5.872159e-05 0.969380886 #> 6958 69.57 0.03054512 5.869223e-05 0.969396192 #> 6959 69.58 0.03052985 5.866290e-05 0.969411490 #> 6960 69.59 0.03051459 5.863357e-05 0.969426780 #> 6961 69.60 0.03049933 5.860426e-05 0.969442063 #> 6962 69.61 0.03048409 5.857497e-05 0.969457338 #> 6963 69.62 0.03046885 5.854569e-05 0.969472606 #> 6964 69.63 0.03045362 5.851642e-05 0.969487866 #> 6965 69.64 0.03043839 5.848717e-05 0.969503118 #> 6966 69.65 0.03042318 5.845793e-05 0.969518363 #> 6967 69.66 0.03040797 5.842871e-05 0.969533600 #> 6968 69.67 0.03039277 5.839951e-05 0.969548829 #> 6969 69.68 0.03037758 5.837031e-05 0.969564051 #> 6970 69.69 0.03036239 5.834114e-05 0.969579265 #> 6971 69.70 0.03034722 5.831197e-05 0.969594472 #> 6972 69.71 0.03033205 5.828282e-05 0.969609671 #> 6973 69.72 0.03031688 5.825369e-05 0.969624862 #> 6974 69.73 0.03030173 5.822457e-05 0.969640046 #> 6975 69.74 0.03028658 5.819546e-05 0.969655222 #> 6976 69.75 0.03027144 5.816637e-05 0.969670390 #> 6977 69.76 0.03025631 5.813730e-05 0.969685551 #> 6978 69.77 0.03024119 5.810824e-05 0.969700705 #> 6979 69.78 0.03022607 5.807919e-05 0.969715851 #> 6980 69.79 0.03021096 5.805016e-05 0.969730989 #> 6981 69.80 0.03019586 5.802114e-05 0.969746120 #> 6982 69.81 0.03018076 5.799214e-05 0.969761243 #> 6983 69.82 0.03016568 5.796315e-05 0.969776359 #> 6984 69.83 0.03015060 5.793417e-05 0.969791467 #> 6985 69.84 0.03013553 5.790521e-05 0.969806567 #> 6986 69.85 0.03012046 5.787627e-05 0.969821660 #> 6987 69.86 0.03010541 5.784734e-05 0.969836745 #> 6988 69.87 0.03009036 5.781842e-05 0.969851823 #> 6989 69.88 0.03007532 5.778952e-05 0.969866894 #> 6990 69.89 0.03006028 5.776063e-05 0.969881956 #> 6991 69.90 0.03004526 5.773176e-05 0.969897012 #> 6992 69.91 0.03003024 5.770290e-05 0.969912059 #> 6993 69.92 0.03001523 5.767406e-05 0.969927100 #> 6994 69.93 0.03000022 5.764523e-05 0.969942132 #> 6995 69.94 0.02998523 5.761641e-05 0.969957157 #> 6996 69.95 0.02997024 5.758761e-05 0.969972175 #> 6997 69.96 0.02995526 5.755882e-05 0.969987185 #> 6998 69.97 0.02994028 5.753005e-05 0.970002188 #> 6999 69.98 0.02992532 5.750129e-05 0.970017183 #> 7000 69.99 0.02991036 5.747255e-05 0.970032171 #> 7001 70.00 0.02989541 5.744382e-05 0.970047151 #> 7002 70.01 0.02988046 5.741511e-05 0.970062124 #> 7003 70.02 0.02986552 5.738641e-05 0.970077089 #> 7004 70.03 0.02985060 5.735772e-05 0.970092047 #> 7005 70.04 0.02983567 5.732905e-05 0.970106997 #> 7006 70.05 0.02982076 5.730039e-05 0.970121940 #> 7007 70.06 0.02980585 5.727175e-05 0.970136875 #> 7008 70.07 0.02979095 5.724312e-05 0.970151803 #> 7009 70.08 0.02977606 5.721450e-05 0.970166723 #> 7010 70.09 0.02976118 5.718590e-05 0.970181636 #> 7011 70.10 0.02974630 5.715732e-05 0.970196541 #> 7012 70.11 0.02973143 5.712875e-05 0.970211439 #> 7013 70.12 0.02971657 5.710019e-05 0.970226330 #> 7014 70.13 0.02970172 5.707165e-05 0.970241213 #> 7015 70.14 0.02968687 5.704312e-05 0.970256089 #> 7016 70.15 0.02967203 5.701460e-05 0.970270957 #> 7017 70.16 0.02965720 5.698610e-05 0.970285818 #> 7018 70.17 0.02964237 5.695762e-05 0.970300671 #> 7019 70.18 0.02962755 5.692915e-05 0.970315517 #> 7020 70.19 0.02961274 5.690069e-05 0.970330356 #> 7021 70.20 0.02959794 5.687224e-05 0.970345187 #> 7022 70.21 0.02958315 5.684382e-05 0.970360010 #> 7023 70.22 0.02956836 5.681540e-05 0.970374827 #> 7024 70.23 0.02955358 5.678700e-05 0.970389636 #> 7025 70.24 0.02953880 5.675861e-05 0.970404437 #> 7026 70.25 0.02952404 5.673024e-05 0.970419231 #> 7027 70.26 0.02950928 5.670188e-05 0.970434018 #> 7028 70.27 0.02949453 5.667354e-05 0.970448797 #> 7029 70.28 0.02947979 5.664521e-05 0.970463569 #> 7030 70.29 0.02946505 5.661689e-05 0.970478334 #> 7031 70.30 0.02945032 5.658859e-05 0.970493091 #> 7032 70.31 0.02943560 5.656031e-05 0.970507841 #> 7033 70.32 0.02942089 5.653203e-05 0.970522583 #> 7034 70.33 0.02940618 5.650377e-05 0.970537318 #> 7035 70.34 0.02939148 5.647553e-05 0.970552046 #> 7036 70.35 0.02937679 5.644730e-05 0.970566766 #> 7037 70.36 0.02936210 5.641908e-05 0.970581479 #> 7038 70.37 0.02934742 5.639088e-05 0.970596184 #> 7039 70.38 0.02933275 5.636269e-05 0.970610883 #> 7040 70.39 0.02931809 5.633452e-05 0.970625574 #> 7041 70.40 0.02930344 5.630636e-05 0.970640257 #> 7042 70.41 0.02928879 5.627821e-05 0.970654933 #> 7043 70.42 0.02927415 5.625008e-05 0.970669602 #> 7044 70.43 0.02925951 5.622196e-05 0.970684264 #> 7045 70.44 0.02924489 5.619386e-05 0.970698918 #> 7046 70.45 0.02923027 5.616577e-05 0.970713565 #> 7047 70.46 0.02921566 5.613769e-05 0.970728204 #> 7048 70.47 0.02920105 5.610963e-05 0.970742837 #> 7049 70.48 0.02918646 5.608158e-05 0.970757462 #> 7050 70.49 0.02917187 5.605355e-05 0.970772079 #> 7051 70.50 0.02915729 5.602553e-05 0.970786689 #> 7052 70.51 0.02914271 5.599752e-05 0.970801292 #> 7053 70.52 0.02912814 5.596953e-05 0.970815888 #> 7054 70.53 0.02911358 5.594155e-05 0.970830477 #> 7055 70.54 0.02909903 5.591359e-05 0.970845058 #> 7056 70.55 0.02908448 5.588564e-05 0.970859631 #> 7057 70.56 0.02906994 5.585770e-05 0.970874198 #> 7058 70.57 0.02905541 5.582978e-05 0.970888757 #> 7059 70.58 0.02904089 5.580187e-05 0.970903309 #> 7060 70.59 0.02902637 5.577398e-05 0.970917854 #> 7061 70.60 0.02901186 5.574610e-05 0.970932391 #> 7062 70.61 0.02899736 5.571823e-05 0.970946922 #> 7063 70.62 0.02898287 5.569038e-05 0.970961445 #> 7064 70.63 0.02896838 5.566254e-05 0.970975960 #> 7065 70.64 0.02895390 5.563472e-05 0.970990469 #> 7066 70.65 0.02893942 5.560691e-05 0.971004970 #> 7067 70.66 0.02892496 5.557911e-05 0.971019464 #> 7068 70.67 0.02891050 5.555133e-05 0.971033950 #> 7069 70.68 0.02889605 5.552356e-05 0.971048430 #> 7070 70.69 0.02888160 5.549580e-05 0.971062902 #> 7071 70.70 0.02886717 5.546806e-05 0.971077367 #> 7072 70.71 0.02885274 5.544034e-05 0.971091824 #> 7073 70.72 0.02883831 5.541262e-05 0.971106275 #> 7074 70.73 0.02882390 5.538492e-05 0.971120718 #> 7075 70.74 0.02880949 5.535724e-05 0.971135154 #> 7076 70.75 0.02879509 5.532957e-05 0.971149583 #> 7077 70.76 0.02878069 5.530191e-05 0.971164005 #> 7078 70.77 0.02876631 5.527426e-05 0.971178419 #> 7079 70.78 0.02875193 5.524663e-05 0.971192826 #> 7080 70.79 0.02873755 5.521902e-05 0.971207226 #> 7081 70.80 0.02872319 5.519142e-05 0.971221619 #> 7082 70.81 0.02870883 5.516383e-05 0.971236005 #> 7083 70.82 0.02869448 5.513625e-05 0.971250383 #> 7084 70.83 0.02868014 5.510869e-05 0.971264754 #> 7085 70.84 0.02866580 5.508114e-05 0.971279118 #> 7086 70.85 0.02865147 5.505361e-05 0.971293475 #> 7087 70.86 0.02863715 5.502609e-05 0.971307825 #> 7088 70.87 0.02862283 5.499858e-05 0.971322167 #> 7089 70.88 0.02860853 5.497109e-05 0.971336503 #> 7090 70.89 0.02859423 5.494361e-05 0.971350831 #> 7091 70.90 0.02857993 5.491615e-05 0.971365152 #> 7092 70.91 0.02856565 5.488870e-05 0.971379466 #> 7093 70.92 0.02855137 5.486126e-05 0.971393772 #> 7094 70.93 0.02853709 5.483383e-05 0.971408072 #> 7095 70.94 0.02852283 5.480642e-05 0.971422364 #> 7096 70.95 0.02850857 5.477903e-05 0.971436649 #> 7097 70.96 0.02849432 5.475165e-05 0.971450928 #> 7098 70.97 0.02848008 5.472428e-05 0.971465199 #> 7099 70.98 0.02846584 5.469692e-05 0.971479462 #> 7100 70.99 0.02845161 5.466958e-05 0.971493719 #> 7101 71.00 0.02843739 5.464225e-05 0.971507969 #> 7102 71.01 0.02842317 5.461494e-05 0.971522211 #> 7103 71.02 0.02840897 5.458764e-05 0.971536446 #> 7104 71.03 0.02839476 5.456035e-05 0.971550675 #> 7105 71.04 0.02838057 5.453308e-05 0.971564896 #> 7106 71.05 0.02836638 5.450582e-05 0.971579110 #> 7107 71.06 0.02835220 5.447857e-05 0.971593317 #> 7108 71.07 0.02833803 5.445134e-05 0.971607516 #> 7109 71.08 0.02832387 5.442412e-05 0.971621709 #> 7110 71.09 0.02830971 5.439691e-05 0.971635895 #> 7111 71.10 0.02829556 5.436972e-05 0.971650073 #> 7112 71.11 0.02828141 5.434254e-05 0.971664245 #> 7113 71.12 0.02826728 5.431538e-05 0.971678409 #> 7114 71.13 0.02825315 5.428823e-05 0.971692566 #> 7115 71.14 0.02823902 5.426109e-05 0.971706716 #> 7116 71.15 0.02822491 5.423397e-05 0.971720860 #> 7117 71.16 0.02821080 5.420686e-05 0.971734996 #> 7118 71.17 0.02819670 5.417976e-05 0.971749125 #> 7119 71.18 0.02818260 5.415268e-05 0.971763246 #> 7120 71.19 0.02816851 5.412561e-05 0.971777361 #> 7121 71.20 0.02815443 5.409855e-05 0.971791469 #> 7122 71.21 0.02814036 5.407151e-05 0.971805570 #> 7123 71.22 0.02812629 5.404448e-05 0.971819664 #> 7124 71.23 0.02811223 5.401747e-05 0.971833750 #> 7125 71.24 0.02809818 5.399046e-05 0.971847830 #> 7126 71.25 0.02808413 5.396347e-05 0.971861902 #> 7127 71.26 0.02807010 5.393650e-05 0.971875968 #> 7128 71.27 0.02805606 5.390954e-05 0.971890026 #> 7129 71.28 0.02804204 5.388259e-05 0.971904078 #> 7130 71.29 0.02802802 5.385566e-05 0.971918122 #> 7131 71.30 0.02801401 5.382873e-05 0.971932160 #> 7132 71.31 0.02800001 5.380183e-05 0.971946190 #> 7133 71.32 0.02798601 5.377493e-05 0.971960214 #> 7134 71.33 0.02797202 5.374805e-05 0.971974230 #> 7135 71.34 0.02795804 5.372118e-05 0.971988239 #> 7136 71.35 0.02794406 5.369433e-05 0.972002242 #> 7137 71.36 0.02793010 5.366749e-05 0.972016237 #> 7138 71.37 0.02791613 5.364066e-05 0.972030225 #> 7139 71.38 0.02790218 5.361385e-05 0.972044207 #> 7140 71.39 0.02788823 5.358705e-05 0.972058181 #> 7141 71.40 0.02787429 5.356026e-05 0.972072149 #> 7142 71.41 0.02786036 5.353349e-05 0.972086109 #> 7143 71.42 0.02784643 5.350673e-05 0.972100063 #> 7144 71.43 0.02783251 5.347998e-05 0.972114009 #> 7145 71.44 0.02781860 5.345325e-05 0.972127949 #> 7146 71.45 0.02780469 5.342653e-05 0.972141881 #> 7147 71.46 0.02779079 5.339982e-05 0.972155807 #> 7148 71.47 0.02777690 5.337313e-05 0.972169725 #> 7149 71.48 0.02776302 5.334645e-05 0.972183637 #> 7150 71.49 0.02774914 5.331978e-05 0.972197542 #> 7151 71.50 0.02773527 5.329313e-05 0.972211439 #> 7152 71.51 0.02772140 5.326649e-05 0.972225330 #> 7153 71.52 0.02770755 5.323986e-05 0.972239214 #> 7154 71.53 0.02769370 5.321325e-05 0.972253091 #> 7155 71.54 0.02767985 5.318665e-05 0.972266961 #> 7156 71.55 0.02766602 5.316006e-05 0.972280824 #> 7157 71.56 0.02765219 5.313349e-05 0.972294680 #> 7158 71.57 0.02763836 5.310693e-05 0.972308529 #> 7159 71.58 0.02762455 5.308038e-05 0.972322372 #> 7160 71.59 0.02761074 5.305385e-05 0.972336207 #> 7161 71.60 0.02759694 5.302733e-05 0.972350035 #> 7162 71.61 0.02758314 5.300082e-05 0.972363857 #> 7163 71.62 0.02756935 5.297433e-05 0.972377672 #> 7164 71.63 0.02755557 5.294785e-05 0.972391479 #> 7165 71.64 0.02754180 5.292138e-05 0.972405280 #> 7166 71.65 0.02752803 5.289493e-05 0.972419074 #> 7167 71.66 0.02751427 5.286849e-05 0.972432861 #> 7168 71.67 0.02750052 5.284206e-05 0.972446641 #> 7169 71.68 0.02748677 5.281564e-05 0.972460414 #> 7170 71.69 0.02747303 5.278924e-05 0.972474181 #> 7171 71.70 0.02745930 5.276285e-05 0.972487940 #> 7172 71.71 0.02744557 5.273648e-05 0.972501693 #> 7173 71.72 0.02743185 5.271012e-05 0.972515439 #> 7174 71.73 0.02741814 5.268377e-05 0.972529177 #> 7175 71.74 0.02740443 5.265743e-05 0.972542909 #> 7176 71.75 0.02739073 5.263111e-05 0.972556635 #> 7177 71.76 0.02737704 5.260480e-05 0.972570353 #> 7178 71.77 0.02736336 5.257851e-05 0.972584064 #> 7179 71.78 0.02734968 5.255222e-05 0.972597769 #> 7180 71.79 0.02733601 5.252595e-05 0.972611466 #> 7181 71.80 0.02732234 5.249970e-05 0.972625157 #> 7182 71.81 0.02730869 5.247346e-05 0.972638841 #> 7183 71.82 0.02729503 5.244722e-05 0.972652518 #> 7184 71.83 0.02728139 5.242101e-05 0.972666189 #> 7185 71.84 0.02726775 5.239480e-05 0.972679852 #> 7186 71.85 0.02725412 5.236861e-05 0.972693509 #> 7187 71.86 0.02724050 5.234244e-05 0.972707159 #> 7188 71.87 0.02722688 5.231627e-05 0.972720802 #> 7189 71.88 0.02721327 5.229012e-05 0.972734438 #> 7190 71.89 0.02719967 5.226398e-05 0.972748067 #> 7191 71.90 0.02718607 5.223786e-05 0.972761690 #> 7192 71.91 0.02717248 5.221174e-05 0.972775306 #> 7193 71.92 0.02715890 5.218564e-05 0.972788915 #> 7194 71.93 0.02714532 5.215956e-05 0.972802517 #> 7195 71.94 0.02713175 5.213348e-05 0.972816112 #> 7196 71.95 0.02711819 5.210742e-05 0.972829701 #> 7197 71.96 0.02710464 5.208138e-05 0.972843282 #> 7198 71.97 0.02709109 5.205534e-05 0.972856857 #> 7199 71.98 0.02707755 5.202932e-05 0.972870425 #> 7200 71.99 0.02706401 5.200331e-05 0.972883987 #> 7201 72.00 0.02705048 5.197732e-05 0.972897541 #> 7202 72.01 0.02703696 5.195134e-05 0.972911089 #> 7203 72.02 0.02702344 5.192537e-05 0.972924630 #> 7204 72.03 0.02700994 5.189941e-05 0.972938165 #> 7205 72.04 0.02699643 5.187347e-05 0.972951692 #> 7206 72.05 0.02698294 5.184754e-05 0.972965213 #> 7207 72.06 0.02696945 5.182162e-05 0.972978727 #> 7208 72.07 0.02695597 5.179572e-05 0.972992234 #> 7209 72.08 0.02694250 5.176982e-05 0.973005735 #> 7210 72.09 0.02692903 5.174395e-05 0.973019229 #> 7211 72.10 0.02691557 5.171808e-05 0.973032716 #> 7212 72.11 0.02690211 5.169223e-05 0.973046196 #> 7213 72.12 0.02688866 5.166639e-05 0.973059669 #> 7214 72.13 0.02687522 5.164056e-05 0.973073136 #> 7215 72.14 0.02686179 5.161475e-05 0.973086596 #> 7216 72.15 0.02684836 5.158895e-05 0.973100050 #> 7217 72.16 0.02683494 5.156316e-05 0.973113496 #> 7218 72.17 0.02682153 5.153738e-05 0.973126936 #> 7219 72.18 0.02680812 5.151162e-05 0.973140369 #> 7220 72.19 0.02679472 5.148587e-05 0.973153796 #> 7221 72.20 0.02678132 5.146013e-05 0.973167215 #> 7222 72.21 0.02676794 5.143441e-05 0.973180629 #> 7223 72.22 0.02675456 5.140870e-05 0.973194035 #> 7224 72.23 0.02674118 5.138300e-05 0.973207434 #> 7225 72.24 0.02672782 5.135732e-05 0.973220827 #> 7226 72.25 0.02671445 5.133165e-05 0.973234214 #> 7227 72.26 0.02670110 5.130599e-05 0.973247593 #> 7228 72.27 0.02668775 5.128034e-05 0.973260966 #> 7229 72.28 0.02667441 5.125471e-05 0.973274332 #> 7230 72.29 0.02666108 5.122908e-05 0.973287692 #> 7231 72.30 0.02664775 5.120348e-05 0.973301045 #> 7232 72.31 0.02663443 5.117788e-05 0.973314391 #> 7233 72.32 0.02662112 5.115230e-05 0.973327730 #> 7234 72.33 0.02660781 5.112673e-05 0.973341063 #> 7235 72.34 0.02659451 5.110117e-05 0.973354389 #> 7236 72.35 0.02658122 5.107563e-05 0.973367709 #> 7237 72.36 0.02656793 5.105010e-05 0.973381021 #> 7238 72.37 0.02655465 5.102458e-05 0.973394328 #> 7239 72.38 0.02654137 5.099907e-05 0.973407627 #> 7240 72.39 0.02652811 5.097358e-05 0.973420920 #> 7241 72.40 0.02651485 5.094810e-05 0.973434206 #> 7242 72.41 0.02650159 5.092263e-05 0.973447486 #> 7243 72.42 0.02648834 5.089718e-05 0.973460759 #> 7244 72.43 0.02647510 5.087173e-05 0.973474025 #> 7245 72.44 0.02646187 5.084630e-05 0.973487285 #> 7246 72.45 0.02644864 5.082089e-05 0.973500538 #> 7247 72.46 0.02643542 5.079548e-05 0.973513784 #> 7248 72.47 0.02642221 5.077009e-05 0.973527024 #> 7249 72.48 0.02640900 5.074471e-05 0.973540257 #> 7250 72.49 0.02639580 5.071935e-05 0.973553484 #> 7251 72.50 0.02638260 5.069399e-05 0.973566704 #> 7252 72.51 0.02636941 5.066865e-05 0.973579917 #> 7253 72.52 0.02635623 5.064332e-05 0.973593124 #> 7254 72.53 0.02634306 5.061801e-05 0.973606324 #> 7255 72.54 0.02632989 5.059271e-05 0.973619517 #> 7256 72.55 0.02631673 5.056742e-05 0.973632704 #> 7257 72.56 0.02630357 5.054214e-05 0.973645885 #> 7258 72.57 0.02629042 5.051687e-05 0.973659058 #> 7259 72.58 0.02627728 5.049162e-05 0.973672226 #> 7260 72.59 0.02626415 5.046638e-05 0.973685386 #> 7261 72.60 0.02625102 5.044116e-05 0.973698540 #> 7262 72.61 0.02623790 5.041594e-05 0.973711688 #> 7263 72.62 0.02622478 5.039074e-05 0.973724829 #> 7264 72.63 0.02621167 5.036555e-05 0.973737963 #> 7265 72.64 0.02619857 5.034037e-05 0.973751091 #> 7266 72.65 0.02618547 5.031521e-05 0.973764212 #> 7267 72.66 0.02617238 5.029006e-05 0.973777326 #> 7268 72.67 0.02615930 5.026492e-05 0.973790434 #> 7269 72.68 0.02614622 5.023979e-05 0.973803536 #> 7270 72.69 0.02613315 5.021468e-05 0.973816631 #> 7271 72.70 0.02612009 5.018958e-05 0.973829719 #> 7272 72.71 0.02610703 5.016449e-05 0.973842801 #> 7273 72.72 0.02609398 5.013941e-05 0.973855877 #> 7274 72.73 0.02608094 5.011435e-05 0.973868945 #> 7275 72.74 0.02606790 5.008930e-05 0.973882008 #> 7276 72.75 0.02605487 5.006426e-05 0.973895063 #> 7277 72.76 0.02604185 5.003924e-05 0.973908112 #> 7278 72.77 0.02602883 5.001422e-05 0.973921155 #> 7279 72.78 0.02601582 4.998922e-05 0.973934191 #> 7280 72.79 0.02600281 4.996423e-05 0.973947221 #> 7281 72.80 0.02598982 4.993926e-05 0.973960244 #> 7282 72.81 0.02597682 4.991429e-05 0.973973261 #> 7283 72.82 0.02596384 4.988934e-05 0.973986271 #> 7284 72.83 0.02595086 4.986441e-05 0.973999274 #> 7285 72.84 0.02593789 4.983948e-05 0.974012272 #> 7286 72.85 0.02592492 4.981457e-05 0.974025262 #> 7287 72.86 0.02591196 4.978966e-05 0.974038246 #> 7288 72.87 0.02589901 4.976478e-05 0.974051224 #> 7289 72.88 0.02588606 4.973990e-05 0.974064195 #> 7290 72.89 0.02587313 4.971504e-05 0.974077160 #> 7291 72.90 0.02586019 4.969018e-05 0.974090118 #> 7292 72.91 0.02584726 4.966535e-05 0.974103070 #> 7293 72.92 0.02583434 4.964052e-05 0.974116015 #> 7294 72.93 0.02582143 4.961571e-05 0.974128954 #> 7295 72.94 0.02580852 4.959090e-05 0.974141886 #> 7296 72.95 0.02579562 4.956611e-05 0.974154812 #> 7297 72.96 0.02578273 4.954134e-05 0.974167731 #> 7298 72.97 0.02576984 4.951657e-05 0.974180644 #> 7299 72.98 0.02575696 4.949182e-05 0.974193550 #> 7300 72.99 0.02574408 4.946708e-05 0.974206450 #> 7301 73.00 0.02573121 4.944235e-05 0.974219344 #> 7302 73.01 0.02571835 4.941764e-05 0.974232231 #> 7303 73.02 0.02570550 4.939294e-05 0.974245112 #> 7304 73.03 0.02569265 4.936825e-05 0.974257986 #> 7305 73.04 0.02567980 4.934357e-05 0.974270854 #> 7306 73.05 0.02566697 4.931890e-05 0.974283715 #> 7307 73.06 0.02565414 4.929425e-05 0.974296570 #> 7308 73.07 0.02564131 4.926961e-05 0.974309419 #> 7309 73.08 0.02562849 4.924498e-05 0.974322261 #> 7310 73.09 0.02561568 4.922036e-05 0.974335096 #> 7311 73.10 0.02560288 4.919576e-05 0.974347926 #> 7312 73.11 0.02559008 4.917117e-05 0.974360748 #> 7313 73.12 0.02557729 4.914659e-05 0.974373565 #> 7314 73.13 0.02556450 4.912202e-05 0.974386375 #> 7315 73.14 0.02555172 4.909747e-05 0.974399178 #> 7316 73.15 0.02553895 4.907292e-05 0.974411976 #> 7317 73.16 0.02552619 4.904839e-05 0.974424767 #> 7318 73.17 0.02551343 4.902387e-05 0.974437551 #> 7319 73.18 0.02550067 4.899937e-05 0.974450329 #> 7320 73.19 0.02548792 4.897488e-05 0.974463101 #> 7321 73.20 0.02547518 4.895039e-05 0.974475866 #> 7322 73.21 0.02546245 4.892593e-05 0.974488625 #> 7323 73.22 0.02544972 4.890147e-05 0.974501377 #> 7324 73.23 0.02543700 4.887702e-05 0.974514123 #> 7325 73.24 0.02542428 4.885259e-05 0.974526863 #> 7326 73.25 0.02541158 4.882817e-05 0.974539597 #> 7327 73.26 0.02539887 4.880376e-05 0.974552324 #> 7328 73.27 0.02538618 4.877937e-05 0.974565044 #> 7329 73.28 0.02537349 4.875498e-05 0.974577758 #> 7330 73.29 0.02536080 4.873061e-05 0.974590466 #> 7331 73.30 0.02534813 4.870625e-05 0.974603168 #> 7332 73.31 0.02533545 4.868191e-05 0.974615863 #> 7333 73.32 0.02532279 4.865757e-05 0.974628552 #> 7334 73.33 0.02531013 4.863325e-05 0.974641235 #> 7335 73.34 0.02529748 4.860894e-05 0.974653911 #> 7336 73.35 0.02528483 4.858464e-05 0.974666581 #> 7337 73.36 0.02527220 4.856035e-05 0.974679244 #> 7338 73.37 0.02525956 4.853608e-05 0.974691902 #> 7339 73.38 0.02524694 4.851182e-05 0.974704552 #> 7340 73.39 0.02523432 4.848757e-05 0.974717197 #> 7341 73.40 0.02522170 4.846333e-05 0.974729835 #> 7342 73.41 0.02520909 4.843910e-05 0.974742467 #> 7343 73.42 0.02519649 4.841489e-05 0.974755093 #> 7344 73.43 0.02518390 4.839069e-05 0.974767712 #> 7345 73.44 0.02517131 4.836650e-05 0.974780325 #> 7346 73.45 0.02515873 4.834232e-05 0.974792932 #> 7347 73.46 0.02514615 4.831816e-05 0.974805532 #> 7348 73.47 0.02513358 4.829400e-05 0.974818126 #> 7349 73.48 0.02512102 4.826986e-05 0.974830714 #> 7350 73.49 0.02510846 4.824573e-05 0.974843296 #> 7351 73.50 0.02509591 4.822162e-05 0.974855871 #> 7352 73.51 0.02508336 4.819751e-05 0.974868440 #> 7353 73.52 0.02507082 4.817342e-05 0.974881002 #> 7354 73.53 0.02505829 4.814934e-05 0.974893559 #> 7355 73.54 0.02504577 4.812527e-05 0.974906109 #> 7356 73.55 0.02503325 4.810121e-05 0.974918653 #> 7357 73.56 0.02502073 4.807717e-05 0.974931190 #> 7358 73.57 0.02500823 4.805314e-05 0.974943721 #> 7359 73.58 0.02499572 4.802912e-05 0.974956246 #> 7360 73.59 0.02498323 4.800511e-05 0.974968765 #> 7361 73.60 0.02497074 4.798111e-05 0.974981278 #> 7362 73.61 0.02495826 4.795713e-05 0.974993784 #> 7363 73.62 0.02494578 4.793315e-05 0.975006284 #> 7364 73.63 0.02493331 4.790919e-05 0.975018778 #> 7365 73.64 0.02492085 4.788525e-05 0.975031265 #> 7366 73.65 0.02490839 4.786131e-05 0.975043746 #> 7367 73.66 0.02489594 4.783738e-05 0.975056221 #> 7368 73.67 0.02488350 4.781347e-05 0.975068690 #> 7369 73.68 0.02487106 4.778957e-05 0.975081153 #> 7370 73.69 0.02485863 4.776568e-05 0.975093609 #> 7371 73.70 0.02484620 4.774180e-05 0.975106059 #> 7372 73.71 0.02483378 4.771794e-05 0.975118503 #> 7373 73.72 0.02482137 4.769409e-05 0.975130941 #> 7374 73.73 0.02480896 4.767025e-05 0.975143372 #> 7375 73.74 0.02479656 4.764642e-05 0.975155797 #> 7376 73.75 0.02478416 4.762260e-05 0.975168216 #> 7377 73.76 0.02477177 4.759879e-05 0.975180629 #> 7378 73.77 0.02475939 4.757500e-05 0.975193036 #> 7379 73.78 0.02474701 4.755122e-05 0.975205436 #> 7380 73.79 0.02473464 4.752745e-05 0.975217830 #> 7381 73.80 0.02472228 4.750369e-05 0.975230218 #> 7382 73.81 0.02470992 4.747995e-05 0.975242600 #> 7383 73.82 0.02469757 4.745621e-05 0.975254975 #> 7384 73.83 0.02468522 4.743249e-05 0.975267345 #> 7385 73.84 0.02467288 4.740878e-05 0.975279708 #> 7386 73.85 0.02466055 4.738508e-05 0.975292065 #> 7387 73.86 0.02464822 4.736139e-05 0.975304416 #> 7388 73.87 0.02463590 4.733772e-05 0.975316761 #> 7389 73.88 0.02462359 4.731406e-05 0.975329099 #> 7390 73.89 0.02461128 4.729041e-05 0.975341432 #> 7391 73.90 0.02459898 4.726677e-05 0.975353758 #> 7392 73.91 0.02458668 4.724314e-05 0.975366078 #> 7393 73.92 0.02457439 4.721952e-05 0.975378392 #> 7394 73.93 0.02456210 4.719592e-05 0.975390700 #> 7395 73.94 0.02454983 4.717233e-05 0.975403001 #> 7396 73.95 0.02453755 4.714875e-05 0.975415297 #> 7397 73.96 0.02452529 4.712518e-05 0.975427586 #> 7398 73.97 0.02451303 4.710162e-05 0.975439869 #> 7399 73.98 0.02450078 4.707808e-05 0.975452146 #> 7400 73.99 0.02448853 4.705454e-05 0.975464417 #> 7401 74.00 0.02447629 4.703102e-05 0.975476682 #> 7402 74.01 0.02446405 4.700751e-05 0.975488940 #> 7403 74.02 0.02445182 4.698401e-05 0.975501193 #> 7404 74.03 0.02443960 4.696053e-05 0.975513439 #> 7405 74.04 0.02442738 4.693705e-05 0.975525679 #> 7406 74.05 0.02441517 4.691359e-05 0.975537913 #> 7407 74.06 0.02440297 4.689014e-05 0.975550141 #> 7408 74.07 0.02439077 4.686670e-05 0.975562363 #> 7409 74.08 0.02437858 4.684327e-05 0.975574579 #> 7410 74.09 0.02436639 4.681986e-05 0.975586789 #> 7411 74.10 0.02435421 4.679645e-05 0.975598992 #> 7412 74.11 0.02434204 4.677306e-05 0.975611190 #> 7413 74.12 0.02432987 4.674968e-05 0.975623381 #> 7414 74.13 0.02431771 4.672631e-05 0.975635566 #> 7415 74.14 0.02430555 4.670295e-05 0.975647745 #> 7416 74.15 0.02429340 4.667961e-05 0.975659918 #> 7417 74.16 0.02428126 4.665627e-05 0.975672085 #> 7418 74.17 0.02426912 4.663295e-05 0.975684246 #> 7419 74.18 0.02425699 4.660964e-05 0.975696401 #> 7420 74.19 0.02424486 4.658634e-05 0.975708550 #> 7421 74.20 0.02423274 4.656306e-05 0.975720693 #> 7422 74.21 0.02422063 4.653978e-05 0.975732829 #> 7423 74.22 0.02420852 4.651652e-05 0.975744960 #> 7424 74.23 0.02419642 4.649326e-05 0.975757084 #> 7425 74.24 0.02418433 4.647002e-05 0.975769203 #> 7426 74.25 0.02417224 4.644679e-05 0.975781315 #> 7427 74.26 0.02416016 4.642358e-05 0.975793421 #> 7428 74.27 0.02414808 4.640037e-05 0.975805522 #> 7429 74.28 0.02413601 4.637718e-05 0.975817616 #> 7430 74.29 0.02412394 4.635399e-05 0.975829704 #> 7431 74.30 0.02411188 4.633082e-05 0.975841786 #> 7432 74.31 0.02409983 4.630766e-05 0.975853862 #> 7433 74.32 0.02408778 4.628451e-05 0.975865932 #> 7434 74.33 0.02407574 4.626138e-05 0.975877996 #> 7435 74.34 0.02406371 4.623825e-05 0.975890054 #> 7436 74.35 0.02405168 4.621514e-05 0.975902106 #> 7437 74.36 0.02403966 4.619204e-05 0.975914152 #> 7438 74.37 0.02402764 4.616895e-05 0.975926192 #> 7439 74.38 0.02401563 4.614587e-05 0.975938226 #> 7440 74.39 0.02400362 4.612280e-05 0.975950254 #> 7441 74.40 0.02399162 4.609975e-05 0.975962276 #> 7442 74.41 0.02397963 4.607670e-05 0.975974292 #> 7443 74.42 0.02396764 4.605367e-05 0.975986301 #> 7444 74.43 0.02395566 4.603065e-05 0.975998305 #> 7445 74.44 0.02394369 4.600764e-05 0.976010303 #> 7446 74.45 0.02393172 4.598464e-05 0.976022295 #> 7447 74.46 0.02391976 4.596165e-05 0.976034281 #> 7448 74.47 0.02390780 4.593868e-05 0.976046261 #> 7449 74.48 0.02389585 4.591571e-05 0.976058235 #> 7450 74.49 0.02388390 4.589276e-05 0.976070203 #> 7451 74.50 0.02387197 4.586982e-05 0.976082164 #> 7452 74.51 0.02386003 4.584689e-05 0.976094120 #> 7453 74.52 0.02384811 4.582397e-05 0.976106070 #> 7454 74.53 0.02383618 4.580107e-05 0.976118014 #> 7455 74.54 0.02382427 4.577817e-05 0.976129952 #> 7456 74.55 0.02381236 4.575529e-05 0.976141884 #> 7457 74.56 0.02380046 4.573242e-05 0.976153810 #> 7458 74.57 0.02378856 4.570956e-05 0.976165731 #> 7459 74.58 0.02377667 4.568671e-05 0.976177645 #> 7460 74.59 0.02376478 4.566387e-05 0.976189553 #> 7461 74.60 0.02375290 4.564104e-05 0.976201455 #> 7462 74.61 0.02374103 4.561823e-05 0.976213351 #> 7463 74.62 0.02372916 4.559543e-05 0.976225242 #> 7464 74.63 0.02371730 4.557263e-05 0.976237126 #> 7465 74.64 0.02370545 4.554985e-05 0.976249005 #> 7466 74.65 0.02369360 4.552708e-05 0.976260877 #> 7467 74.66 0.02368175 4.550433e-05 0.976272744 #> 7468 74.67 0.02366991 4.548158e-05 0.976284604 #> 7469 74.68 0.02365808 4.545885e-05 0.976296459 #> 7470 74.69 0.02364626 4.543612e-05 0.976308308 #> 7471 74.70 0.02363444 4.541341e-05 0.976320151 #> 7472 74.71 0.02362262 4.539071e-05 0.976331988 #> 7473 74.72 0.02361081 4.536802e-05 0.976343819 #> 7474 74.73 0.02359901 4.534534e-05 0.976355644 #> 7475 74.74 0.02358721 4.532267e-05 0.976367463 #> 7476 74.75 0.02357542 4.530002e-05 0.976379277 #> 7477 74.76 0.02356364 4.527737e-05 0.976391084 #> 7478 74.77 0.02355186 4.525474e-05 0.976402885 #> 7479 74.78 0.02354009 4.523212e-05 0.976414681 #> 7480 74.79 0.02352832 4.520951e-05 0.976426471 #> 7481 74.80 0.02351656 4.518691e-05 0.976438255 #> 7482 74.81 0.02350480 4.516432e-05 0.976450033 #> 7483 74.82 0.02349305 4.514174e-05 0.976461805 #> 7484 74.83 0.02348131 4.511918e-05 0.976473571 #> 7485 74.84 0.02346957 4.509663e-05 0.976485331 #> 7486 74.85 0.02345784 4.507408e-05 0.976497085 #> 7487 74.86 0.02344611 4.505155e-05 0.976508834 #> 7488 74.87 0.02343439 4.502903e-05 0.976520577 #> 7489 74.88 0.02342268 4.500652e-05 0.976532313 #> 7490 74.89 0.02341097 4.498402e-05 0.976544044 #> 7491 74.90 0.02339927 4.496154e-05 0.976555769 #> 7492 74.91 0.02338757 4.493906e-05 0.976567489 #> 7493 74.92 0.02337588 4.491660e-05 0.976579202 #> 7494 74.93 0.02336420 4.489415e-05 0.976590909 #> 7495 74.94 0.02335252 4.487171e-05 0.976602611 #> 7496 74.95 0.02334084 4.484927e-05 0.976614307 #> 7497 74.96 0.02332918 4.482686e-05 0.976625997 #> 7498 74.97 0.02331751 4.480445e-05 0.976637681 #> 7499 74.98 0.02330586 4.478205e-05 0.976649359 #> 7500 74.99 0.02329421 4.475967e-05 0.976661031 #> 7501 75.00 0.02328256 4.473729e-05 0.976672698 #> 7502 75.01 0.02327093 4.471493e-05 0.976684359 #> 7503 75.02 0.02325929 4.469258e-05 0.976696014 #> 7504 75.03 0.02324767 4.467024e-05 0.976707663 #> 7505 75.04 0.02323605 4.464791e-05 0.976719306 #> 7506 75.05 0.02322443 4.462559e-05 0.976730943 #> 7507 75.06 0.02321282 4.460328e-05 0.976742575 #> 7508 75.07 0.02320122 4.458098e-05 0.976754201 #> 7509 75.08 0.02318962 4.455870e-05 0.976765821 #> 7510 75.09 0.02317803 4.453643e-05 0.976777435 #> 7511 75.10 0.02316644 4.451416e-05 0.976789043 #> 7512 75.11 0.02315486 4.449191e-05 0.976800646 #> 7513 75.12 0.02314329 4.446967e-05 0.976812243 #> 7514 75.13 0.02313172 4.444744e-05 0.976823834 #> 7515 75.14 0.02312016 4.442522e-05 0.976835419 #> 7516 75.15 0.02310860 4.440302e-05 0.976846998 #> 7517 75.16 0.02309705 4.438082e-05 0.976858572 #> 7518 75.17 0.02308550 4.435864e-05 0.976870140 #> 7519 75.18 0.02307396 4.433646e-05 0.976881702 #> 7520 75.19 0.02306243 4.431430e-05 0.976893258 #> 7521 75.20 0.02305090 4.429215e-05 0.976904808 #> 7522 75.21 0.02303938 4.427001e-05 0.976916353 #> 7523 75.22 0.02302786 4.424788e-05 0.976927892 #> 7524 75.23 0.02301635 4.422576e-05 0.976939425 #> 7525 75.24 0.02300484 4.420365e-05 0.976950953 #> 7526 75.25 0.02299334 4.418156e-05 0.976962474 #> 7527 75.26 0.02298185 4.415947e-05 0.976973990 #> 7528 75.27 0.02297036 4.413740e-05 0.976985500 #> 7529 75.28 0.02295888 4.411533e-05 0.976997005 #> 7530 75.29 0.02294740 4.409328e-05 0.977008503 #> 7531 75.30 0.02293593 4.407124e-05 0.977019996 #> 7532 75.31 0.02292447 4.404921e-05 0.977031483 #> 7533 75.32 0.02291301 4.402719e-05 0.977042965 #> 7534 75.33 0.02290155 4.400518e-05 0.977054440 #> 7535 75.34 0.02289011 4.398319e-05 0.977065910 #> 7536 75.35 0.02287866 4.396120e-05 0.977077374 #> 7537 75.36 0.02286723 4.393922e-05 0.977088833 #> 7538 75.37 0.02285580 4.391726e-05 0.977100286 #> 7539 75.38 0.02284437 4.389531e-05 0.977111733 #> 7540 75.39 0.02283295 4.387337e-05 0.977123174 #> 7541 75.40 0.02282154 4.385143e-05 0.977134609 #> 7542 75.41 0.02281013 4.382951e-05 0.977146039 #> 7543 75.42 0.02279873 4.380760e-05 0.977157463 #> 7544 75.43 0.02278733 4.378571e-05 0.977168882 #> 7545 75.44 0.02277594 4.376382e-05 0.977180295 #> 7546 75.45 0.02276456 4.374194e-05 0.977191702 #> 7547 75.46 0.02275318 4.372008e-05 0.977203103 #> 7548 75.47 0.02274180 4.369822e-05 0.977214498 #> 7549 75.48 0.02273044 4.367638e-05 0.977225888 #> 7550 75.49 0.02271907 4.365455e-05 0.977237273 #> 7551 75.50 0.02270772 4.363272e-05 0.977248651 #> 7552 75.51 0.02269637 4.361091e-05 0.977260024 #> 7553 75.52 0.02268502 4.358911e-05 0.977271391 #> 7554 75.53 0.02267368 4.356732e-05 0.977282753 #> 7555 75.54 0.02266235 4.354555e-05 0.977294108 #> 7556 75.55 0.02265102 4.352378e-05 0.977305458 #> 7557 75.56 0.02263970 4.350202e-05 0.977316803 #> 7558 75.57 0.02262838 4.348028e-05 0.977328142 #> 7559 75.58 0.02261707 4.345854e-05 0.977339475 #> 7560 75.59 0.02260576 4.343682e-05 0.977350802 #> 7561 75.60 0.02259446 4.341510e-05 0.977362124 #> 7562 75.61 0.02258317 4.339340e-05 0.977373440 #> 7563 75.62 0.02257188 4.337171e-05 0.977384750 #> 7564 75.63 0.02256059 4.335003e-05 0.977396055 #> 7565 75.64 0.02254932 4.332836e-05 0.977407354 #> 7566 75.65 0.02253805 4.330670e-05 0.977418648 #> 7567 75.66 0.02252678 4.328505e-05 0.977429936 #> 7568 75.67 0.02251552 4.326342e-05 0.977441218 #> 7569 75.68 0.02250426 4.324179e-05 0.977452495 #> 7570 75.69 0.02249301 4.322018e-05 0.977463765 #> 7571 75.70 0.02248177 4.319857e-05 0.977475031 #> 7572 75.71 0.02247053 4.317698e-05 0.977486290 #> 7573 75.72 0.02245930 4.315539e-05 0.977497545 #> 7574 75.73 0.02244807 4.313382e-05 0.977508793 #> 7575 75.74 0.02243685 4.311226e-05 0.977520036 #> 7576 75.75 0.02242564 4.309071e-05 0.977531273 #> 7577 75.76 0.02241443 4.306917e-05 0.977542504 #> 7578 75.77 0.02240322 4.304764e-05 0.977553730 #> 7579 75.78 0.02239202 4.302612e-05 0.977564951 #> 7580 75.79 0.02238083 4.300461e-05 0.977576165 #> 7581 75.80 0.02236964 4.298312e-05 0.977587375 #> 7582 75.81 0.02235846 4.296163e-05 0.977598578 #> 7583 75.82 0.02234728 4.294016e-05 0.977609776 #> 7584 75.83 0.02233611 4.291869e-05 0.977620968 #> 7585 75.84 0.02232495 4.289724e-05 0.977632155 #> 7586 75.85 0.02231379 4.287579e-05 0.977643336 #> 7587 75.86 0.02230263 4.285436e-05 0.977654512 #> 7588 75.87 0.02229149 4.283294e-05 0.977665682 #> 7589 75.88 0.02228034 4.281153e-05 0.977676846 #> 7590 75.89 0.02226921 4.279013e-05 0.977688005 #> 7591 75.90 0.02225807 4.276874e-05 0.977699158 #> 7592 75.91 0.02224695 4.274736e-05 0.977710306 #> 7593 75.92 0.02223583 4.272599e-05 0.977721448 #> 7594 75.93 0.02222471 4.270463e-05 0.977732584 #> 7595 75.94 0.02221360 4.268329e-05 0.977743715 #> 7596 75.95 0.02220250 4.266195e-05 0.977754840 #> 7597 75.96 0.02219140 4.264062e-05 0.977765960 #> 7598 75.97 0.02218031 4.261931e-05 0.977777075 #> 7599 75.98 0.02216922 4.259800e-05 0.977788183 #> 7600 75.99 0.02215814 4.257671e-05 0.977799286 #> 7601 76.00 0.02214706 4.255543e-05 0.977810384 #> 7602 76.01 0.02213599 4.253416e-05 0.977821476 #> 7603 76.02 0.02212492 4.251289e-05 0.977832562 #> 7604 76.03 0.02211386 4.249164e-05 0.977843643 #> 7605 76.04 0.02210281 4.247040e-05 0.977854719 #> 7606 76.05 0.02209176 4.244917e-05 0.977865789 #> 7607 76.06 0.02208072 4.242795e-05 0.977876853 #> 7608 76.07 0.02206968 4.240674e-05 0.977887912 #> 7609 76.08 0.02205865 4.238555e-05 0.977898965 #> 7610 76.09 0.02204762 4.236436e-05 0.977910013 #> 7611 76.10 0.02203660 4.234318e-05 0.977921055 #> 7612 76.11 0.02202559 4.232202e-05 0.977932092 #> 7613 76.12 0.02201458 4.230086e-05 0.977943123 #> 7614 76.13 0.02200357 4.227971e-05 0.977954149 #> 7615 76.14 0.02199257 4.225858e-05 0.977965169 #> 7616 76.15 0.02198158 4.223746e-05 0.977976184 #> 7617 76.16 0.02197059 4.221634e-05 0.977987193 #> 7618 76.17 0.02195961 4.219524e-05 0.977998196 #> 7619 76.18 0.02194863 4.217415e-05 0.978009194 #> 7620 76.19 0.02193766 4.215307e-05 0.978020187 #> 7621 76.20 0.02192669 4.213199e-05 0.978031174 #> 7622 76.21 0.02191573 4.211093e-05 0.978042156 #> 7623 76.22 0.02190478 4.208988e-05 0.978053132 #> 7624 76.23 0.02189383 4.206884e-05 0.978064103 #> 7625 76.24 0.02188288 4.204781e-05 0.978075068 #> 7626 76.25 0.02187195 4.202680e-05 0.978086028 #> 7627 76.26 0.02186101 4.200579e-05 0.978096982 #> 7628 76.27 0.02185008 4.198479e-05 0.978107931 #> 7629 76.28 0.02183916 4.196380e-05 0.978118874 #> 7630 76.29 0.02182825 4.194283e-05 0.978129812 #> 7631 76.30 0.02181733 4.192186e-05 0.978140744 #> 7632 76.31 0.02180643 4.190090e-05 0.978151671 #> 7633 76.32 0.02179553 4.187996e-05 0.978162593 #> 7634 76.33 0.02178463 4.185902e-05 0.978173509 #> 7635 76.34 0.02177374 4.183810e-05 0.978184419 #> 7636 76.35 0.02176286 4.181719e-05 0.978195324 #> 7637 76.36 0.02175198 4.179628e-05 0.978206224 #> 7638 76.37 0.02174111 4.177539e-05 0.978217118 #> 7639 76.38 0.02173024 4.175451e-05 0.978228007 #> 7640 76.39 0.02171938 4.173364e-05 0.978238890 #> 7641 76.40 0.02170852 4.171277e-05 0.978249768 #> 7642 76.41 0.02169767 4.169192e-05 0.978260640 #> 7643 76.42 0.02168682 4.167108e-05 0.978271507 #> 7644 76.43 0.02167598 4.165025e-05 0.978282369 #> 7645 76.44 0.02166515 4.162943e-05 0.978293225 #> 7646 76.45 0.02165432 4.160862e-05 0.978304075 #> 7647 76.46 0.02164349 4.158782e-05 0.978314921 #> 7648 76.47 0.02163267 4.156703e-05 0.978325761 #> 7649 76.48 0.02162186 4.154626e-05 0.978336595 #> 7650 76.49 0.02161105 4.152549e-05 0.978347424 #> 7651 76.50 0.02160025 4.150473e-05 0.978358248 #> 7652 76.51 0.02158945 4.148398e-05 0.978369066 #> 7653 76.52 0.02157866 4.146325e-05 0.978379878 #> 7654 76.53 0.02156787 4.144252e-05 0.978390686 #> 7655 76.54 0.02155709 4.142180e-05 0.978401488 #> 7656 76.55 0.02154631 4.140110e-05 0.978412284 #> 7657 76.56 0.02153554 4.138040e-05 0.978423075 #> 7658 76.57 0.02152478 4.135972e-05 0.978433861 #> 7659 76.58 0.02151402 4.133904e-05 0.978444642 #> 7660 76.59 0.02150327 4.131838e-05 0.978455417 #> 7661 76.60 0.02149252 4.129773e-05 0.978466186 #> 7662 76.61 0.02148177 4.127708e-05 0.978476950 #> 7663 76.62 0.02147103 4.125645e-05 0.978487709 #> 7664 76.63 0.02146030 4.123582e-05 0.978498463 #> 7665 76.64 0.02144957 4.121521e-05 0.978509211 #> 7666 76.65 0.02143885 4.119461e-05 0.978519954 #> 7667 76.66 0.02142814 4.117402e-05 0.978530691 #> 7668 76.67 0.02141742 4.115344e-05 0.978541423 #> 7669 76.68 0.02140672 4.113286e-05 0.978552149 #> 7670 76.69 0.02139602 4.111230e-05 0.978562871 #> 7671 76.70 0.02138532 4.109175e-05 0.978573587 #> 7672 76.71 0.02137463 4.107121e-05 0.978584297 #> 7673 76.72 0.02136395 4.105068e-05 0.978595002 #> 7674 76.73 0.02135327 4.103016e-05 0.978605702 #> 7675 76.74 0.02134259 4.100965e-05 0.978616397 #> 7676 76.75 0.02133193 4.098915e-05 0.978627086 #> 7677 76.76 0.02132126 4.096866e-05 0.978637769 #> 7678 76.77 0.02131060 4.094818e-05 0.978648448 #> 7679 76.78 0.02129995 4.092771e-05 0.978659121 #> 7680 76.79 0.02128930 4.090725e-05 0.978669789 #> 7681 76.80 0.02127866 4.088681e-05 0.978680451 #> 7682 76.81 0.02126803 4.086637e-05 0.978691108 #> 7683 76.82 0.02125739 4.084594e-05 0.978701760 #> 7684 76.83 0.02124677 4.082552e-05 0.978712407 #> 7685 76.84 0.02123615 4.080511e-05 0.978723048 #> 7686 76.85 0.02122553 4.078472e-05 0.978733684 #> 7687 76.86 0.02121492 4.076433e-05 0.978744314 #> 7688 76.87 0.02120432 4.074395e-05 0.978754939 #> 7689 76.88 0.02119372 4.072359e-05 0.978765559 #> 7690 76.89 0.02118312 4.070323e-05 0.978776174 #> 7691 76.90 0.02117253 4.068288e-05 0.978786783 #> 7692 76.91 0.02116195 4.066255e-05 0.978797387 #> 7693 76.92 0.02115137 4.064222e-05 0.978807986 #> 7694 76.93 0.02114080 4.062190e-05 0.978818579 #> 7695 76.94 0.02113023 4.060160e-05 0.978829167 #> 7696 76.95 0.02111967 4.058130e-05 0.978839750 #> 7697 76.96 0.02110911 4.056102e-05 0.978850327 #> 7698 76.97 0.02109856 4.054074e-05 0.978860899 #> 7699 76.98 0.02108801 4.052048e-05 0.978871466 #> 7700 76.99 0.02107747 4.050022e-05 0.978882028 #> 7701 77.00 0.02106694 4.047998e-05 0.978892584 #> 7702 77.01 0.02105640 4.045974e-05 0.978903135 #> 7703 77.02 0.02104588 4.043952e-05 0.978913681 #> 7704 77.03 0.02103536 4.041930e-05 0.978924222 #> 7705 77.04 0.02102484 4.039910e-05 0.978934757 #> 7706 77.05 0.02101433 4.037890e-05 0.978945287 #> 7707 77.06 0.02100383 4.035872e-05 0.978955812 #> 7708 77.07 0.02099333 4.033854e-05 0.978966331 #> 7709 77.08 0.02098284 4.031838e-05 0.978976845 #> 7710 77.09 0.02097235 4.029822e-05 0.978987354 #> 7711 77.10 0.02096186 4.027808e-05 0.978997858 #> 7712 77.11 0.02095139 4.025795e-05 0.979008356 #> 7713 77.12 0.02094091 4.023782e-05 0.979018850 #> 7714 77.13 0.02093044 4.021771e-05 0.979029338 #> 7715 77.14 0.02091998 4.019760e-05 0.979039820 #> 7716 77.15 0.02090952 4.017751e-05 0.979050298 #> 7717 77.16 0.02089907 4.015743e-05 0.979060770 #> 7718 77.17 0.02088863 4.013735e-05 0.979071237 #> 7719 77.18 0.02087818 4.011729e-05 0.979081699 #> 7720 77.19 0.02086775 4.009724e-05 0.979092155 #> 7721 77.20 0.02085732 4.007719e-05 0.979102607 #> 7722 77.21 0.02084689 4.005716e-05 0.979113053 #> 7723 77.22 0.02083647 4.003714e-05 0.979123494 #> 7724 77.23 0.02082605 4.001712e-05 0.979133929 #> 7725 77.24 0.02081564 3.999712e-05 0.979144360 #> 7726 77.25 0.02080524 3.997712e-05 0.979154785 #> 7727 77.26 0.02079484 3.995714e-05 0.979165205 #> 7728 77.27 0.02078444 3.993717e-05 0.979175620 #> 7729 77.28 0.02077405 3.991720e-05 0.979186029 #> 7730 77.29 0.02076367 3.989725e-05 0.979196434 #> 7731 77.30 0.02075329 3.987731e-05 0.979206833 #> 7732 77.31 0.02074292 3.985737e-05 0.979217227 #> 7733 77.32 0.02073255 3.983745e-05 0.979227616 #> 7734 77.33 0.02072218 3.981754e-05 0.979237999 #> 7735 77.34 0.02071182 3.979763e-05 0.979248378 #> 7736 77.35 0.02070147 3.977774e-05 0.979258751 #> 7737 77.36 0.02069112 3.975785e-05 0.979269119 #> 7738 77.37 0.02068078 3.973798e-05 0.979279482 #> 7739 77.38 0.02067044 3.971812e-05 0.979289839 #> 7740 77.39 0.02066011 3.969826e-05 0.979300192 #> 7741 77.40 0.02064978 3.967842e-05 0.979310539 #> 7742 77.41 0.02063946 3.965858e-05 0.979320881 #> 7743 77.42 0.02062914 3.963876e-05 0.979331218 #> 7744 77.43 0.02061883 3.961895e-05 0.979341550 #> 7745 77.44 0.02060852 3.959914e-05 0.979351877 #> 7746 77.45 0.02059822 3.957935e-05 0.979362198 #> 7747 77.46 0.02058793 3.955956e-05 0.979372515 #> 7748 77.47 0.02057763 3.953979e-05 0.979382826 #> 7749 77.48 0.02056735 3.952002e-05 0.979393132 #> 7750 77.49 0.02055707 3.950027e-05 0.979403433 #> 7751 77.50 0.02054679 3.948052e-05 0.979413728 #> 7752 77.51 0.02053652 3.946079e-05 0.979424019 #> 7753 77.52 0.02052625 3.944106e-05 0.979434304 #> 7754 77.53 0.02051599 3.942134e-05 0.979444585 #> 7755 77.54 0.02050574 3.940164e-05 0.979454860 #> 7756 77.55 0.02049549 3.938194e-05 0.979465130 #> 7757 77.56 0.02048524 3.936226e-05 0.979475395 #> 7758 77.57 0.02047500 3.934258e-05 0.979485654 #> 7759 77.58 0.02046477 3.932291e-05 0.979495909 #> 7760 77.59 0.02045454 3.930326e-05 0.979506158 #> 7761 77.60 0.02044431 3.928361e-05 0.979516403 #> 7762 77.61 0.02043409 3.926397e-05 0.979526642 #> 7763 77.62 0.02042388 3.924435e-05 0.979536876 #> 7764 77.63 0.02041367 3.922473e-05 0.979547105 #> 7765 77.64 0.02040347 3.920512e-05 0.979557329 #> 7766 77.65 0.02039327 3.918552e-05 0.979567548 #> 7767 77.66 0.02038307 3.916594e-05 0.979577761 #> 7768 77.67 0.02037288 3.914636e-05 0.979587970 #> 7769 77.68 0.02036270 3.912679e-05 0.979598173 #> 7770 77.69 0.02035252 3.910723e-05 0.979608372 #> 7771 77.70 0.02034235 3.908768e-05 0.979618565 #> 7772 77.71 0.02033218 3.906814e-05 0.979628753 #> 7773 77.72 0.02032202 3.904862e-05 0.979638936 #> 7774 77.73 0.02031186 3.902910e-05 0.979649114 #> 7775 77.74 0.02030170 3.900959e-05 0.979659287 #> 7776 77.75 0.02029155 3.899009e-05 0.979669455 #> 7777 77.76 0.02028141 3.897060e-05 0.979679618 #> 7778 77.77 0.02027127 3.895112e-05 0.979689775 #> 7779 77.78 0.02026114 3.893164e-05 0.979699928 #> 7780 77.79 0.02025101 3.891218e-05 0.979710076 #> 7781 77.80 0.02024089 3.889273e-05 0.979720218 #> 7782 77.81 0.02023077 3.887329e-05 0.979730355 #> 7783 77.82 0.02022066 3.885386e-05 0.979740488 #> 7784 77.83 0.02021055 3.883444e-05 0.979750615 #> 7785 77.84 0.02020045 3.881502e-05 0.979760737 #> 7786 77.85 0.02019035 3.879562e-05 0.979770854 #> 7787 77.86 0.02018026 3.877623e-05 0.979780966 #> 7788 77.87 0.02017017 3.875685e-05 0.979791073 #> 7789 77.88 0.02016009 3.873747e-05 0.979801175 #> 7790 77.89 0.02015001 3.871811e-05 0.979811272 #> 7791 77.90 0.02013994 3.869875e-05 0.979821364 #> 7792 77.91 0.02012987 3.867941e-05 0.979831451 #> 7793 77.92 0.02011981 3.866007e-05 0.979841532 #> 7794 77.93 0.02010975 3.864075e-05 0.979851609 #> 7795 77.94 0.02009970 3.862143e-05 0.979861681 #> 7796 77.95 0.02008965 3.860213e-05 0.979871747 #> 7797 77.96 0.02007961 3.858283e-05 0.979881809 #> 7798 77.97 0.02006957 3.856355e-05 0.979891866 #> 7799 77.98 0.02005954 3.854427e-05 0.979901917 #> 7800 77.99 0.02004951 3.852500e-05 0.979911964 #> 7801 78.00 0.02003949 3.850574e-05 0.979922005 #> 7802 78.01 0.02002947 3.848650e-05 0.979932042 #> 7803 78.02 0.02001946 3.846726e-05 0.979942073 #> 7804 78.03 0.02000945 3.844803e-05 0.979952100 #> 7805 78.04 0.01999945 3.842881e-05 0.979962121 #> 7806 78.05 0.01998945 3.840960e-05 0.979972137 #> 7807 78.06 0.01997946 3.839040e-05 0.979982149 #> 7808 78.07 0.01996947 3.837121e-05 0.979992155 #> 7809 78.08 0.01995949 3.835203e-05 0.980002157 #> 7810 78.09 0.01994951 3.833286e-05 0.980012153 #> 7811 78.10 0.01993954 3.831370e-05 0.980022145 #> 7812 78.11 0.01992957 3.829454e-05 0.980032131 #> 7813 78.12 0.01991961 3.827540e-05 0.980042112 #> 7814 78.13 0.01990965 3.825627e-05 0.980052089 #> 7815 78.14 0.01989970 3.823714e-05 0.980062060 #> 7816 78.15 0.01988976 3.821803e-05 0.980072027 #> 7817 78.16 0.01987981 3.819893e-05 0.980081988 #> 7818 78.17 0.01986988 3.817983e-05 0.980091945 #> 7819 78.18 0.01985994 3.816075e-05 0.980101896 #> 7820 78.19 0.01985002 3.814167e-05 0.980111843 #> 7821 78.20 0.01984009 3.812260e-05 0.980121785 #> 7822 78.21 0.01983018 3.810355e-05 0.980131721 #> 7823 78.22 0.01982026 3.808450e-05 0.980141653 #> 7824 78.23 0.01981036 3.806546e-05 0.980151580 #> 7825 78.24 0.01980045 3.804644e-05 0.980161501 #> 7826 78.25 0.01979055 3.802742e-05 0.980171418 #> 7827 78.26 0.01978066 3.800841e-05 0.980181330 #> 7828 78.27 0.01977077 3.798941e-05 0.980191237 #> 7829 78.28 0.01976089 3.797042e-05 0.980201139 #> 7830 78.29 0.01975101 3.795144e-05 0.980211036 #> 7831 78.30 0.01974114 3.793247e-05 0.980220928 #> 7832 78.31 0.01973127 3.791351e-05 0.980230815 #> 7833 78.32 0.01972141 3.789455e-05 0.980240697 #> 7834 78.33 0.01971155 3.787561e-05 0.980250574 #> 7835 78.34 0.01970170 3.785668e-05 0.980260446 #> 7836 78.35 0.01969185 3.783775e-05 0.980270313 #> 7837 78.36 0.01968201 3.781884e-05 0.980280176 #> 7838 78.37 0.01967217 3.779994e-05 0.980290033 #> 7839 78.38 0.01966233 3.778104e-05 0.980299886 #> 7840 78.39 0.01965250 3.776216e-05 0.980309733 #> 7841 78.40 0.01964268 3.774328e-05 0.980319576 #> 7842 78.41 0.01963286 3.772441e-05 0.980329414 #> 7843 78.42 0.01962305 3.770555e-05 0.980339247 #> 7844 78.43 0.01961324 3.768671e-05 0.980349075 #> 7845 78.44 0.01960343 3.766787e-05 0.980358898 #> 7846 78.45 0.01959364 3.764904e-05 0.980368716 #> 7847 78.46 0.01958384 3.763022e-05 0.980378529 #> 7848 78.47 0.01957405 3.761141e-05 0.980388337 #> 7849 78.48 0.01956427 3.759261e-05 0.980398141 #> 7850 78.49 0.01955449 3.757382e-05 0.980407939 #> 7851 78.50 0.01954471 3.755503e-05 0.980417733 #> 7852 78.51 0.01953494 3.753626e-05 0.980427521 #> 7853 78.52 0.01952518 3.751750e-05 0.980437305 #> 7854 78.53 0.01951542 3.749874e-05 0.980447084 #> 7855 78.54 0.01950566 3.748000e-05 0.980456858 #> 7856 78.55 0.01949591 3.746126e-05 0.980466627 #> 7857 78.56 0.01948617 3.744254e-05 0.980476391 #> 7858 78.57 0.01947643 3.742382e-05 0.980486151 #> 7859 78.58 0.01946669 3.740511e-05 0.980495905 #> 7860 78.59 0.01945696 3.738642e-05 0.980505655 #> 7861 78.60 0.01944723 3.736773e-05 0.980515400 #> 7862 78.61 0.01943751 3.734905e-05 0.980525139 #> 7863 78.62 0.01942780 3.733038e-05 0.980534874 #> 7864 78.63 0.01941808 3.731172e-05 0.980544605 #> 7865 78.64 0.01940838 3.729307e-05 0.980554330 #> 7866 78.65 0.01939868 3.727442e-05 0.980564050 #> 7867 78.66 0.01938898 3.725579e-05 0.980573766 #> 7868 78.67 0.01937929 3.723717e-05 0.980583477 #> 7869 78.68 0.01936960 3.721855e-05 0.980593182 #> 7870 78.69 0.01935992 3.719995e-05 0.980602883 #> 7871 78.70 0.01935024 3.718135e-05 0.980612579 #> 7872 78.71 0.01934057 3.716277e-05 0.980622271 #> 7873 78.72 0.01933090 3.714419e-05 0.980631957 #> 7874 78.73 0.01932124 3.712562e-05 0.980641639 #> 7875 78.74 0.01931158 3.710707e-05 0.980651316 #> 7876 78.75 0.01930192 3.708852e-05 0.980660987 #> 7877 78.76 0.01929228 3.706998e-05 0.980670655 #> 7878 78.77 0.01928263 3.705145e-05 0.980680317 #> 7879 78.78 0.01927299 3.703293e-05 0.980689974 #> 7880 78.79 0.01926336 3.701441e-05 0.980699627 #> 7881 78.80 0.01925373 3.699591e-05 0.980709275 #> 7882 78.81 0.01924411 3.697742e-05 0.980718918 #> 7883 78.82 0.01923449 3.695893e-05 0.980728556 #> 7884 78.83 0.01922487 3.694046e-05 0.980738189 #> 7885 78.84 0.01921526 3.692199e-05 0.980747818 #> 7886 78.85 0.01920566 3.690354e-05 0.980757441 #> 7887 78.86 0.01919605 3.688509e-05 0.980767060 #> 7888 78.87 0.01918646 3.686665e-05 0.980776674 #> 7889 78.88 0.01917687 3.684822e-05 0.980786283 #> 7890 78.89 0.01916728 3.682980e-05 0.980795888 #> 7891 78.90 0.01915770 3.681139e-05 0.980805488 #> 7892 78.91 0.01914812 3.679299e-05 0.980815082 #> 7893 78.92 0.01913855 3.677460e-05 0.980824672 #> 7894 78.93 0.01912899 3.675622e-05 0.980834258 #> 7895 78.94 0.01911942 3.673784e-05 0.980843838 #> 7896 78.95 0.01910987 3.671948e-05 0.980853414 #> 7897 78.96 0.01910031 3.670113e-05 0.980862985 #> 7898 78.97 0.01909077 3.668278e-05 0.980872551 #> 7899 78.98 0.01908122 3.666444e-05 0.980882112 #> 7900 78.99 0.01907169 3.664611e-05 0.980891669 #> 7901 79.00 0.01906215 3.662780e-05 0.980901221 #> 7902 79.01 0.01905262 3.660949e-05 0.980910768 #> 7903 79.02 0.01904310 3.659119e-05 0.980920310 #> 7904 79.03 0.01903358 3.657290e-05 0.980929847 #> 7905 79.04 0.01902407 3.655461e-05 0.980939380 #> 7906 79.05 0.01901456 3.653634e-05 0.980948908 #> 7907 79.06 0.01900505 3.651808e-05 0.980958431 #> 7908 79.07 0.01899555 3.649982e-05 0.980967949 #> 7909 79.08 0.01898606 3.648158e-05 0.980977463 #> 7910 79.09 0.01897656 3.646334e-05 0.980986972 #> 7911 79.10 0.01896708 3.644511e-05 0.980996476 #> 7912 79.11 0.01895760 3.642690e-05 0.981005976 #> 7913 79.12 0.01894812 3.640869e-05 0.981015470 #> 7914 79.13 0.01893865 3.639049e-05 0.981024960 #> 7915 79.14 0.01892918 3.637230e-05 0.981034445 #> 7916 79.15 0.01891972 3.635412e-05 0.981043926 #> 7917 79.16 0.01891026 3.633594e-05 0.981053401 #> 7918 79.17 0.01890081 3.631778e-05 0.981062872 #> 7919 79.18 0.01889136 3.629962e-05 0.981072338 #> 7920 79.19 0.01888192 3.628148e-05 0.981081800 #> 7921 79.20 0.01887248 3.626334e-05 0.981091257 #> 7922 79.21 0.01886305 3.624522e-05 0.981100709 #> 7923 79.22 0.01885362 3.622710e-05 0.981110156 #> 7924 79.23 0.01884419 3.620899e-05 0.981119598 #> 7925 79.24 0.01883477 3.619089e-05 0.981129036 #> 7926 79.25 0.01882536 3.617280e-05 0.981138469 #> 7927 79.26 0.01881595 3.615472e-05 0.981147898 #> 7928 79.27 0.01880654 3.613664e-05 0.981157322 #> 7929 79.28 0.01879714 3.611858e-05 0.981166741 #> 7930 79.29 0.01878774 3.610053e-05 0.981176155 #> 7931 79.30 0.01877835 3.608248e-05 0.981185564 #> 7932 79.31 0.01876897 3.606444e-05 0.981194969 #> 7933 79.32 0.01875958 3.604641e-05 0.981204369 #> 7934 79.33 0.01875021 3.602840e-05 0.981213765 #> 7935 79.34 0.01874083 3.601039e-05 0.981223156 #> 7936 79.35 0.01873147 3.599239e-05 0.981232542 #> 7937 79.36 0.01872210 3.597439e-05 0.981241923 #> 7938 79.37 0.01871274 3.595641e-05 0.981251300 #> 7939 79.38 0.01870339 3.593844e-05 0.981260672 #> 7940 79.39 0.01869404 3.592047e-05 0.981270039 #> 7941 79.40 0.01868470 3.590252e-05 0.981279402 #> 7942 79.41 0.01867536 3.588457e-05 0.981288760 #> 7943 79.42 0.01866602 3.586663e-05 0.981298113 #> 7944 79.43 0.01865669 3.584870e-05 0.981307462 #> 7945 79.44 0.01864736 3.583078e-05 0.981316806 #> 7946 79.45 0.01863804 3.581287e-05 0.981326145 #> 7947 79.46 0.01862873 3.579497e-05 0.981335479 #> 7948 79.47 0.01861941 3.577708e-05 0.981344809 #> 7949 79.48 0.01861011 3.575919e-05 0.981354135 #> 7950 79.49 0.01860080 3.574132e-05 0.981363455 #> 7951 79.50 0.01859151 3.572345e-05 0.981372771 #> 7952 79.51 0.01858221 3.570560e-05 0.981382082 #> 7953 79.52 0.01857292 3.568775e-05 0.981391389 #> 7954 79.53 0.01856364 3.566991e-05 0.981400691 #> 7955 79.54 0.01855436 3.565208e-05 0.981409988 #> 7956 79.55 0.01854508 3.563426e-05 0.981419281 #> 7957 79.56 0.01853581 3.561644e-05 0.981428569 #> 7958 79.57 0.01852655 3.559864e-05 0.981437852 #> 7959 79.58 0.01851729 3.558084e-05 0.981447131 #> 7960 79.59 0.01850803 3.556306e-05 0.981456405 #> 7961 79.60 0.01849878 3.554528e-05 0.981465675 #> 7962 79.61 0.01848953 3.552751e-05 0.981474940 #> 7963 79.62 0.01848029 3.550975e-05 0.981484200 #> 7964 79.63 0.01847105 3.549200e-05 0.981493455 #> 7965 79.64 0.01846182 3.547426e-05 0.981502706 #> 7966 79.65 0.01845259 3.545653e-05 0.981511953 #> 7967 79.66 0.01844337 3.543881e-05 0.981521194 #> 7968 79.67 0.01843415 3.542109e-05 0.981530432 #> 7969 79.68 0.01842493 3.540338e-05 0.981539664 #> 7970 79.69 0.01841572 3.538569e-05 0.981548892 #> 7971 79.70 0.01840652 3.536800e-05 0.981558115 #> 7972 79.71 0.01839732 3.535032e-05 0.981567334 #> 7973 79.72 0.01838812 3.533265e-05 0.981576548 #> 7974 79.73 0.01837893 3.531499e-05 0.981585757 #> 7975 79.74 0.01836974 3.529733e-05 0.981594962 #> 7976 79.75 0.01836056 3.527969e-05 0.981604162 #> 7977 79.76 0.01835138 3.526205e-05 0.981613358 #> 7978 79.77 0.01834221 3.524443e-05 0.981622549 #> 7979 79.78 0.01833304 3.522681e-05 0.981631735 #> 7980 79.79 0.01832387 3.520920e-05 0.981640917 #> 7981 79.80 0.01831471 3.519160e-05 0.981650094 #> 7982 79.81 0.01830556 3.517401e-05 0.981659267 #> 7983 79.82 0.01829641 3.515643e-05 0.981668435 #> 7984 79.83 0.01828726 3.513885e-05 0.981677599 #> 7985 79.84 0.01827812 3.512129e-05 0.981686758 #> 7986 79.85 0.01826898 3.510373e-05 0.981695912 #> 7987 79.86 0.01825985 3.508618e-05 0.981705062 #> 7988 79.87 0.01825072 3.506864e-05 0.981714207 #> 7989 79.88 0.01824160 3.505111e-05 0.981723347 #> 7990 79.89 0.01823248 3.503359e-05 0.981732484 #> 7991 79.90 0.01822337 3.501608e-05 0.981741615 #> 7992 79.91 0.01821426 3.499858e-05 0.981750742 #> 7993 79.92 0.01820515 3.498108e-05 0.981759864 #> 7994 79.93 0.01819605 3.496360e-05 0.981768982 #> 7995 79.94 0.01818696 3.494612e-05 0.981778095 #> 7996 79.95 0.01817787 3.492865e-05 0.981787204 #> 7997 79.96 0.01816878 3.491119e-05 0.981796308 #> 7998 79.97 0.01815970 3.489374e-05 0.981805408 #> 7999 79.98 0.01815062 3.487630e-05 0.981814503 #> 8000 79.99 0.01814155 3.485886e-05 0.981823593 #> 8001 80.00 0.01813248 3.484144e-05 0.981832679 #> 8002 80.01 0.01812342 3.482402e-05 0.981841760 #> 8003 80.02 0.01811436 3.480661e-05 0.981850837 #> 8004 80.03 0.01810530 3.478921e-05 0.981859910 #> 8005 80.04 0.01809625 3.477182e-05 0.981868977 #> 8006 80.05 0.01808720 3.475444e-05 0.981878041 #> 8007 80.06 0.01807816 3.473707e-05 0.981887099 #> 8008 80.07 0.01806913 3.471971e-05 0.981896154 #> 8009 80.08 0.01806009 3.470235e-05 0.981905203 #> 8010 80.09 0.01805107 3.468500e-05 0.981914248 #> 8011 80.10 0.01804204 3.466767e-05 0.981923289 #> 8012 80.11 0.01803302 3.465034e-05 0.981932325 #> 8013 80.12 0.01802401 3.463301e-05 0.981941357 #> 8014 80.13 0.01801500 3.461570e-05 0.981950384 #> 8015 80.14 0.01800600 3.459840e-05 0.981959406 #> 8016 80.15 0.01799699 3.458110e-05 0.981968424 #> 8017 80.16 0.01798800 3.456382e-05 0.981977438 #> 8018 80.17 0.01797901 3.454654e-05 0.981986447 #> 8019 80.18 0.01797002 3.452927e-05 0.981995451 #> 8020 80.19 0.01796104 3.451201e-05 0.982004451 #> 8021 80.20 0.01795206 3.449476e-05 0.982013447 #> 8022 80.21 0.01794308 3.447752e-05 0.982022438 #> 8023 80.22 0.01793412 3.446028e-05 0.982031424 #> 8024 80.23 0.01792515 3.444306e-05 0.982040407 #> 8025 80.24 0.01791619 3.442584e-05 0.982049384 #> 8026 80.25 0.01790723 3.440863e-05 0.982058357 #> 8027 80.26 0.01789828 3.439143e-05 0.982067326 #> 8028 80.27 0.01788934 3.437424e-05 0.982076290 #> 8029 80.28 0.01788039 3.435706e-05 0.982085249 #> 8030 80.29 0.01787146 3.433988e-05 0.982094205 #> 8031 80.30 0.01786252 3.432272e-05 0.982103155 #> 8032 80.31 0.01785359 3.430556e-05 0.982112101 #> 8033 80.32 0.01784467 3.428841e-05 0.982121043 #> 8034 80.33 0.01783575 3.427127e-05 0.982129980 #> 8035 80.34 0.01782683 3.425414e-05 0.982138913 #> 8036 80.35 0.01781792 3.423702e-05 0.982147841 #> 8037 80.36 0.01780901 3.421990e-05 0.982156765 #> 8038 80.37 0.01780011 3.420280e-05 0.982165685 #> 8039 80.38 0.01779121 3.418570e-05 0.982174600 #> 8040 80.39 0.01778232 3.416861e-05 0.982183510 #> 8041 80.40 0.01777343 3.415153e-05 0.982192416 #> 8042 80.41 0.01776455 3.413446e-05 0.982201318 #> 8043 80.42 0.01775567 3.411740e-05 0.982210215 #> 8044 80.43 0.01774679 3.410034e-05 0.982219107 #> 8045 80.44 0.01773792 3.408330e-05 0.982227996 #> 8046 80.45 0.01772905 3.406626e-05 0.982236879 #> 8047 80.46 0.01772019 3.404923e-05 0.982245759 #> 8048 80.47 0.01771133 3.403221e-05 0.982254634 #> 8049 80.48 0.01770248 3.401520e-05 0.982263504 #> 8050 80.49 0.01769363 3.399819e-05 0.982272370 #> 8051 80.50 0.01768479 3.398120e-05 0.982281232 #> 8052 80.51 0.01767595 3.396421e-05 0.982290089 #> 8053 80.52 0.01766711 3.394724e-05 0.982298942 #> 8054 80.53 0.01765828 3.393027e-05 0.982307790 #> 8055 80.54 0.01764945 3.391330e-05 0.982316634 #> 8056 80.55 0.01764063 3.389635e-05 0.982325473 #> 8057 80.56 0.01763181 3.387941e-05 0.982334308 #> 8058 80.57 0.01762300 3.386247e-05 0.982343139 #> 8059 80.58 0.01761419 3.384555e-05 0.982351965 #> 8060 80.59 0.01760538 3.382863e-05 0.982360787 #> 8061 80.60 0.01759658 3.381172e-05 0.982369605 #> 8062 80.61 0.01758779 3.379482e-05 0.982378418 #> 8063 80.62 0.01757900 3.377792e-05 0.982387226 #> 8064 80.63 0.01757021 3.376104e-05 0.982396030 #> 8065 80.64 0.01756143 3.374416e-05 0.982404830 #> 8066 80.65 0.01755265 3.372729e-05 0.982413626 #> 8067 80.66 0.01754387 3.371043e-05 0.982422416 #> 8068 80.67 0.01753510 3.369358e-05 0.982431203 #> 8069 80.68 0.01752634 3.367674e-05 0.982439985 #> 8070 80.69 0.01751758 3.365991e-05 0.982448763 #> 8071 80.70 0.01750882 3.364308e-05 0.982457537 #> 8072 80.71 0.01750007 3.362626e-05 0.982466306 #> 8073 80.72 0.01749132 3.360945e-05 0.982475070 #> 8074 80.73 0.01748258 3.359265e-05 0.982483830 #> 8075 80.74 0.01747384 3.357586e-05 0.982492586 #> 8076 80.75 0.01746510 3.355908e-05 0.982501338 #> 8077 80.76 0.01745637 3.354230e-05 0.982510085 #> 8078 80.77 0.01744765 3.352554e-05 0.982518828 #> 8079 80.78 0.01743892 3.350878e-05 0.982527566 #> 8080 80.79 0.01743021 3.349203e-05 0.982536300 #> 8081 80.80 0.01742149 3.347529e-05 0.982545030 #> 8082 80.81 0.01741279 3.345855e-05 0.982553755 #> 8083 80.82 0.01740408 3.344183e-05 0.982562476 #> 8084 80.83 0.01739538 3.342511e-05 0.982571193 #> 8085 80.84 0.01738669 3.340840e-05 0.982579905 #> 8086 80.85 0.01737800 3.339170e-05 0.982588613 #> 8087 80.86 0.01736931 3.337501e-05 0.982597316 #> 8088 80.87 0.01736063 3.335833e-05 0.982606016 #> 8089 80.88 0.01735195 3.334165e-05 0.982614710 #> 8090 80.89 0.01734327 3.332498e-05 0.982623401 #> 8091 80.90 0.01733460 3.330833e-05 0.982632087 #> 8092 80.91 0.01732594 3.329168e-05 0.982640769 #> 8093 80.92 0.01731728 3.327503e-05 0.982649446 #> 8094 80.93 0.01730862 3.325840e-05 0.982658119 #> 8095 80.94 0.01729997 3.324178e-05 0.982666788 #> 8096 80.95 0.01729132 3.322516e-05 0.982675453 #> 8097 80.96 0.01728268 3.320855e-05 0.982684113 #> 8098 80.97 0.01727404 3.319195e-05 0.982692768 #> 8099 80.98 0.01726540 3.317536e-05 0.982701420 #> 8100 80.99 0.01725677 3.315878e-05 0.982710067 #> 8101 81.00 0.01724815 3.314220e-05 0.982718710 #> 8102 81.01 0.01723953 3.312563e-05 0.982727348 #> 8103 81.02 0.01723091 3.310907e-05 0.982735982 #> 8104 81.03 0.01722230 3.309252e-05 0.982744612 #> 8105 81.04 0.01721369 3.307598e-05 0.982753238 #> 8106 81.05 0.01720508 3.305945e-05 0.982761859 #> 8107 81.06 0.01719648 3.304292e-05 0.982770476 #> 8108 81.07 0.01718789 3.302641e-05 0.982779089 #> 8109 81.08 0.01717929 3.300990e-05 0.982787697 #> 8110 81.09 0.01717071 3.299340e-05 0.982796301 #> 8111 81.10 0.01716212 3.297690e-05 0.982804901 #> 8112 81.11 0.01715354 3.296042e-05 0.982813496 #> 8113 81.12 0.01714497 3.294394e-05 0.982822087 #> 8114 81.13 0.01713640 3.292747e-05 0.982830674 #> 8115 81.14 0.01712783 3.291102e-05 0.982839256 #> 8116 81.15 0.01711927 3.289456e-05 0.982847835 #> 8117 81.16 0.01711071 3.287812e-05 0.982856409 #> 8118 81.17 0.01710216 3.286169e-05 0.982864978 #> 8119 81.18 0.01709361 3.284526e-05 0.982873544 #> 8120 81.19 0.01708507 3.282884e-05 0.982882105 #> 8121 81.20 0.01707653 3.281243e-05 0.982890662 #> 8122 81.21 0.01706799 3.279603e-05 0.982899214 #> 8123 81.22 0.01705946 3.277963e-05 0.982907762 #> 8124 81.23 0.01705093 3.276325e-05 0.982916306 #> 8125 81.24 0.01704241 3.274687e-05 0.982924846 #> 8126 81.25 0.01703389 3.273050e-05 0.982933381 #> 8127 81.26 0.01702537 3.271414e-05 0.982941913 #> 8128 81.27 0.01701686 3.269779e-05 0.982950440 #> 8129 81.28 0.01700836 3.268144e-05 0.982958962 #> 8130 81.29 0.01699985 3.266511e-05 0.982967481 #> 8131 81.30 0.01699136 3.264878e-05 0.982975995 #> 8132 81.31 0.01698286 3.263246e-05 0.982984505 #> 8133 81.32 0.01697437 3.261614e-05 0.982993010 #> 8134 81.33 0.01696589 3.259984e-05 0.983001512 #> 8135 81.34 0.01695741 3.258354e-05 0.983010009 #> 8136 81.35 0.01694893 3.256726e-05 0.983018502 #> 8137 81.36 0.01694046 3.255098e-05 0.983026990 #> 8138 81.37 0.01693199 3.253471e-05 0.983035475 #> 8139 81.38 0.01692353 3.251844e-05 0.983043955 #> 8140 81.39 0.01691507 3.250219e-05 0.983052431 #> 8141 81.40 0.01690661 3.248594e-05 0.983060902 #> 8142 81.41 0.01689816 3.246970e-05 0.983069370 #> 8143 81.42 0.01688971 3.245347e-05 0.983077833 #> 8144 81.43 0.01688127 3.243725e-05 0.983086292 #> 8145 81.44 0.01687283 3.242103e-05 0.983094747 #> 8146 81.45 0.01686440 3.240483e-05 0.983103197 #> 8147 81.46 0.01685597 3.238863e-05 0.983111643 #> 8148 81.47 0.01684754 3.237244e-05 0.983120085 #> 8149 81.48 0.01683912 3.235626e-05 0.983128523 #> 8150 81.49 0.01683070 3.234008e-05 0.983136957 #> 8151 81.50 0.01682229 3.232392e-05 0.983145386 #> 8152 81.51 0.01681388 3.230776e-05 0.983153812 #> 8153 81.52 0.01680548 3.229161e-05 0.983162233 #> 8154 81.53 0.01679708 3.227547e-05 0.983170649 #> 8155 81.54 0.01678868 3.225933e-05 0.983179062 #> 8156 81.55 0.01678029 3.224321e-05 0.983187470 #> 8157 81.56 0.01677190 3.222709e-05 0.983195874 #> 8158 81.57 0.01676351 3.221098e-05 0.983204274 #> 8159 81.58 0.01675513 3.219488e-05 0.983212670 #> 8160 81.59 0.01674676 3.217879e-05 0.983221062 #> 8161 81.60 0.01673839 3.216270e-05 0.983229449 #> 8162 81.61 0.01673002 3.214662e-05 0.983237832 #> 8163 81.62 0.01672166 3.213055e-05 0.983246211 #> 8164 81.63 0.01671330 3.211449e-05 0.983254586 #> 8165 81.64 0.01670494 3.209844e-05 0.983262957 #> 8166 81.65 0.01669659 3.208239e-05 0.983271323 #> 8167 81.66 0.01668825 3.206636e-05 0.983279685 #> 8168 81.67 0.01667991 3.205033e-05 0.983288043 #> 8169 81.68 0.01667157 3.203431e-05 0.983296397 #> 8170 81.69 0.01666323 3.201829e-05 0.983304747 #> 8171 81.70 0.01665491 3.200229e-05 0.983313093 #> 8172 81.71 0.01664658 3.198629e-05 0.983321434 #> 8173 81.72 0.01663826 3.197030e-05 0.983329771 #> 8174 81.73 0.01662994 3.195432e-05 0.983338104 #> 8175 81.74 0.01662163 3.193835e-05 0.983346433 #> 8176 81.75 0.01661332 3.192238e-05 0.983354758 #> 8177 81.76 0.01660502 3.190643e-05 0.983363078 #> 8178 81.77 0.01659671 3.189048e-05 0.983371395 #> 8179 81.78 0.01658842 3.187453e-05 0.983379707 #> 8180 81.79 0.01658013 3.185860e-05 0.983388015 #> 8181 81.80 0.01657184 3.184268e-05 0.983396319 #> 8182 81.81 0.01656355 3.182676e-05 0.983404619 #> 8183 81.82 0.01655527 3.181085e-05 0.983412914 #> 8184 81.83 0.01654700 3.179495e-05 0.983421206 #> 8185 81.84 0.01653873 3.177905e-05 0.983429493 #> 8186 81.85 0.01653046 3.176317e-05 0.983437776 #> 8187 81.86 0.01652220 3.174729e-05 0.983446055 #> 8188 81.87 0.01651394 3.173142e-05 0.983454330 #> 8189 81.88 0.01650568 3.171556e-05 0.983462601 #> 8190 81.89 0.01649743 3.169971e-05 0.983470868 #> 8191 81.90 0.01648919 3.168386e-05 0.983479130 #> 8192 81.91 0.01648094 3.166802e-05 0.983487388 #> 8193 81.92 0.01647271 3.165219e-05 0.983495643 #> 8194 81.93 0.01646447 3.163637e-05 0.983503893 #> 8195 81.94 0.01645624 3.162056e-05 0.983512139 #> 8196 81.95 0.01644801 3.160475e-05 0.983520381 #> 8197 81.96 0.01643979 3.158895e-05 0.983528618 #> 8198 81.97 0.01643157 3.157316e-05 0.983536852 #> 8199 81.98 0.01642336 3.155738e-05 0.983545082 #> 8200 81.99 0.01641515 3.154160e-05 0.983553307 #> 8201 82.00 0.01640695 3.152584e-05 0.983561528 #> 8202 82.01 0.01639874 3.151008e-05 0.983569745 #> 8203 82.02 0.01639055 3.149433e-05 0.983577959 #> 8204 82.03 0.01638235 3.147858e-05 0.983586168 #> 8205 82.04 0.01637416 3.146285e-05 0.983594372 #> 8206 82.05 0.01636598 3.144712e-05 0.983602573 #> 8207 82.06 0.01635780 3.143140e-05 0.983610770 #> 8208 82.07 0.01634962 3.141569e-05 0.983618962 #> 8209 82.08 0.01634145 3.139998e-05 0.983627151 #> 8210 82.09 0.01633328 3.138429e-05 0.983635335 #> 8211 82.10 0.01632512 3.136860e-05 0.983643516 #> 8212 82.11 0.01631696 3.135292e-05 0.983651692 #> 8213 82.12 0.01630880 3.133725e-05 0.983659864 #> 8214 82.13 0.01630065 3.132158e-05 0.983668032 #> 8215 82.14 0.01629250 3.130593e-05 0.983676196 #> 8216 82.15 0.01628435 3.129028e-05 0.983684356 #> 8217 82.16 0.01627621 3.127464e-05 0.983692511 #> 8218 82.17 0.01626808 3.125900e-05 0.983700663 #> 8219 82.18 0.01625995 3.124338e-05 0.983708811 #> 8220 82.19 0.01625182 3.122776e-05 0.983716954 #> 8221 82.20 0.01624369 3.121215e-05 0.983725094 #> 8222 82.21 0.01623557 3.119655e-05 0.983733229 #> 8223 82.22 0.01622746 3.118095e-05 0.983741361 #> 8224 82.23 0.01621935 3.116537e-05 0.983749488 #> 8225 82.24 0.01621124 3.114979e-05 0.983757611 #> 8226 82.25 0.01620314 3.113422e-05 0.983765730 #> 8227 82.26 0.01619504 3.111865e-05 0.983773845 #> 8228 82.27 0.01618694 3.110310e-05 0.983781956 #> 8229 82.28 0.01617885 3.108755e-05 0.983790063 #> 8230 82.29 0.01617076 3.107201e-05 0.983798166 #> 8231 82.30 0.01616268 3.105648e-05 0.983806265 #> 8232 82.31 0.01615460 3.104095e-05 0.983814360 #> 8233 82.32 0.01614652 3.102544e-05 0.983822451 #> 8234 82.33 0.01613845 3.100993e-05 0.983830538 #> 8235 82.34 0.01613039 3.099443e-05 0.983838620 #> 8236 82.35 0.01612232 3.097893e-05 0.983846699 #> 8237 82.36 0.01611426 3.096345e-05 0.983854774 #> 8238 82.37 0.01610621 3.094797e-05 0.983862844 #> 8239 82.38 0.01609816 3.093250e-05 0.983870911 #> 8240 82.39 0.01609011 3.091704e-05 0.983878973 #> 8241 82.40 0.01608207 3.090158e-05 0.983887032 #> 8242 82.41 0.01607403 3.088614e-05 0.983895086 #> 8243 82.42 0.01606599 3.087070e-05 0.983903137 #> 8244 82.43 0.01605796 3.085527e-05 0.983911183 #> 8245 82.44 0.01604993 3.083984e-05 0.983919226 #> 8246 82.45 0.01604191 3.082443e-05 0.983927264 #> 8247 82.46 0.01603389 3.080902e-05 0.983935298 #> 8248 82.47 0.01602588 3.079362e-05 0.983943329 #> 8249 82.48 0.01601787 3.077822e-05 0.983951355 #> 8250 82.49 0.01600986 3.076284e-05 0.983959377 #> 8251 82.50 0.01600186 3.074746e-05 0.983967396 #> 8252 82.51 0.01599386 3.073209e-05 0.983975410 #> 8253 82.52 0.01598586 3.071673e-05 0.983983420 #> 8254 82.53 0.01597787 3.070137e-05 0.983991427 #> 8255 82.54 0.01596989 3.068603e-05 0.983999429 #> 8256 82.55 0.01596190 3.067069e-05 0.984007427 #> 8257 82.56 0.01595392 3.065536e-05 0.984015421 #> 8258 82.57 0.01594595 3.064003e-05 0.984023412 #> 8259 82.58 0.01593798 3.062472e-05 0.984031398 #> 8260 82.59 0.01593001 3.060941e-05 0.984039380 #> 8261 82.60 0.01592205 3.059411e-05 0.984047359 #> 8262 82.61 0.01591409 3.057881e-05 0.984055333 #> 8263 82.62 0.01590613 3.056353e-05 0.984063303 #> 8264 82.63 0.01589818 3.054825e-05 0.984071270 #> 8265 82.64 0.01589024 3.053298e-05 0.984079232 #> 8266 82.65 0.01588229 3.051772e-05 0.984087190 #> 8267 82.66 0.01587435 3.050246e-05 0.984095145 #> 8268 82.67 0.01586642 3.048721e-05 0.984103095 #> 8269 82.68 0.01585849 3.047197e-05 0.984111042 #> 8270 82.69 0.01585056 3.045674e-05 0.984118984 #> 8271 82.70 0.01584264 3.044152e-05 0.984126923 #> 8272 82.71 0.01583472 3.042630e-05 0.984134857 #> 8273 82.72 0.01582680 3.041109e-05 0.984142788 #> 8274 82.73 0.01581889 3.039589e-05 0.984150714 #> 8275 82.74 0.01581098 3.038070e-05 0.984158637 #> 8276 82.75 0.01580308 3.036551e-05 0.984166556 #> 8277 82.76 0.01579518 3.035033e-05 0.984174471 #> 8278 82.77 0.01578728 3.033516e-05 0.984182381 #> 8279 82.78 0.01577939 3.032000e-05 0.984190288 #> 8280 82.79 0.01577150 3.030484e-05 0.984198191 #> 8281 82.80 0.01576362 3.028969e-05 0.984206090 #> 8282 82.81 0.01575574 3.027455e-05 0.984213985 #> 8283 82.82 0.01574786 3.025942e-05 0.984221876 #> 8284 82.83 0.01573999 3.024429e-05 0.984229763 #> 8285 82.84 0.01573212 3.022917e-05 0.984237646 #> 8286 82.85 0.01572426 3.021406e-05 0.984245525 #> 8287 82.86 0.01571640 3.019896e-05 0.984253401 #> 8288 82.87 0.01570854 3.018386e-05 0.984261272 #> 8289 82.88 0.01570069 3.016877e-05 0.984269139 #> 8290 82.89 0.01569284 3.015369e-05 0.984277003 #> 8291 82.90 0.01568500 3.013862e-05 0.984284862 #> 8292 82.91 0.01567716 3.012355e-05 0.984292718 #> 8293 82.92 0.01566932 3.010850e-05 0.984300570 #> 8294 82.93 0.01566149 3.009345e-05 0.984308418 #> 8295 82.94 0.01565366 3.007840e-05 0.984316261 #> 8296 82.95 0.01564584 3.006337e-05 0.984324101 #> 8297 82.96 0.01563801 3.004834e-05 0.984331937 #> 8298 82.97 0.01563020 3.003332e-05 0.984339769 #> 8299 82.98 0.01562238 3.001831e-05 0.984347597 #> 8300 82.99 0.01561457 3.000330e-05 0.984355422 #> 8301 83.00 0.01560677 2.998830e-05 0.984363242 #> 8302 83.01 0.01559897 2.997331e-05 0.984371058 #> 8303 83.02 0.01559117 2.995833e-05 0.984378871 #> 8304 83.03 0.01558338 2.994335e-05 0.984386680 #> 8305 83.04 0.01557559 2.992839e-05 0.984394484 #> 8306 83.05 0.01556780 2.991343e-05 0.984402285 #> 8307 83.06 0.01556002 2.989847e-05 0.984410082 #> 8308 83.07 0.01555224 2.988353e-05 0.984417875 #> 8309 83.08 0.01554447 2.986859e-05 0.984425664 #> 8310 83.09 0.01553670 2.985366e-05 0.984433449 #> 8311 83.10 0.01552893 2.983874e-05 0.984441231 #> 8312 83.11 0.01552117 2.982382e-05 0.984449008 #> 8313 83.12 0.01551341 2.980891e-05 0.984456782 #> 8314 83.13 0.01550565 2.979401e-05 0.984464551 #> 8315 83.14 0.01549790 2.977912e-05 0.984472317 #> 8316 83.15 0.01549016 2.976423e-05 0.984480079 #> 8317 83.16 0.01548241 2.974935e-05 0.984487837 #> 8318 83.17 0.01547467 2.973448e-05 0.984495591 #> 8319 83.18 0.01546694 2.971962e-05 0.984503341 #> 8320 83.19 0.01545921 2.970476e-05 0.984511088 #> 8321 83.20 0.01545148 2.968991e-05 0.984518830 #> 8322 83.21 0.01544376 2.967507e-05 0.984526569 #> 8323 83.22 0.01543604 2.966024e-05 0.984534304 #> 8324 83.23 0.01542832 2.964541e-05 0.984542035 #> 8325 83.24 0.01542061 2.963059e-05 0.984549762 #> 8326 83.25 0.01541290 2.961578e-05 0.984557485 #> 8327 83.26 0.01540519 2.960098e-05 0.984565204 #> 8328 83.27 0.01539749 2.958618e-05 0.984572920 #> 8329 83.28 0.01538980 2.957139e-05 0.984580631 #> 8330 83.29 0.01538210 2.955661e-05 0.984588339 #> 8331 83.30 0.01537442 2.954184e-05 0.984596043 #> 8332 83.31 0.01536673 2.952707e-05 0.984603743 #> 8333 83.32 0.01535905 2.951231e-05 0.984611439 #> 8334 83.33 0.01535137 2.949756e-05 0.984619132 #> 8335 83.34 0.01534370 2.948281e-05 0.984626820 #> 8336 83.35 0.01533603 2.946807e-05 0.984634505 #> 8337 83.36 0.01532836 2.945334e-05 0.984642186 #> 8338 83.37 0.01532070 2.943862e-05 0.984649863 #> 8339 83.38 0.01531304 2.942390e-05 0.984657536 #> 8340 83.39 0.01530539 2.940920e-05 0.984665205 #> 8341 83.40 0.01529773 2.939449e-05 0.984672871 #> 8342 83.41 0.01529009 2.937980e-05 0.984680532 #> 8343 83.42 0.01528244 2.936511e-05 0.984688190 #> 8344 83.43 0.01527481 2.935044e-05 0.984695844 #> 8345 83.44 0.01526717 2.933576e-05 0.984703494 #> 8346 83.45 0.01525954 2.932110e-05 0.984711141 #> 8347 83.46 0.01525191 2.930644e-05 0.984718783 #> 8348 83.47 0.01524429 2.929179e-05 0.984726422 #> 8349 83.48 0.01523667 2.927715e-05 0.984734057 #> 8350 83.49 0.01522905 2.926252e-05 0.984741688 #> 8351 83.50 0.01522144 2.924789e-05 0.984749315 #> 8352 83.51 0.01521383 2.923327e-05 0.984756938 #> 8353 83.52 0.01520622 2.921866e-05 0.984764558 #> 8354 83.53 0.01519862 2.920405e-05 0.984772174 #> 8355 83.54 0.01519102 2.918945e-05 0.984779786 #> 8356 83.55 0.01518343 2.917486e-05 0.984787394 #> 8357 83.56 0.01517584 2.916028e-05 0.984794998 #> 8358 83.57 0.01516826 2.914570e-05 0.984802599 #> 8359 83.58 0.01516067 2.913113e-05 0.984810196 #> 8360 83.59 0.01515309 2.911657e-05 0.984817789 #> 8361 83.60 0.01514552 2.910201e-05 0.984825378 #> 8362 83.61 0.01513795 2.908747e-05 0.984832964 #> 8363 83.62 0.01513038 2.907293e-05 0.984840545 #> 8364 83.63 0.01512282 2.905839e-05 0.984848123 #> 8365 83.64 0.01511526 2.904387e-05 0.984855697 #> 8366 83.65 0.01510770 2.902935e-05 0.984863267 #> 8367 83.66 0.01510015 2.901484e-05 0.984870834 #> 8368 83.67 0.01509260 2.900034e-05 0.984878396 #> 8369 83.68 0.01508506 2.898584e-05 0.984885955 #> 8370 83.69 0.01507752 2.897135e-05 0.984893511 #> 8371 83.70 0.01506998 2.895687e-05 0.984901062 #> 8372 83.71 0.01506245 2.894239e-05 0.984908609 #> 8373 83.72 0.01505492 2.892793e-05 0.984916153 #> 8374 83.73 0.01504739 2.891346e-05 0.984923693 #> 8375 83.74 0.01503987 2.889901e-05 0.984931230 #> 8376 83.75 0.01503235 2.888457e-05 0.984938762 #> 8377 83.76 0.01502484 2.887013e-05 0.984946291 #> 8378 83.77 0.01501733 2.885570e-05 0.984953816 #> 8379 83.78 0.01500982 2.884127e-05 0.984961337 #> 8380 83.79 0.01500232 2.882685e-05 0.984968854 #> 8381 83.80 0.01499482 2.881244e-05 0.984976368 #> 8382 83.81 0.01498732 2.879804e-05 0.984983878 #> 8383 83.82 0.01497983 2.878365e-05 0.984991384 #> 8384 83.83 0.01497234 2.876926e-05 0.984998887 #> 8385 83.84 0.01496486 2.875488e-05 0.985006385 #> 8386 83.85 0.01495738 2.874050e-05 0.985013880 #> 8387 83.86 0.01494990 2.872614e-05 0.985021371 #> 8388 83.87 0.01494243 2.871178e-05 0.985028859 #> 8389 83.88 0.01493496 2.869743e-05 0.985036343 #> 8390 83.89 0.01492749 2.868308e-05 0.985043823 #> 8391 83.90 0.01492003 2.866874e-05 0.985051299 #> 8392 83.91 0.01491257 2.865441e-05 0.985058771 #> 8393 83.92 0.01490512 2.864009e-05 0.985066240 #> 8394 83.93 0.01489767 2.862577e-05 0.985073705 #> 8395 83.94 0.01489022 2.861146e-05 0.985081166 #> 8396 83.95 0.01488278 2.859716e-05 0.985088624 #> 8397 83.96 0.01487534 2.858286e-05 0.985096078 #> 8398 83.97 0.01486790 2.856858e-05 0.985103528 #> 8399 83.98 0.01486047 2.855430e-05 0.985110974 #> 8400 83.99 0.01485304 2.854002e-05 0.985118417 #> 8401 84.00 0.01484562 2.852576e-05 0.985125856 #> 8402 84.01 0.01483820 2.851150e-05 0.985133291 #> 8403 84.02 0.01483078 2.849724e-05 0.985140722 #> 8404 84.03 0.01482337 2.848300e-05 0.985148150 #> 8405 84.04 0.01481596 2.846876e-05 0.985155574 #> 8406 84.05 0.01480855 2.845453e-05 0.985162995 #> 8407 84.06 0.01480115 2.844031e-05 0.985170411 #> 8408 84.07 0.01479375 2.842609e-05 0.985177824 #> 8409 84.08 0.01478635 2.841188e-05 0.985185234 #> 8410 84.09 0.01477896 2.839768e-05 0.985192639 #> 8411 84.10 0.01477158 2.838348e-05 0.985200041 #> 8412 84.11 0.01476419 2.836930e-05 0.985207439 #> 8413 84.12 0.01475681 2.835511e-05 0.985214833 #> 8414 84.13 0.01474943 2.834094e-05 0.985222224 #> 8415 84.14 0.01474206 2.832677e-05 0.985229611 #> 8416 84.15 0.01473469 2.831261e-05 0.985236995 #> 8417 84.16 0.01472733 2.829846e-05 0.985244374 #> 8418 84.17 0.01471997 2.828431e-05 0.985251750 #> 8419 84.18 0.01471261 2.827018e-05 0.985259122 #> 8420 84.19 0.01470525 2.825604e-05 0.985266491 #> 8421 84.20 0.01469790 2.824192e-05 0.985273856 #> 8422 84.21 0.01469055 2.822780e-05 0.985281217 #> 8423 84.22 0.01468321 2.821369e-05 0.985288575 #> 8424 84.23 0.01467587 2.819959e-05 0.985295929 #> 8425 84.24 0.01466854 2.818549e-05 0.985303279 #> 8426 84.25 0.01466120 2.817140e-05 0.985310625 #> 8427 84.26 0.01465387 2.815732e-05 0.985317968 #> 8428 84.27 0.01464655 2.814325e-05 0.985325307 #> 8429 84.28 0.01463923 2.812918e-05 0.985332643 #> 8430 84.29 0.01463191 2.811512e-05 0.985339975 #> 8431 84.30 0.01462460 2.810106e-05 0.985347303 #> 8432 84.31 0.01461729 2.808702e-05 0.985354627 #> 8433 84.32 0.01460998 2.807298e-05 0.985361948 #> 8434 84.33 0.01460268 2.805894e-05 0.985369266 #> 8435 84.34 0.01459538 2.804492e-05 0.985376579 #> 8436 84.35 0.01458808 2.803090e-05 0.985383889 #> 8437 84.36 0.01458079 2.801689e-05 0.985391195 #> 8438 84.37 0.01457350 2.800288e-05 0.985398498 #> 8439 84.38 0.01456621 2.798888e-05 0.985405797 #> 8440 84.39 0.01455893 2.797489e-05 0.985413092 #> 8441 84.40 0.01455166 2.796091e-05 0.985420384 #> 8442 84.41 0.01454438 2.794693e-05 0.985427672 #> 8443 84.42 0.01453711 2.793296e-05 0.985434956 #> 8444 84.43 0.01452984 2.791900e-05 0.985442237 #> 8445 84.44 0.01452258 2.790504e-05 0.985449514 #> 8446 84.45 0.01451532 2.789109e-05 0.985456787 #> 8447 84.46 0.01450807 2.787715e-05 0.985464057 #> 8448 84.47 0.01450081 2.786322e-05 0.985471323 #> 8449 84.48 0.01449357 2.784929e-05 0.985478586 #> 8450 84.49 0.01448632 2.783537e-05 0.985485844 #> 8451 84.50 0.01447908 2.782145e-05 0.985493100 #> 8452 84.51 0.01447184 2.780755e-05 0.985500351 #> 8453 84.52 0.01446461 2.779365e-05 0.985507599 #> 8454 84.53 0.01445738 2.777975e-05 0.985514844 #> 8455 84.54 0.01445015 2.776587e-05 0.985522085 #> 8456 84.55 0.01444293 2.775199e-05 0.985529322 #> 8457 84.56 0.01443571 2.773811e-05 0.985536555 #> 8458 84.57 0.01442849 2.772425e-05 0.985543785 #> 8459 84.58 0.01442128 2.771039e-05 0.985551011 #> 8460 84.59 0.01441407 2.769654e-05 0.985558234 #> 8461 84.60 0.01440686 2.768269e-05 0.985565453 #> 8462 84.61 0.01439966 2.766885e-05 0.985572669 #> 8463 84.62 0.01439246 2.765502e-05 0.985579881 #> 8464 84.63 0.01438527 2.764120e-05 0.985587089 #> 8465 84.64 0.01437808 2.762738e-05 0.985594293 #> 8466 84.65 0.01437089 2.761357e-05 0.985601494 #> 8467 84.66 0.01436371 2.759977e-05 0.985608692 #> 8468 84.67 0.01435653 2.758597e-05 0.985615886 #> 8469 84.68 0.01434935 2.757218e-05 0.985623076 #> 8470 84.69 0.01434218 2.755840e-05 0.985630263 #> 8471 84.70 0.01433501 2.754462e-05 0.985637446 #> 8472 84.71 0.01432784 2.753086e-05 0.985644625 #> 8473 84.72 0.01432068 2.751709e-05 0.985651801 #> 8474 84.73 0.01431352 2.750334e-05 0.985658973 #> 8475 84.74 0.01430637 2.748959e-05 0.985666142 #> 8476 84.75 0.01429922 2.747585e-05 0.985673307 #> 8477 84.76 0.01429207 2.746211e-05 0.985680469 #> 8478 84.77 0.01428492 2.744839e-05 0.985687627 #> 8479 84.78 0.01427778 2.743467e-05 0.985694781 #> 8480 84.79 0.01427065 2.742095e-05 0.985701932 #> 8481 84.80 0.01426351 2.740725e-05 0.985709079 #> 8482 84.81 0.01425638 2.739354e-05 0.985716223 #> 8483 84.82 0.01424926 2.737985e-05 0.985723363 #> 8484 84.83 0.01424213 2.736617e-05 0.985730500 #> 8485 84.84 0.01423501 2.735249e-05 0.985737633 #> 8486 84.85 0.01422790 2.733881e-05 0.985744762 #> 8487 84.86 0.01422079 2.732515e-05 0.985751888 #> 8488 84.87 0.01421368 2.731149e-05 0.985759010 #> 8489 84.88 0.01420657 2.729784e-05 0.985766129 #> 8490 84.89 0.01419947 2.728419e-05 0.985773244 #> 8491 84.90 0.01419237 2.727055e-05 0.985780356 #> 8492 84.91 0.01418528 2.725692e-05 0.985787464 #> 8493 84.92 0.01417819 2.724329e-05 0.985794568 #> 8494 84.93 0.01417110 2.722968e-05 0.985801669 #> 8495 84.94 0.01416402 2.721606e-05 0.985808766 #> 8496 84.95 0.01415694 2.720246e-05 0.985815860 #> 8497 84.96 0.01414986 2.718886e-05 0.985822951 #> 8498 84.97 0.01414279 2.717527e-05 0.985830037 #> 8499 84.98 0.01413572 2.716169e-05 0.985837121 #> 8500 84.99 0.01412865 2.714811e-05 0.985844200 #> 8501 85.00 0.01412159 2.713454e-05 0.985851276 #> 8502 85.01 0.01411453 2.712097e-05 0.985858349 #> 8503 85.02 0.01410747 2.710742e-05 0.985865418 #> 8504 85.03 0.01410042 2.709387e-05 0.985872484 #> 8505 85.04 0.01409337 2.708032e-05 0.985879546 #> 8506 85.05 0.01408633 2.706679e-05 0.985886604 #> 8507 85.06 0.01407929 2.705326e-05 0.985893659 #> 8508 85.07 0.01407225 2.703973e-05 0.985900710 #> 8509 85.08 0.01406522 2.702622e-05 0.985907758 #> 8510 85.09 0.01405818 2.701271e-05 0.985914803 #> 8511 85.10 0.01405116 2.699920e-05 0.985921843 #> 8512 85.11 0.01404413 2.698571e-05 0.985928881 #> 8513 85.12 0.01403711 2.697222e-05 0.985935915 #> 8514 85.13 0.01403010 2.695874e-05 0.985942945 #> 8515 85.14 0.01402308 2.694526e-05 0.985949972 #> 8516 85.15 0.01401607 2.693179e-05 0.985956995 #> 8517 85.16 0.01400907 2.691833e-05 0.985964015 #> 8518 85.17 0.01400206 2.690487e-05 0.985971031 #> 8519 85.18 0.01399506 2.689142e-05 0.985978044 #> 8520 85.19 0.01398807 2.687798e-05 0.985985053 #> 8521 85.20 0.01398108 2.686455e-05 0.985992059 #> 8522 85.21 0.01397409 2.685112e-05 0.985999061 #> 8523 85.22 0.01396710 2.683769e-05 0.986006059 #> 8524 85.23 0.01396012 2.682428e-05 0.986013055 #> 8525 85.24 0.01395314 2.681087e-05 0.986020046 #> 8526 85.25 0.01394617 2.679747e-05 0.986027035 #> 8527 85.26 0.01393920 2.678407e-05 0.986034019 #> 8528 85.27 0.01393223 2.677068e-05 0.986041001 #> 8529 85.28 0.01392526 2.675730e-05 0.986047978 #> 8530 85.29 0.01391830 2.674393e-05 0.986054953 #> 8531 85.30 0.01391135 2.673056e-05 0.986061923 #> 8532 85.31 0.01390439 2.671720e-05 0.986068891 #> 8533 85.32 0.01389744 2.670384e-05 0.986075855 #> 8534 85.33 0.01389049 2.669049e-05 0.986082815 #> 8535 85.34 0.01388355 2.667715e-05 0.986089772 #> 8536 85.35 0.01387661 2.666381e-05 0.986096725 #> 8537 85.36 0.01386967 2.665049e-05 0.986103675 #> 8538 85.37 0.01386274 2.663716e-05 0.986110621 #> 8539 85.38 0.01385581 2.662385e-05 0.986117564 #> 8540 85.39 0.01384889 2.661054e-05 0.986124504 #> 8541 85.40 0.01384196 2.659724e-05 0.986131440 #> 8542 85.41 0.01383504 2.658394e-05 0.986138372 #> 8543 85.42 0.01382813 2.657065e-05 0.986145302 #> 8544 85.43 0.01382122 2.655737e-05 0.986152227 #> 8545 85.44 0.01381431 2.654410e-05 0.986159149 #> 8546 85.45 0.01380740 2.653083e-05 0.986166068 #> 8547 85.46 0.01380050 2.651757e-05 0.986172983 #> 8548 85.47 0.01379360 2.650431e-05 0.986179895 #> 8549 85.48 0.01378671 2.649106e-05 0.986186803 #> 8550 85.49 0.01377981 2.647782e-05 0.986193708 #> 8551 85.50 0.01377293 2.646458e-05 0.986200610 #> 8552 85.51 0.01376604 2.645136e-05 0.986207508 #> 8553 85.52 0.01375916 2.643813e-05 0.986214402 #> 8554 85.53 0.01375228 2.642492e-05 0.986221293 #> 8555 85.54 0.01374541 2.641171e-05 0.986228181 #> 8556 85.55 0.01373854 2.639851e-05 0.986235065 #> 8557 85.56 0.01373167 2.638531e-05 0.986241946 #> 8558 85.57 0.01372480 2.637212e-05 0.986248823 #> 8559 85.58 0.01371794 2.635894e-05 0.986255697 #> 8560 85.59 0.01371109 2.634576e-05 0.986262567 #> 8561 85.60 0.01370423 2.633259e-05 0.986269434 #> 8562 85.61 0.01369738 2.631943e-05 0.986276298 #> 8563 85.62 0.01369054 2.630627e-05 0.986283158 #> 8564 85.63 0.01368369 2.629312e-05 0.986290015 #> 8565 85.64 0.01367685 2.627998e-05 0.986296868 #> 8566 85.65 0.01367002 2.626684e-05 0.986303718 #> 8567 85.66 0.01366318 2.625371e-05 0.986310564 #> 8568 85.67 0.01365635 2.624059e-05 0.986317407 #> 8569 85.68 0.01364953 2.622747e-05 0.986324247 #> 8570 85.69 0.01364270 2.621436e-05 0.986331083 #> 8571 85.70 0.01363588 2.620126e-05 0.986337916 #> 8572 85.71 0.01362907 2.618816e-05 0.986344745 #> 8573 85.72 0.01362225 2.617507e-05 0.986351571 #> 8574 85.73 0.01361544 2.616198e-05 0.986358394 #> 8575 85.74 0.01360864 2.614891e-05 0.986365213 #> 8576 85.75 0.01360184 2.613584e-05 0.986372028 #> 8577 85.76 0.01359504 2.612277e-05 0.986378841 #> 8578 85.77 0.01358824 2.610971e-05 0.986385650 #> 8579 85.78 0.01358145 2.609666e-05 0.986392455 #> 8580 85.79 0.01357466 2.608362e-05 0.986399257 #> 8581 85.80 0.01356787 2.607058e-05 0.986406056 #> 8582 85.81 0.01356109 2.605755e-05 0.986412851 #> 8583 85.82 0.01355431 2.604452e-05 0.986419643 #> 8584 85.83 0.01354754 2.603150e-05 0.986426431 #> 8585 85.84 0.01354077 2.601849e-05 0.986433217 #> 8586 85.85 0.01353400 2.600548e-05 0.986439998 #> 8587 85.86 0.01352723 2.599248e-05 0.986446777 #> 8588 85.87 0.01352047 2.597949e-05 0.986453551 #> 8589 85.88 0.01351371 2.596650e-05 0.986460323 #> 8590 85.89 0.01350696 2.595352e-05 0.986467091 #> 8591 85.90 0.01350020 2.594055e-05 0.986473856 #> 8592 85.91 0.01349346 2.592758e-05 0.986480617 #> 8593 85.92 0.01348671 2.591462e-05 0.986487375 #> 8594 85.93 0.01347997 2.590167e-05 0.986494130 #> 8595 85.94 0.01347323 2.588872e-05 0.986500881 #> 8596 85.95 0.01346650 2.587578e-05 0.986507629 #> 8597 85.96 0.01345976 2.586285e-05 0.986514373 #> 8598 85.97 0.01345304 2.584992e-05 0.986521115 #> 8599 85.98 0.01344631 2.583700e-05 0.986527852 #> 8600 85.99 0.01343959 2.582408e-05 0.986534587 #> 8601 86.00 0.01343287 2.581117e-05 0.986541318 #> 8602 86.01 0.01342616 2.579827e-05 0.986548045 #> 8603 86.02 0.01341944 2.578537e-05 0.986554770 #> 8604 86.03 0.01341274 2.577248e-05 0.986561491 #> 8605 86.04 0.01340603 2.575960e-05 0.986568208 #> 8606 86.05 0.01339933 2.574672e-05 0.986574922 #> 8607 86.06 0.01339263 2.573385e-05 0.986581633 #> 8608 86.07 0.01338594 2.572099e-05 0.986588341 #> 8609 86.08 0.01337925 2.570813e-05 0.986595045 #> 8610 86.09 0.01337256 2.569528e-05 0.986601746 #> 8611 86.10 0.01336587 2.568244e-05 0.986608443 #> 8612 86.11 0.01335919 2.566960e-05 0.986615137 #> 8613 86.12 0.01335252 2.565677e-05 0.986621828 #> 8614 86.13 0.01334584 2.564394e-05 0.986628516 #> 8615 86.14 0.01333917 2.563112e-05 0.986635200 #> 8616 86.15 0.01333250 2.561831e-05 0.986641880 #> 8617 86.16 0.01332584 2.560551e-05 0.986648558 #> 8618 86.17 0.01331918 2.559271e-05 0.986655232 #> 8619 86.18 0.01331252 2.557991e-05 0.986661902 #> 8620 86.19 0.01330586 2.556713e-05 0.986668570 #> 8621 86.20 0.01329921 2.555435e-05 0.986675234 #> 8622 86.21 0.01329256 2.554157e-05 0.986681895 #> 8623 86.22 0.01328592 2.552880e-05 0.986688552 #> 8624 86.23 0.01327928 2.551604e-05 0.986695206 #> 8625 86.24 0.01327264 2.550329e-05 0.986701857 #> 8626 86.25 0.01326601 2.549054e-05 0.986708504 #> 8627 86.26 0.01325937 2.547780e-05 0.986715148 #> 8628 86.27 0.01325275 2.546506e-05 0.986721789 #> 8629 86.28 0.01324612 2.545233e-05 0.986728427 #> 8630 86.29 0.01323950 2.543961e-05 0.986735061 #> 8631 86.30 0.01323288 2.542689e-05 0.986741691 #> 8632 86.31 0.01322627 2.541418e-05 0.986748319 #> 8633 86.32 0.01321966 2.540148e-05 0.986754943 #> 8634 86.33 0.01321305 2.538878e-05 0.986761564 #> 8635 86.34 0.01320644 2.537609e-05 0.986768182 #> 8636 86.35 0.01319984 2.536341e-05 0.986774796 #> 8637 86.36 0.01319324 2.535073e-05 0.986781407 #> 8638 86.37 0.01318665 2.533805e-05 0.986788014 #> 8639 86.38 0.01318006 2.532539e-05 0.986794619 #> 8640 86.39 0.01317347 2.531273e-05 0.986801220 #> 8641 86.40 0.01316688 2.530008e-05 0.986807818 #> 8642 86.41 0.01316030 2.528743e-05 0.986814412 #> 8643 86.42 0.01315372 2.527479e-05 0.986821003 #> 8644 86.43 0.01314715 2.526215e-05 0.986827591 #> 8645 86.44 0.01314057 2.524953e-05 0.986834176 #> 8646 86.45 0.01313401 2.523690e-05 0.986840757 #> 8647 86.46 0.01312744 2.522429e-05 0.986847335 #> 8648 86.47 0.01312088 2.521168e-05 0.986853910 #> 8649 86.48 0.01311432 2.519908e-05 0.986860481 #> 8650 86.49 0.01310776 2.518648e-05 0.986867049 #> 8651 86.50 0.01310121 2.517389e-05 0.986873614 #> 8652 86.51 0.01309466 2.516131e-05 0.986880175 #> 8653 86.52 0.01308812 2.514873e-05 0.986886734 #> 8654 86.53 0.01308158 2.513616e-05 0.986893289 #> 8655 86.54 0.01307504 2.512359e-05 0.986899840 #> 8656 86.55 0.01306850 2.511104e-05 0.986906389 #> 8657 86.56 0.01306197 2.509848e-05 0.986912934 #> 8658 86.57 0.01305544 2.508594e-05 0.986919476 #> 8659 86.58 0.01304891 2.507340e-05 0.986926015 #> 8660 86.59 0.01304239 2.506086e-05 0.986932550 #> 8661 86.60 0.01303587 2.504834e-05 0.986939082 #> 8662 86.61 0.01302935 2.503581e-05 0.986945611 #> 8663 86.62 0.01302284 2.502330e-05 0.986952136 #> 8664 86.63 0.01301633 2.501079e-05 0.986958659 #> 8665 86.64 0.01300982 2.499829e-05 0.986965178 #> 8666 86.65 0.01300332 2.498579e-05 0.986971693 #> 8667 86.66 0.01299682 2.497330e-05 0.986978206 #> 8668 86.67 0.01299032 2.496082e-05 0.986984715 #> 8669 86.68 0.01298383 2.494834e-05 0.986991221 #> 8670 86.69 0.01297734 2.493587e-05 0.986997724 #> 8671 86.70 0.01297085 2.492341e-05 0.987004224 #> 8672 86.71 0.01296437 2.491095e-05 0.987010720 #> 8673 86.72 0.01295789 2.489850e-05 0.987017213 #> 8674 86.73 0.01295141 2.488605e-05 0.987023703 #> 8675 86.74 0.01294494 2.487361e-05 0.987030189 #> 8676 86.75 0.01293847 2.486118e-05 0.987036672 #> 8677 86.76 0.01293200 2.484875e-05 0.987043152 #> 8678 86.77 0.01292553 2.483633e-05 0.987049629 #> 8679 86.78 0.01291907 2.482391e-05 0.987056103 #> 8680 86.79 0.01291262 2.481150e-05 0.987062573 #> 8681 86.80 0.01290616 2.479910e-05 0.987069040 #> 8682 86.81 0.01289971 2.478670e-05 0.987075504 #> 8683 86.82 0.01289326 2.477431e-05 0.987081965 #> 8684 86.83 0.01288682 2.476193e-05 0.987088422 #> 8685 86.84 0.01288037 2.474955e-05 0.987094876 #> 8686 86.85 0.01287394 2.473718e-05 0.987101327 #> 8687 86.86 0.01286750 2.472481e-05 0.987107775 #> 8688 86.87 0.01286107 2.471246e-05 0.987114220 #> 8689 86.88 0.01285464 2.470010e-05 0.987120661 #> 8690 86.89 0.01284821 2.468776e-05 0.987127099 #> 8691 86.90 0.01284179 2.467541e-05 0.987133534 #> 8692 86.91 0.01283537 2.466308e-05 0.987139965 #> 8693 86.92 0.01282896 2.465075e-05 0.987146394 #> 8694 86.93 0.01282254 2.463843e-05 0.987152819 #> 8695 86.94 0.01281613 2.462611e-05 0.987159241 #> 8696 86.95 0.01280973 2.461380e-05 0.987165660 #> 8697 86.96 0.01280332 2.460150e-05 0.987172075 #> 8698 86.97 0.01279692 2.458920e-05 0.987178488 #> 8699 86.98 0.01279053 2.457691e-05 0.987184897 #> 8700 86.99 0.01278413 2.456462e-05 0.987191303 #> 8701 87.00 0.01277774 2.455235e-05 0.987197705 #> 8702 87.01 0.01277135 2.454007e-05 0.987204105 #> 8703 87.02 0.01276497 2.452781e-05 0.987210501 #> 8704 87.03 0.01275859 2.451554e-05 0.987216895 #> 8705 87.04 0.01275221 2.450329e-05 0.987223284 #> 8706 87.05 0.01274584 2.449104e-05 0.987229671 #> 8707 87.06 0.01273947 2.447880e-05 0.987236055 #> 8708 87.07 0.01273310 2.446656e-05 0.987242435 #> 8709 87.08 0.01272673 2.445433e-05 0.987248812 #> 8710 87.09 0.01272037 2.444211e-05 0.987255186 #> 8711 87.10 0.01271401 2.442989e-05 0.987261557 #> 8712 87.11 0.01270766 2.441768e-05 0.987267925 #> 8713 87.12 0.01270131 2.440547e-05 0.987274289 #> 8714 87.13 0.01269496 2.439327e-05 0.987280651 #> 8715 87.14 0.01268861 2.438108e-05 0.987287009 #> 8716 87.15 0.01268227 2.436889e-05 0.987293364 #> 8717 87.16 0.01267593 2.435671e-05 0.987299715 #> 8718 87.17 0.01266959 2.434454e-05 0.987306064 #> 8719 87.18 0.01266326 2.433237e-05 0.987312409 #> 8720 87.19 0.01265693 2.432020e-05 0.987318751 #> 8721 87.20 0.01265060 2.430805e-05 0.987325090 #> 8722 87.21 0.01264428 2.429589e-05 0.987331426 #> 8723 87.22 0.01263796 2.428375e-05 0.987337759 #> 8724 87.23 0.01263164 2.427161e-05 0.987344089 #> 8725 87.24 0.01262533 2.425948e-05 0.987350415 #> 8726 87.25 0.01261901 2.424735e-05 0.987356738 #> 8727 87.26 0.01261271 2.423523e-05 0.987363058 #> 8728 87.27 0.01260640 2.422312e-05 0.987369375 #> 8729 87.28 0.01260010 2.421101e-05 0.987375689 #> 8730 87.29 0.01259380 2.419891e-05 0.987381999 #> 8731 87.30 0.01258751 2.418681e-05 0.987388307 #> 8732 87.31 0.01258121 2.417472e-05 0.987394611 #> 8733 87.32 0.01257493 2.416263e-05 0.987400912 #> 8734 87.33 0.01256864 2.415056e-05 0.987407210 #> 8735 87.34 0.01256236 2.413848e-05 0.987413505 #> 8736 87.35 0.01255608 2.412642e-05 0.987419797 #> 8737 87.36 0.01254980 2.411436e-05 0.987426085 #> 8738 87.37 0.01254353 2.410230e-05 0.987432371 #> 8739 87.38 0.01253726 2.409025e-05 0.987438653 #> 8740 87.39 0.01253099 2.407821e-05 0.987444932 #> 8741 87.40 0.01252473 2.406618e-05 0.987451208 #> 8742 87.41 0.01251847 2.405415e-05 0.987457481 #> 8743 87.42 0.01251221 2.404212e-05 0.987463750 #> 8744 87.43 0.01250595 2.403010e-05 0.987470017 #> 8745 87.44 0.01249970 2.401809e-05 0.987476280 #> 8746 87.45 0.01249345 2.400609e-05 0.987482541 #> 8747 87.46 0.01248721 2.399409e-05 0.987488798 #> 8748 87.47 0.01248097 2.398209e-05 0.987495052 #> 8749 87.48 0.01247473 2.397010e-05 0.987501303 #> 8750 87.49 0.01246849 2.395812e-05 0.987507551 #> 8751 87.50 0.01246226 2.394615e-05 0.987513795 #> 8752 87.51 0.01245603 2.393418e-05 0.987520037 #> 8753 87.52 0.01244980 2.392221e-05 0.987526275 #> 8754 87.53 0.01244358 2.391025e-05 0.987532511 #> 8755 87.54 0.01243736 2.389830e-05 0.987538743 #> 8756 87.55 0.01243114 2.388636e-05 0.987544972 #> 8757 87.56 0.01242493 2.387442e-05 0.987551198 #> 8758 87.57 0.01241872 2.386248e-05 0.987557421 #> 8759 87.58 0.01241251 2.385055e-05 0.987563640 #> 8760 87.59 0.01240630 2.383863e-05 0.987569857 #> 8761 87.60 0.01240010 2.382671e-05 0.987576070 #> 8762 87.61 0.01239390 2.381480e-05 0.987582281 #> 8763 87.62 0.01238771 2.380290e-05 0.987588488 #> 8764 87.63 0.01238152 2.379100e-05 0.987594692 #> 8765 87.64 0.01237533 2.377911e-05 0.987600894 #> 8766 87.65 0.01236914 2.376722e-05 0.987607092 #> 8767 87.66 0.01236296 2.375534e-05 0.987613286 #> 8768 87.67 0.01235678 2.374347e-05 0.987619478 #> 8769 87.68 0.01235060 2.373160e-05 0.987625667 #> 8770 87.69 0.01234443 2.371973e-05 0.987631853 #> 8771 87.70 0.01233826 2.370788e-05 0.987638035 #> 8772 87.71 0.01233209 2.369603e-05 0.987644215 #> 8773 87.72 0.01232592 2.368418e-05 0.987650391 #> 8774 87.73 0.01231976 2.367234e-05 0.987656564 #> 8775 87.74 0.01231361 2.366051e-05 0.987662734 #> 8776 87.75 0.01230745 2.364868e-05 0.987668901 #> 8777 87.76 0.01230130 2.363686e-05 0.987675065 #> 8778 87.77 0.01229515 2.362505e-05 0.987681226 #> 8779 87.78 0.01228900 2.361324e-05 0.987687384 #> 8780 87.79 0.01228286 2.360143e-05 0.987693539 #> 8781 87.80 0.01227672 2.358963e-05 0.987699691 #> 8782 87.81 0.01227058 2.357784e-05 0.987705839 #> 8783 87.82 0.01226445 2.356606e-05 0.987711985 #> 8784 87.83 0.01225832 2.355428e-05 0.987718127 #> 8785 87.84 0.01225219 2.354250e-05 0.987724267 #> 8786 87.85 0.01224607 2.353073e-05 0.987730403 #> 8787 87.86 0.01223994 2.351897e-05 0.987736536 #> 8788 87.87 0.01223383 2.350721e-05 0.987742666 #> 8789 87.88 0.01222771 2.349546e-05 0.987748794 #> 8790 87.89 0.01222160 2.348372e-05 0.987754918 #> 8791 87.90 0.01221549 2.347198e-05 0.987761039 #> 8792 87.91 0.01220938 2.346025e-05 0.987767157 #> 8793 87.92 0.01220328 2.344852e-05 0.987773272 #> 8794 87.93 0.01219718 2.343680e-05 0.987779383 #> 8795 87.94 0.01219108 2.342508e-05 0.987785492 #> 8796 87.95 0.01218499 2.341337e-05 0.987791598 #> 8797 87.96 0.01217890 2.340167e-05 0.987797701 #> 8798 87.97 0.01217281 2.338997e-05 0.987803800 #> 8799 87.98 0.01216672 2.337828e-05 0.987809897 #> 8800 87.99 0.01216064 2.336659e-05 0.987815990 #> 8801 88.00 0.01215456 2.335491e-05 0.987822081 #> 8802 88.01 0.01214849 2.334324e-05 0.987828168 #> 8803 88.02 0.01214242 2.333157e-05 0.987834253 #> 8804 88.03 0.01213635 2.331991e-05 0.987840334 #> 8805 88.04 0.01213028 2.330825e-05 0.987846412 #> 8806 88.05 0.01212422 2.329660e-05 0.987852488 #> 8807 88.06 0.01211816 2.328495e-05 0.987858560 #> 8808 88.07 0.01211210 2.327331e-05 0.987864629 #> 8809 88.08 0.01210604 2.326168e-05 0.987870695 #> 8810 88.09 0.01209999 2.325005e-05 0.987876758 #> 8811 88.10 0.01209394 2.323843e-05 0.987882818 #> 8812 88.11 0.01208790 2.322681e-05 0.987888875 #> 8813 88.12 0.01208186 2.321520e-05 0.987894930 #> 8814 88.13 0.01207582 2.320360e-05 0.987900981 #> 8815 88.14 0.01206978 2.319200e-05 0.987907029 #> 8816 88.15 0.01206375 2.318041e-05 0.987913074 #> 8817 88.16 0.01205772 2.316882e-05 0.987919115 #> 8818 88.17 0.01205169 2.315724e-05 0.987925154 #> 8819 88.18 0.01204566 2.314566e-05 0.987931190 #> 8820 88.19 0.01203964 2.313409e-05 0.987937223 #> 8821 88.20 0.01203362 2.312253e-05 0.987943253 #> 8822 88.21 0.01202761 2.311097e-05 0.987949280 #> 8823 88.22 0.01202160 2.309942e-05 0.987955304 #> 8824 88.23 0.01201559 2.308787e-05 0.987961325 #> 8825 88.24 0.01200958 2.307633e-05 0.987967343 #> 8826 88.25 0.01200358 2.306479e-05 0.987973357 #> 8827 88.26 0.01199758 2.305326e-05 0.987979369 #> 8828 88.27 0.01199158 2.304174e-05 0.987985378 #> 8829 88.28 0.01198559 2.303022e-05 0.987991384 #> 8830 88.29 0.01197959 2.301871e-05 0.987997387 #> 8831 88.30 0.01197361 2.300720e-05 0.988003386 #> 8832 88.31 0.01196762 2.299570e-05 0.988009383 #> 8833 88.32 0.01196164 2.298421e-05 0.988015377 #> 8834 88.33 0.01195566 2.297272e-05 0.988021368 #> 8835 88.34 0.01194968 2.296124e-05 0.988027356 #> 8836 88.35 0.01194371 2.294976e-05 0.988033340 #> 8837 88.36 0.01193774 2.293829e-05 0.988039322 #> 8838 88.37 0.01193177 2.292682e-05 0.988045301 #> 8839 88.38 0.01192581 2.291536e-05 0.988051277 #> 8840 88.39 0.01191985 2.290390e-05 0.988057250 #> 8841 88.40 0.01191389 2.289246e-05 0.988063220 #> 8842 88.41 0.01190793 2.288101e-05 0.988069187 #> 8843 88.42 0.01190198 2.286957e-05 0.988075151 #> 8844 88.43 0.01189603 2.285814e-05 0.988081112 #> 8845 88.44 0.01189008 2.284672e-05 0.988087069 #> 8846 88.45 0.01188414 2.283530e-05 0.988093024 #> 8847 88.46 0.01187820 2.282388e-05 0.988098976 #> 8848 88.47 0.01187226 2.281247e-05 0.988104925 #> 8849 88.48 0.01186633 2.280107e-05 0.988110872 #> 8850 88.49 0.01186040 2.278967e-05 0.988116815 #> 8851 88.50 0.01185447 2.277828e-05 0.988122755 #> 8852 88.51 0.01184854 2.276689e-05 0.988128692 #> 8853 88.52 0.01184262 2.275551e-05 0.988134626 #> 8854 88.53 0.01183670 2.274414e-05 0.988140557 #> 8855 88.54 0.01183078 2.273277e-05 0.988146485 #> 8856 88.55 0.01182487 2.272140e-05 0.988152411 #> 8857 88.56 0.01181896 2.271005e-05 0.988158333 #> 8858 88.57 0.01181305 2.269869e-05 0.988164252 #> 8859 88.58 0.01180714 2.268735e-05 0.988170169 #> 8860 88.59 0.01180124 2.267601e-05 0.988176082 #> 8861 88.60 0.01179534 2.266467e-05 0.988181993 #> 8862 88.61 0.01178945 2.265334e-05 0.988187900 #> 8863 88.62 0.01178355 2.264202e-05 0.988193805 #> 8864 88.63 0.01177766 2.263070e-05 0.988199706 #> 8865 88.64 0.01177178 2.261939e-05 0.988205605 #> 8866 88.65 0.01176589 2.260808e-05 0.988211501 #> 8867 88.66 0.01176001 2.259678e-05 0.988217394 #> 8868 88.67 0.01175413 2.258548e-05 0.988223283 #> 8869 88.68 0.01174826 2.257419e-05 0.988229170 #> 8870 88.69 0.01174238 2.256291e-05 0.988235054 #> 8871 88.70 0.01173651 2.255163e-05 0.988240935 #> 8872 88.71 0.01173065 2.254036e-05 0.988246813 #> 8873 88.72 0.01172478 2.252909e-05 0.988252688 #> 8874 88.73 0.01171892 2.251783e-05 0.988258561 #> 8875 88.74 0.01171306 2.250657e-05 0.988264430 #> 8876 88.75 0.01170721 2.249532e-05 0.988270296 #> 8877 88.76 0.01170136 2.248408e-05 0.988276160 #> 8878 88.77 0.01169551 2.247284e-05 0.988282020 #> 8879 88.78 0.01168966 2.246160e-05 0.988287878 #> 8880 88.79 0.01168382 2.245038e-05 0.988293732 #> 8881 88.80 0.01167798 2.243915e-05 0.988299584 #> 8882 88.81 0.01167214 2.242794e-05 0.988305433 #> 8883 88.82 0.01166630 2.241673e-05 0.988311278 #> 8884 88.83 0.01166047 2.240552e-05 0.988317121 #> 8885 88.84 0.01165464 2.239432e-05 0.988322961 #> 8886 88.85 0.01164882 2.238313e-05 0.988328798 #> 8887 88.86 0.01164300 2.237194e-05 0.988334633 #> 8888 88.87 0.01163718 2.236075e-05 0.988340464 #> 8889 88.88 0.01163136 2.234958e-05 0.988346292 #> 8890 88.89 0.01162554 2.233840e-05 0.988352117 #> 8891 88.90 0.01161973 2.232724e-05 0.988357940 #> 8892 88.91 0.01161392 2.231608e-05 0.988363760 #> 8893 88.92 0.01160812 2.230492e-05 0.988369576 #> 8894 88.93 0.01160232 2.229377e-05 0.988375390 #> 8895 88.94 0.01159652 2.228263e-05 0.988381201 #> 8896 88.95 0.01159072 2.227149e-05 0.988387009 #> 8897 88.96 0.01158493 2.226036e-05 0.988392814 #> 8898 88.97 0.01157913 2.224923e-05 0.988398616 #> 8899 88.98 0.01157335 2.223811e-05 0.988404415 #> 8900 88.99 0.01156756 2.222699e-05 0.988410211 #> 8901 89.00 0.01156178 2.221588e-05 0.988416005 #> 8902 89.01 0.01155600 2.220478e-05 0.988421795 #> 8903 89.02 0.01155022 2.219368e-05 0.988427583 #> 8904 89.03 0.01154445 2.218258e-05 0.988433368 #> 8905 89.04 0.01153868 2.217149e-05 0.988439150 #> 8906 89.05 0.01153291 2.216041e-05 0.988444929 #> 8907 89.06 0.01152715 2.214933e-05 0.988450705 #> 8908 89.07 0.01152138 2.213826e-05 0.988456478 #> 8909 89.08 0.01151562 2.212719e-05 0.988462248 #> 8910 89.09 0.01150987 2.211613e-05 0.988468016 #> 8911 89.10 0.01150411 2.210508e-05 0.988473780 #> 8912 89.11 0.01149836 2.209403e-05 0.988479542 #> 8913 89.12 0.01149262 2.208298e-05 0.988485301 #> 8914 89.13 0.01148687 2.207195e-05 0.988491057 #> 8915 89.14 0.01148113 2.206091e-05 0.988496810 #> 8916 89.15 0.01147539 2.204989e-05 0.988502560 #> 8917 89.16 0.01146965 2.203886e-05 0.988508307 #> 8918 89.17 0.01146392 2.202785e-05 0.988514052 #> 8919 89.18 0.01145819 2.201684e-05 0.988519793 #> 8920 89.19 0.01145246 2.200583e-05 0.988525532 #> 8921 89.20 0.01144674 2.199483e-05 0.988531268 #> 8922 89.21 0.01144102 2.198383e-05 0.988537001 #> 8923 89.22 0.01143530 2.197285e-05 0.988542731 #> 8924 89.23 0.01142958 2.196186e-05 0.988548458 #> 8925 89.24 0.01142387 2.195088e-05 0.988554182 #> 8926 89.25 0.01141816 2.193991e-05 0.988559904 #> 8927 89.26 0.01141245 2.192894e-05 0.988565622 #> 8928 89.27 0.01140674 2.191798e-05 0.988571338 #> 8929 89.28 0.01140104 2.190703e-05 0.988577051 #> 8930 89.29 0.01139534 2.189607e-05 0.988582761 #> 8931 89.30 0.01138965 2.188513e-05 0.988588468 #> 8932 89.31 0.01138395 2.187419e-05 0.988594173 #> 8933 89.32 0.01137826 2.186326e-05 0.988599874 #> 8934 89.33 0.01137258 2.185233e-05 0.988605573 #> 8935 89.34 0.01136689 2.184140e-05 0.988611268 #> 8936 89.35 0.01136121 2.183049e-05 0.988616961 #> 8937 89.36 0.01135553 2.181957e-05 0.988622651 #> 8938 89.37 0.01134985 2.180867e-05 0.988628339 #> 8939 89.38 0.01134418 2.179776e-05 0.988634023 #> 8940 89.39 0.01133851 2.178687e-05 0.988639705 #> 8941 89.40 0.01133284 2.177598e-05 0.988645383 #> 8942 89.41 0.01132718 2.176509e-05 0.988651059 #> 8943 89.42 0.01132151 2.175421e-05 0.988656732 #> 8944 89.43 0.01131585 2.174334e-05 0.988662403 #> 8945 89.44 0.01131020 2.173247e-05 0.988668070 #> 8946 89.45 0.01130454 2.172161e-05 0.988673735 #> 8947 89.46 0.01129889 2.171075e-05 0.988679396 #> 8948 89.47 0.01129324 2.169989e-05 0.988685055 #> 8949 89.48 0.01128760 2.168905e-05 0.988690711 #> 8950 89.49 0.01128196 2.167821e-05 0.988696364 #> 8951 89.50 0.01127632 2.166737e-05 0.988702015 #> 8952 89.51 0.01127068 2.165654e-05 0.988707662 #> 8953 89.52 0.01126505 2.164571e-05 0.988713307 #> 8954 89.53 0.01125942 2.163489e-05 0.988718949 #> 8955 89.54 0.01125379 2.162408e-05 0.988724588 #> 8956 89.55 0.01124816 2.161327e-05 0.988730225 #> 8957 89.56 0.01124254 2.160246e-05 0.988735858 #> 8958 89.57 0.01123692 2.159167e-05 0.988741489 #> 8959 89.58 0.01123130 2.158087e-05 0.988747116 #> 8960 89.59 0.01122569 2.157008e-05 0.988752742 #> 8961 89.60 0.01122008 2.155930e-05 0.988758364 #> 8962 89.61 0.01121447 2.154853e-05 0.988763983 #> 8963 89.62 0.01120886 2.153775e-05 0.988769600 #> 8964 89.63 0.01120326 2.152699e-05 0.988775214 #> 8965 89.64 0.01119766 2.151623e-05 0.988780825 #> 8966 89.65 0.01119206 2.150547e-05 0.988786433 #> 8967 89.66 0.01118647 2.149472e-05 0.988792038 #> 8968 89.67 0.01118088 2.148398e-05 0.988797641 #> 8969 89.68 0.01117529 2.147324e-05 0.988803240 #> 8970 89.69 0.01116970 2.146250e-05 0.988808837 #> 8971 89.70 0.01116412 2.145177e-05 0.988814432 #> 8972 89.71 0.01115854 2.144105e-05 0.988820023 #> 8973 89.72 0.01115296 2.143033e-05 0.988825612 #> 8974 89.73 0.01114738 2.141962e-05 0.988831197 #> 8975 89.74 0.01114181 2.140891e-05 0.988836780 #> 8976 89.75 0.01113624 2.139821e-05 0.988842361 #> 8977 89.76 0.01113067 2.138752e-05 0.988847938 #> 8978 89.77 0.01112511 2.137682e-05 0.988853513 #> 8979 89.78 0.01111955 2.136614e-05 0.988859085 #> 8980 89.79 0.01111399 2.135546e-05 0.988864654 #> 8981 89.80 0.01110844 2.134478e-05 0.988870220 #> 8982 89.81 0.01110288 2.133411e-05 0.988875783 #> 8983 89.82 0.01109733 2.132345e-05 0.988881344 #> 8984 89.83 0.01109179 2.131279e-05 0.988886902 #> 8985 89.84 0.01108624 2.130214e-05 0.988892457 #> 8986 89.85 0.01108070 2.129149e-05 0.988898010 #> 8987 89.86 0.01107516 2.128085e-05 0.988903559 #> 8988 89.87 0.01106962 2.127021e-05 0.988909106 #> 8989 89.88 0.01106409 2.125958e-05 0.988914650 #> 8990 89.89 0.01105856 2.124895e-05 0.988920191 #> 8991 89.90 0.01105303 2.123833e-05 0.988925730 #> 8992 89.91 0.01104751 2.122771e-05 0.988931266 #> 8993 89.92 0.01104198 2.121710e-05 0.988936799 #> 8994 89.93 0.01103646 2.120649e-05 0.988942329 #> 8995 89.94 0.01103095 2.119589e-05 0.988947856 #> 8996 89.95 0.01102543 2.118530e-05 0.988953381 #> 8997 89.96 0.01101992 2.117471e-05 0.988958903 #> 8998 89.97 0.01101441 2.116412e-05 0.988964422 #> 8999 89.98 0.01100891 2.115354e-05 0.988969939 #> 9000 89.99 0.01100340 2.114297e-05 0.988975452 #> 9001 90.00 0.01099790 2.113240e-05 0.988980963 #>  #> attr(,\"class\") #> [1] \"sir_static_model\""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_subpops_model.html","id":null,"dir":"Reference","previous_headings":"","what":"SIR Model with Interacting Subpopulations — sir_subpops_model","title":"SIR Model with Interacting Subpopulations — sir_subpops_model","text":"extension basic SIR model incorporates interaction sub-populations","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_subpops_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SIR Model with Interacting Subpopulations — sir_subpops_model","text":"","code":"sir_subpops_model(times, state, parameters)"},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_subpops_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SIR Model with Interacting Subpopulations — sir_subpops_model","text":"times time sequence. state initial state model. named vector following s: initial susceptible proportion : initial infected proportion r: initial recovered proportion parameters parameters model. named vector following mu: natural death rate (1/L). beta: WAIFW matrix, dimensions [K, K]. nu: recovery rate","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_subpops_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SIR Model with Interacting Subpopulations — sir_subpops_model","text":"list class sir_subpops_model following items  parameters list parameters used fitting model output matrix proportion compartment time","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_subpops_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SIR Model with Interacting Subpopulations — sir_subpops_model","text":"Follow SIR model sub populations described book Hens et al. (section 3.5.1.) K subpopulations, WAIFW matrix mixing matrix given $$ C = \\begin{bmatrix} \\beta_{11} & \\beta_{12}  & ... & \\beta_{1K} \\\\ \\beta_{21} & \\beta_{22}  & ... & \\beta_{2K} \\\\ \\vdots & \\vdots  & ... & \\vdots \\\\ \\beta_{K1} & \\beta_{K2}  & ... & \\beta_{KK} \\\\ \\end{bmatrix} $$ \\(^{th}\\) sub population described following system differential equations $$ \\begin{cases} \\frac{dS_i(t)}{dt} = -(\\sum^K_{j=1}\\beta_{ij}I_j(t)) S_i(t) + N_i\\mu_i - \\mu_i S_i(t) \\\\ \\frac{dI_i(t)}{dt} = (\\sum^K_{j=1}\\beta_{ij}I_j(t)) S_i(t)  - (\\nu_i + \\mu_i) I_i(t)  \\\\ \\frac{dR_i(t)}{dt} = \\nu_i I_i(t)  - \\mu_i R_i(t) \\end{cases} $$","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_subpops_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SIR Model with Interacting Subpopulations — sir_subpops_model","text":"Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based Serological Social Contact Data: Modern Statistical Perspective. tatistics Biology Health. Springer New York. doi:10.1007/978-1-4614-4072-7 .","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/sir_subpops_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SIR Model with Interacting Subpopulations — sir_subpops_model","text":"","code":"# \\donttest{ state <- c(   s = c(0.8, 0.8),   i = c(0.2, 0.2),   r = c(  0,   0) ) beta_matrix <- matrix(   c(0.05, 0.00,   0.00, 0.05),   2 ) parameters <- list(   beta = beta_matrix,   nu = c(1/30, 1/30),   mu = 0.001 ) times<-seq(0,10000,by=0.5) model <- sir_subpops_model(times, state, parameters) model #> $parameters #> $parameters$beta #>      [,1] [,2] #> [1,] 0.05 0.00 #> [2,] 0.00 0.05 #>  #> $parameters$nu #> [1] 0.03333333 0.03333333 #>  #> $parameters$mu #> [1] 0.001 #>  #> $parameters$k #> [1] 2 #>  #>  #> $output #>         time        s1        s2           i1           i2          r1 #> 1        0.0 0.8000000 0.8000000 2.000000e-01 2.000000e-01 0.000000000 #> 2        0.5 0.7961052 0.7961052 2.005577e-01 2.005577e-01 0.003337146 #> 3        1.0 0.7922208 0.7922208 2.010974e-01 2.010974e-01 0.006681767 #> 4        1.5 0.7883475 0.7883475 2.016190e-01 2.016190e-01 0.010033558 #> 5        2.0 0.7844854 0.7844854 2.021224e-01 2.021224e-01 0.013392251 #> 6        2.5 0.7806350 0.7806350 2.026075e-01 2.026075e-01 0.016757489 #> 7        3.0 0.7767967 0.7767967 2.030743e-01 2.030743e-01 0.020128975 #> 8        3.5 0.7729709 0.7729709 2.035227e-01 2.035227e-01 0.023506400 #> 9        4.0 0.7691580 0.7691580 2.039525e-01 2.039525e-01 0.026889453 #> 10       4.5 0.7653583 0.7653583 2.043639e-01 2.043639e-01 0.030277824 #> 11       5.0 0.7615722 0.7615722 2.047566e-01 2.047566e-01 0.033671200 #> 12       5.5 0.7578000 0.7578000 2.051307e-01 2.051307e-01 0.037069269 #> 13       6.0 0.7540421 0.7540421 2.054862e-01 2.054862e-01 0.040471717 #> 14       6.5 0.7502988 0.7502988 2.058229e-01 2.058229e-01 0.043878231 #> 15       7.0 0.7465705 0.7465705 2.061410e-01 2.061410e-01 0.047288499 #> 16       7.5 0.7428574 0.7428574 2.064404e-01 2.064404e-01 0.050702206 #> 17       8.0 0.7391599 0.7391599 2.067210e-01 2.067210e-01 0.054119038 #> 18       8.5 0.7354784 0.7354784 2.069829e-01 2.069829e-01 0.057538683 #> 19       9.0 0.7318130 0.7318130 2.072262e-01 2.072262e-01 0.060960827 #> 20       9.5 0.7281641 0.7281641 2.074507e-01 2.074507e-01 0.064385157 #> 21      10.0 0.7245320 0.7245320 2.076566e-01 2.076566e-01 0.067811362 #> 22      10.5 0.7209170 0.7209170 2.078439e-01 2.078439e-01 0.071239129 #> 23      11.0 0.7173193 0.7173193 2.080126e-01 2.080126e-01 0.074668149 #> 24      11.5 0.7137392 0.7137392 2.081627e-01 2.081627e-01 0.078098110 #> 25      12.0 0.7101769 0.7101769 2.082944e-01 2.082944e-01 0.081528705 #> 26      12.5 0.7066328 0.7066328 2.084076e-01 2.084076e-01 0.084959625 #> 27      13.0 0.7031070 0.7031070 2.085024e-01 2.085024e-01 0.088390563 #> 28      13.5 0.6995998 0.6995998 2.085790e-01 2.085790e-01 0.091821214 #> 29      14.0 0.6961114 0.6961114 2.086373e-01 2.086373e-01 0.095251274 #> 30      14.5 0.6926420 0.6926420 2.086776e-01 2.086776e-01 0.098680441 #> 31      15.0 0.6891919 0.6891919 2.086997e-01 2.086997e-01 0.102108412 #> 32      15.5 0.6857612 0.6857612 2.087039e-01 2.087039e-01 0.105534890 #> 33      16.0 0.6823501 0.6823501 2.086903e-01 2.086903e-01 0.108959577 #> 34      16.5 0.6789589 0.6789589 2.086589e-01 2.086589e-01 0.112382176 #> 35      17.0 0.6755877 0.6755877 2.086099e-01 2.086099e-01 0.115802395 #> 36      17.5 0.6722366 0.6722366 2.085434e-01 2.085434e-01 0.119219941 #> 37      18.0 0.6689060 0.6689060 2.084595e-01 2.084595e-01 0.122634526 #> 38      18.5 0.6655958 0.6655958 2.083583e-01 2.083583e-01 0.126045862 #> 39      19.0 0.6623063 0.6623063 2.082400e-01 2.082400e-01 0.129453664 #> 40      19.5 0.6590376 0.6590376 2.081047e-01 2.081047e-01 0.132857649 #> 41      20.0 0.6557899 0.6557899 2.079526e-01 2.079526e-01 0.136257538 #> 42      20.5 0.6525632 0.6525632 2.077838e-01 2.077838e-01 0.139653053 #> 43      21.0 0.6493577 0.6493577 2.075983e-01 2.075983e-01 0.143043919 #> 44      21.5 0.6461736 0.6461736 2.073965e-01 2.073965e-01 0.146429864 #> 45      22.0 0.6430109 0.6430109 2.071785e-01 2.071785e-01 0.149810618 #> 46      22.5 0.6398697 0.6398697 2.069444e-01 2.069444e-01 0.153185914 #> 47      23.0 0.6367502 0.6367502 2.066943e-01 2.066943e-01 0.156555490 #> 48      23.5 0.6336524 0.6336524 2.064286e-01 2.064286e-01 0.159919084 #> 49      24.0 0.6305764 0.6305764 2.061472e-01 2.061472e-01 0.163276437 #> 50      24.5 0.6275222 0.6275222 2.058505e-01 2.058505e-01 0.166627296 #> 51      25.0 0.6244901 0.6244901 2.055385e-01 2.055385e-01 0.169971409 #> 52      25.5 0.6214799 0.6214799 2.052115e-01 2.052115e-01 0.173308527 #> 53      26.0 0.6184919 0.6184919 2.048697e-01 2.048697e-01 0.176638404 #> 54      26.5 0.6155259 0.6155259 2.045133e-01 2.045133e-01 0.179960799 #> 55      27.0 0.6125822 0.6125822 2.041423e-01 2.041423e-01 0.183275473 #> 56      27.5 0.6096607 0.6096607 2.037571e-01 2.037571e-01 0.186582190 #> 57      28.0 0.6067614 0.6067614 2.033579e-01 2.033579e-01 0.189880719 #> 58      28.5 0.6038844 0.6038844 2.029447e-01 2.029447e-01 0.193170830 #> 59      29.0 0.6010298 0.6010298 2.025179e-01 2.025179e-01 0.196452298 #> 60      29.5 0.5981974 0.5981974 2.020777e-01 2.020777e-01 0.199724902 #> 61      30.0 0.5953875 0.5953875 2.016241e-01 2.016241e-01 0.202988423 #> 62      30.5 0.5925998 0.5925998 2.011575e-01 2.011575e-01 0.206242646 #> 63      31.0 0.5898346 0.5898346 2.006781e-01 2.006781e-01 0.209487361 #> 64      31.5 0.5870917 0.5870917 2.001860e-01 2.001860e-01 0.212722359 #> 65      32.0 0.5843711 0.5843711 1.996815e-01 1.996815e-01 0.215947437 #> 66      32.5 0.5816729 0.5816729 1.991647e-01 1.991647e-01 0.219162394 #> 67      33.0 0.5789970 0.5789970 1.986360e-01 1.986360e-01 0.222367034 #> 68      33.5 0.5763434 0.5763434 1.980954e-01 1.980954e-01 0.225561163 #> 69      34.0 0.5737121 0.5737121 1.975433e-01 1.975433e-01 0.228744592 #> 70      34.5 0.5711030 0.5711030 1.969798e-01 1.969798e-01 0.231917135 #> 71      35.0 0.5685162 0.5685162 1.964052e-01 1.964052e-01 0.235078610 #> 72      35.5 0.5659515 0.5659515 1.958197e-01 1.958197e-01 0.238228838 #> 73      36.0 0.5634090 0.5634090 1.952234e-01 1.952234e-01 0.241367645 #> 74      36.5 0.5608886 0.5608886 1.946166e-01 1.946166e-01 0.244494860 #> 75      37.0 0.5583902 0.5583902 1.939995e-01 1.939995e-01 0.247610314 #> 76      37.5 0.5559138 0.5559138 1.933724e-01 1.933724e-01 0.250713846 #> 77      38.0 0.5534593 0.5534593 1.927354e-01 1.927354e-01 0.253805294 #> 78      38.5 0.5510267 0.5510267 1.920888e-01 1.920888e-01 0.256884502 #> 79      39.0 0.5486160 0.5486160 1.914327e-01 1.914327e-01 0.259951318 #> 80      39.5 0.5462269 0.5462269 1.907675e-01 1.907675e-01 0.263005592 #> 81      40.0 0.5438596 0.5438596 1.900932e-01 1.900932e-01 0.266047180 #> 82      40.5 0.5415139 0.5415139 1.894102e-01 1.894102e-01 0.269075939 #> 83      41.0 0.5391896 0.5391896 1.887186e-01 1.887186e-01 0.272091732 #> 84      41.5 0.5368869 0.5368869 1.880187e-01 1.880187e-01 0.275094424 #> 85      42.0 0.5346055 0.5346055 1.873106e-01 1.873106e-01 0.278083884 #> 86      42.5 0.5323454 0.5323454 1.865946e-01 1.865946e-01 0.281059985 #> 87      43.0 0.5301065 0.5301065 1.858709e-01 1.858709e-01 0.284022604 #> 88      43.5 0.5278887 0.5278887 1.851397e-01 1.851397e-01 0.286971620 #> 89      44.0 0.5256919 0.5256919 1.844012e-01 1.844012e-01 0.289906917 #> 90      44.5 0.5235161 0.5235161 1.836556e-01 1.836556e-01 0.292828382 #> 91      45.0 0.5213610 0.5213610 1.829031e-01 1.829031e-01 0.295735905 #> 92      45.5 0.5192267 0.5192267 1.821439e-01 1.821439e-01 0.298629380 #> 93      46.0 0.5171131 0.5171131 1.813782e-01 1.813782e-01 0.301508705 #> 94      46.5 0.5150199 0.5150199 1.806063e-01 1.806063e-01 0.304373780 #> 95      47.0 0.5129472 0.5129472 1.798283e-01 1.798283e-01 0.307224510 #> 96      47.5 0.5108948 0.5108948 1.790444e-01 1.790444e-01 0.310060801 #> 97      48.0 0.5088626 0.5088626 1.782548e-01 1.782548e-01 0.312882565 #> 98      48.5 0.5068506 0.5068506 1.774597e-01 1.774597e-01 0.315689717 #> 99      49.0 0.5048585 0.5048585 1.766593e-01 1.766593e-01 0.318482173 #> 100     49.5 0.5028863 0.5028863 1.758539e-01 1.758539e-01 0.321259854 #> 101     50.0 0.5009338 0.5009338 1.750435e-01 1.750435e-01 0.324022684 #> 102     50.5 0.4990010 0.4990010 1.742284e-01 1.742284e-01 0.326770590 #> 103     51.0 0.4970878 0.4970878 1.734087e-01 1.734087e-01 0.329503503 #> 104     51.5 0.4951939 0.4951939 1.725847e-01 1.725847e-01 0.332221355 #> 105     52.0 0.4933194 0.4933194 1.717565e-01 1.717565e-01 0.334924084 #> 106     52.5 0.4914640 0.4914640 1.709243e-01 1.709243e-01 0.337611628 #> 107     53.0 0.4896277 0.4896277 1.700883e-01 1.700883e-01 0.340283931 #> 108     53.5 0.4878104 0.4878104 1.692487e-01 1.692487e-01 0.342940938 #> 109     54.0 0.4860118 0.4860118 1.684056e-01 1.684056e-01 0.345582596 #> 110     54.5 0.4842320 0.4842320 1.675592e-01 1.675592e-01 0.348208859 #> 111     55.0 0.4824707 0.4824707 1.667096e-01 1.667096e-01 0.350819678 #> 112     55.5 0.4807278 0.4807278 1.658572e-01 1.658572e-01 0.353415013 #> 113     56.0 0.4790033 0.4790033 1.650019e-01 1.650019e-01 0.355994822 #> 114     56.5 0.4772970 0.4772970 1.641440e-01 1.641440e-01 0.358559069 #> 115     57.0 0.4756087 0.4756087 1.632836e-01 1.632836e-01 0.361107718 #> 116     57.5 0.4739384 0.4739384 1.624209e-01 1.624209e-01 0.363640737 #> 117     58.0 0.4722858 0.4722858 1.615561e-01 1.615561e-01 0.366158098 #> 118     58.5 0.4706510 0.4706510 1.606892e-01 1.606892e-01 0.368659773 #> 119     59.0 0.4690337 0.4690337 1.598205e-01 1.598205e-01 0.371145738 #> 120     59.5 0.4674339 0.4674339 1.589502e-01 1.589502e-01 0.373615972 #> 121     60.0 0.4658513 0.4658513 1.580782e-01 1.580782e-01 0.376070455 #> 122     60.5 0.4642859 0.4642859 1.572049e-01 1.572049e-01 0.378509171 #> 123     61.0 0.4627376 0.4627376 1.563303e-01 1.563303e-01 0.380932105 #> 124     61.5 0.4612061 0.4612061 1.554546e-01 1.554546e-01 0.383339246 #> 125     62.0 0.4596915 0.4596915 1.545779e-01 1.545779e-01 0.385730583 #> 126     62.5 0.4581935 0.4581935 1.537004e-01 1.537004e-01 0.388106110 #> 127     63.0 0.4567120 0.4567120 1.528221e-01 1.528221e-01 0.390465822 #> 128     63.5 0.4552470 0.4552470 1.519433e-01 1.519433e-01 0.392809716 #> 129     64.0 0.4537982 0.4537982 1.510641e-01 1.510641e-01 0.395137790 #> 130     64.5 0.4523655 0.4523655 1.501845e-01 1.501845e-01 0.397450047 #> 131     65.0 0.4509488 0.4509488 1.493047e-01 1.493047e-01 0.399746490 #> 132     65.5 0.4495481 0.4495481 1.484248e-01 1.484248e-01 0.402027125 #> 133     66.0 0.4481630 0.4481630 1.475450e-01 1.475450e-01 0.404291960 #> 134     66.5 0.4467936 0.4467936 1.466654e-01 1.466654e-01 0.406541004 #> 135     67.0 0.4454397 0.4454397 1.457860e-01 1.457860e-01 0.408774268 #> 136     67.5 0.4441012 0.4441012 1.449070e-01 1.449070e-01 0.410991767 #> 137     68.0 0.4427779 0.4427779 1.440286e-01 1.440286e-01 0.413193516 #> 138     68.5 0.4414698 0.4414698 1.431507e-01 1.431507e-01 0.415379531 #> 139     69.0 0.4401766 0.4401766 1.422736e-01 1.422736e-01 0.417549833 #> 140     69.5 0.4388983 0.4388983 1.413973e-01 1.413973e-01 0.419704442 #> 141     70.0 0.4376347 0.4376347 1.405219e-01 1.405219e-01 0.421843379 #> 142     70.5 0.4363857 0.4363857 1.396476e-01 1.396476e-01 0.423966671 #> 143     71.0 0.4351513 0.4351513 1.387744e-01 1.387744e-01 0.426074342 #> 144     71.5 0.4339311 0.4339311 1.379024e-01 1.379024e-01 0.428166419 #> 145     72.0 0.4327253 0.4327253 1.370318e-01 1.370318e-01 0.430242933 #> 146     72.5 0.4315335 0.4315335 1.361626e-01 1.361626e-01 0.432303913 #> 147     73.0 0.4303558 0.4303558 1.352948e-01 1.352948e-01 0.434349392 #> 148     73.5 0.4291919 0.4291919 1.344287e-01 1.344287e-01 0.436379403 #> 149     74.0 0.4280417 0.4280417 1.335643e-01 1.335643e-01 0.438393982 #> 150     74.5 0.4269052 0.4269052 1.327016e-01 1.327016e-01 0.440393164 #> 151     75.0 0.4257823 0.4257823 1.318408e-01 1.318408e-01 0.442376988 #> 152     75.5 0.4246726 0.4246726 1.309819e-01 1.309819e-01 0.444345492 #> 153     76.0 0.4235763 0.4235763 1.301250e-01 1.301250e-01 0.446298718 #> 154     76.5 0.4224931 0.4224931 1.292702e-01 1.292702e-01 0.448236707 #> 155     77.0 0.4214230 0.4214230 1.284175e-01 1.284175e-01 0.450159501 #> 156     77.5 0.4203658 0.4203658 1.275671e-01 1.275671e-01 0.452067145 #> 157     78.0 0.4193214 0.4193214 1.267190e-01 1.267190e-01 0.453959685 #> 158     78.5 0.4182896 0.4182896 1.258732e-01 1.258732e-01 0.455837167 #> 159     79.0 0.4172705 0.4172705 1.250299e-01 1.250299e-01 0.457699638 #> 160     79.5 0.4162637 0.4162637 1.241891e-01 1.241891e-01 0.459547147 #> 161     80.0 0.4152694 0.4152694 1.233509e-01 1.233509e-01 0.461379744 #> 162     80.5 0.4142872 0.4142872 1.225153e-01 1.225153e-01 0.463197480 #> 163     81.0 0.4133172 0.4133172 1.216824e-01 1.216824e-01 0.465000407 #> 164     81.5 0.4123592 0.4123592 1.208522e-01 1.208522e-01 0.466788577 #> 165     82.0 0.4114131 0.4114131 1.200248e-01 1.200248e-01 0.468562043 #> 166     82.5 0.4104788 0.4104788 1.192004e-01 1.192004e-01 0.470320861 #> 167     83.0 0.4095561 0.4095561 1.183788e-01 1.183788e-01 0.472065086 #> 168     83.5 0.4086451 0.4086451 1.175602e-01 1.175602e-01 0.473794774 #> 169     84.0 0.4077454 0.4077454 1.167446e-01 1.167446e-01 0.475509983 #> 170     84.5 0.4068572 0.4068572 1.159321e-01 1.159321e-01 0.477210770 #> 171     85.0 0.4059801 0.4059801 1.151227e-01 1.151227e-01 0.478897194 #> 172     85.5 0.4051143 0.4051143 1.143164e-01 1.143164e-01 0.480569315 #> 173     86.0 0.4042594 0.4042594 1.135134e-01 1.135134e-01 0.482227192 #> 174     86.5 0.4034155 0.4034155 1.127136e-01 1.127136e-01 0.483870887 #> 175     87.0 0.4025825 0.4025825 1.119171e-01 1.119171e-01 0.485500461 #> 176     87.5 0.4017601 0.4017601 1.111239e-01 1.111239e-01 0.487115976 #> 177     88.0 0.4009484 0.4009484 1.103341e-01 1.103341e-01 0.488717496 #> 178     88.5 0.4001472 0.4001472 1.095477e-01 1.095477e-01 0.490305083 #> 179     89.0 0.3993565 0.3993565 1.087647e-01 1.087647e-01 0.491878801 #> 180     89.5 0.3985761 0.3985761 1.079852e-01 1.079852e-01 0.493438716 #> 181     90.0 0.3978059 0.3978059 1.072093e-01 1.072093e-01 0.494984892 #> 182     90.5 0.3970458 0.3970458 1.064368e-01 1.064368e-01 0.496517395 #> 183     91.0 0.3962958 0.3962958 1.056679e-01 1.056679e-01 0.498036291 #> 184     91.5 0.3955557 0.3955557 1.049027e-01 1.049027e-01 0.499541646 #> 185     92.0 0.3948254 0.3948254 1.041410e-01 1.041410e-01 0.501033528 #> 186     92.5 0.3941049 0.3941049 1.033831e-01 1.033831e-01 0.502512003 #> 187     93.0 0.3933941 0.3933941 1.026288e-01 1.026288e-01 0.503977140 #> 188     93.5 0.3926928 0.3926928 1.018782e-01 1.018782e-01 0.505429007 #> 189     94.0 0.3920010 0.3920010 1.011313e-01 1.011313e-01 0.506867673 #> 190     94.5 0.3913186 0.3913186 1.003882e-01 1.003882e-01 0.508293205 #> 191     95.0 0.3906455 0.3906455 9.964882e-02 9.964882e-02 0.509705675 #> 192     95.5 0.3899816 0.3899816 9.891327e-02 9.891327e-02 0.511105150 #> 193     96.0 0.3893268 0.3893268 9.818152e-02 9.818152e-02 0.512491702 #> 194     96.5 0.3886810 0.3886810 9.745361e-02 9.745361e-02 0.513865399 #> 195     97.0 0.3880441 0.3880441 9.672954e-02 9.672954e-02 0.515226314 #> 196     97.5 0.3874162 0.3874162 9.600933e-02 9.600933e-02 0.516574515 #> 197     98.0 0.3867969 0.3867969 9.529300e-02 9.529300e-02 0.517910074 #> 198     98.5 0.3861864 0.3861864 9.458056e-02 9.458056e-02 0.519233062 #> 199     99.0 0.3855844 0.3855844 9.387203e-02 9.387203e-02 0.520543550 #> 200     99.5 0.3849910 0.3849910 9.316741e-02 9.316741e-02 0.521841609 #> 201    100.0 0.3844060 0.3844060 9.246671e-02 9.246671e-02 0.523127312 #> 202    100.5 0.3838293 0.3838293 9.176996e-02 9.176996e-02 0.524400730 #> 203    101.0 0.3832609 0.3832609 9.107715e-02 9.107715e-02 0.525661934 #> 204    101.5 0.3827007 0.3827007 9.038829e-02 9.038829e-02 0.526910997 #> 205    102.0 0.3821486 0.3821486 8.970340e-02 8.970340e-02 0.528147990 #> 206    102.5 0.3816045 0.3816045 8.902248e-02 8.902248e-02 0.529372986 #> 207    103.0 0.3810684 0.3810684 8.834553e-02 8.834553e-02 0.530586057 #> 208    103.5 0.3805402 0.3805402 8.767257e-02 8.767257e-02 0.531787275 #> 209    104.0 0.3800197 0.3800197 8.700359e-02 8.700359e-02 0.532976713 #> 210    104.5 0.3795070 0.3795070 8.633860e-02 8.633860e-02 0.534154442 #> 211    105.0 0.3790019 0.3790019 8.567760e-02 8.567760e-02 0.535320536 #> 212    105.5 0.3785043 0.3785043 8.502060e-02 8.502060e-02 0.536475066 #> 213    106.0 0.3780143 0.3780143 8.436760e-02 8.436760e-02 0.537618105 #> 214    106.5 0.3775317 0.3775317 8.371859e-02 8.371859e-02 0.538749725 #> 215    107.0 0.3770564 0.3770564 8.307358e-02 8.307358e-02 0.539869999 #> 216    107.5 0.3765884 0.3765884 8.243257e-02 8.243257e-02 0.540978999 #> 217    108.0 0.3761277 0.3761277 8.179555e-02 8.179555e-02 0.542076796 #> 218    108.5 0.3756740 0.3756740 8.116253e-02 8.116253e-02 0.543163464 #> 219    109.0 0.3752274 0.3752274 8.053350e-02 8.053350e-02 0.544239075 #> 220    109.5 0.3747878 0.3747878 7.990847e-02 7.990847e-02 0.545303699 #> 221    110.0 0.3743552 0.3743552 7.928741e-02 7.928741e-02 0.546357411 #> 222    110.5 0.3739294 0.3739294 7.867035e-02 7.867035e-02 0.547400280 #> 223    111.0 0.3735104 0.3735104 7.805726e-02 7.805726e-02 0.548432379 #> 224    111.5 0.3730981 0.3730981 7.744814e-02 7.744814e-02 0.549453780 #> 225    112.0 0.3726925 0.3726925 7.684299e-02 7.684299e-02 0.550464554 #> 226    112.5 0.3722934 0.3722934 7.624180e-02 7.624180e-02 0.551464772 #> 227    113.0 0.3719009 0.3719009 7.564457e-02 7.564457e-02 0.552454506 #> 228    113.5 0.3715149 0.3715149 7.505128e-02 7.505128e-02 0.553433827 #> 229    114.0 0.3711353 0.3711353 7.446194e-02 7.446194e-02 0.554402805 #> 230    114.5 0.3707620 0.3707620 7.387652e-02 7.387652e-02 0.555361512 #> 231    115.0 0.3703949 0.3703949 7.329504e-02 7.329504e-02 0.556310018 #> 232    115.5 0.3700341 0.3700341 7.271746e-02 7.271746e-02 0.557248393 #> 233    116.0 0.3696795 0.3696795 7.214380e-02 7.214380e-02 0.558176708 #> 234    116.5 0.3693309 0.3693309 7.157403e-02 7.157403e-02 0.559095033 #> 235    117.0 0.3689884 0.3689884 7.100814e-02 7.100814e-02 0.560003438 #> 236    117.5 0.3686519 0.3686519 7.044614e-02 7.044614e-02 0.560901991 #> 237    118.0 0.3683212 0.3683212 6.988800e-02 6.988800e-02 0.561790763 #> 238    118.5 0.3679965 0.3679965 6.933371e-02 6.933371e-02 0.562669823 #> 239    119.0 0.3676775 0.3676775 6.878326e-02 6.878326e-02 0.563539240 #> 240    119.5 0.3673643 0.3673643 6.823665e-02 6.823665e-02 0.564399082 #> 241    120.0 0.3670567 0.3670567 6.769385e-02 6.769385e-02 0.565249419 #> 242    120.5 0.3667548 0.3667548 6.715486e-02 6.715486e-02 0.566090317 #> 243    121.0 0.3664585 0.3664585 6.661967e-02 6.661967e-02 0.566921846 #> 244    121.5 0.3661677 0.3661677 6.608825e-02 6.608825e-02 0.567744073 #> 245    122.0 0.3658823 0.3658823 6.556060e-02 6.556060e-02 0.568557066 #> 246    122.5 0.3656024 0.3656024 6.503670e-02 6.503670e-02 0.569360892 #> 247    123.0 0.3653278 0.3653278 6.451655e-02 6.451655e-02 0.570155618 #> 248    123.5 0.3650586 0.3650586 6.400012e-02 6.400012e-02 0.570941310 #> 249    124.0 0.3647946 0.3647946 6.348740e-02 6.348740e-02 0.571718036 #> 250    124.5 0.3645358 0.3645358 6.297837e-02 6.297837e-02 0.572485861 #> 251    125.0 0.3642821 0.3642821 6.247303e-02 6.247303e-02 0.573244851 #> 252    125.5 0.3640336 0.3640336 6.197135e-02 6.197135e-02 0.573995072 #> 253    126.0 0.3637901 0.3637901 6.147332e-02 6.147332e-02 0.574736589 #> 254    126.5 0.3635516 0.3635516 6.097893e-02 6.097893e-02 0.575469468 #> 255    127.0 0.3633181 0.3633181 6.048815e-02 6.048815e-02 0.576193772 #> 256    127.5 0.3630894 0.3630894 6.000098e-02 6.000098e-02 0.576909567 #> 257    128.0 0.3628657 0.3628657 5.951740e-02 5.951740e-02 0.577616917 #> 258    128.5 0.3626467 0.3626467 5.903739e-02 5.903739e-02 0.578315885 #> 259    129.0 0.3624325 0.3624325 5.856093e-02 5.856093e-02 0.579006535 #> 260    129.5 0.3622231 0.3622231 5.808801e-02 5.808801e-02 0.579688930 #> 261    130.0 0.3620183 0.3620183 5.761861e-02 5.761861e-02 0.580363134 #> 262    130.5 0.3618181 0.3618181 5.715272e-02 5.715272e-02 0.581029209 #> 263    131.0 0.3616225 0.3616225 5.669031e-02 5.669031e-02 0.581687216 #> 264    131.5 0.3614314 0.3614314 5.623137e-02 5.623137e-02 0.582337219 #> 265    132.0 0.3612448 0.3612448 5.577588e-02 5.577588e-02 0.582979278 #> 266    132.5 0.3610627 0.3610627 5.532383e-02 5.532383e-02 0.583613456 #> 267    133.0 0.3608850 0.3608850 5.487519e-02 5.487519e-02 0.584239813 #> 268    133.5 0.3607116 0.3607116 5.442995e-02 5.442995e-02 0.584858409 #> 269    134.0 0.3605426 0.3605426 5.398810e-02 5.398810e-02 0.585469306 #> 270    134.5 0.3603778 0.3603778 5.354961e-02 5.354961e-02 0.586072563 #> 271    135.0 0.3602173 0.3602173 5.311446e-02 5.311446e-02 0.586668240 #> 272    135.5 0.3600610 0.3600610 5.268264e-02 5.268264e-02 0.587256397 #> 273    136.0 0.3599088 0.3599088 5.225413e-02 5.225413e-02 0.587837092 #> 274    136.5 0.3597607 0.3597607 5.182891e-02 5.182891e-02 0.588410384 #> 275    137.0 0.3596167 0.3596167 5.140697e-02 5.140697e-02 0.588976331 #> 276    137.5 0.3594767 0.3594767 5.098828e-02 5.098828e-02 0.589534992 #> 277    138.0 0.3593408 0.3593408 5.057282e-02 5.057282e-02 0.590086425 #> 278    138.5 0.3592087 0.3592087 5.016058e-02 5.016058e-02 0.590630686 #> 279    139.0 0.3590806 0.3590806 4.975154e-02 4.975154e-02 0.591167832 #> 280    139.5 0.3589564 0.3589564 4.934568e-02 4.934568e-02 0.591697921 #> 281    140.0 0.3588360 0.3588360 4.894298e-02 4.894298e-02 0.592221009 #> 282    140.5 0.3587194 0.3587194 4.854342e-02 4.854342e-02 0.592737151 #> 283    141.0 0.3586066 0.3586066 4.814699e-02 4.814699e-02 0.593246404 #> 284    141.5 0.3584975 0.3584975 4.775366e-02 4.775366e-02 0.593748823 #> 285    142.0 0.3583921 0.3583921 4.736342e-02 4.736342e-02 0.594244463 #> 286    142.5 0.3582904 0.3582904 4.697624e-02 4.697624e-02 0.594733377 #> 287    143.0 0.3581923 0.3581923 4.659212e-02 4.659212e-02 0.595215622 #> 288    143.5 0.3580977 0.3580977 4.621102e-02 4.621102e-02 0.595691250 #> 289    144.0 0.3580067 0.3580067 4.583294e-02 4.583294e-02 0.596160316 #> 290    144.5 0.3579193 0.3579193 4.545784e-02 4.545784e-02 0.596622872 #> 291    145.0 0.3578353 0.3578353 4.508572e-02 4.508572e-02 0.597078972 #> 292    145.5 0.3577548 0.3577548 4.471656e-02 4.471656e-02 0.597528668 #> 293    146.0 0.3576777 0.3576777 4.435033e-02 4.435033e-02 0.597972013 #> 294    146.5 0.3576039 0.3576039 4.398701e-02 4.398701e-02 0.598409058 #> 295    147.0 0.3575336 0.3575336 4.362660e-02 4.362660e-02 0.598839854 #> 296    147.5 0.3574665 0.3574665 4.326906e-02 4.326906e-02 0.599264455 #> 297    148.0 0.3574027 0.3574027 4.291438e-02 4.291438e-02 0.599682909 #> 298    148.5 0.3573422 0.3573422 4.256254e-02 4.256254e-02 0.600095268 #> 299    149.0 0.3572849 0.3572849 4.221353e-02 4.221353e-02 0.600501582 #> 300    149.5 0.3572308 0.3572308 4.186732e-02 4.186732e-02 0.600901900 #> 301    150.0 0.3571798 0.3571798 4.152389e-02 4.152389e-02 0.601296274 #> 302    150.5 0.3571320 0.3571320 4.118323e-02 4.118323e-02 0.601684750 #> 303    151.0 0.3570873 0.3570873 4.084532e-02 4.084532e-02 0.602067379 #> 304    151.5 0.3570457 0.3570457 4.051014e-02 4.051014e-02 0.602444210 #> 305    152.0 0.3570070 0.3570070 4.017767e-02 4.017767e-02 0.602815289 #> 306    152.5 0.3569714 0.3569714 3.984788e-02 3.984788e-02 0.603180666 #> 307    153.0 0.3569388 0.3569388 3.952078e-02 3.952078e-02 0.603540387 #> 308    153.5 0.3569092 0.3569092 3.919632e-02 3.919632e-02 0.603894500 #> 309    154.0 0.3568824 0.3568824 3.887451e-02 3.887451e-02 0.604243052 #> 310    154.5 0.3568586 0.3568586 3.855531e-02 3.855531e-02 0.604586090 #> 311    155.0 0.3568376 0.3568376 3.823871e-02 3.823871e-02 0.604923659 #> 312    155.5 0.3568195 0.3568195 3.792469e-02 3.792469e-02 0.605255805 #> 313    156.0 0.3568042 0.3568042 3.761323e-02 3.761323e-02 0.605582574 #> 314    156.5 0.3567917 0.3567917 3.730432e-02 3.730432e-02 0.605904012 #> 315    157.0 0.3567819 0.3567819 3.699794e-02 3.699794e-02 0.606220163 #> 316    157.5 0.3567749 0.3567749 3.669406e-02 3.669406e-02 0.606531071 #> 317    158.0 0.3567705 0.3567705 3.639268e-02 3.639268e-02 0.606836782 #> 318    158.5 0.3567689 0.3567689 3.609377e-02 3.609377e-02 0.607137338 #> 319    159.0 0.3567699 0.3567699 3.579731e-02 3.579731e-02 0.607432784 #> 320    159.5 0.3567735 0.3567735 3.550329e-02 3.550329e-02 0.607723164 #> 321    160.0 0.3567798 0.3567798 3.521169e-02 3.521169e-02 0.608008519 #> 322    160.5 0.3567886 0.3567886 3.492249e-02 3.492249e-02 0.608288892 #> 323    161.0 0.3568000 0.3568000 3.463568e-02 3.463568e-02 0.608564327 #> 324    161.5 0.3568139 0.3568139 3.435123e-02 3.435123e-02 0.608834864 #> 325    162.0 0.3568303 0.3568303 3.406913e-02 3.406913e-02 0.609100547 #> 326    162.5 0.3568492 0.3568492 3.378936e-02 3.378936e-02 0.609361415 #> 327    163.0 0.3568706 0.3568706 3.351191e-02 3.351191e-02 0.609617511 #> 328    163.5 0.3568944 0.3568944 3.323675e-02 3.323675e-02 0.609868875 #> 329    164.0 0.3569206 0.3569206 3.296387e-02 3.296387e-02 0.610115547 #> 330    164.5 0.3569492 0.3569492 3.269326e-02 3.269326e-02 0.610357569 #> 331    165.0 0.3569801 0.3569801 3.242489e-02 3.242489e-02 0.610594978 #> 332    165.5 0.3570134 0.3570134 3.215875e-02 3.215875e-02 0.610827816 #> 333    166.0 0.3570491 0.3570491 3.189482e-02 3.189482e-02 0.611056122 #> 334    166.5 0.3570870 0.3570870 3.163309e-02 3.163309e-02 0.611279934 #> 335    167.0 0.3571272 0.3571272 3.137354e-02 3.137354e-02 0.611499291 #> 336    167.5 0.3571696 0.3571696 3.111614e-02 3.111614e-02 0.611714232 #> 337    168.0 0.3572143 0.3572143 3.086090e-02 3.086090e-02 0.611924794 #> 338    168.5 0.3572612 0.3572612 3.060778e-02 3.060778e-02 0.612131016 #> 339    169.0 0.3573103 0.3573103 3.035677e-02 3.035677e-02 0.612332935 #> 340    169.5 0.3573615 0.3573615 3.010786e-02 3.010786e-02 0.612530588 #> 341    170.0 0.3574150 0.3574150 2.986103e-02 2.986103e-02 0.612724012 #> 342    170.5 0.3574705 0.3574705 2.961627e-02 2.961627e-02 0.612913244 #> 343    171.0 0.3575281 0.3575281 2.937355e-02 2.937355e-02 0.613098320 #> 344    171.5 0.3575879 0.3575879 2.913287e-02 2.913287e-02 0.613279276 #> 345    172.0 0.3576497 0.3576497 2.889420e-02 2.889420e-02 0.613456148 #> 346    172.5 0.3577135 0.3577135 2.865753e-02 2.865753e-02 0.613628972 #> 347    173.0 0.3577794 0.3577794 2.842284e-02 2.842284e-02 0.613797782 #> 348    173.5 0.3578473 0.3578473 2.819013e-02 2.819013e-02 0.613962614 #> 349    174.0 0.3579171 0.3579171 2.795936e-02 2.795936e-02 0.614123502 #> 350    174.5 0.3579890 0.3579890 2.773054e-02 2.773054e-02 0.614280480 #> 351    175.0 0.3580628 0.3580628 2.750364e-02 2.750364e-02 0.614433584 #> 352    175.5 0.3581385 0.3581385 2.727864e-02 2.727864e-02 0.614582846 #> 353    176.0 0.3582162 0.3582162 2.705554e-02 2.705554e-02 0.614728300 #> 354    176.5 0.3582957 0.3582957 2.683432e-02 2.683432e-02 0.614869980 #> 355    177.0 0.3583771 0.3583771 2.661496e-02 2.661496e-02 0.615007918 #> 356    177.5 0.3584604 0.3584604 2.639744e-02 2.639744e-02 0.615142148 #> 357    178.0 0.3585455 0.3585455 2.618176e-02 2.618176e-02 0.615272702 #> 358    178.5 0.3586325 0.3586325 2.596790e-02 2.596790e-02 0.615399611 #> 359    179.0 0.3587213 0.3587213 2.575584e-02 2.575584e-02 0.615522909 #> 360    179.5 0.3588118 0.3588118 2.554557e-02 2.554557e-02 0.615642627 #> 361    180.0 0.3589041 0.3589041 2.533707e-02 2.533707e-02 0.615758796 #> 362    180.5 0.3589982 0.3589982 2.513034e-02 2.513034e-02 0.615871448 #> 363    181.0 0.3590940 0.3590940 2.492535e-02 2.492535e-02 0.615980613 #> 364    181.5 0.3591916 0.3591916 2.472209e-02 2.472209e-02 0.616086322 #> 365    182.0 0.3592908 0.3592908 2.452055e-02 2.452055e-02 0.616188606 #> 366    182.5 0.3593918 0.3593918 2.432071e-02 2.432071e-02 0.616287495 #> 367    183.0 0.3594944 0.3594944 2.412257e-02 2.412257e-02 0.616383019 #> 368    183.5 0.3595987 0.3595987 2.392610e-02 2.392610e-02 0.616475208 #> 369    184.0 0.3597046 0.3597046 2.373129e-02 2.373129e-02 0.616564090 #> 370    184.5 0.3598122 0.3598122 2.353813e-02 2.353813e-02 0.616649696 #> 371    185.0 0.3599213 0.3599213 2.334661e-02 2.334661e-02 0.616732054 #> 372    185.5 0.3600321 0.3600321 2.315671e-02 2.315671e-02 0.616811194 #> 373    186.0 0.3601444 0.3601444 2.296841e-02 2.296841e-02 0.616887143 #> 374    186.5 0.3602584 0.3602584 2.278172e-02 2.278172e-02 0.616959930 #> 375    187.0 0.3603738 0.3603738 2.259660e-02 2.259660e-02 0.617029583 #> 376    187.5 0.3604908 0.3604908 2.241306e-02 2.241306e-02 0.617096130 #> 377    188.0 0.3606093 0.3606093 2.223107e-02 2.223107e-02 0.617159598 #> 378    188.5 0.3607294 0.3607294 2.205062e-02 2.205062e-02 0.617220015 #> 379    189.0 0.3608509 0.3608509 2.187171e-02 2.187171e-02 0.617277407 #> 380    189.5 0.3609739 0.3609739 2.169431e-02 2.169431e-02 0.617331803 #> 381    190.0 0.3610984 0.3610984 2.151842e-02 2.151842e-02 0.617383228 #> 382    190.5 0.3612243 0.3612243 2.134402e-02 2.134402e-02 0.617431709 #> 383    191.0 0.3613516 0.3613516 2.117110e-02 2.117110e-02 0.617477272 #> 384    191.5 0.3614804 0.3614804 2.099965e-02 2.099965e-02 0.617519944 #> 385    192.0 0.3616106 0.3616106 2.082966e-02 2.082966e-02 0.617559750 #> 386    192.5 0.3617422 0.3617422 2.066111e-02 2.066111e-02 0.617596715 #> 387    193.0 0.3618751 0.3618751 2.049399e-02 2.049399e-02 0.617630865 #> 388    193.5 0.3620095 0.3620095 2.032829e-02 2.032829e-02 0.617662225 #> 389    194.0 0.3621452 0.3621452 2.016400e-02 2.016400e-02 0.617690821 #> 390    194.5 0.3622822 0.3622822 2.000110e-02 2.000110e-02 0.617716676 #> 391    195.0 0.3624206 0.3624206 1.983959e-02 1.983959e-02 0.617739815 #> 392    195.5 0.3625603 0.3625603 1.967945e-02 1.967945e-02 0.617760264 #> 393    196.0 0.3627013 0.3627013 1.952068e-02 1.952068e-02 0.617778045 #> 394    196.5 0.3628436 0.3628436 1.936325e-02 1.936325e-02 0.617793183 #> 395    197.0 0.3629871 0.3629871 1.920716e-02 1.920716e-02 0.617805701 #> 396    197.5 0.3631320 0.3631320 1.905240e-02 1.905240e-02 0.617815624 #> 397    198.0 0.3632781 0.3632781 1.889895e-02 1.889895e-02 0.617822973 #> 398    198.5 0.3634254 0.3634254 1.874681e-02 1.874681e-02 0.617827773 #> 399    199.0 0.3635740 0.3635740 1.859596e-02 1.859596e-02 0.617830047 #> 400    199.5 0.3637238 0.3637238 1.844639e-02 1.844639e-02 0.617829816 #> 401    200.0 0.3638748 0.3638748 1.829810e-02 1.829810e-02 0.617827104 #> 402    200.5 0.3640270 0.3640270 1.815107e-02 1.815107e-02 0.617821933 #> 403    201.0 0.3641804 0.3641804 1.800528e-02 1.800528e-02 0.617814325 #> 404    201.5 0.3643350 0.3643350 1.786074e-02 1.786074e-02 0.617804302 #> 405    202.0 0.3644907 0.3644907 1.771743e-02 1.771743e-02 0.617791886 #> 406    202.5 0.3646476 0.3646476 1.757533e-02 1.757533e-02 0.617777099 #> 407    203.0 0.3648056 0.3648056 1.743444e-02 1.743444e-02 0.617759961 #> 408    203.5 0.3649648 0.3649648 1.729475e-02 1.729475e-02 0.617740494 #> 409    204.0 0.3651250 0.3651250 1.715625e-02 1.715625e-02 0.617718719 #> 410    204.5 0.3652864 0.3652864 1.701893e-02 1.701893e-02 0.617694657 #> 411    205.0 0.3654489 0.3654489 1.688277e-02 1.688277e-02 0.617668329 #> 412    205.5 0.3656125 0.3656125 1.674777e-02 1.674777e-02 0.617639754 #> 413    206.0 0.3657771 0.3657771 1.661392e-02 1.661392e-02 0.617608955 #> 414    206.5 0.3659428 0.3659428 1.648121e-02 1.648121e-02 0.617575950 #> 415    207.0 0.3661096 0.3661096 1.634962e-02 1.634962e-02 0.617540759 #> 416    207.5 0.3662774 0.3662774 1.621915e-02 1.621915e-02 0.617503403 #> 417    208.0 0.3664463 0.3664463 1.608980e-02 1.608980e-02 0.617463901 #> 418    208.5 0.3666162 0.3666162 1.596154e-02 1.596154e-02 0.617422272 #> 419    209.0 0.3667871 0.3667871 1.583437e-02 1.583437e-02 0.617378536 #> 420    209.5 0.3669590 0.3669590 1.570828e-02 1.570828e-02 0.617332712 #> 421    210.0 0.3671319 0.3671319 1.558326e-02 1.558326e-02 0.617284819 #> 422    210.5 0.3673058 0.3673058 1.545931e-02 1.545931e-02 0.617234875 #> 423    211.0 0.3674807 0.3674807 1.533640e-02 1.533640e-02 0.617182900 #> 424    211.5 0.3676565 0.3676565 1.521455e-02 1.521455e-02 0.617128912 #> 425    212.0 0.3678333 0.3678333 1.509372e-02 1.509372e-02 0.617072929 #> 426    212.5 0.3680111 0.3680111 1.497392e-02 1.497392e-02 0.617014969 #> 427    213.0 0.3681898 0.3681898 1.485514e-02 1.485514e-02 0.616955051 #> 428    213.5 0.3683694 0.3683694 1.473737e-02 1.473737e-02 0.616893192 #> 429    214.0 0.3685500 0.3685500 1.462060e-02 1.462060e-02 0.616829409 #> 430    214.5 0.3687315 0.3687315 1.450481e-02 1.450481e-02 0.616763721 #> 431    215.0 0.3689138 0.3689138 1.439001e-02 1.439001e-02 0.616696145 #> 432    215.5 0.3690971 0.3690971 1.427619e-02 1.427619e-02 0.616626698 #> 433    216.0 0.3692813 0.3692813 1.416333e-02 1.416333e-02 0.616555397 #> 434    216.5 0.3694663 0.3694663 1.405142e-02 1.405142e-02 0.616482259 #> 435    217.0 0.3696522 0.3696522 1.394047e-02 1.394047e-02 0.616407301 #> 436    217.5 0.3698390 0.3698390 1.383045e-02 1.383045e-02 0.616330540 #> 437    218.0 0.3700266 0.3700266 1.372137e-02 1.372137e-02 0.616251991 #> 438    218.5 0.3702151 0.3702151 1.361321e-02 1.361321e-02 0.616171672 #> 439    219.0 0.3704044 0.3704044 1.350597e-02 1.350597e-02 0.616089599 #> 440    219.5 0.3705946 0.3705946 1.339964e-02 1.339964e-02 0.616005787 #> 441    220.0 0.3707855 0.3707855 1.329421e-02 1.329421e-02 0.615920253 #> 442    220.5 0.3709773 0.3709773 1.318967e-02 1.318967e-02 0.615833012 #> 443    221.0 0.3711699 0.3711699 1.308601e-02 1.308601e-02 0.615744081 #> 444    221.5 0.3713633 0.3713633 1.298324e-02 1.298324e-02 0.615653474 #> 445    222.0 0.3715575 0.3715575 1.288133e-02 1.288133e-02 0.615561207 #> 446    222.5 0.3717524 0.3717524 1.278028e-02 1.278028e-02 0.615467295 #> 447    223.0 0.3719482 0.3719482 1.268009e-02 1.268009e-02 0.615371754 #> 448    223.5 0.3721447 0.3721447 1.258075e-02 1.258075e-02 0.615274598 #> 449    224.0 0.3723419 0.3723419 1.248224e-02 1.248224e-02 0.615175843 #> 450    224.5 0.3725399 0.3725399 1.238457e-02 1.238457e-02 0.615075502 #> 451    225.0 0.3727387 0.3727387 1.228772e-02 1.228772e-02 0.614973591 #> 452    225.5 0.3729382 0.3729382 1.219170e-02 1.219170e-02 0.614870124 #> 453    226.0 0.3731384 0.3731384 1.209648e-02 1.209648e-02 0.614765115 #> 454    226.5 0.3733394 0.3733394 1.200206e-02 1.200206e-02 0.614658579 #> 455    227.0 0.3735410 0.3735410 1.190845e-02 1.190845e-02 0.614550530 #> 456    227.5 0.3737434 0.3737434 1.181562e-02 1.181562e-02 0.614440982 #> 457    228.0 0.3739465 0.3739465 1.172358e-02 1.172358e-02 0.614329948 #> 458    228.5 0.3741503 0.3741503 1.163231e-02 1.163231e-02 0.614217442 #> 459    229.0 0.3743547 0.3743547 1.154181e-02 1.154181e-02 0.614103479 #> 460    229.5 0.3745599 0.3745599 1.145207e-02 1.145207e-02 0.613988070 #> 461    230.0 0.3747657 0.3747657 1.136309e-02 1.136309e-02 0.613871231 #> 462    230.5 0.3749722 0.3749722 1.127486e-02 1.127486e-02 0.613752973 #> 463    231.0 0.3751793 0.3751793 1.118738e-02 1.118738e-02 0.613633311 #> 464    231.5 0.3753871 0.3753871 1.110062e-02 1.110062e-02 0.613512257 #> 465    232.0 0.3755956 0.3755956 1.101460e-02 1.101460e-02 0.613389824 #> 466    232.5 0.3758047 0.3758047 1.092931e-02 1.092931e-02 0.613266025 #> 467    233.0 0.3760144 0.3760144 1.084473e-02 1.084473e-02 0.613140872 #> 468    233.5 0.3762248 0.3762248 1.076086e-02 1.076086e-02 0.613014379 #> 469    234.0 0.3764358 0.3764358 1.067769e-02 1.067769e-02 0.612886557 #> 470    234.5 0.3766474 0.3766474 1.059523e-02 1.059523e-02 0.612757419 #> 471    235.0 0.3768596 0.3768596 1.051345e-02 1.051345e-02 0.612626978 #> 472    235.5 0.3770724 0.3770724 1.043237e-02 1.043237e-02 0.612495245 #> 473    236.0 0.3772858 0.3772858 1.035196e-02 1.035196e-02 0.612362232 #> 474    236.5 0.3774998 0.3774998 1.027223e-02 1.027223e-02 0.612227952 #> 475    237.0 0.3777144 0.3777144 1.019317e-02 1.019317e-02 0.612092416 #> 476    237.5 0.3779296 0.3779296 1.011477e-02 1.011477e-02 0.611955636 #> 477    238.0 0.3781454 0.3781454 1.003703e-02 1.003703e-02 0.611817623 #> 478    238.5 0.3783617 0.3783617 9.959935e-03 9.959935e-03 0.611678389 #> 479    239.0 0.3785786 0.3785786 9.883490e-03 9.883490e-03 0.611537946 #> 480    239.5 0.3787960 0.3787960 9.807684e-03 9.807684e-03 0.611396305 #> 481    240.0 0.3790140 0.3790140 9.732513e-03 9.732513e-03 0.611253477 #> 482    240.5 0.3792326 0.3792326 9.657971e-03 9.657971e-03 0.611109472 #> 483    241.0 0.3794516 0.3794516 9.584052e-03 9.584052e-03 0.610964303 #> 484    241.5 0.3796713 0.3796713 9.510751e-03 9.510751e-03 0.610817980 #> 485    242.0 0.3798914 0.3798914 9.438062e-03 9.438062e-03 0.610670514 #> 486    242.5 0.3801121 0.3801121 9.365981e-03 9.365981e-03 0.610521915 #> 487    243.0 0.3803333 0.3803333 9.294501e-03 9.294501e-03 0.610372195 #> 488    243.5 0.3805550 0.3805550 9.223618e-03 9.223618e-03 0.610221363 #> 489    244.0 0.3807772 0.3807772 9.153326e-03 9.153326e-03 0.610069431 #> 490    244.5 0.3810000 0.3810000 9.083621e-03 9.083621e-03 0.609916408 #> 491    245.0 0.3812232 0.3812232 9.014497e-03 9.014497e-03 0.609762305 #> 492    245.5 0.3814469 0.3814469 8.945948e-03 8.945948e-03 0.609607132 #> 493    246.0 0.3816711 0.3816711 8.877971e-03 8.877971e-03 0.609450900 #> 494    246.5 0.3818958 0.3818958 8.810560e-03 8.810560e-03 0.609293617 #> 495    247.0 0.3821210 0.3821210 8.743709e-03 8.743709e-03 0.609135295 #> 496    247.5 0.3823466 0.3823466 8.677415e-03 8.677415e-03 0.608975942 #> 497    248.0 0.3825728 0.3825728 8.611672e-03 8.611672e-03 0.608815569 #> 498    248.5 0.3827993 0.3827993 8.546476e-03 8.546476e-03 0.608654185 #> 499    249.0 0.3830264 0.3830264 8.481821e-03 8.481821e-03 0.608491801 #> 500    249.5 0.3832539 0.3832539 8.417703e-03 8.417703e-03 0.608328424 #> 501    250.0 0.3834818 0.3834818 8.354117e-03 8.354117e-03 0.608164065 #> 502    250.5 0.3837102 0.3837102 8.291059e-03 8.291059e-03 0.607998734 #> 503    251.0 0.3839390 0.3839390 8.228524e-03 8.228524e-03 0.607832438 #> 504    251.5 0.3841683 0.3841683 8.166508e-03 8.166508e-03 0.607665188 #> 505    252.0 0.3843980 0.3843980 8.105005e-03 8.105005e-03 0.607496993 #> 506    252.5 0.3846281 0.3846281 8.044012e-03 8.044012e-03 0.607327861 #> 507    253.0 0.3848587 0.3848587 7.983524e-03 7.983524e-03 0.607157802 #> 508    253.5 0.3850896 0.3850896 7.923536e-03 7.923536e-03 0.606986824 #> 509    254.0 0.3853210 0.3853210 7.864044e-03 7.864044e-03 0.606814936 #> 510    254.5 0.3855528 0.3855528 7.805045e-03 7.805045e-03 0.606642147 #> 511    255.0 0.3857850 0.3857850 7.746532e-03 7.746532e-03 0.606468465 #> 512    255.5 0.3860176 0.3860176 7.688504e-03 7.688504e-03 0.606293899 #> 513    256.0 0.3862506 0.3862506 7.630954e-03 7.630954e-03 0.606118457 #> 514    256.5 0.3864840 0.3864840 7.573879e-03 7.573879e-03 0.605942148 #> 515    257.0 0.3867177 0.3867177 7.517275e-03 7.517275e-03 0.605764980 #> 516    257.5 0.3869519 0.3869519 7.461138e-03 7.461138e-03 0.605586962 #> 517    258.0 0.3871864 0.3871864 7.405463e-03 7.405463e-03 0.605408101 #> 518    258.5 0.3874213 0.3874213 7.350247e-03 7.350247e-03 0.605228405 #> 519    259.0 0.3876566 0.3876566 7.295486e-03 7.295486e-03 0.605047883 #> 520    259.5 0.3878923 0.3878923 7.241175e-03 7.241175e-03 0.604866543 #> 521    260.0 0.3881283 0.3881283 7.187310e-03 7.187310e-03 0.604684392 #> 522    260.5 0.3883647 0.3883647 7.133889e-03 7.133889e-03 0.604501438 #> 523    261.0 0.3886014 0.3886014 7.080906e-03 7.080906e-03 0.604317689 #> 524    261.5 0.3888385 0.3888385 7.028359e-03 7.028359e-03 0.604133153 #> 525    262.0 0.3890759 0.3890759 6.976243e-03 6.976243e-03 0.603947837 #> 526    262.5 0.3893137 0.3893137 6.924555e-03 6.924555e-03 0.603761749 #> 527    263.0 0.3895518 0.3895518 6.873290e-03 6.873290e-03 0.603574896 #> 528    263.5 0.3897903 0.3897903 6.822445e-03 6.822445e-03 0.603387286 #> 529    264.0 0.3900291 0.3900291 6.772018e-03 6.772018e-03 0.603198926 #> 530    264.5 0.3902682 0.3902682 6.722003e-03 6.722003e-03 0.603009824 #> 531    265.0 0.3905076 0.3905076 6.672397e-03 6.672397e-03 0.602819986 #> 532    265.5 0.3907474 0.3907474 6.623197e-03 6.623197e-03 0.602629419 #> 533    266.0 0.3909875 0.3909875 6.574399e-03 6.574399e-03 0.602438132 #> 534    266.5 0.3912279 0.3912279 6.526000e-03 6.526000e-03 0.602246130 #> 535    267.0 0.3914686 0.3914686 6.477996e-03 6.477996e-03 0.602053421 #> 536    267.5 0.3917096 0.3917096 6.430384e-03 6.430384e-03 0.601860012 #> 537    268.0 0.3919509 0.3919509 6.383161e-03 6.383161e-03 0.601665910 #> 538    268.5 0.3921926 0.3921926 6.336322e-03 6.336322e-03 0.601471121 #> 539    269.0 0.3924345 0.3924345 6.289865e-03 6.289865e-03 0.601275652 #> 540    269.5 0.3926767 0.3926767 6.243787e-03 6.243787e-03 0.601079509 #> 541    270.0 0.3929192 0.3929192 6.198084e-03 6.198084e-03 0.600882701 #> 542    270.5 0.3931620 0.3931620 6.152752e-03 6.152752e-03 0.600685232 #> 543    271.0 0.3934051 0.3934051 6.107790e-03 6.107790e-03 0.600487109 #> 544    271.5 0.3936485 0.3936485 6.063192e-03 6.063192e-03 0.600288340 #> 545    272.0 0.3938921 0.3938921 6.018957e-03 6.018957e-03 0.600088929 #> 546    272.5 0.3941360 0.3941360 5.975081e-03 5.975081e-03 0.599888885 #> 547    273.0 0.3943802 0.3943802 5.931562e-03 5.931562e-03 0.599688212 #> 548    273.5 0.3946247 0.3946247 5.888395e-03 5.888395e-03 0.599486917 #> 549    274.0 0.3948694 0.3948694 5.845578e-03 5.845578e-03 0.599285007 #> 550    274.5 0.3951144 0.3951144 5.803108e-03 5.803108e-03 0.599082487 #> 551    275.0 0.3953597 0.3953597 5.760981e-03 5.760981e-03 0.598879363 #> 552    275.5 0.3956052 0.3956052 5.719196e-03 5.719196e-03 0.598675642 #> 553    276.0 0.3958509 0.3958509 5.677748e-03 5.677748e-03 0.598471330 #> 554    276.5 0.3960969 0.3960969 5.636636e-03 5.636636e-03 0.598266431 #> 555    277.0 0.3963432 0.3963432 5.595856e-03 5.595856e-03 0.598060953 #> 556    277.5 0.3965897 0.3965897 5.555405e-03 5.555405e-03 0.597854901 #> 557    278.0 0.3968364 0.3968364 5.515280e-03 5.515280e-03 0.597648280 #> 558    278.5 0.3970834 0.3970834 5.475479e-03 5.475479e-03 0.597441097 #> 559    279.0 0.3973306 0.3973306 5.435999e-03 5.435999e-03 0.597233357 #> 560    279.5 0.3975781 0.3975781 5.396836e-03 5.396836e-03 0.597025065 #> 561    280.0 0.3978258 0.3978258 5.357990e-03 5.357990e-03 0.596816228 #> 562    280.5 0.3980737 0.3980737 5.319455e-03 5.319455e-03 0.596606851 #> 563    281.0 0.3983218 0.3983218 5.281231e-03 5.281231e-03 0.596396938 #> 564    281.5 0.3985702 0.3985702 5.243314e-03 5.243314e-03 0.596186497 #> 565    282.0 0.3988188 0.3988188 5.205701e-03 5.205701e-03 0.595975531 #> 566    282.5 0.3990676 0.3990676 5.168390e-03 5.168390e-03 0.595764046 #> 567    283.0 0.3993166 0.3993166 5.131379e-03 5.131379e-03 0.595552048 #> 568    283.5 0.3995658 0.3995658 5.094664e-03 5.094664e-03 0.595339542 #> 569    284.0 0.3998152 0.3998152 5.058244e-03 5.058244e-03 0.595126532 #> 570    284.5 0.4000649 0.4000649 5.022115e-03 5.022115e-03 0.594913025 #> 571    285.0 0.4003147 0.4003147 4.986276e-03 4.986276e-03 0.594699025 #> 572    285.5 0.4005647 0.4005647 4.950723e-03 4.950723e-03 0.594484537 #> 573    286.0 0.4008150 0.4008150 4.915454e-03 4.915454e-03 0.594269566 #> 574    286.5 0.4010654 0.4010654 4.880467e-03 4.880467e-03 0.594054118 #> 575    287.0 0.4013160 0.4013160 4.845760e-03 4.845760e-03 0.593838196 #> 576    287.5 0.4015669 0.4015669 4.811330e-03 4.811330e-03 0.593621806 #> 577    288.0 0.4018179 0.4018179 4.777174e-03 4.777174e-03 0.593404953 #> 578    288.5 0.4020691 0.4020691 4.743290e-03 4.743290e-03 0.593187642 #> 579    289.0 0.4023204 0.4023204 4.709677e-03 4.709677e-03 0.592969877 #> 580    289.5 0.4025720 0.4025720 4.676331e-03 4.676331e-03 0.592751663 #> 581    290.0 0.4028237 0.4028237 4.643250e-03 4.643250e-03 0.592533005 #> 582    290.5 0.4030757 0.4030757 4.610432e-03 4.610432e-03 0.592313906 #> 583    291.0 0.4033278 0.4033278 4.577875e-03 4.577875e-03 0.592094373 #> 584    291.5 0.4035800 0.4035800 4.545577e-03 4.545577e-03 0.591874409 #> 585    292.0 0.4038324 0.4038324 4.513535e-03 4.513535e-03 0.591654020 #> 586    292.5 0.4040850 0.4040850 4.481747e-03 4.481747e-03 0.591433208 #> 587    293.0 0.4043378 0.4043378 4.450211e-03 4.450211e-03 0.591211979 #> 588    293.5 0.4045907 0.4045907 4.418925e-03 4.418925e-03 0.590990338 #> 589    294.0 0.4048438 0.4048438 4.387887e-03 4.387887e-03 0.590768288 #> 590    294.5 0.4050971 0.4050971 4.357094e-03 4.357094e-03 0.590545834 #> 591    295.0 0.4053505 0.4053505 4.326545e-03 4.326545e-03 0.590322980 #> 592    295.5 0.4056040 0.4056040 4.296237e-03 4.296237e-03 0.590099731 #> 593    296.0 0.4058577 0.4058577 4.266169e-03 4.266169e-03 0.589876090 #> 594    296.5 0.4061116 0.4061116 4.236338e-03 4.236338e-03 0.589652061 #> 595    297.0 0.4063656 0.4063656 4.206742e-03 4.206742e-03 0.589427650 #> 596    297.5 0.4066198 0.4066198 4.177379e-03 4.177379e-03 0.589202860 #> 597    298.0 0.4068741 0.4068741 4.148248e-03 4.148248e-03 0.588977695 #> 598    298.5 0.4071285 0.4071285 4.119346e-03 4.119346e-03 0.588752158 #> 599    299.0 0.4073831 0.4073831 4.090672e-03 4.090672e-03 0.588526255 #> 600    299.5 0.4076378 0.4076378 4.062223e-03 4.062223e-03 0.588299989 #> 601    300.0 0.4078926 0.4078926 4.033997e-03 4.033997e-03 0.588073364 #> 602    300.5 0.4081476 0.4081476 4.005993e-03 4.005993e-03 0.587846384 #> 603    301.0 0.4084027 0.4084027 3.978209e-03 3.978209e-03 0.587619052 #> 604    301.5 0.4086580 0.4086580 3.950643e-03 3.950643e-03 0.587391373 #> 605    302.0 0.4089134 0.4089134 3.923293e-03 3.923293e-03 0.587163350 #> 606    302.5 0.4091689 0.4091689 3.896157e-03 3.896157e-03 0.586934987 #> 607    303.0 0.4094245 0.4094245 3.869234e-03 3.869234e-03 0.586706288 #> 608    303.5 0.4096802 0.4096802 3.842521e-03 3.842521e-03 0.586477257 #> 609    304.0 0.4099361 0.4099361 3.816017e-03 3.816017e-03 0.586247896 #> 610    304.5 0.4101921 0.4101921 3.789720e-03 3.789720e-03 0.586018211 #> 611    305.0 0.4104482 0.4104482 3.763628e-03 3.763628e-03 0.585788203 #> 612    305.5 0.4107044 0.4107044 3.737740e-03 3.737740e-03 0.585557878 #> 613    306.0 0.4109607 0.4109607 3.712054e-03 3.712054e-03 0.585327238 #> 614    306.5 0.4112171 0.4112171 3.686568e-03 3.686568e-03 0.585096287 #> 615    307.0 0.4114737 0.4114737 3.661280e-03 3.661280e-03 0.584865028 #> 616    307.5 0.4117303 0.4117303 3.636189e-03 3.636189e-03 0.584633466 #> 617    308.0 0.4119871 0.4119871 3.611293e-03 3.611293e-03 0.584401602 #> 618    308.5 0.4122440 0.4122440 3.586591e-03 3.586591e-03 0.584169442 #> 619    309.0 0.4125009 0.4125009 3.562081e-03 3.562081e-03 0.583936987 #> 620    309.5 0.4127580 0.4127580 3.537761e-03 3.537761e-03 0.583704242 #> 621    310.0 0.4130152 0.4130152 3.513629e-03 3.513629e-03 0.583471209 #> 622    310.5 0.4132724 0.4132724 3.489685e-03 3.489685e-03 0.583237893 #> 623    311.0 0.4135298 0.4135298 3.465926e-03 3.465926e-03 0.583004295 #> 624    311.5 0.4137872 0.4137872 3.442350e-03 3.442350e-03 0.582770420 #> 625    312.0 0.4140448 0.4140448 3.418958e-03 3.418958e-03 0.582536271 #> 626    312.5 0.4143024 0.4143024 3.395746e-03 3.395746e-03 0.582301850 #> 627    313.0 0.4145601 0.4145601 3.372713e-03 3.372713e-03 0.582067162 #> 628    313.5 0.4148179 0.4148179 3.349858e-03 3.349858e-03 0.581832208 #> 629    314.0 0.4150758 0.4150758 3.327180e-03 3.327180e-03 0.581596992 #> 630    314.5 0.4153338 0.4153338 3.304676e-03 3.304676e-03 0.581361518 #> 631    315.0 0.4155919 0.4155919 3.282346e-03 3.282346e-03 0.581125788 #> 632    315.5 0.4158500 0.4158500 3.260188e-03 3.260188e-03 0.580889805 #> 633    316.0 0.4161082 0.4161082 3.238200e-03 3.238200e-03 0.580653572 #> 634    316.5 0.4163665 0.4163665 3.216381e-03 3.216381e-03 0.580417092 #> 635    317.0 0.4166249 0.4166249 3.194730e-03 3.194730e-03 0.580180368 #> 636    317.5 0.4168834 0.4168834 3.173245e-03 3.173245e-03 0.579943404 #> 637    318.0 0.4171419 0.4171419 3.151925e-03 3.151925e-03 0.579706201 #> 638    318.5 0.4174005 0.4174005 3.130769e-03 3.130769e-03 0.579468763 #> 639    319.0 0.4176591 0.4176591 3.109774e-03 3.109774e-03 0.579231092 #> 640    319.5 0.4179179 0.4179179 3.088941e-03 3.088941e-03 0.578993192 #> 641    320.0 0.4181767 0.4181767 3.068266e-03 3.068266e-03 0.578755064 #> 642    320.5 0.4184355 0.4184355 3.047750e-03 3.047750e-03 0.578516713 #> 643    321.0 0.4186945 0.4186945 3.027391e-03 3.027391e-03 0.578278140 #> 644    321.5 0.4189535 0.4189535 3.007187e-03 3.007187e-03 0.578039349 #> 645    322.0 0.4192125 0.4192125 2.987137e-03 2.987137e-03 0.577800341 #> 646    322.5 0.4194716 0.4194716 2.967240e-03 2.967240e-03 0.577561120 #> 647    323.0 0.4197308 0.4197308 2.947495e-03 2.947495e-03 0.577321689 #> 648    323.5 0.4199901 0.4199901 2.927900e-03 2.927900e-03 0.577082049 #> 649    324.0 0.4202493 0.4202493 2.908455e-03 2.908455e-03 0.576842205 #> 650    324.5 0.4205087 0.4205087 2.889157e-03 2.889157e-03 0.576602157 #> 651    325.0 0.4207681 0.4207681 2.870006e-03 2.870006e-03 0.576361908 #> 652    325.5 0.4210275 0.4210275 2.851000e-03 2.851000e-03 0.576121462 #> 653    326.0 0.4212870 0.4212870 2.832138e-03 2.832138e-03 0.575880821 #> 654    326.5 0.4215466 0.4215466 2.813420e-03 2.813420e-03 0.575639987 #> 655    327.0 0.4218062 0.4218062 2.794843e-03 2.794843e-03 0.575398963 #> 656    327.5 0.4220658 0.4220658 2.776407e-03 2.776407e-03 0.575157750 #> 657    328.0 0.4223255 0.4223255 2.758111e-03 2.758111e-03 0.574916353 #> 658    328.5 0.4225853 0.4225853 2.739953e-03 2.739953e-03 0.574674772 #> 659    329.0 0.4228451 0.4228451 2.721932e-03 2.721932e-03 0.574433010 #> 660    329.5 0.4231049 0.4231049 2.704047e-03 2.704047e-03 0.574191071 #> 661    330.0 0.4233647 0.4233647 2.686297e-03 2.686297e-03 0.573948955 #> 662    330.5 0.4236247 0.4236247 2.668682e-03 2.668682e-03 0.573706666 #> 663    331.0 0.4238846 0.4238846 2.651198e-03 2.651198e-03 0.573464205 #> 664    331.5 0.4241446 0.4241446 2.633847e-03 2.633847e-03 0.573221576 #> 665    332.0 0.4244046 0.4244046 2.616626e-03 2.616626e-03 0.572978779 #> 666    332.5 0.4246646 0.4246646 2.599534e-03 2.599534e-03 0.572735818 #> 667    333.0 0.4249247 0.4249247 2.582571e-03 2.582571e-03 0.572492695 #> 668    333.5 0.4251849 0.4251849 2.565736e-03 2.565736e-03 0.572249412 #> 669    334.0 0.4254450 0.4254450 2.549026e-03 2.549026e-03 0.572005971 #> 670    334.5 0.4257052 0.4257052 2.532442e-03 2.532442e-03 0.571762374 #> 671    335.0 0.4259654 0.4259654 2.515982e-03 2.515982e-03 0.571518624 #> 672    335.5 0.4262256 0.4262256 2.499646e-03 2.499646e-03 0.571274723 #> 673    336.0 0.4264859 0.4264859 2.483432e-03 2.483432e-03 0.571030672 #> 674    336.5 0.4267462 0.4267462 2.467339e-03 2.467339e-03 0.570786474 #> 675    337.0 0.4270065 0.4270065 2.451366e-03 2.451366e-03 0.570542131 #> 676    337.5 0.4272668 0.4272668 2.435512e-03 2.435512e-03 0.570297644 #> 677    338.0 0.4275272 0.4275272 2.419777e-03 2.419777e-03 0.570053017 #> 678    338.5 0.4277876 0.4277876 2.404159e-03 2.404159e-03 0.569808251 #> 679    339.0 0.4280480 0.4280480 2.388657e-03 2.388657e-03 0.569563348 #> 680    339.5 0.4283084 0.4283084 2.373271e-03 2.373271e-03 0.569318311 #> 681    340.0 0.4285689 0.4285689 2.357999e-03 2.357999e-03 0.569073140 #> 682    340.5 0.4288293 0.4288293 2.342841e-03 2.342841e-03 0.568827838 #> 683    341.0 0.4290898 0.4290898 2.327795e-03 2.327795e-03 0.568582407 #> 684    341.5 0.4293503 0.4293503 2.312861e-03 2.312861e-03 0.568336849 #> 685    342.0 0.4296108 0.4296108 2.298038e-03 2.298038e-03 0.568091166 #> 686    342.5 0.4298713 0.4298713 2.283325e-03 2.283325e-03 0.567845360 #> 687    343.0 0.4301318 0.4301318 2.268720e-03 2.268720e-03 0.567599432 #> 688    343.5 0.4303924 0.4303924 2.254224e-03 2.254224e-03 0.567353385 #> 689    344.0 0.4306529 0.4306529 2.239835e-03 2.239835e-03 0.567107221 #> 690    344.5 0.4309135 0.4309135 2.225552e-03 2.225552e-03 0.566860940 #> 691    345.0 0.4311741 0.4311741 2.211375e-03 2.211375e-03 0.566614545 #> 692    345.5 0.4314347 0.4314347 2.197303e-03 2.197303e-03 0.566368038 #> 693    346.0 0.4316952 0.4316952 2.183334e-03 2.183334e-03 0.566121421 #> 694    346.5 0.4319558 0.4319558 2.169468e-03 2.169468e-03 0.565874695 #> 695    347.0 0.4322164 0.4322164 2.155704e-03 2.155704e-03 0.565627863 #> 696    347.5 0.4324770 0.4324770 2.142042e-03 2.142042e-03 0.565380925 #> 697    348.0 0.4327376 0.4327376 2.128480e-03 2.128480e-03 0.565133884 #> 698    348.5 0.4329982 0.4329982 2.115017e-03 2.115017e-03 0.564886741 #> 699    349.0 0.4332588 0.4332588 2.101654e-03 2.101654e-03 0.564639498 #> 700    349.5 0.4335195 0.4335195 2.088388e-03 2.088388e-03 0.564392157 #> 701    350.0 0.4337801 0.4337801 2.075220e-03 2.075220e-03 0.564144720 #> 702    350.5 0.4340407 0.4340407 2.062148e-03 2.062148e-03 0.563897187 #> 703    351.0 0.4343013 0.4343013 2.049172e-03 2.049172e-03 0.563649561 #> 704    351.5 0.4345619 0.4345619 2.036291e-03 2.036291e-03 0.563401844 #> 705    352.0 0.4348225 0.4348225 2.023504e-03 2.023504e-03 0.563154036 #> 706    352.5 0.4350830 0.4350830 2.010810e-03 2.010810e-03 0.562906140 #> 707    353.0 0.4353436 0.4353436 1.998209e-03 1.998209e-03 0.562658158 #> 708    353.5 0.4356042 0.4356042 1.985700e-03 1.985700e-03 0.562410090 #> 709    354.0 0.4358648 0.4358648 1.973282e-03 1.973282e-03 0.562161938 #> 710    354.5 0.4361253 0.4361253 1.960955e-03 1.960955e-03 0.561913704 #> 711    355.0 0.4363859 0.4363859 1.948717e-03 1.948717e-03 0.561665390 #> 712    355.5 0.4366464 0.4366464 1.936568e-03 1.936568e-03 0.561416997 #> 713    356.0 0.4369070 0.4369070 1.924508e-03 1.924508e-03 0.561168526 #> 714    356.5 0.4371675 0.4371675 1.912535e-03 1.912535e-03 0.560919979 #> 715    357.0 0.4374280 0.4374280 1.900649e-03 1.900649e-03 0.560671358 #> 716    357.5 0.4376885 0.4376885 1.888849e-03 1.888849e-03 0.560422663 #> 717    358.0 0.4379490 0.4379490 1.877134e-03 1.877134e-03 0.560173897 #> 718    358.5 0.4382094 0.4382094 1.865505e-03 1.865505e-03 0.559925061 #> 719    359.0 0.4384699 0.4384699 1.853959e-03 1.853959e-03 0.559676156 #> 720    359.5 0.4387303 0.4387303 1.842497e-03 1.842497e-03 0.559427184 #> 721    360.0 0.4389907 0.4389907 1.831118e-03 1.831118e-03 0.559178146 #> 722    360.5 0.4392511 0.4392511 1.819821e-03 1.819821e-03 0.558929043 #> 723    361.0 0.4395115 0.4395115 1.808605e-03 1.808605e-03 0.558679878 #> 724    361.5 0.4397719 0.4397719 1.797470e-03 1.797470e-03 0.558430651 #> 725    362.0 0.4400322 0.4400322 1.786416e-03 1.786416e-03 0.558181363 #> 726    362.5 0.4402925 0.4402925 1.775441e-03 1.775441e-03 0.557932017 #> 727    363.0 0.4405528 0.4405528 1.764544e-03 1.764544e-03 0.557682613 #> 728    363.5 0.4408131 0.4408131 1.753726e-03 1.753726e-03 0.557433153 #> 729    364.0 0.4410734 0.4410734 1.742986e-03 1.742986e-03 0.557183638 #> 730    364.5 0.4413336 0.4413336 1.732323e-03 1.732323e-03 0.556934069 #> 731    365.0 0.4415938 0.4415938 1.721736e-03 1.721736e-03 0.556684448 #> 732    365.5 0.4418540 0.4418540 1.711225e-03 1.711225e-03 0.556434777 #> 733    366.0 0.4421142 0.4421142 1.700789e-03 1.700789e-03 0.556185055 #> 734    366.5 0.4423743 0.4423743 1.690428e-03 1.690428e-03 0.555935285 #> 735    367.0 0.4426344 0.4426344 1.680141e-03 1.680141e-03 0.555685468 #> 736    367.5 0.4428945 0.4428945 1.669928e-03 1.669928e-03 0.555435605 #> 737    368.0 0.4431545 0.4431545 1.659787e-03 1.659787e-03 0.555185697 #> 738    368.5 0.4434145 0.4434145 1.649719e-03 1.649719e-03 0.554935745 #> 739    369.0 0.4436745 0.4436745 1.639722e-03 1.639722e-03 0.554685752 #> 740    369.5 0.4439345 0.4439345 1.629796e-03 1.629796e-03 0.554435718 #> 741    370.0 0.4441944 0.4441944 1.619942e-03 1.619942e-03 0.554185643 #> 742    370.5 0.4444543 0.4444543 1.610157e-03 1.610157e-03 0.553935530 #> 743    371.0 0.4447142 0.4447142 1.600441e-03 1.600441e-03 0.553685380 #> 744    371.5 0.4449740 0.4449740 1.590795e-03 1.590795e-03 0.553435193 #> 745    372.0 0.4452338 0.4452338 1.581217e-03 1.581217e-03 0.553184972 #> 746    372.5 0.4454936 0.4454936 1.571707e-03 1.571707e-03 0.552934716 #> 747    373.0 0.4457533 0.4457533 1.562264e-03 1.562264e-03 0.552684428 #> 748    373.5 0.4460130 0.4460130 1.552888e-03 1.552888e-03 0.552434107 #> 749    374.0 0.4462727 0.4462727 1.543579e-03 1.543579e-03 0.552183757 #> 750    374.5 0.4465323 0.4465323 1.534335e-03 1.534335e-03 0.551933377 #> 751    375.0 0.4467919 0.4467919 1.525156e-03 1.525156e-03 0.551682968 #> 752    375.5 0.4470514 0.4470514 1.516042e-03 1.516042e-03 0.551432533 #> 753    376.0 0.4473109 0.4473109 1.506993e-03 1.506993e-03 0.551182071 #> 754    376.5 0.4475704 0.4475704 1.498007e-03 1.498007e-03 0.550931584 #> 755    377.0 0.4478298 0.4478298 1.489084e-03 1.489084e-03 0.550681073 #> 756    377.5 0.4480892 0.4480892 1.480224e-03 1.480224e-03 0.550430539 #> 757    378.0 0.4483486 0.4483486 1.471427e-03 1.471427e-03 0.550179984 #> 758    378.5 0.4486079 0.4486079 1.462691e-03 1.462691e-03 0.549929407 #> 759    379.0 0.4488672 0.4488672 1.454016e-03 1.454016e-03 0.549678811 #> 760    379.5 0.4491264 0.4491264 1.445402e-03 1.445402e-03 0.549428196 #> 761    380.0 0.4493856 0.4493856 1.436849e-03 1.436849e-03 0.549177563 #> 762    380.5 0.4496447 0.4496447 1.428355e-03 1.428355e-03 0.548926914 #> 763    381.0 0.4499038 0.4499038 1.419921e-03 1.419921e-03 0.548676249 #> 764    381.5 0.4501629 0.4501629 1.411546e-03 1.411546e-03 0.548425569 #> 765    382.0 0.4504219 0.4504219 1.403229e-03 1.403229e-03 0.548174875 #> 766    382.5 0.4506809 0.4506809 1.394970e-03 1.394970e-03 0.547924168 #> 767    383.0 0.4509398 0.4509398 1.386769e-03 1.386769e-03 0.547673450 #> 768    383.5 0.4511987 0.4511987 1.378625e-03 1.378625e-03 0.547422721 #> 769    384.0 0.4514575 0.4514575 1.370538e-03 1.370538e-03 0.547171982 #> 770    384.5 0.4517163 0.4517163 1.362507e-03 1.362507e-03 0.546921234 #> 771    385.0 0.4519750 0.4519750 1.354532e-03 1.354532e-03 0.546670478 #> 772    385.5 0.4522337 0.4522337 1.346612e-03 1.346612e-03 0.546419715 #> 773    386.0 0.4524923 0.4524923 1.338747e-03 1.338747e-03 0.546168945 #> 774    386.5 0.4527509 0.4527509 1.330937e-03 1.330937e-03 0.545918171 #> 775    387.0 0.4530094 0.4530094 1.323181e-03 1.323181e-03 0.545667392 #> 776    387.5 0.4532679 0.4532679 1.315478e-03 1.315478e-03 0.545416610 #> 777    388.0 0.4535263 0.4535263 1.307829e-03 1.307829e-03 0.545165825 #> 778    388.5 0.4537847 0.4537847 1.300233e-03 1.300233e-03 0.544915039 #> 779    389.0 0.4540431 0.4540431 1.292689e-03 1.292689e-03 0.544664251 #> 780    389.5 0.4543013 0.4543013 1.285197e-03 1.285197e-03 0.544413464 #> 781    390.0 0.4545596 0.4545596 1.277757e-03 1.277757e-03 0.544162678 #> 782    390.5 0.4548177 0.4548177 1.270368e-03 1.270368e-03 0.543911894 #> 783    391.0 0.4550759 0.4550759 1.263030e-03 1.263030e-03 0.543661112 #> 784    391.5 0.4553339 0.4553339 1.255743e-03 1.255743e-03 0.543410334 #> 785    392.0 0.4555919 0.4555919 1.248505e-03 1.248505e-03 0.543159560 #> 786    392.5 0.4558499 0.4558499 1.241318e-03 1.241318e-03 0.542908792 #> 787    393.0 0.4561078 0.4561078 1.234179e-03 1.234179e-03 0.542658029 #> 788    393.5 0.4563656 0.4563656 1.227090e-03 1.227090e-03 0.542407273 #> 789    394.0 0.4566234 0.4566234 1.220049e-03 1.220049e-03 0.542156525 #> 790    394.5 0.4568812 0.4568812 1.213057e-03 1.213057e-03 0.541905785 #> 791    395.0 0.4571388 0.4571388 1.206112e-03 1.206112e-03 0.541655055 #> 792    395.5 0.4573965 0.4573965 1.199215e-03 1.199215e-03 0.541404334 #> 793    396.0 0.4576540 0.4576540 1.192365e-03 1.192365e-03 0.541153624 #> 794    396.5 0.4579115 0.4579115 1.185561e-03 1.185561e-03 0.540902926 #> 795    397.0 0.4581690 0.4581690 1.178804e-03 1.178804e-03 0.540652240 #> 796    397.5 0.4584263 0.4584263 1.172093e-03 1.172093e-03 0.540401568 #> 797    398.0 0.4586837 0.4586837 1.165428e-03 1.165428e-03 0.540150909 #> 798    398.5 0.4589409 0.4589409 1.158808e-03 1.158808e-03 0.539900265 #> 799    399.0 0.4591981 0.4591981 1.152234e-03 1.152234e-03 0.539649636 #> 800    399.5 0.4594553 0.4594553 1.145703e-03 1.145703e-03 0.539399023 #> 801    400.0 0.4597124 0.4597124 1.139218e-03 1.139218e-03 0.539148427 #> 802    400.5 0.4599694 0.4599694 1.132776e-03 1.132776e-03 0.538897849 #> 803    401.0 0.4602263 0.4602263 1.126378e-03 1.126378e-03 0.538647289 #> 804    401.5 0.4604832 0.4604832 1.120023e-03 1.120023e-03 0.538396748 #> 805    402.0 0.4607401 0.4607401 1.113711e-03 1.113711e-03 0.538146226 #> 806    402.5 0.4609968 0.4609968 1.107442e-03 1.107442e-03 0.537895725 #> 807    403.0 0.4612535 0.4612535 1.101215e-03 1.101215e-03 0.537645246 #> 808    403.5 0.4615102 0.4615102 1.095030e-03 1.095030e-03 0.537394788 #> 809    404.0 0.4617668 0.4617668 1.088887e-03 1.088887e-03 0.537144352 #> 810    404.5 0.4620233 0.4620233 1.082786e-03 1.082786e-03 0.536893940 #> 811    405.0 0.4622797 0.4622797 1.076725e-03 1.076725e-03 0.536643551 #> 812    405.5 0.4625361 0.4625361 1.070705e-03 1.070705e-03 0.536393187 #> 813    406.0 0.4627924 0.4627924 1.064726e-03 1.064726e-03 0.536142848 #> 814    406.5 0.4630487 0.4630487 1.058787e-03 1.058787e-03 0.535892535 #> 815    407.0 0.4633049 0.4633049 1.052888e-03 1.052888e-03 0.535642249 #> 816    407.5 0.4635610 0.4635610 1.047028e-03 1.047028e-03 0.535391990 #> 817    408.0 0.4638170 0.4638170 1.041208e-03 1.041208e-03 0.535141758 #> 818    408.5 0.4640730 0.4640730 1.035427e-03 1.035427e-03 0.534891555 #> 819    409.0 0.4643289 0.4643289 1.029684e-03 1.029684e-03 0.534641381 #> 820    409.5 0.4645848 0.4645848 1.023980e-03 1.023980e-03 0.534391237 #> 821    410.0 0.4648406 0.4648406 1.018313e-03 1.018313e-03 0.534141123 #> 822    410.5 0.4650963 0.4650963 1.012685e-03 1.012685e-03 0.533891039 #> 823    411.0 0.4653519 0.4653519 1.007094e-03 1.007094e-03 0.533640988 #> 824    411.5 0.4656075 0.4656075 1.001541e-03 1.001541e-03 0.533390969 #> 825    412.0 0.4658630 0.4658630 9.960242e-04 9.960242e-04 0.533140982 #> 826    412.5 0.4661184 0.4661184 9.905443e-04 9.905443e-04 0.532891029 #> 827    413.0 0.4663738 0.4663738 9.851010e-04 9.851010e-04 0.532641109 #> 828    413.5 0.4666291 0.4666291 9.796937e-04 9.796937e-04 0.532391224 #> 829    414.0 0.4668843 0.4668843 9.743224e-04 9.743224e-04 0.532141375 #> 830    414.5 0.4671395 0.4671395 9.689867e-04 9.689867e-04 0.531891561 #> 831    415.0 0.4673945 0.4673945 9.636864e-04 9.636864e-04 0.531641783 #> 832    415.5 0.4676495 0.4676495 9.584211e-04 9.584211e-04 0.531392042 #> 833    416.0 0.4679045 0.4679045 9.531907e-04 9.531907e-04 0.531142338 #> 834    416.5 0.4681593 0.4681593 9.479949e-04 9.479949e-04 0.530892673 #> 835    417.0 0.4684141 0.4684141 9.428335e-04 9.428335e-04 0.530643046 #> 836    417.5 0.4686688 0.4686688 9.377061e-04 9.377061e-04 0.530393458 #> 837    418.0 0.4689235 0.4689235 9.326125e-04 9.326125e-04 0.530143909 #> 838    418.5 0.4691780 0.4691780 9.275525e-04 9.275525e-04 0.529894401 #> 839    419.0 0.4694325 0.4694325 9.225258e-04 9.225258e-04 0.529644933 #> 840    419.5 0.4696870 0.4696870 9.175322e-04 9.175322e-04 0.529395507 #> 841    420.0 0.4699413 0.4699413 9.125714e-04 9.125714e-04 0.529146122 #> 842    420.5 0.4701956 0.4701956 9.076432e-04 9.076432e-04 0.528896780 #> 843    421.0 0.4704498 0.4704498 9.027474e-04 9.027474e-04 0.528647481 #> 844    421.5 0.4707039 0.4707039 8.978837e-04 8.978837e-04 0.528398224 #> 845    422.0 0.4709579 0.4709579 8.930519e-04 8.930519e-04 0.528149012 #> 846    422.5 0.4712119 0.4712119 8.882517e-04 8.882517e-04 0.527899844 #> 847    423.0 0.4714658 0.4714658 8.834829e-04 8.834829e-04 0.527650721 #> 848    423.5 0.4717196 0.4717196 8.787453e-04 8.787453e-04 0.527401643 #> 849    424.0 0.4719734 0.4719734 8.740386e-04 8.740386e-04 0.527152611 #> 850    424.5 0.4722270 0.4722270 8.693627e-04 8.693627e-04 0.526903625 #> 851    425.0 0.4724806 0.4724806 8.647173e-04 8.647173e-04 0.526654686 #> 852    425.5 0.4727341 0.4727341 8.601021e-04 8.601021e-04 0.526405794 #> 853    426.0 0.4729875 0.4729875 8.555170e-04 8.555170e-04 0.526156951 #> 854    426.5 0.4732409 0.4732409 8.509617e-04 8.509617e-04 0.525908155 #> 855    427.0 0.4734942 0.4734942 8.464361e-04 8.464361e-04 0.525659408 #> 856    427.5 0.4737474 0.4737474 8.419398e-04 8.419398e-04 0.525410710 #> 857    428.0 0.4740005 0.4740005 8.374727e-04 8.374727e-04 0.525162062 #> 858    428.5 0.4742535 0.4742535 8.330346e-04 8.330346e-04 0.524913464 #> 859    429.0 0.4745065 0.4745065 8.286253e-04 8.286253e-04 0.524664917 #> 860    429.5 0.4747593 0.4747593 8.242445e-04 8.242445e-04 0.524416420 #> 861    430.0 0.4750121 0.4750121 8.198920e-04 8.198920e-04 0.524167975 #> 862    430.5 0.4752649 0.4752649 8.155677e-04 8.155677e-04 0.523919582 #> 863    431.0 0.4755175 0.4755175 8.112713e-04 8.112713e-04 0.523671241 #> 864    431.5 0.4757700 0.4757700 8.070027e-04 8.070027e-04 0.523422953 #> 865    432.0 0.4760225 0.4760225 8.027616e-04 8.027616e-04 0.523174719 #> 866    432.5 0.4762749 0.4762749 7.985478e-04 7.985478e-04 0.522926537 #> 867    433.0 0.4765272 0.4765272 7.943611e-04 7.943611e-04 0.522678410 #> 868    433.5 0.4767795 0.4767795 7.902014e-04 7.902014e-04 0.522430338 #> 869    434.0 0.4770316 0.4770316 7.860684e-04 7.860684e-04 0.522182320 #> 870    434.5 0.4772837 0.4772837 7.819619e-04 7.819619e-04 0.521934358 #> 871    435.0 0.4775357 0.4775357 7.778818e-04 7.778818e-04 0.521686451 #> 872    435.5 0.4777876 0.4777876 7.738279e-04 7.738279e-04 0.521438601 #> 873    436.0 0.4780394 0.4780394 7.698000e-04 7.698000e-04 0.521190807 #> 874    436.5 0.4782911 0.4782911 7.657978e-04 7.657978e-04 0.520943070 #> 875    437.0 0.4785428 0.4785428 7.618212e-04 7.618212e-04 0.520695391 #> 876    437.5 0.4787944 0.4787944 7.578701e-04 7.578701e-04 0.520447769 #> 877    438.0 0.4790459 0.4790459 7.539442e-04 7.539442e-04 0.520200206 #> 878    438.5 0.4792973 0.4792973 7.500433e-04 7.500433e-04 0.519952701 #> 879    439.0 0.4795486 0.4795486 7.461673e-04 7.461673e-04 0.519705254 #> 880    439.5 0.4797998 0.4797998 7.423160e-04 7.423160e-04 0.519457868 #> 881    440.0 0.4800510 0.4800510 7.384892e-04 7.384892e-04 0.519210541 #> 882    440.5 0.4803020 0.4803020 7.346868e-04 7.346868e-04 0.518963274 #> 883    441.0 0.4805530 0.4805530 7.309085e-04 7.309085e-04 0.518716067 #> 884    441.5 0.4808039 0.4808039 7.271542e-04 7.271542e-04 0.518468921 #> 885    442.0 0.4810547 0.4810547 7.234237e-04 7.234237e-04 0.518221837 #> 886    442.5 0.4813055 0.4813055 7.197169e-04 7.197169e-04 0.517974814 #> 887    443.0 0.4815561 0.4815561 7.160336e-04 7.160336e-04 0.517727852 #> 888    443.5 0.4818067 0.4818067 7.123735e-04 7.123735e-04 0.517480954 #> 889    444.0 0.4820571 0.4820571 7.087367e-04 7.087367e-04 0.517234117 #> 890    444.5 0.4823075 0.4823075 7.051228e-04 7.051228e-04 0.516987344 #> 891    445.0 0.4825578 0.4825578 7.015317e-04 7.015317e-04 0.516740634 #> 892    445.5 0.4828080 0.4828080 6.979633e-04 6.979633e-04 0.516493988 #> 893    446.0 0.4830582 0.4830582 6.944174e-04 6.944174e-04 0.516247406 #> 894    446.5 0.4833082 0.4833082 6.908938e-04 6.908938e-04 0.516000888 #> 895    447.0 0.4835582 0.4835582 6.873924e-04 6.873924e-04 0.515754435 #> 896    447.5 0.4838080 0.4838080 6.839130e-04 6.839130e-04 0.515508047 #> 897    448.0 0.4840578 0.4840578 6.804554e-04 6.804554e-04 0.515261724 #> 898    448.5 0.4843075 0.4843075 6.770196e-04 6.770196e-04 0.515015467 #> 899    449.0 0.4845571 0.4845571 6.736053e-04 6.736053e-04 0.514769276 #> 900    449.5 0.4848066 0.4848066 6.702124e-04 6.702124e-04 0.514523151 #> 901    450.0 0.4850561 0.4850561 6.668408e-04 6.668408e-04 0.514277093 #> 902    450.5 0.4853054 0.4853054 6.634903e-04 6.634903e-04 0.514031102 #> 903    451.0 0.4855547 0.4855547 6.601607e-04 6.601607e-04 0.513785179 #> 904    451.5 0.4858038 0.4858038 6.568519e-04 6.568519e-04 0.513539323 #> 905    452.0 0.4860529 0.4860529 6.535638e-04 6.535638e-04 0.513293535 #> 906    452.5 0.4863019 0.4863019 6.502962e-04 6.502962e-04 0.513047815 #> 907    453.0 0.4865508 0.4865508 6.470489e-04 6.470489e-04 0.512802163 #> 908    453.5 0.4867996 0.4867996 6.438219e-04 6.438219e-04 0.512556581 #> 909    454.0 0.4870483 0.4870483 6.406149e-04 6.406149e-04 0.512311068 #> 910    454.5 0.4872969 0.4872969 6.374279e-04 6.374279e-04 0.512065624 #> 911    455.0 0.4875455 0.4875455 6.342607e-04 6.342607e-04 0.511820250 #> 912    455.5 0.4877939 0.4877939 6.311131e-04 6.311131e-04 0.511574946 #> 913    456.0 0.4880423 0.4880423 6.279851e-04 6.279851e-04 0.511329712 #> 914    456.5 0.4882906 0.4882906 6.248764e-04 6.248764e-04 0.511084549 #> 915    457.0 0.4885388 0.4885388 6.217870e-04 6.217870e-04 0.510839457 #> 916    457.5 0.4887868 0.4887868 6.187167e-04 6.187167e-04 0.510594436 #> 917    458.0 0.4890348 0.4890348 6.156654e-04 6.156654e-04 0.510349486 #> 918    458.5 0.4892828 0.4892828 6.126329e-04 6.126329e-04 0.510104609 #> 919    459.0 0.4895306 0.4895306 6.096192e-04 6.096192e-04 0.509859803 #> 920    459.5 0.4897783 0.4897783 6.066240e-04 6.066240e-04 0.509615069 #> 921    460.0 0.4900259 0.4900259 6.036473e-04 6.036473e-04 0.509370409 #> 922    460.5 0.4902735 0.4902735 6.006889e-04 6.006889e-04 0.509125821 #> 923    461.0 0.4905209 0.4905209 5.977487e-04 5.977487e-04 0.508881306 #> 924    461.5 0.4907683 0.4907683 5.948265e-04 5.948265e-04 0.508636864 #> 925    462.0 0.4910156 0.4910156 5.919223e-04 5.919223e-04 0.508392497 #> 926    462.5 0.4912628 0.4912628 5.890360e-04 5.890360e-04 0.508148203 #> 927    463.0 0.4915098 0.4915098 5.861673e-04 5.861673e-04 0.507903983 #> 928    463.5 0.4917568 0.4917568 5.833162e-04 5.833162e-04 0.507659838 #> 929    464.0 0.4920038 0.4920038 5.804826e-04 5.804826e-04 0.507415767 #> 930    464.5 0.4922506 0.4922506 5.776662e-04 5.776662e-04 0.507171772 #> 931    465.0 0.4924973 0.4924973 5.748671e-04 5.748671e-04 0.506927851 #> 932    465.5 0.4927439 0.4927439 5.720851e-04 5.720851e-04 0.506684006 #> 933    466.0 0.4929904 0.4929904 5.693201e-04 5.693201e-04 0.506440237 #> 934    466.5 0.4932369 0.4932369 5.665719e-04 5.665719e-04 0.506196543 #> 935    467.0 0.4934832 0.4934832 5.638404e-04 5.638404e-04 0.505952926 #> 936    467.5 0.4937295 0.4937295 5.611256e-04 5.611256e-04 0.505709385 #> 937    468.0 0.4939757 0.4939757 5.584273e-04 5.584273e-04 0.505465921 #> 938    468.5 0.4942217 0.4942217 5.557454e-04 5.557454e-04 0.505222533 #> 939    469.0 0.4944677 0.4944677 5.530797e-04 5.530797e-04 0.504979223 #> 940    469.5 0.4947136 0.4947136 5.504303e-04 5.504303e-04 0.504735990 #> 941    470.0 0.4949594 0.4949594 5.477969e-04 5.477969e-04 0.504492835 #> 942    470.5 0.4952051 0.4952051 5.451794e-04 5.451794e-04 0.504249757 #> 943    471.0 0.4954507 0.4954507 5.425778e-04 5.425778e-04 0.504006758 #> 944    471.5 0.4956962 0.4956962 5.399919e-04 5.399919e-04 0.503763837 #> 945    472.0 0.4959416 0.4959416 5.374216e-04 5.374216e-04 0.503520994 #> 946    472.5 0.4961869 0.4961869 5.348669e-04 5.348669e-04 0.503278230 #> 947    473.0 0.4964321 0.4964321 5.323275e-04 5.323275e-04 0.503035545 #> 948    473.5 0.4966773 0.4966773 5.298035e-04 5.298035e-04 0.502792939 #> 949    474.0 0.4969223 0.4969223 5.272946e-04 5.272946e-04 0.502550412 #> 950    474.5 0.4971672 0.4971672 5.248009e-04 5.248009e-04 0.502307965 #> 951    475.0 0.4974121 0.4974121 5.223221e-04 5.223221e-04 0.502065597 #> 952    475.5 0.4976568 0.4976568 5.198583e-04 5.198583e-04 0.501823310 #> 953    476.0 0.4979015 0.4979015 5.174092e-04 5.174092e-04 0.501581103 #> 954    476.5 0.4981460 0.4981460 5.149748e-04 5.149748e-04 0.501338976 #> 955    477.0 0.4983905 0.4983905 5.125550e-04 5.125550e-04 0.501096930 #> 956    477.5 0.4986349 0.4986349 5.101497e-04 5.101497e-04 0.500854964 #> 957    478.0 0.4988792 0.4988792 5.077587e-04 5.077587e-04 0.500613080 #> 958    478.5 0.4991233 0.4991233 5.053821e-04 5.053821e-04 0.500371276 #> 959    479.0 0.4993674 0.4993674 5.030196e-04 5.030196e-04 0.500129555 #> 960    479.5 0.4996114 0.4996114 5.006713e-04 5.006713e-04 0.499887914 #> 961    480.0 0.4998553 0.4998553 4.983370e-04 4.983370e-04 0.499646356 #> 962    480.5 0.5000991 0.5000991 4.960165e-04 4.960165e-04 0.499404879 #> 963    481.0 0.5003428 0.5003428 4.937099e-04 4.937099e-04 0.499163485 #> 964    481.5 0.5005864 0.5005864 4.914170e-04 4.914170e-04 0.498922173 #> 965    482.0 0.5008299 0.5008299 4.891377e-04 4.891377e-04 0.498680943 #> 966    482.5 0.5010733 0.5010733 4.868720e-04 4.868720e-04 0.498439797 #> 967    483.0 0.5013166 0.5013166 4.846197e-04 4.846197e-04 0.498198733 #> 968    483.5 0.5015599 0.5015599 4.823807e-04 4.823807e-04 0.497957752 #> 969    484.0 0.5018030 0.5018030 4.801550e-04 4.801550e-04 0.497716854 #> 970    484.5 0.5020460 0.5020460 4.779425e-04 4.779425e-04 0.497476040 #> 971    485.0 0.5022889 0.5022889 4.757431e-04 4.757431e-04 0.497235310 #> 972    485.5 0.5025318 0.5025318 4.735567e-04 4.735567e-04 0.496994663 #> 973    486.0 0.5027745 0.5027745 4.713832e-04 4.713832e-04 0.496754100 #> 974    486.5 0.5030172 0.5030172 4.692225e-04 4.692225e-04 0.496513622 #> 975    487.0 0.5032597 0.5032597 4.670745e-04 4.670745e-04 0.496273228 #> 976    487.5 0.5035021 0.5035021 4.649392e-04 4.649392e-04 0.496032918 #> 977    488.0 0.5037445 0.5037445 4.628165e-04 4.628165e-04 0.495792693 #> 978    488.5 0.5039867 0.5039867 4.607062e-04 4.607062e-04 0.495552552 #> 979    489.0 0.5042289 0.5042289 4.586084e-04 4.586084e-04 0.495312497 #> 980    489.5 0.5044710 0.5044710 4.565228e-04 4.565228e-04 0.495072527 #> 981    490.0 0.5047129 0.5047129 4.544495e-04 4.544495e-04 0.494832642 #> 982    490.5 0.5049548 0.5049548 4.523883e-04 4.523883e-04 0.494592843 #> 983    491.0 0.5051965 0.5051965 4.503393e-04 4.503393e-04 0.494353129 #> 984    491.5 0.5054382 0.5054382 4.483022e-04 4.483022e-04 0.494113501 #> 985    492.0 0.5056798 0.5056798 4.462770e-04 4.462770e-04 0.493873959 #> 986    492.5 0.5059212 0.5059212 4.442636e-04 4.442636e-04 0.493634503 #> 987    493.0 0.5061626 0.5061626 4.422620e-04 4.422620e-04 0.493395133 #> 988    493.5 0.5064039 0.5064039 4.402721e-04 4.402721e-04 0.493155850 #> 989    494.0 0.5066451 0.5066451 4.382938e-04 4.382938e-04 0.492916653 #> 990    494.5 0.5068861 0.5068861 4.363269e-04 4.363269e-04 0.492677543 #> 991    495.0 0.5071271 0.5071271 4.343716e-04 4.343716e-04 0.492438520 #> 992    495.5 0.5073680 0.5073680 4.324276e-04 4.324276e-04 0.492199584 #> 993    496.0 0.5076088 0.5076088 4.304949e-04 4.304949e-04 0.491960734 #> 994    496.5 0.5078495 0.5078495 4.285734e-04 4.285734e-04 0.491721973 #> 995    497.0 0.5080900 0.5080900 4.266630e-04 4.266630e-04 0.491483298 #> 996    497.5 0.5083305 0.5083305 4.247638e-04 4.247638e-04 0.491244712 #> 997    498.0 0.5085709 0.5085709 4.228755e-04 4.228755e-04 0.491006212 #> 998    498.5 0.5088112 0.5088112 4.209981e-04 4.209981e-04 0.490767801 #> 999    499.0 0.5090514 0.5090514 4.191316e-04 4.191316e-04 0.490529478 #> 1000   499.5 0.5092915 0.5092915 4.172759e-04 4.172759e-04 0.490291243 #> 1001   500.0 0.5095315 0.5095315 4.154309e-04 4.154309e-04 0.490053096 #> 1002   500.5 0.5097714 0.5097714 4.135965e-04 4.135965e-04 0.489815037 #> 1003   501.0 0.5100112 0.5100112 4.117727e-04 4.117727e-04 0.489577068 #> 1004   501.5 0.5102509 0.5102509 4.099594e-04 4.099594e-04 0.489339186 #> 1005   502.0 0.5104904 0.5104904 4.081565e-04 4.081565e-04 0.489101394 #> 1006   502.5 0.5107299 0.5107299 4.063640e-04 4.063640e-04 0.488863690 #> 1007   503.0 0.5109693 0.5109693 4.045817e-04 4.045817e-04 0.488626076 #> 1008   503.5 0.5112086 0.5112086 4.028098e-04 4.028098e-04 0.488388550 #> 1009   504.0 0.5114478 0.5114478 4.010479e-04 4.010479e-04 0.488151114 #> 1010   504.5 0.5116869 0.5116869 3.992962e-04 3.992962e-04 0.487913767 #> 1011   505.0 0.5119259 0.5119259 3.975545e-04 3.975545e-04 0.487676510 #> 1012   505.5 0.5121648 0.5121648 3.958227e-04 3.958227e-04 0.487439343 #> 1013   506.0 0.5124036 0.5124036 3.941008e-04 3.941008e-04 0.487202265 #> 1014   506.5 0.5126423 0.5126423 3.923888e-04 3.923888e-04 0.486965277 #> 1015   507.0 0.5128809 0.5128809 3.906866e-04 3.906866e-04 0.486728379 #> 1016   507.5 0.5131194 0.5131194 3.889940e-04 3.889940e-04 0.486491572 #> 1017   508.0 0.5133578 0.5133578 3.873111e-04 3.873111e-04 0.486254854 #> 1018   508.5 0.5135961 0.5135961 3.856378e-04 3.856378e-04 0.486018227 #> 1019   509.0 0.5138343 0.5138343 3.839740e-04 3.839740e-04 0.485781691 #> 1020   509.5 0.5140724 0.5140724 3.823196e-04 3.823196e-04 0.485545245 #> 1021   510.0 0.5143104 0.5143104 3.806746e-04 3.806746e-04 0.485308889 #> 1022   510.5 0.5145483 0.5145483 3.790390e-04 3.790390e-04 0.485072625 #> 1023   511.0 0.5147861 0.5147861 3.774126e-04 3.774126e-04 0.484836451 #> 1024   511.5 0.5150238 0.5150238 3.757955e-04 3.757955e-04 0.484600369 #> 1025   512.0 0.5152614 0.5152614 3.741875e-04 3.741875e-04 0.484364378 #> 1026   512.5 0.5154989 0.5154989 3.725886e-04 3.725886e-04 0.484128478 #> 1027   513.0 0.5157363 0.5157363 3.709987e-04 3.709987e-04 0.483892669 #> 1028   513.5 0.5159736 0.5159736 3.694178e-04 3.694178e-04 0.483656952 #> 1029   514.0 0.5162108 0.5162108 3.678458e-04 3.678458e-04 0.483421326 #> 1030   514.5 0.5164479 0.5164479 3.662827e-04 3.662827e-04 0.483185792 #> 1031   515.0 0.5166849 0.5166849 3.647284e-04 3.647284e-04 0.482950350 #> 1032   515.5 0.5169218 0.5169218 3.631828e-04 3.631828e-04 0.482714999 #> 1033   516.0 0.5171586 0.5171586 3.616460e-04 3.616460e-04 0.482479741 #> 1034   516.5 0.5173953 0.5173953 3.601177e-04 3.601177e-04 0.482244574 #> 1035   517.0 0.5176319 0.5176319 3.585981e-04 3.585981e-04 0.482009500 #> 1036   517.5 0.5178684 0.5178684 3.570869e-04 3.570869e-04 0.481774518 #> 1037   518.0 0.5181048 0.5181048 3.555843e-04 3.555843e-04 0.481539628 #> 1038   518.5 0.5183411 0.5183411 3.540900e-04 3.540900e-04 0.481304831 #> 1039   519.0 0.5185773 0.5185773 3.526041e-04 3.526041e-04 0.481070127 #> 1040   519.5 0.5188134 0.5188134 3.511265e-04 3.511265e-04 0.480835515 #> 1041   520.0 0.5190493 0.5190493 3.496572e-04 3.496572e-04 0.480600995 #> 1042   520.5 0.5192852 0.5192852 3.481961e-04 3.481961e-04 0.480366569 #> 1043   521.0 0.5195210 0.5195210 3.467431e-04 3.467431e-04 0.480132235 #> 1044   521.5 0.5197567 0.5197567 3.452982e-04 3.452982e-04 0.479897995 #> 1045   522.0 0.5199923 0.5199923 3.438614e-04 3.438614e-04 0.479663847 #> 1046   522.5 0.5202278 0.5202278 3.424326e-04 3.424326e-04 0.479429793 #> 1047   523.0 0.5204632 0.5204632 3.410117e-04 3.410117e-04 0.479195832 #> 1048   523.5 0.5206984 0.5206984 3.395987e-04 3.395987e-04 0.478961964 #> 1049   524.0 0.5209336 0.5209336 3.381936e-04 3.381936e-04 0.478728190 #> 1050   524.5 0.5211687 0.5211687 3.367962e-04 3.367962e-04 0.478494509 #> 1051   525.0 0.5214037 0.5214037 3.354066e-04 3.354066e-04 0.478260922 #> 1052   525.5 0.5216385 0.5216385 3.340247e-04 3.340247e-04 0.478027429 #> 1053   526.0 0.5218733 0.5218733 3.326504e-04 3.326504e-04 0.477794029 #> 1054   526.5 0.5221080 0.5221080 3.312837e-04 3.312837e-04 0.477560723 #> 1055   527.0 0.5223426 0.5223426 3.299246e-04 3.299246e-04 0.477327511 #> 1056   527.5 0.5225770 0.5225770 3.285730e-04 3.285730e-04 0.477094393 #> 1057   528.0 0.5228114 0.5228114 3.272288e-04 3.272288e-04 0.476861369 #> 1058   528.5 0.5230457 0.5230457 3.258921e-04 3.258921e-04 0.476628439 #> 1059   529.0 0.5232798 0.5232798 3.245627e-04 3.245627e-04 0.476395604 #> 1060   529.5 0.5235139 0.5235139 3.232406e-04 3.232406e-04 0.476162862 #> 1061   530.0 0.5237479 0.5237479 3.219258e-04 3.219258e-04 0.475930215 #> 1062   530.5 0.5239817 0.5239817 3.206182e-04 3.206182e-04 0.475697663 #> 1063   531.0 0.5242155 0.5242155 3.193178e-04 3.193178e-04 0.475465205 #> 1064   531.5 0.5244491 0.5244491 3.180245e-04 3.180245e-04 0.475232842 #> 1065   532.0 0.5246827 0.5246827 3.167383e-04 3.167383e-04 0.475000573 #> 1066   532.5 0.5249161 0.5249161 3.154591e-04 3.154591e-04 0.474768399 #> 1067   533.0 0.5251495 0.5251495 3.141870e-04 3.141870e-04 0.474536320 #> 1068   533.5 0.5253827 0.5253827 3.129218e-04 3.129218e-04 0.474304336 #> 1069   534.0 0.5256159 0.5256159 3.116635e-04 3.116635e-04 0.474072446 #> 1070   534.5 0.5258489 0.5258489 3.104121e-04 3.104121e-04 0.473840652 #> 1071   535.0 0.5260819 0.5260819 3.091675e-04 3.091675e-04 0.473608953 #> 1072   535.5 0.5263147 0.5263147 3.079297e-04 3.079297e-04 0.473377349 #> 1073   536.0 0.5265475 0.5265475 3.066986e-04 3.066986e-04 0.473145840 #> 1074   536.5 0.5267801 0.5267801 3.054743e-04 3.054743e-04 0.472914426 #> 1075   537.0 0.5270126 0.5270126 3.042566e-04 3.042566e-04 0.472683108 #> 1076   537.5 0.5272451 0.5272451 3.030455e-04 3.030455e-04 0.472451885 #> 1077   538.0 0.5274774 0.5274774 3.018409e-04 3.018409e-04 0.472220758 #> 1078   538.5 0.5277096 0.5277096 3.006430e-04 3.006430e-04 0.471989726 #> 1079   539.0 0.5279418 0.5279418 2.994515e-04 2.994515e-04 0.471758789 #> 1080   539.5 0.5281738 0.5281738 2.982664e-04 2.982664e-04 0.471527949 #> 1081   540.0 0.5284057 0.5284057 2.970878e-04 2.970878e-04 0.471297204 #> 1082   540.5 0.5286375 0.5286375 2.959155e-04 2.959155e-04 0.471066554 #> 1083   541.0 0.5288692 0.5288692 2.947496e-04 2.947496e-04 0.470836001 #> 1084   541.5 0.5291009 0.5291009 2.935900e-04 2.935900e-04 0.470605543 #> 1085   542.0 0.5293324 0.5293324 2.924366e-04 2.924366e-04 0.470375182 #> 1086   542.5 0.5295638 0.5295638 2.912895e-04 2.912895e-04 0.470144916 #> 1087   543.0 0.5297951 0.5297951 2.901485e-04 2.901485e-04 0.469914747 #> 1088   543.5 0.5300263 0.5300263 2.890137e-04 2.890137e-04 0.469684673 #> 1089   544.0 0.5302574 0.5302574 2.878849e-04 2.878849e-04 0.469454696 #> 1090   544.5 0.5304884 0.5304884 2.867622e-04 2.867622e-04 0.469224814 #> 1091   545.0 0.5307193 0.5307193 2.856456e-04 2.856456e-04 0.468995030 #> 1092   545.5 0.5309501 0.5309501 2.845349e-04 2.845349e-04 0.468765341 #> 1093   546.0 0.5311808 0.5311808 2.834303e-04 2.834303e-04 0.468535749 #> 1094   546.5 0.5314114 0.5314114 2.823315e-04 2.823315e-04 0.468306253 #> 1095   547.0 0.5316419 0.5316419 2.812386e-04 2.812386e-04 0.468076854 #> 1096   547.5 0.5318723 0.5318723 2.801515e-04 2.801515e-04 0.467847551 #> 1097   548.0 0.5321026 0.5321026 2.790703e-04 2.790703e-04 0.467618344 #> 1098   548.5 0.5323328 0.5323328 2.779948e-04 2.779948e-04 0.467389235 #> 1099   549.0 0.5325629 0.5325629 2.769250e-04 2.769250e-04 0.467160222 #> 1100   549.5 0.5327928 0.5327928 2.758610e-04 2.758610e-04 0.466931305 #> 1101   550.0 0.5330227 0.5330227 2.748026e-04 2.748026e-04 0.466702486 #> 1102   550.5 0.5332525 0.5332525 2.737499e-04 2.737499e-04 0.466473763 #> 1103   551.0 0.5334822 0.5334822 2.727028e-04 2.727028e-04 0.466245137 #> 1104   551.5 0.5337117 0.5337117 2.716612e-04 2.716612e-04 0.466016608 #> 1105   552.0 0.5339412 0.5339412 2.706252e-04 2.706252e-04 0.465788176 #> 1106   552.5 0.5341706 0.5341706 2.695946e-04 2.695946e-04 0.465559841 #> 1107   553.0 0.5343998 0.5343998 2.685696e-04 2.685696e-04 0.465331602 #> 1108   553.5 0.5346290 0.5346290 2.675499e-04 2.675499e-04 0.465103461 #> 1109   554.0 0.5348580 0.5348580 2.665357e-04 2.665357e-04 0.464875417 #> 1110   554.5 0.5350870 0.5350870 2.655268e-04 2.655268e-04 0.464647470 #> 1111   555.0 0.5353159 0.5353159 2.645232e-04 2.645232e-04 0.464419621 #> 1112   555.5 0.5355446 0.5355446 2.635250e-04 2.635250e-04 0.464191868 #> 1113   556.0 0.5357733 0.5357733 2.625320e-04 2.625320e-04 0.463964213 #> 1114   556.5 0.5360018 0.5360018 2.615442e-04 2.615442e-04 0.463736655 #> 1115   557.0 0.5362302 0.5362302 2.605617e-04 2.605617e-04 0.463509195 #> 1116   557.5 0.5364586 0.5364586 2.595843e-04 2.595843e-04 0.463281831 #> 1117   558.0 0.5366868 0.5366868 2.586121e-04 2.586121e-04 0.463054566 #> 1118   558.5 0.5369150 0.5369150 2.576450e-04 2.576450e-04 0.462827397 #> 1119   559.0 0.5371430 0.5371430 2.566830e-04 2.566830e-04 0.462600326 #> 1120   559.5 0.5373709 0.5373709 2.557260e-04 2.557260e-04 0.462373353 #> 1121   560.0 0.5375987 0.5375987 2.547740e-04 2.547740e-04 0.462146477 #> 1122   560.5 0.5378265 0.5378265 2.538270e-04 2.538270e-04 0.461919699 #> 1123   561.0 0.5380541 0.5380541 2.528850e-04 2.528850e-04 0.461693018 #> 1124   561.5 0.5382816 0.5382816 2.519479e-04 2.519479e-04 0.461466436 #> 1125   562.0 0.5385090 0.5385090 2.510157e-04 2.510157e-04 0.461239950 #> 1126   562.5 0.5387363 0.5387363 2.500884e-04 2.500884e-04 0.461013563 #> 1127   563.0 0.5389636 0.5389636 2.491660e-04 2.491660e-04 0.460787273 #> 1128   563.5 0.5391907 0.5391907 2.482483e-04 2.482483e-04 0.460561081 #> 1129   564.0 0.5394177 0.5394177 2.473354e-04 2.473354e-04 0.460334987 #> 1130   564.5 0.5396446 0.5396446 2.464273e-04 2.464273e-04 0.460108991 #> 1131   565.0 0.5398714 0.5398714 2.455239e-04 2.455239e-04 0.459883092 #> 1132   565.5 0.5400981 0.5400981 2.446252e-04 2.446252e-04 0.459657292 #> 1133   566.0 0.5403247 0.5403247 2.437312e-04 2.437312e-04 0.459431589 #> 1134   566.5 0.5405512 0.5405512 2.428418e-04 2.428418e-04 0.459205984 #> 1135   567.0 0.5407776 0.5407776 2.419570e-04 2.419570e-04 0.458980478 #> 1136   567.5 0.5410039 0.5410039 2.410768e-04 2.410768e-04 0.458755069 #> 1137   568.0 0.5412300 0.5412300 2.402012e-04 2.402012e-04 0.458529759 #> 1138   568.5 0.5414561 0.5414561 2.393301e-04 2.393301e-04 0.458304546 #> 1139   569.0 0.5416821 0.5416821 2.384635e-04 2.384635e-04 0.458079432 #> 1140   569.5 0.5419080 0.5419080 2.376014e-04 2.376014e-04 0.457854415 #> 1141   570.0 0.5421338 0.5421338 2.367438e-04 2.367438e-04 0.457629497 #> 1142   570.5 0.5423594 0.5423594 2.358906e-04 2.358906e-04 0.457404677 #> 1143   571.0 0.5425850 0.5425850 2.350417e-04 2.350417e-04 0.457179956 #> 1144   571.5 0.5428105 0.5428105 2.341973e-04 2.341973e-04 0.456955332 #> 1145   572.0 0.5430358 0.5430358 2.333572e-04 2.333572e-04 0.456730807 #> 1146   572.5 0.5432611 0.5432611 2.325214e-04 2.325214e-04 0.456506380 #> 1147   573.0 0.5434863 0.5434863 2.316899e-04 2.316899e-04 0.456282051 #> 1148   573.5 0.5437113 0.5437113 2.308627e-04 2.308627e-04 0.456057821 #> 1149   574.0 0.5439363 0.5439363 2.300398e-04 2.300398e-04 0.455833689 #> 1150   574.5 0.5441611 0.5441611 2.292210e-04 2.292210e-04 0.455609655 #> 1151   575.0 0.5443859 0.5443859 2.284065e-04 2.284065e-04 0.455385720 #> 1152   575.5 0.5446105 0.5446105 2.275961e-04 2.275961e-04 0.455161883 #> 1153   576.0 0.5448351 0.5448351 2.267899e-04 2.267899e-04 0.454938145 #> 1154   576.5 0.5450595 0.5450595 2.259878e-04 2.259878e-04 0.454714505 #> 1155   577.0 0.5452838 0.5452838 2.251898e-04 2.251898e-04 0.454490963 #> 1156   577.5 0.5455081 0.5455081 2.243959e-04 2.243959e-04 0.454267520 #> 1157   578.0 0.5457322 0.5457322 2.236061e-04 2.236061e-04 0.454044175 #> 1158   578.5 0.5459563 0.5459563 2.228202e-04 2.228202e-04 0.453820929 #> 1159   579.0 0.5461802 0.5461802 2.220384e-04 2.220384e-04 0.453597782 #> 1160   579.5 0.5464040 0.5464040 2.212605e-04 2.212605e-04 0.453374733 #> 1161   580.0 0.5466277 0.5466277 2.204867e-04 2.204867e-04 0.453151782 #> 1162   580.5 0.5468514 0.5468514 2.197167e-04 2.197167e-04 0.452928931 #> 1163   581.0 0.5470749 0.5470749 2.189507e-04 2.189507e-04 0.452706177 #> 1164   581.5 0.5472983 0.5472983 2.181885e-04 2.181885e-04 0.452483523 #> 1165   582.0 0.5475216 0.5475216 2.174302e-04 2.174302e-04 0.452260967 #> 1166   582.5 0.5477448 0.5477448 2.166758e-04 2.166758e-04 0.452038510 #> 1167   583.0 0.5479679 0.5479679 2.159252e-04 2.159252e-04 0.451816151 #> 1168   583.5 0.5481909 0.5481909 2.151784e-04 2.151784e-04 0.451593891 #> 1169   584.0 0.5484138 0.5484138 2.144353e-04 2.144353e-04 0.451371730 #> 1170   584.5 0.5486366 0.5486366 2.136960e-04 2.136960e-04 0.451149667 #> 1171   585.0 0.5488593 0.5488593 2.129605e-04 2.129605e-04 0.450927703 #> 1172   585.5 0.5490819 0.5490819 2.122286e-04 2.122286e-04 0.450705838 #> 1173   586.0 0.5493044 0.5493044 2.115005e-04 2.115005e-04 0.450484072 #> 1174   586.5 0.5495268 0.5495268 2.107760e-04 2.107760e-04 0.450262404 #> 1175   587.0 0.5497491 0.5497491 2.100552e-04 2.100552e-04 0.450040835 #> 1176   587.5 0.5499713 0.5499713 2.093380e-04 2.093380e-04 0.449819365 #> 1177   588.0 0.5501934 0.5501934 2.086244e-04 2.086244e-04 0.449597994 #> 1178   588.5 0.5504154 0.5504154 2.079144e-04 2.079144e-04 0.449376721 #> 1179   589.0 0.5506372 0.5506372 2.072080e-04 2.072080e-04 0.449155547 #> 1180   589.5 0.5508590 0.5508590 2.065051e-04 2.065051e-04 0.448934473 #> 1181   590.0 0.5510807 0.5510807 2.058057e-04 2.058057e-04 0.448713496 #> 1182   590.5 0.5513023 0.5513023 2.051098e-04 2.051098e-04 0.448492619 #> 1183   591.0 0.5515237 0.5515237 2.044175e-04 2.044175e-04 0.448271841 #> 1184   591.5 0.5517451 0.5517451 2.037286e-04 2.037286e-04 0.448051161 #> 1185   592.0 0.5519664 0.5519664 2.030431e-04 2.030431e-04 0.447830581 #> 1186   592.5 0.5521875 0.5521875 2.023611e-04 2.023611e-04 0.447610099 #> 1187   593.0 0.5524086 0.5524086 2.016824e-04 2.016824e-04 0.447389716 #> 1188   593.5 0.5526296 0.5526296 2.010072e-04 2.010072e-04 0.447169432 #> 1189   594.0 0.5528504 0.5528504 2.003353e-04 2.003353e-04 0.446949247 #> 1190   594.5 0.5530712 0.5530712 1.996668e-04 1.996668e-04 0.446729160 #> 1191   595.0 0.5532918 0.5532918 1.990015e-04 1.990015e-04 0.446509173 #> 1192   595.5 0.5535124 0.5535124 1.983397e-04 1.983397e-04 0.446289285 #> 1193   596.0 0.5537328 0.5537328 1.976811e-04 1.976811e-04 0.446069495 #> 1194   596.5 0.5539532 0.5539532 1.970257e-04 1.970257e-04 0.445849805 #> 1195   597.0 0.5541734 0.5541734 1.963736e-04 1.963736e-04 0.445630213 #> 1196   597.5 0.5543936 0.5543936 1.957248e-04 1.957248e-04 0.445410720 #> 1197   598.0 0.5546136 0.5546136 1.950792e-04 1.950792e-04 0.445191326 #> 1198   598.5 0.5548335 0.5548335 1.944368e-04 1.944368e-04 0.444972031 #> 1199   599.0 0.5550534 0.5550534 1.937975e-04 1.937975e-04 0.444752836 #> 1200   599.5 0.5552731 0.5552731 1.931614e-04 1.931614e-04 0.444533739 #> 1201   600.0 0.5554927 0.5554927 1.925285e-04 1.925285e-04 0.444314740 #> 1202   600.5 0.5557123 0.5557123 1.918987e-04 1.918987e-04 0.444095841 #> 1203   601.0 0.5559317 0.5559317 1.912720e-04 1.912720e-04 0.443877041 #> 1204   601.5 0.5561510 0.5561510 1.906484e-04 1.906484e-04 0.443658340 #> 1205   602.0 0.5563702 0.5563702 1.900278e-04 1.900278e-04 0.443439738 #> 1206   602.5 0.5565894 0.5565894 1.894104e-04 1.894104e-04 0.443221235 #> 1207   603.0 0.5568084 0.5568084 1.887959e-04 1.887959e-04 0.443002830 #> 1208   603.5 0.5570273 0.5570273 1.881845e-04 1.881845e-04 0.442784525 #> 1209   604.0 0.5572461 0.5572461 1.875761e-04 1.875761e-04 0.442566319 #> 1210   604.5 0.5574648 0.5574648 1.869707e-04 1.869707e-04 0.442348211 #> 1211   605.0 0.5576834 0.5576834 1.863683e-04 1.863683e-04 0.442130203 #> 1212   605.5 0.5579019 0.5579019 1.857688e-04 1.857688e-04 0.441912293 #> 1213   606.0 0.5581203 0.5581203 1.851722e-04 1.851722e-04 0.441694483 #> 1214   606.5 0.5583387 0.5583387 1.845786e-04 1.845786e-04 0.441476771 #> 1215   607.0 0.5585569 0.5585569 1.839879e-04 1.839879e-04 0.441259159 #> 1216   607.5 0.5587750 0.5587750 1.834001e-04 1.834001e-04 0.441041645 #> 1217   608.0 0.5589930 0.5589930 1.828151e-04 1.828151e-04 0.440824230 #> 1218   608.5 0.5592109 0.5592109 1.822330e-04 1.822330e-04 0.440606915 #> 1219   609.0 0.5594286 0.5594286 1.816538e-04 1.816538e-04 0.440389698 #> 1220   609.5 0.5596463 0.5596463 1.810774e-04 1.810774e-04 0.440172580 #> 1221   610.0 0.5598639 0.5598639 1.805037e-04 1.805037e-04 0.439955561 #> 1222   610.5 0.5600814 0.5600814 1.799329e-04 1.799329e-04 0.439738641 #> 1223   611.0 0.5602988 0.5602988 1.793649e-04 1.793649e-04 0.439521820 #> 1224   611.5 0.5605161 0.5605161 1.787996e-04 1.787996e-04 0.439305098 #> 1225   612.0 0.5607333 0.5607333 1.782371e-04 1.782371e-04 0.439088475 #> 1226   612.5 0.5609504 0.5609504 1.776773e-04 1.776773e-04 0.438871951 #> 1227   613.0 0.5611674 0.5611674 1.771202e-04 1.771202e-04 0.438655526 #> 1228   613.5 0.5613842 0.5613842 1.765659e-04 1.765659e-04 0.438439199 #> 1229   614.0 0.5616010 0.5616010 1.760142e-04 1.760142e-04 0.438222972 #> 1230   614.5 0.5618177 0.5618177 1.754652e-04 1.754652e-04 0.438006844 #> 1231   615.0 0.5620343 0.5620343 1.749189e-04 1.749189e-04 0.437790814 #> 1232   615.5 0.5622507 0.5622507 1.743752e-04 1.743752e-04 0.437574883 #> 1233   616.0 0.5624671 0.5624671 1.738341e-04 1.738341e-04 0.437359052 #> 1234   616.5 0.5626834 0.5626834 1.732957e-04 1.732957e-04 0.437143319 #> 1235   617.0 0.5628996 0.5628996 1.727598e-04 1.727598e-04 0.436927685 #> 1236   617.5 0.5631156 0.5631156 1.722266e-04 1.722266e-04 0.436712150 #> 1237   618.0 0.5633316 0.5633316 1.716959e-04 1.716959e-04 0.436496714 #> 1238   618.5 0.5635475 0.5635475 1.711677e-04 1.711677e-04 0.436281376 #> 1239   619.0 0.5637632 0.5637632 1.706422e-04 1.706422e-04 0.436066138 #> 1240   619.5 0.5639789 0.5639789 1.701191e-04 1.701191e-04 0.435850998 #> 1241   620.0 0.5641944 0.5641944 1.695986e-04 1.695986e-04 0.435635958 #> 1242   620.5 0.5644099 0.5644099 1.690805e-04 1.690805e-04 0.435421016 #> 1243   621.0 0.5646253 0.5646253 1.685650e-04 1.685650e-04 0.435206173 #> 1244   621.5 0.5648405 0.5648405 1.680519e-04 1.680519e-04 0.434991428 #> 1245   622.0 0.5650557 0.5650557 1.675413e-04 1.675413e-04 0.434776783 #> 1246   622.5 0.5652707 0.5652707 1.670332e-04 1.670332e-04 0.434562236 #> 1247   623.0 0.5654857 0.5654857 1.665275e-04 1.665275e-04 0.434347788 #> 1248   623.5 0.5657005 0.5657005 1.660242e-04 1.660242e-04 0.434133439 #> 1249   624.0 0.5659153 0.5659153 1.655233e-04 1.655233e-04 0.433919189 #> 1250   624.5 0.5661299 0.5661299 1.650248e-04 1.650248e-04 0.433705038 #> 1251   625.0 0.5663445 0.5663445 1.645287e-04 1.645287e-04 0.433490985 #> 1252   625.5 0.5665589 0.5665589 1.640350e-04 1.640350e-04 0.433277031 #> 1253   626.0 0.5667733 0.5667733 1.635436e-04 1.635436e-04 0.433063176 #> 1254   626.5 0.5669875 0.5669875 1.630546e-04 1.630546e-04 0.432849419 #> 1255   627.0 0.5672017 0.5672017 1.625679e-04 1.625679e-04 0.432635761 #> 1256   627.5 0.5674157 0.5674157 1.620835e-04 1.620835e-04 0.432422202 #> 1257   628.0 0.5676297 0.5676297 1.616015e-04 1.616015e-04 0.432208742 #> 1258   628.5 0.5678435 0.5678435 1.611217e-04 1.611217e-04 0.431995380 #> 1259   629.0 0.5680572 0.5680572 1.606442e-04 1.606442e-04 0.431782117 #> 1260   629.5 0.5682709 0.5682709 1.601690e-04 1.601690e-04 0.431568953 #> 1261   630.0 0.5684844 0.5684844 1.596960e-04 1.596960e-04 0.431355887 #> 1262   630.5 0.5686979 0.5686979 1.592253e-04 1.592253e-04 0.431142920 #> 1263   631.0 0.5689112 0.5689112 1.587569e-04 1.587569e-04 0.430930052 #> 1264   631.5 0.5691244 0.5691244 1.582906e-04 1.582906e-04 0.430717282 #> 1265   632.0 0.5693376 0.5693376 1.578266e-04 1.578266e-04 0.430504611 #> 1266   632.5 0.5695506 0.5695506 1.573647e-04 1.573647e-04 0.430292038 #> 1267   633.0 0.5697635 0.5697635 1.569051e-04 1.569051e-04 0.430079564 #> 1268   633.5 0.5699764 0.5699764 1.564476e-04 1.564476e-04 0.429867189 #> 1269   634.0 0.5701891 0.5701891 1.559923e-04 1.559923e-04 0.429654912 #> 1270   634.5 0.5704017 0.5704017 1.555391e-04 1.555391e-04 0.429442734 #> 1271   635.0 0.5706143 0.5706143 1.550881e-04 1.550881e-04 0.429230654 #> 1272   635.5 0.5708267 0.5708267 1.546392e-04 1.546392e-04 0.429018673 #> 1273   636.0 0.5710390 0.5710390 1.541924e-04 1.541924e-04 0.428806790 #> 1274   636.5 0.5712512 0.5712512 1.537477e-04 1.537477e-04 0.428595006 #> 1275   637.0 0.5714634 0.5714634 1.533051e-04 1.533051e-04 0.428383320 #> 1276   637.5 0.5716754 0.5716754 1.528646e-04 1.528646e-04 0.428171733 #> 1277   638.0 0.5718873 0.5718873 1.524262e-04 1.524262e-04 0.427960244 #> 1278   638.5 0.5720992 0.5720992 1.519899e-04 1.519899e-04 0.427748853 #> 1279   639.0 0.5723109 0.5723109 1.515556e-04 1.515556e-04 0.427537561 #> 1280   639.5 0.5725225 0.5725225 1.511233e-04 1.511233e-04 0.427326367 #> 1281   640.0 0.5727340 0.5727340 1.506931e-04 1.506931e-04 0.427115272 #> 1282   640.5 0.5729455 0.5729455 1.502649e-04 1.502649e-04 0.426904275 #> 1283   641.0 0.5731568 0.5731568 1.498386e-04 1.498386e-04 0.426693377 #> 1284   641.5 0.5733680 0.5733680 1.494144e-04 1.494144e-04 0.426482576 #> 1285   642.0 0.5735791 0.5735791 1.489922e-04 1.489922e-04 0.426271875 #> 1286   642.5 0.5737902 0.5737902 1.485720e-04 1.485720e-04 0.426061271 #> 1287   643.0 0.5740011 0.5740011 1.481537e-04 1.481537e-04 0.425850766 #> 1288   643.5 0.5742119 0.5742119 1.477374e-04 1.477374e-04 0.425640359 #> 1289   644.0 0.5744226 0.5744226 1.473230e-04 1.473230e-04 0.425430050 #> 1290   644.5 0.5746333 0.5746333 1.469106e-04 1.469106e-04 0.425219839 #> 1291   645.0 0.5748438 0.5748438 1.465001e-04 1.465001e-04 0.425009727 #> 1292   645.5 0.5750542 0.5750542 1.460915e-04 1.460915e-04 0.424799713 #> 1293   646.0 0.5752645 0.5752645 1.456848e-04 1.456848e-04 0.424589797 #> 1294   646.5 0.5754747 0.5754747 1.452800e-04 1.452800e-04 0.424379979 #> 1295   647.0 0.5756849 0.5756849 1.448771e-04 1.448771e-04 0.424170260 #> 1296   647.5 0.5758949 0.5758949 1.444760e-04 1.444760e-04 0.423960638 #> 1297   648.0 0.5761048 0.5761048 1.440769e-04 1.440769e-04 0.423751115 #> 1298   648.5 0.5763146 0.5763146 1.436796e-04 1.436796e-04 0.423541690 #> 1299   649.0 0.5765244 0.5765244 1.432841e-04 1.432841e-04 0.423332363 #> 1300   649.5 0.5767340 0.5767340 1.428905e-04 1.428905e-04 0.423123134 #> 1301   650.0 0.5769435 0.5769435 1.424987e-04 1.424987e-04 0.422914002 #> 1302   650.5 0.5771529 0.5771529 1.421087e-04 1.421087e-04 0.422704969 #> 1303   651.0 0.5773622 0.5773622 1.417205e-04 1.417205e-04 0.422496034 #> 1304   651.5 0.5775715 0.5775715 1.413342e-04 1.413342e-04 0.422287197 #> 1305   652.0 0.5777806 0.5777806 1.409496e-04 1.409496e-04 0.422078458 #> 1306   652.5 0.5779896 0.5779896 1.405668e-04 1.405668e-04 0.421869817 #> 1307   653.0 0.5781985 0.5781985 1.401858e-04 1.401858e-04 0.421661274 #> 1308   653.5 0.5784074 0.5784074 1.398065e-04 1.398065e-04 0.421452829 #> 1309   654.0 0.5786161 0.5786161 1.394290e-04 1.394290e-04 0.421244482 #> 1310   654.5 0.5788247 0.5788247 1.390532e-04 1.390532e-04 0.421036232 #> 1311   655.0 0.5790332 0.5790332 1.386792e-04 1.386792e-04 0.420828080 #> 1312   655.5 0.5792417 0.5792417 1.383069e-04 1.383069e-04 0.420620027 #> 1313   656.0 0.5794500 0.5794500 1.379363e-04 1.379363e-04 0.420412071 #> 1314   656.5 0.5796582 0.5796582 1.375675e-04 1.375675e-04 0.420204212 #> 1315   657.0 0.5798663 0.5798663 1.372003e-04 1.372003e-04 0.419996452 #> 1316   657.5 0.5800744 0.5800744 1.368348e-04 1.368348e-04 0.419788789 #> 1317   658.0 0.5802823 0.5802823 1.364710e-04 1.364710e-04 0.419581224 #> 1318   658.5 0.5804901 0.5804901 1.361089e-04 1.361089e-04 0.419373757 #> 1319   659.0 0.5806979 0.5806979 1.357484e-04 1.357484e-04 0.419166388 #> 1320   659.5 0.5809055 0.5809055 1.353896e-04 1.353896e-04 0.418959116 #> 1321   660.0 0.5811130 0.5811130 1.350325e-04 1.350325e-04 0.418751941 #> 1322   660.5 0.5813205 0.5813205 1.346770e-04 1.346770e-04 0.418544865 #> 1323   661.0 0.5815278 0.5815278 1.343231e-04 1.343231e-04 0.418337886 #> 1324   661.5 0.5817350 0.5817350 1.339708e-04 1.339708e-04 0.418131004 #> 1325   662.0 0.5819422 0.5819422 1.336202e-04 1.336202e-04 0.417924220 #> 1326   662.5 0.5821492 0.5821492 1.332712e-04 1.332712e-04 0.417717534 #> 1327   663.0 0.5823561 0.5823561 1.329237e-04 1.329237e-04 0.417510945 #> 1328   663.5 0.5825630 0.5825630 1.325779e-04 1.325779e-04 0.417304454 #> 1329   664.0 0.5827697 0.5827697 1.322336e-04 1.322336e-04 0.417098060 #> 1330   664.5 0.5829763 0.5829763 1.318910e-04 1.318910e-04 0.416891764 #> 1331   665.0 0.5831829 0.5831829 1.315498e-04 1.315498e-04 0.416685565 #> 1332   665.5 0.5833893 0.5833893 1.312103e-04 1.312103e-04 0.416479463 #> 1333   666.0 0.5835957 0.5835957 1.308723e-04 1.308723e-04 0.416273459 #> 1334   666.5 0.5838019 0.5838019 1.305358e-04 1.305358e-04 0.416067552 #> 1335   667.0 0.5840081 0.5840081 1.302009e-04 1.302009e-04 0.415861742 #> 1336   667.5 0.5842141 0.5842141 1.298675e-04 1.298675e-04 0.415656030 #> 1337   668.0 0.5844200 0.5844200 1.295356e-04 1.295356e-04 0.415450415 #> 1338   668.5 0.5846259 0.5846259 1.292053e-04 1.292053e-04 0.415244898 #> 1339   669.0 0.5848316 0.5848316 1.288764e-04 1.288764e-04 0.415039477 #> 1340   669.5 0.5850373 0.5850373 1.285491e-04 1.285491e-04 0.414834154 #> 1341   670.0 0.5852428 0.5852428 1.282232e-04 1.282232e-04 0.414628928 #> 1342   670.5 0.5854483 0.5854483 1.278988e-04 1.278988e-04 0.414423799 #> 1343   671.0 0.5856537 0.5856537 1.275759e-04 1.275759e-04 0.414218768 #> 1344   671.5 0.5858589 0.5858589 1.272545e-04 1.272545e-04 0.414013833 #> 1345   672.0 0.5860641 0.5860641 1.269345e-04 1.269345e-04 0.413808996 #> 1346   672.5 0.5862691 0.5862691 1.266160e-04 1.266160e-04 0.413604255 #> 1347   673.0 0.5864741 0.5864741 1.262989e-04 1.262989e-04 0.413399612 #> 1348   673.5 0.5866790 0.5866790 1.259833e-04 1.259833e-04 0.413195066 #> 1349   674.0 0.5868837 0.5868837 1.256691e-04 1.256691e-04 0.412990616 #> 1350   674.5 0.5870884 0.5870884 1.253563e-04 1.253563e-04 0.412786264 #> 1351   675.0 0.5872929 0.5872929 1.250449e-04 1.250449e-04 0.412582008 #> 1352   675.5 0.5874974 0.5874974 1.247350e-04 1.247350e-04 0.412377850 #> 1353   676.0 0.5877018 0.5877018 1.244264e-04 1.244264e-04 0.412173788 #> 1354   676.5 0.5879061 0.5879061 1.241193e-04 1.241193e-04 0.411969824 #> 1355   677.0 0.5881102 0.5881102 1.238135e-04 1.238135e-04 0.411765956 #> 1356   677.5 0.5883143 0.5883143 1.235091e-04 1.235091e-04 0.411562185 #> 1357   678.0 0.5885183 0.5885183 1.232061e-04 1.232061e-04 0.411358511 #> 1358   678.5 0.5887222 0.5887222 1.229045e-04 1.229045e-04 0.411154933 #> 1359   679.0 0.5889259 0.5889259 1.226043e-04 1.226043e-04 0.410951453 #> 1360   679.5 0.5891296 0.5891296 1.223053e-04 1.223053e-04 0.410748069 #> 1361   680.0 0.5893332 0.5893332 1.220078e-04 1.220078e-04 0.410544781 #> 1362   680.5 0.5895367 0.5895367 1.217116e-04 1.217116e-04 0.410341591 #> 1363   681.0 0.5897401 0.5897401 1.214167e-04 1.214167e-04 0.410138497 #> 1364   681.5 0.5899434 0.5899434 1.211231e-04 1.211231e-04 0.409935499 #> 1365   682.0 0.5901466 0.5901466 1.208309e-04 1.208309e-04 0.409732599 #> 1366   682.5 0.5903497 0.5903497 1.205400e-04 1.205400e-04 0.409529795 #> 1367   683.0 0.5905527 0.5905527 1.202504e-04 1.202504e-04 0.409327087 #> 1368   683.5 0.5907556 0.5907556 1.199621e-04 1.199621e-04 0.409124476 #> 1369   684.0 0.5909584 0.5909584 1.196751e-04 1.196751e-04 0.408921961 #> 1370   684.5 0.5911611 0.5911611 1.193894e-04 1.193894e-04 0.408719543 #> 1371   685.0 0.5913637 0.5913637 1.191050e-04 1.191050e-04 0.408517221 #> 1372   685.5 0.5915662 0.5915662 1.188219e-04 1.188219e-04 0.408314996 #> 1373   686.0 0.5917686 0.5917686 1.185400e-04 1.185400e-04 0.408112867 #> 1374   686.5 0.5919709 0.5919709 1.182594e-04 1.182594e-04 0.407910834 #> 1375   687.0 0.5921731 0.5921731 1.179801e-04 1.179801e-04 0.407708898 #> 1376   687.5 0.5923752 0.5923752 1.177020e-04 1.177020e-04 0.407507058 #> 1377   688.0 0.5925773 0.5925773 1.174252e-04 1.174252e-04 0.407305314 #> 1378   688.5 0.5927792 0.5927792 1.171496e-04 1.171496e-04 0.407103667 #> 1379   689.0 0.5929810 0.5929810 1.168752e-04 1.168752e-04 0.406902116 #> 1380   689.5 0.5931827 0.5931827 1.166021e-04 1.166021e-04 0.406700661 #> 1381   690.0 0.5933844 0.5933844 1.163302e-04 1.163302e-04 0.406499302 #> 1382   690.5 0.5935859 0.5935859 1.160595e-04 1.160595e-04 0.406298039 #> 1383   691.0 0.5937873 0.5937873 1.157900e-04 1.157900e-04 0.406096872 #> 1384   691.5 0.5939887 0.5939887 1.155218e-04 1.155218e-04 0.405895802 #> 1385   692.0 0.5941899 0.5941899 1.152547e-04 1.152547e-04 0.405694827 #> 1386   692.5 0.5943911 0.5943911 1.149889e-04 1.149889e-04 0.405493949 #> 1387   693.0 0.5945921 0.5945921 1.147242e-04 1.147242e-04 0.405293166 #> 1388   693.5 0.5947931 0.5947931 1.144607e-04 1.144607e-04 0.405092480 #> 1389   694.0 0.5949939 0.5949939 1.141984e-04 1.141984e-04 0.404891889 #> 1390   694.5 0.5951947 0.5951947 1.139373e-04 1.139373e-04 0.404691395 #> 1391   695.0 0.5953953 0.5953953 1.136773e-04 1.136773e-04 0.404490996 #> 1392   695.5 0.5955959 0.5955959 1.134185e-04 1.134185e-04 0.404290693 #> 1393   696.0 0.5957964 0.5957964 1.131608e-04 1.131608e-04 0.404090486 #> 1394   696.5 0.5959967 0.5959967 1.129043e-04 1.129043e-04 0.403890374 #> 1395   697.0 0.5961970 0.5961970 1.126490e-04 1.126490e-04 0.403690359 #> 1396   697.5 0.5963972 0.5963972 1.123948e-04 1.123948e-04 0.403490439 #> 1397   698.0 0.5965972 0.5965972 1.121417e-04 1.121417e-04 0.403290615 #> 1398   698.5 0.5967972 0.5967972 1.118897e-04 1.118897e-04 0.403090886 #> 1399   699.0 0.5969971 0.5969971 1.116389e-04 1.116389e-04 0.402891254 #> 1400   699.5 0.5971969 0.5971969 1.113892e-04 1.113892e-04 0.402691716 #> 1401   700.0 0.5973966 0.5973966 1.111406e-04 1.111406e-04 0.402492275 #> 1402   700.5 0.5975962 0.5975962 1.108931e-04 1.108931e-04 0.402292929 #> 1403   701.0 0.5977957 0.5977957 1.106467e-04 1.106467e-04 0.402093678 #> 1404   701.5 0.5979951 0.5979951 1.104015e-04 1.104015e-04 0.401894523 #> 1405   702.0 0.5981944 0.5981944 1.101573e-04 1.101573e-04 0.401695464 #> 1406   702.5 0.5983936 0.5983936 1.099142e-04 1.099142e-04 0.401496500 #> 1407   703.0 0.5985927 0.5985927 1.096721e-04 1.096721e-04 0.401297631 #> 1408   703.5 0.5987917 0.5987917 1.094312e-04 1.094312e-04 0.401098858 #> 1409   704.0 0.5989906 0.5989906 1.091913e-04 1.091913e-04 0.400900180 #> 1410   704.5 0.5991895 0.5991895 1.089525e-04 1.089525e-04 0.400701597 #> 1411   705.0 0.5993882 0.5993882 1.087148e-04 1.087148e-04 0.400503110 #> 1412   705.5 0.5995868 0.5995868 1.084781e-04 1.084781e-04 0.400304718 #> 1413   706.0 0.5997853 0.5997853 1.082425e-04 1.082425e-04 0.400106421 #> 1414   706.5 0.5999838 0.5999838 1.080079e-04 1.080079e-04 0.399908220 #> 1415   707.0 0.6001821 0.6001821 1.077743e-04 1.077743e-04 0.399710113 #> 1416   707.5 0.6003804 0.6003804 1.075418e-04 1.075418e-04 0.399512102 #> 1417   708.0 0.6005785 0.6005785 1.073104e-04 1.073104e-04 0.399314186 #> 1418   708.5 0.6007766 0.6007766 1.070799e-04 1.070799e-04 0.399116365 #> 1419   709.0 0.6009745 0.6009745 1.068505e-04 1.068505e-04 0.398918639 #> 1420   709.5 0.6011724 0.6011724 1.066221e-04 1.066221e-04 0.398721008 #> 1421   710.0 0.6013701 0.6013701 1.063947e-04 1.063947e-04 0.398523472 #> 1422   710.5 0.6015678 0.6015678 1.061684e-04 1.061684e-04 0.398326031 #> 1423   711.0 0.6017654 0.6017654 1.059430e-04 1.059430e-04 0.398128685 #> 1424   711.5 0.6019628 0.6019628 1.057186e-04 1.057186e-04 0.397931434 #> 1425   712.0 0.6021602 0.6021602 1.054952e-04 1.054952e-04 0.397734277 #> 1426   712.5 0.6023575 0.6023575 1.052729e-04 1.052729e-04 0.397537216 #> 1427   713.0 0.6025547 0.6025547 1.050515e-04 1.050515e-04 0.397340249 #> 1428   713.5 0.6027518 0.6027518 1.048311e-04 1.048311e-04 0.397143377 #> 1429   714.0 0.6029488 0.6029488 1.046116e-04 1.046116e-04 0.396946600 #> 1430   714.5 0.6031457 0.6031457 1.043932e-04 1.043932e-04 0.396749918 #> 1431   715.0 0.6033425 0.6033425 1.041757e-04 1.041757e-04 0.396553330 #> 1432   715.5 0.6035392 0.6035392 1.039591e-04 1.039591e-04 0.396356837 #> 1433   716.0 0.6037358 0.6037358 1.037436e-04 1.037436e-04 0.396160439 #> 1434   716.5 0.6039323 0.6039323 1.035290e-04 1.035290e-04 0.395964135 #> 1435   717.0 0.6041288 0.6041288 1.033153e-04 1.033153e-04 0.395767925 #> 1436   717.5 0.6043251 0.6043251 1.031026e-04 1.031026e-04 0.395571811 #> 1437   718.0 0.6045213 0.6045213 1.028908e-04 1.028908e-04 0.395375790 #> 1438   718.5 0.6047175 0.6047175 1.026800e-04 1.026800e-04 0.395179864 #> 1439   719.0 0.6049135 0.6049135 1.024701e-04 1.024701e-04 0.394984033 #> 1440   719.5 0.6051094 0.6051094 1.022611e-04 1.022611e-04 0.394788296 #> 1441   720.0 0.6053053 0.6053053 1.020531e-04 1.020531e-04 0.394592653 #> 1442   720.5 0.6055010 0.6055010 1.018460e-04 1.018460e-04 0.394397105 #> 1443   721.0 0.6056967 0.6056967 1.016398e-04 1.016398e-04 0.394201651 #> 1444   721.5 0.6058923 0.6058923 1.014345e-04 1.014345e-04 0.394006292 #> 1445   722.0 0.6060877 0.6060877 1.012301e-04 1.012301e-04 0.393811026 #> 1446   722.5 0.6062831 0.6062831 1.010266e-04 1.010266e-04 0.393615855 #> 1447   723.0 0.6064784 0.6064784 1.008241e-04 1.008241e-04 0.393420778 #> 1448   723.5 0.6066736 0.6066736 1.006224e-04 1.006224e-04 0.393225795 #> 1449   724.0 0.6068687 0.6068687 1.004216e-04 1.004216e-04 0.393030906 #> 1450   724.5 0.6070637 0.6070637 1.002217e-04 1.002217e-04 0.392836111 #> 1451   725.0 0.6072586 0.6072586 1.000227e-04 1.000227e-04 0.392641411 #> 1452   725.5 0.6074534 0.6074534 9.982458e-05 9.982458e-05 0.392446804 #> 1453   726.0 0.6076481 0.6076481 9.962733e-05 9.962733e-05 0.392252291 #> 1454   726.5 0.6078427 0.6078427 9.943095e-05 9.943095e-05 0.392057873 #> 1455   727.0 0.6080372 0.6080372 9.923545e-05 9.923545e-05 0.391863548 #> 1456   727.5 0.6082316 0.6082316 9.904081e-05 9.904081e-05 0.391669317 #> 1457   728.0 0.6084260 0.6084260 9.884704e-05 9.884704e-05 0.391475180 #> 1458   728.5 0.6086202 0.6086202 9.865412e-05 9.865412e-05 0.391281137 #> 1459   729.0 0.6088144 0.6088144 9.846205e-05 9.846205e-05 0.391087187 #> 1460   729.5 0.6090084 0.6090084 9.827084e-05 9.827084e-05 0.390893331 #> 1461   730.0 0.6092024 0.6092024 9.808048e-05 9.808048e-05 0.390699569 #> 1462   730.5 0.6093962 0.6093962 9.789095e-05 9.789095e-05 0.390505901 #> 1463   731.0 0.6095900 0.6095900 9.770227e-05 9.770227e-05 0.390312327 #> 1464   731.5 0.6097836 0.6097836 9.751442e-05 9.751442e-05 0.390118846 #> 1465   732.0 0.6099772 0.6099772 9.732741e-05 9.732741e-05 0.389925458 #> 1466   732.5 0.6101707 0.6101707 9.714122e-05 9.714122e-05 0.389732164 #> 1467   733.0 0.6103641 0.6103641 9.695586e-05 9.695586e-05 0.389538964 #> 1468   733.5 0.6105574 0.6105574 9.677132e-05 9.677132e-05 0.389345857 #> 1469   734.0 0.6107506 0.6107506 9.658760e-05 9.658760e-05 0.389152844 #> 1470   734.5 0.6109437 0.6109437 9.640469e-05 9.640469e-05 0.388959924 #> 1471   735.0 0.6111367 0.6111367 9.622260e-05 9.622260e-05 0.388767098 #> 1472   735.5 0.6113296 0.6113296 9.604131e-05 9.604131e-05 0.388574364 #> 1473   736.0 0.6115224 0.6115224 9.586082e-05 9.586082e-05 0.388381725 #> 1474   736.5 0.6117151 0.6117151 9.568114e-05 9.568114e-05 0.388189178 #> 1475   737.0 0.6119078 0.6119078 9.550225e-05 9.550225e-05 0.387996725 #> 1476   737.5 0.6121003 0.6121003 9.532415e-05 9.532415e-05 0.387804365 #> 1477   738.0 0.6122928 0.6122928 9.514685e-05 9.514685e-05 0.387612098 #> 1478   738.5 0.6124851 0.6124851 9.497033e-05 9.497033e-05 0.387419924 #> 1479   739.0 0.6126774 0.6126774 9.479460e-05 9.479460e-05 0.387227844 #> 1480   739.5 0.6128695 0.6128695 9.461964e-05 9.461964e-05 0.387035856 #> 1481   740.0 0.6130616 0.6130616 9.444546e-05 9.444546e-05 0.386843962 #> 1482   740.5 0.6132536 0.6132536 9.427206e-05 9.427206e-05 0.386652160 #> 1483   741.0 0.6134454 0.6134454 9.409942e-05 9.409942e-05 0.386460452 #> 1484   741.5 0.6136372 0.6136372 9.392755e-05 9.392755e-05 0.386268836 #> 1485   742.0 0.6138289 0.6138289 9.375645e-05 9.375645e-05 0.386077314 #> 1486   742.5 0.6140205 0.6140205 9.358610e-05 9.358610e-05 0.385885884 #> 1487   743.0 0.6142120 0.6142120 9.341651e-05 9.341651e-05 0.385694548 #> 1488   743.5 0.6144034 0.6144034 9.324768e-05 9.324768e-05 0.385503304 #> 1489   744.0 0.6145948 0.6145948 9.307959e-05 9.307959e-05 0.385312153 #> 1490   744.5 0.6147860 0.6147860 9.291225e-05 9.291225e-05 0.385121094 #> 1491   745.0 0.6149771 0.6149771 9.274566e-05 9.274566e-05 0.384930128 #> 1492   745.5 0.6151682 0.6151682 9.257981e-05 9.257981e-05 0.384739256 #> 1493   746.0 0.6153591 0.6153591 9.241469e-05 9.241469e-05 0.384548475 #> 1494   746.5 0.6155500 0.6155500 9.225031e-05 9.225031e-05 0.384357788 #> 1495   747.0 0.6157407 0.6157407 9.208667e-05 9.208667e-05 0.384167192 #> 1496   747.5 0.6159314 0.6159314 9.192375e-05 9.192375e-05 0.383976690 #> 1497   748.0 0.6161220 0.6161220 9.176155e-05 9.176155e-05 0.383786280 #> 1498   748.5 0.6163124 0.6163124 9.160008e-05 9.160008e-05 0.383595962 #> 1499   749.0 0.6165028 0.6165028 9.143933e-05 9.143933e-05 0.383405737 #> 1500   749.5 0.6166931 0.6166931 9.127929e-05 9.127929e-05 0.383215605 #> 1501   750.0 0.6168833 0.6168833 9.111997e-05 9.111997e-05 0.383025564 #> 1502   750.5 0.6170734 0.6170734 9.096136e-05 9.096136e-05 0.382835616 #> 1503   751.0 0.6172634 0.6172634 9.080345e-05 9.080345e-05 0.382645761 #> 1504   751.5 0.6174534 0.6174534 9.064626e-05 9.064626e-05 0.382455997 #> 1505   752.0 0.6176432 0.6176432 9.048976e-05 9.048976e-05 0.382266326 #> 1506   752.5 0.6178329 0.6178329 9.033396e-05 9.033396e-05 0.382076747 #> 1507   753.0 0.6180226 0.6180226 9.017886e-05 9.017886e-05 0.381887261 #> 1508   753.5 0.6182121 0.6182121 9.002445e-05 9.002445e-05 0.381697866 #> 1509   754.0 0.6184016 0.6184016 8.987073e-05 8.987073e-05 0.381508564 #> 1510   754.5 0.6185909 0.6185909 8.971769e-05 8.971769e-05 0.381319353 #> 1511   755.0 0.6187802 0.6187802 8.956534e-05 8.956534e-05 0.381130235 #> 1512   755.5 0.6189694 0.6189694 8.941368e-05 8.941368e-05 0.380941208 #> 1513   756.0 0.6191585 0.6191585 8.926269e-05 8.926269e-05 0.380752274 #> 1514   756.5 0.6193475 0.6193475 8.911238e-05 8.911238e-05 0.380563432 #> 1515   757.0 0.6195364 0.6195364 8.896274e-05 8.896274e-05 0.380374681 #> 1516   757.5 0.6197252 0.6197252 8.881377e-05 8.881377e-05 0.380186022 #> 1517   758.0 0.6199139 0.6199139 8.866547e-05 8.866547e-05 0.379997455 #> 1518   758.5 0.6201025 0.6201025 8.851784e-05 8.851784e-05 0.379808980 #> 1519   759.0 0.6202910 0.6202910 8.837087e-05 8.837087e-05 0.379620597 #> 1520   759.5 0.6204795 0.6204795 8.822455e-05 8.822455e-05 0.379432305 #> 1521   760.0 0.6206678 0.6206678 8.807890e-05 8.807890e-05 0.379244105 #> 1522   760.5 0.6208561 0.6208561 8.793390e-05 8.793390e-05 0.379055997 #> 1523   761.0 0.6210442 0.6210442 8.778955e-05 8.778955e-05 0.378867981 #> 1524   761.5 0.6212323 0.6212323 8.764585e-05 8.764585e-05 0.378680056 #> 1525   762.0 0.6214203 0.6214203 8.750280e-05 8.750280e-05 0.378492222 #> 1526   762.5 0.6216082 0.6216082 8.736039e-05 8.736039e-05 0.378304480 #> 1527   763.0 0.6217960 0.6217960 8.721862e-05 8.721862e-05 0.378116830 #> 1528   763.5 0.6219837 0.6219837 8.707749e-05 8.707749e-05 0.377929271 #> 1529   764.0 0.6221713 0.6221713 8.693700e-05 8.693700e-05 0.377741803 #> 1530   764.5 0.6223588 0.6223588 8.679714e-05 8.679714e-05 0.377554427 #> 1531   765.0 0.6225462 0.6225462 8.665791e-05 8.665791e-05 0.377367142 #> 1532   765.5 0.6227335 0.6227335 8.651931e-05 8.651931e-05 0.377179948 #> 1533   766.0 0.6229208 0.6229208 8.638133e-05 8.638133e-05 0.376992846 #> 1534   766.5 0.6231079 0.6231079 8.624398e-05 8.624398e-05 0.376805835 #> 1535   767.0 0.6232950 0.6232950 8.610726e-05 8.610726e-05 0.376618915 #> 1536   767.5 0.6234819 0.6234819 8.597115e-05 8.597115e-05 0.376432086 #> 1537   768.0 0.6236688 0.6236688 8.583565e-05 8.583565e-05 0.376245348 #> 1538   768.5 0.6238556 0.6238556 8.570077e-05 8.570077e-05 0.376058702 #> 1539   769.0 0.6240423 0.6240423 8.556650e-05 8.556650e-05 0.375872146 #> 1540   769.5 0.6242289 0.6242289 8.543284e-05 8.543284e-05 0.375685682 #> 1541   770.0 0.6244154 0.6244154 8.529979e-05 8.529979e-05 0.375499308 #> 1542   770.5 0.6246018 0.6246018 8.516734e-05 8.516734e-05 0.375313026 #> 1543   771.0 0.6247881 0.6247881 8.503549e-05 8.503549e-05 0.375126834 #> 1544   771.5 0.6249744 0.6249744 8.490425e-05 8.490425e-05 0.374940733 #> 1545   772.0 0.6251605 0.6251605 8.477360e-05 8.477360e-05 0.374754724 #> 1546   772.5 0.6253466 0.6253466 8.464354e-05 8.464354e-05 0.374568804 #> 1547   773.0 0.6255325 0.6255325 8.451408e-05 8.451408e-05 0.374382976 #> 1548   773.5 0.6257184 0.6257184 8.438521e-05 8.438521e-05 0.374197239 #> 1549   774.0 0.6259042 0.6259042 8.425692e-05 8.425692e-05 0.374011592 #> 1550   774.5 0.6260898 0.6260898 8.412922e-05 8.412922e-05 0.373826036 #> 1551   775.0 0.6262754 0.6262754 8.400211e-05 8.400211e-05 0.373640570 #> 1552   775.5 0.6264609 0.6264609 8.387557e-05 8.387557e-05 0.373455195 #> 1553   776.0 0.6266463 0.6266463 8.374962e-05 8.374962e-05 0.373269911 #> 1554   776.5 0.6268317 0.6268317 8.362424e-05 8.362424e-05 0.373084717 #> 1555   777.0 0.6270169 0.6270169 8.349943e-05 8.349943e-05 0.372899613 #> 1556   777.5 0.6272020 0.6272020 8.337520e-05 8.337520e-05 0.372714601 #> 1557   778.0 0.6273871 0.6273871 8.325154e-05 8.325154e-05 0.372529678 #> 1558   778.5 0.6275720 0.6275720 8.312844e-05 8.312844e-05 0.372344846 #> 1559   779.0 0.6277569 0.6277569 8.300591e-05 8.300591e-05 0.372160104 #> 1560   779.5 0.6279417 0.6279417 8.288395e-05 8.288395e-05 0.371975453 #> 1561   780.0 0.6281263 0.6281263 8.276255e-05 8.276255e-05 0.371790891 #> 1562   780.5 0.6283109 0.6283109 8.264170e-05 8.264170e-05 0.371606420 #> 1563   781.0 0.6284954 0.6284954 8.252141e-05 8.252141e-05 0.371422040 #> 1564   781.5 0.6286798 0.6286798 8.240168e-05 8.240168e-05 0.371237749 #> 1565   782.0 0.6288642 0.6288642 8.228250e-05 8.228250e-05 0.371053549 #> 1566   782.5 0.6290484 0.6290484 8.216387e-05 8.216387e-05 0.370869438 #> 1567   783.0 0.6292325 0.6292325 8.204580e-05 8.204580e-05 0.370685418 #> 1568   783.5 0.6294166 0.6294166 8.192826e-05 8.192826e-05 0.370501488 #> 1569   784.0 0.6296005 0.6296005 8.181127e-05 8.181127e-05 0.370317647 #> 1570   784.5 0.6297844 0.6297844 8.169483e-05 8.169483e-05 0.370133897 #> 1571   785.0 0.6299682 0.6299682 8.157892e-05 8.157892e-05 0.369950237 #> 1572   785.5 0.6301519 0.6301519 8.146356e-05 8.146356e-05 0.369766666 #> 1573   786.0 0.6303355 0.6303355 8.134873e-05 8.134873e-05 0.369583185 #> 1574   786.5 0.6305190 0.6305190 8.123443e-05 8.123443e-05 0.369399795 #> 1575   787.0 0.6307024 0.6307024 8.112067e-05 8.112067e-05 0.369216494 #> 1576   787.5 0.6308857 0.6308857 8.100744e-05 8.100744e-05 0.369033282 #> 1577   788.0 0.6310689 0.6310689 8.089474e-05 8.089474e-05 0.368850160 #> 1578   788.5 0.6312521 0.6312521 8.078256e-05 8.078256e-05 0.368667128 #> 1579   789.0 0.6314351 0.6314351 8.067091e-05 8.067091e-05 0.368484186 #> 1580   789.5 0.6316181 0.6316181 8.055978e-05 8.055978e-05 0.368301333 #> 1581   790.0 0.6318010 0.6318010 8.044917e-05 8.044917e-05 0.368118570 #> 1582   790.5 0.6319838 0.6319838 8.033909e-05 8.033909e-05 0.367935896 #> 1583   791.0 0.6321665 0.6321665 8.022952e-05 8.022952e-05 0.367753312 #> 1584   791.5 0.6323491 0.6323491 8.012046e-05 8.012046e-05 0.367570817 #> 1585   792.0 0.6325316 0.6325316 8.001192e-05 8.001192e-05 0.367388412 #> 1586   792.5 0.6327140 0.6327140 7.990389e-05 7.990389e-05 0.367206096 #> 1587   793.0 0.6328963 0.6328963 7.979637e-05 7.979637e-05 0.367023869 #> 1588   793.5 0.6330786 0.6330786 7.968935e-05 7.968935e-05 0.366841732 #> 1589   794.0 0.6332607 0.6332607 7.958284e-05 7.958284e-05 0.366659684 #> 1590   794.5 0.6334428 0.6334428 7.947684e-05 7.947684e-05 0.366477725 #> 1591   795.0 0.6336248 0.6336248 7.937134e-05 7.937134e-05 0.366295855 #> 1592   795.5 0.6338067 0.6338067 7.926634e-05 7.926634e-05 0.366114075 #> 1593   796.0 0.6339885 0.6339885 7.916184e-05 7.916184e-05 0.365932384 #> 1594   796.5 0.6341702 0.6341702 7.905784e-05 7.905784e-05 0.365750781 #> 1595   797.0 0.6343518 0.6343518 7.895433e-05 7.895433e-05 0.365569268 #> 1596   797.5 0.6345333 0.6345333 7.885131e-05 7.885131e-05 0.365387844 #> 1597   798.0 0.6347147 0.6347147 7.874879e-05 7.874879e-05 0.365206508 #> 1598   798.5 0.6348961 0.6348961 7.864675e-05 7.864675e-05 0.365025262 #> 1599   799.0 0.6350774 0.6350774 7.854521e-05 7.854521e-05 0.364844105 #> 1600   799.5 0.6352585 0.6352585 7.844415e-05 7.844415e-05 0.364663036 #> 1601   800.0 0.6354396 0.6354396 7.834357e-05 7.834357e-05 0.364482057 #> 1602   800.5 0.6356206 0.6356206 7.824348e-05 7.824348e-05 0.364301166 #> 1603   801.0 0.6358015 0.6358015 7.814387e-05 7.814387e-05 0.364120363 #> 1604   801.5 0.6359823 0.6359823 7.804474e-05 7.804474e-05 0.363939650 #> 1605   802.0 0.6361630 0.6361630 7.794609e-05 7.794609e-05 0.363759025 #> 1606   802.5 0.6363437 0.6363437 7.784791e-05 7.784791e-05 0.363578489 #> 1607   803.0 0.6365242 0.6365242 7.775021e-05 7.775021e-05 0.363398042 #> 1608   803.5 0.6367047 0.6367047 7.765298e-05 7.765298e-05 0.363217683 #> 1609   804.0 0.6368850 0.6368850 7.755622e-05 7.755622e-05 0.363037412 #> 1610   804.5 0.6370653 0.6370653 7.745994e-05 7.745994e-05 0.362857231 #> 1611   805.0 0.6372455 0.6372455 7.736412e-05 7.736412e-05 0.362677137 #> 1612   805.5 0.6374256 0.6374256 7.726877e-05 7.726877e-05 0.362497132 #> 1613   806.0 0.6376056 0.6376056 7.717388e-05 7.717388e-05 0.362317216 #> 1614   806.5 0.6377855 0.6377855 7.707945e-05 7.707945e-05 0.362137387 #> 1615   807.0 0.6379654 0.6379654 7.698549e-05 7.698549e-05 0.361957648 #> 1616   807.5 0.6381451 0.6381451 7.689199e-05 7.689199e-05 0.361777996 #> 1617   808.0 0.6383248 0.6383248 7.679894e-05 7.679894e-05 0.361598433 #> 1618   808.5 0.6385043 0.6385043 7.670636e-05 7.670636e-05 0.361418958 #> 1619   809.0 0.6386838 0.6386838 7.661423e-05 7.661423e-05 0.361239571 #> 1620   809.5 0.6388632 0.6388632 7.652255e-05 7.652255e-05 0.361060272 #> 1621   810.0 0.6390425 0.6390425 7.643132e-05 7.643132e-05 0.360881061 #> 1622   810.5 0.6392217 0.6392217 7.634055e-05 7.634055e-05 0.360701938 #> 1623   811.0 0.6394008 0.6394008 7.625022e-05 7.625022e-05 0.360522904 #> 1624   811.5 0.6395799 0.6395799 7.616035e-05 7.616035e-05 0.360343957 #> 1625   812.0 0.6397588 0.6397588 7.607092e-05 7.607092e-05 0.360165098 #> 1626   812.5 0.6399377 0.6399377 7.598193e-05 7.598193e-05 0.359986328 #> 1627   813.0 0.6401165 0.6401165 7.589339e-05 7.589339e-05 0.359807645 #> 1628   813.5 0.6402951 0.6402951 7.580529e-05 7.580529e-05 0.359629050 #> 1629   814.0 0.6404737 0.6404737 7.571763e-05 7.571763e-05 0.359450543 #> 1630   814.5 0.6406522 0.6406522 7.563041e-05 7.563041e-05 0.359272123 #> 1631   815.0 0.6408307 0.6408307 7.554362e-05 7.554362e-05 0.359093792 #> 1632   815.5 0.6410090 0.6410090 7.545727e-05 7.545727e-05 0.358915548 #> 1633   816.0 0.6411872 0.6411872 7.537136e-05 7.537136e-05 0.358737391 #> 1634   816.5 0.6413654 0.6413654 7.528588e-05 7.528588e-05 0.358559323 #> 1635   817.0 0.6415435 0.6415435 7.520083e-05 7.520083e-05 0.358381341 #> 1636   817.5 0.6417214 0.6417214 7.511622e-05 7.511622e-05 0.358203448 #> 1637   818.0 0.6418993 0.6418993 7.503203e-05 7.503203e-05 0.358025642 #> 1638   818.5 0.6420771 0.6420771 7.494827e-05 7.494827e-05 0.357847923 #> 1639   819.0 0.6422548 0.6422548 7.486493e-05 7.486493e-05 0.357670292 #> 1640   819.5 0.6424325 0.6424325 7.478202e-05 7.478202e-05 0.357492748 #> 1641   820.0 0.6426100 0.6426100 7.469953e-05 7.469953e-05 0.357315292 #> 1642   820.5 0.6427875 0.6427875 7.461747e-05 7.461747e-05 0.357137923 #> 1643   821.0 0.6429648 0.6429648 7.453583e-05 7.453583e-05 0.356960641 #> 1644   821.5 0.6431421 0.6431421 7.445460e-05 7.445460e-05 0.356783447 #> 1645   822.0 0.6433193 0.6433193 7.437380e-05 7.437380e-05 0.356606340 #> 1646   822.5 0.6434964 0.6434964 7.429341e-05 7.429341e-05 0.356429320 #> 1647   823.0 0.6436734 0.6436734 7.421343e-05 7.421343e-05 0.356252387 #> 1648   823.5 0.6438503 0.6438503 7.413387e-05 7.413387e-05 0.356075541 #> 1649   824.0 0.6440272 0.6440272 7.405472e-05 7.405472e-05 0.355898783 #> 1650   824.5 0.6442039 0.6442039 7.397599e-05 7.397599e-05 0.355722111 #> 1651   825.0 0.6443806 0.6443806 7.389766e-05 7.389766e-05 0.355545526 #> 1652   825.5 0.6445572 0.6445572 7.381975e-05 7.381975e-05 0.355369029 #> 1653   826.0 0.6447336 0.6447336 7.374224e-05 7.374224e-05 0.355192618 #> 1654   826.5 0.6449100 0.6449100 7.366513e-05 7.366513e-05 0.355016294 #> 1655   827.0 0.6450864 0.6450864 7.358844e-05 7.358844e-05 0.354840057 #> 1656   827.5 0.6452626 0.6452626 7.351214e-05 7.351214e-05 0.354663907 #> 1657   828.0 0.6454387 0.6454387 7.343625e-05 7.343625e-05 0.354487844 #> 1658   828.5 0.6456148 0.6456148 7.336076e-05 7.336076e-05 0.354311867 #> 1659   829.0 0.6457907 0.6457907 7.328567e-05 7.328567e-05 0.354135977 #> 1660   829.5 0.6459666 0.6459666 7.321098e-05 7.321098e-05 0.353960174 #> 1661   830.0 0.6461424 0.6461424 7.313669e-05 7.313669e-05 0.353784457 #> 1662   830.5 0.6463181 0.6463181 7.306279e-05 7.306279e-05 0.353608827 #> 1663   831.0 0.6464937 0.6464937 7.298929e-05 7.298929e-05 0.353433284 #> 1664   831.5 0.6466693 0.6466693 7.291618e-05 7.291618e-05 0.353257827 #> 1665   832.0 0.6468447 0.6468447 7.284346e-05 7.284346e-05 0.353082457 #> 1666   832.5 0.6470201 0.6470201 7.277114e-05 7.277114e-05 0.352907173 #> 1667   833.0 0.6471953 0.6471953 7.269921e-05 7.269921e-05 0.352731975 #> 1668   833.5 0.6473705 0.6473705 7.262766e-05 7.262766e-05 0.352556864 #> 1669   834.0 0.6475456 0.6475456 7.255651e-05 7.255651e-05 0.352381839 #> 1670   834.5 0.6477206 0.6477206 7.248574e-05 7.248574e-05 0.352206901 #> 1671   835.0 0.6478955 0.6478955 7.241535e-05 7.241535e-05 0.352032048 #> 1672   835.5 0.6480704 0.6480704 7.234536e-05 7.234536e-05 0.351857282 #> 1673   836.0 0.6482451 0.6482451 7.227574e-05 7.227574e-05 0.351682603 #> 1674   836.5 0.6484198 0.6484198 7.220651e-05 7.220651e-05 0.351508009 #> 1675   837.0 0.6485944 0.6485944 7.213766e-05 7.213766e-05 0.351333501 #> 1676   837.5 0.6487689 0.6487689 7.206919e-05 7.206919e-05 0.351159080 #> 1677   838.0 0.6489433 0.6489433 7.200109e-05 7.200109e-05 0.350984745 #> 1678   838.5 0.6491176 0.6491176 7.193338e-05 7.193338e-05 0.350810495 #> 1679   839.0 0.6492918 0.6492918 7.186604e-05 7.186604e-05 0.350636332 #> 1680   839.5 0.6494659 0.6494659 7.179908e-05 7.179908e-05 0.350462254 #> 1681   840.0 0.6496400 0.6496400 7.173249e-05 7.173249e-05 0.350288263 #> 1682   840.5 0.6498140 0.6498140 7.166628e-05 7.166628e-05 0.350114357 #> 1683   841.0 0.6499879 0.6499879 7.160044e-05 7.160044e-05 0.349940537 #> 1684   841.5 0.6501617 0.6501617 7.153497e-05 7.153497e-05 0.349766803 #> 1685   842.0 0.6503354 0.6503354 7.146987e-05 7.146987e-05 0.349593155 #> 1686   842.5 0.6505090 0.6505090 7.140514e-05 7.140514e-05 0.349419593 #> 1687   843.0 0.6506825 0.6506825 7.134077e-05 7.134077e-05 0.349246116 #> 1688   843.5 0.6508560 0.6508560 7.127678e-05 7.127678e-05 0.349072724 #> 1689   844.0 0.6510294 0.6510294 7.121315e-05 7.121315e-05 0.348899419 #> 1690   844.5 0.6512027 0.6512027 7.114989e-05 7.114989e-05 0.348726199 #> 1691   845.0 0.6513758 0.6513758 7.108699e-05 7.108699e-05 0.348553064 #> 1692   845.5 0.6515490 0.6515490 7.102445e-05 7.102445e-05 0.348380015 #> 1693   846.0 0.6517220 0.6517220 7.096228e-05 7.096228e-05 0.348207052 #> 1694   846.5 0.6518949 0.6518949 7.090046e-05 7.090046e-05 0.348034174 #> 1695   847.0 0.6520678 0.6520678 7.083901e-05 7.083901e-05 0.347861381 #> 1696   847.5 0.6522405 0.6522405 7.077791e-05 7.077791e-05 0.347688674 #> 1697   848.0 0.6524132 0.6524132 7.071718e-05 7.071718e-05 0.347516052 #> 1698   848.5 0.6525858 0.6525858 7.065680e-05 7.065680e-05 0.347343515 #> 1699   849.0 0.6527583 0.6527583 7.059678e-05 7.059678e-05 0.347171063 #> 1700   849.5 0.6529308 0.6529308 7.053711e-05 7.053711e-05 0.346998697 #> 1701   850.0 0.6531031 0.6531031 7.047779e-05 7.047779e-05 0.346826416 #> 1702   850.5 0.6532754 0.6532754 7.041883e-05 7.041883e-05 0.346654220 #> 1703   851.0 0.6534475 0.6534475 7.036022e-05 7.036022e-05 0.346482109 #> 1704   851.5 0.6536196 0.6536196 7.030197e-05 7.030197e-05 0.346310083 #> 1705   852.0 0.6537916 0.6537916 7.024406e-05 7.024406e-05 0.346138142 #> 1706   852.5 0.6539635 0.6539635 7.018650e-05 7.018650e-05 0.345966286 #> 1707   853.0 0.6541354 0.6541354 7.012929e-05 7.012929e-05 0.345794515 #> 1708   853.5 0.6543071 0.6543071 7.007243e-05 7.007243e-05 0.345622829 #> 1709   854.0 0.6544788 0.6544788 7.001592e-05 7.001592e-05 0.345451228 #> 1710   854.5 0.6546503 0.6546503 6.995975e-05 6.995975e-05 0.345279712 #> 1711   855.0 0.6548218 0.6548218 6.990393e-05 6.990393e-05 0.345108281 #> 1712   855.5 0.6549932 0.6549932 6.984845e-05 6.984845e-05 0.344936934 #> 1713   856.0 0.6551645 0.6551645 6.979331e-05 6.979331e-05 0.344765672 #> 1714   856.5 0.6553358 0.6553358 6.973851e-05 6.973851e-05 0.344594495 #> 1715   857.0 0.6555069 0.6555069 6.968406e-05 6.968406e-05 0.344423402 #> 1716   857.5 0.6556780 0.6556780 6.962995e-05 6.962995e-05 0.344252394 #> 1717   858.0 0.6558490 0.6558490 6.957617e-05 6.957617e-05 0.344081471 #> 1718   858.5 0.6560198 0.6560198 6.952274e-05 6.952274e-05 0.343910632 #> 1719   859.0 0.6561907 0.6561907 6.946964e-05 6.946964e-05 0.343739877 #> 1720   859.5 0.6563614 0.6563614 6.941688e-05 6.941688e-05 0.343569207 #> 1721   860.0 0.6565320 0.6565320 6.936445e-05 6.936445e-05 0.343398622 #> 1722   860.5 0.6567026 0.6567026 6.931236e-05 6.931236e-05 0.343228121 #> 1723   861.0 0.6568730 0.6568730 6.926061e-05 6.926061e-05 0.343057704 #> 1724   861.5 0.6570434 0.6570434 6.920919e-05 6.920919e-05 0.342887372 #> 1725   862.0 0.6572137 0.6572137 6.915810e-05 6.915810e-05 0.342717124 #> 1726   862.5 0.6573839 0.6573839 6.910734e-05 6.910734e-05 0.342546960 #> 1727   863.0 0.6575541 0.6575541 6.905692e-05 6.905692e-05 0.342376880 #> 1728   863.5 0.6577241 0.6577241 6.900682e-05 6.900682e-05 0.342206885 #> 1729   864.0 0.6578941 0.6578941 6.895705e-05 6.895705e-05 0.342036974 #> 1730   864.5 0.6580639 0.6580639 6.890762e-05 6.890762e-05 0.341867147 #> 1731   865.0 0.6582337 0.6582337 6.885851e-05 6.885851e-05 0.341697404 #> 1732   865.5 0.6584034 0.6584034 6.880972e-05 6.880972e-05 0.341527745 #> 1733   866.0 0.6585731 0.6585731 6.876127e-05 6.876127e-05 0.341358169 #> 1734   866.5 0.6587426 0.6587426 6.871314e-05 6.871314e-05 0.341188678 #> 1735   867.0 0.6589121 0.6589121 6.866533e-05 6.866533e-05 0.341019271 #> 1736   867.5 0.6590814 0.6590814 6.861785e-05 6.861785e-05 0.340849948 #> 1737   868.0 0.6592507 0.6592507 6.857069e-05 6.857069e-05 0.340680709 #> 1738   868.5 0.6594199 0.6594199 6.852385e-05 6.852385e-05 0.340511553 #> 1739   869.0 0.6595890 0.6595890 6.847733e-05 6.847733e-05 0.340342481 #> 1740   869.5 0.6597581 0.6597581 6.843114e-05 6.843114e-05 0.340173493 #> 1741   870.0 0.6599270 0.6599270 6.838527e-05 6.838527e-05 0.340004589 #> 1742   870.5 0.6600959 0.6600959 6.833971e-05 6.833971e-05 0.339835768 #> 1743   871.0 0.6602647 0.6602647 6.829447e-05 6.829447e-05 0.339667031 #> 1744   871.5 0.6604334 0.6604334 6.824955e-05 6.824955e-05 0.339498377 #> 1745   872.0 0.6606020 0.6606020 6.820495e-05 6.820495e-05 0.339329807 #> 1746   872.5 0.6607705 0.6607705 6.816067e-05 6.816067e-05 0.339161321 #> 1747   873.0 0.6609390 0.6609390 6.811670e-05 6.811670e-05 0.338992918 #> 1748   873.5 0.6611073 0.6611073 6.807304e-05 6.807304e-05 0.338824599 #> 1749   874.0 0.6612756 0.6612756 6.802970e-05 6.802970e-05 0.338656363 #> 1750   874.5 0.6614438 0.6614438 6.798667e-05 6.798667e-05 0.338488210 #> 1751   875.0 0.6616119 0.6616119 6.794396e-05 6.794396e-05 0.338320141 #> 1752   875.5 0.6617799 0.6617799 6.790156e-05 6.790156e-05 0.338152155 #> 1753   876.0 0.6619479 0.6619479 6.785947e-05 6.785947e-05 0.337984252 #> 1754   876.5 0.6621157 0.6621157 6.781769e-05 6.781769e-05 0.337816432 #> 1755   877.0 0.6622835 0.6622835 6.777622e-05 6.777622e-05 0.337648696 #> 1756   877.5 0.6624512 0.6624512 6.773506e-05 6.773506e-05 0.337481043 #> 1757   878.0 0.6626188 0.6626188 6.769421e-05 6.769421e-05 0.337313473 #> 1758   878.5 0.6627864 0.6627864 6.765366e-05 6.765366e-05 0.337145986 #> 1759   879.0 0.6629538 0.6629538 6.761343e-05 6.761343e-05 0.336978582 #> 1760   879.5 0.6631212 0.6631212 6.757350e-05 6.757350e-05 0.336811261 #> 1761   880.0 0.6632884 0.6632884 6.753387e-05 6.753387e-05 0.336644023 #> 1762   880.5 0.6634556 0.6634556 6.749456e-05 6.749456e-05 0.336476868 #> 1763   881.0 0.6636227 0.6636227 6.745554e-05 6.745554e-05 0.336309796 #> 1764   881.5 0.6637898 0.6637898 6.741683e-05 6.741683e-05 0.336142807 #> 1765   882.0 0.6639567 0.6639567 6.737843e-05 6.737843e-05 0.335975900 #> 1766   882.5 0.6641236 0.6641236 6.734033e-05 6.734033e-05 0.335809077 #> 1767   883.0 0.6642904 0.6642904 6.730252e-05 6.730252e-05 0.335642336 #> 1768   883.5 0.6644571 0.6644571 6.726503e-05 6.726503e-05 0.335475678 #> 1769   884.0 0.6646237 0.6646237 6.722783e-05 6.722783e-05 0.335309102 #> 1770   884.5 0.6647902 0.6647902 6.719093e-05 6.719093e-05 0.335142610 #> 1771   885.0 0.6649566 0.6649566 6.715433e-05 6.715433e-05 0.334976200 #> 1772   885.5 0.6651230 0.6651230 6.711803e-05 6.711803e-05 0.334809872 #> 1773   886.0 0.6652893 0.6652893 6.708203e-05 6.708203e-05 0.334643627 #> 1774   886.5 0.6654555 0.6654555 6.704633e-05 6.704633e-05 0.334477464 #> 1775   887.0 0.6656216 0.6656216 6.701093e-05 6.701093e-05 0.334311384 #> 1776   887.5 0.6657876 0.6657876 6.697582e-05 6.697582e-05 0.334145387 #> 1777   888.0 0.6659536 0.6659536 6.694101e-05 6.694101e-05 0.333979471 #> 1778   888.5 0.6661195 0.6661195 6.690649e-05 6.690649e-05 0.333813639 #> 1779   889.0 0.6662852 0.6662852 6.687227e-05 6.687227e-05 0.333647888 #> 1780   889.5 0.6664509 0.6664509 6.683834e-05 6.683834e-05 0.333482220 #> 1781   890.0 0.6666166 0.6666166 6.680471e-05 6.680471e-05 0.333316634 #> 1782   890.5 0.6667821 0.6667821 6.677137e-05 6.677137e-05 0.333151130 #> 1783   891.0 0.6669476 0.6669476 6.673832e-05 6.673832e-05 0.332985708 #> 1784   891.5 0.6671129 0.6671129 6.670557e-05 6.670557e-05 0.332820369 #> 1785   892.0 0.6672782 0.6672782 6.667311e-05 6.667311e-05 0.332655111 #> 1786   892.5 0.6674434 0.6674434 6.664094e-05 6.664094e-05 0.332489936 #> 1787   893.0 0.6676085 0.6676085 6.660905e-05 6.660905e-05 0.332324843 #> 1788   893.5 0.6677736 0.6677736 6.657746e-05 6.657746e-05 0.332159832 #> 1789   894.0 0.6679386 0.6679386 6.654616e-05 6.654616e-05 0.331994902 #> 1790   894.5 0.6681034 0.6681034 6.651515e-05 6.651515e-05 0.331830055 #> 1791   895.0 0.6682682 0.6682682 6.648442e-05 6.648442e-05 0.331665289 #> 1792   895.5 0.6684329 0.6684329 6.645399e-05 6.645399e-05 0.331500606 #> 1793   896.0 0.6685976 0.6685976 6.642384e-05 6.642384e-05 0.331336004 #> 1794   896.5 0.6687621 0.6687621 6.639398e-05 6.639398e-05 0.331171484 #> 1795   897.0 0.6689266 0.6689266 6.636440e-05 6.636440e-05 0.331007046 #> 1796   897.5 0.6690910 0.6690910 6.633511e-05 6.633511e-05 0.330842689 #> 1797   898.0 0.6692553 0.6692553 6.630611e-05 6.630611e-05 0.330678414 #> 1798   898.5 0.6694195 0.6694195 6.627739e-05 6.627739e-05 0.330514221 #> 1799   899.0 0.6695836 0.6695836 6.624895e-05 6.624895e-05 0.330350109 #> 1800   899.5 0.6697477 0.6697477 6.622080e-05 6.622080e-05 0.330186079 #> 1801   900.0 0.6699117 0.6699117 6.619293e-05 6.619293e-05 0.330022130 #> 1802   900.5 0.6700756 0.6700756 6.616535e-05 6.616535e-05 0.329858263 #> 1803   901.0 0.6702394 0.6702394 6.613804e-05 6.613804e-05 0.329694478 #> 1804   901.5 0.6704031 0.6704031 6.611102e-05 6.611102e-05 0.329530773 #> 1805   902.0 0.6705668 0.6705668 6.608428e-05 6.608428e-05 0.329367150 #> 1806   902.5 0.6707303 0.6707303 6.605783e-05 6.605783e-05 0.329203609 #> 1807   903.0 0.6708938 0.6708938 6.603165e-05 6.603165e-05 0.329040149 #> 1808   903.5 0.6710572 0.6710572 6.600575e-05 6.600575e-05 0.328876770 #> 1809   904.0 0.6712205 0.6712205 6.598013e-05 6.598013e-05 0.328713472 #> 1810   904.5 0.6713838 0.6713838 6.595479e-05 6.595479e-05 0.328550256 #> 1811   905.0 0.6715469 0.6715469 6.592973e-05 6.592973e-05 0.328387120 #> 1812   905.5 0.6717100 0.6717100 6.590495e-05 6.590495e-05 0.328224066 #> 1813   906.0 0.6718730 0.6718730 6.588045e-05 6.588045e-05 0.328061093 #> 1814   906.5 0.6720359 0.6720359 6.585622e-05 6.585622e-05 0.327898201 #> 1815   907.0 0.6721988 0.6721988 6.583227e-05 6.583227e-05 0.327735390 #> 1816   907.5 0.6723615 0.6723615 6.580860e-05 6.580860e-05 0.327572660 #> 1817   908.0 0.6725242 0.6725242 6.578520e-05 6.578520e-05 0.327410011 #> 1818   908.5 0.6726868 0.6726868 6.576208e-05 6.576208e-05 0.327247443 #> 1819   909.0 0.6728493 0.6728493 6.573923e-05 6.573923e-05 0.327084956 #> 1820   909.5 0.6730117 0.6730117 6.571666e-05 6.571666e-05 0.326922549 #> 1821   910.0 0.6731741 0.6731741 6.569437e-05 6.569437e-05 0.326760224 #> 1822   910.5 0.6733363 0.6733363 6.567234e-05 6.567234e-05 0.326597979 #> 1823   911.0 0.6734985 0.6734985 6.565059e-05 6.565059e-05 0.326435815 #> 1824   911.5 0.6736606 0.6736606 6.562912e-05 6.562912e-05 0.326273731 #> 1825   912.0 0.6738227 0.6738227 6.560792e-05 6.560792e-05 0.326111729 #> 1826   912.5 0.6739846 0.6739846 6.558698e-05 6.558698e-05 0.325949807 #> 1827   913.0 0.6741465 0.6741465 6.556633e-05 6.556633e-05 0.325787965 #> 1828   913.5 0.6743082 0.6743082 6.554594e-05 6.554594e-05 0.325626204 #> 1829   914.0 0.6744700 0.6744700 6.552582e-05 6.552582e-05 0.325464524 #> 1830   914.5 0.6746316 0.6746316 6.550598e-05 6.550598e-05 0.325302924 #> 1831   915.0 0.6747931 0.6747931 6.548640e-05 6.548640e-05 0.325141404 #> 1832   915.5 0.6749546 0.6749546 6.546710e-05 6.546710e-05 0.324979965 #> 1833   916.0 0.6751159 0.6751159 6.544807e-05 6.544807e-05 0.324818607 #> 1834   916.5 0.6752772 0.6752772 6.542930e-05 6.542930e-05 0.324657328 #> 1835   917.0 0.6754385 0.6754385 6.541081e-05 6.541081e-05 0.324496130 #> 1836   917.5 0.6755996 0.6755996 6.539258e-05 6.539258e-05 0.324335012 #> 1837   918.0 0.6757607 0.6757607 6.537462e-05 6.537462e-05 0.324173975 #> 1838   918.5 0.6759216 0.6759216 6.535693e-05 6.535693e-05 0.324013018 #> 1839   919.0 0.6760825 0.6760825 6.533951e-05 6.533951e-05 0.323852140 #> 1840   919.5 0.6762433 0.6762433 6.532235e-05 6.532235e-05 0.323691343 #> 1841   920.0 0.6764041 0.6764041 6.530546e-05 6.530546e-05 0.323530627 #> 1842   920.5 0.6765647 0.6765647 6.528884e-05 6.528884e-05 0.323369990 #> 1843   921.0 0.6767253 0.6767253 6.527249e-05 6.527249e-05 0.323209433 #> 1844   921.5 0.6768858 0.6768858 6.525640e-05 6.525640e-05 0.323048956 #> 1845   922.0 0.6770462 0.6770462 6.524057e-05 6.524057e-05 0.322888559 #> 1846   922.5 0.6772065 0.6772065 6.522502e-05 6.522502e-05 0.322728242 #> 1847   923.0 0.6773668 0.6773668 6.520972e-05 6.520972e-05 0.322568005 #> 1848   923.5 0.6775270 0.6775270 6.519469e-05 6.519469e-05 0.322407848 #> 1849   924.0 0.6776870 0.6776870 6.517993e-05 6.517993e-05 0.322247770 #> 1850   924.5 0.6778471 0.6778471 6.516543e-05 6.516543e-05 0.322087773 #> 1851   925.0 0.6780070 0.6780070 6.515119e-05 6.515119e-05 0.321927855 #> 1852   925.5 0.6781668 0.6781668 6.513722e-05 6.513722e-05 0.321768016 #> 1853   926.0 0.6783266 0.6783266 6.512351e-05 6.512351e-05 0.321608258 #> 1854   926.5 0.6784863 0.6784863 6.511006e-05 6.511006e-05 0.321448579 #> 1855   927.0 0.6786459 0.6786459 6.509688e-05 6.509688e-05 0.321288980 #> 1856   927.5 0.6788055 0.6788055 6.508396e-05 6.508396e-05 0.321129460 #> 1857   928.0 0.6789649 0.6789649 6.507130e-05 6.507130e-05 0.320970020 #> 1858   928.5 0.6791243 0.6791243 6.505890e-05 6.505890e-05 0.320810659 #> 1859   929.0 0.6792836 0.6792836 6.504676e-05 6.504676e-05 0.320651378 #> 1860   929.5 0.6794428 0.6794428 6.503488e-05 6.503488e-05 0.320492176 #> 1861   930.0 0.6796019 0.6796019 6.502327e-05 6.502327e-05 0.320333053 #> 1862   930.5 0.6797610 0.6797610 6.501192e-05 6.501192e-05 0.320174010 #> 1863   931.0 0.6799200 0.6799200 6.500082e-05 6.500082e-05 0.320015046 #> 1864   931.5 0.6800788 0.6800788 6.498999e-05 6.498999e-05 0.319856162 #> 1865   932.0 0.6802377 0.6802377 6.497942e-05 6.497942e-05 0.319697356 #> 1866   932.5 0.6803964 0.6803964 6.496910e-05 6.496910e-05 0.319538630 #> 1867   933.0 0.6805551 0.6805551 6.495905e-05 6.495905e-05 0.319379983 #> 1868   933.5 0.6807136 0.6807136 6.494925e-05 6.494925e-05 0.319221416 #> 1869   934.0 0.6808721 0.6808721 6.493971e-05 6.493971e-05 0.319062927 #> 1870   934.5 0.6810306 0.6810306 6.493044e-05 6.493044e-05 0.318904517 #> 1871   935.0 0.6811889 0.6811889 6.492142e-05 6.492142e-05 0.318746187 #> 1872   935.5 0.6813472 0.6813472 6.491265e-05 6.491265e-05 0.318587935 #> 1873   936.0 0.6815053 0.6815053 6.490415e-05 6.490415e-05 0.318429763 #> 1874   936.5 0.6816634 0.6816634 6.489591e-05 6.489591e-05 0.318271669 #> 1875   937.0 0.6818215 0.6818215 6.488792e-05 6.488792e-05 0.318113654 #> 1876   937.5 0.6819794 0.6819794 6.488019e-05 6.488019e-05 0.317955718 #> 1877   938.0 0.6821373 0.6821373 6.487271e-05 6.487271e-05 0.317797861 #> 1878   938.5 0.6822951 0.6822951 6.486549e-05 6.486549e-05 0.317640083 #> 1879   939.0 0.6824528 0.6824528 6.485853e-05 6.485853e-05 0.317482383 #> 1880   939.5 0.6826104 0.6826104 6.485183e-05 6.485183e-05 0.317324762 #> 1881   940.0 0.6827679 0.6827679 6.484538e-05 6.484538e-05 0.317167220 #> 1882   940.5 0.6829254 0.6829254 6.483919e-05 6.483919e-05 0.317009757 #> 1883   941.0 0.6830828 0.6830828 6.483325e-05 6.483325e-05 0.316852372 #> 1884   941.5 0.6832401 0.6832401 6.482757e-05 6.482757e-05 0.316695065 #> 1885   942.0 0.6833973 0.6833973 6.482214e-05 6.482214e-05 0.316537837 #> 1886   942.5 0.6835545 0.6835545 6.481697e-05 6.481697e-05 0.316380688 #> 1887   943.0 0.6837116 0.6837116 6.481206e-05 6.481206e-05 0.316223617 #> 1888   943.5 0.6838686 0.6838686 6.480740e-05 6.480740e-05 0.316066625 #> 1889   944.0 0.6840255 0.6840255 6.480299e-05 6.480299e-05 0.315909711 #> 1890   944.5 0.6841823 0.6841823 6.479884e-05 6.479884e-05 0.315752875 #> 1891   945.0 0.6843391 0.6843391 6.479494e-05 6.479494e-05 0.315596118 #> 1892   945.5 0.6844958 0.6844958 6.479130e-05 6.479130e-05 0.315439439 #> 1893   946.0 0.6846524 0.6846524 6.478791e-05 6.478791e-05 0.315282838 #> 1894   946.5 0.6848089 0.6848089 6.478477e-05 6.478477e-05 0.315126316 #> 1895   947.0 0.6849653 0.6849653 6.478189e-05 6.478189e-05 0.314969871 #> 1896   947.5 0.6851217 0.6851217 6.477926e-05 6.477926e-05 0.314813505 #> 1897   948.0 0.6852780 0.6852780 6.477689e-05 6.477689e-05 0.314657217 #> 1898   948.5 0.6854342 0.6854342 6.477476e-05 6.477476e-05 0.314501007 #> 1899   949.0 0.6855904 0.6855904 6.477290e-05 6.477290e-05 0.314344875 #> 1900   949.5 0.6857464 0.6857464 6.477128e-05 6.477128e-05 0.314188822 #> 1901   950.0 0.6859024 0.6859024 6.476991e-05 6.476991e-05 0.314032846 #> 1902   950.5 0.6860583 0.6860583 6.476880e-05 6.476880e-05 0.313876948 #> 1903   951.0 0.6862141 0.6862141 6.476794e-05 6.476794e-05 0.313721128 #> 1904   951.5 0.6863698 0.6863698 6.476734e-05 6.476734e-05 0.313565385 #> 1905   952.0 0.6865255 0.6865255 6.476698e-05 6.476698e-05 0.313409721 #> 1906   952.5 0.6866811 0.6866811 6.476688e-05 6.476688e-05 0.313254135 #> 1907   953.0 0.6868366 0.6868366 6.476703e-05 6.476703e-05 0.313098626 #> 1908   953.5 0.6869920 0.6869920 6.476743e-05 6.476743e-05 0.312943195 #> 1909   954.0 0.6871474 0.6871474 6.476808e-05 6.476808e-05 0.312787842 #> 1910   954.5 0.6873027 0.6873027 6.476899e-05 6.476899e-05 0.312632566 #> 1911   955.0 0.6874579 0.6874579 6.477014e-05 6.477014e-05 0.312477368 #> 1912   955.5 0.6876130 0.6876130 6.477155e-05 6.477155e-05 0.312322248 #> 1913   956.0 0.6877680 0.6877680 6.477321e-05 6.477321e-05 0.312167205 #> 1914   956.5 0.6879230 0.6879230 6.477512e-05 6.477512e-05 0.312012239 #> 1915   957.0 0.6880779 0.6880779 6.477728e-05 6.477728e-05 0.311857352 #> 1916   957.5 0.6882327 0.6882327 6.477969e-05 6.477969e-05 0.311702541 #> 1917   958.0 0.6883874 0.6883874 6.478235e-05 6.478235e-05 0.311547808 #> 1918   958.5 0.6885421 0.6885421 6.478526e-05 6.478526e-05 0.311393153 #> 1919   959.0 0.6886966 0.6886966 6.478842e-05 6.478842e-05 0.311238575 #> 1920   959.5 0.6888511 0.6888511 6.479184e-05 6.479184e-05 0.311084074 #> 1921   960.0 0.6890056 0.6890056 6.479550e-05 6.479550e-05 0.310929650 #> 1922   960.5 0.6891599 0.6891599 6.479941e-05 6.479941e-05 0.310775304 #> 1923   961.0 0.6893142 0.6893142 6.480358e-05 6.480358e-05 0.310621035 #> 1924   961.5 0.6894683 0.6894683 6.480799e-05 6.480799e-05 0.310466843 #> 1925   962.0 0.6896225 0.6896225 6.481266e-05 6.481266e-05 0.310312729 #> 1926   962.5 0.6897765 0.6897765 6.481757e-05 6.481757e-05 0.310158691 #> 1927   963.0 0.6899304 0.6899304 6.482274e-05 6.482274e-05 0.310004730 #> 1928   963.5 0.6900843 0.6900843 6.482815e-05 6.482815e-05 0.309850847 #> 1929   964.0 0.6902381 0.6902381 6.483381e-05 6.483381e-05 0.309697040 #> 1930   964.5 0.6903918 0.6903918 6.483973e-05 6.483973e-05 0.309543311 #> 1931   965.0 0.6905455 0.6905455 6.484589e-05 6.484589e-05 0.309389658 #> 1932   965.5 0.6906991 0.6906991 6.485230e-05 6.485230e-05 0.309236083 #> 1933   966.0 0.6908526 0.6908526 6.485897e-05 6.485897e-05 0.309082584 #> 1934   966.5 0.6910060 0.6910060 6.486588e-05 6.486588e-05 0.308929162 #> 1935   967.0 0.6911593 0.6911593 6.487304e-05 6.487304e-05 0.308775817 #> 1936   967.5 0.6913126 0.6913126 6.488045e-05 6.488045e-05 0.308622549 #> 1937   968.0 0.6914658 0.6914658 6.488811e-05 6.488811e-05 0.308469357 #> 1938   968.5 0.6916189 0.6916189 6.489602e-05 6.489602e-05 0.308316242 #> 1939   969.0 0.6917719 0.6917719 6.490418e-05 6.490418e-05 0.308163204 #> 1940   969.5 0.6919248 0.6919248 6.491259e-05 6.491259e-05 0.308010243 #> 1941   970.0 0.6920777 0.6920777 6.492125e-05 6.492125e-05 0.307857358 #> 1942   970.5 0.6922305 0.6922305 6.493015e-05 6.493015e-05 0.307704549 #> 1943   971.0 0.6923832 0.6923832 6.493931e-05 6.493931e-05 0.307551817 #> 1944   971.5 0.6925359 0.6925359 6.494871e-05 6.494871e-05 0.307399162 #> 1945   972.0 0.6926885 0.6926885 6.495837e-05 6.495837e-05 0.307246583 #> 1946   972.5 0.6928410 0.6928410 6.496827e-05 6.496827e-05 0.307094081 #> 1947   973.0 0.6929934 0.6929934 6.497843e-05 6.497843e-05 0.306941655 #> 1948   973.5 0.6931457 0.6931457 6.498883e-05 6.498883e-05 0.306789305 #> 1949   974.0 0.6932980 0.6932980 6.499948e-05 6.499948e-05 0.306637032 #> 1950   974.5 0.6934502 0.6934502 6.501038e-05 6.501038e-05 0.306484835 #> 1951   975.0 0.6936023 0.6936023 6.502153e-05 6.502153e-05 0.306332714 #> 1952   975.5 0.6937543 0.6937543 6.503293e-05 6.503293e-05 0.306180669 #> 1953   976.0 0.6939063 0.6939063 6.504458e-05 6.504458e-05 0.306028701 #> 1954   976.5 0.6940581 0.6940581 6.505647e-05 6.505647e-05 0.305876809 #> 1955   977.0 0.6942099 0.6942099 6.506862e-05 6.506862e-05 0.305724993 #> 1956   977.5 0.6943617 0.6943617 6.508101e-05 6.508101e-05 0.305573253 #> 1957   978.0 0.6945133 0.6945133 6.509366e-05 6.509366e-05 0.305421589 #> 1958   978.5 0.6946649 0.6946649 6.510655e-05 6.510655e-05 0.305270001 #> 1959   979.0 0.6948164 0.6948164 6.511970e-05 6.511970e-05 0.305118489 #> 1960   979.5 0.6949678 0.6949678 6.513309e-05 6.513309e-05 0.304967053 #> 1961   980.0 0.6951192 0.6951192 6.514673e-05 6.514673e-05 0.304815693 #> 1962   980.5 0.6952704 0.6952704 6.516062e-05 6.516062e-05 0.304664409 #> 1963   981.0 0.6954216 0.6954216 6.517476e-05 6.517476e-05 0.304513201 #> 1964   981.5 0.6955727 0.6955727 6.518915e-05 6.518915e-05 0.304362068 #> 1965   982.0 0.6957238 0.6957238 6.520379e-05 6.520379e-05 0.304211012 #> 1966   982.5 0.6958748 0.6958748 6.521868e-05 6.521868e-05 0.304060031 #> 1967   983.0 0.6960256 0.6960256 6.523382e-05 6.523382e-05 0.303909126 #> 1968   983.5 0.6961765 0.6961765 6.524920e-05 6.524920e-05 0.303758296 #> 1969   984.0 0.6963272 0.6963272 6.526484e-05 6.526484e-05 0.303607542 #> 1970   984.5 0.6964779 0.6964779 6.528073e-05 6.528073e-05 0.303456864 #> 1971   985.0 0.6966284 0.6966284 6.529687e-05 6.529687e-05 0.303306261 #> 1972   985.5 0.6967790 0.6967790 6.531325e-05 6.531325e-05 0.303155734 #> 1973   986.0 0.6969294 0.6969294 6.532989e-05 6.532989e-05 0.303005283 #> 1974   986.5 0.6970797 0.6970797 6.534678e-05 6.534678e-05 0.302854907 #> 1975   987.0 0.6972300 0.6972300 6.536391e-05 6.536391e-05 0.302704606 #> 1976   987.5 0.6973802 0.6973802 6.538130e-05 6.538130e-05 0.302554381 #> 1977   988.0 0.6975304 0.6975304 6.539894e-05 6.539894e-05 0.302404231 #> 1978   988.5 0.6976804 0.6976804 6.541682e-05 6.541682e-05 0.302254157 #> 1979   989.0 0.6978304 0.6978304 6.543496e-05 6.543496e-05 0.302104157 #> 1980   989.5 0.6979803 0.6979803 6.545335e-05 6.545335e-05 0.301954234 #> 1981   990.0 0.6981301 0.6981301 6.547199e-05 6.547199e-05 0.301804385 #> 1982   990.5 0.6982799 0.6982799 6.549087e-05 6.549087e-05 0.301654612 #> 1983   991.0 0.6984296 0.6984296 6.551001e-05 6.551001e-05 0.301504913 #> 1984   991.5 0.6985792 0.6985792 6.552940e-05 6.552940e-05 0.301355290 #> 1985   992.0 0.6987287 0.6987287 6.554905e-05 6.554905e-05 0.301205742 #> 1986   992.5 0.6988782 0.6988782 6.556894e-05 6.556894e-05 0.301056269 #> 1987   993.0 0.6990275 0.6990275 6.558908e-05 6.558908e-05 0.300906872 #> 1988   993.5 0.6991768 0.6991768 6.560947e-05 6.560947e-05 0.300757549 #> 1989   994.0 0.6993261 0.6993261 6.563012e-05 6.563012e-05 0.300608301 #> 1990   994.5 0.6994752 0.6994752 6.565102e-05 6.565102e-05 0.300459128 #> 1991   995.0 0.6996243 0.6996243 6.567216e-05 6.567216e-05 0.300310030 #> 1992   995.5 0.6997733 0.6997733 6.569356e-05 6.569356e-05 0.300161007 #> 1993   996.0 0.6999222 0.6999222 6.571521e-05 6.571521e-05 0.300012059 #> 1994   996.5 0.7000711 0.7000711 6.573712e-05 6.573712e-05 0.299863186 #> 1995   997.0 0.7002199 0.7002199 6.575927e-05 6.575927e-05 0.299714387 #> 1996   997.5 0.7003686 0.7003686 6.578168e-05 6.578168e-05 0.299565663 #> 1997   998.0 0.7005172 0.7005172 6.580434e-05 6.580434e-05 0.299417014 #> 1998   998.5 0.7006657 0.7006657 6.582725e-05 6.582725e-05 0.299268440 #> 1999   999.0 0.7008142 0.7008142 6.585042e-05 6.585042e-05 0.299119940 #> 2000   999.5 0.7009626 0.7009626 6.587383e-05 6.587383e-05 0.298971515 #> 2001  1000.0 0.7011109 0.7011109 6.589750e-05 6.589750e-05 0.298823164 #> 2002  1000.5 0.7012592 0.7012592 6.592143e-05 6.592143e-05 0.298674888 #> 2003  1001.0 0.7014074 0.7014074 6.594560e-05 6.594560e-05 0.298526687 #> 2004  1001.5 0.7015555 0.7015555 6.597003e-05 6.597003e-05 0.298378560 #> 2005  1002.0 0.7017035 0.7017035 6.599471e-05 6.599471e-05 0.298230507 #> 2006  1002.5 0.7018515 0.7018515 6.601965e-05 6.601965e-05 0.298082529 #> 2007  1003.0 0.7019993 0.7019993 6.604484e-05 6.604484e-05 0.297934625 #> 2008  1003.5 0.7021471 0.7021471 6.607028e-05 6.607028e-05 0.297786796 #> 2009  1004.0 0.7022949 0.7022949 6.609598e-05 6.609598e-05 0.297639041 #> 2010  1004.5 0.7024425 0.7024425 6.612193e-05 6.612193e-05 0.297491360 #> 2011  1005.0 0.7025901 0.7025901 6.614813e-05 6.614813e-05 0.297343753 #> 2012  1005.5 0.7027376 0.7027376 6.617459e-05 6.617459e-05 0.297196221 #> 2013  1006.0 0.7028850 0.7028850 6.620131e-05 6.620131e-05 0.297048763 #> 2014  1006.5 0.7030324 0.7030324 6.622828e-05 6.622828e-05 0.296901379 #> 2015  1007.0 0.7031797 0.7031797 6.625550e-05 6.625550e-05 0.296754069 #> 2016  1007.5 0.7033269 0.7033269 6.628298e-05 6.628298e-05 0.296606834 #> 2017  1008.0 0.7034740 0.7034740 6.631072e-05 6.631072e-05 0.296459672 #> 2018  1008.5 0.7036211 0.7036211 6.633871e-05 6.633871e-05 0.296312584 #> 2019  1009.0 0.7037681 0.7037681 6.636695e-05 6.636695e-05 0.296165571 #> 2020  1009.5 0.7039150 0.7039150 6.639545e-05 6.639545e-05 0.296018631 #> 2021  1010.0 0.7040618 0.7040618 6.642421e-05 6.642421e-05 0.295871765 #> 2022  1010.5 0.7042086 0.7042086 6.645323e-05 6.645323e-05 0.295724973 #> 2023  1011.0 0.7043553 0.7043553 6.648250e-05 6.648250e-05 0.295578255 #> 2024  1011.5 0.7045019 0.7045019 6.651203e-05 6.651203e-05 0.295431611 #> 2025  1012.0 0.7046484 0.7046484 6.654181e-05 6.654181e-05 0.295285041 #> 2026  1012.5 0.7047949 0.7047949 6.657185e-05 6.657185e-05 0.295138544 #> 2027  1013.0 0.7049413 0.7049413 6.660215e-05 6.660215e-05 0.294992121 #> 2028  1013.5 0.7050876 0.7050876 6.663271e-05 6.663271e-05 0.294845772 #> 2029  1014.0 0.7052338 0.7052338 6.666352e-05 6.666352e-05 0.294699497 #> 2030  1014.5 0.7053800 0.7053800 6.669460e-05 6.669460e-05 0.294553295 #> 2031  1015.0 0.7055261 0.7055261 6.672593e-05 6.672593e-05 0.294407166 #> 2032  1015.5 0.7056721 0.7056721 6.675752e-05 6.675752e-05 0.294261112 #> 2033  1016.0 0.7058181 0.7058181 6.678937e-05 6.678937e-05 0.294115130 #> 2034  1016.5 0.7059640 0.7059640 6.682147e-05 6.682147e-05 0.293969223 #> 2035  1017.0 0.7061098 0.7061098 6.685384e-05 6.685384e-05 0.293823389 #> 2036  1017.5 0.7062555 0.7062555 6.688647e-05 6.688647e-05 0.293677628 #> 2037  1018.0 0.7064011 0.7064011 6.691935e-05 6.691935e-05 0.293531941 #> 2038  1018.5 0.7065467 0.7065467 6.695250e-05 6.695250e-05 0.293386327 #> 2039  1019.0 0.7066922 0.7066922 6.698590e-05 6.698590e-05 0.293240786 #> 2040  1019.5 0.7068377 0.7068377 6.701957e-05 6.701957e-05 0.293095319 #> 2041  1020.0 0.7069830 0.7069830 6.705350e-05 6.705350e-05 0.292949925 #> 2042  1020.5 0.7071283 0.7071283 6.708768e-05 6.708768e-05 0.292804604 #> 2043  1021.0 0.7072735 0.7072735 6.712213e-05 6.712213e-05 0.292659356 #> 2044  1021.5 0.7074187 0.7074187 6.715684e-05 6.715684e-05 0.292514182 #> 2045  1022.0 0.7075637 0.7075637 6.719181e-05 6.719181e-05 0.292369081 #> 2046  1022.5 0.7077087 0.7077087 6.722705e-05 6.722705e-05 0.292224052 #> 2047  1023.0 0.7078536 0.7078536 6.726254e-05 6.726254e-05 0.292079097 #> 2048  1023.5 0.7079985 0.7079985 6.729830e-05 6.729830e-05 0.291934215 #> 2049  1024.0 0.7081433 0.7081433 6.733432e-05 6.733432e-05 0.291789407 #> 2050  1024.5 0.7082880 0.7082880 6.737061e-05 6.737061e-05 0.291644671 #> 2051  1025.0 0.7084326 0.7084326 6.740716e-05 6.740716e-05 0.291500008 #> 2052  1025.5 0.7085771 0.7085771 6.744397e-05 6.744397e-05 0.291355417 #> 2053  1026.0 0.7087216 0.7087216 6.748104e-05 6.748104e-05 0.291210900 #> 2054  1026.5 0.7088660 0.7088660 6.751838e-05 6.751838e-05 0.291066456 #> 2055  1027.0 0.7090104 0.7090104 6.755599e-05 6.755599e-05 0.290922084 #> 2056  1027.5 0.7091546 0.7091546 6.759385e-05 6.759385e-05 0.290777786 #> 2057  1028.0 0.7092988 0.7092988 6.763199e-05 6.763199e-05 0.290633560 #> 2058  1028.5 0.7094429 0.7094429 6.767039e-05 6.767039e-05 0.290489406 #> 2059  1029.0 0.7095870 0.7095870 6.770905e-05 6.770905e-05 0.290345326 #> 2060  1029.5 0.7097309 0.7097309 6.774798e-05 6.774798e-05 0.290201318 #> 2061  1030.0 0.7098748 0.7098748 6.778718e-05 6.778718e-05 0.290057383 #> 2062  1030.5 0.7100187 0.7100187 6.782664e-05 6.782664e-05 0.289913520 #> 2063  1031.0 0.7101624 0.7101624 6.786637e-05 6.786637e-05 0.289769730 #> 2064  1031.5 0.7103061 0.7103061 6.790637e-05 6.790637e-05 0.289626013 #> 2065  1032.0 0.7104497 0.7104497 6.794664e-05 6.794664e-05 0.289482368 #> 2066  1032.5 0.7105932 0.7105932 6.798717e-05 6.798717e-05 0.289338795 #> 2067  1033.0 0.7107367 0.7107367 6.802797e-05 6.802797e-05 0.289195295 #> 2068  1033.5 0.7108801 0.7108801 6.806904e-05 6.806904e-05 0.289051868 #> 2069  1034.0 0.7110234 0.7110234 6.811038e-05 6.811038e-05 0.288908512 #> 2070  1034.5 0.7111666 0.7111666 6.815199e-05 6.815199e-05 0.288765229 #> 2071  1035.0 0.7113098 0.7113098 6.819387e-05 6.819387e-05 0.288622019 #> 2072  1035.5 0.7114529 0.7114529 6.823602e-05 6.823602e-05 0.288478880 #> 2073  1036.0 0.7115959 0.7115959 6.827843e-05 6.827843e-05 0.288335814 #> 2074  1036.5 0.7117389 0.7117389 6.832112e-05 6.832112e-05 0.288192821 #> 2075  1037.0 0.7118817 0.7118817 6.836408e-05 6.836408e-05 0.288049899 #> 2076  1037.5 0.7120245 0.7120245 6.840731e-05 6.840731e-05 0.287907050 #> 2077  1038.0 0.7121673 0.7121673 6.845082e-05 6.845082e-05 0.287764272 #> 2078  1038.5 0.7123099 0.7123099 6.849459e-05 6.849459e-05 0.287621567 #> 2079  1039.0 0.7124525 0.7124525 6.853864e-05 6.853864e-05 0.287478934 #> 2080  1039.5 0.7125950 0.7125950 6.858296e-05 6.858296e-05 0.287336373 #> 2081  1040.0 0.7127375 0.7127375 6.862755e-05 6.862755e-05 0.287193883 #> 2082  1040.5 0.7128799 0.7128799 6.867242e-05 6.867242e-05 0.287051466 #> 2083  1041.0 0.7130222 0.7130222 6.871756e-05 6.871756e-05 0.286909121 #> 2084  1041.5 0.7131644 0.7131644 6.876297e-05 6.876297e-05 0.286766848 #> 2085  1042.0 0.7133065 0.7133065 6.880866e-05 6.880866e-05 0.286624646 #> 2086  1042.5 0.7134486 0.7134486 6.885462e-05 6.885462e-05 0.286482517 #> 2087  1043.0 0.7135906 0.7135906 6.890086e-05 6.890086e-05 0.286340459 #> 2088  1043.5 0.7137326 0.7137326 6.894738e-05 6.894738e-05 0.286198473 #> 2089  1044.0 0.7138744 0.7138744 6.899417e-05 6.899417e-05 0.286056559 #> 2090  1044.5 0.7140162 0.7140162 6.904124e-05 6.904124e-05 0.285914716 #> 2091  1045.0 0.7141580 0.7141580 6.908858e-05 6.908858e-05 0.285772945 #> 2092  1045.5 0.7142996 0.7142996 6.913620e-05 6.913620e-05 0.285631246 #> 2093  1046.0 0.7144412 0.7144412 6.918410e-05 6.918410e-05 0.285489619 #> 2094  1046.5 0.7145827 0.7145827 6.923228e-05 6.923228e-05 0.285348063 #> 2095  1047.0 0.7147241 0.7147241 6.928074e-05 6.928074e-05 0.285206578 #> 2096  1047.5 0.7148655 0.7148655 6.932947e-05 6.932947e-05 0.285065165 #> 2097  1048.0 0.7150068 0.7150068 6.937849e-05 6.937849e-05 0.284923824 #> 2098  1048.5 0.7151480 0.7151480 6.942778e-05 6.942778e-05 0.284782554 #> 2099  1049.0 0.7152892 0.7152892 6.947736e-05 6.947736e-05 0.284641356 #> 2100  1049.5 0.7154302 0.7154302 6.952721e-05 6.952721e-05 0.284500229 #> 2101  1050.0 0.7155712 0.7155712 6.957735e-05 6.957735e-05 0.284359173 #> 2102  1050.5 0.7157122 0.7157122 6.962777e-05 6.962777e-05 0.284218189 #> 2103  1051.0 0.7158530 0.7158530 6.967847e-05 6.967847e-05 0.284077276 #> 2104  1051.5 0.7159938 0.7159938 6.972945e-05 6.972945e-05 0.283936434 #> 2105  1052.0 0.7161346 0.7161346 6.978072e-05 6.978072e-05 0.283795664 #> 2106  1052.5 0.7162752 0.7162752 6.983227e-05 6.983227e-05 0.283654965 #> 2107  1053.0 0.7164158 0.7164158 6.988410e-05 6.988410e-05 0.283514336 #> 2108  1053.5 0.7165563 0.7165563 6.993622e-05 6.993622e-05 0.283373780 #> 2109  1054.0 0.7166967 0.7166967 6.998862e-05 6.998862e-05 0.283233294 #> 2110  1054.5 0.7168371 0.7168371 7.004131e-05 7.004131e-05 0.283092879 #> 2111  1055.0 0.7169774 0.7169774 7.009428e-05 7.009428e-05 0.282952536 #> 2112  1055.5 0.7171176 0.7171176 7.014754e-05 7.014754e-05 0.282812263 #> 2113  1056.0 0.7172577 0.7172577 7.020108e-05 7.020108e-05 0.282672062 #> 2114  1056.5 0.7173978 0.7173978 7.025491e-05 7.025491e-05 0.282531931 #> 2115  1057.0 0.7175378 0.7175378 7.030903e-05 7.030903e-05 0.282391872 #> 2116  1057.5 0.7176778 0.7176778 7.036344e-05 7.036344e-05 0.282251883 #> 2117  1058.0 0.7178176 0.7178176 7.041813e-05 7.041813e-05 0.282111965 #> 2118  1058.5 0.7179574 0.7179574 7.047312e-05 7.047312e-05 0.281972118 #> 2119  1059.0 0.7180971 0.7180971 7.052839e-05 7.052839e-05 0.281832342 #> 2120  1059.5 0.7182368 0.7182368 7.058396e-05 7.058396e-05 0.281692637 #> 2121  1060.0 0.7183764 0.7183764 7.063981e-05 7.063981e-05 0.281553002 #> 2122  1060.5 0.7185159 0.7185159 7.069595e-05 7.069595e-05 0.281413438 #> 2123  1061.0 0.7186553 0.7186553 7.075239e-05 7.075239e-05 0.281273945 #> 2124  1061.5 0.7187947 0.7187947 7.080912e-05 7.080912e-05 0.281134523 #> 2125  1062.0 0.7189340 0.7189340 7.086614e-05 7.086614e-05 0.280995171 #> 2126  1062.5 0.7190732 0.7190732 7.092345e-05 7.092345e-05 0.280855890 #> 2127  1063.0 0.7192123 0.7192123 7.098106e-05 7.098106e-05 0.280716679 #> 2128  1063.5 0.7193514 0.7193514 7.103896e-05 7.103896e-05 0.280577539 #> 2129  1064.0 0.7194904 0.7194904 7.109715e-05 7.109715e-05 0.280438470 #> 2130  1064.5 0.7196294 0.7196294 7.115564e-05 7.115564e-05 0.280299471 #> 2131  1065.0 0.7197682 0.7197682 7.121443e-05 7.121443e-05 0.280160542 #> 2132  1065.5 0.7199070 0.7199070 7.127351e-05 7.127351e-05 0.280021684 #> 2133  1066.0 0.7200458 0.7200458 7.133288e-05 7.133288e-05 0.279882896 #> 2134  1066.5 0.7201844 0.7201844 7.139256e-05 7.139256e-05 0.279744179 #> 2135  1067.0 0.7203230 0.7203230 7.145253e-05 7.145253e-05 0.279605532 #> 2136  1067.5 0.7204615 0.7204615 7.151280e-05 7.151280e-05 0.279466955 #> 2137  1068.0 0.7206000 0.7206000 7.157337e-05 7.157337e-05 0.279328449 #> 2138  1068.5 0.7207384 0.7207384 7.163424e-05 7.163424e-05 0.279190012 #> 2139  1069.0 0.7208767 0.7208767 7.169540e-05 7.169540e-05 0.279051646 #> 2140  1069.5 0.7210149 0.7210149 7.175687e-05 7.175687e-05 0.278913351 #> 2141  1070.0 0.7211531 0.7211531 7.181864e-05 7.181864e-05 0.278775125 #> 2142  1070.5 0.7212911 0.7212911 7.188071e-05 7.188071e-05 0.278636969 #> 2143  1071.0 0.7214292 0.7214292 7.194308e-05 7.194308e-05 0.278498884 #> 2144  1071.5 0.7215671 0.7215671 7.200576e-05 7.200576e-05 0.278360869 #> 2145  1072.0 0.7217050 0.7217050 7.206873e-05 7.206873e-05 0.278222923 #> 2146  1072.5 0.7218428 0.7218428 7.213202e-05 7.213202e-05 0.278085048 #> 2147  1073.0 0.7219806 0.7219806 7.219560e-05 7.219560e-05 0.277947243 #> 2148  1073.5 0.7221182 0.7221182 7.225949e-05 7.225949e-05 0.277809507 #> 2149  1074.0 0.7222558 0.7222558 7.232369e-05 7.232369e-05 0.277671842 #> 2150  1074.5 0.7223934 0.7223934 7.238819e-05 7.238819e-05 0.277534246 #> 2151  1075.0 0.7225308 0.7225308 7.245300e-05 7.245300e-05 0.277396720 #> 2152  1075.5 0.7226682 0.7226682 7.251811e-05 7.251811e-05 0.277259265 #> 2153  1076.0 0.7228055 0.7228055 7.258353e-05 7.258353e-05 0.277121878 #> 2154  1076.5 0.7229428 0.7229428 7.264927e-05 7.264927e-05 0.276984562 #> 2155  1077.0 0.7230800 0.7230800 7.271531e-05 7.271531e-05 0.276847315 #> 2156  1077.5 0.7232171 0.7232171 7.278166e-05 7.278166e-05 0.276710139 #> 2157  1078.0 0.7233541 0.7233541 7.284832e-05 7.284832e-05 0.276573031 #> 2158  1078.5 0.7234911 0.7234911 7.291529e-05 7.291529e-05 0.276435994 #> 2159  1079.0 0.7236280 0.7236280 7.298257e-05 7.298257e-05 0.276299026 #> 2160  1079.5 0.7237648 0.7237648 7.305016e-05 7.305016e-05 0.276162128 #> 2161  1080.0 0.7239016 0.7239016 7.311807e-05 7.311807e-05 0.276025299 #> 2162  1080.5 0.7240383 0.7240383 7.318629e-05 7.318629e-05 0.275888540 #> 2163  1081.0 0.7241749 0.7241749 7.325483e-05 7.325483e-05 0.275751850 #> 2164  1081.5 0.7243114 0.7243114 7.332368e-05 7.332368e-05 0.275615229 #> 2165  1082.0 0.7244479 0.7244479 7.339284e-05 7.339284e-05 0.275478679 #> 2166  1082.5 0.7245843 0.7245843 7.346232e-05 7.346232e-05 0.275342197 #> 2167  1083.0 0.7247207 0.7247207 7.353212e-05 7.353212e-05 0.275205785 #> 2168  1083.5 0.7248570 0.7248570 7.360223e-05 7.360223e-05 0.275069442 #> 2169  1084.0 0.7249932 0.7249932 7.367266e-05 7.367266e-05 0.274933169 #> 2170  1084.5 0.7251293 0.7251293 7.374341e-05 7.374341e-05 0.274796965 #> 2171  1085.0 0.7252654 0.7252654 7.381448e-05 7.381448e-05 0.274660830 #> 2172  1085.5 0.7254013 0.7254013 7.388587e-05 7.388587e-05 0.274524765 #> 2173  1086.0 0.7255373 0.7255373 7.395758e-05 7.395758e-05 0.274388768 #> 2174  1086.5 0.7256731 0.7256731 7.402961e-05 7.402961e-05 0.274252841 #> 2175  1087.0 0.7258089 0.7258089 7.410196e-05 7.410196e-05 0.274116983 #> 2176  1087.5 0.7259446 0.7259446 7.417463e-05 7.417463e-05 0.273981194 #> 2177  1088.0 0.7260803 0.7260803 7.424763e-05 7.424763e-05 0.273845474 #> 2178  1088.5 0.7262159 0.7262159 7.432095e-05 7.432095e-05 0.273709824 #> 2179  1089.0 0.7263514 0.7263514 7.439460e-05 7.439460e-05 0.273574242 #> 2180  1089.5 0.7264868 0.7264868 7.446857e-05 7.446857e-05 0.273438729 #> 2181  1090.0 0.7266222 0.7266222 7.454286e-05 7.454286e-05 0.273303286 #> 2182  1090.5 0.7267575 0.7267575 7.461749e-05 7.461749e-05 0.273167911 #> 2183  1091.0 0.7268927 0.7268927 7.469244e-05 7.469244e-05 0.273032605 #> 2184  1091.5 0.7270279 0.7270279 7.476772e-05 7.476772e-05 0.272897368 #> 2185  1092.0 0.7271630 0.7271630 7.484332e-05 7.484332e-05 0.272762200 #> 2186  1092.5 0.7272980 0.7272980 7.491926e-05 7.491926e-05 0.272627100 #> 2187  1093.0 0.7274329 0.7274329 7.499553e-05 7.499553e-05 0.272492070 #> 2188  1093.5 0.7275678 0.7275678 7.507213e-05 7.507213e-05 0.272357108 #> 2189  1094.0 0.7277026 0.7277026 7.514905e-05 7.514905e-05 0.272222215 #> 2190  1094.5 0.7278374 0.7278374 7.522632e-05 7.522632e-05 0.272087391 #> 2191  1095.0 0.7279721 0.7279721 7.530391e-05 7.530391e-05 0.271952635 #> 2192  1095.5 0.7281067 0.7281067 7.538184e-05 7.538184e-05 0.271817948 #> 2193  1096.0 0.7282412 0.7282412 7.546010e-05 7.546010e-05 0.271683330 #> 2194  1096.5 0.7283757 0.7283757 7.553870e-05 7.553870e-05 0.271548780 #> 2195  1097.0 0.7285101 0.7285101 7.561764e-05 7.561764e-05 0.271414299 #> 2196  1097.5 0.7286444 0.7286444 7.569691e-05 7.569691e-05 0.271279887 #> 2197  1098.0 0.7287787 0.7287787 7.577651e-05 7.577651e-05 0.271145543 #> 2198  1098.5 0.7289129 0.7289129 7.585646e-05 7.585646e-05 0.271011267 #> 2199  1099.0 0.7290470 0.7290470 7.593675e-05 7.593675e-05 0.270877060 #> 2200  1099.5 0.7291811 0.7291811 7.601737e-05 7.601737e-05 0.270742921 #> 2201  1100.0 0.7293151 0.7293151 7.609834e-05 7.609834e-05 0.270608851 #> 2202  1100.5 0.7294490 0.7294490 7.617965e-05 7.617965e-05 0.270474849 #> 2203  1101.0 0.7295828 0.7295828 7.626130e-05 7.626130e-05 0.270340915 #> 2204  1101.5 0.7297166 0.7297166 7.634329e-05 7.634329e-05 0.270207050 #> 2205  1102.0 0.7298503 0.7298503 7.642563e-05 7.642563e-05 0.270073253 #> 2206  1102.5 0.7299840 0.7299840 7.650831e-05 7.650831e-05 0.269939524 #> 2207  1103.0 0.7301175 0.7301175 7.659133e-05 7.659133e-05 0.269805864 #> 2208  1103.5 0.7302511 0.7302511 7.667470e-05 7.667470e-05 0.269672271 #> 2209  1104.0 0.7303845 0.7303845 7.675842e-05 7.675842e-05 0.269538747 #> 2210  1104.5 0.7305179 0.7305179 7.684249e-05 7.684249e-05 0.269405291 #> 2211  1105.0 0.7306512 0.7306512 7.692691e-05 7.692691e-05 0.269271903 #> 2212  1105.5 0.7307844 0.7307844 7.701167e-05 7.701167e-05 0.269138584 #> 2213  1106.0 0.7309176 0.7309176 7.709678e-05 7.709678e-05 0.269005332 #> 2214  1106.5 0.7310507 0.7310507 7.718225e-05 7.718225e-05 0.268872148 #> 2215  1107.0 0.7311837 0.7311837 7.726807e-05 7.726807e-05 0.268739033 #> 2216  1107.5 0.7313167 0.7313167 7.735424e-05 7.735424e-05 0.268605985 #> 2217  1108.0 0.7314496 0.7314496 7.744076e-05 7.744076e-05 0.268473005 #> 2218  1108.5 0.7315824 0.7315824 7.752764e-05 7.752764e-05 0.268340093 #> 2219  1109.0 0.7317151 0.7317151 7.761487e-05 7.761487e-05 0.268207249 #> 2220  1109.5 0.7318478 0.7318478 7.770246e-05 7.770246e-05 0.268074473 #> 2221  1110.0 0.7319804 0.7319804 7.779041e-05 7.779041e-05 0.267941765 #> 2222  1110.5 0.7321130 0.7321130 7.787871e-05 7.787871e-05 0.267809124 #> 2223  1111.0 0.7322455 0.7322455 7.796737e-05 7.796737e-05 0.267676552 #> 2224  1111.5 0.7323779 0.7323779 7.805639e-05 7.805639e-05 0.267544047 #> 2225  1112.0 0.7325102 0.7325102 7.814578e-05 7.814578e-05 0.267411609 #> 2226  1112.5 0.7326425 0.7326425 7.823552e-05 7.823552e-05 0.267279240 #> 2227  1113.0 0.7327747 0.7327747 7.832562e-05 7.832562e-05 0.267146938 #> 2228  1113.5 0.7329069 0.7329069 7.841609e-05 7.841609e-05 0.267014704 #> 2229  1114.0 0.7330390 0.7330390 7.850692e-05 7.850692e-05 0.266882537 #> 2230  1114.5 0.7331710 0.7331710 7.859812e-05 7.859812e-05 0.266750438 #> 2231  1115.0 0.7333029 0.7333029 7.868968e-05 7.868968e-05 0.266618407 #> 2232  1115.5 0.7334348 0.7334348 7.878161e-05 7.878161e-05 0.266486443 #> 2233  1116.0 0.7335666 0.7335666 7.887390e-05 7.887390e-05 0.266354546 #> 2234  1116.5 0.7336983 0.7336983 7.896657e-05 7.896657e-05 0.266222717 #> 2235  1117.0 0.7338300 0.7338300 7.905960e-05 7.905960e-05 0.266090956 #> 2236  1117.5 0.7339616 0.7339616 7.915300e-05 7.915300e-05 0.265959262 #> 2237  1118.0 0.7340931 0.7340931 7.924678e-05 7.924678e-05 0.265827635 #> 2238  1118.5 0.7342246 0.7342246 7.934092e-05 7.934092e-05 0.265696076 #> 2239  1119.0 0.7343560 0.7343560 7.943544e-05 7.943544e-05 0.265564584 #> 2240  1119.5 0.7344873 0.7344873 7.953033e-05 7.953033e-05 0.265433159 #> 2241  1120.0 0.7346186 0.7346186 7.962560e-05 7.962560e-05 0.265301801 #> 2242  1120.5 0.7347498 0.7347498 7.972124e-05 7.972124e-05 0.265170511 #> 2243  1121.0 0.7348809 0.7348809 7.981726e-05 7.981726e-05 0.265039288 #> 2244  1121.5 0.7350120 0.7350120 7.991366e-05 7.991366e-05 0.264908132 #> 2245  1122.0 0.7351429 0.7351429 8.001044e-05 8.001044e-05 0.264777044 #> 2246  1122.5 0.7352739 0.7352739 8.010759e-05 8.010759e-05 0.264646022 #> 2247  1123.0 0.7354047 0.7354047 8.020513e-05 8.020513e-05 0.264515068 #> 2248  1123.5 0.7355355 0.7355355 8.030304e-05 8.030304e-05 0.264384181 #> 2249  1124.0 0.7356662 0.7356662 8.040134e-05 8.040134e-05 0.264253361 #> 2250  1124.5 0.7357969 0.7357969 8.050003e-05 8.050003e-05 0.264122608 #> 2251  1125.0 0.7359275 0.7359275 8.059909e-05 8.059909e-05 0.263991921 #> 2252  1125.5 0.7360580 0.7360580 8.069855e-05 8.069855e-05 0.263861302 #> 2253  1126.0 0.7361885 0.7361885 8.079838e-05 8.079838e-05 0.263730750 #> 2254  1126.5 0.7363188 0.7363188 8.089861e-05 8.089861e-05 0.263600265 #> 2255  1127.0 0.7364492 0.7364492 8.099922e-05 8.099922e-05 0.263469846 #> 2256  1127.5 0.7365794 0.7365794 8.110023e-05 8.110023e-05 0.263339495 #> 2257  1128.0 0.7367096 0.7367096 8.120162e-05 8.120162e-05 0.263209210 #> 2258  1128.5 0.7368397 0.7368397 8.130341e-05 8.130341e-05 0.263078992 #> 2259  1129.0 0.7369698 0.7369698 8.140558e-05 8.140558e-05 0.262948841 #> 2260  1129.5 0.7370997 0.7370997 8.150815e-05 8.150815e-05 0.262818757 #> 2261  1130.0 0.7372296 0.7372296 8.161112e-05 8.161112e-05 0.262688739 #> 2262  1130.5 0.7373595 0.7373595 8.171448e-05 8.171448e-05 0.262558789 #> 2263  1131.0 0.7374893 0.7374893 8.181824e-05 8.181824e-05 0.262428905 #> 2264  1131.5 0.7376190 0.7376190 8.192239e-05 8.192239e-05 0.262299087 #> 2265  1132.0 0.7377486 0.7377486 8.202694e-05 8.202694e-05 0.262169336 #> 2266  1132.5 0.7378782 0.7378782 8.213190e-05 8.213190e-05 0.262039652 #> 2267  1133.0 0.7380077 0.7380077 8.223725e-05 8.223725e-05 0.261910034 #> 2268  1133.5 0.7381372 0.7381372 8.234300e-05 8.234300e-05 0.261780483 #> 2269  1134.0 0.7382666 0.7382666 8.244916e-05 8.244916e-05 0.261650998 #> 2270  1134.5 0.7383959 0.7383959 8.255572e-05 8.255572e-05 0.261521580 #> 2271  1135.0 0.7385251 0.7385251 8.266269e-05 8.266269e-05 0.261392229 #> 2272  1135.5 0.7386543 0.7386543 8.277006e-05 8.277006e-05 0.261262944 #> 2273  1136.0 0.7387834 0.7387834 8.287784e-05 8.287784e-05 0.261133725 #> 2274  1136.5 0.7389124 0.7389124 8.298603e-05 8.298603e-05 0.261004572 #> 2275  1137.0 0.7390414 0.7390414 8.309462e-05 8.309462e-05 0.260875486 #> 2276  1137.5 0.7391703 0.7391703 8.320363e-05 8.320363e-05 0.260746467 #> 2277  1138.0 0.7392992 0.7392992 8.331305e-05 8.331305e-05 0.260617513 #> 2278  1138.5 0.7394280 0.7394280 8.342288e-05 8.342288e-05 0.260488626 #> 2279  1139.0 0.7395567 0.7395567 8.353312e-05 8.353312e-05 0.260359806 #> 2280  1139.5 0.7396853 0.7396853 8.364378e-05 8.364378e-05 0.260231051 #> 2281  1140.0 0.7398139 0.7398139 8.375486e-05 8.375486e-05 0.260102363 #> 2282  1140.5 0.7399424 0.7399424 8.386635e-05 8.386635e-05 0.259973740 #> 2283  1141.0 0.7400708 0.7400708 8.397826e-05 8.397826e-05 0.259845184 #> 2284  1141.5 0.7401992 0.7401992 8.409059e-05 8.409059e-05 0.259716695 #> 2285  1142.0 0.7403275 0.7403275 8.420334e-05 8.420334e-05 0.259588271 #> 2286  1142.5 0.7404558 0.7404558 8.431651e-05 8.431651e-05 0.259459913 #> 2287  1143.0 0.7405839 0.7405839 8.443011e-05 8.443011e-05 0.259331621 #> 2288  1143.5 0.7407121 0.7407121 8.454412e-05 8.454412e-05 0.259203396 #> 2289  1144.0 0.7408401 0.7408401 8.465857e-05 8.465857e-05 0.259075236 #> 2290  1144.5 0.7409681 0.7409681 8.477344e-05 8.477344e-05 0.258947142 #> 2291  1145.0 0.7410960 0.7410960 8.488873e-05 8.488873e-05 0.258819115 #> 2292  1145.5 0.7412238 0.7412238 8.500446e-05 8.500446e-05 0.258691153 #> 2293  1146.0 0.7413516 0.7413516 8.512061e-05 8.512061e-05 0.258563257 #> 2294  1146.5 0.7414793 0.7414793 8.523720e-05 8.523720e-05 0.258435427 #> 2295  1147.0 0.7416070 0.7416070 8.535422e-05 8.535422e-05 0.258307663 #> 2296  1147.5 0.7417346 0.7417346 8.547167e-05 8.547167e-05 0.258179964 #> 2297  1148.0 0.7418621 0.7418621 8.558956e-05 8.558956e-05 0.258052332 #> 2298  1148.5 0.7419895 0.7419895 8.570788e-05 8.570788e-05 0.257924765 #> 2299  1149.0 0.7421169 0.7421169 8.582664e-05 8.582664e-05 0.257797264 #> 2300  1149.5 0.7422442 0.7422442 8.594583e-05 8.594583e-05 0.257669829 #> 2301  1150.0 0.7423715 0.7423715 8.606547e-05 8.606547e-05 0.257542459 #> 2302  1150.5 0.7424987 0.7424987 8.618555e-05 8.618555e-05 0.257415155 #> 2303  1151.0 0.7426258 0.7426258 8.630607e-05 8.630607e-05 0.257287917 #> 2304  1151.5 0.7427528 0.7427528 8.642703e-05 8.642703e-05 0.257160744 #> 2305  1152.0 0.7428798 0.7428798 8.654844e-05 8.654844e-05 0.257033637 #> 2306  1152.5 0.7430067 0.7430067 8.667029e-05 8.667029e-05 0.256906595 #> 2307  1153.0 0.7431336 0.7431336 8.679259e-05 8.679259e-05 0.256779619 #> 2308  1153.5 0.7432604 0.7432604 8.691533e-05 8.691533e-05 0.256652709 #> 2309  1154.0 0.7433871 0.7433871 8.703853e-05 8.703853e-05 0.256525864 #> 2310  1154.5 0.7435138 0.7435138 8.716218e-05 8.716218e-05 0.256399084 #> 2311  1155.0 0.7436403 0.7436403 8.728628e-05 8.728628e-05 0.256272370 #> 2312  1155.5 0.7437669 0.7437669 8.741083e-05 8.741083e-05 0.256145721 #> 2313  1156.0 0.7438933 0.7438933 8.753583e-05 8.753583e-05 0.256019138 #> 2314  1156.5 0.7440197 0.7440197 8.766130e-05 8.766130e-05 0.255892620 #> 2315  1157.0 0.7441460 0.7441460 8.778722e-05 8.778722e-05 0.255766167 #> 2316  1157.5 0.7442723 0.7442723 8.791360e-05 8.791360e-05 0.255639780 #> 2317  1158.0 0.7443985 0.7443985 8.804043e-05 8.804043e-05 0.255513458 #> 2318  1158.5 0.7445246 0.7445246 8.816773e-05 8.816773e-05 0.255387201 #> 2319  1159.0 0.7446507 0.7446507 8.829549e-05 8.829549e-05 0.255261010 #> 2320  1159.5 0.7447767 0.7447767 8.842372e-05 8.842372e-05 0.255134884 #> 2321  1160.0 0.7449026 0.7449026 8.855241e-05 8.855241e-05 0.255008822 #> 2322  1160.5 0.7450285 0.7450285 8.868157e-05 8.868157e-05 0.254882826 #> 2323  1161.0 0.7451543 0.7451543 8.881119e-05 8.881119e-05 0.254756896 #> 2324  1161.5 0.7452800 0.7452800 8.894129e-05 8.894129e-05 0.254631030 #> 2325  1162.0 0.7454057 0.7454057 8.907185e-05 8.907185e-05 0.254505229 #> 2326  1162.5 0.7455313 0.7455313 8.920289e-05 8.920289e-05 0.254379494 #> 2327  1163.0 0.7456568 0.7456568 8.933439e-05 8.933439e-05 0.254253823 #> 2328  1163.5 0.7457823 0.7457823 8.946638e-05 8.946638e-05 0.254128218 #> 2329  1164.0 0.7459077 0.7459077 8.959884e-05 8.959884e-05 0.254002677 #> 2330  1164.5 0.7460331 0.7460331 8.973178e-05 8.973178e-05 0.253877202 #> 2331  1165.0 0.7461583 0.7461583 8.986519e-05 8.986519e-05 0.253751791 #> 2332  1165.5 0.7462836 0.7462836 8.999909e-05 8.999909e-05 0.253626445 #> 2333  1166.0 0.7464087 0.7464087 9.013347e-05 9.013347e-05 0.253501165 #> 2334  1166.5 0.7465338 0.7465338 9.026833e-05 9.026833e-05 0.253375949 #> 2335  1167.0 0.7466588 0.7466588 9.040367e-05 9.040367e-05 0.253250798 #> 2336  1167.5 0.7467837 0.7467837 9.053950e-05 9.053950e-05 0.253125711 #> 2337  1168.0 0.7469086 0.7469086 9.067582e-05 9.067582e-05 0.253000690 #> 2338  1168.5 0.7470335 0.7470335 9.081263e-05 9.081263e-05 0.252875733 #> 2339  1169.0 0.7471582 0.7471582 9.094992e-05 9.094992e-05 0.252750841 #> 2340  1169.5 0.7472829 0.7472829 9.108771e-05 9.108771e-05 0.252626014 #> 2341  1170.0 0.7474075 0.7474075 9.122599e-05 9.122599e-05 0.252501251 #> 2342  1170.5 0.7475321 0.7475321 9.136477e-05 9.136477e-05 0.252376554 #> 2343  1171.0 0.7476566 0.7476566 9.150404e-05 9.150404e-05 0.252251920 #> 2344  1171.5 0.7477810 0.7477810 9.164381e-05 9.164381e-05 0.252127352 #> 2345  1172.0 0.7479054 0.7479054 9.178408e-05 9.178408e-05 0.252002848 #> 2346  1172.5 0.7480297 0.7480297 9.192485e-05 9.192485e-05 0.251878408 #> 2347  1173.0 0.7481539 0.7481539 9.206612e-05 9.206612e-05 0.251754034 #> 2348  1173.5 0.7482781 0.7482781 9.220789e-05 9.220789e-05 0.251629723 #> 2349  1174.0 0.7484022 0.7484022 9.235017e-05 9.235017e-05 0.251505477 #> 2350  1174.5 0.7485262 0.7485262 9.249296e-05 9.249296e-05 0.251381296 #> 2351  1175.0 0.7486502 0.7486502 9.263625e-05 9.263625e-05 0.251257179 #> 2352  1175.5 0.7487741 0.7487741 9.278006e-05 9.278006e-05 0.251133127 #> 2353  1176.0 0.7488979 0.7488979 9.292437e-05 9.292437e-05 0.251009139 #> 2354  1176.5 0.7490217 0.7490217 9.306920e-05 9.306920e-05 0.250885215 #> 2355  1177.0 0.7491454 0.7491454 9.321454e-05 9.321454e-05 0.250761356 #> 2356  1177.5 0.7492691 0.7492691 9.336039e-05 9.336039e-05 0.250637561 #> 2357  1178.0 0.7493927 0.7493927 9.350677e-05 9.350677e-05 0.250513830 #> 2358  1178.5 0.7495162 0.7495162 9.365366e-05 9.365366e-05 0.250390164 #> 2359  1179.0 0.7496396 0.7496396 9.380107e-05 9.380107e-05 0.250266562 #> 2360  1179.5 0.7497630 0.7497630 9.394901e-05 9.394901e-05 0.250143024 #> 2361  1180.0 0.7498864 0.7498864 9.409746e-05 9.409746e-05 0.250019550 #> 2362  1180.5 0.7500096 0.7500096 9.424645e-05 9.424645e-05 0.249896141 #> 2363  1181.0 0.7501328 0.7501328 9.439596e-05 9.439596e-05 0.249772796 #> 2364  1181.5 0.7502559 0.7502559 9.454599e-05 9.454599e-05 0.249649515 #> 2365  1182.0 0.7503790 0.7503790 9.469656e-05 9.469656e-05 0.249526298 #> 2366  1182.5 0.7505020 0.7505020 9.484766e-05 9.484766e-05 0.249403145 #> 2367  1183.0 0.7506249 0.7506249 9.499930e-05 9.499930e-05 0.249280056 #> 2368  1183.5 0.7507478 0.7507478 9.515146e-05 9.515146e-05 0.249157032 #> 2369  1184.0 0.7508706 0.7508706 9.530417e-05 9.530417e-05 0.249034071 #> 2370  1184.5 0.7509934 0.7509934 9.545741e-05 9.545741e-05 0.248911174 #> 2371  1185.0 0.7511160 0.7511160 9.561119e-05 9.561119e-05 0.248788342 #> 2372  1185.5 0.7512387 0.7512387 9.576551e-05 9.576551e-05 0.248665573 #> 2373  1186.0 0.7513612 0.7513612 9.592038e-05 9.592038e-05 0.248542868 #> 2374  1186.5 0.7514837 0.7514837 9.607579e-05 9.607579e-05 0.248420228 #> 2375  1187.0 0.7516061 0.7516061 9.623175e-05 9.623175e-05 0.248297651 #> 2376  1187.5 0.7517285 0.7517285 9.638825e-05 9.638825e-05 0.248175138 #> 2377  1188.0 0.7518508 0.7518508 9.654531e-05 9.654531e-05 0.248052688 #> 2378  1188.5 0.7519730 0.7519730 9.670291e-05 9.670291e-05 0.247930303 #> 2379  1189.0 0.7520952 0.7520952 9.686107e-05 9.686107e-05 0.247807982 #> 2380  1189.5 0.7522173 0.7522173 9.701979e-05 9.701979e-05 0.247685724 #> 2381  1190.0 0.7523393 0.7523393 9.717906e-05 9.717906e-05 0.247563530 #> 2382  1190.5 0.7524613 0.7524613 9.733889e-05 9.733889e-05 0.247441400 #> 2383  1191.0 0.7525832 0.7525832 9.749928e-05 9.749928e-05 0.247319333 #> 2384  1191.5 0.7527050 0.7527050 9.766023e-05 9.766023e-05 0.247197330 #> 2385  1192.0 0.7528268 0.7528268 9.782175e-05 9.782175e-05 0.247075391 #> 2386  1192.5 0.7529485 0.7529485 9.798383e-05 9.798383e-05 0.246953516 #> 2387  1193.0 0.7530701 0.7530701 9.814647e-05 9.814647e-05 0.246831704 #> 2388  1193.5 0.7531917 0.7531917 9.830969e-05 9.830969e-05 0.246709955 #> 2389  1194.0 0.7533133 0.7533133 9.847348e-05 9.847348e-05 0.246588271 #> 2390  1194.5 0.7534347 0.7534347 9.863784e-05 9.863784e-05 0.246466650 #> 2391  1195.0 0.7535561 0.7535561 9.880277e-05 9.880277e-05 0.246345092 #> 2392  1195.5 0.7536774 0.7536774 9.896828e-05 9.896828e-05 0.246223598 #> 2393  1196.0 0.7537987 0.7537987 9.913437e-05 9.913437e-05 0.246102167 #> 2394  1196.5 0.7539199 0.7539199 9.930103e-05 9.930103e-05 0.245980800 #> 2395  1197.0 0.7540410 0.7540410 9.946828e-05 9.946828e-05 0.245859497 #> 2396  1197.5 0.7541621 0.7541621 9.963612e-05 9.963612e-05 0.245738256 #> 2397  1198.0 0.7542831 0.7542831 9.980453e-05 9.980453e-05 0.245617079 #> 2398  1198.5 0.7544041 0.7544041 9.997354e-05 9.997354e-05 0.245495966 #> 2399  1199.0 0.7545249 0.7545249 1.001431e-04 1.001431e-04 0.245374916 #> 2400  1199.5 0.7546458 0.7546458 1.003133e-04 1.003133e-04 0.245253929 #> 2401  1200.0 0.7547665 0.7547665 1.004841e-04 1.004841e-04 0.245133006 #> 2402  1200.5 0.7548872 0.7548872 1.006555e-04 1.006555e-04 0.245012146 #> 2403  1201.0 0.7550078 0.7550078 1.008274e-04 1.008274e-04 0.244891349 #> 2404  1201.5 0.7551284 0.7551284 1.010000e-04 1.010000e-04 0.244770615 #> 2405  1202.0 0.7552489 0.7552489 1.011732e-04 1.011732e-04 0.244649945 #> 2406  1202.5 0.7553693 0.7553693 1.013469e-04 1.013469e-04 0.244529338 #> 2407  1203.0 0.7554897 0.7554897 1.015213e-04 1.015213e-04 0.244408794 #> 2408  1203.5 0.7556100 0.7556100 1.016963e-04 1.016963e-04 0.244288313 #> 2409  1204.0 0.7557302 0.7557302 1.018719e-04 1.018719e-04 0.244167895 #> 2410  1204.5 0.7558504 0.7558504 1.020481e-04 1.020481e-04 0.244047541 #> 2411  1205.0 0.7559705 0.7559705 1.022249e-04 1.022249e-04 0.243927249 #> 2412  1205.5 0.7560906 0.7560906 1.024023e-04 1.024023e-04 0.243807021 #> 2413  1206.0 0.7562106 0.7562106 1.025803e-04 1.025803e-04 0.243686856 #> 2414  1206.5 0.7563305 0.7563305 1.027590e-04 1.027590e-04 0.243566753 #> 2415  1207.0 0.7564503 0.7564503 1.029383e-04 1.029383e-04 0.243446714 #> 2416  1207.5 0.7565701 0.7565701 1.031182e-04 1.031182e-04 0.243326738 #> 2417  1208.0 0.7566899 0.7566899 1.032987e-04 1.032987e-04 0.243206825 #> 2418  1208.5 0.7568095 0.7568095 1.034798e-04 1.034798e-04 0.243086974 #> 2419  1209.0 0.7569292 0.7569292 1.036616e-04 1.036616e-04 0.242967187 #> 2420  1209.5 0.7570487 0.7570487 1.038440e-04 1.038440e-04 0.242847463 #> 2421  1210.0 0.7571682 0.7571682 1.040270e-04 1.040270e-04 0.242727801 #> 2422  1210.5 0.7572876 0.7572876 1.042107e-04 1.042107e-04 0.242608202 #> 2423  1211.0 0.7574069 0.7574069 1.043950e-04 1.043950e-04 0.242488667 #> 2424  1211.5 0.7575262 0.7575262 1.045800e-04 1.045800e-04 0.242369194 #> 2425  1212.0 0.7576455 0.7576455 1.047655e-04 1.047655e-04 0.242249783 #> 2426  1212.5 0.7577646 0.7577646 1.049518e-04 1.049518e-04 0.242130436 #> 2427  1213.0 0.7578837 0.7578837 1.051386e-04 1.051386e-04 0.242011151 #> 2428  1213.5 0.7580027 0.7580027 1.053261e-04 1.053261e-04 0.241891929 #> 2429  1214.0 0.7581217 0.7581217 1.055143e-04 1.055143e-04 0.241772770 #> 2430  1214.5 0.7582406 0.7582406 1.057031e-04 1.057031e-04 0.241653674 #> 2431  1215.0 0.7583595 0.7583595 1.058926e-04 1.058926e-04 0.241534640 #> 2432  1215.5 0.7584782 0.7584782 1.060827e-04 1.060827e-04 0.241415669 #> 2433  1216.0 0.7585970 0.7585970 1.062735e-04 1.062735e-04 0.241296760 #> 2434  1216.5 0.7587156 0.7587156 1.064649e-04 1.064649e-04 0.241177915 #> 2435  1217.0 0.7588342 0.7588342 1.066570e-04 1.066570e-04 0.241059131 #> 2436  1217.5 0.7589527 0.7589527 1.068498e-04 1.068498e-04 0.240940411 #> 2437  1218.0 0.7590712 0.7590712 1.070432e-04 1.070432e-04 0.240821753 #> 2438  1218.5 0.7591896 0.7591896 1.072373e-04 1.072373e-04 0.240703157 #> 2439  1219.0 0.7593079 0.7593079 1.074321e-04 1.074321e-04 0.240584624 #> 2440  1219.5 0.7594262 0.7594262 1.076275e-04 1.076275e-04 0.240466153 #> 2441  1220.0 0.7595444 0.7595444 1.078236e-04 1.078236e-04 0.240347745 #> 2442  1220.5 0.7596626 0.7596626 1.080204e-04 1.080204e-04 0.240229400 #> 2443  1221.0 0.7597807 0.7597807 1.082179e-04 1.082179e-04 0.240111117 #> 2444  1221.5 0.7598987 0.7598987 1.084160e-04 1.084160e-04 0.239992896 #> 2445  1222.0 0.7600166 0.7600166 1.086148e-04 1.086148e-04 0.239874738 #> 2446  1222.5 0.7601345 0.7601345 1.088144e-04 1.088144e-04 0.239756642 #> 2447  1223.0 0.7602524 0.7602524 1.090146e-04 1.090146e-04 0.239638608 #> 2448  1223.5 0.7603701 0.7603701 1.092155e-04 1.092155e-04 0.239520637 #> 2449  1224.0 0.7604879 0.7604879 1.094170e-04 1.094170e-04 0.239402728 #> 2450  1224.5 0.7606055 0.7606055 1.096193e-04 1.096193e-04 0.239284881 #> 2451  1225.0 0.7607231 0.7607231 1.098223e-04 1.098223e-04 0.239167097 #> 2452  1225.5 0.7608406 0.7608406 1.100260e-04 1.100260e-04 0.239049375 #> 2453  1226.0 0.7609581 0.7609581 1.102304e-04 1.102304e-04 0.238931715 #> 2454  1226.5 0.7610754 0.7610754 1.104354e-04 1.104354e-04 0.238814118 #> 2455  1227.0 0.7611928 0.7611928 1.106412e-04 1.106412e-04 0.238696582 #> 2456  1227.5 0.7613100 0.7613100 1.108477e-04 1.108477e-04 0.238579109 #> 2457  1228.0 0.7614272 0.7614272 1.110549e-04 1.110549e-04 0.238461698 #> 2458  1228.5 0.7615444 0.7615444 1.112628e-04 1.112628e-04 0.238344349 #> 2459  1229.0 0.7616615 0.7616615 1.114715e-04 1.114715e-04 0.238227063 #> 2460  1229.5 0.7617785 0.7617785 1.116808e-04 1.116808e-04 0.238109838 #> 2461  1230.0 0.7618954 0.7618954 1.118909e-04 1.118909e-04 0.237992675 #> 2462  1230.5 0.7620123 0.7620123 1.121017e-04 1.121017e-04 0.237875575 #> 2463  1231.0 0.7621292 0.7621292 1.123132e-04 1.123132e-04 0.237758537 #> 2464  1231.5 0.7622459 0.7622459 1.125255e-04 1.125255e-04 0.237641560 #> 2465  1232.0 0.7623626 0.7623626 1.127385e-04 1.127385e-04 0.237524646 #> 2466  1232.5 0.7624793 0.7624793 1.129522e-04 1.129522e-04 0.237407793 #> 2467  1233.0 0.7625958 0.7625958 1.131666e-04 1.131666e-04 0.237291003 #> 2468  1233.5 0.7627123 0.7627123 1.133818e-04 1.133818e-04 0.237174275 #> 2469  1234.0 0.7628288 0.7628288 1.135977e-04 1.135977e-04 0.237057608 #> 2470  1234.5 0.7629452 0.7629452 1.138144e-04 1.138144e-04 0.236941004 #> 2471  1235.0 0.7630615 0.7630615 1.140318e-04 1.140318e-04 0.236824461 #> 2472  1235.5 0.7631778 0.7631778 1.142500e-04 1.142500e-04 0.236707980 #> 2473  1236.0 0.7632940 0.7632940 1.144689e-04 1.144689e-04 0.236591561 #> 2474  1236.5 0.7634101 0.7634101 1.146885e-04 1.146885e-04 0.236475204 #> 2475  1237.0 0.7635262 0.7635262 1.149090e-04 1.149090e-04 0.236358909 #> 2476  1237.5 0.7636422 0.7636422 1.151301e-04 1.151301e-04 0.236242676 #> 2477  1238.0 0.7637581 0.7637581 1.153521e-04 1.153521e-04 0.236126504 #> 2478  1238.5 0.7638740 0.7638740 1.155748e-04 1.155748e-04 0.236010394 #> 2479  1239.0 0.7639899 0.7639899 1.157982e-04 1.157982e-04 0.235894346 #> 2480  1239.5 0.7641056 0.7641056 1.160225e-04 1.160225e-04 0.235778360 #> 2481  1240.0 0.7642213 0.7642213 1.162475e-04 1.162475e-04 0.235662435 #> 2482  1240.5 0.7643370 0.7643370 1.164732e-04 1.164732e-04 0.235546572 #> 2483  1241.0 0.7644525 0.7644525 1.166998e-04 1.166998e-04 0.235430771 #> 2484  1241.5 0.7645680 0.7645680 1.169271e-04 1.169271e-04 0.235315032 #> 2485  1242.0 0.7646835 0.7646835 1.171552e-04 1.171552e-04 0.235199354 #> 2486  1242.5 0.7647989 0.7647989 1.173841e-04 1.173841e-04 0.235083737 #> 2487  1243.0 0.7649142 0.7649142 1.176138e-04 1.176138e-04 0.234968183 #> 2488  1243.5 0.7650295 0.7650295 1.178443e-04 1.178443e-04 0.234852690 #> 2489  1244.0 0.7651447 0.7651447 1.180755e-04 1.180755e-04 0.234737258 #> 2490  1244.5 0.7652598 0.7652598 1.183076e-04 1.183076e-04 0.234621888 #> 2491  1245.0 0.7653749 0.7653749 1.185405e-04 1.185405e-04 0.234506580 #> 2492  1245.5 0.7654899 0.7654899 1.187741e-04 1.187741e-04 0.234391333 #> 2493  1246.0 0.7656048 0.7656048 1.190086e-04 1.190086e-04 0.234276148 #> 2494  1246.5 0.7657197 0.7657197 1.192438e-04 1.192438e-04 0.234161024 #> 2495  1247.0 0.7658346 0.7658346 1.194799e-04 1.194799e-04 0.234045961 #> 2496  1247.5 0.7659493 0.7659493 1.197168e-04 1.197168e-04 0.233930960 #> 2497  1248.0 0.7660640 0.7660640 1.199545e-04 1.199545e-04 0.233816021 #> 2498  1248.5 0.7661787 0.7661787 1.201930e-04 1.201930e-04 0.233701143 #> 2499  1249.0 0.7662932 0.7662932 1.204323e-04 1.204323e-04 0.233586326 #> 2500  1249.5 0.7664078 0.7664078 1.206725e-04 1.206725e-04 0.233471571 #> 2501  1250.0 0.7665222 0.7665222 1.209134e-04 1.209134e-04 0.233356877 #> 2502  1250.5 0.7666366 0.7666366 1.211552e-04 1.211552e-04 0.233242245 #> 2503  1251.0 0.7667509 0.7667509 1.213979e-04 1.213979e-04 0.233127673 #> 2504  1251.5 0.7668652 0.7668652 1.216413e-04 1.216413e-04 0.233013164 #> 2505  1252.0 0.7669794 0.7669794 1.218856e-04 1.218856e-04 0.232898715 #> 2506  1252.5 0.7670935 0.7670935 1.221308e-04 1.221308e-04 0.232784328 #> 2507  1253.0 0.7672076 0.7672076 1.223768e-04 1.223768e-04 0.232670002 #> 2508  1253.5 0.7673216 0.7673216 1.226236e-04 1.226236e-04 0.232555737 #> 2509  1254.0 0.7674356 0.7674356 1.228713e-04 1.228713e-04 0.232441533 #> 2510  1254.5 0.7675495 0.7675495 1.231198e-04 1.231198e-04 0.232327391 #> 2511  1255.0 0.7676633 0.7676633 1.233692e-04 1.233692e-04 0.232213310 #> 2512  1255.5 0.7677771 0.7677771 1.236194e-04 1.236194e-04 0.232099290 #> 2513  1256.0 0.7678908 0.7678908 1.238705e-04 1.238705e-04 0.231985331 #> 2514  1256.5 0.7680044 0.7680044 1.241225e-04 1.241225e-04 0.231871434 #> 2515  1257.0 0.7681180 0.7681180 1.243753e-04 1.243753e-04 0.231757597 #> 2516  1257.5 0.7682315 0.7682315 1.246290e-04 1.246290e-04 0.231643822 #> 2517  1258.0 0.7683450 0.7683450 1.248836e-04 1.248836e-04 0.231530108 #> 2518  1258.5 0.7684584 0.7684584 1.251391e-04 1.251391e-04 0.231416455 #> 2519  1259.0 0.7685717 0.7685717 1.253954e-04 1.253954e-04 0.231302863 #> 2520  1259.5 0.7686850 0.7686850 1.256526e-04 1.256526e-04 0.231189332 #> 2521  1260.0 0.7687982 0.7687982 1.259107e-04 1.259107e-04 0.231075862 #> 2522  1260.5 0.7689114 0.7689114 1.261696e-04 1.261696e-04 0.230962453 #> 2523  1261.0 0.7690245 0.7690245 1.264295e-04 1.264295e-04 0.230849105 #> 2524  1261.5 0.7691375 0.7691375 1.266903e-04 1.266903e-04 0.230735818 #> 2525  1262.0 0.7692505 0.7692505 1.269519e-04 1.269519e-04 0.230622592 #> 2526  1262.5 0.7693634 0.7693634 1.272145e-04 1.272145e-04 0.230509427 #> 2527  1263.0 0.7694762 0.7694762 1.274779e-04 1.274779e-04 0.230396323 #> 2528  1263.5 0.7695890 0.7695890 1.277423e-04 1.277423e-04 0.230283280 #> 2529  1264.0 0.7697017 0.7697017 1.280076e-04 1.280076e-04 0.230170298 #> 2530  1264.5 0.7698143 0.7698143 1.282738e-04 1.282738e-04 0.230057377 #> 2531  1265.0 0.7699269 0.7699269 1.285409e-04 1.285409e-04 0.229944516 #> 2532  1265.5 0.7700395 0.7700395 1.288089e-04 1.288089e-04 0.229831717 #> 2533  1266.0 0.7701519 0.7701519 1.290778e-04 1.290778e-04 0.229718978 #> 2534  1266.5 0.7702644 0.7702644 1.293477e-04 1.293477e-04 0.229606300 #> 2535  1267.0 0.7703767 0.7703767 1.296185e-04 1.296185e-04 0.229493683 #> 2536  1267.5 0.7704890 0.7704890 1.298902e-04 1.298902e-04 0.229381127 #> 2537  1268.0 0.7706012 0.7706012 1.301629e-04 1.301629e-04 0.229268632 #> 2538  1268.5 0.7707134 0.7707134 1.304365e-04 1.304365e-04 0.229156197 #> 2539  1269.0 0.7708255 0.7708255 1.307110e-04 1.307110e-04 0.229043824 #> 2540  1269.5 0.7709375 0.7709375 1.309865e-04 1.309865e-04 0.228931511 #> 2541  1270.0 0.7710495 0.7710495 1.312629e-04 1.312629e-04 0.228819258 #> 2542  1270.5 0.7711614 0.7711614 1.315403e-04 1.315403e-04 0.228707067 #> 2543  1271.0 0.7712732 0.7712732 1.318186e-04 1.318186e-04 0.228594936 #> 2544  1271.5 0.7713850 0.7713850 1.320979e-04 1.320979e-04 0.228482866 #> 2545  1272.0 0.7714968 0.7714968 1.323782e-04 1.323782e-04 0.228370856 #> 2546  1272.5 0.7716084 0.7716084 1.326594e-04 1.326594e-04 0.228258908 #> 2547  1273.0 0.7717200 0.7717200 1.329416e-04 1.329416e-04 0.228147019 #> 2548  1273.5 0.7718316 0.7718316 1.332248e-04 1.332248e-04 0.228035192 #> 2549  1274.0 0.7719431 0.7719431 1.335089e-04 1.335089e-04 0.227923425 #> 2550  1274.5 0.7720545 0.7720545 1.337940e-04 1.337940e-04 0.227811719 #> 2551  1275.0 0.7721658 0.7721658 1.340801e-04 1.340801e-04 0.227700073 #> 2552  1275.5 0.7722771 0.7722771 1.343672e-04 1.343672e-04 0.227588488 #> 2553  1276.0 0.7723884 0.7723884 1.346553e-04 1.346553e-04 0.227476964 #> 2554  1276.5 0.7724996 0.7724996 1.349444e-04 1.349444e-04 0.227365500 #> 2555  1277.0 0.7726107 0.7726107 1.352344e-04 1.352344e-04 0.227254096 #> 2556  1277.5 0.7727217 0.7727217 1.355255e-04 1.355255e-04 0.227142753 #> 2557  1278.0 0.7728327 0.7728327 1.358176e-04 1.358176e-04 0.227031471 #> 2558  1278.5 0.7729436 0.7729436 1.361106e-04 1.361106e-04 0.226920249 #> 2559  1279.0 0.7730545 0.7730545 1.364047e-04 1.364047e-04 0.226809088 #> 2560  1279.5 0.7731653 0.7731653 1.366998e-04 1.366998e-04 0.226697987 #> 2561  1280.0 0.7732761 0.7732761 1.369960e-04 1.369960e-04 0.226586946 #> 2562  1280.5 0.7733867 0.7733867 1.372931e-04 1.372931e-04 0.226475966 #> 2563  1281.0 0.7734974 0.7734974 1.375913e-04 1.375913e-04 0.226365047 #> 2564  1281.5 0.7736079 0.7736079 1.378905e-04 1.378905e-04 0.226254188 #> 2565  1282.0 0.7737184 0.7737184 1.381907e-04 1.381907e-04 0.226143389 #> 2566  1282.5 0.7738289 0.7738289 1.384919e-04 1.384919e-04 0.226032651 #> 2567  1283.0 0.7739392 0.7739392 1.387942e-04 1.387942e-04 0.225921973 #> 2568  1283.5 0.7740495 0.7740495 1.390976e-04 1.390976e-04 0.225811355 #> 2569  1284.0 0.7741598 0.7741598 1.394020e-04 1.394020e-04 0.225700798 #> 2570  1284.5 0.7742700 0.7742700 1.397074e-04 1.397074e-04 0.225590301 #> 2571  1285.0 0.7743801 0.7743801 1.400139e-04 1.400139e-04 0.225479865 #> 2572  1285.5 0.7744902 0.7744902 1.403215e-04 1.403215e-04 0.225369488 #> 2573  1286.0 0.7746002 0.7746002 1.406301e-04 1.406301e-04 0.225259173 #> 2574  1286.5 0.7747101 0.7747101 1.409398e-04 1.409398e-04 0.225148917 #> 2575  1287.0 0.7748200 0.7748200 1.412505e-04 1.412505e-04 0.225038722 #> 2576  1287.5 0.7749299 0.7749299 1.415624e-04 1.415624e-04 0.224928587 #> 2577  1288.0 0.7750396 0.7750396 1.418753e-04 1.418753e-04 0.224818512 #> 2578  1288.5 0.7751493 0.7751493 1.421893e-04 1.421893e-04 0.224708497 #> 2579  1289.0 0.7752590 0.7752590 1.425043e-04 1.425043e-04 0.224598543 #> 2580  1289.5 0.7753685 0.7753685 1.428205e-04 1.428205e-04 0.224488649 #> 2581  1290.0 0.7754780 0.7754780 1.431378e-04 1.431378e-04 0.224378815 #> 2582  1290.5 0.7755875 0.7755875 1.434561e-04 1.434561e-04 0.224269041 #> 2583  1291.0 0.7756969 0.7756969 1.437756e-04 1.437756e-04 0.224159328 #> 2584  1291.5 0.7758062 0.7758062 1.440961e-04 1.440961e-04 0.224049674 #> 2585  1292.0 0.7759155 0.7759155 1.444178e-04 1.444178e-04 0.223940081 #> 2586  1292.5 0.7760247 0.7760247 1.447406e-04 1.447406e-04 0.223830548 #> 2587  1293.0 0.7761339 0.7761339 1.450645e-04 1.450645e-04 0.223721075 #> 2588  1293.5 0.7762429 0.7762429 1.453895e-04 1.453895e-04 0.223611663 #> 2589  1294.0 0.7763520 0.7763520 1.457157e-04 1.457157e-04 0.223502310 #> 2590  1294.5 0.7764609 0.7764609 1.460430e-04 1.460430e-04 0.223393018 #> 2591  1295.0 0.7765698 0.7765698 1.463714e-04 1.463714e-04 0.223283785 #> 2592  1295.5 0.7766787 0.7766787 1.467009e-04 1.467009e-04 0.223174613 #> 2593  1296.0 0.7767875 0.7767875 1.470316e-04 1.470316e-04 0.223065501 #> 2594  1296.5 0.7768962 0.7768962 1.473635e-04 1.473635e-04 0.222956448 #> 2595  1297.0 0.7770048 0.7770048 1.476964e-04 1.476964e-04 0.222847456 #> 2596  1297.5 0.7771134 0.7771134 1.480306e-04 1.480306e-04 0.222738524 #> 2597  1298.0 0.7772220 0.7772220 1.483659e-04 1.483659e-04 0.222629652 #> 2598  1298.5 0.7773305 0.7773305 1.487024e-04 1.487024e-04 0.222520840 #> 2599  1299.0 0.7774389 0.7774389 1.490400e-04 1.490400e-04 0.222412088 #> 2600  1299.5 0.7775472 0.7775472 1.493788e-04 1.493788e-04 0.222303396 #> 2601  1300.0 0.7776555 0.7776555 1.497188e-04 1.497188e-04 0.222194764 #> 2602  1300.5 0.7777637 0.7777637 1.500599e-04 1.500599e-04 0.222086192 #> 2603  1301.0 0.7778719 0.7778719 1.504023e-04 1.504023e-04 0.221977680 #> 2604  1301.5 0.7779800 0.7779800 1.507458e-04 1.507458e-04 0.221869227 #> 2605  1302.0 0.7780881 0.7780881 1.510905e-04 1.510905e-04 0.221760835 #> 2606  1302.5 0.7781961 0.7781961 1.514365e-04 1.514365e-04 0.221652503 #> 2607  1303.0 0.7783040 0.7783040 1.517836e-04 1.517836e-04 0.221544231 #> 2608  1303.5 0.7784118 0.7784118 1.521319e-04 1.521319e-04 0.221436018 #> 2609  1304.0 0.7785197 0.7785197 1.524814e-04 1.524814e-04 0.221327866 #> 2610  1304.5 0.7786274 0.7786274 1.528322e-04 1.528322e-04 0.221219773 #> 2611  1305.0 0.7787351 0.7787351 1.531842e-04 1.531842e-04 0.221111740 #> 2612  1305.5 0.7788427 0.7788427 1.535374e-04 1.535374e-04 0.221003767 #> 2613  1306.0 0.7789503 0.7789503 1.538918e-04 1.538918e-04 0.220895854 #> 2614  1306.5 0.7790578 0.7790578 1.542475e-04 1.542475e-04 0.220788001 #> 2615  1307.0 0.7791652 0.7791652 1.546044e-04 1.546044e-04 0.220680208 #> 2616  1307.5 0.7792726 0.7792726 1.549625e-04 1.549625e-04 0.220572475 #> 2617  1308.0 0.7793799 0.7793799 1.553219e-04 1.553219e-04 0.220464801 #> 2618  1308.5 0.7794871 0.7794871 1.556825e-04 1.556825e-04 0.220357187 #> 2619  1309.0 0.7795943 0.7795943 1.560444e-04 1.560444e-04 0.220249633 #> 2620  1309.5 0.7797015 0.7797015 1.564076e-04 1.564076e-04 0.220142139 #> 2621  1310.0 0.7798085 0.7798085 1.567720e-04 1.567720e-04 0.220034705 #> 2622  1310.5 0.7799155 0.7799155 1.571377e-04 1.571377e-04 0.219927330 #> 2623  1311.0 0.7800225 0.7800225 1.575046e-04 1.575046e-04 0.219820015 #> 2624  1311.5 0.7801294 0.7801294 1.578729e-04 1.578729e-04 0.219712760 #> 2625  1312.0 0.7802362 0.7802362 1.582424e-04 1.582424e-04 0.219605565 #> 2626  1312.5 0.7803430 0.7803430 1.586132e-04 1.586132e-04 0.219498429 #> 2627  1313.0 0.7804497 0.7804497 1.589853e-04 1.589853e-04 0.219391354 #> 2628  1313.5 0.7805563 0.7805563 1.593587e-04 1.593587e-04 0.219284338 #> 2629  1314.0 0.7806629 0.7806629 1.597334e-04 1.597334e-04 0.219177381 #> 2630  1314.5 0.7807694 0.7807694 1.601094e-04 1.601094e-04 0.219070485 #> 2631  1315.0 0.7808759 0.7808759 1.604868e-04 1.604868e-04 0.218963648 #> 2632  1315.5 0.7809823 0.7809823 1.608654e-04 1.608654e-04 0.218856870 #> 2633  1316.0 0.7810886 0.7810886 1.612454e-04 1.612454e-04 0.218750153 #> 2634  1316.5 0.7811949 0.7811949 1.616266e-04 1.616266e-04 0.218643495 #> 2635  1317.0 0.7813011 0.7813011 1.620093e-04 1.620093e-04 0.218536897 #> 2636  1317.5 0.7814072 0.7814072 1.623932e-04 1.623932e-04 0.218430359 #> 2637  1318.0 0.7815133 0.7815133 1.627785e-04 1.627785e-04 0.218323880 #> 2638  1318.5 0.7816194 0.7816194 1.631652e-04 1.631652e-04 0.218217461 #> 2639  1319.0 0.7817253 0.7817253 1.635532e-04 1.635532e-04 0.218111101 #> 2640  1319.5 0.7818313 0.7818313 1.639425e-04 1.639425e-04 0.218004801 #> 2641  1320.0 0.7819371 0.7819371 1.643333e-04 1.643333e-04 0.217898561 #> 2642  1320.5 0.7820429 0.7820429 1.647254e-04 1.647254e-04 0.217792381 #> 2643  1321.0 0.7821486 0.7821486 1.651188e-04 1.651188e-04 0.217686260 #> 2644  1321.5 0.7822543 0.7822543 1.655136e-04 1.655136e-04 0.217580198 #> 2645  1322.0 0.7823599 0.7823599 1.659099e-04 1.659099e-04 0.217474197 #> 2646  1322.5 0.7824654 0.7824654 1.663075e-04 1.663075e-04 0.217368254 #> 2647  1323.0 0.7825709 0.7825709 1.667065e-04 1.667065e-04 0.217262372 #> 2648  1323.5 0.7826763 0.7826763 1.671069e-04 1.671069e-04 0.217156549 #> 2649  1324.0 0.7827817 0.7827817 1.675087e-04 1.675087e-04 0.217050786 #> 2650  1324.5 0.7828870 0.7828870 1.679119e-04 1.679119e-04 0.216945082 #> 2651  1325.0 0.7829922 0.7829922 1.683165e-04 1.683165e-04 0.216839437 #> 2652  1325.5 0.7830974 0.7830974 1.687225e-04 1.687225e-04 0.216733853 #> 2653  1326.0 0.7832025 0.7832025 1.691300e-04 1.691300e-04 0.216628328 #> 2654  1326.5 0.7833076 0.7833076 1.695389e-04 1.695389e-04 0.216522862 #> 2655  1327.0 0.7834126 0.7834126 1.699492e-04 1.699492e-04 0.216417456 #> 2656  1327.5 0.7835175 0.7835175 1.703610e-04 1.703610e-04 0.216312110 #> 2657  1328.0 0.7836224 0.7836224 1.707742e-04 1.707742e-04 0.216206823 #> 2658  1328.5 0.7837272 0.7837272 1.711889e-04 1.711889e-04 0.216101595 #> 2659  1329.0 0.7838320 0.7838320 1.716050e-04 1.716050e-04 0.215996427 #> 2660  1329.5 0.7839367 0.7839367 1.720226e-04 1.720226e-04 0.215891319 #> 2661  1330.0 0.7840413 0.7840413 1.724416e-04 1.724416e-04 0.215786270 #> 2662  1330.5 0.7841459 0.7841459 1.728621e-04 1.728621e-04 0.215681281 #> 2663  1331.0 0.7842504 0.7842504 1.732841e-04 1.732841e-04 0.215576351 #> 2664  1331.5 0.7843548 0.7843548 1.737076e-04 1.737076e-04 0.215471481 #> 2665  1332.0 0.7844592 0.7844592 1.741326e-04 1.741326e-04 0.215366670 #> 2666  1332.5 0.7845635 0.7845635 1.745591e-04 1.745591e-04 0.215261918 #> 2667  1333.0 0.7846678 0.7846678 1.749870e-04 1.749870e-04 0.215157226 #> 2668  1333.5 0.7847720 0.7847720 1.754165e-04 1.754165e-04 0.215052594 #> 2669  1334.0 0.7848761 0.7848761 1.758475e-04 1.758475e-04 0.214948021 #> 2670  1334.5 0.7849802 0.7849802 1.762800e-04 1.762800e-04 0.214843508 #> 2671  1335.0 0.7850842 0.7850842 1.767141e-04 1.767141e-04 0.214739054 #> 2672  1335.5 0.7851882 0.7851882 1.771496e-04 1.771496e-04 0.214634659 #> 2673  1336.0 0.7852921 0.7852921 1.775867e-04 1.775867e-04 0.214530324 #> 2674  1336.5 0.7853959 0.7853959 1.780253e-04 1.780253e-04 0.214426048 #> 2675  1337.0 0.7854997 0.7854997 1.784655e-04 1.784655e-04 0.214321832 #> 2676  1337.5 0.7856034 0.7856034 1.789073e-04 1.789073e-04 0.214217675 #> 2677  1338.0 0.7857071 0.7857071 1.793506e-04 1.793506e-04 0.214113578 #> 2678  1338.5 0.7858107 0.7858107 1.797954e-04 1.797954e-04 0.214009540 #> 2679  1339.0 0.7859142 0.7859142 1.802418e-04 1.802418e-04 0.213905562 #> 2680  1339.5 0.7860177 0.7860177 1.806898e-04 1.806898e-04 0.213801643 #> 2681  1340.0 0.7861211 0.7861211 1.811394e-04 1.811394e-04 0.213697783 #> 2682  1340.5 0.7862244 0.7862244 1.815906e-04 1.815906e-04 0.213593983 #> 2683  1341.0 0.7863277 0.7863277 1.820434e-04 1.820434e-04 0.213490242 #> 2684  1341.5 0.7864309 0.7864309 1.824977e-04 1.824977e-04 0.213386561 #> 2685  1342.0 0.7865341 0.7865341 1.829537e-04 1.829537e-04 0.213282939 #> 2686  1342.5 0.7866372 0.7866372 1.834113e-04 1.834113e-04 0.213179376 #> 2687  1343.0 0.7867403 0.7867403 1.838705e-04 1.838705e-04 0.213075873 #> 2688  1343.5 0.7868432 0.7868432 1.843313e-04 1.843313e-04 0.212972429 #> 2689  1344.0 0.7869462 0.7869462 1.847938e-04 1.847938e-04 0.212869045 #> 2690  1344.5 0.7870490 0.7870490 1.852579e-04 1.852579e-04 0.212765720 #> 2691  1345.0 0.7871518 0.7871518 1.857236e-04 1.857236e-04 0.212662454 #> 2692  1345.5 0.7872546 0.7872546 1.861910e-04 1.861910e-04 0.212559248 #> 2693  1346.0 0.7873572 0.7873572 1.866600e-04 1.866600e-04 0.212456102 #> 2694  1346.5 0.7874599 0.7874599 1.871307e-04 1.871307e-04 0.212353014 #> 2695  1347.0 0.7875624 0.7875624 1.876031e-04 1.876031e-04 0.212249986 #> 2696  1347.5 0.7876649 0.7876649 1.880772e-04 1.880772e-04 0.212147018 #> 2697  1348.0 0.7877673 0.7877673 1.885529e-04 1.885529e-04 0.212044108 #> 2698  1348.5 0.7878697 0.7878697 1.890303e-04 1.890303e-04 0.211941259 #> 2699  1349.0 0.7879720 0.7879720 1.895094e-04 1.895094e-04 0.211838468 #> 2700  1349.5 0.7880743 0.7880743 1.899902e-04 1.899902e-04 0.211735737 #> 2701  1350.0 0.7881765 0.7881765 1.904727e-04 1.904727e-04 0.211633066 #> 2702  1350.5 0.7882786 0.7882786 1.909570e-04 1.909570e-04 0.211530453 #> 2703  1351.0 0.7883807 0.7883807 1.914429e-04 1.914429e-04 0.211427900 #> 2704  1351.5 0.7884827 0.7884827 1.919306e-04 1.919306e-04 0.211325407 #> 2705  1352.0 0.7885846 0.7885846 1.924200e-04 1.924200e-04 0.211222973 #> 2706  1352.5 0.7886865 0.7886865 1.929111e-04 1.929111e-04 0.211120598 #> 2707  1353.0 0.7887883 0.7887883 1.934040e-04 1.934040e-04 0.211018282 #> 2708  1353.5 0.7888901 0.7888901 1.938987e-04 1.938987e-04 0.210916026 #> 2709  1354.0 0.7889918 0.7889918 1.943951e-04 1.943951e-04 0.210813830 #> 2710  1354.5 0.7890934 0.7890934 1.948933e-04 1.948933e-04 0.210711692 #> 2711  1355.0 0.7891950 0.7891950 1.953932e-04 1.953932e-04 0.210609614 #> 2712  1355.5 0.7892965 0.7892965 1.958949e-04 1.958949e-04 0.210507596 #> 2713  1356.0 0.7893980 0.7893980 1.963984e-04 1.963984e-04 0.210405637 #> 2714  1356.5 0.7894994 0.7894994 1.969037e-04 1.969037e-04 0.210303737 #> 2715  1357.0 0.7896007 0.7896007 1.974109e-04 1.974109e-04 0.210201896 #> 2716  1357.5 0.7897020 0.7897020 1.979198e-04 1.979198e-04 0.210100115 #> 2717  1358.0 0.7898032 0.7898032 1.984305e-04 1.984305e-04 0.209998394 #> 2718  1358.5 0.7899043 0.7899043 1.989430e-04 1.989430e-04 0.209896731 #> 2719  1359.0 0.7900054 0.7900054 1.994574e-04 1.994574e-04 0.209795128 #> 2720  1359.5 0.7901064 0.7901064 1.999736e-04 1.999736e-04 0.209693585 #> 2721  1360.0 0.7902074 0.7902074 2.004917e-04 2.004917e-04 0.209592100 #> 2722  1360.5 0.7903083 0.7903083 2.010116e-04 2.010116e-04 0.209490676 #> 2723  1361.0 0.7904092 0.7904092 2.015333e-04 2.015333e-04 0.209389310 #> 2724  1361.5 0.7905099 0.7905099 2.020570e-04 2.020570e-04 0.209288004 #> 2725  1362.0 0.7906107 0.7906107 2.025825e-04 2.025825e-04 0.209186757 #> 2726  1362.5 0.7907113 0.7907113 2.031098e-04 2.031098e-04 0.209085570 #> 2727  1363.0 0.7908119 0.7908119 2.036391e-04 2.036391e-04 0.208984442 #> 2728  1363.5 0.7909125 0.7909125 2.041702e-04 2.041702e-04 0.208883374 #> 2729  1364.0 0.7910129 0.7910129 2.047033e-04 2.047033e-04 0.208782364 #> 2730  1364.5 0.7911133 0.7911133 2.052382e-04 2.052382e-04 0.208681415 #> 2731  1365.0 0.7912137 0.7912137 2.057751e-04 2.057751e-04 0.208580524 #> 2732  1365.5 0.7913140 0.7913140 2.063139e-04 2.063139e-04 0.208479693 #> 2733  1366.0 0.7914142 0.7914142 2.068546e-04 2.068546e-04 0.208378922 #> 2734  1366.5 0.7915144 0.7915144 2.073973e-04 2.073973e-04 0.208278210 #> 2735  1367.0 0.7916145 0.7916145 2.079419e-04 2.079419e-04 0.208177557 #> 2736  1367.5 0.7917145 0.7917145 2.084884e-04 2.084884e-04 0.208076963 #> 2737  1368.0 0.7918145 0.7918145 2.090369e-04 2.090369e-04 0.207976429 #> 2738  1368.5 0.7919145 0.7919145 2.095874e-04 2.095874e-04 0.207875955 #> 2739  1369.0 0.7920143 0.7920143 2.101399e-04 2.101399e-04 0.207775540 #> 2740  1369.5 0.7921141 0.7921141 2.106943e-04 2.106943e-04 0.207675184 #> 2741  1370.0 0.7922139 0.7922139 2.112507e-04 2.112507e-04 0.207574888 #> 2742  1370.5 0.7923135 0.7923135 2.118092e-04 2.118092e-04 0.207474651 #> 2743  1371.0 0.7924132 0.7924132 2.123696e-04 2.123696e-04 0.207374473 #> 2744  1371.5 0.7925127 0.7925127 2.129320e-04 2.129320e-04 0.207274355 #> 2745  1372.0 0.7926122 0.7926122 2.134965e-04 2.134965e-04 0.207174297 #> 2746  1372.5 0.7927116 0.7927116 2.140630e-04 2.140630e-04 0.207074298 #> 2747  1373.0 0.7928110 0.7928110 2.146315e-04 2.146315e-04 0.206974358 #> 2748  1373.5 0.7929103 0.7929103 2.152021e-04 2.152021e-04 0.206874478 #> 2749  1374.0 0.7930096 0.7930096 2.157747e-04 2.157747e-04 0.206774657 #> 2750  1374.5 0.7931088 0.7931088 2.163494e-04 2.163494e-04 0.206674895 #> 2751  1375.0 0.7932079 0.7932079 2.169261e-04 2.169261e-04 0.206575194 #> 2752  1375.5 0.7933069 0.7933069 2.175050e-04 2.175050e-04 0.206475551 #> 2753  1376.0 0.7934059 0.7934059 2.180859e-04 2.180859e-04 0.206375968 #> 2754  1376.5 0.7935049 0.7935049 2.186689e-04 2.186689e-04 0.206276445 #> 2755  1377.0 0.7936038 0.7936038 2.192540e-04 2.192540e-04 0.206176981 #> 2756  1377.5 0.7937026 0.7937026 2.198412e-04 2.198412e-04 0.206077576 #> 2757  1378.0 0.7938013 0.7938013 2.204305e-04 2.204305e-04 0.205978231 #> 2758  1378.5 0.7939000 0.7939000 2.210220e-04 2.210220e-04 0.205878946 #> 2759  1379.0 0.7939987 0.7939987 2.216156e-04 2.216156e-04 0.205779720 #> 2760  1379.5 0.7940972 0.7940972 2.222113e-04 2.222113e-04 0.205680553 #> 2761  1380.0 0.7941957 0.7941957 2.228092e-04 2.228092e-04 0.205581446 #> 2762  1380.5 0.7942942 0.7942942 2.234093e-04 2.234093e-04 0.205482399 #> 2763  1381.0 0.7943926 0.7943926 2.240115e-04 2.240115e-04 0.205383411 #> 2764  1381.5 0.7944909 0.7944909 2.246158e-04 2.246158e-04 0.205284482 #> 2765  1382.0 0.7945892 0.7945892 2.252224e-04 2.252224e-04 0.205185613 #> 2766  1382.5 0.7946874 0.7946874 2.258312e-04 2.258312e-04 0.205086804 #> 2767  1383.0 0.7947855 0.7947855 2.264421e-04 2.264421e-04 0.204988054 #> 2768  1383.5 0.7948836 0.7948836 2.270553e-04 2.270553e-04 0.204889364 #> 2769  1384.0 0.7949816 0.7949816 2.276707e-04 2.276707e-04 0.204790733 #> 2770  1384.5 0.7950795 0.7950795 2.282883e-04 2.282883e-04 0.204692162 #> 2771  1385.0 0.7951774 0.7951774 2.289082e-04 2.289082e-04 0.204593651 #> 2772  1385.5 0.7952753 0.7952753 2.295303e-04 2.295303e-04 0.204495199 #> 2773  1386.0 0.7953730 0.7953730 2.301546e-04 2.301546e-04 0.204396807 #> 2774  1386.5 0.7954707 0.7954707 2.307812e-04 2.307812e-04 0.204298474 #> 2775  1387.0 0.7955684 0.7955684 2.314101e-04 2.314101e-04 0.204200201 #> 2776  1387.5 0.7956660 0.7956660 2.320413e-04 2.320413e-04 0.204101987 #> 2777  1388.0 0.7957635 0.7957635 2.326748e-04 2.326748e-04 0.204003834 #> 2778  1388.5 0.7958610 0.7958610 2.333105e-04 2.333105e-04 0.203905739 #> 2779  1389.0 0.7959583 0.7959583 2.339486e-04 2.339486e-04 0.203807705 #> 2780  1389.5 0.7960557 0.7960557 2.345889e-04 2.345889e-04 0.203709730 #> 2781  1390.0 0.7961530 0.7961530 2.352316e-04 2.352316e-04 0.203611815 #> 2782  1390.5 0.7962502 0.7962502 2.358767e-04 2.358767e-04 0.203513959 #> 2783  1391.0 0.7963473 0.7963473 2.365240e-04 2.365240e-04 0.203416163 #> 2784  1391.5 0.7964444 0.7964444 2.371738e-04 2.371738e-04 0.203318427 #> 2785  1392.0 0.7965414 0.7965414 2.378259e-04 2.378259e-04 0.203220751 #> 2786  1392.5 0.7966384 0.7966384 2.384803e-04 2.384803e-04 0.203123134 #> 2787  1393.0 0.7967353 0.7967353 2.391372e-04 2.391372e-04 0.203025577 #> 2788  1393.5 0.7968321 0.7968321 2.397964e-04 2.397964e-04 0.202928080 #> 2789  1394.0 0.7969289 0.7969289 2.404580e-04 2.404580e-04 0.202830642 #> 2790  1394.5 0.7970256 0.7970256 2.411221e-04 2.411221e-04 0.202733264 #> 2791  1395.0 0.7971223 0.7971223 2.417886e-04 2.417886e-04 0.202635946 #> 2792  1395.5 0.7972189 0.7972189 2.424574e-04 2.424574e-04 0.202538688 #> 2793  1396.0 0.7973154 0.7973154 2.431288e-04 2.431288e-04 0.202441490 #> 2794  1396.5 0.7974118 0.7974118 2.438025e-04 2.438025e-04 0.202344351 #> 2795  1397.0 0.7975082 0.7975082 2.444788e-04 2.444788e-04 0.202247272 #> 2796  1397.5 0.7976046 0.7976046 2.451575e-04 2.451575e-04 0.202150253 #> 2797  1398.0 0.7977009 0.7977009 2.458386e-04 2.458386e-04 0.202053294 #> 2798  1398.5 0.7977971 0.7977971 2.465223e-04 2.465223e-04 0.201956394 #> 2799  1399.0 0.7978932 0.7978932 2.472084e-04 2.472084e-04 0.201859555 #> 2800  1399.5 0.7979893 0.7979893 2.478971e-04 2.478971e-04 0.201762775 #> 2801  1400.0 0.7980854 0.7980854 2.485883e-04 2.485883e-04 0.201666055 #> 2802  1400.5 0.7981813 0.7981813 2.492820e-04 2.492820e-04 0.201569395 #> 2803  1401.0 0.7982772 0.7982772 2.499782e-04 2.499782e-04 0.201472795 #> 2804  1401.5 0.7983731 0.7983731 2.506770e-04 2.506770e-04 0.201376255 #> 2805  1402.0 0.7984688 0.7984688 2.513783e-04 2.513783e-04 0.201279775 #> 2806  1402.5 0.7985646 0.7985646 2.520822e-04 2.520822e-04 0.201183354 #> 2807  1403.0 0.7986602 0.7986602 2.527887e-04 2.527887e-04 0.201086994 #> 2808  1403.5 0.7987558 0.7987558 2.534978e-04 2.534978e-04 0.200990694 #> 2809  1404.0 0.7988513 0.7988513 2.542094e-04 2.542094e-04 0.200894453 #> 2810  1404.5 0.7989468 0.7989468 2.549237e-04 2.549237e-04 0.200798273 #> 2811  1405.0 0.7990422 0.7990422 2.556406e-04 2.556406e-04 0.200702153 #> 2812  1405.5 0.7991375 0.7991375 2.563601e-04 2.563601e-04 0.200606092 #> 2813  1406.0 0.7992328 0.7992328 2.570822e-04 2.570822e-04 0.200510092 #> 2814  1406.5 0.7993280 0.7993280 2.578070e-04 2.578070e-04 0.200414151 #> 2815  1407.0 0.7994232 0.7994232 2.585345e-04 2.585345e-04 0.200318271 #> 2816  1407.5 0.7995183 0.7995183 2.592646e-04 2.592646e-04 0.200222451 #> 2817  1408.0 0.7996133 0.7996133 2.599974e-04 2.599974e-04 0.200126691 #> 2818  1408.5 0.7997083 0.7997083 2.607329e-04 2.607329e-04 0.200030991 #> 2819  1409.0 0.7998032 0.7998032 2.614711e-04 2.614711e-04 0.199935351 #> 2820  1409.5 0.7998980 0.7998980 2.622120e-04 2.622120e-04 0.199839771 #> 2821  1410.0 0.7999928 0.7999928 2.629556e-04 2.629556e-04 0.199744252 #> 2822  1410.5 0.8000875 0.8000875 2.637020e-04 2.637020e-04 0.199648792 #> 2823  1411.0 0.8001822 0.8001822 2.644511e-04 2.644511e-04 0.199553393 #> 2824  1411.5 0.8002767 0.8002767 2.652029e-04 2.652029e-04 0.199458054 #> 2825  1412.0 0.8003713 0.8003713 2.659576e-04 2.659576e-04 0.199362775 #> 2826  1412.5 0.8004657 0.8004657 2.667150e-04 2.667150e-04 0.199267556 #> 2827  1413.0 0.8005601 0.8005601 2.674752e-04 2.674752e-04 0.199172398 #> 2828  1413.5 0.8006545 0.8006545 2.682382e-04 2.682382e-04 0.199077300 #> 2829  1414.0 0.8007487 0.8007487 2.690040e-04 2.690040e-04 0.198982262 #> 2830  1414.5 0.8008429 0.8008429 2.697726e-04 2.697726e-04 0.198887284 #> 2831  1415.0 0.8009371 0.8009371 2.705441e-04 2.705441e-04 0.198792367 #> 2832  1415.5 0.8010312 0.8010312 2.713184e-04 2.713184e-04 0.198697510 #> 2833  1416.0 0.8011252 0.8011252 2.720955e-04 2.720955e-04 0.198602714 #> 2834  1416.5 0.8012191 0.8012191 2.728755e-04 2.728755e-04 0.198507977 #> 2835  1417.0 0.8013130 0.8013130 2.736585e-04 2.736585e-04 0.198413301 #> 2836  1417.5 0.8014069 0.8014069 2.744442e-04 2.744442e-04 0.198318686 #> 2837  1418.0 0.8015006 0.8015006 2.752329e-04 2.752329e-04 0.198224131 #> 2838  1418.5 0.8015943 0.8015943 2.760246e-04 2.760246e-04 0.198129636 #> 2839  1419.0 0.8016880 0.8016880 2.768191e-04 2.768191e-04 0.198035202 #> 2840  1419.5 0.8017816 0.8017816 2.776166e-04 2.776166e-04 0.197940828 #> 2841  1420.0 0.8018751 0.8018751 2.784170e-04 2.784170e-04 0.197846515 #> 2842  1420.5 0.8019685 0.8019685 2.792204e-04 2.792204e-04 0.197752262 #> 2843  1421.0 0.8020619 0.8020619 2.800267e-04 2.800267e-04 0.197658070 #> 2844  1421.5 0.8021552 0.8021552 2.808361e-04 2.808361e-04 0.197563939 #> 2845  1422.0 0.8022485 0.8022485 2.816484e-04 2.816484e-04 0.197469867 #> 2846  1422.5 0.8023417 0.8023417 2.824637e-04 2.824637e-04 0.197375857 #> 2847  1423.0 0.8024348 0.8024348 2.832821e-04 2.832821e-04 0.197281907 #> 2848  1423.5 0.8025279 0.8025279 2.841035e-04 2.841035e-04 0.197188018 #> 2849  1424.0 0.8026209 0.8026209 2.849279e-04 2.849279e-04 0.197094189 #> 2850  1424.5 0.8027138 0.8027138 2.857554e-04 2.857554e-04 0.197000421 #> 2851  1425.0 0.8028067 0.8028067 2.865860e-04 2.865860e-04 0.196906714 #> 2852  1425.5 0.8028995 0.8028995 2.874196e-04 2.874196e-04 0.196813067 #> 2853  1426.0 0.8029923 0.8029923 2.882564e-04 2.882564e-04 0.196719482 #> 2854  1426.5 0.8030849 0.8030849 2.890962e-04 2.890962e-04 0.196625956 #> 2855  1427.0 0.8031776 0.8031776 2.899392e-04 2.899392e-04 0.196532492 #> 2856  1427.5 0.8032701 0.8032701 2.907853e-04 2.907853e-04 0.196439089 #> 2857  1428.0 0.8033626 0.8033626 2.916345e-04 2.916345e-04 0.196345746 #> 2858  1428.5 0.8034550 0.8034550 2.924869e-04 2.924869e-04 0.196252464 #> 2859  1429.0 0.8035474 0.8035474 2.933425e-04 2.933425e-04 0.196159243 #> 2860  1429.5 0.8036397 0.8036397 2.942012e-04 2.942012e-04 0.196066083 #> 2861  1430.0 0.8037320 0.8037320 2.950632e-04 2.950632e-04 0.195972984 #> 2862  1430.5 0.8038241 0.8038241 2.959283e-04 2.959283e-04 0.195879945 #> 2863  1431.0 0.8039162 0.8039162 2.967967e-04 2.967967e-04 0.195786968 #> 2864  1431.5 0.8040083 0.8040083 2.976683e-04 2.976683e-04 0.195694052 #> 2865  1432.0 0.8041003 0.8041003 2.985431e-04 2.985431e-04 0.195601196 #> 2866  1432.5 0.8041922 0.8041922 2.994212e-04 2.994212e-04 0.195508402 #> 2867  1433.0 0.8042840 0.8042840 3.003026e-04 3.003026e-04 0.195415669 #> 2868  1433.5 0.8043758 0.8043758 3.011873e-04 3.011873e-04 0.195322996 #> 2869  1434.0 0.8044675 0.8044675 3.020752e-04 3.020752e-04 0.195230385 #> 2870  1434.5 0.8045592 0.8045592 3.029665e-04 3.029665e-04 0.195137835 #> 2871  1435.0 0.8046508 0.8046508 3.038611e-04 3.038611e-04 0.195045346 #> 2872  1435.5 0.8047423 0.8047423 3.047591e-04 3.047591e-04 0.194952918 #> 2873  1436.0 0.8048338 0.8048338 3.056603e-04 3.056603e-04 0.194860552 #> 2874  1436.5 0.8049252 0.8049252 3.065650e-04 3.065650e-04 0.194768247 #> 2875  1437.0 0.8050165 0.8050165 3.074730e-04 3.074730e-04 0.194676002 #> 2876  1437.5 0.8051078 0.8051078 3.083845e-04 3.083845e-04 0.194583820 #> 2877  1438.0 0.8051990 0.8051990 3.092993e-04 3.092993e-04 0.194491698 #> 2878  1438.5 0.8052901 0.8052901 3.102176e-04 3.102176e-04 0.194399638 #> 2879  1439.0 0.8053812 0.8053812 3.111393e-04 3.111393e-04 0.194307639 #> 2880  1439.5 0.8054722 0.8054722 3.120644e-04 3.120644e-04 0.194215702 #> 2881  1440.0 0.8055632 0.8055632 3.129930e-04 3.129930e-04 0.194123826 #> 2882  1440.5 0.8056541 0.8056541 3.139251e-04 3.139251e-04 0.194032011 #> 2883  1441.0 0.8057449 0.8057449 3.148607e-04 3.148607e-04 0.193940258 #> 2884  1441.5 0.8058356 0.8058356 3.157998e-04 3.157998e-04 0.193848566 #> 2885  1442.0 0.8059263 0.8059263 3.167424e-04 3.167424e-04 0.193756936 #> 2886  1442.5 0.8060169 0.8060169 3.176885e-04 3.176885e-04 0.193665367 #> 2887  1443.0 0.8061075 0.8061075 3.186382e-04 3.186382e-04 0.193573860 #> 2888  1443.5 0.8061980 0.8061980 3.195914e-04 3.195914e-04 0.193482415 #> 2889  1444.0 0.8062884 0.8062884 3.205483e-04 3.205483e-04 0.193391031 #> 2890  1444.5 0.8063788 0.8063788 3.215087e-04 3.215087e-04 0.193299709 #> 2891  1445.0 0.8064691 0.8064691 3.224727e-04 3.224727e-04 0.193208448 #> 2892  1445.5 0.8065593 0.8065593 3.234403e-04 3.234403e-04 0.193117249 #> 2893  1446.0 0.8066495 0.8066495 3.244116e-04 3.244116e-04 0.193026112 #> 2894  1446.5 0.8067396 0.8067396 3.253865e-04 3.253865e-04 0.192935037 #> 2895  1447.0 0.8068296 0.8068296 3.263651e-04 3.263651e-04 0.192844023 #> 2896  1447.5 0.8069196 0.8069196 3.273474e-04 3.273474e-04 0.192753072 #> 2897  1448.0 0.8070095 0.8070095 3.283333e-04 3.283333e-04 0.192662182 #> 2898  1448.5 0.8070993 0.8070993 3.293230e-04 3.293230e-04 0.192571354 #> 2899  1449.0 0.8071891 0.8071891 3.303164e-04 3.303164e-04 0.192480588 #> 2900  1449.5 0.8072788 0.8072788 3.313135e-04 3.313135e-04 0.192389884 #> 2901  1450.0 0.8073684 0.8073684 3.323144e-04 3.323144e-04 0.192299242 #> 2902  1450.5 0.8074580 0.8074580 3.333191e-04 3.333191e-04 0.192208662 #> 2903  1451.0 0.8075475 0.8075475 3.343275e-04 3.343275e-04 0.192118144 #> 2904  1451.5 0.8076370 0.8076370 3.353398e-04 3.353398e-04 0.192027688 #> 2905  1452.0 0.8077264 0.8077264 3.363559e-04 3.363559e-04 0.191937294 #> 2906  1452.5 0.8078157 0.8078157 3.373758e-04 3.373758e-04 0.191846962 #> 2907  1453.0 0.8079049 0.8079049 3.383995e-04 3.383995e-04 0.191756693 #> 2908  1453.5 0.8079941 0.8079941 3.394271e-04 3.394271e-04 0.191666486 #> 2909  1454.0 0.8080832 0.8080832 3.404586e-04 3.404586e-04 0.191576341 #> 2910  1454.5 0.8081722 0.8081722 3.414940e-04 3.414940e-04 0.191486258 #> 2911  1455.0 0.8082612 0.8082612 3.425333e-04 3.425333e-04 0.191396238 #> 2912  1455.5 0.8083501 0.8083501 3.435765e-04 3.435765e-04 0.191306280 #> 2913  1456.0 0.8084390 0.8084390 3.446237e-04 3.446237e-04 0.191216384 #> 2914  1456.5 0.8085278 0.8085278 3.456748e-04 3.456748e-04 0.191126551 #> 2915  1457.0 0.8086165 0.8086165 3.467299e-04 3.467299e-04 0.191036780 #> 2916  1457.5 0.8087051 0.8087051 3.477890e-04 3.477890e-04 0.190947072 #> 2917  1458.0 0.8087937 0.8087937 3.488521e-04 3.488521e-04 0.190857426 #> 2918  1458.5 0.8088822 0.8088822 3.499192e-04 3.499192e-04 0.190767843 #> 2919  1459.0 0.8089707 0.8089707 3.509903e-04 3.509903e-04 0.190678322 #> 2920  1459.5 0.8090591 0.8090591 3.520656e-04 3.520656e-04 0.190588864 #> 2921  1460.0 0.8091474 0.8091474 3.531449e-04 3.531449e-04 0.190499469 #> 2922  1460.5 0.8092356 0.8092356 3.542282e-04 3.542282e-04 0.190410136 #> 2923  1461.0 0.8093238 0.8093238 3.553157e-04 3.553157e-04 0.190320866 #> 2924  1461.5 0.8094119 0.8094119 3.564073e-04 3.564073e-04 0.190231659 #> 2925  1462.0 0.8095000 0.8095000 3.575031e-04 3.575031e-04 0.190142515 #> 2926  1462.5 0.8095880 0.8095880 3.586030e-04 3.586030e-04 0.190053433 #> 2927  1463.0 0.8096759 0.8096759 3.597071e-04 3.597071e-04 0.189964415 #> 2928  1463.5 0.8097637 0.8097637 3.608154e-04 3.608154e-04 0.189875459 #> 2929  1464.0 0.8098515 0.8098515 3.619279e-04 3.619279e-04 0.189786566 #> 2930  1464.5 0.8099392 0.8099392 3.630446e-04 3.630446e-04 0.189697737 #> 2931  1465.0 0.8100269 0.8100269 3.641656e-04 3.641656e-04 0.189608970 #> 2932  1465.5 0.8101144 0.8101144 3.652908e-04 3.652908e-04 0.189520267 #> 2933  1466.0 0.8102020 0.8102020 3.664203e-04 3.664203e-04 0.189431626 #> 2934  1466.5 0.8102894 0.8102894 3.675541e-04 3.675541e-04 0.189343049 #> 2935  1467.0 0.8103768 0.8103768 3.686922e-04 3.686922e-04 0.189254535 #> 2936  1467.5 0.8104641 0.8104641 3.698346e-04 3.698346e-04 0.189166084 #> 2937  1468.0 0.8105513 0.8105513 3.709814e-04 3.709814e-04 0.189077697 #> 2938  1468.5 0.8106385 0.8106385 3.721326e-04 3.721326e-04 0.188989373 #> 2939  1469.0 0.8107256 0.8107256 3.732881e-04 3.732881e-04 0.188901112 #> 2940  1469.5 0.8108126 0.8108126 3.744481e-04 3.744481e-04 0.188812915 #> 2941  1470.0 0.8108996 0.8108996 3.756124e-04 3.756124e-04 0.188724781 #> 2942  1470.5 0.8109865 0.8109865 3.767813e-04 3.767813e-04 0.188636710 #> 2943  1471.0 0.8110733 0.8110733 3.779545e-04 3.779545e-04 0.188548703 #> 2944  1471.5 0.8111601 0.8111601 3.791323e-04 3.791323e-04 0.188460760 #> 2945  1472.0 0.8112468 0.8112468 3.803145e-04 3.803145e-04 0.188372880 #> 2946  1472.5 0.8113334 0.8113334 3.815012e-04 3.815012e-04 0.188285064 #> 2947  1473.0 0.8114200 0.8114200 3.826925e-04 3.826925e-04 0.188197312 #> 2948  1473.5 0.8115065 0.8115065 3.838884e-04 3.838884e-04 0.188109623 #> 2949  1474.0 0.8115929 0.8115929 3.850888e-04 3.850888e-04 0.188021999 #> 2950  1474.5 0.8116793 0.8116793 3.862938e-04 3.862938e-04 0.187934438 #> 2951  1475.0 0.8117656 0.8117656 3.875033e-04 3.875033e-04 0.187846941 #> 2952  1475.5 0.8118518 0.8118518 3.887176e-04 3.887176e-04 0.187759508 #> 2953  1476.0 0.8119379 0.8119379 3.899364e-04 3.899364e-04 0.187672138 #> 2954  1476.5 0.8120240 0.8120240 3.911600e-04 3.911600e-04 0.187584833 #> 2955  1477.0 0.8121100 0.8121100 3.923882e-04 3.923882e-04 0.187497592 #> 2956  1477.5 0.8121960 0.8121960 3.936211e-04 3.936211e-04 0.187410415 #> 2957  1478.0 0.8122818 0.8122818 3.948587e-04 3.948587e-04 0.187323303 #> 2958  1478.5 0.8123676 0.8123676 3.961011e-04 3.961011e-04 0.187236254 #> 2959  1479.0 0.8124534 0.8124534 3.973483e-04 3.973483e-04 0.187149270 #> 2960  1479.5 0.8125391 0.8125391 3.986002e-04 3.986002e-04 0.187062350 #> 2961  1480.0 0.8126246 0.8126246 3.998569e-04 3.998569e-04 0.186975494 #> 2962  1480.5 0.8127102 0.8127102 4.011184e-04 4.011184e-04 0.186888703 #> 2963  1481.0 0.8127956 0.8127956 4.023848e-04 4.023848e-04 0.186801976 #> 2964  1481.5 0.8128810 0.8128810 4.036561e-04 4.036561e-04 0.186715314 #> 2965  1482.0 0.8129664 0.8129664 4.049322e-04 4.049322e-04 0.186628716 #> 2966  1482.5 0.8130516 0.8130516 4.062132e-04 4.062132e-04 0.186542183 #> 2967  1483.0 0.8131368 0.8131368 4.074992e-04 4.074992e-04 0.186455714 #> 2968  1483.5 0.8132219 0.8132219 4.087901e-04 4.087901e-04 0.186369310 #> 2969  1484.0 0.8133069 0.8133069 4.100859e-04 4.100859e-04 0.186282971 #> 2970  1484.5 0.8133919 0.8133919 4.113867e-04 4.113867e-04 0.186196697 #> 2971  1485.0 0.8134768 0.8134768 4.126926e-04 4.126926e-04 0.186110487 #> 2972  1485.5 0.8135617 0.8135617 4.140034e-04 4.140034e-04 0.186024343 #> 2973  1486.0 0.8136464 0.8136464 4.153193e-04 4.153193e-04 0.185938263 #> 2974  1486.5 0.8137311 0.8137311 4.166403e-04 4.166403e-04 0.185852249 #> 2975  1487.0 0.8138157 0.8138157 4.179663e-04 4.179663e-04 0.185766299 #> 2976  1487.5 0.8139003 0.8139003 4.192975e-04 4.192975e-04 0.185680414 #> 2977  1488.0 0.8139848 0.8139848 4.206338e-04 4.206338e-04 0.185594595 #> 2978  1488.5 0.8140692 0.8140692 4.219752e-04 4.219752e-04 0.185508841 #> 2979  1489.0 0.8141535 0.8141535 4.233218e-04 4.233218e-04 0.185423152 #> 2980  1489.5 0.8142378 0.8142378 4.246736e-04 4.246736e-04 0.185337529 #> 2981  1490.0 0.8143220 0.8143220 4.260306e-04 4.260306e-04 0.185251970 #> 2982  1490.5 0.8144061 0.8144061 4.273929e-04 4.273929e-04 0.185166478 #> 2983  1491.0 0.8144902 0.8144902 4.287604e-04 4.287604e-04 0.185081050 #> 2984  1491.5 0.8145742 0.8145742 4.301332e-04 4.301332e-04 0.184995689 #> 2985  1492.0 0.8146581 0.8146581 4.315113e-04 4.315113e-04 0.184910392 #> 2986  1492.5 0.8147419 0.8147419 4.328947e-04 4.328947e-04 0.184825162 #> 2987  1493.0 0.8148257 0.8148257 4.342834e-04 4.342834e-04 0.184739997 #> 2988  1493.5 0.8149094 0.8149094 4.356775e-04 4.356775e-04 0.184654898 #> 2989  1494.0 0.8149931 0.8149931 4.370770e-04 4.370770e-04 0.184569865 #> 2990  1494.5 0.8150766 0.8150766 4.384820e-04 4.384820e-04 0.184484898 #> 2991  1495.0 0.8151601 0.8151601 4.398923e-04 4.398923e-04 0.184399996 #> 2992  1495.5 0.8152435 0.8152435 4.413081e-04 4.413081e-04 0.184315161 #> 2993  1496.0 0.8153269 0.8153269 4.427294e-04 4.427294e-04 0.184230391 #> 2994  1496.5 0.8154102 0.8154102 4.441562e-04 4.441562e-04 0.184145688 #> 2995  1497.0 0.8154934 0.8154934 4.455886e-04 4.455886e-04 0.184061051 #> 2996  1497.5 0.8155765 0.8155765 4.470264e-04 4.470264e-04 0.183976480 #> 2997  1498.0 0.8156596 0.8156596 4.484699e-04 4.484699e-04 0.183891975 #> 2998  1498.5 0.8157425 0.8157425 4.499189e-04 4.499189e-04 0.183807537 #> 2999  1499.0 0.8158255 0.8158255 4.513736e-04 4.513736e-04 0.183723165 #> 3000  1499.5 0.8159083 0.8159083 4.528339e-04 4.528339e-04 0.183638859 #> 3001  1500.0 0.8159911 0.8159911 4.542998e-04 4.542998e-04 0.183554621 #> 3002  1500.5 0.8160738 0.8160738 4.557715e-04 4.557715e-04 0.183470448 #> 3003  1501.0 0.8161564 0.8161564 4.572488e-04 4.572488e-04 0.183386343 #> 3004  1501.5 0.8162390 0.8162390 4.587319e-04 4.587319e-04 0.183302304 #> 3005  1502.0 0.8163214 0.8163214 4.602208e-04 4.602208e-04 0.183218331 #> 3006  1502.5 0.8164039 0.8164039 4.617154e-04 4.617154e-04 0.183134426 #> 3007  1503.0 0.8164862 0.8164862 4.632159e-04 4.632159e-04 0.183050587 #> 3008  1503.5 0.8165685 0.8165685 4.647222e-04 4.647222e-04 0.182966816 #> 3009  1504.0 0.8166507 0.8166507 4.662343e-04 4.662343e-04 0.182883111 #> 3010  1504.5 0.8167328 0.8167328 4.677523e-04 4.677523e-04 0.182799474 #> 3011  1505.0 0.8168148 0.8168148 4.692762e-04 4.692762e-04 0.182715904 #> 3012  1505.5 0.8168968 0.8168968 4.708061e-04 4.708061e-04 0.182632401 #> 3013  1506.0 0.8169787 0.8169787 4.723419e-04 4.723419e-04 0.182548965 #> 3014  1506.5 0.8170605 0.8170605 4.738837e-04 4.738837e-04 0.182465596 #> 3015  1507.0 0.8171423 0.8171423 4.754315e-04 4.754315e-04 0.182382295 #> 3016  1507.5 0.8172240 0.8172240 4.769853e-04 4.769853e-04 0.182299062 #> 3017  1508.0 0.8173056 0.8173056 4.785452e-04 4.785452e-04 0.182215896 #> 3018  1508.5 0.8173871 0.8173871 4.801112e-04 4.801112e-04 0.182132797 #> 3019  1509.0 0.8174686 0.8174686 4.816832e-04 4.816832e-04 0.182049767 #> 3020  1509.5 0.8175499 0.8175499 4.832614e-04 4.832614e-04 0.181966804 #> 3021  1510.0 0.8176312 0.8176312 4.848458e-04 4.848458e-04 0.181883909 #> 3022  1510.5 0.8177125 0.8177125 4.864363e-04 4.864363e-04 0.181801081 #> 3023  1511.0 0.8177936 0.8177936 4.880330e-04 4.880330e-04 0.181718322 #> 3024  1511.5 0.8178747 0.8178747 4.896360e-04 4.896360e-04 0.181635631 #> 3025  1512.0 0.8179557 0.8179557 4.912453e-04 4.912453e-04 0.181553008 #> 3026  1512.5 0.8180367 0.8180367 4.928608e-04 4.928608e-04 0.181470453 #> 3027  1513.0 0.8181176 0.8181176 4.944826e-04 4.944826e-04 0.181387966 #> 3028  1513.5 0.8181983 0.8181983 4.961108e-04 4.961108e-04 0.181305548 #> 3029  1514.0 0.8182791 0.8182791 4.977454e-04 4.977454e-04 0.181223198 #> 3030  1514.5 0.8183597 0.8183597 4.993863e-04 4.993863e-04 0.181140916 #> 3031  1515.0 0.8184403 0.8184403 5.010336e-04 5.010336e-04 0.181058703 #> 3032  1515.5 0.8185208 0.8185208 5.026875e-04 5.026875e-04 0.180976558 #> 3033  1516.0 0.8186012 0.8186012 5.043477e-04 5.043477e-04 0.180894483 #> 3034  1516.5 0.8186815 0.8186815 5.060145e-04 5.060145e-04 0.180812475 #> 3035  1517.0 0.8187618 0.8187618 5.076878e-04 5.076878e-04 0.180730537 #> 3036  1517.5 0.8188420 0.8188420 5.093677e-04 5.093677e-04 0.180648668 #> 3037  1518.0 0.8189221 0.8189221 5.110541e-04 5.110541e-04 0.180566868 #> 3038  1518.5 0.8190021 0.8190021 5.127472e-04 5.127472e-04 0.180485136 #> 3039  1519.0 0.8190821 0.8190821 5.144468e-04 5.144468e-04 0.180403474 #> 3040  1519.5 0.8191620 0.8191620 5.161532e-04 5.161532e-04 0.180321881 #> 3041  1520.0 0.8192418 0.8192418 5.178663e-04 5.178663e-04 0.180240357 #> 3042  1520.5 0.8193215 0.8193215 5.195860e-04 5.195860e-04 0.180158903 #> 3043  1521.0 0.8194012 0.8194012 5.213125e-04 5.213125e-04 0.180077518 #> 3044  1521.5 0.8194808 0.8194808 5.230458e-04 5.230458e-04 0.179996202 #> 3045  1522.0 0.8195603 0.8195603 5.247859e-04 5.247859e-04 0.179914957 #> 3046  1522.5 0.8196397 0.8196397 5.265329e-04 5.265329e-04 0.179833780 #> 3047  1523.0 0.8197190 0.8197190 5.282867e-04 5.282867e-04 0.179752674 #> 3048  1523.5 0.8197983 0.8197983 5.300474e-04 5.300474e-04 0.179671637 #> 3049  1524.0 0.8198775 0.8198775 5.318150e-04 5.318150e-04 0.179590671 #> 3050  1524.5 0.8199566 0.8199566 5.335896e-04 5.335896e-04 0.179509774 #> 3051  1525.0 0.8200357 0.8200357 5.353711e-04 5.353711e-04 0.179428947 #> 3052  1525.5 0.8201146 0.8201146 5.371597e-04 5.371597e-04 0.179348191 #> 3053  1526.0 0.8201935 0.8201935 5.389553e-04 5.389553e-04 0.179267504 #> 3054  1526.5 0.8202724 0.8202724 5.407579e-04 5.407579e-04 0.179186888 #> 3055  1527.0 0.8203511 0.8203511 5.425677e-04 5.425677e-04 0.179106343 #> 3056  1527.5 0.8204297 0.8204297 5.443846e-04 5.443846e-04 0.179025868 #> 3057  1528.0 0.8205083 0.8205083 5.462086e-04 5.462086e-04 0.178945463 #> 3058  1528.5 0.8205868 0.8205868 5.480399e-04 5.480399e-04 0.178865129 #> 3059  1529.0 0.8206653 0.8206653 5.498783e-04 5.498783e-04 0.178784866 #> 3060  1529.5 0.8207436 0.8207436 5.517240e-04 5.517240e-04 0.178704674 #> 3061  1530.0 0.8208219 0.8208219 5.535770e-04 5.535770e-04 0.178624552 #> 3062  1530.5 0.8209001 0.8209001 5.554373e-04 5.554373e-04 0.178544502 #> 3063  1531.0 0.8209782 0.8209782 5.573049e-04 5.573049e-04 0.178464522 #> 3064  1531.5 0.8210562 0.8210562 5.591799e-04 5.591799e-04 0.178384614 #> 3065  1532.0 0.8211342 0.8211342 5.610623e-04 5.610623e-04 0.178304777 #> 3066  1532.5 0.8212120 0.8212120 5.629522e-04 5.629522e-04 0.178225011 #> 3067  1533.0 0.8212898 0.8212898 5.648495e-04 5.648495e-04 0.178145317 #> 3068  1533.5 0.8213676 0.8213676 5.667543e-04 5.667543e-04 0.178065694 #> 3069  1534.0 0.8214452 0.8214452 5.686666e-04 5.686666e-04 0.177986143 #> 3070  1534.5 0.8215227 0.8215227 5.705865e-04 5.705865e-04 0.177906664 #> 3071  1535.0 0.8216002 0.8216002 5.725140e-04 5.725140e-04 0.177827256 #> 3072  1535.5 0.8216776 0.8216776 5.744491e-04 5.744491e-04 0.177747920 #> 3073  1536.0 0.8217550 0.8217550 5.763918e-04 5.763918e-04 0.177668656 #> 3074  1536.5 0.8218322 0.8218322 5.783423e-04 5.783423e-04 0.177589465 #> 3075  1537.0 0.8219094 0.8219094 5.803004e-04 5.803004e-04 0.177510345 #> 3076  1537.5 0.8219864 0.8219864 5.822663e-04 5.822663e-04 0.177431298 #> 3077  1538.0 0.8220634 0.8220634 5.842400e-04 5.842400e-04 0.177352323 #> 3078  1538.5 0.8221404 0.8221404 5.862216e-04 5.862216e-04 0.177273420 #> 3079  1539.0 0.8222172 0.8222172 5.882109e-04 5.882109e-04 0.177194590 #> 3080  1539.5 0.8222940 0.8222940 5.902082e-04 5.902082e-04 0.177115833 #> 3081  1540.0 0.8223706 0.8223706 5.922134e-04 5.922134e-04 0.177037148 #> 3082  1540.5 0.8224472 0.8224472 5.942265e-04 5.942265e-04 0.176958536 #> 3083  1541.0 0.8225238 0.8225238 5.962476e-04 5.962476e-04 0.176879997 #> 3084  1541.5 0.8226002 0.8226002 5.982767e-04 5.982767e-04 0.176801531 #> 3085  1542.0 0.8226765 0.8226765 6.003139e-04 6.003139e-04 0.176723138 #> 3086  1542.5 0.8227528 0.8227528 6.023592e-04 6.023592e-04 0.176644818 #> 3087  1543.0 0.8228290 0.8228290 6.044126e-04 6.044126e-04 0.176566572 #> 3088  1543.5 0.8229051 0.8229051 6.064741e-04 6.064741e-04 0.176488399 #> 3089  1544.0 0.8229812 0.8229812 6.085438e-04 6.085438e-04 0.176410299 #> 3090  1544.5 0.8230571 0.8230571 6.106218e-04 6.106218e-04 0.176332273 #> 3091  1545.0 0.8231330 0.8231330 6.127080e-04 6.127080e-04 0.176254321 #> 3092  1545.5 0.8232088 0.8232088 6.148025e-04 6.148025e-04 0.176176443 #> 3093  1546.0 0.8232845 0.8232845 6.169053e-04 6.169053e-04 0.176098638 #> 3094  1546.5 0.8233601 0.8233601 6.190165e-04 6.190165e-04 0.176020908 #> 3095  1547.0 0.8234356 0.8234356 6.211361e-04 6.211361e-04 0.175943251 #> 3096  1547.5 0.8235111 0.8235111 6.232641e-04 6.232641e-04 0.175865669 #> 3097  1548.0 0.8235864 0.8235864 6.254006e-04 6.254006e-04 0.175788161 #> 3098  1548.5 0.8236617 0.8236617 6.275456e-04 6.275456e-04 0.175710727 #> 3099  1549.0 0.8237369 0.8237369 6.296992e-04 6.296992e-04 0.175633368 #> 3100  1549.5 0.8238121 0.8238121 6.318613e-04 6.318613e-04 0.175556084 #> 3101  1550.0 0.8238871 0.8238871 6.340320e-04 6.340320e-04 0.175478874 #> 3102  1550.5 0.8239620 0.8239620 6.362114e-04 6.362114e-04 0.175401740 #> 3103  1551.0 0.8240369 0.8240369 6.383995e-04 6.383995e-04 0.175324680 #> 3104  1551.5 0.8241117 0.8241117 6.405963e-04 6.405963e-04 0.175247695 #> 3105  1552.0 0.8241864 0.8241864 6.428018e-04 6.428018e-04 0.175170785 #> 3106  1552.5 0.8242610 0.8242610 6.450162e-04 6.450162e-04 0.175093951 #> 3107  1553.0 0.8243356 0.8243356 6.472394e-04 6.472394e-04 0.175017192 #> 3108  1553.5 0.8244100 0.8244100 6.494714e-04 6.494714e-04 0.174940508 #> 3109  1554.0 0.8244844 0.8244844 6.517124e-04 6.517124e-04 0.174863901 #> 3110  1554.5 0.8245587 0.8245587 6.539623e-04 6.539623e-04 0.174787368 #> 3111  1555.0 0.8246329 0.8246329 6.562212e-04 6.562212e-04 0.174710912 #> 3112  1555.5 0.8247070 0.8247070 6.584891e-04 6.584891e-04 0.174634531 #> 3113  1556.0 0.8247810 0.8247810 6.607661e-04 6.607661e-04 0.174558227 #> 3114  1556.5 0.8248549 0.8248549 6.630522e-04 6.630522e-04 0.174481999 #> 3115  1557.0 0.8249288 0.8249288 6.653475e-04 6.653475e-04 0.174405847 #> 3116  1557.5 0.8250026 0.8250026 6.676519e-04 6.676519e-04 0.174329771 #> 3117  1558.0 0.8250763 0.8250763 6.699655e-04 6.699655e-04 0.174253772 #> 3118  1558.5 0.8251499 0.8251499 6.722884e-04 6.722884e-04 0.174177850 #> 3119  1559.0 0.8252234 0.8252234 6.746205e-04 6.746205e-04 0.174102004 #> 3120  1559.5 0.8252968 0.8252968 6.769620e-04 6.769620e-04 0.174026235 #> 3121  1560.0 0.8253701 0.8253701 6.793129e-04 6.793129e-04 0.173950543 #> 3122  1560.5 0.8254434 0.8254434 6.816732e-04 6.816732e-04 0.173874928 #> 3123  1561.0 0.8255166 0.8255166 6.840429e-04 6.840429e-04 0.173799391 #> 3124  1561.5 0.8255896 0.8255896 6.864222e-04 6.864222e-04 0.173723930 #> 3125  1562.0 0.8256626 0.8256626 6.888109e-04 6.888109e-04 0.173648548 #> 3126  1562.5 0.8257355 0.8257355 6.912093e-04 6.912093e-04 0.173573242 #> 3127  1563.0 0.8258084 0.8258084 6.936172e-04 6.936172e-04 0.173498015 #> 3128  1563.5 0.8258811 0.8258811 6.960348e-04 6.960348e-04 0.173422865 #> 3129  1564.0 0.8259537 0.8259537 6.984622e-04 6.984622e-04 0.173347793 #> 3130  1564.5 0.8260263 0.8260263 7.008992e-04 7.008992e-04 0.173272799 #> 3131  1565.0 0.8260988 0.8260988 7.033460e-04 7.033460e-04 0.173197884 #> 3132  1565.5 0.8261712 0.8261712 7.058027e-04 7.058027e-04 0.173123046 #> 3133  1566.0 0.8262434 0.8262434 7.082692e-04 7.082692e-04 0.173048287 #> 3134  1566.5 0.8263156 0.8263156 7.107456e-04 7.107456e-04 0.172973607 #> 3135  1567.0 0.8263878 0.8263878 7.132319e-04 7.132319e-04 0.172899005 #> 3136  1567.5 0.8264598 0.8264598 7.157283e-04 7.157283e-04 0.172824482 #> 3137  1568.0 0.8265317 0.8265317 7.182346e-04 7.182346e-04 0.172750038 #> 3138  1568.5 0.8266036 0.8266036 7.207511e-04 7.207511e-04 0.172675674 #> 3139  1569.0 0.8266753 0.8266753 7.232776e-04 7.232776e-04 0.172601388 #> 3140  1569.5 0.8267470 0.8267470 7.258143e-04 7.258143e-04 0.172527181 #> 3141  1570.0 0.8268186 0.8268186 7.283613e-04 7.283613e-04 0.172453055 #> 3142  1570.5 0.8268901 0.8268901 7.309184e-04 7.309184e-04 0.172379007 #> 3143  1571.0 0.8269615 0.8269615 7.334859e-04 7.334859e-04 0.172305040 #> 3144  1571.5 0.8270328 0.8270328 7.360637e-04 7.360637e-04 0.172231152 #> 3145  1572.0 0.8271040 0.8271040 7.386518e-04 7.386518e-04 0.172157344 #> 3146  1572.5 0.8271751 0.8271751 7.412504e-04 7.412504e-04 0.172083616 #> 3147  1573.0 0.8272462 0.8272462 7.438594e-04 7.438594e-04 0.172009969 #> 3148  1573.5 0.8273171 0.8273171 7.464790e-04 7.464790e-04 0.171936402 #> 3149  1574.0 0.8273880 0.8273880 7.491091e-04 7.491091e-04 0.171862915 #> 3150  1574.5 0.8274587 0.8274587 7.517498e-04 7.517498e-04 0.171789509 #> 3151  1575.0 0.8275294 0.8275294 7.544011e-04 7.544011e-04 0.171716184 #> 3152  1575.5 0.8276000 0.8276000 7.570631e-04 7.570631e-04 0.171642940 #> 3153  1576.0 0.8276705 0.8276705 7.597359e-04 7.597359e-04 0.171569776 #> 3154  1576.5 0.8277409 0.8277409 7.624194e-04 7.624194e-04 0.171496694 #> 3155  1577.0 0.8278112 0.8278112 7.651138e-04 7.651138e-04 0.171423694 #> 3156  1577.5 0.8278814 0.8278814 7.678190e-04 7.678190e-04 0.171350774 #> 3157  1578.0 0.8279515 0.8279515 7.705352e-04 7.705352e-04 0.171277937 #> 3158  1578.5 0.8280216 0.8280216 7.732623e-04 7.732623e-04 0.171205181 #> 3159  1579.0 0.8280915 0.8280915 7.760004e-04 7.760004e-04 0.171132507 #> 3160  1579.5 0.8281613 0.8281613 7.787496e-04 7.787496e-04 0.171059915 #> 3161  1580.0 0.8282311 0.8282311 7.815099e-04 7.815099e-04 0.170987406 #> 3162  1580.5 0.8283007 0.8283007 7.842813e-04 7.842813e-04 0.170914978 #> 3163  1581.0 0.8283703 0.8283703 7.870639e-04 7.870639e-04 0.170842633 #> 3164  1581.5 0.8284398 0.8284398 7.898578e-04 7.898578e-04 0.170770371 #> 3165  1582.0 0.8285091 0.8285091 7.926630e-04 7.926630e-04 0.170698192 #> 3166  1582.5 0.8285784 0.8285784 7.954795e-04 7.954795e-04 0.170626095 #> 3167  1583.0 0.8286476 0.8286476 7.983074e-04 7.983074e-04 0.170554082 #> 3168  1583.5 0.8287167 0.8287167 8.011467e-04 8.011467e-04 0.170482151 #> 3169  1584.0 0.8287857 0.8287857 8.039976e-04 8.039976e-04 0.170410304 #> 3170  1584.5 0.8288546 0.8288546 8.068599e-04 8.068599e-04 0.170338541 #> 3171  1585.0 0.8289234 0.8289234 8.097339e-04 8.097339e-04 0.170266861 #> 3172  1585.5 0.8289921 0.8289921 8.126194e-04 8.126194e-04 0.170195265 #> 3173  1586.0 0.8290607 0.8290607 8.155167e-04 8.155167e-04 0.170123753 #> 3174  1586.5 0.8291292 0.8291292 8.184257e-04 8.184257e-04 0.170052326 #> 3175  1587.0 0.8291977 0.8291977 8.213465e-04 8.213465e-04 0.169980982 #> 3176  1587.5 0.8292660 0.8292660 8.242791e-04 8.242791e-04 0.169909723 #> 3177  1588.0 0.8293342 0.8293342 8.272236e-04 8.272236e-04 0.169838548 #> 3178  1588.5 0.8294024 0.8294024 8.301800e-04 8.301800e-04 0.169767458 #> 3179  1589.0 0.8294704 0.8294704 8.331484e-04 8.331484e-04 0.169696453 #> 3180  1589.5 0.8295383 0.8295383 8.361288e-04 8.361288e-04 0.169625534 #> 3181  1590.0 0.8296062 0.8296062 8.391214e-04 8.391214e-04 0.169554699 #> 3182  1590.5 0.8296739 0.8296739 8.421260e-04 8.421260e-04 0.169483950 #> 3183  1591.0 0.8297416 0.8297416 8.451429e-04 8.451429e-04 0.169413286 #> 3184  1591.5 0.8298091 0.8298091 8.481720e-04 8.481720e-04 0.169342708 #> 3185  1592.0 0.8298766 0.8298766 8.512134e-04 8.512134e-04 0.169272216 #> 3186  1592.5 0.8299439 0.8299439 8.542671e-04 8.542671e-04 0.169201810 #> 3187  1593.0 0.8300112 0.8300112 8.573332e-04 8.573332e-04 0.169131490 #> 3188  1593.5 0.8300783 0.8300783 8.604118e-04 8.604118e-04 0.169061256 #> 3189  1594.0 0.8301454 0.8301454 8.635029e-04 8.635029e-04 0.168991109 #> 3190  1594.5 0.8302123 0.8302123 8.666066e-04 8.666066e-04 0.168921048 #> 3191  1595.0 0.8302792 0.8302792 8.697228e-04 8.697228e-04 0.168851075 #> 3192  1595.5 0.8303460 0.8303460 8.728517e-04 8.728517e-04 0.168781188 #> 3193  1596.0 0.8304126 0.8304126 8.759934e-04 8.759934e-04 0.168711389 #> 3194  1596.5 0.8304792 0.8304792 8.791478e-04 8.791478e-04 0.168641676 #> 3195  1597.0 0.8305456 0.8305456 8.823150e-04 8.823150e-04 0.168572052 #> 3196  1597.5 0.8306120 0.8306120 8.854951e-04 8.854951e-04 0.168502515 #> 3197  1598.0 0.8306782 0.8306782 8.886882e-04 8.886882e-04 0.168433066 #> 3198  1598.5 0.8307444 0.8307444 8.918942e-04 8.918942e-04 0.168363705 #> 3199  1599.0 0.8308105 0.8308105 8.951133e-04 8.951133e-04 0.168294432 #> 3200  1599.5 0.8308764 0.8308764 8.983455e-04 8.983455e-04 0.168225248 #> 3201  1600.0 0.8309423 0.8309423 9.015908e-04 9.015908e-04 0.168156152 #> 3202  1600.5 0.8310080 0.8310080 9.048494e-04 9.048494e-04 0.168087145 #> 3203  1601.0 0.8310737 0.8310737 9.081212e-04 9.081212e-04 0.168018226 #> 3204  1601.5 0.8311392 0.8311392 9.114064e-04 9.114064e-04 0.167949397 #> 3205  1602.0 0.8312046 0.8312046 9.147049e-04 9.147049e-04 0.167880657 #> 3206  1602.5 0.8312700 0.8312700 9.180169e-04 9.180169e-04 0.167812007 #> 3207  1603.0 0.8313352 0.8313352 9.213423e-04 9.213423e-04 0.167743446 #> 3208  1603.5 0.8314003 0.8314003 9.246813e-04 9.246813e-04 0.167674975 #> 3209  1604.0 0.8314654 0.8314654 9.280340e-04 9.280340e-04 0.167606594 #> 3210  1604.5 0.8315303 0.8315303 9.314003e-04 9.314003e-04 0.167538303 #> 3211  1605.0 0.8315951 0.8315951 9.347803e-04 9.347803e-04 0.167470102 #> 3212  1605.5 0.8316598 0.8316598 9.381741e-04 9.381741e-04 0.167401992 #> 3213  1606.0 0.8317244 0.8317244 9.415817e-04 9.415817e-04 0.167333973 #> 3214  1606.5 0.8317890 0.8317890 9.450033e-04 9.450033e-04 0.167266044 #> 3215  1607.0 0.8318534 0.8318534 9.484388e-04 9.484388e-04 0.167198207 #> 3216  1607.5 0.8319177 0.8319177 9.518884e-04 9.518884e-04 0.167130461 #> 3217  1608.0 0.8319818 0.8319818 9.553520e-04 9.553520e-04 0.167062806 #> 3218  1608.5 0.8320459 0.8320459 9.588297e-04 9.588297e-04 0.166995243 #> 3219  1609.0 0.8321099 0.8321099 9.623217e-04 9.623217e-04 0.166927772 #> 3220  1609.5 0.8321738 0.8321738 9.658279e-04 9.658279e-04 0.166860393 #> 3221  1610.0 0.8322375 0.8322375 9.693485e-04 9.693485e-04 0.166793106 #> 3222  1610.5 0.8323012 0.8323012 9.728834e-04 9.728834e-04 0.166725911 #> 3223  1611.0 0.8323648 0.8323648 9.764327e-04 9.764327e-04 0.166658809 #> 3224  1611.5 0.8324282 0.8324282 9.799966e-04 9.799966e-04 0.166591800 #> 3225  1612.0 0.8324915 0.8324915 9.835751e-04 9.835751e-04 0.166524884 #> 3226  1612.5 0.8325548 0.8325548 9.871681e-04 9.871681e-04 0.166458061 #> 3227  1613.0 0.8326179 0.8326179 9.907759e-04 9.907759e-04 0.166391332 #> 3228  1613.5 0.8326809 0.8326809 9.943984e-04 9.943984e-04 0.166324696 #> 3229  1614.0 0.8327438 0.8327438 9.980357e-04 9.980357e-04 0.166258154 #> 3230  1614.5 0.8328066 0.8328066 1.001688e-03 1.001688e-03 0.166191705 #> 3231  1615.0 0.8328693 0.8328693 1.005355e-03 1.005355e-03 0.166125352 #> 3232  1615.5 0.8329319 0.8329319 1.009037e-03 1.009037e-03 0.166059092 #> 3233  1616.0 0.8329943 0.8329943 1.012734e-03 1.012734e-03 0.165992927 #> 3234  1616.5 0.8330567 0.8330567 1.016447e-03 1.016447e-03 0.165926857 #> 3235  1617.0 0.8331189 0.8331189 1.020174e-03 1.020174e-03 0.165860882 #> 3236  1617.5 0.8331811 0.8331811 1.023917e-03 1.023917e-03 0.165795002 #> 3237  1618.0 0.8332431 0.8332431 1.027675e-03 1.027675e-03 0.165729218 #> 3238  1618.5 0.8333050 0.8333050 1.031449e-03 1.031449e-03 0.165663529 #> 3239  1619.0 0.8333668 0.8333668 1.035238e-03 1.035238e-03 0.165597936 #> 3240  1619.5 0.8334285 0.8334285 1.039042e-03 1.039042e-03 0.165532439 #> 3241  1620.0 0.8334901 0.8334901 1.042862e-03 1.042862e-03 0.165467038 #> 3242  1620.5 0.8335516 0.8335516 1.046698e-03 1.046698e-03 0.165401734 #> 3243  1621.0 0.8336129 0.8336129 1.050550e-03 1.050550e-03 0.165336526 #> 3244  1621.5 0.8336742 0.8336742 1.054417e-03 1.054417e-03 0.165271416 #> 3245  1622.0 0.8337353 0.8337353 1.058300e-03 1.058300e-03 0.165206402 #> 3246  1622.5 0.8337963 0.8337963 1.062199e-03 1.062199e-03 0.165141486 #> 3247  1623.0 0.8338572 0.8338572 1.066114e-03 1.066114e-03 0.165076667 #> 3248  1623.5 0.8339180 0.8339180 1.070045e-03 1.070045e-03 0.165011947 #> 3249  1624.0 0.8339787 0.8339787 1.073992e-03 1.073992e-03 0.164947324 #> 3250  1624.5 0.8340392 0.8340392 1.077956e-03 1.077956e-03 0.164882799 #> 3251  1625.0 0.8340997 0.8340997 1.081935e-03 1.081935e-03 0.164818373 #> 3252  1625.5 0.8341600 0.8341600 1.085931e-03 1.085931e-03 0.164754045 #> 3253  1626.0 0.8342202 0.8342202 1.089944e-03 1.089944e-03 0.164689817 #> 3254  1626.5 0.8342803 0.8342803 1.093973e-03 1.093973e-03 0.164625687 #> 3255  1627.0 0.8343403 0.8343403 1.098018e-03 1.098018e-03 0.164561657 #> 3256  1627.5 0.8344002 0.8344002 1.102080e-03 1.102080e-03 0.164497726 #> 3257  1628.0 0.8344599 0.8344599 1.106159e-03 1.106159e-03 0.164433895 #> 3258  1628.5 0.8345196 0.8345196 1.110254e-03 1.110254e-03 0.164370164 #> 3259  1629.0 0.8345791 0.8345791 1.114367e-03 1.114367e-03 0.164306534 #> 3260  1629.5 0.8346385 0.8346385 1.118496e-03 1.118496e-03 0.164243003 #> 3261  1630.0 0.8346978 0.8346978 1.122642e-03 1.122642e-03 0.164179574 #> 3262  1630.5 0.8347569 0.8347569 1.126805e-03 1.126805e-03 0.164116245 #> 3263  1631.0 0.8348160 0.8348160 1.130985e-03 1.130985e-03 0.164053018 #> 3264  1631.5 0.8348749 0.8348749 1.135183e-03 1.135183e-03 0.163989892 #> 3265  1632.0 0.8349337 0.8349337 1.139398e-03 1.139398e-03 0.163926867 #> 3266  1632.5 0.8349924 0.8349924 1.143630e-03 1.143630e-03 0.163863945 #> 3267  1633.0 0.8350510 0.8350510 1.147879e-03 1.147879e-03 0.163801125 #> 3268  1633.5 0.8351094 0.8351094 1.152146e-03 1.152146e-03 0.163738407 #> 3269  1634.0 0.8351678 0.8351678 1.156431e-03 1.156431e-03 0.163675791 #> 3270  1634.5 0.8352260 0.8352260 1.160733e-03 1.160733e-03 0.163613279 #> 3271  1635.0 0.8352841 0.8352841 1.165052e-03 1.165052e-03 0.163550869 #> 3272  1635.5 0.8353420 0.8353420 1.169390e-03 1.169390e-03 0.163488563 #> 3273  1636.0 0.8353999 0.8353999 1.173745e-03 1.173745e-03 0.163426360 #> 3274  1636.5 0.8354576 0.8354576 1.178119e-03 1.178119e-03 0.163364261 #> 3275  1637.0 0.8355152 0.8355152 1.182510e-03 1.182510e-03 0.163302267 #> 3276  1637.5 0.8355727 0.8355727 1.186920e-03 1.186920e-03 0.163240376 #> 3277  1638.0 0.8356301 0.8356301 1.191347e-03 1.191347e-03 0.163178590 #> 3278  1638.5 0.8356873 0.8356873 1.195793e-03 1.195793e-03 0.163116909 #> 3279  1639.0 0.8357444 0.8357444 1.200257e-03 1.200257e-03 0.163055333 #> 3280  1639.5 0.8358014 0.8358014 1.204740e-03 1.204740e-03 0.162993863 #> 3281  1640.0 0.8358583 0.8358583 1.209241e-03 1.209241e-03 0.162932497 #> 3282  1640.5 0.8359150 0.8359150 1.213760e-03 1.213760e-03 0.162871238 #> 3283  1641.0 0.8359716 0.8359716 1.218298e-03 1.218298e-03 0.162810085 #> 3284  1641.5 0.8360281 0.8360281 1.222855e-03 1.222855e-03 0.162749038 #> 3285  1642.0 0.8360845 0.8360845 1.227431e-03 1.227431e-03 0.162688098 #> 3286  1642.5 0.8361407 0.8361407 1.232025e-03 1.232025e-03 0.162627264 #> 3287  1643.0 0.8361968 0.8361968 1.236638e-03 1.236638e-03 0.162566538 #> 3288  1643.5 0.8362528 0.8362528 1.241271e-03 1.241271e-03 0.162505919 #> 3289  1644.0 0.8363087 0.8363087 1.245922e-03 1.245922e-03 0.162445408 #> 3290  1644.5 0.8363644 0.8363644 1.250593e-03 1.250593e-03 0.162385005 #> 3291  1645.0 0.8364200 0.8364200 1.255283e-03 1.255283e-03 0.162324710 #> 3292  1645.5 0.8364755 0.8364755 1.259992e-03 1.259992e-03 0.162264523 #> 3293  1646.0 0.8365308 0.8365308 1.264721e-03 1.264721e-03 0.162204445 #> 3294  1646.5 0.8365861 0.8365861 1.269469e-03 1.269469e-03 0.162144476 #> 3295  1647.0 0.8366411 0.8366411 1.274237e-03 1.274237e-03 0.162084616 #> 3296  1647.5 0.8366961 0.8366961 1.279024e-03 1.279024e-03 0.162024866 #> 3297  1648.0 0.8367509 0.8367509 1.283831e-03 1.283831e-03 0.161965225 #> 3298  1648.5 0.8368056 0.8368056 1.288658e-03 1.288658e-03 0.161905695 #> 3299  1649.0 0.8368602 0.8368602 1.293505e-03 1.293505e-03 0.161846275 #> 3300  1649.5 0.8369147 0.8369147 1.298372e-03 1.298372e-03 0.161786966 #> 3301  1650.0 0.8369690 0.8369690 1.303259e-03 1.303259e-03 0.161727767 #> 3302  1650.5 0.8370232 0.8370232 1.308166e-03 1.308166e-03 0.161668680 #> 3303  1651.0 0.8370772 0.8370772 1.313094e-03 1.313094e-03 0.161609704 #> 3304  1651.5 0.8371311 0.8371311 1.318041e-03 1.318041e-03 0.161550840 #> 3305  1652.0 0.8371849 0.8371849 1.323010e-03 1.323010e-03 0.161492088 #> 3306  1652.5 0.8372386 0.8372386 1.327998e-03 1.327998e-03 0.161433449 #> 3307  1653.0 0.8372921 0.8372921 1.333008e-03 1.333008e-03 0.161374922 #> 3308  1653.5 0.8373455 0.8373455 1.338038e-03 1.338038e-03 0.161316507 #> 3309  1654.0 0.8373987 0.8373987 1.343088e-03 1.343088e-03 0.161258206 #> 3310  1654.5 0.8374518 0.8374518 1.348160e-03 1.348160e-03 0.161200019 #> 3311  1655.0 0.8375048 0.8375048 1.353253e-03 1.353253e-03 0.161141945 #> 3312  1655.5 0.8375576 0.8375576 1.358366e-03 1.358366e-03 0.161083985 #> 3313  1656.0 0.8376104 0.8376104 1.363501e-03 1.363501e-03 0.161026140 #> 3314  1656.5 0.8376629 0.8376629 1.368657e-03 1.368657e-03 0.160968409 #> 3315  1657.0 0.8377154 0.8377154 1.373834e-03 1.373834e-03 0.160910794 #> 3316  1657.5 0.8377677 0.8377677 1.379033e-03 1.379033e-03 0.160853293 #> 3317  1658.0 0.8378198 0.8378198 1.384253e-03 1.384253e-03 0.160795908 #> 3318  1658.5 0.8378719 0.8378719 1.389494e-03 1.389494e-03 0.160738639 #> 3319  1659.0 0.8379238 0.8379238 1.394758e-03 1.394758e-03 0.160681486 #> 3320  1659.5 0.8379755 0.8379755 1.400043e-03 1.400043e-03 0.160624450 #> 3321  1660.0 0.8380271 0.8380271 1.405350e-03 1.405350e-03 0.160567530 #> 3322  1660.5 0.8380786 0.8380786 1.410678e-03 1.410678e-03 0.160510727 #> 3323  1661.0 0.8381299 0.8381299 1.416029e-03 1.416029e-03 0.160454042 #> 3324  1661.5 0.8381811 0.8381811 1.421402e-03 1.421402e-03 0.160397474 #> 3325  1662.0 0.8382322 0.8382322 1.426797e-03 1.426797e-03 0.160341025 #> 3326  1662.5 0.8382831 0.8382831 1.432215e-03 1.432215e-03 0.160284693 #> 3327  1663.0 0.8383339 0.8383339 1.437655e-03 1.437655e-03 0.160228481 #> 3328  1663.5 0.8383845 0.8383845 1.443117e-03 1.443117e-03 0.160172387 #> 3329  1664.0 0.8384350 0.8384350 1.448602e-03 1.448602e-03 0.160116412 #> 3330  1664.5 0.8384853 0.8384853 1.454110e-03 1.454110e-03 0.160060557 #> 3331  1665.0 0.8385355 0.8385355 1.459640e-03 1.459640e-03 0.160004822 #> 3332  1665.5 0.8385856 0.8385856 1.465194e-03 1.465194e-03 0.159949207 #> 3333  1666.0 0.8386355 0.8386355 1.470770e-03 1.470770e-03 0.159893713 #> 3334  1666.5 0.8386853 0.8386853 1.476369e-03 1.476369e-03 0.159838339 #> 3335  1667.0 0.8387349 0.8387349 1.481992e-03 1.481992e-03 0.159783087 #> 3336  1667.5 0.8387844 0.8387844 1.487637e-03 1.487637e-03 0.159727956 #> 3337  1668.0 0.8388337 0.8388337 1.493306e-03 1.493306e-03 0.159672947 #> 3338  1668.5 0.8388829 0.8388829 1.498999e-03 1.498999e-03 0.159618060 #> 3339  1669.0 0.8389320 0.8389320 1.504715e-03 1.504715e-03 0.159563296 #> 3340  1669.5 0.8389809 0.8389809 1.510455e-03 1.510455e-03 0.159508654 #> 3341  1670.0 0.8390296 0.8390296 1.516218e-03 1.516218e-03 0.159454135 #> 3342  1670.5 0.8390783 0.8390783 1.522006e-03 1.522006e-03 0.159399741 #> 3343  1671.0 0.8391267 0.8391267 1.527817e-03 1.527817e-03 0.159345469 #> 3344  1671.5 0.8391750 0.8391750 1.533652e-03 1.533652e-03 0.159291322 #> 3345  1672.0 0.8392232 0.8392232 1.539512e-03 1.539512e-03 0.159237300 #> 3346  1672.5 0.8392712 0.8392712 1.545395e-03 1.545395e-03 0.159183402 #> 3347  1673.0 0.8393191 0.8393191 1.551303e-03 1.551303e-03 0.159129630 #> 3348  1673.5 0.8393668 0.8393668 1.557236e-03 1.557236e-03 0.159075983 #> 3349  1674.0 0.8394143 0.8394143 1.563193e-03 1.563193e-03 0.159022462 #> 3350  1674.5 0.8394618 0.8394618 1.569175e-03 1.569175e-03 0.158969067 #> 3351  1675.0 0.8395090 0.8395090 1.575181e-03 1.575181e-03 0.158915799 #> 3352  1675.5 0.8395561 0.8395561 1.581212e-03 1.581212e-03 0.158862657 #> 3353  1676.0 0.8396031 0.8396031 1.587269e-03 1.587269e-03 0.158809643 #> 3354  1676.5 0.8396499 0.8396499 1.593350e-03 1.593350e-03 0.158756757 #> 3355  1677.0 0.8396965 0.8396965 1.599457e-03 1.599457e-03 0.158703998 #> 3356  1677.5 0.8397430 0.8397430 1.605588e-03 1.605588e-03 0.158651368 #> 3357  1678.0 0.8397894 0.8397894 1.611745e-03 1.611745e-03 0.158598867 #> 3358  1678.5 0.8398356 0.8398356 1.617928e-03 1.617928e-03 0.158546494 #> 3359  1679.0 0.8398816 0.8398816 1.624136e-03 1.624136e-03 0.158494251 #> 3360  1679.5 0.8399275 0.8399275 1.630370e-03 1.630370e-03 0.158442138 #> 3361  1680.0 0.8399732 0.8399732 1.636630e-03 1.636630e-03 0.158390155 #> 3362  1680.5 0.8400188 0.8400188 1.642916e-03 1.642916e-03 0.158338302 #> 3363  1681.0 0.8400642 0.8400642 1.649227e-03 1.649227e-03 0.158286581 #> 3364  1681.5 0.8401094 0.8401094 1.655565e-03 1.655565e-03 0.158234990 #> 3365  1682.0 0.8401545 0.8401545 1.661929e-03 1.661929e-03 0.158183531 #> 3366  1682.5 0.8401995 0.8401995 1.668319e-03 1.668319e-03 0.158132204 #> 3367  1683.0 0.8402443 0.8402443 1.674736e-03 1.674736e-03 0.158081010 #> 3368  1683.5 0.8402889 0.8402889 1.681179e-03 1.681179e-03 0.158029948 #> 3369  1684.0 0.8403333 0.8403333 1.687649e-03 1.687649e-03 0.157979019 #> 3370  1684.5 0.8403776 0.8403776 1.694146e-03 1.694146e-03 0.157928224 #> 3371  1685.0 0.8404218 0.8404218 1.700670e-03 1.700670e-03 0.157877563 #> 3372  1685.5 0.8404657 0.8404657 1.707221e-03 1.707221e-03 0.157827036 #> 3373  1686.0 0.8405096 0.8405096 1.713798e-03 1.713798e-03 0.157776643 #> 3374  1686.5 0.8405532 0.8405532 1.720403e-03 1.720403e-03 0.157726386 #> 3375  1687.0 0.8405967 0.8405967 1.727036e-03 1.727036e-03 0.157676264 #> 3376  1687.5 0.8406400 0.8406400 1.733696e-03 1.733696e-03 0.157626277 #> 3377  1688.0 0.8406832 0.8406832 1.740383e-03 1.740383e-03 0.157576427 #> 3378  1688.5 0.8407262 0.8407262 1.747098e-03 1.747098e-03 0.157526714 #> 3379  1689.0 0.8407690 0.8407690 1.753841e-03 1.753841e-03 0.157477137 #> 3380  1689.5 0.8408117 0.8408117 1.760612e-03 1.760612e-03 0.157427698 #> 3381  1690.0 0.8408542 0.8408542 1.767410e-03 1.767410e-03 0.157378397 #> 3382  1690.5 0.8408965 0.8408965 1.774237e-03 1.774237e-03 0.157329234 #> 3383  1691.0 0.8409387 0.8409387 1.781092e-03 1.781092e-03 0.157280209 #> 3384  1691.5 0.8409807 0.8409807 1.787976e-03 1.787976e-03 0.157231323 #> 3385  1692.0 0.8410225 0.8410225 1.794888e-03 1.794888e-03 0.157182577 #> 3386  1692.5 0.8410642 0.8410642 1.801829e-03 1.801829e-03 0.157133970 #> 3387  1693.0 0.8411057 0.8411057 1.808798e-03 1.808798e-03 0.157085504 #> 3388  1693.5 0.8411470 0.8411470 1.815796e-03 1.815796e-03 0.157037178 #> 3389  1694.0 0.8411882 0.8411882 1.822823e-03 1.822823e-03 0.156988994 #> 3390  1694.5 0.8412292 0.8412292 1.829879e-03 1.829879e-03 0.156940950 #> 3391  1695.0 0.8412700 0.8412700 1.836965e-03 1.836965e-03 0.156893049 #> 3392  1695.5 0.8413106 0.8413106 1.844079e-03 1.844079e-03 0.156845290 #> 3393  1696.0 0.8413511 0.8413511 1.851224e-03 1.851224e-03 0.156797673 #> 3394  1696.5 0.8413914 0.8413914 1.858397e-03 1.858397e-03 0.156750199 #> 3395  1697.0 0.8414315 0.8414315 1.865601e-03 1.865601e-03 0.156702869 #> 3396  1697.5 0.8414715 0.8414715 1.872834e-03 1.872834e-03 0.156655683 #> 3397  1698.0 0.8415113 0.8415113 1.880097e-03 1.880097e-03 0.156608642 #> 3398  1698.5 0.8415509 0.8415509 1.887390e-03 1.887390e-03 0.156561745 #> 3399  1699.0 0.8415903 0.8415903 1.894713e-03 1.894713e-03 0.156514993 #> 3400  1699.5 0.8416295 0.8416295 1.902067e-03 1.902067e-03 0.156468387 #> 3401  1700.0 0.8416686 0.8416686 1.909451e-03 1.909451e-03 0.156421927 #> 3402  1700.5 0.8417075 0.8417075 1.916865e-03 1.916865e-03 0.156375614 #> 3403  1701.0 0.8417462 0.8417462 1.924310e-03 1.924310e-03 0.156329447 #> 3404  1701.5 0.8417848 0.8417848 1.931786e-03 1.931786e-03 0.156283428 #> 3405  1702.0 0.8418232 0.8418232 1.939293e-03 1.939293e-03 0.156237557 #> 3406  1702.5 0.8418613 0.8418613 1.946831e-03 1.946831e-03 0.156191834 #> 3407  1703.0 0.8418993 0.8418993 1.954400e-03 1.954400e-03 0.156146259 #> 3408  1703.5 0.8419372 0.8419372 1.962001e-03 1.962001e-03 0.156100834 #> 3409  1704.0 0.8419748 0.8419748 1.969632e-03 1.969632e-03 0.156055559 #> 3410  1704.5 0.8420123 0.8420123 1.977296e-03 1.977296e-03 0.156010433 #> 3411  1705.0 0.8420496 0.8420496 1.984991e-03 1.984991e-03 0.155965458 #> 3412  1705.5 0.8420866 0.8420866 1.992717e-03 1.992717e-03 0.155920634 #> 3413  1706.0 0.8421236 0.8421236 2.000476e-03 2.000476e-03 0.155875962 #> 3414  1706.5 0.8421603 0.8421603 2.008267e-03 2.008267e-03 0.155831441 #> 3415  1707.0 0.8421968 0.8421968 2.016090e-03 2.016090e-03 0.155787073 #> 3416  1707.5 0.8422332 0.8422332 2.023945e-03 2.023945e-03 0.155742857 #> 3417  1708.0 0.8422694 0.8422694 2.031833e-03 2.031833e-03 0.155698795 #> 3418  1708.5 0.8423054 0.8423054 2.039753e-03 2.039753e-03 0.155654886 #> 3419  1709.0 0.8423412 0.8423412 2.047706e-03 2.047706e-03 0.155611132 #> 3420  1709.5 0.8423768 0.8423768 2.055692e-03 2.055692e-03 0.155567532 #> 3421  1710.0 0.8424122 0.8424122 2.063711e-03 2.063711e-03 0.155524088 #> 3422  1710.5 0.8424474 0.8424474 2.071762e-03 2.071762e-03 0.155480799 #> 3423  1711.0 0.8424825 0.8424825 2.079848e-03 2.079848e-03 0.155437666 #> 3424  1711.5 0.8425173 0.8425173 2.087966e-03 2.087966e-03 0.155394689 #> 3425  1712.0 0.8425520 0.8425520 2.096118e-03 2.096118e-03 0.155351870 #> 3426  1712.5 0.8425865 0.8425865 2.104304e-03 2.104304e-03 0.155309208 #> 3427  1713.0 0.8426208 0.8426208 2.112523e-03 2.112523e-03 0.155266704 #> 3428  1713.5 0.8426549 0.8426549 2.120777e-03 2.120777e-03 0.155224359 #> 3429  1714.0 0.8426888 0.8426888 2.129064e-03 2.129064e-03 0.155182173 #> 3430  1714.5 0.8427225 0.8427225 2.137386e-03 2.137386e-03 0.155140146 #> 3431  1715.0 0.8427560 0.8427560 2.145742e-03 2.145742e-03 0.155098279 #> 3432  1715.5 0.8427893 0.8427893 2.154132e-03 2.154132e-03 0.155056573 #> 3433  1716.0 0.8428224 0.8428224 2.162557e-03 2.162557e-03 0.155015027 #> 3434  1716.5 0.8428553 0.8428553 2.171017e-03 2.171017e-03 0.154973643 #> 3435  1717.0 0.8428881 0.8428881 2.179512e-03 2.179512e-03 0.154932421 #> 3436  1717.5 0.8429206 0.8429206 2.188041e-03 2.188041e-03 0.154891361 #> 3437  1718.0 0.8429529 0.8429529 2.196606e-03 2.196606e-03 0.154850464 #> 3438  1718.5 0.8429851 0.8429851 2.205206e-03 2.205206e-03 0.154809731 #> 3439  1719.0 0.8430170 0.8430170 2.213842e-03 2.213842e-03 0.154769162 #> 3440  1719.5 0.8430487 0.8430487 2.222513e-03 2.222513e-03 0.154728757 #> 3441  1720.0 0.8430803 0.8430803 2.231220e-03 2.231220e-03 0.154688517 #> 3442  1720.5 0.8431116 0.8431116 2.239963e-03 2.239963e-03 0.154648442 #> 3443  1721.0 0.8431427 0.8431427 2.248742e-03 2.248742e-03 0.154608534 #> 3444  1721.5 0.8431737 0.8431737 2.257557e-03 2.257557e-03 0.154568792 #> 3445  1722.0 0.8432044 0.8432044 2.266408e-03 2.266408e-03 0.154529217 #> 3446  1722.5 0.8432349 0.8432349 2.275296e-03 2.275296e-03 0.154489810 #> 3447  1723.0 0.8432652 0.8432652 2.284220e-03 2.284220e-03 0.154450571 #> 3448  1723.5 0.8432953 0.8432953 2.293181e-03 2.293181e-03 0.154411500 #> 3449  1724.0 0.8433252 0.8433252 2.302179e-03 2.302179e-03 0.154372599 #> 3450  1724.5 0.8433549 0.8433549 2.311214e-03 2.311214e-03 0.154333867 #> 3451  1725.0 0.8433844 0.8433844 2.320286e-03 2.320286e-03 0.154295306 #> 3452  1725.5 0.8434137 0.8434137 2.329395e-03 2.329395e-03 0.154256915 #> 3453  1726.0 0.8434428 0.8434428 2.338542e-03 2.338542e-03 0.154218695 #> 3454  1726.5 0.8434716 0.8434716 2.347727e-03 2.347727e-03 0.154180648 #> 3455  1727.0 0.8435003 0.8435003 2.356949e-03 2.356949e-03 0.154142772 #> 3456  1727.5 0.8435287 0.8435287 2.366209e-03 2.366209e-03 0.154105070 #> 3457  1728.0 0.8435570 0.8435570 2.375508e-03 2.375508e-03 0.154067541 #> 3458  1728.5 0.8435850 0.8435850 2.384844e-03 2.384844e-03 0.154030186 #> 3459  1729.0 0.8436128 0.8436128 2.394219e-03 2.394219e-03 0.153993006 #> 3460  1729.5 0.8436404 0.8436404 2.403632e-03 2.403632e-03 0.153956001 #> 3461  1730.0 0.8436677 0.8436677 2.413084e-03 2.413084e-03 0.153919171 #> 3462  1730.5 0.8436949 0.8436949 2.422575e-03 2.422575e-03 0.153882518 #> 3463  1731.0 0.8437219 0.8437219 2.432105e-03 2.432105e-03 0.153846041 #> 3464  1731.5 0.8437486 0.8437486 2.441674e-03 2.441674e-03 0.153809742 #> 3465  1732.0 0.8437751 0.8437751 2.451282e-03 2.451282e-03 0.153773621 #> 3466  1732.5 0.8438014 0.8438014 2.460930e-03 2.460930e-03 0.153737678 #> 3467  1733.0 0.8438275 0.8438275 2.470617e-03 2.470617e-03 0.153701915 #> 3468  1733.5 0.8438533 0.8438533 2.480344e-03 2.480344e-03 0.153666330 #> 3469  1734.0 0.8438790 0.8438790 2.490111e-03 2.490111e-03 0.153630927 #> 3470  1734.5 0.8439044 0.8439044 2.499918e-03 2.499918e-03 0.153595703 #> 3471  1735.0 0.8439296 0.8439296 2.509765e-03 2.509765e-03 0.153560662 #> 3472  1735.5 0.8439545 0.8439545 2.519653e-03 2.519653e-03 0.153525802 #> 3473  1736.0 0.8439793 0.8439793 2.529581e-03 2.529581e-03 0.153491124 #> 3474  1736.5 0.8440038 0.8440038 2.539550e-03 2.539550e-03 0.153456630 #> 3475  1737.0 0.8440281 0.8440281 2.549559e-03 2.549559e-03 0.153422320 #> 3476  1737.5 0.8440522 0.8440522 2.559610e-03 2.559610e-03 0.153388193 #> 3477  1738.0 0.8440760 0.8440760 2.569701e-03 2.569701e-03 0.153354252 #> 3478  1738.5 0.8440997 0.8440997 2.579835e-03 2.579835e-03 0.153320496 #> 3479  1739.0 0.8441231 0.8441231 2.590009e-03 2.590009e-03 0.153286926 #> 3480  1739.5 0.8441462 0.8441462 2.600225e-03 2.600225e-03 0.153253543 #> 3481  1740.0 0.8441692 0.8441692 2.610483e-03 2.610483e-03 0.153220347 #> 3482  1740.5 0.8441919 0.8441919 2.620783e-03 2.620783e-03 0.153187339 #> 3483  1741.0 0.8442144 0.8442144 2.631125e-03 2.631125e-03 0.153154519 #> 3484  1741.5 0.8442366 0.8442366 2.641510e-03 2.641510e-03 0.153121889 #> 3485  1742.0 0.8442586 0.8442586 2.651936e-03 2.651936e-03 0.153089448 #> 3486  1742.5 0.8442804 0.8442804 2.662406e-03 2.662406e-03 0.153057197 #> 3487  1743.0 0.8443019 0.8443019 2.672918e-03 2.672918e-03 0.153025138 #> 3488  1743.5 0.8443233 0.8443233 2.683473e-03 2.683473e-03 0.152993270 #> 3489  1744.0 0.8443443 0.8443443 2.694071e-03 2.694071e-03 0.152961594 #> 3490  1744.5 0.8443652 0.8443652 2.704713e-03 2.704713e-03 0.152930111 #> 3491  1745.0 0.8443858 0.8443858 2.715398e-03 2.715398e-03 0.152898821 #> 3492  1745.5 0.8444061 0.8444061 2.726127e-03 2.726127e-03 0.152867725 #> 3493  1746.0 0.8444263 0.8444263 2.736899e-03 2.736899e-03 0.152836824 #> 3494  1746.5 0.8444462 0.8444462 2.747715e-03 2.747715e-03 0.152806119 #> 3495  1747.0 0.8444658 0.8444658 2.758576e-03 2.758576e-03 0.152775609 #> 3496  1747.5 0.8444852 0.8444852 2.769480e-03 2.769480e-03 0.152745296 #> 3497  1748.0 0.8445044 0.8445044 2.780429e-03 2.780429e-03 0.152715180 #> 3498  1748.5 0.8445233 0.8445233 2.791423e-03 2.791423e-03 0.152685262 #> 3499  1749.0 0.8445420 0.8445420 2.802462e-03 2.802462e-03 0.152655542 #> 3500  1749.5 0.8445604 0.8445604 2.813545e-03 2.813545e-03 0.152626022 #> 3501  1750.0 0.8445786 0.8445786 2.824674e-03 2.824674e-03 0.152596701 #> 3502  1750.5 0.8445966 0.8445966 2.835848e-03 2.835848e-03 0.152567581 #> 3503  1751.0 0.8446143 0.8446143 2.847067e-03 2.847067e-03 0.152538662 #> 3504  1751.5 0.8446317 0.8446317 2.858332e-03 2.858332e-03 0.152509945 #> 3505  1752.0 0.8446489 0.8446489 2.869643e-03 2.869643e-03 0.152481430 #> 3506  1752.5 0.8446659 0.8446659 2.881000e-03 2.881000e-03 0.152453119 #> 3507  1753.0 0.8446826 0.8446826 2.892403e-03 2.892403e-03 0.152425011 #> 3508  1753.5 0.8446990 0.8446990 2.903852e-03 2.903852e-03 0.152397107 #> 3509  1754.0 0.8447152 0.8447152 2.915348e-03 2.915348e-03 0.152369409 #> 3510  1754.5 0.8447312 0.8447312 2.926891e-03 2.926891e-03 0.152341916 #> 3511  1755.0 0.8447469 0.8447469 2.938480e-03 2.938480e-03 0.152314630 #> 3512  1755.5 0.8447623 0.8447623 2.950117e-03 2.950117e-03 0.152287551 #> 3513  1756.0 0.8447775 0.8447775 2.961800e-03 2.961800e-03 0.152260680 #> 3514  1756.5 0.8447925 0.8447925 2.973531e-03 2.973531e-03 0.152234018 #> 3515  1757.0 0.8448071 0.8448071 2.985310e-03 2.985310e-03 0.152207564 #> 3516  1757.5 0.8448215 0.8448215 2.997136e-03 2.997136e-03 0.152181321 #> 3517  1758.0 0.8448357 0.8448357 3.009010e-03 3.009010e-03 0.152155288 #> 3518  1758.5 0.8448496 0.8448496 3.020933e-03 3.020933e-03 0.152129466 #> 3519  1759.0 0.8448632 0.8448632 3.032904e-03 3.032904e-03 0.152103856 #> 3520  1759.5 0.8448766 0.8448766 3.044923e-03 3.044923e-03 0.152078459 #> 3521  1760.0 0.8448897 0.8448897 3.056990e-03 3.056990e-03 0.152053275 #> 3522  1760.5 0.8449026 0.8449026 3.069107e-03 3.069107e-03 0.152028306 #> 3523  1761.0 0.8449152 0.8449152 3.081273e-03 3.081273e-03 0.152003551 #> 3524  1761.5 0.8449275 0.8449275 3.093487e-03 3.093487e-03 0.151979011 #> 3525  1762.0 0.8449396 0.8449396 3.105752e-03 3.105752e-03 0.151954688 #> 3526  1762.5 0.8449514 0.8449514 3.118065e-03 3.118065e-03 0.151930582 #> 3527  1763.0 0.8449629 0.8449629 3.130429e-03 3.130429e-03 0.151906693 #> 3528  1763.5 0.8449741 0.8449741 3.142842e-03 3.142842e-03 0.151883023 #> 3529  1764.0 0.8449851 0.8449851 3.155306e-03 3.155306e-03 0.151859572 #> 3530  1764.5 0.8449958 0.8449958 3.167819e-03 3.167819e-03 0.151836341 #> 3531  1765.0 0.8450063 0.8450063 3.180384e-03 3.180384e-03 0.151813330 #> 3532  1765.5 0.8450165 0.8450165 3.192998e-03 3.192998e-03 0.151790540 #> 3533  1766.0 0.8450264 0.8450264 3.205664e-03 3.205664e-03 0.151767973 #> 3534  1766.5 0.8450360 0.8450360 3.218381e-03 3.218381e-03 0.151745628 #> 3535  1767.0 0.8450453 0.8450453 3.231149e-03 3.231149e-03 0.151723507 #> 3536  1767.5 0.8450544 0.8450544 3.243968e-03 3.243968e-03 0.151701610 #> 3537  1768.0 0.8450632 0.8450632 3.256839e-03 3.256839e-03 0.151679938 #> 3538  1768.5 0.8450717 0.8450717 3.269762e-03 3.269762e-03 0.151658491 #> 3539  1769.0 0.8450800 0.8450800 3.282737e-03 3.282737e-03 0.151637272 #> 3540  1769.5 0.8450880 0.8450880 3.295763e-03 3.295763e-03 0.151616279 #> 3541  1770.0 0.8450956 0.8450956 3.308843e-03 3.308843e-03 0.151595514 #> 3542  1770.5 0.8451030 0.8451030 3.321974e-03 3.321974e-03 0.151574979 #> 3543  1771.0 0.8451102 0.8451102 3.335159e-03 3.335159e-03 0.151554672 #> 3544  1771.5 0.8451170 0.8451170 3.348396e-03 3.348396e-03 0.151534596 #> 3545  1772.0 0.8451236 0.8451236 3.361687e-03 3.361687e-03 0.151514751 #> 3546  1772.5 0.8451298 0.8451298 3.375030e-03 3.375030e-03 0.151495138 #> 3547  1773.0 0.8451358 0.8451358 3.388427e-03 3.388427e-03 0.151475757 #> 3548  1773.5 0.8451415 0.8451415 3.401878e-03 3.401878e-03 0.151456610 #> 3549  1774.0 0.8451469 0.8451469 3.415383e-03 3.415383e-03 0.151437697 #> 3550  1774.5 0.8451520 0.8451520 3.428942e-03 3.428942e-03 0.151419019 #> 3551  1775.0 0.8451569 0.8451569 3.442555e-03 3.442555e-03 0.151400576 #> 3552  1775.5 0.8451614 0.8451614 3.456223e-03 3.456223e-03 0.151382370 #> 3553  1776.0 0.8451657 0.8451657 3.469945e-03 3.469945e-03 0.151364401 #> 3554  1776.5 0.8451696 0.8451696 3.483722e-03 3.483722e-03 0.151346671 #> 3555  1777.0 0.8451733 0.8451733 3.497554e-03 3.497554e-03 0.151329179 #> 3556  1777.5 0.8451766 0.8451766 3.511441e-03 3.511441e-03 0.151311927 #> 3557  1778.0 0.8451797 0.8451797 3.525384e-03 3.525384e-03 0.151294915 #> 3558  1778.5 0.8451825 0.8451825 3.539382e-03 3.539382e-03 0.151278145 #> 3559  1779.0 0.8451849 0.8451849 3.553436e-03 3.553436e-03 0.151261617 #> 3560  1779.5 0.8451871 0.8451871 3.567546e-03 3.567546e-03 0.151245332 #> 3561  1780.0 0.8451890 0.8451890 3.581713e-03 3.581713e-03 0.151229290 #> 3562  1780.5 0.8451906 0.8451906 3.595936e-03 3.595936e-03 0.151213493 #> 3563  1781.0 0.8451918 0.8451918 3.610215e-03 3.610215e-03 0.151197941 #> 3564  1781.5 0.8451928 0.8451928 3.624551e-03 3.624551e-03 0.151182636 #> 3565  1782.0 0.8451935 0.8451935 3.638944e-03 3.638944e-03 0.151167577 #> 3566  1782.5 0.8451938 0.8451938 3.653395e-03 3.653395e-03 0.151152767 #> 3567  1783.0 0.8451939 0.8451939 3.667903e-03 3.667903e-03 0.151138205 #> 3568  1783.5 0.8451936 0.8451936 3.682468e-03 3.682468e-03 0.151123892 #> 3569  1784.0 0.8451931 0.8451931 3.697091e-03 3.697091e-03 0.151109830 #> 3570  1784.5 0.8451922 0.8451922 3.711772e-03 3.711772e-03 0.151096019 #> 3571  1785.0 0.8451910 0.8451910 3.726512e-03 3.726512e-03 0.151082460 #> 3572  1785.5 0.8451895 0.8451895 3.741310e-03 3.741310e-03 0.151069154 #> 3573  1786.0 0.8451877 0.8451877 3.756166e-03 3.756166e-03 0.151056101 #> 3574  1786.5 0.8451856 0.8451856 3.771081e-03 3.771081e-03 0.151043304 #> 3575  1787.0 0.8451832 0.8451832 3.786055e-03 3.786055e-03 0.151030761 #> 3576  1787.5 0.8451804 0.8451804 3.801089e-03 3.801089e-03 0.151018475 #> 3577  1788.0 0.8451774 0.8451774 3.816182e-03 3.816182e-03 0.151006446 #> 3578  1788.5 0.8451740 0.8451740 3.831334e-03 3.831334e-03 0.150994675 #> 3579  1789.0 0.8451703 0.8451703 3.846547e-03 3.846547e-03 0.150983163 #> 3580  1789.5 0.8451663 0.8451663 3.861819e-03 3.861819e-03 0.150971910 #> 3581  1790.0 0.8451619 0.8451619 3.877151e-03 3.877151e-03 0.150960918 #> 3582  1790.5 0.8451573 0.8451573 3.892544e-03 3.892544e-03 0.150950188 #> 3583  1791.0 0.8451523 0.8451523 3.907998e-03 3.907998e-03 0.150939720 #> 3584  1791.5 0.8451470 0.8451470 3.923513e-03 3.923513e-03 0.150929515 #> 3585  1792.0 0.8451413 0.8451413 3.939088e-03 3.939088e-03 0.150919575 #> 3586  1792.5 0.8451354 0.8451354 3.954725e-03 3.954725e-03 0.150909899 #> 3587  1793.0 0.8451291 0.8451291 3.970423e-03 3.970423e-03 0.150900489 #> 3588  1793.5 0.8451225 0.8451225 3.986183e-03 3.986183e-03 0.150891346 #> 3589  1794.0 0.8451155 0.8451155 4.002005e-03 4.002005e-03 0.150882471 #> 3590  1794.5 0.8451082 0.8451082 4.017889e-03 4.017889e-03 0.150873864 #> 3591  1795.0 0.8451006 0.8451006 4.033835e-03 4.033835e-03 0.150865527 #> 3592  1795.5 0.8450927 0.8450927 4.049844e-03 4.049844e-03 0.150857460 #> 3593  1796.0 0.8450844 0.8450844 4.065915e-03 4.065915e-03 0.150849665 #> 3594  1796.5 0.8450758 0.8450758 4.082050e-03 4.082050e-03 0.150842141 #> 3595  1797.0 0.8450669 0.8450669 4.098247e-03 4.098247e-03 0.150834891 #> 3596  1797.5 0.8450576 0.8450576 4.114508e-03 4.114508e-03 0.150827915 #> 3597  1798.0 0.8450480 0.8450480 4.130832e-03 4.130832e-03 0.150821214 #> 3598  1798.5 0.8450380 0.8450380 4.147220e-03 4.147220e-03 0.150814788 #> 3599  1799.0 0.8450277 0.8450277 4.163672e-03 4.163672e-03 0.150808640 #> 3600  1799.5 0.8450170 0.8450170 4.180188e-03 4.180188e-03 0.150802769 #> 3601  1800.0 0.8450061 0.8450061 4.196769e-03 4.196769e-03 0.150797177 #> 3602  1800.5 0.8449947 0.8449947 4.213414e-03 4.213414e-03 0.150791865 #> 3603  1801.0 0.8449830 0.8449830 4.230124e-03 4.230124e-03 0.150786833 #> 3604  1801.5 0.8449710 0.8449710 4.246899e-03 4.246899e-03 0.150782082 #> 3605  1802.0 0.8449586 0.8449586 4.263739e-03 4.263739e-03 0.150777614 #> 3606  1802.5 0.8449459 0.8449459 4.280645e-03 4.280645e-03 0.150773429 #> 3607  1803.0 0.8449329 0.8449329 4.297616e-03 4.297616e-03 0.150769529 #> 3608  1803.5 0.8449194 0.8449194 4.314653e-03 4.314653e-03 0.150765914 #> 3609  1804.0 0.8449057 0.8449057 4.331757e-03 4.331757e-03 0.150762585 #> 3610  1804.5 0.8448915 0.8448915 4.348926e-03 4.348926e-03 0.150759544 #> 3611  1805.0 0.8448770 0.8448770 4.366162e-03 4.366162e-03 0.150756790 #> 3612  1805.5 0.8448622 0.8448622 4.383465e-03 4.383465e-03 0.150754326 #> 3613  1806.0 0.8448470 0.8448470 4.400834e-03 4.400834e-03 0.150752152 #> 3614  1806.5 0.8448315 0.8448315 4.418271e-03 4.418271e-03 0.150750269 #> 3615  1807.0 0.8448155 0.8448155 4.435775e-03 4.435775e-03 0.150748678 #> 3616  1807.5 0.8447993 0.8447993 4.453347e-03 4.453347e-03 0.150747380 #> 3617  1808.0 0.8447826 0.8447826 4.470986e-03 4.470986e-03 0.150746375 #> 3618  1808.5 0.8447656 0.8447656 4.488694e-03 4.488694e-03 0.150745666 #> 3619  1809.0 0.8447483 0.8447483 4.506469e-03 4.506469e-03 0.150745253 #> 3620  1809.5 0.8447305 0.8447305 4.524313e-03 4.524313e-03 0.150745137 #> 3621  1810.0 0.8447125 0.8447125 4.542226e-03 4.542226e-03 0.150745319 #> 3622  1810.5 0.8446940 0.8446940 4.560207e-03 4.560207e-03 0.150745800 #> 3623  1811.0 0.8446752 0.8446752 4.578258e-03 4.578258e-03 0.150746580 #> 3624  1811.5 0.8446560 0.8446560 4.596378e-03 4.596378e-03 0.150747662 #> 3625  1812.0 0.8446364 0.8446364 4.614567e-03 4.614567e-03 0.150749046 #> 3626  1812.5 0.8446164 0.8446164 4.632826e-03 4.632826e-03 0.150750732 #> 3627  1813.0 0.8445961 0.8445961 4.651155e-03 4.651155e-03 0.150752723 #> 3628  1813.5 0.8445754 0.8445754 4.669553e-03 4.669553e-03 0.150755018 #> 3629  1814.0 0.8445544 0.8445544 4.688023e-03 4.688023e-03 0.150757620 #> 3630  1814.5 0.8445329 0.8445329 4.706563e-03 4.706563e-03 0.150760528 #> 3631  1815.0 0.8445111 0.8445111 4.725173e-03 4.725173e-03 0.150763745 #> 3632  1815.5 0.8444889 0.8444889 4.743855e-03 4.743855e-03 0.150767271 #> 3633  1816.0 0.8444663 0.8444663 4.762608e-03 4.762608e-03 0.150771107 #> 3634  1816.5 0.8444433 0.8444433 4.781432e-03 4.781432e-03 0.150775254 #> 3635  1817.0 0.8444200 0.8444200 4.800328e-03 4.800328e-03 0.150779713 #> 3636  1817.5 0.8443962 0.8443962 4.819295e-03 4.819295e-03 0.150784485 #> 3637  1818.0 0.8443721 0.8443721 4.838335e-03 4.838335e-03 0.150789572 #> 3638  1818.5 0.8443476 0.8443476 4.857447e-03 4.857447e-03 0.150794974 #> 3639  1819.0 0.8443227 0.8443227 4.876631e-03 4.876631e-03 0.150800692 #> 3640  1819.5 0.8442974 0.8442974 4.895888e-03 4.895888e-03 0.150806728 #> 3641  1820.0 0.8442717 0.8442717 4.915218e-03 4.915218e-03 0.150813082 #> 3642  1820.5 0.8442456 0.8442456 4.934622e-03 4.934622e-03 0.150819756 #> 3643  1821.0 0.8442192 0.8442192 4.954098e-03 4.954098e-03 0.150826750 #> 3644  1821.5 0.8441923 0.8441923 4.973648e-03 4.973648e-03 0.150834066 #> 3645  1822.0 0.8441650 0.8441650 4.993272e-03 4.993272e-03 0.150841704 #> 3646  1822.5 0.8441374 0.8441374 5.012970e-03 5.012970e-03 0.150849667 #> 3647  1823.0 0.8441093 0.8441093 5.032742e-03 5.032742e-03 0.150857954 #> 3648  1823.5 0.8440808 0.8440808 5.052588e-03 5.052588e-03 0.150866567 #> 3649  1824.0 0.8440520 0.8440520 5.072509e-03 5.072509e-03 0.150875508 #> 3650  1824.5 0.8440227 0.8440227 5.092505e-03 5.092505e-03 0.150884776 #> 3651  1825.0 0.8439931 0.8439931 5.112576e-03 5.112576e-03 0.150894373 #> 3652  1825.5 0.8439630 0.8439630 5.132723e-03 5.132723e-03 0.150904301 #> 3653  1826.0 0.8439325 0.8439325 5.152944e-03 5.152944e-03 0.150914560 #> 3654  1826.5 0.8439016 0.8439016 5.173242e-03 5.173242e-03 0.150925152 #> 3655  1827.0 0.8438703 0.8438703 5.193615e-03 5.193615e-03 0.150936077 #> 3656  1827.5 0.8438386 0.8438386 5.214065e-03 5.214065e-03 0.150947336 #> 3657  1828.0 0.8438065 0.8438065 5.234590e-03 5.234590e-03 0.150958932 #> 3658  1828.5 0.8437739 0.8437739 5.255193e-03 5.255193e-03 0.150970864 #> 3659  1829.0 0.8437410 0.8437410 5.275872e-03 5.275872e-03 0.150983135 #> 3660  1829.5 0.8437076 0.8437076 5.296628e-03 5.296628e-03 0.150995744 #> 3661  1830.0 0.8436738 0.8436738 5.317461e-03 5.317461e-03 0.151008693 #> 3662  1830.5 0.8436396 0.8436396 5.338372e-03 5.338372e-03 0.151021984 #> 3663  1831.0 0.8436050 0.8436050 5.359361e-03 5.359361e-03 0.151035618 #> 3664  1831.5 0.8435700 0.8435700 5.380427e-03 5.380427e-03 0.151049594 #> 3665  1832.0 0.8435345 0.8435345 5.401571e-03 5.401571e-03 0.151063916 #> 3666  1832.5 0.8434986 0.8434986 5.422793e-03 5.422793e-03 0.151078583 #> 3667  1833.0 0.8434623 0.8434623 5.444094e-03 5.444094e-03 0.151093598 #> 3668  1833.5 0.8434256 0.8434256 5.465474e-03 5.465474e-03 0.151108960 #> 3669  1834.0 0.8433884 0.8433884 5.486933e-03 5.486933e-03 0.151124671 #> 3670  1834.5 0.8433508 0.8433508 5.508470e-03 5.508470e-03 0.151140733 #> 3671  1835.0 0.8433128 0.8433128 5.530087e-03 5.530087e-03 0.151157147 #> 3672  1835.5 0.8432743 0.8432743 5.551784e-03 5.551784e-03 0.151173913 #> 3673  1836.0 0.8432354 0.8432354 5.573560e-03 5.573560e-03 0.151191033 #> 3674  1836.5 0.8431961 0.8431961 5.595416e-03 5.595416e-03 0.151208507 #> 3675  1837.0 0.8431563 0.8431563 5.617352e-03 5.617352e-03 0.151226338 #> 3676  1837.5 0.8431161 0.8431161 5.639369e-03 5.639369e-03 0.151244527 #> 3677  1838.0 0.8430755 0.8430755 5.661466e-03 5.661466e-03 0.151263073 #> 3678  1838.5 0.8430344 0.8430344 5.683644e-03 5.683644e-03 0.151281979 #> 3679  1839.0 0.8429929 0.8429929 5.705903e-03 5.705903e-03 0.151301246 #> 3680  1839.5 0.8429509 0.8429509 5.728243e-03 5.728243e-03 0.151320875 #> 3681  1840.0 0.8429085 0.8429085 5.750665e-03 5.750665e-03 0.151340867 #> 3682  1840.5 0.8428656 0.8428656 5.773168e-03 5.773168e-03 0.151361224 #> 3683  1841.0 0.8428223 0.8428223 5.795753e-03 5.795753e-03 0.151381945 #> 3684  1841.5 0.8427785 0.8427785 5.818420e-03 5.818420e-03 0.151403034 #> 3685  1842.0 0.8427343 0.8427343 5.841169e-03 5.841169e-03 0.151424490 #> 3686  1842.5 0.8426897 0.8426897 5.864001e-03 5.864001e-03 0.151446315 #> 3687  1843.0 0.8426446 0.8426446 5.886915e-03 5.886915e-03 0.151468511 #> 3688  1843.5 0.8425990 0.8425990 5.909912e-03 5.909912e-03 0.151491078 #> 3689  1844.0 0.8425530 0.8425530 5.932993e-03 5.932993e-03 0.151514017 #> 3690  1844.5 0.8425065 0.8425065 5.956156e-03 5.956156e-03 0.151537331 #> 3691  1845.0 0.8424596 0.8424596 5.979403e-03 5.979403e-03 0.151561019 #> 3692  1845.5 0.8424122 0.8424122 6.002734e-03 6.002734e-03 0.151585083 #> 3693  1846.0 0.8423643 0.8423643 6.026148e-03 6.026148e-03 0.151609525 #> 3694  1846.5 0.8423160 0.8423160 6.049647e-03 6.049647e-03 0.151634346 #> 3695  1847.0 0.8422672 0.8422672 6.073229e-03 6.073229e-03 0.151659546 #> 3696  1847.5 0.8422180 0.8422180 6.096897e-03 6.096897e-03 0.151685128 #> 3697  1848.0 0.8421683 0.8421683 6.120648e-03 6.120648e-03 0.151711091 #> 3698  1848.5 0.8421181 0.8421181 6.144485e-03 6.144485e-03 0.151737439 #> 3699  1849.0 0.8420674 0.8420674 6.168407e-03 6.168407e-03 0.151764171 #> 3700  1849.5 0.8420163 0.8420163 6.192414e-03 6.192414e-03 0.151791289 #> 3701  1850.0 0.8419647 0.8419647 6.216506e-03 6.216506e-03 0.151818794 #> 3702  1850.5 0.8419126 0.8419126 6.240685e-03 6.240685e-03 0.151846687 #> 3703  1851.0 0.8418601 0.8418601 6.264949e-03 6.264949e-03 0.151874970 #> 3704  1851.5 0.8418071 0.8418071 6.289299e-03 6.289299e-03 0.151903644 #> 3705  1852.0 0.8417536 0.8417536 6.313735e-03 6.313735e-03 0.151932710 #> 3706  1852.5 0.8416996 0.8416996 6.338258e-03 6.338258e-03 0.151962170 #> 3707  1853.0 0.8416451 0.8416451 6.362867e-03 6.362867e-03 0.151992024 #> 3708  1853.5 0.8415902 0.8415902 6.387563e-03 6.387563e-03 0.152022274 #> 3709  1854.0 0.8415347 0.8415347 6.412346e-03 6.412346e-03 0.152052921 #> 3710  1854.5 0.8414788 0.8414788 6.437216e-03 6.437216e-03 0.152083966 #> 3711  1855.0 0.8414224 0.8414224 6.462174e-03 6.462174e-03 0.152115411 #> 3712  1855.5 0.8413655 0.8413655 6.487219e-03 6.487219e-03 0.152147257 #> 3713  1856.0 0.8413081 0.8413081 6.512352e-03 6.512352e-03 0.152179505 #> 3714  1856.5 0.8412503 0.8412503 6.537573e-03 6.537573e-03 0.152212156 #> 3715  1857.0 0.8411919 0.8411919 6.562883e-03 6.562883e-03 0.152245212 #> 3716  1857.5 0.8411330 0.8411330 6.588280e-03 6.588280e-03 0.152278674 #> 3717  1858.0 0.8410737 0.8410737 6.613766e-03 6.613766e-03 0.152312543 #> 3718  1858.5 0.8410138 0.8410138 6.639341e-03 6.639341e-03 0.152346821 #> 3719  1859.0 0.8409535 0.8409535 6.665004e-03 6.665004e-03 0.152381508 #> 3720  1859.5 0.8408926 0.8408926 6.690757e-03 6.690757e-03 0.152416607 #> 3721  1860.0 0.8408313 0.8408313 6.716599e-03 6.716599e-03 0.152452117 #> 3722  1860.5 0.8407694 0.8407694 6.742530e-03 6.742530e-03 0.152488041 #> 3723  1861.0 0.8407071 0.8407071 6.768551e-03 6.768551e-03 0.152524381 #> 3724  1861.5 0.8406442 0.8406442 6.794662e-03 6.794662e-03 0.152561136 #> 3725  1862.0 0.8405808 0.8405808 6.820862e-03 6.820862e-03 0.152598309 #> 3726  1862.5 0.8405169 0.8405169 6.847153e-03 6.847153e-03 0.152635900 #> 3727  1863.0 0.8404526 0.8404526 6.873534e-03 6.873534e-03 0.152673912 #> 3728  1863.5 0.8403876 0.8403876 6.900006e-03 6.900006e-03 0.152712344 #> 3729  1864.0 0.8403222 0.8403222 6.926568e-03 6.926568e-03 0.152751200 #> 3730  1864.5 0.8402563 0.8402563 6.953221e-03 6.953221e-03 0.152790479 #> 3731  1865.0 0.8401899 0.8401899 6.979965e-03 6.979965e-03 0.152830184 #> 3732  1865.5 0.8401229 0.8401229 7.006800e-03 7.006800e-03 0.152870315 #> 3733  1866.0 0.8400554 0.8400554 7.033727e-03 7.033727e-03 0.152910874 #> 3734  1866.5 0.8399874 0.8399874 7.060745e-03 7.060745e-03 0.152951862 #> 3735  1867.0 0.8399189 0.8399189 7.087855e-03 7.087855e-03 0.152993281 #> 3736  1867.5 0.8398498 0.8398498 7.115057e-03 7.115057e-03 0.153035131 #> 3737  1868.0 0.8397802 0.8397802 7.142350e-03 7.142350e-03 0.153077415 #> 3738  1868.5 0.8397101 0.8397101 7.169736e-03 7.169736e-03 0.153120132 #> 3739  1869.0 0.8396395 0.8396395 7.197215e-03 7.197215e-03 0.153163286 #> 3740  1869.5 0.8395683 0.8395683 7.224785e-03 7.224785e-03 0.153206877 #> 3741  1870.0 0.8394966 0.8394966 7.252449e-03 7.252449e-03 0.153250906 #> 3742  1870.5 0.8394244 0.8394244 7.280205e-03 7.280205e-03 0.153295375 #> 3743  1871.0 0.8393517 0.8393517 7.308054e-03 7.308054e-03 0.153340284 #> 3744  1871.5 0.8392784 0.8392784 7.335997e-03 7.335997e-03 0.153385637 #> 3745  1872.0 0.8392045 0.8392045 7.364032e-03 7.364032e-03 0.153431432 #> 3746  1872.5 0.8391302 0.8391302 7.392161e-03 7.392161e-03 0.153477673 #> 3747  1873.0 0.8390553 0.8390553 7.420384e-03 7.420384e-03 0.153524361 #> 3748  1873.5 0.8389798 0.8389798 7.448700e-03 7.448700e-03 0.153571496 #> 3749  1874.0 0.8389038 0.8389038 7.477111e-03 7.477111e-03 0.153619080 #> 3750  1874.5 0.8388273 0.8388273 7.505615e-03 7.505615e-03 0.153667114 #> 3751  1875.0 0.8387502 0.8387502 7.534214e-03 7.534214e-03 0.153715600 #> 3752  1875.5 0.8386726 0.8386726 7.562907e-03 7.562907e-03 0.153764539 #> 3753  1876.0 0.8385944 0.8385944 7.591694e-03 7.591694e-03 0.153813933 #> 3754  1876.5 0.8385156 0.8385156 7.620576e-03 7.620576e-03 0.153863782 #> 3755  1877.0 0.8384364 0.8384364 7.649553e-03 7.649553e-03 0.153914089 #> 3756  1877.5 0.8383565 0.8383565 7.678625e-03 7.678625e-03 0.153964854 #> 3757  1878.0 0.8382761 0.8382761 7.707792e-03 7.707792e-03 0.154016078 #> 3758  1878.5 0.8381952 0.8381952 7.737053e-03 7.737053e-03 0.154067764 #> 3759  1879.0 0.8381137 0.8381137 7.766411e-03 7.766411e-03 0.154119913 #> 3760  1879.5 0.8380316 0.8380316 7.795863e-03 7.795863e-03 0.154172525 #> 3761  1880.0 0.8379490 0.8379490 7.825412e-03 7.825412e-03 0.154225603 #> 3762  1880.5 0.8378658 0.8378658 7.855056e-03 7.855056e-03 0.154279147 #> 3763  1881.0 0.8377820 0.8377820 7.884796e-03 7.884796e-03 0.154333159 #> 3764  1881.5 0.8376977 0.8376977 7.914632e-03 7.914632e-03 0.154387641 #> 3765  1882.0 0.8376128 0.8376128 7.944564e-03 7.944564e-03 0.154442593 #> 3766  1882.5 0.8375274 0.8375274 7.974592e-03 7.974592e-03 0.154498018 #> 3767  1883.0 0.8374414 0.8374414 8.004716e-03 8.004716e-03 0.154553916 #> 3768  1883.5 0.8373548 0.8373548 8.034937e-03 8.034937e-03 0.154610288 #> 3769  1884.0 0.8372676 0.8372676 8.065255e-03 8.065255e-03 0.154667137 #> 3770  1884.5 0.8371799 0.8371799 8.095669e-03 8.095669e-03 0.154724463 #> 3771  1885.0 0.8370916 0.8370916 8.126180e-03 8.126180e-03 0.154782269 #> 3772  1885.5 0.8370027 0.8370027 8.156788e-03 8.156788e-03 0.154840554 #> 3773  1886.0 0.8369132 0.8369132 8.187493e-03 8.187493e-03 0.154899321 #> 3774  1886.5 0.8368231 0.8368231 8.218295e-03 8.218295e-03 0.154958572 #> 3775  1887.0 0.8367325 0.8367325 8.249195e-03 8.249195e-03 0.155018306 #> 3776  1887.5 0.8366413 0.8366413 8.280191e-03 8.280191e-03 0.155078527 #> 3777  1888.0 0.8365495 0.8365495 8.311286e-03 8.311286e-03 0.155139235 #> 3778  1888.5 0.8364571 0.8364571 8.342477e-03 8.342477e-03 0.155200431 #> 3779  1889.0 0.8363641 0.8363641 8.373767e-03 8.373767e-03 0.155262117 #> 3780  1889.5 0.8362706 0.8362706 8.405154e-03 8.405154e-03 0.155324295 #> 3781  1890.0 0.8361764 0.8361764 8.436639e-03 8.436639e-03 0.155386965 #> 3782  1890.5 0.8360816 0.8360816 8.468222e-03 8.468222e-03 0.155450130 #> 3783  1891.0 0.8359863 0.8359863 8.499903e-03 8.499903e-03 0.155513790 #> 3784  1891.5 0.8358904 0.8358904 8.531682e-03 8.531682e-03 0.155577947 #> 3785  1892.0 0.8357938 0.8357938 8.563559e-03 8.563559e-03 0.155642602 #> 3786  1892.5 0.8356967 0.8356967 8.595535e-03 8.595535e-03 0.155707756 #> 3787  1893.0 0.8355990 0.8355990 8.627609e-03 8.627609e-03 0.155773412 #> 3788  1893.5 0.8355006 0.8355006 8.659782e-03 8.659782e-03 0.155839570 #> 3789  1894.0 0.8354017 0.8354017 8.692053e-03 8.692053e-03 0.155906232 #> 3790  1894.5 0.8353022 0.8353022 8.724422e-03 8.724422e-03 0.155973400 #> 3791  1895.0 0.8352020 0.8352020 8.756891e-03 8.756891e-03 0.156041074 #> 3792  1895.5 0.8351013 0.8351013 8.789458e-03 8.789458e-03 0.156109255 #> 3793  1896.0 0.8349999 0.8349999 8.822124e-03 8.822124e-03 0.156177947 #> 3794  1896.5 0.8348980 0.8348980 8.854888e-03 8.854888e-03 0.156247149 #> 3795  1897.0 0.8347954 0.8347954 8.887752e-03 8.887752e-03 0.156316863 #> 3796  1897.5 0.8346922 0.8346922 8.920715e-03 8.920715e-03 0.156387091 #> 3797  1898.0 0.8345884 0.8345884 8.953777e-03 8.953777e-03 0.156457834 #> 3798  1898.5 0.8344840 0.8344840 8.986938e-03 8.986938e-03 0.156529093 #> 3799  1899.0 0.8343789 0.8343789 9.020198e-03 9.020198e-03 0.156600870 #> 3800  1899.5 0.8342733 0.8342733 9.053558e-03 9.053558e-03 0.156673166 #> 3801  1900.0 0.8341670 0.8341670 9.087016e-03 9.087016e-03 0.156745982 #> 3802  1900.5 0.8340601 0.8340601 9.120575e-03 9.120575e-03 0.156819321 #> 3803  1901.0 0.8339526 0.8339526 9.154232e-03 9.154232e-03 0.156893182 #> 3804  1901.5 0.8338444 0.8338444 9.187989e-03 9.187989e-03 0.156967569 #> 3805  1902.0 0.8337357 0.8337357 9.221845e-03 9.221845e-03 0.157042482 #> 3806  1902.5 0.8336263 0.8336263 9.255801e-03 9.255801e-03 0.157117922 #> 3807  1903.0 0.8335163 0.8335163 9.289857e-03 9.289857e-03 0.157193891 #> 3808  1903.5 0.8334056 0.8334056 9.324012e-03 9.324012e-03 0.157270390 #> 3809  1904.0 0.8332943 0.8332943 9.358267e-03 9.358267e-03 0.157347421 #> 3810  1904.5 0.8331824 0.8331824 9.392621e-03 9.392621e-03 0.157424985 #> 3811  1905.0 0.8330698 0.8330698 9.427075e-03 9.427075e-03 0.157503084 #> 3812  1905.5 0.8329567 0.8329567 9.461629e-03 9.461629e-03 0.157581719 #> 3813  1906.0 0.8328428 0.8328428 9.496282e-03 9.496282e-03 0.157660891 #> 3814  1906.5 0.8327284 0.8327284 9.531035e-03 9.531035e-03 0.157740601 #> 3815  1907.0 0.8326133 0.8326133 9.565888e-03 9.565888e-03 0.157820852 #> 3816  1907.5 0.8324975 0.8324975 9.600841e-03 9.600841e-03 0.157901644 #> 3817  1908.0 0.8323811 0.8323811 9.635893e-03 9.635893e-03 0.157982978 #> 3818  1908.5 0.8322641 0.8322641 9.671045e-03 9.671045e-03 0.158064857 #> 3819  1909.0 0.8321464 0.8321464 9.706297e-03 9.706297e-03 0.158147282 #> 3820  1909.5 0.8320281 0.8320281 9.741649e-03 9.741649e-03 0.158230254 #> 3821  1910.0 0.8319091 0.8319091 9.777100e-03 9.777100e-03 0.158313774 #> 3822  1910.5 0.8317895 0.8317895 9.812651e-03 9.812651e-03 0.158397844 #> 3823  1911.0 0.8316692 0.8316692 9.848302e-03 9.848302e-03 0.158482465 #> 3824  1911.5 0.8315483 0.8315483 9.884053e-03 9.884053e-03 0.158567639 #> 3825  1912.0 0.8314267 0.8314267 9.919903e-03 9.919903e-03 0.158653366 #> 3826  1912.5 0.8313045 0.8313045 9.955853e-03 9.955853e-03 0.158739649 #> 3827  1913.0 0.8311816 0.8311816 9.991902e-03 9.991902e-03 0.158826489 #> 3828  1913.5 0.8310581 0.8310581 1.002805e-02 1.002805e-02 0.158913887 #> 3829  1914.0 0.8309339 0.8309339 1.006430e-02 1.006430e-02 0.159001844 #> 3830  1914.5 0.8308090 0.8308090 1.010065e-02 1.010065e-02 0.159090362 #> 3831  1915.0 0.8306835 0.8306835 1.013710e-02 1.013710e-02 0.159179442 #> 3832  1915.5 0.8305573 0.8305573 1.017364e-02 1.017364e-02 0.159269086 #> 3833  1916.0 0.8304304 0.8304304 1.021029e-02 1.021029e-02 0.159359295 #> 3834  1916.5 0.8303029 0.8303029 1.024704e-02 1.024704e-02 0.159450070 #> 3835  1917.0 0.8301747 0.8301747 1.028388e-02 1.028388e-02 0.159541413 #> 3836  1917.5 0.8300458 0.8300458 1.032083e-02 1.032083e-02 0.159633326 #> 3837  1918.0 0.8299163 0.8299163 1.035787e-02 1.035787e-02 0.159725808 #> 3838  1918.5 0.8297861 0.8297861 1.039502e-02 1.039502e-02 0.159818863 #> 3839  1919.0 0.8296552 0.8296552 1.043226e-02 1.043226e-02 0.159912490 #> 3840  1919.5 0.8295237 0.8295237 1.046960e-02 1.046960e-02 0.160006693 #> 3841  1920.0 0.8293915 0.8293915 1.050704e-02 1.050704e-02 0.160101471 #> 3842  1920.5 0.8292586 0.8292586 1.054458e-02 1.054458e-02 0.160196826 #> 3843  1921.0 0.8291250 0.8291250 1.058222e-02 1.058222e-02 0.160292761 #> 3844  1921.5 0.8289908 0.8289908 1.061996e-02 1.061996e-02 0.160389275 #> 3845  1922.0 0.8288558 0.8288558 1.065780e-02 1.065780e-02 0.160486370 #> 3846  1922.5 0.8287202 0.8287202 1.069573e-02 1.069573e-02 0.160584049 #> 3847  1923.0 0.8285839 0.8285839 1.073376e-02 1.073376e-02 0.160682311 #> 3848  1923.5 0.8284469 0.8284469 1.077190e-02 1.077190e-02 0.160781159 #> 3849  1924.0 0.8283093 0.8283093 1.081013e-02 1.081013e-02 0.160880594 #> 3850  1924.5 0.8281709 0.8281709 1.084845e-02 1.084845e-02 0.160980616 #> 3851  1925.0 0.8280319 0.8280319 1.088688e-02 1.088688e-02 0.161081229 #> 3852  1925.5 0.8278922 0.8278922 1.092541e-02 1.092541e-02 0.161182432 #> 3853  1926.0 0.8277517 0.8277517 1.096403e-02 1.096403e-02 0.161284227 #> 3854  1926.5 0.8276106 0.8276106 1.100275e-02 1.100275e-02 0.161386615 #> 3855  1927.0 0.8274688 0.8274688 1.104157e-02 1.104157e-02 0.161489599 #> 3856  1927.5 0.8273263 0.8273263 1.108048e-02 1.108048e-02 0.161593178 #> 3857  1928.0 0.8271831 0.8271831 1.111950e-02 1.111950e-02 0.161697355 #> 3858  1928.5 0.8270393 0.8270393 1.115861e-02 1.115861e-02 0.161802131 #> 3859  1929.0 0.8268947 0.8268947 1.119781e-02 1.119781e-02 0.161907507 #> 3860  1929.5 0.8267494 0.8267494 1.123712e-02 1.123712e-02 0.162013485 #> 3861  1930.0 0.8266034 0.8266034 1.127652e-02 1.127652e-02 0.162120065 #> 3862  1930.5 0.8264567 0.8264567 1.131602e-02 1.131602e-02 0.162227249 #> 3863  1931.0 0.8263093 0.8263093 1.135561e-02 1.135561e-02 0.162335039 #> 3864  1931.5 0.8261613 0.8261613 1.139530e-02 1.139530e-02 0.162443435 #> 3865  1932.0 0.8260125 0.8260125 1.143509e-02 1.143509e-02 0.162552439 #> 3866  1932.5 0.8258630 0.8258630 1.147498e-02 1.147498e-02 0.162662053 #> 3867  1933.0 0.8257128 0.8257128 1.151496e-02 1.151496e-02 0.162772277 #> 3868  1933.5 0.8255619 0.8255619 1.155503e-02 1.155503e-02 0.162883113 #> 3869  1934.0 0.8254102 0.8254102 1.159520e-02 1.159520e-02 0.162994562 #> 3870  1934.5 0.8252579 0.8252579 1.163547e-02 1.163547e-02 0.163106625 #> 3871  1935.0 0.8251049 0.8251049 1.167583e-02 1.167583e-02 0.163219305 #> 3872  1935.5 0.8249511 0.8249511 1.171629e-02 1.171629e-02 0.163332601 #> 3873  1936.0 0.8247966 0.8247966 1.175684e-02 1.175684e-02 0.163446515 #> 3874  1936.5 0.8246415 0.8246415 1.179749e-02 1.179749e-02 0.163561049 #> 3875  1937.0 0.8244856 0.8244856 1.183823e-02 1.183823e-02 0.163676204 #> 3876  1937.5 0.8243290 0.8243290 1.187906e-02 1.187906e-02 0.163791981 #> 3877  1938.0 0.8241716 0.8241716 1.191999e-02 1.191999e-02 0.163908381 #> 3878  1938.5 0.8240136 0.8240136 1.196102e-02 1.196102e-02 0.164025406 #> 3879  1939.0 0.8238548 0.8238548 1.200213e-02 1.200213e-02 0.164143057 #> 3880  1939.5 0.8236953 0.8236953 1.204334e-02 1.204334e-02 0.164261335 #> 3881  1940.0 0.8235351 0.8235351 1.208465e-02 1.208465e-02 0.164380241 #> 3882  1940.5 0.8233742 0.8233742 1.212604e-02 1.212604e-02 0.164499776 #> 3883  1941.0 0.8232125 0.8232125 1.216753e-02 1.216753e-02 0.164619943 #> 3884  1941.5 0.8230501 0.8230501 1.220912e-02 1.220912e-02 0.164740741 #> 3885  1942.0 0.8228870 0.8228870 1.225079e-02 1.225079e-02 0.164862173 #> 3886  1942.5 0.8227232 0.8227232 1.229256e-02 1.229256e-02 0.164984239 #> 3887  1943.0 0.8225586 0.8225586 1.233441e-02 1.233441e-02 0.165106941 #> 3888  1943.5 0.8223934 0.8223934 1.237636e-02 1.237636e-02 0.165230280 #> 3889  1944.0 0.8222273 0.8222273 1.241840e-02 1.241840e-02 0.165354257 #> 3890  1944.5 0.8220606 0.8220606 1.246053e-02 1.246053e-02 0.165478873 #> 3891  1945.0 0.8218931 0.8218931 1.250276e-02 1.250276e-02 0.165604129 #> 3892  1945.5 0.8217249 0.8217249 1.254507e-02 1.254507e-02 0.165730028 #> 3893  1946.0 0.8215560 0.8215560 1.258747e-02 1.258747e-02 0.165856569 #> 3894  1946.5 0.8213863 0.8213863 1.262997e-02 1.262997e-02 0.165983754 #> 3895  1947.0 0.8212159 0.8212159 1.267255e-02 1.267255e-02 0.166111584 #> 3896  1947.5 0.8210447 0.8210447 1.271522e-02 1.271522e-02 0.166240061 #> 3897  1948.0 0.8208728 0.8208728 1.275798e-02 1.275798e-02 0.166369185 #> 3898  1948.5 0.8207002 0.8207002 1.280083e-02 1.280083e-02 0.166498958 #> 3899  1949.0 0.8205269 0.8205269 1.284377e-02 1.284377e-02 0.166629381 #> 3900  1949.5 0.8203528 0.8203528 1.288679e-02 1.288679e-02 0.166760455 #> 3901  1950.0 0.8201779 0.8201779 1.292991e-02 1.292991e-02 0.166892181 #> 3902  1950.5 0.8200023 0.8200023 1.297311e-02 1.297311e-02 0.167024560 #> 3903  1951.0 0.8198260 0.8198260 1.301640e-02 1.301640e-02 0.167157593 #> 3904  1951.5 0.8196489 0.8196489 1.305977e-02 1.305977e-02 0.167291282 #> 3905  1952.0 0.8194711 0.8194711 1.310323e-02 1.310323e-02 0.167425628 #> 3906  1952.5 0.8192926 0.8192926 1.314678e-02 1.314678e-02 0.167560632 #> 3907  1953.0 0.8191133 0.8191133 1.319041e-02 1.319041e-02 0.167696294 #> 3908  1953.5 0.8189333 0.8189333 1.323413e-02 1.323413e-02 0.167832616 #> 3909  1954.0 0.8187525 0.8187525 1.327793e-02 1.327793e-02 0.167969599 #> 3910  1954.5 0.8185709 0.8185709 1.332182e-02 1.332182e-02 0.168107244 #> 3911  1955.0 0.8183887 0.8183887 1.336579e-02 1.336579e-02 0.168245553 #> 3912  1955.5 0.8182056 0.8182056 1.340985e-02 1.340985e-02 0.168384526 #> 3913  1956.0 0.8180218 0.8180218 1.345399e-02 1.345399e-02 0.168524164 #> 3914  1956.5 0.8178373 0.8178373 1.349821e-02 1.349821e-02 0.168664468 #> 3915  1957.0 0.8176520 0.8176520 1.354252e-02 1.354252e-02 0.168805440 #> 3916  1957.5 0.8174660 0.8174660 1.358690e-02 1.358690e-02 0.168947080 #> 3917  1958.0 0.8172792 0.8172792 1.363137e-02 1.363137e-02 0.169089390 #> 3918  1958.5 0.8170917 0.8170917 1.367593e-02 1.367593e-02 0.169232370 #> 3919  1959.0 0.8169034 0.8169034 1.372056e-02 1.372056e-02 0.169376022 #> 3920  1959.5 0.8167144 0.8167144 1.376527e-02 1.376527e-02 0.169520347 #> 3921  1960.0 0.8165246 0.8165246 1.381007e-02 1.381007e-02 0.169665345 #> 3922  1960.5 0.8163340 0.8163340 1.385494e-02 1.385494e-02 0.169811017 #> 3923  1961.0 0.8161427 0.8161427 1.389989e-02 1.389989e-02 0.169957365 #> 3924  1961.5 0.8159507 0.8159507 1.394492e-02 1.394492e-02 0.170104390 #> 3925  1962.0 0.8157579 0.8157579 1.399003e-02 1.399003e-02 0.170252092 #> 3926  1962.5 0.8155643 0.8155643 1.403522e-02 1.403522e-02 0.170400473 #> 3927  1963.0 0.8153700 0.8153700 1.408049e-02 1.408049e-02 0.170549533 #> 3928  1963.5 0.8151749 0.8151749 1.412583e-02 1.412583e-02 0.170699273 #> 3929  1964.0 0.8149791 0.8149791 1.417125e-02 1.417125e-02 0.170849695 #> 3930  1964.5 0.8147825 0.8147825 1.421675e-02 1.421675e-02 0.171000799 #> 3931  1965.0 0.8145851 0.8145851 1.426232e-02 1.426232e-02 0.171152586 #> 3932  1965.5 0.8143870 0.8143870 1.430797e-02 1.430797e-02 0.171305057 #> 3933  1966.0 0.8141881 0.8141881 1.435369e-02 1.435369e-02 0.171458214 #> 3934  1966.5 0.8139885 0.8139885 1.439949e-02 1.439949e-02 0.171612056 #> 3935  1967.0 0.8137881 0.8137881 1.444536e-02 1.444536e-02 0.171766585 #> 3936  1967.5 0.8135869 0.8135869 1.449131e-02 1.449131e-02 0.171921802 #> 3937  1968.0 0.8133850 0.8133850 1.453732e-02 1.453732e-02 0.172077707 #> 3938  1968.5 0.8131823 0.8131823 1.458341e-02 1.458341e-02 0.172234302 #> 3939  1969.0 0.8129788 0.8129788 1.462958e-02 1.462958e-02 0.172391587 #> 3940  1969.5 0.8127746 0.8127746 1.467581e-02 1.467581e-02 0.172549563 #> 3941  1970.0 0.8125697 0.8125697 1.472211e-02 1.472211e-02 0.172708231 #> 3942  1970.5 0.8123639 0.8123639 1.476849e-02 1.476849e-02 0.172867592 #> 3943  1971.0 0.8121574 0.8121574 1.481493e-02 1.481493e-02 0.173027647 #> 3944  1971.5 0.8119502 0.8119502 1.486144e-02 1.486144e-02 0.173188396 #> 3945  1972.0 0.8117421 0.8117421 1.490802e-02 1.490802e-02 0.173349840 #> 3946  1972.5 0.8115333 0.8115333 1.495467e-02 1.495467e-02 0.173511980 #> 3947  1973.0 0.8113238 0.8113238 1.500139e-02 1.500139e-02 0.173674817 #> 3948  1973.5 0.8111135 0.8111135 1.504818e-02 1.504818e-02 0.173838352 #> 3949  1974.0 0.8109024 0.8109024 1.509503e-02 1.509503e-02 0.174002585 #> 3950  1974.5 0.8106905 0.8106905 1.514194e-02 1.514194e-02 0.174167517 #> 3951  1975.0 0.8104779 0.8104779 1.518892e-02 1.518892e-02 0.174333149 #> 3952  1975.5 0.8102645 0.8102645 1.523597e-02 1.523597e-02 0.174499482 #> 3953  1976.0 0.8100504 0.8100504 1.528308e-02 1.528308e-02 0.174666516 #> 3954  1976.5 0.8098355 0.8098355 1.533025e-02 1.533025e-02 0.174834251 #> 3955  1977.0 0.8096198 0.8096198 1.537749e-02 1.537749e-02 0.175002690 #> 3956  1977.5 0.8094034 0.8094034 1.542479e-02 1.542479e-02 0.175171832 #> 3957  1978.0 0.8091862 0.8091862 1.547215e-02 1.547215e-02 0.175341678 #> 3958  1978.5 0.8089682 0.8089682 1.551957e-02 1.551957e-02 0.175512229 #> 3959  1979.0 0.8087495 0.8087495 1.556705e-02 1.556705e-02 0.175683485 #> 3960  1979.5 0.8085300 0.8085300 1.561459e-02 1.561459e-02 0.175855447 #> 3961  1980.0 0.8083097 0.8083097 1.566219e-02 1.566219e-02 0.176028116 #> 3962  1980.5 0.8080887 0.8080887 1.570985e-02 1.570985e-02 0.176201493 #> 3963  1981.0 0.8078669 0.8078669 1.575757e-02 1.575757e-02 0.176375577 #> 3964  1981.5 0.8076443 0.8076443 1.580534e-02 1.580534e-02 0.176550370 #> 3965  1982.0 0.8074210 0.8074210 1.585318e-02 1.585318e-02 0.176725871 #> 3966  1982.5 0.8071969 0.8071969 1.590106e-02 1.590106e-02 0.176902083 #> 3967  1983.0 0.8069720 0.8069720 1.594900e-02 1.594900e-02 0.177079005 #> 3968  1983.5 0.8067464 0.8067464 1.599700e-02 1.599700e-02 0.177256638 #> 3969  1984.0 0.8065200 0.8065200 1.604505e-02 1.604505e-02 0.177434982 #> 3970  1984.5 0.8062928 0.8062928 1.609315e-02 1.609315e-02 0.177614038 #> 3971  1985.0 0.8060649 0.8060649 1.614131e-02 1.614131e-02 0.177793806 #> 3972  1985.5 0.8058362 0.8058362 1.618951e-02 1.618951e-02 0.177974288 #> 3973  1986.0 0.8056067 0.8056067 1.623777e-02 1.623777e-02 0.178155483 #> 3974  1986.5 0.8053765 0.8053765 1.628608e-02 1.628608e-02 0.178337391 #> 3975  1987.0 0.8051455 0.8051455 1.633444e-02 1.633444e-02 0.178520015 #> 3976  1987.5 0.8049138 0.8049138 1.638284e-02 1.638284e-02 0.178703353 #> 3977  1988.0 0.8046813 0.8046813 1.643130e-02 1.643130e-02 0.178887406 #> 3978  1988.5 0.8044480 0.8044480 1.647980e-02 1.647980e-02 0.179072175 #> 3979  1989.0 0.8042140 0.8042140 1.652835e-02 1.652835e-02 0.179257661 #> 3980  1989.5 0.8039792 0.8039792 1.657694e-02 1.657694e-02 0.179443863 #> 3981  1990.0 0.8037436 0.8037436 1.662558e-02 1.662558e-02 0.179630782 #> 3982  1990.5 0.8035073 0.8035073 1.667427e-02 1.667427e-02 0.179818418 #> 3983  1991.0 0.8032702 0.8032702 1.672299e-02 1.672299e-02 0.180006772 #> 3984  1991.5 0.8030324 0.8030324 1.677176e-02 1.677176e-02 0.180195845 #> 3985  1992.0 0.8027938 0.8027938 1.682058e-02 1.682058e-02 0.180385635 #> 3986  1992.5 0.8025544 0.8025544 1.686943e-02 1.686943e-02 0.180576145 #> 3987  1993.0 0.8023143 0.8023143 1.691832e-02 1.691832e-02 0.180767374 #> 3988  1993.5 0.8020734 0.8020734 1.696726e-02 1.696726e-02 0.180959322 #> 3989  1994.0 0.8018318 0.8018318 1.701623e-02 1.701623e-02 0.181151990 #> 3990  1994.5 0.8015894 0.8015894 1.706524e-02 1.706524e-02 0.181345378 #> 3991  1995.0 0.8013462 0.8013462 1.711429e-02 1.711429e-02 0.181539486 #> 3992  1995.5 0.8011023 0.8011023 1.716337e-02 1.716337e-02 0.181734315 #> 3993  1996.0 0.8008576 0.8008576 1.721249e-02 1.721249e-02 0.181929864 #> 3994  1996.5 0.8006122 0.8006122 1.726165e-02 1.726165e-02 0.182126135 #> 3995  1997.0 0.8003660 0.8003660 1.731084e-02 1.731084e-02 0.182323126 #> 3996  1997.5 0.8001191 0.8001191 1.736006e-02 1.736006e-02 0.182520839 #> 3997  1998.0 0.7998714 0.7998714 1.740931e-02 1.740931e-02 0.182719274 #> 3998  1998.5 0.7996230 0.7996230 1.745860e-02 1.745860e-02 0.182918431 #> 3999  1999.0 0.7993738 0.7993738 1.750792e-02 1.750792e-02 0.183118309 #> 4000  1999.5 0.7991238 0.7991238 1.755726e-02 1.755726e-02 0.183318910 #> 4001  2000.0 0.7988731 0.7988731 1.760664e-02 1.760664e-02 0.183520232 #> 4002  2000.5 0.7986217 0.7986217 1.765604e-02 1.765604e-02 0.183722277 #> 4003  2001.0 0.7983695 0.7983695 1.770547e-02 1.770547e-02 0.183925045 #> 4004  2001.5 0.7981165 0.7981165 1.775493e-02 1.775493e-02 0.184128535 #> 4005  2002.0 0.7978628 0.7978628 1.780441e-02 1.780441e-02 0.184332747 #> 4006  2002.5 0.7976084 0.7976084 1.785392e-02 1.785392e-02 0.184537683 #> 4007  2003.0 0.7973532 0.7973532 1.790345e-02 1.790345e-02 0.184743340 #> 4008  2003.5 0.7970973 0.7970973 1.795300e-02 1.795300e-02 0.184949721 #> 4009  2004.0 0.7968406 0.7968406 1.800258e-02 1.800258e-02 0.185156824 #> 4010  2004.5 0.7965832 0.7965832 1.805217e-02 1.805217e-02 0.185364650 #> 4011  2005.0 0.7963250 0.7963250 1.810179e-02 1.810179e-02 0.185573198 #> 4012  2005.5 0.7960661 0.7960661 1.815142e-02 1.815142e-02 0.185782470 #> 4013  2006.0 0.7958065 0.7958065 1.820108e-02 1.820108e-02 0.185992463 #> 4014  2006.5 0.7955461 0.7955461 1.825075e-02 1.825075e-02 0.186203180 #> 4015  2007.0 0.7952850 0.7952850 1.830043e-02 1.830043e-02 0.186414618 #> 4016  2007.5 0.7950231 0.7950231 1.835013e-02 1.835013e-02 0.186626779 #> 4017  2008.0 0.7947605 0.7947605 1.839985e-02 1.839985e-02 0.186839663 #> 4018  2008.5 0.7944972 0.7944972 1.844958e-02 1.844958e-02 0.187053268 #> 4019  2009.0 0.7942331 0.7942331 1.849932e-02 1.849932e-02 0.187267595 #> 4020  2009.5 0.7939683 0.7939683 1.854907e-02 1.854907e-02 0.187482644 #> 4021  2010.0 0.7937027 0.7937027 1.859884e-02 1.859884e-02 0.187698415 #> 4022  2010.5 0.7934365 0.7934365 1.864861e-02 1.864861e-02 0.187914907 #> 4023  2011.0 0.7931695 0.7931695 1.869839e-02 1.869839e-02 0.188132120 #> 4024  2011.5 0.7929018 0.7929018 1.874818e-02 1.874818e-02 0.188350055 #> 4025  2012.0 0.7926333 0.7926333 1.879798e-02 1.879798e-02 0.188568710 #> 4026  2012.5 0.7923641 0.7923641 1.884778e-02 1.884778e-02 0.188788085 #> 4027  2013.0 0.7920942 0.7920942 1.889759e-02 1.889759e-02 0.189008181 #> 4028  2013.5 0.7918236 0.7918236 1.894740e-02 1.894740e-02 0.189228996 #> 4029  2014.0 0.7915523 0.7915523 1.899721e-02 1.899721e-02 0.189450531 #> 4030  2014.5 0.7912802 0.7912802 1.904702e-02 1.904702e-02 0.189672786 #> 4031  2015.0 0.7910074 0.7910074 1.909684e-02 1.909684e-02 0.189895759 #> 4032  2015.5 0.7907339 0.7907339 1.914665e-02 1.914665e-02 0.190119451 #> 4033  2016.0 0.7904597 0.7904597 1.919646e-02 1.919646e-02 0.190343861 #> 4034  2016.5 0.7901847 0.7901847 1.924627e-02 1.924627e-02 0.190568989 #> 4035  2017.0 0.7899091 0.7899091 1.929608e-02 1.929608e-02 0.190794835 #> 4036  2017.5 0.7896327 0.7896327 1.934588e-02 1.934588e-02 0.191021397 #> 4037  2018.0 0.7893556 0.7893556 1.939568e-02 1.939568e-02 0.191248676 #> 4038  2018.5 0.7890779 0.7890779 1.944547e-02 1.944547e-02 0.191476671 #> 4039  2019.0 0.7887994 0.7887994 1.949525e-02 1.949525e-02 0.191705381 #> 4040  2019.5 0.7885202 0.7885202 1.954502e-02 1.954502e-02 0.191934807 #> 4041  2020.0 0.7882403 0.7882403 1.959478e-02 1.959478e-02 0.192164947 #> 4042  2020.5 0.7879597 0.7879597 1.964454e-02 1.964454e-02 0.192395801 #> 4043  2021.0 0.7876784 0.7876784 1.969428e-02 1.969428e-02 0.192627369 #> 4044  2021.5 0.7873963 0.7873963 1.974400e-02 1.974400e-02 0.192859649 #> 4045  2022.0 0.7871136 0.7871136 1.979372e-02 1.979372e-02 0.193092642 #> 4046  2022.5 0.7868302 0.7868302 1.984341e-02 1.984341e-02 0.193326347 #> 4047  2023.0 0.7865461 0.7865461 1.989310e-02 1.989310e-02 0.193560763 #> 4048  2023.5 0.7862614 0.7862614 1.994276e-02 1.994276e-02 0.193795889 #> 4049  2024.0 0.7859759 0.7859759 1.999241e-02 1.999241e-02 0.194031726 #> 4050  2024.5 0.7856897 0.7856897 2.004203e-02 2.004203e-02 0.194268271 #> 4051  2025.0 0.7854028 0.7854028 2.009164e-02 2.009164e-02 0.194505525 #> 4052  2025.5 0.7851153 0.7851153 2.014122e-02 2.014122e-02 0.194743486 #> 4053  2026.0 0.7848271 0.7848271 2.019078e-02 2.019078e-02 0.194982155 #> 4054  2026.5 0.7845382 0.7845382 2.024032e-02 2.024032e-02 0.195221530 #> 4055  2027.0 0.7842486 0.7842486 2.028983e-02 2.028983e-02 0.195461610 #> 4056  2027.5 0.7839583 0.7839583 2.033931e-02 2.033931e-02 0.195702396 #> 4057  2028.0 0.7836673 0.7836673 2.038877e-02 2.038877e-02 0.195943885 #> 4058  2028.5 0.7833757 0.7833757 2.043820e-02 2.043820e-02 0.196186077 #> 4059  2029.0 0.7830834 0.7830834 2.048760e-02 2.048760e-02 0.196428972 #> 4060  2029.5 0.7827905 0.7827905 2.053696e-02 2.053696e-02 0.196672568 #> 4061  2030.0 0.7824968 0.7824968 2.058630e-02 2.058630e-02 0.196916864 #> 4062  2030.5 0.7822025 0.7822025 2.063560e-02 2.063560e-02 0.197161860 #> 4063  2031.0 0.7819076 0.7819076 2.068487e-02 2.068487e-02 0.197407555 #> 4064  2031.5 0.7816119 0.7816119 2.073410e-02 2.073410e-02 0.197653948 #> 4065  2032.0 0.7813157 0.7813157 2.078330e-02 2.078330e-02 0.197901038 #> 4066  2032.5 0.7810187 0.7810187 2.083246e-02 2.083246e-02 0.198148824 #> 4067  2033.0 0.7807211 0.7807211 2.088158e-02 2.088158e-02 0.198397304 #> 4068  2033.5 0.7804229 0.7804229 2.093066e-02 2.093066e-02 0.198646479 #> 4069  2034.0 0.7801240 0.7801240 2.097970e-02 2.097970e-02 0.198896346 #> 4070  2034.5 0.7798244 0.7798244 2.102870e-02 2.102870e-02 0.199146905 #> 4071  2035.0 0.7795242 0.7795242 2.107765e-02 2.107765e-02 0.199398155 #> 4072  2035.5 0.7792233 0.7792233 2.112656e-02 2.112656e-02 0.199650095 #> 4073  2036.0 0.7789219 0.7789219 2.117542e-02 2.117542e-02 0.199902723 #> 4074  2036.5 0.7786197 0.7786197 2.122423e-02 2.122423e-02 0.200156039 #> 4075  2037.0 0.7783170 0.7783170 2.127300e-02 2.127300e-02 0.200410041 #> 4076  2037.5 0.7780136 0.7780136 2.132172e-02 2.132172e-02 0.200664729 #> 4077  2038.0 0.7777095 0.7777095 2.137038e-02 2.137038e-02 0.200920100 #> 4078  2038.5 0.7774048 0.7774048 2.141900e-02 2.141900e-02 0.201176154 #> 4079  2039.0 0.7770996 0.7770996 2.146756e-02 2.146756e-02 0.201432890 #> 4080  2039.5 0.7767936 0.7767936 2.151606e-02 2.151606e-02 0.201690306 #> 4081  2040.0 0.7764871 0.7764871 2.156452e-02 2.156452e-02 0.201948402 #> 4082  2040.5 0.7761799 0.7761799 2.161291e-02 2.161291e-02 0.202207175 #> 4083  2041.0 0.7758721 0.7758721 2.166125e-02 2.166125e-02 0.202466624 #> 4084  2041.5 0.7755637 0.7755637 2.170953e-02 2.170953e-02 0.202726749 #> 4085  2042.0 0.7752547 0.7752547 2.175774e-02 2.175774e-02 0.202987548 #> 4086  2042.5 0.7749451 0.7749451 2.180590e-02 2.180590e-02 0.203249019 #> 4087  2043.0 0.7746348 0.7746348 2.185399e-02 2.185399e-02 0.203511162 #> 4088  2043.5 0.7743240 0.7743240 2.190202e-02 2.190202e-02 0.203773974 #> 4089  2044.0 0.7740126 0.7740126 2.194999e-02 2.194999e-02 0.204037455 #> 4090  2044.5 0.7737005 0.7737005 2.199788e-02 2.199788e-02 0.204301602 #> 4091  2045.0 0.7733879 0.7733879 2.204571e-02 2.204571e-02 0.204566415 #> 4092  2045.5 0.7730746 0.7730746 2.209347e-02 2.209347e-02 0.204831893 #> 4093  2046.0 0.7727608 0.7727608 2.214117e-02 2.214117e-02 0.205098032 #> 4094  2046.5 0.7724464 0.7724464 2.218879e-02 2.218879e-02 0.205364833 #> 4095  2047.0 0.7721314 0.7721314 2.223633e-02 2.223633e-02 0.205632293 #> 4096  2047.5 0.7718158 0.7718158 2.228381e-02 2.228381e-02 0.205900411 #> 4097  2048.0 0.7714996 0.7714996 2.233120e-02 2.233120e-02 0.206169186 #> 4098  2048.5 0.7711829 0.7711829 2.237853e-02 2.237853e-02 0.206438615 #> 4099  2049.0 0.7708655 0.7708655 2.242577e-02 2.242577e-02 0.206708697 #> 4100  2049.5 0.7705476 0.7705476 2.247294e-02 2.247294e-02 0.206979431 #> 4101  2050.0 0.7702292 0.7702292 2.252002e-02 2.252002e-02 0.207250815 #> 4102  2050.5 0.7699101 0.7699101 2.256703e-02 2.256703e-02 0.207522847 #> 4103  2051.0 0.7695905 0.7695905 2.261395e-02 2.261395e-02 0.207795526 #> 4104  2051.5 0.7692704 0.7692704 2.266079e-02 2.266079e-02 0.208068850 #> 4105  2052.0 0.7689496 0.7689496 2.270754e-02 2.270754e-02 0.208342816 #> 4106  2052.5 0.7686284 0.7686284 2.275421e-02 2.275421e-02 0.208617424 #> 4107  2053.0 0.7683065 0.7683065 2.280079e-02 2.280079e-02 0.208892672 #> 4108  2053.5 0.7679842 0.7679842 2.284728e-02 2.284728e-02 0.209168557 #> 4109  2054.0 0.7676612 0.7676612 2.289369e-02 2.289369e-02 0.209445079 #> 4110  2054.5 0.7673378 0.7673378 2.294000e-02 2.294000e-02 0.209722235 #> 4111  2055.0 0.7670138 0.7670138 2.298622e-02 2.298622e-02 0.210000023 #> 4112  2055.5 0.7666892 0.7666892 2.303234e-02 2.303234e-02 0.210278441 #> 4113  2056.0 0.7663641 0.7663641 2.307837e-02 2.307837e-02 0.210557488 #> 4114  2056.5 0.7660385 0.7660385 2.312430e-02 2.312430e-02 0.210837162 #> 4115  2057.0 0.7657124 0.7657124 2.317014e-02 2.317014e-02 0.211117461 #> 4116  2057.5 0.7653857 0.7653857 2.321588e-02 2.321588e-02 0.211398382 #> 4117  2058.0 0.7650586 0.7650586 2.326152e-02 2.326152e-02 0.211679924 #> 4118  2058.5 0.7647309 0.7647309 2.330705e-02 2.330705e-02 0.211962085 #> 4119  2059.0 0.7644026 0.7644026 2.335249e-02 2.335249e-02 0.212244863 #> 4120  2059.5 0.7640739 0.7640739 2.339782e-02 2.339782e-02 0.212528256 #> 4121  2060.0 0.7637447 0.7637447 2.344305e-02 2.344305e-02 0.212812262 #> 4122  2060.5 0.7634150 0.7634150 2.348817e-02 2.348817e-02 0.213096878 #> 4123  2061.0 0.7630847 0.7630847 2.353318e-02 2.353318e-02 0.213382103 #> 4124  2061.5 0.7627540 0.7627540 2.357808e-02 2.357808e-02 0.213667934 #> 4125  2062.0 0.7624228 0.7624228 2.362288e-02 2.362288e-02 0.213954370 #> 4126  2062.5 0.7620910 0.7620910 2.366756e-02 2.366756e-02 0.214241409 #> 4127  2063.0 0.7617588 0.7617588 2.371213e-02 2.371213e-02 0.214529047 #> 4128  2063.5 0.7614261 0.7614261 2.375659e-02 2.375659e-02 0.214817283 #> 4129  2064.0 0.7610929 0.7610929 2.380094e-02 2.380094e-02 0.215106115 #> 4130  2064.5 0.7607593 0.7607593 2.384516e-02 2.384516e-02 0.215395541 #> 4131  2065.0 0.7604252 0.7604252 2.388927e-02 2.388927e-02 0.215685558 #> 4132  2065.5 0.7600906 0.7600906 2.393326e-02 2.393326e-02 0.215976164 #> 4133  2066.0 0.7597555 0.7597555 2.397714e-02 2.397714e-02 0.216267356 #> 4134  2066.5 0.7594200 0.7594200 2.402089e-02 2.402089e-02 0.216559133 #> 4135  2067.0 0.7590840 0.7590840 2.406452e-02 2.406452e-02 0.216851493 #> 4136  2067.5 0.7587475 0.7587475 2.410802e-02 2.410802e-02 0.217144432 #> 4137  2068.0 0.7584107 0.7584107 2.415140e-02 2.415140e-02 0.217437948 #> 4138  2068.5 0.7580733 0.7580733 2.419466e-02 2.419466e-02 0.217732040 #> 4139  2069.0 0.7577355 0.7577355 2.423778e-02 2.423778e-02 0.218026704 #> 4140  2069.5 0.7573973 0.7573973 2.428078e-02 2.428078e-02 0.218321938 #> 4141  2070.0 0.7570586 0.7570586 2.432365e-02 2.432365e-02 0.218617740 #> 4142  2070.5 0.7567195 0.7567195 2.436639e-02 2.436639e-02 0.218914108 #> 4143  2071.0 0.7563800 0.7563800 2.440900e-02 2.440900e-02 0.219211039 #> 4144  2071.5 0.7560400 0.7560400 2.445147e-02 2.445147e-02 0.219508530 #> 4145  2072.0 0.7556996 0.7556996 2.449381e-02 2.449381e-02 0.219806578 #> 4146  2072.5 0.7553588 0.7553588 2.453602e-02 2.453602e-02 0.220105183 #> 4147  2073.0 0.7550176 0.7550176 2.457808e-02 2.457808e-02 0.220404340 #> 4148  2073.5 0.7546759 0.7546759 2.462001e-02 2.462001e-02 0.220704047 #> 4149  2074.0 0.7543339 0.7543339 2.466180e-02 2.466180e-02 0.221004302 #> 4150  2074.5 0.7539914 0.7539914 2.470345e-02 2.470345e-02 0.221305102 #> 4151  2075.0 0.7536486 0.7536486 2.474496e-02 2.474496e-02 0.221606444 #> 4152  2075.5 0.7533053 0.7533053 2.478633e-02 2.478633e-02 0.221908326 #> 4153  2076.0 0.7529617 0.7529617 2.482755e-02 2.482755e-02 0.222210746 #> 4154  2076.5 0.7526177 0.7526177 2.486862e-02 2.486862e-02 0.222513700 #> 4155  2077.0 0.7522733 0.7522733 2.490955e-02 2.490955e-02 0.222817185 #> 4156  2077.5 0.7519285 0.7519285 2.495033e-02 2.495033e-02 0.223121200 #> 4157  2078.0 0.7515833 0.7515833 2.499097e-02 2.499097e-02 0.223425741 #> 4158  2078.5 0.7512377 0.7512377 2.503145e-02 2.503145e-02 0.223730805 #> 4159  2079.0 0.7508918 0.7508918 2.507178e-02 2.507178e-02 0.224036391 #> 4160  2079.5 0.7505455 0.7505455 2.511196e-02 2.511196e-02 0.224342494 #> 4161  2080.0 0.7501989 0.7501989 2.515199e-02 2.515199e-02 0.224649113 #> 4162  2080.5 0.7498519 0.7498519 2.519186e-02 2.519186e-02 0.224956244 #> 4163  2081.0 0.7495045 0.7495045 2.523157e-02 2.523157e-02 0.225263884 #> 4164  2081.5 0.7491568 0.7491568 2.527113e-02 2.527113e-02 0.225572031 #> 4165  2082.0 0.7488088 0.7488088 2.531053e-02 2.531053e-02 0.225880682 #> 4166  2082.5 0.7484604 0.7484604 2.534978e-02 2.534978e-02 0.226189834 #> 4167  2083.0 0.7481117 0.7481117 2.538886e-02 2.538886e-02 0.226499484 #> 4168  2083.5 0.7477626 0.7477626 2.542778e-02 2.542778e-02 0.226809629 #> 4169  2084.0 0.7474132 0.7474132 2.546653e-02 2.546653e-02 0.227120266 #> 4170  2084.5 0.7470635 0.7470635 2.550513e-02 2.550513e-02 0.227431392 #> 4171  2085.0 0.7467134 0.7467134 2.554355e-02 2.554355e-02 0.227743004 #> 4172  2085.5 0.7463631 0.7463631 2.558182e-02 2.558182e-02 0.228055100 #> 4173  2086.0 0.7460124 0.7460124 2.561991e-02 2.561991e-02 0.228367675 #> 4174  2086.5 0.7456614 0.7456614 2.565784e-02 2.565784e-02 0.228680728 #> 4175  2087.0 0.7453102 0.7453102 2.569559e-02 2.569559e-02 0.228994255 #> 4176  2087.5 0.7449586 0.7449586 2.573318e-02 2.573318e-02 0.229308253 #> 4177  2088.0 0.7446067 0.7446067 2.577060e-02 2.577060e-02 0.229622718 #> 4178  2088.5 0.7442545 0.7442545 2.580784e-02 2.580784e-02 0.229937649 #> 4179  2089.0 0.7439021 0.7439021 2.584491e-02 2.584491e-02 0.230253041 #> 4180  2089.5 0.7435493 0.7435493 2.588180e-02 2.588180e-02 0.230568892 #> 4181  2090.0 0.7431963 0.7431963 2.591852e-02 2.591852e-02 0.230885198 #> 4182  2090.5 0.7428430 0.7428430 2.595506e-02 2.595506e-02 0.231201956 #> 4183  2091.0 0.7424894 0.7424894 2.599142e-02 2.599142e-02 0.231519163 #> 4184  2091.5 0.7421356 0.7421356 2.602760e-02 2.602760e-02 0.231836816 #> 4185  2092.0 0.7417815 0.7417815 2.606361e-02 2.606361e-02 0.232154912 #> 4186  2092.5 0.7414271 0.7414271 2.609943e-02 2.609943e-02 0.232473447 #> 4187  2093.0 0.7410725 0.7410725 2.613507e-02 2.613507e-02 0.232792418 #> 4188  2093.5 0.7407177 0.7407177 2.617052e-02 2.617052e-02 0.233111822 #> 4189  2094.0 0.7403626 0.7403626 2.620579e-02 2.620579e-02 0.233431656 #> 4190  2094.5 0.7400072 0.7400072 2.624088e-02 2.624088e-02 0.233751916 #> 4191  2095.0 0.7396516 0.7396516 2.627578e-02 2.627578e-02 0.234072599 #> 4192  2095.5 0.7392958 0.7392958 2.631049e-02 2.631049e-02 0.234393702 #> 4193  2096.0 0.7389398 0.7389398 2.634501e-02 2.634501e-02 0.234715221 #> 4194  2096.5 0.7385835 0.7385835 2.637935e-02 2.637935e-02 0.235037152 #> 4195  2097.0 0.7382270 0.7382270 2.641349e-02 2.641349e-02 0.235359494 #> 4196  2097.5 0.7378703 0.7378703 2.644744e-02 2.644744e-02 0.235682241 #> 4197  2098.0 0.7375134 0.7375134 2.648120e-02 2.648120e-02 0.236005392 #> 4198  2098.5 0.7371563 0.7371563 2.651476e-02 2.651476e-02 0.236328941 #> 4199  2099.0 0.7367990 0.7367990 2.654813e-02 2.654813e-02 0.236652887 #> 4200  2099.5 0.7364415 0.7364415 2.658131e-02 2.658131e-02 0.236977225 #> 4201  2100.0 0.7360838 0.7360838 2.661429e-02 2.661429e-02 0.237301952 #> 4202  2100.5 0.7357259 0.7357259 2.664707e-02 2.664707e-02 0.237627065 #> 4203  2101.0 0.7353678 0.7353678 2.667965e-02 2.667965e-02 0.237952560 #> 4204  2101.5 0.7350095 0.7350095 2.671203e-02 2.671203e-02 0.238278433 #> 4205  2102.0 0.7346511 0.7346511 2.674422e-02 2.674422e-02 0.238604681 #> 4206  2102.5 0.7342925 0.7342925 2.677620e-02 2.677620e-02 0.238931301 #> 4207  2103.0 0.7339337 0.7339337 2.680798e-02 2.680798e-02 0.239258289 #> 4208  2103.5 0.7335748 0.7335748 2.683956e-02 2.683956e-02 0.239585641 #> 4209  2104.0 0.7332157 0.7332157 2.687093e-02 2.687093e-02 0.239913354 #> 4210  2104.5 0.7328565 0.7328565 2.690210e-02 2.690210e-02 0.240241424 #> 4211  2105.0 0.7324971 0.7324971 2.693306e-02 2.693306e-02 0.240569848 #> 4212  2105.5 0.7321376 0.7321376 2.696381e-02 2.696381e-02 0.240898622 #> 4213  2106.0 0.7317779 0.7317779 2.699436e-02 2.699436e-02 0.241227742 #> 4214  2106.5 0.7314181 0.7314181 2.702470e-02 2.702470e-02 0.241557205 #> 4215  2107.0 0.7310582 0.7310582 2.705483e-02 2.705483e-02 0.241887007 #> 4216  2107.5 0.7306981 0.7306981 2.708475e-02 2.708475e-02 0.242217144 #> 4217  2108.0 0.7303379 0.7303379 2.711446e-02 2.711446e-02 0.242547613 #> 4218  2108.5 0.7299776 0.7299776 2.714396e-02 2.714396e-02 0.242878411 #> 4219  2109.0 0.7296172 0.7296172 2.717324e-02 2.717324e-02 0.243209532 #> 4220  2109.5 0.7292567 0.7292567 2.720231e-02 2.720231e-02 0.243540975 #> 4221  2110.0 0.7288961 0.7288961 2.723117e-02 2.723117e-02 0.243872734 #> 4222  2110.5 0.7285354 0.7285354 2.725981e-02 2.725981e-02 0.244204806 #> 4223  2111.0 0.7281746 0.7281746 2.728823e-02 2.728823e-02 0.244537188 #> 4224  2111.5 0.7278137 0.7278137 2.731644e-02 2.731644e-02 0.244869876 #> 4225  2112.0 0.7274527 0.7274527 2.734443e-02 2.734443e-02 0.245202865 #> 4226  2112.5 0.7270916 0.7270916 2.737221e-02 2.737221e-02 0.245536153 #> 4227  2113.0 0.7267305 0.7267305 2.739976e-02 2.739976e-02 0.245869734 #> 4228  2113.5 0.7263693 0.7263693 2.742709e-02 2.742709e-02 0.246203607 #> 4229  2114.0 0.7260080 0.7260080 2.745421e-02 2.745421e-02 0.246537766 #> 4230  2114.5 0.7256467 0.7256467 2.748110e-02 2.748110e-02 0.246872208 #> 4231  2115.0 0.7252853 0.7252853 2.750777e-02 2.750777e-02 0.247206929 #> 4232  2115.5 0.7249239 0.7249239 2.753421e-02 2.753421e-02 0.247541925 #> 4233  2116.0 0.7245624 0.7245624 2.756044e-02 2.756044e-02 0.247877193 #> 4234  2116.5 0.7242008 0.7242008 2.758643e-02 2.758643e-02 0.248212728 #> 4235  2117.0 0.7238393 0.7238393 2.761221e-02 2.761221e-02 0.248548527 #> 4236  2117.5 0.7234777 0.7234777 2.763775e-02 2.763775e-02 0.248884585 #> 4237  2118.0 0.7231160 0.7231160 2.766308e-02 2.766308e-02 0.249220899 #> 4238  2118.5 0.7227544 0.7227544 2.768817e-02 2.768817e-02 0.249557465 #> 4239  2119.0 0.7223927 0.7223927 2.771303e-02 2.771303e-02 0.249894280 #> 4240  2119.5 0.7220310 0.7220310 2.773767e-02 2.773767e-02 0.250231338 #> 4241  2120.0 0.7216693 0.7216693 2.776208e-02 2.776208e-02 0.250568636 #> 4242  2120.5 0.7213076 0.7213076 2.778626e-02 2.778626e-02 0.250906170 #> 4243  2121.0 0.7209459 0.7209459 2.781021e-02 2.781021e-02 0.251243937 #> 4244  2121.5 0.7205841 0.7205841 2.783392e-02 2.783392e-02 0.251581932 #> 4245  2122.0 0.7202224 0.7202224 2.785741e-02 2.785741e-02 0.251920151 #> 4246  2122.5 0.7198607 0.7198607 2.788066e-02 2.788066e-02 0.252258591 #> 4247  2123.0 0.7194991 0.7194991 2.790368e-02 2.790368e-02 0.252597247 #> 4248  2123.5 0.7191374 0.7191374 2.792647e-02 2.792647e-02 0.252936115 #> 4249  2124.0 0.7187758 0.7187758 2.794902e-02 2.794902e-02 0.253275192 #> 4250  2124.5 0.7184142 0.7184142 2.797134e-02 2.797134e-02 0.253614473 #> 4251  2125.0 0.7180526 0.7180526 2.799342e-02 2.799342e-02 0.253953954 #> 4252  2125.5 0.7176911 0.7176911 2.801527e-02 2.801527e-02 0.254293632 #> 4253  2126.0 0.7173296 0.7173296 2.803688e-02 2.803688e-02 0.254633501 #> 4254  2126.5 0.7169682 0.7169682 2.805826e-02 2.805826e-02 0.254973559 #> 4255  2127.0 0.7166068 0.7166068 2.807939e-02 2.807939e-02 0.255313802 #> 4256  2127.5 0.7162455 0.7162455 2.810029e-02 2.810029e-02 0.255654224 #> 4257  2128.0 0.7158842 0.7158842 2.812095e-02 2.812095e-02 0.255994822 #> 4258  2128.5 0.7155230 0.7155230 2.814137e-02 2.814137e-02 0.256335593 #> 4259  2129.0 0.7151619 0.7151619 2.816155e-02 2.816155e-02 0.256676531 #> 4260  2129.5 0.7148009 0.7148009 2.818150e-02 2.818150e-02 0.257017633 #> 4261  2130.0 0.7144399 0.7144399 2.820120e-02 2.820120e-02 0.257358895 #> 4262  2130.5 0.7140790 0.7140790 2.822066e-02 2.822066e-02 0.257700313 #> 4263  2131.0 0.7137182 0.7137182 2.823988e-02 2.823988e-02 0.258041882 #> 4264  2131.5 0.7133575 0.7133575 2.825885e-02 2.825885e-02 0.258383599 #> 4265  2132.0 0.7129970 0.7129970 2.827759e-02 2.827759e-02 0.258725459 #> 4266  2132.5 0.7126365 0.7126365 2.829608e-02 2.829608e-02 0.259067458 #> 4267  2133.0 0.7122761 0.7122761 2.831433e-02 2.831433e-02 0.259409593 #> 4268  2133.5 0.7119158 0.7119158 2.833234e-02 2.833234e-02 0.259751859 #> 4269  2134.0 0.7115557 0.7115557 2.835010e-02 2.835010e-02 0.260094251 #> 4270  2134.5 0.7111956 0.7111956 2.836762e-02 2.836762e-02 0.260436766 #> 4271  2135.0 0.7108357 0.7108357 2.838489e-02 2.838489e-02 0.260779400 #> 4272  2135.5 0.7104759 0.7104759 2.840192e-02 2.840192e-02 0.261122148 #> 4273  2136.0 0.7101163 0.7101163 2.841870e-02 2.841870e-02 0.261465007 #> 4274  2136.5 0.7097568 0.7097568 2.843524e-02 2.843524e-02 0.261807972 #> 4275  2137.0 0.7093974 0.7093974 2.845153e-02 2.845153e-02 0.262151039 #> 4276  2137.5 0.7090382 0.7090382 2.846757e-02 2.846757e-02 0.262494204 #> 4277  2138.0 0.7086792 0.7086792 2.848337e-02 2.848337e-02 0.262837463 #> 4278  2138.5 0.7083203 0.7083203 2.849892e-02 2.849892e-02 0.263180811 #> 4279  2139.0 0.7079615 0.7079615 2.851423e-02 2.851423e-02 0.263524245 #> 4280  2139.5 0.7076030 0.7076030 2.852928e-02 2.852928e-02 0.263867760 #> 4281  2140.0 0.7072446 0.7072446 2.854409e-02 2.854409e-02 0.264211352 #> 4282  2140.5 0.7068863 0.7068863 2.855865e-02 2.855865e-02 0.264555017 #> 4283  2141.0 0.7065283 0.7065283 2.857297e-02 2.857297e-02 0.264898750 #> 4284  2141.5 0.7061704 0.7061704 2.858703e-02 2.858703e-02 0.265242549 #> 4285  2142.0 0.7058127 0.7058127 2.860084e-02 2.860084e-02 0.265586407 #> 4286  2142.5 0.7054553 0.7054553 2.861441e-02 2.861441e-02 0.265930322 #> 4287  2143.0 0.7050980 0.7050980 2.862772e-02 2.862772e-02 0.266274289 #> 4288  2143.5 0.7047409 0.7047409 2.864079e-02 2.864079e-02 0.266618304 #> 4289  2144.0 0.7043840 0.7043840 2.865361e-02 2.865361e-02 0.266962363 #> 4290  2144.5 0.7040274 0.7040274 2.866617e-02 2.866617e-02 0.267306460 #> 4291  2145.0 0.7036709 0.7036709 2.867849e-02 2.867849e-02 0.267650594 #> 4292  2145.5 0.7033147 0.7033147 2.869056e-02 2.869056e-02 0.267994758 #> 4293  2146.0 0.7029587 0.7029587 2.870237e-02 2.870237e-02 0.268338949 #> 4294  2146.5 0.7026029 0.7026029 2.871394e-02 2.871394e-02 0.268683164 #> 4295  2147.0 0.7022474 0.7022474 2.872525e-02 2.872525e-02 0.269027396 #> 4296  2147.5 0.7018920 0.7018920 2.873632e-02 2.873632e-02 0.269371643 #> 4297  2148.0 0.7015370 0.7015370 2.874713e-02 2.874713e-02 0.269715900 #> 4298  2148.5 0.7011821 0.7011821 2.875769e-02 2.875769e-02 0.270060163 #> 4299  2149.0 0.7008276 0.7008276 2.876800e-02 2.876800e-02 0.270404428 #> 4300  2149.5 0.7004733 0.7004733 2.877806e-02 2.877806e-02 0.270748691 #> 4301  2150.0 0.7001192 0.7001192 2.878787e-02 2.878787e-02 0.271092947 #> 4302  2150.5 0.6997654 0.6997654 2.879742e-02 2.879742e-02 0.271437192 #> 4303  2151.0 0.6994119 0.6994119 2.880673e-02 2.880673e-02 0.271781422 #> 4304  2151.5 0.6990586 0.6990586 2.881578e-02 2.881578e-02 0.272125633 #> 4305  2152.0 0.6987056 0.6987056 2.882458e-02 2.882458e-02 0.272469821 #> 4306  2152.5 0.6983529 0.6983529 2.883313e-02 2.883313e-02 0.272813982 #> 4307  2153.0 0.6980005 0.6980005 2.884143e-02 2.884143e-02 0.273158110 #> 4308  2153.5 0.6976483 0.6976483 2.884947e-02 2.884947e-02 0.273502203 #> 4309  2154.0 0.6972965 0.6972965 2.885727e-02 2.885727e-02 0.273846256 #> 4310  2154.5 0.6969449 0.6969449 2.886481e-02 2.886481e-02 0.274190264 #> 4311  2155.0 0.6965937 0.6965937 2.887210e-02 2.887210e-02 0.274534224 #> 4312  2155.5 0.6962427 0.6962427 2.887914e-02 2.887914e-02 0.274878132 #> 4313  2156.0 0.6958921 0.6958921 2.888593e-02 2.888593e-02 0.275221983 #> 4314  2156.5 0.6955418 0.6955418 2.889247e-02 2.889247e-02 0.275565773 #> 4315  2157.0 0.6951918 0.6951918 2.889875e-02 2.889875e-02 0.275909498 #> 4316  2157.5 0.6948421 0.6948421 2.890478e-02 2.890478e-02 0.276253154 #> 4317  2158.0 0.6944927 0.6944927 2.891057e-02 2.891057e-02 0.276596736 #> 4318  2158.5 0.6941437 0.6941437 2.891610e-02 2.891610e-02 0.276940241 #> 4319  2159.0 0.6937950 0.6937950 2.892138e-02 2.892138e-02 0.277283665 #> 4320  2159.5 0.6934466 0.6934466 2.892641e-02 2.892641e-02 0.277627002 #> 4321  2160.0 0.6930986 0.6930986 2.893118e-02 2.893118e-02 0.277970250 #> 4322  2160.5 0.6927509 0.6927509 2.893571e-02 2.893571e-02 0.278313403 #> 4323  2161.0 0.6924036 0.6924036 2.893999e-02 2.893999e-02 0.278656459 #> 4324  2161.5 0.6920566 0.6920566 2.894401e-02 2.894401e-02 0.278999412 #> 4325  2162.0 0.6917100 0.6917100 2.894779e-02 2.894779e-02 0.279342258 #> 4326  2162.5 0.6913637 0.6913637 2.895131e-02 2.895131e-02 0.279684994 #> 4327  2163.0 0.6910178 0.6910178 2.895458e-02 2.895458e-02 0.280027616 #> 4328  2163.5 0.6906723 0.6906723 2.895761e-02 2.895761e-02 0.280370118 #> 4329  2164.0 0.6903271 0.6903271 2.896038e-02 2.896038e-02 0.280712498 #> 4330  2164.5 0.6899823 0.6899823 2.896291e-02 2.896291e-02 0.281054750 #> 4331  2165.0 0.6896379 0.6896379 2.896519e-02 2.896519e-02 0.281396872 #> 4332  2165.5 0.6892939 0.6892939 2.896721e-02 2.896721e-02 0.281738858 #> 4333  2166.0 0.6889503 0.6889503 2.896899e-02 2.896899e-02 0.282080705 #> 4334  2166.5 0.6886071 0.6886071 2.897052e-02 2.897052e-02 0.282422409 #> 4335  2167.0 0.6882642 0.6882642 2.897180e-02 2.897180e-02 0.282763966 #> 4336  2167.5 0.6879218 0.6879218 2.897284e-02 2.897284e-02 0.283105371 #> 4337  2168.0 0.6875798 0.6875798 2.897362e-02 2.897362e-02 0.283446620 #> 4338  2168.5 0.6872381 0.6872381 2.897416e-02 2.897416e-02 0.283787710 #> 4339  2169.0 0.6868969 0.6868969 2.897445e-02 2.897445e-02 0.284128636 #> 4340  2169.5 0.6865561 0.6865561 2.897449e-02 2.897449e-02 0.284469395 #> 4341  2170.0 0.6862157 0.6862157 2.897429e-02 2.897429e-02 0.284809982 #> 4342  2170.5 0.6858758 0.6858758 2.897384e-02 2.897384e-02 0.285150393 #> 4343  2171.0 0.6855362 0.6855362 2.897314e-02 2.897314e-02 0.285490625 #> 4344  2171.5 0.6851971 0.6851971 2.897220e-02 2.897220e-02 0.285830673 #> 4345  2172.0 0.6848585 0.6848585 2.897101e-02 2.897101e-02 0.286170533 #> 4346  2172.5 0.6845202 0.6845202 2.896958e-02 2.896958e-02 0.286510201 #> 4347  2173.0 0.6841824 0.6841824 2.896791e-02 2.896791e-02 0.286849674 #> 4348  2173.5 0.6838451 0.6838451 2.896598e-02 2.896598e-02 0.287188947 #> 4349  2174.0 0.6835082 0.6835082 2.896382e-02 2.896382e-02 0.287528017 #> 4350  2174.5 0.6831717 0.6831717 2.896141e-02 2.896141e-02 0.287866879 #> 4351  2175.0 0.6828357 0.6828357 2.895876e-02 2.895876e-02 0.288205529 #> 4352  2175.5 0.6825002 0.6825002 2.895586e-02 2.895586e-02 0.288543964 #> 4353  2176.0 0.6821651 0.6821651 2.895273e-02 2.895273e-02 0.288882179 #> 4354  2176.5 0.6818305 0.6818305 2.894935e-02 2.894935e-02 0.289220171 #> 4355  2177.0 0.6814963 0.6814963 2.894573e-02 2.894573e-02 0.289557936 #> 4356  2177.5 0.6811627 0.6811627 2.894186e-02 2.894186e-02 0.289895469 #> 4357  2178.0 0.6808295 0.6808295 2.893776e-02 2.893776e-02 0.290232768 #> 4358  2178.5 0.6804968 0.6804968 2.893342e-02 2.893342e-02 0.290569827 #> 4359  2179.0 0.6801645 0.6801645 2.892884e-02 2.892884e-02 0.290906644 #> 4360  2179.5 0.6798328 0.6798328 2.892401e-02 2.892401e-02 0.291243214 #> 4361  2180.0 0.6795015 0.6795015 2.891895e-02 2.891895e-02 0.291579533 #> 4362  2180.5 0.6791707 0.6791707 2.891365e-02 2.891365e-02 0.291915598 #> 4363  2181.0 0.6788405 0.6788405 2.890812e-02 2.890812e-02 0.292251405 #> 4364  2181.5 0.6785107 0.6785107 2.890234e-02 2.890234e-02 0.292586949 #> 4365  2182.0 0.6781814 0.6781814 2.889633e-02 2.889633e-02 0.292922228 #> 4366  2182.5 0.6778527 0.6778527 2.889008e-02 2.889008e-02 0.293257237 #> 4367  2183.0 0.6775244 0.6775244 2.888360e-02 2.888360e-02 0.293591972 #> 4368  2183.5 0.6771967 0.6771967 2.887688e-02 2.887688e-02 0.293926430 #> 4369  2184.0 0.6768695 0.6768695 2.886993e-02 2.886993e-02 0.294260607 #> 4370  2184.5 0.6765428 0.6765428 2.886274e-02 2.886274e-02 0.294594499 #> 4371  2185.0 0.6762166 0.6762166 2.885532e-02 2.885532e-02 0.294928103 #> 4372  2185.5 0.6758909 0.6758909 2.884766e-02 2.884766e-02 0.295261414 #> 4373  2186.0 0.6755658 0.6755658 2.883977e-02 2.883977e-02 0.295594429 #> 4374  2186.5 0.6752412 0.6752412 2.883165e-02 2.883165e-02 0.295927144 #> 4375  2187.0 0.6749171 0.6749171 2.882330e-02 2.882330e-02 0.296259555 #> 4376  2187.5 0.6745936 0.6745936 2.881472e-02 2.881472e-02 0.296591660 #> 4377  2188.0 0.6742706 0.6742706 2.880591e-02 2.880591e-02 0.296923453 #> 4378  2188.5 0.6739482 0.6739482 2.879687e-02 2.879687e-02 0.297254932 #> 4379  2189.0 0.6736263 0.6736263 2.878760e-02 2.878760e-02 0.297586093 #> 4380  2189.5 0.6733050 0.6733050 2.877810e-02 2.877810e-02 0.297916931 #> 4381  2190.0 0.6729842 0.6729842 2.876837e-02 2.876837e-02 0.298247444 #> 4382  2190.5 0.6726640 0.6726640 2.875842e-02 2.875842e-02 0.298577628 #> 4383  2191.0 0.6723443 0.6723443 2.874824e-02 2.874824e-02 0.298907479 #> 4384  2191.5 0.6720252 0.6720252 2.873783e-02 2.873783e-02 0.299236994 #> 4385  2192.0 0.6717066 0.6717066 2.872720e-02 2.872720e-02 0.299566169 #> 4386  2192.5 0.6713887 0.6713887 2.871634e-02 2.871634e-02 0.299895000 #> 4387  2193.0 0.6710713 0.6710713 2.870526e-02 2.870526e-02 0.300223484 #> 4388  2193.5 0.6707544 0.6707544 2.869396e-02 2.869396e-02 0.300551617 #> 4389  2194.0 0.6704382 0.6704382 2.868243e-02 2.868243e-02 0.300879397 #> 4390  2194.5 0.6701225 0.6701225 2.867068e-02 2.867068e-02 0.301206818 #> 4391  2195.0 0.6698074 0.6698074 2.865872e-02 2.865872e-02 0.301533878 #> 4392  2195.5 0.6694929 0.6694929 2.864653e-02 2.864653e-02 0.301860573 #> 4393  2196.0 0.6691790 0.6691790 2.863412e-02 2.863412e-02 0.302186901 #> 4394  2196.5 0.6688657 0.6688657 2.862149e-02 2.862149e-02 0.302512856 #> 4395  2197.0 0.6685529 0.6685529 2.860864e-02 2.860864e-02 0.302838436 #> 4396  2197.5 0.6682408 0.6682408 2.859558e-02 2.859558e-02 0.303163638 #> 4397  2198.0 0.6679292 0.6679292 2.858230e-02 2.858230e-02 0.303488457 #> 4398  2198.5 0.6676183 0.6676183 2.856880e-02 2.856880e-02 0.303812891 #> 4399  2199.0 0.6673080 0.6673080 2.855509e-02 2.855509e-02 0.304136937 #> 4400  2199.5 0.6669982 0.6669982 2.854116e-02 2.854116e-02 0.304460590 #> 4401  2200.0 0.6666891 0.6666891 2.852702e-02 2.852702e-02 0.304783847 #> 4402  2200.5 0.6663806 0.6663806 2.851267e-02 2.851267e-02 0.305106705 #> 4403  2201.0 0.6660727 0.6660727 2.849810e-02 2.849810e-02 0.305429162 #> 4404  2201.5 0.6657655 0.6657655 2.848332e-02 2.848332e-02 0.305751212 #> 4405  2202.0 0.6654588 0.6654588 2.846833e-02 2.846833e-02 0.306072853 #> 4406  2202.5 0.6651528 0.6651528 2.845314e-02 2.845314e-02 0.306394082 #> 4407  2203.0 0.6648474 0.6648474 2.843773e-02 2.843773e-02 0.306714896 #> 4408  2203.5 0.6645426 0.6645426 2.842211e-02 2.842211e-02 0.307035291 #> 4409  2204.0 0.6642384 0.6642384 2.840629e-02 2.840629e-02 0.307355263 #> 4410  2204.5 0.6639349 0.6639349 2.839026e-02 2.839026e-02 0.307674811 #> 4411  2205.0 0.6636321 0.6636321 2.837402e-02 2.837402e-02 0.307993929 #> 4412  2205.5 0.6633298 0.6633298 2.835758e-02 2.835758e-02 0.308312616 #> 4413  2206.0 0.6630282 0.6630282 2.834093e-02 2.834093e-02 0.308630868 #> 4414  2206.5 0.6627272 0.6627272 2.832408e-02 2.832408e-02 0.308948682 #> 4415  2207.0 0.6624269 0.6624269 2.830703e-02 2.830703e-02 0.309266055 #> 4416  2207.5 0.6621272 0.6621272 2.828977e-02 2.828977e-02 0.309582983 #> 4417  2208.0 0.6618282 0.6618282 2.827232e-02 2.827232e-02 0.309899463 #> 4418  2208.5 0.6615298 0.6615298 2.825466e-02 2.825466e-02 0.310215493 #> 4419  2209.0 0.6612321 0.6612321 2.823681e-02 2.823681e-02 0.310531069 #> 4420  2209.5 0.6609351 0.6609351 2.821875e-02 2.821875e-02 0.310846188 #> 4421  2210.0 0.6606387 0.6606387 2.820050e-02 2.820050e-02 0.311160847 #> 4422  2210.5 0.6603429 0.6603429 2.818205e-02 2.818205e-02 0.311475042 #> 4423  2211.0 0.6600478 0.6600478 2.816341e-02 2.816341e-02 0.311788772 #> 4424  2211.5 0.6597534 0.6597534 2.814457e-02 2.814457e-02 0.312102033 #> 4425  2212.0 0.6594596 0.6594596 2.812554e-02 2.812554e-02 0.312414822 #> 4426  2212.5 0.6591666 0.6591666 2.810631e-02 2.810631e-02 0.312727135 #> 4427  2213.0 0.6588741 0.6588741 2.808689e-02 2.808689e-02 0.313038970 #> 4428  2213.5 0.6585824 0.6585824 2.806728e-02 2.806728e-02 0.313350325 #> 4429  2214.0 0.6582913 0.6582913 2.804748e-02 2.804748e-02 0.313661195 #> 4430  2214.5 0.6580009 0.6580009 2.802749e-02 2.802749e-02 0.313971578 #> 4431  2215.0 0.6577112 0.6577112 2.800731e-02 2.800731e-02 0.314281472 #> 4432  2215.5 0.6574222 0.6574222 2.798694e-02 2.798694e-02 0.314590873 #> 4433  2216.0 0.6571338 0.6571338 2.796638e-02 2.796638e-02 0.314899778 #> 4434  2216.5 0.6568462 0.6568462 2.794564e-02 2.794564e-02 0.315208185 #> 4435  2217.0 0.6565592 0.6565592 2.792472e-02 2.792472e-02 0.315516091 #> 4436  2217.5 0.6562729 0.6562729 2.790361e-02 2.790361e-02 0.315823492 #> 4437  2218.0 0.6559873 0.6559873 2.788231e-02 2.788231e-02 0.316130386 #> 4438  2218.5 0.6557024 0.6557024 2.786084e-02 2.786084e-02 0.316436771 #> 4439  2219.0 0.6554182 0.6554182 2.783918e-02 2.783918e-02 0.316742643 #> 4440  2219.5 0.6551347 0.6551347 2.781734e-02 2.781734e-02 0.317048000 #> 4441  2220.0 0.6548518 0.6548518 2.779532e-02 2.779532e-02 0.317352839 #> 4442  2220.5 0.6545697 0.6545697 2.777312e-02 2.777312e-02 0.317657157 #> 4443  2221.0 0.6542883 0.6542883 2.775075e-02 2.775075e-02 0.317960952 #> 4444  2221.5 0.6540076 0.6540076 2.772820e-02 2.772820e-02 0.318264220 #> 4445  2222.0 0.6537276 0.6537276 2.770547e-02 2.770547e-02 0.318566960 #> 4446  2222.5 0.6534483 0.6534483 2.768257e-02 2.768257e-02 0.318869168 #> 4447  2223.0 0.6531697 0.6531697 2.765949e-02 2.765949e-02 0.319170842 #> 4448  2223.5 0.6528918 0.6528918 2.763624e-02 2.763624e-02 0.319471979 #> 4449  2224.0 0.6526146 0.6526146 2.761282e-02 2.761282e-02 0.319772577 #> 4450  2224.5 0.6523381 0.6523381 2.758923e-02 2.758923e-02 0.320072633 #> 4451  2225.0 0.6520624 0.6520624 2.756547e-02 2.756547e-02 0.320372145 #> 4452  2225.5 0.6517874 0.6517874 2.754154e-02 2.754154e-02 0.320671109 #> 4453  2226.0 0.6515130 0.6515130 2.751744e-02 2.751744e-02 0.320969524 #> 4454  2226.5 0.6512394 0.6512394 2.749317e-02 2.749317e-02 0.321267387 #> 4455  2227.0 0.6509666 0.6509666 2.746874e-02 2.746874e-02 0.321564695 #> 4456  2227.5 0.6506944 0.6506944 2.744414e-02 2.744414e-02 0.321861446 #> 4457  2228.0 0.6504230 0.6504230 2.741938e-02 2.741938e-02 0.322157637 #> 4458  2228.5 0.6501523 0.6501523 2.739445e-02 2.739445e-02 0.322453267 #> 4459  2229.0 0.6498823 0.6498823 2.736936e-02 2.736936e-02 0.322748332 #> 4460  2229.5 0.6496131 0.6496131 2.734411e-02 2.734411e-02 0.323042830 #> 4461  2230.0 0.6493445 0.6493445 2.731870e-02 2.731870e-02 0.323336758 #> 4462  2230.5 0.6490768 0.6490768 2.729313e-02 2.729313e-02 0.323630115 #> 4463  2231.0 0.6488097 0.6488097 2.726741e-02 2.726741e-02 0.323922899 #> 4464  2231.5 0.6485434 0.6485434 2.724152e-02 2.724152e-02 0.324215105 #> 4465  2232.0 0.6482778 0.6482778 2.721548e-02 2.721548e-02 0.324506733 #> 4466  2232.5 0.6480129 0.6480129 2.718928e-02 2.718928e-02 0.324797780 #> 4467  2233.0 0.6477488 0.6477488 2.716293e-02 2.716293e-02 0.325088244 #> 4468  2233.5 0.6474855 0.6474855 2.713643e-02 2.713643e-02 0.325378122 #> 4469  2234.0 0.6472228 0.6472228 2.710977e-02 2.710977e-02 0.325667413 #> 4470  2234.5 0.6469609 0.6469609 2.708296e-02 2.708296e-02 0.325956113 #> 4471  2235.0 0.6466998 0.6466998 2.705600e-02 2.705600e-02 0.326244221 #> 4472  2235.5 0.6464394 0.6464394 2.702889e-02 2.702889e-02 0.326531735 #> 4473  2236.0 0.6461797 0.6461797 2.700164e-02 2.700164e-02 0.326818652 #> 4474  2236.5 0.6459208 0.6459208 2.697423e-02 2.697423e-02 0.327104970 #> 4475  2237.0 0.6456626 0.6456626 2.694668e-02 2.694668e-02 0.327390688 #> 4476  2237.5 0.6454052 0.6454052 2.691899e-02 2.691899e-02 0.327675802 #> 4477  2238.0 0.6451485 0.6451485 2.689115e-02 2.689115e-02 0.327960311 #> 4478  2238.5 0.6448926 0.6448926 2.686316e-02 2.686316e-02 0.328244212 #> 4479  2239.0 0.6446375 0.6446375 2.683504e-02 2.683504e-02 0.328527505 #> 4480  2239.5 0.6443830 0.6443830 2.680677e-02 2.680677e-02 0.328810185 #> 4481  2240.0 0.6441294 0.6441294 2.677837e-02 2.677837e-02 0.329092253 #> 4482  2240.5 0.6438765 0.6438765 2.674982e-02 2.674982e-02 0.329373705 #> 4483  2241.0 0.6436243 0.6436243 2.672113e-02 2.672113e-02 0.329654539 #> 4484  2241.5 0.6433729 0.6433729 2.669231e-02 2.669231e-02 0.329934754 #> 4485  2242.0 0.6431223 0.6431223 2.666335e-02 2.666335e-02 0.330214347 #> 4486  2242.5 0.6428724 0.6428724 2.663426e-02 2.663426e-02 0.330493317 #> 4487  2243.0 0.6426233 0.6426233 2.660503e-02 2.660503e-02 0.330771662 #> 4488  2243.5 0.6423749 0.6423749 2.657567e-02 2.657567e-02 0.331049380 #> 4489  2244.0 0.6421274 0.6421274 2.654618e-02 2.654618e-02 0.331326468 #> 4490  2244.5 0.6418805 0.6418805 2.651655e-02 2.651655e-02 0.331602925 #> 4491  2245.0 0.6416344 0.6416344 2.648680e-02 2.648680e-02 0.331878749 #> 4492  2245.5 0.6413891 0.6413891 2.645692e-02 2.645692e-02 0.332153939 #> 4493  2246.0 0.6411446 0.6411446 2.642691e-02 2.642691e-02 0.332428492 #> 4494  2246.5 0.6409008 0.6409008 2.639677e-02 2.639677e-02 0.332702407 #> 4495  2247.0 0.6406578 0.6406578 2.636650e-02 2.636650e-02 0.332975681 #> 4496  2247.5 0.6404156 0.6404156 2.633611e-02 2.633611e-02 0.333248314 #> 4497  2248.0 0.6401741 0.6401741 2.630560e-02 2.630560e-02 0.333520303 #> 4498  2248.5 0.6399334 0.6399334 2.627496e-02 2.627496e-02 0.333791646 #> 4499  2249.0 0.6396935 0.6396935 2.624420e-02 2.624420e-02 0.334062343 #> 4500  2249.5 0.6394543 0.6394543 2.621332e-02 2.621332e-02 0.334332390 #> 4501  2250.0 0.6392159 0.6392159 2.618232e-02 2.618232e-02 0.334601787 #> 4502  2250.5 0.6389783 0.6389783 2.615121e-02 2.615121e-02 0.334870532 #> 4503  2251.0 0.6387414 0.6387414 2.611997e-02 2.611997e-02 0.335138623 #> 4504  2251.5 0.6385053 0.6385053 2.608862e-02 2.608862e-02 0.335406059 #> 4505  2252.0 0.6382700 0.6382700 2.605715e-02 2.605715e-02 0.335672837 #> 4506  2252.5 0.6380355 0.6380355 2.602556e-02 2.602556e-02 0.335938957 #> 4507  2253.0 0.6378017 0.6378017 2.599386e-02 2.599386e-02 0.336204416 #> 4508  2253.5 0.6375687 0.6375687 2.596205e-02 2.596205e-02 0.336469214 #> 4509  2254.0 0.6373365 0.6373365 2.593013e-02 2.593013e-02 0.336733348 #> 4510  2254.5 0.6371051 0.6371051 2.589810e-02 2.589810e-02 0.336996818 #> 4511  2255.0 0.6368744 0.6368744 2.586595e-02 2.586595e-02 0.337259621 #> 4512  2255.5 0.6366445 0.6366445 2.583370e-02 2.583370e-02 0.337521756 #> 4513  2256.0 0.6364154 0.6364154 2.580134e-02 2.580134e-02 0.337783222 #> 4514  2256.5 0.6361871 0.6361871 2.576887e-02 2.576887e-02 0.338044017 #> 4515  2257.0 0.6359596 0.6359596 2.573630e-02 2.573630e-02 0.338304140 #> 4516  2257.5 0.6357328 0.6357328 2.570362e-02 2.570362e-02 0.338563589 #> 4517  2258.0 0.6355068 0.6355068 2.567084e-02 2.567084e-02 0.338822363 #> 4518  2258.5 0.6352816 0.6352816 2.563796e-02 2.563796e-02 0.339080461 #> 4519  2259.0 0.6350571 0.6350571 2.560497e-02 2.560497e-02 0.339337881 #> 4520  2259.5 0.6348335 0.6348335 2.557189e-02 2.557189e-02 0.339594622 #> 4521  2260.0 0.6346106 0.6346106 2.553870e-02 2.553870e-02 0.339850682 #> 4522  2260.5 0.6343885 0.6343885 2.550541e-02 2.550541e-02 0.340106061 #> 4523  2261.0 0.6341672 0.6341672 2.547203e-02 2.547203e-02 0.340360756 #> 4524  2261.5 0.6339467 0.6339467 2.543855e-02 2.543855e-02 0.340614767 #> 4525  2262.0 0.6337269 0.6337269 2.540497e-02 2.540497e-02 0.340868093 #> 4526  2262.5 0.6335080 0.6335080 2.537130e-02 2.537130e-02 0.341120731 #> 4527  2263.0 0.6332898 0.6332898 2.533754e-02 2.533754e-02 0.341372682 #> 4528  2263.5 0.6330724 0.6330724 2.530368e-02 2.530368e-02 0.341623943 #> 4529  2264.0 0.6328558 0.6328558 2.526973e-02 2.526973e-02 0.341874513 #> 4530  2264.5 0.6326399 0.6326399 2.523569e-02 2.523569e-02 0.342124392 #> 4531  2265.0 0.6324249 0.6324249 2.520156e-02 2.520156e-02 0.342373578 #> 4532  2265.5 0.6322106 0.6322106 2.516734e-02 2.516734e-02 0.342622070 #> 4533  2266.0 0.6319971 0.6319971 2.513304e-02 2.513304e-02 0.342869867 #> 4534  2266.5 0.6317844 0.6317844 2.509864e-02 2.509864e-02 0.343116968 #> 4535  2267.0 0.6315725 0.6315725 2.506416e-02 2.506416e-02 0.343363372 #> 4536  2267.5 0.6313613 0.6313613 2.502960e-02 2.502960e-02 0.343609077 #> 4537  2268.0 0.6311510 0.6311510 2.499495e-02 2.499495e-02 0.343854082 #> 4538  2268.5 0.6309414 0.6309414 2.496022e-02 2.496022e-02 0.344098387 #> 4539  2269.0 0.6307326 0.6307326 2.492541e-02 2.492541e-02 0.344341991 #> 4540  2269.5 0.6305246 0.6305246 2.489051e-02 2.489051e-02 0.344584892 #> 4541  2270.0 0.6303174 0.6303174 2.485554e-02 2.485554e-02 0.344827089 #> 4542  2270.5 0.6301109 0.6301109 2.482048e-02 2.482048e-02 0.345068582 #> 4543  2271.0 0.6299053 0.6299053 2.478535e-02 2.478535e-02 0.345309370 #> 4544  2271.5 0.6297004 0.6297004 2.475014e-02 2.475014e-02 0.345549451 #> 4545  2272.0 0.6294963 0.6294963 2.471485e-02 2.471485e-02 0.345788825 #> 4546  2272.5 0.6292930 0.6292930 2.467949e-02 2.467949e-02 0.346027490 #> 4547  2273.0 0.6290905 0.6290905 2.464406e-02 2.464406e-02 0.346265447 #> 4548  2273.5 0.6288888 0.6288888 2.460855e-02 2.460855e-02 0.346502693 #> 4549  2274.0 0.6286878 0.6286878 2.457297e-02 2.457297e-02 0.346739229 #> 4550  2274.5 0.6284876 0.6284876 2.453731e-02 2.453731e-02 0.346975052 #> 4551  2275.0 0.6282882 0.6282882 2.450159e-02 2.450159e-02 0.347210164 #> 4552  2275.5 0.6280896 0.6280896 2.446579e-02 2.446579e-02 0.347444562 #> 4553  2276.0 0.6278918 0.6278918 2.442993e-02 2.442993e-02 0.347678245 #> 4554  2276.5 0.6276948 0.6276948 2.439400e-02 2.439400e-02 0.347911214 #> 4555  2277.0 0.6274985 0.6274985 2.435800e-02 2.435800e-02 0.348143467 #> 4556  2277.5 0.6273031 0.6273031 2.432194e-02 2.432194e-02 0.348375004 #> 4557  2278.0 0.6271084 0.6271084 2.428581e-02 2.428581e-02 0.348605823 #> 4558  2278.5 0.6269145 0.6269145 2.424962e-02 2.424962e-02 0.348835925 #> 4559  2279.0 0.6267213 0.6267213 2.421336e-02 2.421336e-02 0.349065308 #> 4560  2279.5 0.6265290 0.6265290 2.417705e-02 2.417705e-02 0.349293971 #> 4561  2280.0 0.6263374 0.6263374 2.414067e-02 2.414067e-02 0.349521915 #> 4562  2280.5 0.6261466 0.6261466 2.410423e-02 2.410423e-02 0.349749138 #> 4563  2281.0 0.6259566 0.6259566 2.406773e-02 2.406773e-02 0.349975640 #> 4564  2281.5 0.6257674 0.6257674 2.403117e-02 2.403117e-02 0.350201420 #> 4565  2282.0 0.6255790 0.6255790 2.399455e-02 2.399455e-02 0.350426477 #> 4566  2282.5 0.6253913 0.6253913 2.395788e-02 2.395788e-02 0.350650812 #> 4567  2283.0 0.6252044 0.6252044 2.392115e-02 2.392115e-02 0.350874422 #> 4568  2283.5 0.6250183 0.6250183 2.388437e-02 2.388437e-02 0.351097309 #> 4569  2284.0 0.6248330 0.6248330 2.384753e-02 2.384753e-02 0.351319471 #> 4570  2284.5 0.6246485 0.6246485 2.381064e-02 2.381064e-02 0.351540907 #> 4571  2285.0 0.6244647 0.6244647 2.377370e-02 2.377370e-02 0.351761618 #> 4572  2285.5 0.6242817 0.6242817 2.373670e-02 2.373670e-02 0.351981602 #> 4573  2286.0 0.6240995 0.6240995 2.369966e-02 2.369966e-02 0.352200859 #> 4574  2286.5 0.6239180 0.6239180 2.366256e-02 2.366256e-02 0.352419389 #> 4575  2287.0 0.6237374 0.6237374 2.362542e-02 2.362542e-02 0.352637192 #> 4576  2287.5 0.6235575 0.6235575 2.358823e-02 2.358823e-02 0.352854266 #> 4577  2288.0 0.6233784 0.6233784 2.355099e-02 2.355099e-02 0.353070611 #> 4578  2288.5 0.6232001 0.6232001 2.351370e-02 2.351370e-02 0.353286228 #> 4579  2289.0 0.6230225 0.6230225 2.347637e-02 2.347637e-02 0.353501115 #> 4580  2289.5 0.6228457 0.6228457 2.343899e-02 2.343899e-02 0.353715272 #> 4581  2290.0 0.6226697 0.6226697 2.340157e-02 2.340157e-02 0.353928700 #> 4582  2290.5 0.6224945 0.6224945 2.336411e-02 2.336411e-02 0.354141396 #> 4583  2291.0 0.6223200 0.6223200 2.332661e-02 2.332661e-02 0.354353362 #> 4584  2291.5 0.6221463 0.6221463 2.328906e-02 2.328906e-02 0.354564596 #> 4585  2292.0 0.6219734 0.6219734 2.325148e-02 2.325148e-02 0.354775099 #> 4586  2292.5 0.6218013 0.6218013 2.321385e-02 2.321385e-02 0.354984870 #> 4587  2293.0 0.6216299 0.6216299 2.317619e-02 2.317619e-02 0.355193909 #> 4588  2293.5 0.6214593 0.6214593 2.313849e-02 2.313849e-02 0.355402216 #> 4589  2294.0 0.6212895 0.6212895 2.310075e-02 2.310075e-02 0.355609790 #> 4590  2294.5 0.6211204 0.6211204 2.306298e-02 2.306298e-02 0.355816631 #> 4591  2295.0 0.6209521 0.6209521 2.302517e-02 2.302517e-02 0.356022739 #> 4592  2295.5 0.6207846 0.6207846 2.298732e-02 2.298732e-02 0.356228114 #> 4593  2296.0 0.6206178 0.6206178 2.294944e-02 2.294944e-02 0.356432755 #> 4594  2296.5 0.6204518 0.6204518 2.291153e-02 2.291153e-02 0.356636662 #> 4595  2297.0 0.6202866 0.6202866 2.287359e-02 2.287359e-02 0.356839836 #> 4596  2297.5 0.6201221 0.6201221 2.283562e-02 2.283562e-02 0.357042276 #> 4597  2298.0 0.6199584 0.6199584 2.279761e-02 2.279761e-02 0.357243981 #> 4598  2298.5 0.6197955 0.6197955 2.275958e-02 2.275958e-02 0.357444952 #> 4599  2299.0 0.6196333 0.6196333 2.272151e-02 2.272151e-02 0.357645188 #> 4600  2299.5 0.6194719 0.6194719 2.268342e-02 2.268342e-02 0.357844690 #> 4601  2300.0 0.6193112 0.6193112 2.264530e-02 2.264530e-02 0.358043458 #> 4602  2300.5 0.6191514 0.6191514 2.260716e-02 2.260716e-02 0.358241490 #> 4603  2301.0 0.6189922 0.6189922 2.256899e-02 2.256899e-02 0.358438788 #> 4604  2301.5 0.6188339 0.6188339 2.253079e-02 2.253079e-02 0.358635351 #> 4605  2302.0 0.6186763 0.6186763 2.249257e-02 2.249257e-02 0.358831179 #> 4606  2302.5 0.6185194 0.6185194 2.245433e-02 2.245433e-02 0.359026272 #> 4607  2303.0 0.6183633 0.6183633 2.241606e-02 2.241606e-02 0.359220630 #> 4608  2303.5 0.6182080 0.6182080 2.237777e-02 2.237777e-02 0.359414254 #> 4609  2304.0 0.6180534 0.6180534 2.233946e-02 2.233946e-02 0.359607142 #> 4610  2304.5 0.6178996 0.6178996 2.230113e-02 2.230113e-02 0.359799295 #> 4611  2305.0 0.6177465 0.6177465 2.226278e-02 2.226278e-02 0.359990714 #> 4612  2305.5 0.6175942 0.6175942 2.222442e-02 2.222442e-02 0.360181397 #> 4613  2306.0 0.6174426 0.6174426 2.218603e-02 2.218603e-02 0.360371346 #> 4614  2306.5 0.6172918 0.6172918 2.214763e-02 2.214763e-02 0.360560560 #> 4615  2307.0 0.6171418 0.6171418 2.210921e-02 2.210921e-02 0.360749040 #> 4616  2307.5 0.6169924 0.6169924 2.207077e-02 2.207077e-02 0.360936785 #> 4617  2308.0 0.6168439 0.6168439 2.203232e-02 2.203232e-02 0.361123795 #> 4618  2308.5 0.6166961 0.6166961 2.199385e-02 2.199385e-02 0.361310072 #> 4619  2309.0 0.6165490 0.6165490 2.195537e-02 2.195537e-02 0.361495614 #> 4620  2309.5 0.6164027 0.6164027 2.191688e-02 2.191688e-02 0.361680422 #> 4621  2310.0 0.6162571 0.6162571 2.187837e-02 2.187837e-02 0.361864496 #> 4622  2310.5 0.6161123 0.6161123 2.183986e-02 2.183986e-02 0.362047837 #> 4623  2311.0 0.6159682 0.6159682 2.180133e-02 2.180133e-02 0.362230443 #> 4624  2311.5 0.6158249 0.6158249 2.176279e-02 2.176279e-02 0.362412317 #> 4625  2312.0 0.6156823 0.6156823 2.172424e-02 2.172424e-02 0.362593458 #> 4626  2312.5 0.6155404 0.6155404 2.168569e-02 2.168569e-02 0.362773865 #> 4627  2313.0 0.6153993 0.6153993 2.164712e-02 2.164712e-02 0.362953540 #> 4628  2313.5 0.6152590 0.6152590 2.160855e-02 2.160855e-02 0.363132482 #> 4629  2314.0 0.6151193 0.6151193 2.156997e-02 2.156997e-02 0.363310693 #> 4630  2314.5 0.6149804 0.6149804 2.153139e-02 2.153139e-02 0.363488171 #> 4631  2315.0 0.6148423 0.6148423 2.149280e-02 2.149280e-02 0.363664918 #> 4632  2315.5 0.6147049 0.6147049 2.145420e-02 2.145420e-02 0.363840933 #> 4633  2316.0 0.6145682 0.6145682 2.141560e-02 2.141560e-02 0.364016217 #> 4634  2316.5 0.6144322 0.6144322 2.137700e-02 2.137700e-02 0.364190770 #> 4635  2317.0 0.6142970 0.6142970 2.133840e-02 2.133840e-02 0.364364593 #> 4636  2317.5 0.6141625 0.6141625 2.129979e-02 2.129979e-02 0.364537685 #> 4637  2318.0 0.6140288 0.6140288 2.126118e-02 2.126118e-02 0.364710048 #> 4638  2318.5 0.6138957 0.6138957 2.122257e-02 2.122257e-02 0.364881681 #> 4639  2319.0 0.6137635 0.6137635 2.118396e-02 2.118396e-02 0.365052586 #> 4640  2319.5 0.6136319 0.6136319 2.114535e-02 2.114535e-02 0.365222761 #> 4641  2320.0 0.6135010 0.6135010 2.110674e-02 2.110674e-02 0.365392208 #> 4642  2320.5 0.6133709 0.6133709 2.106814e-02 2.106814e-02 0.365560927 #> 4643  2321.0 0.6132415 0.6132415 2.102953e-02 2.102953e-02 0.365728918 #> 4644  2321.5 0.6131129 0.6131129 2.099093e-02 2.099093e-02 0.365896183 #> 4645  2322.0 0.6129849 0.6129849 2.095234e-02 2.095234e-02 0.366062720 #> 4646  2322.5 0.6128577 0.6128577 2.091374e-02 2.091374e-02 0.366228531 #> 4647  2323.0 0.6127312 0.6127312 2.087515e-02 2.087515e-02 0.366393617 #> 4648  2323.5 0.6126055 0.6126055 2.083657e-02 2.083657e-02 0.366557976 #> 4649  2324.0 0.6124804 0.6124804 2.079800e-02 2.079800e-02 0.366721611 #> 4650  2324.5 0.6123561 0.6123561 2.075943e-02 2.075943e-02 0.366884521 #> 4651  2325.0 0.6122324 0.6122324 2.072086e-02 2.072086e-02 0.367046708 #> 4652  2325.5 0.6121095 0.6121095 2.068231e-02 2.068231e-02 0.367208170 #> 4653  2326.0 0.6119873 0.6119873 2.064376e-02 2.064376e-02 0.367368910 #> 4654  2326.5 0.6118658 0.6118658 2.060523e-02 2.060523e-02 0.367528927 #> 4655  2327.0 0.6117451 0.6117451 2.056670e-02 2.056670e-02 0.367688222 #> 4656  2327.5 0.6116250 0.6116250 2.052818e-02 2.052818e-02 0.367846796 #> 4657  2328.0 0.6115057 0.6115057 2.048968e-02 2.048968e-02 0.368004648 #> 4658  2328.5 0.6113870 0.6113870 2.045118e-02 2.045118e-02 0.368161780 #> 4659  2329.0 0.6112691 0.6112691 2.041270e-02 2.041270e-02 0.368318193 #> 4660  2329.5 0.6111519 0.6111519 2.037423e-02 2.037423e-02 0.368473886 #> 4661  2330.0 0.6110354 0.6110354 2.033577e-02 2.033577e-02 0.368628860 #> 4662  2330.5 0.6109196 0.6109196 2.029733e-02 2.029733e-02 0.368783116 #> 4663  2331.0 0.6108044 0.6108044 2.025890e-02 2.025890e-02 0.368936654 #> 4664  2331.5 0.6106900 0.6106900 2.022048e-02 2.022048e-02 0.369089476 #> 4665  2332.0 0.6105763 0.6105763 2.018208e-02 2.018208e-02 0.369241581 #> 4666  2332.5 0.6104633 0.6104633 2.014370e-02 2.014370e-02 0.369392971 #> 4667  2333.0 0.6103510 0.6103510 2.010533e-02 2.010533e-02 0.369543645 #> 4668  2333.5 0.6102394 0.6102394 2.006698e-02 2.006698e-02 0.369693605 #> 4669  2334.0 0.6101285 0.6101285 2.002865e-02 2.002865e-02 0.369842851 #> 4670  2334.5 0.6100183 0.6100183 1.999033e-02 1.999033e-02 0.369991384 #> 4671  2335.0 0.6099088 0.6099088 1.995204e-02 1.995204e-02 0.370139205 #> 4672  2335.5 0.6097999 0.6097999 1.991376e-02 1.991376e-02 0.370286313 #> 4673  2336.0 0.6096918 0.6096918 1.987550e-02 1.987550e-02 0.370432710 #> 4674  2336.5 0.6095843 0.6095843 1.983727e-02 1.983727e-02 0.370578397 #> 4675  2337.0 0.6094776 0.6094776 1.979905e-02 1.979905e-02 0.370723374 #> 4676  2337.5 0.6093715 0.6093715 1.976085e-02 1.976085e-02 0.370867643 #> 4677  2338.0 0.6092661 0.6092661 1.972268e-02 1.972268e-02 0.371011202 #> 4678  2338.5 0.6091614 0.6091614 1.968453e-02 1.968453e-02 0.371154054 #> 4679  2339.0 0.6090574 0.6090574 1.964640e-02 1.964640e-02 0.371296199 #> 4680  2339.5 0.6089541 0.6089541 1.960829e-02 1.960829e-02 0.371437638 #> 4681  2340.0 0.6088514 0.6088514 1.957020e-02 1.957020e-02 0.371578371 #> 4682  2340.5 0.6087495 0.6087495 1.953215e-02 1.953215e-02 0.371718400 #> 4683  2341.0 0.6086482 0.6086482 1.949411e-02 1.949411e-02 0.371857725 #> 4684  2341.5 0.6085476 0.6085476 1.945610e-02 1.945610e-02 0.371996346 #> 4685  2342.0 0.6084476 0.6084476 1.941812e-02 1.941812e-02 0.372134265 #> 4686  2342.5 0.6083484 0.6083484 1.938016e-02 1.938016e-02 0.372271483 #> 4687  2343.0 0.6082498 0.6082498 1.934222e-02 1.934222e-02 0.372407999 #> 4688  2343.5 0.6081519 0.6081519 1.930432e-02 1.930432e-02 0.372543816 #> 4689  2344.0 0.6080546 0.6080546 1.926644e-02 1.926644e-02 0.372678933 #> 4690  2344.5 0.6079581 0.6079581 1.922859e-02 1.922859e-02 0.372813352 #> 4691  2345.0 0.6078622 0.6078622 1.919077e-02 1.919077e-02 0.372947073 #> 4692  2345.5 0.6077669 0.6077669 1.915298e-02 1.915298e-02 0.373080097 #> 4693  2346.0 0.6076724 0.6076724 1.911521e-02 1.911521e-02 0.373212426 #> 4694  2346.5 0.6075785 0.6075785 1.907748e-02 1.907748e-02 0.373344059 #> 4695  2347.0 0.6074852 0.6074852 1.903977e-02 1.903977e-02 0.373474998 #> 4696  2347.5 0.6073927 0.6073927 1.900210e-02 1.900210e-02 0.373605243 #> 4697  2348.0 0.6073008 0.6073008 1.896445e-02 1.896445e-02 0.373734796 #> 4698  2348.5 0.6072095 0.6072095 1.892684e-02 1.892684e-02 0.373863657 #> 4699  2349.0 0.6071189 0.6071189 1.888926e-02 1.888926e-02 0.373991827 #> 4700  2349.5 0.6070290 0.6070290 1.885171e-02 1.885171e-02 0.374119308 #> 4701  2350.0 0.6069397 0.6069397 1.881419e-02 1.881419e-02 0.374246099 #> 4702  2350.5 0.6068511 0.6068511 1.877671e-02 1.877671e-02 0.374372202 #> 4703  2351.0 0.6067631 0.6067631 1.873926e-02 1.873926e-02 0.374497617 #> 4704  2351.5 0.6066758 0.6066758 1.870184e-02 1.870184e-02 0.374622346 #> 4705  2352.0 0.6065891 0.6065891 1.866446e-02 1.866446e-02 0.374746390 #> 4706  2352.5 0.6065031 0.6065031 1.862711e-02 1.862711e-02 0.374869749 #> 4707  2353.0 0.6064178 0.6064178 1.858980e-02 1.858980e-02 0.374992424 #> 4708  2353.5 0.6063331 0.6063331 1.855252e-02 1.855252e-02 0.375114417 #> 4709  2354.0 0.6062490 0.6062490 1.851528e-02 1.851528e-02 0.375235728 #> 4710  2354.5 0.6061656 0.6061656 1.847808e-02 1.847808e-02 0.375356357 #> 4711  2355.0 0.6060828 0.6060828 1.844091e-02 1.844091e-02 0.375476307 #> 4712  2355.5 0.6060006 0.6060006 1.840377e-02 1.840377e-02 0.375595578 #> 4713  2356.0 0.6059192 0.6059192 1.836668e-02 1.836668e-02 0.375714171 #> 4714  2356.5 0.6058383 0.6058383 1.832962e-02 1.832962e-02 0.375832087 #> 4715  2357.0 0.6057581 0.6057581 1.829260e-02 1.829260e-02 0.375949327 #> 4716  2357.5 0.6056785 0.6056785 1.825562e-02 1.825562e-02 0.376065891 #> 4717  2358.0 0.6055995 0.6055995 1.821868e-02 1.821868e-02 0.376181782 #> 4718  2358.5 0.6055212 0.6055212 1.818177e-02 1.818177e-02 0.376296999 #> 4719  2359.0 0.6054435 0.6054435 1.814491e-02 1.814491e-02 0.376411544 #> 4720  2359.5 0.6053665 0.6053665 1.810808e-02 1.810808e-02 0.376525418 #> 4721  2360.0 0.6052901 0.6052901 1.807130e-02 1.807130e-02 0.376638622 #> 4722  2360.5 0.6052143 0.6052143 1.803455e-02 1.803455e-02 0.376751157 #> 4723  2361.0 0.6051391 0.6051391 1.799785e-02 1.799785e-02 0.376863023 #> 4724  2361.5 0.6050646 0.6050646 1.796119e-02 1.796119e-02 0.376974222 #> 4725  2362.0 0.6049907 0.6049907 1.792457e-02 1.792457e-02 0.377084755 #> 4726  2362.5 0.6049174 0.6049174 1.788799e-02 1.788799e-02 0.377194623 #> 4727  2363.0 0.6048447 0.6048447 1.785145e-02 1.785145e-02 0.377303827 #> 4728  2363.5 0.6047727 0.6047727 1.781496e-02 1.781496e-02 0.377412368 #> 4729  2364.0 0.6047012 0.6047012 1.777850e-02 1.777850e-02 0.377520247 #> 4730  2364.5 0.6046304 0.6046304 1.774210e-02 1.774210e-02 0.377627465 #> 4731  2365.0 0.6045602 0.6045602 1.770573e-02 1.770573e-02 0.377734023 #> 4732  2365.5 0.6044907 0.6044907 1.766941e-02 1.766941e-02 0.377839923 #> 4733  2366.0 0.6044217 0.6044217 1.763313e-02 1.763313e-02 0.377945164 #> 4734  2366.5 0.6043534 0.6043534 1.759690e-02 1.759690e-02 0.378049749 #> 4735  2367.0 0.6042856 0.6042856 1.756071e-02 1.756071e-02 0.378153678 #> 4736  2367.5 0.6042185 0.6042185 1.752456e-02 1.752456e-02 0.378256952 #> 4737  2368.0 0.6041520 0.6041520 1.748846e-02 1.748846e-02 0.378359573 #> 4738  2368.5 0.6040860 0.6040860 1.745241e-02 1.745241e-02 0.378461542 #> 4739  2369.0 0.6040207 0.6040207 1.741640e-02 1.741640e-02 0.378562859 #> 4740  2369.5 0.6039560 0.6039560 1.738044e-02 1.738044e-02 0.378663526 #> 4741  2370.0 0.6038919 0.6038919 1.734452e-02 1.734452e-02 0.378763544 #> 4742  2370.5 0.6038284 0.6038284 1.730866e-02 1.730866e-02 0.378862914 #> 4743  2371.0 0.6037655 0.6037655 1.727283e-02 1.727283e-02 0.378961637 #> 4744  2371.5 0.6037032 0.6037032 1.723706e-02 1.723706e-02 0.379059714 #> 4745  2372.0 0.6036415 0.6036415 1.720133e-02 1.720133e-02 0.379157146 #> 4746  2372.5 0.6035804 0.6035804 1.716565e-02 1.716565e-02 0.379253935 #> 4747  2373.0 0.6035199 0.6035199 1.713002e-02 1.713002e-02 0.379350081 #> 4748  2373.5 0.6034600 0.6034600 1.709444e-02 1.709444e-02 0.379445586 #> 4749  2374.0 0.6034006 0.6034006 1.705890e-02 1.705890e-02 0.379540450 #> 4750  2374.5 0.6033419 0.6033419 1.702342e-02 1.702342e-02 0.379634676 #> 4751  2375.0 0.6032838 0.6032838 1.698798e-02 1.698798e-02 0.379728263 #> 4752  2375.5 0.6032262 0.6032262 1.695259e-02 1.695259e-02 0.379821214 #> 4753  2376.0 0.6031692 0.6031692 1.691725e-02 1.691725e-02 0.379913528 #> 4754  2376.5 0.6031128 0.6031128 1.688196e-02 1.688196e-02 0.380005209 #> 4755  2377.0 0.6030570 0.6030570 1.684672e-02 1.684672e-02 0.380096256 #> 4756  2377.5 0.6030018 0.6030018 1.681154e-02 1.681154e-02 0.380186670 #> 4757  2378.0 0.6029471 0.6029471 1.677640e-02 1.677640e-02 0.380276454 #> 4758  2378.5 0.6028931 0.6028931 1.674131e-02 1.674131e-02 0.380365608 #> 4759  2379.0 0.6028396 0.6028396 1.670627e-02 1.670627e-02 0.380454132 #> 4760  2379.5 0.6027867 0.6027867 1.667129e-02 1.667129e-02 0.380542030 #> 4761  2380.0 0.6027343 0.6027343 1.663635e-02 1.663635e-02 0.380629300 #> 4762  2380.5 0.6026826 0.6026826 1.660147e-02 1.660147e-02 0.380715946 #> 4763  2381.0 0.6026314 0.6026314 1.656664e-02 1.656664e-02 0.380801967 #> 4764  2381.5 0.6025808 0.6025808 1.653186e-02 1.653186e-02 0.380887366 #> 4765  2382.0 0.6025307 0.6025307 1.649714e-02 1.649714e-02 0.380972142 #> 4766  2382.5 0.6024812 0.6024812 1.646246e-02 1.646246e-02 0.381056299 #> 4767  2383.0 0.6024323 0.6024323 1.642784e-02 1.642784e-02 0.381139835 #> 4768  2383.5 0.6023840 0.6023840 1.639327e-02 1.639327e-02 0.381222754 #> 4769  2384.0 0.6023362 0.6023362 1.635876e-02 1.635876e-02 0.381305055 #> 4770  2384.5 0.6022890 0.6022890 1.632430e-02 1.632430e-02 0.381386741 #> 4771  2385.0 0.6022423 0.6022423 1.628989e-02 1.628989e-02 0.381467812 #> 4772  2385.5 0.6021962 0.6021962 1.625553e-02 1.625553e-02 0.381548270 #> 4773  2386.0 0.6021507 0.6021507 1.622123e-02 1.622123e-02 0.381628115 #> 4774  2386.5 0.6021057 0.6021057 1.618699e-02 1.618699e-02 0.381707350 #> 4775  2387.0 0.6020612 0.6020612 1.615279e-02 1.615279e-02 0.381785975 #> 4776  2387.5 0.6020174 0.6020174 1.611866e-02 1.611866e-02 0.381863991 #> 4777  2388.0 0.6019740 0.6019740 1.608457e-02 1.608457e-02 0.381941400 #> 4778  2388.5 0.6019313 0.6019313 1.605054e-02 1.605054e-02 0.382018202 #> 4779  2389.0 0.6018890 0.6018890 1.601657e-02 1.601657e-02 0.382094400 #> 4780  2389.5 0.6018474 0.6018474 1.598265e-02 1.598265e-02 0.382169994 #> 4781  2390.0 0.6018062 0.6018062 1.594879e-02 1.594879e-02 0.382244985 #> 4782  2390.5 0.6017656 0.6017656 1.591498e-02 1.591498e-02 0.382319376 #> 4783  2391.0 0.6017256 0.6017256 1.588123e-02 1.588123e-02 0.382393166 #> 4784  2391.5 0.6016861 0.6016861 1.584753e-02 1.584753e-02 0.382466357 #> 4785  2392.0 0.6016472 0.6016472 1.581389e-02 1.581389e-02 0.382538951 #> 4786  2392.5 0.6016087 0.6016087 1.578031e-02 1.578031e-02 0.382610948 #> 4787  2393.0 0.6015709 0.6015709 1.574678e-02 1.574678e-02 0.382682351 #> 4788  2393.5 0.6015335 0.6015335 1.571331e-02 1.571331e-02 0.382753159 #> 4789  2394.0 0.6014967 0.6014967 1.567989e-02 1.567989e-02 0.382823375 #> 4790  2394.5 0.6014605 0.6014605 1.564654e-02 1.564654e-02 0.382892999 #> 4791  2395.0 0.6014247 0.6014247 1.561323e-02 1.561323e-02 0.382962033 #> 4792  2395.5 0.6013895 0.6013895 1.557999e-02 1.557999e-02 0.383030479 #> 4793  2396.0 0.6013549 0.6013549 1.554680e-02 1.554680e-02 0.383098336 #> 4794  2396.5 0.6013207 0.6013207 1.551367e-02 1.551367e-02 0.383165607 #> 4795  2397.0 0.6012871 0.6012871 1.548060e-02 1.548060e-02 0.383232294 #> 4796  2397.5 0.6012540 0.6012540 1.544759e-02 1.544759e-02 0.383298396 #> 4797  2398.0 0.6012215 0.6012215 1.541463e-02 1.541463e-02 0.383363915 #> 4798  2398.5 0.6011894 0.6011894 1.538173e-02 1.538173e-02 0.383428853 #> 4799  2399.0 0.6011579 0.6011579 1.534889e-02 1.534889e-02 0.383493211 #> 4800  2399.5 0.6011269 0.6011269 1.531611e-02 1.531611e-02 0.383556990 #> 4801  2400.0 0.6010964 0.6010964 1.528338e-02 1.528338e-02 0.383620191 #> 4802  2400.5 0.6010665 0.6010665 1.525072e-02 1.525072e-02 0.383682816 #> 4803  2401.0 0.6010370 0.6010370 1.521811e-02 1.521811e-02 0.383744866 #> 4804  2401.5 0.6010081 0.6010081 1.518556e-02 1.518556e-02 0.383806342 #> 4805  2402.0 0.6009797 0.6009797 1.515307e-02 1.515307e-02 0.383867246 #> 4806  2402.5 0.6009518 0.6009518 1.512064e-02 1.512064e-02 0.383927578 #> 4807  2403.0 0.6009244 0.6009244 1.508827e-02 1.508827e-02 0.383987340 #> 4808  2403.5 0.6008975 0.6008975 1.505596e-02 1.505596e-02 0.384046533 #> 4809  2404.0 0.6008711 0.6008711 1.502370e-02 1.502370e-02 0.384105159 #> 4810  2404.5 0.6008453 0.6008453 1.499151e-02 1.499151e-02 0.384163219 #> 4811  2405.0 0.6008199 0.6008199 1.495937e-02 1.495937e-02 0.384220713 #> 4812  2405.5 0.6007951 0.6007951 1.492730e-02 1.492730e-02 0.384277644 #> 4813  2406.0 0.6007707 0.6007707 1.489528e-02 1.489528e-02 0.384334013 #> 4814  2406.5 0.6007469 0.6007469 1.486333e-02 1.486333e-02 0.384389820 #> 4815  2407.0 0.6007235 0.6007235 1.483143e-02 1.483143e-02 0.384445068 #> 4816  2407.5 0.6007007 0.6007007 1.479959e-02 1.479959e-02 0.384499756 #> 4817  2408.0 0.6006783 0.6006783 1.476782e-02 1.476782e-02 0.384553888 #> 4818  2408.5 0.6006564 0.6006564 1.473610e-02 1.473610e-02 0.384607464 #> 4819  2409.0 0.6006351 0.6006351 1.470444e-02 1.470444e-02 0.384660484 #> 4820  2409.5 0.6006142 0.6006142 1.467285e-02 1.467285e-02 0.384712952 #> 4821  2410.0 0.6005938 0.6005938 1.464131e-02 1.464131e-02 0.384764867 #> 4822  2410.5 0.6005739 0.6005739 1.460984e-02 1.460984e-02 0.384816231 #> 4823  2411.0 0.6005545 0.6005545 1.457842e-02 1.457842e-02 0.384867045 #> 4824  2411.5 0.6005356 0.6005356 1.454707e-02 1.454707e-02 0.384917312 #> 4825  2412.0 0.6005172 0.6005172 1.451577e-02 1.451577e-02 0.384967031 #> 4826  2412.5 0.6004993 0.6004993 1.448454e-02 1.448454e-02 0.385016204 #> 4827  2413.0 0.6004818 0.6004818 1.445337e-02 1.445337e-02 0.385064833 #> 4828  2413.5 0.6004648 0.6004648 1.442226e-02 1.442226e-02 0.385112919 #> 4829  2414.0 0.6004483 0.6004483 1.439121e-02 1.439121e-02 0.385160462 #> 4830  2414.5 0.6004323 0.6004323 1.436022e-02 1.436022e-02 0.385207466 #> 4831  2415.0 0.6004168 0.6004168 1.432929e-02 1.432929e-02 0.385253929 #> 4832  2415.5 0.6004017 0.6004017 1.429842e-02 1.429842e-02 0.385299855 #> 4833  2416.0 0.6003871 0.6003871 1.426762e-02 1.426762e-02 0.385345244 #> 4834  2416.5 0.6003730 0.6003730 1.423687e-02 1.423687e-02 0.385390098 #> 4835  2417.0 0.6003594 0.6003594 1.420619e-02 1.420619e-02 0.385434417 #> 4836  2417.5 0.6003462 0.6003462 1.417557e-02 1.417557e-02 0.385478203 #> 4837  2418.0 0.6003335 0.6003335 1.414501e-02 1.414501e-02 0.385521458 #> 4838  2418.5 0.6003213 0.6003213 1.411451e-02 1.411451e-02 0.385564183 #> 4839  2419.0 0.6003095 0.6003095 1.408407e-02 1.408407e-02 0.385606378 #> 4840  2419.5 0.6002983 0.6002983 1.405370e-02 1.405370e-02 0.385648046 #> 4841  2420.0 0.6002874 0.6002874 1.402338e-02 1.402338e-02 0.385689187 #> 4842  2420.5 0.6002771 0.6002771 1.399313e-02 1.399313e-02 0.385729803 #> 4843  2421.0 0.6002672 0.6002672 1.396294e-02 1.396294e-02 0.385769895 #> 4844  2421.5 0.6002577 0.6002577 1.393281e-02 1.393281e-02 0.385809465 #> 4845  2422.0 0.6002487 0.6002487 1.390274e-02 1.390274e-02 0.385848513 #> 4846  2422.5 0.6002402 0.6002402 1.387274e-02 1.387274e-02 0.385887042 #> 4847  2423.0 0.6002322 0.6002322 1.384280e-02 1.384280e-02 0.385925051 #> 4848  2423.5 0.6002245 0.6002245 1.381291e-02 1.381291e-02 0.385962543 #> 4849  2424.0 0.6002174 0.6002174 1.378309e-02 1.378309e-02 0.385999520 #> 4850  2424.5 0.6002107 0.6002107 1.375334e-02 1.375334e-02 0.386035981 #> 4851  2425.0 0.6002044 0.6002044 1.372364e-02 1.372364e-02 0.386071929 #> 4852  2425.5 0.6001986 0.6001986 1.369401e-02 1.369401e-02 0.386107364 #> 4853  2426.0 0.6001933 0.6001933 1.366444e-02 1.366444e-02 0.386142289 #> 4854  2426.5 0.6001884 0.6001884 1.363493e-02 1.363493e-02 0.386176703 #> 4855  2427.0 0.6001839 0.6001839 1.360548e-02 1.360548e-02 0.386210610 #> 4856  2427.5 0.6001799 0.6001799 1.357610e-02 1.357610e-02 0.386244009 #> 4857  2428.0 0.6001763 0.6001763 1.354677e-02 1.354677e-02 0.386276903 #> 4858  2428.5 0.6001732 0.6001732 1.351751e-02 1.351751e-02 0.386309292 #> 4859  2429.0 0.6001705 0.6001705 1.348831e-02 1.348831e-02 0.386341178 #> 4860  2429.5 0.6001683 0.6001683 1.345918e-02 1.345918e-02 0.386372562 #> 4861  2430.0 0.6001665 0.6001665 1.343010e-02 1.343010e-02 0.386403445 #> 4862  2430.5 0.6001651 0.6001651 1.340109e-02 1.340109e-02 0.386433829 #> 4863  2431.0 0.6001641 0.6001641 1.337214e-02 1.337214e-02 0.386463715 #> 4864  2431.5 0.6001636 0.6001636 1.334326e-02 1.334326e-02 0.386493104 #> 4865  2432.0 0.6001636 0.6001636 1.331443e-02 1.331443e-02 0.386521997 #> 4866  2432.5 0.6001639 0.6001639 1.328567e-02 1.328567e-02 0.386550396 #> 4867  2433.0 0.6001647 0.6001647 1.325697e-02 1.325697e-02 0.386578303 #> 4868  2433.5 0.6001660 0.6001660 1.322833e-02 1.322833e-02 0.386605717 #> 4869  2434.0 0.6001676 0.6001676 1.319976e-02 1.319976e-02 0.386632642 #> 4870  2434.5 0.6001697 0.6001697 1.317124e-02 1.317124e-02 0.386659077 #> 4871  2435.0 0.6001722 0.6001722 1.314279e-02 1.314279e-02 0.386685025 #> 4872  2435.5 0.6001751 0.6001751 1.311440e-02 1.311440e-02 0.386710486 #> 4873  2436.0 0.6001785 0.6001785 1.308608e-02 1.308608e-02 0.386735461 #> 4874  2436.5 0.6001822 0.6001822 1.305781e-02 1.305781e-02 0.386759953 #> 4875  2437.0 0.6001864 0.6001864 1.302961e-02 1.302961e-02 0.386783962 #> 4876  2437.5 0.6001910 0.6001910 1.300147e-02 1.300147e-02 0.386807490 #> 4877  2438.0 0.6001961 0.6001961 1.297340e-02 1.297340e-02 0.386830538 #> 4878  2438.5 0.6002015 0.6002015 1.294538e-02 1.294538e-02 0.386853107 #> 4879  2439.0 0.6002074 0.6002074 1.291743e-02 1.291743e-02 0.386875198 #> 4880  2439.5 0.6002136 0.6002136 1.288954e-02 1.288954e-02 0.386896813 #> 4881  2440.0 0.6002203 0.6002203 1.286171e-02 1.286171e-02 0.386917953 #> 4882  2440.5 0.6002274 0.6002274 1.283395e-02 1.283395e-02 0.386938619 #> 4883  2441.0 0.6002349 0.6002349 1.280625e-02 1.280625e-02 0.386958813 #> 4884  2441.5 0.6002429 0.6002429 1.277861e-02 1.277861e-02 0.386978536 #> 4885  2442.0 0.6002512 0.6002512 1.275103e-02 1.275103e-02 0.386997788 #> 4886  2442.5 0.6002599 0.6002599 1.272351e-02 1.272351e-02 0.387016573 #> 4887  2443.0 0.6002691 0.6002691 1.269606e-02 1.269606e-02 0.387034889 #> 4888  2443.5 0.6002786 0.6002786 1.266867e-02 1.266867e-02 0.387052740 #> 4889  2444.0 0.6002885 0.6002885 1.264134e-02 1.264134e-02 0.387070126 #> 4890  2444.5 0.6002989 0.6002989 1.261407e-02 1.261407e-02 0.387087048 #> 4891  2445.0 0.6003096 0.6003096 1.258686e-02 1.258686e-02 0.387103508 #> 4892  2445.5 0.6003208 0.6003208 1.255972e-02 1.255972e-02 0.387119507 #> 4893  2446.0 0.6003323 0.6003323 1.253264e-02 1.253264e-02 0.387135047 #> 4894  2446.5 0.6003443 0.6003443 1.250562e-02 1.250562e-02 0.387150127 #> 4895  2447.0 0.6003566 0.6003566 1.247866e-02 1.247866e-02 0.387164751 #> 4896  2447.5 0.6003693 0.6003693 1.245177e-02 1.245177e-02 0.387178919 #> 4897  2448.0 0.6003824 0.6003824 1.242494e-02 1.242494e-02 0.387192632 #> 4898  2448.5 0.6003959 0.6003959 1.239817e-02 1.239817e-02 0.387205891 #> 4899  2449.0 0.6004098 0.6004098 1.237146e-02 1.237146e-02 0.387218698 #> 4900  2449.5 0.6004241 0.6004241 1.234481e-02 1.234481e-02 0.387231055 #> 4901  2450.0 0.6004388 0.6004388 1.231822e-02 1.231822e-02 0.387242961 #> 4902  2450.5 0.6004539 0.6004539 1.229170e-02 1.229170e-02 0.387254420 #> 4903  2451.0 0.6004693 0.6004693 1.226524e-02 1.226524e-02 0.387265431 #> 4904  2451.5 0.6004852 0.6004852 1.223884e-02 1.223884e-02 0.387275996 #> 4905  2452.0 0.6005014 0.6005014 1.221250e-02 1.221250e-02 0.387286116 #> 4906  2452.5 0.6005180 0.6005180 1.218623e-02 1.218623e-02 0.387295794 #> 4907  2453.0 0.6005350 0.6005350 1.216001e-02 1.216001e-02 0.387305029 #> 4908  2453.5 0.6005523 0.6005523 1.213386e-02 1.213386e-02 0.387313823 #> 4909  2454.0 0.6005701 0.6005701 1.210777e-02 1.210777e-02 0.387322177 #> 4910  2454.5 0.6005882 0.6005882 1.208174e-02 1.208174e-02 0.387330093 #> 4911  2455.0 0.6006067 0.6006067 1.205577e-02 1.205577e-02 0.387337572 #> 4912  2455.5 0.6006255 0.6006255 1.202986e-02 1.202986e-02 0.387344615 #> 4913  2456.0 0.6006448 0.6006448 1.200401e-02 1.200401e-02 0.387351223 #> 4914  2456.5 0.6006644 0.6006644 1.197823e-02 1.197823e-02 0.387357398 #> 4915  2457.0 0.6006844 0.6006844 1.195251e-02 1.195251e-02 0.387363140 #> 4916  2457.5 0.6007047 0.6007047 1.192684e-02 1.192684e-02 0.387368452 #> 4917  2458.0 0.6007254 0.6007254 1.190124e-02 1.190124e-02 0.387373334 #> 4918  2458.5 0.6007465 0.6007465 1.187570e-02 1.187570e-02 0.387377787 #> 4919  2459.0 0.6007680 0.6007680 1.185022e-02 1.185022e-02 0.387381813 #> 4920  2459.5 0.6007898 0.6007898 1.182481e-02 1.182481e-02 0.387385413 #> 4921  2460.0 0.6008120 0.6008120 1.179945e-02 1.179945e-02 0.387388588 #> 4922  2460.5 0.6008345 0.6008345 1.177416e-02 1.177416e-02 0.387391340 #> 4923  2461.0 0.6008574 0.6008574 1.174892e-02 1.174892e-02 0.387393669 #> 4924  2461.5 0.6008807 0.6008807 1.172375e-02 1.172375e-02 0.387395577 #> 4925  2462.0 0.6009043 0.6009043 1.169863e-02 1.169863e-02 0.387397066 #> 4926  2462.5 0.6009283 0.6009283 1.167358e-02 1.167358e-02 0.387398135 #> 4927  2463.0 0.6009526 0.6009526 1.164859e-02 1.164859e-02 0.387398787 #> 4928  2463.5 0.6009773 0.6009773 1.162366e-02 1.162366e-02 0.387399023 #> 4929  2464.0 0.6010024 0.6010024 1.159879e-02 1.159879e-02 0.387398844 #> 4930  2464.5 0.6010278 0.6010278 1.157398e-02 1.157398e-02 0.387398251 #> 4931  2465.0 0.6010535 0.6010535 1.154923e-02 1.154923e-02 0.387397245 #> 4932  2465.5 0.6010796 0.6010796 1.152454e-02 1.152454e-02 0.387395828 #> 4933  2466.0 0.6011061 0.6011061 1.149991e-02 1.149991e-02 0.387394001 #> 4934  2466.5 0.6011329 0.6011329 1.147534e-02 1.147534e-02 0.387391765 #> 4935  2467.0 0.6011600 0.6011600 1.145084e-02 1.145084e-02 0.387389121 #> 4936  2467.5 0.6011875 0.6011875 1.142639e-02 1.142639e-02 0.387386071 #> 4937  2468.0 0.6012154 0.6012154 1.140200e-02 1.140200e-02 0.387382615 #> 4938  2468.5 0.6012436 0.6012436 1.137767e-02 1.137767e-02 0.387378756 #> 4939  2469.0 0.6012721 0.6012721 1.135340e-02 1.135340e-02 0.387374493 #> 4940  2469.5 0.6013010 0.6013010 1.132920e-02 1.132920e-02 0.387369828 #> 4941  2470.0 0.6013302 0.6013302 1.130505e-02 1.130505e-02 0.387364763 #> 4942  2470.5 0.6013597 0.6013597 1.128096e-02 1.128096e-02 0.387359299 #> 4943  2471.0 0.6013896 0.6013896 1.125693e-02 1.125693e-02 0.387353436 #> 4944  2471.5 0.6014199 0.6014199 1.123296e-02 1.123296e-02 0.387347177 #> 4945  2472.0 0.6014504 0.6014504 1.120905e-02 1.120905e-02 0.387340522 #> 4946  2472.5 0.6014813 0.6014813 1.118520e-02 1.118520e-02 0.387333472 #> 4947  2473.0 0.6015126 0.6015126 1.116141e-02 1.116141e-02 0.387326029 #> 4948  2473.5 0.6015441 0.6015441 1.113768e-02 1.113768e-02 0.387318193 #> 4949  2474.0 0.6015760 0.6015760 1.111401e-02 1.111401e-02 0.387309967 #> 4950  2474.5 0.6016083 0.6016083 1.109039e-02 1.109039e-02 0.387301350 #> 4951  2475.0 0.6016408 0.6016408 1.106684e-02 1.106684e-02 0.387292346 #> 4952  2475.5 0.6016737 0.6016737 1.104335e-02 1.104335e-02 0.387282953 #> 4953  2476.0 0.6017069 0.6017069 1.101991e-02 1.101991e-02 0.387273175 #> 4954  2476.5 0.6017405 0.6017405 1.099653e-02 1.099653e-02 0.387263011 #> 4955  2477.0 0.6017743 0.6017743 1.097322e-02 1.097322e-02 0.387252463 #> 4956  2477.5 0.6018085 0.6018085 1.094996e-02 1.094996e-02 0.387241533 #> 4957  2478.0 0.6018430 0.6018430 1.092676e-02 1.092676e-02 0.387230220 #> 4958  2478.5 0.6018779 0.6018779 1.090361e-02 1.090361e-02 0.387218528 #> 4959  2479.0 0.6019130 0.6019130 1.088053e-02 1.088053e-02 0.387206456 #> 4960  2479.5 0.6019485 0.6019485 1.085751e-02 1.085751e-02 0.387194006 #> 4961  2480.0 0.6019843 0.6019843 1.083454e-02 1.083454e-02 0.387181179 #> 4962  2480.5 0.6020204 0.6020204 1.081163e-02 1.081163e-02 0.387167977 #> 4963  2481.0 0.6020568 0.6020568 1.078878e-02 1.078878e-02 0.387154400 #> 4964  2481.5 0.6020936 0.6020936 1.076599e-02 1.076599e-02 0.387140449 #> 4965  2482.0 0.6021306 0.6021306 1.074326e-02 1.074326e-02 0.387126126 #> 4966  2482.5 0.6021680 0.6021680 1.072058e-02 1.072058e-02 0.387111432 #> 4967  2483.0 0.6022057 0.6022057 1.069796e-02 1.069796e-02 0.387096367 #> 4968  2483.5 0.6022437 0.6022437 1.067540e-02 1.067540e-02 0.387080934 #> 4969  2484.0 0.6022820 0.6022820 1.065290e-02 1.065290e-02 0.387065134 #> 4970  2484.5 0.6023206 0.6023206 1.063045e-02 1.063045e-02 0.387048966 #> 4971  2485.0 0.6023595 0.6023595 1.060807e-02 1.060807e-02 0.387032433 #> 4972  2485.5 0.6023987 0.6023987 1.058574e-02 1.058574e-02 0.387015536 #> 4973  2486.0 0.6024383 0.6024383 1.056347e-02 1.056347e-02 0.386998276 #> 4974  2486.5 0.6024781 0.6024781 1.054125e-02 1.054125e-02 0.386980654 #> 4975  2487.0 0.6025182 0.6025182 1.051909e-02 1.051909e-02 0.386962671 #> 4976  2487.5 0.6025587 0.6025587 1.049699e-02 1.049699e-02 0.386944328 #> 4977  2488.0 0.6025994 0.6025994 1.047495e-02 1.047495e-02 0.386925627 #> 4978  2488.5 0.6026405 0.6026405 1.045296e-02 1.045296e-02 0.386906568 #> 4979  2489.0 0.6026818 0.6026818 1.043103e-02 1.043103e-02 0.386887153 #> 4980  2489.5 0.6027235 0.6027235 1.040916e-02 1.040916e-02 0.386867382 #> 4981  2490.0 0.6027654 0.6027654 1.038734e-02 1.038734e-02 0.386847258 #> 4982  2490.5 0.6028076 0.6028076 1.036558e-02 1.036558e-02 0.386826780 #> 4983  2491.0 0.6028502 0.6028502 1.034388e-02 1.034388e-02 0.386805951 #> 4984  2491.5 0.6028930 0.6028930 1.032223e-02 1.032223e-02 0.386784771 #> 4985  2492.0 0.6029361 0.6029361 1.030064e-02 1.030064e-02 0.386763241 #> 4986  2492.5 0.6029795 0.6029795 1.027911e-02 1.027911e-02 0.386741363 #> 4987  2493.0 0.6030232 0.6030232 1.025763e-02 1.025763e-02 0.386719137 #> 4988  2493.5 0.6030672 0.6030672 1.023621e-02 1.023621e-02 0.386696565 #> 4989  2494.0 0.6031115 0.6031115 1.021485e-02 1.021485e-02 0.386673648 #> 4990  2494.5 0.6031561 0.6031561 1.019354e-02 1.019354e-02 0.386650386 #> 4991  2495.0 0.6032009 0.6032009 1.017228e-02 1.017228e-02 0.386626782 #> 4992  2495.5 0.6032461 0.6032461 1.015108e-02 1.015108e-02 0.386602836 #> 4993  2496.0 0.6032915 0.6032915 1.012994e-02 1.012994e-02 0.386578549 #> 4994  2496.5 0.6033372 0.6033372 1.010886e-02 1.010886e-02 0.386553923 #> 4995  2497.0 0.6033832 0.6033832 1.008782e-02 1.008782e-02 0.386528958 #> 4996  2497.5 0.6034295 0.6034295 1.006685e-02 1.006685e-02 0.386503655 #> 4997  2498.0 0.6034761 0.6034761 1.004593e-02 1.004593e-02 0.386478016 #> 4998  2498.5 0.6035229 0.6035229 1.002506e-02 1.002506e-02 0.386452041 #> 4999  2499.0 0.6035700 0.6035700 1.000425e-02 1.000425e-02 0.386425733 #> 5000  2499.5 0.6036174 0.6036174 9.983495e-03 9.983495e-03 0.386399091 #> 5001  2500.0 0.6036651 0.6036651 9.962795e-03 9.962795e-03 0.386372117 #> 5002  2500.5 0.6037130 0.6037130 9.942149e-03 9.942149e-03 0.386344813 #> 5003  2501.0 0.6037613 0.6037613 9.921558e-03 9.921558e-03 0.386317178 #> 5004  2501.5 0.6038098 0.6038098 9.901021e-03 9.901021e-03 0.386289214 #> 5005  2502.0 0.6038585 0.6038585 9.880539e-03 9.880539e-03 0.386260923 #> 5006  2502.5 0.6039076 0.6039076 9.860112e-03 9.860112e-03 0.386232305 #> 5007  2503.0 0.6039569 0.6039569 9.839739e-03 9.839739e-03 0.386203361 #> 5008  2503.5 0.6040065 0.6040065 9.819420e-03 9.819420e-03 0.386174093 #> 5009  2504.0 0.6040563 0.6040563 9.799155e-03 9.799155e-03 0.386144502 #> 5010  2504.5 0.6041065 0.6041065 9.778944e-03 9.778944e-03 0.386114588 #> 5011  2505.0 0.6041569 0.6041569 9.758787e-03 9.758787e-03 0.386084352 #> 5012  2505.5 0.6042075 0.6042075 9.738684e-03 9.738684e-03 0.386053797 #> 5013  2506.0 0.6042584 0.6042584 9.718635e-03 9.718635e-03 0.386022922 #> 5014  2506.5 0.6043096 0.6043096 9.698640e-03 9.698640e-03 0.385991729 #> 5015  2507.0 0.6043611 0.6043611 9.678698e-03 9.678698e-03 0.385960218 #> 5016  2507.5 0.6044128 0.6044128 9.658809e-03 9.658809e-03 0.385928392 #> 5017  2508.0 0.6044648 0.6044648 9.638974e-03 9.638974e-03 0.385896251 #> 5018  2508.5 0.6045170 0.6045170 9.619192e-03 9.619192e-03 0.385863795 #> 5019  2509.0 0.6045695 0.6045695 9.599463e-03 9.599463e-03 0.385831027 #> 5020  2509.5 0.6046223 0.6046223 9.579787e-03 9.579787e-03 0.385797947 #> 5021  2510.0 0.6046753 0.6046753 9.560165e-03 9.560165e-03 0.385764555 #> 5022  2510.5 0.6047286 0.6047286 9.540595e-03 9.540595e-03 0.385730855 #> 5023  2511.0 0.6047821 0.6047821 9.521078e-03 9.521078e-03 0.385696845 #> 5024  2511.5 0.6048359 0.6048359 9.501613e-03 9.501613e-03 0.385662527 #> 5025  2512.0 0.6048899 0.6048899 9.482201e-03 9.482201e-03 0.385627903 #> 5026  2512.5 0.6049442 0.6049442 9.462842e-03 9.462842e-03 0.385592973 #> 5027  2513.0 0.6049987 0.6049987 9.443535e-03 9.443535e-03 0.385557738 #> 5028  2513.5 0.6050535 0.6050535 9.424280e-03 9.424280e-03 0.385522200 #> 5029  2514.0 0.6051086 0.6051086 9.405078e-03 9.405078e-03 0.385486359 #> 5030  2514.5 0.6051639 0.6051639 9.385927e-03 9.385927e-03 0.385450217 #> 5031  2515.0 0.6052194 0.6052194 9.366829e-03 9.366829e-03 0.385413774 #> 5032  2515.5 0.6052752 0.6052752 9.347782e-03 9.347782e-03 0.385377031 #> 5033  2516.0 0.6053312 0.6053312 9.328787e-03 9.328787e-03 0.385339990 #> 5034  2516.5 0.6053875 0.6053875 9.309844e-03 9.309844e-03 0.385302651 #> 5035  2517.0 0.6054440 0.6054440 9.290952e-03 9.290952e-03 0.385265016 #> 5036  2517.5 0.6055008 0.6055008 9.272112e-03 9.272112e-03 0.385227085 #> 5037  2518.0 0.6055578 0.6055578 9.253323e-03 9.253323e-03 0.385188859 #> 5038  2518.5 0.6056151 0.6056151 9.234586e-03 9.234586e-03 0.385150340 #> 5039  2519.0 0.6056726 0.6056726 9.215899e-03 9.215899e-03 0.385111529 #> 5040  2519.5 0.6057303 0.6057303 9.197264e-03 9.197264e-03 0.385072426 #> 5041  2520.0 0.6057883 0.6057883 9.178680e-03 9.178680e-03 0.385033032 #> 5042  2520.5 0.6058465 0.6058465 9.160146e-03 9.160146e-03 0.384993349 #> 5043  2521.0 0.6059050 0.6059050 9.141664e-03 9.141664e-03 0.384953377 #> 5044  2521.5 0.6059637 0.6059637 9.123231e-03 9.123231e-03 0.384913118 #> 5045  2522.0 0.6060226 0.6060226 9.104850e-03 9.104850e-03 0.384872572 #> 5046  2522.5 0.6060817 0.6060817 9.086519e-03 9.086519e-03 0.384831741 #> 5047  2523.0 0.6061411 0.6061411 9.068238e-03 9.068238e-03 0.384790625 #> 5048  2523.5 0.6062008 0.6062008 9.050008e-03 9.050008e-03 0.384749225 #> 5049  2524.0 0.6062606 0.6062606 9.031827e-03 9.031827e-03 0.384707543 #> 5050  2524.5 0.6063207 0.6063207 9.013697e-03 9.013697e-03 0.384665579 #> 5051  2525.0 0.6063810 0.6063810 8.995616e-03 8.995616e-03 0.384623334 #> 5052  2525.5 0.6064416 0.6064416 8.977586e-03 8.977586e-03 0.384580810 #> 5053  2526.0 0.6065024 0.6065024 8.959605e-03 8.959605e-03 0.384538006 #> 5054  2526.5 0.6065634 0.6065634 8.941674e-03 8.941674e-03 0.384494925 #> 5055  2527.0 0.6066246 0.6066246 8.923792e-03 8.923792e-03 0.384451568 #> 5056  2527.5 0.6066861 0.6066861 8.905960e-03 8.905960e-03 0.384407934 #> 5057  2528.0 0.6067478 0.6067478 8.888177e-03 8.888177e-03 0.384364025 #> 5058  2528.5 0.6068097 0.6068097 8.870443e-03 8.870443e-03 0.384319843 #> 5059  2529.0 0.6068719 0.6068719 8.852759e-03 8.852759e-03 0.384275387 #> 5060  2529.5 0.6069342 0.6069342 8.835123e-03 8.835123e-03 0.384230660 #> 5061  2530.0 0.6069968 0.6069968 8.817536e-03 8.817536e-03 0.384185661 #> 5062  2530.5 0.6070596 0.6070596 8.799998e-03 8.799998e-03 0.384140392 #> 5063  2531.0 0.6071226 0.6071226 8.782509e-03 8.782509e-03 0.384094854 #> 5064  2531.5 0.6071859 0.6071859 8.765068e-03 8.765068e-03 0.384049048 #> 5065  2532.0 0.6072493 0.6072493 8.747676e-03 8.747676e-03 0.384002974 #> 5066  2532.5 0.6073130 0.6073130 8.730333e-03 8.730333e-03 0.383956635 #> 5067  2533.0 0.6073769 0.6073769 8.713037e-03 8.713037e-03 0.383910029 #> 5068  2533.5 0.6074411 0.6074411 8.695790e-03 8.695790e-03 0.383863159 #> 5069  2534.0 0.6075054 0.6075054 8.678591e-03 8.678591e-03 0.383816026 #> 5070  2534.5 0.6075699 0.6075699 8.661439e-03 8.661439e-03 0.383768630 #> 5071  2535.0 0.6076347 0.6076347 8.644336e-03 8.644336e-03 0.383720972 #> 5072  2535.5 0.6076997 0.6076997 8.627280e-03 8.627280e-03 0.383673054 #> 5073  2536.0 0.6077649 0.6077649 8.610272e-03 8.610272e-03 0.383624876 #> 5074  2536.5 0.6078302 0.6078302 8.593312e-03 8.593312e-03 0.383576438 #> 5075  2537.0 0.6078959 0.6078959 8.576399e-03 8.576399e-03 0.383527743 #> 5076  2537.5 0.6079617 0.6079617 8.559533e-03 8.559533e-03 0.383478791 #> 5077  2538.0 0.6080277 0.6080277 8.542715e-03 8.542715e-03 0.383429582 #> 5078  2538.5 0.6080939 0.6080939 8.525944e-03 8.525944e-03 0.383380119 #> 5079  2539.0 0.6081604 0.6081604 8.509220e-03 8.509220e-03 0.383330401 #> 5080  2539.5 0.6082270 0.6082270 8.492543e-03 8.492543e-03 0.383280429 #> 5081  2540.0 0.6082939 0.6082939 8.475912e-03 8.475912e-03 0.383230205 #> 5082  2540.5 0.6083609 0.6083609 8.459328e-03 8.459328e-03 0.383179730 #> 5083  2541.0 0.6084282 0.6084282 8.442791e-03 8.442791e-03 0.383129004 #> 5084  2541.5 0.6084957 0.6084957 8.426301e-03 8.426301e-03 0.383078028 #> 5085  2542.0 0.6085633 0.6085633 8.409857e-03 8.409857e-03 0.383026803 #> 5086  2542.5 0.6086312 0.6086312 8.393459e-03 8.393459e-03 0.382975330 #> 5087  2543.0 0.6086993 0.6086993 8.377107e-03 8.377107e-03 0.382923610 #> 5088  2543.5 0.6087676 0.6087676 8.360802e-03 8.360802e-03 0.382871643 #> 5089  2544.0 0.6088360 0.6088360 8.344542e-03 8.344542e-03 0.382819432 #> 5090  2544.5 0.6089047 0.6089047 8.328329e-03 8.328329e-03 0.382766975 #> 5091  2545.0 0.6089736 0.6089736 8.312161e-03 8.312161e-03 0.382714276 #> 5092  2545.5 0.6090426 0.6090426 8.296039e-03 8.296039e-03 0.382661334 #> 5093  2546.0 0.6091119 0.6091119 8.279962e-03 8.279962e-03 0.382608149 #> 5094  2546.5 0.6091813 0.6091813 8.263931e-03 8.263931e-03 0.382554724 #> 5095  2547.0 0.6092510 0.6092510 8.247946e-03 8.247946e-03 0.382501059 #> 5096  2547.5 0.6093208 0.6093208 8.232005e-03 8.232005e-03 0.382447155 #> 5097  2548.0 0.6093909 0.6093909 8.216110e-03 8.216110e-03 0.382393013 #> 5098  2548.5 0.6094611 0.6094611 8.200260e-03 8.200260e-03 0.382338633 #> 5099  2549.0 0.6095315 0.6095315 8.184455e-03 8.184455e-03 0.382284016 #> 5100  2549.5 0.6096021 0.6096021 8.168694e-03 8.168694e-03 0.382229164 #> 5101  2550.0 0.6096729 0.6096729 8.152979e-03 8.152979e-03 0.382174077 #> 5102  2550.5 0.6097439 0.6097439 8.137308e-03 8.137308e-03 0.382118756 #> 5103  2551.0 0.6098151 0.6098151 8.121682e-03 8.121682e-03 0.382063202 #> 5104  2551.5 0.6098865 0.6098865 8.106100e-03 8.106100e-03 0.382007416 #> 5105  2552.0 0.6099580 0.6099580 8.090562e-03 8.090562e-03 0.381951399 #> 5106  2552.5 0.6100298 0.6100298 8.075069e-03 8.075069e-03 0.381895150 #> 5107  2553.0 0.6101017 0.6101017 8.059620e-03 8.059620e-03 0.381838673 #> 5108  2553.5 0.6101738 0.6101738 8.044215e-03 8.044215e-03 0.381781966 #> 5109  2554.0 0.6102461 0.6102461 8.028853e-03 8.028853e-03 0.381725031 #> 5110  2554.5 0.6103186 0.6103186 8.013536e-03 8.013536e-03 0.381667870 #> 5111  2555.0 0.6103913 0.6103913 7.998263e-03 7.998263e-03 0.381610482 #> 5112  2555.5 0.6104641 0.6104641 7.983033e-03 7.983033e-03 0.381552868 #> 5113  2556.0 0.6105371 0.6105371 7.967846e-03 7.967846e-03 0.381495030 #> 5114  2556.5 0.6106103 0.6106103 7.952703e-03 7.952703e-03 0.381436968 #> 5115  2557.0 0.6106837 0.6106837 7.937603e-03 7.937603e-03 0.381378684 #> 5116  2557.5 0.6107573 0.6107573 7.922547e-03 7.922547e-03 0.381320177 #> 5117  2558.0 0.6108310 0.6108310 7.907534e-03 7.907534e-03 0.381261449 #> 5118  2558.5 0.6109049 0.6109049 7.892563e-03 7.892563e-03 0.381202500 #> 5119  2559.0 0.6109790 0.6109790 7.877636e-03 7.877636e-03 0.381143332 #> 5120  2559.5 0.6110533 0.6110533 7.862751e-03 7.862751e-03 0.381083945 #> 5121  2560.0 0.6111278 0.6111278 7.847909e-03 7.847909e-03 0.381024340 #> 5122  2560.5 0.6112024 0.6112024 7.833110e-03 7.833110e-03 0.380964518 #> 5123  2561.0 0.6112772 0.6112772 7.818353e-03 7.818353e-03 0.380904479 #> 5124  2561.5 0.6113521 0.6113521 7.803639e-03 7.803639e-03 0.380844225 #> 5125  2562.0 0.6114273 0.6114273 7.788967e-03 7.788967e-03 0.380783757 #> 5126  2562.5 0.6115026 0.6115026 7.774337e-03 7.774337e-03 0.380723074 #> 5127  2563.0 0.6115781 0.6115781 7.759749e-03 7.759749e-03 0.380662178 #> 5128  2563.5 0.6116537 0.6116537 7.745204e-03 7.745204e-03 0.380601071 #> 5129  2564.0 0.6117295 0.6117295 7.730700e-03 7.730700e-03 0.380539751 #> 5130  2564.5 0.6118055 0.6118055 7.716238e-03 7.716238e-03 0.380478221 #> 5131  2565.0 0.6118817 0.6118817 7.701817e-03 7.701817e-03 0.380416481 #> 5132  2565.5 0.6119580 0.6119580 7.687439e-03 7.687439e-03 0.380354532 #> 5133  2566.0 0.6120345 0.6120345 7.673102e-03 7.673102e-03 0.380292375 #> 5134  2566.5 0.6121112 0.6121112 7.658806e-03 7.658806e-03 0.380230010 #> 5135  2567.0 0.6121880 0.6121880 7.644551e-03 7.644551e-03 0.380167439 #> 5136  2567.5 0.6122650 0.6122650 7.630338e-03 7.630338e-03 0.380104661 #> 5137  2568.0 0.6123422 0.6123422 7.616166e-03 7.616166e-03 0.380041679 #> 5138  2568.5 0.6124195 0.6124195 7.602034e-03 7.602034e-03 0.379978492 #> 5139  2569.0 0.6124970 0.6124970 7.587944e-03 7.587944e-03 0.379915102 #> 5140  2569.5 0.6125746 0.6125746 7.573895e-03 7.573895e-03 0.379851509 #> 5141  2570.0 0.6126524 0.6126524 7.559886e-03 7.559886e-03 0.379787714 #> 5142  2570.5 0.6127304 0.6127304 7.545918e-03 7.545918e-03 0.379723717 #> 5143  2571.0 0.6128085 0.6128085 7.531990e-03 7.531990e-03 0.379659521 #> 5144  2571.5 0.6128868 0.6128868 7.518103e-03 7.518103e-03 0.379595124 #> 5145  2572.0 0.6129652 0.6129652 7.504256e-03 7.504256e-03 0.379530529 #> 5146  2572.5 0.6130438 0.6130438 7.490449e-03 7.490449e-03 0.379465736 #> 5147  2573.0 0.6131226 0.6131226 7.476682e-03 7.476682e-03 0.379400745 #> 5148  2573.5 0.6132015 0.6132015 7.462956e-03 7.462956e-03 0.379335558 #> 5149  2574.0 0.6132806 0.6132806 7.449269e-03 7.449269e-03 0.379270175 #> 5150  2574.5 0.6133598 0.6133598 7.435622e-03 7.435622e-03 0.379204597 #> 5151  2575.0 0.6134392 0.6134392 7.422015e-03 7.422015e-03 0.379138825 #> 5152  2575.5 0.6135187 0.6135187 7.408447e-03 7.408447e-03 0.379072859 #> 5153  2576.0 0.6135984 0.6135984 7.394919e-03 7.394919e-03 0.379006701 #> 5154  2576.5 0.6136782 0.6136782 7.381430e-03 7.381430e-03 0.378940350 #> 5155  2577.0 0.6137582 0.6137582 7.367981e-03 7.367981e-03 0.378873808 #> 5156  2577.5 0.6138384 0.6138384 7.354571e-03 7.354571e-03 0.378807076 #> 5157  2578.0 0.6139186 0.6139186 7.341200e-03 7.341200e-03 0.378740154 #> 5158  2578.5 0.6139991 0.6139991 7.327868e-03 7.327868e-03 0.378673043 #> 5159  2579.0 0.6140797 0.6140797 7.314575e-03 7.314575e-03 0.378605743 #> 5160  2579.5 0.6141604 0.6141604 7.301321e-03 7.301321e-03 0.378538256 #> 5161  2580.0 0.6142413 0.6142413 7.288105e-03 7.288105e-03 0.378470583 #> 5162  2580.5 0.6143223 0.6143223 7.274929e-03 7.274929e-03 0.378402723 #> 5163  2581.0 0.6144035 0.6144035 7.261790e-03 7.261790e-03 0.378334678 #> 5164  2581.5 0.6144849 0.6144849 7.248691e-03 7.248691e-03 0.378266448 #> 5165  2582.0 0.6145663 0.6145663 7.235629e-03 7.235629e-03 0.378198035 #> 5166  2582.5 0.6146480 0.6146480 7.222606e-03 7.222606e-03 0.378129438 #> 5167  2583.0 0.6147297 0.6147297 7.209621e-03 7.209621e-03 0.378060659 #> 5168  2583.5 0.6148116 0.6148116 7.196674e-03 7.196674e-03 0.377991698 #> 5169  2584.0 0.6148937 0.6148937 7.183765e-03 7.183765e-03 0.377922557 #> 5170  2584.5 0.6149759 0.6149759 7.170894e-03 7.170894e-03 0.377853235 #> 5171  2585.0 0.6150582 0.6150582 7.158061e-03 7.158061e-03 0.377783733 #> 5172  2585.5 0.6151407 0.6151407 7.145265e-03 7.145265e-03 0.377714053 #> 5173  2586.0 0.6152233 0.6152233 7.132507e-03 7.132507e-03 0.377644195 #> 5174  2586.5 0.6153061 0.6153061 7.119787e-03 7.119787e-03 0.377574160 #> 5175  2587.0 0.6153889 0.6153889 7.107103e-03 7.107103e-03 0.377503947 #> 5176  2587.5 0.6154720 0.6154720 7.094458e-03 7.094458e-03 0.377433559 #> 5177  2588.0 0.6155552 0.6155552 7.081849e-03 7.081849e-03 0.377362996 #> 5178  2588.5 0.6156385 0.6156385 7.069278e-03 7.069278e-03 0.377292258 #> 5179  2589.0 0.6157219 0.6157219 7.056743e-03 7.056743e-03 0.377221346 #> 5180  2589.5 0.6158055 0.6158055 7.044246e-03 7.044246e-03 0.377150262 #> 5181  2590.0 0.6158892 0.6158892 7.031785e-03 7.031785e-03 0.377079005 #> 5182  2590.5 0.6159731 0.6159731 7.019361e-03 7.019361e-03 0.377007576 #> 5183  2591.0 0.6160571 0.6160571 7.006974e-03 7.006974e-03 0.376935976 #> 5184  2591.5 0.6161412 0.6161412 6.994623e-03 6.994623e-03 0.376864206 #> 5185  2592.0 0.6162254 0.6162254 6.982309e-03 6.982309e-03 0.376792266 #> 5186  2592.5 0.6163098 0.6163098 6.970031e-03 6.970031e-03 0.376720157 #> 5187  2593.0 0.6163943 0.6163943 6.957789e-03 6.957789e-03 0.376647881 #> 5188  2593.5 0.6164790 0.6164790 6.945584e-03 6.945584e-03 0.376575436 #> 5189  2594.0 0.6165638 0.6165638 6.933414e-03 6.933414e-03 0.376502825 #> 5190  2594.5 0.6166487 0.6166487 6.921281e-03 6.921281e-03 0.376430047 #> 5191  2595.0 0.6167337 0.6167337 6.909184e-03 6.909184e-03 0.376357104 #> 5192  2595.5 0.6168189 0.6168189 6.897122e-03 6.897122e-03 0.376283997 #> 5193  2596.0 0.6169042 0.6169042 6.885096e-03 6.885096e-03 0.376210725 #> 5194  2596.5 0.6169896 0.6169896 6.873106e-03 6.873106e-03 0.376137289 #> 5195  2597.0 0.6170752 0.6170752 6.861151e-03 6.861151e-03 0.376063691 #> 5196  2597.5 0.6171608 0.6171608 6.849232e-03 6.849232e-03 0.375989931 #> 5197  2598.0 0.6172466 0.6172466 6.837348e-03 6.837348e-03 0.375916009 #> 5198  2598.5 0.6173326 0.6173326 6.825500e-03 6.825500e-03 0.375841927 #> 5199  2599.0 0.6174186 0.6174186 6.813686e-03 6.813686e-03 0.375767684 #> 5200  2599.5 0.6175048 0.6175048 6.801908e-03 6.801908e-03 0.375693282 #> 5201  2600.0 0.6175911 0.6175911 6.790164e-03 6.790164e-03 0.375618721 #> 5202  2600.5 0.6176775 0.6176775 6.778456e-03 6.778456e-03 0.375544002 #> 5203  2601.0 0.6177641 0.6177641 6.766782e-03 6.766782e-03 0.375469126 #> 5204  2601.5 0.6178508 0.6178508 6.755144e-03 6.755144e-03 0.375394093 #> 5205  2602.0 0.6179376 0.6179376 6.743539e-03 6.743539e-03 0.375318904 #> 5206  2602.5 0.6180245 0.6180245 6.731970e-03 6.731970e-03 0.375243559 #> 5207  2603.0 0.6181115 0.6181115 6.720434e-03 6.720434e-03 0.375168059 #> 5208  2603.5 0.6181987 0.6181987 6.708934e-03 6.708934e-03 0.375092405 #> 5209  2604.0 0.6182859 0.6182859 6.697467e-03 6.697467e-03 0.375016598 #> 5210  2604.5 0.6183733 0.6183733 6.686035e-03 6.686035e-03 0.374940638 #> 5211  2605.0 0.6184608 0.6184608 6.674636e-03 6.674636e-03 0.374864526 #> 5212  2605.5 0.6185485 0.6185485 6.663272e-03 6.663272e-03 0.374788262 #> 5213  2606.0 0.6186362 0.6186362 6.651942e-03 6.651942e-03 0.374711847 #> 5214  2606.5 0.6187241 0.6187241 6.640645e-03 6.640645e-03 0.374635281 #> 5215  2607.0 0.6188121 0.6188121 6.629383e-03 6.629383e-03 0.374558566 #> 5216  2607.5 0.6189001 0.6189001 6.618154e-03 6.618154e-03 0.374481702 #> 5217  2608.0 0.6189884 0.6189884 6.606958e-03 6.606958e-03 0.374404690 #> 5218  2608.5 0.6190767 0.6190767 6.595796e-03 6.595796e-03 0.374327530 #> 5219  2609.0 0.6191651 0.6191651 6.584668e-03 6.584668e-03 0.374250223 #> 5220  2609.5 0.6192537 0.6192537 6.573572e-03 6.573572e-03 0.374172769 #> 5221  2610.0 0.6193423 0.6193423 6.562510e-03 6.562510e-03 0.374095169 #> 5222  2610.5 0.6194311 0.6194311 6.551482e-03 6.551482e-03 0.374017424 #> 5223  2611.0 0.6195200 0.6195200 6.540486e-03 6.540486e-03 0.373939535 #> 5224  2611.5 0.6196090 0.6196090 6.529523e-03 6.529523e-03 0.373861501 #> 5225  2612.0 0.6196981 0.6196981 6.518593e-03 6.518593e-03 0.373783324 #> 5226  2612.5 0.6197873 0.6197873 6.507696e-03 6.507696e-03 0.373705004 #> 5227  2613.0 0.6198766 0.6198766 6.496832e-03 6.496832e-03 0.373626542 #> 5228  2613.5 0.6199661 0.6199661 6.486000e-03 6.486000e-03 0.373547939 #> 5229  2614.0 0.6200556 0.6200556 6.475200e-03 6.475200e-03 0.373469195 #> 5230  2614.5 0.6201453 0.6201453 6.464434e-03 6.464434e-03 0.373390310 #> 5231  2615.0 0.6202350 0.6202350 6.453699e-03 6.453699e-03 0.373311286 #> 5232  2615.5 0.6203249 0.6203249 6.442997e-03 6.442997e-03 0.373232122 #> 5233  2616.0 0.6204149 0.6204149 6.432327e-03 6.432327e-03 0.373152820 #> 5234  2616.5 0.6205049 0.6205049 6.421689e-03 6.421689e-03 0.373073381 #> 5235  2617.0 0.6205951 0.6205951 6.411084e-03 6.411084e-03 0.372993804 #> 5236  2617.5 0.6206854 0.6206854 6.400510e-03 6.400510e-03 0.372914090 #> 5237  2618.0 0.6207758 0.6207758 6.389968e-03 6.389968e-03 0.372834240 #> 5238  2618.5 0.6208663 0.6208663 6.379458e-03 6.379458e-03 0.372754255 #> 5239  2619.0 0.6209569 0.6209569 6.368979e-03 6.368979e-03 0.372674135 #> 5240  2619.5 0.6210476 0.6210476 6.358533e-03 6.358533e-03 0.372593880 #> 5241  2620.0 0.6211384 0.6211384 6.348117e-03 6.348117e-03 0.372513492 #> 5242  2620.5 0.6212293 0.6212293 6.337734e-03 6.337734e-03 0.372432971 #> 5243  2621.0 0.6213203 0.6213203 6.327381e-03 6.327381e-03 0.372352317 #> 5244  2621.5 0.6214114 0.6214114 6.317060e-03 6.317060e-03 0.372271531 #> 5245  2622.0 0.6215026 0.6215026 6.306770e-03 6.306770e-03 0.372190614 #> 5246  2622.5 0.6215939 0.6215939 6.296511e-03 6.296511e-03 0.372109567 #> 5247  2623.0 0.6216853 0.6216853 6.286283e-03 6.286283e-03 0.372028389 #> 5248  2623.5 0.6217768 0.6217768 6.276087e-03 6.276087e-03 0.371947081 #> 5249  2624.0 0.6218684 0.6218684 6.265921e-03 6.265921e-03 0.371865645 #> 5250  2624.5 0.6219601 0.6219601 6.255786e-03 6.255786e-03 0.371784080 #> 5251  2625.0 0.6220519 0.6220519 6.245681e-03 6.245681e-03 0.371702387 #> 5252  2625.5 0.6221438 0.6221438 6.235607e-03 6.235607e-03 0.371620567 #> 5253  2626.0 0.6222358 0.6222358 6.225564e-03 6.225564e-03 0.371538620 #> 5254  2626.5 0.6223279 0.6223279 6.215551e-03 6.215551e-03 0.371456547 #> 5255  2627.0 0.6224201 0.6224201 6.205569e-03 6.205569e-03 0.371374349 #> 5256  2627.5 0.6225124 0.6225124 6.195617e-03 6.195617e-03 0.371292025 #> 5257  2628.0 0.6226047 0.6226047 6.185695e-03 6.185695e-03 0.371209578 #> 5258  2628.5 0.6226972 0.6226972 6.175804e-03 6.175804e-03 0.371127006 #> 5259  2629.0 0.6227897 0.6227897 6.165942e-03 6.165942e-03 0.371044311 #> 5260  2629.5 0.6228824 0.6228824 6.156111e-03 6.156111e-03 0.370961493 #> 5261  2630.0 0.6229751 0.6229751 6.146309e-03 6.146309e-03 0.370878553 #> 5262  2630.5 0.6230680 0.6230680 6.136537e-03 6.136537e-03 0.370795492 #> 5263  2631.0 0.6231609 0.6231609 6.126795e-03 6.126795e-03 0.370712309 #> 5264  2631.5 0.6232539 0.6232539 6.117083e-03 6.117083e-03 0.370629006 #> 5265  2632.0 0.6233470 0.6233470 6.107400e-03 6.107400e-03 0.370545583 #> 5266  2632.5 0.6234402 0.6234402 6.097747e-03 6.097747e-03 0.370462041 #> 5267  2633.0 0.6235335 0.6235335 6.088123e-03 6.088123e-03 0.370378379 #> 5268  2633.5 0.6236269 0.6236269 6.078529e-03 6.078529e-03 0.370294600 #> 5269  2634.0 0.6237203 0.6237203 6.068964e-03 6.068964e-03 0.370210703 #> 5270  2634.5 0.6238139 0.6238139 6.059428e-03 6.059428e-03 0.370126688 #> 5271  2635.0 0.6239075 0.6239075 6.049921e-03 6.049921e-03 0.370042557 #> 5272  2635.5 0.6240012 0.6240012 6.040443e-03 6.040443e-03 0.369958310 #> 5273  2636.0 0.6240951 0.6240951 6.030995e-03 6.030995e-03 0.369873947 #> 5274  2636.5 0.6241890 0.6241890 6.021575e-03 6.021575e-03 0.369789469 #> 5275  2637.0 0.6242829 0.6242829 6.012184e-03 6.012184e-03 0.369704877 #> 5276  2637.5 0.6243770 0.6243770 6.002822e-03 6.002822e-03 0.369620171 #> 5277  2638.0 0.6244712 0.6244712 5.993488e-03 5.993488e-03 0.369535351 #> 5278  2638.5 0.6245654 0.6245654 5.984183e-03 5.984183e-03 0.369450418 #> 5279  2639.0 0.6246597 0.6246597 5.974907e-03 5.974907e-03 0.369365374 #> 5280  2639.5 0.6247541 0.6247541 5.965659e-03 5.965659e-03 0.369280217 #> 5281  2640.0 0.6248486 0.6248486 5.956439e-03 5.956439e-03 0.369194949 #> 5282  2640.5 0.6249432 0.6249432 5.947248e-03 5.947248e-03 0.369109570 #> 5283  2641.0 0.6250378 0.6250378 5.938085e-03 5.938085e-03 0.369024081 #> 5284  2641.5 0.6251326 0.6251326 5.928950e-03 5.928950e-03 0.368938482 #> 5285  2642.0 0.6252274 0.6252274 5.919843e-03 5.919843e-03 0.368852774 #> 5286  2642.5 0.6253223 0.6253223 5.910765e-03 5.910765e-03 0.368766958 #> 5287  2643.0 0.6254173 0.6254173 5.901714e-03 5.901714e-03 0.368681033 #> 5288  2643.5 0.6255123 0.6255123 5.892691e-03 5.892691e-03 0.368595001 #> 5289  2644.0 0.6256074 0.6256074 5.883696e-03 5.883696e-03 0.368508861 #> 5290  2644.5 0.6257027 0.6257027 5.874728e-03 5.874728e-03 0.368422615 #> 5291  2645.0 0.6257979 0.6257979 5.865788e-03 5.865788e-03 0.368336263 #> 5292  2645.5 0.6258933 0.6258933 5.856876e-03 5.856876e-03 0.368249805 #> 5293  2646.0 0.6259888 0.6259888 5.847991e-03 5.847991e-03 0.368163243 #> 5294  2646.5 0.6260843 0.6260843 5.839134e-03 5.839134e-03 0.368076575 #> 5295  2647.0 0.6261799 0.6261799 5.830304e-03 5.830304e-03 0.367989804 #> 5296  2647.5 0.6262756 0.6262756 5.821501e-03 5.821501e-03 0.367902929 #> 5297  2648.0 0.6263713 0.6263713 5.812725e-03 5.812725e-03 0.367815951 #> 5298  2648.5 0.6264672 0.6264672 5.803977e-03 5.803977e-03 0.367728871 #> 5299  2649.0 0.6265631 0.6265631 5.795255e-03 5.795255e-03 0.367641688 #> 5300  2649.5 0.6266590 0.6266590 5.786561e-03 5.786561e-03 0.367554405 #> 5301  2650.0 0.6267551 0.6267551 5.777894e-03 5.777894e-03 0.367467020 #> 5302  2650.5 0.6268512 0.6268512 5.769253e-03 5.769253e-03 0.367379534 #> 5303  2651.0 0.6269474 0.6269474 5.760639e-03 5.760639e-03 0.367291949 #> 5304  2651.5 0.6270437 0.6270437 5.752052e-03 5.752052e-03 0.367204264 #> 5305  2652.0 0.6271400 0.6271400 5.743491e-03 5.743491e-03 0.367116480 #> 5306  2652.5 0.6272364 0.6272364 5.734957e-03 5.734957e-03 0.367028597 #> 5307  2653.0 0.6273329 0.6273329 5.726450e-03 5.726450e-03 0.366940616 #> 5308  2653.5 0.6274295 0.6274295 5.717969e-03 5.717969e-03 0.366852538 #> 5309  2654.0 0.6275261 0.6275261 5.709514e-03 5.709514e-03 0.366764363 #> 5310  2654.5 0.6276228 0.6276228 5.701085e-03 5.701085e-03 0.366676091 #> 5311  2655.0 0.6277196 0.6277196 5.692683e-03 5.692683e-03 0.366587723 #> 5312  2655.5 0.6278164 0.6278164 5.684307e-03 5.684307e-03 0.366499260 #> 5313  2656.0 0.6279133 0.6279133 5.675957e-03 5.675957e-03 0.366410701 #> 5314  2656.5 0.6280103 0.6280103 5.667633e-03 5.667633e-03 0.366322048 #> 5315  2657.0 0.6281074 0.6281074 5.659335e-03 5.659335e-03 0.366233300 #> 5316  2657.5 0.6282045 0.6282045 5.651063e-03 5.651063e-03 0.366144459 #> 5317  2658.0 0.6283017 0.6283017 5.642816e-03 5.642816e-03 0.366055525 #> 5318  2658.5 0.6283989 0.6283989 5.634595e-03 5.634595e-03 0.365966498 #> 5319  2659.0 0.6284962 0.6284962 5.626400e-03 5.626400e-03 0.365877378 #> 5320  2659.5 0.6285936 0.6285936 5.618231e-03 5.618231e-03 0.365788167 #> 5321  2660.0 0.6286910 0.6286910 5.610087e-03 5.610087e-03 0.365698865 #> 5322  2660.5 0.6287886 0.6287886 5.601968e-03 5.601968e-03 0.365609471 #> 5323  2661.0 0.6288861 0.6288861 5.593875e-03 5.593875e-03 0.365519988 #> 5324  2661.5 0.6289838 0.6289838 5.585808e-03 5.585808e-03 0.365430414 #> 5325  2662.0 0.6290815 0.6290815 5.577765e-03 5.577765e-03 0.365340751 #> 5326  2662.5 0.6291793 0.6291793 5.569748e-03 5.569748e-03 0.365250999 #> 5327  2663.0 0.6292771 0.6292771 5.561756e-03 5.561756e-03 0.365161158 #> 5328  2663.5 0.6293750 0.6293750 5.553788e-03 5.553788e-03 0.365071230 #> 5329  2664.0 0.6294729 0.6294729 5.545846e-03 5.545846e-03 0.364981214 #> 5330  2664.5 0.6295710 0.6295710 5.537929e-03 5.537929e-03 0.364891110 #> 5331  2665.0 0.6296690 0.6296690 5.530037e-03 5.530037e-03 0.364800920 #> 5332  2665.5 0.6297672 0.6297672 5.522169e-03 5.522169e-03 0.364710644 #> 5333  2666.0 0.6298654 0.6298654 5.514326e-03 5.514326e-03 0.364620282 #> 5334  2666.5 0.6299637 0.6299637 5.506508e-03 5.506508e-03 0.364529835 #> 5335  2667.0 0.6300620 0.6300620 5.498714e-03 5.498714e-03 0.364439303 #> 5336  2667.5 0.6301604 0.6301604 5.490945e-03 5.490945e-03 0.364348686 #> 5337  2668.0 0.6302588 0.6302588 5.483201e-03 5.483201e-03 0.364257986 #> 5338  2668.5 0.6303573 0.6303573 5.475480e-03 5.475480e-03 0.364167202 #> 5339  2669.0 0.6304559 0.6304559 5.467785e-03 5.467785e-03 0.364076334 #> 5340  2669.5 0.6305545 0.6305545 5.460113e-03 5.460113e-03 0.363985385 #> 5341  2670.0 0.6306532 0.6306532 5.452466e-03 5.452466e-03 0.363894353 #> 5342  2670.5 0.6307519 0.6307519 5.444842e-03 5.444842e-03 0.363803239 #> 5343  2671.0 0.6308507 0.6308507 5.437243e-03 5.437243e-03 0.363712045 #> 5344  2671.5 0.6309496 0.6309496 5.429668e-03 5.429668e-03 0.363620769 #> 5345  2672.0 0.6310485 0.6310485 5.422117e-03 5.422117e-03 0.363529413 #> 5346  2672.5 0.6311474 0.6311474 5.414590e-03 5.414590e-03 0.363437977 #> 5347  2673.0 0.6312465 0.6312465 5.407086e-03 5.407086e-03 0.363346461 #> 5348  2673.5 0.6313455 0.6313455 5.399607e-03 5.399607e-03 0.363254867 #> 5349  2674.0 0.6314447 0.6314447 5.392151e-03 5.392151e-03 0.363163194 #> 5350  2674.5 0.6315438 0.6315438 5.384718e-03 5.384718e-03 0.363071442 #> 5351  2675.0 0.6316431 0.6316431 5.377310e-03 5.377310e-03 0.362979613 #> 5352  2675.5 0.6317424 0.6317424 5.369924e-03 5.369924e-03 0.362887706 #> 5353  2676.0 0.6318417 0.6318417 5.362563e-03 5.362563e-03 0.362795723 #> 5354  2676.5 0.6319411 0.6319411 5.355224e-03 5.355224e-03 0.362703663 #> 5355  2677.0 0.6320406 0.6320406 5.347909e-03 5.347909e-03 0.362611527 #> 5356  2677.5 0.6321401 0.6321401 5.340617e-03 5.340617e-03 0.362519315 #> 5357  2678.0 0.6322396 0.6322396 5.333349e-03 5.333349e-03 0.362427028 #> 5358  2678.5 0.6323392 0.6323392 5.326103e-03 5.326103e-03 0.362334667 #> 5359  2679.0 0.6324389 0.6324389 5.318881e-03 5.318881e-03 0.362242230 #> 5360  2679.5 0.6325386 0.6325386 5.311682e-03 5.311682e-03 0.362149720 #> 5361  2680.0 0.6326384 0.6326384 5.304506e-03 5.304506e-03 0.362057137 #> 5362  2680.5 0.6327382 0.6327382 5.297352e-03 5.297352e-03 0.361964480 #> 5363  2681.0 0.6328380 0.6328380 5.290222e-03 5.290222e-03 0.361871751 #> 5364  2681.5 0.6329379 0.6329379 5.283114e-03 5.283114e-03 0.361778949 #> 5365  2682.0 0.6330379 0.6330379 5.276029e-03 5.276029e-03 0.361686076 #> 5366  2682.5 0.6331379 0.6331379 5.268967e-03 5.268967e-03 0.361593131 #> 5367  2683.0 0.6332380 0.6332380 5.261927e-03 5.261927e-03 0.361500115 #> 5368  2683.5 0.6333381 0.6333381 5.254910e-03 5.254910e-03 0.361407029 #> 5369  2684.0 0.6334382 0.6334382 5.247915e-03 5.247915e-03 0.361313872 #> 5370  2684.5 0.6335384 0.6335384 5.240943e-03 5.240943e-03 0.361220645 #> 5371  2685.0 0.6336387 0.6336387 5.233993e-03 5.233993e-03 0.361127350 #> 5372  2685.5 0.6337390 0.6337390 5.227065e-03 5.227065e-03 0.361033985 #> 5373  2686.0 0.6338393 0.6338393 5.220160e-03 5.220160e-03 0.360940551 #> 5374  2686.5 0.6339397 0.6339397 5.213277e-03 5.213277e-03 0.360847050 #> 5375  2687.0 0.6340401 0.6340401 5.206416e-03 5.206416e-03 0.360753480 #> 5376  2687.5 0.6341406 0.6341406 5.199577e-03 5.199577e-03 0.360659843 #> 5377  2688.0 0.6342411 0.6342411 5.192760e-03 5.192760e-03 0.360566140 #> 5378  2688.5 0.6343417 0.6343417 5.185965e-03 5.185965e-03 0.360472369 #> 5379  2689.0 0.6344423 0.6344423 5.179192e-03 5.179192e-03 0.360378533 #> 5380  2689.5 0.6345429 0.6345429 5.172441e-03 5.172441e-03 0.360284631 #> 5381  2690.0 0.6346436 0.6346436 5.165712e-03 5.165712e-03 0.360190663 #> 5382  2690.5 0.6347444 0.6347444 5.159005e-03 5.159005e-03 0.360096631 #> 5383  2691.0 0.6348451 0.6348451 5.152319e-03 5.152319e-03 0.360002534 #> 5384  2691.5 0.6349460 0.6349460 5.145654e-03 5.145654e-03 0.359908372 #> 5385  2692.0 0.6350468 0.6350468 5.139012e-03 5.139012e-03 0.359814147 #> 5386  2692.5 0.6351478 0.6351478 5.132391e-03 5.132391e-03 0.359719859 #> 5387  2693.0 0.6352487 0.6352487 5.125791e-03 5.125791e-03 0.359625507 #> 5388  2693.5 0.6353497 0.6353497 5.119213e-03 5.119213e-03 0.359531093 #> 5389  2694.0 0.6354507 0.6354507 5.112656e-03 5.112656e-03 0.359436617 #> 5390  2694.5 0.6355518 0.6355518 5.106120e-03 5.106120e-03 0.359342078 #> 5391  2695.0 0.6356529 0.6356529 5.099606e-03 5.099606e-03 0.359247479 #> 5392  2695.5 0.6357541 0.6357541 5.093113e-03 5.093113e-03 0.359152818 #> 5393  2696.0 0.6358553 0.6358553 5.086641e-03 5.086641e-03 0.359058096 #> 5394  2696.5 0.6359565 0.6359565 5.080190e-03 5.080190e-03 0.358963315 #> 5395  2697.0 0.6360578 0.6360578 5.073760e-03 5.073760e-03 0.358868473 #> 5396  2697.5 0.6361591 0.6361591 5.067351e-03 5.067351e-03 0.358773572 #> 5397  2698.0 0.6362604 0.6362604 5.060963e-03 5.060963e-03 0.358678611 #> 5398  2698.5 0.6363618 0.6363618 5.054596e-03 5.054596e-03 0.358583592 #> 5399  2699.0 0.6364632 0.6364632 5.048249e-03 5.048249e-03 0.358488514 #> 5400  2699.5 0.6365647 0.6365647 5.041924e-03 5.041924e-03 0.358393378 #> 5401  2700.0 0.6366662 0.6366662 5.035619e-03 5.035619e-03 0.358298185 #> 5402  2700.5 0.6367677 0.6367677 5.029334e-03 5.029334e-03 0.358202934 #> 5403  2701.0 0.6368693 0.6368693 5.023071e-03 5.023071e-03 0.358107627 #> 5404  2701.5 0.6369709 0.6369709 5.016828e-03 5.016828e-03 0.358012262 #> 5405  2702.0 0.6370726 0.6370726 5.010605e-03 5.010605e-03 0.357916842 #> 5406  2702.5 0.6371742 0.6371742 5.004403e-03 5.004403e-03 0.357821366 #> 5407  2703.0 0.6372759 0.6372759 4.998221e-03 4.998221e-03 0.357725834 #> 5408  2703.5 0.6373777 0.6373777 4.992060e-03 4.992060e-03 0.357630248 #> 5409  2704.0 0.6374795 0.6374795 4.985918e-03 4.985918e-03 0.357534606 #> 5410  2704.5 0.6375813 0.6375813 4.979797e-03 4.979797e-03 0.357438910 #> 5411  2705.0 0.6376831 0.6376831 4.973697e-03 4.973697e-03 0.357343161 #> 5412  2705.5 0.6377850 0.6377850 4.967616e-03 4.967616e-03 0.357247357 #> 5413  2706.0 0.6378869 0.6378869 4.961555e-03 4.961555e-03 0.357151501 #> 5414  2706.5 0.6379889 0.6379889 4.955515e-03 4.955515e-03 0.357055591 #> 5415  2707.0 0.6380909 0.6380909 4.949494e-03 4.949494e-03 0.356959629 #> 5416  2707.5 0.6381929 0.6381929 4.943493e-03 4.943493e-03 0.356863615 #> 5417  2708.0 0.6382949 0.6382949 4.937513e-03 4.937513e-03 0.356767549 #> 5418  2708.5 0.6383970 0.6383970 4.931552e-03 4.931552e-03 0.356671431 #> 5419  2709.0 0.6384991 0.6384991 4.925610e-03 4.925610e-03 0.356575262 #> 5420  2709.5 0.6386013 0.6386013 4.919689e-03 4.919689e-03 0.356479043 #> 5421  2710.0 0.6387034 0.6387034 4.913787e-03 4.913787e-03 0.356382773 #> 5422  2710.5 0.6388056 0.6388056 4.907905e-03 4.907905e-03 0.356286453 #> 5423  2711.0 0.6389079 0.6389079 4.902042e-03 4.902042e-03 0.356190083 #> 5424  2711.5 0.6390101 0.6390101 4.896199e-03 4.896199e-03 0.356093665 #> 5425  2712.0 0.6391124 0.6391124 4.890375e-03 4.890375e-03 0.355997197 #> 5426  2712.5 0.6392147 0.6392147 4.884571e-03 4.884571e-03 0.355900680 #> 5427  2713.0 0.6393171 0.6393171 4.878786e-03 4.878786e-03 0.355804115 #> 5428  2713.5 0.6394195 0.6394195 4.873021e-03 4.873021e-03 0.355707502 #> 5429  2714.0 0.6395219 0.6395219 4.867274e-03 4.867274e-03 0.355610842 #> 5430  2714.5 0.6396243 0.6396243 4.861547e-03 4.861547e-03 0.355514134 #> 5431  2715.0 0.6397268 0.6397268 4.855839e-03 4.855839e-03 0.355417379 #> 5432  2715.5 0.6398293 0.6398293 4.850151e-03 4.850151e-03 0.355320578 #> 5433  2716.0 0.6399318 0.6399318 4.844481e-03 4.844481e-03 0.355223730 #> 5434  2716.5 0.6400343 0.6400343 4.838830e-03 4.838830e-03 0.355126837 #> 5435  2717.0 0.6401369 0.6401369 4.833199e-03 4.833199e-03 0.355029898 #> 5436  2717.5 0.6402395 0.6402395 4.827586e-03 4.827586e-03 0.354932914 #> 5437  2718.0 0.6403421 0.6403421 4.821992e-03 4.821992e-03 0.354835885 #> 5438  2718.5 0.6404448 0.6404448 4.816417e-03 4.816417e-03 0.354738811 #> 5439  2719.0 0.6405474 0.6405474 4.810861e-03 4.810861e-03 0.354641693 #> 5440  2719.5 0.6406501 0.6406501 4.805323e-03 4.805323e-03 0.354544532 #> 5441  2720.0 0.6407529 0.6407529 4.799805e-03 4.799805e-03 0.354447326 #> 5442  2720.5 0.6408556 0.6408556 4.794305e-03 4.794305e-03 0.354350078 #> 5443  2721.0 0.6409584 0.6409584 4.788823e-03 4.788823e-03 0.354252787 #> 5444  2721.5 0.6410612 0.6410612 4.783360e-03 4.783360e-03 0.354155453 #> 5445  2722.0 0.6411640 0.6411640 4.777916e-03 4.777916e-03 0.354058077 #> 5446  2722.5 0.6412669 0.6412669 4.772490e-03 4.772490e-03 0.353960659 #> 5447  2723.0 0.6413697 0.6413697 4.767082e-03 4.767082e-03 0.353863199 #> 5448  2723.5 0.6414726 0.6414726 4.761693e-03 4.761693e-03 0.353765698 #> 5449  2724.0 0.6415755 0.6415755 4.756322e-03 4.756322e-03 0.353668156 #> 5450  2724.5 0.6416785 0.6416785 4.750970e-03 4.750970e-03 0.353570574 #> 5451  2725.0 0.6417814 0.6417814 4.745635e-03 4.745635e-03 0.353472952 #> 5452  2725.5 0.6418844 0.6418844 4.740319e-03 4.740319e-03 0.353375289 #> 5453  2726.0 0.6419874 0.6419874 4.735021e-03 4.735021e-03 0.353277587 #> 5454  2726.5 0.6420904 0.6420904 4.729741e-03 4.729741e-03 0.353179846 #> 5455  2727.0 0.6421935 0.6421935 4.724479e-03 4.724479e-03 0.353082065 #> 5456  2727.5 0.6422965 0.6422965 4.719236e-03 4.719236e-03 0.352984246 #> 5457  2728.0 0.6423996 0.6423996 4.714010e-03 4.714010e-03 0.352886389 #> 5458  2728.5 0.6425027 0.6425027 4.708802e-03 4.708802e-03 0.352788494 #> 5459  2729.0 0.6426058 0.6426058 4.703612e-03 4.703612e-03 0.352690561 #> 5460  2729.5 0.6427090 0.6427090 4.698439e-03 4.698439e-03 0.352592590 #> 5461  2730.0 0.6428121 0.6428121 4.693285e-03 4.693285e-03 0.352494583 #> 5462  2730.5 0.6429153 0.6429153 4.688148e-03 4.688148e-03 0.352396539 #> 5463  2731.0 0.6430185 0.6430185 4.683029e-03 4.683029e-03 0.352298458 #> 5464  2731.5 0.6431217 0.6431217 4.677928e-03 4.677928e-03 0.352200341 #> 5465  2732.0 0.6432250 0.6432250 4.672844e-03 4.672844e-03 0.352102189 #> 5466  2732.5 0.6433282 0.6433282 4.667778e-03 4.667778e-03 0.352004001 #> 5467  2733.0 0.6434315 0.6434315 4.662730e-03 4.662730e-03 0.351905778 #> 5468  2733.5 0.6435348 0.6435348 4.657699e-03 4.657699e-03 0.351807519 #> 5469  2734.0 0.6436381 0.6436381 4.652685e-03 4.652685e-03 0.351709227 #> 5470  2734.5 0.6437414 0.6437414 4.647689e-03 4.647689e-03 0.351610900 #> 5471  2735.0 0.6438448 0.6438448 4.642710e-03 4.642710e-03 0.351512539 #> 5472  2735.5 0.6439481 0.6439481 4.637748e-03 4.637748e-03 0.351414144 #> 5473  2736.0 0.6440515 0.6440515 4.632804e-03 4.632804e-03 0.351315716 #> 5474  2736.5 0.6441549 0.6441549 4.627877e-03 4.627877e-03 0.351217256 #> 5475  2737.0 0.6442583 0.6442583 4.622967e-03 4.622967e-03 0.351118762 #> 5476  2737.5 0.6443617 0.6443617 4.618074e-03 4.618074e-03 0.351020236 #> 5477  2738.0 0.6444651 0.6444651 4.613198e-03 4.613198e-03 0.350921678 #> 5478  2738.5 0.6445686 0.6445686 4.608340e-03 4.608340e-03 0.350823087 #> 5479  2739.0 0.6446720 0.6446720 4.603498e-03 4.603498e-03 0.350724466 #> 5480  2739.5 0.6447755 0.6447755 4.598674e-03 4.598674e-03 0.350625813 #> 5481  2740.0 0.6448790 0.6448790 4.593866e-03 4.593866e-03 0.350527129 #> 5482  2740.5 0.6449825 0.6449825 4.589075e-03 4.589075e-03 0.350428415 #> 5483  2741.0 0.6450860 0.6450860 4.584301e-03 4.584301e-03 0.350329670 #> 5484  2741.5 0.6451896 0.6451896 4.579544e-03 4.579544e-03 0.350230895 #> 5485  2742.0 0.6452931 0.6452931 4.574804e-03 4.574804e-03 0.350132091 #> 5486  2742.5 0.6453967 0.6453967 4.570081e-03 4.570081e-03 0.350033257 #> 5487  2743.0 0.6455002 0.6455002 4.565374e-03 4.565374e-03 0.349934394 #> 5488  2743.5 0.6456038 0.6456038 4.560684e-03 4.560684e-03 0.349835502 #> 5489  2744.0 0.6457074 0.6457074 4.556010e-03 4.556010e-03 0.349736581 #> 5490  2744.5 0.6458110 0.6458110 4.551353e-03 4.551353e-03 0.349637632 #> 5491  2745.0 0.6459146 0.6459146 4.546713e-03 4.546713e-03 0.349538655 #> 5492  2745.5 0.6460183 0.6460183 4.542089e-03 4.542089e-03 0.349439651 #> 5493  2746.0 0.6461219 0.6461219 4.537481e-03 4.537481e-03 0.349340619 #> 5494  2746.5 0.6462256 0.6462256 4.532890e-03 4.532890e-03 0.349241560 #> 5495  2747.0 0.6463292 0.6463292 4.528315e-03 4.528315e-03 0.349142474 #> 5496  2747.5 0.6464329 0.6464329 4.523757e-03 4.523757e-03 0.349043361 #> 5497  2748.0 0.6465366 0.6465366 4.519215e-03 4.519215e-03 0.348944222 #> 5498  2748.5 0.6466403 0.6466403 4.514689e-03 4.514689e-03 0.348845057 #> 5499  2749.0 0.6467440 0.6467440 4.510180e-03 4.510180e-03 0.348745867 #> 5500  2749.5 0.6468477 0.6468477 4.505686e-03 4.505686e-03 0.348646651 #> 5501  2750.0 0.6469514 0.6469514 4.501209e-03 4.501209e-03 0.348547410 #> 5502  2750.5 0.6470551 0.6470551 4.496748e-03 4.496748e-03 0.348448144 #> 5503  2751.0 0.6471588 0.6471588 4.492303e-03 4.492303e-03 0.348348853 #> 5504  2751.5 0.6472626 0.6472626 4.487874e-03 4.487874e-03 0.348249539 #> 5505  2752.0 0.6473663 0.6473663 4.483461e-03 4.483461e-03 0.348150200 #> 5506  2752.5 0.6474701 0.6474701 4.479064e-03 4.479064e-03 0.348050837 #> 5507  2753.0 0.6475739 0.6475739 4.474683e-03 4.474683e-03 0.347951451 #> 5508  2753.5 0.6476776 0.6476776 4.470317e-03 4.470317e-03 0.347852042 #> 5509  2754.0 0.6477814 0.6477814 4.465968e-03 4.465968e-03 0.347752610 #> 5510  2754.5 0.6478852 0.6478852 4.461634e-03 4.461634e-03 0.347653155 #> 5511  2755.0 0.6479890 0.6479890 4.457316e-03 4.457316e-03 0.347553678 #> 5512  2755.5 0.6480928 0.6480928 4.453014e-03 4.453014e-03 0.347454179 #> 5513  2756.0 0.6481966 0.6481966 4.448728e-03 4.448728e-03 0.347354658 #> 5514  2756.5 0.6483004 0.6483004 4.444457e-03 4.444457e-03 0.347255115 #> 5515  2757.0 0.6484042 0.6484042 4.440202e-03 4.440202e-03 0.347155551 #> 5516  2757.5 0.6485081 0.6485081 4.435963e-03 4.435963e-03 0.347055966 #> 5517  2758.0 0.6486119 0.6486119 4.431739e-03 4.431739e-03 0.346956361 #> 5518  2758.5 0.6487157 0.6487157 4.427530e-03 4.427530e-03 0.346856735 #> 5519  2759.0 0.6488196 0.6488196 4.423337e-03 4.423337e-03 0.346757088 #> 5520  2759.5 0.6489234 0.6489234 4.419160e-03 4.419160e-03 0.346657422 #> 5521  2760.0 0.6490273 0.6490273 4.414997e-03 4.414997e-03 0.346557736 #> 5522  2760.5 0.6491311 0.6491311 4.410851e-03 4.410851e-03 0.346458031 #> 5523  2761.0 0.6492350 0.6492350 4.406719e-03 4.406719e-03 0.346358307 #> 5524  2761.5 0.6493388 0.6493388 4.402603e-03 4.402603e-03 0.346258564 #> 5525  2762.0 0.6494427 0.6494427 4.398502e-03 4.398502e-03 0.346158802 #> 5526  2762.5 0.6495466 0.6495466 4.394417e-03 4.394417e-03 0.346059022 #> 5527  2763.0 0.6496504 0.6496504 4.390346e-03 4.390346e-03 0.345959223 #> 5528  2763.5 0.6497543 0.6497543 4.386291e-03 4.386291e-03 0.345859407 #> 5529  2764.0 0.6498582 0.6498582 4.382251e-03 4.382251e-03 0.345759574 #> 5530  2764.5 0.6499621 0.6499621 4.378226e-03 4.378226e-03 0.345659723 #> 5531  2765.0 0.6500659 0.6500659 4.374216e-03 4.374216e-03 0.345559855 #> 5532  2765.5 0.6501698 0.6501698 4.370221e-03 4.370221e-03 0.345459970 #> 5533  2766.0 0.6502737 0.6502737 4.366241e-03 4.366241e-03 0.345360069 #> 5534  2766.5 0.6503776 0.6503776 4.362276e-03 4.362276e-03 0.345260152 #> 5535  2767.0 0.6504815 0.6504815 4.358326e-03 4.358326e-03 0.345160218 #> 5536  2767.5 0.6505853 0.6505853 4.354390e-03 4.354390e-03 0.345060269 #> 5537  2768.0 0.6506892 0.6506892 4.350470e-03 4.350470e-03 0.344960304 #> 5538  2768.5 0.6507931 0.6507931 4.346564e-03 4.346564e-03 0.344860324 #> 5539  2769.0 0.6508970 0.6508970 4.342674e-03 4.342674e-03 0.344760330 #> 5540  2769.5 0.6510009 0.6510009 4.338798e-03 4.338798e-03 0.344660320 #> 5541  2770.0 0.6511048 0.6511048 4.334936e-03 4.334936e-03 0.344560296 #> 5542  2770.5 0.6512087 0.6512087 4.331090e-03 4.331090e-03 0.344460258 #> 5543  2771.0 0.6513125 0.6513125 4.327258e-03 4.327258e-03 0.344360205 #> 5544  2771.5 0.6514164 0.6514164 4.323440e-03 4.323440e-03 0.344260139 #> 5545  2772.0 0.6515203 0.6515203 4.319637e-03 4.319637e-03 0.344160060 #> 5546  2772.5 0.6516242 0.6516242 4.315849e-03 4.315849e-03 0.344059967 #> 5547  2773.0 0.6517281 0.6517281 4.312076e-03 4.312076e-03 0.343959862 #> 5548  2773.5 0.6518319 0.6518319 4.308316e-03 4.308316e-03 0.343859743 #> 5549  2774.0 0.6519358 0.6519358 4.304572e-03 4.304572e-03 0.343759613 #> 5550  2774.5 0.6520397 0.6520397 4.300841e-03 4.300841e-03 0.343659470 #> 5551  2775.0 0.6521436 0.6521436 4.297125e-03 4.297125e-03 0.343559315 #> 5552  2775.5 0.6522474 0.6522474 4.293424e-03 4.293424e-03 0.343459148 #> 5553  2776.0 0.6523513 0.6523513 4.289736e-03 4.289736e-03 0.343358970 #> 5554  2776.5 0.6524552 0.6524552 4.286064e-03 4.286064e-03 0.343258780 #> 5555  2777.0 0.6525590 0.6525590 4.282405e-03 4.282405e-03 0.343158580 #> 5556  2777.5 0.6526629 0.6526629 4.278760e-03 4.278760e-03 0.343058369 #> 5557  2778.0 0.6527667 0.6527667 4.275130e-03 4.275130e-03 0.342958147 #> 5558  2778.5 0.6528706 0.6528706 4.271514e-03 4.271514e-03 0.342857915 #> 5559  2779.0 0.6529744 0.6529744 4.267912e-03 4.267912e-03 0.342757673 #> 5560  2779.5 0.6530783 0.6530783 4.264324e-03 4.264324e-03 0.342657421 #> 5561  2780.0 0.6531821 0.6531821 4.260750e-03 4.260750e-03 0.342557160 #> 5562  2780.5 0.6532859 0.6532859 4.257191e-03 4.257191e-03 0.342456889 #> 5563  2781.0 0.6533897 0.6533897 4.253645e-03 4.253645e-03 0.342356609 #> 5564  2781.5 0.6534936 0.6534936 4.250113e-03 4.250113e-03 0.342256321 #> 5565  2782.0 0.6535974 0.6535974 4.246596e-03 4.246596e-03 0.342156023 #> 5566  2782.5 0.6537012 0.6537012 4.243092e-03 4.243092e-03 0.342055718 #> 5567  2783.0 0.6538050 0.6538050 4.239602e-03 4.239602e-03 0.341955404 #> 5568  2783.5 0.6539088 0.6539088 4.236126e-03 4.236126e-03 0.341855083 #> 5569  2784.0 0.6540126 0.6540126 4.232663e-03 4.232663e-03 0.341754753 #> 5570  2784.5 0.6541164 0.6541164 4.229215e-03 4.229215e-03 0.341654417 #> 5571  2785.0 0.6542201 0.6542201 4.225780e-03 4.225780e-03 0.341554073 #> 5572  2785.5 0.6543239 0.6543239 4.222359e-03 4.222359e-03 0.341453722 #> 5573  2786.0 0.6544277 0.6544277 4.218952e-03 4.218952e-03 0.341353364 #> 5574  2786.5 0.6545314 0.6545314 4.215559e-03 4.215559e-03 0.341253000 #> 5575  2787.0 0.6546352 0.6546352 4.212179e-03 4.212179e-03 0.341152630 #> 5576  2787.5 0.6547389 0.6547389 4.208813e-03 4.208813e-03 0.341052254 #> 5577  2788.0 0.6548427 0.6548427 4.205460e-03 4.205460e-03 0.340951872 #> 5578  2788.5 0.6549464 0.6549464 4.202121e-03 4.202121e-03 0.340851484 #> 5579  2789.0 0.6550501 0.6550501 4.198795e-03 4.198795e-03 0.340751091 #> 5580  2789.5 0.6551538 0.6551538 4.195483e-03 4.195483e-03 0.340650693 #> 5581  2790.0 0.6552575 0.6552575 4.192185e-03 4.192185e-03 0.340550290 #> 5582  2790.5 0.6553612 0.6553612 4.188900e-03 4.188900e-03 0.340449882 #> 5583  2791.0 0.6554649 0.6554649 4.185628e-03 4.185628e-03 0.340349470 #> 5584  2791.5 0.6555686 0.6555686 4.182370e-03 4.182370e-03 0.340249054 #> 5585  2792.0 0.6556722 0.6556722 4.179125e-03 4.179125e-03 0.340148633 #> 5586  2792.5 0.6557759 0.6557759 4.175893e-03 4.175893e-03 0.340048209 #> 5587  2793.0 0.6558795 0.6558795 4.172675e-03 4.172675e-03 0.339947782 #> 5588  2793.5 0.6559832 0.6559832 4.169470e-03 4.169470e-03 0.339847351 #> 5589  2794.0 0.6560868 0.6560868 4.166278e-03 4.166278e-03 0.339746917 #> 5590  2794.5 0.6561904 0.6561904 4.163100e-03 4.163100e-03 0.339646480 #> 5591  2795.0 0.6562940 0.6562940 4.159934e-03 4.159934e-03 0.339546040 #> 5592  2795.5 0.6563976 0.6563976 4.156782e-03 4.156782e-03 0.339445598 #> 5593  2796.0 0.6565012 0.6565012 4.153643e-03 4.153643e-03 0.339345154 #> 5594  2796.5 0.6566048 0.6566048 4.150517e-03 4.150517e-03 0.339244708 #> 5595  2797.0 0.6567083 0.6567083 4.147405e-03 4.147405e-03 0.339144260 #> 5596  2797.5 0.6568119 0.6568119 4.144305e-03 4.144305e-03 0.339043811 #> 5597  2798.0 0.6569154 0.6569154 4.141218e-03 4.141218e-03 0.338943360 #> 5598  2798.5 0.6570189 0.6570189 4.138145e-03 4.138145e-03 0.338842908 #> 5599  2799.0 0.6571225 0.6571225 4.135084e-03 4.135084e-03 0.338742455 #> 5600  2799.5 0.6572260 0.6572260 4.132036e-03 4.132036e-03 0.338642002 #> 5601  2800.0 0.6573295 0.6573295 4.129001e-03 4.129001e-03 0.338541548 #> 5602  2800.5 0.6574329 0.6574329 4.125980e-03 4.125980e-03 0.338441094 #> 5603  2801.0 0.6575364 0.6575364 4.122971e-03 4.122971e-03 0.338340639 #> 5604  2801.5 0.6576398 0.6576398 4.119974e-03 4.119974e-03 0.338240185 #> 5605  2802.0 0.6577433 0.6577433 4.116991e-03 4.116991e-03 0.338139732 #> 5606  2802.5 0.6578467 0.6578467 4.114020e-03 4.114020e-03 0.338039279 #> 5607  2803.0 0.6579501 0.6579501 4.111063e-03 4.111063e-03 0.337938827 #> 5608  2803.5 0.6580535 0.6580535 4.108118e-03 4.108118e-03 0.337838375 #> 5609  2804.0 0.6581569 0.6581569 4.105185e-03 4.105185e-03 0.337737926 #> 5610  2804.5 0.6582603 0.6582603 4.102266e-03 4.102266e-03 0.337637477 #> 5611  2805.0 0.6583636 0.6583636 4.099359e-03 4.099359e-03 0.337537030 #> 5612  2805.5 0.6584669 0.6584669 4.096465e-03 4.096465e-03 0.337436585 #> 5613  2806.0 0.6585703 0.6585703 4.093583e-03 4.093583e-03 0.337336143 #> 5614  2806.5 0.6586736 0.6586736 4.090714e-03 4.090714e-03 0.337235702 #> 5615  2807.0 0.6587769 0.6587769 4.087857e-03 4.087857e-03 0.337135264 #> 5616  2807.5 0.6588802 0.6588802 4.085013e-03 4.085013e-03 0.337034829 #> 5617  2808.0 0.6589834 0.6589834 4.082182e-03 4.082182e-03 0.336934397 #> 5618  2808.5 0.6590867 0.6590867 4.079363e-03 4.079363e-03 0.336833967 #> 5619  2809.0 0.6591899 0.6591899 4.076556e-03 4.076556e-03 0.336733541 #> 5620  2809.5 0.6592931 0.6592931 4.073762e-03 4.073762e-03 0.336633119 #> 5621  2810.0 0.6593963 0.6593963 4.070981e-03 4.070981e-03 0.336532700 #> 5622  2810.5 0.6594995 0.6594995 4.068212e-03 4.068212e-03 0.336432286 #> 5623  2811.0 0.6596027 0.6596027 4.065455e-03 4.065455e-03 0.336331875 #> 5624  2811.5 0.6597058 0.6597058 4.062710e-03 4.062710e-03 0.336231469 #> 5625  2812.0 0.6598090 0.6598090 4.059978e-03 4.059978e-03 0.336131068 #> 5626  2812.5 0.6599121 0.6599121 4.057258e-03 4.057258e-03 0.336030671 #> 5627  2813.0 0.6600152 0.6600152 4.054550e-03 4.054550e-03 0.335930279 #> 5628  2813.5 0.6601183 0.6601183 4.051855e-03 4.051855e-03 0.335829892 #> 5629  2814.0 0.6602213 0.6602213 4.049172e-03 4.049172e-03 0.335729511 #> 5630  2814.5 0.6603244 0.6603244 4.046501e-03 4.046501e-03 0.335629135 #> 5631  2815.0 0.6604274 0.6604274 4.043842e-03 4.043842e-03 0.335528765 #> 5632  2815.5 0.6605304 0.6605304 4.041196e-03 4.041196e-03 0.335428401 #> 5633  2816.0 0.6606334 0.6606334 4.038561e-03 4.038561e-03 0.335328043 #> 5634  2816.5 0.6607364 0.6607364 4.035939e-03 4.035939e-03 0.335227692 #> 5635  2817.0 0.6608393 0.6608393 4.033328e-03 4.033328e-03 0.335127347 #> 5636  2817.5 0.6609423 0.6609423 4.030730e-03 4.030730e-03 0.335027009 #> 5637  2818.0 0.6610452 0.6610452 4.028144e-03 4.028144e-03 0.334926678 #> 5638  2818.5 0.6611481 0.6611481 4.025570e-03 4.025570e-03 0.334826354 #> 5639  2819.0 0.6612510 0.6612510 4.023008e-03 4.023008e-03 0.334726037 #> 5640  2819.5 0.6613538 0.6613538 4.020457e-03 4.020457e-03 0.334625728 #> 5641  2820.0 0.6614567 0.6614567 4.017919e-03 4.017919e-03 0.334525427 #> 5642  2820.5 0.6615595 0.6615595 4.015393e-03 4.015393e-03 0.334425133 #> 5643  2821.0 0.6616623 0.6616623 4.012878e-03 4.012878e-03 0.334324848 #> 5644  2821.5 0.6617651 0.6617651 4.010376e-03 4.010376e-03 0.334224571 #> 5645  2822.0 0.6618678 0.6618678 4.007885e-03 4.007885e-03 0.334124303 #> 5646  2822.5 0.6619706 0.6619706 4.005406e-03 4.005406e-03 0.334024043 #> 5647  2823.0 0.6620733 0.6620733 4.002939e-03 4.002939e-03 0.333923792 #> 5648  2823.5 0.6621760 0.6621760 4.000484e-03 4.000484e-03 0.333823551 #> 5649  2824.0 0.6622786 0.6622786 3.998041e-03 3.998041e-03 0.333723318 #> 5650  2824.5 0.6623813 0.6623813 3.995609e-03 3.995609e-03 0.333623095 #> 5651  2825.0 0.6624839 0.6624839 3.993189e-03 3.993189e-03 0.333522882 #> 5652  2825.5 0.6625865 0.6625865 3.990780e-03 3.990780e-03 0.333422679 #> 5653  2826.0 0.6626891 0.6626891 3.988384e-03 3.988384e-03 0.333322486 #> 5654  2826.5 0.6627917 0.6627917 3.985999e-03 3.985999e-03 0.333222303 #> 5655  2827.0 0.6628942 0.6628942 3.983626e-03 3.983626e-03 0.333122130 #> 5656  2827.5 0.6629968 0.6629968 3.981264e-03 3.981264e-03 0.333021968 #> 5657  2828.0 0.6630993 0.6630993 3.978914e-03 3.978914e-03 0.332921817 #> 5658  2828.5 0.6632017 0.6632017 3.976575e-03 3.976575e-03 0.332821677 #> 5659  2829.0 0.6633042 0.6633042 3.974248e-03 3.974248e-03 0.332721548 #> 5660  2829.5 0.6634066 0.6634066 3.971933e-03 3.971933e-03 0.332621430 #> 5661  2830.0 0.6635090 0.6635090 3.969629e-03 3.969629e-03 0.332521324 #> 5662  2830.5 0.6636114 0.6636114 3.967336e-03 3.967336e-03 0.332421230 #> 5663  2831.0 0.6637138 0.6637138 3.965055e-03 3.965055e-03 0.332321148 #> 5664  2831.5 0.6638161 0.6638161 3.962786e-03 3.962786e-03 0.332221077 #> 5665  2832.0 0.6639185 0.6639185 3.960528e-03 3.960528e-03 0.332121019 #> 5666  2832.5 0.6640207 0.6640207 3.958281e-03 3.958281e-03 0.332020974 #> 5667  2833.0 0.6641230 0.6641230 3.956046e-03 3.956046e-03 0.331920941 #> 5668  2833.5 0.6642253 0.6642253 3.953822e-03 3.953822e-03 0.331820921 #> 5669  2834.0 0.6643275 0.6643275 3.951609e-03 3.951609e-03 0.331720914 #> 5670  2834.5 0.6644297 0.6644297 3.949408e-03 3.949408e-03 0.331620921 #> 5671  2835.0 0.6645318 0.6645318 3.947218e-03 3.947218e-03 0.331520940 #> 5672  2835.5 0.6646340 0.6646340 3.945040e-03 3.945040e-03 0.331420974 #> 5673  2836.0 0.6647361 0.6647361 3.942872e-03 3.942872e-03 0.331321021 #> 5674  2836.5 0.6648382 0.6648382 3.940716e-03 3.940716e-03 0.331221082 #> 5675  2837.0 0.6649403 0.6649403 3.938571e-03 3.938571e-03 0.331121157 #> 5676  2837.5 0.6650423 0.6650423 3.936438e-03 3.936438e-03 0.331021247 #> 5677  2838.0 0.6651443 0.6651443 3.934315e-03 3.934315e-03 0.330921350 #> 5678  2838.5 0.6652463 0.6652463 3.932204e-03 3.932204e-03 0.330821469 #> 5679  2839.0 0.6653483 0.6653483 3.930104e-03 3.930104e-03 0.330721603 #> 5680  2839.5 0.6654502 0.6654502 3.928015e-03 3.928015e-03 0.330621751 #> 5681  2840.0 0.6655521 0.6655521 3.925937e-03 3.925937e-03 0.330521915 #> 5682  2840.5 0.6656540 0.6656540 3.923870e-03 3.923870e-03 0.330422094 #> 5683  2841.0 0.6657559 0.6657559 3.921814e-03 3.921814e-03 0.330322288 #> 5684  2841.5 0.6658577 0.6658577 3.919770e-03 3.919770e-03 0.330222499 #> 5685  2842.0 0.6659595 0.6659595 3.917736e-03 3.917736e-03 0.330122725 #> 5686  2842.5 0.6660613 0.6660613 3.915713e-03 3.915713e-03 0.330022967 #> 5687  2843.0 0.6661631 0.6661631 3.913702e-03 3.913702e-03 0.329923226 #> 5688  2843.5 0.6662648 0.6662648 3.911701e-03 3.911701e-03 0.329823501 #> 5689  2844.0 0.6663665 0.6663665 3.909711e-03 3.909711e-03 0.329723792 #> 5690  2844.5 0.6664682 0.6664682 3.907733e-03 3.907733e-03 0.329624101 #> 5691  2845.0 0.6665698 0.6665698 3.905765e-03 3.905765e-03 0.329524426 #> 5692  2845.5 0.6666714 0.6666714 3.903808e-03 3.903808e-03 0.329424768 #> 5693  2846.0 0.6667730 0.6667730 3.901862e-03 3.901862e-03 0.329325128 #> 5694  2846.5 0.6668746 0.6668746 3.899927e-03 3.899927e-03 0.329225505 #> 5695  2847.0 0.6669761 0.6669761 3.898003e-03 3.898003e-03 0.329125900 #> 5696  2847.5 0.6670776 0.6670776 3.896089e-03 3.896089e-03 0.329026313 #> 5697  2848.0 0.6671791 0.6671791 3.894186e-03 3.894186e-03 0.328926744 #> 5698  2848.5 0.6672805 0.6672805 3.892295e-03 3.892295e-03 0.328827193 #> 5699  2849.0 0.6673819 0.6673819 3.890414e-03 3.890414e-03 0.328727660 #> 5700  2849.5 0.6674833 0.6674833 3.888543e-03 3.888543e-03 0.328628145 #> 5701  2850.0 0.6675847 0.6675847 3.886684e-03 3.886684e-03 0.328528650 #> 5702  2850.5 0.6676860 0.6676860 3.884835e-03 3.884835e-03 0.328429173 #> 5703  2851.0 0.6677873 0.6677873 3.882997e-03 3.882997e-03 0.328329715 #> 5704  2851.5 0.6678886 0.6678886 3.881170e-03 3.881170e-03 0.328230277 #> 5705  2852.0 0.6679898 0.6679898 3.879353e-03 3.879353e-03 0.328130857 #> 5706  2852.5 0.6680910 0.6680910 3.877547e-03 3.877547e-03 0.328031457 #> 5707  2853.0 0.6681922 0.6681922 3.875752e-03 3.875752e-03 0.327932077 #> 5708  2853.5 0.6682933 0.6682933 3.873967e-03 3.873967e-03 0.327832717 #> 5709  2854.0 0.6683944 0.6683944 3.872193e-03 3.872193e-03 0.327733377 #> 5710  2854.5 0.6684955 0.6684955 3.870429e-03 3.870429e-03 0.327634057 #> 5711  2855.0 0.6685966 0.6685966 3.868676e-03 3.868676e-03 0.327534757 #> 5712  2855.5 0.6686976 0.6686976 3.866934e-03 3.866934e-03 0.327435478 #> 5713  2856.0 0.6687986 0.6687986 3.865202e-03 3.865202e-03 0.327336220 #> 5714  2856.5 0.6688995 0.6688995 3.863481e-03 3.863481e-03 0.327236982 #> 5715  2857.0 0.6690005 0.6690005 3.861770e-03 3.861770e-03 0.327137765 #> 5716  2857.5 0.6691014 0.6691014 3.860069e-03 3.860069e-03 0.327038570 #> 5717  2858.0 0.6692022 0.6692022 3.858380e-03 3.858380e-03 0.326939396 #> 5718  2858.5 0.6693031 0.6693031 3.856700e-03 3.856700e-03 0.326840243 #> 5719  2859.0 0.6694039 0.6694039 3.855031e-03 3.855031e-03 0.326741112 #> 5720  2859.5 0.6695046 0.6695046 3.853373e-03 3.853373e-03 0.326642003 #> 5721  2860.0 0.6696054 0.6696054 3.851725e-03 3.851725e-03 0.326542916 #> 5722  2860.5 0.6697061 0.6697061 3.850087e-03 3.850087e-03 0.326443851 #> 5723  2861.0 0.6698067 0.6698067 3.848460e-03 3.848460e-03 0.326344809 #> 5724  2861.5 0.6699074 0.6699074 3.846843e-03 3.846843e-03 0.326245788 #> 5725  2862.0 0.6700080 0.6700080 3.845236e-03 3.845236e-03 0.326146791 #> 5726  2862.5 0.6701085 0.6701085 3.843640e-03 3.843640e-03 0.326047816 #> 5727  2863.0 0.6702091 0.6702091 3.842054e-03 3.842054e-03 0.325948865 #> 5728  2863.5 0.6703096 0.6703096 3.840478e-03 3.840478e-03 0.325849936 #> 5729  2864.0 0.6704101 0.6704101 3.838913e-03 3.838913e-03 0.325751031 #> 5730  2864.5 0.6705105 0.6705105 3.837358e-03 3.837358e-03 0.325652149 #> 5731  2865.0 0.6706109 0.6706109 3.835813e-03 3.835813e-03 0.325553290 #> 5732  2865.5 0.6707113 0.6707113 3.834279e-03 3.834279e-03 0.325454456 #> 5733  2866.0 0.6708116 0.6708116 3.832754e-03 3.832754e-03 0.325355645 #> 5734  2866.5 0.6709119 0.6709119 3.831240e-03 3.831240e-03 0.325256859 #> 5735  2867.0 0.6710122 0.6710122 3.829736e-03 3.829736e-03 0.325158096 #> 5736  2867.5 0.6711124 0.6711124 3.828243e-03 3.828243e-03 0.325059359 #> 5737  2868.0 0.6712126 0.6712126 3.826759e-03 3.826759e-03 0.324960645 #> 5738  2868.5 0.6713128 0.6713128 3.825286e-03 3.825286e-03 0.324861957 #> 5739  2869.0 0.6714129 0.6714129 3.823822e-03 3.823822e-03 0.324763293 #> 5740  2869.5 0.6715130 0.6715130 3.822369e-03 3.822369e-03 0.324664654 #> 5741  2870.0 0.6716130 0.6716130 3.820926e-03 3.820926e-03 0.324566041 #> 5742  2870.5 0.6717131 0.6717131 3.819493e-03 3.819493e-03 0.324467452 #> 5743  2871.0 0.6718130 0.6718130 3.818070e-03 3.818070e-03 0.324368890 #> 5744  2871.5 0.6719130 0.6719130 3.816658e-03 3.816658e-03 0.324270353 #> 5745  2872.0 0.6720129 0.6720129 3.815255e-03 3.815255e-03 0.324171841 #> 5746  2872.5 0.6721128 0.6721128 3.813862e-03 3.813862e-03 0.324073356 #> 5747  2873.0 0.6722126 0.6722126 3.812480e-03 3.812480e-03 0.323974897 #> 5748  2873.5 0.6723124 0.6723124 3.811107e-03 3.811107e-03 0.323876464 #> 5749  2874.0 0.6724122 0.6724122 3.809744e-03 3.809744e-03 0.323778057 #> 5750  2874.5 0.6725119 0.6725119 3.808392e-03 3.808392e-03 0.323679677 #> 5751  2875.0 0.6726116 0.6726116 3.807049e-03 3.807049e-03 0.323581324 #> 5752  2875.5 0.6727113 0.6727113 3.805716e-03 3.805716e-03 0.323482998 #> 5753  2876.0 0.6728109 0.6728109 3.804393e-03 3.804393e-03 0.323384698 #> 5754  2876.5 0.6729105 0.6729105 3.803081e-03 3.803081e-03 0.323286426 #> 5755  2877.0 0.6730100 0.6730100 3.801778e-03 3.801778e-03 0.323188181 #> 5756  2877.5 0.6731096 0.6731096 3.800485e-03 3.800485e-03 0.323089964 #> 5757  2878.0 0.6732090 0.6732090 3.799201e-03 3.799201e-03 0.322991774 #> 5758  2878.5 0.6733085 0.6733085 3.797928e-03 3.797928e-03 0.322893612 #> 5759  2879.0 0.6734079 0.6734079 3.796665e-03 3.796665e-03 0.322795478 #> 5760  2879.5 0.6735072 0.6735072 3.795411e-03 3.795411e-03 0.322697372 #> 5761  2880.0 0.6736065 0.6736065 3.794167e-03 3.794167e-03 0.322599295 #> 5762  2880.5 0.6737058 0.6737058 3.792934e-03 3.792934e-03 0.322501245 #> 5763  2881.0 0.6738051 0.6738051 3.791710e-03 3.791710e-03 0.322403224 #> 5764  2881.5 0.6739043 0.6739043 3.790495e-03 3.790495e-03 0.322305232 #> 5765  2882.0 0.6740034 0.6740034 3.789291e-03 3.789291e-03 0.322207269 #> 5766  2882.5 0.6741026 0.6741026 3.788096e-03 3.788096e-03 0.322109335 #> 5767  2883.0 0.6742017 0.6742017 3.786911e-03 3.786911e-03 0.322011430 #> 5768  2883.5 0.6743007 0.6743007 3.785736e-03 3.785736e-03 0.321913554 #> 5769  2884.0 0.6743997 0.6743997 3.784570e-03 3.784570e-03 0.321815707 #> 5770  2884.5 0.6744987 0.6744987 3.783415e-03 3.783415e-03 0.321717890 #> 5771  2885.0 0.6745976 0.6745976 3.782268e-03 3.782268e-03 0.321620103 #> 5772  2885.5 0.6746965 0.6746965 3.781132e-03 3.781132e-03 0.321522346 #> 5773  2886.0 0.6747954 0.6747954 3.780005e-03 3.780005e-03 0.321424619 #> 5774  2886.5 0.6748942 0.6748942 3.778888e-03 3.778888e-03 0.321326922 #> 5775  2887.0 0.6749930 0.6749930 3.777781e-03 3.777781e-03 0.321229255 #> 5776  2887.5 0.6750917 0.6750917 3.776683e-03 3.776683e-03 0.321131618 #> 5777  2888.0 0.6751904 0.6751904 3.775595e-03 3.775595e-03 0.321034013 #> 5778  2888.5 0.6752890 0.6752890 3.774517e-03 3.774517e-03 0.320936438 #> 5779  2889.0 0.6753877 0.6753877 3.773448e-03 3.773448e-03 0.320838894 #> 5780  2889.5 0.6754862 0.6754862 3.772389e-03 3.772389e-03 0.320741380 #> 5781  2890.0 0.6755848 0.6755848 3.771339e-03 3.771339e-03 0.320643899 #> 5782  2890.5 0.6756833 0.6756833 3.770299e-03 3.770299e-03 0.320546448 #> 5783  2891.0 0.6757817 0.6757817 3.769269e-03 3.769269e-03 0.320449029 #> 5784  2891.5 0.6758801 0.6758801 3.768248e-03 3.768248e-03 0.320351641 #> 5785  2892.0 0.6759785 0.6759785 3.767236e-03 3.767236e-03 0.320254285 #> 5786  2892.5 0.6760768 0.6760768 3.766234e-03 3.766234e-03 0.320156962 #> 5787  2893.0 0.6761751 0.6761751 3.765242e-03 3.765242e-03 0.320059670 #> 5788  2893.5 0.6762733 0.6762733 3.764259e-03 3.764259e-03 0.319962410 #> 5789  2894.0 0.6763715 0.6763715 3.763286e-03 3.763286e-03 0.319865183 #> 5790  2894.5 0.6764697 0.6764697 3.762322e-03 3.762322e-03 0.319767988 #> 5791  2895.0 0.6765678 0.6765678 3.761368e-03 3.761368e-03 0.319670825 #> 5792  2895.5 0.6766659 0.6766659 3.760423e-03 3.760423e-03 0.319573696 #> 5793  2896.0 0.6767639 0.6767639 3.759487e-03 3.759487e-03 0.319476599 #> 5794  2896.5 0.6768619 0.6768619 3.758561e-03 3.758561e-03 0.319379536 #> 5795  2897.0 0.6769599 0.6769599 3.757645e-03 3.757645e-03 0.319282505 #> 5796  2897.5 0.6770578 0.6770578 3.756738e-03 3.756738e-03 0.319185508 #> 5797  2898.0 0.6771556 0.6771556 3.755840e-03 3.755840e-03 0.319088544 #> 5798  2898.5 0.6772534 0.6772534 3.754952e-03 3.754952e-03 0.318991614 #> 5799  2899.0 0.6773512 0.6773512 3.754073e-03 3.754073e-03 0.318894718 #> 5800  2899.5 0.6774489 0.6774489 3.753203e-03 3.753203e-03 0.318797855 #> 5801  2900.0 0.6775466 0.6775466 3.752343e-03 3.752343e-03 0.318701027 #> 5802  2900.5 0.6776443 0.6776443 3.751492e-03 3.751492e-03 0.318604232 #> 5803  2901.0 0.6777419 0.6777419 3.750650e-03 3.750650e-03 0.318507472 #> 5804  2901.5 0.6778394 0.6778394 3.749818e-03 3.749818e-03 0.318410747 #> 5805  2902.0 0.6779369 0.6779369 3.748995e-03 3.748995e-03 0.318314055 #> 5806  2902.5 0.6780344 0.6780344 3.748182e-03 3.748182e-03 0.318217399 #> 5807  2903.0 0.6781318 0.6781318 3.747378e-03 3.747378e-03 0.318120777 #> 5808  2903.5 0.6782292 0.6782292 3.746583e-03 3.746583e-03 0.318024191 #> 5809  2904.0 0.6783266 0.6783266 3.745797e-03 3.745797e-03 0.317927639 #> 5810  2904.5 0.6784239 0.6784239 3.745021e-03 3.745021e-03 0.317831123 #> 5811  2905.0 0.6785211 0.6785211 3.744254e-03 3.744254e-03 0.317734642 #> 5812  2905.5 0.6786183 0.6786183 3.743496e-03 3.743496e-03 0.317638197 #> 5813  2906.0 0.6787155 0.6787155 3.742747e-03 3.742747e-03 0.317541787 #> 5814  2906.5 0.6788126 0.6788126 3.742008e-03 3.742008e-03 0.317445414 #> 5815  2907.0 0.6789096 0.6789096 3.741278e-03 3.741278e-03 0.317349076 #> 5816  2907.5 0.6790067 0.6790067 3.740557e-03 3.740557e-03 0.317252774 #> 5817  2908.0 0.6791036 0.6791036 3.739845e-03 3.739845e-03 0.317156508 #> 5818  2908.5 0.6792006 0.6792006 3.739142e-03 3.739142e-03 0.317060279 #> 5819  2909.0 0.6792975 0.6792975 3.738449e-03 3.738449e-03 0.316964086 #> 5820  2909.5 0.6793943 0.6793943 3.737765e-03 3.737765e-03 0.316867930 #> 5821  2910.0 0.6794911 0.6794911 3.737090e-03 3.737090e-03 0.316771810 #> 5822  2910.5 0.6795878 0.6795878 3.736424e-03 3.736424e-03 0.316675728 #> 5823  2911.0 0.6796846 0.6796846 3.735768e-03 3.735768e-03 0.316579682 #> 5824  2911.5 0.6797812 0.6797812 3.735120e-03 3.735120e-03 0.316483674 #> 5825  2912.0 0.6798778 0.6798778 3.734482e-03 3.734482e-03 0.316387702 #> 5826  2912.5 0.6799744 0.6799744 3.733852e-03 3.733852e-03 0.316291769 #> 5827  2913.0 0.6800709 0.6800709 3.733232e-03 3.733232e-03 0.316195872 #> 5828  2913.5 0.6801674 0.6801674 3.732621e-03 3.732621e-03 0.316100014 #> 5829  2914.0 0.6802638 0.6802638 3.732019e-03 3.732019e-03 0.316004193 #> 5830  2914.5 0.6803602 0.6803602 3.731426e-03 3.731426e-03 0.315908410 #> 5831  2915.0 0.6804565 0.6804565 3.730843e-03 3.730843e-03 0.315812666 #> 5832  2915.5 0.6805528 0.6805528 3.730268e-03 3.730268e-03 0.315716959 #> 5833  2916.0 0.6806490 0.6806490 3.729702e-03 3.729702e-03 0.315621291 #> 5834  2916.5 0.6807452 0.6807452 3.729146e-03 3.729146e-03 0.315525661 #> 5835  2917.0 0.6808413 0.6808413 3.728598e-03 3.728598e-03 0.315430070 #> 5836  2917.5 0.6809374 0.6809374 3.728060e-03 3.728060e-03 0.315334518 #> 5837  2918.0 0.6810335 0.6810335 3.727530e-03 3.727530e-03 0.315239004 #> 5838  2918.5 0.6811295 0.6811295 3.727010e-03 3.727010e-03 0.315143530 #> 5839  2919.0 0.6812254 0.6812254 3.726498e-03 3.726498e-03 0.315048095 #> 5840  2919.5 0.6813213 0.6813213 3.725996e-03 3.725996e-03 0.314952699 #> 5841  2920.0 0.6814172 0.6814172 3.725503e-03 3.725503e-03 0.314857342 #> 5842  2920.5 0.6815130 0.6815130 3.725018e-03 3.725018e-03 0.314762025 #> 5843  2921.0 0.6816087 0.6816087 3.724543e-03 3.724543e-03 0.314666747 #> 5844  2921.5 0.6817044 0.6817044 3.724076e-03 3.724076e-03 0.314571509 #> 5845  2922.0 0.6818001 0.6818001 3.723619e-03 3.723619e-03 0.314476312 #> 5846  2922.5 0.6818957 0.6818957 3.723170e-03 3.723170e-03 0.314381154 #> 5847  2923.0 0.6819912 0.6819912 3.722730e-03 3.722730e-03 0.314286036 #> 5848  2923.5 0.6820867 0.6820867 3.722300e-03 3.722300e-03 0.314190959 #> 5849  2924.0 0.6821822 0.6821822 3.721878e-03 3.721878e-03 0.314095922 #> 5850  2924.5 0.6822776 0.6822776 3.721465e-03 3.721465e-03 0.314000925 #> 5851  2925.0 0.6823730 0.6823730 3.721061e-03 3.721061e-03 0.313905970 #> 5852  2925.5 0.6824683 0.6824683 3.720666e-03 3.720666e-03 0.313811055 #> 5853  2926.0 0.6825635 0.6825635 3.720280e-03 3.720280e-03 0.313716181 #> 5854  2926.5 0.6826587 0.6826587 3.719903e-03 3.719903e-03 0.313621348 #> 5855  2927.0 0.6827539 0.6827539 3.719535e-03 3.719535e-03 0.313526556 #> 5856  2927.5 0.6828490 0.6828490 3.719176e-03 3.719176e-03 0.313431806 #> 5857  2928.0 0.6829441 0.6829441 3.718825e-03 3.718825e-03 0.313337097 #> 5858  2928.5 0.6830391 0.6830391 3.718483e-03 3.718483e-03 0.313242430 #> 5859  2929.0 0.6831340 0.6831340 3.718151e-03 3.718151e-03 0.313147804 #> 5860  2929.5 0.6832290 0.6832290 3.717827e-03 3.717827e-03 0.313053220 #> 5861  2930.0 0.6833238 0.6833238 3.717511e-03 3.717511e-03 0.312958679 #> 5862  2930.5 0.6834186 0.6834186 3.717205e-03 3.717205e-03 0.312864179 #> 5863  2931.0 0.6835134 0.6835134 3.716908e-03 3.716908e-03 0.312769721 #> 5864  2931.5 0.6836081 0.6836081 3.716619e-03 3.716619e-03 0.312675306 #> 5865  2932.0 0.6837027 0.6837027 3.716339e-03 3.716339e-03 0.312580933 #> 5866  2932.5 0.6837973 0.6837973 3.716068e-03 3.716068e-03 0.312486603 #> 5867  2933.0 0.6838919 0.6838919 3.715806e-03 3.715806e-03 0.312392316 #> 5868  2933.5 0.6839864 0.6839864 3.715553e-03 3.715553e-03 0.312298071 #> 5869  2934.0 0.6840808 0.6840808 3.715308e-03 3.715308e-03 0.312203870 #> 5870  2934.5 0.6841752 0.6841752 3.715073e-03 3.715073e-03 0.312109711 #> 5871  2935.0 0.6842696 0.6842696 3.714846e-03 3.714846e-03 0.312015596 #> 5872  2935.5 0.6843638 0.6843638 3.714627e-03 3.714627e-03 0.311921524 #> 5873  2936.0 0.6844581 0.6844581 3.714418e-03 3.714418e-03 0.311827495 #> 5874  2936.5 0.6845523 0.6845523 3.714217e-03 3.714217e-03 0.311733510 #> 5875  2937.0 0.6846464 0.6846464 3.714025e-03 3.714025e-03 0.311639569 #> 5876  2937.5 0.6847405 0.6847405 3.713842e-03 3.713842e-03 0.311545671 #> 5877  2938.0 0.6848345 0.6848345 3.713667e-03 3.713667e-03 0.311451818 #> 5878  2938.5 0.6849285 0.6849285 3.713502e-03 3.713502e-03 0.311358009 #> 5879  2939.0 0.6850224 0.6850224 3.713345e-03 3.713345e-03 0.311264243 #> 5880  2939.5 0.6851163 0.6851163 3.713196e-03 3.713196e-03 0.311170522 #> 5881  2940.0 0.6852101 0.6852101 3.713057e-03 3.713057e-03 0.311076846 #> 5882  2940.5 0.6853039 0.6853039 3.712926e-03 3.712926e-03 0.310983214 #> 5883  2941.0 0.6853976 0.6853976 3.712804e-03 3.712804e-03 0.310889627 #> 5884  2941.5 0.6854912 0.6854912 3.712690e-03 3.712690e-03 0.310796085 #> 5885  2942.0 0.6855848 0.6855848 3.712586e-03 3.712586e-03 0.310702587 #> 5886  2942.5 0.6856784 0.6856784 3.712490e-03 3.712490e-03 0.310609135 #> 5887  2943.0 0.6857719 0.6857719 3.712402e-03 3.712402e-03 0.310515728 #> 5888  2943.5 0.6858653 0.6858653 3.712324e-03 3.712324e-03 0.310422366 #> 5889  2944.0 0.6859587 0.6859587 3.712253e-03 3.712253e-03 0.310329050 #> 5890  2944.5 0.6860520 0.6860520 3.712192e-03 3.712192e-03 0.310235779 #> 5891  2945.0 0.6861453 0.6861453 3.712139e-03 3.712139e-03 0.310142554 #> 5892  2945.5 0.6862385 0.6862385 3.712095e-03 3.712095e-03 0.310049374 #> 5893  2946.0 0.6863317 0.6863317 3.712060e-03 3.712060e-03 0.309956241 #> 5894  2946.5 0.6864248 0.6864248 3.712033e-03 3.712033e-03 0.309863153 #> 5895  2947.0 0.6865179 0.6865179 3.712015e-03 3.712015e-03 0.309770112 #> 5896  2947.5 0.6866109 0.6866109 3.712006e-03 3.712006e-03 0.309677117 #> 5897  2948.0 0.6867038 0.6867038 3.712005e-03 3.712005e-03 0.309584169 #> 5898  2948.5 0.6867967 0.6867967 3.712012e-03 3.712012e-03 0.309491267 #> 5899  2949.0 0.6868896 0.6868896 3.712029e-03 3.712029e-03 0.309398411 #> 5900  2949.5 0.6869823 0.6869823 3.712054e-03 3.712054e-03 0.309305603 #> 5901  2950.0 0.6870751 0.6870751 3.712087e-03 3.712087e-03 0.309212841 #> 5902  2950.5 0.6871677 0.6871677 3.712130e-03 3.712130e-03 0.309120126 #> 5903  2951.0 0.6872604 0.6872604 3.712180e-03 3.712180e-03 0.309027458 #> 5904  2951.5 0.6873529 0.6873529 3.712240e-03 3.712240e-03 0.308934838 #> 5905  2952.0 0.6874454 0.6874454 3.712308e-03 3.712308e-03 0.308842265 #> 5906  2952.5 0.6875379 0.6875379 3.712384e-03 3.712384e-03 0.308749739 #> 5907  2953.0 0.6876303 0.6876303 3.712470e-03 3.712470e-03 0.308657261 #> 5908  2953.5 0.6877226 0.6877226 3.712563e-03 3.712563e-03 0.308564831 #> 5909  2954.0 0.6878149 0.6878149 3.712666e-03 3.712666e-03 0.308472449 #> 5910  2954.5 0.6879071 0.6879071 3.712776e-03 3.712776e-03 0.308380114 #> 5911  2955.0 0.6879993 0.6879993 3.712896e-03 3.712896e-03 0.308287828 #> 5912  2955.5 0.6880914 0.6880914 3.713024e-03 3.713024e-03 0.308195590 #> 5913  2956.0 0.6881834 0.6881834 3.713160e-03 3.713160e-03 0.308103400 #> 5914  2956.5 0.6882754 0.6882754 3.713305e-03 3.713305e-03 0.308011258 #> 5915  2957.0 0.6883674 0.6883674 3.713459e-03 3.713459e-03 0.307919165 #> 5916  2957.5 0.6884593 0.6884593 3.713621e-03 3.713621e-03 0.307827121 #> 5917  2958.0 0.6885511 0.6885511 3.713792e-03 3.713792e-03 0.307735126 #> 5918  2958.5 0.6886428 0.6886428 3.713971e-03 3.713971e-03 0.307643179 #> 5919  2959.0 0.6887346 0.6887346 3.714159e-03 3.714159e-03 0.307551281 #> 5920  2959.5 0.6888262 0.6888262 3.714355e-03 3.714355e-03 0.307459433 #> 5921  2960.0 0.6889178 0.6889178 3.714560e-03 3.714560e-03 0.307367634 #> 5922  2960.5 0.6890093 0.6890093 3.714773e-03 3.714773e-03 0.307275884 #> 5923  2961.0 0.6891008 0.6891008 3.714995e-03 3.714995e-03 0.307184184 #> 5924  2961.5 0.6891922 0.6891922 3.715225e-03 3.715225e-03 0.307092533 #> 5925  2962.0 0.6892836 0.6892836 3.715464e-03 3.715464e-03 0.307000932 #> 5926  2962.5 0.6893749 0.6893749 3.715711e-03 3.715711e-03 0.306909381 #> 5927  2963.0 0.6894662 0.6894662 3.715967e-03 3.715967e-03 0.306817880 #> 5928  2963.5 0.6895573 0.6895573 3.716232e-03 3.716232e-03 0.306726429 #> 5929  2964.0 0.6896485 0.6896485 3.716504e-03 3.716504e-03 0.306635028 #> 5930  2964.5 0.6897395 0.6897395 3.716786e-03 3.716786e-03 0.306543677 #> 5931  2965.0 0.6898305 0.6898305 3.717075e-03 3.717075e-03 0.306452377 #> 5932  2965.5 0.6899215 0.6899215 3.717374e-03 3.717374e-03 0.306361127 #> 5933  2966.0 0.6900124 0.6900124 3.717680e-03 3.717680e-03 0.306269928 #> 5934  2966.5 0.6901032 0.6901032 3.717996e-03 3.717996e-03 0.306178780 #> 5935  2967.0 0.6901940 0.6901940 3.718319e-03 3.718319e-03 0.306087683 #> 5936  2967.5 0.6902847 0.6902847 3.718651e-03 3.718651e-03 0.305996637 #> 5937  2968.0 0.6903754 0.6903754 3.718992e-03 3.718992e-03 0.305905641 #> 5938  2968.5 0.6904660 0.6904660 3.719341e-03 3.719341e-03 0.305814697 #> 5939  2969.0 0.6905565 0.6905565 3.719699e-03 3.719699e-03 0.305723805 #> 5940  2969.5 0.6906470 0.6906470 3.720065e-03 3.720065e-03 0.305632964 #> 5941  2970.0 0.6907374 0.6907374 3.720439e-03 3.720439e-03 0.305542174 #> 5942  2970.5 0.6908277 0.6908277 3.720822e-03 3.720822e-03 0.305451436 #> 5943  2971.0 0.6909180 0.6909180 3.721213e-03 3.721213e-03 0.305360750 #> 5944  2971.5 0.6910083 0.6910083 3.721613e-03 3.721613e-03 0.305270116 #> 5945  2972.0 0.6910984 0.6910984 3.722021e-03 3.722021e-03 0.305179534 #> 5946  2972.5 0.6911886 0.6911886 3.722438e-03 3.722438e-03 0.305089004 #> 5947  2973.0 0.6912786 0.6912786 3.722863e-03 3.722863e-03 0.304998526 #> 5948  2973.5 0.6913686 0.6913686 3.723296e-03 3.723296e-03 0.304908101 #> 5949  2974.0 0.6914585 0.6914585 3.723738e-03 3.723738e-03 0.304817728 #> 5950  2974.5 0.6915484 0.6915484 3.724188e-03 3.724188e-03 0.304727408 #> 5951  2975.0 0.6916382 0.6916382 3.724647e-03 3.724647e-03 0.304637140 #> 5952  2975.5 0.6917280 0.6917280 3.725114e-03 3.725114e-03 0.304546925 #> 5953  2976.0 0.6918176 0.6918176 3.725590e-03 3.725590e-03 0.304456764 #> 5954  2976.5 0.6919073 0.6919073 3.726074e-03 3.726074e-03 0.304366655 #> 5955  2977.0 0.6919968 0.6919968 3.726566e-03 3.726566e-03 0.304276599 #> 5956  2977.5 0.6920863 0.6920863 3.727067e-03 3.727067e-03 0.304186597 #> 5957  2978.0 0.6921758 0.6921758 3.727576e-03 3.727576e-03 0.304096648 #> 5958  2978.5 0.6922652 0.6922652 3.728094e-03 3.728094e-03 0.304006753 #> 5959  2979.0 0.6923545 0.6923545 3.728619e-03 3.728619e-03 0.303916911 #> 5960  2979.5 0.6924437 0.6924437 3.729154e-03 3.729154e-03 0.303827124 #> 5961  2980.0 0.6925329 0.6925329 3.729697e-03 3.729697e-03 0.303737390 #> 5962  2980.5 0.6926220 0.6926220 3.730248e-03 3.730248e-03 0.303647709 #> 5963  2981.0 0.6927111 0.6927111 3.730807e-03 3.730807e-03 0.303558083 #> 5964  2981.5 0.6928001 0.6928001 3.731375e-03 3.731375e-03 0.303468512 #> 5965  2982.0 0.6928891 0.6928891 3.731952e-03 3.731952e-03 0.303378994 #> 5966  2982.5 0.6929779 0.6929779 3.732536e-03 3.732536e-03 0.303289531 #> 5967  2983.0 0.6930667 0.6930667 3.733130e-03 3.733130e-03 0.303200123 #> 5968  2983.5 0.6931555 0.6931555 3.733731e-03 3.733731e-03 0.303110769 #> 5969  2984.0 0.6932442 0.6932442 3.734341e-03 3.734341e-03 0.303021470 #> 5970  2984.5 0.6933328 0.6933328 3.734959e-03 3.734959e-03 0.302932225 #> 5971  2985.0 0.6934214 0.6934214 3.735586e-03 3.735586e-03 0.302843036 #> 5972  2985.5 0.6935099 0.6935099 3.736221e-03 3.736221e-03 0.302753902 #> 5973  2986.0 0.6935983 0.6935983 3.736864e-03 3.736864e-03 0.302664823 #> 5974  2986.5 0.6936867 0.6936867 3.737516e-03 3.737516e-03 0.302575799 #> 5975  2987.0 0.6937750 0.6937750 3.738176e-03 3.738176e-03 0.302486831 #> 5976  2987.5 0.6938632 0.6938632 3.738845e-03 3.738845e-03 0.302397918 #> 5977  2988.0 0.6939514 0.6939514 3.739521e-03 3.739521e-03 0.302309061 #> 5978  2988.5 0.6940395 0.6940395 3.740207e-03 3.740207e-03 0.302220260 #> 5979  2989.0 0.6941276 0.6941276 3.740900e-03 3.740900e-03 0.302131515 #> 5980  2989.5 0.6942156 0.6942156 3.741602e-03 3.741602e-03 0.302042825 #> 5981  2990.0 0.6943035 0.6943035 3.742313e-03 3.742313e-03 0.301954192 #> 5982  2990.5 0.6943914 0.6943914 3.743031e-03 3.743031e-03 0.301865615 #> 5983  2991.0 0.6944791 0.6944791 3.743758e-03 3.743758e-03 0.301777094 #> 5984  2991.5 0.6945669 0.6945669 3.744494e-03 3.744494e-03 0.301688630 #> 5985  2992.0 0.6946545 0.6946545 3.745237e-03 3.745237e-03 0.301600222 #> 5986  2992.5 0.6947421 0.6947421 3.745989e-03 3.745989e-03 0.301511871 #> 5987  2993.0 0.6948297 0.6948297 3.746750e-03 3.746750e-03 0.301423576 #> 5988  2993.5 0.6949171 0.6949171 3.747519e-03 3.747519e-03 0.301335339 #> 5989  2994.0 0.6950045 0.6950045 3.748296e-03 3.748296e-03 0.301247158 #> 5990  2994.5 0.6950919 0.6950919 3.749081e-03 3.749081e-03 0.301159035 #> 5991  2995.0 0.6951792 0.6951792 3.749875e-03 3.749875e-03 0.301070969 #> 5992  2995.5 0.6952664 0.6952664 3.750677e-03 3.750677e-03 0.300982960 #> 5993  2996.0 0.6953535 0.6953535 3.751488e-03 3.751488e-03 0.300895008 #> 5994  2996.5 0.6954406 0.6954406 3.752307e-03 3.752307e-03 0.300807114 #> 5995  2997.0 0.6955276 0.6955276 3.753134e-03 3.753134e-03 0.300719278 #> 5996  2997.5 0.6956145 0.6956145 3.753970e-03 3.753970e-03 0.300631500 #> 5997  2998.0 0.6957014 0.6957014 3.754813e-03 3.754813e-03 0.300543779 #> 5998  2998.5 0.6957882 0.6957882 3.755666e-03 3.755666e-03 0.300456116 #> 5999  2999.0 0.6958750 0.6958750 3.756526e-03 3.756526e-03 0.300368512 #> 6000  2999.5 0.6959616 0.6959616 3.757395e-03 3.757395e-03 0.300280965 #> 6001  3000.0 0.6960483 0.6960483 3.758273e-03 3.758273e-03 0.300193477 #> 6002  3000.5 0.6961348 0.6961348 3.759158e-03 3.759158e-03 0.300106048 #> 6003  3001.0 0.6962213 0.6962213 3.760052e-03 3.760052e-03 0.300018677 #> 6004  3001.5 0.6963077 0.6963077 3.760955e-03 3.760955e-03 0.299931364 #> 6005  3002.0 0.6963940 0.6963940 3.761865e-03 3.761865e-03 0.299844110 #> 6006  3002.5 0.6964803 0.6964803 3.762784e-03 3.762784e-03 0.299756916 #> 6007  3003.0 0.6965665 0.6965665 3.763711e-03 3.763711e-03 0.299669780 #> 6008  3003.5 0.6966526 0.6966526 3.764647e-03 3.764647e-03 0.299582703 #> 6009  3004.0 0.6967387 0.6967387 3.765591e-03 3.765591e-03 0.299495685 #> 6010  3004.5 0.6968247 0.6968247 3.766544e-03 3.766544e-03 0.299408727 #> 6011  3005.0 0.6969107 0.6969107 3.767504e-03 3.767504e-03 0.299321828 #> 6012  3005.5 0.6969965 0.6969965 3.768473e-03 3.768473e-03 0.299234989 #> 6013  3006.0 0.6970823 0.6970823 3.769451e-03 3.769451e-03 0.299148209 #> 6014  3006.5 0.6971681 0.6971681 3.770436e-03 3.770436e-03 0.299061489 #> 6015  3007.0 0.6972537 0.6972537 3.771430e-03 3.771430e-03 0.298974829 #> 6016  3007.5 0.6973393 0.6973393 3.772433e-03 3.772433e-03 0.298888228 #> 6017  3008.0 0.6974249 0.6974249 3.773443e-03 3.773443e-03 0.298801688 #> 6018  3008.5 0.6975103 0.6975103 3.774463e-03 3.774463e-03 0.298715208 #> 6019  3009.0 0.6975957 0.6975957 3.775490e-03 3.775490e-03 0.298628788 #> 6020  3009.5 0.6976810 0.6976810 3.776526e-03 3.776526e-03 0.298542429 #> 6021  3010.0 0.6977663 0.6977663 3.777570e-03 3.777570e-03 0.298456130 #> 6022  3010.5 0.6978515 0.6978515 3.778622e-03 3.778622e-03 0.298369892 #> 6023  3011.0 0.6979366 0.6979366 3.779683e-03 3.779683e-03 0.298283714 #> 6024  3011.5 0.6980217 0.6980217 3.780752e-03 3.780752e-03 0.298197598 #> 6025  3012.0 0.6981066 0.6981066 3.781829e-03 3.781829e-03 0.298111542 #> 6026  3012.5 0.6981915 0.6981915 3.782915e-03 3.782915e-03 0.298025547 #> 6027  3013.0 0.6982764 0.6982764 3.784009e-03 3.784009e-03 0.297939614 #> 6028  3013.5 0.6983611 0.6983611 3.785112e-03 3.785112e-03 0.297853741 #> 6029  3014.0 0.6984458 0.6984458 3.786223e-03 3.786223e-03 0.297767930 #> 6030  3014.5 0.6985305 0.6985305 3.787342e-03 3.787342e-03 0.297682181 #> 6031  3015.0 0.6986150 0.6986150 3.788469e-03 3.788469e-03 0.297596493 #> 6032  3015.5 0.6986995 0.6986995 3.789605e-03 3.789605e-03 0.297510867 #> 6033  3016.0 0.6987839 0.6987839 3.790749e-03 3.790749e-03 0.297425302 #> 6034  3016.5 0.6988683 0.6988683 3.791902e-03 3.791902e-03 0.297339800 #> 6035  3017.0 0.6989526 0.6989526 3.793062e-03 3.793062e-03 0.297254359 #> 6036  3017.5 0.6990368 0.6990368 3.794232e-03 3.794232e-03 0.297168981 #> 6037  3018.0 0.6991209 0.6991209 3.795409e-03 3.795409e-03 0.297083665 #> 6038  3018.5 0.6992050 0.6992050 3.796595e-03 3.796595e-03 0.296998411 #> 6039  3019.0 0.6992890 0.6992890 3.797789e-03 3.797789e-03 0.296913219 #> 6040  3019.5 0.6993729 0.6993729 3.798992e-03 3.798992e-03 0.296828091 #> 6041  3020.0 0.6994568 0.6994568 3.800203e-03 3.800203e-03 0.296743024 #> 6042  3020.5 0.6995406 0.6995406 3.801422e-03 3.801422e-03 0.296658021 #> 6043  3021.0 0.6996243 0.6996243 3.802650e-03 3.802650e-03 0.296573080 #> 6044  3021.5 0.6997079 0.6997079 3.803886e-03 3.803886e-03 0.296488203 #> 6045  3022.0 0.6997915 0.6997915 3.805130e-03 3.805130e-03 0.296403388 #> 6046  3022.5 0.6998750 0.6998750 3.806383e-03 3.806383e-03 0.296318637 #> 6047  3023.0 0.6999584 0.6999584 3.807644e-03 3.807644e-03 0.296233949 #> 6048  3023.5 0.7000418 0.7000418 3.808913e-03 3.808913e-03 0.296149325 #> 6049  3024.0 0.7001250 0.7001250 3.810191e-03 3.810191e-03 0.296064764 #> 6050  3024.5 0.7002083 0.7002083 3.811477e-03 3.811477e-03 0.295980266 #> 6051  3025.0 0.7002914 0.7002914 3.812772e-03 3.812772e-03 0.295895833 #> 6052  3025.5 0.7003745 0.7003745 3.814075e-03 3.814075e-03 0.295811463 #> 6053  3026.0 0.7004575 0.7004575 3.815386e-03 3.815386e-03 0.295727157 #> 6054  3026.5 0.7005404 0.7005404 3.816706e-03 3.816706e-03 0.295642915 #> 6055  3027.0 0.7006232 0.7006232 3.818034e-03 3.818034e-03 0.295558738 #> 6056  3027.5 0.7007060 0.7007060 3.819370e-03 3.819370e-03 0.295474625 #> 6057  3028.0 0.7007887 0.7007887 3.820715e-03 3.820715e-03 0.295390576 #> 6058  3028.5 0.7008713 0.7008713 3.822068e-03 3.822068e-03 0.295306591 #> 6059  3029.0 0.7009539 0.7009539 3.823429e-03 3.823429e-03 0.295222671 #> 6060  3029.5 0.7010364 0.7010364 3.824799e-03 3.824799e-03 0.295138816 #> 6061  3030.0 0.7011188 0.7011188 3.826177e-03 3.826177e-03 0.295055026 #> 6062  3030.5 0.7012011 0.7012011 3.827564e-03 3.827564e-03 0.294971300 #> 6063  3031.0 0.7012834 0.7012834 3.828959e-03 3.828959e-03 0.294887640 #> 6064  3031.5 0.7013656 0.7013656 3.830362e-03 3.830362e-03 0.294804045 #> 6065  3032.0 0.7014477 0.7014477 3.831774e-03 3.831774e-03 0.294720515 #> 6066  3032.5 0.7015298 0.7015298 3.833194e-03 3.833194e-03 0.294637050 #> 6067  3033.0 0.7016117 0.7016117 3.834623e-03 3.834623e-03 0.294553651 #> 6068  3033.5 0.7016936 0.7016936 3.836060e-03 3.836060e-03 0.294470317 #> 6069  3034.0 0.7017754 0.7017754 3.837505e-03 3.837505e-03 0.294387049 #> 6070  3034.5 0.7018572 0.7018572 3.838959e-03 3.838959e-03 0.294303847 #> 6071  3035.0 0.7019389 0.7019389 3.840421e-03 3.840421e-03 0.294220711 #> 6072  3035.5 0.7020205 0.7020205 3.841891e-03 3.841891e-03 0.294137640 #> 6073  3036.0 0.7021020 0.7021020 3.843370e-03 3.843370e-03 0.294054636 #> 6074  3036.5 0.7021834 0.7021834 3.844857e-03 3.844857e-03 0.293971698 #> 6075  3037.0 0.7022648 0.7022648 3.846353e-03 3.846353e-03 0.293888826 #> 6076  3037.5 0.7023461 0.7023461 3.847857e-03 3.847857e-03 0.293806021 #> 6077  3038.0 0.7024273 0.7024273 3.849370e-03 3.849370e-03 0.293723282 #> 6078  3038.5 0.7025085 0.7025085 3.850891e-03 3.850891e-03 0.293640610 #> 6079  3039.0 0.7025896 0.7025896 3.852420e-03 3.852420e-03 0.293558005 #> 6080  3039.5 0.7026706 0.7026706 3.853958e-03 3.853958e-03 0.293475466 #> 6081  3040.0 0.7027515 0.7027515 3.855504e-03 3.855504e-03 0.293392995 #> 6082  3040.5 0.7028324 0.7028324 3.857059e-03 3.857059e-03 0.293310590 #> 6083  3041.0 0.7029131 0.7029131 3.858622e-03 3.858622e-03 0.293228253 #> 6084  3041.5 0.7029938 0.7029938 3.860193e-03 3.860193e-03 0.293145983 #> 6085  3042.0 0.7030744 0.7030744 3.861773e-03 3.861773e-03 0.293063780 #> 6086  3042.5 0.7031550 0.7031550 3.863361e-03 3.863361e-03 0.292981645 #> 6087  3043.0 0.7032355 0.7032355 3.864958e-03 3.864958e-03 0.292899577 #> 6088  3043.5 0.7033159 0.7033159 3.866563e-03 3.866563e-03 0.292817577 #> 6089  3044.0 0.7033962 0.7033962 3.868177e-03 3.868177e-03 0.292735645 #> 6090  3044.5 0.7034764 0.7034764 3.869799e-03 3.869799e-03 0.292653781 #> 6091  3045.0 0.7035566 0.7035566 3.871429e-03 3.871429e-03 0.292571984 #> 6092  3045.5 0.7036367 0.7036367 3.873068e-03 3.873068e-03 0.292490256 #> 6093  3046.0 0.7037167 0.7037167 3.874715e-03 3.874715e-03 0.292408596 #> 6094  3046.5 0.7037966 0.7037966 3.876371e-03 3.876371e-03 0.292327005 #> 6095  3047.0 0.7038765 0.7038765 3.878036e-03 3.878036e-03 0.292245482 #> 6096  3047.5 0.7039563 0.7039563 3.879708e-03 3.879708e-03 0.292164027 #> 6097  3048.0 0.7040360 0.7040360 3.881389e-03 3.881389e-03 0.292082641 #> 6098  3048.5 0.7041156 0.7041156 3.883079e-03 3.883079e-03 0.292001324 #> 6099  3049.0 0.7041951 0.7041951 3.884777e-03 3.884777e-03 0.291920076 #> 6100  3049.5 0.7042746 0.7042746 3.886484e-03 3.886484e-03 0.291838897 #> 6101  3050.0 0.7043540 0.7043540 3.888199e-03 3.888199e-03 0.291757787 #> 6102  3050.5 0.7044333 0.7044333 3.889922e-03 3.889922e-03 0.291676746 #> 6103  3051.0 0.7045126 0.7045126 3.891654e-03 3.891654e-03 0.291595774 #> 6104  3051.5 0.7045917 0.7045917 3.893395e-03 3.893395e-03 0.291514872 #> 6105  3052.0 0.7046708 0.7046708 3.895144e-03 3.895144e-03 0.291434039 #> 6106  3052.5 0.7047498 0.7047498 3.896901e-03 3.896901e-03 0.291353276 #> 6107  3053.0 0.7048288 0.7048288 3.898667e-03 3.898667e-03 0.291272583 #> 6108  3053.5 0.7049076 0.7049076 3.900442e-03 3.900442e-03 0.291191959 #> 6109  3054.0 0.7049864 0.7049864 3.902225e-03 3.902225e-03 0.291111405 #> 6110  3054.5 0.7050651 0.7050651 3.904016e-03 3.904016e-03 0.291030922 #> 6111  3055.0 0.7051437 0.7051437 3.905816e-03 3.905816e-03 0.290950508 #> 6112  3055.5 0.7052222 0.7052222 3.907625e-03 3.907625e-03 0.290870165 #> 6113  3056.0 0.7053007 0.7053007 3.909441e-03 3.909441e-03 0.290789892 #> 6114  3056.5 0.7053790 0.7053790 3.911267e-03 3.911267e-03 0.290709690 #> 6115  3057.0 0.7054573 0.7054573 3.913101e-03 3.913101e-03 0.290629558 #> 6116  3057.5 0.7055356 0.7055356 3.914943e-03 3.914943e-03 0.290549497 #> 6117  3058.0 0.7056137 0.7056137 3.916794e-03 3.916794e-03 0.290469507 #> 6118  3058.5 0.7056918 0.7056918 3.918654e-03 3.918654e-03 0.290389588 #> 6119  3059.0 0.7057697 0.7057697 3.920522e-03 3.920522e-03 0.290309739 #> 6120  3059.5 0.7058476 0.7058476 3.922399e-03 3.922399e-03 0.290229962 #> 6121  3060.0 0.7059255 0.7059255 3.924284e-03 3.924284e-03 0.290150256 #> 6122  3060.5 0.7060032 0.7060032 3.926178e-03 3.926178e-03 0.290070621 #> 6123  3061.0 0.7060809 0.7060809 3.928080e-03 3.928080e-03 0.289991058 #> 6124  3061.5 0.7061584 0.7061584 3.929991e-03 3.929991e-03 0.289911566 #> 6125  3062.0 0.7062359 0.7062359 3.931910e-03 3.931910e-03 0.289832146 #> 6126  3062.5 0.7063134 0.7063134 3.933838e-03 3.933838e-03 0.289752798 #> 6127  3063.0 0.7063907 0.7063907 3.935774e-03 3.935774e-03 0.289673521 #> 6128  3063.5 0.7064680 0.7064680 3.937719e-03 3.937719e-03 0.289594317 #> 6129  3064.0 0.7065451 0.7065451 3.939673e-03 3.939673e-03 0.289515184 #> 6130  3064.5 0.7066222 0.7066222 3.941635e-03 3.941635e-03 0.289436124 #> 6131  3065.0 0.7066993 0.7066993 3.943606e-03 3.943606e-03 0.289357136 #> 6132  3065.5 0.7067762 0.7067762 3.945585e-03 3.945585e-03 0.289278220 #> 6133  3066.0 0.7068530 0.7068530 3.947573e-03 3.947573e-03 0.289199377 #> 6134  3066.5 0.7069298 0.7069298 3.949569e-03 3.949569e-03 0.289120607 #> 6135  3067.0 0.7070065 0.7070065 3.951575e-03 3.951575e-03 0.289041909 #> 6136  3067.5 0.7070831 0.7070831 3.953588e-03 3.953588e-03 0.288963284 #> 6137  3068.0 0.7071597 0.7071597 3.955610e-03 3.955610e-03 0.288884732 #> 6138  3068.5 0.7072361 0.7072361 3.957641e-03 3.957641e-03 0.288806253 #> 6139  3069.0 0.7073125 0.7073125 3.959681e-03 3.959681e-03 0.288727847 #> 6140  3069.5 0.7073888 0.7073888 3.961729e-03 3.961729e-03 0.288649515 #> 6141  3070.0 0.7074650 0.7074650 3.963786e-03 3.963786e-03 0.288571255 #> 6142  3070.5 0.7075411 0.7075411 3.965851e-03 3.965851e-03 0.288493070 #> 6143  3071.0 0.7076171 0.7076171 3.967925e-03 3.967925e-03 0.288414957 #> 6144  3071.5 0.7076931 0.7076931 3.970007e-03 3.970007e-03 0.288336919 #> 6145  3072.0 0.7077689 0.7077689 3.972099e-03 3.972099e-03 0.288258954 #> 6146  3072.5 0.7078447 0.7078447 3.974198e-03 3.974198e-03 0.288181063 #> 6147  3073.0 0.7079204 0.7079204 3.976307e-03 3.976307e-03 0.288103246 #> 6148  3073.5 0.7079961 0.7079961 3.978424e-03 3.978424e-03 0.288025504 #> 6149  3074.0 0.7080716 0.7080716 3.980550e-03 3.980550e-03 0.287947835 #> 6150  3074.5 0.7081471 0.7081471 3.982684e-03 3.982684e-03 0.287870241 #> 6151  3075.0 0.7082225 0.7082225 3.984827e-03 3.984827e-03 0.287792721 #> 6152  3075.5 0.7082977 0.7082977 3.986979e-03 3.986979e-03 0.287715276 #> 6153  3076.0 0.7083730 0.7083730 3.989139e-03 3.989139e-03 0.287637905 #> 6154  3076.5 0.7084481 0.7084481 3.991308e-03 3.991308e-03 0.287560609 #> 6155  3077.0 0.7085231 0.7085231 3.993486e-03 3.993486e-03 0.287483388 #> 6156  3077.5 0.7085981 0.7085981 3.995673e-03 3.995673e-03 0.287406242 #> 6157  3078.0 0.7086730 0.7086730 3.997868e-03 3.997868e-03 0.287329171 #> 6158  3078.5 0.7087478 0.7087478 4.000071e-03 4.000071e-03 0.287252175 #> 6159  3079.0 0.7088225 0.7088225 4.002284e-03 4.002284e-03 0.287175254 #> 6160  3079.5 0.7088971 0.7088971 4.004505e-03 4.004505e-03 0.287098409 #> 6161  3080.0 0.7089716 0.7089716 4.006735e-03 4.006735e-03 0.287021640 #> 6162  3080.5 0.7090461 0.7090461 4.008974e-03 4.008974e-03 0.286944946 #> 6163  3081.0 0.7091205 0.7091205 4.011221e-03 4.011221e-03 0.286868327 #> 6164  3081.5 0.7091947 0.7091947 4.013477e-03 4.013477e-03 0.286791785 #> 6165  3082.0 0.7092689 0.7092689 4.015742e-03 4.015742e-03 0.286715318 #> 6166  3082.5 0.7093431 0.7093431 4.018015e-03 4.018015e-03 0.286638927 #> 6167  3083.0 0.7094171 0.7094171 4.020297e-03 4.020297e-03 0.286562613 #> 6168  3083.5 0.7094910 0.7094910 4.022588e-03 4.022588e-03 0.286486375 #> 6169  3084.0 0.7095649 0.7095649 4.024888e-03 4.024888e-03 0.286410213 #> 6170  3084.5 0.7096387 0.7096387 4.027197e-03 4.027197e-03 0.286334127 #> 6171  3085.0 0.7097124 0.7097124 4.029514e-03 4.029514e-03 0.286258119 #> 6172  3085.5 0.7097860 0.7097860 4.031840e-03 4.031840e-03 0.286182186 #> 6173  3086.0 0.7098595 0.7098595 4.034174e-03 4.034174e-03 0.286106331 #> 6174  3086.5 0.7099329 0.7099329 4.036518e-03 4.036518e-03 0.286030553 #> 6175  3087.0 0.7100063 0.7100063 4.038870e-03 4.038870e-03 0.285954851 #> 6176  3087.5 0.7100795 0.7100795 4.041231e-03 4.041231e-03 0.285879227 #> 6177  3088.0 0.7101527 0.7101527 4.043601e-03 4.043601e-03 0.285803680 #> 6178  3088.5 0.7102258 0.7102258 4.045980e-03 4.045980e-03 0.285728210 #> 6179  3089.0 0.7102988 0.7102988 4.048367e-03 4.048367e-03 0.285652818 #> 6180  3089.5 0.7103717 0.7103717 4.050763e-03 4.050763e-03 0.285577503 #> 6181  3090.0 0.7104446 0.7104446 4.053168e-03 4.053168e-03 0.285502266 #> 6182  3090.5 0.7105173 0.7105173 4.055582e-03 4.055582e-03 0.285427106 #> 6183  3091.0 0.7105900 0.7105900 4.058005e-03 4.058005e-03 0.285352025 #> 6184  3091.5 0.7106625 0.7106625 4.060436e-03 4.060436e-03 0.285277021 #> 6185  3092.0 0.7107350 0.7107350 4.062876e-03 4.062876e-03 0.285202095 #> 6186  3092.5 0.7108074 0.7108074 4.065325e-03 4.065325e-03 0.285127248 #> 6187  3093.0 0.7108797 0.7108797 4.067783e-03 4.067783e-03 0.285052479 #> 6188  3093.5 0.7109520 0.7109520 4.070250e-03 4.070250e-03 0.284977788 #> 6189  3094.0 0.7110241 0.7110241 4.072726e-03 4.072726e-03 0.284903176 #> 6190  3094.5 0.7110961 0.7110961 4.075210e-03 4.075210e-03 0.284828643 #> 6191  3095.0 0.7111681 0.7111681 4.077703e-03 4.077703e-03 0.284754188 #> 6192  3095.5 0.7112400 0.7112400 4.080206e-03 4.080206e-03 0.284679812 #> 6193  3096.0 0.7113118 0.7113118 4.082717e-03 4.082717e-03 0.284605515 #> 6194  3096.5 0.7113835 0.7113835 4.085237e-03 4.085237e-03 0.284531297 #> 6195  3097.0 0.7114551 0.7114551 4.087765e-03 4.087765e-03 0.284457158 #> 6196  3097.5 0.7115266 0.7115266 4.090303e-03 4.090303e-03 0.284383099 #> 6197  3098.0 0.7115980 0.7115980 4.092850e-03 4.092850e-03 0.284309119 #> 6198  3098.5 0.7116694 0.7116694 4.095405e-03 4.095405e-03 0.284235218 #> 6199  3099.0 0.7117406 0.7117406 4.097969e-03 4.097969e-03 0.284161397 #> 6200  3099.5 0.7118118 0.7118118 4.100543e-03 4.100543e-03 0.284087656 #> 6201  3100.0 0.7118829 0.7118829 4.103125e-03 4.103125e-03 0.284013994 #> 6202  3100.5 0.7119539 0.7119539 4.105716e-03 4.105716e-03 0.283940413 #> 6203  3101.0 0.7120248 0.7120248 4.108316e-03 4.108316e-03 0.283866911 #> 6204  3101.5 0.7120956 0.7120956 4.110925e-03 4.110925e-03 0.283793490 #> 6205  3102.0 0.7121663 0.7121663 4.113543e-03 4.113543e-03 0.283720148 #> 6206  3102.5 0.7122369 0.7122369 4.116170e-03 4.116170e-03 0.283646888 #> 6207  3103.0 0.7123075 0.7123075 4.118805e-03 4.118805e-03 0.283573707 #> 6208  3103.5 0.7123779 0.7123779 4.121450e-03 4.121450e-03 0.283500607 #> 6209  3104.0 0.7124483 0.7124483 4.124104e-03 4.124104e-03 0.283427588 #> 6210  3104.5 0.7125186 0.7125186 4.126766e-03 4.126766e-03 0.283354650 #> 6211  3105.0 0.7125888 0.7125888 4.129438e-03 4.129438e-03 0.283281793 #> 6212  3105.5 0.7126589 0.7126589 4.132119e-03 4.132119e-03 0.283209016 #> 6213  3106.0 0.7127289 0.7127289 4.134808e-03 4.134808e-03 0.283136321 #> 6214  3106.5 0.7127988 0.7127988 4.137507e-03 4.137507e-03 0.283063707 #> 6215  3107.0 0.7128686 0.7128686 4.140214e-03 4.140214e-03 0.282991174 #> 6216  3107.5 0.7129383 0.7129383 4.142931e-03 4.142931e-03 0.282918723 #> 6217  3108.0 0.7130080 0.7130080 4.145656e-03 4.145656e-03 0.282846353 #> 6218  3108.5 0.7130775 0.7130775 4.148391e-03 4.148391e-03 0.282774065 #> 6219  3109.0 0.7131470 0.7131470 4.151134e-03 4.151134e-03 0.282701859 #> 6220  3109.5 0.7132164 0.7132164 4.153887e-03 4.153887e-03 0.282629734 #> 6221  3110.0 0.7132857 0.7132857 4.156649e-03 4.156649e-03 0.282557692 #> 6222  3110.5 0.7133548 0.7133548 4.159419e-03 4.159419e-03 0.282485732 #> 6223  3111.0 0.7134239 0.7134239 4.162199e-03 4.162199e-03 0.282413854 #> 6224  3111.5 0.7134930 0.7134930 4.164988e-03 4.164988e-03 0.282342058 #> 6225  3112.0 0.7135619 0.7135619 4.167786e-03 4.167786e-03 0.282270344 #> 6226  3112.5 0.7136307 0.7136307 4.170592e-03 4.170592e-03 0.282198714 #> 6227  3113.0 0.7136994 0.7136994 4.173408e-03 4.173408e-03 0.282127166 #> 6228  3113.5 0.7137681 0.7137681 4.176233e-03 4.176233e-03 0.282055700 #> 6229  3114.0 0.7138366 0.7138366 4.179067e-03 4.179067e-03 0.281984318 #> 6230  3114.5 0.7139051 0.7139051 4.181911e-03 4.181911e-03 0.281913018 #> 6231  3115.0 0.7139734 0.7139734 4.184763e-03 4.184763e-03 0.281841802 #> 6232  3115.5 0.7140417 0.7140417 4.187624e-03 4.187624e-03 0.281770668 #> 6233  3116.0 0.7141099 0.7141099 4.190495e-03 4.190495e-03 0.281699619 #> 6234  3116.5 0.7141780 0.7141780 4.193374e-03 4.193374e-03 0.281628652 #> 6235  3117.0 0.7142460 0.7142460 4.196263e-03 4.196263e-03 0.281557769 #> 6236  3117.5 0.7143139 0.7143139 4.199161e-03 4.199161e-03 0.281486970 #> 6237  3118.0 0.7143817 0.7143817 4.202068e-03 4.202068e-03 0.281416254 #> 6238  3118.5 0.7144494 0.7144494 4.204984e-03 4.204984e-03 0.281345622 #> 6239  3119.0 0.7145170 0.7145170 4.207909e-03 4.207909e-03 0.281275075 #> 6240  3119.5 0.7145845 0.7145845 4.210843e-03 4.210843e-03 0.281204611 #> 6241  3120.0 0.7146520 0.7146520 4.213787e-03 4.213787e-03 0.281134232 #> 6242  3120.5 0.7147193 0.7147193 4.216740e-03 4.216740e-03 0.281063936 #> 6243  3121.0 0.7147866 0.7147866 4.219701e-03 4.219701e-03 0.280993726 #> 6244  3121.5 0.7148537 0.7148537 4.222672e-03 4.222672e-03 0.280923599 #> 6245  3122.0 0.7149208 0.7149208 4.225653e-03 4.225653e-03 0.280853558 #> 6246  3122.5 0.7149878 0.7149878 4.228642e-03 4.228642e-03 0.280783601 #> 6247  3123.0 0.7150546 0.7150546 4.231641e-03 4.231641e-03 0.280713729 #> 6248  3123.5 0.7151214 0.7151214 4.234648e-03 4.234648e-03 0.280643942 #> 6249  3124.0 0.7151881 0.7151881 4.237665e-03 4.237665e-03 0.280574240 #> 6250  3124.5 0.7152547 0.7152547 4.240692e-03 4.240692e-03 0.280504623 #> 6251  3125.0 0.7153212 0.7153212 4.243727e-03 4.243727e-03 0.280435092 #> 6252  3125.5 0.7153876 0.7153876 4.246772e-03 4.246772e-03 0.280365646 #> 6253  3126.0 0.7154539 0.7154539 4.249826e-03 4.249826e-03 0.280296285 #> 6254  3126.5 0.7155201 0.7155201 4.252889e-03 4.252889e-03 0.280227010 #> 6255  3127.0 0.7155862 0.7155862 4.255961e-03 4.255961e-03 0.280157821 #> 6256  3127.5 0.7156522 0.7156522 4.259043e-03 4.259043e-03 0.280088718 #> 6257  3128.0 0.7157182 0.7157182 4.262134e-03 4.262134e-03 0.280019701 #> 6258  3128.5 0.7157840 0.7157840 4.265234e-03 4.265234e-03 0.279950770 #> 6259  3129.0 0.7158497 0.7158497 4.268343e-03 4.268343e-03 0.279881924 #> 6260  3129.5 0.7159154 0.7159154 4.271462e-03 4.271462e-03 0.279813166 #> 6261  3130.0 0.7159809 0.7159809 4.274590e-03 4.274590e-03 0.279744493 #> 6262  3130.5 0.7160464 0.7160464 4.277727e-03 4.277727e-03 0.279675908 #> 6263  3131.0 0.7161117 0.7161117 4.280874e-03 4.280874e-03 0.279607408 #> 6264  3131.5 0.7161770 0.7161770 4.284030e-03 4.284030e-03 0.279538996 #> 6265  3132.0 0.7162421 0.7162421 4.287195e-03 4.287195e-03 0.279470670 #> 6266  3132.5 0.7163072 0.7163072 4.290370e-03 4.290370e-03 0.279402432 #> 6267  3133.0 0.7163722 0.7163722 4.293554e-03 4.293554e-03 0.279334280 #> 6268  3133.5 0.7164370 0.7164370 4.296747e-03 4.296747e-03 0.279266216 #> 6269  3134.0 0.7165018 0.7165018 4.299949e-03 4.299949e-03 0.279198239 #> 6270  3134.5 0.7165665 0.7165665 4.303161e-03 4.303161e-03 0.279130350 #> 6271  3135.0 0.7166311 0.7166311 4.306383e-03 4.306383e-03 0.279062548 #> 6272  3135.5 0.7166956 0.7166956 4.309613e-03 4.309613e-03 0.278994833 #> 6273  3136.0 0.7167599 0.7167599 4.312853e-03 4.312853e-03 0.278927207 #> 6274  3136.5 0.7168242 0.7168242 4.316103e-03 4.316103e-03 0.278859668 #> 6275  3137.0 0.7168884 0.7168884 4.319361e-03 4.319361e-03 0.278792217 #> 6276  3137.5 0.7169525 0.7169525 4.322630e-03 4.322630e-03 0.278724854 #> 6277  3138.0 0.7170165 0.7170165 4.325907e-03 4.325907e-03 0.278657580 #> 6278  3138.5 0.7170804 0.7170804 4.329194e-03 4.329194e-03 0.278590394 #> 6279  3139.0 0.7171442 0.7171442 4.332491e-03 4.332491e-03 0.278523296 #> 6280  3139.5 0.7172079 0.7172079 4.335796e-03 4.335796e-03 0.278456287 #> 6281  3140.0 0.7172715 0.7172715 4.339112e-03 4.339112e-03 0.278389366 #> 6282  3140.5 0.7173350 0.7173350 4.342436e-03 4.342436e-03 0.278322534 #> 6283  3141.0 0.7173984 0.7173984 4.345770e-03 4.345770e-03 0.278255792 #> 6284  3141.5 0.7174617 0.7174617 4.349114e-03 4.349114e-03 0.278189138 #> 6285  3142.0 0.7175250 0.7175250 4.352467e-03 4.352467e-03 0.278122573 #> 6286  3142.5 0.7175881 0.7175881 4.355829e-03 4.355829e-03 0.278056097 #> 6287  3143.0 0.7176511 0.7176511 4.359201e-03 4.359201e-03 0.277989711 #> 6288  3143.5 0.7177140 0.7177140 4.362583e-03 4.362583e-03 0.277923414 #> 6289  3144.0 0.7177768 0.7177768 4.365974e-03 4.365974e-03 0.277857207 #> 6290  3144.5 0.7178395 0.7178395 4.369374e-03 4.369374e-03 0.277791090 #> 6291  3145.0 0.7179022 0.7179022 4.372784e-03 4.372784e-03 0.277725062 #> 6292  3145.5 0.7179647 0.7179647 4.376203e-03 4.376203e-03 0.277659124 #> 6293  3146.0 0.7180271 0.7180271 4.379632e-03 4.379632e-03 0.277593276 #> 6294  3146.5 0.7180894 0.7180894 4.383071e-03 4.383071e-03 0.277527519 #> 6295  3147.0 0.7181516 0.7181516 4.386519e-03 4.386519e-03 0.277461851 #> 6296  3147.5 0.7182137 0.7182137 4.389976e-03 4.389976e-03 0.277396274 #> 6297  3148.0 0.7182758 0.7182758 4.393443e-03 4.393443e-03 0.277330788 #> 6298  3148.5 0.7183377 0.7183377 4.396920e-03 4.396920e-03 0.277265392 #> 6299  3149.0 0.7183995 0.7183995 4.400406e-03 4.400406e-03 0.277200086 #> 6300  3149.5 0.7184612 0.7184612 4.403902e-03 4.403902e-03 0.277134872 #> 6301  3150.0 0.7185228 0.7185228 4.407407e-03 4.407407e-03 0.277069748 #> 6302  3150.5 0.7185844 0.7185844 4.410922e-03 4.410922e-03 0.277004716 #> 6303  3151.0 0.7186458 0.7186458 4.414446e-03 4.414446e-03 0.276939774 #> 6304  3151.5 0.7187071 0.7187071 4.417980e-03 4.417980e-03 0.276874924 #> 6305  3152.0 0.7187683 0.7187683 4.421524e-03 4.421524e-03 0.276810165 #> 6306  3152.5 0.7188294 0.7188294 4.425077e-03 4.425077e-03 0.276745498 #> 6307  3153.0 0.7188904 0.7188904 4.428640e-03 4.428640e-03 0.276680922 #> 6308  3153.5 0.7189513 0.7189513 4.432213e-03 4.432213e-03 0.276616438 #> 6309  3154.0 0.7190122 0.7190122 4.435795e-03 4.435795e-03 0.276552046 #> 6310  3154.5 0.7190729 0.7190729 4.439387e-03 4.439387e-03 0.276487746 #> 6311  3155.0 0.7191335 0.7191335 4.442988e-03 4.442988e-03 0.276423538 #> 6312  3155.5 0.7191940 0.7191940 4.446599e-03 4.446599e-03 0.276359422 #> 6313  3156.0 0.7192544 0.7192544 4.450220e-03 4.450220e-03 0.276295399 #> 6314  3156.5 0.7193147 0.7193147 4.453850e-03 4.453850e-03 0.276231468 #> 6315  3157.0 0.7193749 0.7193749 4.457490e-03 4.457490e-03 0.276167629 #> 6316  3157.5 0.7194350 0.7194350 4.461140e-03 4.461140e-03 0.276103883 #> 6317  3158.0 0.7194950 0.7194950 4.464799e-03 4.464799e-03 0.276040230 #> 6318  3158.5 0.7195549 0.7195549 4.468469e-03 4.468469e-03 0.275976670 #> 6319  3159.0 0.7196147 0.7196147 4.472147e-03 4.472147e-03 0.275913202 #> 6320  3159.5 0.7196743 0.7196743 4.475836e-03 4.475836e-03 0.275849828 #> 6321  3160.0 0.7197339 0.7197339 4.479534e-03 4.479534e-03 0.275786547 #> 6322  3160.5 0.7197934 0.7197934 4.483242e-03 4.483242e-03 0.275723359 #> 6323  3161.0 0.7198528 0.7198528 4.486960e-03 4.486960e-03 0.275660265 #> 6324  3161.5 0.7199120 0.7199120 4.490688e-03 4.490688e-03 0.275597264 #> 6325  3162.0 0.7199712 0.7199712 4.494425e-03 4.494425e-03 0.275534357 #> 6326  3162.5 0.7200303 0.7200303 4.498172e-03 4.498172e-03 0.275471544 #> 6327  3163.0 0.7200892 0.7200892 4.501929e-03 4.501929e-03 0.275408825 #> 6328  3163.5 0.7201481 0.7201481 4.505695e-03 4.505695e-03 0.275346200 #> 6329  3164.0 0.7202069 0.7202069 4.509471e-03 4.509471e-03 0.275283669 #> 6330  3164.5 0.7202655 0.7202655 4.513257e-03 4.513257e-03 0.275221232 #> 6331  3165.0 0.7203241 0.7203241 4.517053e-03 4.517053e-03 0.275158889 #> 6332  3165.5 0.7203825 0.7203825 4.520859e-03 4.520859e-03 0.275096641 #> 6333  3166.0 0.7204408 0.7204408 4.524675e-03 4.524675e-03 0.275034488 #> 6334  3166.5 0.7204991 0.7204991 4.528500e-03 4.528500e-03 0.274972429 #> 6335  3167.0 0.7205572 0.7205572 4.532335e-03 4.532335e-03 0.274910466 #> 6336  3167.5 0.7206152 0.7206152 4.536180e-03 4.536180e-03 0.274848597 #> 6337  3168.0 0.7206731 0.7206731 4.540035e-03 4.540035e-03 0.274786823 #> 6338  3168.5 0.7207310 0.7207310 4.543900e-03 4.543900e-03 0.274725145 #> 6339  3169.0 0.7207887 0.7207887 4.547774e-03 4.547774e-03 0.274663561 #> 6340  3169.5 0.7208463 0.7208463 4.551659e-03 4.551659e-03 0.274602074 #> 6341  3170.0 0.7209038 0.7209038 4.555553e-03 4.555553e-03 0.274540681 #> 6342  3170.5 0.7209612 0.7209612 4.559457e-03 4.559457e-03 0.274479385 #> 6343  3171.0 0.7210184 0.7210184 4.563371e-03 4.563371e-03 0.274418184 #> 6344  3171.5 0.7210756 0.7210756 4.567295e-03 4.567295e-03 0.274357079 #> 6345  3172.0 0.7211327 0.7211327 4.571229e-03 4.571229e-03 0.274296070 #> 6346  3172.5 0.7211897 0.7211897 4.575173e-03 4.575173e-03 0.274235157 #> 6347  3173.0 0.7212465 0.7212465 4.579126e-03 4.579126e-03 0.274174341 #> 6348  3173.5 0.7213033 0.7213033 4.583090e-03 4.583090e-03 0.274113620 #> 6349  3174.0 0.7213599 0.7213599 4.587063e-03 4.587063e-03 0.274052997 #> 6350  3174.5 0.7214165 0.7214165 4.591047e-03 4.591047e-03 0.273992470 #> 6351  3175.0 0.7214729 0.7214729 4.595040e-03 4.595040e-03 0.273932039 #> 6352  3175.5 0.7215293 0.7215293 4.599044e-03 4.599044e-03 0.273871706 #> 6353  3176.0 0.7215855 0.7215855 4.603057e-03 4.603057e-03 0.273811469 #> 6354  3176.5 0.7216416 0.7216416 4.607080e-03 4.607080e-03 0.273751329 #> 6355  3177.0 0.7216976 0.7216976 4.611114e-03 4.611114e-03 0.273691287 #> 6356  3177.5 0.7217535 0.7217535 4.615157e-03 4.615157e-03 0.273631342 #> 6357  3178.0 0.7218093 0.7218093 4.619210e-03 4.619210e-03 0.273571494 #> 6358  3178.5 0.7218650 0.7218650 4.623274e-03 4.623274e-03 0.273511744 #> 6359  3179.0 0.7219206 0.7219206 4.627347e-03 4.627347e-03 0.273452092 #> 6360  3179.5 0.7219760 0.7219760 4.631430e-03 4.631430e-03 0.273392537 #> 6361  3180.0 0.7220314 0.7220314 4.635524e-03 4.635524e-03 0.273333080 #> 6362  3180.5 0.7220867 0.7220867 4.639627e-03 4.639627e-03 0.273273721 #> 6363  3181.0 0.7221418 0.7221418 4.643740e-03 4.643740e-03 0.273214461 #> 6364  3181.5 0.7221968 0.7221968 4.647864e-03 4.647864e-03 0.273155298 #> 6365  3182.0 0.7222518 0.7222518 4.651997e-03 4.651997e-03 0.273096234 #> 6366  3182.5 0.7223066 0.7223066 4.656141e-03 4.656141e-03 0.273037269 #> 6367  3183.0 0.7223613 0.7223613 4.660295e-03 4.660295e-03 0.272978402 #> 6368  3183.5 0.7224159 0.7224159 4.664458e-03 4.664458e-03 0.272919633 #> 6369  3184.0 0.7224704 0.7224704 4.668632e-03 4.668632e-03 0.272860964 #> 6370  3184.5 0.7225248 0.7225248 4.672816e-03 4.672816e-03 0.272802394 #> 6371  3185.0 0.7225791 0.7225791 4.677010e-03 4.677010e-03 0.272743922 #> 6372  3185.5 0.7226332 0.7226332 4.681214e-03 4.681214e-03 0.272685550 #> 6373  3186.0 0.7226873 0.7226873 4.685428e-03 4.685428e-03 0.272627277 #> 6374  3186.5 0.7227412 0.7227412 4.689653e-03 4.689653e-03 0.272569104 #> 6375  3187.0 0.7227951 0.7227951 4.693887e-03 4.693887e-03 0.272511030 #> 6376  3187.5 0.7228488 0.7228488 4.698132e-03 4.698132e-03 0.272453056 #> 6377  3188.0 0.7229024 0.7229024 4.702387e-03 4.702387e-03 0.272395181 #> 6378  3188.5 0.7229559 0.7229559 4.706652e-03 4.706652e-03 0.272337407 #> 6379  3189.0 0.7230093 0.7230093 4.710927e-03 4.710927e-03 0.272279732 #> 6380  3189.5 0.7230626 0.7230626 4.715212e-03 4.715212e-03 0.272222158 #> 6381  3190.0 0.7231158 0.7231158 4.719507e-03 4.719507e-03 0.272164684 #> 6382  3190.5 0.7231689 0.7231689 4.723813e-03 4.723813e-03 0.272107310 #> 6383  3191.0 0.7232218 0.7232218 4.728129e-03 4.728129e-03 0.272050037 #> 6384  3191.5 0.7232747 0.7232747 4.732455e-03 4.732455e-03 0.271992865 #> 6385  3192.0 0.7233274 0.7233274 4.736791e-03 4.736791e-03 0.271935793 #> 6386  3192.5 0.7233800 0.7233800 4.741138e-03 4.741138e-03 0.271878822 #> 6387  3193.0 0.7234326 0.7234326 4.745494e-03 4.745494e-03 0.271821952 #> 6388  3193.5 0.7234850 0.7234850 4.749861e-03 4.749861e-03 0.271765183 #> 6389  3194.0 0.7235372 0.7235372 4.754238e-03 4.754238e-03 0.271708515 #> 6390  3194.5 0.7235894 0.7235894 4.758626e-03 4.758626e-03 0.271651949 #> 6391  3195.0 0.7236415 0.7236415 4.763024e-03 4.763024e-03 0.271595484 #> 6392  3195.5 0.7236934 0.7236934 4.767431e-03 4.767431e-03 0.271539121 #> 6393  3196.0 0.7237453 0.7237453 4.771850e-03 4.771850e-03 0.271482860 #> 6394  3196.5 0.7237970 0.7237970 4.776278e-03 4.776278e-03 0.271426700 #> 6395  3197.0 0.7238486 0.7238486 4.780717e-03 4.780717e-03 0.271370642 #> 6396  3197.5 0.7239001 0.7239001 4.785166e-03 4.785166e-03 0.271314687 #> 6397  3198.0 0.7239515 0.7239515 4.789625e-03 4.789625e-03 0.271258833 #> 6398  3198.5 0.7240028 0.7240028 4.794095e-03 4.794095e-03 0.271203082 #> 6399  3199.0 0.7240540 0.7240540 4.798575e-03 4.798575e-03 0.271147433 #> 6400  3199.5 0.7241050 0.7241050 4.803065e-03 4.803065e-03 0.271091887 #> 6401  3200.0 0.7241560 0.7241560 4.807566e-03 4.807566e-03 0.271036444 #> 6402  3200.5 0.7242068 0.7242068 4.812077e-03 4.812077e-03 0.270981103 #> 6403  3201.0 0.7242575 0.7242575 4.816598e-03 4.816598e-03 0.270925865 #> 6404  3201.5 0.7243081 0.7243081 4.821130e-03 4.821130e-03 0.270870730 #> 6405  3202.0 0.7243586 0.7243586 4.825672e-03 4.825672e-03 0.270815699 #> 6406  3202.5 0.7244090 0.7244090 4.830224e-03 4.830224e-03 0.270760770 #> 6407  3203.0 0.7244593 0.7244593 4.834787e-03 4.834787e-03 0.270705946 #> 6408  3203.5 0.7245094 0.7245094 4.839360e-03 4.839360e-03 0.270651224 #> 6409  3204.0 0.7245594 0.7245594 4.843944e-03 4.843944e-03 0.270596606 #> 6410  3204.5 0.7246094 0.7246094 4.848538e-03 4.848538e-03 0.270542092 #> 6411  3205.0 0.7246592 0.7246592 4.853142e-03 4.853142e-03 0.270487682 #> 6412  3205.5 0.7247089 0.7247089 4.857757e-03 4.857757e-03 0.270433376 #> 6413  3206.0 0.7247584 0.7247584 4.862382e-03 4.862382e-03 0.270379174 #> 6414  3206.5 0.7248079 0.7248079 4.867018e-03 4.867018e-03 0.270325076 #> 6415  3207.0 0.7248573 0.7248573 4.871664e-03 4.871664e-03 0.270271083 #> 6416  3207.5 0.7249065 0.7249065 4.876320e-03 4.876320e-03 0.270217194 #> 6417  3208.0 0.7249556 0.7249556 4.880987e-03 4.880987e-03 0.270163410 #> 6418  3208.5 0.7250046 0.7250046 4.885665e-03 4.885665e-03 0.270109730 #> 6419  3209.0 0.7250535 0.7250535 4.890353e-03 4.890353e-03 0.270056156 #> 6420  3209.5 0.7251023 0.7251023 4.895051e-03 4.895051e-03 0.270002686 #> 6421  3210.0 0.7251509 0.7251509 4.899760e-03 4.899760e-03 0.269949321 #> 6422  3210.5 0.7251995 0.7251995 4.904479e-03 4.904479e-03 0.269896062 #> 6423  3211.0 0.7252479 0.7252479 4.909209e-03 4.909209e-03 0.269842908 #> 6424  3211.5 0.7252962 0.7252962 4.913949e-03 4.913949e-03 0.269789860 #> 6425  3212.0 0.7253444 0.7253444 4.918700e-03 4.918700e-03 0.269736917 #> 6426  3212.5 0.7253925 0.7253925 4.923462e-03 4.923462e-03 0.269684079 #> 6427  3213.0 0.7254404 0.7254404 4.928234e-03 4.928234e-03 0.269631348 #> 6428  3213.5 0.7254883 0.7254883 4.933016e-03 4.933016e-03 0.269578722 #> 6429  3214.0 0.7255360 0.7255360 4.937809e-03 4.937809e-03 0.269526203 #> 6430  3214.5 0.7255836 0.7255836 4.942612e-03 4.942612e-03 0.269473790 #> 6431  3215.0 0.7256311 0.7256311 4.947427e-03 4.947427e-03 0.269421483 #> 6432  3215.5 0.7256785 0.7256785 4.952251e-03 4.952251e-03 0.269369283 #> 6433  3216.0 0.7257257 0.7257257 4.957086e-03 4.957086e-03 0.269317189 #> 6434  3216.5 0.7257729 0.7257729 4.961932e-03 4.961932e-03 0.269265202 #> 6435  3217.0 0.7258199 0.7258199 4.966788e-03 4.966788e-03 0.269213321 #> 6436  3217.5 0.7258668 0.7258668 4.971655e-03 4.971655e-03 0.269161548 #> 6437  3218.0 0.7259136 0.7259136 4.976533e-03 4.976533e-03 0.269109882 #> 6438  3218.5 0.7259603 0.7259603 4.981421e-03 4.981421e-03 0.269058323 #> 6439  3219.0 0.7260068 0.7260068 4.986320e-03 4.986320e-03 0.269006871 #> 6440  3219.5 0.7260532 0.7260532 4.991229e-03 4.991229e-03 0.268955527 #> 6441  3220.0 0.7260996 0.7260996 4.996149e-03 4.996149e-03 0.268904290 #> 6442  3220.5 0.7261458 0.7261458 5.001080e-03 5.001080e-03 0.268853161 #> 6443  3221.0 0.7261918 0.7261918 5.006021e-03 5.006021e-03 0.268802139 #> 6444  3221.5 0.7262378 0.7262378 5.010973e-03 5.010973e-03 0.268751226 #> 6445  3222.0 0.7262836 0.7262836 5.015936e-03 5.015936e-03 0.268700420 #> 6446  3222.5 0.7263294 0.7263294 5.020909e-03 5.020909e-03 0.268649723 #> 6447  3223.0 0.7263750 0.7263750 5.025893e-03 5.025893e-03 0.268599134 #> 6448  3223.5 0.7264205 0.7264205 5.030887e-03 5.030887e-03 0.268548654 #> 6449  3224.0 0.7264658 0.7264658 5.035893e-03 5.035893e-03 0.268498282 #> 6450  3224.5 0.7265111 0.7265111 5.040909e-03 5.040909e-03 0.268448019 #> 6451  3225.0 0.7265562 0.7265562 5.045935e-03 5.045935e-03 0.268397864 #> 6452  3225.5 0.7266012 0.7266012 5.050973e-03 5.050973e-03 0.268347819 #> 6453  3226.0 0.7266461 0.7266461 5.056021e-03 5.056021e-03 0.268297882 #> 6454  3226.5 0.7266909 0.7266909 5.061079e-03 5.061079e-03 0.268248055 #> 6455  3227.0 0.7267355 0.7267355 5.066149e-03 5.066149e-03 0.268198337 #> 6456  3227.5 0.7267800 0.7267800 5.071229e-03 5.071229e-03 0.268148728 #> 6457  3228.0 0.7268245 0.7268245 5.076320e-03 5.076320e-03 0.268099229 #> 6458  3228.5 0.7268687 0.7268687 5.081422e-03 5.081422e-03 0.268049840 #> 6459  3229.0 0.7269129 0.7269129 5.086534e-03 5.086534e-03 0.268000560 #> 6460  3229.5 0.7269570 0.7269570 5.091658e-03 5.091658e-03 0.267951390 #> 6461  3230.0 0.7270009 0.7270009 5.096791e-03 5.096791e-03 0.267902331 #> 6462  3230.5 0.7270447 0.7270447 5.101936e-03 5.101936e-03 0.267853381 #> 6463  3231.0 0.7270884 0.7270884 5.107092e-03 5.107092e-03 0.267804542 #> 6464  3231.5 0.7271319 0.7271319 5.112258e-03 5.112258e-03 0.267755813 #> 6465  3232.0 0.7271754 0.7271754 5.117435e-03 5.117435e-03 0.267707195 #> 6466  3232.5 0.7272187 0.7272187 5.122623e-03 5.122623e-03 0.267658687 #> 6467  3233.0 0.7272619 0.7272619 5.127822e-03 5.127822e-03 0.267610290 #> 6468  3233.5 0.7273050 0.7273050 5.133031e-03 5.133031e-03 0.267562004 #> 6469  3234.0 0.7273479 0.7273479 5.138252e-03 5.138252e-03 0.267513829 #> 6470  3234.5 0.7273908 0.7273908 5.143483e-03 5.143483e-03 0.267465766 #> 6471  3235.0 0.7274335 0.7274335 5.148725e-03 5.148725e-03 0.267417813 #> 6472  3235.5 0.7274761 0.7274761 5.153978e-03 5.153978e-03 0.267369972 #> 6473  3236.0 0.7275185 0.7275185 5.159241e-03 5.159241e-03 0.267322242 #> 6474  3236.5 0.7275609 0.7275609 5.164516e-03 5.164516e-03 0.267274624 #> 6475  3237.0 0.7276031 0.7276031 5.169801e-03 5.169801e-03 0.267227118 #> 6476  3237.5 0.7276452 0.7276452 5.175098e-03 5.175098e-03 0.267179724 #> 6477  3238.0 0.7276872 0.7276872 5.180405e-03 5.180405e-03 0.267132442 #> 6478  3238.5 0.7277290 0.7277290 5.185723e-03 5.185723e-03 0.267085272 #> 6479  3239.0 0.7277707 0.7277707 5.191052e-03 5.191052e-03 0.267038214 #> 6480  3239.5 0.7278123 0.7278123 5.196391e-03 5.196391e-03 0.266991269 #> 6481  3240.0 0.7278538 0.7278538 5.201742e-03 5.201742e-03 0.266944436 #> 6482  3240.5 0.7278952 0.7278952 5.207104e-03 5.207104e-03 0.266897716 #> 6483  3241.0 0.7279364 0.7279364 5.212476e-03 5.212476e-03 0.266851108 #> 6484  3241.5 0.7279775 0.7279775 5.217860e-03 5.217860e-03 0.266804614 #> 6485  3242.0 0.7280185 0.7280185 5.223254e-03 5.223254e-03 0.266758232 #> 6486  3242.5 0.7280594 0.7280594 5.228659e-03 5.228659e-03 0.266711964 #> 6487  3243.0 0.7281001 0.7281001 5.234075e-03 5.234075e-03 0.266665809 #> 6488  3243.5 0.7281407 0.7281407 5.239502e-03 5.239502e-03 0.266619768 #> 6489  3244.0 0.7281812 0.7281812 5.244941e-03 5.244941e-03 0.266573840 #> 6490  3244.5 0.7282216 0.7282216 5.250390e-03 5.250390e-03 0.266528025 #> 6491  3245.0 0.7282618 0.7282618 5.255850e-03 5.255850e-03 0.266482325 #> 6492  3245.5 0.7283019 0.7283019 5.261320e-03 5.261320e-03 0.266436738 #> 6493  3246.0 0.7283419 0.7283419 5.266802e-03 5.266802e-03 0.266391265 #> 6494  3246.5 0.7283818 0.7283818 5.272295e-03 5.272295e-03 0.266345907 #> 6495  3247.0 0.7284215 0.7284215 5.277799e-03 5.277799e-03 0.266300662 #> 6496  3247.5 0.7284612 0.7284612 5.283314e-03 5.283314e-03 0.266255533 #> 6497  3248.0 0.7285006 0.7285006 5.288840e-03 5.288840e-03 0.266210517 #> 6498  3248.5 0.7285400 0.7285400 5.294377e-03 5.294377e-03 0.266165617 #> 6499  3249.0 0.7285792 0.7285792 5.299924e-03 5.299924e-03 0.266120831 #> 6500  3249.5 0.7286184 0.7286184 5.305483e-03 5.305483e-03 0.266076160 #> 6501  3250.0 0.7286573 0.7286573 5.311053e-03 5.311053e-03 0.266031604 #> 6502  3250.5 0.7286962 0.7286962 5.316634e-03 5.316634e-03 0.265987164 #> 6503  3251.0 0.7287349 0.7287349 5.322226e-03 5.322226e-03 0.265942838 #> 6504  3251.5 0.7287735 0.7287735 5.327829e-03 5.327829e-03 0.265898629 #> 6505  3252.0 0.7288120 0.7288120 5.333443e-03 5.333443e-03 0.265854534 #> 6506  3252.5 0.7288504 0.7288504 5.339068e-03 5.339068e-03 0.265810555 #> 6507  3253.0 0.7288886 0.7288886 5.344704e-03 5.344704e-03 0.265766693 #> 6508  3253.5 0.7289267 0.7289267 5.350351e-03 5.350351e-03 0.265722946 #> 6509  3254.0 0.7289647 0.7289647 5.356009e-03 5.356009e-03 0.265679315 #> 6510  3254.5 0.7290025 0.7290025 5.361678e-03 5.361678e-03 0.265635800 #> 6511  3255.0 0.7290402 0.7290402 5.367359e-03 5.367359e-03 0.265592402 #> 6512  3255.5 0.7290778 0.7290778 5.373050e-03 5.373050e-03 0.265549120 #> 6513  3256.0 0.7291153 0.7291153 5.378753e-03 5.378753e-03 0.265505954 #> 6514  3256.5 0.7291526 0.7291526 5.384466e-03 5.384466e-03 0.265462905 #> 6515  3257.0 0.7291898 0.7291898 5.390191e-03 5.390191e-03 0.265419973 #> 6516  3257.5 0.7292269 0.7292269 5.395927e-03 5.395927e-03 0.265377159 #> 6517  3258.0 0.7292639 0.7292639 5.401673e-03 5.401673e-03 0.265334461 #> 6518  3258.5 0.7293007 0.7293007 5.407431e-03 5.407431e-03 0.265291880 #> 6519  3259.0 0.7293374 0.7293374 5.413200e-03 5.413200e-03 0.265249416 #> 6520  3259.5 0.7293739 0.7293739 5.418981e-03 5.418981e-03 0.265207071 #> 6521  3260.0 0.7294104 0.7294104 5.424772e-03 5.424772e-03 0.265164842 #> 6522  3260.5 0.7294467 0.7294467 5.430574e-03 5.430574e-03 0.265122731 #> 6523  3261.0 0.7294829 0.7294829 5.436388e-03 5.436388e-03 0.265080739 #> 6524  3261.5 0.7295189 0.7295189 5.442213e-03 5.442213e-03 0.265038864 #> 6525  3262.0 0.7295548 0.7295548 5.448049e-03 5.448049e-03 0.264997107 #> 6526  3262.5 0.7295906 0.7295906 5.453896e-03 5.453896e-03 0.264955468 #> 6527  3263.0 0.7296263 0.7296263 5.459754e-03 5.459754e-03 0.264913948 #> 6528  3263.5 0.7296618 0.7296618 5.465623e-03 5.465623e-03 0.264872546 #> 6529  3264.0 0.7296972 0.7296972 5.471503e-03 5.471503e-03 0.264831263 #> 6530  3264.5 0.7297325 0.7297325 5.477395e-03 5.477395e-03 0.264790098 #> 6531  3265.0 0.7297676 0.7297676 5.483298e-03 5.483298e-03 0.264749053 #> 6532  3265.5 0.7298027 0.7298027 5.489212e-03 5.489212e-03 0.264708126 #> 6533  3266.0 0.7298375 0.7298375 5.495137e-03 5.495137e-03 0.264667318 #> 6534  3266.5 0.7298723 0.7298723 5.501073e-03 5.501073e-03 0.264626630 #> 6535  3267.0 0.7299069 0.7299069 5.507021e-03 5.507021e-03 0.264586061 #> 6536  3267.5 0.7299414 0.7299414 5.512980e-03 5.512980e-03 0.264545611 #> 6537  3268.0 0.7299758 0.7299758 5.518950e-03 5.518950e-03 0.264505281 #> 6538  3268.5 0.7300100 0.7300100 5.524931e-03 5.524931e-03 0.264465071 #> 6539  3269.0 0.7300441 0.7300441 5.530923e-03 5.530923e-03 0.264424981 #> 6540  3269.5 0.7300781 0.7300781 5.536927e-03 5.536927e-03 0.264385010 #> 6541  3270.0 0.7301119 0.7301119 5.542941e-03 5.542941e-03 0.264345160 #> 6542  3270.5 0.7301456 0.7301456 5.548967e-03 5.548967e-03 0.264305430 #> 6543  3271.0 0.7301792 0.7301792 5.555004e-03 5.555004e-03 0.264265820 #> 6544  3271.5 0.7302126 0.7302126 5.561053e-03 5.561053e-03 0.264226331 #> 6545  3272.0 0.7302459 0.7302459 5.567112e-03 5.567112e-03 0.264186962 #> 6546  3272.5 0.7302791 0.7302791 5.573183e-03 5.573183e-03 0.264147714 #> 6547  3273.0 0.7303121 0.7303121 5.579265e-03 5.579265e-03 0.264108587 #> 6548  3273.5 0.7303451 0.7303451 5.585359e-03 5.585359e-03 0.264069581 #> 6549  3274.0 0.7303778 0.7303778 5.591463e-03 5.591463e-03 0.264030696 #> 6550  3274.5 0.7304105 0.7304105 5.597579e-03 5.597579e-03 0.263991933 #> 6551  3275.0 0.7304430 0.7304430 5.603706e-03 5.603706e-03 0.263953290 #> 6552  3275.5 0.7304754 0.7304754 5.609845e-03 5.609845e-03 0.263914770 #> 6553  3276.0 0.7305076 0.7305076 5.615994e-03 5.615994e-03 0.263876371 #> 6554  3276.5 0.7305398 0.7305398 5.622155e-03 5.622155e-03 0.263838093 #> 6555  3277.0 0.7305717 0.7305717 5.628328e-03 5.628328e-03 0.263799938 #> 6556  3277.5 0.7306036 0.7306036 5.634511e-03 5.634511e-03 0.263761904 #> 6557  3278.0 0.7306353 0.7306353 5.640706e-03 5.640706e-03 0.263723993 #> 6558  3278.5 0.7306669 0.7306669 5.646912e-03 5.646912e-03 0.263686204 #> 6559  3279.0 0.7306983 0.7306983 5.653129e-03 5.653129e-03 0.263648537 #> 6560  3279.5 0.7307296 0.7307296 5.659357e-03 5.659357e-03 0.263610993 #> 6561  3280.0 0.7307608 0.7307608 5.665597e-03 5.665597e-03 0.263573571 #> 6562  3280.5 0.7307919 0.7307919 5.671848e-03 5.671848e-03 0.263536273 #> 6563  3281.0 0.7308228 0.7308228 5.678111e-03 5.678111e-03 0.263499097 #> 6564  3281.5 0.7308536 0.7308536 5.684385e-03 5.684385e-03 0.263462044 #> 6565  3282.0 0.7308842 0.7308842 5.690670e-03 5.690670e-03 0.263425114 #> 6566  3282.5 0.7309147 0.7309147 5.696966e-03 5.696966e-03 0.263388308 #> 6567  3283.0 0.7309451 0.7309451 5.703274e-03 5.703274e-03 0.263351625 #> 6568  3283.5 0.7309753 0.7309753 5.709593e-03 5.709593e-03 0.263315065 #> 6569  3284.0 0.7310054 0.7310054 5.715923e-03 5.715923e-03 0.263278630 #> 6570  3284.5 0.7310354 0.7310354 5.722264e-03 5.722264e-03 0.263242318 #> 6571  3285.0 0.7310653 0.7310653 5.728617e-03 5.728617e-03 0.263206129 #> 6572  3285.5 0.7310950 0.7310950 5.734982e-03 5.734982e-03 0.263170065 #> 6573  3286.0 0.7311245 0.7311245 5.741357e-03 5.741357e-03 0.263134125 #> 6574  3286.5 0.7311539 0.7311539 5.747744e-03 5.747744e-03 0.263098310 #> 6575  3287.0 0.7311832 0.7311832 5.754142e-03 5.754142e-03 0.263062619 #> 6576  3287.5 0.7312124 0.7312124 5.760552e-03 5.760552e-03 0.263027052 #> 6577  3288.0 0.7312414 0.7312414 5.766973e-03 5.766973e-03 0.262991610 #> 6578  3288.5 0.7312703 0.7312703 5.773405e-03 5.773405e-03 0.262956293 #> 6579  3289.0 0.7312991 0.7312991 5.779848e-03 5.779848e-03 0.262921101 #> 6580  3289.5 0.7313277 0.7313277 5.786303e-03 5.786303e-03 0.262886033 #> 6581  3290.0 0.7313561 0.7313561 5.792769e-03 5.792769e-03 0.262851091 #> 6582  3290.5 0.7313845 0.7313845 5.799247e-03 5.799247e-03 0.262816275 #> 6583  3291.0 0.7314127 0.7314127 5.805736e-03 5.805736e-03 0.262781583 #> 6584  3291.5 0.7314407 0.7314407 5.812236e-03 5.812236e-03 0.262747018 #> 6585  3292.0 0.7314687 0.7314687 5.818748e-03 5.818748e-03 0.262712578 #> 6586  3292.5 0.7314965 0.7314965 5.825271e-03 5.825271e-03 0.262678263 #> 6587  3293.0 0.7315241 0.7315241 5.831805e-03 5.831805e-03 0.262644075 #> 6588  3293.5 0.7315516 0.7315516 5.838351e-03 5.838351e-03 0.262610013 #> 6589  3294.0 0.7315790 0.7315790 5.844908e-03 5.844908e-03 0.262576077 #> 6590  3294.5 0.7316063 0.7316063 5.851476e-03 5.851476e-03 0.262542267 #> 6591  3295.0 0.7316334 0.7316334 5.858056e-03 5.858056e-03 0.262508584 #> 6592  3295.5 0.7316603 0.7316603 5.864647e-03 5.864647e-03 0.262475027 #> 6593  3296.0 0.7316872 0.7316872 5.871250e-03 5.871250e-03 0.262441597 #> 6594  3296.5 0.7317138 0.7317138 5.877863e-03 5.877863e-03 0.262408294 #> 6595  3297.0 0.7317404 0.7317404 5.884489e-03 5.884489e-03 0.262375118 #> 6596  3297.5 0.7317668 0.7317668 5.891125e-03 5.891125e-03 0.262342068 #> 6597  3298.0 0.7317931 0.7317931 5.897773e-03 5.897773e-03 0.262309146 #> 6598  3298.5 0.7318192 0.7318192 5.904432e-03 5.904432e-03 0.262276352 #> 6599  3299.0 0.7318452 0.7318452 5.911103e-03 5.911103e-03 0.262243685 #> 6600  3299.5 0.7318711 0.7318711 5.917785e-03 5.917785e-03 0.262211145 #> 6601  3300.0 0.7318968 0.7318968 5.924479e-03 5.924479e-03 0.262178733 #> 6602  3300.5 0.7319224 0.7319224 5.931183e-03 5.931183e-03 0.262146449 #> 6603  3301.0 0.7319478 0.7319478 5.937900e-03 5.937900e-03 0.262114293 #> 6604  3301.5 0.7319731 0.7319731 5.944627e-03 5.944627e-03 0.262082265 #> 6605  3302.0 0.7319983 0.7319983 5.951366e-03 5.951366e-03 0.262050365 #> 6606  3302.5 0.7320233 0.7320233 5.958116e-03 5.958116e-03 0.262018593 #> 6607  3303.0 0.7320482 0.7320482 5.964878e-03 5.964878e-03 0.261986950 #> 6608  3303.5 0.7320729 0.7320729 5.971651e-03 5.971651e-03 0.261955435 #> 6609  3304.0 0.7320975 0.7320975 5.978436e-03 5.978436e-03 0.261924050 #> 6610  3304.5 0.7321220 0.7321220 5.985232e-03 5.985232e-03 0.261892793 #> 6611  3305.0 0.7321463 0.7321463 5.992039e-03 5.992039e-03 0.261861665 #> 6612  3305.5 0.7321705 0.7321705 5.998857e-03 5.998857e-03 0.261830666 #> 6613  3306.0 0.7321945 0.7321945 6.005687e-03 6.005687e-03 0.261799796 #> 6614  3306.5 0.7322184 0.7322184 6.012529e-03 6.012529e-03 0.261769055 #> 6615  3307.0 0.7322422 0.7322422 6.019381e-03 6.019381e-03 0.261738444 #> 6616  3307.5 0.7322658 0.7322658 6.026245e-03 6.026245e-03 0.261707963 #> 6617  3308.0 0.7322893 0.7322893 6.033121e-03 6.033121e-03 0.261677611 #> 6618  3308.5 0.7323126 0.7323126 6.040008e-03 6.040008e-03 0.261647390 #> 6619  3309.0 0.7323358 0.7323358 6.046906e-03 6.046906e-03 0.261617298 #> 6620  3309.5 0.7323588 0.7323588 6.053816e-03 6.053816e-03 0.261587336 #> 6621  3310.0 0.7323818 0.7323818 6.060737e-03 6.060737e-03 0.261557504 #> 6622  3310.5 0.7324045 0.7324045 6.067669e-03 6.067669e-03 0.261527803 #> 6623  3311.0 0.7324272 0.7324272 6.074613e-03 6.074613e-03 0.261498232 #> 6624  3311.5 0.7324496 0.7324496 6.081568e-03 6.081568e-03 0.261468792 #> 6625  3312.0 0.7324720 0.7324720 6.088534e-03 6.088534e-03 0.261439482 #> 6626  3312.5 0.7324942 0.7324942 6.095512e-03 6.095512e-03 0.261410304 #> 6627  3313.0 0.7325162 0.7325162 6.102501e-03 6.102501e-03 0.261381256 #> 6628  3313.5 0.7325382 0.7325382 6.109502e-03 6.109502e-03 0.261352339 #> 6629  3314.0 0.7325599 0.7325599 6.116514e-03 6.116514e-03 0.261323554 #> 6630  3314.5 0.7325816 0.7325816 6.123537e-03 6.123537e-03 0.261294899 #> 6631  3315.0 0.7326031 0.7326031 6.130572e-03 6.130572e-03 0.261266377 #> 6632  3315.5 0.7326244 0.7326244 6.137618e-03 6.137618e-03 0.261237985 #> 6633  3316.0 0.7326456 0.7326456 6.144676e-03 6.144676e-03 0.261209726 #> 6634  3316.5 0.7326667 0.7326667 6.151744e-03 6.151744e-03 0.261181598 #> 6635  3317.0 0.7326876 0.7326876 6.158825e-03 6.158825e-03 0.261153602 #> 6636  3317.5 0.7327083 0.7327083 6.165916e-03 6.165916e-03 0.261125739 #> 6637  3318.0 0.7327290 0.7327290 6.173019e-03 6.173019e-03 0.261098007 #> 6638  3318.5 0.7327495 0.7327495 6.180133e-03 6.180133e-03 0.261070408 #> 6639  3319.0 0.7327698 0.7327698 6.187259e-03 6.187259e-03 0.261042941 #> 6640  3319.5 0.7327900 0.7327900 6.194396e-03 6.194396e-03 0.261015607 #> 6641  3320.0 0.7328100 0.7328100 6.201544e-03 6.201544e-03 0.260988406 #> 6642  3320.5 0.7328300 0.7328300 6.208704e-03 6.208704e-03 0.260961337 #> 6643  3321.0 0.7328497 0.7328497 6.215875e-03 6.215875e-03 0.260934401 #> 6644  3321.5 0.7328693 0.7328693 6.223057e-03 6.223057e-03 0.260907598 #> 6645  3322.0 0.7328888 0.7328888 6.230251e-03 6.230251e-03 0.260880929 #> 6646  3322.5 0.7329082 0.7329082 6.237456e-03 6.237456e-03 0.260854393 #> 6647  3323.0 0.7329273 0.7329273 6.244673e-03 6.244673e-03 0.260827990 #> 6648  3323.5 0.7329464 0.7329464 6.251900e-03 6.251900e-03 0.260801720 #> 6649  3324.0 0.7329653 0.7329653 6.259139e-03 6.259139e-03 0.260775585 #> 6650  3324.5 0.7329840 0.7329840 6.266390e-03 6.266390e-03 0.260749583 #> 6651  3325.0 0.7330026 0.7330026 6.273652e-03 6.273652e-03 0.260723715 #> 6652  3325.5 0.7330211 0.7330211 6.280925e-03 6.280925e-03 0.260697981 #> 6653  3326.0 0.7330394 0.7330394 6.288209e-03 6.288209e-03 0.260672381 #> 6654  3326.5 0.7330576 0.7330576 6.295505e-03 6.295505e-03 0.260646916 #> 6655  3327.0 0.7330756 0.7330756 6.302812e-03 6.302812e-03 0.260621584 #> 6656  3327.5 0.7330935 0.7330935 6.310130e-03 6.310130e-03 0.260596388 #> 6657  3328.0 0.7331112 0.7331112 6.317460e-03 6.317460e-03 0.260571326 #> 6658  3328.5 0.7331288 0.7331288 6.324801e-03 6.324801e-03 0.260546398 #> 6659  3329.0 0.7331462 0.7331462 6.332153e-03 6.332153e-03 0.260521606 #> 6660  3329.5 0.7331635 0.7331635 6.339516e-03 6.339516e-03 0.260496949 #> 6661  3330.0 0.7331807 0.7331807 6.346891e-03 6.346891e-03 0.260472426 #> 6662  3330.5 0.7331977 0.7331977 6.354277e-03 6.354277e-03 0.260448039 #> 6663  3331.0 0.7332145 0.7332145 6.361675e-03 6.361675e-03 0.260423788 #> 6664  3331.5 0.7332312 0.7332312 6.369083e-03 6.369083e-03 0.260399671 #> 6665  3332.0 0.7332478 0.7332478 6.376503e-03 6.376503e-03 0.260375691 #> 6666  3332.5 0.7332642 0.7332642 6.383935e-03 6.383935e-03 0.260351846 #> 6667  3333.0 0.7332805 0.7332805 6.391377e-03 6.391377e-03 0.260328137 #> 6668  3333.5 0.7332966 0.7332966 6.398831e-03 6.398831e-03 0.260304564 #> 6669  3334.0 0.7333126 0.7333126 6.406296e-03 6.406296e-03 0.260281127 #> 6670  3334.5 0.7333284 0.7333284 6.413772e-03 6.413772e-03 0.260257826 #> 6671  3335.0 0.7333441 0.7333441 6.421260e-03 6.421260e-03 0.260234661 #> 6672  3335.5 0.7333596 0.7333596 6.428759e-03 6.428759e-03 0.260211633 #> 6673  3336.0 0.7333750 0.7333750 6.436269e-03 6.436269e-03 0.260188742 #> 6674  3336.5 0.7333902 0.7333902 6.443790e-03 6.443790e-03 0.260165987 #> 6675  3337.0 0.7334053 0.7334053 6.451323e-03 6.451323e-03 0.260143369 #> 6676  3337.5 0.7334202 0.7334202 6.458866e-03 6.458866e-03 0.260120887 #> 6677  3338.0 0.7334350 0.7334350 6.466421e-03 6.466421e-03 0.260098543 #> 6678  3338.5 0.7334497 0.7334497 6.473988e-03 6.473988e-03 0.260076336 #> 6679  3339.0 0.7334642 0.7334642 6.481565e-03 6.481565e-03 0.260054267 #> 6680  3339.5 0.7334785 0.7334785 6.489154e-03 6.489154e-03 0.260032334 #> 6681  3340.0 0.7334927 0.7334927 6.496753e-03 6.496753e-03 0.260010539 #> 6682  3340.5 0.7335068 0.7335068 6.504365e-03 6.504365e-03 0.259988882 #> 6683  3341.0 0.7335207 0.7335207 6.511987e-03 6.511987e-03 0.259967363 #> 6684  3341.5 0.7335344 0.7335344 6.519620e-03 6.519620e-03 0.259945981 #> 6685  3342.0 0.7335480 0.7335480 6.527265e-03 6.527265e-03 0.259924737 #> 6686  3342.5 0.7335614 0.7335614 6.534920e-03 6.534920e-03 0.259903632 #> 6687  3343.0 0.7335747 0.7335747 6.542587e-03 6.542587e-03 0.259882665 #> 6688  3343.5 0.7335879 0.7335879 6.550265e-03 6.550265e-03 0.259861836 #> 6689  3344.0 0.7336009 0.7336009 6.557955e-03 6.557955e-03 0.259841145 #> 6690  3344.5 0.7336138 0.7336138 6.565655e-03 6.565655e-03 0.259820593 #> 6691  3345.0 0.7336265 0.7336265 6.573367e-03 6.573367e-03 0.259800180 #> 6692  3345.5 0.7336390 0.7336390 6.581089e-03 6.581089e-03 0.259779905 #> 6693  3346.0 0.7336514 0.7336514 6.588823e-03 6.588823e-03 0.259759769 #> 6694  3346.5 0.7336637 0.7336637 6.596568e-03 6.596568e-03 0.259739773 #> 6695  3347.0 0.7336758 0.7336758 6.604324e-03 6.604324e-03 0.259719915 #> 6696  3347.5 0.7336877 0.7336877 6.612091e-03 6.612091e-03 0.259700197 #> 6697  3348.0 0.7336995 0.7336995 6.619869e-03 6.619869e-03 0.259680618 #> 6698  3348.5 0.7337112 0.7337112 6.627659e-03 6.627659e-03 0.259661179 #> 6699  3349.0 0.7337227 0.7337227 6.635459e-03 6.635459e-03 0.259641879 #> 6700  3349.5 0.7337340 0.7337340 6.643271e-03 6.643271e-03 0.259622719 #> 6701  3350.0 0.7337452 0.7337452 6.651093e-03 6.651093e-03 0.259603699 #> 6702  3350.5 0.7337563 0.7337563 6.658927e-03 6.658927e-03 0.259584818 #> 6703  3351.0 0.7337672 0.7337672 6.666772e-03 6.666772e-03 0.259566078 #> 6704  3351.5 0.7337779 0.7337779 6.674627e-03 6.674627e-03 0.259547478 #> 6705  3352.0 0.7337885 0.7337885 6.682494e-03 6.682494e-03 0.259529018 #> 6706  3352.5 0.7337989 0.7337989 6.690372e-03 6.690372e-03 0.259510699 #> 6707  3353.0 0.7338092 0.7338092 6.698261e-03 6.698261e-03 0.259492520 #> 6708  3353.5 0.7338194 0.7338194 6.706160e-03 6.706160e-03 0.259474482 #> 6709  3354.0 0.7338293 0.7338293 6.714071e-03 6.714071e-03 0.259456584 #> 6710  3354.5 0.7338392 0.7338392 6.721993e-03 6.721993e-03 0.259438827 #> 6711  3355.0 0.7338489 0.7338489 6.729926e-03 6.729926e-03 0.259421212 #> 6712  3355.5 0.7338584 0.7338584 6.737870e-03 6.737870e-03 0.259403737 #> 6713  3356.0 0.7338678 0.7338678 6.745825e-03 6.745825e-03 0.259386404 #> 6714  3356.5 0.7338770 0.7338770 6.753790e-03 6.753790e-03 0.259369212 #> 6715  3357.0 0.7338861 0.7338861 6.761767e-03 6.761767e-03 0.259352161 #> 6716  3357.5 0.7338950 0.7338950 6.769755e-03 6.769755e-03 0.259335252 #> 6717  3358.0 0.7339038 0.7339038 6.777753e-03 6.777753e-03 0.259318484 #> 6718  3358.5 0.7339124 0.7339124 6.785763e-03 6.785763e-03 0.259301858 #> 6719  3359.0 0.7339208 0.7339208 6.793783e-03 6.793783e-03 0.259285374 #> 6720  3359.5 0.7339292 0.7339292 6.801814e-03 6.801814e-03 0.259269032 #> 6721  3360.0 0.7339373 0.7339373 6.809857e-03 6.809857e-03 0.259252833 #> 6722  3360.5 0.7339453 0.7339453 6.817910e-03 6.817910e-03 0.259236775 #> 6723  3361.0 0.7339532 0.7339532 6.825974e-03 6.825974e-03 0.259220860 #> 6724  3361.5 0.7339609 0.7339609 6.834049e-03 6.834049e-03 0.259205087 #> 6725  3362.0 0.7339684 0.7339684 6.842134e-03 6.842134e-03 0.259189456 #> 6726  3362.5 0.7339758 0.7339758 6.850231e-03 6.850231e-03 0.259173968 #> 6727  3363.0 0.7339830 0.7339830 6.858338e-03 6.858338e-03 0.259158623 #> 6728  3363.5 0.7339901 0.7339901 6.866456e-03 6.866456e-03 0.259143421 #> 6729  3364.0 0.7339971 0.7339971 6.874585e-03 6.874585e-03 0.259128362 #> 6730  3364.5 0.7340038 0.7340038 6.882725e-03 6.882725e-03 0.259113445 #> 6731  3365.0 0.7340105 0.7340105 6.890876e-03 6.890876e-03 0.259098672 #> 6732  3365.5 0.7340169 0.7340169 6.899037e-03 6.899037e-03 0.259084043 #> 6733  3366.0 0.7340232 0.7340232 6.907209e-03 6.907209e-03 0.259069556 #> 6734  3366.5 0.7340294 0.7340294 6.915392e-03 6.915392e-03 0.259055213 #> 6735  3367.0 0.7340354 0.7340354 6.923586e-03 6.923586e-03 0.259041014 #> 6736  3367.5 0.7340413 0.7340413 6.931790e-03 6.931790e-03 0.259026959 #> 6737  3368.0 0.7340469 0.7340469 6.940005e-03 6.940005e-03 0.259013047 #> 6738  3368.5 0.7340525 0.7340525 6.948231e-03 6.948231e-03 0.258999279 #> 6739  3369.0 0.7340579 0.7340579 6.956468e-03 6.956468e-03 0.258985655 #> 6740  3369.5 0.7340631 0.7340631 6.964715e-03 6.964715e-03 0.258972176 #> 6741  3370.0 0.7340682 0.7340682 6.972973e-03 6.972973e-03 0.258958840 #> 6742  3370.5 0.7340731 0.7340731 6.981242e-03 6.981242e-03 0.258945649 #> 6743  3371.0 0.7340779 0.7340779 6.989521e-03 6.989521e-03 0.258932603 #> 6744  3371.5 0.7340825 0.7340825 6.997811e-03 6.997811e-03 0.258919701 #> 6745  3372.0 0.7340869 0.7340869 7.006111e-03 7.006111e-03 0.258906944 #> 6746  3372.5 0.7340912 0.7340912 7.014422e-03 7.014422e-03 0.258894331 #> 6747  3373.0 0.7340954 0.7340954 7.022744e-03 7.022744e-03 0.258881863 #> 6748  3373.5 0.7340994 0.7340994 7.031076e-03 7.031076e-03 0.258869541 #> 6749  3374.0 0.7341032 0.7341032 7.039419e-03 7.039419e-03 0.258857363 #> 6750  3374.5 0.7341069 0.7341069 7.047772e-03 7.047772e-03 0.258845331 #> 6751  3375.0 0.7341104 0.7341104 7.056136e-03 7.056136e-03 0.258833444 #> 6752  3375.5 0.7341138 0.7341138 7.064511e-03 7.064511e-03 0.258821702 #> 6753  3376.0 0.7341170 0.7341170 7.072896e-03 7.072896e-03 0.258810106 #> 6754  3376.5 0.7341201 0.7341201 7.081292e-03 7.081292e-03 0.258798655 #> 6755  3377.0 0.7341230 0.7341230 7.089698e-03 7.089698e-03 0.258787350 #> 6756  3377.5 0.7341257 0.7341257 7.098114e-03 7.098114e-03 0.258776191 #> 6757  3378.0 0.7341283 0.7341283 7.106541e-03 7.106541e-03 0.258765178 #> 6758  3378.5 0.7341307 0.7341307 7.114978e-03 7.114978e-03 0.258754310 #> 6759  3379.0 0.7341330 0.7341330 7.123426e-03 7.123426e-03 0.258743589 #> 6760  3379.5 0.7341351 0.7341351 7.131885e-03 7.131885e-03 0.258733014 #> 6761  3380.0 0.7341371 0.7341371 7.140353e-03 7.140353e-03 0.258722586 #> 6762  3380.5 0.7341389 0.7341389 7.148832e-03 7.148832e-03 0.258712304 #> 6763  3381.0 0.7341405 0.7341405 7.157322e-03 7.157322e-03 0.258702168 #> 6764  3381.5 0.7341420 0.7341420 7.165822e-03 7.165822e-03 0.258692179 #> 6765  3382.0 0.7341433 0.7341433 7.174332e-03 7.174332e-03 0.258682337 #> 6766  3382.5 0.7341445 0.7341445 7.182852e-03 7.182852e-03 0.258672641 #> 6767  3383.0 0.7341455 0.7341455 7.191383e-03 7.191383e-03 0.258663092 #> 6768  3383.5 0.7341464 0.7341464 7.199924e-03 7.199924e-03 0.258653691 #> 6769  3384.0 0.7341471 0.7341471 7.208476e-03 7.208476e-03 0.258644436 #> 6770  3384.5 0.7341476 0.7341476 7.217037e-03 7.217037e-03 0.258635329 #> 6771  3385.0 0.7341480 0.7341480 7.225609e-03 7.225609e-03 0.258626369 #> 6772  3385.5 0.7341483 0.7341483 7.234191e-03 7.234191e-03 0.258617556 #> 6773  3386.0 0.7341483 0.7341483 7.242784e-03 7.242784e-03 0.258608891 #> 6774  3386.5 0.7341482 0.7341482 7.251386e-03 7.251386e-03 0.258600373 #> 6775  3387.0 0.7341480 0.7341480 7.259999e-03 7.259999e-03 0.258592003 #> 6776  3387.5 0.7341476 0.7341476 7.268622e-03 7.268622e-03 0.258583781 #> 6777  3388.0 0.7341470 0.7341470 7.277255e-03 7.277255e-03 0.258575707 #> 6778  3388.5 0.7341463 0.7341463 7.285898e-03 7.285898e-03 0.258567781 #> 6779  3389.0 0.7341454 0.7341454 7.294552e-03 7.294552e-03 0.258560003 #> 6780  3389.5 0.7341444 0.7341444 7.303215e-03 7.303215e-03 0.258552373 #> 6781  3390.0 0.7341432 0.7341432 7.311889e-03 7.311889e-03 0.258544891 #> 6782  3390.5 0.7341419 0.7341419 7.320572e-03 7.320572e-03 0.258537557 #> 6783  3391.0 0.7341404 0.7341404 7.329266e-03 7.329266e-03 0.258530372 #> 6784  3391.5 0.7341387 0.7341387 7.337970e-03 7.337970e-03 0.258523336 #> 6785  3392.0 0.7341369 0.7341369 7.346683e-03 7.346683e-03 0.258516448 #> 6786  3392.5 0.7341349 0.7341349 7.355407e-03 7.355407e-03 0.258509709 #> 6787  3393.0 0.7341327 0.7341327 7.364141e-03 7.364141e-03 0.258503119 #> 6788  3393.5 0.7341304 0.7341304 7.372884e-03 7.372884e-03 0.258496677 #> 6789  3394.0 0.7341280 0.7341280 7.381638e-03 7.381638e-03 0.258490385 #> 6790  3394.5 0.7341254 0.7341254 7.390402e-03 7.390402e-03 0.258484241 #> 6791  3395.0 0.7341226 0.7341226 7.399175e-03 7.399175e-03 0.258478247 #> 6792  3395.5 0.7341196 0.7341196 7.407958e-03 7.407958e-03 0.258472402 #> 6793  3396.0 0.7341165 0.7341165 7.416751e-03 7.416751e-03 0.258466707 #> 6794  3396.5 0.7341133 0.7341133 7.425554e-03 7.425554e-03 0.258461161 #> 6795  3397.0 0.7341099 0.7341099 7.434367e-03 7.434367e-03 0.258455764 #> 6796  3397.5 0.7341063 0.7341063 7.443190e-03 7.443190e-03 0.258450517 #> 6797  3398.0 0.7341026 0.7341026 7.452022e-03 7.452022e-03 0.258445420 #> 6798  3398.5 0.7340987 0.7340987 7.460865e-03 7.460865e-03 0.258440473 #> 6799  3399.0 0.7340946 0.7340946 7.469717e-03 7.469717e-03 0.258435675 #> 6800  3399.5 0.7340904 0.7340904 7.478578e-03 7.478578e-03 0.258431028 #> 6801  3400.0 0.7340860 0.7340860 7.487450e-03 7.487450e-03 0.258426530 #> 6802  3400.5 0.7340815 0.7340815 7.496331e-03 7.496331e-03 0.258422183 #> 6803  3401.0 0.7340768 0.7340768 7.505222e-03 7.505222e-03 0.258417986 #> 6804  3401.5 0.7340719 0.7340719 7.514122e-03 7.514122e-03 0.258413939 #> 6805  3402.0 0.7340669 0.7340669 7.523032e-03 7.523032e-03 0.258410042 #> 6806  3402.5 0.7340618 0.7340618 7.531952e-03 7.531952e-03 0.258406297 #> 6807  3403.0 0.7340564 0.7340564 7.540881e-03 7.540881e-03 0.258402701 #> 6808  3403.5 0.7340509 0.7340509 7.549820e-03 7.549820e-03 0.258399257 #> 6809  3404.0 0.7340453 0.7340453 7.558768e-03 7.558768e-03 0.258395963 #> 6810  3404.5 0.7340395 0.7340395 7.567726e-03 7.567726e-03 0.258392820 #> 6811  3405.0 0.7340335 0.7340335 7.576693e-03 7.576693e-03 0.258389827 #> 6812  3405.5 0.7340273 0.7340273 7.585670e-03 7.585670e-03 0.258386986 #> 6813  3406.0 0.7340210 0.7340210 7.594656e-03 7.594656e-03 0.258384296 #> 6814  3406.5 0.7340146 0.7340146 7.603652e-03 7.603652e-03 0.258381757 #> 6815  3407.0 0.7340080 0.7340080 7.612657e-03 7.612657e-03 0.258379369 #> 6816  3407.5 0.7340012 0.7340012 7.621672e-03 7.621672e-03 0.258377133 #> 6817  3408.0 0.7339943 0.7339943 7.630696e-03 7.630696e-03 0.258375048 #> 6818  3408.5 0.7339872 0.7339872 7.639729e-03 7.639729e-03 0.258373115 #> 6819  3409.0 0.7339799 0.7339799 7.648771e-03 7.648771e-03 0.258371333 #> 6820  3409.5 0.7339725 0.7339725 7.657823e-03 7.657823e-03 0.258369702 #> 6821  3410.0 0.7339649 0.7339649 7.666884e-03 7.666884e-03 0.258368224 #> 6822  3410.5 0.7339571 0.7339571 7.675955e-03 7.675955e-03 0.258366897 #> 6823  3411.0 0.7339492 0.7339492 7.685034e-03 7.685034e-03 0.258365722 #> 6824  3411.5 0.7339412 0.7339412 7.694123e-03 7.694123e-03 0.258364699 #> 6825  3412.0 0.7339330 0.7339330 7.703221e-03 7.703221e-03 0.258363828 #> 6826  3412.5 0.7339246 0.7339246 7.712328e-03 7.712328e-03 0.258363109 #> 6827  3413.0 0.7339160 0.7339160 7.721444e-03 7.721444e-03 0.258362543 #> 6828  3413.5 0.7339073 0.7339073 7.730570e-03 7.730570e-03 0.258362128 #> 6829  3414.0 0.7338984 0.7338984 7.739704e-03 7.739704e-03 0.258361866 #> 6830  3414.5 0.7338894 0.7338894 7.748848e-03 7.748848e-03 0.258361756 #> 6831  3415.0 0.7338802 0.7338802 7.758000e-03 7.758000e-03 0.258361799 #> 6832  3415.5 0.7338708 0.7338708 7.767162e-03 7.767162e-03 0.258361995 #> 6833  3416.0 0.7338613 0.7338613 7.776333e-03 7.776333e-03 0.258362343 #> 6834  3416.5 0.7338516 0.7338516 7.785512e-03 7.785512e-03 0.258362843 #> 6835  3417.0 0.7338418 0.7338418 7.794700e-03 7.794700e-03 0.258363497 #> 6836  3417.5 0.7338318 0.7338318 7.803898e-03 7.803898e-03 0.258364303 #> 6837  3418.0 0.7338216 0.7338216 7.813104e-03 7.813104e-03 0.258365263 #> 6838  3418.5 0.7338113 0.7338113 7.822319e-03 7.822319e-03 0.258366375 #> 6839  3419.0 0.7338008 0.7338008 7.831543e-03 7.831543e-03 0.258367640 #> 6840  3419.5 0.7337902 0.7337902 7.840776e-03 7.840776e-03 0.258369059 #> 6841  3420.0 0.7337794 0.7337794 7.850017e-03 7.850017e-03 0.258370630 #> 6842  3420.5 0.7337684 0.7337684 7.859268e-03 7.859268e-03 0.258372355 #> 6843  3421.0 0.7337572 0.7337572 7.868527e-03 7.868527e-03 0.258374234 #> 6844  3421.5 0.7337459 0.7337459 7.877794e-03 7.877794e-03 0.258376265 #> 6845  3422.0 0.7337345 0.7337345 7.887071e-03 7.887071e-03 0.258378451 #> 6846  3422.5 0.7337229 0.7337229 7.896356e-03 7.896356e-03 0.258380789 #> 6847  3423.0 0.7337111 0.7337111 7.905649e-03 7.905649e-03 0.258383282 #> 6848  3423.5 0.7336991 0.7336991 7.914951e-03 7.914951e-03 0.258385928 #> 6849  3424.0 0.7336870 0.7336870 7.924262e-03 7.924262e-03 0.258388727 #> 6850  3424.5 0.7336747 0.7336747 7.933581e-03 7.933581e-03 0.258391681 #> 6851  3425.0 0.7336623 0.7336623 7.942909e-03 7.942909e-03 0.258394789 #> 6852  3425.5 0.7336497 0.7336497 7.952246e-03 7.952246e-03 0.258398050 #> 6853  3426.0 0.7336369 0.7336369 7.961590e-03 7.961590e-03 0.258401465 #> 6854  3426.5 0.7336240 0.7336240 7.970943e-03 7.970943e-03 0.258405035 #> 6855  3427.0 0.7336109 0.7336109 7.980305e-03 7.980305e-03 0.258408758 #> 6856  3427.5 0.7335977 0.7335977 7.989675e-03 7.989675e-03 0.258412636 #> 6857  3428.0 0.7335843 0.7335843 7.999053e-03 7.999053e-03 0.258416668 #> 6858  3428.5 0.7335707 0.7335707 8.008440e-03 8.008440e-03 0.258420855 #> 6859  3429.0 0.7335570 0.7335570 8.017835e-03 8.017835e-03 0.258425195 #> 6860  3429.5 0.7335431 0.7335431 8.027238e-03 8.027238e-03 0.258429691 #> 6861  3430.0 0.7335290 0.7335290 8.036649e-03 8.036649e-03 0.258434340 #> 6862  3430.5 0.7335148 0.7335148 8.046068e-03 8.046068e-03 0.258439145 #> 6863  3431.0 0.7335004 0.7335004 8.055496e-03 8.055496e-03 0.258444104 #> 6864  3431.5 0.7334859 0.7334859 8.064932e-03 8.064932e-03 0.258449217 #> 6865  3432.0 0.7334711 0.7334711 8.074376e-03 8.074376e-03 0.258454485 #> 6866  3432.5 0.7334563 0.7334563 8.083828e-03 8.083828e-03 0.258459909 #> 6867  3433.0 0.7334412 0.7334412 8.093288e-03 8.093288e-03 0.258465486 #> 6868  3433.5 0.7334260 0.7334260 8.102756e-03 8.102756e-03 0.258471219 #> 6869  3434.0 0.7334107 0.7334107 8.112232e-03 8.112232e-03 0.258477107 #> 6870  3434.5 0.7333951 0.7333951 8.121716e-03 8.121716e-03 0.258483150 #> 6871  3435.0 0.7333794 0.7333794 8.131207e-03 8.131207e-03 0.258489348 #> 6872  3435.5 0.7333636 0.7333636 8.140707e-03 8.140707e-03 0.258495701 #> 6873  3436.0 0.7333476 0.7333476 8.150215e-03 8.150215e-03 0.258502209 #> 6874  3436.5 0.7333314 0.7333314 8.159730e-03 8.159730e-03 0.258508872 #> 6875  3437.0 0.7333151 0.7333151 8.169253e-03 8.169253e-03 0.258515691 #> 6876  3437.5 0.7332986 0.7332986 8.178784e-03 8.178784e-03 0.258522665 #> 6877  3438.0 0.7332819 0.7332819 8.188323e-03 8.188323e-03 0.258529795 #> 6878  3438.5 0.7332651 0.7332651 8.197869e-03 8.197869e-03 0.258537080 #> 6879  3439.0 0.7332481 0.7332481 8.207423e-03 8.207423e-03 0.258544520 #> 6880  3439.5 0.7332309 0.7332309 8.216985e-03 8.216985e-03 0.258552116 #> 6881  3440.0 0.7332136 0.7332136 8.226554e-03 8.226554e-03 0.258559867 #> 6882  3440.5 0.7331961 0.7331961 8.236131e-03 8.236131e-03 0.258567774 #> 6883  3441.0 0.7331784 0.7331784 8.245715e-03 8.245715e-03 0.258575837 #> 6884  3441.5 0.7331606 0.7331606 8.255307e-03 8.255307e-03 0.258584056 #> 6885  3442.0 0.7331427 0.7331427 8.264906e-03 8.264906e-03 0.258592430 #> 6886  3442.5 0.7331245 0.7331245 8.274513e-03 8.274513e-03 0.258600960 #> 6887  3443.0 0.7331062 0.7331062 8.284127e-03 8.284127e-03 0.258609646 #> 6888  3443.5 0.7330878 0.7330878 8.293749e-03 8.293749e-03 0.258618488 #> 6889  3444.0 0.7330691 0.7330691 8.303377e-03 8.303377e-03 0.258627486 #> 6890  3444.5 0.7330503 0.7330503 8.313013e-03 8.313013e-03 0.258636640 #> 6891  3445.0 0.7330314 0.7330314 8.322657e-03 8.322657e-03 0.258645950 #> 6892  3445.5 0.7330123 0.7330123 8.332307e-03 8.332307e-03 0.258655416 #> 6893  3446.0 0.7329930 0.7329930 8.341965e-03 8.341965e-03 0.258665038 #> 6894  3446.5 0.7329736 0.7329736 8.351630e-03 8.351630e-03 0.258674816 #> 6895  3447.0 0.7329539 0.7329539 8.361302e-03 8.361302e-03 0.258684751 #> 6896  3447.5 0.7329342 0.7329342 8.370981e-03 8.370981e-03 0.258694842 #> 6897  3448.0 0.7329142 0.7329142 8.380667e-03 8.380667e-03 0.258705089 #> 6898  3448.5 0.7328941 0.7328941 8.390360e-03 8.390360e-03 0.258715492 #> 6899  3449.0 0.7328739 0.7328739 8.400060e-03 8.400060e-03 0.258726052 #> 6900  3449.5 0.7328535 0.7328535 8.409767e-03 8.409767e-03 0.258736768 #> 6901  3450.0 0.7328329 0.7328329 8.419481e-03 8.419481e-03 0.258747641 #> 6902  3450.5 0.7328121 0.7328121 8.429202e-03 8.429202e-03 0.258758670 #> 6903  3451.0 0.7327912 0.7327912 8.438930e-03 8.438930e-03 0.258769856 #> 6904  3451.5 0.7327701 0.7327701 8.448664e-03 8.448664e-03 0.258781198 #> 6905  3452.0 0.7327489 0.7327489 8.458405e-03 8.458405e-03 0.258792697 #> 6906  3452.5 0.7327275 0.7327275 8.468153e-03 8.468153e-03 0.258804352 #> 6907  3453.0 0.7327059 0.7327059 8.477908e-03 8.477908e-03 0.258816164 #> 6908  3453.5 0.7326842 0.7326842 8.487669e-03 8.487669e-03 0.258828133 #> 6909  3454.0 0.7326623 0.7326623 8.497437e-03 8.497437e-03 0.258840258 #> 6910  3454.5 0.7326402 0.7326402 8.507211e-03 8.507211e-03 0.258852540 #> 6911  3455.0 0.7326180 0.7326180 8.516992e-03 8.516992e-03 0.258864979 #> 6912  3455.5 0.7325956 0.7325956 8.526780e-03 8.526780e-03 0.258877575 #> 6913  3456.0 0.7325731 0.7325731 8.536574e-03 8.536574e-03 0.258890328 #> 6914  3456.5 0.7325504 0.7325504 8.546374e-03 8.546374e-03 0.258903237 #> 6915  3457.0 0.7325275 0.7325275 8.556181e-03 8.556181e-03 0.258916304 #> 6916  3457.5 0.7325045 0.7325045 8.565994e-03 8.565994e-03 0.258929527 #> 6917  3458.0 0.7324813 0.7324813 8.575813e-03 8.575813e-03 0.258942907 #> 6918  3458.5 0.7324579 0.7324579 8.585638e-03 8.585638e-03 0.258956445 #> 6919  3459.0 0.7324344 0.7324344 8.595470e-03 8.595470e-03 0.258970139 #> 6920  3459.5 0.7324107 0.7324107 8.605308e-03 8.605308e-03 0.258983990 #> 6921  3460.0 0.7323868 0.7323868 8.615152e-03 8.615152e-03 0.258997998 #> 6922  3460.5 0.7323628 0.7323628 8.625003e-03 8.625003e-03 0.259012164 #> 6923  3461.0 0.7323387 0.7323387 8.634859e-03 8.634859e-03 0.259026486 #> 6924  3461.5 0.7323143 0.7323143 8.644721e-03 8.644721e-03 0.259040966 #> 6925  3462.0 0.7322898 0.7322898 8.654589e-03 8.654589e-03 0.259055603 #> 6926  3462.5 0.7322651 0.7322651 8.664464e-03 8.664464e-03 0.259070397 #> 6927  3463.0 0.7322403 0.7322403 8.674344e-03 8.674344e-03 0.259085348 #> 6928  3463.5 0.7322153 0.7322153 8.684230e-03 8.684230e-03 0.259100456 #> 6929  3464.0 0.7321902 0.7321902 8.694121e-03 8.694121e-03 0.259115722 #> 6930  3464.5 0.7321648 0.7321648 8.704019e-03 8.704019e-03 0.259131144 #> 6931  3465.0 0.7321394 0.7321394 8.713922e-03 8.713922e-03 0.259146725 #> 6932  3465.5 0.7321137 0.7321137 8.723831e-03 8.723831e-03 0.259162462 #> 6933  3466.0 0.7320879 0.7320879 8.733746e-03 8.733746e-03 0.259178356 #> 6934  3466.5 0.7320619 0.7320619 8.743666e-03 8.743666e-03 0.259194408 #> 6935  3467.0 0.7320358 0.7320358 8.753592e-03 8.753592e-03 0.259210618 #> 6936  3467.5 0.7320095 0.7320095 8.763523e-03 8.763523e-03 0.259226984 #> 6937  3468.0 0.7319830 0.7319830 8.773460e-03 8.773460e-03 0.259243508 #> 6938  3468.5 0.7319564 0.7319564 8.783402e-03 8.783402e-03 0.259260189 #> 6939  3469.0 0.7319296 0.7319296 8.793350e-03 8.793350e-03 0.259277028 #> 6940  3469.5 0.7319027 0.7319027 8.803303e-03 8.803303e-03 0.259294024 #> 6941  3470.0 0.7318756 0.7318756 8.813261e-03 8.813261e-03 0.259311177 #> 6942  3470.5 0.7318483 0.7318483 8.823225e-03 8.823225e-03 0.259328488 #> 6943  3471.0 0.7318209 0.7318209 8.833194e-03 8.833194e-03 0.259345956 #> 6944  3471.5 0.7317933 0.7317933 8.843167e-03 8.843167e-03 0.259363582 #> 6945  3472.0 0.7317655 0.7317655 8.853147e-03 8.853147e-03 0.259381365 #> 6946  3472.5 0.7317376 0.7317376 8.863131e-03 8.863131e-03 0.259399305 #> 6947  3473.0 0.7317095 0.7317095 8.873120e-03 8.873120e-03 0.259417403 #> 6948  3473.5 0.7316812 0.7316812 8.883114e-03 8.883114e-03 0.259435659 #> 6949  3474.0 0.7316528 0.7316528 8.893114e-03 8.893114e-03 0.259454072 #> 6950  3474.5 0.7316242 0.7316242 8.903118e-03 8.903118e-03 0.259472642 #> 6951  3475.0 0.7315955 0.7315955 8.913127e-03 8.913127e-03 0.259491369 #> 6952  3475.5 0.7315666 0.7315666 8.923140e-03 8.923140e-03 0.259510255 #> 6953  3476.0 0.7315375 0.7315375 8.933159e-03 8.933159e-03 0.259529297 #> 6954  3476.5 0.7315083 0.7315083 8.943182e-03 8.943182e-03 0.259548497 #> 6955  3477.0 0.7314789 0.7314789 8.953210e-03 8.953210e-03 0.259567855 #> 6956  3477.5 0.7314494 0.7314494 8.963243e-03 8.963243e-03 0.259587370 #> 6957  3478.0 0.7314197 0.7314197 8.973280e-03 8.973280e-03 0.259607042 #> 6958  3478.5 0.7313898 0.7313898 8.983322e-03 8.983322e-03 0.259626872 #> 6959  3479.0 0.7313598 0.7313598 8.993368e-03 8.993368e-03 0.259646859 #> 6960  3479.5 0.7313296 0.7313296 9.003419e-03 9.003419e-03 0.259667004 #> 6961  3480.0 0.7312992 0.7312992 9.013474e-03 9.013474e-03 0.259687306 #> 6962  3480.5 0.7312687 0.7312687 9.023534e-03 9.023534e-03 0.259707766 #> 6963  3481.0 0.7312380 0.7312380 9.033598e-03 9.033598e-03 0.259728383 #> 6964  3481.5 0.7312072 0.7312072 9.043666e-03 9.043666e-03 0.259749158 #> 6965  3482.0 0.7311762 0.7311762 9.053738e-03 9.053738e-03 0.259770089 #> 6966  3482.5 0.7311450 0.7311450 9.063815e-03 9.063815e-03 0.259791179 #> 6967  3483.0 0.7311137 0.7311137 9.073895e-03 9.073895e-03 0.259812425 #> 6968  3483.5 0.7310822 0.7310822 9.083980e-03 9.083980e-03 0.259833829 #> 6969  3484.0 0.7310505 0.7310505 9.094069e-03 9.094069e-03 0.259855391 #> 6970  3484.5 0.7310187 0.7310187 9.104162e-03 9.104162e-03 0.259877110 #> 6971  3485.0 0.7309868 0.7309868 9.114258e-03 9.114258e-03 0.259898986 #> 6972  3485.5 0.7309546 0.7309546 9.124359e-03 9.124359e-03 0.259921019 #> 6973  3486.0 0.7309223 0.7309223 9.134463e-03 9.134463e-03 0.259943210 #> 6974  3486.5 0.7308899 0.7308899 9.144571e-03 9.144571e-03 0.259965558 #> 6975  3487.0 0.7308573 0.7308573 9.154683e-03 9.154683e-03 0.259988064 #> 6976  3487.5 0.7308245 0.7308245 9.164799e-03 9.164799e-03 0.260010726 #> 6977  3488.0 0.7307915 0.7307915 9.174918e-03 9.174918e-03 0.260033546 #> 6978  3488.5 0.7307584 0.7307584 9.185041e-03 9.185041e-03 0.260056523 #> 6979  3489.0 0.7307252 0.7307252 9.195167e-03 9.195167e-03 0.260079658 #> 6980  3489.5 0.7306918 0.7306918 9.205297e-03 9.205297e-03 0.260102949 #> 6981  3490.0 0.7306582 0.7306582 9.215430e-03 9.215430e-03 0.260126398 #> 6982  3490.5 0.7306244 0.7306244 9.225567e-03 9.225567e-03 0.260150004 #> 6983  3491.0 0.7305905 0.7305905 9.235707e-03 9.235707e-03 0.260173767 #> 6984  3491.5 0.7305565 0.7305565 9.245851e-03 9.245851e-03 0.260197687 #> 6985  3492.0 0.7305222 0.7305222 9.255997e-03 9.255997e-03 0.260221764 #> 6986  3492.5 0.7304879 0.7304879 9.266147e-03 9.266147e-03 0.260245998 #> 6987  3493.0 0.7304533 0.7304533 9.276300e-03 9.276300e-03 0.260270390 #> 6988  3493.5 0.7304186 0.7304186 9.286456e-03 9.286456e-03 0.260294938 #> 6989  3494.0 0.7303837 0.7303837 9.296615e-03 9.296615e-03 0.260319643 #> 6990  3494.5 0.7303487 0.7303487 9.306777e-03 9.306777e-03 0.260344506 #> 6991  3495.0 0.7303135 0.7303135 9.316942e-03 9.316942e-03 0.260369525 #> 6992  3495.5 0.7302782 0.7302782 9.327110e-03 9.327110e-03 0.260394701 #> 6993  3496.0 0.7302427 0.7302427 9.337280e-03 9.337280e-03 0.260420034 #> 6994  3496.5 0.7302070 0.7302070 9.347454e-03 9.347454e-03 0.260445523 #> 6995  3497.0 0.7301712 0.7301712 9.357630e-03 9.357630e-03 0.260471170 #> 6996  3497.5 0.7301352 0.7301352 9.367809e-03 9.367809e-03 0.260496973 #> 6997  3498.0 0.7300991 0.7300991 9.377990e-03 9.377990e-03 0.260522933 #> 6998  3498.5 0.7300628 0.7300628 9.388174e-03 9.388174e-03 0.260549050 #> 6999  3499.0 0.7300263 0.7300263 9.398360e-03 9.398360e-03 0.260575323 #> 7000  3499.5 0.7299897 0.7299897 9.408549e-03 9.408549e-03 0.260601753 #> 7001  3500.0 0.7299529 0.7299529 9.418741e-03 9.418741e-03 0.260628340 #> 7002  3500.5 0.7299160 0.7299160 9.428935e-03 9.428935e-03 0.260655083 #> 7003  3501.0 0.7298789 0.7298789 9.439131e-03 9.439131e-03 0.260681983 #> 7004  3501.5 0.7298416 0.7298416 9.449329e-03 9.449329e-03 0.260709039 #> 7005  3502.0 0.7298042 0.7298042 9.459529e-03 9.459529e-03 0.260736251 #> 7006  3502.5 0.7297666 0.7297666 9.469732e-03 9.469732e-03 0.260763620 #> 7007  3503.0 0.7297289 0.7297289 9.479936e-03 9.479936e-03 0.260791145 #> 7008  3503.5 0.7296910 0.7296910 9.490143e-03 9.490143e-03 0.260818827 #> 7009  3504.0 0.7296530 0.7296530 9.500352e-03 9.500352e-03 0.260846664 #> 7010  3504.5 0.7296148 0.7296148 9.510562e-03 9.510562e-03 0.260874658 #> 7011  3505.0 0.7295764 0.7295764 9.520775e-03 9.520775e-03 0.260902809 #> 7012  3505.5 0.7295379 0.7295379 9.530989e-03 9.530989e-03 0.260931115 #> 7013  3506.0 0.7294992 0.7294992 9.541205e-03 9.541205e-03 0.260959577 #> 7014  3506.5 0.7294604 0.7294604 9.551423e-03 9.551423e-03 0.260988195 #> 7015  3507.0 0.7294214 0.7294214 9.561642e-03 9.561642e-03 0.261016970 #> 7016  3507.5 0.7293822 0.7293822 9.571863e-03 9.571863e-03 0.261045900 #> 7017  3508.0 0.7293429 0.7293429 9.582085e-03 9.582085e-03 0.261074986 #> 7018  3508.5 0.7293035 0.7293035 9.592309e-03 9.592309e-03 0.261104228 #> 7019  3509.0 0.7292638 0.7292638 9.602535e-03 9.602535e-03 0.261133625 #> 7020  3509.5 0.7292241 0.7292241 9.612761e-03 9.612761e-03 0.261163178 #> 7021  3510.0 0.7291841 0.7291841 9.622989e-03 9.622989e-03 0.261192887 #> 7022  3510.5 0.7291440 0.7291440 9.633219e-03 9.633219e-03 0.261222752 #> 7023  3511.0 0.7291038 0.7291038 9.643449e-03 9.643449e-03 0.261252772 #> 7024  3511.5 0.7290634 0.7290634 9.653681e-03 9.653681e-03 0.261282947 #> 7025  3512.0 0.7290228 0.7290228 9.663913e-03 9.663913e-03 0.261313278 #> 7026  3512.5 0.7289821 0.7289821 9.674147e-03 9.674147e-03 0.261343764 #> 7027  3513.0 0.7289412 0.7289412 9.684382e-03 9.684382e-03 0.261374406 #> 7028  3513.5 0.7289002 0.7289002 9.694617e-03 9.694617e-03 0.261405203 #> 7029  3514.0 0.7288590 0.7288590 9.704853e-03 9.704853e-03 0.261436155 #> 7030  3514.5 0.7288176 0.7288176 9.715091e-03 9.715091e-03 0.261467262 #> 7031  3515.0 0.7287761 0.7287761 9.725328e-03 9.725328e-03 0.261498524 #> 7032  3515.5 0.7287345 0.7287345 9.735567e-03 9.735567e-03 0.261529941 #> 7033  3516.0 0.7286927 0.7286927 9.745806e-03 9.745806e-03 0.261561513 #> 7034  3516.5 0.7286507 0.7286507 9.756046e-03 9.756046e-03 0.261593239 #> 7035  3517.0 0.7286086 0.7286086 9.766286e-03 9.766286e-03 0.261625121 #> 7036  3517.5 0.7285663 0.7285663 9.776527e-03 9.776527e-03 0.261657157 #> 7037  3518.0 0.7285239 0.7285239 9.786768e-03 9.786768e-03 0.261689348 #> 7038  3518.5 0.7284813 0.7284813 9.797009e-03 9.797009e-03 0.261721693 #> 7039  3519.0 0.7284386 0.7284386 9.807251e-03 9.807251e-03 0.261754193 #> 7040  3519.5 0.7283957 0.7283957 9.817492e-03 9.817492e-03 0.261786848 #> 7041  3520.0 0.7283526 0.7283526 9.827734e-03 9.827734e-03 0.261819656 #> 7042  3520.5 0.7283094 0.7283094 9.837976e-03 9.837976e-03 0.261852619 #> 7043  3521.0 0.7282660 0.7282660 9.848218e-03 9.848218e-03 0.261885736 #> 7044  3521.5 0.7282225 0.7282225 9.858460e-03 9.858460e-03 0.261919007 #> 7045  3522.0 0.7281789 0.7281789 9.868702e-03 9.868702e-03 0.261952432 #> 7046  3522.5 0.7281350 0.7281350 9.878944e-03 9.878944e-03 0.261986011 #> 7047  3523.0 0.7280911 0.7280911 9.889185e-03 9.889185e-03 0.262019744 #> 7048  3523.5 0.7280469 0.7280469 9.899426e-03 9.899426e-03 0.262053631 #> 7049  3524.0 0.7280027 0.7280027 9.909667e-03 9.909667e-03 0.262087672 #> 7050  3524.5 0.7279582 0.7279582 9.919908e-03 9.919908e-03 0.262121866 #> 7051  3525.0 0.7279136 0.7279136 9.930148e-03 9.930148e-03 0.262156213 #> 7052  3525.5 0.7278689 0.7278689 9.940387e-03 9.940387e-03 0.262190714 #> 7053  3526.0 0.7278240 0.7278240 9.950626e-03 9.950626e-03 0.262225369 #> 7054  3526.5 0.7277790 0.7277790 9.960864e-03 9.960864e-03 0.262260177 #> 7055  3527.0 0.7277338 0.7277338 9.971102e-03 9.971102e-03 0.262295137 #> 7056  3527.5 0.7276884 0.7276884 9.981338e-03 9.981338e-03 0.262330251 #> 7057  3528.0 0.7276429 0.7276429 9.991574e-03 9.991574e-03 0.262365518 #> 7058  3528.5 0.7275973 0.7275973 1.000181e-02 1.000181e-02 0.262400938 #> 7059  3529.0 0.7275514 0.7275514 1.001204e-02 1.001204e-02 0.262436511 #> 7060  3529.5 0.7275055 0.7275055 1.002228e-02 1.002228e-02 0.262472237 #> 7061  3530.0 0.7274594 0.7274594 1.003251e-02 1.003251e-02 0.262508115 #> 7062  3530.5 0.7274131 0.7274131 1.004274e-02 1.004274e-02 0.262544145 #> 7063  3531.0 0.7273667 0.7273667 1.005297e-02 1.005297e-02 0.262580328 #> 7064  3531.5 0.7273201 0.7273201 1.006320e-02 1.006320e-02 0.262616664 #> 7065  3532.0 0.7272734 0.7272734 1.007342e-02 1.007342e-02 0.262653152 #> 7066  3532.5 0.7272266 0.7272266 1.008365e-02 1.008365e-02 0.262689792 #> 7067  3533.0 0.7271795 0.7271795 1.009387e-02 1.009387e-02 0.262726583 #> 7068  3533.5 0.7271324 0.7271324 1.010410e-02 1.010410e-02 0.262763527 #> 7069  3534.0 0.7270851 0.7270851 1.011432e-02 1.011432e-02 0.262800623 #> 7070  3534.5 0.7270376 0.7270376 1.012454e-02 1.012454e-02 0.262837871 #> 7071  3535.0 0.7269900 0.7269900 1.013475e-02 1.013475e-02 0.262875270 #> 7072  3535.5 0.7269422 0.7269422 1.014497e-02 1.014497e-02 0.262912820 #> 7073  3536.0 0.7268943 0.7268943 1.015518e-02 1.015518e-02 0.262950522 #> 7074  3536.5 0.7268462 0.7268462 1.016539e-02 1.016539e-02 0.262988376 #> 7075  3537.0 0.7267980 0.7267980 1.017561e-02 1.017561e-02 0.263026381 #> 7076  3537.5 0.7267497 0.7267497 1.018581e-02 1.018581e-02 0.263064536 #> 7077  3538.0 0.7267011 0.7267011 1.019602e-02 1.019602e-02 0.263102843 #> 7078  3538.5 0.7266525 0.7266525 1.020622e-02 1.020622e-02 0.263141301 #> 7079  3539.0 0.7266037 0.7266037 1.021642e-02 1.021642e-02 0.263179909 #> 7080  3539.5 0.7265547 0.7265547 1.022662e-02 1.022662e-02 0.263218668 #> 7081  3540.0 0.7265056 0.7265056 1.023682e-02 1.023682e-02 0.263257578 #> 7082  3540.5 0.7264563 0.7264563 1.024701e-02 1.024701e-02 0.263296638 #> 7083  3541.0 0.7264069 0.7264069 1.025721e-02 1.025721e-02 0.263335848 #> 7084  3541.5 0.7263574 0.7263574 1.026740e-02 1.026740e-02 0.263375209 #> 7085  3542.0 0.7263077 0.7263077 1.027758e-02 1.027758e-02 0.263414720 #> 7086  3542.5 0.7262579 0.7262579 1.028777e-02 1.028777e-02 0.263454380 #> 7087  3543.0 0.7262079 0.7262079 1.029795e-02 1.029795e-02 0.263494191 #> 7088  3543.5 0.7261577 0.7261577 1.030813e-02 1.030813e-02 0.263534151 #> 7089  3544.0 0.7261074 0.7261074 1.031830e-02 1.031830e-02 0.263574261 #> 7090  3544.5 0.7260570 0.7260570 1.032847e-02 1.032847e-02 0.263614520 #> 7091  3545.0 0.7260064 0.7260064 1.033864e-02 1.033864e-02 0.263654929 #> 7092  3545.5 0.7259557 0.7259557 1.034881e-02 1.034881e-02 0.263695487 #> 7093  3546.0 0.7259048 0.7259048 1.035897e-02 1.035897e-02 0.263736193 #> 7094  3546.5 0.7258538 0.7258538 1.036913e-02 1.036913e-02 0.263777049 #> 7095  3547.0 0.7258027 0.7258027 1.037929e-02 1.037929e-02 0.263818054 #> 7096  3547.5 0.7257513 0.7257513 1.038944e-02 1.038944e-02 0.263859208 #> 7097  3548.0 0.7256999 0.7256999 1.039959e-02 1.039959e-02 0.263900510 #> 7098  3548.5 0.7256483 0.7256483 1.040974e-02 1.040974e-02 0.263941960 #> 7099  3549.0 0.7255966 0.7255966 1.041988e-02 1.041988e-02 0.263983559 #> 7100  3549.5 0.7255447 0.7255447 1.043002e-02 1.043002e-02 0.264025306 #> 7101  3550.0 0.7254926 0.7254926 1.044016e-02 1.044016e-02 0.264067201 #> 7102  3550.5 0.7254405 0.7254405 1.045029e-02 1.045029e-02 0.264109244 #> 7103  3551.0 0.7253881 0.7253881 1.046042e-02 1.046042e-02 0.264151435 #> 7104  3551.5 0.7253357 0.7253357 1.047054e-02 1.047054e-02 0.264193773 #> 7105  3552.0 0.7252831 0.7252831 1.048066e-02 1.048066e-02 0.264236259 #> 7106  3552.5 0.7252303 0.7252303 1.049078e-02 1.049078e-02 0.264278892 #> 7107  3553.0 0.7251774 0.7251774 1.050089e-02 1.050089e-02 0.264321673 #> 7108  3553.5 0.7251244 0.7251244 1.051100e-02 1.051100e-02 0.264364600 #> 7109  3554.0 0.7250712 0.7250712 1.052110e-02 1.052110e-02 0.264407675 #> 7110  3554.5 0.7250179 0.7250179 1.053120e-02 1.053120e-02 0.264450896 #> 7111  3555.0 0.7249644 0.7249644 1.054130e-02 1.054130e-02 0.264494264 #> 7112  3555.5 0.7249108 0.7249108 1.055139e-02 1.055139e-02 0.264537778 #> 7113  3556.0 0.7248571 0.7248571 1.056147e-02 1.056147e-02 0.264581439 #> 7114  3556.5 0.7248032 0.7248032 1.057155e-02 1.057155e-02 0.264625246 #> 7115  3557.0 0.7247492 0.7247492 1.058163e-02 1.058163e-02 0.264669199 #> 7116  3557.5 0.7246950 0.7246950 1.059170e-02 1.059170e-02 0.264713297 #> 7117  3558.0 0.7246407 0.7246407 1.060177e-02 1.060177e-02 0.264757542 #> 7118  3558.5 0.7245862 0.7245862 1.061183e-02 1.061183e-02 0.264801932 #> 7119  3559.0 0.7245316 0.7245316 1.062189e-02 1.062189e-02 0.264846468 #> 7120  3559.5 0.7244769 0.7244769 1.063194e-02 1.063194e-02 0.264891149 #> 7121  3560.0 0.7244220 0.7244220 1.064199e-02 1.064199e-02 0.264935975 #> 7122  3560.5 0.7243670 0.7243670 1.065203e-02 1.065203e-02 0.264980945 #> 7123  3561.0 0.7243119 0.7243119 1.066207e-02 1.066207e-02 0.265026061 #> 7124  3561.5 0.7242566 0.7242566 1.067210e-02 1.067210e-02 0.265071322 #> 7125  3562.0 0.7242011 0.7242011 1.068213e-02 1.068213e-02 0.265116727 #> 7126  3562.5 0.7241456 0.7241456 1.069215e-02 1.069215e-02 0.265162276 #> 7127  3563.0 0.7240899 0.7240899 1.070216e-02 1.070216e-02 0.265207969 #> 7128  3563.5 0.7240340 0.7240340 1.071217e-02 1.071217e-02 0.265253806 #> 7129  3564.0 0.7239780 0.7239780 1.072218e-02 1.072218e-02 0.265299788 #> 7130  3564.5 0.7239219 0.7239219 1.073218e-02 1.073218e-02 0.265345913 #> 7131  3565.0 0.7238657 0.7238657 1.074217e-02 1.074217e-02 0.265392181 #> 7132  3565.5 0.7238093 0.7238093 1.075216e-02 1.075216e-02 0.265438593 #> 7133  3566.0 0.7237527 0.7237527 1.076214e-02 1.076214e-02 0.265485147 #> 7134  3566.5 0.7236960 0.7236960 1.077211e-02 1.077211e-02 0.265531845 #> 7135  3567.0 0.7236392 0.7236392 1.078208e-02 1.078208e-02 0.265578686 #> 7136  3567.5 0.7235823 0.7235823 1.079204e-02 1.079204e-02 0.265625669 #> 7137  3568.0 0.7235252 0.7235252 1.080200e-02 1.080200e-02 0.265672795 #> 7138  3568.5 0.7234680 0.7234680 1.081195e-02 1.081195e-02 0.265720063 #> 7139  3569.0 0.7234106 0.7234106 1.082190e-02 1.082190e-02 0.265767473 #> 7140  3569.5 0.7233531 0.7233531 1.083183e-02 1.083183e-02 0.265815025 #> 7141  3570.0 0.7232955 0.7232955 1.084176e-02 1.084176e-02 0.265862719 #> 7142  3570.5 0.7232378 0.7232378 1.085169e-02 1.085169e-02 0.265910555 #> 7143  3571.0 0.7231799 0.7231799 1.086161e-02 1.086161e-02 0.265958532 #> 7144  3571.5 0.7231218 0.7231218 1.087152e-02 1.087152e-02 0.266006650 #> 7145  3572.0 0.7230637 0.7230637 1.088142e-02 1.088142e-02 0.266054909 #> 7146  3572.5 0.7230054 0.7230054 1.089132e-02 1.089132e-02 0.266103309 #> 7147  3573.0 0.7229469 0.7229469 1.090121e-02 1.090121e-02 0.266151849 #> 7148  3573.5 0.7228884 0.7228884 1.091110e-02 1.091110e-02 0.266200531 #> 7149  3574.0 0.7228297 0.7228297 1.092097e-02 1.092097e-02 0.266249352 #> 7150  3574.5 0.7227708 0.7227708 1.093084e-02 1.093084e-02 0.266298314 #> 7151  3575.0 0.7227119 0.7227119 1.094071e-02 1.094071e-02 0.266347415 #> 7152  3575.5 0.7226528 0.7226528 1.095056e-02 1.095056e-02 0.266396656 #> 7153  3576.0 0.7225936 0.7225936 1.096041e-02 1.096041e-02 0.266446037 #> 7154  3576.5 0.7225342 0.7225342 1.097025e-02 1.097025e-02 0.266495557 #> 7155  3577.0 0.7224747 0.7224747 1.098008e-02 1.098008e-02 0.266545216 #> 7156  3577.5 0.7224151 0.7224151 1.098991e-02 1.098991e-02 0.266595015 #> 7157  3578.0 0.7223553 0.7223553 1.099973e-02 1.099973e-02 0.266644952 #> 7158  3578.5 0.7222954 0.7222954 1.100954e-02 1.100954e-02 0.266695027 #> 7159  3579.0 0.7222354 0.7222354 1.101934e-02 1.101934e-02 0.266745241 #> 7160  3579.5 0.7221753 0.7221753 1.102913e-02 1.102913e-02 0.266795593 #> 7161  3580.0 0.7221150 0.7221150 1.103892e-02 1.103892e-02 0.266846083 #> 7162  3580.5 0.7220546 0.7220546 1.104870e-02 1.104870e-02 0.266896711 #> 7163  3581.0 0.7219941 0.7219941 1.105847e-02 1.105847e-02 0.266947476 #> 7164  3581.5 0.7219334 0.7219334 1.106823e-02 1.106823e-02 0.266998379 #> 7165  3582.0 0.7218726 0.7218726 1.107799e-02 1.107799e-02 0.267049419 #> 7166  3582.5 0.7218117 0.7218117 1.108773e-02 1.108773e-02 0.267100596 #> 7167  3583.0 0.7217506 0.7217506 1.109747e-02 1.109747e-02 0.267151910 #> 7168  3583.5 0.7216894 0.7216894 1.110720e-02 1.110720e-02 0.267203360 #> 7169  3584.0 0.7216281 0.7216281 1.111692e-02 1.111692e-02 0.267254946 #> 7170  3584.5 0.7215667 0.7215667 1.112663e-02 1.112663e-02 0.267306669 #> 7171  3585.0 0.7215051 0.7215051 1.113634e-02 1.113634e-02 0.267358527 #> 7172  3585.5 0.7214434 0.7214434 1.114603e-02 1.114603e-02 0.267410521 #> 7173  3586.0 0.7213816 0.7213816 1.115572e-02 1.115572e-02 0.267462651 #> 7174  3586.5 0.7213197 0.7213197 1.116540e-02 1.116540e-02 0.267514916 #> 7175  3587.0 0.7212576 0.7212576 1.117506e-02 1.117506e-02 0.267567316 #> 7176  3587.5 0.7211954 0.7211954 1.118472e-02 1.118472e-02 0.267619851 #> 7177  3588.0 0.7211331 0.7211331 1.119437e-02 1.119437e-02 0.267672520 #> 7178  3588.5 0.7210707 0.7210707 1.120401e-02 1.120401e-02 0.267725324 #> 7179  3589.0 0.7210081 0.7210081 1.121365e-02 1.121365e-02 0.267778262 #> 7180  3589.5 0.7209454 0.7209454 1.122327e-02 1.122327e-02 0.267831334 #> 7181  3590.0 0.7208826 0.7208826 1.123288e-02 1.123288e-02 0.267884539 #> 7182  3590.5 0.7208196 0.7208196 1.124249e-02 1.124249e-02 0.267937878 #> 7183  3591.0 0.7207566 0.7207566 1.125208e-02 1.125208e-02 0.267991351 #> 7184  3591.5 0.7206934 0.7206934 1.126167e-02 1.126167e-02 0.268044956 #> 7185  3592.0 0.7206301 0.7206301 1.127124e-02 1.127124e-02 0.268098695 #> 7186  3592.5 0.7205666 0.7205666 1.128081e-02 1.128081e-02 0.268152566 #> 7187  3593.0 0.7205031 0.7205031 1.129036e-02 1.129036e-02 0.268206569 #> 7188  3593.5 0.7204394 0.7204394 1.129991e-02 1.129991e-02 0.268260704 #> 7189  3594.0 0.7203756 0.7203756 1.130944e-02 1.130944e-02 0.268314972 #> 7190  3594.5 0.7203117 0.7203117 1.131897e-02 1.131897e-02 0.268369371 #> 7191  3595.0 0.7202476 0.7202476 1.132848e-02 1.132848e-02 0.268423901 #> 7192  3595.5 0.7201834 0.7201834 1.133799e-02 1.133799e-02 0.268478563 #> 7193  3596.0 0.7201192 0.7201192 1.134748e-02 1.134748e-02 0.268533356 #> 7194  3596.5 0.7200548 0.7200548 1.135697e-02 1.135697e-02 0.268588279 #> 7195  3597.0 0.7199902 0.7199902 1.136644e-02 1.136644e-02 0.268643333 #> 7196  3597.5 0.7199256 0.7199256 1.137591e-02 1.137591e-02 0.268698517 #> 7197  3598.0 0.7198608 0.7198608 1.138536e-02 1.138536e-02 0.268753831 #> 7198  3598.5 0.7197959 0.7197959 1.139480e-02 1.139480e-02 0.268809275 #> 7199  3599.0 0.7197309 0.7197309 1.140424e-02 1.140424e-02 0.268864849 #> 7200  3599.5 0.7196658 0.7196658 1.141366e-02 1.141366e-02 0.268920551 #> 7201  3600.0 0.7196005 0.7196005 1.142307e-02 1.142307e-02 0.268976383 #> 7202  3600.5 0.7195352 0.7195352 1.143247e-02 1.143247e-02 0.269032344 #> 7203  3601.0 0.7194697 0.7194697 1.144186e-02 1.144186e-02 0.269088433 #> 7204  3601.5 0.7194041 0.7194041 1.145123e-02 1.145123e-02 0.269144651 #> 7205  3602.0 0.7193384 0.7193384 1.146060e-02 1.146060e-02 0.269200996 #> 7206  3602.5 0.7192726 0.7192726 1.146996e-02 1.146996e-02 0.269257470 #> 7207  3603.0 0.7192066 0.7192066 1.147930e-02 1.147930e-02 0.269314071 #> 7208  3603.5 0.7191406 0.7191406 1.148863e-02 1.148863e-02 0.269370799 #> 7209  3604.0 0.7190744 0.7190744 1.149795e-02 1.149795e-02 0.269427654 #> 7210  3604.5 0.7190081 0.7190081 1.150726e-02 1.150726e-02 0.269484636 #> 7211  3605.0 0.7189417 0.7189417 1.151656e-02 1.151656e-02 0.269541745 #> 7212  3605.5 0.7188752 0.7188752 1.152585e-02 1.152585e-02 0.269598980 #> 7213  3606.0 0.7188085 0.7188085 1.153512e-02 1.153512e-02 0.269656341 #> 7214  3606.5 0.7187418 0.7187418 1.154439e-02 1.154439e-02 0.269713828 #> 7215  3607.0 0.7186749 0.7186749 1.155364e-02 1.155364e-02 0.269771440 #> 7216  3607.5 0.7186079 0.7186079 1.156288e-02 1.156288e-02 0.269829177 #> 7217  3608.0 0.7185409 0.7185409 1.157211e-02 1.157211e-02 0.269887040 #> 7218  3608.5 0.7184737 0.7184737 1.158132e-02 1.158132e-02 0.269945027 #> 7219  3609.0 0.7184063 0.7184063 1.159052e-02 1.159052e-02 0.270003139 #> 7220  3609.5 0.7183389 0.7183389 1.159971e-02 1.159971e-02 0.270061375 #> 7221  3610.0 0.7182714 0.7182714 1.160889e-02 1.160889e-02 0.270119734 #> 7222  3610.5 0.7182037 0.7182037 1.161806e-02 1.161806e-02 0.270178218 #> 7223  3611.0 0.7181360 0.7181360 1.162721e-02 1.162721e-02 0.270236825 #> 7224  3611.5 0.7180681 0.7180681 1.163635e-02 1.163635e-02 0.270295555 #> 7225  3612.0 0.7180001 0.7180001 1.164548e-02 1.164548e-02 0.270354408 #> 7226  3612.5 0.7179320 0.7179320 1.165460e-02 1.165460e-02 0.270413383 #> 7227  3613.0 0.7178638 0.7178638 1.166370e-02 1.166370e-02 0.270472481 #> 7228  3613.5 0.7177955 0.7177955 1.167279e-02 1.167279e-02 0.270531701 #> 7229  3614.0 0.7177271 0.7177271 1.168187e-02 1.168187e-02 0.270591042 #> 7230  3614.5 0.7176586 0.7176586 1.169093e-02 1.169093e-02 0.270650505 #> 7231  3615.0 0.7175899 0.7175899 1.169999e-02 1.169999e-02 0.270710089 #> 7232  3615.5 0.7175212 0.7175212 1.170902e-02 1.170902e-02 0.270769794 #> 7233  3616.0 0.7174523 0.7174523 1.171805e-02 1.171805e-02 0.270829620 #> 7234  3616.5 0.7173834 0.7173834 1.172706e-02 1.172706e-02 0.270889566 #> 7235  3617.0 0.7173143 0.7173143 1.173606e-02 1.173606e-02 0.270949633 #> 7236  3617.5 0.7172451 0.7172451 1.174505e-02 1.174505e-02 0.271009819 #> 7237  3618.0 0.7171759 0.7171759 1.175402e-02 1.175402e-02 0.271070124 #> 7238  3618.5 0.7171065 0.7171065 1.176298e-02 1.176298e-02 0.271130549 #> 7239  3619.0 0.7170370 0.7170370 1.177192e-02 1.177192e-02 0.271191093 #> 7240  3619.5 0.7169674 0.7169674 1.178085e-02 1.178085e-02 0.271251755 #> 7241  3620.0 0.7168977 0.7168977 1.178977e-02 1.178977e-02 0.271312536 #> 7242  3620.5 0.7168279 0.7168279 1.179867e-02 1.179867e-02 0.271373435 #> 7243  3621.0 0.7167580 0.7167580 1.180756e-02 1.180756e-02 0.271434452 #> 7244  3621.5 0.7166880 0.7166880 1.181644e-02 1.181644e-02 0.271495586 #> 7245  3622.0 0.7166179 0.7166179 1.182530e-02 1.182530e-02 0.271556837 #> 7246  3622.5 0.7165476 0.7165476 1.183415e-02 1.183415e-02 0.271618206 #> 7247  3623.0 0.7164773 0.7164773 1.184298e-02 1.184298e-02 0.271679690 #> 7248  3623.5 0.7164069 0.7164069 1.185180e-02 1.185180e-02 0.271741292 #> 7249  3624.0 0.7163364 0.7163364 1.186060e-02 1.186060e-02 0.271803009 #> 7250  3624.5 0.7162658 0.7162658 1.186939e-02 1.186939e-02 0.271864842 #> 7251  3625.0 0.7161950 0.7161950 1.187817e-02 1.187817e-02 0.271926791 #> 7252  3625.5 0.7161242 0.7161242 1.188693e-02 1.188693e-02 0.271988854 #> 7253  3626.0 0.7160533 0.7160533 1.189568e-02 1.189568e-02 0.272051033 #> 7254  3626.5 0.7159823 0.7159823 1.190441e-02 1.190441e-02 0.272113326 #> 7255  3627.0 0.7159111 0.7159111 1.191313e-02 1.191313e-02 0.272175733 #> 7256  3627.5 0.7158399 0.7158399 1.192183e-02 1.192183e-02 0.272238254 #> 7257  3628.0 0.7157686 0.7157686 1.193052e-02 1.193052e-02 0.272300889 #> 7258  3628.5 0.7156972 0.7156972 1.193919e-02 1.193919e-02 0.272363637 #> 7259  3629.0 0.7156257 0.7156257 1.194785e-02 1.194785e-02 0.272426498 #> 7260  3629.5 0.7155540 0.7155540 1.195649e-02 1.195649e-02 0.272489472 #> 7261  3630.0 0.7154823 0.7154823 1.196512e-02 1.196512e-02 0.272552558 #> 7262  3630.5 0.7154105 0.7154105 1.197373e-02 1.197373e-02 0.272615757 #> 7263  3631.0 0.7153386 0.7153386 1.198233e-02 1.198233e-02 0.272679067 #> 7264  3631.5 0.7152666 0.7152666 1.199091e-02 1.199091e-02 0.272742488 #> 7265  3632.0 0.7151945 0.7151945 1.199947e-02 1.199947e-02 0.272806021 #> 7266  3632.5 0.7151223 0.7151223 1.200802e-02 1.200802e-02 0.272869665 #> 7267  3633.0 0.7150500 0.7150500 1.201656e-02 1.201656e-02 0.272933419 #> 7268  3633.5 0.7149776 0.7149776 1.202508e-02 1.202508e-02 0.272997283 #> 7269  3634.0 0.7149052 0.7149052 1.203358e-02 1.203358e-02 0.273061257 #> 7270  3634.5 0.7148326 0.7148326 1.204207e-02 1.204207e-02 0.273125341 #> 7271  3635.0 0.7147599 0.7147599 1.205054e-02 1.205054e-02 0.273189534 #> 7272  3635.5 0.7146872 0.7146872 1.205899e-02 1.205899e-02 0.273253836 #> 7273  3636.0 0.7146143 0.7146143 1.206743e-02 1.206743e-02 0.273318247 #> 7274  3636.5 0.7145414 0.7145414 1.207586e-02 1.207586e-02 0.273382765 #> 7275  3637.0 0.7144683 0.7144683 1.208426e-02 1.208426e-02 0.273447392 #> 7276  3637.5 0.7143952 0.7143952 1.209265e-02 1.209265e-02 0.273512127 #> 7277  3638.0 0.7143220 0.7143220 1.210103e-02 1.210103e-02 0.273576968 #> 7278  3638.5 0.7142487 0.7142487 1.210939e-02 1.210939e-02 0.273641917 #> 7279  3639.0 0.7141753 0.7141753 1.211773e-02 1.211773e-02 0.273706973 #> 7280  3639.5 0.7141018 0.7141018 1.212605e-02 1.212605e-02 0.273772134 #> 7281  3640.0 0.7140282 0.7140282 1.213436e-02 1.213436e-02 0.273837402 #> 7282  3640.5 0.7139546 0.7139546 1.214266e-02 1.214266e-02 0.273902776 #> 7283  3641.0 0.7138808 0.7138808 1.215093e-02 1.215093e-02 0.273968255 #> 7284  3641.5 0.7138070 0.7138070 1.215919e-02 1.215919e-02 0.274033838 #> 7285  3642.0 0.7137330 0.7137330 1.216743e-02 1.216743e-02 0.274099527 #> 7286  3642.5 0.7136590 0.7136590 1.217566e-02 1.217566e-02 0.274165320 #> 7287  3643.0 0.7135849 0.7135849 1.218386e-02 1.218386e-02 0.274231217 #> 7288  3643.5 0.7135107 0.7135107 1.219205e-02 1.219205e-02 0.274297217 #> 7289  3644.0 0.7134365 0.7134365 1.220023e-02 1.220023e-02 0.274363321 #> 7290  3644.5 0.7133621 0.7133621 1.220838e-02 1.220838e-02 0.274429528 #> 7291  3645.0 0.7132876 0.7132876 1.221652e-02 1.221652e-02 0.274495838 #> 7292  3645.5 0.7132131 0.7132131 1.222464e-02 1.222464e-02 0.274562250 #> 7293  3646.0 0.7131385 0.7131385 1.223275e-02 1.223275e-02 0.274628763 #> 7294  3646.5 0.7130638 0.7130638 1.224084e-02 1.224084e-02 0.274695379 #> 7295  3647.0 0.7129890 0.7129890 1.224891e-02 1.224891e-02 0.274762096 #> 7296  3647.5 0.7129141 0.7129141 1.225696e-02 1.225696e-02 0.274828914 #> 7297  3648.0 0.7128392 0.7128392 1.226499e-02 1.226499e-02 0.274895832 #> 7298  3648.5 0.7127641 0.7127641 1.227301e-02 1.227301e-02 0.274962851 #> 7299  3649.0 0.7126890 0.7126890 1.228101e-02 1.228101e-02 0.275029969 #> 7300  3649.5 0.7126138 0.7126138 1.228899e-02 1.228899e-02 0.275097187 #> 7301  3650.0 0.7125385 0.7125385 1.229695e-02 1.229695e-02 0.275164505 #> 7302  3650.5 0.7124632 0.7124632 1.230489e-02 1.230489e-02 0.275231921 #> 7303  3651.0 0.7123877 0.7123877 1.231282e-02 1.231282e-02 0.275299436 #> 7304  3651.5 0.7123122 0.7123122 1.232073e-02 1.232073e-02 0.275367049 #> 7305  3652.0 0.7122366 0.7122366 1.232862e-02 1.232862e-02 0.275434760 #> 7306  3652.5 0.7121609 0.7121609 1.233649e-02 1.233649e-02 0.275502568 #> 7307  3653.0 0.7120852 0.7120852 1.234434e-02 1.234434e-02 0.275570473 #> 7308  3653.5 0.7120093 0.7120093 1.235218e-02 1.235218e-02 0.275638475 #> 7309  3654.0 0.7119334 0.7119334 1.236000e-02 1.236000e-02 0.275706574 #> 7310  3654.5 0.7118574 0.7118574 1.236779e-02 1.236779e-02 0.275774769 #> 7311  3655.0 0.7117814 0.7117814 1.237557e-02 1.237557e-02 0.275843059 #> 7312  3655.5 0.7117052 0.7117052 1.238333e-02 1.238333e-02 0.275911445 #> 7313  3656.0 0.7116290 0.7116290 1.239108e-02 1.239108e-02 0.275979925 #> 7314  3656.5 0.7115527 0.7115527 1.239880e-02 1.239880e-02 0.276048500 #> 7315  3657.0 0.7114763 0.7114763 1.240650e-02 1.240650e-02 0.276117170 #> 7316  3657.5 0.7113999 0.7113999 1.241419e-02 1.241419e-02 0.276185933 #> 7317  3658.0 0.7113234 0.7113234 1.242186e-02 1.242186e-02 0.276254790 #> 7318  3658.5 0.7112468 0.7112468 1.242950e-02 1.242950e-02 0.276323740 #> 7319  3659.0 0.7111701 0.7111701 1.243713e-02 1.243713e-02 0.276392783 #> 7320  3659.5 0.7110933 0.7110933 1.244474e-02 1.244474e-02 0.276461918 #> 7321  3660.0 0.7110165 0.7110165 1.245233e-02 1.245233e-02 0.276531146 #> 7322  3660.5 0.7109396 0.7109396 1.245990e-02 1.245990e-02 0.276600465 #> 7323  3661.0 0.7108627 0.7108627 1.246745e-02 1.246745e-02 0.276669875 #> 7324  3661.5 0.7107856 0.7107856 1.247499e-02 1.247499e-02 0.276739377 #> 7325  3662.0 0.7107085 0.7107085 1.248250e-02 1.248250e-02 0.276808969 #> 7326  3662.5 0.7106314 0.7106314 1.248999e-02 1.248999e-02 0.276878651 #> 7327  3663.0 0.7105541 0.7105541 1.249746e-02 1.249746e-02 0.276948423 #> 7328  3663.5 0.7104768 0.7104768 1.250492e-02 1.250492e-02 0.277018284 #> 7329  3664.0 0.7103994 0.7103994 1.251235e-02 1.251235e-02 0.277088235 #> 7330  3664.5 0.7103220 0.7103220 1.251976e-02 1.251976e-02 0.277158274 #> 7331  3665.0 0.7102444 0.7102444 1.252716e-02 1.252716e-02 0.277228402 #> 7332  3665.5 0.7101668 0.7101668 1.253453e-02 1.253453e-02 0.277298618 #> 7333  3666.0 0.7100892 0.7100892 1.254189e-02 1.254189e-02 0.277368921 #> 7334  3666.5 0.7100115 0.7100115 1.254922e-02 1.254922e-02 0.277439312 #> 7335  3667.0 0.7099337 0.7099337 1.255653e-02 1.255653e-02 0.277509789 #> 7336  3667.5 0.7098558 0.7098558 1.256383e-02 1.256383e-02 0.277580353 #> 7337  3668.0 0.7097779 0.7097779 1.257110e-02 1.257110e-02 0.277651003 #> 7338  3668.5 0.7096999 0.7096999 1.257835e-02 1.257835e-02 0.277721738 #> 7339  3669.0 0.7096219 0.7096219 1.258559e-02 1.258559e-02 0.277792559 #> 7340  3669.5 0.7095437 0.7095437 1.259280e-02 1.259280e-02 0.277863465 #> 7341  3670.0 0.7094656 0.7094656 1.259999e-02 1.259999e-02 0.277934456 #> 7342  3670.5 0.7093873 0.7093873 1.260716e-02 1.260716e-02 0.278005530 #> 7343  3671.0 0.7093090 0.7093090 1.261431e-02 1.261431e-02 0.278076689 #> 7344  3671.5 0.7092306 0.7092306 1.262144e-02 1.262144e-02 0.278147931 #> 7345  3672.0 0.7091522 0.7091522 1.262855e-02 1.262855e-02 0.278219256 #> 7346  3672.5 0.7090737 0.7090737 1.263564e-02 1.263564e-02 0.278290663 #> 7347  3673.0 0.7089951 0.7089951 1.264271e-02 1.264271e-02 0.278362153 #> 7348  3673.5 0.7089165 0.7089165 1.264976e-02 1.264976e-02 0.278433725 #> 7349  3674.0 0.7088378 0.7088378 1.265678e-02 1.265678e-02 0.278505378 #> 7350  3674.5 0.7087591 0.7087591 1.266379e-02 1.266379e-02 0.278577112 #> 7351  3675.0 0.7086803 0.7086803 1.267077e-02 1.267077e-02 0.278648927 #> 7352  3675.5 0.7086014 0.7086014 1.267773e-02 1.267773e-02 0.278720822 #> 7353  3676.0 0.7085225 0.7085225 1.268468e-02 1.268468e-02 0.278792797 #> 7354  3676.5 0.7084436 0.7084436 1.269160e-02 1.269160e-02 0.278864851 #> 7355  3677.0 0.7083645 0.7083645 1.269849e-02 1.269849e-02 0.278936985 #> 7356  3677.5 0.7082854 0.7082854 1.270537e-02 1.270537e-02 0.279009197 #> 7357  3678.0 0.7082063 0.7082063 1.271223e-02 1.271223e-02 0.279081488 #> 7358  3678.5 0.7081271 0.7081271 1.271906e-02 1.271906e-02 0.279153857 #> 7359  3679.0 0.7080478 0.7080478 1.272588e-02 1.272588e-02 0.279226303 #> 7360  3679.5 0.7079685 0.7079685 1.273267e-02 1.273267e-02 0.279298826 #> 7361  3680.0 0.7078891 0.7078891 1.273944e-02 1.273944e-02 0.279371426 #> 7362  3680.5 0.7078097 0.7078097 1.274619e-02 1.274619e-02 0.279444103 #> 7363  3681.0 0.7077302 0.7077302 1.275291e-02 1.275291e-02 0.279516855 #> 7364  3681.5 0.7076507 0.7076507 1.275962e-02 1.275962e-02 0.279589683 #> 7365  3682.0 0.7075711 0.7075711 1.276630e-02 1.276630e-02 0.279662586 #> 7366  3682.5 0.7074915 0.7074915 1.277296e-02 1.277296e-02 0.279735563 #> 7367  3683.0 0.7074118 0.7074118 1.277960e-02 1.277960e-02 0.279808615 #> 7368  3683.5 0.7073320 0.7073320 1.278622e-02 1.278622e-02 0.279881741 #> 7369  3684.0 0.7072522 0.7072522 1.279281e-02 1.279281e-02 0.279954941 #> 7370  3684.5 0.7071724 0.7071724 1.279938e-02 1.279938e-02 0.280028213 #> 7371  3685.0 0.7070925 0.7070925 1.280593e-02 1.280593e-02 0.280101558 #> 7372  3685.5 0.7070126 0.7070126 1.281246e-02 1.281246e-02 0.280174976 #> 7373  3686.0 0.7069326 0.7069326 1.281897e-02 1.281897e-02 0.280248465 #> 7374  3686.5 0.7068525 0.7068525 1.282545e-02 1.282545e-02 0.280322026 #> 7375  3687.0 0.7067724 0.7067724 1.283191e-02 1.283191e-02 0.280395658 #> 7376  3687.5 0.7066923 0.7066923 1.283835e-02 1.283835e-02 0.280469361 #> 7377  3688.0 0.7066121 0.7066121 1.284477e-02 1.284477e-02 0.280543134 #> 7378  3688.5 0.7065319 0.7065319 1.285116e-02 1.285116e-02 0.280616977 #> 7379  3689.0 0.7064516 0.7064516 1.285753e-02 1.285753e-02 0.280690889 #> 7380  3689.5 0.7063712 0.7063712 1.286388e-02 1.286388e-02 0.280764870 #> 7381  3690.0 0.7062909 0.7062909 1.287021e-02 1.287021e-02 0.280838920 #> 7382  3690.5 0.7062105 0.7062105 1.287651e-02 1.287651e-02 0.280913038 #> 7383  3691.0 0.7061300 0.7061300 1.288279e-02 1.288279e-02 0.280987223 #> 7384  3691.5 0.7060495 0.7060495 1.288905e-02 1.288905e-02 0.281061477 #> 7385  3692.0 0.7059689 0.7059689 1.289528e-02 1.289528e-02 0.281135797 #> 7386  3692.5 0.7058883 0.7058883 1.290149e-02 1.290149e-02 0.281210183 #> 7387  3693.0 0.7058077 0.7058077 1.290768e-02 1.290768e-02 0.281284636 #> 7388  3693.5 0.7057270 0.7057270 1.291384e-02 1.291384e-02 0.281359155 #> 7389  3694.0 0.7056463 0.7056463 1.291999e-02 1.291999e-02 0.281433738 #> 7390  3694.5 0.7055655 0.7055655 1.292611e-02 1.292611e-02 0.281508387 #> 7391  3695.0 0.7054847 0.7054847 1.293220e-02 1.293220e-02 0.281583100 #> 7392  3695.5 0.7054038 0.7054038 1.293827e-02 1.293827e-02 0.281657877 #> 7393  3696.0 0.7053230 0.7053230 1.294432e-02 1.294432e-02 0.281732718 #> 7394  3696.5 0.7052420 0.7052420 1.295035e-02 1.295035e-02 0.281807622 #> 7395  3697.0 0.7051611 0.7051611 1.295635e-02 1.295635e-02 0.281882588 #> 7396  3697.5 0.7050801 0.7050801 1.296233e-02 1.296233e-02 0.281957617 #> 7397  3698.0 0.7049990 0.7049990 1.296828e-02 1.296828e-02 0.282032708 #> 7398  3698.5 0.7049179 0.7049179 1.297422e-02 1.297422e-02 0.282107860 #> 7399  3699.0 0.7048368 0.7048368 1.298012e-02 1.298012e-02 0.282183074 #> 7400  3699.5 0.7047556 0.7047556 1.298601e-02 1.298601e-02 0.282258348 #> 7401  3700.0 0.7046744 0.7046744 1.299187e-02 1.299187e-02 0.282333682 #> 7402  3700.5 0.7045932 0.7045932 1.299771e-02 1.299771e-02 0.282409076 #> 7403  3701.0 0.7045120 0.7045120 1.300352e-02 1.300352e-02 0.282484530 #> 7404  3701.5 0.7044306 0.7044306 1.300931e-02 1.300931e-02 0.282560042 #> 7405  3702.0 0.7043493 0.7043493 1.301507e-02 1.301507e-02 0.282635613 #> 7406  3702.5 0.7042679 0.7042679 1.302082e-02 1.302082e-02 0.282711242 #> 7407  3703.0 0.7041865 0.7041865 1.302653e-02 1.302653e-02 0.282786929 #> 7408  3703.5 0.7041051 0.7041051 1.303223e-02 1.303223e-02 0.282862673 #> 7409  3704.0 0.7040236 0.7040236 1.303790e-02 1.303790e-02 0.282938474 #> 7410  3704.5 0.7039421 0.7039421 1.304354e-02 1.304354e-02 0.283014331 #> 7411  3705.0 0.7038606 0.7038606 1.304916e-02 1.304916e-02 0.283090244 #> 7412  3705.5 0.7037790 0.7037790 1.305476e-02 1.305476e-02 0.283166213 #> 7413  3706.0 0.7036974 0.7036974 1.306033e-02 1.306033e-02 0.283242236 #> 7414  3706.5 0.7036158 0.7036158 1.306588e-02 1.306588e-02 0.283318315 #> 7415  3707.0 0.7035341 0.7035341 1.307141e-02 1.307141e-02 0.283394447 #> 7416  3707.5 0.7034525 0.7034525 1.307691e-02 1.307691e-02 0.283470634 #> 7417  3708.0 0.7033707 0.7033707 1.308238e-02 1.308238e-02 0.283546873 #> 7418  3708.5 0.7032890 0.7032890 1.308783e-02 1.308783e-02 0.283623166 #> 7419  3709.0 0.7032072 0.7032072 1.309326e-02 1.309326e-02 0.283699511 #> 7420  3709.5 0.7031254 0.7031254 1.309866e-02 1.309866e-02 0.283775908 #> 7421  3710.0 0.7030436 0.7030436 1.310404e-02 1.310404e-02 0.283852357 #> 7422  3710.5 0.7029618 0.7029618 1.310939e-02 1.310939e-02 0.283928857 #> 7423  3711.0 0.7028799 0.7028799 1.311472e-02 1.311472e-02 0.284005408 #> 7424  3711.5 0.7027980 0.7027980 1.312002e-02 1.312002e-02 0.284082009 #> 7425  3712.0 0.7027160 0.7027160 1.312530e-02 1.312530e-02 0.284158660 #> 7426  3712.5 0.7026341 0.7026341 1.313055e-02 1.313055e-02 0.284235360 #> 7427  3713.0 0.7025521 0.7025521 1.313578e-02 1.313578e-02 0.284312109 #> 7428  3713.5 0.7024701 0.7024701 1.314099e-02 1.314099e-02 0.284388907 #> 7429  3714.0 0.7023881 0.7023881 1.314617e-02 1.314617e-02 0.284465753 #> 7430  3714.5 0.7023060 0.7023060 1.315132e-02 1.315132e-02 0.284542647 #> 7431  3715.0 0.7022240 0.7022240 1.315645e-02 1.315645e-02 0.284619588 #> 7432  3715.5 0.7021419 0.7021419 1.316156e-02 1.316156e-02 0.284696576 #> 7433  3716.0 0.7020598 0.7020598 1.316664e-02 1.316664e-02 0.284773610 #> 7434  3716.5 0.7019776 0.7019776 1.317169e-02 1.317169e-02 0.284850690 #> 7435  3717.0 0.7018955 0.7018955 1.317672e-02 1.317672e-02 0.284927815 #> 7436  3717.5 0.7018133 0.7018133 1.318172e-02 1.318172e-02 0.285004986 #> 7437  3718.0 0.7017311 0.7017311 1.318670e-02 1.318670e-02 0.285082201 #> 7438  3718.5 0.7016489 0.7016489 1.319166e-02 1.319166e-02 0.285159460 #> 7439  3719.0 0.7015667 0.7015667 1.319658e-02 1.319658e-02 0.285236763 #> 7440  3719.5 0.7014844 0.7014844 1.320149e-02 1.320149e-02 0.285314109 #> 7441  3720.0 0.7014021 0.7014021 1.320636e-02 1.320636e-02 0.285391498 #> 7442  3720.5 0.7013199 0.7013199 1.321122e-02 1.321122e-02 0.285468930 #> 7443  3721.0 0.7012376 0.7012376 1.321604e-02 1.321604e-02 0.285546403 #> 7444  3721.5 0.7011552 0.7011552 1.322085e-02 1.322085e-02 0.285623918 #> 7445  3722.0 0.7010729 0.7010729 1.322562e-02 1.322562e-02 0.285701474 #> 7446  3722.5 0.7009906 0.7009906 1.323037e-02 1.323037e-02 0.285779070 #> 7447  3723.0 0.7009082 0.7009082 1.323510e-02 1.323510e-02 0.285856707 #> 7448  3723.5 0.7008258 0.7008258 1.323980e-02 1.323980e-02 0.285934383 #> 7449  3724.0 0.7007434 0.7007434 1.324447e-02 1.324447e-02 0.286012099 #> 7450  3724.5 0.7006610 0.7006610 1.324912e-02 1.324912e-02 0.286089853 #> 7451  3725.0 0.7005786 0.7005786 1.325374e-02 1.325374e-02 0.286167646 #> 7452  3725.5 0.7004962 0.7004962 1.325834e-02 1.325834e-02 0.286245477 #> 7453  3726.0 0.7004137 0.7004137 1.326291e-02 1.326291e-02 0.286323345 #> 7454  3726.5 0.7003313 0.7003313 1.326745e-02 1.326745e-02 0.286401251 #> 7455  3727.0 0.7002488 0.7002488 1.327197e-02 1.327197e-02 0.286479192 #> 7456  3727.5 0.7001664 0.7001664 1.327647e-02 1.327647e-02 0.286557170 #> 7457  3728.0 0.7000839 0.7000839 1.328093e-02 1.328093e-02 0.286635184 #> 7458  3728.5 0.7000014 0.7000014 1.328538e-02 1.328538e-02 0.286713233 #> 7459  3729.0 0.6999189 0.6999189 1.328979e-02 1.328979e-02 0.286791316 #> 7460  3729.5 0.6998364 0.6998364 1.329418e-02 1.329418e-02 0.286869434 #> 7461  3730.0 0.6997539 0.6997539 1.329855e-02 1.329855e-02 0.286947586 #> 7462  3730.5 0.6996713 0.6996713 1.330288e-02 1.330288e-02 0.287025771 #> 7463  3731.0 0.6995888 0.6995888 1.330720e-02 1.330720e-02 0.287103990 #> 7464  3731.5 0.6995063 0.6995063 1.331148e-02 1.331148e-02 0.287182241 #> 7465  3732.0 0.6994237 0.6994237 1.331574e-02 1.331574e-02 0.287260523 #> 7466  3732.5 0.6993412 0.6993412 1.331998e-02 1.331998e-02 0.287338838 #> 7467  3733.0 0.6992586 0.6992586 1.332418e-02 1.332418e-02 0.287417184 #> 7468  3733.5 0.6991761 0.6991761 1.332836e-02 1.332836e-02 0.287495560 #> 7469  3734.0 0.6990935 0.6990935 1.333252e-02 1.333252e-02 0.287573967 #> 7470  3734.5 0.6990109 0.6990109 1.333665e-02 1.333665e-02 0.287652403 #> 7471  3735.0 0.6989284 0.6989284 1.334075e-02 1.334075e-02 0.287730869 #> 7472  3735.5 0.6988458 0.6988458 1.334483e-02 1.334483e-02 0.287809364 #> 7473  3736.0 0.6987632 0.6987632 1.334888e-02 1.334888e-02 0.287887887 #> 7474  3736.5 0.6986807 0.6986807 1.335290e-02 1.335290e-02 0.287966439 #> 7475  3737.0 0.6985981 0.6985981 1.335690e-02 1.335690e-02 0.288045017 #> 7476  3737.5 0.6985155 0.6985155 1.336087e-02 1.336087e-02 0.288123623 #> 7477  3738.0 0.6984329 0.6984329 1.336481e-02 1.336481e-02 0.288202256 #> 7478  3738.5 0.6983504 0.6983504 1.336873e-02 1.336873e-02 0.288280915 #> 7479  3739.0 0.6982678 0.6982678 1.337262e-02 1.337262e-02 0.288359599 #> 7480  3739.5 0.6981852 0.6981852 1.337649e-02 1.337649e-02 0.288438309 #> 7481  3740.0 0.6981026 0.6981026 1.338033e-02 1.338033e-02 0.288517044 #> 7482  3740.5 0.6980201 0.6980201 1.338414e-02 1.338414e-02 0.288595803 #> 7483  3741.0 0.6979375 0.6979375 1.338793e-02 1.338793e-02 0.288674586 #> 7484  3741.5 0.6978549 0.6978549 1.339168e-02 1.339168e-02 0.288753392 #> 7485  3742.0 0.6977724 0.6977724 1.339542e-02 1.339542e-02 0.288832222 #> 7486  3742.5 0.6976898 0.6976898 1.339912e-02 1.339912e-02 0.288911074 #> 7487  3743.0 0.6976072 0.6976072 1.340280e-02 1.340280e-02 0.288989948 #> 7488  3743.5 0.6975247 0.6975247 1.340645e-02 1.340645e-02 0.289068844 #> 7489  3744.0 0.6974422 0.6974422 1.341008e-02 1.341008e-02 0.289147761 #> 7490  3744.5 0.6973596 0.6973596 1.341368e-02 1.341368e-02 0.289226699 #> 7491  3745.0 0.6972771 0.6972771 1.341725e-02 1.341725e-02 0.289305657 #> 7492  3745.5 0.6971946 0.6971946 1.342080e-02 1.342080e-02 0.289384634 #> 7493  3746.0 0.6971121 0.6971121 1.342432e-02 1.342432e-02 0.289463632 #> 7494  3746.5 0.6970295 0.6970295 1.342781e-02 1.342781e-02 0.289542648 #> 7495  3747.0 0.6969470 0.6969470 1.343127e-02 1.343127e-02 0.289621683 #> 7496  3747.5 0.6968646 0.6968646 1.343471e-02 1.343471e-02 0.289700735 #> 7497  3748.0 0.6967821 0.6967821 1.343812e-02 1.343812e-02 0.289779805 #> 7498  3748.5 0.6966996 0.6966996 1.344151e-02 1.344151e-02 0.289858893 #> 7499  3749.0 0.6966171 0.6966171 1.344487e-02 1.344487e-02 0.289937997 #> 7500  3749.5 0.6965347 0.6965347 1.344820e-02 1.344820e-02 0.290017117 #> 7501  3750.0 0.6964522 0.6964522 1.345150e-02 1.345150e-02 0.290096253 #> 7502  3750.5 0.6963698 0.6963698 1.345478e-02 1.345478e-02 0.290175404 #> 7503  3751.0 0.6962874 0.6962874 1.345803e-02 1.345803e-02 0.290254570 #> 7504  3751.5 0.6962050 0.6962050 1.346125e-02 1.346125e-02 0.290333750 #> 7505  3752.0 0.6961226 0.6961226 1.346445e-02 1.346445e-02 0.290412944 #> 7506  3752.5 0.6960402 0.6960402 1.346762e-02 1.346762e-02 0.290492152 #> 7507  3753.0 0.6959579 0.6959579 1.347076e-02 1.347076e-02 0.290571373 #> 7508  3753.5 0.6958755 0.6958755 1.347388e-02 1.347388e-02 0.290650606 #> 7509  3754.0 0.6957932 0.6957932 1.347697e-02 1.347697e-02 0.290729851 #> 7510  3754.5 0.6957109 0.6957109 1.348003e-02 1.348003e-02 0.290809108 #> 7511  3755.0 0.6956286 0.6956286 1.348306e-02 1.348306e-02 0.290888376 #> 7512  3755.5 0.6955463 0.6955463 1.348607e-02 1.348607e-02 0.290967655 #> 7513  3756.0 0.6954640 0.6954640 1.348905e-02 1.348905e-02 0.291046944 #> 7514  3756.5 0.6953818 0.6953818 1.349200e-02 1.349200e-02 0.291126243 #> 7515  3757.0 0.6952995 0.6952995 1.349493e-02 1.349493e-02 0.291205551 #> 7516  3757.5 0.6952173 0.6952173 1.349783e-02 1.349783e-02 0.291284868 #> 7517  3758.0 0.6951351 0.6951351 1.350070e-02 1.350070e-02 0.291364194 #> 7518  3758.5 0.6950529 0.6950529 1.350354e-02 1.350354e-02 0.291443527 #> 7519  3759.0 0.6949708 0.6949708 1.350636e-02 1.350636e-02 0.291522868 #> 7520  3759.5 0.6948886 0.6948886 1.350915e-02 1.350915e-02 0.291602216 #> 7521  3760.0 0.6948065 0.6948065 1.351192e-02 1.351192e-02 0.291681571 #> 7522  3760.5 0.6947244 0.6947244 1.351465e-02 1.351465e-02 0.291760932 #> 7523  3761.0 0.6946423 0.6946423 1.351736e-02 1.351736e-02 0.291840298 #> 7524  3761.5 0.6945603 0.6945603 1.352004e-02 1.352004e-02 0.291919670 #> 7525  3762.0 0.6944783 0.6944783 1.352270e-02 1.352270e-02 0.291999046 #> 7526  3762.5 0.6943962 0.6943962 1.352532e-02 1.352532e-02 0.292078427 #> 7527  3763.0 0.6943143 0.6943143 1.352792e-02 1.352792e-02 0.292157812 #> 7528  3763.5 0.6942323 0.6942323 1.353050e-02 1.353050e-02 0.292237200 #> 7529  3764.0 0.6941504 0.6941504 1.353304e-02 1.353304e-02 0.292316591 #> 7530  3764.5 0.6940685 0.6940685 1.353556e-02 1.353556e-02 0.292395985 #> 7531  3765.0 0.6939866 0.6939866 1.353805e-02 1.353805e-02 0.292475380 #> 7532  3765.5 0.6939047 0.6939047 1.354052e-02 1.354052e-02 0.292554778 #> 7533  3766.0 0.6938229 0.6938229 1.354295e-02 1.354295e-02 0.292634176 #> 7534  3766.5 0.6937411 0.6937411 1.354536e-02 1.354536e-02 0.292713575 #> 7535  3767.0 0.6936593 0.6936593 1.354774e-02 1.354774e-02 0.292792974 #> 7536  3767.5 0.6935775 0.6935775 1.355010e-02 1.355010e-02 0.292872373 #> 7537  3768.0 0.6934958 0.6934958 1.355243e-02 1.355243e-02 0.292951772 #> 7538  3768.5 0.6934141 0.6934141 1.355473e-02 1.355473e-02 0.293031169 #> 7539  3769.0 0.6933324 0.6933324 1.355700e-02 1.355700e-02 0.293110564 #> 7540  3769.5 0.6932508 0.6932508 1.355924e-02 1.355924e-02 0.293189958 #> 7541  3770.0 0.6931692 0.6931692 1.356146e-02 1.356146e-02 0.293269349 #> 7542  3770.5 0.6930876 0.6930876 1.356365e-02 1.356365e-02 0.293348737 #> 7543  3771.0 0.6930061 0.6930061 1.356582e-02 1.356582e-02 0.293428122 #> 7544  3771.5 0.6929245 0.6929245 1.356795e-02 1.356795e-02 0.293507503 #> 7545  3772.0 0.6928431 0.6928431 1.357006e-02 1.357006e-02 0.293586879 #> 7546  3772.5 0.6927616 0.6927616 1.357214e-02 1.357214e-02 0.293666251 #> 7547  3773.0 0.6926802 0.6926802 1.357420e-02 1.357420e-02 0.293745618 #> 7548  3773.5 0.6925988 0.6925988 1.357622e-02 1.357622e-02 0.293824979 #> 7549  3774.0 0.6925174 0.6925174 1.357822e-02 1.357822e-02 0.293904334 #> 7550  3774.5 0.6924361 0.6924361 1.358020e-02 1.358020e-02 0.293983682 #> 7551  3775.0 0.6923548 0.6923548 1.358214e-02 1.358214e-02 0.294063023 #> 7552  3775.5 0.6922736 0.6922736 1.358406e-02 1.358406e-02 0.294142357 #> 7553  3776.0 0.6921924 0.6921924 1.358595e-02 1.358595e-02 0.294221682 #> 7554  3776.5 0.6921112 0.6921112 1.358781e-02 1.358781e-02 0.294301000 #> 7555  3777.0 0.6920300 0.6920300 1.358965e-02 1.358965e-02 0.294380308 #> 7556  3777.5 0.6919489 0.6919489 1.359146e-02 1.359146e-02 0.294459608 #> 7557  3778.0 0.6918679 0.6918679 1.359324e-02 1.359324e-02 0.294538897 #> 7558  3778.5 0.6917868 0.6917868 1.359499e-02 1.359499e-02 0.294618177 #> 7559  3779.0 0.6917058 0.6917058 1.359672e-02 1.359672e-02 0.294697445 #> 7560  3779.5 0.6916249 0.6916249 1.359842e-02 1.359842e-02 0.294776703 #> 7561  3780.0 0.6915440 0.6915440 1.360009e-02 1.360009e-02 0.294855949 #> 7562  3780.5 0.6914631 0.6914631 1.360174e-02 1.360174e-02 0.294935183 #> 7563  3781.0 0.6913822 0.6913822 1.360335e-02 1.360335e-02 0.295014405 #> 7564  3781.5 0.6913014 0.6913014 1.360494e-02 1.360494e-02 0.295093614 #> 7565  3782.0 0.6912207 0.6912207 1.360651e-02 1.360651e-02 0.295172809 #> 7566  3782.5 0.6911400 0.6911400 1.360804e-02 1.360804e-02 0.295251991 #> 7567  3783.0 0.6910593 0.6910593 1.360955e-02 1.360955e-02 0.295331159 #> 7568  3783.5 0.6909787 0.6909787 1.361103e-02 1.361103e-02 0.295410311 #> 7569  3784.0 0.6908981 0.6908981 1.361248e-02 1.361248e-02 0.295489449 #> 7570  3784.5 0.6908175 0.6908175 1.361391e-02 1.361391e-02 0.295568571 #> 7571  3785.0 0.6907370 0.6907370 1.361531e-02 1.361531e-02 0.295647677 #> 7572  3785.5 0.6906566 0.6906566 1.361668e-02 1.361668e-02 0.295726767 #> 7573  3786.0 0.6905761 0.6905761 1.361803e-02 1.361803e-02 0.295805840 #> 7574  3786.5 0.6904958 0.6904958 1.361934e-02 1.361934e-02 0.295884895 #> 7575  3787.0 0.6904154 0.6904154 1.362063e-02 1.362063e-02 0.295963933 #> 7576  3787.5 0.6903352 0.6903352 1.362190e-02 1.362190e-02 0.296042952 #> 7577  3788.0 0.6902549 0.6902549 1.362313e-02 1.362313e-02 0.296121953 #> 7578  3788.5 0.6901747 0.6901747 1.362434e-02 1.362434e-02 0.296200935 #> 7579  3789.0 0.6900946 0.6900946 1.362552e-02 1.362552e-02 0.296279897 #> 7580  3789.5 0.6900145 0.6900145 1.362668e-02 1.362668e-02 0.296358839 #> 7581  3790.0 0.6899344 0.6899344 1.362780e-02 1.362780e-02 0.296437760 #> 7582  3790.5 0.6898544 0.6898544 1.362890e-02 1.362890e-02 0.296516661 #> 7583  3791.0 0.6897745 0.6897745 1.362997e-02 1.362997e-02 0.296595540 #> 7584  3791.5 0.6896946 0.6896946 1.363102e-02 1.363102e-02 0.296674397 #> 7585  3792.0 0.6896147 0.6896147 1.363204e-02 1.363204e-02 0.296753233 #> 7586  3792.5 0.6895349 0.6895349 1.363303e-02 1.363303e-02 0.296832045 #> 7587  3793.0 0.6894552 0.6894552 1.363399e-02 1.363399e-02 0.296910835 #> 7588  3793.5 0.6893755 0.6893755 1.363493e-02 1.363493e-02 0.296989601 #> 7589  3794.0 0.6892958 0.6892958 1.363584e-02 1.363584e-02 0.297068343 #> 7590  3794.5 0.6892162 0.6892162 1.363672e-02 1.363672e-02 0.297147060 #> 7591  3795.0 0.6891367 0.6891367 1.363758e-02 1.363758e-02 0.297225753 #> 7592  3795.5 0.6890572 0.6890572 1.363841e-02 1.363841e-02 0.297304420 #> 7593  3796.0 0.6889777 0.6889777 1.363921e-02 1.363921e-02 0.297383062 #> 7594  3796.5 0.6888983 0.6888983 1.363998e-02 1.363998e-02 0.297461677 #> 7595  3797.0 0.6888190 0.6888190 1.364073e-02 1.364073e-02 0.297540266 #> 7596  3797.5 0.6887397 0.6887397 1.364145e-02 1.364145e-02 0.297618828 #> 7597  3798.0 0.6886605 0.6886605 1.364214e-02 1.364214e-02 0.297697362 #> 7598  3798.5 0.6885813 0.6885813 1.364281e-02 1.364281e-02 0.297775869 #> 7599  3799.0 0.6885022 0.6885022 1.364345e-02 1.364345e-02 0.297854347 #> 7600  3799.5 0.6884231 0.6884231 1.364406e-02 1.364406e-02 0.297932796 #> 7601  3800.0 0.6883441 0.6883441 1.364465e-02 1.364465e-02 0.298011216 #> 7602  3800.5 0.6882652 0.6882652 1.364521e-02 1.364521e-02 0.298089606 #> 7603  3801.0 0.6881863 0.6881863 1.364574e-02 1.364574e-02 0.298167966 #> 7604  3801.5 0.6881075 0.6881075 1.364624e-02 1.364624e-02 0.298246296 #> 7605  3802.0 0.6880287 0.6880287 1.364672e-02 1.364672e-02 0.298324595 #> 7606  3802.5 0.6879500 0.6879500 1.364717e-02 1.364717e-02 0.298402862 #> 7607  3803.0 0.6878713 0.6878713 1.364760e-02 1.364760e-02 0.298481098 #> 7608  3803.5 0.6877927 0.6877927 1.364800e-02 1.364800e-02 0.298559301 #> 7609  3804.0 0.6877142 0.6877142 1.364837e-02 1.364837e-02 0.298637471 #> 7610  3804.5 0.6876357 0.6876357 1.364871e-02 1.364871e-02 0.298715609 #> 7611  3805.0 0.6875573 0.6875573 1.364903e-02 1.364903e-02 0.298793713 #> 7612  3805.5 0.6874789 0.6874789 1.364932e-02 1.364932e-02 0.298871783 #> 7613  3806.0 0.6874006 0.6874006 1.364958e-02 1.364958e-02 0.298949818 #> 7614  3806.5 0.6873224 0.6873224 1.364982e-02 1.364982e-02 0.299027819 #> 7615  3807.0 0.6872442 0.6872442 1.365003e-02 1.365003e-02 0.299105784 #> 7616  3807.5 0.6871661 0.6871661 1.365021e-02 1.365021e-02 0.299183714 #> 7617  3808.0 0.6870880 0.6870880 1.365037e-02 1.365037e-02 0.299261607 #> 7618  3808.5 0.6870100 0.6870100 1.365050e-02 1.365050e-02 0.299339464 #> 7619  3809.0 0.6869321 0.6869321 1.365061e-02 1.365061e-02 0.299417284 #> 7620  3809.5 0.6868543 0.6868543 1.365068e-02 1.365068e-02 0.299495067 #> 7621  3810.0 0.6867765 0.6867765 1.365073e-02 1.365073e-02 0.299572812 #> 7622  3810.5 0.6866987 0.6866987 1.365076e-02 1.365076e-02 0.299650519 #> 7623  3811.0 0.6866211 0.6866211 1.365076e-02 1.365076e-02 0.299728187 #> 7624  3811.5 0.6865435 0.6865435 1.365073e-02 1.365073e-02 0.299805815 #> 7625  3812.0 0.6864659 0.6864659 1.365067e-02 1.365067e-02 0.299883405 #> 7626  3812.5 0.6863885 0.6863885 1.365059e-02 1.365059e-02 0.299960954 #> 7627  3813.0 0.6863111 0.6863111 1.365048e-02 1.365048e-02 0.300038463 #> 7628  3813.5 0.6862337 0.6862337 1.365035e-02 1.365035e-02 0.300115932 #> 7629  3814.0 0.6861565 0.6861565 1.365019e-02 1.365019e-02 0.300193359 #> 7630  3814.5 0.6860793 0.6860793 1.365000e-02 1.365000e-02 0.300270744 #> 7631  3815.0 0.6860021 0.6860021 1.364978e-02 1.364978e-02 0.300348088 #> 7632  3815.5 0.6859251 0.6859251 1.364954e-02 1.364954e-02 0.300425389 #> 7633  3816.0 0.6858481 0.6858481 1.364928e-02 1.364928e-02 0.300502647 #> 7634  3816.5 0.6857712 0.6857712 1.364899e-02 1.364899e-02 0.300579862 #> 7635  3817.0 0.6856943 0.6856943 1.364867e-02 1.364867e-02 0.300657033 #> 7636  3817.5 0.6856175 0.6856175 1.364832e-02 1.364832e-02 0.300734161 #> 7637  3818.0 0.6855408 0.6855408 1.364795e-02 1.364795e-02 0.300811243 #> 7638  3818.5 0.6854642 0.6854642 1.364755e-02 1.364755e-02 0.300888281 #> 7639  3819.0 0.6853876 0.6853876 1.364713e-02 1.364713e-02 0.300965273 #> 7640  3819.5 0.6853111 0.6853111 1.364668e-02 1.364668e-02 0.301042220 #> 7641  3820.0 0.6852347 0.6852347 1.364621e-02 1.364621e-02 0.301119120 #> 7642  3820.5 0.6851583 0.6851583 1.364570e-02 1.364570e-02 0.301195974 #> 7643  3821.0 0.6850820 0.6850820 1.364518e-02 1.364518e-02 0.301272781 #> 7644  3821.5 0.6850058 0.6850058 1.364462e-02 1.364462e-02 0.301349540 #> 7645  3822.0 0.6849297 0.6849297 1.364404e-02 1.364404e-02 0.301426252 #> 7646  3822.5 0.6848536 0.6848536 1.364344e-02 1.364344e-02 0.301502915 #> 7647  3823.0 0.6847777 0.6847777 1.364281e-02 1.364281e-02 0.301579530 #> 7648  3823.5 0.6847018 0.6847018 1.364215e-02 1.364215e-02 0.301656096 #> 7649  3824.0 0.6846259 0.6846259 1.364147e-02 1.364147e-02 0.301732612 #> 7650  3824.5 0.6845502 0.6845502 1.364076e-02 1.364076e-02 0.301809079 #> 7651  3825.0 0.6844745 0.6844745 1.364002e-02 1.364002e-02 0.301885495 #> 7652  3825.5 0.6843989 0.6843989 1.363926e-02 1.363926e-02 0.301961861 #> 7653  3826.0 0.6843233 0.6843233 1.363848e-02 1.363848e-02 0.302038175 #> 7654  3826.5 0.6842479 0.6842479 1.363766e-02 1.363766e-02 0.302114438 #> 7655  3827.0 0.6841725 0.6841725 1.363683e-02 1.363683e-02 0.302190649 #> 7656  3827.5 0.6840972 0.6840972 1.363596e-02 1.363596e-02 0.302266808 #> 7657  3828.0 0.6840220 0.6840220 1.363508e-02 1.363508e-02 0.302342915 #> 7658  3828.5 0.6839469 0.6839469 1.363416e-02 1.363416e-02 0.302418968 #> 7659  3829.0 0.6838718 0.6838718 1.363322e-02 1.363322e-02 0.302494968 #> 7660  3829.5 0.6837968 0.6837968 1.363226e-02 1.363226e-02 0.302570913 #> 7661  3830.0 0.6837219 0.6837219 1.363127e-02 1.363127e-02 0.302646805 #> 7662  3830.5 0.6836471 0.6836471 1.363025e-02 1.363025e-02 0.302722642 #> 7663  3831.0 0.6835724 0.6835724 1.362921e-02 1.362921e-02 0.302798424 #> 7664  3831.5 0.6834977 0.6834977 1.362814e-02 1.362814e-02 0.302874150 #> 7665  3832.0 0.6834231 0.6834231 1.362705e-02 1.362705e-02 0.302949821 #> 7666  3832.5 0.6833486 0.6833486 1.362593e-02 1.362593e-02 0.303025435 #> 7667  3833.0 0.6832742 0.6832742 1.362479e-02 1.362479e-02 0.303100993 #> 7668  3833.5 0.6831999 0.6831999 1.362362e-02 1.362362e-02 0.303176494 #> 7669  3834.0 0.6831256 0.6831256 1.362243e-02 1.362243e-02 0.303251937 #> 7670  3834.5 0.6830515 0.6830515 1.362121e-02 1.362121e-02 0.303327323 #> 7671  3835.0 0.6829774 0.6829774 1.361996e-02 1.361996e-02 0.303402650 #> 7672  3835.5 0.6829034 0.6829034 1.361870e-02 1.361870e-02 0.303477919 #> 7673  3836.0 0.6828295 0.6828295 1.361740e-02 1.361740e-02 0.303553128 #> 7674  3836.5 0.6827556 0.6827556 1.361608e-02 1.361608e-02 0.303628279 #> 7675  3837.0 0.6826819 0.6826819 1.361474e-02 1.361474e-02 0.303703369 #> 7676  3837.5 0.6826082 0.6826082 1.361337e-02 1.361337e-02 0.303778400 #> 7677  3838.0 0.6825347 0.6825347 1.361198e-02 1.361198e-02 0.303853370 #> 7678  3838.5 0.6824612 0.6824612 1.361056e-02 1.361056e-02 0.303928279 #> 7679  3839.0 0.6823878 0.6823878 1.360911e-02 1.360911e-02 0.304003127 #> 7680  3839.5 0.6823144 0.6823144 1.360765e-02 1.360765e-02 0.304077913 #> 7681  3840.0 0.6822412 0.6822412 1.360615e-02 1.360615e-02 0.304152637 #> 7682  3840.5 0.6821681 0.6821681 1.360464e-02 1.360464e-02 0.304227299 #> 7683  3841.0 0.6820950 0.6820950 1.360309e-02 1.360309e-02 0.304301897 #> 7684  3841.5 0.6820220 0.6820220 1.360153e-02 1.360153e-02 0.304376433 #> 7685  3842.0 0.6819492 0.6819492 1.359993e-02 1.359993e-02 0.304450905 #> 7686  3842.5 0.6818764 0.6818764 1.359832e-02 1.359832e-02 0.304525313 #> 7687  3843.0 0.6818037 0.6818037 1.359668e-02 1.359668e-02 0.304599657 #> 7688  3843.5 0.6817311 0.6817311 1.359501e-02 1.359501e-02 0.304673936 #> 7689  3844.0 0.6816585 0.6816585 1.359332e-02 1.359332e-02 0.304748150 #> 7690  3844.5 0.6815861 0.6815861 1.359161e-02 1.359161e-02 0.304822298 #> 7691  3845.0 0.6815137 0.6815137 1.358987e-02 1.358987e-02 0.304896381 #> 7692  3845.5 0.6814415 0.6814415 1.358811e-02 1.358811e-02 0.304970398 #> 7693  3846.0 0.6813693 0.6813693 1.358632e-02 1.358632e-02 0.305044347 #> 7694  3846.5 0.6812973 0.6812973 1.358451e-02 1.358451e-02 0.305118230 #> 7695  3847.0 0.6812253 0.6812253 1.358267e-02 1.358267e-02 0.305192046 #> 7696  3847.5 0.6811534 0.6811534 1.358081e-02 1.358081e-02 0.305265794 #> 7697  3848.0 0.6810816 0.6810816 1.357893e-02 1.357893e-02 0.305339474 #> 7698  3848.5 0.6810099 0.6810099 1.357702e-02 1.357702e-02 0.305413085 #> 7699  3849.0 0.6809383 0.6809383 1.357509e-02 1.357509e-02 0.305486628 #> 7700  3849.5 0.6808668 0.6808668 1.357313e-02 1.357313e-02 0.305560102 #> 7701  3850.0 0.6807953 0.6807953 1.357115e-02 1.357115e-02 0.305633506 #> 7702  3850.5 0.6807240 0.6807240 1.356915e-02 1.356915e-02 0.305706840 #> 7703  3851.0 0.6806528 0.6806528 1.356712e-02 1.356712e-02 0.305780104 #> 7704  3851.5 0.6805816 0.6805816 1.356507e-02 1.356507e-02 0.305853297 #> 7705  3852.0 0.6805106 0.6805106 1.356299e-02 1.356299e-02 0.305926419 #> 7706  3852.5 0.6804396 0.6804396 1.356089e-02 1.356089e-02 0.305999470 #> 7707  3853.0 0.6803688 0.6803688 1.355877e-02 1.355877e-02 0.306072449 #> 7708  3853.5 0.6802980 0.6802980 1.355663e-02 1.355663e-02 0.306145357 #> 7709  3854.0 0.6802274 0.6802274 1.355445e-02 1.355445e-02 0.306218191 #> 7710  3854.5 0.6801568 0.6801568 1.355226e-02 1.355226e-02 0.306290953 #> 7711  3855.0 0.6800863 0.6800863 1.355004e-02 1.355004e-02 0.306363642 #> 7712  3855.5 0.6800159 0.6800159 1.354780e-02 1.354780e-02 0.306436258 #> 7713  3856.0 0.6799457 0.6799457 1.354554e-02 1.354554e-02 0.306508799 #> 7714  3856.5 0.6798755 0.6798755 1.354325e-02 1.354325e-02 0.306581267 #> 7715  3857.0 0.6798054 0.6798054 1.354094e-02 1.354094e-02 0.306653660 #> 7716  3857.5 0.6797354 0.6797354 1.353861e-02 1.353861e-02 0.306725978 #> 7717  3858.0 0.6796655 0.6796655 1.353625e-02 1.353625e-02 0.306798221 #> 7718  3858.5 0.6795957 0.6795957 1.353387e-02 1.353387e-02 0.306870388 #> 7719  3859.0 0.6795261 0.6795261 1.353146e-02 1.353146e-02 0.306942479 #> 7720  3859.5 0.6794565 0.6794565 1.352904e-02 1.352904e-02 0.307014494 #> 7721  3860.0 0.6793870 0.6793870 1.352659e-02 1.352659e-02 0.307086432 #> 7722  3860.5 0.6793176 0.6793176 1.352411e-02 1.352411e-02 0.307158294 #> 7723  3861.0 0.6792483 0.6792483 1.352162e-02 1.352162e-02 0.307230078 #> 7724  3861.5 0.6791791 0.6791791 1.351910e-02 1.351910e-02 0.307301784 #> 7725  3862.0 0.6791100 0.6791100 1.351656e-02 1.351656e-02 0.307373412 #> 7726  3862.5 0.6790410 0.6790410 1.351399e-02 1.351399e-02 0.307444962 #> 7727  3863.0 0.6789722 0.6789722 1.351140e-02 1.351140e-02 0.307516433 #> 7728  3863.5 0.6789034 0.6789034 1.350879e-02 1.350879e-02 0.307587826 #> 7729  3864.0 0.6788347 0.6788347 1.350616e-02 1.350616e-02 0.307659139 #> 7730  3864.5 0.6787661 0.6787661 1.350350e-02 1.350350e-02 0.307730372 #> 7731  3865.0 0.6786977 0.6786977 1.350082e-02 1.350082e-02 0.307801525 #> 7732  3865.5 0.6786293 0.6786293 1.349812e-02 1.349812e-02 0.307872598 #> 7733  3866.0 0.6785610 0.6785610 1.349540e-02 1.349540e-02 0.307943590 #> 7734  3866.5 0.6784928 0.6784928 1.349265e-02 1.349265e-02 0.308014501 #> 7735  3867.0 0.6784248 0.6784248 1.348989e-02 1.348989e-02 0.308085330 #> 7736  3867.5 0.6783568 0.6783568 1.348710e-02 1.348710e-02 0.308156078 #> 7737  3868.0 0.6782890 0.6782890 1.348428e-02 1.348428e-02 0.308226744 #> 7738  3868.5 0.6782212 0.6782212 1.348145e-02 1.348145e-02 0.308297327 #> 7739  3869.0 0.6781536 0.6781536 1.347859e-02 1.347859e-02 0.308367828 #> 7740  3869.5 0.6780860 0.6780860 1.347571e-02 1.347571e-02 0.308438246 #> 7741  3870.0 0.6780186 0.6780186 1.347281e-02 1.347281e-02 0.308508580 #> 7742  3870.5 0.6779513 0.6779513 1.346988e-02 1.346988e-02 0.308578830 #> 7743  3871.0 0.6778841 0.6778841 1.346694e-02 1.346694e-02 0.308648997 #> 7744  3871.5 0.6778170 0.6778170 1.346397e-02 1.346397e-02 0.308719079 #> 7745  3872.0 0.6777499 0.6777499 1.346098e-02 1.346098e-02 0.308789077 #> 7746  3872.5 0.6776830 0.6776830 1.345797e-02 1.345797e-02 0.308858989 #> 7747  3873.0 0.6776162 0.6776162 1.345494e-02 1.345494e-02 0.308928817 #> 7748  3873.5 0.6775496 0.6775496 1.345188e-02 1.345188e-02 0.308998558 #> 7749  3874.0 0.6774830 0.6774830 1.344880e-02 1.344880e-02 0.309068214 #> 7750  3874.5 0.6774165 0.6774165 1.344570e-02 1.344570e-02 0.309137784 #> 7751  3875.0 0.6773501 0.6773501 1.344258e-02 1.344258e-02 0.309207266 #> 7752  3875.5 0.6772839 0.6772839 1.343944e-02 1.343944e-02 0.309276662 #> 7753  3876.0 0.6772177 0.6772177 1.343628e-02 1.343628e-02 0.309345971 #> 7754  3876.5 0.6771517 0.6771517 1.343309e-02 1.343309e-02 0.309415192 #> 7755  3877.0 0.6770858 0.6770858 1.342989e-02 1.342989e-02 0.309484326 #> 7756  3877.5 0.6770200 0.6770200 1.342666e-02 1.342666e-02 0.309553371 #> 7757  3878.0 0.6769543 0.6769543 1.342341e-02 1.342341e-02 0.309622327 #> 7758  3878.5 0.6768887 0.6768887 1.342014e-02 1.342014e-02 0.309691195 #> 7759  3879.0 0.6768232 0.6768232 1.341685e-02 1.341685e-02 0.309759974 #> 7760  3879.5 0.6767578 0.6767578 1.341354e-02 1.341354e-02 0.309828664 #> 7761  3880.0 0.6766925 0.6766925 1.341021e-02 1.341021e-02 0.309897263 #> 7762  3880.5 0.6766274 0.6766274 1.340685e-02 1.340685e-02 0.309965773 #> 7763  3881.0 0.6765623 0.6765623 1.340348e-02 1.340348e-02 0.310034193 #> 7764  3881.5 0.6764974 0.6764974 1.340008e-02 1.340008e-02 0.310102521 #> 7765  3882.0 0.6764326 0.6764326 1.339666e-02 1.339666e-02 0.310170759 #> 7766  3882.5 0.6763679 0.6763679 1.339322e-02 1.339322e-02 0.310238906 #> 7767  3883.0 0.6763033 0.6763033 1.338977e-02 1.338977e-02 0.310306961 #> 7768  3883.5 0.6762388 0.6762388 1.338629e-02 1.338629e-02 0.310374925 #> 7769  3884.0 0.6761744 0.6761744 1.338279e-02 1.338279e-02 0.310442796 #> 7770  3884.5 0.6761102 0.6761102 1.337927e-02 1.337927e-02 0.310510575 #> 7771  3885.0 0.6760460 0.6760460 1.337572e-02 1.337572e-02 0.310578261 #> 7772  3885.5 0.6759820 0.6759820 1.337216e-02 1.337216e-02 0.310645854 #> 7773  3886.0 0.6759181 0.6759181 1.336858e-02 1.336858e-02 0.310713353 #> 7774  3886.5 0.6758543 0.6758543 1.336498e-02 1.336498e-02 0.310780760 #> 7775  3887.0 0.6757906 0.6757906 1.336136e-02 1.336136e-02 0.310848072 #> 7776  3887.5 0.6757270 0.6757270 1.335771e-02 1.335771e-02 0.310915290 #> 7777  3888.0 0.6756635 0.6756635 1.335405e-02 1.335405e-02 0.310982414 #> 7778  3888.5 0.6756002 0.6756002 1.335037e-02 1.335037e-02 0.311049442 #> 7779  3889.0 0.6755370 0.6755370 1.334666e-02 1.334666e-02 0.311116376 #> 7780  3889.5 0.6754738 0.6754738 1.334294e-02 1.334294e-02 0.311183215 #> 7781  3890.0 0.6754108 0.6754108 1.333919e-02 1.333919e-02 0.311249957 #> 7782  3890.5 0.6753480 0.6753480 1.333543e-02 1.333543e-02 0.311316604 #> 7783  3891.0 0.6752852 0.6752852 1.333165e-02 1.333165e-02 0.311383155 #> 7784  3891.5 0.6752225 0.6752225 1.332784e-02 1.332784e-02 0.311449609 #> 7785  3892.0 0.6751600 0.6751600 1.332402e-02 1.332402e-02 0.311515967 #> 7786  3892.5 0.6750976 0.6750976 1.332018e-02 1.332018e-02 0.311582227 #> 7787  3893.0 0.6750353 0.6750353 1.331632e-02 1.331632e-02 0.311648391 #> 7788  3893.5 0.6749731 0.6749731 1.331243e-02 1.331243e-02 0.311714456 #> 7789  3894.0 0.6749110 0.6749110 1.330853e-02 1.330853e-02 0.311780424 #> 7790  3894.5 0.6748491 0.6748491 1.330461e-02 1.330461e-02 0.311846293 #> 7791  3895.0 0.6747873 0.6747873 1.330067e-02 1.330067e-02 0.311912065 #> 7792  3895.5 0.6747256 0.6747256 1.329671e-02 1.329671e-02 0.311977737 #> 7793  3896.0 0.6746640 0.6746640 1.329273e-02 1.329273e-02 0.312043310 #> 7794  3896.5 0.6746025 0.6746025 1.328873e-02 1.328873e-02 0.312108785 #> 7795  3897.0 0.6745411 0.6745411 1.328472e-02 1.328472e-02 0.312174159 #> 7796  3897.5 0.6744799 0.6744799 1.328068e-02 1.328068e-02 0.312239434 #> 7797  3898.0 0.6744188 0.6744188 1.327662e-02 1.327662e-02 0.312304609 #> 7798  3898.5 0.6743578 0.6743578 1.327255e-02 1.327255e-02 0.312369684 #> 7799  3899.0 0.6742969 0.6742969 1.326846e-02 1.326846e-02 0.312434658 #> 7800  3899.5 0.6742361 0.6742361 1.326434e-02 1.326434e-02 0.312499531 #> 7801  3900.0 0.6741755 0.6741755 1.326021e-02 1.326021e-02 0.312564303 #> 7802  3900.5 0.6741150 0.6741150 1.325606e-02 1.325606e-02 0.312628974 #> 7803  3901.0 0.6740546 0.6740546 1.325189e-02 1.325189e-02 0.312693543 #> 7804  3901.5 0.6739943 0.6739943 1.324770e-02 1.324770e-02 0.312758010 #> 7805  3902.0 0.6739341 0.6739341 1.324350e-02 1.324350e-02 0.312822375 #> 7806  3902.5 0.6738741 0.6738741 1.323927e-02 1.323927e-02 0.312886637 #> 7807  3903.0 0.6738142 0.6738142 1.323503e-02 1.323503e-02 0.312950797 #> 7808  3903.5 0.6737544 0.6737544 1.323077e-02 1.323077e-02 0.313014854 #> 7809  3904.0 0.6736947 0.6736947 1.322649e-02 1.322649e-02 0.313078808 #> 7810  3904.5 0.6736352 0.6736352 1.322219e-02 1.322219e-02 0.313142658 #> 7811  3905.0 0.6735757 0.6735757 1.321787e-02 1.321787e-02 0.313206405 #> 7812  3905.5 0.6735164 0.6735164 1.321354e-02 1.321354e-02 0.313270047 #> 7813  3906.0 0.6734572 0.6734572 1.320919e-02 1.320919e-02 0.313333586 #> 7814  3906.5 0.6733982 0.6733982 1.320481e-02 1.320481e-02 0.313397020 #> 7815  3907.0 0.6733392 0.6733392 1.320042e-02 1.320042e-02 0.313460349 #> 7816  3907.5 0.6732804 0.6732804 1.319602e-02 1.319602e-02 0.313523574 #> 7817  3908.0 0.6732217 0.6732217 1.319159e-02 1.319159e-02 0.313586693 #> 7818  3908.5 0.6731631 0.6731631 1.318715e-02 1.318715e-02 0.313649706 #> 7819  3909.0 0.6731047 0.6731047 1.318269e-02 1.318269e-02 0.313712615 #> 7820  3909.5 0.6730464 0.6730464 1.317821e-02 1.317821e-02 0.313775417 #> 7821  3910.0 0.6729882 0.6729882 1.317371e-02 1.317371e-02 0.313838113 #> 7822  3910.5 0.6729301 0.6729301 1.316920e-02 1.316920e-02 0.313900702 #> 7823  3911.0 0.6728721 0.6728721 1.316467e-02 1.316467e-02 0.313963185 #> 7824  3911.5 0.6728143 0.6728143 1.316012e-02 1.316012e-02 0.314025561 #> 7825  3912.0 0.6727566 0.6727566 1.315555e-02 1.315555e-02 0.314087830 #> 7826  3912.5 0.6726990 0.6726990 1.315097e-02 1.315097e-02 0.314149992 #> 7827  3913.0 0.6726416 0.6726416 1.314637e-02 1.314637e-02 0.314212046 #> 7828  3913.5 0.6725843 0.6725843 1.314175e-02 1.314175e-02 0.314273992 #> 7829  3914.0 0.6725271 0.6725271 1.313712e-02 1.313712e-02 0.314335830 #> 7830  3914.5 0.6724700 0.6724700 1.313246e-02 1.313246e-02 0.314397560 #> 7831  3915.0 0.6724130 0.6724130 1.312779e-02 1.312779e-02 0.314459182 #> 7832  3915.5 0.6723562 0.6723562 1.312311e-02 1.312311e-02 0.314520694 #> 7833  3916.0 0.6722995 0.6722995 1.311841e-02 1.311841e-02 0.314582098 #> 7834  3916.5 0.6722429 0.6722429 1.311368e-02 1.311368e-02 0.314643392 #> 7835  3917.0 0.6721865 0.6721865 1.310895e-02 1.310895e-02 0.314704577 #> 7836  3917.5 0.6721302 0.6721302 1.310419e-02 1.310419e-02 0.314765652 #> 7837  3918.0 0.6720740 0.6720740 1.309942e-02 1.309942e-02 0.314826618 #> 7838  3918.5 0.6720179 0.6720179 1.309464e-02 1.309464e-02 0.314887473 #> 7839  3919.0 0.6719619 0.6719619 1.308983e-02 1.308983e-02 0.314948218 #> 7840  3919.5 0.6719061 0.6719061 1.308501e-02 1.308501e-02 0.315008853 #> 7841  3920.0 0.6718504 0.6718504 1.308018e-02 1.308018e-02 0.315069376 #> 7842  3920.5 0.6717949 0.6717949 1.307532e-02 1.307532e-02 0.315129789 #> 7843  3921.0 0.6717395 0.6717395 1.307045e-02 1.307045e-02 0.315190090 #> 7844  3921.5 0.6716842 0.6716842 1.306557e-02 1.306557e-02 0.315250281 #> 7845  3922.0 0.6716290 0.6716290 1.306066e-02 1.306066e-02 0.315310359 #> 7846  3922.5 0.6715739 0.6715739 1.305575e-02 1.305575e-02 0.315370326 #> 7847  3923.0 0.6715190 0.6715190 1.305081e-02 1.305081e-02 0.315430180 #> 7848  3923.5 0.6714642 0.6714642 1.304586e-02 1.304586e-02 0.315489922 #> 7849  3924.0 0.6714096 0.6714096 1.304090e-02 1.304090e-02 0.315549552 #> 7850  3924.5 0.6713550 0.6713550 1.303591e-02 1.303591e-02 0.315609069 #> 7851  3925.0 0.6713006 0.6713006 1.303092e-02 1.303092e-02 0.315668474 #> 7852  3925.5 0.6712463 0.6712463 1.302590e-02 1.302590e-02 0.315727765 #> 7853  3926.0 0.6711922 0.6711922 1.302087e-02 1.302087e-02 0.315786942 #> 7854  3926.5 0.6711382 0.6711382 1.301583e-02 1.301583e-02 0.315846007 #> 7855  3927.0 0.6710843 0.6710843 1.301077e-02 1.301077e-02 0.315904957 #> 7856  3927.5 0.6710305 0.6710305 1.300569e-02 1.300569e-02 0.315963794 #> 7857  3928.0 0.6709769 0.6709769 1.300060e-02 1.300060e-02 0.316022516 #> 7858  3928.5 0.6709234 0.6709234 1.299549e-02 1.299549e-02 0.316081125 #> 7859  3929.0 0.6708700 0.6708700 1.299037e-02 1.299037e-02 0.316139618 #> 7860  3929.5 0.6708168 0.6708168 1.298523e-02 1.298523e-02 0.316197997 #> 7861  3930.0 0.6707637 0.6707637 1.298008e-02 1.298008e-02 0.316256261 #> 7862  3930.5 0.6707107 0.6707107 1.297491e-02 1.297491e-02 0.316314410 #> 7863  3931.0 0.6706578 0.6706578 1.296973e-02 1.296973e-02 0.316372444 #> 7864  3931.5 0.6706051 0.6706051 1.296453e-02 1.296453e-02 0.316430362 #> 7865  3932.0 0.6705525 0.6705525 1.295932e-02 1.295932e-02 0.316488164 #> 7866  3932.5 0.6705001 0.6705001 1.295409e-02 1.295409e-02 0.316545851 #> 7867  3933.0 0.6704477 0.6704477 1.294884e-02 1.294884e-02 0.316603421 #> 7868  3933.5 0.6703955 0.6703955 1.294359e-02 1.294359e-02 0.316660876 #> 7869  3934.0 0.6703435 0.6703435 1.293831e-02 1.293831e-02 0.316718213 #> 7870  3934.5 0.6702915 0.6702915 1.293303e-02 1.293303e-02 0.316775435 #> 7871  3935.0 0.6702397 0.6702397 1.292773e-02 1.292773e-02 0.316832539 #> 7872  3935.5 0.6701881 0.6701881 1.292241e-02 1.292241e-02 0.316889526 #> 7873  3936.0 0.6701365 0.6701365 1.291708e-02 1.291708e-02 0.316946396 #> 7874  3936.5 0.6700851 0.6700851 1.291173e-02 1.291173e-02 0.317003149 #> 7875  3937.0 0.6700338 0.6700338 1.290637e-02 1.290637e-02 0.317059784 #> 7876  3937.5 0.6699827 0.6699827 1.290100e-02 1.290100e-02 0.317116302 #> 7877  3938.0 0.6699317 0.6699317 1.289561e-02 1.289561e-02 0.317172701 #> 7878  3938.5 0.6698808 0.6698808 1.289021e-02 1.289021e-02 0.317228982 #> 7879  3939.0 0.6698301 0.6698301 1.288479e-02 1.288479e-02 0.317285146 #> 7880  3939.5 0.6697794 0.6697794 1.287936e-02 1.287936e-02 0.317341190 #> 7881  3940.0 0.6697290 0.6697290 1.287392e-02 1.287392e-02 0.317397116 #> 7882  3940.5 0.6696786 0.6696786 1.286846e-02 1.286846e-02 0.317452924 #> 7883  3941.0 0.6696284 0.6696284 1.286299e-02 1.286299e-02 0.317508612 #> 7884  3941.5 0.6695783 0.6695783 1.285750e-02 1.285750e-02 0.317564181 #> 7885  3942.0 0.6695284 0.6695284 1.285200e-02 1.285200e-02 0.317619631 #> 7886  3942.5 0.6694785 0.6694785 1.284649e-02 1.284649e-02 0.317674962 #> 7887  3943.0 0.6694289 0.6694289 1.284096e-02 1.284096e-02 0.317730173 #> 7888  3943.5 0.6693793 0.6693793 1.283542e-02 1.283542e-02 0.317785264 #> 7889  3944.0 0.6693299 0.6693299 1.282987e-02 1.282987e-02 0.317840235 #> 7890  3944.5 0.6692806 0.6692806 1.282430e-02 1.282430e-02 0.317895086 #> 7891  3945.0 0.6692315 0.6692315 1.281872e-02 1.281872e-02 0.317949816 #> 7892  3945.5 0.6691824 0.6691824 1.281312e-02 1.281312e-02 0.318004426 #> 7893  3946.0 0.6691336 0.6691336 1.280752e-02 1.280752e-02 0.318058916 #> 7894  3946.5 0.6690848 0.6690848 1.280190e-02 1.280190e-02 0.318113285 #> 7895  3947.0 0.6690362 0.6690362 1.279626e-02 1.279626e-02 0.318167532 #> 7896  3947.5 0.6689877 0.6689877 1.279062e-02 1.279062e-02 0.318221659 #> 7897  3948.0 0.6689394 0.6689394 1.278496e-02 1.278496e-02 0.318275665 #> 7898  3948.5 0.6688912 0.6688912 1.277928e-02 1.277928e-02 0.318329549 #> 7899  3949.0 0.6688431 0.6688431 1.277360e-02 1.277360e-02 0.318383311 #> 7900  3949.5 0.6687951 0.6687951 1.276790e-02 1.276790e-02 0.318436952 #> 7901  3950.0 0.6687473 0.6687473 1.276219e-02 1.276219e-02 0.318490471 #> 7902  3950.5 0.6686997 0.6686997 1.275647e-02 1.275647e-02 0.318543868 #> 7903  3951.0 0.6686521 0.6686521 1.275073e-02 1.275073e-02 0.318597143 #> 7904  3951.5 0.6686047 0.6686047 1.274498e-02 1.274498e-02 0.318650295 #> 7905  3952.0 0.6685575 0.6685575 1.273922e-02 1.273922e-02 0.318703325 #> 7906  3952.5 0.6685103 0.6685103 1.273345e-02 1.273345e-02 0.318756232 #> 7907  3953.0 0.6684633 0.6684633 1.272766e-02 1.272766e-02 0.318809017 #> 7908  3953.5 0.6684165 0.6684165 1.272186e-02 1.272186e-02 0.318861679 #> 7909  3954.0 0.6683697 0.6683697 1.271605e-02 1.271605e-02 0.318914217 #> 7910  3954.5 0.6683231 0.6683231 1.271023e-02 1.271023e-02 0.318966633 #> 7911  3955.0 0.6682767 0.6682767 1.270439e-02 1.270439e-02 0.319018925 #> 7912  3955.5 0.6682304 0.6682304 1.269855e-02 1.269855e-02 0.319071094 #> 7913  3956.0 0.6681842 0.6681842 1.269269e-02 1.269269e-02 0.319123139 #> 7914  3956.5 0.6681381 0.6681381 1.268682e-02 1.268682e-02 0.319175060 #> 7915  3957.0 0.6680922 0.6680922 1.268094e-02 1.268094e-02 0.319226857 #> 7916  3957.5 0.6680464 0.6680464 1.267504e-02 1.267504e-02 0.319278531 #> 7917  3958.0 0.6680008 0.6680008 1.266914e-02 1.266914e-02 0.319330080 #> 7918  3958.5 0.6679553 0.6679553 1.266322e-02 1.266322e-02 0.319381505 #> 7919  3959.0 0.6679099 0.6679099 1.265729e-02 1.265729e-02 0.319432806 #> 7920  3959.5 0.6678647 0.6678647 1.265135e-02 1.265135e-02 0.319483982 #> 7921  3960.0 0.6678196 0.6678196 1.264539e-02 1.264539e-02 0.319535034 #> 7922  3960.5 0.6677746 0.6677746 1.263943e-02 1.263943e-02 0.319585960 #> 7923  3961.0 0.6677298 0.6677298 1.263346e-02 1.263346e-02 0.319636762 #> 7924  3961.5 0.6676851 0.6676851 1.262747e-02 1.262747e-02 0.319687439 #> 7925  3962.0 0.6676405 0.6676405 1.262147e-02 1.262147e-02 0.319737990 #> 7926  3962.5 0.6675961 0.6675961 1.261546e-02 1.261546e-02 0.319788416 #> 7927  3963.0 0.6675518 0.6675518 1.260944e-02 1.260944e-02 0.319838717 #> 7928  3963.5 0.6675077 0.6675077 1.260341e-02 1.260341e-02 0.319888892 #> 7929  3964.0 0.6674637 0.6674637 1.259737e-02 1.259737e-02 0.319938942 #> 7930  3964.5 0.6674198 0.6674198 1.259131e-02 1.259131e-02 0.319988865 #> 7931  3965.0 0.6673761 0.6673761 1.258525e-02 1.258525e-02 0.320038663 #> 7932  3965.5 0.6673325 0.6673325 1.257918e-02 1.257918e-02 0.320088335 #> 7933  3966.0 0.6672890 0.6672890 1.257309e-02 1.257309e-02 0.320137881 #> 7934  3966.5 0.6672457 0.6672457 1.256699e-02 1.256699e-02 0.320187300 #> 7935  3967.0 0.6672025 0.6672025 1.256089e-02 1.256089e-02 0.320236593 #> 7936  3967.5 0.6671595 0.6671595 1.255477e-02 1.255477e-02 0.320285760 #> 7937  3968.0 0.6671166 0.6671166 1.254864e-02 1.254864e-02 0.320334800 #> 7938  3968.5 0.6670738 0.6670738 1.254250e-02 1.254250e-02 0.320383713 #> 7939  3969.0 0.6670311 0.6670311 1.253635e-02 1.253635e-02 0.320432499 #> 7940  3969.5 0.6669886 0.6669886 1.253020e-02 1.253020e-02 0.320481159 #> 7941  3970.0 0.6669463 0.6669463 1.252403e-02 1.252403e-02 0.320529691 #> 7942  3970.5 0.6669041 0.6669041 1.251785e-02 1.251785e-02 0.320578097 #> 7943  3971.0 0.6668620 0.6668620 1.251166e-02 1.251166e-02 0.320626375 #> 7944  3971.5 0.6668200 0.6668200 1.250546e-02 1.250546e-02 0.320674526 #> 7945  3972.0 0.6667782 0.6667782 1.249925e-02 1.249925e-02 0.320722549 #> 7946  3972.5 0.6667365 0.6667365 1.249303e-02 1.249303e-02 0.320770445 #> 7947  3973.0 0.6666950 0.6666950 1.248680e-02 1.248680e-02 0.320818213 #> 7948  3973.5 0.6666536 0.6666536 1.248056e-02 1.248056e-02 0.320865853 #> 7949  3974.0 0.6666123 0.6666123 1.247431e-02 1.247431e-02 0.320913365 #> 7950  3974.5 0.6665712 0.6665712 1.246805e-02 1.246805e-02 0.320960750 #> 7951  3975.0 0.6665302 0.6665302 1.246178e-02 1.246178e-02 0.321008006 #> 7952  3975.5 0.6664894 0.6664894 1.245550e-02 1.245550e-02 0.321055135 #> 7953  3976.0 0.6664486 0.6664486 1.244922e-02 1.244922e-02 0.321102135 #> 7954  3976.5 0.6664081 0.6664081 1.244292e-02 1.244292e-02 0.321149006 #> 7955  3977.0 0.6663676 0.6663676 1.243661e-02 1.243661e-02 0.321195750 #> 7956  3977.5 0.6663273 0.6663273 1.243030e-02 1.243030e-02 0.321242364 #> 7957  3978.0 0.6662872 0.6662872 1.242397e-02 1.242397e-02 0.321288850 #> 7958  3978.5 0.6662472 0.6662472 1.241764e-02 1.241764e-02 0.321335208 #> 7959  3979.0 0.6662073 0.6662073 1.241129e-02 1.241129e-02 0.321381436 #> 7960  3979.5 0.6661675 0.6661675 1.240494e-02 1.240494e-02 0.321427536 #> 7961  3980.0 0.6661279 0.6661279 1.239858e-02 1.239858e-02 0.321473507 #> 7962  3980.5 0.6660884 0.6660884 1.239221e-02 1.239221e-02 0.321519348 #> 7963  3981.0 0.6660491 0.6660491 1.238583e-02 1.238583e-02 0.321565061 #> 7964  3981.5 0.6660099 0.6660099 1.237944e-02 1.237944e-02 0.321610644 #> 7965  3982.0 0.6659709 0.6659709 1.237304e-02 1.237304e-02 0.321656098 #> 7966  3982.5 0.6659319 0.6659319 1.236664e-02 1.236664e-02 0.321701423 #> 7967  3983.0 0.6658932 0.6658932 1.236022e-02 1.236022e-02 0.321746618 #> 7968  3983.5 0.6658545 0.6658545 1.235380e-02 1.235380e-02 0.321791684 #> 7969  3984.0 0.6658160 0.6658160 1.234737e-02 1.234737e-02 0.321836620 #> 7970  3984.5 0.6657776 0.6657776 1.234093e-02 1.234093e-02 0.321881426 #> 7971  3985.0 0.6657394 0.6657394 1.233448e-02 1.233448e-02 0.321926102 #> 7972  3985.5 0.6657013 0.6657013 1.232802e-02 1.232802e-02 0.321970649 #> 7973  3986.0 0.6656634 0.6656634 1.232155e-02 1.232155e-02 0.322015066 #> 7974  3986.5 0.6656256 0.6656256 1.231508e-02 1.231508e-02 0.322059352 #> 7975  3987.0 0.6655879 0.6655879 1.230860e-02 1.230860e-02 0.322103509 #> 7976  3987.5 0.6655504 0.6655504 1.230211e-02 1.230211e-02 0.322147535 #> 7977  3988.0 0.6655130 0.6655130 1.229561e-02 1.229561e-02 0.322191432 #> 7978  3988.5 0.6654757 0.6654757 1.228910e-02 1.228910e-02 0.322235198 #> 7979  3989.0 0.6654386 0.6654386 1.228259e-02 1.228259e-02 0.322278833 #> 7980  3989.5 0.6654016 0.6654016 1.227607e-02 1.227607e-02 0.322322338 #> 7981  3990.0 0.6653648 0.6653648 1.226953e-02 1.226953e-02 0.322365713 #> 7982  3990.5 0.6653280 0.6653280 1.226300e-02 1.226300e-02 0.322408957 #> 7983  3991.0 0.6652915 0.6652915 1.225645e-02 1.225645e-02 0.322452070 #> 7984  3991.5 0.6652550 0.6652550 1.224990e-02 1.224990e-02 0.322495053 #> 7985  3992.0 0.6652188 0.6652188 1.224334e-02 1.224334e-02 0.322537905 #> 7986  3992.5 0.6651826 0.6651826 1.223677e-02 1.223677e-02 0.322580627 #> 7987  3993.0 0.6651466 0.6651466 1.223019e-02 1.223019e-02 0.322623217 #> 7988  3993.5 0.6651107 0.6651107 1.222361e-02 1.222361e-02 0.322665676 #> 7989  3994.0 0.6650750 0.6650750 1.221702e-02 1.221702e-02 0.322708005 #> 7990  3994.5 0.6650394 0.6650394 1.221042e-02 1.221042e-02 0.322750202 #> 7991  3995.0 0.6650039 0.6650039 1.220381e-02 1.220381e-02 0.322792269 #> 7992  3995.5 0.6649686 0.6649686 1.219720e-02 1.219720e-02 0.322834204 #> 7993  3996.0 0.6649334 0.6649334 1.219058e-02 1.219058e-02 0.322876008 #> 7994  3996.5 0.6648984 0.6648984 1.218395e-02 1.218395e-02 0.322917680 #> 7995  3997.0 0.6648635 0.6648635 1.217732e-02 1.217732e-02 0.322959222 #> 7996  3997.5 0.6648287 0.6648287 1.217068e-02 1.217068e-02 0.323000632 #> 7997  3998.0 0.6647941 0.6647941 1.216403e-02 1.216403e-02 0.323041911 #> 7998  3998.5 0.6647596 0.6647596 1.215738e-02 1.215738e-02 0.323083058 #> 7999  3999.0 0.6647252 0.6647252 1.215071e-02 1.215071e-02 0.323124073 #> 8000  3999.5 0.6646910 0.6646910 1.214405e-02 1.214405e-02 0.323164957 #> 8001  4000.0 0.6646569 0.6646569 1.213737e-02 1.213737e-02 0.323205710 #> 8002  4000.5 0.6646230 0.6646230 1.213069e-02 1.213069e-02 0.323246331 #> 8003  4001.0 0.6645892 0.6645892 1.212400e-02 1.212400e-02 0.323286820 #> 8004  4001.5 0.6645555 0.6645555 1.211731e-02 1.211731e-02 0.323327177 #> 8005  4002.0 0.6645220 0.6645220 1.211060e-02 1.211060e-02 0.323367403 #> 8006  4002.5 0.6644886 0.6644886 1.210390e-02 1.210390e-02 0.323407497 #> 8007  4003.0 0.6644554 0.6644554 1.209718e-02 1.209718e-02 0.323447458 #> 8008  4003.5 0.6644223 0.6644223 1.209046e-02 1.209046e-02 0.323487288 #> 8009  4004.0 0.6643893 0.6643893 1.208373e-02 1.208373e-02 0.323526986 #> 8010  4004.5 0.6643564 0.6643564 1.207700e-02 1.207700e-02 0.323566553 #> 8011  4005.0 0.6643238 0.6643238 1.207026e-02 1.207026e-02 0.323605987 #> 8012  4005.5 0.6642912 0.6642912 1.206352e-02 1.206352e-02 0.323645289 #> 8013  4006.0 0.6642588 0.6642588 1.205677e-02 1.205677e-02 0.323684459 #> 8014  4006.5 0.6642265 0.6642265 1.205001e-02 1.205001e-02 0.323723496 #> 8015  4007.0 0.6641944 0.6641944 1.204325e-02 1.204325e-02 0.323762402 #> 8016  4007.5 0.6641623 0.6641623 1.203648e-02 1.203648e-02 0.323801175 #> 8017  4008.0 0.6641305 0.6641305 1.202970e-02 1.202970e-02 0.323839817 #> 8018  4008.5 0.6640988 0.6640988 1.202292e-02 1.202292e-02 0.323878326 #> 8019  4009.0 0.6640672 0.6640672 1.201614e-02 1.201614e-02 0.323916702 #> 8020  4009.5 0.6640357 0.6640357 1.200934e-02 1.200934e-02 0.323954947 #> 8021  4010.0 0.6640044 0.6640044 1.200255e-02 1.200255e-02 0.323993059 #> 8022  4010.5 0.6639732 0.6639732 1.199574e-02 1.199574e-02 0.324031039 #> 8023  4011.0 0.6639422 0.6639422 1.198894e-02 1.198894e-02 0.324068886 #> 8024  4011.5 0.6639113 0.6639113 1.198212e-02 1.198212e-02 0.324106601 #> 8025  4012.0 0.6638805 0.6638805 1.197530e-02 1.197530e-02 0.324144183 #> 8026  4012.5 0.6638499 0.6638499 1.196848e-02 1.196848e-02 0.324181634 #> 8027  4013.0 0.6638194 0.6638194 1.196165e-02 1.196165e-02 0.324218951 #> 8028  4013.5 0.6637891 0.6637891 1.195481e-02 1.195481e-02 0.324256136 #> 8029  4014.0 0.6637588 0.6637588 1.194797e-02 1.194797e-02 0.324293189 #> 8030  4014.5 0.6637288 0.6637288 1.194113e-02 1.194113e-02 0.324330109 #> 8031  4015.0 0.6636988 0.6636988 1.193428e-02 1.193428e-02 0.324366896 #> 8032  4015.5 0.6636690 0.6636690 1.192742e-02 1.192742e-02 0.324403551 #> 8033  4016.0 0.6636394 0.6636394 1.192056e-02 1.192056e-02 0.324440073 #> 8034  4016.5 0.6636098 0.6636098 1.191370e-02 1.191370e-02 0.324476463 #> 8035  4017.0 0.6635805 0.6635805 1.190683e-02 1.190683e-02 0.324512720 #> 8036  4017.5 0.6635512 0.6635512 1.189995e-02 1.189995e-02 0.324548845 #> 8037  4018.0 0.6635221 0.6635221 1.189307e-02 1.189307e-02 0.324584837 #> 8038  4018.5 0.6634931 0.6634931 1.188619e-02 1.188619e-02 0.324620696 #> 8039  4019.0 0.6634643 0.6634643 1.187930e-02 1.187930e-02 0.324656422 #> 8040  4019.5 0.6634356 0.6634356 1.187241e-02 1.187241e-02 0.324692016 #> 8041  4020.0 0.6634070 0.6634070 1.186551e-02 1.186551e-02 0.324727477 #> 8042  4020.5 0.6633786 0.6633786 1.185861e-02 1.185861e-02 0.324762806 #> 8043  4021.0 0.6633503 0.6633503 1.185170e-02 1.185170e-02 0.324798001 #> 8044  4021.5 0.6633221 0.6633221 1.184479e-02 1.184479e-02 0.324833064 #> 8045  4022.0 0.6632941 0.6632941 1.183788e-02 1.183788e-02 0.324867995 #> 8046  4022.5 0.6632662 0.6632662 1.183096e-02 1.183096e-02 0.324902792 #> 8047  4023.0 0.6632385 0.6632385 1.182404e-02 1.182404e-02 0.324937457 #> 8048  4023.5 0.6632109 0.6632109 1.181711e-02 1.181711e-02 0.324971989 #> 8049  4024.0 0.6631834 0.6631834 1.181018e-02 1.181018e-02 0.325006389 #> 8050  4024.5 0.6631561 0.6631561 1.180324e-02 1.180324e-02 0.325040656 #> 8051  4025.0 0.6631289 0.6631289 1.179630e-02 1.179630e-02 0.325074790 #> 8052  4025.5 0.6631019 0.6631019 1.178936e-02 1.178936e-02 0.325108791 #> 8053  4026.0 0.6630749 0.6630749 1.178241e-02 1.178241e-02 0.325142659 #> 8054  4026.5 0.6630481 0.6630481 1.177546e-02 1.177546e-02 0.325176395 #> 8055  4027.0 0.6630215 0.6630215 1.176851e-02 1.176851e-02 0.325209998 #> 8056  4027.5 0.6629950 0.6629950 1.176155e-02 1.176155e-02 0.325243469 #> 8057  4028.0 0.6629686 0.6629686 1.175458e-02 1.175458e-02 0.325276807 #> 8058  4028.5 0.6629424 0.6629424 1.174762e-02 1.174762e-02 0.325310011 #> 8059  4029.0 0.6629163 0.6629163 1.174065e-02 1.174065e-02 0.325343084 #> 8060  4029.5 0.6628903 0.6628903 1.173368e-02 1.173368e-02 0.325376023 #> 8061  4030.0 0.6628645 0.6628645 1.172670e-02 1.172670e-02 0.325408830 #> 8062  4030.5 0.6628388 0.6628388 1.171972e-02 1.171972e-02 0.325441505 #> 8063  4031.0 0.6628132 0.6628132 1.171274e-02 1.171274e-02 0.325474046 #> 8064  4031.5 0.6627878 0.6627878 1.170575e-02 1.170575e-02 0.325506455 #> 8065  4032.0 0.6627625 0.6627625 1.169876e-02 1.169876e-02 0.325538731 #> 8066  4032.5 0.6627374 0.6627374 1.169177e-02 1.169177e-02 0.325570875 #> 8067  4033.0 0.6627123 0.6627123 1.168477e-02 1.168477e-02 0.325602886 #> 8068  4033.5 0.6626875 0.6626875 1.167777e-02 1.167777e-02 0.325634765 #> 8069  4034.0 0.6626627 0.6626627 1.167077e-02 1.167077e-02 0.325666511 #> 8070  4034.5 0.6626381 0.6626381 1.166377e-02 1.166377e-02 0.325698124 #> 8071  4035.0 0.6626136 0.6626136 1.165676e-02 1.165676e-02 0.325729605 #> 8072  4035.5 0.6625893 0.6625893 1.164975e-02 1.164975e-02 0.325760953 #> 8073  4036.0 0.6625651 0.6625651 1.164273e-02 1.164273e-02 0.325792169 #> 8074  4036.5 0.6625410 0.6625410 1.163572e-02 1.163572e-02 0.325823252 #> 8075  4037.0 0.6625171 0.6625171 1.162870e-02 1.162870e-02 0.325854203 #> 8076  4037.5 0.6624933 0.6624933 1.162168e-02 1.162168e-02 0.325885021 #> 8077  4038.0 0.6624696 0.6624696 1.161465e-02 1.161465e-02 0.325915707 #> 8078  4038.5 0.6624461 0.6624461 1.160762e-02 1.160762e-02 0.325946260 #> 8079  4039.0 0.6624227 0.6624227 1.160059e-02 1.160059e-02 0.325976681 #> 8080  4039.5 0.6623995 0.6623995 1.159356e-02 1.159356e-02 0.326006970 #> 8081  4040.0 0.6623763 0.6623763 1.158653e-02 1.158653e-02 0.326037126 #> 8082  4040.5 0.6623534 0.6623534 1.157949e-02 1.157949e-02 0.326067150 #> 8083  4041.0 0.6623305 0.6623305 1.157245e-02 1.157245e-02 0.326097042 #> 8084  4041.5 0.6623078 0.6623078 1.156541e-02 1.156541e-02 0.326126802 #> 8085  4042.0 0.6622852 0.6622852 1.155836e-02 1.155836e-02 0.326156429 #> 8086  4042.5 0.6622628 0.6622628 1.155132e-02 1.155132e-02 0.326185924 #> 8087  4043.0 0.6622404 0.6622404 1.154427e-02 1.154427e-02 0.326215287 #> 8088  4043.5 0.6622183 0.6622183 1.153722e-02 1.153722e-02 0.326244518 #> 8089  4044.0 0.6621962 0.6621962 1.153017e-02 1.153017e-02 0.326273616 #> 8090  4044.5 0.6621743 0.6621743 1.152311e-02 1.152311e-02 0.326302583 #> 8091  4045.0 0.6621525 0.6621525 1.151605e-02 1.151605e-02 0.326331418 #> 8092  4045.5 0.6621309 0.6621309 1.150900e-02 1.150900e-02 0.326360120 #> 8093  4046.0 0.6621094 0.6621094 1.150194e-02 1.150194e-02 0.326388691 #> 8094  4046.5 0.6620880 0.6620880 1.149487e-02 1.149487e-02 0.326417129 #> 8095  4047.0 0.6620668 0.6620668 1.148781e-02 1.148781e-02 0.326445436 #> 8096  4047.5 0.6620456 0.6620456 1.148074e-02 1.148074e-02 0.326473611 #> 8097  4048.0 0.6620247 0.6620247 1.147368e-02 1.147368e-02 0.326501654 #> 8098  4048.5 0.6620038 0.6620038 1.146661e-02 1.146661e-02 0.326529565 #> 8099  4049.0 0.6619831 0.6619831 1.145954e-02 1.145954e-02 0.326557345 #> 8100  4049.5 0.6619625 0.6619625 1.145246e-02 1.145246e-02 0.326584992 #> 8101  4050.0 0.6619421 0.6619421 1.144539e-02 1.144539e-02 0.326612508 #> 8102  4050.5 0.6619218 0.6619218 1.143831e-02 1.143831e-02 0.326639893 #> 8103  4051.0 0.6619016 0.6619016 1.143124e-02 1.143124e-02 0.326667146 #> 8104  4051.5 0.6618816 0.6618816 1.142416e-02 1.142416e-02 0.326694267 #> 8105  4052.0 0.6618617 0.6618617 1.141708e-02 1.141708e-02 0.326721257 #> 8106  4052.5 0.6618419 0.6618419 1.141000e-02 1.141000e-02 0.326748115 #> 8107  4053.0 0.6618222 0.6618222 1.140292e-02 1.140292e-02 0.326774842 #> 8108  4053.5 0.6618027 0.6618027 1.139583e-02 1.139583e-02 0.326801437 #> 8109  4054.0 0.6617833 0.6617833 1.138875e-02 1.138875e-02 0.326827902 #> 8110  4054.5 0.6617641 0.6617641 1.138166e-02 1.138166e-02 0.326854234 #> 8111  4055.0 0.6617450 0.6617450 1.137458e-02 1.137458e-02 0.326880436 #> 8112  4055.5 0.6617260 0.6617260 1.136749e-02 1.136749e-02 0.326906507 #> 8113  4056.0 0.6617072 0.6617072 1.136040e-02 1.136040e-02 0.326932446 #> 8114  4056.5 0.6616884 0.6616884 1.135331e-02 1.135331e-02 0.326958254 #> 8115  4057.0 0.6616698 0.6616698 1.134622e-02 1.134622e-02 0.326983932 #> 8116  4057.5 0.6616514 0.6616514 1.133913e-02 1.133913e-02 0.327009478 #> 8117  4058.0 0.6616331 0.6616331 1.133204e-02 1.133204e-02 0.327034893 #> 8118  4058.5 0.6616149 0.6616149 1.132495e-02 1.132495e-02 0.327060178 #> 8119  4059.0 0.6615968 0.6615968 1.131786e-02 1.131786e-02 0.327085331 #> 8120  4059.5 0.6615789 0.6615789 1.131076e-02 1.131076e-02 0.327110354 #> 8121  4060.0 0.6615611 0.6615611 1.130367e-02 1.130367e-02 0.327135246 #> 8122  4060.5 0.6615434 0.6615434 1.129657e-02 1.129657e-02 0.327160008 #> 8123  4061.0 0.6615259 0.6615259 1.128948e-02 1.128948e-02 0.327184639 #> 8124  4061.5 0.6615085 0.6615085 1.128238e-02 1.128238e-02 0.327209139 #> 8125  4062.0 0.6614912 0.6614912 1.127529e-02 1.127529e-02 0.327233509 #> 8126  4062.5 0.6614741 0.6614741 1.126819e-02 1.126819e-02 0.327257749 #> 8127  4063.0 0.6614571 0.6614571 1.126109e-02 1.126109e-02 0.327281858 #> 8128  4063.5 0.6614402 0.6614402 1.125399e-02 1.125399e-02 0.327305836 #> 8129  4064.0 0.6614234 0.6614234 1.124690e-02 1.124690e-02 0.327329685 #> 8130  4064.5 0.6614068 0.6614068 1.123980e-02 1.123980e-02 0.327353403 #> 8131  4065.0 0.6613903 0.6613903 1.123270e-02 1.123270e-02 0.327376992 #> 8132  4065.5 0.6613739 0.6613739 1.122560e-02 1.122560e-02 0.327400450 #> 8133  4066.0 0.6613577 0.6613577 1.121850e-02 1.121850e-02 0.327423778 #> 8134  4066.5 0.6613416 0.6613416 1.121141e-02 1.121141e-02 0.327446976 #> 8135  4067.0 0.6613256 0.6613256 1.120431e-02 1.120431e-02 0.327470044 #> 8136  4067.5 0.6613098 0.6613098 1.119721e-02 1.119721e-02 0.327492983 #> 8137  4068.0 0.6612941 0.6612941 1.119011e-02 1.119011e-02 0.327515792 #> 8138  4068.5 0.6612785 0.6612785 1.118301e-02 1.118301e-02 0.327538471 #> 8139  4069.0 0.6612631 0.6612631 1.117592e-02 1.117592e-02 0.327561021 #> 8140  4069.5 0.6612477 0.6612477 1.116882e-02 1.116882e-02 0.327583441 #> 8141  4070.0 0.6612325 0.6612325 1.116172e-02 1.116172e-02 0.327605731 #> 8142  4070.5 0.6612175 0.6612175 1.115462e-02 1.115462e-02 0.327627892 #> 8143  4071.0 0.6612025 0.6612025 1.114753e-02 1.114753e-02 0.327649924 #> 8144  4071.5 0.6611877 0.6611877 1.114043e-02 1.114043e-02 0.327671827 #> 8145  4072.0 0.6611731 0.6611731 1.113333e-02 1.113333e-02 0.327693600 #> 8146  4072.5 0.6611585 0.6611585 1.112624e-02 1.112624e-02 0.327715244 #> 8147  4073.0 0.6611441 0.6611441 1.111914e-02 1.111914e-02 0.327736759 #> 8148  4073.5 0.6611298 0.6611298 1.111205e-02 1.111205e-02 0.327758146 #> 8149  4074.0 0.6611156 0.6611156 1.110496e-02 1.110496e-02 0.327779403 #> 8150  4074.5 0.6611016 0.6611016 1.109786e-02 1.109786e-02 0.327800531 #> 8151  4075.0 0.6610877 0.6610877 1.109077e-02 1.109077e-02 0.327821531 #> 8152  4075.5 0.6610739 0.6610739 1.108368e-02 1.108368e-02 0.327842402 #> 8153  4076.0 0.6610603 0.6610603 1.107659e-02 1.107659e-02 0.327863145 #> 8154  4076.5 0.6610467 0.6610467 1.106950e-02 1.106950e-02 0.327883759 #> 8155  4077.0 0.6610333 0.6610333 1.106241e-02 1.106241e-02 0.327904244 #> 8156  4077.5 0.6610201 0.6610201 1.105532e-02 1.105532e-02 0.327924601 #> 8157  4078.0 0.6610069 0.6610069 1.104823e-02 1.104823e-02 0.327944830 #> 8158  4078.5 0.6609939 0.6609939 1.104114e-02 1.104114e-02 0.327964931 #> 8159  4079.0 0.6609810 0.6609810 1.103406e-02 1.103406e-02 0.327984903 #> 8160  4079.5 0.6609683 0.6609683 1.102697e-02 1.102697e-02 0.328004748 #> 8161  4080.0 0.6609556 0.6609556 1.101989e-02 1.101989e-02 0.328024464 #> 8162  4080.5 0.6609431 0.6609431 1.101281e-02 1.101281e-02 0.328044053 #> 8163  4081.0 0.6609308 0.6609308 1.100572e-02 1.100572e-02 0.328063514 #> 8164  4081.5 0.6609185 0.6609185 1.099864e-02 1.099864e-02 0.328082847 #> 8165  4082.0 0.6609064 0.6609064 1.099156e-02 1.099156e-02 0.328102053 #> 8166  4082.5 0.6608944 0.6608944 1.098449e-02 1.098449e-02 0.328121131 #> 8167  4083.0 0.6608825 0.6608825 1.097741e-02 1.097741e-02 0.328140081 #> 8168  4083.5 0.6608708 0.6608708 1.097033e-02 1.097033e-02 0.328158904 #> 8169  4084.0 0.6608591 0.6608591 1.096326e-02 1.096326e-02 0.328177600 #> 8170  4084.5 0.6608476 0.6608476 1.095619e-02 1.095619e-02 0.328196169 #> 8171  4085.0 0.6608363 0.6608363 1.094912e-02 1.094912e-02 0.328214610 #> 8172  4085.5 0.6608250 0.6608250 1.094205e-02 1.094205e-02 0.328232925 #> 8173  4086.0 0.6608139 0.6608139 1.093498e-02 1.093498e-02 0.328251112 #> 8174  4086.5 0.6608029 0.6608029 1.092791e-02 1.092791e-02 0.328269173 #> 8175  4087.0 0.6607920 0.6607920 1.092085e-02 1.092085e-02 0.328287107 #> 8176  4087.5 0.6607813 0.6607813 1.091378e-02 1.091378e-02 0.328304914 #> 8177  4088.0 0.6607707 0.6607707 1.090672e-02 1.090672e-02 0.328322595 #> 8178  4088.5 0.6607602 0.6607602 1.089966e-02 1.089966e-02 0.328340149 #> 8179  4089.0 0.6607498 0.6607498 1.089260e-02 1.089260e-02 0.328357577 #> 8180  4089.5 0.6607396 0.6607396 1.088555e-02 1.088555e-02 0.328374878 #> 8181  4090.0 0.6607295 0.6607295 1.087849e-02 1.087849e-02 0.328392053 #> 8182  4090.5 0.6607195 0.6607195 1.087144e-02 1.087144e-02 0.328409102 #> 8183  4091.0 0.6607096 0.6607096 1.086439e-02 1.086439e-02 0.328426025 #> 8184  4091.5 0.6606998 0.6606998 1.085734e-02 1.085734e-02 0.328442823 #> 8185  4092.0 0.6606902 0.6606902 1.085029e-02 1.085029e-02 0.328459494 #> 8186  4092.5 0.6606807 0.6606807 1.084325e-02 1.084325e-02 0.328476039 #> 8187  4093.0 0.6606713 0.6606713 1.083620e-02 1.083620e-02 0.328492459 #> 8188  4093.5 0.6606621 0.6606621 1.082916e-02 1.082916e-02 0.328508754 #> 8189  4094.0 0.6606530 0.6606530 1.082212e-02 1.082212e-02 0.328524923 #> 8190  4094.5 0.6606439 0.6606439 1.081508e-02 1.081508e-02 0.328540966 #> 8191  4095.0 0.6606351 0.6606351 1.080805e-02 1.080805e-02 0.328556885 #> 8192  4095.5 0.6606263 0.6606263 1.080102e-02 1.080102e-02 0.328572678 #> 8193  4096.0 0.6606177 0.6606177 1.079399e-02 1.079399e-02 0.328588346 #> 8194  4096.5 0.6606092 0.6606092 1.078696e-02 1.078696e-02 0.328603889 #> 8195  4097.0 0.6606008 0.6606008 1.077993e-02 1.077993e-02 0.328619307 #> 8196  4097.5 0.6605925 0.6605925 1.077291e-02 1.077291e-02 0.328634601 #> 8197  4098.0 0.6605843 0.6605843 1.076589e-02 1.076589e-02 0.328649770 #> 8198  4098.5 0.6605763 0.6605763 1.075887e-02 1.075887e-02 0.328664814 #> 8199  4099.0 0.6605684 0.6605684 1.075185e-02 1.075185e-02 0.328679734 #> 8200  4099.5 0.6605606 0.6605606 1.074484e-02 1.074484e-02 0.328694529 #> 8201  4100.0 0.6605530 0.6605530 1.073783e-02 1.073783e-02 0.328709201 #> 8202  4100.5 0.6605454 0.6605454 1.073082e-02 1.073082e-02 0.328723748 #> 8203  4101.0 0.6605380 0.6605380 1.072381e-02 1.072381e-02 0.328738171 #> 8204  4101.5 0.6605307 0.6605307 1.071681e-02 1.071681e-02 0.328752470 #> 8205  4102.0 0.6605235 0.6605235 1.070981e-02 1.070981e-02 0.328766645 #> 8206  4102.5 0.6605165 0.6605165 1.070281e-02 1.070281e-02 0.328780697 #> 8207  4103.0 0.6605096 0.6605096 1.069581e-02 1.069581e-02 0.328794625 #> 8208  4103.5 0.6605027 0.6605027 1.068882e-02 1.068882e-02 0.328808430 #> 8209  4104.0 0.6604961 0.6604961 1.068183e-02 1.068183e-02 0.328822111 #> 8210  4104.5 0.6604895 0.6604895 1.067484e-02 1.067484e-02 0.328835669 #> 8211  4105.0 0.6604830 0.6604830 1.066786e-02 1.066786e-02 0.328849103 #> 8212  4105.5 0.6604767 0.6604767 1.066088e-02 1.066088e-02 0.328862415 #> 8213  4106.0 0.6604705 0.6604705 1.065390e-02 1.065390e-02 0.328875603 #> 8214  4106.5 0.6604644 0.6604644 1.064692e-02 1.064692e-02 0.328888669 #> 8215  4107.0 0.6604584 0.6604584 1.063995e-02 1.063995e-02 0.328901612 #> 8216  4107.5 0.6604526 0.6604526 1.063298e-02 1.063298e-02 0.328914433 #> 8217  4108.0 0.6604469 0.6604469 1.062601e-02 1.062601e-02 0.328927131 #> 8218  4108.5 0.6604412 0.6604412 1.061905e-02 1.061905e-02 0.328939706 #> 8219  4109.0 0.6604358 0.6604358 1.061209e-02 1.061209e-02 0.328952159 #> 8220  4109.5 0.6604304 0.6604304 1.060513e-02 1.060513e-02 0.328964490 #> 8221  4110.0 0.6604251 0.6604251 1.059818e-02 1.059818e-02 0.328976699 #> 8222  4110.5 0.6604200 0.6604200 1.059123e-02 1.059123e-02 0.328988786 #> 8223  4111.0 0.6604150 0.6604150 1.058428e-02 1.058428e-02 0.329000751 #> 8224  4111.5 0.6604101 0.6604101 1.057733e-02 1.057733e-02 0.329012595 #> 8225  4112.0 0.6604053 0.6604053 1.057039e-02 1.057039e-02 0.329024316 #> 8226  4112.5 0.6604006 0.6604006 1.056345e-02 1.056345e-02 0.329035917 #> 8227  4113.0 0.6603961 0.6603961 1.055652e-02 1.055652e-02 0.329047396 #> 8228  4113.5 0.6603917 0.6603917 1.054959e-02 1.054959e-02 0.329058753 #> 8229  4114.0 0.6603874 0.6603874 1.054266e-02 1.054266e-02 0.329069990 #> 8230  4114.5 0.6603832 0.6603832 1.053573e-02 1.053573e-02 0.329081105 #> 8231  4115.0 0.6603791 0.6603791 1.052881e-02 1.052881e-02 0.329092100 #> 8232  4115.5 0.6603751 0.6603751 1.052190e-02 1.052190e-02 0.329102974 #> 8233  4116.0 0.6603713 0.6603713 1.051498e-02 1.051498e-02 0.329113727 #> 8234  4116.5 0.6603676 0.6603676 1.050807e-02 1.050807e-02 0.329124359 #> 8235  4117.0 0.6603640 0.6603640 1.050116e-02 1.050116e-02 0.329134872 #> 8236  4117.5 0.6603605 0.6603605 1.049426e-02 1.049426e-02 0.329145263 #> 8237  4118.0 0.6603571 0.6603571 1.048736e-02 1.048736e-02 0.329155535 #> 8238  4118.5 0.6603538 0.6603538 1.048046e-02 1.048046e-02 0.329165687 #> 8239  4119.0 0.6603507 0.6603507 1.047357e-02 1.047357e-02 0.329175718 #> 8240  4119.5 0.6603477 0.6603477 1.046668e-02 1.046668e-02 0.329185630 #> 8241  4120.0 0.6603448 0.6603448 1.045980e-02 1.045980e-02 0.329195422 #> 8242  4120.5 0.6603420 0.6603420 1.045292e-02 1.045292e-02 0.329205094 #> 8243  4121.0 0.6603393 0.6603393 1.044604e-02 1.044604e-02 0.329214647 #> 8244  4121.5 0.6603368 0.6603368 1.043917e-02 1.043917e-02 0.329224081 #> 8245  4122.0 0.6603343 0.6603343 1.043230e-02 1.043230e-02 0.329233396 #> 8246  4122.5 0.6603320 0.6603320 1.042543e-02 1.042543e-02 0.329242591 #> 8247  4123.0 0.6603298 0.6603298 1.041857e-02 1.041857e-02 0.329251667 #> 8248  4123.5 0.6603277 0.6603277 1.041171e-02 1.041171e-02 0.329260625 #> 8249  4124.0 0.6603257 0.6603257 1.040486e-02 1.040486e-02 0.329269464 #> 8250  4124.5 0.6603238 0.6603238 1.039801e-02 1.039801e-02 0.329278184 #> 8251  4125.0 0.6603221 0.6603221 1.039116e-02 1.039116e-02 0.329286786 #> 8252  4125.5 0.6603204 0.6603204 1.038432e-02 1.038432e-02 0.329295270 #> 8253  4126.0 0.6603189 0.6603189 1.037748e-02 1.037748e-02 0.329303635 #> 8254  4126.5 0.6603175 0.6603175 1.037065e-02 1.037065e-02 0.329311882 #> 8255  4127.0 0.6603162 0.6603162 1.036382e-02 1.036382e-02 0.329320011 #> 8256  4127.5 0.6603150 0.6603150 1.035699e-02 1.035699e-02 0.329328023 #> 8257  4128.0 0.6603139 0.6603139 1.035017e-02 1.035017e-02 0.329335916 #> 8258  4128.5 0.6603130 0.6603130 1.034336e-02 1.034336e-02 0.329343693 #> 8259  4129.0 0.6603121 0.6603121 1.033654e-02 1.033654e-02 0.329351351 #> 8260  4129.5 0.6603114 0.6603114 1.032974e-02 1.032974e-02 0.329358893 #> 8261  4130.0 0.6603108 0.6603108 1.032293e-02 1.032293e-02 0.329366317 #> 8262  4130.5 0.6603102 0.6603102 1.031613e-02 1.031613e-02 0.329373624 #> 8263  4131.0 0.6603098 0.6603098 1.030934e-02 1.030934e-02 0.329380815 #> 8264  4131.5 0.6603096 0.6603096 1.030255e-02 1.030255e-02 0.329387888 #> 8265  4132.0 0.6603094 0.6603094 1.029576e-02 1.029576e-02 0.329394845 #> 8266  4132.5 0.6603093 0.6603093 1.028898e-02 1.028898e-02 0.329401685 #> 8267  4133.0 0.6603094 0.6603094 1.028220e-02 1.028220e-02 0.329408409 #> 8268  4133.5 0.6603096 0.6603096 1.027543e-02 1.027543e-02 0.329415017 #> 8269  4134.0 0.6603098 0.6603098 1.026866e-02 1.026866e-02 0.329421508 #> 8270  4134.5 0.6603102 0.6603102 1.026189e-02 1.026189e-02 0.329427884 #> 8271  4135.0 0.6603107 0.6603107 1.025514e-02 1.025514e-02 0.329434144 #> 8272  4135.5 0.6603113 0.6603113 1.024838e-02 1.024838e-02 0.329440288 #> 8273  4136.0 0.6603121 0.6603121 1.024163e-02 1.024163e-02 0.329446316 #> 8274  4136.5 0.6603129 0.6603129 1.023488e-02 1.023488e-02 0.329452229 #> 8275  4137.0 0.6603138 0.6603138 1.022814e-02 1.022814e-02 0.329458027 #> 8276  4137.5 0.6603149 0.6603149 1.022141e-02 1.022141e-02 0.329463709 #> 8277  4138.0 0.6603160 0.6603160 1.021468e-02 1.021468e-02 0.329469277 #> 8278  4138.5 0.6603173 0.6603173 1.020795e-02 1.020795e-02 0.329474729 #> 8279  4139.0 0.6603187 0.6603187 1.020123e-02 1.020123e-02 0.329480067 #> 8280  4139.5 0.6603202 0.6603202 1.019451e-02 1.019451e-02 0.329485290 #> 8281  4140.0 0.6603218 0.6603218 1.018780e-02 1.018780e-02 0.329490399 #> 8282  4140.5 0.6603235 0.6603235 1.018109e-02 1.018109e-02 0.329495393 #> 8283  4141.0 0.6603253 0.6603253 1.017439e-02 1.017439e-02 0.329500273 #> 8284  4141.5 0.6603273 0.6603273 1.016769e-02 1.016769e-02 0.329505039 #> 8285  4142.0 0.6603293 0.6603293 1.016100e-02 1.016100e-02 0.329509691 #> 8286  4142.5 0.6603315 0.6603315 1.015431e-02 1.015431e-02 0.329514230 #> 8287  4143.0 0.6603337 0.6603337 1.014763e-02 1.014763e-02 0.329518654 #> 8288  4143.5 0.6603361 0.6603361 1.014095e-02 1.014095e-02 0.329522965 #> 8289  4144.0 0.6603386 0.6603386 1.013428e-02 1.013428e-02 0.329527163 #> 8290  4144.5 0.6603411 0.6603411 1.012761e-02 1.012761e-02 0.329531247 #> 8291  4145.0 0.6603438 0.6603438 1.012095e-02 1.012095e-02 0.329535219 #> 8292  4145.5 0.6603466 0.6603466 1.011429e-02 1.011429e-02 0.329539077 #> 8293  4146.0 0.6603495 0.6603495 1.010764e-02 1.010764e-02 0.329542822 #> 8294  4146.5 0.6603526 0.6603526 1.010099e-02 1.010099e-02 0.329546455 #> 8295  4147.0 0.6603557 0.6603557 1.009435e-02 1.009435e-02 0.329549976 #> 8296  4147.5 0.6603589 0.6603589 1.008771e-02 1.008771e-02 0.329553384 #> 8297  4148.0 0.6603622 0.6603622 1.008108e-02 1.008108e-02 0.329556679 #> 8298  4148.5 0.6603657 0.6603657 1.007445e-02 1.007445e-02 0.329559863 #> 8299  4149.0 0.6603692 0.6603692 1.006783e-02 1.006783e-02 0.329562934 #> 8300  4149.5 0.6603729 0.6603729 1.006121e-02 1.006121e-02 0.329565894 #> 8301  4150.0 0.6603767 0.6603767 1.005460e-02 1.005460e-02 0.329568742 #> 8302  4150.5 0.6603805 0.6603805 1.004799e-02 1.004799e-02 0.329571479 #> 8303  4151.0 0.6603845 0.6603845 1.004139e-02 1.004139e-02 0.329574104 #> 8304  4151.5 0.6603886 0.6603886 1.003480e-02 1.003480e-02 0.329576618 #> 8305  4152.0 0.6603928 0.6603928 1.002821e-02 1.002821e-02 0.329579021 #> 8306  4152.5 0.6603971 0.6603971 1.002162e-02 1.002162e-02 0.329581313 #> 8307  4153.0 0.6604015 0.6604015 1.001505e-02 1.001505e-02 0.329583494 #> 8308  4153.5 0.6604060 0.6604060 1.000847e-02 1.000847e-02 0.329585564 #> 8309  4154.0 0.6604106 0.6604106 1.000190e-02 1.000190e-02 0.329587524 #> 8310  4154.5 0.6604153 0.6604153 9.995342e-03 9.995342e-03 0.329589373 #> 8311  4155.0 0.6604201 0.6604201 9.988785e-03 9.988785e-03 0.329591113 #> 8312  4155.5 0.6604250 0.6604250 9.982233e-03 9.982233e-03 0.329592742 #> 8313  4156.0 0.6604301 0.6604301 9.975688e-03 9.975688e-03 0.329594261 #> 8314  4156.5 0.6604352 0.6604352 9.969147e-03 9.969147e-03 0.329595670 #> 8315  4157.0 0.6604404 0.6604404 9.962612e-03 9.962612e-03 0.329596970 #> 8316  4157.5 0.6604458 0.6604458 9.956083e-03 9.956083e-03 0.329598161 #> 8317  4158.0 0.6604512 0.6604512 9.949559e-03 9.949559e-03 0.329599242 #> 8318  4158.5 0.6604567 0.6604567 9.943041e-03 9.943041e-03 0.329600213 #> 8319  4159.0 0.6604624 0.6604624 9.936529e-03 9.936529e-03 0.329601076 #> 8320  4159.5 0.6604681 0.6604681 9.930022e-03 9.930022e-03 0.329601830 #> 8321  4160.0 0.6604740 0.6604740 9.923521e-03 9.923521e-03 0.329602475 #> 8322  4160.5 0.6604800 0.6604800 9.917026e-03 9.917026e-03 0.329603012 #> 8323  4161.0 0.6604860 0.6604860 9.910537e-03 9.910537e-03 0.329603440 #> 8324  4161.5 0.6604922 0.6604922 9.904053e-03 9.904053e-03 0.329603759 #> 8325  4162.0 0.6604985 0.6604985 9.897575e-03 9.897575e-03 0.329603971 #> 8326  4162.5 0.6605048 0.6605048 9.891103e-03 9.891103e-03 0.329604075 #> 8327  4163.0 0.6605113 0.6605113 9.884637e-03 9.884637e-03 0.329604070 #> 8328  4163.5 0.6605179 0.6605179 9.878176e-03 9.878176e-03 0.329603959 #> 8329  4164.0 0.6605245 0.6605245 9.871722e-03 9.871722e-03 0.329603739 #> 8330  4164.5 0.6605313 0.6605313 9.865273e-03 9.865273e-03 0.329603412 #> 8331  4165.0 0.6605382 0.6605382 9.858830e-03 9.858830e-03 0.329602978 #> 8332  4165.5 0.6605452 0.6605452 9.852393e-03 9.852393e-03 0.329602437 #> 8333  4166.0 0.6605522 0.6605522 9.845962e-03 9.845962e-03 0.329601789 #> 8334  4166.5 0.6605594 0.6605594 9.839537e-03 9.839537e-03 0.329601034 #> 8335  4167.0 0.6605667 0.6605667 9.833118e-03 9.833118e-03 0.329600172 #> 8336  4167.5 0.6605741 0.6605741 9.826705e-03 9.826705e-03 0.329599204 #> 8337  4168.0 0.6605816 0.6605816 9.820298e-03 9.820298e-03 0.329598130 #> 8338  4168.5 0.6605892 0.6605892 9.813897e-03 9.813897e-03 0.329596950 #> 8339  4169.0 0.6605968 0.6605968 9.807502e-03 9.807502e-03 0.329595663 #> 8340  4169.5 0.6606046 0.6606046 9.801113e-03 9.801113e-03 0.329594271 #> 8341  4170.0 0.6606125 0.6606125 9.794730e-03 9.794730e-03 0.329592773 #> 8342  4170.5 0.6606205 0.6606205 9.788353e-03 9.788353e-03 0.329591169 #> 8343  4171.0 0.6606286 0.6606286 9.781982e-03 9.781982e-03 0.329589460 #> 8344  4171.5 0.6606367 0.6606367 9.775618e-03 9.775618e-03 0.329587646 #> 8345  4172.0 0.6606450 0.6606450 9.769259e-03 9.769259e-03 0.329585726 #> 8346  4172.5 0.6606534 0.6606534 9.762907e-03 9.762907e-03 0.329583702 #> 8347  4173.0 0.6606619 0.6606619 9.756561e-03 9.756561e-03 0.329581573 #> 8348  4173.5 0.6606704 0.6606704 9.750221e-03 9.750221e-03 0.329579339 #> 8349  4174.0 0.6606791 0.6606791 9.743888e-03 9.743888e-03 0.329577001 #> 8350  4174.5 0.6606879 0.6606879 9.737560e-03 9.737560e-03 0.329574558 #> 8351  4175.0 0.6606967 0.6606967 9.731239e-03 9.731239e-03 0.329572012 #> 8352  4175.5 0.6607057 0.6607057 9.724924e-03 9.724924e-03 0.329569361 #> 8353  4176.0 0.6607148 0.6607148 9.718616e-03 9.718616e-03 0.329566607 #> 8354  4176.5 0.6607239 0.6607239 9.712313e-03 9.712313e-03 0.329563748 #> 8355  4177.0 0.6607332 0.6607332 9.706017e-03 9.706017e-03 0.329560787 #> 8356  4177.5 0.6607426 0.6607426 9.699728e-03 9.699728e-03 0.329557722 #> 8357  4178.0 0.6607520 0.6607520 9.693445e-03 9.693445e-03 0.329554553 #> 8358  4178.5 0.6607616 0.6607616 9.687168e-03 9.687168e-03 0.329551282 #> 8359  4179.0 0.6607712 0.6607712 9.680897e-03 9.680897e-03 0.329547908 #> 8360  4179.5 0.6607809 0.6607809 9.674633e-03 9.674633e-03 0.329544431 #> 8361  4180.0 0.6607908 0.6607908 9.668376e-03 9.668376e-03 0.329540851 #> 8362  4180.5 0.6608007 0.6608007 9.662124e-03 9.662124e-03 0.329537169 #> 8363  4181.0 0.6608107 0.6608107 9.655880e-03 9.655880e-03 0.329533385 #> 8364  4181.5 0.6608209 0.6608209 9.649641e-03 9.649641e-03 0.329529498 #> 8365  4182.0 0.6608311 0.6608311 9.643409e-03 9.643409e-03 0.329525510 #> 8366  4182.5 0.6608414 0.6608414 9.637184e-03 9.637184e-03 0.329521420 #> 8367  4183.0 0.6608518 0.6608518 9.630965e-03 9.630965e-03 0.329517228 #> 8368  4183.5 0.6608623 0.6608623 9.624753e-03 9.624753e-03 0.329512935 #> 8369  4184.0 0.6608729 0.6608729 9.618547e-03 9.618547e-03 0.329508540 #> 8370  4184.5 0.6608836 0.6608836 9.612348e-03 9.612348e-03 0.329504045 #> 8371  4185.0 0.6608944 0.6608944 9.606155e-03 9.606155e-03 0.329499448 #> 8372  4185.5 0.6609053 0.6609053 9.599969e-03 9.599969e-03 0.329494750 #> 8373  4186.0 0.6609163 0.6609163 9.593790e-03 9.593790e-03 0.329489952 #> 8374  4186.5 0.6609273 0.6609273 9.587617e-03 9.587617e-03 0.329485054 #> 8375  4187.0 0.6609385 0.6609385 9.581451e-03 9.581451e-03 0.329480054 #> 8376  4187.5 0.6609498 0.6609498 9.575292e-03 9.575292e-03 0.329474955 #> 8377  4188.0 0.6609611 0.6609611 9.569139e-03 9.569139e-03 0.329469756 #> 8378  4188.5 0.6609726 0.6609726 9.562993e-03 9.562993e-03 0.329464457 #> 8379  4189.0 0.6609841 0.6609841 9.556853e-03 9.556853e-03 0.329459058 #> 8380  4189.5 0.6609957 0.6609957 9.550720e-03 9.550720e-03 0.329453560 #> 8381  4190.0 0.6610074 0.6610074 9.544594e-03 9.544594e-03 0.329447962 #> 8382  4190.5 0.6610193 0.6610193 9.538475e-03 9.538475e-03 0.329442265 #> 8383  4191.0 0.6610312 0.6610312 9.532362e-03 9.532362e-03 0.329436469 #> 8384  4191.5 0.6610432 0.6610432 9.526257e-03 9.526257e-03 0.329430574 #> 8385  4192.0 0.6610553 0.6610553 9.520158e-03 9.520158e-03 0.329424580 #> 8386  4192.5 0.6610674 0.6610674 9.514065e-03 9.514065e-03 0.329418488 #> 8387  4193.0 0.6610797 0.6610797 9.507980e-03 9.507980e-03 0.329412297 #> 8388  4193.5 0.6610921 0.6610921 9.501901e-03 9.501901e-03 0.329406008 #> 8389  4194.0 0.6611045 0.6611045 9.495830e-03 9.495830e-03 0.329399621 #> 8390  4194.5 0.6611171 0.6611171 9.489765e-03 9.489765e-03 0.329393136 #> 8391  4195.0 0.6611297 0.6611297 9.483707e-03 9.483707e-03 0.329386554 #> 8392  4195.5 0.6611425 0.6611425 9.477656e-03 9.477656e-03 0.329379873 #> 8393  4196.0 0.6611553 0.6611553 9.471611e-03 9.471611e-03 0.329373096 #> 8394  4196.5 0.6611682 0.6611682 9.465574e-03 9.465574e-03 0.329366221 #> 8395  4197.0 0.6611812 0.6611812 9.459543e-03 9.459543e-03 0.329359249 #> 8396  4197.5 0.6611943 0.6611943 9.453520e-03 9.453520e-03 0.329352180 #> 8397  4198.0 0.6612075 0.6612075 9.447503e-03 9.447503e-03 0.329345014 #> 8398  4198.5 0.6612208 0.6612208 9.441494e-03 9.441494e-03 0.329337752 #> 8399  4199.0 0.6612341 0.6612341 9.435491e-03 9.435491e-03 0.329330393 #> 8400  4199.5 0.6612476 0.6612476 9.429495e-03 9.429495e-03 0.329322938 #> 8401  4200.0 0.6612611 0.6612611 9.423506e-03 9.423506e-03 0.329315386 #> 8402  4200.5 0.6612747 0.6612747 9.417525e-03 9.417525e-03 0.329307739 #> 8403  4201.0 0.6612885 0.6612885 9.411550e-03 9.411550e-03 0.329299996 #> 8404  4201.5 0.6613023 0.6613023 9.405582e-03 9.405582e-03 0.329292158 #> 8405  4202.0 0.6613162 0.6613162 9.399622e-03 9.399622e-03 0.329284223 #> 8406  4202.5 0.6613301 0.6613301 9.393668e-03 9.393668e-03 0.329276194 #> 8407  4203.0 0.6613442 0.6613442 9.387721e-03 9.387721e-03 0.329268070 #> 8408  4203.5 0.6613584 0.6613584 9.381782e-03 9.381782e-03 0.329259850 #> 8409  4204.0 0.6613726 0.6613726 9.375850e-03 9.375850e-03 0.329251536 #> 8410  4204.5 0.6613869 0.6613869 9.369924e-03 9.369924e-03 0.329243127 #> 8411  4205.0 0.6614014 0.6614014 9.364006e-03 9.364006e-03 0.329234624 #> 8412  4205.5 0.6614159 0.6614159 9.358095e-03 9.358095e-03 0.329226026 #> 8413  4206.0 0.6614305 0.6614305 9.352191e-03 9.352191e-03 0.329217334 #> 8414  4206.5 0.6614452 0.6614452 9.346294e-03 9.346294e-03 0.329208548 #> 8415  4207.0 0.6614599 0.6614599 9.340405e-03 9.340405e-03 0.329199669 #> 8416  4207.5 0.6614748 0.6614748 9.334522e-03 9.334522e-03 0.329190696 #> 8417  4208.0 0.6614897 0.6614897 9.328647e-03 9.328647e-03 0.329181629 #> 8418  4208.5 0.6615048 0.6615048 9.322779e-03 9.322779e-03 0.329172469 #> 8419  4209.0 0.6615199 0.6615199 9.316918e-03 9.316918e-03 0.329163216 #> 8420  4209.5 0.6615351 0.6615351 9.311065e-03 9.311065e-03 0.329153870 #> 8421  4210.0 0.6615504 0.6615504 9.305218e-03 9.305218e-03 0.329144431 #> 8422  4210.5 0.6615657 0.6615657 9.299379e-03 9.299379e-03 0.329134899 #> 8423  4211.0 0.6615812 0.6615812 9.293547e-03 9.293547e-03 0.329125275 #> 8424  4211.5 0.6615967 0.6615967 9.287722e-03 9.287722e-03 0.329115559 #> 8425  4212.0 0.6616123 0.6616123 9.281905e-03 9.281905e-03 0.329105750 #> 8426  4212.5 0.6616281 0.6616281 9.276095e-03 9.276095e-03 0.329095850 #> 8427  4213.0 0.6616439 0.6616439 9.270292e-03 9.270292e-03 0.329085858 #> 8428  4213.5 0.6616597 0.6616597 9.264496e-03 9.264496e-03 0.329075774 #> 8429  4214.0 0.6616757 0.6616757 9.258708e-03 9.258708e-03 0.329065599 #> 8430  4214.5 0.6616917 0.6616917 9.252927e-03 9.252927e-03 0.329055332 #> 8431  4215.0 0.6617079 0.6617079 9.247153e-03 9.247153e-03 0.329044974 #> 8432  4215.5 0.6617241 0.6617241 9.241387e-03 9.241387e-03 0.329034526 #> 8433  4216.0 0.6617404 0.6617404 9.235628e-03 9.235628e-03 0.329023986 #> 8434  4216.5 0.6617568 0.6617568 9.229877e-03 9.229877e-03 0.329013356 #> 8435  4217.0 0.6617732 0.6617732 9.224132e-03 9.224132e-03 0.329002635 #> 8436  4217.5 0.6617898 0.6617898 9.218396e-03 9.218396e-03 0.328991824 #> 8437  4218.0 0.6618064 0.6618064 9.212666e-03 9.212666e-03 0.328980923 #> 8438  4218.5 0.6618231 0.6618231 9.206944e-03 9.206944e-03 0.328969932 #> 8439  4219.0 0.6618399 0.6618399 9.201230e-03 9.201230e-03 0.328958852 #> 8440  4219.5 0.6618568 0.6618568 9.195522e-03 9.195522e-03 0.328947681 #> 8441  4220.0 0.6618738 0.6618738 9.189823e-03 9.189823e-03 0.328936421 #> 8442  4220.5 0.6618908 0.6618908 9.184130e-03 9.184130e-03 0.328925072 #> 8443  4221.0 0.6619079 0.6619079 9.178445e-03 9.178445e-03 0.328913634 #> 8444  4221.5 0.6619251 0.6619251 9.172768e-03 9.172768e-03 0.328902107 #> 8445  4222.0 0.6619424 0.6619424 9.167098e-03 9.167098e-03 0.328890491 #> 8446  4222.5 0.6619598 0.6619598 9.161435e-03 9.161435e-03 0.328878786 #> 8447  4223.0 0.6619772 0.6619772 9.155780e-03 9.155780e-03 0.328866993 #> 8448  4223.5 0.6619948 0.6619948 9.150133e-03 9.150133e-03 0.328855112 #> 8449  4224.0 0.6620124 0.6620124 9.144493e-03 9.144493e-03 0.328843143 #> 8450  4224.5 0.6620301 0.6620301 9.138860e-03 9.138860e-03 0.328831085 #> 8451  4225.0 0.6620478 0.6620478 9.133235e-03 9.133235e-03 0.328818940 #> 8452  4225.5 0.6620657 0.6620657 9.127618e-03 9.127618e-03 0.328806708 #> 8453  4226.0 0.6620836 0.6620836 9.122008e-03 9.122008e-03 0.328794388 #> 8454  4226.5 0.6621016 0.6621016 9.116405e-03 9.116405e-03 0.328781980 #> 8455  4227.0 0.6621197 0.6621197 9.110811e-03 9.110811e-03 0.328769486 #> 8456  4227.5 0.6621379 0.6621379 9.105223e-03 9.105223e-03 0.328756905 #> 8457  4228.0 0.6621561 0.6621561 9.099644e-03 9.099644e-03 0.328744237 #> 8458  4228.5 0.6621744 0.6621744 9.094071e-03 9.094071e-03 0.328731482 #> 8459  4229.0 0.6621929 0.6621929 9.088507e-03 9.088507e-03 0.328718641 #> 8460  4229.5 0.6622113 0.6622113 9.082950e-03 9.082950e-03 0.328705714 #> 8461  4230.0 0.6622299 0.6622299 9.077401e-03 9.077401e-03 0.328692700 #> 8462  4230.5 0.6622485 0.6622485 9.071859e-03 9.071859e-03 0.328679601 #> 8463  4231.0 0.6622673 0.6622673 9.066325e-03 9.066325e-03 0.328666416 #> 8464  4231.5 0.6622861 0.6622861 9.060798e-03 9.060798e-03 0.328653145 #> 8465  4232.0 0.6623049 0.6623049 9.055279e-03 9.055279e-03 0.328639789 #> 8466  4232.5 0.6623239 0.6623239 9.049768e-03 9.049768e-03 0.328626348 #> 8467  4233.0 0.6623429 0.6623429 9.044264e-03 9.044264e-03 0.328612822 #> 8468  4233.5 0.6623620 0.6623620 9.038769e-03 9.038769e-03 0.328599211 #> 8469  4234.0 0.6623812 0.6623812 9.033280e-03 9.033280e-03 0.328585515 #> 8470  4234.5 0.6624005 0.6624005 9.027800e-03 9.027800e-03 0.328571735 #> 8471  4235.0 0.6624198 0.6624198 9.022327e-03 9.022327e-03 0.328557870 #> 8472  4235.5 0.6624392 0.6624392 9.016862e-03 9.016862e-03 0.328543921 #> 8473  4236.0 0.6624587 0.6624587 9.011404e-03 9.011404e-03 0.328529888 #> 8474  4236.5 0.6624783 0.6624783 9.005954e-03 9.005954e-03 0.328515772 #> 8475  4237.0 0.6624979 0.6624979 9.000512e-03 9.000512e-03 0.328501571 #> 8476  4237.5 0.6625176 0.6625176 8.995078e-03 8.995078e-03 0.328487287 #> 8477  4238.0 0.6625374 0.6625374 8.989651e-03 8.989651e-03 0.328472920 #> 8478  4238.5 0.6625573 0.6625573 8.984232e-03 8.984232e-03 0.328458469 #> 8479  4239.0 0.6625772 0.6625772 8.978821e-03 8.978821e-03 0.328443936 #> 8480  4239.5 0.6625973 0.6625973 8.973417e-03 8.973417e-03 0.328429319 #> 8481  4240.0 0.6626174 0.6626174 8.968021e-03 8.968021e-03 0.328414620 #> 8482  4240.5 0.6626375 0.6626375 8.962633e-03 8.962633e-03 0.328399839 #> 8483  4241.0 0.6626578 0.6626578 8.957253e-03 8.957253e-03 0.328384975 #> 8484  4241.5 0.6626781 0.6626781 8.951881e-03 8.951881e-03 0.328370029 #> 8485  4242.0 0.6626985 0.6626985 8.946516e-03 8.946516e-03 0.328355001 #> 8486  4242.5 0.6627189 0.6627189 8.941159e-03 8.941159e-03 0.328339891 #> 8487  4243.0 0.6627395 0.6627395 8.935810e-03 8.935810e-03 0.328324700 #> 8488  4243.5 0.6627601 0.6627601 8.930469e-03 8.930469e-03 0.328309427 #> 8489  4244.0 0.6627808 0.6627808 8.925135e-03 8.925135e-03 0.328294073 #> 8490  4244.5 0.6628016 0.6628016 8.919810e-03 8.919810e-03 0.328278638 #> 8491  4245.0 0.6628224 0.6628224 8.914492e-03 8.914492e-03 0.328263121 #> 8492  4245.5 0.6628433 0.6628433 8.909182e-03 8.909182e-03 0.328247524 #> 8493  4246.0 0.6628643 0.6628643 8.903879e-03 8.903879e-03 0.328231847 #> 8494  4246.5 0.6628853 0.6628853 8.898585e-03 8.898585e-03 0.328216088 #> 8495  4247.0 0.6629065 0.6629065 8.893298e-03 8.893298e-03 0.328200250 #> 8496  4247.5 0.6629276 0.6629276 8.888020e-03 8.888020e-03 0.328184332 #> 8497  4248.0 0.6629489 0.6629489 8.882749e-03 8.882749e-03 0.328168333 #> 8498  4248.5 0.6629703 0.6629703 8.877486e-03 8.877486e-03 0.328152255 #> 8499  4249.0 0.6629917 0.6629917 8.872230e-03 8.872230e-03 0.328136097 #> 8500  4249.5 0.6630132 0.6630132 8.866983e-03 8.866983e-03 0.328119860 #> 8501  4250.0 0.6630347 0.6630347 8.861744e-03 8.861744e-03 0.328103543 #> 8502  4250.5 0.6630563 0.6630563 8.856512e-03 8.856512e-03 0.328087148 #> 8503  4251.0 0.6630780 0.6630780 8.851288e-03 8.851288e-03 0.328070673 #> 8504  4251.5 0.6630998 0.6630998 8.846073e-03 8.846073e-03 0.328054120 #> 8505  4252.0 0.6631216 0.6631216 8.840865e-03 8.840865e-03 0.328037488 #> 8506  4252.5 0.6631436 0.6631436 8.835665e-03 8.835665e-03 0.328020778 #> 8507  4253.0 0.6631655 0.6631655 8.830473e-03 8.830473e-03 0.328003990 #> 8508  4253.5 0.6631876 0.6631876 8.825288e-03 8.825288e-03 0.327987123 #> 8509  4254.0 0.6632097 0.6632097 8.820112e-03 8.820112e-03 0.327970179 #> 8510  4254.5 0.6632319 0.6632319 8.814944e-03 8.814944e-03 0.327953157 #> 8511  4255.0 0.6632542 0.6632542 8.809783e-03 8.809783e-03 0.327936057 #> 8512  4255.5 0.6632765 0.6632765 8.804631e-03 8.804631e-03 0.327918880 #> 8513  4256.0 0.6632989 0.6632989 8.799486e-03 8.799486e-03 0.327901626 #> 8514  4256.5 0.6633214 0.6633214 8.794350e-03 8.794350e-03 0.327884295 #> 8515  4257.0 0.6633439 0.6633439 8.789221e-03 8.789221e-03 0.327866887 #> 8516  4257.5 0.6633665 0.6633665 8.784100e-03 8.784100e-03 0.327849402 #> 8517  4258.0 0.6633892 0.6633892 8.778987e-03 8.778987e-03 0.327831841 #> 8518  4258.5 0.6634119 0.6634119 8.773882e-03 8.773882e-03 0.327814203 #> 8519  4259.0 0.6634347 0.6634347 8.768786e-03 8.768786e-03 0.327796490 #> 8520  4259.5 0.6634576 0.6634576 8.763697e-03 8.763697e-03 0.327778700 #> 8521  4260.0 0.6634806 0.6634806 8.758616e-03 8.758616e-03 0.327760834 #> 8522  4260.5 0.6635036 0.6635036 8.753543e-03 8.753543e-03 0.327742893 #> 8523  4261.0 0.6635266 0.6635266 8.748478e-03 8.748478e-03 0.327724876 #> 8524  4261.5 0.6635498 0.6635498 8.743421e-03 8.743421e-03 0.327706784 #> 8525  4262.0 0.6635730 0.6635730 8.738372e-03 8.738372e-03 0.327688617 #> 8526  4262.5 0.6635963 0.6635963 8.733331e-03 8.733331e-03 0.327670375 #> 8527  4263.0 0.6636196 0.6636196 8.728298e-03 8.728298e-03 0.327652057 #> 8528  4263.5 0.6636431 0.6636431 8.723273e-03 8.723273e-03 0.327633666 #> 8529  4264.0 0.6636665 0.6636665 8.718256e-03 8.718256e-03 0.327615200 #> 8530  4264.5 0.6636901 0.6636901 8.713247e-03 8.713247e-03 0.327596659 #> 8531  4265.0 0.6637137 0.6637137 8.708246e-03 8.708246e-03 0.327578045 #> 8532  4265.5 0.6637374 0.6637374 8.703253e-03 8.703253e-03 0.327559356 #> 8533  4266.0 0.6637611 0.6637611 8.698268e-03 8.698268e-03 0.327540594 #> 8534  4266.5 0.6637850 0.6637850 8.693291e-03 8.693291e-03 0.327521758 #> 8535  4267.0 0.6638088 0.6638088 8.688322e-03 8.688322e-03 0.327502848 #> 8536  4267.5 0.6638328 0.6638328 8.683361e-03 8.683361e-03 0.327483866 #> 8537  4268.0 0.6638568 0.6638568 8.678408e-03 8.678408e-03 0.327464810 #> 8538  4268.5 0.6638809 0.6638809 8.673463e-03 8.673463e-03 0.327445681 #> 8539  4269.0 0.6639050 0.6639050 8.668526e-03 8.668526e-03 0.327426480 #> 8540  4269.5 0.6639292 0.6639292 8.663598e-03 8.663598e-03 0.327407206 #> 8541  4270.0 0.6639535 0.6639535 8.658677e-03 8.658677e-03 0.327387859 #> 8542  4270.5 0.6639778 0.6639778 8.653764e-03 8.653764e-03 0.327368440 #> 8543  4271.0 0.6640022 0.6640022 8.648860e-03 8.648860e-03 0.327348950 #> 8544  4271.5 0.6640266 0.6640266 8.643963e-03 8.643963e-03 0.327329387 #> 8545  4272.0 0.6640512 0.6640512 8.639075e-03 8.639075e-03 0.327309752 #> 8546  4272.5 0.6640758 0.6640758 8.634194e-03 8.634194e-03 0.327290046 #> 8547  4273.0 0.6641004 0.6641004 8.629322e-03 8.629322e-03 0.327270269 #> 8548  4273.5 0.6641251 0.6641251 8.624458e-03 8.624458e-03 0.327250420 #> 8549  4274.0 0.6641499 0.6641499 8.619602e-03 8.619602e-03 0.327230500 #> 8550  4274.5 0.6641747 0.6641747 8.614754e-03 8.614754e-03 0.327210510 #> 8551  4275.0 0.6641996 0.6641996 8.609914e-03 8.609914e-03 0.327190448 #> 8552  4275.5 0.6642246 0.6642246 8.605082e-03 8.605082e-03 0.327170317 #> 8553  4276.0 0.6642496 0.6642496 8.600258e-03 8.600258e-03 0.327150114 #> 8554  4276.5 0.6642747 0.6642747 8.595442e-03 8.595442e-03 0.327129842 #> 8555  4277.0 0.6642999 0.6642999 8.590634e-03 8.590634e-03 0.327109499 #> 8556  4277.5 0.6643251 0.6643251 8.585835e-03 8.585835e-03 0.327089087 #> 8557  4278.0 0.6643504 0.6643504 8.581043e-03 8.581043e-03 0.327068605 #> 8558  4278.5 0.6643757 0.6643757 8.576260e-03 8.576260e-03 0.327048053 #> 8559  4279.0 0.6644011 0.6644011 8.571485e-03 8.571485e-03 0.327027432 #> 8560  4279.5 0.6644265 0.6644265 8.566717e-03 8.566717e-03 0.327006742 #> 8561  4280.0 0.6644521 0.6644521 8.561958e-03 8.561958e-03 0.326985983 #> 8562  4280.5 0.6644776 0.6644776 8.557207e-03 8.557207e-03 0.326965154 #> 8563  4281.0 0.6645033 0.6645033 8.552465e-03 8.552465e-03 0.326944258 #> 8564  4281.5 0.6645290 0.6645290 8.547730e-03 8.547730e-03 0.326923292 #> 8565  4282.0 0.6645547 0.6645547 8.543003e-03 8.543003e-03 0.326902259 #> 8566  4282.5 0.6645806 0.6645806 8.538285e-03 8.538285e-03 0.326881157 #> 8567  4283.0 0.6646064 0.6646064 8.533574e-03 8.533574e-03 0.326859987 #> 8568  4283.5 0.6646324 0.6646324 8.528872e-03 8.528872e-03 0.326838750 #> 8569  4284.0 0.6646584 0.6646584 8.524178e-03 8.524178e-03 0.326817444 #> 8570  4284.5 0.6646844 0.6646844 8.519492e-03 8.519492e-03 0.326796071 #> 8571  4285.0 0.6647106 0.6647106 8.514814e-03 8.514814e-03 0.326774631 #> 8572  4285.5 0.6647367 0.6647367 8.510144e-03 8.510144e-03 0.326753124 #> 8573  4286.0 0.6647630 0.6647630 8.505483e-03 8.505483e-03 0.326731550 #> 8574  4286.5 0.6647893 0.6647893 8.500829e-03 8.500829e-03 0.326709909 #> 8575  4287.0 0.6648156 0.6648156 8.496184e-03 8.496184e-03 0.326688201 #> 8576  4287.5 0.6648420 0.6648420 8.491547e-03 8.491547e-03 0.326666427 #> 8577  4288.0 0.6648685 0.6648685 8.486918e-03 8.486918e-03 0.326644586 #> 8578  4288.5 0.6648950 0.6648950 8.482297e-03 8.482297e-03 0.326622679 #> 8579  4289.0 0.6649216 0.6649216 8.477684e-03 8.477684e-03 0.326600707 #> 8580  4289.5 0.6649483 0.6649483 8.473079e-03 8.473079e-03 0.326578668 #> 8581  4290.0 0.6649750 0.6649750 8.468483e-03 8.468483e-03 0.326556564 #> 8582  4290.5 0.6650017 0.6650017 8.463895e-03 8.463895e-03 0.326534394 #> 8583  4291.0 0.6650285 0.6650285 8.459314e-03 8.459314e-03 0.326512159 #> 8584  4291.5 0.6650554 0.6650554 8.454742e-03 8.454742e-03 0.326489859 #> 8585  4292.0 0.6650823 0.6650823 8.450179e-03 8.450179e-03 0.326467494 #> 8586  4292.5 0.6651093 0.6651093 8.445623e-03 8.445623e-03 0.326445065 #> 8587  4293.0 0.6651364 0.6651364 8.441075e-03 8.441075e-03 0.326422570 #> 8588  4293.5 0.6651635 0.6651635 8.436536e-03 8.436536e-03 0.326400011 #> 8589  4294.0 0.6651906 0.6651906 8.432005e-03 8.432005e-03 0.326377388 #> 8590  4294.5 0.6652178 0.6652178 8.427482e-03 8.427482e-03 0.326354701 #> 8591  4295.0 0.6652451 0.6652451 8.422967e-03 8.422967e-03 0.326331949 #> 8592  4295.5 0.6652724 0.6652724 8.418460e-03 8.418460e-03 0.326309134 #> 8593  4296.0 0.6652998 0.6652998 8.413961e-03 8.413961e-03 0.326286256 #> 8594  4296.5 0.6653272 0.6653272 8.409471e-03 8.409471e-03 0.326263314 #> 8595  4297.0 0.6653547 0.6653547 8.404989e-03 8.404989e-03 0.326240308 #> 8596  4297.5 0.6653822 0.6653822 8.400515e-03 8.400515e-03 0.326217240 #> 8597  4298.0 0.6654098 0.6654098 8.396049e-03 8.396049e-03 0.326194108 #> 8598  4298.5 0.6654375 0.6654375 8.391591e-03 8.391591e-03 0.326170914 #> 8599  4299.0 0.6654652 0.6654652 8.387141e-03 8.387141e-03 0.326147657 #> 8600  4299.5 0.6654930 0.6654930 8.382700e-03 8.382700e-03 0.326124338 #> 8601  4300.0 0.6655208 0.6655208 8.378267e-03 8.378267e-03 0.326100956 #> 8602  4300.5 0.6655486 0.6655486 8.373842e-03 8.373842e-03 0.326077512 #> 8603  4301.0 0.6655766 0.6655766 8.369425e-03 8.369425e-03 0.326054007 #> 8604  4301.5 0.6656045 0.6656045 8.365016e-03 8.365016e-03 0.326030439 #> 8605  4302.0 0.6656326 0.6656326 8.360616e-03 8.360616e-03 0.326006810 #> 8606  4302.5 0.6656607 0.6656607 8.356223e-03 8.356223e-03 0.325983120 #> 8607  4303.0 0.6656888 0.6656888 8.351839e-03 8.351839e-03 0.325959368 #> 8608  4303.5 0.6657170 0.6657170 8.347463e-03 8.347463e-03 0.325935555 #> 8609  4304.0 0.6657452 0.6657452 8.343095e-03 8.343095e-03 0.325911681 #> 8610  4304.5 0.6657735 0.6657735 8.338735e-03 8.338735e-03 0.325887746 #> 8611  4305.0 0.6658019 0.6658019 8.334384e-03 8.334384e-03 0.325863751 #> 8612  4305.5 0.6658303 0.6658303 8.330041e-03 8.330041e-03 0.325839696 #> 8613  4306.0 0.6658587 0.6658587 8.325706e-03 8.325706e-03 0.325815580 #> 8614  4306.5 0.6658872 0.6658872 8.321379e-03 8.321379e-03 0.325791404 #> 8615  4307.0 0.6659158 0.6659158 8.317060e-03 8.317060e-03 0.325767168 #> 8616  4307.5 0.6659444 0.6659444 8.312749e-03 8.312749e-03 0.325742872 #> 8617  4308.0 0.6659730 0.6659730 8.308447e-03 8.308447e-03 0.325718516 #> 8618  4308.5 0.6660017 0.6660017 8.304153e-03 8.304153e-03 0.325694102 #> 8619  4309.0 0.6660305 0.6660305 8.299867e-03 8.299867e-03 0.325669627 #> 8620  4309.5 0.6660593 0.6660593 8.295589e-03 8.295589e-03 0.325645094 #> 8621  4310.0 0.6660882 0.6660882 8.291319e-03 8.291319e-03 0.325620502 #> 8622  4310.5 0.6661171 0.6661171 8.287058e-03 8.287058e-03 0.325595851 #> 8623  4311.0 0.6661461 0.6661461 8.282804e-03 8.282804e-03 0.325571141 #> 8624  4311.5 0.6661751 0.6661751 8.278559e-03 8.278559e-03 0.325546373 #> 8625  4312.0 0.6662041 0.6662041 8.274322e-03 8.274322e-03 0.325521547 #> 8626  4312.5 0.6662332 0.6662332 8.270094e-03 8.270094e-03 0.325496663 #> 8627  4313.0 0.6662624 0.6662624 8.265873e-03 8.265873e-03 0.325471720 #> 8628  4313.5 0.6662916 0.6662916 8.261661e-03 8.261661e-03 0.325446720 #> 8629  4314.0 0.6663209 0.6663209 8.257457e-03 8.257457e-03 0.325421662 #> 8630  4314.5 0.6663502 0.6663502 8.253260e-03 8.253260e-03 0.325396547 #> 8631  4315.0 0.6663796 0.6663796 8.249073e-03 8.249073e-03 0.325371375 #> 8632  4315.5 0.6664090 0.6664090 8.244893e-03 8.244893e-03 0.325346145 #> 8633  4316.0 0.6664384 0.6664384 8.240722e-03 8.240722e-03 0.325320858 #> 8634  4316.5 0.6664679 0.6664679 8.236558e-03 8.236558e-03 0.325295515 #> 8635  4317.0 0.6664975 0.6664975 8.232403e-03 8.232403e-03 0.325270115 #> 8636  4317.5 0.6665271 0.6665271 8.228256e-03 8.228256e-03 0.325244658 #> 8637  4318.0 0.6665567 0.6665567 8.224117e-03 8.224117e-03 0.325219145 #> 8638  4318.5 0.6665864 0.6665864 8.219987e-03 8.219987e-03 0.325193576 #> 8639  4319.0 0.6666162 0.6666162 8.215864e-03 8.215864e-03 0.325167951 #> 8640  4319.5 0.6666460 0.6666460 8.211750e-03 8.211750e-03 0.325142271 #> 8641  4320.0 0.6666758 0.6666758 8.207644e-03 8.207644e-03 0.325116534 #> 8642  4320.5 0.6667057 0.6667057 8.203546e-03 8.203546e-03 0.325090742 #> 8643  4321.0 0.6667356 0.6667356 8.199457e-03 8.199457e-03 0.325064895 #> 8644  4321.5 0.6667656 0.6667656 8.195375e-03 8.195375e-03 0.325038993 #> 8645  4322.0 0.6667957 0.6667957 8.191302e-03 8.191302e-03 0.325013035 #> 8646  4322.5 0.6668257 0.6668257 8.187237e-03 8.187237e-03 0.324987023 #> 8647  4323.0 0.6668559 0.6668559 8.183180e-03 8.183180e-03 0.324960956 #> 8648  4323.5 0.6668860 0.6668860 8.179131e-03 8.179131e-03 0.324934835 #> 8649  4324.0 0.6669163 0.6669163 8.175090e-03 8.175090e-03 0.324908659 #> 8650  4324.5 0.6669465 0.6669465 8.171058e-03 8.171058e-03 0.324882429 #> 8651  4325.0 0.6669768 0.6669768 8.167034e-03 8.167034e-03 0.324856145 #> 8652  4325.5 0.6670072 0.6670072 8.163018e-03 8.163018e-03 0.324829808 #> 8653  4326.0 0.6670376 0.6670376 8.159010e-03 8.159010e-03 0.324803416 #> 8654  4326.5 0.6670680 0.6670680 8.155010e-03 8.155010e-03 0.324776971 #> 8655  4327.0 0.6670985 0.6670985 8.151018e-03 8.151018e-03 0.324750473 #> 8656  4327.5 0.6671290 0.6671290 8.147035e-03 8.147035e-03 0.324723921 #> 8657  4328.0 0.6671596 0.6671596 8.143060e-03 8.143060e-03 0.324697317 #> 8658  4328.5 0.6671902 0.6671902 8.139092e-03 8.139092e-03 0.324670659 #> 8659  4329.0 0.6672209 0.6672209 8.135134e-03 8.135134e-03 0.324643949 #> 8660  4329.5 0.6672516 0.6672516 8.131183e-03 8.131183e-03 0.324617187 #> 8661  4330.0 0.6672824 0.6672824 8.127240e-03 8.127240e-03 0.324590372 #> 8662  4330.5 0.6673132 0.6673132 8.123306e-03 8.123306e-03 0.324563504 #> 8663  4331.0 0.6673440 0.6673440 8.119379e-03 8.119379e-03 0.324536585 #> 8664  4331.5 0.6673749 0.6673749 8.115461e-03 8.115461e-03 0.324509614 #> 8665  4332.0 0.6674059 0.6674059 8.111551e-03 8.111551e-03 0.324482591 #> 8666  4332.5 0.6674368 0.6674368 8.107650e-03 8.107650e-03 0.324455516 #> 8667  4333.0 0.6674679 0.6674679 8.103756e-03 8.103756e-03 0.324428390 #> 8668  4333.5 0.6674989 0.6674989 8.099870e-03 8.099870e-03 0.324401213 #> 8669  4334.0 0.6675300 0.6675300 8.095993e-03 8.095993e-03 0.324373985 #> 8670  4334.5 0.6675612 0.6675612 8.092124e-03 8.092124e-03 0.324346706 #> 8671  4335.0 0.6675924 0.6675924 8.088263e-03 8.088263e-03 0.324319376 #> 8672  4335.5 0.6676236 0.6676236 8.084410e-03 8.084410e-03 0.324291995 #> 8673  4336.0 0.6676549 0.6676549 8.080565e-03 8.080565e-03 0.324264564 #> 8674  4336.5 0.6676862 0.6676862 8.076729e-03 8.076729e-03 0.324237083 #> 8675  4337.0 0.6677175 0.6677175 8.072900e-03 8.072900e-03 0.324209551 #> 8676  4337.5 0.6677490 0.6677490 8.069080e-03 8.069080e-03 0.324181970 #> 8677  4338.0 0.6677804 0.6677804 8.065268e-03 8.065268e-03 0.324154339 #> 8678  4338.5 0.6678119 0.6678119 8.061464e-03 8.061464e-03 0.324126658 #> 8679  4339.0 0.6678434 0.6678434 8.057668e-03 8.057668e-03 0.324098928 #> 8680  4339.5 0.6678750 0.6678750 8.053880e-03 8.053880e-03 0.324071148 #> 8681  4340.0 0.6679066 0.6679066 8.050101e-03 8.050101e-03 0.324043319 #> 8682  4340.5 0.6679382 0.6679382 8.046329e-03 8.046329e-03 0.324015441 #> 8683  4341.0 0.6679699 0.6679699 8.042566e-03 8.042566e-03 0.323987515 #> 8684  4341.5 0.6680016 0.6680016 8.038811e-03 8.038811e-03 0.323959539 #> 8685  4342.0 0.6680334 0.6680334 8.035064e-03 8.035064e-03 0.323931516 #> 8686  4342.5 0.6680652 0.6680652 8.031325e-03 8.031325e-03 0.323903443 #> 8687  4343.0 0.6680971 0.6680971 8.027594e-03 8.027594e-03 0.323875323 #> 8688  4343.5 0.6681290 0.6681290 8.023871e-03 8.023871e-03 0.323847155 #> 8689  4344.0 0.6681609 0.6681609 8.020157e-03 8.020157e-03 0.323818938 #> 8690  4344.5 0.6681929 0.6681929 8.016450e-03 8.016450e-03 0.323790674 #> 8691  4345.0 0.6682249 0.6682249 8.012752e-03 8.012752e-03 0.323762363 #> 8692  4345.5 0.6682569 0.6682569 8.009062e-03 8.009062e-03 0.323734004 #> 8693  4346.0 0.6682890 0.6682890 8.005380e-03 8.005380e-03 0.323705598 #> 8694  4346.5 0.6683211 0.6683211 8.001706e-03 8.001706e-03 0.323677144 #> 8695  4347.0 0.6683533 0.6683533 7.998040e-03 7.998040e-03 0.323648644 #> 8696  4347.5 0.6683855 0.6683855 7.994382e-03 7.994382e-03 0.323620097 #> 8697  4348.0 0.6684178 0.6684178 7.990733e-03 7.990733e-03 0.323591503 #> 8698  4348.5 0.6684500 0.6684500 7.987091e-03 7.987091e-03 0.323562863 #> 8699  4349.0 0.6684824 0.6684824 7.983458e-03 7.983458e-03 0.323534177 #> 8700  4349.5 0.6685147 0.6685147 7.979833e-03 7.979833e-03 0.323505444 #> 8701  4350.0 0.6685471 0.6685471 7.976215e-03 7.976215e-03 0.323476666 #> 8702  4350.5 0.6685796 0.6685796 7.972606e-03 7.972606e-03 0.323447842 #> 8703  4351.0 0.6686120 0.6686120 7.969005e-03 7.969005e-03 0.323418972 #> 8704  4351.5 0.6686445 0.6686445 7.965412e-03 7.965412e-03 0.323390056 #> 8705  4352.0 0.6686771 0.6686771 7.961828e-03 7.961828e-03 0.323361095 #> 8706  4352.5 0.6687097 0.6687097 7.958251e-03 7.958251e-03 0.323332089 #> 8707  4353.0 0.6687423 0.6687423 7.954682e-03 7.954682e-03 0.323303038 #> 8708  4353.5 0.6687749 0.6687749 7.951122e-03 7.951122e-03 0.323273943 #> 8709  4354.0 0.6688076 0.6688076 7.947569e-03 7.947569e-03 0.323244802 #> 8710  4354.5 0.6688404 0.6688404 7.944025e-03 7.944025e-03 0.323215617 #> 8711  4355.0 0.6688731 0.6688731 7.940489e-03 7.940489e-03 0.323186387 #> 8712  4355.5 0.6689059 0.6689059 7.936961e-03 7.936961e-03 0.323157113 #> 8713  4356.0 0.6689388 0.6689388 7.933440e-03 7.933440e-03 0.323127795 #> 8714  4356.5 0.6689716 0.6689716 7.929928e-03 7.929928e-03 0.323098434 #> 8715  4357.0 0.6690045 0.6690045 7.926424e-03 7.926424e-03 0.323069028 #> 8716  4357.5 0.6690375 0.6690375 7.922928e-03 7.922928e-03 0.323039579 #> 8717  4358.0 0.6690705 0.6690705 7.919441e-03 7.919441e-03 0.323010086 #> 8718  4358.5 0.6691035 0.6691035 7.915961e-03 7.915961e-03 0.322980550 #> 8719  4359.0 0.6691365 0.6691365 7.912489e-03 7.912489e-03 0.322950971 #> 8720  4359.5 0.6691696 0.6691696 7.909026e-03 7.909026e-03 0.322921349 #> 8721  4360.0 0.6692027 0.6692027 7.905570e-03 7.905570e-03 0.322891684 #> 8722  4360.5 0.6692359 0.6692359 7.902122e-03 7.902122e-03 0.322861976 #> 8723  4361.0 0.6692691 0.6692691 7.898683e-03 7.898683e-03 0.322832226 #> 8724  4361.5 0.6693023 0.6693023 7.895252e-03 7.895252e-03 0.322802433 #> 8725  4362.0 0.6693356 0.6693356 7.891828e-03 7.891828e-03 0.322772599 #> 8726  4362.5 0.6693689 0.6693689 7.888413e-03 7.888413e-03 0.322742722 #> 8727  4363.0 0.6694022 0.6694022 7.885006e-03 7.885006e-03 0.322712803 #> 8728  4363.5 0.6694356 0.6694356 7.881606e-03 7.881606e-03 0.322682843 #> 8729  4364.0 0.6694689 0.6694689 7.878215e-03 7.878215e-03 0.322652841 #> 8730  4364.5 0.6695024 0.6695024 7.874832e-03 7.874832e-03 0.322622797 #> 8731  4365.0 0.6695358 0.6695358 7.871457e-03 7.871457e-03 0.322592712 #> 8732  4365.5 0.6695693 0.6695693 7.868090e-03 7.868090e-03 0.322562586 #> 8733  4366.0 0.6696029 0.6696029 7.864731e-03 7.864731e-03 0.322532419 #> 8734  4366.5 0.6696364 0.6696364 7.861380e-03 7.861380e-03 0.322502212 #> 8735  4367.0 0.6696700 0.6696700 7.858037e-03 7.858037e-03 0.322471963 #> 8736  4367.5 0.6697036 0.6697036 7.854702e-03 7.854702e-03 0.322441674 #> 8737  4368.0 0.6697373 0.6697373 7.851375e-03 7.851375e-03 0.322411345 #> 8738  4368.5 0.6697710 0.6697710 7.848056e-03 7.848056e-03 0.322380975 #> 8739  4369.0 0.6698047 0.6698047 7.844745e-03 7.844745e-03 0.322350566 #> 8740  4369.5 0.6698384 0.6698384 7.841442e-03 7.841442e-03 0.322320116 #> 8741  4370.0 0.6698722 0.6698722 7.838147e-03 7.838147e-03 0.322289627 #> 8742  4370.5 0.6699060 0.6699060 7.834860e-03 7.834860e-03 0.322259098 #> 8743  4371.0 0.6699399 0.6699399 7.831581e-03 7.831581e-03 0.322228530 #> 8744  4371.5 0.6699738 0.6699738 7.828310e-03 7.828310e-03 0.322197923 #> 8745  4372.0 0.6700077 0.6700077 7.825047e-03 7.825047e-03 0.322167276 #> 8746  4372.5 0.6700416 0.6700416 7.821792e-03 7.821792e-03 0.322136590 #> 8747  4373.0 0.6700756 0.6700756 7.818545e-03 7.818545e-03 0.322105866 #> 8748  4373.5 0.6701096 0.6701096 7.815306e-03 7.815306e-03 0.322075103 #> 8749  4374.0 0.6701436 0.6701436 7.812075e-03 7.812075e-03 0.322044301 #> 8750  4374.5 0.6701777 0.6701777 7.808852e-03 7.808852e-03 0.322013461 #> 8751  4375.0 0.6702118 0.6702118 7.805637e-03 7.805637e-03 0.321982583 #> 8752  4375.5 0.6702459 0.6702459 7.802430e-03 7.802430e-03 0.321951666 #> 8753  4376.0 0.6702801 0.6702801 7.799231e-03 7.799231e-03 0.321920712 #> 8754  4376.5 0.6703142 0.6703142 7.796040e-03 7.796040e-03 0.321889720 #> 8755  4377.0 0.6703485 0.6703485 7.792857e-03 7.792857e-03 0.321858690 #> 8756  4377.5 0.6703827 0.6703827 7.789682e-03 7.789682e-03 0.321827623 #> 8757  4378.0 0.6704170 0.6704170 7.786515e-03 7.786515e-03 0.321796519 #> 8758  4378.5 0.6704513 0.6704513 7.783355e-03 7.783355e-03 0.321765377 #> 8759  4379.0 0.6704856 0.6704856 7.780204e-03 7.780204e-03 0.321734199 #> 8760  4379.5 0.6705200 0.6705200 7.777061e-03 7.777061e-03 0.321702983 #> 8761  4380.0 0.6705543 0.6705543 7.773925e-03 7.773925e-03 0.321671731 #> 8762  4380.5 0.6705888 0.6705888 7.770798e-03 7.770798e-03 0.321640442 #> 8763  4381.0 0.6706232 0.6706232 7.767678e-03 7.767678e-03 0.321609117 #> 8764  4381.5 0.6706577 0.6706577 7.764567e-03 7.764567e-03 0.321577756 #> 8765  4382.0 0.6706922 0.6706922 7.761463e-03 7.761463e-03 0.321546359 #> 8766  4382.5 0.6707267 0.6707267 7.758368e-03 7.758368e-03 0.321514925 #> 8767  4383.0 0.6707613 0.6707613 7.755280e-03 7.755280e-03 0.321483456 #> 8768  4383.5 0.6707958 0.6707958 7.752200e-03 7.752200e-03 0.321451951 #> 8769  4384.0 0.6708305 0.6708305 7.749128e-03 7.749128e-03 0.321420411 #> 8770  4384.5 0.6708651 0.6708651 7.746064e-03 7.746064e-03 0.321388835 #> 8771  4385.0 0.6708998 0.6708998 7.743008e-03 7.743008e-03 0.321357224 #> 8772  4385.5 0.6709345 0.6709345 7.739960e-03 7.739960e-03 0.321325578 #> 8773  4386.0 0.6709692 0.6709692 7.736920e-03 7.736920e-03 0.321293897 #> 8774  4386.5 0.6710039 0.6710039 7.733888e-03 7.733888e-03 0.321262182 #> 8775  4387.0 0.6710387 0.6710387 7.730863e-03 7.730863e-03 0.321230431 #> 8776  4387.5 0.6710735 0.6710735 7.727847e-03 7.727847e-03 0.321198647 #> 8777  4388.0 0.6711083 0.6711083 7.724838e-03 7.724838e-03 0.321166828 #> 8778  4388.5 0.6711432 0.6711432 7.721837e-03 7.721837e-03 0.321134974 #> 8779  4389.0 0.6711781 0.6711781 7.718845e-03 7.718845e-03 0.321103087 #> 8780  4389.5 0.6712130 0.6712130 7.715860e-03 7.715860e-03 0.321071166 #> 8781  4390.0 0.6712479 0.6712479 7.712883e-03 7.712883e-03 0.321039211 #> 8782  4390.5 0.6712829 0.6712829 7.709914e-03 7.709914e-03 0.321007223 #> 8783  4391.0 0.6713178 0.6713178 7.706952e-03 7.706952e-03 0.320975201 #> 8784  4391.5 0.6713529 0.6713529 7.703999e-03 7.703999e-03 0.320943146 #> 8785  4392.0 0.6713879 0.6713879 7.701053e-03 7.701053e-03 0.320911058 #> 8786  4392.5 0.6714229 0.6714229 7.698116e-03 7.698116e-03 0.320878937 #> 8787  4393.0 0.6714580 0.6714580 7.695186e-03 7.695186e-03 0.320846783 #> 8788  4393.5 0.6714931 0.6714931 7.692264e-03 7.692264e-03 0.320814597 #> 8789  4394.0 0.6715283 0.6715283 7.689350e-03 7.689350e-03 0.320782377 #> 8790  4394.5 0.6715634 0.6715634 7.686444e-03 7.686444e-03 0.320750126 #> 8791  4395.0 0.6715986 0.6715986 7.683546e-03 7.683546e-03 0.320717842 #> 8792  4395.5 0.6716338 0.6716338 7.680655e-03 7.680655e-03 0.320685526 #> 8793  4396.0 0.6716690 0.6716690 7.677772e-03 7.677772e-03 0.320653179 #> 8794  4396.5 0.6717043 0.6717043 7.674898e-03 7.674898e-03 0.320620799 #> 8795  4397.0 0.6717396 0.6717396 7.672031e-03 7.672031e-03 0.320588388 #> 8796  4397.5 0.6717749 0.6717749 7.669172e-03 7.669172e-03 0.320555945 #> 8797  4398.0 0.6718102 0.6718102 7.666320e-03 7.666320e-03 0.320523471 #> 8798  4398.5 0.6718456 0.6718456 7.663477e-03 7.663477e-03 0.320490966 #> 8799  4399.0 0.6718809 0.6718809 7.660641e-03 7.660641e-03 0.320458429 #> 8800  4399.5 0.6719163 0.6719163 7.657814e-03 7.657814e-03 0.320425862 #> 8801  4400.0 0.6719517 0.6719517 7.654994e-03 7.654994e-03 0.320393264 #> 8802  4400.5 0.6719872 0.6719872 7.652181e-03 7.652181e-03 0.320360635 #> 8803  4401.0 0.6720226 0.6720226 7.649377e-03 7.649377e-03 0.320327976 #> 8804  4401.5 0.6720581 0.6720581 7.646581e-03 7.646581e-03 0.320295286 #> 8805  4402.0 0.6720936 0.6720936 7.643792e-03 7.643792e-03 0.320262567 #> 8806  4402.5 0.6721292 0.6721292 7.641011e-03 7.641011e-03 0.320229817 #> 8807  4403.0 0.6721647 0.6721647 7.638238e-03 7.638238e-03 0.320197037 #> 8808  4403.5 0.6722003 0.6722003 7.635472e-03 7.635472e-03 0.320164228 #> 8809  4404.0 0.6722359 0.6722359 7.632715e-03 7.632715e-03 0.320131389 #> 8810  4404.5 0.6722715 0.6722715 7.629965e-03 7.629965e-03 0.320098520 #> 8811  4405.0 0.6723072 0.6723072 7.627223e-03 7.627223e-03 0.320065623 #> 8812  4405.5 0.6723428 0.6723428 7.624489e-03 7.624489e-03 0.320032696 #> 8813  4406.0 0.6723785 0.6723785 7.621763e-03 7.621763e-03 0.319999740 #> 8814  4406.5 0.6724142 0.6724142 7.619044e-03 7.619044e-03 0.319966755 #> 8815  4407.0 0.6724499 0.6724499 7.616333e-03 7.616333e-03 0.319933741 #> 8816  4407.5 0.6724857 0.6724857 7.613630e-03 7.613630e-03 0.319900699 #> 8817  4408.0 0.6725214 0.6725214 7.610935e-03 7.610935e-03 0.319867628 #> 8818  4408.5 0.6725572 0.6725572 7.608247e-03 7.608247e-03 0.319834529 #> 8819  4409.0 0.6725930 0.6725930 7.605567e-03 7.605567e-03 0.319801402 #> 8820  4409.5 0.6726289 0.6726289 7.602895e-03 7.602895e-03 0.319768247 #> 8821  4410.0 0.6726647 0.6726647 7.600231e-03 7.600231e-03 0.319735063 #> 8822  4410.5 0.6727006 0.6727006 7.597574e-03 7.597574e-03 0.319701853 #> 8823  4411.0 0.6727365 0.6727365 7.594925e-03 7.594925e-03 0.319668614 #> 8824  4411.5 0.6727724 0.6727724 7.592284e-03 7.592284e-03 0.319635348 #> 8825  4412.0 0.6728083 0.6728083 7.589651e-03 7.589651e-03 0.319602055 #> 8826  4412.5 0.6728442 0.6728442 7.587025e-03 7.587025e-03 0.319568735 #> 8827  4413.0 0.6728802 0.6728802 7.584407e-03 7.584407e-03 0.319535387 #> 8828  4413.5 0.6729162 0.6729162 7.581797e-03 7.581797e-03 0.319502013 #> 8829  4414.0 0.6729522 0.6729522 7.579195e-03 7.579195e-03 0.319468612 #> 8830  4414.5 0.6729882 0.6729882 7.576600e-03 7.576600e-03 0.319435184 #> 8831  4415.0 0.6730243 0.6730243 7.574013e-03 7.574013e-03 0.319401730 #> 8832  4415.5 0.6730603 0.6730603 7.571433e-03 7.571433e-03 0.319368249 #> 8833  4416.0 0.6730964 0.6730964 7.568862e-03 7.568862e-03 0.319334743 #> 8834  4416.5 0.6731325 0.6731325 7.566298e-03 7.566298e-03 0.319301210 #> 8835  4417.0 0.6731686 0.6731686 7.563742e-03 7.563742e-03 0.319267652 #> 8836  4417.5 0.6732047 0.6732047 7.561193e-03 7.561193e-03 0.319234067 #> 8837  4418.0 0.6732409 0.6732409 7.558652e-03 7.558652e-03 0.319200457 #> 8838  4418.5 0.6732771 0.6732771 7.556119e-03 7.556119e-03 0.319166822 #> 8839  4419.0 0.6733132 0.6733132 7.553594e-03 7.553594e-03 0.319133161 #> 8840  4419.5 0.6733494 0.6733494 7.551076e-03 7.551076e-03 0.319099475 #> 8841  4420.0 0.6733857 0.6733857 7.548566e-03 7.548566e-03 0.319065764 #> 8842  4420.5 0.6734219 0.6734219 7.546063e-03 7.546063e-03 0.319032028 #> 8843  4421.0 0.6734582 0.6734582 7.543568e-03 7.543568e-03 0.318998268 #> 8844  4421.5 0.6734944 0.6734944 7.541081e-03 7.541081e-03 0.318964482 #> 8845  4422.0 0.6735307 0.6735307 7.538602e-03 7.538602e-03 0.318930673 #> 8846  4422.5 0.6735670 0.6735670 7.536130e-03 7.536130e-03 0.318896839 #> 8847  4423.0 0.6736034 0.6736034 7.533666e-03 7.533666e-03 0.318862980 #> 8848  4423.5 0.6736397 0.6736397 7.531209e-03 7.531209e-03 0.318829098 #> 8849  4424.0 0.6736760 0.6736760 7.528760e-03 7.528760e-03 0.318795191 #> 8850  4424.5 0.6737124 0.6737124 7.526319e-03 7.526319e-03 0.318761261 #> 8851  4425.0 0.6737488 0.6737488 7.523885e-03 7.523885e-03 0.318727308 #> 8852  4425.5 0.6737852 0.6737852 7.521459e-03 7.521459e-03 0.318693330 #> 8853  4426.0 0.6738216 0.6738216 7.519041e-03 7.519041e-03 0.318659330 #> 8854  4426.5 0.6738581 0.6738581 7.516630e-03 7.516630e-03 0.318625306 #> 8855  4427.0 0.6738945 0.6738945 7.514227e-03 7.514227e-03 0.318591259 #> 8856  4427.5 0.6739310 0.6739310 7.511832e-03 7.511832e-03 0.318557189 #> 8857  4428.0 0.6739675 0.6739675 7.509444e-03 7.509444e-03 0.318523096 #> 8858  4428.5 0.6740040 0.6740040 7.507063e-03 7.507063e-03 0.318488980 #> 8859  4429.0 0.6740405 0.6740405 7.504691e-03 7.504691e-03 0.318454842 #> 8860  4429.5 0.6740770 0.6740770 7.502326e-03 7.502326e-03 0.318420682 #> 8861  4430.0 0.6741135 0.6741135 7.499968e-03 7.499968e-03 0.318386499 #> 8862  4430.5 0.6741501 0.6741501 7.497618e-03 7.497618e-03 0.318352294 #> 8863  4431.0 0.6741867 0.6741867 7.495276e-03 7.495276e-03 0.318318068 #> 8864  4431.5 0.6742232 0.6742232 7.492941e-03 7.492941e-03 0.318283819 #> 8865  4432.0 0.6742598 0.6742598 7.490614e-03 7.490614e-03 0.318249549 #> 8866  4432.5 0.6742964 0.6742964 7.488294e-03 7.488294e-03 0.318215257 #> 8867  4433.0 0.6743331 0.6743331 7.485982e-03 7.485982e-03 0.318180943 #> 8868  4433.5 0.6743697 0.6743697 7.483678e-03 7.483678e-03 0.318146608 #> 8869  4434.0 0.6744064 0.6744064 7.481381e-03 7.481381e-03 0.318112253 #> 8870  4434.5 0.6744430 0.6744430 7.479092e-03 7.479092e-03 0.318077876 #> 8871  4435.0 0.6744797 0.6744797 7.476810e-03 7.476810e-03 0.318043478 #> 8872  4435.5 0.6745164 0.6745164 7.474536e-03 7.474536e-03 0.318009059 #> 8873  4436.0 0.6745531 0.6745531 7.472269e-03 7.472269e-03 0.317974620 #> 8874  4436.5 0.6745898 0.6745898 7.470010e-03 7.470010e-03 0.317940160 #> 8875  4437.0 0.6746266 0.6746266 7.467758e-03 7.467758e-03 0.317905680 #> 8876  4437.5 0.6746633 0.6746633 7.465514e-03 7.465514e-03 0.317871180 #> 8877  4438.0 0.6747001 0.6747001 7.463278e-03 7.463278e-03 0.317836659 #> 8878  4438.5 0.6747368 0.6747368 7.461049e-03 7.461049e-03 0.317802119 #> 8879  4439.0 0.6747736 0.6747736 7.458827e-03 7.458827e-03 0.317767559 #> 8880  4439.5 0.6748104 0.6748104 7.456613e-03 7.456613e-03 0.317732979 #> 8881  4440.0 0.6748472 0.6748472 7.454407e-03 7.454407e-03 0.317698380 #> 8882  4440.5 0.6748840 0.6748840 7.452208e-03 7.452208e-03 0.317663761 #> 8883  4441.0 0.6749209 0.6749209 7.450017e-03 7.450017e-03 0.317629123 #> 8884  4441.5 0.6749577 0.6749577 7.447833e-03 7.447833e-03 0.317594466 #> 8885  4442.0 0.6749946 0.6749946 7.445656e-03 7.445656e-03 0.317559790 #> 8886  4442.5 0.6750314 0.6750314 7.443487e-03 7.443487e-03 0.317525095 #> 8887  4443.0 0.6750683 0.6750683 7.441326e-03 7.441326e-03 0.317490381 #> 8888  4443.5 0.6751052 0.6751052 7.439172e-03 7.439172e-03 0.317455649 #> 8889  4444.0 0.6751421 0.6751421 7.437026e-03 7.437026e-03 0.317420898 #> 8890  4444.5 0.6751790 0.6751790 7.434887e-03 7.434887e-03 0.317386129 #> 8891  4445.0 0.6752159 0.6752159 7.432755e-03 7.432755e-03 0.317351341 #> 8892  4445.5 0.6752528 0.6752528 7.430631e-03 7.430631e-03 0.317316536 #> 8893  4446.0 0.6752898 0.6752898 7.428514e-03 7.428514e-03 0.317281712 #> 8894  4446.5 0.6753267 0.6753267 7.426405e-03 7.426405e-03 0.317246871 #> 8895  4447.0 0.6753637 0.6753637 7.424304e-03 7.424304e-03 0.317212012 #> 8896  4447.5 0.6754007 0.6754007 7.422210e-03 7.422210e-03 0.317177136 #> 8897  4448.0 0.6754376 0.6754376 7.420123e-03 7.420123e-03 0.317142242 #> 8898  4448.5 0.6754746 0.6754746 7.418043e-03 7.418043e-03 0.317107331 #> 8899  4449.0 0.6755116 0.6755116 7.415972e-03 7.415972e-03 0.317072403 #> 8900  4449.5 0.6755486 0.6755486 7.413907e-03 7.413907e-03 0.317037458 #> 8901  4450.0 0.6755857 0.6755857 7.411850e-03 7.411850e-03 0.317002496 #> 8902  4450.5 0.6756227 0.6756227 7.409801e-03 7.409801e-03 0.316967517 #> 8903  4451.0 0.6756597 0.6756597 7.407759e-03 7.407759e-03 0.316932522 #> 8904  4451.5 0.6756968 0.6756968 7.405724e-03 7.405724e-03 0.316897510 #> 8905  4452.0 0.6757338 0.6757338 7.403697e-03 7.403697e-03 0.316862482 #> 8906  4452.5 0.6757709 0.6757709 7.401677e-03 7.401677e-03 0.316827437 #> 8907  4453.0 0.6758080 0.6758080 7.399664e-03 7.399664e-03 0.316792377 #> 8908  4453.5 0.6758450 0.6758450 7.397659e-03 7.397659e-03 0.316757301 #> 8909  4454.0 0.6758821 0.6758821 7.395661e-03 7.395661e-03 0.316722208 #> 8910  4454.5 0.6759192 0.6759192 7.393671e-03 7.393671e-03 0.316687101 #> 8911  4455.0 0.6759563 0.6759563 7.391688e-03 7.391688e-03 0.316651977 #> 8912  4455.5 0.6759934 0.6759934 7.389713e-03 7.389713e-03 0.316616838 #> 8913  4456.0 0.6760306 0.6760306 7.387745e-03 7.387745e-03 0.316581684 #> 8914  4456.5 0.6760677 0.6760677 7.385784e-03 7.385784e-03 0.316546515 #> 8915  4457.0 0.6761048 0.6761048 7.383831e-03 7.383831e-03 0.316511330 #> 8916  4457.5 0.6761420 0.6761420 7.381885e-03 7.381885e-03 0.316476131 #> 8917  4458.0 0.6761791 0.6761791 7.379946e-03 7.379946e-03 0.316440917 #> 8918  4458.5 0.6762163 0.6762163 7.378015e-03 7.378015e-03 0.316405688 #> 8919  4459.0 0.6762535 0.6762535 7.376091e-03 7.376091e-03 0.316370445 #> 8920  4459.5 0.6762906 0.6762906 7.374174e-03 7.374174e-03 0.316335188 #> 8921  4460.0 0.6763278 0.6763278 7.372265e-03 7.372265e-03 0.316299916 #> 8922  4460.5 0.6763650 0.6763650 7.370363e-03 7.370363e-03 0.316264630 #> 8923  4461.0 0.6764022 0.6764022 7.368469e-03 7.368469e-03 0.316229330 #> 8924  4461.5 0.6764394 0.6764394 7.366582e-03 7.366582e-03 0.316194016 #> 8925  4462.0 0.6764766 0.6764766 7.364702e-03 7.364702e-03 0.316158689 #> 8926  4462.5 0.6765138 0.6765138 7.362829e-03 7.362829e-03 0.316123348 #> 8927  4463.0 0.6765510 0.6765510 7.360964e-03 7.360964e-03 0.316087993 #> 8928  4463.5 0.6765883 0.6765883 7.359106e-03 7.359106e-03 0.316052625 #> 8929  4464.0 0.6766255 0.6766255 7.357256e-03 7.357256e-03 0.316017244 #> 8930  4464.5 0.6766627 0.6766627 7.355412e-03 7.355412e-03 0.315981850 #> 8931  4465.0 0.6767000 0.6767000 7.353577e-03 7.353577e-03 0.315946443 #> 8932  4465.5 0.6767372 0.6767372 7.351748e-03 7.351748e-03 0.315911023 #> 8933  4466.0 0.6767745 0.6767745 7.349927e-03 7.349927e-03 0.315875590 #> 8934  4466.5 0.6768117 0.6768117 7.348113e-03 7.348113e-03 0.315840145 #> 8935  4467.0 0.6768490 0.6768490 7.346306e-03 7.346306e-03 0.315804687 #> 8936  4467.5 0.6768863 0.6768863 7.344506e-03 7.344506e-03 0.315769217 #> 8937  4468.0 0.6769236 0.6769236 7.342714e-03 7.342714e-03 0.315733735 #> 8938  4468.5 0.6769608 0.6769608 7.340929e-03 7.340929e-03 0.315698240 #> 8939  4469.0 0.6769981 0.6769981 7.339152e-03 7.339152e-03 0.315662734 #> 8940  4469.5 0.6770354 0.6770354 7.337381e-03 7.337381e-03 0.315627216 #> 8941  4470.0 0.6770727 0.6770727 7.335618e-03 7.335618e-03 0.315591686 #> 8942  4470.5 0.6771100 0.6771100 7.333863e-03 7.333863e-03 0.315556145 #> 8943  4471.0 0.6771473 0.6771473 7.332114e-03 7.332114e-03 0.315520592 #> 8944  4471.5 0.6771846 0.6771846 7.330373e-03 7.330373e-03 0.315485028 #> 8945  4472.0 0.6772219 0.6772219 7.328639e-03 7.328639e-03 0.315449453 #> 8946  4472.5 0.6772592 0.6772592 7.326912e-03 7.326912e-03 0.315413867 #> 8947  4473.0 0.6772965 0.6772965 7.325192e-03 7.325192e-03 0.315378270 #> 8948  4473.5 0.6773339 0.6773339 7.323480e-03 7.323480e-03 0.315342662 #> 8949  4474.0 0.6773712 0.6773712 7.321775e-03 7.321775e-03 0.315307043 #> 8950  4474.5 0.6774085 0.6774085 7.320077e-03 7.320077e-03 0.315271414 #> 8951  4475.0 0.6774458 0.6774458 7.318386e-03 7.318386e-03 0.315235774 #> 8952  4475.5 0.6774832 0.6774832 7.316703e-03 7.316703e-03 0.315200124 #> 8953  4476.0 0.6775205 0.6775205 7.315027e-03 7.315027e-03 0.315164464 #> 8954  4476.5 0.6775578 0.6775578 7.313358e-03 7.313358e-03 0.315128794 #> 8955  4477.0 0.6775952 0.6775952 7.311696e-03 7.311696e-03 0.315093114 #> 8956  4477.5 0.6776325 0.6776325 7.310041e-03 7.310041e-03 0.315057424 #> 8957  4478.0 0.6776699 0.6776699 7.308394e-03 7.308394e-03 0.315021725 #> 8958  4478.5 0.6777072 0.6777072 7.306754e-03 7.306754e-03 0.314986016 #> 8959  4479.0 0.6777446 0.6777446 7.305121e-03 7.305121e-03 0.314950297 #> 8960  4479.5 0.6777819 0.6777819 7.303495e-03 7.303495e-03 0.314914570 #> 8961  4480.0 0.6778193 0.6778193 7.301876e-03 7.301876e-03 0.314878833 #> 8962  4480.5 0.6778566 0.6778566 7.300265e-03 7.300265e-03 0.314843087 #> 8963  4481.0 0.6778940 0.6778940 7.298660e-03 7.298660e-03 0.314807332 #> 8964  4481.5 0.6779314 0.6779314 7.297063e-03 7.297063e-03 0.314771568 #> 8965  4482.0 0.6779687 0.6779687 7.295473e-03 7.295473e-03 0.314735796 #> 8966  4482.5 0.6780061 0.6780061 7.293890e-03 7.293890e-03 0.314700015 #> 8967  4483.0 0.6780435 0.6780435 7.292315e-03 7.292315e-03 0.314664225 #> 8968  4483.5 0.6780808 0.6780808 7.290746e-03 7.290746e-03 0.314628428 #> 8969  4484.0 0.6781182 0.6781182 7.289185e-03 7.289185e-03 0.314592622 #> 8970  4484.5 0.6781556 0.6781556 7.287631e-03 7.287631e-03 0.314556808 #> 8971  4485.0 0.6781929 0.6781929 7.286084e-03 7.286084e-03 0.314520986 #> 8972  4485.5 0.6782303 0.6782303 7.284544e-03 7.284544e-03 0.314485156 #> 8973  4486.0 0.6782677 0.6782677 7.283011e-03 7.283011e-03 0.314449319 #> 8974  4486.5 0.6783050 0.6783050 7.281485e-03 7.281485e-03 0.314413474 #> 8975  4487.0 0.6783424 0.6783424 7.279967e-03 7.279967e-03 0.314377622 #> 8976  4487.5 0.6783798 0.6783798 7.278455e-03 7.278455e-03 0.314341762 #> 8977  4488.0 0.6784172 0.6784172 7.276951e-03 7.276951e-03 0.314305895 #> 8978  4488.5 0.6784545 0.6784545 7.275454e-03 7.275454e-03 0.314270021 #> 8979  4489.0 0.6784919 0.6784919 7.273963e-03 7.273963e-03 0.314234140 #> 8980  4489.5 0.6785293 0.6785293 7.272480e-03 7.272480e-03 0.314198253 #> 8981  4490.0 0.6785666 0.6785666 7.271005e-03 7.271005e-03 0.314162358 #> 8982  4490.5 0.6786040 0.6786040 7.269536e-03 7.269536e-03 0.314126457 #> 8983  4491.0 0.6786414 0.6786414 7.268074e-03 7.268074e-03 0.314090550 #> 8984  4491.5 0.6786787 0.6786787 7.266619e-03 7.266619e-03 0.314054636 #> 8985  4492.0 0.6787161 0.6787161 7.265172e-03 7.265172e-03 0.314018716 #> 8986  4492.5 0.6787535 0.6787535 7.263731e-03 7.263731e-03 0.313982789 #> 8987  4493.0 0.6787908 0.6787908 7.262298e-03 7.262298e-03 0.313946857 #> 8988  4493.5 0.6788282 0.6788282 7.260871e-03 7.260871e-03 0.313910919 #> 8989  4494.0 0.6788656 0.6788656 7.259452e-03 7.259452e-03 0.313874975 #> 8990  4494.5 0.6789029 0.6789029 7.258040e-03 7.258040e-03 0.313839026 #> 8991  4495.0 0.6789403 0.6789403 7.256635e-03 7.256635e-03 0.313803071 #> 8992  4495.5 0.6789777 0.6789777 7.255236e-03 7.255236e-03 0.313767111 #> 8993  4496.0 0.6790150 0.6790150 7.253845e-03 7.253845e-03 0.313731145 #> 8994  4496.5 0.6790524 0.6790524 7.252461e-03 7.252461e-03 0.313695175 #> 8995  4497.0 0.6790897 0.6790897 7.251084e-03 7.251084e-03 0.313659199 #> 8996  4497.5 0.6791271 0.6791271 7.249714e-03 7.249714e-03 0.313623218 #> 8997  4498.0 0.6791644 0.6791644 7.248351e-03 7.248351e-03 0.313587233 #> 8998  4498.5 0.6792018 0.6792018 7.246995e-03 7.246995e-03 0.313551243 #> 8999  4499.0 0.6792391 0.6792391 7.245646e-03 7.245646e-03 0.313515248 #> 9000  4499.5 0.6792764 0.6792764 7.244304e-03 7.244304e-03 0.313479249 #> 9001  4500.0 0.6793138 0.6793138 7.242969e-03 7.242969e-03 0.313443246 #> 9002  4500.5 0.6793511 0.6793511 7.241641e-03 7.241641e-03 0.313407238 #> 9003  4501.0 0.6793885 0.6793885 7.240321e-03 7.240321e-03 0.313371227 #> 9004  4501.5 0.6794258 0.6794258 7.239007e-03 7.239007e-03 0.313335211 #> 9005  4502.0 0.6794631 0.6794631 7.237700e-03 7.237700e-03 0.313299192 #> 9006  4502.5 0.6795004 0.6795004 7.236400e-03 7.236400e-03 0.313263168 #> 9007  4503.0 0.6795378 0.6795378 7.235107e-03 7.235107e-03 0.313227142 #> 9008  4503.5 0.6795751 0.6795751 7.233821e-03 7.233821e-03 0.313191112 #> 9009  4504.0 0.6796124 0.6796124 7.232542e-03 7.232542e-03 0.313155078 #> 9010  4504.5 0.6796497 0.6796497 7.231270e-03 7.231270e-03 0.313119041 #> 9011  4505.0 0.6796870 0.6796870 7.230005e-03 7.230005e-03 0.313083001 #> 9012  4505.5 0.6797243 0.6797243 7.228747e-03 7.228747e-03 0.313046959 #> 9013  4506.0 0.6797616 0.6797616 7.227496e-03 7.227496e-03 0.313010913 #> 9014  4506.5 0.6797989 0.6797989 7.226251e-03 7.226251e-03 0.312974864 #> 9015  4507.0 0.6798362 0.6798362 7.225014e-03 7.225014e-03 0.312938813 #> 9016  4507.5 0.6798735 0.6798735 7.223784e-03 7.223784e-03 0.312902759 #> 9017  4508.0 0.6799107 0.6799107 7.222560e-03 7.222560e-03 0.312866703 #> 9018  4508.5 0.6799480 0.6799480 7.221344e-03 7.221344e-03 0.312830645 #> 9019  4509.0 0.6799853 0.6799853 7.220135e-03 7.220135e-03 0.312794584 #> 9020  4509.5 0.6800225 0.6800225 7.218932e-03 7.218932e-03 0.312758521 #> 9021  4510.0 0.6800598 0.6800598 7.217736e-03 7.217736e-03 0.312722456 #> 9022  4510.5 0.6800971 0.6800971 7.216548e-03 7.216548e-03 0.312686390 #> 9023  4511.0 0.6801343 0.6801343 7.215366e-03 7.215366e-03 0.312650322 #> 9024  4511.5 0.6801716 0.6801716 7.214191e-03 7.214191e-03 0.312614252 #> 9025  4512.0 0.6802088 0.6802088 7.213023e-03 7.213023e-03 0.312578181 #> 9026  4512.5 0.6802460 0.6802460 7.211862e-03 7.211862e-03 0.312542108 #> 9027  4513.0 0.6802833 0.6802833 7.210708e-03 7.210708e-03 0.312506034 #> 9028  4513.5 0.6803205 0.6803205 7.209561e-03 7.209561e-03 0.312469959 #> 9029  4514.0 0.6803577 0.6803577 7.208420e-03 7.208420e-03 0.312433883 #> 9030  4514.5 0.6803949 0.6803949 7.207287e-03 7.207287e-03 0.312397806 #> 9031  4515.0 0.6804321 0.6804321 7.206160e-03 7.206160e-03 0.312361728 #> 9032  4515.5 0.6804693 0.6804693 7.205040e-03 7.205040e-03 0.312325649 #> 9033  4516.0 0.6805065 0.6805065 7.203927e-03 7.203927e-03 0.312289570 #> 9034  4516.5 0.6805437 0.6805437 7.202821e-03 7.202821e-03 0.312253491 #> 9035  4517.0 0.6805809 0.6805809 7.201722e-03 7.201722e-03 0.312217411 #> 9036  4517.5 0.6806180 0.6806180 7.200630e-03 7.200630e-03 0.312181331 #> 9037  4518.0 0.6806552 0.6806552 7.199545e-03 7.199545e-03 0.312145250 #> 9038  4518.5 0.6806924 0.6806924 7.198466e-03 7.198466e-03 0.312109170 #> 9039  4519.0 0.6807295 0.6807295 7.197394e-03 7.197394e-03 0.312073090 #> 9040  4519.5 0.6807667 0.6807667 7.196329e-03 7.196329e-03 0.312037010 #> 9041  4520.0 0.6808038 0.6808038 7.195271e-03 7.195271e-03 0.312000931 #> 9042  4520.5 0.6808409 0.6808409 7.194220e-03 7.194220e-03 0.311964852 #> 9043  4521.0 0.6808781 0.6808781 7.193176e-03 7.193176e-03 0.311928773 #> 9044  4521.5 0.6809152 0.6809152 7.192138e-03 7.192138e-03 0.311892696 #> 9045  4522.0 0.6809523 0.6809523 7.191108e-03 7.191108e-03 0.311856619 #> 9046  4522.5 0.6809894 0.6809894 7.190084e-03 7.190084e-03 0.311820543 #> 9047  4523.0 0.6810265 0.6810265 7.189066e-03 7.189066e-03 0.311784468 #> 9048  4523.5 0.6810636 0.6810636 7.188056e-03 7.188056e-03 0.311748394 #> 9049  4524.0 0.6811006 0.6811006 7.187053e-03 7.187053e-03 0.311712321 #> 9050  4524.5 0.6811377 0.6811377 7.186056e-03 7.186056e-03 0.311676250 #> 9051  4525.0 0.6811748 0.6811748 7.185066e-03 7.185066e-03 0.311640180 #> 9052  4525.5 0.6812118 0.6812118 7.184083e-03 7.184083e-03 0.311604112 #> 9053  4526.0 0.6812488 0.6812488 7.183107e-03 7.183107e-03 0.311568045 #> 9054  4526.5 0.6812859 0.6812859 7.182137e-03 7.182137e-03 0.311531981 #> 9055  4527.0 0.6813229 0.6813229 7.181174e-03 7.181174e-03 0.311495918 #> 9056  4527.5 0.6813599 0.6813599 7.180218e-03 7.180218e-03 0.311459857 #> 9057  4528.0 0.6813969 0.6813969 7.179269e-03 7.179269e-03 0.311423799 #> 9058  4528.5 0.6814339 0.6814339 7.178327e-03 7.178327e-03 0.311387743 #> 9059  4529.0 0.6814709 0.6814709 7.177391e-03 7.177391e-03 0.311351689 #> 9060  4529.5 0.6815079 0.6815079 7.176462e-03 7.176462e-03 0.311315637 #> 9061  4530.0 0.6815449 0.6815449 7.175540e-03 7.175540e-03 0.311279589 #> 9062  4530.5 0.6815818 0.6815818 7.174625e-03 7.174625e-03 0.311243542 #> 9063  4531.0 0.6816188 0.6816188 7.173716e-03 7.173716e-03 0.311207499 #> 9064  4531.5 0.6816557 0.6816557 7.172814e-03 7.172814e-03 0.311171459 #> 9065  4532.0 0.6816927 0.6816927 7.171919e-03 7.171919e-03 0.311135422 #> 9066  4532.5 0.6817296 0.6817296 7.171030e-03 7.171030e-03 0.311099387 #> 9067  4533.0 0.6817665 0.6817665 7.170149e-03 7.170149e-03 0.311063357 #> 9068  4533.5 0.6818034 0.6818034 7.169274e-03 7.169274e-03 0.311027329 #> 9069  4534.0 0.6818403 0.6818403 7.168405e-03 7.168405e-03 0.310991305 #> 9070  4534.5 0.6818772 0.6818772 7.167544e-03 7.167544e-03 0.310955285 #> 9071  4535.0 0.6819140 0.6819140 7.166689e-03 7.166689e-03 0.310919268 #> 9072  4535.5 0.6819509 0.6819509 7.165841e-03 7.165841e-03 0.310883255 #> 9073  4536.0 0.6819878 0.6819878 7.164999e-03 7.164999e-03 0.310847246 #> 9074  4536.5 0.6820246 0.6820246 7.164165e-03 7.164165e-03 0.310811241 #> 9075  4537.0 0.6820614 0.6820614 7.163336e-03 7.163336e-03 0.310775240 #> 9076  4537.5 0.6820982 0.6820982 7.162515e-03 7.162515e-03 0.310739244 #> 9077  4538.0 0.6821350 0.6821350 7.161700e-03 7.161700e-03 0.310703252 #> 9078  4538.5 0.6821718 0.6821718 7.160892e-03 7.160892e-03 0.310667264 #> 9079  4539.0 0.6822086 0.6822086 7.160091e-03 7.160091e-03 0.310631281 #> 9080  4539.5 0.6822454 0.6822454 7.159296e-03 7.159296e-03 0.310595302 #> 9081  4540.0 0.6822822 0.6822822 7.158508e-03 7.158508e-03 0.310559329 #> 9082  4540.5 0.6823189 0.6823189 7.157727e-03 7.157727e-03 0.310523360 #> 9083  4541.0 0.6823557 0.6823557 7.156952e-03 7.156952e-03 0.310487396 #> 9084  4541.5 0.6823924 0.6823924 7.156184e-03 7.156184e-03 0.310451438 #> 9085  4542.0 0.6824291 0.6824291 7.155423e-03 7.155423e-03 0.310415484 #> 9086  4542.5 0.6824658 0.6824658 7.154668e-03 7.154668e-03 0.310379536 #> 9087  4543.0 0.6825025 0.6825025 7.153920e-03 7.153920e-03 0.310343594 #> 9088  4543.5 0.6825392 0.6825392 7.153179e-03 7.153179e-03 0.310307657 #> 9089  4544.0 0.6825758 0.6825758 7.152444e-03 7.152444e-03 0.310271726 #> 9090  4544.5 0.6826125 0.6826125 7.151716e-03 7.151716e-03 0.310235800 #> 9091  4545.0 0.6826491 0.6826491 7.150994e-03 7.150994e-03 0.310199880 #> 9092  4545.5 0.6826858 0.6826858 7.150279e-03 7.150279e-03 0.310163967 #> 9093  4546.0 0.6827224 0.6827224 7.149571e-03 7.149571e-03 0.310128059 #> 9094  4546.5 0.6827590 0.6827590 7.148869e-03 7.148869e-03 0.310092158 #> 9095  4547.0 0.6827956 0.6827956 7.148174e-03 7.148174e-03 0.310056263 #> 9096  4547.5 0.6828321 0.6828321 7.147486e-03 7.147486e-03 0.310020374 #> 9097  4548.0 0.6828687 0.6828687 7.146804e-03 7.146804e-03 0.309984492 #> 9098  4548.5 0.6829053 0.6829053 7.146129e-03 7.146129e-03 0.309948616 #> 9099  4549.0 0.6829418 0.6829418 7.145460e-03 7.145460e-03 0.309912747 #> 9100  4549.5 0.6829783 0.6829783 7.144798e-03 7.144798e-03 0.309876886 #> 9101  4550.0 0.6830148 0.6830148 7.144142e-03 7.144142e-03 0.309841031 #> 9102  4550.5 0.6830513 0.6830513 7.143493e-03 7.143493e-03 0.309805183 #> 9103  4551.0 0.6830878 0.6830878 7.142851e-03 7.142851e-03 0.309769342 #> 9104  4551.5 0.6831243 0.6831243 7.142215e-03 7.142215e-03 0.309733508 #> 9105  4552.0 0.6831607 0.6831607 7.141586e-03 7.141586e-03 0.309697682 #> 9106  4552.5 0.6831972 0.6831972 7.140963e-03 7.140963e-03 0.309661864 #> 9107  4553.0 0.6832336 0.6832336 7.140347e-03 7.140347e-03 0.309626052 #> 9108  4553.5 0.6832700 0.6832700 7.139738e-03 7.139738e-03 0.309590249 #> 9109  4554.0 0.6833064 0.6833064 7.139135e-03 7.139135e-03 0.309554453 #> 9110  4554.5 0.6833428 0.6833428 7.138538e-03 7.138538e-03 0.309518666 #> 9111  4555.0 0.6833792 0.6833792 7.137948e-03 7.137948e-03 0.309482886 #> 9112  4555.5 0.6834155 0.6834155 7.137365e-03 7.137365e-03 0.309447115 #> 9113  4556.0 0.6834519 0.6834519 7.136788e-03 7.136788e-03 0.309411351 #> 9114  4556.5 0.6834882 0.6834882 7.136218e-03 7.136218e-03 0.309375596 #> 9115  4557.0 0.6835245 0.6835245 7.135654e-03 7.135654e-03 0.309339850 #> 9116  4557.5 0.6835608 0.6835608 7.135097e-03 7.135097e-03 0.309304112 #> 9117  4558.0 0.6835971 0.6835971 7.134546e-03 7.134546e-03 0.309268382 #> 9118  4558.5 0.6836333 0.6836333 7.134002e-03 7.134002e-03 0.309232661 #> 9119  4559.0 0.6836696 0.6836696 7.133464e-03 7.133464e-03 0.309196949 #> 9120  4559.5 0.6837058 0.6837058 7.132933e-03 7.132933e-03 0.309161247 #> 9121  4560.0 0.6837420 0.6837420 7.132408e-03 7.132408e-03 0.309125553 #> 9122  4560.5 0.6837782 0.6837782 7.131890e-03 7.131890e-03 0.309089868 #> 9123  4561.0 0.6838144 0.6838144 7.131378e-03 7.131378e-03 0.309054193 #> 9124  4561.5 0.6838506 0.6838506 7.130873e-03 7.130873e-03 0.309018526 #> 9125  4562.0 0.6838868 0.6838868 7.130374e-03 7.130374e-03 0.308982870 #> 9126  4562.5 0.6839229 0.6839229 7.129881e-03 7.129881e-03 0.308947223 #> 9127  4563.0 0.6839590 0.6839590 7.129396e-03 7.129396e-03 0.308911585 #> 9128  4563.5 0.6839951 0.6839951 7.128916e-03 7.128916e-03 0.308875958 #> 9129  4564.0 0.6840312 0.6840312 7.128443e-03 7.128443e-03 0.308840340 #> 9130  4564.5 0.6840673 0.6840673 7.127977e-03 7.127977e-03 0.308804732 #> 9131  4565.0 0.6841033 0.6841033 7.127517e-03 7.127517e-03 0.308769134 #> 9132  4565.5 0.6841394 0.6841394 7.127063e-03 7.127063e-03 0.308733547 #> 9133  4566.0 0.6841754 0.6841754 7.126616e-03 7.126616e-03 0.308697970 #> 9134  4566.5 0.6842114 0.6842114 7.126176e-03 7.126176e-03 0.308662403 #> 9135  4567.0 0.6842474 0.6842474 7.125742e-03 7.125742e-03 0.308626847 #> 9136  4567.5 0.6842834 0.6842834 7.125314e-03 7.125314e-03 0.308591301 #> 9137  4568.0 0.6843193 0.6843193 7.124892e-03 7.124892e-03 0.308555766 #> 9138  4568.5 0.6843553 0.6843553 7.124478e-03 7.124478e-03 0.308520241 #> 9139  4569.0 0.6843912 0.6843912 7.124069e-03 7.124069e-03 0.308484728 #> 9140  4569.5 0.6844271 0.6844271 7.123667e-03 7.123667e-03 0.308449226 #> 9141  4570.0 0.6844630 0.6844630 7.123271e-03 7.123271e-03 0.308413734 #> 9142  4570.5 0.6844989 0.6844989 7.122882e-03 7.122882e-03 0.308378254 #> 9143  4571.0 0.6845347 0.6845347 7.122499e-03 7.122499e-03 0.308342786 #> 9144  4571.5 0.6845705 0.6845705 7.122123e-03 7.122123e-03 0.308307328 #> 9145  4572.0 0.6846064 0.6846064 7.121753e-03 7.121753e-03 0.308271882 #> 9146  4572.5 0.6846422 0.6846422 7.121389e-03 7.121389e-03 0.308236448 #> 9147  4573.0 0.6846779 0.6846779 7.121032e-03 7.121032e-03 0.308201026 #> 9148  4573.5 0.6847137 0.6847137 7.120681e-03 7.120681e-03 0.308165615 #> 9149  4574.0 0.6847494 0.6847494 7.120337e-03 7.120337e-03 0.308130216 #> 9150  4574.5 0.6847852 0.6847852 7.119999e-03 7.119999e-03 0.308094829 #> 9151  4575.0 0.6848209 0.6848209 7.119667e-03 7.119667e-03 0.308059455 #> 9152  4575.5 0.6848566 0.6848566 7.119342e-03 7.119342e-03 0.308024092 #> 9153  4576.0 0.6848922 0.6848922 7.119023e-03 7.119023e-03 0.307988742 #> 9154  4576.5 0.6849279 0.6849279 7.118710e-03 7.118710e-03 0.307953404 #> 9155  4577.0 0.6849635 0.6849635 7.118404e-03 7.118404e-03 0.307918079 #> 9156  4577.5 0.6849991 0.6849991 7.118104e-03 7.118104e-03 0.307882766 #> 9157  4578.0 0.6850347 0.6850347 7.117810e-03 7.117810e-03 0.307847466 #> 9158  4578.5 0.6850703 0.6850703 7.117523e-03 7.117523e-03 0.307812179 #> 9159  4579.0 0.6851059 0.6851059 7.117242e-03 7.117242e-03 0.307776905 #> 9160  4579.5 0.6851414 0.6851414 7.116968e-03 7.116968e-03 0.307741644 #> 9161  4580.0 0.6851769 0.6851769 7.116699e-03 7.116699e-03 0.307706396 #> 9162  4580.5 0.6852124 0.6852124 7.116437e-03 7.116437e-03 0.307671161 #> 9163  4581.0 0.6852479 0.6852479 7.116182e-03 7.116182e-03 0.307635939 #> 9164  4581.5 0.6852833 0.6852833 7.115933e-03 7.115933e-03 0.307600731 #> 9165  4582.0 0.6853188 0.6853188 7.115690e-03 7.115690e-03 0.307565536 #> 9166  4582.5 0.6853542 0.6853542 7.115453e-03 7.115453e-03 0.307530355 #> 9167  4583.0 0.6853896 0.6853896 7.115223e-03 7.115223e-03 0.307495188 #> 9168  4583.5 0.6854250 0.6854250 7.114999e-03 7.114999e-03 0.307460034 #> 9169  4584.0 0.6854603 0.6854603 7.114781e-03 7.114781e-03 0.307424894 #> 9170  4584.5 0.6854957 0.6854957 7.114570e-03 7.114570e-03 0.307389768 #> 9171  4585.0 0.6855310 0.6855310 7.114364e-03 7.114364e-03 0.307354657 #> 9172  4585.5 0.6855663 0.6855663 7.114166e-03 7.114166e-03 0.307319559 #> 9173  4586.0 0.6856016 0.6856016 7.113973e-03 7.113973e-03 0.307284476 #> 9174  4586.5 0.6856368 0.6856368 7.113787e-03 7.113787e-03 0.307249407 #> 9175  4587.0 0.6856720 0.6856720 7.113607e-03 7.113607e-03 0.307214353 #> 9176  4587.5 0.6857073 0.6857073 7.113433e-03 7.113433e-03 0.307179313 #> 9177  4588.0 0.6857424 0.6857424 7.113265e-03 7.113265e-03 0.307144288 #> 9178  4588.5 0.6857776 0.6857776 7.113104e-03 7.113104e-03 0.307109278 #> 9179  4589.0 0.6858128 0.6858128 7.112949e-03 7.112949e-03 0.307074282 #> 9180  4589.5 0.6858479 0.6858479 7.112801e-03 7.112801e-03 0.307039302 #> 9181  4590.0 0.6858830 0.6858830 7.112658e-03 7.112658e-03 0.307004336 #> 9182  4590.5 0.6859181 0.6859181 7.112522e-03 7.112522e-03 0.306969386 #> 9183  4591.0 0.6859532 0.6859532 7.112392e-03 7.112392e-03 0.306934451 #> 9184  4591.5 0.6859882 0.6859882 7.112268e-03 7.112268e-03 0.306899531 #> 9185  4592.0 0.6860232 0.6860232 7.112151e-03 7.112151e-03 0.306864627 #> 9186  4592.5 0.6860582 0.6860582 7.112039e-03 7.112039e-03 0.306829739 #> 9187  4593.0 0.6860932 0.6860932 7.111934e-03 7.111934e-03 0.306794865 #> 9188  4593.5 0.6861282 0.6861282 7.111835e-03 7.111835e-03 0.306760008 #> 9189  4594.0 0.6861631 0.6861631 7.111743e-03 7.111743e-03 0.306725167 #> 9190  4594.5 0.6861980 0.6861980 7.111656e-03 7.111656e-03 0.306690341 #> 9191  4595.0 0.6862329 0.6862329 7.111576e-03 7.111576e-03 0.306655532 #> 9192  4595.5 0.6862678 0.6862678 7.111502e-03 7.111502e-03 0.306620738 #> 9193  4596.0 0.6863026 0.6863026 7.111434e-03 7.111434e-03 0.306585961 #> 9194  4596.5 0.6863374 0.6863374 7.111373e-03 7.111373e-03 0.306551200 #> 9195  4597.0 0.6863722 0.6863722 7.111317e-03 7.111317e-03 0.306516456 #> 9196  4597.5 0.6864070 0.6864070 7.111268e-03 7.111268e-03 0.306481727 #> 9197  4598.0 0.6864418 0.6864418 7.111225e-03 7.111225e-03 0.306447016 #> 9198  4598.5 0.6864765 0.6864765 7.111188e-03 7.111188e-03 0.306412321 #> 9199  4599.0 0.6865112 0.6865112 7.111157e-03 7.111157e-03 0.306377643 #> 9200  4599.5 0.6865459 0.6865459 7.111133e-03 7.111133e-03 0.306342982 #> 9201  4600.0 0.6865805 0.6865805 7.111114e-03 7.111114e-03 0.306308338 #> 9202  4600.5 0.6866152 0.6866152 7.111102e-03 7.111102e-03 0.306273711 #> 9203  4601.0 0.6866498 0.6866498 7.111096e-03 7.111096e-03 0.306239101 #> 9204  4601.5 0.6866844 0.6866844 7.111096e-03 7.111096e-03 0.306204509 #> 9205  4602.0 0.6867190 0.6867190 7.111102e-03 7.111102e-03 0.306169933 #> 9206  4602.5 0.6867535 0.6867535 7.111115e-03 7.111115e-03 0.306135375 #> 9207  4603.0 0.6867880 0.6867880 7.111133e-03 7.111133e-03 0.306100835 #> 9208  4603.5 0.6868225 0.6868225 7.111158e-03 7.111158e-03 0.306066312 #> 9209  4604.0 0.6868570 0.6868570 7.111189e-03 7.111189e-03 0.306031807 #> 9210  4604.5 0.6868915 0.6868915 7.111225e-03 7.111225e-03 0.305997320 #> 9211  4605.0 0.6869259 0.6869259 7.111268e-03 7.111268e-03 0.305962851 #> 9212  4605.5 0.6869603 0.6869603 7.111318e-03 7.111318e-03 0.305928400 #> 9213  4606.0 0.6869947 0.6869947 7.111373e-03 7.111373e-03 0.305893967 #> 9214  4606.5 0.6870290 0.6870290 7.111434e-03 7.111434e-03 0.305859552 #> 9215  4607.0 0.6870633 0.6870633 7.111502e-03 7.111502e-03 0.305825155 #> 9216  4607.5 0.6870976 0.6870976 7.111575e-03 7.111575e-03 0.305790776 #> 9217  4608.0 0.6871319 0.6871319 7.111655e-03 7.111655e-03 0.305756417 #> 9218  4608.5 0.6871662 0.6871662 7.111741e-03 7.111741e-03 0.305722075 #> 9219  4609.0 0.6872004 0.6872004 7.111833e-03 7.111833e-03 0.305687753 #> 9220  4609.5 0.6872346 0.6872346 7.111931e-03 7.111931e-03 0.305653449 #> 9221  4610.0 0.6872688 0.6872688 7.112035e-03 7.112035e-03 0.305619163 #> 9222  4610.5 0.6873030 0.6873030 7.112145e-03 7.112145e-03 0.305584897 #> 9223  4611.0 0.6873371 0.6873371 7.112261e-03 7.112261e-03 0.305550650 #> 9224  4611.5 0.6873712 0.6873712 7.112383e-03 7.112383e-03 0.305516422 #> 9225  4612.0 0.6874053 0.6874053 7.112511e-03 7.112511e-03 0.305482213 #> 9226  4612.5 0.6874393 0.6874393 7.112646e-03 7.112646e-03 0.305448024 #> 9227  4613.0 0.6874734 0.6874734 7.112786e-03 7.112786e-03 0.305413853 #> 9228  4613.5 0.6875074 0.6875074 7.112933e-03 7.112933e-03 0.305379703 #> 9229  4614.0 0.6875413 0.6875413 7.113085e-03 7.113085e-03 0.305345571 #> 9230  4614.5 0.6875753 0.6875753 7.113244e-03 7.113244e-03 0.305311460 #> 9231  4615.0 0.6876092 0.6876092 7.113408e-03 7.113408e-03 0.305277368 #> 9232  4615.5 0.6876431 0.6876431 7.113579e-03 7.113579e-03 0.305243296 #> 9233  4616.0 0.6876770 0.6876770 7.113756e-03 7.113756e-03 0.305209244 #> 9234  4616.5 0.6877108 0.6877108 7.113938e-03 7.113938e-03 0.305175212 #> 9235  4617.0 0.6877447 0.6877447 7.114127e-03 7.114127e-03 0.305141200 #> 9236  4617.5 0.6877785 0.6877785 7.114322e-03 7.114322e-03 0.305107209 #> 9237  4618.0 0.6878122 0.6878122 7.114522e-03 7.114522e-03 0.305073237 #> 9238  4618.5 0.6878460 0.6878460 7.114729e-03 7.114729e-03 0.305039286 #> 9239  4619.0 0.6878797 0.6878797 7.114942e-03 7.114942e-03 0.305005356 #> 9240  4619.5 0.6879134 0.6879134 7.115161e-03 7.115161e-03 0.304971445 #> 9241  4620.0 0.6879471 0.6879471 7.115386e-03 7.115386e-03 0.304937556 #> 9242  4620.5 0.6879807 0.6879807 7.115616e-03 7.115616e-03 0.304903687 #> 9243  4621.0 0.6880143 0.6880143 7.115853e-03 7.115853e-03 0.304869840 #> 9244  4621.5 0.6880479 0.6880479 7.116096e-03 7.116096e-03 0.304836013 #> 9245  4622.0 0.6880814 0.6880814 7.116344e-03 7.116344e-03 0.304802207 #> 9246  4622.5 0.6881150 0.6881150 7.116599e-03 7.116599e-03 0.304768422 #> 9247  4623.0 0.6881485 0.6881485 7.116860e-03 7.116860e-03 0.304734658 #> 9248  4623.5 0.6881820 0.6881820 7.117126e-03 7.117126e-03 0.304700916 #> 9249  4624.0 0.6882154 0.6882154 7.117399e-03 7.117399e-03 0.304667195 #> 9250  4624.5 0.6882488 0.6882488 7.117678e-03 7.117678e-03 0.304633496 #> 9251  4625.0 0.6882822 0.6882822 7.117962e-03 7.117962e-03 0.304599818 #> 9252  4625.5 0.6883156 0.6883156 7.118253e-03 7.118253e-03 0.304566161 #> 9253  4626.0 0.6883489 0.6883489 7.118549e-03 7.118549e-03 0.304532527 #> 9254  4626.5 0.6883822 0.6883822 7.118851e-03 7.118851e-03 0.304498914 #> 9255  4627.0 0.6884155 0.6884155 7.119160e-03 7.119160e-03 0.304465323 #> 9256  4627.5 0.6884488 0.6884488 7.119474e-03 7.119474e-03 0.304431754 #> 9257  4628.0 0.6884820 0.6884820 7.119794e-03 7.119794e-03 0.304398207 #> 9258  4628.5 0.6885152 0.6885152 7.120120e-03 7.120120e-03 0.304364682 #> 9259  4629.0 0.6885484 0.6885484 7.120452e-03 7.120452e-03 0.304331179 #> 9260  4629.5 0.6885815 0.6885815 7.120790e-03 7.120790e-03 0.304297699 #> 9261  4630.0 0.6886146 0.6886146 7.121134e-03 7.121134e-03 0.304264241 #> 9262  4630.5 0.6886477 0.6886477 7.121484e-03 7.121484e-03 0.304230806 #> 9263  4631.0 0.6886808 0.6886808 7.121839e-03 7.121839e-03 0.304197393 #> 9264  4631.5 0.6887138 0.6887138 7.122201e-03 7.122201e-03 0.304164003 #> 9265  4632.0 0.6887468 0.6887468 7.122568e-03 7.122568e-03 0.304130636 #> 9266  4632.5 0.6887798 0.6887798 7.122942e-03 7.122942e-03 0.304097292 #> 9267  4633.0 0.6888127 0.6888127 7.123321e-03 7.123321e-03 0.304063970 #> 9268  4633.5 0.6888456 0.6888456 7.123706e-03 7.123706e-03 0.304030672 #> 9269  4634.0 0.6888785 0.6888785 7.124097e-03 7.124097e-03 0.303997397 #> 9270  4634.5 0.6889114 0.6889114 7.124494e-03 7.124494e-03 0.303964144 #> 9271  4635.0 0.6889442 0.6889442 7.124897e-03 7.124897e-03 0.303930916 #> 9272  4635.5 0.6889770 0.6889770 7.125305e-03 7.125305e-03 0.303897710 #> 9273  4636.0 0.6890098 0.6890098 7.125720e-03 7.125720e-03 0.303864528 #> 9274  4636.5 0.6890425 0.6890425 7.126140e-03 7.126140e-03 0.303831370 #> 9275  4637.0 0.6890752 0.6890752 7.126566e-03 7.126566e-03 0.303798235 #> 9276  4637.5 0.6891079 0.6891079 7.126998e-03 7.126998e-03 0.303765124 #> 9277  4638.0 0.6891405 0.6891405 7.127436e-03 7.127436e-03 0.303732036 #> 9278  4638.5 0.6891731 0.6891731 7.127880e-03 7.127880e-03 0.303698973 #> 9279  4639.0 0.6892057 0.6892057 7.128329e-03 7.128329e-03 0.303665933 #> 9280  4639.5 0.6892383 0.6892383 7.128785e-03 7.128785e-03 0.303632918 #> 9281  4640.0 0.6892708 0.6892708 7.129246e-03 7.129246e-03 0.303599927 #> 9282  4640.5 0.6893033 0.6893033 7.129713e-03 7.129713e-03 0.303566959 #> 9283  4641.0 0.6893358 0.6893358 7.130186e-03 7.130186e-03 0.303534017 #> 9284  4641.5 0.6893682 0.6893682 7.130665e-03 7.130665e-03 0.303501098 #> 9285  4642.0 0.6894006 0.6894006 7.131149e-03 7.131149e-03 0.303468204 #> 9286  4642.5 0.6894330 0.6894330 7.131639e-03 7.131639e-03 0.303435335 #> 9287  4643.0 0.6894654 0.6894654 7.132136e-03 7.132136e-03 0.303402490 #> 9288  4643.5 0.6894977 0.6894977 7.132637e-03 7.132637e-03 0.303369671 #> 9289  4644.0 0.6895300 0.6895300 7.133145e-03 7.133145e-03 0.303336875 #> 9290  4644.5 0.6895622 0.6895622 7.133659e-03 7.133659e-03 0.303304105 #> 9291  4645.0 0.6895945 0.6895945 7.134178e-03 7.134178e-03 0.303271360 #> 9292  4645.5 0.6896267 0.6896267 7.134703e-03 7.134703e-03 0.303238640 #> 9293  4646.0 0.6896588 0.6896588 7.135234e-03 7.135234e-03 0.303205945 #> 9294  4646.5 0.6896910 0.6896910 7.135771e-03 7.135771e-03 0.303173275 #> 9295  4647.0 0.6897231 0.6897231 7.136313e-03 7.136313e-03 0.303140631 #> 9296  4647.5 0.6897551 0.6897551 7.136861e-03 7.136861e-03 0.303108011 #> 9297  4648.0 0.6897872 0.6897872 7.137415e-03 7.137415e-03 0.303075418 #> 9298  4648.5 0.6898192 0.6898192 7.137975e-03 7.137975e-03 0.303042850 #> 9299  4649.0 0.6898512 0.6898512 7.138540e-03 7.138540e-03 0.303010308 #> 9300  4649.5 0.6898831 0.6898831 7.139111e-03 7.139111e-03 0.302977791 #> 9301  4650.0 0.6899150 0.6899150 7.139688e-03 7.139688e-03 0.302945300 #> 9302  4650.5 0.6899469 0.6899469 7.140271e-03 7.140271e-03 0.302912835 #> 9303  4651.0 0.6899787 0.6899787 7.140859e-03 7.140859e-03 0.302880396 #> 9304  4651.5 0.6900106 0.6900106 7.141454e-03 7.141454e-03 0.302847984 #> 9305  4652.0 0.6900423 0.6900423 7.142053e-03 7.142053e-03 0.302815597 #> 9306  4652.5 0.6900741 0.6900741 7.142659e-03 7.142659e-03 0.302783236 #> 9307  4653.0 0.6901058 0.6901058 7.143270e-03 7.143270e-03 0.302750902 #> 9308  4653.5 0.6901375 0.6901375 7.143887e-03 7.143887e-03 0.302718595 #> 9309  4654.0 0.6901692 0.6901692 7.144510e-03 7.144510e-03 0.302686313 #> 9310  4654.5 0.6902008 0.6902008 7.145139e-03 7.145139e-03 0.302654059 #> 9311  4655.0 0.6902324 0.6902324 7.145773e-03 7.145773e-03 0.302621831 #> 9312  4655.5 0.6902640 0.6902640 7.146413e-03 7.146413e-03 0.302589629 #> 9313  4656.0 0.6902955 0.6902955 7.147058e-03 7.147058e-03 0.302557455 #> 9314  4656.5 0.6903270 0.6903270 7.147709e-03 7.147709e-03 0.302525307 #> 9315  4657.0 0.6903584 0.6903584 7.148366e-03 7.148366e-03 0.302493186 #> 9316  4657.5 0.6903899 0.6903899 7.149029e-03 7.149029e-03 0.302461093 #> 9317  4658.0 0.6904213 0.6904213 7.149697e-03 7.149697e-03 0.302429026 #> 9318  4658.5 0.6904526 0.6904526 7.150371e-03 7.150371e-03 0.302396987 #> 9319  4659.0 0.6904840 0.6904840 7.151051e-03 7.151051e-03 0.302364975 #> 9320  4659.5 0.6905153 0.6905153 7.151736e-03 7.151736e-03 0.302332990 #> 9321  4660.0 0.6905465 0.6905465 7.152427e-03 7.152427e-03 0.302301033 #> 9322  4660.5 0.6905778 0.6905778 7.153123e-03 7.153123e-03 0.302269104 #> 9323  4661.0 0.6906090 0.6906090 7.153826e-03 7.153826e-03 0.302237202 #> 9324  4661.5 0.6906401 0.6906401 7.154534e-03 7.154534e-03 0.302205327 #> 9325  4662.0 0.6906713 0.6906713 7.155247e-03 7.155247e-03 0.302173481 #> 9326  4662.5 0.6907024 0.6907024 7.155966e-03 7.155966e-03 0.302141662 #> 9327  4663.0 0.6907334 0.6907334 7.156691e-03 7.156691e-03 0.302109871 #> 9328  4663.5 0.6907645 0.6907645 7.157421e-03 7.157421e-03 0.302078109 #> 9329  4664.0 0.6907955 0.6907955 7.158158e-03 7.158158e-03 0.302046374 #> 9330  4664.5 0.6908264 0.6908264 7.158899e-03 7.158899e-03 0.302014668 #> 9331  4665.0 0.6908574 0.6908574 7.159646e-03 7.159646e-03 0.301982989 #> 9332  4665.5 0.6908883 0.6908883 7.160399e-03 7.160399e-03 0.301951340 #> 9333  4666.0 0.6909191 0.6909191 7.161158e-03 7.161158e-03 0.301919718 #> 9334  4666.5 0.6909500 0.6909500 7.161922e-03 7.161922e-03 0.301888125 #> 9335  4667.0 0.6909807 0.6909807 7.162692e-03 7.162692e-03 0.301856561 #> 9336  4667.5 0.6910115 0.6910115 7.163467e-03 7.163467e-03 0.301825025 #> 9337  4668.0 0.6910422 0.6910422 7.164248e-03 7.164248e-03 0.301793518 #> 9338  4668.5 0.6910729 0.6910729 7.165034e-03 7.165034e-03 0.301762040 #> 9339  4669.0 0.6911036 0.6911036 7.165826e-03 7.165826e-03 0.301730590 #> 9340  4669.5 0.6911342 0.6911342 7.166624e-03 7.166624e-03 0.301699170 #> 9341  4670.0 0.6911648 0.6911648 7.167427e-03 7.167427e-03 0.301667779 #> 9342  4670.5 0.6911953 0.6911953 7.168236e-03 7.168236e-03 0.301636417 #> 9343  4671.0 0.6912259 0.6912259 7.169050e-03 7.169050e-03 0.301605084 #> 9344  4671.5 0.6912563 0.6912563 7.169870e-03 7.169870e-03 0.301573780 #> 9345  4672.0 0.6912868 0.6912868 7.170696e-03 7.170696e-03 0.301542505 #> 9346  4672.5 0.6913172 0.6913172 7.171527e-03 7.171527e-03 0.301511261 #> 9347  4673.0 0.6913476 0.6913476 7.172363e-03 7.172363e-03 0.301480045 #> 9348  4673.5 0.6913779 0.6913779 7.173206e-03 7.173206e-03 0.301448859 #> 9349  4674.0 0.6914082 0.6914082 7.174053e-03 7.174053e-03 0.301417703 #> 9350  4674.5 0.6914385 0.6914385 7.174906e-03 7.174906e-03 0.301386577 #> 9351  4675.0 0.6914688 0.6914688 7.175765e-03 7.175765e-03 0.301355480 #> 9352  4675.5 0.6914990 0.6914990 7.176629e-03 7.176629e-03 0.301324413 #> 9353  4676.0 0.6915291 0.6915291 7.177499e-03 7.177499e-03 0.301293377 #> 9354  4676.5 0.6915593 0.6915593 7.178374e-03 7.178374e-03 0.301262370 #> 9355  4677.0 0.6915894 0.6915894 7.179255e-03 7.179255e-03 0.301231394 #> 9356  4677.5 0.6916194 0.6916194 7.180141e-03 7.180141e-03 0.301200447 #> 9357  4678.0 0.6916494 0.6916494 7.181033e-03 7.181033e-03 0.301169531 #> 9358  4678.5 0.6916794 0.6916794 7.181930e-03 7.181930e-03 0.301138645 #> 9359  4679.0 0.6917094 0.6917094 7.182833e-03 7.182833e-03 0.301107790 #> 9360  4679.5 0.6917393 0.6917393 7.183741e-03 7.183741e-03 0.301076965 #> 9361  4680.0 0.6917692 0.6917692 7.184655e-03 7.184655e-03 0.301046171 #> 9362  4680.5 0.6917990 0.6917990 7.185574e-03 7.185574e-03 0.301015408 #> 9363  4681.0 0.6918288 0.6918288 7.186499e-03 7.186499e-03 0.300984675 #> 9364  4681.5 0.6918586 0.6918586 7.187429e-03 7.187429e-03 0.300953973 #> 9365  4682.0 0.6918883 0.6918883 7.188365e-03 7.188365e-03 0.300923302 #> 9366  4682.5 0.6919180 0.6919180 7.189306e-03 7.189306e-03 0.300892662 #> 9367  4683.0 0.6919477 0.6919477 7.190253e-03 7.190253e-03 0.300862053 #> 9368  4683.5 0.6919773 0.6919773 7.191205e-03 7.191205e-03 0.300831475 #> 9369  4684.0 0.6920069 0.6920069 7.192162e-03 7.192162e-03 0.300800928 #> 9370  4684.5 0.6920365 0.6920365 7.193125e-03 7.193125e-03 0.300770413 #> 9371  4685.0 0.6920660 0.6920660 7.194093e-03 7.194093e-03 0.300739929 #> 9372  4685.5 0.6920955 0.6920955 7.195067e-03 7.195067e-03 0.300709476 #> 9373  4686.0 0.6921249 0.6921249 7.196046e-03 7.196046e-03 0.300679055 #> 9374  4686.5 0.6921543 0.6921543 7.197031e-03 7.197031e-03 0.300648665 #> 9375  4687.0 0.6921837 0.6921837 7.198021e-03 7.198021e-03 0.300618307 #> 9376  4687.5 0.6922130 0.6922130 7.199017e-03 7.199017e-03 0.300587981 #> 9377  4688.0 0.6922423 0.6922423 7.200018e-03 7.200018e-03 0.300557687 #> 9378  4688.5 0.6922716 0.6922716 7.201024e-03 7.201024e-03 0.300527424 #> 9379  4689.0 0.6923008 0.6923008 7.202036e-03 7.202036e-03 0.300497193 #> 9380  4689.5 0.6923300 0.6923300 7.203053e-03 7.203053e-03 0.300466995 #> 9381  4690.0 0.6923591 0.6923591 7.204075e-03 7.204075e-03 0.300436828 #> 9382  4690.5 0.6923882 0.6923882 7.205103e-03 7.205103e-03 0.300406694 #> 9383  4691.0 0.6924173 0.6924173 7.206137e-03 7.206137e-03 0.300376592 #> 9384  4691.5 0.6924463 0.6924463 7.207175e-03 7.207175e-03 0.300346522 #> 9385  4692.0 0.6924753 0.6924753 7.208219e-03 7.208219e-03 0.300316484 #> 9386  4692.5 0.6925043 0.6925043 7.209269e-03 7.209269e-03 0.300286479 #> 9387  4693.0 0.6925332 0.6925332 7.210323e-03 7.210323e-03 0.300256507 #> 9388  4693.5 0.6925620 0.6925620 7.211384e-03 7.211384e-03 0.300226567 #> 9389  4694.0 0.6925909 0.6925909 7.212449e-03 7.212449e-03 0.300196660 #> 9390  4694.5 0.6926197 0.6926197 7.213520e-03 7.213520e-03 0.300166785 #> 9391  4695.0 0.6926485 0.6926485 7.214596e-03 7.214596e-03 0.300136944 #> 9392  4695.5 0.6926772 0.6926772 7.215678e-03 7.215678e-03 0.300107135 #> 9393  4696.0 0.6927059 0.6927059 7.216765e-03 7.216765e-03 0.300077359 #> 9394  4696.5 0.6927345 0.6927345 7.217857e-03 7.217857e-03 0.300047616 #> 9395  4697.0 0.6927631 0.6927631 7.218955e-03 7.218955e-03 0.300017907 #> 9396  4697.5 0.6927917 0.6927917 7.220058e-03 7.220058e-03 0.299988230 #> 9397  4698.0 0.6928202 0.6928202 7.221166e-03 7.221166e-03 0.299958587 #> 9398  4698.5 0.6928487 0.6928487 7.222279e-03 7.222279e-03 0.299928977 #> 9399  4699.0 0.6928772 0.6928772 7.223398e-03 7.223398e-03 0.299899401 #> 9400  4699.5 0.6929056 0.6929056 7.224522e-03 7.224522e-03 0.299869858 #> 9401  4700.0 0.6929340 0.6929340 7.225652e-03 7.225652e-03 0.299840348 #> 9402  4700.5 0.6929623 0.6929623 7.226787e-03 7.226787e-03 0.299810873 #> 9403  4701.0 0.6929906 0.6929906 7.227927e-03 7.227927e-03 0.299781430 #> 9404  4701.5 0.6930189 0.6930189 7.229072e-03 7.229072e-03 0.299752022 #> 9405  4702.0 0.6930471 0.6930471 7.230223e-03 7.230223e-03 0.299722647 #> 9406  4702.5 0.6930753 0.6930753 7.231379e-03 7.231379e-03 0.299693307 #> 9407  4703.0 0.6931035 0.6931035 7.232540e-03 7.232540e-03 0.299664000 #> 9408  4703.5 0.6931316 0.6931316 7.233706e-03 7.233706e-03 0.299634728 #> 9409  4704.0 0.6931596 0.6931596 7.234878e-03 7.234878e-03 0.299605489 #> 9410  4704.5 0.6931877 0.6931877 7.236055e-03 7.236055e-03 0.299576285 #> 9411  4705.0 0.6932156 0.6932156 7.237238e-03 7.237238e-03 0.299547115 #> 9412  4705.5 0.6932436 0.6932436 7.238425e-03 7.238425e-03 0.299517979 #> 9413  4706.0 0.6932715 0.6932715 7.239618e-03 7.239618e-03 0.299488877 #> 9414  4706.5 0.6932994 0.6932994 7.240816e-03 7.240816e-03 0.299459811 #> 9415  4707.0 0.6933272 0.6933272 7.242019e-03 7.242019e-03 0.299430778 #> 9416  4707.5 0.6933550 0.6933550 7.243228e-03 7.243228e-03 0.299401780 #> 9417  4708.0 0.6933827 0.6933827 7.244441e-03 7.244441e-03 0.299372817 #> 9418  4708.5 0.6934105 0.6934105 7.245660e-03 7.245660e-03 0.299343889 #> 9419  4709.0 0.6934381 0.6934381 7.246885e-03 7.246885e-03 0.299314996 #> 9420  4709.5 0.6934657 0.6934657 7.248114e-03 7.248114e-03 0.299286137 #> 9421  4710.0 0.6934933 0.6934933 7.249349e-03 7.249349e-03 0.299257313 #> 9422  4710.5 0.6935209 0.6935209 7.250588e-03 7.250588e-03 0.299228525 #> 9423  4711.0 0.6935484 0.6935484 7.251833e-03 7.251833e-03 0.299199771 #> 9424  4711.5 0.6935759 0.6935759 7.253084e-03 7.253084e-03 0.299171053 #> 9425  4712.0 0.6936033 0.6936033 7.254339e-03 7.254339e-03 0.299142369 #> 9426  4712.5 0.6936307 0.6936307 7.255600e-03 7.255600e-03 0.299113722 #> 9427  4713.0 0.6936580 0.6936580 7.256866e-03 7.256866e-03 0.299085109 #> 9428  4713.5 0.6936853 0.6936853 7.258136e-03 7.258136e-03 0.299056532 #> 9429  4714.0 0.6937126 0.6937126 7.259413e-03 7.259413e-03 0.299027990 #> 9430  4714.5 0.6937398 0.6937398 7.260694e-03 7.260694e-03 0.298999484 #> 9431  4715.0 0.6937670 0.6937670 7.261980e-03 7.261980e-03 0.298971014 #> 9432  4715.5 0.6937941 0.6937941 7.263272e-03 7.263272e-03 0.298942579 #> 9433  4716.0 0.6938213 0.6938213 7.264569e-03 7.264569e-03 0.298914181 #> 9434  4716.5 0.6938483 0.6938483 7.265871e-03 7.265871e-03 0.298885818 #> 9435  4717.0 0.6938753 0.6938753 7.267178e-03 7.267178e-03 0.298857490 #> 9436  4717.5 0.6939023 0.6939023 7.268490e-03 7.268490e-03 0.298829199 #> 9437  4718.0 0.6939292 0.6939292 7.269808e-03 7.269808e-03 0.298800944 #> 9438  4718.5 0.6939561 0.6939561 7.271130e-03 7.271130e-03 0.298772725 #> 9439  4719.0 0.6939830 0.6939830 7.272458e-03 7.272458e-03 0.298744543 #> 9440  4719.5 0.6940098 0.6940098 7.273790e-03 7.273790e-03 0.298716396 #> 9441  4720.0 0.6940366 0.6940366 7.275128e-03 7.275128e-03 0.298688286 #> 9442  4720.5 0.6940633 0.6940633 7.276471e-03 7.276471e-03 0.298660212 #> 9443  4721.0 0.6940900 0.6940900 7.277819e-03 7.277819e-03 0.298632175 #> 9444  4721.5 0.6941167 0.6941167 7.279173e-03 7.279173e-03 0.298604174 #> 9445  4722.0 0.6941433 0.6941433 7.280531e-03 7.280531e-03 0.298576210 #> 9446  4722.5 0.6941698 0.6941698 7.281894e-03 7.281894e-03 0.298548282 #> 9447  4723.0 0.6941963 0.6941963 7.283263e-03 7.283263e-03 0.298520391 #> 9448  4723.5 0.6942228 0.6942228 7.284636e-03 7.284636e-03 0.298492537 #> 9449  4724.0 0.6942493 0.6942493 7.286015e-03 7.286015e-03 0.298464720 #> 9450  4724.5 0.6942757 0.6942757 7.287399e-03 7.287399e-03 0.298436940 #> 9451  4725.0 0.6943020 0.6943020 7.288787e-03 7.288787e-03 0.298409196 #> 9452  4725.5 0.6943283 0.6943283 7.290181e-03 7.290181e-03 0.298381490 #> 9453  4726.0 0.6943546 0.6943546 7.291580e-03 7.291580e-03 0.298353821 #> 9454  4726.5 0.6943808 0.6943808 7.292984e-03 7.292984e-03 0.298326189 #> 9455  4727.0 0.6944070 0.6944070 7.294393e-03 7.294393e-03 0.298298594 #> 9456  4727.5 0.6944332 0.6944332 7.295807e-03 7.295807e-03 0.298271037 #> 9457  4728.0 0.6944593 0.6944593 7.297226e-03 7.297226e-03 0.298243517 #> 9458  4728.5 0.6944853 0.6944853 7.298650e-03 7.298650e-03 0.298216034 #> 9459  4729.0 0.6945113 0.6945113 7.300080e-03 7.300080e-03 0.298188589 #> 9460  4729.5 0.6945373 0.6945373 7.301514e-03 7.301514e-03 0.298161182 #> 9461  4730.0 0.6945632 0.6945632 7.302953e-03 7.302953e-03 0.298133812 #> 9462  4730.5 0.6945891 0.6945891 7.304397e-03 7.304397e-03 0.298106480 #> 9463  4731.0 0.6946150 0.6946150 7.305846e-03 7.305846e-03 0.298079185 #> 9464  4731.5 0.6946408 0.6946408 7.307301e-03 7.307301e-03 0.298051929 #> 9465  4732.0 0.6946665 0.6946665 7.308760e-03 7.308760e-03 0.298024710 #> 9466  4732.5 0.6946922 0.6946922 7.310224e-03 7.310224e-03 0.297997529 #> 9467  4733.0 0.6947179 0.6947179 7.311693e-03 7.311693e-03 0.297970387 #> 9468  4733.5 0.6947436 0.6947436 7.313167e-03 7.313167e-03 0.297943282 #> 9469  4734.0 0.6947691 0.6947691 7.314647e-03 7.314647e-03 0.297916216 #> 9470  4734.5 0.6947947 0.6947947 7.316131e-03 7.316131e-03 0.297889187 #> 9471  4735.0 0.6948202 0.6948202 7.317620e-03 7.317620e-03 0.297862197 #> 9472  4735.5 0.6948456 0.6948456 7.319114e-03 7.319114e-03 0.297835246 #> 9473  4736.0 0.6948711 0.6948711 7.320613e-03 7.320613e-03 0.297808333 #> 9474  4736.5 0.6948964 0.6948964 7.322117e-03 7.322117e-03 0.297781458 #> 9475  4737.0 0.6949218 0.6949218 7.323626e-03 7.323626e-03 0.297754622 #> 9476  4737.5 0.6949470 0.6949470 7.325140e-03 7.325140e-03 0.297727824 #> 9477  4738.0 0.6949723 0.6949723 7.326659e-03 7.326659e-03 0.297701065 #> 9478  4738.5 0.6949975 0.6949975 7.328182e-03 7.328182e-03 0.297674345 #> 9479  4739.0 0.6950226 0.6950226 7.329711e-03 7.329711e-03 0.297647664 #> 9480  4739.5 0.6950477 0.6950477 7.331245e-03 7.331245e-03 0.297621021 #> 9481  4740.0 0.6950728 0.6950728 7.332783e-03 7.332783e-03 0.297594418 #> 9482  4740.5 0.6950978 0.6950978 7.334327e-03 7.334327e-03 0.297567853 #> 9483  4741.0 0.6951228 0.6951228 7.335875e-03 7.335875e-03 0.297541327 #> 9484  4741.5 0.6951477 0.6951477 7.337429e-03 7.337429e-03 0.297514841 #> 9485  4742.0 0.6951726 0.6951726 7.338987e-03 7.338987e-03 0.297488393 #> 9486  4742.5 0.6951975 0.6951975 7.340550e-03 7.340550e-03 0.297461985 #> 9487  4743.0 0.6952223 0.6952223 7.342118e-03 7.342118e-03 0.297435616 #> 9488  4743.5 0.6952470 0.6952470 7.343691e-03 7.343691e-03 0.297409287 #> 9489  4744.0 0.6952717 0.6952717 7.345268e-03 7.345268e-03 0.297382997 #> 9490  4744.5 0.6952964 0.6952964 7.346851e-03 7.346851e-03 0.297356746 #> 9491  4745.0 0.6953210 0.6953210 7.348438e-03 7.348438e-03 0.297330535 #> 9492  4745.5 0.6953456 0.6953456 7.350031e-03 7.350031e-03 0.297304364 #> 9493  4746.0 0.6953701 0.6953701 7.351628e-03 7.351628e-03 0.297278232 #> 9494  4746.5 0.6953946 0.6953946 7.353230e-03 7.353230e-03 0.297252140 #> 9495  4747.0 0.6954191 0.6954191 7.354837e-03 7.354837e-03 0.297226087 #> 9496  4747.5 0.6954435 0.6954435 7.356449e-03 7.356449e-03 0.297200075 #> 9497  4748.0 0.6954678 0.6954678 7.358065e-03 7.358065e-03 0.297174102 #> 9498  4748.5 0.6954921 0.6954921 7.359687e-03 7.359687e-03 0.297148170 #> 9499  4749.0 0.6955164 0.6955164 7.361313e-03 7.361313e-03 0.297122277 #> 9500  4749.5 0.6955406 0.6955406 7.362944e-03 7.362944e-03 0.297096424 #> 9501  4750.0 0.6955648 0.6955648 7.364580e-03 7.364580e-03 0.297070612 #> 9502  4750.5 0.6955889 0.6955889 7.366220e-03 7.366220e-03 0.297044840 #> 9503  4751.0 0.6956130 0.6956130 7.367866e-03 7.367866e-03 0.297019108 #> 9504  4751.5 0.6956371 0.6956371 7.369516e-03 7.369516e-03 0.296993416 #> 9505  4752.0 0.6956611 0.6956611 7.371171e-03 7.371171e-03 0.296967765 #> 9506  4752.5 0.6956850 0.6956850 7.372831e-03 7.372831e-03 0.296942154 #> 9507  4753.0 0.6957089 0.6957089 7.374496e-03 7.374496e-03 0.296916584 #> 9508  4753.5 0.6957328 0.6957328 7.376165e-03 7.376165e-03 0.296891054 #> 9509  4754.0 0.6957566 0.6957566 7.377839e-03 7.377839e-03 0.296865565 #> 9510  4754.5 0.6957804 0.6957804 7.379518e-03 7.379518e-03 0.296840117 #> 9511  4755.0 0.6958041 0.6958041 7.381202e-03 7.381202e-03 0.296814709 #> 9512  4755.5 0.6958278 0.6958278 7.382891e-03 7.382891e-03 0.296789342 #> 9513  4756.0 0.6958514 0.6958514 7.384584e-03 7.384584e-03 0.296764016 #> 9514  4756.5 0.6958750 0.6958750 7.386282e-03 7.386282e-03 0.296738731 #> 9515  4757.0 0.6958985 0.6958985 7.387985e-03 7.387985e-03 0.296713487 #> 9516  4757.5 0.6959220 0.6959220 7.389692e-03 7.389692e-03 0.296688284 #> 9517  4758.0 0.6959455 0.6959455 7.391404e-03 7.391404e-03 0.296663121 #> 9518  4758.5 0.6959689 0.6959689 7.393121e-03 7.393121e-03 0.296638001 #> 9519  4759.0 0.6959922 0.6959922 7.394843e-03 7.394843e-03 0.296612921 #> 9520  4759.5 0.6960155 0.6960155 7.396569e-03 7.396569e-03 0.296587882 #> 9521  4760.0 0.6960388 0.6960388 7.398300e-03 7.398300e-03 0.296562885 #> 9522  4760.5 0.6960620 0.6960620 7.400036e-03 7.400036e-03 0.296537930 #> 9523  4761.0 0.6960852 0.6960852 7.401777e-03 7.401777e-03 0.296513015 #> 9524  4761.5 0.6961083 0.6961083 7.403522e-03 7.403522e-03 0.296488142 #> 9525  4762.0 0.6961314 0.6961314 7.405272e-03 7.405272e-03 0.296463311 #> 9526  4762.5 0.6961545 0.6961545 7.407026e-03 7.407026e-03 0.296438522 #> 9527  4763.0 0.6961774 0.6961774 7.408786e-03 7.408786e-03 0.296413774 #> 9528  4763.5 0.6962004 0.6962004 7.410550e-03 7.410550e-03 0.296389067 #> 9529  4764.0 0.6962233 0.6962233 7.412318e-03 7.412318e-03 0.296364403 #> 9530  4764.5 0.6962461 0.6962461 7.414091e-03 7.414091e-03 0.296339780 #> 9531  4765.0 0.6962689 0.6962689 7.415869e-03 7.415869e-03 0.296315200 #> 9532  4765.5 0.6962917 0.6962917 7.417652e-03 7.417652e-03 0.296290661 #> 9533  4766.0 0.6963144 0.6963144 7.419439e-03 7.419439e-03 0.296266164 #> 9534  4766.5 0.6963371 0.6963371 7.421231e-03 7.421231e-03 0.296241709 #> 9535  4767.0 0.6963597 0.6963597 7.423027e-03 7.423027e-03 0.296217297 #> 9536  4767.5 0.6963822 0.6963822 7.424828e-03 7.424828e-03 0.296192926 #> 9537  4768.0 0.6964048 0.6964048 7.426634e-03 7.426634e-03 0.296168598 #> 9538  4768.5 0.6964272 0.6964272 7.428445e-03 7.428445e-03 0.296144312 #> 9539  4769.0 0.6964497 0.6964497 7.430260e-03 7.430260e-03 0.296120069 #> 9540  4769.5 0.6964721 0.6964721 7.432079e-03 7.432079e-03 0.296095867 #> 9541  4770.0 0.6964944 0.6964944 7.433903e-03 7.433903e-03 0.296071709 #> 9542  4770.5 0.6965167 0.6965167 7.435732e-03 7.435732e-03 0.296047592 #> 9543  4771.0 0.6965389 0.6965389 7.437565e-03 7.437565e-03 0.296023519 #> 9544  4771.5 0.6965611 0.6965611 7.439403e-03 7.439403e-03 0.295999488 #> 9545  4772.0 0.6965833 0.6965833 7.441246e-03 7.441246e-03 0.295975499 #> 9546  4772.5 0.6966054 0.6966054 7.443093e-03 7.443093e-03 0.295951554 #> 9547  4773.0 0.6966274 0.6966274 7.444944e-03 7.444944e-03 0.295927651 #> 9548  4773.5 0.6966494 0.6966494 7.446801e-03 7.446801e-03 0.295903791 #> 9549  4774.0 0.6966714 0.6966714 7.448661e-03 7.448661e-03 0.295879974 #> 9550  4774.5 0.6966933 0.6966933 7.450527e-03 7.450527e-03 0.295856200 #> 9551  4775.0 0.6967151 0.6967151 7.452397e-03 7.452397e-03 0.295832469 #> 9552  4775.5 0.6967369 0.6967369 7.454271e-03 7.454271e-03 0.295808781 #> 9553  4776.0 0.6967587 0.6967587 7.456150e-03 7.456150e-03 0.295785136 #> 9554  4776.5 0.6967804 0.6967804 7.458033e-03 7.458033e-03 0.295761534 #> 9555  4777.0 0.6968021 0.6968021 7.459921e-03 7.459921e-03 0.295737975 #> 9556  4777.5 0.6968237 0.6968237 7.461814e-03 7.461814e-03 0.295714460 #> 9557  4778.0 0.6968453 0.6968453 7.463711e-03 7.463711e-03 0.295690988 #> 9558  4778.5 0.6968668 0.6968668 7.465612e-03 7.465612e-03 0.295667559 #> 9559  4779.0 0.6968883 0.6968883 7.467518e-03 7.467518e-03 0.295644174 #> 9560  4779.5 0.6969097 0.6969097 7.469429e-03 7.469429e-03 0.295620832 #> 9561  4780.0 0.6969311 0.6969311 7.471344e-03 7.471344e-03 0.295597534 #> 9562  4780.5 0.6969525 0.6969525 7.473263e-03 7.473263e-03 0.295574280 #> 9563  4781.0 0.6969737 0.6969737 7.475187e-03 7.475187e-03 0.295551069 #> 9564  4781.5 0.6969950 0.6969950 7.477116e-03 7.477116e-03 0.295527901 #> 9565  4782.0 0.6970162 0.6970162 7.479049e-03 7.479049e-03 0.295504778 #> 9566  4782.5 0.6970373 0.6970373 7.480986e-03 7.480986e-03 0.295481698 #> 9567  4783.0 0.6970584 0.6970584 7.482928e-03 7.482928e-03 0.295458663 #> 9568  4783.5 0.6970795 0.6970795 7.484874e-03 7.484874e-03 0.295435671 #> 9569  4784.0 0.6971005 0.6971005 7.486825e-03 7.486825e-03 0.295412723 #> 9570  4784.5 0.6971214 0.6971214 7.488780e-03 7.488780e-03 0.295389819 #> 9571  4785.0 0.6971423 0.6971423 7.490739e-03 7.490739e-03 0.295366959 #> 9572  4785.5 0.6971632 0.6971632 7.492703e-03 7.492703e-03 0.295344143 #> 9573  4786.0 0.6971840 0.6971840 7.494672e-03 7.494672e-03 0.295321372 #> 9574  4786.5 0.6972047 0.6972047 7.496644e-03 7.496644e-03 0.295298644 #> 9575  4787.0 0.6972254 0.6972254 7.498622e-03 7.498622e-03 0.295275961 #> 9576  4787.5 0.6972461 0.6972461 7.500603e-03 7.500603e-03 0.295253322 #> 9577  4788.0 0.6972667 0.6972667 7.502589e-03 7.502589e-03 0.295230728 #> 9578  4788.5 0.6972872 0.6972872 7.504580e-03 7.504580e-03 0.295208178 #> 9579  4789.0 0.6973078 0.6973078 7.506574e-03 7.506574e-03 0.295185672 #> 9580  4789.5 0.6973282 0.6973282 7.508574e-03 7.508574e-03 0.295163211 #> 9581  4790.0 0.6973486 0.6973486 7.510577e-03 7.510577e-03 0.295140795 #> 9582  4790.5 0.6973690 0.6973690 7.512585e-03 7.512585e-03 0.295118423 #> 9583  4791.0 0.6973893 0.6973893 7.514597e-03 7.514597e-03 0.295096096 #> 9584  4791.5 0.6974096 0.6974096 7.516614e-03 7.516614e-03 0.295073814 #> 9585  4792.0 0.6974298 0.6974298 7.518635e-03 7.518635e-03 0.295051576 #> 9586  4792.5 0.6974500 0.6974500 7.520660e-03 7.520660e-03 0.295029383 #> 9587  4793.0 0.6974701 0.6974701 7.522690e-03 7.522690e-03 0.295007235 #> 9588  4793.5 0.6974901 0.6974901 7.524723e-03 7.524723e-03 0.294985132 #> 9589  4794.0 0.6975102 0.6975102 7.526762e-03 7.526762e-03 0.294963074 #> 9590  4794.5 0.6975301 0.6975301 7.528804e-03 7.528804e-03 0.294941061 #> 9591  4795.0 0.6975501 0.6975501 7.530851e-03 7.530851e-03 0.294919093 #> 9592  4795.5 0.6975699 0.6975699 7.532902e-03 7.532902e-03 0.294897171 #> 9593  4796.0 0.6975897 0.6975897 7.534958e-03 7.534958e-03 0.294875293 #> 9594  4796.5 0.6976095 0.6976095 7.537018e-03 7.537018e-03 0.294853461 #> 9595  4797.0 0.6976292 0.6976292 7.539082e-03 7.539082e-03 0.294831673 #> 9596  4797.5 0.6976489 0.6976489 7.541150e-03 7.541150e-03 0.294809932 #> 9597  4798.0 0.6976685 0.6976685 7.543223e-03 7.543223e-03 0.294788235 #> 9598  4798.5 0.6976881 0.6976881 7.545299e-03 7.545299e-03 0.294766584 #> 9599  4799.0 0.6977076 0.6977076 7.547381e-03 7.547381e-03 0.294744979 #> 9600  4799.5 0.6977271 0.6977271 7.549466e-03 7.549466e-03 0.294723419 #> 9601  4800.0 0.6977465 0.6977465 7.551556e-03 7.551556e-03 0.294701904 #> 9602  4800.5 0.6977659 0.6977659 7.553650e-03 7.553650e-03 0.294680435 #> 9603  4801.0 0.6977852 0.6977852 7.555748e-03 7.555748e-03 0.294659012 #> 9604  4801.5 0.6978045 0.6978045 7.557850e-03 7.557850e-03 0.294637634 #> 9605  4802.0 0.6978237 0.6978237 7.559957e-03 7.559957e-03 0.294616303 #> 9606  4802.5 0.6978429 0.6978429 7.562067e-03 7.562067e-03 0.294595017 #> 9607  4803.0 0.6978620 0.6978620 7.564182e-03 7.564182e-03 0.294573777 #> 9608  4803.5 0.6978811 0.6978811 7.566302e-03 7.566302e-03 0.294552582 #> 9609  4804.0 0.6979001 0.6979001 7.568425e-03 7.568425e-03 0.294531434 #> 9610  4804.5 0.6979191 0.6979191 7.570552e-03 7.570552e-03 0.294510332 #> 9611  4805.0 0.6979380 0.6979380 7.572684e-03 7.572684e-03 0.294489276 #> 9612  4805.5 0.6979569 0.6979569 7.574820e-03 7.574820e-03 0.294468265 #> 9613  4806.0 0.6979757 0.6979757 7.576960e-03 7.576960e-03 0.294447301 #> 9614  4806.5 0.6979945 0.6979945 7.579105e-03 7.579105e-03 0.294426383 #> 9615  4807.0 0.6980132 0.6980132 7.581253e-03 7.581253e-03 0.294405512 #> 9616  4807.5 0.6980319 0.6980319 7.583406e-03 7.583406e-03 0.294384686 #> 9617  4808.0 0.6980505 0.6980505 7.585562e-03 7.585562e-03 0.294363907 #> 9618  4808.5 0.6980691 0.6980691 7.587723e-03 7.587723e-03 0.294343175 #> 9619  4809.0 0.6980876 0.6980876 7.589888e-03 7.589888e-03 0.294322488 #> 9620  4809.5 0.6981061 0.6981061 7.592058e-03 7.592058e-03 0.294301848 #> 9621  4810.0 0.6981245 0.6981245 7.594231e-03 7.594231e-03 0.294281255 #> 9622  4810.5 0.6981429 0.6981429 7.596408e-03 7.596408e-03 0.294260708 #> 9623  4811.0 0.6981612 0.6981612 7.598590e-03 7.598590e-03 0.294240208 #> 9624  4811.5 0.6981795 0.6981795 7.600775e-03 7.600775e-03 0.294219754 #> 9625  4812.0 0.6981977 0.6981977 7.602965e-03 7.602965e-03 0.294199347 #> 9626  4812.5 0.6982159 0.6982159 7.605159e-03 7.605159e-03 0.294178987 #> 9627  4813.0 0.6982340 0.6982340 7.607357e-03 7.607357e-03 0.294158674 #> 9628  4813.5 0.6982520 0.6982520 7.609559e-03 7.609559e-03 0.294138407 #> 9629  4814.0 0.6982700 0.6982700 7.611765e-03 7.611765e-03 0.294118187 #> 9630  4814.5 0.6982880 0.6982880 7.613975e-03 7.613975e-03 0.294098014 #> 9631  4815.0 0.6983059 0.6983059 7.616189e-03 7.616189e-03 0.294077888 #> 9632  4815.5 0.6983238 0.6983238 7.618407e-03 7.618407e-03 0.294057809 #> 9633  4816.0 0.6983416 0.6983416 7.620629e-03 7.620629e-03 0.294037777 #> 9634  4816.5 0.6983594 0.6983594 7.622856e-03 7.622856e-03 0.294017793 #> 9635  4817.0 0.6983771 0.6983771 7.625086e-03 7.625086e-03 0.293997855 #> 9636  4817.5 0.6983947 0.6983947 7.627320e-03 7.627320e-03 0.293977964 #> 9637  4818.0 0.6984123 0.6984123 7.629559e-03 7.629559e-03 0.293958121 #> 9638  4818.5 0.6984299 0.6984299 7.631801e-03 7.631801e-03 0.293938325 #> 9639  4819.0 0.6984474 0.6984474 7.634047e-03 7.634047e-03 0.293918576 #> 9640  4819.5 0.6984648 0.6984648 7.636298e-03 7.636298e-03 0.293898875 #> 9641  4820.0 0.6984822 0.6984822 7.638552e-03 7.638552e-03 0.293879220 #> 9642  4820.5 0.6984996 0.6984996 7.640810e-03 7.640810e-03 0.293859614 #> 9643  4821.0 0.6985169 0.6985169 7.643073e-03 7.643073e-03 0.293840054 #> 9644  4821.5 0.6985341 0.6985341 7.645339e-03 7.645339e-03 0.293820543 #> 9645  4822.0 0.6985513 0.6985513 7.647609e-03 7.647609e-03 0.293801079 #> 9646  4822.5 0.6985685 0.6985685 7.649883e-03 7.649883e-03 0.293781662 #> 9647  4823.0 0.6985855 0.6985855 7.652162e-03 7.652162e-03 0.293762293 #> 9648  4823.5 0.6986026 0.6986026 7.654444e-03 7.654444e-03 0.293742972 #> 9649  4824.0 0.6986196 0.6986196 7.656730e-03 7.656730e-03 0.293723698 #> 9650  4824.5 0.6986365 0.6986365 7.659020e-03 7.659020e-03 0.293704472 #> 9651  4825.0 0.6986534 0.6986534 7.661314e-03 7.661314e-03 0.293685294 #> 9652  4825.5 0.6986702 0.6986702 7.663611e-03 7.663611e-03 0.293666164 #> 9653  4826.0 0.6986870 0.6986870 7.665913e-03 7.665913e-03 0.293647082 #> 9654  4826.5 0.6987037 0.6987037 7.668219e-03 7.668219e-03 0.293628048 #> 9655  4827.0 0.6987204 0.6987204 7.670528e-03 7.670528e-03 0.293609061 #> 9656  4827.5 0.6987370 0.6987370 7.672842e-03 7.672842e-03 0.293590123 #> 9657  4828.0 0.6987536 0.6987536 7.675159e-03 7.675159e-03 0.293571233 #> 9658  4828.5 0.6987701 0.6987701 7.677480e-03 7.677480e-03 0.293552390 #> 9659  4829.0 0.6987866 0.6987866 7.679805e-03 7.679805e-03 0.293533596 #> 9660  4829.5 0.6988030 0.6988030 7.682134e-03 7.682134e-03 0.293514850 #> 9661  4830.0 0.6988194 0.6988194 7.684467e-03 7.684467e-03 0.293496152 #> 9662  4830.5 0.6988357 0.6988357 7.686803e-03 7.686803e-03 0.293477503 #> 9663  4831.0 0.6988520 0.6988520 7.689144e-03 7.689144e-03 0.293458902 #> 9664  4831.5 0.6988682 0.6988682 7.691488e-03 7.691488e-03 0.293440349 #> 9665  4832.0 0.6988843 0.6988843 7.693836e-03 7.693836e-03 0.293421844 #> 9666  4832.5 0.6989004 0.6989004 7.696188e-03 7.696188e-03 0.293403388 #> 9667  4833.0 0.6989165 0.6989165 7.698544e-03 7.698544e-03 0.293384981 #> 9668  4833.5 0.6989325 0.6989325 7.700903e-03 7.700903e-03 0.293366621 #> 9669  4834.0 0.6989484 0.6989484 7.703267e-03 7.703267e-03 0.293348311 #> 9670  4834.5 0.6989643 0.6989643 7.705634e-03 7.705634e-03 0.293330049 #> 9671  4835.0 0.6989802 0.6989802 7.708005e-03 7.708005e-03 0.293311835 #> 9672  4835.5 0.6989960 0.6989960 7.710379e-03 7.710379e-03 0.293293670 #> 9673  4836.0 0.6990117 0.6990117 7.712758e-03 7.712758e-03 0.293275554 #> 9674  4836.5 0.6990274 0.6990274 7.715140e-03 7.715140e-03 0.293257487 #> 9675  4837.0 0.6990430 0.6990430 7.717526e-03 7.717526e-03 0.293239468 #> 9676  4837.5 0.6990586 0.6990586 7.719916e-03 7.719916e-03 0.293221498 #> 9677  4838.0 0.6990741 0.6990741 7.722309e-03 7.722309e-03 0.293203577 #> 9678  4838.5 0.6990896 0.6990896 7.724707e-03 7.724707e-03 0.293185705 #> 9679  4839.0 0.6991050 0.6991050 7.727108e-03 7.727108e-03 0.293167882 #> 9680  4839.5 0.6991204 0.6991204 7.729512e-03 7.729512e-03 0.293150107 #> 9681  4840.0 0.6991357 0.6991357 7.731921e-03 7.731921e-03 0.293132382 #> 9682  4840.5 0.6991510 0.6991510 7.734333e-03 7.734333e-03 0.293114706 #> 9683  4841.0 0.6991662 0.6991662 7.736749e-03 7.736749e-03 0.293097078 #> 9684  4841.5 0.6991813 0.6991813 7.739168e-03 7.739168e-03 0.293079500 #> 9685  4842.0 0.6991964 0.6991964 7.741591e-03 7.741591e-03 0.293061971 #> 9686  4842.5 0.6992115 0.6992115 7.744018e-03 7.744018e-03 0.293044491 #> 9687  4843.0 0.6992265 0.6992265 7.746449e-03 7.746449e-03 0.293027061 #> 9688  4843.5 0.6992414 0.6992414 7.748883e-03 7.748883e-03 0.293009679 #> 9689  4844.0 0.6992563 0.6992563 7.751321e-03 7.751321e-03 0.292992347 #> 9690  4844.5 0.6992712 0.6992712 7.753762e-03 7.753762e-03 0.292975064 #> 9691  4845.0 0.6992860 0.6992860 7.756207e-03 7.756207e-03 0.292957831 #> 9692  4845.5 0.6993007 0.6993007 7.758656e-03 7.758656e-03 0.292940647 #> 9693  4846.0 0.6993154 0.6993154 7.761108e-03 7.761108e-03 0.292923512 #> 9694  4846.5 0.6993300 0.6993300 7.763564e-03 7.763564e-03 0.292906427 #> 9695  4847.0 0.6993446 0.6993446 7.766024e-03 7.766024e-03 0.292889391 #> 9696  4847.5 0.6993591 0.6993591 7.768487e-03 7.768487e-03 0.292872405 #> 9697  4848.0 0.6993736 0.6993736 7.770954e-03 7.770954e-03 0.292855468 #> 9698  4848.5 0.6993880 0.6993880 7.773424e-03 7.773424e-03 0.292838581 #> 9699  4849.0 0.6994024 0.6994024 7.775898e-03 7.775898e-03 0.292821744 #> 9700  4849.5 0.6994167 0.6994167 7.778376e-03 7.778376e-03 0.292804956 #> 9701  4850.0 0.6994309 0.6994309 7.780857e-03 7.780857e-03 0.292788218 #> 9702  4850.5 0.6994451 0.6994451 7.783342e-03 7.783342e-03 0.292771530 #> 9703  4851.0 0.6994593 0.6994593 7.785830e-03 7.785830e-03 0.292754891 #> 9704  4851.5 0.6994734 0.6994734 7.788322e-03 7.788322e-03 0.292738302 #> 9705  4852.0 0.6994874 0.6994874 7.790817e-03 7.790817e-03 0.292721764 #> 9706  4852.5 0.6995014 0.6995014 7.793316e-03 7.793316e-03 0.292705275 #> 9707  4853.0 0.6995153 0.6995153 7.795818e-03 7.795818e-03 0.292688836 #> 9708  4853.5 0.6995292 0.6995292 7.798324e-03 7.798324e-03 0.292672446 #> 9709  4854.0 0.6995431 0.6995431 7.800834e-03 7.800834e-03 0.292656107 #> 9710  4854.5 0.6995568 0.6995568 7.803347e-03 7.803347e-03 0.292639818 #> 9711  4855.0 0.6995706 0.6995706 7.805863e-03 7.805863e-03 0.292623579 #> 9712  4855.5 0.6995842 0.6995842 7.808383e-03 7.808383e-03 0.292607390 #> 9713  4856.0 0.6995978 0.6995978 7.810906e-03 7.810906e-03 0.292591251 #> 9714  4856.5 0.6996114 0.6996114 7.813433e-03 7.813433e-03 0.292575162 #> 9715  4857.0 0.6996249 0.6996249 7.815964e-03 7.815964e-03 0.292559124 #> 9716  4857.5 0.6996384 0.6996384 7.818497e-03 7.818497e-03 0.292543135 #> 9717  4858.0 0.6996518 0.6996518 7.821034e-03 7.821034e-03 0.292527197 #> 9718  4858.5 0.6996651 0.6996651 7.823575e-03 7.823575e-03 0.292511309 #> 9719  4859.0 0.6996784 0.6996784 7.826119e-03 7.826119e-03 0.292495472 #> 9720  4859.5 0.6996916 0.6996916 7.828667e-03 7.828667e-03 0.292479684 #> 9721  4860.0 0.6997048 0.6997048 7.831218e-03 7.831218e-03 0.292463948 #> 9722  4860.5 0.6997180 0.6997180 7.833772e-03 7.833772e-03 0.292448261 #> 9723  4861.0 0.6997310 0.6997310 7.836330e-03 7.836330e-03 0.292432625 #> 9724  4861.5 0.6997441 0.6997441 7.838891e-03 7.838891e-03 0.292417039 #> 9725  4862.0 0.6997570 0.6997570 7.841455e-03 7.841455e-03 0.292401504 #> 9726  4862.5 0.6997700 0.6997700 7.844023e-03 7.844023e-03 0.292386020 #> 9727  4863.0 0.6997828 0.6997828 7.846594e-03 7.846594e-03 0.292370586 #> 9728  4863.5 0.6997956 0.6997956 7.849169e-03 7.849169e-03 0.292355202 #> 9729  4864.0 0.6998084 0.6998084 7.851747e-03 7.851747e-03 0.292339870 #> 9730  4864.5 0.6998211 0.6998211 7.854328e-03 7.854328e-03 0.292324587 #> 9731  4865.0 0.6998337 0.6998337 7.856913e-03 7.856913e-03 0.292309356 #> 9732  4865.5 0.6998463 0.6998463 7.859501e-03 7.859501e-03 0.292294175 #> 9733  4866.0 0.6998589 0.6998589 7.862092e-03 7.862092e-03 0.292279045 #> 9734  4866.5 0.6998713 0.6998713 7.864687e-03 7.864687e-03 0.292263966 #> 9735  4867.0 0.6998838 0.6998838 7.867285e-03 7.867285e-03 0.292248937 #> 9736  4867.5 0.6998962 0.6998962 7.869886e-03 7.869886e-03 0.292233960 #> 9737  4868.0 0.6999085 0.6999085 7.872490e-03 7.872490e-03 0.292219033 #> 9738  4868.5 0.6999207 0.6999207 7.875098e-03 7.875098e-03 0.292204157 #> 9739  4869.0 0.6999330 0.6999330 7.877709e-03 7.877709e-03 0.292189332 #> 9740  4869.5 0.6999451 0.6999451 7.880324e-03 7.880324e-03 0.292174558 #> 9741  4870.0 0.6999572 0.6999572 7.882941e-03 7.882941e-03 0.292159835 #> 9742  4870.5 0.6999693 0.6999693 7.885562e-03 7.885562e-03 0.292145163 #> 9743  4871.0 0.6999813 0.6999813 7.888186e-03 7.888186e-03 0.292130542 #> 9744  4871.5 0.6999932 0.6999932 7.890813e-03 7.890813e-03 0.292115972 #> 9745  4872.0 0.7000051 0.7000051 7.893444e-03 7.893444e-03 0.292101453 #> 9746  4872.5 0.7000169 0.7000169 7.896078e-03 7.896078e-03 0.292086985 #> 9747  4873.0 0.7000287 0.7000287 7.898715e-03 7.898715e-03 0.292072569 #> 9748  4873.5 0.7000404 0.7000404 7.901355e-03 7.901355e-03 0.292058203 #> 9749  4874.0 0.7000521 0.7000521 7.903998e-03 7.903998e-03 0.292043889 #> 9750  4874.5 0.7000637 0.7000637 7.906645e-03 7.906645e-03 0.292029626 #> 9751  4875.0 0.7000753 0.7000753 7.909295e-03 7.909295e-03 0.292015414 #> 9752  4875.5 0.7000868 0.7000868 7.911947e-03 7.911947e-03 0.292001254 #> 9753  4876.0 0.7000983 0.7000983 7.914604e-03 7.914604e-03 0.291987145 #> 9754  4876.5 0.7001097 0.7001097 7.917263e-03 7.917263e-03 0.291973087 #> 9755  4877.0 0.7001210 0.7001210 7.919925e-03 7.919925e-03 0.291959080 #> 9756  4877.5 0.7001323 0.7001323 7.922591e-03 7.922591e-03 0.291945125 #> 9757  4878.0 0.7001435 0.7001435 7.925259e-03 7.925259e-03 0.291931221 #> 9758  4878.5 0.7001547 0.7001547 7.927931e-03 7.927931e-03 0.291917369 #> 9759  4879.0 0.7001658 0.7001658 7.930606e-03 7.930606e-03 0.291903568 #> 9760  4879.5 0.7001769 0.7001769 7.933284e-03 7.933284e-03 0.291889819 #> 9761  4880.0 0.7001879 0.7001879 7.935965e-03 7.935965e-03 0.291876121 #> 9762  4880.5 0.7001989 0.7001989 7.938649e-03 7.938649e-03 0.291862475 #> 9763  4881.0 0.7002098 0.7002098 7.941336e-03 7.941336e-03 0.291848881 #> 9764  4881.5 0.7002206 0.7002206 7.944027e-03 7.944027e-03 0.291835338 #> 9765  4882.0 0.7002314 0.7002314 7.946720e-03 7.946720e-03 0.291821846 #> 9766  4882.5 0.7002422 0.7002422 7.949416e-03 7.949416e-03 0.291808406 #> 9767  4883.0 0.7002529 0.7002529 7.952116e-03 7.952116e-03 0.291795018 #> 9768  4883.5 0.7002635 0.7002635 7.954818e-03 7.954818e-03 0.291781682 #> 9769  4884.0 0.7002741 0.7002741 7.957524e-03 7.957524e-03 0.291768397 #> 9770  4884.5 0.7002846 0.7002846 7.960232e-03 7.960232e-03 0.291755164 #> 9771  4885.0 0.7002951 0.7002951 7.962944e-03 7.962944e-03 0.291741983 #> 9772  4885.5 0.7003055 0.7003055 7.965658e-03 7.965658e-03 0.291728854 #> 9773  4886.0 0.7003158 0.7003158 7.968376e-03 7.968376e-03 0.291715776 #> 9774  4886.5 0.7003262 0.7003262 7.971097e-03 7.971097e-03 0.291702751 #> 9775  4887.0 0.7003364 0.7003364 7.973820e-03 7.973820e-03 0.291689777 #> 9776  4887.5 0.7003466 0.7003466 7.976547e-03 7.976547e-03 0.291676855 #> 9777  4888.0 0.7003567 0.7003567 7.979276e-03 7.979276e-03 0.291663985 #> 9778  4888.5 0.7003668 0.7003668 7.982008e-03 7.982008e-03 0.291651167 #> 9779  4889.0 0.7003769 0.7003769 7.984744e-03 7.984744e-03 0.291638401 #> 9780  4889.5 0.7003868 0.7003868 7.987482e-03 7.987482e-03 0.291625686 #> 9781  4890.0 0.7003968 0.7003968 7.990223e-03 7.990223e-03 0.291613024 #> 9782  4890.5 0.7004066 0.7004066 7.992967e-03 7.992967e-03 0.291600414 #> 9783  4891.0 0.7004164 0.7004164 7.995714e-03 7.995714e-03 0.291587856 #> 9784  4891.5 0.7004262 0.7004262 7.998464e-03 7.998464e-03 0.291575350 #> 9785  4892.0 0.7004359 0.7004359 8.001217e-03 8.001217e-03 0.291562896 #> 9786  4892.5 0.7004455 0.7004455 8.003973e-03 8.003973e-03 0.291550494 #> 9787  4893.0 0.7004551 0.7004551 8.006731e-03 8.006731e-03 0.291538145 #> 9788  4893.5 0.7004647 0.7004647 8.009493e-03 8.009493e-03 0.291525847 #> 9789  4894.0 0.7004741 0.7004741 8.012257e-03 8.012257e-03 0.291513602 #> 9790  4894.5 0.7004836 0.7004836 8.015024e-03 8.015024e-03 0.291501409 #> 9791  4895.0 0.7004929 0.7004929 8.017794e-03 8.017794e-03 0.291489268 #> 9792  4895.5 0.7005023 0.7005023 8.020567e-03 8.020567e-03 0.291477180 #> 9793  4896.0 0.7005115 0.7005115 8.023343e-03 8.023343e-03 0.291465143 #> 9794  4896.5 0.7005207 0.7005207 8.026121e-03 8.026121e-03 0.291453159 #> 9795  4897.0 0.7005299 0.7005299 8.028902e-03 8.028902e-03 0.291441227 #> 9796  4897.5 0.7005390 0.7005390 8.031686e-03 8.031686e-03 0.291429348 #> 9797  4898.0 0.7005480 0.7005480 8.034473e-03 8.034473e-03 0.291417521 #> 9798  4898.5 0.7005570 0.7005570 8.037263e-03 8.037263e-03 0.291405746 #> 9799  4899.0 0.7005659 0.7005659 8.040055e-03 8.040055e-03 0.291394024 #> 9800  4899.5 0.7005748 0.7005748 8.042850e-03 8.042850e-03 0.291382354 #> 9801  4900.0 0.7005836 0.7005836 8.045648e-03 8.045648e-03 0.291370737 #> 9802  4900.5 0.7005924 0.7005924 8.048449e-03 8.048449e-03 0.291359172 #> 9803  4901.0 0.7006011 0.7006011 8.051252e-03 8.051252e-03 0.291347659 #> 9804  4901.5 0.7006097 0.7006097 8.054058e-03 8.054058e-03 0.291336199 #> 9805  4902.0 0.7006183 0.7006183 8.056867e-03 8.056867e-03 0.291324792 #> 9806  4902.5 0.7006269 0.7006269 8.059679e-03 8.059679e-03 0.291313437 #> 9807  4903.0 0.7006354 0.7006354 8.062493e-03 8.062493e-03 0.291302134 #> 9808  4903.5 0.7006438 0.7006438 8.065310e-03 8.065310e-03 0.291290884 #> 9809  4904.0 0.7006522 0.7006522 8.068130e-03 8.068130e-03 0.291279687 #> 9810  4904.5 0.7006605 0.7006605 8.070952e-03 8.070952e-03 0.291268542 #> 9811  4905.0 0.7006688 0.7006688 8.073777e-03 8.073777e-03 0.291257450 #> 9812  4905.5 0.7006770 0.7006770 8.076604e-03 8.076604e-03 0.291246411 #> 9813  4906.0 0.7006851 0.7006851 8.079434e-03 8.079434e-03 0.291235424 #> 9814  4906.5 0.7006932 0.7006932 8.082267e-03 8.082267e-03 0.291224490 #> 9815  4907.0 0.7007013 0.7007013 8.085103e-03 8.085103e-03 0.291213608 #> 9816  4907.5 0.7007093 0.7007093 8.087941e-03 8.087941e-03 0.291202780 #> 9817  4908.0 0.7007172 0.7007172 8.090782e-03 8.090782e-03 0.291192004 #> 9818  4908.5 0.7007251 0.7007251 8.093625e-03 8.093625e-03 0.291181280 #> 9819  4909.0 0.7007329 0.7007329 8.096471e-03 8.096471e-03 0.291170610 #> 9820  4909.5 0.7007407 0.7007407 8.099319e-03 8.099319e-03 0.291159992 #> 9821  4910.0 0.7007484 0.7007484 8.102170e-03 8.102170e-03 0.291149427 #> 9822  4910.5 0.7007561 0.7007561 8.105024e-03 8.105024e-03 0.291138915 #> 9823  4911.0 0.7007637 0.7007637 8.107880e-03 8.107880e-03 0.291128456 #> 9824  4911.5 0.7007712 0.7007712 8.110739e-03 8.110739e-03 0.291118049 #> 9825  4912.0 0.7007787 0.7007787 8.113600e-03 8.113600e-03 0.291107696 #> 9826  4912.5 0.7007861 0.7007861 8.116464e-03 8.116464e-03 0.291097395 #> 9827  4913.0 0.7007935 0.7007935 8.119330e-03 8.119330e-03 0.291087147 #> 9828  4913.5 0.7008008 0.7008008 8.122199e-03 8.122199e-03 0.291076952 #> 9829  4914.0 0.7008081 0.7008081 8.125070e-03 8.125070e-03 0.291066810 #> 9830  4914.5 0.7008153 0.7008153 8.127944e-03 8.127944e-03 0.291056721 #> 9831  4915.0 0.7008225 0.7008225 8.130820e-03 8.130820e-03 0.291046685 #> 9832  4915.5 0.7008296 0.7008296 8.133699e-03 8.133699e-03 0.291036701 #> 9833  4916.0 0.7008366 0.7008366 8.136580e-03 8.136580e-03 0.291026771 #> 9834  4916.5 0.7008436 0.7008436 8.139463e-03 8.139463e-03 0.291016894 #> 9835  4917.0 0.7008506 0.7008506 8.142349e-03 8.142349e-03 0.291007070 #> 9836  4917.5 0.7008575 0.7008575 8.145238e-03 8.145238e-03 0.290997299 #> 9837  4918.0 0.7008643 0.7008643 8.148129e-03 8.148129e-03 0.290987580 #> 9838  4918.5 0.7008711 0.7008711 8.151022e-03 8.151022e-03 0.290977915 #> 9839  4919.0 0.7008778 0.7008778 8.153918e-03 8.153918e-03 0.290968303 #> 9840  4919.5 0.7008844 0.7008844 8.156816e-03 8.156816e-03 0.290958744 #> 9841  4920.0 0.7008910 0.7008910 8.159716e-03 8.159716e-03 0.290949238 #> 9842  4920.5 0.7008976 0.7008976 8.162619e-03 8.162619e-03 0.290939786 #> 9843  4921.0 0.7009041 0.7009041 8.165524e-03 8.165524e-03 0.290930386 #> 9844  4921.5 0.7009105 0.7009105 8.168432e-03 8.168432e-03 0.290921039 #> 9845  4922.0 0.7009169 0.7009169 8.171342e-03 8.171342e-03 0.290911746 #> 9846  4922.5 0.7009232 0.7009232 8.174254e-03 8.174254e-03 0.290902506 #> 9847  4923.0 0.7009295 0.7009295 8.177169e-03 8.177169e-03 0.290893319 #> 9848  4923.5 0.7009357 0.7009357 8.180086e-03 8.180086e-03 0.290884185 #> 9849  4924.0 0.7009419 0.7009419 8.183005e-03 8.183005e-03 0.290875104 #> 9850  4924.5 0.7009480 0.7009480 8.185926e-03 8.185926e-03 0.290866076 #> 9851  4925.0 0.7009540 0.7009540 8.188850e-03 8.188850e-03 0.290857102 #> 9852  4925.5 0.7009600 0.7009600 8.191776e-03 8.191776e-03 0.290848181 #> 9853  4926.0 0.7009660 0.7009660 8.194705e-03 8.194705e-03 0.290839313 #> 9854  4926.5 0.7009719 0.7009719 8.197635e-03 8.197635e-03 0.290830498 #> 9855  4927.0 0.7009777 0.7009777 8.200568e-03 8.200568e-03 0.290821737 #> 9856  4927.5 0.7009835 0.7009835 8.203503e-03 8.203503e-03 0.290813029 #> 9857  4928.0 0.7009892 0.7009892 8.206440e-03 8.206440e-03 0.290804374 #> 9858  4928.5 0.7009948 0.7009948 8.209380e-03 8.209380e-03 0.290795773 #> 9859  4929.0 0.7010005 0.7010005 8.212322e-03 8.212322e-03 0.290787224 #> 9860  4929.5 0.7010060 0.7010060 8.215266e-03 8.215266e-03 0.290778730 #> 9861  4930.0 0.7010115 0.7010115 8.218212e-03 8.218212e-03 0.290770288 #> 9862  4930.5 0.7010169 0.7010169 8.221160e-03 8.221160e-03 0.290761900 #> 9863  4931.0 0.7010223 0.7010223 8.224111e-03 8.224111e-03 0.290753565 #> 9864  4931.5 0.7010277 0.7010277 8.227063e-03 8.227063e-03 0.290745283 #> 9865  4932.0 0.7010329 0.7010329 8.230018e-03 8.230018e-03 0.290737055 #> 9866  4932.5 0.7010381 0.7010381 8.232975e-03 8.232975e-03 0.290728880 #> 9867  4933.0 0.7010433 0.7010433 8.235934e-03 8.235934e-03 0.290720758 #> 9868  4933.5 0.7010484 0.7010484 8.238895e-03 8.238895e-03 0.290712690 #> 9869  4934.0 0.7010535 0.7010535 8.241858e-03 8.241858e-03 0.290704676 #> 9870  4934.5 0.7010585 0.7010585 8.244824e-03 8.244824e-03 0.290696714 #> 9871  4935.0 0.7010634 0.7010634 8.247791e-03 8.247791e-03 0.290688806 #> 9872  4935.5 0.7010683 0.7010683 8.250761e-03 8.250761e-03 0.290680952 #> 9873  4936.0 0.7010731 0.7010731 8.253733e-03 8.253733e-03 0.290673151 #> 9874  4936.5 0.7010779 0.7010779 8.256706e-03 8.256706e-03 0.290665403 #> 9875  4937.0 0.7010826 0.7010826 8.259682e-03 8.259682e-03 0.290657709 #> 9876  4937.5 0.7010873 0.7010873 8.262660e-03 8.262660e-03 0.290650068 #> 9877  4938.0 0.7010919 0.7010919 8.265640e-03 8.265640e-03 0.290642481 #> 9878  4938.5 0.7010964 0.7010964 8.268622e-03 8.268622e-03 0.290634947 #> 9879  4939.0 0.7011009 0.7011009 8.271605e-03 8.271605e-03 0.290627467 #> 9880  4939.5 0.7011054 0.7011054 8.274591e-03 8.274591e-03 0.290620040 #> 9881  4940.0 0.7011098 0.7011098 8.277579e-03 8.277579e-03 0.290612666 #> 9882  4940.5 0.7011141 0.7011141 8.280569e-03 8.280569e-03 0.290605346 #> 9883  4941.0 0.7011184 0.7011184 8.283561e-03 8.283561e-03 0.290598080 #> 9884  4941.5 0.7011226 0.7011226 8.286554e-03 8.286554e-03 0.290590867 #> 9885  4942.0 0.7011267 0.7011267 8.289550e-03 8.289550e-03 0.290583708 #> 9886  4942.5 0.7011309 0.7011309 8.292548e-03 8.292548e-03 0.290576602 #> 9887  4943.0 0.7011349 0.7011349 8.295547e-03 8.295547e-03 0.290569549 #> 9888  4943.5 0.7011389 0.7011389 8.298549e-03 8.298549e-03 0.290562550 #> 9889  4944.0 0.7011428 0.7011428 8.301552e-03 8.301552e-03 0.290555605 #> 9890  4944.5 0.7011467 0.7011467 8.304558e-03 8.304558e-03 0.290548713 #> 9891  4945.0 0.7011506 0.7011506 8.307565e-03 8.307565e-03 0.290541875 #> 9892  4945.5 0.7011543 0.7011543 8.310574e-03 8.310574e-03 0.290535090 #> 9893  4946.0 0.7011581 0.7011581 8.313585e-03 8.313585e-03 0.290528359 #> 9894  4946.5 0.7011617 0.7011617 8.316598e-03 8.316598e-03 0.290521681 #> 9895  4947.0 0.7011653 0.7011653 8.319612e-03 8.319612e-03 0.290515057 #> 9896  4947.5 0.7011689 0.7011689 8.322629e-03 8.322629e-03 0.290508487 #> 9897  4948.0 0.7011724 0.7011724 8.325647e-03 8.325647e-03 0.290501970 #> 9898  4948.5 0.7011758 0.7011758 8.328667e-03 8.328667e-03 0.290495506 #> 9899  4949.0 0.7011792 0.7011792 8.331689e-03 8.331689e-03 0.290489096 #> 9900  4949.5 0.7011825 0.7011825 8.334713e-03 8.334713e-03 0.290482740 #> 9901  4950.0 0.7011858 0.7011858 8.337739e-03 8.337739e-03 0.290476437 #> 9902  4950.5 0.7011890 0.7011890 8.340766e-03 8.340766e-03 0.290470188 #> 9903  4951.0 0.7011922 0.7011922 8.343795e-03 8.343795e-03 0.290463993 #> 9904  4951.5 0.7011953 0.7011953 8.346826e-03 8.346826e-03 0.290457851 #> 9905  4952.0 0.7011984 0.7011984 8.349858e-03 8.349858e-03 0.290451762 #> 9906  4952.5 0.7012014 0.7012014 8.352893e-03 8.352893e-03 0.290445728 #> 9907  4953.0 0.7012043 0.7012043 8.355929e-03 8.355929e-03 0.290439746 #> 9908  4953.5 0.7012072 0.7012072 8.358966e-03 8.358966e-03 0.290433819 #> 9909  4954.0 0.7012100 0.7012100 8.362006e-03 8.362006e-03 0.290427945 #> 9910  4954.5 0.7012128 0.7012128 8.365047e-03 8.365047e-03 0.290422124 #> 9911  4955.0 0.7012156 0.7012156 8.368090e-03 8.368090e-03 0.290416358 #> 9912  4955.5 0.7012182 0.7012182 8.371134e-03 8.371134e-03 0.290410644 #> 9913  4956.0 0.7012208 0.7012208 8.374181e-03 8.374181e-03 0.290404985 #> 9914  4956.5 0.7012234 0.7012234 8.377228e-03 8.377228e-03 0.290399379 #> 9915  4957.0 0.7012259 0.7012259 8.380278e-03 8.380278e-03 0.290393826 #> 9916  4957.5 0.7012283 0.7012283 8.383329e-03 8.383329e-03 0.290388327 #> 9917  4958.0 0.7012307 0.7012307 8.386382e-03 8.386382e-03 0.290382882 #> 9918  4958.5 0.7012331 0.7012331 8.389436e-03 8.389436e-03 0.290377491 #> 9919  4959.0 0.7012354 0.7012354 8.392492e-03 8.392492e-03 0.290372153 #> 9920  4959.5 0.7012376 0.7012376 8.395549e-03 8.395549e-03 0.290366868 #> 9921  4960.0 0.7012398 0.7012398 8.398608e-03 8.398608e-03 0.290361637 #> 9922  4960.5 0.7012419 0.7012419 8.401669e-03 8.401669e-03 0.290356460 #> 9923  4961.0 0.7012439 0.7012439 8.404731e-03 8.404731e-03 0.290351337 #> 9924  4961.5 0.7012459 0.7012459 8.407795e-03 8.407795e-03 0.290346267 #> 9925  4962.0 0.7012479 0.7012479 8.410860e-03 8.410860e-03 0.290341250 #> 9926  4962.5 0.7012498 0.7012498 8.413927e-03 8.413927e-03 0.290336287 #> 9927  4963.0 0.7012516 0.7012516 8.416995e-03 8.416995e-03 0.290331378 #> 9928  4963.5 0.7012534 0.7012534 8.420065e-03 8.420065e-03 0.290326522 #> 9929  4964.0 0.7012551 0.7012551 8.423136e-03 8.423136e-03 0.290321720 #> 9930  4964.5 0.7012568 0.7012568 8.426209e-03 8.426209e-03 0.290316972 #> 9931  4965.0 0.7012584 0.7012584 8.429283e-03 8.429283e-03 0.290312277 #> 9932  4965.5 0.7012600 0.7012600 8.432359e-03 8.432359e-03 0.290307636 #> 9933  4966.0 0.7012615 0.7012615 8.435436e-03 8.435436e-03 0.290303048 #> 9934  4966.5 0.7012630 0.7012630 8.438515e-03 8.438515e-03 0.290298514 #> 9935  4967.0 0.7012644 0.7012644 8.441595e-03 8.441595e-03 0.290294033 #> 9936  4967.5 0.7012657 0.7012657 8.444676e-03 8.444676e-03 0.290289606 #> 9937  4968.0 0.7012670 0.7012670 8.447759e-03 8.447759e-03 0.290285233 #> 9938  4968.5 0.7012682 0.7012682 8.450843e-03 8.450843e-03 0.290280913 #> 9939  4969.0 0.7012694 0.7012694 8.453929e-03 8.453929e-03 0.290276647 #> 9940  4969.5 0.7012706 0.7012706 8.457015e-03 8.457015e-03 0.290272434 #> 9941  4970.0 0.7012716 0.7012716 8.460104e-03 8.460104e-03 0.290268275 #> 9942  4970.5 0.7012726 0.7012726 8.463193e-03 8.463193e-03 0.290264169 #> 9943  4971.0 0.7012736 0.7012736 8.466284e-03 8.466284e-03 0.290260117 #> 9944  4971.5 0.7012745 0.7012745 8.469377e-03 8.469377e-03 0.290256119 #> 9945  4972.0 0.7012754 0.7012754 8.472470e-03 8.472470e-03 0.290252174 #> 9946  4972.5 0.7012762 0.7012762 8.475565e-03 8.475565e-03 0.290248282 #> 9947  4973.0 0.7012769 0.7012769 8.478662e-03 8.478662e-03 0.290244444 #> 9948  4973.5 0.7012776 0.7012776 8.481759e-03 8.481759e-03 0.290240660 #> 9949  4974.0 0.7012782 0.7012782 8.484858e-03 8.484858e-03 0.290236929 #> 9950  4974.5 0.7012788 0.7012788 8.487958e-03 8.487958e-03 0.290233251 #> 9951  4975.0 0.7012793 0.7012793 8.491059e-03 8.491059e-03 0.290229627 #> 9952  4975.5 0.7012798 0.7012798 8.494162e-03 8.494162e-03 0.290226057 #> 9953  4976.0 0.7012802 0.7012802 8.497266e-03 8.497266e-03 0.290222540 #> 9954  4976.5 0.7012806 0.7012806 8.500371e-03 8.500371e-03 0.290219077 #> 9955  4977.0 0.7012809 0.7012809 8.503477e-03 8.503477e-03 0.290215667 #> 9956  4977.5 0.7012811 0.7012811 8.506584e-03 8.506584e-03 0.290212310 #> 9957  4978.0 0.7012813 0.7012813 8.509693e-03 8.509693e-03 0.290209007 #> 9958  4978.5 0.7012814 0.7012814 8.512802e-03 8.512802e-03 0.290205758 #> 9959  4979.0 0.7012815 0.7012815 8.515913e-03 8.515913e-03 0.290202562 #> 9960  4979.5 0.7012816 0.7012816 8.519025e-03 8.519025e-03 0.290199419 #> 9961  4980.0 0.7012815 0.7012815 8.522139e-03 8.522139e-03 0.290196330 #> 9962  4980.5 0.7012815 0.7012815 8.525253e-03 8.525253e-03 0.290193294 #> 9963  4981.0 0.7012813 0.7012813 8.528368e-03 8.528368e-03 0.290190311 #> 9964  4981.5 0.7012811 0.7012811 8.531485e-03 8.531485e-03 0.290187382 #> 9965  4982.0 0.7012809 0.7012809 8.534602e-03 8.534602e-03 0.290184507 #> 9966  4982.5 0.7012806 0.7012806 8.537721e-03 8.537721e-03 0.290181685 #> 9967  4983.0 0.7012802 0.7012802 8.540841e-03 8.540841e-03 0.290178916 #> 9968  4983.5 0.7012798 0.7012798 8.543962e-03 8.543962e-03 0.290176200 #> 9969  4984.0 0.7012794 0.7012794 8.547084e-03 8.547084e-03 0.290173538 #> 9970  4984.5 0.7012789 0.7012789 8.550207e-03 8.550207e-03 0.290170930 #> 9971  4985.0 0.7012783 0.7012783 8.553330e-03 8.553330e-03 0.290168374 #> 9972  4985.5 0.7012777 0.7012777 8.556455e-03 8.556455e-03 0.290165872 #> 9973  4986.0 0.7012770 0.7012770 8.559581e-03 8.559581e-03 0.290163424 #> 9974  4986.5 0.7012763 0.7012763 8.562708e-03 8.562708e-03 0.290161028 #> 9975  4987.0 0.7012755 0.7012755 8.565836e-03 8.565836e-03 0.290158686 #> 9976  4987.5 0.7012746 0.7012746 8.568965e-03 8.568965e-03 0.290156397 #> 9977  4988.0 0.7012737 0.7012737 8.572095e-03 8.572095e-03 0.290154162 #> 9978  4988.5 0.7012728 0.7012728 8.575226e-03 8.575226e-03 0.290151980 #> 9979  4989.0 0.7012718 0.7012718 8.578358e-03 8.578358e-03 0.290149851 #> 9980  4989.5 0.7012707 0.7012707 8.581490e-03 8.581490e-03 0.290147775 #> 9981  4990.0 0.7012696 0.7012696 8.584624e-03 8.584624e-03 0.290145753 #> 9982  4990.5 0.7012685 0.7012685 8.587758e-03 8.587758e-03 0.290143784 #> 9983  4991.0 0.7012672 0.7012672 8.590894e-03 8.590894e-03 0.290141868 #> 9984  4991.5 0.7012660 0.7012660 8.594030e-03 8.594030e-03 0.290140005 #> 9985  4992.0 0.7012646 0.7012646 8.597167e-03 8.597167e-03 0.290138195 #> 9986  4992.5 0.7012633 0.7012633 8.600305e-03 8.600305e-03 0.290136439 #> 9987  4993.0 0.7012618 0.7012618 8.603444e-03 8.603444e-03 0.290134736 #> 9988  4993.5 0.7012603 0.7012603 8.606583e-03 8.606583e-03 0.290133086 #> 9989  4994.0 0.7012588 0.7012588 8.609724e-03 8.609724e-03 0.290131489 #> 9990  4994.5 0.7012572 0.7012572 8.612865e-03 8.612865e-03 0.290129945 #> 9991  4995.0 0.7012555 0.7012555 8.616007e-03 8.616007e-03 0.290128454 #> 9992  4995.5 0.7012538 0.7012538 8.619150e-03 8.619150e-03 0.290127017 #> 9993  4996.0 0.7012521 0.7012521 8.622294e-03 8.622294e-03 0.290125632 #> 9994  4996.5 0.7012503 0.7012503 8.625438e-03 8.625438e-03 0.290124301 #> 9995  4997.0 0.7012484 0.7012484 8.628583e-03 8.628583e-03 0.290123022 #> 9996  4997.5 0.7012465 0.7012465 8.631729e-03 8.631729e-03 0.290121797 #> 9997  4998.0 0.7012445 0.7012445 8.634875e-03 8.634875e-03 0.290120625 #> 9998  4998.5 0.7012425 0.7012425 8.638023e-03 8.638023e-03 0.290119506 #> 9999  4999.0 0.7012404 0.7012404 8.641171e-03 8.641171e-03 0.290118440 #> 10000 4999.5 0.7012383 0.7012383 8.644319e-03 8.644319e-03 0.290117426 #> 10001 5000.0 0.7012361 0.7012361 8.647469e-03 8.647469e-03 0.290116466 #> 10002 5000.5 0.7012338 0.7012338 8.650619e-03 8.650619e-03 0.290115559 #> 10003 5001.0 0.7012315 0.7012315 8.653770e-03 8.653770e-03 0.290114705 #> 10004 5001.5 0.7012292 0.7012292 8.656921e-03 8.656921e-03 0.290113903 #> 10005 5002.0 0.7012268 0.7012268 8.660073e-03 8.660073e-03 0.290113155 #> 10006 5002.5 0.7012243 0.7012243 8.663225e-03 8.663225e-03 0.290112459 #> 10007 5003.0 0.7012218 0.7012218 8.666379e-03 8.666379e-03 0.290111816 #> 10008 5003.5 0.7012192 0.7012192 8.669532e-03 8.669532e-03 0.290111227 #> 10009 5004.0 0.7012166 0.7012166 8.672687e-03 8.672687e-03 0.290110690 #> 10010 5004.5 0.7012140 0.7012140 8.675842e-03 8.675842e-03 0.290110205 #> 10011 5005.0 0.7012112 0.7012112 8.678997e-03 8.678997e-03 0.290109774 #> 10012 5005.5 0.7012085 0.7012085 8.682153e-03 8.682153e-03 0.290109396 #> 10013 5006.0 0.7012056 0.7012056 8.685310e-03 8.685310e-03 0.290109070 #> 10014 5006.5 0.7012027 0.7012027 8.688467e-03 8.688467e-03 0.290108797 #> 10015 5007.0 0.7011998 0.7011998 8.691625e-03 8.691625e-03 0.290108577 #> 10016 5007.5 0.7011968 0.7011968 8.694783e-03 8.694783e-03 0.290108409 #> 10017 5008.0 0.7011938 0.7011938 8.697942e-03 8.697942e-03 0.290108294 #> 10018 5008.5 0.7011907 0.7011907 8.701101e-03 8.701101e-03 0.290108232 #> 10019 5009.0 0.7011875 0.7011875 8.704260e-03 8.704260e-03 0.290108223 #> 10020 5009.5 0.7011843 0.7011843 8.707420e-03 8.707420e-03 0.290108266 #> 10021 5010.0 0.7011811 0.7011811 8.710581e-03 8.710581e-03 0.290108362 #> 10022 5010.5 0.7011777 0.7011777 8.713742e-03 8.713742e-03 0.290108510 #> 10023 5011.0 0.7011744 0.7011744 8.716903e-03 8.716903e-03 0.290108711 #> 10024 5011.5 0.7011710 0.7011710 8.720065e-03 8.720065e-03 0.290108965 #> 10025 5012.0 0.7011675 0.7011675 8.723227e-03 8.723227e-03 0.290109271 #> 10026 5012.5 0.7011640 0.7011640 8.726390e-03 8.726390e-03 0.290109630 #> 10027 5013.0 0.7011604 0.7011604 8.729553e-03 8.729553e-03 0.290110041 #> 10028 5013.5 0.7011568 0.7011568 8.732716e-03 8.732716e-03 0.290110505 #> 10029 5014.0 0.7011531 0.7011531 8.735880e-03 8.735880e-03 0.290111021 #> 10030 5014.5 0.7011494 0.7011494 8.739044e-03 8.739044e-03 0.290111590 #> 10031 5015.0 0.7011456 0.7011456 8.742208e-03 8.742208e-03 0.290112211 #> 10032 5015.5 0.7011417 0.7011417 8.745372e-03 8.745372e-03 0.290112885 #> 10033 5016.0 0.7011379 0.7011379 8.748537e-03 8.748537e-03 0.290113611 #> 10034 5016.5 0.7011339 0.7011339 8.751702e-03 8.751702e-03 0.290114389 #> 10035 5017.0 0.7011299 0.7011299 8.754868e-03 8.754868e-03 0.290115220 #> 10036 5017.5 0.7011259 0.7011259 8.758034e-03 8.758034e-03 0.290116103 #> 10037 5018.0 0.7011218 0.7011218 8.761200e-03 8.761200e-03 0.290117038 #> 10038 5018.5 0.7011176 0.7011176 8.764366e-03 8.764366e-03 0.290118026 #> 10039 5019.0 0.7011134 0.7011134 8.767532e-03 8.767532e-03 0.290119065 #> 10040 5019.5 0.7011091 0.7011091 8.770699e-03 8.770699e-03 0.290120158 #> 10041 5020.0 0.7011048 0.7011048 8.773866e-03 8.773866e-03 0.290121302 #> 10042 5020.5 0.7011005 0.7011005 8.777033e-03 8.777033e-03 0.290122498 #> 10043 5021.0 0.7010961 0.7010961 8.780200e-03 8.780200e-03 0.290123747 #> 10044 5021.5 0.7010916 0.7010916 8.783368e-03 8.783368e-03 0.290125048 #> 10045 5022.0 0.7010871 0.7010871 8.786535e-03 8.786535e-03 0.290126401 #> 10046 5022.5 0.7010825 0.7010825 8.789703e-03 8.789703e-03 0.290127806 #> 10047 5023.0 0.7010779 0.7010779 8.792871e-03 8.792871e-03 0.290129263 #> 10048 5023.5 0.7010732 0.7010732 8.796039e-03 8.796039e-03 0.290130773 #> 10049 5024.0 0.7010685 0.7010685 8.799207e-03 8.799207e-03 0.290132334 #> 10050 5024.5 0.7010637 0.7010637 8.802375e-03 8.802375e-03 0.290133947 #> 10051 5025.0 0.7010588 0.7010588 8.805543e-03 8.805543e-03 0.290135612 #> 10052 5025.5 0.7010540 0.7010540 8.808711e-03 8.808711e-03 0.290137330 #> 10053 5026.0 0.7010490 0.7010490 8.811880e-03 8.811880e-03 0.290139099 #> 10054 5026.5 0.7010440 0.7010440 8.815048e-03 8.815048e-03 0.290140920 #> 10055 5027.0 0.7010390 0.7010390 8.818217e-03 8.818217e-03 0.290142793 #> 10056 5027.5 0.7010339 0.7010339 8.821385e-03 8.821385e-03 0.290144718 #> 10057 5028.0 0.7010288 0.7010288 8.824553e-03 8.824553e-03 0.290146694 #> 10058 5028.5 0.7010236 0.7010236 8.827722e-03 8.827722e-03 0.290148723 #> 10059 5029.0 0.7010183 0.7010183 8.830890e-03 8.830890e-03 0.290150803 #> 10060 5029.5 0.7010130 0.7010130 8.834059e-03 8.834059e-03 0.290152935 #> 10061 5030.0 0.7010077 0.7010077 8.837227e-03 8.837227e-03 0.290155119 #> 10062 5030.5 0.7010023 0.7010023 8.840396e-03 8.840396e-03 0.290157354 #> 10063 5031.0 0.7009968 0.7009968 8.843564e-03 8.843564e-03 0.290159641 #> 10064 5031.5 0.7009913 0.7009913 8.846732e-03 8.846732e-03 0.290161980 #> 10065 5032.0 0.7009857 0.7009857 8.849900e-03 8.849900e-03 0.290164370 #> 10066 5032.5 0.7009801 0.7009801 8.853068e-03 8.853068e-03 0.290166812 #> 10067 5033.0 0.7009745 0.7009745 8.856236e-03 8.856236e-03 0.290169306 #> 10068 5033.5 0.7009687 0.7009687 8.859404e-03 8.859404e-03 0.290171851 #> 10069 5034.0 0.7009630 0.7009630 8.862571e-03 8.862571e-03 0.290174447 #> 10070 5034.5 0.7009572 0.7009572 8.865739e-03 8.865739e-03 0.290177095 #> 10071 5035.0 0.7009513 0.7009513 8.868906e-03 8.868906e-03 0.290179795 #> 10072 5035.5 0.7009454 0.7009454 8.872073e-03 8.872073e-03 0.290182546 #> 10073 5036.0 0.7009394 0.7009394 8.875240e-03 8.875240e-03 0.290185348 #> 10074 5036.5 0.7009334 0.7009334 8.878407e-03 8.878407e-03 0.290188202 #> 10075 5037.0 0.7009273 0.7009273 8.881574e-03 8.881574e-03 0.290191107 #> 10076 5037.5 0.7009212 0.7009212 8.884740e-03 8.884740e-03 0.290194063 #> 10077 5038.0 0.7009150 0.7009150 8.887906e-03 8.887906e-03 0.290197070 #> 10078 5038.5 0.7009088 0.7009088 8.891072e-03 8.891072e-03 0.290200129 #> 10079 5039.0 0.7009025 0.7009025 8.894237e-03 8.894237e-03 0.290203239 #> 10080 5039.5 0.7008962 0.7008962 8.897403e-03 8.897403e-03 0.290206401 #> 10081 5040.0 0.7008898 0.7008898 8.900568e-03 8.900568e-03 0.290209613 #> 10082 5040.5 0.7008834 0.7008834 8.903732e-03 8.903732e-03 0.290212877 #> 10083 5041.0 0.7008769 0.7008769 8.906897e-03 8.906897e-03 0.290216191 #> 10084 5041.5 0.7008704 0.7008704 8.910061e-03 8.910061e-03 0.290219557 #> 10085 5042.0 0.7008638 0.7008638 8.913225e-03 8.913225e-03 0.290222974 #> 10086 5042.5 0.7008572 0.7008572 8.916388e-03 8.916388e-03 0.290226441 #> 10087 5043.0 0.7008505 0.7008505 8.919551e-03 8.919551e-03 0.290229960 #> 10088 5043.5 0.7008438 0.7008438 8.922714e-03 8.922714e-03 0.290233530 #> 10089 5044.0 0.7008370 0.7008370 8.925876e-03 8.925876e-03 0.290237150 #> 10090 5044.5 0.7008301 0.7008301 8.929038e-03 8.929038e-03 0.290240822 #> 10091 5045.0 0.7008233 0.7008233 8.932199e-03 8.932199e-03 0.290244544 #> 10092 5045.5 0.7008163 0.7008163 8.935360e-03 8.935360e-03 0.290248317 #> 10093 5046.0 0.7008093 0.7008093 8.938521e-03 8.938521e-03 0.290252141 #> 10094 5046.5 0.7008023 0.7008023 8.941681e-03 8.941681e-03 0.290256016 #> 10095 5047.0 0.7007952 0.7007952 8.944841e-03 8.944841e-03 0.290259941 #> 10096 5047.5 0.7007881 0.7007881 8.948000e-03 8.948000e-03 0.290263917 #> 10097 5048.0 0.7007809 0.7007809 8.951158e-03 8.951158e-03 0.290267944 #> 10098 5048.5 0.7007737 0.7007737 8.954317e-03 8.954317e-03 0.290272021 #> 10099 5049.0 0.7007664 0.7007664 8.957474e-03 8.957474e-03 0.290276149 #> 10100 5049.5 0.7007590 0.7007590 8.960632e-03 8.960632e-03 0.290280328 #> 10101 5050.0 0.7007517 0.7007517 8.963788e-03 8.963788e-03 0.290284557 #> 10102 5050.5 0.7007442 0.7007442 8.966944e-03 8.966944e-03 0.290288836 #> 10103 5051.0 0.7007367 0.7007367 8.970100e-03 8.970100e-03 0.290293166 #> 10104 5051.5 0.7007292 0.7007292 8.973255e-03 8.973255e-03 0.290297546 #> 10105 5052.0 0.7007216 0.7007216 8.976409e-03 8.976409e-03 0.290301977 #> 10106 5052.5 0.7007140 0.7007140 8.979563e-03 8.979563e-03 0.290306458 #> 10107 5053.0 0.7007063 0.7007063 8.982716e-03 8.982716e-03 0.290310989 #> 10108 5053.5 0.7006986 0.7006986 8.985869e-03 8.985869e-03 0.290315571 #> 10109 5054.0 0.7006908 0.7006908 8.989021e-03 8.989021e-03 0.290320202 #> 10110 5054.5 0.7006829 0.7006829 8.992172e-03 8.992172e-03 0.290324884 #> 10111 5055.0 0.7006751 0.7006751 8.995322e-03 8.995322e-03 0.290329617 #> 10112 5055.5 0.7006671 0.7006671 8.998472e-03 8.998472e-03 0.290334399 #> 10113 5056.0 0.7006591 0.7006591 9.001622e-03 9.001622e-03 0.290339231 #> 10114 5056.5 0.7006511 0.7006511 9.004770e-03 9.004770e-03 0.290344114 #> 10115 5057.0 0.7006430 0.7006430 9.007918e-03 9.007918e-03 0.290349046 #> 10116 5057.5 0.7006349 0.7006349 9.011065e-03 9.011065e-03 0.290354029 #> 10117 5058.0 0.7006267 0.7006267 9.014211e-03 9.014211e-03 0.290359061 #> 10118 5058.5 0.7006185 0.7006185 9.017357e-03 9.017357e-03 0.290364143 #> 10119 5059.0 0.7006102 0.7006102 9.020502e-03 9.020502e-03 0.290369275 #> 10120 5059.5 0.7006019 0.7006019 9.023646e-03 9.023646e-03 0.290374457 #> 10121 5060.0 0.7005935 0.7005935 9.026789e-03 9.026789e-03 0.290379689 #> 10122 5060.5 0.7005851 0.7005851 9.029931e-03 9.029931e-03 0.290384971 #> 10123 5061.0 0.7005766 0.7005766 9.033073e-03 9.033073e-03 0.290390302 #> 10124 5061.5 0.7005681 0.7005681 9.036214e-03 9.036214e-03 0.290395683 #> 10125 5062.0 0.7005595 0.7005595 9.039354e-03 9.039354e-03 0.290401113 #> 10126 5062.5 0.7005509 0.7005509 9.042493e-03 9.042493e-03 0.290406593 #> 10127 5063.0 0.7005422 0.7005422 9.045631e-03 9.045631e-03 0.290412123 #> 10128 5063.5 0.7005335 0.7005335 9.048769e-03 9.048769e-03 0.290417702 #> 10129 5064.0 0.7005248 0.7005248 9.051905e-03 9.051905e-03 0.290423331 #> 10130 5064.5 0.7005160 0.7005160 9.055041e-03 9.055041e-03 0.290429009 #> 10131 5065.0 0.7005071 0.7005071 9.058176e-03 9.058176e-03 0.290434737 #> 10132 5065.5 0.7004982 0.7004982 9.061309e-03 9.061309e-03 0.290440514 #> 10133 5066.0 0.7004892 0.7004892 9.064442e-03 9.064442e-03 0.290446340 #> 10134 5066.5 0.7004802 0.7004802 9.067574e-03 9.067574e-03 0.290452215 #> 10135 5067.0 0.7004712 0.7004712 9.070705e-03 9.070705e-03 0.290458140 #> 10136 5067.5 0.7004621 0.7004621 9.073835e-03 9.073835e-03 0.290464114 #> 10137 5068.0 0.7004529 0.7004529 9.076964e-03 9.076964e-03 0.290470137 #> 10138 5068.5 0.7004437 0.7004437 9.080092e-03 9.080092e-03 0.290476209 #> 10139 5069.0 0.7004345 0.7004345 9.083219e-03 9.083219e-03 0.290482331 #> 10140 5069.5 0.7004252 0.7004252 9.086344e-03 9.086344e-03 0.290488501 #> 10141 5070.0 0.7004158 0.7004158 9.089469e-03 9.089469e-03 0.290494720 #> 10142 5070.5 0.7004064 0.7004064 9.092593e-03 9.092593e-03 0.290500988 #> 10143 5071.0 0.7003970 0.7003970 9.095716e-03 9.095716e-03 0.290507306 #> 10144 5071.5 0.7003875 0.7003875 9.098837e-03 9.098837e-03 0.290513672 #> 10145 5072.0 0.7003780 0.7003780 9.101958e-03 9.101958e-03 0.290520086 #> 10146 5072.5 0.7003684 0.7003684 9.105077e-03 9.105077e-03 0.290526550 #> 10147 5073.0 0.7003587 0.7003587 9.108196e-03 9.108196e-03 0.290533063 #> 10148 5073.5 0.7003491 0.7003491 9.111313e-03 9.111313e-03 0.290539624 #> 10149 5074.0 0.7003393 0.7003393 9.114429e-03 9.114429e-03 0.290546233 #> 10150 5074.5 0.7003296 0.7003296 9.117544e-03 9.117544e-03 0.290552892 #> 10151 5075.0 0.7003197 0.7003197 9.120658e-03 9.120658e-03 0.290559599 #> 10152 5075.5 0.7003099 0.7003099 9.123770e-03 9.123770e-03 0.290566354 #> 10153 5076.0 0.7003000 0.7003000 9.126881e-03 9.126881e-03 0.290573158 #> 10154 5076.5 0.7002900 0.7002900 9.129992e-03 9.129992e-03 0.290580010 #> 10155 5077.0 0.7002800 0.7002800 9.133100e-03 9.133100e-03 0.290586911 #> 10156 5077.5 0.7002699 0.7002699 9.136208e-03 9.136208e-03 0.290593860 #> 10157 5078.0 0.7002598 0.7002598 9.139315e-03 9.139315e-03 0.290600857 #> 10158 5078.5 0.7002497 0.7002497 9.142420e-03 9.142420e-03 0.290607903 #> 10159 5079.0 0.7002395 0.7002395 9.145524e-03 9.145524e-03 0.290614997 #> 10160 5079.5 0.7002292 0.7002292 9.148626e-03 9.148626e-03 0.290622139 #> 10161 5080.0 0.7002189 0.7002189 9.151728e-03 9.151728e-03 0.290629329 #> 10162 5080.5 0.7002086 0.7002086 9.154828e-03 9.154828e-03 0.290636567 #> 10163 5081.0 0.7001982 0.7001982 9.157926e-03 9.157926e-03 0.290643853 #> 10164 5081.5 0.7001878 0.7001878 9.161024e-03 9.161024e-03 0.290651187 #> 10165 5082.0 0.7001773 0.7001773 9.164120e-03 9.164120e-03 0.290658569 #> 10166 5082.5 0.7001668 0.7001668 9.167214e-03 9.167214e-03 0.290665999 #> 10167 5083.0 0.7001562 0.7001562 9.170308e-03 9.170308e-03 0.290673477 #> 10168 5083.5 0.7001456 0.7001456 9.173399e-03 9.173399e-03 0.290681003 #> 10169 5084.0 0.7001349 0.7001349 9.176490e-03 9.176490e-03 0.290688576 #> 10170 5084.5 0.7001242 0.7001242 9.179579e-03 9.179579e-03 0.290696197 #> 10171 5085.0 0.7001135 0.7001135 9.182667e-03 9.182667e-03 0.290703866 #> 10172 5085.5 0.7001027 0.7001027 9.185753e-03 9.185753e-03 0.290711582 #> 10173 5086.0 0.7000918 0.7000918 9.188838e-03 9.188838e-03 0.290719346 #> 10174 5086.5 0.7000809 0.7000809 9.191921e-03 9.191921e-03 0.290727157 #> 10175 5087.0 0.7000700 0.7000700 9.195003e-03 9.195003e-03 0.290735016 #> 10176 5087.5 0.7000590 0.7000590 9.198083e-03 9.198083e-03 0.290742922 #> 10177 5088.0 0.7000480 0.7000480 9.201162e-03 9.201162e-03 0.290750876 #> 10178 5088.5 0.7000369 0.7000369 9.204239e-03 9.204239e-03 0.290758877 #> 10179 5089.0 0.7000258 0.7000258 9.207315e-03 9.207315e-03 0.290766925 #> 10180 5089.5 0.7000146 0.7000146 9.210389e-03 9.210389e-03 0.290775020 #> 10181 5090.0 0.7000034 0.7000034 9.213462e-03 9.213462e-03 0.290783163 #> 10182 5090.5 0.6999921 0.6999921 9.216533e-03 9.216533e-03 0.290791353 #> 10183 5091.0 0.6999808 0.6999808 9.219603e-03 9.219603e-03 0.290799589 #> 10184 5091.5 0.6999695 0.6999695 9.222671e-03 9.222671e-03 0.290807873 #> 10185 5092.0 0.6999581 0.6999581 9.225737e-03 9.225737e-03 0.290816204 #> 10186 5092.5 0.6999466 0.6999466 9.228802e-03 9.228802e-03 0.290824582 #> 10187 5093.0 0.6999351 0.6999351 9.231865e-03 9.231865e-03 0.290833006 #> 10188 5093.5 0.6999236 0.6999236 9.234927e-03 9.234927e-03 0.290841477 #> 10189 5094.0 0.6999120 0.6999120 9.237986e-03 9.237986e-03 0.290849995 #> 10190 5094.5 0.6999004 0.6999004 9.241045e-03 9.241045e-03 0.290858560 #> 10191 5095.0 0.6998887 0.6998887 9.244101e-03 9.244101e-03 0.290867172 #> 10192 5095.5 0.6998770 0.6998770 9.247156e-03 9.247156e-03 0.290875830 #> 10193 5096.0 0.6998653 0.6998653 9.250209e-03 9.250209e-03 0.290884534 #> 10194 5096.5 0.6998535 0.6998535 9.253260e-03 9.253260e-03 0.290893285 #> 10195 5097.0 0.6998416 0.6998416 9.256310e-03 9.256310e-03 0.290902083 #> 10196 5097.5 0.6998297 0.6998297 9.259358e-03 9.259358e-03 0.290910927 #> 10197 5098.0 0.6998178 0.6998178 9.262404e-03 9.262404e-03 0.290919817 #> 10198 5098.5 0.6998058 0.6998058 9.265448e-03 9.265448e-03 0.290928754 #> 10199 5099.0 0.6997938 0.6997938 9.268491e-03 9.268491e-03 0.290937737 #> 10200 5099.5 0.6997817 0.6997817 9.271532e-03 9.271532e-03 0.290946766 #> 10201 5100.0 0.6997696 0.6997696 9.274571e-03 9.274571e-03 0.290955841 #> 10202 5100.5 0.6997574 0.6997574 9.277608e-03 9.277608e-03 0.290964962 #> 10203 5101.0 0.6997452 0.6997452 9.280643e-03 9.280643e-03 0.290974130 #> 10204 5101.5 0.6997330 0.6997330 9.283677e-03 9.283677e-03 0.290983343 #> 10205 5102.0 0.6997207 0.6997207 9.286709e-03 9.286709e-03 0.290992602 #> 10206 5102.5 0.6997084 0.6997084 9.289738e-03 9.289738e-03 0.291001907 #> 10207 5103.0 0.6996960 0.6996960 9.292766e-03 9.292766e-03 0.291011258 #> 10208 5103.5 0.6996836 0.6996836 9.295792e-03 9.295792e-03 0.291020655 #> 10209 5104.0 0.6996711 0.6996711 9.298816e-03 9.298816e-03 0.291030097 #> 10210 5104.5 0.6996586 0.6996586 9.301839e-03 9.301839e-03 0.291039585 #> 10211 5105.0 0.6996460 0.6996460 9.304859e-03 9.304859e-03 0.291049119 #> 10212 5105.5 0.6996334 0.6996334 9.307877e-03 9.307877e-03 0.291058698 #> 10213 5106.0 0.6996208 0.6996208 9.310893e-03 9.310893e-03 0.291068323 #> 10214 5106.5 0.6996081 0.6996081 9.313908e-03 9.313908e-03 0.291077993 #> 10215 5107.0 0.6995954 0.6995954 9.316920e-03 9.316920e-03 0.291087709 #> 10216 5107.5 0.6995826 0.6995826 9.319931e-03 9.319931e-03 0.291097469 #> 10217 5108.0 0.6995698 0.6995698 9.322939e-03 9.322939e-03 0.291107275 #> 10218 5108.5 0.6995569 0.6995569 9.325945e-03 9.325945e-03 0.291117127 #> 10219 5109.0 0.6995440 0.6995440 9.328950e-03 9.328950e-03 0.291127023 #> 10220 5109.5 0.6995311 0.6995311 9.331952e-03 9.331952e-03 0.291136965 #> 10221 5110.0 0.6995181 0.6995181 9.334952e-03 9.334952e-03 0.291146951 #> 10222 5110.5 0.6995051 0.6995051 9.337950e-03 9.337950e-03 0.291156983 #> 10223 5111.0 0.6994920 0.6994920 9.340946e-03 9.340946e-03 0.291167059 #> 10224 5111.5 0.6994789 0.6994789 9.343940e-03 9.343940e-03 0.291177180 #> 10225 5112.0 0.6994657 0.6994657 9.346932e-03 9.346932e-03 0.291187347 #> 10226 5112.5 0.6994525 0.6994525 9.349922e-03 9.349922e-03 0.291197558 #> 10227 5113.0 0.6994393 0.6994393 9.352910e-03 9.352910e-03 0.291207813 #> 10228 5113.5 0.6994260 0.6994260 9.355895e-03 9.355895e-03 0.291218113 #> 10229 5114.0 0.6994127 0.6994127 9.358878e-03 9.358878e-03 0.291228458 #> 10230 5114.5 0.6993993 0.6993993 9.361859e-03 9.361859e-03 0.291238847 #> 10231 5115.0 0.6993859 0.6993859 9.364838e-03 9.364838e-03 0.291249281 #> 10232 5115.5 0.6993724 0.6993724 9.367815e-03 9.367815e-03 0.291259759 #> 10233 5116.0 0.6993589 0.6993589 9.370790e-03 9.370790e-03 0.291270282 #> 10234 5116.5 0.6993454 0.6993454 9.373762e-03 9.373762e-03 0.291280849 #> 10235 5117.0 0.6993318 0.6993318 9.376732e-03 9.376732e-03 0.291291460 #> 10236 5117.5 0.6993182 0.6993182 9.379700e-03 9.379700e-03 0.291302115 #> 10237 5118.0 0.6993045 0.6993045 9.382665e-03 9.382665e-03 0.291312814 #> 10238 5118.5 0.6992908 0.6992908 9.385629e-03 9.385629e-03 0.291323558 #> 10239 5119.0 0.6992771 0.6992771 9.388590e-03 9.388590e-03 0.291334345 #> 10240 5119.5 0.6992633 0.6992633 9.391548e-03 9.391548e-03 0.291345176 #> 10241 5120.0 0.6992494 0.6992494 9.394505e-03 9.394505e-03 0.291356051 #> 10242 5120.5 0.6992356 0.6992356 9.397459e-03 9.397459e-03 0.291366970 #> 10243 5121.0 0.6992217 0.6992217 9.400410e-03 9.400410e-03 0.291377933 #> 10244 5121.5 0.6992077 0.6992077 9.403360e-03 9.403360e-03 0.291388939 #> 10245 5122.0 0.6991937 0.6991937 9.406307e-03 9.406307e-03 0.291399989 #> 10246 5122.5 0.6991797 0.6991797 9.409251e-03 9.409251e-03 0.291411083 #> 10247 5123.0 0.6991656 0.6991656 9.412194e-03 9.412194e-03 0.291422220 #> 10248 5123.5 0.6991515 0.6991515 9.415134e-03 9.415134e-03 0.291433401 #> 10249 5124.0 0.6991373 0.6991373 9.418071e-03 9.418071e-03 0.291444624 #> 10250 5124.5 0.6991231 0.6991231 9.421006e-03 9.421006e-03 0.291455892 #> 10251 5125.0 0.6991089 0.6991089 9.423939e-03 9.423939e-03 0.291467202 #> 10252 5125.5 0.6990946 0.6990946 9.426869e-03 9.426869e-03 0.291478556 #> 10253 5126.0 0.6990803 0.6990803 9.429796e-03 9.429796e-03 0.291489952 #> 10254 5126.5 0.6990659 0.6990659 9.432722e-03 9.432722e-03 0.291501392 #> 10255 5127.0 0.6990515 0.6990515 9.435644e-03 9.435644e-03 0.291512875 #> 10256 5127.5 0.6990370 0.6990370 9.438565e-03 9.438565e-03 0.291524401 #> 10257 5128.0 0.6990225 0.6990225 9.441482e-03 9.441482e-03 0.291535970 #> 10258 5128.5 0.6990080 0.6990080 9.444397e-03 9.444397e-03 0.291547581 #> 10259 5129.0 0.6989935 0.6989935 9.447310e-03 9.447310e-03 0.291559235 #> 10260 5129.5 0.6989788 0.6989788 9.450220e-03 9.450220e-03 0.291570932 #> 10261 5130.0 0.6989642 0.6989642 9.453128e-03 9.453128e-03 0.291582672 #> 10262 5130.5 0.6989495 0.6989495 9.456033e-03 9.456033e-03 0.291594454 #> 10263 5131.0 0.6989348 0.6989348 9.458935e-03 9.458935e-03 0.291606278 #> 10264 5131.5 0.6989200 0.6989200 9.461835e-03 9.461835e-03 0.291618145 #> 10265 5132.0 0.6989052 0.6989052 9.464732e-03 9.464732e-03 0.291630054 #> 10266 5132.5 0.6988904 0.6988904 9.467627e-03 9.467627e-03 0.291642006 #> 10267 5133.0 0.6988755 0.6988755 9.470519e-03 9.470519e-03 0.291654000 #> 10268 5133.5 0.6988606 0.6988606 9.473408e-03 9.473408e-03 0.291666036 #> 10269 5134.0 0.6988456 0.6988456 9.476294e-03 9.476294e-03 0.291678114 #> 10270 5134.5 0.6988306 0.6988306 9.479178e-03 9.479178e-03 0.291690234 #> 10271 5135.0 0.6988155 0.6988155 9.482060e-03 9.482060e-03 0.291702397 #> 10272 5135.5 0.6988005 0.6988005 9.484938e-03 9.484938e-03 0.291714601 #> 10273 5136.0 0.6987853 0.6987853 9.487814e-03 9.487814e-03 0.291726847 #> 10274 5136.5 0.6987702 0.6987702 9.490687e-03 9.490687e-03 0.291739134 #> 10275 5137.0 0.6987550 0.6987550 9.493558e-03 9.493558e-03 0.291751464 #> 10276 5137.5 0.6987397 0.6987397 9.496425e-03 9.496425e-03 0.291763835 #> 10277 5138.0 0.6987245 0.6987245 9.499290e-03 9.499290e-03 0.291776247 #> 10278 5138.5 0.6987091 0.6987091 9.502152e-03 9.502152e-03 0.291788701 #> 10279 5139.0 0.6986938 0.6986938 9.505012e-03 9.505012e-03 0.291801197 #> 10280 5139.5 0.6986784 0.6986784 9.507868e-03 9.507868e-03 0.291813734 #> 10281 5140.0 0.6986630 0.6986630 9.510722e-03 9.510722e-03 0.291826312 #> 10282 5140.5 0.6986475 0.6986475 9.513573e-03 9.513573e-03 0.291838932 #> 10283 5141.0 0.6986320 0.6986320 9.516421e-03 9.516421e-03 0.291851592 #> 10284 5141.5 0.6986164 0.6986164 9.519266e-03 9.519266e-03 0.291864294 #> 10285 5142.0 0.6986009 0.6986009 9.522109e-03 9.522109e-03 0.291877037 #> 10286 5142.5 0.6985852 0.6985852 9.524948e-03 9.524948e-03 0.291889821 #> 10287 5143.0 0.6985696 0.6985696 9.527785e-03 9.527785e-03 0.291902645 #> 10288 5143.5 0.6985539 0.6985539 9.530618e-03 9.530618e-03 0.291915511 #> 10289 5144.0 0.6985381 0.6985381 9.533449e-03 9.533449e-03 0.291928417 #> 10290 5144.5 0.6985224 0.6985224 9.536277e-03 9.536277e-03 0.291941364 #> 10291 5145.0 0.6985065 0.6985065 9.539102e-03 9.539102e-03 0.291954352 #> 10292 5145.5 0.6984907 0.6984907 9.541924e-03 9.541924e-03 0.291967380 #> 10293 5146.0 0.6984748 0.6984748 9.544743e-03 9.544743e-03 0.291980448 #> 10294 5146.5 0.6984589 0.6984589 9.547560e-03 9.547560e-03 0.291993557 #> 10295 5147.0 0.6984429 0.6984429 9.550373e-03 9.550373e-03 0.292006707 #> 10296 5147.5 0.6984269 0.6984269 9.553183e-03 9.553183e-03 0.292019896 #> 10297 5148.0 0.6984109 0.6984109 9.555990e-03 9.555990e-03 0.292033126 #> 10298 5148.5 0.6983948 0.6983948 9.558794e-03 9.558794e-03 0.292046396 #> 10299 5149.0 0.6983787 0.6983787 9.561596e-03 9.561596e-03 0.292059706 #> 10300 5149.5 0.6983626 0.6983626 9.564394e-03 9.564394e-03 0.292073056 #> 10301 5150.0 0.6983464 0.6983464 9.567189e-03 9.567189e-03 0.292086446 #> 10302 5150.5 0.6983301 0.6983301 9.569981e-03 9.569981e-03 0.292099876 #> 10303 5151.0 0.6983139 0.6983139 9.572770e-03 9.572770e-03 0.292113346 #> 10304 5151.5 0.6982976 0.6982976 9.575556e-03 9.575556e-03 0.292126855 #> 10305 5152.0 0.6982813 0.6982813 9.578338e-03 9.578338e-03 0.292140404 #> 10306 5152.5 0.6982649 0.6982649 9.581118e-03 9.581118e-03 0.292153993 #> 10307 5153.0 0.6982485 0.6982485 9.583894e-03 9.583894e-03 0.292167621 #> 10308 5153.5 0.6982320 0.6982320 9.586668e-03 9.586668e-03 0.292181288 #> 10309 5154.0 0.6982156 0.6982156 9.589438e-03 9.589438e-03 0.292194995 #> 10310 5154.5 0.6981991 0.6981991 9.592205e-03 9.592205e-03 0.292208741 #> 10311 5155.0 0.6981825 0.6981825 9.594969e-03 9.594969e-03 0.292222526 #> 10312 5155.5 0.6981659 0.6981659 9.597730e-03 9.597730e-03 0.292236351 #> 10313 5156.0 0.6981493 0.6981493 9.600488e-03 9.600488e-03 0.292250214 #> 10314 5156.5 0.6981326 0.6981326 9.603242e-03 9.603242e-03 0.292264117 #> 10315 5157.0 0.6981159 0.6981159 9.605993e-03 9.605993e-03 0.292278058 #> 10316 5157.5 0.6980992 0.6980992 9.608741e-03 9.608741e-03 0.292292038 #> 10317 5158.0 0.6980825 0.6980825 9.611486e-03 9.611486e-03 0.292306058 #> 10318 5158.5 0.6980657 0.6980657 9.614227e-03 9.614227e-03 0.292320115 #> 10319 5159.0 0.6980488 0.6980488 9.616965e-03 9.616965e-03 0.292334212 #> 10320 5159.5 0.6980320 0.6980320 9.619700e-03 9.619700e-03 0.292348347 #> 10321 5160.0 0.6980150 0.6980150 9.622432e-03 9.622432e-03 0.292362520 #> 10322 5160.5 0.6979981 0.6979981 9.625160e-03 9.625160e-03 0.292376732 #> 10323 5161.0 0.6979811 0.6979811 9.627885e-03 9.627885e-03 0.292390982 #> 10324 5161.5 0.6979641 0.6979641 9.630607e-03 9.630607e-03 0.292405270 #> 10325 5162.0 0.6979471 0.6979471 9.633325e-03 9.633325e-03 0.292419597 #> 10326 5162.5 0.6979300 0.6979300 9.636040e-03 9.636040e-03 0.292433961 #> 10327 5163.0 0.6979129 0.6979129 9.638752e-03 9.638752e-03 0.292448364 #> 10328 5163.5 0.6978957 0.6978957 9.641460e-03 9.641460e-03 0.292462805 #> 10329 5164.0 0.6978786 0.6978786 9.644165e-03 9.644165e-03 0.292477283 #> 10330 5164.5 0.6978613 0.6978613 9.646867e-03 9.646867e-03 0.292491800 #> 10331 5165.0 0.6978441 0.6978441 9.649565e-03 9.649565e-03 0.292506354 #> 10332 5165.5 0.6978268 0.6978268 9.652260e-03 9.652260e-03 0.292520945 #> 10333 5166.0 0.6978095 0.6978095 9.654951e-03 9.654951e-03 0.292535575 #> 10334 5166.5 0.6977921 0.6977921 9.657639e-03 9.657639e-03 0.292550242 #> 10335 5167.0 0.6977747 0.6977747 9.660323e-03 9.660323e-03 0.292564946 #> 10336 5167.5 0.6977573 0.6977573 9.663004e-03 9.663004e-03 0.292579687 #> 10337 5168.0 0.6977399 0.6977399 9.665682e-03 9.665682e-03 0.292594466 #> 10338 5168.5 0.6977224 0.6977224 9.668356e-03 9.668356e-03 0.292609282 #> 10339 5169.0 0.6977048 0.6977048 9.671026e-03 9.671026e-03 0.292624135 #> 10340 5169.5 0.6976873 0.6976873 9.673693e-03 9.673693e-03 0.292639026 #> 10341 5170.0 0.6976697 0.6976697 9.676357e-03 9.676357e-03 0.292653953 #> 10342 5170.5 0.6976521 0.6976521 9.679017e-03 9.679017e-03 0.292668917 #> 10343 5171.0 0.6976344 0.6976344 9.681673e-03 9.681673e-03 0.292683918 #> 10344 5171.5 0.6976167 0.6976167 9.684326e-03 9.684326e-03 0.292698955 #> 10345 5172.0 0.6975990 0.6975990 9.686975e-03 9.686975e-03 0.292714030 #> 10346 5172.5 0.6975812 0.6975812 9.689621e-03 9.689621e-03 0.292729140 #> 10347 5173.0 0.6975634 0.6975634 9.692263e-03 9.692263e-03 0.292744288 #> 10348 5173.5 0.6975456 0.6975456 9.694902e-03 9.694902e-03 0.292759472 #> 10349 5174.0 0.6975278 0.6975278 9.697537e-03 9.697537e-03 0.292774692 #> 10350 5174.5 0.6975099 0.6975099 9.700168e-03 9.700168e-03 0.292789948 #> 10351 5175.0 0.6974920 0.6974920 9.702796e-03 9.702796e-03 0.292805241 #> 10352 5175.5 0.6974740 0.6974740 9.705420e-03 9.705420e-03 0.292820569 #> 10353 5176.0 0.6974560 0.6974560 9.708040e-03 9.708040e-03 0.292835934 #> 10354 5176.5 0.6974380 0.6974380 9.710657e-03 9.710657e-03 0.292851335 #> 10355 5177.0 0.6974200 0.6974200 9.713270e-03 9.713270e-03 0.292866771 #> 10356 5177.5 0.6974019 0.6974019 9.715879e-03 9.715879e-03 0.292882244 #> 10357 5178.0 0.6973838 0.6973838 9.718485e-03 9.718485e-03 0.292897752 #> 10358 5178.5 0.6973656 0.6973656 9.721087e-03 9.721087e-03 0.292913295 #> 10359 5179.0 0.6973474 0.6973474 9.723685e-03 9.723685e-03 0.292928874 #> 10360 5179.5 0.6973292 0.6973292 9.726280e-03 9.726280e-03 0.292944489 #> 10361 5180.0 0.6973110 0.6973110 9.728871e-03 9.728871e-03 0.292960139 #> 10362 5180.5 0.6972927 0.6972927 9.731458e-03 9.731458e-03 0.292975825 #> 10363 5181.0 0.6972744 0.6972744 9.734041e-03 9.734041e-03 0.292991545 #> 10364 5181.5 0.6972561 0.6972561 9.736621e-03 9.736621e-03 0.293007301 #> 10365 5182.0 0.6972377 0.6972377 9.739196e-03 9.739196e-03 0.293023092 #> 10366 5182.5 0.6972193 0.6972193 9.741768e-03 9.741768e-03 0.293038918 #> 10367 5183.0 0.6972009 0.6972009 9.744336e-03 9.744336e-03 0.293054779 #> 10368 5183.5 0.6971824 0.6971824 9.746901e-03 9.746901e-03 0.293070674 #> 10369 5184.0 0.6971639 0.6971639 9.749461e-03 9.749461e-03 0.293086605 #> 10370 5184.5 0.6971454 0.6971454 9.752018e-03 9.752018e-03 0.293102570 #> 10371 5185.0 0.6971269 0.6971269 9.754571e-03 9.754571e-03 0.293118569 #> 10372 5185.5 0.6971083 0.6971083 9.757119e-03 9.757119e-03 0.293134604 #> 10373 5186.0 0.6970897 0.6970897 9.759665e-03 9.759665e-03 0.293150672 #> 10374 5186.5 0.6970710 0.6970710 9.762206e-03 9.762206e-03 0.293166775 #> 10375 5187.0 0.6970523 0.6970523 9.764743e-03 9.764743e-03 0.293182912 #> 10376 5187.5 0.6970336 0.6970336 9.767276e-03 9.767276e-03 0.293199083 #> 10377 5188.0 0.6970149 0.6970149 9.769806e-03 9.769806e-03 0.293215289 #> 10378 5188.5 0.6969961 0.6969961 9.772331e-03 9.772331e-03 0.293231528 #> 10379 5189.0 0.6969773 0.6969773 9.774853e-03 9.774853e-03 0.293247802 #> 10380 5189.5 0.6969585 0.6969585 9.777371e-03 9.777371e-03 0.293264109 #> 10381 5190.0 0.6969397 0.6969397 9.779884e-03 9.779884e-03 0.293280450 #> 10382 5190.5 0.6969208 0.6969208 9.782394e-03 9.782394e-03 0.293296825 #> 10383 5191.0 0.6969019 0.6969019 9.784900e-03 9.784900e-03 0.293313233 #> 10384 5191.5 0.6968829 0.6968829 9.787402e-03 9.787402e-03 0.293329675 #> 10385 5192.0 0.6968640 0.6968640 9.789899e-03 9.789899e-03 0.293346150 #> 10386 5192.5 0.6968449 0.6968449 9.792393e-03 9.792393e-03 0.293362658 #> 10387 5193.0 0.6968259 0.6968259 9.794883e-03 9.794883e-03 0.293379200 #> 10388 5193.5 0.6968069 0.6968069 9.797369e-03 9.797369e-03 0.293395775 #> 10389 5194.0 0.6967878 0.6967878 9.799850e-03 9.799850e-03 0.293412383 #> 10390 5194.5 0.6967686 0.6967686 9.802328e-03 9.802328e-03 0.293429025 #> 10391 5195.0 0.6967495 0.6967495 9.804801e-03 9.804801e-03 0.293445699 #> 10392 5195.5 0.6967303 0.6967303 9.807271e-03 9.807271e-03 0.293462406 #> 10393 5196.0 0.6967111 0.6967111 9.809736e-03 9.809736e-03 0.293479145 #> 10394 5196.5 0.6966919 0.6966919 9.812198e-03 9.812198e-03 0.293495918 #> 10395 5197.0 0.6966726 0.6966726 9.814655e-03 9.814655e-03 0.293512723 #> 10396 5197.5 0.6966533 0.6966533 9.817108e-03 9.817108e-03 0.293529560 #> 10397 5198.0 0.6966340 0.6966340 9.819557e-03 9.819557e-03 0.293546430 #> 10398 5198.5 0.6966147 0.6966147 9.822002e-03 9.822002e-03 0.293563332 #> 10399 5199.0 0.6965953 0.6965953 9.824442e-03 9.824442e-03 0.293580267 #> 10400 5199.5 0.6965759 0.6965759 9.826879e-03 9.826879e-03 0.293597233 #> 10401 5200.0 0.6965565 0.6965565 9.829311e-03 9.829311e-03 0.293614232 #> 10402 5200.5 0.6965370 0.6965370 9.831739e-03 9.831739e-03 0.293631263 #> 10403 5201.0 0.6965175 0.6965175 9.834163e-03 9.834163e-03 0.293648325 #> 10404 5201.5 0.6964980 0.6964980 9.836583e-03 9.836583e-03 0.293665420 #> 10405 5202.0 0.6964785 0.6964785 9.838998e-03 9.838998e-03 0.293682546 #> 10406 5202.5 0.6964589 0.6964589 9.841410e-03 9.841410e-03 0.293699704 #> 10407 5203.0 0.6964393 0.6964393 9.843817e-03 9.843817e-03 0.293716893 #> 10408 5203.5 0.6964197 0.6964197 9.846220e-03 9.846220e-03 0.293734114 #> 10409 5204.0 0.6964000 0.6964000 9.848618e-03 9.848618e-03 0.293751366 #> 10410 5204.5 0.6963803 0.6963803 9.851013e-03 9.851013e-03 0.293768650 #> 10411 5205.0 0.6963606 0.6963606 9.853403e-03 9.853403e-03 0.293785965 #> 10412 5205.5 0.6963409 0.6963409 9.855789e-03 9.855789e-03 0.293803311 #> 10413 5206.0 0.6963211 0.6963211 9.858170e-03 9.858170e-03 0.293820688 #> 10414 5206.5 0.6963014 0.6963014 9.860547e-03 9.860547e-03 0.293838096 #> 10415 5207.0 0.6962815 0.6962815 9.862920e-03 9.862920e-03 0.293855535 #> 10416 5207.5 0.6962617 0.6962617 9.865289e-03 9.865289e-03 0.293873004 #> 10417 5208.0 0.6962418 0.6962418 9.867653e-03 9.867653e-03 0.293890505 #> 10418 5208.5 0.6962220 0.6962220 9.870013e-03 9.870013e-03 0.293908035 #> 10419 5209.0 0.6962020 0.6962020 9.872369e-03 9.872369e-03 0.293925597 #> 10420 5209.5 0.6961821 0.6961821 9.874720e-03 9.874720e-03 0.293943189 #> 10421 5210.0 0.6961621 0.6961621 9.877067e-03 9.877067e-03 0.293960811 #> 10422 5210.5 0.6961421 0.6961421 9.879409e-03 9.879409e-03 0.293978463 #> 10423 5211.0 0.6961221 0.6961221 9.881748e-03 9.881748e-03 0.293996146 #> 10424 5211.5 0.6961021 0.6961021 9.884081e-03 9.884081e-03 0.294013859 #> 10425 5212.0 0.6960820 0.6960820 9.886411e-03 9.886411e-03 0.294031602 #> 10426 5212.5 0.6960619 0.6960619 9.888735e-03 9.888735e-03 0.294049374 #> 10427 5213.0 0.6960418 0.6960418 9.891056e-03 9.891056e-03 0.294067177 #> 10428 5213.5 0.6960216 0.6960216 9.893372e-03 9.893372e-03 0.294085009 #> 10429 5214.0 0.6960014 0.6960014 9.895683e-03 9.895683e-03 0.294102871 #> 10430 5214.5 0.6959812 0.6959812 9.897991e-03 9.897991e-03 0.294120762 #> 10431 5215.0 0.6959610 0.6959610 9.900293e-03 9.900293e-03 0.294138683 #> 10432 5215.5 0.6959408 0.6959408 9.902591e-03 9.902591e-03 0.294156633 #> 10433 5216.0 0.6959205 0.6959205 9.904885e-03 9.904885e-03 0.294174613 #> 10434 5216.5 0.6959002 0.6959002 9.907174e-03 9.907174e-03 0.294192621 #> 10435 5217.0 0.6958799 0.6958799 9.909459e-03 9.909459e-03 0.294210659 #> 10436 5217.5 0.6958595 0.6958595 9.911739e-03 9.911739e-03 0.294228726 #> 10437 5218.0 0.6958392 0.6958392 9.914015e-03 9.914015e-03 0.294246822 #> 10438 5218.5 0.6958188 0.6958188 9.916286e-03 9.916286e-03 0.294264946 #> 10439 5219.0 0.6957983 0.6957983 9.918553e-03 9.918553e-03 0.294283100 #> 10440 5219.5 0.6957779 0.6957779 9.920815e-03 9.920815e-03 0.294301282 #> 10441 5220.0 0.6957574 0.6957574 9.923072e-03 9.923072e-03 0.294319492 #> 10442 5220.5 0.6957369 0.6957369 9.925325e-03 9.925325e-03 0.294337731 #> 10443 5221.0 0.6957164 0.6957164 9.927573e-03 9.927573e-03 0.294355999 #> 10444 5221.5 0.6956959 0.6956959 9.929817e-03 9.929817e-03 0.294374294 #> 10445 5222.0 0.6956753 0.6956753 9.932056e-03 9.932056e-03 0.294392618 #> 10446 5222.5 0.6956547 0.6956547 9.934291e-03 9.934291e-03 0.294410970 #> 10447 5223.0 0.6956341 0.6956341 9.936521e-03 9.936521e-03 0.294429350 #> 10448 5223.5 0.6956135 0.6956135 9.938746e-03 9.938746e-03 0.294447758 #> 10449 5224.0 0.6955928 0.6955928 9.940967e-03 9.940967e-03 0.294466194 #> 10450 5224.5 0.6955722 0.6955722 9.943183e-03 9.943183e-03 0.294484657 #> 10451 5225.0 0.6955515 0.6955515 9.945394e-03 9.945394e-03 0.294503149 #> 10452 5225.5 0.6955307 0.6955307 9.947601e-03 9.947601e-03 0.294521667 #> 10453 5226.0 0.6955100 0.6955100 9.949803e-03 9.949803e-03 0.294540214 #> 10454 5226.5 0.6954892 0.6954892 9.952001e-03 9.952001e-03 0.294558787 #> 10455 5227.0 0.6954684 0.6954684 9.954193e-03 9.954193e-03 0.294577388 #> 10456 5227.5 0.6954476 0.6954476 9.956381e-03 9.956381e-03 0.294596016 #> 10457 5228.0 0.6954268 0.6954268 9.958565e-03 9.958565e-03 0.294614672 #> 10458 5228.5 0.6954059 0.6954059 9.960743e-03 9.960743e-03 0.294633354 #> 10459 5229.0 0.6953850 0.6953850 9.962917e-03 9.962917e-03 0.294652063 #> 10460 5229.5 0.6953641 0.6953641 9.965086e-03 9.965086e-03 0.294670799 #> 10461 5230.0 0.6953432 0.6953432 9.967251e-03 9.967251e-03 0.294689562 #> 10462 5230.5 0.6953222 0.6953222 9.969410e-03 9.969410e-03 0.294708351 #> 10463 5231.0 0.6953013 0.6953013 9.971565e-03 9.971565e-03 0.294727167 #> 10464 5231.5 0.6952803 0.6952803 9.973715e-03 9.973715e-03 0.294746009 #> 10465 5232.0 0.6952593 0.6952593 9.975861e-03 9.975861e-03 0.294764878 #> 10466 5232.5 0.6952382 0.6952382 9.978001e-03 9.978001e-03 0.294783773 #> 10467 5233.0 0.6952172 0.6952172 9.980137e-03 9.980137e-03 0.294802694 #> 10468 5233.5 0.6951961 0.6951961 9.982268e-03 9.982268e-03 0.294821642 #> 10469 5234.0 0.6951750 0.6951750 9.984394e-03 9.984394e-03 0.294840615 #> 10470 5234.5 0.6951539 0.6951539 9.986515e-03 9.986515e-03 0.294859614 #> 10471 5235.0 0.6951327 0.6951327 9.988632e-03 9.988632e-03 0.294878639 #> 10472 5235.5 0.6951116 0.6951116 9.990744e-03 9.990744e-03 0.294897690 #> 10473 5236.0 0.6950904 0.6950904 9.992850e-03 9.992850e-03 0.294916766 #> 10474 5236.5 0.6950692 0.6950692 9.994952e-03 9.994952e-03 0.294935868 #> 10475 5237.0 0.6950480 0.6950480 9.997050e-03 9.997050e-03 0.294954995 #> 10476 5237.5 0.6950267 0.6950267 9.999142e-03 9.999142e-03 0.294974148 #> 10477 5238.0 0.6950054 0.6950054 1.000123e-02 1.000123e-02 0.294993326 #> 10478 5238.5 0.6949842 0.6949842 1.000331e-02 1.000331e-02 0.295012529 #> 10479 5239.0 0.6949629 0.6949629 1.000539e-02 1.000539e-02 0.295031757 #> 10480 5239.5 0.6949415 0.6949415 1.000746e-02 1.000746e-02 0.295051010 #> 10481 5240.0 0.6949202 0.6949202 1.000953e-02 1.000953e-02 0.295070288 #> 10482 5240.5 0.6948988 0.6948988 1.001159e-02 1.001159e-02 0.295089591 #> 10483 5241.0 0.6948774 0.6948774 1.001365e-02 1.001365e-02 0.295108918 #> 10484 5241.5 0.6948560 0.6948560 1.001570e-02 1.001570e-02 0.295128270 #> 10485 5242.0 0.6948346 0.6948346 1.001775e-02 1.001775e-02 0.295147647 #> 10486 5242.5 0.6948132 0.6948132 1.001980e-02 1.001980e-02 0.295167048 #> 10487 5243.0 0.6947917 0.6947917 1.002183e-02 1.002183e-02 0.295186473 #> 10488 5243.5 0.6947702 0.6947702 1.002387e-02 1.002387e-02 0.295205922 #> 10489 5244.0 0.6947487 0.6947487 1.002589e-02 1.002589e-02 0.295225396 #> 10490 5244.5 0.6947272 0.6947272 1.002792e-02 1.002792e-02 0.295244893 #> 10491 5245.0 0.6947056 0.6947056 1.002994e-02 1.002994e-02 0.295264415 #> 10492 5245.5 0.6946841 0.6946841 1.003195e-02 1.003195e-02 0.295283960 #> 10493 5246.0 0.6946625 0.6946625 1.003396e-02 1.003396e-02 0.295303529 #> 10494 5246.5 0.6946409 0.6946409 1.003596e-02 1.003596e-02 0.295323122 #> 10495 5247.0 0.6946193 0.6946193 1.003796e-02 1.003796e-02 0.295342738 #> 10496 5247.5 0.6945977 0.6945977 1.003995e-02 1.003995e-02 0.295362378 #> 10497 5248.0 0.6945760 0.6945760 1.004194e-02 1.004194e-02 0.295382041 #> 10498 5248.5 0.6945543 0.6945543 1.004392e-02 1.004392e-02 0.295401727 #> 10499 5249.0 0.6945327 0.6945327 1.004590e-02 1.004590e-02 0.295421436 #> 10500 5249.5 0.6945110 0.6945110 1.004787e-02 1.004787e-02 0.295441169 #> 10501 5250.0 0.6944892 0.6944892 1.004984e-02 1.004984e-02 0.295460924 #> 10502 5250.5 0.6944675 0.6944675 1.005181e-02 1.005181e-02 0.295480703 #> 10503 5251.0 0.6944457 0.6944457 1.005376e-02 1.005376e-02 0.295500504 #> 10504 5251.5 0.6944240 0.6944240 1.005572e-02 1.005572e-02 0.295520327 #> 10505 5252.0 0.6944022 0.6944022 1.005766e-02 1.005766e-02 0.295540174 #> 10506 5252.5 0.6943804 0.6943804 1.005961e-02 1.005961e-02 0.295560043 #> 10507 5253.0 0.6943585 0.6943585 1.006154e-02 1.006154e-02 0.295579934 #> 10508 5253.5 0.6943367 0.6943367 1.006348e-02 1.006348e-02 0.295599848 #> 10509 5254.0 0.6943148 0.6943148 1.006540e-02 1.006540e-02 0.295619783 #> 10510 5254.5 0.6942929 0.6942929 1.006732e-02 1.006732e-02 0.295639741 #> 10511 5255.0 0.6942710 0.6942710 1.006924e-02 1.006924e-02 0.295659721 #> 10512 5255.5 0.6942491 0.6942491 1.007115e-02 1.007115e-02 0.295679723 #> 10513 5256.0 0.6942272 0.6942272 1.007306e-02 1.007306e-02 0.295699746 #> 10514 5256.5 0.6942052 0.6942052 1.007496e-02 1.007496e-02 0.295719792 #> 10515 5257.0 0.6941833 0.6941833 1.007686e-02 1.007686e-02 0.295739859 #> 10516 5257.5 0.6941613 0.6941613 1.007875e-02 1.007875e-02 0.295759947 #> 10517 5258.0 0.6941393 0.6941393 1.008063e-02 1.008063e-02 0.295780057 #> 10518 5258.5 0.6941173 0.6941173 1.008251e-02 1.008251e-02 0.295800188 #> 10519 5259.0 0.6940953 0.6940953 1.008439e-02 1.008439e-02 0.295820340 #> 10520 5259.5 0.6940732 0.6940732 1.008626e-02 1.008626e-02 0.295840514 #> 10521 5260.0 0.6940512 0.6940512 1.008813e-02 1.008813e-02 0.295860709 #> 10522 5260.5 0.6940291 0.6940291 1.008999e-02 1.008999e-02 0.295880924 #> 10523 5261.0 0.6940070 0.6940070 1.009184e-02 1.009184e-02 0.295901160 #> 10524 5261.5 0.6939849 0.6939849 1.009369e-02 1.009369e-02 0.295921418 #> 10525 5262.0 0.6939628 0.6939628 1.009553e-02 1.009553e-02 0.295941695 #> 10526 5262.5 0.6939406 0.6939406 1.009737e-02 1.009737e-02 0.295961994 #> 10527 5263.0 0.6939185 0.6939185 1.009921e-02 1.009921e-02 0.295982312 #> 10528 5263.5 0.6938963 0.6938963 1.010103e-02 1.010103e-02 0.296002651 #> 10529 5264.0 0.6938741 0.6938741 1.010286e-02 1.010286e-02 0.296023011 #> 10530 5264.5 0.6938519 0.6938519 1.010467e-02 1.010467e-02 0.296043390 #> 10531 5265.0 0.6938297 0.6938297 1.010649e-02 1.010649e-02 0.296063790 #> 10532 5265.5 0.6938075 0.6938075 1.010829e-02 1.010829e-02 0.296084209 #> 10533 5266.0 0.6937853 0.6937853 1.011010e-02 1.011010e-02 0.296104649 #> 10534 5266.5 0.6937630 0.6937630 1.011189e-02 1.011189e-02 0.296125108 #> 10535 5267.0 0.6937407 0.6937407 1.011368e-02 1.011368e-02 0.296145587 #> 10536 5267.5 0.6937184 0.6937184 1.011547e-02 1.011547e-02 0.296166085 #> 10537 5268.0 0.6936961 0.6936961 1.011725e-02 1.011725e-02 0.296186603 #> 10538 5268.5 0.6936738 0.6936738 1.011903e-02 1.011903e-02 0.296207140 #> 10539 5269.0 0.6936515 0.6936515 1.012080e-02 1.012080e-02 0.296227697 #> 10540 5269.5 0.6936292 0.6936292 1.012256e-02 1.012256e-02 0.296248272 #> 10541 5270.0 0.6936068 0.6936068 1.012432e-02 1.012432e-02 0.296268867 #> 10542 5270.5 0.6935844 0.6935844 1.012607e-02 1.012607e-02 0.296289481 #> 10543 5271.0 0.6935621 0.6935621 1.012782e-02 1.012782e-02 0.296310113 #> 10544 5271.5 0.6935397 0.6935397 1.012957e-02 1.012957e-02 0.296330765 #> 10545 5272.0 0.6935173 0.6935173 1.013130e-02 1.013130e-02 0.296351435 #> 10546 5272.5 0.6934948 0.6934948 1.013304e-02 1.013304e-02 0.296372123 #> 10547 5273.0 0.6934724 0.6934724 1.013476e-02 1.013476e-02 0.296392830 #> 10548 5273.5 0.6934500 0.6934500 1.013648e-02 1.013648e-02 0.296413556 #> 10549 5274.0 0.6934275 0.6934275 1.013820e-02 1.013820e-02 0.296434300 #> 10550 5274.5 0.6934050 0.6934050 1.013991e-02 1.013991e-02 0.296455061 #> 10551 5275.0 0.6933825 0.6933825 1.014162e-02 1.014162e-02 0.296475841 #> 10552 5275.5 0.6933600 0.6933600 1.014332e-02 1.014332e-02 0.296496639 #> 10553 5276.0 0.6933375 0.6933375 1.014501e-02 1.014501e-02 0.296517455 #> 10554 5276.5 0.6933150 0.6933150 1.014670e-02 1.014670e-02 0.296538289 #> 10555 5277.0 0.6932925 0.6932925 1.014838e-02 1.014838e-02 0.296559140 #> 10556 5277.5 0.6932699 0.6932699 1.015006e-02 1.015006e-02 0.296580009 #> 10557 5278.0 0.6932474 0.6932474 1.015174e-02 1.015174e-02 0.296600896 #> 10558 5278.5 0.6932248 0.6932248 1.015340e-02 1.015340e-02 0.296621800 #> 10559 5279.0 0.6932022 0.6932022 1.015507e-02 1.015507e-02 0.296642721 #> 10560 5279.5 0.6931796 0.6931796 1.015672e-02 1.015672e-02 0.296663659 #> 10561 5280.0 0.6931570 0.6931570 1.015837e-02 1.015837e-02 0.296684614 #> 10562 5280.5 0.6931344 0.6931344 1.016002e-02 1.016002e-02 0.296705587 #> 10563 5281.0 0.6931118 0.6931118 1.016166e-02 1.016166e-02 0.296726576 #> 10564 5281.5 0.6930891 0.6930891 1.016329e-02 1.016329e-02 0.296747582 #> 10565 5282.0 0.6930665 0.6930665 1.016492e-02 1.016492e-02 0.296768605 #> 10566 5282.5 0.6930438 0.6930438 1.016655e-02 1.016655e-02 0.296789644 #> 10567 5283.0 0.6930211 0.6930211 1.016816e-02 1.016816e-02 0.296810700 #> 10568 5283.5 0.6929985 0.6929985 1.016978e-02 1.016978e-02 0.296831772 #> 10569 5284.0 0.6929758 0.6929758 1.017138e-02 1.017138e-02 0.296852861 #> 10570 5284.5 0.6929530 0.6929530 1.017298e-02 1.017298e-02 0.296873965 #> 10571 5285.0 0.6929303 0.6929303 1.017458e-02 1.017458e-02 0.296895086 #> 10572 5285.5 0.6929076 0.6929076 1.017617e-02 1.017617e-02 0.296916223 #> 10573 5286.0 0.6928849 0.6928849 1.017776e-02 1.017776e-02 0.296937376 #> 10574 5286.5 0.6928621 0.6928621 1.017934e-02 1.017934e-02 0.296958544 #> 10575 5287.0 0.6928394 0.6928394 1.018091e-02 1.018091e-02 0.296979728 #> 10576 5287.5 0.6928166 0.6928166 1.018248e-02 1.018248e-02 0.297000928 #> 10577 5288.0 0.6927938 0.6927938 1.018404e-02 1.018404e-02 0.297022143 #> 10578 5288.5 0.6927710 0.6927710 1.018560e-02 1.018560e-02 0.297043374 #> 10579 5289.0 0.6927482 0.6927482 1.018715e-02 1.018715e-02 0.297064620 #> 10580 5289.5 0.6927254 0.6927254 1.018870e-02 1.018870e-02 0.297085881 #> 10581 5290.0 0.6927026 0.6927026 1.019024e-02 1.019024e-02 0.297107157 #> 10582 5290.5 0.6926798 0.6926798 1.019177e-02 1.019177e-02 0.297128448 #> 10583 5291.0 0.6926569 0.6926569 1.019330e-02 1.019330e-02 0.297149754 #> 10584 5291.5 0.6926341 0.6926341 1.019482e-02 1.019482e-02 0.297171075 #> 10585 5292.0 0.6926112 0.6926112 1.019634e-02 1.019634e-02 0.297192411 #> 10586 5292.5 0.6925884 0.6925884 1.019785e-02 1.019785e-02 0.297213761 #> 10587 5293.0 0.6925655 0.6925655 1.019936e-02 1.019936e-02 0.297235125 #> 10588 5293.5 0.6925426 0.6925426 1.020086e-02 1.020086e-02 0.297256504 #> 10589 5294.0 0.6925197 0.6925197 1.020236e-02 1.020236e-02 0.297277898 #> 10590 5294.5 0.6924968 0.6924968 1.020385e-02 1.020385e-02 0.297299305 #> 10591 5295.0 0.6924739 0.6924739 1.020533e-02 1.020533e-02 0.297320726 #> 10592 5295.5 0.6924510 0.6924510 1.020681e-02 1.020681e-02 0.297342162 #> 10593 5296.0 0.6924281 0.6924281 1.020828e-02 1.020828e-02 0.297363611 #> 10594 5296.5 0.6924052 0.6924052 1.020975e-02 1.020975e-02 0.297385074 #> 10595 5297.0 0.6923822 0.6923822 1.021121e-02 1.021121e-02 0.297406551 #> 10596 5297.5 0.6923593 0.6923593 1.021267e-02 1.021267e-02 0.297428042 #> 10597 5298.0 0.6923363 0.6923363 1.021412e-02 1.021412e-02 0.297449546 #> 10598 5298.5 0.6923134 0.6923134 1.021557e-02 1.021557e-02 0.297471063 #> 10599 5299.0 0.6922904 0.6922904 1.021701e-02 1.021701e-02 0.297492593 #> 10600 5299.5 0.6922674 0.6922674 1.021844e-02 1.021844e-02 0.297514137 #> 10601 5300.0 0.6922444 0.6922444 1.021987e-02 1.021987e-02 0.297535694 #> 10602 5300.5 0.6922214 0.6922214 1.022129e-02 1.022129e-02 0.297557264 #> 10603 5301.0 0.6921984 0.6921984 1.022271e-02 1.022271e-02 0.297578846 #> 10604 5301.5 0.6921754 0.6921754 1.022412e-02 1.022412e-02 0.297600442 #> 10605 5302.0 0.6921524 0.6921524 1.022552e-02 1.022552e-02 0.297622050 #> 10606 5302.5 0.6921294 0.6921294 1.022692e-02 1.022692e-02 0.297643670 #> 10607 5303.0 0.6921064 0.6921064 1.022832e-02 1.022832e-02 0.297665303 #> 10608 5303.5 0.6920833 0.6920833 1.022970e-02 1.022970e-02 0.297686949 #> 10609 5304.0 0.6920603 0.6920603 1.023109e-02 1.023109e-02 0.297708606 #> 10610 5304.5 0.6920373 0.6920373 1.023246e-02 1.023246e-02 0.297730276 #> 10611 5305.0 0.6920142 0.6920142 1.023383e-02 1.023383e-02 0.297751958 #> 10612 5305.5 0.6919911 0.6919911 1.023520e-02 1.023520e-02 0.297773652 #> 10613 5306.0 0.6919681 0.6919681 1.023656e-02 1.023656e-02 0.297795358 #> 10614 5306.5 0.6919450 0.6919450 1.023791e-02 1.023791e-02 0.297817075 #> 10615 5307.0 0.6919219 0.6919219 1.023926e-02 1.023926e-02 0.297838804 #> 10616 5307.5 0.6918989 0.6918989 1.024060e-02 1.024060e-02 0.297860545 #> 10617 5308.0 0.6918758 0.6918758 1.024194e-02 1.024194e-02 0.297882297 #> 10618 5308.5 0.6918527 0.6918527 1.024327e-02 1.024327e-02 0.297904061 #> 10619 5309.0 0.6918296 0.6918296 1.024460e-02 1.024460e-02 0.297925835 #> 10620 5309.5 0.6918065 0.6918065 1.024591e-02 1.024591e-02 0.297947621 #> 10621 5310.0 0.6917834 0.6917834 1.024723e-02 1.024723e-02 0.297969418 #> 10622 5310.5 0.6917602 0.6917602 1.024854e-02 1.024854e-02 0.297991226 #> 10623 5311.0 0.6917371 0.6917371 1.024984e-02 1.024984e-02 0.298013045 #> 10624 5311.5 0.6917140 0.6917140 1.025114e-02 1.025114e-02 0.298034874 #> 10625 5312.0 0.6916909 0.6916909 1.025243e-02 1.025243e-02 0.298056715 #> 10626 5312.5 0.6916677 0.6916677 1.025371e-02 1.025371e-02 0.298078565 #> 10627 5313.0 0.6916446 0.6916446 1.025499e-02 1.025499e-02 0.298100426 #> 10628 5313.5 0.6916214 0.6916214 1.025626e-02 1.025626e-02 0.298122298 #> 10629 5314.0 0.6915983 0.6915983 1.025753e-02 1.025753e-02 0.298144179 #> 10630 5314.5 0.6915751 0.6915751 1.025879e-02 1.025879e-02 0.298166071 #> 10631 5315.0 0.6915520 0.6915520 1.026005e-02 1.026005e-02 0.298187973 #> 10632 5315.5 0.6915288 0.6915288 1.026130e-02 1.026130e-02 0.298209885 #> 10633 5316.0 0.6915057 0.6915057 1.026254e-02 1.026254e-02 0.298231806 #> 10634 5316.5 0.6914825 0.6914825 1.026378e-02 1.026378e-02 0.298253738 #> 10635 5317.0 0.6914593 0.6914593 1.026501e-02 1.026501e-02 0.298275679 #> 10636 5317.5 0.6914361 0.6914361 1.026624e-02 1.026624e-02 0.298297629 #> 10637 5318.0 0.6914129 0.6914129 1.026746e-02 1.026746e-02 0.298319589 #> 10638 5318.5 0.6913898 0.6913898 1.026868e-02 1.026868e-02 0.298341558 #> 10639 5319.0 0.6913666 0.6913666 1.026989e-02 1.026989e-02 0.298363537 #> 10640 5319.5 0.6913434 0.6913434 1.027109e-02 1.027109e-02 0.298385524 #> 10641 5320.0 0.6913202 0.6913202 1.027229e-02 1.027229e-02 0.298407521 #> 10642 5320.5 0.6912970 0.6912970 1.027348e-02 1.027348e-02 0.298429526 #> 10643 5321.0 0.6912738 0.6912738 1.027467e-02 1.027467e-02 0.298451541 #> 10644 5321.5 0.6912506 0.6912506 1.027585e-02 1.027585e-02 0.298473564 #> 10645 5322.0 0.6912274 0.6912274 1.027702e-02 1.027702e-02 0.298495595 #> 10646 5322.5 0.6912042 0.6912042 1.027819e-02 1.027819e-02 0.298517636 #> 10647 5323.0 0.6911810 0.6911810 1.027935e-02 1.027935e-02 0.298539684 #> 10648 5323.5 0.6911577 0.6911577 1.028051e-02 1.028051e-02 0.298561741 #> 10649 5324.0 0.6911345 0.6911345 1.028166e-02 1.028166e-02 0.298583806 #> 10650 5324.5 0.6911113 0.6911113 1.028281e-02 1.028281e-02 0.298605879 #> 10651 5325.0 0.6910881 0.6910881 1.028395e-02 1.028395e-02 0.298627960 #> 10652 5325.5 0.6910649 0.6910649 1.028508e-02 1.028508e-02 0.298650050 #> 10653 5326.0 0.6910416 0.6910416 1.028621e-02 1.028621e-02 0.298672146 #> 10654 5326.5 0.6910184 0.6910184 1.028733e-02 1.028733e-02 0.298694251 #> 10655 5327.0 0.6909952 0.6909952 1.028845e-02 1.028845e-02 0.298716363 #> 10656 5327.5 0.6909720 0.6909720 1.028956e-02 1.028956e-02 0.298738483 #> 10657 5328.0 0.6909487 0.6909487 1.029066e-02 1.029066e-02 0.298760610 #> 10658 5328.5 0.6909255 0.6909255 1.029176e-02 1.029176e-02 0.298782744 #> 10659 5329.0 0.6909023 0.6909023 1.029286e-02 1.029286e-02 0.298804886 #> 10660 5329.5 0.6908790 0.6908790 1.029394e-02 1.029394e-02 0.298827035 #> 10661 5330.0 0.6908558 0.6908558 1.029502e-02 1.029502e-02 0.298849190 #> 10662 5330.5 0.6908325 0.6908325 1.029610e-02 1.029610e-02 0.298871353 #> 10663 5331.0 0.6908093 0.6908093 1.029717e-02 1.029717e-02 0.298893522 #> 10664 5331.5 0.6907861 0.6907861 1.029823e-02 1.029823e-02 0.298915698 #> 10665 5332.0 0.6907628 0.6907628 1.029929e-02 1.029929e-02 0.298937881 #> 10666 5332.5 0.6907396 0.6907396 1.030034e-02 1.030034e-02 0.298960070 #> 10667 5333.0 0.6907163 0.6907163 1.030139e-02 1.030139e-02 0.298982265 #> 10668 5333.5 0.6906931 0.6906931 1.030243e-02 1.030243e-02 0.299004467 #> 10669 5334.0 0.6906699 0.6906699 1.030346e-02 1.030346e-02 0.299026675 #> 10670 5334.5 0.6906466 0.6906466 1.030449e-02 1.030449e-02 0.299048889 #> 10671 5335.0 0.6906234 0.6906234 1.030551e-02 1.030551e-02 0.299071109 #> 10672 5335.5 0.6906001 0.6906001 1.030653e-02 1.030653e-02 0.299093335 #> 10673 5336.0 0.6905769 0.6905769 1.030754e-02 1.030754e-02 0.299115567 #> 10674 5336.5 0.6905537 0.6905537 1.030854e-02 1.030854e-02 0.299137804 #> 10675 5337.0 0.6905304 0.6905304 1.030954e-02 1.030954e-02 0.299160047 #> 10676 5337.5 0.6905072 0.6905072 1.031053e-02 1.031053e-02 0.299182295 #> 10677 5338.0 0.6904839 0.6904839 1.031152e-02 1.031152e-02 0.299204549 #> 10678 5338.5 0.6904607 0.6904607 1.031250e-02 1.031250e-02 0.299226808 #> 10679 5339.0 0.6904374 0.6904374 1.031348e-02 1.031348e-02 0.299249072 #> 10680 5339.5 0.6904142 0.6904142 1.031445e-02 1.031445e-02 0.299271342 #> 10681 5340.0 0.6903910 0.6903910 1.031541e-02 1.031541e-02 0.299293616 #> 10682 5340.5 0.6903677 0.6903677 1.031637e-02 1.031637e-02 0.299315895 #> 10683 5341.0 0.6903445 0.6903445 1.031732e-02 1.031732e-02 0.299338179 #> 10684 5341.5 0.6903213 0.6903213 1.031827e-02 1.031827e-02 0.299360468 #> 10685 5342.0 0.6902980 0.6902980 1.031921e-02 1.031921e-02 0.299382761 #> 10686 5342.5 0.6902748 0.6902748 1.032014e-02 1.032014e-02 0.299405058 #> 10687 5343.0 0.6902516 0.6902516 1.032107e-02 1.032107e-02 0.299427360 #> 10688 5343.5 0.6902283 0.6902283 1.032199e-02 1.032199e-02 0.299449666 #> 10689 5344.0 0.6902051 0.6902051 1.032291e-02 1.032291e-02 0.299471977 #> 10690 5344.5 0.6901819 0.6901819 1.032382e-02 1.032382e-02 0.299494291 #> 10691 5345.0 0.6901587 0.6901587 1.032472e-02 1.032472e-02 0.299516610 #> 10692 5345.5 0.6901354 0.6901354 1.032562e-02 1.032562e-02 0.299538932 #> 10693 5346.0 0.6901122 0.6901122 1.032651e-02 1.032651e-02 0.299561258 #> 10694 5346.5 0.6900890 0.6900890 1.032740e-02 1.032740e-02 0.299583588 #> 10695 5347.0 0.6900658 0.6900658 1.032828e-02 1.032828e-02 0.299605921 #> 10696 5347.5 0.6900426 0.6900426 1.032915e-02 1.032915e-02 0.299628258 #> 10697 5348.0 0.6900194 0.6900194 1.033002e-02 1.033002e-02 0.299650598 #> 10698 5348.5 0.6899962 0.6899962 1.033088e-02 1.033088e-02 0.299672941 #> 10699 5349.0 0.6899730 0.6899730 1.033174e-02 1.033174e-02 0.299695288 #> 10700 5349.5 0.6899498 0.6899498 1.033259e-02 1.033259e-02 0.299717637 #> 10701 5350.0 0.6899266 0.6899266 1.033344e-02 1.033344e-02 0.299739990 #> 10702 5350.5 0.6899034 0.6899034 1.033428e-02 1.033428e-02 0.299762345 #> 10703 5351.0 0.6898802 0.6898802 1.033511e-02 1.033511e-02 0.299784703 #> 10704 5351.5 0.6898570 0.6898570 1.033594e-02 1.033594e-02 0.299807064 #> 10705 5352.0 0.6898338 0.6898338 1.033676e-02 1.033676e-02 0.299829427 #> 10706 5352.5 0.6898106 0.6898106 1.033757e-02 1.033757e-02 0.299851793 #> 10707 5353.0 0.6897875 0.6897875 1.033838e-02 1.033838e-02 0.299874161 #> 10708 5353.5 0.6897643 0.6897643 1.033919e-02 1.033919e-02 0.299896532 #> 10709 5354.0 0.6897411 0.6897411 1.033998e-02 1.033998e-02 0.299918904 #> 10710 5354.5 0.6897179 0.6897179 1.034078e-02 1.034078e-02 0.299941279 #> 10711 5355.0 0.6896948 0.6896948 1.034156e-02 1.034156e-02 0.299963655 #> 10712 5355.5 0.6896716 0.6896716 1.034234e-02 1.034234e-02 0.299986034 #> 10713 5356.0 0.6896485 0.6896485 1.034312e-02 1.034312e-02 0.300008414 #> 10714 5356.5 0.6896253 0.6896253 1.034388e-02 1.034388e-02 0.300030796 #> 10715 5357.0 0.6896022 0.6896022 1.034465e-02 1.034465e-02 0.300053179 #> 10716 5357.5 0.6895790 0.6895790 1.034540e-02 1.034540e-02 0.300075564 #> 10717 5358.0 0.6895559 0.6895559 1.034615e-02 1.034615e-02 0.300097951 #> 10718 5358.5 0.6895328 0.6895328 1.034690e-02 1.034690e-02 0.300120338 #> 10719 5359.0 0.6895096 0.6895096 1.034764e-02 1.034764e-02 0.300142727 #> 10720 5359.5 0.6894865 0.6894865 1.034837e-02 1.034837e-02 0.300165117 #> 10721 5360.0 0.6894634 0.6894634 1.034909e-02 1.034909e-02 0.300187507 #> 10722 5360.5 0.6894403 0.6894403 1.034982e-02 1.034982e-02 0.300209899 #> 10723 5361.0 0.6894172 0.6894172 1.035053e-02 1.035053e-02 0.300232291 #> 10724 5361.5 0.6893941 0.6893941 1.035124e-02 1.035124e-02 0.300254684 #> 10725 5362.0 0.6893710 0.6893710 1.035194e-02 1.035194e-02 0.300277078 #> 10726 5362.5 0.6893479 0.6893479 1.035264e-02 1.035264e-02 0.300299472 #> 10727 5363.0 0.6893248 0.6893248 1.035333e-02 1.035333e-02 0.300321866 #> 10728 5363.5 0.6893017 0.6893017 1.035401e-02 1.035401e-02 0.300344261 #> 10729 5364.0 0.6892787 0.6892787 1.035469e-02 1.035469e-02 0.300366656 #> 10730 5364.5 0.6892556 0.6892556 1.035537e-02 1.035537e-02 0.300389051 #> 10731 5365.0 0.6892325 0.6892325 1.035603e-02 1.035603e-02 0.300411446 #> 10732 5365.5 0.6892095 0.6892095 1.035670e-02 1.035670e-02 0.300433840 #> 10733 5366.0 0.6891864 0.6891864 1.035735e-02 1.035735e-02 0.300456235 #> 10734 5366.5 0.6891634 0.6891634 1.035800e-02 1.035800e-02 0.300478629 #> 10735 5367.0 0.6891403 0.6891403 1.035864e-02 1.035864e-02 0.300501023 #> 10736 5367.5 0.6891173 0.6891173 1.035928e-02 1.035928e-02 0.300523416 #> 10737 5368.0 0.6890943 0.6890943 1.035991e-02 1.035991e-02 0.300545809 #> 10738 5368.5 0.6890713 0.6890713 1.036054e-02 1.036054e-02 0.300568201 #> 10739 5369.0 0.6890482 0.6890482 1.036116e-02 1.036116e-02 0.300590592 #> 10740 5369.5 0.6890252 0.6890252 1.036177e-02 1.036177e-02 0.300612982 #> 10741 5370.0 0.6890022 0.6890022 1.036238e-02 1.036238e-02 0.300635371 #> 10742 5370.5 0.6889793 0.6889793 1.036298e-02 1.036298e-02 0.300657759 #> 10743 5371.0 0.6889563 0.6889563 1.036358e-02 1.036358e-02 0.300680146 #> 10744 5371.5 0.6889333 0.6889333 1.036417e-02 1.036417e-02 0.300702532 #> 10745 5372.0 0.6889103 0.6889103 1.036475e-02 1.036475e-02 0.300724916 #> 10746 5372.5 0.6888874 0.6888874 1.036533e-02 1.036533e-02 0.300747299 #> 10747 5373.0 0.6888644 0.6888644 1.036590e-02 1.036590e-02 0.300769680 #> 10748 5373.5 0.6888415 0.6888415 1.036647e-02 1.036647e-02 0.300792059 #> 10749 5374.0 0.6888185 0.6888185 1.036703e-02 1.036703e-02 0.300814437 #> 10750 5374.5 0.6887956 0.6887956 1.036759e-02 1.036759e-02 0.300836813 #> 10751 5375.0 0.6887727 0.6887727 1.036814e-02 1.036814e-02 0.300859186 #> 10752 5375.5 0.6887498 0.6887498 1.036868e-02 1.036868e-02 0.300881558 #> 10753 5376.0 0.6887269 0.6887269 1.036922e-02 1.036922e-02 0.300903927 #> 10754 5376.5 0.6887040 0.6887040 1.036975e-02 1.036975e-02 0.300926294 #> 10755 5377.0 0.6886811 0.6886811 1.037027e-02 1.037027e-02 0.300948659 #> 10756 5377.5 0.6886582 0.6886582 1.037079e-02 1.037079e-02 0.300971021 #> 10757 5378.0 0.6886353 0.6886353 1.037130e-02 1.037130e-02 0.300993381 #> 10758 5378.5 0.6886124 0.6886124 1.037181e-02 1.037181e-02 0.301015738 #> 10759 5379.0 0.6885896 0.6885896 1.037231e-02 1.037231e-02 0.301038092 #> 10760 5379.5 0.6885667 0.6885667 1.037281e-02 1.037281e-02 0.301060444 #> 10761 5380.0 0.6885439 0.6885439 1.037330e-02 1.037330e-02 0.301082792 #> 10762 5380.5 0.6885211 0.6885211 1.037378e-02 1.037378e-02 0.301105138 #> 10763 5381.0 0.6884983 0.6884983 1.037426e-02 1.037426e-02 0.301127480 #> 10764 5381.5 0.6884754 0.6884754 1.037473e-02 1.037473e-02 0.301149819 #> 10765 5382.0 0.6884526 0.6884526 1.037520e-02 1.037520e-02 0.301172154 #> 10766 5382.5 0.6884299 0.6884299 1.037566e-02 1.037566e-02 0.301194487 #> 10767 5383.0 0.6884071 0.6884071 1.037611e-02 1.037611e-02 0.301216815 #> 10768 5383.5 0.6883843 0.6883843 1.037656e-02 1.037656e-02 0.301239140 #> 10769 5384.0 0.6883615 0.6883615 1.037701e-02 1.037701e-02 0.301261461 #> 10770 5384.5 0.6883388 0.6883388 1.037744e-02 1.037744e-02 0.301283779 #> 10771 5385.0 0.6883160 0.6883160 1.037787e-02 1.037787e-02 0.301306092 #> 10772 5385.5 0.6882933 0.6882933 1.037830e-02 1.037830e-02 0.301328402 #> 10773 5386.0 0.6882706 0.6882706 1.037872e-02 1.037872e-02 0.301350707 #> 10774 5386.5 0.6882479 0.6882479 1.037913e-02 1.037913e-02 0.301373008 #> 10775 5387.0 0.6882252 0.6882252 1.037954e-02 1.037954e-02 0.301395305 #> 10776 5387.5 0.6882025 0.6882025 1.037994e-02 1.037994e-02 0.301417598 #> 10777 5388.0 0.6881798 0.6881798 1.038033e-02 1.038033e-02 0.301439886 #> 10778 5388.5 0.6881571 0.6881571 1.038072e-02 1.038072e-02 0.301462169 #> 10779 5389.0 0.6881344 0.6881344 1.038111e-02 1.038111e-02 0.301484447 #> 10780 5389.5 0.6881118 0.6881118 1.038149e-02 1.038149e-02 0.301506721 #> 10781 5390.0 0.6880892 0.6880892 1.038186e-02 1.038186e-02 0.301528990 #> 10782 5390.5 0.6880665 0.6880665 1.038222e-02 1.038222e-02 0.301551254 #> 10783 5391.0 0.6880439 0.6880439 1.038258e-02 1.038258e-02 0.301573513 #> 10784 5391.5 0.6880213 0.6880213 1.038294e-02 1.038294e-02 0.301595767 #> 10785 5392.0 0.6879987 0.6879987 1.038329e-02 1.038329e-02 0.301618015 #> 10786 5392.5 0.6879761 0.6879761 1.038363e-02 1.038363e-02 0.301640258 #> 10787 5393.0 0.6879535 0.6879535 1.038397e-02 1.038397e-02 0.301662496 #> 10788 5393.5 0.6879310 0.6879310 1.038430e-02 1.038430e-02 0.301684728 #> 10789 5394.0 0.6879084 0.6879084 1.038462e-02 1.038462e-02 0.301706955 #> 10790 5394.5 0.6878859 0.6878859 1.038494e-02 1.038494e-02 0.301729175 #> 10791 5395.0 0.6878634 0.6878634 1.038526e-02 1.038526e-02 0.301751390 #> 10792 5395.5 0.6878408 0.6878408 1.038557e-02 1.038557e-02 0.301773599 #> 10793 5396.0 0.6878183 0.6878183 1.038587e-02 1.038587e-02 0.301795802 #> 10794 5396.5 0.6877958 0.6877958 1.038616e-02 1.038616e-02 0.301817999 #> 10795 5397.0 0.6877734 0.6877734 1.038645e-02 1.038645e-02 0.301840189 #> 10796 5397.5 0.6877509 0.6877509 1.038674e-02 1.038674e-02 0.301862374 #> 10797 5398.0 0.6877284 0.6877284 1.038702e-02 1.038702e-02 0.301884552 #> 10798 5398.5 0.6877060 0.6877060 1.038729e-02 1.038729e-02 0.301906723 #> 10799 5399.0 0.6876836 0.6876836 1.038756e-02 1.038756e-02 0.301928888 #> 10800 5399.5 0.6876611 0.6876611 1.038782e-02 1.038782e-02 0.301951046 #> 10801 5400.0 0.6876387 0.6876387 1.038807e-02 1.038807e-02 0.301973197 #> 10802 5400.5 0.6876163 0.6876163 1.038832e-02 1.038832e-02 0.301995342 #> 10803 5401.0 0.6875940 0.6875940 1.038857e-02 1.038857e-02 0.302017479 #> 10804 5401.5 0.6875716 0.6875716 1.038880e-02 1.038880e-02 0.302039610 #> 10805 5402.0 0.6875492 0.6875492 1.038904e-02 1.038904e-02 0.302061733 #> 10806 5402.5 0.6875269 0.6875269 1.038926e-02 1.038926e-02 0.302083849 #> 10807 5403.0 0.6875046 0.6875046 1.038948e-02 1.038948e-02 0.302105958 #> 10808 5403.5 0.6874822 0.6874822 1.038970e-02 1.038970e-02 0.302128060 #> 10809 5404.0 0.6874599 0.6874599 1.038991e-02 1.038991e-02 0.302150153 #> 10810 5404.5 0.6874377 0.6874377 1.039011e-02 1.039011e-02 0.302172240 #> 10811 5405.0 0.6874154 0.6874154 1.039031e-02 1.039031e-02 0.302194318 #> 10812 5405.5 0.6873931 0.6873931 1.039050e-02 1.039050e-02 0.302216389 #> 10813 5406.0 0.6873709 0.6873709 1.039068e-02 1.039068e-02 0.302238452 #> 10814 5406.5 0.6873486 0.6873486 1.039086e-02 1.039086e-02 0.302260506 #> 10815 5407.0 0.6873264 0.6873264 1.039104e-02 1.039104e-02 0.302282553 #> 10816 5407.5 0.6873042 0.6873042 1.039121e-02 1.039121e-02 0.302304592 #> 10817 5408.0 0.6872820 0.6872820 1.039137e-02 1.039137e-02 0.302326622 #> 10818 5408.5 0.6872598 0.6872598 1.039153e-02 1.039153e-02 0.302348644 #> 10819 5409.0 0.6872377 0.6872377 1.039168e-02 1.039168e-02 0.302370658 #> 10820 5409.5 0.6872155 0.6872155 1.039182e-02 1.039182e-02 0.302392663 #> 10821 5410.0 0.6871934 0.6871934 1.039196e-02 1.039196e-02 0.302414659 #> 10822 5410.5 0.6871713 0.6871713 1.039210e-02 1.039210e-02 0.302436647 #> 10823 5411.0 0.6871491 0.6871491 1.039223e-02 1.039223e-02 0.302458626 #> 10824 5411.5 0.6871271 0.6871271 1.039235e-02 1.039235e-02 0.302480596 #> 10825 5412.0 0.6871050 0.6871050 1.039247e-02 1.039247e-02 0.302502557 #> 10826 5412.5 0.6870829 0.6870829 1.039258e-02 1.039258e-02 0.302524509 #> 10827 5413.0 0.6870609 0.6870609 1.039268e-02 1.039268e-02 0.302546451 #> 10828 5413.5 0.6870388 0.6870388 1.039278e-02 1.039278e-02 0.302568385 #> 10829 5414.0 0.6870168 0.6870168 1.039287e-02 1.039287e-02 0.302590309 #> 10830 5414.5 0.6869948 0.6869948 1.039296e-02 1.039296e-02 0.302612224 #> 10831 5415.0 0.6869728 0.6869728 1.039305e-02 1.039305e-02 0.302634129 #> 10832 5415.5 0.6869509 0.6869509 1.039312e-02 1.039312e-02 0.302656024 #> 10833 5416.0 0.6869289 0.6869289 1.039319e-02 1.039319e-02 0.302677910 #> 10834 5416.5 0.6869070 0.6869070 1.039326e-02 1.039326e-02 0.302699786 #> 10835 5417.0 0.6868850 0.6868850 1.039332e-02 1.039332e-02 0.302721652 #> 10836 5417.5 0.6868631 0.6868631 1.039337e-02 1.039337e-02 0.302743509 #> 10837 5418.0 0.6868412 0.6868412 1.039342e-02 1.039342e-02 0.302765355 #> 10838 5418.5 0.6868193 0.6868193 1.039346e-02 1.039346e-02 0.302787190 #> 10839 5419.0 0.6867975 0.6867975 1.039350e-02 1.039350e-02 0.302809016 #> 10840 5419.5 0.6867756 0.6867756 1.039353e-02 1.039353e-02 0.302830831 #> 10841 5420.0 0.6867538 0.6867538 1.039356e-02 1.039356e-02 0.302852636 #> 10842 5420.5 0.6867320 0.6867320 1.039358e-02 1.039358e-02 0.302874431 #> 10843 5421.0 0.6867102 0.6867102 1.039359e-02 1.039359e-02 0.302896214 #> 10844 5421.5 0.6866884 0.6866884 1.039360e-02 1.039360e-02 0.302917987 #> 10845 5422.0 0.6866666 0.6866666 1.039360e-02 1.039360e-02 0.302939750 #> 10846 5422.5 0.6866449 0.6866449 1.039360e-02 1.039360e-02 0.302961501 #> 10847 5423.0 0.6866232 0.6866232 1.039359e-02 1.039359e-02 0.302983241 #> 10848 5423.5 0.6866015 0.6866015 1.039358e-02 1.039358e-02 0.303004971 #> 10849 5424.0 0.6865798 0.6865798 1.039356e-02 1.039356e-02 0.303026689 #> 10850 5424.5 0.6865581 0.6865581 1.039353e-02 1.039353e-02 0.303048396 #> 10851 5425.0 0.6865364 0.6865364 1.039350e-02 1.039350e-02 0.303070091 #> 10852 5425.5 0.6865148 0.6865148 1.039346e-02 1.039346e-02 0.303091776 #> 10853 5426.0 0.6864931 0.6864931 1.039342e-02 1.039342e-02 0.303113448 #> 10854 5426.5 0.6864715 0.6864715 1.039337e-02 1.039337e-02 0.303135110 #> 10855 5427.0 0.6864499 0.6864499 1.039332e-02 1.039332e-02 0.303156759 #> 10856 5427.5 0.6864283 0.6864283 1.039326e-02 1.039326e-02 0.303178397 #> 10857 5428.0 0.6864068 0.6864068 1.039320e-02 1.039320e-02 0.303200023 #> 10858 5428.5 0.6863852 0.6863852 1.039313e-02 1.039313e-02 0.303221637 #> 10859 5429.0 0.6863637 0.6863637 1.039305e-02 1.039305e-02 0.303243238 #> 10860 5429.5 0.6863422 0.6863422 1.039297e-02 1.039297e-02 0.303264828 #> 10861 5430.0 0.6863207 0.6863207 1.039288e-02 1.039288e-02 0.303286406 #> 10862 5430.5 0.6862992 0.6862992 1.039279e-02 1.039279e-02 0.303307971 #> 10863 5431.0 0.6862778 0.6862778 1.039269e-02 1.039269e-02 0.303329524 #> 10864 5431.5 0.6862563 0.6862563 1.039259e-02 1.039259e-02 0.303351065 #> 10865 5432.0 0.6862349 0.6862349 1.039248e-02 1.039248e-02 0.303372593 #> 10866 5432.5 0.6862135 0.6862135 1.039236e-02 1.039236e-02 0.303394108 #> 10867 5433.0 0.6861921 0.6861921 1.039224e-02 1.039224e-02 0.303415610 #> 10868 5433.5 0.6861708 0.6861708 1.039211e-02 1.039211e-02 0.303437100 #> 10869 5434.0 0.6861494 0.6861494 1.039198e-02 1.039198e-02 0.303458577 #> 10870 5434.5 0.6861281 0.6861281 1.039185e-02 1.039185e-02 0.303480041 #> 10871 5435.0 0.6861068 0.6861068 1.039170e-02 1.039170e-02 0.303501492 #> 10872 5435.5 0.6860855 0.6860855 1.039156e-02 1.039156e-02 0.303522930 #> 10873 5436.0 0.6860642 0.6860642 1.039140e-02 1.039140e-02 0.303544354 #> 10874 5436.5 0.6860430 0.6860430 1.039124e-02 1.039124e-02 0.303565765 #> 10875 5437.0 0.6860218 0.6860218 1.039108e-02 1.039108e-02 0.303587163 #> 10876 5437.5 0.6860005 0.6860005 1.039091e-02 1.039091e-02 0.303608547 #> 10877 5438.0 0.6859794 0.6859794 1.039073e-02 1.039073e-02 0.303629918 #> 10878 5438.5 0.6859582 0.6859582 1.039055e-02 1.039055e-02 0.303651275 #> 10879 5439.0 0.6859370 0.6859370 1.039036e-02 1.039036e-02 0.303672619 #> 10880 5439.5 0.6859159 0.6859159 1.039017e-02 1.039017e-02 0.303693948 #> 10881 5440.0 0.6858948 0.6858948 1.038997e-02 1.038997e-02 0.303715264 #> 10882 5440.5 0.6858737 0.6858737 1.038977e-02 1.038977e-02 0.303736565 #> 10883 5441.0 0.6858526 0.6858526 1.038956e-02 1.038956e-02 0.303757853 #> 10884 5441.5 0.6858315 0.6858315 1.038935e-02 1.038935e-02 0.303779126 #> 10885 5442.0 0.6858105 0.6858105 1.038913e-02 1.038913e-02 0.303800385 #> 10886 5442.5 0.6857895 0.6857895 1.038890e-02 1.038890e-02 0.303821630 #> 10887 5443.0 0.6857685 0.6857685 1.038867e-02 1.038867e-02 0.303842860 #> 10888 5443.5 0.6857475 0.6857475 1.038844e-02 1.038844e-02 0.303864076 #> 10889 5444.0 0.6857265 0.6857265 1.038819e-02 1.038819e-02 0.303885277 #> 10890 5444.5 0.6857056 0.6857056 1.038795e-02 1.038795e-02 0.303906464 #> 10891 5445.0 0.6856847 0.6856847 1.038770e-02 1.038770e-02 0.303927636 #> 10892 5445.5 0.6856638 0.6856638 1.038744e-02 1.038744e-02 0.303948793 #> 10893 5446.0 0.6856429 0.6856429 1.038717e-02 1.038717e-02 0.303969935 #> 10894 5446.5 0.6856220 0.6856220 1.038691e-02 1.038691e-02 0.303991062 #> 10895 5447.0 0.6856012 0.6856012 1.038663e-02 1.038663e-02 0.304012174 #> 10896 5447.5 0.6855804 0.6855804 1.038635e-02 1.038635e-02 0.304033271 #> 10897 5448.0 0.6855596 0.6855596 1.038607e-02 1.038607e-02 0.304054352 #> 10898 5448.5 0.6855388 0.6855388 1.038578e-02 1.038578e-02 0.304075419 #> 10899 5449.0 0.6855180 0.6855180 1.038548e-02 1.038548e-02 0.304096469 #> 10900 5449.5 0.6854973 0.6854973 1.038518e-02 1.038518e-02 0.304117505 #> 10901 5450.0 0.6854766 0.6854766 1.038487e-02 1.038487e-02 0.304138525 #> 10902 5450.5 0.6854559 0.6854559 1.038456e-02 1.038456e-02 0.304159529 #> 10903 5451.0 0.6854352 0.6854352 1.038425e-02 1.038425e-02 0.304180517 #> 10904 5451.5 0.6854146 0.6854146 1.038392e-02 1.038392e-02 0.304201490 #> 10905 5452.0 0.6853940 0.6853940 1.038360e-02 1.038360e-02 0.304222446 #> 10906 5452.5 0.6853733 0.6853733 1.038326e-02 1.038326e-02 0.304243387 #> 10907 5453.0 0.6853528 0.6853528 1.038292e-02 1.038292e-02 0.304264312 #> 10908 5453.5 0.6853322 0.6853322 1.038258e-02 1.038258e-02 0.304285220 #> 10909 5454.0 0.6853117 0.6853117 1.038223e-02 1.038223e-02 0.304306113 #> 10910 5454.5 0.6852911 0.6852911 1.038188e-02 1.038188e-02 0.304326989 #> 10911 5455.0 0.6852706 0.6852706 1.038152e-02 1.038152e-02 0.304347848 #> 10912 5455.5 0.6852502 0.6852502 1.038115e-02 1.038115e-02 0.304368691 #> 10913 5456.0 0.6852297 0.6852297 1.038078e-02 1.038078e-02 0.304389518 #> 10914 5456.5 0.6852093 0.6852093 1.038041e-02 1.038041e-02 0.304410328 #> 10915 5457.0 0.6851889 0.6851889 1.038003e-02 1.038003e-02 0.304431121 #> 10916 5457.5 0.6851685 0.6851685 1.037964e-02 1.037964e-02 0.304451898 #> 10917 5458.0 0.6851481 0.6851481 1.037925e-02 1.037925e-02 0.304472657 #> 10918 5458.5 0.6851277 0.6851277 1.037885e-02 1.037885e-02 0.304493400 #> 10919 5459.0 0.6851074 0.6851074 1.037845e-02 1.037845e-02 0.304514125 #> 10920 5459.5 0.6850871 0.6850871 1.037804e-02 1.037804e-02 0.304534834 #> 10921 5460.0 0.6850668 0.6850668 1.037763e-02 1.037763e-02 0.304555525 #> 10922 5460.5 0.6850466 0.6850466 1.037721e-02 1.037721e-02 0.304576199 #> 10923 5461.0 0.6850264 0.6850264 1.037679e-02 1.037679e-02 0.304596856 #> 10924 5461.5 0.6850061 0.6850061 1.037636e-02 1.037636e-02 0.304617496 #> 10925 5462.0 0.6849860 0.6849860 1.037593e-02 1.037593e-02 0.304638117 #> 10926 5462.5 0.6849658 0.6849658 1.037549e-02 1.037549e-02 0.304658722 #> 10927 5463.0 0.6849456 0.6849456 1.037505e-02 1.037505e-02 0.304679308 #> 10928 5463.5 0.6849255 0.6849255 1.037460e-02 1.037460e-02 0.304699877 #> 10929 5464.0 0.6849054 0.6849054 1.037414e-02 1.037414e-02 0.304720428 #> 10930 5464.5 0.6848854 0.6848854 1.037368e-02 1.037368e-02 0.304740962 #> 10931 5465.0 0.6848653 0.6848653 1.037322e-02 1.037322e-02 0.304761477 #> 10932 5465.5 0.6848453 0.6848453 1.037275e-02 1.037275e-02 0.304781974 #> 10933 5466.0 0.6848253 0.6848253 1.037227e-02 1.037227e-02 0.304802453 #> 10934 5466.5 0.6848053 0.6848053 1.037179e-02 1.037179e-02 0.304822914 #> 10935 5467.0 0.6847853 0.6847853 1.037131e-02 1.037131e-02 0.304843357 #> 10936 5467.5 0.6847654 0.6847654 1.037082e-02 1.037082e-02 0.304863781 #> 10937 5468.0 0.6847455 0.6847455 1.037032e-02 1.037032e-02 0.304884187 #> 10938 5468.5 0.6847256 0.6847256 1.036982e-02 1.036982e-02 0.304904574 #> 10939 5469.0 0.6847057 0.6847057 1.036932e-02 1.036932e-02 0.304924943 #> 10940 5469.5 0.6846859 0.6846859 1.036881e-02 1.036881e-02 0.304945293 #> 10941 5470.0 0.6846661 0.6846661 1.036829e-02 1.036829e-02 0.304965624 #> 10942 5470.5 0.6846463 0.6846463 1.036777e-02 1.036777e-02 0.304985937 #> 10943 5471.0 0.6846265 0.6846265 1.036724e-02 1.036724e-02 0.305006231 #> 10944 5471.5 0.6846068 0.6846068 1.036671e-02 1.036671e-02 0.305026506 #> 10945 5472.0 0.6845871 0.6845871 1.036617e-02 1.036617e-02 0.305046761 #> 10946 5472.5 0.6845674 0.6845674 1.036563e-02 1.036563e-02 0.305066998 #> 10947 5473.0 0.6845477 0.6845477 1.036509e-02 1.036509e-02 0.305087215 #> 10948 5473.5 0.6845281 0.6845281 1.036454e-02 1.036454e-02 0.305107414 #> 10949 5474.0 0.6845084 0.6845084 1.036398e-02 1.036398e-02 0.305127592 #> 10950 5474.5 0.6844888 0.6844888 1.036342e-02 1.036342e-02 0.305147752 #> 10951 5475.0 0.6844693 0.6844693 1.036285e-02 1.036285e-02 0.305167892 #> 10952 5475.5 0.6844497 0.6844497 1.036228e-02 1.036228e-02 0.305188012 #> 10953 5476.0 0.6844302 0.6844302 1.036170e-02 1.036170e-02 0.305208113 #> 10954 5476.5 0.6844107 0.6844107 1.036112e-02 1.036112e-02 0.305228194 #> 10955 5477.0 0.6843912 0.6843912 1.036053e-02 1.036053e-02 0.305248256 #> 10956 5477.5 0.6843718 0.6843718 1.035994e-02 1.035994e-02 0.305268297 #> 10957 5478.0 0.6843523 0.6843523 1.035934e-02 1.035934e-02 0.305288319 #> 10958 5478.5 0.6843329 0.6843329 1.035874e-02 1.035874e-02 0.305308320 #> 10959 5479.0 0.6843136 0.6843136 1.035814e-02 1.035814e-02 0.305328302 #> 10960 5479.5 0.6842942 0.6842942 1.035752e-02 1.035752e-02 0.305348263 #> 10961 5480.0 0.6842749 0.6842749 1.035691e-02 1.035691e-02 0.305368204 #> 10962 5480.5 0.6842556 0.6842556 1.035628e-02 1.035628e-02 0.305388125 #> 10963 5481.0 0.6842363 0.6842363 1.035566e-02 1.035566e-02 0.305408025 #> 10964 5481.5 0.6842171 0.6842171 1.035503e-02 1.035503e-02 0.305427906 #> 10965 5482.0 0.6841978 0.6841978 1.035439e-02 1.035439e-02 0.305447765 #> 10966 5482.5 0.6841786 0.6841786 1.035375e-02 1.035375e-02 0.305467604 #> 10967 5483.0 0.6841595 0.6841595 1.035310e-02 1.035310e-02 0.305487422 #> 10968 5483.5 0.6841403 0.6841403 1.035245e-02 1.035245e-02 0.305507220 #> 10969 5484.0 0.6841212 0.6841212 1.035179e-02 1.035179e-02 0.305526997 #> 10970 5484.5 0.6841021 0.6841021 1.035113e-02 1.035113e-02 0.305546753 #> 10971 5485.0 0.6840830 0.6840830 1.035047e-02 1.035047e-02 0.305566488 #> 10972 5485.5 0.6840640 0.6840640 1.034980e-02 1.034980e-02 0.305586202 #> 10973 5486.0 0.6840450 0.6840450 1.034912e-02 1.034912e-02 0.305605895 #> 10974 5486.5 0.6840260 0.6840260 1.034844e-02 1.034844e-02 0.305625567 #> 10975 5487.0 0.6840070 0.6840070 1.034775e-02 1.034775e-02 0.305645217 #> 10976 5487.5 0.6839881 0.6839881 1.034706e-02 1.034706e-02 0.305664846 #> 10977 5488.0 0.6839692 0.6839692 1.034637e-02 1.034637e-02 0.305684454 #> 10978 5488.5 0.6839503 0.6839503 1.034567e-02 1.034567e-02 0.305704041 #> 10979 5489.0 0.6839314 0.6839314 1.034496e-02 1.034496e-02 0.305723606 #> 10980 5489.5 0.6839126 0.6839126 1.034425e-02 1.034425e-02 0.305743149 #> 10981 5490.0 0.6838938 0.6838938 1.034354e-02 1.034354e-02 0.305762671 #> 10982 5490.5 0.6838750 0.6838750 1.034282e-02 1.034282e-02 0.305782171 #> 10983 5491.0 0.6838563 0.6838563 1.034209e-02 1.034209e-02 0.305801650 #> 10984 5491.5 0.6838375 0.6838375 1.034137e-02 1.034137e-02 0.305821106 #> 10985 5492.0 0.6838188 0.6838188 1.034063e-02 1.034063e-02 0.305840541 #> 10986 5492.5 0.6838002 0.6838002 1.033989e-02 1.033989e-02 0.305859953 #> 10987 5493.0 0.6837815 0.6837815 1.033915e-02 1.033915e-02 0.305879344 #> 10988 5493.5 0.6837629 0.6837629 1.033840e-02 1.033840e-02 0.305898712 #> 10989 5494.0 0.6837443 0.6837443 1.033765e-02 1.033765e-02 0.305918058 #> 10990 5494.5 0.6837257 0.6837257 1.033689e-02 1.033689e-02 0.305937382 #> 10991 5495.0 0.6837072 0.6837072 1.033613e-02 1.033613e-02 0.305956684 #> 10992 5495.5 0.6836887 0.6836887 1.033536e-02 1.033536e-02 0.305975963 #> 10993 5496.0 0.6836702 0.6836702 1.033459e-02 1.033459e-02 0.305995220 #> 10994 5496.5 0.6836517 0.6836517 1.033381e-02 1.033381e-02 0.306014454 #> 10995 5497.0 0.6836333 0.6836333 1.033303e-02 1.033303e-02 0.306033666 #> 10996 5497.5 0.6836149 0.6836149 1.033225e-02 1.033225e-02 0.306052855 #> 10997 5498.0 0.6835965 0.6835965 1.033146e-02 1.033146e-02 0.306072021 #> 10998 5498.5 0.6835782 0.6835782 1.033066e-02 1.033066e-02 0.306091165 #> 10999 5499.0 0.6835599 0.6835599 1.032986e-02 1.032986e-02 0.306110286 #> 11000 5499.5 0.6835416 0.6835416 1.032906e-02 1.032906e-02 0.306129383 #> 11001 5500.0 0.6835233 0.6835233 1.032825e-02 1.032825e-02 0.306148458 #> 11002 5500.5 0.6835051 0.6835051 1.032743e-02 1.032743e-02 0.306167510 #> 11003 5501.0 0.6834868 0.6834868 1.032661e-02 1.032661e-02 0.306186538 #> 11004 5501.5 0.6834687 0.6834687 1.032579e-02 1.032579e-02 0.306205544 #> 11005 5502.0 0.6834505 0.6834505 1.032496e-02 1.032496e-02 0.306224526 #> 11006 5502.5 0.6834324 0.6834324 1.032413e-02 1.032413e-02 0.306243484 #> 11007 5503.0 0.6834143 0.6834143 1.032329e-02 1.032329e-02 0.306262420 #> 11008 5503.5 0.6833962 0.6833962 1.032245e-02 1.032245e-02 0.306281332 #> 11009 5504.0 0.6833782 0.6833782 1.032160e-02 1.032160e-02 0.306300220 #> 11010 5504.5 0.6833602 0.6833602 1.032075e-02 1.032075e-02 0.306319085 #> 11011 5505.0 0.6833422 0.6833422 1.031990e-02 1.031990e-02 0.306337926 #> 11012 5505.5 0.6833242 0.6833242 1.031904e-02 1.031904e-02 0.306356744 #> 11013 5506.0 0.6833063 0.6833063 1.031817e-02 1.031817e-02 0.306375537 #> 11014 5506.5 0.6832884 0.6832884 1.031730e-02 1.031730e-02 0.306394307 #> 11015 5507.0 0.6832705 0.6832705 1.031643e-02 1.031643e-02 0.306413053 #> 11016 5507.5 0.6832527 0.6832527 1.031555e-02 1.031555e-02 0.306431775 #> 11017 5508.0 0.6832349 0.6832349 1.031467e-02 1.031467e-02 0.306450473 #> 11018 5508.5 0.6832171 0.6832171 1.031378e-02 1.031378e-02 0.306469147 #> 11019 5509.0 0.6831993 0.6831993 1.031289e-02 1.031289e-02 0.306487797 #> 11020 5509.5 0.6831816 0.6831816 1.031200e-02 1.031200e-02 0.306506422 #> 11021 5510.0 0.6831639 0.6831639 1.031109e-02 1.031109e-02 0.306525023 #> 11022 5510.5 0.6831462 0.6831462 1.031019e-02 1.031019e-02 0.306543600 #> 11023 5511.0 0.6831286 0.6831286 1.030928e-02 1.030928e-02 0.306562153 #> 11024 5511.5 0.6831110 0.6831110 1.030837e-02 1.030837e-02 0.306580681 #> 11025 5512.0 0.6830934 0.6830934 1.030745e-02 1.030745e-02 0.306599184 #> 11026 5512.5 0.6830758 0.6830758 1.030652e-02 1.030652e-02 0.306617663 #> 11027 5513.0 0.6830583 0.6830583 1.030560e-02 1.030560e-02 0.306636117 #> 11028 5513.5 0.6830408 0.6830408 1.030466e-02 1.030466e-02 0.306654547 #> 11029 5514.0 0.6830233 0.6830233 1.030373e-02 1.030373e-02 0.306672952 #> 11030 5514.5 0.6830059 0.6830059 1.030279e-02 1.030279e-02 0.306691332 #> 11031 5515.0 0.6829885 0.6829885 1.030184e-02 1.030184e-02 0.306709687 #> 11032 5515.5 0.6829711 0.6829711 1.030089e-02 1.030089e-02 0.306728017 #> 11033 5516.0 0.6829537 0.6829537 1.029994e-02 1.029994e-02 0.306746322 #> 11034 5516.5 0.6829364 0.6829364 1.029898e-02 1.029898e-02 0.306764602 #> 11035 5517.0 0.6829191 0.6829191 1.029802e-02 1.029802e-02 0.306782856 #> 11036 5517.5 0.6829019 0.6829019 1.029705e-02 1.029705e-02 0.306801086 #> 11037 5518.0 0.6828846 0.6828846 1.029608e-02 1.029608e-02 0.306819290 #> 11038 5518.5 0.6828674 0.6828674 1.029510e-02 1.029510e-02 0.306837469 #> 11039 5519.0 0.6828503 0.6828503 1.029412e-02 1.029412e-02 0.306855623 #> 11040 5519.5 0.6828331 0.6828331 1.029314e-02 1.029314e-02 0.306873751 #> 11041 5520.0 0.6828160 0.6828160 1.029215e-02 1.029215e-02 0.306891854 #> 11042 5520.5 0.6827989 0.6827989 1.029116e-02 1.029116e-02 0.306909931 #> 11043 5521.0 0.6827819 0.6827819 1.029016e-02 1.029016e-02 0.306927983 #> 11044 5521.5 0.6827648 0.6827648 1.028916e-02 1.028916e-02 0.306946008 #> 11045 5522.0 0.6827478 0.6827478 1.028815e-02 1.028815e-02 0.306964009 #> 11046 5522.5 0.6827309 0.6827309 1.028714e-02 1.028714e-02 0.306981983 #> 11047 5523.0 0.6827139 0.6827139 1.028613e-02 1.028613e-02 0.306999931 #> 11048 5523.5 0.6826970 0.6826970 1.028511e-02 1.028511e-02 0.307017854 #> 11049 5524.0 0.6826802 0.6826802 1.028409e-02 1.028409e-02 0.307035751 #> 11050 5524.5 0.6826633 0.6826633 1.028306e-02 1.028306e-02 0.307053621 #> 11051 5525.0 0.6826465 0.6826465 1.028203e-02 1.028203e-02 0.307071466 #> 11052 5525.5 0.6826297 0.6826297 1.028099e-02 1.028099e-02 0.307089284 #> 11053 5526.0 0.6826130 0.6826130 1.027995e-02 1.027995e-02 0.307107076 #> 11054 5526.5 0.6825962 0.6825962 1.027891e-02 1.027891e-02 0.307124842 #> 11055 5527.0 0.6825796 0.6825796 1.027786e-02 1.027786e-02 0.307142582 #> 11056 5527.5 0.6825629 0.6825629 1.027681e-02 1.027681e-02 0.307160295 #> 11057 5528.0 0.6825463 0.6825463 1.027575e-02 1.027575e-02 0.307177982 #> 11058 5528.5 0.6825297 0.6825297 1.027469e-02 1.027469e-02 0.307195642 #> 11059 5529.0 0.6825131 0.6825131 1.027363e-02 1.027363e-02 0.307213276 #> 11060 5529.5 0.6824966 0.6824966 1.027256e-02 1.027256e-02 0.307230883 #> 11061 5530.0 0.6824800 0.6824800 1.027149e-02 1.027149e-02 0.307248464 #> 11062 5530.5 0.6824636 0.6824636 1.027041e-02 1.027041e-02 0.307266017 #> 11063 5531.0 0.6824471 0.6824471 1.026933e-02 1.026933e-02 0.307283544 #> 11064 5531.5 0.6824307 0.6824307 1.026824e-02 1.026824e-02 0.307301045 #> 11065 5532.0 0.6824143 0.6824143 1.026715e-02 1.026715e-02 0.307318518 #> 11066 5532.5 0.6823980 0.6823980 1.026606e-02 1.026606e-02 0.307335965 #> 11067 5533.0 0.6823817 0.6823817 1.026496e-02 1.026496e-02 0.307353384 #> 11068 5533.5 0.6823654 0.6823654 1.026386e-02 1.026386e-02 0.307370777 #> 11069 5534.0 0.6823491 0.6823491 1.026276e-02 1.026276e-02 0.307388142 #> 11070 5534.5 0.6823329 0.6823329 1.026165e-02 1.026165e-02 0.307405480 #> 11071 5535.0 0.6823167 0.6823167 1.026053e-02 1.026053e-02 0.307422791 #> 11072 5535.5 0.6823005 0.6823005 1.025941e-02 1.025941e-02 0.307440075 #> 11073 5536.0 0.6822844 0.6822844 1.025829e-02 1.025829e-02 0.307457332 #> 11074 5536.5 0.6822683 0.6822683 1.025717e-02 1.025717e-02 0.307474561 #> 11075 5537.0 0.6822522 0.6822522 1.025604e-02 1.025604e-02 0.307491763 #> 11076 5537.5 0.6822362 0.6822362 1.025490e-02 1.025490e-02 0.307508937 #> 11077 5538.0 0.6822202 0.6822202 1.025376e-02 1.025376e-02 0.307526084 #> 11078 5538.5 0.6822042 0.6822042 1.025262e-02 1.025262e-02 0.307543203 #> 11079 5539.0 0.6821882 0.6821882 1.025148e-02 1.025148e-02 0.307560295 #> 11080 5539.5 0.6821723 0.6821723 1.025033e-02 1.025033e-02 0.307577359 #> 11081 5540.0 0.6821564 0.6821564 1.024917e-02 1.024917e-02 0.307594395 #> 11082 5540.5 0.6821406 0.6821406 1.024802e-02 1.024802e-02 0.307611403 #> 11083 5541.0 0.6821248 0.6821248 1.024685e-02 1.024685e-02 0.307628384 #> 11084 5541.5 0.6821090 0.6821090 1.024569e-02 1.024569e-02 0.307645337 #> 11085 5542.0 0.6820932 0.6820932 1.024452e-02 1.024452e-02 0.307662262 #> 11086 5542.5 0.6820775 0.6820775 1.024335e-02 1.024335e-02 0.307679158 #> 11087 5543.0 0.6820618 0.6820618 1.024217e-02 1.024217e-02 0.307696027 #> 11088 5543.5 0.6820461 0.6820461 1.024099e-02 1.024099e-02 0.307712868 #> 11089 5544.0 0.6820305 0.6820305 1.023980e-02 1.023980e-02 0.307729681 #> 11090 5544.5 0.6820149 0.6820149 1.023861e-02 1.023861e-02 0.307746465 #> 11091 5545.0 0.6819994 0.6819994 1.023742e-02 1.023742e-02 0.307763221 #> 11092 5545.5 0.6819838 0.6819838 1.023623e-02 1.023623e-02 0.307779949 #> 11093 5546.0 0.6819683 0.6819683 1.023502e-02 1.023502e-02 0.307796649 #> 11094 5546.5 0.6819529 0.6819529 1.023382e-02 1.023382e-02 0.307813320 #> 11095 5547.0 0.6819374 0.6819374 1.023261e-02 1.023261e-02 0.307829963 #> 11096 5547.5 0.6819220 0.6819220 1.023140e-02 1.023140e-02 0.307846577 #> 11097 5548.0 0.6819067 0.6819067 1.023019e-02 1.023019e-02 0.307863163 #> 11098 5548.5 0.6818913 0.6818913 1.022897e-02 1.022897e-02 0.307879720 #> 11099 5549.0 0.6818760 0.6818760 1.022774e-02 1.022774e-02 0.307896249 #> 11100 5549.5 0.6818607 0.6818607 1.022652e-02 1.022652e-02 0.307912749 #> 11101 5550.0 0.6818455 0.6818455 1.022529e-02 1.022529e-02 0.307929220 #> 11102 5550.5 0.6818303 0.6818303 1.022405e-02 1.022405e-02 0.307945662 #> 11103 5551.0 0.6818151 0.6818151 1.022281e-02 1.022281e-02 0.307962076 #> 11104 5551.5 0.6818000 0.6818000 1.022157e-02 1.022157e-02 0.307978461 #> 11105 5552.0 0.6817849 0.6817849 1.022033e-02 1.022033e-02 0.307994817 #> 11106 5552.5 0.6817698 0.6817698 1.021908e-02 1.021908e-02 0.308011143 #> 11107 5553.0 0.6817547 0.6817547 1.021782e-02 1.021782e-02 0.308027441 #> 11108 5553.5 0.6817397 0.6817397 1.021657e-02 1.021657e-02 0.308043710 #> 11109 5554.0 0.6817247 0.6817247 1.021531e-02 1.021531e-02 0.308059950 #> 11110 5554.5 0.6817098 0.6817098 1.021404e-02 1.021404e-02 0.308076160 #> 11111 5555.0 0.6816949 0.6816949 1.021278e-02 1.021278e-02 0.308092342 #> 11112 5555.5 0.6816800 0.6816800 1.021150e-02 1.021150e-02 0.308108494 #> 11113 5556.0 0.6816652 0.6816652 1.021023e-02 1.021023e-02 0.308124617 #> 11114 5556.5 0.6816503 0.6816503 1.020895e-02 1.020895e-02 0.308140710 #> 11115 5557.0 0.6816356 0.6816356 1.020767e-02 1.020767e-02 0.308156774 #> 11116 5557.5 0.6816208 0.6816208 1.020638e-02 1.020638e-02 0.308172809 #> 11117 5558.0 0.6816061 0.6816061 1.020509e-02 1.020509e-02 0.308188814 #> 11118 5558.5 0.6815914 0.6815914 1.020380e-02 1.020380e-02 0.308204790 #> 11119 5559.0 0.6815768 0.6815768 1.020250e-02 1.020250e-02 0.308220736 #> 11120 5559.5 0.6815621 0.6815621 1.020120e-02 1.020120e-02 0.308236653 #> 11121 5560.0 0.6815476 0.6815476 1.019990e-02 1.019990e-02 0.308252540 #> 11122 5560.5 0.6815330 0.6815330 1.019859e-02 1.019859e-02 0.308268397 #> 11123 5561.0 0.6815185 0.6815185 1.019728e-02 1.019728e-02 0.308284225 #> 11124 5561.5 0.6815040 0.6815040 1.019597e-02 1.019597e-02 0.308300022 #> 11125 5562.0 0.6814896 0.6814896 1.019465e-02 1.019465e-02 0.308315790 #> 11126 5562.5 0.6814751 0.6814751 1.019333e-02 1.019333e-02 0.308331528 #> 11127 5563.0 0.6814608 0.6814608 1.019201e-02 1.019201e-02 0.308347236 #> 11128 5563.5 0.6814464 0.6814464 1.019068e-02 1.019068e-02 0.308362914 #> 11129 5564.0 0.6814321 0.6814321 1.018935e-02 1.018935e-02 0.308378563 #> 11130 5564.5 0.6814178 0.6814178 1.018801e-02 1.018801e-02 0.308394181 #> 11131 5565.0 0.6814036 0.6814036 1.018667e-02 1.018667e-02 0.308409769 #> 11132 5565.5 0.6813893 0.6813893 1.018533e-02 1.018533e-02 0.308425327 #> 11133 5566.0 0.6813752 0.6813752 1.018398e-02 1.018398e-02 0.308440854 #> 11134 5566.5 0.6813610 0.6813610 1.018264e-02 1.018264e-02 0.308456352 #> 11135 5567.0 0.6813469 0.6813469 1.018128e-02 1.018128e-02 0.308471819 #> 11136 5567.5 0.6813328 0.6813328 1.017993e-02 1.017993e-02 0.308487256 #> 11137 5568.0 0.6813188 0.6813188 1.017857e-02 1.017857e-02 0.308502663 #> 11138 5568.5 0.6813048 0.6813048 1.017721e-02 1.017721e-02 0.308518039 #> 11139 5569.0 0.6812908 0.6812908 1.017584e-02 1.017584e-02 0.308533385 #> 11140 5569.5 0.6812768 0.6812768 1.017447e-02 1.017447e-02 0.308548700 #> 11141 5570.0 0.6812629 0.6812629 1.017310e-02 1.017310e-02 0.308563985 #> 11142 5570.5 0.6812490 0.6812490 1.017172e-02 1.017172e-02 0.308579240 #> 11143 5571.0 0.6812352 0.6812352 1.017034e-02 1.017034e-02 0.308594463 #> 11144 5571.5 0.6812214 0.6812214 1.016896e-02 1.016896e-02 0.308609657 #> 11145 5572.0 0.6812076 0.6812076 1.016757e-02 1.016757e-02 0.308624819 #> 11146 5572.5 0.6811939 0.6811939 1.016618e-02 1.016618e-02 0.308639951 #> 11147 5573.0 0.6811802 0.6811802 1.016479e-02 1.016479e-02 0.308655052 #> 11148 5573.5 0.6811665 0.6811665 1.016340e-02 1.016340e-02 0.308670122 #> 11149 5574.0 0.6811528 0.6811528 1.016200e-02 1.016200e-02 0.308685162 #> 11150 5574.5 0.6811392 0.6811392 1.016059e-02 1.016059e-02 0.308700170 #> 11151 5575.0 0.6811257 0.6811257 1.015919e-02 1.015919e-02 0.308715148 #> 11152 5575.5 0.6811121 0.6811121 1.015778e-02 1.015778e-02 0.308730095 #> 11153 5576.0 0.6810986 0.6810986 1.015637e-02 1.015637e-02 0.308745011 #> 11154 5576.5 0.6810852 0.6810852 1.015495e-02 1.015495e-02 0.308759895 #> 11155 5577.0 0.6810717 0.6810717 1.015353e-02 1.015353e-02 0.308774749 #> 11156 5577.5 0.6810583 0.6810583 1.015211e-02 1.015211e-02 0.308789572 #> 11157 5578.0 0.6810449 0.6810449 1.015069e-02 1.015069e-02 0.308804363 #> 11158 5578.5 0.6810316 0.6810316 1.014926e-02 1.014926e-02 0.308819124 #> 11159 5579.0 0.6810183 0.6810183 1.014783e-02 1.014783e-02 0.308833853 #> 11160 5579.5 0.6810051 0.6810051 1.014639e-02 1.014639e-02 0.308848551 #> 11161 5580.0 0.6809918 0.6809918 1.014496e-02 1.014496e-02 0.308863217 #> 11162 5580.5 0.6809786 0.6809786 1.014351e-02 1.014351e-02 0.308877853 #> 11163 5581.0 0.6809655 0.6809655 1.014207e-02 1.014207e-02 0.308892456 #> 11164 5581.5 0.6809523 0.6809523 1.014062e-02 1.014062e-02 0.308907029 #> 11165 5582.0 0.6809393 0.6809393 1.013917e-02 1.013917e-02 0.308921570 #> 11166 5582.5 0.6809262 0.6809262 1.013772e-02 1.013772e-02 0.308936080 #> 11167 5583.0 0.6809132 0.6809132 1.013626e-02 1.013626e-02 0.308950558 #> 11168 5583.5 0.6809002 0.6809002 1.013480e-02 1.013480e-02 0.308965005 #> 11169 5584.0 0.6808872 0.6808872 1.013334e-02 1.013334e-02 0.308979420 #> 11170 5584.5 0.6808743 0.6808743 1.013188e-02 1.013188e-02 0.308993803 #> 11171 5585.0 0.6808614 0.6808614 1.013041e-02 1.013041e-02 0.309008155 #> 11172 5585.5 0.6808486 0.6808486 1.012894e-02 1.012894e-02 0.309022475 #> 11173 5586.0 0.6808358 0.6808358 1.012746e-02 1.012746e-02 0.309036764 #> 11174 5586.5 0.6808230 0.6808230 1.012598e-02 1.012598e-02 0.309051021 #> 11175 5587.0 0.6808103 0.6808103 1.012450e-02 1.012450e-02 0.309065246 #> 11176 5587.5 0.6807975 0.6807975 1.012302e-02 1.012302e-02 0.309079439 #> 11177 5588.0 0.6807849 0.6807849 1.012153e-02 1.012153e-02 0.309093600 #> 11178 5588.5 0.6807722 0.6807722 1.012004e-02 1.012004e-02 0.309107730 #> 11179 5589.0 0.6807596 0.6807596 1.011855e-02 1.011855e-02 0.309121827 #> 11180 5589.5 0.6807471 0.6807471 1.011705e-02 1.011705e-02 0.309135893 #> 11181 5590.0 0.6807345 0.6807345 1.011555e-02 1.011555e-02 0.309149926 #> 11182 5590.5 0.6807220 0.6807220 1.011405e-02 1.011405e-02 0.309163928 #> 11183 5591.0 0.6807096 0.6807096 1.011255e-02 1.011255e-02 0.309177897 #> 11184 5591.5 0.6806971 0.6806971 1.011104e-02 1.011104e-02 0.309191835 #> 11185 5592.0 0.6806847 0.6806847 1.010953e-02 1.010953e-02 0.309205740 #> 11186 5592.5 0.6806724 0.6806724 1.010802e-02 1.010802e-02 0.309219613 #> 11187 5593.0 0.6806600 0.6806600 1.010650e-02 1.010650e-02 0.309233454 #> 11188 5593.5 0.6806478 0.6806478 1.010498e-02 1.010498e-02 0.309247263 #> 11189 5594.0 0.6806355 0.6806355 1.010346e-02 1.010346e-02 0.309261040 #> 11190 5594.5 0.6806233 0.6806233 1.010193e-02 1.010193e-02 0.309274784 #> 11191 5595.0 0.6806111 0.6806111 1.010041e-02 1.010041e-02 0.309288496 #> 11192 5595.5 0.6805989 0.6805989 1.009888e-02 1.009888e-02 0.309302176 #> 11193 5596.0 0.6805868 0.6805868 1.009734e-02 1.009734e-02 0.309315823 #> 11194 5596.5 0.6805748 0.6805748 1.009581e-02 1.009581e-02 0.309329438 #> 11195 5597.0 0.6805627 0.6805627 1.009427e-02 1.009427e-02 0.309343021 #> 11196 5597.5 0.6805507 0.6805507 1.009273e-02 1.009273e-02 0.309356571 #> 11197 5598.0 0.6805387 0.6805387 1.009118e-02 1.009118e-02 0.309370088 #> 11198 5598.5 0.6805268 0.6805268 1.008963e-02 1.008963e-02 0.309383573 #> 11199 5599.0 0.6805149 0.6805149 1.008808e-02 1.008808e-02 0.309397026 #> 11200 5599.5 0.6805030 0.6805030 1.008653e-02 1.008653e-02 0.309410446 #> 11201 5600.0 0.6804912 0.6804912 1.008498e-02 1.008498e-02 0.309423833 #> 11202 5600.5 0.6804794 0.6804794 1.008342e-02 1.008342e-02 0.309437188 #> 11203 5601.0 0.6804676 0.6804676 1.008186e-02 1.008186e-02 0.309450510 #> 11204 5601.5 0.6804559 0.6804559 1.008029e-02 1.008029e-02 0.309463799 #> 11205 5602.0 0.6804442 0.6804442 1.007873e-02 1.007873e-02 0.309477056 #> 11206 5602.5 0.6804326 0.6804326 1.007716e-02 1.007716e-02 0.309490280 #> 11207 5603.0 0.6804209 0.6804209 1.007558e-02 1.007558e-02 0.309503471 #> 11208 5603.5 0.6804094 0.6804094 1.007401e-02 1.007401e-02 0.309516629 #> 11209 5604.0 0.6803978 0.6803978 1.007243e-02 1.007243e-02 0.309529754 #> 11210 5604.5 0.6803863 0.6803863 1.007085e-02 1.007085e-02 0.309542847 #> 11211 5605.0 0.6803748 0.6803748 1.006927e-02 1.006927e-02 0.309555907 #> 11212 5605.5 0.6803634 0.6803634 1.006769e-02 1.006769e-02 0.309568933 #> 11213 5606.0 0.6803520 0.6803520 1.006610e-02 1.006610e-02 0.309581927 #> 11214 5606.5 0.6803406 0.6803406 1.006451e-02 1.006451e-02 0.309594888 #> 11215 5607.0 0.6803293 0.6803293 1.006291e-02 1.006291e-02 0.309607816 #> 11216 5607.5 0.6803180 0.6803180 1.006132e-02 1.006132e-02 0.309620711 #> 11217 5608.0 0.6803067 0.6803067 1.005972e-02 1.005972e-02 0.309633572 #> 11218 5608.5 0.6802955 0.6802955 1.005812e-02 1.005812e-02 0.309646401 #> 11219 5609.0 0.6802843 0.6802843 1.005652e-02 1.005652e-02 0.309659197 #> 11220 5609.5 0.6802731 0.6802731 1.005491e-02 1.005491e-02 0.309671959 #> 11221 5610.0 0.6802620 0.6802620 1.005330e-02 1.005330e-02 0.309684688 #> 11222 5610.5 0.6802509 0.6802509 1.005169e-02 1.005169e-02 0.309697384 #> 11223 5611.0 0.6802399 0.6802399 1.005008e-02 1.005008e-02 0.309710047 #> 11224 5611.5 0.6802289 0.6802289 1.004846e-02 1.004846e-02 0.309722677 #> 11225 5612.0 0.6802179 0.6802179 1.004684e-02 1.004684e-02 0.309735273 #> 11226 5612.5 0.6802069 0.6802069 1.004522e-02 1.004522e-02 0.309747836 #> 11227 5613.0 0.6801960 0.6801960 1.004360e-02 1.004360e-02 0.309760366 #> 11228 5613.5 0.6801852 0.6801852 1.004197e-02 1.004197e-02 0.309772863 #> 11229 5614.0 0.6801743 0.6801743 1.004034e-02 1.004034e-02 0.309785326 #> 11230 5614.5 0.6801635 0.6801635 1.003871e-02 1.003871e-02 0.309797756 #> 11231 5615.0 0.6801528 0.6801528 1.003708e-02 1.003708e-02 0.309810152 #> 11232 5615.5 0.6801420 0.6801420 1.003544e-02 1.003544e-02 0.309822515 #> 11233 5616.0 0.6801313 0.6801313 1.003381e-02 1.003381e-02 0.309834844 #> 11234 5616.5 0.6801207 0.6801207 1.003217e-02 1.003217e-02 0.309847140 #> 11235 5617.0 0.6801101 0.6801101 1.003052e-02 1.003052e-02 0.309859402 #> 11236 5617.5 0.6800995 0.6800995 1.002888e-02 1.002888e-02 0.309871631 #> 11237 5618.0 0.6800889 0.6800889 1.002723e-02 1.002723e-02 0.309883827 #> 11238 5618.5 0.6800784 0.6800784 1.002558e-02 1.002558e-02 0.309895988 #> 11239 5619.0 0.6800680 0.6800680 1.002393e-02 1.002393e-02 0.309908117 #> 11240 5619.5 0.6800575 0.6800575 1.002227e-02 1.002227e-02 0.309920211 #> 11241 5620.0 0.6800471 0.6800471 1.002061e-02 1.002061e-02 0.309932272 #> 11242 5620.5 0.6800367 0.6800367 1.001896e-02 1.001896e-02 0.309944299 #> 11243 5621.0 0.6800264 0.6800264 1.001729e-02 1.001729e-02 0.309956293 #> 11244 5621.5 0.6800161 0.6800161 1.001563e-02 1.001563e-02 0.309968253 #> 11245 5622.0 0.6800059 0.6800059 1.001396e-02 1.001396e-02 0.309980179 #> 11246 5622.5 0.6799956 0.6799956 1.001229e-02 1.001229e-02 0.309992072 #> 11247 5623.0 0.6799854 0.6799854 1.001062e-02 1.001062e-02 0.310003930 #> 11248 5623.5 0.6799753 0.6799753 1.000895e-02 1.000895e-02 0.310015755 #> 11249 5624.0 0.6799652 0.6799652 1.000727e-02 1.000727e-02 0.310027546 #> 11250 5624.5 0.6799551 0.6799551 1.000560e-02 1.000560e-02 0.310039303 #> 11251 5625.0 0.6799451 0.6799451 1.000392e-02 1.000392e-02 0.310051027 #> 11252 5625.5 0.6799350 0.6799350 1.000224e-02 1.000224e-02 0.310062716 #> 11253 5626.0 0.6799251 0.6799251 1.000055e-02 1.000055e-02 0.310074372 #> 11254 5626.5 0.6799151 0.6799151 9.998864e-03 9.998864e-03 0.310085994 #> 11255 5627.0 0.6799052 0.6799052 9.997175e-03 9.997175e-03 0.310097581 #> 11256 5627.5 0.6798954 0.6798954 9.995484e-03 9.995484e-03 0.310109135 #> 11257 5628.0 0.6798856 0.6798856 9.993791e-03 9.993791e-03 0.310120655 #> 11258 5628.5 0.6798758 0.6798758 9.992096e-03 9.992096e-03 0.310132141 #> 11259 5629.0 0.6798660 0.6798660 9.990398e-03 9.990398e-03 0.310143593 #> 11260 5629.5 0.6798563 0.6798563 9.988699e-03 9.988699e-03 0.310155011 #> 11261 5630.0 0.6798466 0.6798466 9.986997e-03 9.986997e-03 0.310166394 #> 11262 5630.5 0.6798370 0.6798370 9.985293e-03 9.985293e-03 0.310177744 #> 11263 5631.0 0.6798274 0.6798274 9.983587e-03 9.983587e-03 0.310189060 #> 11264 5631.5 0.6798178 0.6798178 9.981879e-03 9.981879e-03 0.310200341 #> 11265 5632.0 0.6798082 0.6798082 9.980169e-03 9.980169e-03 0.310211589 #> 11266 5632.5 0.6797987 0.6797987 9.978457e-03 9.978457e-03 0.310222802 #> 11267 5633.0 0.6797893 0.6797893 9.976742e-03 9.976742e-03 0.310233981 #> 11268 5633.5 0.6797798 0.6797798 9.975026e-03 9.975026e-03 0.310245126 #> 11269 5634.0 0.6797705 0.6797705 9.973308e-03 9.973308e-03 0.310256237 #> 11270 5634.5 0.6797611 0.6797611 9.971587e-03 9.971587e-03 0.310267313 #> 11271 5635.0 0.6797518 0.6797518 9.969865e-03 9.969865e-03 0.310278356 #> 11272 5635.5 0.6797425 0.6797425 9.968140e-03 9.968140e-03 0.310289364 #> 11273 5636.0 0.6797332 0.6797332 9.966414e-03 9.966414e-03 0.310300338 #> 11274 5636.5 0.6797240 0.6797240 9.964685e-03 9.964685e-03 0.310311277 #> 11275 5637.0 0.6797149 0.6797149 9.962955e-03 9.962955e-03 0.310322183 #> 11276 5637.5 0.6797057 0.6797057 9.961222e-03 9.961222e-03 0.310333054 #> 11277 5638.0 0.6796966 0.6796966 9.959488e-03 9.959488e-03 0.310343890 #> 11278 5638.5 0.6796876 0.6796876 9.957751e-03 9.957751e-03 0.310354693 #> 11279 5639.0 0.6796785 0.6796785 9.956013e-03 9.956013e-03 0.310365461 #> 11280 5639.5 0.6796695 0.6796695 9.954272e-03 9.954272e-03 0.310376194 #> 11281 5640.0 0.6796606 0.6796606 9.952530e-03 9.952530e-03 0.310386893 #> 11282 5640.5 0.6796517 0.6796517 9.950786e-03 9.950786e-03 0.310397558 #> 11283 5641.0 0.6796428 0.6796428 9.949040e-03 9.949040e-03 0.310408189 #> 11284 5641.5 0.6796339 0.6796339 9.947292e-03 9.947292e-03 0.310418785 #> 11285 5642.0 0.6796251 0.6796251 9.945542e-03 9.945542e-03 0.310429346 #> 11286 5642.5 0.6796163 0.6796163 9.943790e-03 9.943790e-03 0.310439874 #> 11287 5643.0 0.6796076 0.6796076 9.942037e-03 9.942037e-03 0.310450366 #> 11288 5643.5 0.6795989 0.6795989 9.940281e-03 9.940281e-03 0.310460824 #> 11289 5644.0 0.6795902 0.6795902 9.938524e-03 9.938524e-03 0.310471248 #> 11290 5644.5 0.6795816 0.6795816 9.936765e-03 9.936765e-03 0.310481637 #> 11291 5645.0 0.6795730 0.6795730 9.935004e-03 9.935004e-03 0.310491992 #> 11292 5645.5 0.6795644 0.6795644 9.933241e-03 9.933241e-03 0.310502312 #> 11293 5646.0 0.6795559 0.6795559 9.931477e-03 9.931477e-03 0.310512598 #> 11294 5646.5 0.6795474 0.6795474 9.929710e-03 9.929710e-03 0.310522849 #> 11295 5647.0 0.6795390 0.6795390 9.927942e-03 9.927942e-03 0.310533065 #> 11296 5647.5 0.6795306 0.6795306 9.926172e-03 9.926172e-03 0.310543247 #> 11297 5648.0 0.6795222 0.6795222 9.924400e-03 9.924400e-03 0.310553394 #> 11298 5648.5 0.6795139 0.6795139 9.922627e-03 9.922627e-03 0.310563507 #> 11299 5649.0 0.6795056 0.6795056 9.920851e-03 9.920851e-03 0.310573585 #> 11300 5649.5 0.6794973 0.6794973 9.919075e-03 9.919075e-03 0.310583628 #> 11301 5650.0 0.6794891 0.6794891 9.917296e-03 9.917296e-03 0.310593637 #> 11302 5650.5 0.6794809 0.6794809 9.915515e-03 9.915515e-03 0.310603611 #> 11303 5651.0 0.6794727 0.6794727 9.913733e-03 9.913733e-03 0.310613551 #> 11304 5651.5 0.6794646 0.6794646 9.911949e-03 9.911949e-03 0.310623456 #> 11305 5652.0 0.6794565 0.6794565 9.910164e-03 9.910164e-03 0.310633326 #> 11306 5652.5 0.6794485 0.6794485 9.908377e-03 9.908377e-03 0.310643161 #> 11307 5653.0 0.6794405 0.6794405 9.906588e-03 9.906588e-03 0.310652962 #> 11308 5653.5 0.6794325 0.6794325 9.904797e-03 9.904797e-03 0.310662728 #> 11309 5654.0 0.6794245 0.6794245 9.903005e-03 9.903005e-03 0.310672459 #> 11310 5654.5 0.6794166 0.6794166 9.901211e-03 9.901211e-03 0.310682155 #> 11311 5655.0 0.6794088 0.6794088 9.899416e-03 9.899416e-03 0.310691817 #> 11312 5655.5 0.6794009 0.6794009 9.897619e-03 9.897619e-03 0.310701444 #> 11313 5656.0 0.6793931 0.6793931 9.895820e-03 9.895820e-03 0.310711036 #> 11314 5656.5 0.6793854 0.6793854 9.894020e-03 9.894020e-03 0.310720594 #> 11315 5657.0 0.6793777 0.6793777 9.892218e-03 9.892218e-03 0.310730116 #> 11316 5657.5 0.6793700 0.6793700 9.890415e-03 9.890415e-03 0.310739604 #> 11317 5658.0 0.6793623 0.6793623 9.888610e-03 9.888610e-03 0.310749057 #> 11318 5658.5 0.6793547 0.6793547 9.886803e-03 9.886803e-03 0.310758475 #> 11319 5659.0 0.6793471 0.6793471 9.884995e-03 9.884995e-03 0.310767859 #> 11320 5659.5 0.6793396 0.6793396 9.883186e-03 9.883186e-03 0.310777207 #> 11321 5660.0 0.6793321 0.6793321 9.881375e-03 9.881375e-03 0.310786521 #> 11322 5660.5 0.6793246 0.6793246 9.879562e-03 9.879562e-03 0.310795800 #> 11323 5661.0 0.6793172 0.6793172 9.877748e-03 9.877748e-03 0.310805044 #> 11324 5661.5 0.6793098 0.6793098 9.875932e-03 9.875932e-03 0.310814253 #> 11325 5662.0 0.6793025 0.6793025 9.874115e-03 9.874115e-03 0.310823428 #> 11326 5662.5 0.6792951 0.6792951 9.872296e-03 9.872296e-03 0.310832567 #> 11327 5663.0 0.6792879 0.6792879 9.870476e-03 9.870476e-03 0.310841672 #> 11328 5663.5 0.6792806 0.6792806 9.868655e-03 9.868655e-03 0.310850741 #> 11329 5664.0 0.6792734 0.6792734 9.866832e-03 9.866832e-03 0.310859776 #> 11330 5664.5 0.6792662 0.6792662 9.865007e-03 9.865007e-03 0.310868776 #> 11331 5665.0 0.6792591 0.6792591 9.863182e-03 9.863182e-03 0.310877741 #> 11332 5665.5 0.6792520 0.6792520 9.861354e-03 9.861354e-03 0.310886671 #> 11333 5666.0 0.6792449 0.6792449 9.859526e-03 9.859526e-03 0.310895566 #> 11334 5666.5 0.6792379 0.6792379 9.857696e-03 9.857696e-03 0.310904426 #> 11335 5667.0 0.6792309 0.6792309 9.855864e-03 9.855864e-03 0.310913251 #> 11336 5667.5 0.6792239 0.6792239 9.854031e-03 9.854031e-03 0.310922042 #> 11337 5668.0 0.6792170 0.6792170 9.852197e-03 9.852197e-03 0.310930797 #> 11338 5668.5 0.6792101 0.6792101 9.850362e-03 9.850362e-03 0.310939517 #> 11339 5669.0 0.6792033 0.6792033 9.848525e-03 9.848525e-03 0.310948203 #> 11340 5669.5 0.6791965 0.6791965 9.846686e-03 9.846686e-03 0.310956853 #> 11341 5670.0 0.6791897 0.6791897 9.844847e-03 9.844847e-03 0.310965469 #> 11342 5670.5 0.6791829 0.6791829 9.843006e-03 9.843006e-03 0.310974049 #> 11343 5671.0 0.6791762 0.6791762 9.841164e-03 9.841164e-03 0.310982595 #> 11344 5671.5 0.6791696 0.6791696 9.839320e-03 9.839320e-03 0.310991106 #> 11345 5672.0 0.6791629 0.6791629 9.837475e-03 9.837475e-03 0.310999581 #> 11346 5672.5 0.6791563 0.6791563 9.835629e-03 9.835629e-03 0.311008022 #> 11347 5673.0 0.6791498 0.6791498 9.833782e-03 9.833782e-03 0.311016428 #> 11348 5673.5 0.6791433 0.6791433 9.831933e-03 9.831933e-03 0.311024798 #> 11349 5674.0 0.6791368 0.6791368 9.830083e-03 9.830083e-03 0.311033134 #> 11350 5674.5 0.6791303 0.6791303 9.828232e-03 9.828232e-03 0.311041435 #> 11351 5675.0 0.6791239 0.6791239 9.826380e-03 9.826380e-03 0.311049700 #> 11352 5675.5 0.6791175 0.6791175 9.824526e-03 9.824526e-03 0.311057931 #> 11353 5676.0 0.6791112 0.6791112 9.822672e-03 9.822672e-03 0.311066126 #> 11354 5676.5 0.6791049 0.6791049 9.820816e-03 9.820816e-03 0.311074287 #> 11355 5677.0 0.6790986 0.6790986 9.818959e-03 9.818959e-03 0.311082413 #> 11356 5677.5 0.6790924 0.6790924 9.817100e-03 9.817100e-03 0.311090503 #> 11357 5678.0 0.6790862 0.6790862 9.815241e-03 9.815241e-03 0.311098559 #> 11358 5678.5 0.6790800 0.6790800 9.813380e-03 9.813380e-03 0.311106579 #> 11359 5679.0 0.6790739 0.6790739 9.811518e-03 9.811518e-03 0.311114565 #> 11360 5679.5 0.6790678 0.6790678 9.809655e-03 9.809655e-03 0.311122515 #> 11361 5680.0 0.6790618 0.6790618 9.807791e-03 9.807791e-03 0.311130431 #> 11362 5680.5 0.6790558 0.6790558 9.805926e-03 9.805926e-03 0.311138311 #> 11363 5681.0 0.6790498 0.6790498 9.804059e-03 9.804059e-03 0.311146157 #> 11364 5681.5 0.6790438 0.6790438 9.802192e-03 9.802192e-03 0.311153967 #> 11365 5682.0 0.6790379 0.6790379 9.800323e-03 9.800323e-03 0.311161743 #> 11366 5682.5 0.6790321 0.6790321 9.798454e-03 9.798454e-03 0.311169483 #> 11367 5683.0 0.6790262 0.6790262 9.796583e-03 9.796583e-03 0.311177188 #> 11368 5683.5 0.6790204 0.6790204 9.794711e-03 9.794711e-03 0.311184859 #> 11369 5684.0 0.6790147 0.6790147 9.792838e-03 9.792838e-03 0.311192494 #> 11370 5684.5 0.6790089 0.6790089 9.790965e-03 9.790965e-03 0.311200094 #> 11371 5685.0 0.6790033 0.6790033 9.789090e-03 9.789090e-03 0.311207659 #> 11372 5685.5 0.6789976 0.6789976 9.787214e-03 9.787214e-03 0.311215189 #> 11373 5686.0 0.6789920 0.6789920 9.785337e-03 9.785337e-03 0.311222684 #> 11374 5686.5 0.6789864 0.6789864 9.783459e-03 9.783459e-03 0.311230145 #> 11375 5687.0 0.6789809 0.6789809 9.781580e-03 9.781580e-03 0.311237570 #> 11376 5687.5 0.6789753 0.6789753 9.779700e-03 9.779700e-03 0.311244960 #> 11377 5688.0 0.6789699 0.6789699 9.777819e-03 9.777819e-03 0.311252315 #> 11378 5688.5 0.6789644 0.6789644 9.775937e-03 9.775937e-03 0.311259635 #> 11379 5689.0 0.6789590 0.6789590 9.774054e-03 9.774054e-03 0.311266920 #> 11380 5689.5 0.6789537 0.6789537 9.772170e-03 9.772170e-03 0.311274169 #> 11381 5690.0 0.6789483 0.6789483 9.770285e-03 9.770285e-03 0.311281384 #> 11382 5690.5 0.6789430 0.6789430 9.768400e-03 9.768400e-03 0.311288564 #> 11383 5691.0 0.6789378 0.6789378 9.766513e-03 9.766513e-03 0.311295709 #> 11384 5691.5 0.6789326 0.6789326 9.764625e-03 9.764625e-03 0.311302819 #> 11385 5692.0 0.6789274 0.6789274 9.762737e-03 9.762737e-03 0.311309894 #> 11386 5692.5 0.6789222 0.6789222 9.760848e-03 9.760848e-03 0.311316934 #> 11387 5693.0 0.6789171 0.6789171 9.758957e-03 9.758957e-03 0.311323938 #> 11388 5693.5 0.6789120 0.6789120 9.757066e-03 9.757066e-03 0.311330908 #> 11389 5694.0 0.6789070 0.6789070 9.755174e-03 9.755174e-03 0.311337843 #> 11390 5694.5 0.6789020 0.6789020 9.753281e-03 9.753281e-03 0.311344743 #> 11391 5695.0 0.6788970 0.6788970 9.751388e-03 9.751388e-03 0.311351608 #> 11392 5695.5 0.6788921 0.6788921 9.749493e-03 9.749493e-03 0.311358438 #> 11393 5696.0 0.6788872 0.6788872 9.747598e-03 9.747598e-03 0.311365232 #> 11394 5696.5 0.6788823 0.6788823 9.745702e-03 9.745702e-03 0.311371992 #> 11395 5697.0 0.6788775 0.6788775 9.743804e-03 9.743804e-03 0.311378717 #> 11396 5697.5 0.6788727 0.6788727 9.741907e-03 9.741907e-03 0.311385407 #> 11397 5698.0 0.6788679 0.6788679 9.740008e-03 9.740008e-03 0.311392062 #> 11398 5698.5 0.6788632 0.6788632 9.738109e-03 9.738109e-03 0.311398682 #> 11399 5699.0 0.6788585 0.6788585 9.736209e-03 9.736209e-03 0.311405267 #> 11400 5699.5 0.6788539 0.6788539 9.734308e-03 9.734308e-03 0.311411817 #> 11401 5700.0 0.6788493 0.6788493 9.732406e-03 9.732406e-03 0.311418332 #> 11402 5700.5 0.6788447 0.6788447 9.730504e-03 9.730504e-03 0.311424812 #> 11403 5701.0 0.6788401 0.6788401 9.728600e-03 9.728600e-03 0.311431257 #> 11404 5701.5 0.6788356 0.6788356 9.726696e-03 9.726696e-03 0.311437668 #> 11405 5702.0 0.6788312 0.6788312 9.724792e-03 9.724792e-03 0.311444043 #> 11406 5702.5 0.6788267 0.6788267 9.722887e-03 9.722887e-03 0.311450383 #> 11407 5703.0 0.6788223 0.6788223 9.720981e-03 9.720981e-03 0.311456689 #> 11408 5703.5 0.6788180 0.6788180 9.719074e-03 9.719074e-03 0.311462959 #> 11409 5704.0 0.6788136 0.6788136 9.717166e-03 9.717166e-03 0.311469195 #> 11410 5704.5 0.6788093 0.6788093 9.715258e-03 9.715258e-03 0.311475396 #> 11411 5705.0 0.6788051 0.6788051 9.713350e-03 9.713350e-03 0.311481561 #> 11412 5705.5 0.6788009 0.6788009 9.711440e-03 9.711440e-03 0.311487692 #> 11413 5706.0 0.6787967 0.6787967 9.709530e-03 9.709530e-03 0.311493788 #> 11414 5706.5 0.6787925 0.6787925 9.707620e-03 9.707620e-03 0.311499850 #> 11415 5707.0 0.6787884 0.6787884 9.705708e-03 9.705708e-03 0.311505876 #> 11416 5707.5 0.6787843 0.6787843 9.703796e-03 9.703796e-03 0.311511867 #> 11417 5708.0 0.6787803 0.6787803 9.701884e-03 9.701884e-03 0.311517824 #> 11418 5708.5 0.6787763 0.6787763 9.699971e-03 9.699971e-03 0.311523746 #> 11419 5709.0 0.6787723 0.6787723 9.698057e-03 9.698057e-03 0.311529632 #> 11420 5709.5 0.6787684 0.6787684 9.696143e-03 9.696143e-03 0.311535485 #> 11421 5710.0 0.6787645 0.6787645 9.694228e-03 9.694228e-03 0.311541302 #> 11422 5710.5 0.6787606 0.6787606 9.692312e-03 9.692312e-03 0.311547084 #> 11423 5711.0 0.6787568 0.6787568 9.690396e-03 9.690396e-03 0.311552832 #> 11424 5711.5 0.6787530 0.6787530 9.688480e-03 9.688480e-03 0.311558545 #> 11425 5712.0 0.6787492 0.6787492 9.686563e-03 9.686563e-03 0.311564223 #> 11426 5712.5 0.6787455 0.6787455 9.684645e-03 9.684645e-03 0.311569866 #> 11427 5713.0 0.6787418 0.6787418 9.682727e-03 9.682727e-03 0.311575474 #> 11428 5713.5 0.6787381 0.6787381 9.680809e-03 9.680809e-03 0.311581048 #> 11429 5714.0 0.6787345 0.6787345 9.678889e-03 9.678889e-03 0.311586587 #> 11430 5714.5 0.6787309 0.6787309 9.676970e-03 9.676970e-03 0.311592091 #> 11431 5715.0 0.6787274 0.6787274 9.675050e-03 9.675050e-03 0.311597560 #> 11432 5715.5 0.6787239 0.6787239 9.673129e-03 9.673129e-03 0.311602995 #> 11433 5716.0 0.6787204 0.6787204 9.671208e-03 9.671208e-03 0.311608395 #> 11434 5716.5 0.6787170 0.6787170 9.669287e-03 9.669287e-03 0.311613760 #> 11435 5717.0 0.6787135 0.6787135 9.667365e-03 9.667365e-03 0.311619091 #> 11436 5717.5 0.6787102 0.6787102 9.665442e-03 9.665442e-03 0.311624387 #> 11437 5718.0 0.6787068 0.6787068 9.663520e-03 9.663520e-03 0.311629648 #> 11438 5718.5 0.6787035 0.6787035 9.661596e-03 9.661596e-03 0.311634874 #> 11439 5719.0 0.6787003 0.6787003 9.659673e-03 9.659673e-03 0.311640066 #> 11440 5719.5 0.6786970 0.6786970 9.657749e-03 9.657749e-03 0.311645223 #> 11441 5720.0 0.6786938 0.6786938 9.655824e-03 9.655824e-03 0.311650346 #> 11442 5720.5 0.6786907 0.6786907 9.653900e-03 9.653900e-03 0.311655434 #> 11443 5721.0 0.6786875 0.6786875 9.651974e-03 9.651974e-03 0.311660487 #> 11444 5721.5 0.6786844 0.6786844 9.650049e-03 9.650049e-03 0.311665506 #> 11445 5722.0 0.6786814 0.6786814 9.648123e-03 9.648123e-03 0.311670490 #> 11446 5722.5 0.6786784 0.6786784 9.646197e-03 9.646197e-03 0.311675439 #> 11447 5723.0 0.6786754 0.6786754 9.644270e-03 9.644270e-03 0.311680354 #> 11448 5723.5 0.6786724 0.6786724 9.642343e-03 9.642343e-03 0.311685235 #> 11449 5724.0 0.6786695 0.6786695 9.640416e-03 9.640416e-03 0.311690081 #> 11450 5724.5 0.6786666 0.6786666 9.638488e-03 9.638488e-03 0.311694892 #> 11451 5725.0 0.6786638 0.6786638 9.636561e-03 9.636561e-03 0.311699669 #> 11452 5725.5 0.6786610 0.6786610 9.634632e-03 9.634632e-03 0.311704411 #> 11453 5726.0 0.6786582 0.6786582 9.632704e-03 9.632704e-03 0.311709119 #> 11454 5726.5 0.6786554 0.6786554 9.630775e-03 9.630775e-03 0.311713792 #> 11455 5727.0 0.6786527 0.6786527 9.628846e-03 9.628846e-03 0.311718431 #> 11456 5727.5 0.6786500 0.6786500 9.626917e-03 9.626917e-03 0.311723035 #> 11457 5728.0 0.6786474 0.6786474 9.624988e-03 9.624988e-03 0.311727605 #> 11458 5728.5 0.6786448 0.6786448 9.623058e-03 9.623058e-03 0.311732140 #> 11459 5729.0 0.6786422 0.6786422 9.621128e-03 9.621128e-03 0.311736641 #> 11460 5729.5 0.6786397 0.6786397 9.619198e-03 9.619198e-03 0.311741108 #> 11461 5730.0 0.6786372 0.6786372 9.617267e-03 9.617267e-03 0.311745540 #> 11462 5730.5 0.6786347 0.6786347 9.615336e-03 9.615336e-03 0.311749938 #> 11463 5731.0 0.6786323 0.6786323 9.613406e-03 9.613406e-03 0.311754301 #> 11464 5731.5 0.6786299 0.6786299 9.611475e-03 9.611475e-03 0.311758631 #> 11465 5732.0 0.6786275 0.6786275 9.609543e-03 9.609543e-03 0.311762925 #> 11466 5732.5 0.6786252 0.6786252 9.607612e-03 9.607612e-03 0.311767186 #> 11467 5733.0 0.6786229 0.6786229 9.605680e-03 9.605680e-03 0.311771412 #> 11468 5733.5 0.6786206 0.6786206 9.603749e-03 9.603749e-03 0.311775604 #> 11469 5734.0 0.6786184 0.6786184 9.601817e-03 9.601817e-03 0.311779761 #> 11470 5734.5 0.6786162 0.6786162 9.599885e-03 9.599885e-03 0.311783885 #> 11471 5735.0 0.6786141 0.6786141 9.597953e-03 9.597953e-03 0.311787974 #> 11472 5735.5 0.6786120 0.6786120 9.596020e-03 9.596020e-03 0.311792028 #> 11473 5736.0 0.6786099 0.6786099 9.594088e-03 9.594088e-03 0.311796049 #> 11474 5736.5 0.6786078 0.6786078 9.592156e-03 9.592156e-03 0.311800035 #> 11475 5737.0 0.6786058 0.6786058 9.590223e-03 9.590223e-03 0.311803987 #> 11476 5737.5 0.6786038 0.6786038 9.588290e-03 9.588290e-03 0.311807905 #> 11477 5738.0 0.6786019 0.6786019 9.586357e-03 9.586357e-03 0.311811789 #> 11478 5738.5 0.6785999 0.6785999 9.584425e-03 9.584425e-03 0.311815639 #> 11479 5739.0 0.6785981 0.6785981 9.582492e-03 9.582492e-03 0.311819454 #> 11480 5739.5 0.6785962 0.6785962 9.580559e-03 9.580559e-03 0.311823236 #> 11481 5740.0 0.6785944 0.6785944 9.578626e-03 9.578626e-03 0.311826983 #> 11482 5740.5 0.6785926 0.6785926 9.576693e-03 9.576693e-03 0.311830696 #> 11483 5741.0 0.6785909 0.6785909 9.574760e-03 9.574760e-03 0.311834375 #> 11484 5741.5 0.6785892 0.6785892 9.572826e-03 9.572826e-03 0.311838021 #> 11485 5742.0 0.6785875 0.6785875 9.570893e-03 9.570893e-03 0.311841632 #> 11486 5742.5 0.6785858 0.6785858 9.568960e-03 9.568960e-03 0.311845209 #> 11487 5743.0 0.6785842 0.6785842 9.567027e-03 9.567027e-03 0.311848752 #> 11488 5743.5 0.6785826 0.6785826 9.565094e-03 9.565094e-03 0.311852261 #> 11489 5744.0 0.6785811 0.6785811 9.563161e-03 9.563161e-03 0.311855736 #> 11490 5744.5 0.6785796 0.6785796 9.561228e-03 9.561228e-03 0.311859177 #> 11491 5745.0 0.6785781 0.6785781 9.559295e-03 9.559295e-03 0.311862584 #> 11492 5745.5 0.6785767 0.6785767 9.557362e-03 9.557362e-03 0.311865958 #> 11493 5746.0 0.6785753 0.6785753 9.555429e-03 9.555429e-03 0.311869297 #> 11494 5746.5 0.6785739 0.6785739 9.553496e-03 9.553496e-03 0.311872603 #> 11495 5747.0 0.6785726 0.6785726 9.551563e-03 9.551563e-03 0.311875874 #> 11496 5747.5 0.6785713 0.6785713 9.549630e-03 9.549630e-03 0.311879112 #> 11497 5748.0 0.6785700 0.6785700 9.547698e-03 9.547698e-03 0.311882316 #> 11498 5748.5 0.6785687 0.6785687 9.545765e-03 9.545765e-03 0.311885487 #> 11499 5749.0 0.6785675 0.6785675 9.543833e-03 9.543833e-03 0.311888623 #> 11500 5749.5 0.6785664 0.6785664 9.541900e-03 9.541900e-03 0.311891726 #> 11501 5750.0 0.6785652 0.6785652 9.539968e-03 9.539968e-03 0.311894795 #> 11502 5750.5 0.6785641 0.6785641 9.538036e-03 9.538036e-03 0.311897830 #> 11503 5751.0 0.6785631 0.6785631 9.536104e-03 9.536104e-03 0.311900831 #> 11504 5751.5 0.6785620 0.6785620 9.534172e-03 9.534172e-03 0.311903799 #> 11505 5752.0 0.6785610 0.6785610 9.532240e-03 9.532240e-03 0.311906733 #> 11506 5752.5 0.6785601 0.6785601 9.530309e-03 9.530309e-03 0.311909634 #> 11507 5753.0 0.6785591 0.6785591 9.528378e-03 9.528378e-03 0.311912501 #> 11508 5753.5 0.6785582 0.6785582 9.526446e-03 9.526446e-03 0.311915334 #> 11509 5754.0 0.6785574 0.6785574 9.524515e-03 9.524515e-03 0.311918134 #> 11510 5754.5 0.6785565 0.6785565 9.522584e-03 9.522584e-03 0.311920900 #> 11511 5755.0 0.6785557 0.6785557 9.520654e-03 9.520654e-03 0.311923632 #> 11512 5755.5 0.6785549 0.6785549 9.518723e-03 9.518723e-03 0.311926331 #> 11513 5756.0 0.6785542 0.6785542 9.516793e-03 9.516793e-03 0.311928997 #> 11514 5756.5 0.6785535 0.6785535 9.514863e-03 9.514863e-03 0.311931629 #> 11515 5757.0 0.6785528 0.6785528 9.512933e-03 9.512933e-03 0.311934227 #> 11516 5757.5 0.6785522 0.6785522 9.511004e-03 9.511004e-03 0.311936792 #> 11517 5758.0 0.6785516 0.6785516 9.509075e-03 9.509075e-03 0.311939324 #> 11518 5758.5 0.6785510 0.6785510 9.507145e-03 9.507145e-03 0.311941822 #> 11519 5759.0 0.6785505 0.6785505 9.505217e-03 9.505217e-03 0.311944287 #> 11520 5759.5 0.6785500 0.6785500 9.503288e-03 9.503288e-03 0.311946718 #> 11521 5760.0 0.6785495 0.6785495 9.501360e-03 9.501360e-03 0.311949116 #> 11522 5760.5 0.6785491 0.6785491 9.499432e-03 9.499432e-03 0.311951481 #> 11523 5761.0 0.6785487 0.6785487 9.497504e-03 9.497504e-03 0.311953813 #> 11524 5761.5 0.6785483 0.6785483 9.495577e-03 9.495577e-03 0.311956111 #> 11525 5762.0 0.6785480 0.6785480 9.493650e-03 9.493650e-03 0.311958376 #> 11526 5762.5 0.6785477 0.6785477 9.491723e-03 9.491723e-03 0.311960607 #> 11527 5763.0 0.6785474 0.6785474 9.489797e-03 9.489797e-03 0.311962806 #> 11528 5763.5 0.6785472 0.6785472 9.487871e-03 9.487871e-03 0.311964971 #> 11529 5764.0 0.6785470 0.6785470 9.485945e-03 9.485945e-03 0.311967103 #> 11530 5764.5 0.6785468 0.6785468 9.484020e-03 9.484020e-03 0.311969202 #> 11531 5765.0 0.6785466 0.6785466 9.482094e-03 9.482094e-03 0.311971268 #> 11532 5765.5 0.6785465 0.6785465 9.480170e-03 9.480170e-03 0.311973301 #> 11533 5766.0 0.6785465 0.6785465 9.478245e-03 9.478245e-03 0.311975300 #> 11534 5766.5 0.6785464 0.6785464 9.476322e-03 9.476322e-03 0.311977267 #> 11535 5767.0 0.6785464 0.6785464 9.474398e-03 9.474398e-03 0.311979201 #> 11536 5767.5 0.6785464 0.6785464 9.472475e-03 9.472475e-03 0.311981101 #> 11537 5768.0 0.6785465 0.6785465 9.470552e-03 9.470552e-03 0.311982969 #> 11538 5768.5 0.6785466 0.6785466 9.468630e-03 9.468630e-03 0.311984803 #> 11539 5769.0 0.6785467 0.6785467 9.466708e-03 9.466708e-03 0.311986605 #> 11540 5769.5 0.6785468 0.6785468 9.464786e-03 9.464786e-03 0.311988374 #> 11541 5770.0 0.6785470 0.6785470 9.462865e-03 9.462865e-03 0.311990109 #> 11542 5770.5 0.6785472 0.6785472 9.460944e-03 9.460944e-03 0.311991812 #> 11543 5771.0 0.6785475 0.6785475 9.459024e-03 9.459024e-03 0.311993482 #> 11544 5771.5 0.6785478 0.6785478 9.457104e-03 9.457104e-03 0.311995120 #> 11545 5772.0 0.6785481 0.6785481 9.455185e-03 9.455185e-03 0.311996724 #> 11546 5772.5 0.6785484 0.6785484 9.453266e-03 9.453266e-03 0.311998296 #> 11547 5773.0 0.6785488 0.6785488 9.451348e-03 9.451348e-03 0.311999835 #> 11548 5773.5 0.6785492 0.6785492 9.449430e-03 9.449430e-03 0.312001341 #> 11549 5774.0 0.6785497 0.6785497 9.447513e-03 9.447513e-03 0.312002814 #> 11550 5774.5 0.6785501 0.6785501 9.445596e-03 9.445596e-03 0.312004255 #> 11551 5775.0 0.6785507 0.6785507 9.443679e-03 9.443679e-03 0.312005663 #> 11552 5775.5 0.6785512 0.6785512 9.441764e-03 9.441764e-03 0.312007039 #> 11553 5776.0 0.6785518 0.6785518 9.439848e-03 9.439848e-03 0.312008382 #> 11554 5776.5 0.6785524 0.6785524 9.437933e-03 9.437933e-03 0.312009692 #> 11555 5777.0 0.6785530 0.6785530 9.436019e-03 9.436019e-03 0.312010970 #> 11556 5777.5 0.6785537 0.6785537 9.434105e-03 9.434105e-03 0.312012215 #> 11557 5778.0 0.6785544 0.6785544 9.432192e-03 9.432192e-03 0.312013428 #> 11558 5778.5 0.6785551 0.6785551 9.430280e-03 9.430280e-03 0.312014608 #> 11559 5779.0 0.6785559 0.6785559 9.428367e-03 9.428367e-03 0.312015756 #> 11560 5779.5 0.6785567 0.6785567 9.426456e-03 9.426456e-03 0.312016871 #> 11561 5780.0 0.6785575 0.6785575 9.424545e-03 9.424545e-03 0.312017954 #> 11562 5780.5 0.6785584 0.6785584 9.422635e-03 9.422635e-03 0.312019005 #> 11563 5781.0 0.6785593 0.6785593 9.420725e-03 9.420725e-03 0.312020023 #> 11564 5781.5 0.6785602 0.6785602 9.418816e-03 9.418816e-03 0.312021009 #> 11565 5782.0 0.6785611 0.6785611 9.416907e-03 9.416907e-03 0.312021962 #> 11566 5782.5 0.6785621 0.6785621 9.414999e-03 9.414999e-03 0.312022884 #> 11567 5783.0 0.6785631 0.6785631 9.413092e-03 9.413092e-03 0.312023773 #> 11568 5783.5 0.6785642 0.6785642 9.411185e-03 9.411185e-03 0.312024630 #> 11569 5784.0 0.6785653 0.6785653 9.409279e-03 9.409279e-03 0.312025454 #> 11570 5784.5 0.6785664 0.6785664 9.407374e-03 9.407374e-03 0.312026247 #> 11571 5785.0 0.6785675 0.6785675 9.405469e-03 9.405469e-03 0.312027007 #> 11572 5785.5 0.6785687 0.6785687 9.403565e-03 9.403565e-03 0.312027736 #> 11573 5786.0 0.6785699 0.6785699 9.401662e-03 9.401662e-03 0.312028432 #> 11574 5786.5 0.6785711 0.6785711 9.399759e-03 9.399759e-03 0.312029096 #> 11575 5787.0 0.6785724 0.6785724 9.397857e-03 9.397857e-03 0.312029728 #> 11576 5787.5 0.6785737 0.6785737 9.395956e-03 9.395956e-03 0.312030328 #> 11577 5788.0 0.6785750 0.6785750 9.394055e-03 9.394055e-03 0.312030896 #> 11578 5788.5 0.6785764 0.6785764 9.392155e-03 9.392155e-03 0.312031432 #> 11579 5789.0 0.6785778 0.6785778 9.390256e-03 9.390256e-03 0.312031937 #> 11580 5789.5 0.6785792 0.6785792 9.388357e-03 9.388357e-03 0.312032409 #> 11581 5790.0 0.6785807 0.6785807 9.386459e-03 9.386459e-03 0.312032849 #> 11582 5790.5 0.6785822 0.6785822 9.384562e-03 9.384562e-03 0.312033258 #> 11583 5791.0 0.6785837 0.6785837 9.382666e-03 9.382666e-03 0.312033635 #> 11584 5791.5 0.6785852 0.6785852 9.380770e-03 9.380770e-03 0.312033980 #> 11585 5792.0 0.6785868 0.6785868 9.378875e-03 9.378875e-03 0.312034293 #> 11586 5792.5 0.6785884 0.6785884 9.376981e-03 9.376981e-03 0.312034575 #> 11587 5793.0 0.6785901 0.6785901 9.375088e-03 9.375088e-03 0.312034825 #> 11588 5793.5 0.6785918 0.6785918 9.373195e-03 9.373195e-03 0.312035043 #> 11589 5794.0 0.6785935 0.6785935 9.371304e-03 9.371304e-03 0.312035230 #> 11590 5794.5 0.6785952 0.6785952 9.369413e-03 9.369413e-03 0.312035385 #> 11591 5795.0 0.6785970 0.6785970 9.367522e-03 9.367522e-03 0.312035508 #> 11592 5795.5 0.6785988 0.6785988 9.365633e-03 9.365633e-03 0.312035600 #> 11593 5796.0 0.6786006 0.6786006 9.363744e-03 9.363744e-03 0.312035660 #> 11594 5796.5 0.6786025 0.6786025 9.361856e-03 9.361856e-03 0.312035689 #> 11595 5797.0 0.6786043 0.6786043 9.359969e-03 9.359969e-03 0.312035686 #> 11596 5797.5 0.6786063 0.6786063 9.358083e-03 9.358083e-03 0.312035652 #> 11597 5798.0 0.6786082 0.6786082 9.356198e-03 9.356198e-03 0.312035587 #> 11598 5798.5 0.6786102 0.6786102 9.354313e-03 9.354313e-03 0.312035490 #> 11599 5799.0 0.6786122 0.6786122 9.352430e-03 9.352430e-03 0.312035362 #> 11600 5799.5 0.6786143 0.6786143 9.350547e-03 9.350547e-03 0.312035202 #> 11601 5800.0 0.6786163 0.6786163 9.348665e-03 9.348665e-03 0.312035012 #> 11602 5800.5 0.6786184 0.6786184 9.346784e-03 9.346784e-03 0.312034789 #> 11603 5801.0 0.6786206 0.6786206 9.344904e-03 9.344904e-03 0.312034536 #> 11604 5801.5 0.6786227 0.6786227 9.343025e-03 9.343025e-03 0.312034252 #> 11605 5802.0 0.6786249 0.6786249 9.341146e-03 9.341146e-03 0.312033936 #> 11606 5802.5 0.6786271 0.6786271 9.339269e-03 9.339269e-03 0.312033589 #> 11607 5803.0 0.6786294 0.6786294 9.337392e-03 9.337392e-03 0.312033212 #> 11608 5803.5 0.6786317 0.6786317 9.335516e-03 9.335516e-03 0.312032803 #> 11609 5804.0 0.6786340 0.6786340 9.333641e-03 9.333641e-03 0.312032363 #> 11610 5804.5 0.6786363 0.6786363 9.331768e-03 9.331768e-03 0.312031892 #> 11611 5805.0 0.6786387 0.6786387 9.329895e-03 9.329895e-03 0.312031390 #> 11612 5805.5 0.6786411 0.6786411 9.328023e-03 9.328023e-03 0.312030857 #> 11613 5806.0 0.6786436 0.6786436 9.326151e-03 9.326151e-03 0.312030293 #> 11614 5806.5 0.6786460 0.6786460 9.324281e-03 9.324281e-03 0.312029698 #> 11615 5807.0 0.6786485 0.6786485 9.322412e-03 9.322412e-03 0.312029073 #> 11616 5807.5 0.6786510 0.6786510 9.320544e-03 9.320544e-03 0.312028416 #> 11617 5808.0 0.6786536 0.6786536 9.318677e-03 9.318677e-03 0.312027729 #> 11618 5808.5 0.6786562 0.6786562 9.316810e-03 9.316810e-03 0.312027011 #> 11619 5809.0 0.6786588 0.6786588 9.314945e-03 9.314945e-03 0.312026262 #> 11620 5809.5 0.6786614 0.6786614 9.313081e-03 9.313081e-03 0.312025483 #> 11621 5810.0 0.6786641 0.6786641 9.311217e-03 9.311217e-03 0.312024673 #> 11622 5810.5 0.6786668 0.6786668 9.309355e-03 9.309355e-03 0.312023832 #> 11623 5811.0 0.6786695 0.6786695 9.307494e-03 9.307494e-03 0.312022961 #> 11624 5811.5 0.6786723 0.6786723 9.305633e-03 9.305633e-03 0.312022059 #> 11625 5812.0 0.6786751 0.6786751 9.303774e-03 9.303774e-03 0.312021127 #> 11626 5812.5 0.6786779 0.6786779 9.301916e-03 9.301916e-03 0.312020164 #> 11627 5813.0 0.6786808 0.6786808 9.300058e-03 9.300058e-03 0.312019170 #> 11628 5813.5 0.6786837 0.6786837 9.298202e-03 9.298202e-03 0.312018146 #> 11629 5814.0 0.6786866 0.6786866 9.296347e-03 9.296347e-03 0.312017092 #> 11630 5814.5 0.6786895 0.6786895 9.294493e-03 9.294493e-03 0.312016008 #> 11631 5815.0 0.6786925 0.6786925 9.292640e-03 9.292640e-03 0.312014893 #> 11632 5815.5 0.6786955 0.6786955 9.290788e-03 9.290788e-03 0.312013747 #> 11633 5816.0 0.6786985 0.6786985 9.288937e-03 9.288937e-03 0.312012572 #> 11634 5816.5 0.6787015 0.6787015 9.287087e-03 9.287087e-03 0.312011366 #> 11635 5817.0 0.6787046 0.6787046 9.285238e-03 9.285238e-03 0.312010130 #> 11636 5817.5 0.6787077 0.6787077 9.283391e-03 9.283391e-03 0.312008864 #> 11637 5818.0 0.6787109 0.6787109 9.281544e-03 9.281544e-03 0.312007568 #> 11638 5818.5 0.6787141 0.6787141 9.279699e-03 9.279699e-03 0.312006241 #> 11639 5819.0 0.6787173 0.6787173 9.277854e-03 9.277854e-03 0.312004885 #> 11640 5819.5 0.6787205 0.6787205 9.276011e-03 9.276011e-03 0.312003498 #> 11641 5820.0 0.6787237 0.6787237 9.274169e-03 9.274169e-03 0.312002081 #> 11642 5820.5 0.6787270 0.6787270 9.272328e-03 9.272328e-03 0.312000635 #> 11643 5821.0 0.6787304 0.6787304 9.270488e-03 9.270488e-03 0.311999158 #> 11644 5821.5 0.6787337 0.6787337 9.268649e-03 9.268649e-03 0.311997652 #> 11645 5822.0 0.6787371 0.6787371 9.266811e-03 9.266811e-03 0.311996116 #> 11646 5822.5 0.6787405 0.6787405 9.264975e-03 9.264975e-03 0.311994550 #> 11647 5823.0 0.6787439 0.6787439 9.263140e-03 9.263140e-03 0.311992954 #> 11648 5823.5 0.6787474 0.6787474 9.261305e-03 9.261305e-03 0.311991328 #> 11649 5824.0 0.6787509 0.6787509 9.259472e-03 9.259472e-03 0.311989673 #> 11650 5824.5 0.6787544 0.6787544 9.257641e-03 9.257641e-03 0.311987987 #> 11651 5825.0 0.6787579 0.6787579 9.255810e-03 9.255810e-03 0.311986273 #> 11652 5825.5 0.6787615 0.6787615 9.253981e-03 9.253981e-03 0.311984528 #> 11653 5826.0 0.6787651 0.6787651 9.252152e-03 9.252152e-03 0.311982754 #> 11654 5826.5 0.6787687 0.6787687 9.250325e-03 9.250325e-03 0.311980951 #> 11655 5827.0 0.6787724 0.6787724 9.248499e-03 9.248499e-03 0.311979117 #> 11656 5827.5 0.6787761 0.6787761 9.246675e-03 9.246675e-03 0.311977255 #> 11657 5828.0 0.6787798 0.6787798 9.244851e-03 9.244851e-03 0.311975363 #> 11658 5828.5 0.6787835 0.6787835 9.243029e-03 9.243029e-03 0.311973441 #> 11659 5829.0 0.6787873 0.6787873 9.241208e-03 9.241208e-03 0.311971490 #> 11660 5829.5 0.6787911 0.6787911 9.239388e-03 9.239388e-03 0.311969510 #> 11661 5830.0 0.6787949 0.6787949 9.237570e-03 9.237570e-03 0.311967500 #> 11662 5830.5 0.6787988 0.6787988 9.235753e-03 9.235753e-03 0.311965461 #> 11663 5831.0 0.6788027 0.6788027 9.233937e-03 9.233937e-03 0.311963393 #> 11664 5831.5 0.6788066 0.6788066 9.232122e-03 9.232122e-03 0.311961296 #> 11665 5832.0 0.6788105 0.6788105 9.230308e-03 9.230308e-03 0.311959169 #> 11666 5832.5 0.6788145 0.6788145 9.228496e-03 9.228496e-03 0.311957014 #> 11667 5833.0 0.6788185 0.6788185 9.226685e-03 9.226685e-03 0.311954829 #> 11668 5833.5 0.6788225 0.6788225 9.224875e-03 9.224875e-03 0.311952615 #> 11669 5834.0 0.6788266 0.6788266 9.223067e-03 9.223067e-03 0.311950372 #> 11670 5834.5 0.6788306 0.6788306 9.221260e-03 9.221260e-03 0.311948100 #> 11671 5835.0 0.6788347 0.6788347 9.219454e-03 9.219454e-03 0.311945799 #> 11672 5835.5 0.6788389 0.6788389 9.217650e-03 9.217650e-03 0.311943470 #> 11673 5836.0 0.6788430 0.6788430 9.215846e-03 9.215846e-03 0.311941111 #> 11674 5836.5 0.6788472 0.6788472 9.214045e-03 9.214045e-03 0.311938723 #> 11675 5837.0 0.6788514 0.6788514 9.212244e-03 9.212244e-03 0.311936307 #> 11676 5837.5 0.6788557 0.6788557 9.210445e-03 9.210445e-03 0.311933862 #> 11677 5838.0 0.6788600 0.6788600 9.208647e-03 9.208647e-03 0.311931388 #> 11678 5838.5 0.6788643 0.6788643 9.206850e-03 9.206850e-03 0.311928885 #> 11679 5839.0 0.6788686 0.6788686 9.205055e-03 9.205055e-03 0.311926354 #> 11680 5839.5 0.6788729 0.6788729 9.203261e-03 9.203261e-03 0.311923794 #> 11681 5840.0 0.6788773 0.6788773 9.201469e-03 9.201469e-03 0.311921206 #> 11682 5840.5 0.6788817 0.6788817 9.199678e-03 9.199678e-03 0.311918589 #> 11683 5841.0 0.6788862 0.6788862 9.197888e-03 9.197888e-03 0.311915943 #> 11684 5841.5 0.6788906 0.6788906 9.196099e-03 9.196099e-03 0.311913269 #> 11685 5842.0 0.6788951 0.6788951 9.194312e-03 9.194312e-03 0.311910566 #> 11686 5842.5 0.6788996 0.6788996 9.192527e-03 9.192527e-03 0.311907836 #> 11687 5843.0 0.6789042 0.6789042 9.190742e-03 9.190742e-03 0.311905076 #> 11688 5843.5 0.6789088 0.6789088 9.188959e-03 9.188959e-03 0.311902289 #> 11689 5844.0 0.6789133 0.6789133 9.187178e-03 9.187178e-03 0.311899473 #> 11690 5844.5 0.6789180 0.6789180 9.185398e-03 9.185398e-03 0.311896628 #> 11691 5845.0 0.6789226 0.6789226 9.183619e-03 9.183619e-03 0.311893756 #> 11692 5845.5 0.6789273 0.6789273 9.181842e-03 9.181842e-03 0.311890855 #> 11693 5846.0 0.6789320 0.6789320 9.180066e-03 9.180066e-03 0.311887926 #> 11694 5846.5 0.6789367 0.6789367 9.178292e-03 9.178292e-03 0.311884970 #> 11695 5847.0 0.6789415 0.6789415 9.176519e-03 9.176519e-03 0.311881985 #> 11696 5847.5 0.6789463 0.6789463 9.174747e-03 9.174747e-03 0.311878972 #> 11697 5848.0 0.6789511 0.6789511 9.172977e-03 9.172977e-03 0.311875930 #> 11698 5848.5 0.6789559 0.6789559 9.171209e-03 9.171209e-03 0.311872861 #> 11699 5849.0 0.6789608 0.6789608 9.169441e-03 9.169441e-03 0.311869765 #> 11700 5849.5 0.6789657 0.6789657 9.167676e-03 9.167676e-03 0.311866640 #> 11701 5850.0 0.6789706 0.6789706 9.165911e-03 9.165911e-03 0.311863487 #> 11702 5850.5 0.6789755 0.6789755 9.164149e-03 9.164149e-03 0.311860307 #> 11703 5851.0 0.6789805 0.6789805 9.162387e-03 9.162387e-03 0.311857098 #> 11704 5851.5 0.6789855 0.6789855 9.160627e-03 9.160627e-03 0.311853863 #> 11705 5852.0 0.6789905 0.6789905 9.158869e-03 9.158869e-03 0.311850599 #> 11706 5852.5 0.6789956 0.6789956 9.157112e-03 9.157112e-03 0.311847308 #> 11707 5853.0 0.6790007 0.6790007 9.155357e-03 9.155357e-03 0.311843989 #> 11708 5853.5 0.6790058 0.6790058 9.153603e-03 9.153603e-03 0.311840642 #> 11709 5854.0 0.6790109 0.6790109 9.151851e-03 9.151851e-03 0.311837268 #> 11710 5854.5 0.6790160 0.6790160 9.150100e-03 9.150100e-03 0.311833867 #> 11711 5855.0 0.6790212 0.6790212 9.148350e-03 9.148350e-03 0.311830438 #> 11712 5855.5 0.6790264 0.6790264 9.146602e-03 9.146602e-03 0.311826981 #> 11713 5856.0 0.6790316 0.6790316 9.144856e-03 9.144856e-03 0.311823497 #> 11714 5856.5 0.6790369 0.6790369 9.143111e-03 9.143111e-03 0.311819986 #> 11715 5857.0 0.6790422 0.6790422 9.141368e-03 9.141368e-03 0.311816448 #> 11716 5857.5 0.6790475 0.6790475 9.139627e-03 9.139627e-03 0.311812882 #> 11717 5858.0 0.6790528 0.6790528 9.137886e-03 9.137886e-03 0.311809289 #> 11718 5858.5 0.6790582 0.6790582 9.136148e-03 9.136148e-03 0.311805669 #> 11719 5859.0 0.6790636 0.6790636 9.134411e-03 9.134411e-03 0.311802022 #> 11720 5859.5 0.6790690 0.6790690 9.132675e-03 9.132675e-03 0.311798347 #> 11721 5860.0 0.6790744 0.6790744 9.130941e-03 9.130941e-03 0.311794646 #> 11722 5860.5 0.6790799 0.6790799 9.129209e-03 9.129209e-03 0.311790917 #> 11723 5861.0 0.6790854 0.6790854 9.127478e-03 9.127478e-03 0.311787162 #> 11724 5861.5 0.6790909 0.6790909 9.125749e-03 9.125749e-03 0.311783380 #> 11725 5862.0 0.6790964 0.6790964 9.124022e-03 9.124022e-03 0.311779570 #> 11726 5862.5 0.6791020 0.6791020 9.122296e-03 9.122296e-03 0.311775734 #> 11727 5863.0 0.6791076 0.6791076 9.120571e-03 9.120571e-03 0.311771871 #> 11728 5863.5 0.6791132 0.6791132 9.118848e-03 9.118848e-03 0.311767981 #> 11729 5864.0 0.6791188 0.6791188 9.117127e-03 9.117127e-03 0.311764065 #> 11730 5864.5 0.6791245 0.6791245 9.115408e-03 9.115408e-03 0.311760121 #> 11731 5865.0 0.6791302 0.6791302 9.113690e-03 9.113690e-03 0.311756152 #> 11732 5865.5 0.6791359 0.6791359 9.111973e-03 9.111973e-03 0.311752155 #> 11733 5866.0 0.6791416 0.6791416 9.110259e-03 9.110259e-03 0.311748132 #> 11734 5866.5 0.6791474 0.6791474 9.108546e-03 9.108546e-03 0.311744082 #> 11735 5867.0 0.6791532 0.6791532 9.106834e-03 9.106834e-03 0.311740006 #> 11736 5867.5 0.6791590 0.6791590 9.105124e-03 9.105124e-03 0.311735903 #> 11737 5868.0 0.6791648 0.6791648 9.103416e-03 9.103416e-03 0.311731774 #> 11738 5868.5 0.6791707 0.6791707 9.101710e-03 9.101710e-03 0.311727619 #> 11739 5869.0 0.6791766 0.6791766 9.100005e-03 9.100005e-03 0.311723437 #> 11740 5869.5 0.6791825 0.6791825 9.098302e-03 9.098302e-03 0.311719229 #> 11741 5870.0 0.6791884 0.6791884 9.096600e-03 9.096600e-03 0.311714994 #> 11742 5870.5 0.6791944 0.6791944 9.094901e-03 9.094901e-03 0.311710734 #> 11743 5871.0 0.6792004 0.6792004 9.093202e-03 9.093202e-03 0.311706447 #> 11744 5871.5 0.6792064 0.6792064 9.091506e-03 9.091506e-03 0.311702134 #> 11745 5872.0 0.6792124 0.6792124 9.089811e-03 9.089811e-03 0.311697795 #> 11746 5872.5 0.6792185 0.6792185 9.088118e-03 9.088118e-03 0.311693430 #> 11747 5873.0 0.6792245 0.6792245 9.086427e-03 9.086427e-03 0.311689039 #> 11748 5873.5 0.6792306 0.6792306 9.084737e-03 9.084737e-03 0.311684622 #> 11749 5874.0 0.6792368 0.6792368 9.083049e-03 9.083049e-03 0.311680179 #> 11750 5874.5 0.6792429 0.6792429 9.081363e-03 9.081363e-03 0.311675710 #> 11751 5875.0 0.6792491 0.6792491 9.079678e-03 9.079678e-03 0.311671215 #> 11752 5875.5 0.6792553 0.6792553 9.077995e-03 9.077995e-03 0.311666695 #> 11753 5876.0 0.6792615 0.6792615 9.076314e-03 9.076314e-03 0.311662148 #> 11754 5876.5 0.6792678 0.6792678 9.074635e-03 9.074635e-03 0.311657576 #> 11755 5877.0 0.6792741 0.6792741 9.072957e-03 9.072957e-03 0.311652979 #> 11756 5877.5 0.6792804 0.6792804 9.071281e-03 9.071281e-03 0.311648355 #> 11757 5878.0 0.6792867 0.6792867 9.069607e-03 9.069607e-03 0.311643706 #> 11758 5878.5 0.6792930 0.6792930 9.067934e-03 9.067934e-03 0.311639032 #> 11759 5879.0 0.6792994 0.6792994 9.066264e-03 9.066264e-03 0.311634332 #> 11760 5879.5 0.6793058 0.6793058 9.064595e-03 9.064595e-03 0.311629606 #> 11761 5880.0 0.6793122 0.6793122 9.062927e-03 9.062927e-03 0.311624855 #> 11762 5880.5 0.6793187 0.6793187 9.061262e-03 9.061262e-03 0.311620079 #> 11763 5881.0 0.6793251 0.6793251 9.059598e-03 9.059598e-03 0.311615277 #> 11764 5881.5 0.6793316 0.6793316 9.057936e-03 9.057936e-03 0.311610450 #> 11765 5882.0 0.6793381 0.6793381 9.056276e-03 9.056276e-03 0.311605598 #> 11766 5882.5 0.6793447 0.6793447 9.054618e-03 9.054618e-03 0.311600721 #> 11767 5883.0 0.6793512 0.6793512 9.052961e-03 9.052961e-03 0.311595818 #> 11768 5883.5 0.6793578 0.6793578 9.051307e-03 9.051307e-03 0.311590890 #> 11769 5884.0 0.6793644 0.6793644 9.049654e-03 9.049654e-03 0.311585937 #> 11770 5884.5 0.6793710 0.6793710 9.048002e-03 9.048002e-03 0.311580960 #> 11771 5885.0 0.6793777 0.6793777 9.046353e-03 9.046353e-03 0.311575957 #> 11772 5885.5 0.6793844 0.6793844 9.044706e-03 9.044706e-03 0.311570929 #> 11773 5886.0 0.6793911 0.6793911 9.043060e-03 9.043060e-03 0.311565876 #> 11774 5886.5 0.6793978 0.6793978 9.041416e-03 9.041416e-03 0.311560798 #> 11775 5887.0 0.6794045 0.6794045 9.039774e-03 9.039774e-03 0.311555696 #> 11776 5887.5 0.6794113 0.6794113 9.038133e-03 9.038133e-03 0.311550568 #> 11777 5888.0 0.6794181 0.6794181 9.036495e-03 9.036495e-03 0.311545416 #> 11778 5888.5 0.6794249 0.6794249 9.034858e-03 9.034858e-03 0.311540239 #> 11779 5889.0 0.6794317 0.6794317 9.033224e-03 9.033224e-03 0.311535038 #> 11780 5889.5 0.6794386 0.6794386 9.031591e-03 9.031591e-03 0.311529812 #> 11781 5890.0 0.6794455 0.6794455 9.029960e-03 9.029960e-03 0.311524561 #> 11782 5890.5 0.6794524 0.6794524 9.028330e-03 9.028330e-03 0.311519286 #> 11783 5891.0 0.6794593 0.6794593 9.026703e-03 9.026703e-03 0.311513986 #> 11784 5891.5 0.6794663 0.6794663 9.025077e-03 9.025077e-03 0.311508662 #> 11785 5892.0 0.6794732 0.6794732 9.023454e-03 9.023454e-03 0.311503313 #> 11786 5892.5 0.6794802 0.6794802 9.021832e-03 9.021832e-03 0.311497941 #> 11787 5893.0 0.6794872 0.6794872 9.020212e-03 9.020212e-03 0.311492543 #> 11788 5893.5 0.6794943 0.6794943 9.018594e-03 9.018594e-03 0.311487122 #> 11789 5894.0 0.6795013 0.6795013 9.016978e-03 9.016978e-03 0.311481676 #> 11790 5894.5 0.6795084 0.6795084 9.015363e-03 9.015363e-03 0.311476206 #> 11791 5895.0 0.6795155 0.6795155 9.013751e-03 9.013751e-03 0.311470712 #> 11792 5895.5 0.6795227 0.6795227 9.012140e-03 9.012140e-03 0.311465194 #> 11793 5896.0 0.6795298 0.6795298 9.010532e-03 9.010532e-03 0.311459651 #> 11794 5896.5 0.6795370 0.6795370 9.008925e-03 9.008925e-03 0.311454085 #> 11795 5897.0 0.6795442 0.6795442 9.007320e-03 9.007320e-03 0.311448495 #> 11796 5897.5 0.6795514 0.6795514 9.005717e-03 9.005717e-03 0.311442881 #> 11797 5898.0 0.6795586 0.6795586 9.004116e-03 9.004116e-03 0.311437243 #> 11798 5898.5 0.6795659 0.6795659 9.002517e-03 9.002517e-03 0.311431581 #> 11799 5899.0 0.6795732 0.6795732 9.000920e-03 9.000920e-03 0.311425895 #> 11800 5899.5 0.6795805 0.6795805 8.999325e-03 8.999325e-03 0.311420185 #> 11801 5900.0 0.6795878 0.6795878 8.997732e-03 8.997732e-03 0.311414452 #> 11802 5900.5 0.6795952 0.6795952 8.996140e-03 8.996140e-03 0.311408695 #> 11803 5901.0 0.6796025 0.6796025 8.994551e-03 8.994551e-03 0.311402915 #> 11804 5901.5 0.6796099 0.6796099 8.992963e-03 8.992963e-03 0.311397111 #> 11805 5902.0 0.6796173 0.6796173 8.991378e-03 8.991378e-03 0.311391283 #> 11806 5902.5 0.6796248 0.6796248 8.989794e-03 8.989794e-03 0.311385432 #> 11807 5903.0 0.6796322 0.6796322 8.988213e-03 8.988213e-03 0.311379558 #> 11808 5903.5 0.6796397 0.6796397 8.986633e-03 8.986633e-03 0.311373660 #> 11809 5904.0 0.6796472 0.6796472 8.985055e-03 8.985055e-03 0.311367738 #> 11810 5904.5 0.6796547 0.6796547 8.983479e-03 8.983479e-03 0.311361794 #> 11811 5905.0 0.6796623 0.6796623 8.981905e-03 8.981905e-03 0.311355826 #> 11812 5905.5 0.6796698 0.6796698 8.980334e-03 8.980334e-03 0.311349835 #> 11813 5906.0 0.6796774 0.6796774 8.978764e-03 8.978764e-03 0.311343821 #> 11814 5906.5 0.6796850 0.6796850 8.977196e-03 8.977196e-03 0.311337783 #> 11815 5907.0 0.6796926 0.6796926 8.975630e-03 8.975630e-03 0.311331723 #> 11816 5907.5 0.6797003 0.6797003 8.974066e-03 8.974066e-03 0.311325639 #> 11817 5908.0 0.6797080 0.6797080 8.972504e-03 8.972504e-03 0.311319533 #> 11818 5908.5 0.6797157 0.6797157 8.970944e-03 8.970944e-03 0.311313403 #> 11819 5909.0 0.6797234 0.6797234 8.969386e-03 8.969386e-03 0.311307251 #> 11820 5909.5 0.6797311 0.6797311 8.967830e-03 8.967830e-03 0.311301075 #> 11821 5910.0 0.6797388 0.6797388 8.966276e-03 8.966276e-03 0.311294877 #> 11822 5910.5 0.6797466 0.6797466 8.964725e-03 8.964725e-03 0.311288656 #> 11823 5911.0 0.6797544 0.6797544 8.963175e-03 8.963175e-03 0.311282413 #> 11824 5911.5 0.6797622 0.6797622 8.961627e-03 8.961627e-03 0.311276146 #> 11825 5912.0 0.6797701 0.6797701 8.960081e-03 8.960081e-03 0.311269858 #> 11826 5912.5 0.6797779 0.6797779 8.958537e-03 8.958537e-03 0.311263546 #> 11827 5913.0 0.6797858 0.6797858 8.956995e-03 8.956995e-03 0.311257212 #> 11828 5913.5 0.6797937 0.6797937 8.955455e-03 8.955455e-03 0.311250855 #> 11829 5914.0 0.6798016 0.6798016 8.953918e-03 8.953918e-03 0.311244476 #> 11830 5914.5 0.6798095 0.6798095 8.952382e-03 8.952382e-03 0.311238075 #> 11831 5915.0 0.6798175 0.6798175 8.950848e-03 8.950848e-03 0.311231651 #> 11832 5915.5 0.6798255 0.6798255 8.949317e-03 8.949317e-03 0.311225205 #> 11833 5916.0 0.6798335 0.6798335 8.947787e-03 8.947787e-03 0.311218736 #> 11834 5916.5 0.6798415 0.6798415 8.946260e-03 8.946260e-03 0.311212246 #> 11835 5917.0 0.6798495 0.6798495 8.944734e-03 8.944734e-03 0.311205733 #> 11836 5917.5 0.6798576 0.6798576 8.943211e-03 8.943211e-03 0.311199198 #> 11837 5918.0 0.6798657 0.6798657 8.941689e-03 8.941689e-03 0.311192641 #> 11838 5918.5 0.6798738 0.6798738 8.940170e-03 8.940170e-03 0.311186061 #> 11839 5919.0 0.6798819 0.6798819 8.938653e-03 8.938653e-03 0.311179460 #> 11840 5919.5 0.6798900 0.6798900 8.937138e-03 8.937138e-03 0.311172837 #> 11841 5920.0 0.6798982 0.6798982 8.935625e-03 8.935625e-03 0.311166192 #> 11842 5920.5 0.6799064 0.6799064 8.934114e-03 8.934114e-03 0.311159525 #> 11843 5921.0 0.6799146 0.6799146 8.932605e-03 8.932605e-03 0.311152836 #> 11844 5921.5 0.6799228 0.6799228 8.931098e-03 8.931098e-03 0.311146126 #> 11845 5922.0 0.6799310 0.6799310 8.929593e-03 8.929593e-03 0.311139393 #> 11846 5922.5 0.6799393 0.6799393 8.928091e-03 8.928091e-03 0.311132639 #> 11847 5923.0 0.6799475 0.6799475 8.926590e-03 8.926590e-03 0.311125864 #> 11848 5923.5 0.6799558 0.6799558 8.925092e-03 8.925092e-03 0.311119067 #> 11849 5924.0 0.6799642 0.6799642 8.923595e-03 8.923595e-03 0.311112248 #> 11850 5924.5 0.6799725 0.6799725 8.922101e-03 8.922101e-03 0.311105408 #> 11851 5925.0 0.6799808 0.6799808 8.920609e-03 8.920609e-03 0.311098546 #> 11852 5925.5 0.6799892 0.6799892 8.919119e-03 8.919119e-03 0.311091663 #> 11853 5926.0 0.6799976 0.6799976 8.917631e-03 8.917631e-03 0.311084758 #> 11854 5926.5 0.6800060 0.6800060 8.916146e-03 8.916146e-03 0.311077832 #> 11855 5927.0 0.6800145 0.6800145 8.914662e-03 8.914662e-03 0.311070885 #> 11856 5927.5 0.6800229 0.6800229 8.913180e-03 8.913180e-03 0.311063917 #> 11857 5928.0 0.6800314 0.6800314 8.911701e-03 8.911701e-03 0.311056927 #> 11858 5928.5 0.6800399 0.6800399 8.910224e-03 8.910224e-03 0.311049917 #> 11859 5929.0 0.6800484 0.6800484 8.908749e-03 8.908749e-03 0.311042885 #> 11860 5929.5 0.6800569 0.6800569 8.907276e-03 8.907276e-03 0.311035832 #> 11861 5930.0 0.6800654 0.6800654 8.905805e-03 8.905805e-03 0.311028758 #> 11862 5930.5 0.6800740 0.6800740 8.904336e-03 8.904336e-03 0.311021663 #> 11863 5931.0 0.6800826 0.6800826 8.902870e-03 8.902870e-03 0.311014548 #> 11864 5931.5 0.6800912 0.6800912 8.901406e-03 8.901406e-03 0.311007411 #> 11865 5932.0 0.6800998 0.6800998 8.899943e-03 8.899943e-03 0.311000254 #> 11866 5932.5 0.6801084 0.6801084 8.898483e-03 8.898483e-03 0.310993076 #> 11867 5933.0 0.6801171 0.6801171 8.897025e-03 8.897025e-03 0.310985877 #> 11868 5933.5 0.6801258 0.6801258 8.895570e-03 8.895570e-03 0.310978657 #> 11869 5934.0 0.6801345 0.6801345 8.894116e-03 8.894116e-03 0.310971417 #> 11870 5934.5 0.6801432 0.6801432 8.892665e-03 8.892665e-03 0.310964157 #> 11871 5935.0 0.6801519 0.6801519 8.891216e-03 8.891216e-03 0.310956875 #> 11872 5935.5 0.6801607 0.6801607 8.889769e-03 8.889769e-03 0.310949574 #> 11873 5936.0 0.6801694 0.6801694 8.888324e-03 8.888324e-03 0.310942251 #> 11874 5936.5 0.6801782 0.6801782 8.886881e-03 8.886881e-03 0.310934909 #> 11875 5937.0 0.6801870 0.6801870 8.885441e-03 8.885441e-03 0.310927546 #> 11876 5937.5 0.6801958 0.6801958 8.884002e-03 8.884002e-03 0.310920163 #> 11877 5938.0 0.6802047 0.6802047 8.882566e-03 8.882566e-03 0.310912759 #> 11878 5938.5 0.6802135 0.6802135 8.881132e-03 8.881132e-03 0.310905335 #> 11879 5939.0 0.6802224 0.6802224 8.879701e-03 8.879701e-03 0.310897892 #> 11880 5939.5 0.6802313 0.6802313 8.878271e-03 8.878271e-03 0.310890428 #> 11881 5940.0 0.6802402 0.6802402 8.876844e-03 8.876844e-03 0.310882943 #> 11882 5940.5 0.6802491 0.6802491 8.875419e-03 8.875419e-03 0.310875439 #> 11883 5941.0 0.6802581 0.6802581 8.873996e-03 8.873996e-03 0.310867915 #> 11884 5941.5 0.6802671 0.6802671 8.872575e-03 8.872575e-03 0.310860371 #> 11885 5942.0 0.6802760 0.6802760 8.871157e-03 8.871157e-03 0.310852808 #> 11886 5942.5 0.6802850 0.6802850 8.869741e-03 8.869741e-03 0.310845224 #> 11887 5943.0 0.6802941 0.6802941 8.868327e-03 8.868327e-03 0.310837620 #> 11888 5943.5 0.6803031 0.6803031 8.866915e-03 8.866915e-03 0.310829997 #> 11889 5944.0 0.6803121 0.6803121 8.865506e-03 8.865506e-03 0.310822354 #> 11890 5944.5 0.6803212 0.6803212 8.864098e-03 8.864098e-03 0.310814692 #> 11891 5945.0 0.6803303 0.6803303 8.862693e-03 8.862693e-03 0.310807009 #> 11892 5945.5 0.6803394 0.6803394 8.861290e-03 8.861290e-03 0.310799308 #> 11893 5946.0 0.6803485 0.6803485 8.859890e-03 8.859890e-03 0.310791587 #> 11894 5946.5 0.6803577 0.6803577 8.858492e-03 8.858492e-03 0.310783846 #> 11895 5947.0 0.6803668 0.6803668 8.857095e-03 8.857095e-03 0.310776086 #> 11896 5947.5 0.6803760 0.6803760 8.855702e-03 8.855702e-03 0.310768306 #> 11897 5948.0 0.6803852 0.6803852 8.854310e-03 8.854310e-03 0.310760508 #> 11898 5948.5 0.6803944 0.6803944 8.852921e-03 8.852921e-03 0.310752689 #> 11899 5949.0 0.6804036 0.6804036 8.851534e-03 8.851534e-03 0.310744852 #> 11900 5949.5 0.6804129 0.6804129 8.850149e-03 8.850149e-03 0.310736996 #> 11901 5950.0 0.6804221 0.6804221 8.848766e-03 8.848766e-03 0.310729120 #> 11902 5950.5 0.6804314 0.6804314 8.847386e-03 8.847386e-03 0.310721226 #> 11903 5951.0 0.6804407 0.6804407 8.846008e-03 8.846008e-03 0.310713312 #> 11904 5951.5 0.6804500 0.6804500 8.844632e-03 8.844632e-03 0.310705379 #> 11905 5952.0 0.6804593 0.6804593 8.843259e-03 8.843259e-03 0.310697428 #> 11906 5952.5 0.6804687 0.6804687 8.841888e-03 8.841888e-03 0.310689457 #> 11907 5953.0 0.6804780 0.6804780 8.840519e-03 8.840519e-03 0.310681468 #> 11908 5953.5 0.6804874 0.6804874 8.839152e-03 8.839152e-03 0.310673460 #> 11909 5954.0 0.6804968 0.6804968 8.837788e-03 8.837788e-03 0.310665433 #> 11910 5954.5 0.6805062 0.6805062 8.836426e-03 8.836426e-03 0.310657387 #> 11911 5955.0 0.6805156 0.6805156 8.835066e-03 8.835066e-03 0.310649323 #> 11912 5955.5 0.6805251 0.6805251 8.833708e-03 8.833708e-03 0.310641240 #> 11913 5956.0 0.6805345 0.6805345 8.832353e-03 8.832353e-03 0.310633139 #> 11914 5956.5 0.6805440 0.6805440 8.831000e-03 8.831000e-03 0.310625019 #> 11915 5957.0 0.6805535 0.6805535 8.829650e-03 8.829650e-03 0.310616880 #> 11916 5957.5 0.6805630 0.6805630 8.828301e-03 8.828301e-03 0.310608723 #> 11917 5958.0 0.6805725 0.6805725 8.826956e-03 8.826956e-03 0.310600548 #> 11918 5958.5 0.6805820 0.6805820 8.825612e-03 8.825612e-03 0.310592355 #> 11919 5959.0 0.6805916 0.6805916 8.824270e-03 8.824270e-03 0.310584143 #> 11920 5959.5 0.6806012 0.6806012 8.822931e-03 8.822931e-03 0.310575913 #> 11921 5960.0 0.6806107 0.6806107 8.821595e-03 8.821595e-03 0.310567665 #> 11922 5960.5 0.6806203 0.6806203 8.820260e-03 8.820260e-03 0.310559398 #> 11923 5961.0 0.6806300 0.6806300 8.818928e-03 8.818928e-03 0.310551114 #> 11924 5961.5 0.6806396 0.6806396 8.817598e-03 8.817598e-03 0.310542812 #> 11925 5962.0 0.6806492 0.6806492 8.816271e-03 8.816271e-03 0.310534491 #> 11926 5962.5 0.6806589 0.6806589 8.814946e-03 8.814946e-03 0.310526153 #> 11927 5963.0 0.6806686 0.6806686 8.813623e-03 8.813623e-03 0.310517797 #> 11928 5963.5 0.6806783 0.6806783 8.812303e-03 8.812303e-03 0.310509423 #> 11929 5964.0 0.6806880 0.6806880 8.810985e-03 8.810985e-03 0.310501031 #> 11930 5964.5 0.6806977 0.6806977 8.809669e-03 8.809669e-03 0.310492621 #> 11931 5965.0 0.6807075 0.6807075 8.808355e-03 8.808355e-03 0.310484194 #> 11932 5965.5 0.6807172 0.6807172 8.807044e-03 8.807044e-03 0.310475749 #> 11933 5966.0 0.6807270 0.6807270 8.805735e-03 8.805735e-03 0.310467286 #> 11934 5966.5 0.6807368 0.6807368 8.804429e-03 8.804429e-03 0.310458806 #> 11935 5967.0 0.6807466 0.6807466 8.803125e-03 8.803125e-03 0.310450308 #> 11936 5967.5 0.6807564 0.6807564 8.801823e-03 8.801823e-03 0.310441793 #> 11937 5968.0 0.6807662 0.6807662 8.800524e-03 8.800524e-03 0.310433261 #> 11938 5968.5 0.6807761 0.6807761 8.799227e-03 8.799227e-03 0.310424711 #> 11939 5969.0 0.6807859 0.6807859 8.797932e-03 8.797932e-03 0.310416143 #> 11940 5969.5 0.6807958 0.6807958 8.796640e-03 8.796640e-03 0.310407559 #> 11941 5970.0 0.6808057 0.6808057 8.795350e-03 8.795350e-03 0.310398957 #> 11942 5970.5 0.6808156 0.6808156 8.794063e-03 8.794063e-03 0.310390338 #> 11943 5971.0 0.6808255 0.6808255 8.792777e-03 8.792777e-03 0.310381702 #> 11944 5971.5 0.6808355 0.6808355 8.791495e-03 8.791495e-03 0.310373049 #> 11945 5972.0 0.6808454 0.6808454 8.790214e-03 8.790214e-03 0.310364379 #> 11946 5972.5 0.6808554 0.6808554 8.788936e-03 8.788936e-03 0.310355692 #> 11947 5973.0 0.6808654 0.6808654 8.787660e-03 8.787660e-03 0.310346988 #> 11948 5973.5 0.6808753 0.6808753 8.786387e-03 8.786387e-03 0.310338267 #> 11949 5974.0 0.6808854 0.6808854 8.785116e-03 8.785116e-03 0.310329529 #> 11950 5974.5 0.6808954 0.6808954 8.783848e-03 8.783848e-03 0.310320775 #> 11951 5975.0 0.6809054 0.6809054 8.782581e-03 8.782581e-03 0.310312003 #> 11952 5975.5 0.6809155 0.6809155 8.781318e-03 8.781318e-03 0.310303215 #> 11953 5976.0 0.6809255 0.6809255 8.780056e-03 8.780056e-03 0.310294411 #> 11954 5976.5 0.6809356 0.6809356 8.778797e-03 8.778797e-03 0.310285590 #> 11955 5977.0 0.6809457 0.6809457 8.777541e-03 8.777541e-03 0.310276752 #> 11956 5977.5 0.6809558 0.6809558 8.776286e-03 8.776286e-03 0.310267898 #> 11957 5978.0 0.6809659 0.6809659 8.775035e-03 8.775035e-03 0.310259027 #> 11958 5978.5 0.6809761 0.6809761 8.773785e-03 8.773785e-03 0.310250140 #> 11959 5979.0 0.6809862 0.6809862 8.772538e-03 8.772538e-03 0.310241236 #> 11960 5979.5 0.6809964 0.6809964 8.771294e-03 8.771294e-03 0.310232316 #> 11961 5980.0 0.6810066 0.6810066 8.770051e-03 8.770051e-03 0.310223380 #> 11962 5980.5 0.6810168 0.6810168 8.768812e-03 8.768812e-03 0.310214428 #> 11963 5981.0 0.6810270 0.6810270 8.767574e-03 8.767574e-03 0.310205460 #> 11964 5981.5 0.6810372 0.6810372 8.766339e-03 8.766339e-03 0.310196475 #> 11965 5982.0 0.6810474 0.6810474 8.765107e-03 8.765107e-03 0.310187475 #> 11966 5982.5 0.6810577 0.6810577 8.763877e-03 8.763877e-03 0.310178458 #> 11967 5983.0 0.6810679 0.6810679 8.762649e-03 8.762649e-03 0.310169425 #> 11968 5983.5 0.6810782 0.6810782 8.761424e-03 8.761424e-03 0.310160377 #> 11969 5984.0 0.6810885 0.6810885 8.760201e-03 8.760201e-03 0.310151312 #> 11970 5984.5 0.6810988 0.6810988 8.758980e-03 8.758980e-03 0.310142232 #> 11971 5985.0 0.6811091 0.6811091 8.757762e-03 8.757762e-03 0.310133136 #> 11972 5985.5 0.6811194 0.6811194 8.756547e-03 8.756547e-03 0.310124024 #> 11973 5986.0 0.6811298 0.6811298 8.755334e-03 8.755334e-03 0.310114897 #> 11974 5986.5 0.6811401 0.6811401 8.754123e-03 8.754123e-03 0.310105754 #> 11975 5987.0 0.6811505 0.6811505 8.752915e-03 8.752915e-03 0.310096595 #> 11976 5987.5 0.6811609 0.6811609 8.751709e-03 8.751709e-03 0.310087421 #> 11977 5988.0 0.6811713 0.6811713 8.750505e-03 8.750505e-03 0.310078232 #> 11978 5988.5 0.6811817 0.6811817 8.749304e-03 8.749304e-03 0.310069027 #> 11979 5989.0 0.6811921 0.6811921 8.748106e-03 8.748106e-03 0.310059806 #> 11980 5989.5 0.6812025 0.6812025 8.746910e-03 8.746910e-03 0.310050570 #> 11981 5990.0 0.6812130 0.6812130 8.745716e-03 8.745716e-03 0.310041319 #> 11982 5990.5 0.6812234 0.6812234 8.744525e-03 8.744525e-03 0.310032053 #> 11983 5991.0 0.6812339 0.6812339 8.743336e-03 8.743336e-03 0.310022771 #> 11984 5991.5 0.6812444 0.6812444 8.742150e-03 8.742150e-03 0.310013475 #> 11985 5992.0 0.6812549 0.6812549 8.740966e-03 8.740966e-03 0.310004163 #> 11986 5992.5 0.6812654 0.6812654 8.739785e-03 8.739785e-03 0.309994836 #> 11987 5993.0 0.6812759 0.6812759 8.738606e-03 8.738606e-03 0.309985494 #> 11988 5993.5 0.6812864 0.6812864 8.737429e-03 8.737429e-03 0.309976137 #> 11989 5994.0 0.6812970 0.6812970 8.736255e-03 8.736255e-03 0.309966766 #> 11990 5994.5 0.6813075 0.6813075 8.735084e-03 8.735084e-03 0.309957379 #> 11991 5995.0 0.6813181 0.6813181 8.733915e-03 8.733915e-03 0.309947978 #> 11992 5995.5 0.6813287 0.6813287 8.732748e-03 8.732748e-03 0.309938562 #> 11993 5996.0 0.6813393 0.6813393 8.731584e-03 8.731584e-03 0.309929131 #> 11994 5996.5 0.6813499 0.6813499 8.730422e-03 8.730422e-03 0.309919685 #> 11995 5997.0 0.6813605 0.6813605 8.729263e-03 8.729263e-03 0.309910225 #> 11996 5997.5 0.6813711 0.6813711 8.728106e-03 8.728106e-03 0.309900751 #> 11997 5998.0 0.6813818 0.6813818 8.726952e-03 8.726952e-03 0.309891261 #> 11998 5998.5 0.6813924 0.6813924 8.725800e-03 8.725800e-03 0.309881758 #> 11999 5999.0 0.6814031 0.6814031 8.724651e-03 8.724651e-03 0.309872240 #> 12000 5999.5 0.6814138 0.6814138 8.723504e-03 8.723504e-03 0.309862707 #> 12001 6000.0 0.6814245 0.6814245 8.722360e-03 8.722360e-03 0.309853161 #> 12002 6000.5 0.6814352 0.6814352 8.721218e-03 8.721218e-03 0.309843599 #> 12003 6001.0 0.6814459 0.6814459 8.720079e-03 8.720079e-03 0.309834024 #> 12004 6001.5 0.6814566 0.6814566 8.718942e-03 8.718942e-03 0.309824435 #> 12005 6002.0 0.6814674 0.6814674 8.717807e-03 8.717807e-03 0.309814831 #> 12006 6002.5 0.6814781 0.6814781 8.716675e-03 8.716675e-03 0.309805214 #> 12007 6003.0 0.6814889 0.6814889 8.715546e-03 8.715546e-03 0.309795582 #> 12008 6003.5 0.6814996 0.6814996 8.714419e-03 8.714419e-03 0.309785936 #> 12009 6004.0 0.6815104 0.6815104 8.713295e-03 8.713295e-03 0.309776277 #> 12010 6004.5 0.6815212 0.6815212 8.712173e-03 8.712173e-03 0.309766603 #> 12011 6005.0 0.6815320 0.6815320 8.711053e-03 8.711053e-03 0.309756916 #> 12012 6005.5 0.6815428 0.6815428 8.709936e-03 8.709936e-03 0.309747215 #> 12013 6006.0 0.6815537 0.6815537 8.708822e-03 8.708822e-03 0.309737500 #> 12014 6006.5 0.6815645 0.6815645 8.707710e-03 8.707710e-03 0.309727771 #> 12015 6007.0 0.6815754 0.6815754 8.706600e-03 8.706600e-03 0.309718029 #> 12016 6007.5 0.6815862 0.6815862 8.705493e-03 8.705493e-03 0.309708273 #> 12017 6008.0 0.6815971 0.6815971 8.704389e-03 8.704389e-03 0.309698504 #> 12018 6008.5 0.6816080 0.6816080 8.703287e-03 8.703287e-03 0.309688721 #> 12019 6009.0 0.6816189 0.6816189 8.702188e-03 8.702188e-03 0.309678925 #> 12020 6009.5 0.6816298 0.6816298 8.701091e-03 8.701091e-03 0.309669115 #> 12021 6010.0 0.6816407 0.6816407 8.699996e-03 8.699996e-03 0.309659292 #> 12022 6010.5 0.6816516 0.6816516 8.698904e-03 8.698904e-03 0.309649456 #> 12023 6011.0 0.6816626 0.6816626 8.697815e-03 8.697815e-03 0.309639606 #> 12024 6011.5 0.6816735 0.6816735 8.696728e-03 8.696728e-03 0.309629743 #> 12025 6012.0 0.6816845 0.6816845 8.695644e-03 8.695644e-03 0.309619867 #> 12026 6012.5 0.6816955 0.6816955 8.694562e-03 8.694562e-03 0.309609978 #> 12027 6013.0 0.6817064 0.6817064 8.693483e-03 8.693483e-03 0.309600076 #> 12028 6013.5 0.6817174 0.6817174 8.692406e-03 8.692406e-03 0.309590161 #> 12029 6014.0 0.6817284 0.6817284 8.691332e-03 8.691332e-03 0.309580233 #> 12030 6014.5 0.6817394 0.6817394 8.690260e-03 8.690260e-03 0.309570292 #> 12031 6015.0 0.6817505 0.6817505 8.689191e-03 8.689191e-03 0.309560338 #> 12032 6015.5 0.6817615 0.6817615 8.688124e-03 8.688124e-03 0.309550371 #> 12033 6016.0 0.6817725 0.6817725 8.687060e-03 8.687060e-03 0.309540391 #> 12034 6016.5 0.6817836 0.6817836 8.685998e-03 8.685998e-03 0.309530399 #> 12035 6017.0 0.6817947 0.6817947 8.684939e-03 8.684939e-03 0.309520394 #> 12036 6017.5 0.6818057 0.6818057 8.683883e-03 8.683883e-03 0.309510377 #> 12037 6018.0 0.6818168 0.6818168 8.682829e-03 8.682829e-03 0.309500347 #> 12038 6018.5 0.6818279 0.6818279 8.681777e-03 8.681777e-03 0.309490304 #> 12039 6019.0 0.6818390 0.6818390 8.680728e-03 8.680728e-03 0.309480249 #> 12040 6019.5 0.6818501 0.6818501 8.679682e-03 8.679682e-03 0.309470182 #> 12041 6020.0 0.6818613 0.6818613 8.678638e-03 8.678638e-03 0.309460102 #> 12042 6020.5 0.6818724 0.6818724 8.677597e-03 8.677597e-03 0.309450009 #> 12043 6021.0 0.6818835 0.6818835 8.676558e-03 8.676558e-03 0.309439905 #> 12044 6021.5 0.6818947 0.6818947 8.675521e-03 8.675521e-03 0.309429788 #> 12045 6022.0 0.6819059 0.6819059 8.674488e-03 8.674488e-03 0.309419659 #> 12046 6022.5 0.6819170 0.6819170 8.673457e-03 8.673457e-03 0.309409518 #> 12047 6023.0 0.6819282 0.6819282 8.672428e-03 8.672428e-03 0.309399365 #> 12048 6023.5 0.6819394 0.6819394 8.671402e-03 8.671402e-03 0.309389200 #> 12049 6024.0 0.6819506 0.6819506 8.670378e-03 8.670378e-03 0.309379022 #> 12050 6024.5 0.6819618 0.6819618 8.669357e-03 8.669357e-03 0.309368833 #> 12051 6025.0 0.6819730 0.6819730 8.668339e-03 8.668339e-03 0.309358632 #> 12052 6025.5 0.6819843 0.6819843 8.667323e-03 8.667323e-03 0.309348419 #> 12053 6026.0 0.6819955 0.6819955 8.666310e-03 8.666310e-03 0.309338195 #> 12054 6026.5 0.6820067 0.6820067 8.665299e-03 8.665299e-03 0.309327958 #> 12055 6027.0 0.6820180 0.6820180 8.664291e-03 8.664291e-03 0.309317710 #> 12056 6027.5 0.6820293 0.6820293 8.663285e-03 8.663285e-03 0.309307450 #> 12057 6028.0 0.6820405 0.6820405 8.662282e-03 8.662282e-03 0.309297179 #> 12058 6028.5 0.6820518 0.6820518 8.661281e-03 8.661281e-03 0.309286896 #> 12059 6029.0 0.6820631 0.6820631 8.660284e-03 8.660284e-03 0.309276601 #> 12060 6029.5 0.6820744 0.6820744 8.659288e-03 8.659288e-03 0.309266295 #> 12061 6030.0 0.6820857 0.6820857 8.658295e-03 8.658295e-03 0.309255978 #> 12062 6030.5 0.6820970 0.6820970 8.657305e-03 8.657305e-03 0.309245649 #> 12063 6031.0 0.6821084 0.6821084 8.656317e-03 8.656317e-03 0.309235309 #> 12064 6031.5 0.6821197 0.6821197 8.655332e-03 8.655332e-03 0.309224958 #> 12065 6032.0 0.6821311 0.6821311 8.654349e-03 8.654349e-03 0.309214595 #> 12066 6032.5 0.6821424 0.6821424 8.653369e-03 8.653369e-03 0.309204221 #> 12067 6033.0 0.6821538 0.6821538 8.652392e-03 8.652392e-03 0.309193837 #> 12068 6033.5 0.6821651 0.6821651 8.651417e-03 8.651417e-03 0.309183441 #> 12069 6034.0 0.6821765 0.6821765 8.650445e-03 8.650445e-03 0.309173034 #> 12070 6034.5 0.6821879 0.6821879 8.649475e-03 8.649475e-03 0.309162616 #> 12071 6035.0 0.6821993 0.6821993 8.648508e-03 8.648508e-03 0.309152187 #> 12072 6035.5 0.6822107 0.6822107 8.647543e-03 8.647543e-03 0.309141747 #> 12073 6036.0 0.6822221 0.6822221 8.646581e-03 8.646581e-03 0.309131297 #> 12074 6036.5 0.6822335 0.6822335 8.645622e-03 8.645622e-03 0.309120835 #> 12075 6037.0 0.6822450 0.6822450 8.644665e-03 8.644665e-03 0.309110363 #> 12076 6037.5 0.6822564 0.6822564 8.643710e-03 8.643710e-03 0.309099880 #> 12077 6038.0 0.6822679 0.6822679 8.642759e-03 8.642759e-03 0.309089387 #> 12078 6038.5 0.6822793 0.6822793 8.641810e-03 8.641810e-03 0.309078883 #> 12079 6039.0 0.6822908 0.6822908 8.640863e-03 8.640863e-03 0.309068368 #> 12080 6039.5 0.6823022 0.6823022 8.639919e-03 8.639919e-03 0.309057843 #> 12081 6040.0 0.6823137 0.6823137 8.638978e-03 8.638978e-03 0.309047308 #> 12082 6040.5 0.6823252 0.6823252 8.638039e-03 8.638039e-03 0.309036762 #> 12083 6041.0 0.6823367 0.6823367 8.637102e-03 8.637102e-03 0.309026206 #> 12084 6041.5 0.6823482 0.6823482 8.636169e-03 8.636169e-03 0.309015639 #> 12085 6042.0 0.6823597 0.6823597 8.635238e-03 8.635238e-03 0.309005062 #> 12086 6042.5 0.6823712 0.6823712 8.634309e-03 8.634309e-03 0.308994475 #> 12087 6043.0 0.6823827 0.6823827 8.633383e-03 8.633383e-03 0.308983878 #> 12088 6043.5 0.6823943 0.6823943 8.632460e-03 8.632460e-03 0.308973271 #> 12089 6044.0 0.6824058 0.6824058 8.631539e-03 8.631539e-03 0.308962654 #> 12090 6044.5 0.6824174 0.6824174 8.630621e-03 8.630621e-03 0.308952026 #> 12091 6045.0 0.6824289 0.6824289 8.629706e-03 8.629706e-03 0.308941389 #> 12092 6045.5 0.6824405 0.6824405 8.628793e-03 8.628793e-03 0.308930742 #> 12093 6046.0 0.6824520 0.6824520 8.627882e-03 8.627882e-03 0.308920085 #> 12094 6046.5 0.6824636 0.6824636 8.626974e-03 8.626974e-03 0.308909418 #> 12095 6047.0 0.6824752 0.6824752 8.626069e-03 8.626069e-03 0.308898741 #> 12096 6047.5 0.6824868 0.6824868 8.625167e-03 8.625167e-03 0.308888055 #> 12097 6048.0 0.6824984 0.6824984 8.624267e-03 8.624267e-03 0.308877359 #> 12098 6048.5 0.6825100 0.6825100 8.623369e-03 8.623369e-03 0.308866653 #> 12099 6049.0 0.6825216 0.6825216 8.622474e-03 8.622474e-03 0.308855938 #> 12100 6049.5 0.6825332 0.6825332 8.621582e-03 8.621582e-03 0.308845213 #> 12101 6050.0 0.6825448 0.6825448 8.620692e-03 8.620692e-03 0.308834479 #> 12102 6050.5 0.6825565 0.6825565 8.619805e-03 8.619805e-03 0.308823735 #> 12103 6051.0 0.6825681 0.6825681 8.618921e-03 8.618921e-03 0.308812982 #> 12104 6051.5 0.6825797 0.6825797 8.618039e-03 8.618039e-03 0.308802219 #> 12105 6052.0 0.6825914 0.6825914 8.617160e-03 8.617160e-03 0.308791447 #> 12106 6052.5 0.6826031 0.6826031 8.616283e-03 8.616283e-03 0.308780666 #> 12107 6053.0 0.6826147 0.6826147 8.615409e-03 8.615409e-03 0.308769876 #> 12108 6053.5 0.6826264 0.6826264 8.614538e-03 8.614538e-03 0.308759077 #> 12109 6054.0 0.6826381 0.6826381 8.613669e-03 8.613669e-03 0.308748268 #> 12110 6054.5 0.6826497 0.6826497 8.612803e-03 8.612803e-03 0.308737451 #> 12111 6055.0 0.6826614 0.6826614 8.611939e-03 8.611939e-03 0.308726624 #> 12112 6055.5 0.6826731 0.6826731 8.611078e-03 8.611078e-03 0.308715789 #> 12113 6056.0 0.6826848 0.6826848 8.610220e-03 8.610220e-03 0.308704945 #> 12114 6056.5 0.6826965 0.6826965 8.609364e-03 8.609364e-03 0.308694091 #> 12115 6057.0 0.6827083 0.6827083 8.608511e-03 8.608511e-03 0.308683229 #> 12116 6057.5 0.6827200 0.6827200 8.607660e-03 8.607660e-03 0.308672359 #> 12117 6058.0 0.6827317 0.6827317 8.606812e-03 8.606812e-03 0.308661479 #> 12118 6058.5 0.6827434 0.6827434 8.605967e-03 8.605967e-03 0.308650591 #> 12119 6059.0 0.6827552 0.6827552 8.605124e-03 8.605124e-03 0.308639694 #> 12120 6059.5 0.6827669 0.6827669 8.604284e-03 8.604284e-03 0.308628789 #> 12121 6060.0 0.6827787 0.6827787 8.603446e-03 8.603446e-03 0.308617875 #> 12122 6060.5 0.6827904 0.6827904 8.602611e-03 8.602611e-03 0.308606952 #> 12123 6061.0 0.6828022 0.6828022 8.601779e-03 8.601779e-03 0.308596021 #> 12124 6061.5 0.6828140 0.6828140 8.600949e-03 8.600949e-03 0.308585082 #> 12125 6062.0 0.6828257 0.6828257 8.600122e-03 8.600122e-03 0.308574135 #> 12126 6062.5 0.6828375 0.6828375 8.599298e-03 8.599298e-03 0.308563179 #> 12127 6063.0 0.6828493 0.6828493 8.598476e-03 8.598476e-03 0.308552215 #> 12128 6063.5 0.6828611 0.6828611 8.597656e-03 8.597656e-03 0.308541243 #> 12129 6064.0 0.6828729 0.6828729 8.596840e-03 8.596840e-03 0.308530262 #> 12130 6064.5 0.6828847 0.6828847 8.596026e-03 8.596026e-03 0.308519274 #> 12131 6065.0 0.6828965 0.6828965 8.595214e-03 8.595214e-03 0.308508277 #> 12132 6065.5 0.6829083 0.6829083 8.594405e-03 8.594405e-03 0.308497272 #> 12133 6066.0 0.6829201 0.6829201 8.593599e-03 8.593599e-03 0.308486260 #> 12134 6066.5 0.6829320 0.6829320 8.592796e-03 8.592796e-03 0.308475240 #> 12135 6067.0 0.6829438 0.6829438 8.591995e-03 8.591995e-03 0.308464211 #> 12136 6067.5 0.6829556 0.6829556 8.591196e-03 8.591196e-03 0.308453175 #> 12137 6068.0 0.6829675 0.6829675 8.590401e-03 8.590401e-03 0.308442131 #> 12138 6068.5 0.6829793 0.6829793 8.589607e-03 8.589607e-03 0.308431080 #> 12139 6069.0 0.6829912 0.6829912 8.588817e-03 8.588817e-03 0.308420021 #> 12140 6069.5 0.6830030 0.6830030 8.588029e-03 8.588029e-03 0.308408954 #> 12141 6070.0 0.6830149 0.6830149 8.587244e-03 8.587244e-03 0.308397879 #> 12142 6070.5 0.6830267 0.6830267 8.586461e-03 8.586461e-03 0.308386797 #> 12143 6071.0 0.6830386 0.6830386 8.585681e-03 8.585681e-03 0.308375708 #> 12144 6071.5 0.6830505 0.6830505 8.584904e-03 8.584904e-03 0.308364611 #> 12145 6072.0 0.6830624 0.6830624 8.584129e-03 8.584129e-03 0.308353507 #> 12146 6072.5 0.6830742 0.6830742 8.583357e-03 8.583357e-03 0.308342395 #> 12147 6073.0 0.6830861 0.6830861 8.582587e-03 8.582587e-03 0.308331276 #> 12148 6073.5 0.6830980 0.6830980 8.581820e-03 8.581820e-03 0.308320150 #> 12149 6074.0 0.6831099 0.6831099 8.581056e-03 8.581056e-03 0.308309017 #> 12150 6074.5 0.6831218 0.6831218 8.580294e-03 8.580294e-03 0.308297876 #> 12151 6075.0 0.6831337 0.6831337 8.579535e-03 8.579535e-03 0.308286729 #> 12152 6075.5 0.6831456 0.6831456 8.578779e-03 8.578779e-03 0.308275574 #> 12153 6076.0 0.6831576 0.6831576 8.578025e-03 8.578025e-03 0.308264412 #> 12154 6076.5 0.6831695 0.6831695 8.577274e-03 8.577274e-03 0.308253244 #> 12155 6077.0 0.6831814 0.6831814 8.576525e-03 8.576525e-03 0.308242068 #> 12156 6077.5 0.6831933 0.6831933 8.575779e-03 8.575779e-03 0.308230886 #> 12157 6078.0 0.6832053 0.6832053 8.575036e-03 8.575036e-03 0.308219697 #> 12158 6078.5 0.6832172 0.6832172 8.574295e-03 8.574295e-03 0.308208501 #> 12159 6079.0 0.6832291 0.6832291 8.573557e-03 8.573557e-03 0.308197298 #> 12160 6079.5 0.6832411 0.6832411 8.572821e-03 8.572821e-03 0.308186089 #> 12161 6080.0 0.6832530 0.6832530 8.572088e-03 8.572088e-03 0.308174873 #> 12162 6080.5 0.6832650 0.6832650 8.571358e-03 8.571358e-03 0.308163650 #> 12163 6081.0 0.6832769 0.6832769 8.570631e-03 8.570631e-03 0.308152421 #> 12164 6081.5 0.6832889 0.6832889 8.569906e-03 8.569906e-03 0.308141185 #> 12165 6082.0 0.6833009 0.6833009 8.569183e-03 8.569183e-03 0.308129943 #> 12166 6082.5 0.6833128 0.6833128 8.568463e-03 8.568463e-03 0.308118695 #> 12167 6083.0 0.6833248 0.6833248 8.567746e-03 8.567746e-03 0.308107440 #> 12168 6083.5 0.6833368 0.6833368 8.567032e-03 8.567032e-03 0.308096179 #> 12169 6084.0 0.6833488 0.6833488 8.566320e-03 8.566320e-03 0.308084912 #> 12170 6084.5 0.6833608 0.6833608 8.565611e-03 8.565611e-03 0.308073638 #> 12171 6085.0 0.6833727 0.6833727 8.564904e-03 8.564904e-03 0.308062358 #> 12172 6085.5 0.6833847 0.6833847 8.564200e-03 8.564200e-03 0.308051073 #> 12173 6086.0 0.6833967 0.6833967 8.563499e-03 8.563499e-03 0.308039781 #> 12174 6086.5 0.6834087 0.6834087 8.562800e-03 8.562800e-03 0.308028483 #> 12175 6087.0 0.6834207 0.6834207 8.562104e-03 8.562104e-03 0.308017179 #> 12176 6087.5 0.6834327 0.6834327 8.561410e-03 8.561410e-03 0.308005869 #> 12177 6088.0 0.6834447 0.6834447 8.560720e-03 8.560720e-03 0.307994553 #> 12178 6088.5 0.6834567 0.6834567 8.560031e-03 8.560031e-03 0.307983232 #> 12179 6089.0 0.6834687 0.6834687 8.559346e-03 8.559346e-03 0.307971905 #> 12180 6089.5 0.6834808 0.6834808 8.558663e-03 8.558663e-03 0.307960571 #> 12181 6090.0 0.6834928 0.6834928 8.557982e-03 8.557982e-03 0.307949233 #> 12182 6090.5 0.6835048 0.6835048 8.557305e-03 8.557305e-03 0.307937888 #> 12183 6091.0 0.6835168 0.6835168 8.556630e-03 8.556630e-03 0.307926538 #> 12184 6091.5 0.6835289 0.6835289 8.555957e-03 8.555957e-03 0.307915183 #> 12185 6092.0 0.6835409 0.6835409 8.555287e-03 8.555287e-03 0.307903822 #> 12186 6092.5 0.6835529 0.6835529 8.554620e-03 8.554620e-03 0.307892455 #> 12187 6093.0 0.6835650 0.6835650 8.553955e-03 8.553955e-03 0.307881083 #> 12188 6093.5 0.6835770 0.6835770 8.553293e-03 8.553293e-03 0.307869706 #> 12189 6094.0 0.6835890 0.6835890 8.552634e-03 8.552634e-03 0.307858323 #> 12190 6094.5 0.6836011 0.6836011 8.551977e-03 8.551977e-03 0.307846936 #> 12191 6095.0 0.6836131 0.6836131 8.551323e-03 8.551323e-03 0.307835542 #> 12192 6095.5 0.6836252 0.6836252 8.550672e-03 8.550672e-03 0.307824144 #> 12193 6096.0 0.6836372 0.6836372 8.550023e-03 8.550023e-03 0.307812741 #> 12194 6096.5 0.6836493 0.6836493 8.549377e-03 8.549377e-03 0.307801332 #> 12195 6097.0 0.6836613 0.6836613 8.548733e-03 8.548733e-03 0.307789919 #> 12196 6097.5 0.6836734 0.6836734 8.548092e-03 8.548092e-03 0.307778500 #> 12197 6098.0 0.6836855 0.6836855 8.547454e-03 8.547454e-03 0.307767076 #> 12198 6098.5 0.6836975 0.6836975 8.546818e-03 8.546818e-03 0.307755648 #> 12199 6099.0 0.6837096 0.6837096 8.546185e-03 8.546185e-03 0.307744215 #> 12200 6099.5 0.6837217 0.6837217 8.545554e-03 8.545554e-03 0.307732777 #> 12201 6100.0 0.6837337 0.6837337 8.544926e-03 8.544926e-03 0.307721334 #> 12202 6100.5 0.6837458 0.6837458 8.544301e-03 8.544301e-03 0.307709886 #> 12203 6101.0 0.6837579 0.6837579 8.543679e-03 8.543679e-03 0.307698434 #> 12204 6101.5 0.6837700 0.6837700 8.543059e-03 8.543059e-03 0.307686977 #> 12205 6102.0 0.6837820 0.6837820 8.542441e-03 8.542441e-03 0.307675516 #> 12206 6102.5 0.6837941 0.6837941 8.541827e-03 8.541827e-03 0.307664050 #> 12207 6103.0 0.6838062 0.6838062 8.541214e-03 8.541214e-03 0.307652579 #> 12208 6103.5 0.6838183 0.6838183 8.540605e-03 8.540605e-03 0.307641104 #> 12209 6104.0 0.6838304 0.6838304 8.539998e-03 8.539998e-03 0.307629625 #> 12210 6104.5 0.6838425 0.6838425 8.539394e-03 8.539394e-03 0.307618141 #> 12211 6105.0 0.6838546 0.6838546 8.538792e-03 8.538792e-03 0.307606653 #> 12212 6105.5 0.6838666 0.6838666 8.538193e-03 8.538193e-03 0.307595161 #> 12213 6106.0 0.6838787 0.6838787 8.537597e-03 8.537597e-03 0.307583665 #> 12214 6106.5 0.6838908 0.6838908 8.537003e-03 8.537003e-03 0.307572164 #> 12215 6107.0 0.6839029 0.6839029 8.536412e-03 8.536412e-03 0.307560659 #> 12216 6107.5 0.6839150 0.6839150 8.535824e-03 8.535824e-03 0.307549150 #> 12217 6108.0 0.6839271 0.6839271 8.535238e-03 8.535238e-03 0.307537637 #> 12218 6108.5 0.6839392 0.6839392 8.534654e-03 8.534654e-03 0.307526121 #> 12219 6109.0 0.6839513 0.6839513 8.534074e-03 8.534074e-03 0.307514600 #> 12220 6109.5 0.6839634 0.6839634 8.533496e-03 8.533496e-03 0.307503075 #> 12221 6110.0 0.6839755 0.6839755 8.532920e-03 8.532920e-03 0.307491547 #> 12222 6110.5 0.6839876 0.6839876 8.532348e-03 8.532348e-03 0.307480014 #> 12223 6111.0 0.6839997 0.6839997 8.531777e-03 8.531777e-03 0.307468478 #> 12224 6111.5 0.6840119 0.6840119 8.531210e-03 8.531210e-03 0.307456938 #> 12225 6112.0 0.6840240 0.6840240 8.530645e-03 8.530645e-03 0.307445395 #> 12226 6112.5 0.6840361 0.6840361 8.530083e-03 8.530083e-03 0.307433848 #> 12227 6113.0 0.6840482 0.6840482 8.529523e-03 8.529523e-03 0.307422297 #> 12228 6113.5 0.6840603 0.6840603 8.528966e-03 8.528966e-03 0.307410743 #> 12229 6114.0 0.6840724 0.6840724 8.528412e-03 8.528412e-03 0.307399185 #> 12230 6114.5 0.6840845 0.6840845 8.527860e-03 8.527860e-03 0.307387624 #> 12231 6115.0 0.6840966 0.6840966 8.527311e-03 8.527311e-03 0.307376060 #> 12232 6115.5 0.6841087 0.6841087 8.526764e-03 8.526764e-03 0.307364492 #> 12233 6116.0 0.6841209 0.6841209 8.526220e-03 8.526220e-03 0.307352921 #> 12234 6116.5 0.6841330 0.6841330 8.525679e-03 8.525679e-03 0.307341346 #> 12235 6117.0 0.6841451 0.6841451 8.525140e-03 8.525140e-03 0.307329769 #> 12236 6117.5 0.6841572 0.6841572 8.524604e-03 8.524604e-03 0.307318188 #> 12237 6118.0 0.6841693 0.6841693 8.524070e-03 8.524070e-03 0.307306604 #> 12238 6118.5 0.6841814 0.6841814 8.523539e-03 8.523539e-03 0.307295017 #> 12239 6119.0 0.6841936 0.6841936 8.523011e-03 8.523011e-03 0.307283427 #> 12240 6119.5 0.6842057 0.6842057 8.522486e-03 8.522486e-03 0.307271834 #> 12241 6120.0 0.6842178 0.6842178 8.521963e-03 8.521963e-03 0.307260238 #> 12242 6120.5 0.6842299 0.6842299 8.521442e-03 8.521442e-03 0.307248639 #> 12243 6121.0 0.6842420 0.6842420 8.520924e-03 8.520924e-03 0.307237037 #> 12244 6121.5 0.6842542 0.6842542 8.520409e-03 8.520409e-03 0.307225433 #> 12245 6122.0 0.6842663 0.6842663 8.519897e-03 8.519897e-03 0.307213826 #> 12246 6122.5 0.6842784 0.6842784 8.519387e-03 8.519387e-03 0.307202216 #> 12247 6123.0 0.6842905 0.6842905 8.518879e-03 8.518879e-03 0.307190603 #> 12248 6123.5 0.6843026 0.6843026 8.518374e-03 8.518374e-03 0.307178988 #> 12249 6124.0 0.6843148 0.6843148 8.517872e-03 8.517872e-03 0.307167370 #> 12250 6124.5 0.6843269 0.6843269 8.517373e-03 8.517373e-03 0.307155749 #> 12251 6125.0 0.6843390 0.6843390 8.516876e-03 8.516876e-03 0.307144127 #> 12252 6125.5 0.6843511 0.6843511 8.516382e-03 8.516382e-03 0.307132501 #> 12253 6126.0 0.6843632 0.6843632 8.515890e-03 8.515890e-03 0.307120873 #> 12254 6126.5 0.6843754 0.6843754 8.515401e-03 8.515401e-03 0.307109243 #> 12255 6127.0 0.6843875 0.6843875 8.514914e-03 8.514914e-03 0.307097611 #> 12256 6127.5 0.6843996 0.6843996 8.514430e-03 8.514430e-03 0.307085976 #> 12257 6128.0 0.6844117 0.6844117 8.513949e-03 8.513949e-03 0.307074339 #> 12258 6128.5 0.6844238 0.6844238 8.513470e-03 8.513470e-03 0.307062700 #> 12259 6129.0 0.6844359 0.6844359 8.512994e-03 8.512994e-03 0.307051059 #> 12260 6129.5 0.6844481 0.6844481 8.512521e-03 8.512521e-03 0.307039416 #> 12261 6130.0 0.6844602 0.6844602 8.512050e-03 8.512050e-03 0.307027770 #> 12262 6130.5 0.6844723 0.6844723 8.511582e-03 8.511582e-03 0.307016123 #> 12263 6131.0 0.6844844 0.6844844 8.511116e-03 8.511116e-03 0.307004474 #> 12264 6131.5 0.6844965 0.6844965 8.510653e-03 8.510653e-03 0.306992822 #> 12265 6132.0 0.6845086 0.6845086 8.510193e-03 8.510193e-03 0.306981169 #> 12266 6132.5 0.6845208 0.6845208 8.509735e-03 8.509735e-03 0.306969514 #> 12267 6133.0 0.6845329 0.6845329 8.509280e-03 8.509280e-03 0.306957858 #> 12268 6133.5 0.6845450 0.6845450 8.508827e-03 8.508827e-03 0.306946199 #> 12269 6134.0 0.6845571 0.6845571 8.508377e-03 8.508377e-03 0.306934539 #> 12270 6134.5 0.6845692 0.6845692 8.507930e-03 8.507930e-03 0.306922877 #> 12271 6135.0 0.6845813 0.6845813 8.507485e-03 8.507485e-03 0.306911214 #> 12272 6135.5 0.6845934 0.6845934 8.507043e-03 8.507043e-03 0.306899549 #> 12273 6136.0 0.6846055 0.6846055 8.506603e-03 8.506603e-03 0.306887882 #> 12274 6136.5 0.6846176 0.6846176 8.506166e-03 8.506166e-03 0.306876214 #> 12275 6137.0 0.6846297 0.6846297 8.505731e-03 8.505731e-03 0.306864545 #> 12276 6137.5 0.6846418 0.6846418 8.505300e-03 8.505300e-03 0.306852874 #> 12277 6138.0 0.6846539 0.6846539 8.504870e-03 8.504870e-03 0.306841202 #> 12278 6138.5 0.6846660 0.6846660 8.504444e-03 8.504444e-03 0.306829529 #> 12279 6139.0 0.6846781 0.6846781 8.504020e-03 8.504020e-03 0.306817854 #> 12280 6139.5 0.6846902 0.6846902 8.503598e-03 8.503598e-03 0.306806178 #> 12281 6140.0 0.6847023 0.6847023 8.503179e-03 8.503179e-03 0.306794501 #> 12282 6140.5 0.6847144 0.6847144 8.502763e-03 8.502763e-03 0.306782823 #> 12283 6141.0 0.6847265 0.6847265 8.502349e-03 8.502349e-03 0.306771144 #> 12284 6141.5 0.6847386 0.6847386 8.501938e-03 8.501938e-03 0.306759463 #> 12285 6142.0 0.6847507 0.6847507 8.501530e-03 8.501530e-03 0.306747782 #> 12286 6142.5 0.6847628 0.6847628 8.501124e-03 8.501124e-03 0.306736100 #> 12287 6143.0 0.6847749 0.6847749 8.500720e-03 8.500720e-03 0.306724417 #> 12288 6143.5 0.6847869 0.6847869 8.500320e-03 8.500320e-03 0.306712733 #> 12289 6144.0 0.6847990 0.6847990 8.499922e-03 8.499922e-03 0.306701048 #> 12290 6144.5 0.6848111 0.6848111 8.499526e-03 8.499526e-03 0.306689363 #> 12291 6145.0 0.6848232 0.6848232 8.499133e-03 8.499133e-03 0.306677676 #> 12292 6145.5 0.6848353 0.6848353 8.498743e-03 8.498743e-03 0.306665989 #> 12293 6146.0 0.6848473 0.6848473 8.498355e-03 8.498355e-03 0.306654302 #> 12294 6146.5 0.6848594 0.6848594 8.497969e-03 8.497969e-03 0.306642614 #> 12295 6147.0 0.6848715 0.6848715 8.497587e-03 8.497587e-03 0.306630925 #> 12296 6147.5 0.6848836 0.6848836 8.497207e-03 8.497207e-03 0.306619236 #> 12297 6148.0 0.6848956 0.6848956 8.496829e-03 8.496829e-03 0.306607546 #> 12298 6148.5 0.6849077 0.6849077 8.496454e-03 8.496454e-03 0.306595856 #> 12299 6149.0 0.6849198 0.6849198 8.496082e-03 8.496082e-03 0.306584165 #> 12300 6149.5 0.6849318 0.6849318 8.495712e-03 8.495712e-03 0.306572474 #> 12301 6150.0 0.6849439 0.6849439 8.495345e-03 8.495345e-03 0.306560783 #> 12302 6150.5 0.6849559 0.6849559 8.494980e-03 8.494980e-03 0.306549092 #> 12303 6151.0 0.6849680 0.6849680 8.494618e-03 8.494618e-03 0.306537400 #> 12304 6151.5 0.6849800 0.6849800 8.494259e-03 8.494259e-03 0.306525708 #> 12305 6152.0 0.6849921 0.6849921 8.493902e-03 8.493902e-03 0.306514016 #> 12306 6152.5 0.6850041 0.6850041 8.493548e-03 8.493548e-03 0.306502324 #> 12307 6153.0 0.6850162 0.6850162 8.493196e-03 8.493196e-03 0.306490632 #> 12308 6153.5 0.6850282 0.6850282 8.492847e-03 8.492847e-03 0.306478940 #> 12309 6154.0 0.6850403 0.6850403 8.492500e-03 8.492500e-03 0.306467248 #> 12310 6154.5 0.6850523 0.6850523 8.492156e-03 8.492156e-03 0.306455556 #> 12311 6155.0 0.6850643 0.6850643 8.491815e-03 8.491815e-03 0.306443865 #> 12312 6155.5 0.6850764 0.6850764 8.491476e-03 8.491476e-03 0.306432173 #> 12313 6156.0 0.6850884 0.6850884 8.491140e-03 8.491140e-03 0.306420482 #> 12314 6156.5 0.6851004 0.6851004 8.490806e-03 8.490806e-03 0.306408791 #> 12315 6157.0 0.6851124 0.6851124 8.490475e-03 8.490475e-03 0.306397100 #> 12316 6157.5 0.6851244 0.6851244 8.490146e-03 8.490146e-03 0.306385409 #> 12317 6158.0 0.6851365 0.6851365 8.489820e-03 8.489820e-03 0.306373719 #> 12318 6158.5 0.6851485 0.6851485 8.489496e-03 8.489496e-03 0.306362030 #> 12319 6159.0 0.6851605 0.6851605 8.489176e-03 8.489176e-03 0.306350340 #> 12320 6159.5 0.6851725 0.6851725 8.488857e-03 8.488857e-03 0.306338652 #> 12321 6160.0 0.6851845 0.6851845 8.488541e-03 8.488541e-03 0.306326964 #> 12322 6160.5 0.6851965 0.6851965 8.488228e-03 8.488228e-03 0.306315276 #> 12323 6161.0 0.6852085 0.6852085 8.487917e-03 8.487917e-03 0.306303589 #> 12324 6161.5 0.6852205 0.6852205 8.487609e-03 8.487609e-03 0.306291903 #> 12325 6162.0 0.6852325 0.6852325 8.487304e-03 8.487304e-03 0.306280218 #> 12326 6162.5 0.6852445 0.6852445 8.487001e-03 8.487001e-03 0.306268533 #> 12327 6163.0 0.6852565 0.6852565 8.486700e-03 8.486700e-03 0.306256849 #> 12328 6163.5 0.6852684 0.6852684 8.486402e-03 8.486402e-03 0.306245166 #> 12329 6164.0 0.6852804 0.6852804 8.486107e-03 8.486107e-03 0.306233484 #> 12330 6164.5 0.6852924 0.6852924 8.485814e-03 8.485814e-03 0.306221803 #> 12331 6165.0 0.6853044 0.6853044 8.485524e-03 8.485524e-03 0.306210123 #> 12332 6165.5 0.6853163 0.6853163 8.485236e-03 8.485236e-03 0.306198444 #> 12333 6166.0 0.6853283 0.6853283 8.484951e-03 8.484951e-03 0.306186766 #> 12334 6166.5 0.6853402 0.6853402 8.484668e-03 8.484668e-03 0.306175089 #> 12335 6167.0 0.6853522 0.6853522 8.484388e-03 8.484388e-03 0.306163413 #> 12336 6167.5 0.6853642 0.6853642 8.484111e-03 8.484111e-03 0.306151738 #> 12337 6168.0 0.6853761 0.6853761 8.483836e-03 8.483836e-03 0.306140065 #> 12338 6168.5 0.6853880 0.6853880 8.483563e-03 8.483563e-03 0.306128393 #> 12339 6169.0 0.6854000 0.6854000 8.483293e-03 8.483293e-03 0.306116722 #> 12340 6169.5 0.6854119 0.6854119 8.483026e-03 8.483026e-03 0.306105052 #> 12341 6170.0 0.6854239 0.6854239 8.482761e-03 8.482761e-03 0.306093384 #> 12342 6170.5 0.6854358 0.6854358 8.482499e-03 8.482499e-03 0.306081718 #> 12343 6171.0 0.6854477 0.6854477 8.482239e-03 8.482239e-03 0.306070053 #> 12344 6171.5 0.6854596 0.6854596 8.481982e-03 8.481982e-03 0.306058389 #> 12345 6172.0 0.6854715 0.6854715 8.481727e-03 8.481727e-03 0.306046727 #> 12346 6172.5 0.6854835 0.6854835 8.481475e-03 8.481475e-03 0.306035066 #> 12347 6173.0 0.6854954 0.6854954 8.481225e-03 8.481225e-03 0.306023408 #> 12348 6173.5 0.6855073 0.6855073 8.480978e-03 8.480978e-03 0.306011751 #> 12349 6174.0 0.6855192 0.6855192 8.480734e-03 8.480734e-03 0.306000095 #> 12350 6174.5 0.6855311 0.6855311 8.480492e-03 8.480492e-03 0.305988442 #> 12351 6175.0 0.6855430 0.6855430 8.480252e-03 8.480252e-03 0.305976790 #> 12352 6175.5 0.6855548 0.6855548 8.480015e-03 8.480015e-03 0.305965140 #> 12353 6176.0 0.6855667 0.6855667 8.479781e-03 8.479781e-03 0.305953492 #> 12354 6176.5 0.6855786 0.6855786 8.479549e-03 8.479549e-03 0.305941846 #> 12355 6177.0 0.6855905 0.6855905 8.479319e-03 8.479319e-03 0.305930202 #> 12356 6177.5 0.6856023 0.6856023 8.479093e-03 8.479093e-03 0.305918560 #> 12357 6178.0 0.6856142 0.6856142 8.478868e-03 8.478868e-03 0.305906920 #> 12358 6178.5 0.6856261 0.6856261 8.478646e-03 8.478646e-03 0.305895282 #> 12359 6179.0 0.6856379 0.6856379 8.478427e-03 8.478427e-03 0.305883646 #> 12360 6179.5 0.6856498 0.6856498 8.478210e-03 8.478210e-03 0.305872012 #> 12361 6180.0 0.6856616 0.6856616 8.477996e-03 8.477996e-03 0.305860381 #> 12362 6180.5 0.6856735 0.6856735 8.477784e-03 8.477784e-03 0.305848752 #> 12363 6181.0 0.6856853 0.6856853 8.477575e-03 8.477575e-03 0.305837125 #> 12364 6181.5 0.6856971 0.6856971 8.477368e-03 8.477368e-03 0.305825500 #> 12365 6182.0 0.6857090 0.6857090 8.477164e-03 8.477164e-03 0.305813878 #> 12366 6182.5 0.6857208 0.6857208 8.476962e-03 8.476962e-03 0.305802259 #> 12367 6183.0 0.6857326 0.6857326 8.476763e-03 8.476763e-03 0.305790642 #> 12368 6183.5 0.6857444 0.6857444 8.476566e-03 8.476566e-03 0.305779027 #> 12369 6184.0 0.6857562 0.6857562 8.476372e-03 8.476372e-03 0.305767415 #> 12370 6184.5 0.6857680 0.6857680 8.476181e-03 8.476181e-03 0.305755805 #> 12371 6185.0 0.6857798 0.6857798 8.475991e-03 8.475991e-03 0.305744198 #> 12372 6185.5 0.6857916 0.6857916 8.475805e-03 8.475805e-03 0.305732594 #> 12373 6186.0 0.6858034 0.6858034 8.475621e-03 8.475621e-03 0.305720993 #> 12374 6186.5 0.6858152 0.6858152 8.475439e-03 8.475439e-03 0.305709394 #> 12375 6187.0 0.6858269 0.6858269 8.475260e-03 8.475260e-03 0.305697798 #> 12376 6187.5 0.6858387 0.6858387 8.475083e-03 8.475083e-03 0.305686205 #> 12377 6188.0 0.6858505 0.6858505 8.474909e-03 8.474909e-03 0.305674614 #> 12378 6188.5 0.6858622 0.6858622 8.474737e-03 8.474737e-03 0.305663027 #> 12379 6189.0 0.6858740 0.6858740 8.474568e-03 8.474568e-03 0.305651443 #> 12380 6189.5 0.6858857 0.6858857 8.474401e-03 8.474401e-03 0.305639861 #> 12381 6190.0 0.6858975 0.6858975 8.474237e-03 8.474237e-03 0.305628283 #> 12382 6190.5 0.6859092 0.6859092 8.474075e-03 8.474075e-03 0.305616708 #> 12383 6191.0 0.6859209 0.6859209 8.473916e-03 8.473916e-03 0.305605135 #> 12384 6191.5 0.6859327 0.6859327 8.473759e-03 8.473759e-03 0.305593566 #> 12385 6192.0 0.6859444 0.6859444 8.473605e-03 8.473605e-03 0.305582000 #> 12386 6192.5 0.6859561 0.6859561 8.473453e-03 8.473453e-03 0.305570438 #> 12387 6193.0 0.6859678 0.6859678 8.473304e-03 8.473304e-03 0.305558878 #> 12388 6193.5 0.6859795 0.6859795 8.473157e-03 8.473157e-03 0.305547322 #> 12389 6194.0 0.6859912 0.6859912 8.473013e-03 8.473013e-03 0.305535770 #> 12390 6194.5 0.6860029 0.6860029 8.472871e-03 8.472871e-03 0.305524220 #> 12391 6195.0 0.6860146 0.6860146 8.472732e-03 8.472732e-03 0.305512675 #> 12392 6195.5 0.6860263 0.6860263 8.472595e-03 8.472595e-03 0.305501132 #> 12393 6196.0 0.6860379 0.6860379 8.472461e-03 8.472461e-03 0.305489593 #> 12394 6196.5 0.6860496 0.6860496 8.472329e-03 8.472329e-03 0.305478058 #> 12395 6197.0 0.6860613 0.6860613 8.472199e-03 8.472199e-03 0.305466526 #> 12396 6197.5 0.6860729 0.6860729 8.472072e-03 8.472072e-03 0.305454998 #> 12397 6198.0 0.6860846 0.6860846 8.471948e-03 8.471948e-03 0.305443474 #> 12398 6198.5 0.6860962 0.6860962 8.471826e-03 8.471826e-03 0.305431953 #> 12399 6199.0 0.6861079 0.6861079 8.471706e-03 8.471706e-03 0.305420436 #> 12400 6199.5 0.6861195 0.6861195 8.471589e-03 8.471589e-03 0.305408923 #> 12401 6200.0 0.6861311 0.6861311 8.471474e-03 8.471474e-03 0.305397413 #> 12402 6200.5 0.6861427 0.6861427 8.471362e-03 8.471362e-03 0.305385908 #> 12403 6201.0 0.6861543 0.6861543 8.471252e-03 8.471252e-03 0.305374406 #> 12404 6201.5 0.6861659 0.6861659 8.471145e-03 8.471145e-03 0.305362908 #> 12405 6202.0 0.6861775 0.6861775 8.471040e-03 8.471040e-03 0.305351415 #> 12406 6202.5 0.6861891 0.6861891 8.470938e-03 8.470938e-03 0.305339925 #> 12407 6203.0 0.6862007 0.6862007 8.470838e-03 8.470838e-03 0.305328439 #> 12408 6203.5 0.6862123 0.6862123 8.470741e-03 8.470741e-03 0.305316958 #> 12409 6204.0 0.6862239 0.6862239 8.470646e-03 8.470646e-03 0.305305480 #> 12410 6204.5 0.6862354 0.6862354 8.470553e-03 8.470553e-03 0.305294007 #> 12411 6205.0 0.6862470 0.6862470 8.470463e-03 8.470463e-03 0.305282538 #> 12412 6205.5 0.6862586 0.6862586 8.470375e-03 8.470375e-03 0.305271073 #> 12413 6206.0 0.6862701 0.6862701 8.470290e-03 8.470290e-03 0.305259613 #> 12414 6206.5 0.6862816 0.6862816 8.470207e-03 8.470207e-03 0.305248157 #> 12415 6207.0 0.6862932 0.6862932 8.470127e-03 8.470127e-03 0.305236705 #> 12416 6207.5 0.6863047 0.6863047 8.470049e-03 8.470049e-03 0.305225258 #> 12417 6208.0 0.6863162 0.6863162 8.469974e-03 8.469974e-03 0.305213815 #> 12418 6208.5 0.6863277 0.6863277 8.469901e-03 8.469901e-03 0.305202376 #> 12419 6209.0 0.6863392 0.6863392 8.469830e-03 8.469830e-03 0.305190942 #> 12420 6209.5 0.6863507 0.6863507 8.469762e-03 8.469762e-03 0.305179513 #> 12421 6210.0 0.6863622 0.6863622 8.469696e-03 8.469696e-03 0.305168088 #> 12422 6210.5 0.6863737 0.6863737 8.469633e-03 8.469633e-03 0.305156668 #> 12423 6211.0 0.6863852 0.6863852 8.469572e-03 8.469572e-03 0.305145253 #> 12424 6211.5 0.6863966 0.6863966 8.469514e-03 8.469514e-03 0.305133842 #> 12425 6212.0 0.6864081 0.6864081 8.469458e-03 8.469458e-03 0.305122436 #> 12426 6212.5 0.6864196 0.6864196 8.469404e-03 8.469404e-03 0.305111035 #> 12427 6213.0 0.6864310 0.6864310 8.469353e-03 8.469353e-03 0.305099639 #> 12428 6213.5 0.6864424 0.6864424 8.469305e-03 8.469305e-03 0.305088247 #> 12429 6214.0 0.6864539 0.6864539 8.469258e-03 8.469258e-03 0.305076860 #> 12430 6214.5 0.6864653 0.6864653 8.469214e-03 8.469214e-03 0.305065479 #> 12431 6215.0 0.6864767 0.6864767 8.469173e-03 8.469173e-03 0.305054102 #> 12432 6215.5 0.6864881 0.6864881 8.469134e-03 8.469134e-03 0.305042730 #> 12433 6216.0 0.6864995 0.6864995 8.469097e-03 8.469097e-03 0.305031364 #> 12434 6216.5 0.6865109 0.6865109 8.469063e-03 8.469063e-03 0.305020002 #> 12435 6217.0 0.6865223 0.6865223 8.469032e-03 8.469032e-03 0.305008646 #> 12436 6217.5 0.6865337 0.6865337 8.469002e-03 8.469002e-03 0.304997295 #> 12437 6218.0 0.6865451 0.6865451 8.468975e-03 8.468975e-03 0.304985949 #> 12438 6218.5 0.6865564 0.6865564 8.468951e-03 8.468951e-03 0.304974608 #> 12439 6219.0 0.6865678 0.6865678 8.468929e-03 8.468929e-03 0.304963273 #> 12440 6219.5 0.6865791 0.6865791 8.468909e-03 8.468909e-03 0.304951942 #> 12441 6220.0 0.6865905 0.6865905 8.468891e-03 8.468891e-03 0.304940618 #> 12442 6220.5 0.6866018 0.6866018 8.468877e-03 8.468877e-03 0.304929298 #> 12443 6221.0 0.6866132 0.6866132 8.468864e-03 8.468864e-03 0.304917984 #> 12444 6221.5 0.6866245 0.6866245 8.468854e-03 8.468854e-03 0.304906676 #> 12445 6222.0 0.6866358 0.6866358 8.468846e-03 8.468846e-03 0.304895373 #> 12446 6222.5 0.6866471 0.6866471 8.468841e-03 8.468841e-03 0.304884075 #> 12447 6223.0 0.6866584 0.6866584 8.468838e-03 8.468838e-03 0.304872783 #> 12448 6223.5 0.6866697 0.6866697 8.468837e-03 8.468837e-03 0.304861497 #> 12449 6224.0 0.6866809 0.6866809 8.468839e-03 8.468839e-03 0.304850216 #> 12450 6224.5 0.6866922 0.6866922 8.468843e-03 8.468843e-03 0.304838941 #> 12451 6225.0 0.6867035 0.6867035 8.468850e-03 8.468850e-03 0.304827672 #> 12452 6225.5 0.6867147 0.6867147 8.468859e-03 8.468859e-03 0.304816409 #> 12453 6226.0 0.6867260 0.6867260 8.468870e-03 8.468870e-03 0.304805151 #> 12454 6226.5 0.6867372 0.6867372 8.468884e-03 8.468884e-03 0.304793899 #> 12455 6227.0 0.6867484 0.6867484 8.468900e-03 8.468900e-03 0.304782653 #> 12456 6227.5 0.6867597 0.6867597 8.468919e-03 8.468919e-03 0.304771413 #> 12457 6228.0 0.6867709 0.6867709 8.468940e-03 8.468940e-03 0.304760179 #> 12458 6228.5 0.6867821 0.6867821 8.468963e-03 8.468963e-03 0.304748951 #> 12459 6229.0 0.6867933 0.6867933 8.468988e-03 8.468988e-03 0.304737729 #> 12460 6229.5 0.6868045 0.6868045 8.469016e-03 8.469016e-03 0.304726513 #> 12461 6230.0 0.6868157 0.6868157 8.469047e-03 8.469047e-03 0.304715303 #> 12462 6230.5 0.6868268 0.6868268 8.469079e-03 8.469079e-03 0.304704099 #> 12463 6231.0 0.6868380 0.6868380 8.469115e-03 8.469115e-03 0.304692902 #> 12464 6231.5 0.6868491 0.6868491 8.469152e-03 8.469152e-03 0.304681710 #> 12465 6232.0 0.6868603 0.6868603 8.469192e-03 8.469192e-03 0.304670525 #> 12466 6232.5 0.6868714 0.6868714 8.469234e-03 8.469234e-03 0.304659346 #> 12467 6233.0 0.6868825 0.6868825 8.469279e-03 8.469279e-03 0.304648174 #> 12468 6233.5 0.6868937 0.6868937 8.469325e-03 8.469325e-03 0.304637007 #> 12469 6234.0 0.6869048 0.6869048 8.469375e-03 8.469375e-03 0.304625847 #> 12470 6234.5 0.6869159 0.6869159 8.469426e-03 8.469426e-03 0.304614694 #> 12471 6235.0 0.6869270 0.6869270 8.469480e-03 8.469480e-03 0.304603547 #> 12472 6235.5 0.6869381 0.6869381 8.469537e-03 8.469537e-03 0.304592406 #> 12473 6236.0 0.6869491 0.6869491 8.469595e-03 8.469595e-03 0.304581272 #> 12474 6236.5 0.6869602 0.6869602 8.469656e-03 8.469656e-03 0.304570145 #> 12475 6237.0 0.6869713 0.6869713 8.469719e-03 8.469719e-03 0.304559024 #> 12476 6237.5 0.6869823 0.6869823 8.469785e-03 8.469785e-03 0.304547910 #> 12477 6238.0 0.6869933 0.6869933 8.469853e-03 8.469853e-03 0.304536802 #> 12478 6238.5 0.6870044 0.6870044 8.469923e-03 8.469923e-03 0.304525702 #> 12479 6239.0 0.6870154 0.6870154 8.469996e-03 8.469996e-03 0.304514607 #> 12480 6239.5 0.6870264 0.6870264 8.470071e-03 8.470071e-03 0.304503520 #> 12481 6240.0 0.6870374 0.6870374 8.470148e-03 8.470148e-03 0.304492440 #> 12482 6240.5 0.6870484 0.6870484 8.470228e-03 8.470228e-03 0.304481366 #> 12483 6241.0 0.6870594 0.6870594 8.470310e-03 8.470310e-03 0.304470299 #> 12484 6241.5 0.6870704 0.6870704 8.470394e-03 8.470394e-03 0.304459239 #> 12485 6242.0 0.6870813 0.6870813 8.470481e-03 8.470481e-03 0.304448187 #> 12486 6242.5 0.6870923 0.6870923 8.470570e-03 8.470570e-03 0.304437141 #> 12487 6243.0 0.6871032 0.6871032 8.470661e-03 8.470661e-03 0.304426102 #> 12488 6243.5 0.6871142 0.6871142 8.470755e-03 8.470755e-03 0.304415070 #> 12489 6244.0 0.6871251 0.6871251 8.470851e-03 8.470851e-03 0.304404045 #> 12490 6244.5 0.6871360 0.6871360 8.470949e-03 8.470949e-03 0.304393028 #> 12491 6245.0 0.6871469 0.6871469 8.471050e-03 8.471050e-03 0.304382017 #> 12492 6245.5 0.6871578 0.6871578 8.471153e-03 8.471153e-03 0.304371014 #> 12493 6246.0 0.6871687 0.6871687 8.471258e-03 8.471258e-03 0.304360018 #> 12494 6246.5 0.6871796 0.6871796 8.471365e-03 8.471365e-03 0.304349029 #> 12495 6247.0 0.6871905 0.6871905 8.471475e-03 8.471475e-03 0.304338048 #> 12496 6247.5 0.6872013 0.6872013 8.471587e-03 8.471587e-03 0.304327074 #> 12497 6248.0 0.6872122 0.6872122 8.471701e-03 8.471701e-03 0.304316107 #> 12498 6248.5 0.6872230 0.6872230 8.471818e-03 8.471818e-03 0.304305148 #> 12499 6249.0 0.6872339 0.6872339 8.471937e-03 8.471937e-03 0.304294196 #> 12500 6249.5 0.6872447 0.6872447 8.472058e-03 8.472058e-03 0.304283251 #> 12501 6250.0 0.6872555 0.6872555 8.472182e-03 8.472182e-03 0.304272314 #> 12502 6250.5 0.6872663 0.6872663 8.472308e-03 8.472308e-03 0.304261385 #> 12503 6251.0 0.6872771 0.6872771 8.472436e-03 8.472436e-03 0.304250463 #> 12504 6251.5 0.6872879 0.6872879 8.472566e-03 8.472566e-03 0.304239549 #> 12505 6252.0 0.6872987 0.6872987 8.472699e-03 8.472699e-03 0.304228643 #> 12506 6252.5 0.6873094 0.6873094 8.472834e-03 8.472834e-03 0.304217744 #> 12507 6253.0 0.6873202 0.6873202 8.472971e-03 8.472971e-03 0.304206853 #> 12508 6253.5 0.6873309 0.6873309 8.473111e-03 8.473111e-03 0.304195969 #> 12509 6254.0 0.6873417 0.6873417 8.473253e-03 8.473253e-03 0.304185094 #> 12510 6254.5 0.6873524 0.6873524 8.473397e-03 8.473397e-03 0.304174226 #> 12511 6255.0 0.6873631 0.6873631 8.473543e-03 8.473543e-03 0.304163366 #> 12512 6255.5 0.6873738 0.6873738 8.473692e-03 8.473692e-03 0.304152514 #> 12513 6256.0 0.6873845 0.6873845 8.473843e-03 8.473843e-03 0.304141670 #> 12514 6256.5 0.6873952 0.6873952 8.473996e-03 8.473996e-03 0.304130834 #> 12515 6257.0 0.6874058 0.6874058 8.474152e-03 8.474152e-03 0.304120006 #> 12516 6257.5 0.6874165 0.6874165 8.474309e-03 8.474309e-03 0.304109186 #> 12517 6258.0 0.6874272 0.6874272 8.474469e-03 8.474469e-03 0.304098374 #> 12518 6258.5 0.6874378 0.6874378 8.474632e-03 8.474632e-03 0.304087570 #> 12519 6259.0 0.6874484 0.6874484 8.474796e-03 8.474796e-03 0.304076774 #> 12520 6259.5 0.6874591 0.6874591 8.474963e-03 8.474963e-03 0.304065986 #> 12521 6260.0 0.6874697 0.6874697 8.475132e-03 8.475132e-03 0.304055207 #> 12522 6260.5 0.6874803 0.6874803 8.475303e-03 8.475303e-03 0.304044435 #> 12523 6261.0 0.6874909 0.6874909 8.475477e-03 8.475477e-03 0.304033672 #> 12524 6261.5 0.6875014 0.6875014 8.475652e-03 8.475652e-03 0.304022917 #> 12525 6262.0 0.6875120 0.6875120 8.475830e-03 8.475830e-03 0.304012171 #> 12526 6262.5 0.6875226 0.6875226 8.476011e-03 8.476011e-03 0.304001433 #> 12527 6263.0 0.6875331 0.6875331 8.476193e-03 8.476193e-03 0.303990703 #> 12528 6263.5 0.6875436 0.6875436 8.476378e-03 8.476378e-03 0.303979982 #> 12529 6264.0 0.6875542 0.6875542 8.476565e-03 8.476565e-03 0.303969269 #> 12530 6264.5 0.6875647 0.6875647 8.476754e-03 8.476754e-03 0.303958565 #> 12531 6265.0 0.6875752 0.6875752 8.476945e-03 8.476945e-03 0.303947869 #> 12532 6265.5 0.6875857 0.6875857 8.477139e-03 8.477139e-03 0.303937182 #> 12533 6266.0 0.6875962 0.6875962 8.477335e-03 8.477335e-03 0.303926503 #> 12534 6266.5 0.6876066 0.6876066 8.477533e-03 8.477533e-03 0.303915833 #> 12535 6267.0 0.6876171 0.6876171 8.477733e-03 8.477733e-03 0.303905172 #> 12536 6267.5 0.6876275 0.6876275 8.477936e-03 8.477936e-03 0.303894519 #> 12537 6268.0 0.6876380 0.6876380 8.478141e-03 8.478141e-03 0.303883875 #> 12538 6268.5 0.6876484 0.6876484 8.478348e-03 8.478348e-03 0.303873240 #> 12539 6269.0 0.6876588 0.6876588 8.478557e-03 8.478557e-03 0.303862614 #> 12540 6269.5 0.6876692 0.6876692 8.478768e-03 8.478768e-03 0.303851996 #> 12541 6270.0 0.6876796 0.6876796 8.478982e-03 8.478982e-03 0.303841387 #> 12542 6270.5 0.6876900 0.6876900 8.479198e-03 8.479198e-03 0.303830787 #> 12543 6271.0 0.6877004 0.6877004 8.479416e-03 8.479416e-03 0.303820196 #> 12544 6271.5 0.6877107 0.6877107 8.479636e-03 8.479636e-03 0.303809614 #> 12545 6272.0 0.6877211 0.6877211 8.479858e-03 8.479858e-03 0.303799041 #> 12546 6272.5 0.6877314 0.6877314 8.480083e-03 8.480083e-03 0.303788477 #> 12547 6273.0 0.6877418 0.6877418 8.480310e-03 8.480310e-03 0.303777922 #> 12548 6273.5 0.6877521 0.6877521 8.480539e-03 8.480539e-03 0.303767376 #> 12549 6274.0 0.6877624 0.6877624 8.480770e-03 8.480770e-03 0.303756840 #> 12550 6274.5 0.6877727 0.6877727 8.481004e-03 8.481004e-03 0.303746312 #> 12551 6275.0 0.6877830 0.6877830 8.481239e-03 8.481239e-03 0.303735793 #> 12552 6275.5 0.6877932 0.6877932 8.481477e-03 8.481477e-03 0.303725284 #> 12553 6276.0 0.6878035 0.6878035 8.481717e-03 8.481717e-03 0.303714784 #> 12554 6276.5 0.6878137 0.6878137 8.481959e-03 8.481959e-03 0.303704293 #> 12555 6277.0 0.6878240 0.6878240 8.482203e-03 8.482203e-03 0.303693812 #> 12556 6277.5 0.6878342 0.6878342 8.482450e-03 8.482450e-03 0.303683340 #> 12557 6278.0 0.6878444 0.6878444 8.482699e-03 8.482699e-03 0.303672877 #> 12558 6278.5 0.6878546 0.6878546 8.482949e-03 8.482949e-03 0.303662423 #> 12559 6279.0 0.6878648 0.6878648 8.483202e-03 8.483202e-03 0.303651979 #> 12560 6279.5 0.6878750 0.6878750 8.483458e-03 8.483458e-03 0.303641545 #> 12561 6280.0 0.6878852 0.6878852 8.483715e-03 8.483715e-03 0.303631120 #> 12562 6280.5 0.6878953 0.6878953 8.483975e-03 8.483975e-03 0.303620704 #> 12563 6281.0 0.6879055 0.6879055 8.484236e-03 8.484236e-03 0.303610298 #> 12564 6281.5 0.6879156 0.6879156 8.484500e-03 8.484500e-03 0.303599902 #> 12565 6282.0 0.6879257 0.6879257 8.484766e-03 8.484766e-03 0.303589515 #> 12566 6282.5 0.6879358 0.6879358 8.485034e-03 8.485034e-03 0.303579138 #> 12567 6283.0 0.6879459 0.6879459 8.485304e-03 8.485304e-03 0.303568770 #> 12568 6283.5 0.6879560 0.6879560 8.485577e-03 8.485577e-03 0.303558412 #> 12569 6284.0 0.6879661 0.6879661 8.485852e-03 8.485852e-03 0.303548064 #> 12570 6284.5 0.6879761 0.6879761 8.486128e-03 8.486128e-03 0.303537726 #> 12571 6285.0 0.6879862 0.6879862 8.486407e-03 8.486407e-03 0.303527398 #> 12572 6285.5 0.6879962 0.6879962 8.486688e-03 8.486688e-03 0.303517079 #> 12573 6286.0 0.6880063 0.6880063 8.486971e-03 8.486971e-03 0.303506770 #> 12574 6286.5 0.6880163 0.6880163 8.487257e-03 8.487257e-03 0.303496471 #> 12575 6287.0 0.6880263 0.6880263 8.487544e-03 8.487544e-03 0.303486182 #> 12576 6287.5 0.6880363 0.6880363 8.487834e-03 8.487834e-03 0.303475903 #> 12577 6288.0 0.6880462 0.6880462 8.488125e-03 8.488125e-03 0.303465634 #> 12578 6288.5 0.6880562 0.6880562 8.488419e-03 8.488419e-03 0.303455375 #> 12579 6289.0 0.6880662 0.6880662 8.488715e-03 8.488715e-03 0.303445126 #> 12580 6289.5 0.6880761 0.6880761 8.489013e-03 8.489013e-03 0.303434887 #> 12581 6290.0 0.6880860 0.6880860 8.489313e-03 8.489313e-03 0.303424658 #> 12582 6290.5 0.6880959 0.6880959 8.489616e-03 8.489616e-03 0.303414440 #> 12583 6291.0 0.6881058 0.6881058 8.489920e-03 8.489920e-03 0.303404231 #> 12584 6291.5 0.6881157 0.6881157 8.490226e-03 8.490226e-03 0.303394033 #> 12585 6292.0 0.6881256 0.6881256 8.490535e-03 8.490535e-03 0.303383845 #> 12586 6292.5 0.6881355 0.6881355 8.490846e-03 8.490846e-03 0.303373667 #> 12587 6293.0 0.6881453 0.6881453 8.491159e-03 8.491159e-03 0.303363499 #> 12588 6293.5 0.6881552 0.6881552 8.491474e-03 8.491474e-03 0.303353342 #> 12589 6294.0 0.6881650 0.6881650 8.491791e-03 8.491791e-03 0.303343195 #> 12590 6294.5 0.6881748 0.6881748 8.492110e-03 8.492110e-03 0.303333058 #> 12591 6295.0 0.6881846 0.6881846 8.492431e-03 8.492431e-03 0.303322932 #> 12592 6295.5 0.6881944 0.6881944 8.492754e-03 8.492754e-03 0.303312816 #> 12593 6296.0 0.6882042 0.6882042 8.493080e-03 8.493080e-03 0.303302711 #> 12594 6296.5 0.6882140 0.6882140 8.493407e-03 8.493407e-03 0.303292616 #> 12595 6297.0 0.6882237 0.6882237 8.493737e-03 8.493737e-03 0.303282532 #> 12596 6297.5 0.6882335 0.6882335 8.494069e-03 8.494069e-03 0.303272458 #> 12597 6298.0 0.6882432 0.6882432 8.494402e-03 8.494402e-03 0.303262395 #> 12598 6298.5 0.6882529 0.6882529 8.494738e-03 8.494738e-03 0.303252343 #> 12599 6299.0 0.6882626 0.6882626 8.495076e-03 8.495076e-03 0.303242301 #> 12600 6299.5 0.6882723 0.6882723 8.495416e-03 8.495416e-03 0.303232270 #> 12601 6300.0 0.6882820 0.6882820 8.495758e-03 8.495758e-03 0.303222249 #> 12602 6300.5 0.6882917 0.6882917 8.496102e-03 8.496102e-03 0.303212239 #> 12603 6301.0 0.6883013 0.6883013 8.496448e-03 8.496448e-03 0.303202240 #> 12604 6301.5 0.6883110 0.6883110 8.496797e-03 8.496797e-03 0.303192252 #> 12605 6302.0 0.6883206 0.6883206 8.497147e-03 8.497147e-03 0.303182275 #> 12606 6302.5 0.6883302 0.6883302 8.497499e-03 8.497499e-03 0.303172308 #> 12607 6303.0 0.6883398 0.6883398 8.497854e-03 8.497854e-03 0.303162352 #> 12608 6303.5 0.6883494 0.6883494 8.498210e-03 8.498210e-03 0.303152408 #> 12609 6304.0 0.6883590 0.6883590 8.498569e-03 8.498569e-03 0.303142474 #> 12610 6304.5 0.6883685 0.6883685 8.498929e-03 8.498929e-03 0.303132551 #> 12611 6305.0 0.6883781 0.6883781 8.499292e-03 8.499292e-03 0.303122639 #> 12612 6305.5 0.6883876 0.6883876 8.499657e-03 8.499657e-03 0.303112738 #> 12613 6306.0 0.6883971 0.6883971 8.500023e-03 8.500023e-03 0.303102848 #> 12614 6306.5 0.6884066 0.6884066 8.500392e-03 8.500392e-03 0.303092969 #> 12615 6307.0 0.6884161 0.6884161 8.500763e-03 8.500763e-03 0.303083101 #> 12616 6307.5 0.6884256 0.6884256 8.501136e-03 8.501136e-03 0.303073245 #> 12617 6308.0 0.6884351 0.6884351 8.501510e-03 8.501510e-03 0.303063399 #> 12618 6308.5 0.6884445 0.6884445 8.501887e-03 8.501887e-03 0.303053565 #> 12619 6309.0 0.6884540 0.6884540 8.502266e-03 8.502266e-03 0.303043742 #> 12620 6309.5 0.6884634 0.6884634 8.502647e-03 8.502647e-03 0.303033930 #> 12621 6310.0 0.6884728 0.6884728 8.503030e-03 8.503030e-03 0.303024130 #> 12622 6310.5 0.6884822 0.6884822 8.503415e-03 8.503415e-03 0.303014340 #> 12623 6311.0 0.6884916 0.6884916 8.503802e-03 8.503802e-03 0.303004563 #> 12624 6311.5 0.6885010 0.6885010 8.504191e-03 8.504191e-03 0.302994796 #> 12625 6312.0 0.6885104 0.6885104 8.504582e-03 8.504582e-03 0.302985041 #> 12626 6312.5 0.6885197 0.6885197 8.504975e-03 8.504975e-03 0.302975297 #> 12627 6313.0 0.6885291 0.6885291 8.505370e-03 8.505370e-03 0.302965565 #> 12628 6313.5 0.6885384 0.6885384 8.505767e-03 8.505767e-03 0.302955844 #> 12629 6314.0 0.6885477 0.6885477 8.506166e-03 8.506166e-03 0.302946134 #> 12630 6314.5 0.6885570 0.6885570 8.506567e-03 8.506567e-03 0.302936437 #> 12631 6315.0 0.6885663 0.6885663 8.506970e-03 8.506970e-03 0.302926750 #> 12632 6315.5 0.6885755 0.6885755 8.507375e-03 8.507375e-03 0.302917075 #> 12633 6316.0 0.6885848 0.6885848 8.507782e-03 8.507782e-03 0.302907412 #> 12634 6316.5 0.6885940 0.6885940 8.508191e-03 8.508191e-03 0.302897761 #> 12635 6317.0 0.6886033 0.6886033 8.508602e-03 8.508602e-03 0.302888121 #> 12636 6317.5 0.6886125 0.6886125 8.509015e-03 8.509015e-03 0.302878493 #> 12637 6318.0 0.6886217 0.6886217 8.509430e-03 8.509430e-03 0.302868876 #> 12638 6318.5 0.6886309 0.6886309 8.509847e-03 8.509847e-03 0.302859272 #> 12639 6319.0 0.6886401 0.6886401 8.510266e-03 8.510266e-03 0.302849679 #> 12640 6319.5 0.6886492 0.6886492 8.510686e-03 8.510686e-03 0.302840097 #> 12641 6320.0 0.6886584 0.6886584 8.511109e-03 8.511109e-03 0.302830528 #> 12642 6320.5 0.6886675 0.6886675 8.511534e-03 8.511534e-03 0.302820970 #> 12643 6321.0 0.6886766 0.6886766 8.511961e-03 8.511961e-03 0.302811425 #> 12644 6321.5 0.6886857 0.6886857 8.512389e-03 8.512389e-03 0.302801891 #> 12645 6322.0 0.6886948 0.6886948 8.512820e-03 8.512820e-03 0.302792369 #> 12646 6322.5 0.6887039 0.6887039 8.513253e-03 8.513253e-03 0.302782859 #> 12647 6323.0 0.6887130 0.6887130 8.513687e-03 8.513687e-03 0.302773362 #> 12648 6323.5 0.6887220 0.6887220 8.514124e-03 8.514124e-03 0.302763876 #> 12649 6324.0 0.6887310 0.6887310 8.514562e-03 8.514562e-03 0.302754402 #> 12650 6324.5 0.6887401 0.6887401 8.515003e-03 8.515003e-03 0.302744940 #> 12651 6325.0 0.6887491 0.6887491 8.515445e-03 8.515445e-03 0.302735490 #> 12652 6325.5 0.6887581 0.6887581 8.515889e-03 8.515889e-03 0.302726053 #> 12653 6326.0 0.6887670 0.6887670 8.516335e-03 8.516335e-03 0.302716627 #> 12654 6326.5 0.6887760 0.6887760 8.516784e-03 8.516784e-03 0.302707214 #> 12655 6327.0 0.6887850 0.6887850 8.517234e-03 8.517234e-03 0.302697813 #> 12656 6327.5 0.6887939 0.6887939 8.517686e-03 8.517686e-03 0.302688424 #> 12657 6328.0 0.6888028 0.6888028 8.518140e-03 8.518140e-03 0.302679047 #> 12658 6328.5 0.6888117 0.6888117 8.518595e-03 8.518595e-03 0.302669683 #> 12659 6329.0 0.6888206 0.6888206 8.519053e-03 8.519053e-03 0.302660331 #> 12660 6329.5 0.6888295 0.6888295 8.519513e-03 8.519513e-03 0.302650991 #> 12661 6330.0 0.6888384 0.6888384 8.519975e-03 8.519975e-03 0.302641664 #> 12662 6330.5 0.6888472 0.6888472 8.520438e-03 8.520438e-03 0.302632348 #> 12663 6331.0 0.6888561 0.6888561 8.520904e-03 8.520904e-03 0.302623046 #> 12664 6331.5 0.6888649 0.6888649 8.521371e-03 8.521371e-03 0.302613756 #> 12665 6332.0 0.6888737 0.6888737 8.521840e-03 8.521840e-03 0.302604478 #> 12666 6332.5 0.6888825 0.6888825 8.522311e-03 8.522311e-03 0.302595212 #> 12667 6333.0 0.6888913 0.6888913 8.522784e-03 8.522784e-03 0.302585960 #> 12668 6333.5 0.6889000 0.6889000 8.523259e-03 8.523259e-03 0.302576719 #> 12669 6334.0 0.6889088 0.6889088 8.523736e-03 8.523736e-03 0.302567491 #> 12670 6334.5 0.6889175 0.6889175 8.524215e-03 8.524215e-03 0.302558276 #> 12671 6335.0 0.6889262 0.6889262 8.524695e-03 8.524695e-03 0.302549074 #> 12672 6335.5 0.6889349 0.6889349 8.525178e-03 8.525178e-03 0.302539884 #> 12673 6336.0 0.6889436 0.6889436 8.525662e-03 8.525662e-03 0.302530706 #> 12674 6336.5 0.6889523 0.6889523 8.526149e-03 8.526149e-03 0.302521542 #> 12675 6337.0 0.6889610 0.6889610 8.526637e-03 8.526637e-03 0.302512390 #> 12676 6337.5 0.6889696 0.6889696 8.527127e-03 8.527127e-03 0.302503251 #> 12677 6338.0 0.6889783 0.6889783 8.527619e-03 8.527619e-03 0.302494124 #> 12678 6338.5 0.6889869 0.6889869 8.528112e-03 8.528112e-03 0.302485010 #> 12679 6339.0 0.6889955 0.6889955 8.528608e-03 8.528608e-03 0.302475910 #> 12680 6339.5 0.6890041 0.6890041 8.529105e-03 8.529105e-03 0.302466822 #> 12681 6340.0 0.6890126 0.6890126 8.529605e-03 8.529605e-03 0.302457746 #> 12682 6340.5 0.6890212 0.6890212 8.530106e-03 8.530106e-03 0.302448684 #> 12683 6341.0 0.6890298 0.6890298 8.530609e-03 8.530609e-03 0.302439634 #> 12684 6341.5 0.6890383 0.6890383 8.531114e-03 8.531114e-03 0.302430598 #> 12685 6342.0 0.6890468 0.6890468 8.531621e-03 8.531621e-03 0.302421574 #> 12686 6342.5 0.6890553 0.6890553 8.532129e-03 8.532129e-03 0.302412564 #> 12687 6343.0 0.6890638 0.6890638 8.532640e-03 8.532640e-03 0.302403566 #> 12688 6343.5 0.6890723 0.6890723 8.533152e-03 8.533152e-03 0.302394581 #> 12689 6344.0 0.6890807 0.6890807 8.533666e-03 8.533666e-03 0.302385610 #> 12690 6344.5 0.6890892 0.6890892 8.534182e-03 8.534182e-03 0.302376651 #> 12691 6345.0 0.6890976 0.6890976 8.534700e-03 8.534700e-03 0.302367706 #> 12692 6345.5 0.6891060 0.6891060 8.535219e-03 8.535219e-03 0.302358774 #> 12693 6346.0 0.6891144 0.6891144 8.535741e-03 8.535741e-03 0.302349855 #> 12694 6346.5 0.6891228 0.6891228 8.536264e-03 8.536264e-03 0.302340949 #> 12695 6347.0 0.6891312 0.6891312 8.536789e-03 8.536789e-03 0.302332056 #> 12696 6347.5 0.6891395 0.6891395 8.537316e-03 8.537316e-03 0.302323176 #> 12697 6348.0 0.6891478 0.6891478 8.537845e-03 8.537845e-03 0.302314310 #> 12698 6348.5 0.6891562 0.6891562 8.538375e-03 8.538375e-03 0.302305457 #> 12699 6349.0 0.6891645 0.6891645 8.538907e-03 8.538907e-03 0.302296617 #> 12700 6349.5 0.6891728 0.6891728 8.539441e-03 8.539441e-03 0.302287790 #> 12701 6350.0 0.6891810 0.6891810 8.539977e-03 8.539977e-03 0.302278977 #> 12702 6350.5 0.6891893 0.6891893 8.540515e-03 8.540515e-03 0.302270177 #> 12703 6351.0 0.6891976 0.6891976 8.541055e-03 8.541055e-03 0.302261391 #> 12704 6351.5 0.6892058 0.6892058 8.541596e-03 8.541596e-03 0.302252618 #> 12705 6352.0 0.6892140 0.6892140 8.542139e-03 8.542139e-03 0.302243858 #> 12706 6352.5 0.6892222 0.6892222 8.542684e-03 8.542684e-03 0.302235112 #> 12707 6353.0 0.6892304 0.6892304 8.543231e-03 8.543231e-03 0.302226379 #> 12708 6353.5 0.6892386 0.6892386 8.543779e-03 8.543779e-03 0.302217660 #> 12709 6354.0 0.6892467 0.6892467 8.544329e-03 8.544329e-03 0.302208954 #> 12710 6354.5 0.6892549 0.6892549 8.544881e-03 8.544881e-03 0.302200262 #> 12711 6355.0 0.6892630 0.6892630 8.545435e-03 8.545435e-03 0.302191583 #> 12712 6355.5 0.6892711 0.6892711 8.545991e-03 8.545991e-03 0.302182918 #> 12713 6356.0 0.6892792 0.6892792 8.546548e-03 8.546548e-03 0.302174267 #> 12714 6356.5 0.6892873 0.6892873 8.547107e-03 8.547107e-03 0.302165629 #> 12715 6357.0 0.6892953 0.6892953 8.547668e-03 8.547668e-03 0.302157005 #> 12716 6357.5 0.6893034 0.6893034 8.548230e-03 8.548230e-03 0.302148394 #> 12717 6358.0 0.6893114 0.6893114 8.548795e-03 8.548795e-03 0.302139797 #> 12718 6358.5 0.6893194 0.6893194 8.549361e-03 8.549361e-03 0.302131214 #> 12719 6359.0 0.6893274 0.6893274 8.549929e-03 8.549929e-03 0.302122645 #> 12720 6359.5 0.6893354 0.6893354 8.550498e-03 8.550498e-03 0.302114089 #> 12721 6360.0 0.6893434 0.6893434 8.551070e-03 8.551070e-03 0.302105547 #> 12722 6360.5 0.6893513 0.6893513 8.551643e-03 8.551643e-03 0.302097019 #> 12723 6361.0 0.6893593 0.6893593 8.552218e-03 8.552218e-03 0.302088505 #> 12724 6361.5 0.6893672 0.6893672 8.552794e-03 8.552794e-03 0.302080005 #> 12725 6362.0 0.6893751 0.6893751 8.553372e-03 8.553372e-03 0.302071518 #> 12726 6362.5 0.6893830 0.6893830 8.553953e-03 8.553953e-03 0.302063045 #> 12727 6363.0 0.6893909 0.6893909 8.554534e-03 8.554534e-03 0.302054587 #> 12728 6363.5 0.6893987 0.6893987 8.555118e-03 8.555118e-03 0.302046142 #> 12729 6364.0 0.6894066 0.6894066 8.555703e-03 8.555703e-03 0.302037711 #> 12730 6364.5 0.6894144 0.6894144 8.556290e-03 8.556290e-03 0.302029294 #> 12731 6365.0 0.6894222 0.6894222 8.556878e-03 8.556878e-03 0.302020892 #> 12732 6365.5 0.6894300 0.6894300 8.557469e-03 8.557469e-03 0.302012503 #> 12733 6366.0 0.6894378 0.6894378 8.558061e-03 8.558061e-03 0.302004128 #> 12734 6366.5 0.6894456 0.6894456 8.558655e-03 8.558655e-03 0.301995767 #> 12735 6367.0 0.6894533 0.6894533 8.559250e-03 8.559250e-03 0.301987421 #> 12736 6367.5 0.6894611 0.6894611 8.559847e-03 8.559847e-03 0.301979088 #> 12737 6368.0 0.6894688 0.6894688 8.560446e-03 8.560446e-03 0.301970770 #> 12738 6368.5 0.6894765 0.6894765 8.561047e-03 8.561047e-03 0.301962466 #> 12739 6369.0 0.6894842 0.6894842 8.561649e-03 8.561649e-03 0.301954176 #> 12740 6369.5 0.6894918 0.6894918 8.562253e-03 8.562253e-03 0.301945900 #> 12741 6370.0 0.6894995 0.6894995 8.562858e-03 8.562858e-03 0.301937638 #> 12742 6370.5 0.6895071 0.6895071 8.563465e-03 8.563465e-03 0.301929391 #> 12743 6371.0 0.6895148 0.6895148 8.564074e-03 8.564074e-03 0.301921158 #> 12744 6371.5 0.6895224 0.6895224 8.564685e-03 8.564685e-03 0.301912939 #> 12745 6372.0 0.6895300 0.6895300 8.565297e-03 8.565297e-03 0.301904734 #> 12746 6372.5 0.6895375 0.6895375 8.565911e-03 8.565911e-03 0.301896544 #> 12747 6373.0 0.6895451 0.6895451 8.566527e-03 8.566527e-03 0.301888368 #> 12748 6373.5 0.6895526 0.6895526 8.567144e-03 8.567144e-03 0.301880207 #> 12749 6374.0 0.6895602 0.6895602 8.567763e-03 8.567763e-03 0.301872059 #> 12750 6374.5 0.6895677 0.6895677 8.568384e-03 8.568384e-03 0.301863927 #> 12751 6375.0 0.6895752 0.6895752 8.569006e-03 8.569006e-03 0.301855808 #> 12752 6375.5 0.6895827 0.6895827 8.569630e-03 8.569630e-03 0.301847704 #> 12753 6376.0 0.6895901 0.6895901 8.570255e-03 8.570255e-03 0.301839615 #> 12754 6376.5 0.6895976 0.6895976 8.570883e-03 8.570883e-03 0.301831540 #> 12755 6377.0 0.6896050 0.6896050 8.571511e-03 8.571511e-03 0.301823479 #> 12756 6377.5 0.6896124 0.6896124 8.572142e-03 8.572142e-03 0.301815433 #> 12757 6378.0 0.6896198 0.6896198 8.572774e-03 8.572774e-03 0.301807402 #> 12758 6378.5 0.6896272 0.6896272 8.573408e-03 8.573408e-03 0.301799385 #> 12759 6379.0 0.6896346 0.6896346 8.574043e-03 8.574043e-03 0.301791383 #> 12760 6379.5 0.6896419 0.6896419 8.574680e-03 8.574680e-03 0.301783395 #> 12761 6380.0 0.6896493 0.6896493 8.575319e-03 8.575319e-03 0.301775422 #> 12762 6380.5 0.6896566 0.6896566 8.575959e-03 8.575959e-03 0.301767464 #> 12763 6381.0 0.6896639 0.6896639 8.576601e-03 8.576601e-03 0.301759520 #> 12764 6381.5 0.6896712 0.6896712 8.577244e-03 8.577244e-03 0.301751591 #> 12765 6382.0 0.6896784 0.6896784 8.577889e-03 8.577889e-03 0.301743677 #> 12766 6382.5 0.6896857 0.6896857 8.578536e-03 8.578536e-03 0.301735777 #> 12767 6383.0 0.6896929 0.6896929 8.579184e-03 8.579184e-03 0.301727892 #> 12768 6383.5 0.6897001 0.6897001 8.579834e-03 8.579834e-03 0.301720022 #> 12769 6384.0 0.6897073 0.6897073 8.580486e-03 8.580486e-03 0.301712167 #> 12770 6384.5 0.6897145 0.6897145 8.581139e-03 8.581139e-03 0.301704326 #> 12771 6385.0 0.6897217 0.6897217 8.581793e-03 8.581793e-03 0.301696500 #> 12772 6385.5 0.6897289 0.6897289 8.582450e-03 8.582450e-03 0.301688689 #> 12773 6386.0 0.6897360 0.6897360 8.583107e-03 8.583107e-03 0.301680893 #> 12774 6386.5 0.6897431 0.6897431 8.583767e-03 8.583767e-03 0.301673112 #> 12775 6387.0 0.6897502 0.6897502 8.584428e-03 8.584428e-03 0.301665346 #> 12776 6387.5 0.6897573 0.6897573 8.585090e-03 8.585090e-03 0.301657594 #> 12777 6388.0 0.6897644 0.6897644 8.585754e-03 8.585754e-03 0.301649858 #> 12778 6388.5 0.6897714 0.6897714 8.586420e-03 8.586420e-03 0.301642136 #> 12779 6389.0 0.6897785 0.6897785 8.587087e-03 8.587087e-03 0.301634430 #> 12780 6389.5 0.6897855 0.6897855 8.587756e-03 8.587756e-03 0.301626738 #> 12781 6390.0 0.6897925 0.6897925 8.588427e-03 8.588427e-03 0.301619061 #> 12782 6390.5 0.6897995 0.6897995 8.589098e-03 8.589098e-03 0.301611400 #> 12783 6391.0 0.6898065 0.6898065 8.589772e-03 8.589772e-03 0.301603753 #> 12784 6391.5 0.6898134 0.6898134 8.590447e-03 8.590447e-03 0.301596122 #> 12785 6392.0 0.6898204 0.6898204 8.591124e-03 8.591124e-03 0.301588505 #> 12786 6392.5 0.6898273 0.6898273 8.591802e-03 8.591802e-03 0.301580904 #> 12787 6393.0 0.6898342 0.6898342 8.592481e-03 8.592481e-03 0.301573318 #> 12788 6393.5 0.6898411 0.6898411 8.593163e-03 8.593163e-03 0.301565747 #> 12789 6394.0 0.6898480 0.6898480 8.593845e-03 8.593845e-03 0.301558191 #> 12790 6394.5 0.6898548 0.6898548 8.594530e-03 8.594530e-03 0.301550650 #> 12791 6395.0 0.6898617 0.6898617 8.595215e-03 8.595215e-03 0.301543125 #> 12792 6395.5 0.6898685 0.6898685 8.595903e-03 8.595903e-03 0.301535614 #> 12793 6396.0 0.6898753 0.6898753 8.596591e-03 8.596591e-03 0.301528119 #> 12794 6396.5 0.6898821 0.6898821 8.597282e-03 8.597282e-03 0.301520639 #> 12795 6397.0 0.6898889 0.6898889 8.597974e-03 8.597974e-03 0.301513174 #> 12796 6397.5 0.6898956 0.6898956 8.598667e-03 8.598667e-03 0.301505725 #> 12797 6398.0 0.6899023 0.6899023 8.599362e-03 8.599362e-03 0.301498291 #> 12798 6398.5 0.6899091 0.6899091 8.600058e-03 8.600058e-03 0.301490872 #> 12799 6399.0 0.6899158 0.6899158 8.600756e-03 8.600756e-03 0.301483469 #> 12800 6399.5 0.6899225 0.6899225 8.601455e-03 8.601455e-03 0.301476081 #> 12801 6400.0 0.6899291 0.6899291 8.602156e-03 8.602156e-03 0.301468708 #> 12802 6400.5 0.6899358 0.6899358 8.602859e-03 8.602859e-03 0.301461350 #> 12803 6401.0 0.6899424 0.6899424 8.603562e-03 8.603562e-03 0.301454008 #> 12804 6401.5 0.6899491 0.6899491 8.604268e-03 8.604268e-03 0.301446682 #> 12805 6402.0 0.6899557 0.6899557 8.604975e-03 8.604975e-03 0.301439371 #> 12806 6402.5 0.6899622 0.6899622 8.605683e-03 8.605683e-03 0.301432075 #> 12807 6403.0 0.6899688 0.6899688 8.606393e-03 8.606393e-03 0.301424795 #> 12808 6403.5 0.6899754 0.6899754 8.607104e-03 8.607104e-03 0.301417530 #> 12809 6404.0 0.6899819 0.6899819 8.607817e-03 8.607817e-03 0.301410281 #> 12810 6404.5 0.6899884 0.6899884 8.608531e-03 8.608531e-03 0.301403047 #> 12811 6405.0 0.6899949 0.6899949 8.609246e-03 8.609246e-03 0.301395829 #> 12812 6405.5 0.6900014 0.6900014 8.609963e-03 8.609963e-03 0.301388626 #> 12813 6406.0 0.6900079 0.6900079 8.610682e-03 8.610682e-03 0.301381439 #> 12814 6406.5 0.6900143 0.6900143 8.611402e-03 8.611402e-03 0.301374267 #> 12815 6407.0 0.6900208 0.6900208 8.612123e-03 8.612123e-03 0.301367111 #> 12816 6407.5 0.6900272 0.6900272 8.612846e-03 8.612846e-03 0.301359971 #> 12817 6408.0 0.6900336 0.6900336 8.613571e-03 8.613571e-03 0.301352846 #> 12818 6408.5 0.6900400 0.6900400 8.614296e-03 8.614296e-03 0.301345737 #> 12819 6409.0 0.6900463 0.6900463 8.615023e-03 8.615023e-03 0.301338644 #> 12820 6409.5 0.6900527 0.6900527 8.615752e-03 8.615752e-03 0.301331566 #> 12821 6410.0 0.6900590 0.6900590 8.616482e-03 8.616482e-03 0.301324504 #> 12822 6410.5 0.6900653 0.6900653 8.617214e-03 8.617214e-03 0.301317457 #> 12823 6411.0 0.6900716 0.6900716 8.617946e-03 8.617946e-03 0.301310427 #> 12824 6411.5 0.6900779 0.6900779 8.618681e-03 8.618681e-03 0.301303412 #> 12825 6412.0 0.6900842 0.6900842 8.619416e-03 8.619416e-03 0.301296413 #> 12826 6412.5 0.6900904 0.6900904 8.620154e-03 8.620154e-03 0.301289429 #> 12827 6413.0 0.6900966 0.6900966 8.620892e-03 8.620892e-03 0.301282462 #> 12828 6413.5 0.6901029 0.6901029 8.621632e-03 8.621632e-03 0.301275510 #> 12829 6414.0 0.6901091 0.6901091 8.622373e-03 8.622373e-03 0.301268574 #> 12830 6414.5 0.6901152 0.6901152 8.623116e-03 8.623116e-03 0.301261654 #> 12831 6415.0 0.6901214 0.6901214 8.623860e-03 8.623860e-03 0.301254750 #> 12832 6415.5 0.6901275 0.6901275 8.624606e-03 8.624606e-03 0.301247861 #> 12833 6416.0 0.6901337 0.6901337 8.625353e-03 8.625353e-03 0.301240989 #> 12834 6416.5 0.6901398 0.6901398 8.626101e-03 8.626101e-03 0.301234132 #> 12835 6417.0 0.6901459 0.6901459 8.626851e-03 8.626851e-03 0.301227291 #> 12836 6417.5 0.6901519 0.6901519 8.627602e-03 8.627602e-03 0.301220466 #> 12837 6418.0 0.6901580 0.6901580 8.628354e-03 8.628354e-03 0.301213657 #> 12838 6418.5 0.6901640 0.6901640 8.629108e-03 8.629108e-03 0.301206864 #> 12839 6419.0 0.6901700 0.6901700 8.629863e-03 8.629863e-03 0.301200087 #> 12840 6419.5 0.6901761 0.6901761 8.630620e-03 8.630620e-03 0.301193326 #> 12841 6420.0 0.6901820 0.6901820 8.631378e-03 8.631378e-03 0.301186581 #> 12842 6420.5 0.6901880 0.6901880 8.632137e-03 8.632137e-03 0.301179852 #> 12843 6421.0 0.6901940 0.6901940 8.632897e-03 8.632897e-03 0.301173140 #> 12844 6421.5 0.6901999 0.6901999 8.633659e-03 8.633659e-03 0.301166443 #> 12845 6422.0 0.6902058 0.6902058 8.634423e-03 8.634423e-03 0.301159762 #> 12846 6422.5 0.6902117 0.6902117 8.635187e-03 8.635187e-03 0.301153097 #> 12847 6423.0 0.6902176 0.6902176 8.635953e-03 8.635953e-03 0.301146448 #> 12848 6423.5 0.6902235 0.6902235 8.636720e-03 8.636720e-03 0.301139816 #> 12849 6424.0 0.6902293 0.6902293 8.637489e-03 8.637489e-03 0.301133199 #> 12850 6424.5 0.6902351 0.6902351 8.638259e-03 8.638259e-03 0.301126599 #> 12851 6425.0 0.6902410 0.6902410 8.639030e-03 8.639030e-03 0.301120014 #> 12852 6425.5 0.6902468 0.6902468 8.639803e-03 8.639803e-03 0.301113446 #> 12853 6426.0 0.6902525 0.6902525 8.640577e-03 8.640577e-03 0.301106894 #> 12854 6426.5 0.6902583 0.6902583 8.641352e-03 8.641352e-03 0.301100359 #> 12855 6427.0 0.6902640 0.6902640 8.642129e-03 8.642129e-03 0.301093839 #> 12856 6427.5 0.6902698 0.6902698 8.642907e-03 8.642907e-03 0.301087336 #> 12857 6428.0 0.6902755 0.6902755 8.643686e-03 8.643686e-03 0.301080849 #> 12858 6428.5 0.6902812 0.6902812 8.644466e-03 8.644466e-03 0.301074378 #> 12859 6429.0 0.6902868 0.6902868 8.645248e-03 8.645248e-03 0.301067923 #> 12860 6429.5 0.6902925 0.6902925 8.646031e-03 8.646031e-03 0.301061485 #> 12861 6430.0 0.6902981 0.6902981 8.646816e-03 8.646816e-03 0.301055063 #> 12862 6430.5 0.6903037 0.6903037 8.647601e-03 8.647601e-03 0.301048657 #> 12863 6431.0 0.6903093 0.6903093 8.648388e-03 8.648388e-03 0.301042268 #> 12864 6431.5 0.6903149 0.6903149 8.649176e-03 8.649176e-03 0.301035894 #> 12865 6432.0 0.6903205 0.6903205 8.649966e-03 8.649966e-03 0.301029537 #> 12866 6432.5 0.6903260 0.6903260 8.650757e-03 8.650757e-03 0.301023197 #> 12867 6433.0 0.6903316 0.6903316 8.651549e-03 8.651549e-03 0.301016873 #> 12868 6433.5 0.6903371 0.6903371 8.652342e-03 8.652342e-03 0.301010565 #> 12869 6434.0 0.6903426 0.6903426 8.653137e-03 8.653137e-03 0.301004274 #> 12870 6434.5 0.6903481 0.6903481 8.653932e-03 8.653932e-03 0.300997999 #> 12871 6435.0 0.6903535 0.6903535 8.654730e-03 8.654730e-03 0.300991740 #> 12872 6435.5 0.6903590 0.6903590 8.655528e-03 8.655528e-03 0.300985498 #> 12873 6436.0 0.6903644 0.6903644 8.656327e-03 8.656327e-03 0.300979272 #> 12874 6436.5 0.6903698 0.6903698 8.657128e-03 8.657128e-03 0.300973063 #> 12875 6437.0 0.6903752 0.6903752 8.657930e-03 8.657930e-03 0.300966870 #> 12876 6437.5 0.6903806 0.6903806 8.658734e-03 8.658734e-03 0.300960694 #> 12877 6438.0 0.6903859 0.6903859 8.659538e-03 8.659538e-03 0.300954534 #> 12878 6438.5 0.6903913 0.6903913 8.660344e-03 8.660344e-03 0.300948390 #> 12879 6439.0 0.6903966 0.6903966 8.661151e-03 8.661151e-03 0.300942264 #> 12880 6439.5 0.6904019 0.6904019 8.661959e-03 8.661959e-03 0.300936153 #> 12881 6440.0 0.6904072 0.6904072 8.662769e-03 8.662769e-03 0.300930059 #> 12882 6440.5 0.6904124 0.6904124 8.663579e-03 8.663579e-03 0.300923982 #> 12883 6441.0 0.6904177 0.6904177 8.664391e-03 8.664391e-03 0.300917921 #> 12884 6441.5 0.6904229 0.6904229 8.665204e-03 8.665204e-03 0.300911877 #> 12885 6442.0 0.6904281 0.6904281 8.666019e-03 8.666019e-03 0.300905850 #> 12886 6442.5 0.6904333 0.6904333 8.666834e-03 8.666834e-03 0.300899839 #> 12887 6443.0 0.6904385 0.6904385 8.667651e-03 8.667651e-03 0.300893844 #> 12888 6443.5 0.6904437 0.6904437 8.668469e-03 8.668469e-03 0.300887867 #> 12889 6444.0 0.6904488 0.6904488 8.669288e-03 8.669288e-03 0.300881905 #> 12890 6444.5 0.6904539 0.6904539 8.670108e-03 8.670108e-03 0.300875961 #> 12891 6445.0 0.6904590 0.6904590 8.670930e-03 8.670930e-03 0.300870033 #> 12892 6445.5 0.6904641 0.6904641 8.671753e-03 8.671753e-03 0.300864122 #> 12893 6446.0 0.6904692 0.6904692 8.672576e-03 8.672576e-03 0.300858227 #> 12894 6446.5 0.6904742 0.6904742 8.673401e-03 8.673401e-03 0.300852350 #> 12895 6447.0 0.6904793 0.6904793 8.674228e-03 8.674228e-03 0.300846488 #> 12896 6447.5 0.6904843 0.6904843 8.675055e-03 8.675055e-03 0.300840644 #> 12897 6448.0 0.6904893 0.6904893 8.675884e-03 8.675884e-03 0.300834816 #> 12898 6448.5 0.6904943 0.6904943 8.676713e-03 8.676713e-03 0.300829005 #> 12899 6449.0 0.6904992 0.6904992 8.677544e-03 8.677544e-03 0.300823211 #> 12900 6449.5 0.6905042 0.6905042 8.678376e-03 8.678376e-03 0.300817434 #> 12901 6450.0 0.6905091 0.6905091 8.679209e-03 8.679209e-03 0.300811673 #> 12902 6450.5 0.6905140 0.6905140 8.680044e-03 8.680044e-03 0.300805929 #> 12903 6451.0 0.6905189 0.6905189 8.680879e-03 8.680879e-03 0.300800202 #> 12904 6451.5 0.6905238 0.6905238 8.681716e-03 8.681716e-03 0.300794491 #> 12905 6452.0 0.6905286 0.6905286 8.682553e-03 8.682553e-03 0.300788798 #> 12906 6452.5 0.6905335 0.6905335 8.683392e-03 8.683392e-03 0.300783121 #> 12907 6453.0 0.6905383 0.6905383 8.684232e-03 8.684232e-03 0.300777461 #> 12908 6453.5 0.6905431 0.6905431 8.685073e-03 8.685073e-03 0.300771818 #> 12909 6454.0 0.6905479 0.6905479 8.685916e-03 8.685916e-03 0.300766192 #> 12910 6454.5 0.6905527 0.6905527 8.686759e-03 8.686759e-03 0.300760582 #> 12911 6455.0 0.6905574 0.6905574 8.687603e-03 8.687603e-03 0.300754990 #> 12912 6455.5 0.6905621 0.6905621 8.688449e-03 8.688449e-03 0.300749414 #> 12913 6456.0 0.6905668 0.6905668 8.689296e-03 8.689296e-03 0.300743855 #> 12914 6456.5 0.6905715 0.6905715 8.690144e-03 8.690144e-03 0.300738313 #> 12915 6457.0 0.6905762 0.6905762 8.690992e-03 8.690992e-03 0.300732788 #> 12916 6457.5 0.6905809 0.6905809 8.691842e-03 8.691842e-03 0.300727280 #> 12917 6458.0 0.6905855 0.6905855 8.692693e-03 8.692693e-03 0.300721789 #> 12918 6458.5 0.6905901 0.6905901 8.693546e-03 8.693546e-03 0.300716315 #> 12919 6459.0 0.6905947 0.6905947 8.694399e-03 8.694399e-03 0.300710858 #> 12920 6459.5 0.6905993 0.6905993 8.695253e-03 8.695253e-03 0.300705417 #> 12921 6460.0 0.6906039 0.6906039 8.696109e-03 8.696109e-03 0.300699994 #> 12922 6460.5 0.6906084 0.6906084 8.696965e-03 8.696965e-03 0.300694588 #> 12923 6461.0 0.6906130 0.6906130 8.697823e-03 8.697823e-03 0.300689198 #> 12924 6461.5 0.6906175 0.6906175 8.698681e-03 8.698681e-03 0.300683826 #> 12925 6462.0 0.6906220 0.6906220 8.699541e-03 8.699541e-03 0.300678471 #> 12926 6462.5 0.6906265 0.6906265 8.700402e-03 8.700402e-03 0.300673132 #> 12927 6463.0 0.6906309 0.6906309 8.701264e-03 8.701264e-03 0.300667811 #> 12928 6463.5 0.6906354 0.6906354 8.702127e-03 8.702127e-03 0.300662507 #> 12929 6464.0 0.6906398 0.6906398 8.702991e-03 8.702991e-03 0.300657219 #> 12930 6464.5 0.6906442 0.6906442 8.703855e-03 8.703855e-03 0.300651949 #> 12931 6465.0 0.6906486 0.6906486 8.704722e-03 8.704722e-03 0.300646696 #> 12932 6465.5 0.6906530 0.6906530 8.705589e-03 8.705589e-03 0.300641460 #> 12933 6466.0 0.6906573 0.6906573 8.706457e-03 8.706457e-03 0.300636241 #> 12934 6466.5 0.6906616 0.6906616 8.707326e-03 8.707326e-03 0.300631039 #> 12935 6467.0 0.6906660 0.6906660 8.708196e-03 8.708196e-03 0.300625854 #> 12936 6467.5 0.6906702 0.6906702 8.709067e-03 8.709067e-03 0.300620686 #> 12937 6468.0 0.6906745 0.6906745 8.709939e-03 8.709939e-03 0.300615535 #> 12938 6468.5 0.6906788 0.6906788 8.710813e-03 8.710813e-03 0.300610402 #> 12939 6469.0 0.6906830 0.6906830 8.711687e-03 8.711687e-03 0.300605285 #> 12940 6469.5 0.6906873 0.6906873 8.712562e-03 8.712562e-03 0.300600186 #> 12941 6470.0 0.6906915 0.6906915 8.713438e-03 8.713438e-03 0.300595104 #> 12942 6470.5 0.6906956 0.6906956 8.714315e-03 8.714315e-03 0.300590039 #> 12943 6471.0 0.6906998 0.6906998 8.715194e-03 8.715194e-03 0.300584991 #> 12944 6471.5 0.6907040 0.6907040 8.716073e-03 8.716073e-03 0.300579960 #> 12945 6472.0 0.6907081 0.6907081 8.716953e-03 8.716953e-03 0.300574947 #> 12946 6472.5 0.6907122 0.6907122 8.717834e-03 8.717834e-03 0.300569951 #> 12947 6473.0 0.6907163 0.6907163 8.718717e-03 8.718717e-03 0.300564971 #> 12948 6473.5 0.6907204 0.6907204 8.719600e-03 8.719600e-03 0.300560010 #> 12949 6474.0 0.6907245 0.6907245 8.720484e-03 8.720484e-03 0.300555065 #> 12950 6474.5 0.6907285 0.6907285 8.721369e-03 8.721369e-03 0.300550137 #> 12951 6475.0 0.6907325 0.6907325 8.722255e-03 8.722255e-03 0.300545227 #> 12952 6475.5 0.6907365 0.6907365 8.723142e-03 8.723142e-03 0.300540334 #> 12953 6476.0 0.6907405 0.6907405 8.724030e-03 8.724030e-03 0.300535458 #> 12954 6476.5 0.6907445 0.6907445 8.724919e-03 8.724919e-03 0.300530599 #> 12955 6477.0 0.6907484 0.6907484 8.725809e-03 8.725809e-03 0.300525758 #> 12956 6477.5 0.6907524 0.6907524 8.726700e-03 8.726700e-03 0.300520934 #> 12957 6478.0 0.6907563 0.6907563 8.727592e-03 8.727592e-03 0.300516127 #> 12958 6478.5 0.6907602 0.6907602 8.728485e-03 8.728485e-03 0.300511338 #> 12959 6479.0 0.6907641 0.6907641 8.729378e-03 8.729378e-03 0.300506565 #> 12960 6479.5 0.6907679 0.6907679 8.730273e-03 8.730273e-03 0.300501810 #> 12961 6480.0 0.6907718 0.6907718 8.731169e-03 8.731169e-03 0.300497073 #> 12962 6480.5 0.6907756 0.6907756 8.732065e-03 8.732065e-03 0.300492352 #> 12963 6481.0 0.6907794 0.6907794 8.732963e-03 8.732963e-03 0.300487649 #> 12964 6481.5 0.6907832 0.6907832 8.733861e-03 8.733861e-03 0.300482963 #> 12965 6482.0 0.6907869 0.6907869 8.734760e-03 8.734760e-03 0.300478295 #> 12966 6482.5 0.6907907 0.6907907 8.735661e-03 8.735661e-03 0.300473644 #> 12967 6483.0 0.6907944 0.6907944 8.736562e-03 8.736562e-03 0.300469010 #> 12968 6483.5 0.6907981 0.6907981 8.737464e-03 8.737464e-03 0.300464393 #> 12969 6484.0 0.6908018 0.6908018 8.738367e-03 8.738367e-03 0.300459794 #> 12970 6484.5 0.6908055 0.6908055 8.739270e-03 8.739270e-03 0.300455212 #> 12971 6485.0 0.6908092 0.6908092 8.740175e-03 8.740175e-03 0.300450648 #> 12972 6485.5 0.6908128 0.6908128 8.741081e-03 8.741081e-03 0.300446101 #> 12973 6486.0 0.6908164 0.6908164 8.741987e-03 8.741987e-03 0.300441571 #> 12974 6486.5 0.6908200 0.6908200 8.742895e-03 8.742895e-03 0.300437059 #> 12975 6487.0 0.6908236 0.6908236 8.743803e-03 8.743803e-03 0.300432564 #> 12976 6487.5 0.6908272 0.6908272 8.744712e-03 8.744712e-03 0.300428087 #> 12977 6488.0 0.6908308 0.6908308 8.745622e-03 8.745622e-03 0.300423626 #> 12978 6488.5 0.6908343 0.6908343 8.746533e-03 8.746533e-03 0.300419184 #> 12979 6489.0 0.6908378 0.6908378 8.747445e-03 8.747445e-03 0.300414758 #> 12980 6489.5 0.6908413 0.6908413 8.748357e-03 8.748357e-03 0.300410350 #> 12981 6490.0 0.6908448 0.6908448 8.749271e-03 8.749271e-03 0.300405960 #> 12982 6490.5 0.6908482 0.6908482 8.750185e-03 8.750185e-03 0.300401587 #> 12983 6491.0 0.6908517 0.6908517 8.751100e-03 8.751100e-03 0.300397231 #> 12984 6491.5 0.6908551 0.6908551 8.752016e-03 8.752016e-03 0.300392893 #> 12985 6492.0 0.6908585 0.6908585 8.752933e-03 8.752933e-03 0.300388572 #> 12986 6492.5 0.6908619 0.6908619 8.753851e-03 8.753851e-03 0.300384269 #> 12987 6493.0 0.6908652 0.6908652 8.754769e-03 8.754769e-03 0.300379983 #> 12988 6493.5 0.6908686 0.6908686 8.755688e-03 8.755688e-03 0.300375714 #> 12989 6494.0 0.6908719 0.6908719 8.756609e-03 8.756609e-03 0.300371463 #> 12990 6494.5 0.6908752 0.6908752 8.757530e-03 8.757530e-03 0.300367230 #> 12991 6495.0 0.6908785 0.6908785 8.758451e-03 8.758451e-03 0.300363014 #> 12992 6495.5 0.6908818 0.6908818 8.759374e-03 8.759374e-03 0.300358815 #> 12993 6496.0 0.6908851 0.6908851 8.760298e-03 8.760298e-03 0.300354634 #> 12994 6496.5 0.6908883 0.6908883 8.761222e-03 8.761222e-03 0.300350471 #> 12995 6497.0 0.6908915 0.6908915 8.762147e-03 8.762147e-03 0.300346324 #> 12996 6497.5 0.6908947 0.6908947 8.763073e-03 8.763073e-03 0.300342196 #> 12997 6498.0 0.6908979 0.6908979 8.763999e-03 8.763999e-03 0.300338085 #> 12998 6498.5 0.6909011 0.6909011 8.764927e-03 8.764927e-03 0.300333991 #> 12999 6499.0 0.6909042 0.6909042 8.765855e-03 8.765855e-03 0.300329915 #> 13000 6499.5 0.6909074 0.6909074 8.766784e-03 8.766784e-03 0.300325856 #> 13001 6500.0 0.6909105 0.6909105 8.767714e-03 8.767714e-03 0.300321815 #> 13002 6500.5 0.6909136 0.6909136 8.768645e-03 8.768645e-03 0.300317792 #> 13003 6501.0 0.6909166 0.6909166 8.769576e-03 8.769576e-03 0.300313786 #> 13004 6501.5 0.6909197 0.6909197 8.770508e-03 8.770508e-03 0.300309797 #> 13005 6502.0 0.6909227 0.6909227 8.771441e-03 8.771441e-03 0.300305826 #> 13006 6502.5 0.6909258 0.6909258 8.772375e-03 8.772375e-03 0.300301873 #> 13007 6503.0 0.6909288 0.6909288 8.773309e-03 8.773309e-03 0.300297937 #> 13008 6503.5 0.6909317 0.6909317 8.774244e-03 8.774244e-03 0.300294018 #> 13009 6504.0 0.6909347 0.6909347 8.775180e-03 8.775180e-03 0.300290117 #> 13010 6504.5 0.6909376 0.6909376 8.776117e-03 8.776117e-03 0.300286234 #> 13011 6505.0 0.6909406 0.6909406 8.777054e-03 8.777054e-03 0.300282368 #> 13012 6505.5 0.6909435 0.6909435 8.777993e-03 8.777993e-03 0.300278520 #> 13013 6506.0 0.6909464 0.6909464 8.778932e-03 8.778932e-03 0.300274690 #> 13014 6506.5 0.6909493 0.6909493 8.779871e-03 8.779871e-03 0.300270877 #> 13015 6507.0 0.6909521 0.6909521 8.780812e-03 8.780812e-03 0.300267081 #> 13016 6507.5 0.6909549 0.6909549 8.781753e-03 8.781753e-03 0.300263303 #> 13017 6508.0 0.6909578 0.6909578 8.782695e-03 8.782695e-03 0.300259543 #> 13018 6508.5 0.6909606 0.6909606 8.783637e-03 8.783637e-03 0.300255800 #> 13019 6509.0 0.6909633 0.6909633 8.784580e-03 8.784580e-03 0.300252075 #> 13020 6509.5 0.6909661 0.6909661 8.785524e-03 8.785524e-03 0.300248367 #> 13021 6510.0 0.6909689 0.6909689 8.786469e-03 8.786469e-03 0.300244677 #> 13022 6510.5 0.6909716 0.6909716 8.787414e-03 8.787414e-03 0.300241005 #> 13023 6511.0 0.6909743 0.6909743 8.788360e-03 8.788360e-03 0.300237350 #> 13024 6511.5 0.6909770 0.6909770 8.789307e-03 8.789307e-03 0.300233713 #> 13025 6512.0 0.6909797 0.6909797 8.790255e-03 8.790255e-03 0.300230093 #> 13026 6512.5 0.6909823 0.6909823 8.791203e-03 8.791203e-03 0.300226491 #> 13027 6513.0 0.6909849 0.6909849 8.792152e-03 8.792152e-03 0.300222907 #> 13028 6513.5 0.6909876 0.6909876 8.793101e-03 8.793101e-03 0.300219340 #> 13029 6514.0 0.6909902 0.6909902 8.794051e-03 8.794051e-03 0.300215791 #> 13030 6514.5 0.6909927 0.6909927 8.795002e-03 8.795002e-03 0.300212260 #> 13031 6515.0 0.6909953 0.6909953 8.795954e-03 8.795954e-03 0.300208746 #> 13032 6515.5 0.6909978 0.6909978 8.796906e-03 8.796906e-03 0.300205249 #> 13033 6516.0 0.6910004 0.6910004 8.797859e-03 8.797859e-03 0.300201771 #> 13034 6516.5 0.6910029 0.6910029 8.798812e-03 8.798812e-03 0.300198309 #> 13035 6517.0 0.6910054 0.6910054 8.799766e-03 8.799766e-03 0.300194866 #> 13036 6517.5 0.6910078 0.6910078 8.800721e-03 8.800721e-03 0.300191440 #> 13037 6518.0 0.6910103 0.6910103 8.801677e-03 8.801677e-03 0.300188032 #> 13038 6518.5 0.6910127 0.6910127 8.802633e-03 8.802633e-03 0.300184641 #> 13039 6519.0 0.6910151 0.6910151 8.803590e-03 8.803590e-03 0.300181268 #> 13040 6519.5 0.6910175 0.6910175 8.804547e-03 8.804547e-03 0.300177913 #> 13041 6520.0 0.6910199 0.6910199 8.805505e-03 8.805505e-03 0.300174575 #> 13042 6520.5 0.6910223 0.6910223 8.806464e-03 8.806464e-03 0.300171255 #> 13043 6521.0 0.6910246 0.6910246 8.807423e-03 8.807423e-03 0.300167953 #> 13044 6521.5 0.6910269 0.6910269 8.808383e-03 8.808383e-03 0.300164668 #> 13045 6522.0 0.6910293 0.6910293 8.809343e-03 8.809343e-03 0.300161401 #> 13046 6522.5 0.6910315 0.6910315 8.810304e-03 8.810304e-03 0.300158151 #> 13047 6523.0 0.6910338 0.6910338 8.811266e-03 8.811266e-03 0.300154920 #> 13048 6523.5 0.6910361 0.6910361 8.812228e-03 8.812228e-03 0.300151705 #> 13049 6524.0 0.6910383 0.6910383 8.813191e-03 8.813191e-03 0.300148509 #> 13050 6524.5 0.6910405 0.6910405 8.814155e-03 8.814155e-03 0.300145330 #> 13051 6525.0 0.6910427 0.6910427 8.815119e-03 8.815119e-03 0.300142169 #> 13052 6525.5 0.6910449 0.6910449 8.816083e-03 8.816083e-03 0.300139025 #> 13053 6526.0 0.6910471 0.6910471 8.817049e-03 8.817049e-03 0.300135899 #> 13054 6526.5 0.6910492 0.6910492 8.818015e-03 8.818015e-03 0.300132791 #> 13055 6527.0 0.6910513 0.6910513 8.818981e-03 8.818981e-03 0.300129700 #> 13056 6527.5 0.6910534 0.6910534 8.819948e-03 8.819948e-03 0.300126627 #> 13057 6528.0 0.6910555 0.6910555 8.820915e-03 8.820915e-03 0.300123572 #> 13058 6528.5 0.6910576 0.6910576 8.821884e-03 8.821884e-03 0.300120534 #> 13059 6529.0 0.6910596 0.6910596 8.822852e-03 8.822852e-03 0.300117514 #> 13060 6529.5 0.6910617 0.6910617 8.823822e-03 8.823822e-03 0.300114512 #> 13061 6530.0 0.6910637 0.6910637 8.824791e-03 8.824791e-03 0.300111527 #> 13062 6530.5 0.6910657 0.6910657 8.825762e-03 8.825762e-03 0.300108560 #> 13063 6531.0 0.6910677 0.6910677 8.826733e-03 8.826733e-03 0.300105610 #> 13064 6531.5 0.6910696 0.6910696 8.827704e-03 8.827704e-03 0.300102679 #> 13065 6532.0 0.6910716 0.6910716 8.828676e-03 8.828676e-03 0.300099765 #> 13066 6532.5 0.6910735 0.6910735 8.829648e-03 8.829648e-03 0.300096868 #> 13067 6533.0 0.6910754 0.6910754 8.830621e-03 8.830621e-03 0.300093989 #> 13068 6533.5 0.6910773 0.6910773 8.831595e-03 8.831595e-03 0.300091128 #> 13069 6534.0 0.6910791 0.6910791 8.832569e-03 8.832569e-03 0.300088285 #> 13070 6534.5 0.6910810 0.6910810 8.833544e-03 8.833544e-03 0.300085459 #> 13071 6535.0 0.6910828 0.6910828 8.834519e-03 8.834519e-03 0.300082651 #> 13072 6535.5 0.6910846 0.6910846 8.835494e-03 8.835494e-03 0.300079860 #> 13073 6536.0 0.6910864 0.6910864 8.836471e-03 8.836471e-03 0.300077087 #> 13074 6536.5 0.6910882 0.6910882 8.837447e-03 8.837447e-03 0.300074332 #> 13075 6537.0 0.6910900 0.6910900 8.838424e-03 8.838424e-03 0.300071594 #> 13076 6537.5 0.6910917 0.6910917 8.839402e-03 8.839402e-03 0.300068875 #> 13077 6538.0 0.6910934 0.6910934 8.840380e-03 8.840380e-03 0.300066172 #> 13078 6538.5 0.6910952 0.6910952 8.841359e-03 8.841359e-03 0.300063488 #> 13079 6539.0 0.6910968 0.6910968 8.842338e-03 8.842338e-03 0.300060821 #> 13080 6539.5 0.6910985 0.6910985 8.843317e-03 8.843317e-03 0.300058172 #> 13081 6540.0 0.6911002 0.6911002 8.844297e-03 8.844297e-03 0.300055540 #> 13082 6540.5 0.6911018 0.6911018 8.845278e-03 8.845278e-03 0.300052926 #> 13083 6541.0 0.6911034 0.6911034 8.846259e-03 8.846259e-03 0.300050330 #> 13084 6541.5 0.6911050 0.6911050 8.847240e-03 8.847240e-03 0.300047751 #> 13085 6542.0 0.6911066 0.6911066 8.848222e-03 8.848222e-03 0.300045190 #> 13086 6542.5 0.6911081 0.6911081 8.849205e-03 8.849205e-03 0.300042646 #> 13087 6543.0 0.6911097 0.6911097 8.850187e-03 8.850187e-03 0.300040121 #> 13088 6543.5 0.6911112 0.6911112 8.851171e-03 8.851171e-03 0.300037613 #> 13089 6544.0 0.6911127 0.6911127 8.852154e-03 8.852154e-03 0.300035122 #> 13090 6544.5 0.6911142 0.6911142 8.853139e-03 8.853139e-03 0.300032649 #> 13091 6545.0 0.6911157 0.6911157 8.854123e-03 8.854123e-03 0.300030194 #> 13092 6545.5 0.6911171 0.6911171 8.855108e-03 8.855108e-03 0.300027756 #> 13093 6546.0 0.6911186 0.6911186 8.856094e-03 8.856094e-03 0.300025337 #> 13094 6546.5 0.6911200 0.6911200 8.857079e-03 8.857079e-03 0.300022934 #> 13095 6547.0 0.6911214 0.6911214 8.858066e-03 8.858066e-03 0.300020550 #> 13096 6547.5 0.6911228 0.6911228 8.859052e-03 8.859052e-03 0.300018183 #> 13097 6548.0 0.6911241 0.6911241 8.860040e-03 8.860040e-03 0.300015833 #> 13098 6548.5 0.6911255 0.6911255 8.861027e-03 8.861027e-03 0.300013501 #> 13099 6549.0 0.6911268 0.6911268 8.862015e-03 8.862015e-03 0.300011187 #> 13100 6549.5 0.6911281 0.6911281 8.863003e-03 8.863003e-03 0.300008891 #> 13101 6550.0 0.6911294 0.6911294 8.863992e-03 8.863992e-03 0.300006612 #> 13102 6550.5 0.6911307 0.6911307 8.864981e-03 8.864981e-03 0.300004350 #> 13103 6551.0 0.6911319 0.6911319 8.865971e-03 8.865971e-03 0.300002107 #> 13104 6551.5 0.6911332 0.6911332 8.866961e-03 8.866961e-03 0.299999881 #> 13105 6552.0 0.6911344 0.6911344 8.867951e-03 8.867951e-03 0.299997672 #> 13106 6552.5 0.6911356 0.6911356 8.868942e-03 8.868942e-03 0.299995481 #> 13107 6553.0 0.6911368 0.6911368 8.869933e-03 8.869933e-03 0.299993308 #> 13108 6553.5 0.6911379 0.6911379 8.870924e-03 8.870924e-03 0.299991153 #> 13109 6554.0 0.6911391 0.6911391 8.871916e-03 8.871916e-03 0.299989015 #> 13110 6554.5 0.6911402 0.6911402 8.872908e-03 8.872908e-03 0.299986894 #> 13111 6555.0 0.6911413 0.6911413 8.873901e-03 8.873901e-03 0.299984791 #> 13112 6555.5 0.6911424 0.6911424 8.874893e-03 8.874893e-03 0.299982706 #> 13113 6556.0 0.6911435 0.6911435 8.875887e-03 8.875887e-03 0.299980638 #> 13114 6556.5 0.6911445 0.6911445 8.876880e-03 8.876880e-03 0.299978588 #> 13115 6557.0 0.6911456 0.6911456 8.877874e-03 8.877874e-03 0.299976556 #> 13116 6557.5 0.6911466 0.6911466 8.878868e-03 8.878868e-03 0.299974541 #> 13117 6558.0 0.6911476 0.6911476 8.879863e-03 8.879863e-03 0.299972543 #> 13118 6558.5 0.6911486 0.6911486 8.880858e-03 8.880858e-03 0.299970564 #> 13119 6559.0 0.6911495 0.6911495 8.881853e-03 8.881853e-03 0.299968601 #> 13120 6559.5 0.6911505 0.6911505 8.882849e-03 8.882849e-03 0.299966657 #> 13121 6560.0 0.6911514 0.6911514 8.883845e-03 8.883845e-03 0.299964730 #> 13122 6560.5 0.6911523 0.6911523 8.884841e-03 8.884841e-03 0.299962820 #> 13123 6561.0 0.6911532 0.6911532 8.885837e-03 8.885837e-03 0.299960928 #> 13124 6561.5 0.6911541 0.6911541 8.886834e-03 8.886834e-03 0.299959054 #> 13125 6562.0 0.6911550 0.6911550 8.887831e-03 8.887831e-03 0.299957197 #> 13126 6562.5 0.6911558 0.6911558 8.888829e-03 8.888829e-03 0.299955358 #> 13127 6563.0 0.6911566 0.6911566 8.889826e-03 8.889826e-03 0.299953536 #> 13128 6563.5 0.6911574 0.6911574 8.890824e-03 8.890824e-03 0.299951732 #> 13129 6564.0 0.6911582 0.6911582 8.891823e-03 8.891823e-03 0.299949945 #> 13130 6564.5 0.6911590 0.6911590 8.892821e-03 8.892821e-03 0.299948176 #> 13131 6565.0 0.6911598 0.6911598 8.893820e-03 8.893820e-03 0.299946424 #> 13132 6565.5 0.6911605 0.6911605 8.894819e-03 8.894819e-03 0.299944690 #> 13133 6566.0 0.6911612 0.6911612 8.895819e-03 8.895819e-03 0.299942974 #> 13134 6566.5 0.6911619 0.6911619 8.896819e-03 8.896819e-03 0.299941274 #> 13135 6567.0 0.6911626 0.6911626 8.897819e-03 8.897819e-03 0.299939593 #> 13136 6567.5 0.6911633 0.6911633 8.898819e-03 8.898819e-03 0.299937929 #> 13137 6568.0 0.6911639 0.6911639 8.899819e-03 8.899819e-03 0.299936282 #> 13138 6568.5 0.6911645 0.6911645 8.900820e-03 8.900820e-03 0.299934653 #> 13139 6569.0 0.6911651 0.6911651 8.901821e-03 8.901821e-03 0.299933042 #> 13140 6569.5 0.6911657 0.6911657 8.902822e-03 8.902822e-03 0.299931448 #> 13141 6570.0 0.6911663 0.6911663 8.903824e-03 8.903824e-03 0.299929871 #> 13142 6570.5 0.6911669 0.6911669 8.904825e-03 8.904825e-03 0.299928312 #> 13143 6571.0 0.6911674 0.6911674 8.905827e-03 8.905827e-03 0.299926770 #> 13144 6571.5 0.6911679 0.6911679 8.906830e-03 8.906830e-03 0.299925246 #> 13145 6572.0 0.6911684 0.6911684 8.907832e-03 8.907832e-03 0.299923739 #> 13146 6572.5 0.6911689 0.6911689 8.908835e-03 8.908835e-03 0.299922250 #> 13147 6573.0 0.6911694 0.6911694 8.909837e-03 8.909837e-03 0.299920778 #> 13148 6573.5 0.6911698 0.6911698 8.910840e-03 8.910840e-03 0.299919324 #> 13149 6574.0 0.6911703 0.6911703 8.911844e-03 8.911844e-03 0.299917887 #> 13150 6574.5 0.6911707 0.6911707 8.912847e-03 8.912847e-03 0.299916467 #> 13151 6575.0 0.6911711 0.6911711 8.913851e-03 8.913851e-03 0.299915065 #> 13152 6575.5 0.6911715 0.6911715 8.914855e-03 8.914855e-03 0.299913681 #> 13153 6576.0 0.6911718 0.6911718 8.915859e-03 8.915859e-03 0.299912313 #> 13154 6576.5 0.6911722 0.6911722 8.916863e-03 8.916863e-03 0.299910964 #> 13155 6577.0 0.6911725 0.6911725 8.917868e-03 8.917868e-03 0.299909631 #> 13156 6577.5 0.6911728 0.6911728 8.918872e-03 8.918872e-03 0.299908316 #> 13157 6578.0 0.6911731 0.6911731 8.919877e-03 8.919877e-03 0.299907019 #> 13158 6578.5 0.6911734 0.6911734 8.920882e-03 8.920882e-03 0.299905738 #> 13159 6579.0 0.6911736 0.6911736 8.921887e-03 8.921887e-03 0.299904476 #> 13160 6579.5 0.6911739 0.6911739 8.922893e-03 8.922893e-03 0.299903230 #> 13161 6580.0 0.6911741 0.6911741 8.923898e-03 8.923898e-03 0.299902002 #> 13162 6580.5 0.6911743 0.6911743 8.924904e-03 8.924904e-03 0.299900791 #> 13163 6581.0 0.6911745 0.6911745 8.925910e-03 8.925910e-03 0.299899598 #> 13164 6581.5 0.6911747 0.6911747 8.926916e-03 8.926916e-03 0.299898422 #> 13165 6582.0 0.6911748 0.6911748 8.927922e-03 8.927922e-03 0.299897264 #> 13166 6582.5 0.6911749 0.6911749 8.928928e-03 8.928928e-03 0.299896122 #> 13167 6583.0 0.6911751 0.6911751 8.929935e-03 8.929935e-03 0.299894998 #> 13168 6583.5 0.6911752 0.6911752 8.930941e-03 8.930941e-03 0.299893892 #> 13169 6584.0 0.6911752 0.6911752 8.931948e-03 8.931948e-03 0.299892803 #> 13170 6584.5 0.6911753 0.6911753 8.932955e-03 8.932955e-03 0.299891731 #> 13171 6585.0 0.6911754 0.6911754 8.933962e-03 8.933962e-03 0.299890676 #> 13172 6585.5 0.6911754 0.6911754 8.934969e-03 8.934969e-03 0.299889639 #> 13173 6586.0 0.6911754 0.6911754 8.935976e-03 8.935976e-03 0.299888619 #> 13174 6586.5 0.6911754 0.6911754 8.936983e-03 8.936983e-03 0.299887616 #> 13175 6587.0 0.6911754 0.6911754 8.937991e-03 8.937991e-03 0.299886630 #> 13176 6587.5 0.6911753 0.6911753 8.938998e-03 8.938998e-03 0.299885662 #> 13177 6588.0 0.6911753 0.6911753 8.940006e-03 8.940006e-03 0.299884711 #> 13178 6588.5 0.6911752 0.6911752 8.941013e-03 8.941013e-03 0.299883778 #> 13179 6589.0 0.6911751 0.6911751 8.942021e-03 8.942021e-03 0.299882861 #> 13180 6589.5 0.6911750 0.6911750 8.943029e-03 8.943029e-03 0.299881962 #> 13181 6590.0 0.6911749 0.6911749 8.944037e-03 8.944037e-03 0.299881080 #> 13182 6590.5 0.6911747 0.6911747 8.945045e-03 8.945045e-03 0.299880216 #> 13183 6591.0 0.6911746 0.6911746 8.946053e-03 8.946053e-03 0.299879368 #> 13184 6591.5 0.6911744 0.6911744 8.947062e-03 8.947062e-03 0.299878538 #> 13185 6592.0 0.6911742 0.6911742 8.948070e-03 8.948070e-03 0.299877725 #> 13186 6592.5 0.6911740 0.6911740 8.949078e-03 8.949078e-03 0.299876929 #> 13187 6593.0 0.6911738 0.6911738 8.950087e-03 8.950087e-03 0.299876151 #> 13188 6593.5 0.6911735 0.6911735 8.951095e-03 8.951095e-03 0.299875389 #> 13189 6594.0 0.6911733 0.6911733 8.952104e-03 8.952104e-03 0.299874645 #> 13190 6594.5 0.6911730 0.6911730 8.953112e-03 8.953112e-03 0.299873918 #> 13191 6595.0 0.6911727 0.6911727 8.954121e-03 8.954121e-03 0.299873208 #> 13192 6595.5 0.6911724 0.6911724 8.955130e-03 8.955130e-03 0.299872516 #> 13193 6596.0 0.6911720 0.6911720 8.956139e-03 8.956139e-03 0.299871840 #> 13194 6596.5 0.6911717 0.6911717 8.957147e-03 8.957147e-03 0.299871182 #> 13195 6597.0 0.6911713 0.6911713 8.958156e-03 8.958156e-03 0.299870541 #> 13196 6597.5 0.6911709 0.6911709 8.959165e-03 8.959165e-03 0.299869917 #> 13197 6598.0 0.6911705 0.6911705 8.960174e-03 8.960174e-03 0.299869310 #> 13198 6598.5 0.6911701 0.6911701 8.961183e-03 8.961183e-03 0.299868720 #> 13199 6599.0 0.6911697 0.6911697 8.962192e-03 8.962192e-03 0.299868147 #> 13200 6599.5 0.6911692 0.6911692 8.963201e-03 8.963201e-03 0.299867591 #> 13201 6600.0 0.6911687 0.6911687 8.964210e-03 8.964210e-03 0.299867053 #> 13202 6600.5 0.6911683 0.6911683 8.965218e-03 8.965218e-03 0.299866531 #> 13203 6601.0 0.6911677 0.6911677 8.966227e-03 8.966227e-03 0.299866027 #> 13204 6601.5 0.6911672 0.6911672 8.967236e-03 8.967236e-03 0.299865539 #> 13205 6602.0 0.6911667 0.6911667 8.968245e-03 8.968245e-03 0.299865069 #> 13206 6602.5 0.6911661 0.6911661 8.969254e-03 8.969254e-03 0.299864616 #> 13207 6603.0 0.6911656 0.6911656 8.970263e-03 8.970263e-03 0.299864180 #> 13208 6603.5 0.6911650 0.6911650 8.971272e-03 8.971272e-03 0.299863760 #> 13209 6604.0 0.6911644 0.6911644 8.972281e-03 8.972281e-03 0.299863358 #> 13210 6604.5 0.6911637 0.6911637 8.973290e-03 8.973290e-03 0.299862973 #> 13211 6605.0 0.6911631 0.6911631 8.974299e-03 8.974299e-03 0.299862605 #> 13212 6605.5 0.6911624 0.6911624 8.975307e-03 8.975307e-03 0.299862254 #> 13213 6606.0 0.6911618 0.6911618 8.976316e-03 8.976316e-03 0.299861920 #> 13214 6606.5 0.6911611 0.6911611 8.977325e-03 8.977325e-03 0.299861602 #> 13215 6607.0 0.6911604 0.6911604 8.978334e-03 8.978334e-03 0.299861302 #> 13216 6607.5 0.6911596 0.6911596 8.979342e-03 8.979342e-03 0.299861019 #> 13217 6608.0 0.6911589 0.6911589 8.980351e-03 8.980351e-03 0.299860753 #> 13218 6608.5 0.6911581 0.6911581 8.981359e-03 8.981359e-03 0.299860503 #> 13219 6609.0 0.6911574 0.6911574 8.982368e-03 8.982368e-03 0.299860271 #> 13220 6609.5 0.6911566 0.6911566 8.983376e-03 8.983376e-03 0.299860055 #> 13221 6610.0 0.6911558 0.6911558 8.984384e-03 8.984384e-03 0.299859856 #> 13222 6610.5 0.6911549 0.6911549 8.985393e-03 8.985393e-03 0.299859675 #> 13223 6611.0 0.6911541 0.6911541 8.986401e-03 8.986401e-03 0.299859510 #> 13224 6611.5 0.6911532 0.6911532 8.987409e-03 8.987409e-03 0.299859362 #> 13225 6612.0 0.6911524 0.6911524 8.988417e-03 8.988417e-03 0.299859231 #> 13226 6612.5 0.6911515 0.6911515 8.989425e-03 8.989425e-03 0.299859117 #> 13227 6613.0 0.6911505 0.6911505 8.990433e-03 8.990433e-03 0.299859019 #> 13228 6613.5 0.6911496 0.6911496 8.991441e-03 8.991441e-03 0.299858939 #> 13229 6614.0 0.6911487 0.6911487 8.992448e-03 8.992448e-03 0.299858875 #> 13230 6614.5 0.6911477 0.6911477 8.993456e-03 8.993456e-03 0.299858828 #> 13231 6615.0 0.6911467 0.6911467 8.994463e-03 8.994463e-03 0.299858798 #> 13232 6615.5 0.6911457 0.6911457 8.995471e-03 8.995471e-03 0.299858785 #> 13233 6616.0 0.6911447 0.6911447 8.996478e-03 8.996478e-03 0.299858788 #> 13234 6616.5 0.6911437 0.6911437 8.997485e-03 8.997485e-03 0.299858809 #> 13235 6617.0 0.6911427 0.6911427 8.998492e-03 8.998492e-03 0.299858846 #> 13236 6617.5 0.6911416 0.6911416 8.999499e-03 8.999499e-03 0.299858899 #> 13237 6618.0 0.6911405 0.6911405 9.000505e-03 9.000505e-03 0.299858970 #> 13238 6618.5 0.6911394 0.6911394 9.001512e-03 9.001512e-03 0.299859057 #> 13239 6619.0 0.6911383 0.6911383 9.002519e-03 9.002519e-03 0.299859161 #> 13240 6619.5 0.6911372 0.6911372 9.003525e-03 9.003525e-03 0.299859282 #> 13241 6620.0 0.6911360 0.6911360 9.004531e-03 9.004531e-03 0.299859420 #> 13242 6620.5 0.6911349 0.6911349 9.005537e-03 9.005537e-03 0.299859574 #> 13243 6621.0 0.6911337 0.6911337 9.006543e-03 9.006543e-03 0.299859744 #> 13244 6621.5 0.6911325 0.6911325 9.007549e-03 9.007549e-03 0.299859932 #> 13245 6622.0 0.6911313 0.6911313 9.008554e-03 9.008554e-03 0.299860136 #> 13246 6622.5 0.6911301 0.6911301 9.009560e-03 9.009560e-03 0.299860357 #> 13247 6623.0 0.6911288 0.6911288 9.010565e-03 9.010565e-03 0.299860594 #> 13248 6623.5 0.6911276 0.6911276 9.011570e-03 9.011570e-03 0.299860849 #> 13249 6624.0 0.6911263 0.6911263 9.012575e-03 9.012575e-03 0.299861119 #> 13250 6624.5 0.6911250 0.6911250 9.013580e-03 9.013580e-03 0.299861407 #> 13251 6625.0 0.6911237 0.6911237 9.014584e-03 9.014584e-03 0.299861711 #> 13252 6625.5 0.6911224 0.6911224 9.015589e-03 9.015589e-03 0.299862031 #> 13253 6626.0 0.6911210 0.6911210 9.016593e-03 9.016593e-03 0.299862368 #> 13254 6626.5 0.6911197 0.6911197 9.017597e-03 9.017597e-03 0.299862722 #> 13255 6627.0 0.6911183 0.6911183 9.018601e-03 9.018601e-03 0.299863092 #> 13256 6627.5 0.6911169 0.6911169 9.019604e-03 9.019604e-03 0.299863479 #> 13257 6628.0 0.6911155 0.6911155 9.020608e-03 9.020608e-03 0.299863882 #> 13258 6628.5 0.6911141 0.6911141 9.021611e-03 9.021611e-03 0.299864302 #> 13259 6629.0 0.6911126 0.6911126 9.022614e-03 9.022614e-03 0.299864738 #> 13260 6629.5 0.6911112 0.6911112 9.023616e-03 9.023616e-03 0.299865191 #> 13261 6630.0 0.6911097 0.6911097 9.024619e-03 9.024619e-03 0.299865660 #> 13262 6630.5 0.6911082 0.6911082 9.025621e-03 9.025621e-03 0.299866146 #> 13263 6631.0 0.6911067 0.6911067 9.026623e-03 9.026623e-03 0.299866648 #> 13264 6631.5 0.6911052 0.6911052 9.027625e-03 9.027625e-03 0.299867167 #> 13265 6632.0 0.6911037 0.6911037 9.028627e-03 9.028627e-03 0.299867702 #> 13266 6632.5 0.6911021 0.6911021 9.029628e-03 9.029628e-03 0.299868253 #> 13267 6633.0 0.6911005 0.6911005 9.030629e-03 9.030629e-03 0.299868821 #> 13268 6633.5 0.6910990 0.6910990 9.031630e-03 9.031630e-03 0.299869405 #> 13269 6634.0 0.6910974 0.6910974 9.032631e-03 9.032631e-03 0.299870006 #> 13270 6634.5 0.6910957 0.6910957 9.033631e-03 9.033631e-03 0.299870623 #> 13271 6635.0 0.6910941 0.6910941 9.034631e-03 9.034631e-03 0.299871256 #> 13272 6635.5 0.6910925 0.6910925 9.035631e-03 9.035631e-03 0.299871906 #> 13273 6636.0 0.6910908 0.6910908 9.036631e-03 9.036631e-03 0.299872572 #> 13274 6636.5 0.6910891 0.6910891 9.037630e-03 9.037630e-03 0.299873254 #> 13275 6637.0 0.6910874 0.6910874 9.038629e-03 9.038629e-03 0.299873953 #> 13276 6637.5 0.6910857 0.6910857 9.039628e-03 9.039628e-03 0.299874668 #> 13277 6638.0 0.6910840 0.6910840 9.040627e-03 9.040627e-03 0.299875399 #> 13278 6638.5 0.6910822 0.6910822 9.041625e-03 9.041625e-03 0.299876147 #> 13279 6639.0 0.6910805 0.6910805 9.042623e-03 9.042623e-03 0.299876911 #> 13280 6639.5 0.6910787 0.6910787 9.043621e-03 9.043621e-03 0.299877691 #> 13281 6640.0 0.6910769 0.6910769 9.044618e-03 9.044618e-03 0.299878487 #> 13282 6640.5 0.6910751 0.6910751 9.045615e-03 9.045615e-03 0.299879300 #> 13283 6641.0 0.6910733 0.6910733 9.046612e-03 9.046612e-03 0.299880128 #> 13284 6641.5 0.6910714 0.6910714 9.047608e-03 9.047608e-03 0.299880973 #> 13285 6642.0 0.6910696 0.6910696 9.048604e-03 9.048604e-03 0.299881834 #> 13286 6642.5 0.6910677 0.6910677 9.049600e-03 9.049600e-03 0.299882712 #> 13287 6643.0 0.6910658 0.6910658 9.050596e-03 9.050596e-03 0.299883605 #> 13288 6643.5 0.6910639 0.6910639 9.051591e-03 9.051591e-03 0.299884514 #> 13289 6644.0 0.6910620 0.6910620 9.052586e-03 9.052586e-03 0.299885440 #> 13290 6644.5 0.6910600 0.6910600 9.053580e-03 9.053580e-03 0.299886382 #> 13291 6645.0 0.6910581 0.6910581 9.054575e-03 9.054575e-03 0.299887340 #> 13292 6645.5 0.6910561 0.6910561 9.055569e-03 9.055569e-03 0.299888314 #> 13293 6646.0 0.6910541 0.6910541 9.056562e-03 9.056562e-03 0.299889304 #> 13294 6646.5 0.6910521 0.6910521 9.057555e-03 9.057555e-03 0.299890310 #> 13295 6647.0 0.6910501 0.6910501 9.058548e-03 9.058548e-03 0.299891332 #> 13296 6647.5 0.6910481 0.6910481 9.059541e-03 9.059541e-03 0.299892370 #> 13297 6648.0 0.6910460 0.6910460 9.060533e-03 9.060533e-03 0.299893424 #> 13298 6648.5 0.6910440 0.6910440 9.061525e-03 9.061525e-03 0.299894494 #> 13299 6649.0 0.6910419 0.6910419 9.062516e-03 9.062516e-03 0.299895581 #> 13300 6649.5 0.6910398 0.6910398 9.063507e-03 9.063507e-03 0.299896683 #> 13301 6650.0 0.6910377 0.6910377 9.064498e-03 9.064498e-03 0.299897801 #> 13302 6650.5 0.6910356 0.6910356 9.065488e-03 9.065488e-03 0.299898935 #> 13303 6651.0 0.6910334 0.6910334 9.066478e-03 9.066478e-03 0.299900085 #> 13304 6651.5 0.6910313 0.6910313 9.067468e-03 9.067468e-03 0.299901251 #> 13305 6652.0 0.6910291 0.6910291 9.068457e-03 9.068457e-03 0.299902432 #> 13306 6652.5 0.6910269 0.6910269 9.069446e-03 9.069446e-03 0.299903630 #> 13307 6653.0 0.6910247 0.6910247 9.070434e-03 9.070434e-03 0.299904844 #> 13308 6653.5 0.6910225 0.6910225 9.071422e-03 9.071422e-03 0.299906073 #> 13309 6654.0 0.6910203 0.6910203 9.072410e-03 9.072410e-03 0.299907318 #> 13310 6654.5 0.6910180 0.6910180 9.073397e-03 9.073397e-03 0.299908579 #> 13311 6655.0 0.6910158 0.6910158 9.074384e-03 9.074384e-03 0.299909856 #> 13312 6655.5 0.6910135 0.6910135 9.075370e-03 9.075370e-03 0.299911149 #> 13313 6656.0 0.6910112 0.6910112 9.076356e-03 9.076356e-03 0.299912457 #> 13314 6656.5 0.6910089 0.6910089 9.077342e-03 9.077342e-03 0.299913782 #> 13315 6657.0 0.6910066 0.6910066 9.078327e-03 9.078327e-03 0.299915122 #> 13316 6657.5 0.6910042 0.6910042 9.079312e-03 9.079312e-03 0.299916478 #> 13317 6658.0 0.6910019 0.6910019 9.080296e-03 9.080296e-03 0.299917849 #> 13318 6658.5 0.6909995 0.6909995 9.081280e-03 9.081280e-03 0.299919236 #> 13319 6659.0 0.6909971 0.6909971 9.082264e-03 9.082264e-03 0.299920639 #> 13320 6659.5 0.6909947 0.6909947 9.083247e-03 9.083247e-03 0.299922058 #> 13321 6660.0 0.6909923 0.6909923 9.084229e-03 9.084229e-03 0.299923492 #> 13322 6660.5 0.6909898 0.6909898 9.085211e-03 9.085211e-03 0.299924942 #> 13323 6661.0 0.6909874 0.6909874 9.086193e-03 9.086193e-03 0.299926407 #> 13324 6661.5 0.6909849 0.6909849 9.087174e-03 9.087174e-03 0.299927889 #> 13325 6662.0 0.6909825 0.6909825 9.088155e-03 9.088155e-03 0.299929385 #> 13326 6662.5 0.6909800 0.6909800 9.089136e-03 9.089136e-03 0.299930898 #> 13327 6663.0 0.6909775 0.6909775 9.090115e-03 9.090115e-03 0.299932426 #> 13328 6663.5 0.6909749 0.6909749 9.091095e-03 9.091095e-03 0.299933969 #> 13329 6664.0 0.6909724 0.6909724 9.092074e-03 9.092074e-03 0.299935528 #> 13330 6664.5 0.6909698 0.6909698 9.093052e-03 9.093052e-03 0.299937103 #> 13331 6665.0 0.6909673 0.6909673 9.094030e-03 9.094030e-03 0.299938693 #> 13332 6665.5 0.6909647 0.6909647 9.095008e-03 9.095008e-03 0.299940298 #> 13333 6666.0 0.6909621 0.6909621 9.095985e-03 9.095985e-03 0.299941919 #> 13334 6666.5 0.6909595 0.6909595 9.096961e-03 9.096961e-03 0.299943556 #> 13335 6667.0 0.6909569 0.6909569 9.097937e-03 9.097937e-03 0.299945208 #> 13336 6667.5 0.6909542 0.6909542 9.098913e-03 9.098913e-03 0.299946875 #> 13337 6668.0 0.6909516 0.6909516 9.099888e-03 9.099888e-03 0.299948558 #> 13338 6668.5 0.6909489 0.6909489 9.100862e-03 9.100862e-03 0.299950256 #> 13339 6669.0 0.6909462 0.6909462 9.101837e-03 9.101837e-03 0.299951970 #> 13340 6669.5 0.6909435 0.6909435 9.102810e-03 9.102810e-03 0.299953699 #> 13341 6670.0 0.6909408 0.6909408 9.103783e-03 9.103783e-03 0.299955443 #> 13342 6670.5 0.6909380 0.6909380 9.104756e-03 9.104756e-03 0.299957203 #> 13343 6671.0 0.6909353 0.6909353 9.105728e-03 9.105728e-03 0.299958978 #> 13344 6671.5 0.6909325 0.6909325 9.106699e-03 9.106699e-03 0.299960768 #> 13345 6672.0 0.6909298 0.6909298 9.107670e-03 9.107670e-03 0.299962574 #> 13346 6672.5 0.6909270 0.6909270 9.108640e-03 9.108640e-03 0.299964395 #> 13347 6673.0 0.6909242 0.6909242 9.109610e-03 9.109610e-03 0.299966231 #> 13348 6673.5 0.6909213 0.6909213 9.110580e-03 9.110580e-03 0.299968082 #> 13349 6674.0 0.6909185 0.6909185 9.111548e-03 9.111548e-03 0.299969949 #> 13350 6674.5 0.6909157 0.6909157 9.112517e-03 9.112517e-03 0.299971830 #> 13351 6675.0 0.6909128 0.6909128 9.113484e-03 9.113484e-03 0.299973727 #> 13352 6675.5 0.6909099 0.6909099 9.114451e-03 9.114451e-03 0.299975639 #> 13353 6676.0 0.6909070 0.6909070 9.115418e-03 9.115418e-03 0.299977567 #> 13354 6676.5 0.6909041 0.6909041 9.116384e-03 9.116384e-03 0.299979509 #> 13355 6677.0 0.6909012 0.6909012 9.117350e-03 9.117350e-03 0.299981467 #> 13356 6677.5 0.6908982 0.6908982 9.118314e-03 9.118314e-03 0.299983439 #> 13357 6678.0 0.6908953 0.6908953 9.119279e-03 9.119279e-03 0.299985427 #> 13358 6678.5 0.6908923 0.6908923 9.120243e-03 9.120243e-03 0.299987430 #> 13359 6679.0 0.6908893 0.6908893 9.121206e-03 9.121206e-03 0.299989448 #> 13360 6679.5 0.6908864 0.6908864 9.122168e-03 9.122168e-03 0.299991481 #> 13361 6680.0 0.6908833 0.6908833 9.123130e-03 9.123130e-03 0.299993528 #> 13362 6680.5 0.6908803 0.6908803 9.124092e-03 9.124092e-03 0.299995591 #> 13363 6681.0 0.6908773 0.6908773 9.125053e-03 9.125053e-03 0.299997669 #> 13364 6681.5 0.6908742 0.6908742 9.126013e-03 9.126013e-03 0.299999762 #> 13365 6682.0 0.6908712 0.6908712 9.126973e-03 9.126973e-03 0.300001870 #> 13366 6682.5 0.6908681 0.6908681 9.127932e-03 9.127932e-03 0.300003993 #> 13367 6683.0 0.6908650 0.6908650 9.128890e-03 9.128890e-03 0.300006130 #> 13368 6683.5 0.6908619 0.6908619 9.129848e-03 9.129848e-03 0.300008283 #> 13369 6684.0 0.6908587 0.6908587 9.130805e-03 9.130805e-03 0.300010450 #> 13370 6684.5 0.6908556 0.6908556 9.131762e-03 9.131762e-03 0.300012633 #> 13371 6685.0 0.6908525 0.6908525 9.132718e-03 9.132718e-03 0.300014830 #> 13372 6685.5 0.6908493 0.6908493 9.133673e-03 9.133673e-03 0.300017042 #> 13373 6686.0 0.6908461 0.6908461 9.134628e-03 9.134628e-03 0.300019268 #> 13374 6686.5 0.6908429 0.6908429 9.135582e-03 9.135582e-03 0.300021510 #> 13375 6687.0 0.6908397 0.6908397 9.136536e-03 9.136536e-03 0.300023766 #> 13376 6687.5 0.6908365 0.6908365 9.137488e-03 9.137488e-03 0.300026037 #> 13377 6688.0 0.6908332 0.6908332 9.138441e-03 9.138441e-03 0.300028323 #> 13378 6688.5 0.6908300 0.6908300 9.139392e-03 9.139392e-03 0.300030624 #> 13379 6689.0 0.6908267 0.6908267 9.140343e-03 9.140343e-03 0.300032939 #> 13380 6689.5 0.6908234 0.6908234 9.141293e-03 9.141293e-03 0.300035269 #> 13381 6690.0 0.6908201 0.6908201 9.142243e-03 9.142243e-03 0.300037613 #> 13382 6690.5 0.6908168 0.6908168 9.143192e-03 9.143192e-03 0.300039973 #> 13383 6691.0 0.6908135 0.6908135 9.144140e-03 9.144140e-03 0.300042347 #> 13384 6691.5 0.6908102 0.6908102 9.145088e-03 9.145088e-03 0.300044735 #> 13385 6692.0 0.6908068 0.6908068 9.146035e-03 9.146035e-03 0.300047138 #> 13386 6692.5 0.6908035 0.6908035 9.146981e-03 9.146981e-03 0.300049556 #> 13387 6693.0 0.6908001 0.6908001 9.147927e-03 9.147927e-03 0.300051988 #> 13388 6693.5 0.6907967 0.6907967 9.148872e-03 9.148872e-03 0.300054435 #> 13389 6694.0 0.6907933 0.6907933 9.149816e-03 9.149816e-03 0.300056896 #> 13390 6694.5 0.6907899 0.6907899 9.150760e-03 9.150760e-03 0.300059372 #> 13391 6695.0 0.6907864 0.6907864 9.151703e-03 9.151703e-03 0.300061862 #> 13392 6695.5 0.6907830 0.6907830 9.152645e-03 9.152645e-03 0.300064366 #> 13393 6696.0 0.6907795 0.6907795 9.153586e-03 9.153586e-03 0.300066886 #> 13394 6696.5 0.6907761 0.6907761 9.154527e-03 9.154527e-03 0.300069419 #> 13395 6697.0 0.6907726 0.6907726 9.155467e-03 9.155467e-03 0.300071967 #> 13396 6697.5 0.6907691 0.6907691 9.156407e-03 9.156407e-03 0.300074529 #> 13397 6698.0 0.6907655 0.6907655 9.157346e-03 9.157346e-03 0.300077106 #> 13398 6698.5 0.6907620 0.6907620 9.158284e-03 9.158284e-03 0.300079697 #> 13399 6699.0 0.6907585 0.6907585 9.159221e-03 9.159221e-03 0.300082303 #> 13400 6699.5 0.6907549 0.6907549 9.160157e-03 9.160157e-03 0.300084922 #> 13401 6700.0 0.6907514 0.6907514 9.161093e-03 9.161093e-03 0.300087556 #> 13402 6700.5 0.6907478 0.6907478 9.162029e-03 9.162029e-03 0.300090204 #> 13403 6701.0 0.6907442 0.6907442 9.162963e-03 9.162963e-03 0.300092867 #> 13404 6701.5 0.6907406 0.6907406 9.163897e-03 9.163897e-03 0.300095544 #> 13405 6702.0 0.6907369 0.6907369 9.164830e-03 9.164830e-03 0.300098235 #> 13406 6702.5 0.6907333 0.6907333 9.165762e-03 9.165762e-03 0.300100940 #> 13407 6703.0 0.6907296 0.6907296 9.166693e-03 9.166693e-03 0.300103659 #> 13408 6703.5 0.6907260 0.6907260 9.167624e-03 9.167624e-03 0.300106392 #> 13409 6704.0 0.6907223 0.6907223 9.168554e-03 9.168554e-03 0.300109140 #> 13410 6704.5 0.6907186 0.6907186 9.169483e-03 9.169483e-03 0.300111902 #> 13411 6705.0 0.6907149 0.6907149 9.170412e-03 9.170412e-03 0.300114678 #> 13412 6705.5 0.6907112 0.6907112 9.171339e-03 9.171339e-03 0.300117468 #> 13413 6706.0 0.6907075 0.6907075 9.172266e-03 9.172266e-03 0.300120271 #> 13414 6706.5 0.6907037 0.6907037 9.173193e-03 9.173193e-03 0.300123089 #> 13415 6707.0 0.6907000 0.6907000 9.174118e-03 9.174118e-03 0.300125921 #> 13416 6707.5 0.6906962 0.6906962 9.175043e-03 9.175043e-03 0.300128767 #> 13417 6708.0 0.6906924 0.6906924 9.175967e-03 9.175967e-03 0.300131627 #> 13418 6708.5 0.6906886 0.6906886 9.176890e-03 9.176890e-03 0.300134501 #> 13419 6709.0 0.6906848 0.6906848 9.177812e-03 9.177812e-03 0.300137389 #> 13420 6709.5 0.6906810 0.6906810 9.178734e-03 9.178734e-03 0.300140291 #> 13421 6710.0 0.6906771 0.6906771 9.179654e-03 9.179654e-03 0.300143207 #> 13422 6710.5 0.6906733 0.6906733 9.180574e-03 9.180574e-03 0.300146136 #> 13423 6711.0 0.6906694 0.6906694 9.181493e-03 9.181493e-03 0.300149080 #> 13424 6711.5 0.6906656 0.6906656 9.182412e-03 9.182412e-03 0.300152037 #> 13425 6712.0 0.6906617 0.6906617 9.183329e-03 9.183329e-03 0.300155008 #> 13426 6712.5 0.6906578 0.6906578 9.184246e-03 9.184246e-03 0.300157993 #> 13427 6713.0 0.6906538 0.6906538 9.185162e-03 9.185162e-03 0.300160992 #> 13428 6713.5 0.6906499 0.6906499 9.186077e-03 9.186077e-03 0.300164004 #> 13429 6714.0 0.6906460 0.6906460 9.186992e-03 9.186992e-03 0.300167030 #> 13430 6714.5 0.6906420 0.6906420 9.187905e-03 9.187905e-03 0.300170070 #> 13431 6715.0 0.6906381 0.6906381 9.188818e-03 9.188818e-03 0.300173124 #> 13432 6715.5 0.6906341 0.6906341 9.189730e-03 9.189730e-03 0.300176191 #> 13433 6716.0 0.6906301 0.6906301 9.190641e-03 9.190641e-03 0.300179272 #> 13434 6716.5 0.6906261 0.6906261 9.191551e-03 9.191551e-03 0.300182366 #> 13435 6717.0 0.6906221 0.6906221 9.192461e-03 9.192461e-03 0.300185474 #> 13436 6717.5 0.6906180 0.6906180 9.193369e-03 9.193369e-03 0.300188596 #> 13437 6718.0 0.6906140 0.6906140 9.194277e-03 9.194277e-03 0.300191731 #> 13438 6718.5 0.6906099 0.6906099 9.195184e-03 9.195184e-03 0.300194880 #> 13439 6719.0 0.6906059 0.6906059 9.196090e-03 9.196090e-03 0.300198043 #> 13440 6719.5 0.6906018 0.6906018 9.196995e-03 9.196995e-03 0.300201219 #> 13441 6720.0 0.6905977 0.6905977 9.197899e-03 9.197899e-03 0.300204408 #> 13442 6720.5 0.6905936 0.6905936 9.198803e-03 9.198803e-03 0.300207611 #> 13443 6721.0 0.6905895 0.6905895 9.199706e-03 9.199706e-03 0.300210827 #> 13444 6721.5 0.6905853 0.6905853 9.200607e-03 9.200607e-03 0.300214057 #> 13445 6722.0 0.6905812 0.6905812 9.201508e-03 9.201508e-03 0.300217300 #> 13446 6722.5 0.6905770 0.6905770 9.202408e-03 9.202408e-03 0.300220556 #> 13447 6723.0 0.6905729 0.6905729 9.203308e-03 9.203308e-03 0.300223826 #> 13448 6723.5 0.6905687 0.6905687 9.204206e-03 9.204206e-03 0.300227110 #> 13449 6724.0 0.6905645 0.6905645 9.205103e-03 9.205103e-03 0.300230406 #> 13450 6724.5 0.6905603 0.6905603 9.206000e-03 9.206000e-03 0.300233716 #> 13451 6725.0 0.6905561 0.6905561 9.206895e-03 9.206895e-03 0.300237039 #> 13452 6725.5 0.6905518 0.6905518 9.207790e-03 9.207790e-03 0.300240375 #> 13453 6726.0 0.6905476 0.6905476 9.208684e-03 9.208684e-03 0.300243725 #> 13454 6726.5 0.6905433 0.6905433 9.209577e-03 9.209577e-03 0.300247088 #> 13455 6727.0 0.6905391 0.6905391 9.210469e-03 9.210469e-03 0.300250464 #> 13456 6727.5 0.6905348 0.6905348 9.211361e-03 9.211361e-03 0.300253853 #> 13457 6728.0 0.6905305 0.6905305 9.212251e-03 9.212251e-03 0.300257255 #> 13458 6728.5 0.6905262 0.6905262 9.213140e-03 9.213140e-03 0.300260671 #> 13459 6729.0 0.6905219 0.6905219 9.214029e-03 9.214029e-03 0.300264099 #> 13460 6729.5 0.6905175 0.6905175 9.214916e-03 9.214916e-03 0.300267541 #> 13461 6730.0 0.6905132 0.6905132 9.215803e-03 9.215803e-03 0.300270996 #> 13462 6730.5 0.6905088 0.6905088 9.216689e-03 9.216689e-03 0.300274463 #> 13463 6731.0 0.6905045 0.6905045 9.217574e-03 9.217574e-03 0.300277944 #> 13464 6731.5 0.6905001 0.6905001 9.218458e-03 9.218458e-03 0.300281438 #> 13465 6732.0 0.6904957 0.6904957 9.219341e-03 9.219341e-03 0.300284945 #> 13466 6732.5 0.6904913 0.6904913 9.220223e-03 9.220223e-03 0.300288464 #> 13467 6733.0 0.6904869 0.6904869 9.221104e-03 9.221104e-03 0.300291997 #> 13468 6733.5 0.6904825 0.6904825 9.221984e-03 9.221984e-03 0.300295542 #> 13469 6734.0 0.6904780 0.6904780 9.222863e-03 9.222863e-03 0.300299101 #> 13470 6734.5 0.6904736 0.6904736 9.223742e-03 9.223742e-03 0.300302672 #> 13471 6735.0 0.6904691 0.6904691 9.224619e-03 9.224619e-03 0.300306256 #> 13472 6735.5 0.6904647 0.6904647 9.225495e-03 9.225495e-03 0.300309853 #> 13473 6736.0 0.6904602 0.6904602 9.226371e-03 9.226371e-03 0.300313463 #> 13474 6736.5 0.6904557 0.6904557 9.227245e-03 9.227245e-03 0.300317085 #> 13475 6737.0 0.6904512 0.6904512 9.228119e-03 9.228119e-03 0.300320721 #> 13476 6737.5 0.6904466 0.6904466 9.228992e-03 9.228992e-03 0.300324369 #> 13477 6738.0 0.6904421 0.6904421 9.229863e-03 9.229863e-03 0.300328029 #> 13478 6738.5 0.6904376 0.6904376 9.230734e-03 9.230734e-03 0.300331703 #> 13479 6739.0 0.6904330 0.6904330 9.231604e-03 9.231604e-03 0.300335389 #> 13480 6739.5 0.6904284 0.6904284 9.232472e-03 9.232472e-03 0.300339087 #> 13481 6740.0 0.6904239 0.6904239 9.233340e-03 9.233340e-03 0.300342799 #> 13482 6740.5 0.6904193 0.6904193 9.234207e-03 9.234207e-03 0.300346523 #> 13483 6741.0 0.6904147 0.6904147 9.235073e-03 9.235073e-03 0.300350259 #> 13484 6741.5 0.6904101 0.6904101 9.235938e-03 9.235938e-03 0.300354008 #> 13485 6742.0 0.6904054 0.6904054 9.236801e-03 9.236801e-03 0.300357770 #> 13486 6742.5 0.6904008 0.6904008 9.237664e-03 9.237664e-03 0.300361544 #> 13487 6743.0 0.6903961 0.6903961 9.238526e-03 9.238526e-03 0.300365330 #> 13488 6743.5 0.6903915 0.6903915 9.239387e-03 9.239387e-03 0.300369129 #> 13489 6744.0 0.6903868 0.6903868 9.240247e-03 9.240247e-03 0.300372941 #> 13490 6744.5 0.6903821 0.6903821 9.241106e-03 9.241106e-03 0.300376764 #> 13491 6745.0 0.6903774 0.6903774 9.241964e-03 9.241964e-03 0.300380601 #> 13492 6745.5 0.6903727 0.6903727 9.242821e-03 9.242821e-03 0.300384449 #> 13493 6746.0 0.6903680 0.6903680 9.243676e-03 9.243676e-03 0.300388310 #> 13494 6746.5 0.6903633 0.6903633 9.244531e-03 9.244531e-03 0.300392184 #> 13495 6747.0 0.6903585 0.6903585 9.245385e-03 9.245385e-03 0.300396069 #> 13496 6747.5 0.6903538 0.6903538 9.246238e-03 9.246238e-03 0.300399967 #> 13497 6748.0 0.6903490 0.6903490 9.247090e-03 9.247090e-03 0.300403877 #> 13498 6748.5 0.6903443 0.6903443 9.247940e-03 9.247940e-03 0.300407799 #> 13499 6749.0 0.6903395 0.6903395 9.248790e-03 9.248790e-03 0.300411734 #> 13500 6749.5 0.6903347 0.6903347 9.249639e-03 9.249639e-03 0.300415681 #> 13501 6750.0 0.6903299 0.6903299 9.250487e-03 9.250487e-03 0.300419640 #> 13502 6750.5 0.6903251 0.6903251 9.251333e-03 9.251333e-03 0.300423611 #> 13503 6751.0 0.6903202 0.6903202 9.252179e-03 9.252179e-03 0.300427594 #> 13504 6751.5 0.6903154 0.6903154 9.253023e-03 9.253023e-03 0.300431589 #> 13505 6752.0 0.6903105 0.6903105 9.253867e-03 9.253867e-03 0.300435596 #> 13506 6752.5 0.6903057 0.6903057 9.254709e-03 9.254709e-03 0.300439616 #> 13507 6753.0 0.6903008 0.6903008 9.255551e-03 9.255551e-03 0.300443647 #> 13508 6753.5 0.6902959 0.6902959 9.256391e-03 9.256391e-03 0.300447690 #> 13509 6754.0 0.6902910 0.6902910 9.257230e-03 9.257230e-03 0.300451746 #> 13510 6754.5 0.6902861 0.6902861 9.258069e-03 9.258069e-03 0.300455813 #> 13511 6755.0 0.6902812 0.6902812 9.258906e-03 9.258906e-03 0.300459892 #> 13512 6755.5 0.6902763 0.6902763 9.259742e-03 9.259742e-03 0.300463983 #> 13513 6756.0 0.6902713 0.6902713 9.260577e-03 9.260577e-03 0.300468086 #> 13514 6756.5 0.6902664 0.6902664 9.261411e-03 9.261411e-03 0.300472201 #> 13515 6757.0 0.6902614 0.6902614 9.262244e-03 9.262244e-03 0.300476328 #> 13516 6757.5 0.6902565 0.6902565 9.263076e-03 9.263076e-03 0.300480466 #> 13517 6758.0 0.6902515 0.6902515 9.263907e-03 9.263907e-03 0.300484616 #> 13518 6758.5 0.6902465 0.6902465 9.264736e-03 9.264736e-03 0.300488778 #> 13519 6759.0 0.6902415 0.6902415 9.265565e-03 9.265565e-03 0.300492952 #> 13520 6759.5 0.6902365 0.6902365 9.266393e-03 9.266393e-03 0.300497138 #> 13521 6760.0 0.6902314 0.6902314 9.267219e-03 9.267219e-03 0.300501335 #> 13522 6760.5 0.6902264 0.6902264 9.268044e-03 9.268044e-03 0.300505543 #> 13523 6761.0 0.6902214 0.6902214 9.268869e-03 9.268869e-03 0.300509764 #> 13524 6761.5 0.6902163 0.6902163 9.269692e-03 9.269692e-03 0.300513996 #> 13525 6762.0 0.6902112 0.6902112 9.270514e-03 9.270514e-03 0.300518240 #> 13526 6762.5 0.6902062 0.6902062 9.271335e-03 9.271335e-03 0.300522495 #> 13527 6763.0 0.6902011 0.6902011 9.272155e-03 9.272155e-03 0.300526762 #> 13528 6763.5 0.6901960 0.6901960 9.272973e-03 9.272973e-03 0.300531040 #> 13529 6764.0 0.6901909 0.6901909 9.273791e-03 9.273791e-03 0.300535330 #> 13530 6764.5 0.6901858 0.6901858 9.274607e-03 9.274607e-03 0.300539631 #> 13531 6765.0 0.6901806 0.6901806 9.275423e-03 9.275423e-03 0.300543944 #> 13532 6765.5 0.6901755 0.6901755 9.276237e-03 9.276237e-03 0.300548268 #> 13533 6766.0 0.6901703 0.6901703 9.277050e-03 9.277050e-03 0.300552603 #> 13534 6766.5 0.6901652 0.6901652 9.277862e-03 9.277862e-03 0.300556950 #> 13535 6767.0 0.6901600 0.6901600 9.278673e-03 9.278673e-03 0.300561308 #> 13536 6767.5 0.6901548 0.6901548 9.279483e-03 9.279483e-03 0.300565678 #> 13537 6768.0 0.6901496 0.6901496 9.280292e-03 9.280292e-03 0.300570059 #> 13538 6768.5 0.6901444 0.6901444 9.281099e-03 9.281099e-03 0.300574451 #> 13539 6769.0 0.6901392 0.6901392 9.281905e-03 9.281905e-03 0.300578854 #> 13540 6769.5 0.6901340 0.6901340 9.282711e-03 9.282711e-03 0.300583269 #> 13541 6770.0 0.6901288 0.6901288 9.283515e-03 9.283515e-03 0.300587695 #> 13542 6770.5 0.6901236 0.6901236 9.284318e-03 9.284318e-03 0.300592132 #> 13543 6771.0 0.6901183 0.6901183 9.285120e-03 9.285120e-03 0.300596580 #> 13544 6771.5 0.6901130 0.6901130 9.285920e-03 9.285920e-03 0.300601039 #> 13545 6772.0 0.6901078 0.6901078 9.286720e-03 9.286720e-03 0.300605509 #> 13546 6772.5 0.6901025 0.6901025 9.287518e-03 9.287518e-03 0.300609991 #> 13547 6773.0 0.6900972 0.6900972 9.288315e-03 9.288315e-03 0.300614483 #> 13548 6773.5 0.6900919 0.6900919 9.289111e-03 9.289111e-03 0.300618987 #> 13549 6774.0 0.6900866 0.6900866 9.289906e-03 9.289906e-03 0.300623501 #> 13550 6774.5 0.6900813 0.6900813 9.290700e-03 9.290700e-03 0.300628027 #> 13551 6775.0 0.6900759 0.6900759 9.291492e-03 9.291492e-03 0.300632563 #> 13552 6775.5 0.6900706 0.6900706 9.292284e-03 9.292284e-03 0.300637111 #> 13553 6776.0 0.6900653 0.6900653 9.293074e-03 9.293074e-03 0.300641669 #> 13554 6776.5 0.6900599 0.6900599 9.293863e-03 9.293863e-03 0.300646238 #> 13555 6777.0 0.6900545 0.6900545 9.294651e-03 9.294651e-03 0.300650818 #> 13556 6777.5 0.6900492 0.6900492 9.295437e-03 9.295437e-03 0.300655409 #> 13557 6778.0 0.6900438 0.6900438 9.296223e-03 9.296223e-03 0.300660011 #> 13558 6778.5 0.6900384 0.6900384 9.297007e-03 9.297007e-03 0.300664623 #> 13559 6779.0 0.6900330 0.6900330 9.297790e-03 9.297790e-03 0.300669246 #> 13560 6779.5 0.6900275 0.6900275 9.298572e-03 9.298572e-03 0.300673880 #> 13561 6780.0 0.6900221 0.6900221 9.299353e-03 9.299353e-03 0.300678525 #> 13562 6780.5 0.6900167 0.6900167 9.300132e-03 9.300132e-03 0.300683180 #> 13563 6781.0 0.6900112 0.6900112 9.300910e-03 9.300910e-03 0.300687846 #> 13564 6781.5 0.6900058 0.6900058 9.301687e-03 9.301687e-03 0.300692523 #> 13565 6782.0 0.6900003 0.6900003 9.302463e-03 9.302463e-03 0.300697210 #> 13566 6782.5 0.6899949 0.6899949 9.303238e-03 9.303238e-03 0.300701908 #> 13567 6783.0 0.6899894 0.6899894 9.304011e-03 9.304011e-03 0.300706616 #> 13568 6783.5 0.6899839 0.6899839 9.304784e-03 9.304784e-03 0.300711335 #> 13569 6784.0 0.6899784 0.6899784 9.305555e-03 9.305555e-03 0.300716064 #> 13570 6784.5 0.6899729 0.6899729 9.306325e-03 9.306325e-03 0.300720804 #> 13571 6785.0 0.6899674 0.6899674 9.307093e-03 9.307093e-03 0.300725554 #> 13572 6785.5 0.6899618 0.6899618 9.307861e-03 9.307861e-03 0.300730315 #> 13573 6786.0 0.6899563 0.6899563 9.308627e-03 9.308627e-03 0.300735086 #> 13574 6786.5 0.6899507 0.6899507 9.309392e-03 9.309392e-03 0.300739867 #> 13575 6787.0 0.6899452 0.6899452 9.310155e-03 9.310155e-03 0.300744659 #> 13576 6787.5 0.6899396 0.6899396 9.310918e-03 9.310918e-03 0.300749461 #> 13577 6788.0 0.6899340 0.6899340 9.311679e-03 9.311679e-03 0.300754273 #> 13578 6788.5 0.6899285 0.6899285 9.312439e-03 9.312439e-03 0.300759096 #> 13579 6789.0 0.6899229 0.6899229 9.313198e-03 9.313198e-03 0.300763929 #> 13580 6789.5 0.6899173 0.6899173 9.313955e-03 9.313955e-03 0.300768772 #> 13581 6790.0 0.6899117 0.6899117 9.314712e-03 9.314712e-03 0.300773625 #> 13582 6790.5 0.6899060 0.6899060 9.315467e-03 9.315467e-03 0.300778489 #> 13583 6791.0 0.6899004 0.6899004 9.316220e-03 9.316220e-03 0.300783362 #> 13584 6791.5 0.6898948 0.6898948 9.316973e-03 9.316973e-03 0.300788246 #> 13585 6792.0 0.6898891 0.6898891 9.317724e-03 9.317724e-03 0.300793140 #> 13586 6792.5 0.6898835 0.6898835 9.318474e-03 9.318474e-03 0.300798044 #> 13587 6793.0 0.6898778 0.6898778 9.319223e-03 9.319223e-03 0.300802958 #> 13588 6793.5 0.6898721 0.6898721 9.319971e-03 9.319971e-03 0.300807881 #> 13589 6794.0 0.6898665 0.6898665 9.320717e-03 9.320717e-03 0.300812815 #> 13590 6794.5 0.6898608 0.6898608 9.321462e-03 9.321462e-03 0.300817759 #> 13591 6795.0 0.6898551 0.6898551 9.322206e-03 9.322206e-03 0.300822713 #> 13592 6795.5 0.6898494 0.6898494 9.322948e-03 9.322948e-03 0.300827677 #> 13593 6796.0 0.6898437 0.6898437 9.323689e-03 9.323689e-03 0.300832650 #> 13594 6796.5 0.6898379 0.6898379 9.324429e-03 9.324429e-03 0.300837634 #> 13595 6797.0 0.6898322 0.6898322 9.325168e-03 9.325168e-03 0.300842627 #> 13596 6797.5 0.6898265 0.6898265 9.325905e-03 9.325905e-03 0.300847630 #> 13597 6798.0 0.6898207 0.6898207 9.326641e-03 9.326641e-03 0.300852643 #> 13598 6798.5 0.6898150 0.6898150 9.327376e-03 9.327376e-03 0.300857665 #> 13599 6799.0 0.6898092 0.6898092 9.328109e-03 9.328109e-03 0.300862698 #> 13600 6799.5 0.6898034 0.6898034 9.328842e-03 9.328842e-03 0.300867740 #> 13601 6800.0 0.6897976 0.6897976 9.329572e-03 9.329572e-03 0.300872791 #> 13602 6800.5 0.6897918 0.6897918 9.330302e-03 9.330302e-03 0.300877852 #> 13603 6801.0 0.6897860 0.6897860 9.331030e-03 9.331030e-03 0.300882923 #> 13604 6801.5 0.6897802 0.6897802 9.331757e-03 9.331757e-03 0.300888004 #> 13605 6802.0 0.6897744 0.6897744 9.332483e-03 9.332483e-03 0.300893094 #> 13606 6802.5 0.6897686 0.6897686 9.333208e-03 9.333208e-03 0.300898194 #> 13607 6803.0 0.6897628 0.6897628 9.333931e-03 9.333931e-03 0.300903303 #> 13608 6803.5 0.6897569 0.6897569 9.334652e-03 9.334652e-03 0.300908421 #> 13609 6804.0 0.6897511 0.6897511 9.335373e-03 9.335373e-03 0.300913549 #> 13610 6804.5 0.6897452 0.6897452 9.336092e-03 9.336092e-03 0.300918687 #> 13611 6805.0 0.6897394 0.6897394 9.336810e-03 9.336810e-03 0.300923834 #> 13612 6805.5 0.6897335 0.6897335 9.337527e-03 9.337527e-03 0.300928990 #> 13613 6806.0 0.6897276 0.6897276 9.338242e-03 9.338242e-03 0.300934156 #> 13614 6806.5 0.6897217 0.6897217 9.338956e-03 9.338956e-03 0.300939331 #> 13615 6807.0 0.6897158 0.6897158 9.339668e-03 9.339668e-03 0.300944515 #> 13616 6807.5 0.6897099 0.6897099 9.340380e-03 9.340380e-03 0.300949708 #> 13617 6808.0 0.6897040 0.6897040 9.341090e-03 9.341090e-03 0.300954911 #> 13618 6808.5 0.6896981 0.6896981 9.341798e-03 9.341798e-03 0.300960123 #> 13619 6809.0 0.6896922 0.6896922 9.342506e-03 9.342506e-03 0.300965344 #> 13620 6809.5 0.6896862 0.6896862 9.343212e-03 9.343212e-03 0.300970575 #> 13621 6810.0 0.6896803 0.6896803 9.343916e-03 9.343916e-03 0.300975814 #> 13622 6810.5 0.6896743 0.6896743 9.344620e-03 9.344620e-03 0.300981063 #> 13623 6811.0 0.6896684 0.6896684 9.345322e-03 9.345322e-03 0.300986320 #> 13624 6811.5 0.6896624 0.6896624 9.346022e-03 9.346022e-03 0.300991587 #> 13625 6812.0 0.6896564 0.6896564 9.346721e-03 9.346721e-03 0.300996863 #> 13626 6812.5 0.6896504 0.6896504 9.347419e-03 9.347419e-03 0.301002147 #> 13627 6813.0 0.6896444 0.6896444 9.348116e-03 9.348116e-03 0.301007441 #> 13628 6813.5 0.6896384 0.6896384 9.348811e-03 9.348811e-03 0.301012744 #> 13629 6814.0 0.6896324 0.6896324 9.349505e-03 9.349505e-03 0.301018056 #> 13630 6814.5 0.6896264 0.6896264 9.350198e-03 9.350198e-03 0.301023376 #> 13631 6815.0 0.6896204 0.6896204 9.350889e-03 9.350889e-03 0.301028705 #> 13632 6815.5 0.6896144 0.6896144 9.351579e-03 9.351579e-03 0.301034044 #> 13633 6816.0 0.6896083 0.6896083 9.352267e-03 9.352267e-03 0.301039391 #> 13634 6816.5 0.6896023 0.6896023 9.352954e-03 9.352954e-03 0.301044746 #> 13635 6817.0 0.6895962 0.6895962 9.353640e-03 9.353640e-03 0.301050111 #> 13636 6817.5 0.6895902 0.6895902 9.354324e-03 9.354324e-03 0.301055484 #> 13637 6818.0 0.6895841 0.6895841 9.355007e-03 9.355007e-03 0.301060866 #> 13638 6818.5 0.6895781 0.6895781 9.355689e-03 9.355689e-03 0.301066257 #> 13639 6819.0 0.6895720 0.6895720 9.356369e-03 9.356369e-03 0.301071656 #> 13640 6819.5 0.6895659 0.6895659 9.357048e-03 9.357048e-03 0.301077064 #> 13641 6820.0 0.6895598 0.6895598 9.357726e-03 9.357726e-03 0.301082481 #> 13642 6820.5 0.6895537 0.6895537 9.358402e-03 9.358402e-03 0.301087906 #> 13643 6821.0 0.6895476 0.6895476 9.359076e-03 9.359076e-03 0.301093340 #> 13644 6821.5 0.6895415 0.6895415 9.359750e-03 9.359750e-03 0.301098782 #> 13645 6822.0 0.6895353 0.6895353 9.360422e-03 9.360422e-03 0.301104233 #> 13646 6822.5 0.6895292 0.6895292 9.361092e-03 9.361092e-03 0.301109692 #> 13647 6823.0 0.6895231 0.6895231 9.361762e-03 9.361762e-03 0.301115159 #> 13648 6823.5 0.6895169 0.6895169 9.362430e-03 9.362430e-03 0.301120635 #> 13649 6824.0 0.6895108 0.6895108 9.363096e-03 9.363096e-03 0.301126120 #> 13650 6824.5 0.6895046 0.6895046 9.363761e-03 9.363761e-03 0.301131612 #> 13651 6825.0 0.6894985 0.6894985 9.364425e-03 9.364425e-03 0.301137113 #> 13652 6825.5 0.6894923 0.6894923 9.365087e-03 9.365087e-03 0.301142623 #> 13653 6826.0 0.6894861 0.6894861 9.365748e-03 9.365748e-03 0.301148140 #> 13654 6826.5 0.6894799 0.6894799 9.366407e-03 9.366407e-03 0.301153666 #> 13655 6827.0 0.6894737 0.6894737 9.367065e-03 9.367065e-03 0.301159200 #> 13656 6827.5 0.6894675 0.6894675 9.367722e-03 9.367722e-03 0.301164742 #> 13657 6828.0 0.6894613 0.6894613 9.368377e-03 9.368377e-03 0.301170293 #> 13658 6828.5 0.6894551 0.6894551 9.369031e-03 9.369031e-03 0.301175851 #> 13659 6829.0 0.6894489 0.6894489 9.369683e-03 9.369683e-03 0.301181418 #> 13660 6829.5 0.6894427 0.6894427 9.370334e-03 9.370334e-03 0.301186993 #> 13661 6830.0 0.6894364 0.6894364 9.370984e-03 9.370984e-03 0.301192575 #> 13662 6830.5 0.6894302 0.6894302 9.371632e-03 9.371632e-03 0.301198166 #> 13663 6831.0 0.6894240 0.6894240 9.372279e-03 9.372279e-03 0.301203765 #> 13664 6831.5 0.6894177 0.6894177 9.372924e-03 9.372924e-03 0.301209372 #> 13665 6832.0 0.6894114 0.6894114 9.373568e-03 9.373568e-03 0.301214986 #> 13666 6832.5 0.6894052 0.6894052 9.374211e-03 9.374211e-03 0.301220609 #> 13667 6833.0 0.6893989 0.6893989 9.374852e-03 9.374852e-03 0.301226240 #> 13668 6833.5 0.6893926 0.6893926 9.375491e-03 9.375491e-03 0.301231878 #> 13669 6834.0 0.6893863 0.6893863 9.376130e-03 9.376130e-03 0.301237524 #> 13670 6834.5 0.6893801 0.6893801 9.376766e-03 9.376766e-03 0.301243178 #> 13671 6835.0 0.6893738 0.6893738 9.377402e-03 9.377402e-03 0.301248840 #> 13672 6835.5 0.6893675 0.6893675 9.378036e-03 9.378036e-03 0.301254509 #> 13673 6836.0 0.6893611 0.6893611 9.378668e-03 9.378668e-03 0.301260186 #> 13674 6836.5 0.6893548 0.6893548 9.379299e-03 9.379299e-03 0.301265871 #> 13675 6837.0 0.6893485 0.6893485 9.379929e-03 9.379929e-03 0.301271564 #> 13676 6837.5 0.6893422 0.6893422 9.380557e-03 9.380557e-03 0.301277264 #> 13677 6838.0 0.6893358 0.6893358 9.381184e-03 9.381184e-03 0.301282972 #> 13678 6838.5 0.6893295 0.6893295 9.381809e-03 9.381809e-03 0.301288687 #> 13679 6839.0 0.6893232 0.6893232 9.382433e-03 9.382433e-03 0.301294410 #> 13680 6839.5 0.6893168 0.6893168 9.383055e-03 9.383055e-03 0.301300141 #> 13681 6840.0 0.6893104 0.6893104 9.383676e-03 9.383676e-03 0.301305878 #> 13682 6840.5 0.6893041 0.6893041 9.384296e-03 9.384296e-03 0.301311624 #> 13683 6841.0 0.6892977 0.6892977 9.384914e-03 9.384914e-03 0.301317377 #> 13684 6841.5 0.6892913 0.6892913 9.385530e-03 9.385530e-03 0.301323137 #> 13685 6842.0 0.6892850 0.6892850 9.386145e-03 9.386145e-03 0.301328905 #> 13686 6842.5 0.6892786 0.6892786 9.386759e-03 9.386759e-03 0.301334679 #> 13687 6843.0 0.6892722 0.6892722 9.387371e-03 9.387371e-03 0.301340462 #> 13688 6843.5 0.6892658 0.6892658 9.387982e-03 9.387982e-03 0.301346251 #> 13689 6844.0 0.6892594 0.6892594 9.388591e-03 9.388591e-03 0.301352048 #> 13690 6844.5 0.6892529 0.6892529 9.389199e-03 9.389199e-03 0.301357852 #> 13691 6845.0 0.6892465 0.6892465 9.389805e-03 9.389805e-03 0.301363664 #> 13692 6845.5 0.6892401 0.6892401 9.390410e-03 9.390410e-03 0.301369482 #> 13693 6846.0 0.6892337 0.6892337 9.391014e-03 9.391014e-03 0.301375308 #> 13694 6846.5 0.6892272 0.6892272 9.391616e-03 9.391616e-03 0.301381141 #> 13695 6847.0 0.6892208 0.6892208 9.392216e-03 9.392216e-03 0.301386981 #> 13696 6847.5 0.6892144 0.6892144 9.392815e-03 9.392815e-03 0.301392828 #> 13697 6848.0 0.6892079 0.6892079 9.393413e-03 9.393413e-03 0.301398682 #> 13698 6848.5 0.6892014 0.6892014 9.394009e-03 9.394009e-03 0.301404543 #> 13699 6849.0 0.6891950 0.6891950 9.394603e-03 9.394603e-03 0.301410411 #> 13700 6849.5 0.6891885 0.6891885 9.395196e-03 9.395196e-03 0.301416286 #> 13701 6850.0 0.6891820 0.6891820 9.395788e-03 9.395788e-03 0.301422168 #> 13702 6850.5 0.6891756 0.6891756 9.396378e-03 9.396378e-03 0.301428056 #> 13703 6851.0 0.6891691 0.6891691 9.396967e-03 9.396967e-03 0.301433952 #> 13704 6851.5 0.6891626 0.6891626 9.397554e-03 9.397554e-03 0.301439855 #> 13705 6852.0 0.6891561 0.6891561 9.398139e-03 9.398139e-03 0.301445764 #> 13706 6852.5 0.6891496 0.6891496 9.398724e-03 9.398724e-03 0.301451680 #> 13707 6853.0 0.6891431 0.6891431 9.399306e-03 9.399306e-03 0.301457603 #> 13708 6853.5 0.6891366 0.6891366 9.399887e-03 9.399887e-03 0.301463533 #> 13709 6854.0 0.6891301 0.6891301 9.400467e-03 9.400467e-03 0.301469469 #> 13710 6854.5 0.6891235 0.6891235 9.401045e-03 9.401045e-03 0.301475412 #> 13711 6855.0 0.6891170 0.6891170 9.401622e-03 9.401622e-03 0.301481362 #> 13712 6855.5 0.6891105 0.6891105 9.402197e-03 9.402197e-03 0.301487318 #> 13713 6856.0 0.6891039 0.6891039 9.402771e-03 9.402771e-03 0.301493281 #> 13714 6856.5 0.6890974 0.6890974 9.403343e-03 9.403343e-03 0.301499251 #> 13715 6857.0 0.6890909 0.6890909 9.403914e-03 9.403914e-03 0.301505227 #> 13716 6857.5 0.6890843 0.6890843 9.404483e-03 9.404483e-03 0.301511209 #> 13717 6858.0 0.6890778 0.6890778 9.405050e-03 9.405050e-03 0.301517198 #> 13718 6858.5 0.6890712 0.6890712 9.405617e-03 9.405617e-03 0.301523194 #> 13719 6859.0 0.6890646 0.6890646 9.406181e-03 9.406181e-03 0.301529196 #> 13720 6859.5 0.6890581 0.6890581 9.406744e-03 9.406744e-03 0.301535204 #> 13721 6860.0 0.6890515 0.6890515 9.407306e-03 9.407306e-03 0.301541218 #> 13722 6860.5 0.6890449 0.6890449 9.407866e-03 9.407866e-03 0.301547239 #> 13723 6861.0 0.6890383 0.6890383 9.408425e-03 9.408425e-03 0.301553267 #> 13724 6861.5 0.6890317 0.6890317 9.408982e-03 9.408982e-03 0.301559300 #> 13725 6862.0 0.6890251 0.6890251 9.409537e-03 9.409537e-03 0.301565340 #> 13726 6862.5 0.6890185 0.6890185 9.410091e-03 9.410091e-03 0.301571386 #> 13727 6863.0 0.6890119 0.6890119 9.410644e-03 9.410644e-03 0.301577439 #> 13728 6863.5 0.6890053 0.6890053 9.411195e-03 9.411195e-03 0.301583497 #> 13729 6864.0 0.6889987 0.6889987 9.411744e-03 9.411744e-03 0.301589561 #> 13730 6864.5 0.6889921 0.6889921 9.412292e-03 9.412292e-03 0.301595632 #> 13731 6865.0 0.6889855 0.6889855 9.412839e-03 9.412839e-03 0.301601709 #> 13732 6865.5 0.6889788 0.6889788 9.413384e-03 9.413384e-03 0.301607792 #> 13733 6866.0 0.6889722 0.6889722 9.413927e-03 9.413927e-03 0.301613881 #> 13734 6866.5 0.6889656 0.6889656 9.414469e-03 9.414469e-03 0.301619975 #> 13735 6867.0 0.6889589 0.6889589 9.415009e-03 9.415009e-03 0.301626076 #> 13736 6867.5 0.6889523 0.6889523 9.415548e-03 9.415548e-03 0.301632183 #> 13737 6868.0 0.6889456 0.6889456 9.416085e-03 9.416085e-03 0.301638296 #> 13738 6868.5 0.6889390 0.6889390 9.416621e-03 9.416621e-03 0.301644414 #> 13739 6869.0 0.6889323 0.6889323 9.417155e-03 9.417155e-03 0.301650539 #> 13740 6869.5 0.6889256 0.6889256 9.417688e-03 9.417688e-03 0.301656669 #> 13741 6870.0 0.6889190 0.6889190 9.418219e-03 9.418219e-03 0.301662805 #> 13742 6870.5 0.6889123 0.6889123 9.418748e-03 9.418748e-03 0.301668947 #> 13743 6871.0 0.6889056 0.6889056 9.419276e-03 9.419276e-03 0.301675094 #> 13744 6871.5 0.6888989 0.6888989 9.419803e-03 9.419803e-03 0.301681247 #> 13745 6872.0 0.6888923 0.6888923 9.420328e-03 9.420328e-03 0.301687406 #> 13746 6872.5 0.6888856 0.6888856 9.420851e-03 9.420851e-03 0.301693571 #> 13747 6873.0 0.6888789 0.6888789 9.421373e-03 9.421373e-03 0.301699741 #> 13748 6873.5 0.6888722 0.6888722 9.421893e-03 9.421893e-03 0.301705917 #> 13749 6874.0 0.6888655 0.6888655 9.422412e-03 9.422412e-03 0.301712098 #> 13750 6874.5 0.6888588 0.6888588 9.422929e-03 9.422929e-03 0.301718285 #> 13751 6875.0 0.6888521 0.6888521 9.423445e-03 9.423445e-03 0.301724478 #> 13752 6875.5 0.6888454 0.6888454 9.423959e-03 9.423959e-03 0.301730676 #> 13753 6876.0 0.6888386 0.6888386 9.424471e-03 9.424471e-03 0.301736879 #> 13754 6876.5 0.6888319 0.6888319 9.424982e-03 9.424982e-03 0.301743088 #> 13755 6877.0 0.6888252 0.6888252 9.425492e-03 9.425492e-03 0.301749302 #> 13756 6877.5 0.6888185 0.6888185 9.426000e-03 9.426000e-03 0.301755521 #> 13757 6878.0 0.6888117 0.6888117 9.426506e-03 9.426506e-03 0.301761746 #> 13758 6878.5 0.6888050 0.6888050 9.427011e-03 9.427011e-03 0.301767977 #> 13759 6879.0 0.6887983 0.6887983 9.427514e-03 9.427514e-03 0.301774212 #> 13760 6879.5 0.6887915 0.6887915 9.428015e-03 9.428015e-03 0.301780453 #> 13761 6880.0 0.6887848 0.6887848 9.428515e-03 9.428515e-03 0.301786699 #> 13762 6880.5 0.6887780 0.6887780 9.429014e-03 9.429014e-03 0.301792950 #> 13763 6881.0 0.6887713 0.6887713 9.429511e-03 9.429511e-03 0.301799206 #> 13764 6881.5 0.6887645 0.6887645 9.430006e-03 9.430006e-03 0.301805468 #> 13765 6882.0 0.6887578 0.6887578 9.430500e-03 9.430500e-03 0.301811734 #> 13766 6882.5 0.6887510 0.6887510 9.430992e-03 9.430992e-03 0.301818006 #> 13767 6883.0 0.6887442 0.6887442 9.431483e-03 9.431483e-03 0.301824283 #> 13768 6883.5 0.6887375 0.6887375 9.431972e-03 9.431972e-03 0.301830564 #> 13769 6884.0 0.6887307 0.6887307 9.432459e-03 9.432459e-03 0.301836851 #> 13770 6884.5 0.6887239 0.6887239 9.432945e-03 9.432945e-03 0.301843143 #> 13771 6885.0 0.6887171 0.6887171 9.433430e-03 9.433430e-03 0.301849440 #> 13772 6885.5 0.6887103 0.6887103 9.433912e-03 9.433912e-03 0.301855741 #> 13773 6886.0 0.6887036 0.6887036 9.434394e-03 9.434394e-03 0.301862048 #> 13774 6886.5 0.6886968 0.6886968 9.434873e-03 9.434873e-03 0.301868359 #> 13775 6887.0 0.6886900 0.6886900 9.435351e-03 9.435351e-03 0.301874675 #> 13776 6887.5 0.6886832 0.6886832 9.435828e-03 9.435828e-03 0.301880996 #> 13777 6888.0 0.6886764 0.6886764 9.436303e-03 9.436303e-03 0.301887322 #> 13778 6888.5 0.6886696 0.6886696 9.436776e-03 9.436776e-03 0.301893652 #> 13779 6889.0 0.6886628 0.6886628 9.437248e-03 9.437248e-03 0.301899987 #> 13780 6889.5 0.6886560 0.6886560 9.437718e-03 9.437718e-03 0.301906327 #> 13781 6890.0 0.6886491 0.6886491 9.438186e-03 9.438186e-03 0.301912671 #> 13782 6890.5 0.6886423 0.6886423 9.438653e-03 9.438653e-03 0.301919020 #> 13783 6891.0 0.6886355 0.6886355 9.439119e-03 9.439119e-03 0.301925374 #> 13784 6891.5 0.6886287 0.6886287 9.439583e-03 9.439583e-03 0.301931732 #> 13785 6892.0 0.6886219 0.6886219 9.440045e-03 9.440045e-03 0.301938095 #> 13786 6892.5 0.6886150 0.6886150 9.440506e-03 9.440506e-03 0.301944462 #> 13787 6893.0 0.6886082 0.6886082 9.440965e-03 9.440965e-03 0.301950834 #> 13788 6893.5 0.6886014 0.6886014 9.441422e-03 9.441422e-03 0.301957210 #> 13789 6894.0 0.6885945 0.6885945 9.441878e-03 9.441878e-03 0.301963591 #> 13790 6894.5 0.6885877 0.6885877 9.442332e-03 9.442332e-03 0.301969976 #> 13791 6895.0 0.6885808 0.6885808 9.442785e-03 9.442785e-03 0.301976365 #> 13792 6895.5 0.6885740 0.6885740 9.443236e-03 9.443236e-03 0.301982759 #> 13793 6896.0 0.6885672 0.6885672 9.443685e-03 9.443685e-03 0.301989157 #> 13794 6896.5 0.6885603 0.6885603 9.444133e-03 9.444133e-03 0.301995559 #> 13795 6897.0 0.6885535 0.6885535 9.444580e-03 9.444580e-03 0.302001966 #> 13796 6897.5 0.6885466 0.6885466 9.445024e-03 9.445024e-03 0.302008377 #> 13797 6898.0 0.6885397 0.6885397 9.445467e-03 9.445467e-03 0.302014792 #> 13798 6898.5 0.6885329 0.6885329 9.445909e-03 9.445909e-03 0.302021211 #> 13799 6899.0 0.6885260 0.6885260 9.446349e-03 9.446349e-03 0.302027634 #> 13800 6899.5 0.6885192 0.6885192 9.446787e-03 9.446787e-03 0.302034062 #> 13801 6900.0 0.6885123 0.6885123 9.447224e-03 9.447224e-03 0.302040493 #> 13802 6900.5 0.6885054 0.6885054 9.447659e-03 9.447659e-03 0.302046929 #> 13803 6901.0 0.6884985 0.6884985 9.448092e-03 9.448092e-03 0.302053368 #> 13804 6901.5 0.6884917 0.6884917 9.448524e-03 9.448524e-03 0.302059812 #> 13805 6902.0 0.6884848 0.6884848 9.448955e-03 9.448955e-03 0.302066259 #> 13806 6902.5 0.6884779 0.6884779 9.449383e-03 9.449383e-03 0.302072710 #> 13807 6903.0 0.6884710 0.6884710 9.449810e-03 9.449810e-03 0.302079166 #> 13808 6903.5 0.6884641 0.6884641 9.450236e-03 9.450236e-03 0.302085625 #> 13809 6904.0 0.6884573 0.6884573 9.450660e-03 9.450660e-03 0.302092088 #> 13810 6904.5 0.6884504 0.6884504 9.451082e-03 9.451082e-03 0.302098555 #> 13811 6905.0 0.6884435 0.6884435 9.451503e-03 9.451503e-03 0.302105025 #> 13812 6905.5 0.6884366 0.6884366 9.451922e-03 9.451922e-03 0.302111500 #> 13813 6906.0 0.6884297 0.6884297 9.452339e-03 9.452339e-03 0.302117978 #> 13814 6906.5 0.6884228 0.6884228 9.452755e-03 9.452755e-03 0.302124460 #> 13815 6907.0 0.6884159 0.6884159 9.453169e-03 9.453169e-03 0.302130945 #> 13816 6907.5 0.6884090 0.6884090 9.453582e-03 9.453582e-03 0.302137435 #> 13817 6908.0 0.6884021 0.6884021 9.453993e-03 9.453993e-03 0.302143927 #> 13818 6908.5 0.6883952 0.6883952 9.454402e-03 9.454402e-03 0.302150424 #> 13819 6909.0 0.6883883 0.6883883 9.454810e-03 9.454810e-03 0.302156924 #> 13820 6909.5 0.6883814 0.6883814 9.455216e-03 9.455216e-03 0.302163427 #> 13821 6910.0 0.6883744 0.6883744 9.455620e-03 9.455620e-03 0.302169934 #> 13822 6910.5 0.6883675 0.6883675 9.456023e-03 9.456023e-03 0.302176444 #> 13823 6911.0 0.6883606 0.6883606 9.456424e-03 9.456424e-03 0.302182958 #> 13824 6911.5 0.6883537 0.6883537 9.456824e-03 9.456824e-03 0.302189476 #> 13825 6912.0 0.6883468 0.6883468 9.457222e-03 9.457222e-03 0.302195996 #> 13826 6912.5 0.6883399 0.6883399 9.457619e-03 9.457619e-03 0.302202520 #> 13827 6913.0 0.6883329 0.6883329 9.458013e-03 9.458013e-03 0.302209048 #> 13828 6913.5 0.6883260 0.6883260 9.458407e-03 9.458407e-03 0.302215578 #> 13829 6914.0 0.6883191 0.6883191 9.458798e-03 9.458798e-03 0.302222112 #> 13830 6914.5 0.6883122 0.6883122 9.459188e-03 9.459188e-03 0.302228650 #> 13831 6915.0 0.6883052 0.6883052 9.459576e-03 9.459576e-03 0.302235190 #> 13832 6915.5 0.6882983 0.6882983 9.459963e-03 9.459963e-03 0.302241734 #> 13833 6916.0 0.6882914 0.6882914 9.460348e-03 9.460348e-03 0.302248280 #> 13834 6916.5 0.6882844 0.6882844 9.460732e-03 9.460732e-03 0.302254830 #> 13835 6917.0 0.6882775 0.6882775 9.461113e-03 9.461113e-03 0.302261383 #> 13836 6917.5 0.6882706 0.6882706 9.461494e-03 9.461494e-03 0.302267939 #> 13837 6918.0 0.6882636 0.6882636 9.461872e-03 9.461872e-03 0.302274498 #> 13838 6918.5 0.6882567 0.6882567 9.462249e-03 9.462249e-03 0.302281061 #> 13839 6919.0 0.6882497 0.6882497 9.462624e-03 9.462624e-03 0.302287626 #> 13840 6919.5 0.6882428 0.6882428 9.462998e-03 9.462998e-03 0.302294194 #> 13841 6920.0 0.6882359 0.6882359 9.463370e-03 9.463370e-03 0.302300765 #> 13842 6920.5 0.6882289 0.6882289 9.463741e-03 9.463741e-03 0.302307339 #> 13843 6921.0 0.6882220 0.6882220 9.464109e-03 9.464109e-03 0.302313915 #> 13844 6921.5 0.6882150 0.6882150 9.464477e-03 9.464477e-03 0.302320495 #> 13845 6922.0 0.6882081 0.6882081 9.464842e-03 9.464842e-03 0.302327077 #> 13846 6922.5 0.6882011 0.6882011 9.465206e-03 9.465206e-03 0.302333663 #> 13847 6923.0 0.6881942 0.6881942 9.465568e-03 9.465568e-03 0.302340251 #> 13848 6923.5 0.6881872 0.6881872 9.465929e-03 9.465929e-03 0.302346841 #> 13849 6924.0 0.6881803 0.6881803 9.466288e-03 9.466288e-03 0.302353435 #> 13850 6924.5 0.6881733 0.6881733 9.466645e-03 9.466645e-03 0.302360031 #> 13851 6925.0 0.6881664 0.6881664 9.467001e-03 9.467001e-03 0.302366630 #> 13852 6925.5 0.6881594 0.6881594 9.467355e-03 9.467355e-03 0.302373231 #> 13853 6926.0 0.6881525 0.6881525 9.467708e-03 9.467708e-03 0.302379835 #> 13854 6926.5 0.6881455 0.6881455 9.468059e-03 9.468059e-03 0.302386441 #> 13855 6927.0 0.6881385 0.6881385 9.468408e-03 9.468408e-03 0.302393050 #> 13856 6927.5 0.6881316 0.6881316 9.468755e-03 9.468755e-03 0.302399662 #> 13857 6928.0 0.6881246 0.6881246 9.469101e-03 9.469101e-03 0.302406276 #> 13858 6928.5 0.6881177 0.6881177 9.469446e-03 9.469446e-03 0.302412892 #> 13859 6929.0 0.6881107 0.6881107 9.469788e-03 9.469788e-03 0.302419511 #> 13860 6929.5 0.6881037 0.6881037 9.470129e-03 9.470129e-03 0.302426132 #> 13861 6930.0 0.6880968 0.6880968 9.470469e-03 9.470469e-03 0.302432756 #> 13862 6930.5 0.6880898 0.6880898 9.470807e-03 9.470807e-03 0.302439382 #> 13863 6931.0 0.6880828 0.6880828 9.471143e-03 9.471143e-03 0.302446010 #> 13864 6931.5 0.6880759 0.6880759 9.471477e-03 9.471477e-03 0.302452641 #> 13865 6932.0 0.6880689 0.6880689 9.471810e-03 9.471810e-03 0.302459273 #> 13866 6932.5 0.6880620 0.6880620 9.472141e-03 9.472141e-03 0.302465908 #> 13867 6933.0 0.6880550 0.6880550 9.472471e-03 9.472471e-03 0.302472546 #> 13868 6933.5 0.6880480 0.6880480 9.472799e-03 9.472799e-03 0.302479185 #> 13869 6934.0 0.6880410 0.6880410 9.473125e-03 9.473125e-03 0.302485826 #> 13870 6934.5 0.6880341 0.6880341 9.473450e-03 9.473450e-03 0.302492470 #> 13871 6935.0 0.6880271 0.6880271 9.473773e-03 9.473773e-03 0.302499115 #> 13872 6935.5 0.6880201 0.6880201 9.474094e-03 9.474094e-03 0.302505763 #> 13873 6936.0 0.6880132 0.6880132 9.474414e-03 9.474414e-03 0.302512413 #> 13874 6936.5 0.6880062 0.6880062 9.474732e-03 9.474732e-03 0.302519065 #> 13875 6937.0 0.6879992 0.6879992 9.475049e-03 9.475049e-03 0.302525718 #> 13876 6937.5 0.6879923 0.6879923 9.475364e-03 9.475364e-03 0.302532374 #> 13877 6938.0 0.6879853 0.6879853 9.475677e-03 9.475677e-03 0.302539031 #> 13878 6938.5 0.6879783 0.6879783 9.475988e-03 9.475988e-03 0.302545691 #> 13879 6939.0 0.6879713 0.6879713 9.476298e-03 9.476298e-03 0.302552352 #> 13880 6939.5 0.6879644 0.6879644 9.476606e-03 9.476606e-03 0.302559015 #> 13881 6940.0 0.6879574 0.6879574 9.476913e-03 9.476913e-03 0.302565680 #> 13882 6940.5 0.6879504 0.6879504 9.477218e-03 9.477218e-03 0.302572346 #> 13883 6941.0 0.6879435 0.6879435 9.477521e-03 9.477521e-03 0.302579015 #> 13884 6941.5 0.6879365 0.6879365 9.477823e-03 9.477823e-03 0.302585685 #> 13885 6942.0 0.6879295 0.6879295 9.478123e-03 9.478123e-03 0.302592356 #> 13886 6942.5 0.6879225 0.6879225 9.478422e-03 9.478422e-03 0.302599030 #> 13887 6943.0 0.6879156 0.6879156 9.478718e-03 9.478718e-03 0.302605705 #> 13888 6943.5 0.6879086 0.6879086 9.479013e-03 9.479013e-03 0.302612381 #> 13889 6944.0 0.6879016 0.6879016 9.479307e-03 9.479307e-03 0.302619059 #> 13890 6944.5 0.6878947 0.6878947 9.479599e-03 9.479599e-03 0.302625739 #> 13891 6945.0 0.6878877 0.6878877 9.479889e-03 9.479889e-03 0.302632420 #> 13892 6945.5 0.6878807 0.6878807 9.480178e-03 9.480178e-03 0.302639103 #> 13893 6946.0 0.6878737 0.6878737 9.480464e-03 9.480464e-03 0.302645787 #> 13894 6946.5 0.6878668 0.6878668 9.480750e-03 9.480750e-03 0.302652473 #> 13895 6947.0 0.6878598 0.6878598 9.481033e-03 9.481033e-03 0.302659160 #> 13896 6947.5 0.6878528 0.6878528 9.481315e-03 9.481315e-03 0.302665848 #> 13897 6948.0 0.6878459 0.6878459 9.481596e-03 9.481596e-03 0.302672538 #> 13898 6948.5 0.6878389 0.6878389 9.481874e-03 9.481874e-03 0.302679229 #> 13899 6949.0 0.6878319 0.6878319 9.482151e-03 9.482151e-03 0.302685921 #> 13900 6949.5 0.6878250 0.6878250 9.482427e-03 9.482427e-03 0.302692614 #> 13901 6950.0 0.6878180 0.6878180 9.482701e-03 9.482701e-03 0.302699309 #> 13902 6950.5 0.6878110 0.6878110 9.482973e-03 9.482973e-03 0.302706005 #> 13903 6951.0 0.6878041 0.6878041 9.483243e-03 9.483243e-03 0.302712702 #> 13904 6951.5 0.6877971 0.6877971 9.483512e-03 9.483512e-03 0.302719400 #> 13905 6952.0 0.6877901 0.6877901 9.483779e-03 9.483779e-03 0.302726100 #> 13906 6952.5 0.6877832 0.6877832 9.484045e-03 9.484045e-03 0.302732800 #> 13907 6953.0 0.6877762 0.6877762 9.484309e-03 9.484309e-03 0.302739502 #> 13908 6953.5 0.6877692 0.6877692 9.484571e-03 9.484571e-03 0.302746204 #> 13909 6954.0 0.6877623 0.6877623 9.484832e-03 9.484832e-03 0.302752908 #> 13910 6954.5 0.6877553 0.6877553 9.485091e-03 9.485091e-03 0.302759613 #> 13911 6955.0 0.6877483 0.6877483 9.485348e-03 9.485348e-03 0.302766318 #> 13912 6955.5 0.6877414 0.6877414 9.485604e-03 9.485604e-03 0.302773025 #> 13913 6956.0 0.6877344 0.6877344 9.485858e-03 9.485858e-03 0.302779732 #> 13914 6956.5 0.6877275 0.6877275 9.486110e-03 9.486110e-03 0.302786440 #> 13915 6957.0 0.6877205 0.6877205 9.486361e-03 9.486361e-03 0.302793149 #> 13916 6957.5 0.6877135 0.6877135 9.486610e-03 9.486610e-03 0.302799859 #> 13917 6958.0 0.6877066 0.6877066 9.486857e-03 9.486857e-03 0.302806570 #> 13918 6958.5 0.6876996 0.6876996 9.487103e-03 9.487103e-03 0.302813281 #> 13919 6959.0 0.6876927 0.6876927 9.487347e-03 9.487347e-03 0.302819993 #> 13920 6959.5 0.6876857 0.6876857 9.487590e-03 9.487590e-03 0.302826706 #> 13921 6960.0 0.6876788 0.6876788 9.487831e-03 9.487831e-03 0.302833419 #> 13922 6960.5 0.6876718 0.6876718 9.488070e-03 9.488070e-03 0.302840133 #> 13923 6961.0 0.6876648 0.6876648 9.488307e-03 9.488307e-03 0.302846848 #> 13924 6961.5 0.6876579 0.6876579 9.488543e-03 9.488543e-03 0.302853563 #> 13925 6962.0 0.6876509 0.6876509 9.488778e-03 9.488778e-03 0.302860279 #> 13926 6962.5 0.6876440 0.6876440 9.489010e-03 9.489010e-03 0.302866996 #> 13927 6963.0 0.6876370 0.6876370 9.489241e-03 9.489241e-03 0.302873713 #> 13928 6963.5 0.6876301 0.6876301 9.489471e-03 9.489471e-03 0.302880430 #> 13929 6964.0 0.6876232 0.6876232 9.489699e-03 9.489699e-03 0.302887148 #> 13930 6964.5 0.6876162 0.6876162 9.489925e-03 9.489925e-03 0.302893866 #> 13931 6965.0 0.6876093 0.6876093 9.490149e-03 9.490149e-03 0.302900585 #> 13932 6965.5 0.6876023 0.6876023 9.490372e-03 9.490372e-03 0.302907304 #> 13933 6966.0 0.6875954 0.6875954 9.490593e-03 9.490593e-03 0.302914023 #> 13934 6966.5 0.6875884 0.6875884 9.490813e-03 9.490813e-03 0.302920743 #> 13935 6967.0 0.6875815 0.6875815 9.491031e-03 9.491031e-03 0.302927463 #> 13936 6967.5 0.6875746 0.6875746 9.491247e-03 9.491247e-03 0.302934183 #> 13937 6968.0 0.6875676 0.6875676 9.491461e-03 9.491461e-03 0.302940904 #> 13938 6968.5 0.6875607 0.6875607 9.491674e-03 9.491674e-03 0.302947624 #> 13939 6969.0 0.6875538 0.6875538 9.491886e-03 9.491886e-03 0.302954345 #> 13940 6969.5 0.6875468 0.6875468 9.492095e-03 9.492095e-03 0.302961066 #> 13941 6970.0 0.6875399 0.6875399 9.492303e-03 9.492303e-03 0.302967787 #> 13942 6970.5 0.6875330 0.6875330 9.492510e-03 9.492510e-03 0.302974508 #> 13943 6971.0 0.6875261 0.6875261 9.492715e-03 9.492715e-03 0.302981230 #> 13944 6971.5 0.6875191 0.6875191 9.492918e-03 9.492918e-03 0.302987951 #> 13945 6972.0 0.6875122 0.6875122 9.493119e-03 9.493119e-03 0.302994672 #> 13946 6972.5 0.6875053 0.6875053 9.493319e-03 9.493319e-03 0.303001394 #> 13947 6973.0 0.6874984 0.6874984 9.493517e-03 9.493517e-03 0.303008115 #> 13948 6973.5 0.6874915 0.6874915 9.493714e-03 9.493714e-03 0.303014836 #> 13949 6974.0 0.6874845 0.6874845 9.493909e-03 9.493909e-03 0.303021557 #> 13950 6974.5 0.6874776 0.6874776 9.494102e-03 9.494102e-03 0.303028278 #> 13951 6975.0 0.6874707 0.6874707 9.494294e-03 9.494294e-03 0.303034999 #> 13952 6975.5 0.6874638 0.6874638 9.494484e-03 9.494484e-03 0.303041720 #> 13953 6976.0 0.6874569 0.6874569 9.494672e-03 9.494672e-03 0.303048440 #> 13954 6976.5 0.6874500 0.6874500 9.494859e-03 9.494859e-03 0.303055160 #> 13955 6977.0 0.6874431 0.6874431 9.495044e-03 9.495044e-03 0.303061880 #> 13956 6977.5 0.6874362 0.6874362 9.495228e-03 9.495228e-03 0.303068600 #> 13957 6978.0 0.6874293 0.6874293 9.495409e-03 9.495409e-03 0.303075319 #> 13958 6978.5 0.6874224 0.6874224 9.495590e-03 9.495590e-03 0.303082038 #> 13959 6979.0 0.6874155 0.6874155 9.495768e-03 9.495768e-03 0.303088757 #> 13960 6979.5 0.6874086 0.6874086 9.495945e-03 9.495945e-03 0.303095475 #> 13961 6980.0 0.6874017 0.6874017 9.496120e-03 9.496120e-03 0.303102193 #> 13962 6980.5 0.6873948 0.6873948 9.496294e-03 9.496294e-03 0.303108910 #> 13963 6981.0 0.6873879 0.6873879 9.496466e-03 9.496466e-03 0.303115627 #> 13964 6981.5 0.6873810 0.6873810 9.496637e-03 9.496637e-03 0.303122343 #> 13965 6982.0 0.6873741 0.6873741 9.496805e-03 9.496805e-03 0.303129059 #> 13966 6982.5 0.6873673 0.6873673 9.496972e-03 9.496972e-03 0.303135774 #> 13967 6983.0 0.6873604 0.6873604 9.497138e-03 9.497138e-03 0.303142489 #> 13968 6983.5 0.6873535 0.6873535 9.497302e-03 9.497302e-03 0.303149203 #> 13969 6984.0 0.6873466 0.6873466 9.497464e-03 9.497464e-03 0.303155917 #> 13970 6984.5 0.6873397 0.6873397 9.497625e-03 9.497625e-03 0.303162629 #> 13971 6985.0 0.6873329 0.6873329 9.497784e-03 9.497784e-03 0.303169342 #> 13972 6985.5 0.6873260 0.6873260 9.497941e-03 9.497941e-03 0.303176053 #> 13973 6986.0 0.6873191 0.6873191 9.498097e-03 9.498097e-03 0.303182764 #> 13974 6986.5 0.6873123 0.6873123 9.498251e-03 9.498251e-03 0.303189473 #> 13975 6987.0 0.6873054 0.6873054 9.498404e-03 9.498404e-03 0.303196182 #> 13976 6987.5 0.6872986 0.6872986 9.498554e-03 9.498554e-03 0.303202891 #> 13977 6988.0 0.6872917 0.6872917 9.498704e-03 9.498704e-03 0.303209598 #> 13978 6988.5 0.6872848 0.6872848 9.498851e-03 9.498851e-03 0.303216304 #> 13979 6989.0 0.6872780 0.6872780 9.498997e-03 9.498997e-03 0.303223010 #> 13980 6989.5 0.6872711 0.6872711 9.499142e-03 9.499142e-03 0.303229715 #> 13981 6990.0 0.6872643 0.6872643 9.499284e-03 9.499284e-03 0.303236418 #> 13982 6990.5 0.6872575 0.6872575 9.499425e-03 9.499425e-03 0.303243121 #> 13983 6991.0 0.6872506 0.6872506 9.499565e-03 9.499565e-03 0.303249823 #> 13984 6991.5 0.6872438 0.6872438 9.499703e-03 9.499703e-03 0.303256523 #> 13985 6992.0 0.6872369 0.6872369 9.499839e-03 9.499839e-03 0.303263223 #> 13986 6992.5 0.6872301 0.6872301 9.499974e-03 9.499974e-03 0.303269921 #> 13987 6993.0 0.6872233 0.6872233 9.500107e-03 9.500107e-03 0.303276619 #> 13988 6993.5 0.6872164 0.6872164 9.500238e-03 9.500238e-03 0.303283315 #> 13989 6994.0 0.6872096 0.6872096 9.500368e-03 9.500368e-03 0.303290010 #> 13990 6994.5 0.6872028 0.6872028 9.500496e-03 9.500496e-03 0.303296704 #> 13991 6995.0 0.6871960 0.6871960 9.500622e-03 9.500622e-03 0.303303397 #> 13992 6995.5 0.6871892 0.6871892 9.500747e-03 9.500747e-03 0.303310088 #> 13993 6996.0 0.6871824 0.6871824 9.500871e-03 9.500871e-03 0.303316778 #> 13994 6996.5 0.6871755 0.6871755 9.500992e-03 9.500992e-03 0.303323467 #> 13995 6997.0 0.6871687 0.6871687 9.501112e-03 9.501112e-03 0.303330154 #> 13996 6997.5 0.6871619 0.6871619 9.501231e-03 9.501231e-03 0.303336840 #> 13997 6998.0 0.6871551 0.6871551 9.501348e-03 9.501348e-03 0.303343525 #> 13998 6998.5 0.6871483 0.6871483 9.501463e-03 9.501463e-03 0.303350209 #> 13999 6999.0 0.6871415 0.6871415 9.501577e-03 9.501577e-03 0.303356890 #> 14000 6999.5 0.6871347 0.6871347 9.501689e-03 9.501689e-03 0.303363571 #> 14001 7000.0 0.6871280 0.6871280 9.501799e-03 9.501799e-03 0.303370250 #> 14002 7000.5 0.6871212 0.6871212 9.501908e-03 9.501908e-03 0.303376927 #> 14003 7001.0 0.6871144 0.6871144 9.502015e-03 9.502015e-03 0.303383603 #> 14004 7001.5 0.6871076 0.6871076 9.502120e-03 9.502120e-03 0.303390277 #> 14005 7002.0 0.6871008 0.6871008 9.502224e-03 9.502224e-03 0.303396950 #> 14006 7002.5 0.6870941 0.6870941 9.502327e-03 9.502327e-03 0.303403621 #> 14007 7003.0 0.6870873 0.6870873 9.502427e-03 9.502427e-03 0.303410291 #> 14008 7003.5 0.6870805 0.6870805 9.502527e-03 9.502527e-03 0.303416959 #> 14009 7004.0 0.6870738 0.6870738 9.502624e-03 9.502624e-03 0.303423625 #> 14010 7004.5 0.6870670 0.6870670 9.502720e-03 9.502720e-03 0.303430289 #> 14011 7005.0 0.6870602 0.6870602 9.502814e-03 9.502814e-03 0.303436952 #> 14012 7005.5 0.6870535 0.6870535 9.502907e-03 9.502907e-03 0.303443613 #> 14013 7006.0 0.6870467 0.6870467 9.502998e-03 9.502998e-03 0.303450272 #> 14014 7006.5 0.6870400 0.6870400 9.503088e-03 9.503088e-03 0.303456929 #> 14015 7007.0 0.6870332 0.6870332 9.503175e-03 9.503175e-03 0.303463584 #> 14016 7007.5 0.6870265 0.6870265 9.503262e-03 9.503262e-03 0.303470238 #> 14017 7008.0 0.6870198 0.6870198 9.503346e-03 9.503346e-03 0.303476889 #> 14018 7008.5 0.6870130 0.6870130 9.503429e-03 9.503429e-03 0.303483539 #> 14019 7009.0 0.6870063 0.6870063 9.503511e-03 9.503511e-03 0.303490187 #> 14020 7009.5 0.6869996 0.6869996 9.503591e-03 9.503591e-03 0.303496833 #> 14021 7010.0 0.6869929 0.6869929 9.503669e-03 9.503669e-03 0.303503476 #> 14022 7010.5 0.6869861 0.6869861 9.503746e-03 9.503746e-03 0.303510118 #> 14023 7011.0 0.6869794 0.6869794 9.503821e-03 9.503821e-03 0.303516758 #> 14024 7011.5 0.6869727 0.6869727 9.503894e-03 9.503894e-03 0.303523395 #> 14025 7012.0 0.6869660 0.6869660 9.503966e-03 9.503966e-03 0.303530031 #> 14026 7012.5 0.6869593 0.6869593 9.504037e-03 9.504037e-03 0.303536664 #> 14027 7013.0 0.6869526 0.6869526 9.504105e-03 9.504105e-03 0.303543295 #> 14028 7013.5 0.6869459 0.6869459 9.504173e-03 9.504173e-03 0.303549924 #> 14029 7014.0 0.6869392 0.6869392 9.504238e-03 9.504238e-03 0.303556551 #> 14030 7014.5 0.6869325 0.6869325 9.504302e-03 9.504302e-03 0.303563176 #> 14031 7015.0 0.6869258 0.6869258 9.504365e-03 9.504365e-03 0.303569798 #> 14032 7015.5 0.6869192 0.6869192 9.504425e-03 9.504425e-03 0.303576418 #> 14033 7016.0 0.6869125 0.6869125 9.504484e-03 9.504484e-03 0.303583036 #> 14034 7016.5 0.6869058 0.6869058 9.504542e-03 9.504542e-03 0.303589651 #> 14035 7017.0 0.6868991 0.6868991 9.504598e-03 9.504598e-03 0.303596264 #> 14036 7017.5 0.6868925 0.6868925 9.504653e-03 9.504653e-03 0.303602875 #> 14037 7018.0 0.6868858 0.6868858 9.504705e-03 9.504705e-03 0.303609483 #> 14038 7018.5 0.6868792 0.6868792 9.504757e-03 9.504757e-03 0.303616089 #> 14039 7019.0 0.6868725 0.6868725 9.504806e-03 9.504806e-03 0.303622692 #> 14040 7019.5 0.6868659 0.6868659 9.504855e-03 9.504855e-03 0.303629293 #> 14041 7020.0 0.6868592 0.6868592 9.504901e-03 9.504901e-03 0.303635891 #> 14042 7020.5 0.6868526 0.6868526 9.504946e-03 9.504946e-03 0.303642487 #> 14043 7021.0 0.6868459 0.6868459 9.504989e-03 9.504989e-03 0.303649081 #> 14044 7021.5 0.6868393 0.6868393 9.505031e-03 9.505031e-03 0.303655671 #> 14045 7022.0 0.6868327 0.6868327 9.505071e-03 9.505071e-03 0.303662259 #> 14046 7022.5 0.6868260 0.6868260 9.505110e-03 9.505110e-03 0.303668845 #> 14047 7023.0 0.6868194 0.6868194 9.505147e-03 9.505147e-03 0.303675427 #> 14048 7023.5 0.6868128 0.6868128 9.505183e-03 9.505183e-03 0.303682007 #> 14049 7024.0 0.6868062 0.6868062 9.505217e-03 9.505217e-03 0.303688585 #> 14050 7024.5 0.6867996 0.6867996 9.505249e-03 9.505249e-03 0.303695159 #> 14051 7025.0 0.6867930 0.6867930 9.505280e-03 9.505280e-03 0.303701731 #> 14052 7025.5 0.6867864 0.6867864 9.505309e-03 9.505309e-03 0.303708300 #> 14053 7026.0 0.6867798 0.6867798 9.505337e-03 9.505337e-03 0.303714867 #> 14054 7026.5 0.6867732 0.6867732 9.505363e-03 9.505363e-03 0.303721430 #> 14055 7027.0 0.6867666 0.6867666 9.505387e-03 9.505387e-03 0.303727991 #> 14056 7027.5 0.6867600 0.6867600 9.505410e-03 9.505410e-03 0.303734548 #> 14057 7028.0 0.6867535 0.6867535 9.505432e-03 9.505432e-03 0.303741103 #> 14058 7028.5 0.6867469 0.6867469 9.505452e-03 9.505452e-03 0.303747655 #> 14059 7029.0 0.6867403 0.6867403 9.505470e-03 9.505470e-03 0.303754204 #> 14060 7029.5 0.6867338 0.6867338 9.505487e-03 9.505487e-03 0.303760750 #> 14061 7030.0 0.6867272 0.6867272 9.505502e-03 9.505502e-03 0.303767293 #> 14062 7030.5 0.6867207 0.6867207 9.505515e-03 9.505515e-03 0.303773832 #> 14063 7031.0 0.6867141 0.6867141 9.505527e-03 9.505527e-03 0.303780369 #> 14064 7031.5 0.6867076 0.6867076 9.505538e-03 9.505538e-03 0.303786903 #> 14065 7032.0 0.6867010 0.6867010 9.505547e-03 9.505547e-03 0.303793434 #> 14066 7032.5 0.6866945 0.6866945 9.505554e-03 9.505554e-03 0.303799961 #> 14067 7033.0 0.6866880 0.6866880 9.505560e-03 9.505560e-03 0.303806485 #> 14068 7033.5 0.6866814 0.6866814 9.505564e-03 9.505564e-03 0.303813007 #> 14069 7034.0 0.6866749 0.6866749 9.505567e-03 9.505567e-03 0.303819525 #> 14070 7034.5 0.6866684 0.6866684 9.505568e-03 9.505568e-03 0.303826039 #> 14071 7035.0 0.6866619 0.6866619 9.505568e-03 9.505568e-03 0.303832551 #> 14072 7035.5 0.6866554 0.6866554 9.505566e-03 9.505566e-03 0.303839059 #> 14073 7036.0 0.6866489 0.6866489 9.505563e-03 9.505563e-03 0.303845564 #> 14074 7036.5 0.6866424 0.6866424 9.505558e-03 9.505558e-03 0.303852066 #> 14075 7037.0 0.6866359 0.6866359 9.505551e-03 9.505551e-03 0.303858564 #> 14076 7037.5 0.6866294 0.6866294 9.505543e-03 9.505543e-03 0.303865059 #> 14077 7038.0 0.6866229 0.6866229 9.505533e-03 9.505533e-03 0.303871550 #> 14078 7038.5 0.6866164 0.6866164 9.505522e-03 9.505522e-03 0.303878038 #> 14079 7039.0 0.6866100 0.6866100 9.505509e-03 9.505509e-03 0.303884523 #> 14080 7039.5 0.6866035 0.6866035 9.505495e-03 9.505495e-03 0.303891004 #> 14081 7040.0 0.6865970 0.6865970 9.505479e-03 9.505479e-03 0.303897482 #> 14082 7040.5 0.6865906 0.6865906 9.505462e-03 9.505462e-03 0.303903956 #> 14083 7041.0 0.6865841 0.6865841 9.505443e-03 9.505443e-03 0.303910426 #> 14084 7041.5 0.6865777 0.6865777 9.505423e-03 9.505423e-03 0.303916893 #> 14085 7042.0 0.6865712 0.6865712 9.505401e-03 9.505401e-03 0.303923357 #> 14086 7042.5 0.6865648 0.6865648 9.505378e-03 9.505378e-03 0.303929817 #> 14087 7043.0 0.6865584 0.6865584 9.505353e-03 9.505353e-03 0.303936273 #> 14088 7043.5 0.6865519 0.6865519 9.505326e-03 9.505326e-03 0.303942726 #> 14089 7044.0 0.6865455 0.6865455 9.505298e-03 9.505298e-03 0.303949175 #> 14090 7044.5 0.6865391 0.6865391 9.505269e-03 9.505269e-03 0.303955620 #> 14091 7045.0 0.6865327 0.6865327 9.505237e-03 9.505237e-03 0.303962061 #> 14092 7045.5 0.6865263 0.6865263 9.505205e-03 9.505205e-03 0.303968499 #> 14093 7046.0 0.6865199 0.6865199 9.505171e-03 9.505171e-03 0.303974933 #> 14094 7046.5 0.6865135 0.6865135 9.505135e-03 9.505135e-03 0.303981363 #> 14095 7047.0 0.6865071 0.6865071 9.505098e-03 9.505098e-03 0.303987789 #> 14096 7047.5 0.6865007 0.6865007 9.505059e-03 9.505059e-03 0.303994212 #> 14097 7048.0 0.6864944 0.6864944 9.505019e-03 9.505019e-03 0.304000630 #> 14098 7048.5 0.6864880 0.6864880 9.504977e-03 9.504977e-03 0.304007045 #> 14099 7049.0 0.6864816 0.6864816 9.504934e-03 9.504934e-03 0.304013456 #> 14100 7049.5 0.6864752 0.6864752 9.504889e-03 9.504889e-03 0.304019863 #> 14101 7050.0 0.6864689 0.6864689 9.504843e-03 9.504843e-03 0.304026266 #> 14102 7050.5 0.6864625 0.6864625 9.504795e-03 9.504795e-03 0.304032665 #> 14103 7051.0 0.6864562 0.6864562 9.504746e-03 9.504746e-03 0.304039060 #> 14104 7051.5 0.6864499 0.6864499 9.504695e-03 9.504695e-03 0.304045450 #> 14105 7052.0 0.6864435 0.6864435 9.504643e-03 9.504643e-03 0.304051837 #> 14106 7052.5 0.6864372 0.6864372 9.504589e-03 9.504589e-03 0.304058220 #> 14107 7053.0 0.6864309 0.6864309 9.504534e-03 9.504534e-03 0.304064599 #> 14108 7053.5 0.6864245 0.6864245 9.504477e-03 9.504477e-03 0.304070973 #> 14109 7054.0 0.6864182 0.6864182 9.504419e-03 9.504419e-03 0.304077344 #> 14110 7054.5 0.6864119 0.6864119 9.504359e-03 9.504359e-03 0.304083710 #> 14111 7055.0 0.6864056 0.6864056 9.504298e-03 9.504298e-03 0.304090072 #> 14112 7055.5 0.6863993 0.6863993 9.504235e-03 9.504235e-03 0.304096430 #> 14113 7056.0 0.6863930 0.6863930 9.504171e-03 9.504171e-03 0.304102783 #> 14114 7056.5 0.6863868 0.6863868 9.504105e-03 9.504105e-03 0.304109133 #> 14115 7057.0 0.6863805 0.6863805 9.504038e-03 9.504038e-03 0.304115478 #> 14116 7057.5 0.6863742 0.6863742 9.503969e-03 9.503969e-03 0.304121818 #> 14117 7058.0 0.6863679 0.6863679 9.503899e-03 9.503899e-03 0.304128155 #> 14118 7058.5 0.6863617 0.6863617 9.503827e-03 9.503827e-03 0.304134487 #> 14119 7059.0 0.6863554 0.6863554 9.503754e-03 9.503754e-03 0.304140815 #> 14120 7059.5 0.6863492 0.6863492 9.503679e-03 9.503679e-03 0.304147138 #> 14121 7060.0 0.6863429 0.6863429 9.503603e-03 9.503603e-03 0.304153457 #> 14122 7060.5 0.6863367 0.6863367 9.503526e-03 9.503526e-03 0.304159771 #> 14123 7061.0 0.6863305 0.6863305 9.503446e-03 9.503446e-03 0.304166081 #> 14124 7061.5 0.6863242 0.6863242 9.503366e-03 9.503366e-03 0.304172387 #> 14125 7062.0 0.6863180 0.6863180 9.503284e-03 9.503284e-03 0.304178688 #> 14126 7062.5 0.6863118 0.6863118 9.503200e-03 9.503200e-03 0.304184984 #> 14127 7063.0 0.6863056 0.6863056 9.503115e-03 9.503115e-03 0.304191276 #> 14128 7063.5 0.6862994 0.6862994 9.503029e-03 9.503029e-03 0.304197563 #> 14129 7064.0 0.6862932 0.6862932 9.502941e-03 9.502941e-03 0.304203846 #> 14130 7064.5 0.6862870 0.6862870 9.502851e-03 9.502851e-03 0.304210124 #> 14131 7065.0 0.6862808 0.6862808 9.502760e-03 9.502760e-03 0.304216398 #> 14132 7065.5 0.6862747 0.6862747 9.502668e-03 9.502668e-03 0.304222666 #> 14133 7066.0 0.6862685 0.6862685 9.502574e-03 9.502574e-03 0.304228930 #> 14134 7066.5 0.6862623 0.6862623 9.502479e-03 9.502479e-03 0.304235190 #> 14135 7067.0 0.6862562 0.6862562 9.502382e-03 9.502382e-03 0.304241445 #> 14136 7067.5 0.6862500 0.6862500 9.502284e-03 9.502284e-03 0.304247694 #> 14137 7068.0 0.6862439 0.6862439 9.502184e-03 9.502184e-03 0.304253940 #> 14138 7068.5 0.6862377 0.6862377 9.502083e-03 9.502083e-03 0.304260180 #> 14139 7069.0 0.6862316 0.6862316 9.501980e-03 9.501980e-03 0.304266416 #> 14140 7069.5 0.6862255 0.6862255 9.501876e-03 9.501876e-03 0.304272646 #> 14141 7070.0 0.6862194 0.6862194 9.501770e-03 9.501770e-03 0.304278872 #> 14142 7070.5 0.6862132 0.6862132 9.501663e-03 9.501663e-03 0.304285093 #> 14143 7071.0 0.6862071 0.6862071 9.501555e-03 9.501555e-03 0.304291309 #> 14144 7071.5 0.6862010 0.6862010 9.501445e-03 9.501445e-03 0.304297520 #> 14145 7072.0 0.6861949 0.6861949 9.501334e-03 9.501334e-03 0.304303726 #> 14146 7072.5 0.6861889 0.6861889 9.501221e-03 9.501221e-03 0.304309928 #> 14147 7073.0 0.6861828 0.6861828 9.501107e-03 9.501107e-03 0.304316124 #> 14148 7073.5 0.6861767 0.6861767 9.500991e-03 9.500991e-03 0.304322315 #> 14149 7074.0 0.6861706 0.6861706 9.500874e-03 9.500874e-03 0.304328501 #> 14150 7074.5 0.6861646 0.6861646 9.500756e-03 9.500756e-03 0.304334682 #> 14151 7075.0 0.6861585 0.6861585 9.500636e-03 9.500636e-03 0.304340858 #> 14152 7075.5 0.6861525 0.6861525 9.500514e-03 9.500514e-03 0.304347029 #> 14153 7076.0 0.6861464 0.6861464 9.500391e-03 9.500391e-03 0.304353195 #> 14154 7076.5 0.6861404 0.6861404 9.500267e-03 9.500267e-03 0.304359356 #> 14155 7077.0 0.6861343 0.6861343 9.500141e-03 9.500141e-03 0.304365511 #> 14156 7077.5 0.6861283 0.6861283 9.500014e-03 9.500014e-03 0.304371662 #> 14157 7078.0 0.6861223 0.6861223 9.499886e-03 9.499886e-03 0.304377807 #> 14158 7078.5 0.6861163 0.6861163 9.499756e-03 9.499756e-03 0.304383947 #> 14159 7079.0 0.6861103 0.6861103 9.499624e-03 9.499624e-03 0.304390081 #> 14160 7079.5 0.6861043 0.6861043 9.499491e-03 9.499491e-03 0.304396211 #> 14161 7080.0 0.6860983 0.6860983 9.499357e-03 9.499357e-03 0.304402335 #> 14162 7080.5 0.6860923 0.6860923 9.499221e-03 9.499221e-03 0.304408454 #> 14163 7081.0 0.6860863 0.6860863 9.499084e-03 9.499084e-03 0.304414567 #> 14164 7081.5 0.6860804 0.6860804 9.498946e-03 9.498946e-03 0.304420675 #> 14165 7082.0 0.6860744 0.6860744 9.498806e-03 9.498806e-03 0.304426778 #> 14166 7082.5 0.6860685 0.6860685 9.498664e-03 9.498664e-03 0.304432875 #> 14167 7083.0 0.6860625 0.6860625 9.498522e-03 9.498522e-03 0.304438967 #> 14168 7083.5 0.6860566 0.6860566 9.498378e-03 9.498378e-03 0.304445054 #> 14169 7084.0 0.6860506 0.6860506 9.498232e-03 9.498232e-03 0.304451135 #> 14170 7084.5 0.6860447 0.6860447 9.498085e-03 9.498085e-03 0.304457210 #> 14171 7085.0 0.6860388 0.6860388 9.497937e-03 9.497937e-03 0.304463280 #> 14172 7085.5 0.6860329 0.6860329 9.497787e-03 9.497787e-03 0.304469345 #> 14173 7086.0 0.6860270 0.6860270 9.497636e-03 9.497636e-03 0.304475404 #> 14174 7086.5 0.6860211 0.6860211 9.497483e-03 9.497483e-03 0.304481457 #> 14175 7087.0 0.6860152 0.6860152 9.497329e-03 9.497329e-03 0.304487505 #> 14176 7087.5 0.6860093 0.6860093 9.497174e-03 9.497174e-03 0.304493547 #> 14177 7088.0 0.6860034 0.6860034 9.497017e-03 9.497017e-03 0.304499584 #> 14178 7088.5 0.6859975 0.6859975 9.496859e-03 9.496859e-03 0.304505615 #> 14179 7089.0 0.6859917 0.6859917 9.496699e-03 9.496699e-03 0.304511640 #> 14180 7089.5 0.6859858 0.6859858 9.496538e-03 9.496538e-03 0.304517660 #> 14181 7090.0 0.6859800 0.6859800 9.496376e-03 9.496376e-03 0.304523674 #> 14182 7090.5 0.6859741 0.6859741 9.496212e-03 9.496212e-03 0.304529682 #> 14183 7091.0 0.6859683 0.6859683 9.496047e-03 9.496047e-03 0.304535685 #> 14184 7091.5 0.6859624 0.6859624 9.495881e-03 9.495881e-03 0.304541681 #> 14185 7092.0 0.6859566 0.6859566 9.495713e-03 9.495713e-03 0.304547672 #> 14186 7092.5 0.6859508 0.6859508 9.495543e-03 9.495543e-03 0.304553658 #> 14187 7093.0 0.6859450 0.6859450 9.495373e-03 9.495373e-03 0.304559637 #> 14188 7093.5 0.6859392 0.6859392 9.495201e-03 9.495201e-03 0.304565610 #> 14189 7094.0 0.6859334 0.6859334 9.495027e-03 9.495027e-03 0.304571578 #> 14190 7094.5 0.6859276 0.6859276 9.494853e-03 9.494853e-03 0.304577540 #> 14191 7095.0 0.6859218 0.6859218 9.494677e-03 9.494677e-03 0.304583495 #> 14192 7095.5 0.6859161 0.6859161 9.494499e-03 9.494499e-03 0.304589445 #> 14193 7096.0 0.6859103 0.6859103 9.494320e-03 9.494320e-03 0.304595389 #> 14194 7096.5 0.6859045 0.6859045 9.494140e-03 9.494140e-03 0.304601327 #> 14195 7097.0 0.6858988 0.6858988 9.493958e-03 9.493958e-03 0.304607259 #> 14196 7097.5 0.6858930 0.6858930 9.493775e-03 9.493775e-03 0.304613185 #> 14197 7098.0 0.6858873 0.6858873 9.493591e-03 9.493591e-03 0.304619105 #> 14198 7098.5 0.6858816 0.6858816 9.493406e-03 9.493406e-03 0.304625019 #> 14199 7099.0 0.6858759 0.6858759 9.493219e-03 9.493219e-03 0.304630927 #> 14200 7099.5 0.6858701 0.6858701 9.493030e-03 9.493030e-03 0.304636829 #> 14201 7100.0 0.6858644 0.6858644 9.492840e-03 9.492840e-03 0.304642725 #> 14202 7100.5 0.6858587 0.6858587 9.492649e-03 9.492649e-03 0.304648614 #> 14203 7101.0 0.6858530 0.6858530 9.492457e-03 9.492457e-03 0.304654498 #> 14204 7101.5 0.6858474 0.6858474 9.492263e-03 9.492263e-03 0.304660375 #> 14205 7102.0 0.6858417 0.6858417 9.492068e-03 9.492068e-03 0.304666246 #> 14206 7102.5 0.6858360 0.6858360 9.491872e-03 9.491872e-03 0.304672111 #> 14207 7103.0 0.6858304 0.6858304 9.491674e-03 9.491674e-03 0.304677970 #> 14208 7103.5 0.6858247 0.6858247 9.491475e-03 9.491475e-03 0.304683822 #> 14209 7104.0 0.6858191 0.6858191 9.491274e-03 9.491274e-03 0.304689668 #> 14210 7104.5 0.6858134 0.6858134 9.491073e-03 9.491073e-03 0.304695508 #> 14211 7105.0 0.6858078 0.6858078 9.490869e-03 9.490869e-03 0.304701342 #> 14212 7105.5 0.6858022 0.6858022 9.490665e-03 9.490665e-03 0.304707169 #> 14213 7106.0 0.6857966 0.6857966 9.490459e-03 9.490459e-03 0.304712990 #> 14214 7106.5 0.6857909 0.6857909 9.490252e-03 9.490252e-03 0.304718805 #> 14215 7107.0 0.6857853 0.6857853 9.490044e-03 9.490044e-03 0.304724613 #> 14216 7107.5 0.6857798 0.6857798 9.489834e-03 9.489834e-03 0.304730415 #> 14217 7108.0 0.6857742 0.6857742 9.489623e-03 9.489623e-03 0.304736211 #> 14218 7108.5 0.6857686 0.6857686 9.489410e-03 9.489410e-03 0.304742000 #> 14219 7109.0 0.6857630 0.6857630 9.489197e-03 9.489197e-03 0.304747782 #> 14220 7109.5 0.6857575 0.6857575 9.488982e-03 9.488982e-03 0.304753558 #> 14221 7110.0 0.6857519 0.6857519 9.488765e-03 9.488765e-03 0.304759328 #> 14222 7110.5 0.6857464 0.6857464 9.488548e-03 9.488548e-03 0.304765091 #> 14223 7111.0 0.6857408 0.6857408 9.488329e-03 9.488329e-03 0.304770848 #> 14224 7111.5 0.6857353 0.6857353 9.488108e-03 9.488108e-03 0.304776598 #> 14225 7112.0 0.6857298 0.6857298 9.487887e-03 9.487887e-03 0.304782342 #> 14226 7112.5 0.6857243 0.6857243 9.487664e-03 9.487664e-03 0.304788079 #> 14227 7113.0 0.6857188 0.6857188 9.487440e-03 9.487440e-03 0.304793809 #> 14228 7113.5 0.6857133 0.6857133 9.487214e-03 9.487214e-03 0.304799533 #> 14229 7114.0 0.6857078 0.6857078 9.486988e-03 9.486988e-03 0.304805250 #> 14230 7114.5 0.6857023 0.6857023 9.486759e-03 9.486759e-03 0.304810960 #> 14231 7115.0 0.6856968 0.6856968 9.486530e-03 9.486530e-03 0.304816664 #> 14232 7115.5 0.6856913 0.6856913 9.486299e-03 9.486299e-03 0.304822361 #> 14233 7116.0 0.6856859 0.6856859 9.486067e-03 9.486067e-03 0.304828052 #> 14234 7116.5 0.6856804 0.6856804 9.485834e-03 9.485834e-03 0.304833736 #> 14235 7117.0 0.6856750 0.6856750 9.485600e-03 9.485600e-03 0.304839413 #> 14236 7117.5 0.6856696 0.6856696 9.485364e-03 9.485364e-03 0.304845083 #> 14237 7118.0 0.6856641 0.6856641 9.485127e-03 9.485127e-03 0.304850746 #> 14238 7118.5 0.6856587 0.6856587 9.484888e-03 9.484888e-03 0.304856403 #> 14239 7119.0 0.6856533 0.6856533 9.484649e-03 9.484649e-03 0.304862053 #> 14240 7119.5 0.6856479 0.6856479 9.484408e-03 9.484408e-03 0.304867696 #> 14241 7120.0 0.6856425 0.6856425 9.484166e-03 9.484166e-03 0.304873332 #> 14242 7120.5 0.6856371 0.6856371 9.483922e-03 9.483922e-03 0.304878961 #> 14243 7121.0 0.6856317 0.6856317 9.483677e-03 9.483677e-03 0.304884584 #> 14244 7121.5 0.6856264 0.6856264 9.483431e-03 9.483431e-03 0.304890199 #> 14245 7122.0 0.6856210 0.6856210 9.483184e-03 9.483184e-03 0.304895808 #> 14246 7122.5 0.6856157 0.6856157 9.482936e-03 9.482936e-03 0.304901410 #> 14247 7123.0 0.6856103 0.6856103 9.482686e-03 9.482686e-03 0.304907004 #> 14248 7123.5 0.6856050 0.6856050 9.482435e-03 9.482435e-03 0.304912592 #> 14249 7124.0 0.6855996 0.6855996 9.482182e-03 9.482182e-03 0.304918173 #> 14250 7124.5 0.6855943 0.6855943 9.481929e-03 9.481929e-03 0.304923747 #> 14251 7125.0 0.6855890 0.6855890 9.481674e-03 9.481674e-03 0.304929314 #> 14252 7125.5 0.6855837 0.6855837 9.481418e-03 9.481418e-03 0.304934873 #> 14253 7126.0 0.6855784 0.6855784 9.481161e-03 9.481161e-03 0.304940426 #> 14254 7126.5 0.6855731 0.6855731 9.480902e-03 9.480902e-03 0.304945972 #> 14255 7127.0 0.6855678 0.6855678 9.480642e-03 9.480642e-03 0.304951510 #> 14256 7127.5 0.6855626 0.6855626 9.480381e-03 9.480381e-03 0.304957041 #> 14257 7128.0 0.6855573 0.6855573 9.480119e-03 9.480119e-03 0.304962566 #> 14258 7128.5 0.6855521 0.6855521 9.479855e-03 9.479855e-03 0.304968083 #> 14259 7129.0 0.6855468 0.6855468 9.479591e-03 9.479591e-03 0.304973593 #> 14260 7129.5 0.6855416 0.6855416 9.479325e-03 9.479325e-03 0.304979096 #> 14261 7130.0 0.6855364 0.6855364 9.479057e-03 9.479057e-03 0.304984591 #> 14262 7130.5 0.6855311 0.6855311 9.478789e-03 9.478789e-03 0.304990080 #> 14263 7131.0 0.6855259 0.6855259 9.478519e-03 9.478519e-03 0.304995561 #> 14264 7131.5 0.6855207 0.6855207 9.478248e-03 9.478248e-03 0.305001035 #> 14265 7132.0 0.6855155 0.6855155 9.477976e-03 9.477976e-03 0.305006501 #> 14266 7132.5 0.6855103 0.6855103 9.477703e-03 9.477703e-03 0.305011961 #> 14267 7133.0 0.6855052 0.6855052 9.477428e-03 9.477428e-03 0.305017413 #> 14268 7133.5 0.6855000 0.6855000 9.477152e-03 9.477152e-03 0.305022857 #> 14269 7134.0 0.6854948 0.6854948 9.476875e-03 9.476875e-03 0.305028295 #> 14270 7134.5 0.6854897 0.6854897 9.476597e-03 9.476597e-03 0.305033725 #> 14271 7135.0 0.6854845 0.6854845 9.476318e-03 9.476318e-03 0.305039148 #> 14272 7135.5 0.6854794 0.6854794 9.476037e-03 9.476037e-03 0.305044563 #> 14273 7136.0 0.6854743 0.6854743 9.475755e-03 9.475755e-03 0.305049971 #> 14274 7136.5 0.6854692 0.6854692 9.475472e-03 9.475472e-03 0.305055372 #> 14275 7137.0 0.6854640 0.6854640 9.475188e-03 9.475188e-03 0.305060765 #> 14276 7137.5 0.6854589 0.6854589 9.474902e-03 9.474902e-03 0.305066150 #> 14277 7138.0 0.6854539 0.6854539 9.474616e-03 9.474616e-03 0.305071529 #> 14278 7138.5 0.6854488 0.6854488 9.474328e-03 9.474328e-03 0.305076899 #> 14279 7139.0 0.6854437 0.6854437 9.474039e-03 9.474039e-03 0.305082263 #> 14280 7139.5 0.6854386 0.6854386 9.473748e-03 9.473748e-03 0.305087618 #> 14281 7140.0 0.6854336 0.6854336 9.473457e-03 9.473457e-03 0.305092967 #> 14282 7140.5 0.6854285 0.6854285 9.473164e-03 9.473164e-03 0.305098307 #> 14283 7141.0 0.6854235 0.6854235 9.472871e-03 9.472871e-03 0.305103641 #> 14284 7141.5 0.6854185 0.6854185 9.472576e-03 9.472576e-03 0.305108966 #> 14285 7142.0 0.6854134 0.6854134 9.472279e-03 9.472279e-03 0.305114284 #>                r2 #> 1     0.000000000 #> 2     0.003337146 #> 3     0.006681767 #> 4     0.010033558 #> 5     0.013392251 #> 6     0.016757489 #> 7     0.020128975 #> 8     0.023506400 #> 9     0.026889453 #> 10    0.030277824 #> 11    0.033671200 #> 12    0.037069269 #> 13    0.040471717 #> 14    0.043878231 #> 15    0.047288499 #> 16    0.050702206 #> 17    0.054119038 #> 18    0.057538683 #> 19    0.060960827 #> 20    0.064385157 #> 21    0.067811362 #> 22    0.071239129 #> 23    0.074668149 #> 24    0.078098110 #> 25    0.081528705 #> 26    0.084959625 #> 27    0.088390563 #> 28    0.091821214 #> 29    0.095251274 #> 30    0.098680441 #> 31    0.102108412 #> 32    0.105534890 #> 33    0.108959577 #> 34    0.112382176 #> 35    0.115802395 #> 36    0.119219941 #> 37    0.122634526 #> 38    0.126045862 #> 39    0.129453664 #> 40    0.132857649 #> 41    0.136257538 #> 42    0.139653053 #> 43    0.143043919 #> 44    0.146429864 #> 45    0.149810618 #> 46    0.153185914 #> 47    0.156555490 #> 48    0.159919084 #> 49    0.163276437 #> 50    0.166627296 #> 51    0.169971409 #> 52    0.173308527 #> 53    0.176638404 #> 54    0.179960799 #> 55    0.183275473 #> 56    0.186582190 #> 57    0.189880719 #> 58    0.193170830 #> 59    0.196452298 #> 60    0.199724902 #> 61    0.202988423 #> 62    0.206242646 #> 63    0.209487361 #> 64    0.212722359 #> 65    0.215947437 #> 66    0.219162394 #> 67    0.222367034 #> 68    0.225561163 #> 69    0.228744592 #> 70    0.231917135 #> 71    0.235078610 #> 72    0.238228838 #> 73    0.241367645 #> 74    0.244494860 #> 75    0.247610314 #> 76    0.250713846 #> 77    0.253805294 #> 78    0.256884502 #> 79    0.259951318 #> 80    0.263005592 #> 81    0.266047180 #> 82    0.269075939 #> 83    0.272091732 #> 84    0.275094424 #> 85    0.278083884 #> 86    0.281059985 #> 87    0.284022604 #> 88    0.286971620 #> 89    0.289906917 #> 90    0.292828382 #> 91    0.295735905 #> 92    0.298629380 #> 93    0.301508705 #> 94    0.304373780 #> 95    0.307224510 #> 96    0.310060801 #> 97    0.312882565 #> 98    0.315689717 #> 99    0.318482173 #> 100   0.321259854 #> 101   0.324022684 #> 102   0.326770590 #> 103   0.329503503 #> 104   0.332221355 #> 105   0.334924084 #> 106   0.337611628 #> 107   0.340283931 #> 108   0.342940938 #> 109   0.345582596 #> 110   0.348208859 #> 111   0.350819678 #> 112   0.353415013 #> 113   0.355994822 #> 114   0.358559069 #> 115   0.361107718 #> 116   0.363640737 #> 117   0.366158098 #> 118   0.368659773 #> 119   0.371145738 #> 120   0.373615972 #> 121   0.376070455 #> 122   0.378509171 #> 123   0.380932105 #> 124   0.383339246 #> 125   0.385730583 #> 126   0.388106110 #> 127   0.390465822 #> 128   0.392809716 #> 129   0.395137790 #> 130   0.397450047 #> 131   0.399746490 #> 132   0.402027125 #> 133   0.404291960 #> 134   0.406541004 #> 135   0.408774268 #> 136   0.410991767 #> 137   0.413193516 #> 138   0.415379531 #> 139   0.417549833 #> 140   0.419704442 #> 141   0.421843379 #> 142   0.423966671 #> 143   0.426074342 #> 144   0.428166419 #> 145   0.430242933 #> 146   0.432303913 #> 147   0.434349392 #> 148   0.436379403 #> 149   0.438393982 #> 150   0.440393164 #> 151   0.442376988 #> 152   0.444345492 #> 153   0.446298718 #> 154   0.448236707 #> 155   0.450159501 #> 156   0.452067145 #> 157   0.453959685 #> 158   0.455837167 #> 159   0.457699638 #> 160   0.459547147 #> 161   0.461379744 #> 162   0.463197480 #> 163   0.465000407 #> 164   0.466788577 #> 165   0.468562043 #> 166   0.470320861 #> 167   0.472065086 #> 168   0.473794774 #> 169   0.475509983 #> 170   0.477210770 #> 171   0.478897194 #> 172   0.480569315 #> 173   0.482227192 #> 174   0.483870887 #> 175   0.485500461 #> 176   0.487115976 #> 177   0.488717496 #> 178   0.490305083 #> 179   0.491878801 #> 180   0.493438716 #> 181   0.494984892 #> 182   0.496517395 #> 183   0.498036291 #> 184   0.499541646 #> 185   0.501033528 #> 186   0.502512003 #> 187   0.503977140 #> 188   0.505429007 #> 189   0.506867673 #> 190   0.508293205 #> 191   0.509705675 #> 192   0.511105150 #> 193   0.512491702 #> 194   0.513865399 #> 195   0.515226314 #> 196   0.516574515 #> 197   0.517910074 #> 198   0.519233062 #> 199   0.520543550 #> 200   0.521841609 #> 201   0.523127312 #> 202   0.524400730 #> 203   0.525661934 #> 204   0.526910997 #> 205   0.528147990 #> 206   0.529372986 #> 207   0.530586057 #> 208   0.531787275 #> 209   0.532976713 #> 210   0.534154442 #> 211   0.535320536 #> 212   0.536475066 #> 213   0.537618105 #> 214   0.538749725 #> 215   0.539869999 #> 216   0.540978999 #> 217   0.542076796 #> 218   0.543163464 #> 219   0.544239075 #> 220   0.545303699 #> 221   0.546357411 #> 222   0.547400280 #> 223   0.548432379 #> 224   0.549453780 #> 225   0.550464554 #> 226   0.551464772 #> 227   0.552454506 #> 228   0.553433827 #> 229   0.554402805 #> 230   0.555361512 #> 231   0.556310018 #> 232   0.557248393 #> 233   0.558176708 #> 234   0.559095033 #> 235   0.560003438 #> 236   0.560901991 #> 237   0.561790763 #> 238   0.562669823 #> 239   0.563539240 #> 240   0.564399082 #> 241   0.565249419 #> 242   0.566090317 #> 243   0.566921846 #> 244   0.567744073 #> 245   0.568557066 #> 246   0.569360892 #> 247   0.570155618 #> 248   0.570941310 #> 249   0.571718036 #> 250   0.572485861 #> 251   0.573244851 #> 252   0.573995072 #> 253   0.574736589 #> 254   0.575469468 #> 255   0.576193772 #> 256   0.576909567 #> 257   0.577616917 #> 258   0.578315885 #> 259   0.579006535 #> 260   0.579688930 #> 261   0.580363134 #> 262   0.581029209 #> 263   0.581687216 #> 264   0.582337219 #> 265   0.582979278 #> 266   0.583613456 #> 267   0.584239813 #> 268   0.584858409 #> 269   0.585469306 #> 270   0.586072563 #> 271   0.586668240 #> 272   0.587256397 #> 273   0.587837092 #> 274   0.588410384 #> 275   0.588976331 #> 276   0.589534992 #> 277   0.590086425 #> 278   0.590630686 #> 279   0.591167832 #> 280   0.591697921 #> 281   0.592221009 #> 282   0.592737151 #> 283   0.593246404 #> 284   0.593748823 #> 285   0.594244463 #> 286   0.594733377 #> 287   0.595215622 #> 288   0.595691250 #> 289   0.596160316 #> 290   0.596622872 #> 291   0.597078972 #> 292   0.597528668 #> 293   0.597972013 #> 294   0.598409058 #> 295   0.598839854 #> 296   0.599264455 #> 297   0.599682909 #> 298   0.600095268 #> 299   0.600501582 #> 300   0.600901900 #> 301   0.601296274 #> 302   0.601684750 #> 303   0.602067379 #> 304   0.602444210 #> 305   0.602815289 #> 306   0.603180666 #> 307   0.603540387 #> 308   0.603894500 #> 309   0.604243052 #> 310   0.604586090 #> 311   0.604923659 #> 312   0.605255805 #> 313   0.605582574 #> 314   0.605904012 #> 315   0.606220163 #> 316   0.606531071 #> 317   0.606836782 #> 318   0.607137338 #> 319   0.607432784 #> 320   0.607723164 #> 321   0.608008519 #> 322   0.608288892 #> 323   0.608564327 #> 324   0.608834864 #> 325   0.609100547 #> 326   0.609361415 #> 327   0.609617511 #> 328   0.609868875 #> 329   0.610115547 #> 330   0.610357569 #> 331   0.610594978 #> 332   0.610827816 #> 333   0.611056122 #> 334   0.611279934 #> 335   0.611499291 #> 336   0.611714232 #> 337   0.611924794 #> 338   0.612131016 #> 339   0.612332935 #> 340   0.612530588 #> 341   0.612724012 #> 342   0.612913244 #> 343   0.613098320 #> 344   0.613279276 #> 345   0.613456148 #> 346   0.613628972 #> 347   0.613797782 #> 348   0.613962614 #> 349   0.614123502 #> 350   0.614280480 #> 351   0.614433584 #> 352   0.614582846 #> 353   0.614728300 #> 354   0.614869980 #> 355   0.615007918 #> 356   0.615142148 #> 357   0.615272702 #> 358   0.615399611 #> 359   0.615522909 #> 360   0.615642627 #> 361   0.615758796 #> 362   0.615871448 #> 363   0.615980613 #> 364   0.616086322 #> 365   0.616188606 #> 366   0.616287495 #> 367   0.616383019 #> 368   0.616475208 #> 369   0.616564090 #> 370   0.616649696 #> 371   0.616732054 #> 372   0.616811194 #> 373   0.616887143 #> 374   0.616959930 #> 375   0.617029583 #> 376   0.617096130 #> 377   0.617159598 #> 378   0.617220015 #> 379   0.617277407 #> 380   0.617331803 #> 381   0.617383228 #> 382   0.617431709 #> 383   0.617477272 #> 384   0.617519944 #> 385   0.617559750 #> 386   0.617596715 #> 387   0.617630865 #> 388   0.617662225 #> 389   0.617690821 #> 390   0.617716676 #> 391   0.617739815 #> 392   0.617760264 #> 393   0.617778045 #> 394   0.617793183 #> 395   0.617805701 #> 396   0.617815624 #> 397   0.617822973 #> 398   0.617827773 #> 399   0.617830047 #> 400   0.617829816 #> 401   0.617827104 #> 402   0.617821933 #> 403   0.617814325 #> 404   0.617804302 #> 405   0.617791886 #> 406   0.617777099 #> 407   0.617759961 #> 408   0.617740494 #> 409   0.617718719 #> 410   0.617694657 #> 411   0.617668329 #> 412   0.617639754 #> 413   0.617608955 #> 414   0.617575950 #> 415   0.617540759 #> 416   0.617503403 #> 417   0.617463901 #> 418   0.617422272 #> 419   0.617378536 #> 420   0.617332712 #> 421   0.617284819 #> 422   0.617234875 #> 423   0.617182900 #> 424   0.617128912 #> 425   0.617072929 #> 426   0.617014969 #> 427   0.616955051 #> 428   0.616893192 #> 429   0.616829409 #> 430   0.616763721 #> 431   0.616696145 #> 432   0.616626698 #> 433   0.616555397 #> 434   0.616482259 #> 435   0.616407301 #> 436   0.616330540 #> 437   0.616251991 #> 438   0.616171672 #> 439   0.616089599 #> 440   0.616005787 #> 441   0.615920253 #> 442   0.615833012 #> 443   0.615744081 #> 444   0.615653474 #> 445   0.615561207 #> 446   0.615467295 #> 447   0.615371754 #> 448   0.615274598 #> 449   0.615175843 #> 450   0.615075502 #> 451   0.614973591 #> 452   0.614870124 #> 453   0.614765115 #> 454   0.614658579 #> 455   0.614550530 #> 456   0.614440982 #> 457   0.614329948 #> 458   0.614217442 #> 459   0.614103479 #> 460   0.613988070 #> 461   0.613871231 #> 462   0.613752973 #> 463   0.613633311 #> 464   0.613512257 #> 465   0.613389824 #> 466   0.613266025 #> 467   0.613140872 #> 468   0.613014379 #> 469   0.612886557 #> 470   0.612757419 #> 471   0.612626978 #> 472   0.612495245 #> 473   0.612362232 #> 474   0.612227952 #> 475   0.612092416 #> 476   0.611955636 #> 477   0.611817623 #> 478   0.611678389 #> 479   0.611537946 #> 480   0.611396305 #> 481   0.611253477 #> 482   0.611109472 #> 483   0.610964303 #> 484   0.610817980 #> 485   0.610670514 #> 486   0.610521915 #> 487   0.610372195 #> 488   0.610221363 #> 489   0.610069431 #> 490   0.609916408 #> 491   0.609762305 #> 492   0.609607132 #> 493   0.609450900 #> 494   0.609293617 #> 495   0.609135295 #> 496   0.608975942 #> 497   0.608815569 #> 498   0.608654185 #> 499   0.608491801 #> 500   0.608328424 #> 501   0.608164065 #> 502   0.607998734 #> 503   0.607832438 #> 504   0.607665188 #> 505   0.607496993 #> 506   0.607327861 #> 507   0.607157802 #> 508   0.606986824 #> 509   0.606814936 #> 510   0.606642147 #> 511   0.606468465 #> 512   0.606293899 #> 513   0.606118457 #> 514   0.605942148 #> 515   0.605764980 #> 516   0.605586962 #> 517   0.605408101 #> 518   0.605228405 #> 519   0.605047883 #> 520   0.604866543 #> 521   0.604684392 #> 522   0.604501438 #> 523   0.604317689 #> 524   0.604133153 #> 525   0.603947837 #> 526   0.603761749 #> 527   0.603574896 #> 528   0.603387286 #> 529   0.603198926 #> 530   0.603009824 #> 531   0.602819986 #> 532   0.602629419 #> 533   0.602438132 #> 534   0.602246130 #> 535   0.602053421 #> 536   0.601860012 #> 537   0.601665910 #> 538   0.601471121 #> 539   0.601275652 #> 540   0.601079509 #> 541   0.600882701 #> 542   0.600685232 #> 543   0.600487109 #> 544   0.600288340 #> 545   0.600088929 #> 546   0.599888885 #> 547   0.599688212 #> 548   0.599486917 #> 549   0.599285007 #> 550   0.599082487 #> 551   0.598879363 #> 552   0.598675642 #> 553   0.598471330 #> 554   0.598266431 #> 555   0.598060953 #> 556   0.597854901 #> 557   0.597648280 #> 558   0.597441097 #> 559   0.597233357 #> 560   0.597025065 #> 561   0.596816228 #> 562   0.596606851 #> 563   0.596396938 #> 564   0.596186497 #> 565   0.595975531 #> 566   0.595764046 #> 567   0.595552048 #> 568   0.595339542 #> 569   0.595126532 #> 570   0.594913025 #> 571   0.594699025 #> 572   0.594484537 #> 573   0.594269566 #> 574   0.594054118 #> 575   0.593838196 #> 576   0.593621806 #> 577   0.593404953 #> 578   0.593187642 #> 579   0.592969877 #> 580   0.592751663 #> 581   0.592533005 #> 582   0.592313906 #> 583   0.592094373 #> 584   0.591874409 #> 585   0.591654020 #> 586   0.591433208 #> 587   0.591211979 #> 588   0.590990338 #> 589   0.590768288 #> 590   0.590545834 #> 591   0.590322980 #> 592   0.590099731 #> 593   0.589876090 #> 594   0.589652061 #> 595   0.589427650 #> 596   0.589202860 #> 597   0.588977695 #> 598   0.588752158 #> 599   0.588526255 #> 600   0.588299989 #> 601   0.588073364 #> 602   0.587846384 #> 603   0.587619052 #> 604   0.587391373 #> 605   0.587163350 #> 606   0.586934987 #> 607   0.586706288 #> 608   0.586477257 #> 609   0.586247896 #> 610   0.586018211 #> 611   0.585788203 #> 612   0.585557878 #> 613   0.585327238 #> 614   0.585096287 #> 615   0.584865028 #> 616   0.584633466 #> 617   0.584401602 #> 618   0.584169442 #> 619   0.583936987 #> 620   0.583704242 #> 621   0.583471209 #> 622   0.583237893 #> 623   0.583004295 #> 624   0.582770420 #> 625   0.582536271 #> 626   0.582301850 #> 627   0.582067162 #> 628   0.581832208 #> 629   0.581596992 #> 630   0.581361518 #> 631   0.581125788 #> 632   0.580889805 #> 633   0.580653572 #> 634   0.580417092 #> 635   0.580180368 #> 636   0.579943404 #> 637   0.579706201 #> 638   0.579468763 #> 639   0.579231092 #> 640   0.578993192 #> 641   0.578755064 #> 642   0.578516713 #> 643   0.578278140 #> 644   0.578039349 #> 645   0.577800341 #> 646   0.577561120 #> 647   0.577321689 #> 648   0.577082049 #> 649   0.576842205 #> 650   0.576602157 #> 651   0.576361908 #> 652   0.576121462 #> 653   0.575880821 #> 654   0.575639987 #> 655   0.575398963 #> 656   0.575157750 #> 657   0.574916353 #> 658   0.574674772 #> 659   0.574433010 #> 660   0.574191071 #> 661   0.573948955 #> 662   0.573706666 #> 663   0.573464205 #> 664   0.573221576 #> 665   0.572978779 #> 666   0.572735818 #> 667   0.572492695 #> 668   0.572249412 #> 669   0.572005971 #> 670   0.571762374 #> 671   0.571518624 #> 672   0.571274723 #> 673   0.571030672 #> 674   0.570786474 #> 675   0.570542131 #> 676   0.570297644 #> 677   0.570053017 #> 678   0.569808251 #> 679   0.569563348 #> 680   0.569318311 #> 681   0.569073140 #> 682   0.568827838 #> 683   0.568582407 #> 684   0.568336849 #> 685   0.568091166 #> 686   0.567845360 #> 687   0.567599432 #> 688   0.567353385 #> 689   0.567107221 #> 690   0.566860940 #> 691   0.566614545 #> 692   0.566368038 #> 693   0.566121421 #> 694   0.565874695 #> 695   0.565627863 #> 696   0.565380925 #> 697   0.565133884 #> 698   0.564886741 #> 699   0.564639498 #> 700   0.564392157 #> 701   0.564144720 #> 702   0.563897187 #> 703   0.563649561 #> 704   0.563401844 #> 705   0.563154036 #> 706   0.562906140 #> 707   0.562658158 #> 708   0.562410090 #> 709   0.562161938 #> 710   0.561913704 #> 711   0.561665390 #> 712   0.561416997 #> 713   0.561168526 #> 714   0.560919979 #> 715   0.560671358 #> 716   0.560422663 #> 717   0.560173897 #> 718   0.559925061 #> 719   0.559676156 #> 720   0.559427184 #> 721   0.559178146 #> 722   0.558929043 #> 723   0.558679878 #> 724   0.558430651 #> 725   0.558181363 #> 726   0.557932017 #> 727   0.557682613 #> 728   0.557433153 #> 729   0.557183638 #> 730   0.556934069 #> 731   0.556684448 #> 732   0.556434777 #> 733   0.556185055 #> 734   0.555935285 #> 735   0.555685468 #> 736   0.555435605 #> 737   0.555185697 #> 738   0.554935745 #> 739   0.554685752 #> 740   0.554435718 #> 741   0.554185643 #> 742   0.553935530 #> 743   0.553685380 #> 744   0.553435193 #> 745   0.553184972 #> 746   0.552934716 #> 747   0.552684428 #> 748   0.552434107 #> 749   0.552183757 #> 750   0.551933377 #> 751   0.551682968 #> 752   0.551432533 #> 753   0.551182071 #> 754   0.550931584 #> 755   0.550681073 #> 756   0.550430539 #> 757   0.550179984 #> 758   0.549929407 #> 759   0.549678811 #> 760   0.549428196 #> 761   0.549177563 #> 762   0.548926914 #> 763   0.548676249 #> 764   0.548425569 #> 765   0.548174875 #> 766   0.547924168 #> 767   0.547673450 #> 768   0.547422721 #> 769   0.547171982 #> 770   0.546921234 #> 771   0.546670478 #> 772   0.546419715 #> 773   0.546168945 #> 774   0.545918171 #> 775   0.545667392 #> 776   0.545416610 #> 777   0.545165825 #> 778   0.544915039 #> 779   0.544664251 #> 780   0.544413464 #> 781   0.544162678 #> 782   0.543911894 #> 783   0.543661112 #> 784   0.543410334 #> 785   0.543159560 #> 786   0.542908792 #> 787   0.542658029 #> 788   0.542407273 #> 789   0.542156525 #> 790   0.541905785 #> 791   0.541655055 #> 792   0.541404334 #> 793   0.541153624 #> 794   0.540902926 #> 795   0.540652240 #> 796   0.540401568 #> 797   0.540150909 #> 798   0.539900265 #> 799   0.539649636 #> 800   0.539399023 #> 801   0.539148427 #> 802   0.538897849 #> 803   0.538647289 #> 804   0.538396748 #> 805   0.538146226 #> 806   0.537895725 #> 807   0.537645246 #> 808   0.537394788 #> 809   0.537144352 #> 810   0.536893940 #> 811   0.536643551 #> 812   0.536393187 #> 813   0.536142848 #> 814   0.535892535 #> 815   0.535642249 #> 816   0.535391990 #> 817   0.535141758 #> 818   0.534891555 #> 819   0.534641381 #> 820   0.534391237 #> 821   0.534141123 #> 822   0.533891039 #> 823   0.533640988 #> 824   0.533390969 #> 825   0.533140982 #> 826   0.532891029 #> 827   0.532641109 #> 828   0.532391224 #> 829   0.532141375 #> 830   0.531891561 #> 831   0.531641783 #> 832   0.531392042 #> 833   0.531142338 #> 834   0.530892673 #> 835   0.530643046 #> 836   0.530393458 #> 837   0.530143909 #> 838   0.529894401 #> 839   0.529644933 #> 840   0.529395507 #> 841   0.529146122 #> 842   0.528896780 #> 843   0.528647481 #> 844   0.528398224 #> 845   0.528149012 #> 846   0.527899844 #> 847   0.527650721 #> 848   0.527401643 #> 849   0.527152611 #> 850   0.526903625 #> 851   0.526654686 #> 852   0.526405794 #> 853   0.526156951 #> 854   0.525908155 #> 855   0.525659408 #> 856   0.525410710 #> 857   0.525162062 #> 858   0.524913464 #> 859   0.524664917 #> 860   0.524416420 #> 861   0.524167975 #> 862   0.523919582 #> 863   0.523671241 #> 864   0.523422953 #> 865   0.523174719 #> 866   0.522926537 #> 867   0.522678410 #> 868   0.522430338 #> 869   0.522182320 #> 870   0.521934358 #> 871   0.521686451 #> 872   0.521438601 #> 873   0.521190807 #> 874   0.520943070 #> 875   0.520695391 #> 876   0.520447769 #> 877   0.520200206 #> 878   0.519952701 #> 879   0.519705254 #> 880   0.519457868 #> 881   0.519210541 #> 882   0.518963274 #> 883   0.518716067 #> 884   0.518468921 #> 885   0.518221837 #> 886   0.517974814 #> 887   0.517727852 #> 888   0.517480954 #> 889   0.517234117 #> 890   0.516987344 #> 891   0.516740634 #> 892   0.516493988 #> 893   0.516247406 #> 894   0.516000888 #> 895   0.515754435 #> 896   0.515508047 #> 897   0.515261724 #> 898   0.515015467 #> 899   0.514769276 #> 900   0.514523151 #> 901   0.514277093 #> 902   0.514031102 #> 903   0.513785179 #> 904   0.513539323 #> 905   0.513293535 #> 906   0.513047815 #> 907   0.512802163 #> 908   0.512556581 #> 909   0.512311068 #> 910   0.512065624 #> 911   0.511820250 #> 912   0.511574946 #> 913   0.511329712 #> 914   0.511084549 #> 915   0.510839457 #> 916   0.510594436 #> 917   0.510349486 #> 918   0.510104609 #> 919   0.509859803 #> 920   0.509615069 #> 921   0.509370409 #> 922   0.509125821 #> 923   0.508881306 #> 924   0.508636864 #> 925   0.508392497 #> 926   0.508148203 #> 927   0.507903983 #> 928   0.507659838 #> 929   0.507415767 #> 930   0.507171772 #> 931   0.506927851 #> 932   0.506684006 #> 933   0.506440237 #> 934   0.506196543 #> 935   0.505952926 #> 936   0.505709385 #> 937   0.505465921 #> 938   0.505222533 #> 939   0.504979223 #> 940   0.504735990 #> 941   0.504492835 #> 942   0.504249757 #> 943   0.504006758 #> 944   0.503763837 #> 945   0.503520994 #> 946   0.503278230 #> 947   0.503035545 #> 948   0.502792939 #> 949   0.502550412 #> 950   0.502307965 #> 951   0.502065597 #> 952   0.501823310 #> 953   0.501581103 #> 954   0.501338976 #> 955   0.501096930 #> 956   0.500854964 #> 957   0.500613080 #> 958   0.500371276 #> 959   0.500129555 #> 960   0.499887914 #> 961   0.499646356 #> 962   0.499404879 #> 963   0.499163485 #> 964   0.498922173 #> 965   0.498680943 #> 966   0.498439797 #> 967   0.498198733 #> 968   0.497957752 #> 969   0.497716854 #> 970   0.497476040 #> 971   0.497235310 #> 972   0.496994663 #> 973   0.496754100 #> 974   0.496513622 #> 975   0.496273228 #> 976   0.496032918 #> 977   0.495792693 #> 978   0.495552552 #> 979   0.495312497 #> 980   0.495072527 #> 981   0.494832642 #> 982   0.494592843 #> 983   0.494353129 #> 984   0.494113501 #> 985   0.493873959 #> 986   0.493634503 #> 987   0.493395133 #> 988   0.493155850 #> 989   0.492916653 #> 990   0.492677543 #> 991   0.492438520 #> 992   0.492199584 #> 993   0.491960734 #> 994   0.491721973 #> 995   0.491483298 #> 996   0.491244712 #> 997   0.491006212 #> 998   0.490767801 #> 999   0.490529478 #> 1000  0.490291243 #> 1001  0.490053096 #> 1002  0.489815037 #> 1003  0.489577068 #> 1004  0.489339186 #> 1005  0.489101394 #> 1006  0.488863690 #> 1007  0.488626076 #> 1008  0.488388550 #> 1009  0.488151114 #> 1010  0.487913767 #> 1011  0.487676510 #> 1012  0.487439343 #> 1013  0.487202265 #> 1014  0.486965277 #> 1015  0.486728379 #> 1016  0.486491572 #> 1017  0.486254854 #> 1018  0.486018227 #> 1019  0.485781691 #> 1020  0.485545245 #> 1021  0.485308889 #> 1022  0.485072625 #> 1023  0.484836451 #> 1024  0.484600369 #> 1025  0.484364378 #> 1026  0.484128478 #> 1027  0.483892669 #> 1028  0.483656952 #> 1029  0.483421326 #> 1030  0.483185792 #> 1031  0.482950350 #> 1032  0.482714999 #> 1033  0.482479741 #> 1034  0.482244574 #> 1035  0.482009500 #> 1036  0.481774518 #> 1037  0.481539628 #> 1038  0.481304831 #> 1039  0.481070127 #> 1040  0.480835515 #> 1041  0.480600995 #> 1042  0.480366569 #> 1043  0.480132235 #> 1044  0.479897995 #> 1045  0.479663847 #> 1046  0.479429793 #> 1047  0.479195832 #> 1048  0.478961964 #> 1049  0.478728190 #> 1050  0.478494509 #> 1051  0.478260922 #> 1052  0.478027429 #> 1053  0.477794029 #> 1054  0.477560723 #> 1055  0.477327511 #> 1056  0.477094393 #> 1057  0.476861369 #> 1058  0.476628439 #> 1059  0.476395604 #> 1060  0.476162862 #> 1061  0.475930215 #> 1062  0.475697663 #> 1063  0.475465205 #> 1064  0.475232842 #> 1065  0.475000573 #> 1066  0.474768399 #> 1067  0.474536320 #> 1068  0.474304336 #> 1069  0.474072446 #> 1070  0.473840652 #> 1071  0.473608953 #> 1072  0.473377349 #> 1073  0.473145840 #> 1074  0.472914426 #> 1075  0.472683108 #> 1076  0.472451885 #> 1077  0.472220758 #> 1078  0.471989726 #> 1079  0.471758789 #> 1080  0.471527949 #> 1081  0.471297204 #> 1082  0.471066554 #> 1083  0.470836001 #> 1084  0.470605543 #> 1085  0.470375182 #> 1086  0.470144916 #> 1087  0.469914747 #> 1088  0.469684673 #> 1089  0.469454696 #> 1090  0.469224814 #> 1091  0.468995030 #> 1092  0.468765341 #> 1093  0.468535749 #> 1094  0.468306253 #> 1095  0.468076854 #> 1096  0.467847551 #> 1097  0.467618344 #> 1098  0.467389235 #> 1099  0.467160222 #> 1100  0.466931305 #> 1101  0.466702486 #> 1102  0.466473763 #> 1103  0.466245137 #> 1104  0.466016608 #> 1105  0.465788176 #> 1106  0.465559841 #> 1107  0.465331602 #> 1108  0.465103461 #> 1109  0.464875417 #> 1110  0.464647470 #> 1111  0.464419621 #> 1112  0.464191868 #> 1113  0.463964213 #> 1114  0.463736655 #> 1115  0.463509195 #> 1116  0.463281831 #> 1117  0.463054566 #> 1118  0.462827397 #> 1119  0.462600326 #> 1120  0.462373353 #> 1121  0.462146477 #> 1122  0.461919699 #> 1123  0.461693018 #> 1124  0.461466436 #> 1125  0.461239950 #> 1126  0.461013563 #> 1127  0.460787273 #> 1128  0.460561081 #> 1129  0.460334987 #> 1130  0.460108991 #> 1131  0.459883092 #> 1132  0.459657292 #> 1133  0.459431589 #> 1134  0.459205984 #> 1135  0.458980478 #> 1136  0.458755069 #> 1137  0.458529759 #> 1138  0.458304546 #> 1139  0.458079432 #> 1140  0.457854415 #> 1141  0.457629497 #> 1142  0.457404677 #> 1143  0.457179956 #> 1144  0.456955332 #> 1145  0.456730807 #> 1146  0.456506380 #> 1147  0.456282051 #> 1148  0.456057821 #> 1149  0.455833689 #> 1150  0.455609655 #> 1151  0.455385720 #> 1152  0.455161883 #> 1153  0.454938145 #> 1154  0.454714505 #> 1155  0.454490963 #> 1156  0.454267520 #> 1157  0.454044175 #> 1158  0.453820929 #> 1159  0.453597782 #> 1160  0.453374733 #> 1161  0.453151782 #> 1162  0.452928931 #> 1163  0.452706177 #> 1164  0.452483523 #> 1165  0.452260967 #> 1166  0.452038510 #> 1167  0.451816151 #> 1168  0.451593891 #> 1169  0.451371730 #> 1170  0.451149667 #> 1171  0.450927703 #> 1172  0.450705838 #> 1173  0.450484072 #> 1174  0.450262404 #> 1175  0.450040835 #> 1176  0.449819365 #> 1177  0.449597994 #> 1178  0.449376721 #> 1179  0.449155547 #> 1180  0.448934473 #> 1181  0.448713496 #> 1182  0.448492619 #> 1183  0.448271841 #> 1184  0.448051161 #> 1185  0.447830581 #> 1186  0.447610099 #> 1187  0.447389716 #> 1188  0.447169432 #> 1189  0.446949247 #> 1190  0.446729160 #> 1191  0.446509173 #> 1192  0.446289285 #> 1193  0.446069495 #> 1194  0.445849805 #> 1195  0.445630213 #> 1196  0.445410720 #> 1197  0.445191326 #> 1198  0.444972031 #> 1199  0.444752836 #> 1200  0.444533739 #> 1201  0.444314740 #> 1202  0.444095841 #> 1203  0.443877041 #> 1204  0.443658340 #> 1205  0.443439738 #> 1206  0.443221235 #> 1207  0.443002830 #> 1208  0.442784525 #> 1209  0.442566319 #> 1210  0.442348211 #> 1211  0.442130203 #> 1212  0.441912293 #> 1213  0.441694483 #> 1214  0.441476771 #> 1215  0.441259159 #> 1216  0.441041645 #> 1217  0.440824230 #> 1218  0.440606915 #> 1219  0.440389698 #> 1220  0.440172580 #> 1221  0.439955561 #> 1222  0.439738641 #> 1223  0.439521820 #> 1224  0.439305098 #> 1225  0.439088475 #> 1226  0.438871951 #> 1227  0.438655526 #> 1228  0.438439199 #> 1229  0.438222972 #> 1230  0.438006844 #> 1231  0.437790814 #> 1232  0.437574883 #> 1233  0.437359052 #> 1234  0.437143319 #> 1235  0.436927685 #> 1236  0.436712150 #> 1237  0.436496714 #> 1238  0.436281376 #> 1239  0.436066138 #> 1240  0.435850998 #> 1241  0.435635958 #> 1242  0.435421016 #> 1243  0.435206173 #> 1244  0.434991428 #> 1245  0.434776783 #> 1246  0.434562236 #> 1247  0.434347788 #> 1248  0.434133439 #> 1249  0.433919189 #> 1250  0.433705038 #> 1251  0.433490985 #> 1252  0.433277031 #> 1253  0.433063176 #> 1254  0.432849419 #> 1255  0.432635761 #> 1256  0.432422202 #> 1257  0.432208742 #> 1258  0.431995380 #> 1259  0.431782117 #> 1260  0.431568953 #> 1261  0.431355887 #> 1262  0.431142920 #> 1263  0.430930052 #> 1264  0.430717282 #> 1265  0.430504611 #> 1266  0.430292038 #> 1267  0.430079564 #> 1268  0.429867189 #> 1269  0.429654912 #> 1270  0.429442734 #> 1271  0.429230654 #> 1272  0.429018673 #> 1273  0.428806790 #> 1274  0.428595006 #> 1275  0.428383320 #> 1276  0.428171733 #> 1277  0.427960244 #> 1278  0.427748853 #> 1279  0.427537561 #> 1280  0.427326367 #> 1281  0.427115272 #> 1282  0.426904275 #> 1283  0.426693377 #> 1284  0.426482576 #> 1285  0.426271875 #> 1286  0.426061271 #> 1287  0.425850766 #> 1288  0.425640359 #> 1289  0.425430050 #> 1290  0.425219839 #> 1291  0.425009727 #> 1292  0.424799713 #> 1293  0.424589797 #> 1294  0.424379979 #> 1295  0.424170260 #> 1296  0.423960638 #> 1297  0.423751115 #> 1298  0.423541690 #> 1299  0.423332363 #> 1300  0.423123134 #> 1301  0.422914002 #> 1302  0.422704969 #> 1303  0.422496034 #> 1304  0.422287197 #> 1305  0.422078458 #> 1306  0.421869817 #> 1307  0.421661274 #> 1308  0.421452829 #> 1309  0.421244482 #> 1310  0.421036232 #> 1311  0.420828080 #> 1312  0.420620027 #> 1313  0.420412071 #> 1314  0.420204212 #> 1315  0.419996452 #> 1316  0.419788789 #> 1317  0.419581224 #> 1318  0.419373757 #> 1319  0.419166388 #> 1320  0.418959116 #> 1321  0.418751941 #> 1322  0.418544865 #> 1323  0.418337886 #> 1324  0.418131004 #> 1325  0.417924220 #> 1326  0.417717534 #> 1327  0.417510945 #> 1328  0.417304454 #> 1329  0.417098060 #> 1330  0.416891764 #> 1331  0.416685565 #> 1332  0.416479463 #> 1333  0.416273459 #> 1334  0.416067552 #> 1335  0.415861742 #> 1336  0.415656030 #> 1337  0.415450415 #> 1338  0.415244898 #> 1339  0.415039477 #> 1340  0.414834154 #> 1341  0.414628928 #> 1342  0.414423799 #> 1343  0.414218768 #> 1344  0.414013833 #> 1345  0.413808996 #> 1346  0.413604255 #> 1347  0.413399612 #> 1348  0.413195066 #> 1349  0.412990616 #> 1350  0.412786264 #> 1351  0.412582008 #> 1352  0.412377850 #> 1353  0.412173788 #> 1354  0.411969824 #> 1355  0.411765956 #> 1356  0.411562185 #> 1357  0.411358511 #> 1358  0.411154933 #> 1359  0.410951453 #> 1360  0.410748069 #> 1361  0.410544781 #> 1362  0.410341591 #> 1363  0.410138497 #> 1364  0.409935499 #> 1365  0.409732599 #> 1366  0.409529795 #> 1367  0.409327087 #> 1368  0.409124476 #> 1369  0.408921961 #> 1370  0.408719543 #> 1371  0.408517221 #> 1372  0.408314996 #> 1373  0.408112867 #> 1374  0.407910834 #> 1375  0.407708898 #> 1376  0.407507058 #> 1377  0.407305314 #> 1378  0.407103667 #> 1379  0.406902116 #> 1380  0.406700661 #> 1381  0.406499302 #> 1382  0.406298039 #> 1383  0.406096872 #> 1384  0.405895802 #> 1385  0.405694827 #> 1386  0.405493949 #> 1387  0.405293166 #> 1388  0.405092480 #> 1389  0.404891889 #> 1390  0.404691395 #> 1391  0.404490996 #> 1392  0.404290693 #> 1393  0.404090486 #> 1394  0.403890374 #> 1395  0.403690359 #> 1396  0.403490439 #> 1397  0.403290615 #> 1398  0.403090886 #> 1399  0.402891254 #> 1400  0.402691716 #> 1401  0.402492275 #> 1402  0.402292929 #> 1403  0.402093678 #> 1404  0.401894523 #> 1405  0.401695464 #> 1406  0.401496500 #> 1407  0.401297631 #> 1408  0.401098858 #> 1409  0.400900180 #> 1410  0.400701597 #> 1411  0.400503110 #> 1412  0.400304718 #> 1413  0.400106421 #> 1414  0.399908220 #> 1415  0.399710113 #> 1416  0.399512102 #> 1417  0.399314186 #> 1418  0.399116365 #> 1419  0.398918639 #> 1420  0.398721008 #> 1421  0.398523472 #> 1422  0.398326031 #> 1423  0.398128685 #> 1424  0.397931434 #> 1425  0.397734277 #> 1426  0.397537216 #> 1427  0.397340249 #> 1428  0.397143377 #> 1429  0.396946600 #> 1430  0.396749918 #> 1431  0.396553330 #> 1432  0.396356837 #> 1433  0.396160439 #> 1434  0.395964135 #> 1435  0.395767925 #> 1436  0.395571811 #> 1437  0.395375790 #> 1438  0.395179864 #> 1439  0.394984033 #> 1440  0.394788296 #> 1441  0.394592653 #> 1442  0.394397105 #> 1443  0.394201651 #> 1444  0.394006292 #> 1445  0.393811026 #> 1446  0.393615855 #> 1447  0.393420778 #> 1448  0.393225795 #> 1449  0.393030906 #> 1450  0.392836111 #> 1451  0.392641411 #> 1452  0.392446804 #> 1453  0.392252291 #> 1454  0.392057873 #> 1455  0.391863548 #> 1456  0.391669317 #> 1457  0.391475180 #> 1458  0.391281137 #> 1459  0.391087187 #> 1460  0.390893331 #> 1461  0.390699569 #> 1462  0.390505901 #> 1463  0.390312327 #> 1464  0.390118846 #> 1465  0.389925458 #> 1466  0.389732164 #> 1467  0.389538964 #> 1468  0.389345857 #> 1469  0.389152844 #> 1470  0.388959924 #> 1471  0.388767098 #> 1472  0.388574364 #> 1473  0.388381725 #> 1474  0.388189178 #> 1475  0.387996725 #> 1476  0.387804365 #> 1477  0.387612098 #> 1478  0.387419924 #> 1479  0.387227844 #> 1480  0.387035856 #> 1481  0.386843962 #> 1482  0.386652160 #> 1483  0.386460452 #> 1484  0.386268836 #> 1485  0.386077314 #> 1486  0.385885884 #> 1487  0.385694548 #> 1488  0.385503304 #> 1489  0.385312153 #> 1490  0.385121094 #> 1491  0.384930128 #> 1492  0.384739256 #> 1493  0.384548475 #> 1494  0.384357788 #> 1495  0.384167192 #> 1496  0.383976690 #> 1497  0.383786280 #> 1498  0.383595962 #> 1499  0.383405737 #> 1500  0.383215605 #> 1501  0.383025564 #> 1502  0.382835616 #> 1503  0.382645761 #> 1504  0.382455997 #> 1505  0.382266326 #> 1506  0.382076747 #> 1507  0.381887261 #> 1508  0.381697866 #> 1509  0.381508564 #> 1510  0.381319353 #> 1511  0.381130235 #> 1512  0.380941208 #> 1513  0.380752274 #> 1514  0.380563432 #> 1515  0.380374681 #> 1516  0.380186022 #> 1517  0.379997455 #> 1518  0.379808980 #> 1519  0.379620597 #> 1520  0.379432305 #> 1521  0.379244105 #> 1522  0.379055997 #> 1523  0.378867981 #> 1524  0.378680056 #> 1525  0.378492222 #> 1526  0.378304480 #> 1527  0.378116830 #> 1528  0.377929271 #> 1529  0.377741803 #> 1530  0.377554427 #> 1531  0.377367142 #> 1532  0.377179948 #> 1533  0.376992846 #> 1534  0.376805835 #> 1535  0.376618915 #> 1536  0.376432086 #> 1537  0.376245348 #> 1538  0.376058702 #> 1539  0.375872146 #> 1540  0.375685682 #> 1541  0.375499308 #> 1542  0.375313026 #> 1543  0.375126834 #> 1544  0.374940733 #> 1545  0.374754724 #> 1546  0.374568804 #> 1547  0.374382976 #> 1548  0.374197239 #> 1549  0.374011592 #> 1550  0.373826036 #> 1551  0.373640570 #> 1552  0.373455195 #> 1553  0.373269911 #> 1554  0.373084717 #> 1555  0.372899613 #> 1556  0.372714601 #> 1557  0.372529678 #> 1558  0.372344846 #> 1559  0.372160104 #> 1560  0.371975453 #> 1561  0.371790891 #> 1562  0.371606420 #> 1563  0.371422040 #> 1564  0.371237749 #> 1565  0.371053549 #> 1566  0.370869438 #> 1567  0.370685418 #> 1568  0.370501488 #> 1569  0.370317647 #> 1570  0.370133897 #> 1571  0.369950237 #> 1572  0.369766666 #> 1573  0.369583185 #> 1574  0.369399795 #> 1575  0.369216494 #> 1576  0.369033282 #> 1577  0.368850160 #> 1578  0.368667128 #> 1579  0.368484186 #> 1580  0.368301333 #> 1581  0.368118570 #> 1582  0.367935896 #> 1583  0.367753312 #> 1584  0.367570817 #> 1585  0.367388412 #> 1586  0.367206096 #> 1587  0.367023869 #> 1588  0.366841732 #> 1589  0.366659684 #> 1590  0.366477725 #> 1591  0.366295855 #> 1592  0.366114075 #> 1593  0.365932384 #> 1594  0.365750781 #> 1595  0.365569268 #> 1596  0.365387844 #> 1597  0.365206508 #> 1598  0.365025262 #> 1599  0.364844105 #> 1600  0.364663036 #> 1601  0.364482057 #> 1602  0.364301166 #> 1603  0.364120363 #> 1604  0.363939650 #> 1605  0.363759025 #> 1606  0.363578489 #> 1607  0.363398042 #> 1608  0.363217683 #> 1609  0.363037412 #> 1610  0.362857231 #> 1611  0.362677137 #> 1612  0.362497132 #> 1613  0.362317216 #> 1614  0.362137387 #> 1615  0.361957648 #> 1616  0.361777996 #> 1617  0.361598433 #> 1618  0.361418958 #> 1619  0.361239571 #> 1620  0.361060272 #> 1621  0.360881061 #> 1622  0.360701938 #> 1623  0.360522904 #> 1624  0.360343957 #> 1625  0.360165098 #> 1626  0.359986328 #> 1627  0.359807645 #> 1628  0.359629050 #> 1629  0.359450543 #> 1630  0.359272123 #> 1631  0.359093792 #> 1632  0.358915548 #> 1633  0.358737391 #> 1634  0.358559323 #> 1635  0.358381341 #> 1636  0.358203448 #> 1637  0.358025642 #> 1638  0.357847923 #> 1639  0.357670292 #> 1640  0.357492748 #> 1641  0.357315292 #> 1642  0.357137923 #> 1643  0.356960641 #> 1644  0.356783447 #> 1645  0.356606340 #> 1646  0.356429320 #> 1647  0.356252387 #> 1648  0.356075541 #> 1649  0.355898783 #> 1650  0.355722111 #> 1651  0.355545526 #> 1652  0.355369029 #> 1653  0.355192618 #> 1654  0.355016294 #> 1655  0.354840057 #> 1656  0.354663907 #> 1657  0.354487844 #> 1658  0.354311867 #> 1659  0.354135977 #> 1660  0.353960174 #> 1661  0.353784457 #> 1662  0.353608827 #> 1663  0.353433284 #> 1664  0.353257827 #> 1665  0.353082457 #> 1666  0.352907173 #> 1667  0.352731975 #> 1668  0.352556864 #> 1669  0.352381839 #> 1670  0.352206901 #> 1671  0.352032048 #> 1672  0.351857282 #> 1673  0.351682603 #> 1674  0.351508009 #> 1675  0.351333501 #> 1676  0.351159080 #> 1677  0.350984745 #> 1678  0.350810495 #> 1679  0.350636332 #> 1680  0.350462254 #> 1681  0.350288263 #> 1682  0.350114357 #> 1683  0.349940537 #> 1684  0.349766803 #> 1685  0.349593155 #> 1686  0.349419593 #> 1687  0.349246116 #> 1688  0.349072724 #> 1689  0.348899419 #> 1690  0.348726199 #> 1691  0.348553064 #> 1692  0.348380015 #> 1693  0.348207052 #> 1694  0.348034174 #> 1695  0.347861381 #> 1696  0.347688674 #> 1697  0.347516052 #> 1698  0.347343515 #> 1699  0.347171063 #> 1700  0.346998697 #> 1701  0.346826416 #> 1702  0.346654220 #> 1703  0.346482109 #> 1704  0.346310083 #> 1705  0.346138142 #> 1706  0.345966286 #> 1707  0.345794515 #> 1708  0.345622829 #> 1709  0.345451228 #> 1710  0.345279712 #> 1711  0.345108281 #> 1712  0.344936934 #> 1713  0.344765672 #> 1714  0.344594495 #> 1715  0.344423402 #> 1716  0.344252394 #> 1717  0.344081471 #> 1718  0.343910632 #> 1719  0.343739877 #> 1720  0.343569207 #> 1721  0.343398622 #> 1722  0.343228121 #> 1723  0.343057704 #> 1724  0.342887372 #> 1725  0.342717124 #> 1726  0.342546960 #> 1727  0.342376880 #> 1728  0.342206885 #> 1729  0.342036974 #> 1730  0.341867147 #> 1731  0.341697404 #> 1732  0.341527745 #> 1733  0.341358169 #> 1734  0.341188678 #> 1735  0.341019271 #> 1736  0.340849948 #> 1737  0.340680709 #> 1738  0.340511553 #> 1739  0.340342481 #> 1740  0.340173493 #> 1741  0.340004589 #> 1742  0.339835768 #> 1743  0.339667031 #> 1744  0.339498377 #> 1745  0.339329807 #> 1746  0.339161321 #> 1747  0.338992918 #> 1748  0.338824599 #> 1749  0.338656363 #> 1750  0.338488210 #> 1751  0.338320141 #> 1752  0.338152155 #> 1753  0.337984252 #> 1754  0.337816432 #> 1755  0.337648696 #> 1756  0.337481043 #> 1757  0.337313473 #> 1758  0.337145986 #> 1759  0.336978582 #> 1760  0.336811261 #> 1761  0.336644023 #> 1762  0.336476868 #> 1763  0.336309796 #> 1764  0.336142807 #> 1765  0.335975900 #> 1766  0.335809077 #> 1767  0.335642336 #> 1768  0.335475678 #> 1769  0.335309102 #> 1770  0.335142610 #> 1771  0.334976200 #> 1772  0.334809872 #> 1773  0.334643627 #> 1774  0.334477464 #> 1775  0.334311384 #> 1776  0.334145387 #> 1777  0.333979471 #> 1778  0.333813639 #> 1779  0.333647888 #> 1780  0.333482220 #> 1781  0.333316634 #> 1782  0.333151130 #> 1783  0.332985708 #> 1784  0.332820369 #> 1785  0.332655111 #> 1786  0.332489936 #> 1787  0.332324843 #> 1788  0.332159832 #> 1789  0.331994902 #> 1790  0.331830055 #> 1791  0.331665289 #> 1792  0.331500606 #> 1793  0.331336004 #> 1794  0.331171484 #> 1795  0.331007046 #> 1796  0.330842689 #> 1797  0.330678414 #> 1798  0.330514221 #> 1799  0.330350109 #> 1800  0.330186079 #> 1801  0.330022130 #> 1802  0.329858263 #> 1803  0.329694478 #> 1804  0.329530773 #> 1805  0.329367150 #> 1806  0.329203609 #> 1807  0.329040149 #> 1808  0.328876770 #> 1809  0.328713472 #> 1810  0.328550256 #> 1811  0.328387120 #> 1812  0.328224066 #> 1813  0.328061093 #> 1814  0.327898201 #> 1815  0.327735390 #> 1816  0.327572660 #> 1817  0.327410011 #> 1818  0.327247443 #> 1819  0.327084956 #> 1820  0.326922549 #> 1821  0.326760224 #> 1822  0.326597979 #> 1823  0.326435815 #> 1824  0.326273731 #> 1825  0.326111729 #> 1826  0.325949807 #> 1827  0.325787965 #> 1828  0.325626204 #> 1829  0.325464524 #> 1830  0.325302924 #> 1831  0.325141404 #> 1832  0.324979965 #> 1833  0.324818607 #> 1834  0.324657328 #> 1835  0.324496130 #> 1836  0.324335012 #> 1837  0.324173975 #> 1838  0.324013018 #> 1839  0.323852140 #> 1840  0.323691343 #> 1841  0.323530627 #> 1842  0.323369990 #> 1843  0.323209433 #> 1844  0.323048956 #> 1845  0.322888559 #> 1846  0.322728242 #> 1847  0.322568005 #> 1848  0.322407848 #> 1849  0.322247770 #> 1850  0.322087773 #> 1851  0.321927855 #> 1852  0.321768016 #> 1853  0.321608258 #> 1854  0.321448579 #> 1855  0.321288980 #> 1856  0.321129460 #> 1857  0.320970020 #> 1858  0.320810659 #> 1859  0.320651378 #> 1860  0.320492176 #> 1861  0.320333053 #> 1862  0.320174010 #> 1863  0.320015046 #> 1864  0.319856162 #> 1865  0.319697356 #> 1866  0.319538630 #> 1867  0.319379983 #> 1868  0.319221416 #> 1869  0.319062927 #> 1870  0.318904517 #> 1871  0.318746187 #> 1872  0.318587935 #> 1873  0.318429763 #> 1874  0.318271669 #> 1875  0.318113654 #> 1876  0.317955718 #> 1877  0.317797861 #> 1878  0.317640083 #> 1879  0.317482383 #> 1880  0.317324762 #> 1881  0.317167220 #> 1882  0.317009757 #> 1883  0.316852372 #> 1884  0.316695065 #> 1885  0.316537837 #> 1886  0.316380688 #> 1887  0.316223617 #> 1888  0.316066625 #> 1889  0.315909711 #> 1890  0.315752875 #> 1891  0.315596118 #> 1892  0.315439439 #> 1893  0.315282838 #> 1894  0.315126316 #> 1895  0.314969871 #> 1896  0.314813505 #> 1897  0.314657217 #> 1898  0.314501007 #> 1899  0.314344875 #> 1900  0.314188822 #> 1901  0.314032846 #> 1902  0.313876948 #> 1903  0.313721128 #> 1904  0.313565385 #> 1905  0.313409721 #> 1906  0.313254135 #> 1907  0.313098626 #> 1908  0.312943195 #> 1909  0.312787842 #> 1910  0.312632566 #> 1911  0.312477368 #> 1912  0.312322248 #> 1913  0.312167205 #> 1914  0.312012239 #> 1915  0.311857352 #> 1916  0.311702541 #> 1917  0.311547808 #> 1918  0.311393153 #> 1919  0.311238575 #> 1920  0.311084074 #> 1921  0.310929650 #> 1922  0.310775304 #> 1923  0.310621035 #> 1924  0.310466843 #> 1925  0.310312729 #> 1926  0.310158691 #> 1927  0.310004730 #> 1928  0.309850847 #> 1929  0.309697040 #> 1930  0.309543311 #> 1931  0.309389658 #> 1932  0.309236083 #> 1933  0.309082584 #> 1934  0.308929162 #> 1935  0.308775817 #> 1936  0.308622549 #> 1937  0.308469357 #> 1938  0.308316242 #> 1939  0.308163204 #> 1940  0.308010243 #> 1941  0.307857358 #> 1942  0.307704549 #> 1943  0.307551817 #> 1944  0.307399162 #> 1945  0.307246583 #> 1946  0.307094081 #> 1947  0.306941655 #> 1948  0.306789305 #> 1949  0.306637032 #> 1950  0.306484835 #> 1951  0.306332714 #> 1952  0.306180669 #> 1953  0.306028701 #> 1954  0.305876809 #> 1955  0.305724993 #> 1956  0.305573253 #> 1957  0.305421589 #> 1958  0.305270001 #> 1959  0.305118489 #> 1960  0.304967053 #> 1961  0.304815693 #> 1962  0.304664409 #> 1963  0.304513201 #> 1964  0.304362068 #> 1965  0.304211012 #> 1966  0.304060031 #> 1967  0.303909126 #> 1968  0.303758296 #> 1969  0.303607542 #> 1970  0.303456864 #> 1971  0.303306261 #> 1972  0.303155734 #> 1973  0.303005283 #> 1974  0.302854907 #> 1975  0.302704606 #> 1976  0.302554381 #> 1977  0.302404231 #> 1978  0.302254157 #> 1979  0.302104157 #> 1980  0.301954234 #> 1981  0.301804385 #> 1982  0.301654612 #> 1983  0.301504913 #> 1984  0.301355290 #> 1985  0.301205742 #> 1986  0.301056269 #> 1987  0.300906872 #> 1988  0.300757549 #> 1989  0.300608301 #> 1990  0.300459128 #> 1991  0.300310030 #> 1992  0.300161007 #> 1993  0.300012059 #> 1994  0.299863186 #> 1995  0.299714387 #> 1996  0.299565663 #> 1997  0.299417014 #> 1998  0.299268440 #> 1999  0.299119940 #> 2000  0.298971515 #> 2001  0.298823164 #> 2002  0.298674888 #> 2003  0.298526687 #> 2004  0.298378560 #> 2005  0.298230507 #> 2006  0.298082529 #> 2007  0.297934625 #> 2008  0.297786796 #> 2009  0.297639041 #> 2010  0.297491360 #> 2011  0.297343753 #> 2012  0.297196221 #> 2013  0.297048763 #> 2014  0.296901379 #> 2015  0.296754069 #> 2016  0.296606834 #> 2017  0.296459672 #> 2018  0.296312584 #> 2019  0.296165571 #> 2020  0.296018631 #> 2021  0.295871765 #> 2022  0.295724973 #> 2023  0.295578255 #> 2024  0.295431611 #> 2025  0.295285041 #> 2026  0.295138544 #> 2027  0.294992121 #> 2028  0.294845772 #> 2029  0.294699497 #> 2030  0.294553295 #> 2031  0.294407166 #> 2032  0.294261112 #> 2033  0.294115130 #> 2034  0.293969223 #> 2035  0.293823389 #> 2036  0.293677628 #> 2037  0.293531941 #> 2038  0.293386327 #> 2039  0.293240786 #> 2040  0.293095319 #> 2041  0.292949925 #> 2042  0.292804604 #> 2043  0.292659356 #> 2044  0.292514182 #> 2045  0.292369081 #> 2046  0.292224052 #> 2047  0.292079097 #> 2048  0.291934215 #> 2049  0.291789407 #> 2050  0.291644671 #> 2051  0.291500008 #> 2052  0.291355417 #> 2053  0.291210900 #> 2054  0.291066456 #> 2055  0.290922084 #> 2056  0.290777786 #> 2057  0.290633560 #> 2058  0.290489406 #> 2059  0.290345326 #> 2060  0.290201318 #> 2061  0.290057383 #> 2062  0.289913520 #> 2063  0.289769730 #> 2064  0.289626013 #> 2065  0.289482368 #> 2066  0.289338795 #> 2067  0.289195295 #> 2068  0.289051868 #> 2069  0.288908512 #> 2070  0.288765229 #> 2071  0.288622019 #> 2072  0.288478880 #> 2073  0.288335814 #> 2074  0.288192821 #> 2075  0.288049899 #> 2076  0.287907050 #> 2077  0.287764272 #> 2078  0.287621567 #> 2079  0.287478934 #> 2080  0.287336373 #> 2081  0.287193883 #> 2082  0.287051466 #> 2083  0.286909121 #> 2084  0.286766848 #> 2085  0.286624646 #> 2086  0.286482517 #> 2087  0.286340459 #> 2088  0.286198473 #> 2089  0.286056559 #> 2090  0.285914716 #> 2091  0.285772945 #> 2092  0.285631246 #> 2093  0.285489619 #> 2094  0.285348063 #> 2095  0.285206578 #> 2096  0.285065165 #> 2097  0.284923824 #> 2098  0.284782554 #> 2099  0.284641356 #> 2100  0.284500229 #> 2101  0.284359173 #> 2102  0.284218189 #> 2103  0.284077276 #> 2104  0.283936434 #> 2105  0.283795664 #> 2106  0.283654965 #> 2107  0.283514336 #> 2108  0.283373780 #> 2109  0.283233294 #> 2110  0.283092879 #> 2111  0.282952536 #> 2112  0.282812263 #> 2113  0.282672062 #> 2114  0.282531931 #> 2115  0.282391872 #> 2116  0.282251883 #> 2117  0.282111965 #> 2118  0.281972118 #> 2119  0.281832342 #> 2120  0.281692637 #> 2121  0.281553002 #> 2122  0.281413438 #> 2123  0.281273945 #> 2124  0.281134523 #> 2125  0.280995171 #> 2126  0.280855890 #> 2127  0.280716679 #> 2128  0.280577539 #> 2129  0.280438470 #> 2130  0.280299471 #> 2131  0.280160542 #> 2132  0.280021684 #> 2133  0.279882896 #> 2134  0.279744179 #> 2135  0.279605532 #> 2136  0.279466955 #> 2137  0.279328449 #> 2138  0.279190012 #> 2139  0.279051646 #> 2140  0.278913351 #> 2141  0.278775125 #> 2142  0.278636969 #> 2143  0.278498884 #> 2144  0.278360869 #> 2145  0.278222923 #> 2146  0.278085048 #> 2147  0.277947243 #> 2148  0.277809507 #> 2149  0.277671842 #> 2150  0.277534246 #> 2151  0.277396720 #> 2152  0.277259265 #> 2153  0.277121878 #> 2154  0.276984562 #> 2155  0.276847315 #> 2156  0.276710139 #> 2157  0.276573031 #> 2158  0.276435994 #> 2159  0.276299026 #> 2160  0.276162128 #> 2161  0.276025299 #> 2162  0.275888540 #> 2163  0.275751850 #> 2164  0.275615229 #> 2165  0.275478679 #> 2166  0.275342197 #> 2167  0.275205785 #> 2168  0.275069442 #> 2169  0.274933169 #> 2170  0.274796965 #> 2171  0.274660830 #> 2172  0.274524765 #> 2173  0.274388768 #> 2174  0.274252841 #> 2175  0.274116983 #> 2176  0.273981194 #> 2177  0.273845474 #> 2178  0.273709824 #> 2179  0.273574242 #> 2180  0.273438729 #> 2181  0.273303286 #> 2182  0.273167911 #> 2183  0.273032605 #> 2184  0.272897368 #> 2185  0.272762200 #> 2186  0.272627100 #> 2187  0.272492070 #> 2188  0.272357108 #> 2189  0.272222215 #> 2190  0.272087391 #> 2191  0.271952635 #> 2192  0.271817948 #> 2193  0.271683330 #> 2194  0.271548780 #> 2195  0.271414299 #> 2196  0.271279887 #> 2197  0.271145543 #> 2198  0.271011267 #> 2199  0.270877060 #> 2200  0.270742921 #> 2201  0.270608851 #> 2202  0.270474849 #> 2203  0.270340915 #> 2204  0.270207050 #> 2205  0.270073253 #> 2206  0.269939524 #> 2207  0.269805864 #> 2208  0.269672271 #> 2209  0.269538747 #> 2210  0.269405291 #> 2211  0.269271903 #> 2212  0.269138584 #> 2213  0.269005332 #> 2214  0.268872148 #> 2215  0.268739033 #> 2216  0.268605985 #> 2217  0.268473005 #> 2218  0.268340093 #> 2219  0.268207249 #> 2220  0.268074473 #> 2221  0.267941765 #> 2222  0.267809124 #> 2223  0.267676552 #> 2224  0.267544047 #> 2225  0.267411609 #> 2226  0.267279240 #> 2227  0.267146938 #> 2228  0.267014704 #> 2229  0.266882537 #> 2230  0.266750438 #> 2231  0.266618407 #> 2232  0.266486443 #> 2233  0.266354546 #> 2234  0.266222717 #> 2235  0.266090956 #> 2236  0.265959262 #> 2237  0.265827635 #> 2238  0.265696076 #> 2239  0.265564584 #> 2240  0.265433159 #> 2241  0.265301801 #> 2242  0.265170511 #> 2243  0.265039288 #> 2244  0.264908132 #> 2245  0.264777044 #> 2246  0.264646022 #> 2247  0.264515068 #> 2248  0.264384181 #> 2249  0.264253361 #> 2250  0.264122608 #> 2251  0.263991921 #> 2252  0.263861302 #> 2253  0.263730750 #> 2254  0.263600265 #> 2255  0.263469846 #> 2256  0.263339495 #> 2257  0.263209210 #> 2258  0.263078992 #> 2259  0.262948841 #> 2260  0.262818757 #> 2261  0.262688739 #> 2262  0.262558789 #> 2263  0.262428905 #> 2264  0.262299087 #> 2265  0.262169336 #> 2266  0.262039652 #> 2267  0.261910034 #> 2268  0.261780483 #> 2269  0.261650998 #> 2270  0.261521580 #> 2271  0.261392229 #> 2272  0.261262944 #> 2273  0.261133725 #> 2274  0.261004572 #> 2275  0.260875486 #> 2276  0.260746467 #> 2277  0.260617513 #> 2278  0.260488626 #> 2279  0.260359806 #> 2280  0.260231051 #> 2281  0.260102363 #> 2282  0.259973740 #> 2283  0.259845184 #> 2284  0.259716695 #> 2285  0.259588271 #> 2286  0.259459913 #> 2287  0.259331621 #> 2288  0.259203396 #> 2289  0.259075236 #> 2290  0.258947142 #> 2291  0.258819115 #> 2292  0.258691153 #> 2293  0.258563257 #> 2294  0.258435427 #> 2295  0.258307663 #> 2296  0.258179964 #> 2297  0.258052332 #> 2298  0.257924765 #> 2299  0.257797264 #> 2300  0.257669829 #> 2301  0.257542459 #> 2302  0.257415155 #> 2303  0.257287917 #> 2304  0.257160744 #> 2305  0.257033637 #> 2306  0.256906595 #> 2307  0.256779619 #> 2308  0.256652709 #> 2309  0.256525864 #> 2310  0.256399084 #> 2311  0.256272370 #> 2312  0.256145721 #> 2313  0.256019138 #> 2314  0.255892620 #> 2315  0.255766167 #> 2316  0.255639780 #> 2317  0.255513458 #> 2318  0.255387201 #> 2319  0.255261010 #> 2320  0.255134884 #> 2321  0.255008822 #> 2322  0.254882826 #> 2323  0.254756896 #> 2324  0.254631030 #> 2325  0.254505229 #> 2326  0.254379494 #> 2327  0.254253823 #> 2328  0.254128218 #> 2329  0.254002677 #> 2330  0.253877202 #> 2331  0.253751791 #> 2332  0.253626445 #> 2333  0.253501165 #> 2334  0.253375949 #> 2335  0.253250798 #> 2336  0.253125711 #> 2337  0.253000690 #> 2338  0.252875733 #> 2339  0.252750841 #> 2340  0.252626014 #> 2341  0.252501251 #> 2342  0.252376554 #> 2343  0.252251920 #> 2344  0.252127352 #> 2345  0.252002848 #> 2346  0.251878408 #> 2347  0.251754034 #> 2348  0.251629723 #> 2349  0.251505477 #> 2350  0.251381296 #> 2351  0.251257179 #> 2352  0.251133127 #> 2353  0.251009139 #> 2354  0.250885215 #> 2355  0.250761356 #> 2356  0.250637561 #> 2357  0.250513830 #> 2358  0.250390164 #> 2359  0.250266562 #> 2360  0.250143024 #> 2361  0.250019550 #> 2362  0.249896141 #> 2363  0.249772796 #> 2364  0.249649515 #> 2365  0.249526298 #> 2366  0.249403145 #> 2367  0.249280056 #> 2368  0.249157032 #> 2369  0.249034071 #> 2370  0.248911174 #> 2371  0.248788342 #> 2372  0.248665573 #> 2373  0.248542868 #> 2374  0.248420228 #> 2375  0.248297651 #> 2376  0.248175138 #> 2377  0.248052688 #> 2378  0.247930303 #> 2379  0.247807982 #> 2380  0.247685724 #> 2381  0.247563530 #> 2382  0.247441400 #> 2383  0.247319333 #> 2384  0.247197330 #> 2385  0.247075391 #> 2386  0.246953516 #> 2387  0.246831704 #> 2388  0.246709955 #> 2389  0.246588271 #> 2390  0.246466650 #> 2391  0.246345092 #> 2392  0.246223598 #> 2393  0.246102167 #> 2394  0.245980800 #> 2395  0.245859497 #> 2396  0.245738256 #> 2397  0.245617079 #> 2398  0.245495966 #> 2399  0.245374916 #> 2400  0.245253929 #> 2401  0.245133006 #> 2402  0.245012146 #> 2403  0.244891349 #> 2404  0.244770615 #> 2405  0.244649945 #> 2406  0.244529338 #> 2407  0.244408794 #> 2408  0.244288313 #> 2409  0.244167895 #> 2410  0.244047541 #> 2411  0.243927249 #> 2412  0.243807021 #> 2413  0.243686856 #> 2414  0.243566753 #> 2415  0.243446714 #> 2416  0.243326738 #> 2417  0.243206825 #> 2418  0.243086974 #> 2419  0.242967187 #> 2420  0.242847463 #> 2421  0.242727801 #> 2422  0.242608202 #> 2423  0.242488667 #> 2424  0.242369194 #> 2425  0.242249783 #> 2426  0.242130436 #> 2427  0.242011151 #> 2428  0.241891929 #> 2429  0.241772770 #> 2430  0.241653674 #> 2431  0.241534640 #> 2432  0.241415669 #> 2433  0.241296760 #> 2434  0.241177915 #> 2435  0.241059131 #> 2436  0.240940411 #> 2437  0.240821753 #> 2438  0.240703157 #> 2439  0.240584624 #> 2440  0.240466153 #> 2441  0.240347745 #> 2442  0.240229400 #> 2443  0.240111117 #> 2444  0.239992896 #> 2445  0.239874738 #> 2446  0.239756642 #> 2447  0.239638608 #> 2448  0.239520637 #> 2449  0.239402728 #> 2450  0.239284881 #> 2451  0.239167097 #> 2452  0.239049375 #> 2453  0.238931715 #> 2454  0.238814118 #> 2455  0.238696582 #> 2456  0.238579109 #> 2457  0.238461698 #> 2458  0.238344349 #> 2459  0.238227063 #> 2460  0.238109838 #> 2461  0.237992675 #> 2462  0.237875575 #> 2463  0.237758537 #> 2464  0.237641560 #> 2465  0.237524646 #> 2466  0.237407793 #> 2467  0.237291003 #> 2468  0.237174275 #> 2469  0.237057608 #> 2470  0.236941004 #> 2471  0.236824461 #> 2472  0.236707980 #> 2473  0.236591561 #> 2474  0.236475204 #> 2475  0.236358909 #> 2476  0.236242676 #> 2477  0.236126504 #> 2478  0.236010394 #> 2479  0.235894346 #> 2480  0.235778360 #> 2481  0.235662435 #> 2482  0.235546572 #> 2483  0.235430771 #> 2484  0.235315032 #> 2485  0.235199354 #> 2486  0.235083737 #> 2487  0.234968183 #> 2488  0.234852690 #> 2489  0.234737258 #> 2490  0.234621888 #> 2491  0.234506580 #> 2492  0.234391333 #> 2493  0.234276148 #> 2494  0.234161024 #> 2495  0.234045961 #> 2496  0.233930960 #> 2497  0.233816021 #> 2498  0.233701143 #> 2499  0.233586326 #> 2500  0.233471571 #> 2501  0.233356877 #> 2502  0.233242245 #> 2503  0.233127673 #> 2504  0.233013164 #> 2505  0.232898715 #> 2506  0.232784328 #> 2507  0.232670002 #> 2508  0.232555737 #> 2509  0.232441533 #> 2510  0.232327391 #> 2511  0.232213310 #> 2512  0.232099290 #> 2513  0.231985331 #> 2514  0.231871434 #> 2515  0.231757597 #> 2516  0.231643822 #> 2517  0.231530108 #> 2518  0.231416455 #> 2519  0.231302863 #> 2520  0.231189332 #> 2521  0.231075862 #> 2522  0.230962453 #> 2523  0.230849105 #> 2524  0.230735818 #> 2525  0.230622592 #> 2526  0.230509427 #> 2527  0.230396323 #> 2528  0.230283280 #> 2529  0.230170298 #> 2530  0.230057377 #> 2531  0.229944516 #> 2532  0.229831717 #> 2533  0.229718978 #> 2534  0.229606300 #> 2535  0.229493683 #> 2536  0.229381127 #> 2537  0.229268632 #> 2538  0.229156197 #> 2539  0.229043824 #> 2540  0.228931511 #> 2541  0.228819258 #> 2542  0.228707067 #> 2543  0.228594936 #> 2544  0.228482866 #> 2545  0.228370856 #> 2546  0.228258908 #> 2547  0.228147019 #> 2548  0.228035192 #> 2549  0.227923425 #> 2550  0.227811719 #> 2551  0.227700073 #> 2552  0.227588488 #> 2553  0.227476964 #> 2554  0.227365500 #> 2555  0.227254096 #> 2556  0.227142753 #> 2557  0.227031471 #> 2558  0.226920249 #> 2559  0.226809088 #> 2560  0.226697987 #> 2561  0.226586946 #> 2562  0.226475966 #> 2563  0.226365047 #> 2564  0.226254188 #> 2565  0.226143389 #> 2566  0.226032651 #> 2567  0.225921973 #> 2568  0.225811355 #> 2569  0.225700798 #> 2570  0.225590301 #> 2571  0.225479865 #> 2572  0.225369488 #> 2573  0.225259173 #> 2574  0.225148917 #> 2575  0.225038722 #> 2576  0.224928587 #> 2577  0.224818512 #> 2578  0.224708497 #> 2579  0.224598543 #> 2580  0.224488649 #> 2581  0.224378815 #> 2582  0.224269041 #> 2583  0.224159328 #> 2584  0.224049674 #> 2585  0.223940081 #> 2586  0.223830548 #> 2587  0.223721075 #> 2588  0.223611663 #> 2589  0.223502310 #> 2590  0.223393018 #> 2591  0.223283785 #> 2592  0.223174613 #> 2593  0.223065501 #> 2594  0.222956448 #> 2595  0.222847456 #> 2596  0.222738524 #> 2597  0.222629652 #> 2598  0.222520840 #> 2599  0.222412088 #> 2600  0.222303396 #> 2601  0.222194764 #> 2602  0.222086192 #> 2603  0.221977680 #> 2604  0.221869227 #> 2605  0.221760835 #> 2606  0.221652503 #> 2607  0.221544231 #> 2608  0.221436018 #> 2609  0.221327866 #> 2610  0.221219773 #> 2611  0.221111740 #> 2612  0.221003767 #> 2613  0.220895854 #> 2614  0.220788001 #> 2615  0.220680208 #> 2616  0.220572475 #> 2617  0.220464801 #> 2618  0.220357187 #> 2619  0.220249633 #> 2620  0.220142139 #> 2621  0.220034705 #> 2622  0.219927330 #> 2623  0.219820015 #> 2624  0.219712760 #> 2625  0.219605565 #> 2626  0.219498429 #> 2627  0.219391354 #> 2628  0.219284338 #> 2629  0.219177381 #> 2630  0.219070485 #> 2631  0.218963648 #> 2632  0.218856870 #> 2633  0.218750153 #> 2634  0.218643495 #> 2635  0.218536897 #> 2636  0.218430359 #> 2637  0.218323880 #> 2638  0.218217461 #> 2639  0.218111101 #> 2640  0.218004801 #> 2641  0.217898561 #> 2642  0.217792381 #> 2643  0.217686260 #> 2644  0.217580198 #> 2645  0.217474197 #> 2646  0.217368254 #> 2647  0.217262372 #> 2648  0.217156549 #> 2649  0.217050786 #> 2650  0.216945082 #> 2651  0.216839437 #> 2652  0.216733853 #> 2653  0.216628328 #> 2654  0.216522862 #> 2655  0.216417456 #> 2656  0.216312110 #> 2657  0.216206823 #> 2658  0.216101595 #> 2659  0.215996427 #> 2660  0.215891319 #> 2661  0.215786270 #> 2662  0.215681281 #> 2663  0.215576351 #> 2664  0.215471481 #> 2665  0.215366670 #> 2666  0.215261918 #> 2667  0.215157226 #> 2668  0.215052594 #> 2669  0.214948021 #> 2670  0.214843508 #> 2671  0.214739054 #> 2672  0.214634659 #> 2673  0.214530324 #> 2674  0.214426048 #> 2675  0.214321832 #> 2676  0.214217675 #> 2677  0.214113578 #> 2678  0.214009540 #> 2679  0.213905562 #> 2680  0.213801643 #> 2681  0.213697783 #> 2682  0.213593983 #> 2683  0.213490242 #> 2684  0.213386561 #> 2685  0.213282939 #> 2686  0.213179376 #> 2687  0.213075873 #> 2688  0.212972429 #> 2689  0.212869045 #> 2690  0.212765720 #> 2691  0.212662454 #> 2692  0.212559248 #> 2693  0.212456102 #> 2694  0.212353014 #> 2695  0.212249986 #> 2696  0.212147018 #> 2697  0.212044108 #> 2698  0.211941259 #> 2699  0.211838468 #> 2700  0.211735737 #> 2701  0.211633066 #> 2702  0.211530453 #> 2703  0.211427900 #> 2704  0.211325407 #> 2705  0.211222973 #> 2706  0.211120598 #> 2707  0.211018282 #> 2708  0.210916026 #> 2709  0.210813830 #> 2710  0.210711692 #> 2711  0.210609614 #> 2712  0.210507596 #> 2713  0.210405637 #> 2714  0.210303737 #> 2715  0.210201896 #> 2716  0.210100115 #> 2717  0.209998394 #> 2718  0.209896731 #> 2719  0.209795128 #> 2720  0.209693585 #> 2721  0.209592100 #> 2722  0.209490676 #> 2723  0.209389310 #> 2724  0.209288004 #> 2725  0.209186757 #> 2726  0.209085570 #> 2727  0.208984442 #> 2728  0.208883374 #> 2729  0.208782364 #> 2730  0.208681415 #> 2731  0.208580524 #> 2732  0.208479693 #> 2733  0.208378922 #> 2734  0.208278210 #> 2735  0.208177557 #> 2736  0.208076963 #> 2737  0.207976429 #> 2738  0.207875955 #> 2739  0.207775540 #> 2740  0.207675184 #> 2741  0.207574888 #> 2742  0.207474651 #> 2743  0.207374473 #> 2744  0.207274355 #> 2745  0.207174297 #> 2746  0.207074298 #> 2747  0.206974358 #> 2748  0.206874478 #> 2749  0.206774657 #> 2750  0.206674895 #> 2751  0.206575194 #> 2752  0.206475551 #> 2753  0.206375968 #> 2754  0.206276445 #> 2755  0.206176981 #> 2756  0.206077576 #> 2757  0.205978231 #> 2758  0.205878946 #> 2759  0.205779720 #> 2760  0.205680553 #> 2761  0.205581446 #> 2762  0.205482399 #> 2763  0.205383411 #> 2764  0.205284482 #> 2765  0.205185613 #> 2766  0.205086804 #> 2767  0.204988054 #> 2768  0.204889364 #> 2769  0.204790733 #> 2770  0.204692162 #> 2771  0.204593651 #> 2772  0.204495199 #> 2773  0.204396807 #> 2774  0.204298474 #> 2775  0.204200201 #> 2776  0.204101987 #> 2777  0.204003834 #> 2778  0.203905739 #> 2779  0.203807705 #> 2780  0.203709730 #> 2781  0.203611815 #> 2782  0.203513959 #> 2783  0.203416163 #> 2784  0.203318427 #> 2785  0.203220751 #> 2786  0.203123134 #> 2787  0.203025577 #> 2788  0.202928080 #> 2789  0.202830642 #> 2790  0.202733264 #> 2791  0.202635946 #> 2792  0.202538688 #> 2793  0.202441490 #> 2794  0.202344351 #> 2795  0.202247272 #> 2796  0.202150253 #> 2797  0.202053294 #> 2798  0.201956394 #> 2799  0.201859555 #> 2800  0.201762775 #> 2801  0.201666055 #> 2802  0.201569395 #> 2803  0.201472795 #> 2804  0.201376255 #> 2805  0.201279775 #> 2806  0.201183354 #> 2807  0.201086994 #> 2808  0.200990694 #> 2809  0.200894453 #> 2810  0.200798273 #> 2811  0.200702153 #> 2812  0.200606092 #> 2813  0.200510092 #> 2814  0.200414151 #> 2815  0.200318271 #> 2816  0.200222451 #> 2817  0.200126691 #> 2818  0.200030991 #> 2819  0.199935351 #> 2820  0.199839771 #> 2821  0.199744252 #> 2822  0.199648792 #> 2823  0.199553393 #> 2824  0.199458054 #> 2825  0.199362775 #> 2826  0.199267556 #> 2827  0.199172398 #> 2828  0.199077300 #> 2829  0.198982262 #> 2830  0.198887284 #> 2831  0.198792367 #> 2832  0.198697510 #> 2833  0.198602714 #> 2834  0.198507977 #> 2835  0.198413301 #> 2836  0.198318686 #> 2837  0.198224131 #> 2838  0.198129636 #> 2839  0.198035202 #> 2840  0.197940828 #> 2841  0.197846515 #> 2842  0.197752262 #> 2843  0.197658070 #> 2844  0.197563939 #> 2845  0.197469867 #> 2846  0.197375857 #> 2847  0.197281907 #> 2848  0.197188018 #> 2849  0.197094189 #> 2850  0.197000421 #> 2851  0.196906714 #> 2852  0.196813067 #> 2853  0.196719482 #> 2854  0.196625956 #> 2855  0.196532492 #> 2856  0.196439089 #> 2857  0.196345746 #> 2858  0.196252464 #> 2859  0.196159243 #> 2860  0.196066083 #> 2861  0.195972984 #> 2862  0.195879945 #> 2863  0.195786968 #> 2864  0.195694052 #> 2865  0.195601196 #> 2866  0.195508402 #> 2867  0.195415669 #> 2868  0.195322996 #> 2869  0.195230385 #> 2870  0.195137835 #> 2871  0.195045346 #> 2872  0.194952918 #> 2873  0.194860552 #> 2874  0.194768247 #> 2875  0.194676002 #> 2876  0.194583820 #> 2877  0.194491698 #> 2878  0.194399638 #> 2879  0.194307639 #> 2880  0.194215702 #> 2881  0.194123826 #> 2882  0.194032011 #> 2883  0.193940258 #> 2884  0.193848566 #> 2885  0.193756936 #> 2886  0.193665367 #> 2887  0.193573860 #> 2888  0.193482415 #> 2889  0.193391031 #> 2890  0.193299709 #> 2891  0.193208448 #> 2892  0.193117249 #> 2893  0.193026112 #> 2894  0.192935037 #> 2895  0.192844023 #> 2896  0.192753072 #> 2897  0.192662182 #> 2898  0.192571354 #> 2899  0.192480588 #> 2900  0.192389884 #> 2901  0.192299242 #> 2902  0.192208662 #> 2903  0.192118144 #> 2904  0.192027688 #> 2905  0.191937294 #> 2906  0.191846962 #> 2907  0.191756693 #> 2908  0.191666486 #> 2909  0.191576341 #> 2910  0.191486258 #> 2911  0.191396238 #> 2912  0.191306280 #> 2913  0.191216384 #> 2914  0.191126551 #> 2915  0.191036780 #> 2916  0.190947072 #> 2917  0.190857426 #> 2918  0.190767843 #> 2919  0.190678322 #> 2920  0.190588864 #> 2921  0.190499469 #> 2922  0.190410136 #> 2923  0.190320866 #> 2924  0.190231659 #> 2925  0.190142515 #> 2926  0.190053433 #> 2927  0.189964415 #> 2928  0.189875459 #> 2929  0.189786566 #> 2930  0.189697737 #> 2931  0.189608970 #> 2932  0.189520267 #> 2933  0.189431626 #> 2934  0.189343049 #> 2935  0.189254535 #> 2936  0.189166084 #> 2937  0.189077697 #> 2938  0.188989373 #> 2939  0.188901112 #> 2940  0.188812915 #> 2941  0.188724781 #> 2942  0.188636710 #> 2943  0.188548703 #> 2944  0.188460760 #> 2945  0.188372880 #> 2946  0.188285064 #> 2947  0.188197312 #> 2948  0.188109623 #> 2949  0.188021999 #> 2950  0.187934438 #> 2951  0.187846941 #> 2952  0.187759508 #> 2953  0.187672138 #> 2954  0.187584833 #> 2955  0.187497592 #> 2956  0.187410415 #> 2957  0.187323303 #> 2958  0.187236254 #> 2959  0.187149270 #> 2960  0.187062350 #> 2961  0.186975494 #> 2962  0.186888703 #> 2963  0.186801976 #> 2964  0.186715314 #> 2965  0.186628716 #> 2966  0.186542183 #> 2967  0.186455714 #> 2968  0.186369310 #> 2969  0.186282971 #> 2970  0.186196697 #> 2971  0.186110487 #> 2972  0.186024343 #> 2973  0.185938263 #> 2974  0.185852249 #> 2975  0.185766299 #> 2976  0.185680414 #> 2977  0.185594595 #> 2978  0.185508841 #> 2979  0.185423152 #> 2980  0.185337529 #> 2981  0.185251970 #> 2982  0.185166478 #> 2983  0.185081050 #> 2984  0.184995689 #> 2985  0.184910392 #> 2986  0.184825162 #> 2987  0.184739997 #> 2988  0.184654898 #> 2989  0.184569865 #> 2990  0.184484898 #> 2991  0.184399996 #> 2992  0.184315161 #> 2993  0.184230391 #> 2994  0.184145688 #> 2995  0.184061051 #> 2996  0.183976480 #> 2997  0.183891975 #> 2998  0.183807537 #> 2999  0.183723165 #> 3000  0.183638859 #> 3001  0.183554621 #> 3002  0.183470448 #> 3003  0.183386343 #> 3004  0.183302304 #> 3005  0.183218331 #> 3006  0.183134426 #> 3007  0.183050587 #> 3008  0.182966816 #> 3009  0.182883111 #> 3010  0.182799474 #> 3011  0.182715904 #> 3012  0.182632401 #> 3013  0.182548965 #> 3014  0.182465596 #> 3015  0.182382295 #> 3016  0.182299062 #> 3017  0.182215896 #> 3018  0.182132797 #> 3019  0.182049767 #> 3020  0.181966804 #> 3021  0.181883909 #> 3022  0.181801081 #> 3023  0.181718322 #> 3024  0.181635631 #> 3025  0.181553008 #> 3026  0.181470453 #> 3027  0.181387966 #> 3028  0.181305548 #> 3029  0.181223198 #> 3030  0.181140916 #> 3031  0.181058703 #> 3032  0.180976558 #> 3033  0.180894483 #> 3034  0.180812475 #> 3035  0.180730537 #> 3036  0.180648668 #> 3037  0.180566868 #> 3038  0.180485136 #> 3039  0.180403474 #> 3040  0.180321881 #> 3041  0.180240357 #> 3042  0.180158903 #> 3043  0.180077518 #> 3044  0.179996202 #> 3045  0.179914957 #> 3046  0.179833780 #> 3047  0.179752674 #> 3048  0.179671637 #> 3049  0.179590671 #> 3050  0.179509774 #> 3051  0.179428947 #> 3052  0.179348191 #> 3053  0.179267504 #> 3054  0.179186888 #> 3055  0.179106343 #> 3056  0.179025868 #> 3057  0.178945463 #> 3058  0.178865129 #> 3059  0.178784866 #> 3060  0.178704674 #> 3061  0.178624552 #> 3062  0.178544502 #> 3063  0.178464522 #> 3064  0.178384614 #> 3065  0.178304777 #> 3066  0.178225011 #> 3067  0.178145317 #> 3068  0.178065694 #> 3069  0.177986143 #> 3070  0.177906664 #> 3071  0.177827256 #> 3072  0.177747920 #> 3073  0.177668656 #> 3074  0.177589465 #> 3075  0.177510345 #> 3076  0.177431298 #> 3077  0.177352323 #> 3078  0.177273420 #> 3079  0.177194590 #> 3080  0.177115833 #> 3081  0.177037148 #> 3082  0.176958536 #> 3083  0.176879997 #> 3084  0.176801531 #> 3085  0.176723138 #> 3086  0.176644818 #> 3087  0.176566572 #> 3088  0.176488399 #> 3089  0.176410299 #> 3090  0.176332273 #> 3091  0.176254321 #> 3092  0.176176443 #> 3093  0.176098638 #> 3094  0.176020908 #> 3095  0.175943251 #> 3096  0.175865669 #> 3097  0.175788161 #> 3098  0.175710727 #> 3099  0.175633368 #> 3100  0.175556084 #> 3101  0.175478874 #> 3102  0.175401740 #> 3103  0.175324680 #> 3104  0.175247695 #> 3105  0.175170785 #> 3106  0.175093951 #> 3107  0.175017192 #> 3108  0.174940508 #> 3109  0.174863901 #> 3110  0.174787368 #> 3111  0.174710912 #> 3112  0.174634531 #> 3113  0.174558227 #> 3114  0.174481999 #> 3115  0.174405847 #> 3116  0.174329771 #> 3117  0.174253772 #> 3118  0.174177850 #> 3119  0.174102004 #> 3120  0.174026235 #> 3121  0.173950543 #> 3122  0.173874928 #> 3123  0.173799391 #> 3124  0.173723930 #> 3125  0.173648548 #> 3126  0.173573242 #> 3127  0.173498015 #> 3128  0.173422865 #> 3129  0.173347793 #> 3130  0.173272799 #> 3131  0.173197884 #> 3132  0.173123046 #> 3133  0.173048287 #> 3134  0.172973607 #> 3135  0.172899005 #> 3136  0.172824482 #> 3137  0.172750038 #> 3138  0.172675674 #> 3139  0.172601388 #> 3140  0.172527181 #> 3141  0.172453055 #> 3142  0.172379007 #> 3143  0.172305040 #> 3144  0.172231152 #> 3145  0.172157344 #> 3146  0.172083616 #> 3147  0.172009969 #> 3148  0.171936402 #> 3149  0.171862915 #> 3150  0.171789509 #> 3151  0.171716184 #> 3152  0.171642940 #> 3153  0.171569776 #> 3154  0.171496694 #> 3155  0.171423694 #> 3156  0.171350774 #> 3157  0.171277937 #> 3158  0.171205181 #> 3159  0.171132507 #> 3160  0.171059915 #> 3161  0.170987406 #> 3162  0.170914978 #> 3163  0.170842633 #> 3164  0.170770371 #> 3165  0.170698192 #> 3166  0.170626095 #> 3167  0.170554082 #> 3168  0.170482151 #> 3169  0.170410304 #> 3170  0.170338541 #> 3171  0.170266861 #> 3172  0.170195265 #> 3173  0.170123753 #> 3174  0.170052326 #> 3175  0.169980982 #> 3176  0.169909723 #> 3177  0.169838548 #> 3178  0.169767458 #> 3179  0.169696453 #> 3180  0.169625534 #> 3181  0.169554699 #> 3182  0.169483950 #> 3183  0.169413286 #> 3184  0.169342708 #> 3185  0.169272216 #> 3186  0.169201810 #> 3187  0.169131490 #> 3188  0.169061256 #> 3189  0.168991109 #> 3190  0.168921048 #> 3191  0.168851075 #> 3192  0.168781188 #> 3193  0.168711389 #> 3194  0.168641676 #> 3195  0.168572052 #> 3196  0.168502515 #> 3197  0.168433066 #> 3198  0.168363705 #> 3199  0.168294432 #> 3200  0.168225248 #> 3201  0.168156152 #> 3202  0.168087145 #> 3203  0.168018226 #> 3204  0.167949397 #> 3205  0.167880657 #> 3206  0.167812007 #> 3207  0.167743446 #> 3208  0.167674975 #> 3209  0.167606594 #> 3210  0.167538303 #> 3211  0.167470102 #> 3212  0.167401992 #> 3213  0.167333973 #> 3214  0.167266044 #> 3215  0.167198207 #> 3216  0.167130461 #> 3217  0.167062806 #> 3218  0.166995243 #> 3219  0.166927772 #> 3220  0.166860393 #> 3221  0.166793106 #> 3222  0.166725911 #> 3223  0.166658809 #> 3224  0.166591800 #> 3225  0.166524884 #> 3226  0.166458061 #> 3227  0.166391332 #> 3228  0.166324696 #> 3229  0.166258154 #> 3230  0.166191705 #> 3231  0.166125352 #> 3232  0.166059092 #> 3233  0.165992927 #> 3234  0.165926857 #> 3235  0.165860882 #> 3236  0.165795002 #> 3237  0.165729218 #> 3238  0.165663529 #> 3239  0.165597936 #> 3240  0.165532439 #> 3241  0.165467038 #> 3242  0.165401734 #> 3243  0.165336526 #> 3244  0.165271416 #> 3245  0.165206402 #> 3246  0.165141486 #> 3247  0.165076667 #> 3248  0.165011947 #> 3249  0.164947324 #> 3250  0.164882799 #> 3251  0.164818373 #> 3252  0.164754045 #> 3253  0.164689817 #> 3254  0.164625687 #> 3255  0.164561657 #> 3256  0.164497726 #> 3257  0.164433895 #> 3258  0.164370164 #> 3259  0.164306534 #> 3260  0.164243003 #> 3261  0.164179574 #> 3262  0.164116245 #> 3263  0.164053018 #> 3264  0.163989892 #> 3265  0.163926867 #> 3266  0.163863945 #> 3267  0.163801125 #> 3268  0.163738407 #> 3269  0.163675791 #> 3270  0.163613279 #> 3271  0.163550869 #> 3272  0.163488563 #> 3273  0.163426360 #> 3274  0.163364261 #> 3275  0.163302267 #> 3276  0.163240376 #> 3277  0.163178590 #> 3278  0.163116909 #> 3279  0.163055333 #> 3280  0.162993863 #> 3281  0.162932497 #> 3282  0.162871238 #> 3283  0.162810085 #> 3284  0.162749038 #> 3285  0.162688098 #> 3286  0.162627264 #> 3287  0.162566538 #> 3288  0.162505919 #> 3289  0.162445408 #> 3290  0.162385005 #> 3291  0.162324710 #> 3292  0.162264523 #> 3293  0.162204445 #> 3294  0.162144476 #> 3295  0.162084616 #> 3296  0.162024866 #> 3297  0.161965225 #> 3298  0.161905695 #> 3299  0.161846275 #> 3300  0.161786966 #> 3301  0.161727767 #> 3302  0.161668680 #> 3303  0.161609704 #> 3304  0.161550840 #> 3305  0.161492088 #> 3306  0.161433449 #> 3307  0.161374922 #> 3308  0.161316507 #> 3309  0.161258206 #> 3310  0.161200019 #> 3311  0.161141945 #> 3312  0.161083985 #> 3313  0.161026140 #> 3314  0.160968409 #> 3315  0.160910794 #> 3316  0.160853293 #> 3317  0.160795908 #> 3318  0.160738639 #> 3319  0.160681486 #> 3320  0.160624450 #> 3321  0.160567530 #> 3322  0.160510727 #> 3323  0.160454042 #> 3324  0.160397474 #> 3325  0.160341025 #> 3326  0.160284693 #> 3327  0.160228481 #> 3328  0.160172387 #> 3329  0.160116412 #> 3330  0.160060557 #> 3331  0.160004822 #> 3332  0.159949207 #> 3333  0.159893713 #> 3334  0.159838339 #> 3335  0.159783087 #> 3336  0.159727956 #> 3337  0.159672947 #> 3338  0.159618060 #> 3339  0.159563296 #> 3340  0.159508654 #> 3341  0.159454135 #> 3342  0.159399741 #> 3343  0.159345469 #> 3344  0.159291322 #> 3345  0.159237300 #> 3346  0.159183402 #> 3347  0.159129630 #> 3348  0.159075983 #> 3349  0.159022462 #> 3350  0.158969067 #> 3351  0.158915799 #> 3352  0.158862657 #> 3353  0.158809643 #> 3354  0.158756757 #> 3355  0.158703998 #> 3356  0.158651368 #> 3357  0.158598867 #> 3358  0.158546494 #> 3359  0.158494251 #> 3360  0.158442138 #> 3361  0.158390155 #> 3362  0.158338302 #> 3363  0.158286581 #> 3364  0.158234990 #> 3365  0.158183531 #> 3366  0.158132204 #> 3367  0.158081010 #> 3368  0.158029948 #> 3369  0.157979019 #> 3370  0.157928224 #> 3371  0.157877563 #> 3372  0.157827036 #> 3373  0.157776643 #> 3374  0.157726386 #> 3375  0.157676264 #> 3376  0.157626277 #> 3377  0.157576427 #> 3378  0.157526714 #> 3379  0.157477137 #> 3380  0.157427698 #> 3381  0.157378397 #> 3382  0.157329234 #> 3383  0.157280209 #> 3384  0.157231323 #> 3385  0.157182577 #> 3386  0.157133970 #> 3387  0.157085504 #> 3388  0.157037178 #> 3389  0.156988994 #> 3390  0.156940950 #> 3391  0.156893049 #> 3392  0.156845290 #> 3393  0.156797673 #> 3394  0.156750199 #> 3395  0.156702869 #> 3396  0.156655683 #> 3397  0.156608642 #> 3398  0.156561745 #> 3399  0.156514993 #> 3400  0.156468387 #> 3401  0.156421927 #> 3402  0.156375614 #> 3403  0.156329447 #> 3404  0.156283428 #> 3405  0.156237557 #> 3406  0.156191834 #> 3407  0.156146259 #> 3408  0.156100834 #> 3409  0.156055559 #> 3410  0.156010433 #> 3411  0.155965458 #> 3412  0.155920634 #> 3413  0.155875962 #> 3414  0.155831441 #> 3415  0.155787073 #> 3416  0.155742857 #> 3417  0.155698795 #> 3418  0.155654886 #> 3419  0.155611132 #> 3420  0.155567532 #> 3421  0.155524088 #> 3422  0.155480799 #> 3423  0.155437666 #> 3424  0.155394689 #> 3425  0.155351870 #> 3426  0.155309208 #> 3427  0.155266704 #> 3428  0.155224359 #> 3429  0.155182173 #> 3430  0.155140146 #> 3431  0.155098279 #> 3432  0.155056573 #> 3433  0.155015027 #> 3434  0.154973643 #> 3435  0.154932421 #> 3436  0.154891361 #> 3437  0.154850464 #> 3438  0.154809731 #> 3439  0.154769162 #> 3440  0.154728757 #> 3441  0.154688517 #> 3442  0.154648442 #> 3443  0.154608534 #> 3444  0.154568792 #> 3445  0.154529217 #> 3446  0.154489810 #> 3447  0.154450571 #> 3448  0.154411500 #> 3449  0.154372599 #> 3450  0.154333867 #> 3451  0.154295306 #> 3452  0.154256915 #> 3453  0.154218695 #> 3454  0.154180648 #> 3455  0.154142772 #> 3456  0.154105070 #> 3457  0.154067541 #> 3458  0.154030186 #> 3459  0.153993006 #> 3460  0.153956001 #> 3461  0.153919171 #> 3462  0.153882518 #> 3463  0.153846041 #> 3464  0.153809742 #> 3465  0.153773621 #> 3466  0.153737678 #> 3467  0.153701915 #> 3468  0.153666330 #> 3469  0.153630927 #> 3470  0.153595703 #> 3471  0.153560662 #> 3472  0.153525802 #> 3473  0.153491124 #> 3474  0.153456630 #> 3475  0.153422320 #> 3476  0.153388193 #> 3477  0.153354252 #> 3478  0.153320496 #> 3479  0.153286926 #> 3480  0.153253543 #> 3481  0.153220347 #> 3482  0.153187339 #> 3483  0.153154519 #> 3484  0.153121889 #> 3485  0.153089448 #> 3486  0.153057197 #> 3487  0.153025138 #> 3488  0.152993270 #> 3489  0.152961594 #> 3490  0.152930111 #> 3491  0.152898821 #> 3492  0.152867725 #> 3493  0.152836824 #> 3494  0.152806119 #> 3495  0.152775609 #> 3496  0.152745296 #> 3497  0.152715180 #> 3498  0.152685262 #> 3499  0.152655542 #> 3500  0.152626022 #> 3501  0.152596701 #> 3502  0.152567581 #> 3503  0.152538662 #> 3504  0.152509945 #> 3505  0.152481430 #> 3506  0.152453119 #> 3507  0.152425011 #> 3508  0.152397107 #> 3509  0.152369409 #> 3510  0.152341916 #> 3511  0.152314630 #> 3512  0.152287551 #> 3513  0.152260680 #> 3514  0.152234018 #> 3515  0.152207564 #> 3516  0.152181321 #> 3517  0.152155288 #> 3518  0.152129466 #> 3519  0.152103856 #> 3520  0.152078459 #> 3521  0.152053275 #> 3522  0.152028306 #> 3523  0.152003551 #> 3524  0.151979011 #> 3525  0.151954688 #> 3526  0.151930582 #> 3527  0.151906693 #> 3528  0.151883023 #> 3529  0.151859572 #> 3530  0.151836341 #> 3531  0.151813330 #> 3532  0.151790540 #> 3533  0.151767973 #> 3534  0.151745628 #> 3535  0.151723507 #> 3536  0.151701610 #> 3537  0.151679938 #> 3538  0.151658491 #> 3539  0.151637272 #> 3540  0.151616279 #> 3541  0.151595514 #> 3542  0.151574979 #> 3543  0.151554672 #> 3544  0.151534596 #> 3545  0.151514751 #> 3546  0.151495138 #> 3547  0.151475757 #> 3548  0.151456610 #> 3549  0.151437697 #> 3550  0.151419019 #> 3551  0.151400576 #> 3552  0.151382370 #> 3553  0.151364401 #> 3554  0.151346671 #> 3555  0.151329179 #> 3556  0.151311927 #> 3557  0.151294915 #> 3558  0.151278145 #> 3559  0.151261617 #> 3560  0.151245332 #> 3561  0.151229290 #> 3562  0.151213493 #> 3563  0.151197941 #> 3564  0.151182636 #> 3565  0.151167577 #> 3566  0.151152767 #> 3567  0.151138205 #> 3568  0.151123892 #> 3569  0.151109830 #> 3570  0.151096019 #> 3571  0.151082460 #> 3572  0.151069154 #> 3573  0.151056101 #> 3574  0.151043304 #> 3575  0.151030761 #> 3576  0.151018475 #> 3577  0.151006446 #> 3578  0.150994675 #> 3579  0.150983163 #> 3580  0.150971910 #> 3581  0.150960918 #> 3582  0.150950188 #> 3583  0.150939720 #> 3584  0.150929515 #> 3585  0.150919575 #> 3586  0.150909899 #> 3587  0.150900489 #> 3588  0.150891346 #> 3589  0.150882471 #> 3590  0.150873864 #> 3591  0.150865527 #> 3592  0.150857460 #> 3593  0.150849665 #> 3594  0.150842141 #> 3595  0.150834891 #> 3596  0.150827915 #> 3597  0.150821214 #> 3598  0.150814788 #> 3599  0.150808640 #> 3600  0.150802769 #> 3601  0.150797177 #> 3602  0.150791865 #> 3603  0.150786833 #> 3604  0.150782082 #> 3605  0.150777614 #> 3606  0.150773429 #> 3607  0.150769529 #> 3608  0.150765914 #> 3609  0.150762585 #> 3610  0.150759544 #> 3611  0.150756790 #> 3612  0.150754326 #> 3613  0.150752152 #> 3614  0.150750269 #> 3615  0.150748678 #> 3616  0.150747380 #> 3617  0.150746375 #> 3618  0.150745666 #> 3619  0.150745253 #> 3620  0.150745137 #> 3621  0.150745319 #> 3622  0.150745800 #> 3623  0.150746580 #> 3624  0.150747662 #> 3625  0.150749046 #> 3626  0.150750732 #> 3627  0.150752723 #> 3628  0.150755018 #> 3629  0.150757620 #> 3630  0.150760528 #> 3631  0.150763745 #> 3632  0.150767271 #> 3633  0.150771107 #> 3634  0.150775254 #> 3635  0.150779713 #> 3636  0.150784485 #> 3637  0.150789572 #> 3638  0.150794974 #> 3639  0.150800692 #> 3640  0.150806728 #> 3641  0.150813082 #> 3642  0.150819756 #> 3643  0.150826750 #> 3644  0.150834066 #> 3645  0.150841704 #> 3646  0.150849667 #> 3647  0.150857954 #> 3648  0.150866567 #> 3649  0.150875508 #> 3650  0.150884776 #> 3651  0.150894373 #> 3652  0.150904301 #> 3653  0.150914560 #> 3654  0.150925152 #> 3655  0.150936077 #> 3656  0.150947336 #> 3657  0.150958932 #> 3658  0.150970864 #> 3659  0.150983135 #> 3660  0.150995744 #> 3661  0.151008693 #> 3662  0.151021984 #> 3663  0.151035618 #> 3664  0.151049594 #> 3665  0.151063916 #> 3666  0.151078583 #> 3667  0.151093598 #> 3668  0.151108960 #> 3669  0.151124671 #> 3670  0.151140733 #> 3671  0.151157147 #> 3672  0.151173913 #> 3673  0.151191033 #> 3674  0.151208507 #> 3675  0.151226338 #> 3676  0.151244527 #> 3677  0.151263073 #> 3678  0.151281979 #> 3679  0.151301246 #> 3680  0.151320875 #> 3681  0.151340867 #> 3682  0.151361224 #> 3683  0.151381945 #> 3684  0.151403034 #> 3685  0.151424490 #> 3686  0.151446315 #> 3687  0.151468511 #> 3688  0.151491078 #> 3689  0.151514017 #> 3690  0.151537331 #> 3691  0.151561019 #> 3692  0.151585083 #> 3693  0.151609525 #> 3694  0.151634346 #> 3695  0.151659546 #> 3696  0.151685128 #> 3697  0.151711091 #> 3698  0.151737439 #> 3699  0.151764171 #> 3700  0.151791289 #> 3701  0.151818794 #> 3702  0.151846687 #> 3703  0.151874970 #> 3704  0.151903644 #> 3705  0.151932710 #> 3706  0.151962170 #> 3707  0.151992024 #> 3708  0.152022274 #> 3709  0.152052921 #> 3710  0.152083966 #> 3711  0.152115411 #> 3712  0.152147257 #> 3713  0.152179505 #> 3714  0.152212156 #> 3715  0.152245212 #> 3716  0.152278674 #> 3717  0.152312543 #> 3718  0.152346821 #> 3719  0.152381508 #> 3720  0.152416607 #> 3721  0.152452117 #> 3722  0.152488041 #> 3723  0.152524381 #> 3724  0.152561136 #> 3725  0.152598309 #> 3726  0.152635900 #> 3727  0.152673912 #> 3728  0.152712344 #> 3729  0.152751200 #> 3730  0.152790479 #> 3731  0.152830184 #> 3732  0.152870315 #> 3733  0.152910874 #> 3734  0.152951862 #> 3735  0.152993281 #> 3736  0.153035131 #> 3737  0.153077415 #> 3738  0.153120132 #> 3739  0.153163286 #> 3740  0.153206877 #> 3741  0.153250906 #> 3742  0.153295375 #> 3743  0.153340284 #> 3744  0.153385637 #> 3745  0.153431432 #> 3746  0.153477673 #> 3747  0.153524361 #> 3748  0.153571496 #> 3749  0.153619080 #> 3750  0.153667114 #> 3751  0.153715600 #> 3752  0.153764539 #> 3753  0.153813933 #> 3754  0.153863782 #> 3755  0.153914089 #> 3756  0.153964854 #> 3757  0.154016078 #> 3758  0.154067764 #> 3759  0.154119913 #> 3760  0.154172525 #> 3761  0.154225603 #> 3762  0.154279147 #> 3763  0.154333159 #> 3764  0.154387641 #> 3765  0.154442593 #> 3766  0.154498018 #> 3767  0.154553916 #> 3768  0.154610288 #> 3769  0.154667137 #> 3770  0.154724463 #> 3771  0.154782269 #> 3772  0.154840554 #> 3773  0.154899321 #> 3774  0.154958572 #> 3775  0.155018306 #> 3776  0.155078527 #> 3777  0.155139235 #> 3778  0.155200431 #> 3779  0.155262117 #> 3780  0.155324295 #> 3781  0.155386965 #> 3782  0.155450130 #> 3783  0.155513790 #> 3784  0.155577947 #> 3785  0.155642602 #> 3786  0.155707756 #> 3787  0.155773412 #> 3788  0.155839570 #> 3789  0.155906232 #> 3790  0.155973400 #> 3791  0.156041074 #> 3792  0.156109255 #> 3793  0.156177947 #> 3794  0.156247149 #> 3795  0.156316863 #> 3796  0.156387091 #> 3797  0.156457834 #> 3798  0.156529093 #> 3799  0.156600870 #> 3800  0.156673166 #> 3801  0.156745982 #> 3802  0.156819321 #> 3803  0.156893182 #> 3804  0.156967569 #> 3805  0.157042482 #> 3806  0.157117922 #> 3807  0.157193891 #> 3808  0.157270390 #> 3809  0.157347421 #> 3810  0.157424985 #> 3811  0.157503084 #> 3812  0.157581719 #> 3813  0.157660891 #> 3814  0.157740601 #> 3815  0.157820852 #> 3816  0.157901644 #> 3817  0.157982978 #> 3818  0.158064857 #> 3819  0.158147282 #> 3820  0.158230254 #> 3821  0.158313774 #> 3822  0.158397844 #> 3823  0.158482465 #> 3824  0.158567639 #> 3825  0.158653366 #> 3826  0.158739649 #> 3827  0.158826489 #> 3828  0.158913887 #> 3829  0.159001844 #> 3830  0.159090362 #> 3831  0.159179442 #> 3832  0.159269086 #> 3833  0.159359295 #> 3834  0.159450070 #> 3835  0.159541413 #> 3836  0.159633326 #> 3837  0.159725808 #> 3838  0.159818863 #> 3839  0.159912490 #> 3840  0.160006693 #> 3841  0.160101471 #> 3842  0.160196826 #> 3843  0.160292761 #> 3844  0.160389275 #> 3845  0.160486370 #> 3846  0.160584049 #> 3847  0.160682311 #> 3848  0.160781159 #> 3849  0.160880594 #> 3850  0.160980616 #> 3851  0.161081229 #> 3852  0.161182432 #> 3853  0.161284227 #> 3854  0.161386615 #> 3855  0.161489599 #> 3856  0.161593178 #> 3857  0.161697355 #> 3858  0.161802131 #> 3859  0.161907507 #> 3860  0.162013485 #> 3861  0.162120065 #> 3862  0.162227249 #> 3863  0.162335039 #> 3864  0.162443435 #> 3865  0.162552439 #> 3866  0.162662053 #> 3867  0.162772277 #> 3868  0.162883113 #> 3869  0.162994562 #> 3870  0.163106625 #> 3871  0.163219305 #> 3872  0.163332601 #> 3873  0.163446515 #> 3874  0.163561049 #> 3875  0.163676204 #> 3876  0.163791981 #> 3877  0.163908381 #> 3878  0.164025406 #> 3879  0.164143057 #> 3880  0.164261335 #> 3881  0.164380241 #> 3882  0.164499776 #> 3883  0.164619943 #> 3884  0.164740741 #> 3885  0.164862173 #> 3886  0.164984239 #> 3887  0.165106941 #> 3888  0.165230280 #> 3889  0.165354257 #> 3890  0.165478873 #> 3891  0.165604129 #> 3892  0.165730028 #> 3893  0.165856569 #> 3894  0.165983754 #> 3895  0.166111584 #> 3896  0.166240061 #> 3897  0.166369185 #> 3898  0.166498958 #> 3899  0.166629381 #> 3900  0.166760455 #> 3901  0.166892181 #> 3902  0.167024560 #> 3903  0.167157593 #> 3904  0.167291282 #> 3905  0.167425628 #> 3906  0.167560632 #> 3907  0.167696294 #> 3908  0.167832616 #> 3909  0.167969599 #> 3910  0.168107244 #> 3911  0.168245553 #> 3912  0.168384526 #> 3913  0.168524164 #> 3914  0.168664468 #> 3915  0.168805440 #> 3916  0.168947080 #> 3917  0.169089390 #> 3918  0.169232370 #> 3919  0.169376022 #> 3920  0.169520347 #> 3921  0.169665345 #> 3922  0.169811017 #> 3923  0.169957365 #> 3924  0.170104390 #> 3925  0.170252092 #> 3926  0.170400473 #> 3927  0.170549533 #> 3928  0.170699273 #> 3929  0.170849695 #> 3930  0.171000799 #> 3931  0.171152586 #> 3932  0.171305057 #> 3933  0.171458214 #> 3934  0.171612056 #> 3935  0.171766585 #> 3936  0.171921802 #> 3937  0.172077707 #> 3938  0.172234302 #> 3939  0.172391587 #> 3940  0.172549563 #> 3941  0.172708231 #> 3942  0.172867592 #> 3943  0.173027647 #> 3944  0.173188396 #> 3945  0.173349840 #> 3946  0.173511980 #> 3947  0.173674817 #> 3948  0.173838352 #> 3949  0.174002585 #> 3950  0.174167517 #> 3951  0.174333149 #> 3952  0.174499482 #> 3953  0.174666516 #> 3954  0.174834251 #> 3955  0.175002690 #> 3956  0.175171832 #> 3957  0.175341678 #> 3958  0.175512229 #> 3959  0.175683485 #> 3960  0.175855447 #> 3961  0.176028116 #> 3962  0.176201493 #> 3963  0.176375577 #> 3964  0.176550370 #> 3965  0.176725871 #> 3966  0.176902083 #> 3967  0.177079005 #> 3968  0.177256638 #> 3969  0.177434982 #> 3970  0.177614038 #> 3971  0.177793806 #> 3972  0.177974288 #> 3973  0.178155483 #> 3974  0.178337391 #> 3975  0.178520015 #> 3976  0.178703353 #> 3977  0.178887406 #> 3978  0.179072175 #> 3979  0.179257661 #> 3980  0.179443863 #> 3981  0.179630782 #> 3982  0.179818418 #> 3983  0.180006772 #> 3984  0.180195845 #> 3985  0.180385635 #> 3986  0.180576145 #> 3987  0.180767374 #> 3988  0.180959322 #> 3989  0.181151990 #> 3990  0.181345378 #> 3991  0.181539486 #> 3992  0.181734315 #> 3993  0.181929864 #> 3994  0.182126135 #> 3995  0.182323126 #> 3996  0.182520839 #> 3997  0.182719274 #> 3998  0.182918431 #> 3999  0.183118309 #> 4000  0.183318910 #> 4001  0.183520232 #> 4002  0.183722277 #> 4003  0.183925045 #> 4004  0.184128535 #> 4005  0.184332747 #> 4006  0.184537683 #> 4007  0.184743340 #> 4008  0.184949721 #> 4009  0.185156824 #> 4010  0.185364650 #> 4011  0.185573198 #> 4012  0.185782470 #> 4013  0.185992463 #> 4014  0.186203180 #> 4015  0.186414618 #> 4016  0.186626779 #> 4017  0.186839663 #> 4018  0.187053268 #> 4019  0.187267595 #> 4020  0.187482644 #> 4021  0.187698415 #> 4022  0.187914907 #> 4023  0.188132120 #> 4024  0.188350055 #> 4025  0.188568710 #> 4026  0.188788085 #> 4027  0.189008181 #> 4028  0.189228996 #> 4029  0.189450531 #> 4030  0.189672786 #> 4031  0.189895759 #> 4032  0.190119451 #> 4033  0.190343861 #> 4034  0.190568989 #> 4035  0.190794835 #> 4036  0.191021397 #> 4037  0.191248676 #> 4038  0.191476671 #> 4039  0.191705381 #> 4040  0.191934807 #> 4041  0.192164947 #> 4042  0.192395801 #> 4043  0.192627369 #> 4044  0.192859649 #> 4045  0.193092642 #> 4046  0.193326347 #> 4047  0.193560763 #> 4048  0.193795889 #> 4049  0.194031726 #> 4050  0.194268271 #> 4051  0.194505525 #> 4052  0.194743486 #> 4053  0.194982155 #> 4054  0.195221530 #> 4055  0.195461610 #> 4056  0.195702396 #> 4057  0.195943885 #> 4058  0.196186077 #> 4059  0.196428972 #> 4060  0.196672568 #> 4061  0.196916864 #> 4062  0.197161860 #> 4063  0.197407555 #> 4064  0.197653948 #> 4065  0.197901038 #> 4066  0.198148824 #> 4067  0.198397304 #> 4068  0.198646479 #> 4069  0.198896346 #> 4070  0.199146905 #> 4071  0.199398155 #> 4072  0.199650095 #> 4073  0.199902723 #> 4074  0.200156039 #> 4075  0.200410041 #> 4076  0.200664729 #> 4077  0.200920100 #> 4078  0.201176154 #> 4079  0.201432890 #> 4080  0.201690306 #> 4081  0.201948402 #> 4082  0.202207175 #> 4083  0.202466624 #> 4084  0.202726749 #> 4085  0.202987548 #> 4086  0.203249019 #> 4087  0.203511162 #> 4088  0.203773974 #> 4089  0.204037455 #> 4090  0.204301602 #> 4091  0.204566415 #> 4092  0.204831893 #> 4093  0.205098032 #> 4094  0.205364833 #> 4095  0.205632293 #> 4096  0.205900411 #> 4097  0.206169186 #> 4098  0.206438615 #> 4099  0.206708697 #> 4100  0.206979431 #> 4101  0.207250815 #> 4102  0.207522847 #> 4103  0.207795526 #> 4104  0.208068850 #> 4105  0.208342816 #> 4106  0.208617424 #> 4107  0.208892672 #> 4108  0.209168557 #> 4109  0.209445079 #> 4110  0.209722235 #> 4111  0.210000023 #> 4112  0.210278441 #> 4113  0.210557488 #> 4114  0.210837162 #> 4115  0.211117461 #> 4116  0.211398382 #> 4117  0.211679924 #> 4118  0.211962085 #> 4119  0.212244863 #> 4120  0.212528256 #> 4121  0.212812262 #> 4122  0.213096878 #> 4123  0.213382103 #> 4124  0.213667934 #> 4125  0.213954370 #> 4126  0.214241409 #> 4127  0.214529047 #> 4128  0.214817283 #> 4129  0.215106115 #> 4130  0.215395541 #> 4131  0.215685558 #> 4132  0.215976164 #> 4133  0.216267356 #> 4134  0.216559133 #> 4135  0.216851493 #> 4136  0.217144432 #> 4137  0.217437948 #> 4138  0.217732040 #> 4139  0.218026704 #> 4140  0.218321938 #> 4141  0.218617740 #> 4142  0.218914108 #> 4143  0.219211039 #> 4144  0.219508530 #> 4145  0.219806578 #> 4146  0.220105183 #> 4147  0.220404340 #> 4148  0.220704047 #> 4149  0.221004302 #> 4150  0.221305102 #> 4151  0.221606444 #> 4152  0.221908326 #> 4153  0.222210746 #> 4154  0.222513700 #> 4155  0.222817185 #> 4156  0.223121200 #> 4157  0.223425741 #> 4158  0.223730805 #> 4159  0.224036391 #> 4160  0.224342494 #> 4161  0.224649113 #> 4162  0.224956244 #> 4163  0.225263884 #> 4164  0.225572031 #> 4165  0.225880682 #> 4166  0.226189834 #> 4167  0.226499484 #> 4168  0.226809629 #> 4169  0.227120266 #> 4170  0.227431392 #> 4171  0.227743004 #> 4172  0.228055100 #> 4173  0.228367675 #> 4174  0.228680728 #> 4175  0.228994255 #> 4176  0.229308253 #> 4177  0.229622718 #> 4178  0.229937649 #> 4179  0.230253041 #> 4180  0.230568892 #> 4181  0.230885198 #> 4182  0.231201956 #> 4183  0.231519163 #> 4184  0.231836816 #> 4185  0.232154912 #> 4186  0.232473447 #> 4187  0.232792418 #> 4188  0.233111822 #> 4189  0.233431656 #> 4190  0.233751916 #> 4191  0.234072599 #> 4192  0.234393702 #> 4193  0.234715221 #> 4194  0.235037152 #> 4195  0.235359494 #> 4196  0.235682241 #> 4197  0.236005392 #> 4198  0.236328941 #> 4199  0.236652887 #> 4200  0.236977225 #> 4201  0.237301952 #> 4202  0.237627065 #> 4203  0.237952560 #> 4204  0.238278433 #> 4205  0.238604681 #> 4206  0.238931301 #> 4207  0.239258289 #> 4208  0.239585641 #> 4209  0.239913354 #> 4210  0.240241424 #> 4211  0.240569848 #> 4212  0.240898622 #> 4213  0.241227742 #> 4214  0.241557205 #> 4215  0.241887007 #> 4216  0.242217144 #> 4217  0.242547613 #> 4218  0.242878411 #> 4219  0.243209532 #> 4220  0.243540975 #> 4221  0.243872734 #> 4222  0.244204806 #> 4223  0.244537188 #> 4224  0.244869876 #> 4225  0.245202865 #> 4226  0.245536153 #> 4227  0.245869734 #> 4228  0.246203607 #> 4229  0.246537766 #> 4230  0.246872208 #> 4231  0.247206929 #> 4232  0.247541925 #> 4233  0.247877193 #> 4234  0.248212728 #> 4235  0.248548527 #> 4236  0.248884585 #> 4237  0.249220899 #> 4238  0.249557465 #> 4239  0.249894280 #> 4240  0.250231338 #> 4241  0.250568636 #> 4242  0.250906170 #> 4243  0.251243937 #> 4244  0.251581932 #> 4245  0.251920151 #> 4246  0.252258591 #> 4247  0.252597247 #> 4248  0.252936115 #> 4249  0.253275192 #> 4250  0.253614473 #> 4251  0.253953954 #> 4252  0.254293632 #> 4253  0.254633501 #> 4254  0.254973559 #> 4255  0.255313802 #> 4256  0.255654224 #> 4257  0.255994822 #> 4258  0.256335593 #> 4259  0.256676531 #> 4260  0.257017633 #> 4261  0.257358895 #> 4262  0.257700313 #> 4263  0.258041882 #> 4264  0.258383599 #> 4265  0.258725459 #> 4266  0.259067458 #> 4267  0.259409593 #> 4268  0.259751859 #> 4269  0.260094251 #> 4270  0.260436766 #> 4271  0.260779400 #> 4272  0.261122148 #> 4273  0.261465007 #> 4274  0.261807972 #> 4275  0.262151039 #> 4276  0.262494204 #> 4277  0.262837463 #> 4278  0.263180811 #> 4279  0.263524245 #> 4280  0.263867760 #> 4281  0.264211352 #> 4282  0.264555017 #> 4283  0.264898750 #> 4284  0.265242549 #> 4285  0.265586407 #> 4286  0.265930322 #> 4287  0.266274289 #> 4288  0.266618304 #> 4289  0.266962363 #> 4290  0.267306460 #> 4291  0.267650594 #> 4292  0.267994758 #> 4293  0.268338949 #> 4294  0.268683164 #> 4295  0.269027396 #> 4296  0.269371643 #> 4297  0.269715900 #> 4298  0.270060163 #> 4299  0.270404428 #> 4300  0.270748691 #> 4301  0.271092947 #> 4302  0.271437192 #> 4303  0.271781422 #> 4304  0.272125633 #> 4305  0.272469821 #> 4306  0.272813982 #> 4307  0.273158110 #> 4308  0.273502203 #> 4309  0.273846256 #> 4310  0.274190264 #> 4311  0.274534224 #> 4312  0.274878132 #> 4313  0.275221983 #> 4314  0.275565773 #> 4315  0.275909498 #> 4316  0.276253154 #> 4317  0.276596736 #> 4318  0.276940241 #> 4319  0.277283665 #> 4320  0.277627002 #> 4321  0.277970250 #> 4322  0.278313403 #> 4323  0.278656459 #> 4324  0.278999412 #> 4325  0.279342258 #> 4326  0.279684994 #> 4327  0.280027616 #> 4328  0.280370118 #> 4329  0.280712498 #> 4330  0.281054750 #> 4331  0.281396872 #> 4332  0.281738858 #> 4333  0.282080705 #> 4334  0.282422409 #> 4335  0.282763966 #> 4336  0.283105371 #> 4337  0.283446620 #> 4338  0.283787710 #> 4339  0.284128636 #> 4340  0.284469395 #> 4341  0.284809982 #> 4342  0.285150393 #> 4343  0.285490625 #> 4344  0.285830673 #> 4345  0.286170533 #> 4346  0.286510201 #> 4347  0.286849674 #> 4348  0.287188947 #> 4349  0.287528017 #> 4350  0.287866879 #> 4351  0.288205529 #> 4352  0.288543964 #> 4353  0.288882179 #> 4354  0.289220171 #> 4355  0.289557936 #> 4356  0.289895469 #> 4357  0.290232768 #> 4358  0.290569827 #> 4359  0.290906644 #> 4360  0.291243214 #> 4361  0.291579533 #> 4362  0.291915598 #> 4363  0.292251405 #> 4364  0.292586949 #> 4365  0.292922228 #> 4366  0.293257237 #> 4367  0.293591972 #> 4368  0.293926430 #> 4369  0.294260607 #> 4370  0.294594499 #> 4371  0.294928103 #> 4372  0.295261414 #> 4373  0.295594429 #> 4374  0.295927144 #> 4375  0.296259555 #> 4376  0.296591660 #> 4377  0.296923453 #> 4378  0.297254932 #> 4379  0.297586093 #> 4380  0.297916931 #> 4381  0.298247444 #> 4382  0.298577628 #> 4383  0.298907479 #> 4384  0.299236994 #> 4385  0.299566169 #> 4386  0.299895000 #> 4387  0.300223484 #> 4388  0.300551617 #> 4389  0.300879397 #> 4390  0.301206818 #> 4391  0.301533878 #> 4392  0.301860573 #> 4393  0.302186901 #> 4394  0.302512856 #> 4395  0.302838436 #> 4396  0.303163638 #> 4397  0.303488457 #> 4398  0.303812891 #> 4399  0.304136937 #> 4400  0.304460590 #> 4401  0.304783847 #> 4402  0.305106705 #> 4403  0.305429162 #> 4404  0.305751212 #> 4405  0.306072853 #> 4406  0.306394082 #> 4407  0.306714896 #> 4408  0.307035291 #> 4409  0.307355263 #> 4410  0.307674811 #> 4411  0.307993929 #> 4412  0.308312616 #> 4413  0.308630868 #> 4414  0.308948682 #> 4415  0.309266055 #> 4416  0.309582983 #> 4417  0.309899463 #> 4418  0.310215493 #> 4419  0.310531069 #> 4420  0.310846188 #> 4421  0.311160847 #> 4422  0.311475042 #> 4423  0.311788772 #> 4424  0.312102033 #> 4425  0.312414822 #> 4426  0.312727135 #> 4427  0.313038970 #> 4428  0.313350325 #> 4429  0.313661195 #> 4430  0.313971578 #> 4431  0.314281472 #> 4432  0.314590873 #> 4433  0.314899778 #> 4434  0.315208185 #> 4435  0.315516091 #> 4436  0.315823492 #> 4437  0.316130386 #> 4438  0.316436771 #> 4439  0.316742643 #> 4440  0.317048000 #> 4441  0.317352839 #> 4442  0.317657157 #> 4443  0.317960952 #> 4444  0.318264220 #> 4445  0.318566960 #> 4446  0.318869168 #> 4447  0.319170842 #> 4448  0.319471979 #> 4449  0.319772577 #> 4450  0.320072633 #> 4451  0.320372145 #> 4452  0.320671109 #> 4453  0.320969524 #> 4454  0.321267387 #> 4455  0.321564695 #> 4456  0.321861446 #> 4457  0.322157637 #> 4458  0.322453267 #> 4459  0.322748332 #> 4460  0.323042830 #> 4461  0.323336758 #> 4462  0.323630115 #> 4463  0.323922899 #> 4464  0.324215105 #> 4465  0.324506733 #> 4466  0.324797780 #> 4467  0.325088244 #> 4468  0.325378122 #> 4469  0.325667413 #> 4470  0.325956113 #> 4471  0.326244221 #> 4472  0.326531735 #> 4473  0.326818652 #> 4474  0.327104970 #> 4475  0.327390688 #> 4476  0.327675802 #> 4477  0.327960311 #> 4478  0.328244212 #> 4479  0.328527505 #> 4480  0.328810185 #> 4481  0.329092253 #> 4482  0.329373705 #> 4483  0.329654539 #> 4484  0.329934754 #> 4485  0.330214347 #> 4486  0.330493317 #> 4487  0.330771662 #> 4488  0.331049380 #> 4489  0.331326468 #> 4490  0.331602925 #> 4491  0.331878749 #> 4492  0.332153939 #> 4493  0.332428492 #> 4494  0.332702407 #> 4495  0.332975681 #> 4496  0.333248314 #> 4497  0.333520303 #> 4498  0.333791646 #> 4499  0.334062343 #> 4500  0.334332390 #> 4501  0.334601787 #> 4502  0.334870532 #> 4503  0.335138623 #> 4504  0.335406059 #> 4505  0.335672837 #> 4506  0.335938957 #> 4507  0.336204416 #> 4508  0.336469214 #> 4509  0.336733348 #> 4510  0.336996818 #> 4511  0.337259621 #> 4512  0.337521756 #> 4513  0.337783222 #> 4514  0.338044017 #> 4515  0.338304140 #> 4516  0.338563589 #> 4517  0.338822363 #> 4518  0.339080461 #> 4519  0.339337881 #> 4520  0.339594622 #> 4521  0.339850682 #> 4522  0.340106061 #> 4523  0.340360756 #> 4524  0.340614767 #> 4525  0.340868093 #> 4526  0.341120731 #> 4527  0.341372682 #> 4528  0.341623943 #> 4529  0.341874513 #> 4530  0.342124392 #> 4531  0.342373578 #> 4532  0.342622070 #> 4533  0.342869867 #> 4534  0.343116968 #> 4535  0.343363372 #> 4536  0.343609077 #> 4537  0.343854082 #> 4538  0.344098387 #> 4539  0.344341991 #> 4540  0.344584892 #> 4541  0.344827089 #> 4542  0.345068582 #> 4543  0.345309370 #> 4544  0.345549451 #> 4545  0.345788825 #> 4546  0.346027490 #> 4547  0.346265447 #> 4548  0.346502693 #> 4549  0.346739229 #> 4550  0.346975052 #> 4551  0.347210164 #> 4552  0.347444562 #> 4553  0.347678245 #> 4554  0.347911214 #> 4555  0.348143467 #> 4556  0.348375004 #> 4557  0.348605823 #> 4558  0.348835925 #> 4559  0.349065308 #> 4560  0.349293971 #> 4561  0.349521915 #> 4562  0.349749138 #> 4563  0.349975640 #> 4564  0.350201420 #> 4565  0.350426477 #> 4566  0.350650812 #> 4567  0.350874422 #> 4568  0.351097309 #> 4569  0.351319471 #> 4570  0.351540907 #> 4571  0.351761618 #> 4572  0.351981602 #> 4573  0.352200859 #> 4574  0.352419389 #> 4575  0.352637192 #> 4576  0.352854266 #> 4577  0.353070611 #> 4578  0.353286228 #> 4579  0.353501115 #> 4580  0.353715272 #> 4581  0.353928700 #> 4582  0.354141396 #> 4583  0.354353362 #> 4584  0.354564596 #> 4585  0.354775099 #> 4586  0.354984870 #> 4587  0.355193909 #> 4588  0.355402216 #> 4589  0.355609790 #> 4590  0.355816631 #> 4591  0.356022739 #> 4592  0.356228114 #> 4593  0.356432755 #> 4594  0.356636662 #> 4595  0.356839836 #> 4596  0.357042276 #> 4597  0.357243981 #> 4598  0.357444952 #> 4599  0.357645188 #> 4600  0.357844690 #> 4601  0.358043458 #> 4602  0.358241490 #> 4603  0.358438788 #> 4604  0.358635351 #> 4605  0.358831179 #> 4606  0.359026272 #> 4607  0.359220630 #> 4608  0.359414254 #> 4609  0.359607142 #> 4610  0.359799295 #> 4611  0.359990714 #> 4612  0.360181397 #> 4613  0.360371346 #> 4614  0.360560560 #> 4615  0.360749040 #> 4616  0.360936785 #> 4617  0.361123795 #> 4618  0.361310072 #> 4619  0.361495614 #> 4620  0.361680422 #> 4621  0.361864496 #> 4622  0.362047837 #> 4623  0.362230443 #> 4624  0.362412317 #> 4625  0.362593458 #> 4626  0.362773865 #> 4627  0.362953540 #> 4628  0.363132482 #> 4629  0.363310693 #> 4630  0.363488171 #> 4631  0.363664918 #> 4632  0.363840933 #> 4633  0.364016217 #> 4634  0.364190770 #> 4635  0.364364593 #> 4636  0.364537685 #> 4637  0.364710048 #> 4638  0.364881681 #> 4639  0.365052586 #> 4640  0.365222761 #> 4641  0.365392208 #> 4642  0.365560927 #> 4643  0.365728918 #> 4644  0.365896183 #> 4645  0.366062720 #> 4646  0.366228531 #> 4647  0.366393617 #> 4648  0.366557976 #> 4649  0.366721611 #> 4650  0.366884521 #> 4651  0.367046708 #> 4652  0.367208170 #> 4653  0.367368910 #> 4654  0.367528927 #> 4655  0.367688222 #> 4656  0.367846796 #> 4657  0.368004648 #> 4658  0.368161780 #> 4659  0.368318193 #> 4660  0.368473886 #> 4661  0.368628860 #> 4662  0.368783116 #> 4663  0.368936654 #> 4664  0.369089476 #> 4665  0.369241581 #> 4666  0.369392971 #> 4667  0.369543645 #> 4668  0.369693605 #> 4669  0.369842851 #> 4670  0.369991384 #> 4671  0.370139205 #> 4672  0.370286313 #> 4673  0.370432710 #> 4674  0.370578397 #> 4675  0.370723374 #> 4676  0.370867643 #> 4677  0.371011202 #> 4678  0.371154054 #> 4679  0.371296199 #> 4680  0.371437638 #> 4681  0.371578371 #> 4682  0.371718400 #> 4683  0.371857725 #> 4684  0.371996346 #> 4685  0.372134265 #> 4686  0.372271483 #> 4687  0.372407999 #> 4688  0.372543816 #> 4689  0.372678933 #> 4690  0.372813352 #> 4691  0.372947073 #> 4692  0.373080097 #> 4693  0.373212426 #> 4694  0.373344059 #> 4695  0.373474998 #> 4696  0.373605243 #> 4697  0.373734796 #> 4698  0.373863657 #> 4699  0.373991827 #> 4700  0.374119308 #> 4701  0.374246099 #> 4702  0.374372202 #> 4703  0.374497617 #> 4704  0.374622346 #> 4705  0.374746390 #> 4706  0.374869749 #> 4707  0.374992424 #> 4708  0.375114417 #> 4709  0.375235728 #> 4710  0.375356357 #> 4711  0.375476307 #> 4712  0.375595578 #> 4713  0.375714171 #> 4714  0.375832087 #> 4715  0.375949327 #> 4716  0.376065891 #> 4717  0.376181782 #> 4718  0.376296999 #> 4719  0.376411544 #> 4720  0.376525418 #> 4721  0.376638622 #> 4722  0.376751157 #> 4723  0.376863023 #> 4724  0.376974222 #> 4725  0.377084755 #> 4726  0.377194623 #> 4727  0.377303827 #> 4728  0.377412368 #> 4729  0.377520247 #> 4730  0.377627465 #> 4731  0.377734023 #> 4732  0.377839923 #> 4733  0.377945164 #> 4734  0.378049749 #> 4735  0.378153678 #> 4736  0.378256952 #> 4737  0.378359573 #> 4738  0.378461542 #> 4739  0.378562859 #> 4740  0.378663526 #> 4741  0.378763544 #> 4742  0.378862914 #> 4743  0.378961637 #> 4744  0.379059714 #> 4745  0.379157146 #> 4746  0.379253935 #> 4747  0.379350081 #> 4748  0.379445586 #> 4749  0.379540450 #> 4750  0.379634676 #> 4751  0.379728263 #> 4752  0.379821214 #> 4753  0.379913528 #> 4754  0.380005209 #> 4755  0.380096256 #> 4756  0.380186670 #> 4757  0.380276454 #> 4758  0.380365608 #> 4759  0.380454132 #> 4760  0.380542030 #> 4761  0.380629300 #> 4762  0.380715946 #> 4763  0.380801967 #> 4764  0.380887366 #> 4765  0.380972142 #> 4766  0.381056299 #> 4767  0.381139835 #> 4768  0.381222754 #> 4769  0.381305055 #> 4770  0.381386741 #> 4771  0.381467812 #> 4772  0.381548270 #> 4773  0.381628115 #> 4774  0.381707350 #> 4775  0.381785975 #> 4776  0.381863991 #> 4777  0.381941400 #> 4778  0.382018202 #> 4779  0.382094400 #> 4780  0.382169994 #> 4781  0.382244985 #> 4782  0.382319376 #> 4783  0.382393166 #> 4784  0.382466357 #> 4785  0.382538951 #> 4786  0.382610948 #> 4787  0.382682351 #> 4788  0.382753159 #> 4789  0.382823375 #> 4790  0.382892999 #> 4791  0.382962033 #> 4792  0.383030479 #> 4793  0.383098336 #> 4794  0.383165607 #> 4795  0.383232294 #> 4796  0.383298396 #> 4797  0.383363915 #> 4798  0.383428853 #> 4799  0.383493211 #> 4800  0.383556990 #> 4801  0.383620191 #> 4802  0.383682816 #> 4803  0.383744866 #> 4804  0.383806342 #> 4805  0.383867246 #> 4806  0.383927578 #> 4807  0.383987340 #> 4808  0.384046533 #> 4809  0.384105159 #> 4810  0.384163219 #> 4811  0.384220713 #> 4812  0.384277644 #> 4813  0.384334013 #> 4814  0.384389820 #> 4815  0.384445068 #> 4816  0.384499756 #> 4817  0.384553888 #> 4818  0.384607464 #> 4819  0.384660484 #> 4820  0.384712952 #> 4821  0.384764867 #> 4822  0.384816231 #> 4823  0.384867045 #> 4824  0.384917312 #> 4825  0.384967031 #> 4826  0.385016204 #> 4827  0.385064833 #> 4828  0.385112919 #> 4829  0.385160462 #> 4830  0.385207466 #> 4831  0.385253929 #> 4832  0.385299855 #> 4833  0.385345244 #> 4834  0.385390098 #> 4835  0.385434417 #> 4836  0.385478203 #> 4837  0.385521458 #> 4838  0.385564183 #> 4839  0.385606378 #> 4840  0.385648046 #> 4841  0.385689187 #> 4842  0.385729803 #> 4843  0.385769895 #> 4844  0.385809465 #> 4845  0.385848513 #> 4846  0.385887042 #> 4847  0.385925051 #> 4848  0.385962543 #> 4849  0.385999520 #> 4850  0.386035981 #> 4851  0.386071929 #> 4852  0.386107364 #> 4853  0.386142289 #> 4854  0.386176703 #> 4855  0.386210610 #> 4856  0.386244009 #> 4857  0.386276903 #> 4858  0.386309292 #> 4859  0.386341178 #> 4860  0.386372562 #> 4861  0.386403445 #> 4862  0.386433829 #> 4863  0.386463715 #> 4864  0.386493104 #> 4865  0.386521997 #> 4866  0.386550396 #> 4867  0.386578303 #> 4868  0.386605717 #> 4869  0.386632642 #> 4870  0.386659077 #> 4871  0.386685025 #> 4872  0.386710486 #> 4873  0.386735461 #> 4874  0.386759953 #> 4875  0.386783962 #> 4876  0.386807490 #> 4877  0.386830538 #> 4878  0.386853107 #> 4879  0.386875198 #> 4880  0.386896813 #> 4881  0.386917953 #> 4882  0.386938619 #> 4883  0.386958813 #> 4884  0.386978536 #> 4885  0.386997788 #> 4886  0.387016573 #> 4887  0.387034889 #> 4888  0.387052740 #> 4889  0.387070126 #> 4890  0.387087048 #> 4891  0.387103508 #> 4892  0.387119507 #> 4893  0.387135047 #> 4894  0.387150127 #> 4895  0.387164751 #> 4896  0.387178919 #> 4897  0.387192632 #> 4898  0.387205891 #> 4899  0.387218698 #> 4900  0.387231055 #> 4901  0.387242961 #> 4902  0.387254420 #> 4903  0.387265431 #> 4904  0.387275996 #> 4905  0.387286116 #> 4906  0.387295794 #> 4907  0.387305029 #> 4908  0.387313823 #> 4909  0.387322177 #> 4910  0.387330093 #> 4911  0.387337572 #> 4912  0.387344615 #> 4913  0.387351223 #> 4914  0.387357398 #> 4915  0.387363140 #> 4916  0.387368452 #> 4917  0.387373334 #> 4918  0.387377787 #> 4919  0.387381813 #> 4920  0.387385413 #> 4921  0.387388588 #> 4922  0.387391340 #> 4923  0.387393669 #> 4924  0.387395577 #> 4925  0.387397066 #> 4926  0.387398135 #> 4927  0.387398787 #> 4928  0.387399023 #> 4929  0.387398844 #> 4930  0.387398251 #> 4931  0.387397245 #> 4932  0.387395828 #> 4933  0.387394001 #> 4934  0.387391765 #> 4935  0.387389121 #> 4936  0.387386071 #> 4937  0.387382615 #> 4938  0.387378756 #> 4939  0.387374493 #> 4940  0.387369828 #> 4941  0.387364763 #> 4942  0.387359299 #> 4943  0.387353436 #> 4944  0.387347177 #> 4945  0.387340522 #> 4946  0.387333472 #> 4947  0.387326029 #> 4948  0.387318193 #> 4949  0.387309967 #> 4950  0.387301350 #> 4951  0.387292346 #> 4952  0.387282953 #> 4953  0.387273175 #> 4954  0.387263011 #> 4955  0.387252463 #> 4956  0.387241533 #> 4957  0.387230220 #> 4958  0.387218528 #> 4959  0.387206456 #> 4960  0.387194006 #> 4961  0.387181179 #> 4962  0.387167977 #> 4963  0.387154400 #> 4964  0.387140449 #> 4965  0.387126126 #> 4966  0.387111432 #> 4967  0.387096367 #> 4968  0.387080934 #> 4969  0.387065134 #> 4970  0.387048966 #> 4971  0.387032433 #> 4972  0.387015536 #> 4973  0.386998276 #> 4974  0.386980654 #> 4975  0.386962671 #> 4976  0.386944328 #> 4977  0.386925627 #> 4978  0.386906568 #> 4979  0.386887153 #> 4980  0.386867382 #> 4981  0.386847258 #> 4982  0.386826780 #> 4983  0.386805951 #> 4984  0.386784771 #> 4985  0.386763241 #> 4986  0.386741363 #> 4987  0.386719137 #> 4988  0.386696565 #> 4989  0.386673648 #> 4990  0.386650386 #> 4991  0.386626782 #> 4992  0.386602836 #> 4993  0.386578549 #> 4994  0.386553923 #> 4995  0.386528958 #> 4996  0.386503655 #> 4997  0.386478016 #> 4998  0.386452041 #> 4999  0.386425733 #> 5000  0.386399091 #> 5001  0.386372117 #> 5002  0.386344813 #> 5003  0.386317178 #> 5004  0.386289214 #> 5005  0.386260923 #> 5006  0.386232305 #> 5007  0.386203361 #> 5008  0.386174093 #> 5009  0.386144502 #> 5010  0.386114588 #> 5011  0.386084352 #> 5012  0.386053797 #> 5013  0.386022922 #> 5014  0.385991729 #> 5015  0.385960218 #> 5016  0.385928392 #> 5017  0.385896251 #> 5018  0.385863795 #> 5019  0.385831027 #> 5020  0.385797947 #> 5021  0.385764555 #> 5022  0.385730855 #> 5023  0.385696845 #> 5024  0.385662527 #> 5025  0.385627903 #> 5026  0.385592973 #> 5027  0.385557738 #> 5028  0.385522200 #> 5029  0.385486359 #> 5030  0.385450217 #> 5031  0.385413774 #> 5032  0.385377031 #> 5033  0.385339990 #> 5034  0.385302651 #> 5035  0.385265016 #> 5036  0.385227085 #> 5037  0.385188859 #> 5038  0.385150340 #> 5039  0.385111529 #> 5040  0.385072426 #> 5041  0.385033032 #> 5042  0.384993349 #> 5043  0.384953377 #> 5044  0.384913118 #> 5045  0.384872572 #> 5046  0.384831741 #> 5047  0.384790625 #> 5048  0.384749225 #> 5049  0.384707543 #> 5050  0.384665579 #> 5051  0.384623334 #> 5052  0.384580810 #> 5053  0.384538006 #> 5054  0.384494925 #> 5055  0.384451568 #> 5056  0.384407934 #> 5057  0.384364025 #> 5058  0.384319843 #> 5059  0.384275387 #> 5060  0.384230660 #> 5061  0.384185661 #> 5062  0.384140392 #> 5063  0.384094854 #> 5064  0.384049048 #> 5065  0.384002974 #> 5066  0.383956635 #> 5067  0.383910029 #> 5068  0.383863159 #> 5069  0.383816026 #> 5070  0.383768630 #> 5071  0.383720972 #> 5072  0.383673054 #> 5073  0.383624876 #> 5074  0.383576438 #> 5075  0.383527743 #> 5076  0.383478791 #> 5077  0.383429582 #> 5078  0.383380119 #> 5079  0.383330401 #> 5080  0.383280429 #> 5081  0.383230205 #> 5082  0.383179730 #> 5083  0.383129004 #> 5084  0.383078028 #> 5085  0.383026803 #> 5086  0.382975330 #> 5087  0.382923610 #> 5088  0.382871643 #> 5089  0.382819432 #> 5090  0.382766975 #> 5091  0.382714276 #> 5092  0.382661334 #> 5093  0.382608149 #> 5094  0.382554724 #> 5095  0.382501059 #> 5096  0.382447155 #> 5097  0.382393013 #> 5098  0.382338633 #> 5099  0.382284016 #> 5100  0.382229164 #> 5101  0.382174077 #> 5102  0.382118756 #> 5103  0.382063202 #> 5104  0.382007416 #> 5105  0.381951399 #> 5106  0.381895150 #> 5107  0.381838673 #> 5108  0.381781966 #> 5109  0.381725031 #> 5110  0.381667870 #> 5111  0.381610482 #> 5112  0.381552868 #> 5113  0.381495030 #> 5114  0.381436968 #> 5115  0.381378684 #> 5116  0.381320177 #> 5117  0.381261449 #> 5118  0.381202500 #> 5119  0.381143332 #> 5120  0.381083945 #> 5121  0.381024340 #> 5122  0.380964518 #> 5123  0.380904479 #> 5124  0.380844225 #> 5125  0.380783757 #> 5126  0.380723074 #> 5127  0.380662178 #> 5128  0.380601071 #> 5129  0.380539751 #> 5130  0.380478221 #> 5131  0.380416481 #> 5132  0.380354532 #> 5133  0.380292375 #> 5134  0.380230010 #> 5135  0.380167439 #> 5136  0.380104661 #> 5137  0.380041679 #> 5138  0.379978492 #> 5139  0.379915102 #> 5140  0.379851509 #> 5141  0.379787714 #> 5142  0.379723717 #> 5143  0.379659521 #> 5144  0.379595124 #> 5145  0.379530529 #> 5146  0.379465736 #> 5147  0.379400745 #> 5148  0.379335558 #> 5149  0.379270175 #> 5150  0.379204597 #> 5151  0.379138825 #> 5152  0.379072859 #> 5153  0.379006701 #> 5154  0.378940350 #> 5155  0.378873808 #> 5156  0.378807076 #> 5157  0.378740154 #> 5158  0.378673043 #> 5159  0.378605743 #> 5160  0.378538256 #> 5161  0.378470583 #> 5162  0.378402723 #> 5163  0.378334678 #> 5164  0.378266448 #> 5165  0.378198035 #> 5166  0.378129438 #> 5167  0.378060659 #> 5168  0.377991698 #> 5169  0.377922557 #> 5170  0.377853235 #> 5171  0.377783733 #> 5172  0.377714053 #> 5173  0.377644195 #> 5174  0.377574160 #> 5175  0.377503947 #> 5176  0.377433559 #> 5177  0.377362996 #> 5178  0.377292258 #> 5179  0.377221346 #> 5180  0.377150262 #> 5181  0.377079005 #> 5182  0.377007576 #> 5183  0.376935976 #> 5184  0.376864206 #> 5185  0.376792266 #> 5186  0.376720157 #> 5187  0.376647881 #> 5188  0.376575436 #> 5189  0.376502825 #> 5190  0.376430047 #> 5191  0.376357104 #> 5192  0.376283997 #> 5193  0.376210725 #> 5194  0.376137289 #> 5195  0.376063691 #> 5196  0.375989931 #> 5197  0.375916009 #> 5198  0.375841927 #> 5199  0.375767684 #> 5200  0.375693282 #> 5201  0.375618721 #> 5202  0.375544002 #> 5203  0.375469126 #> 5204  0.375394093 #> 5205  0.375318904 #> 5206  0.375243559 #> 5207  0.375168059 #> 5208  0.375092405 #> 5209  0.375016598 #> 5210  0.374940638 #> 5211  0.374864526 #> 5212  0.374788262 #> 5213  0.374711847 #> 5214  0.374635281 #> 5215  0.374558566 #> 5216  0.374481702 #> 5217  0.374404690 #> 5218  0.374327530 #> 5219  0.374250223 #> 5220  0.374172769 #> 5221  0.374095169 #> 5222  0.374017424 #> 5223  0.373939535 #> 5224  0.373861501 #> 5225  0.373783324 #> 5226  0.373705004 #> 5227  0.373626542 #> 5228  0.373547939 #> 5229  0.373469195 #> 5230  0.373390310 #> 5231  0.373311286 #> 5232  0.373232122 #> 5233  0.373152820 #> 5234  0.373073381 #> 5235  0.372993804 #> 5236  0.372914090 #> 5237  0.372834240 #> 5238  0.372754255 #> 5239  0.372674135 #> 5240  0.372593880 #> 5241  0.372513492 #> 5242  0.372432971 #> 5243  0.372352317 #> 5244  0.372271531 #> 5245  0.372190614 #> 5246  0.372109567 #> 5247  0.372028389 #> 5248  0.371947081 #> 5249  0.371865645 #> 5250  0.371784080 #> 5251  0.371702387 #> 5252  0.371620567 #> 5253  0.371538620 #> 5254  0.371456547 #> 5255  0.371374349 #> 5256  0.371292025 #> 5257  0.371209578 #> 5258  0.371127006 #> 5259  0.371044311 #> 5260  0.370961493 #> 5261  0.370878553 #> 5262  0.370795492 #> 5263  0.370712309 #> 5264  0.370629006 #> 5265  0.370545583 #> 5266  0.370462041 #> 5267  0.370378379 #> 5268  0.370294600 #> 5269  0.370210703 #> 5270  0.370126688 #> 5271  0.370042557 #> 5272  0.369958310 #> 5273  0.369873947 #> 5274  0.369789469 #> 5275  0.369704877 #> 5276  0.369620171 #> 5277  0.369535351 #> 5278  0.369450418 #> 5279  0.369365374 #> 5280  0.369280217 #> 5281  0.369194949 #> 5282  0.369109570 #> 5283  0.369024081 #> 5284  0.368938482 #> 5285  0.368852774 #> 5286  0.368766958 #> 5287  0.368681033 #> 5288  0.368595001 #> 5289  0.368508861 #> 5290  0.368422615 #> 5291  0.368336263 #> 5292  0.368249805 #> 5293  0.368163243 #> 5294  0.368076575 #> 5295  0.367989804 #> 5296  0.367902929 #> 5297  0.367815951 #> 5298  0.367728871 #> 5299  0.367641688 #> 5300  0.367554405 #> 5301  0.367467020 #> 5302  0.367379534 #> 5303  0.367291949 #> 5304  0.367204264 #> 5305  0.367116480 #> 5306  0.367028597 #> 5307  0.366940616 #> 5308  0.366852538 #> 5309  0.366764363 #> 5310  0.366676091 #> 5311  0.366587723 #> 5312  0.366499260 #> 5313  0.366410701 #> 5314  0.366322048 #> 5315  0.366233300 #> 5316  0.366144459 #> 5317  0.366055525 #> 5318  0.365966498 #> 5319  0.365877378 #> 5320  0.365788167 #> 5321  0.365698865 #> 5322  0.365609471 #> 5323  0.365519988 #> 5324  0.365430414 #> 5325  0.365340751 #> 5326  0.365250999 #> 5327  0.365161158 #> 5328  0.365071230 #> 5329  0.364981214 #> 5330  0.364891110 #> 5331  0.364800920 #> 5332  0.364710644 #> 5333  0.364620282 #> 5334  0.364529835 #> 5335  0.364439303 #> 5336  0.364348686 #> 5337  0.364257986 #> 5338  0.364167202 #> 5339  0.364076334 #> 5340  0.363985385 #> 5341  0.363894353 #> 5342  0.363803239 #> 5343  0.363712045 #> 5344  0.363620769 #> 5345  0.363529413 #> 5346  0.363437977 #> 5347  0.363346461 #> 5348  0.363254867 #> 5349  0.363163194 #> 5350  0.363071442 #> 5351  0.362979613 #> 5352  0.362887706 #> 5353  0.362795723 #> 5354  0.362703663 #> 5355  0.362611527 #> 5356  0.362519315 #> 5357  0.362427028 #> 5358  0.362334667 #> 5359  0.362242230 #> 5360  0.362149720 #> 5361  0.362057137 #> 5362  0.361964480 #> 5363  0.361871751 #> 5364  0.361778949 #> 5365  0.361686076 #> 5366  0.361593131 #> 5367  0.361500115 #> 5368  0.361407029 #> 5369  0.361313872 #> 5370  0.361220645 #> 5371  0.361127350 #> 5372  0.361033985 #> 5373  0.360940551 #> 5374  0.360847050 #> 5375  0.360753480 #> 5376  0.360659843 #> 5377  0.360566140 #> 5378  0.360472369 #> 5379  0.360378533 #> 5380  0.360284631 #> 5381  0.360190663 #> 5382  0.360096631 #> 5383  0.360002534 #> 5384  0.359908372 #> 5385  0.359814147 #> 5386  0.359719859 #> 5387  0.359625507 #> 5388  0.359531093 #> 5389  0.359436617 #> 5390  0.359342078 #> 5391  0.359247479 #> 5392  0.359152818 #> 5393  0.359058096 #> 5394  0.358963315 #> 5395  0.358868473 #> 5396  0.358773572 #> 5397  0.358678611 #> 5398  0.358583592 #> 5399  0.358488514 #> 5400  0.358393378 #> 5401  0.358298185 #> 5402  0.358202934 #> 5403  0.358107627 #> 5404  0.358012262 #> 5405  0.357916842 #> 5406  0.357821366 #> 5407  0.357725834 #> 5408  0.357630248 #> 5409  0.357534606 #> 5410  0.357438910 #> 5411  0.357343161 #> 5412  0.357247357 #> 5413  0.357151501 #> 5414  0.357055591 #> 5415  0.356959629 #> 5416  0.356863615 #> 5417  0.356767549 #> 5418  0.356671431 #> 5419  0.356575262 #> 5420  0.356479043 #> 5421  0.356382773 #> 5422  0.356286453 #> 5423  0.356190083 #> 5424  0.356093665 #> 5425  0.355997197 #> 5426  0.355900680 #> 5427  0.355804115 #> 5428  0.355707502 #> 5429  0.355610842 #> 5430  0.355514134 #> 5431  0.355417379 #> 5432  0.355320578 #> 5433  0.355223730 #> 5434  0.355126837 #> 5435  0.355029898 #> 5436  0.354932914 #> 5437  0.354835885 #> 5438  0.354738811 #> 5439  0.354641693 #> 5440  0.354544532 #> 5441  0.354447326 #> 5442  0.354350078 #> 5443  0.354252787 #> 5444  0.354155453 #> 5445  0.354058077 #> 5446  0.353960659 #> 5447  0.353863199 #> 5448  0.353765698 #> 5449  0.353668156 #> 5450  0.353570574 #> 5451  0.353472952 #> 5452  0.353375289 #> 5453  0.353277587 #> 5454  0.353179846 #> 5455  0.353082065 #> 5456  0.352984246 #> 5457  0.352886389 #> 5458  0.352788494 #> 5459  0.352690561 #> 5460  0.352592590 #> 5461  0.352494583 #> 5462  0.352396539 #> 5463  0.352298458 #> 5464  0.352200341 #> 5465  0.352102189 #> 5466  0.352004001 #> 5467  0.351905778 #> 5468  0.351807519 #> 5469  0.351709227 #> 5470  0.351610900 #> 5471  0.351512539 #> 5472  0.351414144 #> 5473  0.351315716 #> 5474  0.351217256 #> 5475  0.351118762 #> 5476  0.351020236 #> 5477  0.350921678 #> 5478  0.350823087 #> 5479  0.350724466 #> 5480  0.350625813 #> 5481  0.350527129 #> 5482  0.350428415 #> 5483  0.350329670 #> 5484  0.350230895 #> 5485  0.350132091 #> 5486  0.350033257 #> 5487  0.349934394 #> 5488  0.349835502 #> 5489  0.349736581 #> 5490  0.349637632 #> 5491  0.349538655 #> 5492  0.349439651 #> 5493  0.349340619 #> 5494  0.349241560 #> 5495  0.349142474 #> 5496  0.349043361 #> 5497  0.348944222 #> 5498  0.348845057 #> 5499  0.348745867 #> 5500  0.348646651 #> 5501  0.348547410 #> 5502  0.348448144 #> 5503  0.348348853 #> 5504  0.348249539 #> 5505  0.348150200 #> 5506  0.348050837 #> 5507  0.347951451 #> 5508  0.347852042 #> 5509  0.347752610 #> 5510  0.347653155 #> 5511  0.347553678 #> 5512  0.347454179 #> 5513  0.347354658 #> 5514  0.347255115 #> 5515  0.347155551 #> 5516  0.347055966 #> 5517  0.346956361 #> 5518  0.346856735 #> 5519  0.346757088 #> 5520  0.346657422 #> 5521  0.346557736 #> 5522  0.346458031 #> 5523  0.346358307 #> 5524  0.346258564 #> 5525  0.346158802 #> 5526  0.346059022 #> 5527  0.345959223 #> 5528  0.345859407 #> 5529  0.345759574 #> 5530  0.345659723 #> 5531  0.345559855 #> 5532  0.345459970 #> 5533  0.345360069 #> 5534  0.345260152 #> 5535  0.345160218 #> 5536  0.345060269 #> 5537  0.344960304 #> 5538  0.344860324 #> 5539  0.344760330 #> 5540  0.344660320 #> 5541  0.344560296 #> 5542  0.344460258 #> 5543  0.344360205 #> 5544  0.344260139 #> 5545  0.344160060 #> 5546  0.344059967 #> 5547  0.343959862 #> 5548  0.343859743 #> 5549  0.343759613 #> 5550  0.343659470 #> 5551  0.343559315 #> 5552  0.343459148 #> 5553  0.343358970 #> 5554  0.343258780 #> 5555  0.343158580 #> 5556  0.343058369 #> 5557  0.342958147 #> 5558  0.342857915 #> 5559  0.342757673 #> 5560  0.342657421 #> 5561  0.342557160 #> 5562  0.342456889 #> 5563  0.342356609 #> 5564  0.342256321 #> 5565  0.342156023 #> 5566  0.342055718 #> 5567  0.341955404 #> 5568  0.341855083 #> 5569  0.341754753 #> 5570  0.341654417 #> 5571  0.341554073 #> 5572  0.341453722 #> 5573  0.341353364 #> 5574  0.341253000 #> 5575  0.341152630 #> 5576  0.341052254 #> 5577  0.340951872 #> 5578  0.340851484 #> 5579  0.340751091 #> 5580  0.340650693 #> 5581  0.340550290 #> 5582  0.340449882 #> 5583  0.340349470 #> 5584  0.340249054 #> 5585  0.340148633 #> 5586  0.340048209 #> 5587  0.339947782 #> 5588  0.339847351 #> 5589  0.339746917 #> 5590  0.339646480 #> 5591  0.339546040 #> 5592  0.339445598 #> 5593  0.339345154 #> 5594  0.339244708 #> 5595  0.339144260 #> 5596  0.339043811 #> 5597  0.338943360 #> 5598  0.338842908 #> 5599  0.338742455 #> 5600  0.338642002 #> 5601  0.338541548 #> 5602  0.338441094 #> 5603  0.338340639 #> 5604  0.338240185 #> 5605  0.338139732 #> 5606  0.338039279 #> 5607  0.337938827 #> 5608  0.337838375 #> 5609  0.337737926 #> 5610  0.337637477 #> 5611  0.337537030 #> 5612  0.337436585 #> 5613  0.337336143 #> 5614  0.337235702 #> 5615  0.337135264 #> 5616  0.337034829 #> 5617  0.336934397 #> 5618  0.336833967 #> 5619  0.336733541 #> 5620  0.336633119 #> 5621  0.336532700 #> 5622  0.336432286 #> 5623  0.336331875 #> 5624  0.336231469 #> 5625  0.336131068 #> 5626  0.336030671 #> 5627  0.335930279 #> 5628  0.335829892 #> 5629  0.335729511 #> 5630  0.335629135 #> 5631  0.335528765 #> 5632  0.335428401 #> 5633  0.335328043 #> 5634  0.335227692 #> 5635  0.335127347 #> 5636  0.335027009 #> 5637  0.334926678 #> 5638  0.334826354 #> 5639  0.334726037 #> 5640  0.334625728 #> 5641  0.334525427 #> 5642  0.334425133 #> 5643  0.334324848 #> 5644  0.334224571 #> 5645  0.334124303 #> 5646  0.334024043 #> 5647  0.333923792 #> 5648  0.333823551 #> 5649  0.333723318 #> 5650  0.333623095 #> 5651  0.333522882 #> 5652  0.333422679 #> 5653  0.333322486 #> 5654  0.333222303 #> 5655  0.333122130 #> 5656  0.333021968 #> 5657  0.332921817 #> 5658  0.332821677 #> 5659  0.332721548 #> 5660  0.332621430 #> 5661  0.332521324 #> 5662  0.332421230 #> 5663  0.332321148 #> 5664  0.332221077 #> 5665  0.332121019 #> 5666  0.332020974 #> 5667  0.331920941 #> 5668  0.331820921 #> 5669  0.331720914 #> 5670  0.331620921 #> 5671  0.331520940 #> 5672  0.331420974 #> 5673  0.331321021 #> 5674  0.331221082 #> 5675  0.331121157 #> 5676  0.331021247 #> 5677  0.330921350 #> 5678  0.330821469 #> 5679  0.330721603 #> 5680  0.330621751 #> 5681  0.330521915 #> 5682  0.330422094 #> 5683  0.330322288 #> 5684  0.330222499 #> 5685  0.330122725 #> 5686  0.330022967 #> 5687  0.329923226 #> 5688  0.329823501 #> 5689  0.329723792 #> 5690  0.329624101 #> 5691  0.329524426 #> 5692  0.329424768 #> 5693  0.329325128 #> 5694  0.329225505 #> 5695  0.329125900 #> 5696  0.329026313 #> 5697  0.328926744 #> 5698  0.328827193 #> 5699  0.328727660 #> 5700  0.328628145 #> 5701  0.328528650 #> 5702  0.328429173 #> 5703  0.328329715 #> 5704  0.328230277 #> 5705  0.328130857 #> 5706  0.328031457 #> 5707  0.327932077 #> 5708  0.327832717 #> 5709  0.327733377 #> 5710  0.327634057 #> 5711  0.327534757 #> 5712  0.327435478 #> 5713  0.327336220 #> 5714  0.327236982 #> 5715  0.327137765 #> 5716  0.327038570 #> 5717  0.326939396 #> 5718  0.326840243 #> 5719  0.326741112 #> 5720  0.326642003 #> 5721  0.326542916 #> 5722  0.326443851 #> 5723  0.326344809 #> 5724  0.326245788 #> 5725  0.326146791 #> 5726  0.326047816 #> 5727  0.325948865 #> 5728  0.325849936 #> 5729  0.325751031 #> 5730  0.325652149 #> 5731  0.325553290 #> 5732  0.325454456 #> 5733  0.325355645 #> 5734  0.325256859 #> 5735  0.325158096 #> 5736  0.325059359 #> 5737  0.324960645 #> 5738  0.324861957 #> 5739  0.324763293 #> 5740  0.324664654 #> 5741  0.324566041 #> 5742  0.324467452 #> 5743  0.324368890 #> 5744  0.324270353 #> 5745  0.324171841 #> 5746  0.324073356 #> 5747  0.323974897 #> 5748  0.323876464 #> 5749  0.323778057 #> 5750  0.323679677 #> 5751  0.323581324 #> 5752  0.323482998 #> 5753  0.323384698 #> 5754  0.323286426 #> 5755  0.323188181 #> 5756  0.323089964 #> 5757  0.322991774 #> 5758  0.322893612 #> 5759  0.322795478 #> 5760  0.322697372 #> 5761  0.322599295 #> 5762  0.322501245 #> 5763  0.322403224 #> 5764  0.322305232 #> 5765  0.322207269 #> 5766  0.322109335 #> 5767  0.322011430 #> 5768  0.321913554 #> 5769  0.321815707 #> 5770  0.321717890 #> 5771  0.321620103 #> 5772  0.321522346 #> 5773  0.321424619 #> 5774  0.321326922 #> 5775  0.321229255 #> 5776  0.321131618 #> 5777  0.321034013 #> 5778  0.320936438 #> 5779  0.320838894 #> 5780  0.320741380 #> 5781  0.320643899 #> 5782  0.320546448 #> 5783  0.320449029 #> 5784  0.320351641 #> 5785  0.320254285 #> 5786  0.320156962 #> 5787  0.320059670 #> 5788  0.319962410 #> 5789  0.319865183 #> 5790  0.319767988 #> 5791  0.319670825 #> 5792  0.319573696 #> 5793  0.319476599 #> 5794  0.319379536 #> 5795  0.319282505 #> 5796  0.319185508 #> 5797  0.319088544 #> 5798  0.318991614 #> 5799  0.318894718 #> 5800  0.318797855 #> 5801  0.318701027 #> 5802  0.318604232 #> 5803  0.318507472 #> 5804  0.318410747 #> 5805  0.318314055 #> 5806  0.318217399 #> 5807  0.318120777 #> 5808  0.318024191 #> 5809  0.317927639 #> 5810  0.317831123 #> 5811  0.317734642 #> 5812  0.317638197 #> 5813  0.317541787 #> 5814  0.317445414 #> 5815  0.317349076 #> 5816  0.317252774 #> 5817  0.317156508 #> 5818  0.317060279 #> 5819  0.316964086 #> 5820  0.316867930 #> 5821  0.316771810 #> 5822  0.316675728 #> 5823  0.316579682 #> 5824  0.316483674 #> 5825  0.316387702 #> 5826  0.316291769 #> 5827  0.316195872 #> 5828  0.316100014 #> 5829  0.316004193 #> 5830  0.315908410 #> 5831  0.315812666 #> 5832  0.315716959 #> 5833  0.315621291 #> 5834  0.315525661 #> 5835  0.315430070 #> 5836  0.315334518 #> 5837  0.315239004 #> 5838  0.315143530 #> 5839  0.315048095 #> 5840  0.314952699 #> 5841  0.314857342 #> 5842  0.314762025 #> 5843  0.314666747 #> 5844  0.314571509 #> 5845  0.314476312 #> 5846  0.314381154 #> 5847  0.314286036 #> 5848  0.314190959 #> 5849  0.314095922 #> 5850  0.314000925 #> 5851  0.313905970 #> 5852  0.313811055 #> 5853  0.313716181 #> 5854  0.313621348 #> 5855  0.313526556 #> 5856  0.313431806 #> 5857  0.313337097 #> 5858  0.313242430 #> 5859  0.313147804 #> 5860  0.313053220 #> 5861  0.312958679 #> 5862  0.312864179 #> 5863  0.312769721 #> 5864  0.312675306 #> 5865  0.312580933 #> 5866  0.312486603 #> 5867  0.312392316 #> 5868  0.312298071 #> 5869  0.312203870 #> 5870  0.312109711 #> 5871  0.312015596 #> 5872  0.311921524 #> 5873  0.311827495 #> 5874  0.311733510 #> 5875  0.311639569 #> 5876  0.311545671 #> 5877  0.311451818 #> 5878  0.311358009 #> 5879  0.311264243 #> 5880  0.311170522 #> 5881  0.311076846 #> 5882  0.310983214 #> 5883  0.310889627 #> 5884  0.310796085 #> 5885  0.310702587 #> 5886  0.310609135 #> 5887  0.310515728 #> 5888  0.310422366 #> 5889  0.310329050 #> 5890  0.310235779 #> 5891  0.310142554 #> 5892  0.310049374 #> 5893  0.309956241 #> 5894  0.309863153 #> 5895  0.309770112 #> 5896  0.309677117 #> 5897  0.309584169 #> 5898  0.309491267 #> 5899  0.309398411 #> 5900  0.309305603 #> 5901  0.309212841 #> 5902  0.309120126 #> 5903  0.309027458 #> 5904  0.308934838 #> 5905  0.308842265 #> 5906  0.308749739 #> 5907  0.308657261 #> 5908  0.308564831 #> 5909  0.308472449 #> 5910  0.308380114 #> 5911  0.308287828 #> 5912  0.308195590 #> 5913  0.308103400 #> 5914  0.308011258 #> 5915  0.307919165 #> 5916  0.307827121 #> 5917  0.307735126 #> 5918  0.307643179 #> 5919  0.307551281 #> 5920  0.307459433 #> 5921  0.307367634 #> 5922  0.307275884 #> 5923  0.307184184 #> 5924  0.307092533 #> 5925  0.307000932 #> 5926  0.306909381 #> 5927  0.306817880 #> 5928  0.306726429 #> 5929  0.306635028 #> 5930  0.306543677 #> 5931  0.306452377 #> 5932  0.306361127 #> 5933  0.306269928 #> 5934  0.306178780 #> 5935  0.306087683 #> 5936  0.305996637 #> 5937  0.305905641 #> 5938  0.305814697 #> 5939  0.305723805 #> 5940  0.305632964 #> 5941  0.305542174 #> 5942  0.305451436 #> 5943  0.305360750 #> 5944  0.305270116 #> 5945  0.305179534 #> 5946  0.305089004 #> 5947  0.304998526 #> 5948  0.304908101 #> 5949  0.304817728 #> 5950  0.304727408 #> 5951  0.304637140 #> 5952  0.304546925 #> 5953  0.304456764 #> 5954  0.304366655 #> 5955  0.304276599 #> 5956  0.304186597 #> 5957  0.304096648 #> 5958  0.304006753 #> 5959  0.303916911 #> 5960  0.303827124 #> 5961  0.303737390 #> 5962  0.303647709 #> 5963  0.303558083 #> 5964  0.303468512 #> 5965  0.303378994 #> 5966  0.303289531 #> 5967  0.303200123 #> 5968  0.303110769 #> 5969  0.303021470 #> 5970  0.302932225 #> 5971  0.302843036 #> 5972  0.302753902 #> 5973  0.302664823 #> 5974  0.302575799 #> 5975  0.302486831 #> 5976  0.302397918 #> 5977  0.302309061 #> 5978  0.302220260 #> 5979  0.302131515 #> 5980  0.302042825 #> 5981  0.301954192 #> 5982  0.301865615 #> 5983  0.301777094 #> 5984  0.301688630 #> 5985  0.301600222 #> 5986  0.301511871 #> 5987  0.301423576 #> 5988  0.301335339 #> 5989  0.301247158 #> 5990  0.301159035 #> 5991  0.301070969 #> 5992  0.300982960 #> 5993  0.300895008 #> 5994  0.300807114 #> 5995  0.300719278 #> 5996  0.300631500 #> 5997  0.300543779 #> 5998  0.300456116 #> 5999  0.300368512 #> 6000  0.300280965 #> 6001  0.300193477 #> 6002  0.300106048 #> 6003  0.300018677 #> 6004  0.299931364 #> 6005  0.299844110 #> 6006  0.299756916 #> 6007  0.299669780 #> 6008  0.299582703 #> 6009  0.299495685 #> 6010  0.299408727 #> 6011  0.299321828 #> 6012  0.299234989 #> 6013  0.299148209 #> 6014  0.299061489 #> 6015  0.298974829 #> 6016  0.298888228 #> 6017  0.298801688 #> 6018  0.298715208 #> 6019  0.298628788 #> 6020  0.298542429 #> 6021  0.298456130 #> 6022  0.298369892 #> 6023  0.298283714 #> 6024  0.298197598 #> 6025  0.298111542 #> 6026  0.298025547 #> 6027  0.297939614 #> 6028  0.297853741 #> 6029  0.297767930 #> 6030  0.297682181 #> 6031  0.297596493 #> 6032  0.297510867 #> 6033  0.297425302 #> 6034  0.297339800 #> 6035  0.297254359 #> 6036  0.297168981 #> 6037  0.297083665 #> 6038  0.296998411 #> 6039  0.296913219 #> 6040  0.296828091 #> 6041  0.296743024 #> 6042  0.296658021 #> 6043  0.296573080 #> 6044  0.296488203 #> 6045  0.296403388 #> 6046  0.296318637 #> 6047  0.296233949 #> 6048  0.296149325 #> 6049  0.296064764 #> 6050  0.295980266 #> 6051  0.295895833 #> 6052  0.295811463 #> 6053  0.295727157 #> 6054  0.295642915 #> 6055  0.295558738 #> 6056  0.295474625 #> 6057  0.295390576 #> 6058  0.295306591 #> 6059  0.295222671 #> 6060  0.295138816 #> 6061  0.295055026 #> 6062  0.294971300 #> 6063  0.294887640 #> 6064  0.294804045 #> 6065  0.294720515 #> 6066  0.294637050 #> 6067  0.294553651 #> 6068  0.294470317 #> 6069  0.294387049 #> 6070  0.294303847 #> 6071  0.294220711 #> 6072  0.294137640 #> 6073  0.294054636 #> 6074  0.293971698 #> 6075  0.293888826 #> 6076  0.293806021 #> 6077  0.293723282 #> 6078  0.293640610 #> 6079  0.293558005 #> 6080  0.293475466 #> 6081  0.293392995 #> 6082  0.293310590 #> 6083  0.293228253 #> 6084  0.293145983 #> 6085  0.293063780 #> 6086  0.292981645 #> 6087  0.292899577 #> 6088  0.292817577 #> 6089  0.292735645 #> 6090  0.292653781 #> 6091  0.292571984 #> 6092  0.292490256 #> 6093  0.292408596 #> 6094  0.292327005 #> 6095  0.292245482 #> 6096  0.292164027 #> 6097  0.292082641 #> 6098  0.292001324 #> 6099  0.291920076 #> 6100  0.291838897 #> 6101  0.291757787 #> 6102  0.291676746 #> 6103  0.291595774 #> 6104  0.291514872 #> 6105  0.291434039 #> 6106  0.291353276 #> 6107  0.291272583 #> 6108  0.291191959 #> 6109  0.291111405 #> 6110  0.291030922 #> 6111  0.290950508 #> 6112  0.290870165 #> 6113  0.290789892 #> 6114  0.290709690 #> 6115  0.290629558 #> 6116  0.290549497 #> 6117  0.290469507 #> 6118  0.290389588 #> 6119  0.290309739 #> 6120  0.290229962 #> 6121  0.290150256 #> 6122  0.290070621 #> 6123  0.289991058 #> 6124  0.289911566 #> 6125  0.289832146 #> 6126  0.289752798 #> 6127  0.289673521 #> 6128  0.289594317 #> 6129  0.289515184 #> 6130  0.289436124 #> 6131  0.289357136 #> 6132  0.289278220 #> 6133  0.289199377 #> 6134  0.289120607 #> 6135  0.289041909 #> 6136  0.288963284 #> 6137  0.288884732 #> 6138  0.288806253 #> 6139  0.288727847 #> 6140  0.288649515 #> 6141  0.288571255 #> 6142  0.288493070 #> 6143  0.288414957 #> 6144  0.288336919 #> 6145  0.288258954 #> 6146  0.288181063 #> 6147  0.288103246 #> 6148  0.288025504 #> 6149  0.287947835 #> 6150  0.287870241 #> 6151  0.287792721 #> 6152  0.287715276 #> 6153  0.287637905 #> 6154  0.287560609 #> 6155  0.287483388 #> 6156  0.287406242 #> 6157  0.287329171 #> 6158  0.287252175 #> 6159  0.287175254 #> 6160  0.287098409 #> 6161  0.287021640 #> 6162  0.286944946 #> 6163  0.286868327 #> 6164  0.286791785 #> 6165  0.286715318 #> 6166  0.286638927 #> 6167  0.286562613 #> 6168  0.286486375 #> 6169  0.286410213 #> 6170  0.286334127 #> 6171  0.286258119 #> 6172  0.286182186 #> 6173  0.286106331 #> 6174  0.286030553 #> 6175  0.285954851 #> 6176  0.285879227 #> 6177  0.285803680 #> 6178  0.285728210 #> 6179  0.285652818 #> 6180  0.285577503 #> 6181  0.285502266 #> 6182  0.285427106 #> 6183  0.285352025 #> 6184  0.285277021 #> 6185  0.285202095 #> 6186  0.285127248 #> 6187  0.285052479 #> 6188  0.284977788 #> 6189  0.284903176 #> 6190  0.284828643 #> 6191  0.284754188 #> 6192  0.284679812 #> 6193  0.284605515 #> 6194  0.284531297 #> 6195  0.284457158 #> 6196  0.284383099 #> 6197  0.284309119 #> 6198  0.284235218 #> 6199  0.284161397 #> 6200  0.284087656 #> 6201  0.284013994 #> 6202  0.283940413 #> 6203  0.283866911 #> 6204  0.283793490 #> 6205  0.283720148 #> 6206  0.283646888 #> 6207  0.283573707 #> 6208  0.283500607 #> 6209  0.283427588 #> 6210  0.283354650 #> 6211  0.283281793 #> 6212  0.283209016 #> 6213  0.283136321 #> 6214  0.283063707 #> 6215  0.282991174 #> 6216  0.282918723 #> 6217  0.282846353 #> 6218  0.282774065 #> 6219  0.282701859 #> 6220  0.282629734 #> 6221  0.282557692 #> 6222  0.282485732 #> 6223  0.282413854 #> 6224  0.282342058 #> 6225  0.282270344 #> 6226  0.282198714 #> 6227  0.282127166 #> 6228  0.282055700 #> 6229  0.281984318 #> 6230  0.281913018 #> 6231  0.281841802 #> 6232  0.281770668 #> 6233  0.281699619 #> 6234  0.281628652 #> 6235  0.281557769 #> 6236  0.281486970 #> 6237  0.281416254 #> 6238  0.281345622 #> 6239  0.281275075 #> 6240  0.281204611 #> 6241  0.281134232 #> 6242  0.281063936 #> 6243  0.280993726 #> 6244  0.280923599 #> 6245  0.280853558 #> 6246  0.280783601 #> 6247  0.280713729 #> 6248  0.280643942 #> 6249  0.280574240 #> 6250  0.280504623 #> 6251  0.280435092 #> 6252  0.280365646 #> 6253  0.280296285 #> 6254  0.280227010 #> 6255  0.280157821 #> 6256  0.280088718 #> 6257  0.280019701 #> 6258  0.279950770 #> 6259  0.279881924 #> 6260  0.279813166 #> 6261  0.279744493 #> 6262  0.279675908 #> 6263  0.279607408 #> 6264  0.279538996 #> 6265  0.279470670 #> 6266  0.279402432 #> 6267  0.279334280 #> 6268  0.279266216 #> 6269  0.279198239 #> 6270  0.279130350 #> 6271  0.279062548 #> 6272  0.278994833 #> 6273  0.278927207 #> 6274  0.278859668 #> 6275  0.278792217 #> 6276  0.278724854 #> 6277  0.278657580 #> 6278  0.278590394 #> 6279  0.278523296 #> 6280  0.278456287 #> 6281  0.278389366 #> 6282  0.278322534 #> 6283  0.278255792 #> 6284  0.278189138 #> 6285  0.278122573 #> 6286  0.278056097 #> 6287  0.277989711 #> 6288  0.277923414 #> 6289  0.277857207 #> 6290  0.277791090 #> 6291  0.277725062 #> 6292  0.277659124 #> 6293  0.277593276 #> 6294  0.277527519 #> 6295  0.277461851 #> 6296  0.277396274 #> 6297  0.277330788 #> 6298  0.277265392 #> 6299  0.277200086 #> 6300  0.277134872 #> 6301  0.277069748 #> 6302  0.277004716 #> 6303  0.276939774 #> 6304  0.276874924 #> 6305  0.276810165 #> 6306  0.276745498 #> 6307  0.276680922 #> 6308  0.276616438 #> 6309  0.276552046 #> 6310  0.276487746 #> 6311  0.276423538 #> 6312  0.276359422 #> 6313  0.276295399 #> 6314  0.276231468 #> 6315  0.276167629 #> 6316  0.276103883 #> 6317  0.276040230 #> 6318  0.275976670 #> 6319  0.275913202 #> 6320  0.275849828 #> 6321  0.275786547 #> 6322  0.275723359 #> 6323  0.275660265 #> 6324  0.275597264 #> 6325  0.275534357 #> 6326  0.275471544 #> 6327  0.275408825 #> 6328  0.275346200 #> 6329  0.275283669 #> 6330  0.275221232 #> 6331  0.275158889 #> 6332  0.275096641 #> 6333  0.275034488 #> 6334  0.274972429 #> 6335  0.274910466 #> 6336  0.274848597 #> 6337  0.274786823 #> 6338  0.274725145 #> 6339  0.274663561 #> 6340  0.274602074 #> 6341  0.274540681 #> 6342  0.274479385 #> 6343  0.274418184 #> 6344  0.274357079 #> 6345  0.274296070 #> 6346  0.274235157 #> 6347  0.274174341 #> 6348  0.274113620 #> 6349  0.274052997 #> 6350  0.273992470 #> 6351  0.273932039 #> 6352  0.273871706 #> 6353  0.273811469 #> 6354  0.273751329 #> 6355  0.273691287 #> 6356  0.273631342 #> 6357  0.273571494 #> 6358  0.273511744 #> 6359  0.273452092 #> 6360  0.273392537 #> 6361  0.273333080 #> 6362  0.273273721 #> 6363  0.273214461 #> 6364  0.273155298 #> 6365  0.273096234 #> 6366  0.273037269 #> 6367  0.272978402 #> 6368  0.272919633 #> 6369  0.272860964 #> 6370  0.272802394 #> 6371  0.272743922 #> 6372  0.272685550 #> 6373  0.272627277 #> 6374  0.272569104 #> 6375  0.272511030 #> 6376  0.272453056 #> 6377  0.272395181 #> 6378  0.272337407 #> 6379  0.272279732 #> 6380  0.272222158 #> 6381  0.272164684 #> 6382  0.272107310 #> 6383  0.272050037 #> 6384  0.271992865 #> 6385  0.271935793 #> 6386  0.271878822 #> 6387  0.271821952 #> 6388  0.271765183 #> 6389  0.271708515 #> 6390  0.271651949 #> 6391  0.271595484 #> 6392  0.271539121 #> 6393  0.271482860 #> 6394  0.271426700 #> 6395  0.271370642 #> 6396  0.271314687 #> 6397  0.271258833 #> 6398  0.271203082 #> 6399  0.271147433 #> 6400  0.271091887 #> 6401  0.271036444 #> 6402  0.270981103 #> 6403  0.270925865 #> 6404  0.270870730 #> 6405  0.270815699 #> 6406  0.270760770 #> 6407  0.270705946 #> 6408  0.270651224 #> 6409  0.270596606 #> 6410  0.270542092 #> 6411  0.270487682 #> 6412  0.270433376 #> 6413  0.270379174 #> 6414  0.270325076 #> 6415  0.270271083 #> 6416  0.270217194 #> 6417  0.270163410 #> 6418  0.270109730 #> 6419  0.270056156 #> 6420  0.270002686 #> 6421  0.269949321 #> 6422  0.269896062 #> 6423  0.269842908 #> 6424  0.269789860 #> 6425  0.269736917 #> 6426  0.269684079 #> 6427  0.269631348 #> 6428  0.269578722 #> 6429  0.269526203 #> 6430  0.269473790 #> 6431  0.269421483 #> 6432  0.269369283 #> 6433  0.269317189 #> 6434  0.269265202 #> 6435  0.269213321 #> 6436  0.269161548 #> 6437  0.269109882 #> 6438  0.269058323 #> 6439  0.269006871 #> 6440  0.268955527 #> 6441  0.268904290 #> 6442  0.268853161 #> 6443  0.268802139 #> 6444  0.268751226 #> 6445  0.268700420 #> 6446  0.268649723 #> 6447  0.268599134 #> 6448  0.268548654 #> 6449  0.268498282 #> 6450  0.268448019 #> 6451  0.268397864 #> 6452  0.268347819 #> 6453  0.268297882 #> 6454  0.268248055 #> 6455  0.268198337 #> 6456  0.268148728 #> 6457  0.268099229 #> 6458  0.268049840 #> 6459  0.268000560 #> 6460  0.267951390 #> 6461  0.267902331 #> 6462  0.267853381 #> 6463  0.267804542 #> 6464  0.267755813 #> 6465  0.267707195 #> 6466  0.267658687 #> 6467  0.267610290 #> 6468  0.267562004 #> 6469  0.267513829 #> 6470  0.267465766 #> 6471  0.267417813 #> 6472  0.267369972 #> 6473  0.267322242 #> 6474  0.267274624 #> 6475  0.267227118 #> 6476  0.267179724 #> 6477  0.267132442 #> 6478  0.267085272 #> 6479  0.267038214 #> 6480  0.266991269 #> 6481  0.266944436 #> 6482  0.266897716 #> 6483  0.266851108 #> 6484  0.266804614 #> 6485  0.266758232 #> 6486  0.266711964 #> 6487  0.266665809 #> 6488  0.266619768 #> 6489  0.266573840 #> 6490  0.266528025 #> 6491  0.266482325 #> 6492  0.266436738 #> 6493  0.266391265 #> 6494  0.266345907 #> 6495  0.266300662 #> 6496  0.266255533 #> 6497  0.266210517 #> 6498  0.266165617 #> 6499  0.266120831 #> 6500  0.266076160 #> 6501  0.266031604 #> 6502  0.265987164 #> 6503  0.265942838 #> 6504  0.265898629 #> 6505  0.265854534 #> 6506  0.265810555 #> 6507  0.265766693 #> 6508  0.265722946 #> 6509  0.265679315 #> 6510  0.265635800 #> 6511  0.265592402 #> 6512  0.265549120 #> 6513  0.265505954 #> 6514  0.265462905 #> 6515  0.265419973 #> 6516  0.265377159 #> 6517  0.265334461 #> 6518  0.265291880 #> 6519  0.265249416 #> 6520  0.265207071 #> 6521  0.265164842 #> 6522  0.265122731 #> 6523  0.265080739 #> 6524  0.265038864 #> 6525  0.264997107 #> 6526  0.264955468 #> 6527  0.264913948 #> 6528  0.264872546 #> 6529  0.264831263 #> 6530  0.264790098 #> 6531  0.264749053 #> 6532  0.264708126 #> 6533  0.264667318 #> 6534  0.264626630 #> 6535  0.264586061 #> 6536  0.264545611 #> 6537  0.264505281 #> 6538  0.264465071 #> 6539  0.264424981 #> 6540  0.264385010 #> 6541  0.264345160 #> 6542  0.264305430 #> 6543  0.264265820 #> 6544  0.264226331 #> 6545  0.264186962 #> 6546  0.264147714 #> 6547  0.264108587 #> 6548  0.264069581 #> 6549  0.264030696 #> 6550  0.263991933 #> 6551  0.263953290 #> 6552  0.263914770 #> 6553  0.263876371 #> 6554  0.263838093 #> 6555  0.263799938 #> 6556  0.263761904 #> 6557  0.263723993 #> 6558  0.263686204 #> 6559  0.263648537 #> 6560  0.263610993 #> 6561  0.263573571 #> 6562  0.263536273 #> 6563  0.263499097 #> 6564  0.263462044 #> 6565  0.263425114 #> 6566  0.263388308 #> 6567  0.263351625 #> 6568  0.263315065 #> 6569  0.263278630 #> 6570  0.263242318 #> 6571  0.263206129 #> 6572  0.263170065 #> 6573  0.263134125 #> 6574  0.263098310 #> 6575  0.263062619 #> 6576  0.263027052 #> 6577  0.262991610 #> 6578  0.262956293 #> 6579  0.262921101 #> 6580  0.262886033 #> 6581  0.262851091 #> 6582  0.262816275 #> 6583  0.262781583 #> 6584  0.262747018 #> 6585  0.262712578 #> 6586  0.262678263 #> 6587  0.262644075 #> 6588  0.262610013 #> 6589  0.262576077 #> 6590  0.262542267 #> 6591  0.262508584 #> 6592  0.262475027 #> 6593  0.262441597 #> 6594  0.262408294 #> 6595  0.262375118 #> 6596  0.262342068 #> 6597  0.262309146 #> 6598  0.262276352 #> 6599  0.262243685 #> 6600  0.262211145 #> 6601  0.262178733 #> 6602  0.262146449 #> 6603  0.262114293 #> 6604  0.262082265 #> 6605  0.262050365 #> 6606  0.262018593 #> 6607  0.261986950 #> 6608  0.261955435 #> 6609  0.261924050 #> 6610  0.261892793 #> 6611  0.261861665 #> 6612  0.261830666 #> 6613  0.261799796 #> 6614  0.261769055 #> 6615  0.261738444 #> 6616  0.261707963 #> 6617  0.261677611 #> 6618  0.261647390 #> 6619  0.261617298 #> 6620  0.261587336 #> 6621  0.261557504 #> 6622  0.261527803 #> 6623  0.261498232 #> 6624  0.261468792 #> 6625  0.261439482 #> 6626  0.261410304 #> 6627  0.261381256 #> 6628  0.261352339 #> 6629  0.261323554 #> 6630  0.261294899 #> 6631  0.261266377 #> 6632  0.261237985 #> 6633  0.261209726 #> 6634  0.261181598 #> 6635  0.261153602 #> 6636  0.261125739 #> 6637  0.261098007 #> 6638  0.261070408 #> 6639  0.261042941 #> 6640  0.261015607 #> 6641  0.260988406 #> 6642  0.260961337 #> 6643  0.260934401 #> 6644  0.260907598 #> 6645  0.260880929 #> 6646  0.260854393 #> 6647  0.260827990 #> 6648  0.260801720 #> 6649  0.260775585 #> 6650  0.260749583 #> 6651  0.260723715 #> 6652  0.260697981 #> 6653  0.260672381 #> 6654  0.260646916 #> 6655  0.260621584 #> 6656  0.260596388 #> 6657  0.260571326 #> 6658  0.260546398 #> 6659  0.260521606 #> 6660  0.260496949 #> 6661  0.260472426 #> 6662  0.260448039 #> 6663  0.260423788 #> 6664  0.260399671 #> 6665  0.260375691 #> 6666  0.260351846 #> 6667  0.260328137 #> 6668  0.260304564 #> 6669  0.260281127 #> 6670  0.260257826 #> 6671  0.260234661 #> 6672  0.260211633 #> 6673  0.260188742 #> 6674  0.260165987 #> 6675  0.260143369 #> 6676  0.260120887 #> 6677  0.260098543 #> 6678  0.260076336 #> 6679  0.260054267 #> 6680  0.260032334 #> 6681  0.260010539 #> 6682  0.259988882 #> 6683  0.259967363 #> 6684  0.259945981 #> 6685  0.259924737 #> 6686  0.259903632 #> 6687  0.259882665 #> 6688  0.259861836 #> 6689  0.259841145 #> 6690  0.259820593 #> 6691  0.259800180 #> 6692  0.259779905 #> 6693  0.259759769 #> 6694  0.259739773 #> 6695  0.259719915 #> 6696  0.259700197 #> 6697  0.259680618 #> 6698  0.259661179 #> 6699  0.259641879 #> 6700  0.259622719 #> 6701  0.259603699 #> 6702  0.259584818 #> 6703  0.259566078 #> 6704  0.259547478 #> 6705  0.259529018 #> 6706  0.259510699 #> 6707  0.259492520 #> 6708  0.259474482 #> 6709  0.259456584 #> 6710  0.259438827 #> 6711  0.259421212 #> 6712  0.259403737 #> 6713  0.259386404 #> 6714  0.259369212 #> 6715  0.259352161 #> 6716  0.259335252 #> 6717  0.259318484 #> 6718  0.259301858 #> 6719  0.259285374 #> 6720  0.259269032 #> 6721  0.259252833 #> 6722  0.259236775 #> 6723  0.259220860 #> 6724  0.259205087 #> 6725  0.259189456 #> 6726  0.259173968 #> 6727  0.259158623 #> 6728  0.259143421 #> 6729  0.259128362 #> 6730  0.259113445 #> 6731  0.259098672 #> 6732  0.259084043 #> 6733  0.259069556 #> 6734  0.259055213 #> 6735  0.259041014 #> 6736  0.259026959 #> 6737  0.259013047 #> 6738  0.258999279 #> 6739  0.258985655 #> 6740  0.258972176 #> 6741  0.258958840 #> 6742  0.258945649 #> 6743  0.258932603 #> 6744  0.258919701 #> 6745  0.258906944 #> 6746  0.258894331 #> 6747  0.258881863 #> 6748  0.258869541 #> 6749  0.258857363 #> 6750  0.258845331 #> 6751  0.258833444 #> 6752  0.258821702 #> 6753  0.258810106 #> 6754  0.258798655 #> 6755  0.258787350 #> 6756  0.258776191 #> 6757  0.258765178 #> 6758  0.258754310 #> 6759  0.258743589 #> 6760  0.258733014 #> 6761  0.258722586 #> 6762  0.258712304 #> 6763  0.258702168 #> 6764  0.258692179 #> 6765  0.258682337 #> 6766  0.258672641 #> 6767  0.258663092 #> 6768  0.258653691 #> 6769  0.258644436 #> 6770  0.258635329 #> 6771  0.258626369 #> 6772  0.258617556 #> 6773  0.258608891 #> 6774  0.258600373 #> 6775  0.258592003 #> 6776  0.258583781 #> 6777  0.258575707 #> 6778  0.258567781 #> 6779  0.258560003 #> 6780  0.258552373 #> 6781  0.258544891 #> 6782  0.258537557 #> 6783  0.258530372 #> 6784  0.258523336 #> 6785  0.258516448 #> 6786  0.258509709 #> 6787  0.258503119 #> 6788  0.258496677 #> 6789  0.258490385 #> 6790  0.258484241 #> 6791  0.258478247 #> 6792  0.258472402 #> 6793  0.258466707 #> 6794  0.258461161 #> 6795  0.258455764 #> 6796  0.258450517 #> 6797  0.258445420 #> 6798  0.258440473 #> 6799  0.258435675 #> 6800  0.258431028 #> 6801  0.258426530 #> 6802  0.258422183 #> 6803  0.258417986 #> 6804  0.258413939 #> 6805  0.258410042 #> 6806  0.258406297 #> 6807  0.258402701 #> 6808  0.258399257 #> 6809  0.258395963 #> 6810  0.258392820 #> 6811  0.258389827 #> 6812  0.258386986 #> 6813  0.258384296 #> 6814  0.258381757 #> 6815  0.258379369 #> 6816  0.258377133 #> 6817  0.258375048 #> 6818  0.258373115 #> 6819  0.258371333 #> 6820  0.258369702 #> 6821  0.258368224 #> 6822  0.258366897 #> 6823  0.258365722 #> 6824  0.258364699 #> 6825  0.258363828 #> 6826  0.258363109 #> 6827  0.258362543 #> 6828  0.258362128 #> 6829  0.258361866 #> 6830  0.258361756 #> 6831  0.258361799 #> 6832  0.258361995 #> 6833  0.258362343 #> 6834  0.258362843 #> 6835  0.258363497 #> 6836  0.258364303 #> 6837  0.258365263 #> 6838  0.258366375 #> 6839  0.258367640 #> 6840  0.258369059 #> 6841  0.258370630 #> 6842  0.258372355 #> 6843  0.258374234 #> 6844  0.258376265 #> 6845  0.258378451 #> 6846  0.258380789 #> 6847  0.258383282 #> 6848  0.258385928 #> 6849  0.258388727 #> 6850  0.258391681 #> 6851  0.258394789 #> 6852  0.258398050 #> 6853  0.258401465 #> 6854  0.258405035 #> 6855  0.258408758 #> 6856  0.258412636 #> 6857  0.258416668 #> 6858  0.258420855 #> 6859  0.258425195 #> 6860  0.258429691 #> 6861  0.258434340 #> 6862  0.258439145 #> 6863  0.258444104 #> 6864  0.258449217 #> 6865  0.258454485 #> 6866  0.258459909 #> 6867  0.258465486 #> 6868  0.258471219 #> 6869  0.258477107 #> 6870  0.258483150 #> 6871  0.258489348 #> 6872  0.258495701 #> 6873  0.258502209 #> 6874  0.258508872 #> 6875  0.258515691 #> 6876  0.258522665 #> 6877  0.258529795 #> 6878  0.258537080 #> 6879  0.258544520 #> 6880  0.258552116 #> 6881  0.258559867 #> 6882  0.258567774 #> 6883  0.258575837 #> 6884  0.258584056 #> 6885  0.258592430 #> 6886  0.258600960 #> 6887  0.258609646 #> 6888  0.258618488 #> 6889  0.258627486 #> 6890  0.258636640 #> 6891  0.258645950 #> 6892  0.258655416 #> 6893  0.258665038 #> 6894  0.258674816 #> 6895  0.258684751 #> 6896  0.258694842 #> 6897  0.258705089 #> 6898  0.258715492 #> 6899  0.258726052 #> 6900  0.258736768 #> 6901  0.258747641 #> 6902  0.258758670 #> 6903  0.258769856 #> 6904  0.258781198 #> 6905  0.258792697 #> 6906  0.258804352 #> 6907  0.258816164 #> 6908  0.258828133 #> 6909  0.258840258 #> 6910  0.258852540 #> 6911  0.258864979 #> 6912  0.258877575 #> 6913  0.258890328 #> 6914  0.258903237 #> 6915  0.258916304 #> 6916  0.258929527 #> 6917  0.258942907 #> 6918  0.258956445 #> 6919  0.258970139 #> 6920  0.258983990 #> 6921  0.258997998 #> 6922  0.259012164 #> 6923  0.259026486 #> 6924  0.259040966 #> 6925  0.259055603 #> 6926  0.259070397 #> 6927  0.259085348 #> 6928  0.259100456 #> 6929  0.259115722 #> 6930  0.259131144 #> 6931  0.259146725 #> 6932  0.259162462 #> 6933  0.259178356 #> 6934  0.259194408 #> 6935  0.259210618 #> 6936  0.259226984 #> 6937  0.259243508 #> 6938  0.259260189 #> 6939  0.259277028 #> 6940  0.259294024 #> 6941  0.259311177 #> 6942  0.259328488 #> 6943  0.259345956 #> 6944  0.259363582 #> 6945  0.259381365 #> 6946  0.259399305 #> 6947  0.259417403 #> 6948  0.259435659 #> 6949  0.259454072 #> 6950  0.259472642 #> 6951  0.259491369 #> 6952  0.259510255 #> 6953  0.259529297 #> 6954  0.259548497 #> 6955  0.259567855 #> 6956  0.259587370 #> 6957  0.259607042 #> 6958  0.259626872 #> 6959  0.259646859 #> 6960  0.259667004 #> 6961  0.259687306 #> 6962  0.259707766 #> 6963  0.259728383 #> 6964  0.259749158 #> 6965  0.259770089 #> 6966  0.259791179 #> 6967  0.259812425 #> 6968  0.259833829 #> 6969  0.259855391 #> 6970  0.259877110 #> 6971  0.259898986 #> 6972  0.259921019 #> 6973  0.259943210 #> 6974  0.259965558 #> 6975  0.259988064 #> 6976  0.260010726 #> 6977  0.260033546 #> 6978  0.260056523 #> 6979  0.260079658 #> 6980  0.260102949 #> 6981  0.260126398 #> 6982  0.260150004 #> 6983  0.260173767 #> 6984  0.260197687 #> 6985  0.260221764 #> 6986  0.260245998 #> 6987  0.260270390 #> 6988  0.260294938 #> 6989  0.260319643 #> 6990  0.260344506 #> 6991  0.260369525 #> 6992  0.260394701 #> 6993  0.260420034 #> 6994  0.260445523 #> 6995  0.260471170 #> 6996  0.260496973 #> 6997  0.260522933 #> 6998  0.260549050 #> 6999  0.260575323 #> 7000  0.260601753 #> 7001  0.260628340 #> 7002  0.260655083 #> 7003  0.260681983 #> 7004  0.260709039 #> 7005  0.260736251 #> 7006  0.260763620 #> 7007  0.260791145 #> 7008  0.260818827 #> 7009  0.260846664 #> 7010  0.260874658 #> 7011  0.260902809 #> 7012  0.260931115 #> 7013  0.260959577 #> 7014  0.260988195 #> 7015  0.261016970 #> 7016  0.261045900 #> 7017  0.261074986 #> 7018  0.261104228 #> 7019  0.261133625 #> 7020  0.261163178 #> 7021  0.261192887 #> 7022  0.261222752 #> 7023  0.261252772 #> 7024  0.261282947 #> 7025  0.261313278 #> 7026  0.261343764 #> 7027  0.261374406 #> 7028  0.261405203 #> 7029  0.261436155 #> 7030  0.261467262 #> 7031  0.261498524 #> 7032  0.261529941 #> 7033  0.261561513 #> 7034  0.261593239 #> 7035  0.261625121 #> 7036  0.261657157 #> 7037  0.261689348 #> 7038  0.261721693 #> 7039  0.261754193 #> 7040  0.261786848 #> 7041  0.261819656 #> 7042  0.261852619 #> 7043  0.261885736 #> 7044  0.261919007 #> 7045  0.261952432 #> 7046  0.261986011 #> 7047  0.262019744 #> 7048  0.262053631 #> 7049  0.262087672 #> 7050  0.262121866 #> 7051  0.262156213 #> 7052  0.262190714 #> 7053  0.262225369 #> 7054  0.262260177 #> 7055  0.262295137 #> 7056  0.262330251 #> 7057  0.262365518 #> 7058  0.262400938 #> 7059  0.262436511 #> 7060  0.262472237 #> 7061  0.262508115 #> 7062  0.262544145 #> 7063  0.262580328 #> 7064  0.262616664 #> 7065  0.262653152 #> 7066  0.262689792 #> 7067  0.262726583 #> 7068  0.262763527 #> 7069  0.262800623 #> 7070  0.262837871 #> 7071  0.262875270 #> 7072  0.262912820 #> 7073  0.262950522 #> 7074  0.262988376 #> 7075  0.263026381 #> 7076  0.263064536 #> 7077  0.263102843 #> 7078  0.263141301 #> 7079  0.263179909 #> 7080  0.263218668 #> 7081  0.263257578 #> 7082  0.263296638 #> 7083  0.263335848 #> 7084  0.263375209 #> 7085  0.263414720 #> 7086  0.263454380 #> 7087  0.263494191 #> 7088  0.263534151 #> 7089  0.263574261 #> 7090  0.263614520 #> 7091  0.263654929 #> 7092  0.263695487 #> 7093  0.263736193 #> 7094  0.263777049 #> 7095  0.263818054 #> 7096  0.263859208 #> 7097  0.263900510 #> 7098  0.263941960 #> 7099  0.263983559 #> 7100  0.264025306 #> 7101  0.264067201 #> 7102  0.264109244 #> 7103  0.264151435 #> 7104  0.264193773 #> 7105  0.264236259 #> 7106  0.264278892 #> 7107  0.264321673 #> 7108  0.264364600 #> 7109  0.264407675 #> 7110  0.264450896 #> 7111  0.264494264 #> 7112  0.264537778 #> 7113  0.264581439 #> 7114  0.264625246 #> 7115  0.264669199 #> 7116  0.264713297 #> 7117  0.264757542 #> 7118  0.264801932 #> 7119  0.264846468 #> 7120  0.264891149 #> 7121  0.264935975 #> 7122  0.264980945 #> 7123  0.265026061 #> 7124  0.265071322 #> 7125  0.265116727 #> 7126  0.265162276 #> 7127  0.265207969 #> 7128  0.265253806 #> 7129  0.265299788 #> 7130  0.265345913 #> 7131  0.265392181 #> 7132  0.265438593 #> 7133  0.265485147 #> 7134  0.265531845 #> 7135  0.265578686 #> 7136  0.265625669 #> 7137  0.265672795 #> 7138  0.265720063 #> 7139  0.265767473 #> 7140  0.265815025 #> 7141  0.265862719 #> 7142  0.265910555 #> 7143  0.265958532 #> 7144  0.266006650 #> 7145  0.266054909 #> 7146  0.266103309 #> 7147  0.266151849 #> 7148  0.266200531 #> 7149  0.266249352 #> 7150  0.266298314 #> 7151  0.266347415 #> 7152  0.266396656 #> 7153  0.266446037 #> 7154  0.266495557 #> 7155  0.266545216 #> 7156  0.266595015 #> 7157  0.266644952 #> 7158  0.266695027 #> 7159  0.266745241 #> 7160  0.266795593 #> 7161  0.266846083 #> 7162  0.266896711 #> 7163  0.266947476 #> 7164  0.266998379 #> 7165  0.267049419 #> 7166  0.267100596 #> 7167  0.267151910 #> 7168  0.267203360 #> 7169  0.267254946 #> 7170  0.267306669 #> 7171  0.267358527 #> 7172  0.267410521 #> 7173  0.267462651 #> 7174  0.267514916 #> 7175  0.267567316 #> 7176  0.267619851 #> 7177  0.267672520 #> 7178  0.267725324 #> 7179  0.267778262 #> 7180  0.267831334 #> 7181  0.267884539 #> 7182  0.267937878 #> 7183  0.267991351 #> 7184  0.268044956 #> 7185  0.268098695 #> 7186  0.268152566 #> 7187  0.268206569 #> 7188  0.268260704 #> 7189  0.268314972 #> 7190  0.268369371 #> 7191  0.268423901 #> 7192  0.268478563 #> 7193  0.268533356 #> 7194  0.268588279 #> 7195  0.268643333 #> 7196  0.268698517 #> 7197  0.268753831 #> 7198  0.268809275 #> 7199  0.268864849 #> 7200  0.268920551 #> 7201  0.268976383 #> 7202  0.269032344 #> 7203  0.269088433 #> 7204  0.269144651 #> 7205  0.269200996 #> 7206  0.269257470 #> 7207  0.269314071 #> 7208  0.269370799 #> 7209  0.269427654 #> 7210  0.269484636 #> 7211  0.269541745 #> 7212  0.269598980 #> 7213  0.269656341 #> 7214  0.269713828 #> 7215  0.269771440 #> 7216  0.269829177 #> 7217  0.269887040 #> 7218  0.269945027 #> 7219  0.270003139 #> 7220  0.270061375 #> 7221  0.270119734 #> 7222  0.270178218 #> 7223  0.270236825 #> 7224  0.270295555 #> 7225  0.270354408 #> 7226  0.270413383 #> 7227  0.270472481 #> 7228  0.270531701 #> 7229  0.270591042 #> 7230  0.270650505 #> 7231  0.270710089 #> 7232  0.270769794 #> 7233  0.270829620 #> 7234  0.270889566 #> 7235  0.270949633 #> 7236  0.271009819 #> 7237  0.271070124 #> 7238  0.271130549 #> 7239  0.271191093 #> 7240  0.271251755 #> 7241  0.271312536 #> 7242  0.271373435 #> 7243  0.271434452 #> 7244  0.271495586 #> 7245  0.271556837 #> 7246  0.271618206 #> 7247  0.271679690 #> 7248  0.271741292 #> 7249  0.271803009 #> 7250  0.271864842 #> 7251  0.271926791 #> 7252  0.271988854 #> 7253  0.272051033 #> 7254  0.272113326 #> 7255  0.272175733 #> 7256  0.272238254 #> 7257  0.272300889 #> 7258  0.272363637 #> 7259  0.272426498 #> 7260  0.272489472 #> 7261  0.272552558 #> 7262  0.272615757 #> 7263  0.272679067 #> 7264  0.272742488 #> 7265  0.272806021 #> 7266  0.272869665 #> 7267  0.272933419 #> 7268  0.272997283 #> 7269  0.273061257 #> 7270  0.273125341 #> 7271  0.273189534 #> 7272  0.273253836 #> 7273  0.273318247 #> 7274  0.273382765 #> 7275  0.273447392 #> 7276  0.273512127 #> 7277  0.273576968 #> 7278  0.273641917 #> 7279  0.273706973 #> 7280  0.273772134 #> 7281  0.273837402 #> 7282  0.273902776 #> 7283  0.273968255 #> 7284  0.274033838 #> 7285  0.274099527 #> 7286  0.274165320 #> 7287  0.274231217 #> 7288  0.274297217 #> 7289  0.274363321 #> 7290  0.274429528 #> 7291  0.274495838 #> 7292  0.274562250 #> 7293  0.274628763 #> 7294  0.274695379 #> 7295  0.274762096 #> 7296  0.274828914 #> 7297  0.274895832 #> 7298  0.274962851 #> 7299  0.275029969 #> 7300  0.275097187 #> 7301  0.275164505 #> 7302  0.275231921 #> 7303  0.275299436 #> 7304  0.275367049 #> 7305  0.275434760 #> 7306  0.275502568 #> 7307  0.275570473 #> 7308  0.275638475 #> 7309  0.275706574 #> 7310  0.275774769 #> 7311  0.275843059 #> 7312  0.275911445 #> 7313  0.275979925 #> 7314  0.276048500 #> 7315  0.276117170 #> 7316  0.276185933 #> 7317  0.276254790 #> 7318  0.276323740 #> 7319  0.276392783 #> 7320  0.276461918 #> 7321  0.276531146 #> 7322  0.276600465 #> 7323  0.276669875 #> 7324  0.276739377 #> 7325  0.276808969 #> 7326  0.276878651 #> 7327  0.276948423 #> 7328  0.277018284 #> 7329  0.277088235 #> 7330  0.277158274 #> 7331  0.277228402 #> 7332  0.277298618 #> 7333  0.277368921 #> 7334  0.277439312 #> 7335  0.277509789 #> 7336  0.277580353 #> 7337  0.277651003 #> 7338  0.277721738 #> 7339  0.277792559 #> 7340  0.277863465 #> 7341  0.277934456 #> 7342  0.278005530 #> 7343  0.278076689 #> 7344  0.278147931 #> 7345  0.278219256 #> 7346  0.278290663 #> 7347  0.278362153 #> 7348  0.278433725 #> 7349  0.278505378 #> 7350  0.278577112 #> 7351  0.278648927 #> 7352  0.278720822 #> 7353  0.278792797 #> 7354  0.278864851 #> 7355  0.278936985 #> 7356  0.279009197 #> 7357  0.279081488 #> 7358  0.279153857 #> 7359  0.279226303 #> 7360  0.279298826 #> 7361  0.279371426 #> 7362  0.279444103 #> 7363  0.279516855 #> 7364  0.279589683 #> 7365  0.279662586 #> 7366  0.279735563 #> 7367  0.279808615 #> 7368  0.279881741 #> 7369  0.279954941 #> 7370  0.280028213 #> 7371  0.280101558 #> 7372  0.280174976 #> 7373  0.280248465 #> 7374  0.280322026 #> 7375  0.280395658 #> 7376  0.280469361 #> 7377  0.280543134 #> 7378  0.280616977 #> 7379  0.280690889 #> 7380  0.280764870 #> 7381  0.280838920 #> 7382  0.280913038 #> 7383  0.280987223 #> 7384  0.281061477 #> 7385  0.281135797 #> 7386  0.281210183 #> 7387  0.281284636 #> 7388  0.281359155 #> 7389  0.281433738 #> 7390  0.281508387 #> 7391  0.281583100 #> 7392  0.281657877 #> 7393  0.281732718 #> 7394  0.281807622 #> 7395  0.281882588 #> 7396  0.281957617 #> 7397  0.282032708 #> 7398  0.282107860 #> 7399  0.282183074 #> 7400  0.282258348 #> 7401  0.282333682 #> 7402  0.282409076 #> 7403  0.282484530 #> 7404  0.282560042 #> 7405  0.282635613 #> 7406  0.282711242 #> 7407  0.282786929 #> 7408  0.282862673 #> 7409  0.282938474 #> 7410  0.283014331 #> 7411  0.283090244 #> 7412  0.283166213 #> 7413  0.283242236 #> 7414  0.283318315 #> 7415  0.283394447 #> 7416  0.283470634 #> 7417  0.283546873 #> 7418  0.283623166 #> 7419  0.283699511 #> 7420  0.283775908 #> 7421  0.283852357 #> 7422  0.283928857 #> 7423  0.284005408 #> 7424  0.284082009 #> 7425  0.284158660 #> 7426  0.284235360 #> 7427  0.284312109 #> 7428  0.284388907 #> 7429  0.284465753 #> 7430  0.284542647 #> 7431  0.284619588 #> 7432  0.284696576 #> 7433  0.284773610 #> 7434  0.284850690 #> 7435  0.284927815 #> 7436  0.285004986 #> 7437  0.285082201 #> 7438  0.285159460 #> 7439  0.285236763 #> 7440  0.285314109 #> 7441  0.285391498 #> 7442  0.285468930 #> 7443  0.285546403 #> 7444  0.285623918 #> 7445  0.285701474 #> 7446  0.285779070 #> 7447  0.285856707 #> 7448  0.285934383 #> 7449  0.286012099 #> 7450  0.286089853 #> 7451  0.286167646 #> 7452  0.286245477 #> 7453  0.286323345 #> 7454  0.286401251 #> 7455  0.286479192 #> 7456  0.286557170 #> 7457  0.286635184 #> 7458  0.286713233 #> 7459  0.286791316 #> 7460  0.286869434 #> 7461  0.286947586 #> 7462  0.287025771 #> 7463  0.287103990 #> 7464  0.287182241 #> 7465  0.287260523 #> 7466  0.287338838 #> 7467  0.287417184 #> 7468  0.287495560 #> 7469  0.287573967 #> 7470  0.287652403 #> 7471  0.287730869 #> 7472  0.287809364 #> 7473  0.287887887 #> 7474  0.287966439 #> 7475  0.288045017 #> 7476  0.288123623 #> 7477  0.288202256 #> 7478  0.288280915 #> 7479  0.288359599 #> 7480  0.288438309 #> 7481  0.288517044 #> 7482  0.288595803 #> 7483  0.288674586 #> 7484  0.288753392 #> 7485  0.288832222 #> 7486  0.288911074 #> 7487  0.288989948 #> 7488  0.289068844 #> 7489  0.289147761 #> 7490  0.289226699 #> 7491  0.289305657 #> 7492  0.289384634 #> 7493  0.289463632 #> 7494  0.289542648 #> 7495  0.289621683 #> 7496  0.289700735 #> 7497  0.289779805 #> 7498  0.289858893 #> 7499  0.289937997 #> 7500  0.290017117 #> 7501  0.290096253 #> 7502  0.290175404 #> 7503  0.290254570 #> 7504  0.290333750 #> 7505  0.290412944 #> 7506  0.290492152 #> 7507  0.290571373 #> 7508  0.290650606 #> 7509  0.290729851 #> 7510  0.290809108 #> 7511  0.290888376 #> 7512  0.290967655 #> 7513  0.291046944 #> 7514  0.291126243 #> 7515  0.291205551 #> 7516  0.291284868 #> 7517  0.291364194 #> 7518  0.291443527 #> 7519  0.291522868 #> 7520  0.291602216 #> 7521  0.291681571 #> 7522  0.291760932 #> 7523  0.291840298 #> 7524  0.291919670 #> 7525  0.291999046 #> 7526  0.292078427 #> 7527  0.292157812 #> 7528  0.292237200 #> 7529  0.292316591 #> 7530  0.292395985 #> 7531  0.292475380 #> 7532  0.292554778 #> 7533  0.292634176 #> 7534  0.292713575 #> 7535  0.292792974 #> 7536  0.292872373 #> 7537  0.292951772 #> 7538  0.293031169 #> 7539  0.293110564 #> 7540  0.293189958 #> 7541  0.293269349 #> 7542  0.293348737 #> 7543  0.293428122 #> 7544  0.293507503 #> 7545  0.293586879 #> 7546  0.293666251 #> 7547  0.293745618 #> 7548  0.293824979 #> 7549  0.293904334 #> 7550  0.293983682 #> 7551  0.294063023 #> 7552  0.294142357 #> 7553  0.294221682 #> 7554  0.294301000 #> 7555  0.294380308 #> 7556  0.294459608 #> 7557  0.294538897 #> 7558  0.294618177 #> 7559  0.294697445 #> 7560  0.294776703 #> 7561  0.294855949 #> 7562  0.294935183 #> 7563  0.295014405 #> 7564  0.295093614 #> 7565  0.295172809 #> 7566  0.295251991 #> 7567  0.295331159 #> 7568  0.295410311 #> 7569  0.295489449 #> 7570  0.295568571 #> 7571  0.295647677 #> 7572  0.295726767 #> 7573  0.295805840 #> 7574  0.295884895 #> 7575  0.295963933 #> 7576  0.296042952 #> 7577  0.296121953 #> 7578  0.296200935 #> 7579  0.296279897 #> 7580  0.296358839 #> 7581  0.296437760 #> 7582  0.296516661 #> 7583  0.296595540 #> 7584  0.296674397 #> 7585  0.296753233 #> 7586  0.296832045 #> 7587  0.296910835 #> 7588  0.296989601 #> 7589  0.297068343 #> 7590  0.297147060 #> 7591  0.297225753 #> 7592  0.297304420 #> 7593  0.297383062 #> 7594  0.297461677 #> 7595  0.297540266 #> 7596  0.297618828 #> 7597  0.297697362 #> 7598  0.297775869 #> 7599  0.297854347 #> 7600  0.297932796 #> 7601  0.298011216 #> 7602  0.298089606 #> 7603  0.298167966 #> 7604  0.298246296 #> 7605  0.298324595 #> 7606  0.298402862 #> 7607  0.298481098 #> 7608  0.298559301 #> 7609  0.298637471 #> 7610  0.298715609 #> 7611  0.298793713 #> 7612  0.298871783 #> 7613  0.298949818 #> 7614  0.299027819 #> 7615  0.299105784 #> 7616  0.299183714 #> 7617  0.299261607 #> 7618  0.299339464 #> 7619  0.299417284 #> 7620  0.299495067 #> 7621  0.299572812 #> 7622  0.299650519 #> 7623  0.299728187 #> 7624  0.299805815 #> 7625  0.299883405 #> 7626  0.299960954 #> 7627  0.300038463 #> 7628  0.300115932 #> 7629  0.300193359 #> 7630  0.300270744 #> 7631  0.300348088 #> 7632  0.300425389 #> 7633  0.300502647 #> 7634  0.300579862 #> 7635  0.300657033 #> 7636  0.300734161 #> 7637  0.300811243 #> 7638  0.300888281 #> 7639  0.300965273 #> 7640  0.301042220 #> 7641  0.301119120 #> 7642  0.301195974 #> 7643  0.301272781 #> 7644  0.301349540 #> 7645  0.301426252 #> 7646  0.301502915 #> 7647  0.301579530 #> 7648  0.301656096 #> 7649  0.301732612 #> 7650  0.301809079 #> 7651  0.301885495 #> 7652  0.301961861 #> 7653  0.302038175 #> 7654  0.302114438 #> 7655  0.302190649 #> 7656  0.302266808 #> 7657  0.302342915 #> 7658  0.302418968 #> 7659  0.302494968 #> 7660  0.302570913 #> 7661  0.302646805 #> 7662  0.302722642 #> 7663  0.302798424 #> 7664  0.302874150 #> 7665  0.302949821 #> 7666  0.303025435 #> 7667  0.303100993 #> 7668  0.303176494 #> 7669  0.303251937 #> 7670  0.303327323 #> 7671  0.303402650 #> 7672  0.303477919 #> 7673  0.303553128 #> 7674  0.303628279 #> 7675  0.303703369 #> 7676  0.303778400 #> 7677  0.303853370 #> 7678  0.303928279 #> 7679  0.304003127 #> 7680  0.304077913 #> 7681  0.304152637 #> 7682  0.304227299 #> 7683  0.304301897 #> 7684  0.304376433 #> 7685  0.304450905 #> 7686  0.304525313 #> 7687  0.304599657 #> 7688  0.304673936 #> 7689  0.304748150 #> 7690  0.304822298 #> 7691  0.304896381 #> 7692  0.304970398 #> 7693  0.305044347 #> 7694  0.305118230 #> 7695  0.305192046 #> 7696  0.305265794 #> 7697  0.305339474 #> 7698  0.305413085 #> 7699  0.305486628 #> 7700  0.305560102 #> 7701  0.305633506 #> 7702  0.305706840 #> 7703  0.305780104 #> 7704  0.305853297 #> 7705  0.305926419 #> 7706  0.305999470 #> 7707  0.306072449 #> 7708  0.306145357 #> 7709  0.306218191 #> 7710  0.306290953 #> 7711  0.306363642 #> 7712  0.306436258 #> 7713  0.306508799 #> 7714  0.306581267 #> 7715  0.306653660 #> 7716  0.306725978 #> 7717  0.306798221 #> 7718  0.306870388 #> 7719  0.306942479 #> 7720  0.307014494 #> 7721  0.307086432 #> 7722  0.307158294 #> 7723  0.307230078 #> 7724  0.307301784 #> 7725  0.307373412 #> 7726  0.307444962 #> 7727  0.307516433 #> 7728  0.307587826 #> 7729  0.307659139 #> 7730  0.307730372 #> 7731  0.307801525 #> 7732  0.307872598 #> 7733  0.307943590 #> 7734  0.308014501 #> 7735  0.308085330 #> 7736  0.308156078 #> 7737  0.308226744 #> 7738  0.308297327 #> 7739  0.308367828 #> 7740  0.308438246 #> 7741  0.308508580 #> 7742  0.308578830 #> 7743  0.308648997 #> 7744  0.308719079 #> 7745  0.308789077 #> 7746  0.308858989 #> 7747  0.308928817 #> 7748  0.308998558 #> 7749  0.309068214 #> 7750  0.309137784 #> 7751  0.309207266 #> 7752  0.309276662 #> 7753  0.309345971 #> 7754  0.309415192 #> 7755  0.309484326 #> 7756  0.309553371 #> 7757  0.309622327 #> 7758  0.309691195 #> 7759  0.309759974 #> 7760  0.309828664 #> 7761  0.309897263 #> 7762  0.309965773 #> 7763  0.310034193 #> 7764  0.310102521 #> 7765  0.310170759 #> 7766  0.310238906 #> 7767  0.310306961 #> 7768  0.310374925 #> 7769  0.310442796 #> 7770  0.310510575 #> 7771  0.310578261 #> 7772  0.310645854 #> 7773  0.310713353 #> 7774  0.310780760 #> 7775  0.310848072 #> 7776  0.310915290 #> 7777  0.310982414 #> 7778  0.311049442 #> 7779  0.311116376 #> 7780  0.311183215 #> 7781  0.311249957 #> 7782  0.311316604 #> 7783  0.311383155 #> 7784  0.311449609 #> 7785  0.311515967 #> 7786  0.311582227 #> 7787  0.311648391 #> 7788  0.311714456 #> 7789  0.311780424 #> 7790  0.311846293 #> 7791  0.311912065 #> 7792  0.311977737 #> 7793  0.312043310 #> 7794  0.312108785 #> 7795  0.312174159 #> 7796  0.312239434 #> 7797  0.312304609 #> 7798  0.312369684 #> 7799  0.312434658 #> 7800  0.312499531 #> 7801  0.312564303 #> 7802  0.312628974 #> 7803  0.312693543 #> 7804  0.312758010 #> 7805  0.312822375 #> 7806  0.312886637 #> 7807  0.312950797 #> 7808  0.313014854 #> 7809  0.313078808 #> 7810  0.313142658 #> 7811  0.313206405 #> 7812  0.313270047 #> 7813  0.313333586 #> 7814  0.313397020 #> 7815  0.313460349 #> 7816  0.313523574 #> 7817  0.313586693 #> 7818  0.313649706 #> 7819  0.313712615 #> 7820  0.313775417 #> 7821  0.313838113 #> 7822  0.313900702 #> 7823  0.313963185 #> 7824  0.314025561 #> 7825  0.314087830 #> 7826  0.314149992 #> 7827  0.314212046 #> 7828  0.314273992 #> 7829  0.314335830 #> 7830  0.314397560 #> 7831  0.314459182 #> 7832  0.314520694 #> 7833  0.314582098 #> 7834  0.314643392 #> 7835  0.314704577 #> 7836  0.314765652 #> 7837  0.314826618 #> 7838  0.314887473 #> 7839  0.314948218 #> 7840  0.315008853 #> 7841  0.315069376 #> 7842  0.315129789 #> 7843  0.315190090 #> 7844  0.315250281 #> 7845  0.315310359 #> 7846  0.315370326 #> 7847  0.315430180 #> 7848  0.315489922 #> 7849  0.315549552 #> 7850  0.315609069 #> 7851  0.315668474 #> 7852  0.315727765 #> 7853  0.315786942 #> 7854  0.315846007 #> 7855  0.315904957 #> 7856  0.315963794 #> 7857  0.316022516 #> 7858  0.316081125 #> 7859  0.316139618 #> 7860  0.316197997 #> 7861  0.316256261 #> 7862  0.316314410 #> 7863  0.316372444 #> 7864  0.316430362 #> 7865  0.316488164 #> 7866  0.316545851 #> 7867  0.316603421 #> 7868  0.316660876 #> 7869  0.316718213 #> 7870  0.316775435 #> 7871  0.316832539 #> 7872  0.316889526 #> 7873  0.316946396 #> 7874  0.317003149 #> 7875  0.317059784 #> 7876  0.317116302 #> 7877  0.317172701 #> 7878  0.317228982 #> 7879  0.317285146 #> 7880  0.317341190 #> 7881  0.317397116 #> 7882  0.317452924 #> 7883  0.317508612 #> 7884  0.317564181 #> 7885  0.317619631 #> 7886  0.317674962 #> 7887  0.317730173 #> 7888  0.317785264 #> 7889  0.317840235 #> 7890  0.317895086 #> 7891  0.317949816 #> 7892  0.318004426 #> 7893  0.318058916 #> 7894  0.318113285 #> 7895  0.318167532 #> 7896  0.318221659 #> 7897  0.318275665 #> 7898  0.318329549 #> 7899  0.318383311 #> 7900  0.318436952 #> 7901  0.318490471 #> 7902  0.318543868 #> 7903  0.318597143 #> 7904  0.318650295 #> 7905  0.318703325 #> 7906  0.318756232 #> 7907  0.318809017 #> 7908  0.318861679 #> 7909  0.318914217 #> 7910  0.318966633 #> 7911  0.319018925 #> 7912  0.319071094 #> 7913  0.319123139 #> 7914  0.319175060 #> 7915  0.319226857 #> 7916  0.319278531 #> 7917  0.319330080 #> 7918  0.319381505 #> 7919  0.319432806 #> 7920  0.319483982 #> 7921  0.319535034 #> 7922  0.319585960 #> 7923  0.319636762 #> 7924  0.319687439 #> 7925  0.319737990 #> 7926  0.319788416 #> 7927  0.319838717 #> 7928  0.319888892 #> 7929  0.319938942 #> 7930  0.319988865 #> 7931  0.320038663 #> 7932  0.320088335 #> 7933  0.320137881 #> 7934  0.320187300 #> 7935  0.320236593 #> 7936  0.320285760 #> 7937  0.320334800 #> 7938  0.320383713 #> 7939  0.320432499 #> 7940  0.320481159 #> 7941  0.320529691 #> 7942  0.320578097 #> 7943  0.320626375 #> 7944  0.320674526 #> 7945  0.320722549 #> 7946  0.320770445 #> 7947  0.320818213 #> 7948  0.320865853 #> 7949  0.320913365 #> 7950  0.320960750 #> 7951  0.321008006 #> 7952  0.321055135 #> 7953  0.321102135 #> 7954  0.321149006 #> 7955  0.321195750 #> 7956  0.321242364 #> 7957  0.321288850 #> 7958  0.321335208 #> 7959  0.321381436 #> 7960  0.321427536 #> 7961  0.321473507 #> 7962  0.321519348 #> 7963  0.321565061 #> 7964  0.321610644 #> 7965  0.321656098 #> 7966  0.321701423 #> 7967  0.321746618 #> 7968  0.321791684 #> 7969  0.321836620 #> 7970  0.321881426 #> 7971  0.321926102 #> 7972  0.321970649 #> 7973  0.322015066 #> 7974  0.322059352 #> 7975  0.322103509 #> 7976  0.322147535 #> 7977  0.322191432 #> 7978  0.322235198 #> 7979  0.322278833 #> 7980  0.322322338 #> 7981  0.322365713 #> 7982  0.322408957 #> 7983  0.322452070 #> 7984  0.322495053 #> 7985  0.322537905 #> 7986  0.322580627 #> 7987  0.322623217 #> 7988  0.322665676 #> 7989  0.322708005 #> 7990  0.322750202 #> 7991  0.322792269 #> 7992  0.322834204 #> 7993  0.322876008 #> 7994  0.322917680 #> 7995  0.322959222 #> 7996  0.323000632 #> 7997  0.323041911 #> 7998  0.323083058 #> 7999  0.323124073 #> 8000  0.323164957 #> 8001  0.323205710 #> 8002  0.323246331 #> 8003  0.323286820 #> 8004  0.323327177 #> 8005  0.323367403 #> 8006  0.323407497 #> 8007  0.323447458 #> 8008  0.323487288 #> 8009  0.323526986 #> 8010  0.323566553 #> 8011  0.323605987 #> 8012  0.323645289 #> 8013  0.323684459 #> 8014  0.323723496 #> 8015  0.323762402 #> 8016  0.323801175 #> 8017  0.323839817 #> 8018  0.323878326 #> 8019  0.323916702 #> 8020  0.323954947 #> 8021  0.323993059 #> 8022  0.324031039 #> 8023  0.324068886 #> 8024  0.324106601 #> 8025  0.324144183 #> 8026  0.324181634 #> 8027  0.324218951 #> 8028  0.324256136 #> 8029  0.324293189 #> 8030  0.324330109 #> 8031  0.324366896 #> 8032  0.324403551 #> 8033  0.324440073 #> 8034  0.324476463 #> 8035  0.324512720 #> 8036  0.324548845 #> 8037  0.324584837 #> 8038  0.324620696 #> 8039  0.324656422 #> 8040  0.324692016 #> 8041  0.324727477 #> 8042  0.324762806 #> 8043  0.324798001 #> 8044  0.324833064 #> 8045  0.324867995 #> 8046  0.324902792 #> 8047  0.324937457 #> 8048  0.324971989 #> 8049  0.325006389 #> 8050  0.325040656 #> 8051  0.325074790 #> 8052  0.325108791 #> 8053  0.325142659 #> 8054  0.325176395 #> 8055  0.325209998 #> 8056  0.325243469 #> 8057  0.325276807 #> 8058  0.325310011 #> 8059  0.325343084 #> 8060  0.325376023 #> 8061  0.325408830 #> 8062  0.325441505 #> 8063  0.325474046 #> 8064  0.325506455 #> 8065  0.325538731 #> 8066  0.325570875 #> 8067  0.325602886 #> 8068  0.325634765 #> 8069  0.325666511 #> 8070  0.325698124 #> 8071  0.325729605 #> 8072  0.325760953 #> 8073  0.325792169 #> 8074  0.325823252 #> 8075  0.325854203 #> 8076  0.325885021 #> 8077  0.325915707 #> 8078  0.325946260 #> 8079  0.325976681 #> 8080  0.326006970 #> 8081  0.326037126 #> 8082  0.326067150 #> 8083  0.326097042 #> 8084  0.326126802 #> 8085  0.326156429 #> 8086  0.326185924 #> 8087  0.326215287 #> 8088  0.326244518 #> 8089  0.326273616 #> 8090  0.326302583 #> 8091  0.326331418 #> 8092  0.326360120 #> 8093  0.326388691 #> 8094  0.326417129 #> 8095  0.326445436 #> 8096  0.326473611 #> 8097  0.326501654 #> 8098  0.326529565 #> 8099  0.326557345 #> 8100  0.326584992 #> 8101  0.326612508 #> 8102  0.326639893 #> 8103  0.326667146 #> 8104  0.326694267 #> 8105  0.326721257 #> 8106  0.326748115 #> 8107  0.326774842 #> 8108  0.326801437 #> 8109  0.326827902 #> 8110  0.326854234 #> 8111  0.326880436 #> 8112  0.326906507 #> 8113  0.326932446 #> 8114  0.326958254 #> 8115  0.326983932 #> 8116  0.327009478 #> 8117  0.327034893 #> 8118  0.327060178 #> 8119  0.327085331 #> 8120  0.327110354 #> 8121  0.327135246 #> 8122  0.327160008 #> 8123  0.327184639 #> 8124  0.327209139 #> 8125  0.327233509 #> 8126  0.327257749 #> 8127  0.327281858 #> 8128  0.327305836 #> 8129  0.327329685 #> 8130  0.327353403 #> 8131  0.327376992 #> 8132  0.327400450 #> 8133  0.327423778 #> 8134  0.327446976 #> 8135  0.327470044 #> 8136  0.327492983 #> 8137  0.327515792 #> 8138  0.327538471 #> 8139  0.327561021 #> 8140  0.327583441 #> 8141  0.327605731 #> 8142  0.327627892 #> 8143  0.327649924 #> 8144  0.327671827 #> 8145  0.327693600 #> 8146  0.327715244 #> 8147  0.327736759 #> 8148  0.327758146 #> 8149  0.327779403 #> 8150  0.327800531 #> 8151  0.327821531 #> 8152  0.327842402 #> 8153  0.327863145 #> 8154  0.327883759 #> 8155  0.327904244 #> 8156  0.327924601 #> 8157  0.327944830 #> 8158  0.327964931 #> 8159  0.327984903 #> 8160  0.328004748 #> 8161  0.328024464 #> 8162  0.328044053 #> 8163  0.328063514 #> 8164  0.328082847 #> 8165  0.328102053 #> 8166  0.328121131 #> 8167  0.328140081 #> 8168  0.328158904 #> 8169  0.328177600 #> 8170  0.328196169 #> 8171  0.328214610 #> 8172  0.328232925 #> 8173  0.328251112 #> 8174  0.328269173 #> 8175  0.328287107 #> 8176  0.328304914 #> 8177  0.328322595 #> 8178  0.328340149 #> 8179  0.328357577 #> 8180  0.328374878 #> 8181  0.328392053 #> 8182  0.328409102 #> 8183  0.328426025 #> 8184  0.328442823 #> 8185  0.328459494 #> 8186  0.328476039 #> 8187  0.328492459 #> 8188  0.328508754 #> 8189  0.328524923 #> 8190  0.328540966 #> 8191  0.328556885 #> 8192  0.328572678 #> 8193  0.328588346 #> 8194  0.328603889 #> 8195  0.328619307 #> 8196  0.328634601 #> 8197  0.328649770 #> 8198  0.328664814 #> 8199  0.328679734 #> 8200  0.328694529 #> 8201  0.328709201 #> 8202  0.328723748 #> 8203  0.328738171 #> 8204  0.328752470 #> 8205  0.328766645 #> 8206  0.328780697 #> 8207  0.328794625 #> 8208  0.328808430 #> 8209  0.328822111 #> 8210  0.328835669 #> 8211  0.328849103 #> 8212  0.328862415 #> 8213  0.328875603 #> 8214  0.328888669 #> 8215  0.328901612 #> 8216  0.328914433 #> 8217  0.328927131 #> 8218  0.328939706 #> 8219  0.328952159 #> 8220  0.328964490 #> 8221  0.328976699 #> 8222  0.328988786 #> 8223  0.329000751 #> 8224  0.329012595 #> 8225  0.329024316 #> 8226  0.329035917 #> 8227  0.329047396 #> 8228  0.329058753 #> 8229  0.329069990 #> 8230  0.329081105 #> 8231  0.329092100 #> 8232  0.329102974 #> 8233  0.329113727 #> 8234  0.329124359 #> 8235  0.329134872 #> 8236  0.329145263 #> 8237  0.329155535 #> 8238  0.329165687 #> 8239  0.329175718 #> 8240  0.329185630 #> 8241  0.329195422 #> 8242  0.329205094 #> 8243  0.329214647 #> 8244  0.329224081 #> 8245  0.329233396 #> 8246  0.329242591 #> 8247  0.329251667 #> 8248  0.329260625 #> 8249  0.329269464 #> 8250  0.329278184 #> 8251  0.329286786 #> 8252  0.329295270 #> 8253  0.329303635 #> 8254  0.329311882 #> 8255  0.329320011 #> 8256  0.329328023 #> 8257  0.329335916 #> 8258  0.329343693 #> 8259  0.329351351 #> 8260  0.329358893 #> 8261  0.329366317 #> 8262  0.329373624 #> 8263  0.329380815 #> 8264  0.329387888 #> 8265  0.329394845 #> 8266  0.329401685 #> 8267  0.329408409 #> 8268  0.329415017 #> 8269  0.329421508 #> 8270  0.329427884 #> 8271  0.329434144 #> 8272  0.329440288 #> 8273  0.329446316 #> 8274  0.329452229 #> 8275  0.329458027 #> 8276  0.329463709 #> 8277  0.329469277 #> 8278  0.329474729 #> 8279  0.329480067 #> 8280  0.329485290 #> 8281  0.329490399 #> 8282  0.329495393 #> 8283  0.329500273 #> 8284  0.329505039 #> 8285  0.329509691 #> 8286  0.329514230 #> 8287  0.329518654 #> 8288  0.329522965 #> 8289  0.329527163 #> 8290  0.329531247 #> 8291  0.329535219 #> 8292  0.329539077 #> 8293  0.329542822 #> 8294  0.329546455 #> 8295  0.329549976 #> 8296  0.329553384 #> 8297  0.329556679 #> 8298  0.329559863 #> 8299  0.329562934 #> 8300  0.329565894 #> 8301  0.329568742 #> 8302  0.329571479 #> 8303  0.329574104 #> 8304  0.329576618 #> 8305  0.329579021 #> 8306  0.329581313 #> 8307  0.329583494 #> 8308  0.329585564 #> 8309  0.329587524 #> 8310  0.329589373 #> 8311  0.329591113 #> 8312  0.329592742 #> 8313  0.329594261 #> 8314  0.329595670 #> 8315  0.329596970 #> 8316  0.329598161 #> 8317  0.329599242 #> 8318  0.329600213 #> 8319  0.329601076 #> 8320  0.329601830 #> 8321  0.329602475 #> 8322  0.329603012 #> 8323  0.329603440 #> 8324  0.329603759 #> 8325  0.329603971 #> 8326  0.329604075 #> 8327  0.329604070 #> 8328  0.329603959 #> 8329  0.329603739 #> 8330  0.329603412 #> 8331  0.329602978 #> 8332  0.329602437 #> 8333  0.329601789 #> 8334  0.329601034 #> 8335  0.329600172 #> 8336  0.329599204 #> 8337  0.329598130 #> 8338  0.329596950 #> 8339  0.329595663 #> 8340  0.329594271 #> 8341  0.329592773 #> 8342  0.329591169 #> 8343  0.329589460 #> 8344  0.329587646 #> 8345  0.329585726 #> 8346  0.329583702 #> 8347  0.329581573 #> 8348  0.329579339 #> 8349  0.329577001 #> 8350  0.329574558 #> 8351  0.329572012 #> 8352  0.329569361 #> 8353  0.329566607 #> 8354  0.329563748 #> 8355  0.329560787 #> 8356  0.329557722 #> 8357  0.329554553 #> 8358  0.329551282 #> 8359  0.329547908 #> 8360  0.329544431 #> 8361  0.329540851 #> 8362  0.329537169 #> 8363  0.329533385 #> 8364  0.329529498 #> 8365  0.329525510 #> 8366  0.329521420 #> 8367  0.329517228 #> 8368  0.329512935 #> 8369  0.329508540 #> 8370  0.329504045 #> 8371  0.329499448 #> 8372  0.329494750 #> 8373  0.329489952 #> 8374  0.329485054 #> 8375  0.329480054 #> 8376  0.329474955 #> 8377  0.329469756 #> 8378  0.329464457 #> 8379  0.329459058 #> 8380  0.329453560 #> 8381  0.329447962 #> 8382  0.329442265 #> 8383  0.329436469 #> 8384  0.329430574 #> 8385  0.329424580 #> 8386  0.329418488 #> 8387  0.329412297 #> 8388  0.329406008 #> 8389  0.329399621 #> 8390  0.329393136 #> 8391  0.329386554 #> 8392  0.329379873 #> 8393  0.329373096 #> 8394  0.329366221 #> 8395  0.329359249 #> 8396  0.329352180 #> 8397  0.329345014 #> 8398  0.329337752 #> 8399  0.329330393 #> 8400  0.329322938 #> 8401  0.329315386 #> 8402  0.329307739 #> 8403  0.329299996 #> 8404  0.329292158 #> 8405  0.329284223 #> 8406  0.329276194 #> 8407  0.329268070 #> 8408  0.329259850 #> 8409  0.329251536 #> 8410  0.329243127 #> 8411  0.329234624 #> 8412  0.329226026 #> 8413  0.329217334 #> 8414  0.329208548 #> 8415  0.329199669 #> 8416  0.329190696 #> 8417  0.329181629 #> 8418  0.329172469 #> 8419  0.329163216 #> 8420  0.329153870 #> 8421  0.329144431 #> 8422  0.329134899 #> 8423  0.329125275 #> 8424  0.329115559 #> 8425  0.329105750 #> 8426  0.329095850 #> 8427  0.329085858 #> 8428  0.329075774 #> 8429  0.329065599 #> 8430  0.329055332 #> 8431  0.329044974 #> 8432  0.329034526 #> 8433  0.329023986 #> 8434  0.329013356 #> 8435  0.329002635 #> 8436  0.328991824 #> 8437  0.328980923 #> 8438  0.328969932 #> 8439  0.328958852 #> 8440  0.328947681 #> 8441  0.328936421 #> 8442  0.328925072 #> 8443  0.328913634 #> 8444  0.328902107 #> 8445  0.328890491 #> 8446  0.328878786 #> 8447  0.328866993 #> 8448  0.328855112 #> 8449  0.328843143 #> 8450  0.328831085 #> 8451  0.328818940 #> 8452  0.328806708 #> 8453  0.328794388 #> 8454  0.328781980 #> 8455  0.328769486 #> 8456  0.328756905 #> 8457  0.328744237 #> 8458  0.328731482 #> 8459  0.328718641 #> 8460  0.328705714 #> 8461  0.328692700 #> 8462  0.328679601 #> 8463  0.328666416 #> 8464  0.328653145 #> 8465  0.328639789 #> 8466  0.328626348 #> 8467  0.328612822 #> 8468  0.328599211 #> 8469  0.328585515 #> 8470  0.328571735 #> 8471  0.328557870 #> 8472  0.328543921 #> 8473  0.328529888 #> 8474  0.328515772 #> 8475  0.328501571 #> 8476  0.328487287 #> 8477  0.328472920 #> 8478  0.328458469 #> 8479  0.328443936 #> 8480  0.328429319 #> 8481  0.328414620 #> 8482  0.328399839 #> 8483  0.328384975 #> 8484  0.328370029 #> 8485  0.328355001 #> 8486  0.328339891 #> 8487  0.328324700 #> 8488  0.328309427 #> 8489  0.328294073 #> 8490  0.328278638 #> 8491  0.328263121 #> 8492  0.328247524 #> 8493  0.328231847 #> 8494  0.328216088 #> 8495  0.328200250 #> 8496  0.328184332 #> 8497  0.328168333 #> 8498  0.328152255 #> 8499  0.328136097 #> 8500  0.328119860 #> 8501  0.328103543 #> 8502  0.328087148 #> 8503  0.328070673 #> 8504  0.328054120 #> 8505  0.328037488 #> 8506  0.328020778 #> 8507  0.328003990 #> 8508  0.327987123 #> 8509  0.327970179 #> 8510  0.327953157 #> 8511  0.327936057 #> 8512  0.327918880 #> 8513  0.327901626 #> 8514  0.327884295 #> 8515  0.327866887 #> 8516  0.327849402 #> 8517  0.327831841 #> 8518  0.327814203 #> 8519  0.327796490 #> 8520  0.327778700 #> 8521  0.327760834 #> 8522  0.327742893 #> 8523  0.327724876 #> 8524  0.327706784 #> 8525  0.327688617 #> 8526  0.327670375 #> 8527  0.327652057 #> 8528  0.327633666 #> 8529  0.327615200 #> 8530  0.327596659 #> 8531  0.327578045 #> 8532  0.327559356 #> 8533  0.327540594 #> 8534  0.327521758 #> 8535  0.327502848 #> 8536  0.327483866 #> 8537  0.327464810 #> 8538  0.327445681 #> 8539  0.327426480 #> 8540  0.327407206 #> 8541  0.327387859 #> 8542  0.327368440 #> 8543  0.327348950 #> 8544  0.327329387 #> 8545  0.327309752 #> 8546  0.327290046 #> 8547  0.327270269 #> 8548  0.327250420 #> 8549  0.327230500 #> 8550  0.327210510 #> 8551  0.327190448 #> 8552  0.327170317 #> 8553  0.327150114 #> 8554  0.327129842 #> 8555  0.327109499 #> 8556  0.327089087 #> 8557  0.327068605 #> 8558  0.327048053 #> 8559  0.327027432 #> 8560  0.327006742 #> 8561  0.326985983 #> 8562  0.326965154 #> 8563  0.326944258 #> 8564  0.326923292 #> 8565  0.326902259 #> 8566  0.326881157 #> 8567  0.326859987 #> 8568  0.326838750 #> 8569  0.326817444 #> 8570  0.326796071 #> 8571  0.326774631 #> 8572  0.326753124 #> 8573  0.326731550 #> 8574  0.326709909 #> 8575  0.326688201 #> 8576  0.326666427 #> 8577  0.326644586 #> 8578  0.326622679 #> 8579  0.326600707 #> 8580  0.326578668 #> 8581  0.326556564 #> 8582  0.326534394 #> 8583  0.326512159 #> 8584  0.326489859 #> 8585  0.326467494 #> 8586  0.326445065 #> 8587  0.326422570 #> 8588  0.326400011 #> 8589  0.326377388 #> 8590  0.326354701 #> 8591  0.326331949 #> 8592  0.326309134 #> 8593  0.326286256 #> 8594  0.326263314 #> 8595  0.326240308 #> 8596  0.326217240 #> 8597  0.326194108 #> 8598  0.326170914 #> 8599  0.326147657 #> 8600  0.326124338 #> 8601  0.326100956 #> 8602  0.326077512 #> 8603  0.326054007 #> 8604  0.326030439 #> 8605  0.326006810 #> 8606  0.325983120 #> 8607  0.325959368 #> 8608  0.325935555 #> 8609  0.325911681 #> 8610  0.325887746 #> 8611  0.325863751 #> 8612  0.325839696 #> 8613  0.325815580 #> 8614  0.325791404 #> 8615  0.325767168 #> 8616  0.325742872 #> 8617  0.325718516 #> 8618  0.325694102 #> 8619  0.325669627 #> 8620  0.325645094 #> 8621  0.325620502 #> 8622  0.325595851 #> 8623  0.325571141 #> 8624  0.325546373 #> 8625  0.325521547 #> 8626  0.325496663 #> 8627  0.325471720 #> 8628  0.325446720 #> 8629  0.325421662 #> 8630  0.325396547 #> 8631  0.325371375 #> 8632  0.325346145 #> 8633  0.325320858 #> 8634  0.325295515 #> 8635  0.325270115 #> 8636  0.325244658 #> 8637  0.325219145 #> 8638  0.325193576 #> 8639  0.325167951 #> 8640  0.325142271 #> 8641  0.325116534 #> 8642  0.325090742 #> 8643  0.325064895 #> 8644  0.325038993 #> 8645  0.325013035 #> 8646  0.324987023 #> 8647  0.324960956 #> 8648  0.324934835 #> 8649  0.324908659 #> 8650  0.324882429 #> 8651  0.324856145 #> 8652  0.324829808 #> 8653  0.324803416 #> 8654  0.324776971 #> 8655  0.324750473 #> 8656  0.324723921 #> 8657  0.324697317 #> 8658  0.324670659 #> 8659  0.324643949 #> 8660  0.324617187 #> 8661  0.324590372 #> 8662  0.324563504 #> 8663  0.324536585 #> 8664  0.324509614 #> 8665  0.324482591 #> 8666  0.324455516 #> 8667  0.324428390 #> 8668  0.324401213 #> 8669  0.324373985 #> 8670  0.324346706 #> 8671  0.324319376 #> 8672  0.324291995 #> 8673  0.324264564 #> 8674  0.324237083 #> 8675  0.324209551 #> 8676  0.324181970 #> 8677  0.324154339 #> 8678  0.324126658 #> 8679  0.324098928 #> 8680  0.324071148 #> 8681  0.324043319 #> 8682  0.324015441 #> 8683  0.323987515 #> 8684  0.323959539 #> 8685  0.323931516 #> 8686  0.323903443 #> 8687  0.323875323 #> 8688  0.323847155 #> 8689  0.323818938 #> 8690  0.323790674 #> 8691  0.323762363 #> 8692  0.323734004 #> 8693  0.323705598 #> 8694  0.323677144 #> 8695  0.323648644 #> 8696  0.323620097 #> 8697  0.323591503 #> 8698  0.323562863 #> 8699  0.323534177 #> 8700  0.323505444 #> 8701  0.323476666 #> 8702  0.323447842 #> 8703  0.323418972 #> 8704  0.323390056 #> 8705  0.323361095 #> 8706  0.323332089 #> 8707  0.323303038 #> 8708  0.323273943 #> 8709  0.323244802 #> 8710  0.323215617 #> 8711  0.323186387 #> 8712  0.323157113 #> 8713  0.323127795 #> 8714  0.323098434 #> 8715  0.323069028 #> 8716  0.323039579 #> 8717  0.323010086 #> 8718  0.322980550 #> 8719  0.322950971 #> 8720  0.322921349 #> 8721  0.322891684 #> 8722  0.322861976 #> 8723  0.322832226 #> 8724  0.322802433 #> 8725  0.322772599 #> 8726  0.322742722 #> 8727  0.322712803 #> 8728  0.322682843 #> 8729  0.322652841 #> 8730  0.322622797 #> 8731  0.322592712 #> 8732  0.322562586 #> 8733  0.322532419 #> 8734  0.322502212 #> 8735  0.322471963 #> 8736  0.322441674 #> 8737  0.322411345 #> 8738  0.322380975 #> 8739  0.322350566 #> 8740  0.322320116 #> 8741  0.322289627 #> 8742  0.322259098 #> 8743  0.322228530 #> 8744  0.322197923 #> 8745  0.322167276 #> 8746  0.322136590 #> 8747  0.322105866 #> 8748  0.322075103 #> 8749  0.322044301 #> 8750  0.322013461 #> 8751  0.321982583 #> 8752  0.321951666 #> 8753  0.321920712 #> 8754  0.321889720 #> 8755  0.321858690 #> 8756  0.321827623 #> 8757  0.321796519 #> 8758  0.321765377 #> 8759  0.321734199 #> 8760  0.321702983 #> 8761  0.321671731 #> 8762  0.321640442 #> 8763  0.321609117 #> 8764  0.321577756 #> 8765  0.321546359 #> 8766  0.321514925 #> 8767  0.321483456 #> 8768  0.321451951 #> 8769  0.321420411 #> 8770  0.321388835 #> 8771  0.321357224 #> 8772  0.321325578 #> 8773  0.321293897 #> 8774  0.321262182 #> 8775  0.321230431 #> 8776  0.321198647 #> 8777  0.321166828 #> 8778  0.321134974 #> 8779  0.321103087 #> 8780  0.321071166 #> 8781  0.321039211 #> 8782  0.321007223 #> 8783  0.320975201 #> 8784  0.320943146 #> 8785  0.320911058 #> 8786  0.320878937 #> 8787  0.320846783 #> 8788  0.320814597 #> 8789  0.320782377 #> 8790  0.320750126 #> 8791  0.320717842 #> 8792  0.320685526 #> 8793  0.320653179 #> 8794  0.320620799 #> 8795  0.320588388 #> 8796  0.320555945 #> 8797  0.320523471 #> 8798  0.320490966 #> 8799  0.320458429 #> 8800  0.320425862 #> 8801  0.320393264 #> 8802  0.320360635 #> 8803  0.320327976 #> 8804  0.320295286 #> 8805  0.320262567 #> 8806  0.320229817 #> 8807  0.320197037 #> 8808  0.320164228 #> 8809  0.320131389 #> 8810  0.320098520 #> 8811  0.320065623 #> 8812  0.320032696 #> 8813  0.319999740 #> 8814  0.319966755 #> 8815  0.319933741 #> 8816  0.319900699 #> 8817  0.319867628 #> 8818  0.319834529 #> 8819  0.319801402 #> 8820  0.319768247 #> 8821  0.319735063 #> 8822  0.319701853 #> 8823  0.319668614 #> 8824  0.319635348 #> 8825  0.319602055 #> 8826  0.319568735 #> 8827  0.319535387 #> 8828  0.319502013 #> 8829  0.319468612 #> 8830  0.319435184 #> 8831  0.319401730 #> 8832  0.319368249 #> 8833  0.319334743 #> 8834  0.319301210 #> 8835  0.319267652 #> 8836  0.319234067 #> 8837  0.319200457 #> 8838  0.319166822 #> 8839  0.319133161 #> 8840  0.319099475 #> 8841  0.319065764 #> 8842  0.319032028 #> 8843  0.318998268 #> 8844  0.318964482 #> 8845  0.318930673 #> 8846  0.318896839 #> 8847  0.318862980 #> 8848  0.318829098 #> 8849  0.318795191 #> 8850  0.318761261 #> 8851  0.318727308 #> 8852  0.318693330 #> 8853  0.318659330 #> 8854  0.318625306 #> 8855  0.318591259 #> 8856  0.318557189 #> 8857  0.318523096 #> 8858  0.318488980 #> 8859  0.318454842 #> 8860  0.318420682 #> 8861  0.318386499 #> 8862  0.318352294 #> 8863  0.318318068 #> 8864  0.318283819 #> 8865  0.318249549 #> 8866  0.318215257 #> 8867  0.318180943 #> 8868  0.318146608 #> 8869  0.318112253 #> 8870  0.318077876 #> 8871  0.318043478 #> 8872  0.318009059 #> 8873  0.317974620 #> 8874  0.317940160 #> 8875  0.317905680 #> 8876  0.317871180 #> 8877  0.317836659 #> 8878  0.317802119 #> 8879  0.317767559 #> 8880  0.317732979 #> 8881  0.317698380 #> 8882  0.317663761 #> 8883  0.317629123 #> 8884  0.317594466 #> 8885  0.317559790 #> 8886  0.317525095 #> 8887  0.317490381 #> 8888  0.317455649 #> 8889  0.317420898 #> 8890  0.317386129 #> 8891  0.317351341 #> 8892  0.317316536 #> 8893  0.317281712 #> 8894  0.317246871 #> 8895  0.317212012 #> 8896  0.317177136 #> 8897  0.317142242 #> 8898  0.317107331 #> 8899  0.317072403 #> 8900  0.317037458 #> 8901  0.317002496 #> 8902  0.316967517 #> 8903  0.316932522 #> 8904  0.316897510 #> 8905  0.316862482 #> 8906  0.316827437 #> 8907  0.316792377 #> 8908  0.316757301 #> 8909  0.316722208 #> 8910  0.316687101 #> 8911  0.316651977 #> 8912  0.316616838 #> 8913  0.316581684 #> 8914  0.316546515 #> 8915  0.316511330 #> 8916  0.316476131 #> 8917  0.316440917 #> 8918  0.316405688 #> 8919  0.316370445 #> 8920  0.316335188 #> 8921  0.316299916 #> 8922  0.316264630 #> 8923  0.316229330 #> 8924  0.316194016 #> 8925  0.316158689 #> 8926  0.316123348 #> 8927  0.316087993 #> 8928  0.316052625 #> 8929  0.316017244 #> 8930  0.315981850 #> 8931  0.315946443 #> 8932  0.315911023 #> 8933  0.315875590 #> 8934  0.315840145 #> 8935  0.315804687 #> 8936  0.315769217 #> 8937  0.315733735 #> 8938  0.315698240 #> 8939  0.315662734 #> 8940  0.315627216 #> 8941  0.315591686 #> 8942  0.315556145 #> 8943  0.315520592 #> 8944  0.315485028 #> 8945  0.315449453 #> 8946  0.315413867 #> 8947  0.315378270 #> 8948  0.315342662 #> 8949  0.315307043 #> 8950  0.315271414 #> 8951  0.315235774 #> 8952  0.315200124 #> 8953  0.315164464 #> 8954  0.315128794 #> 8955  0.315093114 #> 8956  0.315057424 #> 8957  0.315021725 #> 8958  0.314986016 #> 8959  0.314950297 #> 8960  0.314914570 #> 8961  0.314878833 #> 8962  0.314843087 #> 8963  0.314807332 #> 8964  0.314771568 #> 8965  0.314735796 #> 8966  0.314700015 #> 8967  0.314664225 #> 8968  0.314628428 #> 8969  0.314592622 #> 8970  0.314556808 #> 8971  0.314520986 #> 8972  0.314485156 #> 8973  0.314449319 #> 8974  0.314413474 #> 8975  0.314377622 #> 8976  0.314341762 #> 8977  0.314305895 #> 8978  0.314270021 #> 8979  0.314234140 #> 8980  0.314198253 #> 8981  0.314162358 #> 8982  0.314126457 #> 8983  0.314090550 #> 8984  0.314054636 #> 8985  0.314018716 #> 8986  0.313982789 #> 8987  0.313946857 #> 8988  0.313910919 #> 8989  0.313874975 #> 8990  0.313839026 #> 8991  0.313803071 #> 8992  0.313767111 #> 8993  0.313731145 #> 8994  0.313695175 #> 8995  0.313659199 #> 8996  0.313623218 #> 8997  0.313587233 #> 8998  0.313551243 #> 8999  0.313515248 #> 9000  0.313479249 #> 9001  0.313443246 #> 9002  0.313407238 #> 9003  0.313371227 #> 9004  0.313335211 #> 9005  0.313299192 #> 9006  0.313263168 #> 9007  0.313227142 #> 9008  0.313191112 #> 9009  0.313155078 #> 9010  0.313119041 #> 9011  0.313083001 #> 9012  0.313046959 #> 9013  0.313010913 #> 9014  0.312974864 #> 9015  0.312938813 #> 9016  0.312902759 #> 9017  0.312866703 #> 9018  0.312830645 #> 9019  0.312794584 #> 9020  0.312758521 #> 9021  0.312722456 #> 9022  0.312686390 #> 9023  0.312650322 #> 9024  0.312614252 #> 9025  0.312578181 #> 9026  0.312542108 #> 9027  0.312506034 #> 9028  0.312469959 #> 9029  0.312433883 #> 9030  0.312397806 #> 9031  0.312361728 #> 9032  0.312325649 #> 9033  0.312289570 #> 9034  0.312253491 #> 9035  0.312217411 #> 9036  0.312181331 #> 9037  0.312145250 #> 9038  0.312109170 #> 9039  0.312073090 #> 9040  0.312037010 #> 9041  0.312000931 #> 9042  0.311964852 #> 9043  0.311928773 #> 9044  0.311892696 #> 9045  0.311856619 #> 9046  0.311820543 #> 9047  0.311784468 #> 9048  0.311748394 #> 9049  0.311712321 #> 9050  0.311676250 #> 9051  0.311640180 #> 9052  0.311604112 #> 9053  0.311568045 #> 9054  0.311531981 #> 9055  0.311495918 #> 9056  0.311459857 #> 9057  0.311423799 #> 9058  0.311387743 #> 9059  0.311351689 #> 9060  0.311315637 #> 9061  0.311279589 #> 9062  0.311243542 #> 9063  0.311207499 #> 9064  0.311171459 #> 9065  0.311135422 #> 9066  0.311099387 #> 9067  0.311063357 #> 9068  0.311027329 #> 9069  0.310991305 #> 9070  0.310955285 #> 9071  0.310919268 #> 9072  0.310883255 #> 9073  0.310847246 #> 9074  0.310811241 #> 9075  0.310775240 #> 9076  0.310739244 #> 9077  0.310703252 #> 9078  0.310667264 #> 9079  0.310631281 #> 9080  0.310595302 #> 9081  0.310559329 #> 9082  0.310523360 #> 9083  0.310487396 #> 9084  0.310451438 #> 9085  0.310415484 #> 9086  0.310379536 #> 9087  0.310343594 #> 9088  0.310307657 #> 9089  0.310271726 #> 9090  0.310235800 #> 9091  0.310199880 #> 9092  0.310163967 #> 9093  0.310128059 #> 9094  0.310092158 #> 9095  0.310056263 #> 9096  0.310020374 #> 9097  0.309984492 #> 9098  0.309948616 #> 9099  0.309912747 #> 9100  0.309876886 #> 9101  0.309841031 #> 9102  0.309805183 #> 9103  0.309769342 #> 9104  0.309733508 #> 9105  0.309697682 #> 9106  0.309661864 #> 9107  0.309626052 #> 9108  0.309590249 #> 9109  0.309554453 #> 9110  0.309518666 #> 9111  0.309482886 #> 9112  0.309447115 #> 9113  0.309411351 #> 9114  0.309375596 #> 9115  0.309339850 #> 9116  0.309304112 #> 9117  0.309268382 #> 9118  0.309232661 #> 9119  0.309196949 #> 9120  0.309161247 #> 9121  0.309125553 #> 9122  0.309089868 #> 9123  0.309054193 #> 9124  0.309018526 #> 9125  0.308982870 #> 9126  0.308947223 #> 9127  0.308911585 #> 9128  0.308875958 #> 9129  0.308840340 #> 9130  0.308804732 #> 9131  0.308769134 #> 9132  0.308733547 #> 9133  0.308697970 #> 9134  0.308662403 #> 9135  0.308626847 #> 9136  0.308591301 #> 9137  0.308555766 #> 9138  0.308520241 #> 9139  0.308484728 #> 9140  0.308449226 #> 9141  0.308413734 #> 9142  0.308378254 #> 9143  0.308342786 #> 9144  0.308307328 #> 9145  0.308271882 #> 9146  0.308236448 #> 9147  0.308201026 #> 9148  0.308165615 #> 9149  0.308130216 #> 9150  0.308094829 #> 9151  0.308059455 #> 9152  0.308024092 #> 9153  0.307988742 #> 9154  0.307953404 #> 9155  0.307918079 #> 9156  0.307882766 #> 9157  0.307847466 #> 9158  0.307812179 #> 9159  0.307776905 #> 9160  0.307741644 #> 9161  0.307706396 #> 9162  0.307671161 #> 9163  0.307635939 #> 9164  0.307600731 #> 9165  0.307565536 #> 9166  0.307530355 #> 9167  0.307495188 #> 9168  0.307460034 #> 9169  0.307424894 #> 9170  0.307389768 #> 9171  0.307354657 #> 9172  0.307319559 #> 9173  0.307284476 #> 9174  0.307249407 #> 9175  0.307214353 #> 9176  0.307179313 #> 9177  0.307144288 #> 9178  0.307109278 #> 9179  0.307074282 #> 9180  0.307039302 #> 9181  0.307004336 #> 9182  0.306969386 #> 9183  0.306934451 #> 9184  0.306899531 #> 9185  0.306864627 #> 9186  0.306829739 #> 9187  0.306794865 #> 9188  0.306760008 #> 9189  0.306725167 #> 9190  0.306690341 #> 9191  0.306655532 #> 9192  0.306620738 #> 9193  0.306585961 #> 9194  0.306551200 #> 9195  0.306516456 #> 9196  0.306481727 #> 9197  0.306447016 #> 9198  0.306412321 #> 9199  0.306377643 #> 9200  0.306342982 #> 9201  0.306308338 #> 9202  0.306273711 #> 9203  0.306239101 #> 9204  0.306204509 #> 9205  0.306169933 #> 9206  0.306135375 #> 9207  0.306100835 #> 9208  0.306066312 #> 9209  0.306031807 #> 9210  0.305997320 #> 9211  0.305962851 #> 9212  0.305928400 #> 9213  0.305893967 #> 9214  0.305859552 #> 9215  0.305825155 #> 9216  0.305790776 #> 9217  0.305756417 #> 9218  0.305722075 #> 9219  0.305687753 #> 9220  0.305653449 #> 9221  0.305619163 #> 9222  0.305584897 #> 9223  0.305550650 #> 9224  0.305516422 #> 9225  0.305482213 #> 9226  0.305448024 #> 9227  0.305413853 #> 9228  0.305379703 #> 9229  0.305345571 #> 9230  0.305311460 #> 9231  0.305277368 #> 9232  0.305243296 #> 9233  0.305209244 #> 9234  0.305175212 #> 9235  0.305141200 #> 9236  0.305107209 #> 9237  0.305073237 #> 9238  0.305039286 #> 9239  0.305005356 #> 9240  0.304971445 #> 9241  0.304937556 #> 9242  0.304903687 #> 9243  0.304869840 #> 9244  0.304836013 #> 9245  0.304802207 #> 9246  0.304768422 #> 9247  0.304734658 #> 9248  0.304700916 #> 9249  0.304667195 #> 9250  0.304633496 #> 9251  0.304599818 #> 9252  0.304566161 #> 9253  0.304532527 #> 9254  0.304498914 #> 9255  0.304465323 #> 9256  0.304431754 #> 9257  0.304398207 #> 9258  0.304364682 #> 9259  0.304331179 #> 9260  0.304297699 #> 9261  0.304264241 #> 9262  0.304230806 #> 9263  0.304197393 #> 9264  0.304164003 #> 9265  0.304130636 #> 9266  0.304097292 #> 9267  0.304063970 #> 9268  0.304030672 #> 9269  0.303997397 #> 9270  0.303964144 #> 9271  0.303930916 #> 9272  0.303897710 #> 9273  0.303864528 #> 9274  0.303831370 #> 9275  0.303798235 #> 9276  0.303765124 #> 9277  0.303732036 #> 9278  0.303698973 #> 9279  0.303665933 #> 9280  0.303632918 #> 9281  0.303599927 #> 9282  0.303566959 #> 9283  0.303534017 #> 9284  0.303501098 #> 9285  0.303468204 #> 9286  0.303435335 #> 9287  0.303402490 #> 9288  0.303369671 #> 9289  0.303336875 #> 9290  0.303304105 #> 9291  0.303271360 #> 9292  0.303238640 #> 9293  0.303205945 #> 9294  0.303173275 #> 9295  0.303140631 #> 9296  0.303108011 #> 9297  0.303075418 #> 9298  0.303042850 #> 9299  0.303010308 #> 9300  0.302977791 #> 9301  0.302945300 #> 9302  0.302912835 #> 9303  0.302880396 #> 9304  0.302847984 #> 9305  0.302815597 #> 9306  0.302783236 #> 9307  0.302750902 #> 9308  0.302718595 #> 9309  0.302686313 #> 9310  0.302654059 #> 9311  0.302621831 #> 9312  0.302589629 #> 9313  0.302557455 #> 9314  0.302525307 #> 9315  0.302493186 #> 9316  0.302461093 #> 9317  0.302429026 #> 9318  0.302396987 #> 9319  0.302364975 #> 9320  0.302332990 #> 9321  0.302301033 #> 9322  0.302269104 #> 9323  0.302237202 #> 9324  0.302205327 #> 9325  0.302173481 #> 9326  0.302141662 #> 9327  0.302109871 #> 9328  0.302078109 #> 9329  0.302046374 #> 9330  0.302014668 #> 9331  0.301982989 #> 9332  0.301951340 #> 9333  0.301919718 #> 9334  0.301888125 #> 9335  0.301856561 #> 9336  0.301825025 #> 9337  0.301793518 #> 9338  0.301762040 #> 9339  0.301730590 #> 9340  0.301699170 #> 9341  0.301667779 #> 9342  0.301636417 #> 9343  0.301605084 #> 9344  0.301573780 #> 9345  0.301542505 #> 9346  0.301511261 #> 9347  0.301480045 #> 9348  0.301448859 #> 9349  0.301417703 #> 9350  0.301386577 #> 9351  0.301355480 #> 9352  0.301324413 #> 9353  0.301293377 #> 9354  0.301262370 #> 9355  0.301231394 #> 9356  0.301200447 #> 9357  0.301169531 #> 9358  0.301138645 #> 9359  0.301107790 #> 9360  0.301076965 #> 9361  0.301046171 #> 9362  0.301015408 #> 9363  0.300984675 #> 9364  0.300953973 #> 9365  0.300923302 #> 9366  0.300892662 #> 9367  0.300862053 #> 9368  0.300831475 #> 9369  0.300800928 #> 9370  0.300770413 #> 9371  0.300739929 #> 9372  0.300709476 #> 9373  0.300679055 #> 9374  0.300648665 #> 9375  0.300618307 #> 9376  0.300587981 #> 9377  0.300557687 #> 9378  0.300527424 #> 9379  0.300497193 #> 9380  0.300466995 #> 9381  0.300436828 #> 9382  0.300406694 #> 9383  0.300376592 #> 9384  0.300346522 #> 9385  0.300316484 #> 9386  0.300286479 #> 9387  0.300256507 #> 9388  0.300226567 #> 9389  0.300196660 #> 9390  0.300166785 #> 9391  0.300136944 #> 9392  0.300107135 #> 9393  0.300077359 #> 9394  0.300047616 #> 9395  0.300017907 #> 9396  0.299988230 #> 9397  0.299958587 #> 9398  0.299928977 #> 9399  0.299899401 #> 9400  0.299869858 #> 9401  0.299840348 #> 9402  0.299810873 #> 9403  0.299781430 #> 9404  0.299752022 #> 9405  0.299722647 #> 9406  0.299693307 #> 9407  0.299664000 #> 9408  0.299634728 #> 9409  0.299605489 #> 9410  0.299576285 #> 9411  0.299547115 #> 9412  0.299517979 #> 9413  0.299488877 #> 9414  0.299459811 #> 9415  0.299430778 #> 9416  0.299401780 #> 9417  0.299372817 #> 9418  0.299343889 #> 9419  0.299314996 #> 9420  0.299286137 #> 9421  0.299257313 #> 9422  0.299228525 #> 9423  0.299199771 #> 9424  0.299171053 #> 9425  0.299142369 #> 9426  0.299113722 #> 9427  0.299085109 #> 9428  0.299056532 #> 9429  0.299027990 #> 9430  0.298999484 #> 9431  0.298971014 #> 9432  0.298942579 #> 9433  0.298914181 #> 9434  0.298885818 #> 9435  0.298857490 #> 9436  0.298829199 #> 9437  0.298800944 #> 9438  0.298772725 #> 9439  0.298744543 #> 9440  0.298716396 #> 9441  0.298688286 #> 9442  0.298660212 #> 9443  0.298632175 #> 9444  0.298604174 #> 9445  0.298576210 #> 9446  0.298548282 #> 9447  0.298520391 #> 9448  0.298492537 #> 9449  0.298464720 #> 9450  0.298436940 #> 9451  0.298409196 #> 9452  0.298381490 #> 9453  0.298353821 #> 9454  0.298326189 #> 9455  0.298298594 #> 9456  0.298271037 #> 9457  0.298243517 #> 9458  0.298216034 #> 9459  0.298188589 #> 9460  0.298161182 #> 9461  0.298133812 #> 9462  0.298106480 #> 9463  0.298079185 #> 9464  0.298051929 #> 9465  0.298024710 #> 9466  0.297997529 #> 9467  0.297970387 #> 9468  0.297943282 #> 9469  0.297916216 #> 9470  0.297889187 #> 9471  0.297862197 #> 9472  0.297835246 #> 9473  0.297808333 #> 9474  0.297781458 #> 9475  0.297754622 #> 9476  0.297727824 #> 9477  0.297701065 #> 9478  0.297674345 #> 9479  0.297647664 #> 9480  0.297621021 #> 9481  0.297594418 #> 9482  0.297567853 #> 9483  0.297541327 #> 9484  0.297514841 #> 9485  0.297488393 #> 9486  0.297461985 #> 9487  0.297435616 #> 9488  0.297409287 #> 9489  0.297382997 #> 9490  0.297356746 #> 9491  0.297330535 #> 9492  0.297304364 #> 9493  0.297278232 #> 9494  0.297252140 #> 9495  0.297226087 #> 9496  0.297200075 #> 9497  0.297174102 #> 9498  0.297148170 #> 9499  0.297122277 #> 9500  0.297096424 #> 9501  0.297070612 #> 9502  0.297044840 #> 9503  0.297019108 #> 9504  0.296993416 #> 9505  0.296967765 #> 9506  0.296942154 #> 9507  0.296916584 #> 9508  0.296891054 #> 9509  0.296865565 #> 9510  0.296840117 #> 9511  0.296814709 #> 9512  0.296789342 #> 9513  0.296764016 #> 9514  0.296738731 #> 9515  0.296713487 #> 9516  0.296688284 #> 9517  0.296663121 #> 9518  0.296638001 #> 9519  0.296612921 #> 9520  0.296587882 #> 9521  0.296562885 #> 9522  0.296537930 #> 9523  0.296513015 #> 9524  0.296488142 #> 9525  0.296463311 #> 9526  0.296438522 #> 9527  0.296413774 #> 9528  0.296389067 #> 9529  0.296364403 #> 9530  0.296339780 #> 9531  0.296315200 #> 9532  0.296290661 #> 9533  0.296266164 #> 9534  0.296241709 #> 9535  0.296217297 #> 9536  0.296192926 #> 9537  0.296168598 #> 9538  0.296144312 #> 9539  0.296120069 #> 9540  0.296095867 #> 9541  0.296071709 #> 9542  0.296047592 #> 9543  0.296023519 #> 9544  0.295999488 #> 9545  0.295975499 #> 9546  0.295951554 #> 9547  0.295927651 #> 9548  0.295903791 #> 9549  0.295879974 #> 9550  0.295856200 #> 9551  0.295832469 #> 9552  0.295808781 #> 9553  0.295785136 #> 9554  0.295761534 #> 9555  0.295737975 #> 9556  0.295714460 #> 9557  0.295690988 #> 9558  0.295667559 #> 9559  0.295644174 #> 9560  0.295620832 #> 9561  0.295597534 #> 9562  0.295574280 #> 9563  0.295551069 #> 9564  0.295527901 #> 9565  0.295504778 #> 9566  0.295481698 #> 9567  0.295458663 #> 9568  0.295435671 #> 9569  0.295412723 #> 9570  0.295389819 #> 9571  0.295366959 #> 9572  0.295344143 #> 9573  0.295321372 #> 9574  0.295298644 #> 9575  0.295275961 #> 9576  0.295253322 #> 9577  0.295230728 #> 9578  0.295208178 #> 9579  0.295185672 #> 9580  0.295163211 #> 9581  0.295140795 #> 9582  0.295118423 #> 9583  0.295096096 #> 9584  0.295073814 #> 9585  0.295051576 #> 9586  0.295029383 #> 9587  0.295007235 #> 9588  0.294985132 #> 9589  0.294963074 #> 9590  0.294941061 #> 9591  0.294919093 #> 9592  0.294897171 #> 9593  0.294875293 #> 9594  0.294853461 #> 9595  0.294831673 #> 9596  0.294809932 #> 9597  0.294788235 #> 9598  0.294766584 #> 9599  0.294744979 #> 9600  0.294723419 #> 9601  0.294701904 #> 9602  0.294680435 #> 9603  0.294659012 #> 9604  0.294637634 #> 9605  0.294616303 #> 9606  0.294595017 #> 9607  0.294573777 #> 9608  0.294552582 #> 9609  0.294531434 #> 9610  0.294510332 #> 9611  0.294489276 #> 9612  0.294468265 #> 9613  0.294447301 #> 9614  0.294426383 #> 9615  0.294405512 #> 9616  0.294384686 #> 9617  0.294363907 #> 9618  0.294343175 #> 9619  0.294322488 #> 9620  0.294301848 #> 9621  0.294281255 #> 9622  0.294260708 #> 9623  0.294240208 #> 9624  0.294219754 #> 9625  0.294199347 #> 9626  0.294178987 #> 9627  0.294158674 #> 9628  0.294138407 #> 9629  0.294118187 #> 9630  0.294098014 #> 9631  0.294077888 #> 9632  0.294057809 #> 9633  0.294037777 #> 9634  0.294017793 #> 9635  0.293997855 #> 9636  0.293977964 #> 9637  0.293958121 #> 9638  0.293938325 #> 9639  0.293918576 #> 9640  0.293898875 #> 9641  0.293879220 #> 9642  0.293859614 #> 9643  0.293840054 #> 9644  0.293820543 #> 9645  0.293801079 #> 9646  0.293781662 #> 9647  0.293762293 #> 9648  0.293742972 #> 9649  0.293723698 #> 9650  0.293704472 #> 9651  0.293685294 #> 9652  0.293666164 #> 9653  0.293647082 #> 9654  0.293628048 #> 9655  0.293609061 #> 9656  0.293590123 #> 9657  0.293571233 #> 9658  0.293552390 #> 9659  0.293533596 #> 9660  0.293514850 #> 9661  0.293496152 #> 9662  0.293477503 #> 9663  0.293458902 #> 9664  0.293440349 #> 9665  0.293421844 #> 9666  0.293403388 #> 9667  0.293384981 #> 9668  0.293366621 #> 9669  0.293348311 #> 9670  0.293330049 #> 9671  0.293311835 #> 9672  0.293293670 #> 9673  0.293275554 #> 9674  0.293257487 #> 9675  0.293239468 #> 9676  0.293221498 #> 9677  0.293203577 #> 9678  0.293185705 #> 9679  0.293167882 #> 9680  0.293150107 #> 9681  0.293132382 #> 9682  0.293114706 #> 9683  0.293097078 #> 9684  0.293079500 #> 9685  0.293061971 #> 9686  0.293044491 #> 9687  0.293027061 #> 9688  0.293009679 #> 9689  0.292992347 #> 9690  0.292975064 #> 9691  0.292957831 #> 9692  0.292940647 #> 9693  0.292923512 #> 9694  0.292906427 #> 9695  0.292889391 #> 9696  0.292872405 #> 9697  0.292855468 #> 9698  0.292838581 #> 9699  0.292821744 #> 9700  0.292804956 #> 9701  0.292788218 #> 9702  0.292771530 #> 9703  0.292754891 #> 9704  0.292738302 #> 9705  0.292721764 #> 9706  0.292705275 #> 9707  0.292688836 #> 9708  0.292672446 #> 9709  0.292656107 #> 9710  0.292639818 #> 9711  0.292623579 #> 9712  0.292607390 #> 9713  0.292591251 #> 9714  0.292575162 #> 9715  0.292559124 #> 9716  0.292543135 #> 9717  0.292527197 #> 9718  0.292511309 #> 9719  0.292495472 #> 9720  0.292479684 #> 9721  0.292463948 #> 9722  0.292448261 #> 9723  0.292432625 #> 9724  0.292417039 #> 9725  0.292401504 #> 9726  0.292386020 #> 9727  0.292370586 #> 9728  0.292355202 #> 9729  0.292339870 #> 9730  0.292324587 #> 9731  0.292309356 #> 9732  0.292294175 #> 9733  0.292279045 #> 9734  0.292263966 #> 9735  0.292248937 #> 9736  0.292233960 #> 9737  0.292219033 #> 9738  0.292204157 #> 9739  0.292189332 #> 9740  0.292174558 #> 9741  0.292159835 #> 9742  0.292145163 #> 9743  0.292130542 #> 9744  0.292115972 #> 9745  0.292101453 #> 9746  0.292086985 #> 9747  0.292072569 #> 9748  0.292058203 #> 9749  0.292043889 #> 9750  0.292029626 #> 9751  0.292015414 #> 9752  0.292001254 #> 9753  0.291987145 #> 9754  0.291973087 #> 9755  0.291959080 #> 9756  0.291945125 #> 9757  0.291931221 #> 9758  0.291917369 #> 9759  0.291903568 #> 9760  0.291889819 #> 9761  0.291876121 #> 9762  0.291862475 #> 9763  0.291848881 #> 9764  0.291835338 #> 9765  0.291821846 #> 9766  0.291808406 #> 9767  0.291795018 #> 9768  0.291781682 #> 9769  0.291768397 #> 9770  0.291755164 #> 9771  0.291741983 #> 9772  0.291728854 #> 9773  0.291715776 #> 9774  0.291702751 #> 9775  0.291689777 #> 9776  0.291676855 #> 9777  0.291663985 #> 9778  0.291651167 #> 9779  0.291638401 #> 9780  0.291625686 #> 9781  0.291613024 #> 9782  0.291600414 #> 9783  0.291587856 #> 9784  0.291575350 #> 9785  0.291562896 #> 9786  0.291550494 #> 9787  0.291538145 #> 9788  0.291525847 #> 9789  0.291513602 #> 9790  0.291501409 #> 9791  0.291489268 #> 9792  0.291477180 #> 9793  0.291465143 #> 9794  0.291453159 #> 9795  0.291441227 #> 9796  0.291429348 #> 9797  0.291417521 #> 9798  0.291405746 #> 9799  0.291394024 #> 9800  0.291382354 #> 9801  0.291370737 #> 9802  0.291359172 #> 9803  0.291347659 #> 9804  0.291336199 #> 9805  0.291324792 #> 9806  0.291313437 #> 9807  0.291302134 #> 9808  0.291290884 #> 9809  0.291279687 #> 9810  0.291268542 #> 9811  0.291257450 #> 9812  0.291246411 #> 9813  0.291235424 #> 9814  0.291224490 #> 9815  0.291213608 #> 9816  0.291202780 #> 9817  0.291192004 #> 9818  0.291181280 #> 9819  0.291170610 #> 9820  0.291159992 #> 9821  0.291149427 #> 9822  0.291138915 #> 9823  0.291128456 #> 9824  0.291118049 #> 9825  0.291107696 #> 9826  0.291097395 #> 9827  0.291087147 #> 9828  0.291076952 #> 9829  0.291066810 #> 9830  0.291056721 #> 9831  0.291046685 #> 9832  0.291036701 #> 9833  0.291026771 #> 9834  0.291016894 #> 9835  0.291007070 #> 9836  0.290997299 #> 9837  0.290987580 #> 9838  0.290977915 #> 9839  0.290968303 #> 9840  0.290958744 #> 9841  0.290949238 #> 9842  0.290939786 #> 9843  0.290930386 #> 9844  0.290921039 #> 9845  0.290911746 #> 9846  0.290902506 #> 9847  0.290893319 #> 9848  0.290884185 #> 9849  0.290875104 #> 9850  0.290866076 #> 9851  0.290857102 #> 9852  0.290848181 #> 9853  0.290839313 #> 9854  0.290830498 #> 9855  0.290821737 #> 9856  0.290813029 #> 9857  0.290804374 #> 9858  0.290795773 #> 9859  0.290787224 #> 9860  0.290778730 #> 9861  0.290770288 #> 9862  0.290761900 #> 9863  0.290753565 #> 9864  0.290745283 #> 9865  0.290737055 #> 9866  0.290728880 #> 9867  0.290720758 #> 9868  0.290712690 #> 9869  0.290704676 #> 9870  0.290696714 #> 9871  0.290688806 #> 9872  0.290680952 #> 9873  0.290673151 #> 9874  0.290665403 #> 9875  0.290657709 #> 9876  0.290650068 #> 9877  0.290642481 #> 9878  0.290634947 #> 9879  0.290627467 #> 9880  0.290620040 #> 9881  0.290612666 #> 9882  0.290605346 #> 9883  0.290598080 #> 9884  0.290590867 #> 9885  0.290583708 #> 9886  0.290576602 #> 9887  0.290569549 #> 9888  0.290562550 #> 9889  0.290555605 #> 9890  0.290548713 #> 9891  0.290541875 #> 9892  0.290535090 #> 9893  0.290528359 #> 9894  0.290521681 #> 9895  0.290515057 #> 9896  0.290508487 #> 9897  0.290501970 #> 9898  0.290495506 #> 9899  0.290489096 #> 9900  0.290482740 #> 9901  0.290476437 #> 9902  0.290470188 #> 9903  0.290463993 #> 9904  0.290457851 #> 9905  0.290451762 #> 9906  0.290445728 #> 9907  0.290439746 #> 9908  0.290433819 #> 9909  0.290427945 #> 9910  0.290422124 #> 9911  0.290416358 #> 9912  0.290410644 #> 9913  0.290404985 #> 9914  0.290399379 #> 9915  0.290393826 #> 9916  0.290388327 #> 9917  0.290382882 #> 9918  0.290377491 #> 9919  0.290372153 #> 9920  0.290366868 #> 9921  0.290361637 #> 9922  0.290356460 #> 9923  0.290351337 #> 9924  0.290346267 #> 9925  0.290341250 #> 9926  0.290336287 #> 9927  0.290331378 #> 9928  0.290326522 #> 9929  0.290321720 #> 9930  0.290316972 #> 9931  0.290312277 #> 9932  0.290307636 #> 9933  0.290303048 #> 9934  0.290298514 #> 9935  0.290294033 #> 9936  0.290289606 #> 9937  0.290285233 #> 9938  0.290280913 #> 9939  0.290276647 #> 9940  0.290272434 #> 9941  0.290268275 #> 9942  0.290264169 #> 9943  0.290260117 #> 9944  0.290256119 #> 9945  0.290252174 #> 9946  0.290248282 #> 9947  0.290244444 #> 9948  0.290240660 #> 9949  0.290236929 #> 9950  0.290233251 #> 9951  0.290229627 #> 9952  0.290226057 #> 9953  0.290222540 #> 9954  0.290219077 #> 9955  0.290215667 #> 9956  0.290212310 #> 9957  0.290209007 #> 9958  0.290205758 #> 9959  0.290202562 #> 9960  0.290199419 #> 9961  0.290196330 #> 9962  0.290193294 #> 9963  0.290190311 #> 9964  0.290187382 #> 9965  0.290184507 #> 9966  0.290181685 #> 9967  0.290178916 #> 9968  0.290176200 #> 9969  0.290173538 #> 9970  0.290170930 #> 9971  0.290168374 #> 9972  0.290165872 #> 9973  0.290163424 #> 9974  0.290161028 #> 9975  0.290158686 #> 9976  0.290156397 #> 9977  0.290154162 #> 9978  0.290151980 #> 9979  0.290149851 #> 9980  0.290147775 #> 9981  0.290145753 #> 9982  0.290143784 #> 9983  0.290141868 #> 9984  0.290140005 #> 9985  0.290138195 #> 9986  0.290136439 #> 9987  0.290134736 #> 9988  0.290133086 #> 9989  0.290131489 #> 9990  0.290129945 #> 9991  0.290128454 #> 9992  0.290127017 #> 9993  0.290125632 #> 9994  0.290124301 #> 9995  0.290123022 #> 9996  0.290121797 #> 9997  0.290120625 #> 9998  0.290119506 #> 9999  0.290118440 #> 10000 0.290117426 #> 10001 0.290116466 #> 10002 0.290115559 #> 10003 0.290114705 #> 10004 0.290113903 #> 10005 0.290113155 #> 10006 0.290112459 #> 10007 0.290111816 #> 10008 0.290111227 #> 10009 0.290110690 #> 10010 0.290110205 #> 10011 0.290109774 #> 10012 0.290109396 #> 10013 0.290109070 #> 10014 0.290108797 #> 10015 0.290108577 #> 10016 0.290108409 #> 10017 0.290108294 #> 10018 0.290108232 #> 10019 0.290108223 #> 10020 0.290108266 #> 10021 0.290108362 #> 10022 0.290108510 #> 10023 0.290108711 #> 10024 0.290108965 #> 10025 0.290109271 #> 10026 0.290109630 #> 10027 0.290110041 #> 10028 0.290110505 #> 10029 0.290111021 #> 10030 0.290111590 #> 10031 0.290112211 #> 10032 0.290112885 #> 10033 0.290113611 #> 10034 0.290114389 #> 10035 0.290115220 #> 10036 0.290116103 #> 10037 0.290117038 #> 10038 0.290118026 #> 10039 0.290119065 #> 10040 0.290120158 #> 10041 0.290121302 #> 10042 0.290122498 #> 10043 0.290123747 #> 10044 0.290125048 #> 10045 0.290126401 #> 10046 0.290127806 #> 10047 0.290129263 #> 10048 0.290130773 #> 10049 0.290132334 #> 10050 0.290133947 #> 10051 0.290135612 #> 10052 0.290137330 #> 10053 0.290139099 #> 10054 0.290140920 #> 10055 0.290142793 #> 10056 0.290144718 #> 10057 0.290146694 #> 10058 0.290148723 #> 10059 0.290150803 #> 10060 0.290152935 #> 10061 0.290155119 #> 10062 0.290157354 #> 10063 0.290159641 #> 10064 0.290161980 #> 10065 0.290164370 #> 10066 0.290166812 #> 10067 0.290169306 #> 10068 0.290171851 #> 10069 0.290174447 #> 10070 0.290177095 #> 10071 0.290179795 #> 10072 0.290182546 #> 10073 0.290185348 #> 10074 0.290188202 #> 10075 0.290191107 #> 10076 0.290194063 #> 10077 0.290197070 #> 10078 0.290200129 #> 10079 0.290203239 #> 10080 0.290206401 #> 10081 0.290209613 #> 10082 0.290212877 #> 10083 0.290216191 #> 10084 0.290219557 #> 10085 0.290222974 #> 10086 0.290226441 #> 10087 0.290229960 #> 10088 0.290233530 #> 10089 0.290237150 #> 10090 0.290240822 #> 10091 0.290244544 #> 10092 0.290248317 #> 10093 0.290252141 #> 10094 0.290256016 #> 10095 0.290259941 #> 10096 0.290263917 #> 10097 0.290267944 #> 10098 0.290272021 #> 10099 0.290276149 #> 10100 0.290280328 #> 10101 0.290284557 #> 10102 0.290288836 #> 10103 0.290293166 #> 10104 0.290297546 #> 10105 0.290301977 #> 10106 0.290306458 #> 10107 0.290310989 #> 10108 0.290315571 #> 10109 0.290320202 #> 10110 0.290324884 #> 10111 0.290329617 #> 10112 0.290334399 #> 10113 0.290339231 #> 10114 0.290344114 #> 10115 0.290349046 #> 10116 0.290354029 #> 10117 0.290359061 #> 10118 0.290364143 #> 10119 0.290369275 #> 10120 0.290374457 #> 10121 0.290379689 #> 10122 0.290384971 #> 10123 0.290390302 #> 10124 0.290395683 #> 10125 0.290401113 #> 10126 0.290406593 #> 10127 0.290412123 #> 10128 0.290417702 #> 10129 0.290423331 #> 10130 0.290429009 #> 10131 0.290434737 #> 10132 0.290440514 #> 10133 0.290446340 #> 10134 0.290452215 #> 10135 0.290458140 #> 10136 0.290464114 #> 10137 0.290470137 #> 10138 0.290476209 #> 10139 0.290482331 #> 10140 0.290488501 #> 10141 0.290494720 #> 10142 0.290500988 #> 10143 0.290507306 #> 10144 0.290513672 #> 10145 0.290520086 #> 10146 0.290526550 #> 10147 0.290533063 #> 10148 0.290539624 #> 10149 0.290546233 #> 10150 0.290552892 #> 10151 0.290559599 #> 10152 0.290566354 #> 10153 0.290573158 #> 10154 0.290580010 #> 10155 0.290586911 #> 10156 0.290593860 #> 10157 0.290600857 #> 10158 0.290607903 #> 10159 0.290614997 #> 10160 0.290622139 #> 10161 0.290629329 #> 10162 0.290636567 #> 10163 0.290643853 #> 10164 0.290651187 #> 10165 0.290658569 #> 10166 0.290665999 #> 10167 0.290673477 #> 10168 0.290681003 #> 10169 0.290688576 #> 10170 0.290696197 #> 10171 0.290703866 #> 10172 0.290711582 #> 10173 0.290719346 #> 10174 0.290727157 #> 10175 0.290735016 #> 10176 0.290742922 #> 10177 0.290750876 #> 10178 0.290758877 #> 10179 0.290766925 #> 10180 0.290775020 #> 10181 0.290783163 #> 10182 0.290791353 #> 10183 0.290799589 #> 10184 0.290807873 #> 10185 0.290816204 #> 10186 0.290824582 #> 10187 0.290833006 #> 10188 0.290841477 #> 10189 0.290849995 #> 10190 0.290858560 #> 10191 0.290867172 #> 10192 0.290875830 #> 10193 0.290884534 #> 10194 0.290893285 #> 10195 0.290902083 #> 10196 0.290910927 #> 10197 0.290919817 #> 10198 0.290928754 #> 10199 0.290937737 #> 10200 0.290946766 #> 10201 0.290955841 #> 10202 0.290964962 #> 10203 0.290974130 #> 10204 0.290983343 #> 10205 0.290992602 #> 10206 0.291001907 #> 10207 0.291011258 #> 10208 0.291020655 #> 10209 0.291030097 #> 10210 0.291039585 #> 10211 0.291049119 #> 10212 0.291058698 #> 10213 0.291068323 #> 10214 0.291077993 #> 10215 0.291087709 #> 10216 0.291097469 #> 10217 0.291107275 #> 10218 0.291117127 #> 10219 0.291127023 #> 10220 0.291136965 #> 10221 0.291146951 #> 10222 0.291156983 #> 10223 0.291167059 #> 10224 0.291177180 #> 10225 0.291187347 #> 10226 0.291197558 #> 10227 0.291207813 #> 10228 0.291218113 #> 10229 0.291228458 #> 10230 0.291238847 #> 10231 0.291249281 #> 10232 0.291259759 #> 10233 0.291270282 #> 10234 0.291280849 #> 10235 0.291291460 #> 10236 0.291302115 #> 10237 0.291312814 #> 10238 0.291323558 #> 10239 0.291334345 #> 10240 0.291345176 #> 10241 0.291356051 #> 10242 0.291366970 #> 10243 0.291377933 #> 10244 0.291388939 #> 10245 0.291399989 #> 10246 0.291411083 #> 10247 0.291422220 #> 10248 0.291433401 #> 10249 0.291444624 #> 10250 0.291455892 #> 10251 0.291467202 #> 10252 0.291478556 #> 10253 0.291489952 #> 10254 0.291501392 #> 10255 0.291512875 #> 10256 0.291524401 #> 10257 0.291535970 #> 10258 0.291547581 #> 10259 0.291559235 #> 10260 0.291570932 #> 10261 0.291582672 #> 10262 0.291594454 #> 10263 0.291606278 #> 10264 0.291618145 #> 10265 0.291630054 #> 10266 0.291642006 #> 10267 0.291654000 #> 10268 0.291666036 #> 10269 0.291678114 #> 10270 0.291690234 #> 10271 0.291702397 #> 10272 0.291714601 #> 10273 0.291726847 #> 10274 0.291739134 #> 10275 0.291751464 #> 10276 0.291763835 #> 10277 0.291776247 #> 10278 0.291788701 #> 10279 0.291801197 #> 10280 0.291813734 #> 10281 0.291826312 #> 10282 0.291838932 #> 10283 0.291851592 #> 10284 0.291864294 #> 10285 0.291877037 #> 10286 0.291889821 #> 10287 0.291902645 #> 10288 0.291915511 #> 10289 0.291928417 #> 10290 0.291941364 #> 10291 0.291954352 #> 10292 0.291967380 #> 10293 0.291980448 #> 10294 0.291993557 #> 10295 0.292006707 #> 10296 0.292019896 #> 10297 0.292033126 #> 10298 0.292046396 #> 10299 0.292059706 #> 10300 0.292073056 #> 10301 0.292086446 #> 10302 0.292099876 #> 10303 0.292113346 #> 10304 0.292126855 #> 10305 0.292140404 #> 10306 0.292153993 #> 10307 0.292167621 #> 10308 0.292181288 #> 10309 0.292194995 #> 10310 0.292208741 #> 10311 0.292222526 #> 10312 0.292236351 #> 10313 0.292250214 #> 10314 0.292264117 #> 10315 0.292278058 #> 10316 0.292292038 #> 10317 0.292306058 #> 10318 0.292320115 #> 10319 0.292334212 #> 10320 0.292348347 #> 10321 0.292362520 #> 10322 0.292376732 #> 10323 0.292390982 #> 10324 0.292405270 #> 10325 0.292419597 #> 10326 0.292433961 #> 10327 0.292448364 #> 10328 0.292462805 #> 10329 0.292477283 #> 10330 0.292491800 #> 10331 0.292506354 #> 10332 0.292520945 #> 10333 0.292535575 #> 10334 0.292550242 #> 10335 0.292564946 #> 10336 0.292579687 #> 10337 0.292594466 #> 10338 0.292609282 #> 10339 0.292624135 #> 10340 0.292639026 #> 10341 0.292653953 #> 10342 0.292668917 #> 10343 0.292683918 #> 10344 0.292698955 #> 10345 0.292714030 #> 10346 0.292729140 #> 10347 0.292744288 #> 10348 0.292759472 #> 10349 0.292774692 #> 10350 0.292789948 #> 10351 0.292805241 #> 10352 0.292820569 #> 10353 0.292835934 #> 10354 0.292851335 #> 10355 0.292866771 #> 10356 0.292882244 #> 10357 0.292897752 #> 10358 0.292913295 #> 10359 0.292928874 #> 10360 0.292944489 #> 10361 0.292960139 #> 10362 0.292975825 #> 10363 0.292991545 #> 10364 0.293007301 #> 10365 0.293023092 #> 10366 0.293038918 #> 10367 0.293054779 #> 10368 0.293070674 #> 10369 0.293086605 #> 10370 0.293102570 #> 10371 0.293118569 #> 10372 0.293134604 #> 10373 0.293150672 #> 10374 0.293166775 #> 10375 0.293182912 #> 10376 0.293199083 #> 10377 0.293215289 #> 10378 0.293231528 #> 10379 0.293247802 #> 10380 0.293264109 #> 10381 0.293280450 #> 10382 0.293296825 #> 10383 0.293313233 #> 10384 0.293329675 #> 10385 0.293346150 #> 10386 0.293362658 #> 10387 0.293379200 #> 10388 0.293395775 #> 10389 0.293412383 #> 10390 0.293429025 #> 10391 0.293445699 #> 10392 0.293462406 #> 10393 0.293479145 #> 10394 0.293495918 #> 10395 0.293512723 #> 10396 0.293529560 #> 10397 0.293546430 #> 10398 0.293563332 #> 10399 0.293580267 #> 10400 0.293597233 #> 10401 0.293614232 #> 10402 0.293631263 #> 10403 0.293648325 #> 10404 0.293665420 #> 10405 0.293682546 #> 10406 0.293699704 #> 10407 0.293716893 #> 10408 0.293734114 #> 10409 0.293751366 #> 10410 0.293768650 #> 10411 0.293785965 #> 10412 0.293803311 #> 10413 0.293820688 #> 10414 0.293838096 #> 10415 0.293855535 #> 10416 0.293873004 #> 10417 0.293890505 #> 10418 0.293908035 #> 10419 0.293925597 #> 10420 0.293943189 #> 10421 0.293960811 #> 10422 0.293978463 #> 10423 0.293996146 #> 10424 0.294013859 #> 10425 0.294031602 #> 10426 0.294049374 #> 10427 0.294067177 #> 10428 0.294085009 #> 10429 0.294102871 #> 10430 0.294120762 #> 10431 0.294138683 #> 10432 0.294156633 #> 10433 0.294174613 #> 10434 0.294192621 #> 10435 0.294210659 #> 10436 0.294228726 #> 10437 0.294246822 #> 10438 0.294264946 #> 10439 0.294283100 #> 10440 0.294301282 #> 10441 0.294319492 #> 10442 0.294337731 #> 10443 0.294355999 #> 10444 0.294374294 #> 10445 0.294392618 #> 10446 0.294410970 #> 10447 0.294429350 #> 10448 0.294447758 #> 10449 0.294466194 #> 10450 0.294484657 #> 10451 0.294503149 #> 10452 0.294521667 #> 10453 0.294540214 #> 10454 0.294558787 #> 10455 0.294577388 #> 10456 0.294596016 #> 10457 0.294614672 #> 10458 0.294633354 #> 10459 0.294652063 #> 10460 0.294670799 #> 10461 0.294689562 #> 10462 0.294708351 #> 10463 0.294727167 #> 10464 0.294746009 #> 10465 0.294764878 #> 10466 0.294783773 #> 10467 0.294802694 #> 10468 0.294821642 #> 10469 0.294840615 #> 10470 0.294859614 #> 10471 0.294878639 #> 10472 0.294897690 #> 10473 0.294916766 #> 10474 0.294935868 #> 10475 0.294954995 #> 10476 0.294974148 #> 10477 0.294993326 #> 10478 0.295012529 #> 10479 0.295031757 #> 10480 0.295051010 #> 10481 0.295070288 #> 10482 0.295089591 #> 10483 0.295108918 #> 10484 0.295128270 #> 10485 0.295147647 #> 10486 0.295167048 #> 10487 0.295186473 #> 10488 0.295205922 #> 10489 0.295225396 #> 10490 0.295244893 #> 10491 0.295264415 #> 10492 0.295283960 #> 10493 0.295303529 #> 10494 0.295323122 #> 10495 0.295342738 #> 10496 0.295362378 #> 10497 0.295382041 #> 10498 0.295401727 #> 10499 0.295421436 #> 10500 0.295441169 #> 10501 0.295460924 #> 10502 0.295480703 #> 10503 0.295500504 #> 10504 0.295520327 #> 10505 0.295540174 #> 10506 0.295560043 #> 10507 0.295579934 #> 10508 0.295599848 #> 10509 0.295619783 #> 10510 0.295639741 #> 10511 0.295659721 #> 10512 0.295679723 #> 10513 0.295699746 #> 10514 0.295719792 #> 10515 0.295739859 #> 10516 0.295759947 #> 10517 0.295780057 #> 10518 0.295800188 #> 10519 0.295820340 #> 10520 0.295840514 #> 10521 0.295860709 #> 10522 0.295880924 #> 10523 0.295901160 #> 10524 0.295921418 #> 10525 0.295941695 #> 10526 0.295961994 #> 10527 0.295982312 #> 10528 0.296002651 #> 10529 0.296023011 #> 10530 0.296043390 #> 10531 0.296063790 #> 10532 0.296084209 #> 10533 0.296104649 #> 10534 0.296125108 #> 10535 0.296145587 #> 10536 0.296166085 #> 10537 0.296186603 #> 10538 0.296207140 #> 10539 0.296227697 #> 10540 0.296248272 #> 10541 0.296268867 #> 10542 0.296289481 #> 10543 0.296310113 #> 10544 0.296330765 #> 10545 0.296351435 #> 10546 0.296372123 #> 10547 0.296392830 #> 10548 0.296413556 #> 10549 0.296434300 #> 10550 0.296455061 #> 10551 0.296475841 #> 10552 0.296496639 #> 10553 0.296517455 #> 10554 0.296538289 #> 10555 0.296559140 #> 10556 0.296580009 #> 10557 0.296600896 #> 10558 0.296621800 #> 10559 0.296642721 #> 10560 0.296663659 #> 10561 0.296684614 #> 10562 0.296705587 #> 10563 0.296726576 #> 10564 0.296747582 #> 10565 0.296768605 #> 10566 0.296789644 #> 10567 0.296810700 #> 10568 0.296831772 #> 10569 0.296852861 #> 10570 0.296873965 #> 10571 0.296895086 #> 10572 0.296916223 #> 10573 0.296937376 #> 10574 0.296958544 #> 10575 0.296979728 #> 10576 0.297000928 #> 10577 0.297022143 #> 10578 0.297043374 #> 10579 0.297064620 #> 10580 0.297085881 #> 10581 0.297107157 #> 10582 0.297128448 #> 10583 0.297149754 #> 10584 0.297171075 #> 10585 0.297192411 #> 10586 0.297213761 #> 10587 0.297235125 #> 10588 0.297256504 #> 10589 0.297277898 #> 10590 0.297299305 #> 10591 0.297320726 #> 10592 0.297342162 #> 10593 0.297363611 #> 10594 0.297385074 #> 10595 0.297406551 #> 10596 0.297428042 #> 10597 0.297449546 #> 10598 0.297471063 #> 10599 0.297492593 #> 10600 0.297514137 #> 10601 0.297535694 #> 10602 0.297557264 #> 10603 0.297578846 #> 10604 0.297600442 #> 10605 0.297622050 #> 10606 0.297643670 #> 10607 0.297665303 #> 10608 0.297686949 #> 10609 0.297708606 #> 10610 0.297730276 #> 10611 0.297751958 #> 10612 0.297773652 #> 10613 0.297795358 #> 10614 0.297817075 #> 10615 0.297838804 #> 10616 0.297860545 #> 10617 0.297882297 #> 10618 0.297904061 #> 10619 0.297925835 #> 10620 0.297947621 #> 10621 0.297969418 #> 10622 0.297991226 #> 10623 0.298013045 #> 10624 0.298034874 #> 10625 0.298056715 #> 10626 0.298078565 #> 10627 0.298100426 #> 10628 0.298122298 #> 10629 0.298144179 #> 10630 0.298166071 #> 10631 0.298187973 #> 10632 0.298209885 #> 10633 0.298231806 #> 10634 0.298253738 #> 10635 0.298275679 #> 10636 0.298297629 #> 10637 0.298319589 #> 10638 0.298341558 #> 10639 0.298363537 #> 10640 0.298385524 #> 10641 0.298407521 #> 10642 0.298429526 #> 10643 0.298451541 #> 10644 0.298473564 #> 10645 0.298495595 #> 10646 0.298517636 #> 10647 0.298539684 #> 10648 0.298561741 #> 10649 0.298583806 #> 10650 0.298605879 #> 10651 0.298627960 #> 10652 0.298650050 #> 10653 0.298672146 #> 10654 0.298694251 #> 10655 0.298716363 #> 10656 0.298738483 #> 10657 0.298760610 #> 10658 0.298782744 #> 10659 0.298804886 #> 10660 0.298827035 #> 10661 0.298849190 #> 10662 0.298871353 #> 10663 0.298893522 #> 10664 0.298915698 #> 10665 0.298937881 #> 10666 0.298960070 #> 10667 0.298982265 #> 10668 0.299004467 #> 10669 0.299026675 #> 10670 0.299048889 #> 10671 0.299071109 #> 10672 0.299093335 #> 10673 0.299115567 #> 10674 0.299137804 #> 10675 0.299160047 #> 10676 0.299182295 #> 10677 0.299204549 #> 10678 0.299226808 #> 10679 0.299249072 #> 10680 0.299271342 #> 10681 0.299293616 #> 10682 0.299315895 #> 10683 0.299338179 #> 10684 0.299360468 #> 10685 0.299382761 #> 10686 0.299405058 #> 10687 0.299427360 #> 10688 0.299449666 #> 10689 0.299471977 #> 10690 0.299494291 #> 10691 0.299516610 #> 10692 0.299538932 #> 10693 0.299561258 #> 10694 0.299583588 #> 10695 0.299605921 #> 10696 0.299628258 #> 10697 0.299650598 #> 10698 0.299672941 #> 10699 0.299695288 #> 10700 0.299717637 #> 10701 0.299739990 #> 10702 0.299762345 #> 10703 0.299784703 #> 10704 0.299807064 #> 10705 0.299829427 #> 10706 0.299851793 #> 10707 0.299874161 #> 10708 0.299896532 #> 10709 0.299918904 #> 10710 0.299941279 #> 10711 0.299963655 #> 10712 0.299986034 #> 10713 0.300008414 #> 10714 0.300030796 #> 10715 0.300053179 #> 10716 0.300075564 #> 10717 0.300097951 #> 10718 0.300120338 #> 10719 0.300142727 #> 10720 0.300165117 #> 10721 0.300187507 #> 10722 0.300209899 #> 10723 0.300232291 #> 10724 0.300254684 #> 10725 0.300277078 #> 10726 0.300299472 #> 10727 0.300321866 #> 10728 0.300344261 #> 10729 0.300366656 #> 10730 0.300389051 #> 10731 0.300411446 #> 10732 0.300433840 #> 10733 0.300456235 #> 10734 0.300478629 #> 10735 0.300501023 #> 10736 0.300523416 #> 10737 0.300545809 #> 10738 0.300568201 #> 10739 0.300590592 #> 10740 0.300612982 #> 10741 0.300635371 #> 10742 0.300657759 #> 10743 0.300680146 #> 10744 0.300702532 #> 10745 0.300724916 #> 10746 0.300747299 #> 10747 0.300769680 #> 10748 0.300792059 #> 10749 0.300814437 #> 10750 0.300836813 #> 10751 0.300859186 #> 10752 0.300881558 #> 10753 0.300903927 #> 10754 0.300926294 #> 10755 0.300948659 #> 10756 0.300971021 #> 10757 0.300993381 #> 10758 0.301015738 #> 10759 0.301038092 #> 10760 0.301060444 #> 10761 0.301082792 #> 10762 0.301105138 #> 10763 0.301127480 #> 10764 0.301149819 #> 10765 0.301172154 #> 10766 0.301194487 #> 10767 0.301216815 #> 10768 0.301239140 #> 10769 0.301261461 #> 10770 0.301283779 #> 10771 0.301306092 #> 10772 0.301328402 #> 10773 0.301350707 #> 10774 0.301373008 #> 10775 0.301395305 #> 10776 0.301417598 #> 10777 0.301439886 #> 10778 0.301462169 #> 10779 0.301484447 #> 10780 0.301506721 #> 10781 0.301528990 #> 10782 0.301551254 #> 10783 0.301573513 #> 10784 0.301595767 #> 10785 0.301618015 #> 10786 0.301640258 #> 10787 0.301662496 #> 10788 0.301684728 #> 10789 0.301706955 #> 10790 0.301729175 #> 10791 0.301751390 #> 10792 0.301773599 #> 10793 0.301795802 #> 10794 0.301817999 #> 10795 0.301840189 #> 10796 0.301862374 #> 10797 0.301884552 #> 10798 0.301906723 #> 10799 0.301928888 #> 10800 0.301951046 #> 10801 0.301973197 #> 10802 0.301995342 #> 10803 0.302017479 #> 10804 0.302039610 #> 10805 0.302061733 #> 10806 0.302083849 #> 10807 0.302105958 #> 10808 0.302128060 #> 10809 0.302150153 #> 10810 0.302172240 #> 10811 0.302194318 #> 10812 0.302216389 #> 10813 0.302238452 #> 10814 0.302260506 #> 10815 0.302282553 #> 10816 0.302304592 #> 10817 0.302326622 #> 10818 0.302348644 #> 10819 0.302370658 #> 10820 0.302392663 #> 10821 0.302414659 #> 10822 0.302436647 #> 10823 0.302458626 #> 10824 0.302480596 #> 10825 0.302502557 #> 10826 0.302524509 #> 10827 0.302546451 #> 10828 0.302568385 #> 10829 0.302590309 #> 10830 0.302612224 #> 10831 0.302634129 #> 10832 0.302656024 #> 10833 0.302677910 #> 10834 0.302699786 #> 10835 0.302721652 #> 10836 0.302743509 #> 10837 0.302765355 #> 10838 0.302787190 #> 10839 0.302809016 #> 10840 0.302830831 #> 10841 0.302852636 #> 10842 0.302874431 #> 10843 0.302896214 #> 10844 0.302917987 #> 10845 0.302939750 #> 10846 0.302961501 #> 10847 0.302983241 #> 10848 0.303004971 #> 10849 0.303026689 #> 10850 0.303048396 #> 10851 0.303070091 #> 10852 0.303091776 #> 10853 0.303113448 #> 10854 0.303135110 #> 10855 0.303156759 #> 10856 0.303178397 #> 10857 0.303200023 #> 10858 0.303221637 #> 10859 0.303243238 #> 10860 0.303264828 #> 10861 0.303286406 #> 10862 0.303307971 #> 10863 0.303329524 #> 10864 0.303351065 #> 10865 0.303372593 #> 10866 0.303394108 #> 10867 0.303415610 #> 10868 0.303437100 #> 10869 0.303458577 #> 10870 0.303480041 #> 10871 0.303501492 #> 10872 0.303522930 #> 10873 0.303544354 #> 10874 0.303565765 #> 10875 0.303587163 #> 10876 0.303608547 #> 10877 0.303629918 #> 10878 0.303651275 #> 10879 0.303672619 #> 10880 0.303693948 #> 10881 0.303715264 #> 10882 0.303736565 #> 10883 0.303757853 #> 10884 0.303779126 #> 10885 0.303800385 #> 10886 0.303821630 #> 10887 0.303842860 #> 10888 0.303864076 #> 10889 0.303885277 #> 10890 0.303906464 #> 10891 0.303927636 #> 10892 0.303948793 #> 10893 0.303969935 #> 10894 0.303991062 #> 10895 0.304012174 #> 10896 0.304033271 #> 10897 0.304054352 #> 10898 0.304075419 #> 10899 0.304096469 #> 10900 0.304117505 #> 10901 0.304138525 #> 10902 0.304159529 #> 10903 0.304180517 #> 10904 0.304201490 #> 10905 0.304222446 #> 10906 0.304243387 #> 10907 0.304264312 #> 10908 0.304285220 #> 10909 0.304306113 #> 10910 0.304326989 #> 10911 0.304347848 #> 10912 0.304368691 #> 10913 0.304389518 #> 10914 0.304410328 #> 10915 0.304431121 #> 10916 0.304451898 #> 10917 0.304472657 #> 10918 0.304493400 #> 10919 0.304514125 #> 10920 0.304534834 #> 10921 0.304555525 #> 10922 0.304576199 #> 10923 0.304596856 #> 10924 0.304617496 #> 10925 0.304638117 #> 10926 0.304658722 #> 10927 0.304679308 #> 10928 0.304699877 #> 10929 0.304720428 #> 10930 0.304740962 #> 10931 0.304761477 #> 10932 0.304781974 #> 10933 0.304802453 #> 10934 0.304822914 #> 10935 0.304843357 #> 10936 0.304863781 #> 10937 0.304884187 #> 10938 0.304904574 #> 10939 0.304924943 #> 10940 0.304945293 #> 10941 0.304965624 #> 10942 0.304985937 #> 10943 0.305006231 #> 10944 0.305026506 #> 10945 0.305046761 #> 10946 0.305066998 #> 10947 0.305087215 #> 10948 0.305107414 #> 10949 0.305127592 #> 10950 0.305147752 #> 10951 0.305167892 #> 10952 0.305188012 #> 10953 0.305208113 #> 10954 0.305228194 #> 10955 0.305248256 #> 10956 0.305268297 #> 10957 0.305288319 #> 10958 0.305308320 #> 10959 0.305328302 #> 10960 0.305348263 #> 10961 0.305368204 #> 10962 0.305388125 #> 10963 0.305408025 #> 10964 0.305427906 #> 10965 0.305447765 #> 10966 0.305467604 #> 10967 0.305487422 #> 10968 0.305507220 #> 10969 0.305526997 #> 10970 0.305546753 #> 10971 0.305566488 #> 10972 0.305586202 #> 10973 0.305605895 #> 10974 0.305625567 #> 10975 0.305645217 #> 10976 0.305664846 #> 10977 0.305684454 #> 10978 0.305704041 #> 10979 0.305723606 #> 10980 0.305743149 #> 10981 0.305762671 #> 10982 0.305782171 #> 10983 0.305801650 #> 10984 0.305821106 #> 10985 0.305840541 #> 10986 0.305859953 #> 10987 0.305879344 #> 10988 0.305898712 #> 10989 0.305918058 #> 10990 0.305937382 #> 10991 0.305956684 #> 10992 0.305975963 #> 10993 0.305995220 #> 10994 0.306014454 #> 10995 0.306033666 #> 10996 0.306052855 #> 10997 0.306072021 #> 10998 0.306091165 #> 10999 0.306110286 #> 11000 0.306129383 #> 11001 0.306148458 #> 11002 0.306167510 #> 11003 0.306186538 #> 11004 0.306205544 #> 11005 0.306224526 #> 11006 0.306243484 #> 11007 0.306262420 #> 11008 0.306281332 #> 11009 0.306300220 #> 11010 0.306319085 #> 11011 0.306337926 #> 11012 0.306356744 #> 11013 0.306375537 #> 11014 0.306394307 #> 11015 0.306413053 #> 11016 0.306431775 #> 11017 0.306450473 #> 11018 0.306469147 #> 11019 0.306487797 #> 11020 0.306506422 #> 11021 0.306525023 #> 11022 0.306543600 #> 11023 0.306562153 #> 11024 0.306580681 #> 11025 0.306599184 #> 11026 0.306617663 #> 11027 0.306636117 #> 11028 0.306654547 #> 11029 0.306672952 #> 11030 0.306691332 #> 11031 0.306709687 #> 11032 0.306728017 #> 11033 0.306746322 #> 11034 0.306764602 #> 11035 0.306782856 #> 11036 0.306801086 #> 11037 0.306819290 #> 11038 0.306837469 #> 11039 0.306855623 #> 11040 0.306873751 #> 11041 0.306891854 #> 11042 0.306909931 #> 11043 0.306927983 #> 11044 0.306946008 #> 11045 0.306964009 #> 11046 0.306981983 #> 11047 0.306999931 #> 11048 0.307017854 #> 11049 0.307035751 #> 11050 0.307053621 #> 11051 0.307071466 #> 11052 0.307089284 #> 11053 0.307107076 #> 11054 0.307124842 #> 11055 0.307142582 #> 11056 0.307160295 #> 11057 0.307177982 #> 11058 0.307195642 #> 11059 0.307213276 #> 11060 0.307230883 #> 11061 0.307248464 #> 11062 0.307266017 #> 11063 0.307283544 #> 11064 0.307301045 #> 11065 0.307318518 #> 11066 0.307335965 #> 11067 0.307353384 #> 11068 0.307370777 #> 11069 0.307388142 #> 11070 0.307405480 #> 11071 0.307422791 #> 11072 0.307440075 #> 11073 0.307457332 #> 11074 0.307474561 #> 11075 0.307491763 #> 11076 0.307508937 #> 11077 0.307526084 #> 11078 0.307543203 #> 11079 0.307560295 #> 11080 0.307577359 #> 11081 0.307594395 #> 11082 0.307611403 #> 11083 0.307628384 #> 11084 0.307645337 #> 11085 0.307662262 #> 11086 0.307679158 #> 11087 0.307696027 #> 11088 0.307712868 #> 11089 0.307729681 #> 11090 0.307746465 #> 11091 0.307763221 #> 11092 0.307779949 #> 11093 0.307796649 #> 11094 0.307813320 #> 11095 0.307829963 #> 11096 0.307846577 #> 11097 0.307863163 #> 11098 0.307879720 #> 11099 0.307896249 #> 11100 0.307912749 #> 11101 0.307929220 #> 11102 0.307945662 #> 11103 0.307962076 #> 11104 0.307978461 #> 11105 0.307994817 #> 11106 0.308011143 #> 11107 0.308027441 #> 11108 0.308043710 #> 11109 0.308059950 #> 11110 0.308076160 #> 11111 0.308092342 #> 11112 0.308108494 #> 11113 0.308124617 #> 11114 0.308140710 #> 11115 0.308156774 #> 11116 0.308172809 #> 11117 0.308188814 #> 11118 0.308204790 #> 11119 0.308220736 #> 11120 0.308236653 #> 11121 0.308252540 #> 11122 0.308268397 #> 11123 0.308284225 #> 11124 0.308300022 #> 11125 0.308315790 #> 11126 0.308331528 #> 11127 0.308347236 #> 11128 0.308362914 #> 11129 0.308378563 #> 11130 0.308394181 #> 11131 0.308409769 #> 11132 0.308425327 #> 11133 0.308440854 #> 11134 0.308456352 #> 11135 0.308471819 #> 11136 0.308487256 #> 11137 0.308502663 #> 11138 0.308518039 #> 11139 0.308533385 #> 11140 0.308548700 #> 11141 0.308563985 #> 11142 0.308579240 #> 11143 0.308594463 #> 11144 0.308609657 #> 11145 0.308624819 #> 11146 0.308639951 #> 11147 0.308655052 #> 11148 0.308670122 #> 11149 0.308685162 #> 11150 0.308700170 #> 11151 0.308715148 #> 11152 0.308730095 #> 11153 0.308745011 #> 11154 0.308759895 #> 11155 0.308774749 #> 11156 0.308789572 #> 11157 0.308804363 #> 11158 0.308819124 #> 11159 0.308833853 #> 11160 0.308848551 #> 11161 0.308863217 #> 11162 0.308877853 #> 11163 0.308892456 #> 11164 0.308907029 #> 11165 0.308921570 #> 11166 0.308936080 #> 11167 0.308950558 #> 11168 0.308965005 #> 11169 0.308979420 #> 11170 0.308993803 #> 11171 0.309008155 #> 11172 0.309022475 #> 11173 0.309036764 #> 11174 0.309051021 #> 11175 0.309065246 #> 11176 0.309079439 #> 11177 0.309093600 #> 11178 0.309107730 #> 11179 0.309121827 #> 11180 0.309135893 #> 11181 0.309149926 #> 11182 0.309163928 #> 11183 0.309177897 #> 11184 0.309191835 #> 11185 0.309205740 #> 11186 0.309219613 #> 11187 0.309233454 #> 11188 0.309247263 #> 11189 0.309261040 #> 11190 0.309274784 #> 11191 0.309288496 #> 11192 0.309302176 #> 11193 0.309315823 #> 11194 0.309329438 #> 11195 0.309343021 #> 11196 0.309356571 #> 11197 0.309370088 #> 11198 0.309383573 #> 11199 0.309397026 #> 11200 0.309410446 #> 11201 0.309423833 #> 11202 0.309437188 #> 11203 0.309450510 #> 11204 0.309463799 #> 11205 0.309477056 #> 11206 0.309490280 #> 11207 0.309503471 #> 11208 0.309516629 #> 11209 0.309529754 #> 11210 0.309542847 #> 11211 0.309555907 #> 11212 0.309568933 #> 11213 0.309581927 #> 11214 0.309594888 #> 11215 0.309607816 #> 11216 0.309620711 #> 11217 0.309633572 #> 11218 0.309646401 #> 11219 0.309659197 #> 11220 0.309671959 #> 11221 0.309684688 #> 11222 0.309697384 #> 11223 0.309710047 #> 11224 0.309722677 #> 11225 0.309735273 #> 11226 0.309747836 #> 11227 0.309760366 #> 11228 0.309772863 #> 11229 0.309785326 #> 11230 0.309797756 #> 11231 0.309810152 #> 11232 0.309822515 #> 11233 0.309834844 #> 11234 0.309847140 #> 11235 0.309859402 #> 11236 0.309871631 #> 11237 0.309883827 #> 11238 0.309895988 #> 11239 0.309908117 #> 11240 0.309920211 #> 11241 0.309932272 #> 11242 0.309944299 #> 11243 0.309956293 #> 11244 0.309968253 #> 11245 0.309980179 #> 11246 0.309992072 #> 11247 0.310003930 #> 11248 0.310015755 #> 11249 0.310027546 #> 11250 0.310039303 #> 11251 0.310051027 #> 11252 0.310062716 #> 11253 0.310074372 #> 11254 0.310085994 #> 11255 0.310097581 #> 11256 0.310109135 #> 11257 0.310120655 #> 11258 0.310132141 #> 11259 0.310143593 #> 11260 0.310155011 #> 11261 0.310166394 #> 11262 0.310177744 #> 11263 0.310189060 #> 11264 0.310200341 #> 11265 0.310211589 #> 11266 0.310222802 #> 11267 0.310233981 #> 11268 0.310245126 #> 11269 0.310256237 #> 11270 0.310267313 #> 11271 0.310278356 #> 11272 0.310289364 #> 11273 0.310300338 #> 11274 0.310311277 #> 11275 0.310322183 #> 11276 0.310333054 #> 11277 0.310343890 #> 11278 0.310354693 #> 11279 0.310365461 #> 11280 0.310376194 #> 11281 0.310386893 #> 11282 0.310397558 #> 11283 0.310408189 #> 11284 0.310418785 #> 11285 0.310429346 #> 11286 0.310439874 #> 11287 0.310450366 #> 11288 0.310460824 #> 11289 0.310471248 #> 11290 0.310481637 #> 11291 0.310491992 #> 11292 0.310502312 #> 11293 0.310512598 #> 11294 0.310522849 #> 11295 0.310533065 #> 11296 0.310543247 #> 11297 0.310553394 #> 11298 0.310563507 #> 11299 0.310573585 #> 11300 0.310583628 #> 11301 0.310593637 #> 11302 0.310603611 #> 11303 0.310613551 #> 11304 0.310623456 #> 11305 0.310633326 #> 11306 0.310643161 #> 11307 0.310652962 #> 11308 0.310662728 #> 11309 0.310672459 #> 11310 0.310682155 #> 11311 0.310691817 #> 11312 0.310701444 #> 11313 0.310711036 #> 11314 0.310720594 #> 11315 0.310730116 #> 11316 0.310739604 #> 11317 0.310749057 #> 11318 0.310758475 #> 11319 0.310767859 #> 11320 0.310777207 #> 11321 0.310786521 #> 11322 0.310795800 #> 11323 0.310805044 #> 11324 0.310814253 #> 11325 0.310823428 #> 11326 0.310832567 #> 11327 0.310841672 #> 11328 0.310850741 #> 11329 0.310859776 #> 11330 0.310868776 #> 11331 0.310877741 #> 11332 0.310886671 #> 11333 0.310895566 #> 11334 0.310904426 #> 11335 0.310913251 #> 11336 0.310922042 #> 11337 0.310930797 #> 11338 0.310939517 #> 11339 0.310948203 #> 11340 0.310956853 #> 11341 0.310965469 #> 11342 0.310974049 #> 11343 0.310982595 #> 11344 0.310991106 #> 11345 0.310999581 #> 11346 0.311008022 #> 11347 0.311016428 #> 11348 0.311024798 #> 11349 0.311033134 #> 11350 0.311041435 #> 11351 0.311049700 #> 11352 0.311057931 #> 11353 0.311066126 #> 11354 0.311074287 #> 11355 0.311082413 #> 11356 0.311090503 #> 11357 0.311098559 #> 11358 0.311106579 #> 11359 0.311114565 #> 11360 0.311122515 #> 11361 0.311130431 #> 11362 0.311138311 #> 11363 0.311146157 #> 11364 0.311153967 #> 11365 0.311161743 #> 11366 0.311169483 #> 11367 0.311177188 #> 11368 0.311184859 #> 11369 0.311192494 #> 11370 0.311200094 #> 11371 0.311207659 #> 11372 0.311215189 #> 11373 0.311222684 #> 11374 0.311230145 #> 11375 0.311237570 #> 11376 0.311244960 #> 11377 0.311252315 #> 11378 0.311259635 #> 11379 0.311266920 #> 11380 0.311274169 #> 11381 0.311281384 #> 11382 0.311288564 #> 11383 0.311295709 #> 11384 0.311302819 #> 11385 0.311309894 #> 11386 0.311316934 #> 11387 0.311323938 #> 11388 0.311330908 #> 11389 0.311337843 #> 11390 0.311344743 #> 11391 0.311351608 #> 11392 0.311358438 #> 11393 0.311365232 #> 11394 0.311371992 #> 11395 0.311378717 #> 11396 0.311385407 #> 11397 0.311392062 #> 11398 0.311398682 #> 11399 0.311405267 #> 11400 0.311411817 #> 11401 0.311418332 #> 11402 0.311424812 #> 11403 0.311431257 #> 11404 0.311437668 #> 11405 0.311444043 #> 11406 0.311450383 #> 11407 0.311456689 #> 11408 0.311462959 #> 11409 0.311469195 #> 11410 0.311475396 #> 11411 0.311481561 #> 11412 0.311487692 #> 11413 0.311493788 #> 11414 0.311499850 #> 11415 0.311505876 #> 11416 0.311511867 #> 11417 0.311517824 #> 11418 0.311523746 #> 11419 0.311529632 #> 11420 0.311535485 #> 11421 0.311541302 #> 11422 0.311547084 #> 11423 0.311552832 #> 11424 0.311558545 #> 11425 0.311564223 #> 11426 0.311569866 #> 11427 0.311575474 #> 11428 0.311581048 #> 11429 0.311586587 #> 11430 0.311592091 #> 11431 0.311597560 #> 11432 0.311602995 #> 11433 0.311608395 #> 11434 0.311613760 #> 11435 0.311619091 #> 11436 0.311624387 #> 11437 0.311629648 #> 11438 0.311634874 #> 11439 0.311640066 #> 11440 0.311645223 #> 11441 0.311650346 #> 11442 0.311655434 #> 11443 0.311660487 #> 11444 0.311665506 #> 11445 0.311670490 #> 11446 0.311675439 #> 11447 0.311680354 #> 11448 0.311685235 #> 11449 0.311690081 #> 11450 0.311694892 #> 11451 0.311699669 #> 11452 0.311704411 #> 11453 0.311709119 #> 11454 0.311713792 #> 11455 0.311718431 #> 11456 0.311723035 #> 11457 0.311727605 #> 11458 0.311732140 #> 11459 0.311736641 #> 11460 0.311741108 #> 11461 0.311745540 #> 11462 0.311749938 #> 11463 0.311754301 #> 11464 0.311758631 #> 11465 0.311762925 #> 11466 0.311767186 #> 11467 0.311771412 #> 11468 0.311775604 #> 11469 0.311779761 #> 11470 0.311783885 #> 11471 0.311787974 #> 11472 0.311792028 #> 11473 0.311796049 #> 11474 0.311800035 #> 11475 0.311803987 #> 11476 0.311807905 #> 11477 0.311811789 #> 11478 0.311815639 #> 11479 0.311819454 #> 11480 0.311823236 #> 11481 0.311826983 #> 11482 0.311830696 #> 11483 0.311834375 #> 11484 0.311838021 #> 11485 0.311841632 #> 11486 0.311845209 #> 11487 0.311848752 #> 11488 0.311852261 #> 11489 0.311855736 #> 11490 0.311859177 #> 11491 0.311862584 #> 11492 0.311865958 #> 11493 0.311869297 #> 11494 0.311872603 #> 11495 0.311875874 #> 11496 0.311879112 #> 11497 0.311882316 #> 11498 0.311885487 #> 11499 0.311888623 #> 11500 0.311891726 #> 11501 0.311894795 #> 11502 0.311897830 #> 11503 0.311900831 #> 11504 0.311903799 #> 11505 0.311906733 #> 11506 0.311909634 #> 11507 0.311912501 #> 11508 0.311915334 #> 11509 0.311918134 #> 11510 0.311920900 #> 11511 0.311923632 #> 11512 0.311926331 #> 11513 0.311928997 #> 11514 0.311931629 #> 11515 0.311934227 #> 11516 0.311936792 #> 11517 0.311939324 #> 11518 0.311941822 #> 11519 0.311944287 #> 11520 0.311946718 #> 11521 0.311949116 #> 11522 0.311951481 #> 11523 0.311953813 #> 11524 0.311956111 #> 11525 0.311958376 #> 11526 0.311960607 #> 11527 0.311962806 #> 11528 0.311964971 #> 11529 0.311967103 #> 11530 0.311969202 #> 11531 0.311971268 #> 11532 0.311973301 #> 11533 0.311975300 #> 11534 0.311977267 #> 11535 0.311979201 #> 11536 0.311981101 #> 11537 0.311982969 #> 11538 0.311984803 #> 11539 0.311986605 #> 11540 0.311988374 #> 11541 0.311990109 #> 11542 0.311991812 #> 11543 0.311993482 #> 11544 0.311995120 #> 11545 0.311996724 #> 11546 0.311998296 #> 11547 0.311999835 #> 11548 0.312001341 #> 11549 0.312002814 #> 11550 0.312004255 #> 11551 0.312005663 #> 11552 0.312007039 #> 11553 0.312008382 #> 11554 0.312009692 #> 11555 0.312010970 #> 11556 0.312012215 #> 11557 0.312013428 #> 11558 0.312014608 #> 11559 0.312015756 #> 11560 0.312016871 #> 11561 0.312017954 #> 11562 0.312019005 #> 11563 0.312020023 #> 11564 0.312021009 #> 11565 0.312021962 #> 11566 0.312022884 #> 11567 0.312023773 #> 11568 0.312024630 #> 11569 0.312025454 #> 11570 0.312026247 #> 11571 0.312027007 #> 11572 0.312027736 #> 11573 0.312028432 #> 11574 0.312029096 #> 11575 0.312029728 #> 11576 0.312030328 #> 11577 0.312030896 #> 11578 0.312031432 #> 11579 0.312031937 #> 11580 0.312032409 #> 11581 0.312032849 #> 11582 0.312033258 #> 11583 0.312033635 #> 11584 0.312033980 #> 11585 0.312034293 #> 11586 0.312034575 #> 11587 0.312034825 #> 11588 0.312035043 #> 11589 0.312035230 #> 11590 0.312035385 #> 11591 0.312035508 #> 11592 0.312035600 #> 11593 0.312035660 #> 11594 0.312035689 #> 11595 0.312035686 #> 11596 0.312035652 #> 11597 0.312035587 #> 11598 0.312035490 #> 11599 0.312035362 #> 11600 0.312035202 #> 11601 0.312035012 #> 11602 0.312034789 #> 11603 0.312034536 #> 11604 0.312034252 #> 11605 0.312033936 #> 11606 0.312033589 #> 11607 0.312033212 #> 11608 0.312032803 #> 11609 0.312032363 #> 11610 0.312031892 #> 11611 0.312031390 #> 11612 0.312030857 #> 11613 0.312030293 #> 11614 0.312029698 #> 11615 0.312029073 #> 11616 0.312028416 #> 11617 0.312027729 #> 11618 0.312027011 #> 11619 0.312026262 #> 11620 0.312025483 #> 11621 0.312024673 #> 11622 0.312023832 #> 11623 0.312022961 #> 11624 0.312022059 #> 11625 0.312021127 #> 11626 0.312020164 #> 11627 0.312019170 #> 11628 0.312018146 #> 11629 0.312017092 #> 11630 0.312016008 #> 11631 0.312014893 #> 11632 0.312013747 #> 11633 0.312012572 #> 11634 0.312011366 #> 11635 0.312010130 #> 11636 0.312008864 #> 11637 0.312007568 #> 11638 0.312006241 #> 11639 0.312004885 #> 11640 0.312003498 #> 11641 0.312002081 #> 11642 0.312000635 #> 11643 0.311999158 #> 11644 0.311997652 #> 11645 0.311996116 #> 11646 0.311994550 #> 11647 0.311992954 #> 11648 0.311991328 #> 11649 0.311989673 #> 11650 0.311987987 #> 11651 0.311986273 #> 11652 0.311984528 #> 11653 0.311982754 #> 11654 0.311980951 #> 11655 0.311979117 #> 11656 0.311977255 #> 11657 0.311975363 #> 11658 0.311973441 #> 11659 0.311971490 #> 11660 0.311969510 #> 11661 0.311967500 #> 11662 0.311965461 #> 11663 0.311963393 #> 11664 0.311961296 #> 11665 0.311959169 #> 11666 0.311957014 #> 11667 0.311954829 #> 11668 0.311952615 #> 11669 0.311950372 #> 11670 0.311948100 #> 11671 0.311945799 #> 11672 0.311943470 #> 11673 0.311941111 #> 11674 0.311938723 #> 11675 0.311936307 #> 11676 0.311933862 #> 11677 0.311931388 #> 11678 0.311928885 #> 11679 0.311926354 #> 11680 0.311923794 #> 11681 0.311921206 #> 11682 0.311918589 #> 11683 0.311915943 #> 11684 0.311913269 #> 11685 0.311910566 #> 11686 0.311907836 #> 11687 0.311905076 #> 11688 0.311902289 #> 11689 0.311899473 #> 11690 0.311896628 #> 11691 0.311893756 #> 11692 0.311890855 #> 11693 0.311887926 #> 11694 0.311884970 #> 11695 0.311881985 #> 11696 0.311878972 #> 11697 0.311875930 #> 11698 0.311872861 #> 11699 0.311869765 #> 11700 0.311866640 #> 11701 0.311863487 #> 11702 0.311860307 #> 11703 0.311857098 #> 11704 0.311853863 #> 11705 0.311850599 #> 11706 0.311847308 #> 11707 0.311843989 #> 11708 0.311840642 #> 11709 0.311837268 #> 11710 0.311833867 #> 11711 0.311830438 #> 11712 0.311826981 #> 11713 0.311823497 #> 11714 0.311819986 #> 11715 0.311816448 #> 11716 0.311812882 #> 11717 0.311809289 #> 11718 0.311805669 #> 11719 0.311802022 #> 11720 0.311798347 #> 11721 0.311794646 #> 11722 0.311790917 #> 11723 0.311787162 #> 11724 0.311783380 #> 11725 0.311779570 #> 11726 0.311775734 #> 11727 0.311771871 #> 11728 0.311767981 #> 11729 0.311764065 #> 11730 0.311760121 #> 11731 0.311756152 #> 11732 0.311752155 #> 11733 0.311748132 #> 11734 0.311744082 #> 11735 0.311740006 #> 11736 0.311735903 #> 11737 0.311731774 #> 11738 0.311727619 #> 11739 0.311723437 #> 11740 0.311719229 #> 11741 0.311714994 #> 11742 0.311710734 #> 11743 0.311706447 #> 11744 0.311702134 #> 11745 0.311697795 #> 11746 0.311693430 #> 11747 0.311689039 #> 11748 0.311684622 #> 11749 0.311680179 #> 11750 0.311675710 #> 11751 0.311671215 #> 11752 0.311666695 #> 11753 0.311662148 #> 11754 0.311657576 #> 11755 0.311652979 #> 11756 0.311648355 #> 11757 0.311643706 #> 11758 0.311639032 #> 11759 0.311634332 #> 11760 0.311629606 #> 11761 0.311624855 #> 11762 0.311620079 #> 11763 0.311615277 #> 11764 0.311610450 #> 11765 0.311605598 #> 11766 0.311600721 #> 11767 0.311595818 #> 11768 0.311590890 #> 11769 0.311585937 #> 11770 0.311580960 #> 11771 0.311575957 #> 11772 0.311570929 #> 11773 0.311565876 #> 11774 0.311560798 #> 11775 0.311555696 #> 11776 0.311550568 #> 11777 0.311545416 #> 11778 0.311540239 #> 11779 0.311535038 #> 11780 0.311529812 #> 11781 0.311524561 #> 11782 0.311519286 #> 11783 0.311513986 #> 11784 0.311508662 #> 11785 0.311503313 #> 11786 0.311497941 #> 11787 0.311492543 #> 11788 0.311487122 #> 11789 0.311481676 #> 11790 0.311476206 #> 11791 0.311470712 #> 11792 0.311465194 #> 11793 0.311459651 #> 11794 0.311454085 #> 11795 0.311448495 #> 11796 0.311442881 #> 11797 0.311437243 #> 11798 0.311431581 #> 11799 0.311425895 #> 11800 0.311420185 #> 11801 0.311414452 #> 11802 0.311408695 #> 11803 0.311402915 #> 11804 0.311397111 #> 11805 0.311391283 #> 11806 0.311385432 #> 11807 0.311379558 #> 11808 0.311373660 #> 11809 0.311367738 #> 11810 0.311361794 #> 11811 0.311355826 #> 11812 0.311349835 #> 11813 0.311343821 #> 11814 0.311337783 #> 11815 0.311331723 #> 11816 0.311325639 #> 11817 0.311319533 #> 11818 0.311313403 #> 11819 0.311307251 #> 11820 0.311301075 #> 11821 0.311294877 #> 11822 0.311288656 #> 11823 0.311282413 #> 11824 0.311276146 #> 11825 0.311269858 #> 11826 0.311263546 #> 11827 0.311257212 #> 11828 0.311250855 #> 11829 0.311244476 #> 11830 0.311238075 #> 11831 0.311231651 #> 11832 0.311225205 #> 11833 0.311218736 #> 11834 0.311212246 #> 11835 0.311205733 #> 11836 0.311199198 #> 11837 0.311192641 #> 11838 0.311186061 #> 11839 0.311179460 #> 11840 0.311172837 #> 11841 0.311166192 #> 11842 0.311159525 #> 11843 0.311152836 #> 11844 0.311146126 #> 11845 0.311139393 #> 11846 0.311132639 #> 11847 0.311125864 #> 11848 0.311119067 #> 11849 0.311112248 #> 11850 0.311105408 #> 11851 0.311098546 #> 11852 0.311091663 #> 11853 0.311084758 #> 11854 0.311077832 #> 11855 0.311070885 #> 11856 0.311063917 #> 11857 0.311056927 #> 11858 0.311049917 #> 11859 0.311042885 #> 11860 0.311035832 #> 11861 0.311028758 #> 11862 0.311021663 #> 11863 0.311014548 #> 11864 0.311007411 #> 11865 0.311000254 #> 11866 0.310993076 #> 11867 0.310985877 #> 11868 0.310978657 #> 11869 0.310971417 #> 11870 0.310964157 #> 11871 0.310956875 #> 11872 0.310949574 #> 11873 0.310942251 #> 11874 0.310934909 #> 11875 0.310927546 #> 11876 0.310920163 #> 11877 0.310912759 #> 11878 0.310905335 #> 11879 0.310897892 #> 11880 0.310890428 #> 11881 0.310882943 #> 11882 0.310875439 #> 11883 0.310867915 #> 11884 0.310860371 #> 11885 0.310852808 #> 11886 0.310845224 #> 11887 0.310837620 #> 11888 0.310829997 #> 11889 0.310822354 #> 11890 0.310814692 #> 11891 0.310807009 #> 11892 0.310799308 #> 11893 0.310791587 #> 11894 0.310783846 #> 11895 0.310776086 #> 11896 0.310768306 #> 11897 0.310760508 #> 11898 0.310752689 #> 11899 0.310744852 #> 11900 0.310736996 #> 11901 0.310729120 #> 11902 0.310721226 #> 11903 0.310713312 #> 11904 0.310705379 #> 11905 0.310697428 #> 11906 0.310689457 #> 11907 0.310681468 #> 11908 0.310673460 #> 11909 0.310665433 #> 11910 0.310657387 #> 11911 0.310649323 #> 11912 0.310641240 #> 11913 0.310633139 #> 11914 0.310625019 #> 11915 0.310616880 #> 11916 0.310608723 #> 11917 0.310600548 #> 11918 0.310592355 #> 11919 0.310584143 #> 11920 0.310575913 #> 11921 0.310567665 #> 11922 0.310559398 #> 11923 0.310551114 #> 11924 0.310542812 #> 11925 0.310534491 #> 11926 0.310526153 #> 11927 0.310517797 #> 11928 0.310509423 #> 11929 0.310501031 #> 11930 0.310492621 #> 11931 0.310484194 #> 11932 0.310475749 #> 11933 0.310467286 #> 11934 0.310458806 #> 11935 0.310450308 #> 11936 0.310441793 #> 11937 0.310433261 #> 11938 0.310424711 #> 11939 0.310416143 #> 11940 0.310407559 #> 11941 0.310398957 #> 11942 0.310390338 #> 11943 0.310381702 #> 11944 0.310373049 #> 11945 0.310364379 #> 11946 0.310355692 #> 11947 0.310346988 #> 11948 0.310338267 #> 11949 0.310329529 #> 11950 0.310320775 #> 11951 0.310312003 #> 11952 0.310303215 #> 11953 0.310294411 #> 11954 0.310285590 #> 11955 0.310276752 #> 11956 0.310267898 #> 11957 0.310259027 #> 11958 0.310250140 #> 11959 0.310241236 #> 11960 0.310232316 #> 11961 0.310223380 #> 11962 0.310214428 #> 11963 0.310205460 #> 11964 0.310196475 #> 11965 0.310187475 #> 11966 0.310178458 #> 11967 0.310169425 #> 11968 0.310160377 #> 11969 0.310151312 #> 11970 0.310142232 #> 11971 0.310133136 #> 11972 0.310124024 #> 11973 0.310114897 #> 11974 0.310105754 #> 11975 0.310096595 #> 11976 0.310087421 #> 11977 0.310078232 #> 11978 0.310069027 #> 11979 0.310059806 #> 11980 0.310050570 #> 11981 0.310041319 #> 11982 0.310032053 #> 11983 0.310022771 #> 11984 0.310013475 #> 11985 0.310004163 #> 11986 0.309994836 #> 11987 0.309985494 #> 11988 0.309976137 #> 11989 0.309966766 #> 11990 0.309957379 #> 11991 0.309947978 #> 11992 0.309938562 #> 11993 0.309929131 #> 11994 0.309919685 #> 11995 0.309910225 #> 11996 0.309900751 #> 11997 0.309891261 #> 11998 0.309881758 #> 11999 0.309872240 #> 12000 0.309862707 #> 12001 0.309853161 #> 12002 0.309843599 #> 12003 0.309834024 #> 12004 0.309824435 #> 12005 0.309814831 #> 12006 0.309805214 #> 12007 0.309795582 #> 12008 0.309785936 #> 12009 0.309776277 #> 12010 0.309766603 #> 12011 0.309756916 #> 12012 0.309747215 #> 12013 0.309737500 #> 12014 0.309727771 #> 12015 0.309718029 #> 12016 0.309708273 #> 12017 0.309698504 #> 12018 0.309688721 #> 12019 0.309678925 #> 12020 0.309669115 #> 12021 0.309659292 #> 12022 0.309649456 #> 12023 0.309639606 #> 12024 0.309629743 #> 12025 0.309619867 #> 12026 0.309609978 #> 12027 0.309600076 #> 12028 0.309590161 #> 12029 0.309580233 #> 12030 0.309570292 #> 12031 0.309560338 #> 12032 0.309550371 #> 12033 0.309540391 #> 12034 0.309530399 #> 12035 0.309520394 #> 12036 0.309510377 #> 12037 0.309500347 #> 12038 0.309490304 #> 12039 0.309480249 #> 12040 0.309470182 #> 12041 0.309460102 #> 12042 0.309450009 #> 12043 0.309439905 #> 12044 0.309429788 #> 12045 0.309419659 #> 12046 0.309409518 #> 12047 0.309399365 #> 12048 0.309389200 #> 12049 0.309379022 #> 12050 0.309368833 #> 12051 0.309358632 #> 12052 0.309348419 #> 12053 0.309338195 #> 12054 0.309327958 #> 12055 0.309317710 #> 12056 0.309307450 #> 12057 0.309297179 #> 12058 0.309286896 #> 12059 0.309276601 #> 12060 0.309266295 #> 12061 0.309255978 #> 12062 0.309245649 #> 12063 0.309235309 #> 12064 0.309224958 #> 12065 0.309214595 #> 12066 0.309204221 #> 12067 0.309193837 #> 12068 0.309183441 #> 12069 0.309173034 #> 12070 0.309162616 #> 12071 0.309152187 #> 12072 0.309141747 #> 12073 0.309131297 #> 12074 0.309120835 #> 12075 0.309110363 #> 12076 0.309099880 #> 12077 0.309089387 #> 12078 0.309078883 #> 12079 0.309068368 #> 12080 0.309057843 #> 12081 0.309047308 #> 12082 0.309036762 #> 12083 0.309026206 #> 12084 0.309015639 #> 12085 0.309005062 #> 12086 0.308994475 #> 12087 0.308983878 #> 12088 0.308973271 #> 12089 0.308962654 #> 12090 0.308952026 #> 12091 0.308941389 #> 12092 0.308930742 #> 12093 0.308920085 #> 12094 0.308909418 #> 12095 0.308898741 #> 12096 0.308888055 #> 12097 0.308877359 #> 12098 0.308866653 #> 12099 0.308855938 #> 12100 0.308845213 #> 12101 0.308834479 #> 12102 0.308823735 #> 12103 0.308812982 #> 12104 0.308802219 #> 12105 0.308791447 #> 12106 0.308780666 #> 12107 0.308769876 #> 12108 0.308759077 #> 12109 0.308748268 #> 12110 0.308737451 #> 12111 0.308726624 #> 12112 0.308715789 #> 12113 0.308704945 #> 12114 0.308694091 #> 12115 0.308683229 #> 12116 0.308672359 #> 12117 0.308661479 #> 12118 0.308650591 #> 12119 0.308639694 #> 12120 0.308628789 #> 12121 0.308617875 #> 12122 0.308606952 #> 12123 0.308596021 #> 12124 0.308585082 #> 12125 0.308574135 #> 12126 0.308563179 #> 12127 0.308552215 #> 12128 0.308541243 #> 12129 0.308530262 #> 12130 0.308519274 #> 12131 0.308508277 #> 12132 0.308497272 #> 12133 0.308486260 #> 12134 0.308475240 #> 12135 0.308464211 #> 12136 0.308453175 #> 12137 0.308442131 #> 12138 0.308431080 #> 12139 0.308420021 #> 12140 0.308408954 #> 12141 0.308397879 #> 12142 0.308386797 #> 12143 0.308375708 #> 12144 0.308364611 #> 12145 0.308353507 #> 12146 0.308342395 #> 12147 0.308331276 #> 12148 0.308320150 #> 12149 0.308309017 #> 12150 0.308297876 #> 12151 0.308286729 #> 12152 0.308275574 #> 12153 0.308264412 #> 12154 0.308253244 #> 12155 0.308242068 #> 12156 0.308230886 #> 12157 0.308219697 #> 12158 0.308208501 #> 12159 0.308197298 #> 12160 0.308186089 #> 12161 0.308174873 #> 12162 0.308163650 #> 12163 0.308152421 #> 12164 0.308141185 #> 12165 0.308129943 #> 12166 0.308118695 #> 12167 0.308107440 #> 12168 0.308096179 #> 12169 0.308084912 #> 12170 0.308073638 #> 12171 0.308062358 #> 12172 0.308051073 #> 12173 0.308039781 #> 12174 0.308028483 #> 12175 0.308017179 #> 12176 0.308005869 #> 12177 0.307994553 #> 12178 0.307983232 #> 12179 0.307971905 #> 12180 0.307960571 #> 12181 0.307949233 #> 12182 0.307937888 #> 12183 0.307926538 #> 12184 0.307915183 #> 12185 0.307903822 #> 12186 0.307892455 #> 12187 0.307881083 #> 12188 0.307869706 #> 12189 0.307858323 #> 12190 0.307846936 #> 12191 0.307835542 #> 12192 0.307824144 #> 12193 0.307812741 #> 12194 0.307801332 #> 12195 0.307789919 #> 12196 0.307778500 #> 12197 0.307767076 #> 12198 0.307755648 #> 12199 0.307744215 #> 12200 0.307732777 #> 12201 0.307721334 #> 12202 0.307709886 #> 12203 0.307698434 #> 12204 0.307686977 #> 12205 0.307675516 #> 12206 0.307664050 #> 12207 0.307652579 #> 12208 0.307641104 #> 12209 0.307629625 #> 12210 0.307618141 #> 12211 0.307606653 #> 12212 0.307595161 #> 12213 0.307583665 #> 12214 0.307572164 #> 12215 0.307560659 #> 12216 0.307549150 #> 12217 0.307537637 #> 12218 0.307526121 #> 12219 0.307514600 #> 12220 0.307503075 #> 12221 0.307491547 #> 12222 0.307480014 #> 12223 0.307468478 #> 12224 0.307456938 #> 12225 0.307445395 #> 12226 0.307433848 #> 12227 0.307422297 #> 12228 0.307410743 #> 12229 0.307399185 #> 12230 0.307387624 #> 12231 0.307376060 #> 12232 0.307364492 #> 12233 0.307352921 #> 12234 0.307341346 #> 12235 0.307329769 #> 12236 0.307318188 #> 12237 0.307306604 #> 12238 0.307295017 #> 12239 0.307283427 #> 12240 0.307271834 #> 12241 0.307260238 #> 12242 0.307248639 #> 12243 0.307237037 #> 12244 0.307225433 #> 12245 0.307213826 #> 12246 0.307202216 #> 12247 0.307190603 #> 12248 0.307178988 #> 12249 0.307167370 #> 12250 0.307155749 #> 12251 0.307144127 #> 12252 0.307132501 #> 12253 0.307120873 #> 12254 0.307109243 #> 12255 0.307097611 #> 12256 0.307085976 #> 12257 0.307074339 #> 12258 0.307062700 #> 12259 0.307051059 #> 12260 0.307039416 #> 12261 0.307027770 #> 12262 0.307016123 #> 12263 0.307004474 #> 12264 0.306992822 #> 12265 0.306981169 #> 12266 0.306969514 #> 12267 0.306957858 #> 12268 0.306946199 #> 12269 0.306934539 #> 12270 0.306922877 #> 12271 0.306911214 #> 12272 0.306899549 #> 12273 0.306887882 #> 12274 0.306876214 #> 12275 0.306864545 #> 12276 0.306852874 #> 12277 0.306841202 #> 12278 0.306829529 #> 12279 0.306817854 #> 12280 0.306806178 #> 12281 0.306794501 #> 12282 0.306782823 #> 12283 0.306771144 #> 12284 0.306759463 #> 12285 0.306747782 #> 12286 0.306736100 #> 12287 0.306724417 #> 12288 0.306712733 #> 12289 0.306701048 #> 12290 0.306689363 #> 12291 0.306677676 #> 12292 0.306665989 #> 12293 0.306654302 #> 12294 0.306642614 #> 12295 0.306630925 #> 12296 0.306619236 #> 12297 0.306607546 #> 12298 0.306595856 #> 12299 0.306584165 #> 12300 0.306572474 #> 12301 0.306560783 #> 12302 0.306549092 #> 12303 0.306537400 #> 12304 0.306525708 #> 12305 0.306514016 #> 12306 0.306502324 #> 12307 0.306490632 #> 12308 0.306478940 #> 12309 0.306467248 #> 12310 0.306455556 #> 12311 0.306443865 #> 12312 0.306432173 #> 12313 0.306420482 #> 12314 0.306408791 #> 12315 0.306397100 #> 12316 0.306385409 #> 12317 0.306373719 #> 12318 0.306362030 #> 12319 0.306350340 #> 12320 0.306338652 #> 12321 0.306326964 #> 12322 0.306315276 #> 12323 0.306303589 #> 12324 0.306291903 #> 12325 0.306280218 #> 12326 0.306268533 #> 12327 0.306256849 #> 12328 0.306245166 #> 12329 0.306233484 #> 12330 0.306221803 #> 12331 0.306210123 #> 12332 0.306198444 #> 12333 0.306186766 #> 12334 0.306175089 #> 12335 0.306163413 #> 12336 0.306151738 #> 12337 0.306140065 #> 12338 0.306128393 #> 12339 0.306116722 #> 12340 0.306105052 #> 12341 0.306093384 #> 12342 0.306081718 #> 12343 0.306070053 #> 12344 0.306058389 #> 12345 0.306046727 #> 12346 0.306035066 #> 12347 0.306023408 #> 12348 0.306011751 #> 12349 0.306000095 #> 12350 0.305988442 #> 12351 0.305976790 #> 12352 0.305965140 #> 12353 0.305953492 #> 12354 0.305941846 #> 12355 0.305930202 #> 12356 0.305918560 #> 12357 0.305906920 #> 12358 0.305895282 #> 12359 0.305883646 #> 12360 0.305872012 #> 12361 0.305860381 #> 12362 0.305848752 #> 12363 0.305837125 #> 12364 0.305825500 #> 12365 0.305813878 #> 12366 0.305802259 #> 12367 0.305790642 #> 12368 0.305779027 #> 12369 0.305767415 #> 12370 0.305755805 #> 12371 0.305744198 #> 12372 0.305732594 #> 12373 0.305720993 #> 12374 0.305709394 #> 12375 0.305697798 #> 12376 0.305686205 #> 12377 0.305674614 #> 12378 0.305663027 #> 12379 0.305651443 #> 12380 0.305639861 #> 12381 0.305628283 #> 12382 0.305616708 #> 12383 0.305605135 #> 12384 0.305593566 #> 12385 0.305582000 #> 12386 0.305570438 #> 12387 0.305558878 #> 12388 0.305547322 #> 12389 0.305535770 #> 12390 0.305524220 #> 12391 0.305512675 #> 12392 0.305501132 #> 12393 0.305489593 #> 12394 0.305478058 #> 12395 0.305466526 #> 12396 0.305454998 #> 12397 0.305443474 #> 12398 0.305431953 #> 12399 0.305420436 #> 12400 0.305408923 #> 12401 0.305397413 #> 12402 0.305385908 #> 12403 0.305374406 #> 12404 0.305362908 #> 12405 0.305351415 #> 12406 0.305339925 #> 12407 0.305328439 #> 12408 0.305316958 #> 12409 0.305305480 #> 12410 0.305294007 #> 12411 0.305282538 #> 12412 0.305271073 #> 12413 0.305259613 #> 12414 0.305248157 #> 12415 0.305236705 #> 12416 0.305225258 #> 12417 0.305213815 #> 12418 0.305202376 #> 12419 0.305190942 #> 12420 0.305179513 #> 12421 0.305168088 #> 12422 0.305156668 #> 12423 0.305145253 #> 12424 0.305133842 #> 12425 0.305122436 #> 12426 0.305111035 #> 12427 0.305099639 #> 12428 0.305088247 #> 12429 0.305076860 #> 12430 0.305065479 #> 12431 0.305054102 #> 12432 0.305042730 #> 12433 0.305031364 #> 12434 0.305020002 #> 12435 0.305008646 #> 12436 0.304997295 #> 12437 0.304985949 #> 12438 0.304974608 #> 12439 0.304963273 #> 12440 0.304951942 #> 12441 0.304940618 #> 12442 0.304929298 #> 12443 0.304917984 #> 12444 0.304906676 #> 12445 0.304895373 #> 12446 0.304884075 #> 12447 0.304872783 #> 12448 0.304861497 #> 12449 0.304850216 #> 12450 0.304838941 #> 12451 0.304827672 #> 12452 0.304816409 #> 12453 0.304805151 #> 12454 0.304793899 #> 12455 0.304782653 #> 12456 0.304771413 #> 12457 0.304760179 #> 12458 0.304748951 #> 12459 0.304737729 #> 12460 0.304726513 #> 12461 0.304715303 #> 12462 0.304704099 #> 12463 0.304692902 #> 12464 0.304681710 #> 12465 0.304670525 #> 12466 0.304659346 #> 12467 0.304648174 #> 12468 0.304637007 #> 12469 0.304625847 #> 12470 0.304614694 #> 12471 0.304603547 #> 12472 0.304592406 #> 12473 0.304581272 #> 12474 0.304570145 #> 12475 0.304559024 #> 12476 0.304547910 #> 12477 0.304536802 #> 12478 0.304525702 #> 12479 0.304514607 #> 12480 0.304503520 #> 12481 0.304492440 #> 12482 0.304481366 #> 12483 0.304470299 #> 12484 0.304459239 #> 12485 0.304448187 #> 12486 0.304437141 #> 12487 0.304426102 #> 12488 0.304415070 #> 12489 0.304404045 #> 12490 0.304393028 #> 12491 0.304382017 #> 12492 0.304371014 #> 12493 0.304360018 #> 12494 0.304349029 #> 12495 0.304338048 #> 12496 0.304327074 #> 12497 0.304316107 #> 12498 0.304305148 #> 12499 0.304294196 #> 12500 0.304283251 #> 12501 0.304272314 #> 12502 0.304261385 #> 12503 0.304250463 #> 12504 0.304239549 #> 12505 0.304228643 #> 12506 0.304217744 #> 12507 0.304206853 #> 12508 0.304195969 #> 12509 0.304185094 #> 12510 0.304174226 #> 12511 0.304163366 #> 12512 0.304152514 #> 12513 0.304141670 #> 12514 0.304130834 #> 12515 0.304120006 #> 12516 0.304109186 #> 12517 0.304098374 #> 12518 0.304087570 #> 12519 0.304076774 #> 12520 0.304065986 #> 12521 0.304055207 #> 12522 0.304044435 #> 12523 0.304033672 #> 12524 0.304022917 #> 12525 0.304012171 #> 12526 0.304001433 #> 12527 0.303990703 #> 12528 0.303979982 #> 12529 0.303969269 #> 12530 0.303958565 #> 12531 0.303947869 #> 12532 0.303937182 #> 12533 0.303926503 #> 12534 0.303915833 #> 12535 0.303905172 #> 12536 0.303894519 #> 12537 0.303883875 #> 12538 0.303873240 #> 12539 0.303862614 #> 12540 0.303851996 #> 12541 0.303841387 #> 12542 0.303830787 #> 12543 0.303820196 #> 12544 0.303809614 #> 12545 0.303799041 #> 12546 0.303788477 #> 12547 0.303777922 #> 12548 0.303767376 #> 12549 0.303756840 #> 12550 0.303746312 #> 12551 0.303735793 #> 12552 0.303725284 #> 12553 0.303714784 #> 12554 0.303704293 #> 12555 0.303693812 #> 12556 0.303683340 #> 12557 0.303672877 #> 12558 0.303662423 #> 12559 0.303651979 #> 12560 0.303641545 #> 12561 0.303631120 #> 12562 0.303620704 #> 12563 0.303610298 #> 12564 0.303599902 #> 12565 0.303589515 #> 12566 0.303579138 #> 12567 0.303568770 #> 12568 0.303558412 #> 12569 0.303548064 #> 12570 0.303537726 #> 12571 0.303527398 #> 12572 0.303517079 #> 12573 0.303506770 #> 12574 0.303496471 #> 12575 0.303486182 #> 12576 0.303475903 #> 12577 0.303465634 #> 12578 0.303455375 #> 12579 0.303445126 #> 12580 0.303434887 #> 12581 0.303424658 #> 12582 0.303414440 #> 12583 0.303404231 #> 12584 0.303394033 #> 12585 0.303383845 #> 12586 0.303373667 #> 12587 0.303363499 #> 12588 0.303353342 #> 12589 0.303343195 #> 12590 0.303333058 #> 12591 0.303322932 #> 12592 0.303312816 #> 12593 0.303302711 #> 12594 0.303292616 #> 12595 0.303282532 #> 12596 0.303272458 #> 12597 0.303262395 #> 12598 0.303252343 #> 12599 0.303242301 #> 12600 0.303232270 #> 12601 0.303222249 #> 12602 0.303212239 #> 12603 0.303202240 #> 12604 0.303192252 #> 12605 0.303182275 #> 12606 0.303172308 #> 12607 0.303162352 #> 12608 0.303152408 #> 12609 0.303142474 #> 12610 0.303132551 #> 12611 0.303122639 #> 12612 0.303112738 #> 12613 0.303102848 #> 12614 0.303092969 #> 12615 0.303083101 #> 12616 0.303073245 #> 12617 0.303063399 #> 12618 0.303053565 #> 12619 0.303043742 #> 12620 0.303033930 #> 12621 0.303024130 #> 12622 0.303014340 #> 12623 0.303004563 #> 12624 0.302994796 #> 12625 0.302985041 #> 12626 0.302975297 #> 12627 0.302965565 #> 12628 0.302955844 #> 12629 0.302946134 #> 12630 0.302936437 #> 12631 0.302926750 #> 12632 0.302917075 #> 12633 0.302907412 #> 12634 0.302897761 #> 12635 0.302888121 #> 12636 0.302878493 #> 12637 0.302868876 #> 12638 0.302859272 #> 12639 0.302849679 #> 12640 0.302840097 #> 12641 0.302830528 #> 12642 0.302820970 #> 12643 0.302811425 #> 12644 0.302801891 #> 12645 0.302792369 #> 12646 0.302782859 #> 12647 0.302773362 #> 12648 0.302763876 #> 12649 0.302754402 #> 12650 0.302744940 #> 12651 0.302735490 #> 12652 0.302726053 #> 12653 0.302716627 #> 12654 0.302707214 #> 12655 0.302697813 #> 12656 0.302688424 #> 12657 0.302679047 #> 12658 0.302669683 #> 12659 0.302660331 #> 12660 0.302650991 #> 12661 0.302641664 #> 12662 0.302632348 #> 12663 0.302623046 #> 12664 0.302613756 #> 12665 0.302604478 #> 12666 0.302595212 #> 12667 0.302585960 #> 12668 0.302576719 #> 12669 0.302567491 #> 12670 0.302558276 #> 12671 0.302549074 #> 12672 0.302539884 #> 12673 0.302530706 #> 12674 0.302521542 #> 12675 0.302512390 #> 12676 0.302503251 #> 12677 0.302494124 #> 12678 0.302485010 #> 12679 0.302475910 #> 12680 0.302466822 #> 12681 0.302457746 #> 12682 0.302448684 #> 12683 0.302439634 #> 12684 0.302430598 #> 12685 0.302421574 #> 12686 0.302412564 #> 12687 0.302403566 #> 12688 0.302394581 #> 12689 0.302385610 #> 12690 0.302376651 #> 12691 0.302367706 #> 12692 0.302358774 #> 12693 0.302349855 #> 12694 0.302340949 #> 12695 0.302332056 #> 12696 0.302323176 #> 12697 0.302314310 #> 12698 0.302305457 #> 12699 0.302296617 #> 12700 0.302287790 #> 12701 0.302278977 #> 12702 0.302270177 #> 12703 0.302261391 #> 12704 0.302252618 #> 12705 0.302243858 #> 12706 0.302235112 #> 12707 0.302226379 #> 12708 0.302217660 #> 12709 0.302208954 #> 12710 0.302200262 #> 12711 0.302191583 #> 12712 0.302182918 #> 12713 0.302174267 #> 12714 0.302165629 #> 12715 0.302157005 #> 12716 0.302148394 #> 12717 0.302139797 #> 12718 0.302131214 #> 12719 0.302122645 #> 12720 0.302114089 #> 12721 0.302105547 #> 12722 0.302097019 #> 12723 0.302088505 #> 12724 0.302080005 #> 12725 0.302071518 #> 12726 0.302063045 #> 12727 0.302054587 #> 12728 0.302046142 #> 12729 0.302037711 #> 12730 0.302029294 #> 12731 0.302020892 #> 12732 0.302012503 #> 12733 0.302004128 #> 12734 0.301995767 #> 12735 0.301987421 #> 12736 0.301979088 #> 12737 0.301970770 #> 12738 0.301962466 #> 12739 0.301954176 #> 12740 0.301945900 #> 12741 0.301937638 #> 12742 0.301929391 #> 12743 0.301921158 #> 12744 0.301912939 #> 12745 0.301904734 #> 12746 0.301896544 #> 12747 0.301888368 #> 12748 0.301880207 #> 12749 0.301872059 #> 12750 0.301863927 #> 12751 0.301855808 #> 12752 0.301847704 #> 12753 0.301839615 #> 12754 0.301831540 #> 12755 0.301823479 #> 12756 0.301815433 #> 12757 0.301807402 #> 12758 0.301799385 #> 12759 0.301791383 #> 12760 0.301783395 #> 12761 0.301775422 #> 12762 0.301767464 #> 12763 0.301759520 #> 12764 0.301751591 #> 12765 0.301743677 #> 12766 0.301735777 #> 12767 0.301727892 #> 12768 0.301720022 #> 12769 0.301712167 #> 12770 0.301704326 #> 12771 0.301696500 #> 12772 0.301688689 #> 12773 0.301680893 #> 12774 0.301673112 #> 12775 0.301665346 #> 12776 0.301657594 #> 12777 0.301649858 #> 12778 0.301642136 #> 12779 0.301634430 #> 12780 0.301626738 #> 12781 0.301619061 #> 12782 0.301611400 #> 12783 0.301603753 #> 12784 0.301596122 #> 12785 0.301588505 #> 12786 0.301580904 #> 12787 0.301573318 #> 12788 0.301565747 #> 12789 0.301558191 #> 12790 0.301550650 #> 12791 0.301543125 #> 12792 0.301535614 #> 12793 0.301528119 #> 12794 0.301520639 #> 12795 0.301513174 #> 12796 0.301505725 #> 12797 0.301498291 #> 12798 0.301490872 #> 12799 0.301483469 #> 12800 0.301476081 #> 12801 0.301468708 #> 12802 0.301461350 #> 12803 0.301454008 #> 12804 0.301446682 #> 12805 0.301439371 #> 12806 0.301432075 #> 12807 0.301424795 #> 12808 0.301417530 #> 12809 0.301410281 #> 12810 0.301403047 #> 12811 0.301395829 #> 12812 0.301388626 #> 12813 0.301381439 #> 12814 0.301374267 #> 12815 0.301367111 #> 12816 0.301359971 #> 12817 0.301352846 #> 12818 0.301345737 #> 12819 0.301338644 #> 12820 0.301331566 #> 12821 0.301324504 #> 12822 0.301317457 #> 12823 0.301310427 #> 12824 0.301303412 #> 12825 0.301296413 #> 12826 0.301289429 #> 12827 0.301282462 #> 12828 0.301275510 #> 12829 0.301268574 #> 12830 0.301261654 #> 12831 0.301254750 #> 12832 0.301247861 #> 12833 0.301240989 #> 12834 0.301234132 #> 12835 0.301227291 #> 12836 0.301220466 #> 12837 0.301213657 #> 12838 0.301206864 #> 12839 0.301200087 #> 12840 0.301193326 #> 12841 0.301186581 #> 12842 0.301179852 #> 12843 0.301173140 #> 12844 0.301166443 #> 12845 0.301159762 #> 12846 0.301153097 #> 12847 0.301146448 #> 12848 0.301139816 #> 12849 0.301133199 #> 12850 0.301126599 #> 12851 0.301120014 #> 12852 0.301113446 #> 12853 0.301106894 #> 12854 0.301100359 #> 12855 0.301093839 #> 12856 0.301087336 #> 12857 0.301080849 #> 12858 0.301074378 #> 12859 0.301067923 #> 12860 0.301061485 #> 12861 0.301055063 #> 12862 0.301048657 #> 12863 0.301042268 #> 12864 0.301035894 #> 12865 0.301029537 #> 12866 0.301023197 #> 12867 0.301016873 #> 12868 0.301010565 #> 12869 0.301004274 #> 12870 0.300997999 #> 12871 0.300991740 #> 12872 0.300985498 #> 12873 0.300979272 #> 12874 0.300973063 #> 12875 0.300966870 #> 12876 0.300960694 #> 12877 0.300954534 #> 12878 0.300948390 #> 12879 0.300942264 #> 12880 0.300936153 #> 12881 0.300930059 #> 12882 0.300923982 #> 12883 0.300917921 #> 12884 0.300911877 #> 12885 0.300905850 #> 12886 0.300899839 #> 12887 0.300893844 #> 12888 0.300887867 #> 12889 0.300881905 #> 12890 0.300875961 #> 12891 0.300870033 #> 12892 0.300864122 #> 12893 0.300858227 #> 12894 0.300852350 #> 12895 0.300846488 #> 12896 0.300840644 #> 12897 0.300834816 #> 12898 0.300829005 #> 12899 0.300823211 #> 12900 0.300817434 #> 12901 0.300811673 #> 12902 0.300805929 #> 12903 0.300800202 #> 12904 0.300794491 #> 12905 0.300788798 #> 12906 0.300783121 #> 12907 0.300777461 #> 12908 0.300771818 #> 12909 0.300766192 #> 12910 0.300760582 #> 12911 0.300754990 #> 12912 0.300749414 #> 12913 0.300743855 #> 12914 0.300738313 #> 12915 0.300732788 #> 12916 0.300727280 #> 12917 0.300721789 #> 12918 0.300716315 #> 12919 0.300710858 #> 12920 0.300705417 #> 12921 0.300699994 #> 12922 0.300694588 #> 12923 0.300689198 #> 12924 0.300683826 #> 12925 0.300678471 #> 12926 0.300673132 #> 12927 0.300667811 #> 12928 0.300662507 #> 12929 0.300657219 #> 12930 0.300651949 #> 12931 0.300646696 #> 12932 0.300641460 #> 12933 0.300636241 #> 12934 0.300631039 #> 12935 0.300625854 #> 12936 0.300620686 #> 12937 0.300615535 #> 12938 0.300610402 #> 12939 0.300605285 #> 12940 0.300600186 #> 12941 0.300595104 #> 12942 0.300590039 #> 12943 0.300584991 #> 12944 0.300579960 #> 12945 0.300574947 #> 12946 0.300569951 #> 12947 0.300564971 #> 12948 0.300560010 #> 12949 0.300555065 #> 12950 0.300550137 #> 12951 0.300545227 #> 12952 0.300540334 #> 12953 0.300535458 #> 12954 0.300530599 #> 12955 0.300525758 #> 12956 0.300520934 #> 12957 0.300516127 #> 12958 0.300511338 #> 12959 0.300506565 #> 12960 0.300501810 #> 12961 0.300497073 #> 12962 0.300492352 #> 12963 0.300487649 #> 12964 0.300482963 #> 12965 0.300478295 #> 12966 0.300473644 #> 12967 0.300469010 #> 12968 0.300464393 #> 12969 0.300459794 #> 12970 0.300455212 #> 12971 0.300450648 #> 12972 0.300446101 #> 12973 0.300441571 #> 12974 0.300437059 #> 12975 0.300432564 #> 12976 0.300428087 #> 12977 0.300423626 #> 12978 0.300419184 #> 12979 0.300414758 #> 12980 0.300410350 #> 12981 0.300405960 #> 12982 0.300401587 #> 12983 0.300397231 #> 12984 0.300392893 #> 12985 0.300388572 #> 12986 0.300384269 #> 12987 0.300379983 #> 12988 0.300375714 #> 12989 0.300371463 #> 12990 0.300367230 #> 12991 0.300363014 #> 12992 0.300358815 #> 12993 0.300354634 #> 12994 0.300350471 #> 12995 0.300346324 #> 12996 0.300342196 #> 12997 0.300338085 #> 12998 0.300333991 #> 12999 0.300329915 #> 13000 0.300325856 #> 13001 0.300321815 #> 13002 0.300317792 #> 13003 0.300313786 #> 13004 0.300309797 #> 13005 0.300305826 #> 13006 0.300301873 #> 13007 0.300297937 #> 13008 0.300294018 #> 13009 0.300290117 #> 13010 0.300286234 #> 13011 0.300282368 #> 13012 0.300278520 #> 13013 0.300274690 #> 13014 0.300270877 #> 13015 0.300267081 #> 13016 0.300263303 #> 13017 0.300259543 #> 13018 0.300255800 #> 13019 0.300252075 #> 13020 0.300248367 #> 13021 0.300244677 #> 13022 0.300241005 #> 13023 0.300237350 #> 13024 0.300233713 #> 13025 0.300230093 #> 13026 0.300226491 #> 13027 0.300222907 #> 13028 0.300219340 #> 13029 0.300215791 #> 13030 0.300212260 #> 13031 0.300208746 #> 13032 0.300205249 #> 13033 0.300201771 #> 13034 0.300198309 #> 13035 0.300194866 #> 13036 0.300191440 #> 13037 0.300188032 #> 13038 0.300184641 #> 13039 0.300181268 #> 13040 0.300177913 #> 13041 0.300174575 #> 13042 0.300171255 #> 13043 0.300167953 #> 13044 0.300164668 #> 13045 0.300161401 #> 13046 0.300158151 #> 13047 0.300154920 #> 13048 0.300151705 #> 13049 0.300148509 #> 13050 0.300145330 #> 13051 0.300142169 #> 13052 0.300139025 #> 13053 0.300135899 #> 13054 0.300132791 #> 13055 0.300129700 #> 13056 0.300126627 #> 13057 0.300123572 #> 13058 0.300120534 #> 13059 0.300117514 #> 13060 0.300114512 #> 13061 0.300111527 #> 13062 0.300108560 #> 13063 0.300105610 #> 13064 0.300102679 #> 13065 0.300099765 #> 13066 0.300096868 #> 13067 0.300093989 #> 13068 0.300091128 #> 13069 0.300088285 #> 13070 0.300085459 #> 13071 0.300082651 #> 13072 0.300079860 #> 13073 0.300077087 #> 13074 0.300074332 #> 13075 0.300071594 #> 13076 0.300068875 #> 13077 0.300066172 #> 13078 0.300063488 #> 13079 0.300060821 #> 13080 0.300058172 #> 13081 0.300055540 #> 13082 0.300052926 #> 13083 0.300050330 #> 13084 0.300047751 #> 13085 0.300045190 #> 13086 0.300042646 #> 13087 0.300040121 #> 13088 0.300037613 #> 13089 0.300035122 #> 13090 0.300032649 #> 13091 0.300030194 #> 13092 0.300027756 #> 13093 0.300025337 #> 13094 0.300022934 #> 13095 0.300020550 #> 13096 0.300018183 #> 13097 0.300015833 #> 13098 0.300013501 #> 13099 0.300011187 #> 13100 0.300008891 #> 13101 0.300006612 #> 13102 0.300004350 #> 13103 0.300002107 #> 13104 0.299999881 #> 13105 0.299997672 #> 13106 0.299995481 #> 13107 0.299993308 #> 13108 0.299991153 #> 13109 0.299989015 #> 13110 0.299986894 #> 13111 0.299984791 #> 13112 0.299982706 #> 13113 0.299980638 #> 13114 0.299978588 #> 13115 0.299976556 #> 13116 0.299974541 #> 13117 0.299972543 #> 13118 0.299970564 #> 13119 0.299968601 #> 13120 0.299966657 #> 13121 0.299964730 #> 13122 0.299962820 #> 13123 0.299960928 #> 13124 0.299959054 #> 13125 0.299957197 #> 13126 0.299955358 #> 13127 0.299953536 #> 13128 0.299951732 #> 13129 0.299949945 #> 13130 0.299948176 #> 13131 0.299946424 #> 13132 0.299944690 #> 13133 0.299942974 #> 13134 0.299941274 #> 13135 0.299939593 #> 13136 0.299937929 #> 13137 0.299936282 #> 13138 0.299934653 #> 13139 0.299933042 #> 13140 0.299931448 #> 13141 0.299929871 #> 13142 0.299928312 #> 13143 0.299926770 #> 13144 0.299925246 #> 13145 0.299923739 #> 13146 0.299922250 #> 13147 0.299920778 #> 13148 0.299919324 #> 13149 0.299917887 #> 13150 0.299916467 #> 13151 0.299915065 #> 13152 0.299913681 #> 13153 0.299912313 #> 13154 0.299910964 #> 13155 0.299909631 #> 13156 0.299908316 #> 13157 0.299907019 #> 13158 0.299905738 #> 13159 0.299904476 #> 13160 0.299903230 #> 13161 0.299902002 #> 13162 0.299900791 #> 13163 0.299899598 #> 13164 0.299898422 #> 13165 0.299897264 #> 13166 0.299896122 #> 13167 0.299894998 #> 13168 0.299893892 #> 13169 0.299892803 #> 13170 0.299891731 #> 13171 0.299890676 #> 13172 0.299889639 #> 13173 0.299888619 #> 13174 0.299887616 #> 13175 0.299886630 #> 13176 0.299885662 #> 13177 0.299884711 #> 13178 0.299883778 #> 13179 0.299882861 #> 13180 0.299881962 #> 13181 0.299881080 #> 13182 0.299880216 #> 13183 0.299879368 #> 13184 0.299878538 #> 13185 0.299877725 #> 13186 0.299876929 #> 13187 0.299876151 #> 13188 0.299875389 #> 13189 0.299874645 #> 13190 0.299873918 #> 13191 0.299873208 #> 13192 0.299872516 #> 13193 0.299871840 #> 13194 0.299871182 #> 13195 0.299870541 #> 13196 0.299869917 #> 13197 0.299869310 #> 13198 0.299868720 #> 13199 0.299868147 #> 13200 0.299867591 #> 13201 0.299867053 #> 13202 0.299866531 #> 13203 0.299866027 #> 13204 0.299865539 #> 13205 0.299865069 #> 13206 0.299864616 #> 13207 0.299864180 #> 13208 0.299863760 #> 13209 0.299863358 #> 13210 0.299862973 #> 13211 0.299862605 #> 13212 0.299862254 #> 13213 0.299861920 #> 13214 0.299861602 #> 13215 0.299861302 #> 13216 0.299861019 #> 13217 0.299860753 #> 13218 0.299860503 #> 13219 0.299860271 #> 13220 0.299860055 #> 13221 0.299859856 #> 13222 0.299859675 #> 13223 0.299859510 #> 13224 0.299859362 #> 13225 0.299859231 #> 13226 0.299859117 #> 13227 0.299859019 #> 13228 0.299858939 #> 13229 0.299858875 #> 13230 0.299858828 #> 13231 0.299858798 #> 13232 0.299858785 #> 13233 0.299858788 #> 13234 0.299858809 #> 13235 0.299858846 #> 13236 0.299858899 #> 13237 0.299858970 #> 13238 0.299859057 #> 13239 0.299859161 #> 13240 0.299859282 #> 13241 0.299859420 #> 13242 0.299859574 #> 13243 0.299859744 #> 13244 0.299859932 #> 13245 0.299860136 #> 13246 0.299860357 #> 13247 0.299860594 #> 13248 0.299860849 #> 13249 0.299861119 #> 13250 0.299861407 #> 13251 0.299861711 #> 13252 0.299862031 #> 13253 0.299862368 #> 13254 0.299862722 #> 13255 0.299863092 #> 13256 0.299863479 #> 13257 0.299863882 #> 13258 0.299864302 #> 13259 0.299864738 #> 13260 0.299865191 #> 13261 0.299865660 #> 13262 0.299866146 #> 13263 0.299866648 #> 13264 0.299867167 #> 13265 0.299867702 #> 13266 0.299868253 #> 13267 0.299868821 #> 13268 0.299869405 #> 13269 0.299870006 #> 13270 0.299870623 #> 13271 0.299871256 #> 13272 0.299871906 #> 13273 0.299872572 #> 13274 0.299873254 #> 13275 0.299873953 #> 13276 0.299874668 #> 13277 0.299875399 #> 13278 0.299876147 #> 13279 0.299876911 #> 13280 0.299877691 #> 13281 0.299878487 #> 13282 0.299879300 #> 13283 0.299880128 #> 13284 0.299880973 #> 13285 0.299881834 #> 13286 0.299882712 #> 13287 0.299883605 #> 13288 0.299884514 #> 13289 0.299885440 #> 13290 0.299886382 #> 13291 0.299887340 #> 13292 0.299888314 #> 13293 0.299889304 #> 13294 0.299890310 #> 13295 0.299891332 #> 13296 0.299892370 #> 13297 0.299893424 #> 13298 0.299894494 #> 13299 0.299895581 #> 13300 0.299896683 #> 13301 0.299897801 #> 13302 0.299898935 #> 13303 0.299900085 #> 13304 0.299901251 #> 13305 0.299902432 #> 13306 0.299903630 #> 13307 0.299904844 #> 13308 0.299906073 #> 13309 0.299907318 #> 13310 0.299908579 #> 13311 0.299909856 #> 13312 0.299911149 #> 13313 0.299912457 #> 13314 0.299913782 #> 13315 0.299915122 #> 13316 0.299916478 #> 13317 0.299917849 #> 13318 0.299919236 #> 13319 0.299920639 #> 13320 0.299922058 #> 13321 0.299923492 #> 13322 0.299924942 #> 13323 0.299926407 #> 13324 0.299927889 #> 13325 0.299929385 #> 13326 0.299930898 #> 13327 0.299932426 #> 13328 0.299933969 #> 13329 0.299935528 #> 13330 0.299937103 #> 13331 0.299938693 #> 13332 0.299940298 #> 13333 0.299941919 #> 13334 0.299943556 #> 13335 0.299945208 #> 13336 0.299946875 #> 13337 0.299948558 #> 13338 0.299950256 #> 13339 0.299951970 #> 13340 0.299953699 #> 13341 0.299955443 #> 13342 0.299957203 #> 13343 0.299958978 #> 13344 0.299960768 #> 13345 0.299962574 #> 13346 0.299964395 #> 13347 0.299966231 #> 13348 0.299968082 #> 13349 0.299969949 #> 13350 0.299971830 #> 13351 0.299973727 #> 13352 0.299975639 #> 13353 0.299977567 #> 13354 0.299979509 #> 13355 0.299981467 #> 13356 0.299983439 #> 13357 0.299985427 #> 13358 0.299987430 #> 13359 0.299989448 #> 13360 0.299991481 #> 13361 0.299993528 #> 13362 0.299995591 #> 13363 0.299997669 #> 13364 0.299999762 #> 13365 0.300001870 #> 13366 0.300003993 #> 13367 0.300006130 #> 13368 0.300008283 #> 13369 0.300010450 #> 13370 0.300012633 #> 13371 0.300014830 #> 13372 0.300017042 #> 13373 0.300019268 #> 13374 0.300021510 #> 13375 0.300023766 #> 13376 0.300026037 #> 13377 0.300028323 #> 13378 0.300030624 #> 13379 0.300032939 #> 13380 0.300035269 #> 13381 0.300037613 #> 13382 0.300039973 #> 13383 0.300042347 #> 13384 0.300044735 #> 13385 0.300047138 #> 13386 0.300049556 #> 13387 0.300051988 #> 13388 0.300054435 #> 13389 0.300056896 #> 13390 0.300059372 #> 13391 0.300061862 #> 13392 0.300064366 #> 13393 0.300066886 #> 13394 0.300069419 #> 13395 0.300071967 #> 13396 0.300074529 #> 13397 0.300077106 #> 13398 0.300079697 #> 13399 0.300082303 #> 13400 0.300084922 #> 13401 0.300087556 #> 13402 0.300090204 #> 13403 0.300092867 #> 13404 0.300095544 #> 13405 0.300098235 #> 13406 0.300100940 #> 13407 0.300103659 #> 13408 0.300106392 #> 13409 0.300109140 #> 13410 0.300111902 #> 13411 0.300114678 #> 13412 0.300117468 #> 13413 0.300120271 #> 13414 0.300123089 #> 13415 0.300125921 #> 13416 0.300128767 #> 13417 0.300131627 #> 13418 0.300134501 #> 13419 0.300137389 #> 13420 0.300140291 #> 13421 0.300143207 #> 13422 0.300146136 #> 13423 0.300149080 #> 13424 0.300152037 #> 13425 0.300155008 #> 13426 0.300157993 #> 13427 0.300160992 #> 13428 0.300164004 #> 13429 0.300167030 #> 13430 0.300170070 #> 13431 0.300173124 #> 13432 0.300176191 #> 13433 0.300179272 #> 13434 0.300182366 #> 13435 0.300185474 #> 13436 0.300188596 #> 13437 0.300191731 #> 13438 0.300194880 #> 13439 0.300198043 #> 13440 0.300201219 #> 13441 0.300204408 #> 13442 0.300207611 #> 13443 0.300210827 #> 13444 0.300214057 #> 13445 0.300217300 #> 13446 0.300220556 #> 13447 0.300223826 #> 13448 0.300227110 #> 13449 0.300230406 #> 13450 0.300233716 #> 13451 0.300237039 #> 13452 0.300240375 #> 13453 0.300243725 #> 13454 0.300247088 #> 13455 0.300250464 #> 13456 0.300253853 #> 13457 0.300257255 #> 13458 0.300260671 #> 13459 0.300264099 #> 13460 0.300267541 #> 13461 0.300270996 #> 13462 0.300274463 #> 13463 0.300277944 #> 13464 0.300281438 #> 13465 0.300284945 #> 13466 0.300288464 #> 13467 0.300291997 #> 13468 0.300295542 #> 13469 0.300299101 #> 13470 0.300302672 #> 13471 0.300306256 #> 13472 0.300309853 #> 13473 0.300313463 #> 13474 0.300317085 #> 13475 0.300320721 #> 13476 0.300324369 #> 13477 0.300328029 #> 13478 0.300331703 #> 13479 0.300335389 #> 13480 0.300339087 #> 13481 0.300342799 #> 13482 0.300346523 #> 13483 0.300350259 #> 13484 0.300354008 #> 13485 0.300357770 #> 13486 0.300361544 #> 13487 0.300365330 #> 13488 0.300369129 #> 13489 0.300372941 #> 13490 0.300376764 #> 13491 0.300380601 #> 13492 0.300384449 #> 13493 0.300388310 #> 13494 0.300392184 #> 13495 0.300396069 #> 13496 0.300399967 #> 13497 0.300403877 #> 13498 0.300407799 #> 13499 0.300411734 #> 13500 0.300415681 #> 13501 0.300419640 #> 13502 0.300423611 #> 13503 0.300427594 #> 13504 0.300431589 #> 13505 0.300435596 #> 13506 0.300439616 #> 13507 0.300443647 #> 13508 0.300447690 #> 13509 0.300451746 #> 13510 0.300455813 #> 13511 0.300459892 #> 13512 0.300463983 #> 13513 0.300468086 #> 13514 0.300472201 #> 13515 0.300476328 #> 13516 0.300480466 #> 13517 0.300484616 #> 13518 0.300488778 #> 13519 0.300492952 #> 13520 0.300497138 #> 13521 0.300501335 #> 13522 0.300505543 #> 13523 0.300509764 #> 13524 0.300513996 #> 13525 0.300518240 #> 13526 0.300522495 #> 13527 0.300526762 #> 13528 0.300531040 #> 13529 0.300535330 #> 13530 0.300539631 #> 13531 0.300543944 #> 13532 0.300548268 #> 13533 0.300552603 #> 13534 0.300556950 #> 13535 0.300561308 #> 13536 0.300565678 #> 13537 0.300570059 #> 13538 0.300574451 #> 13539 0.300578854 #> 13540 0.300583269 #> 13541 0.300587695 #> 13542 0.300592132 #> 13543 0.300596580 #> 13544 0.300601039 #> 13545 0.300605509 #> 13546 0.300609991 #> 13547 0.300614483 #> 13548 0.300618987 #> 13549 0.300623501 #> 13550 0.300628027 #> 13551 0.300632563 #> 13552 0.300637111 #> 13553 0.300641669 #> 13554 0.300646238 #> 13555 0.300650818 #> 13556 0.300655409 #> 13557 0.300660011 #> 13558 0.300664623 #> 13559 0.300669246 #> 13560 0.300673880 #> 13561 0.300678525 #> 13562 0.300683180 #> 13563 0.300687846 #> 13564 0.300692523 #> 13565 0.300697210 #> 13566 0.300701908 #> 13567 0.300706616 #> 13568 0.300711335 #> 13569 0.300716064 #> 13570 0.300720804 #> 13571 0.300725554 #> 13572 0.300730315 #> 13573 0.300735086 #> 13574 0.300739867 #> 13575 0.300744659 #> 13576 0.300749461 #> 13577 0.300754273 #> 13578 0.300759096 #> 13579 0.300763929 #> 13580 0.300768772 #> 13581 0.300773625 #> 13582 0.300778489 #> 13583 0.300783362 #> 13584 0.300788246 #> 13585 0.300793140 #> 13586 0.300798044 #> 13587 0.300802958 #> 13588 0.300807881 #> 13589 0.300812815 #> 13590 0.300817759 #> 13591 0.300822713 #> 13592 0.300827677 #> 13593 0.300832650 #> 13594 0.300837634 #> 13595 0.300842627 #> 13596 0.300847630 #> 13597 0.300852643 #> 13598 0.300857665 #> 13599 0.300862698 #> 13600 0.300867740 #> 13601 0.300872791 #> 13602 0.300877852 #> 13603 0.300882923 #> 13604 0.300888004 #> 13605 0.300893094 #> 13606 0.300898194 #> 13607 0.300903303 #> 13608 0.300908421 #> 13609 0.300913549 #> 13610 0.300918687 #> 13611 0.300923834 #> 13612 0.300928990 #> 13613 0.300934156 #> 13614 0.300939331 #> 13615 0.300944515 #> 13616 0.300949708 #> 13617 0.300954911 #> 13618 0.300960123 #> 13619 0.300965344 #> 13620 0.300970575 #> 13621 0.300975814 #> 13622 0.300981063 #> 13623 0.300986320 #> 13624 0.300991587 #> 13625 0.300996863 #> 13626 0.301002147 #> 13627 0.301007441 #> 13628 0.301012744 #> 13629 0.301018056 #> 13630 0.301023376 #> 13631 0.301028705 #> 13632 0.301034044 #> 13633 0.301039391 #> 13634 0.301044746 #> 13635 0.301050111 #> 13636 0.301055484 #> 13637 0.301060866 #> 13638 0.301066257 #> 13639 0.301071656 #> 13640 0.301077064 #> 13641 0.301082481 #> 13642 0.301087906 #> 13643 0.301093340 #> 13644 0.301098782 #> 13645 0.301104233 #> 13646 0.301109692 #> 13647 0.301115159 #> 13648 0.301120635 #> 13649 0.301126120 #> 13650 0.301131612 #> 13651 0.301137113 #> 13652 0.301142623 #> 13653 0.301148140 #> 13654 0.301153666 #> 13655 0.301159200 #> 13656 0.301164742 #> 13657 0.301170293 #> 13658 0.301175851 #> 13659 0.301181418 #> 13660 0.301186993 #> 13661 0.301192575 #> 13662 0.301198166 #> 13663 0.301203765 #> 13664 0.301209372 #> 13665 0.301214986 #> 13666 0.301220609 #> 13667 0.301226240 #> 13668 0.301231878 #> 13669 0.301237524 #> 13670 0.301243178 #> 13671 0.301248840 #> 13672 0.301254509 #> 13673 0.301260186 #> 13674 0.301265871 #> 13675 0.301271564 #> 13676 0.301277264 #> 13677 0.301282972 #> 13678 0.301288687 #> 13679 0.301294410 #> 13680 0.301300141 #> 13681 0.301305878 #> 13682 0.301311624 #> 13683 0.301317377 #> 13684 0.301323137 #> 13685 0.301328905 #> 13686 0.301334679 #> 13687 0.301340462 #> 13688 0.301346251 #> 13689 0.301352048 #> 13690 0.301357852 #> 13691 0.301363664 #> 13692 0.301369482 #> 13693 0.301375308 #> 13694 0.301381141 #> 13695 0.301386981 #> 13696 0.301392828 #> 13697 0.301398682 #> 13698 0.301404543 #> 13699 0.301410411 #> 13700 0.301416286 #> 13701 0.301422168 #> 13702 0.301428056 #> 13703 0.301433952 #> 13704 0.301439855 #> 13705 0.301445764 #> 13706 0.301451680 #> 13707 0.301457603 #> 13708 0.301463533 #> 13709 0.301469469 #> 13710 0.301475412 #> 13711 0.301481362 #> 13712 0.301487318 #> 13713 0.301493281 #> 13714 0.301499251 #> 13715 0.301505227 #> 13716 0.301511209 #> 13717 0.301517198 #> 13718 0.301523194 #> 13719 0.301529196 #> 13720 0.301535204 #> 13721 0.301541218 #> 13722 0.301547239 #> 13723 0.301553267 #> 13724 0.301559300 #> 13725 0.301565340 #> 13726 0.301571386 #> 13727 0.301577439 #> 13728 0.301583497 #> 13729 0.301589561 #> 13730 0.301595632 #> 13731 0.301601709 #> 13732 0.301607792 #> 13733 0.301613881 #> 13734 0.301619975 #> 13735 0.301626076 #> 13736 0.301632183 #> 13737 0.301638296 #> 13738 0.301644414 #> 13739 0.301650539 #> 13740 0.301656669 #> 13741 0.301662805 #> 13742 0.301668947 #> 13743 0.301675094 #> 13744 0.301681247 #> 13745 0.301687406 #> 13746 0.301693571 #> 13747 0.301699741 #> 13748 0.301705917 #> 13749 0.301712098 #> 13750 0.301718285 #> 13751 0.301724478 #> 13752 0.301730676 #> 13753 0.301736879 #> 13754 0.301743088 #> 13755 0.301749302 #> 13756 0.301755521 #> 13757 0.301761746 #> 13758 0.301767977 #> 13759 0.301774212 #> 13760 0.301780453 #> 13761 0.301786699 #> 13762 0.301792950 #> 13763 0.301799206 #> 13764 0.301805468 #> 13765 0.301811734 #> 13766 0.301818006 #> 13767 0.301824283 #> 13768 0.301830564 #> 13769 0.301836851 #> 13770 0.301843143 #> 13771 0.301849440 #> 13772 0.301855741 #> 13773 0.301862048 #> 13774 0.301868359 #> 13775 0.301874675 #> 13776 0.301880996 #> 13777 0.301887322 #> 13778 0.301893652 #> 13779 0.301899987 #> 13780 0.301906327 #> 13781 0.301912671 #> 13782 0.301919020 #> 13783 0.301925374 #> 13784 0.301931732 #> 13785 0.301938095 #> 13786 0.301944462 #> 13787 0.301950834 #> 13788 0.301957210 #> 13789 0.301963591 #> 13790 0.301969976 #> 13791 0.301976365 #> 13792 0.301982759 #> 13793 0.301989157 #> 13794 0.301995559 #> 13795 0.302001966 #> 13796 0.302008377 #> 13797 0.302014792 #> 13798 0.302021211 #> 13799 0.302027634 #> 13800 0.302034062 #> 13801 0.302040493 #> 13802 0.302046929 #> 13803 0.302053368 #> 13804 0.302059812 #> 13805 0.302066259 #> 13806 0.302072710 #> 13807 0.302079166 #> 13808 0.302085625 #> 13809 0.302092088 #> 13810 0.302098555 #> 13811 0.302105025 #> 13812 0.302111500 #> 13813 0.302117978 #> 13814 0.302124460 #> 13815 0.302130945 #> 13816 0.302137435 #> 13817 0.302143927 #> 13818 0.302150424 #> 13819 0.302156924 #> 13820 0.302163427 #> 13821 0.302169934 #> 13822 0.302176444 #> 13823 0.302182958 #> 13824 0.302189476 #> 13825 0.302195996 #> 13826 0.302202520 #> 13827 0.302209048 #> 13828 0.302215578 #> 13829 0.302222112 #> 13830 0.302228650 #> 13831 0.302235190 #> 13832 0.302241734 #> 13833 0.302248280 #> 13834 0.302254830 #> 13835 0.302261383 #> 13836 0.302267939 #> 13837 0.302274498 #> 13838 0.302281061 #> 13839 0.302287626 #> 13840 0.302294194 #> 13841 0.302300765 #> 13842 0.302307339 #> 13843 0.302313915 #> 13844 0.302320495 #> 13845 0.302327077 #> 13846 0.302333663 #> 13847 0.302340251 #> 13848 0.302346841 #> 13849 0.302353435 #> 13850 0.302360031 #> 13851 0.302366630 #> 13852 0.302373231 #> 13853 0.302379835 #> 13854 0.302386441 #> 13855 0.302393050 #> 13856 0.302399662 #> 13857 0.302406276 #> 13858 0.302412892 #> 13859 0.302419511 #> 13860 0.302426132 #> 13861 0.302432756 #> 13862 0.302439382 #> 13863 0.302446010 #> 13864 0.302452641 #> 13865 0.302459273 #> 13866 0.302465908 #> 13867 0.302472546 #> 13868 0.302479185 #> 13869 0.302485826 #> 13870 0.302492470 #> 13871 0.302499115 #> 13872 0.302505763 #> 13873 0.302512413 #> 13874 0.302519065 #> 13875 0.302525718 #> 13876 0.302532374 #> 13877 0.302539031 #> 13878 0.302545691 #> 13879 0.302552352 #> 13880 0.302559015 #> 13881 0.302565680 #> 13882 0.302572346 #> 13883 0.302579015 #> 13884 0.302585685 #> 13885 0.302592356 #> 13886 0.302599030 #> 13887 0.302605705 #> 13888 0.302612381 #> 13889 0.302619059 #> 13890 0.302625739 #> 13891 0.302632420 #> 13892 0.302639103 #> 13893 0.302645787 #> 13894 0.302652473 #> 13895 0.302659160 #> 13896 0.302665848 #> 13897 0.302672538 #> 13898 0.302679229 #> 13899 0.302685921 #> 13900 0.302692614 #> 13901 0.302699309 #> 13902 0.302706005 #> 13903 0.302712702 #> 13904 0.302719400 #> 13905 0.302726100 #> 13906 0.302732800 #> 13907 0.302739502 #> 13908 0.302746204 #> 13909 0.302752908 #> 13910 0.302759613 #> 13911 0.302766318 #> 13912 0.302773025 #> 13913 0.302779732 #> 13914 0.302786440 #> 13915 0.302793149 #> 13916 0.302799859 #> 13917 0.302806570 #> 13918 0.302813281 #> 13919 0.302819993 #> 13920 0.302826706 #> 13921 0.302833419 #> 13922 0.302840133 #> 13923 0.302846848 #> 13924 0.302853563 #> 13925 0.302860279 #> 13926 0.302866996 #> 13927 0.302873713 #> 13928 0.302880430 #> 13929 0.302887148 #> 13930 0.302893866 #> 13931 0.302900585 #> 13932 0.302907304 #> 13933 0.302914023 #> 13934 0.302920743 #> 13935 0.302927463 #> 13936 0.302934183 #> 13937 0.302940904 #> 13938 0.302947624 #> 13939 0.302954345 #> 13940 0.302961066 #> 13941 0.302967787 #> 13942 0.302974508 #> 13943 0.302981230 #> 13944 0.302987951 #> 13945 0.302994672 #> 13946 0.303001394 #> 13947 0.303008115 #> 13948 0.303014836 #> 13949 0.303021557 #> 13950 0.303028278 #> 13951 0.303034999 #> 13952 0.303041720 #> 13953 0.303048440 #> 13954 0.303055160 #> 13955 0.303061880 #> 13956 0.303068600 #> 13957 0.303075319 #> 13958 0.303082038 #> 13959 0.303088757 #> 13960 0.303095475 #> 13961 0.303102193 #> 13962 0.303108910 #> 13963 0.303115627 #> 13964 0.303122343 #> 13965 0.303129059 #> 13966 0.303135774 #> 13967 0.303142489 #> 13968 0.303149203 #> 13969 0.303155917 #> 13970 0.303162629 #> 13971 0.303169342 #> 13972 0.303176053 #> 13973 0.303182764 #> 13974 0.303189473 #> 13975 0.303196182 #> 13976 0.303202891 #> 13977 0.303209598 #> 13978 0.303216304 #> 13979 0.303223010 #> 13980 0.303229715 #> 13981 0.303236418 #> 13982 0.303243121 #> 13983 0.303249823 #> 13984 0.303256523 #> 13985 0.303263223 #> 13986 0.303269921 #> 13987 0.303276619 #> 13988 0.303283315 #> 13989 0.303290010 #> 13990 0.303296704 #> 13991 0.303303397 #> 13992 0.303310088 #> 13993 0.303316778 #> 13994 0.303323467 #> 13995 0.303330154 #> 13996 0.303336840 #> 13997 0.303343525 #> 13998 0.303350209 #> 13999 0.303356890 #> 14000 0.303363571 #> 14001 0.303370250 #> 14002 0.303376927 #> 14003 0.303383603 #> 14004 0.303390277 #> 14005 0.303396950 #> 14006 0.303403621 #> 14007 0.303410291 #> 14008 0.303416959 #> 14009 0.303423625 #> 14010 0.303430289 #> 14011 0.303436952 #> 14012 0.303443613 #> 14013 0.303450272 #> 14014 0.303456929 #> 14015 0.303463584 #> 14016 0.303470238 #> 14017 0.303476889 #> 14018 0.303483539 #> 14019 0.303490187 #> 14020 0.303496833 #> 14021 0.303503476 #> 14022 0.303510118 #> 14023 0.303516758 #> 14024 0.303523395 #> 14025 0.303530031 #> 14026 0.303536664 #> 14027 0.303543295 #> 14028 0.303549924 #> 14029 0.303556551 #> 14030 0.303563176 #> 14031 0.303569798 #> 14032 0.303576418 #> 14033 0.303583036 #> 14034 0.303589651 #> 14035 0.303596264 #> 14036 0.303602875 #> 14037 0.303609483 #> 14038 0.303616089 #> 14039 0.303622692 #> 14040 0.303629293 #> 14041 0.303635891 #> 14042 0.303642487 #> 14043 0.303649081 #> 14044 0.303655671 #> 14045 0.303662259 #> 14046 0.303668845 #> 14047 0.303675427 #> 14048 0.303682007 #> 14049 0.303688585 #> 14050 0.303695159 #> 14051 0.303701731 #> 14052 0.303708300 #> 14053 0.303714867 #> 14054 0.303721430 #> 14055 0.303727991 #> 14056 0.303734548 #> 14057 0.303741103 #> 14058 0.303747655 #> 14059 0.303754204 #> 14060 0.303760750 #> 14061 0.303767293 #> 14062 0.303773832 #> 14063 0.303780369 #> 14064 0.303786903 #> 14065 0.303793434 #> 14066 0.303799961 #> 14067 0.303806485 #> 14068 0.303813007 #> 14069 0.303819525 #> 14070 0.303826039 #> 14071 0.303832551 #> 14072 0.303839059 #> 14073 0.303845564 #> 14074 0.303852066 #> 14075 0.303858564 #> 14076 0.303865059 #> 14077 0.303871550 #> 14078 0.303878038 #> 14079 0.303884523 #> 14080 0.303891004 #> 14081 0.303897482 #> 14082 0.303903956 #> 14083 0.303910426 #> 14084 0.303916893 #> 14085 0.303923357 #> 14086 0.303929817 #> 14087 0.303936273 #> 14088 0.303942726 #> 14089 0.303949175 #> 14090 0.303955620 #> 14091 0.303962061 #> 14092 0.303968499 #> 14093 0.303974933 #> 14094 0.303981363 #> 14095 0.303987789 #> 14096 0.303994212 #> 14097 0.304000630 #> 14098 0.304007045 #> 14099 0.304013456 #> 14100 0.304019863 #> 14101 0.304026266 #> 14102 0.304032665 #> 14103 0.304039060 #> 14104 0.304045450 #> 14105 0.304051837 #> 14106 0.304058220 #> 14107 0.304064599 #> 14108 0.304070973 #> 14109 0.304077344 #> 14110 0.304083710 #> 14111 0.304090072 #> 14112 0.304096430 #> 14113 0.304102783 #> 14114 0.304109133 #> 14115 0.304115478 #> 14116 0.304121818 #> 14117 0.304128155 #> 14118 0.304134487 #> 14119 0.304140815 #> 14120 0.304147138 #> 14121 0.304153457 #> 14122 0.304159771 #> 14123 0.304166081 #> 14124 0.304172387 #> 14125 0.304178688 #> 14126 0.304184984 #> 14127 0.304191276 #> 14128 0.304197563 #> 14129 0.304203846 #> 14130 0.304210124 #> 14131 0.304216398 #> 14132 0.304222666 #> 14133 0.304228930 #> 14134 0.304235190 #> 14135 0.304241445 #> 14136 0.304247694 #> 14137 0.304253940 #> 14138 0.304260180 #> 14139 0.304266416 #> 14140 0.304272646 #> 14141 0.304278872 #> 14142 0.304285093 #> 14143 0.304291309 #> 14144 0.304297520 #> 14145 0.304303726 #> 14146 0.304309928 #> 14147 0.304316124 #> 14148 0.304322315 #> 14149 0.304328501 #> 14150 0.304334682 #> 14151 0.304340858 #> 14152 0.304347029 #> 14153 0.304353195 #> 14154 0.304359356 #> 14155 0.304365511 #> 14156 0.304371662 #> 14157 0.304377807 #> 14158 0.304383947 #> 14159 0.304390081 #> 14160 0.304396211 #> 14161 0.304402335 #> 14162 0.304408454 #> 14163 0.304414567 #> 14164 0.304420675 #> 14165 0.304426778 #> 14166 0.304432875 #> 14167 0.304438967 #> 14168 0.304445054 #> 14169 0.304451135 #> 14170 0.304457210 #> 14171 0.304463280 #> 14172 0.304469345 #> 14173 0.304475404 #> 14174 0.304481457 #> 14175 0.304487505 #> 14176 0.304493547 #> 14177 0.304499584 #> 14178 0.304505615 #> 14179 0.304511640 #> 14180 0.304517660 #> 14181 0.304523674 #> 14182 0.304529682 #> 14183 0.304535685 #> 14184 0.304541681 #> 14185 0.304547672 #> 14186 0.304553658 #> 14187 0.304559637 #> 14188 0.304565610 #> 14189 0.304571578 #> 14190 0.304577540 #> 14191 0.304583495 #> 14192 0.304589445 #> 14193 0.304595389 #> 14194 0.304601327 #> 14195 0.304607259 #> 14196 0.304613185 #> 14197 0.304619105 #> 14198 0.304625019 #> 14199 0.304630927 #> 14200 0.304636829 #> 14201 0.304642725 #> 14202 0.304648614 #> 14203 0.304654498 #> 14204 0.304660375 #> 14205 0.304666246 #> 14206 0.304672111 #> 14207 0.304677970 #> 14208 0.304683822 #> 14209 0.304689668 #> 14210 0.304695508 #> 14211 0.304701342 #> 14212 0.304707169 #> 14213 0.304712990 #> 14214 0.304718805 #> 14215 0.304724613 #> 14216 0.304730415 #> 14217 0.304736211 #> 14218 0.304742000 #> 14219 0.304747782 #> 14220 0.304753558 #> 14221 0.304759328 #> 14222 0.304765091 #> 14223 0.304770848 #> 14224 0.304776598 #> 14225 0.304782342 #> 14226 0.304788079 #> 14227 0.304793809 #> 14228 0.304799533 #> 14229 0.304805250 #> 14230 0.304810960 #> 14231 0.304816664 #> 14232 0.304822361 #> 14233 0.304828052 #> 14234 0.304833736 #> 14235 0.304839413 #> 14236 0.304845083 #> 14237 0.304850746 #> 14238 0.304856403 #> 14239 0.304862053 #> 14240 0.304867696 #> 14241 0.304873332 #> 14242 0.304878961 #> 14243 0.304884584 #> 14244 0.304890199 #> 14245 0.304895808 #> 14246 0.304901410 #> 14247 0.304907004 #> 14248 0.304912592 #> 14249 0.304918173 #> 14250 0.304923747 #> 14251 0.304929314 #> 14252 0.304934873 #> 14253 0.304940426 #> 14254 0.304945972 #> 14255 0.304951510 #> 14256 0.304957041 #> 14257 0.304962566 #> 14258 0.304968083 #> 14259 0.304973593 #> 14260 0.304979096 #> 14261 0.304984591 #> 14262 0.304990080 #> 14263 0.304995561 #> 14264 0.305001035 #> 14265 0.305006501 #> 14266 0.305011961 #> 14267 0.305017413 #> 14268 0.305022857 #> 14269 0.305028295 #> 14270 0.305033725 #> 14271 0.305039148 #> 14272 0.305044563 #> 14273 0.305049971 #> 14274 0.305055372 #> 14275 0.305060765 #> 14276 0.305066150 #> 14277 0.305071529 #> 14278 0.305076899 #> 14279 0.305082263 #> 14280 0.305087618 #> 14281 0.305092967 #> 14282 0.305098307 #> 14283 0.305103641 #> 14284 0.305108966 #> 14285 0.305114284 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 5716 rows ] #>  #> attr(,\"class\") #> [1] \"sir_subpops_model\" # }"},{"path":"https://oucru-modelling.github.io/serosv/reference/standardize_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocess data — standardize_data","title":"Preprocess data — standardize_data","text":"Preprocess data","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/standardize_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocess data — standardize_data","text":"","code":"standardize_data(   df,   plate_id_col = \"PLATE_ID\",   id_col = \"SAMPLE_ID\",   result_col = \"RESULT\",   dilution_fct_col = \"DILUTION_FACTORS\",   antitoxin_label = \"Anti_toxin\",   negative_col = \"^NEGATIVE_*\" )"},{"path":"https://oucru-modelling.github.io/serosv/reference/standardize_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocess data — standardize_data","text":"df data.frame columns plate id, sample id, result, dilution factor, (optionally) negative controls plate_id_col name column storing plates id id_col name column storing sample id result_col name column storing result dilution_fct_col name column storing dilution factors antitoxin_label antitoxin label sample id column negative_col regex columns negative controls, assumed label followed dilution factor (e.g. NEGATIVE_50, NEGATIVE_100)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/standardize_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocess data — standardize_data","text":"standardized data.frame can passed `to_titer()`","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/tb_nl_1966_1973.html","id":null,"dir":"Reference","previous_headings":"","what":"Tuberculosis serological data from the Netherlands 1966-1973 (aggregated) — tb_nl_1966_1973","title":"Tuberculosis serological data from the Netherlands 1966-1973 (aggregated) — tb_nl_1966_1973","text":"study tuberculosis conducted Netherlands. Schoolchildren, aged 6 18 years, tested using tuberculin skin test.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/tb_nl_1966_1973.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tuberculosis serological data from the Netherlands 1966-1973 (aggregated) — tb_nl_1966_1973","text":"","code":"tb_nl_1966_1973"},{"path":"https://oucru-modelling.github.io/serosv/reference/tb_nl_1966_1973.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Tuberculosis serological data from the Netherlands 1966-1973 (aggregated) — tb_nl_1966_1973","text":"data frame 5 variables: age Age group pos Number seropositive individuals tot Total number individuals surveyed gender Gender cohort (unsure 0 1 means) birthyr Birth year cohort","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/tb_nl_1966_1973.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Tuberculosis serological data from the Netherlands 1966-1973 (aggregated) — tb_nl_1966_1973","text":"Nagelkerke, N., Heisterkamp, S., Borgdorff, M., Broekmans, J. Van Houwelingen, H. (1999), Semi-parametric estimation age-time specific infection incidence serial prevalence data. Statist. Med., 18: 307-320. doi:10.1002/(SICI)1097-0258(19990215)18:3<307::AID-SIM15>3.0.CO;2-Z","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/tb_nl_1966_1973.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tuberculosis serological data from the Netherlands 1966-1973 (aggregated) — tb_nl_1966_1973","text":"","code":"with(tb_nl_1966_1973,   plot(age, pos / tot,     pch = 16, cex = 0.01 * sqrt(tot), xlab = \"age\",     ylab = \"prevalence\", xlim = c(6, 18)   ) )   with(tb_nl_1966_1973,   plot(birthyr, pos / tot,     pch = 16, cex = 0.01 * sqrt(tot), xlab = \"year\", ylab = \"prevalence\"   ) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/to_titer.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert assay readings to titers — to_titer","title":"Convert assay readings to titers — to_titer","text":"to_titer() converts raw assay readings (e.g., OD, fluorescence intensity) titer fitting calibrating model","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/to_titer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert assay readings to titers — to_titer","text":"","code":"to_titer(   df,   model = \"4PL\",   positive_threshold = NULL,   ci = 0.95,   negative_control = TRUE )"},{"path":"https://oucru-modelling.github.io/serosv/reference/to_titer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert assay readings to titers — to_titer","text":"df standardized data.frame returned `standardize_data()` model either: string naming built-model (currently supported: \"4PL\"), named list two functions: $mod curve fitting     $quantify_ci titer estimation confidence intervals. positive_threshold NULL, processed_data serostatus labeled ci confidence interval titer estimates (default .95 .e., 95% CI) negative_control TRUE, output tibble include result negative controls","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/to_titer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert assay readings to titers — to_titer","text":"data.frame 8 columns plate_id id plate data list `data.frame`s containing raw sample results plate antitoxin_df list `data.frame`s containing raw results antitoxins plate standard_curve_func list functions mapping assay reading titer plate std_crv_midpoint midpoint standard curve, qualitative analysis processed_data list `tibble`s containing samples titer estimates (lower, median, upper) negative_control list `tibble`s containing negative control check results (`negative_control=TRUE`)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/transform.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a dataframe with `t`, `pos` and `tot` columns from\n`t` and `seropositive` vectors. — transform","title":"Generate a dataframe with `t`, `pos` and `tot` columns from\n`t` and `seropositive` vectors. — transform","text":"Generate dataframe `t`, `pos` `tot` columns `t` `seropositive` vectors.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a dataframe with `t`, `pos` and `tot` columns from\n`t` and `seropositive` vectors. — transform","text":"","code":"transform(t, spos)"},{"path":"https://oucru-modelling.github.io/serosv/reference/transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a dataframe with `t`, `pos` and `tot` columns from\n`t` and `seropositive` vectors. — transform","text":"t time vector. spos seropositive vector.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a dataframe with `t`, `pos` and `tot` columns from\n`t` and `seropositive` vectors. — transform","text":"","code":"df <- hcv_be_2006 hcv_df <- transform(df$dur, df$seropositive) hcv_df #> # A tibble: 116 × 3 #>        t   pos   tot #>    <dbl> <int> <int> #>  1   0.1     0     1 #>  2   0.3     0     1 #>  3   0.4     0     3 #>  4   0.5     0     1 #>  5   0.7     2     5 #>  6   0.8     3     4 #>  7   0.9     3     4 #>  8   1       5     7 #>  9   1.1     1     2 #> 10   1.2     3     4 #> # ℹ 106 more rows"},{"path":"https://oucru-modelling.github.io/serosv/reference/transform_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Aggregate data — transform_data","title":"Aggregate data — transform_data","text":"Generate dataframe `t`, `pos` `tot` columns `t` `seropositive` vectors.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/transform_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregate data — transform_data","text":"","code":"transform_data(data, stratum_col = \"age\", status_col = \"status\")"},{"path":"https://oucru-modelling.github.io/serosv/reference/transform_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aggregate data — transform_data","text":"data data frame columns age serostatus stratum_col name column stratify (default \"age\") status_col name column serostatus","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/transform_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aggregate data — transform_data","text":"dataframe aggregated format","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/transform_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aggregate data — transform_data","text":"","code":"df <- hcv_be_2006 hcv_df <- transform_data(df, stratum_col=\"dur\", status_col=\"seropositive\") hcv_df #> # A tibble: 116 × 3 #>      age   pos   tot #>    <dbl> <int> <int> #>  1   0.1     0     1 #>  2   0.3     0     1 #>  3   0.4     0     3 #>  4   0.5     0     1 #>  5   0.7     2     5 #>  6   0.8     3     4 #>  7   0.9     3     4 #>  8   1       5     7 #>  9   1.1     1     2 #> 10   1.2     3     4 #> # ℹ 106 more rows"},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_1999_2000.html","id":null,"dir":"Reference","previous_headings":"","what":"VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated) — vzv_be_1999_2000","title":"VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated) — vzv_be_1999_2000","text":"Age-specific seroprevalence VZV antibodies, assessed Flanders (Belgium) October 1999 April 2000. population stratified age order obtain approximately 100 observations per age group.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_1999_2000.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated) — vzv_be_1999_2000","text":"","code":"vzv_be_1999_2000"},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_1999_2000.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated) — vzv_be_1999_2000","text":"data frame 3 variables: age Age group pos Number seropositive individuals tot Total number individuals surveyed","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_1999_2000.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated) — vzv_be_1999_2000","text":"Thiry, N., Beutels, P., Shkedy, Z. et al. seroepidemiology primary varicella-zoster virus infection Flanders (Belgium). Eur J Pediatr 161, 588-593 (2002). doi:10.1007/s00431-002-1053-2","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_1999_2000.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated) — vzv_be_1999_2000","text":"","code":"with(vzv_be_1999_2000,   plot(age, pos / tot,     cex = 0.1 * sqrt(tot), pch = 19, xlab = \"age\", ylab = \"seroprevalence\",     xlim = c(0, 45), ylim = c(0, 1)   ) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_2001_2003.html","id":null,"dir":"Reference","previous_headings":"","what":"VZV serological data from Belgium from 2001-2003 (line listing) — vzv_be_2001_2003","title":"VZV serological data from Belgium from 2001-2003 (line listing) — vzv_be_2001_2003","text":"survey one used study seroprevalence parvovirus B19 Belgium, described .","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_2001_2003.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"VZV serological data from Belgium from 2001-2003 (line listing) — vzv_be_2001_2003","text":"","code":"vzv_be_2001_2003"},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_2001_2003.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"VZV serological data from Belgium from 2001-2003 (line listing) — vzv_be_2001_2003","text":"data frame 4 variables: age Age individual seropositive individual seropositive gender Gender individual VZVmIUml VZV milli international units per ml","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_2001_2003.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"VZV serological data from Belgium from 2001-2003 (line listing) — vzv_be_2001_2003","text":"MOSSONG, J., N. HENS, V. FRIEDERICHS, . DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. \"Parvovirus B19 Infection Five European Countries: Seroepidemiology, Force Infection Maternal Risk Infection.\" Epidemiology Infection 136, . 8 (2008): 1059-68. doi:10.1017/S0950268807009661","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_be_2001_2003.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"VZV serological data from Belgium from 2001-2003 (line listing) — vzv_be_2001_2003","text":"","code":"library(dplyr) df <- vzv_be_2001_2003 %>%   mutate(age = round(age)) %>%   group_by(age) %>%   summarise(pos = sum(seropositive), tot = n()) plot(df$age, df$pos / df$tot,   cex = 0.1 * sqrt(df$tot), pch = 19, xlab = \"age\", ylab = \"seroprevalence\",   xlim = c(0, 45), ylim = c(0, 1) )"},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_parvo_be.html","id":null,"dir":"Reference","previous_headings":"","what":"VZV and Parvovirus B19 serological data in Belgium (line listing) — vzv_parvo_be","title":"VZV and Parvovirus B19 serological data in Belgium (line listing) — vzv_parvo_be","text":"VZV Parvovirus B19 serological data Belgium (line listing)","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_parvo_be.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"VZV and Parvovirus B19 serological data in Belgium (line listing) — vzv_parvo_be","text":"","code":"vzv_parvo_be"},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_parvo_be.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"VZV and Parvovirus B19 serological data in Belgium (line listing) — vzv_parvo_be","text":"data frame 7 variables: id ID individual age Age individual gender Gender individual parvouml Parvo B19 antibody units per ml parvo_res individual positive parvovirus B19 VZVmUIml VZV milli international units per ml vzv_res individual positive VZV","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/vzv_parvo_be.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"VZV and Parvovirus B19 serological data in Belgium (line listing) — vzv_parvo_be","text":"MOSSONG, J., N. HENS, V. FRIEDERICHS, . DAVIDKIN, M. BROMAN, B. LITWINSKA, J. SIENNICKA, et al. \"Parvovirus B19 Infection Five European Countries: Seroepidemiology, Force Infection Maternal Risk Infection.\" Epidemiology Infection 136, . 8 (2008): 1059-68. doi:10.1017/S0950268807009661","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/weibull_model.html","id":null,"dir":"Reference","previous_headings":"","what":"The Weibull model. — weibull_model","title":"The Weibull model. — weibull_model","text":"Model seroprevalence function duration since vaccination using Weibull model, force infection assumed vary monotonically duration.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/weibull_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Weibull model. — weibull_model","text":"","code":"weibull_model(   data,   t_lab = \"t\",   pos_col = \"pos\",   tot_col = \"tot\",   status_col = \"status\" )"},{"path":"https://oucru-modelling.github.io/serosv/reference/weibull_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Weibull model. — weibull_model","text":"data input data frame, must either columns `t`, `pos`, `tot` (aggregated data) `t`, `status` (linelisting data) t_lab name `t` column (default t_lab=\"t\"). pos_col name `pos` column (default pos_col=\"pos\"). tot_col name `tot` column (default tot_col=\"tot\"). status_col name `status` column (default status_col=\"status\").","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/weibull_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Weibull model. — weibull_model","text":"list class weibull_model following items datatype type datatype used model fitting (aggregated linelisting) df dataframe used fitting model info fitted \"glm\" object sp seroprevalence foi force infection","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/weibull_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The Weibull model. — weibull_model","text":"Weibull model, prevalence given $$  \\pi (d) = 1 - e^{ - \\beta_0 d ^ {\\beta_1}} $$ \\(d\\) exposure time (difference age vaccination age test) implies force infection monotonic function $$  \\lambda(d) = \\beta_0 \\beta_1 d^{\\beta_1 - 1} $$ Refer section 6.1.2. book Hens et al. (2012) details.","code":""},{"path":"https://oucru-modelling.github.io/serosv/reference/weibull_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The Weibull model. — weibull_model","text":"Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based Serological Social Contact Data: Modern Statistical Perspective. tatistics Biology Health. Springer New York. doi:10.1007/978-1-4614-4072-7 .","code":""},{"path":[]},{"path":"https://oucru-modelling.github.io/serosv/reference/weibull_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Weibull model. — weibull_model","text":"","code":"df <- hcv_be_2006[order(hcv_be_2006$dur), ] df$t <- df$dur df$status <- df$seropositive model <- weibull_model(df, t_lab=\"dur\", status_col=\"seropositive\") plot(model)"},{"path":"https://oucru-modelling.github.io/serosv/news/index.html","id":"serosv-development-version","dir":"Changelog","previous_headings":"","what":"serosv (development version)","title":"serosv (development version)","text":"clean documentation","code":""},{"path":"https://oucru-modelling.github.io/serosv/news/index.html","id":"serosv-120","dir":"Changelog","previous_headings":"","what":"serosv 1.2.0","title":"serosv 1.2.0","text":"add to_titer() function convert assay reading titer update docs update article to_titer() website","code":""},{"path":"https://oucru-modelling.github.io/serosv/news/index.html","id":"serosv-110","dir":"Changelog","previous_headings":"","what":"serosv 1.1.0","title":"serosv 1.1.0","text":"CRAN release: 2025-04-09 add correct_prevalence() function estimate real prevalence imperfect test add compare_models() function model selection update API takes data.frame input","code":""},{"path":"https://oucru-modelling.github.io/serosv/news/index.html","id":"serosv-101","dir":"Changelog","previous_headings":"","what":"serosv 1.0.1","title":"serosv 1.0.1","text":"CRAN release: 2024-10-18","code":""},{"path":"https://oucru-modelling.github.io/serosv/news/index.html","id":"serosv-100","dir":"Changelog","previous_headings":"","what":"serosv 1.0.0","title":"serosv 1.0.0","text":"CRAN release: 2024-10-07 Initial CRAN submission.","code":""}]
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index e347825..39ac79b 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -13,6 +13,7 @@
 <url><loc>https://oucru-modelling.github.io/serosv/articles/model_selection.html</loc></url>
 <url><loc>https://oucru-modelling.github.io/serosv/articles/nonparametric_model.html</loc></url>
 <url><loc>https://oucru-modelling.github.io/serosv/articles/parametric_model.html</loc></url>
+<url><loc>https://oucru-modelling.github.io/serosv/articles/reading_to_titer.html</loc></url>
 <url><loc>https://oucru-modelling.github.io/serosv/articles/repeated_cross_sectional.html</loc></url>
 <url><loc>https://oucru-modelling.github.io/serosv/articles/semiparametric_model.html</loc></url>
 <url><loc>https://oucru-modelling.github.io/serosv/articles/sir_model.html</loc></url>
@@ -26,6 +27,7 @@
 <url><loc>https://oucru-modelling.github.io/serosv/reference/bivariate_dale_model.html</loc></url>
 <url><loc>https://oucru-modelling.github.io/serosv/reference/compare_models.html</loc></url>
 <url><loc>https://oucru-modelling.github.io/serosv/reference/compute_ci.age_time_model.html</loc></url>
+<url><loc>https://oucru-modelling.github.io/serosv/reference/compute_ci.default.html</loc></url>
 <url><loc>https://oucru-modelling.github.io/serosv/reference/compute_ci.fp_model.html</loc></url>
 <url><loc>https://oucru-modelling.github.io/serosv/reference/compute_ci.glmm_ps_model.html</loc></url>
 <url><loc>https://oucru-modelling.github.io/serosv/reference/compute_ci.hierarchical_bayesian_model.html</loc></url>
@@ -82,6 +84,14 @@
 <url><loc>https://oucru-modelling.github.io/serosv/reference/plot_standard_curve.html</loc></url>
 <url><loc>https://oucru-modelling.github.io/serosv/reference/plot_titer_qc.html</loc></url>
 <url><loc>https://oucru-modelling.github.io/serosv/reference/polynomial_model.html</loc></url>
+<url><loc>https://oucru-modelling.github.io/serosv/reference/predict.age_time_model.html</loc></url>
+<url><loc>https://oucru-modelling.github.io/serosv/reference/predict.farrington_model.html</loc></url>
+<url><loc>https://oucru-modelling.github.io/serosv/reference/predict.fp_model.html</loc></url>
+<url><loc>https://oucru-modelling.github.io/serosv/reference/predict.hierarchical_bayesian_model.html</loc></url>
+<url><loc>https://oucru-modelling.github.io/serosv/reference/predict.lp_model.html</loc></url>
+<url><loc>https://oucru-modelling.github.io/serosv/reference/predict.penalized_spline_model.html</loc></url>
+<url><loc>https://oucru-modelling.github.io/serosv/reference/predict.polynomial_model.html</loc></url>
+<url><loc>https://oucru-modelling.github.io/serosv/reference/predict.weibull_model.html</loc></url>
 <url><loc>https://oucru-modelling.github.io/serosv/reference/rubella_mumps_uk.html</loc></url>
 <url><loc>https://oucru-modelling.github.io/serosv/reference/rubella_uk_1986_1987.html</loc></url>
 <url><loc>https://oucru-modelling.github.io/serosv/reference/serosv-package.html</loc></url>
diff --git a/man/age_time_model.Rd b/man/age_time_model.Rd
index e0aa715..1248326 100644
--- a/man/age_time_model.Rd
+++ b/man/age_time_model.Rd
@@ -6,6 +6,10 @@
 \usage{
 age_time_model(
   data,
+  age_col = "age",
+  status_col = "status",
+  pos_col = "pos",
+  tot_col = "tot",
   time_col = "date",
   grouping_col = "group",
   age_correct = F,
@@ -15,19 +19,27 @@ age_time_model(
 )
 }
 \arguments{
-\item{data}{- input data, must have`age`, `status`, time, group columns, where group column determines how data is aggregated}
+\item{data}{input data, must have age, status, time, group columns, where group column determines how data is aggregated}
 
-\item{time_col}{- name of the column for time (default to `date`)}
+\item{age_col}{name of the `age` column (default age_col="age").}
 
-\item{grouping_col}{- name of the column for time (default to `group`)}
+\item{status_col}{name of the `status` column (default status_col="status").}
 
-\item{age_correct}{- a boolean, if `TRUE`, monotonize age-specific prevalence. Monotonize birth cohort-specific seroprevalence otherwise.}
+\item{pos_col}{name of the `pos` column (default pos_col="pos").}
 
-\item{le}{- number of bins to generate age grid, used when monotonizing data}
+\item{tot_col}{name of the `tot` column (default tot_col="tot").}
 
-\item{ci}{- confidence interval for smoothing}
+\item{time_col}{name of the column for time (default to "date")}
 
-\item{monotonize_method}{- either "pava" or "scam"}
+\item{grouping_col}{name of the column for time (default to "group")}
+
+\item{age_correct}{a boolean, if `TRUE`, monotonize age-specific prevalence. Monotonize birth cohort-specific seroprevalence otherwise.}
+
+\item{le}{number of bins to generate age grid, used when monotonizing data}
+
+\item{ci}{confidence interval for smoothing}
+
+\item{monotonize_method}{either "pava" or "scam"}
 }
 \value{
 a list of class time_age_model with 4 items
diff --git a/man/compare_models.Rd b/man/compare_models.Rd
index 93a5a74..ca8838d 100644
--- a/man/compare_models.Rd
+++ b/man/compare_models.Rd
@@ -4,14 +4,18 @@
 \alias{compare_models}
 \title{Compare models}
 \usage{
-compare_models(...)
+compare_models(data, method = "AIC/BIC", ...)
 }
 \arguments{
+\item{data}{input data to fit into the models}
+
+\item{method}{method to compare models. Can be one of the built-in methods or a function to compute the returned metrics (see Details).}
+
 \item{...}{models to be compared. Must be models created by serosv. If models' names are not provided, indices will be used instead for the `model` column in the returned data.frame.}
 }
 \value{
 a data.frame of 4 columns
-  \item{model}{name or index of the model}
+  \item{label}{name or index of the model}
   \item{type}{model type of the given model (a serosv model name)}
   \item{AIC}{AIC value for the model (lower value indicates better fit)}
   \item{BIC}{BIC value for the model (lower value indicates better fit)}
@@ -19,3 +23,8 @@ a data.frame of 4 columns
 \description{
 Compare models
 }
+\details{
+Built-in comparison methods include:
+- computing AIC and BIC, which returns AIC, BIC values of the model if available
+- cross validation, which reutns
+}
diff --git a/man/compute_ci.Rd b/man/compute_ci.Rd
deleted file mode 100644
index 09c2034..0000000
--- a/man/compute_ci.Rd
+++ /dev/null
@@ -1,23 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/compute_ci.R
-\name{compute_ci}
-\alias{compute_ci}
-\title{Compute confidence interval}
-\usage{
-compute_ci(x, ci = 0.95, le = 100, ...)
-}
-\arguments{
-\item{x}{- serosv models}
-
-\item{ci}{- confidence interval}
-
-\item{le}{- number of data for computing confidence interval}
-
-\item{...}{- arbitrary argument}
-}
-\value{
-confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval
-}
-\description{
-Compute confidence interval
-}
diff --git a/man/compute_ci.age_time_model.Rd b/man/compute_ci.age_time_model.Rd
index 0cb076b..b4632d2 100644
--- a/man/compute_ci.age_time_model.Rd
+++ b/man/compute_ci.age_time_model.Rd
@@ -4,16 +4,16 @@
 \alias{compute_ci.age_time_model}
 \title{Compute confidence interval for time age model}
 \usage{
-compute_ci.age_time_model(x, ci = 0.95, le = 100, ...)
+\method{compute_ci}{age_time_model}(x, ci = 0.95, le = 100, ...)
 }
 \arguments{
-\item{x}{- serosv models}
+\item{x}{serosv models}
 
-\item{ci}{- confidence interval}
+\item{ci}{confidence interval}
 
-\item{le}{- number of data for computing confidence interval}
+\item{le}{number of data for computing confidence interval}
 
-\item{...}{- arbitrary argument}
+\item{...}{arbitrary argument}
 }
 \value{
 confidence interval dataframe with n_group x 3 cols, the columns are `group`, `sp_df`, `foi_df`
diff --git a/man/compute_ci.default.Rd b/man/compute_ci.default.Rd
new file mode 100644
index 0000000..40b9737
--- /dev/null
+++ b/man/compute_ci.default.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/compute_ci.R
+\name{compute_ci.default}
+\alias{compute_ci.default}
+\title{Compute confidence interval for a model of serosv}
+\usage{
+\method{compute_ci}{default}(x, ci = 0.95, le = 100, ...)
+}
+\arguments{
+\item{x}{serosv models}
+
+\item{ci}{confidence interval}
+
+\item{le}{number of data for computing confidence interval}
+
+\item{...}{arbitrary argument}
+}
+\value{
+confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval
+}
+\description{
+Compute confidence interval for a model of serosv
+}
diff --git a/man/compute_ci.fp_model.Rd b/man/compute_ci.fp_model.Rd
index 27f2274..90e4185 100644
--- a/man/compute_ci.fp_model.Rd
+++ b/man/compute_ci.fp_model.Rd
@@ -4,16 +4,16 @@
 \alias{compute_ci.fp_model}
 \title{Compute confidence interval for fractional polynomial model}
 \usage{
-compute_ci.fp_model(x, ci = 0.95, le = 100, ...)
+\method{compute_ci}{fp_model}(x, ci = 0.95, le = 100, ...)
 }
 \arguments{
-\item{x}{- serosv models}
+\item{x}{serosv models}
 
-\item{ci}{- confidence interval}
+\item{ci}{confidence interval}
 
-\item{le}{- number of data for computing confidence interval}
+\item{le}{number of data for computing confidence interval}
 
-\item{...}{- arbitrary argument}
+\item{...}{arbitrary argument}
 }
 \value{
 confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval
diff --git a/man/compute_ci.hierarchical_bayesian_model.Rd b/man/compute_ci.hierarchical_bayesian_model.Rd
index fe02dab..9a4daa3 100644
--- a/man/compute_ci.hierarchical_bayesian_model.Rd
+++ b/man/compute_ci.hierarchical_bayesian_model.Rd
@@ -4,12 +4,12 @@
 \alias{compute_ci.hierarchical_bayesian_model}
 \title{Compute 95\% credible interval for hierarchical Bayesian model}
 \usage{
-compute_ci.hierarchical_bayesian_model(x, ...)
+\method{compute_ci}{hierarchical_bayesian_model}(x, ...)
 }
 \arguments{
-\item{x}{- serosv models}
+\item{x}{serosv models}
 
-\item{...}{- arbitrary arguments}
+\item{...}{arbitrary arguments}
 }
 \value{
 list of confidence interval for seroprevalence and foi. Each confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval
diff --git a/man/compute_ci.lp_model.Rd b/man/compute_ci.lp_model.Rd
index 2983cff..76b2f21 100644
--- a/man/compute_ci.lp_model.Rd
+++ b/man/compute_ci.lp_model.Rd
@@ -4,14 +4,14 @@
 \alias{compute_ci.lp_model}
 \title{Compute confidence interval for local polynomial model}
 \usage{
-compute_ci.lp_model(x, ci = 0.95, ...)
+\method{compute_ci}{lp_model}(x, ci = 0.95, ...)
 }
 \arguments{
-\item{x}{- serosv models}
+\item{x}{serosv models}
 
-\item{ci}{- confidence interval}
+\item{ci}{confidence interval}
 
-\item{...}{- arbitrary arguments}
+\item{...}{arbitrary arguments}
 }
 \value{
 confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval
diff --git a/man/compute_ci.mixture_model.Rd b/man/compute_ci.mixture_model.Rd
index 6835cc2..7b0c180 100644
--- a/man/compute_ci.mixture_model.Rd
+++ b/man/compute_ci.mixture_model.Rd
@@ -4,14 +4,14 @@
 \alias{compute_ci.mixture_model}
 \title{Compute confidence interval for mixture model}
 \usage{
-compute_ci.mixture_model(x, ci = 0.95, ...)
+\method{compute_ci}{mixture_model}(x, ci = 0.95, ...)
 }
 \arguments{
-\item{x}{- serosv mixture_model object}
+\item{x}{serosv mixture_model object}
 
-\item{ci}{- confidence interval}
+\item{ci}{confidence interval}
 
-\item{...}{- arbitrary arguments}
+\item{...}{arbitrary arguments}
 }
 \value{
 list of confidence interval for susceptible and infected. Each confidence interval is a list with 2 items for lower and upper bound of the interval.
diff --git a/man/compute_ci.penalized_spline_model.Rd b/man/compute_ci.penalized_spline_model.Rd
index 5dc5dbf..c6b0b54 100644
--- a/man/compute_ci.penalized_spline_model.Rd
+++ b/man/compute_ci.penalized_spline_model.Rd
@@ -4,14 +4,14 @@
 \alias{compute_ci.penalized_spline_model}
 \title{Compute confidence interval for penalized_spline_model}
 \usage{
-compute_ci.penalized_spline_model(x, ci = 0.95, ...)
+\method{compute_ci}{penalized_spline_model}(x, ci = 0.95, ...)
 }
 \arguments{
-\item{x}{- serosv models}
+\item{x}{serosv models}
 
-\item{ci}{- confidence interval}
+\item{ci}{confidence interval}
 
-\item{...}{- arbitrary arguments}
+\item{...}{arbitrary arguments}
 }
 \value{
 list of confidence interval for seroprevalence and foi Each confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval
diff --git a/man/compute_ci.weibull_model.Rd b/man/compute_ci.weibull_model.Rd
index 2d6bf84..53000b5 100644
--- a/man/compute_ci.weibull_model.Rd
+++ b/man/compute_ci.weibull_model.Rd
@@ -4,14 +4,14 @@
 \alias{compute_ci.weibull_model}
 \title{Compute confidence interval for Weibull model}
 \usage{
-compute_ci.weibull_model(x, ci = 0.95, ...)
+\method{compute_ci}{weibull_model}(x, ci = 0.95, ...)
 }
 \arguments{
-\item{x}{- serosv models}
+\item{x}{serosv models}
 
-\item{ci}{- confidence interval}
+\item{ci}{confidence interval}
 
-\item{...}{- arbitrary argument}
+\item{...}{arbitrary argument}
 }
 \value{
 confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval
diff --git a/man/correct_prevalence.Rd b/man/correct_prevalence.Rd
index 3a1ffae..950904a 100644
--- a/man/correct_prevalence.Rd
+++ b/man/correct_prevalence.Rd
@@ -7,6 +7,10 @@
 correct_prevalence(
   data,
   bayesian = TRUE,
+  age_col = "age",
+  pos_col = "pos",
+  tot_col = "tot",
+  status_col = "status",
   init_se = 0.95,
   init_sp = 0.8,
   study_size_se = 1000,
@@ -17,10 +21,18 @@ correct_prevalence(
 )
 }
 \arguments{
-\item{data}{the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)}
+\item{data}{the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR `age`, `status` (for linelisting data)}
 
 \item{bayesian}{whether to adjust sero-prevalence using the Bayesian or frequentist approach. If set to `TRUE`, true sero-prevalence is estimated using MCMC.}
 
+\item{age_col}{name of the `age` column (default age_col="age").}
+
+\item{pos_col}{name of the `pos` column (default pos_col="pos").}
+
+\item{tot_col}{name of the `tot` column (default tot_col="tot").}
+
+\item{status_col}{name of the `status` column (default status_col="status").}
+
 \item{init_se}{sensitivity of the serological test}
 
 \item{init_sp}{specificity of the serological test}
@@ -38,7 +50,7 @@ correct_prevalence(
 \value{
 a list of 3 items
   \item{info}{estimated parameters (when `bayesian = TRUE`) or formula to compute corrected prevalence (when `bayesian = FALSE`)}
-  \item{df}{data.frame of input data (in aggregated form)}
+  \item{df}{data.frame of input data (in aggregated form) with the 95\% confidence interval for apparent (i.e. observed) seroprevalence}
   \item{corrected_sero}{data.frame containing age, the corresponding estimated seroprevalance with 95\% confidence/credible interval, and adjusted tot and pos}
 }
 \description{
diff --git a/man/estimate_from_mixture.Rd b/man/estimate_from_mixture.Rd
index d87522e..6c3ee6b 100644
--- a/man/estimate_from_mixture.Rd
+++ b/man/estimate_from_mixture.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/mixture_model.R
 \name{estimate_from_mixture}
 \alias{estimate_from_mixture}
-\title{Estimate seroprevalence and foi by combining mixture model and regression}
+\title{Estimate seroprevalence and FOI from a fixed mixture model}
 \usage{
 estimate_from_mixture(
   age,
@@ -15,19 +15,19 @@ estimate_from_mixture(
 )
 }
 \arguments{
-\item{age}{- vector of age}
+\item{age}{vector of age}
 
-\item{antibody_level}{- vector of the corresponding raw antibody level}
+\item{antibody_level}{vector of the corresponding raw antibody level}
 
-\item{threshold_status}{- sero status using threshold approach in line listing (optional, for visualization and comparison only)}
+\item{threshold_status}{sero status using threshold approach in line listing (optional, for visualization and comparison only)}
 
-\item{mixture_model}{- mixture_model object generated by serosv::mixture_model()}
+\item{mixture_model}{mixture_model object generated by serosv::mixture_model()}
 
-\item{s}{- smoothing basis used to fit antibody level}
+\item{s}{smoothing basis used to fit antibody level}
 
-\item{sp}{- smoothing parameter}
+\item{sp}{smoothing parameter}
 
-\item{monotonize}{- whether to monotonize seroprevalence (default to TRUE)}
+\item{monotonize}{whether to monotonize seroprevalence (default to TRUE)}
 }
 \value{
 a list of class estimated_from_mixture with the following items
@@ -38,7 +38,43 @@ a list of class estimated_from_mixture with the following items
   \item{threshold_status}{serostatus using threshold method only if provided}
 }
 \description{
-Refers to section 11.2 - 11.4
+Estimate age-specific seroprevalence and FOI given
+a fitted mixture model (generated by [serosv::mixture_model()])
+}
+\details{
+Antibody level (denoted \eqn{Z}) is modeled using a 2-component Gaussian
+mixture model. Each component \eqn{Z_j} (\eqn{j \in \{I, S\}}) represents the
+antibody level of the latent Infected and Susceptible sub-populations, following density
+\eqn{f_j(z_j|\theta_j)}
+
+Let \eqn{\pi_{\text{TRUE}}(a)} denotes the age-dependent mixing probability
+(i.e., the true prevalence), the density of the mixture is formulated as
+
+\deqn{f(z|z_I, z_S,a) = (1-\pi_{\text{TRUE}}(a))f_S(z_S|\theta_S)+\pi_{\text{TRUE}}(a)f_I(z_I|\theta_I)}
+
+The mean \eqn{E(Z|a)} thus equals
+\deqn{\mu(a) = (1-\pi_{\text{TRUE}}(a))\mu_S+\pi_{\text{TRUE}}(a)\mu_I}
+
+From which true prevalence can be computed as
+\deqn{\pi_{\text{TRUE}}(a) = \frac{\mu(a) - \mu_S}{\mu_I - \mu_S}}
+
+And FOI can then be inferred as
+\deqn{\lambda_{TRUE} = \frac{\mu'(a)}{\mu_I - \mu(a)}}
+
+Function [serosv::mixture_model()] fits antibody level data to \eqn{f_S(z_S|\theta_S)} and
+\eqn{f_I(z_I|\theta_I)}
+
+Function [serosv::estimate_mixture()] will then estimate age-specific antibody level \eqn{\mu(a)}
+and infer the estimation for \eqn{\pi_{\text{TRUE}}(a)} and \eqn{\lambda_{TRUE}}
+
+Refer to section 11.3. of the the book by Hens et al. (2012) for further details.
+}
+\references{
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+\doi{https://doi.org/10.1007/978-1-4614-4072-7}.
 }
 \seealso{
 [mgcv::gam()] for more information about the fitted gam object
diff --git a/man/farrington_model.Rd b/man/farrington_model.Rd
index 91679a3..9306655 100644
--- a/man/farrington_model.Rd
+++ b/man/farrington_model.Rd
@@ -1,19 +1,35 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/polynomial_models.R
+% Please edit documentation in R/farrington_model.R
 \name{farrington_model}
 \alias{farrington_model}
 \title{The Farrington (1990) model.}
 \usage{
-farrington_model(data, start, fixed = list())
+farrington_model(
+  data,
+  start,
+  fixed = list(),
+  age_col = "age",
+  pos_col = "pos",
+  tot_col = "tot",
+  status_col = "status"
+)
 }
 \arguments{
-\item{data}{the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)}
+\item{data}{the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR `age`, `status` (for linelisting data)}
 
 \item{start}{Named list of vectors or single vector.
 Initial values for optimizer.}
 
 \item{fixed}{Named list of vectors or single vector.
 Parameter values to keep fixed during optimization.}
+
+\item{age_col}{name of the `age` column (default age_col="age").}
+
+\item{pos_col}{name of the `pos` column (default pos_col="pos").}
+
+\item{tot_col}{name of the `tot` column (default tot_col="tot").}
+
+\item{status_col}{name of the `status` column (default status_col="status").}
 }
 \value{
 a list of class farrington_model with 5 items
@@ -24,7 +40,26 @@ a list of class farrington_model with 5 items
   \item{foi}{force of infection}
 }
 \description{
-Refers to section 6.1.2.
+Fit age-stratified seroprevalence data using the Farrington (1990) model, which assumes the
+force of infection increases linearly with age and subsequently decreases exponentially.
+}
+\details{
+The force of infection is defined as followed
+
+\deqn{
+\lambda(a) = (\alpha a - \gamma)e^{-\beta a} + \gamma
+}
+Where \eqn{\gamma} is called the long term residual for FOI,
+as \eqn{a \rightarrow \infty} , \eqn{\lambda (a) \rightarrow \gamma}
+
+The seroprevalence can thus be estimated using the non-linear model
+\deqn{
+ \pi(a) = 1 - exp\{ \frac{\alpha}{\beta}ae^{-\beta a} +
+ \frac{1}{\beta}(\frac{\alpha}{\beta} -
+ \gamma)(e^{-\beta a} - 1) -\gamma a \}
+}
+
+Refer to section 6.1.2. of the the book by Hens et al. (2012) for further details.
 }
 \examples{
 df <- rubella_uk_1986_1987
@@ -34,6 +69,13 @@ model <- farrington_model(
   )
 plot(model)
 
+}
+\references{
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+\doi{https://doi.org/10.1007/978-1-4614-4072-7}.
 }
 \seealso{
 [stats4::mle()] for more information on the fitted mle object
diff --git a/man/find_best_fp_powers.Rd b/man/find_best_fp_powers.Rd
index d7aa389..df8118c 100644
--- a/man/find_best_fp_powers.Rd
+++ b/man/find_best_fp_powers.Rd
@@ -2,18 +2,19 @@
 % Please edit documentation in R/fractional_polynomial_models.R
 \name{find_best_fp_powers}
 \alias{find_best_fp_powers}
-\title{Returns the powers of the GLM fitted model which has the lowest deviance score.}
+\title{Returns the powers of the fractional polynomial model which has the lowest deviance score.}
 \usage{
 find_best_fp_powers(data, p, mc, degree, link = "logit")
 }
 \arguments{
-\item{data}{the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)}
+\item{data}{the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR
+`age`, `status` (for linelisting data)}
 
-\item{p}{a powers sequence.}
+\item{p}{a powers sequence to be tested.}
 
 \item{mc}{indicates if the returned model should be monotonic.}
 
-\item{degree}{the degree of the model. Recommended to be <= 2.}
+\item{degree}{the maximum degree (i.e. number of power terms) to search for the best model. Recommended to be <= 2.}
 
 \item{link}{the link function. Defaulted to "logit".}
 }
@@ -24,14 +25,5 @@ list of 3 elements:
   \item{model}{The best model fitted}
 }
 \description{
-Refers to section 6.2.
-}
-\examples{
-df <- hav_be_1993_1994
-best_p <- find_best_fp_powers(
-df,
-p=seq(-2,3,0.1), mc=FALSE, degree=2, link="cloglog"
-)
-best_p
-
+Return the best powers for a given degree
 }
diff --git a/man/fp_model.Rd b/man/fp_model.Rd
index b95f394..804d1a9 100644
--- a/man/fp_model.Rd
+++ b/man/fp_model.Rd
@@ -4,14 +4,41 @@
 \alias{fp_model}
 \title{A fractional polynomial model.}
 \usage{
-fp_model(data, p, link = "logit")
+fp_model(
+  data,
+  p,
+  monotonic = FALSE,
+  link = "logit",
+  age_col = "age",
+  pos_col = "pos",
+  tot_col = "tot",
+  status_col = "status"
+)
 }
 \arguments{
 \item{data}{the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)}
 
-\item{p}{the powers of the predictor.}
+\item{p}{is either:
+\itemize{
+ \item{a numeric vector specifying the powers to apply to the predictors}
+ \item{
+   a named list with two elements, \code{"p_range"} and \code{"degree"}. \code{"p_range"}
+   is a sequence of powers and \code{"degree"} is the maximum degree.
+   In which case the package will search for the best degree and power combinations
+ }
+}}
+
+\item{monotonic}{whether the returned model should be monotonic (if a search is specified)}
 
 \item{link}{the link function for model. Defaulted to "logit".}
+
+\item{age_col}{name of the `age` column (default age_col="age").}
+
+\item{pos_col}{name of the `pos` column (default pos_col="pos").}
+
+\item{tot_col}{name of the `tot` column (default tot_col="tot").}
+
+\item{status_col}{name of the `status` column (default status_col="status").}
 }
 \value{
 a list of class fp_model with 5 items
@@ -22,7 +49,26 @@ a list of class fp_model with 5 items
   \item{foi}{force of infection}
 }
 \description{
-Refers to section 6.2.
+Fractional polynomial model is a generalization of polynomial models
+where the power of the terms can be fractions, allowing more flexibility and better
+fit for data where asymptotic behavior is expected.
+}
+\details{
+Instead of a polynomial, the linear predictor is now defined as
+\deqn{
+ \eta_m(a, \beta, p_1, p_2, ...,p_m) = \Sigma^m_{i=0} \beta_i H_i(a)
+}
+Where \eqn{m} is an integer, \eqn{p_1 \le p_2 \le... \le p_m} is a sequence of powers,
+and \eqn{H_i(a)} is a transformation given by
+
+\deqn{
+H_i = \begin{cases}
+a^{p_i} & \text{ if } p_i \neq p_{i-1},
+\\ H_{i-1}(a) \times log(a)  & \text{ if } p_i = p_{i-1},
+\end{cases}
+}
+
+Refers to section 6.2. of the the book by Hens et al. (2012) for further details.
 }
 \examples{
 df <- hav_be_1993_1994
@@ -31,7 +77,16 @@ model <- fp_model(
   p=c(1.5, 1.6), link="cloglog")
 plot(model)
 
+}
+\references{
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+\doi{https://doi.org/10.1007/978-1-4614-4072-7}.
 }
 \seealso{
 [stats::glm()] for more information on glm object
+
+[polynomial_models()]
 }
diff --git a/man/hav_be_1993_1994.Rd b/man/hav_be_1993_1994.Rd
index 7053195..4b6f9f7 100644
--- a/man/hav_be_1993_1994.Rd
+++ b/man/hav_be_1993_1994.Rd
@@ -25,14 +25,12 @@ A study of the prevalence of HAV antibodies conducted in the Flemish
 Community of Belgium in 1993 and early 1994
 }
 \examples{
-# Reproduce Fig 4.1 (upper left panel), p. 63
-age <- hav_be_1993_1994$age
-pos <- hav_be_1993_1994$pos
-tot <- hav_be_1993_1994$tot
-plot(
-  age, pos / tot,
-  pty = "s", cex = 0.06 * tot, pch = 16, xlab = "age",
-  ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
+with(hav_be_1993_1994,
+  plot(
+    age, pos / tot,
+    pty = "s", cex = 0.34 * sqrt(tot), pch = 16, xlab = "age",
+    ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
+  )
 )
 
 }
diff --git a/man/hav_be_2002.Rd b/man/hav_be_2002.Rd
index 3c4bec3..a353f1a 100644
--- a/man/hav_be_2002.Rd
+++ b/man/hav_be_2002.Rd
@@ -26,15 +26,18 @@ Parvovirus B19 in Belgium where only individuals living in Flanders were
 selected
 }
 \examples{
-# Reproduce Fig 4.1 (upper right panel), p. 63
 library(dplyr)
 df <- hav_be_2002 \%>\%
   group_by(age) \%>\%
   summarise(pos = sum(seropositive), tot = n())
-plot(
-  df$age, df$pos / df$tot,
-  pty = "s", cex = 0.06 * df$tot, pch = 16, xlab = "age",
-  ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
+
+with(
+  df,
+  plot(
+    age, pos / tot,
+    pty = "s", cex = 0.34 * sqrt(tot), pch = 16, xlab = "age",
+    ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
+  )
 )
 
 }
diff --git a/man/hav_bg_1964.Rd b/man/hav_bg_1964.Rd
index 6163be3..a2b7082 100644
--- a/man/hav_bg_1964.Rd
+++ b/man/hav_bg_1964.Rd
@@ -26,14 +26,13 @@ A cross-sectional survey conducted in 1964 in Bulgaria. Samples were
 collected from schoolchildren and blood donors.
 }
 \examples{
-# Reproduce Fig 4.1 (lower panel), p. 63
-age <- hav_bg_1964$age
-pos <- hav_bg_1964$pos
-tot <- hav_bg_1964$tot
-plot(
+with(
+  hav_bg_1964,
+  plot(
     age, pos / tot,
-    pty = "s", cex = 0.08 * tot, pch = 16, xlab = "age",
+    pty = "s", cex = 0.6 * sqrt(tot), pch = 16, xlab = "age",
     ylab = "seroprevalence", xlim = c(0, 86), ylim = c(0, 1)
+  )
 )
 
 }
diff --git a/man/hbv_ru_1999.Rd b/man/hbv_ru_1999.Rd
index c97e1ea..07a8097 100644
--- a/man/hbv_ru_1999.Rd
+++ b/man/hbv_ru_1999.Rd
@@ -27,7 +27,6 @@ A seroprevalence study conducted in St. Petersburg
 (more information in the book)
 }
 \examples{
-# Reproduce Fig 4.2, p. 65
 library(dplyr)
 hbv_ru_1999$age <- trunc(hbv_ru_1999$age / 1) * 1
 hbv_ru_1999$age[hbv_ru_1999$age > 40] <- trunc(
@@ -36,9 +35,10 @@ hbv_ru_1999$age[hbv_ru_1999$age > 40] <- trunc(
 df <- hbv_ru_1999 \%>\%
   group_by(age) \%>\%
   summarise(pos = sum(pos), tot = sum(tot))
+
 plot(
   df$age, df$pos / df$tot,
-  cex = 0.05 * df$tot, pch = 16, xlab = "age",
+  cex = 0.32 * sqrt(df$tot), pch = 16, xlab = "age",
   ylab = "seroprevalence", xlim = c(0, 72)
 )
 
diff --git a/man/hcv_be_2006.Rd b/man/hcv_be_2006.Rd
index e3eb36e..74ba600 100644
--- a/man/hcv_be_2006.Rd
+++ b/man/hcv_be_2006.Rd
@@ -29,7 +29,6 @@ information on their socio-demographic status, drug use history, drug use,
 and related risk behavior was recorded
 }
 \examples{
-# Reproduce Fig 4.3, p. 66
 library(dplyr)
 # snapping age to aggregated age group
 # (credit: https://stackoverflow.com/a/12861810)
@@ -48,11 +47,13 @@ hcv_be_2006 <- hcv_be_2006 \%>\%
   group_by(dur) \%>\%
   summarise(tot = n(), pos = sum(seropositive))
 
-plot(
-  hcv_be_2006$dur, hcv_be_2006$pos / hcv_be_2006$tot,
-  cex = 0.1 * hcv_be_2006$tot, pch = 16,
-  xlab = "duration of injection (years)",
-  ylab = "seroprevalence", xlim = c(0, 25), ylim = c(0, 1)
+with(hcv_be_2006,
+  plot(
+    dur, pos / tot,
+    cex = 0.42 * sqrt(tot), pch = 16,
+    xlab = "duration of injection (years)",
+    ylab = "seroprevalence", xlim = c(0, 25), ylim = c(0, 1)
+  )
 )
 
 }
diff --git a/man/hierarchical_bayesian_model.Rd b/man/hierarchical_bayesian_model.Rd
index 90e9000..ebcf7a0 100644
--- a/man/hierarchical_bayesian_model.Rd
+++ b/man/hierarchical_bayesian_model.Rd
@@ -6,6 +6,10 @@
 \usage{
 hierarchical_bayesian_model(
   data,
+  age_col = "age",
+  pos_col = "pos",
+  tot_col = "tot",
+  status_col = "status",
   type = "far3",
   chains = 1,
   warmup = 1500,
@@ -13,7 +17,15 @@ hierarchical_bayesian_model(
 )
 }
 \arguments{
-\item{data}{the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)}
+\item{data}{the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR `age`, `status` (for linelisting data)}
+
+\item{age_col}{name of the `age` column (default age_col="age").}
+
+\item{pos_col}{name of the `pos` column (default pos_col="pos").}
+
+\item{tot_col}{name of the `tot` column (default tot_col="tot").}
+
+\item{status_col}{name of the `status` column (default status_col="status").}
 
 \item{type}{type of model ("far2", "far3" or "log_logistic")}
 
@@ -35,7 +47,53 @@ a list of class hierarchical_bayesian_model with 6 items
   \item{foi}{function to compute force of infection given age and model parameters}
 }
 \description{
-Refers to section 10.3
+Fit age-stratified seroprevalence to parametric hierarchical Bayesian models.
+Supported models including Farrington model (2 and 3 parameters variants)
+and Log Logistic model
+}
+\details{
+Consider a model for prevalence that has a parametric form
+\eqn{\pi(a_i, \alpha)} where \eqn{\alpha} is a parameter vector
+
+Under a Bayesian framework, we can constraint the parameter space of the prior distribution \eqn{P(\alpha)}
+to achieve monotonicity of the posterior distribution \eqn{P(\pi_1, \pi_2, ..., \pi_m|y,n)}
+
+Where:
+
+- \eqn{n = (n_1, n_2, ..., n_m)} and \eqn{n_i} is the sample size at age \eqn{a_i}
+
+- \eqn{y = (y_1, y_2, ..., y_m)} and \eqn{y_i} is the number of infected individual from the \eqn{n_i} sampled subjects
+
+For \bold{Farrington} model with 3 parameters, prevalence is formulated as follow
+
+\deqn{
+\pi (a) = 1 - exp\{ \frac{\alpha_1}{\alpha_2}ae^{-\alpha_2 a} +
+\frac{1}{\alpha_2}(\frac{\alpha_1}{\alpha_2} - \alpha_3)(e^{-\alpha_2 a} - 1) -\alpha_3 a \}
+}
+
+The likelihood model is defined as \eqn{y_i \sim Bin(n_i, \pi_i), \text{  for } i = 1,2,3,...m}
+
+The constraint on the parameter space can be incorporated by assuming
+ truncated normal distribution for the components of \eqn{\alpha},
+\eqn{\alpha = (\alpha_1, \alpha_2, \alpha_3)} in \eqn{\pi_i = \pi(a_i,\alpha)}
+
+The flat hyperpriors are defined as \eqn{\mu_j \sim \mathcal{N}(0, 10000)} and
+\eqn{\tau^{-2}_j \sim \Gamma(100,100)}
+
+For \bold{Farrington} model with 2 parameters, it is equivalent to the previous model with \eqn{\alpha_3 = 0}
+
+For \bold{Log logistic model}, seroprevalence is instead defined as
+
+\deqn{\pi(a) = \frac{\beta a^\alpha}{1 + \beta a^\alpha}, \text{ } \alpha, \beta > 0}
+
+The likelihood is similarly defined as \eqn{y_i \sim Bin(n_i, \pi_i))}
+
+The prior model of \eqn{\alpha_1} is specified as \eqn{\alpha_1 \sim \text{truncated  } \mathcal{N}(\mu_1, \tau_1)}
+with flat hyperpriors as in Farrington model
+
+\eqn{\beta} is constrained to be positive by specifying \eqn{\alpha_2 \sim \mathcal{N}(\mu_2, \tau_2)}
+
+Refer to section Chapter 10.3 of the the book by Hens et al. (2012) for further details.
 }
 \examples{
 \donttest{
@@ -45,3 +103,10 @@ model$info
 plot(model)
 }
 }
+\references{
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+\doi{https://doi.org/10.1007/978-1-4614-4072-7}.
+}
diff --git a/man/lp_model.Rd b/man/lp_model.Rd
index 7fe492d..0460eca 100644
--- a/man/lp_model.Rd
+++ b/man/lp_model.Rd
@@ -4,10 +4,20 @@
 \alias{lp_model}
 \title{A local polynomial model.}
 \usage{
-lp_model(data, kern = "tcub", nn = 0, h = 0, deg = 2)
+lp_model(
+  data,
+  kern = "tcub",
+  nn = 0,
+  h = 0,
+  deg = 2,
+  age_col = "age",
+  pos_col = "pos",
+  tot_col = "tot",
+  status_col = "status"
+)
 }
 \arguments{
-\item{data}{the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)}
+\item{data}{the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR `age`, `status` (for linelisting data)}
 
 \item{kern}{Weight function, default = "tcub".
 Other choices are "rect", "trwt", "tria", "epan", "bisq" and "gauss".
@@ -17,9 +27,17 @@ e.g. for confidence bands and some bandwidth selectors.}
 \item{nn}{Nearest neighbor component of the smoothing parameter.
 Default value is 0.7, unless either h is provided, in which case the default is 0.}
 
-\item{h}{The constant component of the smoothing parameter. Default: 0.}
+\item{h}{The constant bandwidth of the smoothing parameter. Default: 0.}
 
 \item{deg}{Degree of polynomial to use. Default: 2.}
+
+\item{age_col}{name of the `age` column (default age_col="age").}
+
+\item{pos_col}{name of the `pos` column (default pos_col="pos").}
+
+\item{tot_col}{name of the `tot` column (default tot_col="tot").}
+
+\item{status_col}{name of the `status` column (default status_col="status").}
 }
 \value{
 a list of class lp_model with 6 items
@@ -31,7 +49,43 @@ a list of class lp_model with 6 items
   \item{foi}{force of infection}
 }
 \description{
-Refers to section 7.1. and 7.2.
+Fit the age-specific seroprevalence to a local polynomial model,
+where the linear predictor is approximated locally at one particular age.
+}
+\details{
+Consider a linear predictor \eqn{\eta(a)} approximated locally at one particular value \eqn{a_0}.
+
+For a general degree \eqn{p}, the linear predictor for a neighbor of \eqn{a_0}, labeled \eqn{a_i} is equivalent to the Taylor approximation
+\deqn{
+\eta(a_i) = \eta(a_0) + \eta^{(1)}(a_0)(a_i - a_0) +
+\frac{\eta^{(2)}(a_0)}{2}(a_i - a_0)^2 + ... + \frac{\eta^{(p)}(a_0)}{p!}(a_i - a_0)^p
+}
+
+\eqn{\eta(a_i)} can be estimated by maximizing
+\deqn{
+ \Sigma_{i=1}^{N} \ell_i \{Y_i, g^{-1} (\beta_0 + \beta_1(a_i-a_0)+ \beta_2(a_i-a_0)^2 ... +
+ \beta_p(a_i-a_0)^p) \} K_h(a_i - a_0)
+}
+
+The estimator for the \eqn{k}-th derivative of \eqn{\eta(a_0)}, for \eqn{k = 0,1,…,p}
+(degree of local polynomial) is thus:
+\deqn{
+ \hat{\eta}^{(k)}(a_0) = k!\hat{\beta}_k(a_0)
+}
+
+The estimator for the prevalence at age \eqn{a_0} is then given by
+\deqn{
+\hat{\pi}(a_0) = g^{-1}\{ \hat{\beta}_0(a_0) \}
+}
+Where \eqn{g} is the link function
+
+The estimator for the force of infection at age \eqn{a_0} by assuming \eqn{p \ge 1} is as followed
+\deqn{
+\hat{\lambda}(a_0) = \hat{\beta}_1(a_0) \delta \{ \hat{\beta}_0 (a_0) \}
+}
+Where \eqn{\delta \{ \hat{\beta}_0(a_0) \} = \frac{dg^{-1} \{ \hat{\beta}_0(a_0) \} } {d\hat{\beta}_0(a_0)}}
+
+Refer to section 7.1 and 7.2. of the the book by Hens et al. (2012) for further details.
 }
 \examples{
 df <- mumps_uk_1986_1987
@@ -41,6 +95,13 @@ model <- lp_model(
   )
 plot(model)
 
+}
+\references{
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+\doi{https://doi.org/10.1007/978-1-4614-4072-7}.
 }
 \seealso{
 [locfit::locfit()] for more information on the fitted locfit object
diff --git a/man/mixture_model.Rd b/man/mixture_model.Rd
index 2b0ec5e..cc78b2d 100644
--- a/man/mixture_model.Rd
+++ b/man/mixture_model.Rd
@@ -13,15 +13,15 @@ mixture_model(
 )
 }
 \arguments{
-\item{antibody_level}{- vector of the corresponding raw antibody level}
+\item{antibody_level}{vector of the corresponding raw antibody level}
 
-\item{breaks}{- number of intervals which the antibody_level are grouped into}
+\item{breaks}{number of intervals which the antibody_level are grouped into}
 
-\item{pi}{- proportion of susceptible, infected}
+\item{pi}{proportion of susceptible, infected}
 
-\item{mu}{- a vector of means of component distributions (vector of 2 numbers in ascending order)}
+\item{mu}{a vector of means of component distributions (vector of 2 numbers in ascending order)}
 
-\item{sigma}{-  a vector of standard deviations of component distributions (vector of 2 number)}
+\item{sigma}{a vector of standard deviations of component distributions (vector of 2 number)}
 }
 \value{
 a list of class mixture_model with the following items
@@ -31,7 +31,35 @@ a list of class mixture_model with the following items
   \item{infected}{fitted distribution for infected}
 }
 \description{
-Refers to section 11.1 - 11.4
+Fit the antibody level data to a 2-component Gaussian mixture model
+}
+\details{
+Antibody level (denoted \eqn{Z}) is modeled using a 2-component Gaussian
+mixture model. Each component \eqn{Z_j} (\eqn{j \in \{I, S\}}) represents the
+antibody level of the latent Infected and Susceptible sub-populations, following density
+\eqn{f_j(z_j|\theta_j)}
+
+Let \eqn{\pi_{\text{TRUE}}(a)} denotes the age-dependent mixing probability
+(i.e., the true prevalence), the density of the mixture is formulated as
+
+\deqn{f(z|z_I, z_S,a) = (1-\pi_{\text{TRUE}}(a))f_S(z_S|\theta_S)+\pi_{\text{TRUE}}(a)f_I(z_I|\theta_I)}
+
+The mean \eqn{E(Z|a)} thus equals
+\deqn{\mu(a) = (1-\pi_{\text{TRUE}}(a))\mu_S+\pi_{\text{TRUE}}(a)\mu_I}
+
+From which true prevalence can be computed as
+\deqn{\pi_{\text{TRUE}}(a) = \frac{\mu(a) - \mu_S}{\mu_I - \mu_S}}
+
+And FOI can then be inferred as
+\deqn{\lambda_{TRUE} = \frac{\mu'(a)}{\mu_I - \mu(a)}}
+
+Function [serosv::mixture_model()] fits antibody level data to \eqn{f_S(z_S|\theta_S)} and
+\eqn{f_I(z_I|\theta_I)}
+
+Function [serosv::estimate_mixture()] will then estimate age-specific antibody level \eqn{\mu(a)}
+and infer the estimation for \eqn{\pi_{\text{TRUE}}(a)} and \eqn{\lambda_{TRUE}}
+
+Refer to section 11.3. of the the book by Hens et al. (2012) for further details.
 }
 \examples{
 df <- vzv_be_2001_2003[vzv_be_2001_2003$age < 40.5,]
@@ -40,3 +68,10 @@ model <- mixture_model(antibody_level = data)
 model$info
 plot(model)
 }
+\references{
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+\doi{https://doi.org/10.1007/978-1-4614-4072-7}.
+}
diff --git a/man/mseir_model.Rd b/man/mseir_model.Rd
deleted file mode 100644
index 6643ce7..0000000
--- a/man/mseir_model.Rd
+++ /dev/null
@@ -1,38 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/mseir_model.R
-\name{mseir_model}
-\alias{mseir_model}
-\title{MSEIR model}
-\usage{
-mseir_model(a, gamma, lambda, sigma, nu)
-}
-\arguments{
-\item{a}{age sequence}
-
-\item{gamma}{time in maternal class.}
-
-\item{lambda}{time in susceptible class.}
-
-\item{sigma}{time in latent class.}
-
-\item{nu}{time in infected class.}
-}
-\value{
-list of class mseir_model with the following parameters
-  \item{parameters}{list of parameters used for fitting the model}
-  \item{output}{matrix of proportion for each compartment over time}
-}
-\description{
-Refers to section 3.4.
-}
-\examples{
-model <- mseir_model(
-  a=seq(from=1,to=20,length=500), # age range from 0 -> 20 yo
-  gamma=1/0.5, # 6 months in the maternal antibodies
-  lambda=0.2,  # 5 years in the susceptible class
-  sigma=26.07, # 14 days in the latent class
-  nu=36.5      # 10 days in the infected class
-)
-model
-
-}
diff --git a/man/mumps_uk_1986_1987.Rd b/man/mumps_uk_1986_1987.Rd
index b57c904..3d5be4e 100644
--- a/man/mumps_uk_1986_1987.Rd
+++ b/man/mumps_uk_1986_1987.Rd
@@ -7,7 +7,7 @@
 \format{
 A data frame with 3 variables:
 \describe{
- \item{age}{Age group}
+ \item{age}{Midpoint of the age group (e.g. 1.5 = 1-2 years old, 2.5 = 2-3 years old)}
  \item{pos}{Number of seropositive individuals}
  \item{tot}{Total number of individuals surveyed}
 }
@@ -26,13 +26,11 @@ the UK. The survey, covering subjects from 1 to over 65 years of age,
 provides information on the prevalence of antibody by age
 }
 \examples{
-# Reproduce Fig 4.4 (left panel), p. 67
-age <- mumps_uk_1986_1987$age
-pos <- mumps_uk_1986_1987$pos
-tot <- mumps_uk_1986_1987$tot
-plot(age, pos / tot,
-  cex = 0.008 * tot, pch = 16, xlab = "age", ylab = "seroprevalence",
-  xlim = c(0, 45), ylim = c(0, 1)
+with(mumps_uk_1986_1987,
+  plot(age, pos / tot,
+    cex = 0.1 * sqrt(tot), pch = 16, xlab = "age", ylab = "seroprevalence",
+    xlim = c(0, 45), ylim = c(0, 1)
+  )
 )
 
 }
diff --git a/man/parvob19_be_2001_2003.Rd b/man/parvob19_be_2001_2003.Rd
index 345b2e7..e1b129b 100644
--- a/man/parvob19_be_2001_2003.Rd
+++ b/man/parvob19_be_2001_2003.Rd
@@ -31,13 +31,12 @@ Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and
 were obtained from residual sera submitted for routine laboratory testing.
 }
 \examples{
-# Reproduce Fig 4.5 (left upper panel), p. 68
 library(dplyr)
 df <- parvob19_be_2001_2003 \%>\%
   group_by(age) \%>\%
   summarise(pos = sum(seropositive), tot = n())
 plot(df$age, df$pos / df$tot,
-  cex = 0.02 * df$tot, pch = 16, xlab = "age", ylab = "seroprevalence",
+  cex = 0.3 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
   xlim = c(0, 82), ylim = c(0, 1)
 )
 
diff --git a/man/parvob19_ew_1996.Rd b/man/parvob19_ew_1996.Rd
index 6b4b5d5..69b6a18 100644
--- a/man/parvob19_ew_1996.Rd
+++ b/man/parvob19_ew_1996.Rd
@@ -31,17 +31,17 @@ Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and
 were obtained from residual sera submitted for routine laboratory testing.
 }
 \examples{
-# Reproduce Fig 4.5 (right upper panel), p. 68
-# NB: This figure will look different to that of in the book, since we
+# Note: This figure will look different to that of in the book, since we
 # believe that the original authors has made some errors in specifying
 # the sample size of the dots.
+
 library(dplyr)
 df <- parvob19_ew_1996 \%>\%
   mutate(age = round(age)) \%>\%
   group_by(age) \%>\%
   summarise(pos = sum(seropositive), tot = n())
 plot(df$age, df$pos / df$tot,
-  cex = 0.02 * df$tot, pch = 16, xlab = "age", ylab = "seroprevalence",
+  cex = 0.3 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
   xlim = c(0, 82), ylim = c(0, 1)
 )
 
diff --git a/man/parvob19_fi_1997_1998.Rd b/man/parvob19_fi_1997_1998.Rd
index 6e0a918..2f23621 100644
--- a/man/parvob19_fi_1997_1998.Rd
+++ b/man/parvob19_fi_1997_1998.Rd
@@ -31,17 +31,17 @@ Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and
 were obtained from residual sera submitted for routine laboratory testing.
 }
 \examples{
-# Reproduce Fig 4.5 (left bottom panel), p. 68
-# NB: This figure will look different to that of in the book, since we
+# Note: This figure will look different to that of in the book, since we
 # believe that the original authors has made some errors in specifying
 # the sample size of the dots.
+
 library(dplyr)
 df <- parvob19_fi_1997_1998 \%>\%
   mutate(age = round(age)) \%>\%
   group_by(age) \%>\%
   summarise(pos = sum(seropositive), tot = n())
 plot(df$age, df$pos / df$tot,
-  cex = 0.07 * df$tot, pch = 16, xlab = "age", ylab = "seroprevalence",
+  cex = 0.4 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
   xlim = c(0, 82), ylim = c(0, 1)
 )
 
diff --git a/man/parvob19_it_2003_2004.Rd b/man/parvob19_it_2003_2004.Rd
index 798d623..0adb21e 100644
--- a/man/parvob19_it_2003_2004.Rd
+++ b/man/parvob19_it_2003_2004.Rd
@@ -31,16 +31,12 @@ Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and
 were obtained from residual sera submitted for routine laboratory testing.
 }
 \examples{
-# Reproduce Fig 4.5 (middle bottom panel), p. 68
-# NB: This figure will look different to that of in the book, since we
-# believe that the original authors has made some errors in specifying
-# the sample size of the dots.
 library(dplyr)
 df <- parvob19_it_2003_2004 \%>\%
   group_by(age) \%>\%
   summarise(pos = sum(seropositive), tot = n())
 plot(df$age, df$pos / df$tot,
-  cex = 0.07 * df$tot, pch = 16, xlab = "age", ylab = "seroprevalence",
+  cex = 0.32 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
   xlim = c(0, 82), ylim = c(0, 1)
 )
 
diff --git a/man/parvob19_pl_1995_2004.Rd b/man/parvob19_pl_1995_2004.Rd
index db10c3e..4d8a2c8 100644
--- a/man/parvob19_pl_1995_2004.Rd
+++ b/man/parvob19_pl_1995_2004.Rd
@@ -31,17 +31,17 @@ Finland, Italy, and Poland. The sera were collected between 1995 and 2004 and
 were obtained from residual sera submitted for routine laboratory testing.
 }
 \examples{
-# Reproduce Fig 4.5 (right bottom panel), p. 68
-# NB: This figure will look different to that of in the book, since we
+# Note: This figure will look different to that of in the book, since we
 # believe that the original authors has made some errors in specifying
 # the sample size of the dots.
+
 library(dplyr)
 df <- parvob19_pl_1995_2004 \%>\%
   mutate(age = round(age)) \%>\%
   group_by(age) \%>\%
   summarise(pos = sum(seropositive), tot = n())
 plot(df$age, df$pos / df$tot,
-  cex = 0.07 * df$tot, pch = 16, xlab = "age", ylab = "seroprevalence",
+  cex = 0.32 * sqrt(df$tot), pch = 16, xlab = "age", ylab = "seroprevalence",
   xlim = c(0, 82), ylim = c(0, 1)
 )
 
diff --git a/man/penalized_spline_model.Rd b/man/penalized_spline_model.Rd
index a71ee0c..5cb2940 100644
--- a/man/penalized_spline_model.Rd
+++ b/man/penalized_spline_model.Rd
@@ -6,6 +6,10 @@
 \usage{
 penalized_spline_model(
   data,
+  age_col = "age",
+  pos_col = "pos",
+  tot_col = "tot",
+  status_col = "status",
   s = "bs",
   link = "logit",
   framework = "pl",
@@ -13,7 +17,16 @@ penalized_spline_model(
 )
 }
 \arguments{
-\item{data}{the input data frame, must either have `age`, `pos`, `tot` column for aggregated data OR `age`, `status` for linelisting data}
+\item{data}{the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR
+columns for `age`, `status` (for linelisting data)}
+
+\item{age_col}{name of the `age` column (default age_col="age").}
+
+\item{pos_col}{name of the `pos` column (default pos_col="pos").}
+
+\item{tot_col}{name of the `tot` column (default tot_col="tot").}
+
+\item{status_col}{name of the `status` column (default status_col="status").}
 
 \item{s}{smoothing basis to use}
 
@@ -33,7 +46,82 @@ a list of class penalized_spline_model with 6 attributes
   \item{foi}{force of infection}
 }
 \description{
-Penalized Spline model
+Fit age-specific seroprevalence to a semi-parametric model where
+predictor is modeled with penalized splines. The penalized splines can be estimated by either
+(1) penalized likelihood framework or (2) mixed model framework
+}
+\details{
+In the semi-parametric model, the predictor is formulated as a penalized spline
+with truncated power basis functions of degree \eqn{p}
+and fixed knots \eqn{\kappa_1,..., \kappa_k} as followed
+
+\deqn{
+\eta(a_i) = \beta_0 + \beta_1a_i + ... + \beta_p a_i^p + \Sigma_{k=1}^ku_k(a_i - \kappa_k)^p_+
+}
+
+- Where:
+\deqn{
+(a_i - \kappa_k)^p_+ = \begin{cases}
+0, & a_i \le \kappa_k \\
+(a_i - \kappa_k)^p, & a_i > \kappa_k
+\end{cases}
+}
+
+FOI can then be derived by
+
+\deqn{\hat{\lambda}(a_i) = [\hat{\beta_1} , 2\hat{\beta_2}a_i, ...,
+p \hat{\beta} a_i ^{p-1} + \Sigma^k_{k=1} p \hat{u}_k(a_i - \kappa_k)^{p-1}_+] \delta(\hat{\eta}(a_i))
+}
+
+Where \eqn{\delta(.)} is determined by the link function used in the model
+
+In matrix annotation, the mean structure model for \eqn{\eta(a_i)} becomes
+\deqn{\eta = \textbf{X}\beta + \textbf{Zu}}
+
+Where \eqn{\eta = [\eta(a_i) ... \eta(a_N) ]^T}, \eqn{\beta = [\beta_0 \beta_1 .... \beta_p]^T},
+and \eqn{\textbf{u} = [u_1 u_2 ... u_k]^T} are the regression with corresponding design matrices
+\deqn{
+
+\textbf{X} = \begin{bmatrix}
+1 & a_1 & a_1^2 & ... & a_1^p \\
+1 & a_2 & a_2^2 & ... & a_2^p \\
+\vdots & \vdots & \vdots & \dots & \vdots \\
+1 & a_N & a_N^2 & ... & a_N^p
+\end{bmatrix}, \textbf{Z} = \begin{bmatrix}
+(a_1 - \kappa_1 )_+^p & (a_1 - \kappa_2 )_+^p & \dots & (a_1 - \kappa_k)_+^p \\
+(a_2 - \kappa_1 )_+^p & (a_2 - \kappa_2 )_+^p & \dots & (a_2 - \kappa_k)_+^p \\
+\vdots & \vdots & \dots & \vdots \\
+(a_N - \kappa_1 )_+^p & (a_N - \kappa_2 )_+^p & \dots & (a_N - \kappa_k)_+^p
+\end{bmatrix}
+}
+
+Under \bold{penalized likelihood framework}, the model is fitted by maximizing
+the following likelihood
+
+\deqn{
+\phi^{-1}[y^T(\textbf{X}\beta + \textbf{Zu} ) -  \textbf{1}^Tc(\textbf{X}\beta + \textbf{Zu} )] - \frac{1}{2}\lambda^2
+\begin{bmatrix} \beta \\ \textbf{u} \end{bmatrix}^T D\begin{bmatrix} \beta \\ \textbf{u} \end{bmatrix}
+}
+
+Where:
+
+- \eqn{X\beta + Zu} is the predictor
+
+- \eqn{D} is a known semi-definite penalty matrix [@Wahba1978], [@Green1993]
+
+- \eqn{y} is the response vector
+
+- \eqn{\textbf{1}} the unit vector, \eqn{c(.)} is determined by the link function used
+
+- \eqn{\lambda} is the smoothing parameter (larger values –> smoother curves)
+
+- \eqn{\phi} is the overdispersion parameter and equals 1 if there is no overdispersion
+
+Under the \bold{mixed model} framework,
+the model instead treats the coefficients \eqn{\textbf{u}} in the likelihood formulation
+as random effects with \eqn{\textbf{u} \sim N(\textbf{0}, \mathbf{\sigma}^2_u \textbf{I})}
+
+Refer to section 8.1 and 8.2 of the the book by Hens et al. (2012) for further details.
 }
 \examples{
 data <- parvob19_be_2001_2003
@@ -42,6 +130,13 @@ model <- penalized_spline_model(data, framework="glmm")
 model$info$gam
 plot(model)
 }
+\references{
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+\doi{https://doi.org/10.1007/978-1-4614-4072-7}.
+}
 \seealso{
 [mgcv::gam()], [mgcv::gamm()] for more information the fitted gam and gamm model
 }
diff --git a/man/plot.mseir_model.Rd b/man/plot.mseir_model.Rd
deleted file mode 100644
index 70b016e..0000000
--- a/man/plot.mseir_model.Rd
+++ /dev/null
@@ -1,19 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/plots.R
-\name{plot.mseir_model}
-\alias{plot.mseir_model}
-\title{plot() overloading for MSEIR model}
-\usage{
-\method{plot}{mseir_model}(x, ...)
-}
-\arguments{
-\item{x}{the mseir_model object.}
-
-\item{...}{arbitrary params.}
-}
-\value{
-ggplot object
-}
-\description{
-plot() overloading for MSEIR model
-}
diff --git a/man/plot.sir_basic_model.Rd b/man/plot.sir_basic_model.Rd
deleted file mode 100644
index 32d17ca..0000000
--- a/man/plot.sir_basic_model.Rd
+++ /dev/null
@@ -1,19 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/plots.R
-\name{plot.sir_basic_model}
-\alias{plot.sir_basic_model}
-\title{plot() overloading for SIR model}
-\usage{
-\method{plot}{sir_basic_model}(x, ...)
-}
-\arguments{
-\item{x}{the sir_basic_model object.}
-
-\item{...}{arbitrary params.}
-}
-\value{
-ggplot object
-}
-\description{
-plot() overloading for SIR model
-}
diff --git a/man/plot.sir_static_model.Rd b/man/plot.sir_static_model.Rd
deleted file mode 100644
index 68dba3f..0000000
--- a/man/plot.sir_static_model.Rd
+++ /dev/null
@@ -1,19 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/plots.R
-\name{plot.sir_static_model}
-\alias{plot.sir_static_model}
-\title{plot() overloading for SIR static model}
-\usage{
-\method{plot}{sir_static_model}(x, ...)
-}
-\arguments{
-\item{x}{the sir_static_model object.}
-
-\item{...}{arbitrary params.}
-}
-\value{
-ggplot object
-}
-\description{
-plot() overloading for SIR static model
-}
diff --git a/man/plot.sir_subpops_model.Rd b/man/plot.sir_subpops_model.Rd
deleted file mode 100644
index 3eceefa..0000000
--- a/man/plot.sir_subpops_model.Rd
+++ /dev/null
@@ -1,19 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/plots.R
-\name{plot.sir_subpops_model}
-\alias{plot.sir_subpops_model}
-\title{plot() overloading for SIR sub populations model}
-\usage{
-\method{plot}{sir_subpops_model}(x, ...)
-}
-\arguments{
-\item{x}{the sir_subpops_models object.}
-
-\item{...}{arbitrary params.}
-}
-\value{
-list of ggplot objects, each object is the plot for the corresponding subpopulation
-}
-\description{
-plot() overloading for SIR sub populations model
-}
diff --git a/man/plot_standard_curve.Rd b/man/plot_standard_curve.Rd
index c57cd07..4d73b90 100644
--- a/man/plot_standard_curve.Rd
+++ b/man/plot_standard_curve.Rd
@@ -13,15 +13,15 @@ plot_standard_curve(
 )
 }
 \arguments{
-\item{x}{- output of `to_titer()`}
+\item{x}{output of `to_titer()`}
 
-\item{facet}{- whether to faceted by plates or plot all standard curves on a single plot}
+\item{facet}{whether to faceted by plates or plot all standard curves on a single plot}
 
-\item{xlab}{- label of the x axis}
+\item{xlab}{label of the x axis}
 
-\item{ylab}{- label of the y axis}
+\item{ylab}{label of the y axis}
 
-\item{datapoint_size}{- size of the data point (only applicable when `facet=TRUE`)}
+\item{datapoint_size}{size of the data point (only applicable when `facet=TRUE`)}
 }
 \description{
 Visualize standard curves for each plate
diff --git a/man/polynomial_model.Rd b/man/polynomial_model.Rd
index f03eb29..ceca55a 100644
--- a/man/polynomial_model.Rd
+++ b/man/polynomial_model.Rd
@@ -4,16 +4,30 @@
 \alias{polynomial_model}
 \title{Polynomial models}
 \usage{
-polynomial_model(data, k, type, link = "log")
+polynomial_model(
+  data,
+  k,
+  link = "log",
+  age_col = "age",
+  pos_col = "pos",
+  tot_col = "tot",
+  status_col = "status"
+)
 }
 \arguments{
-\item{data}{the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)}
+\item{data}{the input data frame, must either have columns for `age`, `pos`, `tot` (for aggregated data) OR `age`, `status` (for linelisting data)}
 
-\item{k}{degree of the model.}
+\item{k}{degree of the polynomial. (k=1 for Muench model, k=2 for Griffith model, k=3 for Grenfell model).}
 
-\item{type}{name of method (Muench, Giffith, Grenfell).}
+\item{link}{link function (default link="log").}
 
-\item{link}{link function.}
+\item{age_col}{name of the `age` column (default age_col="age").}
+
+\item{pos_col}{name of the `pos` column (default pos_col="pos").}
+
+\item{tot_col}{name of the `tot` column (default tot_col="tot").}
+
+\item{status_col}{name of the `status` column (default status_col="status").}
 }
 \value{
 a list of class polynomial_model with 5 items
@@ -24,17 +38,59 @@ a list of class polynomial_model with 5 items
   \item{foi}{force of infection}
 }
 \description{
-Refers to section 6.1.1
+Fit age-stratified seroprevalence data to serocatalytic models formulated as polynomials.
+}
+\details{
+The seroprevalence is assumed to follow the general format
+\deqn{
+\pi(a)  = 1 - e^{-\Sigma_{i=1}^k \beta_i a^i}
+}
+Which implies the force of infection to be \eqn{\lambda(a) = \Sigma_{i=1}^k \beta_i i a^{i-1}}
+
+Where:
+
+- \eqn{\pi} is the seroprevalence at age \eqn{a}
+
+- \eqn{a} is the variable age
+
+- \eqn{k} is the degree of the polynomial
+
+The seroprevalence \eqn{\pi(a)} is fitted using a GLM with log link with
+the linear predictor \eqn{\eta(a) = \Sigma_{i=1}^k \beta_i a^{i}}
+
+Muench (1934) model is equivalent to a degree 1 (\eqn{k=1}) linear predictor
+
+Griffith model is equivalent to a degree 2 (\eqn{k=2}) linear predictor
+
+Grenfell & Anderson (1985) suggested a higher order polynomials (\eqn{k \geq 3})
+
+Refer to section 6.1.1. of the the book by Hens et al. (2012) for further details.
 }
 \examples{
 data <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age), ]
-data$status <- data$seropositive
-aggregated <- transform_data(data$age, data$seropositive, stratum_col = "age")
+aggregated <- transform_data(data, stratum_col = "age", status_col="seropositive")
 
 # fit with aggregated data
-model <- polynomial_model(aggregated, type = "Muench")
+model <- polynomial_model(aggregated, k = 1)
 # fit with linelisting data
-model <- polynomial_model(data, type = "Muench")
+model <- polynomial_model(data,
+    status_col = "seropositive",
+    k = 1)
 plot(model)
 
 }
+\references{
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+\doi{https://doi.org/10.1007/978-1-4614-4072-7}.
+
+Grenfell, B. T., and R. M. Anderson. 1985. “The Estimation of
+Age-Related Rates of Infection from Case Notifications and Serological Data.”
+The Journal of Hygiene 95 (2): 419–36. \doi{https://doi.org/10.1017/s0022172400062859}.
+
+Muench, Hugo. 1934. “Derivation of Rates from Summation Data by the Catalytic Curve.”
+Journal of the American Statistical Association 29 (185):
+25–38. \doi{https://doi.org/10.1080/01621459.1934.10502684}.
+}
diff --git a/man/predict.age_time_model.Rd b/man/predict.age_time_model.Rd
new file mode 100644
index 0000000..54b07f4
--- /dev/null
+++ b/man/predict.age_time_model.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/predict.R
+\name{predict.age_time_model}
+\alias{predict.age_time_model}
+\title{Predict from the age_time_mdoel}
+\usage{
+\method{predict}{age_time_model}(object, newdata, modtype = "monotonized", ...)
+}
+\arguments{
+\item{object}{serosv models}
+
+\item{newdata}{data.frame with age column to generate prediction}
+
+\item{modtype}{either "monotonized" (to predict using monotonized model) or "non-monotonized"}
+
+\item{...}{arbitrary argument}
+}
+\value{
+confidence interval dataframe with n_group x 3 cols, the columns are `group`, `sp_df`, `foi_df`
+}
+\description{
+Predict from the age_time_mdoel
+}
diff --git a/man/predict.farrington_model.Rd b/man/predict.farrington_model.Rd
new file mode 100644
index 0000000..5303118
--- /dev/null
+++ b/man/predict.farrington_model.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/predict.R
+\name{predict.farrington_model}
+\alias{predict.farrington_model}
+\title{Prediction for serosv Farrington model}
+\usage{
+\method{predict}{farrington_model}(object, newdata = NULL, ...)
+}
+\arguments{
+\item{object}{serosv models}
+
+\item{newdata}{data.frame with age column to generate prediction}
+
+\item{...}{arbitrary argument}
+}
+\value{
+prediction output
+}
+\description{
+Prediction for serosv Farrington model
+}
diff --git a/man/predict.fp_model.Rd b/man/predict.fp_model.Rd
new file mode 100644
index 0000000..0a43ff2
--- /dev/null
+++ b/man/predict.fp_model.Rd
@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/predict.R
+\name{predict.fp_model}
+\alias{predict.fp_model}
+\title{Prediction for serosv fractional polynomial model}
+\usage{
+\method{predict}{fp_model}(object, newdata = NULL, ...)
+}
+\arguments{
+\item{object}{serosv models}
+
+\item{newdata}{data.frame with age column to generate prediction}
+
+\item{...}{arbitrary argument}
+}
+\value{
+prediction output
+}
+\description{
+Prediction for serosv fractional polynomial model
+}
+\seealso{
+[stats::predict.glm()] for more information on the predict function
+}
diff --git a/man/predict.hierarchical_bayesian_model.Rd b/man/predict.hierarchical_bayesian_model.Rd
new file mode 100644
index 0000000..e5dc770
--- /dev/null
+++ b/man/predict.hierarchical_bayesian_model.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/predict.R
+\name{predict.hierarchical_bayesian_model}
+\alias{predict.hierarchical_bayesian_model}
+\title{Predict from an hierarchical bayesian model}
+\usage{
+\method{predict}{hierarchical_bayesian_model}(object, newdata = NULL, ...)
+}
+\arguments{
+\item{object}{serosv models}
+
+\item{newdata}{data.frame with age column to generate prediction}
+
+\item{...}{arbitrary arguments}
+}
+\value{
+list of confidence interval for seroprevalence and foi. Each confidence interval dataframe with 4 variables, x and y for the fitted values and ymin and ymax for the confidence interval
+}
+\description{
+Predict from an hierarchical bayesian model
+}
diff --git a/man/predict.lp_model.Rd b/man/predict.lp_model.Rd
new file mode 100644
index 0000000..9e0d04f
--- /dev/null
+++ b/man/predict.lp_model.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/predict.R
+\name{predict.lp_model}
+\alias{predict.lp_model}
+\title{Prediction for serosv local polynomial model}
+\usage{
+\method{predict}{lp_model}(object, newdata = NULL, ...)
+}
+\arguments{
+\item{object}{serosv models}
+
+\item{newdata}{data.frame with age column to generate prediction}
+
+\item{...}{arbitrary argument}
+}
+\value{
+prediction output
+}
+\description{
+Prediction for serosv local polynomial model
+}
diff --git a/man/predict.penalized_spline_model.Rd b/man/predict.penalized_spline_model.Rd
new file mode 100644
index 0000000..4968ea6
--- /dev/null
+++ b/man/predict.penalized_spline_model.Rd
@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/predict.R
+\name{predict.penalized_spline_model}
+\alias{predict.penalized_spline_model}
+\title{Prediction for serosv penalized spline model}
+\usage{
+\method{predict}{penalized_spline_model}(object, newdata = NULL, ...)
+}
+\arguments{
+\item{object}{serosv models}
+
+\item{newdata}{data.frame with age column to generate prediction}
+
+\item{...}{arbitrary argument}
+}
+\value{
+prediction output
+}
+\description{
+Prediction for serosv penalized spline model
+}
+\seealso{
+[mgcv::predict.gam()] for more information on the predict function
+}
diff --git a/man/predict.polynomial_model.Rd b/man/predict.polynomial_model.Rd
new file mode 100644
index 0000000..c83ab89
--- /dev/null
+++ b/man/predict.polynomial_model.Rd
@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/predict.R
+\name{predict.polynomial_model}
+\alias{predict.polynomial_model}
+\title{Prediction for serosv polynomial model}
+\usage{
+\method{predict}{polynomial_model}(object, newdata = NULL, ...)
+}
+\arguments{
+\item{object}{serosv models}
+
+\item{newdata}{data.frame with age column to generate prediction}
+
+\item{...}{arbitrary argument}
+}
+\value{
+prediction output
+}
+\description{
+A wrapper of predict.glm for direct prediction from polynomial_model object
+}
+\seealso{
+[stats::predict.glm()] for more information on the predict function
+}
diff --git a/man/predict.weibull_model.Rd b/man/predict.weibull_model.Rd
new file mode 100644
index 0000000..e84ad35
--- /dev/null
+++ b/man/predict.weibull_model.Rd
@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/predict.R
+\name{predict.weibull_model}
+\alias{predict.weibull_model}
+\title{Prediction for serosv Weibull model}
+\usage{
+\method{predict}{weibull_model}(object, newdata = NULL, ...)
+}
+\arguments{
+\item{object}{serosv models}
+
+\item{newdata}{data.frame with age column to generate prediction}
+
+\item{...}{arbitrary argument}
+}
+\value{
+prediction output
+}
+\description{
+Prediction for serosv Weibull model
+}
+\seealso{
+[stats::predict.glm()] for more information on the predict function
+}
diff --git a/man/rubella_uk_1986_1987.Rd b/man/rubella_uk_1986_1987.Rd
index c5726e6..6d58216 100644
--- a/man/rubella_uk_1986_1987.Rd
+++ b/man/rubella_uk_1986_1987.Rd
@@ -7,7 +7,7 @@
 \format{
 A data frame with 3 variables:
 \describe{
- \item{age}{Age group}
+ \item{age}{Midpoint of the age group (e.g. 1.5 = 1-2 years old, 2.5 = 2-3 years old)}
  \item{pos}{Number of seropositive individuals}
  \item{tot}{Total number of individuals surveyed}
 }
@@ -25,13 +25,11 @@ Prevalence of rubella in the UK, obtained from a large survey of prevalence
 of antibodies to both mumps and rubella viruses.
 }
 \examples{
-# Reproduce Fig 4.4 (middle panel), p. 67
-age <- rubella_uk_1986_1987$age
-pos <- rubella_uk_1986_1987$pos
-tot <- rubella_uk_1986_1987$tot
-plot(age, pos / tot,
-  cex = 0.008 * tot, pch = 16, xlab = "age", ylab = "seroprevalence",
-  xlim = c(0, 45), ylim = c(0, 1)
+with(rubella_uk_1986_1987,
+  plot(age, pos / tot,
+    cex = 0.2 * sqrt(tot), pch = 16, xlab = "age", ylab = "seroprevalence",
+    xlim = c(0, 45), ylim = c(0, 1)
+  )
 )
 
 }
diff --git a/man/sir_basic_model.Rd b/man/sir_basic_model.Rd
deleted file mode 100644
index 70e453e..0000000
--- a/man/sir_basic_model.Rd
+++ /dev/null
@@ -1,58 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/sir_basic_model.R
-\name{sir_basic_model}
-\alias{sir_basic_model}
-\title{Basic SIR model}
-\usage{
-sir_basic_model(times, state, parameters)
-}
-\arguments{
-\item{times}{time sequence.}
-
-\item{state}{the initial state of the model.}
-
-\item{parameters}{the parameters of the model.}
-}
-\value{
-list of class sir_basic_model with the following items
-  \item{parameters}{list of parameters used for fitting the model}
-  \item{output}{matrix of population for each compartment over time}
-}
-\description{
-Refers to section 3.1.3.
-}
-\details{
-In \code{state}:
-
- - \code{S}: number of susceptible
-
- - \code{I}: number of infected
-
- - \code{R}: number of recovered
-
-In \code{parameters}:
-
- - \code{alpha}: disease-related death rate
-
- - \code{mu}: natural death rate (= 1/life expectancy)
-
- - \code{beta}: transmission rate
-
- - \code{nu}: recovery rate
-
- - \code{p}: percent of population vaccinated at birth
-}
-\examples{
-state <- c(S=4999, I=1, R=0)
-parameters <- c(
-  mu=1/75, # 1 divided by life expectancy (75 years old)
-  alpha=0, # no disease-related death
-  beta=0.0005, # transmission rate
-  nu=1, # 1 year for infected to recover
-  p=0 # no vaccination at birth
-)
-times <- seq(0, 250, by=0.1)
-model <- sir_basic_model(times, state, parameters)
-model
-
-}
diff --git a/man/sir_static_model.Rd b/man/sir_static_model.Rd
deleted file mode 100644
index c276511..0000000
--- a/man/sir_static_model.Rd
+++ /dev/null
@@ -1,49 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/sir_static_model.R
-\name{sir_static_model}
-\alias{sir_static_model}
-\title{SIR static model (age-heterogeneous, endemic equilibrium)}
-\usage{
-sir_static_model(a, state, parameters)
-}
-\arguments{
-\item{a}{age sequence.}
-
-\item{state}{the initial state of the system.}
-
-\item{parameters}{the model's parameter.}
-}
-\value{
-list of class sir_static_model with the following items
-  \item{parameters}{list of parameters used for fitting the model}
-  \item{output}{matrix of proportion for each compartment over time}
-}
-\description{
-Refers to section 3.2.2.
-}
-\details{
-In \code{state}:
-
-  - \code{s}: proportion susceptible
-
-  - \code{i}: proportion infected
-
-  - \code{r}: proportion recovered
-
-In \code{parameters}:
-
-  - \code{lambda}: natural death rate
-
-  - \code{nu}: recovery rate
-}
-\examples{
-state <- c(s=0.99,i=0.01,r=0)
-parameters <- c(
-  lambda = 0.05,
-  nu=1/(14/365) # 2 weeks to recover
-)
-ages<-seq(0, 90, by=0.01)
-model = sir_static_model(ages, state, parameters)
-model
-
-}
diff --git a/man/sir_subpops_model.Rd b/man/sir_subpops_model.Rd
deleted file mode 100644
index 6fb44e6..0000000
--- a/man/sir_subpops_model.Rd
+++ /dev/null
@@ -1,67 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/sir_subpops_model.R
-\name{sir_subpops_model}
-\alias{sir_subpops_model}
-\title{SIR Model with Interacting Subpopulations}
-\usage{
-sir_subpops_model(times, state, parameters)
-}
-\arguments{
-\item{times}{time sequence.}
-
-\item{state}{the initial state of the model.}
-
-\item{parameters}{the parameters of the model.}
-}
-\value{
-list of class sir_subpops_model with the following items
-
-  \item{parameters}{list of parameters used for fitting the model}
-  \item{output}{matrix of proportion for each compartment over time}
-}
-\description{
-Refers to section 3.5.1.
-}
-\details{
-In \code{state}:
-
-- \code{s}: Percent susceptible
-
-- \code{i}: Percent infected
-
-- \code{r}: Percent recovered
-
-In \code{parameters}:
-
-- \code{mu}: natural death rate (1/L).
-
-- \code{beta}: transmission rate w.r.t population (beta tilde)
-
-- \code{nu}: recovery rate
-
-- \code{k}: number of subpopulations
-}
-\examples{
-\donttest{
-k <- 2
-state <- c(
-  s = c(0.8, 0.8),
-  i = c(0.2, 0.2),
-  r = c(  0,   0)
-)
-beta_matrix <- c(
-  c(0.05, 0.00),
-  c(0.00, 0.05)
-)
-parameters <- list(
-  beta = matrix(beta_matrix, nrow=k, ncol=k, byrow=TRUE),
-  nu = c(1/30, 1/30),
-  mu = 0.001,
-  k = k
-)
-times<-seq(0,10000,by=0.5)
-model <- sir_subpops_model(times, state, parameters)
-model
-}
-
-}
diff --git a/man/standardize_data.Rd b/man/standardize_data.Rd
index a289db6..c359100 100644
--- a/man/standardize_data.Rd
+++ b/man/standardize_data.Rd
@@ -15,19 +15,19 @@ standardize_data(
 )
 }
 \arguments{
-\item{df}{- data.frame with columns for plate id, sample id, result, dilution factor, and (optionally) negative controls}
+\item{df}{data.frame with columns for plate id, sample id, result, dilution factor, and (optionally) negative controls}
 
-\item{plate_id_col}{- name of the column storing plates id}
+\item{plate_id_col}{name of the column storing plates id}
 
-\item{id_col}{- name of the column storing sample id}
+\item{id_col}{name of the column storing sample id}
 
-\item{result_col}{- name of the column storing result}
+\item{result_col}{name of the column storing result}
 
-\item{dilution_fct_col}{- name of the column storing dilution factors}
+\item{dilution_fct_col}{name of the column storing dilution factors}
 
-\item{antitoxin_label}{- how antitoxin is label in the sample id column}
+\item{antitoxin_label}{how antitoxin is label in the sample id column}
 
-\item{negative_col}{- regex for columns for negative controls, assumed to be a label followed by the dilution factor (e.g. NEGATIVE_50, NEGATIVE_100)}
+\item{negative_col}{regex for columns for negative controls, assumed to be a label followed by the dilution factor (e.g. NEGATIVE_50, NEGATIVE_100)}
 }
 \value{
 a standardized data.frame that can be passed to `to_titer()`
diff --git a/man/tb_nl_1966_1973.Rd b/man/tb_nl_1966_1973.Rd
index d43bf47..a094ed4 100644
--- a/man/tb_nl_1966_1973.Rd
+++ b/man/tb_nl_1966_1973.Rd
@@ -28,19 +28,17 @@ A study of tuberculosis conducted in the Netherlands. Schoolchildren, aged
 between 6 and 18 years, were tested using the tuberculin skin test.
 }
 \examples{
-# Reproduce Fig 4.6, p.70
-age <- tb_nl_1966_1973$age
-birthyr <- tb_nl_1966_1973$birthyr
-pos <- tb_nl_1966_1973$pos
-tot <- tb_nl_1966_1973$tot
-# left panel
-plot(age, pos / tot,
-  pch = 16, cex = 0.00005 * tot, xlab = "age",
-  ylab = "prevalence", xlim = c(6, 18)
+with(tb_nl_1966_1973,
+  plot(age, pos / tot,
+    pch = 16, cex = 0.01 * sqrt(tot), xlab = "age",
+    ylab = "prevalence", xlim = c(6, 18)
+  )
 )
-# right panel
-plot(birthyr, pos / tot,
-  pch = 16, cex = 0.00005 * tot, xlab = "year", ylab = "prevalence"
+
+with(tb_nl_1966_1973,
+  plot(birthyr, pos / tot,
+    pch = 16, cex = 0.01 * sqrt(tot), xlab = "year", ylab = "prevalence"
+  )
 )
 
 }
diff --git a/man/to_titer.Rd b/man/to_titer.Rd
index 1b854da..4177008 100644
--- a/man/to_titer.Rd
+++ b/man/to_titer.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/to_titer.R
 \name{to_titer}
 \alias{to_titer}
-\title{Process assay test result to titer}
+\title{Convert assay readings to titers}
 \usage{
 to_titer(
   df,
@@ -13,26 +13,32 @@ to_titer(
 )
 }
 \arguments{
-\item{df}{- a standardized data.frame generated by`standardize_data()`}
+\item{df}{a standardized data.frame returned by`standardize_data()`}
 
-\item{model}{- either the name of a built-in model to fit standard curve or a named list of 2 functions for "mod" and "quantify_ci"}
+\item{model}{either:
+\enumerate{
+  \item {A string naming a built-in model (currently supported: \code{"4PL"}), or}
+  \item  {A named list with two functions: \code{$mod} for curve fitting and
+    \code{$quantify_ci} for titer estimation with confidence intervals.}
+}}
 
-\item{positive_threshold}{- if not NULL, processed_data will have the serostatus labeled}
+\item{positive_threshold}{if not NULL, processed_data will have the serostatus labeled}
 
-\item{ci}{- confidence interval for the titer estimates}
+\item{ci}{confidence interval for the titer estimates (default is .95 i.e., 95\% CI)}
 
-\item{negative_control}{- if TRUE, output tibble will include the result for negative controls}
+\item{negative_control}{if TRUE, output tibble will include the result for negative controls}
 }
 \value{
 a data.frame with 8 columns
   \item{plate_id}{id of the plate}
-  \item{data}{list of `data.frame`s containing the results from each plate}
-  \item{antitoxin_df}{list of `data.frame`s containing the results for antitoxins from each plate}
-  \item{standard_curve_func}{list of functions mapping from OD to titer for each plate}
+  \item{data}{list of `data.frame`s containing the raw sample results from each plate}
+  \item{antitoxin_df}{list of `data.frame`s containing the raw results for antitoxins from each plate}
+  \item{standard_curve_func}{list of functions mapping from assay reading to titer for each plate}
   \item{std_crv_midpoint}{midpoint of the standard curve, for qualitative analysis}
   \item{processed_data}{list of `tibble`s containing samples with titer estimates (lower, median, upper)}
   \item{negative_control}{list of `tibble`s containing negative control check results (if `negative_control=TRUE`)}
 }
 \description{
-Process assay test result to titer
+to_titer() converts raw assay readings (e.g., OD, fluorescence intensity) to titer by
+fitting a calibrating model
 }
diff --git a/man/transform_data.Rd b/man/transform_data.Rd
index a45092b..15acb7f 100644
--- a/man/transform_data.Rd
+++ b/man/transform_data.Rd
@@ -4,14 +4,14 @@
 \alias{transform_data}
 \title{Aggregate data}
 \usage{
-transform_data(t, spos, stratum_col = "t")
+transform_data(data, stratum_col = "age", status_col = "status")
 }
 \arguments{
-\item{t}{the time vector (for stratification).}
+\item{data}{a data frame with columns for age and serostatus}
 
-\item{spos}{the seropositive vector.}
+\item{stratum_col}{name of the column to stratify by (default to "age")}
 
-\item{stratum_col}{new name for the time vector (default to "t")}
+\item{status_col}{name of the column for serostatus}
 }
 \value{
 dataframe in aggregated format
@@ -22,7 +22,7 @@ Generate a dataframe with `t`, `pos` and `tot` columns from
 }
 \examples{
 df <- hcv_be_2006
-hcv_df <- transform_data(df$dur, df$seropositive)
+hcv_df <- transform_data(df, stratum_col="dur", status_col="seropositive")
 hcv_df
 
 }
diff --git a/man/vzv_be_1999_2000.Rd b/man/vzv_be_1999_2000.Rd
index 5ba0451..e5f113f 100644
--- a/man/vzv_be_1999_2000.Rd
+++ b/man/vzv_be_1999_2000.Rd
@@ -27,14 +27,11 @@ between October 1999 and April 2000. This population was stratified by age
 in order to obtain approximately 100 observations per age group.
 }
 \examples{
-# Reproduce Fig 4.7 (left panel), p.71
-age <- vzv_be_1999_2000$age
-pos <- vzv_be_1999_2000$pos
-tot <- vzv_be_1999_2000$tot
-plot(age, pos / tot,
-  cex = 0.036 * tot, pch = 19, xlab = "age", ylab = "seroprevalence",
-  xlim = c(0, 45), ylim = c(0, 1)
+with(vzv_be_1999_2000,
+  plot(age, pos / tot,
+    cex = 0.1 * sqrt(tot), pch = 19, xlab = "age", ylab = "seroprevalence",
+    xlim = c(0, 45), ylim = c(0, 1)
+  )
 )
-
 }
 \keyword{datasets}
diff --git a/man/vzv_be_2001_2003.Rd b/man/vzv_be_2001_2003.Rd
index fd625f8..158c949 100644
--- a/man/vzv_be_2001_2003.Rd
+++ b/man/vzv_be_2001_2003.Rd
@@ -28,14 +28,13 @@ The survey is the same as the one used to study the seroprevalence of
 parvovirus B19 in Belgium, as described above.
 }
 \examples{
-# Reproduce Fig 4.7 (right panel), p.71
 library(dplyr)
 df <- vzv_be_2001_2003 \%>\%
   mutate(age = round(age)) \%>\%
   group_by(age) \%>\%
   summarise(pos = sum(seropositive), tot = n())
 plot(df$age, df$pos / df$tot,
-  cex = 0.036 * df$tot, pch = 19, xlab = "age", ylab = "seroprevalence",
+  cex = 0.1 * sqrt(df$tot), pch = 19, xlab = "age", ylab = "seroprevalence",
   xlim = c(0, 45), ylim = c(0, 1)
 )
 
diff --git a/man/weibull_model.Rd b/man/weibull_model.Rd
index edb010d..e1a9a4c 100644
--- a/man/weibull_model.Rd
+++ b/man/weibull_model.Rd
@@ -4,10 +4,25 @@
 \alias{weibull_model}
 \title{The Weibull model.}
 \usage{
-weibull_model(data)
+weibull_model(
+  data,
+  t_lab = "t",
+  pos_col = "pos",
+  tot_col = "tot",
+  status_col = "status"
+)
 }
 \arguments{
-\item{data}{the input data frame, must either have `t`, `pos`, `tot` column for aggregated data OR `t`, `status` for linelisting data}
+\item{data}{the input data frame, must either have columns for `t`, `pos`, `tot` (for aggregated data) OR
+`t`, `status` (for linelisting data)}
+
+\item{t_lab}{name of the `t` column (default t_lab="t").}
+
+\item{pos_col}{name of the `pos` column (default pos_col="pos").}
+
+\item{tot_col}{name of the `tot` column (default tot_col="tot").}
+
+\item{status_col}{name of the `status` column (default status_col="status").}
 }
 \value{
 list of class weibull_model with the following items
@@ -18,15 +33,37 @@ list of class weibull_model with the following items
   \item{foi}{force of infection}
 }
 \description{
-Refers to section 6.1.2.
+Model seroprevalence as a function of duration since vaccination using the Weibull
+model, where the force of infection is assumed to vary monotonically with duration.
+}
+\details{
+For a Weibull model, the prevalence is given by
+\deqn{
+ \pi (d) = 1 - e^{ - \beta_0 d ^ {\beta_1}}
+}
+Where \eqn{d} is exposure time (difference between age of vaccination and age at test)
+
+Which implies the force of infection to be the monotonic function
+\deqn{
+ \lambda(d) = \beta_0 \beta_1 d^{\beta_1 - 1}
+}
+
+Refer to section 6.1.2. of the the book by Hens et al. (2012) for further details.
 }
 \examples{
 df <- hcv_be_2006[order(hcv_be_2006$dur), ]
 df$t <- df$dur
 df$status <- df$seropositive
-model <- weibull_model(df)
+model <- weibull_model(df, t_lab="dur", status_col="seropositive")
 plot(model)
 
+}
+\references{
+Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme,
+and Philippe Beutels. 2012. Modeling Infectious Disease Parameters Based on
+Serological and Social Contact Data: A Modern Statistical Perspective.
+tatistics for Biology and Health. Springer New York.
+\doi{https://doi.org/10.1007/978-1-4614-4072-7}.
 }
 \seealso{
 [stats::glm()] for more information on the fitted "glm" object
diff --git a/renv.lock b/renv.lock
index 36612af..c900776 100644
--- a/renv.lock
+++ b/renv.lock
@@ -11,18 +11,19 @@
   "Packages": {
     "BH": {
       "Package": "BH",
-      "Version": "1.84.0-0",
+      "Version": "1.87.0-1",
       "Source": "Repository",
       "Type": "Package",
       "Title": "Boost C++ Header Files",
-      "Date": "2024-01-09",
-      "Author": "Dirk Eddelbuettel, John W. Emerson and Michael J. Kane",
-      "Maintainer": "Dirk Eddelbuettel <edd@debian.org>",
+      "Date": "2024-12-17",
+      "Authors@R": "c(person(\"Dirk\", \"Eddelbuettel\", role = c(\"aut\", \"cre\"), email = \"edd@debian.org\", comment = c(ORCID = \"0000-0001-6419-907X\")), person(\"John W.\", \"Emerson\", role = \"aut\"),  person(\"Michael J.\", \"Kane\", role = \"aut\",  comment = c(ORCID = \"0000-0003-1899-6662\")))",
       "Description": "Boost provides free peer-reviewed portable C++ source  libraries.  A large part of Boost is provided as C++ template code which is resolved entirely at compile-time without linking.  This  package aims to provide the most useful subset of Boost libraries  for template use among CRAN packages. By placing these libraries in  this package, we offer a more efficient distribution system for CRAN  as replication of this code in the sources of other packages is  avoided. As of release 1.84.0-0, the following Boost libraries are included: 'accumulators' 'algorithm' 'align' 'any' 'atomic' 'beast' 'bimap' 'bind' 'circular_buffer' 'compute' 'concept' 'config' 'container' 'date_time' 'detail' 'dynamic_bitset' 'exception' 'flyweight' 'foreach' 'functional' 'fusion' 'geometry' 'graph' 'heap' 'icl' 'integer' 'interprocess' 'intrusive' 'io' 'iostreams' 'iterator' 'lambda2' 'math' 'move' 'mp11' 'mpl' 'multiprecision' 'numeric' 'pending' 'phoenix' 'polygon' 'preprocessor' 'process' 'propery_tree' 'qvm' 'random' 'range' 'scope_exit' 'smart_ptr' 'sort' 'spirit' 'tuple' 'type_traits' 'typeof' 'unordered' 'url' 'utility' 'uuid'.",
       "License": "BSL-1.0",
       "URL": "https://github.com/eddelbuettel/bh, https://dirk.eddelbuettel.com/code/bh.html",
       "BugReports": "https://github.com/eddelbuettel/bh/issues",
       "NeedsCompilation": "no",
+      "Author": "Dirk Eddelbuettel [aut, cre] (<https://orcid.org/0000-0001-6419-907X>), John W. Emerson [aut], Michael J. Kane [aut] (<https://orcid.org/0000-0003-1899-6662>)",
+      "Maintainer": "Dirk Eddelbuettel <edd@debian.org>",
       "Repository": "CRAN"
     },
     "MASS": {
@@ -111,57 +112,53 @@
     },
     "QuickJSR": {
       "Package": "QuickJSR",
-      "Version": "1.1.3",
+      "Version": "1.8.0",
       "Source": "Repository",
-      "Title": "Interface for the 'QuickJS' Lightweight 'JavaScript' Engine",
-      "Authors@R": "c( person(given = c(\"Andrew\", \"R.\"), family = \"Johnson\", role = c(\"aut\", \"cre\"), email = \"andrew.johnson@arjohnsonau.com\", comment = c(ORCID = \"0000-0001-7000-8065\")), person(given = \"Fabrice\", family = \"Bellard\", role = c(\"ctb\", \"cph\"), comment = \"Author of QuickJS sources and headers\"), person(given = \"Charlie\", family = \"Gordon\", role = c(\"ctb\", \"cph\"), comment = \"Author of QuickJS sources and headers\") )",
-      "Description": "An 'R' interface to the 'QuickJS' portable 'JavaScript' engine. The engine is bundled entirely within the package, requiring no external system dependencies beyond a 'C' compiler.",
+      "Title": "Interface for the 'QuickJS-NG' Lightweight 'JavaScript' Engine",
+      "Authors@R": "c( person(c(\"Andrew\", \"R.\"), \"Johnson\", , \"andrew.johnson@arjohnsonau.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0001-7000-8065\")), person(\"QuickJS\", \"Authors\", role = c(\"cph\"), comment = \"QuickJS sources and headers\"), person(\"QuickJS-NG\", \"Authors\", role = c(\"cph\"), comment = \"QuickJS-NG sources and headers\") )",
+      "Description": "An 'R' interface to the 'QuickJS' portable 'JavaScript' engine.  The engine and all 'R' to 'JavaScript' interoperability is bundled within the package, requiring no dependencies beyond a 'C' compiler.",
       "License": "MIT + file LICENSE",
-      "Encoding": "UTF-8",
-      "RoxygenNote": "7.3.1",
-      "NeedsCompilation": "yes",
-      "SystemRequirements": "GNU make",
-      "Imports": [
-        "jsonlite",
-        "R6",
-        "Rcpp"
-      ],
-      "LinkingTo": [
-        "Rcpp"
-      ],
-      "URL": "https://github.com/andrjohns/QuickJSR, https://bellard.org/quickjs/",
+      "URL": "https://github.com/andrjohns/QuickJSR, https://github.com/quickjs-ng/quickjs",
       "BugReports": "https://github.com/andrjohns/QuickJSR/issues",
       "Suggests": [
-        "testthat (>= 3.0.0)"
+        "knitr",
+        "rmarkdown",
+        "tinytest"
       ],
-      "Config/testthat/edition": "3",
-      "Config/testthat/parallel": "true",
-      "Language": "en-GB",
-      "Author": "Andrew R. Johnson [aut, cre] (<https://orcid.org/0000-0001-7000-8065>), Fabrice Bellard [ctb, cph] (Author of QuickJS sources and headers), Charlie Gordon [ctb, cph] (Author of QuickJS sources and headers)",
+      "Encoding": "UTF-8",
+      "Language": "en-AU",
+      "NeedsCompilation": "yes",
+      "RoxygenNote": "7.3.2",
+      "SystemRequirements": "GNU make",
+      "VignetteBuilder": "knitr",
+      "Author": "Andrew R. Johnson [aut, cre] (ORCID: <https://orcid.org/0000-0001-7000-8065>), QuickJS Authors [cph] (QuickJS sources and headers), QuickJS-NG Authors [cph] (QuickJS-NG sources and headers)",
       "Maintainer": "Andrew R. Johnson <andrew.johnson@arjohnsonau.com>",
       "Repository": "CRAN"
     },
     "R6": {
       "Package": "R6",
-      "Version": "2.5.1",
+      "Version": "2.6.1",
       "Source": "Repository",
       "Title": "Encapsulated Classes with Reference Semantics",
-      "Authors@R": "person(\"Winston\", \"Chang\", role = c(\"aut\", \"cre\"), email = \"winston@stdout.org\")",
+      "Authors@R": "c( person(\"Winston\", \"Chang\", , \"winston@posit.co\", role = c(\"aut\", \"cre\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )",
       "Description": "Creates classes with reference semantics, similar to R's built-in reference classes. Compared to reference classes, R6 classes are simpler and lighter-weight, and they are not built on S4 classes so they do not require the methods package. These classes allow public and private members, and they support inheritance, even when the classes are defined in different packages.",
+      "License": "MIT + file LICENSE",
+      "URL": "https://r6.r-lib.org, https://github.com/r-lib/R6",
+      "BugReports": "https://github.com/r-lib/R6/issues",
       "Depends": [
-        "R (>= 3.0)"
+        "R (>= 3.6)"
       ],
       "Suggests": [
-        "testthat",
-        "pryr"
+        "lobstr",
+        "testthat (>= 3.0.0)"
       ],
-      "License": "MIT + file LICENSE",
-      "URL": "https://r6.r-lib.org, https://github.com/r-lib/R6/",
-      "BugReports": "https://github.com/r-lib/R6/issues",
-      "RoxygenNote": "7.1.1",
+      "Config/Needs/website": "tidyverse/tidytemplate, ggplot2, microbenchmark, scales",
+      "Config/testthat/edition": "3",
+      "Encoding": "UTF-8",
+      "RoxygenNote": "7.3.2",
       "NeedsCompilation": "no",
-      "Author": "Winston Chang [aut, cre]",
-      "Maintainer": "Winston Chang <winston@stdout.org>",
+      "Author": "Winston Chang [aut, cre], Posit Software, PBC [cph, fnd]",
+      "Maintainer": "Winston Chang <winston@posit.co>",
       "Repository": "CRAN"
     },
     "RColorBrewer": {
@@ -184,12 +181,11 @@
     },
     "Rcpp": {
       "Package": "Rcpp",
-      "Version": "1.0.12",
+      "Version": "1.1.0",
       "Source": "Repository",
       "Title": "Seamless R and C++ Integration",
-      "Date": "2024-01-08",
-      "Author": "Dirk Eddelbuettel, Romain Francois, JJ Allaire, Kevin Ushey, Qiang Kou, Nathan Russell, Inaki Ucar, Douglas Bates and John Chambers",
-      "Maintainer": "Dirk Eddelbuettel <edd@debian.org>",
+      "Date": "2025-07-01",
+      "Authors@R": "c(person(\"Dirk\", \"Eddelbuettel\", role = c(\"aut\", \"cre\"), email = \"edd@debian.org\", comment = c(ORCID = \"0000-0001-6419-907X\")), person(\"Romain\", \"Francois\", role = \"aut\", comment = c(ORCID = \"0000-0002-2444-4226\")), person(\"JJ\", \"Allaire\", role = \"aut\", comment = c(ORCID = \"0000-0003-0174-9868\")), person(\"Kevin\", \"Ushey\", role = \"aut\", comment = c(ORCID = \"0000-0003-2880-7407\")), person(\"Qiang\", \"Kou\", role = \"aut\", comment = c(ORCID = \"0000-0001-6786-5453\")), person(\"Nathan\", \"Russell\", role = \"aut\"), person(\"Iñaki\", \"Ucar\", role = \"aut\", comment = c(ORCID = \"0000-0001-6403-5550\")), person(\"Doug\", \"Bates\", role = \"aut\", comment = c(ORCID = \"0000-0001-8316-9503\")), person(\"John\", \"Chambers\", role = \"aut\"))",
       "Description": "The 'Rcpp' package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about 'Rcpp' is provided by several vignettes included in this package, via the 'Rcpp Gallery' site at <https://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, <doi:10.18637/jss.v040.i08>), the book by Eddelbuettel (2013, <doi:10.1007/978-1-4614-6868-4>) and the paper by Eddelbuettel and Balamuta (2018, <doi:10.1080/00031305.2017.1375990>); see 'citation(\"Rcpp\")' for details.",
       "Imports": [
         "methods",
@@ -208,17 +204,18 @@
       "RoxygenNote": "6.1.1",
       "Encoding": "UTF-8",
       "NeedsCompilation": "yes",
-      "Repository": "RSPM"
+      "Author": "Dirk Eddelbuettel [aut, cre] (ORCID: <https://orcid.org/0000-0001-6419-907X>), Romain Francois [aut] (ORCID: <https://orcid.org/0000-0002-2444-4226>), JJ Allaire [aut] (ORCID: <https://orcid.org/0000-0003-0174-9868>), Kevin Ushey [aut] (ORCID: <https://orcid.org/0000-0003-2880-7407>), Qiang Kou [aut] (ORCID: <https://orcid.org/0000-0001-6786-5453>), Nathan Russell [aut], Iñaki Ucar [aut] (ORCID: <https://orcid.org/0000-0001-6403-5550>), Doug Bates [aut] (ORCID: <https://orcid.org/0000-0001-8316-9503>), John Chambers [aut]",
+      "Maintainer": "Dirk Eddelbuettel <edd@debian.org>",
+      "Repository": "CRAN"
     },
     "RcppEigen": {
       "Package": "RcppEigen",
-      "Version": "0.3.4.0.0",
+      "Version": "0.3.4.0.2",
       "Source": "Repository",
       "Type": "Package",
       "Title": "'Rcpp' Integration for the 'Eigen' Templated Linear Algebra Library",
-      "Date": "2024-02-28",
-      "Author": "Douglas Bates, Dirk Eddelbuettel, Romain Francois, and Yixuan Qiu; the authors of Eigen for the included version of Eigen",
-      "Maintainer": "Dirk Eddelbuettel <edd@debian.org>",
+      "Date": "2024-08-23",
+      "Authors@R": "c(person(\"Doug\", \"Bates\", role = \"aut\", comment = c(ORCID = \"0000-0001-8316-9503\")), person(\"Dirk\", \"Eddelbuettel\", role = c(\"aut\", \"cre\"), email = \"edd@debian.org\", comment = c(ORCID = \"0000-0001-6419-907X\")), person(\"Romain\", \"Francois\", role = \"aut\", comment = c(ORCID = \"0000-0002-2444-4226\")), person(\"Yixuan\", \"Qiu\", role = \"aut\", comment = c(ORCID = \"0000-0003-0109-6692\")), person(\"Authors of\", \"Eigen\", role = \"cph\", comment = \"Authorship and copyright in included Eigen library as detailed in inst/COPYRIGHTS\"))",
       "Copyright": "See the file COPYRIGHTS for various Eigen copyright details",
       "Description": "R and 'Eigen' integration using 'Rcpp'. 'Eigen' is a C++ template library for linear algebra: matrices, vectors, numerical solvers and related algorithms.  It supports dense and sparse matrices on integer, floating point and complex numbers, decompositions of such matrices, and solutions of linear systems. Its performance on many algorithms is comparable with some of the best implementations based on 'Lapack' and level-3 'BLAS'. The 'RcppEigen' package includes the header files from the 'Eigen' C++ template library. Thus users do not need to install 'Eigen' itself in order to use 'RcppEigen'. Since version 3.1.1, 'Eigen' is licensed under the Mozilla Public License (version 2); earlier version were licensed under the GNU LGPL version 3 or later. 'RcppEigen' (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'.",
       "License": "GPL (>= 2) | file LICENSE",
@@ -244,12 +241,13 @@
       "URL": "https://github.com/RcppCore/RcppEigen, https://dirk.eddelbuettel.com/code/rcpp.eigen.html",
       "BugReports": "https://github.com/RcppCore/RcppEigen/issues",
       "NeedsCompilation": "yes",
-      "Repository": "RSPM",
-      "Encoding": "UTF-8"
+      "Author": "Doug Bates [aut] (<https://orcid.org/0000-0001-8316-9503>), Dirk Eddelbuettel [aut, cre] (<https://orcid.org/0000-0001-6419-907X>), Romain Francois [aut] (<https://orcid.org/0000-0002-2444-4226>), Yixuan Qiu [aut] (<https://orcid.org/0000-0003-0109-6692>), Authors of Eigen [cph] (Authorship and copyright in included Eigen library as detailed in inst/COPYRIGHTS)",
+      "Maintainer": "Dirk Eddelbuettel <edd@debian.org>",
+      "Repository": "CRAN"
     },
     "RcppParallel": {
       "Package": "RcppParallel",
-      "Version": "5.1.7",
+      "Version": "5.1.10",
       "Source": "Repository",
       "Type": "Package",
       "Title": "Parallel Programming Tools for 'Rcpp'",
@@ -278,7 +276,7 @@
     },
     "StanHeaders": {
       "Package": "StanHeaders",
-      "Version": "2.32.6",
+      "Version": "2.32.10",
       "Source": "Repository",
       "Title": "C++ Header Files for Stan",
       "Authors@R": "c(person(\"Ben\",family=\"Goodrich\", email=\"benjamin.goodrich@columbia.edu\", role=c('cre','aut')), person(\"Joshua\", \"Pritikin\", role = \"ctb\"), person(\"Andrew\", \"Gelman\", role = \"aut\"), person(\"Bob\", \"Carpenter\",  role = \"aut\"), person(\"Matt\", \"Hoffman\", role = \"aut\"), person(\"Daniel\", \"Lee\", role = \"aut\"), person(\"Michael\", \"Betancourt\", role = \"aut\"), person(\"Marcus\", \"Brubaker\", role = \"aut\"), person(\"Jiqiang\", \"Guo\", role = \"aut\"), person(\"Peter\", \"Li\", role = \"aut\"), person(\"Allen\", \"Riddell\", role = \"aut\"), person(\"Marco\", \"Inacio\", role = \"aut\"), person(\"Mitzi\", \"Morris\", role = \"aut\"), person(\"Jeffrey\", \"Arnold\", role = \"aut\"), person(\"Rob\", \"Goedman\", role = \"aut\"), person(\"Brian\", \"Lau\", role = \"aut\"), person(\"Rob\", \"Trangucci\", role = \"aut\"), person(\"Jonah\", \"Gabry\", role = \"aut\"), person(\"Alp\", \"Kucukelbir\", role = \"aut\"), person(\"Robert\", \"Grant\", role = \"aut\"), person(\"Dustin\", \"Tran\", role = \"aut\"), person(\"Michael\", \"Malecki\", role = \"aut\"), person(\"Yuanjun\", \"Gao\", role = \"aut\"), person(\"Hamada S.\", \"Badr\", role = c(\"aut\"), email = \"badr@jhu.edu\", comment = c(ORCID = \"0000-0002-9808-2344\")), person(\"Trustees of\", \"Columbia University\", role = \"cph\"), person(\"Lawrence Livermore\", \"National Security\", role = \"cph\", comment = \"CVODES\"), person(\"The Regents of the\", \"University of California\", role = \"cph\", comment = \"CVODES\"), person(\"Southern Methodist\", \"University\", role = \"cph\", comment = \"CVODES\"))",
@@ -304,7 +302,7 @@
       "VignetteBuilder": "knitr",
       "SystemRequirements": "GNU make, pandoc",
       "Depends": [
-        "R (>= 3.6.0)"
+        "R (>= 3.4.0)"
       ],
       "License": "BSD_3_clause + file LICENSE",
       "Encoding": "UTF-8",
@@ -316,13 +314,13 @@
     },
     "abind": {
       "Package": "abind",
-      "Version": "1.4-5",
+      "Version": "1.4-8",
       "Source": "Repository",
-      "Date": "2016-06-19",
+      "Date": "2024-09-08",
       "Title": "Combine Multidimensional Arrays",
-      "Author": "Tony Plate <tplate@acm.org> and Richard Heiberger",
+      "Authors@R": "c(person(\"Tony\", \"Plate\", email = \"tplate@acm.org\", role = c(\"aut\", \"cre\")), person(\"Richard\", \"Heiberger\", role = c(\"aut\")))",
       "Maintainer": "Tony Plate <tplate@acm.org>",
-      "Description": "Combine multidimensional arrays into a single array. This is a generalization of 'cbind' and 'rbind'.  Works with vectors, matrices, and higher-dimensional arrays.  Also provides functions 'adrop', 'asub', and 'afill' for manipulating, extracting and replacing data in arrays.",
+      "Description": "Combine multidimensional arrays into a single array. This is a generalization of 'cbind' and 'rbind'.  Works with vectors, matrices, and higher-dimensional arrays (aka tensors). Also provides functions 'adrop', 'asub', and 'afill' for manipulating, extracting and replacing data in arrays.",
       "Depends": [
         "R (>= 1.5.0)"
       ],
@@ -330,10 +328,10 @@
         "methods",
         "utils"
       ],
-      "License": "LGPL (>= 2)",
+      "License": "MIT + file LICENSE",
       "NeedsCompilation": "no",
-      "Repository": "RSPM",
-      "Encoding": "UTF-8"
+      "Author": "Tony Plate [aut, cre], Richard Heiberger [aut]",
+      "Repository": "CRAN"
     },
     "assertthat": {
       "Package": "assertthat",
@@ -359,11 +357,11 @@
     },
     "backports": {
       "Package": "backports",
-      "Version": "1.4.1",
+      "Version": "1.5.0",
       "Source": "Repository",
       "Type": "Package",
       "Title": "Reimplementations of Functions Introduced Since R-3.0.0",
-      "Authors@R": "c( person(\"Michel\", \"Lang\", NULL, \"michellang@gmail.com\", role = c(\"cre\", \"aut\"), comment = c(ORCID = \"0000-0001-9754-0393\")), person(\"R Core Team\", role = \"aut\"))",
+      "Authors@R": "c( person(\"Michel\", \"Lang\", NULL, \"michellang@gmail.com\", role = c(\"cre\", \"aut\"), comment = c(ORCID = \"0000-0001-9754-0393\")), person(\"Duncan\", \"Murdoch\", NULL, \"murdoch.duncan@gmail.com\", role = c(\"aut\")), person(\"R Core Team\", role = \"aut\"))",
       "Maintainer": "Michel Lang <michellang@gmail.com>",
       "Description": "Functions introduced or changed since R v3.0.0 are re-implemented in this package. The backports are conditionally exported in order to let R resolve the function name to either the implemented backport, or the respective base version, if available. Package developers can make use of new functions or arguments by selectively importing specific backports to support older installations.",
       "URL": "https://github.com/r-lib/backports",
@@ -375,9 +373,9 @@
         "R (>= 3.0.0)"
       ],
       "Encoding": "UTF-8",
-      "RoxygenNote": "7.1.2",
-      "Author": "Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), R Core Team [aut]",
-      "Repository": "RSPM"
+      "RoxygenNote": "7.3.1",
+      "Author": "Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), Duncan Murdoch [aut], R Core Team [aut]",
+      "Repository": "CRAN"
     },
     "boot": {
       "Package": "boot",
@@ -408,7 +406,7 @@
     },
     "callr": {
       "Package": "callr",
-      "Version": "3.7.5",
+      "Version": "3.7.6",
       "Source": "Repository",
       "Title": "Call R from R",
       "Authors@R": "c( person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\", \"cph\"), comment = c(ORCID = \"0000-0001-7098-9676\")), person(\"Winston\", \"Chang\", role = \"aut\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")), person(\"Ascent Digital Services\", role = c(\"cph\", \"fnd\")) )",
@@ -427,7 +425,6 @@
       "Suggests": [
         "asciicast (>= 2.3.1)",
         "cli (>= 1.1.0)",
-        "covr",
         "mockery",
         "ps",
         "rprojroot",
@@ -439,15 +436,15 @@
       "Config/testthat/edition": "3",
       "Encoding": "UTF-8",
       "Language": "en-US",
-      "RoxygenNote": "7.2.3",
+      "RoxygenNote": "7.3.1.9000",
       "NeedsCompilation": "no",
       "Author": "Gábor Csárdi [aut, cre, cph] (<https://orcid.org/0000-0001-7098-9676>), Winston Chang [aut], Posit Software, PBC [cph, fnd], Ascent Digital Services [cph, fnd]",
       "Maintainer": "Gábor Csárdi <csardi.gabor@gmail.com>",
-      "Repository": "RSPM"
+      "Repository": "CRAN"
     },
     "checkmate": {
       "Package": "checkmate",
-      "Version": "2.3.1",
+      "Version": "2.3.2",
       "Source": "Repository",
       "Type": "Package",
       "Title": "Fast and Versatile Argument Checks",
@@ -482,7 +479,7 @@
       ],
       "License": "BSD_3_clause + file LICENSE",
       "VignetteBuilder": "knitr",
-      "RoxygenNote": "7.2.3",
+      "RoxygenNote": "7.3.2",
       "Collate": "'AssertCollection.R' 'allMissing.R' 'anyInfinite.R' 'anyMissing.R' 'anyNaN.R' 'asInteger.R' 'assert.R' 'helper.R' 'makeExpectation.R' 'makeTest.R' 'makeAssertion.R' 'checkAccess.R' 'checkArray.R' 'checkAtomic.R' 'checkAtomicVector.R' 'checkCharacter.R' 'checkChoice.R' 'checkClass.R' 'checkComplex.R' 'checkCount.R' 'checkDataFrame.R' 'checkDataTable.R' 'checkDate.R' 'checkDirectoryExists.R' 'checkDisjunct.R' 'checkDouble.R' 'checkEnvironment.R' 'checkFALSE.R' 'checkFactor.R' 'checkFileExists.R' 'checkFlag.R' 'checkFormula.R' 'checkFunction.R' 'checkInt.R' 'checkInteger.R' 'checkIntegerish.R' 'checkList.R' 'checkLogical.R' 'checkMatrix.R' 'checkMultiClass.R' 'checkNamed.R' 'checkNames.R' 'checkNull.R' 'checkNumber.R' 'checkNumeric.R' 'checkOS.R' 'checkPOSIXct.R' 'checkPathForOutput.R' 'checkPermutation.R' 'checkR6.R' 'checkRaw.R' 'checkScalar.R' 'checkScalarNA.R' 'checkSetEqual.R' 'checkString.R' 'checkSubset.R' 'checkTRUE.R' 'checkTibble.R' 'checkVector.R' 'coalesce.R' 'isIntegerish.R' 'matchArg.R' 'qassert.R' 'qassertr.R' 'vname.R' 'wfwl.R' 'zzz.R'",
       "Author": "Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), Bernd Bischl [ctb], Dénes Tóth [ctb] (<https://orcid.org/0000-0003-4262-3217>)",
       "Maintainer": "Michel Lang <michellang@gmail.com>",
@@ -490,10 +487,10 @@
     },
     "cli": {
       "Package": "cli",
-      "Version": "3.6.2",
+      "Version": "3.6.5",
       "Source": "Repository",
       "Title": "Helpers for Developing Command Line Interfaces",
-      "Authors@R": "c( person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Hadley\", \"Wickham\", role = \"ctb\"), person(\"Kirill\", \"Müller\", role = \"ctb\"), person(\"Salim\", \"Brüggemann\", , \"salim-b@pm.me\", role = \"ctb\", comment = c(ORCID = \"0000-0002-5329-5987\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )",
+      "Authors@R": "c( person(\"Gábor\", \"Csárdi\", , \"gabor@posit.co\", role = c(\"aut\", \"cre\")), person(\"Hadley\", \"Wickham\", role = \"ctb\"), person(\"Kirill\", \"Müller\", role = \"ctb\"), person(\"Salim\", \"Brüggemann\", , \"salim-b@pm.me\", role = \"ctb\", comment = c(ORCID = \"0000-0002-5329-5987\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )",
       "Description": "A suite of tools to build attractive command line interfaces ('CLIs'), from semantic elements: headings, lists, alerts, paragraphs, etc. Supports custom themes via a 'CSS'-like language. It also contains a number of lower level 'CLI' elements: rules, boxes, trees, and 'Unicode' symbols with 'ASCII' alternatives. It support ANSI colors and text styles as well.",
       "License": "MIT + file LICENSE",
       "URL": "https://cli.r-lib.org, https://github.com/r-lib/cli",
@@ -515,14 +512,13 @@
         "htmlwidgets",
         "knitr",
         "methods",
-        "mockery",
         "processx",
         "ps (>= 1.3.4.9000)",
         "rlang (>= 1.0.2.9003)",
         "rmarkdown",
         "rprojroot",
         "rstudioapi",
-        "testthat",
+        "testthat (>= 3.2.0)",
         "tibble",
         "whoami",
         "withr"
@@ -530,64 +526,57 @@
       "Config/Needs/website": "r-lib/asciicast, bench, brio, cpp11, decor, desc, fansi, prettyunits, sessioninfo, tidyverse/tidytemplate, usethis, vctrs",
       "Config/testthat/edition": "3",
       "Encoding": "UTF-8",
-      "RoxygenNote": "7.2.3",
+      "RoxygenNote": "7.3.2",
       "NeedsCompilation": "yes",
       "Author": "Gábor Csárdi [aut, cre], Hadley Wickham [ctb], Kirill Müller [ctb], Salim Brüggemann [ctb] (<https://orcid.org/0000-0002-5329-5987>), Posit Software, PBC [cph, fnd]",
-      "Maintainer": "Gábor Csárdi <csardi.gabor@gmail.com>",
+      "Maintainer": "Gábor Csárdi <gabor@posit.co>",
       "Repository": "CRAN"
     },
-    "colorspace": {
-      "Package": "colorspace",
-      "Version": "2.1-0",
+    "cpp11": {
+      "Package": "cpp11",
+      "Version": "0.4.7",
       "Source": "Repository",
-      "Date": "2023-01-23",
-      "Title": "A Toolbox for Manipulating and Assessing Colors and Palettes",
-      "Authors@R": "c(person(given = \"Ross\", family = \"Ihaka\", role = \"aut\", email = \"ihaka@stat.auckland.ac.nz\"), person(given = \"Paul\", family = \"Murrell\", role = \"aut\", email = \"paul@stat.auckland.ac.nz\", comment = c(ORCID = \"0000-0002-3224-8858\")), person(given = \"Kurt\", family = \"Hornik\", role = \"aut\", email = \"Kurt.Hornik@R-project.org\", comment = c(ORCID = \"0000-0003-4198-9911\")), person(given = c(\"Jason\", \"C.\"), family = \"Fisher\", role = \"aut\", email = \"jfisher@usgs.gov\", comment = c(ORCID = \"0000-0001-9032-8912\")), person(given = \"Reto\", family = \"Stauffer\", role = \"aut\", email = \"Reto.Stauffer@uibk.ac.at\", comment = c(ORCID = \"0000-0002-3798-5507\")), person(given = c(\"Claus\", \"O.\"), family = \"Wilke\", role = \"aut\", email = \"wilke@austin.utexas.edu\", comment = c(ORCID = \"0000-0002-7470-9261\")), person(given = c(\"Claire\", \"D.\"), family = \"McWhite\", role = \"aut\", email = \"claire.mcwhite@utmail.utexas.edu\", comment = c(ORCID = \"0000-0001-7346-3047\")), person(given = \"Achim\", family = \"Zeileis\", role = c(\"aut\", \"cre\"), email = \"Achim.Zeileis@R-project.org\", comment = c(ORCID = \"0000-0003-0918-3766\")))",
-      "Description": "Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB, and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with corresponding ggplot2 color scales. Color palette choice is aided by an interactive app (with either a Tcl/Tk or a shiny graphical user interface) and shiny apps with an HCL color picker and a color vision deficiency emulator. Plotting functions for displaying and assessing palettes include color swatches, visualizations of the HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation functions include: desaturation, lightening/darkening, mixing, and simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly). Details can be found on the project web page at <https://colorspace.R-Forge.R-project.org/> and in the accompanying scientific paper: Zeileis et al. (2020, Journal of Statistical Software, <doi:10.18637/jss.v096.i01>).",
+      "Title": "A C++11 Interface for R's C Interface",
+      "Authors@R": "c( person(\"Davis\", \"Vaughan\", email = \"davis@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-4777-038X\")), person(\"Jim\",\"Hester\", role = \"aut\", comment = c(ORCID = \"0000-0002-2739-7082\")), person(\"Romain\", \"François\", role = \"aut\", comment = c(ORCID = \"0000-0002-2444-4226\")), person(\"Benjamin\", \"Kietzman\", role = \"ctb\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )",
+      "Description": "Provides a header only, C++11 interface to R's C interface.  Compared to other approaches 'cpp11' strives to be safe against long jumps from the C API as well as C++ exceptions, conform to normal R function semantics and supports interaction with 'ALTREP' vectors.",
+      "License": "MIT + file LICENSE",
+      "URL": "https://cpp11.r-lib.org, https://github.com/r-lib/cpp11",
+      "BugReports": "https://github.com/r-lib/cpp11/issues",
       "Depends": [
-        "R (>= 3.0.0)",
-        "methods"
-      ],
-      "Imports": [
-        "graphics",
-        "grDevices",
-        "stats"
+        "R (>= 3.5.0)"
       ],
       "Suggests": [
-        "datasets",
-        "utils",
-        "KernSmooth",
-        "MASS",
-        "kernlab",
-        "mvtnorm",
-        "vcd",
-        "tcltk",
-        "shiny",
-        "shinyjs",
+        "bench",
+        "brio",
+        "callr",
+        "cli",
+        "covr",
+        "decor",
+        "desc",
         "ggplot2",
-        "dplyr",
-        "scales",
-        "grid",
-        "png",
-        "jpeg",
+        "glue",
         "knitr",
+        "lobstr",
+        "mockery",
+        "progress",
         "rmarkdown",
-        "RColorBrewer",
-        "rcartocolor",
-        "scico",
-        "viridis",
-        "wesanderson"
+        "scales",
+        "Rcpp",
+        "testthat (>= 3.2.0)",
+        "tibble",
+        "utils",
+        "vctrs",
+        "withr"
       ],
       "VignetteBuilder": "knitr",
-      "License": "BSD_3_clause + file LICENSE",
-      "URL": "https://colorspace.R-Forge.R-project.org/, https://hclwizard.org/",
-      "BugReports": "https://colorspace.R-Forge.R-project.org/contact.html",
-      "LazyData": "yes",
+      "Config/Needs/website": "tidyverse/tidytemplate",
+      "Config/testthat/edition": "3",
+      "Config/Needs/cpp11/cpp_register": "brio, cli, decor, desc, glue, tibble, vctrs",
       "Encoding": "UTF-8",
       "RoxygenNote": "7.2.3",
-      "NeedsCompilation": "yes",
-      "Author": "Ross Ihaka [aut], Paul Murrell [aut] (<https://orcid.org/0000-0002-3224-8858>), Kurt Hornik [aut] (<https://orcid.org/0000-0003-4198-9911>), Jason C. Fisher [aut] (<https://orcid.org/0000-0001-9032-8912>), Reto Stauffer [aut] (<https://orcid.org/0000-0002-3798-5507>), Claus O. Wilke [aut] (<https://orcid.org/0000-0002-7470-9261>), Claire D. McWhite [aut] (<https://orcid.org/0000-0001-7346-3047>), Achim Zeileis [aut, cre] (<https://orcid.org/0000-0003-0918-3766>)",
-      "Maintainer": "Achim Zeileis <Achim.Zeileis@R-project.org>",
+      "NeedsCompilation": "no",
+      "Author": "Davis Vaughan [aut, cre] (<https://orcid.org/0000-0003-4777-038X>), Jim Hester [aut] (<https://orcid.org/0000-0002-2739-7082>), Romain François [aut] (<https://orcid.org/0000-0002-2444-4226>), Benjamin Kietzman [ctb], Posit Software, PBC [cph, fnd]",
+      "Maintainer": "Davis Vaughan <davis@posit.co>",
       "Repository": "RSPM"
     },
     "deSolve": {
@@ -658,10 +647,10 @@
     },
     "distributional": {
       "Package": "distributional",
-      "Version": "0.4.0",
+      "Version": "0.5.0",
       "Source": "Repository",
       "Title": "Vectorised Probability Distributions",
-      "Authors@R": "c(person(given = \"Mitchell\", family = \"O'Hara-Wild\", role = c(\"aut\", \"cre\"), email = \"mail@mitchelloharawild.com\", comment = c(ORCID = \"0000-0001-6729-7695\")), person(given = \"Matthew\", family = \"Kay\", role = c(\"aut\"), comment = c(ORCID = \"0000-0001-9446-0419\")), person(given = \"Alex\", family = \"Hayes\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-4985-5160\")), person(given = \"Earo\", family = \"Wang\", role = c(\"ctb\"), comment = c(ORCID = \"0000-0001-6448-5260\")))",
+      "Authors@R": "c(person(given = \"Mitchell\", family = \"O'Hara-Wild\", role = c(\"aut\", \"cre\"), email = \"mail@mitchelloharawild.com\", comment = c(ORCID = \"0000-0001-6729-7695\")), person(given = \"Matthew\", family = \"Kay\", role = c(\"aut\"), comment = c(ORCID = \"0000-0001-9446-0419\")), person(given = \"Alex\", family = \"Hayes\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-4985-5160\")), person(given = \"Rob\", family = \"Hyndman\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-2140-5352\")), person(given = \"Earo\", family = \"Wang\", role = c(\"ctb\"), comment = c(ORCID = \"0000-0001-6448-5260\")), person(given = \"Vencislav\", family = \"Popov\", role = c(\"ctb\"), comment = c(ORCID = \"0000-0002-8073-4199\")))",
       "Description": "Vectorised distribution objects with tools for manipulating,  visualising, and using probability distributions. Designed to allow model prediction outputs to return distributions rather than their parameters,  allowing users to directly interact with predictive distributions in a data-oriented workflow. In addition to providing generic replacements for p/d/q/r functions, other useful statistics can be computed including means, variances, intervals, and highest density regions.",
       "License": "GPL-3",
       "Imports": [
@@ -671,24 +660,27 @@
         "stats",
         "numDeriv",
         "utils",
-        "lifecycle"
+        "lifecycle",
+        "pillar"
       ],
       "Suggests": [
         "testthat (>= 2.1.0)",
         "covr",
         "mvtnorm",
         "actuar (>= 2.0.0)",
+        "evd",
         "ggdist",
-        "ggplot2"
+        "ggplot2",
+        "gk"
       ],
       "RdMacros": "lifecycle",
       "URL": "https://pkg.mitchelloharawild.com/distributional/, https://github.com/mitchelloharawild/distributional",
       "BugReports": "https://github.com/mitchelloharawild/distributional/issues",
       "Encoding": "UTF-8",
       "Language": "en-GB",
-      "RoxygenNote": "7.2.3",
+      "RoxygenNote": "7.3.2",
       "NeedsCompilation": "no",
-      "Author": "Mitchell O'Hara-Wild [aut, cre] (<https://orcid.org/0000-0001-6729-7695>), Matthew Kay [aut] (<https://orcid.org/0000-0001-9446-0419>), Alex Hayes [aut] (<https://orcid.org/0000-0002-4985-5160>), Earo Wang [ctb] (<https://orcid.org/0000-0001-6448-5260>)",
+      "Author": "Mitchell O'Hara-Wild [aut, cre] (<https://orcid.org/0000-0001-6729-7695>), Matthew Kay [aut] (<https://orcid.org/0000-0001-9446-0419>), Alex Hayes [aut] (<https://orcid.org/0000-0002-4985-5160>), Rob Hyndman [aut] (<https://orcid.org/0000-0002-2140-5352>), Earo Wang [ctb] (<https://orcid.org/0000-0001-6448-5260>), Vencislav Popov [ctb] (<https://orcid.org/0000-0002-8073-4199>)",
       "Maintainer": "Mitchell O'Hara-Wild <mail@mitchelloharawild.com>",
       "Repository": "CRAN"
     },
@@ -755,45 +747,13 @@
       "Maintainer": "Hadley Wickham <hadley@posit.co>",
       "Repository": "CRAN"
     },
-    "fansi": {
-      "Package": "fansi",
-      "Version": "1.0.6",
-      "Source": "Repository",
-      "Title": "ANSI Control Sequence Aware String Functions",
-      "Description": "Counterparts to R string manipulation functions that account for the effects of ANSI text formatting control sequences.",
-      "Authors@R": "c( person(\"Brodie\", \"Gaslam\", email=\"brodie.gaslam@yahoo.com\", role=c(\"aut\", \"cre\")), person(\"Elliott\", \"Sales De Andrade\", role=\"ctb\"), person(family=\"R Core Team\", email=\"R-core@r-project.org\", role=\"cph\", comment=\"UTF8 byte length calcs from src/util.c\" ))",
-      "Depends": [
-        "R (>= 3.1.0)"
-      ],
-      "License": "GPL-2 | GPL-3",
-      "URL": "https://github.com/brodieG/fansi",
-      "BugReports": "https://github.com/brodieG/fansi/issues",
-      "VignetteBuilder": "knitr",
-      "Suggests": [
-        "unitizer",
-        "knitr",
-        "rmarkdown"
-      ],
-      "Imports": [
-        "grDevices",
-        "utils"
-      ],
-      "RoxygenNote": "7.2.3",
-      "Encoding": "UTF-8",
-      "Collate": "'constants.R' 'fansi-package.R' 'internal.R' 'load.R' 'misc.R' 'nchar.R' 'strwrap.R' 'strtrim.R' 'strsplit.R' 'substr2.R' 'trimws.R' 'tohtml.R' 'unhandled.R' 'normalize.R' 'sgr.R'",
-      "NeedsCompilation": "yes",
-      "Author": "Brodie Gaslam [aut, cre], Elliott Sales De Andrade [ctb], R Core Team [cph] (UTF8 byte length calcs from src/util.c)",
-      "Maintainer": "Brodie Gaslam <brodie.gaslam@yahoo.com>",
-      "Repository": "CRAN"
-    },
     "farver": {
       "Package": "farver",
-      "Version": "2.1.1",
+      "Version": "2.1.2",
       "Source": "Repository",
       "Type": "Package",
       "Title": "High Performance Colour Space Manipulation",
-      "Authors@R": "c( person(\"Thomas Lin\", \"Pedersen\", , \"thomasp85@gmail.com\", role = c(\"cre\", \"aut\"), comment = c(ORCID = \"0000-0002-5147-4711\")), person(\"Berendea\", \"Nicolae\", role = \"aut\", comment = \"Author of the ColorSpace C++ library\"), person(\"Romain\", \"François\", , \"romain@purrple.cat\", role = \"aut\", comment = c(ORCID = \"0000-0002-2444-4226\")) )",
-      "Maintainer": "Thomas Lin Pedersen <thomasp85@gmail.com>",
+      "Authors@R": "c( person(\"Thomas Lin\", \"Pedersen\", , \"thomas.pedersen@posit.co\", role = c(\"cre\", \"aut\"), comment = c(ORCID = \"0000-0002-5147-4711\")), person(\"Berendea\", \"Nicolae\", role = \"aut\", comment = \"Author of the ColorSpace C++ library\"), person(\"Romain\", \"François\", , \"romain@purrple.cat\", role = \"aut\", comment = c(ORCID = \"0000-0002-2444-4226\")), person(\"Posit, PBC\", role = c(\"cph\", \"fnd\")) )",
       "Description": "The encoding of colour can be handled in many different ways, using different colour spaces. As different colour spaces have different uses, efficient conversion between these representations are important. The 'farver' package provides a set of functions that gives access to very fast colour space conversion and comparisons implemented in C++, and offers speed improvements over the 'convertColor' function in the 'grDevices' package.",
       "License": "MIT + file LICENSE",
       "URL": "https://farver.data-imaginist.com, https://github.com/thomasp85/farver",
@@ -802,26 +762,26 @@
         "covr",
         "testthat (>= 3.0.0)"
       ],
-      "Encoding": "UTF-8",
-      "RoxygenNote": "7.2.0",
-      "SystemRequirements": "C++11",
       "Config/testthat/edition": "3",
+      "Encoding": "UTF-8",
+      "RoxygenNote": "7.3.1",
       "NeedsCompilation": "yes",
-      "Author": "Thomas Lin Pedersen [cre, aut] (<https://orcid.org/0000-0002-5147-4711>), Berendea Nicolae [aut] (Author of the ColorSpace C++ library), Romain François [aut] (<https://orcid.org/0000-0002-2444-4226>)",
-      "Repository": "RSPM"
+      "Author": "Thomas Lin Pedersen [cre, aut] (<https://orcid.org/0000-0002-5147-4711>), Berendea Nicolae [aut] (Author of the ColorSpace C++ library), Romain François [aut] (<https://orcid.org/0000-0002-2444-4226>), Posit, PBC [cph, fnd]",
+      "Maintainer": "Thomas Lin Pedersen <thomas.pedersen@posit.co>",
+      "Repository": "CRAN"
     },
     "generics": {
       "Package": "generics",
-      "Version": "0.1.3",
+      "Version": "0.1.4",
       "Source": "Repository",
       "Title": "Common S3 Generics not Provided by Base R Methods Related to Model Fitting",
-      "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@rstudio.com\", role = c(\"aut\", \"cre\")), person(\"Max\", \"Kuhn\", , \"max@rstudio.com\", role = \"aut\"), person(\"Davis\", \"Vaughan\", , \"davis@rstudio.com\", role = \"aut\"), person(\"RStudio\", role = \"cph\") )",
+      "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-4757-117X\")), person(\"Max\", \"Kuhn\", , \"max@posit.co\", role = \"aut\"), person(\"Davis\", \"Vaughan\", , \"davis@posit.co\", role = \"aut\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"https://ror.org/03wc8by49\")) )",
       "Description": "In order to reduce potential package dependencies and conflicts, generics provides a number of commonly used S3 generics.",
       "License": "MIT + file LICENSE",
       "URL": "https://generics.r-lib.org, https://github.com/r-lib/generics",
       "BugReports": "https://github.com/r-lib/generics/issues",
       "Depends": [
-        "R (>= 3.2)"
+        "R (>= 3.6)"
       ],
       "Imports": [
         "methods"
@@ -836,15 +796,15 @@
       "Config/Needs/website": "tidyverse/tidytemplate",
       "Config/testthat/edition": "3",
       "Encoding": "UTF-8",
-      "RoxygenNote": "7.2.0",
+      "RoxygenNote": "7.3.2",
       "NeedsCompilation": "no",
-      "Author": "Hadley Wickham [aut, cre], Max Kuhn [aut], Davis Vaughan [aut], RStudio [cph]",
-      "Maintainer": "Hadley Wickham <hadley@rstudio.com>",
+      "Author": "Hadley Wickham [aut, cre] (ORCID: <https://orcid.org/0000-0003-4757-117X>), Max Kuhn [aut], Davis Vaughan [aut], Posit Software, PBC [cph, fnd] (ROR: <https://ror.org/03wc8by49>)",
+      "Maintainer": "Hadley Wickham <hadley@posit.co>",
       "Repository": "CRAN"
     },
     "ggplot2": {
       "Package": "ggplot2",
-      "Version": "3.5.0",
+      "Version": "3.5.2",
       "Source": "Repository",
       "Title": "Create Elegant Data Visualisations Using the Grammar of Graphics",
       "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = \"aut\", comment = c(ORCID = \"0000-0003-4757-117X\")), person(\"Winston\", \"Chang\", role = \"aut\", comment = c(ORCID = \"0000-0002-1576-2126\")), person(\"Lionel\", \"Henry\", role = \"aut\"), person(\"Thomas Lin\", \"Pedersen\", , \"thomas.pedersen@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-5147-4711\")), person(\"Kohske\", \"Takahashi\", role = \"aut\"), person(\"Claus\", \"Wilke\", role = \"aut\", comment = c(ORCID = \"0000-0002-7470-9261\")), person(\"Kara\", \"Woo\", role = \"aut\", comment = c(ORCID = \"0000-0002-5125-4188\")), person(\"Hiroaki\", \"Yutani\", role = \"aut\", comment = c(ORCID = \"0000-0002-3385-7233\")), person(\"Dewey\", \"Dunnington\", role = \"aut\", comment = c(ORCID = \"0000-0002-9415-4582\")), person(\"Teun\", \"van den Brand\", role = \"aut\", comment = c(ORCID = \"0000-0002-9335-7468\")), person(\"Posit, PBC\", role = c(\"cph\", \"fnd\")) )",
@@ -869,7 +829,7 @@
         "scales (>= 1.3.0)",
         "stats",
         "tibble",
-        "vctrs (>= 0.5.0)",
+        "vctrs (>= 0.6.0)",
         "withr (>= 2.5.0)"
       ],
       "Suggests": [
@@ -904,16 +864,16 @@
       "Config/testthat/edition": "3",
       "Encoding": "UTF-8",
       "LazyData": "true",
-      "RoxygenNote": "7.3.0",
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       "NeedsCompilation": "no",
       "Author": "Hadley Wickham [aut] (<https://orcid.org/0000-0003-4757-117X>), Winston Chang [aut] (<https://orcid.org/0000-0002-1576-2126>), Lionel Henry [aut], Thomas Lin Pedersen [aut, cre] (<https://orcid.org/0000-0002-5147-4711>), Kohske Takahashi [aut], Claus Wilke [aut] (<https://orcid.org/0000-0002-7470-9261>), Kara Woo [aut] (<https://orcid.org/0000-0002-5125-4188>), Hiroaki Yutani [aut] (<https://orcid.org/0000-0002-3385-7233>), Dewey Dunnington [aut] (<https://orcid.org/0000-0002-9415-4582>), Teun van den Brand [aut] (<https://orcid.org/0000-0002-9335-7468>), Posit, PBC [cph, fnd]",
       "Maintainer": "Thomas Lin Pedersen <thomas.pedersen@posit.co>",
-      "Repository": "RSPM"
+      "Repository": "CRAN"
     },
     "glue": {
       "Package": "glue",
-      "Version": "1.7.0",
+      "Version": "1.8.0",
       "Source": "Repository",
       "Title": "Interpreted String Literals",
       "Authors@R": "c( person(\"Jim\", \"Hester\", role = \"aut\", comment = c(ORCID = \"0000-0002-2739-7082\")), person(\"Jennifer\", \"Bryan\", , \"jenny@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-6983-2759\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )",
@@ -938,7 +898,7 @@
         "RSQLite",
         "testthat (>= 3.2.0)",
         "vctrs (>= 0.3.0)",
-        "waldo (>= 0.3.0)",
+        "waldo (>= 0.5.3)",
         "withr"
       ],
       "VignetteBuilder": "knitr",
@@ -946,7 +906,7 @@
       "Config/Needs/website": "bench, forcats, ggbeeswarm, ggplot2, R.utils, rprintf, tidyr, tidyverse/tidytemplate",
       "Config/testthat/edition": "3",
       "Encoding": "UTF-8",
-      "RoxygenNote": "7.2.3.9000",
+      "RoxygenNote": "7.3.2",
       "NeedsCompilation": "yes",
       "Author": "Jim Hester [aut] (<https://orcid.org/0000-0002-2739-7082>), Jennifer Bryan [aut, cre] (<https://orcid.org/0000-0002-6983-2759>), Posit Software, PBC [cph, fnd]",
       "Maintainer": "Jennifer Bryan <jenny@posit.co>",
@@ -985,21 +945,24 @@
     },
     "gtable": {
       "Package": "gtable",
-      "Version": "0.3.4",
+      "Version": "0.3.6",
       "Source": "Repository",
       "Title": "Arrange 'Grobs' in Tables",
-      "Authors@R": "c(person(given = \"Hadley\", family = \"Wickham\", role = \"aut\", email = \"hadley@posit.co\"), person(given = \"Thomas Lin\", family = \"Pedersen\", role = c(\"aut\", \"cre\"), email = \"thomas.pedersen@posit.co\"), person(given = \"Posit Software, PBC\", role = \"cph\"))",
-      "Description": "Tools to make it easier to work with \"tables\" of 'grobs'. The 'gtable' package defines a 'gtable' grob class that specifies a grid along with a list of grobs and their placement in the grid. Further the package makes it easy to manipulate and combine 'gtable' objects so that  complex compositions can be built up sequentially.",
+      "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = \"aut\"), person(\"Thomas Lin\", \"Pedersen\", , \"thomas.pedersen@posit.co\", role = c(\"aut\", \"cre\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )",
+      "Description": "Tools to make it easier to work with \"tables\" of 'grobs'. The 'gtable' package defines a 'gtable' grob class that specifies a grid along with a list of grobs and their placement in the grid. Further the package makes it easy to manipulate and combine 'gtable' objects so that complex compositions can be built up sequentially.",
       "License": "MIT + file LICENSE",
+      "URL": "https://gtable.r-lib.org, https://github.com/r-lib/gtable",
+      "BugReports": "https://github.com/r-lib/gtable/issues",
       "Depends": [
-        "R (>= 3.5)"
+        "R (>= 4.0)"
       ],
       "Imports": [
         "cli",
         "glue",
         "grid",
         "lifecycle",
-        "rlang (>= 1.1.0)"
+        "rlang (>= 1.1.0)",
+        "stats"
       ],
       "Suggests": [
         "covr",
@@ -1010,30 +973,63 @@
         "testthat (>= 3.0.0)"
       ],
       "VignetteBuilder": "knitr",
+      "Config/Needs/website": "tidyverse/tidytemplate",
+      "Config/testthat/edition": "3",
+      "Config/usethis/last-upkeep": "2024-10-25",
       "Encoding": "UTF-8",
+      "RoxygenNote": "7.3.2",
+      "NeedsCompilation": "no",
+      "Author": "Hadley Wickham [aut], Thomas Lin Pedersen [aut, cre], Posit Software, PBC [cph, fnd]",
+      "Maintainer": "Thomas Lin Pedersen <thomas.pedersen@posit.co>",
+      "Repository": "CRAN"
+    },
+    "hms": {
+      "Package": "hms",
+      "Version": "1.1.3",
+      "Source": "Repository",
+      "Title": "Pretty Time of Day",
+      "Date": "2023-03-21",
+      "Authors@R": "c( person(\"Kirill\", \"Müller\", role = c(\"aut\", \"cre\"), email = \"kirill@cynkra.com\", comment = c(ORCID = \"0000-0002-1416-3412\")), person(\"R Consortium\", role = \"fnd\"), person(\"RStudio\", role = \"fnd\") )",
+      "Description": "Implements an S3 class for storing and formatting time-of-day values, based on the 'difftime' class.",
+      "Imports": [
+        "lifecycle",
+        "methods",
+        "pkgconfig",
+        "rlang (>= 1.0.2)",
+        "vctrs (>= 0.3.8)"
+      ],
+      "Suggests": [
+        "crayon",
+        "lubridate",
+        "pillar (>= 1.1.0)",
+        "testthat (>= 3.0.0)"
+      ],
+      "License": "MIT + file LICENSE",
+      "Encoding": "UTF-8",
+      "URL": "https://hms.tidyverse.org/, https://github.com/tidyverse/hms",
+      "BugReports": "https://github.com/tidyverse/hms/issues",
       "RoxygenNote": "7.2.3",
-      "URL": "https://gtable.r-lib.org, https://github.com/r-lib/gtable",
-      "BugReports": "https://github.com/r-lib/gtable/issues",
       "Config/testthat/edition": "3",
+      "Config/autostyle/scope": "line_breaks",
+      "Config/autostyle/strict": "false",
       "Config/Needs/website": "tidyverse/tidytemplate",
       "NeedsCompilation": "no",
-      "Author": "Hadley Wickham [aut], Thomas Lin Pedersen [aut, cre], Posit Software, PBC [cph]",
-      "Maintainer": "Thomas Lin Pedersen <thomas.pedersen@posit.co>",
+      "Author": "Kirill Müller [aut, cre] (<https://orcid.org/0000-0002-1416-3412>), R Consortium [fnd], RStudio [fnd]",
+      "Maintainer": "Kirill Müller <kirill@cynkra.com>",
       "Repository": "RSPM"
     },
     "inline": {
       "Package": "inline",
-      "Version": "0.3.19",
+      "Version": "0.3.21",
       "Source": "Repository",
-      "Date": "2021-05-25",
+      "Date": "2025-01-08",
       "Title": "Functions to Inline C, C++, Fortran Function Calls from R",
-      "Author": "Oleg Sklyar, Duncan Murdoch, Mike Smith, Dirk Eddelbuettel,  Romain Francois, Karline Soetaert, Johannes Ranke",
-      "Maintainer": "Dirk Eddelbuettel <edd@debian.org>",
+      "Authors@R": "c(person(\"Oleg\", \"Sklyar\", role = \"aut\"), person(\"Dirk\", \"Eddelbuettel\", role = c(\"aut\", \"cre\"), email = \"edd@debian.org\", comment = c(ORCID = \"0000-0001-6419-907X\")), person(\"Romain\", \"Francois\", role = \"aut\", comment = c(ORCID = \"0000-0002-2444-4226\")), person(\"Mike\", \"Smith\", role = \"ctb\"), person(\"Duncan\", \"Murdoch\", role = \"ctb\"), person(\"Karline\", \"Soetaert\", role = \"ctb\", comment = c(ORCID = \"0000-0003-4603-7100\")), person(\"Johannes\", \"Ranke\", role = \"ctb\", comment = c(ORCID = \"0000-0003-4371-6538\")))",
       "Imports": [
         "methods"
       ],
       "Suggests": [
-        "Rcpp (>= 0.11.0)",
+        "Rcpp",
         "tinytest"
       ],
       "Description": "Functionality to dynamically define R functions and S4 methods with 'inlined' C, C++ or Fortran code supporting the .C and .Call calling conventions.",
@@ -1043,6 +1039,8 @@
       "URL": "https://github.com/eddelbuettel/inline, https://dirk.eddelbuettel.com/code/inline.html",
       "BugReports": "https://github.com/eddelbuettel/inline/issues",
       "NeedsCompilation": "no",
+      "Author": "Oleg Sklyar [aut], Dirk Eddelbuettel [aut, cre] (<https://orcid.org/0000-0001-6419-907X>), Romain Francois [aut] (<https://orcid.org/0000-0002-2444-4226>), Mike Smith [ctb], Duncan Murdoch [ctb], Karline Soetaert [ctb] (<https://orcid.org/0000-0003-4603-7100>), Johannes Ranke [ctb] (<https://orcid.org/0000-0003-4371-6538>)",
+      "Maintainer": "Dirk Eddelbuettel <edd@debian.org>",
       "Repository": "CRAN"
     },
     "isoband": {
@@ -1081,35 +1079,51 @@
       "Maintainer": "Hadley Wickham <hadley@posit.co>",
       "Repository": "RSPM"
     },
-    "jsonlite": {
-      "Package": "jsonlite",
-      "Version": "1.8.8",
+    "janitor": {
+      "Package": "janitor",
+      "Version": "2.2.0",
       "Source": "Repository",
-      "Title": "A Simple and Robust JSON Parser and Generator for R",
-      "License": "MIT + file LICENSE",
+      "Title": "Simple Tools for Examining and Cleaning Dirty Data",
+      "Authors@R": "c(person(\"Sam\", \"Firke\", email = \"samuel.firke@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Bill\", \"Denney\", email = \"wdenney@humanpredictions.com\", role = \"ctb\"), person(\"Chris\", \"Haid\", email = \"chrishaid@gmail.com\", role = \"ctb\"), person(\"Ryan\", \"Knight\", email = \"ryangknight@gmail.com\", role = \"ctb\"), person(\"Malte\", \"Grosser\", email = \"malte.grosser@gmail.com\", role = \"ctb\"), person(\"Jonathan\", \"Zadra\", email = \"jonathan.zadra@sorensonimpact.com\", role = \"ctb\"))",
+      "Description": "The main janitor functions can: perfectly format data.frame column names; provide quick counts of variable combinations (i.e., frequency tables and crosstabs); and explore duplicate records. Other janitor functions nicely format the tabulation results. These tabulate-and-report functions approximate popular features of SPSS and Microsoft Excel. This package follows the principles of the \"tidyverse\" and works well with the pipe function %>%. janitor was built with beginning-to-intermediate R users in mind and is optimized for user-friendliness.",
+      "URL": "https://github.com/sfirke/janitor, https://sfirke.github.io/janitor/",
+      "BugReports": "https://github.com/sfirke/janitor/issues",
       "Depends": [
-        "methods"
+        "R (>= 3.1.2)"
       ],
-      "Authors@R": "c( person(\"Jeroen\", \"Ooms\", role = c(\"aut\", \"cre\"), email = \"jeroen@berkeley.edu\", comment = c(ORCID = \"0000-0002-4035-0289\")), person(\"Duncan\", \"Temple Lang\", role = \"ctb\"), person(\"Lloyd\", \"Hilaiel\", role = \"cph\", comment=\"author of bundled libyajl\"))",
-      "URL": "https://jeroen.r-universe.dev/jsonlite https://arxiv.org/abs/1403.2805",
-      "BugReports": "https://github.com/jeroen/jsonlite/issues",
-      "Maintainer": "Jeroen Ooms <jeroen@berkeley.edu>",
-      "VignetteBuilder": "knitr, R.rsp",
-      "Description": "A reasonably fast JSON parser and generator, optimized for statistical  data and the web. Offers simple, flexible tools for working with JSON in R, and is particularly powerful for building pipelines and interacting with a web API.  The implementation is based on the mapping described in the vignette (Ooms, 2014). In addition to converting JSON data from/to R objects, 'jsonlite' contains  functions to stream, validate, and prettify JSON data. The unit tests included  with the package verify that all edge cases are encoded and decoded consistently  for use with dynamic data in systems and applications.",
+      "Imports": [
+        "dplyr (>= 1.0.0)",
+        "hms",
+        "lifecycle",
+        "lubridate",
+        "magrittr",
+        "purrr",
+        "rlang",
+        "stringi",
+        "stringr",
+        "snakecase (>= 0.9.2)",
+        "tidyselect (>= 1.0.0)",
+        "tidyr (>= 0.7.0)"
+      ],
+      "License": "MIT + file LICENSE",
+      "RoxygenNote": "7.2.3",
       "Suggests": [
-        "httr",
-        "vctrs",
-        "testthat",
+        "dbplyr",
         "knitr",
         "rmarkdown",
-        "R.rsp",
-        "sf"
+        "RSQLite",
+        "sf",
+        "testthat (>= 3.0.0)",
+        "tibble",
+        "tidygraph"
       ],
-      "RoxygenNote": "7.2.3",
+      "VignetteBuilder": "knitr",
       "Encoding": "UTF-8",
-      "NeedsCompilation": "yes",
-      "Author": "Jeroen Ooms [aut, cre] (<https://orcid.org/0000-0002-4035-0289>), Duncan Temple Lang [ctb], Lloyd Hilaiel [cph] (author of bundled libyajl)",
-      "Repository": "CRAN"
+      "Config/testthat/edition": "3",
+      "NeedsCompilation": "no",
+      "Author": "Sam Firke [aut, cre], Bill Denney [ctb], Chris Haid [ctb], Ryan Knight [ctb], Malte Grosser [ctb], Jonathan Zadra [ctb]",
+      "Maintainer": "Sam Firke <samuel.firke@gmail.com>",
+      "Repository": "RSPM"
     },
     "labeling": {
       "Package": "labeling",
@@ -1212,10 +1226,10 @@
     },
     "locfit": {
       "Package": "locfit",
-      "Version": "1.5-9.8",
+      "Version": "1.5-9.12",
       "Source": "Repository",
       "Title": "Local Regression, Likelihood and Density Estimation",
-      "Date": "2023-06-11",
+      "Date": "2025-03-05",
       "Authors@R": "c(person(\"Catherine\", \"Loader\", role = \"aut\"), person(\"Jiayang\", \"Sun\", role = \"ctb\"), person(\"Lucent Technologies\", role = \"cph\"), person(\"Andy\", \"Liaw\", role = \"cre\",  email=\"andy_liaw@merck.com\"))",
       "Author": "Catherine Loader [aut], Jiayang Sun [ctb], Lucent Technologies [cph], Andy Liaw [cre]",
       "Maintainer": "Andy Liaw <andy_liaw@merck.com>",
@@ -1237,12 +1251,12 @@
     },
     "loo": {
       "Package": "loo",
-      "Version": "2.7.0",
+      "Version": "2.8.0",
       "Source": "Repository",
       "Type": "Package",
       "Title": "Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models",
-      "Date": "2024-02-24",
-      "Authors@R": "c(person(\"Aki\", \"Vehtari\", email = \"Aki.Vehtari@aalto.fi\", role = c(\"aut\")), person(\"Jonah\", \"Gabry\", email = \"jsg2201@columbia.edu\", role = c(\"cre\", \"aut\")), person(\"Mans\", \"Magnusson\", role = c(\"aut\")), person(\"Yuling\", \"Yao\", role = c(\"aut\")), person(\"Paul-Christian\", \"Bürkner\", role = c(\"aut\")), person(\"Topi\", \"Paananen\", role = c(\"aut\")), person(\"Andrew\", \"Gelman\", role = c(\"aut\")), person(\"Ben\", \"Goodrich\", role = c(\"ctb\")), person(\"Juho\", \"Piironen\", role = c(\"ctb\")),  person(\"Bruno\", \"Nicenboim\", role = c(\"ctb\")),  person(\"Leevi\", \"Lindgren\", role = c(\"ctb\")))",
+      "Date": "2024-07-03",
+      "Authors@R": "c(person(\"Aki\", \"Vehtari\", email = \"Aki.Vehtari@aalto.fi\", role = c(\"aut\")), person(\"Jonah\", \"Gabry\", email = \"jsg2201@columbia.edu\", role = c(\"cre\", \"aut\")), person(\"Måns\", \"Magnusson\", role = c(\"aut\")), person(\"Yuling\", \"Yao\", role = c(\"aut\")), person(\"Paul-Christian\", \"Bürkner\", role = c(\"aut\")), person(\"Topi\", \"Paananen\", role = c(\"aut\")), person(\"Andrew\", \"Gelman\", role = c(\"aut\")), person(\"Ben\", \"Goodrich\", role = c(\"ctb\")), person(\"Juho\", \"Piironen\", role = c(\"ctb\")),  person(\"Bruno\", \"Nicenboim\", role = c(\"ctb\")),  person(\"Leevi\", \"Lindgren\", role = c(\"ctb\")))",
       "Maintainer": "Jonah Gabry <jsg2201@columbia.edu>",
       "URL": "https://mc-stan.org/loo/, https://discourse.mc-stan.org",
       "BugReports": "https://github.com/stan-dev/loo/issues",
@@ -1275,11 +1289,57 @@
       "VignetteBuilder": "knitr",
       "Encoding": "UTF-8",
       "SystemRequirements": "pandoc (>= 1.12.3), pandoc-citeproc",
-      "RoxygenNote": "7.3.0",
+      "RoxygenNote": "7.3.1",
       "NeedsCompilation": "no",
-      "Author": "Aki Vehtari [aut], Jonah Gabry [cre, aut], Mans Magnusson [aut], Yuling Yao [aut], Paul-Christian Bürkner [aut], Topi Paananen [aut], Andrew Gelman [aut], Ben Goodrich [ctb], Juho Piironen [ctb], Bruno Nicenboim [ctb], Leevi Lindgren [ctb]",
+      "Author": "Aki Vehtari [aut], Jonah Gabry [cre, aut], Måns Magnusson [aut], Yuling Yao [aut], Paul-Christian Bürkner [aut], Topi Paananen [aut], Andrew Gelman [aut], Ben Goodrich [ctb], Juho Piironen [ctb], Bruno Nicenboim [ctb], Leevi Lindgren [ctb]",
       "Repository": "CRAN"
     },
+    "lubridate": {
+      "Package": "lubridate",
+      "Version": "1.9.3",
+      "Source": "Repository",
+      "Type": "Package",
+      "Title": "Make Dealing with Dates a Little Easier",
+      "Authors@R": "c( person(\"Vitalie\", \"Spinu\", , \"spinuvit@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Garrett\", \"Grolemund\", role = \"aut\"), person(\"Hadley\", \"Wickham\", role = \"aut\"), person(\"Davis\", \"Vaughan\", role = \"ctb\"), person(\"Ian\", \"Lyttle\", role = \"ctb\"), person(\"Imanuel\", \"Costigan\", role = \"ctb\"), person(\"Jason\", \"Law\", role = \"ctb\"), person(\"Doug\", \"Mitarotonda\", role = \"ctb\"), person(\"Joseph\", \"Larmarange\", role = \"ctb\"), person(\"Jonathan\", \"Boiser\", role = \"ctb\"), person(\"Chel Hee\", \"Lee\", role = \"ctb\") )",
+      "Maintainer": "Vitalie Spinu <spinuvit@gmail.com>",
+      "Description": "Functions to work with date-times and time-spans: fast and user friendly parsing of date-time data, extraction and updating of components of a date-time (years, months, days, hours, minutes, and seconds), algebraic manipulation on date-time and time-span objects. The 'lubridate' package has a consistent and memorable syntax that makes working with dates easy and fun.",
+      "License": "GPL (>= 2)",
+      "URL": "https://lubridate.tidyverse.org, https://github.com/tidyverse/lubridate",
+      "BugReports": "https://github.com/tidyverse/lubridate/issues",
+      "Depends": [
+        "methods",
+        "R (>= 3.2)"
+      ],
+      "Imports": [
+        "generics",
+        "timechange (>= 0.1.1)"
+      ],
+      "Suggests": [
+        "covr",
+        "knitr",
+        "rmarkdown",
+        "testthat (>= 2.1.0)",
+        "vctrs (>= 0.5.0)"
+      ],
+      "Enhances": [
+        "chron",
+        "data.table",
+        "timeDate",
+        "tis",
+        "zoo"
+      ],
+      "VignetteBuilder": "knitr",
+      "Config/Needs/website": "tidyverse/tidytemplate",
+      "Config/testthat/edition": "3",
+      "Encoding": "UTF-8",
+      "LazyData": "true",
+      "RoxygenNote": "7.2.3",
+      "SystemRequirements": "C++11, A system with zoneinfo data (e.g. /usr/share/zoneinfo). On Windows the zoneinfo included with R is used.",
+      "Collate": "'Dates.r' 'POSIXt.r' 'util.r' 'parse.r' 'timespans.r' 'intervals.r' 'difftimes.r' 'durations.r' 'periods.r' 'accessors-date.R' 'accessors-day.r' 'accessors-dst.r' 'accessors-hour.r' 'accessors-minute.r' 'accessors-month.r' 'accessors-quarter.r' 'accessors-second.r' 'accessors-tz.r' 'accessors-week.r' 'accessors-year.r' 'am-pm.r' 'time-zones.r' 'numeric.r' 'coercion.r' 'constants.r' 'cyclic_encoding.r' 'data.r' 'decimal-dates.r' 'deprecated.r' 'format_ISO8601.r' 'guess.r' 'hidden.r' 'instants.r' 'leap-years.r' 'ops-addition.r' 'ops-compare.r' 'ops-division.r' 'ops-integer-division.r' 'ops-m+.r' 'ops-modulo.r' 'ops-multiplication.r' 'ops-subtraction.r' 'package.r' 'pretty.r' 'round.r' 'stamp.r' 'tzdir.R' 'update.r' 'vctrs.R' 'zzz.R'",
+      "NeedsCompilation": "yes",
+      "Author": "Vitalie Spinu [aut, cre], Garrett Grolemund [aut], Hadley Wickham [aut], Davis Vaughan [ctb], Ian Lyttle [ctb], Imanuel Costigan [ctb], Jason Law [ctb], Doug Mitarotonda [ctb], Joseph Larmarange [ctb], Jonathan Boiser [ctb], Chel Hee Lee [ctb]",
+      "Repository": "RSPM"
+    },
     "magrittr": {
       "Package": "magrittr",
       "Version": "2.0.3",
@@ -1313,12 +1373,13 @@
     },
     "matrixStats": {
       "Package": "matrixStats",
-      "Version": "1.2.0",
+      "Version": "1.5.0",
       "Source": "Repository",
       "Depends": [
-        "R (>= 2.12.0)"
+        "R (>= 3.4.0)"
       ],
       "Suggests": [
+        "utils",
         "base64enc",
         "ggplot2",
         "knitr",
@@ -1339,7 +1400,7 @@
       "ByteCompile": "TRUE",
       "URL": "https://github.com/HenrikBengtsson/matrixStats",
       "BugReports": "https://github.com/HenrikBengtsson/matrixStats/issues",
-      "RoxygenNote": "7.2.3",
+      "RoxygenNote": "7.3.2",
       "Repository": "CRAN"
     },
     "mgcv": {
@@ -1395,29 +1456,30 @@
       "NeedsCompilation": "no",
       "Repository": "CRAN"
     },
-    "munsell": {
-      "Package": "munsell",
-      "Version": "0.5.0",
+    "mvtnorm": {
+      "Package": "mvtnorm",
+      "Version": "1.3-3",
       "Source": "Repository",
-      "Type": "Package",
-      "Title": "Utilities for Using Munsell Colours",
-      "Author": "Charlotte Wickham <cwickham@gmail.com>",
-      "Maintainer": "Charlotte Wickham <cwickham@gmail.com>",
-      "Description": "Provides easy access to, and manipulation of, the Munsell  colours. Provides a mapping between Munsell's  original notation (e.g. \"5R 5/10\") and hexadecimal strings suitable  for use directly in R graphics. Also provides utilities  to explore slices through the Munsell colour tree, to transform  Munsell colours and display colour palettes.",
-      "Suggests": [
-        "ggplot2",
-        "testthat"
-      ],
+      "Title": "Multivariate Normal and t Distributions",
+      "Date": "2025-01-09",
+      "Authors@R": "c(person(\"Alan\", \"Genz\", role = \"aut\"), person(\"Frank\", \"Bretz\", role = \"aut\"), person(\"Tetsuhisa\", \"Miwa\", role = \"aut\"), person(\"Xuefei\", \"Mi\", role = \"aut\"), person(\"Friedrich\", \"Leisch\", role = \"ctb\"), person(\"Fabian\", \"Scheipl\", role = \"ctb\"), person(\"Bjoern\", \"Bornkamp\", role = \"ctb\", comment = c(ORCID = \"0000-0002-6294-8185\")), person(\"Martin\", \"Maechler\", role = \"ctb\", comment = c(ORCID = \"0000-0002-8685-9910\")), person(\"Torsten\", \"Hothorn\", role = c(\"aut\", \"cre\"), email = \"Torsten.Hothorn@R-project.org\", comment = c(ORCID = \"0000-0001-8301-0471\")))",
+      "Description": "Computes multivariate normal and t probabilities, quantiles, random deviates,  and densities. Log-likelihoods for multivariate Gaussian models and Gaussian copulae  parameterised by Cholesky factors of covariance or precision matrices are implemented  for interval-censored and exact data, or a mix thereof. Score functions for these  log-likelihoods are available. A class representing multiple lower triangular matrices  and corresponding methods are part of this package.",
       "Imports": [
-        "colorspace",
-        "methods"
+        "stats"
       ],
-      "License": "MIT + file LICENSE",
-      "URL": "https://cran.r-project.org/package=munsell, https://github.com/cwickham/munsell/",
-      "RoxygenNote": "6.0.1",
-      "NeedsCompilation": "no",
-      "Repository": "RSPM",
-      "Encoding": "UTF-8"
+      "Depends": [
+        "R(>= 3.5.0)"
+      ],
+      "Suggests": [
+        "qrng",
+        "numDeriv"
+      ],
+      "License": "GPL-2",
+      "URL": "http://mvtnorm.R-forge.R-project.org",
+      "NeedsCompilation": "yes",
+      "Author": "Alan Genz [aut], Frank Bretz [aut], Tetsuhisa Miwa [aut], Xuefei Mi [aut], Friedrich Leisch [ctb], Fabian Scheipl [ctb], Bjoern Bornkamp [ctb] (<https://orcid.org/0000-0002-6294-8185>), Martin Maechler [ctb] (<https://orcid.org/0000-0002-8685-9910>), Torsten Hothorn [aut, cre] (<https://orcid.org/0000-0001-8301-0471>)",
+      "Maintainer": "Torsten Hothorn <Torsten.Hothorn@R-project.org>",
+      "Repository": "CRAN"
     },
     "nlme": {
       "Package": "nlme",
@@ -1476,7 +1538,7 @@
     },
     "patchwork": {
       "Package": "patchwork",
-      "Version": "1.2.0",
+      "Version": "1.3.1",
       "Source": "Repository",
       "Type": "Package",
       "Title": "The Composer of Plots",
@@ -1487,16 +1549,17 @@
       "Encoding": "UTF-8",
       "Imports": [
         "ggplot2 (>= 3.0.0)",
-        "gtable",
+        "gtable (>= 0.3.6)",
         "grid",
         "stats",
         "grDevices",
         "utils",
         "graphics",
-        "rlang",
-        "cli"
+        "rlang (>= 1.0.0)",
+        "cli",
+        "farver"
       ],
-      "RoxygenNote": "7.2.3",
+      "RoxygenNote": "7.3.2",
       "URL": "https://patchwork.data-imaginist.com, https://github.com/thomasp85/patchwork",
       "BugReports": "https://github.com/thomasp85/patchwork/issues",
       "Suggests": [
@@ -1508,17 +1571,18 @@
         "testthat (>= 2.1.0)",
         "vdiffr",
         "covr",
-        "png"
+        "png",
+        "gt (>= 0.11.0)"
       ],
       "VignetteBuilder": "knitr",
       "Config/Needs/website": "gifski",
       "NeedsCompilation": "no",
-      "Author": "Thomas Lin Pedersen [cre, aut] (<https://orcid.org/0000-0002-5147-4711>)",
+      "Author": "Thomas Lin Pedersen [cre, aut] (ORCID: <https://orcid.org/0000-0002-5147-4711>)",
       "Repository": "CRAN"
     },
     "pillar": {
       "Package": "pillar",
-      "Version": "1.9.0",
+      "Version": "1.11.0",
       "Source": "Repository",
       "Title": "Coloured Formatting for Columns",
       "Authors@R": "c(person(given = \"Kirill\", family = \"M\\u00fcller\", role = c(\"aut\", \"cre\"), email = \"kirill@cynkra.com\", comment = c(ORCID = \"0000-0002-1416-3412\")), person(given = \"Hadley\", family = \"Wickham\", role = \"aut\"), person(given = \"RStudio\", role = \"cph\"))",
@@ -1528,7 +1592,6 @@
       "BugReports": "https://github.com/r-lib/pillar/issues",
       "Imports": [
         "cli (>= 2.3.0)",
-        "fansi",
         "glue",
         "lifecycle",
         "rlang (>= 1.0.2)",
@@ -1561,25 +1624,25 @@
       ],
       "VignetteBuilder": "knitr",
       "Encoding": "UTF-8",
-      "RoxygenNote": "7.2.3",
+      "RoxygenNote": "7.3.2.9000",
       "Config/testthat/edition": "3",
       "Config/testthat/parallel": "true",
       "Config/testthat/start-first": "format_multi_fuzz, format_multi_fuzz_2, format_multi, ctl_colonnade, ctl_colonnade_1, ctl_colonnade_2",
       "Config/autostyle/scope": "line_breaks",
       "Config/autostyle/strict": "true",
-      "Config/gha/extra-packages": "DiagrammeR=?ignore-before-r=3.5.0",
+      "Config/gha/extra-packages": "units=?ignore-before-r=4.3.0",
       "Config/Needs/website": "tidyverse/tidytemplate",
       "NeedsCompilation": "no",
-      "Author": "Kirill Müller [aut, cre] (<https://orcid.org/0000-0002-1416-3412>), Hadley Wickham [aut], RStudio [cph]",
+      "Author": "Kirill Müller [aut, cre] (ORCID: <https://orcid.org/0000-0002-1416-3412>), Hadley Wickham [aut], RStudio [cph]",
       "Maintainer": "Kirill Müller <kirill@cynkra.com>",
       "Repository": "CRAN"
     },
     "pkgbuild": {
       "Package": "pkgbuild",
-      "Version": "1.4.3",
+      "Version": "1.4.8",
       "Source": "Repository",
       "Title": "Find Tools Needed to Build R Packages",
-      "Authors@R": "c( person(\"Hadley\", \"Wickham\", role = \"aut\"), person(\"Jim\", \"Hester\", role = \"aut\"), person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )",
+      "Authors@R": "c( person(\"Hadley\", \"Wickham\", role = \"aut\"), person(\"Jim\", \"Hester\", role = \"aut\"), person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"03wc8by49\")) )",
       "Description": "Provides functions used to build R packages. Locates compilers needed to build R packages on various platforms and ensures the PATH is configured appropriately so R can use them.",
       "License": "MIT + file LICENSE",
       "URL": "https://github.com/r-lib/pkgbuild, https://pkgbuild.r-lib.org",
@@ -1598,20 +1661,20 @@
         "covr",
         "cpp11",
         "knitr",
-        "mockery",
         "Rcpp",
         "rmarkdown",
-        "testthat (>= 3.0.0)",
+        "testthat (>= 3.2.0)",
         "withr (>= 2.3.0)"
       ],
       "Config/Needs/website": "tidyverse/tidytemplate",
       "Config/testthat/edition": "3",
+      "Config/usethis/last-upkeep": "2025-04-30",
       "Encoding": "UTF-8",
-      "RoxygenNote": "7.2.3",
+      "RoxygenNote": "7.3.2",
       "NeedsCompilation": "no",
-      "Author": "Hadley Wickham [aut], Jim Hester [aut], Gábor Csárdi [aut, cre], Posit Software, PBC [cph, fnd]",
+      "Author": "Hadley Wickham [aut], Jim Hester [aut], Gábor Csárdi [aut, cre], Posit Software, PBC [cph, fnd] (ROR: <https://ror.org/03wc8by49>)",
       "Maintainer": "Gábor Csárdi <csardi.gabor@gmail.com>",
-      "Repository": "RSPM"
+      "Repository": "CRAN"
     },
     "pkgconfig": {
       "Package": "pkgconfig",
@@ -1639,11 +1702,11 @@
     },
     "posterior": {
       "Package": "posterior",
-      "Version": "1.5.0",
+      "Version": "1.6.1",
       "Source": "Repository",
       "Title": "Tools for Working with Posterior Distributions",
-      "Date": "2023-10-31",
-      "Authors@R": "c(person(\"Paul-Christian\", \"Bürkner\", email = \"paul.buerkner@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Jonah\", \"Gabry\", email = \"jsg2201@columbia.edu\", role = c(\"aut\")), person(\"Matthew\", \"Kay\", email = \"mjskay@northwestern.edu\", role = c(\"aut\")), person(\"Aki\", \"Vehtari\", email = \"Aki.Vehtari@aalto.fi\", role = c(\"aut\")), person(\"Måns\", \"Magnusson\", role = c(\"ctb\")), person(\"Rok\", \"Češnovar\", role = c(\"ctb\")), person(\"Ben\", \"Lambert\", role = c(\"ctb\")), person(\"Ozan\", \"Adıgüzel\", role = c(\"ctb\")),  person(\"Jacob\", \"Socolar\", role = c(\"ctb\")))",
+      "Date": "2025-02-27",
+      "Authors@R": "c(person(\"Paul-Christian\", \"Bürkner\", email = \"paul.buerkner@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Jonah\", \"Gabry\", email = \"jsg2201@columbia.edu\", role = c(\"aut\")), person(\"Matthew\", \"Kay\", email = \"mjskay@northwestern.edu\", role = c(\"aut\")), person(\"Aki\", \"Vehtari\", email = \"Aki.Vehtari@aalto.fi\", role = c(\"aut\")), person(\"Måns\", \"Magnusson\", role = c(\"ctb\")), person(\"Rok\", \"Češnovar\", role = c(\"ctb\")), person(\"Ben\", \"Lambert\", role = c(\"ctb\")), person(\"Ozan\", \"Adıgüzel\", role = c(\"ctb\")),  person(\"Jacob\", \"Socolar\", role = c(\"ctb\")), person(\"Noa\", \"Kallioinen\", role = c(\"ctb\")))",
       "Description": "Provides useful tools for both users and developers of packages  for fitting Bayesian models or working with output from Bayesian models.  The primary goals of the package are to:  (a) Efficiently convert between many different useful formats of draws (samples) from posterior or prior distributions. (b) Provide consistent methods for operations commonly performed on draws,  for example, subsetting, binding, or mutating draws. (c) Provide various summaries of draws in convenient formats. (d) Provide lightweight implementations of state of the art posterior  inference diagnostics. References: Vehtari et al. (2021)  <doi:10.1214/20-BA1221>.",
       "Depends": [
         "R (>= 3.2.0)"
@@ -1664,10 +1727,10 @@
       ],
       "Suggests": [
         "testthat (>= 2.1.0)",
-        "caret (>= 6.0.84)",
+        "caret (>= 6.0-84)",
         "gbm (>= 2.1.8)",
         "randomForest (>= 4.6.14)",
-        "e1071 (>= 1.7.3)",
+        "e1071 (>= 1.7-3)",
         "dplyr",
         "tidyr",
         "knitr",
@@ -1679,19 +1742,19 @@
       "Encoding": "UTF-8",
       "URL": "https://mc-stan.org/posterior/, https://discourse.mc-stan.org/",
       "BugReports": "https://github.com/stan-dev/posterior/issues",
-      "RoxygenNote": "7.2.3",
+      "RoxygenNote": "7.3.2",
       "VignetteBuilder": "knitr",
       "NeedsCompilation": "no",
-      "Author": "Paul-Christian Bürkner [aut, cre], Jonah Gabry [aut], Matthew Kay [aut], Aki Vehtari [aut], Måns Magnusson [ctb], Rok Češnovar [ctb], Ben Lambert [ctb], Ozan Adıgüzel [ctb], Jacob Socolar [ctb]",
+      "Author": "Paul-Christian Bürkner [aut, cre], Jonah Gabry [aut], Matthew Kay [aut], Aki Vehtari [aut], Måns Magnusson [ctb], Rok Češnovar [ctb], Ben Lambert [ctb], Ozan Adıgüzel [ctb], Jacob Socolar [ctb], Noa Kallioinen [ctb]",
       "Maintainer": "Paul-Christian Bürkner <paul.buerkner@gmail.com>",
       "Repository": "CRAN"
     },
     "processx": {
       "Package": "processx",
-      "Version": "3.8.3",
+      "Version": "3.8.6",
       "Source": "Repository",
       "Title": "Execute and Control System Processes",
-      "Authors@R": "c( person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\", \"cph\"), comment = c(ORCID = \"0000-0001-7098-9676\")), person(\"Winston\", \"Chang\", role = \"aut\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")), person(\"Mango Solutions\", role = c(\"cph\", \"fnd\")) )",
+      "Authors@R": "c( person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\", \"cph\"), comment = c(ORCID = \"0000-0001-7098-9676\")), person(\"Winston\", \"Chang\", role = \"aut\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")), person(\"Ascent Digital Services\", role = c(\"cph\", \"fnd\")) )",
       "Description": "Tools to run system processes in the background.  It can check if a background process is running; wait on a background process to finish; get the exit status of finished processes; kill background processes. It can read the standard output and error of the processes, using non-blocking connections. 'processx' can poll a process for standard output or error, with a timeout. It can also poll several processes at once.",
       "License": "MIT + file LICENSE",
       "URL": "https://processx.r-lib.org, https://github.com/r-lib/processx",
@@ -1720,15 +1783,15 @@
       "Config/Needs/website": "tidyverse/tidytemplate",
       "Config/testthat/edition": "3",
       "Encoding": "UTF-8",
-      "RoxygenNote": "7.2.3",
+      "RoxygenNote": "7.3.1.9000",
       "NeedsCompilation": "yes",
-      "Author": "Gábor Csárdi [aut, cre, cph] (<https://orcid.org/0000-0001-7098-9676>), Winston Chang [aut], Posit Software, PBC [cph, fnd], Mango Solutions [cph, fnd]",
+      "Author": "Gábor Csárdi [aut, cre, cph] (<https://orcid.org/0000-0001-7098-9676>), Winston Chang [aut], Posit Software, PBC [cph, fnd], Ascent Digital Services [cph, fnd]",
       "Maintainer": "Gábor Csárdi <csardi.gabor@gmail.com>",
-      "Repository": "RSPM"
+      "Repository": "CRAN"
     },
     "ps": {
       "Package": "ps",
-      "Version": "1.7.6",
+      "Version": "1.9.1",
       "Source": "Repository",
       "Title": "List, Query, Manipulate System Processes",
       "Authors@R": "c( person(\"Jay\", \"Loden\", role = \"aut\"), person(\"Dave\", \"Daeschler\", role = \"aut\"), person(\"Giampaolo\", \"Rodola'\", role = \"aut\"), person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )",
@@ -1752,30 +1815,31 @@
         "R6",
         "rlang",
         "testthat (>= 3.0.0)",
-        "webfakes"
+        "webfakes",
+        "withr"
       ],
       "Biarch": "true",
       "Config/Needs/website": "tidyverse/tidytemplate",
       "Config/testthat/edition": "3",
       "Encoding": "UTF-8",
-      "RoxygenNote": "7.2.3",
+      "RoxygenNote": "7.3.2",
       "NeedsCompilation": "yes",
       "Author": "Jay Loden [aut], Dave Daeschler [aut], Giampaolo Rodola' [aut], Gábor Csárdi [aut, cre], Posit Software, PBC [cph, fnd]",
       "Maintainer": "Gábor Csárdi <csardi.gabor@gmail.com>",
-      "Repository": "RSPM"
+      "Repository": "CRAN"
     },
     "purrr": {
       "Package": "purrr",
-      "Version": "1.0.2",
+      "Version": "1.0.4",
       "Source": "Repository",
       "Title": "Functional Programming Tools",
-      "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@rstudio.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-4757-117X\")), person(\"Lionel\", \"Henry\", , \"lionel@rstudio.com\", role = \"aut\"), person(\"RStudio\", role = c(\"cph\", \"fnd\")) )",
+      "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-4757-117X\")), person(\"Lionel\", \"Henry\", , \"lionel@posit.co\", role = \"aut\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"03wc8by49\")) )",
       "Description": "A complete and consistent functional programming toolkit for R.",
       "License": "MIT + file LICENSE",
       "URL": "https://purrr.tidyverse.org/, https://github.com/tidyverse/purrr",
       "BugReports": "https://github.com/tidyverse/purrr/issues",
       "Depends": [
-        "R (>= 3.5.0)"
+        "R (>= 4.0)"
       ],
       "Imports": [
         "cli (>= 3.6.1)",
@@ -1800,14 +1864,16 @@
       ],
       "VignetteBuilder": "knitr",
       "Biarch": "true",
+      "Config/build/compilation-database": "true",
       "Config/Needs/website": "tidyverse/tidytemplate, tidyr",
       "Config/testthat/edition": "3",
+      "Config/testthat/parallel": "TRUE",
       "Encoding": "UTF-8",
-      "RoxygenNote": "7.2.3",
+      "RoxygenNote": "7.3.2",
       "NeedsCompilation": "yes",
-      "Author": "Hadley Wickham [aut, cre] (<https://orcid.org/0000-0003-4757-117X>), Lionel Henry [aut], RStudio [cph, fnd]",
-      "Maintainer": "Hadley Wickham <hadley@rstudio.com>",
-      "Repository": "RSPM"
+      "Author": "Hadley Wickham [aut, cre] (<https://orcid.org/0000-0003-4757-117X>), Lionel Henry [aut], Posit Software, PBC [cph, fnd] (03wc8by49)",
+      "Maintainer": "Hadley Wickham <hadley@posit.co>",
+      "Repository": "CRAN"
     },
     "renv": {
       "Package": "renv",
@@ -1864,11 +1930,11 @@
     },
     "rlang": {
       "Package": "rlang",
-      "Version": "1.1.3",
+      "Version": "1.1.6",
       "Source": "Repository",
       "Title": "Functions for Base Types and Core R and 'Tidyverse' Features",
       "Description": "A toolbox for working with base types, core R features like the condition system, and core 'Tidyverse' features like tidy evaluation.",
-      "Authors@R": "c( person(\"Lionel\", \"Henry\", ,\"lionel@posit.co\", c(\"aut\", \"cre\")), person(\"Hadley\", \"Wickham\", ,\"hadley@posit.co\", \"aut\"), person(given = \"mikefc\", email = \"mikefc@coolbutuseless.com\",  role = \"cph\",  comment = \"Hash implementation based on Mike's xxhashlite\"), person(given = \"Yann\", family = \"Collet\", role = \"cph\",  comment = \"Author of the embedded xxHash library\"), person(given = \"Posit, PBC\", role = c(\"cph\", \"fnd\")) )",
+      "Authors@R": "c( person(\"Lionel\", \"Henry\", ,\"lionel@posit.co\", c(\"aut\", \"cre\")), person(\"Hadley\", \"Wickham\", ,\"hadley@posit.co\", \"aut\"), person(given = \"mikefc\", email = \"mikefc@coolbutuseless.com\", role = \"cph\", comment = \"Hash implementation based on Mike's xxhashlite\"), person(given = \"Yann\", family = \"Collet\", role = \"cph\", comment = \"Author of the embedded xxHash library\"), person(given = \"Posit, PBC\", role = c(\"cph\", \"fnd\")) )",
       "License": "MIT + file LICENSE",
       "ByteCompile": "true",
       "Biarch": "true",
@@ -1882,15 +1948,17 @@
         "cli (>= 3.1.0)",
         "covr",
         "crayon",
+        "desc",
         "fs",
         "glue",
         "knitr",
         "magrittr",
         "methods",
         "pillar",
+        "pkgload",
         "rmarkdown",
         "stats",
-        "testthat (>= 3.0.0)",
+        "testthat (>= 3.2.0)",
         "tibble",
         "usethis",
         "vctrs (>= 0.2.3)",
@@ -1900,9 +1968,10 @@
         "winch"
       ],
       "Encoding": "UTF-8",
-      "RoxygenNote": "7.2.3",
+      "RoxygenNote": "7.3.2",
       "URL": "https://rlang.r-lib.org, https://github.com/r-lib/rlang",
       "BugReports": "https://github.com/r-lib/rlang/issues",
+      "Config/build/compilation-database": "true",
       "Config/testthat/edition": "3",
       "Config/Needs/website": "dplyr, tidyverse/tidytemplate",
       "NeedsCompilation": "yes",
@@ -2009,16 +2078,16 @@
     },
     "scales": {
       "Package": "scales",
-      "Version": "1.3.0",
+      "Version": "1.4.0",
       "Source": "Repository",
       "Title": "Scale Functions for Visualization",
-      "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = c(\"aut\")), person(\"Thomas Lin\", \"Pedersen\", , \"thomas.pedersen@posit.co\", role = c(\"cre\", \"aut\"), comment = c(ORCID = \"0000-0002-5147-4711\")), person(\"Dana\", \"Seidel\", role = \"aut\"), person(\"Posit, PBC\", role = c(\"cph\", \"fnd\")) )",
+      "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = \"aut\"), person(\"Thomas Lin\", \"Pedersen\", , \"thomas.pedersen@posit.co\", role = c(\"cre\", \"aut\"), comment = c(ORCID = \"0000-0002-5147-4711\")), person(\"Dana\", \"Seidel\", role = \"aut\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"03wc8by49\")) )",
       "Description": "Graphical scales map data to aesthetics, and provide methods for automatically determining breaks and labels for axes and legends.",
       "License": "MIT + file LICENSE",
       "URL": "https://scales.r-lib.org, https://github.com/r-lib/scales",
       "BugReports": "https://github.com/r-lib/scales/issues",
       "Depends": [
-        "R (>= 3.6)"
+        "R (>= 4.1)"
       ],
       "Imports": [
         "cli",
@@ -2026,10 +2095,9 @@
         "glue",
         "labeling",
         "lifecycle",
-        "munsell (>= 0.5)",
         "R6",
         "RColorBrewer",
-        "rlang (>= 1.0.0)",
+        "rlang (>= 1.1.0)",
         "viridisLite"
       ],
       "Suggests": [
@@ -2043,19 +2111,84 @@
       ],
       "Config/Needs/website": "tidyverse/tidytemplate",
       "Config/testthat/edition": "3",
+      "Config/usethis/last-upkeep": "2025-04-23",
       "Encoding": "UTF-8",
       "LazyLoad": "yes",
-      "RoxygenNote": "7.2.3",
-      "NeedsCompilation": "yes",
-      "Author": "Hadley Wickham [aut], Thomas Lin Pedersen [cre, aut] (<https://orcid.org/0000-0002-5147-4711>), Dana Seidel [aut], Posit, PBC [cph, fnd]",
+      "RoxygenNote": "7.3.2",
+      "NeedsCompilation": "no",
+      "Author": "Hadley Wickham [aut], Thomas Lin Pedersen [cre, aut] (<https://orcid.org/0000-0002-5147-4711>), Dana Seidel [aut], Posit Software, PBC [cph, fnd] (03wc8by49)",
       "Maintainer": "Thomas Lin Pedersen <thomas.pedersen@posit.co>",
+      "Repository": "CRAN"
+    },
+    "scam": {
+      "Package": "scam",
+      "Version": "1.2-19",
+      "Source": "Repository",
+      "Authors@R": "person(given = \"Natalya\", family = \"Pya\", role = c(\"aut\", \"cre\"), email = \"nat.pya@gmail.com\")",
+      "Maintainer": "Natalya Pya <nat.pya@gmail.com>",
+      "Title": "Shape Constrained Additive Models",
+      "Date": "2025-05-23",
+      "Description": "Generalized additive models under shape constraints on the component functions of the linear predictor.        Models can include multiple shape-constrained (univariate and bivariate) and unconstrained terms. Routines of the  package 'mgcv' are used to set up the model matrix, print,  and plot the results. Multiple smoothing parameter  estimation by the Generalized Cross Validation or similar. See Pya and Wood (2015) <doi:10.1007/s11222-013-9448-7>  for an overview. A broad selection of shape-constrained  smoothers, linear functionals of smooths with shape constraints, and Gaussian models with AR1 residuals.",
+      "Depends": [
+        "R (>= 3.6.0)"
+      ],
+      "Imports": [
+        "mgcv (>= 1.8-2)",
+        "methods",
+        "stats",
+        "graphics",
+        "Matrix",
+        "splines"
+      ],
+      "Suggests": [
+        "nlme"
+      ],
+      "License": "GPL (>= 2)",
+      "LazyLoad": "yes",
+      "NeedsCompilation": "yes",
+      "Author": "Natalya Pya [aut, cre]",
+      "Repository": "CRAN"
+    },
+    "snakecase": {
+      "Package": "snakecase",
+      "Version": "0.11.1",
+      "Source": "Repository",
+      "Date": "2023-08-27",
+      "Title": "Convert Strings into any Case",
+      "Description": "A consistent, flexible and easy to use tool to parse and convert strings into cases like snake or camel among others.",
+      "Authors@R": "c( person(\"Malte\", \"Grosser\", , \"malte.grosser@gmail.com\", role = c(\"aut\", \"cre\")))",
+      "Maintainer": "Malte Grosser <malte.grosser@gmail.com>",
+      "Depends": [
+        "R (>= 3.2)"
+      ],
+      "Imports": [
+        "stringr",
+        "stringi"
+      ],
+      "Suggests": [
+        "testthat",
+        "covr",
+        "tibble",
+        "purrrlyr",
+        "knitr",
+        "rmarkdown",
+        "magrittr"
+      ],
+      "URL": "https://github.com/Tazinho/snakecase",
+      "BugReports": "https://github.com/Tazinho/snakecase/issues",
+      "Encoding": "UTF-8",
+      "License": "GPL-3",
+      "RoxygenNote": "6.1.1",
+      "VignetteBuilder": "knitr",
+      "NeedsCompilation": "no",
+      "Author": "Malte Grosser [aut, cre]",
       "Repository": "RSPM"
     },
     "stringi": {
       "Package": "stringi",
-      "Version": "1.8.3",
+      "Version": "1.8.7",
       "Source": "Repository",
-      "Date": "2023-12-10",
+      "Date": "2025-03-27",
       "Title": "Fast and Portable Character String Processing Facilities",
       "Description": "A collection of character string/text/natural language processing tools for pattern searching (e.g., with 'Java'-like regular expressions or the 'Unicode' collation algorithm), random string generation, case mapping, string transliteration, concatenation, sorting, padding, wrapping, Unicode normalisation, date-time formatting and parsing, and many more. They are fast, consistent, convenient, and - thanks to 'ICU' (International Components for Unicode) - portable across all locales and platforms. Documentation about 'stringi' is provided via its website at <https://stringi.gagolewski.com/> and the paper by Gagolewski (2022, <doi:10.18637/jss.v103.i02>).",
       "URL": "https://stringi.gagolewski.com/, https://github.com/gagolews/stringi, https://icu.unicode.org/",
@@ -2072,11 +2205,12 @@
       ],
       "Biarch": "TRUE",
       "License": "file LICENSE",
-      "Author": "Marek Gagolewski [aut, cre, cph] (<https://orcid.org/0000-0003-0637-6028>), Bartek Tartanus [ctb], and others (stringi source code); Unicode, Inc. and others (ICU4C source code, Unicode Character Database)",
-      "Maintainer": "Marek Gagolewski <marek@gagolewski.com>",
-      "RoxygenNote": "7.2.3",
+      "Authors@R": "c(person(given = \"Marek\", family = \"Gagolewski\", role = c(\"aut\", \"cre\", \"cph\"), email = \"marek@gagolewski.com\", comment = c(ORCID = \"0000-0003-0637-6028\")), person(given = \"Bartek\", family = \"Tartanus\", role = \"ctb\"), person(\"Unicode, Inc. and others\", role=\"ctb\", comment = \"ICU4C source code, Unicode Character Database\") )",
+      "RoxygenNote": "7.3.2",
       "Encoding": "UTF-8",
       "NeedsCompilation": "yes",
+      "Author": "Marek Gagolewski [aut, cre, cph] (<https://orcid.org/0000-0003-0637-6028>), Bartek Tartanus [ctb], Unicode, Inc. and others [ctb] (ICU4C source code, Unicode Character Database)",
+      "Maintainer": "Marek Gagolewski <marek@gagolewski.com>",
       "License_is_FOSS": "yes",
       "Repository": "CRAN"
     },
@@ -2144,7 +2278,7 @@
     },
     "tibble": {
       "Package": "tibble",
-      "Version": "3.2.1",
+      "Version": "3.3.0",
       "Source": "Repository",
       "Title": "Simple Data Frames",
       "Authors@R": "c(person(given = \"Kirill\", family = \"M\\u00fcller\", role = c(\"aut\", \"cre\"), email = \"kirill@cynkra.com\", comment = c(ORCID = \"0000-0002-1416-3412\")), person(given = \"Hadley\", family = \"Wickham\", role = \"aut\", email = \"hadley@rstudio.com\"), person(given = \"Romain\", family = \"Francois\", role = \"ctb\", email = \"romain@r-enthusiasts.com\"), person(given = \"Jennifer\", family = \"Bryan\", role = \"ctb\", email = \"jenny@rstudio.com\"), person(given = \"RStudio\", role = c(\"cph\", \"fnd\")))",
@@ -2156,7 +2290,7 @@
         "R (>= 3.4.0)"
       ],
       "Imports": [
-        "fansi (>= 0.4.0)",
+        "cli",
         "lifecycle (>= 1.0.0)",
         "magrittr",
         "methods",
@@ -2164,7 +2298,7 @@
         "pkgconfig",
         "rlang (>= 1.0.2)",
         "utils",
-        "vctrs (>= 0.4.2)"
+        "vctrs (>= 0.5.0)"
       ],
       "Suggests": [
         "bench",
@@ -2172,9 +2306,6 @@
         "blob",
         "brio",
         "callr",
-        "cli",
-        "covr",
-        "crayon (>= 1.3.4)",
         "DiagrammeR",
         "dplyr",
         "evaluate",
@@ -2185,9 +2316,7 @@
         "htmltools",
         "knitr",
         "lubridate",
-        "mockr",
         "nycflights13",
-        "pkgbuild",
         "pkgload",
         "purrr",
         "rmarkdown",
@@ -2198,7 +2327,7 @@
       ],
       "VignetteBuilder": "knitr",
       "Encoding": "UTF-8",
-      "RoxygenNote": "7.2.3",
+      "RoxygenNote": "7.3.2.9000",
       "Config/testthat/edition": "3",
       "Config/testthat/parallel": "true",
       "Config/testthat/start-first": "vignette-formats, as_tibble, add, invariants",
@@ -2207,10 +2336,60 @@
       "Config/autostyle/rmd": "false",
       "Config/Needs/website": "tidyverse/tidytemplate",
       "NeedsCompilation": "yes",
-      "Author": "Kirill Müller [aut, cre] (<https://orcid.org/0000-0002-1416-3412>), Hadley Wickham [aut], Romain Francois [ctb], Jennifer Bryan [ctb], RStudio [cph, fnd]",
+      "Author": "Kirill Müller [aut, cre] (ORCID: <https://orcid.org/0000-0002-1416-3412>), Hadley Wickham [aut], Romain Francois [ctb], Jennifer Bryan [ctb], RStudio [cph, fnd]",
       "Maintainer": "Kirill Müller <kirill@cynkra.com>",
       "Repository": "CRAN"
     },
+    "tidyr": {
+      "Package": "tidyr",
+      "Version": "1.3.1",
+      "Source": "Repository",
+      "Title": "Tidy Messy Data",
+      "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = c(\"aut\", \"cre\")), person(\"Davis\", \"Vaughan\", , \"davis@posit.co\", role = \"aut\"), person(\"Maximilian\", \"Girlich\", role = \"aut\"), person(\"Kevin\", \"Ushey\", , \"kevin@posit.co\", role = \"ctb\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )",
+      "Description": "Tools to help to create tidy data, where each column is a variable, each row is an observation, and each cell contains a single value.  'tidyr' contains tools for changing the shape (pivoting) and hierarchy (nesting and 'unnesting') of a dataset, turning deeply nested lists into rectangular data frames ('rectangling'), and extracting values out of string columns. It also includes tools for working with missing values (both implicit and explicit).",
+      "License": "MIT + file LICENSE",
+      "URL": "https://tidyr.tidyverse.org, https://github.com/tidyverse/tidyr",
+      "BugReports": "https://github.com/tidyverse/tidyr/issues",
+      "Depends": [
+        "R (>= 3.6)"
+      ],
+      "Imports": [
+        "cli (>= 3.4.1)",
+        "dplyr (>= 1.0.10)",
+        "glue",
+        "lifecycle (>= 1.0.3)",
+        "magrittr",
+        "purrr (>= 1.0.1)",
+        "rlang (>= 1.1.1)",
+        "stringr (>= 1.5.0)",
+        "tibble (>= 2.1.1)",
+        "tidyselect (>= 1.2.0)",
+        "utils",
+        "vctrs (>= 0.5.2)"
+      ],
+      "Suggests": [
+        "covr",
+        "data.table",
+        "knitr",
+        "readr",
+        "repurrrsive (>= 1.1.0)",
+        "rmarkdown",
+        "testthat (>= 3.0.0)"
+      ],
+      "LinkingTo": [
+        "cpp11 (>= 0.4.0)"
+      ],
+      "VignetteBuilder": "knitr",
+      "Config/Needs/website": "tidyverse/tidytemplate",
+      "Config/testthat/edition": "3",
+      "Encoding": "UTF-8",
+      "LazyData": "true",
+      "RoxygenNote": "7.3.0",
+      "NeedsCompilation": "yes",
+      "Author": "Hadley Wickham [aut, cre], Davis Vaughan [aut], Maximilian Girlich [aut], Kevin Ushey [ctb], Posit Software, PBC [cph, fnd]",
+      "Maintainer": "Hadley Wickham <hadley@posit.co>",
+      "Repository": "RSPM"
+    },
     "tidyselect": {
       "Package": "tidyselect",
       "Version": "1.2.1",
@@ -2254,16 +2433,44 @@
       "Maintainer": "Lionel Henry <lionel@posit.co>",
       "Repository": "CRAN"
     },
+    "timechange": {
+      "Package": "timechange",
+      "Version": "0.3.0",
+      "Source": "Repository",
+      "Title": "Efficient Manipulation of Date-Times",
+      "Authors@R": "c(person(\"Vitalie\", \"Spinu\", email = \"spinuvit@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Google Inc.\", role = c(\"ctb\", \"cph\")))",
+      "Description": "Efficient routines for manipulation of date-time objects while accounting for time-zones and daylight saving times. The package includes utilities for updating of date-time components (year, month, day etc.), modification of time-zones, rounding of date-times, period addition and subtraction etc. Parts of the 'CCTZ' source code, released under the Apache 2.0 License, are included in this package. See <https://github.com/google/cctz> for more details.",
+      "Depends": [
+        "R (>= 3.3)"
+      ],
+      "License": "GPL (>= 3)",
+      "Encoding": "UTF-8",
+      "LinkingTo": [
+        "cpp11 (>= 0.2.7)"
+      ],
+      "Suggests": [
+        "testthat (>= 0.7.1.99)",
+        "knitr"
+      ],
+      "SystemRequirements": "A system with zoneinfo data (e.g. /usr/share/zoneinfo) as well as a recent-enough C++11 compiler (such as g++-4.8 or later). On Windows the zoneinfo included with R is used.",
+      "BugReports": "https://github.com/vspinu/timechange/issues",
+      "URL": "https://github.com/vspinu/timechange/",
+      "RoxygenNote": "7.2.1",
+      "NeedsCompilation": "yes",
+      "Author": "Vitalie Spinu [aut, cre], Google Inc. [ctb, cph]",
+      "Maintainer": "Vitalie Spinu <spinuvit@gmail.com>",
+      "Repository": "RSPM"
+    },
     "utf8": {
       "Package": "utf8",
-      "Version": "1.2.4",
+      "Version": "1.2.6",
       "Source": "Repository",
       "Title": "Unicode Text Processing",
-      "Authors@R": "c(person(given = c(\"Patrick\", \"O.\"), family = \"Perry\", role = c(\"aut\", \"cph\")), person(given = \"Kirill\", family = \"M\\u00fcller\", role = \"cre\", email = \"kirill@cynkra.com\"), person(given = \"Unicode, Inc.\", role = c(\"cph\", \"dtc\"), comment = \"Unicode Character Database\"))",
+      "Authors@R": "c(person(given = c(\"Patrick\", \"O.\"), family = \"Perry\", role = c(\"aut\", \"cph\")), person(given = \"Kirill\", family = \"M\\u00fcller\", role = \"cre\", email = \"kirill@cynkra.com\", comment = c(ORCID = \"0000-0002-1416-3412\")), person(given = \"Unicode, Inc.\", role = c(\"cph\", \"dtc\"), comment = \"Unicode Character Database\"))",
       "Description": "Process and print 'UTF-8' encoded international text (Unicode). Input, validate, normalize, encode, format, and display.",
       "License": "Apache License (== 2.0) | file LICENSE",
-      "URL": "https://ptrckprry.com/r-utf8/, https://github.com/patperry/r-utf8",
-      "BugReports": "https://github.com/patperry/r-utf8/issues",
+      "URL": "https://krlmlr.github.io/utf8/, https://github.com/krlmlr/utf8",
+      "BugReports": "https://github.com/krlmlr/utf8/issues",
       "Depends": [
         "R (>= 2.10)"
       ],
@@ -2279,9 +2486,9 @@
       "VignetteBuilder": "knitr, rmarkdown",
       "Config/testthat/edition": "3",
       "Encoding": "UTF-8",
-      "RoxygenNote": "7.2.3",
+      "RoxygenNote": "7.3.2.9000",
       "NeedsCompilation": "yes",
-      "Author": "Patrick O. Perry [aut, cph], Kirill Müller [cre], Unicode, Inc. [cph, dtc] (Unicode Character Database)",
+      "Author": "Patrick O. Perry [aut, cph], Kirill Müller [cre] (ORCID: <https://orcid.org/0000-0002-1416-3412>), Unicode, Inc. [cph, dtc] (Unicode Character Database)",
       "Maintainer": "Kirill Müller <kirill@cynkra.com>",
       "Repository": "CRAN"
     },
@@ -2362,16 +2569,16 @@
     },
     "withr": {
       "Package": "withr",
-      "Version": "3.0.0",
+      "Version": "3.0.2",
       "Source": "Repository",
       "Title": "Run Code 'With' Temporarily Modified Global State",
-      "Authors@R": "c(person(given = \"Jim\", family = \"Hester\", role = \"aut\"), person(given = \"Lionel\", family = \"Henry\", role = c(\"aut\", \"cre\"), email = \"lionel@posit.co\"), person(given = \"Kirill\", family = \"Müller\", role = \"aut\", email = \"krlmlr+r@mailbox.org\"), person(given = \"Kevin\", family = \"Ushey\", role = \"aut\", email = \"kevinushey@gmail.com\"), person(given = \"Hadley\", family = \"Wickham\", role = \"aut\", email = \"hadley@posit.co\"), person(given = \"Winston\", family = \"Chang\", role = \"aut\"), person(given = \"Jennifer\", family = \"Bryan\", role = \"ctb\"), person(given = \"Richard\", family = \"Cotton\", role = \"ctb\"), person(given = \"Posit Software, PBC\", role = c(\"cph\", \"fnd\")))",
+      "Authors@R": "c( person(\"Jim\", \"Hester\", role = \"aut\"), person(\"Lionel\", \"Henry\", , \"lionel@posit.co\", role = c(\"aut\", \"cre\")), person(\"Kirill\", \"Müller\", , \"krlmlr+r@mailbox.org\", role = \"aut\"), person(\"Kevin\", \"Ushey\", , \"kevinushey@gmail.com\", role = \"aut\"), person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = \"aut\"), person(\"Winston\", \"Chang\", role = \"aut\"), person(\"Jennifer\", \"Bryan\", role = \"ctb\"), person(\"Richard\", \"Cotton\", role = \"ctb\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )",
       "Description": "A set of functions to run code 'with' safely and temporarily modified global state. Many of these functions were originally a part of the 'devtools' package, this provides a simple package with limited dependencies to provide access to these functions.",
       "License": "MIT + file LICENSE",
       "URL": "https://withr.r-lib.org, https://github.com/r-lib/withr#readme",
       "BugReports": "https://github.com/r-lib/withr/issues",
       "Depends": [
-        "R (>= 3.5.0)"
+        "R (>= 3.6.0)"
       ],
       "Imports": [
         "graphics",
@@ -2379,10 +2586,8 @@
       ],
       "Suggests": [
         "callr",
-        "covr",
         "DBI",
         "knitr",
-        "lattice",
         "methods",
         "rlang",
         "rmarkdown (>= 2.12)",
@@ -2390,11 +2595,11 @@
         "testthat (>= 3.0.0)"
       ],
       "VignetteBuilder": "knitr",
-      "Encoding": "UTF-8",
-      "RoxygenNote": "7.3.0.9000",
-      "Collate": "'aaa.R' 'collate.R' 'connection.R' 'db.R' 'defer-exit.R' 'standalone-defer.R' 'defer.R' 'wrap.R' 'local_.R' 'with_.R' 'devices.R' 'dir.R' 'env.R' 'file.R' 'language.R' 'libpaths.R' 'locale.R' 'makevars.R' 'namespace.R' 'options.R' 'par.R' 'path.R' 'rng.R' 'seed.R' 'sink.R' 'tempfile.R' 'timezone.R' 'torture.R' 'utils.R' 'with.R'",
-      "Config/testthat/edition": "3",
       "Config/Needs/website": "tidyverse/tidytemplate",
+      "Config/testthat/edition": "3",
+      "Encoding": "UTF-8",
+      "RoxygenNote": "7.3.2",
+      "Collate": "'aaa.R' 'collate.R' 'connection.R' 'db.R' 'defer-exit.R' 'standalone-defer.R' 'defer.R' 'devices.R' 'local_.R' 'with_.R' 'dir.R' 'env.R' 'file.R' 'language.R' 'libpaths.R' 'locale.R' 'makevars.R' 'namespace.R' 'options.R' 'par.R' 'path.R' 'rng.R' 'seed.R' 'wrap.R' 'sink.R' 'tempfile.R' 'timezone.R' 'torture.R' 'utils.R' 'with.R'",
       "NeedsCompilation": "no",
       "Author": "Jim Hester [aut], Lionel Henry [aut, cre], Kirill Müller [aut], Kevin Ushey [aut], Hadley Wickham [aut], Winston Chang [aut], Jennifer Bryan [ctb], Richard Cotton [ctb], Posit Software, PBC [cph, fnd]",
       "Maintainer": "Lionel Henry <lionel@posit.co>",
diff --git a/tests/testthat/test-mseir-model.R b/tests/testthat/test-mseir-model.R
deleted file mode 100644
index 6b4ea40..0000000
--- a/tests/testthat/test-mseir-model.R
+++ /dev/null
@@ -1,22 +0,0 @@
-test_that("mseir_model returns expected results", {
-  expected = list(
-    a=4.000000e+01,
-    m=1.804851e-35,
-    s=3.727363e-04,
-    e=2.881610e-06,
-    i=2.069520e-06,
-    r=9.996223e-01)
-
-  # last.age,     d, lambda, sigma, ni
-  #       40, 1/0.5,    0.2, 26.07, 36.5
-  output <- mseir_model(
-    a=seq(from=1,to=40,length=500), # age range from 0 -> 40 yo
-    gamma=1/0.5, # 6 months in the maternal antibodies
-    lambda=0.2,  # 5 years in the susceptible class
-    sigma=26.07, # 14 days in the latent class
-    nu=36.5      # 10 days in the infected class
-  )$output
-  actual <- as.list(tail(output, 1))
-
-  expect_equal(actual, expected, tolerance = 0.000001)
-})
diff --git a/tests/testthat/test-polynomial-models.R b/tests/testthat/test-polynomial-models.R
index df4cade..f808071 100644
--- a/tests/testthat/test-polynomial-models.R
+++ b/tests/testthat/test-polynomial-models.R
@@ -40,29 +40,28 @@ test_that("farrington_model works with linelisting data", {
 test_that("polynomial_model works with line listing data", {
 
   data <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age),]
-  data$status <- data$seropositive
-  expect_no_error(polynomial_model(data, type = "Muench"))
-
+  expect_no_error(polynomial_model(data, k=1,
+                                   status_col = "seropositive"))
 
 })
 
-test_that("polynomial_model returns same result as in the book (Muench)", {
-  expected <- c(-0.0505004)
-
-  model <- polynomial_model(
-    hav_bg_1964,
-    type= "Muench"
-  )
-  actual <- unname(c(
-    coef(model$info)[1]
-  ))
-
-  expect_equal(actual, expected, tolerance=0.000001)
-
-  # make sure utilities work
-  expect_no_error(compute_ci(model))
-  expect_no_error(plot(model))
-})
+# test_that("polynomial_model returns same result as in the book (Muench)", {
+#   expected <- c(-0.0505004)
+#
+#   model <- polynomial_model(
+#     hav_bg_1964,
+#     type= "Muench"
+#   )
+#   actual <- unname(c(
+#     coef(model$info)[1]
+#   ))
+#
+#   expect_equal(actual, expected, tolerance=0.000001)
+#
+#   # make sure utilities work
+#   expect_no_error(compute_ci(model))
+#   expect_no_error(plot(model))
+# })
 
 test_that("polynomial_model returns same result as in the book (Muench)", {
   expected <- c(-0.0505004)
@@ -78,20 +77,20 @@ test_that("polynomial_model returns same result as in the book (Muench)", {
   expect_equal(actual, expected, tolerance=0.000001)
 })
 
-test_that("polynomial_model returns same result as in the book (Griffiths)", {
-  expected <- c(-0.0442615740, -0.0001888796)
-
-  model <- polynomial_model(
-    hav_bg_1964,
-    type = "Griffith"
-  )
-  actual <- unname(c(
-    coef(model$info)[1],
-    coef(model$info)[2]
-  ))
-
-  expect_equal(actual, expected, tolerance=0.000001)
-})
+# test_that("polynomial_model returns same result as in the book (Griffiths)", {
+#   expected <- c(-0.0442615740, -0.0001888796)
+#
+#   model <- polynomial_model(
+#     hav_bg_1964,
+#     type = "Griffith"
+#   )
+#   actual <- unname(c(
+#     coef(model$info)[1],
+#     coef(model$info)[2]
+#   ))
+#
+#   expect_equal(actual, expected, tolerance=0.000001)
+# })
 
 
 
@@ -110,21 +109,21 @@ test_that("polynomial_model returns same result as in the book (Griffiths)", {
   expect_equal(actual, expected, tolerance=0.000001)
 })
 
-test_that("polynomial_model returns same result as in the book (Grenfell & Anderson)", {
-  expected <- c(-5.325918e-02, 5.065095e-04, -1.018736e-05)
-
-  model <- polynomial_model(
-    hav_bg_1964,
-    type = "Grenfell"
-  )
-  actual <- unname(c(
-    coef(model$info)[1],
-    coef(model$info)[2],
-    coef(model$info)[3]
-  ))
-
-  expect_equal(actual, expected, tolerance=0.000001)
-})
+# test_that("polynomial_model returns same result as in the book (Grenfell & Anderson)", {
+#   expected <- c(-5.325918e-02, 5.065095e-04, -1.018736e-05)
+#
+#   model <- polynomial_model(
+#     hav_bg_1964,
+#     type = "Grenfell"
+#   )
+#   actual <- unname(c(
+#     coef(model$info)[1],
+#     coef(model$info)[2],
+#     coef(model$info)[3]
+#   ))
+#
+#   expect_equal(actual, expected, tolerance=0.000001)
+# })
 
 test_that("polynomial_model returns same result as in the book (Grenfell & Anderson)", {
   expected <- c(-5.325918e-02, 5.065095e-04, -1.018736e-05)
diff --git a/tests/testthat/test-sir-basic-model.R b/tests/testthat/test-sir-basic-model.R
deleted file mode 100644
index 68f5bf8..0000000
--- a/tests/testthat/test-sir-basic-model.R
+++ /dev/null
@@ -1,23 +0,0 @@
-library(testthat)
-
-test_that("sir_basic_model returns expected results", {
-  S_equilibrium = 2026.67
-  I_equilibrium =   39.12
-  state <- c(S=4999, I=1, R=0)
-  parameters <- c(
-    mu=1/75,
-    alpha=0,
-    beta=0.0005,
-    nu=1,
-    p=0
-  )
-  times <- seq(0, 1000, by=0.1)
-
-  output <- sir_basic_model(times, state, parameters)$output
-
-  expect_equal(round(tail(output, 1)$S, 2), S_equilibrium)
-  expect_equal(round(tail(output, 1)$I, 2), I_equilibrium)
-
-  # make sure utilities work
-  expect_no_error(plot(output))
-})
diff --git a/tests/testthat/test-sir-static-model.R b/tests/testthat/test-sir-static-model.R
deleted file mode 100644
index 6c8b8f4..0000000
--- a/tests/testthat/test-sir-static-model.R
+++ /dev/null
@@ -1,20 +0,0 @@
-library(testthat)
-
-test_that("sir_static_model returns expected results", {
-  expected = list(
-    time=40,
-    s=0.01829732,
-    i=5.026737e-05,
-    r=0.9816524)
-  state <- c(s=0.999,i=0.001,r=0)
-  parameters <- c(lambda = 0.1, nu=36.5)
-  times <- seq(0, 40, by=0.01)
-
-  output <- sir_static_model(times, state, parameters)$output
-  actual <- as.list(tail(output, 1))
-
-  expect_equal(actual, expected, tolerance = 0.000001)
-
-  # make sure utilities work
-  expect_no_error(plot(output))
-})
diff --git a/tests/testthat/test-sir-subpops-model.R b/tests/testthat/test-sir-subpops-model.R
deleted file mode 100644
index 16297a2..0000000
--- a/tests/testthat/test-sir-subpops-model.R
+++ /dev/null
@@ -1,36 +0,0 @@
-test_that("sir_subpops_model returns expected results", {
-  expected <- list(
-    time=10000,
-    s1=0.6869925,
-    s2=0.6869925,
-    i1=0.009141676,
-    i2=0.009141676,
-    r1=0.3038659,
-    r2=0.3038659
-  )
-
-  k <- 2
-  state <- c(
-    s = c(0.8, 0.8),
-    i = c(0.2, 0.2),
-    r = c(  0,   0)
-  )
-  beta_matrix <- c(
-    c(0.05, 0.00),
-    c(0.00, 0.05)
-  )
-  parameters <- list(
-    beta = matrix(beta_matrix, nrow=k, ncol=k, byrow=TRUE),
-    nu = c(1/30, 1/30),
-    mu = 0.001,
-    k = k
-  )
-  times<-seq(0,10000,by=10)
-
-  output <- sir_subpops_model(times, state, parameters)$output
-  actual <- as.list(tail(output, 1))
-
-  expect_equal(actual, expected, tolerance = 0.000001)
-  # make sure utilities work
-  expect_no_error(plot(output))
-})
diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R
index 2a31b38..138de6d 100644
--- a/tests/testthat/test-utils.R
+++ b/tests/testthat/test-utils.R
@@ -1,22 +1,28 @@
 library(testthat)
 
 test_that("transform_data returns a data frame", {
-  df <- transform_data(c(1, 2, 3), c(1, 0, 1))
+  df <- transform_data(data.frame(
+    age=c(1, 2, 3),
+    status=c(1, 0, 1)
+  ))
   expect_type(df, "list")
 })
 
 test_that("transform_data returns the correct number of rows", {
-  df <- transform_data(c(1, 2, 3), c(1, 0, 1))
+  df <- transform_data(data.frame(
+    age=c(1, 2, 3),
+    status=c(1, 0, 1)
+  ))
   expect_equal(nrow(df), 3)
 })
 
-test_that("transform_data returns the correct column names", {
-  df <- transform_data(c(1, 2, 3), c(1, 0, 1))
-  expect_equal(colnames(df), c("t", "pos", "tot"))
-})
-
 test_that("transform_data returns the correct values", {
-  df <- transform_data(c(1, 1, 2, 2, 3), c(1, 0, 1, 1, 0))
+  df <- transform_data(
+    data.frame(
+      age=c(1, 1, 2, 2, 3),
+      status=c(1, 0, 1, 1, 0)
+    )
+  )
   expect_equal(df$pos, c(1, 2, 0))
   expect_equal(df$tot, c(2, 2, 1))
 })
diff --git a/vignettes/data_transformation.Rmd b/vignettes/data_transformation.Rmd
index f0fb5f1..8fae131 100644
--- a/vignettes/data_transformation.Rmd
+++ b/vignettes/data_transformation.Rmd
@@ -32,6 +32,6 @@ Arguments:
 
 ```{r}
 linelisting_data <- hcv_be_2006
-aggregated_data <- transform_data(linelisting_data$dur, linelisting_data$seropositive)
+aggregated_data <- transform_data(linelisting_data, stratum_col = "dur", status_col="seropositive")
 aggregated_data
 ```
diff --git a/vignettes/hierarchical_model.Rmd b/vignettes/hierarchical_model.Rmd
deleted file mode 100644
index 1bad388..0000000
--- a/vignettes/hierarchical_model.Rmd
+++ /dev/null
@@ -1,135 +0,0 @@
----
-title: "Hierarchical Bayesian models"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Hierarchical Bayesian models}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r, include = FALSE}
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>"
-)
-```
-
-```{r setup, output=FALSE}
-library(serosv)
-```
-
-## Parametric Bayesian framework
-
-Currently, `serosv` only has models under parametric Bayesian framework
-
-**Proposed approach**
-
-Prevalence has a parametric form $\pi(a_i, \alpha)$ where $\alpha$ is a parameter vector
-
-One can constraint the parameter space of the prior distribution $P(\alpha)$ in order to achieve the desired monotonicity of the posterior distribution $P(\pi_1, \pi_2, ..., \pi_m|y,n)$
-
-Where:
-
--   $n = (n_1, n_2, ..., n_m)$ and $n_i$ is the sample size at age $a_i$
-
-
--   $y = (y_1, y_2, ..., y_m)$ and $y_i$ is the number of infected individual from the $n_i$ sampled subjects
-
-### Farrington
-
-Refer to `Chapter 10.3.1`
-
-**Proposed model**
-
-The model for prevalence is as followed
-
-$$
-\pi (a) = 1 - exp\{ \frac{\alpha_1}{\alpha_2}ae^{-\alpha_2 a} + \frac{1}{\alpha_2}(\frac{\alpha_1}{\alpha_2} - \alpha_3)(e^{-\alpha_2 a} - 1) -\alpha_3 a \}
-$$
-
-For likelihood model, independent binomial distribution are assumed for the number of infected individuals at age $a_i$
-
-$$
-y_i \sim Bin(n_i, \pi_i), \text{  for } i = 1,2,3,...m
-$$
-
-The constraint on the parameter space can be incorporated by assuming truncated normal distribution for the components of $\alpha$, $\alpha = (\alpha_1, \alpha_2, \alpha_3)$ in $\pi_i = \pi(a_i,\alpha)$
-
-$$
-\alpha_j \sim \text{truncated  } \mathcal{N}(\mu_j, \tau_j), \text{ } j = 1,2,3
-$$
-
-The joint posterior distribution for $\alpha$ can be derived by combining the likelihood and prior as followed
-
-$$
-P(\alpha|y) \propto \prod^m_{i=1} \text{Bin}(y_i|n_i, \pi(a_i, \alpha)) \prod^3_{i=1}-\frac{1}{\tau_j}\text{exp}(\frac{1}{2\tau^2_j} (\alpha_j - \mu_j)^2)
-$$
-
--   Where the flat hyperprior distribution is defined as followed:
-
-    -   $\mu_j \sim \mathcal{N}(0, 10000)$
-
-    -   $\tau^{-2}_j \sim \Gamma(100,100)$
-
-The full conditional distribution of $\alpha_i$ is thus $$
-P(\alpha_i|\alpha_j,\alpha_k, k, j \neq i) \propto  -\frac{1}{\tau_i}\text{exp}(\frac{1}{2\tau^2_i} (\alpha_i - \mu_i)^2) \prod^m_{i=1} \text{Bin}(y_i|n_i, \pi(a_i, \alpha))
-$$
-
-**Fitting data**
-
-To fit Farrington model, use `hierarchical_bayesian_model()` and define `type = "far2"` or `type = "far3"` where
-
--   `type = "far2"` refers to Farrington model with 2 parameters ($\alpha_3 = 0$)
-
--   `type = "far3"` refers to Farrington model with 3 parameters ($\alpha_3 > 0$)
-
-```{r}
-df <- mumps_uk_1986_1987
-model <- hierarchical_bayesian_model(df, type="far3")
-
-model$info
-plot(model)
-```
-
-### Log-logistic
-
-**Proposed approach**
-
-The model for seroprevalence is as followed
-
-$$
-\pi(a) = \frac{\beta a^\alpha}{1 + \beta a^\alpha}, \text{ } \alpha, \beta > 0
-$$
-
-The likelihood is specified to be the same as Farrington model ($y_i \sim Bin(n_i, \pi_i)$) with
-
-$$
-\text{logit}(\pi(a)) = \alpha_2 + \alpha_1\log(a)
-$$
-
--   Where $\alpha_2 = \text{log}(\beta)$
-
-The prior model of $\alpha_1$ is specified as $\alpha_1 \sim \text{truncated  } \mathcal{N}(\mu_1, \tau_1)$ with flat hyperprior as in Farrington model
-
-$\beta$ is constrained to be positive by specifying $\alpha_2 \sim \mathcal{N}(\mu_2, \tau_2)$
-
-The full conditional distribution of $\alpha_1$ is thus
-
-$$
-P(\alpha_1|\alpha_2) \propto -\frac{1}{\tau_1} \text{exp} (\frac{1}{2 \tau_1^2} (\alpha_1 - \mu_1)^2)
-\prod_{i=1}^m \text{Bin}(y_i|n_i,\pi(a_i, \alpha_1, \alpha_2) )
-$$
-
-And $\alpha_2$ can be derived in the same way
-
-**Fitting data**
-
-To fit Log-logistic model, use `hierarchical_bayesian_model()` and define `type = "log_logistic"`
-
-```{r}
-df <- rubella_uk_1986_1987
-model <- hierarchical_bayesian_model(df, type="log_logistic")
-
-model$type
-plot(model)
-```
diff --git a/vignettes/imperfect_test.Rmd b/vignettes/imperfect_test.Rmd
index 86703ba..142d657 100644
--- a/vignettes/imperfect_test.Rmd
+++ b/vignettes/imperfect_test.Rmd
@@ -16,7 +16,6 @@ knitr::opts_chunk$set(
 
 ```{r setup, output=FALSE}
 library(serosv)
-library(ggplot2)
 ```
 
 ## Imperfect test
@@ -27,9 +26,11 @@ Arguments:
 
 -   `data` the input data frame, must either have:
 
-    -   `age`, `pos`, `tot` columns (for aggregated data)
+    -   age, pos, tot columns (for aggregated data)
 
-    -   **OR** `age`, `status` columns for (linelisting data)
+    -   **OR** age, status columns for (linelisting data)
+
+    -   Users can specifiy the name for these columns in the input data frame using arguments `age_col`, `pos_col`, `tot_col`or `age_col`, `status_col` respectively
 
 -   `bayesian` whether to adjust sero-prevalence using the Bayesian or frequentist approach. If set to `TRUE`, true sero-prevalence is estimated using MCMC.
 
@@ -72,7 +73,8 @@ freq_output <- correct_prevalence(data, bayesian = FALSE, init_se=0.9, init_sp =
 freq_output$info
 ```
 
-User can then visualize the output using `plot_corrected_prev()` function 
+User can then visualize the output using `plot_corrected_prev()` function
+
 ```{r}
 # Plot output of the frequentist approach
 plot_corrected_prev(freq_output)
@@ -80,7 +82,8 @@ plot_corrected_prev(freq_output)
 # Plot output of the bayesian approach 
 plot_corrected_prev(output)
 ```
-To compare both correction methods in a single plot, provide the output from the second method as the optional `y` argument in `plot_corrected_prev()`  
+
+To compare both correction methods in a single plot, provide the output from the second method as the optional `y` argument in `plot_corrected_prev()`
 
 ```{r}
 plot_corrected_prev(output, freq_output)
@@ -89,7 +92,6 @@ plot_corrected_prev(output, freq_output)
 plot_corrected_prev(output, freq_output, facet = TRUE)
 ```
 
-
 ### Fitting corrected data
 
 **Data after seroprevalence correction**
diff --git a/vignettes/linelisting_vs_aggregated.Rmd b/vignettes/linelisting_vs_aggregated.Rmd
index 21ef123..f19761c 100644
--- a/vignettes/linelisting_vs_aggregated.Rmd
+++ b/vignettes/linelisting_vs_aggregated.Rmd
@@ -16,29 +16,28 @@ knitr::opts_chunk$set(
 
 ```{r setup}
 library(serosv)
-library(dplyr)
 library(magrittr)
 ```
 
 ## Input data format
 
-Most `*_model()` functions in `serosv` require `data` argument as the input data to be fitted.
+All `*_model()` functions in `serosv` require `data` argument as the input data to be fitted.
 
-The package can handle both linelisting and aggregated data, and it infers the format from the column names of the input data frame. This means that input data is [expected to follow a specific format.]{.underline}
+The package can handle both linelisting and aggregated data, and it infers the format from the structure of the input data frame. This means that input data is [expected to follow a specific format.]{.underline}
 
-For linelisting data: data must have `age`, `pos` and `tot` columns, where
+For linelisting data: data must have 3 columns as follows
 
--   `age` is the age vector
+-   the age vector (column name specified via `age_col` parameter)
 
--   `pos` is the vector of counts of sero positives of that age group
+-   the vector of counts of sero positives of that age group (column name specified via `pos_col` parameter)
 
--   `tot` is the vector is the total population of that age group
+-   the vector is the total population of that age group (column name specified via `tot_col` parameter)
 
-For aggregated data: data must have `age`, `status` columns, where
+For aggregated data: data must have 2 columns as follows
 
--   `age` is the age vector of individuals
+-   the age vector of individuals 
 
--   `status` is the vector for the sero positivity of that individual
+-   is the vector indicating the serostatus of that individual (column name specified via `status_col` parameter)
 
 **Example:** Fitting linelisting and aggregated data using `polynomial_model()`
 
@@ -51,12 +50,10 @@ head(linelisting)
 head(aggregated)
 
 # fit with aggregated data
-model1 <- polynomial_model(aggregated, type = "Muench")
+model1 <- polynomial_model(aggregated, k=1)
 plot(model1)
 # fit with linelisting data
-model2 <- linelisting %>% 
-  rename(status = seropositive) %>% 
-  polynomial_model(type = "Muench")
+model2 <- polynomial_model(linelisting, k=1, status_col = "seropositive")
 plot(model2)
 ```
 
@@ -66,9 +63,9 @@ plot(model2)
 
 ```{r}
 transform_data(
-  linelisting$age, 
-  linelisting$seropositive,
-  stratum_col = "age") %>% 
-  polynomial_model(type = "Muench") %>% 
+  linelisting,
+  stratum_col="age", 
+  status_col="seropositive") %>% 
+  polynomial_model(k=1) %>% 
   plot()
 ```
diff --git a/vignettes/model_quantitative_data.Rmd b/vignettes/model_quantitative_data.Rmd
index 9064b29..7062817 100644
--- a/vignettes/model_quantitative_data.Rmd
+++ b/vignettes/model_quantitative_data.Rmd
@@ -1,6 +1,7 @@
 ---
 title: "Modeling directly from antibody levels"
 output: rmarkdown::html_vignette
+bibliography: references.bib
 vignette: >
   %\VignetteIndexEntry{Modeling directly from antibody levels}
   %\VignetteEngine{knitr::rmarkdown}
@@ -22,13 +23,17 @@ library(serosv)
 
 **Proposed model**
 
-Two-component mixture model for test result $Z$ with $Z_j (j = \{I, S\})$ being the latent mixing component having density $f_j(z_j|\theta_j)$ and with $\pi_{\text{TRUE}}(a)$ being the age-dependent mixing probability can be represented as
+Consider a two-component Gaussian mixture model for the antibody level $Z$, where each component $Z_j$ represent antibody level arising from the 2 latent sub-populations $j \in \{I, S\}$ (i.e., Infected and Susceptible groups).
+
+Let $f_j(z_j|\theta_j)$ denotes the density of component $Z_j$, where $\theta_I$ and $\theta_S$ are the parameters for the Susceptible and Infected components respectively.
+
+With $\pi_{\text{TRUE}}(a)$ being the age-dependent mixing probability (i.e., the true prevalence), the density of the mixture is formulated as
 
 $$
 f(z|z_I, z_S,a) = (1-\pi_{\text{TRUE}}(a))f_S(z_S|\theta_S)+\pi_{\text{TRUE}}(a)f_I(z_I|\theta_I)
 $$
 
-The mean $E(Z|a)$ thus equals
+The age-specific mean antibody level $E(Z|a)$ thus equals
 
 $$
 \mu(a) = (1-\pi_{\text{TRUE}}(a))\mu_S+\pi_{\text{TRUE}}(a)\mu_I$$
@@ -39,22 +44,30 @@ $$
 \pi_{\text{TRUE}}(a) = \frac{\mu(a) - \mu_S}{\mu_I - \mu_S}
 $$
 
-Force of infection can then be calculated by
+Force of infection can then be inferred by
 
 $$
 \lambda_{TRUE} = \frac{\mu'(a)}{\mu_I - \mu(a)}
 $$
 
+Refer to Chapter `11.3` of the book by @Hens2012 for a more detailed explanation of the method.
+
 **Fitting data**
 
-To fit the mixture data, use `mixture_model` function
+General workflow:
+
+-   Step 1: Fit the antibody level data to a 2-component mixture model
+
+-   Step 2: From the fitted mixture model, estimate the seroprevalence and FOI
+
+To fit the antibody data, use `mixture_model` function
 
 ```{r}
 df <- vzv_be_2001_2003[vzv_be_2001_2003$age < 40.5,]
 df <- df[order(df$age),]
 data <- df$VZVmIUml
 model <- mixture_model(antibody_level = data)
-model$info
+print(model)
 ```
 
 ```{r}
@@ -65,5 +78,6 @@ sero-prevalence and FOI can then be esimated using function `estimate_from_mixtu
 
 ```{r}
 est_mixture <- estimate_from_mixture(df$age, data, mixture_model = model, threshold_status = df$seropositive, sp=83, monotonize = FALSE)
+est_mixture
 plot(est_mixture)
 ```
diff --git a/vignettes/model_selection.Rmd b/vignettes/model_selection.Rmd
index dce13ad..4345805 100644
--- a/vignettes/model_selection.Rmd
+++ b/vignettes/model_selection.Rmd
@@ -7,7 +7,7 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r setup, output=FALSE}
+```{r setup, output=FALSE, warning=FALSE, message=FALSE}
 library(serosv)
 library(dplyr)
 library(magrittr)
@@ -15,45 +15,150 @@ library(magrittr)
 
 ```{r, warning=FALSE}
 data <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age), ] %>% 
-  rename(status = seropositive)
-  
-aggregated <- transform_data(data$age, data$status, stratum_col = "age")
-
-# fit with linelisting data
-model1 <- polynomial_model(data, type = "Muench")
-# fit with aggregated data
-model2 <- polynomial_model(aggregated, type = "Muench")
-# fit with aggregated data
-model3 <- polynomial_model(aggregated, type = "Griffith")
-# fit with gam
-model4 <- penalized_spline_model(aggregated)
+  rename(status = seropositive) 
+
+aggregated <- transform_data(data, stratum_col = "age", status_col="status")
 ```
 
 ## Generate models comparison `data.frame`
 
-Function `compare_models()` is used for quickly computing AIC and BIC values for given model(s).
+Function `compare_models()` is used for quickly computing comparison metrics for a set of models on a given dataset.
+
+The function takes the following arguments:
+
+-   `data` the dataset that will be fitted to the models
 
-The function can take an arbitrary number of models and all models must be created from `serosv`'s set of `*_model()` functions. It will then return a `data.frame` of 4 columns:
+-   `method` the method to generate comparison metrics. It can be the name of one of the built-in methods, or a user-defined function.
 
--   `model` model identifier. Either user defined name or index based on the order provided.
+-   `…` functions that take a data and return a fitted `serosv` model.
+
+It will then return a `data.frame` with the following columns
+
+-   `label` model identifier. Either user defined name or index based on the order provided.
 
 -   `type` type of model (a `serosv` model class)
 
--   `AIC` AIC value for the fitted model (if applicable)
+-   metrics depending on the method selected
+
+**Built-in `method`**
+
+`serosv` currently provide 2 built-in metrics generating methods
 
--   `BIC` AIC value for the fitted model (if applicable)
+-   `"AIC/BIC"` which returns fitted model's AIC, BIC and Log-likelihood (where applicable)
+
+-   `"CV"` which split the data into "train" and "test" set then return logloss and AUC from model's prediction on the test set
 
 **Sample usage**
 
-Compare 4 models defined above
+```{r message=FALSE}
+# ----- Return AIC, BIC ----- 
+aic_bic_out <- compare_models(
+  data = data,
+  method = "AIC/BIC",
+  griffith = ~polynomial_model(.x, k=2),
+  penalized_spline = penalized_spline_model,
+  farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
+  local_polynomial = lp_model # expect to not return any values
+  ) %>% suppressWarnings()
+
+# ----- Return Cross-validation metrics ----- 
+cv_out <- compare_models(
+    data,
+    method = "CV",
+    griffith = ~polynomial_model(.x, k=2),
+    penalized_spline = penalized_spline_model,
+    farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
+    local_polynomial = lp_model 
+  ) %>% suppressWarnings()
+
+aic_bic_out
+cv_out
+```
+
+With aggregated data
+
+```{r message=FALSE}
+# ----- Return AIC, BIC ----- 
+aic_bic_out <- compare_models(
+  data = aggregated,
+  method = "AIC/BIC",
+  griffith = ~polynomial_model(.x, k=2),
+  penalized_spline = penalized_spline_model,
+  farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
+  local_polynomial = lp_model # expect to not return any values
+  ) %>% suppressWarnings()
+
+# ----- Return Cross-validation metrics (default with 4 folds) ----- 
+cv_out <- compare_models(
+    data = aggregated,
+    method = "CV",
+    griffith = ~polynomial_model(.x, k=2),
+    penalized_spline = penalized_spline_model,
+    farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
+    local_polynomial = lp_model 
+  ) %>% suppressWarnings()
+
+
+aic_bic_out
+cv_out
+```
+
+## Generate custom metrics
+
+The users can also provide a custom function to generate the comparison metrics.
+
+This function must accepts 2 parameters:
+
+-   `dat` the input data
+
+-   `model_func` a function that takes an input data and returns a `serosv` model
+
+And it must returns a data frame with 1 row where each column represents one metric.
+
+***Example:***
+
+The following implements holdout validation and returns MAE:
 
 ```{r}
-# provide models with name
-compare_models(muench_linelist = model1, muench_aggregated = model2, griffith = model3, penalized_spline = model4)
+generate_mae <- function(dat, model_func){
+  n_train <- round(nrow(dat)*0.8)
+  train <- dat[1:n_train,]
+  test <- dat[n_train:nrow(dat),]
+  
+  fit <- model_func(dat)
+  pred <- predict(fit, test[, 1, drop=FALSE])
+  
+  # handle error differently depending on datatype
+  mae <- if(fit$datatype == "linelisting"){
+    sum(abs(test$status - pred), na.rm=TRUE)/nrow(test)
+  }else{
+    sum(abs(test$pos/test$tot - pred), na.rm=TRUE)/nrow(test)
+  }
+  
+  data.frame(
+    mae = mae
+  )
+}
+```
 
-# provide models without name
-compare_models(model1, model2, model3, model4)
+We can then run `compare_models` with the custom metrics function
+
+```{r}
+compare_models(
+    data = aggregated,
+    method = generate_mae,
+    griffith = ~polynomial_model(.x, k=2),
+    penalized_spline = penalized_spline_model,
+    farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
+    local_polynomial = lp_model 
+  ) %>% suppressWarnings()
 
-# user can provide arbitrary number of models
-compare_models(model3, model4)
+compare_models(
+    data = data,
+    method = generate_mae,
+    griffith = ~polynomial_model(.x, k=2),
+    penalized_spline = penalized_spline_model,
+    farrington = ~farrington_model(.x, start=list(alpha=0.07,beta=0.1,gamma=0.03)),
+    local_polynomial = lp_model 
+  ) %>% suppressWarnings()
 ```
diff --git a/vignettes/nonparametric_model.Rmd b/vignettes/nonparametric_model.Rmd
index 704e60a..f39311d 100644
--- a/vignettes/nonparametric_model.Rmd
+++ b/vignettes/nonparametric_model.Rmd
@@ -1,6 +1,7 @@
 ---
 title: "Nonparametric model"
 output: rmarkdown::html_vignette
+bibliography: references.bib
 vignette: >
   %\VignetteIndexEntry{Nonparametric model}
   %\VignetteEngine{knitr::rmarkdown}
@@ -16,17 +17,35 @@ knitr::opts_chunk$set(
 
 ```{r setup, output=FALSE}
 library(serosv)
+library(magrittr)
 ```
 
 ## Local estimation by polynomial
 
-Refer to `Chapter 7.1`
-
 **Proposed model**
 
-Within the local polynomial framework, the linear predictor $\eta(a)$ is approximated locally at one particular value $a_0$ for age by a line (local linear) or a parabola (local quadratic).
+Within the local polynomial framework, the linear predictor $\eta(a)$ is approximated locally at one particular value $a_0$ for age by a line (local linear, degree $p=1$) or a parabola (local quadratic, degree $p=2$).
+
+For a general degree $p$, the linear predictor for a neighbor of $a_0$, labeled $a_i$ is equivalent to the Taylor approximation
+
+$$
+\eta(a_i) = \eta(a_0) + \eta^{(1)}(a_0)(a_i - a_0) + \frac{\eta^{(2)}(a_0)}{2}(a_i - a_0)^2 + ... + \frac{\eta^{(p)}(a_0)}{p!}(a_i - a_0)^p
+$$
 
-The estimator for the $k$-th derivative of $\eta(a_0)$, for $k = 0,1,…,p$ (degree of local polynomial) is as followed:
+$\eta(a_i)$ can be estimated by maximizing
+
+$$
+\Sigma_{i=1}^{N} \ell_i \{Y_i, g^{-1} (\beta_0 + \beta_1(a_i-a_0)+ \beta_2(a_i-a_0)^2 ... +
+\beta_p(a_i-a_0)^p) \} K_h(a_i - a_0)
+$$
+
+Where:
+
+-   $\ell_i$ is the binomial log-likelihood $\ell_i\{Y_i, \pi\} = Y_ilog\{\pi\} + (1-Y_i)log\{1-\pi\}$
+
+-   $K_h$ is the kernel function with the specified smoothing parameter $h$, also called "bandwidth" or "window width"
+
+The estimator for the $k$-th derivative of $\eta(a_0)$, for $k = 0,1,…,p$ (degree of local polynomial) is thus
 
 $$
 \hat{\eta}^{(k)}(a_0) = k!\hat{\beta}_k(a_0) 
@@ -48,13 +67,43 @@ $$
 
 -   Where $\delta \{ \hat{\beta}_0(a_0) \} = \frac{dg^{-1} \{ \hat{\beta}_0(a_0) \} } {d\hat{\beta}_0(a_0)}$
 
+Refer to `Chapter 7.1` of the book by @Hens2012 for a more detailed explanation of the method.
+
 **Fitting data**
 
 ```{r}
 mump <- mumps_uk_1986_1987
 ```
 
-Use `plot_gcv()` to show GCV curves for the nearest neighbor method (left) and constant bandwidth (right).
+To fit a local estimation by polynomials, use `lp_model()` function with model parameters include:
+
+-   `nn` the number of nearest neighbors to be included in the approximation (i.e., the nearest neighbor component of the smoothing parameter)
+
+-   `h` the constant component of the smoothing parameter
+
+-   `deg` the degree of the polynomial
+
+-   `kern` the type of kernel
+
+```{r}
+lp <- lp_model(mump, kern="tcub", nn = 0.5, deg=2)
+lp
+plot(lp)
+```
+
+The users can also provide `h` or `nn` as a numeric vector, in which case the package will use the parameter value that minimize the generalized cross-validation (GCV) criterion.
+
+```{r}
+# ----- Tune nearest neighbor (nn) parameter
+lp_model(mump, kern="tcub", nn = seq(0.2, 0.8, 0.1), deg=2) %>% 
+  plot()
+
+# ----- Tune bandwidth (h) parameter
+lp_model(mump, kern="tcub", h = seq(5,25), deg=2) %>% 
+  plot()
+```
+
+Alternatively, use `plot_gcv()` to show GCV curves for the nearest neighbor method (left) and constant bandwidth (right) for manual parameter selection.
 
 ```{r, fig.width=7, fig.height=3}
 plot_gcv(
@@ -64,9 +113,12 @@ plot_gcv(
  )
 ```
 
-Use `lp_model()` to fit a local estimation by polynomials.
+Based on the plot, we can then fit the model using the parameter value that give the lowest GCV (`nn = 0.3` and `h = 14`)
 
 ```{r}
-lp <- lp_model(mump, kern="tcub", nn=0.7, deg=2)
-plot(lp)
+lp_model(mump, kern="tcub", nn = 0.3, deg=2) %>% 
+  plot()
+
+lp_model(mump, kern="tcub", h = 14, deg=2)  %>% 
+  plot()
 ```
diff --git a/vignettes/parametric_model.Rmd b/vignettes/parametric_model.Rmd
index b47a0bf..7553828 100644
--- a/vignettes/parametric_model.Rmd
+++ b/vignettes/parametric_model.Rmd
@@ -9,7 +9,7 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r, include = FALSE}
+```{r include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>"
@@ -22,102 +22,139 @@ library(dplyr)
 library(magrittr)
 ```
 
+# Frequentist methods
+
 ## Polynomial models
 
-Refer to `Chapter 6.1.1`
+Seroprevalence is modeled using the following serocatalytic model
 
-Use `polynomial_model()` to fit a polynomial model.
+$$
+\pi(a)  = 1 - \text{exp}({-\Sigma_{i=1}^k \beta_i a^i})
+$$
 
-We will use the `Hepatitis A` data from Belgium 1993--1994 for this example.
+Where:
 
-```{r}
-data <- hav_bg_1964
-```
+-   $a$ is the variable age
 
-### Muench model
+-   $\pi$ is the seroprevalence of the population at age $a$
 
-**Proposed model**
+-   $k$ is the degree of the polynomial
 
-[@muench_derivation_1934] suggested to model the infection process with so-called "catalytic model", in which the distribution of the time spent in the susceptible class in SIR model is exponential with rate $\beta$
+-   $\beta_i$ are the model parameters
 
-$$
-\pi(a) = k(1 - e^{-\beta a} )
-$$
+Which implies the force of infection is $\lambda(a) = \Sigma_{i=1}^k \beta_i i a^{i-1}$
 
-Where:
+This generalization encompasses several classical serocatalytic model including
 
--   $\pi$ is the seroprevalence at age $a$
--   $1 - k$ is the proportion of population that stay uninfected for a lifetime
--   $a$ is the variable age
+-   **Muench model** (assuming $k=1$) [@muench_derivation_1934]
+
+-   **Griffith model** (assuming $k = 2$)
+
+-   **Grenfell and Anderson model** (assuming higher degree $k$) [@grenfell_estimation_1985]
 
-Under this catalytic model and assuming that $k = 1$, force infection would be $\lambda(a) = \beta$
+Refer to `Chapter 6.1.1` of the book by @Hens2012 for a more detailed explanation of the methods.
 
 **Fitting data**
 
-**Muench**'s model can be estimated by either defining `k = 1` (a degree one linear predictor, note that it is irrelevant to the k in the proposed model) or setting the `type = "Muench"`.
+We will use the `Parvo B19` data from Finland 1997--1998 for this example.
 
 ```{r}
-muench1 <- polynomial_model(data, k = 1)
-summary(muench1$info)
+data <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age), ]
+```
+
+To fit a polynomial model, use the `polynomial_model()` function.
 
-muench2 <- polynomial_model(data, type = "Muench")
-summary(muench2$info)
+```{r}
+# Fit a Muench model
+muench <- polynomial_model(data, k = 1, status_col="seropositive")
+summary(muench$info)
+plot(muench) 
 ```
 
-We can plot any model with the `plot()` function.
+The users can also choose to provide a range of values for $k$ in which case the package will try to find the best $k$ parameter determined by Loglikelihood Ratio test (LRT)
 
 ```{r}
-plot(muench2) 
+# Provide a range of values for k
+best_param <- polynomial_model(data, k = 1:5, status_col = "seropositive")
+plot(best_param)
+# View the best model here which suggests k = 4 is the best parameter value
+best_param
 ```
 
-### Griffith model
+------------------------------------------------------------------------
+
+## Fractional polynomial model
 
 **Proposed model**
 
-Griffith proposed a model for force of infection as followed
+Fractional polynomial model generalize conventional polynomial class of functions. In the context of binary responses, a fractional polynomial of degree $m$ for the linear predictor is defined as followed
 
 $$
- \lambda(a) = \beta_1 + 2\beta_2a  
+\eta_m(a, \beta, p_1, p_2, ...,p_m) = \Sigma^m_{i=0} \beta_i H_i(a)
 $$
 
-Which can be estimated using a GLM where the for which the linear predictor was $\eta(a) = \beta_1 + \beta_2a^{2}$
-
-**Fitting data**
+Where $m$ is an integer, $p_1 \le p_2 \le... \le p_m$ is a sequence of powers, and $H_i(a)$ is a transformation given by
 
-Similarly, we can estimate **Griffith**'s model either by defining `k = 2`, or setting the `type = "Griffith"`
+$$
+H_i = \begin{cases}
+ a^{p_i} & \text{ if } p_i \neq p_{i-1},
+ \\ H_{i-1}(a) \times log(a)  & \text{ if } p_i = p_{i-1},
+\end{cases}
+$$
 
-```{r}
-gf_model <- polynomial_model(data, type = "Griffith")
-plot(gf_model)
-```
+Refer to `Chapter 6.2` of the book by @Hens2012 for a more detailed explanation of the methods.
 
-### Grenfell and Anderson model
+**Fitting data**
 
-**Proposed model**
+Use `fp_model()` to fit a fractional polynomial model.
 
-[@grenfell_estimation_1985] extended the models of Muench and Griffiths further suggest the use of higher order polynomial functions to model the force of infection which assumes prevalence model as followed
+The parameter `p` specifies the powers of each polynomial term (length of `p` is thus the model's degree)
 
-$$
-\pi(a)  = 1 - e^{-\Sigma_i \beta_i a^i}
-$$
+```{r}
+hav <- hav_be_1993_1994
+model <- fp_model(hav, p=c(1, 1.5), link="cloglog")
+model
+plot(model)
+```
 
-Which implies that force of infection equals $\lambda(a) = \Sigma \beta_i i a^{i-1}$
+The users can also tell the package to perform parameter selection by providing `p` as a named list with 2 elements:
 
-**Fitting data**
+-   `degree` the maximum number of terms to search over
+-   `p_range` the possible powers for each term
 
-And Grenfell and Anderson's model.
+```{r, warning=FALSE}
+model <- fp_model(hav, 
+                  p=list(
+                    p_range=seq(-2,3,0.1),
+                    degree=2
+                  ), 
+                  monotonic=FALSE,
+                  link="cloglog")
+plot(model)
+# the best set of powers for this dataset is 1.5 and 1.6
+model
+```
 
-```{r}
-grf_model <- polynomial_model(data, type = "Grenfell")
-plot(grf_model)
+To restrict the parameters search such that the predictions are monotonic (thus ensuring the FOI to be $\lambda \geq 0$) set `monotonic=TRUE`
+
+```{r warning=FALSE}
+# ---- Best model with the monotonic constraint -----
+model <- fp_model(hav, 
+                  p=list(
+                    p_range=seq(-2,3,0.1),
+                    degree=2
+                  ), 
+                  monotonic=TRUE,
+                  link="cloglog")
+plot(model)
+# the best set of powers with the monotonic constraint is 0.5 and 1.1
+model
 ```
 
 ------------------------------------------------------------------------
 
 ## Nonlinear models
 
-Refer to `Chapter 6.1.2`
-
 ### Farrington model
 
 **Proposed model**
@@ -136,6 +173,8 @@ $$
 \pi (a) = 1 - e^{-\int_0^a \lambda(s) ds} \\ = 1 - exp\{ \frac{\alpha}{\beta}ae^{-\beta a} + \frac{1}{\beta}(\frac{\alpha}{\beta} - \gamma)(e^{-\beta a} - 1) -\gamma a \}
 $$
 
+Refer to `Chapter 6.1.2` of the book by @Hens2012 for a more detailed explanation of the methods.
+
 **Fitting data**
 
 Use `farrington_model()` to fit a **Farrington**'s model.
@@ -145,6 +184,7 @@ farrington_md <- farrington_model(
    rubella_uk_1986_1987,
    start=list(alpha=0.07,beta=0.1,gamma=0.03)
    )
+farrington_md
 plot(farrington_md)
 ```
 
@@ -170,6 +210,8 @@ $$
 \lambda(d) = \beta_0 \beta_1 d^{\beta_1 - 1}
 $$
 
+Refer to `Chapter 6.1.2` of the book by @Hens2012 for a more detailed explanation of the methods.
+
 **Fitting data**
 
 Use `weibull_model()` to fit a Weibull model.
@@ -177,54 +219,126 @@ Use `weibull_model()` to fit a Weibull model.
 ```{r}
 hcv <- hcv_be_2006[order(hcv_be_2006$dur), ]
 
-wb_md <- hcv %>% 
-  rename(
-    t = dur, status = seropositive
-  ) %>% weibull_model()
+wb_md <- hcv %>% weibull_model(t_lab = "dur", status_col="seropositive")
+wb_md
 plot(wb_md) 
 ```
 
-------------------------------------------------------------------------
+# Bayesian methods
 
-## Fractional polynomial model
+**Proposed approach**
+
+Consider a model for prevalence that has a parametric form $\pi(a_i, \alpha)$ where $\alpha$ is a parameter vector
+
+One can constraint the parameter space of the prior distribution $P(\alpha)$ in order to achieve the desired monotonicity of the posterior distribution $P(\pi_1, \pi_2, ..., \pi_m|y,n)$
+
+Where:
+
+-   $n = (n_1, n_2, ..., n_m)$ and $n_i$ is the sample size at age $a_i$
 
-Refer to `Chapter 6.2`
+-   $y = (y_1, y_2, ..., y_m)$ and $y_i$ is the number of infected individual from the $n_i$ sampled subjects
+
+## Farrington
 
 **Proposed model**
 
-Fractional polynomial model generalize conventional polynomial class of functions. In the context of binary responses, a fractional polynomial of degree $m$ for the linear predictor is defined as followed
+The model for prevalence is as followed
 
 $$
-\eta_m(a, \beta, p_1, p_2, ...,p_m) = \Sigma^m_{i=0} \beta_i H_i(a)
+\pi (a) = 1 - exp\{ \frac{\alpha_1}{\alpha_2}ae^{-\alpha_2 a} + \frac{1}{\alpha_2}(\frac{\alpha_1}{\alpha_2} - \alpha_3)(e^{-\alpha_2 a} - 1) -\alpha_3 a \}
 $$
 
-Where $m$ is an integer, $p_1 \le p_2 \le... \le p_m$ is a sequence of powers, and $H_i(a)$ is a transformation given by
+For likelihood model, independent binomial distribution are assumed for the number of infected individuals at age $a_i$
 
 $$
-H_i = \begin{cases}
- a^{p_i} & \text{ if } p_i \neq p_{i-1},
- \\ H_{i-1}(a) \times log(a)  & \text{ if } p_i = p_{i-1},
-\end{cases}
+y_i \sim Bin(n_i, \pi_i), \text{  for } i = 1,2,3,...m
 $$
 
-**Best power selection**
+The constraint on the parameter space can be incorporated by assuming truncated normal distribution for the components of $\alpha$, $\alpha = (\alpha_1, \alpha_2, \alpha_3)$ in $\pi_i = \pi(a_i,\alpha)$
 
-Use `find_best_fp_powers()` to find the powers which gives the lowest deviance score
+$$
+\alpha_j \sim \text{truncated  } \mathcal{N}(\mu_j, \tau_j), \text{ } j = 1,2,3
+$$
 
-```{r, warning=FALSE}
-hav <- hav_be_1993_1994
-best_p <- find_best_fp_powers(
-  hav,
-  p=seq(-2,3,0.1), mc=FALSE, degree=2, link="cloglog"
-)
-best_p
+The joint posterior distribution for $\alpha$ can be derived by combining the likelihood and prior as followed
+
+$$
+P(\alpha|y) \propto \prod^m_{i=1} \text{Bin}(y_i|n_i, \pi(a_i, \alpha)) \prod^3_{i=1}-\frac{1}{\tau_j}\text{exp}(\frac{1}{2\tau^2_j} (\alpha_j - \mu_j)^2)
+$$
+
+-   Where the flat hyperprior distribution is defined as followed:
+
+    -   $\mu_j \sim \mathcal{N}(0, 10000)$
+
+    -   $\tau^{-2}_j \sim \Gamma(100,100)$
+
+The full conditional distribution of $\alpha_i$ is thus $$
+P(\alpha_i|\alpha_j,\alpha_k, k, j \neq i) \propto  -\frac{1}{\tau_i}\text{exp}(\frac{1}{2\tau^2_i} (\alpha_i - \mu_i)^2) \prod^m_{i=1} \text{Bin}(y_i|n_i, \pi(a_i, \alpha))
+$$
+
+Refer to `Chapter 10.3.1` of the book by @Hens2012 for a more detailed explanation of the method.
+
+**Fitting data**
+
+To fit Farrington model, use `hierarchical_bayesian_model()` and define `type = "far2"` or `type = "far3"` where
+
+-   `type = "far2"` refers to Farrington model with 2 parameters ($\alpha_3 = 0$)
+
+-   `type = "far3"` refers to Farrington model with 3 parameters ($\alpha_3 > 0$)
+
+```{r message=FALSE, output=FALSE, warning=FALSE}
+df <- mumps_uk_1986_1987
+model <- hierarchical_bayesian_model(df, type="far3")
 ```
 
+```{r}
+model
+plot(model)
+```
+
+## Log-logistic
+
+**Proposed approach**
+
+The model for seroprevalence is as followed
+
+$$
+\pi(a) = \frac{\beta a^\alpha}{1 + \beta a^\alpha}, \text{ } \alpha, \beta > 0
+$$
+
+The likelihood is specified to be the same as Farrington model ($y_i \sim Bin(n_i, \pi_i)$) with
+
+$$
+\text{logit}(\pi(a)) = \alpha_2 + \alpha_1\log(a)
+$$
+
+-   Where $\alpha_2 = \text{log}(\beta)$
+
+The prior model of $\alpha_1$ is specified as $\alpha_1 \sim \text{truncated  } \mathcal{N}(\mu_1, \tau_1)$ with flat hyperprior as in Farrington model
+
+$\beta$ is constrained to be positive by specifying $\alpha_2 \sim \mathcal{N}(\mu_2, \tau_2)$
+
+The full conditional distribution of $\alpha_1$ is thus
+
+$$
+P(\alpha_1|\alpha_2) \propto -\frac{1}{\tau_1} \text{exp} (\frac{1}{2 \tau_1^2} (\alpha_1 - \mu_1)^2)
+\prod_{i=1}^m \text{Bin}(y_i|n_i,\pi(a_i, \alpha_1, \alpha_2) )
+$$
+
+And $\alpha_2$ can be derived in the same way
+
+Refer to `Chapter 10.3.3` of the book by @Hens2012 for a more detailed explanation of the method.
+
 **Fitting data**
 
-Use `fp_model()` to fit a fractional polynomial model
+To fit Log-logistic model, use `hierarchical_bayesian_model()` and define `type = "log_logistic"`
+
+```{r message=FALSE, output=FALSE, warning=FALSE}
+df <- rubella_uk_1986_1987
+model <- hierarchical_bayesian_model(df, type="log_logistic")
+```
 
 ```{r}
-model <- fp_model(hav, p=c(1.5, 1.6), link="cloglog")
+model
 plot(model)
 ```
diff --git a/vignettes/reading_to_titer.Rmd b/vignettes/reading_to_titer.Rmd
new file mode 100644
index 0000000..1754da0
--- /dev/null
+++ b/vignettes/reading_to_titer.Rmd
@@ -0,0 +1,352 @@
+---
+title: "Convert assay readings to titer"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Convert assay readings to titer}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r setup, output=FALSE, warning=FALSE, message=FALSE}
+library(serosv)
+library(dplyr)
+library(tidyr)
+library(magrittr)
+```
+
+## Overview
+
+Serological assays often produce raw signals such as optical density (OD) or fluorescence intensity. These measurements are not directly interpretable and must be converted to titers (e.g., IU/ml) using a calibration model.
+
+`serosv` provides the function `to_titer()` perform this conversion by fitting a standard curve (per plate) and mapping assay readings to estimated titers
+
+## Convert to titer workflow
+
+The input data is expected to have the following information
+
+-   Sample id containing id for samples **or** label for antitoxins
+
+-   Plate id the id of each plate (the standard curve will be fitted for each plate)
+
+-   Dilution level (e.g., 50, 100, 200)
+
+-   (Optionally) the columns for result for negative control at different dilution level (e.g., negative_50, negative_100, and negative_200)
+
+To demonstrate the use of this function, we will use a simulated dataset
+
+```{=html}
+<details>
+  <summary>Code for simulating data</summary>
+```
+
+```{r `generate mock data`, message=FALSE, warning=FALSE, output=FALSE}
+set.seed(1)
+# Config
+n_samples <- 50
+n_plates  <- 5
+dilutions <- c(50, 100, 200)
+ref_conc_antitoxin <- 10
+
+# 4PL function: OD = D + (A - D) / (1 + 10^((log10(conc) - c) * b))
+mock_4pl <- function(conc, A = 0, B = 1.8, C = -2.5, D = 4) {
+  D + (A - D) / (1 + 10^(( log10(conc) - C)*B))
+}
+
+# Assign each sample a "true" concentration (UI/ml)
+# ~60% positive (conc > 0.1), ~40% negative
+sample_meta <- tibble(
+  SAMPLE_ID    = sprintf("S%03d", 1:n_samples),
+  PLATE_ID     = rep(1:n_plates, length.out = n_samples),
+  true_conc    = c(
+    runif(round(n_samples * 0.8), 0.15, 100),   # positives
+    runif(round(n_samples * 0.2), 0.001, 0.09) # negatives
+  ) %>% sample()                               # shuffle
+)
+
+# Negative control concentrations per plate (one fixed OD per plate per dilution)
+neg_ctrl_conc <- tibble(
+  PLATE_ID      = 1:n_plates,
+  true_neg_conc = runif(n_plates, 0.001, 0.04),
+  neg_conc_50   = true_neg_conc/50,
+  neg_conc_100  = true_neg_conc/100,
+  neg_conc_200  = true_neg_conc/200
+)
+neg_ctrl <- neg_ctrl_conc %>%
+  mutate(
+    NEGATIVE_50  = round(mock_4pl(neg_conc_50)  + rnorm(n_plates, 0, 0.003), 4),
+    NEGATIVE_100 = round(mock_4pl(neg_conc_100) + rnorm(n_plates, 0, 0.003), 4),
+    NEGATIVE_200 = round(mock_4pl(neg_conc_200) + rnorm(n_plates, 0, 0.003), 4)
+  ) %>%
+  select(PLATE_ID, NEGATIVE_50, NEGATIVE_100, NEGATIVE_200)
+
+# Antitoxin 
+antitoxin_conc <-  c(50, 100, 200, 400, 800, 1600, 3200, 6400, 12800, 25600, 51200, 102400)
+antitoxin <- tibble(
+  SAMPLE_ID    = rep("Antitoxin", n_plates),
+  true_conc    = rep(ref_conc_antitoxin, n_plates),
+  PLATE_ID = 1:n_plates
+) %>% 
+  crossing(
+    DILUTION = antitoxin_conc
+  ) %>% 
+  mutate(
+    eff_conc = true_conc/DILUTION
+  )
+
+# Generate one row per sample × dilution, compute OD via 4PL + noise
+simulated_data <- sample_meta %>%
+  crossing(DILUTION = dilutions) %>%
+  mutate(
+    eff_conc  = true_conc / DILUTION
+  ) %>% 
+  bind_rows(
+    antitoxin
+  ) %>% 
+  mutate(
+    # Effective concentration decreases with dilution
+    od_mean   = mock_4pl(eff_conc,
+                         B = runif(n(), 1.7, 1.9),
+                         C = runif(n(), -2.7, -2.4)),
+    noise     = rnorm(n(), 0, 0.008),
+    RESULT    = round(pmax(od_mean + noise, 0.02), 4),
+
+    # Blanks: clearly below result (reagent background only)
+    BLANK_1   = round(runif(n(), 0.010, pmin(RESULT - 0.01, 0.045)), 4),
+    BLANK_2   = round(runif(n(), 0.010, pmin(RESULT - 0.01, 0.045)), 4),
+    BLANK_3   = round(runif(n(), 0.010, pmin(RESULT - 0.01, 0.045)), 4)
+  ) %>%
+  left_join(neg_ctrl, by = "PLATE_ID") %>%
+  select(
+    SAMPLE_ID, PLATE_ID, 
+    DILUTION, RESULT,
+    # BLANK_1, BLANK_2, BLANK_3,
+    NEGATIVE_50, NEGATIVE_100, NEGATIVE_200
+  ) %>%
+  arrange(PLATE_ID, SAMPLE_ID, DILUTION)
+```
+
+</details>
+
+```{r}
+simulated_data
+```
+
+**Standardize data**
+
+Before fitting the standard curve, the input data must be standardized into the required format. This is handled by the function `standardize_data()`.
+
+```{r}
+standardized_dat <- standardize_data(
+  simulated_data,
+  plate_id_col = "PLATE_ID", # specify the column for plate id
+  id_col = "SAMPLE_ID", # specify the column for sample id
+  result_col = "RESULT", # specify the column for raw assay readings
+  dilution_fct_col= "DILUTION", # specify the column for dilution factor
+  antitoxin_label = "Antitoxin", # specify the label for antitoxin (in the sample id column)
+  negative_col = "^NEGATIVE_*" # (optionally) specify the regex (i.e., pattern) for columns for negative controls
+)
+
+standardized_dat
+```
+
+**Fitting data**
+
+The function `to_titer()` fits a standard curve for each plate and converts assay readings into titer estimates.
+
+The users can configure the following:
+
+-   `model` either the name of a built-in model (`"4PL"` for 4 parameters log-logistic model) or a named list with 2 items `"mod"` and `"quantify_ci"`. Section [Custom models] will provide more details on these functions.
+
+<!-- -->
+
+-   `ci` confidence interval of the titer estimate (default to `.95` for 95% CI)
+
+-   `negative_control` whether to include the result for negative controls
+
+-   (optionally) `positive_threshold` the threshold of titer for sample to be considered positive. If provided, the output will include serostatus
+
+```{r}
+out <- to_titer(
+  standardized_dat,
+  model = "4PL",
+  ci = 0.95,
+  positive_threshold = 0.1,
+  negative_control = TRUE
+)
+```
+
+**Output format**
+
+```{r}
+out
+```
+
+The output is a nested tibble with the following columns
+
+-   `plate_id` ids of the plate
+
+-   `data` list of `data.frame`s containing the input samples data corresponding to each plate
+
+-   `antitoxin_df` list of `data.frame`s containing the input reference data corresponding to each plate
+
+-   `standard_curve_func` list of functions mapping from assay reading to titer for each plate
+
+-   `std_crv_midpoint` midpoint of the standard curve, for qualitative analysis
+
+-   `processed_data` list of `tibble`s containing samples with titer estimates (lower, median, upper)
+
+-   `negative_control` list of `tibble`s containing negative control check results (if `negative_control=TRUE`)
+
+To access the estimated titers, simply unnest the `processed_data` column.
+
+```{r}
+out %>% 
+  select(plate_id, processed_data) %>% 
+  unnest(processed_data) %>% 
+  select(plate_id, sample_id, result, lower, median, upper, positive)
+```
+
+The columns for titer estimates are
+
+-   `lower` lower bound of the confidence interval for the titer estimate.
+
+-   `median` median (predicted) titer value.
+
+-   `upper` upper bound of the confidence interval for titer estimates
+
+**Visualize standard curves**
+
+Standard curves can be visualized using the function `plot_standard_curve()`
+
+```{r}
+# visualize standard curves with datapoints for antitoxin
+plot_standard_curve(out, facet=TRUE)
+# set facet to FALSE to view all the curves together
+plot_standard_curve(out, facet=FALSE)
+```
+
+Positive threshold at different dilutions and also be added to the plot using `add_threshold()` function, note that this only works when `facet=FALSE`
+
+```{r}
+# set facet to FALSE to view all the curves together
+plot_standard_curve(out, facet=FALSE) +
+  add_thresholds(
+    dilution_factors = c(50, 100, 200),
+    positive_threshold = 0.1
+  )
+```
+
+**Visualizing estimate availability**
+
+The function `plot_titer_qc()` visualizes whether titer estimates can be computed for each sample across the tested dilutions.
+
+Each sample is represented as a grid of size `n_estimates × n_dilutions`, where the cell color indicates estimate availability:
+
+-   **Green**: estimate available
+
+-   **Orange**: assay reading is too low to estimate
+
+-   **Red**: assay reading is too high to estimate
+
+The sample grids are arranged by plate, with each column corresponding to a single plate and containing samples from that plate.
+
+```{r}
+plot_titer_qc(
+  out,
+  n_plates = NULL, # maximum number of plates to visualize, if NULL then plot all
+  n_samples = 10, # maximum number of samples per plate to visualize
+  n_dilutions = 3 # number of dilutions used for testing
+)
+```
+
+## Custom models {#custom-mod}
+
+The users can also define their own model for fitting the standard curve and a function for quantifying the confidence intervals.
+
+To do this, define the `model` argument of `serosv::to_titer()` as a named list of 2 items where:
+
+-   `mod` is a function that takes a `df` as input and return a fitted standard curve model
+
+-   `quantify_ci` is a function to compute the confidence intervals, with the following required arguments
+
+    -   `object` the fitted model
+
+    -   `newdata` the new predictor values for which predictions are made
+
+    -   `nb` the number of samples (required for bootstrapping, but may be unused otherwise)
+
+    -   `alpha` the significance level
+
+    `quantify_ci` must return a data.frame/tibble with the following columns:
+
+    -   `lower` lower bound of the confidence interval for the titer estimate.
+
+    -   `median` median (predicted) titer value.
+
+    -   `upper` upper bound of the confidence interval for titer estimate.
+
+**Example**
+
+For demonstration purpose, below is an example for the `mod` and `quantify_ci` functions for a 4PL model
+
+```{r message=FALSE, warning=FALSE}
+library(mvtnorm)
+library(purrr)
+# custom model function
+custom_4PL <- function (df){
+  good_guess4PL <- function(x, y, eps = 0.3) {
+    nb_rep <- unique(table(x))
+    the_order <- order(x)
+    x <- x[the_order]
+    y <- y[the_order]
+    a <- min(y)
+    d <- max(y)
+    c <- approx(y, x, (d - a)/2, ties = "ordered")$y
+    list(a = a, c = c, d = d, b = (approx(x, y, c + eps, ties = "ordered")$y - 
+        approx(x, y, c - eps, ties = "ordered")$y)/(2 * eps))
+  }
+  
+  nls(
+    result ~ d + (a - d) / (1 + 10 ^ ((log10(concentration) - c) * b)),
+    data = df,
+    start = with(df, good_guess4PL(log10(concentration), result))
+  )
+}
+
+# custom quantify CI function for the model
+custom_quantify_ci <- function(object, newdata, nb = 9999, alpha = 0.025){
+  rowsplit <- function(df) split(df, 1:nrow(df))
+
+  nb |>
+    rmvnorm(mean = coef(object), sigma = vcov(object)) |>
+    as.data.frame() |>
+    rowsplit() |>
+    map(as.list) |>
+    map(~ c(.x, newdata)) |>
+    map_dfc(eval, expr = parse(text = as.character(formula(object))[3])) %>%
+    apply(1, quantile, c(alpha, .5, 1 - alpha))  %>%
+    t() %>%  as.data.frame() %>%
+    setNames(c("lower", "median", "upper"))
+}
+```
+
+The custom model can be provided as followed
+
+```{r}
+# use the custom model and quantify ci function
+custom_mod <- list(
+  "mod" = custom_4PL,
+  "quantify_ci" = custom_quantify_ci
+)
+custom_mod_out <- to_titer(
+  standardized_dat,
+  model = custom_mod,
+  positive_threshold = 0.1, ci = 0.95,
+  negative_control = TRUE)
+```
+
+Visualize standard curves
+
+```{r}
+plot_standard_curve(custom_mod_out, facet=TRUE)
+```
diff --git a/vignettes/repeated_cross_sectional.Rmd b/vignettes/repeated_cross_sectional.Rmd
index 753d51a..5fe27d4 100644
--- a/vignettes/repeated_cross_sectional.Rmd
+++ b/vignettes/repeated_cross_sectional.Rmd
@@ -53,6 +53,8 @@ tb_nl <- tb_nl_1966_1973 %>%
   filter(survey_year > 1966) %>% 
   group_by(age, survey_year, survey_time) %>% 
   summarize(pos = sum(pos), tot = sum(tot), .groups = "drop")
+
+head(tb_nl)
 ```
 
 The monotonization method can be specified via the `monotonize_method` argument, `serosv` currently supports 2 options:
@@ -108,7 +110,8 @@ The output is a `data.frame` with dimension [number of survey, 9], where each ro
 -   `monotonized_foi` - estimated force of infection from `monotonized_sp`
 
 ```{r}
-out_pava$out
+out_pava
+out_scam
 ```
 
 For visualization, the plot function for `age_time_model` offers the following configurations
@@ -119,7 +122,7 @@ For visualization, the plot function for `age_time_model` offers the following c
 
 ***Example:*** output for model with PAVA for monotonization
 
-```{r warning=FALSE}
+```{r warning=FALSE, message=FALSE}
 plot(out_pava, facet = TRUE, modtype = "non-monotonized") + ylim(c(0, 0.07))
 plot(out_pava, facet = TRUE, modtype = "monotonized") + ylim(c(0, 0.07))
 
@@ -128,7 +131,7 @@ plot(out_pava, facet = FALSE, modtype = "monotonized") + ylim(c(0, 0.07))
 
 ***Example:*** output for model with SCAM for monotonization
 
-```{r warning=FALSE}
+```{r warning=FALSE, message=FALSE}
 plot(out_scam, facet = TRUE, modtype = "non-monotonized") + ylim(c(0, 0.07))
 plot(out_scam, facet = TRUE, modtype = "monotonized") + ylim(c(0, 0.07))
 ```
diff --git a/vignettes/semiparametric_model.Rmd b/vignettes/semiparametric_model.Rmd
index 43b4b48..d2f223f 100644
--- a/vignettes/semiparametric_model.Rmd
+++ b/vignettes/semiparametric_model.Rmd
@@ -21,7 +21,7 @@ knitr::opts_chunk$set(
 )
 ```
 
-```{r setup, output=FALSE}
+```{r setup, output=FALSE, message=FALSE, warning=FALSE}
 library(serosv)
 library(dplyr)
 ```
@@ -30,10 +30,6 @@ library(dplyr)
 
 **Proposed model**
 
-```{=html}
-<details>
-  <summary> <code> Penalized splines </code> </summary>
-```
 A general model relating the prevalence to age can be written as a GLM
 
 $$
@@ -59,18 +55,18 @@ $$
 In matrix notation, the mean structure model for $\eta(a_i)$ becomes
 
 $$
-\eta = X\beta + Zu
+\eta = \textbf{X}\beta + \textbf{Zu}
 $$
 
-Where $\eta = [\eta(a_i) ... \eta(a_N) ]^T$, $\beta = [\beta_0 \beta_1 .... \beta_p]^T$, and $u = [u_1 u_2 ... u_k]^T$ are the regression with corresponding design matrices
+Where $\eta = [\eta(a_i) ... \eta(a_N) ]^T$, $\beta = [\beta_0 \beta_1 .... \beta_p]^T$, and $\textbf{u} = [u_1 u_2 ... u_k]^T$ are the regression with corresponding design matrices
 
 $$
-X = \begin{bmatrix}
+\textbf{X} = \begin{bmatrix}
 1 & a_1 & a_1^2 & ... & a_1^p \\
 1 & a_2 & a_2^2 & ... & a_2^p \\
 \vdots & \vdots & \vdots & \dots & \vdots \\
 1 & a_N & a_N^2 & ... & a_N^p
-\end{bmatrix}, Z = \begin{bmatrix} 
+\end{bmatrix}, \textbf{Z} = \begin{bmatrix} 
 (a_1 - \kappa_1 )_+^p & (a_1 - \kappa_2 )_+^p & \dots & (a_1 - \kappa_k)_+^p \\
 (a_2 - \kappa_1 )_+^p & (a_2 - \kappa_2 )_+^p & \dots & (a_2 - \kappa_k)_+^p \\
 \vdots & \vdots & \dots & \vdots \\
@@ -84,42 +80,39 @@ $$
 \hat{\lambda}(a_i) = [\hat{\beta_1} , 2\hat{\beta_2}a_i, ..., p \hat{\beta} a_i ^{p-1} + \Sigma^k_{k=1} p \hat{u}_k(a_i - \kappa_k)^{p-1}_+] \delta(\hat{\eta}(a_i))
 $$
 
--   Where $\delta(.)$ is determined by the link function use in the model
-
-</details>
+-   Where $\delta(.)$ is determined by the link function used in the model
 
 ------------------------------------------------------------------------
 
 ### Penalized likelihood framework
 
-Refer to Chapter `8.2.1`
-
 **Proposed approach**
 
-A first approach to fit the model is by maximizing the following penalized likelihood
+The first approach to fit the model is by maximizing the following penalized likelihood
 
-```{=tex}
 \begin{equation}
-\phi^{-1}[y^T(X\beta + Zu ) -  1^Tc(X\beta + Zu )] - \frac{1}{2}\lambda^2 
-\begin{bmatrix} \beta \\u \end{bmatrix}^T D\begin{bmatrix} \beta \\u \end{bmatrix}
+\phi^{-1}[y^T(\textbf{X}\beta + \textbf{Zu} ) -  \textbf{1}^Tc(\textbf{X}\beta + \textbf{Zu} )] - \frac{1}{2}\lambda^2 
+\begin{bmatrix} \beta \\ \textbf{u} \end{bmatrix}^T D\begin{bmatrix} \beta \\ \textbf{u} \end{bmatrix}
 
 (\#eq:penlikelihood)
 \end{equation}
-```
+
 Where:
 
--   $X\beta + Zu$ is the linear predictor
+-   $X\beta + Zu$ is the predictor
 
 -   $D$ is a known semi-definite penalty matrix [@Wahba1978], [@Green1993]
 
 -   $y$ is the response vector
 
--   1 the unit vector, $c(.)$ is determined by the link function used
+-   $\textbf{1}$ is the unit vector, $c(.)$ is determined by the link function used
 
 -   $\lambda$ is the smoothing parameter (larger values --\> smoother curves)
 
 -   $\phi$ is the overdispersion parameter and equals 1 if there is no overdispersion
 
+Refer to Chapter `8.2.1` of the book by @Hens2012 for a more detailed explanation of the method.
+
 **Fitting data**
 
 To fit the data using the penalized likelihood framework, specify `framework = "pl"`
@@ -138,9 +131,8 @@ For more options, refer to the [mgcv documentation](https://stat.ethz.ch/R-manua
 
 ```{r}
 pl <- parvob19_be_2001_2003 %>% 
-  rename(status = seropositive) %>% 
-  penalized_spline_model(s = "tp", framework = "pl") 
-pl$info
+  penalized_spline_model(status_col = "seropositive", s = "tp", framework = "pl") 
+pl
 ```
 
 ```{r, fig.width=7, fig.height=4}
@@ -151,13 +143,11 @@ plot(pl)
 
 ### Generalized Linear Mixed Model framework
 
-Refer to Chapter `8.2.2`
-
 **Proposed approach**
 
-Looking back at \@ref(eq:penlikelihood), a constraint for $u$ would be $\Sigma_ku_k^2 < C$ for some positive value $C$
+Looking back at \@ref(eq:penlikelihood), a constraint for $\textbf{u}$ would be $\Sigma_ku_k^2 < C$ for some positive value $C$
 
-This is equivalent to choosing $(\beta, u)$ to maximise \@ref(eq:penlikelihood) with $D = diag(0, 1)$ where $0$ denotes zero vector length $p+1$ and 1 denotes the unit vector of length $K$
+This is equivalent to choosing $(\beta, \textbf{u})$ to maximise \@ref(eq:penlikelihood) with $D = diag(\textbf{0}, \textbf{1})$ where $\textbf{0}$ denotes zero vector length $p+1$ and $\textbf{1}$ denotes the unit vector of length $K$
 
 For a fixed value for $\lambda$ this is equivalent to fitting the following generalized linear mixed model [@Ruppert2003 , @Wand2003, @Ngo2004]
 
@@ -168,7 +158,9 @@ $$
 
 -   With similar notations as before and $G = \sigma^2_uI_{K \times K}$
 
-Thus $Z$ is penalized by assuming the corresponding coefficients $u$ are random effect with $u \sim N(0, \sigma^2_uI)$.
+Thus $Z$ is penalized by assuming the corresponding coefficients $\textbf{u}$ are random effect with $\textbf{u} \sim N(\textbf{0}, \mathbf{\sigma}^2_u \textbf{I})$.
+
+Refer to Chapter `8.2.2` of the book by @Hens2012 for a more detailed explanation of the method.
 
 **Fitting data**
 
@@ -176,9 +168,8 @@ To fit the data using the penalized likelihood framework, specify `framework = "
 
 ```{r}
 glmm <- parvob19_be_2001_2003 %>% 
-  rename(status = seropositive) %>% 
-  penalized_spline_model(s = "tp", framework = "glmm") 
-glmm$info$gam
+  penalized_spline_model(status_col = "seropositive", s = "tp", framework = "glmm") 
+glmm
 ```
 
 ```{r, fig.width=7, fig.height=4}
diff --git a/vignettes/sir_model.Rmd b/vignettes/sir_model.Rmd
deleted file mode 100644
index 4fb18a8..0000000
--- a/vignettes/sir_model.Rmd
+++ /dev/null
@@ -1,245 +0,0 @@
----
-title: "SIR model"
-output: rmarkdown::html_vignette
-bibliography: references.bib
-nocite: "@Hens2012"
-link-citations: TRUE
-vignette: >
-  %\VignetteIndexEntry{SIR model}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r setup, output=FALSE}
-library(serosv)
-```
-
-```{r, include = FALSE}
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>"
-)
-```
-
-## Basic SIR model
-
-**Proposed model**
-
-A transmission model consists of 3 compartments: susceptible (S), infected (I), recovered (R)
-
-With the following assumptions:
-
--   Individuals are born into susceptible group (exposure time is age of the individual) then transfer to infected class and recovered class
-
--   Recovered individuals gained lifelong immunity
-
--   Age homogeneity
-
-And described by a system of 3 differential equations
-
-$$
-\begin{cases}
-\frac{dS(t)}{dt} = B(t) (1-p) - \lambda(t)S(t) - \mu S(t) \\
-\frac{dI(t)}{dt} = \lambda(t)S(t) - \nu I(t) - \mu I(t) - \alpha I(t)  \\
-\frac{dR(t)}{dt} =  B(t) p + \nu I(t) - \mu R(t)
-\end{cases}
-$$
-
-Where:
-
--   $B(t) = \mu N(t)$
--   $\lambda(t) = \beta I(t)$ with $\beta$ is the transmission rate
--   $\mu$ is the natural death rate
--   $\nu$ is the recovery rate
--   $\alpha$ is the disease related death rate
--   $p$ is the proportion of newborn vaccinated and moved directly to the recovered compartment
-
-**Fitting data**
-
-To fit a basic SIR model, use `sir_basic_model()` and specify the following parameters
-
--   `state` - initial population of each compartment
-
--   `times` - a time sequence
-
--   `parameters` - parameters for SIR model
-
-```{r}
-state <- c(S=4999, I=1, R=0)
-parameters <- c(
-  mu=1/75, # 1 divided by life expectancy (75 years old)
-  alpha=0, # no disease-related death
-  beta=0.0005, # transmission rate
-  nu=1, # 1 year for infected to recover
-  p=0 # no vaccination at birth
-)
-times <- seq(0, 250, by=0.1)
-model <- sir_basic_model(times, state, parameters)
-model$parameters
-plot(model)
-```
-
-## SIR model with constant Force of Infection at Endemic state
-
-**Proposed model**
-
-A transmission model consists of 3 compartments: susceptible (S), infected (I), recovered (R)
-
-With the following assumptions:
-
--   Time homogeneity
-
--   Age heterogeneity
-
-Described by a system of 3 differential equations
-
-$$
-\begin{cases}
-\frac{ds(a)}{da} = -\lambda s(a) \\
-\frac{di(a)}{da} = \lambda s(a) - \nu i(a)  \\
-\frac{dr(a)}{da} =  \nu i(a) 
-\end{cases}
-$$
-
-Where:
-
--   $s(a), i(a), r(a)$ are proportion of susceptible, infected, recovered population of age group $a$ respectively
--   $\lambda$ is the force of infection
--   $\nu$ is the recovery rate
-
-**FItting data**
-
-To fit an SIR model with constant FOI, use `sir_static_model()` and specify the following parameters
-
--   `state` - initial proportion of each compartment
-
--   `ages` - an age sequence
-
--   `parameters` - parameters for the model
-
-```{r}
-state <- c(s=0.99,i=0.01,r=0)
-parameters <- c(
-  lambda = 0.05,
-  nu=1/(14/365) # 2 weeks to recover
-)
-ages<-seq(0, 90, by=0.01)
-model <- sir_static_model(ages, state, parameters)
-model$parameters
-plot(model)
-```
-
-## SIR model with sub populations
-
-**Proposed model**
-
-Extends on the SIR model by having interacting sub-populations (different age groups)
-
-With K subpopulations, the WAIFW matrix or mixing matrix is given by
-
-$$
-C = \begin{bmatrix} 
-\beta_{11} & \beta_{12}  & ... & \beta_{1K} \\
-\beta_{21} & \beta_{22}  & ... & \beta_{2K} \\
-\vdots & \vdots  & ... & \vdots \\
-\beta_{K1} & \beta_{K2}  & ... & \beta_{KK} \\
-\end{bmatrix}
-$$
-
-The system of differential equations for the i$th$ subpopulation is given by
-
-$$
-\begin{cases}
-\frac{dS_i(t)}{dt} = -(\sum^K_{j=1}\beta_{ij}I_j(t)) S_i(t) + N_i\mu_i - \mu_i S_i(t) \\
-\frac{dI_i(t)}{dt} = (\sum^K_{j=1}\beta_{ij}I_j(t)) S_i(t)  - (\nu_i + \mu_i) I_i(t)  \\
-\frac{dR_i(t)}{dt} = \nu_i I_i(t)  - \mu_i R_i(t)
-\end{cases}
-$$
-
-**FItting data**
-
-To fit a SIR model with subpopulations, use `sir_subpops_model()` and specify the following parameters
-
--   `state` - initial proportion of each compartment for each population
-
--   `beta_matrix` - the WAIFW matrix
-
--   `times` - a time sequence
-
--   `parameters` - parameters for the model
-
-```{r}
-k <- 2 # number of population
-state <- c(
-  # proportion of each compartment for each population
-  s = c(0.8, 0.6), 
-  i = c(0.2, 0.4),
-  r = c(  0,   0)
-)
-beta_matrix <- c(
-  c(0.05, 0.00),
-  c(0.00, 0.05)
-)
-parameters <- list(
-  beta = matrix(beta_matrix, nrow=k, ncol=k, byrow=TRUE),
-  nu = c(1/30, 1/30),
-  mu = 0.001,
-  k = k
-)
-times<-seq(0,10000,by=0.5)
-model <- sir_subpops_model(times, state, parameters)
-model$parameters
-plot(model) # returns plot for each population
-```
-
-## MSEIR model
-
-**Proposed model**
-
-Extends on SIR model with 2 additional compartments: maternal antibody (M) and exposed period (E)
-
-And described by the following system of ordinary differential equation
-
-$$
-\begin{cases}
-\frac{dM(a)}{da} = -(\gamma + \mu(a))M(a) \\
-\frac{dS(a)}{da} = \gamma M(a) - (\lambda(a) + \mu(a)) S(a) \\
-\frac{dE(a)}{da} = \lambda(a) S(a)  - (\sigma + \mu(a)) E(a) \\
-\frac{dI(a)}{da} = \sigma(a) E(a)  - (\nu + \mu(a)) I(a)  \\
-\frac{dR(a)}{da} = \nu I(a)  - \mu(a) R(a)
-\end{cases}
-$$
-
-Where
-
--   $M(0)$ = B, the number of births in the population
-
--   $\gamma$ is the rate of antibody decaying
-
--   $\lambda(a)$ is the force of infection at age $a$
-
--   $\mu(a)$ is the natural death rate at age $a$
-
--   $\sigma$ is the rate of becoming infected after being exposed
-
--   $\nu$ is the recovery rate
-
-**Fitting data**
-
-To fit a MSEIR, use `mseir_model()` and specify the following parameters
-
--   `a` - age sequence
-
--   And model parameters including `gamma`, `lambda`, `sigma`, `nu`
-
-```{r}
-model <- mseir_model(
-  a=seq(from=1,to=20,length=500), # age range from 0 -> 20 yo
-  gamma=1/0.5, # 6 months in the maternal antibodies
-  lambda=0.2,  # 5 years in the susceptible class
-  sigma=26.07, # 14 days in the latent class
-  nu=36.5      # 10 days in the infected class
-)
-model$parameters
-plot(model)
-```
diff --git a/vignettes/visualizing_model.Rmd b/vignettes/visualizing_model.Rmd
index 102bdd3..f86a6b6 100644
--- a/vignettes/visualizing_model.Rmd
+++ b/vignettes/visualizing_model.Rmd
@@ -44,15 +44,17 @@ plot(farrington_md)
 
 ## Customize the plot
 
-Function `set_plot_style()` is provided to customize some attributes of the plot.
+### Built-in function
+
+`serosv` offers the function `set_plot_style()` to customize some key attributes of the plot.
 
 Current modifiable attributes include color, linetype for seroprevalence, foi and fill color for confidence interval
 
 ```{r}
-gf_model <- polynomial_model(hav_bg_1964, type = "Griffith")
+hav_mod <- polynomial_model(hav_bg_1964, k=3)
 
 # customize plot
-plot(gf_model) +
+plot(hav_mod) +
   set_plot_style(
     sero = "#3de071",
     foi = "#2f22e0",
@@ -61,3 +63,36 @@ plot(gf_model) +
     sero_line = "dotdash"
   )
 ```
+
+### `ggplot2` functions
+
+Since `serosv` uses `ggplot2` for plotting, the returned plot is a `gg` object meaning any standard `ggplot2` layer can be appended with `+` for further configurations. 
+
+```{r}
+# return gg object
+class(plot(hav_mod))
+```
+
+**Examples**
+
+```{r warning=FALSE}
+library(ggplot2)
+
+# Set x and y limits
+plot(hav_mod) +
+  coord_cartesian(xlim = c(0, 50), ylim = c(0, 1.5))
+
+# Configure the plot theme
+plot(hav_mod) +
+  theme_bw()
+
+# Set titles and omit legends
+plot(hav_mod) +
+  theme_bw() +
+  ggtitle("Age-stratified Hepatitis A prevalence in Bulgaria (1964)") +
+  guides(
+    colour = "none",
+    linetype = "none",
+    fill = "none"
+  )
+```